From 21f7627d58a50d4ceb13f8420506fcde690c6945 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Wed, 9 Aug 2023 17:02:50 +0200 Subject: [PATCH 01/17] Refactor KWDGValueSet and prepare new hierarchy of classes MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Preparation à la nouvelle hierarchie de classes - KWDGPartValues: classe ancetre - KWDGInterval - KWDGValueSet - KWDGSymbolValueSet - KWDGVarPartSet Etape 1 de preparation - nouvelle classe - KWDGPartValues - KWDGInterval herite de KWDGPartValues - nouvelle classes temporaires - KWDGGenericValueSet - KWDGGenericValue - nettoyage de KWValueSet - supression de la methode GetValueNumber - en partie redondante avec GetTRueValueNumber - peu utilisee - permet de ne garder que GetTrueValueNumber - pour unifier avec GetVarPartNumber, en une methode commune GetValueNumber - AddValue: incrementation de ValueNumber, sauf si StarValue - DeleteValue: decrementation de ValueNumber, sauf si StarValue - GetTrueValueNumber: renvoie toujours nValueNumber - KWDGPODiscretizer::InitializePartFrequencyVector: supression du parametrage inutile des ValueNumber - KWDataGridUnivariateCosts::ComputePartCost: amelioration des commentaires sur l'utilisation du nValueNumber Rajout de override manquants dans les reimplementations de GetEmulated dans KWDataGridPostOptimizer Rajout de KWTest dans la compilation, pour garder l'ensemble des sources compilables a tout momment - ajout dans le CMakeLists.txt - ajout d'un CMakeLists.txt dans le repertoire KWTest - ExportGranularizedPartsForSymbolAttribute: mise a jour minimaliste pour que cette classe compile a nouveau --- src/Learning/CMakeLists.txt | 2 + src/Learning/KWDataPreparation/KWDataGrid.cpp | 353 ++++++++++-------- src/Learning/KWDataPreparation/KWDataGrid.h | 199 ++++++++-- .../KWDataPreparation/KWDataGridManager.cpp | 1 + .../KWDataGridPostOptimizer.cpp | 27 +- .../KWDataGridPostOptimizer.h | 12 +- src/Learning/KWTest/CMakeLists.txt | 4 + .../KWTest/KWHierarchicalMultinomialStudy.cpp | 121 +++++- .../KWTest/KWHierarchicalMultinomialStudy.h | 3 + src/Learning/KWTest/Test.cpp | 2 +- src/Learning/KWTest/Test.h | 2 +- .../CCCoclusteringBuilder.cpp | 2 +- .../MODL_Coclustering/MODL_Coclustering.cpp | 3 +- 13 files changed, 513 insertions(+), 218 deletions(-) create mode 100644 src/Learning/KWTest/CMakeLists.txt diff --git a/src/Learning/CMakeLists.txt b/src/Learning/CMakeLists.txt index 15281aad7..ac1ec5114 100644 --- a/src/Learning/CMakeLists.txt +++ b/src/Learning/CMakeLists.txt @@ -20,6 +20,8 @@ add_subdirectory(MODL_Coclustering) add_subdirectory(SNBPredictor) add_subdirectory(samples/sample3) +add_subdirectory(KWTest) + # build norm.jar if(BUILD_JARS) include(UseJava) diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index 7ca41a6ad..7d71692df 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -1230,14 +1230,14 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const attributeGrouping->SetCatchAllValueNumber(attribute->GetCatchAllValueNumber()); attributeGrouping->SetGarbageModalityNumber(attribute->GetGarbageModalityNumber()); - // Comptage du nombre total de valeurs + // Comptage du nombre total de valeurs, en partant de 1 pour compter la StarValue nSuppressedValueNumber = 0; - nValueNumber = 0; + nValueNumber = 1; part = attribute->GetHeadPart(); while (part != NULL) { valueSet = part->GetValueSet(); - nValueNumber += valueSet->GetValueNumber(); + nValueNumber += valueSet->GetTrueValueNumber(); // Calcul du nombre eventuel de modalites qui seront nettoyees (non memorisees pour // l'affichage) En presence d'un fourre-tout, les modalites du fourre-tout autres que @@ -1293,6 +1293,7 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const else if (part == attribute->GetGarbagePart()) { attributeGrouping->SetGarbageGroupIndex(nPart); + // Cas de la presence d'un groupe poubelle sans presence de fourre-tout if (attribute->GetCatchAllValueNumber() == 0) { @@ -1320,6 +1321,7 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const nValue++; cleanedValueSet->GetNextValue(value); } + // Nettoyage delete cleanedValueSet; cleanedValueSet = NULL; @@ -1349,7 +1351,6 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const attributeDiscretization = new KWDGSAttributeDiscretization; attributePartition = attributeDiscretization; attributePartition->SetAttributeName(attribute->GetAttributeName()); - attributePartition->SetInitialValueNumber(attribute->GetInitialValueNumber()); attributePartition->SetGranularizedValueNumber(attribute->GetGranularizedValueNumber()); @@ -3984,7 +3985,7 @@ void KWDGPart::Write(ostream& ost) const ost << GetClassLabel() << "\t" << GetObjectLabel() << "\t" << GetPartFrequency() << "\n"; // Valeurs et cellules de la partie - if (GetPartType() == KWType::Symbol and valueSet->GetValueNumber() > 0) + if (GetPartType() == KWType::Symbol and valueSet->GetTrueValueNumber() > 0) WriteValues(ost); if (GetPartType() == KWType::VarPart and varPartSet->GetVarPartNumber() > 0) @@ -4237,28 +4238,6 @@ boolean KWDGInterval::IsSubInterval(const KWDGInterval* otherInterval) const return GetLowerBound() >= otherInterval->GetLowerBound() and GetUpperBound() <= otherInterval->GetUpperBound(); } -boolean KWDGInterval::Check() const -{ - boolean bOk = true; - - // Test des bornes: cas missing - if (cUpperBound == KWContinuous::GetMissingValue()) - { - if (cLowerBound != KWContinuous::GetMissingValue()) - { - AddError("The lower and upper bounds must be equal for the missing value interval"); - bOk = false; - } - } - // Cas standard (tolerance si egalite des bornes, possible dans les limites de precision numerique) - else if (cLowerBound > cUpperBound) - { - AddError("The lower bound must be less than the upper bound"); - bOk = false; - } - return bOk; -} - void KWDGInterval::Import(KWDGInterval* sourceInterval) { require(Check()); @@ -4297,8 +4276,50 @@ void KWDGInterval::UpgradeFrom(const KWDGInterval* sourceInterval) ensure(Check()); } - // CH IV End + +boolean KWDGInterval::IsSubPartValues(const KWDGPartValues* otherPartValues) const +{ + return IsSubInterval(cast(KWDGInterval*, otherPartValues)); +} + +void KWDGInterval::Import(KWDGPartValues* sourcePartValues) +{ + Import(cast(KWDGInterval*, sourcePartValues)); +} + +void KWDGInterval::UpgradeFrom(const KWDGPartValues* sourcePartValues) +{ + UpgradeFrom(cast(KWDGInterval*, sourcePartValues)); +} + +void KWDGInterval::CopyFrom(const KWDGPartValues* sourcePartValues) +{ + CopyFrom(cast(KWDGInterval*, sourcePartValues)); +} + +boolean KWDGInterval::Check() const +{ + boolean bOk = true; + + // Test des bornes: cas missing + if (cUpperBound == KWContinuous::GetMissingValue()) + { + if (cLowerBound != KWContinuous::GetMissingValue()) + { + AddError("The lower and upper bounds must be equal for the missing value interval"); + bOk = false; + } + } + // Cas standard (tolerance si egalite des bornes, possible dans les limites de precision numerique) + else if (cLowerBound > cUpperBound) + { + AddError("The lower bound must be less than the upper bound"); + bOk = false; + } + return bOk; +} + void KWDGInterval::Write(ostream& ost) const { // Identification @@ -4343,7 +4364,6 @@ KWDGValue* KWDGValueSet::AddValue(const Symbol& sValue) value = NewValue(sValue); // Ajout en fin de la liste des valeurs - nValueNumber++; if (headValue == NULL) headValue = value; if (tailValue != NULL) @@ -4356,6 +4376,9 @@ KWDGValue* KWDGValueSet::AddValue(const Symbol& sValue) // Partie par defaut si la valeur est la valeur par defaut if (sValue == Symbol::GetStarValue()) bIsDefaultPart = true; + // Incrementation du nombre de valeurs sinon + else + nValueNumber++; // On retourne la valeur cree return value; @@ -4366,7 +4389,6 @@ void KWDGValueSet::DeleteValue(KWDGValue* value) require(value != NULL); // Supression de la liste des valuees - nValueNumber--; if (value->prevValue != NULL) value->prevValue->nextValue = value->nextValue; if (value->nextValue != NULL) @@ -4379,6 +4401,9 @@ void KWDGValueSet::DeleteValue(KWDGValue* value) // Partie "standard" si la valeur detruite est la valeur par defaut if (value->GetValue() == Symbol::GetStarValue()) bIsDefaultPart = false; + // Decrementation du nombre de valeurs sinon + else + nValueNumber--; // Destruction de la valeur delete value; @@ -4493,19 +4518,15 @@ KWDGValueSet* KWDGValueSet::ComputeCleanedValueSet() const void KWDGValueSet::ExportValues(ObjectArray* oaValues) const { KWDGValue* value; - int nValue; require(oaValues != NULL); require(oaValues->GetSize() == 0); // Ajout des parties dans le tableau - oaValues->SetSize(GetValueNumber()); - nValue = 0; value = headValue; while (value != NULL) { - oaValues->SetAt(nValue, value); - nValue++; + oaValues->Add(value); value = value->nextValue; } } @@ -4517,7 +4538,7 @@ boolean KWDGValueSet::IsSubValueSet(const KWDGValueSet* otherValueSet) const KWDGValue* value; // On doit avoir moins de valeurs - if (GetValueNumber() > otherValueSet->GetValueNumber()) + if (GetTrueValueNumber() > otherValueSet->GetTrueValueNumber()) bOk = false; // Sinon, on teste effectivement l'inclusion else @@ -4545,80 +4566,6 @@ boolean KWDGValueSet::IsSubValueSet(const KWDGValueSet* otherValueSet) const return bOk; } -boolean KWDGValueSet::Check() const -{ - boolean bOk = true; - boolean bStarValuePresent; - NumericKeyDictionary nkdCheckValues; - KWDGValue* value; - boolean bCheckFrequencies; - ALString sTmp; - - // Test d'existence d'au moins une valeur - if (nValueNumber == 0) - { - AddError("No value specified"); - bOk = false; - } - - // Test des valeurs de la partie - if (bOk) - { - // On ne verifie les effectifs que si au moins un est specifie - bCheckFrequencies = ComputeTotalFrequency() > 0; - - // Parcours des valeurs de la partie - bStarValuePresent = false; - value = GetHeadValue(); - while (value != NULL) - { - // Detection de la star value - if (value->GetValue() == Symbol::GetStarValue()) - bStarValuePresent = true; - - // Erreur si partie deja enregistree avec cette valeur - if (nkdCheckValues.Lookup(value->GetValue().GetNumericKey()) != NULL) - { - AddError(sTmp + "Value " + value->GetValue() + " already exists in the part"); - bOk = false; - break; - } - // Erreur si effectif a 0 pour une valeur qui n'est pas la star value - else if (bCheckFrequencies and value->GetValue() != Symbol::GetStarValue() and - value->GetValueFrequency() == 0) - { - AddError(sTmp + "Value " + value->GetValue() + " should have a non-zero frequency"); - bOk = false; - break; - } - // On continue si pas d'erreur - else - { - // Ajout de la partie avec la valeur pour cle - nkdCheckValues.SetAt(value->GetValue().GetNumericKey(), value); - - // Valeur suivante - GetNextValue(value); - } - } - - // Test d'integrite sur la star value - if (bStarValuePresent and not bIsDefaultPart) - { - AddError(sTmp + "Special grouping value " + Symbol::GetStarValue() + - " is used used in a standard part"); - bOk = false; - } - else if (not bStarValuePresent and bIsDefaultPart) - { - AddError(sTmp + "Special grouping value " + Symbol::GetStarValue() + - " is missing in default part"); - bOk = false; - } - } - return bOk; -} - int KWDGValueSet::ComputeTotalFrequency() const { int nTotalFrequency; @@ -4750,6 +4697,80 @@ boolean KWDGValueSet::AreValuesSorted() const return bIsSorted; } +boolean KWDGValueSet::Check() const +{ + boolean bOk = true; + boolean bStarValuePresent; + NumericKeyDictionary nkdCheckValues; + KWDGValue* value; + boolean bCheckFrequencies; + ALString sTmp; + + // Test d'existence d'au moins une valeur + if (nValueNumber == 0) + { + AddError("No value specified"); + bOk = false; + } + + // Test des valeurs de la partie + if (bOk) + { + // On ne verifie les effectifs que si au moins un est specifie + bCheckFrequencies = ComputeTotalFrequency() > 0; + + // Parcours des valeurs de la partie + bStarValuePresent = false; + value = GetHeadValue(); + while (value != NULL) + { + // Detection de la star value + if (value->GetValue() == Symbol::GetStarValue()) + bStarValuePresent = true; + + // Erreur si partie deja enregistree avec cette valeur + if (nkdCheckValues.Lookup(value->GetValue().GetNumericKey()) != NULL) + { + AddError(sTmp + "Value " + value->GetValue() + " already exists in the part"); + bOk = false; + break; + } + // Erreur si effectif a 0 pour une valeur qui n'est pas la star value + else if (bCheckFrequencies and value->GetValue() != Symbol::GetStarValue() and + value->GetValueFrequency() == 0) + { + AddError(sTmp + "Value " + value->GetValue() + " should have a non-zero frequency"); + bOk = false; + break; + } + // On continue si pas d'erreur + else + { + // Ajout de la partie avec la valeur pour cle + nkdCheckValues.SetAt(value->GetValue().GetNumericKey(), value); + + // Valeur suivante + GetNextValue(value); + } + } + + // Test d'integrite sur la star value + if (bStarValuePresent and not bIsDefaultPart) + { + AddError(sTmp + "Special grouping value " + Symbol::GetStarValue() + + " is used used in a standard part"); + bOk = false; + } + else if (not bStarValuePresent and bIsDefaultPart) + { + AddError(sTmp + "Special grouping value " + Symbol::GetStarValue() + + " is missing in default part"); + bOk = false; + } + } + return bOk; +} + void KWDGValueSet::Write(ostream& ost) const { // Identification @@ -4765,7 +4786,7 @@ void KWDGValueSet::WriteValues(ostream& ost) const // Affichage des valeurs cout << "Values" - << "\t" << GetValueNumber() << "\n"; + << "\t" << GetTrueValueNumber() << "\n"; value = GetHeadValue(); while (value != NULL) { @@ -5134,61 +5155,6 @@ boolean KWDGVarPartSet::IsSubVarPartSet(const KWDGVarPartSet* otherVarPartSet) c return bOk; } -boolean KWDGVarPartSet::Check() const -{ - boolean bOk = true; - NumericKeyDictionary nkdCheckVarParts; - KWDGVarPartValue* varPartValue; - boolean bCheckFrequencies; - ALString sTmp; - - // Test d'existence d'au moins une partie - if (nVarPartNumber == 0) - { - AddError("No var part specified"); - bOk = false; - } - - // Test des valeurs de la partie - if (bOk) - { - // On ne verifie les effectifs que si au moins un est specifie - bCheckFrequencies = ComputeTotalFrequency() > 0; - - // Parcours des parties - varPartValue = GetHeadVarPart(); - while (varPartValue != NULL) - { - // Erreur si partie deja enregistree avec cette valeur - if (nkdCheckVarParts.Lookup((NUMERIC)varPartValue) != NULL) - { - AddError(sTmp + "VarPart " + varPartValue->GetObjectLabel() + - " already exists in the cluster"); - bOk = false; - break; - } - // Erreur si effectif a 0 - else if (bCheckFrequencies and varPartValue->GetVarPartFrequency() == 0) - { - AddError(sTmp + "VarPart " + varPartValue->GetObjectLabel() + - " should have a non-zero frequency"); - bOk = false; - break; - } - // On continue si pas d'erreur - else - { - // Ajout de la partie - nkdCheckVarParts.SetAt((NUMERIC)varPartValue, varPartValue); - - // Valeur suivante - GetNextVarPart(varPartValue); - } - } - } - return bOk; -} - int KWDGVarPartSet::ComputeTotalFrequency() const { int nTotalFrequency; @@ -5302,6 +5268,61 @@ boolean KWDGVarPartSet::AreVarPartValuesSorted() const return bIsSorted; } +boolean KWDGVarPartSet::Check() const +{ + boolean bOk = true; + NumericKeyDictionary nkdCheckVarParts; + KWDGVarPartValue* varPartValue; + boolean bCheckFrequencies; + ALString sTmp; + + // Test d'existence d'au moins une partie + if (nVarPartNumber == 0) + { + AddError("No var part specified"); + bOk = false; + } + + // Test des valeurs de la partie + if (bOk) + { + // On ne verifie les effectifs que si au moins un est specifie + bCheckFrequencies = ComputeTotalFrequency() > 0; + + // Parcours des parties + varPartValue = GetHeadVarPart(); + while (varPartValue != NULL) + { + // Erreur si partie deja enregistree avec cette valeur + if (nkdCheckVarParts.Lookup((NUMERIC)varPartValue) != NULL) + { + AddError(sTmp + "VarPart " + varPartValue->GetObjectLabel() + + " already exists in the cluster"); + bOk = false; + break; + } + // Erreur si effectif a 0 + else if (bCheckFrequencies and varPartValue->GetVarPartFrequency() == 0) + { + AddError(sTmp + "VarPart " + varPartValue->GetObjectLabel() + + " should have a non-zero frequency"); + bOk = false; + break; + } + // On continue si pas d'erreur + else + { + // Ajout de la partie + nkdCheckVarParts.SetAt((NUMERIC)varPartValue, varPartValue); + + // Valeur suivante + GetNextVarPart(varPartValue); + } + } + } + return bOk; +} + void KWDGVarPartSet::Write(ostream& ost) const { // Identification diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index 5235e3c07..f3953d36e 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -12,6 +12,9 @@ class KWDGValueSet; class KWDGValue; class KWDGCell; // CH IV Begin +class KWDGPartValues; +class KWDGGenericValueSet; +class KWDGGenericValue; class KWDGVarPartSet; class KWDGVarPartValue; class KWDGInnerAttributes; @@ -897,16 +900,162 @@ int KWDGPartVarPartCompare(const void* elem1, const void* elem2); // Comparaison de deux parties de parties de variables, par effectif decroissant int KWDGPartVarPartCompareDecreasingFrequency(const void* elem1, const void* elem2); // CH IV End + +////////////////////////////////////////////////////////////////////////////// +// Classe KWDGPartValues +// Composant de KWDGPart dedie a la gestion des valeurs effectives d'une partie +// Classe abstraite, ancetre des intervalles et groupes de valeur +class KWDGPartValues : public Object +{ +public: + // Type de valeur gere par la partie + virtual int GetValueType() const = 0; + + // Test si la partie de valeur est un sous-ensemble de la partie en parametre + virtual boolean IsSubPartValues(const KWDGPartValues* otherPartValues) const = 0; + + // Import des valeurs d'une autre partie + // La partie source est reinitialisee + virtual void Import(KWDGPartValues* sourcePartValues) = 0; + + // Mise a jour des valeurs a partir des valeurs d'une partie source, devant etre adjacente + // La partie source n'est pas modifiee + virtual void UpgradeFrom(const KWDGPartValues* sourcePartValues) = 0; + + // Copie + virtual void CopyFrom(const KWDGPartValues* sourcePartValues) = 0; + + // Controle d'integrite + boolean Check() const override = 0; + + // Affichage + void Write(ostream& ost) const override = 0; +}; + +/*DDD +////////////////////////////////////////////////////////////////////////////// +// Classe KWDGGenericValueSet +// Sous-classe virtuelle de KWDGPartValues dediee aux groupes de valeurs +// pour mutualiser la gestion des valeur de type Symbol ou VarPart +class KWDGGenericValueSet : public KWDGPartValues +{ +public: + //////////////////////////////////////////////////////////////// + // Gestion des valeurs de la parties sous forme de liste + // Memoire: les valeurs appartiennent a la partie + + // Destruction d'une valeur de la liste + void DeleteValue(KWDGGenericValue* value); + + // Destruction de toutes les valeurs + void DeleteAllValues(); + + // Test de validite d'une valeur (si elle appartient a la partie) + boolean CheckValue(KWDGGenericValue* value) const; + + // Nombre de valeurs + int GetValueNumber() const; + + // Parcours de tous les valeurs + KWDGGenericValue* GetHeadValue() const; + KWDGGenericValue* GetTailValue() const; + void GetNextValue(KWDGGenericValue*& value) const; + void GetPrevValue(KWDGGenericValue*& value) const; + + /////////////////////////////// + // Services divers + + // Export des valeurs dans un tableau (initialement vide) + void ExportValues(ObjectArray* oaValues) const; + + // Test si l'ensemble de valeur est inclus dans l'autre ensemble de valeurs en parametres + // On ne tient pas compte de la StarValue, et on effectue un test d'inclusion exhaustif pour toutes les valeurs + boolean IsSubValueSet(const KWDGGenericValueSet* otherValueSet) const; + + // Calcul de l'effectif cumule des valeurs + int ComputeTotalFrequency() const; + + // Import des valeurs d'une partie source, devant etre disjointe de la premiere + // partie. La partie source est reinitialise + void Import(KWDGGenericValueSet* sourceValueSet); + + // Copie + void CopyFrom(const KWDGGenericValueSet* sourceValueSet); + + // Ajout de nouvelles valeurs recopiees depuis une source + void UpgradeFrom(const KWDGGenericValueSet* sourceValueSet); + + // Tri des valeurs par effectif decroissant, pour preparer l'affichage + void SortValues(); + + // Verification du tri des valeurs par effectif decroissant + // Couteux, a utiliser essentiellement dans les assertions + boolean AreValuesSorted() const; + + // Controle d'integrite + boolean Check() const override; + + // Affichage + void Write(ostream& ost) const override; + void WriteValues(ostream& ost) const; + + // Libelles utilisateur + const ALString GetClassLabel() const override; + const ALString GetObjectLabel() const override; + + /////////////////////////////// + ///// Implementation +protected: + // Tri des valeurs selon une fonction de tri + // La valeur speciale est toujours mise en dernier, independament du critere de tri + void InternalSortValues(CompareFunction fCompare); + + // Methode indiquant si les donnees sont emulee + virtual boolean GetEmulated() const; + + // Gestion de la liste doublement chainee des cellules + KWDGGenericValue* headValue; + KWDGGenericValue* tailValue; + int nValueNumber; +}; +*/ + +////////////////////////////////////////////////////////////////////////////// +// Classe KWGenericValue +// Classe virtuelle pour mutualiser la gestion des valeur de type Sumbol ou VarPart +class KWDGGenericValue : public Object +{ +public: + // Effectif lie a la partie de variable, qui est gere par la partie de variable elle-meme + virtual void SetVarPartFrequency(int nFrequency) = 0; + virtual int GetVarPartFrequency() const = 0; + + // Affichage + void Write(ostream& ost) const override = 0; + + /////////////////////////////// + ///// Implementation +protected: + friend class KWDGGenericValueSet; + + // Attributs + KWDGVarPartValue* prevVarPartValue; + KWDGVarPartValue* nextVarPartValue; +}; + ////////////////////////////////////////////////////////////////////////////// // Classe KWDGInterval // Intervalle de valeurs -class KWDGInterval : public Object +class KWDGInterval : public KWDGPartValues { public: // Constructeur KWDGInterval(); ~KWDGInterval(); + // Type de valeur gere par la partie + int GetValueType() const; + // Borne inf de l'intervalle // (doit etre egal a KWDGInterval::GetMinLowerBound pour le premier intervalle) void SetLowerBound(Continuous cValue); @@ -927,9 +1076,6 @@ class KWDGInterval : public Object // Test si l'intervalle est un sous-intervalle boolean IsSubInterval(const KWDGInterval* otherInterval) const; - // Controle d'integrite - boolean Check() const override; - // Import des valeurs d'un intervalle source, devant etre adjacent au premier // intervalle. L'intervalle source est reinitialise void Import(KWDGInterval* sourceInterval); @@ -937,11 +1083,21 @@ class KWDGInterval : public Object // CH IV Begin // Mise a jour des valeurs de l'intervalle a partir des valeurs d'un intervalle source, devant etre adjacent au // premier intervalle. L'intervalle source n'est pas modifie + // CH IV Refactoring: UpgradeFrom est tres proche de Import: a mutualiser, ou remplacer Import par UpgradeFrom puis Reset??? void UpgradeFrom(const KWDGInterval* sourceInterval); // CH IV End // Copie void CopyFrom(const KWDGInterval* sourceInterval); + // Redefinition des methodes virtuelles a partir des methodes specifiques + boolean IsSubPartValues(const KWDGPartValues* otherPartValues) const override; + void Import(KWDGPartValues* sourcePartValues) override; + void UpgradeFrom(const KWDGPartValues* sourcePartValues) override; + void CopyFrom(const KWDGPartValues* sourcePartValues) override; + + // Controle d'integrite + boolean Check() const override; + // Affichage void Write(ostream& ost) const override; @@ -984,9 +1140,6 @@ class KWDGValueSet : public Object // Test de validite d'une valeur (si elle appartient a la partie) boolean CheckValue(KWDGValue* value) const; - // Nombre de valeurs, y compris l'eventuelle modalite speciale StarValue - int GetValueNumber() const; - // Test si la partie est la partie par defaut (si elle contient la valeur speciale) boolean IsDefaultPart() const; @@ -1026,9 +1179,6 @@ class KWDGValueSet : public Object // On ne tine spas compte de la StarValue, et on effectue un test d'inclusion exhaustif pour toutes les valeurs boolean IsSubValueSet(const KWDGValueSet* otherValueSet) const; - // Controle d'integrite - boolean Check() const override; - // Calcul de l'effectif cumule des valeurs int ComputeTotalFrequency() const; @@ -1049,6 +1199,9 @@ class KWDGValueSet : public Object // assertions boolean AreValuesSorted() const; + // Controle d'integrite + boolean Check() const override; + // Affichage void Write(ostream& ost) const override; void WriteValues(ostream& ost) const; @@ -1164,9 +1317,6 @@ class KWDGVarPartSet : public Object // Test si l'ensemble de VarPart est inclus dans l'autre ensemble de VarPart en parametres boolean IsSubVarPartSet(const KWDGVarPartSet* otherVarPartSet) const; - // Controle d'integrite - boolean Check() const override; - // Calcul de l'effectif cumule des parties de variable int ComputeTotalFrequency() const; @@ -1187,6 +1337,9 @@ class KWDGVarPartSet : public Object // Verification du tri des parties de variable : couteux, a utiliser essentiellement dans les assertions boolean AreVarPartValuesSorted() const; + // Controle d'integrite + boolean Check() const override; + // Affichage void Write(ostream& ost) const override; void WriteVarParts(ostream& ost) const; @@ -1820,7 +1973,10 @@ inline int KWDGAttribute::GetCatchAllValueNumber() const else if (nCatchAllValueNumber != -1) return nCatchAllValueNumber; else - return GetCatchAllValueSet()->GetValueNumber(); + { + assert(not GetCatchAllValueSet()->IsDefaultPart()); + return GetCatchAllValueSet()->GetTrueValueNumber(); + } } inline boolean KWDGAttribute::IsIndexed() const @@ -1934,6 +2090,11 @@ inline KWDGInterval::KWDGInterval() inline KWDGInterval::~KWDGInterval() {} +inline int KWDGInterval::GetValueType() const +{ + return KWType::Continuous; +} + inline void KWDGInterval::SetLowerBound(Continuous cValue) { cLowerBound = cValue; @@ -1988,11 +2149,6 @@ inline KWDGValueSet::~KWDGValueSet() DeleteAllValues(); } -inline int KWDGValueSet::GetValueNumber() const -{ - return nValueNumber; -} - inline boolean KWDGValueSet::IsDefaultPart() const { return bIsDefaultPart; @@ -2000,10 +2156,7 @@ inline boolean KWDGValueSet::IsDefaultPart() const inline int KWDGValueSet::GetTrueValueNumber() const { - if (bIsDefaultPart and nValueNumber > 1) - return nValueNumber - 1; - else - return nValueNumber; + return nValueNumber; } inline KWDGValue* KWDGValueSet::GetHeadValue() const diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index 3b72976ad..fbca9e7e5 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -3423,6 +3423,7 @@ void KWDataGridManager::InitialiseAttributeGranularizedSymbolParts(const KWDGAtt targetAttribute->InitializeCatchAllValueSet(cleanedValueSet); delete cleanedValueSet; } + // Tri des valeurs du fourre tout if (targetPart->GetValueSet()->IsDefaultPart()) targetPart->GetValueSet()->SortValues(); diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp index 927528bf2..aa1c5af80 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp @@ -498,21 +498,15 @@ void KWDGPODiscretizer::InitializePartFrequencyVector(KWDGPOPartFrequencyVector* require(partFrequencyVector != NULL); require(partFrequencyVector->GetCellCost() == 0); require(partFrequencyVector->ComputeTotalFrequency() == 0); + require(partFrequencyVector->GetModalityNumber() == 0); require(part != NULL); + require(part->GetPartType() == KWType::Continuous); require(part->GetAttribute()->GetAttributeName() == GetPostOptimizationAttributeName()); require(nkdHashCells != NULL); // Initialisation "standard" du vecteur d'effectif InitializeFrequencyVector(partFrequencyVector); - // Memorisation du nombre de valeurs associees a la partie - if (part->GetPartType() == KWType::Symbol) - partFrequencyVector->SetModalityNumber(part->GetValueSet()->GetValueNumber()); - // CH IV Begin - else if (part->GetPartType() == KWType::VarPart) - partFrequencyVector->SetModalityNumber(part->GetVarPartSet()->GetVarPartNumber()); - // CH IV End - // Parcours des cellules de la partie pour creer les vecteurs d'effectif par cellule cell = part->GetHeadCell(); while (cell != NULL) @@ -1436,7 +1430,9 @@ void KWDGPOGrouper::InitializePartFrequencyVector(KWDGPOPartFrequencyVector* par require(partFrequencyVector != NULL); require(partFrequencyVector->GetCellCost() == 0); require(partFrequencyVector->ComputeTotalFrequency() == 0); + require(partFrequencyVector->GetModalityNumber() == 0); require(part != NULL); + require(part->GetPartType() == KWType::Symbol or part->GetPartType() == KWType::VarPart); require(part->GetAttribute()->GetAttributeName() == GetPostOptimizationAttributeName()); require(nkdHashCells != NULL); @@ -2651,9 +2647,12 @@ double KWDataGridUnivariateCosts::ComputePartCost(const KWFrequencyVector* part) // Specification du nombre de valeurs pour une partie symbolique // Attention: on utilise ici un acces direct au nombre de valeur du ValueSet pour permettre - // d'utiliser ce parametre dans les calculs de cout (mais il y a inconsistance du ValueSet) + // d'utiliser ce parametre dans les calculs de cout + // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) if (partCostParameter->GetPartType() == KWType::Symbol) { + // N'etant jamais la partie par defaut, on a le TrueValueNumber egal au ValueNumber + assert(not partCostParameter->GetValueSet()->IsDefaultPart()); cast(KWDGValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = cast(KWDGPOPartFrequencyVector*, part)->GetModalityNumber(); } @@ -2724,7 +2723,8 @@ double KWDataGridUnivariateCosts::ComputePartUnionCost(const KWFrequencyVector* // Specification du nombre de valeurs pour une partie symbolique // Attention: on utilise ici un acces direct au nombre de valeur du ValueSet pour permettre - // d'utiliser ce parametre dans les calculs de cout (mais il y a inconsistance du ValueSet) + // d'utiliser ce parametre dans les calculs de cout + // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) if (partCostParameter->GetPartType() == KWType::Symbol) { cast(KWDGValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = @@ -2764,7 +2764,8 @@ double KWDataGridUnivariateCosts::ComputePartDiffCost(const KWFrequencyVector* s // Specification du nombre de valeurs pour une partie symbolique // Attention: on utilise ici un acces direct au nombre de valeur du ValueSet pour permettre - // d'utiliser ce parametre dans les calculs de cout (mais il y a inconsistance du ValueSet) + // d'utiliser ce parametre dans les calculs de cout + // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) if (partCostParameter->GetPartType() == KWType::Symbol) { cast(KWDGValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = @@ -4076,8 +4077,8 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K // cluster de depart innerOutOptimizedPart = innerOptimizedAttribute->LookupSymbolPart( innerPart->GetValueSet()->GetHeadValue()->GetValue()); - if (innerOutOptimizedPart->GetValueSet()->GetValueNumber() == - innerPart->GetValueSet()->GetValueNumber()) + if (innerOutOptimizedPart->GetValueSet()->GetTrueValueNumber() == + innerPart->GetValueSet()->GetTrueValueNumber()) nDeltaVarPartNumber = 1; // Parcours des clusters d'arrivee eligibles diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h index a7808cf32..f00144217 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h @@ -426,7 +426,7 @@ class KWDataGridCostParameter : public KWDataGrid { protected: friend class KWDataGridUnivariateCosts; - boolean GetEmulated() const; + boolean GetEmulated() const override; }; //////////////////////////////////////////////////////////////////////////////////////////// @@ -445,7 +445,7 @@ class KWDGAttributeCostParameter : public KWDGAttribute protected: KWDGPart* NewPart() const; friend class KWDataGridUnivariateCosts; - boolean GetEmulated() const; + boolean GetEmulated() const override; int nGarbageModalityNumber; }; @@ -456,7 +456,7 @@ class KWDGPartCostParameter : public KWDGPart protected: friend class KWDataGridUnivariateCosts; void SetPartType(int nValue); - boolean GetEmulated() const; + boolean GetEmulated() const override; }; //////////////////////////////////////////////////////////////////////////////////////////// @@ -465,7 +465,7 @@ class KWDGValueSetCostParameter : public KWDGValueSet { protected: friend class KWDataGridUnivariateCosts; - boolean GetEmulated() const; + boolean GetEmulated() const override; }; //////////////////////////////////////////////////////////////////////////////////////////// @@ -475,7 +475,7 @@ class KWDGVarPartSetCostParameter : public KWDGVarPartSet { protected: friend class KWDataGridUnivariateCosts; - boolean GetEmulated() const; + boolean GetEmulated() const override; }; // CH IV End @@ -485,7 +485,7 @@ class KWDGCellCostParameter : public KWDGCell { protected: friend class KWDataGridUnivariateCosts; - boolean GetEmulated() const; + boolean GetEmulated() const override; }; //////////////////////////////////////////////////////////////////////////////////// diff --git a/src/Learning/KWTest/CMakeLists.txt b/src/Learning/KWTest/CMakeLists.txt new file mode 100644 index 000000000..ef9268c3d --- /dev/null +++ b/src/Learning/KWTest/CMakeLists.txt @@ -0,0 +1,4 @@ +file(GLOB cppfiles ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp) +add_executable(KWTest ${cppfiles}) +set_khiops_options(KWTest) +target_link_libraries(KWTest PUBLIC DTForest KMDRRuleLibrary KWLearningProblem PLParallelTask) diff --git a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp index 69ba3624b..77e757c0d 100644 --- a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp +++ b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp @@ -1029,8 +1029,7 @@ boolean KWAttributePairStatsStudy::ComputeStats(const KWTupleTable* tupleTable) // Parametrage de quantile builder dataGridManager.SetSourceDataGrid(dataGrid); - dataGridManager.InitializeQuantileBuildersBeforeGranularization(&odQuantilesBuilders, - &ivMaxPartNumbers); + dataGridManager.InitializeQuantileBuilders(&odQuantilesBuilders, &ivMaxPartNumbers); // Effectif total nTotalFrequency = dataGrid->GetGridFrequency(); @@ -1282,7 +1281,7 @@ void KWAttributePairStatsStudy::ExportGranularizedParts(KWDataGrid* targetDataGr { targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); - dataGridManager.ExportPartsForAttribute(targetDataGrid, sourceAttribute->GetAttributeName()); + dataGridManager.ExportAttributeParts(targetDataGrid, sourceAttribute->GetAttributeName()); if (bDisplayResults) { cout << "Attribut cible " << targetAttribute->GetAttributeName() << endl; @@ -1311,9 +1310,9 @@ void KWAttributePairStatsStudy::ExportGranularizedParts(KWDataGrid* targetDataGr cast(KWQuantileGroupBuilder*, odQuantileBuilders->Lookup(sourceAttribute->GetAttributeName())); - dataGridManager.ExportGranularizedPartsForSymbolAttribute( - targetDataGrid, sourceAttribute, targetAttribute, nPartileNumber, - quantileGroupBuilder); + ExportGranularizedPartsForSymbolAttribute(targetDataGrid, sourceAttribute, + targetAttribute, nPartileNumber, + quantileGroupBuilder); } } } @@ -1390,6 +1389,116 @@ void KWAttributePairStatsStudy::ExportGranularizedPartsForContinuousAttribute( targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); } +void KWAttributePairStatsStudy::ExportGranularizedPartsForSymbolAttribute(KWDataGrid* targetDataGrid, + KWDGAttribute* sourceAttribute, + KWDGAttribute* targetAttribute, + int nGranularity, + KWQuantileGroupBuilder* quantileBuilder) const +{ + ObjectArray oaSourceParts; + KWDGPart* sourcePart; + KWDGPart* targetPart; + KWDGValueSet* cleanedValueSet; + int nValueNumber; + int nPartileNumber; + int nActualPartileNumber; + int nPartileIndex; + int nSourceIndex; + + require(quantileBuilder != NULL); + + nValueNumber = dataGridManager.GetSourceDataGrid()->GetGridFrequency(); + + // Nombre potentiel de partiles associes a cette granularite + nPartileNumber = (int)pow(2, nGranularity); + if (nPartileNumber > nValueNumber) + nPartileNumber = nValueNumber; + // Initialisation + nActualPartileNumber = nPartileNumber; + + // Cas ou la granularisation n'est pas appliquee : non prise en compte de la granularite + if (nGranularity == 0) + { + dataGridManager.ExportAttributeParts(targetDataGrid, sourceAttribute->GetAttributeName()); + targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); + } + + // Granularisation + else + { + // Export des parties de l'attribut source + sourceAttribute->ExportParts(&oaSourceParts); + + // Cas du nombre de partiles associe a la granularite maximale + if (nPartileNumber == nValueNumber) + // Seuillage de nPartileNumber au nombre de partiles associe a la granularite precedente + // pour que la granularisation rassemble les eventuelles valeurs sources + // singletons dans le fourre-tout + // Pour G tel que 2^G < N <= 2^(G+1) on aura 1 < N/2^G <= 2 c'est a dire un effectif minimal par + // partile de 2 (donc pas de singleton apres granularisation) + nPartileNumber = (int)pow(2, nGranularity - 1); + + // Calcul des quantiles + quantileBuilder->ComputeQuantiles(nPartileNumber); + + // Initialisation du nombre effectif de partiles (peut etre inferieur au nombre theorique du fait de + // doublons) + nActualPartileNumber = quantileBuilder->GetGroupNumber(); + + // Creation des partiles + for (nPartileIndex = 0; nPartileIndex < nActualPartileNumber; nPartileIndex++) + { + targetPart = targetAttribute->AddPart(); + + // Parcours des instances du partile + for (nSourceIndex = quantileBuilder->GetGroupFirstValueIndexAt(nPartileIndex); + nSourceIndex <= quantileBuilder->GetGroupLastValueIndexAt(nPartileIndex); nSourceIndex++) + { + // Extraction de la partie a ajouter dans le groupe + sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSourceIndex)); + + // Ajout de ses valeurs + targetPart->GetValueSet()->UpgradeFrom(sourcePart->GetValueSet()); + + // CH IV Begin + // Cas de la granularisation d'un attribut interne dans un attribut de grille de type + // VarPart + if (sourceAttribute->IsInnerAttribute()) + targetPart->SetPartFrequency(targetPart->GetPartFrequency() + + sourcePart->GetPartFrequency()); + // CH IV End + } + + // Compression et memorisation du fourre-tout si necessaire (mode supervise, attribut non cible) + // La partie qui contient la StarValue est compressee uniquement si elle contient plus d'une + // modalite (cas d'un vrai fourre-tout) + if ((targetDataGrid->GetTargetValueNumber() > 0 or + (targetDataGrid->GetTargetAttribute() != NULL and + not sourceAttribute->GetAttributeTargetFunction())) and + targetPart->GetValueSet()->IsDefaultPart() and + targetPart->GetValueSet()->GetTrueValueNumber() > 1) + { + // Compression du fourre-tout et memorisation de ses valeurs + cleanedValueSet = targetPart->GetValueSet()->ConvertToCleanedValueSet(); + targetAttribute->InitializeCatchAllValueSet(cleanedValueSet); + delete cleanedValueSet; + } + // Tri des valeurs du fourre tout + if (targetPart->GetValueSet()->IsDefaultPart()) + targetPart->GetValueSet()->SortValues(); + } + } + + // Cas d'un attribut explicatif dans le cadre d'une analyse supervisee + // Mise a jour du parametrage du nombre de partiles par le nombre effectif de groupes distincts + if ((targetDataGrid->GetTargetValueNumber() > 0 or + (targetDataGrid->GetTargetAttribute() != NULL and not sourceAttribute->GetAttributeTargetFunction()))) + targetAttribute->SetGranularizedValueNumber(nActualPartileNumber); + // Sinon, la granularisation n'est qu'un procede de construction d'une grille initiale + else + targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); +} + //////////////////////////////////////////////////////////////////////////////////////// // Classe KWDataGridClusteringCostsBivariate diff --git a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h index 1747ac4dc..6f7cad3c9 100644 --- a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h +++ b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h @@ -138,6 +138,9 @@ class KWAttributePairStatsStudy : public KWAttributePairStats void ExportGranularizedPartsForContinuousAttribute(KWDataGrid* targetDataGrid, KWDGAttribute* sourceAttribute, KWDGAttribute* targetAttribute, int nPartileNumber, KWQuantileIntervalBuilder* quantileIntervalBuilder) const; + void ExportGranularizedPartsForSymbolAttribute(KWDataGrid* targetDataGrid, KWDGAttribute* sourceAttribute, + KWDGAttribute* targetAttribute, int nGranularity, + KWQuantileGroupBuilder* quantileGroupBuilder) const; // Variables boolean bHierarchicalMultinomialPrior; diff --git a/src/Learning/KWTest/Test.cpp b/src/Learning/KWTest/Test.cpp index 235924857..5104f1a5d 100644 --- a/src/Learning/KWTest/Test.cpp +++ b/src/Learning/KWTest/Test.cpp @@ -265,7 +265,7 @@ int main(int argc, char** argv) boolean bBigBatch; // Parametrage de l'utilisation de MPI - UseMPI(); + //DDD UseMPI(); // Initialisation de l'environnement learningProject.Begin(); diff --git a/src/Learning/KWTest/Test.h b/src/Learning/KWTest/Test.h index e3356b9bd..a1f307975 100644 --- a/src/Learning/KWTest/Test.h +++ b/src/Learning/KWTest/Test.h @@ -4,7 +4,7 @@ #pragma once -#include "PLUseMPI.h" +//DDD #include "PLUseMPI.h" #include "UserInterface.h" #include "KWContinuous.h" diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp index 84f7ec56c..81f39fc75 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp @@ -3506,7 +3506,7 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn { // Acces a la valeur dgValuePart = cast(KWDGPart*, oaValueParts.GetAt(nValue)); - assert(dgValuePart->GetValueSet()->GetValueNumber() == 1 or + assert(dgValuePart->GetValueSet()->GetTrueValueNumber() == 1 or // n'est plus garanti car la grille initiale contient un fourre-tout //(dgValuePart->GetValueSet()->GetValueNumber() == 2 and dgValuePart->GetValueSet()->GetTailValue()->GetValue() == Symbol::GetStarValue()); diff --git a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp index 348fa7beb..a187ebec3 100644 --- a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp +++ b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp @@ -34,7 +34,8 @@ int main(int argc, char** argv) Global::ActivateSignalErrorManagement(); // Choix du repertoire de lancement pour le debugage sous Windows (a commenter apres fin du debug) - SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); + //SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); + SetWindowsDebugDir("Standard", "Iris"); // Point d'arret sur l'allocation d'un bloc memoire // MemSetAllocIndexExit(17362); From f18f14a08d91755eb9576b2f3b87ac021fb49b0f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Wed, 23 Aug 2023 17:20:59 +0200 Subject: [PATCH 02/17] New class hierarchy, with KWDGSymbolValueSet sub-class of KWDGValueSet MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Corrections suites à quelques problème d'assertion, detectes apres lancement de LearningTest en mode debug - KWDGValueSet::GetTrueValueNumber: tolerance avec ValueNumber a 1 dans le cas d'une partie par defaut sans valeurs autre que la StarValue - KWDGValueSet::Check: assouplissement des controle dans le cas du groupe contenant la StarValue Mise en place de la hierarchie - KWDGValueSet - KWDGSymbolValueSet - KWDGValue - KWDGSymbolValue Propagation des impacts sur l'utilisation des nouvelles classes - nombreux impacts - KWDataPreparation - KWAttributeSubsetStats - KWDataGrid - KWDataGridCosts - KWDataGridDeployment - KWDataGridMerger - KWDataGridPostOptimizer - MODL_Coclustering - CCCoclusteringBuilder - CCCoclusteringReport - CCHierarchicalDataGrid - CCLearningProblem Hierarchie de classe KWDGSymbolValueSet et KWDGSymbolValue - finalisation d'une premiere implementation complete - gestion d'une partie par defaut dans la classe ancetre KWDGValueSet, pour uniformiser l'implementation - KWDGValueSet - IsDefaultPart - KWDGValue - IsDefaultValue - version minimaliste de KWDGSymbolValueSet, qui ne contient plus que des methodes vraiment specifiques --- .../KWAttributeSubsetStats.cpp | 4 +- src/Learning/KWDataPreparation/KWDataGrid.cpp | 527 ++++++++++-------- src/Learning/KWDataPreparation/KWDataGrid.h | 497 ++++++++++------- .../KWDataPreparation/KWDataGridCosts.cpp | 32 +- .../KWDataGridDeployment.cpp | 2 +- .../KWDataPreparation/KWDataGridManager.cpp | 50 +- .../KWDataPreparation/KWDataGridMerger.cpp | 10 +- .../KWDataGridPostOptimizer.cpp | 44 +- .../KWDataGridPostOptimizer.h | 4 +- .../KWTest/KWHierarchicalMultinomialStudy.cpp | 5 +- .../CCCoclusteringBuilder.cpp | 36 +- .../CCCoclusteringReport.cpp | 57 +- .../CCHierarchicalDataGrid.cpp | 47 +- .../CCHierarchicalDataGrid.h | 27 +- .../MODL_Coclustering/CCLearningProblem.cpp | 12 +- 15 files changed, 750 insertions(+), 604 deletions(-) diff --git a/src/Learning/KWDataPreparation/KWAttributeSubsetStats.cpp b/src/Learning/KWDataPreparation/KWAttributeSubsetStats.cpp index 147b2ea5b..befde8810 100644 --- a/src/Learning/KWDataPreparation/KWAttributeSubsetStats.cpp +++ b/src/Learning/KWDataPreparation/KWAttributeSubsetStats.cpp @@ -801,7 +801,7 @@ boolean KWAttributeSubsetStats::CreateAttributeValueSets(const KWTupleTable* tup // Creation d'une nouvelle partie mono-valeur part = dgAttribute->AddPart(); - value = part->GetValueSet()->AddValue(tuple->GetSymbolAt(0)); + value = part->GetSymbolValueSet()->AddSymbolValue(tuple->GetSymbolAt(0)); value->SetValueFrequency(tuple->GetFrequency()); } assert(attributeTupleTable.GetSize() == dgAttribute->GetPartNumber()); @@ -809,7 +809,7 @@ boolean KWAttributeSubsetStats::CreateAttributeValueSets(const KWTupleTable* tup // Initialisation de la partie par defaut, contenant la modalite speciale // Compte-tenu du tri prealable des tuples, il s'agit de la derniere partie de l'attribut - dgAttribute->GetTailPart()->GetValueSet()->AddValue(Symbol::GetStarValue()); + dgAttribute->GetTailPart()->GetSymbolValueSet()->AddSymbolValue(Symbol::GetStarValue()); // Parametrage du nombre total de valeurs // Pour un attribut categoriel, l'InitialValueNumber ne contient plus la StarValue diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index 7d71692df..9a67d7918 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -943,7 +943,7 @@ void KWDataGrid::ImportDataGridStats(const KWDataGridStats* dataGridStats) int nPart; KWDGPart* part; KWDGInterval* interval; - KWDGValueSet* valueSet; + KWDGSymbolValueSet* symbolValueSet; int nValue; ObjectArray oaAttributePartitions; ObjectArray* oaPartition; @@ -1080,12 +1080,13 @@ void KWDataGrid::ImportDataGridStats(const KWDataGridStats* dataGridStats) { part = attribute->AddPart(); oaPartition->SetAt(nPart, part); - valueSet = part->GetValueSet(); + symbolValueSet = part->GetSymbolValueSet(); // Initialisation des valeurs du groupe for (nValue = attributeGrouping->GetGroupFirstValueIndexAt(nPart); nValue <= attributeGrouping->GetGroupLastValueIndexAt(nPart); nValue++) - valueSet->AddValue(attributeGrouping->GetValueAt(nValue)); + symbolValueSet->AddSymbolValue(attributeGrouping->GetValueAt(nValue)); + // Cas du groupe poubelle : memorisation de la partie au niveau de l'attribut if (nPart == attributeGrouping->GetGarbageGroupIndex()) attribute->SetGarbagePart(part); @@ -1103,17 +1104,17 @@ void KWDataGrid::ImportDataGridStats(const KWDataGridStats* dataGridStats) { part = attribute->AddPart(); oaPartition->SetAt(nPart, part); - valueSet = part->GetValueSet(); + symbolValueSet = part->GetSymbolValueSet(); // Initialisation de la valeur du groupe assert(attributeSymbolValues->GetValueAt(nPart) != Symbol::GetStarValue()); - valueSet->AddValue(attributeSymbolValues->GetValueAt(nPart)); + symbolValueSet->AddSymbolValue(attributeSymbolValues->GetValueAt(nPart)); } // Ajout de la valeur speciale assert(attributeSymbolValues->GetValueNumber() > 0); - valueSet = attribute->GetTailPart()->GetValueSet(); - valueSet->AddValue(Symbol::GetStarValue()); + symbolValueSet = attribute->GetTailPart()->GetSymbolValueSet(); + symbolValueSet->AddSymbolValue(Symbol::GetStarValue()); } } @@ -1189,7 +1190,7 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const int nPart; KWDGPart* part; KWDGInterval* interval; - KWDGValueSet* valueSet; + KWDGSymbolValueSet* symbolValueSet; int nValue; int nValueNumber; int nSuppressedValueNumber; @@ -1198,7 +1199,7 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const IntVector ivPartIndexes; NumericKeyDictionary nkdPartIndexes; KWSortableIndex* partIndex; - KWDGValueSet* cleanedValueSet; + KWDGSymbolValueSet* cleanedSymbolValueSet; require(Check()); require(dataGridStats != NULL); @@ -1206,7 +1207,7 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const dataGridStats->SetGranularity(nGranularity); - cleanedValueSet = NULL; + cleanedSymbolValueSet = NULL; // Creation des attributs sources de la grille for (nAttribute = 0; nAttribute < GetAttributeNumber(); nAttribute++) @@ -1236,8 +1237,8 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const part = attribute->GetHeadPart(); while (part != NULL) { - valueSet = part->GetValueSet(); - nValueNumber += valueSet->GetTrueValueNumber(); + symbolValueSet = part->GetSymbolValueSet(); + nValueNumber += symbolValueSet->GetValueNumber(); // Calcul du nombre eventuel de modalites qui seront nettoyees (non memorisees pour // l'affichage) En presence d'un fourre-tout, les modalites du fourre-tout autres que @@ -1247,7 +1248,7 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const // groupe poubelle, ce groupe poubelle est represente par la modalite la plus frequente // + StarValue Cas de la partie poubelle en l'absence de fourre-tout if (attribute->GetGarbagePart() == part and attribute->GetCatchAllValueNumber() == 0) - nSuppressedValueNumber = valueSet->GetTrueValueNumber() - 1; + nSuppressedValueNumber = symbolValueSet->GetValueNumber() - 1; attribute->GetNextPart(part); } @@ -1261,7 +1262,7 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const nValue = 0; while (part != NULL) { - valueSet = part->GetValueSet(); + symbolValueSet = part->GetSymbolValueSet(); // Regles d'affectation de la StarValue // - dans le fourre-tout en presence d'un fourre-tout et en l'absence d'un groupe @@ -1278,14 +1279,14 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const attribute->GetGarbageModalityNumber() > 0) { // Creation d'un valueSet nettoye sans la StarValue - cleanedValueSet = new KWDGValueSet; - value = valueSet->GetHeadValue(); + cleanedSymbolValueSet = cast(KWDGSymbolValueSet*, symbolValueSet->Create()); + value = symbolValueSet->GetHeadValue(); while (value != NULL) { // Cas d'une modalite differente de la StarValue - if (value->GetValue() != Symbol::GetStarValue()) - cleanedValueSet->AddValue(value->GetValue()); - valueSet->GetNextValue(value); + if (value->GetSymbolValue() != Symbol::GetStarValue()) + cleanedSymbolValueSet->AddSymbolValue(value->GetSymbolValue()); + symbolValueSet->GetNextValue(value); } } @@ -1299,7 +1300,8 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const { // Creation d'un ValueSet nettoye pour un affichage reduit a la modalite // la plus frequente - cleanedValueSet = valueSet->ComputeCleanedValueSet(); + cleanedSymbolValueSet = + cast(KWDGSymbolValueSet*, symbolValueSet->ComputeCleanedValueSet()); } } @@ -1312,29 +1314,29 @@ void KWDataGrid::ExportDataGridStats(KWDataGridStats* dataGridStats) const attributeGrouping->SetGroupFirstValueIndexAt(nPart, nValue); // Cas ou une version nettoyee du valueSet de la partie a ete cree - if (cleanedValueSet != NULL) + if (cleanedSymbolValueSet != NULL) { - value = cleanedValueSet->GetHeadValue(); + value = cleanedSymbolValueSet->GetHeadValue(); while (value != NULL) { - attributeGrouping->SetValueAt(nValue, value->GetValue()); + attributeGrouping->SetValueAt(nValue, value->GetSymbolValue()); nValue++; - cleanedValueSet->GetNextValue(value); + cleanedSymbolValueSet->GetNextValue(value); } // Nettoyage - delete cleanedValueSet; - cleanedValueSet = NULL; + delete cleanedSymbolValueSet; + cleanedSymbolValueSet = NULL; } // Sinon else { - value = valueSet->GetHeadValue(); + value = symbolValueSet->GetHeadValue(); while (value != NULL) { - attributeGrouping->SetValueAt(nValue, value->GetValue()); + attributeGrouping->SetValueAt(nValue, value->GetSymbolValue()); nValue++; - valueSet->GetNextValue(value); + symbolValueSet->GetNextValue(value); } } @@ -1583,7 +1585,7 @@ void KWDataGrid::WriteAttributeParts(ostream& ost) const break; } else - ost << "\t" << value->GetValue(); + ost << "\t" << value->GetSymbolValue(); part->GetValueSet()->GetNextValue(value); } } @@ -2563,10 +2565,10 @@ void KWDGAttribute::BuildIndexingStructure() while (value != NULL) { // Ajout de la partie avec la valeur pour cle - nkdParts.SetAt(value->GetValue().GetNumericKey(), part); + nkdParts.SetAt(value->GetNumericKeyValue(), part); // Memorisation de la partie associe a la valeur speciale - if (value->GetValue() == Symbol::GetStarValue()) + if (value->GetSymbolValue() == Symbol::GetStarValue()) starValuePart = part; // Valeur suivante @@ -2773,7 +2775,6 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con KWDGPart* part; KWDGPart* otherPart; KWDGValue* value; - Symbol sFirstValue; KWDGVarPartValue* varPartValue; KWDGPart* firstVarPartValue; NumericKeyDictionary nkdOtherPartsPerValue; @@ -2834,7 +2835,7 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con value = otherPart->GetValueSet()->GetHeadValue(); while (value != NULL) { - nkdOtherPartsPerValue.SetAt(value->GetValue().GetNumericKey(), otherPart); + nkdOtherPartsPerValue.SetAt(value->GetNumericKeyValue(), otherPart); otherPart->GetValueSet()->GetNextValue(value); } @@ -2847,8 +2848,9 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con while (part != NULL) { // Recherche de l'autre partie sur la base de la premiere valeur de la partie en cours - sFirstValue = part->GetValueSet()->GetHeadValue()->GetValue(); - otherPart = cast(KWDGPart*, nkdOtherPartsPerValue.Lookup(sFirstValue.GetNumericKey())); + otherPart = + cast(KWDGPart*, nkdOtherPartsPerValue.Lookup( + part->GetValueSet()->GetHeadValue()->GetNumericKeyValue())); // Pas d'inclusion si partie non trouvee ou non inclusante if (otherPart == NULL or not part->IsSubPart(otherPart)) @@ -3148,12 +3150,12 @@ boolean KWDGAttribute::Check() const while (value != NULL) { // Recherche si la valeur est deja enregistree - searchedPart = cast(KWDGPart*, nkdCheckParts.Lookup(value->GetValue().GetNumericKey())); + searchedPart = cast(KWDGPart*, nkdCheckParts.Lookup(value->GetNumericKeyValue())); // Erreur si partie deja enregistree avec cette valeur if (searchedPart != NULL) { - part->AddError(sTmp + "Value " + value->GetValue() + + part->AddError(sTmp + "Value " + value->GetObjectLabel() + " already belongs to part " + part->GetObjectLabel()); bOk = false; break; @@ -3162,10 +3164,10 @@ boolean KWDGAttribute::Check() const else { // Ajout de la partie avec la valeur pour cle - nkdCheckParts.SetAt(value->GetValue().GetNumericKey(), part); + nkdCheckParts.SetAt(value->GetNumericKeyValue(), part); // Test si valeur speciale - if (value->GetValue() == Symbol::GetStarValue()) + if (value->GetSymbolValue() == Symbol::GetStarValue()) bStarValueFound = true; // Valeur suivante @@ -3426,7 +3428,7 @@ void KWDGAttribute::Write(ostream& ost) const ost << "\t" << (GetInitialValueNumber() > KWFrequencyTable::GetMinimumNumberOfModalitiesForGarbage()) << "\t" << (GetGarbagePart() != NULL); if (GetGarbagePart() != NULL) - ost << "\t" << cast(KWDGValueSet*, GetGarbagePart())->GetTrueValueNumber() << "\n"; + ost << "\t" << cast(KWDGValueSet*, GetGarbagePart())->GetValueNumber() << "\n"; else ost << "\n"; // Parties de l'attribut @@ -3512,7 +3514,7 @@ KWDGAttribute* KWDGAttribute::CreateTestSymbolAttribute(int nValueNumber, int nP KWDGAttribute* attribute; int nValue; KWDGPart* part; - KWDGValueSet* valueSet; + KWDGSymbolValueSet* symbolValueSet; KWDGValue* value; require(nValueNumber > 0); @@ -3538,10 +3540,10 @@ KWDGAttribute* KWDGAttribute::CreateTestSymbolAttribute(int nValueNumber, int nP if (part == NULL) part = attribute->GetHeadPart(); check(part); - valueSet = part->GetValueSet(); + symbolValueSet = part->GetSymbolValueSet(); // Ajout d'une valeur a la partie courant - value = valueSet->AddValue((Symbol)(sValuePrefix + IntToString(nValue))); + value = symbolValueSet->AddSymbolValue((Symbol)(sValuePrefix + IntToString(nValue))); value->SetValueFrequency(0); nValue++; } @@ -3551,8 +3553,8 @@ KWDGAttribute* KWDGAttribute::CreateTestSymbolAttribute(int nValueNumber, int nP if (part == NULL) part = attribute->GetHeadPart(); check(part); - valueSet = part->GetValueSet(); - value = valueSet->AddValue(Symbol::GetStarValue()); + symbolValueSet = part->GetSymbolValueSet(); + value = symbolValueSet->AddSymbolValue(Symbol::GetStarValue()); value->SetValueFrequency(0); ensure(attribute->Check()); @@ -3705,7 +3707,7 @@ KWDGPart::KWDGPart() tailCell = NULL; nCellNumber = 0; interval = NULL; - valueSet = NULL; + symbolValueSet = NULL; // CH IV Begin varPartSet = NULL; // CH IV End @@ -3715,15 +3717,13 @@ KWDGPart::~KWDGPart() { if (interval != NULL) delete interval; - if (valueSet != NULL) - delete valueSet; + if (symbolValueSet != NULL) + delete symbolValueSet; // CH IV Begin if (varPartSet != NULL) - { delete varPartSet; - varPartSet = NULL; - } // CH IV End + // Reinitialisation pour faciliter le debug debug(attribute = NULL); debug(prevPart = NULL); @@ -3733,7 +3733,8 @@ KWDGPart::~KWDGPart() debug(tailCell = NULL); debug(nCellNumber = 0); debug(interval = NULL); - debug(valueSet = NULL); + debug(symbolValueSet = NULL); + debug(varPartSet = NULL); } void KWDGPart::SetPartType(int nValue) @@ -3746,7 +3747,7 @@ void KWDGPart::SetPartType(int nValue) if (nValue == KWType::Continuous) interval = NewInterval(); else if (nValue == KWType::Symbol) - valueSet = NewValueSet(); + symbolValueSet = NewSymbolValueSet(); else varPartSet = NewVarPartSet(); // CH IV End @@ -3844,7 +3845,7 @@ boolean KWDGPart::Check() const // Verification de l'ensemble de valeurs else if (GetPartType() == KWType::Symbol) { - bOk = bOk and valueSet->Check(); + bOk = bOk and symbolValueSet->Check(); // Verification de la compatibilite entre l'effectif de la partie // et l'effectif cumule de ses valeurs @@ -3856,7 +3857,7 @@ boolean KWDGPart::Check() const // Cela permet egalement de verifier la validite d'une grille // construite pour le deploiement de modele, qui n'a pas besoin // des effectifs par valeur. - nTotalValueFrequency = valueSet->ComputeTotalFrequency(); + nTotalValueFrequency = symbolValueSet->ComputeTotalFrequency(); if (bOk and GetPartFrequency() > 0 and nTotalValueFrequency > 0 and GetPartFrequency() != nTotalValueFrequency) { @@ -3973,8 +3974,10 @@ longint KWDGPart::GetUsedMemory() const lUsedMemory = sizeof(KWDGPart); if (interval != NULL) lUsedMemory += sizeof(KWDGInterval); - if (valueSet != NULL) - lUsedMemory += sizeof(KWDGValueSet); + if (symbolValueSet != NULL) + lUsedMemory += sizeof(KWDGSymbolValueSet); + if (varPartSet != NULL) + lUsedMemory += sizeof(KWDGVarPartSet); return lUsedMemory; } @@ -3985,7 +3988,7 @@ void KWDGPart::Write(ostream& ost) const ost << GetClassLabel() << "\t" << GetObjectLabel() << "\t" << GetPartFrequency() << "\n"; // Valeurs et cellules de la partie - if (GetPartType() == KWType::Symbol and valueSet->GetTrueValueNumber() > 0) + if (GetPartType() == KWType::Symbol and symbolValueSet->GetValueNumber() > 0) WriteValues(ost); if (GetPartType() == KWType::VarPart and varPartSet->GetVarPartNumber() > 0) @@ -4000,7 +4003,7 @@ void KWDGPart::WriteValues(ostream& ost) const // Des valeurs sont a afficher uniquement dans le cas symbolique et VarPart // (l'intervalle est le libelle de la partie dans le cas continu) if (GetPartType() == KWType::Symbol) - valueSet->WriteValues(ost); + symbolValueSet->WriteValues(ost); else if (GetPartType() == KWType::VarPart) varPartSet->WriteVarParts(ost); } @@ -4034,7 +4037,7 @@ const ALString KWDGPart::GetClassLabel() const if (GetPartType() == KWType::Continuous) return interval->GetClassLabel(); else if (GetPartType() == KWType::Symbol) - return valueSet->GetClassLabel(); + return symbolValueSet->GetClassLabel(); // CH IV Begin else if (GetPartType() == KWType::VarPart) return varPartSet->GetClassLabel(); @@ -4048,7 +4051,7 @@ const ALString KWDGPart::GetObjectLabel() const if (GetPartType() == KWType::Continuous) return interval->GetObjectLabel(); else if (GetPartType() == KWType::Symbol) - return valueSet->GetObjectLabel(); + return symbolValueSet->GetObjectLabel(); // CH IV Begin else if (GetPartType() == KWType::VarPart) return varPartSet->GetObjectLabel(); @@ -4062,9 +4065,9 @@ KWDGInterval* KWDGPart::NewInterval() const return new KWDGInterval; } -KWDGValueSet* KWDGPart::NewValueSet() const +KWDGSymbolValueSet* KWDGPart::NewSymbolValueSet() const { - return new KWDGValueSet; + return new KWDGSymbolValueSet; } // CH IV Begin KWDGVarPartSet* KWDGPart::NewVarPartSet() const @@ -4141,8 +4144,8 @@ int KWDGPartSymbolCompare(const void* elem1, const void* elem2) else if (part2->GetValueSet()->GetHeadValue() == NULL) return 1; else - return part1->GetValueSet()->GetHeadValue()->GetValue().CompareValue( - part2->GetValueSet()->GetHeadValue()->GetValue()); + return part1->GetValueSet()->GetHeadValue()->GetSymbolValue().CompareValue( + part2->GetValueSet()->GetHeadValue()->GetSymbolValue()); } int KWDGPartSymbolCompareDecreasingFrequency(const void* elem1, const void* elem2) @@ -4356,39 +4359,36 @@ const ALString KWDGInterval::GetObjectLabel() const ////////////////////////////////////////////////////////////////////////////// // Classe KWDGValueSet -KWDGValue* KWDGValueSet::AddValue(const Symbol& sValue) +void KWDGValueSet::CopyFrom(const KWDGValueSet* sourceValueSet) { KWDGValue* value; + KWDGValue* valueCopy; - // Creation de la valeur - value = NewValue(sValue); + require(sourceValueSet != NULL); + require(sourceValueSet->GetValueType() == GetValueType()); - // Ajout en fin de la liste des valeurs - if (headValue == NULL) - headValue = value; - if (tailValue != NULL) + // Nettoyage des valeurs actuelles + DeleteAllValues(); + + // Recopie de la liste de valeurs source + value = sourceValueSet->GetHeadValue(); + while (value != NULL) { - tailValue->nextValue = value; - value->prevValue = tailValue; + valueCopy = AddValueCopy(value); + valueCopy->SetValueFrequency(value->GetValueFrequency()); + sourceValueSet->GetNextValue(value); } - tailValue = value; - - // Partie par defaut si la valeur est la valeur par defaut - if (sValue == Symbol::GetStarValue()) - bIsDefaultPart = true; - // Incrementation du nombre de valeurs sinon - else - nValueNumber++; + bIsDefaultPart = sourceValueSet->IsDefaultPart(); - // On retourne la valeur cree - return value; + // Pour garantir la valeur correcte de nValueNumber si le sourceValueSet a ete compresse avant la copie + nValueNumber = sourceValueSet->nValueNumber; } void KWDGValueSet::DeleteValue(KWDGValue* value) { require(value != NULL); - // Supression de la liste des valuees + // Supression de la liste des values if (value->prevValue != NULL) value->prevValue->nextValue = value->nextValue; if (value->nextValue != NULL) @@ -4399,7 +4399,7 @@ void KWDGValueSet::DeleteValue(KWDGValue* value) tailValue = value->prevValue; // Partie "standard" si la valeur detruite est la valeur par defaut - if (value->GetValue() == Symbol::GetStarValue()) + if (value->IsDefaultValue()) bIsDefaultPart = false; // Decrementation du nombre de valeurs sinon else @@ -4452,69 +4452,6 @@ boolean KWDGValueSet::CheckValue(KWDGValue* value) const return bOk; } -void KWDGValueSet::CompressValueSet() -{ - int nCurrentValueNumber; - int nCurrentPartFrequency; - KWDGValue* value; - - require(bIsDefaultPart); - - // Memorisation des statistique - nCurrentValueNumber = nValueNumber; - nCurrentPartFrequency = ComputeTotalFrequency(); - - // Remplacement de l'ensemble des valeurs par une seule valeur - DeleteAllValues(); - value = AddValue(Symbol::GetStarValue()); - value->SetValueFrequency(nCurrentPartFrequency); - nValueNumber = nCurrentValueNumber; -} - -KWDGValueSet* KWDGValueSet::ConvertToCleanedValueSet() -{ - int nCurrentPartFrequency; - KWDGValue* value; - KWDGValueSet* valueSet; - - require(bIsDefaultPart); - - // Memorisation des statistique - nCurrentPartFrequency = ComputeTotalFrequency(); - valueSet = new KWDGValueSet; - valueSet->CopyFrom(this); - - // Remplacement de l'ensemble des valeurs par une seule valeur + modalite StarValue - DeleteAllValues(); - value = AddValue(valueSet->GetHeadValue()->GetValue()); - value->SetValueFrequency(valueSet->GetHeadValue()->GetValueFrequency()); - value = AddValue(Symbol::GetStarValue()); - value->SetValueFrequency(nCurrentPartFrequency - valueSet->GetHeadValue()->GetValueFrequency()); - - // Suppression dans le fourre tout des valeurs conservees dans la partie granularisee (pour eviter la redondance - // des modalites) - valueSet->DeleteValue(valueSet->GetHeadValue()); - valueSet->DeleteValue(valueSet->GetTailValue()); - - return valueSet; -} - -KWDGValueSet* KWDGValueSet::ComputeCleanedValueSet() const -{ - KWDGValueSet* valueSet; - KWDGValue* value; - - // Creation d'un nouveau ValueSet - valueSet = new KWDGValueSet; - - value = valueSet->AddValue(GetHeadValue()->GetValue()); - value->SetValueFrequency(GetHeadValue()->GetValueFrequency()); - value = valueSet->AddValue(Symbol::GetStarValue()); - value->SetValueFrequency(ComputeTotalFrequency() - GetHeadValue()->GetValueFrequency()); - - return valueSet; -} - void KWDGValueSet::ExportValues(ObjectArray* oaValues) const { KWDGValue* value; @@ -4538,7 +4475,7 @@ boolean KWDGValueSet::IsSubValueSet(const KWDGValueSet* otherValueSet) const KWDGValue* value; // On doit avoir moins de valeurs - if (GetTrueValueNumber() > otherValueSet->GetTrueValueNumber()) + if (GetValueNumber() > otherValueSet->GetValueNumber()) bOk = false; // Sinon, on teste effectivement l'inclusion else @@ -4547,7 +4484,7 @@ boolean KWDGValueSet::IsSubValueSet(const KWDGValueSet* otherValueSet) const value = otherValueSet->GetHeadValue(); while (value != NULL) { - nkdOtherValues.SetAt(value->GetValue().GetNumericKey(), value); + nkdOtherValues.SetAt(value->GetNumericKeyValue(), value); otherValueSet->GetNextValue(value); } @@ -4555,7 +4492,7 @@ boolean KWDGValueSet::IsSubValueSet(const KWDGValueSet* otherValueSet) const value = GetHeadValue(); while (value != NULL) { - if (nkdOtherValues.Lookup(value->GetValue().GetNumericKey()) == NULL) + if (nkdOtherValues.Lookup(value->GetNumericKeyValue()) == NULL) { bOk = false; break; @@ -4604,30 +4541,6 @@ void KWDGValueSet::Import(KWDGValueSet* sourceValueSet) ensure(Check()); } -void KWDGValueSet::CopyFrom(const KWDGValueSet* sourceValueSet) -{ - KWDGValue* value; - KWDGValue* valueCopy; - - require(sourceValueSet != NULL); - - // Nettoyage des valeurs actuelles - DeleteAllValues(); - - // Recopie de la liste de valeurs source - value = sourceValueSet->GetHeadValue(); - while (value != NULL) - { - valueCopy = AddValue(value->GetValue()); - valueCopy->SetValueFrequency(value->GetValueFrequency()); - sourceValueSet->GetNextValue(value); - } - bIsDefaultPart = sourceValueSet->IsDefaultPart(); - - // Pour garantir la valeur correcte de nValueNumber si le sourceValueSet a ete compresse avant la copie - nValueNumber = sourceValueSet->nValueNumber; -} - void KWDGValueSet::UpgradeFrom(const KWDGValueSet* sourceValueSet) { KWDGValue* value; @@ -4643,7 +4556,7 @@ void KWDGValueSet::UpgradeFrom(const KWDGValueSet* sourceValueSet) value = sourceValueSet->GetHeadValue(); while (value != NULL) { - valueUpgrade = AddValue(value->GetValue()); + valueUpgrade = AddValueCopy(value); valueUpgrade->SetValueFrequency(value->GetValueFrequency()); sourceValueSet->GetNextValue(value); } @@ -4670,13 +4583,13 @@ boolean KWDGValueSet::AreValuesSorted() const // Parcours des valeurs while (value2 != NULL) { - if (value1->GetValue() == Symbol::GetStarValue()) + if (value1->IsDefaultValue()) { bIsSorted = false; break; } - if (value2->GetValue() == Symbol::GetStarValue()) + if (value2->IsDefaultValue()) { if (value2 != GetTailValue()) bIsSorted = false; @@ -4697,17 +4610,37 @@ boolean KWDGValueSet::AreValuesSorted() const return bIsSorted; } +boolean KWDGValueSet::IsSubPartValues(const KWDGPartValues* otherPartValues) const +{ + return IsSubValueSet(cast(KWDGValueSet*, otherPartValues)); +} + +void KWDGValueSet::Import(KWDGPartValues* sourcePartValues) +{ + Import(cast(KWDGValueSet*, sourcePartValues)); +} + +void KWDGValueSet::UpgradeFrom(const KWDGPartValues* sourcePartValues) +{ + UpgradeFrom(cast(KWDGValueSet*, sourcePartValues)); +} + +void KWDGValueSet::CopyFrom(const KWDGPartValues* sourcePartValues) +{ + CopyFrom(cast(KWDGValueSet*, sourcePartValues)); +} + boolean KWDGValueSet::Check() const { boolean bOk = true; - boolean bStarValuePresent; + boolean bDefaultValuePresent; NumericKeyDictionary nkdCheckValues; KWDGValue* value; boolean bCheckFrequencies; ALString sTmp; // Test d'existence d'au moins une valeur - if (nValueNumber == 0) + if (GetValueNumber() == 0) { AddError("No value specified"); bOk = false; @@ -4716,30 +4649,33 @@ boolean KWDGValueSet::Check() const // Test des valeurs de la partie if (bOk) { - // On ne verifie les effectifs que si au moins un est specifie - bCheckFrequencies = ComputeTotalFrequency() > 0; + // On ne verifie les effectifs que si au moins un est specifie et si on n'est pas le groupe par defaut + // En effet, le groupe par defaut pouvant etre compresse, il peut y a voir quelques incoherences + // sur les effectifs des valeurs des valeur du groupe + bCheckFrequencies = not bIsDefaultPart and ComputeTotalFrequency() > 0; // Parcours des valeurs de la partie - bStarValuePresent = false; + bDefaultValuePresent = false; value = GetHeadValue(); while (value != NULL) { // Detection de la star value - if (value->GetValue() == Symbol::GetStarValue()) - bStarValuePresent = true; + if (value->IsDefaultValue()) + bDefaultValuePresent = true; // Erreur si partie deja enregistree avec cette valeur - if (nkdCheckValues.Lookup(value->GetValue().GetNumericKey()) != NULL) + if (nkdCheckValues.Lookup(value->GetNumericKeyValue()) != NULL) { - AddError(sTmp + "Value " + value->GetValue() + " already exists in the part"); + AddError(sTmp + value->GetClassLabel() + " " + value->GetObjectLabel() + + " already exists in the part"); bOk = false; break; } - // Erreur si effectif a 0 pour une valeur qui n'est pas la star value - else if (bCheckFrequencies and value->GetValue() != Symbol::GetStarValue() and - value->GetValueFrequency() == 0) + // Erreur si effectif a 0 pour une valeur qui n'est pas la valeur par defaut + else if (bCheckFrequencies and not value->IsDefaultValue() and value->GetValueFrequency() == 0) { - AddError(sTmp + "Value " + value->GetValue() + " should have a non-zero frequency"); + AddError(sTmp + value->GetClassLabel() + " " + value->GetObjectLabel() + + " should have a non-zero frequency"); bOk = false; break; } @@ -4747,24 +4683,22 @@ boolean KWDGValueSet::Check() const else { // Ajout de la partie avec la valeur pour cle - nkdCheckValues.SetAt(value->GetValue().GetNumericKey(), value); + nkdCheckValues.SetAt(value->GetNumericKeyValue(), value); // Valeur suivante GetNextValue(value); } } - // Test d'integrite sur la star value - if (bStarValuePresent and not bIsDefaultPart) + // Test d'integrite sur la valeur par defaut + if (bDefaultValuePresent and not bIsDefaultPart) { - AddError(sTmp + "Special grouping value " + Symbol::GetStarValue() + - " is used used in a standard part"); + AddError(sTmp + "Special default value is used used in a standard part"); bOk = false; } - else if (not bStarValuePresent and bIsDefaultPart) + else if (not bDefaultValuePresent and bIsDefaultPart) { - AddError(sTmp + "Special grouping value " + Symbol::GetStarValue() + - " is missing in default part"); + AddError(sTmp + "Special default value is missing in default part"); bOk = false; } } @@ -4786,7 +4720,7 @@ void KWDGValueSet::WriteValues(ostream& ost) const // Affichage des valeurs cout << "Values" - << "\t" << GetTrueValueNumber() << "\n"; + << "\t" << GetValueNumber() << "\n"; value = GetHeadValue(); while (value != NULL) { @@ -4812,15 +4746,15 @@ const ALString KWDGValueSet::GetObjectLabel() const dgValue = GetHeadValue(); while (dgValue != NULL) { - // On n'utilise la modalite speciale pour fabriquer le libelle - if (dgValue->GetValue() != Symbol::GetStarValue()) + // On n'utilise la valeur par defaut pour fabriquer le libelle + if (not dgValue->IsDefaultValue()) { // Prise en compte si moins de trois valeurs if (nValue < 3) { if (nValue > 0) sLabel += ", "; - sLabel += GetExternalValue(dgValue->GetValue()); + sLabel += dgValue->GetExternalValueLabel(); nValue++; } // Arret si au moins quatre valeurs @@ -4836,31 +4770,27 @@ const ALString KWDGValueSet::GetObjectLabel() const return sLabel; } -const ALString KWDGValueSet::GetExternalValue(const Symbol& sValue) const +void KWDGValueSet::AddTailValue(KWDGValue* value) { - ALString sResult; - int nLength; - char c; - int i; - - // On prend la valeur telle quelle - sResult = sValue; + require(value != NULL); + require(value->GetValueType() == GetValueType()); - // On la met entre quotes si elle contient un caractere special utilise par ObjectLabel - if (sResult.FindOneOf("'{},") != -1) + // Ajout en fin de la liste des valeurs + if (headValue == NULL) + headValue = value; + if (tailValue != NULL) { - sResult = '\''; - nLength = sValue.GetLength(); - for (i = 0; i < nLength; i++) - { - c = sValue.GetAt(i); - if (c == '\'') - sResult += '\''; - sResult += c; - } - sResult += '\''; + tailValue->nextValue = value; + value->prevValue = tailValue; } - return sResult; + tailValue = value; + + // Partie par defaut si la valeur est la valeur par defaut + if (value->IsDefaultValue()) + bIsDefaultPart = true; + // Incrementation du nombre de valeurs sinon + else + nValueNumber++; } void KWDGValueSet::InternalSortValues(CompareFunction fCompare) @@ -4879,7 +4809,7 @@ void KWDGValueSet::InternalSortValues(CompareFunction fCompare) value = GetHeadValue(); while (value != NULL) { - if (value->GetValue() != Symbol::GetStarValue()) + if (not value->IsDefaultValue()) oaValues.Add(value); else defaultValue = value; @@ -4917,24 +4847,104 @@ void KWDGValueSet::InternalSortValues(CompareFunction fCompare) } } -KWDGValue* KWDGValueSet::NewValue(const Symbol& sValue) const -{ - return new KWDGValue(sValue); -} - boolean KWDGValueSet::GetEmulated() const { return false; } ////////////////////////////////////////////////////////////////////////////// -// Classe KWDGValue +// Classe KWDGSymbolValueSet -void KWDGValue::Write(ostream& ost) const +KWDGValue* KWDGSymbolValueSet::AddSymbolValue(const Symbol& sValue) { - ost << sSymbolValue << "\t" << nValueFrequency; + KWDGValue* value; + + // Creation de la valeur + value = NewSymbolValue(sValue); + + // Ajout en fin de la liste des valeurs + AddTailValue(value); + + // On retourne la valeur cree + return value; +} + +KWDGValue* KWDGSymbolValueSet::AddValueCopy(const KWDGValue* sourceValue) +{ + return AddSymbolValue(cast(KWDGSymbolValue*, sourceValue)->GetSymbolValue()); +} + +void KWDGSymbolValueSet::CompressValueSet() +{ + int nCurrentValueNumber; + int nCurrentPartFrequency; + KWDGValue* value; + + require(bIsDefaultPart); + + // Memorisation des statistique + nCurrentValueNumber = nValueNumber; + nCurrentPartFrequency = ComputeTotalFrequency(); + + // Remplacement de l'ensemble des valeurs par une seule valeur + DeleteAllValues(); + value = AddSymbolValue(Symbol::GetStarValue()); + value->SetValueFrequency(nCurrentPartFrequency); + nValueNumber = nCurrentValueNumber; } +KWDGValueSet* KWDGSymbolValueSet::ConvertToCleanedValueSet() +{ + int nCurrentPartFrequency; + KWDGValue* value; + KWDGValueSet* valueSet; + + require(bIsDefaultPart); + + // Memorisation des statistique + nCurrentPartFrequency = ComputeTotalFrequency(); + valueSet = Clone(); + + // Remplacement de l'ensemble des valeurs par une seule valeur + modalite StarValue + DeleteAllValues(); + value = AddSymbolValue(valueSet->GetHeadValue()->GetSymbolValue()); + value->SetValueFrequency(valueSet->GetHeadValue()->GetValueFrequency()); + value = AddSymbolValue(Symbol::GetStarValue()); + value->SetValueFrequency(nCurrentPartFrequency - valueSet->GetHeadValue()->GetValueFrequency()); + + // Suppression dans le fourre tout des valeurs conservees dans la partie granularisee + // (pour eviter la redondance des modalites) + valueSet->DeleteValue(valueSet->GetHeadValue()); + valueSet->DeleteValue(valueSet->GetTailValue()); + + return valueSet; +} + +KWDGValueSet* KWDGSymbolValueSet::ComputeCleanedValueSet() const +{ + KWDGSymbolValueSet* symbolValueSet; + KWDGValue* value; + + // Creation d'un nouveau ValueSet + symbolValueSet = cast(KWDGSymbolValueSet*, Create()); + + // Remplacement de l'ensemble des valeurs par une seule valeur + modalite StarValue + value = symbolValueSet->AddSymbolValue(GetHeadValue()->GetSymbolValue()); + value->SetValueFrequency(GetHeadValue()->GetValueFrequency()); + value = symbolValueSet->AddSymbolValue(Symbol::GetStarValue()); + value->SetValueFrequency(ComputeTotalFrequency() - GetHeadValue()->GetValueFrequency()); + + return symbolValueSet; +} + +KWDGValue* KWDGSymbolValueSet::NewSymbolValue(const Symbol& sValue) const +{ + return new KWDGSymbolValue(sValue); +} + +////////////////////////////////////////////////////////////////////////////// +// Classe KWDGValue + int KWDGValueCompareDecreasingFrequency(const void* elem1, const void* elem2) { KWDGValue* value1; @@ -4953,7 +4963,52 @@ int KWDGValueCompareDecreasingFrequency(const void* elem1, const void* elem2) if (nCompare != 0) return nCompare; else - return value1->GetValue().CompareValue(value2->GetValue()); + return value1->CompareValue(value2); +} + +////////////////////////////////////////////////////////////////////////////// +// Classe KWDGSymbolValue + +void KWDGSymbolValue::Write(ostream& ost) const +{ + ost << sSymbolValue << "\t" << nValueFrequency; +} + +const ALString KWDGSymbolValue::GetExternalValueLabel() const +{ + ALString sResult; + int nLength; + char c; + int i; + + // On prend la valeur telle quelle + sResult = sSymbolValue; + + // On la met entre quotes si elle contient un caractere special utilise par ObjectLabel + if (sResult.FindOneOf("'{},") != -1) + { + sResult = '\''; + nLength = sSymbolValue.GetLength(); + for (i = 0; i < nLength; i++) + { + c = sSymbolValue.GetAt(i); + if (c == '\'') + sResult += '\''; + sResult += c; + } + sResult += '\''; + } + return sResult; +} + +const ALString KWDGSymbolValue::GetClassLabel() const +{ + return "Value"; +} + +const ALString KWDGSymbolValue::GetObjectLabel() const +{ + return sSymbolValue.GetValue(); } ////////////////////////////////////////////////////////////////////////////// diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index f3953d36e..9f2e20d90 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -13,8 +13,8 @@ class KWDGValue; class KWDGCell; // CH IV Begin class KWDGPartValues; -class KWDGGenericValueSet; -class KWDGGenericValue; +class KWDGSymbolValueSet; +class KWDGSymbolValue; class KWDGVarPartSet; class KWDGVarPartValue; class KWDGInnerAttributes; @@ -780,6 +780,7 @@ class KWDGPart : public Object // Acces aux valeurs, selon le type KWDGInterval* GetInterval() const; KWDGValueSet* GetValueSet() const; + KWDGSymbolValueSet* GetSymbolValueSet() const; // CH IV Begin KWDGVarPartSet* GetVarPartSet() const; // CH IV End @@ -844,7 +845,7 @@ class KWDGPart : public Object // Methodes de creations virtuelles, permettant de specialiser la composition d'une partie // dans une sous-classe virtual KWDGInterval* NewInterval() const; - virtual KWDGValueSet* NewValueSet() const; + virtual KWDGSymbolValueSet* NewSymbolValueSet() const; // CH IV Begin virtual KWDGVarPartSet* NewVarPartSet() const; // CH IV End @@ -876,7 +877,7 @@ class KWDGPart : public Object // Gestion des valeurs de la parties // Un seul objet, correspondant au type de la partie, doit etre non nul KWDGInterval* interval; - KWDGValueSet* valueSet; + KWDGSymbolValueSet* symbolValueSet; // CH IV Begin KWDGVarPartSet* varPartSet; // CH IV End @@ -932,35 +933,57 @@ class KWDGPartValues : public Object void Write(ostream& ost) const override = 0; }; -/*DDD ////////////////////////////////////////////////////////////////////////////// -// Classe KWDGGenericValueSet +// Classe KWDGValueSet // Sous-classe virtuelle de KWDGPartValues dediee aux groupes de valeurs -// pour mutualiser la gestion des valeur de type Symbol ou VarPart -class KWDGGenericValueSet : public KWDGPartValues +// pour mutualiser la gestion des valeurs de type Symbol ou VarPart +class KWDGValueSet : public KWDGPartValues { public: + // Constructeur + KWDGValueSet(); + ~KWDGValueSet(); + + // Type de valeur gere + virtual int GetValueType() const = 0; + + // Creation + virtual KWDGValueSet* Create() const = 0; + + // Copie + void CopyFrom(const KWDGValueSet* sourceValueSet); + + // Duplication + KWDGValueSet* Clone() const; + //////////////////////////////////////////////////////////////// // Gestion des valeurs de la parties sous forme de liste // Memoire: les valeurs appartiennent a la partie + // Creation d'une valeur a partir de la copie d'une valeur existante et ajout en fin de liste + // Renvoie la valeur cree + virtual KWDGValue* AddValueCopy(const KWDGValue* sourceValue) = 0; + // Destruction d'une valeur de la liste - void DeleteValue(KWDGGenericValue* value); + void DeleteValue(KWDGValue* value); // Destruction de toutes les valeurs void DeleteAllValues(); // Test de validite d'une valeur (si elle appartient a la partie) - boolean CheckValue(KWDGGenericValue* value) const; + boolean CheckValue(KWDGValue* value) const; + + // Test si la partie est la partie par defaut + boolean IsDefaultPart() const; // Nombre de valeurs int GetValueNumber() const; // Parcours de tous les valeurs - KWDGGenericValue* GetHeadValue() const; - KWDGGenericValue* GetTailValue() const; - void GetNextValue(KWDGGenericValue*& value) const; - void GetPrevValue(KWDGGenericValue*& value) const; + KWDGValue* GetHeadValue() const; + KWDGValue* GetTailValue() const; + void GetNextValue(KWDGValue*& value) const; + void GetPrevValue(KWDGValue*& value) const; /////////////////////////////// // Services divers @@ -970,20 +993,17 @@ class KWDGGenericValueSet : public KWDGPartValues // Test si l'ensemble de valeur est inclus dans l'autre ensemble de valeurs en parametres // On ne tient pas compte de la StarValue, et on effectue un test d'inclusion exhaustif pour toutes les valeurs - boolean IsSubValueSet(const KWDGGenericValueSet* otherValueSet) const; + boolean IsSubValueSet(const KWDGValueSet* otherValueSet) const; // Calcul de l'effectif cumule des valeurs int ComputeTotalFrequency() const; // Import des valeurs d'une partie source, devant etre disjointe de la premiere // partie. La partie source est reinitialise - void Import(KWDGGenericValueSet* sourceValueSet); - - // Copie - void CopyFrom(const KWDGGenericValueSet* sourceValueSet); + void Import(KWDGValueSet* sourceValueSet); // Ajout de nouvelles valeurs recopiees depuis une source - void UpgradeFrom(const KWDGGenericValueSet* sourceValueSet); + void UpgradeFrom(const KWDGValueSet* sourceValueSet); // Tri des valeurs par effectif decroissant, pour preparer l'affichage void SortValues(); @@ -992,6 +1012,12 @@ class KWDGGenericValueSet : public KWDGPartValues // Couteux, a utiliser essentiellement dans les assertions boolean AreValuesSorted() const; + // Redefinition des methodes virtuelles + boolean IsSubPartValues(const KWDGPartValues* otherPartValues) const override; + void Import(KWDGPartValues* sourcePartValues) override; + void UpgradeFrom(const KWDGPartValues* sourcePartValues) override; + void CopyFrom(const KWDGPartValues* sourcePartValues) override; + // Controle d'integrite boolean Check() const override; @@ -1006,6 +1032,9 @@ class KWDGGenericValueSet : public KWDGPartValues /////////////////////////////// ///// Implementation protected: + // Ajout d'une valeur en fin de liste + void AddTailValue(KWDGValue* value); + // Tri des valeurs selon une fonction de tri // La valeur speciale est toujours mise en dernier, independament du critere de tri void InternalSortValues(CompareFunction fCompare); @@ -1014,143 +1043,86 @@ class KWDGGenericValueSet : public KWDGPartValues virtual boolean GetEmulated() const; // Gestion de la liste doublement chainee des cellules - KWDGGenericValue* headValue; - KWDGGenericValue* tailValue; + KWDGValue* headValue; + KWDGValue* tailValue; int nValueNumber; -}; -*/ - -////////////////////////////////////////////////////////////////////////////// -// Classe KWGenericValue -// Classe virtuelle pour mutualiser la gestion des valeur de type Sumbol ou VarPart -class KWDGGenericValue : public Object -{ -public: - // Effectif lie a la partie de variable, qui est gere par la partie de variable elle-meme - virtual void SetVarPartFrequency(int nFrequency) = 0; - virtual int GetVarPartFrequency() const = 0; - - // Affichage - void Write(ostream& ost) const override = 0; - - /////////////////////////////// - ///// Implementation -protected: - friend class KWDGGenericValueSet; - - // Attributs - KWDGVarPartValue* prevVarPartValue; - KWDGVarPartValue* nextVarPartValue; + boolean bIsDefaultPart; }; ////////////////////////////////////////////////////////////////////////////// -// Classe KWDGInterval -// Intervalle de valeurs -class KWDGInterval : public KWDGPartValues +// Classe KWDGValue +// Classe virtuelle pour mutualiser la gestion des valeur de type Symbol ou VarPart +class KWDGValue : public Object { public: // Constructeur - KWDGInterval(); - ~KWDGInterval(); - - // Type de valeur gere par la partie - int GetValueType() const; - - // Borne inf de l'intervalle - // (doit etre egal a KWDGInterval::GetMinLowerBound pour le premier intervalle) - void SetLowerBound(Continuous cValue); - Continuous GetLowerBound() const; - - // Borne sup de l'intervalle - // (doit etre egal a KWDGInterval::GetMaxUpperBound pour le dernier intervalle) - void SetUpperBound(Continuous cValue); - Continuous GetUpperBound() const; - - // Valeurs extremes des bornes des intervalles - static Continuous GetMinLowerBound(); - static Continuous GetMaxUpperBound(); + KWDGValue(); + ~KWDGValue(); - /////////////////////////////// - // Services divers + // Type de valeur + virtual int GetValueType() const = 0; - // Test si l'intervalle est un sous-intervalle - boolean IsSubInterval(const KWDGInterval* otherInterval) const; + // Valeur + virtual Symbol& GetSymbolValue() const; + virtual KWDGPart* GetVarPart() const; - // Import des valeurs d'un intervalle source, devant etre adjacent au premier - // intervalle. L'intervalle source est reinitialise - void Import(KWDGInterval* sourceInterval); + // Valeur en tant que cle numerique + virtual NUMERIC GetNumericKeyValue() const = 0; - // CH IV Begin - // Mise a jour des valeurs de l'intervalle a partir des valeurs d'un intervalle source, devant etre adjacent au - // premier intervalle. L'intervalle source n'est pas modifie - // CH IV Refactoring: UpgradeFrom est tres proche de Import: a mutualiser, ou remplacer Import par UpgradeFrom puis Reset??? - void UpgradeFrom(const KWDGInterval* sourceInterval); - // CH IV End - // Copie - void CopyFrom(const KWDGInterval* sourceInterval); + // Test si on est la valeur par defaut, qui fait partie de la partie par defaut + virtual boolean IsDefaultValue() const = 0; - // Redefinition des methodes virtuelles a partir des methodes specifiques - boolean IsSubPartValues(const KWDGPartValues* otherPartValues) const override; - void Import(KWDGPartValues* sourcePartValues) override; - void UpgradeFrom(const KWDGPartValues* sourcePartValues) override; - void CopyFrom(const KWDGPartValues* sourcePartValues) override; + // Effectif de la valeur + virtual void SetValueFrequency(int nFrequency) = 0; + virtual int GetValueFrequency() const = 0; - // Controle d'integrite - boolean Check() const override; + // Comparaison de valeur + virtual int CompareValue(const KWDGValue* otherValue) const = 0; // Affichage - void Write(ostream& ost) const override; + void Write(ostream& ost) const override = 0; - // Libelles utilisateur - const ALString GetClassLabel() const override; - const ALString GetObjectLabel() const override; + // Libelle externe base sur la valeur + virtual const ALString GetExternalValueLabel() const = 0; /////////////////////////////// ///// Implementation protected: - // Bornes de l'intervalle - Continuous cLowerBound; - Continuous cUpperBound; + friend class KWDGValueSet; + + // Attributs + KWDGValue* prevValue; + KWDGValue* nextValue; }; +// Comparaison de deux valeurs, par effectif decroissant +int KWDGValueCompareDecreasingFrequency(const void* elem1, const void* elem2); + ////////////////////////////////////////////////////////////////////////////// -// Classe KWDGValueSet +// Classe KWDGSymbolValueSet // Ensemble de valeurs d'une partie symbolique -class KWDGValueSet : public Object +class KWDGSymbolValueSet : public KWDGValueSet { public: // Constructeur - KWDGValueSet(); - ~KWDGValueSet(); + KWDGSymbolValueSet(); + ~KWDGSymbolValueSet(); - //////////////////////////////////////////////////////////////// - // Gestion des valeurs de la parties sous forme de liste - // Memoire: les valeurs appartiennent a la partie + // Type de valeur gere par la partie + int GetValueType() const override; + + // Creation + KWDGValueSet* Create() const override; // Creation d'une valeur et ajout en fin de liste // Renvoie la valeur cree - KWDGValue* AddValue(const Symbol& sValue); - - // Destruction d'une valeur de la liste - void DeleteValue(KWDGValue* value); - - // Destruction de toutes les valeurs - void DeleteAllValues(); - - // Test de validite d'une valeur (si elle appartient a la partie) - boolean CheckValue(KWDGValue* value) const; + KWDGValue* AddSymbolValue(const Symbol& sValue); - // Test si la partie est la partie par defaut (si elle contient la valeur speciale) - boolean IsDefaultPart() const; - - // Nombre de valeurs reelles (valeurs sauf l'eventuelle valeur speciale) - int GetTrueValueNumber() const; + // Redefinition de la methode virtuelle d'ajout de la copie d'une valeur existante + KWDGValue* AddValueCopy(const KWDGValue* sourceValue) override; - // Parcours de tous les valeurs - KWDGValue* GetHeadValue() const; - KWDGValue* GetTailValue() const; - void GetNextValue(KWDGValue*& value) const; - void GetPrevValue(KWDGValue*& value) const; + /////////////////////////////// + // Services avances // Compression des valeurs (pour raison de memoire) // La liste des valeurs est remplacee par une seule valeur (la valeur speciale StarValue) @@ -1170,41 +1142,47 @@ class KWDGValueSet : public Object KWDGValueSet* ComputeCleanedValueSet() const; /////////////////////////////// - // Services divers - - // Export des valeurs dans un tableau (initialement vide) - void ExportValues(ObjectArray* oaValues) const; - - // Test si l'ensemble de valeur est inclus dans l'autre ensemble de valeurs en parametres - // On ne tine spas compte de la StarValue, et on effectue un test d'inclusion exhaustif pour toutes les valeurs - boolean IsSubValueSet(const KWDGValueSet* otherValueSet) const; + ///// Implementation +protected: + // Methodes de creations virtuelles, permettant de specialiser la creation d'une valeur + // dans une sous-classe + virtual KWDGValue* NewSymbolValue(const Symbol& sValue) const; +}; - // Calcul de l'effectif cumule des valeurs - int ComputeTotalFrequency() const; +////////////////////////////////////////////////////////////////////////////// +// Classe KWDGSymbolValue +// Valeur symbolique appartenant a une partie de valeur +class KWDGSymbolValue : public KWDGValue +{ +public: + // Constructeur + KWDGSymbolValue(const Symbol& sValue); + ~KWDGSymbolValue(); - // Import des valeurs d'une partie source, devant etre disjointe de la premiere - // partie. La partie source est reinitialise - void Import(KWDGValueSet* sourceValueSet); + // Type de valeur + int GetValueType() const override; - // Copie - void CopyFrom(const KWDGValueSet* sourceValueSet); + // Valeur + Symbol& GetSymbolValue() const override; - // Ajout de nouvelles valeurs recopiees depuis une source - void UpgradeFrom(const KWDGValueSet* sourceValueSet); + // Valeur en tant que cle numerique + NUMERIC GetNumericKeyValue() const override; - // Tri des valeurs par effectif decroissant, pour preparer l'affichage - void SortValues(); + // Test si on est la valeur par defaut, qui fait partie de la partie par defaut + boolean IsDefaultValue() const override; - // Verification du tri des valeurs par effectif decroissant : couteux, a utiliser essentiellement dans les - // assertions - boolean AreValuesSorted() const; + // Effectif lie a la valeur + void SetValueFrequency(int nFrequency) override; + int GetValueFrequency() const override; - // Controle d'integrite - boolean Check() const override; + // Comparaison de valeur + int CompareValue(const KWDGValue* otherValue) const override; // Affichage void Write(ostream& ost) const override; - void WriteValues(ostream& ost) const; + + // Libelle externe base sur la valeur + const ALString GetExternalValueLabel() const override; // Libelles utilisateur const ALString GetClassLabel() const override; @@ -1213,64 +1191,80 @@ class KWDGValueSet : public Object /////////////////////////////// ///// Implementation protected: - // Nom externe d'une valeur, de facon a assurer que l'unicite des ObjectLabel - // Les valeurs sont entourees de quotes, si elle contiennent un des caracteres impliquee dans la construction du - // ObjectLabel - const ALString GetExternalValue(const Symbol& sValue) const; - - // Tri des valeurs selon une fonction de tri - // La valeur speciale est toujours mise en dernier, independament du critere de tri - void InternalSortValues(CompareFunction fCompare); - - // Methodes de creations virtuelles, permettant de specialiser la creation d'une valeur - // dans une sous-classe - virtual KWDGValue* NewValue(const Symbol& sValue) const; - - // Methode indiquant si les donnees sont emulee - virtual boolean GetEmulated() const; - - // Gestion de la liste doublement chainee des cellules - KWDGValue* headValue; - KWDGValue* tailValue; - int nValueNumber; - boolean bIsDefaultPart; + mutable Symbol sSymbolValue; + int nValueFrequency; }; ////////////////////////////////////////////////////////////////////////////// -// Classe KWDGValue -// Valeur symbolique appartenant a une partie de valeur -class KWDGValue : public Object +// Classe KWDGInterval +// Intervalle de valeurs +class KWDGInterval : public KWDGPartValues { public: // Constructeur - KWDGValue(const Symbol& sValue); - ~KWDGValue(); + KWDGInterval(); + ~KWDGInterval(); - // Valeur - Symbol& GetValue() const; + // Type de valeur gere par la partie + int GetValueType() const; - // Effectif lie a la valeur - void SetValueFrequency(int nFrequency); - int GetValueFrequency() const; + // Borne inf de l'intervalle + // (doit etre egal a KWDGInterval::GetMinLowerBound pour le premier intervalle) + void SetLowerBound(Continuous cValue); + Continuous GetLowerBound() const; + + // Borne sup de l'intervalle + // (doit etre egal a KWDGInterval::GetMaxUpperBound pour le dernier intervalle) + void SetUpperBound(Continuous cValue); + Continuous GetUpperBound() const; + + // Valeurs extremes des bornes des intervalles + static Continuous GetMinLowerBound(); + static Continuous GetMaxUpperBound(); + + /////////////////////////////// + // Services divers + + // Test si l'intervalle est un sous-intervalle + boolean IsSubInterval(const KWDGInterval* otherInterval) const; + + // Import des valeurs d'un intervalle source, devant etre adjacent au premier + // intervalle. L'intervalle source est reinitialise + void Import(KWDGInterval* sourceInterval); + + // CH IV Begin + // Mise a jour des valeurs de l'intervalle a partir des valeurs d'un intervalle source, devant etre adjacent au + // premier intervalle. L'intervalle source n'est pas modifie + // CH IV Refactoring: UpgradeFrom est tres proche de Import: a mutualiser, ou remplacer Import par UpgradeFrom puis Reset??? + void UpgradeFrom(const KWDGInterval* sourceInterval); + // CH IV End + // Copie + void CopyFrom(const KWDGInterval* sourceInterval); + + // Redefinition des methodes virtuelles + boolean IsSubPartValues(const KWDGPartValues* otherPartValues) const override; + void Import(KWDGPartValues* sourcePartValues) override; + void UpgradeFrom(const KWDGPartValues* sourcePartValues) override; + void CopyFrom(const KWDGPartValues* sourcePartValues) override; + + // Controle d'integrite + boolean Check() const override; // Affichage void Write(ostream& ost) const override; + // Libelles utilisateur + const ALString GetClassLabel() const override; + const ALString GetObjectLabel() const override; + /////////////////////////////// ///// Implementation protected: - friend class KWDGValueSet; - - // Attributs - KWDGValue* prevValue; - KWDGValue* nextValue; - mutable Symbol sSymbolValue; - int nValueFrequency; + // Bornes de l'intervalle + Continuous cLowerBound; + Continuous cUpperBound; }; -// Comparaison de deux valeurs symboliques, par effectif decroissant -int KWDGValueCompareDecreasingFrequency(const void* elem1, const void* elem2); - ////////////////////////////////////////////////////////////////////////////// // CH IV Begin // Classe KWDGVarPartSet @@ -1927,7 +1921,7 @@ inline int KWDGAttribute::GetGarbageModalityNumber() const // Sinon // CH IV Begin else if (nAttributeType == KWType::Symbol) - return GetGarbagePart()->GetValueSet()->GetTrueValueNumber(); + return GetGarbagePart()->GetValueSet()->GetValueNumber(); else return GetGarbagePart()->GetVarPartSet()->GetVarPartNumber(); // CH IV End @@ -1951,10 +1945,7 @@ inline void KWDGAttribute::InitializeCatchAllValueSet(KWDGValueSet* valueSet) if (valueSet == NULL) catchAllValueSet = NULL; else - { - catchAllValueSet = new KWDGValueSet; - catchAllValueSet->CopyFrom(valueSet); - } + catchAllValueSet = valueSet->Clone(); } inline void KWDGAttribute::SetCatchAllValueNumber(int nValue) @@ -1975,7 +1966,7 @@ inline int KWDGAttribute::GetCatchAllValueNumber() const else { assert(not GetCatchAllValueSet()->IsDefaultPart()); - return GetCatchAllValueSet()->GetTrueValueNumber(); + return GetCatchAllValueSet()->GetValueNumber(); } } @@ -2001,12 +1992,12 @@ inline int KWDGPart::GetPartFrequency() const inline int KWDGPart::GetPartType() const { - require(interval == NULL or valueSet == NULL or varPartSet == NULL); - require((interval == NULL) + (valueSet == NULL) + (varPartSet == NULL) >= 2); + require(interval == NULL or symbolValueSet == NULL or varPartSet == NULL); + require((interval == NULL) + (symbolValueSet == NULL) + (varPartSet == NULL) >= 2); if (interval != NULL) return KWType::Continuous; - else if (valueSet != NULL) + else if (symbolValueSet != NULL) return KWType::Symbol; // CH IV Begin else if (varPartSet != NULL) @@ -2025,11 +2016,21 @@ inline KWDGInterval* KWDGPart::GetInterval() const } inline KWDGValueSet* KWDGPart::GetValueSet() const +{ + require(GetPartType() == KWType::Symbol or GetPartType() == KWType::VarPart); + + if (GetPartType() == KWType::Symbol) + return symbolValueSet; + else + return NULL; //DDD +} + +inline KWDGSymbolValueSet* KWDGPart::GetSymbolValueSet() const { require(GetPartType() == KWType::Symbol); - ensure(valueSet != NULL); - return valueSet; + ensure(symbolValueSet != NULL); + return symbolValueSet; } // CH IV Begin inline KWDGVarPartSet* KWDGPart::GetVarPartSet() const @@ -2149,14 +2150,33 @@ inline KWDGValueSet::~KWDGValueSet() DeleteAllValues(); } +inline KWDGValueSet* KWDGValueSet::Clone() const +{ + KWDGValueSet* cloneValueSet; + cloneValueSet = Create(); + cloneValueSet->CopyFrom(this); + return cloneValueSet; +} + inline boolean KWDGValueSet::IsDefaultPart() const { return bIsDefaultPart; } -inline int KWDGValueSet::GetTrueValueNumber() const +inline int KWDGValueSet::GetValueNumber() const { - return nValueNumber; + assert(nValueNumber > 0 or IsDefaultPart()); + + // On assure que le seul cas sans aucune valeur est le cas de la partie par defaut reduite a la StarValue + // Cela peut arriver si la partie a ete "nettoyee" pour gagner de la place, notamment quand on exporte + // une grille dans un dictionnaire et qu'on la reimporte pour le deploiement + // Dans ce cas, ou rend une nombre d evaleur egal 1 a, ce qui a le merite d'avoir des couts de grille + // valides numeriquement, meme s'il ne corrrespondnent pas au vrau model qui aurait du memoriser + // le nombre exacte de valeurs du groupe + if (nValueNumber == 0 and IsDefaultPart()) + return 1; + else + return nValueNumber; } inline KWDGValue* KWDGValueSet::GetHeadValue() const @@ -2180,6 +2200,23 @@ inline void KWDGValueSet::GetPrevValue(KWDGValue*& value) const require(value != NULL); value = value->prevValue; } + +// Classe KWDGSymbolValueSet + +inline KWDGSymbolValueSet::KWDGSymbolValueSet() {} + +inline KWDGSymbolValueSet::~KWDGSymbolValueSet() {} + +inline int KWDGSymbolValueSet::GetValueType() const +{ + return KWType::Symbol; +} + +inline KWDGValueSet* KWDGSymbolValueSet::Create() const +{ + return new KWDGSymbolValueSet; +} + // CH IV Begin // Classe KWDGVarPartValue @@ -2268,34 +2305,76 @@ inline int KWDGInnerAttributes::GetInnerAttributeNumber() const } // CH IV End + // Classe KWDGValue -inline KWDGValue::KWDGValue(const Symbol& sValue) +inline KWDGValue::KWDGValue() { - sSymbolValue = sValue; - nValueFrequency = 0; prevValue = NULL; nextValue = NULL; } inline KWDGValue::~KWDGValue() {} -inline Symbol& KWDGValue::GetValue() const +inline Symbol& KWDGValue::GetSymbolValue() const +{ + static Symbol sEmpty; + assert(false); + return sEmpty; +} + +inline KWDGPart* KWDGValue::GetVarPart() const +{ + assert(false); + return NULL; +} + +// Classe KWDGSymbolValue + +inline KWDGSymbolValue::KWDGSymbolValue(const Symbol& sValue) +{ + sSymbolValue = sValue; + nValueFrequency = 0; +} + +inline KWDGSymbolValue::~KWDGSymbolValue() {} + +inline int KWDGSymbolValue::GetValueType() const +{ + return KWType::Symbol; +} + +inline Symbol& KWDGSymbolValue::GetSymbolValue() const { return sSymbolValue; } -inline void KWDGValue::SetValueFrequency(int nFrequency) +inline NUMERIC KWDGSymbolValue::GetNumericKeyValue() const +{ + return sSymbolValue.GetNumericKey(); +} + +inline boolean KWDGSymbolValue::IsDefaultValue() const +{ + return sSymbolValue == Symbol::GetStarValue(); +} + +inline void KWDGSymbolValue::SetValueFrequency(int nFrequency) { require(nFrequency >= 0); nValueFrequency = nFrequency; } -inline int KWDGValue::GetValueFrequency() const +inline int KWDGSymbolValue::GetValueFrequency() const { return nValueFrequency; } +inline int KWDGSymbolValue::CompareValue(const KWDGValue* otherValue) const +{ + return GetSymbolValue().CompareValue(otherValue->GetSymbolValue()); +} + // Classe KWDGCell inline KWDGCell::KWDGCell() diff --git a/src/Learning/KWDataPreparation/KWDataGridCosts.cpp b/src/Learning/KWDataPreparation/KWDataGridCosts.cpp index eec16b4aa..3b1fec96f 100644 --- a/src/Learning/KWDataPreparation/KWDataGridCosts.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridCosts.cpp @@ -283,7 +283,7 @@ void KWDataGridCosts::InitializeDefaultCosts(const KWDataGrid* dataGrid) defaultPart = defaultAttribute->GetHeadPart(); // Remplacement de ses valeurs par une valeur unique - defaultPart->GetValueSet()->CompressValueSet(); + cast(KWDGSymbolValueSet*, defaultPart->GetValueSet())->CompressValueSet(); } } } @@ -743,7 +743,7 @@ void KWDataGridCosts::WritePartCostLine(const KWDGPart* part, ostream& ost) cons ost << part->GetObjectLabel() << "\t"; ost << part->GetCellNumber() << "\t" << part->GetPartFrequency() << "\t"; if (part->GetPartType() == KWType::Symbol) - ost << part->GetValueSet()->GetTrueValueNumber() << "\t"; + ost << part->GetValueSet()->GetValueNumber() << "\t"; // CH IV Begin else if (part->GetPartType() == KWType::Continuous) ost << part->GetPartFrequency() << "\t"; @@ -1550,7 +1550,7 @@ double KWDataGridClusteringCosts::ComputePartCost(const KWDGPart* part) const dPartCost = 0; if (part->GetPartFrequency() > 0) { - nValueNumber = part->GetValueSet()->GetTrueValueNumber(); + nValueNumber = part->GetValueSet()->GetValueNumber(); dPartCost = KWStat::LnFactorial(part->GetPartFrequency()); // Distribution des valeurs dans la partie @@ -1583,7 +1583,7 @@ double KWDataGridClusteringCosts::ComputePartUnionCost(const KWDGPart* part1, co // On ignore le cout LnFactorial par valeur, qui est constant quelle que soit la grille else { - nValueNumber = part1->GetValueSet()->GetTrueValueNumber() + part2->GetValueSet()->GetTrueValueNumber(); + nValueNumber = part1->GetValueSet()->GetValueNumber() + part2->GetValueSet()->GetValueNumber(); dPartUnionCost = KWStat::LnFactorial(nPartFrequency); // Distribution des valeurs dans la partie @@ -1686,7 +1686,7 @@ double KWDataGridClusteringCosts::ComputePartModelCost(const KWDGPart* part) con { if (part->GetPartFrequency() > 0) { - nValueNumber = part->GetValueSet()->GetTrueValueNumber(); + nValueNumber = part->GetValueSet()->GetValueNumber(); // Distribution des valeurs dans la partie dPartCost += KWStat::LnFactorial(part->GetPartFrequency() + nValueNumber - 1); @@ -1981,7 +1981,7 @@ double KWVarPartDataGridClusteringCosts::ComputeInnerAttributePartCost(const KWD if (part->GetPartType() == KWType::Symbol) { - nValueNumber = part->GetValueSet()->GetTrueValueNumber(); + nValueNumber = part->GetValueSet()->GetValueNumber(); // Distribution des valeurs dans la partie dInnerAttributePartCost += KWStat::LnFactorial(part->GetPartFrequency() + nValueNumber - 1); dInnerAttributePartCost -= KWStat::LnFactorial(nValueNumber - 1); @@ -2011,7 +2011,7 @@ double KWVarPartDataGridClusteringCosts::ComputePartCost(const KWDGPart* part) c if (part->GetPartFrequency() > 0) { if (part->GetAttribute()->GetAttributeType() == KWType::Symbol) - nValueNumber = part->GetValueSet()->GetTrueValueNumber(); + nValueNumber = part->GetValueSet()->GetValueNumber(); else nValueNumber = part->GetVarPartSet()->GetVarPartNumber(); dPartCost = KWStat::LnFactorial(part->GetPartFrequency()); @@ -2048,7 +2048,7 @@ double KWVarPartDataGridClusteringCosts::ComputePartUnionCost(const KWDGPart* pa // On ignore le cout LnFactorial par valeur, qui est constant quelle que soit la grille else if (part1->GetAttribute()->GetAttributeType() == KWType::Symbol) { - nValueNumber = part1->GetValueSet()->GetTrueValueNumber() + part2->GetValueSet()->GetTrueValueNumber(); + nValueNumber = part1->GetValueSet()->GetValueNumber() + part2->GetValueSet()->GetValueNumber(); dPartUnionCost = KWStat::LnFactorial(nPartFrequency); // Distribution des valeurs dans la partie @@ -2176,7 +2176,7 @@ double KWVarPartDataGridClusteringCosts::ComputePartModelCost(const KWDGPart* pa { if (part->GetPartFrequency() > 0) { - nValueNumber = part->GetValueSet()->GetTrueValueNumber(); + nValueNumber = part->GetValueSet()->GetValueNumber(); // Distribution des valeurs dans la partie dPartCost += KWStat::LnFactorial(part->GetPartFrequency() + nValueNumber - 1); @@ -2718,13 +2718,13 @@ double KWDataGridGeneralizedClassificationCosts::ComputePartCost(const KWDGPart* else { assert(part->GetAttribute()->GetAttributeType() == KWType::Symbol); - assert(part->GetPartFrequency() >= part->GetValueSet()->GetTrueValueNumber() - 1); + assert(part->GetPartFrequency() >= part->GetValueSet()->GetValueNumber() - 1); dPartCost = 0; if (part->GetPartFrequency() > 0) { dPartCost = KWStat::LnFactorial(part->GetPartFrequency() + - part->GetValueSet()->GetTrueValueNumber() - 1); - dPartCost -= KWStat::LnFactorial(part->GetValueSet()->GetTrueValueNumber() - 1); + part->GetValueSet()->GetValueNumber() - 1); + dPartCost -= KWStat::LnFactorial(part->GetValueSet()->GetValueNumber() - 1); } } } @@ -2773,7 +2773,7 @@ double KWDataGridGeneralizedClassificationCosts::ComputePartUnionCost(const KWDG { assert(part1->GetAttribute()->GetAttributeType() == KWType::Symbol); nPartValueNumber = - part1->GetValueSet()->GetTrueValueNumber() + part2->GetValueSet()->GetTrueValueNumber(); + part1->GetValueSet()->GetValueNumber() + part2->GetValueSet()->GetValueNumber(); assert(nPartFrequency >= nPartValueNumber - 1); dPartUnionCost = 0; if (nPartFrequency > 0) @@ -2974,13 +2974,13 @@ double KWDataGridGeneralizedClassificationCosts::ComputePartModelCost(const KWDG else { assert(part->GetAttribute()->GetAttributeType() == KWType::Symbol); - assert(part->GetPartFrequency() >= part->GetValueSet()->GetTrueValueNumber() - 1); + assert(part->GetPartFrequency() >= part->GetValueSet()->GetValueNumber() - 1); dPartCost = 0; if (part->GetPartFrequency() > 0) { dPartCost = KWStat::LnFactorial(part->GetPartFrequency() + - part->GetValueSet()->GetTrueValueNumber() - 1); - dPartCost -= KWStat::LnFactorial(part->GetValueSet()->GetTrueValueNumber() - 1); + part->GetValueSet()->GetValueNumber() - 1); + dPartCost -= KWStat::LnFactorial(part->GetValueSet()->GetValueNumber() - 1); dPartCost -= KWStat::LnFactorial(part->GetPartFrequency()); } } diff --git a/src/Learning/KWDataPreparation/KWDataGridDeployment.cpp b/src/Learning/KWDataPreparation/KWDataGridDeployment.cpp index ead8fa5a3..cf4cf237a 100644 --- a/src/Learning/KWDataPreparation/KWDataGridDeployment.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridDeployment.cpp @@ -81,7 +81,7 @@ void KWDataGridDeployment::PrepareForDeployment() // Ajout d'une valeur fictive dans le cas categoriel if (GetDeploymentAttribute()->GetAttributeType() == KWType::Symbol) { - dgNewDeploymentPart->GetValueSet()->AddValue( + dgNewDeploymentPart->GetSymbolValueSet()->AddSymbolValue( Symbol::BuildNewSymbol(" Coclustering New Deployment Part ")); // On n'oublie pas de mettre a jour le nombre de valeurs diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index fbca9e7e5..5565cf49c 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -716,7 +716,7 @@ void KWDataGridManager::ExportFrequencyTableFromOneAttribute(KWFrequencyTable* k if (oneAttributeDataGrid.GetAttributeAt(0)->GetAttributeType() == KWType::Symbol) { // Recopie du nombre de modalites - kwdfvFrequencyVector->SetModalityNumber(dgPart->GetValueSet()->GetTrueValueNumber()); + kwdfvFrequencyVector->SetModalityNumber(dgPart->GetValueSet()->GetValueNumber()); } // CH IV Begin else if (oneAttributeDataGrid.GetAttributeAt(0)->GetAttributeType() == KWType::VarPart) @@ -934,7 +934,7 @@ void KWDataGridManager::ExportDataGridWithReferenceVarPartClusters(KWDataGrid* r { // Recherche d'une valeur typique: la premiere valeur assert(initialVarPart->GetValueSet()->GetHeadValue() != NULL); - sValue = initialVarPart->GetValueSet()->GetHeadValue()->GetValue(); + sValue = initialVarPart->GetValueSet()->GetHeadValue()->GetSymbolValue(); // Recherche du groupe de valeurs contenant cette modalite dans l'attribut interne // de la grille de reference @@ -1057,7 +1057,7 @@ void KWDataGridManager::ExportCells(KWDataGrid* targetDataGrid) const { // Recherche d'une valeur typique: la premiere valeur assert(sourcePart->GetValueSet()->GetHeadValue() != NULL); - sValue = sourcePart->GetValueSet()->GetHeadValue()->GetValue(); + sValue = sourcePart->GetValueSet()->GetHeadValue()->GetSymbolValue(); // Recherche du groupe de valeurs cible correspondant targetPart = targetAttribute->LookupSymbolPart(sValue); @@ -1095,7 +1095,7 @@ void KWDataGridManager::ExportCells(KWDataGrid* targetDataGrid) const { // Recherche d'une valeur typique: la premiere valeur assert(sourceVarPart->GetValueSet()->GetHeadValue() != NULL); - sValue = sourceVarPart->GetValueSet()->GetHeadValue()->GetValue(); + sValue = sourceVarPart->GetValueSet()->GetHeadValue()->GetSymbolValue(); // Recherche du groupe de valeurs cible correspondant targetVarPart = innerAttribute->LookupSymbolPart(sValue); @@ -1717,10 +1717,10 @@ void KWDataGridManager::BuildPartsOfSymbolAttributeFromGroupsIndex(KWDGAttribute // Memorisation de la partie comme partie poubelle. // Si elle existe, elle maximise le nombre de modalites - if (nGarbageModalityNumber > 0 and targetPart->GetValueSet()->GetTrueValueNumber() > nMaxValueNumber) + if (nGarbageModalityNumber > 0 and targetPart->GetValueSet()->GetValueNumber() > nMaxValueNumber) { targetAttribute->SetGarbagePart(targetPart); - nMaxValueNumber = targetPart->GetValueSet()->GetTrueValueNumber(); + nMaxValueNumber = targetPart->GetValueSet()->GetValueNumber(); } // Partie initiale suivante @@ -1753,7 +1753,7 @@ void KWDataGridManager::BuildDataGridAttributeFromUnivariateStats(KWDGAttribute* int nPart; KWDGPart* part; KWDGInterval* interval; - KWDGValueSet* valueSet; + KWDGSymbolValueSet* symbolValueSet; int nValue; KWAttribute* attribute; KWDGAttribute* sourceAttribute; @@ -1822,12 +1822,12 @@ void KWDataGridManager::BuildDataGridAttributeFromUnivariateStats(KWDGAttribute* for (nPart = 0; nPart < attributeGrouping->GetPartNumber(); nPart++) { part = targetAttribute->AddPart(); - valueSet = part->GetValueSet(); + symbolValueSet = part->GetSymbolValueSet(); // Initialisation des valeurs du groupe for (nValue = attributeGrouping->GetGroupFirstValueIndexAt(nPart); nValue <= attributeGrouping->GetGroupLastValueIndexAt(nPart); nValue++) - valueSet->AddValue(attributeGrouping->GetValueAt(nValue)); + symbolValueSet->AddSymbolValue(attributeGrouping->GetValueAt(nValue)); // Memorisation du groupe poubelle if (nPart == attributeGrouping->GetGarbageGroupIndex()) @@ -2204,7 +2204,7 @@ boolean KWDataGridManager::CheckParts(const KWDataGrid* targetDataGrid) const // Recherche de la partie cible associee a la premiere valeur source sourceValue = sourceValueSet->GetHeadValue(); check(sourceValue); - headTargetPart = targetAttribute->LookupSymbolPart(sourceValue->GetValue()); + headTargetPart = targetAttribute->LookupSymbolPart(sourceValue->GetSymbolValue()); check(headTargetPart); // Parcours des valeurs de la partie source @@ -2212,15 +2212,16 @@ boolean KWDataGridManager::CheckParts(const KWDataGrid* targetDataGrid) const while (sourceValue != NULL) { // Recherche de la partie cible associee a la valeur source - targetPart = targetAttribute->LookupSymbolPart(sourceValue->GetValue()); + targetPart = targetAttribute->LookupSymbolPart(sourceValue->GetSymbolValue()); check(targetPart); // Erreur si la partie est differente de la premiere partie // Tolerance pour la valeur speciale if (targetPart != headTargetPart and - sourceValue->GetValue() != Symbol::GetStarValue()) + sourceValue->GetSymbolValue() != Symbol::GetStarValue()) { - sourcePart->AddError(sTmp + "Input value (" + sourceValue->GetValue() + + sourcePart->AddError(sTmp + "Input value (" + + sourceValue->GetSymbolValue() + ") belongs to an output group different from that " "of the first input value"); bOk = false; @@ -2357,7 +2358,7 @@ boolean KWDataGridManager::CheckCells(const KWDataGrid* targetDataGrid) const { // Recherche d'une valeur typique: la premiere valeur assert(targetPart->GetValueSet()->GetHeadValue() != NULL); - sValue = targetPart->GetValueSet()->GetHeadValue()->GetValue(); + sValue = targetPart->GetValueSet()->GetHeadValue()->GetSymbolValue(); // Recherche du groupe de valeurs cible correspondant checkPart = checkAttribute->LookupSymbolPart(sValue); @@ -3415,11 +3416,12 @@ void KWDataGridManager::InitialiseAttributeGranularizedSymbolParts(const KWDGAtt // La partie qui contient la StarValue est compressee uniquement si elle contient plus d'une // modalite (cas d'un vrai fourre-tout) if (targetPart->GetValueSet()->IsDefaultPart() and - targetPart->GetValueSet()->GetTrueValueNumber() > 1 and + targetPart->GetValueSet()->GetValueNumber() > 1 and IsSupervisedInputAttribute(targetAttribute)) { // Compression du fourre-tout et memorisation de ses valeurs - cleanedValueSet = targetPart->GetValueSet()->ConvertToCleanedValueSet(); + cleanedValueSet = + cast(KWDGSymbolValueSet*, targetPart->GetValueSet())->ConvertToCleanedValueSet(); targetAttribute->InitializeCatchAllValueSet(cleanedValueSet); delete cleanedValueSet; } @@ -3519,7 +3521,7 @@ void KWDataGridManager::InitialiseAttributeGranularizedVarPartParts(const KWDGAt // if ((targetDataGrid->GetTargetValueNumber() > 0 or(targetDataGrid->GetTargetAttribute() != // NULL and not sourceAttribute->GetAttributeTargetFunction())) and // targetPart->GetVarPartSet()->IsDefaultPart() and - // targetPart->GetVarPartSet()->GetTrueValueNumber() > 1) + // targetPart->GetVarPartSet()->GetValueNumber() > 1) //{ // // Compression du fourre-tout et memorisation de ses valeurs // targetAttribute->SetCatchAllValueSet(targetPart->GetValueSet()->ConvertToCleanedValueSet()); @@ -3934,7 +3936,7 @@ void KWDataGridManager::ExportAttributeSymbolValueFrequencies(KWDGAttribute* tar value = valueSet->GetHeadValue(); while (value != NULL) { - nkdSourceValues.SetAt(value->GetValue().GetNumericKey(), value); + nkdSourceValues.SetAt(value->GetNumericKeyValue(), value); valueSet->GetNextValue(value); } @@ -3954,7 +3956,7 @@ void KWDataGridManager::ExportAttributeSymbolValueFrequencies(KWDGAttribute* tar while (value != NULL) { // Recherche de son effectif, precedement collecte a partir de l'attribut source - sourceValue = cast(KWDGValue*, nkdSourceValues.Lookup(value->GetValue().GetNumericKey())); + sourceValue = cast(KWDGValue*, nkdSourceValues.Lookup(value->GetNumericKeyValue())); // Cas ou la sourceValue est bien presente // Dans le cas particulier ou sourceAttribute provient d'une grille construite a partir d'un @@ -3966,7 +3968,7 @@ void KWDataGridManager::ExportAttributeSymbolValueFrequencies(KWDGAttribute* tar value->SetValueFrequency(sourceValue->GetValueFrequency()); // On test si on est sur la valeur par defaut - if (value->GetValue() == Symbol::GetStarValue()) + if (value->GetSymbolValue() == Symbol::GetStarValue()) defaultValue = value; // Sinon cumul de l'effectif hors fourre-tout else @@ -4024,13 +4026,14 @@ void KWDataGridManager::SortAttributeParts(const KWDGAttribute* sourceAttribute, sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSource)); // Recherche de la partie groupee correspondante - groupedPart = groupedAttribute->LookupSymbolPart(sourcePart->GetValueSet()->GetHeadValue()->GetValue()); + groupedPart = + groupedAttribute->LookupSymbolPart(sourcePart->GetValueSet()->GetHeadValue()->GetSymbolValue()); // Creation de l'association entre index de partie et premiere valeur du groupe association = new KWSortableSymbol; oaAssociations.SetAt(nSource, association); association->SetIndex(nSource); - association->SetSortValue(groupedPart->GetValueSet()->GetHeadValue()->GetValue()); + association->SetSortValue(groupedPart->GetValueSet()->GetHeadValue()->GetSymbolValue()); } // Tri des association, apres une randomisation pour avoir un ordre aleatoire par groupe @@ -4051,7 +4054,8 @@ void KWDataGridManager::SortAttributeParts(const KWDGAttribute* sourceAttribute, sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSource)); // Recherche de la partie groupee correspondante - groupedPart = groupedAttribute->LookupSymbolPart(sourcePart->GetValueSet()->GetHeadValue()->GetValue()); + groupedPart = + groupedAttribute->LookupSymbolPart(sourcePart->GetValueSet()->GetHeadValue()->GetSymbolValue()); // Rangement dans les tableaux en sortie oaSortedSourceParts->SetAt(n, sourcePart); diff --git a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp index 75c4b3131..257920552 100644 --- a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp @@ -955,11 +955,11 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b // Cas ou les deux parties fusionnees deviennent le groupe poubelle if (attribute->GetAttributeType() == KWType::Symbol) { - if (partMerge->GetPart1()->GetValueSet()->GetTrueValueNumber() + - partMerge->GetPart2()->GetValueSet()->GetTrueValueNumber() > + if (partMerge->GetPart1()->GetValueSet()->GetValueNumber() + + partMerge->GetPart2()->GetValueSet()->GetValueNumber() > cast(KWDGPart*, attributeM->slPartValueNumbers->GetHead()) ->GetValueSet() - ->GetTrueValueNumber()) + ->GetValueNumber()) { // Creation du nouveau groupe poubelle garbagePart = new KWDGPart; @@ -1934,7 +1934,7 @@ boolean KWDGMPart::Check() const // de la grille (les parties et leurs valeurs sont creees avant // la creation des cellules). // La verification n'est pas faite dans tous les cas dans la classe ancetre - nTotalValueFrequency = valueSet->ComputeTotalFrequency(); + nTotalValueFrequency = symbolValueSet->ComputeTotalFrequency(); if (bOk and GetPartFrequency() > 0 and nTotalValueFrequency > 0 and GetPartFrequency() != nTotalValueFrequency) { @@ -2009,7 +2009,7 @@ int KWDGMPartValueNumberCompare(const void* elem1, const void* elem2) // Comparaison du nombre de modalites par valeurs decroissantes if (partM1->GetPartType() == KWType::Symbol) - return (partM2->GetValueSet()->GetTrueValueNumber() - partM1->GetValueSet()->GetTrueValueNumber()); + return (partM2->GetValueSet()->GetValueNumber() - partM1->GetValueSet()->GetValueNumber()); else return (partM2->GetVarPartSet()->GetVarPartNumber() - partM1->GetVarPartSet()->GetVarPartNumber()); // CH IV End diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp index aa1c5af80..bd891d246 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp @@ -1441,7 +1441,7 @@ void KWDGPOGrouper::InitializePartFrequencyVector(KWDGPOPartFrequencyVector* par // Memorisation du nombre de valeurs associees a la partie if (part->GetPartType() == KWType::Symbol) - partFrequencyVector->SetModalityNumber(part->GetValueSet()->GetTrueValueNumber()); + partFrequencyVector->SetModalityNumber(part->GetValueSet()->GetValueNumber()); // CH IV Begin else if (part->GetPartType() == KWType::VarPart) partFrequencyVector->SetModalityNumber(part->GetVarPartSet()->GetVarPartNumber()); @@ -1660,7 +1660,7 @@ void KWDGPOGrouper::InitializeGroupIndexes(IntVector* ivGroups, const KWDataGrid if (optimizedAttribute->GetAttributeType() == KWType::Symbol) // Recherche de sa partie groupee correspondante optimizedPart = optimizedAttribute->LookupSymbolPart( - initialPart->GetValueSet()->GetHeadValue()->GetValue()); + initialPart->GetValueSet()->GetHeadValue()->GetSymbolValue()); else optimizedPart = optimizedAttribute->LookupVarPart( initialPart->GetVarPartSet()->GetHeadVarPart()->GetVarPart()); @@ -1745,7 +1745,7 @@ int KWDGPOGrouper::InitializeGroupIndexesAndGarbageIndex(IntVector* ivGroups, co if (optimizedAttribute->GetAttributeType() == KWType::Symbol) { optimizedPart = optimizedAttribute->LookupSymbolPart( - initialPart->GetValueSet()->GetHeadValue()->GetValue()); + initialPart->GetValueSet()->GetHeadValue()->GetSymbolValue()); } else if (optimizedAttribute->GetAttributeType() == KWType::VarPart) { @@ -1875,7 +1875,7 @@ void KWDGPOGrouper::UpdateDataGridWithGarbageFromGroups(KWDataGrid* optimizedDat optimizedPart->GetValueSet()->UpgradeFrom(initialPart->GetValueSet()); // Mise a jour du groupe poubelle comme le groupe contenant le plus de modalites - if (optimizedPart->GetValueSet()->GetTrueValueNumber() > + if (optimizedPart->GetValueSet()->GetValueNumber() > optimizedAttribute->GetGarbageModalityNumber()) optimizedAttribute->SetGarbagePart(optimizedPart); } @@ -2653,7 +2653,7 @@ double KWDataGridUnivariateCosts::ComputePartCost(const KWFrequencyVector* part) { // N'etant jamais la partie par defaut, on a le TrueValueNumber egal au ValueNumber assert(not partCostParameter->GetValueSet()->IsDefaultPart()); - cast(KWDGValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = + cast(KWDGSymbolValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = cast(KWDGPOPartFrequencyVector*, part)->GetModalityNumber(); } // CH IV Begin @@ -2727,7 +2727,7 @@ double KWDataGridUnivariateCosts::ComputePartUnionCost(const KWFrequencyVector* // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) if (partCostParameter->GetPartType() == KWType::Symbol) { - cast(KWDGValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = + cast(KWDGSymbolValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = cast(KWDGPOPartFrequencyVector*, sourcePart1)->GetModalityNumber() + cast(KWDGPOPartFrequencyVector*, sourcePart2)->GetModalityNumber(); } @@ -2768,7 +2768,7 @@ double KWDataGridUnivariateCosts::ComputePartDiffCost(const KWFrequencyVector* s // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) if (partCostParameter->GetPartType() == KWType::Symbol) { - cast(KWDGValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = + cast(KWDGSymbolValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = cast(KWDGPOPartFrequencyVector*, sourcePart)->GetModalityNumber() - cast(KWDGPOPartFrequencyVector*, removedPart)->GetModalityNumber(); } @@ -2865,7 +2865,7 @@ void KWDGPartCostParameter::SetPartType(int nValue) if (nValue == KWType::Continuous) interval = new KWDGInterval; else if (nValue == KWType::Symbol) - valueSet = new KWDGValueSetCostParameter; + symbolValueSet = new KWDGSymbolValueSetCostParameter; else varPartSet = new KWDGVarPartSetCostParameter; // Avant integration : pour quel cas ? valueSet = new KWDGValueSetCostParameter; @@ -2878,7 +2878,7 @@ boolean KWDGPartCostParameter::GetEmulated() const return true; } -boolean KWDGValueSetCostParameter::GetEmulated() const +boolean KWDGSymbolValueSetCostParameter::GetEmulated() const { return true; } @@ -4076,9 +4076,9 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K // Cas ou la PV deplacee etait la seule PV de l'attribut interne presente dans son // cluster de depart innerOutOptimizedPart = innerOptimizedAttribute->LookupSymbolPart( - innerPart->GetValueSet()->GetHeadValue()->GetValue()); - if (innerOutOptimizedPart->GetValueSet()->GetTrueValueNumber() == - innerPart->GetValueSet()->GetTrueValueNumber()) + innerPart->GetValueSet()->GetHeadValue()->GetSymbolValue()); + if (innerOutOptimizedPart->GetValueSet()->GetValueNumber() == + innerPart->GetValueSet()->GetValueNumber()) nDeltaVarPartNumber = 1; // Parcours des clusters d'arrivee eligibles @@ -4435,7 +4435,7 @@ double CCVarPartDataGridPostOptimizer::ComputeVarPartsSymbolAttributeVariationCo // Variation de cout de la partie de variable de depart // On retire le cout avant deplacement - nValueNumber = varPartOut->GetValueSet()->GetTrueValueNumber(); + nValueNumber = varPartOut->GetValueSet()->GetValueNumber(); nPartFrequency = varPartOut->GetPartFrequency(); // Distribution des valeurs dans la partie dVariationAttributeCost -= KWStat::LnFactorial(nPartFrequency + nValueNumber - 1); @@ -4444,8 +4444,8 @@ double CCVarPartDataGridPostOptimizer::ComputeVarPartsSymbolAttributeVariationCo // On ajoute le cout de la partie de depart apres deplacement // Si la partie n'est pas vide apres deplacement - nValueNumber = varPartOut->GetValueSet()->GetTrueValueNumber() - - innerPart->GetValueSet()->GetTrueValueNumber(); + nValueNumber = + varPartOut->GetValueSet()->GetValueNumber() - innerPart->GetValueSet()->GetValueNumber(); if (nValueNumber > 0) { nPartFrequency = varPartOut->GetPartFrequency() - innerPart->GetPartFrequency(); @@ -4458,7 +4458,7 @@ double CCVarPartDataGridPostOptimizer::ComputeVarPartsSymbolAttributeVariationCo // Variation de cout de la partie de variable d'arrivee // On retire le cout de la partie d'arrivee avant deplacement - nValueNumber = varPartIn->GetValueSet()->GetTrueValueNumber(); + nValueNumber = varPartIn->GetValueSet()->GetValueNumber(); nPartFrequency = varPartIn->GetPartFrequency(); // Distribution des valeurs dans la partie dVariationAttributeCost -= KWStat::LnFactorial(nPartFrequency + nValueNumber - 1); @@ -4466,8 +4466,8 @@ double CCVarPartDataGridPostOptimizer::ComputeVarPartsSymbolAttributeVariationCo dVariationAttributeCost += KWStat::LnFactorial(nPartFrequency); // On ajoute le cout de la partie d'arrivee apres deplacement - nValueNumber = varPartIn->GetValueSet()->GetTrueValueNumber() + - innerPart->GetValueSet()->GetTrueValueNumber(); + nValueNumber = + varPartIn->GetValueSet()->GetValueNumber() + innerPart->GetValueSet()->GetValueNumber(); nPartFrequency = varPartIn->GetPartFrequency() + innerPart->GetPartFrequency(); // Distribution des valeurs dans la partie dVariationAttributeCost += KWStat::LnFactorial(nPartFrequency + nValueNumber - 1); @@ -4752,7 +4752,7 @@ double CCVarPartDataGridPostOptimizer::ComputeClusterCellVariationCost(KWDGPart* // Extraction du cluster Identifier identifierPart = clusterCell->GetPartAt(0); assert(identifierPart->GetPartType() == KWType::Symbol); - nkdCells.SetAt((NUMERIC)identifierPart->GetValueSet()->GetHeadValue()->GetValue().GetNumericKey(), + nkdCells.SetAt((NUMERIC)identifierPart->GetValueSet()->GetHeadValue()->GetNumericKeyValue(), clusterCell); // Partie suivante @@ -4765,9 +4765,9 @@ double CCVarPartDataGridPostOptimizer::ComputeClusterCellVariationCost(KWDGPart* { identifierPart = varPartCell->GetPartAt(0); assert(identifierPart->GetPartType() == KWType::Symbol); - clusterCell = cast( - KWDGCell*, nkdCells.Lookup( - (NUMERIC)identifierPart->GetValueSet()->GetHeadValue()->GetValue().GetNumericKey())); + clusterCell = + cast(KWDGCell*, + nkdCells.Lookup((NUMERIC)identifierPart->GetValueSet()->GetHeadValue()->GetNumericKeyValue())); // Calcul de la variation de cout du a la variation d'effectifs (croissant ou decroissant selon le // depart ou l'arrivee de la PV) diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h index f00144217..5d77f398d 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h @@ -18,7 +18,7 @@ class KWDataGridUnivariateCosts; class KWDataGridCostParameter; class KWDGAttributeCostParameter; class KWDGPartCostParameter; -class KWDGValueSetCostParameter; +class KWDGSymbolValueSetCostParameter; class KWDGVarPartSetCostParameter; class KWDGCellCostParameter; class KWDGPOCellFrequencyVector; @@ -461,7 +461,7 @@ class KWDGPartCostParameter : public KWDGPart //////////////////////////////////////////////////////////////////////////////////////////// // Sous-classes de ValueSet de grille servant de parametre de cout -class KWDGValueSetCostParameter : public KWDGValueSet +class KWDGSymbolValueSetCostParameter : public KWDGSymbolValueSet { protected: friend class KWDataGridUnivariateCosts; diff --git a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp index 77e757c0d..cdccb6f2e 100644 --- a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp +++ b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp @@ -1476,10 +1476,11 @@ void KWAttributePairStatsStudy::ExportGranularizedPartsForSymbolAttribute(KWData (targetDataGrid->GetTargetAttribute() != NULL and not sourceAttribute->GetAttributeTargetFunction())) and targetPart->GetValueSet()->IsDefaultPart() and - targetPart->GetValueSet()->GetTrueValueNumber() > 1) + targetPart->GetValueSet()->GetValueNumber() > 1) { // Compression du fourre-tout et memorisation de ses valeurs - cleanedValueSet = targetPart->GetValueSet()->ConvertToCleanedValueSet(); + cleanedValueSet = + cast(KWDGSymbolValueSet*, targetPart->GetValueSet())->ConvertToCleanedValueSet(); targetAttribute->InitializeCatchAllValueSet(cleanedValueSet); delete cleanedValueSet; } diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp index 81f39fc75..0a2173c28 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp @@ -1264,7 +1264,7 @@ void CCCoclusteringBuilder::CleanVarPartDataGrid(KWDataGrid* dataGrid) // Si necessaire, initialisation de la partie par defaut, contenant la modalite speciale // Compte-tenu du tri prealable des tuples, il s'agit de la derniere partie de l'attribut if (bIsDefaultPartDeleted and innerAttribute->GetPartNumber() > 0) - innerAttribute->GetTailPart()->GetValueSet()->AddValue(Symbol::GetStarValue()); + innerAttribute->GetTailPart()->GetSymbolValueSet()->AddSymbolValue(Symbol::GetStarValue()); } // DDDDD dataGrid->GetInnerAttributes()->CleanEmptyInnerAttributes(); dataGrid->UpdateAllStatistics(); @@ -2537,7 +2537,7 @@ boolean CCCoclusteringBuilder::CreateIdentifierAttributeValueSets(const KWTupleT // Creation d'une nouvelle partie mono-valeur part = dgAttribute->AddPart(); - value = part->GetValueSet()->AddValue(tuple->GetSymbolAt(0)); + value = part->GetSymbolValueSet()->AddSymbolValue(tuple->GetSymbolAt(0)); value->SetValueFrequency( cast(IntObject*, odObservationNumbers.Lookup(tuple->GetSymbolAt(0)))->GetInt()); } @@ -2546,7 +2546,7 @@ boolean CCCoclusteringBuilder::CreateIdentifierAttributeValueSets(const KWTupleT // Initialisation de la partie par defaut, contenant la modalite speciale // Compte-tenu du tri prealable des tuples, il s'agit de la derniere partie de l'attribut - dgAttribute->GetTailPart()->GetValueSet()->AddValue(Symbol::GetStarValue()); + dgAttribute->GetTailPart()->GetSymbolValueSet()->AddSymbolValue(Symbol::GetStarValue()); // Parametrage du nombre total de valeurs // Pour un attribut categoriel, l'InitialValueNumber ne contient plus la StarValue @@ -3228,7 +3228,7 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn KWDGPart* dgValuePart; KWDGValue* dgValue; NumericKeyDictionary nkdOptimizedAttributeValues; - CCHDGValue* hdgValue; + CCHDGSymbolValue* hdgValue; DoubleVector dvTypicalities; DoubleVector dvMaxTypicalities; int nGarbageGroupIndex; @@ -3306,17 +3306,17 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn dgValue = dgValuePart->GetValueSet()->GetHeadValue(); while (dgValue != NULL) { - nkdOptimizedAttributeValues.SetAt((dgValue->GetValue().GetNumericKey()), dgValue); + nkdOptimizedAttributeValues.SetAt((dgValue->GetNumericKeyValue()), dgValue); dgValuePart->GetValueSet()->GetNextValue(dgValue); } // Memorisation du nombre de modalites du groupe - ivGroupModalityNumber.Add(dgValuePart->GetValueSet()->GetTrueValueNumber()); + ivGroupModalityNumber.Add(dgValuePart->GetValueSet()->GetValueNumber()); // Cas du groupe poubelle : memorisation du nombre de modalites if (nGroup == nGarbageGroupIndex) { - nGarbageModalityNumber = dgValuePart->GetValueSet()->GetTrueValueNumber(); + nGarbageModalityNumber = dgValuePart->GetValueSet()->GetValueNumber(); // CH AB AF adaptation eventuelle VarPart } } @@ -3481,9 +3481,8 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn for (nIntraCatchAllValue = 0; nIntraCatchAllValue < nValueModalityNumber; nIntraCatchAllValue++) { // Recherche de la valeur correspondante pour l'attribut optimise - hdgValue = - cast(CCHDGValue*, - nkdOptimizedAttributeValues.Lookup((dgValue->GetValue().GetNumericKey()))); + hdgValue = cast(CCHDGSymbolValue*, + nkdOptimizedAttributeValues.Lookup((dgValue->GetNumericKeyValue()))); // Calcul de la typicite elementaire = la typicite du fourre-tout * effectif de la // modalite elementaire / effectif total du fourre-tout @@ -3506,10 +3505,10 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn { // Acces a la valeur dgValuePart = cast(KWDGPart*, oaValueParts.GetAt(nValue)); - assert(dgValuePart->GetValueSet()->GetTrueValueNumber() == 1 or + assert(dgValuePart->GetValueSet()->GetValueNumber() == 1 or // n'est plus garanti car la grille initiale contient un fourre-tout //(dgValuePart->GetValueSet()->GetValueNumber() == 2 and - dgValuePart->GetValueSet()->GetTailValue()->GetValue() == Symbol::GetStarValue()); + dgValuePart->GetValueSet()->GetTailValue()->GetSymbolValue() == Symbol::GetStarValue()); // Acces au nombre de modalites de la valeur nValueModalityNumber = initialFrequencyTable.GetFrequencyVectorAt(nValue)->GetModalityNumber(); @@ -3518,13 +3517,13 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn nOutGroup = ivGroups.GetAt(nValue); // Cas d'une modalite non fourre-tout - if (dgValuePart->GetValueSet()->GetTrueValueNumber() == 1) + if (dgValuePart->GetValueSet()->GetValueNumber() == 1) { dgValue = dgValuePart->GetValueSet()->GetHeadValue(); // Recherche de la valeur correspondante pour l'attribut optimise - hdgValue = cast(CCHDGValue*, - nkdOptimizedAttributeValues.Lookup((dgValue->GetValue().GetNumericKey()))); + hdgValue = cast(CCHDGSymbolValue*, + nkdOptimizedAttributeValues.Lookup((dgValue->GetNumericKeyValue()))); // Memorisation de la typicalite normalisee // Mis en defaut avec Breast pour nValue = 14 @@ -3545,9 +3544,8 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn for (nIntraCatchAllValue = 0; nIntraCatchAllValue < nValueModalityNumber; nIntraCatchAllValue++) { // Recherche de la valeur correspondante pour l'attribut optimise - hdgValue = - cast(CCHDGValue*, - nkdOptimizedAttributeValues.Lookup((dgValue->GetValue().GetNumericKey()))); + hdgValue = cast(CCHDGSymbolValue*, + nkdOptimizedAttributeValues.Lookup((dgValue->GetNumericKeyValue()))); if (dvMaxTypicalities.GetAt(nOutGroup) > 0) hdgValue->SetTypicality(dvTypicalities.GetAt(nValue + nIntraCatchAllValue) / @@ -4063,7 +4061,7 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* { // Tri des valeurs de la partie si attribut categoriel if (dgAttribute->GetAttributeType() == KWType::Symbol) - cast(CCHDGValueSet*, dgPart->GetValueSet())->SortValuesByTypicality(); + cast(CCHDGSymbolValueSet*, dgPart->GetValueSet())->SortValuesByTypicality(); // Initialisation du nom de la partie cast(CCHDGPart*, dgPart)->SetPartName(dgPart->GetObjectLabel()); diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp index e2ccf3174..2fa57c701 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp @@ -1552,8 +1552,8 @@ boolean CCCoclusteringReport::ReadComposition(CCHierarchicalDataGrid* coclusteri ALString sTmp; CCHDGAttribute* dgAttribute; KWDGPart* dgPart; - CCHDGValueSet* dgValueSet; - CCHDGValue* dgValue; + CCHDGSymbolValueSet* dgValueSet; + CCHDGSymbolValue* dgValue; int nAttribute; ObjectDictionary* odPartDictionary; ALString sAttributeName; @@ -1643,8 +1643,8 @@ boolean CCCoclusteringReport::ReadComposition(CCHierarchicalDataGrid* coclusteri // On rajoute si necessaire la StarValue au dernier groupe specifie if (dgPart != NULL and not bStarValueFound) { - dgValueSet = cast(CCHDGValueSet*, dgPart->GetValueSet()); - dgValueSet->AddValue(Symbol::GetStarValue()); + dgValueSet = cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()); + dgValueSet->AddSymbolValue(Symbol::GetStarValue()); } break; } @@ -1726,8 +1726,8 @@ boolean CCCoclusteringReport::ReadComposition(CCHierarchicalDataGrid* coclusteri sValue = (Symbol)sValueName; // Memorisation de la valeur - dgValueSet = cast(CCHDGValueSet*, dgPart->GetValueSet()); - dgValue = cast(CCHDGValue*, dgValueSet->AddValue(sValue)); + dgValueSet = cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()); + dgValue = cast(CCHDGSymbolValue*, dgValueSet->AddSymbolValue(sValue)); dgValue->SetValueFrequency(nFrequency); dgValue->SetTypicality(dTypicality); @@ -1754,7 +1754,7 @@ boolean CCCoclusteringReport::ReadComposition(CCHierarchicalDataGrid* coclusteri { // Verification de la compatibilite entre l'effectif de la partie // et l'effectif cumule de ses valeurs - dgValueSet = cast(CCHDGValueSet*, dgPart->GetValueSet()); + dgValueSet = cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()); nTotalValueFrequency = dgValueSet->ComputeTotalFrequency(); if (dgPart->GetPartFrequency() != nTotalValueFrequency) { @@ -2225,9 +2225,9 @@ void CCCoclusteringReport::WriteComposition(const CCHierarchicalDataGrid* coclus int nAttribute; KWDGPart* dgPart; CCHDGPart* hdgPart; - CCHDGValueSet* hdgValueSet; + CCHDGSymbolValueSet* hdgValueSet; KWDGValue* dgValue; - CCHDGValue* hdgValue; + CCHDGSymbolValue* hdgValue; // CH IV Begin CCHDGVarPartSet* hdgVarPartSet; KWDGVarPartValue* dgVarPartValue; @@ -2255,15 +2255,15 @@ void CCCoclusteringReport::WriteComposition(const CCHierarchicalDataGrid* coclus hdgPart = cast(CCHDGPart*, dgPart); // Parcours des valeurs - hdgValueSet = cast(CCHDGValueSet*, hdgPart->GetValueSet()); + hdgValueSet = cast(CCHDGSymbolValueSet*, hdgPart->GetValueSet()); dgValue = hdgValueSet->GetHeadValue(); while (dgValue != NULL) { - hdgValue = cast(CCHDGValue*, dgValue); + hdgValue = cast(CCHDGSymbolValue*, dgValue); // Caracteristiques des valeurs // (y compris la valeur par defaut, pour etre coherent avec l'export JSON) - ost << hdgPart->GetPartName() << "\t" << hdgValue->GetValue() << "\t" + ost << hdgPart->GetPartName() << "\t" << hdgValue->GetSymbolValue() << "\t" << hdgValue->GetValueFrequency() << "\t" << hdgValue->GetTypicality() << "\n"; @@ -3419,7 +3419,8 @@ boolean CCCoclusteringReport::ReadJSONDimensionPartitions(CCHierarchicalDataGrid // Ajout de la valeur par defaut dans ce groupe assert(dgPart != NULL); - cast(CCHDGValueSet*, dgPart->GetValueSet())->AddValue(Symbol::GetStarValue()); + cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()) + ->AddSymbolValue(Symbol::GetStarValue()); } } // CH IV Begin @@ -3523,8 +3524,8 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(CCHDGAttribute* dgAttribute, KW boolean bOk = true; ObjectDictionary odChekedValues; boolean bIsEnd; - CCHDGValueSet* dgValueSet; - CCHDGValue* dgValue; + CCHDGSymbolValueSet* dgValueSet; + CCHDGSymbolValue* dgValue; CCHDGPart* hdgPart; ALString sClusterName; ALString sValue; @@ -3631,7 +3632,7 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(CCHDGAttribute* dgAttribute, KW if (bOk) { hdgPart = cast(CCHDGPart*, dgPart); - dgValueSet = cast(CCHDGValueSet*, dgPart->GetValueSet()); + dgValueSet = cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()); hdgPart->SetPartName(sClusterName); hdgPart->SetShortDescription(""); hdgPart->SetDescription(""); @@ -3639,7 +3640,7 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(CCHDGAttribute* dgAttribute, KW // Memorisation des valeurs for (i = 0; i < svValues.GetSize(); i++) { - dgValue = cast(CCHDGValue*, dgValueSet->AddValue((Symbol)svValues.GetAt(i))); + dgValue = cast(CCHDGSymbolValue*, dgValueSet->AddSymbolValue((Symbol)svValues.GetAt(i))); dgValue->SetValueFrequency(ivValueFrequencies.GetAt(i)); dgValue->SetTypicality(dvValueTypicalities.GetAt(i)); } @@ -3838,9 +3839,9 @@ boolean CCCoclusteringReport::ReadJSONInnerAttributeValueGroups(KWDGAttribute* i { // Traitement particulier de la Star value if (sValue == Symbol::GetStarValue().GetValue()) - dgPart->GetValueSet()->AddValue(Symbol::GetStarValue()); + dgPart->GetSymbolValueSet()->AddSymbolValue(Symbol::GetStarValue()); else - dgPart->GetValueSet()->AddValue((Symbol)sValue); + dgPart->GetSymbolValueSet()->AddSymbolValue((Symbol)sValue); } bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); } @@ -4624,9 +4625,9 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData int nAttribute; KWDGPart* dgPart; CCHDGPart* hdgPart; - CCHDGValueSet* hdgValueSet; + CCHDGSymbolValueSet* hdgValueSet; KWDGValue* dgValue; - CCHDGValue* hdgValue; + CCHDGSymbolValue* hdgValue; int nIndex; int nDefaultGroupIndex; KWDGInterval* dgInterval; @@ -4705,7 +4706,7 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData while (dgPart != NULL) { hdgPart = cast(CCHDGPart*, dgPart); - hdgValueSet = cast(CCHDGValueSet*, hdgPart->GetValueSet()); + hdgValueSet = cast(CCHDGSymbolValueSet*, hdgPart->GetValueSet()); // Memorisation de l'index du groupe par defaut if (hdgValueSet->IsDefaultPart()) @@ -4719,9 +4720,9 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData dgValue = hdgValueSet->GetHeadValue(); while (dgValue != NULL) { - hdgValue = cast(CCHDGValue*, dgValue); + hdgValue = cast(CCHDGSymbolValue*, dgValue); if (hdgValue->GetValueFrequency() > 0) - fJSON->WriteString(hdgValue->GetValue().GetValue()); + fJSON->WriteString(hdgValue->GetSymbolValue().GetValue()); hdgValueSet->GetNextValue(dgValue); } fJSON->EndList(); @@ -4731,7 +4732,7 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData dgValue = hdgValueSet->GetHeadValue(); while (dgValue != NULL) { - hdgValue = cast(CCHDGValue*, dgValue); + hdgValue = cast(CCHDGSymbolValue*, dgValue); if (hdgValue->GetValueFrequency() > 0) fJSON->WriteInt(hdgValue->GetValueFrequency()); hdgValueSet->GetNextValue(dgValue); @@ -4743,7 +4744,7 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData dgValue = hdgValueSet->GetHeadValue(); while (dgValue != NULL) { - hdgValue = cast(CCHDGValue*, dgValue); + hdgValue = cast(CCHDGSymbolValue*, dgValue); if (hdgValue->GetValueFrequency() > 0) fJSON->WriteDouble(hdgValue->GetTypicality()); hdgValueSet->GetNextValue(dgValue); @@ -4924,7 +4925,7 @@ void CCCoclusteringReport::WriteJSONInnerAttributePartition(const CCHierarchical currentValue = currentPart->GetValueSet()->GetHeadValue(); while (currentValue != NULL) { - fJSON->WriteString(currentValue->GetValue().GetValue()); + fJSON->WriteString(currentValue->GetSymbolValue().GetValue()); // Valeur suivante currentPart->GetValueSet()->GetNextValue(currentValue); @@ -4983,7 +4984,7 @@ void CCCoclusteringReport::WriteJSONInnerAttributePartition(const CCHierarchical { dgValue = cast(KWDGValue*, oaAllValues.GetAt(nValue)); if (dgValue->GetValueFrequency() > 0) - fJSON->WriteString(dgValue->GetValue().GetValue()); + fJSON->WriteString(dgValue->GetSymbolValue().GetValue()); } fJSON->EndList(); diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp index 3469dde51..5eb184f22 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp @@ -856,7 +856,7 @@ boolean CCHDGPart::CheckHierarchy() const // Verification de l'ensemble de valeurs dans le cas categoriel if (GetPartType() == KWType::Symbol) - bOk = bOk and cast(CCHDGValueSet*, valueSet)->CheckHierarchy(); + bOk = bOk and cast(CCHDGSymbolValueSet*, symbolValueSet)->CheckHierarchy(); // Verifications complementaires de chainahe coherent de la hierarchie, sans message d'erreur assert(not bOk or parentPart == NULL or parentPart->childPart1 == this or parentPart->childPart2 == this); @@ -865,9 +865,9 @@ boolean CCHDGPart::CheckHierarchy() const return bOk; } -KWDGValueSet* CCHDGPart::NewValueSet() const +KWDGSymbolValueSet* CCHDGPart::NewSymbolValueSet() const { - return new CCHDGValueSet(); + return new CCHDGSymbolValueSet(); } // CH IV Begin @@ -949,18 +949,23 @@ int CCHDGPartCompareHierarchicalRank(const void* elem1, const void* elem2) } ////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGValueSet +// Classe CCHDGSymbolValueSet -CCHDGValueSet::CCHDGValueSet() {} +CCHDGSymbolValueSet::CCHDGSymbolValueSet() {} -CCHDGValueSet::~CCHDGValueSet() {} +CCHDGSymbolValueSet::~CCHDGSymbolValueSet() {} -void CCHDGValueSet::SortValuesByTypicality() +KWDGValueSet* CCHDGSymbolValueSet::Create() const +{ + return new CCHDGSymbolValueSet; +} + +void CCHDGSymbolValueSet::SortValuesByTypicality() { InternalSortValues(CCHDGValueCompareDecreasingTypicality); } -boolean CCHDGValueSet::CheckHierarchy() const +boolean CCHDGSymbolValueSet::CheckHierarchy() const { boolean bOk = true; KWDGValue* value; @@ -974,7 +979,7 @@ boolean CCHDGValueSet::CheckHierarchy() const while (value != NULL) { // Test de la valeur - bOk = cast(CCHDGValue*, value)->CheckHierarchy(); + bOk = cast(CCHDGSymbolValue*, value)->CheckHierarchy(); if (not bOk) break; @@ -985,39 +990,39 @@ boolean CCHDGValueSet::CheckHierarchy() const return bOk; } -KWDGValue* CCHDGValueSet::NewValue(const Symbol& sValue) const +KWDGValue* CCHDGSymbolValueSet::NewSymbolValue(const Symbol& sValue) const { - return new CCHDGValue(sValue); + return new CCHDGSymbolValue(sValue); } ////////////////////////////////////////////////////////////////////////////// // Classe CCHDGValue -CCHDGValue::CCHDGValue(const Symbol& sValue) : KWDGValue(sValue) +CCHDGSymbolValue::CCHDGSymbolValue(const Symbol& sValue) : KWDGSymbolValue(sValue) { dTypicality = 0; } -CCHDGValue::~CCHDGValue() {} +CCHDGSymbolValue::~CCHDGSymbolValue() {} -void CCHDGValue::SetTypicality(double dValue) +void CCHDGSymbolValue::SetTypicality(double dValue) { require(0 <= dTypicality and dTypicality <= 1); dTypicality = dValue; } -double CCHDGValue::GetTypicality() const +double CCHDGSymbolValue::GetTypicality() const { return dTypicality; } -boolean CCHDGValue::CheckHierarchy() const +boolean CCHDGSymbolValue::CheckHierarchy() const { boolean bOk = true; ALString sTmp; // Methode d'integrite de base - require(KWDGValue::Check()); + require(KWDGSymbolValue::Check()); // Typicalite if (bOk and (dTypicality < 0 or dTypicality > 1)) @@ -1031,8 +1036,8 @@ boolean CCHDGValue::CheckHierarchy() const int CCHDGValueCompareDecreasingTypicality(const void* elem1, const void* elem2) { const double dPrecision = 1e-7; - CCHDGValue* value1; - CCHDGValue* value2; + CCHDGSymbolValue* value1; + CCHDGSymbolValue* value2; double dCompare; int nCompare; @@ -1040,8 +1045,8 @@ int CCHDGValueCompareDecreasingTypicality(const void* elem1, const void* elem2) require(elem2 != NULL); // Acces a la valueies - value1 = cast(CCHDGValue*, *(Object**)elem1); - value2 = cast(CCHDGValue*, *(Object**)elem2); + value1 = cast(CCHDGSymbolValue*, *(Object**)elem1); + value2 = cast(CCHDGSymbolValue*, *(Object**)elem2); // Comparaison a une precision pres (compatible avec l'affichage) dCompare = -value1->GetTypicality() + value2->GetTypicality(); diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h index 96bbf37ad..d24420d5e 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h @@ -7,8 +7,8 @@ class CCHierarchicalDataGrid; class CCHDGAttribute; class CCHDGPart; -class CCHDGValueSet; -class CCHDGValue; +class CCHDGSymbolValueSet; +class CCHDGSymbolValue; class CCHDGCell; // CH IV Begin class CCHDGVarPartSet; @@ -278,7 +278,7 @@ class CCHDGPart : public KWDGPart ///// Implementation protected: // Reimplementation des methodes virtuelles - KWDGValueSet* NewValueSet() const override; + KWDGSymbolValueSet* NewSymbolValueSet() const override; // CH IV Begin KWDGVarPartSet* NewVarPartSet() const override; // CH IV End @@ -311,14 +311,17 @@ int CCHDGPartCompareLeafRank(const void* elem1, const void* elem2); int CCHDGPartCompareHierarchicalRank(const void* elem1, const void* elem2); ////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGValueSet +// Classe CCHDGSymbolValueSet // Ensemble de valeurs d'une partie symbolique d'un HierarchicalDataGrid -class CCHDGValueSet : public KWDGValueSet +class CCHDGSymbolValueSet : public KWDGSymbolValueSet { public: // Constructeur - CCHDGValueSet(); - ~CCHDGValueSet(); + CCHDGSymbolValueSet(); + ~CCHDGSymbolValueSet(); + + // Creation + KWDGValueSet* Create() const override; // Tri des valeurs par typicalite decroissante void SortValuesByTypicality(); @@ -330,18 +333,18 @@ class CCHDGValueSet : public KWDGValueSet ///// Implementation protected: // Reimplementation des methodes virtuelles - KWDGValue* NewValue(const Symbol& sValue) const override; + KWDGValue* NewSymbolValue(const Symbol& sValue) const override; }; ////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGValue +// Classe CCHDGSymbolValue // Valeur symbolique d'un HierarchicalDataGrid -class CCHDGValue : public KWDGValue +class CCHDGSymbolValue : public KWDGSymbolValue { public: // Constructeur - CCHDGValue(const Symbol& sValue); - ~CCHDGValue(); + CCHDGSymbolValue(const Symbol& sValue); + ~CCHDGSymbolValue(); // Typicalite void SetTypicality(double dValue); diff --git a/src/Learning/MODL_Coclustering/CCLearningProblem.cpp b/src/Learning/MODL_Coclustering/CCLearningProblem.cpp index dce50f24c..b5901ce1b 100644 --- a/src/Learning/MODL_Coclustering/CCLearningProblem.cpp +++ b/src/Learning/MODL_Coclustering/CCLearningProblem.cpp @@ -1036,9 +1036,9 @@ void CCLearningProblem::WriteSymbolClusters(const CCHDGAttribute* symbolCocluste { KWDGPart* dgPart; CCHDGPart* hdgPart; - CCHDGValueSet* hdgValueSet; + CCHDGSymbolValueSet* hdgValueSet; KWDGValue* dgValue; - CCHDGValue* hdgValue; + CCHDGSymbolValue* hdgValue; require(symbolCoclusteringAttribute != NULL); require(symbolCoclusteringAttribute->GetAttributeType() == KWType::Symbol); @@ -1053,14 +1053,14 @@ void CCLearningProblem::WriteSymbolClusters(const CCHDGAttribute* symbolCocluste hdgPart = cast(CCHDGPart*, dgPart); // Parcours des valeurs - hdgValueSet = cast(CCHDGValueSet*, hdgPart->GetValueSet()); + hdgValueSet = cast(CCHDGSymbolValueSet*, hdgPart->GetValueSet()); dgValue = hdgValueSet->GetHeadValue(); while (dgValue != NULL) { - hdgValue = cast(CCHDGValue*, dgValue); + hdgValue = cast(CCHDGSymbolValue*, dgValue); // Caracteristiques des valeurs - ost << hdgPart->GetUserLabel() << "\t" << hdgValue->GetValue() << "\t" + ost << hdgPart->GetUserLabel() << "\t" << hdgValue->GetSymbolValue() << "\t" << hdgValue->GetValueFrequency() << "\t" << hdgValue->GetTypicality() << "\n"; // Valeur suivante @@ -1291,7 +1291,7 @@ void CCLearningProblem::WriteSymbolInnerAttribute(KWDGAttribute* symbolCocluster { // Caracteristiques des valeurs ost << symbolCoclusteringAttribute->GetAttributeName() + " " + dgPart->GetObjectLabel() << "\t" - << dgValue->GetValue() << "\n"; + << dgValue->GetSymbolValue() << "\n"; // Valeur suivante dgValueSet->GetNextValue(dgValue); From 5f44568c9da1a26791a91bb3d877ad13b4728694 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Mon, 28 Aug 2023 18:20:10 +0200 Subject: [PATCH 03/17] New class hierarchy, with KWDGVarPartSet sub-class of KWDGValueSet MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Ratachement des classes KWDGVarPartSet et KWDGVarPartValue a leur nouvelle hierarchie - KWDGValueSet - KWDGVarPartSet - KWDGValue - KWDGVarPartValue Propagation des impacts sur l'utilisation des nouvelles classes - nombreux impacts - KWDataPreparation - KWDataGrid - KWDataGridCosts - KWDataGridManager - KWDataGridMerger - KWDataGridPostOptimizer - MODL_Coclustering - CCCoclusteringReport - CCHierarchicalDataGrid - CCLearningProblem Ajustements: - KWDGValueSet::GetValueNumber - reimplementation en une methdoe unique, mais legerement specialisee pour le cas des valeur du type Symbol (cas de la StarValue) - KWDGValueSet::SortValues - definition d'une methode virtuelle, avec implementation dedie - SymbolValueSet: KWDGValueCompareDecreasingFrequency, effectif decroissant, puis valeur Symbol - VarPartSet: KWDGVarPartValueCompareAttributeNameAndVarPart: attribut, puis partie intervalle (KWDGPartContinuousCompare) ou valueSet (KWDGPartSymbolCompare) - necessaire, sinon bug suivant - cela ne s'arrete jamais sur LearningTest\TestCoclustering\y_CoclusteringIV_Standard\Iris - ce n'est pas une boucle infinir simple - CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid trouve sans arret des ameliorations du cout - c'est du au tri par effectif puis par valeur, alors que cette précisement a besoin de trier uniquement par valeurs - a faire: refactorer la gestion des trus des parties et des valeurs, en distingautn les cas par valeur, et par valeur et effectif decroissant Tests effectues - LearningTest\TestCoclustering\y_CoclusteringIV_Standard en release - LearningTest\TestCoclustering\y_CoclusteringIV_Standard\Iris en debug --- src/Learning/KWDataPreparation/KWDataGrid.cpp | 619 +++--------------- src/Learning/KWDataPreparation/KWDataGrid.h | 243 ++++--- .../KWDataPreparation/KWDataGridCosts.cpp | 12 +- .../KWDataPreparation/KWDataGridManager.cpp | 94 +-- .../KWDataPreparation/KWDataGridMerger.cpp | 8 +- .../KWDataGridPostOptimizer.cpp | 39 +- .../CCCoclusteringReport.cpp | 30 +- .../CCHierarchicalDataGrid.cpp | 8 +- .../MODL_Coclustering/CCLearningProblem.cpp | 8 +- .../MODL_Coclustering/MODL_Coclustering.cpp | 4 +- 10 files changed, 321 insertions(+), 744 deletions(-) diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index 9a67d7918..847618a62 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -1549,9 +1549,6 @@ void KWDataGrid::WriteAttributeParts(ostream& ost) const KWDGValue* value; boolean bDisplayAll = false; boolean bDisplayPartDetails = true; - // CH IV Begin - KWDGVarPartValue* varPartValue; - // CH IV End // Liste des attributs et de leurs parties ost << "Parts by variable" @@ -1567,9 +1564,7 @@ void KWDataGrid::WriteAttributeParts(ostream& ost) const part = attribute->GetHeadPart(); while (part != NULL) { - // CH IV Begin ost << "\t\t" << part->GetObjectLabel() << "\t" << part->GetPartFrequency(); - // CH IV End // Affichage des premieres valeurs dans le cas d'un attribut Symbol if (attribute->GetAttributeType() == KWType::Symbol) @@ -1593,8 +1588,8 @@ void KWDataGrid::WriteAttributeParts(ostream& ost) const else if (attribute->GetAttributeType() == KWType::VarPart) { nDisplayedValue = 0; - varPartValue = part->GetVarPartSet()->GetHeadVarPart(); - while (varPartValue != NULL) + value = part->GetVarPartSet()->GetHeadValue(); + while (value != NULL) { nDisplayedValue++; if (nDisplayedValue > nMaxDisplayedValue) @@ -1604,11 +1599,9 @@ void KWDataGrid::WriteAttributeParts(ostream& ost) const } else ost << "\t" - << varPartValue->GetVarPart() - ->GetAttribute() - ->GetAttributeName() - << "\t" << *(varPartValue->GetVarPart()); - part->GetVarPartSet()->GetNextVarPart(varPartValue); + << value->GetVarPart()->GetAttribute()->GetAttributeName() + << "\t" << *(value->GetVarPart()); + part->GetVarPartSet()->GetNextValue(value); } } // CH IV End @@ -2517,16 +2510,10 @@ void KWDGAttribute::BuildIndexingStructure() KWDGPart* part; KWDGValueSet* valueSet; KWDGValue* value; - // CH IV Begin - KWDGVarPartSet* varPartSet; - KWDGVarPartValue* varPartValue; KWDGAttribute* innerAttribute; int nInnerAttribute; - // CH IV End - // CH IV Begin require(KWType::IsCoclusteringType(GetAttributeType())); - // CH IV End require(Check()); // Indexation si necessaire @@ -2568,7 +2555,7 @@ void KWDGAttribute::BuildIndexingStructure() nkdParts.SetAt(value->GetNumericKeyValue(), part); // Memorisation de la partie associe a la valeur speciale - if (value->GetSymbolValue() == Symbol::GetStarValue()) + if (value->IsDefaultValue()) starValuePart = part; // Valeur suivante @@ -2592,15 +2579,15 @@ void KWDGAttribute::BuildIndexingStructure() while (part != NULL) { // Parcours des parties de variable de la partie - varPartSet = part->GetVarPartSet(); - varPartValue = varPartSet->GetHeadVarPart(); - while (varPartValue != NULL) + valueSet = part->GetValueSet(); + value = valueSet->GetHeadValue(); + while (value != NULL) { // Ajout de la partie avec la partie de variable pour cle - nkdVarPartSets.SetAt((NUMERIC)varPartValue->GetVarPart(), part); + nkdVarPartSets.SetAt(value->GetNumericKeyValue(), part); // Partie de variable suivante - varPartSet->GetNextVarPart(varPartValue); + valueSet->GetNextValue(value); } // Partie suivante @@ -2775,8 +2762,6 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con KWDGPart* part; KWDGPart* otherPart; KWDGValue* value; - KWDGVarPartValue* varPartValue; - KWDGPart* firstVarPartValue; NumericKeyDictionary nkdOtherPartsPerValue; require(otherAttribute != NULL); @@ -2843,7 +2828,7 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con otherAttribute->GetNextPart(otherPart); } - // Parcours des partie pour verifier leur inclusion + // Parcours des parties pour verifier leur inclusion part = GetHeadPart(); while (part != NULL) { @@ -2873,11 +2858,11 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con while (otherPart != NULL) { // Parcours de valeurs de l'autre partie pour memoriser l'association - varPartValue = otherPart->GetVarPartSet()->GetHeadVarPart(); - while (varPartValue != NULL) + value = otherPart->GetVarPartSet()->GetHeadValue(); + while (value != NULL) { - nkdOtherPartsPerValue.SetAt((NUMERIC)(varPartValue->GetVarPart()), otherPart); - otherPart->GetVarPartSet()->GetNextVarPart(varPartValue); + nkdOtherPartsPerValue.SetAt(value->GetNumericKeyValue(), otherPart); + otherPart->GetVarPartSet()->GetNextValue(value); } // Autre partie suivante @@ -2889,8 +2874,9 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con while (part != NULL) { // Recherche de l'autre partie sur la base de la premiere valeur de la partie en cours - firstVarPartValue = part->GetVarPartSet()->GetHeadVarPart()->GetVarPart(); - otherPart = cast(KWDGPart*, nkdOtherPartsPerValue.Lookup((NUMERIC)firstVarPartValue)); + otherPart = + cast(KWDGPart*, nkdOtherPartsPerValue.Lookup( + part->GetValueSet()->GetHeadValue()->GetNumericKeyValue())); // Pas d'inclusion si partie non trouvee ou non inclusante if (otherPart == NULL or not part->IsSubPart(otherPart)) @@ -2915,8 +2901,6 @@ boolean KWDGAttribute::Check() const KWDGPart* searchedPart; KWDGValueSet* valueSet; KWDGValue* value; - KWDGVarPartSet* varPartSet; - KWDGVarPartValue* varPartValue; KWDGPart* usedVarPart; ObjectArray oaCheckIntervals; int nInterval; @@ -3197,21 +3181,18 @@ boolean KWDGAttribute::Check() const while (part != NULL) { // Parcours des VarPart de la partie pour verifier qu'elle ne sont utilisees qu'une seule fois - varPartSet = part->GetVarPartSet(); - varPartValue = varPartSet->GetHeadVarPart(); - while (varPartValue != NULL) + valueSet = part->GetValueSet(); + value = valueSet->GetHeadValue(); + while (value != NULL) { - usedVarPart = varPartValue->GetVarPart(); - // Recherche si la VarPart est deja enregistree - searchedPart = cast(KWDGPart*, nkdCheckParts.Lookup((NUMERIC)usedVarPart)); + searchedPart = cast(KWDGPart*, nkdCheckParts.Lookup(value->GetNumericKeyValue())); // Erreur si VarPart deja enregistree if (searchedPart != NULL) { - part->AddError(sTmp + "Inner variable var part " + - usedVarPart->GetObjectLabel() + " already belongs to part " + - part->GetObjectLabel()); + part->AddError(sTmp + "Inner variable var part " + value->GetObjectLabel() + + " already belongs to part " + part->GetObjectLabel()); bOk = false; break; } @@ -3219,10 +3200,10 @@ boolean KWDGAttribute::Check() const else { // Ajout de la partie avec la valeur pour cle - nkdCheckParts.SetAt((NUMERIC)usedVarPart, part); + nkdCheckParts.SetAt(value->GetNumericKeyValue(), part); // Valeur suivante - varPartSet->GetNextVarPart(varPartValue); + valueSet->GetNextValue(value); } } @@ -3316,7 +3297,7 @@ void KWDGAttribute::SortParts() part = GetHeadPart(); while (part != NULL) { - part->GetVarPartSet()->SortVarPartValues(); + part->GetValueSet()->SortValues(); GetNextPart(part); } } @@ -3358,7 +3339,7 @@ boolean KWDGAttribute::ArePartsSorted() const part = GetHeadPart(); while (part != NULL) { - bIsSorted = part->GetVarPartSet()->AreVarPartValuesSorted(); + bIsSorted = part->GetValueSet()->AreValuesSorted(); if (not bIsSorted) break; GetNextPart(part); @@ -3818,7 +3799,7 @@ boolean KWDGPart::IsSubPart(const KWDGPart* otherPart) const else if (GetPartType() == KWType::Symbol) return GetValueSet()->IsSubValueSet(otherPart->GetValueSet()); else - return GetVarPartSet()->IsSubVarPartSet(otherPart->GetVarPartSet()); + return GetValueSet()->IsSubValueSet(otherPart->GetValueSet()); } boolean KWDGPart::Check() const @@ -3991,8 +3972,8 @@ void KWDGPart::Write(ostream& ost) const if (GetPartType() == KWType::Symbol and symbolValueSet->GetValueNumber() > 0) WriteValues(ost); - if (GetPartType() == KWType::VarPart and varPartSet->GetVarPartNumber() > 0) - varPartSet->WriteVarParts(ost); + if (GetPartType() == KWType::VarPart and varPartSet->GetValueNumber() > 0) + WriteValues(ost); // CH IV End if (GetCellNumber() > 0) WriteCells(ost); @@ -4005,7 +3986,7 @@ void KWDGPart::WriteValues(ostream& ost) const if (GetPartType() == KWType::Symbol) symbolValueSet->WriteValues(ost); else if (GetPartType() == KWType::VarPart) - varPartSet->WriteVarParts(ost); + varPartSet->WriteValues(ost); } void KWDGPart::WriteCells(ostream& ost) const @@ -4176,8 +4157,8 @@ int KWDGPartVarPartCompare(const void* elem1, const void* elem2) { KWDGPart* part1; KWDGPart* part2; - KWDGVarPartValue* varPartValue1; - KWDGVarPartValue* varPartValue2; + KWDGValue* value1; + KWDGValue* value2; require(elem1 != NULL); require(elem2 != NULL); @@ -4189,20 +4170,20 @@ int KWDGPartVarPartCompare(const void* elem1, const void* elem2) assert(part2->GetPartType() == KWType::VarPart); // Comparaison de la premiere partie de variable de la partie - if (part1->GetVarPartSet()->GetHeadVarPart() == NULL) + if (part1->GetVarPartSet()->GetHeadValue() == NULL) { - if (part2->GetVarPartSet()->GetHeadVarPart() == NULL) + if (part2->GetVarPartSet()->GetHeadValue() == NULL) return 0; else return -1; } - else if (part2->GetVarPartSet()->GetHeadVarPart() == NULL) + else if (part2->GetVarPartSet()->GetHeadValue() == NULL) return 1; else { - varPartValue1 = part1->GetVarPartSet()->GetHeadVarPart(); - varPartValue2 = part2->GetVarPartSet()->GetHeadVarPart(); - return KWDGVarPartValueCompareAttributeNameAndVarPart(&varPartValue1, &varPartValue2); + value1 = part1->GetVarPartSet()->GetHeadValue(); + value2 = part2->GetVarPartSet()->GetHeadValue(); + return value1->CompareValue(value2); } } @@ -4724,7 +4705,7 @@ void KWDGValueSet::WriteValues(ostream& ost) const value = GetHeadValue(); while (value != NULL) { - ost << "\t" << *value << "\n"; + ost << "\t" << value->GetObjectLabel() << "\t" << value->GetValueFrequency() << "\n"; GetNextValue(value); } } @@ -4855,25 +4836,6 @@ boolean KWDGValueSet::GetEmulated() const ////////////////////////////////////////////////////////////////////////////// // Classe KWDGSymbolValueSet -KWDGValue* KWDGSymbolValueSet::AddSymbolValue(const Symbol& sValue) -{ - KWDGValue* value; - - // Creation de la valeur - value = NewSymbolValue(sValue); - - // Ajout en fin de la liste des valeurs - AddTailValue(value); - - // On retourne la valeur cree - return value; -} - -KWDGValue* KWDGSymbolValueSet::AddValueCopy(const KWDGValue* sourceValue) -{ - return AddSymbolValue(cast(KWDGSymbolValue*, sourceValue)->GetSymbolValue()); -} - void KWDGSymbolValueSet::CompressValueSet() { int nCurrentValueNumber; @@ -4937,11 +4899,6 @@ KWDGValueSet* KWDGSymbolValueSet::ComputeCleanedValueSet() const return symbolValueSet; } -KWDGValue* KWDGSymbolValueSet::NewSymbolValue(const Symbol& sValue) const -{ - return new KWDGSymbolValue(sValue); -} - ////////////////////////////////////////////////////////////////////////////// // Classe KWDGValue @@ -5013,38 +4970,29 @@ const ALString KWDGSymbolValue::GetObjectLabel() const ////////////////////////////////////////////////////////////////////////////// // CH IV Begin -// Classe KWDGVarPartValue -void KWDGVarPartValue::Write(ostream& ost) const -{ - ost << *varPart << "\t" << varPart->GetPartFrequency() << "\n"; -} +////////////////////////////////////////////////////////////////////////////// +// Classe KWDGVarPartValue -int KWDGVarPartValueCompareAttributeNameAndVarPart(const void* elem1, const void* elem2) +int KWDGVarPartValue::CompareValue(const KWDGValue* otherValue) const { - KWDGVarPartValue* value1; - KWDGVarPartValue* value2; + int nCompare; KWDGPart* varPart1; KWDGPart* varPart2; - int nCompare; - - require(elem1 != NULL); - require(elem2 != NULL); - // Acces a la valeur - value1 = cast(KWDGVarPartValue*, *(Object**)elem1); - value2 = cast(KWDGVarPartValue*, *(Object**)elem2); + require(otherValue != NULL); // Comparaison des noms d'attributs des parties de variable - nCompare = value1->GetVarPart()->GetAttribute()->GetAttributeName().Compare( - value2->GetVarPart()->GetAttribute()->GetAttributeName()); + nCompare = GetVarPart()->GetAttribute()->GetAttributeName().Compare( + otherValue->GetVarPart()->GetAttribute()->GetAttributeName()); if (nCompare != 0) return nCompare; // Cas de parties de variable d'un meme attribut else + { - varPart1 = value1->GetVarPart(); - varPart2 = value2->GetVarPart(); + varPart1 = GetVarPart(); + varPart2 = otherValue->GetVarPart(); // Cas d'un attribut numerique : comparaison des intervalles if (varPart1->GetPartType() == KWType::Continuous) return KWDGPartContinuousCompare(&varPart1, &varPart2); @@ -5054,449 +5002,90 @@ int KWDGVarPartValueCompareAttributeNameAndVarPart(const void* elem1, const void } } -int KWSortableObjectCompareVarPart(const void* elem1, const void* elem2) -{ - KWDGVarPartValue* varPartValue1; - KWDGVarPartValue* varPartValue2; - - require(elem1 != NULL); - require(elem2 != NULL); - - // Acces aux parties de variable - varPartValue1 = cast(KWDGVarPartValue*, cast(KWSortableObject*, *(Object**)elem1)->GetSortValue()); - varPartValue2 = cast(KWDGVarPartValue*, cast(KWSortableObject*, *(Object**)elem2)->GetSortValue()); - - return KWDGVarPartValueCompareAttributeNameAndVarPart(&varPartValue1, &varPartValue2); -} - -KWDGVarPartValue* KWDGVarPartSet::AddVarPart(KWDGPart* varPart) -{ - KWDGVarPartValue* varPartValue; - - // Creation de la partie de variable - varPartValue = NewVarPartValue(varPart); - - // Ajout en fin de la liste des valeurs - nVarPartNumber++; - if (headVarPart == NULL) - headVarPart = varPartValue; - if (tailVarPart != NULL) - { - tailVarPart->nextVarPartValue = varPartValue; - varPartValue->prevVarPartValue = tailVarPart; - } - tailVarPart = varPartValue; - - // On retourne la valeur cree - return varPartValue; -} - -void KWDGVarPartSet::DeleteVarPartValue(KWDGVarPartValue* value) -{ - require(value != NULL); - - // Supression de la liste des valuees - nVarPartNumber--; - if (value->prevVarPartValue != NULL) - value->prevVarPartValue->nextVarPartValue = value->nextVarPartValue; - if (value->nextVarPartValue != NULL) - value->nextVarPartValue->prevVarPartValue = value->prevVarPartValue; - if (headVarPart == value) - headVarPart = value->nextVarPartValue; - if (tailVarPart == value) - tailVarPart = value->prevVarPartValue; - - // Destruction de la valeur - delete value; -} - -void KWDGVarPartSet::DeleteAllVarPartValues() -{ - KWDGVarPartValue* value; - KWDGVarPartValue* valueToDelete; - - // Destruction des valeurs - value = headVarPart; - while (value != NULL) - { - valueToDelete = value; - value = value->nextVarPartValue; - delete valueToDelete; - } - - // Reinitialisation de la gestion de la liste des valeurs - headVarPart = NULL; - tailVarPart = NULL; - nVarPartNumber = 0; -} - -boolean KWDGVarPartSet::CheckVarPart(KWDGVarPartValue* value) const -{ - boolean bOk; - KWDGVarPartValue* currentValue; - - require(value != NULL); - - // Test si la partie fait partie de l'ensemble - bOk = false; - currentValue = headVarPart; - while (currentValue != NULL) - { - if (value == currentValue) - { - bOk = true; - break; - } - currentValue = currentValue->nextVarPartValue; - } - return bOk; -} - -void KWDGVarPartSet::ExportVarParts(ObjectArray* oaVarParts) const -{ - KWDGVarPartValue* varPartValue; - int nVarPart; - - require(oaVarParts != NULL); - require(oaVarParts->GetSize() == 0); - - // Ajout des parties dans le tableau - oaVarParts->SetSize(GetVarPartNumber()); - nVarPart = 0; - varPartValue = headVarPart; - while (varPartValue != NULL) - { - oaVarParts->SetAt(nVarPart, varPartValue); - nVarPart++; - varPartValue = varPartValue->nextVarPartValue; - } -} - -boolean KWDGVarPartSet::IsSubVarPartSet(const KWDGVarPartSet* otherVarPartSet) const -{ - boolean bOk = true; - NumericKeyDictionary nkdOtherValues; - KWDGVarPartValue* varPartValue; - KWDGPart* part; - - // On doit avoir moins de valeurs - if (GetVarPartNumber() > otherVarPartSet->GetVarPartNumber()) - bOk = false; - // Sinon, on teste effectivement l'inclusion - else - { - // Memorisation prealable des valeurs de l'autre ensemble de valeur - varPartValue = otherVarPartSet->GetHeadVarPart(); - while (varPartValue != NULL) - { - part = varPartValue->GetVarPart(); - nkdOtherValues.SetAt((NUMERIC)part, part); - otherVarPartSet->GetNextVarPart(varPartValue); - } - - // Parcours des valeurs pour verifie s'i elles existe dans l'autre ensemble de valeur - varPartValue = GetHeadVarPart(); - while (varPartValue != NULL) - { - part = varPartValue->GetVarPart(); - if (nkdOtherValues.Lookup((NUMERIC)part) == NULL) - { - bOk = false; - break; - } - GetNextVarPart(varPartValue); - } - } - return bOk; -} - -int KWDGVarPartSet::ComputeTotalFrequency() const +void KWDGVarPartValue::Write(ostream& ost) const { - int nTotalFrequency; - KWDGVarPartValue* value; - - // Parcours des parties de variable pour calculer l'effectif cumule - nTotalFrequency = 0; - value = GetHeadVarPart(); - while (value != NULL) - { - nTotalFrequency += value->GetVarPartFrequency(); - GetNextVarPart(value); - } - return nTotalFrequency; + ost << *varPart << "\t" << GetValueFrequency(); } -void KWDGVarPartSet::Import(KWDGVarPartSet* sourceVarPartSet) +const ALString KWDGVarPartValue::GetExternalValueLabel() const { - require(Check()); - require(sourceVarPartSet != NULL); - require(sourceVarPartSet->Check()); - - // Transfert des valeurs de la premiere partie en tete du second - sourceVarPartSet->tailVarPart->nextVarPartValue = headVarPart; - headVarPart->prevVarPartValue = sourceVarPartSet->tailVarPart; - headVarPart = sourceVarPartSet->headVarPart; - nVarPartNumber += sourceVarPartSet->nVarPartNumber; - - // On dereference les valeurs de la premiere partie - sourceVarPartSet->headVarPart = NULL; - sourceVarPartSet->tailVarPart = NULL; - sourceVarPartSet->nVarPartNumber = 0; - - ensure(Check()); + return GetObjectLabel(); } -void KWDGVarPartSet::CopyFrom(const KWDGVarPartSet* sourceVarPartSet) +const ALString KWDGVarPartValue::GetClassLabel() const { - KWDGVarPartValue* varPartValue; - KWDGVarPartValue* varPartCopyValue; - - require(sourceVarPartSet != NULL); - - // Nettoyage des parties de variable actuelles - DeleteAllVarPartValues(); - - // Recopie de la liste de valeurs source - varPartValue = sourceVarPartSet->GetHeadVarPart(); - while (varPartValue != NULL) - { - varPartCopyValue = AddVarPart(varPartValue->GetVarPart()); - assert(varPartCopyValue->GetVarPartFrequency() == varPartValue->GetVarPartFrequency()); - sourceVarPartSet->GetNextVarPart(varPartValue); - } - assert(nVarPartNumber == sourceVarPartSet->nVarPartNumber); + return "VarPart"; } -void KWDGVarPartSet::UpgradeFrom(const KWDGVarPartSet* sourceVarPartSet) +const ALString KWDGVarPartValue::GetObjectLabel() const { - KWDGVarPartValue* part; - KWDGVarPartValue* partUpgrade; - - require(sourceVarPartSet != NULL); - - // Pour garantir la valeur correcte de nValueNumber si le sourceValueSet a ete compresse avant la copie - int nOldPartNumber; - nOldPartNumber = nVarPartNumber; - - // Recopie de la liste de valeurs source - part = sourceVarPartSet->GetHeadVarPart(); - while (part != NULL) - { - partUpgrade = AddVarPart(part->GetVarPart()); - // partUpgrade->SetPartFrequency(part->GetPartFrequency()); - sourceVarPartSet->GetNextVarPart(part); - } - // Pour garantir la valeur correcte de nValueNumber si le sourceValueSet a ete compresse avant la copie - nVarPartNumber = nOldPartNumber + sourceVarPartSet->nVarPartNumber; + return varPart->GetVarPartLabel(); } -void KWDGVarPartSet::SortVarPartValues() -{ - InternalSortValues(KWDGVarPartValueCompareAttributeNameAndVarPart); -} +//DDD +//DDD BEGIN Ajout temporaire de methodes, pour debuggage +//DDD -boolean KWDGVarPartSet::AreVarPartValuesSorted() const +//DDD Ajout temporaire de methodes, pour debuggage +int KWDGVarPartValueCompareAttributeNameAndVarPart(const void* elem1, const void* elem2) { KWDGVarPartValue* value1; KWDGVarPartValue* value2; + KWDGPart* varPart1; + KWDGPart* varPart2; int nCompare; - boolean bIsSorted = true; - - value1 = GetHeadVarPart(); - value2 = GetHeadVarPart(); - GetNextVarPart(value2); - - // Parcours des valeurs - while (value2 != NULL) - { - // Comparaison de la paire courante - nCompare = KWDGVarPartValueCompareAttributeNameAndVarPart(&value1, &value2); - - if (nCompare > 0) - { - bIsSorted = false; - break; - } - GetNextVarPart(value1); - GetNextVarPart(value2); - } - return bIsSorted; -} - -boolean KWDGVarPartSet::Check() const -{ - boolean bOk = true; - NumericKeyDictionary nkdCheckVarParts; - KWDGVarPartValue* varPartValue; - boolean bCheckFrequencies; - ALString sTmp; - - // Test d'existence d'au moins une partie - if (nVarPartNumber == 0) - { - AddError("No var part specified"); - bOk = false; - } - - // Test des valeurs de la partie - if (bOk) - { - // On ne verifie les effectifs que si au moins un est specifie - bCheckFrequencies = ComputeTotalFrequency() > 0; - - // Parcours des parties - varPartValue = GetHeadVarPart(); - while (varPartValue != NULL) - { - // Erreur si partie deja enregistree avec cette valeur - if (nkdCheckVarParts.Lookup((NUMERIC)varPartValue) != NULL) - { - AddError(sTmp + "VarPart " + varPartValue->GetObjectLabel() + - " already exists in the cluster"); - bOk = false; - break; - } - // Erreur si effectif a 0 - else if (bCheckFrequencies and varPartValue->GetVarPartFrequency() == 0) - { - AddError(sTmp + "VarPart " + varPartValue->GetObjectLabel() + - " should have a non-zero frequency"); - bOk = false; - break; - } - // On continue si pas d'erreur - else - { - // Ajout de la partie - nkdCheckVarParts.SetAt((NUMERIC)varPartValue, varPartValue); - - // Valeur suivante - GetNextVarPart(varPartValue); - } - } - } - return bOk; -} - -void KWDGVarPartSet::Write(ostream& ost) const -{ - // Identification - ost << GetClassLabel() << "\t" << GetObjectLabel() << "\n"; - // Affichage des valeurs - WriteVarParts(ost); -} + require(elem1 != NULL); + require(elem2 != NULL); -void KWDGVarPartSet::WriteVarParts(ostream& ost) const -{ - KWDGVarPartValue* value; + // Acces a la valeur + value1 = cast(KWDGVarPartValue*, *(Object**)elem1); + value2 = cast(KWDGVarPartValue*, *(Object**)elem2); - // Affichage des valeurs - cout << "VarParts" - << "\t" << GetVarPartNumber() << "\n"; - value = GetHeadVarPart(); - while (value != NULL) + // Comparaison des noms d'attributs des parties de variable + nCompare = value1->GetVarPart()->GetAttribute()->GetAttributeName().Compare( + value2->GetVarPart()->GetAttribute()->GetAttributeName()); + if (nCompare != 0) + return nCompare; + // Cas de parties de variable d'un meme attribut + else { - ost << value->GetVarPart()->GetAttribute()->GetAttributeName() << "\t" << *(value->GetVarPart()) - << "\n"; - GetNextVarPart(value); + varPart1 = value1->GetVarPart(); + varPart2 = value2->GetVarPart(); + // Cas d'un attribut numerique : comparaison des intervalles + if (varPart1->GetPartType() == KWType::Continuous) + return KWDGPartContinuousCompare(&varPart1, &varPart2); + // Cas d'un attribut categoriel : comparaison des effectifs des valueSet + else + return KWDGPartSymbolCompare(&varPart1, &varPart2); } } -const ALString KWDGVarPartSet::GetClassLabel() const +//DDD Ajout temporaire de methodes, pour debuggage +// Tri des parties de variable par nom de variable puis tri des parties de la meme variable, pour preparer +// l'affichage +void KWDGVarPartSet::SortValues() { - return "VarPart set"; + InternalSortValues(KWDGVarPartValueCompareAttributeNameAndVarPart); } -const ALString KWDGVarPartSet::GetObjectLabel() const -{ - ALString sLabel; - int nValue; - KWDGVarPartValue* dgVarPartValue; +//DDD +//DDD END Ajout temporaire de methodes, pour debuggage +//DDD - // Libelle base sur l'ensemble des parties - sLabel = "{"; - nValue = 0; - dgVarPartValue = GetHeadVarPart(); - while (dgVarPartValue != NULL) - { - // Prise en compte si moins de trois valeurs - if (nValue < 3) - { - if (nValue > 0) - sLabel += ", "; - sLabel += dgVarPartValue->GetVarPart()->GetVarPartLabel(); - nValue++; - } - // Arret si au moins quatre valeurs - else - { - sLabel += ", ..."; - break; - } - GetNextVarPart(dgVarPartValue); - } - sLabel += "}"; - return sLabel; -} - -void KWDGVarPartSet::InternalSortValues(CompareFunction fCompare) +int KWSortableObjectCompareVarPart(const void* elem1, const void* elem2) { - ObjectArray oaValues; - int i; - KWDGVarPartValue* value; - KWDGVarPartValue* defaultValue; - - require(fCompare != NULL); - - // Rangement des valeurs dans un tableau (hors valeur speciale) - // CH V9 TODO - // Pour pouvoir traiter les valueSet qui ont ete compressees, la taille est - // deduite du parcours des valeurs - // oaValues.SetSize(GetValueNumber()); - defaultValue = NULL; - value = GetHeadVarPart(); - while (value != NULL) - { - oaValues.Add(value); - GetNextVarPart(value); - } - - // Tri des valeurs selon la fonction de tri - oaValues.SetCompareFunction(fCompare); - oaValues.Sort(); - - // Rangement des valeurs dans la liste, selon l'ordre du tableau trie - headVarPart = NULL; - tailVarPart = NULL; - for (i = 0; i < oaValues.GetSize(); i++) - { - value = cast(KWDGVarPartValue*, oaValues.GetAt(i)); - - // Reinitialisation du chainage de la partie - value->prevVarPartValue = NULL; - value->nextVarPartValue = NULL; + KWDGVarPartValue* varPartValue1; + KWDGVarPartValue* varPartValue2; - // Ajout de la valeur en fin de liste - if (headVarPart == NULL) - headVarPart = value; - if (tailVarPart != NULL) - { - tailVarPart->nextVarPartValue = value; - value->prevVarPartValue = tailVarPart; - } - tailVarPart = value; - } -} + require(elem1 != NULL); + require(elem2 != NULL); -KWDGVarPartValue* KWDGVarPartSet::NewVarPartValue(KWDGPart* varPart) const -{ - return new KWDGVarPartValue(varPart); -} + // Acces aux parties de variable + varPartValue1 = cast(KWDGVarPartValue*, cast(KWSortableObject*, *(Object**)elem1)->GetSortValue()); + varPartValue2 = cast(KWDGVarPartValue*, cast(KWSortableObject*, *(Object**)elem2)->GetSortValue()); -boolean KWDGVarPartSet::GetEmulated() const -{ - return false; + return varPartValue1->CompareValue(varPartValue2); } ////////////////////////////////////////////////////////////////////////////// diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index 9f2e20d90..9546aebac 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -1006,7 +1006,7 @@ class KWDGValueSet : public KWDGPartValues void UpgradeFrom(const KWDGValueSet* sourceValueSet); // Tri des valeurs par effectif decroissant, pour preparer l'affichage - void SortValues(); + virtual void SortValues(); //DDD DEBUG virtual a supprimer // Verification du tri des valeurs par effectif decroissant // Couteux, a utiliser essentiellement dans les assertions @@ -1144,8 +1144,7 @@ class KWDGSymbolValueSet : public KWDGValueSet /////////////////////////////// ///// Implementation protected: - // Methodes de creations virtuelles, permettant de specialiser la creation d'une valeur - // dans une sous-classe + // Methode de creation virtuelle, permettant de specialiser la creation d'une valeur dans une sous-classe virtual KWDGValue* NewSymbolValue(const Symbol& sValue) const; }; @@ -1266,136 +1265,89 @@ class KWDGInterval : public KWDGPartValues }; ////////////////////////////////////////////////////////////////////////////// -// CH IV Begin // Classe KWDGVarPartSet // Ensemble de parties de variables (constitue une partie d'un attribut de grille de type VarPart) -class KWDGVarPartSet : public Object +class KWDGVarPartSet : public KWDGValueSet { public: // Constructeur KWDGVarPartSet(); ~KWDGVarPartSet(); - //////////////////////////////////////////////////////////////// - // Gestion des parties de variable sous forme de liste - // Memoire: les parties de variable appartiennent a la partie - - // Creation d'une partie de variable et ajout en fin de liste - // Renvoie la partie de variable cree - KWDGVarPartValue* AddVarPart(KWDGPart* varPart); - - // Destruction d'une partie de variable de la liste - void DeleteVarPartValue(KWDGVarPartValue* varPartValue); - - // Destruction de toutes les parties de variable - void DeleteAllVarPartValues(); - - // Test de validite d'une partie de variable (si elle appartient a la partie) - boolean CheckVarPart(KWDGVarPartValue* varPartValue) const; - - // Nombre de parties de variable - int GetVarPartNumber() const; - - // Parcours de toutes les parties - KWDGVarPartValue* GetHeadVarPart() const; - KWDGVarPartValue* GetTailVarPart() const; - void GetNextVarPart(KWDGVarPartValue*& varPartValue) const; - void GetPrevVarPart(KWDGVarPartValue*& varPartValue) const; - - /////////////////////////////// - // Services divers - - // Export des VarPart dans un tableau (initialement vide) - void ExportVarParts(ObjectArray* oaVarParts) const; - - // Test si l'ensemble de VarPart est inclus dans l'autre ensemble de VarPart en parametres - boolean IsSubVarPartSet(const KWDGVarPartSet* otherVarPartSet) const; + // Type de valeur gere par la partie + int GetValueType() const override; - // Calcul de l'effectif cumule des parties de variable - int ComputeTotalFrequency() const; + // Creation + KWDGValueSet* Create() const override; - // Import des parties d'un ensemble source, devant etre disjointe de la premiere - // partie. La cible est reinitialisee - void Import(KWDGVarPartSet* sourceVarPartSet); + // Creation d'une valeur de type partie de variable et ajout en fin de liste + // Renvoie la valeur cree + KWDGValue* AddVarPart(KWDGPart* varPart); - // Copie - void CopyFrom(const KWDGVarPartSet* sourceVarPartSet); + // Redefinition de la methode virtuelle d'ajout de la copie d'une valeur existante + KWDGValue* AddValueCopy(const KWDGValue* sourceValue) override; - // Ajout de nouvelles parties recopiees depuis une source - void UpgradeFrom(const KWDGVarPartSet* sourceVarPartSet); + //DDD BEGIN Ajout temporaire de methodes, pour debuggage // Tri des parties de variable par nom de variable puis tri des parties de la meme variable, pour preparer // l'affichage - void SortVarPartValues(); - - // Verification du tri des parties de variable : couteux, a utiliser essentiellement dans les assertions - boolean AreVarPartValuesSorted() const; - - // Controle d'integrite - boolean Check() const override; + void SortValues() override; - // Affichage - void Write(ostream& ost) const override; - void WriteVarParts(ostream& ost) const; - - // Libelles utilisateur - const ALString GetClassLabel() const override; - const ALString GetObjectLabel() const override; + //DDD END Ajout temporaire de methodes, pour debuggage /////////////////////////////// ///// Implementation protected: - // Tri des parties de variable selon une fonction de tri - void InternalSortValues(CompareFunction fCompare); - - // Methodes de creations virtuelles, permettant de specialiser la creation d'une valeur - // dans une sous-classe + // Methode de creation virtuelle, permettant de specialiser la creation d'une valeur dans une sous-classe virtual KWDGVarPartValue* NewVarPartValue(KWDGPart* varPart) const; - - // Methode indiquant si les donnees sont emulees - virtual boolean GetEmulated() const; - - // Gestion de la liste doublement chainee des cellules - KWDGVarPartValue* headVarPart; - KWDGVarPartValue* tailVarPart; - int nVarPartNumber; }; ////////////////////////////////////////////////////////////////////////////// // Classe KWDGVarPartValue // Partie de variable appartenant a une partie de parties de variable -class KWDGVarPartValue : public Object +class KWDGVarPartValue : public KWDGValue { public: // Constructeur - KWDGVarPartValue(KWDGPart* varPart); + KWDGVarPartValue(KWDGPart* varPartValue); ~KWDGVarPartValue(); + // Type de valeur + int GetValueType() const override; + // Partie de variable KWDGPart* GetVarPart() const; - // Effectif lie a la partie de variable, qui est gere par la partie de variable elle-meme - void SetVarPartFrequency(int nFrequency); - int GetVarPartFrequency() const; + // Valeur en tant que cle numerique + NUMERIC GetNumericKeyValue() const override; + + // Test si on est la valeur par defaut, qui fait partie de la partie par defaut + boolean IsDefaultValue() const override; + + // Effectif lie a la valeur + void SetValueFrequency(int nFrequency) override; + int GetValueFrequency() const override; + + // Comparaison de valeur + int CompareValue(const KWDGValue* otherValue) const override; // Affichage void Write(ostream& ost) const override; + // Libelle externe base sur la valeur + const ALString GetExternalValueLabel() const override; + + // Libelles utilisateur + const ALString GetClassLabel() const override; + const ALString GetObjectLabel() const override; + /////////////////////////////// ///// Implementation protected: - friend class KWDGVarPartSet; - // Attributs KWDGPart* varPart; - KWDGVarPartValue* prevVarPartValue; - KWDGVarPartValue* nextVarPartValue; }; -// Comparaison de deux parties de variable, par nom de variable puis par comparaison des parties si elles dependent de -// la meme variable -int KWDGVarPartValueCompareAttributeNameAndVarPart(const void* elem1, const void* elem2); - // Comparaison de deux objets KWSortableObject contenant des KWDGVarPartValue int KWSortableObjectCompareVarPart(const void* elem1, const void* elem2); @@ -1923,7 +1875,7 @@ inline int KWDGAttribute::GetGarbageModalityNumber() const else if (nAttributeType == KWType::Symbol) return GetGarbagePart()->GetValueSet()->GetValueNumber(); else - return GetGarbagePart()->GetVarPartSet()->GetVarPartNumber(); + return GetGarbagePart()->GetVarPartSet()->GetValueNumber(); // CH IV End } @@ -2022,7 +1974,7 @@ inline KWDGValueSet* KWDGPart::GetValueSet() const if (GetPartType() == KWType::Symbol) return symbolValueSet; else - return NULL; //DDD + return varPartSet; } inline KWDGSymbolValueSet* KWDGPart::GetSymbolValueSet() const @@ -2165,13 +2117,13 @@ inline boolean KWDGValueSet::IsDefaultPart() const inline int KWDGValueSet::GetValueNumber() const { - assert(nValueNumber > 0 or IsDefaultPart()); + assert(nValueNumber > 0 or (IsDefaultPart() and GetValueType() == KWType::Symbol)); // On assure que le seul cas sans aucune valeur est le cas de la partie par defaut reduite a la StarValue // Cela peut arriver si la partie a ete "nettoyee" pour gagner de la place, notamment quand on exporte // une grille dans un dictionnaire et qu'on la reimporte pour le deploiement - // Dans ce cas, ou rend une nombre d evaleur egal 1 a, ce qui a le merite d'avoir des couts de grille - // valides numeriquement, meme s'il ne corrrespondnent pas au vrau model qui aurait du memoriser + // Dans ce cas, ou rend une nombre de valeurs egal 1 a, ce qui a le merite d'avoir des couts de grille + // valides numeriquement, meme s'ils ne corrrespondent pas au vrai model qui aurait du memoriser // le nombre exacte de valeurs du groupe if (nValueNumber == 0 and IsDefaultPart()) return 1; @@ -2217,72 +2169,115 @@ inline KWDGValueSet* KWDGSymbolValueSet::Create() const return new KWDGSymbolValueSet; } -// CH IV Begin -// Classe KWDGVarPartValue +inline KWDGValue* KWDGSymbolValueSet::AddSymbolValue(const Symbol& sValue) +{ + KWDGValue* value; -inline KWDGVarPartValue::KWDGVarPartValue(KWDGPart* varPartValue) + // Creation de la valeur + value = NewSymbolValue(sValue); + + // Ajout en fin de la liste des valeurs + AddTailValue(value); + + // On retourne la valeur cree + return value; +} + +inline KWDGValue* KWDGSymbolValueSet::AddValueCopy(const KWDGValue* sourceValue) { - varPart = varPartValue; - prevVarPartValue = NULL; - nextVarPartValue = NULL; + return AddSymbolValue(cast(KWDGSymbolValue*, sourceValue)->GetSymbolValue()); } -inline KWDGVarPartValue::~KWDGVarPartValue() {} +inline KWDGValue* KWDGSymbolValueSet::NewSymbolValue(const Symbol& sValue) const +{ + return new KWDGSymbolValue(sValue); +} -inline KWDGPart* KWDGVarPartValue::GetVarPart() const +// CH IV Begin + +// Class KWDGVarPartSet + +inline KWDGVarPartSet::KWDGVarPartSet() {} + +inline KWDGVarPartSet::~KWDGVarPartSet() {} + +inline int KWDGVarPartSet::KWDGVarPartSet::GetValueType() const { - return varPart; + return KWType::VarPart; } -inline void KWDGVarPartValue::SetVarPartFrequency(int nFrequency) +inline KWDGValueSet* KWDGVarPartSet::Create() const { - varPart->SetPartFrequency(nFrequency); + return new KWDGVarPartSet; } -inline int KWDGVarPartValue::GetVarPartFrequency() const +inline KWDGValue* KWDGVarPartSet::AddVarPart(KWDGPart* varPart) { - return varPart->GetPartFrequency(); + KWDGValue* value; + + require(varPart != NULL); + + // Creation de la valeur + value = NewVarPartValue(varPart); + + // Ajout en fin de la liste des valeurs + AddTailValue(value); + + // On retourne la valeur cree + return value; } -inline KWDGVarPartSet::KWDGVarPartSet() +inline KWDGValue* KWDGVarPartSet::AddValueCopy(const KWDGValue* sourceValue) { - headVarPart = NULL; - tailVarPart = NULL; - nVarPartNumber = 0; + return AddVarPart(cast(KWDGVarPartValue*, sourceValue)->GetVarPart()); } -inline KWDGVarPartSet::~KWDGVarPartSet() +inline KWDGVarPartValue* KWDGVarPartSet::NewVarPartValue(KWDGPart* varPart) const { - DeleteAllVarPartValues(); + require(varPart != NULL); + return new KWDGVarPartValue(varPart); } -inline int KWDGVarPartSet::GetVarPartNumber() const +// Classe KWDGVarPartValue + +inline KWDGVarPartValue::KWDGVarPartValue(KWDGPart* varPartValue) { - return nVarPartNumber; + require(varPartValue != NULL); + varPart = varPartValue; } -inline KWDGVarPartValue* KWDGVarPartSet::GetHeadVarPart() const +inline KWDGVarPartValue::~KWDGVarPartValue() {} + +inline int KWDGVarPartValue::GetValueType() const { - return headVarPart; + return KWType::VarPart; } -inline KWDGVarPartValue* KWDGVarPartSet::GetTailVarPart() const +inline KWDGPart* KWDGVarPartValue::GetVarPart() const { - return tailVarPart; + return varPart; } -inline void KWDGVarPartSet::GetNextVarPart(KWDGVarPartValue*& varPartValue) const +inline NUMERIC KWDGVarPartValue::GetNumericKeyValue() const { - require(varPartValue != NULL); + return varPart; +} - varPartValue = varPartValue->nextVarPartValue; +inline boolean KWDGVarPartValue::IsDefaultValue() const +{ + return false; } -inline void KWDGVarPartSet::GetPrevVarPart(KWDGVarPartValue*& varPartValue) const +inline void KWDGVarPartValue::SetValueFrequency(int nFrequency) { - require(varPartValue != NULL); + require(varPart != NULL); + varPart->SetPartFrequency(nFrequency); +} - varPartValue = varPartValue->prevVarPartValue; +inline int KWDGVarPartValue::GetValueFrequency() const +{ + require(varPart != NULL); + return varPart->GetPartFrequency(); } // Classe KWDGInnerAttributes diff --git a/src/Learning/KWDataPreparation/KWDataGridCosts.cpp b/src/Learning/KWDataPreparation/KWDataGridCosts.cpp index 3b1fec96f..33adf74e0 100644 --- a/src/Learning/KWDataPreparation/KWDataGridCosts.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridCosts.cpp @@ -748,7 +748,7 @@ void KWDataGridCosts::WritePartCostLine(const KWDGPart* part, ostream& ost) cons else if (part->GetPartType() == KWType::Continuous) ost << part->GetPartFrequency() << "\t"; else - ost << part->GetVarPartSet()->GetVarPartNumber() << "\t"; + ost << part->GetValueSet()->GetValueNumber() << "\t"; // Avant integration coclustering IV ost << part->GetPartFrequency() << "\t"; // CH IV End ost << dPartCost << "\t" << dPartCumulativeCost << "\n"; @@ -2013,7 +2013,7 @@ double KWVarPartDataGridClusteringCosts::ComputePartCost(const KWDGPart* part) c if (part->GetAttribute()->GetAttributeType() == KWType::Symbol) nValueNumber = part->GetValueSet()->GetValueNumber(); else - nValueNumber = part->GetVarPartSet()->GetVarPartNumber(); + nValueNumber = part->GetValueSet()->GetValueNumber(); dPartCost = KWStat::LnFactorial(part->GetPartFrequency()); // Distribution des valeurs dans la partie @@ -2031,7 +2031,6 @@ double KWVarPartDataGridClusteringCosts::ComputePartUnionCost(const KWDGPart* pa double dPartUnionCost; int nPartFrequency; int nValueNumber; - int nVarPartNumber; require(part1 != NULL); require(part2 != NULL); @@ -2059,13 +2058,12 @@ double KWVarPartDataGridClusteringCosts::ComputePartUnionCost(const KWDGPart* pa // Cout de distribution des parties de variable dans le cas VarPart else { - nVarPartNumber = - part1->GetVarPartSet()->GetVarPartNumber() + part2->GetVarPartSet()->GetVarPartNumber(); + nValueNumber = part1->GetValueSet()->GetValueNumber() + part2->GetValueSet()->GetValueNumber(); dPartUnionCost = KWStat::LnFactorial(nPartFrequency); // Distribution des parties de variable dans la partie - dPartUnionCost += KWStat::LnFactorial(nPartFrequency + nVarPartNumber - 1); - dPartUnionCost -= KWStat::LnFactorial(nVarPartNumber - 1); + dPartUnionCost += KWStat::LnFactorial(nPartFrequency + nValueNumber - 1); + dPartUnionCost -= KWStat::LnFactorial(nValueNumber - 1); dPartUnionCost -= KWStat::LnFactorial(nPartFrequency); } return dPartUnionCost; diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index 5565cf49c..14d8eb1ac 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -349,9 +349,9 @@ double KWDataGridManager::ExportDataGridWithVarPartMergeOptimization(KWDataGrid* part = targetAttribute->GetHeadPart(); while (part != NULL) { - if (part->GetVarPartSet()->GetVarPartNumber() > nGarbageModalityNumber) + if (part->GetVarPartSet()->GetValueNumber() > nGarbageModalityNumber) { - nGarbageModalityNumber = part->GetVarPartSet()->GetVarPartNumber(); + nGarbageModalityNumber = part->GetVarPartSet()->GetValueNumber(); garbagePart = part; } targetAttribute->GetNextPart(part); @@ -439,7 +439,7 @@ void KWDataGridManager::UpdateVarPartDataGridFromVarPartGroups(KWDataGrid* targe // Mise a jour du groupe poubelle comme le groupe contenant le plus de parties de variables if (GetVarPartAttributeGarbage() and - targetPart->GetVarPartSet()->GetVarPartNumber() > targetAttribute->GetGarbageModalityNumber()) + targetPart->GetVarPartSet()->GetValueNumber() > targetAttribute->GetGarbageModalityNumber()) targetAttribute->SetGarbagePart(targetPart); // Partie initiale suivante @@ -455,7 +455,7 @@ void KWDataGridManager::UpdateVarPartDataGridFromVarPartGroups(KWDataGrid* targe assert(targetPart->GetPartType() == KWType::VarPart); // Destruction si elle est vide - if (targetPart->GetVarPartSet()->GetVarPartNumber() == 0) + if (targetPart->GetVarPartSet()->GetValueNumber() == 0) { targetAttribute->DeletePart(targetPart); nTargetGroupNumber--; @@ -722,7 +722,7 @@ void KWDataGridManager::ExportFrequencyTableFromOneAttribute(KWFrequencyTable* k else if (oneAttributeDataGrid.GetAttributeAt(0)->GetAttributeType() == KWType::VarPart) { // Recopie du nombre de modalites - kwdfvFrequencyVector->SetModalityNumber(dgPart->GetVarPartSet()->GetVarPartNumber()); + kwdfvFrequencyVector->SetModalityNumber(dgPart->GetValueSet()->GetValueNumber()); } // CH IV End } @@ -910,7 +910,7 @@ void KWDataGridManager::ExportDataGridWithReferenceVarPartClusters(KWDataGrid* r while (initialPart != NULL) { // On prend la premiere partie de variable du groupe - initialVarPart = initialPart->GetVarPartSet()->GetHeadVarPart()->GetVarPart(); + initialVarPart = initialPart->GetVarPartSet()->GetHeadValue()->GetVarPart(); // Extraction de l'attribut interne issu de la grille de reference pour cette partie referenceInnerAttribute = @@ -1067,9 +1067,9 @@ void KWDataGridManager::ExportCells(KWDataGrid* targetDataGrid) const else { // Recherche d'une partie de variable typique : la premiere partie de variable - assert(sourcePart->GetVarPartSet()->GetHeadVarPart() != NULL); + assert(sourcePart->GetVarPartSet()->GetHeadValue() != NULL); - sourceVarPart = sourcePart->GetVarPartSet()->GetHeadVarPart()->GetVarPart(); + sourceVarPart = sourcePart->GetVarPartSet()->GetHeadValue()->GetVarPart(); // Recherche de la partie de variable cible qui contient cette partie de variable source // (suite a la granularisation) Extraction de l'attribut de cette partie de variable @@ -2367,8 +2367,8 @@ boolean KWDataGridManager::CheckCells(const KWDataGrid* targetDataGrid) const else { // Recherche d'une partie de variable typique: la premiere - assert(targetPart->GetVarPartSet()->GetHeadVarPart() != NULL); - varPart = targetPart->GetVarPartSet()->GetHeadVarPart()->GetVarPart(); + assert(targetPart->GetVarPartSet()->GetHeadValue() != NULL); + varPart = targetPart->GetVarPartSet()->GetHeadValue()->GetVarPart(); // Recherche du cluster de parties de variables correspondant checkPart = checkAttribute->LookupVarPart(varPart); @@ -2606,7 +2606,7 @@ void KWDataGridManager::InitialiseVarPartAttributeClonedParts(const KWDGAttribut KWDGPart* targetPart; KWDGPart* sourceVarPart; KWDGPart* targetVarPart; - KWDGVarPartValue* sourceVarPartValue; + KWDGValue* sourceValue; require(CheckAttributesConsistency(sourceAttribute, targetAttribute)); require(targetAttribute->GetAttributeType() == KWType::VarPart); @@ -2637,17 +2637,17 @@ void KWDataGridManager::InitialiseVarPartAttributeClonedParts(const KWDGAttribut targetPart = targetAttribute->AddPart(); // Transfert des parties de parties de variable avec de nouvelles parties de variable - sourceVarPartValue = sourcePart->GetVarPartSet()->GetHeadVarPart(); - while (sourceVarPartValue != NULL) + sourceValue = sourcePart->GetVarPartSet()->GetHeadValue(); + while (sourceValue != NULL) { - sourceVarPart = sourceVarPartValue->GetVarPart(); + sourceVarPart = sourceValue->GetVarPart(); // Memorisation de la partie cible correspondante targetVarPart = cast(KWDGPart*, nkdTargetInnerAttributeVarParts.Lookup(sourceVarPart)); targetPart->GetVarPartSet()->AddVarPart(targetVarPart); // Ajout de cette partie pour l'attribut interne - sourcePart->GetVarPartSet()->GetNextVarPart(sourceVarPartValue); + sourcePart->GetVarPartSet()->GetNextValue(sourceValue); } // Transfert du parametrage du groupe poubelle @@ -3720,8 +3720,8 @@ double KWDataGridManager::MergePartsForVarPartAttributes(KWDataGrid* targetDataG KWDGAttribute* innerAttribute; KWDGPart* initialPart; KWDGPart* initialSourcePart; - KWDGVarPartValue* currentVarPart; - KWDGVarPartValue* nextVarPart; + KWDGValue* currentValue; + KWDGValue* nextValue; boolean bNewVarPart; double dEpsilon = 1e-4; double dDeltaClusterCost; @@ -3746,35 +3746,35 @@ double KWDataGridManager::MergePartsForVarPartAttributes(KWDataGrid* targetDataG while (initialPart != NULL) { // Tri des parties de variable du cluster - initialPart->GetVarPartSet()->SortVarPartValues(); + initialPart->GetVarPartSet()->SortValues(); // Initialisation des deux premieres parties de variable - currentVarPart = initialPart->GetVarPartSet()->GetHeadVarPart(); - nextVarPart = currentVarPart; - initialPart->GetVarPartSet()->GetNextVarPart(nextVarPart); + currentValue = initialPart->GetVarPartSet()->GetHeadValue(); + nextValue = currentValue; + initialPart->GetVarPartSet()->GetNextValue(nextValue); // Deplacement avec une partie et la suivante - while (nextVarPart != NULL) + while (nextValue != NULL) { bNewVarPart = false; // Cas de non fusion // Parties d'attributs distincts - if (currentVarPart->GetVarPart()->GetAttribute()->GetAttributeName() != - nextVarPart->GetVarPart()->GetAttribute()->GetAttributeName()) + if (currentValue->GetVarPart()->GetAttribute()->GetAttributeName() != + nextValue->GetVarPart()->GetAttribute()->GetAttributeName()) bNewVarPart = true; // Parties (intervalles) non consecutives d'un attribut numerique - else if (currentVarPart->GetVarPart()->GetPartType() == KWType::Continuous and - abs(currentVarPart->GetVarPart()->GetInterval()->GetUpperBound() - - nextVarPart->GetVarPart()->GetInterval()->GetLowerBound()) > dEpsilon) + else if (currentValue->GetVarPart()->GetPartType() == KWType::Continuous and + abs(currentValue->GetVarPart()->GetInterval()->GetUpperBound() - + nextValue->GetVarPart()->GetInterval()->GetLowerBound()) > dEpsilon) bNewVarPart = true; // Pas de fusion a realiser if (bNewVarPart) { // Parties de variable suivantes - initialPart->GetVarPartSet()->GetNextVarPart(currentVarPart); - initialPart->GetVarPartSet()->GetNextVarPart(nextVarPart); + initialPart->GetVarPartSet()->GetNextValue(currentValue); + initialPart->GetVarPartSet()->GetNextValue(nextValue); } // Cas de fusion else @@ -3783,37 +3783,37 @@ double KWDataGridManager::MergePartsForVarPartAttributes(KWDataGrid* targetDataG targetVarPartAttribute->GetInitialValueNumber() - 1); // Transfert des valeurs de la partie suivante - if (currentVarPart->GetVarPart()->GetPartType() == KWType::Continuous) - currentVarPart->GetVarPart()->GetInterval()->Import( - nextVarPart->GetVarPart()->GetInterval()); + if (currentValue->GetVarPart()->GetPartType() == KWType::Continuous) + currentValue->GetVarPart()->GetInterval()->Import( + nextValue->GetVarPart()->GetInterval()); else - currentVarPart->GetVarPart()->GetValueSet()->Import( - nextVarPart->GetVarPart()->GetValueSet()); + currentValue->GetVarPart()->GetValueSet()->Import( + nextValue->GetVarPart()->GetValueSet()); // Fusion des effectifs - currentVarPart->GetVarPart()->SetPartFrequency( - currentVarPart->GetVarPart()->GetPartFrequency() + - nextVarPart->GetVarPart()->GetPartFrequency()); + currentValue->GetVarPart()->SetPartFrequency( + currentValue->GetVarPart()->GetPartFrequency() + + nextValue->GetVarPart()->GetPartFrequency()); // Suppression de la partie de variable de l'attribut interne innerAttribute = targetDataGrid->GetInnerAttributes()->LookupInnerAttribute( - currentVarPart->GetVarPart()->GetAttribute()->GetAttributeName()); - innerAttribute->DeletePart(nextVarPart->GetVarPart()); + currentValue->GetVarPart()->GetAttribute()->GetAttributeName()); + innerAttribute->DeletePart(nextValue->GetVarPart()); innerAttribute->SetGranularizedValueNumber( innerAttribute->GetGranularizedValueNumber() - 1); // Evaluation de la variation de cout du cluster du fait de la diminution du nombre de // parties dDeltaClusterCost += -log(initialSourcePart->GetPartFrequency() + - initialPart->GetVarPartSet()->GetVarPartNumber() - 1) + - log(initialPart->GetVarPartSet()->GetVarPartNumber() - 1); + initialPart->GetVarPartSet()->GetValueNumber() - 1) + + log(initialPart->GetVarPartSet()->GetValueNumber() - 1); // Suppression de la partie de variable du cluster - initialPart->GetVarPartSet()->DeleteVarPartValue(nextVarPart); + initialPart->GetVarPartSet()->DeleteValue(nextValue); // Parties de variable suivantes - nextVarPart = currentVarPart; - initialPart->GetVarPartSet()->GetNextVarPart(nextVarPart); + nextValue = currentValue; + initialPart->GetVarPartSet()->GetNextValue(nextValue); } } targetVarPartAttribute->GetNextPart(initialPart); @@ -4113,13 +4113,13 @@ void KWDataGridManager::SortVarPartAttributeParts(const KWDGAttribute* sourceAtt // Recherche de la partie groupee correspondante groupedPart = - groupedAttribute->LookupVarPart(sourcePart->GetVarPartSet()->GetHeadVarPart()->GetVarPart()); + groupedAttribute->LookupVarPart(sourcePart->GetVarPartSet()->GetHeadValue()->GetVarPart()); // Creation de l'association entre index de partie et premiere valeur du groupe association = new KWSortableObject; oaAssociations.SetAt(nSource, association); association->SetIndex(nSource); - association->SetSortValue(groupedPart->GetVarPartSet()->GetHeadVarPart()); + association->SetSortValue(groupedPart->GetVarPartSet()->GetHeadValue()); } // Tri des association, apres une randomisation pour avoir un ordre aleatoire par groupe @@ -4140,7 +4140,7 @@ void KWDataGridManager::SortVarPartAttributeParts(const KWDGAttribute* sourceAtt // Recherche de la partie groupee correspondante groupedPart = - groupedAttribute->LookupVarPart(sourcePart->GetVarPartSet()->GetHeadVarPart()->GetVarPart()); + groupedAttribute->LookupVarPart(sourcePart->GetVarPartSet()->GetHeadValue()->GetVarPart()); // Rangement dans les tableaux en sortie oaSortedSourceParts->SetAt(n, sourcePart); diff --git a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp index 257920552..787f36f5e 100644 --- a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp @@ -982,11 +982,11 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b } else if (attribute->GetAttributeType() == KWType::VarPart) { - if (partMerge->GetPart1()->GetVarPartSet()->GetVarPartNumber() + - partMerge->GetPart2()->GetVarPartSet()->GetVarPartNumber() > + if (partMerge->GetPart1()->GetVarPartSet()->GetValueNumber() + + partMerge->GetPart2()->GetVarPartSet()->GetValueNumber() > cast(KWDGPart*, attributeM->slPartValueNumbers->GetHead()) ->GetVarPartSet() - ->GetVarPartNumber()) + ->GetValueNumber()) { // Creation du nouveau groupe poubelle garbagePart = new KWDGPart; @@ -2011,7 +2011,7 @@ int KWDGMPartValueNumberCompare(const void* elem1, const void* elem2) if (partM1->GetPartType() == KWType::Symbol) return (partM2->GetValueSet()->GetValueNumber() - partM1->GetValueSet()->GetValueNumber()); else - return (partM2->GetVarPartSet()->GetVarPartNumber() - partM1->GetVarPartSet()->GetVarPartNumber()); + return (partM2->GetVarPartSet()->GetValueNumber() - partM1->GetVarPartSet()->GetValueNumber()); // CH IV End } ////////////////////////////////////////////////////////////////////////////// diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp index bd891d246..d86d2a611 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp @@ -1444,7 +1444,7 @@ void KWDGPOGrouper::InitializePartFrequencyVector(KWDGPOPartFrequencyVector* par partFrequencyVector->SetModalityNumber(part->GetValueSet()->GetValueNumber()); // CH IV Begin else if (part->GetPartType() == KWType::VarPart) - partFrequencyVector->SetModalityNumber(part->GetVarPartSet()->GetVarPartNumber()); + partFrequencyVector->SetModalityNumber(part->GetVarPartSet()->GetValueNumber()); // CH IV End // Parcours des cellules de la partie pour creer les vecteurs d'effectif par cellule @@ -1663,7 +1663,7 @@ void KWDGPOGrouper::InitializeGroupIndexes(IntVector* ivGroups, const KWDataGrid initialPart->GetValueSet()->GetHeadValue()->GetSymbolValue()); else optimizedPart = optimizedAttribute->LookupVarPart( - initialPart->GetVarPartSet()->GetHeadVarPart()->GetVarPart()); + initialPart->GetVarPartSet()->GetHeadValue()->GetVarPart()); // CH IV End // Memorisation de l'index de ce groupe @@ -1750,7 +1750,7 @@ int KWDGPOGrouper::InitializeGroupIndexesAndGarbageIndex(IntVector* ivGroups, co else if (optimizedAttribute->GetAttributeType() == KWType::VarPart) { optimizedPart = optimizedAttribute->LookupVarPart( - initialPart->GetVarPartSet()->GetHeadVarPart()->GetVarPart()); + initialPart->GetVarPartSet()->GetHeadValue()->GetVarPart()); } // CH IV End @@ -1887,7 +1887,7 @@ void KWDGPOGrouper::UpdateDataGridWithGarbageFromGroups(KWDataGrid* optimizedDat optimizedPart->GetVarPartSet()->UpgradeFrom(initialPart->GetVarPartSet()); // Mise a jour du groupe poubelle comme le groupe contenant le plus de parties de variables - if (optimizedPart->GetVarPartSet()->GetVarPartNumber() > + if (optimizedPart->GetVarPartSet()->GetValueNumber() > optimizedAttribute->GetGarbageModalityNumber()) optimizedAttribute->SetGarbagePart(optimizedPart); } @@ -2659,7 +2659,7 @@ double KWDataGridUnivariateCosts::ComputePartCost(const KWFrequencyVector* part) // CH IV Begin else if (partCostParameter->GetPartType() == KWType::VarPart) { - cast(KWDGVarPartSetCostParameter*, partCostParameter->GetVarPartSet())->nVarPartNumber = + cast(KWDGVarPartSetCostParameter*, partCostParameter->GetVarPartSet())->nValueNumber = cast(KWDGPOPartFrequencyVector*, part)->GetModalityNumber(); // il faut que modalityNumber ait ete alimente par le nombre de parties de variable } @@ -2734,7 +2734,7 @@ double KWDataGridUnivariateCosts::ComputePartUnionCost(const KWFrequencyVector* // CH IV Begin else if (partCostParameter->GetPartType() == KWType::VarPart) { - cast(KWDGVarPartSetCostParameter*, partCostParameter->GetVarPartSet())->nVarPartNumber = + cast(KWDGVarPartSetCostParameter*, partCostParameter->GetVarPartSet())->nValueNumber = cast(KWDGPOPartFrequencyVector*, sourcePart1)->GetModalityNumber() + cast(KWDGPOPartFrequencyVector*, sourcePart2)->GetModalityNumber(); } @@ -2775,7 +2775,7 @@ double KWDataGridUnivariateCosts::ComputePartDiffCost(const KWFrequencyVector* s // CH IV Begin if (partCostParameter->GetPartType() == KWType::VarPart) { - cast(KWDGVarPartSetCostParameter*, partCostParameter->GetVarPartSet())->nVarPartNumber = + cast(KWDGVarPartSetCostParameter*, partCostParameter->GetVarPartSet())->nValueNumber = cast(KWDGPOPartFrequencyVector*, sourcePart)->GetModalityNumber() - cast(KWDGPOPartFrequencyVector*, removedPart)->GetModalityNumber(); } @@ -3445,7 +3445,7 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K KWDGPart* prevPartCluster = NULL; KWDGPart* currentPartCluster = NULL; KWDGPart* nextPartCluster = NULL; - KWDGVarPartValue* varPartValue; + KWDGValue* varPartValue; int nInnerPart; int nPartIndex; int nCurrentClusterIndex; @@ -3492,6 +3492,7 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K // Verification de la compatibilite entre grille optimisee et grille initiale dataGridManager.SetSourceDataGrid(referenceDataGrid); + // CH IV: si cette verification est inutile, la supprimer // require(dataGridManager.CheckDataGrid(optimizedDataGrid)); // Extraction de l'attribut VarPart dans la grille pre-partitionnee de reference @@ -3532,7 +3533,7 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K innerPartCluster = varPartReferenceAttribute->GetHeadPart(); while (innerPartCluster != NULL) { - innerPart = innerPartCluster->GetVarPartSet()->GetHeadVarPart()->GetVarPart(); + innerPart = innerPartCluster->GetVarPartSet()->GetHeadValue()->GetVarPart(); clusterPart = varPartOptimizedAttribute->LookupVarPart(innerPart); ivGroups->Add(cast(IntObject*, nkdClusterIndexes.Lookup((NUMERIC)clusterPart))->GetInt()); clusterIndex = new IntObject; @@ -3550,7 +3551,7 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K for (nPartIndex = 0; nPartIndex < ivGroups->GetSize(); nPartIndex++) { - innerPart = innerPartCluster->GetVarPartSet()->GetHeadVarPart()->GetVarPart(); + innerPart = innerPartCluster->GetVarPartSet()->GetHeadValue()->GetVarPart(); cout << "PV\t" << *innerPart << "\t" << ivGroups->GetAt(nPartIndex) << "\n"; varPartReferenceAttribute->GetNextPart(innerPartCluster); } @@ -3594,8 +3595,8 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K cout << "CCVarPartDataGridPostOptimizer:Clusters des PV de l'attribut\t" << innerAttribute->GetAttributeName() << endl; oaClusterParts.Write(cout); + cout << flush; } - cout << flush; // Indexation des parties de l'attribut pour melange aleatoire ivVarPartIndexes.SetSize(innerAttribute->GetPartNumber()); @@ -3776,8 +3777,6 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K << endl; } - cout << flush; - dBestVariationCost = dVariationCost; nBestVarPartIndex = cast(IntObject*, @@ -3913,8 +3912,6 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K << endl; } - cout << flush; - dBestVariationCost = dVariationCost; nBestVarPartIndex = cast(IntObject*, nkdReferenceClusterIndexes.Lookup( @@ -4007,8 +4004,8 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K { // cout << "CCVarPartDataGridPostOptimizer:Clusters des PV de l'attribut\t" << // innerAttribute->GetAttributeName() << endl; oaClusterParts.Write(cout); + cout << flush; } - cout << flush; // Indexation des parties de l'attribut pour melange aleatoire ivVarPartIndexes.SetSize(innerAttribute->GetPartNumber()); @@ -4092,11 +4089,11 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K { // Extraction de la partie de variable de l'attribut interne qui // accueillerait la partie de variable deplacee - varPartValue = clusterPartIn->GetVarPartSet()->GetHeadVarPart(); + varPartValue = clusterPartIn->GetVarPartSet()->GetHeadValue(); while (varPartValue->GetVarPart()->GetAttribute()->GetAttributeName() != innerPart->GetAttribute()->GetAttributeName()) { - clusterPartIn->GetVarPartSet()->GetNextVarPart(varPartValue); + clusterPartIn->GetVarPartSet()->GetNextValue(varPartValue); } innerInOptimizedPart = varPartValue->GetVarPart(); @@ -4164,8 +4161,6 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K << endl; } - cout << flush; - dBestVariationCost = dVariationCost; nBestVarPartIndex = cast(IntObject*, @@ -4242,7 +4237,7 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K for (nPartIndex = 0; nPartIndex < ivGroups->GetSize(); nPartIndex++) { - innerPart = innerPartCluster->GetVarPartSet()->GetHeadVarPart()->GetVarPart(); + innerPart = innerPartCluster->GetVarPartSet()->GetHeadValue()->GetVarPart(); cout << "PV\t" << *innerPart << "\t" << ivGroups->GetAt(nPartIndex) << "\n"; varPartReferenceAttribute->GetNextPart(innerPartCluster); } @@ -4703,7 +4698,7 @@ double CCVarPartDataGridPostOptimizer::ComputeClusterVariationCost(KWDGPart* par nVariationSign = -1; // Nombre de modalites du cluster de parties de variable - nValueNumber = part->GetVarPartSet()->GetVarPartNumber(); + nValueNumber = part->GetVarPartSet()->GetValueNumber(); nClusterFrequency = part->GetPartFrequency(); // On retire le cout du cluster avec son effectif et son nombre de PV diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp index 2fa57c701..d45095f01 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp @@ -2230,7 +2230,7 @@ void CCCoclusteringReport::WriteComposition(const CCHierarchicalDataGrid* coclus CCHDGSymbolValue* hdgValue; // CH IV Begin CCHDGVarPartSet* hdgVarPartSet; - KWDGVarPartValue* dgVarPartValue; + KWDGValue* dgVarPartValue; CCHDGVarPartValue* hdgVarPartValue; // CH IV End @@ -2292,7 +2292,7 @@ void CCCoclusteringReport::WriteComposition(const CCHierarchicalDataGrid* coclus // Parcours des valeurs hdgVarPartSet = cast(CCHDGVarPartSet*, hdgPart->GetVarPartSet()); - dgVarPartValue = hdgVarPartSet->GetHeadVarPart(); + dgVarPartValue = hdgVarPartSet->GetHeadValue(); while (dgVarPartValue != NULL) { hdgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartValue); @@ -2304,7 +2304,7 @@ void CCCoclusteringReport::WriteComposition(const CCHierarchicalDataGrid* coclus << hdgVarPartValue->GetTypicality() << "\n"; // Valeur suivante - hdgVarPartSet->GetNextVarPart(dgVarPartValue); + hdgVarPartSet->GetNextValue(dgVarPartValue); } // Partie suivante @@ -4009,7 +4009,7 @@ boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(CCHDGAttribute* dgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartSet->AddVarPart(dgVarPart)); // Memorisation de l'effectif et de la typicite - dgVarPartValue->SetVarPartFrequency(ivValueFrequencies.GetAt(i)); + dgVarPartValue->SetValueFrequency(ivValueFrequencies.GetAt(i)); dgVarPartValue->SetTypicality(dvValueTypicalities.GetAt(i)); } } @@ -4633,7 +4633,7 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData KWDGInterval* dgInterval; // CH IV Begin CCHDGVarPartSet* hdgVarPartSet; - KWDGVarPartValue* dgVarPartValue; + KWDGValue* dgVarPartValue; CCHDGVarPartValue* hdgVarPartValue; KWDGAttribute* innerAttribute; // CH IV End @@ -4801,39 +4801,39 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData fJSON->BeginObject(); fJSON->WriteKeyString("cluster", hdgPart->GetPartName()); fJSON->BeginKeyList("values"); - dgVarPartValue = hdgVarPartSet->GetHeadVarPart(); + dgVarPartValue = hdgVarPartSet->GetHeadValue(); while (dgVarPartValue != NULL) { hdgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartValue); - if (hdgVarPartValue->GetVarPartFrequency() > 0) + if (hdgVarPartValue->GetValueFrequency() > 0) fJSON->WriteString(hdgVarPartValue->GetVarPart()->GetVarPartLabel()); - hdgVarPartSet->GetNextVarPart(dgVarPartValue); + hdgVarPartSet->GetNextValue(dgVarPartValue); } fJSON->EndList(); // Effectifs des valeurs de type partie de variable fJSON->BeginKeyList("valueFrequencies"); - dgVarPartValue = hdgVarPartSet->GetHeadVarPart(); + dgVarPartValue = hdgVarPartSet->GetHeadValue(); while (dgVarPartValue != NULL) { hdgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartValue); - if (hdgVarPartValue->GetVarPartFrequency() > 0) - fJSON->WriteInt(hdgVarPartValue->GetVarPartFrequency()); - hdgVarPartSet->GetNextVarPart(dgVarPartValue); + if (hdgVarPartValue->GetValueFrequency() > 0) + fJSON->WriteInt(hdgVarPartValue->GetValueFrequency()); + hdgVarPartSet->GetNextValue(dgVarPartValue); } fJSON->EndList(); // Typicalite des valeurs de type partie de variable fJSON->BeginKeyList("valueTypicalities"); - dgVarPartValue = hdgVarPartSet->GetHeadVarPart(); + dgVarPartValue = hdgVarPartSet->GetHeadValue(); while (dgVarPartValue != NULL) { hdgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartValue); - if (hdgVarPartValue->GetVarPartFrequency() > 0) + if (hdgVarPartValue->GetValueFrequency() > 0) fJSON->WriteDouble(hdgVarPartValue->GetTypicality()); - hdgVarPartSet->GetNextVarPart(dgVarPartValue); + hdgVarPartSet->GetNextValue(dgVarPartValue); } fJSON->EndList(); fJSON->EndObject(); diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp index 5eb184f22..b145eb0b5 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp @@ -1075,14 +1075,14 @@ void CCHDGVarPartSet::SortVarPartsByTypicality() boolean CCHDGVarPartSet::CheckHierarchy() const { boolean bOk = true; - KWDGVarPartValue* varPartValue; + KWDGValue* varPartValue; // Pas de controle d'integrite de base (invalide pour els parties non feuilles) // Test des valeurs de la partie if (bOk) { // Parcours des valeurs de la partie - varPartValue = GetHeadVarPart(); + varPartValue = GetHeadValue(); while (varPartValue != NULL) { // Test de la valeur @@ -1091,7 +1091,7 @@ boolean CCHDGVarPartSet::CheckHierarchy() const break; // Valeur suivante - GetNextVarPart(varPartValue); + GetNextValue(varPartValue); } } return bOk; @@ -1162,7 +1162,7 @@ int CCHDGVarPartValueCompareDecreasingTypicality(const void* elem1, const void* else if (dCompare < -dPrecision) nCompare = -1; else - nCompare = KWDGVarPartValueCompareAttributeNameAndVarPart(elem1, elem2); + nCompare = value1->CompareValue(value2); return nCompare; } // CH IV End diff --git a/src/Learning/MODL_Coclustering/CCLearningProblem.cpp b/src/Learning/MODL_Coclustering/CCLearningProblem.cpp index b5901ce1b..bd919195a 100644 --- a/src/Learning/MODL_Coclustering/CCLearningProblem.cpp +++ b/src/Learning/MODL_Coclustering/CCLearningProblem.cpp @@ -1108,7 +1108,7 @@ void CCLearningProblem::WriteVarPartClusters(const CCHDGAttribute* varPartCoclus KWDGPart* dgPart; CCHDGPart* hdgPart; CCHDGVarPartSet* hdgVarPartSet; - KWDGVarPartValue* dgValue; + KWDGValue* dgValue; CCHDGVarPartValue* hdgValue; require(varPartCoclusteringAttribute != NULL); @@ -1125,17 +1125,17 @@ void CCLearningProblem::WriteVarPartClusters(const CCHDGAttribute* varPartCoclus // Parcours des valeurs hdgVarPartSet = cast(CCHDGVarPartSet*, hdgPart->GetVarPartSet()); - dgValue = hdgVarPartSet->GetHeadVarPart(); + dgValue = hdgVarPartSet->GetHeadValue(); while (dgValue != NULL) { hdgValue = cast(CCHDGVarPartValue*, dgValue); // Caracteristiques des valeurs ost << hdgPart->GetUserLabel() << "\t" << hdgValue->GetVarPart()->GetVarPartLabel() << "\t" - << hdgValue->GetVarPartFrequency() << "\t" << hdgValue->GetTypicality() << "\n"; + << hdgValue->GetValueFrequency() << "\t" << hdgValue->GetTypicality() << "\n"; // Valeur suivante - hdgVarPartSet->GetNextVarPart(dgValue); + hdgVarPartSet->GetNextValue(dgValue); } // Partie suivante diff --git a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp index a187ebec3..f96090600 100644 --- a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp +++ b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp @@ -34,8 +34,8 @@ int main(int argc, char** argv) Global::ActivateSignalErrorManagement(); // Choix du repertoire de lancement pour le debugage sous Windows (a commenter apres fin du debug) - //SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); - SetWindowsDebugDir("Standard", "Iris"); + SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisMissingValues"); + //SetWindowsDebugDir("Standard", "Iris"); // Point d'arret sur l'allocation d'un bloc memoire // MemSetAllocIndexExit(17362); From bc727ef157236eab57590351e69b27ddbc305308 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Fri, 1 Sep 2023 15:54:46 +0200 Subject: [PATCH 04/17] Refactoring of sort methods for DG attributes, parts, value sets, values (Symbol and VarPart) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Refactoring des methodes de tri et comparaison: - utilisation au maximum de methode Compare - variantes au nom explicite: ex: CompareValue vs CompareFrequency - exploitation des hierarchie d'heritage, avec reimplementation des methdoes Compare* virtuelle - utilisation de methodes de tri au nom explicite, dans les container - base sur un nombre minimal de fonction KWDG*Compare, exploitant les methodes Compare et leur reimplementation Methodes de comparaison - KWDGPart - ComparePart: comparaison de partie, par intervalle croissant si numerique, par effectif decroissant puis ensemble de valeurs - ComparePartValues: comparaison de partie, par intervalle croissant si numerique, ensemble de valeur si groupe de valeurs - KWDGPartValues - ComparePartValues: methode de comparaison virtuelle a reimplementer - dans KWDGInterval:comparaison de deux intervalles - dans KWDGValueSet: comparaison de deux ensemble de valeurs sur leur premiere valeur (Symbol ou VarPart selon type de KWDGValue) - KWDGValue - CompareValue: methode virtuelle de comparaison par valeur - dans KWDGSymbolValue: valeur du Symbol - dans KWDGVarPartValue: attribut, puis ComparePartValues pour la partie - CompareFrequency: comparaison par valeur decroissante, puis par valeur Methodes de tri - KWDGAttribute - SortParts: tri des parties (base sur KWDGPartCompare) - KWDGPartCompareValues: fonction de comparaison utiles pour trier des tableaux de parties par valeurs - KWDGValueSet - SortValues: tri des valeurs (base sur KWDGValueCompareValue) - SortValueByDecreasingFrequencies: tri des valeurs par effectrif decroissant (base sur KWDGValueCompareFrequency) - CCHDGSymbolValueSet - SortValuesByTypicality (base sur CCHDGValueCompareDecreasingTypicality) Supression des fonction de comparaison suivantes - KWDGValueCompareDecreasingFrequency - KWDGVarPartValueCompareAttributeNameAndVarPart - KWDGPartSymbolCompareDecreasingFrequency - KWDGPartVarPartCompareDecreasingFrequency - KWDGPartContinuousCompare - KWDGPartSymbolCompare - KWDGPartVarPartCompare Impacts - KWDGPart::GetPartValues: acces generique aux PartValues (interval, valueSet...) - KWDataGrid: toutes les methdoes concernees - CCCoclusteringReport::WriteJSONInnerAttributePartition - KWDataGridManager::BuildPartsOfContinuousAttributeFromFrequencyTable - KWDataGridManager::CheckParts - KWDataGridMerger::InitializeAllPartMerges Résultats ok sur tout LearningTest\TestCoclustering\y_CoclusteringIV_Standard Pas de probleme observe en debug --- src/Learning/KWDataPreparation/KWDataGrid.cpp | 444 +++++++----------- src/Learning/KWDataPreparation/KWDataGrid.h | 120 +++-- .../KWDataPreparation/KWDataGridManager.cpp | 42 +- .../KWDataPreparation/KWDataGridMerger.cpp | 9 +- .../CCCoclusteringReport.cpp | 3 +- .../CCHierarchicalDataGrid.cpp | 2 +- .../MODL_Coclustering/MODL_Coclustering.cpp | 1 + 7 files changed, 281 insertions(+), 340 deletions(-) diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index 847618a62..a03caf56d 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -1734,14 +1734,7 @@ void KWDataGrid::WriteCrossTableStats(ostream& ost, int nTargetIndex) const nPart1++; attribute1->GetNextPart(part1); } - if (attribute1->GetAttributeType() == KWType::Continuous) - oaAttribute1Parts.SetCompareFunction(KWDGPartContinuousCompare); - // CH IV Begin - else if (attribute1->GetAttributeType() == KWType::Symbol) - oaAttribute1Parts.SetCompareFunction(KWDGPartSymbolCompare); - else - oaAttribute1Parts.SetCompareFunction(KWDGPartVarPartCompare); - // CH IV End + oaAttribute1Parts.SetCompareFunction(KWDGPartCompareValues); oaAttribute1Parts.Sort(); // Tri des parties de l'attribut 2 @@ -1754,14 +1747,7 @@ void KWDataGrid::WriteCrossTableStats(ostream& ost, int nTargetIndex) const nPart2++; attribute2->GetNextPart(part2); } - if (attribute2->GetAttributeType() == KWType::Continuous) - oaAttribute2Parts.SetCompareFunction(KWDGPartContinuousCompare); - // CH IV Begin - else if (attribute2->GetAttributeType() == KWType::Symbol) - oaAttribute2Parts.SetCompareFunction(KWDGPartSymbolCompare); - else - oaAttribute2Parts.SetCompareFunction(KWDGPartVarPartCompare); - // CH IV End + oaAttribute1Parts.SetCompareFunction(KWDGPartCompareValues); oaAttribute2Parts.Sort(); // Affichage de la ligne d'entete @@ -2532,7 +2518,7 @@ void KWDGAttribute::BuildIndexingStructure() ExportParts(&oaIntervals); // Tri des intervalles par borne inf - oaIntervals.SetCompareFunction(KWDGPartContinuousCompare); + oaIntervals.SetCompareFunction(KWDGPartCompareValues); oaIntervals.Sort(); } // Sinon, indexation des parties par les valeurs @@ -2958,7 +2944,9 @@ boolean KWDGAttribute::Check() const AddError("Initial value number must be greater or equal than granularized value number"); bOk = false; } - else if (KWType::IsSimple(nAttributeType) and nPartNumber > nGranularizedValueNumber) + // Verification uniquement si on a des infos sur la granularisation + else if (KWType::IsSimple(nAttributeType) and nPartNumber > nGranularizedValueNumber and + nGranularizedValueNumber > 0) // CH IV End { AddError("Granularized value number must be greater or equal than part number"); @@ -3078,7 +3066,7 @@ boolean KWDGAttribute::Check() const } // Tri des intervalles par borne inf - oaCheckIntervals.SetCompareFunction(KWDGPartContinuousCompare); + oaCheckIntervals.SetCompareFunction(KWDGPartCompareValues); oaCheckIntervals.Sort(); // Verification des intervalles @@ -3281,17 +3269,17 @@ void KWDGAttribute::SortParts() ObjectArray oaParts; KWDGPart* part; - // Tri des valeurs dans chaque partie d'attribut symbolique + // Tri des valeurs par effectif decroissant dans chaque partie d'attribut symbolique if (GetAttributeType() == KWType::Symbol) { part = GetHeadPart(); while (part != NULL) { - part->GetValueSet()->SortValues(); + part->GetValueSet()->SortValueByDecreasingFrequencies(); GetNextPart(part); } } - // CH IV Begin + // Tri par partie de variable (attribut, puis valeur de la parties) dans le cas d'un attribut VarPart else if (GetAttributeType() == KWType::VarPart) { part = GetHeadPart(); @@ -3304,13 +3292,7 @@ void KWDGAttribute::SortParts() // Tri des parties par intervalle croissant pour les attribut continus et // ou par effectif decroissant pour les attributs symboliques ou de type varpart - if (GetAttributeType() == KWType::Continuous) - InternalSortParts(KWDGPartContinuousCompare); - else if (GetAttributeType() == KWType::Symbol) - InternalSortParts(KWDGPartSymbolCompareDecreasingFrequency); - else - InternalSortParts(KWDGPartVarPartCompareDecreasingFrequency); - // CH IV End + InternalSortParts(KWDGPartCompare); } boolean KWDGAttribute::ArePartsSorted() const @@ -3327,13 +3309,13 @@ boolean KWDGAttribute::ArePartsSorted() const part = GetHeadPart(); while (part != NULL) { - bIsSorted = part->GetValueSet()->AreValuesSorted(); + bIsSorted = part->GetValueSet()->AreValuesSortedByDecreasingFrequencies(); if (not bIsSorted) break; GetNextPart(part); } } - // CH IV Begin + // Verification du tri des valeurs dans chaque partie d'attribut VarPart else if (GetAttributeType() == KWType::VarPart) { part = GetHeadPart(); @@ -3345,13 +3327,11 @@ boolean KWDGAttribute::ArePartsSorted() const GetNextPart(part); } } - // CH IV End - if (not bIsSorted) - return false; - else + // Verification du tri des partie si ok + if (bIsSorted) { - // Verification Tri des parties par intervalle croissant pour les attribut continus et + // Verification du tri des parties par intervalle croissant pour les attribut continus et // ou par effectif decroissant pour les attributs symboliques // Initialisation @@ -3362,21 +3342,8 @@ boolean KWDGAttribute::ArePartsSorted() const // Parcours des parties while (nextPart != NULL) { - // Comparaison - // Cas continu - if (GetAttributeType() == KWType::Continuous) - nCompare = KWDGPartContinuousCompare(&part, &nextPart); - // CH IV Begin - // Cas categoriel - else if (GetAttributeType() == KWType::Symbol) - nCompare = KWDGPartSymbolCompareDecreasingFrequency(&part, &nextPart); - // Cas VarPart - else - nCompare = KWDGPartVarPartCompareDecreasingFrequency(&part, &nextPart); - - // CH IV Refactoring: nettoyer ligne suivante??? - // Avant integration coclustering IV nCompare = KWDGPartSymbolCompareDecreasingFrequency(&part, - // &nextPart); CH IV End Erreur de tri + // Comparaison des partie par intervalles, ou par effectif decroissant, puis valeurs Symbol ou VarPart) + nCompare = part->ComparePart(nextPart); if (nCompare > 0) { bIsSorted = false; @@ -3387,7 +3354,6 @@ boolean KWDGAttribute::ArePartsSorted() const GetNextPart(part); GetNextPart(nextPart); } - return bIsSorted; } } @@ -3802,6 +3768,35 @@ boolean KWDGPart::IsSubPart(const KWDGPart* otherPart) const return GetValueSet()->IsSubValueSet(otherPart->GetValueSet()); } +int KWDGPart::ComparePart(const KWDGPart* otherPart) const +{ + int nCompare; + + require(otherPart != NULL); + require(GetPartType() == otherPart->GetPartType()); + + if (GetPartType() == KWType::Continuous) + nCompare = GetInterval()->ComparePartValues(otherPart->GetInterval()); + else + { + nCompare = -GetPartFrequency() + otherPart->GetPartFrequency(); + if (nCompare == 0) + nCompare = GetValueSet()->ComparePartValues(otherPart->GetValueSet()); + } + return nCompare; +} + +int KWDGPart::ComparePartValues(const KWDGPart* otherPart) const +{ + int nCompare; + + require(otherPart != NULL); + require(GetPartType() == otherPart->GetPartType()); + + nCompare = GetPartValues()->ComparePartValues(otherPart->GetPartValues()); + return nCompare; +} + boolean KWDGPart::Check() const { boolean bOk = true; @@ -4077,7 +4072,7 @@ boolean KWDGPart::GetEmulated() const return false; } -int KWDGPartContinuousCompare(const void* elem1, const void* elem2) +int KWDGPartCompare(const void* elem1, const void* elem2) { KWDGPart* part1; KWDGPart* part2; @@ -4089,47 +4084,13 @@ int KWDGPartContinuousCompare(const void* elem1, const void* elem2) // Acces a la parties part1 = cast(KWDGPart*, *(Object**)elem1); part2 = cast(KWDGPart*, *(Object**)elem2); - assert(part1->GetPartType() == KWType::Continuous); - assert(part2->GetPartType() == KWType::Continuous); // Comparaison: on compare en priorite sur les bornes inf - nCompare = KWContinuous::Compare(part1->GetInterval()->GetLowerBound(), part2->GetInterval()->GetLowerBound()); - if (nCompare == 0) - nCompare = - KWContinuous::Compare(part1->GetInterval()->GetUpperBound(), part2->GetInterval()->GetUpperBound()); + return part1->ComparePart(part2); return nCompare; } -int KWDGPartSymbolCompare(const void* elem1, const void* elem2) -{ - KWDGPart* part1; - KWDGPart* part2; - - require(elem1 != NULL); - require(elem2 != NULL); - - // Acces aux parties - part1 = cast(KWDGPart*, *(Object**)elem1); - part2 = cast(KWDGPart*, *(Object**)elem2); - assert(part1->GetPartType() == KWType::Symbol); - assert(part2->GetPartType() == KWType::Symbol); - - // Comparaison de la premiere valeur de la partie - if (part1->GetValueSet()->GetHeadValue() == NULL) - { - if (part2->GetValueSet()->GetHeadValue() == NULL) - return 0; - else - return -1; - } - else if (part2->GetValueSet()->GetHeadValue() == NULL) - return 1; - else - return part1->GetValueSet()->GetHeadValue()->GetSymbolValue().CompareValue( - part2->GetValueSet()->GetHeadValue()->GetSymbolValue()); -} - -int KWDGPartSymbolCompareDecreasingFrequency(const void* elem1, const void* elem2) +int KWDGPartCompareValues(const void* elem1, const void* elem2) { KWDGPart* part1; KWDGPart* part2; @@ -4141,77 +4102,12 @@ int KWDGPartSymbolCompareDecreasingFrequency(const void* elem1, const void* elem // Acces a la parties part1 = cast(KWDGPart*, *(Object**)elem1); part2 = cast(KWDGPart*, *(Object**)elem2); - assert(part1->GetPartType() == KWType::Symbol); - assert(part2->GetPartType() == KWType::Symbol); - - // Comparaison - nCompare = -part1->GetPartFrequency() + part2->GetPartFrequency(); - // Comparaison sur la valeur en cas d'egalite - if (nCompare == 0) - nCompare = KWDGPartSymbolCompare(elem1, elem2); - return nCompare; -} -// CH IV Begin -int KWDGPartVarPartCompare(const void* elem1, const void* elem2) -{ - KWDGPart* part1; - KWDGPart* part2; - KWDGValue* value1; - KWDGValue* value2; - - require(elem1 != NULL); - require(elem2 != NULL); - - // Acces a la parties - part1 = cast(KWDGPart*, *(Object**)elem1); - part2 = cast(KWDGPart*, *(Object**)elem2); - assert(part1->GetPartType() == KWType::VarPart); - assert(part2->GetPartType() == KWType::VarPart); - - // Comparaison de la premiere partie de variable de la partie - if (part1->GetVarPartSet()->GetHeadValue() == NULL) - { - if (part2->GetVarPartSet()->GetHeadValue() == NULL) - return 0; - else - return -1; - } - else if (part2->GetVarPartSet()->GetHeadValue() == NULL) - return 1; - else - { - value1 = part1->GetVarPartSet()->GetHeadValue(); - value2 = part2->GetVarPartSet()->GetHeadValue(); - return value1->CompareValue(value2); - } -} - -int KWDGPartVarPartCompareDecreasingFrequency(const void* elem1, const void* elem2) -{ - KWDGPart* part1; - KWDGPart* part2; - int nCompare; - - require(elem1 != NULL); - require(elem2 != NULL); - - // Acces a la parties - part1 = cast(KWDGPart*, *(Object**)elem1); - part2 = cast(KWDGPart*, *(Object**)elem2); - assert(part1->GetPartType() == KWType::VarPart); - assert(part2->GetPartType() == KWType::VarPart); - - // Comparaison - nCompare = -part1->GetPartFrequency() + part2->GetPartFrequency(); - - // Comparaison sur la valeur en cas d'egalite - if (nCompare == 0) - nCompare = KWDGPartVarPartCompare(elem1, elem2); + // Comparaison: on compare en priorite sur les bornes inf + return part1->ComparePartValues(part2); return nCompare; } -// CH IV End ////////////////////////////////////////////////////////////////////////////// // Classe KWDGInterval @@ -4282,6 +4178,22 @@ void KWDGInterval::CopyFrom(const KWDGPartValues* sourcePartValues) CopyFrom(cast(KWDGInterval*, sourcePartValues)); } +int KWDGInterval::ComparePartValues(const KWDGPartValues* otherPartValues) const +{ + int nCompare; + KWDGInterval* otherInterval; + + require(otherPartValues != NULL); + require(otherPartValues->GetValueType() == GetValueType()); + + // Comparaison: on compare en priorite sur les bornes inf + otherInterval = cast(KWDGInterval*, otherPartValues); + nCompare = KWContinuous::Compare(GetLowerBound(), otherInterval->GetLowerBound()); + if (nCompare == 0) + nCompare = KWContinuous::Compare(GetUpperBound(), otherInterval->GetUpperBound()); + return nCompare; +} + boolean KWDGInterval::Check() const { boolean bOk = true; @@ -4547,48 +4459,22 @@ void KWDGValueSet::UpgradeFrom(const KWDGValueSet* sourceValueSet) void KWDGValueSet::SortValues() { - InternalSortValues(KWDGValueCompareDecreasingFrequency); + InternalSortValues(KWDGValueCompareValue); } -boolean KWDGValueSet::AreValuesSorted() const +void KWDGValueSet::SortValueByDecreasingFrequencies() { - KWDGValue* value1; - KWDGValue* value2; - int nCompare; - boolean bIsSorted = true; - - value1 = GetHeadValue(); - value2 = GetHeadValue(); - GetNextValue(value2); - - // Parcours des valeurs - while (value2 != NULL) - { - if (value1->IsDefaultValue()) - { - bIsSorted = false; - break; - } - - if (value2->IsDefaultValue()) - { - if (value2 != GetTailValue()) - bIsSorted = false; - break; - } + InternalSortValues(KWDGValueCompareFrequency); +} - // Comparaison de la paire courante - nCompare = KWDGValueCompareDecreasingFrequency(&value1, &value2); +boolean KWDGValueSet::AreValuesSorted() const +{ + return InternalAreValuesSorted(KWDGValueCompareValue); +} - if (nCompare > 0) - { - bIsSorted = false; - break; - } - GetNextValue(value1); - GetNextValue(value2); - } - return bIsSorted; +boolean KWDGValueSet::AreValuesSortedByDecreasingFrequencies() const +{ + return InternalAreValuesSorted(KWDGValueCompareFrequency); } boolean KWDGValueSet::IsSubPartValues(const KWDGPartValues* otherPartValues) const @@ -4611,6 +4497,30 @@ void KWDGValueSet::CopyFrom(const KWDGPartValues* sourcePartValues) CopyFrom(cast(KWDGValueSet*, sourcePartValues)); } +int KWDGValueSet::ComparePartValues(const KWDGPartValues* otherPartValues) const +{ + int nCompare; + KWDGValueSet* otherValueSet; + + require(otherPartValues != NULL); + require(otherPartValues->GetValueType() == GetValueType()); + + // Comparaison de la premiere valeur de la partie + otherValueSet = cast(KWDGValueSet*, otherPartValues); + if (GetHeadValue() == NULL) + { + if (otherValueSet->GetHeadValue() == NULL) + return 0; + else + return -1; + } + else if (otherValueSet->GetHeadValue() == NULL) + return 1; + else + return GetHeadValue()->CompareValue(otherValueSet->GetHeadValue()); + return nCompare; +} + boolean KWDGValueSet::Check() const { boolean bOk = true; @@ -4828,6 +4738,47 @@ void KWDGValueSet::InternalSortValues(CompareFunction fCompare) } } +boolean KWDGValueSet::InternalAreValuesSorted(CompareFunction fCompare) const +{ + KWDGValue* value1; + KWDGValue* value2; + int nCompare; + boolean bIsSorted = true; + + value1 = GetHeadValue(); + value2 = GetHeadValue(); + GetNextValue(value2); + + // Parcours des valeurs + while (value2 != NULL) + { + // La valeur manquante doit etre en dernier + if (value1->IsDefaultValue()) + { + bIsSorted = false; + break; + } + if (value2->IsDefaultValue()) + { + if (value2 != GetTailValue()) + bIsSorted = false; + break; + } + + // Comparaison de la paire courante + nCompare = fCompare(&value1, &value2); + + if (nCompare > 0) + { + bIsSorted = false; + break; + } + GetNextValue(value1); + GetNextValue(value2); + } + return bIsSorted; +} + boolean KWDGValueSet::GetEmulated() const { return false; @@ -4902,11 +4853,10 @@ KWDGValueSet* KWDGSymbolValueSet::ComputeCleanedValueSet() const ////////////////////////////////////////////////////////////////////////////// // Classe KWDGValue -int KWDGValueCompareDecreasingFrequency(const void* elem1, const void* elem2) +int KWDGValueCompareValue(const void* elem1, const void* elem2) { KWDGValue* value1; KWDGValue* value2; - int nCompare; require(elem1 != NULL); require(elem2 != NULL); @@ -4916,11 +4866,23 @@ int KWDGValueCompareDecreasingFrequency(const void* elem1, const void* elem2) value2 = cast(KWDGValue*, *(Object**)elem2); // Comparaison - nCompare = -value1->GetValueFrequency() + value2->GetValueFrequency(); - if (nCompare != 0) - return nCompare; - else - return value1->CompareValue(value2); + return value1->CompareValue(value2); +} + +int KWDGValueCompareFrequency(const void* elem1, const void* elem2) +{ + KWDGValue* value1; + KWDGValue* value2; + + require(elem1 != NULL); + require(elem2 != NULL); + + // Acces a la valeur + value1 = cast(KWDGValue*, *(Object**)elem1); + value2 = cast(KWDGValue*, *(Object**)elem2); + + // Comparaison + return value1->CompareFrequency(value2); } ////////////////////////////////////////////////////////////////////////////// @@ -4977,29 +4939,17 @@ const ALString KWDGSymbolValue::GetObjectLabel() const int KWDGVarPartValue::CompareValue(const KWDGValue* otherValue) const { int nCompare; - KWDGPart* varPart1; - KWDGPart* varPart2; require(otherValue != NULL); // Comparaison des noms d'attributs des parties de variable nCompare = GetVarPart()->GetAttribute()->GetAttributeName().Compare( otherValue->GetVarPart()->GetAttribute()->GetAttributeName()); - if (nCompare != 0) - return nCompare; - // Cas de parties de variable d'un meme attribut - else - { - varPart1 = GetVarPart(); - varPart2 = otherValue->GetVarPart(); - // Cas d'un attribut numerique : comparaison des intervalles - if (varPart1->GetPartType() == KWType::Continuous) - return KWDGPartContinuousCompare(&varPart1, &varPart2); - // Cas d'un attribut categoriel : comparaison des effectifs des valueSet - else - return KWDGPartSymbolCompare(&varPart1, &varPart2); - } + // Si egalite, comparaison sur les valeurs de la partie (intervalle, ou premiere valeur du groupe) + if (nCompare == 0) + nCompare = GetVarPart()->ComparePartValues(otherValue->GetVarPart()); + return nCompare; } void KWDGVarPartValue::Write(ostream& ost) const @@ -5022,57 +4972,6 @@ const ALString KWDGVarPartValue::GetObjectLabel() const return varPart->GetVarPartLabel(); } -//DDD -//DDD BEGIN Ajout temporaire de methodes, pour debuggage -//DDD - -//DDD Ajout temporaire de methodes, pour debuggage -int KWDGVarPartValueCompareAttributeNameAndVarPart(const void* elem1, const void* elem2) -{ - KWDGVarPartValue* value1; - KWDGVarPartValue* value2; - KWDGPart* varPart1; - KWDGPart* varPart2; - int nCompare; - - require(elem1 != NULL); - require(elem2 != NULL); - - // Acces a la valeur - value1 = cast(KWDGVarPartValue*, *(Object**)elem1); - value2 = cast(KWDGVarPartValue*, *(Object**)elem2); - - // Comparaison des noms d'attributs des parties de variable - nCompare = value1->GetVarPart()->GetAttribute()->GetAttributeName().Compare( - value2->GetVarPart()->GetAttribute()->GetAttributeName()); - if (nCompare != 0) - return nCompare; - // Cas de parties de variable d'un meme attribut - else - { - varPart1 = value1->GetVarPart(); - varPart2 = value2->GetVarPart(); - // Cas d'un attribut numerique : comparaison des intervalles - if (varPart1->GetPartType() == KWType::Continuous) - return KWDGPartContinuousCompare(&varPart1, &varPart2); - // Cas d'un attribut categoriel : comparaison des effectifs des valueSet - else - return KWDGPartSymbolCompare(&varPart1, &varPart2); - } -} - -//DDD Ajout temporaire de methodes, pour debuggage -// Tri des parties de variable par nom de variable puis tri des parties de la meme variable, pour preparer -// l'affichage -void KWDGVarPartSet::SortValues() -{ - InternalSortValues(KWDGVarPartValueCompareAttributeNameAndVarPart); -} - -//DDD -//DDD END Ajout temporaire de methodes, pour debuggage -//DDD - int KWSortableObjectCompareVarPart(const void* elem1, const void* elem2) { KWDGVarPartValue* varPartValue1; @@ -5590,16 +5489,7 @@ int KWDGCellCompareValue(const void* elem1, const void* elem2) assert(part1->GetPartType() == part2->GetPartType()); // Comparaison selon le type de partie - if (part1->GetPartType() == KWType::Continuous) - nPartCompare = KWDGPartContinuousCompare(&part1, &part2); - // CH IV Begin - else if (part1->GetPartType() == KWType::Symbol) - nPartCompare = KWDGPartSymbolCompare(&part1, &part2); - else if (part1->GetPartType() == KWType::VarPart) - nPartCompare = KWDGPartVarPartCompare(&part1, &part2); - else - nPartCompare = -1; - // CH IV End + nPartCompare = part1->ComparePartValues(part2); if (nPartCompare != 0) return nPartCompare; } diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index 9546aebac..9e8aa5448 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -643,11 +643,11 @@ class KWDGAttribute : public Object // Tri des parties pour preparer l'affichage // Les intervalles sont tries par valeur croissante - // CH IV Begin // Les groupes (de valeurs ou de parties de variable) sont tries par effectif decroissant - // Les valeurs dans les groupes sont tries par effectif decroissant - // Les parties de variables dans les groupes sont tries par attribut puis en appliquant le tri des intervalles - // ou des groupes au sein de chaque attribut CH IV End + // Danc chaque partie: + // - Symbol: les valeurs dans les groupes sont tries par effectif decroissant + // - VarPart: les parties de variables dans les groupes sont tries par attribut puis + // par valeur de la partie, intervalles, ou valeurs du groupe void SortParts(); // Verification du tri des parties : couteux, a utiliser essentiellement dans les assertions @@ -778,6 +778,7 @@ class KWDGPart : public Object int GetPartType() const; // Acces aux valeurs, selon le type + KWDGPartValues* GetPartValues() const; KWDGInterval* GetInterval() const; KWDGValueSet* GetValueSet() const; KWDGSymbolValueSet* GetSymbolValueSet() const; @@ -816,6 +817,16 @@ class KWDGPart : public Object // Test si la partie est une sous-partie de l'autre partie en parametre boolean IsSubPart(const KWDGPart* otherPart) const; + // Comparaison de deux parties en exploitant la taille des parties, sauf dans le cas numerique + // - par intervalle si numerique + // - par effectif decroissant puis valeur si ensemble de valeurs + int ComparePart(const KWDGPart* otherPart) const; + + // Comparaison des valeurs des parties + // - par intervalle si numerique + // - par premiere valeur si groupe de valeur categoriel ou VarPart + int ComparePartValues(const KWDGPart* otherPart) const; + // Controle d'integrite local a la partie (valeurs, cellules de la partie) boolean Check() const override; @@ -883,24 +894,11 @@ class KWDGPart : public Object // CH IV End }; -// Comparaison de deux parties numeriques, sur la base de leur borne sup -int KWDGPartContinuousCompare(const void* elem1, const void* elem2); - -// Comparaison de deux parties symboliques, sur la base de leur premiere valeur -int KWDGPartSymbolCompare(const void* elem1, const void* elem2); +// Comparaison de deux parties, par effectif decroissant puis valeur, ou par intervalle (cf. ComparePart) +int KWDGPartCompare(const void* elem1, const void* elem2); -// Comparaison de deux parties symboliques, par effectif decroissant -int KWDGPartSymbolCompareDecreasingFrequency(const void* elem1, const void* elem2); - -// CH IV Begin -// Comparaison de deux parties de type parties de variables sur la base de leur premiere partie de variable : -// - le nom de l'attribut des premieres parties -// - la comparaison des premieres parties si l'attribut est semblable -int KWDGPartVarPartCompare(const void* elem1, const void* elem2); - -// Comparaison de deux parties de parties de variables, par effectif decroissant -int KWDGPartVarPartCompareDecreasingFrequency(const void* elem1, const void* elem2); -// CH IV End +// Comparaison de deux parties, par valeur ou par intervalle (cf. ComparePartValues) +int KWDGPartCompareValues(const void* elem1, const void* elem2); ////////////////////////////////////////////////////////////////////////////// // Classe KWDGPartValues @@ -926,6 +924,9 @@ class KWDGPartValues : public Object // Copie virtual void CopyFrom(const KWDGPartValues* sourcePartValues) = 0; + // Comparaison sur les valeurs de la partie + virtual int ComparePartValues(const KWDGPartValues* otherPartValues) const = 0; + // Controle d'integrite boolean Check() const override = 0; @@ -1005,12 +1006,16 @@ class KWDGValueSet : public KWDGPartValues // Ajout de nouvelles valeurs recopiees depuis une source void UpgradeFrom(const KWDGValueSet* sourceValueSet); + // Tri des valeurs + void SortValues(); + // Tri des valeurs par effectif decroissant, pour preparer l'affichage - virtual void SortValues(); //DDD DEBUG virtual a supprimer + void SortValueByDecreasingFrequencies(); // Verification du tri des valeurs par effectif decroissant // Couteux, a utiliser essentiellement dans les assertions boolean AreValuesSorted() const; + boolean AreValuesSortedByDecreasingFrequencies() const; // Redefinition des methodes virtuelles boolean IsSubPartValues(const KWDGPartValues* otherPartValues) const override; @@ -1018,6 +1023,11 @@ class KWDGValueSet : public KWDGPartValues void UpgradeFrom(const KWDGPartValues* sourcePartValues) override; void CopyFrom(const KWDGPartValues* sourcePartValues) override; + // Comparaison de deux ensembles de valeurs d'apres leur premiere valeur + // - valeur Symbol si categoriel + // - nom d'attribut, puis valeur de partie si VarPart + int ComparePartValues(const KWDGPartValues* otherPartValues) const override; + // Controle d'integrite boolean Check() const override; @@ -1039,6 +1049,10 @@ class KWDGValueSet : public KWDGPartValues // La valeur speciale est toujours mise en dernier, independament du critere de tri void InternalSortValues(CompareFunction fCompare); + // Test si des valeurs sont triees selon une fonction de tri + // La valeur speciale est toujours mise en dernier, independament du critere de tri + boolean InternalAreValuesSorted(CompareFunction fCompare) const; + // Methode indiquant si les donnees sont emulee virtual boolean GetEmulated() const; @@ -1062,7 +1076,9 @@ class KWDGValue : public Object // Type de valeur virtual int GetValueType() const = 0; - // Valeur + // Valeur accesssible depuis la classe ancetre, pour simplifier l'ecriture de code generique + // Implementation par defaut en assert false + // Chaque sous-classe doit reimplementer la methode correspondante a son type de valeur virtual Symbol& GetSymbolValue() const; virtual KWDGPart* GetVarPart() const; @@ -1076,9 +1092,14 @@ class KWDGValue : public Object virtual void SetValueFrequency(int nFrequency) = 0; virtual int GetValueFrequency() const = 0; - // Comparaison de valeur + // Comparaison par valeur + // - valeur Syymbol si categoriel + // - nom d'attribut, puis valeur de partie si VarPart virtual int CompareValue(const KWDGValue* otherValue) const = 0; + // Compare par effectif decroissant, puis par valeur + virtual int CompareFrequency(const KWDGValue* otherValue) const; + // Affichage void Write(ostream& ost) const override = 0; @@ -1095,8 +1116,11 @@ class KWDGValue : public Object KWDGValue* nextValue; }; +// Comparaison de deux valeurs +int KWDGValueCompareValue(const void* elem1, const void* elem2); + // Comparaison de deux valeurs, par effectif decroissant -int KWDGValueCompareDecreasingFrequency(const void* elem1, const void* elem2); +int KWDGValueCompareFrequency(const void* elem1, const void* elem2); ////////////////////////////////////////////////////////////////////////////// // Classe KWDGSymbolValueSet @@ -1174,7 +1198,7 @@ class KWDGSymbolValue : public KWDGValue void SetValueFrequency(int nFrequency) override; int GetValueFrequency() const override; - // Comparaison de valeur + // Comparaison de valeur Symbol int CompareValue(const KWDGValue* otherValue) const override; // Affichage @@ -1246,6 +1270,9 @@ class KWDGInterval : public KWDGPartValues void UpgradeFrom(const KWDGPartValues* sourcePartValues) override; void CopyFrom(const KWDGPartValues* sourcePartValues) override; + // Comparaison de deux intervalles d'apres leur bornes + int ComparePartValues(const KWDGPartValues* otherPartValues) const override; + // Controle d'integrite boolean Check() const override; @@ -1287,14 +1314,6 @@ class KWDGVarPartSet : public KWDGValueSet // Redefinition de la methode virtuelle d'ajout de la copie d'une valeur existante KWDGValue* AddValueCopy(const KWDGValue* sourceValue) override; - //DDD BEGIN Ajout temporaire de methodes, pour debuggage - - // Tri des parties de variable par nom de variable puis tri des parties de la meme variable, pour preparer - // l'affichage - void SortValues() override; - - //DDD END Ajout temporaire de methodes, pour debuggage - /////////////////////////////// ///// Implementation protected: @@ -1328,7 +1347,7 @@ class KWDGVarPartValue : public KWDGValue void SetValueFrequency(int nFrequency) override; int GetValueFrequency() const override; - // Comparaison de valeur + // Comparaison de valeur, par attribut, puis selon les valeurs de la partie, intervalles ou premiere valeur des groupe int CompareValue(const KWDGValue* otherValue) const override; // Affichage @@ -1959,6 +1978,18 @@ inline int KWDGPart::GetPartType() const return KWType::Unknown; } +inline KWDGPartValues* KWDGPart::GetPartValues() const +{ + require(KWType::IsCoclusteringType(GetPartType())); + + if (GetPartType() == KWType::Continuous) + return interval; + else if (GetPartType() == KWType::Symbol) + return symbolValueSet; + else + return varPartSet; +} + inline KWDGInterval* KWDGPart::GetInterval() const { require(GetPartType() == KWType::Continuous); @@ -2117,11 +2148,13 @@ inline boolean KWDGValueSet::IsDefaultPart() const inline int KWDGValueSet::GetValueNumber() const { - assert(nValueNumber > 0 or (IsDefaultPart() and GetValueType() == KWType::Symbol)); + assert(nValueNumber > 0 or IsDefaultPart() or GetValueType() != KWType::Symbol); // On assure que le seul cas sans aucune valeur est le cas de la partie par defaut reduite a la StarValue // Cela peut arriver si la partie a ete "nettoyee" pour gagner de la place, notamment quand on exporte - // une grille dans un dictionnaire et qu'on la reimporte pour le deploiement + // une grille dans un fichier de dictionnaire .kdic. Le modele exporte n'est pas tout a fait valide, + // puisque l'on a perdu le compte des valeurs du groupe poubelle (bug a corriger plus tard). + // Quand on reimporte le model pour le deploiement, il faut contourner ces incoherences. // Dans ce cas, ou rend une nombre de valeurs egal 1 a, ce qui a le merite d'avoir des couts de grille // valides numeriquement, meme s'ils ne corrrespondent pas au vrai model qui aurait du memoriser // le nombre exacte de valeurs du groupe @@ -2324,6 +2357,19 @@ inline KWDGPart* KWDGValue::GetVarPart() const return NULL; } +inline int KWDGValue::CompareFrequency(const KWDGValue* otherValue) const +{ + int nCompare; + + require(otherValue != NULL); + require(otherValue->GetValueType() == GetValueType()); + + nCompare = -GetValueFrequency() + otherValue->GetValueFrequency(); + if (nCompare == 0) + nCompare = CompareValue(otherValue); + return nCompare; +} + // Classe KWDGSymbolValue inline KWDGSymbolValue::KWDGSymbolValue(const Symbol& sValue) diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index 14d8eb1ac..595c04fd2 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -1606,7 +1606,7 @@ void KWDataGridManager::BuildPartsOfContinuousAttributeFromFrequencyTable(KWDGAt sourceAttribute->ExportParts(&oaSourceParts); // Tri des intervalles source par borne inf croissante - oaSourceParts.SetCompareFunction(KWDGPartContinuousCompare); + oaSourceParts.SetCompareFunction(KWDGPartCompareValues); oaSourceParts.Sort(); // Recuperation des effectifs "source" de la table d'effectifs @@ -2114,12 +2114,12 @@ boolean KWDataGridManager::CheckParts(const KWDataGrid* targetDataGrid) const { // Ajout des parties source numeriques dans un tableau d'intervalles sourceAttribute->ExportParts(&oaSourceIntervals); - oaSourceIntervals.SetCompareFunction(KWDGPartContinuousCompare); + oaSourceIntervals.SetCompareFunction(KWDGPartCompareValues); oaSourceIntervals.Sort(); // Ajout des parties cibles numeriques dans un tableau d'intervalles targetAttribute->ExportParts(&oaTargetIntervals); - oaTargetIntervals.SetCompareFunction(KWDGPartContinuousCompare); + oaTargetIntervals.SetCompareFunction(KWDGPartCompareValues); oaTargetIntervals.Sort(); // Parcours des intervalles cibles pour verifier leur compatibilite @@ -2742,7 +2742,7 @@ void KWDataGridManager::InitialiseAttributeRandomParts(const KWDGAttribute* sour sourceAttribute->ExportParts(&oaSourceParts); // Tri des intervalles source par borne inf croissante - oaSourceParts.SetCompareFunction(KWDGPartContinuousCompare); + oaSourceParts.SetCompareFunction(KWDGPartCompareValues); oaSourceParts.Sort(); // Initialisation d'un ensemble de bornes aleatoires @@ -2974,12 +2974,12 @@ void KWDataGridManager::AddAttributeRandomParts(const KWDGAttribute* sourceAttri { // Export des parties de l'attribut source sourceAttribute->ExportParts(&oaSourceParts); - oaSourceParts.SetCompareFunction(KWDGPartContinuousCompare); + oaSourceParts.SetCompareFunction(KWDGPartCompareValues); oaSourceParts.Sort(); // Export des parties de l'attribut obligatoire mandatoryAttribute->ExportParts(&oaMandatoryParts); - oaMandatoryParts.SetCompareFunction(KWDGPartContinuousCompare); + oaMandatoryParts.SetCompareFunction(KWDGPartCompareValues); oaMandatoryParts.Sort(); assert(oaMandatoryParts.GetSize() > 0); @@ -3425,10 +3425,6 @@ void KWDataGridManager::InitialiseAttributeGranularizedSymbolParts(const KWDGAtt targetAttribute->InitializeCatchAllValueSet(cleanedValueSet); delete cleanedValueSet; } - - // Tri des valeurs du fourre tout - if (targetPart->GetValueSet()->IsDefaultPart()) - targetPart->GetValueSet()->SortValues(); } } @@ -3472,7 +3468,6 @@ void KWDataGridManager::InitialiseAttributeGranularizedVarPartParts(const KWDGAt InitialiseAttributeParts(sourceAttribute, targetAttribute); targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); } - // Granularisation else { @@ -3513,6 +3508,7 @@ void KWDataGridManager::InitialiseAttributeGranularizedVarPartParts(const KWDGAt // Ajout de ses valeurs targetPart->GetVarPartSet()->UpgradeFrom(sourcePart->GetVarPartSet()); } + // CH IV Refactoring: nettoyer lignes ci-dessous // CH TODO Inclusion poubelle //// Compression et memorisation du fourre-tout si necessaire (mode supervise, attribut non @@ -3699,6 +3695,11 @@ KWDataGridManager::CreateGranularizedInnerAttributes(const KWDGInnerAttributes* odInnerAttributesQuantilesBuilders->Lookup(targetInnerAttribute->GetAttributeName())); InitialiseAttributeGranularizedParts(sourceInnerAttribute, targetInnerAttribute, nGranularity, quantileBuilder); + + // Tri des parties de l'attribut interne + // Necessaire, car la derniere partie issue de la granularisation peut etre d'effectif plus + // important en raison du groupe par defaut + targetInnerAttribute->SortParts(); } // Memorisation de la granularite @@ -3723,7 +3724,6 @@ double KWDataGridManager::MergePartsForVarPartAttributes(KWDataGrid* targetDataG KWDGValue* currentValue; KWDGValue* nextValue; boolean bNewVarPart; - double dEpsilon = 1e-4; double dDeltaClusterCost; require(sourceDataGrid->IsVarPartDataGrid()); @@ -3746,6 +3746,8 @@ double KWDataGridManager::MergePartsForVarPartAttributes(KWDataGrid* targetDataG while (initialPart != NULL) { // Tri des parties de variable du cluster + // Attention, les VarPart sont ici trie d'abord par attribut, puis par valeurs de la partie, + // de facon a pouvoir detecter la fusion de deux parties consecutives issues du meme attribut initialPart->GetVarPartSet()->SortValues(); // Initialisation des deux premieres parties de variable @@ -3757,6 +3759,8 @@ double KWDataGridManager::MergePartsForVarPartAttributes(KWDataGrid* targetDataG while (nextValue != NULL) { bNewVarPart = false; + assert(currentValue->GetVarPart()->GetAttribute()->GetAttributeName() <= + nextValue->GetVarPart()->GetAttribute()->GetAttributeName()); // Cas de non fusion // Parties d'attributs distincts @@ -3764,10 +3768,14 @@ double KWDataGridManager::MergePartsForVarPartAttributes(KWDataGrid* targetDataG nextValue->GetVarPart()->GetAttribute()->GetAttributeName()) bNewVarPart = true; // Parties (intervalles) non consecutives d'un attribut numerique - else if (currentValue->GetVarPart()->GetPartType() == KWType::Continuous and - abs(currentValue->GetVarPart()->GetInterval()->GetUpperBound() - - nextValue->GetVarPart()->GetInterval()->GetLowerBound()) > dEpsilon) - bNewVarPart = true; + else if (currentValue->GetVarPart()->GetPartType() == KWType::Continuous) + { + assert(currentValue->GetVarPart()->GetInterval()->GetUpperBound() <= + nextValue->GetVarPart()->GetInterval()->GetLowerBound()); + if (currentValue->GetVarPart()->GetInterval()->GetUpperBound() < + nextValue->GetVarPart()->GetInterval()->GetLowerBound()) + bNewVarPart = true; + } // Pas de fusion a realiser if (bNewVarPart) @@ -3790,7 +3798,7 @@ double KWDataGridManager::MergePartsForVarPartAttributes(KWDataGrid* targetDataG currentValue->GetVarPart()->GetValueSet()->Import( nextValue->GetVarPart()->GetValueSet()); - // Fusion des effectifs + // Cumul des effectifs currentValue->GetVarPart()->SetPartFrequency( currentValue->GetVarPart()->GetPartFrequency() + nextValue->GetVarPart()->GetPartFrequency()); diff --git a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp index 787f36f5e..0e6018637 100644 --- a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp @@ -493,7 +493,7 @@ void KWDataGridMerger::InitializeAllPartMerges() if (attribute->GetAttributeType() == KWType::Continuous) { // Tri des intervalles - oaParts.SetCompareFunction(KWDGPartContinuousCompare); + oaParts.SetCompareFunction(KWDGPartCompareValues); oaParts.Sort(); // Creation des fusions entre intervalles adjacents @@ -524,12 +524,7 @@ void KWDataGridMerger::InitializeAllPartMerges() else { // Tri des parties (pour des raisons d'affichage uniquement) - // CH IV Begin - if (attribute->GetAttributeType() == KWType::Symbol) - oaParts.SetCompareFunction(KWDGPartSymbolCompare); - else - oaParts.SetCompareFunction(KWDGPartVarPartCompare); - // CH IV End + oaParts.SetCompareFunction(KWDGPartCompareValues); oaParts.Sort(); // Creation des fusions entre toutes les paires de parties diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp index d45095f01..6b07556cc 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp @@ -296,6 +296,7 @@ boolean CCCoclusteringReport::WriteJSONReport(const ALString& sJSONReportName, ALString sLocalTempFileName; require(coclusteringDataGrid != NULL); + require(coclusteringDataGrid->Check()); // Preparation de la copie sur HDFS si necessaire bOk = PLRemoteFileService::BuildOutputWorkingFile(sJSONReportName, sLocalTempFileName); @@ -4975,7 +4976,7 @@ void CCCoclusteringReport::WriteJSONInnerAttributePartition(const CCHierarchical } // Tri des valeurs - oaAllValues.SetCompareFunction(KWDGValueCompareDecreasingFrequency); + oaAllValues.SetCompareFunction(KWDGValueCompareFrequency); oaAllValues.Sort(); // Ecriture des valeurs diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp index b145eb0b5..3b1c94809 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp @@ -1055,7 +1055,7 @@ int CCHDGValueCompareDecreasingTypicality(const void* elem1, const void* elem2) else if (dCompare < -dPrecision) nCompare = -1; else - nCompare = KWDGValueCompareDecreasingFrequency(elem1, elem2); + nCompare = value1->CompareFrequency(value2); return nCompare; } diff --git a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp index f96090600..02059dc93 100644 --- a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp +++ b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp @@ -34,6 +34,7 @@ int main(int argc, char** argv) Global::ActivateSignalErrorManagement(); // Choix du repertoire de lancement pour le debugage sous Windows (a commenter apres fin du debug) + //DDD SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisMissingValues"); //SetWindowsDebugDir("Standard", "Iris"); From 5781424d4f04a36b35c897782121932e24361226 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Tue, 5 Sep 2023 16:41:22 +0200 Subject: [PATCH 05/17] Exploit KWDGPartValues hierarchy to simplify class KWDGPart KWType - GetSimpleCoclusteringType -> GetCoclusteringSimpleType - IsCoclusteringGroupableType: nouvelle methode pour les type Symbol ou VarPart KWDGPart - KWDGPartValues* partValues: nouvel attribut de classe generique, remplacant les attributs precedents interval, symbolValueSet, varPartSet - GetPartType: reimplementation simplifiee - GetInterval, GetSymbolValueSet, GetVarPartSet: reimplementation des acces specififiques - GetValueSet, GetPartValues: reimplementation des acces generiques KWDGPartValues - GetUsedMemory: nouvelle methodee generique reimplementee dans les sous-classes KWDGValue - GetUsedMemory: nouvelle methodee generique reimplementee dans les sous-classes Etude preliminaire d'impact pour refactoring plus complet - annotaion de tous les impacts potentiels par //DDDSIMPLIFY --- src/Learning/KWData/KWType.h | 14 +- .../KWDGMPartMergeAction.cpp | 2 + src/Learning/KWDataPreparation/KWDataGrid.cpp | 122 ++++++++++-------- src/Learning/KWDataPreparation/KWDataGrid.h | 107 +++++++-------- .../KWDataPreparation/KWDataGridManager.cpp | 3 + .../KWDataPreparation/KWDataGridMerger.cpp | 11 +- .../KWDataGridPostOptimizer.cpp | 14 +- .../CCCoclusteringReport.cpp | 18 +-- .../CCHierarchicalDataGrid.cpp | 2 +- 9 files changed, 160 insertions(+), 133 deletions(-) diff --git a/src/Learning/KWData/KWType.h b/src/Learning/KWData/KWType.h index 85a0bb459..3b1d1f180 100644 --- a/src/Learning/KWData/KWType.h +++ b/src/Learning/KWData/KWType.h @@ -106,8 +106,11 @@ class KWType : public SystemObject // Indique si le type peut etre utilise pour un attribut de grille: (Continuous, Symbol ou VarPart) static boolean IsCoclusteringType(int nType); - // Renvoie le type eementaire d'un attribut de grille: (Continuous, ou Symbol pour un type Symbol ou VarPart) - static int GetSimpleCoclusteringType(int nType); + // Indique si le type peut etre utilise pour un attribut groupable de grille: (Symbol ou VarPart) + static boolean IsCoclusteringGroupableType(int nType); + + // Renvoie le type elementaire d'un attribut de grille: (Continuous, ou Symbol pour un type Symbol ou VarPart) + static int GetCoclusteringSimpleType(int nType); // CH IV End // Indique si le type est un type de predicteur: (Continuous, Symbol ou None) @@ -367,7 +370,12 @@ inline boolean KWType::IsCoclusteringType(int nType) return (nType == Continuous or nType == Symbol or nType == VarPart); } -inline int KWType::GetSimpleCoclusteringType(int nType) +inline boolean KWType::IsCoclusteringGroupableType(int nType) +{ + return (nType == Symbol or nType == VarPart); +} + +inline int KWType::GetCoclusteringSimpleType(int nType) { require(IsCoclusteringType(nType)); if (nType == Continuous) diff --git a/src/Learning/KWDataPreparation/KWDGMPartMergeAction.cpp b/src/Learning/KWDataPreparation/KWDGMPartMergeAction.cpp index 7de035c9f..04773778d 100644 --- a/src/Learning/KWDataPreparation/KWDGMPartMergeAction.cpp +++ b/src/Learning/KWDataPreparation/KWDGMPartMergeAction.cpp @@ -114,6 +114,7 @@ KWDGMPart* KWDGMPartMergeAction::PerformPartMerge(KWDGMPartMerge* partMerge) // Dereferencement des parties de la liste triee par nombre de modalites // Restitution de la poubelle // CH IV Begin + //DDDSIMPLIFY if (attribute->GetAttributeType() == KWType::Symbol or attribute->GetAttributeType() == KWType::VarPart) // CH IV End { @@ -251,6 +252,7 @@ KWDGMPart* KWDGMPartMergeAction::PerformPartMerge(KWDGMPartMerge* partMerge) part2->SetCost(dataGridCosts->ComputePartCost(part2)); // CH IV Begin + //DDDSIMPLIFY if (attribute->GetAttributeType() == KWType::Symbol or attribute->GetAttributeType() == KWType::VarPart) // CH IV End { diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index a03caf56d..5ecd8efa6 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -1518,6 +1518,7 @@ void KWDataGrid::WriteAttributes(ostream& ost) const ost << "\t" << attribute->GetAttributeName() << "\t" << KWType::ToString(attribute->GetAttributeType()); // Cas d'un attribut categoriel : affichage du groupe poubelle eventuel + //DDDSIMPLIFY if (attribute->GetAttributeType() == KWType::Symbol and not attribute->GetAttributeTargetFunction()) { ost << "\tCatch all size " << attribute->GetCatchAllValueNumber() << "\n"; @@ -1567,6 +1568,7 @@ void KWDataGrid::WriteAttributeParts(ostream& ost) const ost << "\t\t" << part->GetObjectLabel() << "\t" << part->GetPartFrequency(); // Affichage des premieres valeurs dans le cas d'un attribut Symbol + //DDDSIMPLIFY if (attribute->GetAttributeType() == KWType::Symbol) { nDisplayedValue = 0; @@ -1610,6 +1612,7 @@ void KWDataGrid::WriteAttributeParts(ostream& ost) const ost << "\n"; // Affichage complet du valueSet dans le cas d'un attribut Symbol + //DDDSIMPLIFY if (bDisplayAll and attribute->GetAttributeType() == KWType::Symbol) { cout << "ValueSet\n"; @@ -2523,6 +2526,7 @@ void KWDGAttribute::BuildIndexingStructure() } // Sinon, indexation des parties par les valeurs // CH IV Begin + //DDDSIMPLIFY else if (GetAttributeType() == KWType::Symbol) // CH IV End { @@ -2610,6 +2614,7 @@ void KWDGAttribute::DeleteIndexingStructure() assert(KWType::IsCoclusteringType(GetAttributeType())); if (GetAttributeType() == KWType::Continuous) oaIntervals.SetSize(0); + //DDDSIMPLIFY else if (GetAttributeType() == KWType::Symbol) { nkdParts.RemoveAll(); @@ -2796,6 +2801,7 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con } } // Test d'inclusion des parties dans le cas categoriel + //DDDSIMPLIFY else if (otherAttribute->GetAttributeType() == KWType::Symbol) { // Indexation prealable des partie cibles @@ -3108,6 +3114,7 @@ boolean KWDGAttribute::Check() const } } // Si attribut categoriel, indexation locale des parties par les valeurs pour validation + //DDDSIMPLIFY else if (bOk and GetAttributeType() == KWType::Symbol) { // Parcours des parties pour les indexer par leurs valeurs @@ -3354,8 +3361,8 @@ boolean KWDGAttribute::ArePartsSorted() const GetNextPart(part); GetNextPart(nextPart); } - return bIsSorted; } + return bIsSorted; } void KWDGAttribute::Write(ostream& ost) const @@ -3653,23 +3660,13 @@ KWDGPart::KWDGPart() headCell = NULL; tailCell = NULL; nCellNumber = 0; - interval = NULL; - symbolValueSet = NULL; - // CH IV Begin - varPartSet = NULL; - // CH IV End + partValues = NULL; } KWDGPart::~KWDGPart() { - if (interval != NULL) - delete interval; - if (symbolValueSet != NULL) - delete symbolValueSet; - // CH IV Begin - if (varPartSet != NULL) - delete varPartSet; - // CH IV End + if (partValues != NULL) + delete partValues; // Reinitialisation pour faciliter le debug debug(attribute = NULL); @@ -3679,9 +3676,7 @@ KWDGPart::~KWDGPart() debug(headCell = NULL); debug(tailCell = NULL); debug(nCellNumber = 0); - debug(interval = NULL); - debug(symbolValueSet = NULL); - debug(varPartSet = NULL); + debug(partValues = NULL); } void KWDGPart::SetPartType(int nValue) @@ -3690,15 +3685,13 @@ void KWDGPart::SetPartType(int nValue) // CH IV Begin require(KWType::IsCoclusteringType(nValue)); - // Creation de l'objet interval ou ensemble de valeur selon le type + // Creation de l'objet interval ou ensemble de valeurs selon le type if (nValue == KWType::Continuous) - interval = NewInterval(); + partValues = NewInterval(); else if (nValue == KWType::Symbol) - symbolValueSet = NewSymbolValueSet(); + partValues = NewSymbolValueSet(); else - varPartSet = NewVarPartSet(); - // CH IV End - + partValues = NewVarPartSet(); ensure(GetPartType() != KWType::Unknown); } @@ -3817,11 +3810,11 @@ boolean KWDGPart::Check() const } // Verification de l'intervalle else if (GetPartType() == KWType::Continuous) - bOk = bOk and interval->Check(); + bOk = bOk and partValues->Check(); // Verification de l'ensemble de valeurs else if (GetPartType() == KWType::Symbol) { - bOk = bOk and symbolValueSet->Check(); + bOk = bOk and partValues->Check(); // Verification de la compatibilite entre l'effectif de la partie // et l'effectif cumule de ses valeurs @@ -3833,7 +3826,7 @@ boolean KWDGPart::Check() const // Cela permet egalement de verifier la validite d'une grille // construite pour le deploiement de modele, qui n'a pas besoin // des effectifs par valeur. - nTotalValueFrequency = symbolValueSet->ComputeTotalFrequency(); + nTotalValueFrequency = GetValueSet()->ComputeTotalFrequency(); if (bOk and GetPartFrequency() > 0 and nTotalValueFrequency > 0 and GetPartFrequency() != nTotalValueFrequency) { @@ -3847,7 +3840,7 @@ boolean KWDGPart::Check() const // Verification de l'ensemble des parties de variables else { - bOk = bOk and varPartSet->Check(); + bOk = bOk and partValues->Check(); // Verification de la compatibilite entre l'effectif de la partie // et l'effectif cumule de ses parties de variable @@ -3859,7 +3852,7 @@ boolean KWDGPart::Check() const // Cela permet egalement de verifier la validite d'une grille // construite pour le deploiement de modele, qui n'a pas besoin // des effectifs par valeur. - nTotalVarPartFrequency = varPartSet->ComputeTotalFrequency(); + nTotalVarPartFrequency = GetValueSet()->ComputeTotalFrequency(); // CH IV Debug // CH IV Refactoring: nettoyer? // cout << "nTotalVarPartFrequency\t" << nTotalVarPartFrequency << "\tGetPartFrequency\t" << @@ -3948,12 +3941,8 @@ longint KWDGPart::GetUsedMemory() const longint lUsedMemory; lUsedMemory = sizeof(KWDGPart); - if (interval != NULL) - lUsedMemory += sizeof(KWDGInterval); - if (symbolValueSet != NULL) - lUsedMemory += sizeof(KWDGSymbolValueSet); - if (varPartSet != NULL) - lUsedMemory += sizeof(KWDGVarPartSet); + if (partValues != NULL) + lUsedMemory += partValues->GetUsedMemory(); return lUsedMemory; } @@ -3964,10 +3953,11 @@ void KWDGPart::Write(ostream& ost) const ost << GetClassLabel() << "\t" << GetObjectLabel() << "\t" << GetPartFrequency() << "\n"; // Valeurs et cellules de la partie - if (GetPartType() == KWType::Symbol and symbolValueSet->GetValueNumber() > 0) + //DDDSIMPLIFY + if (GetPartType() == KWType::Symbol and GetValueSet()->GetValueNumber() > 0) WriteValues(ost); - if (GetPartType() == KWType::VarPart and varPartSet->GetValueNumber() > 0) + if (GetPartType() == KWType::VarPart and GetValueSet()->GetValueNumber() > 0) WriteValues(ost); // CH IV End if (GetCellNumber() > 0) @@ -3978,10 +3968,11 @@ void KWDGPart::WriteValues(ostream& ost) const { // Des valeurs sont a afficher uniquement dans le cas symbolique et VarPart // (l'intervalle est le libelle de la partie dans le cas continu) + //DDDSIMPLIFY if (GetPartType() == KWType::Symbol) - symbolValueSet->WriteValues(ost); + GetValueSet()->WriteValues(ost); else if (GetPartType() == KWType::VarPart) - varPartSet->WriteValues(ost); + GetValueSet()->WriteValues(ost); } void KWDGPart::WriteCells(ostream& ost) const @@ -4010,30 +4001,18 @@ const ALString KWDGPart::GetVarPartLabel() const const ALString KWDGPart::GetClassLabel() const { - if (GetPartType() == KWType::Continuous) - return interval->GetClassLabel(); - else if (GetPartType() == KWType::Symbol) - return symbolValueSet->GetClassLabel(); - // CH IV Begin - else if (GetPartType() == KWType::VarPart) - return varPartSet->GetClassLabel(); - // CH IV End - else + if (partValues == NULL) return "Part"; + else + return partValues->GetClassLabel(); } const ALString KWDGPart::GetObjectLabel() const { - if (GetPartType() == KWType::Continuous) - return interval->GetObjectLabel(); - else if (GetPartType() == KWType::Symbol) - return symbolValueSet->GetObjectLabel(); - // CH IV Begin - else if (GetPartType() == KWType::VarPart) - return varPartSet->GetObjectLabel(); - // CH IV End - else + if (partValues == NULL) return ""; + else + return partValues->GetObjectLabel(); } KWDGInterval* KWDGPart::NewInterval() const @@ -4218,10 +4197,14 @@ boolean KWDGInterval::Check() const void KWDGInterval::Write(ostream& ost) const { - // Identification ost << GetClassLabel() << "\t" << GetObjectLabel() << "\n"; } +longint KWDGInterval::GetUsedMemory() const +{ + return sizeof(KWDGInterval); +} + const ALString KWDGInterval::GetClassLabel() const { return "Interval"; @@ -4620,6 +4603,21 @@ void KWDGValueSet::WriteValues(ostream& ost) const } } +longint KWDGValueSet::GetUsedMemory() const +{ + longint lUsedMemory; + KWDGValue* value; + + lUsedMemory = sizeof(KWDGValueSet); + value = GetHeadValue(); + while (value != NULL) + { + lUsedMemory += value->GetUsedMemory(); + GetNextValue(value); + } + return lUsedMemory; +} + const ALString KWDGValueSet::GetClassLabel() const { return "Value set"; @@ -4920,6 +4918,11 @@ const ALString KWDGSymbolValue::GetExternalValueLabel() const return sResult; } +longint KWDGSymbolValue::GetUsedMemory() const +{ + return sizeof(KWDGSymbolValue); +} + const ALString KWDGSymbolValue::GetClassLabel() const { return "Value"; @@ -4962,6 +4965,11 @@ const ALString KWDGVarPartValue::GetExternalValueLabel() const return GetObjectLabel(); } +longint KWDGVarPartValue::GetUsedMemory() const +{ + return sizeof(KWDGVarPartValue); +} + const ALString KWDGVarPartValue::GetClassLabel() const { return "VarPart"; diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index 9e8aa5448..1b759e194 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -729,12 +729,12 @@ class KWDGAttribute : public Object // Structure d'indexation des parties dans le cas symbolique // Dictionnaire des parties indexe par les valeurs (Symbol) des parties NumericKeyDictionary nkdParts; - KWDGPart* starValuePart; + KWDGPart* starValuePart; //DDDSIMPLIFY boolean bIsIndexed; // CH IV Begin // Structure d'indexation, des parties dans le cas VarPart - NumericKeyDictionary nkdVarPartSets; + NumericKeyDictionary nkdVarPartSets; //DDDSIMPLIFY // Nom de l'attribut de type VarPart dont depend un attribut interne // Par defaut a vide pour un attribut de type Simple (numerique ou categoriel) @@ -777,14 +777,16 @@ class KWDGPart : public Object virtual void SetPartType(int nValue); int GetPartType() const; - // Acces aux valeurs, selon le type - KWDGPartValues* GetPartValues() const; + // Acces specialise aux valeurs, selon le type d'attribut KWDGInterval* GetInterval() const; - KWDGValueSet* GetValueSet() const; KWDGSymbolValueSet* GetSymbolValueSet() const; - // CH IV Begin KWDGVarPartSet* GetVarPartSet() const; - // CH IV End + + // Acces generique au groupe de valeur, dans les cas Symbol ou VarPart + KWDGValueSet* GetValueSet() const; + + // Acces generique aux valeurs via la classe ancetre commune a tous les types + KWDGPartValues* GetPartValues() const; //////////////////////////////////////////////////////////////// // Acces aux cellules liee a la partie courante, @@ -857,9 +859,7 @@ class KWDGPart : public Object // dans une sous-classe virtual KWDGInterval* NewInterval() const; virtual KWDGSymbolValueSet* NewSymbolValueSet() const; - // CH IV Begin virtual KWDGVarPartSet* NewVarPartSet() const; - // CH IV End // Acces a l'index de l'attribut (0 si NULL) int GetAttributeIndex() const; @@ -885,13 +885,9 @@ class KWDGPart : public Object // Effectif total de la partie int nPartFrequency; - // Gestion des valeurs de la parties - // Un seul objet, correspondant au type de la partie, doit etre non nul - KWDGInterval* interval; - KWDGSymbolValueSet* symbolValueSet; - // CH IV Begin - KWDGVarPartSet* varPartSet; - // CH IV End + // Gestion des valeurs de la parties via a un objet ancetre generique, + // dont le type de valeur doit etre le meme que celui de l'attribut + KWDGPartValues* partValues; }; // Comparaison de deux parties, par effectif decroissant puis valeur, ou par intervalle (cf. ComparePart) @@ -932,6 +928,9 @@ class KWDGPartValues : public Object // Affichage void Write(ostream& ost) const override = 0; + + // Memoire utilisee + longint GetUsedMemory() const override = 0; }; ////////////////////////////////////////////////////////////////////////////// @@ -1035,6 +1034,9 @@ class KWDGValueSet : public KWDGPartValues void Write(ostream& ost) const override; void WriteValues(ostream& ost) const; + // Memoire utilisee + longint GetUsedMemory() const override; + // Libelles utilisateur const ALString GetClassLabel() const override; const ALString GetObjectLabel() const override; @@ -1106,6 +1108,9 @@ class KWDGValue : public Object // Libelle externe base sur la valeur virtual const ALString GetExternalValueLabel() const = 0; + // Memoire utilisee + virtual longint GetUsedMemory() const override = 0; + /////////////////////////////// ///// Implementation protected: @@ -1207,6 +1212,9 @@ class KWDGSymbolValue : public KWDGValue // Libelle externe base sur la valeur const ALString GetExternalValueLabel() const override; + // Memoire utilisee + longint GetUsedMemory() const override; + // Libelles utilisateur const ALString GetClassLabel() const override; const ALString GetObjectLabel() const override; @@ -1279,6 +1287,9 @@ class KWDGInterval : public KWDGPartValues // Affichage void Write(ostream& ost) const override; + // Memoire utilisee + longint GetUsedMemory() const override; + // Libelles utilisateur const ALString GetClassLabel() const override; const ALString GetObjectLabel() const override; @@ -1356,6 +1367,9 @@ class KWDGVarPartValue : public KWDGValue // Libelle externe base sur la valeur const ALString GetExternalValueLabel() const override; + // Memoire utilisee + longint GetUsedMemory() const override; + // Libelles utilisateur const ALString GetClassLabel() const override; const ALString GetObjectLabel() const override; @@ -1963,67 +1977,44 @@ inline int KWDGPart::GetPartFrequency() const inline int KWDGPart::GetPartType() const { - require(interval == NULL or symbolValueSet == NULL or varPartSet == NULL); - require((interval == NULL) + (symbolValueSet == NULL) + (varPartSet == NULL) >= 2); + require(partValues == NULL or KWType::IsCoclusteringType(partValues->GetValueType())); - if (interval != NULL) - return KWType::Continuous; - else if (symbolValueSet != NULL) - return KWType::Symbol; - // CH IV Begin - else if (varPartSet != NULL) - return KWType::VarPart; - // CH IV End - else + if (partValues == NULL) return KWType::Unknown; -} - -inline KWDGPartValues* KWDGPart::GetPartValues() const -{ - require(KWType::IsCoclusteringType(GetPartType())); - - if (GetPartType() == KWType::Continuous) - return interval; - else if (GetPartType() == KWType::Symbol) - return symbolValueSet; else - return varPartSet; + return partValues->GetValueType(); } inline KWDGInterval* KWDGPart::GetInterval() const { require(GetPartType() == KWType::Continuous); - - ensure(interval != NULL); - return interval; -} - -inline KWDGValueSet* KWDGPart::GetValueSet() const -{ - require(GetPartType() == KWType::Symbol or GetPartType() == KWType::VarPart); - - if (GetPartType() == KWType::Symbol) - return symbolValueSet; - else - return varPartSet; + return cast(KWDGInterval*, partValues); } inline KWDGSymbolValueSet* KWDGPart::GetSymbolValueSet() const { require(GetPartType() == KWType::Symbol); - - ensure(symbolValueSet != NULL); - return symbolValueSet; + return cast(KWDGSymbolValueSet*, partValues); } -// CH IV Begin + inline KWDGVarPartSet* KWDGPart::GetVarPartSet() const { require(GetPartType() == KWType::VarPart); + return cast(KWDGVarPartSet*, partValues); +} + +inline KWDGValueSet* KWDGPart::GetValueSet() const +{ + require(KWType::IsCoclusteringGroupableType(GetPartType())); + return cast(KWDGValueSet*, partValues); +} - ensure(varPartSet != NULL); - return varPartSet; +inline KWDGPartValues* KWDGPart::GetPartValues() const +{ + require(KWType::IsCoclusteringType(GetPartType())); + return partValues; } -// CH IV End + inline int KWDGPart::GetCellNumber() const { return nCellNumber; diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index 595c04fd2..400317909 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -713,6 +713,7 @@ void KWDataGridManager::ExportFrequencyTableFromOneAttribute(KWFrequencyTable* k ivFrequency->SetAt(nTargetIndex, dgCell->GetTargetFrequencyAt(nTargetIndex)); // Memorisation eventuelle du groupe poubelle + //DDDSIMPLIFY if (oneAttributeDataGrid.GetAttributeAt(0)->GetAttributeType() == KWType::Symbol) { // Recopie du nombre de modalites @@ -2680,6 +2681,7 @@ void KWDataGridManager::InitialiseAttributeNullPart(const KWDGAttribute* sourceA targetPart->GetInterval()->SetUpperBound(KWDGInterval::GetMaxUpperBound()); } // Creation d'une partie unique comportant toutes les valeurs, dans le cas symbolique + //DDDSIMPLIFY else if (sourceAttribute->GetAttributeType() == KWType::Symbol) { // Creation de l'ensemble des valeur cible @@ -3874,6 +3876,7 @@ void KWDataGridManager::CreateAttributeQuantileBuilder(const KWDGAttribute* attr nMaxPartNumber = attribute->GetPartNumber(); } // Cas d'un attribut categoriel ou de type VarPart + //DDDSIMPLIFY else { // Creation du vecteur des frequences par parties diff --git a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp index 0e6018637..a290f6955 100644 --- a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp @@ -416,6 +416,7 @@ void KWDataGridMerger::InitializeAllPartLists() // CH IV Begin // Cas d'un attribut categoriel ou varParts (donc eligible au groupe poubelle) + //DDDSIMPLIFY if (attribute->GetAttributeType() == KWType::Symbol or attribute->GetAttributeType() == KWType::VarPart) // CH IV End { @@ -877,6 +878,7 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b // CH IV Begin // Cas d'un attribut categoriel ou VarPart pour lequel un groupe poubelle est envisageable + //DDDSIMPLIFY if (attribute->GetAttributeType() == KWType::Symbol or attribute->GetAttributeType() == KWType::VarPart) // CH IV End @@ -939,6 +941,7 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b // avoir de groupe poubelle) attribut VarPart ET mode poubelle pour ce type d'attributs + nbre // de parties apres fusion >=3 CH AB voir l'impact de la variation d'un cout avec groupe // poubelle lorsque le nombre de PV est reduit du fait de la post-fusion + //DDDSIMPLIFY if ((attribute->GetAttributeType() == KWType::Symbol or (attribute->GetAttributeType() == KWType::VarPart and GetVarPartAttributeGarbage())) and attributeM->GetPartNumber() - 1 >= 3) @@ -948,6 +951,7 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b // Cas d'un attribut AVEC groupe poubelle apres fusion // Cas ou les deux parties fusionnees deviennent le groupe poubelle + //DDDSIMPLIFY if (attribute->GetAttributeType() == KWType::Symbol) { if (partMerge->GetPart1()->GetValueSet()->GetValueNumber() + @@ -1063,6 +1067,7 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b delete garbagePart; } // CH IV Begin + //DDDSIMPLIFY if (attribute->GetAttributeType() == KWType::Symbol or (attribute->GetAttributeType() == KWType::VarPart and GetVarPartAttributeGarbage())) // CH IV End @@ -1919,6 +1924,7 @@ boolean KWDGMPart::Check() const bOk = KWDGPart::Check(); // Verification de l'ensemble de valeurs + //DDDSIMPLIFY if (bOk and GetPartType() == KWType::Symbol) { // Verification de la compatibilite entre l'effectif de la partie @@ -1929,7 +1935,7 @@ boolean KWDGMPart::Check() const // de la grille (les parties et leurs valeurs sont creees avant // la creation des cellules). // La verification n'est pas faite dans tous les cas dans la classe ancetre - nTotalValueFrequency = symbolValueSet->ComputeTotalFrequency(); + nTotalValueFrequency = GetValueSet()->ComputeTotalFrequency(); if (bOk and GetPartFrequency() > 0 and nTotalValueFrequency > 0 and GetPartFrequency() != nTotalValueFrequency) { @@ -1951,7 +1957,7 @@ boolean KWDGMPart::Check() const // de la grille (les parties et leurs parties de variable sont creees avant // la creation des cellules). // La verification n'est pas faite dans tous les cas dans la classe ancetre - nTotalValueFrequency = varPartSet->ComputeTotalFrequency(); + nTotalValueFrequency = GetValueSet()->ComputeTotalFrequency(); if (bOk and GetPartFrequency() > 0 and nTotalValueFrequency > 0 and GetPartFrequency() != nTotalValueFrequency) { @@ -2003,6 +2009,7 @@ int KWDGMPartValueNumberCompare(const void* elem1, const void* elem2) assert(partM2->GetPartType() == KWType::Symbol or partM2->GetPartType() == KWType::VarPart); // Comparaison du nombre de modalites par valeurs decroissantes + //DDDSIMPLIFY if (partM1->GetPartType() == KWType::Symbol) return (partM2->GetValueSet()->GetValueNumber() - partM1->GetValueSet()->GetValueNumber()); else diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp index d86d2a611..d1f2935b8 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp @@ -1432,6 +1432,7 @@ void KWDGPOGrouper::InitializePartFrequencyVector(KWDGPOPartFrequencyVector* par require(partFrequencyVector->ComputeTotalFrequency() == 0); require(partFrequencyVector->GetModalityNumber() == 0); require(part != NULL); + //DDDSIMPLIFY require(part->GetPartType() == KWType::Symbol or part->GetPartType() == KWType::VarPart); require(part->GetAttribute()->GetAttributeName() == GetPostOptimizationAttributeName()); require(nkdHashCells != NULL); @@ -1440,6 +1441,7 @@ void KWDGPOGrouper::InitializePartFrequencyVector(KWDGPOPartFrequencyVector* par InitializeFrequencyVector(partFrequencyVector); // Memorisation du nombre de valeurs associees a la partie + //DDDSIMPLIFY if (part->GetPartType() == KWType::Symbol) partFrequencyVector->SetModalityNumber(part->GetValueSet()->GetValueNumber()); // CH IV Begin @@ -1742,6 +1744,7 @@ int KWDGPOGrouper::InitializeGroupIndexesAndGarbageIndex(IntVector* ivGroups, co // Recherche de sa partie groupee correspondante // CH IV Begin + //DDDSIMPLIFY if (optimizedAttribute->GetAttributeType() == KWType::Symbol) { optimizedPart = optimizedAttribute->LookupSymbolPart( @@ -1869,6 +1872,7 @@ void KWDGPOGrouper::UpdateDataGridWithGarbageFromGroups(KWDataGrid* optimizedDat // CH IV Begin // Cas d'une partie d'un attribut categoriel + //DDDSIMPLIFY if (optimizedPart->GetPartType() == KWType::Symbol) { // Mise a jour de la definition du group @@ -2649,6 +2653,7 @@ double KWDataGridUnivariateCosts::ComputePartCost(const KWFrequencyVector* part) // Attention: on utilise ici un acces direct au nombre de valeur du ValueSet pour permettre // d'utiliser ce parametre dans les calculs de cout // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) + //DDDSIMPLIFY if (partCostParameter->GetPartType() == KWType::Symbol) { // N'etant jamais la partie par defaut, on a le TrueValueNumber egal au ValueNumber @@ -2725,6 +2730,7 @@ double KWDataGridUnivariateCosts::ComputePartUnionCost(const KWFrequencyVector* // Attention: on utilise ici un acces direct au nombre de valeur du ValueSet pour permettre // d'utiliser ce parametre dans les calculs de cout // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) + //DDDSIMPLIFY if (partCostParameter->GetPartType() == KWType::Symbol) { cast(KWDGSymbolValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = @@ -2766,6 +2772,7 @@ double KWDataGridUnivariateCosts::ComputePartDiffCost(const KWFrequencyVector* s // Attention: on utilise ici un acces direct au nombre de valeur du ValueSet pour permettre // d'utiliser ce parametre dans les calculs de cout // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) + //DDDSIMPLIFY if (partCostParameter->GetPartType() == KWType::Symbol) { cast(KWDGSymbolValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = @@ -2863,11 +2870,12 @@ void KWDGPartCostParameter::SetPartType(int nValue) // Creation de l'objet interval ou ensemble de valeur selon le type if (nValue == KWType::Continuous) - interval = new KWDGInterval; + partValues = new KWDGInterval; + //DDDSIMPLIFY else if (nValue == KWType::Symbol) - symbolValueSet = new KWDGSymbolValueSetCostParameter; + partValues = new KWDGSymbolValueSetCostParameter; else - varPartSet = new KWDGVarPartSetCostParameter; + partValues = new KWDGVarPartSetCostParameter; // Avant integration : pour quel cas ? valueSet = new KWDGValueSetCostParameter; // CH IV End ensure(GetPartType() != KWType::Unknown); diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp index 6b07556cc..0ee116b78 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp @@ -731,7 +731,7 @@ boolean CCCoclusteringReport::ReadDimensions(CCHierarchicalDataGrid* coclusterin // Affichage en mode debug if (bReadDebug) cout << sKeyWordDimensions << "\t" << sAttributeName << "\t" - << KWType::ToString(KWType::GetSimpleCoclusteringType(nAttributeType)) + << KWType::ToString(KWType::GetCoclusteringSimpleType(nAttributeType)) << "\t" << nAttributePartNumber << "\t" << nAttributeValueNumber << "\t" << sAttributeDescription << endl; } @@ -2068,7 +2068,7 @@ void CCCoclusteringReport::WriteDimensions(const CCHierarchicalDataGrid* coclust // Caracteristique des attributs // Le nombre de valeur est diminuer en externe de 1 pour tenir compte de de la StarValue en interne ost << dgAttribute->GetAttributeName() << "\t"; - ost << KWType::ToString(KWType::GetSimpleCoclusteringType(dgAttribute->GetAttributeType())) << "\t"; + ost << KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType())) << "\t"; ost << dgAttribute->GetPartNumber() << "\t"; ost << dgAttribute->GetInitialPartNumber() << "\t"; ost << nValueNumber << "\t"; @@ -3122,12 +3122,12 @@ boolean CCCoclusteringReport::ReadJSONDimensionPartitions(CCHierarchicalDataGrid // Type de l'attribut bOk = bOk and JSONTokenizer::ReadKeyStringValue("type", sAttributeType, bIsAttributeEnd); - if (bOk and KWType::ToString(KWType::GetSimpleCoclusteringType(dgAttribute->GetAttributeType())) != + if (bOk and KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType())) != sAttributeType) { JSONTokenizer::AddParseError( "Read variable type " + sAttributeType + " instead of expected " + - KWType::ToString(KWType::GetSimpleCoclusteringType(dgAttribute->GetAttributeType()))); + KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType()))); bOk = false; } @@ -4096,12 +4096,12 @@ boolean CCCoclusteringReport::ReadJSONDimensionHierarchies(CCHierarchicalDataGri // Type de l'attribut bOk = bOk and JSONTokenizer::ReadKeyStringValue("type", sAttributeType, bIsAttributeEnd); - if (bOk and KWType::ToString(KWType::GetSimpleCoclusteringType(dgAttribute->GetAttributeType())) != + if (bOk and KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType())) != sAttributeType) { JSONTokenizer::AddParseError( "Read variable type " + sAttributeType + " instead of expected " + - KWType::ToString(KWType::GetSimpleCoclusteringType(dgAttribute->GetAttributeType()))); + KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType()))); bOk = false; } @@ -4601,7 +4601,7 @@ void CCCoclusteringReport::WriteJSONDimensionSummaries(const CCHierarchicalDataG if (dgAttribute->GetAttributeType() == KWType::VarPart) fJSON->WriteKeyBoolean("isVarPart", true); fJSON->WriteKeyString( - "type", KWType::ToString(KWType::GetSimpleCoclusteringType(dgAttribute->GetAttributeType()))); + "type", KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType()))); fJSON->WriteKeyInt("parts", dgAttribute->GetPartNumber()); fJSON->WriteKeyInt("initialParts", dgAttribute->GetInitialPartNumber()); fJSON->WriteKeyInt("values", nValueNumber); @@ -4652,7 +4652,7 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData fJSON->BeginObject(); fJSON->WriteKeyString("name", dgAttribute->GetAttributeName()); fJSON->WriteKeyString( - "type", KWType::ToString(KWType::GetSimpleCoclusteringType(dgAttribute->GetAttributeType()))); + "type", KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType()))); // Traitement des attributs numeriques if (dgAttribute->GetAttributeType() == KWType::Continuous) @@ -5024,7 +5024,7 @@ void CCCoclusteringReport::WriteJSONDimensionHierarchies(const CCHierarchicalDat fJSON->BeginObject(); fJSON->WriteKeyString("name", dgAttribute->GetAttributeName()); fJSON->WriteKeyString( - "type", KWType::ToString(KWType::GetSimpleCoclusteringType(dgAttribute->GetAttributeType()))); + "type", KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType()))); // Exports de toutes les parties de la hierarchie en partant de la racine oaParts.SetSize(0); diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp index 3b1c94809..aeeaabf10 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp @@ -856,7 +856,7 @@ boolean CCHDGPart::CheckHierarchy() const // Verification de l'ensemble de valeurs dans le cas categoriel if (GetPartType() == KWType::Symbol) - bOk = bOk and cast(CCHDGSymbolValueSet*, symbolValueSet)->CheckHierarchy(); + bOk = bOk and cast(CCHDGSymbolValueSet*, GetSymbolValueSet())->CheckHierarchy(); // Verifications complementaires de chainahe coherent de la hierarchie, sans message d'erreur assert(not bOk or parentPart == NULL or parentPart->childPart1 == this or parentPart->childPart2 == this); From 435e80ee626c0525d3690a223d5eb979ef6665b9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Tue, 5 Sep 2023 17:15:14 +0200 Subject: [PATCH 06/17] KWDGPartValues: implement generic methods in hierarchy and clean former methods KWDGPartValues: - methodes concernees - IsSubPartValues -> IsSubPart - CopyFrom - Import - UpgradeFrom - remplementation directe dans les sous-classe KWDGInterval et KWDGValueSet - supression des anciennes methodes specifiques, desormais inutiles --- src/Learning/KWDataPreparation/KWDataGrid.cpp | 231 +++++++++--------- src/Learning/KWDataPreparation/KWDataGrid.h | 46 +--- 2 files changed, 117 insertions(+), 160 deletions(-) diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index 5ecd8efa6..6db762d9a 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -3752,13 +3752,7 @@ boolean KWDGPart::IsSubPart(const KWDGPart* otherPart) const require(otherPart->GetPartType() == GetPartType()); require(otherPart->GetAttribute()->GetAttributeName() == GetAttribute()->GetAttributeName()); - // Test d'inclusion selon le ype de la partie - if (GetPartType() == KWType::Continuous) - return GetInterval()->IsSubInterval(otherPart->GetInterval()); - else if (GetPartType() == KWType::Symbol) - return GetValueSet()->IsSubValueSet(otherPart->GetValueSet()); - else - return GetValueSet()->IsSubValueSet(otherPart->GetValueSet()); + return GetPartValues()->IsSubPart(otherPart->GetPartValues()); } int KWDGPart::ComparePart(const KWDGPart* otherPart) const @@ -4090,22 +4084,31 @@ int KWDGPartCompareValues(const void* elem1, const void* elem2) ////////////////////////////////////////////////////////////////////////////// // Classe KWDGInterval -boolean KWDGInterval::IsSubInterval(const KWDGInterval* otherInterval) const +boolean KWDGInterval::IsSubPart(const KWDGPartValues* otherPartValues) const { + KWDGInterval* otherInterval; + + require(otherPartValues != NULL); + require(otherPartValues->GetValueType() == GetValueType()); - require(otherInterval != NULL); + // Comparaison des bornes + otherInterval = cast(KWDGInterval*, otherPartValues); return GetLowerBound() >= otherInterval->GetLowerBound() and GetUpperBound() <= otherInterval->GetUpperBound(); } -void KWDGInterval::Import(KWDGInterval* sourceInterval) +void KWDGInterval::Import(KWDGPartValues* sourcePartValues) { + KWDGInterval* sourceInterval; + require(Check()); - require(sourceInterval != NULL); - require(sourceInterval->Check()); - require(sourceInterval->GetUpperBound() == GetLowerBound() or - GetUpperBound() == sourceInterval->GetLowerBound()); + require(sourcePartValues->Check()); + require(sourcePartValues != NULL); + require(sourcePartValues->GetValueType() == GetValueType()); // Transfert d'une borne pour fusionner les intervalles + sourceInterval = cast(KWDGInterval*, sourcePartValues); + assert(sourceInterval->GetUpperBound() == GetLowerBound() or + GetUpperBound() == sourceInterval->GetLowerBound()); if (sourceInterval->GetUpperBound() == GetLowerBound()) SetLowerBound(sourceInterval->GetLowerBound()); else @@ -4114,47 +4117,40 @@ void KWDGInterval::Import(KWDGInterval* sourceInterval) // Reinitialisation de l'intervalle source sourceInterval->SetLowerBound(0); sourceInterval->SetUpperBound(0); - ensure(Check()); } -// CH IV Begin -void KWDGInterval::UpgradeFrom(const KWDGInterval* sourceInterval) +void KWDGInterval::UpgradeFrom(const KWDGPartValues* sourcePartValues) { + KWDGInterval* sourceInterval; + require(Check()); - require(sourceInterval != NULL); - require(sourceInterval->Check()); - require(sourceInterval->GetUpperBound() == GetLowerBound() or - GetUpperBound() == sourceInterval->GetLowerBound()); + require(sourcePartValues->Check()); + require(sourcePartValues != NULL); + require(sourcePartValues->GetValueType() == GetValueType()); // Transfert d'une borne pour fusionner les intervalles + sourceInterval = cast(KWDGInterval*, sourcePartValues); + assert(sourceInterval->GetUpperBound() == GetLowerBound() or + GetUpperBound() == sourceInterval->GetLowerBound()); if (sourceInterval->GetUpperBound() == GetLowerBound()) SetLowerBound(sourceInterval->GetLowerBound()); else SetUpperBound(sourceInterval->GetUpperBound()); - ensure(Check()); } -// CH IV End -boolean KWDGInterval::IsSubPartValues(const KWDGPartValues* otherPartValues) const -{ - return IsSubInterval(cast(KWDGInterval*, otherPartValues)); -} - -void KWDGInterval::Import(KWDGPartValues* sourcePartValues) +void KWDGInterval::CopyFrom(const KWDGPartValues* sourcePartValues) { - Import(cast(KWDGInterval*, sourcePartValues)); -} + KWDGInterval* sourceInterval; -void KWDGInterval::UpgradeFrom(const KWDGPartValues* sourcePartValues) -{ - UpgradeFrom(cast(KWDGInterval*, sourcePartValues)); -} + require(sourcePartValues != NULL); + require(sourcePartValues->GetValueType() == GetValueType()); -void KWDGInterval::CopyFrom(const KWDGPartValues* sourcePartValues) -{ - CopyFrom(cast(KWDGInterval*, sourcePartValues)); + // Copie des bornes + sourceInterval = cast(KWDGInterval*, sourcePartValues); + cLowerBound = sourceInterval->cLowerBound; + cUpperBound = sourceInterval->cUpperBound; } int KWDGInterval::ComparePartValues(const KWDGPartValues* otherPartValues) const @@ -4235,31 +4231,6 @@ const ALString KWDGInterval::GetObjectLabel() const ////////////////////////////////////////////////////////////////////////////// // Classe KWDGValueSet -void KWDGValueSet::CopyFrom(const KWDGValueSet* sourceValueSet) -{ - KWDGValue* value; - KWDGValue* valueCopy; - - require(sourceValueSet != NULL); - require(sourceValueSet->GetValueType() == GetValueType()); - - // Nettoyage des valeurs actuelles - DeleteAllValues(); - - // Recopie de la liste de valeurs source - value = sourceValueSet->GetHeadValue(); - while (value != NULL) - { - valueCopy = AddValueCopy(value); - valueCopy->SetValueFrequency(value->GetValueFrequency()); - sourceValueSet->GetNextValue(value); - } - bIsDefaultPart = sourceValueSet->IsDefaultPart(); - - // Pour garantir la valeur correcte de nValueNumber si le sourceValueSet a ete compresse avant la copie - nValueNumber = sourceValueSet->nValueNumber; -} - void KWDGValueSet::DeleteValue(KWDGValue* value) { require(value != NULL); @@ -4344,12 +4315,55 @@ void KWDGValueSet::ExportValues(ObjectArray* oaValues) const } } -boolean KWDGValueSet::IsSubValueSet(const KWDGValueSet* otherValueSet) const +int KWDGValueSet::ComputeTotalFrequency() const +{ + int nTotalFrequency; + KWDGValue* value; + + // Parcours des valeurs de la partie pour calcul l'effectif cumule + nTotalFrequency = 0; + value = GetHeadValue(); + while (value != NULL) + { + nTotalFrequency += value->GetValueFrequency(); + GetNextValue(value); + } + return nTotalFrequency; +} + +void KWDGValueSet::SortValues() +{ + InternalSortValues(KWDGValueCompareValue); +} + +void KWDGValueSet::SortValueByDecreasingFrequencies() +{ + InternalSortValues(KWDGValueCompareFrequency); +} + +boolean KWDGValueSet::AreValuesSorted() const +{ + return InternalAreValuesSorted(KWDGValueCompareValue); +} + +boolean KWDGValueSet::AreValuesSortedByDecreasingFrequencies() const +{ + return InternalAreValuesSorted(KWDGValueCompareFrequency); +} + +boolean KWDGValueSet::IsSubPart(const KWDGPartValues* otherPartValues) const { boolean bOk = true; + KWDGValueSet* otherValueSet; NumericKeyDictionary nkdOtherValues; KWDGValue* value; + require(otherPartValues != NULL); + require(otherPartValues->GetValueType() == GetValueType()); + + // Acces a l'autre groupe de valeurs + otherValueSet = cast(KWDGValueSet*, otherPartValues); + // On doit avoir moins de valeurs if (GetValueNumber() > otherValueSet->GetValueNumber()) bOk = false; @@ -4379,27 +4393,15 @@ boolean KWDGValueSet::IsSubValueSet(const KWDGValueSet* otherValueSet) const return bOk; } -int KWDGValueSet::ComputeTotalFrequency() const +void KWDGValueSet::Import(KWDGPartValues* sourcePartValues) { - int nTotalFrequency; - KWDGValue* value; + KWDGValueSet* sourceValueSet; - // Parcours des valeurs de la partie pour calcul l'effectif cumule - nTotalFrequency = 0; - value = GetHeadValue(); - while (value != NULL) - { - nTotalFrequency += value->GetValueFrequency(); - GetNextValue(value); - } - return nTotalFrequency; -} + require(sourcePartValues != NULL); + require(sourcePartValues->GetValueType() == GetValueType()); -void KWDGValueSet::Import(KWDGValueSet* sourceValueSet) -{ - require(Check()); - require(sourceValueSet != NULL); - require(sourceValueSet->Check()); + // Acces au groupe de valeurs source + sourceValueSet = cast(KWDGValueSet*, sourcePartValues); // Transfert des valeurs de la premiere partie en tete du second sourceValueSet->tailValue->nextValue = headValue; @@ -4413,19 +4415,23 @@ void KWDGValueSet::Import(KWDGValueSet* sourceValueSet) sourceValueSet->tailValue = NULL; sourceValueSet->nValueNumber = 0; sourceValueSet->bIsDefaultPart = false; - ensure(Check()); } -void KWDGValueSet::UpgradeFrom(const KWDGValueSet* sourceValueSet) +void KWDGValueSet::UpgradeFrom(const KWDGPartValues* sourcePartValues) { + KWDGValueSet* sourceValueSet; KWDGValue* value; KWDGValue* valueUpgrade; + int nOldValueNumber; + + require(sourcePartValues != NULL); + require(sourcePartValues->GetValueType() == GetValueType()); - require(sourceValueSet != NULL); + // Acces au groupe de valeurs source + sourceValueSet = cast(KWDGValueSet*, sourcePartValues); // Pour garantir la valeur correcte de nValueNumber si le sourceValueSet a ete compresse avant la copie - int nOldValueNumber; nOldValueNumber = nValueNumber; // Recopie de la liste de valeurs source @@ -4440,44 +4446,33 @@ void KWDGValueSet::UpgradeFrom(const KWDGValueSet* sourceValueSet) nValueNumber = nOldValueNumber + sourceValueSet->nValueNumber; } -void KWDGValueSet::SortValues() -{ - InternalSortValues(KWDGValueCompareValue); -} - -void KWDGValueSet::SortValueByDecreasingFrequencies() -{ - InternalSortValues(KWDGValueCompareFrequency); -} - -boolean KWDGValueSet::AreValuesSorted() const +void KWDGValueSet::CopyFrom(const KWDGPartValues* sourcePartValues) { - return InternalAreValuesSorted(KWDGValueCompareValue); -} + KWDGValueSet* sourceValueSet; + KWDGValue* value; + KWDGValue* valueCopy; -boolean KWDGValueSet::AreValuesSortedByDecreasingFrequencies() const -{ - return InternalAreValuesSorted(KWDGValueCompareFrequency); -} + require(sourcePartValues != NULL); + require(sourcePartValues->GetValueType() == GetValueType()); -boolean KWDGValueSet::IsSubPartValues(const KWDGPartValues* otherPartValues) const -{ - return IsSubValueSet(cast(KWDGValueSet*, otherPartValues)); -} + // Acces au groupe de valeurs source + sourceValueSet = cast(KWDGValueSet*, sourcePartValues); -void KWDGValueSet::Import(KWDGPartValues* sourcePartValues) -{ - Import(cast(KWDGValueSet*, sourcePartValues)); -} + // Nettoyage des valeurs actuelles + DeleteAllValues(); -void KWDGValueSet::UpgradeFrom(const KWDGPartValues* sourcePartValues) -{ - UpgradeFrom(cast(KWDGValueSet*, sourcePartValues)); -} + // Recopie de la liste de valeurs source + value = sourceValueSet->GetHeadValue(); + while (value != NULL) + { + valueCopy = AddValueCopy(value); + valueCopy->SetValueFrequency(value->GetValueFrequency()); + sourceValueSet->GetNextValue(value); + } + bIsDefaultPart = sourceValueSet->IsDefaultPart(); -void KWDGValueSet::CopyFrom(const KWDGPartValues* sourcePartValues) -{ - CopyFrom(cast(KWDGValueSet*, sourcePartValues)); + // Pour garantir la valeur correcte de nValueNumber si le sourceValueSet a ete compresse avant la copie + nValueNumber = sourceValueSet->nValueNumber; } int KWDGValueSet::ComparePartValues(const KWDGPartValues* otherPartValues) const diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index 1b759e194..6e6abc73b 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -906,8 +906,8 @@ class KWDGPartValues : public Object // Type de valeur gere par la partie virtual int GetValueType() const = 0; - // Test si la partie de valeur est un sous-ensemble de la partie en parametre - virtual boolean IsSubPartValues(const KWDGPartValues* otherPartValues) const = 0; + // Test si la partie est un sous-ensemble de la partie en parametre + virtual boolean IsSubPart(const KWDGPartValues* otherPartValues) const = 0; // Import des valeurs d'une autre partie // La partie source est reinitialisee @@ -950,9 +950,6 @@ class KWDGValueSet : public KWDGPartValues // Creation virtual KWDGValueSet* Create() const = 0; - // Copie - void CopyFrom(const KWDGValueSet* sourceValueSet); - // Duplication KWDGValueSet* Clone() const; @@ -991,20 +988,9 @@ class KWDGValueSet : public KWDGPartValues // Export des valeurs dans un tableau (initialement vide) void ExportValues(ObjectArray* oaValues) const; - // Test si l'ensemble de valeur est inclus dans l'autre ensemble de valeurs en parametres - // On ne tient pas compte de la StarValue, et on effectue un test d'inclusion exhaustif pour toutes les valeurs - boolean IsSubValueSet(const KWDGValueSet* otherValueSet) const; - // Calcul de l'effectif cumule des valeurs int ComputeTotalFrequency() const; - // Import des valeurs d'une partie source, devant etre disjointe de la premiere - // partie. La partie source est reinitialise - void Import(KWDGValueSet* sourceValueSet); - - // Ajout de nouvelles valeurs recopiees depuis une source - void UpgradeFrom(const KWDGValueSet* sourceValueSet); - // Tri des valeurs void SortValues(); @@ -1017,7 +1003,7 @@ class KWDGValueSet : public KWDGPartValues boolean AreValuesSortedByDecreasingFrequencies() const; // Redefinition des methodes virtuelles - boolean IsSubPartValues(const KWDGPartValues* otherPartValues) const override; + boolean IsSubPart(const KWDGPartValues* otherPartValues) const override; void Import(KWDGPartValues* sourcePartValues) override; void UpgradeFrom(const KWDGPartValues* sourcePartValues) override; void CopyFrom(const KWDGPartValues* sourcePartValues) override; @@ -1256,24 +1242,8 @@ class KWDGInterval : public KWDGPartValues /////////////////////////////// // Services divers - // Test si l'intervalle est un sous-intervalle - boolean IsSubInterval(const KWDGInterval* otherInterval) const; - - // Import des valeurs d'un intervalle source, devant etre adjacent au premier - // intervalle. L'intervalle source est reinitialise - void Import(KWDGInterval* sourceInterval); - - // CH IV Begin - // Mise a jour des valeurs de l'intervalle a partir des valeurs d'un intervalle source, devant etre adjacent au - // premier intervalle. L'intervalle source n'est pas modifie - // CH IV Refactoring: UpgradeFrom est tres proche de Import: a mutualiser, ou remplacer Import par UpgradeFrom puis Reset??? - void UpgradeFrom(const KWDGInterval* sourceInterval); - // CH IV End - // Copie - void CopyFrom(const KWDGInterval* sourceInterval); - // Redefinition des methodes virtuelles - boolean IsSubPartValues(const KWDGPartValues* otherPartValues) const override; + boolean IsSubPart(const KWDGPartValues* otherPartValues) const override; void Import(KWDGPartValues* sourcePartValues) override; void UpgradeFrom(const KWDGPartValues* sourcePartValues) override; void CopyFrom(const KWDGPartValues* sourcePartValues) override; @@ -2101,14 +2071,6 @@ inline Continuous KWDGInterval::GetMaxUpperBound() return KWContinuous::GetMaxValue(); } -inline void KWDGInterval::CopyFrom(const KWDGInterval* sourceInterval) -{ - require(sourceInterval != NULL); - - cLowerBound = sourceInterval->cLowerBound; - cUpperBound = sourceInterval->cUpperBound; -} - // Classe KWDGValueSet inline KWDGValueSet::KWDGValueSet() From 9b18896082a18ecdb830299c4253275243b7bda6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Wed, 6 Sep 2023 09:20:22 +0200 Subject: [PATCH 07/17] Exploit abstract class KWDGValueSet (Symbol and VartPart) to unify code Globalement, au moins une trentaine d'impacts, permettant de traiter les cas des valeurs Symbol et VarPart de facon unfiee, avec si besoin des cas particulier ponctuels seln le type de valeur traite Classes imapcst principales ci-dessous, avec zoom sur certaines methodes: - KWDGAttribute - starValuePart -> defaultPart: unification entre Symbol et VarPart - nkdVarPartSets: supprime, on reutilise nkdParts a la place, pour les types Symbol et VarPart - LookupGroupablePart: nouvelle methode pour un acces generique a une partie depuis une valeur generique KWDGValue - KWDataGrid - KWDataGridMerger - KWDGPOGrouper - InitializeGroupIndexes - KWDataGridUnivariateCosts - KWDGMPartMergeAction - KWDGValueSet - SetValueNumber: nouvelle methode dans l'implementation pour modifier les nombre de valeur en mode emule pour l'utilisation dans KWDataGridUnivariateCosts - KWSortableObjectComparePartValue: nouvelle fonction comparaison de deux objets KWSortableObject contenant des KWDGValue, par valeur - - KWSortableObjectCompareVarPart: suppression de cette methode de comparaion obsolete - KWDataGridManager - SortAttributePartsByTargetGroups: nouvelle methode generique, uniifiant SortAttributeParts et SortVarPartAttributeParts - SortAttributeParts: supression de l'ancienne methdoe dediee Symbol - SortVarPartAttributeParts: supression de l'ancienne methdoe dediee VarPart - InitialiseAttributeRandomParts, AddAttributeRandomParts: unification des cas Symbol et VarPart - InitialiseAttributeGranularizedGroupableParts: nouvelle methode generique, remplacant les methodes precedentes: - InitialiseAttributeGranularizedSymbolParts: supression - InitialiseAttributeGranularizedVarPartParts: supression - CheckCells - KWDGPart - Check - KWVarPartDataGridClusteringCosts - ComputePartCost - ComputePartUnionCost - ComputePartModelCost - KWDataGridPostOptimizer - PostOptimizeDataGrid Correction pour plante en debug dans LearningTest\TestCoclustering\y_CoclusteringIV_Standard\SimplifyCoclustering - le check ne passe pas pour les valeurs des parties des variable internes categorielles, car celles-ci ne sont pas triee par effectif decroissant (il n'y a pas d'effectif specifie dans le json) - correction dans KWDGInnerAttributes::Check() - pas de verification du tri si les effectifs des valeurs ne sont pas disponibles --- .../KWDGMPartMergeAction.cpp | 21 +- src/Learning/KWDataPreparation/KWDataGrid.cpp | 448 +++++---------- src/Learning/KWDataPreparation/KWDataGrid.h | 69 ++- .../KWDataPreparation/KWDataGridCosts.cpp | 38 +- .../KWDataPreparation/KWDataGridManager.cpp | 522 +++--------------- .../KWDataPreparation/KWDataGridManager.h | 34 +- .../KWDataPreparation/KWDataGridMerger.cpp | 144 ++--- .../KWDataGridPostOptimizer.cpp | 150 ++--- .../KWDataGridPostOptimizer.h | 6 +- .../CCHierarchicalDataGrid.cpp | 10 +- .../MODL_Coclustering/CCLearningProblem.cpp | 2 - .../MODL_Coclustering/MODL_Coclustering.cpp | 4 +- 12 files changed, 380 insertions(+), 1068 deletions(-) diff --git a/src/Learning/KWDataPreparation/KWDGMPartMergeAction.cpp b/src/Learning/KWDataPreparation/KWDGMPartMergeAction.cpp index 04773778d..e3d8017c7 100644 --- a/src/Learning/KWDataPreparation/KWDGMPartMergeAction.cpp +++ b/src/Learning/KWDataPreparation/KWDGMPartMergeAction.cpp @@ -113,10 +113,7 @@ KWDGMPart* KWDGMPartMergeAction::PerformPartMerge(KWDGMPartMerge* partMerge) // Dereferencement des parties de la liste triee par nombre de modalites // Restitution de la poubelle - // CH IV Begin - //DDDSIMPLIFY - if (attribute->GetAttributeType() == KWType::Symbol or attribute->GetAttributeType() == KWType::VarPart) - // CH IV End + if (KWType::IsCoclusteringGroupableType(attribute->GetAttributeType())) { attribute->RemovePartFromValueNumberList(part1); attribute->RemovePartFromValueNumberList(part2); @@ -163,14 +160,7 @@ KWDGMPart* KWDGMPartMergeAction::PerformPartMerge(KWDGMPartMerge* partMerge) // Realisation de la fusion des parties // Import des valeurs de la partie origine vers la partie destination - if (attribute->GetAttributeType() == KWType::Continuous) - part2->GetInterval()->Import(part1->GetInterval()); - // CH IV Begin - else if (attribute->GetAttributeType() == KWType::Symbol) - part2->GetValueSet()->Import(part1->GetValueSet()); - else - part2->GetVarPartSet()->Import(part1->GetVarPartSet()); - // CH IV End + part2->GetPartValues()->Import(part1->GetPartValues()); // Transfert des cellules a transferer for (nCell = 0; nCell < oaTransferredCells1.GetSize(); nCell++) @@ -251,13 +241,12 @@ KWDGMPart* KWDGMPartMergeAction::PerformPartMerge(KWDGMPartMerge* partMerge) // Mise a jour du cout de la partie 2 part2->SetCost(dataGridCosts->ComputePartCost(part2)); - // CH IV Begin - //DDDSIMPLIFY - if (attribute->GetAttributeType() == KWType::Symbol or attribute->GetAttributeType() == KWType::VarPart) - // CH IV End + // Finalisation de la gestion de la poubelle + if (KWType::IsCoclusteringGroupableType(attribute->GetAttributeType())) { // Reinsertion de la partie fusionnee dans la liste triee par nombre de modalites attribute->AddPartToValueNumberList(part2); + // Mise a jour du groupe poubelle selon les caracteristiques enregistrees dans le partMerge if (bGarbagePresence) attribute->SetGarbagePart(cast(KWDGPart*, attribute->slPartValueNumbers->GetHead())); diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index 6db762d9a..f84bdb9cb 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -1517,9 +1517,9 @@ void KWDataGrid::WriteAttributes(ostream& ost) const attribute = cast(KWDGAttribute*, oaAttributes.GetAt(nAttribute)); ost << "\t" << attribute->GetAttributeName() << "\t" << KWType::ToString(attribute->GetAttributeType()); - // Cas d'un attribut categoriel : affichage du groupe poubelle eventuel - //DDDSIMPLIFY - if (attribute->GetAttributeType() == KWType::Symbol and not attribute->GetAttributeTargetFunction()) + // Cas d'un attribut groupable : affichage du groupe poubelle eventuel + if (KWType::IsCoclusteringGroupableType(attribute->GetAttributeType()) and + not attribute->GetAttributeTargetFunction()) { ost << "\tCatch all size " << attribute->GetCatchAllValueNumber() << "\n"; ost << "\t" @@ -1528,14 +1528,6 @@ void KWDataGrid::WriteAttributes(ostream& ost) const << "\t" << (attribute->GetGarbagePart() != NULL); ost << "\t" << attribute->GetGarbageModalityNumber(); } - else if (attribute->GetAttributeType() == KWType::VarPart) - { - ost << "\t" - << (attribute->GetInitialValueNumber() > - KWFrequencyTable::GetMinimumNumberOfModalitiesForGarbage()) - << "\t" << (attribute->GetGarbagePart() != NULL); - ost << "\t" << attribute->GetGarbageModalityNumber() << "\n"; - } ost << "\n"; } } @@ -1567,9 +1559,8 @@ void KWDataGrid::WriteAttributeParts(ostream& ost) const { ost << "\t\t" << part->GetObjectLabel() << "\t" << part->GetPartFrequency(); - // Affichage des premieres valeurs dans le cas d'un attribut Symbol - //DDDSIMPLIFY - if (attribute->GetAttributeType() == KWType::Symbol) + // Affichage des premieres valeurs dans le cas d'un attribut groupable + if (KWType::IsCoclusteringGroupableType(attribute->GetAttributeType())) { nDisplayedValue = 0; value = part->GetValueSet()->GetHeadValue(); @@ -1582,40 +1573,18 @@ void KWDataGrid::WriteAttributeParts(ostream& ost) const break; } else - ost << "\t" << value->GetSymbolValue(); + ost << "\t" << value->GetObjectLabel(); part->GetValueSet()->GetNextValue(value); } } - // CH IV Begin - else if (attribute->GetAttributeType() == KWType::VarPart) - { - nDisplayedValue = 0; - value = part->GetVarPartSet()->GetHeadValue(); - while (value != NULL) - { - nDisplayedValue++; - if (nDisplayedValue > nMaxDisplayedValue) - { - ost << "\t..."; - break; - } - else - ost << "\t" - << value->GetVarPart()->GetAttribute()->GetAttributeName() - << "\t" << *(value->GetVarPart()); - part->GetVarPartSet()->GetNextValue(value); - } - } - // CH IV End // Fin de ligne ost << "\n"; - // Affichage complet du valueSet dans le cas d'un attribut Symbol - //DDDSIMPLIFY - if (bDisplayAll and attribute->GetAttributeType() == KWType::Symbol) + // Affichage complet des valeurs dans le cas d'un attribut groupable + if (bDisplayAll and KWType::IsCoclusteringGroupableType(attribute->GetAttributeType())) { - cout << "ValueSet\n"; + cout << part->GetValueSet()->GetClassLabel() << "\n"; part->GetValueSet()->Write(cout); } @@ -2302,7 +2271,7 @@ KWDGAttribute::KWDGAttribute() tailPart = NULL; nPartNumber = 0; bIsIndexed = false; - starValuePart = NULL; + defaultPart = NULL; bTargetAttribute = false; nInitialValueNumber = 0; nGranularizedValueNumber = 0; @@ -2510,9 +2479,6 @@ void KWDGAttribute::BuildIndexingStructure() { assert(oaIntervals.GetSize() == 0); assert(nkdParts.GetCount() == 0); - // CH IV Begin - assert(nkdVarPartSets.GetCount() == 0); - // CH IV End // Indexation des intervalles si attribut numerique if (GetAttributeType() == KWType::Continuous) @@ -2525,14 +2491,13 @@ void KWDGAttribute::BuildIndexingStructure() oaIntervals.Sort(); } // Sinon, indexation des parties par les valeurs - // CH IV Begin - //DDDSIMPLIFY - else if (GetAttributeType() == KWType::Symbol) - // CH IV End + else { + assert(KWType::IsCoclusteringGroupableType(GetAttributeType())); + // Parcours des parties pour les indexer par leurs valeurs nkdParts.RemoveAll(); - starValuePart = NULL; + defaultPart = NULL; part = headPart; while (part != NULL) { @@ -2546,7 +2511,7 @@ void KWDGAttribute::BuildIndexingStructure() // Memorisation de la partie associe a la valeur speciale if (value->IsDefaultValue()) - starValuePart = part; + defaultPart = part; // Valeur suivante valueSet->GetNextValue(value); @@ -2555,45 +2520,21 @@ void KWDGAttribute::BuildIndexingStructure() // Partie suivante part = part->nextPart; } - assert(starValuePart != NULL); - } - // CH IV Begin - // Cas d'un attribut de type VarPart - else - { - assert(GetAttributeType() == KWType::VarPart); + assert(defaultPart != NULL or GetAttributeType() == KWType::VarPart); - // Parcours des parties pour les indexer par leurs parties de variable - nkdVarPartSets.RemoveAll(); - part = headPart; - while (part != NULL) + // Indexation des attributs internes dans le cas d'un attribut VarPart + if (GetAttributeType() == KWType::VarPart) { - // Parcours des parties de variable de la partie - valueSet = part->GetValueSet(); - value = valueSet->GetHeadValue(); - while (value != NULL) + for (nInnerAttribute = 0; nInnerAttribute < GetInnerAttributeNumber(); + nInnerAttribute++) { - // Ajout de la partie avec la partie de variable pour cle - nkdVarPartSets.SetAt(value->GetNumericKeyValue(), part); + innerAttribute = GetInnerAttributeAt(nInnerAttribute); - // Partie de variable suivante - valueSet->GetNextValue(value); + // Indexation des valeurs des parties de l'attribut + innerAttribute->BuildIndexingStructure(); } - - // Partie suivante - part = part->nextPart; - } - - // Parcours des attributs internes - for (nInnerAttribute = 0; nInnerAttribute < GetInnerAttributeNumber(); nInnerAttribute++) - { - innerAttribute = GetInnerAttributeAt(nInnerAttribute); - - // Indexation des valeurs des parties de l'attribut - innerAttribute->BuildIndexingStructure(); } } - // CH Iv End // Memorisation du flag d'indexation bIsIndexed = true; @@ -2614,24 +2555,25 @@ void KWDGAttribute::DeleteIndexingStructure() assert(KWType::IsCoclusteringType(GetAttributeType())); if (GetAttributeType() == KWType::Continuous) oaIntervals.SetSize(0); - //DDDSIMPLIFY - else if (GetAttributeType() == KWType::Symbol) - { - nkdParts.RemoveAll(); - starValuePart = NULL; - } - // Cas d'un attribut de type VarPart else { - assert(GetAttributeType() == KWType::VarPart); - nkdVarPartSets.RemoveAll(); - for (nInnerAttribute = 0; nInnerAttribute < GetInnerAttributeNumber(); nInnerAttribute++) + assert(KWType::IsCoclusteringGroupableType(GetAttributeType())); + + // Nettoyage de l'indexation + nkdParts.RemoveAll(); + defaultPart = NULL; + + // Nettoyage de l'indexation des attributs internes dans le cas d'un attribut VarPart + if (GetAttributeType() == KWType::VarPart) { - innerAttribute = GetInnerAttributeAt(nInnerAttribute); - innerAttribute->DeleteIndexingStructure(); + for (nInnerAttribute = 0; nInnerAttribute < GetInnerAttributeNumber(); + nInnerAttribute++) + { + innerAttribute = GetInnerAttributeAt(nInnerAttribute); + innerAttribute->DeleteIndexingStructure(); + } } } - // CH IV End bIsIndexed = false; } } @@ -2712,11 +2654,11 @@ KWDGPart* KWDGAttribute::LookupSymbolPart(const Symbol& sValue) part = cast(KWDGPart*, nkdParts.Lookup(sValue.GetNumericKey())); if (part == NULL) - part = starValuePart; + part = defaultPart; ensure(part != NULL); return part; } -// CH IV Begin + KWDGPart* KWDGAttribute::LookupVarPart(KWDGPart* varPart) { KWDGPart* part; @@ -2724,12 +2666,25 @@ KWDGPart* KWDGAttribute::LookupVarPart(KWDGPart* varPart) require(IsIndexed()); require(GetAttributeType() == KWType::VarPart); - part = cast(KWDGPart*, nkdVarPartSets.Lookup((NUMERIC)varPart)); + part = cast(KWDGPart*, nkdParts.Lookup((NUMERIC)varPart)); ensure(part != NULL); return part; } -// CH IV End + +KWDGPart* KWDGAttribute::LookupGroupablePart(const KWDGValue* value) +{ + KWDGPart* part; + + require(IsIndexed()); + require(KWType::IsCoclusteringGroupableType(GetAttributeType())); + + part = cast(KWDGPart*, nkdParts.Lookup(value->GetNumericKeyValue())); + if (part == NULL) + part = defaultPart; + ensure(part != NULL); + return part; +} int KWDGAttribute::ComputeTotalPartFrequency() const { @@ -2800,10 +2755,11 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con } } } - // Test d'inclusion des parties dans le cas categoriel - //DDDSIMPLIFY - else if (otherAttribute->GetAttributeType() == KWType::Symbol) + // Test d'inclusion des parties dans le cas d'un attribut groupable + else { + assert(KWType::IsCoclusteringGroupableType(GetAttributeType())); + // Indexation prealable des partie cibles otherPart = otherAttribute->GetHeadPart(); while (otherPart != NULL) @@ -2836,47 +2792,6 @@ boolean KWDGAttribute::ContainsSubParts(const KWDGAttribute* otherAttribute) con break; } - // Partie suivante - GetNextPart(part); - } - } - // Test d'inclusion des parties dans le cas VarPart - else - { - assert(GetAttributeType() == KWType::VarPart); - - // Indexation prealable des partie cibles - otherPart = otherAttribute->GetHeadPart(); - while (otherPart != NULL) - { - // Parcours de valeurs de l'autre partie pour memoriser l'association - value = otherPart->GetVarPartSet()->GetHeadValue(); - while (value != NULL) - { - nkdOtherPartsPerValue.SetAt(value->GetNumericKeyValue(), otherPart); - otherPart->GetVarPartSet()->GetNextValue(value); - } - - // Autre partie suivante - otherAttribute->GetNextPart(otherPart); - } - - // Parcours des partie pour verifier leur inclusion - part = GetHeadPart(); - while (part != NULL) - { - // Recherche de l'autre partie sur la base de la premiere valeur de la partie en cours - otherPart = - cast(KWDGPart*, nkdOtherPartsPerValue.Lookup( - part->GetValueSet()->GetHeadValue()->GetNumericKeyValue())); - - // Pas d'inclusion si partie non trouvee ou non inclusante - if (otherPart == NULL or not part->IsSubPart(otherPart)) - { - bOk = false; - break; - } - // Partie suivante GetNextPart(part); } @@ -2899,7 +2814,7 @@ boolean KWDGAttribute::Check() const Continuous cPreviousUpperBound; NumericKeyDictionary nkdCheckParts; boolean bGarbagePartFound; - boolean bStarValueFound; + boolean bDefaultValueFound; int nInnerAttribute; KWDGAttribute* innerAttribute; ALString sTmp; @@ -2910,9 +2825,7 @@ boolean KWDGAttribute::Check() const AddError("Missing type"); bOk = false; } - // CH IV Begin else if (not KWType::IsCoclusteringType(nAttributeType)) - // CH IV End { AddError("Type must be Numerical, Categorical or VarPart"); bOk = false; @@ -3113,13 +3026,12 @@ boolean KWDGAttribute::Check() const break; } } - // Si attribut categoriel, indexation locale des parties par les valeurs pour validation - //DDDSIMPLIFY - else if (bOk and GetAttributeType() == KWType::Symbol) + // Si attribut groupable, indexation locale des parties par les valeurs pour validation + else if (bOk and KWType::IsCoclusteringGroupableType(GetAttributeType())) { // Parcours des parties pour les indexer par leurs valeurs nkdCheckParts.RemoveAll(); - bStarValueFound = false; + bDefaultValueFound = false; part = headPart; while (part != NULL) { @@ -3134,7 +3046,7 @@ boolean KWDGAttribute::Check() const // Erreur si partie deja enregistree avec cette valeur if (searchedPart != NULL) { - part->AddError(sTmp + "Value " + value->GetObjectLabel() + + part->AddError(sTmp + value->GetClassLabel() + " " + value->GetObjectLabel() + " already belongs to part " + part->GetObjectLabel()); bOk = false; break; @@ -3146,8 +3058,8 @@ boolean KWDGAttribute::Check() const nkdCheckParts.SetAt(value->GetNumericKeyValue(), part); // Test si valeur speciale - if (value->GetSymbolValue() == Symbol::GetStarValue()) - bStarValueFound = true; + if (value->IsDefaultValue()) + bDefaultValueFound = true; // Valeur suivante valueSet->GetNextValue(value); @@ -3158,92 +3070,58 @@ boolean KWDGAttribute::Check() const part = part->nextPart; } - // Test si la valeur speciale est definie dans le groupage - if (bOk and not bStarValueFound) + // Test si la valeur speciale est definie dans le groupage dans le cas d'un attribut Symbol + if (bOk and GetAttributeType() == KWType::Symbol and not bDefaultValueFound) { AddError(sTmp + "Special grouping value " + Symbol::GetStarValue() + " is not specified in any part"); bOk = false; } - } - // Si attribut de type VarPart, indexation locale des parties par les parties de variable internes pour validation - else if (bOk and GetAttributeType() == KWType::VarPart) - { - // Parcours des parties pour les indexer par leurs valeurs - // (pas de Star value pour les partie de variables) - nkdCheckParts.RemoveAll(); - part = headPart; - while (part != NULL) + + // Tests specifiques dans le cas VarPart + if (GetAttributeType() == KWType::VarPart) { - // Parcours des VarPart de la partie pour verifier qu'elle ne sont utilisees qu'une seule fois - valueSet = part->GetValueSet(); - value = valueSet->GetHeadValue(); - while (value != NULL) + // Verification du nombre total de partie de variables indexes dans les partie de la variable de type VarPart + if (bOk and innerAttributes->ComputeTotalInnerAttributeVarParts() != nkdCheckParts.GetCount()) { - // Recherche si la VarPart est deja enregistree - searchedPart = cast(KWDGPart*, nkdCheckParts.Lookup(value->GetNumericKeyValue())); - - // Erreur si VarPart deja enregistree - if (searchedPart != NULL) - { - part->AddError(sTmp + "Inner variable var part " + value->GetObjectLabel() + - " already belongs to part " + part->GetObjectLabel()); - bOk = false; - break; - } - // On continue si pas d'erreur - else - { - // Ajout de la partie avec la valeur pour cle - nkdCheckParts.SetAt(value->GetNumericKeyValue(), part); - - // Valeur suivante - valueSet->GetNextValue(value); - } + AddError(sTmp + "Number of variable parts used in groups (" + + IntToString(nkdCheckParts.GetCount()) + + ") is different from the total number of parts in the inner variables (" + + IntToString(innerAttributes->ComputeTotalInnerAttributeVarParts()) + ")"); + bOk = false; } - // Partie suivante - part = part->nextPart; - } - - // Verification du nombre total de partie de variables indexes dans les partie de la variable de type VarPart - if (bOk and innerAttributes->ComputeTotalInnerAttributeVarParts() != nkdCheckParts.GetCount()) - { - AddError(sTmp + "Number of variable parts used in groups (" + - IntToString(nkdCheckParts.GetCount()) + - ") is different from the total number of parts in the inner variables (" + - IntToString(innerAttributes->ComputeTotalInnerAttributeVarParts()) + ")"); - bOk = false; - } - - // Parcours de VarPart des variables internes pour verifier qu'elles sont referencees dans les parties - // de la variable de type VarPart - if (bOk) - { - // Parcours des attributs internes - for (nInnerAttribute = 0; nInnerAttribute < GetInnerAttributeNumber(); nInnerAttribute++) + // Parcours de VarPart des variables internes pour verifier qu'elles sont referencees dans les parties + // de la variable de type VarPart + if (bOk) { - innerAttribute = GetInnerAttributeAt(nInnerAttribute); - - // Parcours des partie de l'attribut interne - usedVarPart = innerAttribute->GetHeadPart(); - while (usedVarPart != NULL) + // Parcours des attributs internes + for (nInnerAttribute = 0; nInnerAttribute < GetInnerAttributeNumber(); + nInnerAttribute++) { - // Recherche de la VarPart parmi les VarPart des partie de la variable de type VarPart - searchedPart = cast(KWDGPart*, nkdCheckParts.Lookup((NUMERIC)usedVarPart)); + innerAttribute = GetInnerAttributeAt(nInnerAttribute); - // Erreur si VarPart non trouvee - if (searchedPart == NULL) + // Parcours des partie de l'attribut interne + usedVarPart = innerAttribute->GetHeadPart(); + while (usedVarPart != NULL) { - part->AddError(sTmp + "Inner variable var part " + - usedVarPart->GetObjectLabel() + - " not found amond parts of variables"); - bOk = false; - break; - } + // Recherche de la VarPart parmi les VarPart des partie de la variable de type VarPart + searchedPart = + cast(KWDGPart*, nkdCheckParts.Lookup((NUMERIC)usedVarPart)); + + // Erreur si VarPart non trouvee + if (searchedPart == NULL) + { + part->AddError(sTmp + "Inner variable VarPart " + + usedVarPart->GetObjectLabel() + + " not found amond parts of variables"); + bOk = false; + break; + } - // Partie suivante - innerAttribute->GetNextPart(usedVarPart); + // Partie suivante + innerAttribute->GetNextPart(usedVarPart); + } } } } @@ -3257,7 +3135,8 @@ longint KWDGAttribute::GetUsedMemory() const // Memoire de base lUsedMemory = sizeof(KWDGAttribute); - lUsedMemory += sAttributeName.GetLength() + 1; + lUsedMemory += sAttributeName.GetLength(); + lUsedMemory++; // Prise en compte des parties et des valeurs if (headPart != NULL) @@ -3335,7 +3214,7 @@ boolean KWDGAttribute::ArePartsSorted() const } } - // Verification du tri des partie si ok + // Verification du tri des parties si ok if (bIsSorted) { // Verification du tri des parties par intervalle croissant pour les attribut continus et @@ -3805,9 +3684,11 @@ boolean KWDGPart::Check() const // Verification de l'intervalle else if (GetPartType() == KWType::Continuous) bOk = bOk and partValues->Check(); - // Verification de l'ensemble de valeurs - else if (GetPartType() == KWType::Symbol) + // Verification de l'ensemble de valeurs dans le cas groupable + else { + assert(KWType::IsCoclusteringGroupableType(GetPartType())); + bOk = bOk and partValues->Check(); // Verification de la compatibilite entre l'effectif de la partie @@ -3824,43 +3705,14 @@ boolean KWDGPart::Check() const if (bOk and GetPartFrequency() > 0 and nTotalValueFrequency > 0 and GetPartFrequency() != nTotalValueFrequency) { + assert(GetValueSet()->GetHeadValue() != NULL); AddError(sTmp + "Part frequency (" + IntToString(GetPartFrequency()) + - ") different from the cumulated frequency of its values (" + + ") different from the cumulated frequency of its " + + GetValueSet()->GetHeadValue()->GetClassLabel() + "s (" + IntToString(nTotalValueFrequency) + ")"); bOk = false; } } - // CH IV Begin - // Verification de l'ensemble des parties de variables - else - { - bOk = bOk and partValues->Check(); - - // Verification de la compatibilite entre l'effectif de la partie - // et l'effectif cumule de ses parties de variable - // La verification ne se fait que si la partie a un effectif non nul, - // ou si l'effectif cumule des parties de variable est non nul. - // Cela permet des verifications partielles en cours d'alimentation - // de la grille (les parties et leurs parties de variable sont creees avant - // la creation des cellules). - // Cela permet egalement de verifier la validite d'une grille - // construite pour le deploiement de modele, qui n'a pas besoin - // des effectifs par valeur. - nTotalVarPartFrequency = GetValueSet()->ComputeTotalFrequency(); - // CH IV Debug - // CH IV Refactoring: nettoyer? - // cout << "nTotalVarPartFrequency\t" << nTotalVarPartFrequency << "\tGetPartFrequency\t" << - // GetPartFrequency() << "\n"; - if (bOk and GetPartFrequency() > 0 and nTotalVarPartFrequency > 0 and - GetPartFrequency() != nTotalVarPartFrequency) - { - AddError(sTmp + "Part frequency (" + IntToString(GetPartFrequency()) + - ") different from the cumulated frequency of its variable parts (" + - IntToString(nTotalVarPartFrequency) + ")"); - bOk = false; - } - } - // CH IV End // Test de coherence des cellules de la parties if (bOk) @@ -3943,29 +3795,20 @@ longint KWDGPart::GetUsedMemory() const void KWDGPart::Write(ostream& ost) const { // Identification de la partie - // CH IV Begin ost << GetClassLabel() << "\t" << GetObjectLabel() << "\t" << GetPartFrequency() << "\n"; // Valeurs et cellules de la partie - //DDDSIMPLIFY - if (GetPartType() == KWType::Symbol and GetValueSet()->GetValueNumber() > 0) - WriteValues(ost); - - if (GetPartType() == KWType::VarPart and GetValueSet()->GetValueNumber() > 0) + if (KWType::IsCoclusteringGroupableType(GetPartType()) and GetValueSet()->GetValueNumber() > 0) WriteValues(ost); - // CH IV End if (GetCellNumber() > 0) WriteCells(ost); } void KWDGPart::WriteValues(ostream& ost) const { - // Des valeurs sont a afficher uniquement dans le cas symbolique et VarPart + // Des valeurs sont a afficher uniquement dans le cas groupable // (l'intervalle est le libelle de la partie dans le cas continu) - //DDDSIMPLIFY - if (GetPartType() == KWType::Symbol) - GetValueSet()->WriteValues(ost); - else if (GetPartType() == KWType::VarPart) + if (KWType::IsCoclusteringGroupableType(GetPartType())) GetValueSet()->WriteValues(ost); } @@ -4049,7 +3892,6 @@ int KWDGPartCompare(const void* elem1, const void* elem2) { KWDGPart* part1; KWDGPart* part2; - int nCompare; require(elem1 != NULL); require(elem2 != NULL); @@ -4060,14 +3902,12 @@ int KWDGPartCompare(const void* elem1, const void* elem2) // Comparaison: on compare en priorite sur les bornes inf return part1->ComparePart(part2); - return nCompare; } int KWDGPartCompareValues(const void* elem1, const void* elem2) { KWDGPart* part1; KWDGPart* part2; - int nCompare; require(elem1 != NULL); require(elem2 != NULL); @@ -4078,7 +3918,6 @@ int KWDGPartCompareValues(const void* elem1, const void* elem2) // Comparaison: on compare en priorite sur les bornes inf return part1->ComparePartValues(part2); - return nCompare; } ////////////////////////////////////////////////////////////////////////////// @@ -4477,7 +4316,6 @@ void KWDGValueSet::CopyFrom(const KWDGPartValues* sourcePartValues) int KWDGValueSet::ComparePartValues(const KWDGPartValues* otherPartValues) const { - int nCompare; KWDGValueSet* otherValueSet; require(otherPartValues != NULL); @@ -4496,7 +4334,6 @@ int KWDGValueSet::ComparePartValues(const KWDGPartValues* otherPartValues) const return 1; else return GetHeadValue()->CompareValue(otherValueSet->GetHeadValue()); - return nCompare; } boolean KWDGValueSet::Check() const @@ -4654,6 +4491,12 @@ const ALString KWDGValueSet::GetObjectLabel() const return sLabel; } +void KWDGValueSet::SetValueNumber(int nValue) +{ + require(nValue >= 0); + nValueNumber = nValue; +} + void KWDGValueSet::AddTailValue(KWDGValue* value) { require(value != NULL); @@ -4878,6 +4721,21 @@ int KWDGValueCompareFrequency(const void* elem1, const void* elem2) return value1->CompareFrequency(value2); } +int KWSortableObjectComparePartValue(const void* elem1, const void* elem2) +{ + KWDGValue* value1; + KWDGValue* value2; + + require(elem1 != NULL); + require(elem2 != NULL); + + // Acces aux parties de variable + value1 = cast(KWDGValue*, cast(KWSortableObject*, *(Object**)elem1)->GetSortValue()); + value2 = cast(KWDGValue*, cast(KWSortableObject*, *(Object**)elem2)->GetSortValue()); + + return value1->CompareValue(value2); +} + ////////////////////////////////////////////////////////////////////////////// // Classe KWDGSymbolValue @@ -4920,7 +4778,7 @@ longint KWDGSymbolValue::GetUsedMemory() const const ALString KWDGSymbolValue::GetClassLabel() const { - return "Value"; + return "value"; } const ALString KWDGSymbolValue::GetObjectLabel() const @@ -4975,21 +4833,6 @@ const ALString KWDGVarPartValue::GetObjectLabel() const return varPart->GetVarPartLabel(); } -int KWSortableObjectCompareVarPart(const void* elem1, const void* elem2) -{ - KWDGVarPartValue* varPartValue1; - KWDGVarPartValue* varPartValue2; - - require(elem1 != NULL); - require(elem2 != NULL); - - // Acces aux parties de variable - varPartValue1 = cast(KWDGVarPartValue*, cast(KWSortableObject*, *(Object**)elem1)->GetSortValue()); - varPartValue2 = cast(KWDGVarPartValue*, cast(KWSortableObject*, *(Object**)elem2)->GetSortValue()); - - return varPartValue1->CompareValue(varPartValue2); -} - ////////////////////////////////////////////////////////////////////////////// // Classe KWDGInnerAttributes @@ -5196,9 +5039,12 @@ boolean KWDGInnerAttributes::Check() const } // Verification du tri des parties de l'attribut interne, uniquement si l'attribut est completement specifie - // avec des partie d'effectif non vide, pour pouvoir faire des verifications en cours de construction d'une grille + // avec des parties d'effectif non vide, pour pouvoir faire des verifications en cours de construction d'une grille bIsCompletelySpecified = innerAttribute->GetPartNumber() > 0 and innerAttribute->GetHeadPart()->GetPartFrequency() > 0; + if (bIsCompletelySpecified and KWType::IsCoclusteringGroupableType(innerAttribute->GetAttributeType())) + bIsCompletelySpecified = + innerAttribute->GetHeadPart()->GetValueSet()->GetHeadValue()->GetValueFrequency() > 0; if (bOk and bIsCompletelySpecified and not innerAttribute->ArePartsSorted()) { AddError("Parts of inner variable " + innerAttribute->GetAttributeName() + " should be sorted"); @@ -5221,6 +5067,16 @@ void KWDGInnerAttributes::Write(ostream& ost) const } } +const ALString KWDGInnerAttributes::GetClassLabel() const +{ + return "Inner variables"; +} + +const ALString KWDGInnerAttributes::GetObjectLabel() const +{ + return ""; +} + ////////////////////////////////////////////////////////////////////////////// // Classe KWDGCell diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index 6e6abc73b..b1ed235db 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -608,18 +608,17 @@ class KWDGAttribute : public Object // Indicateur d'indexation boolean IsIndexed() const; - // Recherche de la partie contenant une valeur numerique ou symbolique + // Recherche de la partie contenant une valeur numerique, symbolique ou VarPart // (doit etre compatible avec le type de l'attribut) // Attention a ne pas modifier les valeurs (intervalles ou ensemble de valeurs) // pendant l'utilisation de l'indexation KWDGPart* LookupContinuousPart(Continuous cValue); KWDGPart* LookupSymbolPart(const Symbol& sValue); - - // CH IV Begin - // Recherche de la partie contenant une partie de variables - // (doit etre compatible avec le type de l'attribut) KWDGPart* LookupVarPart(KWDGPart* varPart); - // CH IV End + + // Recherche generique de la partie contenant une valeur de ValueSet dans le cas + // d'un attribut de type groupable, symbolique ou VarPart + KWDGPart* LookupGroupablePart(const KWDGValue* value); /////////////////////////////// // Services divers @@ -726,15 +725,13 @@ class KWDGAttribute : public Object // Tableau des parties (intervalles) tries de facon croissante ObjectArray oaIntervals; - // Structure d'indexation des parties dans le cas symbolique - // Dictionnaire des parties indexe par les valeurs (Symbol) des parties + // Structure d'indexation des parties dans le cas groupable + // Dictionnaire des parties indexe par les valeurs des parties, et partie par defaut NumericKeyDictionary nkdParts; - KWDGPart* starValuePart; //DDDSIMPLIFY - boolean bIsIndexed; + KWDGPart* defaultPart; - // CH IV Begin - // Structure d'indexation, des parties dans le cas VarPart - NumericKeyDictionary nkdVarPartSets; //DDDSIMPLIFY + // Indicateur d'indexation + boolean bIsIndexed; // Nom de l'attribut de type VarPart dont depend un attribut interne // Par defaut a vide pour un attribut de type Simple (numerique ou categoriel) @@ -1029,6 +1026,11 @@ class KWDGValueSet : public KWDGPartValues /////////////////////////////// ///// Implementation + + // Modification du nombre de valeurs + // Methode avancee utilisable uniquement en mode emule + void SetValueNumber(int nValue); + protected: // Ajout d'une valeur en fin de liste void AddTailValue(KWDGValue* value); @@ -1041,7 +1043,7 @@ class KWDGValueSet : public KWDGPartValues // La valeur speciale est toujours mise en dernier, independament du critere de tri boolean InternalAreValuesSorted(CompareFunction fCompare) const; - // Methode indiquant si les donnees sont emulee + // Methode indiquant si les donnees sont emulees virtual boolean GetEmulated() const; // Gestion de la liste doublement chainee des cellules @@ -1097,6 +1099,10 @@ class KWDGValue : public Object // Memoire utilisee virtual longint GetUsedMemory() const override = 0; + // Libelles utilisateur + const ALString GetClassLabel() const override = 0; + const ALString GetObjectLabel() const override = 0; + /////////////////////////////// ///// Implementation protected: @@ -1113,6 +1119,9 @@ int KWDGValueCompareValue(const void* elem1, const void* elem2); // Comparaison de deux valeurs, par effectif decroissant int KWDGValueCompareFrequency(const void* elem1, const void* elem2); +// Comparaison de deux objets KWSortableObject contenant des KWDGValue, par valeur +int KWSortableObjectComparePartValue(const void* elem1, const void* elem2); + ////////////////////////////////////////////////////////////////////////////// // Classe KWDGSymbolValueSet // Ensemble de valeurs d'une partie symbolique @@ -1295,6 +1304,9 @@ class KWDGVarPartSet : public KWDGValueSet // Redefinition de la methode virtuelle d'ajout de la copie d'une valeur existante KWDGValue* AddValueCopy(const KWDGValue* sourceValue) override; + // Libelles utilisateur + const ALString GetClassLabel() const override; + /////////////////////////////// ///// Implementation protected: @@ -1351,9 +1363,6 @@ class KWDGVarPartValue : public KWDGValue KWDGPart* varPart; }; -// Comparaison de deux objets KWSortableObject contenant des KWDGVarPartValue -int KWSortableObjectCompareVarPart(const void* elem1, const void* elem2); - ////////////////////////////////////////////////////////////////////////////// // Classe KWDGInnerAttributes // Ensemble des attributs internes d'un attribut de grille de type VarPart @@ -1413,6 +1422,10 @@ class KWDGInnerAttributes : public Object // Affichage void Write(ostream& ost) const override; + // Libelles utilisateur + const ALString GetClassLabel() const override; + const ALString GetObjectLabel() const override; + /////////////////////////////// ///// Implementation protected: @@ -1857,15 +1870,13 @@ inline void KWDGAttribute::GetPrevPart(KWDGPart*& part) const inline KWDGPart* KWDGAttribute::GetGarbagePart() const { // Pas de require strict sur le type d'attribut, pour simplifier l'usage de cette methode - require(nAttributeType == KWType::Symbol or nAttributeType == KWType::VarPart or garbagePart == NULL); + require(KWType::IsCoclusteringGroupableType(nAttributeType) or garbagePart == NULL); return garbagePart; } inline void KWDGAttribute::SetGarbagePart(KWDGPart* part) { - // CH IV Begin - require(nAttributeType == KWType::Symbol or nAttributeType == KWType::VarPart); - // CH IV End + require(KWType::IsCoclusteringGroupableType(nAttributeType)); garbagePart = part; } @@ -1874,12 +1885,11 @@ inline int KWDGAttribute::GetGarbageModalityNumber() const if (GetGarbagePart() == NULL) return 0; // Sinon - // CH IV Begin - else if (nAttributeType == KWType::Symbol) - return GetGarbagePart()->GetValueSet()->GetValueNumber(); else - return GetGarbagePart()->GetVarPartSet()->GetValueNumber(); - // CH IV End + { + assert(KWType::IsCoclusteringGroupableType(nAttributeType)); + return GetGarbagePart()->GetValueSet()->GetValueNumber(); + } } inline KWDGValueSet* KWDGAttribute::GetCatchAllValueSet() const @@ -1911,7 +1921,7 @@ inline void KWDGAttribute::SetCatchAllValueNumber(int nValue) inline int KWDGAttribute::GetCatchAllValueNumber() const { - // require(nAttributeType == KWType::Symbol); + // Pas de require sur le type pour simplifier l'ecriture du code // Cas ou il n'y a pas de groupe fourre-tout if (GetCatchAllValueSet() == NULL) return 0; @@ -2218,6 +2228,11 @@ inline KWDGValue* KWDGVarPartSet::AddValueCopy(const KWDGValue* sourceValue) return AddVarPart(cast(KWDGVarPartValue*, sourceValue)->GetVarPart()); } +inline const ALString KWDGVarPartSet::GetClassLabel() const +{ + return "VarPart set"; +} + inline KWDGVarPartValue* KWDGVarPartSet::NewVarPartValue(KWDGPart* varPart) const { require(varPart != NULL); diff --git a/src/Learning/KWDataPreparation/KWDataGridCosts.cpp b/src/Learning/KWDataPreparation/KWDataGridCosts.cpp index 33adf74e0..1cabfba10 100644 --- a/src/Learning/KWDataPreparation/KWDataGridCosts.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridCosts.cpp @@ -554,10 +554,8 @@ double KWDataGridCosts::ComputeDataGridAllValuesCost(const KWDataGrid* dataGrid) KWDGAttribute* attribute; KWDGPart* part; KWDGValue* value; - // CH IV Begin int nInnerAttribute; KWDGAttribute* innerAttribute; - // CH IV End require(dataGrid != NULL); @@ -589,10 +587,7 @@ double KWDataGridCosts::ComputeDataGridAllValuesCost(const KWDataGrid* dataGrid) attribute->GetNextPart(part); } } - // CH IV Begin - - // Prise en compte du cout des valeurs des attributs symboliques internes dans des attributs de type - // VarPart + // Prise en compte du cout des valeurs des attributs symboliques internes dans des attributs de type VarPart else if (attribute->GetAttributeType() == KWType::VarPart) { for (nInnerAttribute = 0; nInnerAttribute < attribute->GetInnerAttributeNumber(); @@ -624,7 +619,6 @@ double KWDataGridCosts::ComputeDataGridAllValuesCost(const KWDataGrid* dataGrid) } } } - // CH IV End } return dAllValuesCost; } @@ -2003,17 +1997,16 @@ double KWVarPartDataGridClusteringCosts::ComputePartCost(const KWDGPart* part) c { dPartCost = KWStat::LnFactorial(part->GetPartFrequency()); } - // Cout de distribution des valeurs (modele + donnees) dans le cas symbolique + // Cout de distribution des valeurs (modele + donnees) dans le cas groupable // On ignore le cout LnFactorial par valeur, qui est constant quelle que soit la grille else { + assert(KWType::IsCoclusteringGroupableType(part->GetAttribute()->GetAttributeType())); + dPartCost = 0; if (part->GetPartFrequency() > 0) { - if (part->GetAttribute()->GetAttributeType() == KWType::Symbol) - nValueNumber = part->GetValueSet()->GetValueNumber(); - else - nValueNumber = part->GetValueSet()->GetValueNumber(); + nValueNumber = part->GetValueSet()->GetValueNumber(); dPartCost = KWStat::LnFactorial(part->GetPartFrequency()); // Distribution des valeurs dans la partie @@ -2043,25 +2036,16 @@ double KWVarPartDataGridClusteringCosts::ComputePartUnionCost(const KWDGPart* pa { dPartUnionCost = KWStat::LnFactorial(nPartFrequency); } - // Cout de distribution des valeurs (modele + donnees) dans le cas symbolique + // Cout de distribution des valeurs (modele + donnees) dans le cas groupable // On ignore le cout LnFactorial par valeur, qui est constant quelle que soit la grille - else if (part1->GetAttribute()->GetAttributeType() == KWType::Symbol) - { - nValueNumber = part1->GetValueSet()->GetValueNumber() + part2->GetValueSet()->GetValueNumber(); - dPartUnionCost = KWStat::LnFactorial(nPartFrequency); - - // Distribution des valeurs dans la partie - dPartUnionCost += KWStat::LnFactorial(nPartFrequency + nValueNumber - 1); - dPartUnionCost -= KWStat::LnFactorial(nValueNumber - 1); - dPartUnionCost -= KWStat::LnFactorial(nPartFrequency); - } - // Cout de distribution des parties de variable dans le cas VarPart else { + assert(KWType::IsCoclusteringGroupableType(part1->GetAttribute()->GetAttributeType())); + nValueNumber = part1->GetValueSet()->GetValueNumber() + part2->GetValueSet()->GetValueNumber(); dPartUnionCost = KWStat::LnFactorial(nPartFrequency); - // Distribution des parties de variable dans la partie + // Distribution des valeurs dans la partie dPartUnionCost += KWStat::LnFactorial(nPartFrequency + nValueNumber - 1); dPartUnionCost -= KWStat::LnFactorial(nValueNumber - 1); dPartUnionCost -= KWStat::LnFactorial(nPartFrequency); @@ -2168,9 +2152,9 @@ double KWVarPartDataGridClusteringCosts::ComputePartModelCost(const KWDGPart* pa require(part != NULL); require(KWType::IsSimple(part->GetAttribute()->GetAttributeType())); - // Cout de distribution des valeurs dans le cas categoriel + // Cout de distribution des valeurs dans le cas groupable dPartCost = 0; - if (part->GetAttribute()->GetAttributeType() == KWType::Symbol) + if (KWType::IsCoclusteringGroupableType(part->GetAttribute()->GetAttributeType())) { if (part->GetPartFrequency() > 0) { diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index 400317909..64cd19915 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -713,19 +713,11 @@ void KWDataGridManager::ExportFrequencyTableFromOneAttribute(KWFrequencyTable* k ivFrequency->SetAt(nTargetIndex, dgCell->GetTargetFrequencyAt(nTargetIndex)); // Memorisation eventuelle du groupe poubelle - //DDDSIMPLIFY - if (oneAttributeDataGrid.GetAttributeAt(0)->GetAttributeType() == KWType::Symbol) + if (KWType::IsCoclusteringGroupableType(oneAttributeDataGrid.GetAttributeAt(0)->GetAttributeType())) { // Recopie du nombre de modalites kwdfvFrequencyVector->SetModalityNumber(dgPart->GetValueSet()->GetValueNumber()); } - // CH IV Begin - else if (oneAttributeDataGrid.GetAttributeAt(0)->GetAttributeType() == KWType::VarPart) - { - // Recopie du nombre de modalites - kwdfvFrequencyVector->SetModalityNumber(dgPart->GetValueSet()->GetValueNumber()); - } - // CH IV End } if (bDisplayResults) { @@ -1043,6 +1035,8 @@ void KWDataGridManager::ExportCells(KWDataGrid* targetDataGrid) const // Recherche de la partie associee a la cellule selon son type sourcePart = sourceCell->GetPartAt(sourceAttribute->GetAttributeIndex()); + + // Cas d'une partie de type Continuous if (sourcePart->GetPartType() == KWType::Continuous) { // Recherche d'une valeur typique: le milieu de l'intervalle (hors borne inf) @@ -1053,7 +1047,7 @@ void KWDataGridManager::ExportCells(KWDataGrid* targetDataGrid) const targetPart = targetAttribute->LookupContinuousPart(cValue); oaTargetParts.SetAt(nAttribute, targetPart); } - // CH IV Begin + // Cas d'une partie de type Symbol else if (sourcePart->GetPartType() == KWType::Symbol) { // Recherche d'une valeur typique: la premiere valeur @@ -1106,7 +1100,6 @@ void KWDataGridManager::ExportCells(KWDataGrid* targetDataGrid) const oaTargetParts.SetAt(nAttribute, targetPart); } } - // CH IV End } // Creation de la cellule cible si necessaire @@ -2265,8 +2258,6 @@ boolean KWDataGridManager::CheckCells(const KWDataGrid* targetDataGrid) const KWDGCell* targetCell; KWDGCell* checkCell; Continuous cValue; - Symbol sValue; - KWDGPart* varPart; ALString sTmp; require(Check()); @@ -2354,28 +2345,16 @@ boolean KWDataGridManager::CheckCells(const KWDataGrid* targetDataGrid) const checkPart = checkAttribute->LookupContinuousPart(cValue); oaCheckParts.SetAt(nAttribute, checkPart); } - // CH IV Begin - else if (targetPart->GetPartType() == KWType::Symbol) - { - // Recherche d'une valeur typique: la premiere valeur - assert(targetPart->GetValueSet()->GetHeadValue() != NULL); - sValue = targetPart->GetValueSet()->GetHeadValue()->GetSymbolValue(); - - // Recherche du groupe de valeurs cible correspondant - checkPart = checkAttribute->LookupSymbolPart(sValue); - oaCheckParts.SetAt(nAttribute, checkPart); - } else { - // Recherche d'une partie de variable typique: la premiere - assert(targetPart->GetVarPartSet()->GetHeadValue() != NULL); - varPart = targetPart->GetVarPartSet()->GetHeadValue()->GetVarPart(); + assert(KWType::IsCoclusteringGroupableType(targetPart->GetPartType())); - // Recherche du cluster de parties de variables correspondant - checkPart = checkAttribute->LookupVarPart(varPart); + // Recherche du groupe de valeurs cible correspondant a la premiere valeur + assert(targetPart->GetValueSet()->GetHeadValue() != NULL); + checkPart = checkAttribute->LookupGroupablePart( + targetPart->GetValueSet()->GetHeadValue()); oaCheckParts.SetAt(nAttribute, checkPart); } - // CH IV End } // Recherche de la cellule correspondante dans le DataGrid de verification @@ -2568,21 +2547,12 @@ void KWDataGridManager::InitialiseAttributeParts(const KWDGAttribute* sourceAttr // Creation de la partie cible targetPart = targetAttribute->AddPart(); - // Cas continu - if (sourceAttribute->GetAttributeType() == KWType::Continuous) - targetPart->GetInterval()->CopyFrom(sourcePart->GetInterval()); - // Sinon : cas Symbol ou VarPart - else - { - if (sourceAttribute->GetAttributeType() == KWType::Symbol) - targetPart->GetValueSet()->CopyFrom(sourcePart->GetValueSet()); - else - targetPart->GetVarPartSet()->CopyFrom(sourcePart->GetVarPartSet()); + // Copie des valeurs de la partie, quele que soit son type + targetPart->GetPartValues()->CopyFrom(sourcePart->GetPartValues()); - // Transfert du parametrage du groupe poubelle - if (sourcePart == sourceAttribute->GetGarbagePart()) - targetAttribute->SetGarbagePart(targetPart); - } + // Transfert du parametrage du groupe poubelle (methode tolerante au cas continu) + if (sourcePart == sourceAttribute->GetGarbagePart()) + targetAttribute->SetGarbagePart(targetPart); // Memorisation de l'effectif de la partie pour les attribut internes // Pour les autre attribut, c'es calcule a partir des cellules @@ -2681,26 +2651,10 @@ void KWDataGridManager::InitialiseAttributeNullPart(const KWDGAttribute* sourceA targetPart->GetInterval()->SetUpperBound(KWDGInterval::GetMaxUpperBound()); } // Creation d'une partie unique comportant toutes les valeurs, dans le cas symbolique - //DDDSIMPLIFY - else if (sourceAttribute->GetAttributeType() == KWType::Symbol) + else { - // Creation de l'ensemble des valeur cible - targetPart = targetAttribute->AddPart(); + assert(KWType::IsCoclusteringGroupableType(sourceAttribute->GetAttributeType())); - // Transfert des valeurs des parties de l'attribut source - sourcePart = sourceAttribute->GetHeadPart(); - while (sourcePart != NULL) - { - // Concatenation dans la partie cible des valeurs source - targetPart->GetValueSet()->UpgradeFrom(sourcePart->GetValueSet()); - - // Partie suivante - sourceAttribute->GetNextPart(sourcePart); - } - } - // Creation d'une partie unique comportant toutes les valeurs, dans le cas VarPart - else if (sourceAttribute->GetAttributeType() == KWType::VarPart) - { // Creation de l'ensemble des valeur cible targetPart = targetAttribute->AddPart(); @@ -2709,7 +2663,7 @@ void KWDataGridManager::InitialiseAttributeNullPart(const KWDGAttribute* sourceA while (sourcePart != NULL) { // Concatenation dans la partie cible des valeurs source - targetPart->GetVarPartSet()->UpgradeFrom(sourcePart->GetVarPartSet()); + targetPart->GetValueSet()->UpgradeFrom(sourcePart->GetValueSet()); // Partie suivante sourceAttribute->GetNextPart(sourcePart); @@ -2793,13 +2747,14 @@ void KWDataGridManager::InitialiseAttributeRandomParts(const KWDGAttribute* sour } } } - // Partition aleatoire des valeurs dans le cas symbolique - // CH IV Begin - else if (sourceAttribute->GetAttributeType() == KWType::Symbol) - // CH IV End + // Partition aleatoire des valeurs dans le cas d'un attribut groupable + else { + assert(KWType::IsCoclusteringGroupableType(sourceAttribute->GetAttributeType())); + // Recopie du fourre-tout // Transfert du parametrage du fourre-tout + // (methode tolerante au cas sans fourre-tout, ce qui permet de l'utiliser dans les cas Symbol et VarPart targetAttribute->InitializeCatchAllValueSet(sourceAttribute->GetCatchAllValueSet()); // S'il y a moins de valeurs que de partie a constituer, on recopie directement @@ -2863,72 +2818,6 @@ void KWDataGridManager::InitialiseAttributeRandomParts(const KWDGAttribute* sour assert(targetAttribute->GetPartNumber() == ivValueIndexes.GetSize() + 1); } } - // CH IV Begin - // Partition aleatoire dans le cas d'un attribut de type VarPart - else - { - // S'il y a moins de parties que de partie a constituer, on recopie directement - // les meme parties - if (sourceAttribute->GetPartNumber() <= nPartNumber) - { - // Transfert du parametrage des parties de l'attribut - InitialiseAttributeParts(sourceAttribute, targetAttribute); - } - // Sinon, partitionnement aleatoire des parties sources - else - { - // Export des parties de l'attribut source - sourceAttribute->ExportParts(&oaSourceParts); - - // Permutation aleatoire - oaSourceParts.Shuffle(); - - // Initialisation d'un vecteur des index des valeurs - // Ce vecteur permettra de choisir des bornes de partition apres permutation aleatoire - ivValueIndexes.SetSize(oaSourceParts.GetSize()); - for (n = 0; n < ivValueIndexes.GetSize() - 1; n++) - ivValueIndexes.SetAt(n, n); - - // Recherche d'une ensemble de "bornes" de parties aleatoires - ivValueIndexes.Shuffle(); - ivValueIndexes.SetSize(nPartNumber - 1); - ivValueIndexes.Sort(); - - // Creation de la partition cible - targetPart = NULL; - nTargetPart = 0; - for (nSourcePart = 0; nSourcePart < oaSourceParts.GetSize(); nSourcePart++) - { - sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSourcePart)); - - // Creation si necessaire d'une partie cible - if (targetPart == NULL) - { - targetPart = targetAttribute->AddPart(); - - // Initialisation de ses valeurs - targetPart->GetVarPartSet()->CopyFrom(sourcePart->GetVarPartSet()); - } - // Sinon, mise a jour des valeurs de la partie en cours - else - { - targetPart->GetVarPartSet()->UpgradeFrom(sourcePart->GetVarPartSet()); - } - - // La partie cible est valide si son nombre de partie sources est atteint - if (nTargetPart < ivValueIndexes.GetSize() and - nSourcePart >= ivValueIndexes.GetAt(nTargetPart)) - { - nTargetPart++; - - // On reinitialise l'indicateur de creation d'intervalle cible - targetPart = NULL; - } - } - assert(targetAttribute->GetPartNumber() == ivValueIndexes.GetSize() + 1); - } - } - // CH IV End } void KWDataGridManager::AddAttributeRandomParts(const KWDGAttribute* sourceAttribute, KWDGAttribute* mandatoryAttribute, @@ -3054,11 +2943,11 @@ void KWDataGridManager::AddAttributeRandomParts(const KWDGAttribute* sourceAttri } } } - // Partition aleatoire des valeurs dans le cas symbolique - // CH IV Begin - else if (sourceAttribute->GetAttributeType() == KWType::Symbol) - // CH IV End + // Partition aleatoire des valeurs dans le cas d'un attribut groupable + else { + assert(KWType::IsCoclusteringGroupableType(sourceAttribute->GetAttributeType())); + // S'il y a moins de valeurs que de partie a constituer, on recopie directement les meme parties if (sourceAttribute->GetPartNumber() <= mandatoryAttribute->GetPartNumber() + nAddedPartNumber) { @@ -3068,8 +2957,9 @@ void KWDataGridManager::AddAttributeRandomParts(const KWDGAttribute* sourceAttri // Sinon, partitionnement aleatoire des parties sources else { - // Tri des parties sources synchronisee selon les parties mandatorys - SortAttributeParts(sourceAttribute, mandatoryAttribute, &oaSourceParts, &oaMandatoryParts); + // Tri des parties sources synchronisee selon les parties mandatory + SortAttributePartsByTargetGroups(sourceAttribute, mandatoryAttribute, &oaSourceParts, + &oaMandatoryParts); // Reinitialisation d'un vecteur des index des valeurs // Ce vecteur permettra de choisir des bornes de partition apres permutation aleatoire @@ -3126,78 +3016,6 @@ void KWDataGridManager::AddAttributeRandomParts(const KWDGAttribute* sourceAttri assert(targetAttribute->GetPartNumber() >= mandatoryAttribute->GetPartNumber()); } } - // CH IV Begin - else - { - // S'il y a moins de parties sources que de partie a constituer, on recopie directement les meme parties - if (sourceAttribute->GetPartNumber() <= mandatoryAttribute->GetPartNumber() + nAddedPartNumber) - { - // Transfert du parametrage des parties de l'attribut - InitialiseAttributeParts(sourceAttribute, targetAttribute); - } - // Sinon, partitionnement aleatoire des parties sources - else - { - // Tri des parties sources synchronisee selon les parties mandatorys - SortVarPartAttributeParts(sourceAttribute, mandatoryAttribute, &oaSourceParts, - &oaMandatoryParts); - - // Reinitialisation d'un vecteur des index des valeurs - // Ce vecteur permettra de choisir des bornes de partition apres permutation aleatoire - ivValueIndexes.SetSize(oaSourceParts.GetSize()); - for (n = 0; n < ivValueIndexes.GetSize() - 1; n++) - ivValueIndexes.SetAt(n, n); - - // Recherche d'une ensemble de "bornes" de parties aleatoires a ajouter - ivValueIndexes.Shuffle(); - ivValueIndexes.SetSize(nAddedPartNumber); - ivValueIndexes.Sort(); - - // Creation de la partition cible - targetPart = NULL; - nTargetSplit = 0; - mandatoryPart = NULL; - for (nSourcePart = 0; nSourcePart < oaSourceParts.GetSize(); nSourcePart++) - { - sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSourcePart)); - mandatoryPart = cast(KWDGPart*, oaMandatoryParts.GetAt(nSourcePart)); - - // Creation si necessaire d'une partie cible - if (targetPart == NULL) - { - targetPart = targetAttribute->AddPart(); - - // Reinitialisation de ses valeurs - targetPart->GetVarPartSet()->CopyFrom(sourcePart->GetVarPartSet()); - } - // Sinon, mise a jour des valeurs de la partie en cours - else - { - targetPart->GetVarPartSet()->UpgradeFrom(sourcePart->GetVarPartSet()); - } - - // La partie cible est valide si elle finalise une partie mandatorye - if (nSourcePart == oaSourceParts.GetSize() - 1 or - mandatoryPart != cast(KWDGPart*, oaMandatoryParts.GetAt(nSourcePart + 1))) - { - // On reinitialise l'indicateur de creation d'intervalle cible - targetPart = NULL; - } - - // La partie cible est valide si son nombre de partie sources est atteint - if (nTargetSplit < ivValueIndexes.GetSize() and - nSourcePart >= ivValueIndexes.GetAt(nTargetSplit)) - { - nTargetSplit++; - - // On reinitialise l'indicateur de creation d'intervalle cible - targetPart = NULL; - } - } - assert(targetAttribute->GetPartNumber() >= mandatoryAttribute->GetPartNumber()); - } - } - // CH IV End } void KWDataGridManager::InitialiseAttributeGranularizedParts(const KWDGAttribute* sourceAttribute, @@ -3223,18 +3041,12 @@ void KWDataGridManager::InitialiseAttributeGranularizedParts(const KWDGAttribute sourceAttribute, targetAttribute, nGranularity, cast(KWQuantileIntervalBuilder*, quantileBuilder)); } - // Granularisation dans le cas symbolique - // CH IV Begin - else if (sourceAttribute->GetAttributeType() == KWType::Symbol) - { - InitialiseAttributeGranularizedSymbolParts(sourceAttribute, targetAttribute, nGranularity, - cast(KWQuantileGroupBuilder*, quantileBuilder)); - } - // Granularisation d'un attribut de type VarPart + // Granularisation dans le cas d'un attribut groupable else { - InitialiseAttributeGranularizedVarPartParts(sourceAttribute, targetAttribute, nGranularity, - cast(KWQuantileGroupBuilder*, quantileBuilder)); + assert(KWType::IsCoclusteringGroupableType(sourceAttribute->GetAttributeType())); + InitialiseAttributeGranularizedGroupableParts(sourceAttribute, targetAttribute, nGranularity, + cast(KWQuantileGroupBuilder*, quantileBuilder)); } } } @@ -3332,9 +3144,9 @@ void KWDataGridManager::InitialiseAttributeGranularizedContinuousParts( targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); } -void KWDataGridManager::InitialiseAttributeGranularizedSymbolParts(const KWDGAttribute* sourceAttribute, - KWDGAttribute* targetAttribute, int nGranularity, - KWQuantileGroupBuilder* quantileGroupBuilder) const +void KWDataGridManager::InitialiseAttributeGranularizedGroupableParts( + const KWDGAttribute* sourceAttribute, KWDGAttribute* targetAttribute, int nGranularity, + KWQuantileGroupBuilder* quantileGroupBuilder) const { ObjectArray oaSourceParts; KWDGPart* sourcePart; @@ -3347,6 +3159,7 @@ void KWDataGridManager::InitialiseAttributeGranularizedSymbolParts(const KWDGAtt int nSourceIndex; require(CheckAttributesConsistency(sourceAttribute, targetAttribute)); + require(KWType::IsCoclusteringGroupableType(sourceAttribute->GetAttributeType())); require(not targetAttribute->GetAttributeTargetFunction()); require(quantileGroupBuilder != NULL); @@ -3364,7 +3177,6 @@ void KWDataGridManager::InitialiseAttributeGranularizedSymbolParts(const KWDGAtt InitialiseAttributeParts(sourceAttribute, targetAttribute); targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); } - // Granularisation else { @@ -3374,17 +3186,19 @@ void KWDataGridManager::InitialiseAttributeGranularizedSymbolParts(const KWDGAtt // Cas du nombre de partiles associe a la granularite maximale if (nPartileNumber == nValueNumber) // Seuillage de nPartileNumber au nombre de partiles associe a la granularite precedente - // pour que la granularisation rassemble les eventuelles valeurs sources - // singletons dans le fourre-tout + // pour que la granularisation rassemble les eventuelles valeurs sources singletons dans le fourre-tout // Pour G tel que 2^G < N <= 2^(G+1) on aura 1 < N/2^G <= 2 c'est a dire un effectif minimal par // partile de 2 (donc pas de singleton apres granularisation) + // Cas VarPart: + // Les parties de variable categorielles ne contiennent pas de singletons qui sont + // deja groupes dans un fourre-tout lors du pre-partitionnement. En revanche les parties de + // variable numeriques peuvent contenir des singletons : intervalles d'effectif 1 nPartileNumber = (int)pow(2, nGranularity - 1); // Calcul des quantiles quantileGroupBuilder->ComputeQuantiles(nPartileNumber); - // Initialisation du nombre effectif de partiles (peut etre inferieur au nombre theorique du fait de - // doublons) + // Initialisation du nombre effectif de partiles (peut etre inferieur au nombre theorique du fait de doublons) nActualPartileNumber = quantileGroupBuilder->GetGroupNumber(); // Creation des partiles @@ -3404,129 +3218,33 @@ void KWDataGridManager::InitialiseAttributeGranularizedSymbolParts(const KWDGAtt targetPart->GetValueSet()->UpgradeFrom(sourcePart->GetValueSet()); } - // CH IV Begin // Cas de la granularisation d'un attribut interne dans un attribut de grille de type VarPart if (sourceAttribute->IsInnerAttribute()) { + assert(sourceAttribute->GetAttributeType() == KWType::Symbol); + // Memorisation de l'effectif de la partie interne // L'effectif des parties des attributs de grille est lui calcule a partir des cellules targetPart->SetPartFrequency(quantileGroupBuilder->GetGroupFrequencyAt(nPartileIndex)); } - // CH IV End // Compression et memorisation du fourre-tout si necessaire (mode supervise, attribut non cible) // La partie qui contient la StarValue est compressee uniquement si elle contient plus d'une // modalite (cas d'un vrai fourre-tout) - if (targetPart->GetValueSet()->IsDefaultPart() and - targetPart->GetValueSet()->GetValueNumber() > 1 and - IsSupervisedInputAttribute(targetAttribute)) - { - // Compression du fourre-tout et memorisation de ses valeurs - cleanedValueSet = - cast(KWDGSymbolValueSet*, targetPart->GetValueSet())->ConvertToCleanedValueSet(); - targetAttribute->InitializeCatchAllValueSet(cleanedValueSet); - delete cleanedValueSet; - } - } - } - - // Cas d'un attribut explicatif dans le cadre d'une analyse supervisee - // Mise a jour du parametrage du nombre de partiles par le nombre effectif de groupes distincts - if (IsSupervisedInputAttribute(targetAttribute)) - targetAttribute->SetGranularizedValueNumber(nActualPartileNumber); - // Sinon, la granularisation n'est qu'un procede de construction d'une grille initiale - else - targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); -} - -void KWDataGridManager::InitialiseAttributeGranularizedVarPartParts(const KWDGAttribute* sourceAttribute, - KWDGAttribute* targetAttribute, int nGranularity, - KWQuantileGroupBuilder* quantileGroupBuilder) const -{ - ObjectArray oaSourceParts; - KWDGPart* sourcePart; - KWDGPart* targetPart; - int nValueNumber; - int nPartileNumber; - int nActualPartileNumber; - int nPartileIndex; - int nSourceIndex; - - require(CheckAttributesConsistency(sourceAttribute, targetAttribute)); - require(not targetAttribute->GetAttributeTargetFunction()); - require(quantileGroupBuilder != NULL); - - // Nombre potentiel de partiles associes a cette granularite - nValueNumber = sourceDataGrid->GetGridFrequency(); - nPartileNumber = (int)pow(2, nGranularity); - if (nPartileNumber > nValueNumber) - nPartileNumber = nValueNumber; - // Initialisation - nActualPartileNumber = nPartileNumber; - - // Cas ou la granularisation n'est pas appliquee : non prise en compte de la granularite - if (nGranularity == 0) - { - InitialiseAttributeParts(sourceAttribute, targetAttribute); - targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); - } - // Granularisation - else - { - // Export des parties de l'attribut source - sourceAttribute->ExportParts(&oaSourceParts); - - // Cas du nombre de partiles associe a la granularite maximale - if (nPartileNumber == nValueNumber) - // Seuillage de nPartileNumber au nombre de partiles associe a la granularite precedente - // pour que la granularisation rassemble les eventuelles parties de variables singletons dans un - // fourre-tout Les parties de variable categorielles ne contiennent pas de singletons qui sont - // deja groupes dans un fourre-tout lors du pre-partitionnement En revanche les parties de - // variable numeriques peuvent contenir des singletons : intervalles d'effectif 1 Pour G tel que - // 2^G < N <= 2^(G+1) on aura 1 < N/2^G <= 2 c'est a dire un effectif minimal par partile de 2 - // (donc pas de singleton apres granularisation) - nPartileNumber = (int)pow(2, nGranularity - 1); - - // Calcul des quantiles - quantileGroupBuilder->ComputeQuantiles(nPartileNumber); - - // Initialisation du nombre effectif de partiles (peut etre inferieur au nombre theorique du fait de - // doublons) - nActualPartileNumber = quantileGroupBuilder->GetGroupNumber(); - - // Creation des partiles - for (nPartileIndex = 0; nPartileIndex < nActualPartileNumber; nPartileIndex++) - { - targetPart = targetAttribute->AddPart(); - - // Parcours des instances du partile - for (nSourceIndex = quantileGroupBuilder->GetGroupFirstValueIndexAt(nPartileIndex); - nSourceIndex <= quantileGroupBuilder->GetGroupLastValueIndexAt(nPartileIndex); - nSourceIndex++) + // Ce cas est traite uniquement dans le cas Symbol + if (sourceAttribute->GetAttributeType() == KWType::Symbol) { - // Extraction de la partie a ajouter dans le groupe - sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSourceIndex)); - - // Ajout de ses valeurs - targetPart->GetVarPartSet()->UpgradeFrom(sourcePart->GetVarPartSet()); + if (targetPart->GetValueSet()->IsDefaultPart() and + targetPart->GetValueSet()->GetValueNumber() > 1 and + IsSupervisedInputAttribute(targetAttribute)) + { + // Compression du fourre-tout et memorisation de ses valeurs + cleanedValueSet = cast(KWDGSymbolValueSet*, targetPart->GetValueSet()) + ->ConvertToCleanedValueSet(); + targetAttribute->InitializeCatchAllValueSet(cleanedValueSet); + delete cleanedValueSet; + } } - - // CH IV Refactoring: nettoyer lignes ci-dessous - // CH TODO Inclusion poubelle - //// Compression et memorisation du fourre-tout si necessaire (mode supervise, attribut non - /// cible) / La partie qui contient la StarValue est compressee uniquement si elle contient plus - /// d'une modalite (cas d'un vrai fourre-tout) - // if ((targetDataGrid->GetTargetValueNumber() > 0 or(targetDataGrid->GetTargetAttribute() != - // NULL and not sourceAttribute->GetAttributeTargetFunction())) and - // targetPart->GetVarPartSet()->IsDefaultPart() and - // targetPart->GetVarPartSet()->GetValueNumber() > 1) - //{ - // // Compression du fourre-tout et memorisation de ses valeurs - // targetAttribute->SetCatchAllValueSet(targetPart->GetValueSet()->ConvertToCleanedValueSet()); - // } - // Tri des valeurs du fourre tout - // if (targetPart->GetVarPartSet()->IsDefaultPart()) - // targetPart->GetVarPartSet()->SortValues(); } } @@ -3793,12 +3511,8 @@ double KWDataGridManager::MergePartsForVarPartAttributes(KWDataGrid* targetDataG targetVarPartAttribute->GetInitialValueNumber() - 1); // Transfert des valeurs de la partie suivante - if (currentValue->GetVarPart()->GetPartType() == KWType::Continuous) - currentValue->GetVarPart()->GetInterval()->Import( - nextValue->GetVarPart()->GetInterval()); - else - currentValue->GetVarPart()->GetValueSet()->Import( - nextValue->GetVarPart()->GetValueSet()); + currentValue->GetVarPart()->GetPartValues()->Import( + nextValue->GetVarPart()->GetPartValues()); // Cumul des effectifs currentValue->GetVarPart()->SetPartFrequency( @@ -3876,9 +3590,10 @@ void KWDataGridManager::CreateAttributeQuantileBuilder(const KWDGAttribute* attr nMaxPartNumber = attribute->GetPartNumber(); } // Cas d'un attribut categoriel ou de type VarPart - //DDDSIMPLIFY else { + assert(KWType::IsCoclusteringGroupableType(attribute->GetAttributeType())); + // Creation du vecteur des frequences par parties bSingleton = false; nMaxPartNumber = 0; @@ -4000,94 +3715,10 @@ void KWDataGridManager::ExportAttributeSymbolValueFrequencies(KWDGAttribute* tar defaultValue->SetValueFrequency(nInstanceNumber - nTotalValueFrequency); } -void KWDataGridManager::SortAttributeParts(const KWDGAttribute* sourceAttribute, KWDGAttribute* groupedAttribute, - ObjectArray* oaSortedSourceParts, ObjectArray* oaSortedGroupedParts) const -{ - boolean bIsIndexed; - ObjectArray oaSourceParts; - ObjectArray oaAssociations; - KWSortableSymbol* association; - int nSource; - int n; - KWDGPart* sourcePart; - KWDGPart* groupedPart; - - require(sourceAttribute != NULL); - require(groupedAttribute != NULL); - require(sourceAttribute->Check()); - require(groupedAttribute->Check()); - require(oaSortedSourceParts != NULL); - require(oaSortedSourceParts->GetSize() == 0); - require(oaSortedGroupedParts != NULL); - require(oaSortedGroupedParts->GetSize() == 0); - - // Indexation si necessaire de l'attribut groupe - bIsIndexed = groupedAttribute->IsIndexed(); - if (not bIsIndexed) - groupedAttribute->BuildIndexingStructure(); - - // On exporte les parties sources dans un tableau - sourceAttribute->ExportParts(&oaSourceParts); - - // Initialisation d'un tableau d'associations entre index de partie source et - // (premiere) valeur de groupe source - oaAssociations.SetSize(oaSourceParts.GetSize()); - for (nSource = 0; nSource < oaSourceParts.GetSize(); nSource++) - { - sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSource)); - - // Recherche de la partie groupee correspondante - groupedPart = - groupedAttribute->LookupSymbolPart(sourcePart->GetValueSet()->GetHeadValue()->GetSymbolValue()); - - // Creation de l'association entre index de partie et premiere valeur du groupe - association = new KWSortableSymbol; - oaAssociations.SetAt(nSource, association); - association->SetIndex(nSource); - association->SetSortValue(groupedPart->GetValueSet()->GetHeadValue()->GetSymbolValue()); - } - - // Tri des association, apres une randomisation pour avoir un ordre aleatoire par groupe - oaAssociations.Shuffle(); - oaAssociations.SetCompareFunction(KWSortableSymbolCompareValue); - oaAssociations.Sort(); - - // On range dans le tableau en sortie les parties sources, triees par groupe - // (en fait, par leur premiere valeur, ce qui est equivalent), et leur groupe associe - oaSortedSourceParts->SetSize(oaSourceParts.GetSize()); - oaSortedGroupedParts->SetSize(oaSourceParts.GetSize()); - for (n = 0; n < oaAssociations.GetSize(); n++) - { - association = cast(KWSortableSymbol*, oaAssociations.GetAt(n)); - - // Recherche de la partie source - nSource = association->GetIndex(); - sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSource)); - - // Recherche de la partie groupee correspondante - groupedPart = - groupedAttribute->LookupSymbolPart(sourcePart->GetValueSet()->GetHeadValue()->GetSymbolValue()); - - // Rangement dans les tableaux en sortie - oaSortedSourceParts->SetAt(n, sourcePart); - oaSortedGroupedParts->SetAt(n, groupedPart); - } - - // Nettoyage du tableau d'indexation - oaAssociations.DeleteAll(); - - // Nettoyage eventuel de l'indexation - if (not bIsIndexed) - groupedAttribute->DeleteIndexingStructure(); - - ensure(oaSortedSourceParts->GetSize() == sourceAttribute->GetPartNumber()); - ensure(oaSortedGroupedParts->GetSize() == sourceAttribute->GetPartNumber()); -} - -// CH IV Begin -void KWDataGridManager::SortVarPartAttributeParts(const KWDGAttribute* sourceAttribute, KWDGAttribute* groupedAttribute, - ObjectArray* oaSortedSourceParts, - ObjectArray* oaSortedGroupedParts) const +void KWDataGridManager::SortAttributePartsByTargetGroups(const KWDGAttribute* sourceAttribute, + KWDGAttribute* groupedAttribute, + ObjectArray* oaSortedSourceParts, + ObjectArray* oaSortedGroupedParts) const { boolean bIsIndexed; ObjectArray oaSourceParts; @@ -4102,6 +3733,8 @@ void KWDataGridManager::SortVarPartAttributeParts(const KWDGAttribute* sourceAtt require(groupedAttribute != NULL); require(sourceAttribute->Check()); require(groupedAttribute->Check()); + require(KWType::IsCoclusteringGroupableType(sourceAttribute->GetAttributeType())); + require(sourceAttribute->GetAttributeType() == groupedAttribute->GetAttributeType()); require(oaSortedSourceParts != NULL); require(oaSortedSourceParts->GetSize() == 0); require(oaSortedGroupedParts != NULL); @@ -4123,18 +3756,26 @@ void KWDataGridManager::SortVarPartAttributeParts(const KWDGAttribute* sourceAtt sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSource)); // Recherche de la partie groupee correspondante - groupedPart = - groupedAttribute->LookupVarPart(sourcePart->GetVarPartSet()->GetHeadValue()->GetVarPart()); + groupedPart = groupedAttribute->LookupGroupablePart(sourcePart->GetValueSet()->GetHeadValue()); // Creation de l'association entre index de partie et premiere valeur du groupe association = new KWSortableObject; oaAssociations.SetAt(nSource, association); association->SetIndex(nSource); - association->SetSortValue(groupedPart->GetVarPartSet()->GetHeadValue()); + association->SetSortValue(groupedPart->GetValueSet()->GetHeadValue()); } // Tri des association, apres une randomisation pour avoir un ordre aleatoire par groupe - oaAssociations.SetCompareFunction(KWSortableObjectCompareVarPart); + // CH IV Refactoring: Code specifique suite a refactoring et unification avec l'ancienne methode SortVarPartAttributeParts + // CH IV Refactoring: Pourquoi le Shuffle est fait dans le cas VarPart et pas VarPart? + // CH IV Refactoring: Probleme potentiel supplementaire, faire un shuffle suivi d'un sort entraine des instabilite entre + // CH IV Refactoring: windows et linux, car le Sort de windows ne semble pas etre un "stable sort" (qui garantit qu'en + // CH IV Refactoring: cas d'egalite, les items sont dans le meme ordre que l'ordre initial) + if (sourceAttribute->GetAttributeType() == KWType::Symbol) + oaAssociations.Shuffle(); + + // Tri des association, apres une randomisation pour avoir un ordre aleatoire par groupe + oaAssociations.SetCompareFunction(KWSortableObjectComparePartValue); oaAssociations.Sort(); // On range dans le tableau en sortie les parties sources, triees par groupe @@ -4150,8 +3791,7 @@ void KWDataGridManager::SortVarPartAttributeParts(const KWDGAttribute* sourceAtt sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSource)); // Recherche de la partie groupee correspondante - groupedPart = - groupedAttribute->LookupVarPart(sourcePart->GetVarPartSet()->GetHeadValue()->GetVarPart()); + groupedPart = groupedAttribute->LookupGroupablePart(sourcePart->GetValueSet()->GetHeadValue()); // Rangement dans les tableaux en sortie oaSortedSourceParts->SetAt(n, sourcePart); @@ -4168,7 +3808,7 @@ void KWDataGridManager::SortVarPartAttributeParts(const KWDGAttribute* sourceAtt ensure(oaSortedSourceParts->GetSize() == sourceAttribute->GetPartNumber()); ensure(oaSortedGroupedParts->GetSize() == sourceAttribute->GetPartNumber()); } -// CH IV End + void KWDataGridManager::InitRandomIndexVector(IntVector* ivRandomIndexes, int nIndexNumber, int nMaxIndex) const { double dInitialSize; diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.h b/src/Learning/KWDataPreparation/KWDataGridManager.h index 35360fe43..4256c55dc 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.h +++ b/src/Learning/KWDataPreparation/KWDataGridManager.h @@ -207,8 +207,8 @@ class KWDataGridManager : public Object boolean BuildDataGridFromClassStats(KWDataGrid* targetDataGrid, KWClassStats* classStats) const; // Creation d'un attribut de grille a partir d'une partition univariee deja stockee - // Le parametrage de l'attribut source (granularite, poubelle si categoriel) est copie vers celui de l'attribut - // de DataGrid + // Le parametrage de l'attribut source (granularite, poubelle si categoriel) est copie + // vers celui de l'attribut de DataGrid void BuildDataGridAttributeFromUnivariateStats(KWDGAttribute* targetAttribute, KWAttributeStats* attributeStats) const; @@ -333,12 +333,9 @@ class KWDataGridManager : public Object void InitialiseAttributeGranularizedContinuousParts(const KWDGAttribute* sourceAttribute, KWDGAttribute* targetAttribute, int nGranularity, KWQuantileIntervalBuilder* quantileIntervalBuilder) const; - void InitialiseAttributeGranularizedSymbolParts(const KWDGAttribute* sourceAttribute, - KWDGAttribute* targetAttribute, int nGranularity, - KWQuantileGroupBuilder* quantileGroupBuilder) const; - void InitialiseAttributeGranularizedVarPartParts(const KWDGAttribute* sourceAttribute, - KWDGAttribute* targetAttribute, int nGranularity, - KWQuantileGroupBuilder* quantileGroupBuilder) const; + void InitialiseAttributeGranularizedGroupableParts(const KWDGAttribute* sourceAttribute, + KWDGAttribute* targetAttribute, int nGranularity, + KWQuantileGroupBuilder* quantileGroupBuilder) const; // Test si on est dans le cas d'un attribut source pour l'analyse supervisee boolean IsSupervisedInputAttribute(const KWDGAttribute* attribute) const; @@ -376,24 +373,15 @@ class KWDataGridManager : public Object // La valeur speciale recoit pour effectif l'ensemble des effectifs manquants void ExportAttributeSymbolValueFrequencies(KWDGAttribute* targetAttribute) const; - // Tri des parties d'un attribut source symbolique, selon les groupements - // de ces parties dans un attribut cible compatible + // Tri des parties d'un attribut source Symbol ou VarPart, selon les groupements + // de ces parties dans un attribut cible groupe compatible // Les parties sources se trouvent dans le tableau resultat, trie correctement // par groupe, et en ordre aleatoire a l'interieur de chaque groupe - // oaSortedSourceParts: parties sources triees par groupe + // oaSortedSourceParts: parties sources triees par groupe cible // oaSortedGroupedParts: parties groupees associees aux parties source - void SortAttributeParts(const KWDGAttribute* sourceAttribute, KWDGAttribute* groupedAttribute, - ObjectArray* oaSortedSourceParts, ObjectArray* oaSortedGroupedParts) const; - - // Tri des parties d'un attribut source de type VarPart, selon les groupements - // de ces parties dans un attribut cible compatible - // Les parties sources se trouvent dans le tableau resultat, trie correctement - // par groupe, et en ordre aleatoire a l'interieur de chaque groupe - // oaSortedSourceParts: parties sources triees par groupe - // oaSortedGroupedParts: parties groupees associees aux parties source - void SortVarPartAttributeParts(const KWDGAttribute* sourceAttribute, KWDGAttribute* groupedAttribute, - ObjectArray* oaSortedSourceParts, ObjectArray* oaSortedGroupedParts) const; - // CH IV End + void SortAttributePartsByTargetGroups(const KWDGAttribute* sourceAttribute, KWDGAttribute* groupedAttribute, + ObjectArray* oaSortedSourceParts, + ObjectArray* oaSortedGroupedParts) const; // Initialisation d'un vecteur de nombres aleatoires compris entre 0 et max exclu, ordonnes et tous differents void InitRandomIndexVector(IntVector* ivRandomIndexes, int nIndexNumber, int nMaxIndex) const; diff --git a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp index a290f6955..33c3511f7 100644 --- a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp @@ -414,11 +414,8 @@ void KWDataGridMerger::InitializeAllPartLists() attribute = GetAttributeAt(nAttribute); attributeM = cast(KWDGMAttribute*, attribute); - // CH IV Begin - // Cas d'un attribut categoriel ou varParts (donc eligible au groupe poubelle) - //DDDSIMPLIFY - if (attribute->GetAttributeType() == KWType::Symbol or attribute->GetAttributeType() == KWType::VarPart) - // CH IV End + // Cas d'un attribut groupable (donc eligible au groupe poubelle) + if (KWType::IsCoclusteringGroupableType(attribute->GetAttributeType())) { // Parcours des parties de l'attribut part = attribute->GetHeadPart(); @@ -876,12 +873,8 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b nNewInformativeAttributeNumber) - GetCost(); - // CH IV Begin - // Cas d'un attribut categoriel ou VarPart pour lequel un groupe poubelle est envisageable - //DDDSIMPLIFY - if (attribute->GetAttributeType() == KWType::Symbol or - attribute->GetAttributeType() == KWType::VarPart) - // CH IV End + // Cas d'un attribut groupable pour lequel un groupe poubelle est envisageable + if (KWType::IsCoclusteringGroupableType(attribute->GetAttributeType())) { // Memorisation de la presence d'un groupe poubelle pour restitution ulterieure bGarbageBeforeMerge = attributeM->GetGarbageModalityNumber() > 0; @@ -935,13 +928,12 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b bestPartMerge = partMerge; } - // CH IV Begin // Cas ou l'on envisage un groupe poubelle : // attribut categoriel avec un nombre de parties apres fusion est >= 3 (sinon il ne peut pas y // avoir de groupe poubelle) attribut VarPart ET mode poubelle pour ce type d'attributs + nbre - // de parties apres fusion >=3 CH AB voir l'impact de la variation d'un cout avec groupe - // poubelle lorsque le nombre de PV est reduit du fait de la post-fusion - //DDDSIMPLIFY + // de parties apres fusion >=3 + // CH AB voir l'impact de la variation d'un cout avec groupe poubelle lorsque le nombre + // de PV est reduit du fait de la post-fusion if ((attribute->GetAttributeType() == KWType::Symbol or (attribute->GetAttributeType() == KWType::VarPart and GetVarPartAttributeGarbage())) and attributeM->GetPartNumber() - 1 >= 3) @@ -951,61 +943,24 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b // Cas d'un attribut AVEC groupe poubelle apres fusion // Cas ou les deux parties fusionnees deviennent le groupe poubelle - //DDDSIMPLIFY - if (attribute->GetAttributeType() == KWType::Symbol) + if (partMerge->GetPart1()->GetValueSet()->GetValueNumber() + + partMerge->GetPart2()->GetValueSet()->GetValueNumber() > + cast(KWDGPart*, attributeM->slPartValueNumbers->GetHead()) + ->GetValueSet() + ->GetValueNumber()) { - if (partMerge->GetPart1()->GetValueSet()->GetValueNumber() + - partMerge->GetPart2()->GetValueSet()->GetValueNumber() > - cast(KWDGPart*, attributeM->slPartValueNumbers->GetHead()) - ->GetValueSet() - ->GetValueNumber()) - { - // Creation du nouveau groupe poubelle - garbagePart = new KWDGPart; - // Cas categoriel - - garbagePart->SetPartType(KWType::Symbol); - // Copie des valeurs de la partie 1 de la fusion - garbagePart->GetValueSet()->CopyFrom( - partMerge->GetPart1()->GetValueSet()); - // Mise a jour avec les valeurs de la partie 2 de la fusion - garbagePart->GetValueSet()->UpgradeFrom( - partMerge->GetPart2()->GetValueSet()); - - attributeM->SetGarbagePart(cast(KWDGPart*, garbagePart)); - } - // Sinon : on garde le meme groupe poubelle - else - attributeM->SetGarbagePart( - cast(KWDGPart*, attributeM->slPartValueNumbers->GetHead())); - } - else if (attribute->GetAttributeType() == KWType::VarPart) - { - if (partMerge->GetPart1()->GetVarPartSet()->GetValueNumber() + - partMerge->GetPart2()->GetVarPartSet()->GetValueNumber() > - cast(KWDGPart*, attributeM->slPartValueNumbers->GetHead()) - ->GetVarPartSet() - ->GetValueNumber()) - { - // Creation du nouveau groupe poubelle - garbagePart = new KWDGPart; - - garbagePart->SetPartType(KWType::VarPart); - // Copie des valeurs de la partie 1 de la fusion - garbagePart->GetVarPartSet()->CopyFrom( - partMerge->GetPart1()->GetVarPartSet()); - // Mise a jour avec les valeurs de la partie 2 de la fusion - garbagePart->GetVarPartSet()->UpgradeFrom( - partMerge->GetPart2()->GetVarPartSet()); - - attributeM->SetGarbagePart(cast(KWDGPart*, garbagePart)); - } - // Sinon : on garde le meme groupe poubelle - else - attributeM->SetGarbagePart( - cast(KWDGPart*, attributeM->slPartValueNumbers->GetHead())); + // Creation du nouveau groupe poubelle + garbagePart = new KWDGPart; + // Cas categoriel + + garbagePart->SetPartType(attribute->GetAttributeType()); + // Copie des valeurs de la partie 1 de la fusion + garbagePart->GetValueSet()->CopyFrom(partMerge->GetPart1()->GetValueSet()); + // Mise a jour avec les valeurs de la partie 2 de la fusion + garbagePart->GetValueSet()->UpgradeFrom(partMerge->GetPart2()->GetValueSet()); + + attributeM->SetGarbagePart(cast(KWDGPart*, garbagePart)); } - // CH IV End // Sinon : on garde le meme groupe poubelle else attributeM->SetGarbagePart( @@ -1066,11 +1021,10 @@ double KWDataGridMerger::SearchBestPartMergeWithGarbageSearch(KWDGMPartMerge*& b if (garbagePart != NULL) delete garbagePart; } - // CH IV Begin - //DDDSIMPLIFY + + // Finalisation de la gestion du groupe poubelle if (attribute->GetAttributeType() == KWType::Symbol or (attribute->GetAttributeType() == KWType::VarPart and GetVarPartAttributeGarbage())) - // CH IV End { // Restitution de la poubelle initiale if (not bGarbageBeforeMerge) @@ -1918,14 +1872,14 @@ boolean KWDGMPart::Check() const { boolean bOk = true; int nTotalValueFrequency; + ALString sValueClassLabel; ALString sTmp; // Verification de base bOk = KWDGPart::Check(); - // Verification de l'ensemble de valeurs - //DDDSIMPLIFY - if (bOk and GetPartType() == KWType::Symbol) + // Verification de l'ensemble de valeurs dans le cas groupable + if (bOk and KWType::IsCoclusteringGroupableType(GetPartType())) { // Verification de la compatibilite entre l'effectif de la partie // et l'effectif cumule de ses valeurs @@ -1939,35 +1893,13 @@ boolean KWDGMPart::Check() const if (bOk and GetPartFrequency() > 0 and nTotalValueFrequency > 0 and GetPartFrequency() != nTotalValueFrequency) { + sValueClassLabel = GetValueSet()->GetHeadValue()->GetClassLabel(); AddError(sTmp + "Part frequency (" + IntToString(GetPartFrequency()) + - ") different from the cumulated frequency of its values (" + + ") different from the cumulated frequency of its " + sValueClassLabel + "s (" + IntToString(nTotalValueFrequency) + ")"); bOk = false; } } - // CH IV Begin - // Verification de l'ensemble des parties de variable - else if (bOk and GetPartType() == KWType::VarPart) - { - // Verification de la compatibilite entre l'effectif de la partie - // et l'effectif cumule de ses parties de variables - // La verification ne se fait que si la partie a un effectif non nul, - // ou si l'effectif cumule des parties de variable est non nul. - // Cela permet des verifications partielles en cours d'alimentation - // de la grille (les parties et leurs parties de variable sont creees avant - // la creation des cellules). - // La verification n'est pas faite dans tous les cas dans la classe ancetre - nTotalValueFrequency = GetValueSet()->ComputeTotalFrequency(); - if (bOk and GetPartFrequency() > 0 and nTotalValueFrequency > 0 and - GetPartFrequency() != nTotalValueFrequency) - { - AddError(sTmp + "Part frequency (" + IntToString(GetPartFrequency()) + - ") different from the cumulated frequency of its var parts (" + - IntToString(nTotalValueFrequency) + ")"); - bOk = false; - } - } - // CH IV End return bOk; } @@ -2001,20 +1933,16 @@ int KWDGMPartValueNumberCompare(const void* elem1, const void* elem2) KWDGMPart* partM1; KWDGMPart* partM2; + // Acces aux parties partM1 = cast(KWDGMPart*, *(Object**)elem1); partM2 = cast(KWDGMPart*, *(Object**)elem2); + assert(KWType::IsCoclusteringGroupableType(partM1->GetPartType())); + assert(partM1->GetPartType() == partM1->GetPartType()); - // CH IV Begin - assert(partM1->GetPartType() == KWType::Symbol or partM1->GetPartType() == KWType::VarPart); - assert(partM2->GetPartType() == KWType::Symbol or partM2->GetPartType() == KWType::VarPart); + // CH IV Refactoring: faut-il ajouter une fonction de comparaison secondaire sur les valeurs en cas degalite? - // Comparaison du nombre de modalites par valeurs decroissantes - //DDDSIMPLIFY - if (partM1->GetPartType() == KWType::Symbol) - return (partM2->GetValueSet()->GetValueNumber() - partM1->GetValueSet()->GetValueNumber()); - else - return (partM2->GetVarPartSet()->GetValueNumber() - partM1->GetVarPartSet()->GetValueNumber()); - // CH IV End + // Comparaison du nombre de modalites par valeurs decroissantes dans le cas groupable + return partM2->GetValueSet()->GetValueNumber() - partM1->GetValueSet()->GetValueNumber(); } ////////////////////////////////////////////////////////////////////////////// // Classe KWDGMCell diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp index d1f2935b8..98c78e38a 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp @@ -143,24 +143,16 @@ double KWDataGridPostOptimizer::PostOptimizeDataGrid(const KWDataGrid* initialDa dCost = dataGridUnivariateDiscretizer.PostOptimizeDataGrid( univariateInitialDataGrid, dataGridCosts, optimizedDataGrid, bDeepPostOptimization); } - // Groupement de valeur univariee si attribut Symbol - else if (dataGridAttribute->GetAttributeType() == KWType::Symbol) - { - dataGridUnivariateGrouper.SetPostOptimizationAttributeName( - dataGridAttribute->GetAttributeName()); - dCost = dataGridUnivariateGrouper.PostOptimizeDataGrid( - univariateInitialDataGrid, dataGridCosts, optimizedDataGrid, bDeepPostOptimization); - } - // CH IV Begin - // Groupement de parties de variable si attribut VarPart + // Groupement de valeur univariee si attribut groupable else { + assert(KWType::IsCoclusteringGroupableType(dataGridAttribute->GetAttributeType())); + dataGridUnivariateGrouper.SetPostOptimizationAttributeName( dataGridAttribute->GetAttributeName()); dCost = dataGridUnivariateGrouper.PostOptimizeDataGrid( univariateInitialDataGrid, dataGridCosts, optimizedDataGrid, bDeepPostOptimization); } - // CH IV End // Le cout precedent devra etre correct assert(dCost * (1 - dEpsilon) < dataGridCosts->ComputeDataGridTotalCost(optimizedDataGrid)); @@ -1432,8 +1424,7 @@ void KWDGPOGrouper::InitializePartFrequencyVector(KWDGPOPartFrequencyVector* par require(partFrequencyVector->ComputeTotalFrequency() == 0); require(partFrequencyVector->GetModalityNumber() == 0); require(part != NULL); - //DDDSIMPLIFY - require(part->GetPartType() == KWType::Symbol or part->GetPartType() == KWType::VarPart); + require(KWType::IsCoclusteringGroupableType(part->GetPartType())); require(part->GetAttribute()->GetAttributeName() == GetPostOptimizationAttributeName()); require(nkdHashCells != NULL); @@ -1441,13 +1432,7 @@ void KWDGPOGrouper::InitializePartFrequencyVector(KWDGPOPartFrequencyVector* par InitializeFrequencyVector(partFrequencyVector); // Memorisation du nombre de valeurs associees a la partie - //DDDSIMPLIFY - if (part->GetPartType() == KWType::Symbol) - partFrequencyVector->SetModalityNumber(part->GetValueSet()->GetValueNumber()); - // CH IV Begin - else if (part->GetPartType() == KWType::VarPart) - partFrequencyVector->SetModalityNumber(part->GetVarPartSet()->GetValueNumber()); - // CH IV End + partFrequencyVector->SetModalityNumber(part->GetValueSet()->GetValueNumber()); // Parcours des cellules de la partie pour creer les vecteurs d'effectif par cellule cell = part->GetHeadCell(); @@ -1658,15 +1643,8 @@ void KWDGPOGrouper::InitializeGroupIndexes(IntVector* ivGroups, const KWDataGrid nIndex = 0; while (initialPart != NULL) { - // CH IV Begin - if (optimizedAttribute->GetAttributeType() == KWType::Symbol) - // Recherche de sa partie groupee correspondante - optimizedPart = optimizedAttribute->LookupSymbolPart( - initialPart->GetValueSet()->GetHeadValue()->GetSymbolValue()); - else - optimizedPart = optimizedAttribute->LookupVarPart( - initialPart->GetVarPartSet()->GetHeadValue()->GetVarPart()); - // CH IV End + // Recherche de sa partie groupee correspondante + optimizedPart = optimizedAttribute->LookupGroupablePart(initialPart->GetValueSet()->GetHeadValue()); // Memorisation de l'index de ce groupe kwsiOptimizedPartIndex = cast(KWSortableIndex*, nkdOptimizedPartIndexes.Lookup(optimizedPart)); @@ -1706,6 +1684,7 @@ int KWDGPOGrouper::InitializeGroupIndexesAndGarbageIndex(IntVector* ivGroups, co optimizedAttribute = optimizedDataGrid->SearchAttribute(GetPostOptimizationAttributeName()); check(initialAttribute); check(optimizedAttribute); + assert(KWType::IsCoclusteringGroupableType(initialAttribute->GetAttributeType())); // Memorisation des index des parties optimisees optimizedPart = optimizedAttribute->GetHeadPart(); @@ -1741,21 +1720,10 @@ int KWDGPOGrouper::InitializeGroupIndexesAndGarbageIndex(IntVector* ivGroups, co nIndex = 0; while (initialPart != NULL) { - // Recherche de sa partie groupee correspondante + assert(KWType::IsCoclusteringGroupableType(optimizedAttribute->GetAttributeType())); - // CH IV Begin - //DDDSIMPLIFY - if (optimizedAttribute->GetAttributeType() == KWType::Symbol) - { - optimizedPart = optimizedAttribute->LookupSymbolPart( - initialPart->GetValueSet()->GetHeadValue()->GetSymbolValue()); - } - else if (optimizedAttribute->GetAttributeType() == KWType::VarPart) - { - optimizedPart = optimizedAttribute->LookupVarPart( - initialPart->GetVarPartSet()->GetHeadValue()->GetVarPart()); - } - // CH IV End + // Recherche de sa partie groupee correspondante + optimizedPart = optimizedAttribute->LookupGroupablePart(initialPart->GetValueSet()->GetHeadValue()); // Memorisation de l'index de ce groupe kwsiOptimizedPartIndex = cast(KWSortableIndex*, nkdOptimizedPartIndexes.Lookup(optimizedPart)); @@ -1839,6 +1807,7 @@ void KWDGPOGrouper::UpdateDataGridWithGarbageFromGroups(KWDataGrid* optimizedDat // Acces aux attributs des grilles initiale et optimise pour l'attribut de post-optimisation initialAttribute = initialDataGrid->SearchAttribute(sPostOptimizationAttributeName); optimizedAttribute = optimizedDataGrid->SearchAttribute(sPostOptimizationAttributeName); + assert(KWType::IsCoclusteringGroupableType(initialAttribute->GetAttributeType())); // On vide la grille optimisee de ses cellules, en preservant ses attribut et leur partition optimizedDataGrid->DeleteAllCells(); @@ -1869,33 +1838,14 @@ void KWDGPOGrouper::UpdateDataGridWithGarbageFromGroups(KWDataGrid* optimizedDat // Recherche de la partie optimisee a mettre a jour optimizedPart = cast(KWDGPart*, oaOptimizedParts.GetAt(nGroup)); + assert(KWType::IsCoclusteringGroupableType(optimizedAttribute->GetAttributeType())); - // CH IV Begin - // Cas d'une partie d'un attribut categoriel - //DDDSIMPLIFY - if (optimizedPart->GetPartType() == KWType::Symbol) - { - // Mise a jour de la definition du group - optimizedPart->GetValueSet()->UpgradeFrom(initialPart->GetValueSet()); + // Mise a jour de la definition du groupe + optimizedPart->GetValueSet()->UpgradeFrom(initialPart->GetValueSet()); - // Mise a jour du groupe poubelle comme le groupe contenant le plus de modalites - if (optimizedPart->GetValueSet()->GetValueNumber() > - optimizedAttribute->GetGarbageModalityNumber()) - optimizedAttribute->SetGarbagePart(optimizedPart); - } - - // Sinon d'un attribut de type VarPart - else if (optimizedPart->GetPartType() == KWType::VarPart) - { - // Mise a jour de la definition du group - optimizedPart->GetVarPartSet()->UpgradeFrom(initialPart->GetVarPartSet()); - - // Mise a jour du groupe poubelle comme le groupe contenant le plus de parties de variables - if (optimizedPart->GetVarPartSet()->GetValueNumber() > - optimizedAttribute->GetGarbageModalityNumber()) - optimizedAttribute->SetGarbagePart(optimizedPart); - } - // CH IV End + // Mise a jour du groupe poubelle comme le groupe contenant le plus de valeurs + if (optimizedPart->GetValueSet()->GetValueNumber() > optimizedAttribute->GetGarbageModalityNumber()) + optimizedAttribute->SetGarbagePart(optimizedPart); // Partie initiale suivante initialAttribute->GetNextPart(initialPart); @@ -2649,26 +2599,19 @@ double KWDataGridUnivariateCosts::ComputePartCost(const KWFrequencyVector* part) // Initialisation du parametrage de la partie partCostParameter->nPartFrequency = cast(KWDGPOPartFrequencyVector*, part)->GetFrequency(); - // Specification du nombre de valeurs pour une partie symbolique + // Specification du nombre de valeurs pour une partie d'un attribut groupable // Attention: on utilise ici un acces direct au nombre de valeur du ValueSet pour permettre // d'utiliser ce parametre dans les calculs de cout // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) - //DDDSIMPLIFY - if (partCostParameter->GetPartType() == KWType::Symbol) + if (KWType::IsCoclusteringGroupableType(partCostParameter->GetPartType())) { - // N'etant jamais la partie par defaut, on a le TrueValueNumber egal au ValueNumber + // On peut modifier directement le nombre de valeur de facon generique, puisque que l'on est en mode emule + // via les sous-classes KWDGSymbolValueSetCostParameter et KWDGVarPartSetCostParameter + // N'etant jamais la partie par defaut en mode emule, on a le bon nombre de valeurs assert(not partCostParameter->GetValueSet()->IsDefaultPart()); - cast(KWDGSymbolValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = - cast(KWDGPOPartFrequencyVector*, part)->GetModalityNumber(); + cast(KWDGValueSet*, partCostParameter->GetValueSet()) + ->SetValueNumber(cast(KWDGPOPartFrequencyVector*, part)->GetModalityNumber()); } - // CH IV Begin - else if (partCostParameter->GetPartType() == KWType::VarPart) - { - cast(KWDGVarPartSetCostParameter*, partCostParameter->GetVarPartSet())->nValueNumber = - cast(KWDGPOPartFrequencyVector*, part)->GetModalityNumber(); - // il faut que modalityNumber ait ete alimente par le nombre de parties de variable - } - // CH IV End // Cout: cout local de la partie + somme des cout des cellules (maintenu dans la partie) dCost = @@ -2730,21 +2673,14 @@ double KWDataGridUnivariateCosts::ComputePartUnionCost(const KWFrequencyVector* // Attention: on utilise ici un acces direct au nombre de valeur du ValueSet pour permettre // d'utiliser ce parametre dans les calculs de cout // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) - //DDDSIMPLIFY - if (partCostParameter->GetPartType() == KWType::Symbol) + if (KWType::IsCoclusteringGroupableType(partCostParameter->GetPartType())) { - cast(KWDGSymbolValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = - cast(KWDGPOPartFrequencyVector*, sourcePart1)->GetModalityNumber() + - cast(KWDGPOPartFrequencyVector*, sourcePart2)->GetModalityNumber(); + // On peut modifier directement le nombre de valeur de facon generique, puisque que l'on est en mode emule + // via les sous-classes KWDGSymbolValueSetCostParameter et KWDGVarPartSetCostParameter + cast(KWDGValueSet*, partCostParameter->GetValueSet()) + ->SetValueNumber(cast(KWDGPOPartFrequencyVector*, sourcePart1)->GetModalityNumber() + + cast(KWDGPOPartFrequencyVector*, sourcePart2)->GetModalityNumber()); } - // CH IV Begin - else if (partCostParameter->GetPartType() == KWType::VarPart) - { - cast(KWDGVarPartSetCostParameter*, partCostParameter->GetVarPartSet())->nValueNumber = - cast(KWDGPOPartFrequencyVector*, sourcePart1)->GetModalityNumber() + - cast(KWDGPOPartFrequencyVector*, sourcePart2)->GetModalityNumber(); - } - // CH IV End // Cout: cout local de la partie + somme des cout des cellules (maintenu dans la partie) dCost = dataGridCosts->ComputePartCost(partCostParameter); @@ -2772,21 +2708,14 @@ double KWDataGridUnivariateCosts::ComputePartDiffCost(const KWFrequencyVector* s // Attention: on utilise ici un acces direct au nombre de valeur du ValueSet pour permettre // d'utiliser ce parametre dans les calculs de cout // (mais il y a incompletude du ValueSet, qui n'est specifie que pour ce qui est utile pour le calcul des couts) - //DDDSIMPLIFY - if (partCostParameter->GetPartType() == KWType::Symbol) + if (KWType::IsCoclusteringGroupableType(partCostParameter->GetPartType())) { - cast(KWDGSymbolValueSetCostParameter*, partCostParameter->GetValueSet())->nValueNumber = - cast(KWDGPOPartFrequencyVector*, sourcePart)->GetModalityNumber() - - cast(KWDGPOPartFrequencyVector*, removedPart)->GetModalityNumber(); + // On peut modifier directement le nombre de valeur de facon generique, puisque que l'on est en mode emule + // via les sous-classes KWDGSymbolValueSetCostParameter et KWDGVarPartSetCostParameter + cast(KWDGValueSet*, partCostParameter->GetValueSet()) + ->SetValueNumber(cast(KWDGPOPartFrequencyVector*, sourcePart)->GetModalityNumber() - + cast(KWDGPOPartFrequencyVector*, removedPart)->GetModalityNumber()); } - // CH IV Begin - if (partCostParameter->GetPartType() == KWType::VarPart) - { - cast(KWDGVarPartSetCostParameter*, partCostParameter->GetVarPartSet())->nValueNumber = - cast(KWDGPOPartFrequencyVector*, sourcePart)->GetModalityNumber() - - cast(KWDGPOPartFrequencyVector*, removedPart)->GetModalityNumber(); - } - // CH IV End // Cout: cout local de la partie + somme des cout des cellules (maintenu dans la partie) dCost = dataGridCosts->ComputePartCost(partCostParameter); @@ -2866,18 +2795,15 @@ void KWDGPartCostParameter::SetPartType(int nValue) { require(GetPartType() == KWType::Unknown); // CH IV Begin - require(KWType::IsSimple(nValue) or nValue == KWType::VarPart); + require(KWType::IsCoclusteringType(nValue)); // Creation de l'objet interval ou ensemble de valeur selon le type if (nValue == KWType::Continuous) partValues = new KWDGInterval; - //DDDSIMPLIFY else if (nValue == KWType::Symbol) partValues = new KWDGSymbolValueSetCostParameter; else partValues = new KWDGVarPartSetCostParameter; - // Avant integration : pour quel cas ? valueSet = new KWDGValueSetCostParameter; - // CH IV End ensure(GetPartType() != KWType::Unknown); } diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h index 5d77f398d..51de641b0 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h @@ -460,24 +460,20 @@ class KWDGPartCostParameter : public KWDGPart }; //////////////////////////////////////////////////////////////////////////////////////////// -// Sous-classes de ValueSet de grille servant de parametre de cout +// Sous-classes de SymbolValueSet de grille servant de parametre de cout class KWDGSymbolValueSetCostParameter : public KWDGSymbolValueSet { protected: - friend class KWDataGridUnivariateCosts; boolean GetEmulated() const override; }; //////////////////////////////////////////////////////////////////////////////////////////// -// CH IV Begin // Sous-classes de VarPartSet de grille servant de parametre de cout class KWDGVarPartSetCostParameter : public KWDGVarPartSet { protected: - friend class KWDataGridUnivariateCosts; boolean GetEmulated() const override; }; -// CH IV End //////////////////////////////////////////////////////////////////////////////////////////// // Sous-classes de cellule de grille servant de parametre de cout diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp index aeeaabf10..b6528d826 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp @@ -373,15 +373,7 @@ CCHDGPart* CCHDGAttribute::MergePart(CCHDGPart* part) } // Import des valeurs de la partie source vers la partie destination - if (GetAttributeType() == KWType::Continuous) - targetPart->GetInterval()->Import(sourcePart->GetInterval()); - // CH IV Begin - else if (GetAttributeType() == KWType::Symbol) - targetPart->GetValueSet()->Import(sourcePart->GetValueSet()); - // Cas d'un attribute de type VarPart - else - targetPart->GetVarPartSet()->Import(sourcePart->GetVarPartSet()); - // CH IV End + targetPart->GetPartValues()->Import(sourcePart->GetPartValues()); // Import des informations de la partie fusionnees targetPart->SetPartName(part->GetPartName()); diff --git a/src/Learning/MODL_Coclustering/CCLearningProblem.cpp b/src/Learning/MODL_Coclustering/CCLearningProblem.cpp index bd919195a..479fd5d3a 100644 --- a/src/Learning/MODL_Coclustering/CCLearningProblem.cpp +++ b/src/Learning/MODL_Coclustering/CCLearningProblem.cpp @@ -481,7 +481,6 @@ void CCLearningProblem::ExtractClusters(const ALString& sCoclusteringAttributeNa WriteSymbolClusters(coclusteringAttribute, fstClusterTableFile); else if (coclusteringAttribute->GetAttributeType() == KWType::Continuous) WriteContinuousClusters(coclusteringAttribute, fstClusterTableFile); - // CH IV Begin else if (coclusteringAttribute->GetAttributeType() == KWType::VarPart) { // Cas d'une variable de type VarPart : ajout d'un descriptif des parties de variable @@ -489,7 +488,6 @@ void CCLearningProblem::ExtractClusters(const ALString& sCoclusteringAttributeNa WriteVarPartClusters(coclusteringAttribute, fstClusterTableFile); WriteVarPartsInnerAttributes(coclusteringAttribute); } - // CH IV End // Ecriture du rapport bOk = FileService::CloseOutputFile(sLocalFileName, fstClusterTableFile); diff --git a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp index 02059dc93..6902fcd09 100644 --- a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp +++ b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp @@ -34,8 +34,8 @@ int main(int argc, char** argv) Global::ActivateSignalErrorManagement(); // Choix du repertoire de lancement pour le debugage sous Windows (a commenter apres fin du debug) - //DDD SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); - SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisMissingValues"); + SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); + //DDD SetWindowsDebugDir("y_CoclusteringIV_Standard", "SimplifyCoclustering"); //SetWindowsDebugDir("Standard", "Iris"); // Point d'arret sur l'allocation d'un bloc memoire From 74e236d3674233f556a500485d0fa425cc5b2682 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Mon, 11 Sep 2023 11:15:13 +0200 Subject: [PATCH 08/17] Fix case of inner attributes with only missing values in coclustering IV MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Merge des evolutions de Carine Cas d'un attribut interne n'ayant que des valeurs manquantes - LearningTest\TestCoclustering\y_CoclusteringIV_Standard\IrisMissingValues (enrichi a cet effet) - cet attribut n'a aucune partie alimentee - on le supprime des attributs internes - impacts - KWDGInnerAttributes::CleanEmptyInnerAttributes: supression des attribut internes vides - CCCoclusteringBuilder::CleanVarPartDataGrid: appel de la methode precedente KWDataGridOptimizer::OptimizeDataGrid - ajout d'un commentaire pour expliquer pourquoi la DgGranularity est toujours la même dans les log d'optimisation du coclustering Autre fonctionnalite: possibilite de copier un grille, en clonant les attributs internes de l'attribut VarPart - besoin en preparation pour le nouveau format json du coclustering IV - impacts - KWDataGridManager - CopyDataGridWithInnerAttributesCloned: variante de la methode CopyDataGrid - ExportDataGridWithInnerAttributesCloned: variante de la methode CopyDataGrid ExportDataGrid - CloneInnerAttributes: nouveau parametre targetDataGrid, pour specialiser la creation des attributs internes - KWDataGrid::NewAttribute: passe en public --- src/Learning/KWDataPreparation/KWDataGrid.cpp | 43 ++++++++--- src/Learning/KWDataPreparation/KWDataGrid.h | 13 ++-- .../KWDataPreparation/KWDataGridManager.cpp | 71 +++++++++++++++++-- .../KWDataPreparation/KWDataGridManager.h | 16 ++++- .../KWDataPreparation/KWDataGridOptimizer.cpp | 6 ++ .../KWDataGridPostOptimizer.cpp | 4 +- .../CCCoclusteringBuilder.cpp | 2 +- 7 files changed, 131 insertions(+), 24 deletions(-) diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index f84bdb9cb..0cf8fc8af 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -647,6 +647,11 @@ double KWDataGrid::ComputeMutualEntropy() return dResult; } +KWDGAttribute* KWDataGrid::NewAttribute() const +{ + return new KWDGAttribute; +} + boolean KWDataGrid::Check() const { boolean bOk = true; @@ -2195,11 +2200,6 @@ int KWDataGrid::ComputeTotalPartNumber() const return nResult; } -KWDGAttribute* KWDataGrid::NewAttribute() const -{ - return new KWDGAttribute; -} - KWDGCell* KWDataGrid::NewCell() const { KWDGCell* cell; @@ -2898,7 +2898,7 @@ boolean KWDGAttribute::Check() const // Test de coherence entre variable interne et variable de grille if (bOk) { - // Un variable interne doir avoir une variable de grille la contenant + // Une variable interne doit avoir une variable de grille la contenant if (bOk and dataGrid == NULL and sOwnerAttributeName == "") { AddError("Inner variable should have an owner variable"); @@ -3671,9 +3671,6 @@ boolean KWDGPart::Check() const KWDGCell* nextCell; KWDGCell* prevCell; int nTotalValueFrequency; - // CH IV Begin - int nTotalVarPartFrequency; - // CH IV End // Test du type if (GetPartType() == KWType::Unknown) @@ -4882,6 +4879,34 @@ void KWDGInnerAttributes::DeleteAll() oaInnerAttributes.DeleteAll(); } +void KWDGInnerAttributes::CleanEmptyInnerAttributes() +{ + int nInnerAttribute; + int nNewIndex; + KWDGAttribute* innerAttribute; + + // Nettoyage des attributs n'ayant aucune partie + nNewIndex = 0; + for (nInnerAttribute = 0; nInnerAttribute < oaInnerAttributes.GetSize(); nInnerAttribute++) + { + innerAttribute = GetInnerAttributeAt(nInnerAttribute); + if (innerAttribute->GetPartNumber() == 0) + { + // Suppression de l'indexation dans le dictionnaire + odInnerAttributes.RemoveKey(innerAttribute->GetAttributeName()); + + delete innerAttribute; + } + else + { + oaInnerAttributes.SetAt(nNewIndex, innerAttribute); + nNewIndex++; + } + } + oaInnerAttributes.SetSize(nNewIndex); + ensure(oaInnerAttributes.GetSize() == odInnerAttributes.GetCount()); +} + void KWDGInnerAttributes::ExportAllInnerAttributeVarParts(ObjectArray* oaInnerAttributeVarParts) const { int nInnerAttribute; diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index b1ed235db..fd5cae89b 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -304,6 +304,10 @@ class KWDataGrid : public Object /////////////////////////////// // Services divers + // Creation d'un attribut + // Les creations de partie d'attribut se font depuis une methode NewPart dans les attributs + virtual KWDGAttribute* NewAttribute() const; + // Verification de la validite de la structure du DataGrid // Controle d'integrite global (attributs, parties, cellules...) // Attention: operation couteuse en O(k.n^2) @@ -383,10 +387,6 @@ class KWDataGrid : public Object // Methodes de creations virtuelles, permettant de specialiser les entites // d'un DataGrid dans une sous-classe - // Creation d'un attribut - // Les creations de partie d'attribut se font depuis une methode NewPart dans les attributs - virtual KWDGAttribute* NewAttribute() const; - // Creation d'une cellule // Le tableau d'effectif par classe cible doit etre initialise selon GetTagretValueNumber virtual KWDGCell* NewCell() const; @@ -487,7 +487,7 @@ class KWDGAttribute : public Object void SetOwnerAttributeName(ALString sName); // Statut du parametrage des attributs internes: partagee ou non (defaut: false) - // Ce parametrage est detuit avec l'appelant, sauf s'il est partage + // Ce parametrage est detruit avec l'appelant, sauf s'il est partage boolean GetVarPartsShared() const; void SetVarPartsShared(boolean bValue) const; @@ -1395,6 +1395,9 @@ class KWDGInnerAttributes : public Object // Nettoyage void DeleteAll(); + // Nettoyage des attributs qui ne contiennent que des valeurs manquantes + void CleanEmptyInnerAttributes(); + // Export de toutes les parties parties de variables des attributs internes dans un tableau (initialement vide) void ExportAllInnerAttributeVarParts(ObjectArray* oaInnerAttributeVarParts) const; diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index 64cd19915..3a43b6ee4 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -24,6 +24,20 @@ void KWDataGridManager::CopyDataGrid(const KWDataGrid* initialDataGrid, KWDataGr dataGridManager.ExportDataGrid(targetDataGrid); } +void KWDataGridManager::CopyDataGridWithInnerAttributesCloned(const KWDataGrid* initialDataGrid, + KWDataGrid* targetDataGrid) const +{ + KWDataGridManager dataGridManager; + + require(initialDataGrid != NULL); + require(targetDataGrid != NULL); + + // Utilisation d'un manager de grille pour effectuier la copie + dataGridManager.SetSourceDataGrid(initialDataGrid); + targetDataGrid->DeleteAll(); + dataGridManager.ExportDataGridWithInnerAttributesCloned(targetDataGrid); +} + void KWDataGridManager::CopyInformativeDataGrid(const KWDataGrid* initialDataGrid, KWDataGrid* targetDataGrid) const { KWDataGridManager dataGridManager; @@ -75,6 +89,46 @@ void KWDataGridManager::ExportDataGrid(KWDataGrid* targetDataGrid) const sourceDataGrid->GetVarPartAttribute()->GetInnerAttributes()); } +void KWDataGridManager::ExportDataGridWithInnerAttributesCloned(KWDataGrid* targetDataGrid) const +{ + int nAttribute; + KWDGAttribute* sourceAttribute; + KWDGAttribute* targetAttribute; + + require(Check()); + require(targetDataGrid != NULL and targetDataGrid->IsEmpty()); + + // Export de la granularite + targetDataGrid->SetGranularity(sourceDataGrid->GetGranularity()); + + // Export des attributs + ExportAttributes(targetDataGrid); + + // Initialisation des parties des attributs + for (nAttribute = 0; nAttribute < targetDataGrid->GetAttributeNumber(); nAttribute++) + { + targetAttribute = targetDataGrid->GetAttributeAt(nAttribute); + + // Recherche de l'attribut source correspondant + sourceAttribute = sourceDataGrid->SearchAttribute(targetAttribute->GetAttributeName()); + check(sourceAttribute); + + // Pour un attribut simple, export des parties + if (KWType::IsSimple(sourceAttribute->GetAttributeType())) + InitialiseAttributeParts(sourceAttribute, targetAttribute); + // Pour un attribut VarPart, export des parties d'un clone des attributs internes + else + { + InitialiseVarPartAttributeClonedParts(sourceAttribute, targetAttribute); + assert(targetAttribute->GetInnerAttributes() != sourceAttribute->GetInnerAttributes()); + } + } + + // Export des cellules + ExportCells(targetDataGrid); + ensure(CheckDataGrid(targetDataGrid)); +} + // CH IV Begin void KWDataGridManager::ExportDataGridWithSingletonVarParts(const KWDataGrid* referenceDataGrid, KWDataGrid* targetDataGrid, @@ -308,7 +362,7 @@ double KWDataGridManager::ExportDataGridWithVarPartMergeOptimization(KWDataGrid* // Pour un attribut simple, export des partie if (KWType::IsSimple(sourceAttribute->GetAttributeType())) InitialiseAttributeParts(sourceAttribute, targetAttribute); - // Pour un attribut VarPart, export des partie d'un clone des attributs internes + // Pour un attribut VarPart, export des parties d'un clone des attributs internes else { InitialiseVarPartAttributeClonedParts(sourceAttribute, targetAttribute); @@ -2547,7 +2601,7 @@ void KWDataGridManager::InitialiseAttributeParts(const KWDGAttribute* sourceAttr // Creation de la partie cible targetPart = targetAttribute->AddPart(); - // Copie des valeurs de la partie, quele que soit son type + // Copie des valeurs de la partie, quel que soit son type targetPart->GetPartValues()->CopyFrom(sourcePart->GetPartValues()); // Transfert du parametrage du groupe poubelle (methode tolerante au cas continu) @@ -2584,13 +2638,14 @@ void KWDataGridManager::InitialiseVarPartAttributeClonedParts(const KWDGAttribut require(targetAttribute->GetPartNumber() == 0); // Creation d'un clone des attributs internes - clonedInnerAttributes = CloneInnerAttributes(sourceAttribute->GetInnerAttributes()); + clonedInnerAttributes = + CloneInnerAttributes(sourceAttribute->GetInnerAttributes(), targetAttribute->GetDataGrid()); // Parametrage des attribut interne de l'attribut cible de type VarPart targetAttribute->SetInnerAttributes(clonedInnerAttributes); targetAttribute->SetVarPartsShared(false); - // Memorisation de l'association entre VarPart sourcese et cible via un dictionnaire + // Memorisation de l'association entre VarPart source et cible via un dictionnaire sourceAttribute->GetInnerAttributes()->ExportAllInnerAttributeVarParts(&oaSourceInnerAttributeVarParts); targetAttribute->GetInnerAttributes()->ExportAllInnerAttributeVarParts(&oaTargetInnerAttributeVarParts); for (n = 0; n < oaSourceInnerAttributeVarParts.GetSize(); n++) @@ -3294,7 +3349,8 @@ boolean KWDataGridManager::CheckAttributesConsistency(const KWDGAttribute* attri return bOk; } -KWDGInnerAttributes* KWDataGridManager::CloneInnerAttributes(const KWDGInnerAttributes* sourceInnerAttributes) const +KWDGInnerAttributes* KWDataGridManager::CloneInnerAttributes(const KWDGInnerAttributes* sourceInnerAttributes, + const KWDataGrid* targetDataGrid) const { KWDGInnerAttributes* resultInnerAttributes; int nInnerAttribute; @@ -3304,6 +3360,7 @@ KWDGInnerAttributes* KWDataGridManager::CloneInnerAttributes(const KWDGInnerAttr require(sourceInnerAttributes != NULL); require(sourceInnerAttributes->Check()); require(sourceInnerAttributes->AreInnerAttributePartsSorted()); + require(targetDataGrid != NULL); // Partage des partitions de la grille source resultInnerAttributes = new KWDGInnerAttributes; @@ -3314,8 +3371,8 @@ KWDGInnerAttributes* KWDataGridManager::CloneInnerAttributes(const KWDGInnerAttr // Extraction de l'attribut internes source sourceInnerAttribute = sourceInnerAttributes->GetInnerAttributeAt(nInnerAttribute); - // Creation d'un attribut interne identique - targetInnerAttribute = new KWDGAttribute; + // Creation d'un attribut interne identique en exploitant le createur virtuelle de la grille cibke en parametre + targetInnerAttribute = targetDataGrid->NewAttribute(); // Parametrage InitialiseAttribute(sourceInnerAttribute, targetInnerAttribute); diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.h b/src/Learning/KWDataPreparation/KWDataGridManager.h index 4256c55dc..da6115086 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.h +++ b/src/Learning/KWDataPreparation/KWDataGridManager.h @@ -27,8 +27,13 @@ class KWDataGridManager : public Object ~KWDataGridManager(); // Copie du contenu d'une grille source vers une grille cible + // Dans le cas VarPart, les attributs internes sont partages avec la grille source void CopyDataGrid(const KWDataGrid* initialDataGrid, KWDataGrid* targetDataGrid) const; + // Copie profonde du contenu d'une grille source + // Dans le cas VarPart, les attributs internes sont clones a partir de la grille source + void CopyDataGridWithInnerAttributesCloned(const KWDataGrid* initialDataGrid, KWDataGrid* targetDataGrid) const; + // Copie de la partie informative du contenu d'une grille source vers une grille cible // La partie informative est constituee des attributs non reduits a une seule partie void CopyInformativeDataGrid(const KWDataGrid* initialDataGrid, KWDataGrid* targetDataGrid) const; @@ -46,8 +51,13 @@ class KWDataGridManager : public Object // compatible avec celui des attributs initaux de la grille de donnees source // Export total (attribut, parties et cellules) + // Dans le cas VarPart, les attributs internes sont partages avec la grille source void ExportDataGrid(KWDataGrid* targetDataGrid) const; + // Export total (attribut, parties et cellules) + // Dans le cas VarPart, les attributs internes sont clones a partir de la grille source + void ExportDataGridWithInnerAttributesCloned(KWDataGrid* targetDataGrid) const; + // CH IV Begin // Export total (attribut, parties et cellules) en exploitant une grille de reference, // avec une partie par VarPart @@ -81,6 +91,7 @@ class KWDataGridManager : public Object // dedies aux attributs, parties et cellules // Export des attributs uniquement (plus les specifications des classes cibles) + // Dans le cas VarPart, les attributs internes sont partages avec la grille source void ExportAttributes(KWDataGrid* targetDataGrid) const; // Export des attributs informatifs uniquement (non reduits a une seule partie) @@ -347,7 +358,10 @@ class KWDataGridManager : public Object // Services de creation d'attribut internes // Creation d'attributs internes en dupliquant les attributs internes source - KWDGInnerAttributes* CloneInnerAttributes(const KWDGInnerAttributes* sourceInnerAttributes) const; + // Le parametre targetDataGrid permet de specialiser les classes utiliser pour les clonages + // en utilisant lees methode de creation virtuelle specialisees (ex: NewAttribute) + KWDGInnerAttributes* CloneInnerAttributes(const KWDGInnerAttributes* sourceInnerAttributes, + const KWDataGrid* targetDataGrid) const; // Creation d'attributs internes avec une seule partie par attribut KWDGInnerAttributes* CreateNullInnerAttributes(const KWDGInnerAttributes* sourceInnerAttributes) const; diff --git a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp index 1151f1f7e..1ddb9961b 100644 --- a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp @@ -265,6 +265,12 @@ double KWDataGridOptimizer::OptimizeDataGrid(const KWDataGrid* initialDataGrid, } // On ne traite pas les grilles avec un seul attribut informatif + // Dans le cas d'une grille avec un attribut instances et un attribut VarPart, tant que + // l'attribut instances ne contient qu'une seule partie, la granularite n'est pas selectionnee. + // La granularisation de l'attribut instances est reduite au fourre-tout (1 seule partie) + // tant que le nombre d'observations par instances n'est pas superieur a l'effectif minimal N/2^G + // Pour un nombre d'observations egal au nombre de variables pour toutes les instances, + // il faut atteindre G tel que G > Gmax - log(K) / log(2) if (granularizedDataGrid.GetInformativeAttributeNumber() <= 1) bIsGranularitySelected = false; diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp index 98c78e38a..cb61d6c8a 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp @@ -218,8 +218,10 @@ KWDataGridPostOptimizer::BuildUnivariateInitialDataGrid(const KWDataGrid* optimi // Extension pour un attribut de type VarPart : code identique // Il faut une coherence (egalite) entre le KWDGInnerAttributes utilise par la grille optimisee et celui utilise // pour l'attribut de la grille initiale + // (tolerance dans le cas d'une grille hierarchique qui exploite un clone des inner attributes) require(not initialDataGrid->IsVarPartDataGrid() or - initialDataGrid->GetInnerAttributes() == optimizedDataGrid->GetInnerAttributes()); + initialDataGrid->GetInnerAttributes() == optimizedDataGrid->GetInnerAttributes() or + optimizedDataGrid->GetClassLabel() == "Hierarchichal data grid"); // CH IV End // Creation de la grille univariee diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp index 0a2173c28..aeeb28c1b 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp @@ -1266,7 +1266,7 @@ void CCCoclusteringBuilder::CleanVarPartDataGrid(KWDataGrid* dataGrid) if (bIsDefaultPartDeleted and innerAttribute->GetPartNumber() > 0) innerAttribute->GetTailPart()->GetSymbolValueSet()->AddSymbolValue(Symbol::GetStarValue()); } - // DDDDD dataGrid->GetInnerAttributes()->CleanEmptyInnerAttributes(); + dataGrid->GetInnerAttributes()->CleanEmptyInnerAttributes(); dataGrid->UpdateAllStatistics(); } From 9145dfb51dc8c6950df1762bde67832585ceb3ed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Mon, 11 Sep 2023 15:59:54 +0200 Subject: [PATCH 09/17] Export both values and frequencies of internal categorial variables in json MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Merge des evolutions de Carine Exporter les valeurs et effectifs des attributs internes categoriels - necessaire selon le modele, et pour exploiter le côte modele generatif - prototype implemente dans CCCoclusteringReport::WriteJSONInnerAttributePartition, mais desactive - format à choisir, entre l'export de toutes les valeurs de l'attribut (comme dans le prototype), ou par groupe (comme pour les attributs de grille simples) - implementer egalement la relecture des fichiers json - adapter les jeux de test de LearningTest\TestCoclustering\y_CoclusteringIV_Standard Reutiliser le format utilise dans les "dimensionPartitions" du coclustering standard? - format complet - "-cluster": equivalent des varPartIds, mais avec nommage different - "defaultGroupIndex": remplacerait avantageusement la valeur " * " - "values" et valuesFrequencies" par groupe: correspond à ce qu'il faut - "valueTypicalities": seul champ en trop, mais on pourrait soit ne pas le mettre, soit lui donner un sens (dispatcher la valueTypicality au prorata des effectifs) - pros - reutilisation d'un format existant - permet de mutualiser le code de lecture et ecriture des json dans le code de Khiops coclustering - nouvelle methode WriteJSONAttributePartition(attribute, isInner), plus Read correspondant - WriteJSONDimensionPartitions, WriteJSONInnerAttributePartition (plus Read...), reecrits, en reutilisant les methodes precedentes - idem dans Khiops covisualization pour Stephane Bouget - cons - plus verbeux - attention à refaire toutes les references de LearningTest\TestCoclustering\y_CoclusteringIV_Standard - et changer les input coclsuetring pour les methodes dont les tests les relisent Format actuel d'une partion de variable interne { "variable": "Class", "type": "Categorical", "partition": [["Iris-setosa", "Iris-versicolor"],["Iris-virginica"," * "]], "varPartIds": ["Class {Iris-setosa, Iris-versicolor}","Class {Iris-virginica}"] Format de partition d'une variable de grille "name": "Class", "type": "Categorical", "valueGroups": [ { "cluster": "{Iris-setosa, Iris-versicolor}", "values": ["Iris-setosa", "Iris-versicolor"], "valueFrequencies": [50, 50], "valueTypicalities": [1, 0.5] }, { "cluster": "{Iris-virginica}", "values": ["Iris-virginica"], "valueFrequencies": [50], "valueTypicalities": [1] } ], "defaultGroupIndex": 1 } Format possible pour la partition d'une variable interne "name": "Class", "type": "Categorical", "valueGroups": [ { "cluster": "Class {Iris-setosa, Iris-versicolor}", "values": ["Iris-setosa", "Iris-versicolor"], "valueFrequencies": [50, 50], }, { "cluster": "Class {Iris-virginica}", "values": ["Iris-virginica"], "valueFrequencies": [50], } ], "defaultGroupIndex": 1 Impacts dans CCCoclusteringReport - appel de la methode CopyDataGridWithInnerAttributesCloned de KWDataGridManager qui effectue une copie profonde avec clonage des innerAttributes cette methode est appellee lors de la creation de la CCCoclusteringDataGrid ce qui permet de creer des innerAttributes de type CCHDGAttribute - nouvelle methode WriteJSONVarPartAttributeDimensionSummaries afin de decrire les caracteristiques des innerAttributes avant d'ecrire leur partition - nouvelle methode WriteJSONAttributePartition(attribute, coclusteringDataGrid) : - on a besoin de coclusteringDataGrid pour acceder aux domainBounds dans le cas d'un innerAttribute - on a acces a attribute->IsInnerAttribute donc pas besoin de bIsInnerAttribute - adaptation de WriteJSONDimensionPartitions en utilisant WriteJSONAttributePartition y compris pour les innerAttributes - adaptation selon la meme organisation pour les methodes Read --- src/Learning/KWData/KWDatabase.cpp | 2 +- src/Learning/KWData/KWTimestampTZ.h | 2 +- .../KWDataUtils/KWDataTableSliceSet.cpp | 2 +- .../CCCoclusteringBuilder.cpp | 77 +- .../CCCoclusteringReport.cpp | 1497 ++++++++--------- .../MODL_Coclustering/CCCoclusteringReport.h | 23 +- .../CCHierarchicalDataGrid.cpp | 20 +- .../CCHierarchicalDataGrid.h | 10 +- 8 files changed, 847 insertions(+), 786 deletions(-) diff --git a/src/Learning/KWData/KWDatabase.cpp b/src/Learning/KWData/KWDatabase.cpp index b14267b68..5c69ab41f 100644 --- a/src/Learning/KWData/KWDatabase.cpp +++ b/src/Learning/KWData/KWDatabase.cpp @@ -1341,7 +1341,7 @@ void KWDatabase::TestCreateObjects(int nNumber) // Creation d'objets DeleteAll(); for (i = 0; i < nNumber; i++) - GetObjects()->Add(KWObject::CreateObject(kwcCreationClass, i + 1)); + GetObjects()->Add(KWObject::CreateObject(kwcCreationClass, (longint)i + 1)); } void KWDatabase::TestRead() diff --git a/src/Learning/KWData/KWTimestampTZ.h b/src/Learning/KWData/KWTimestampTZ.h index a8bf8efb3..326fb84af 100644 --- a/src/Learning/KWData/KWTimestampTZ.h +++ b/src/Learning/KWData/KWTimestampTZ.h @@ -23,7 +23,7 @@ class KWTimestampTZFormat; // Prefixe pour les variables: tstz // Pas de constructeur pour pouvoir etre utilise dans l'union de KWValue // -// Les TimestampTZ peuvent comporter ou non information de type time zone (time zone aware ou unaware. +// Les TimestampTZ peuvent comporter ou non information de type time zone (time zone aware ou unaware) class TimestampTZ : public SystemObject { public: diff --git a/src/Learning/KWDataUtils/KWDataTableSliceSet.cpp b/src/Learning/KWDataUtils/KWDataTableSliceSet.cpp index 05d50f414..30f70619c 100644 --- a/src/Learning/KWDataUtils/KWDataTableSliceSet.cpp +++ b/src/Learning/KWDataUtils/KWDataTableSliceSet.cpp @@ -1968,7 +1968,7 @@ KWDataTableSliceSet* KWDataTableSliceSet::CreateDataTableSliceSet(const KWClass* nSliceObjectNumber = ((nObjectNumber - nObject) / (nChunkNumber - nChunk)); for (i = 0; i < nSliceObjectNumber; i++) { - kwoObject = KWObject::CreateObject(dataTableSlice->GetClass(), nObject + 1); + kwoObject = KWObject::CreateObject(dataTableSlice->GetClass(), (longint)nObject + 1); nObject++; databaseTextFile.GetObjects()->Add(kwoObject); } diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp index aeeb28c1b..6e40a1abd 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp @@ -1628,25 +1628,18 @@ void CCCoclusteringBuilder::HandleOptimizationStep(const KWDataGrid* optimizedDa if (coclusteringDataGrid != NULL) delete coclusteringDataGrid; coclusteringDataGrid = new CCHierarchicalDataGrid; - dataGridManager.CopyDataGrid(optimizedDataGrid, coclusteringDataGrid); + + // Creation d'innerAttributes propre a la grille hierarchique afin qu'ils soient du type CCHDGAttribute comme les attributs de grille + dataGridManager.CopyDataGridWithInnerAttributesCloned(optimizedDataGrid, coclusteringDataGrid); + assert(not coclusteringDataGrid->GetVarPartsShared()); // Memorisation de la description courte coclusteringDataGrid->SetShortDescription(GetShortDescription()); - // CH IV Begin // Memorisation variable identifiant dans le cas d'un coclustering instances * variables if (optimizedDataGrid->IsVarPartDataGrid()) coclusteringDataGrid->SetIdentifierAttributeName(GetIdentifierAttributeName()); - // CH AB tentative pour rendre coclusteringDataGrid proprietaire de son innerAttribute qd necessaire - // si on arrive ici via une grille post-fusionnee, elle a son innerAttribute sinon, non - if (optimizedDataGrid->IsVarPartDataGrid() and not optimizedDataGrid->GetVarPartsShared()) - { - optimizedDataGrid->SetVarPartsShared(true); - coclusteringDataGrid->SetVarPartsShared(false); - } - // CH IV End - // Calcul de ses infos de hierarchie // Cas sans granularisation if (initialGranularizedDataGrid == NULL) @@ -3114,6 +3107,8 @@ void CCCoclusteringBuilder::ComputeAttributeTypicalities(CCHierarchicalDataGrid* { int nAttribute; CCHDGAttribute* hdgAttribute; + int nInnerAttribute; // CH IV MemBug + CCHDGAttribute* innerAttribute; // CH IV MemBug require(optimizedDataGrid != NULL); @@ -3126,6 +3121,23 @@ void CCCoclusteringBuilder::ComputeAttributeTypicalities(CCHierarchicalDataGrid* // Pour l'instant, on met toutes les typicalites a 1 hdgAttribute->SetInterest(1); + + // CH IV MemBug + if (hdgAttribute->GetAttributeType() == KWType::VarPart and hdgAttribute->GetInnerAttributeNumber() > 0) + { + for (nInnerAttribute = 0; nInnerAttribute < hdgAttribute->GetInnerAttributeNumber(); + nInnerAttribute++) + { + innerAttribute = + cast(CCHDGAttribute*, hdgAttribute->GetInnerAttributeAt(nInnerAttribute)); + + // Memorisation du nombre de parties initiales, eagl ici au nombre de parties + innerAttribute->SetInitialPartNumber(innerAttribute->GetPartNumber()); + + // Pour l'instant, on met toutes les typicalites a 1 + innerAttribute->SetInterest(1); + } + } } } @@ -3173,6 +3185,17 @@ void CCCoclusteringBuilder::ComputeContinuousAttributeBounds(CCHierarchicalDataG descriptiveContinuousStats = cast(KWDescriptiveContinuousStats*, odDescriptiveStats.Lookup(innerAttribute->GetAttributeName())); + + // CH IV MemBug + // Memorisation de ses bornes + if (descriptiveContinuousStats != NULL) + { + cast(CCHDGAttribute*, innerAttribute) + ->SetMin(descriptiveContinuousStats->GetMin()); + cast(CCHDGAttribute*, innerAttribute) + ->SetMax(descriptiveContinuousStats->GetMax()); + } + // CH IV Redondant : a enlever quand le fonctionnement des innerAttributes en tant que CHDGAttribute sera ok domainBounds = new KWDGInterval; domainBounds->SetLowerBound(descriptiveContinuousStats->GetMin()); domainBounds->SetUpperBound(descriptiveContinuousStats->GetMax()); @@ -4044,6 +4067,11 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* int nAttribute; KWDGAttribute* dgAttribute; KWDGPart* dgPart; + // CH IV MemBug + int nInnerAttribute; + KWDGAttribute* innerAttribute; + KWDGPart* innerPart; + // Fin CH IV require(optimizedDataGrid != NULL); @@ -4069,6 +4097,33 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* // Partie suivante dgAttribute->GetNextPart(dgPart); } + + // CH IV MemBug + if (dgAttribute->GetAttributeType() == KWType::VarPart) + { + // Parcours des inner attributes + for (nInnerAttribute = 0; nInnerAttribute < dgAttribute->GetInnerAttributeNumber(); + nInnerAttribute++) + { + innerAttribute = dgAttribute->GetInnerAttributeAt(nInnerAttribute); + // Nommage des parties terminales et tri des valeurs pour les attributs categoriels + innerPart = innerAttribute->GetHeadPart(); + while (innerPart != NULL) + { + // CH IV voir avec Marc si le tri a deja ete fait suite au renommage des methodes de tri + // Tri des valeurs de la partie si attribut categoriel + //if (innerAttribute->GetAttributeType() == KWType::Symbol) + //cast(CCHDGValueSet*, innerPart->GetValueSet())->SortValuesByTypicality(); + + // Initialisation du nom de la partie + cast(CCHDGPart*, innerPart)->SetPartName(innerPart->GetVarPartLabel()); + + // Partie suivante + dgAttribute->GetNextPart(innerPart); + } + } + } + // Fin CH IV } } diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp index 0ee116b78..5eff83590 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp @@ -2481,7 +2481,7 @@ boolean CCCoclusteringReport::OpenInputCoclusteringReportFile(const ALString& sF // Creation d'un buffer de lecture si necessaire if (bOk) - sFileBuffer = NewCharArray(nMaxFieldSize + 1); + sFileBuffer = NewCharArray((longint)nMaxFieldSize + 1); else { sReportFileName = ""; @@ -2704,7 +2704,7 @@ boolean CCCoclusteringReport::InternalReadJSONReport(CCHierarchicalDataGrid* coc if (not bHeaderOnly) { bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - bOk = bOk and ReadJSONDimensionPartitions(coclusteringDataGrid); + bOk = bOk and ReadJSONDimensionPartitionsNew(coclusteringDataGrid); bOk = bOk and JSONTokenizer::ReadExpectedToken(','); bOk = bOk and ReadJSONDimensionHierarchies(coclusteringDataGrid); bOk = bOk and JSONTokenizer::ReadExpectedToken(','); @@ -2837,6 +2837,7 @@ boolean CCCoclusteringReport::ReadJSONDimensionSummaries(CCHierarchicalDataGrid* ALString sValue; int nAttribute; CCHDGAttribute* varPartAttribute; + KWDGInnerAttributes* innerAttributes; ALString sTmp; require(coclusteringDataGrid != NULL); @@ -2983,8 +2984,14 @@ boolean CCCoclusteringReport::ReadJSONDimensionSummaries(CCHierarchicalDataGrid* // Memorisation dans le cas d'un attribut de type VarPart if (nAttributeType == KWType::VarPart) + { varPartAttribute = dgAttribute; + // Creation des attributs internes + innerAttributes = new KWDGInnerAttributes; + dgAttribute->SetInnerAttributes(innerAttributes); + } + // Valeur min et max dans le cas numerique if (nAttributeType == KWType::Continuous) { @@ -3047,34 +3054,139 @@ boolean CCCoclusteringReport::ReadJSONDimensionSummaries(CCHierarchicalDataGrid* return bOk; } -boolean CCCoclusteringReport::ReadJSONDimensionPartitions(CCHierarchicalDataGrid* coclusteringDataGrid) +boolean CCCoclusteringReport::ReadJSONInnerAttributesDimensionSummaries(KWDGAttribute* dgAttribute) +{ + boolean bOk = true; + boolean bIsEnd = false; + CCHDGAttribute* innerAttribute; + ALString sAttributeName; + ALString sAttributeType; + int nAttributeType; + int nAttributePartNumber; + int nAttributeInitialPartNumber; + int nAttributeValueNumber; + double dAttributeInterest; + ALString sAttributeDescription; + Continuous cMin; + Continuous cMax; + int nPart; + ALString sKey; + ALString sValue; + ALString sTmp; + KWDGInnerAttributes* innerAttributes; + + require(dgAttribute != NULL); + + // Attributs internes + innerAttributes = dgAttribute->GetInnerAttributes(); + + // Tableau principal + bOk = bOk and JSONTokenizer::ReadKeyArray("innerAttributesDimensionSummaries"); + + // Lecture des elements du tableau + while (bOk and not bIsEnd) + { + // Initialisations + sAttributeName = ""; + nAttributeType = 0; + nAttributePartNumber = 0; + nAttributeInitialPartNumber = 0; + nAttributeValueNumber = 0; + dAttributeInterest = 0; + cMin = 0; + cMax = 0; + + // Debut de l'objet + bOk = bOk and JSONTokenizer::ReadExpectedToken('{'); + + // Nom de l'attribut + bOk = bOk and JSONTokenizer::ReadKeyStringValue("name", sAttributeName, bIsEnd); + if (bOk and sAttributeName == "") + { + bOk = false; + AddError("Missing variable name"); + } + + if (bOk and innerAttributes->LookupInnerAttribute(sAttributeName) != NULL) + { + bOk = false; + AddError("Inner variable " + sAttributeName + " used twice"); + } + + // Lecture du champ "type" + bOk = bOk and JSONTokenizer::ReadKeyStringValue("type", sAttributeType, bIsEnd); + + // Verification du type de l'attribut + nAttributeType = KWType::ToType(sAttributeType); + if (bOk and not KWType::IsSimple(nAttributeType)) + { + bOk = false; + JSONTokenizer::AddParseError("Type of inner variable " + sAttributeName + " (" + + sAttributeType + ") should be Numerical or Categorical"); + } + + // Champs restants de l'attribut + bOk = bOk and JSONTokenizer::ReadKeyIntValue("parts", true, nAttributePartNumber, bIsEnd); + bOk = bOk and JSONTokenizer::ReadKeyIntValue("initialParts", true, nAttributeInitialPartNumber, bIsEnd); + bOk = bOk and JSONTokenizer::ReadKeyIntValue("values", true, nAttributeValueNumber, bIsEnd); + bOk = bOk and JSONTokenizer::ReadKeyDoubleValue("interest", true, dAttributeInterest, bIsEnd); + bOk = bOk and JSONTokenizer::ReadKeyStringValue("description", sAttributeDescription, bIsEnd); + + // Valeur min et max dans le cas numerique + if (bOk and nAttributeType == KWType::Continuous) + { + bOk = bOk and JSONTokenizer::ReadKeyContinuousValue("min", false, cMin, bIsEnd); + bOk = bOk and JSONTokenizer::ReadKeyContinuousValue("max", false, cMax, bIsEnd); + } + + // Creation et specification de l'attribut interne + if (bOk) + { + innerAttribute = new CCHDGAttribute; + innerAttribute->SetAttributeName(sAttributeName); + innerAttribute->SetAttributeType(nAttributeType); + innerAttribute->SetInitialPartNumber(nAttributeInitialPartNumber); + innerAttribute->SetInitialValueNumber(nAttributeValueNumber); + innerAttribute->SetGranularizedValueNumber(nAttributeValueNumber); + innerAttribute->SetInterest(dAttributeInterest); + innerAttribute->SetDescription(sAttributeDescription); + innerAttribute->SetOwnerAttributeName(dgAttribute->GetAttributeName()); + + // Valeur min et max dans le cas numerique + if (nAttributeType == KWType::Continuous) + { + innerAttribute->SetMin(cMin); + innerAttribute->SetMax(cMax); + } + + // Creation de parties + for (nPart = 0; nPart < nAttributePartNumber; nPart++) + innerAttribute->AddPart(); + + innerAttributes->AddInnerAttribute(innerAttribute); + } + + // Fin de l'objet et test si nouvel objet + bOk = bOk and JSONTokenizer::CheckObjectEnd("innerAttributesDimensionSummary", bIsEnd); + bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); + } + + return bOk; +} + +boolean CCCoclusteringReport::ReadJSONDimensionPartitionsNew(CCHierarchicalDataGrid* coclusteringDataGrid) { boolean bOk = true; ObjectDictionary odCheckedParts; ALString sPartName; boolean bIsAttributeEnd; - // CH IV Begin - boolean bIsInnerAttributeEnd; - // CH IV End - boolean bIsPartEnd; int nAttributeIndex; - int nPartIndex; CCHDGAttribute* dgAttribute; - KWDGPart* dgPart; ALString sAttributeName; ALString sAttributeType; int nAttributeType; int nDefaultGroupIndex; ALString sValue; - // CH IV Begin - KWDGInnerAttributes* innerAttributes; - KWDGAttribute* innerAttribute; - int nInnerAttributeNumber; - ObjectDictionary odInnerAttributesAllVarParts; - ObjectDictionary odVarPartAttributeAllVarParts; - KWDGInterval* domainBounds; - int nInnerAttribute; - // CH IV End ALString sTmp; require(coclusteringDataGrid != NULL); @@ -3131,331 +3243,391 @@ boolean CCCoclusteringReport::ReadJSONDimensionPartitions(CCHierarchicalDataGrid bOk = false; } - // Initialisation des parties - if (bOk) - { - // Tableau d'intervalles ou de groupes de valeurs selon le type d'attribut - if (dgAttribute->GetAttributeType() == KWType::Continuous) - bOk = bOk and JSONTokenizer::ReadKeyArray("intervals"); - else if (dgAttribute->GetAttributeType() == KWType::Symbol) - bOk = bOk and JSONTokenizer::ReadKeyArray("valueGroups"); - // CH IV Begin - // Cas d'un attribut VarPart - else if (dgAttribute->GetAttributeType() == KWType::VarPart) - { - // Tableau des variables internes dans l'attribut de type VarPart - bOk = bOk and JSONTokenizer::ReadKeyArray("innerVariables"); - - // Creation des attributs internes - innerAttributes = new KWDGInnerAttributes; - dgAttribute->SetInnerAttributes(innerAttributes); - nInnerAttributeNumber = 0; + bOk = bOk and ReadJSONAttributePartition(dgAttribute, coclusteringDataGrid); - // Boucle de lecture des attributs internes - bIsInnerAttributeEnd = false; - while (bOk and not bIsInnerAttributeEnd) - { - // Lecture de l'attribut interne - innerAttribute = new KWDGAttribute; - bOk = bOk and - ReadJSONInnerAttribute(innerAttribute, &odInnerAttributesAllVarParts); - if (not bOk) - { - bOk = false; - JSONTokenizer::AddParseError("Inner variable " + - innerAttribute->GetAttributeName() + - " not correctly specified"); - } + // Test si nouvel objet + bOk = bOk and JSONTokenizer::ReadArrayNext(bIsAttributeEnd); + } - // Test d'unicite de la variable interne - if (bOk and dgAttribute->GetInnerAttributes()->LookupInnerAttribute( - innerAttribute->GetAttributeName()) != NULL) - { - bOk = false; - JSONTokenizer::AddParseError("Inner variable " + - innerAttribute->GetAttributeName() + - " used twice"); - } + // Erreur si pas assez de variables + if (bOk and nAttributeIndex <= coclusteringDataGrid->GetAttributeNumber() - 1) + { + JSONTokenizer::AddParseError(sTmp + + "Too few variables in section \"dimensionPartitions\" (expected number: " + + IntToString(coclusteringDataGrid->GetAttributeNumber()) + ")"); + bOk = false; + } - // Memorisation de l'attribut interne si ok - if (bOk) - { - innerAttribute->SetOwnerAttributeName(dgAttribute->GetAttributeName()); - dgAttribute->GetInnerAttributes()->AddInnerAttribute(innerAttribute); - nInnerAttributeNumber++; - } - // Destruction de l'attribut interne sinon - else - { - delete innerAttribute; - dgAttribute->SetInnerAttributes(NULL); - } + return bOk; +} - // Test si nouvel attribut interne - bOk = bOk and JSONTokenizer::ReadArrayNext(bIsInnerAttributeEnd); +boolean CCCoclusteringReport::ReadJSONAttributePartition(KWDGAttribute* dgAttribute, + CCHierarchicalDataGrid* coclusteringDataGrid) +{ + boolean bOk = true; + boolean bIsPartEnd; + KWDGInnerAttributes* innerAttributes; + CCHDGAttribute* innerAttribute; + KWDGPart* dgPart; + int nPartIndex; + boolean bIsInnerAttributeEnd; + ObjectDictionary odInnerAttributesAllVarParts; + ObjectDictionary odVarPartAttributeAllVarParts; + ObjectDictionary odCheckedParts; + ALString sPartName; + KWDGInterval* domainBounds; + int nInnerAttribute; + ALString sAttributeName; + int nInnerAttributeIndex; + ALString sTmp; + int nInnerAttributeType; + int nDefaultGroupIndex; + ALString sInnerAttributeType; + + // Tableau d'intervalles ou de groupes de valeurs selon le type d'attribut + if (dgAttribute->GetAttributeType() == KWType::Continuous) + bOk = bOk and JSONTokenizer::ReadKeyArray("intervals"); + else if (dgAttribute->GetAttributeType() == KWType::Symbol) + bOk = bOk and JSONTokenizer::ReadKeyArray("valueGroups"); + // Cas d'un attribut VarPart + else if (dgAttribute->GetAttributeType() == KWType::VarPart) + { + // Creation des attributs internes + innerAttributes = dgAttribute->GetInnerAttributes(); - // Arret si echec ou pas d'attribut suivant - if (not bOk) - break; - } + // Descriptif des variables internes dans l'attribut de type VarPart + bOk = bOk and ReadJSONInnerAttributesDimensionSummaries(dgAttribute); + bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - // Nettoyage des attributs internes si erreur - if (not bOk) - { - dgAttribute->SetInnerAttributes(NULL); - delete innerAttributes; - } + // Tableau des variables internes dans l'attribut de type VarPart + bOk = bOk and JSONTokenizer::ReadKeyArray("innerVariables"); - // Memorisation des informations sur les bornes des valeurs des attributs internes - // numeriques - if (bOk) - { - coclusteringDataGrid->GetInnerAttributeDomainBounds()->DeleteAll(); - for (nInnerAttribute = 0; - nInnerAttribute < dgAttribute->GetInnerAttributeNumber(); - nInnerAttribute++) - { - innerAttribute = dgAttribute->GetInnerAttributes()->GetInnerAttributeAt( - nInnerAttribute); + // Lecture des elements du tableau + nInnerAttributeIndex = 0; + bIsInnerAttributeEnd = false; + while (bOk and not bIsInnerAttributeEnd) + { + // Recherche de l'attribut courant + if (nInnerAttributeIndex < dgAttribute->GetInnerAttributeNumber()) + { + // Extraction de l'attribut interne + innerAttribute = + cast(CCHDGAttribute*, dgAttribute->GetInnerAttributeAt(nInnerAttributeIndex)); + nInnerAttributeIndex++; + } + // Erreur si trop de variables + else + { + JSONTokenizer::AddParseError( + sTmp + "Too many inner variables in section \"innerVariables\" (expected number: " + + IntToString(dgAttribute->GetInnerAttributeNumber()) + ")"); + bOk = false; + break; + } - // Cas d'un attribut Continuous - if (innerAttribute->GetAttributeType() == KWType::Continuous) - { - domainBounds = new KWDGInterval; - domainBounds->SetLowerBound(innerAttribute->GetHeadPart() - ->GetInterval() - ->GetLowerBound()); - domainBounds->SetUpperBound(innerAttribute->GetTailPart() - ->GetInterval() - ->GetUpperBound()); - coclusteringDataGrid->GetInnerAttributeDomainBounds()->SetAt( - innerAttribute->GetAttributeName(), domainBounds); - - // On modifie les bornes des intervalles extremes de facon a - // pouvoir produire les libelle avec -inf et +inf par - // GetObjectLabel - innerAttribute->GetHeadPart()->GetInterval()->SetLowerBound( - KWDGInterval::GetMinLowerBound()); - innerAttribute->GetTailPart()->GetInterval()->SetUpperBound( - KWDGInterval::GetMaxUpperBound()); - } - } - } + // Initialisations + sAttributeName = ""; + nInnerAttributeType = 0; + nDefaultGroupIndex = 0; - // Fin du tableau des attributs internes - bOk = bOk and JSONTokenizer::ReadExpectedToken(','); + // Debut de l'objet + bOk = bOk and JSONTokenizer::ReadExpectedToken('{'); - // Lecture des parties "valueGroups", groupes de parties de variables - bOk = bOk and JSONTokenizer::ReadKeyArray("valueGroups"); + // Nom de l'attribut + bOk = bOk and JSONTokenizer::ReadKeyStringValue("name", sAttributeName, bIsInnerAttributeEnd); + if (bOk and innerAttribute->GetAttributeName() != sAttributeName) + { + JSONTokenizer::AddParseError("Read variable \"" + sAttributeName + + "\" instead of expected \"" + + innerAttribute->GetAttributeName() + "\""); + bOk = false; } - // CH IV End - // Lecture des parties - dgPart = dgAttribute->GetHeadPart(); - bIsPartEnd = false; - nPartIndex = 0; - while (bOk and not bIsPartEnd) + // Type de l'attribut + bOk = bOk and + JSONTokenizer::ReadKeyStringValue("type", sInnerAttributeType, bIsInnerAttributeEnd); + if (bOk and KWType::ToString(KWType::GetCoclusteringSimpleType( + innerAttribute->GetAttributeType())) != sInnerAttributeType) { - // Acces a la partie si elle est disponible - if (dgPart == NULL) - { - JSONTokenizer::AddParseError( - "Variable " + sAttributeName + " added part (index " + - IntToString(nPartIndex + 1) + ") beyond the expected number of parts (" + - IntToString(dgAttribute->GetPartNumber()) + ")"); - bOk = false; - break; - } - else - nPartIndex++; + JSONTokenizer::AddParseError("Read variable type " + sInnerAttributeType + + " instead of expected " + + KWType::ToString(KWType::GetCoclusteringSimpleType( + innerAttribute->GetAttributeType()))); + bOk = false; + } - // Intervalles, groupes de valeurs ou groupe de parties de variables selon le type - // d'attribut - if (bOk) - { - if (dgAttribute->GetAttributeType() == KWType::Continuous) - bOk = bOk and ReadJSONInterval(dgAttribute, dgPart); - else if (dgAttribute->GetAttributeType() == KWType::Symbol) - bOk = bOk and ReadJSONValueGroup(dgAttribute, dgPart); - // CH IV Begin - else if (dgAttribute->GetAttributeType() == KWType::VarPart) - { - // Dans le cas instances x variables, on procede a des tests pousses sur - // la coherences des varPartIds - // - tous les varPartIds declare dans les variables internes doivent - // etre uniques - // - tous les varPartIds utilises dans les grpoupes de parties de - // variable de l'attribut VarPart doivent - // etre uniques, et correspondre a des varPartIds declares dans les - // attributs internes - // - les deux ensembles de varPartIds doivent etre exactement les meme - // (tous ceux declares sont utilises) - // Par contre, lors de la regeneration du fichier json, ces libellés - // sont déduits des parties, et ne sont pas necessairement ceux qui - // etaient en entree (mais la structure est preservee). - bOk = bOk and ReadJSONVarPartAttributeValueGroup( - dgAttribute, dgPart, &odInnerAttributesAllVarParts, - &odVarPartAttributeAllVarParts); - } - // CH IV End - } + bOk = bOk and ReadJSONAttributePartition(innerAttribute, coclusteringDataGrid); - // Test si identifiant de partie unique - if (bOk) + // Memorisation des informations sur les parties de l'attribut + if (bOk) + { + // Memorisation de chaque partie + dgPart = innerAttribute->GetHeadPart(); + while (dgPart != NULL) { sPartName = cast(CCHDGPart*, dgPart)->GetPartName(); - if (odCheckedParts.Lookup(sPartName) != NULL) + + // Test d'unicite du libelle de partie parmi l'ensemble de tous les libelles de partie + if (odInnerAttributesAllVarParts.Lookup(sPartName) != NULL) { - JSONTokenizer::AddParseError("\"cluster\" " + sPartName + - " used twice"); + JSONTokenizer::AddParseError("Attribute contains value \"" + sPartName + + "\" already used previously"); bOk = false; + break; } - else - odCheckedParts.SetAt(sPartName, dgPart); + + // Memorisation de la PV avec son label dans un dictionnaire temporaire + odInnerAttributesAllVarParts.SetAt(sPartName, dgPart); + + // Partie suivante + innerAttribute->GetNextPart(dgPart); } + } - // Test si nouvelle partie - bOk = bOk and JSONTokenizer::ReadArrayNext(bIsPartEnd); + // Test si nouvel objet + bOk = bOk and JSONTokenizer::ReadArrayNext(bIsInnerAttributeEnd); + } - // Partie suivante - if (bOk) - dgAttribute->GetNextPart(dgPart); - else - break; + // Nettoyage des attributs internes si erreur + if (not bOk) + { + innerAttributes->DeleteAll(); + delete innerAttributes; + dgAttribute->SetInnerAttributes(NULL); + } + + // Memorisation des informations sur les bornes des valeurs des attributs internes + // numeriques + if (bOk) + { + coclusteringDataGrid->GetInnerAttributeDomainBounds()->DeleteAll(); + for (nInnerAttribute = 0; nInnerAttribute < dgAttribute->GetInnerAttributeNumber(); + nInnerAttribute++) + { + innerAttribute = + cast(CCHDGAttribute*, + dgAttribute->GetInnerAttributes()->GetInnerAttributeAt(nInnerAttribute)); + + // Cas d'un attribut Continuous + if (innerAttribute->GetAttributeType() == KWType::Continuous) + { + domainBounds = new KWDGInterval; + domainBounds->SetLowerBound( + innerAttribute->GetHeadPart()->GetInterval()->GetLowerBound()); + domainBounds->SetUpperBound( + innerAttribute->GetTailPart()->GetInterval()->GetUpperBound()); + coclusteringDataGrid->GetInnerAttributeDomainBounds()->SetAt( + innerAttribute->GetAttributeName(), domainBounds); + + // On modifie les bornes des intervalles extremes de facon a + // pouvoir produire les libelle avec -inf et +inf par + // GetObjectLabel + innerAttribute->GetHeadPart()->GetInterval()->SetLowerBound( + KWDGInterval::GetMinLowerBound()); + innerAttribute->GetTailPart()->GetInterval()->SetUpperBound( + KWDGInterval::GetMaxUpperBound()); + } } + } + + // Fin du tableau des attributs internes + bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - // Erreur s'il manque des parties - if (bOk and nPartIndex < dgAttribute->GetPartNumber()) + // Lecture des parties "valueGroups", groupes de parties de variables + bOk = bOk and JSONTokenizer::ReadKeyArray("valueGroups"); + } + // CH IV End + + // Lecture des parties + dgPart = dgAttribute->GetHeadPart(); + bIsPartEnd = false; + nPartIndex = 0; + while (bOk and not bIsPartEnd) + { + // Acces a la partie si elle est disponible + if (dgPart == NULL) + { + // Cas d'un attribut de la grille + if (not dgAttribute->IsInnerAttribute()) { - JSONTokenizer::AddParseError("Variable " + sAttributeName + " with " + - IntToString(nPartIndex) + " specified parts " + - ") below the expected number of parts (" + + + JSONTokenizer::AddParseError("Variable " + sAttributeName + " added part (index " + + IntToString(nPartIndex + 1) + + ") beyond the expected number of parts (" + IntToString(dgAttribute->GetPartNumber()) + ")"); bOk = false; break; } - - // Erreur si le nombre de parties de variable collectees dans les groupes de partie de variable - // est different du nombre de parties de variables specifie dans les variables internes - if (bOk and dgAttribute->GetAttributeType() == KWType::VarPart and - odInnerAttributesAllVarParts.GetCount() != odVarPartAttributeAllVarParts.GetCount()) + // Cas d'un innerAttribute + // CH IV Dans le cas d'un innerAttribute, les parties ne sont pas encore crees, on ne connait pas leur nombre + // L'initialisation des dgAttribute effectuee dans ReadJSONDimensionSummaries n'est pas effectuee pour ces attributes + else { - assert(odVarPartAttributeAllVarParts.GetCount() < - odInnerAttributesAllVarParts.GetCount()); - JSONTokenizer::AddParseError( - "VarPart variable " + sAttributeName + " contains " + - IntToString(odVarPartAttributeAllVarParts.GetCount()) + - " variable parts in its groups, below the expected number of variable parts (" + - IntToString(odInnerAttributesAllVarParts.GetCount()) + - ") specified globally in inner variables"); - bOk = false; - break; + dgAttribute->AddPart(); } } + else + nPartIndex++; - // Fin de l'objet + // Intervalles, groupes de valeurs ou groupe de parties de variables selon le type + // d'attribut if (bOk) { - // Dans le cas numerique if (dgAttribute->GetAttributeType() == KWType::Continuous) - { - // Lecture de la fin de l'objet - bOk = bOk and JSONTokenizer::ReadExpectedToken('}'); - - // Cas particulier ou le premier intervalle est Missing: il faut modifier la borne inf - // du second pour la mettre a missing - if (bOk and dgAttribute->GetPartNumber() >= 2 and - dgAttribute->GetHeadPart()->GetInterval()->GetLowerBound() == - KWContinuous::GetMissingValue()) - { - // Acces au premier intervalle - dgPart = dgAttribute->GetHeadPart(); - assert(dgPart->GetInterval()->GetUpperBound() == - KWContinuous::GetMissingValue()); - - // Modification du deuxieme intervalle - dgAttribute->GetNextPart(dgPart); - assert(dgPart->GetInterval()->GetLowerBound() != - KWContinuous::GetMissingValue()); - dgPart->GetInterval()->SetLowerBound(KWContinuous::GetMissingValue()); - } - } - // Dans le cas categoriel + bOk = bOk and ReadJSONInterval(dgAttribute, dgPart); else if (dgAttribute->GetAttributeType() == KWType::Symbol) - { - // Lecture de l'index du groupe par defaut avant la fin de l'objet - bIsPartEnd = false; - bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - bOk = bOk and JSONTokenizer::ReadKeyIntValue("defaultGroupIndex", true, - nDefaultGroupIndex, bIsPartEnd); - if (bOk and not bIsPartEnd) - { - bOk = false; - JSONTokenizer::AddParseError( - "Read token " + - JSONTokenizer::GetTokenLabel(JSONTokenizer::GetLastToken()) + - " instead of expected '}'"); - bOk = false; - } - - // Finalisation de la partition en groupes de valeurs - if (bOk and - (nDefaultGroupIndex < 0 or nDefaultGroupIndex >= dgAttribute->GetPartNumber())) - { - bOk = false; - JSONTokenizer::AddParseError("Variable " + sAttributeName + - " with invalid default group index (" + - IntToString(nDefaultGroupIndex) + ")"); - } - if (bOk) - { - // Recherche du groupe par defaut - nPartIndex = 0; - dgPart = dgAttribute->GetHeadPart(); - while (nPartIndex < nDefaultGroupIndex) - { - nPartIndex++; - dgAttribute->GetNextPart(dgPart); - } - - // Ajout de la valeur par defaut dans ce groupe - assert(dgPart != NULL); - cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()) - ->AddSymbolValue(Symbol::GetStarValue()); - } - } + bOk = bOk and ReadJSONValueGroup(dgAttribute, dgPart); // CH IV Begin - // Dans le cas VarPart else if (dgAttribute->GetAttributeType() == KWType::VarPart) { - // Lecture de la fin de l'objet - bOk = bOk and JSONTokenizer::ReadExpectedToken('}'); + // Dans le cas instances x variables, on procede a des tests pousses sur + // la coherences des varPartIds + // - tous les varPartIds declare dans les variables internes doivent + // etre uniques + // - tous les varPartIds utilises dans les grpoupes de parties de + // variable de l'attribut VarPart doivent + // etre uniques, et correspondre a des varPartIds declares dans les + // attributs internes + // - les deux ensembles de varPartIds doivent etre exactement les meme + // (tous ceux declares sont utilises) + // Par contre, lors de la regeneration du fichier json, ces libellés + // sont déduits des parties, et ne sont pas necessairement ceux qui + // etaient en entree (mais la structure est preservee). + bOk = bOk and ReadJSONVarPartAttributeValueGroup(dgAttribute, dgPart, + &odInnerAttributesAllVarParts, + &odVarPartAttributeAllVarParts); } // CH IV End } - // Test si nouvel objet - bOk = bOk and JSONTokenizer::ReadArrayNext(bIsAttributeEnd); + // Test si identifiant de partie unique + if (bOk) + { + sPartName = cast(CCHDGPart*, dgPart)->GetPartName(); + if (odCheckedParts.Lookup(sPartName) != NULL) + { + JSONTokenizer::AddParseError("\"cluster\" " + sPartName + " used twice"); + bOk = false; + } + else + odCheckedParts.SetAt(sPartName, dgPart); + } + + // Test si nouvelle partie + bOk = bOk and JSONTokenizer::ReadArrayNext(bIsPartEnd); + + // Partie suivante + if (bOk) + dgAttribute->GetNextPart(dgPart); + else + break; } - // Erreur si pas assez de variables - if (bOk and nAttributeIndex <= coclusteringDataGrid->GetAttributeNumber() - 1) + // Erreur s'il manque des parties + if (bOk and nPartIndex < dgAttribute->GetPartNumber()) { - JSONTokenizer::AddParseError(sTmp + - "Too few variables in section \"dimensionPartitions\" (expected number: " + - IntToString(coclusteringDataGrid->GetAttributeNumber()) + ")"); + JSONTokenizer::AddParseError("Variable " + sAttributeName + " with " + IntToString(nPartIndex) + + " specified parts " + ") below the expected number of parts (" + + IntToString(dgAttribute->GetPartNumber()) + ")"); bOk = false; } - // CH IV Begin - // Nettoyage du dictionnaire temporaire des PV - odInnerAttributesAllVarParts.RemoveAll(); - // CH IV End + // Erreur si le nombre de parties de variable collectees dans les groupes de partie de variable + // est different du nombre de parties de variables specifie dans les variables internes + if (bOk and dgAttribute->GetAttributeType() == KWType::VarPart and + odInnerAttributesAllVarParts.GetCount() != odVarPartAttributeAllVarParts.GetCount()) + { + assert(odVarPartAttributeAllVarParts.GetCount() < odInnerAttributesAllVarParts.GetCount()); + JSONTokenizer::AddParseError( + "VarPart variable " + sAttributeName + " contains " + + IntToString(odVarPartAttributeAllVarParts.GetCount()) + + " variable parts in its groups, below the expected number of variable parts (" + + IntToString(odInnerAttributesAllVarParts.GetCount()) + ") specified globally in inner variables"); + bOk = false; + } + // Fin de l'objet + if (bOk) + { + // Dans le cas numerique + if (dgAttribute->GetAttributeType() == KWType::Continuous) + { + // Lecture de la fin de l'objet + bOk = bOk and JSONTokenizer::ReadExpectedToken('}'); + + // Cas particulier ou le premier intervalle est Missing: il faut modifier la borne inf + // du second pour la mettre a missing + if (bOk and dgAttribute->GetPartNumber() >= 2 and + dgAttribute->GetHeadPart()->GetInterval()->GetLowerBound() == + KWContinuous::GetMissingValue()) + { + // Acces au premier intervalle + dgPart = dgAttribute->GetHeadPart(); + assert(dgPart->GetInterval()->GetUpperBound() == KWContinuous::GetMissingValue()); + + // Modification du deuxieme intervalle + dgAttribute->GetNextPart(dgPart); + assert(dgPart->GetInterval()->GetLowerBound() != KWContinuous::GetMissingValue()); + dgPart->GetInterval()->SetLowerBound(KWContinuous::GetMissingValue()); + } + } + // Dans le cas categoriel + else if (dgAttribute->GetAttributeType() == KWType::Symbol) + { + nDefaultGroupIndex = 0; + + // Lecture de l'index du groupe par defaut avant la fin de l'objet + bIsPartEnd = false; + bOk = bOk and JSONTokenizer::ReadExpectedToken(','); + bOk = bOk and + JSONTokenizer::ReadKeyIntValue("defaultGroupIndex", true, nDefaultGroupIndex, bIsPartEnd); + if (bOk and not bIsPartEnd) + { + bOk = false; + JSONTokenizer::AddParseError( + "Read token " + JSONTokenizer::GetTokenLabel(JSONTokenizer::GetLastToken()) + + " instead of expected '}'"); + bOk = false; + } + + // Finalisation de la partition en groupes de valeurs + if (bOk and (nDefaultGroupIndex < 0 or nDefaultGroupIndex >= dgAttribute->GetPartNumber())) + { + bOk = false; + JSONTokenizer::AddParseError("Variable " + sAttributeName + + " with invalid default group index (" + + IntToString(nDefaultGroupIndex) + ")"); + } + if (bOk) + { + // Recherche du groupe par defaut + nPartIndex = 0; + dgPart = dgAttribute->GetHeadPart(); + while (nPartIndex < nDefaultGroupIndex) + { + nPartIndex++; + dgAttribute->GetNextPart(dgPart); + } + + // Ajout de la valeur par defaut dans ce groupe + assert(dgPart != NULL); + cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()) + ->AddSymbolValue(Symbol::GetStarValue()); + } + } + // CH IV Begin + // Dans le cas VarPart + else if (dgAttribute->GetAttributeType() == KWType::VarPart) + { + // Lecture de la fin de l'objet + bOk = bOk and JSONTokenizer::ReadExpectedToken('}'); + } + // CH IV End + } return bOk; } -boolean CCCoclusteringReport::ReadJSONInterval(CCHDGAttribute* dgAttribute, KWDGPart* dgPart) +boolean CCCoclusteringReport::ReadJSONInterval(KWDGAttribute* dgAttribute, KWDGPart* dgPart) { boolean bOk = true; boolean bIsEnd; @@ -3520,7 +3692,7 @@ boolean CCCoclusteringReport::ReadJSONInterval(CCHDGAttribute* dgAttribute, KWDG return bOk; } -boolean CCCoclusteringReport::ReadJSONValueGroup(CCHDGAttribute* dgAttribute, KWDGPart* dgPart) +boolean CCCoclusteringReport::ReadJSONValueGroup(KWDGAttribute* dgAttribute, KWDGPart* dgPart) { boolean bOk = true; ObjectDictionary odChekedValues; @@ -3591,39 +3763,45 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(CCHDGAttribute* dgAttribute, KW JSONTokenizer::AddParseError("Vector \"valueFrequencies\" should be of same size as vector \"values\""); bOk = false; } - bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - // Tableau des typicalites - bOk = bOk and JSONTokenizer::ReadKeyArray("valueTypicalities"); - bIsEnd = false; - dValueTypicality = 0; - while (bOk and not bIsEnd) + // CH IV pas pour innerVariables + if (not dgAttribute->IsInnerAttribute()) { - bOk = bOk and JSONTokenizer::ReadDoubleValue(false, dValueTypicality); + bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - // Tolerance pour les typicalite negatives - if (dValueTypicality < 0) - AddWarning(sTmp + "Typicality (" + DoubleToString(dValueTypicality) + - ") less than 0 for variable " + dgAttribute->GetAttributeName() + - " in \"valueTypicalities\" line " + - IntToString(JSONTokenizer::GetCurrentLineIndex())); - // Erreur pour les typicalite supereures a 1 - else if (dValueTypicality > 1) + // Tableau des typicalites + bOk = bOk and JSONTokenizer::ReadKeyArray("valueTypicalities"); + bIsEnd = false; + dValueTypicality = 0; + while (bOk and not bIsEnd) { - AddError(sTmp + "Typicality (" + DoubleToString(dValueTypicality) + - ") greater than 1 for variable " + dgAttribute->GetAttributeName() + - " in \"valueTypicalities\" line " + IntToString(JSONTokenizer::GetCurrentLineIndex())); - break; + bOk = bOk and JSONTokenizer::ReadDoubleValue(false, dValueTypicality); + + // Tolerance pour les typicalite negatives + if (dValueTypicality < 0) + AddWarning(sTmp + "Typicality (" + DoubleToString(dValueTypicality) + + ") less than 0 for variable " + dgAttribute->GetAttributeName() + + " in \"valueTypicalities\" line " + + IntToString(JSONTokenizer::GetCurrentLineIndex())); + // Erreur pour les typicalite supereures a 1 + else if (dValueTypicality > 1) + { + AddError(sTmp + "Typicality (" + DoubleToString(dValueTypicality) + + ") greater than 1 for variable " + dgAttribute->GetAttributeName() + + " in \"valueTypicalities\" line " + + IntToString(JSONTokenizer::GetCurrentLineIndex())); + break; + } + bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); + if (bOk) + dvValueTypicalities.Add(dValueTypicality); + } + if (bOk and svValues.GetSize() != dvValueTypicalities.GetSize()) + { + JSONTokenizer::AddParseError( + "Vector \"valueTypicalities\" should be of same size as vector \"values\""); + bOk = false; } - bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); - if (bOk) - dvValueTypicalities.Add(dValueTypicality); - } - if (bOk and svValues.GetSize() != dvValueTypicalities.GetSize()) - { - JSONTokenizer::AddParseError( - "Vector \"valueTypicalities\" should be of same size as vector \"values\""); - bOk = false; } // Fin de l'objet @@ -3636,122 +3814,15 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(CCHDGAttribute* dgAttribute, KW dgValueSet = cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()); hdgPart->SetPartName(sClusterName); hdgPart->SetShortDescription(""); - hdgPart->SetDescription(""); - - // Memorisation des valeurs - for (i = 0; i < svValues.GetSize(); i++) - { - dgValue = cast(CCHDGSymbolValue*, dgValueSet->AddSymbolValue((Symbol)svValues.GetAt(i))); - dgValue->SetValueFrequency(ivValueFrequencies.GetAt(i)); - dgValue->SetTypicality(dvValueTypicalities.GetAt(i)); - } - } - return bOk; -} - -// CH IV Begin - -boolean CCCoclusteringReport::ReadJSONInnerAttribute(KWDGAttribute* innerAttribute, - ObjectDictionary* odInnerAttributesAllVarParts) -{ - boolean bOk = true; - boolean bIsEnd; - ALString sAttributeName; - ALString sAttributeType; - int nAttributeType; - ALString sValue; - StringVector svValues; - DoubleVector dvValueTypicalities; - int i; - ALString sTmp; - KWDGPart* dgPart; - - require(innerAttribute != NULL); - require(odInnerAttributesAllVarParts != NULL); - - // Debut de l'objet - bOk = bOk and JSONTokenizer::ReadExpectedToken('{'); - - // Nom de l'attribut - bIsEnd = false; - bOk = bOk and JSONTokenizer::ReadKeyStringValue("variable", sAttributeName, bIsEnd); - if (bOk and sAttributeName == "") - { - bOk = false; - JSONTokenizer::AddParseError("Name of inner variable should not be empty"); - } - - // Type de l'attribut - bOk = bOk and JSONTokenizer::ReadKeyStringValue("type", sAttributeType, bIsEnd); - nAttributeType = KWType::ToType(sAttributeType); - if (bOk and not(KWType::IsSimple(nAttributeType))) - { - bOk = false; - JSONTokenizer::AddParseError("Type of variable " + sAttributeName + " (" + sAttributeType + - ") should be Numerical or Categorical"); - } - - // Initialisation, notamment du type (necessaire pour la creation des parties de l'attribut) - if (bOk) - { - innerAttribute->SetAttributeName(sAttributeName); - innerAttribute->SetAttributeType(nAttributeType); - } - - // Partition de l'attribut - bOk = bOk and JSONTokenizer::ReadKeyArray("partition"); - // Cas d'un attribut Continuous - if (nAttributeType == KWType::Continuous) - { - bOk = bOk and ReadJSONInnerAttributeIntervals(innerAttribute); - } - // Cas d'un attribut Categorial - else - { - bOk = bOk and ReadJSONInnerAttributeValueGroups(innerAttribute); - } - - // Tableau des labels des parties de variables - bOk = bOk and JSONTokenizer::ReadKeyArray("varPartIds"); - bIsEnd = false; - while (bOk and not bIsEnd) - { - bOk = bOk and JSONTokenizer::ReadStringValue(sValue); - bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); - if (bOk) - svValues.Add(sValue); - } - - if (bOk and svValues.GetSize() != innerAttribute->GetPartNumber()) - { - JSONTokenizer::AddParseError("Vector \"varPartIds\" should be of same size as vector \"partition\""); - bOk = false; - } - - // Fin de l'objet - bOk = bOk and JSONTokenizer::ReadExpectedToken('}'); - - // Memorisation des informations sur l'attribut - if (bOk) - { - // Memorisation de chaque partie - dgPart = innerAttribute->GetHeadPart(); - for (i = 0; i < svValues.GetSize(); i++) - { - // Test d'unicite du libelle de partie parmi l'ensembe de tous les libelles de partie - if (odInnerAttributesAllVarParts->Lookup(svValues.GetAt(i)) != NULL) - { - JSONTokenizer::AddParseError("Vector \"varPartIds\" contains value \"" + - svValues.GetAt(i) + "\" already used previously"); - bOk = false; - break; - } - - // Memorisation de la PV avec son label dans un dictionnaire temporaire - odInnerAttributesAllVarParts->SetAt(svValues.GetAt(i), dgPart); + hdgPart->SetDescription(""); - // Partie suivante - innerAttribute->GetNextPart(dgPart); + // Memorisation des valeurs + for (i = 0; i < svValues.GetSize(); i++) + { + dgValue = cast(CCHDGSymbolValue*, dgValueSet->AddSymbolValue((Symbol)svValues.GetAt(i))); + dgValue->SetValueFrequency(ivValueFrequencies.GetAt(i)); + if (not dgAttribute->IsInnerAttribute()) + dgValue->SetTypicality(dvValueTypicalities.GetAt(i)); } } return bOk; @@ -3855,7 +3926,7 @@ boolean CCCoclusteringReport::ReadJSONInnerAttributeValueGroups(KWDGAttribute* i return bOk; } -boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(CCHDGAttribute* varPartAttribute, KWDGPart* dgPart, +boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(KWDGAttribute* varPartAttribute, KWDGPart* dgPart, const ObjectDictionary* odInnerAttributesAllVarParts, ObjectDictionary* odVarPartAttributeAllVarParts) { @@ -3905,7 +3976,7 @@ boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(CCHDGAttribute* JSONTokenizer::AddParseError("Vector \"values\" of VarPart variable " + varPartAttribute->GetAttributeName() + " contains variable part \"" + sValue + - "\" which was not specified among all the \"varPartIds\" " + "\" which was not specified among all the \"cluster\" " "vectors in the \"innerVariables\" section"); bOk = false; break; @@ -4619,25 +4690,65 @@ void CCCoclusteringReport::WriteJSONDimensionSummaries(const CCHierarchicalDataG fJSON->EndArray(); } +void CCCoclusteringReport::WriteJSONInnerAttributesDimensionSummaries(const KWDGAttribute* varPartAttribute, + JSONFile* fJSON) +{ + int nAttribute; + // CH IV MemBug + CCHDGAttribute* innerAttribute; + //KWDGAttribute* innerAttribute; + int nValueNumber; + + require(varPartAttribute != NULL); + require(varPartAttribute->GetAttributeType() == KWType::VarPart); + require(varPartAttribute->GetInnerAttributes() != NULL); + require(fJSON != NULL); + + // Parcours des innerAttributes pour la section des dimensions + fJSON->BeginKeyArray("innerAttributesDimensionSummaries"); + for (nAttribute = 0; nAttribute < varPartAttribute->GetInnerAttributeNumber(); nAttribute++) + { + // CH IV MemBug + innerAttribute = cast(CCHDGAttribute*, varPartAttribute->GetInnerAttributeAt(nAttribute)); + //innerAttribute = varPartAttribute->GetInnerAttributeAt(nAttribute); + + // Nombre initial de valeurs + nValueNumber = innerAttribute->GetInitialValueNumber(); + + // Caracteristique des attributs + // Le nombre de valeur est diminuer en externe de 1 pour tenir compte de de la StarValue en interne + fJSON->BeginObject(); + fJSON->WriteKeyString("name", innerAttribute->GetAttributeName()); + fJSON->WriteKeyString( + "type", KWType::ToString(KWType::GetCoclusteringSimpleType(innerAttribute->GetAttributeType()))); + fJSON->WriteKeyInt("parts", innerAttribute->GetPartNumber()); + // CH IV MemBug + fJSON->WriteKeyInt("initialParts", innerAttribute->GetInitialPartNumber()); + //fJSON->WriteKeyInt("initialParts", 0); + fJSON->WriteKeyInt("values", nValueNumber); + fJSON->WriteKeyDouble("interest", innerAttribute->GetInterest()); + //fJSON->WriteKeyDouble("interest", 0); + fJSON->WriteKeyString("description", innerAttribute->GetDescription()); + //fJSON->WriteKeyString("description", "0"); + if (KWFrequencyTable::GetWriteGranularityAndGarbage()) + fJSON->WriteKeyBoolean("garbage", (innerAttribute->GetGarbageModalityNumber() > 0)); + if (innerAttribute->GetAttributeType() == KWType::Continuous) + { + fJSON->WriteKeyDouble("min", innerAttribute->GetMin()); + fJSON->WriteKeyDouble("max", innerAttribute->GetMax()); + //fJSON->WriteKeyDouble("min", 0); + //fJSON->WriteKeyDouble("max", 0); + } + fJSON->EndObject(); + } + fJSON->EndArray(); +} + void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON) { - CCHDGAttribute* dgAttribute; int nAttribute; - KWDGPart* dgPart; - CCHDGPart* hdgPart; - CCHDGSymbolValueSet* hdgValueSet; - KWDGValue* dgValue; - CCHDGSymbolValue* hdgValue; - int nIndex; - int nDefaultGroupIndex; - KWDGInterval* dgInterval; - // CH IV Begin - CCHDGVarPartSet* hdgVarPartSet; - KWDGValue* dgVarPartValue; - CCHDGVarPartValue* hdgVarPartValue; - KWDGAttribute* innerAttribute; - // CH IV End + KWDGAttribute* attribute; require(coclusteringDataGrid != NULL); require(fJSON != NULL); @@ -4646,361 +4757,245 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData fJSON->BeginKeyArray("dimensionPartitions"); for (nAttribute = 0; nAttribute < coclusteringDataGrid->GetAttributeNumber(); nAttribute++) { - dgAttribute = cast(CCHDGAttribute*, coclusteringDataGrid->GetAttributeAt(nAttribute)); + attribute = coclusteringDataGrid->GetAttributeAt(nAttribute); // Debut de l'objet fJSON->BeginObject(); - fJSON->WriteKeyString("name", dgAttribute->GetAttributeName()); + fJSON->WriteKeyString("name", attribute->GetAttributeName()); fJSON->WriteKeyString( - "type", KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType()))); - - // Traitement des attributs numeriques - if (dgAttribute->GetAttributeType() == KWType::Continuous) - { - // Parcours des parties - fJSON->BeginKeyArray("intervals"); - nIndex = 0; - dgPart = dgAttribute->GetHeadPart(); - while (dgPart != NULL) - { - hdgPart = cast(CCHDGPart*, dgPart); - dgInterval = cast(KWDGInterval*, hdgPart->GetInterval()); + "type", KWType::ToString(KWType::GetCoclusteringSimpleType(attribute->GetAttributeType()))); - // Ecritures des bornes - fJSON->BeginObject(); - fJSON->WriteKeyString("cluster", hdgPart->GetPartName()); - fJSON->BeginKeyList("bounds"); - if (dgInterval->GetUpperBound() != KWContinuous::GetMissingValue()) - { - // Borne inf, ou min pour le premier intervalle non Missing - if (nIndex == 0) - fJSON->WriteContinuous(dgAttribute->GetMin()); - else - fJSON->WriteContinuous(dgInterval->GetLowerBound()); + // Ecriture de la partition de l'attribut + // Dans le cas d'un attribut de type VarPart, declenche l'ecriture de ses innerAttributes + WriteJSONAttributePartition(attribute, coclusteringDataGrid, fJSON); - // Borne sup, ou max pour le premier intervalle non Missing - if (dgPart == dgAttribute->GetTailPart()) - fJSON->WriteContinuous(dgAttribute->GetMax()); - else - fJSON->WriteContinuous(dgInterval->GetUpperBound()); + // Fin de l'objet + fJSON->EndObject(); + } + fJSON->EndArray(); +} - // Index suivant d'intervalle non missing - nIndex++; - } - fJSON->EndList(); - fJSON->EndObject(); +void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, + const CCHierarchicalDataGrid* coclusteringDataGrid, + JSONFile* fJSON) +{ + CCHDGAttribute* dgAttribute; + KWDGPart* dgPart; + CCHDGPart* hdgPart; + KWDGInterval* dgInterval; + KWDGValue* dgValue; + KWDGValueSet* dgValueSet; + CCHDGSymbolValue* hdgValue; + CCHDGVarPartValue* hdgVarPartValue; + KWDGAttribute* innerAttribute; + int nIndex; + int nDefaultGroupIndex; - // Partie suivante - dgAttribute->GetNextPart(dgPart); - } - fJSON->EndArray(); - } + // Traitement des attributs numeriques + if (attribute->GetAttributeType() == KWType::Continuous) + { + dgAttribute = cast(CCHDGAttribute*, attribute); - // Traitement des attributs categoriels - if (dgAttribute->GetAttributeType() == KWType::Symbol) + // Parcours des parties + fJSON->BeginKeyArray("intervals"); + nIndex = 0; + dgPart = attribute->GetHeadPart(); + while (dgPart != NULL) { - // Parcours des parties - fJSON->BeginKeyArray("valueGroups"); - nDefaultGroupIndex = -1; - nIndex = 0; - dgPart = dgAttribute->GetHeadPart(); - while (dgPart != NULL) + hdgPart = cast(CCHDGPart*, dgPart); + dgInterval = dgPart->GetInterval(); + + // Ecritures des bornes + fJSON->BeginObject(); + fJSON->WriteKeyString("cluster", hdgPart->GetPartName()); + fJSON->BeginKeyList("bounds"); + if (dgInterval->GetUpperBound() != KWContinuous::GetMissingValue()) { - hdgPart = cast(CCHDGPart*, dgPart); - hdgValueSet = cast(CCHDGSymbolValueSet*, hdgPart->GetValueSet()); + // Borne inf, ou min pour le premier intervalle non Missing + if (nIndex == 0) + fJSON->WriteContinuous(dgAttribute->GetMin()); + else + fJSON->WriteContinuous(dgInterval->GetLowerBound()); + + // Borne sup, ou max pour le premier intervalle non Missing + if (dgPart == attribute->GetTailPart()) + fJSON->WriteContinuous(dgAttribute->GetMax()); + else + fJSON->WriteContinuous(dgInterval->GetUpperBound()); - // Memorisation de l'index du groupe par defaut - if (hdgValueSet->IsDefaultPart()) - nDefaultGroupIndex = nIndex; + // Index suivant d'intervalle non missing nIndex++; + } + fJSON->EndList(); + fJSON->EndObject(); - // Parcours des valeurs, sauf si effectif null (cas de la StarValue) - fJSON->BeginObject(); - fJSON->WriteKeyString("cluster", hdgPart->GetPartName()); - fJSON->BeginKeyList("values"); - dgValue = hdgValueSet->GetHeadValue(); - while (dgValue != NULL) - { - hdgValue = cast(CCHDGSymbolValue*, dgValue); - if (hdgValue->GetValueFrequency() > 0) - fJSON->WriteString(hdgValue->GetSymbolValue().GetValue()); - hdgValueSet->GetNextValue(dgValue); - } - fJSON->EndList(); + // Partie suivante + attribute->GetNextPart(dgPart); + } + fJSON->EndArray(); + } - // Effectifs des effectifs - fJSON->BeginKeyList("valueFrequencies"); - dgValue = hdgValueSet->GetHeadValue(); - while (dgValue != NULL) - { - hdgValue = cast(CCHDGSymbolValue*, dgValue); - if (hdgValue->GetValueFrequency() > 0) - fJSON->WriteInt(hdgValue->GetValueFrequency()); - hdgValueSet->GetNextValue(dgValue); - } - fJSON->EndList(); + // Traitement des attributs categoriels + else if (attribute->GetAttributeType() == KWType::Symbol) + { + // Parcours des parties + fJSON->BeginKeyArray("valueGroups"); + nDefaultGroupIndex = -1; + nIndex = 0; + dgPart = attribute->GetHeadPart(); + while (dgPart != NULL) + { + dgValueSet = dgPart->GetValueSet(); + hdgPart = cast(CCHDGPart*, dgPart); - // Typicalite des valeurs - fJSON->BeginKeyList("valueTypicalities"); - dgValue = hdgValueSet->GetHeadValue(); - while (dgValue != NULL) - { - hdgValue = cast(CCHDGSymbolValue*, dgValue); - if (hdgValue->GetValueFrequency() > 0) - fJSON->WriteDouble(hdgValue->GetTypicality()); - hdgValueSet->GetNextValue(dgValue); - } - fJSON->EndList(); - fJSON->EndObject(); + // Memorisation de l'index du groupe par defaut + if (dgValueSet->IsDefaultPart()) + nDefaultGroupIndex = nIndex; + nIndex++; - // Partie suivante - dgAttribute->GetNextPart(dgPart); + // Parcours des valeurs, sauf si effectif nul (cas de la StarValue) + fJSON->BeginObject(); + fJSON->WriteKeyString("cluster", hdgPart->GetPartName()); + fJSON->BeginKeyList("values"); + dgValue = dgValueSet->GetHeadValue(); + while (dgValue != NULL) + { + if (dgValue->GetValueFrequency() > 0) + fJSON->WriteString(dgValue->GetObjectLabel()); + dgValueSet->GetNextValue(dgValue); } - fJSON->EndArray(); + fJSON->EndList(); - // Index du groupe par defaut - assert(nDefaultGroupIndex >= 0); - fJSON->WriteKeyInt("defaultGroupIndex", nDefaultGroupIndex); - } - // CH IV Begin - // Traitement des attributs de type VarPart - if (dgAttribute->GetAttributeType() == KWType::VarPart) - { - // Parcours des variables de l'attribut - fJSON->BeginKeyArray("innerVariables"); - nIndex = 0; - for (nIndex = 0; nIndex < dgAttribute->GetInnerAttributeNumber(); nIndex++) + // Effectifs des valeurs + fJSON->BeginKeyList("valueFrequencies"); + dgValue = dgValueSet->GetHeadValue(); + while (dgValue != NULL) { - // Extraction de l'attribut - innerAttribute = dgAttribute->GetInnerAttributeAt(nIndex); - assert(innerAttribute != NULL); - - // Partition de la variable interne - fJSON->BeginObject(); - WriteJSONInnerAttributePartition(coclusteringDataGrid, innerAttribute, fJSON); - fJSON->EndObject(); + if (dgValue->GetValueFrequency() > 0) + fJSON->WriteInt(dgValue->GetValueFrequency()); + dgValueSet->GetNextValue(dgValue); } - fJSON->EndArray(); + fJSON->EndList(); - // Parcours des groupes de parties de variable - fJSON->BeginKeyArray("valueGroups"); - // nDefaultGroupIndex = -1; - // nIndex = 0; - dgPart = dgAttribute->GetHeadPart(); - while (dgPart != NULL) + if (not attribute->IsInnerAttribute()) { - hdgPart = cast(CCHDGPart*, dgPart); - hdgVarPartSet = cast(CCHDGVarPartSet*, hdgPart->GetVarPartSet()); - - // Memorisation de l'index du groupe par defaut - // CH IV Refactoring: faut-il gere un default groupe index??? - // if (hdgVarPartSet->IsDefaultPart()) - // nDefaultGroupIndex = nIndex; - // nIndex++; - - // Parcours des parties de variable, sauf si effectif null - fJSON->BeginObject(); - fJSON->WriteKeyString("cluster", hdgPart->GetPartName()); - fJSON->BeginKeyList("values"); - dgVarPartValue = hdgVarPartSet->GetHeadValue(); - while (dgVarPartValue != NULL) - { - hdgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartValue); - - if (hdgVarPartValue->GetValueFrequency() > 0) - fJSON->WriteString(hdgVarPartValue->GetVarPart()->GetVarPartLabel()); - - hdgVarPartSet->GetNextValue(dgVarPartValue); - } - fJSON->EndList(); - - // Effectifs des valeurs de type partie de variable - fJSON->BeginKeyList("valueFrequencies"); - dgVarPartValue = hdgVarPartSet->GetHeadValue(); - while (dgVarPartValue != NULL) - { - hdgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartValue); - if (hdgVarPartValue->GetValueFrequency() > 0) - fJSON->WriteInt(hdgVarPartValue->GetValueFrequency()); - hdgVarPartSet->GetNextValue(dgVarPartValue); - } - fJSON->EndList(); - - // Typicalite des valeurs de type partie de variable + // Typicalite des valeurs fJSON->BeginKeyList("valueTypicalities"); - dgVarPartValue = hdgVarPartSet->GetHeadValue(); - while (dgVarPartValue != NULL) + dgValue = dgValueSet->GetHeadValue(); + while (dgValue != NULL) { - hdgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartValue); - if (hdgVarPartValue->GetValueFrequency() > 0) - fJSON->WriteDouble(hdgVarPartValue->GetTypicality()); - hdgVarPartSet->GetNextValue(dgVarPartValue); + hdgValue = cast(CCHDGSymbolValue*, dgValue); + if (hdgValue->GetValueFrequency() > 0) + fJSON->WriteDouble(hdgValue->GetTypicality()); + dgValueSet->GetNextValue(dgValue); } fJSON->EndList(); - fJSON->EndObject(); - - // Partie suivante - dgAttribute->GetNextPart(dgPart); } - fJSON->EndArray(); - - // Index du groupe par defaut - // assert(nDefaultGroupIndex >= 0); - // fJSON->WriteKeyInt("defaultGroupIndex", nDefaultGroupIndex); - } - // CH IV End - // Fin de l'objet - fJSON->EndObject(); - } - fJSON->EndArray(); -} -// CH IV Begin -void CCCoclusteringReport::WriteJSONInnerAttributePartition(const CCHierarchicalDataGrid* coclusteringDataGrid, - KWDGAttribute* innerAttribute, JSONFile* fJSON) -{ - KWDGPart* currentPart; - KWDGValue* currentValue; - KWDGInterval* domainBounds; - ObjectArray oaValues; - ObjectArray oaAllValues; - KWDGValue* dgValue; - int nValue; + fJSON->EndObject(); - require(coclusteringDataGrid != NULL); - require(innerAttribute != NULL); - require(KWType::IsSimple(innerAttribute->GetAttributeType())); - require(fJSON != NULL); + // Partie suivante + attribute->GetNextPart(dgPart); + } + fJSON->EndArray(); - // Entete - fJSON->WriteKeyString("variable", innerAttribute->GetAttributeName()); - fJSON->WriteKeyString("type", KWType::ToString(innerAttribute->GetAttributeType())); + // Index du groupe par defaut + assert(nDefaultGroupIndex >= 0); + fJSON->WriteKeyInt("defaultGroupIndex", nDefaultGroupIndex); + } - // Partition de la variable - // Cas d'un attribut Continuous - if (innerAttribute->GetAttributeType() == KWType::Continuous) + // Traitement des attributs de type VarPart + else if (attribute->GetAttributeType() == KWType::VarPart) { - fJSON->BeginKeyArray("partition"); - - // Acces aux bornes du domaine - domainBounds = cast(KWDGInterval*, coclusteringDataGrid->GetInnerAttributeDomainBounds()->Lookup( - innerAttribute->GetAttributeName())); - assert(domainBounds != NULL); + // Descriptif des innerVariables + WriteJSONInnerAttributesDimensionSummaries(attribute, fJSON); - // Parcours des parties de l'attribut - currentPart = innerAttribute->GetHeadPart(); - while (currentPart != NULL) + // Parcours des variables de l'attribut + fJSON->BeginKeyArray("innerVariables"); + nIndex = 0; + for (nIndex = 0; nIndex < attribute->GetInnerAttributeNumber(); nIndex++) { - fJSON->BeginList(); + // Extraction de l'attribut interne + innerAttribute = attribute->GetInnerAttributeAt(nIndex); + assert(innerAttribute != NULL); - // Borne inf, ou min pour le premier intervalle non Missing - if (currentPart == innerAttribute->GetHeadPart()) - fJSON->WriteContinuous(domainBounds->GetLowerBound()); - else - fJSON->WriteContinuous(currentPart->GetInterval()->GetLowerBound()); + // Debut de l'objet + fJSON->BeginObject(); + fJSON->WriteKeyString("name", innerAttribute->GetAttributeName()); + fJSON->WriteKeyString("type", KWType::ToString(KWType::GetCoclusteringSimpleType( + innerAttribute->GetAttributeType()))); - // Cas de + inf - if (currentPart == innerAttribute->GetTailPart()) - fJSON->WriteContinuous(domainBounds->GetUpperBound()); - else - fJSON->WriteContinuous(currentPart->GetInterval()->GetUpperBound()); - fJSON->EndList(); + // Ecriture de la partition + WriteJSONAttributePartition(innerAttribute, coclusteringDataGrid, fJSON); - // Partie suivante - innerAttribute->GetNextPart(currentPart); + // Fin de l'objet + fJSON->EndObject(); } fJSON->EndArray(); - } - - // Sinon, attribut Categoriel - else - { - fJSON->BeginKeyList("partition"); - currentPart = innerAttribute->GetHeadPart(); - // Parcours des parties de l'attribut - while (currentPart != NULL) + // Parcours des groupes de parties de variable + fJSON->BeginKeyArray("valueGroups"); + // nDefaultGroupIndex = -1; + // nIndex = 0; + dgPart = attribute->GetHeadPart(); + while (dgPart != NULL) { - // Parcours des valeurs de la partie - fJSON->BeginList(); - currentValue = currentPart->GetValueSet()->GetHeadValue(); - while (currentValue != NULL) - { - fJSON->WriteString(currentValue->GetSymbolValue().GetValue()); + dgValueSet = dgPart->GetValueSet(); + hdgPart = cast(CCHDGPart*, dgPart); + + // Memorisation de l'index du groupe par defaut + // CH IV Refactoring: faut-il gere un default groupe index??? + // if (hdgVarPartSet->IsDefaultPart()) + // nDefaultGroupIndex = nIndex; + // nIndex++; - // Valeur suivante - currentPart->GetValueSet()->GetNextValue(currentValue); + // Parcours des parties de variable, sauf si effectif nul + fJSON->BeginObject(); + fJSON->WriteKeyString("cluster", hdgPart->GetPartName()); + fJSON->BeginKeyList("values"); + dgValue = dgValueSet->GetHeadValue(); + while (dgValue != NULL) + { + if (dgValue->GetValueFrequency() > 0) + fJSON->WriteString(dgValue->GetObjectLabel()); + dgValueSet->GetNextValue(dgValue); } fJSON->EndList(); - // Partie suivante - innerAttribute->GetNextPart(currentPart); - } - fJSON->EndList(); - } - - // Identifiant des parties de variable utilises pour decrire les clusters de parties de variable - fJSON->BeginKeyList("varPartIds"); - - // Parcours des parties de l'attribut - currentPart = innerAttribute->GetHeadPart(); - while (currentPart != NULL) - { - // Ecriture de l'identifiant de la partie de variable, sous la forme d'un libelle lisible dont l'unicite - // est assuree par la methode GetObjectLabel des parties de variables - fJSON->WriteString(currentPart->GetVarPartLabel()); - - // Partie suivante - innerAttribute->GetNextPart(currentPart); - } - fJSON->EndList(); + // Effectifs des valeurs + fJSON->BeginKeyList("valueFrequencies"); + dgValue = dgValueSet->GetHeadValue(); + while (dgValue != NULL) + { + if (dgValue->GetValueFrequency() > 0) + fJSON->WriteInt(dgValue->GetValueFrequency()); + dgValueSet->GetNextValue(dgValue); + } + fJSON->EndList(); - // Ecriture des valeurs et de leur effectif dans les cas des attribut Symbol - // CH IV Refactoring: il faut decider d'un formatbd'export des valeurs et leur effectif, - // soit global, soit par groupe de facon similaire a l'existant - // En attendant, on ne fait rien - // Il faudra egalement implementer la relecture de ces valeurs lors de la lecture d'un json - boolean bExportValues = false; - if (bExportValues and innerAttribute->GetAttributeType() == KWType::Symbol) - { - // Export de toutes les valeurs des parties de l'attribut - currentPart = innerAttribute->GetHeadPart(); - while (currentPart != NULL) - { - currentPart->GetValueSet()->ExportValues(&oaValues); - oaAllValues.InsertObjectArrayAt(oaAllValues.GetSize(), &oaValues); - oaValues.SetSize(0); + // Typicalite des valeurs + fJSON->BeginKeyList("valueTypicalities"); + dgValue = dgValueSet->GetHeadValue(); + while (dgValue != NULL) + { + hdgVarPartValue = cast(CCHDGVarPartValue*, dgValue); + if (dgValue->GetValueFrequency() > 0) + fJSON->WriteDouble(hdgVarPartValue->GetTypicality()); + dgValueSet->GetNextValue(dgValue); + } + fJSON->EndList(); + fJSON->EndObject(); // Partie suivante - innerAttribute->GetNextPart(currentPart); + attribute->GetNextPart(dgPart); } + fJSON->EndArray(); - // Tri des valeurs - oaAllValues.SetCompareFunction(KWDGValueCompareFrequency); - oaAllValues.Sort(); - - // Ecriture des valeurs - fJSON->BeginKeyList("values"); - for (nValue = 0; nValue < oaAllValues.GetSize(); nValue++) - { - dgValue = cast(KWDGValue*, oaAllValues.GetAt(nValue)); - if (dgValue->GetValueFrequency() > 0) - fJSON->WriteString(dgValue->GetSymbolValue().GetValue()); - } - fJSON->EndList(); - - // Effectifs des effectifs - fJSON->BeginKeyList("valueFrequencies"); - for (nValue = 0; nValue < oaAllValues.GetSize(); nValue++) - { - dgValue = cast(KWDGValue*, oaAllValues.GetAt(nValue)); - if (dgValue->GetValueFrequency() > 0) - fJSON->WriteInt(dgValue->GetValueFrequency()); - } - fJSON->EndList(); + // Index du groupe par defaut + // assert(nDefaultGroupIndex >= 0); + // fJSON->WriteKeyInt("defaultGroupIndex", nDefaultGroupIndex); } } -// CH IV End void CCCoclusteringReport::WriteJSONDimensionHierarchies(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON) diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.h b/src/Learning/MODL_Coclustering/CCCoclusteringReport.h index 46d57f171..15d1ba7e6 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.h +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.h @@ -160,20 +160,16 @@ class CCCoclusteringReport : public Object boolean InternalReadJSONReport(CCHierarchicalDataGrid* coclusteringDataGrid, boolean bHeaderOnly); boolean ReadJSONSummary(CCHierarchicalDataGrid* coclusteringDataGrid); boolean ReadJSONDimensionSummaries(CCHierarchicalDataGrid* coclusteringDataGrid); - boolean ReadJSONDimensionPartitions(CCHierarchicalDataGrid* coclusteringDataGrid); - boolean ReadJSONInterval(CCHDGAttribute* dgAttribute, KWDGPart* dgPart); - boolean ReadJSONValueGroup(CCHDGAttribute* dgAttribute, KWDGPart* dgPart); - // CH IV Begin - // Est ce que ces methodes doivent prendre en entree des KWDGAttribute ou des CCHDGAttriute - // Les innerAttribute peuvent ils etre des CCHDGAttribute ? (CCHDGAttribute* dgAttribute); - // Pour l'instant on met des KWDGAttribute pour que cela compile - boolean ReadJSONInnerAttribute(KWDGAttribute* innerAttribute, ObjectDictionary* odInnerAttributesAllVarParts); + boolean ReadJSONDimensionPartitionsNew(CCHierarchicalDataGrid* coclusteringDataGrid); + boolean ReadJSONInnerAttributesDimensionSummaries(KWDGAttribute* dgAttribute); + boolean ReadJSONAttributePartition(KWDGAttribute* attribute, CCHierarchicalDataGrid* coclusteringDataGrid); + boolean ReadJSONInterval(KWDGAttribute* dgAttribute, KWDGPart* dgPart); + boolean ReadJSONValueGroup(KWDGAttribute* dgAttribute, KWDGPart* dgPart); boolean ReadJSONInnerAttributeIntervals(KWDGAttribute* innerAttribute); boolean ReadJSONInnerAttributeValueGroups(KWDGAttribute* innerAttribute); - boolean ReadJSONVarPartAttributeValueGroup(CCHDGAttribute* varPartAttribute, KWDGPart* dgPart, + boolean ReadJSONVarPartAttributeValueGroup(KWDGAttribute* varPartAttribute, KWDGPart* dgPart, const ObjectDictionary* odInnerAttributesAllVarParts, ObjectDictionary* odVarPartAttributeAllVarParts); - // CH IV End boolean ReadJSONDimensionHierarchies(CCHierarchicalDataGrid* coclusteringDataGrid); boolean ReadJSONCells(CCHierarchicalDataGrid* coclusteringDataGrid); @@ -181,11 +177,10 @@ class CCCoclusteringReport : public Object void InternalWriteJSONReport(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); void WriteJSONSummary(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); void WriteJSONDimensionSummaries(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); + void WriteJSONInnerAttributesDimensionSummaries(const KWDGAttribute* vartPartAttribute, JSONFile* fJSON); void WriteJSONDimensionPartitions(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); - // CH IV Begin - void WriteJSONInnerAttributePartition(const CCHierarchicalDataGrid* coclusteringDataGrid, - KWDGAttribute* innerAttribute, JSONFile* fJSON); - // CH IV End + void WriteJSONAttributePartition(KWDGAttribute* attribute, const CCHierarchicalDataGrid* coclusteringDataGrid, + JSONFile* fJSON); void WriteJSONDimensionHierarchies(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); void WriteJSONCells(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp index b6528d826..a49a6e07b 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp @@ -179,6 +179,11 @@ boolean CCHierarchicalDataGrid::CheckHierarchy() const return bOk; } +const ALString CCHierarchicalDataGrid::GetClassLabel() const +{ + return "Hierarchichal data grid"; +} + KWDGAttribute* CCHierarchicalDataGrid::NewAttribute() const { return new CCHDGAttribute; @@ -1059,6 +1064,11 @@ CCHDGVarPartSet::CCHDGVarPartSet() {} CCHDGVarPartSet::~CCHDGVarPartSet() {} +KWDGValueSet* CCHDGVarPartSet::Create() const +{ + return new CCHDGVarPartSet; +} + void CCHDGVarPartSet::SortVarPartsByTypicality() { InternalSortValues(CCHDGVarPartValueCompareDecreasingTypicality); @@ -1067,23 +1077,23 @@ void CCHDGVarPartSet::SortVarPartsByTypicality() boolean CCHDGVarPartSet::CheckHierarchy() const { boolean bOk = true; - KWDGValue* varPartValue; + KWDGValue* value; // Pas de controle d'integrite de base (invalide pour els parties non feuilles) // Test des valeurs de la partie if (bOk) { // Parcours des valeurs de la partie - varPartValue = GetHeadValue(); - while (varPartValue != NULL) + value = GetHeadValue(); + while (value != NULL) { // Test de la valeur - bOk = cast(CCHDGVarPartValue*, varPartValue)->CheckHierarchy(); + bOk = cast(CCHDGVarPartValue*, value)->CheckHierarchy(); if (not bOk) break; // Valeur suivante - GetNextValue(varPartValue); + GetNextValue(value); } } return bOk; diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h index d24420d5e..2ecf25e51 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h @@ -76,8 +76,8 @@ class CCHierarchicalDataGrid : public KWDataGrid // Acces aux dictionnaire des bornes de domaine des attribut internes numeriques, via des KWDGInterval // Ce parametrage permet de memoriser les bornes des intervalles extremes pour les attribut internes, - // afin d'ecrire les varies bornes plutot que -in et +inf dans les rapports json - // Memoire; appartient a l'appele, ainsi que son contenu, qui doit etre gere par l'appelant + // afin d'ecrire les vraies bornes plutot que -inf et +inf dans les rapports json + // Memoire appartient a l'appele, ainsi que son contenu, qui doit etre gere par l'appelant ObjectDictionary* GetInnerAttributeDomainBounds() const; // CH IV End @@ -90,6 +90,9 @@ class CCHierarchicalDataGrid : public KWDataGrid // Verification de l'integrite pour les infos supplementaires sur la hierarchie boolean CheckHierarchy() const; + // Libelles utilisateur + const ALString GetClassLabel() const override; + /////////////////////////////// //// Implementation protected: @@ -373,6 +376,9 @@ class CCHDGVarPartSet : public KWDGVarPartSet CCHDGVarPartSet(); ~CCHDGVarPartSet(); + // Creation + KWDGValueSet* Create() const override; + // Tri des parties de variable par typicalite decroissante void SortVarPartsByTypicality(); From 97f08e8bdb57809488de71d17e2f8e8145f5c165 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Wed, 13 Sep 2023 17:25:25 +0200 Subject: [PATCH 10/17] Collect profiling stats for coclustering IV optimization Nouvelle classe Profiler ajoutee dans la librairie KWData - fichier de stats via la collecte des stats par methode tracee (nombre d'appels, duree moyenne) - ajout optionnel de traces dans un fichier json, pour avoir la dynamique des appels - declaration d'un pointeur vers le fichier de trace JSONFile plutot qu'un objet directement - cet objet est alloue/desalloue le temps d'un session de profiling - cela permet de bien controler la memoire, meme si l'on declare des Profiler globaux en statique - Attention: il ne faut pas utiliser les JSONFile en statique directement ou indirectement - la classe JSONFile utiliser deja une instance en statique pour gerer l'inititialisation de des structure globales, et cela ne pose pas de probleme a l'allocateur memoire - par contre, une deuxime instance de JSONFile en statique creer des problemes pour l'allocateur, qui diagnostique a tor des fuites memoire (les variable statique snt allouee avant le debut et desalloue apres le fin du main()): resoudre ce probleme est long et sans interet Utilisation du Profiler via des methodes de classe de KWDataGridOptimizer - uniquement methode GetProfiler - Acces au profiler global permettant d'enregistrer toute une session de profiling de l'optimisation d'une grille de coclustering - choix des methodes a profiler et des noms externes pour faciliter leur interpretation - Compute coclustering - (CCCoclusteringBuilder::ComputeCoclustering) - Optimize VarPart prepartition - (CCCoclusteringBuilder::OptimizeVarPartDataGrid) - Optimize last granularity - (KWDataGridOptimizer::OptimizeDataGrid) - VNS optimize - (KWDataGridVNSOptimizer::IterativeVNSOptimizeDataGrid) - Optimize solution -(KWDataGridVNSOptimizer::VNSOptimize(VarPart)DataGrid) - Pre-optimization - (KWDataGridVNSOptimizer::OptimizeSolution) - Greedy merge optimization - (KWDataGridVNSOptimizer::OptimizeSolution) - Post-optimization - (KWDataGridVNSOptimizer::OptimizeSolution) - Post-optimization IV - (KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid) - Optimize granularity - (KWDataGridOptimizer::OptimizeDataGrid) - pilotage global depuis CCCoclusteringBuilder::ComputeCoclustering - lancement ou non du profiling - stats sur les donnees de la grille initiale Tests sur LearningTest\TestCoclustering\y_CoclusteringIV_Standard et LearningTest\TestCoclustering\Standard\Iris --- src/Learning/KWData/CMakeLists.txt | 1 + src/Learning/KWData/JSONFile.h | 4 +- src/Learning/KWData/Profiler.cpp | 182 ++++++++++++++++++ src/Learning/KWData/Profiler.h | 78 ++++++++ .../KWDataPreparation/KWDataGridOptimizer.cpp | 56 +++++- .../KWDataPreparation/KWDataGridOptimizer.h | 21 ++ .../CCCoclusteringBuilder.cpp | 78 ++++++-- .../MODL_Coclustering/MODL_Coclustering.cpp | 6 +- 8 files changed, 406 insertions(+), 20 deletions(-) create mode 100644 src/Learning/KWData/Profiler.cpp create mode 100644 src/Learning/KWData/Profiler.h diff --git a/src/Learning/KWData/CMakeLists.txt b/src/Learning/KWData/CMakeLists.txt index 3f8cd2a85..5b0d2d870 100644 --- a/src/Learning/KWData/CMakeLists.txt +++ b/src/Learning/KWData/CMakeLists.txt @@ -70,6 +70,7 @@ add_library( KWValueBlock.cpp KWValueDictionary.cpp KWValueSparseVector.cpp + Profiler.cpp ${BISON_KWCParser_OUTPUTS} ${FLEX_KWCScanner_OUTPUTS} ${FLEX_JsonScanner_OUTPUTS}) diff --git a/src/Learning/KWData/JSONFile.h b/src/Learning/KWData/JSONFile.h index 00f788d5d..27c39e421 100644 --- a/src/Learning/KWData/JSONFile.h +++ b/src/Learning/KWData/JSONFile.h @@ -270,6 +270,8 @@ class JSONFile : public Object static KWIntVectorSorter ivsWindows1252ControlCharUtf8CodeSorter; // Instance statique de JSONFile, permettant de forcer l'initialisation des structure d'encodage une fois - // pour toute lors de l'appel du constructuer de cette instance + // pour toute lors de l'appel du constructeur de cette instance + // Ne pas declarer d'autre instances statiques de JSONFile, par exemple via d'autre classes, sinon cela pose + // des probleme de memoire non liberee non diagnostique par les outils de getsion de la memoire static JSONFile jsonFileGlobalInitializer; }; diff --git a/src/Learning/KWData/Profiler.cpp b/src/Learning/KWData/Profiler.cpp new file mode 100644 index 000000000..0c645201d --- /dev/null +++ b/src/Learning/KWData/Profiler.cpp @@ -0,0 +1,182 @@ +// Copyright (c) 2023 Orange. All rights reserved. +// This software is distributed under the BSD 3-Clause-clear License, the text of which is available +// at https://spdx.org/licenses/BSD-3-Clause-Clear.html or see the "LICENSE" file for more details. + +#include "Profiler.h" + +Profiler::Profiler() +{ + bIsTrace = false; + bIsStarted = false; + fJsonTraceFile = NULL; +} + +Profiler::~Profiler() +{ + assert(not bIsStarted); + assert(fJsonTraceFile == NULL); + odMethodTimers.DeleteAll(); + svMethodNames.SetSize(0); +} + +void Profiler::Start(const ALString& sFileName) +{ + require(sFileName != ""); + require(not bIsStarted); + assert(fJsonTraceFile == NULL); + + // Memorisation du nom du fichier + bIsStarted = true; + sProfilingStatsFileName = sFileName; + + // Ouverture du fichier de trace + if (bIsTrace) + { + fJsonTraceFile = new JSONFile; + fJsonTraceFile->SetFileName(sFileName + ".json"); + fJsonTraceFile->OpenForWrite(); + } +} + +const ALString& Profiler::GetFileName() const +{ + return sProfilingStatsFileName; +} + +void Profiler::Stop() +{ + fstream fstProfilingStats; + ALString sMethodName; + Timer* methodTimer; + int i; + boolean bOk; + + require(bIsStarted); + require(odMethodTimers.GetCount() == svMethodNames.GetSize()); + + // Fermeture du fichier de trace + if (bIsTrace) + { + assert(fJsonTraceFile != NULL); + fJsonTraceFile->Close(); + delete fJsonTraceFile; + fJsonTraceFile = NULL; + } + + // Ecriture du fichier de stats + bOk = FileService::OpenOutputFile(sProfilingStatsFileName, fstProfilingStats); + if (bOk) + { + fstProfilingStats << "Method\tCall\tMean time\tTotal time\n"; + for (i = 0; i < svMethodNames.GetSize(); i++) + { + sMethodName = svMethodNames.GetAt(i); + methodTimer = cast(Timer*, odMethodTimers.Lookup(sMethodName)); + assert(methodTimer != NULL); + assert(not methodTimer->IsStarted()); + + // Affichage des stats + fstProfilingStats << sMethodName << "\t"; + fstProfilingStats << methodTimer->GetStartNumber() << "\t"; + fstProfilingStats << methodTimer->GetMeanElapsedTime() << "\t"; + fstProfilingStats << methodTimer->GetElapsedTime() << "\n"; + } + + FileService::CloseOutputFile(sProfilingStatsFileName, fstProfilingStats); + } + + // Nettoyage + sProfilingStatsFileName = ""; + odMethodTimers.DeleteAll(); + svMethodNames.SetSize(0); + bIsStarted = false; +} + +boolean Profiler::IsStarted() +{ + return bIsStarted; +} + +void Profiler::BeginMethod(const ALString& sMethodName) +{ + Timer* methodTimer; + ALString sJsonKey; + + require(sMethodName != ""); + + // Methode active uniquement si le profiling est demarre + if (bIsStarted) + { + // Recherche ou creation du timer correspondant a la methode dans le dictionnaire + methodTimer = cast(Timer*, odMethodTimers.Lookup(sMethodName)); + if (methodTimer == NULL) + { + methodTimer = new Timer; + odMethodTimers.SetAt(sMethodName, methodTimer); + svMethodNames.Add(sMethodName); + } + + // Mise a jour des stats du timer + methodTimer->Start(); + + // Ecriture de la trace + if (bIsTrace) + { + // On suffixe le nom de la methode par le nombre d'appel, d'une part a titre informatif, + // d'autre part pour genere un fichier json valide en evitant les cles identiques + // potentiellement dans les meme sections + sJsonKey = sMethodName; + sJsonKey += "["; + sJsonKey += IntToString(methodTimer->GetStartNumber()); + sJsonKey += "]"; + fJsonTraceFile->BeginKeyObject(sJsonKey); + } + } +} + +void Profiler::EndMethod(const ALString& sMethodName) +{ + Timer* methodTimer; + + require(sMethodName != ""); + + // Methode active uniquement si le profiling est demarre + if (bIsStarted) + { + // Recherche du timer correspondant a la methode dans le dictionnaire + methodTimer = cast(Timer*, odMethodTimers.Lookup(sMethodName)); + assert(methodTimer != NULL); + + // Mise a jour des stats du timer + methodTimer->Stop(); + + // Ecriture de la trace + if (bIsTrace) + fJsonTraceFile->EndObject(); + } +} + +void Profiler::WriteKeyString(const ALString& sKey, const ALString& sValue) +{ + require(sKey != ""); + require(sValue != ""); + + // Methode active uniquement si le profiling est demarre + if (bIsStarted) + { + // Ecriture de la trace + if (bIsTrace) + fJsonTraceFile->WriteKeyString(sKey, sValue); + } +} + +void Profiler::SetTrace(boolean bTrace) +{ + require(not bIsStarted); + bIsTrace = bTrace; +} + +boolean Profiler::GetTrace() const +{ + return bIsTrace; +} diff --git a/src/Learning/KWData/Profiler.h b/src/Learning/KWData/Profiler.h new file mode 100644 index 000000000..cccbe1dc2 --- /dev/null +++ b/src/Learning/KWData/Profiler.h @@ -0,0 +1,78 @@ +// Copyright (c) 2023 Orange. All rights reserved. +// This software is distributed under the BSD 3-Clause-clear License, the text of which is available +// at https://spdx.org/licenses/BSD-3-Clause-Clear.html or see the "LICENSE" file for more details. + +#pragma once + +class Profiler; + +#include "Object.h" +#include "ALString.h" +#include "JSONFile.h" +#include "FileService.h" +#include "Timer.h" + +/////////////////////////////////////////////// +// Collecte de stats et de trace de profiling +class Profiler : public Object +{ +public: + // Constructeur + Profiler(); + ~Profiler(); + + ///////////////////////////////////////////////////////////////////// + // Gestion d'une session de profiling + + // Debut du profiling, en precisant un nom de fichier ou seront ecrites + // les statistiques sur les traces, avec les indicateurs suivants par methode: + // nombre d'appels, temps moyen, temps total + void Start(const ALString& sFileName); + + // Nom du fichier de profiling + const ALString& GetFileName() const; + + // Arret du profiling + // Declenche l'ecriture du fichier de stats resultats et reinitialise le nom du fichier + void Stop(); + + // Indique si le profiling est en cours + boolean IsStarted(); + + // Collecte des debut et fin de methode, pour actualiser les stats de profiling + // Methode sans effet si le profiling n'est pas demarre + void BeginMethod(const ALString& sMethodName); + void EndMethod(const ALString& sMethodName); + + // Ajout d'une information de type cle-valeur dans la trace, si elle est active + void WriteKeyString(const ALString& sKey, const ALString& sValue); + + // Parametrage avance pour exporter toute la trace de profiling (defaut: false) + // Dans ce cas, un fichier de trace complet est ecrit au format json, de nom + // egal au fichier de stats profiling, avec un suffix ".json" additionnel + void SetTrace(boolean bTrace); + boolean GetTrace() const; + + ////////////////////////////////////////////////////////////////////////////// + ///// Implementation +protected: + // Nom du fichier de stats de profiling + ALString sProfilingStatsFileName; + + // Indique si le profiling est en cours + boolean bIsStarted; + + // Dictionnaire de timers par methode + ObjectDictionary odMethodTimers; + + // Vecteur des nom de methode utilisees + StringVector svMethodNames; + + // Fichier json des traces de profiling + // On utilise un pointeur pour pemettre de declarer un objet Profiler en variable statique + // sans impact sur les objet JSONFile, qui ne doivent pas etre declares en statique + JSONFile* fJsonTraceFile; + + // Indique si l'on exporte les traces + boolean bIsTrace; +}; diff --git a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp index 1ddb9961b..a474d0eaa 100644 --- a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp @@ -85,7 +85,6 @@ double KWDataGridOptimizer::OptimizeDataGrid(const KWDataGrid* initialDataGrid, boolean bIsLastGranularity; boolean bOptimizationNeeded; boolean bWithMemoryMode; - ALString sTmp; ObjectDictionary odQuantileBuilders; IntVector ivPreviousPartNumber; IntVector ivCurrentPartNumber; @@ -102,6 +101,7 @@ double KWDataGridOptimizer::OptimizeDataGrid(const KWDataGrid* initialDataGrid, double dFusionDeltaCost; int nCurrentExploredGranularity; int nLastExploredGranularity; + ALString sTmp; dGranularityBestCost = DBL_MAX; dBestMergedCost = dGranularityBestCost; @@ -304,10 +304,27 @@ double KWDataGridOptimizer::OptimizeDataGrid(const KWDataGrid* initialDataGrid, if (bWithMemoryMode and dBestCost < dGranularityBestCost) dataGridManager.CopyDataGrid(optimizedDataGrid, granularizedOptimizedDataGrid); + // Parametrage du profiling + if (bIsLastGranularity) + KWDataGridOptimizer::GetProfiler()->BeginMethod("Optimize last granularity"); + else + KWDataGridOptimizer::GetProfiler()->BeginMethod("Optimize granularity"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Granularity index", + IntToString(nGranularityIndex)); + if (optimizedDataGrid->IsVarPartDataGrid()) + KWDataGridOptimizer::GetProfiler()->WriteKeyString( + "VarPart granularity", + IntToString( + optimizedDataGrid->GetInnerAttributes()->GetVarPartGranularity())); + // Optimisation de la grille granularisee dGranularityBestCost = OptimizeGranularizedDataGrid(&granularizedDataGrid, granularizedOptimizedDataGrid, bIsLastGranularity, dTotalTime); + if (bIsLastGranularity) + KWDataGridOptimizer::GetProfiler()->EndMethod("Optimize last granularity"); + else + KWDataGridOptimizer::GetProfiler()->EndMethod("Optimize granularity"); if (bDisplayResults) { @@ -1396,6 +1413,8 @@ void KWDataGridOptimizer::DisplayOptimizationDetails(const KWDataGrid* optimized } } +Profiler KWDataGridOptimizer::profiler; + ////////////////////////////////////////////////////////////////////////////////// // Classe KWDataGridVNSOptimizer @@ -1530,6 +1549,14 @@ double KWDataGridVNSOptimizer::IterativeVNSOptimizeDataGrid(const KWDataGrid* in // une taille minimale de voisinnage suffisante dDecreaseFactor = 1.0 / pow(dMinNeighbourhoodSize, 1.0 / (nIndexNumber + 1)); + // Parametrage du profiling + KWDataGridOptimizer::GetProfiler()->BeginMethod("VNS optimize"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString( + "Is VarPart", BooleanToString(currentDataGrid.IsVarPartDataGrid())); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Level", IntToString(nLevel)); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Index number", IntToString(nIndexNumber)); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Decrease factor", DoubleToString(dDecreaseFactor)); + // Optimisation a partir de la nouvelle solution // CH IV Begin // Cas d'un coclustering de variables @@ -1566,6 +1593,7 @@ double KWDataGridVNSOptimizer::IterativeVNSOptimizeDataGrid(const KWDataGrid* in dataGridManager.CopyDataGrid(¤tDataGrid, optimizedDataGrid); } } + KWDataGridOptimizer::GetProfiler()->EndMethod("VNS optimize"); // CH IV End // Test de fin de tache @@ -1624,8 +1652,15 @@ double KWDataGridVNSOptimizer::VNSOptimizeDataGrid(const KWDataGrid* initialData // Generation d'une solution dans un voisinnage de la meilleure solution GenerateNeighbourSolution(initialDataGrid, optimizedDataGrid, dNeighbourhoodSize, &neighbourDataGrid); + // Parametrage du profiling + KWDataGridOptimizer::GetProfiler()->BeginMethod("Optimize solution"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Index", IntToString(nIndex)); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("VNS neighbourhood size", + DoubleToString(dVNSNeighbourhoodSize)); + // Optimisation de cette solution dCost = OptimizeSolution(initialDataGrid, &neighbourDataGrid); + KWDataGridOptimizer::GetProfiler()->EndMethod("Optimize solution"); // Si amelioration: on la memorise if (dCost < dBestCost - dEpsilon) @@ -1707,8 +1742,15 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init // Generation d'une solution dans un voisinage de la meilleure solution GenerateNeighbourSolution(initialDataGrid, optimizedDataGrid, dNeighbourhoodSize, &neighbourDataGrid); + // Parametrage du profiling + KWDataGridOptimizer::GetProfiler()->BeginMethod("Optimize solution"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Index", IntToString(nIndex)); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("VNS neighbourhood size", + DoubleToString(dVNSNeighbourhoodSize)); + // Optimisation de cette solution dCost = OptimizeSolution(initialDataGrid, &neighbourDataGrid); + KWDataGridOptimizer::GetProfiler()->EndMethod("Optimize solution"); // CH AB AF a voir avec Marc // Cas ou l'on peut fusionner les parties de variable des cluster @@ -1829,11 +1871,17 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init cout << flush; } + // Parametrage du profiling + KWDataGridOptimizer::GetProfiler()->BeginMethod("Post-optimization IV"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString( + "Improvement number", IntToString(nImprovementNumber)); + // Exploration des deplacements pour tous les attributs GetDataGridOptimizer()->CCTimerPostIVOptimize.Start(); bImprovement = varPartDataGridPostOptimizer.PostOptimizeLightVarPartDataGrid( &partitionedReferencePostMergedDataGrid, &mergedDataGrid, &ivGroups); GetDataGridOptimizer()->CCTimerPostIVOptimize.Stop(); + KWDataGridOptimizer::GetProfiler()->EndMethod("Post-optimization IV"); if (bImprovement) { @@ -2027,21 +2075,25 @@ double KWDataGridVNSOptimizer::OptimizeSolution(const KWDataGrid* initialDataGri and initialDataGrid->GetAttributeNumber() > 1) // Fin CH RefontePrior2 { + KWDataGridOptimizer::GetProfiler()->BeginMethod("Pre-optimization"); GetDataGridOptimizer()->CCTimerPreOptimize.Start(); dCost = dataGridPostOptimizer.PostOptimizeDataGrid(initialDataGrid, dataGridMerger, false); if (bDisplay) cout << dCost << "\n"; GetDataGridOptimizer()->CCTimerPreOptimize.Stop(); + KWDataGridOptimizer::GetProfiler()->EndMethod("Pre-optimization"); } // Optimisation par fusion des groupes if (optimizationParameters.GetOptimize() and not TaskProgression::IsInterruptionRequested()) { + KWDataGridOptimizer::GetProfiler()->BeginMethod("Greedy merge optimization"); GetDataGridOptimizer()->CCTimerMainOptimize.Start(); dCost = dataGridMerger->Merge(); if (bDisplay) cout << dCost << "\n"; GetDataGridOptimizer()->CCTimerMainOptimize.Stop(); + KWDataGridOptimizer::GetProfiler()->EndMethod("Greedy merge optimization"); } // Post-optimisation de la grille if (optimizationParameters.GetPostOptimize() and @@ -2050,6 +2102,7 @@ double KWDataGridVNSOptimizer::OptimizeSolution(const KWDataGrid* initialDataGri and initialDataGrid->GetAttributeNumber() > 1) // Fin CH RefontePrior2) { + KWDataGridOptimizer::GetProfiler()->BeginMethod("Post-optimization"); GetDataGridOptimizer()->CCTimerPostOptimize.Start(); // Cas d'une optimisation legere (pour les granularites intermediaires en co-clustering) if (GetSlightOptimizationMode()) @@ -2058,6 +2111,7 @@ double KWDataGridVNSOptimizer::OptimizeSolution(const KWDataGrid* initialDataGri else dCost = dataGridPostOptimizer.PostOptimizeDataGrid(initialDataGrid, dataGridMerger, true); GetDataGridOptimizer()->CCTimerPostOptimize.Stop(); + KWDataGridOptimizer::GetProfiler()->EndMethod("Post-optimization"); if (bDisplay) cout << dCost << "\n\n"; } diff --git a/src/Learning/KWDataPreparation/KWDataGridOptimizer.h b/src/Learning/KWDataPreparation/KWDataGridOptimizer.h index ccc92210a..d8264aaa2 100644 --- a/src/Learning/KWDataPreparation/KWDataGridOptimizer.h +++ b/src/Learning/KWDataPreparation/KWDataGridOptimizer.h @@ -16,6 +16,7 @@ class CCCoclusteringOptimizer; #include "KWDataGridPostOptimizer.h" #include "KWDataGridOptimizerParameters.h" #include "SortedList.h" +#include "Profiler.h" #include "Timer.h" ////////////////////////////////////////////////////////////////////////////////// @@ -97,6 +98,15 @@ class KWDataGridOptimizer : public Object mutable Timer CCTimerPostOptimize; mutable Timer CCTimerPostIVOptimize; + ////////////////////////////////////////////////////////////////// + // Gestion d'un profiler dedie a l'optimisation des grilles + // Ce profiler doit etre demarre depuis le point d'entree de l'optimisation, + // et utilise par les methodes d'optimisation a profiler + + // Acces au profiler global permettant d'enregistrer toute une session de profiling + // de l'optimisation d'une grille de coclustering + static Profiler* GetProfiler(); + ////////////////////////////////////////////////////////////////////////////////////////////// ///// Implementation protected: @@ -162,6 +172,9 @@ class KWDataGridOptimizer : public Object // Epsilon d'optimisation double dEpsilon; + + // Profiler + static Profiler profiler; }; ////////////////////////////////////////////////////////////////////////////////// @@ -285,3 +298,11 @@ class KWDataGridVNSOptimizer : public Object // Epsilon d'optimisation double dEpsilon; }; + +//////////////////////// +// Methodes en inline + +inline Profiler* KWDataGridOptimizer::GetProfiler() +{ + return &profiler; +} diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp index 6e40a1abd..78aa40249 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp @@ -349,6 +349,7 @@ boolean CCCoclusteringBuilder::CheckVarPartSpecifications() const boolean CCCoclusteringBuilder::ComputeCoclustering() { boolean bOk = true; + boolean bProfileOptimisation = true; KWTupleTable tupleTable; KWTupleTable tupleFrequencyTable; CCCoclusteringOptimizer dataGridOptimizer; @@ -449,10 +450,48 @@ boolean CCCoclusteringBuilder::ComputeCoclustering() //////////////////////////////////////////////////////////////////////////////////// // Optimisation et post-optimisation de la grille + // Lancement du profiler pour l'optimisation des grilles + // Choix du fichier de trace a parametrer + if (bProfileOptimisation) + { + KWDataGridOptimizer::GetProfiler()->SetTrace(true); + KWDataGridOptimizer::GetProfiler()->Start("d:/temp/DataGridOptimizationProfiling" + + GetDatabase()->GetClassName() + ".txt"); + } + + // Trace de debut d'optimisation, avec informations sur la grille initiale + KWDataGridOptimizer::GetProfiler()->BeginMethod("Compute coclustering"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Dictionary", GetDatabase()->GetClassName()); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Database", GetDatabase()->GetDatabaseName()); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Instances", + IntToString(initialDataGrid->GetGridFrequency())); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Variables", + IntToString(initialDataGrid->GetAttributeNumber())); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Is VarPart", + BooleanToString(initialDataGrid->IsVarPartDataGrid())); + if (initialDataGrid->IsVarPartDataGrid()) + { + KWDataGridOptimizer::GetProfiler()->WriteKeyString( + initialDataGrid->GetAttributeAt(0)->GetAttributeName() + " values", + IntToString(initialDataGrid->GetAttributeAt(0)->GetInitialValueNumber())); + KWDataGridOptimizer::GetProfiler()->WriteKeyString( + initialDataGrid->GetAttributeAt(1)->GetAttributeName() + " values", + IntToString(initialDataGrid->GetAttributeAt(1)->GetInitialValueNumber())); + KWDataGridOptimizer::GetProfiler()->WriteKeyString( + "Inner variables", IntToString(initialDataGrid->GetInnerAttributes()->GetInnerAttributeNumber())); + } + // Optimisation de la grille, par appel d'une methode virtuelle if (bOk and not TaskProgression::IsInterruptionRequested()) OptimizeDataGrid(initialDataGrid, &optimizedDataGrid); + // Trace de fin d'optimisation + KWDataGridOptimizer::GetProfiler()->EndMethod("Compute coclustering"); + + // Arret du profiler + if (bProfileOptimisation) + KWDataGridOptimizer::GetProfiler()->Stop(); + // La solution est sauvegardee periodiquement grace au mode anytime // Nettoyage si aucune solution n'a encore ete trouvee if (coclusteringDataGrid == NULL) @@ -746,8 +785,12 @@ void CCCoclusteringBuilder::OptimizeVarPartDataGrid(const KWDataGrid* inputIniti // Le cout dPartitionBestCost est le cout de la grille antecedente de la meilleure // grille post-fusionnee (fusion des parties de variables consecutives dans un meme // cluster) + KWDataGridOptimizer::GetProfiler()->BeginMethod("Optimize VarPart prepartition"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Pre-partition index", + IntToString(nPrePartitionIndex)); dPartitionBestCost = dataGridOptimizer.OptimizeDataGrid(partitionedDataGrid, partitionedOptimizedDataGrid); + KWDataGridOptimizer::GetProfiler()->EndMethod("Optimize VarPart prepartition"); // Calcul du temps d'optimisation (depuis le debut) tAnyTimeTimer.Stop(); @@ -1629,9 +1672,11 @@ void CCCoclusteringBuilder::HandleOptimizationStep(const KWDataGrid* optimizedDa delete coclusteringDataGrid; coclusteringDataGrid = new CCHierarchicalDataGrid; - // Creation d'innerAttributes propre a la grille hierarchique afin qu'ils soient du type CCHDGAttribute comme les attributs de grille + // Copie de la grille pour calculer les infos du rapport en sortie + // Dans le cas VarPart, creation d'innerAttributes propre a la grille hierarchique + // afin qu'ils soient du type CCHDGAttribute comme les attributs de grille dataGridManager.CopyDataGridWithInnerAttributesCloned(optimizedDataGrid, coclusteringDataGrid); - assert(not coclusteringDataGrid->GetVarPartsShared()); + assert(not coclusteringDataGrid->IsVarPartDataGrid() or not coclusteringDataGrid->GetVarPartsShared()); // Memorisation de la description courte coclusteringDataGrid->SetShortDescription(GetShortDescription()); @@ -1730,6 +1775,9 @@ void CCCoclusteringBuilder::HandleOptimizationStep(const KWDataGrid* optimizedDa bWriteOk = coclusteringReport.WriteJSONReport(sReportFileName, coclusteringDataGrid); JSONFile::SetVerboseMode(true); + // Ajout de trace lie au profiling + KWDataGridOptimizer::GetProfiler()->WriteKeyString("NEW BEST LEVEL", DoubleToString(dLevel)); + // Sauvegarde au format Khc necessaire if (bWriteOk and GetExportAsKhc()) { @@ -1810,13 +1858,13 @@ boolean CCCoclusteringBuilder::CheckMemoryForDatabaseRead(KWDatabase* database) lEstimatedRecordNumber = (longint)(database->GetEstimatedObjectNumber() * dDatabasePercentage / 100); lFileMemory = lEstimatedRecordNumber * lRecordSize; lFileMemory += (longint)(lSourceFileSize * - (GetClass()->GetUsedAttributeNumberForType(KWType::Symbol) / + ((double)GetClass()->GetUsedAttributeNumberForType(KWType::Symbol) / GetClass()->GetNativeDataItemNumber()) * dDatabasePercentage / 100); lNecessaryMemory += lFileMemory; // Prise en compte d'une grille initiale "minimale" estimee de facon heuristique - lSizeOfCell = sizeof(KWDGMCell) + (2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*); + lSizeOfCell = sizeof(KWDGMCell) + ((longint)2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*); lInitialDataGridSize = sizeof(KWDataGrid) + nAttributeNumber * sizeof(KWDGAttribute) + (longint)(ceil(sqrt(lEstimatedRecordNumber * 1.0)) * nAttributeNumber * (lSizeOfCell + sizeof(KWDGMPart) + sizeof(KWDGMPartMerge) + @@ -2729,13 +2777,13 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridInitialization(KWDatabase* } if (dMaxCellNumber > nTupleNumber) dMaxCellNumber = nTupleNumber; - lSizeOfCell = sizeof(KWDGMCell) + (2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*); - lInitialDataGridSize += (int)ceil(sqrt(dMaxCellNumber)) * nAttributeNumber * lSizeOfCell; + lSizeOfCell = sizeof(KWDGMCell) + ((longint)2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*); + lInitialDataGridSize += (longint)ceil(sqrt(dMaxCellNumber)) * nAttributeNumber * lSizeOfCell; lNecessaryMemory += lInitialDataGridSize; // Plus une grille de travail, et une pour la meilleure solution (de taille estimee minimale) lWorkingDataGridSize = sizeof(KWDataGridMerger) + nAttributeNumber * sizeof(KWDGMAttribute) + - (int)ceil(sqrt(dMaxCellNumber)) * nAttributeNumber * + (longint)ceil(sqrt(dMaxCellNumber)) * nAttributeNumber * (lSizeOfCell + sizeof(KWDGMPart) + sizeof(KWDGMPartMerge) + sizeof(KWDGInterval) + sizeof(KWDGValueSet) + sizeof(KWDGValue)); lWorkingDataGridSize *= 2; @@ -2820,8 +2868,8 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridOptimization(KWDataGrid* in // Prise en compte d'une grille de travail et d'une grille pour la meilleure solution lNecessaryMemory = 0; - lMaxPartNumber = 1 + (int)ceil(pow(inputInitialDataGrid->GetGridFrequency(), - 1.0 / inputInitialDataGrid->GetAttributeNumber())); + lMaxPartNumber = (longint)1 + (longint)ceil(pow(inputInitialDataGrid->GetGridFrequency(), + 1.0 / inputInitialDataGrid->GetAttributeNumber())); lWorkingDataGridSize = sizeof(KWDataGridMerger) + sizeof(void*); lWorkingDataGridSize += sizeof(KWDataGrid) + sizeof(void*); lTotalPartMergeNumber = 0; @@ -2830,7 +2878,7 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridOptimization(KWDataGrid* in dgAttribute = inputInitialDataGrid->GetAttributeAt(nAttribute); // Prise en compte de la taille de stockage de l'attribut, de ses parties et valeurs - lWorkingDataGridSize += 2 * (sizeof(KWDGMAttribute) + sizeof(void*)); + lWorkingDataGridSize += (longint)2 * (sizeof(KWDGMAttribute) + sizeof(void*)); lPartNumber = dgAttribute->GetPartNumber(); if (lPartNumber > lMaxPartNumber) lPartNumber = lMaxPartNumber; @@ -2839,7 +2887,7 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridOptimization(KWDataGrid* in lWorkingDataGridSize += 2 * lPartNumber * (sizeof(KWDGMPart) + sizeof(KWDGInterval) + sizeof(void*)); else if (dgAttribute->GetAttributeType() == KWType::Symbol) - lWorkingDataGridSize += 2 * dgAttribute->GetPartNumber() * + lWorkingDataGridSize += (longint)2 * dgAttribute->GetPartNumber() * (sizeof(KWDGMPart) + sizeof(KWDGValueSet) + 2 * sizeof(void*)); // CH IV Begin else if (dgAttribute->GetAttributeType() == KWType::VarPart) @@ -2856,17 +2904,17 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridOptimization(KWDataGrid* in nInnerAttributePartNumber = innerAttribute->GetPartNumber(); if (innerAttribute->GetAttributeType() == KWType::Continuous) lWorkingDataGridSize += - 2 * nInnerAttributePartNumber * + (longint)2 * nInnerAttributePartNumber * (sizeof(KWDGMPart) + sizeof(KWDGInterval) + sizeof(void*)); else if (innerAttribute->GetAttributeType() == KWType::Symbol) lWorkingDataGridSize += - 2 * nInnerAttributePartNumber * + (longint)2 * nInnerAttributePartNumber * (sizeof(KWDGMPart) + sizeof(KWDGValueSet) + 2 * sizeof(void*)); } } // CH IV End } - lSizeOfCell = sizeof(KWDGMCell) + (2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*); + lSizeOfCell = sizeof(KWDGMCell) + ((longint)2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*); lWorkingDataGridSize += inputInitialDataGrid->GetCellNumber() * lSizeOfCell + lTotalValueNumber * (sizeof(KWDGValue) + sizeof(void*)); lWorkingDataGridSize += inputInitialDataGrid->GetCellNumber() * lSizeOfCell + @@ -2877,7 +2925,7 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridOptimization(KWDataGrid* in // Prise en compte de la memoire de travail pour post-optimisation lDataGridPostOptimizationSize = inputInitialDataGrid->GetCellNumber() * - (sizeof(KWDGMCell) + (2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*)); + (sizeof(KWDGMCell) + ((longint)2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*)); lDataGridPostOptimizationSize += inputInitialDataGrid->GetCellNumber() * sizeof(KWDGPOCellFrequencyVector); lDataGridPostOptimizationSize += lInitialMaxPartNumber * (sizeof(KWMODLLineDeepOptimization) + 2 * sizeof(int) + sizeof(KWDGPOPartFrequencyVector)); diff --git a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp index 6902fcd09..537eeece5 100644 --- a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp +++ b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp @@ -34,12 +34,12 @@ int main(int argc, char** argv) Global::ActivateSignalErrorManagement(); // Choix du repertoire de lancement pour le debugage sous Windows (a commenter apres fin du debug) - SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); + //SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); //DDD SetWindowsDebugDir("y_CoclusteringIV_Standard", "SimplifyCoclustering"); - //SetWindowsDebugDir("Standard", "Iris"); + SetWindowsDebugDir("Standard", "Iris"); // Point d'arret sur l'allocation d'un bloc memoire - // MemSetAllocIndexExit(17362); + // MemSetAllocIndexExit(77); // Lancement du projet learningProject.Start(argc, argv); From 024f282903fa8c49a3966c49fd83e66da905f9a3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Tue, 19 Sep 2023 13:48:40 +0200 Subject: [PATCH 11/17] Finalization of json format for coclustering IV MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Integration des developements de Carine Nouveau format de rapport json du coclustering individus x variables - nettoyage du code de CCCoclusteringReport - ReadJSONDimensionPartitionsNew -> ReadJSONDimensionPartitions - balises "// CH IV MemBug" ??? : supprimees - nouveau format actuel, base sur les deux section suivantes OK - innerAttributesDimensionSummaries - innerVariables - une seule section innerVariables, avec deux sous-section identiques à l'existant - innerVariables - dimensionSummaries - dimensionPartitions - impacts dans KWLearningReport - reutiliser aux maximum les methodes existantes - ReadJSONDimensionSummaries - ReadJSONDimensionPartitions - WriteJSONAttributeSummary - ReadJSONInnerAttributesDimensionSummaries: toujours utile Suppression du dictionnaire des bornes de domaine des attribut internes numeriques mutable ObjectDictionary odInnerAttributeDomainBounds; car il est devenu inutile maintenant que les attributs internes de la grille hierarchique sont de type CCHDGAttribute --- .../CCCoclusteringBuilder.cpp | 20 +-- .../CCCoclusteringReport.cpp | 130 +++++++----------- .../MODL_Coclustering/CCCoclusteringReport.h | 3 +- .../CCHierarchicalDataGrid.cpp | 18 +-- .../CCHierarchicalDataGrid.h | 14 +- 5 files changed, 59 insertions(+), 126 deletions(-) diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp index 78aa40249..60fa7fd58 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp @@ -3155,8 +3155,8 @@ void CCCoclusteringBuilder::ComputeAttributeTypicalities(CCHierarchicalDataGrid* { int nAttribute; CCHDGAttribute* hdgAttribute; - int nInnerAttribute; // CH IV MemBug - CCHDGAttribute* innerAttribute; // CH IV MemBug + int nInnerAttribute; + CCHDGAttribute* innerAttribute; require(optimizedDataGrid != NULL); @@ -3170,7 +3170,7 @@ void CCCoclusteringBuilder::ComputeAttributeTypicalities(CCHierarchicalDataGrid* // Pour l'instant, on met toutes les typicalites a 1 hdgAttribute->SetInterest(1); - // CH IV MemBug + // Cas d'un attribut VarPart : traitement de ses innerAttributes if (hdgAttribute->GetAttributeType() == KWType::VarPart and hdgAttribute->GetInnerAttributeNumber() > 0) { for (nInnerAttribute = 0; nInnerAttribute < hdgAttribute->GetInnerAttributeNumber(); @@ -3196,7 +3196,6 @@ void CCCoclusteringBuilder::ComputeContinuousAttributeBounds(CCHierarchicalDataG KWDescriptiveContinuousStats* descriptiveContinuousStats; int nInnerAttribute; KWDGAttribute* innerAttribute; - KWDGInterval* domainBounds; require(optimizedDataGrid != NULL); @@ -3221,7 +3220,6 @@ void CCCoclusteringBuilder::ComputeContinuousAttributeBounds(CCHierarchicalDataG // Memorisation des informations sur les bornes des valeurs des attributs internes numeriques if (hdgAttribute->GetAttributeType() == KWType::VarPart and hdgAttribute->GetInnerAttributeNumber() > 0) { - coclusteringDataGrid->GetInnerAttributeDomainBounds()->DeleteAll(); for (nInnerAttribute = 0; nInnerAttribute < hdgAttribute->GetInnerAttributeNumber(); nInnerAttribute++) { @@ -3234,7 +3232,6 @@ void CCCoclusteringBuilder::ComputeContinuousAttributeBounds(CCHierarchicalDataG cast(KWDescriptiveContinuousStats*, odDescriptiveStats.Lookup(innerAttribute->GetAttributeName())); - // CH IV MemBug // Memorisation de ses bornes if (descriptiveContinuousStats != NULL) { @@ -3243,12 +3240,6 @@ void CCCoclusteringBuilder::ComputeContinuousAttributeBounds(CCHierarchicalDataG cast(CCHDGAttribute*, innerAttribute) ->SetMax(descriptiveContinuousStats->GetMax()); } - // CH IV Redondant : a enlever quand le fonctionnement des innerAttributes en tant que CHDGAttribute sera ok - domainBounds = new KWDGInterval; - domainBounds->SetLowerBound(descriptiveContinuousStats->GetMin()); - domainBounds->SetUpperBound(descriptiveContinuousStats->GetMax()); - coclusteringDataGrid->GetInnerAttributeDomainBounds()->SetAt( - innerAttribute->GetAttributeName(), domainBounds); } } } @@ -4115,11 +4106,9 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* int nAttribute; KWDGAttribute* dgAttribute; KWDGPart* dgPart; - // CH IV MemBug int nInnerAttribute; KWDGAttribute* innerAttribute; KWDGPart* innerPart; - // Fin CH IV require(optimizedDataGrid != NULL); @@ -4146,7 +4135,7 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* dgAttribute->GetNextPart(dgPart); } - // CH IV MemBug + // Traitement des innerAttributes dans le cas d'un attribut VarPart if (dgAttribute->GetAttributeType() == KWType::VarPart) { // Parcours des inner attributes @@ -4171,7 +4160,6 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* } } } - // Fin CH IV } } diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp index 5eff83590..6b5f38d55 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp @@ -2704,7 +2704,7 @@ boolean CCCoclusteringReport::InternalReadJSONReport(CCHierarchicalDataGrid* coc if (not bHeaderOnly) { bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - bOk = bOk and ReadJSONDimensionPartitionsNew(coclusteringDataGrid); + bOk = bOk and ReadJSONDimensionPartitions(coclusteringDataGrid); bOk = bOk and JSONTokenizer::ReadExpectedToken(','); bOk = bOk and ReadJSONDimensionHierarchies(coclusteringDataGrid); bOk = bOk and JSONTokenizer::ReadExpectedToken(','); @@ -3005,7 +3005,7 @@ boolean CCCoclusteringReport::ReadJSONDimensionSummaries(CCHierarchicalDataGrid* } // Fin de l'objet et test si nouvel objet - bOk = bOk and JSONTokenizer::CheckObjectEnd("dimensionSummary", bIsEnd); + bOk = bOk and JSONTokenizer::CheckObjectEnd("dimensionSummaries", bIsEnd); bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); } @@ -3081,7 +3081,7 @@ boolean CCCoclusteringReport::ReadJSONInnerAttributesDimensionSummaries(KWDGAttr innerAttributes = dgAttribute->GetInnerAttributes(); // Tableau principal - bOk = bOk and JSONTokenizer::ReadKeyArray("innerAttributesDimensionSummaries"); + bOk = bOk and JSONTokenizer::ReadKeyArray("dimensionSummaries"); // Lecture des elements du tableau while (bOk and not bIsEnd) @@ -3167,14 +3167,14 @@ boolean CCCoclusteringReport::ReadJSONInnerAttributesDimensionSummaries(KWDGAttr } // Fin de l'objet et test si nouvel objet - bOk = bOk and JSONTokenizer::CheckObjectEnd("innerAttributesDimensionSummary", bIsEnd); + bOk = bOk and JSONTokenizer::CheckObjectEnd("dimensionSummaries", bIsEnd); bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); } return bOk; } -boolean CCCoclusteringReport::ReadJSONDimensionPartitionsNew(CCHierarchicalDataGrid* coclusteringDataGrid) +boolean CCCoclusteringReport::ReadJSONDimensionPartitions(CCHierarchicalDataGrid* coclusteringDataGrid) { boolean bOk = true; ObjectDictionary odCheckedParts; @@ -3275,7 +3275,6 @@ boolean CCCoclusteringReport::ReadJSONAttributePartition(KWDGAttribute* dgAttrib ObjectDictionary odVarPartAttributeAllVarParts; ObjectDictionary odCheckedParts; ALString sPartName; - KWDGInterval* domainBounds; int nInnerAttribute; ALString sAttributeName; int nInnerAttributeIndex; @@ -3295,12 +3294,15 @@ boolean CCCoclusteringReport::ReadJSONAttributePartition(KWDGAttribute* dgAttrib // Creation des attributs internes innerAttributes = dgAttribute->GetInnerAttributes(); + // Tableau principal + bOk = bOk and JSONTokenizer::ReadKeyObject("innerVariables"); + // Descriptif des variables internes dans l'attribut de type VarPart bOk = bOk and ReadJSONInnerAttributesDimensionSummaries(dgAttribute); bOk = bOk and JSONTokenizer::ReadExpectedToken(','); // Tableau des variables internes dans l'attribut de type VarPart - bOk = bOk and JSONTokenizer::ReadKeyArray("innerVariables"); + bOk = bOk and JSONTokenizer::ReadKeyArray("dimensionPartitions"); // Lecture des elements du tableau nInnerAttributeIndex = 0; @@ -3400,7 +3402,6 @@ boolean CCCoclusteringReport::ReadJSONAttributePartition(KWDGAttribute* dgAttrib // numeriques if (bOk) { - coclusteringDataGrid->GetInnerAttributeDomainBounds()->DeleteAll(); for (nInnerAttribute = 0; nInnerAttribute < dgAttribute->GetInnerAttributeNumber(); nInnerAttribute++) { @@ -3411,14 +3412,6 @@ boolean CCCoclusteringReport::ReadJSONAttributePartition(KWDGAttribute* dgAttrib // Cas d'un attribut Continuous if (innerAttribute->GetAttributeType() == KWType::Continuous) { - domainBounds = new KWDGInterval; - domainBounds->SetLowerBound( - innerAttribute->GetHeadPart()->GetInterval()->GetLowerBound()); - domainBounds->SetUpperBound( - innerAttribute->GetTailPart()->GetInterval()->GetUpperBound()); - coclusteringDataGrid->GetInnerAttributeDomainBounds()->SetAt( - innerAttribute->GetAttributeName(), domainBounds); - // On modifie les bornes des intervalles extremes de facon a // pouvoir produire les libelle avec -inf et +inf par // GetObjectLabel @@ -3430,13 +3423,13 @@ boolean CCCoclusteringReport::ReadJSONAttributePartition(KWDGAttribute* dgAttrib } } - // Fin du tableau des attributs internes + // Fin de l'objet + bOk = bOk and JSONTokenizer::ReadExpectedToken('}'); bOk = bOk and JSONTokenizer::ReadExpectedToken(','); // Lecture des parties "valueGroups", groupes de parties de variables bOk = bOk and JSONTokenizer::ReadKeyArray("valueGroups"); } - // CH IV End // Lecture des parties dgPart = dgAttribute->GetHeadPart(); @@ -4651,7 +4644,6 @@ void CCCoclusteringReport::WriteJSONDimensionSummaries(const CCHierarchicalDataG { int nAttribute; CCHDGAttribute* dgAttribute; - int nValueNumber; require(coclusteringDataGrid != NULL); require(fJSON != NULL); @@ -4662,30 +4654,7 @@ void CCCoclusteringReport::WriteJSONDimensionSummaries(const CCHierarchicalDataG { dgAttribute = cast(CCHDGAttribute*, coclusteringDataGrid->GetAttributeAt(nAttribute)); - // Nombre initial de valeurs - nValueNumber = dgAttribute->GetInitialValueNumber(); - - // Caracteristique des attributs - // Le nombre de valeur est diminuer en externe de 1 pour tenir compte de de la StarValue en interne - fJSON->BeginObject(); - fJSON->WriteKeyString("name", dgAttribute->GetAttributeName()); - if (dgAttribute->GetAttributeType() == KWType::VarPart) - fJSON->WriteKeyBoolean("isVarPart", true); - fJSON->WriteKeyString( - "type", KWType::ToString(KWType::GetCoclusteringSimpleType(dgAttribute->GetAttributeType()))); - fJSON->WriteKeyInt("parts", dgAttribute->GetPartNumber()); - fJSON->WriteKeyInt("initialParts", dgAttribute->GetInitialPartNumber()); - fJSON->WriteKeyInt("values", nValueNumber); - fJSON->WriteKeyDouble("interest", dgAttribute->GetInterest()); - fJSON->WriteKeyString("description", dgAttribute->GetDescription()); - if (KWFrequencyTable::GetWriteGranularityAndGarbage()) - fJSON->WriteKeyBoolean("garbage", (dgAttribute->GetGarbageModalityNumber() > 0)); - if (dgAttribute->GetAttributeType() == KWType::Continuous) - { - fJSON->WriteKeyDouble("min", dgAttribute->GetMin()); - fJSON->WriteKeyDouble("max", dgAttribute->GetMax()); - } - fJSON->EndObject(); + WriteJSONDimensionSummary(dgAttribute, fJSON); } fJSON->EndArray(); } @@ -4693,11 +4662,8 @@ void CCCoclusteringReport::WriteJSONDimensionSummaries(const CCHierarchicalDataG void CCCoclusteringReport::WriteJSONInnerAttributesDimensionSummaries(const KWDGAttribute* varPartAttribute, JSONFile* fJSON) { - int nAttribute; - // CH IV MemBug CCHDGAttribute* innerAttribute; - //KWDGAttribute* innerAttribute; - int nValueNumber; + int nAttribute; require(varPartAttribute != NULL); require(varPartAttribute->GetAttributeType() == KWType::VarPart); @@ -4705,45 +4671,46 @@ void CCCoclusteringReport::WriteJSONInnerAttributesDimensionSummaries(const KWDG require(fJSON != NULL); // Parcours des innerAttributes pour la section des dimensions - fJSON->BeginKeyArray("innerAttributesDimensionSummaries"); + fJSON->BeginKeyArray("dimensionSummaries"); for (nAttribute = 0; nAttribute < varPartAttribute->GetInnerAttributeNumber(); nAttribute++) { - // CH IV MemBug innerAttribute = cast(CCHDGAttribute*, varPartAttribute->GetInnerAttributeAt(nAttribute)); - //innerAttribute = varPartAttribute->GetInnerAttributeAt(nAttribute); - - // Nombre initial de valeurs - nValueNumber = innerAttribute->GetInitialValueNumber(); - // Caracteristique des attributs - // Le nombre de valeur est diminuer en externe de 1 pour tenir compte de de la StarValue en interne - fJSON->BeginObject(); - fJSON->WriteKeyString("name", innerAttribute->GetAttributeName()); - fJSON->WriteKeyString( - "type", KWType::ToString(KWType::GetCoclusteringSimpleType(innerAttribute->GetAttributeType()))); - fJSON->WriteKeyInt("parts", innerAttribute->GetPartNumber()); - // CH IV MemBug - fJSON->WriteKeyInt("initialParts", innerAttribute->GetInitialPartNumber()); - //fJSON->WriteKeyInt("initialParts", 0); - fJSON->WriteKeyInt("values", nValueNumber); - fJSON->WriteKeyDouble("interest", innerAttribute->GetInterest()); - //fJSON->WriteKeyDouble("interest", 0); - fJSON->WriteKeyString("description", innerAttribute->GetDescription()); - //fJSON->WriteKeyString("description", "0"); - if (KWFrequencyTable::GetWriteGranularityAndGarbage()) - fJSON->WriteKeyBoolean("garbage", (innerAttribute->GetGarbageModalityNumber() > 0)); - if (innerAttribute->GetAttributeType() == KWType::Continuous) - { - fJSON->WriteKeyDouble("min", innerAttribute->GetMin()); - fJSON->WriteKeyDouble("max", innerAttribute->GetMax()); - //fJSON->WriteKeyDouble("min", 0); - //fJSON->WriteKeyDouble("max", 0); - } - fJSON->EndObject(); + WriteJSONDimensionSummary(innerAttribute, fJSON); } fJSON->EndArray(); } +void CCCoclusteringReport::WriteJSONDimensionSummary(CCHDGAttribute* attribute, JSONFile* fJSON) +{ + int nValueNumber; + + // Nombre initial de valeurs + nValueNumber = attribute->GetInitialValueNumber(); + + // Caracteristique des attributs + // Le nombre de valeur est diminuer en externe de 1 pour tenir compte de de la StarValue en interne + fJSON->BeginObject(); + fJSON->WriteKeyString("name", attribute->GetAttributeName()); + if (attribute->GetAttributeType() == KWType::VarPart) + fJSON->WriteKeyBoolean("isVarPart", true); + fJSON->WriteKeyString("type", + KWType::ToString(KWType::GetCoclusteringSimpleType(attribute->GetAttributeType()))); + fJSON->WriteKeyInt("parts", attribute->GetPartNumber()); + fJSON->WriteKeyInt("initialParts", attribute->GetInitialPartNumber()); + fJSON->WriteKeyInt("values", nValueNumber); + fJSON->WriteKeyDouble("interest", attribute->GetInterest()); + fJSON->WriteKeyString("description", attribute->GetDescription()); + if (KWFrequencyTable::GetWriteGranularityAndGarbage()) + fJSON->WriteKeyBoolean("garbage", (attribute->GetGarbageModalityNumber() > 0)); + if (attribute->GetAttributeType() == KWType::Continuous) + { + fJSON->WriteKeyDouble("min", attribute->GetMin()); + fJSON->WriteKeyDouble("max", attribute->GetMax()); + } + fJSON->EndObject(); +} + void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON) { @@ -4907,11 +4874,14 @@ void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, // Traitement des attributs de type VarPart else if (attribute->GetAttributeType() == KWType::VarPart) { + // Parcours des innerVariables de l'attribut + fJSON->BeginKeyObject("innerVariables"); + // Descriptif des innerVariables WriteJSONInnerAttributesDimensionSummaries(attribute, fJSON); // Parcours des variables de l'attribut - fJSON->BeginKeyArray("innerVariables"); + fJSON->BeginKeyArray("dimensionPartitions"); nIndex = 0; for (nIndex = 0; nIndex < attribute->GetInnerAttributeNumber(); nIndex++) { @@ -4933,6 +4903,8 @@ void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, } fJSON->EndArray(); + fJSON->EndObject(); + // Parcours des groupes de parties de variable fJSON->BeginKeyArray("valueGroups"); // nDefaultGroupIndex = -1; diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.h b/src/Learning/MODL_Coclustering/CCCoclusteringReport.h index 15d1ba7e6..a8b93d4e3 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.h +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.h @@ -160,7 +160,7 @@ class CCCoclusteringReport : public Object boolean InternalReadJSONReport(CCHierarchicalDataGrid* coclusteringDataGrid, boolean bHeaderOnly); boolean ReadJSONSummary(CCHierarchicalDataGrid* coclusteringDataGrid); boolean ReadJSONDimensionSummaries(CCHierarchicalDataGrid* coclusteringDataGrid); - boolean ReadJSONDimensionPartitionsNew(CCHierarchicalDataGrid* coclusteringDataGrid); + boolean ReadJSONDimensionPartitions(CCHierarchicalDataGrid* coclusteringDataGrid); boolean ReadJSONInnerAttributesDimensionSummaries(KWDGAttribute* dgAttribute); boolean ReadJSONAttributePartition(KWDGAttribute* attribute, CCHierarchicalDataGrid* coclusteringDataGrid); boolean ReadJSONInterval(KWDGAttribute* dgAttribute, KWDGPart* dgPart); @@ -178,6 +178,7 @@ class CCCoclusteringReport : public Object void WriteJSONSummary(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); void WriteJSONDimensionSummaries(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); void WriteJSONInnerAttributesDimensionSummaries(const KWDGAttribute* vartPartAttribute, JSONFile* fJSON); + void WriteJSONDimensionSummary(CCHDGAttribute* attribute, JSONFile* fJSON); void WriteJSONDimensionPartitions(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); void WriteJSONAttributePartition(KWDGAttribute* attribute, const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp index a49a6e07b..0243dd18e 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp @@ -14,12 +14,7 @@ CCHierarchicalDataGrid::CCHierarchicalDataGrid() nInitialAttributeNumber = 0; } -CCHierarchicalDataGrid::~CCHierarchicalDataGrid() -{ - // CH IV Begin - odInnerAttributeDomainBounds.DeleteAll(); - // CH IV End -} +CCHierarchicalDataGrid::~CCHierarchicalDataGrid() {} void CCHierarchicalDataGrid::SetShortDescription(const ALString& sValue) { @@ -98,7 +93,6 @@ const KWDatabase* CCHierarchicalDataGrid::GetConstDatabaseSpec() const return &databaseSpec; } -// CH IV Begin void CCHierarchicalDataGrid::SetIdentifierAttributeName(const ALString& sValue) { sIdentifierAttributeName = sValue; @@ -109,13 +103,6 @@ const ALString& CCHierarchicalDataGrid::GetIdentifierAttributeName() const return sIdentifierAttributeName; } -ObjectDictionary* CCHierarchicalDataGrid::GetInnerAttributeDomainBounds() const -{ - return &odInnerAttributeDomainBounds; -} - -// CH IV End - void CCHierarchicalDataGrid::DeleteAll() { const KWDatabase nullDatabase; @@ -127,10 +114,7 @@ void CCHierarchicalDataGrid::DeleteAll() nInitialAttributeNumber = 0; sFrequencyAttributeName = ""; databaseSpec.CopyFrom(&nullDatabase); - // CH IV Begin sIdentifierAttributeName = ""; - odInnerAttributeDomainBounds.DeleteAll(); - // CH IV End } boolean CCHierarchicalDataGrid::CheckHierarchy() const diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h index 2ecf25e51..38c3490f2 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h @@ -67,20 +67,10 @@ class CCHierarchicalDataGrid : public KWDataGrid KWDatabase* GetDatabaseSpec(); const KWDatabase* GetConstDatabaseSpec() const; - // CH IV Begin - // // CH IV Refactoring: re-ecrire avec un dictionnaire d'intervalles contenant les valeurs extremes - // Nom de l'attribute d'identifiant (optionnel) void SetIdentifierAttributeName(const ALString& sValue); const ALString& GetIdentifierAttributeName() const; - // Acces aux dictionnaire des bornes de domaine des attribut internes numeriques, via des KWDGInterval - // Ce parametrage permet de memoriser les bornes des intervalles extremes pour les attribut internes, - // afin d'ecrire les vraies bornes plutot que -inf et +inf dans les rapports json - // Memoire appartient a l'appele, ainsi que son contenu, qui doit etre gere par l'appelant - ObjectDictionary* GetInnerAttributeDomainBounds() const; - // CH IV End - ///////////////////////////////////////////////////////////////////////// // Services divers @@ -107,11 +97,9 @@ class CCHierarchicalDataGrid : public KWDataGrid int nInitialAttributeNumber; ALString sFrequencyAttributeName; KWDatabase databaseSpec; - // CH IV Begin + // CH IV Refactoring: le sIdentifierAttributeName est-il toujours utile??? ALString sIdentifierAttributeName; - mutable ObjectDictionary odInnerAttributeDomainBounds; - // CH IV End }; ////////////////////////////////////////////////////////////////////////////// From c6d53f3f7898dd1d03946e7a53ab6b53bbcb1686 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Tue, 19 Sep 2023 15:24:49 +0200 Subject: [PATCH 12/17] Refactor KWDGValueSet hierarchy to remove classes CCHDG*ValueSet, CHDG*Value MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Extension des classe KWDGValue et KWDGValueSet - KWGGValueSet - SortValuesByDecreasingTypicality - Check: ajout de la verification de la typicalite - KWDGValue - CompareTypicality: methodes virtuelle, reimplementee dans les sous-classes - KWDGValueCompareTypicality: comparaison de deux valeurs par typicalite Objectif: supression des classes de CCHierarchicalDataGrid, sous-classes de KWDGValueSet et KWDGValue - classes concernees - CCHDGSymbolValueSet, CCHDGVarPartSet, sous classes indirectement de KWDGValueSet - ces classes sont quasiment identiques, avec tres peu de methodes ayant la meme implementation - Create, SortVarPartsByTypicality, CheckHierarchy - CCHDGSymbolValue, CCHDGVarPartValue, sous classes indirectement de KWDGValue - ces classes sont quasiment identiques, avec tres peu de methodes ayant la meme implementation - SetTypicality, GetTypicality, CheckHierarchy - depuis la mise en place de la hierarchie KWDGValyeSet et KWDGValue, ces classes ne sont necessaires que parce qu'elles heritent de sous-classes distinctes - refactoring - remonter les methodes de ces classe dans les classe génériques ancetres KWDGValueSet et KWDGValue - supprimer les quatres classes spécialisée - impact pour factoriser le code existant entre les types Symbol et VarPart - permettra de gérer simplement la typicalité dans le cas VarPart Supression des classes - CCHDGSymbolValueSet - CCHDGSymbolValue - CCHDGVarPartSet - CCHDGVarPartValue - CCHDGCell (inutile) Impacts de la supression sur les methodes existantes - CCCoclusteringBuilder - ComputeValueTypicalitiesAt - SortAttributePartsAndValues - CCCoclusteringReport - ReadComposition - WriteComposition - ReadJSONAttributePartition - ReadJSONValueGroup - ReadJSONVarPartAttributeValueGroup - WriteJSONAttributePartition - CCLearningProblem - WriteSymbolClusters - WriteVarPartClusters CCCoclusteringReport - WriteJSONInnerAttributes - nouvelle methode d'ecriture des attributs internes - WriteJSONAttributePartition - supression du deuxieme parametre inutile de type CCHierarchicalDataGrid - WriteJSONAttributePartition: mutualisation des cas Symbol et VarPart - Supression des methodes suivantes desormais inutiles - WriteJSONInnerAttributesDimensionSummaries - ReadJSONInnerAttributeIntervals - ReadJSONInnerAttributeValueGroups KWDGSymbolValue::CompareTypicality, KWDGVarPartValue::CompareTypicality - comparaison a en utilisant une conversion en Continuous pour statbiliser les erreur d'arrondi CCCoclusteringBuilder::ComputeValueTypicalitiesAt -Il se peut qu'une modalite ait une typicalite negative, si la post-optimisation des grille n'a pas pu aller jusqu'a la convergence. Dans ce cas, on met une typicalite a 0, ce qui indique que la modalite n'est pas a sap lace (de justesse) dans son cluster CCLearningProblem - WriteGroupableClusters: ajout, en remplacement des deux methodes identiques suivantes - WriteSymbolClusters - WriteVarPartClusters Jeux de donnees modifies - Adult2var - echantillonage à 25% pour accelerer les traitements - renomme en TooLongAdult2vars: pour executer ce jeu de test en dernier dans les test globaux - Breast - deux typicalites sont passees de negative à 9 - IrisMissingValues - CoclusteringIrisAllValuesMissingPetalWidth - une typicalite est passee de negative à 9 - ReadInputErroneousJSONCoclustering - modification cosmetique des libelles d'erreur dans le err.txt - ExtractClustersIris - le libelle d'entete de ClustersVariables.txt est renommer de VarPart en Value (comme pour les variables categorielles) Tests sur tout LearningTest\TestCoclustering\y_CoclusteringIV_Standard, y compris en debug --- src/Learning/KWDataPreparation/KWDataGrid.cpp | 66 +++ src/Learning/KWDataPreparation/KWDataGrid.h | 38 +- .../CCCoclusteringBuilder.cpp | 87 +-- .../CCCoclusteringReport.cpp | 519 +++++------------- .../MODL_Coclustering/CCCoclusteringReport.h | 8 +- .../CCHierarchicalDataGrid.cpp | 252 --------- .../CCHierarchicalDataGrid.h | 136 ----- .../MODL_Coclustering/CCLearningProblem.cpp | 71 +-- .../MODL_Coclustering/CCLearningProblem.h | 5 +- .../MODL_Coclustering/MODL_Coclustering.cpp | 6 +- 10 files changed, 319 insertions(+), 869 deletions(-) diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index 0cf8fc8af..5685cf94d 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -4177,6 +4177,11 @@ void KWDGValueSet::SortValueByDecreasingFrequencies() InternalSortValues(KWDGValueCompareFrequency); } +void KWDGValueSet::SortValuesByDecreasingTypicalities() +{ + InternalSortValues(KWDGValueCompareTypicality); +} + boolean KWDGValueSet::AreValuesSorted() const { return InternalAreValuesSorted(KWDGValueCompareValue); @@ -4187,6 +4192,11 @@ boolean KWDGValueSet::AreValuesSortedByDecreasingFrequencies() const return InternalAreValuesSorted(KWDGValueCompareFrequency); } +boolean KWDGValueSet::AreValuesSortedByDecreasingTypicalities() const +{ + return InternalAreValuesSorted(KWDGValueCompareTypicality); +} + boolean KWDGValueSet::IsSubPart(const KWDGPartValues* otherPartValues) const { boolean bOk = true; @@ -4382,6 +4392,14 @@ boolean KWDGValueSet::Check() const bOk = false; break; } + // Erreur si typivcalite incorrecte + else if (value->GetTypicality() < 0 or value->GetTypicality() > 1) + { + AddError(sTmp + value->GetClassLabel() + " " + value->GetObjectLabel() + + " should have a typicality (" + DoubleToString(value->GetTypicality()) + + ") between 0 and 1"); + bOk = false; + } // On continue si pas d'erreur else { @@ -4718,6 +4736,22 @@ int KWDGValueCompareFrequency(const void* elem1, const void* elem2) return value1->CompareFrequency(value2); } +int KWDGValueCompareTypicality(const void* elem1, const void* elem2) +{ + KWDGValue* value1; + KWDGValue* value2; + + require(elem1 != NULL); + require(elem2 != NULL); + + // Acces a la valeur + value1 = cast(KWDGValue*, *(Object**)elem1); + value2 = cast(KWDGValue*, *(Object**)elem2); + + // Comparaison + return value1->CompareTypicality(value2); +} + int KWSortableObjectComparePartValue(const void* elem1, const void* elem2) { KWDGValue* value1; @@ -4736,6 +4770,22 @@ int KWSortableObjectComparePartValue(const void* elem1, const void* elem2) ////////////////////////////////////////////////////////////////////////////// // Classe KWDGSymbolValue +int KWDGSymbolValue::CompareTypicality(const KWDGValue* otherValue) const +{ + int nCompare; + + require(otherValue != NULL); + + // Comparaison de la typicalite selon la precison du type Continuous, pour eviter les differences a epsilon pres + nCompare = -KWContinuous::Compare(KWContinuous::DoubleToContinuous(GetTypicality()), + KWContinuous::DoubleToContinuous(otherValue->GetTypicality())); + + // Comparaison par effectif decroissaqnt si egalite + if (nCompare == 0) + nCompare = CompareFrequency(otherValue); + return nCompare; +} + void KWDGSymbolValue::Write(ostream& ost) const { ost << sSymbolValue << "\t" << nValueFrequency; @@ -4805,6 +4855,22 @@ int KWDGVarPartValue::CompareValue(const KWDGValue* otherValue) const return nCompare; } +int KWDGVarPartValue::CompareTypicality(const KWDGValue* otherValue) const +{ + int nCompare; + + require(otherValue != NULL); + + // Comparaison de la typicalite selon la precison du type Continuous, pour eviter les differences a epsilon pres + nCompare = -KWContinuous::Compare(KWContinuous::DoubleToContinuous(GetTypicality()), + KWContinuous::DoubleToContinuous(otherValue->GetTypicality())); + + // Comparaison par valeur si egalite + if (nCompare == 0) + nCompare = CompareValue(otherValue); + return nCompare; +} + void KWDGVarPartValue::Write(ostream& ost) const { ost << *varPart << "\t" << GetValueFrequency(); diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index fd5cae89b..ff3776621 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -994,10 +994,14 @@ class KWDGValueSet : public KWDGPartValues // Tri des valeurs par effectif decroissant, pour preparer l'affichage void SortValueByDecreasingFrequencies(); + // Tri des valeurs par typicalite decroissante + void SortValuesByDecreasingTypicalities(); + // Verification du tri des valeurs par effectif decroissant // Couteux, a utiliser essentiellement dans les assertions boolean AreValuesSorted() const; boolean AreValuesSortedByDecreasingFrequencies() const; + boolean AreValuesSortedByDecreasingTypicalities() const; // Redefinition des methodes virtuelles boolean IsSubPart(const KWDGPartValues* otherPartValues) const override; @@ -1082,14 +1086,23 @@ class KWDGValue : public Object virtual void SetValueFrequency(int nFrequency) = 0; virtual int GetValueFrequency() const = 0; + // Typicalite de la valeur + void SetTypicality(double dValue); + double GetTypicality() const; + // Comparaison par valeur - // - valeur Syymbol si categoriel + // - valeur Symbol si categoriel // - nom d'attribut, puis valeur de partie si VarPart virtual int CompareValue(const KWDGValue* otherValue) const = 0; // Compare par effectif decroissant, puis par valeur virtual int CompareFrequency(const KWDGValue* otherValue) const; + // Comparaison par typicalite, puis + // - par effectif si categoriel + // - par valeur si VarPart + virtual int CompareTypicality(const KWDGValue* otherValue) const = 0; + // Affichage void Write(ostream& ost) const override = 0; @@ -1109,6 +1122,7 @@ class KWDGValue : public Object friend class KWDGValueSet; // Attributs + double dTypicality; KWDGValue* prevValue; KWDGValue* nextValue; }; @@ -1119,6 +1133,9 @@ int KWDGValueCompareValue(const void* elem1, const void* elem2); // Comparaison de deux valeurs, par effectif decroissant int KWDGValueCompareFrequency(const void* elem1, const void* elem2); +// Comparaison de deux typicalite +int KWDGValueCompareTypicality(const void* elem1, const void* elem2); + // Comparaison de deux objets KWSortableObject contenant des KWDGValue, par valeur int KWSortableObjectComparePartValue(const void* elem1, const void* elem2); @@ -1201,6 +1218,9 @@ class KWDGSymbolValue : public KWDGValue // Comparaison de valeur Symbol int CompareValue(const KWDGValue* otherValue) const override; + // Comparaison part typicalite, puis par effectif decroissant + int CompareTypicality(const KWDGValue* otherValue) const override; + // Affichage void Write(ostream& ost) const override; @@ -1343,6 +1363,9 @@ class KWDGVarPartValue : public KWDGValue // Comparaison de valeur, par attribut, puis selon les valeurs de la partie, intervalles ou premiere valeur des groupe int CompareValue(const KWDGValue* otherValue) const override; + // Comparaison part typicalite, puis par valeur + int CompareTypicality(const KWDGValue* otherValue) const override; + // Affichage void Write(ostream& ost) const override; @@ -2309,6 +2332,7 @@ inline int KWDGInnerAttributes::GetInnerAttributeNumber() const inline KWDGValue::KWDGValue() { + dTypicality = 0; prevValue = NULL; nextValue = NULL; } @@ -2341,6 +2365,17 @@ inline int KWDGValue::CompareFrequency(const KWDGValue* otherValue) const return nCompare; } +inline void KWDGValue::SetTypicality(double dValue) +{ + require(0 <= dValue and dValue <= 1); + dTypicality = dValue; +} + +inline double KWDGValue::GetTypicality() const +{ + return dTypicality; +} + // Classe KWDGSymbolValue inline KWDGSymbolValue::KWDGSymbolValue(const Symbol& sValue) @@ -2384,6 +2419,7 @@ inline int KWDGSymbolValue::GetValueFrequency() const inline int KWDGSymbolValue::CompareValue(const KWDGValue* otherValue) const { + require(otherValue != NULL); return GetSymbolValue().CompareValue(otherValue->GetSymbolValue()); } diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp index 60fa7fd58..7d9f5e590 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp @@ -463,22 +463,26 @@ boolean CCCoclusteringBuilder::ComputeCoclustering() KWDataGridOptimizer::GetProfiler()->BeginMethod("Compute coclustering"); KWDataGridOptimizer::GetProfiler()->WriteKeyString("Dictionary", GetDatabase()->GetClassName()); KWDataGridOptimizer::GetProfiler()->WriteKeyString("Database", GetDatabase()->GetDatabaseName()); - KWDataGridOptimizer::GetProfiler()->WriteKeyString("Instances", - IntToString(initialDataGrid->GetGridFrequency())); - KWDataGridOptimizer::GetProfiler()->WriteKeyString("Variables", - IntToString(initialDataGrid->GetAttributeNumber())); - KWDataGridOptimizer::GetProfiler()->WriteKeyString("Is VarPart", - BooleanToString(initialDataGrid->IsVarPartDataGrid())); - if (initialDataGrid->IsVarPartDataGrid()) + if (initialDataGrid != NULL) { + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Instances", + IntToString(initialDataGrid->GetGridFrequency())); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Variables", + IntToString(initialDataGrid->GetAttributeNumber())); KWDataGridOptimizer::GetProfiler()->WriteKeyString( - initialDataGrid->GetAttributeAt(0)->GetAttributeName() + " values", - IntToString(initialDataGrid->GetAttributeAt(0)->GetInitialValueNumber())); - KWDataGridOptimizer::GetProfiler()->WriteKeyString( - initialDataGrid->GetAttributeAt(1)->GetAttributeName() + " values", - IntToString(initialDataGrid->GetAttributeAt(1)->GetInitialValueNumber())); - KWDataGridOptimizer::GetProfiler()->WriteKeyString( - "Inner variables", IntToString(initialDataGrid->GetInnerAttributes()->GetInnerAttributeNumber())); + "Is VarPart", BooleanToString(initialDataGrid->IsVarPartDataGrid())); + if (initialDataGrid->IsVarPartDataGrid()) + { + KWDataGridOptimizer::GetProfiler()->WriteKeyString( + initialDataGrid->GetAttributeAt(0)->GetAttributeName() + " values", + IntToString(initialDataGrid->GetAttributeAt(0)->GetInitialValueNumber())); + KWDataGridOptimizer::GetProfiler()->WriteKeyString( + initialDataGrid->GetAttributeAt(1)->GetAttributeName() + " values", + IntToString(initialDataGrid->GetAttributeAt(1)->GetInitialValueNumber())); + KWDataGridOptimizer::GetProfiler()->WriteKeyString( + "Inner variables", + IntToString(initialDataGrid->GetInnerAttributes()->GetInnerAttributeNumber())); + } } // Optimisation de la grille, par appel d'une methode virtuelle @@ -3290,7 +3294,7 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn KWDGPart* dgValuePart; KWDGValue* dgValue; NumericKeyDictionary nkdOptimizedAttributeValues; - CCHDGSymbolValue* hdgValue; + KWDGValue* dgOptimizedValue; DoubleVector dvTypicalities; DoubleVector dvMaxTypicalities; int nGarbageGroupIndex; @@ -3539,16 +3543,17 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn dgValuePart = cast(KWDGPart*, oaValueParts.GetAt(nValue)); dgValue = dgValuePart->GetValueSet()->GetHeadValue(); + // Repartition de la typicite du fourre-tout entre ses modalites elementaires for (nIntraCatchAllValue = 0; nIntraCatchAllValue < nValueModalityNumber; nIntraCatchAllValue++) { // Recherche de la valeur correspondante pour l'attribut optimise - hdgValue = cast(CCHDGSymbolValue*, - nkdOptimizedAttributeValues.Lookup((dgValue->GetNumericKeyValue()))); + dgOptimizedValue = cast( + KWDGValue*, nkdOptimizedAttributeValues.Lookup((dgValue->GetNumericKeyValue()))); // Calcul de la typicite elementaire = la typicite du fourre-tout * effectif de la // modalite elementaire / effectif total du fourre-tout - dElementaryTypicality = (dTypicality * hdgValue->GetValueFrequency()) / + dElementaryTypicality = (dTypicality * dgOptimizedValue->GetValueFrequency()) / (1.0 * dgValuePart->GetPartFrequency()); dvTypicalities.SetAt(nValue + nIntraCatchAllValue, dElementaryTypicality); @@ -3584,19 +3589,22 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn dgValue = dgValuePart->GetValueSet()->GetHeadValue(); // Recherche de la valeur correspondante pour l'attribut optimise - hdgValue = cast(CCHDGSymbolValue*, - nkdOptimizedAttributeValues.Lookup((dgValue->GetNumericKeyValue()))); + dgOptimizedValue = + cast(KWDGValue*, nkdOptimizedAttributeValues.Lookup((dgValue->GetNumericKeyValue()))); // Memorisation de la typicalite normalisee - // Mis en defaut avec Breast pour nValue = 14 - // assert(0 <= dvTypicalities.GetAt(nValue) and dvTypicalities.GetAt(nValue) <= - // dvMaxTypicalities.GetAt(nOutGroup)); - - if (dvMaxTypicalities.GetAt(nOutGroup) > 0) - hdgValue->SetTypicality(dvTypicalities.GetAt(nValue) / - dvMaxTypicalities.GetAt(nOutGroup)); + // Il se peut qu'une modalite ait une typicalite negative, si la post-optimisation des grille + // n'a pas pu aller jusqu'a la convergence. Dans ce cas, on met une typicalite a 0, ce qui + // indique que la modalite n'est pas a sap lace (de justesse) dans son cluster + if (dvTypicalities.GetAt(nValue) <= 0) + dgOptimizedValue->SetTypicality(0); + // Cas general ou on normalise la typicalite par sa valeur max else - hdgValue->SetTypicality(1); + { + assert(dvMaxTypicalities.GetAt(nOutGroup) > 0); + dgOptimizedValue->SetTypicality(dvTypicalities.GetAt(nValue) / + dvMaxTypicalities.GetAt(nOutGroup)); + } } // Sinon : cas du fourre-tout else @@ -3606,14 +3614,20 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn for (nIntraCatchAllValue = 0; nIntraCatchAllValue < nValueModalityNumber; nIntraCatchAllValue++) { // Recherche de la valeur correspondante pour l'attribut optimise - hdgValue = cast(CCHDGSymbolValue*, - nkdOptimizedAttributeValues.Lookup((dgValue->GetNumericKeyValue()))); + dgOptimizedValue = cast( + KWDGValue*, nkdOptimizedAttributeValues.Lookup((dgValue->GetNumericKeyValue()))); - if (dvMaxTypicalities.GetAt(nOutGroup) > 0) - hdgValue->SetTypicality(dvTypicalities.GetAt(nValue + nIntraCatchAllValue) / - dvMaxTypicalities.GetAt(nOutGroup)); + // Memorisation de la typicalite normalisee dans le cas du fourre-tout + if (dvTypicalities.GetAt(nValue + nIntraCatchAllValue) <= 0) + dgOptimizedValue->SetTypicality(0); + // Cas general ou on normalise la typicalite par sa valeur max else - hdgValue->SetTypicality(1); + { + assert(dvMaxTypicalities.GetAt(nOutGroup) > 0); + dgOptimizedValue->SetTypicality( + dvTypicalities.GetAt(nValue + nIntraCatchAllValue) / + dvMaxTypicalities.GetAt(nOutGroup)); + } // Modalite suivante dgValuePart->GetValueSet()->GetNextValue(dgValue); @@ -4126,7 +4140,7 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* { // Tri des valeurs de la partie si attribut categoriel if (dgAttribute->GetAttributeType() == KWType::Symbol) - cast(CCHDGSymbolValueSet*, dgPart->GetValueSet())->SortValuesByTypicality(); + dgPart->GetValueSet()->SortValuesByDecreasingTypicalities(); // Initialisation du nom de la partie cast(CCHDGPart*, dgPart)->SetPartName(dgPart->GetObjectLabel()); @@ -4143,10 +4157,12 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* nInnerAttribute++) { innerAttribute = dgAttribute->GetInnerAttributeAt(nInnerAttribute); + // Nommage des parties terminales et tri des valeurs pour les attributs categoriels innerPart = innerAttribute->GetHeadPart(); while (innerPart != NULL) { + // CH IV Refactoring // CH IV voir avec Marc si le tri a deja ete fait suite au renommage des methodes de tri // Tri des valeurs de la partie si attribut categoriel //if (innerAttribute->GetAttributeType() == KWType::Symbol) @@ -4160,6 +4176,7 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* } } } + // Fin CH IV } } diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp index 6b5f38d55..93c1e871d 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp @@ -1553,8 +1553,8 @@ boolean CCCoclusteringReport::ReadComposition(CCHierarchicalDataGrid* coclusteri ALString sTmp; CCHDGAttribute* dgAttribute; KWDGPart* dgPart; - CCHDGSymbolValueSet* dgValueSet; - CCHDGSymbolValue* dgValue; + KWDGSymbolValueSet* dgValueSet; + KWDGSymbolValue* dgValue; int nAttribute; ObjectDictionary* odPartDictionary; ALString sAttributeName; @@ -1644,7 +1644,7 @@ boolean CCCoclusteringReport::ReadComposition(CCHierarchicalDataGrid* coclusteri // On rajoute si necessaire la StarValue au dernier groupe specifie if (dgPart != NULL and not bStarValueFound) { - dgValueSet = cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()); + dgValueSet = cast(KWDGSymbolValueSet*, dgPart->GetValueSet()); dgValueSet->AddSymbolValue(Symbol::GetStarValue()); } break; @@ -1727,8 +1727,8 @@ boolean CCCoclusteringReport::ReadComposition(CCHierarchicalDataGrid* coclusteri sValue = (Symbol)sValueName; // Memorisation de la valeur - dgValueSet = cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()); - dgValue = cast(CCHDGSymbolValue*, dgValueSet->AddSymbolValue(sValue)); + dgValueSet = cast(KWDGSymbolValueSet*, dgPart->GetValueSet()); + dgValue = cast(KWDGSymbolValue*, dgValueSet->AddSymbolValue(sValue)); dgValue->SetValueFrequency(nFrequency); dgValue->SetTypicality(dTypicality); @@ -1755,7 +1755,7 @@ boolean CCCoclusteringReport::ReadComposition(CCHierarchicalDataGrid* coclusteri { // Verification de la compatibilite entre l'effectif de la partie // et l'effectif cumule de ses valeurs - dgValueSet = cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()); + dgValueSet = cast(KWDGSymbolValueSet*, dgPart->GetValueSet()); nTotalValueFrequency = dgValueSet->ComputeTotalFrequency(); if (dgPart->GetPartFrequency() != nTotalValueFrequency) { @@ -2226,14 +2226,8 @@ void CCCoclusteringReport::WriteComposition(const CCHierarchicalDataGrid* coclus int nAttribute; KWDGPart* dgPart; CCHDGPart* hdgPart; - CCHDGSymbolValueSet* hdgValueSet; + KWDGValueSet* dgValueSet; KWDGValue* dgValue; - CCHDGSymbolValue* hdgValue; - // CH IV Begin - CCHDGVarPartSet* hdgVarPartSet; - KWDGValue* dgVarPartValue; - CCHDGVarPartValue* hdgVarPartValue; - // CH IV End require(coclusteringDataGrid != NULL); @@ -2242,8 +2236,8 @@ void CCCoclusteringReport::WriteComposition(const CCHierarchicalDataGrid* coclus { dgAttribute = cast(CCHDGAttribute*, coclusteringDataGrid->GetAttributeAt(nAttribute)); - // Traitement uniquement des attributs categoriels - if (dgAttribute->GetAttributeType() == KWType::Symbol) + // Traitement uniquement des attributs groupables + if (KWType::IsCoclusteringGroupableType(dgAttribute->GetAttributeType())) { // Entete ost << sKeyWordComposition << "\t" << dgAttribute->GetAttributeName() << "\n"; @@ -2256,56 +2250,17 @@ void CCCoclusteringReport::WriteComposition(const CCHierarchicalDataGrid* coclus hdgPart = cast(CCHDGPart*, dgPart); // Parcours des valeurs - hdgValueSet = cast(CCHDGSymbolValueSet*, hdgPart->GetValueSet()); - dgValue = hdgValueSet->GetHeadValue(); + dgValueSet = hdgPart->GetValueSet(); + dgValue = dgValueSet->GetHeadValue(); while (dgValue != NULL) { - hdgValue = cast(CCHDGSymbolValue*, dgValue); - // Caracteristiques des valeurs // (y compris la valeur par defaut, pour etre coherent avec l'export JSON) - ost << hdgPart->GetPartName() << "\t" << hdgValue->GetSymbolValue() << "\t" - << hdgValue->GetValueFrequency() << "\t" << hdgValue->GetTypicality() - << "\n"; + ost << hdgPart->GetPartName() << "\t" << dgValue->GetObjectLabel() << "\t" + << dgValue->GetValueFrequency() << "\t" << dgValue->GetTypicality() << "\n"; // Valeur suivante - hdgValueSet->GetNextValue(dgValue); - } - - // Partie suivante - dgAttribute->GetNextPart(dgPart); - } - ost << "\n"; - } - // CH IV Begin - // Traitement des attributs de type VarPart - else if (dgAttribute->GetAttributeType() == KWType::VarPart) - { - // Entete - ost << sKeyWordComposition << "\t" << dgAttribute->GetAttributeName() << "\n"; - ost << "Cluster Value Frequency Typicality\n"; - - // Parcours des parties - dgPart = dgAttribute->GetHeadPart(); - while (dgPart != NULL) - { - hdgPart = cast(CCHDGPart*, dgPart); - - // Parcours des valeurs - hdgVarPartSet = cast(CCHDGVarPartSet*, hdgPart->GetVarPartSet()); - dgVarPartValue = hdgVarPartSet->GetHeadValue(); - while (dgVarPartValue != NULL) - { - hdgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartValue); - - // Caracteristiques de la partie de variable - ost << hdgPart->GetPartName() << "\t"; - ost << hdgVarPartValue->GetVarPart()->GetVarPartLabel(); - ost << "\t" << hdgVarPartValue->GetVarPart()->GetPartFrequency() << "\t" - << hdgVarPartValue->GetTypicality() << "\n"; - - // Valeur suivante - hdgVarPartSet->GetNextValue(dgVarPartValue); + dgValueSet->GetNextValue(dgValue); } // Partie suivante @@ -2313,7 +2268,6 @@ void CCCoclusteringReport::WriteComposition(const CCHierarchicalDataGrid* coclus } ost << "\n"; } - // CH IV End } } @@ -3604,7 +3558,7 @@ boolean CCCoclusteringReport::ReadJSONAttributePartition(KWDGAttribute* dgAttrib // Ajout de la valeur par defaut dans ce groupe assert(dgPart != NULL); - cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()) + cast(KWDGSymbolValueSet*, dgPart->GetValueSet()) ->AddSymbolValue(Symbol::GetStarValue()); } } @@ -3690,13 +3644,12 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(KWDGAttribute* dgAttribute, KWD boolean bOk = true; ObjectDictionary odChekedValues; boolean bIsEnd; - CCHDGSymbolValueSet* dgValueSet; - CCHDGSymbolValue* dgValue; + KWDGSymbolValueSet* dgValueSet; + KWDGValue* dgValue; CCHDGPart* hdgPart; ALString sClusterName; ALString sValue; int nValueFrequency; - double dValueTypicality; StringVector svValues; IntVector ivValueFrequencies; DoubleVector dvValueTypicalities; @@ -3729,12 +3682,13 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(KWDGAttribute* dgAttribute, KWD { if (odChekedValues.Lookup(sValue) != NULL) { - JSONTokenizer::AddParseError("\"values\" contains a duplicate values (" + sValue + ")"); + JSONTokenizer::AddParseError("\"values\" contains a duplicate value (" + sValue + ")"); bOk = false; } else { - odChekedValues.SetAt(sValue, &odChekedValues), svValues.Add(sValue); + odChekedValues.SetAt(sValue, &odChekedValues); + svValues.Add(sValue); } } } @@ -3757,44 +3711,13 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(KWDGAttribute* dgAttribute, KWD bOk = false; } - // CH IV pas pour innerVariables + // Typicalite, sauf si la variable est interne if (not dgAttribute->IsInnerAttribute()) { bOk = bOk and JSONTokenizer::ReadExpectedToken(','); // Tableau des typicalites - bOk = bOk and JSONTokenizer::ReadKeyArray("valueTypicalities"); - bIsEnd = false; - dValueTypicality = 0; - while (bOk and not bIsEnd) - { - bOk = bOk and JSONTokenizer::ReadDoubleValue(false, dValueTypicality); - - // Tolerance pour les typicalite negatives - if (dValueTypicality < 0) - AddWarning(sTmp + "Typicality (" + DoubleToString(dValueTypicality) + - ") less than 0 for variable " + dgAttribute->GetAttributeName() + - " in \"valueTypicalities\" line " + - IntToString(JSONTokenizer::GetCurrentLineIndex())); - // Erreur pour les typicalite supereures a 1 - else if (dValueTypicality > 1) - { - AddError(sTmp + "Typicality (" + DoubleToString(dValueTypicality) + - ") greater than 1 for variable " + dgAttribute->GetAttributeName() + - " in \"valueTypicalities\" line " + - IntToString(JSONTokenizer::GetCurrentLineIndex())); - break; - } - bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); - if (bOk) - dvValueTypicalities.Add(dValueTypicality); - } - if (bOk and svValues.GetSize() != dvValueTypicalities.GetSize()) - { - JSONTokenizer::AddParseError( - "Vector \"valueTypicalities\" should be of same size as vector \"values\""); - bOk = false; - } + bOk = bOk and ReadJSONTypicalities(dgAttribute, svValues.GetSize(), &dvValueTypicalities); } // Fin de l'objet @@ -3804,7 +3727,7 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(KWDGAttribute* dgAttribute, KWD if (bOk) { hdgPart = cast(CCHDGPart*, dgPart); - dgValueSet = cast(CCHDGSymbolValueSet*, dgPart->GetValueSet()); + dgValueSet = cast(KWDGSymbolValueSet*, dgPart->GetValueSet()); hdgPart->SetPartName(sClusterName); hdgPart->SetShortDescription(""); hdgPart->SetDescription(""); @@ -3812,7 +3735,7 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(KWDGAttribute* dgAttribute, KWD // Memorisation des valeurs for (i = 0; i < svValues.GetSize(); i++) { - dgValue = cast(CCHDGSymbolValue*, dgValueSet->AddSymbolValue((Symbol)svValues.GetAt(i))); + dgValue = dgValueSet->AddSymbolValue((Symbol)svValues.GetAt(i)); dgValue->SetValueFrequency(ivValueFrequencies.GetAt(i)); if (not dgAttribute->IsInnerAttribute()) dgValue->SetTypicality(dvValueTypicalities.GetAt(i)); @@ -3821,117 +3744,18 @@ boolean CCCoclusteringReport::ReadJSONValueGroup(KWDGAttribute* dgAttribute, KWD return bOk; } -boolean CCCoclusteringReport::ReadJSONInnerAttributeIntervals(KWDGAttribute* innerAttribute) -{ - boolean bOk = true; - boolean bPartitionIsEnd; - KWDGInterval* dgInterval; - ALString sClusterName; - Continuous cLowerBound; - Continuous cUpperBound; - KWDGPart* dgPart; - - require(innerAttribute != NULL); - require(innerAttribute->GetAttributeName() != ""); - require(innerAttribute->GetAttributeType() == KWType::Continuous); - - // Initialisations - cLowerBound = 0; - cUpperBound = 0; - - // Lecture des intervalles - bPartitionIsEnd = false; - while (bOk and not bPartitionIsEnd) - { - // Creation de la partie courante - dgPart = innerAttribute->AddPart(); - - // Lecture de la borne inf - bOk = bOk and JSONTokenizer::ReadExpectedToken('['); - bOk = bOk and JSONTokenizer::ReadContinuousValue(false, cLowerBound); - - // Lecture de la borne sup - bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - bOk = bOk and JSONTokenizer::ReadContinuousValue(false, cUpperBound); - bOk = bOk and JSONTokenizer::ReadExpectedToken(']'); - bOk = bOk and JSONTokenizer::ReadArrayNext(bPartitionIsEnd); - - // Memorisation des informations sur la PV intervalle - if (bOk) - { - dgInterval = dgPart->GetInterval(); - dgInterval->SetLowerBound(cLowerBound); - dgInterval->SetUpperBound(cUpperBound); - } - } - - // Test si section suivante dans le json - bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - - return bOk; -} - -boolean CCCoclusteringReport::ReadJSONInnerAttributeValueGroups(KWDGAttribute* innerAttribute) -{ - boolean bOk = true; - boolean bIsEnd; - ALString sClusterName; - ALString sValue; - IntVector ivValueFrequencies; - DoubleVector dvValueTypicalities; - ALString sTmp; - boolean bPartitionIsEnd; - KWDGPart* dgPart; - - require(innerAttribute != NULL); - require(innerAttribute->GetAttributeName() != ""); - require(innerAttribute->GetAttributeType() == KWType::Symbol); - - // Lecture des groupes de valeurs - bPartitionIsEnd = false; - while (bOk and not bPartitionIsEnd) - { - // Creation de la partie courante - dgPart = innerAttribute->AddPart(); - - // Lecture des valeurs - bIsEnd = false; - bOk = bOk and JSONTokenizer::ReadExpectedToken('['); - while (bOk and not bIsEnd) - { - bOk = bOk and JSONTokenizer::ReadStringValue(sValue); - if (bOk) - { - // Traitement particulier de la Star value - if (sValue == Symbol::GetStarValue().GetValue()) - dgPart->GetSymbolValueSet()->AddSymbolValue(Symbol::GetStarValue()); - else - dgPart->GetSymbolValueSet()->AddSymbolValue((Symbol)sValue); - } - bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); - } - bOk = bOk and JSONTokenizer::ReadArrayNext(bPartitionIsEnd); - } - - // Test si section suivante dans le json - bOk = bOk and JSONTokenizer::ReadExpectedToken(','); - - return bOk; -} - boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(KWDGAttribute* varPartAttribute, KWDGPart* dgPart, const ObjectDictionary* odInnerAttributesAllVarParts, ObjectDictionary* odVarPartAttributeAllVarParts) { boolean bOk = true; boolean bIsEnd; - CCHDGVarPartSet* dgVarPartSet; - CCHDGVarPartValue* dgVarPartValue; + KWDGVarPartSet* dgVarPartSet; + KWDGValue* dgValue; CCHDGPart* hdgPart; ALString sClusterName; ALString sValue; int nValueFrequency; - double dValueTypicality; StringVector svValues; IntVector ivValueFrequencies; DoubleVector dvValueTypicalities; @@ -3952,6 +3776,11 @@ boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(KWDGAttribute* // Nom du cluster bIsEnd = false; bOk = bOk and JSONTokenizer::ReadKeyStringValue("cluster", sClusterName, bIsEnd); + if (bOk and sClusterName == "") + { + JSONTokenizer::AddParseError("\"cluster\" should have a non empty value"); + bOk = false; + } // Tableau des libelles des parties de variable, analogue des valeurs d'un variable categorielle bOk = bOk and JSONTokenizer::ReadKeyArray("values"); @@ -3966,7 +3795,7 @@ boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(KWDGAttribute* dgVarPart = cast(KWDGPart*, odInnerAttributesAllVarParts->Lookup(sValue)); if (dgVarPart == NULL) { - JSONTokenizer::AddParseError("Vector \"values\" of VarPart variable " + + JSONTokenizer::AddParseError("\"values\" of VarPart variable " + varPartAttribute->GetAttributeName() + " contains variable part \"" + sValue + "\" which was not specified among all the \"cluster\" " @@ -3980,7 +3809,7 @@ boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(KWDGAttribute* dgCheckedVarPart = cast(KWDGPart*, odVarPartAttributeAllVarParts->Lookup(sValue)); if (dgCheckedVarPart != NULL) { - JSONTokenizer::AddParseError("Vector \"values\" of VarPart variable " + + JSONTokenizer::AddParseError("\"values\" of VarPart variable " + varPartAttribute->GetAttributeName() + " contains a duplicate variable part (" + sValue + ")"); bOk = false; @@ -4018,6 +3847,53 @@ boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(KWDGAttribute* } bOk = bOk and JSONTokenizer::ReadExpectedToken(','); + // Tableau des typicalites + bOk = bOk and ReadJSONTypicalities(varPartAttribute, svValues.GetSize(), &dvValueTypicalities); + + // Fin de l'objet + bOk = bOk and JSONTokenizer::ReadExpectedToken('}'); + + // Memorisation des informations sur le groupe de parties de variable + if (bOk) + { + hdgPart = cast(CCHDGPart*, dgPart); + dgVarPartSet = dgPart->GetVarPartSet(); + hdgPart->SetPartName(sClusterName); + hdgPart->SetShortDescription(""); + hdgPart->SetDescription(""); + + // Memorisation des parties de variable identifiees par leur label + for (i = 0; i < svValues.GetSize(); i++) + { + // Recherche de la partie de variable d'apres son libelle + dgVarPart = cast(KWDGPart*, odInnerAttributesAllVarParts->Lookup((Symbol)svValues.GetAt(i))); + assert(dgVarPart != NULL); + + // Ajout de la PV identifiee par son Label au cluster de PV + dgValue = dgVarPartSet->AddVarPart(dgVarPart); + + // Memorisation de l'effectif et de la typicite + dgValue->SetValueFrequency(ivValueFrequencies.GetAt(i)); + dgValue->SetTypicality(dvValueTypicalities.GetAt(i)); + } + } + + return bOk; +} + +boolean CCCoclusteringReport::ReadJSONTypicalities(KWDGAttribute* dgAttribute, int nValueNumber, + DoubleVector* dvValueTypicalities) +{ + boolean bOk = true; + boolean bIsEnd; + double dValueTypicality; + ALString sTmp; + + require(dgAttribute != NULL); + require(nValueNumber >= 0); + require(dvValueTypicalities != NULL); + require(dvValueTypicalities->GetSize() == 0); + // Tableau des typicalites bOk = bOk and JSONTokenizer::ReadKeyArray("valueTypicalities"); bIsEnd = false; @@ -4029,56 +3905,28 @@ boolean CCCoclusteringReport::ReadJSONVarPartAttributeValueGroup(KWDGAttribute* // Tolerance pour les typicalite negatives if (dValueTypicality < 0) AddWarning(sTmp + "Typicality (" + DoubleToString(dValueTypicality) + - ") less than 0 for variable " + varPartAttribute->GetAttributeName() + + ") less than 0 for variable " + dgAttribute->GetAttributeName() + " in \"valueTypicalities\" line " + IntToString(JSONTokenizer::GetCurrentLineIndex())); // Erreur pour les typicalite supereures a 1 else if (dValueTypicality > 1) { AddError(sTmp + "Typicality (" + DoubleToString(dValueTypicality) + - ") greater than 1 for variable " + varPartAttribute->GetAttributeName() + + ") greater than 1 for variable " + dgAttribute->GetAttributeName() + " in \"valueTypicalities\" line " + IntToString(JSONTokenizer::GetCurrentLineIndex())); + bOk = false; break; } bOk = bOk and JSONTokenizer::ReadArrayNext(bIsEnd); if (bOk) - dvValueTypicalities.Add(dValueTypicality); + dvValueTypicalities->Add(dValueTypicality); } - if (bOk and svValues.GetSize() != dvValueTypicalities.GetSize()) + if (bOk and nValueNumber != dvValueTypicalities->GetSize()) { JSONTokenizer::AddParseError( "Vector \"valueTypicalities\" should be of same size as vector \"values\""); bOk = false; } - - // Fin de l'objet - bOk = bOk and JSONTokenizer::ReadExpectedToken('}'); - - // Memorisation des informations sur le groupe de parties de variable - if (bOk) - { - hdgPart = cast(CCHDGPart*, dgPart); - dgVarPartSet = cast(CCHDGVarPartSet*, dgPart->GetVarPartSet()); - hdgPart->SetPartName(sClusterName); - hdgPart->SetShortDescription(""); - hdgPart->SetDescription(""); - - // Memorisation des parties de variable identifiees par leur label - for (i = 0; i < svValues.GetSize(); i++) - { - // Recherche de la partie de variable d'apres son libelle - dgVarPart = cast(KWDGPart*, odInnerAttributesAllVarParts->Lookup((Symbol)svValues.GetAt(i))); - assert(dgVarPart != NULL); - - // Ajout de la PV identifiee par son Label au cluster de PV - dgVarPartValue = cast(CCHDGVarPartValue*, dgVarPartSet->AddVarPart(dgVarPart)); - - // Memorisation de l'effectif et de la typicite - dgVarPartValue->SetValueFrequency(ivValueFrequencies.GetAt(i)); - dgVarPartValue->SetTypicality(dvValueTypicalities.GetAt(i)); - } - } - return bOk; } // CH IV End @@ -4659,28 +4507,6 @@ void CCCoclusteringReport::WriteJSONDimensionSummaries(const CCHierarchicalDataG fJSON->EndArray(); } -void CCCoclusteringReport::WriteJSONInnerAttributesDimensionSummaries(const KWDGAttribute* varPartAttribute, - JSONFile* fJSON) -{ - CCHDGAttribute* innerAttribute; - int nAttribute; - - require(varPartAttribute != NULL); - require(varPartAttribute->GetAttributeType() == KWType::VarPart); - require(varPartAttribute->GetInnerAttributes() != NULL); - require(fJSON != NULL); - - // Parcours des innerAttributes pour la section des dimensions - fJSON->BeginKeyArray("dimensionSummaries"); - for (nAttribute = 0; nAttribute < varPartAttribute->GetInnerAttributeNumber(); nAttribute++) - { - innerAttribute = cast(CCHDGAttribute*, varPartAttribute->GetInnerAttributeAt(nAttribute)); - - WriteJSONDimensionSummary(innerAttribute, fJSON); - } - fJSON->EndArray(); -} - void CCCoclusteringReport::WriteJSONDimensionSummary(CCHDGAttribute* attribute, JSONFile* fJSON) { int nValueNumber; @@ -4726,25 +4552,14 @@ void CCCoclusteringReport::WriteJSONDimensionPartitions(const CCHierarchicalData { attribute = coclusteringDataGrid->GetAttributeAt(nAttribute); - // Debut de l'objet - fJSON->BeginObject(); - fJSON->WriteKeyString("name", attribute->GetAttributeName()); - fJSON->WriteKeyString( - "type", KWType::ToString(KWType::GetCoclusteringSimpleType(attribute->GetAttributeType()))); - // Ecriture de la partition de l'attribut // Dans le cas d'un attribut de type VarPart, declenche l'ecriture de ses innerAttributes - WriteJSONAttributePartition(attribute, coclusteringDataGrid, fJSON); - - // Fin de l'objet - fJSON->EndObject(); + WriteJSONAttributePartition(attribute, fJSON); } fJSON->EndArray(); } -void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, - const CCHierarchicalDataGrid* coclusteringDataGrid, - JSONFile* fJSON) +void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, JSONFile* fJSON) { CCHDGAttribute* dgAttribute; KWDGPart* dgPart; @@ -4752,12 +4567,17 @@ void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, KWDGInterval* dgInterval; KWDGValue* dgValue; KWDGValueSet* dgValueSet; - CCHDGSymbolValue* hdgValue; - CCHDGVarPartValue* hdgVarPartValue; - KWDGAttribute* innerAttribute; int nIndex; int nDefaultGroupIndex; + require(attribute != NULL); + + // Debut de l'objet + fJSON->BeginObject(); + fJSON->WriteKeyString("name", attribute->GetAttributeName()); + fJSON->WriteKeyString("type", + KWType::ToString(KWType::GetCoclusteringSimpleType(attribute->GetAttributeType()))); + // Traitement des attributs numeriques if (attribute->GetAttributeType() == KWType::Continuous) { @@ -4801,11 +4621,16 @@ void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, } fJSON->EndArray(); } - - // Traitement des attributs categoriels - else if (attribute->GetAttributeType() == KWType::Symbol) + // Traitement des attributs groupables + else { - // Parcours des parties + assert(KWType::IsCoclusteringGroupableType(attribute->GetAttributeType())); + + // Ecriture des attributs internes dans le cas d'un attribut VarPart + if (attribute->GetAttributeType() == KWType::VarPart) + WriteJSONInnerAttributes(attribute->GetInnerAttributes(), fJSON); + + // Parcours des groupes de parties de variable fJSON->BeginKeyArray("valueGroups"); nDefaultGroupIndex = -1; nIndex = 0; @@ -4820,7 +4645,7 @@ void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, nDefaultGroupIndex = nIndex; nIndex++; - // Parcours des valeurs, sauf si effectif nul (cas de la StarValue) + // Parcours des valeurs, sauf si effectif nul (cas de la valeur par defaut) fJSON->BeginObject(); fJSON->WriteKeyString("cluster", hdgPart->GetPartName()); fJSON->BeginKeyList("values"); @@ -4844,21 +4669,21 @@ void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, } fJSON->EndList(); + // Typicalite des valeurs, sauf pour un attribut interne if (not attribute->IsInnerAttribute()) { - // Typicalite des valeurs fJSON->BeginKeyList("valueTypicalities"); dgValue = dgValueSet->GetHeadValue(); while (dgValue != NULL) { - hdgValue = cast(CCHDGSymbolValue*, dgValue); - if (hdgValue->GetValueFrequency() > 0) - fJSON->WriteDouble(hdgValue->GetTypicality()); + if (dgValue->GetValueFrequency() > 0) + fJSON->WriteDouble(dgValue->GetTypicality()); dgValueSet->GetNextValue(dgValue); } fJSON->EndList(); } + // Fin de l'objet fJSON->EndObject(); // Partie suivante @@ -4866,107 +4691,47 @@ void CCCoclusteringReport::WriteJSONAttributePartition(KWDGAttribute* attribute, } fJSON->EndArray(); - // Index du groupe par defaut - assert(nDefaultGroupIndex >= 0); - fJSON->WriteKeyInt("defaultGroupIndex", nDefaultGroupIndex); + // Index du groupe par defaut, exploite uniquement pour les attribut Symbol + assert(nDefaultGroupIndex >= 0 or attribute->GetAttributeType() != KWType::Symbol); + if (nDefaultGroupIndex >= 0) + fJSON->WriteKeyInt("defaultGroupIndex", nDefaultGroupIndex); } - // Traitement des attributs de type VarPart - else if (attribute->GetAttributeType() == KWType::VarPart) - { - // Parcours des innerVariables de l'attribut - fJSON->BeginKeyObject("innerVariables"); - - // Descriptif des innerVariables - WriteJSONInnerAttributesDimensionSummaries(attribute, fJSON); - - // Parcours des variables de l'attribut - fJSON->BeginKeyArray("dimensionPartitions"); - nIndex = 0; - for (nIndex = 0; nIndex < attribute->GetInnerAttributeNumber(); nIndex++) - { - // Extraction de l'attribut interne - innerAttribute = attribute->GetInnerAttributeAt(nIndex); - assert(innerAttribute != NULL); - - // Debut de l'objet - fJSON->BeginObject(); - fJSON->WriteKeyString("name", innerAttribute->GetAttributeName()); - fJSON->WriteKeyString("type", KWType::ToString(KWType::GetCoclusteringSimpleType( - innerAttribute->GetAttributeType()))); - - // Ecriture de la partition - WriteJSONAttributePartition(innerAttribute, coclusteringDataGrid, fJSON); - - // Fin de l'objet - fJSON->EndObject(); - } - fJSON->EndArray(); - - fJSON->EndObject(); - - // Parcours des groupes de parties de variable - fJSON->BeginKeyArray("valueGroups"); - // nDefaultGroupIndex = -1; - // nIndex = 0; - dgPart = attribute->GetHeadPart(); - while (dgPart != NULL) - { - dgValueSet = dgPart->GetValueSet(); - hdgPart = cast(CCHDGPart*, dgPart); - - // Memorisation de l'index du groupe par defaut - // CH IV Refactoring: faut-il gere un default groupe index??? - // if (hdgVarPartSet->IsDefaultPart()) - // nDefaultGroupIndex = nIndex; - // nIndex++; + // Fin de l'objet + fJSON->EndObject(); +} - // Parcours des parties de variable, sauf si effectif nul - fJSON->BeginObject(); - fJSON->WriteKeyString("cluster", hdgPart->GetPartName()); - fJSON->BeginKeyList("values"); - dgValue = dgValueSet->GetHeadValue(); - while (dgValue != NULL) - { - if (dgValue->GetValueFrequency() > 0) - fJSON->WriteString(dgValue->GetObjectLabel()); - dgValueSet->GetNextValue(dgValue); - } - fJSON->EndList(); +void CCCoclusteringReport::WriteJSONInnerAttributes(const KWDGInnerAttributes* innerAttributes, JSONFile* fJSON) +{ + CCHDGAttribute* innerAttribute; + int nAttribute; - // Effectifs des valeurs - fJSON->BeginKeyList("valueFrequencies"); - dgValue = dgValueSet->GetHeadValue(); - while (dgValue != NULL) - { - if (dgValue->GetValueFrequency() > 0) - fJSON->WriteInt(dgValue->GetValueFrequency()); - dgValueSet->GetNextValue(dgValue); - } - fJSON->EndList(); + require(innerAttributes != NULL); + require(fJSON != NULL); - // Typicalite des valeurs - fJSON->BeginKeyList("valueTypicalities"); - dgValue = dgValueSet->GetHeadValue(); - while (dgValue != NULL) - { - hdgVarPartValue = cast(CCHDGVarPartValue*, dgValue); - if (dgValue->GetValueFrequency() > 0) - fJSON->WriteDouble(hdgVarPartValue->GetTypicality()); - dgValueSet->GetNextValue(dgValue); - } - fJSON->EndList(); - fJSON->EndObject(); + // Section des attributs internes + fJSON->BeginKeyObject("innerVariables"); - // Partie suivante - attribute->GetNextPart(dgPart); - } - fJSON->EndArray(); + // Parcours des innerAttributes pour la section des dimensions + fJSON->BeginKeyArray("dimensionSummaries"); + for (nAttribute = 0; nAttribute < innerAttributes->GetInnerAttributeNumber(); nAttribute++) + { + innerAttribute = cast(CCHDGAttribute*, innerAttributes->GetInnerAttributeAt(nAttribute)); + WriteJSONDimensionSummary(innerAttribute, fJSON); + } + fJSON->EndArray(); - // Index du groupe par defaut - // assert(nDefaultGroupIndex >= 0); - // fJSON->WriteKeyInt("defaultGroupIndex", nDefaultGroupIndex); + // Parcours des innerAttributes pour la section des partitions + fJSON->BeginKeyArray("dimensionPartitions"); + for (nAttribute = 0; nAttribute < innerAttributes->GetInnerAttributeNumber(); nAttribute++) + { + innerAttribute = cast(CCHDGAttribute*, innerAttributes->GetInnerAttributeAt(nAttribute)); + WriteJSONAttributePartition(innerAttribute, fJSON); } + fJSON->EndArray(); + + // Fin de la section + fJSON->EndObject(); } void CCCoclusteringReport::WriteJSONDimensionHierarchies(const CCHierarchicalDataGrid* coclusteringDataGrid, diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.h b/src/Learning/MODL_Coclustering/CCCoclusteringReport.h index a8b93d4e3..1966ac335 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.h +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.h @@ -165,11 +165,10 @@ class CCCoclusteringReport : public Object boolean ReadJSONAttributePartition(KWDGAttribute* attribute, CCHierarchicalDataGrid* coclusteringDataGrid); boolean ReadJSONInterval(KWDGAttribute* dgAttribute, KWDGPart* dgPart); boolean ReadJSONValueGroup(KWDGAttribute* dgAttribute, KWDGPart* dgPart); - boolean ReadJSONInnerAttributeIntervals(KWDGAttribute* innerAttribute); - boolean ReadJSONInnerAttributeValueGroups(KWDGAttribute* innerAttribute); boolean ReadJSONVarPartAttributeValueGroup(KWDGAttribute* varPartAttribute, KWDGPart* dgPart, const ObjectDictionary* odInnerAttributesAllVarParts, ObjectDictionary* odVarPartAttributeAllVarParts); + boolean ReadJSONTypicalities(KWDGAttribute* dgAttribute, int nValueNumber, DoubleVector* dvValueTypicalities); boolean ReadJSONDimensionHierarchies(CCHierarchicalDataGrid* coclusteringDataGrid); boolean ReadJSONCells(CCHierarchicalDataGrid* coclusteringDataGrid); @@ -177,11 +176,10 @@ class CCCoclusteringReport : public Object void InternalWriteJSONReport(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); void WriteJSONSummary(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); void WriteJSONDimensionSummaries(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); - void WriteJSONInnerAttributesDimensionSummaries(const KWDGAttribute* vartPartAttribute, JSONFile* fJSON); void WriteJSONDimensionSummary(CCHDGAttribute* attribute, JSONFile* fJSON); void WriteJSONDimensionPartitions(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); - void WriteJSONAttributePartition(KWDGAttribute* attribute, const CCHierarchicalDataGrid* coclusteringDataGrid, - JSONFile* fJSON); + void WriteJSONAttributePartition(KWDGAttribute* attribute, JSONFile* fJSON); + void WriteJSONInnerAttributes(const KWDGInnerAttributes* innerAttributes, JSONFile* fJSON); void WriteJSONDimensionHierarchies(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); void WriteJSONCells(const CCHierarchicalDataGrid* coclusteringDataGrid, JSONFile* fJSON); diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp index 0243dd18e..fc2f71e31 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.cpp @@ -173,11 +173,6 @@ KWDGAttribute* CCHierarchicalDataGrid::NewAttribute() const return new CCHDGAttribute; } -KWDGCell* CCHierarchicalDataGrid::NewCell() const -{ - return new CCHDGCell; -} - ////////////////////////////////////////////////////////////////////////////// // Classe CCHDGAttribute @@ -835,10 +830,6 @@ boolean CCHDGPart::CheckHierarchy() const bOk = false; } - // Verification de l'ensemble de valeurs dans le cas categoriel - if (GetPartType() == KWType::Symbol) - bOk = bOk and cast(CCHDGSymbolValueSet*, GetSymbolValueSet())->CheckHierarchy(); - // Verifications complementaires de chainahe coherent de la hierarchie, sans message d'erreur assert(not bOk or parentPart == NULL or parentPart->childPart1 == this or parentPart->childPart2 == this); assert(not bOk or childPart1 == NULL or childPart1->parentPart == this); @@ -846,18 +837,6 @@ boolean CCHDGPart::CheckHierarchy() const return bOk; } -KWDGSymbolValueSet* CCHDGPart::NewSymbolValueSet() const -{ - return new CCHDGSymbolValueSet(); -} - -// CH IV Begin -KWDGVarPartSet* CCHDGPart::NewVarPartSet() const -{ - return new CCHDGVarPartSet(); -} -// CH IV End - boolean CCHDGPart::GetEmulated() const { return true; @@ -928,234 +907,3 @@ int CCHDGPartCompareHierarchicalRank(const void* elem1, const void* elem2) } return nCompare; } - -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGSymbolValueSet - -CCHDGSymbolValueSet::CCHDGSymbolValueSet() {} - -CCHDGSymbolValueSet::~CCHDGSymbolValueSet() {} - -KWDGValueSet* CCHDGSymbolValueSet::Create() const -{ - return new CCHDGSymbolValueSet; -} - -void CCHDGSymbolValueSet::SortValuesByTypicality() -{ - InternalSortValues(CCHDGValueCompareDecreasingTypicality); -} - -boolean CCHDGSymbolValueSet::CheckHierarchy() const -{ - boolean bOk = true; - KWDGValue* value; - - // Pas de controle d'integrite de base (invalide pour els parties non feuilles) - // Test des valeurs de la partie - if (bOk) - { - // Parcours des valeurs de la partie - value = GetHeadValue(); - while (value != NULL) - { - // Test de la valeur - bOk = cast(CCHDGSymbolValue*, value)->CheckHierarchy(); - if (not bOk) - break; - - // Valeur suivante - GetNextValue(value); - } - } - return bOk; -} - -KWDGValue* CCHDGSymbolValueSet::NewSymbolValue(const Symbol& sValue) const -{ - return new CCHDGSymbolValue(sValue); -} - -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGValue - -CCHDGSymbolValue::CCHDGSymbolValue(const Symbol& sValue) : KWDGSymbolValue(sValue) -{ - dTypicality = 0; -} - -CCHDGSymbolValue::~CCHDGSymbolValue() {} - -void CCHDGSymbolValue::SetTypicality(double dValue) -{ - require(0 <= dTypicality and dTypicality <= 1); - dTypicality = dValue; -} - -double CCHDGSymbolValue::GetTypicality() const -{ - return dTypicality; -} - -boolean CCHDGSymbolValue::CheckHierarchy() const -{ - boolean bOk = true; - ALString sTmp; - - // Methode d'integrite de base - require(KWDGSymbolValue::Check()); - - // Typicalite - if (bOk and (dTypicality < 0 or dTypicality > 1)) - { - AddError(sTmp + "Typicality (" + DoubleToString(dTypicality) + ") should be between 0 and 1"); - bOk = false; - } - return bOk; -} - -int CCHDGValueCompareDecreasingTypicality(const void* elem1, const void* elem2) -{ - const double dPrecision = 1e-7; - CCHDGSymbolValue* value1; - CCHDGSymbolValue* value2; - double dCompare; - int nCompare; - - require(elem1 != NULL); - require(elem2 != NULL); - - // Acces a la valueies - value1 = cast(CCHDGSymbolValue*, *(Object**)elem1); - value2 = cast(CCHDGSymbolValue*, *(Object**)elem2); - - // Comparaison a une precision pres (compatible avec l'affichage) - dCompare = -value1->GetTypicality() + value2->GetTypicality(); - if (dCompare > dPrecision) - nCompare = 1; - else if (dCompare < -dPrecision) - nCompare = -1; - else - nCompare = value1->CompareFrequency(value2); - return nCompare; -} - -// CH IV Begin -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGVarPartSet - -CCHDGVarPartSet::CCHDGVarPartSet() {} - -CCHDGVarPartSet::~CCHDGVarPartSet() {} - -KWDGValueSet* CCHDGVarPartSet::Create() const -{ - return new CCHDGVarPartSet; -} - -void CCHDGVarPartSet::SortVarPartsByTypicality() -{ - InternalSortValues(CCHDGVarPartValueCompareDecreasingTypicality); -} - -boolean CCHDGVarPartSet::CheckHierarchy() const -{ - boolean bOk = true; - KWDGValue* value; - - // Pas de controle d'integrite de base (invalide pour els parties non feuilles) - // Test des valeurs de la partie - if (bOk) - { - // Parcours des valeurs de la partie - value = GetHeadValue(); - while (value != NULL) - { - // Test de la valeur - bOk = cast(CCHDGVarPartValue*, value)->CheckHierarchy(); - if (not bOk) - break; - - // Valeur suivante - GetNextValue(value); - } - } - return bOk; -} - -KWDGVarPartValue* CCHDGVarPartSet::NewVarPartValue(KWDGPart* varPart) const -{ - return new CCHDGVarPartValue(varPart); -} - -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGVarPartValue - -CCHDGVarPartValue::CCHDGVarPartValue(KWDGPart* varPart) : KWDGVarPartValue(varPart) -{ - dTypicality = 0; -} - -CCHDGVarPartValue::~CCHDGVarPartValue() {} - -void CCHDGVarPartValue::SetTypicality(double dValue) -{ - require(0 <= dTypicality and dTypicality <= 1); - dTypicality = dValue; -} - -double CCHDGVarPartValue::GetTypicality() const -{ - return dTypicality; -} - -boolean CCHDGVarPartValue::CheckHierarchy() const -{ - boolean bOk = true; - ALString sTmp; - - // Methode d'integrite de base - require(KWDGVarPartValue::Check()); - - // Typicalite - if (bOk and (dTypicality < 0 or dTypicality > 1)) - { - AddError(sTmp + "Typicality (" + DoubleToString(dTypicality) + ") should be between 0 and 1"); - bOk = false; - } - return bOk; -} - -int CCHDGVarPartValueCompareDecreasingTypicality(const void* elem1, const void* elem2) -{ - const double dPrecision = 1e-7; - CCHDGVarPartValue* value1; - CCHDGVarPartValue* value2; - double dCompare; - int nCompare; - - require(elem1 != NULL); - require(elem2 != NULL); - - // Acces a la valueies - value1 = cast(CCHDGVarPartValue*, *(Object**)elem1); - value2 = cast(CCHDGVarPartValue*, *(Object**)elem2); - - // Comparaison a une precision pres (compatible avec l'affichage) - dCompare = -value1->GetTypicality() + value2->GetTypicality(); - if (dCompare > dPrecision) - nCompare = 1; - else if (dCompare < -dPrecision) - nCompare = -1; - else - nCompare = value1->CompareValue(value2); - return nCompare; -} -// CH IV End - -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGCell - -CCHDGCell::CCHDGCell() {} - -CCHDGCell::~CCHDGCell() {} diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h index 38c3490f2..c6bcef04c 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h @@ -7,13 +7,6 @@ class CCHierarchicalDataGrid; class CCHDGAttribute; class CCHDGPart; -class CCHDGSymbolValueSet; -class CCHDGSymbolValue; -class CCHDGCell; -// CH IV Begin -class CCHDGVarPartSet; -class CCHDGVarPartValue; -// CH IV End #include "KWDataGrid.h" #include "KWDatabase.h" @@ -88,7 +81,6 @@ class CCHierarchicalDataGrid : public KWDataGrid protected: // Reimplementation des methodes virtuelles KWDGAttribute* NewAttribute() const override; - KWDGCell* NewCell() const override; // Informations synthetique sur la grille de coclustering ALString sShortDescription; @@ -268,12 +260,6 @@ class CCHDGPart : public KWDGPart /////////////////////////////// ///// Implementation protected: - // Reimplementation des methodes virtuelles - KWDGSymbolValueSet* NewSymbolValueSet() const override; - // CH IV Begin - KWDGVarPartSet* NewVarPartSet() const override; - // CH IV End - // Reimplementation de la methode indiquant si les donnees sont emulees // pour gerer les effectifs des parties de la hierarchie, n'ayant pas directement de cellules boolean GetEmulated() const override; @@ -300,125 +286,3 @@ int CCHDGPartCompareLeafRank(const void* elem1, const void* elem2); // Comparaison de deux parties selon leur rang hierarchique, index d'attribut et partie (pointeur) int CCHDGPartCompareHierarchicalRank(const void* elem1, const void* elem2); - -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGSymbolValueSet -// Ensemble de valeurs d'une partie symbolique d'un HierarchicalDataGrid -class CCHDGSymbolValueSet : public KWDGSymbolValueSet -{ -public: - // Constructeur - CCHDGSymbolValueSet(); - ~CCHDGSymbolValueSet(); - - // Creation - KWDGValueSet* Create() const override; - - // Tri des valeurs par typicalite decroissante - void SortValuesByTypicality(); - - // Controle d'integrite pour la hierarchie - boolean CheckHierarchy() const; - - /////////////////////////////// - ///// Implementation -protected: - // Reimplementation des methodes virtuelles - KWDGValue* NewSymbolValue(const Symbol& sValue) const override; -}; - -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGSymbolValue -// Valeur symbolique d'un HierarchicalDataGrid -class CCHDGSymbolValue : public KWDGSymbolValue -{ -public: - // Constructeur - CCHDGSymbolValue(const Symbol& sValue); - ~CCHDGSymbolValue(); - - // Typicalite - void SetTypicality(double dValue); - double GetTypicality() const; - - // Controle d'integrite pour la hierarchie - boolean CheckHierarchy() const; - - /////////////////////////////// - ///// Implementation -protected: - double dTypicality; -}; - -// Comparaison de deux valeurs symboliques, par typicalite decroissante -int CCHDGValueCompareDecreasingTypicality(const void* elem1, const void* elem2); - -// CH IV Begin -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGVarPartSet -// Ensemble de parties de variable d'une partie de type VarPart d'un HierarchicalDataGrid -class CCHDGVarPartSet : public KWDGVarPartSet -{ -public: - // Constructeur - CCHDGVarPartSet(); - ~CCHDGVarPartSet(); - - // Creation - KWDGValueSet* Create() const override; - - // Tri des parties de variable par typicalite decroissante - void SortVarPartsByTypicality(); - - // Controle d'integrite pour la hierarchie - boolean CheckHierarchy() const; - - /////////////////////////////// - ///// Implementation -protected: - // Reimplementation des methodes virtuelles - KWDGVarPartValue* NewVarPartValue(KWDGPart* varPart) const override; -}; - -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGVarPartValue -// Partie de variable d'un HierarchicalDataGrid -class CCHDGVarPartValue : public KWDGVarPartValue -{ -public: - // Constructeur - CCHDGVarPartValue(KWDGPart* varPart); - ~CCHDGVarPartValue(); - - // Typicalite - void SetTypicality(double dValue); - double GetTypicality() const; - - // Controle d'integrite pour la hierarchie - boolean CheckHierarchy() const; - - /////////////////////////////// - ///// Implementation -protected: - double dTypicality; -}; - -// Comparaison de deux valeurs symboliques, par typicalite decroissante -int CCHDGVarPartValueCompareDecreasingTypicality(const void* elem1, const void* elem2); - -// CH IV End - -////////////////////////////////////////////////////////////////////////////// -// Classe CCHDGCell -// Cellule d'un HierarchicalDataGrid -class CCHDGCell : public KWDGCell -{ -public: - // Constructeur - CCHDGCell(); - ~CCHDGCell(); - - /////////////////////////////// - ///// Implementation -protected: -}; diff --git a/src/Learning/MODL_Coclustering/CCLearningProblem.cpp b/src/Learning/MODL_Coclustering/CCLearningProblem.cpp index 479fd5d3a..71607e95d 100644 --- a/src/Learning/MODL_Coclustering/CCLearningProblem.cpp +++ b/src/Learning/MODL_Coclustering/CCLearningProblem.cpp @@ -478,14 +478,14 @@ void CCLearningProblem::ExtractClusters(const ALString& sCoclusteringAttributeNa { // Ecriture des clusters selon le type de la variable if (coclusteringAttribute->GetAttributeType() == KWType::Symbol) - WriteSymbolClusters(coclusteringAttribute, fstClusterTableFile); + WriteGroupableClusters(coclusteringAttribute, fstClusterTableFile); else if (coclusteringAttribute->GetAttributeType() == KWType::Continuous) WriteContinuousClusters(coclusteringAttribute, fstClusterTableFile); else if (coclusteringAttribute->GetAttributeType() == KWType::VarPart) { // Cas d'une variable de type VarPart : ajout d'un descriptif des parties de variable // des attributs internes - WriteVarPartClusters(coclusteringAttribute, fstClusterTableFile); + WriteGroupableClusters(coclusteringAttribute, fstClusterTableFile); WriteVarPartsInnerAttributes(coclusteringAttribute); } @@ -1030,46 +1030,6 @@ KWAttribute* CCLearningProblem::InsertIdentifierAttribute(KWClass* kwcClass) return attribute; } -void CCLearningProblem::WriteSymbolClusters(const CCHDGAttribute* symbolCoclusteringAttribute, ostream& ost) -{ - KWDGPart* dgPart; - CCHDGPart* hdgPart; - CCHDGSymbolValueSet* hdgValueSet; - KWDGValue* dgValue; - CCHDGSymbolValue* hdgValue; - - require(symbolCoclusteringAttribute != NULL); - require(symbolCoclusteringAttribute->GetAttributeType() == KWType::Symbol); - - // Entete - ost << "Cluster\tValue\tFrequency\tTypicality\n"; - - // Parcours des parties - dgPart = symbolCoclusteringAttribute->GetHeadPart(); - while (dgPart != NULL) - { - hdgPart = cast(CCHDGPart*, dgPart); - - // Parcours des valeurs - hdgValueSet = cast(CCHDGSymbolValueSet*, hdgPart->GetValueSet()); - dgValue = hdgValueSet->GetHeadValue(); - while (dgValue != NULL) - { - hdgValue = cast(CCHDGSymbolValue*, dgValue); - - // Caracteristiques des valeurs - ost << hdgPart->GetUserLabel() << "\t" << hdgValue->GetSymbolValue() << "\t" - << hdgValue->GetValueFrequency() << "\t" << hdgValue->GetTypicality() << "\n"; - - // Valeur suivante - hdgValueSet->GetNextValue(dgValue); - } - - // Partie suivante - symbolCoclusteringAttribute->GetNextPart(dgPart); - } -} - void CCLearningProblem::WriteContinuousClusters(const CCHDGAttribute* continuousCoclusteringAttribute, ostream& ost) { KWDGPart* dgPart; @@ -1101,43 +1061,40 @@ void CCLearningProblem::WriteContinuousClusters(const CCHDGAttribute* continuous } } -void CCLearningProblem::WriteVarPartClusters(const CCHDGAttribute* varPartCoclusteringAttribute, ostream& ost) +void CCLearningProblem::WriteGroupableClusters(const CCHDGAttribute* groupableCoclusteringAttribute, ostream& ost) { KWDGPart* dgPart; CCHDGPart* hdgPart; - CCHDGVarPartSet* hdgVarPartSet; + KWDGValueSet* dgValueSet; KWDGValue* dgValue; - CCHDGVarPartValue* hdgValue; - require(varPartCoclusteringAttribute != NULL); - require(varPartCoclusteringAttribute->GetAttributeType() == KWType::VarPart); + require(groupableCoclusteringAttribute != NULL); + require(KWType::IsCoclusteringGroupableType(groupableCoclusteringAttribute->GetAttributeType())); // Entete - ost << "Cluster\tVarPart\tFrequency\tTypicality\n"; + ost << "Cluster\tValue\tFrequency\tTypicality\n"; // Parcours des parties - dgPart = varPartCoclusteringAttribute->GetHeadPart(); + dgPart = groupableCoclusteringAttribute->GetHeadPart(); while (dgPart != NULL) { hdgPart = cast(CCHDGPart*, dgPart); // Parcours des valeurs - hdgVarPartSet = cast(CCHDGVarPartSet*, hdgPart->GetVarPartSet()); - dgValue = hdgVarPartSet->GetHeadValue(); + dgValueSet = hdgPart->GetValueSet(); + dgValue = dgValueSet->GetHeadValue(); while (dgValue != NULL) { - hdgValue = cast(CCHDGVarPartValue*, dgValue); - // Caracteristiques des valeurs - ost << hdgPart->GetUserLabel() << "\t" << hdgValue->GetVarPart()->GetVarPartLabel() << "\t" - << hdgValue->GetValueFrequency() << "\t" << hdgValue->GetTypicality() << "\n"; + ost << hdgPart->GetUserLabel() << "\t" << dgValue->GetObjectLabel() << "\t" + << dgValue->GetValueFrequency() << "\t" << dgValue->GetTypicality() << "\n"; // Valeur suivante - hdgVarPartSet->GetNextValue(dgValue); + dgValueSet->GetNextValue(dgValue); } // Partie suivante - varPartCoclusteringAttribute->GetNextPart(dgPart); + groupableCoclusteringAttribute->GetNextPart(dgPart); } } diff --git a/src/Learning/MODL_Coclustering/CCLearningProblem.h b/src/Learning/MODL_Coclustering/CCLearningProblem.h index 64009885d..143d85e64 100644 --- a/src/Learning/MODL_Coclustering/CCLearningProblem.h +++ b/src/Learning/MODL_Coclustering/CCLearningProblem.h @@ -141,11 +141,10 @@ class CCLearningProblem : public Object // Cet attribut est de type Symbol et contient le le numero de ligne de l'instance dans son fichier KWAttribute* InsertIdentifierAttribute(KWClass* kwcClass); - // Ecriture des clusters categoriels et numeriques - void WriteSymbolClusters(const CCHDGAttribute* symbolCoclusteringAttribute, ostream& ost); + // Ecriture des clusters numeriques ou groupable (Symbol ou VarPart) void WriteContinuousClusters(const CCHDGAttribute* continuousCoclusteringAttribute, ostream& ost); + void WriteGroupableClusters(const CCHDGAttribute* groupableCoclusteringAttribute, ostream& ost); // CH IV Begin - void WriteVarPartClusters(const CCHDGAttribute* varPartCoclusteringAttribute, ostream& ost); void WriteVarPartsInnerAttributes(const CCHDGAttribute* varPartCoclusteringAttribute); void WriteContinuousInnerAttribute(KWDGAttribute* continuousInnerAttribute, ostream& ost); void WriteSymbolInnerAttribute(KWDGAttribute* symbolInnerAttribute, ostream& ost); diff --git a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp index 537eeece5..918bcf0ba 100644 --- a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp +++ b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp @@ -31,12 +31,12 @@ int main(int argc, char** argv) // Activation de la gestion des signaux via des erreurs, pour afficher des messages d'erreur explicites // A potentiellement commenter sur certains IDE lors des phases de debuggage - Global::ActivateSignalErrorManagement(); + //DDD Global::ActivateSignalErrorManagement(); // Choix du repertoire de lancement pour le debugage sous Windows (a commenter apres fin du debug) //SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); - //DDD SetWindowsDebugDir("y_CoclusteringIV_Standard", "SimplifyCoclustering"); - SetWindowsDebugDir("Standard", "Iris"); + //SetWindowsDebugDir("y_CoclusteringIV_Standard", "Iris"); + SetWindowsDebugDir("y_CoclusteringIV_Standard", "ExtractClustersIris"); // Point d'arret sur l'allocation d'un bloc memoire // MemSetAllocIndexExit(77); From d6b11e40dff37bde968ca99a739130025e3703ea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Thu, 21 Sep 2023 15:58:48 +0200 Subject: [PATCH 13/17] Finalization of new profiling of coclustering IV methods Classe Profiler - en mode trace, ajout du temps d'execution de methode elementaire Rajout de collecte de temps pour deux methodes: - "Generate neighbour solution": appel de GenerateNeighbourSolution depuis KWDataGridVNSOptimizer::VNSOptimizeDataGrid - "Post VarPart merge" : KWDataGridManager::ExportDataGridWithVarPartMergeOptimization KWDataGridVNSOptimizer - debut de refactoring pour isoler lune methdoe de post(optimisation des parties de variables // CH IV Refactoring // Methode de post-optimisation d'un grille optimisee en redecoupant ses parties de variables // Methode temporaire permettant de reutiliser le code de la methode principale VNSOptimizeDataGrid // en isolant cette partie de post-optimisation specifique VarPart, et de supprimer l'ancienne // methode VNSOptimizeVarPartDataGrid // Parametre (a faire evoluer si necessaire): // - initialDataGrid: grille initiale // - neighbourDataGrid: grille courante en cours d'optimisation // - dNeighbourDataGridCost: cout de la grille courante // - mergedDataGrid: grille optimisee si amelioration // En sortie, on rend la nouvelle version de la grille optimisee (ou courante) integrant la post-ptimisation et on renvoie son cout // // A terme, il faudra isoler ce service de post-optimisation pour le deplacer en quatrime sous-methode // en fin de la methode OptimizeSolution // - Pre-optimization // - Greedy merge optimization // - Post-optimization // - Post-optimization IV PROTO_VNSDataGridPostOptimizeVarPart - methodes impactees, non activee encore (reste une bug residuel) - PROTO_VNSDataGridPostOptimizeVarPart - PROTO_VNSOptimizeVarPartDataGrid Nettoyage de l'ancienne gestion des Timer (CCTimer*) - impacts dans KWDataGridOptimizer - impact dans les jeux de donnees TestCoclustering\y_CoclusteringIV_Standard\AdultEducation - seuls change les fichiers de log err.txt, nettoyes pour les utilisateur (les information de profiling sont maintenant gere a part) - surprise: l'indicateur VarPartGranularity est systematiquement a 0 dans les log --- src/Learning/KWData/Profiler.cpp | 28 ++ src/Learning/KWData/Profiler.h | 5 +- .../KWDataPreparation/KWDataGridManager.cpp | 7 + .../KWDataPreparation/KWDataGridManager.h | 20 +- .../KWDataPreparation/KWDataGridOptimizer.cpp | 427 ++++++++++++++++-- .../KWDataPreparation/KWDataGridOptimizer.h | 31 +- .../MODL_Coclustering/MODL_Coclustering.cpp | 1 - 7 files changed, 461 insertions(+), 58 deletions(-) diff --git a/src/Learning/KWData/Profiler.cpp b/src/Learning/KWData/Profiler.cpp index 0c645201d..36663f0a4 100644 --- a/src/Learning/KWData/Profiler.cpp +++ b/src/Learning/KWData/Profiler.cpp @@ -89,6 +89,7 @@ void Profiler::Stop() sProfilingStatsFileName = ""; odMethodTimers.DeleteAll(); svMethodNames.SetSize(0); + dvMethodLastStopElapsedTimes.SetSize(0); bIsStarted = false; } @@ -114,6 +115,7 @@ void Profiler::BeginMethod(const ALString& sMethodName) methodTimer = new Timer; odMethodTimers.SetAt(sMethodName, methodTimer); svMethodNames.Add(sMethodName); + dvMethodLastStopElapsedTimes.Add(0); } // Mise a jour des stats du timer @@ -137,6 +139,9 @@ void Profiler::BeginMethod(const ALString& sMethodName) void Profiler::EndMethod(const ALString& sMethodName) { Timer* methodTimer; + int i = 0; + int nMethodIndex; + double dMethodLastElapsedTime; require(sMethodName != ""); @@ -152,7 +157,30 @@ void Profiler::EndMethod(const ALString& sMethodName) // Ecriture de la trace if (bIsTrace) + { + // Recherche de l'index de la methode + // Une recherche sequentielle est ici suffisante + nMethodIndex = -1; + for (i = 0; i < svMethodNames.GetSize(); i++) + { + if (svMethodNames.GetAt(i) == sMethodName) + { + nMethodIndex = i; + break; + } + } + assert(nMethodIndex >= 0); + + // Calcul du temps passe dans le methode depuis son dernier start + dMethodLastElapsedTime = + methodTimer->GetElapsedTime() - dvMethodLastStopElapsedTimes.GetAt(nMethodIndex); + dvMethodLastStopElapsedTimes.SetAt(nMethodIndex, methodTimer->GetElapsedTime()); + assert(dMethodLastElapsedTime >= 0); + + // Fermeture de l'objet, en memorisant le temps passe dans la methode + fJsonTraceFile->WriteKeyString("time", DoubleToString(dMethodLastElapsedTime)); fJsonTraceFile->EndObject(); + } } } diff --git a/src/Learning/KWData/Profiler.h b/src/Learning/KWData/Profiler.h index cccbe1dc2..025fcd5cf 100644 --- a/src/Learning/KWData/Profiler.h +++ b/src/Learning/KWData/Profiler.h @@ -65,9 +65,12 @@ class Profiler : public Object // Dictionnaire de timers par methode ObjectDictionary odMethodTimers; - // Vecteur des nom de methode utilisees + // Vecteur des noms de methode utilisees StringVector svMethodNames; + // Vecteur des temps passes par les methdoes au momment de leur dernier stop + DoubleVector dvMethodLastStopElapsedTimes; + // Fichier json des traces de profiling // On utilise un pointeur pour pemettre de declarer un objet Profiler en variable statique // sans impact sur les objet JSONFile, qui ne doivent pas etre declares en statique diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index 3a43b6ee4..e2496b7c7 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -347,6 +347,9 @@ double KWDataGridManager::ExportDataGridWithVarPartMergeOptimization(KWDataGrid* require(sourceDataGrid->GetVarPartAttribute()->GetPartNumber() > 1); require(targetDataGrid != NULL and targetDataGrid->IsEmpty()); + // Parametrage du profiler + KWDataGridOptimizer::GetProfiler()->BeginMethod("Post VarPart merge"); + // Export des attributs ExportAttributes(targetDataGrid); @@ -427,6 +430,10 @@ double KWDataGridManager::ExportDataGridWithVarPartMergeOptimization(KWDataGrid* dataGridCosts->ComputeAttributeCost(sourceAttribute, sourceAttribute->GetPartNumber()); dFusionDeltaCost += dNewAttributeCost; } + + // Parametrage du profiler + KWDataGridOptimizer::GetProfiler()->EndMethod("Post VarPart merge"); + ensure(CheckDataGrid(targetDataGrid)); ensure(not sourceDataGrid->IsVarPartDataGrid() or targetDataGrid->GetVarPartAttribute()->GetInnerAttributes() != diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.h b/src/Learning/KWDataPreparation/KWDataGridManager.h index da6115086..e308c2a5b 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.h +++ b/src/Learning/KWDataPreparation/KWDataGridManager.h @@ -58,7 +58,6 @@ class KWDataGridManager : public Object // Dans le cas VarPart, les attributs internes sont clones a partir de la grille source void ExportDataGridWithInnerAttributesCloned(KWDataGrid* targetDataGrid) const; - // CH IV Begin // Export total (attribut, parties et cellules) en exploitant une grille de reference, // avec une partie par VarPart // - pour les attributs simples: @@ -84,7 +83,6 @@ class KWDataGridManager : public Object // une seule partie de variable // Attention: creation de nouveaux attributs internes qui doivent etre detruit par l'appelant void ExportNullDataGrid(KWDataGrid* targetDataGrid) const; - // CH IV End ///////////////////////////////////////////////////////////////////////////////////////// // Service elementaires de transfert du contenu de la grille source vers la grille cible @@ -150,10 +148,8 @@ class KWDataGridManager : public Object // Initialisation du dictionnaire des quantile builders // Un quantile builder (Group ou Interval) est initialise par attribut de la grille source. - // CH IV Begin // Pour un attribut categoriel, les groupes sont des groupes de modalites // Pour un attribut de type parties de variables, les groupes sont des parties de variable - // CH IV End // Il est range dans un dictionnaire a partir du nom de l'attribut source // Une fois initialises, ces quantile builders sont utilises pour les granularisations // En sortie, le vecteur ivMaxPartNumbers contient pour chaque attribut le nombre maximal @@ -183,8 +179,7 @@ class KWDataGridManager : public Object //////////////////////////////////////////////////////////////////////////////////////////// // Services avances de construction de grille - // CH IV Begin - // Export d'une grille en optimisantv ses clusters de VarPart par fusion de VarParts + // Export d'une grille en optimisant ses clusters de VarPart par fusion de VarParts // - dans le cas numerique: fusion des intervalles contigues // - dans le cas categoriel: fusion des modalites de la même variable categorielle // Attention: creation de nouveaux attributs internes qui doivent etre detruit par l'appelant @@ -200,7 +195,6 @@ class KWDataGridManager : public Object // et les cellules de la grille cible sont recalculees void UpdateVarPartDataGridFromVarPartGroups(KWDataGrid* targetDataGrid, const IntVector* ivTargetGroupIndexes, int nTargetGroupNumber) const; - // CH IV End /////////////////////////////////////////////////////////////////////////// // Service de recuperation des partitions univariees pour initialiser @@ -226,17 +220,17 @@ class KWDataGridManager : public Object /////////////////////////////////////////////////////////////////////////// // Service de calcul des partitions univariees a la granularite courante // pour initialiser la grille cible (de facon compatible avec la grille source) - // Dans toutes ces methodes, l egalite entre la granularite de la sourceDataGrid et celle de la targetDataGrid - // est assuree + // Dans toutes ces methodes, l egalite entre la granularite de la sourceDataGrid + // et celle de la targetDataGrid est assuree - // Creation d'un attribut de grille a partir d'une partition univariee calculee pour la granularite de - // l'attribut source + // Creation d'un attribut de grille a partir d'une partition univariee calculee + // pour la granularite de l'attribut source void BuildDataGridAttributeFromGranularizedPartition(KWDGAttribute* sourceAttribute, KWDGAttribute* targetAttribute, KWClassStats* classStats) const; - // Creation des parties de l'attribut cible numerique selon une partition univariee specifiee dans une table - // d'effectifs + // Creation des parties de l'attribut cible numerique selon une partition univariee specifiee + // dans une table d'effectifs void BuildPartsOfContinuousAttributeFromFrequencyTable(KWDGAttribute* targetAttribute, KWFrequencyTable* kwftTable, const ALString& sAttributeName) const; diff --git a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp index a474d0eaa..aea050d04 100644 --- a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp @@ -109,14 +109,6 @@ double KWDataGridOptimizer::OptimizeDataGrid(const KWDataGrid* initialDataGrid, nValueNumber = initialDataGrid->GetGridFrequency(); bWithMemoryMode = false; - // CH IV Refactoring: besoin de passer par une directive de compilation??? -#ifdef CC_BUILD_TIMER - CCTimerPreOptimize.Reset(); - CCTimerMainOptimize.Reset(); - CCTimerPostOptimize.Reset(); - CCTimerPostIVOptimize.Reset(); -#endif - // Controle de la graine aleatoire pour avoir des resultats reproductibles SetRandomSeed(1); @@ -532,25 +524,6 @@ double KWDataGridOptimizer::OptimizeDataGrid(const KWDataGrid* initialDataGrid, // ainsi que les resultats de preparation des donnees optimizedDataGrid->SortAttributeParts(); } - - // CH IV Begin - // CH IV Refactoring: utilisation des timer? - // #ifdef CC_BUILD_TIMER - // StopCCTimer(CCTimerPreOptimize); - // StopCCTimer(CCTimerMainOptimize); - // StopCCTimer(CCTimerPostOptimize); - // Memorisation des temps de calcul pour suivi dans le cas d'un coclustering instances x variables - if (optimizedDataGrid->IsVarPartDataGrid()) - AddSimpleMessage( - sTmp + "Granularity : \t" + - IntToString(optimizedDataGrid->GetInnerAttributes()->GetVarPartGranularity()) + - "\tPre-optimization time: \t" + DoubleToString(CCTimerPreOptimize.GetElapsedTime()) + - "\t Main-Optimization time: \t" + DoubleToString(CCTimerMainOptimize.GetElapsedTime()) + - "\t Post-Optimization time: \t" + DoubleToString(CCTimerPostOptimize.GetElapsedTime()) + - "\t PostIV-Optimization time: \t" + DoubleToString(CCTimerPostIVOptimize.GetElapsedTime())); - // #endif - // CH IV End - return dBestCost; } @@ -1650,9 +1623,13 @@ double KWDataGridVNSOptimizer::VNSOptimizeDataGrid(const KWDataGrid* initialData dVNSNeighbourhoodSize = dNeighbourhoodSize; // Generation d'une solution dans un voisinnage de la meilleure solution + KWDataGridOptimizer::GetProfiler()->BeginMethod("Generate neighbour solution"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Neighbourhood size", + DoubleToString(dNeighbourhoodSize)); GenerateNeighbourSolution(initialDataGrid, optimizedDataGrid, dNeighbourhoodSize, &neighbourDataGrid); + KWDataGridOptimizer::GetProfiler()->EndMethod("Generate neighbour solution"); - // Parametrage du profiling + // Parametrage du profiling pour l'optimisation KWDataGridOptimizer::GetProfiler()->BeginMethod("Optimize solution"); KWDataGridOptimizer::GetProfiler()->WriteKeyString("Index", IntToString(nIndex)); KWDataGridOptimizer::GetProfiler()->WriteKeyString("VNS neighbourhood size", @@ -1699,6 +1676,377 @@ double KWDataGridVNSOptimizer::VNSOptimizeDataGrid(const KWDataGrid* initialData } // CH IV Begin +double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWDataGrid* initialDataGrid, + KWDataGridMerger* neighbourDataGrid, + double dNeighbourDataGridCost) const +{ + double dCost; + double dMergedCost; + double dFusionDeltaCost; + KWDataGrid mergedDataGrid; + KWDataGrid partitionedReferencePostMergedDataGrid; + KWDataGridManager dataGridManager; + ALString sLabel; + boolean bDisplayResults = false; + + // On ne reverifie pas les precondition de la methode publique + require(initialDataGrid != NULL); + require(neighbourDataGrid != NULL); + require(neighbourDataGrid->GetDataGridCosts() == dataGridCosts); + require(fabs(dNeighbourDataGridCost - dataGridCosts->ComputeDataGridTotalCost(neighbourDataGrid)) < dEpsilon); + + // Initialisations + dataGridManager.SetSourceDataGrid(initialDataGrid); + dCost = dNeighbourDataGridCost; + dMergedCost = dNeighbourDataGridCost; + + // CH AB AF a voir avec Marc + // Cas ou l'on peut fusionner les parties de variable des cluster + // Probleme rencontre pour fusionner une grille non informative + // Pas de fusion ici mais question qui se pose : pourquoi dans OptimizeMerge on utilise systematiquement + // un CopyInformativeVariables et dans les methodes qui l'appellent cela est fait en fonction de + // bCleanNonInformativeVariables qui est a false + if (neighbourDataGrid->GetInformativeAttributeNumber() > 0 and optimizationParameters.GetVarPartPostMerge()) + { + // Tri des attributs + neighbourDataGrid->SortAttributeParts(); + + dataGridManager.SetSourceDataGrid(neighbourDataGrid); + // Creation d'une nouvelle grille avec nouvelle description des PV fusionnees + dFusionDeltaCost = + dataGridManager.ExportDataGridWithVarPartMergeOptimization(&mergedDataGrid, dataGridCosts); + assert(not mergedDataGrid.GetVarPartsShared()); + + // Calcul et verification du cout + dMergedCost = dCost + dFusionDeltaCost; + // CH IV Refactoring: nettoyer lignes suivantes? DDD? + // CH DDD pour debuggage et passage dans methode visualiser calcul pas a pas + dMergedCost = dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid); + // Le cout precedent devra etre correct + assert(dMergedCost * (1 - dEpsilon) < dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid)); + assert(dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid) < dMergedCost * (1 + dEpsilon)); + + // Cas ou le cout de la grille avec PV voisines fusionnees est plus eleve que le cout avant + // fusion + if (dMergedCost > dCost * (1 + dEpsilon) and bDisplayResults) + { + sLabel = "PROBLEME : degradation du cout lors de la fusion des parties de variables " + "contigues"; + sLabel += DoubleToString(dMergedCost); + sLabel += "\t"; + sLabel += DoubleToString(dMergedCost - dCost); + AddWarning(sLabel); + cout << "PROBLEME : degradation du cout lors de la fusion des parties de variables " + "contigues : cout fusionne\t " + << dMergedCost << "\tcout\t" << dCost << "\n"; + cout << "Grille avant fusion\n"; + neighbourDataGrid->Write(cout); + cout << "Grille apres fusion\n"; + mergedDataGrid.Write(cout); + debug(false); + } + + // Post-optimisation de l'attribut VarPart uniquement dans le cas d'une optimisation approfondie + // (i.e. derniere granularite) + if (mergedDataGrid.GetInformativeAttributeNumber() > 1 and + optimizationParameters.GetVarPartPostOptimize() and not GetSlightOptimizationMode()) + { + CCVarPartDataGridPostOptimizer varPartDataGridPostOptimizer; + KWDataGrid mergedMergedDataGrid; + IntVector ivGroups; + ALString sInnerAttributeName; + double dVarPartFusionDeltaCost; + double dNewMergedCost; + double dMergedMergedCost; + int nGroupNumber; + boolean bImprovement; + + // Parametrage pour l'attribut VarPart + varPartDataGridPostOptimizer.SetPostOptimizationAttributeName( + mergedDataGrid.GetVarPartAttribute()->GetAttributeName()); + + if (bDisplayResults) + { + cout << "VNSOptimizeVarPartDataGrid : grille a post-optimiser" << endl; + mergedDataGrid.Write(cout); + cout << "Debut PostOptimisation VarPart" << endl; + cout << "VNSOptimizeVarPartDataGrid: grille initiale du DataGridOptimizer" << endl; + GetDataGridOptimizer()->GetInitialVarPartDataGrid()->Write(cout); + cout << "VNSOptimizeVarPartDataGrid: grille initiale utilisee pour l'export" << endl; + initialDataGrid->Write(cout); + cout << flush; + } + + // Boucle : on continue a post-optimiser tant qu'au moins un deplacement de VarPart + // permet d'ameliorer le critere + bImprovement = true; + // CH IV Debug + // CH IV Refactoring: nettoyer? + int nImprovementNumber = 0; + while (bImprovement) + { + // CH IV Debug + // CH IV Refactoring: nettoyer? + nImprovementNumber++; + + // Construction d'une grille de reference avec des clusters contenant une seule + // PV a partir des PV apres fusion Parametrage par la grille initiale de + // l'optimiseur + dataGridManager.SetSourceDataGrid(GetDataGridOptimizer()->GetInitialVarPartDataGrid()); + assert(mergedDataGrid.Check()); + dataGridManager.ExportDataGridWithSingletonVarParts( + &mergedDataGrid, &partitionedReferencePostMergedDataGrid, false); + ivGroups.SetSize( + partitionedReferencePostMergedDataGrid.GetVarPartAttribute()->GetPartNumber()); + assert(partitionedReferencePostMergedDataGrid.Check()); + assert(partitionedReferencePostMergedDataGrid.GetVarPartsShared()); + assert(partitionedReferencePostMergedDataGrid.GetInnerAttributes() == + mergedDataGrid.GetInnerAttributes()); + + // Affichage de la grille courante + if (bDisplayResults) + { + cout << "nImprovementNumber\t" << nImprovementNumber << endl; + cout << "VNSOptimizeVarPartDataGrid: grille post-fusionnee cout\t" + << dMergedCost << endl; + mergedDataGrid.Write(cout); + cout << "VNSOptimizeVarPartDataGrid : grille de reference" << endl; + partitionedReferencePostMergedDataGrid.Write(cout); + cout << flush; + } + + // Parametrage du profiling + KWDataGridOptimizer::GetProfiler()->BeginMethod("Post-optimization IV"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Improvement number", + IntToString(nImprovementNumber)); + + // Exploration des deplacements pour tous les attributs + bImprovement = varPartDataGridPostOptimizer.PostOptimizeLightVarPartDataGrid( + &partitionedReferencePostMergedDataGrid, &mergedDataGrid, &ivGroups); + KWDataGridOptimizer::GetProfiler()->EndMethod("Post-optimization IV"); + + if (bImprovement) + { + // Mise a jour de la grille pour l'optimisation de cet attribut + nGroupNumber = mergedDataGrid.GetVarPartAttribute()->GetPartNumber(); + dataGridManager.SetSourceDataGrid(&partitionedReferencePostMergedDataGrid); + dataGridManager.UpdateVarPartDataGridFromVarPartGroups(&mergedDataGrid, + &ivGroups, nGroupNumber); + + if (bDisplayResults) + { + dNewMergedCost = + dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid); + cout << "VNSOptimizeVarPartDataGrid: grille mise a jour best " + "deplacement\tCout\t" + << dNewMergedCost << endl; + mergedDataGrid.Write(cout); + assert(mergedDataGrid.Check()); + } + + // Mise a jour de la grille fusionnee courante par la grille obtenue par + // fusion de la grille comportant les deplacements + dataGridManager.SetSourceDataGrid(&mergedDataGrid); + dVarPartFusionDeltaCost = + dataGridManager.ExportDataGridWithVarPartMergeOptimization( + &mergedMergedDataGrid, dataGridCosts); + dMergedMergedCost = + dataGridCosts->ComputeDataGridTotalCost(&mergedMergedDataGrid); + if (bDisplayResults) + { + cout << "VNSOptimizeVarPartDataGrid: grille best deplacement " + "et fusionnee\tCout\t" + << dMergedMergedCost << endl; + mergedMergedDataGrid.Write(cout); + } + + // Cas ou la post-optimisation permet d'ameliorer le cout + if (dMergedMergedCost < dMergedCost) + { + dMergedCost = dMergedMergedCost; + + // Export de la grille avec les clusters post-optimises et les + // PV de la grille initiale. Grille sans fusion des PV voisines. + // C'est cette grille antecedent de la meilleure grille qui sera + // memorisee + neighbourDataGrid->DeleteAll(); + dataGridManager.SetSourceDataGrid(initialDataGrid); + dataGridManager.ExportDataGridWithReferenceVarPartClusters( + &mergedDataGrid, neighbourDataGrid); + if (bDisplayResults) + { + cout << "VNSOptimizeVarPartDataGrid: grille best " + "deplacement et initiale" + << endl; + neighbourDataGrid->Write(cout); + } + dCost = dataGridCosts->ComputeDataGridTotalCost(neighbourDataGrid); + } + + // On remplace mergedDataGrid par la nouvelle grille integrant les + // deplacements de post-optimisation puis fusionnee 23/11/22 : pourquoi + // effectuer ce remplacement systematiquement et pas uniquement dans le + // cas ou la poste optimisation permet d'ameliorer le cout ? on est + // quand meme dans le cas if(bImprovement) donc il y a amelioration du + // fait de la post-optimisation + mergedDataGrid.DeleteAll(); + dataGridManager.SetSourceDataGrid(&mergedMergedDataGrid); + dataGridManager.ExportDataGrid(&mergedDataGrid); + mergedDataGrid.SetVarPartsShared(false); + mergedMergedDataGrid.SetVarPartsShared(true); + mergedMergedDataGrid.DeleteAll(); + assert(mergedDataGrid.Check()); + } + // Nettoyage -> peut etre a supprimer pour mutualiser avec utilisation de cette + // grille pour HangleOptimizationStep + partitionedReferencePostMergedDataGrid.DeleteAll(); + ivGroups.SetSize(0); + } + if (bDisplayResults) + cout << "Fin PostOptimisation VarPart" << endl; + } + } + + // Si amelioration: on la memorise + if (dMergedCost < dNeighbourDataGridCost - dEpsilon) + { + // Sauvegarde de la meilleure solution + if (bCleanNonInformativeVariables) + { + // On sauvegarde l'antecedent de la meilleure grille post mergee + // On ne sauvegarde donc pas la meilleure grille post mergee car cela necessiterait de + // modifier la grille initiale qui devrait etre en coherence avec cette grille post + // mergee. La grille initiale est conservee pour une granularite donnee La grille de + // reference est necessaire pour HandleOptimizationStep + dataGridManager.CopyInformativeDataGrid(&mergedDataGrid, neighbourDataGrid); + } + + else + { + dataGridManager.CopyDataGrid(&mergedDataGrid, neighbourDataGrid); + } + + // Gestion de la meilleure solution + if (dataGridOptimizer != NULL) + { + assert(mergedDataGrid.GetInformativeAttributeNumber() > 0); + dataGridManager.SetSourceDataGrid(GetDataGridOptimizer()->GetInitialVarPartDataGrid()); + dataGridManager.ExportDataGridWithSingletonVarParts( + &mergedDataGrid, &partitionedReferencePostMergedDataGrid, true); + dataGridOptimizer->HandleOptimizationStep(&mergedDataGrid, + &partitionedReferencePostMergedDataGrid, false); + + // Nettoyage + partitionedReferencePostMergedDataGrid.DeleteAll(); + } + } + ensure(fabs(dMergedCost - dataGridCosts->ComputeDataGridTotalCost(neighbourDataGrid)) < dEpsilon); + return dMergedCost; +} + +double KWDataGridVNSOptimizer::PROTO_VNSOptimizeVarPartDataGrid(const KWDataGrid* initialDataGrid, + double dDecreaseFactor, int nMinIndex, int nMaxIndex, + KWDataGrid* optimizedDataGrid, + double dOptimizedDataGridCost, + double& dBestMergedDataGridCost) const +{ + double dBestCost; + double dCost; + KWDataGridManager dataGridManager; + KWDataGridMerger neighbourDataGrid; + int nIndex; + double dNeighbourhoodSize; + ALString sLabel; + + // On ne reverifie pas les precondition de la methode publique + require(dDecreaseFactor > 1); + require(0 <= nMinIndex); + require(nMinIndex <= nMaxIndex); + require(fabs(dOptimizedDataGridCost - dataGridCosts->ComputeDataGridTotalCost(optimizedDataGrid)) < dEpsilon); + + // Initialisations + dataGridManager.SetSourceDataGrid(initialDataGrid); + neighbourDataGrid.SetDataGridCosts(dataGridCosts); + dBestCost = dOptimizedDataGridCost; + + // On optimize tant qu'on ne depasse pas la taille max de voisinage + nVNSMaxLevel = nMaxIndex; + nIndex = nMinIndex; + while (nIndex <= nMaxIndex) + { + nVNSLevel = nIndex; + dNeighbourhoodSize = pow(1.0 / dDecreaseFactor, nIndex); + dVNSNeighbourhoodSize = dNeighbourhoodSize; + + // Generation d'une solution dans un voisinnage de la meilleure solution + KWDataGridOptimizer::GetProfiler()->BeginMethod("Generate neighbour solution"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Neighbourhood size", + DoubleToString(dNeighbourhoodSize)); + GenerateNeighbourSolution(initialDataGrid, optimizedDataGrid, dNeighbourhoodSize, &neighbourDataGrid); + KWDataGridOptimizer::GetProfiler()->EndMethod("Generate neighbour solution"); + + // Parametrage du profiling pour l'optimisation + KWDataGridOptimizer::GetProfiler()->BeginMethod("Optimize solution"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Index", IntToString(nIndex)); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("VNS neighbourhood size", + DoubleToString(dVNSNeighbourhoodSize)); + + // Optimisation de cette solution + dCost = OptimizeSolution(initialDataGrid, &neighbourDataGrid); + KWDataGridOptimizer::GetProfiler()->EndMethod("Optimize solution"); + + // Post-optimisation des parties de variables de la grille + // A terme, a deplacer dans OptimizeSolution + if (initialDataGrid->IsVarPartDataGrid()) + dCost = PROTO_VNSDataGridPostOptimizeVarPart(initialDataGrid, &neighbourDataGrid, dCost); + + // Si amelioration: on la memorise + if (dCost < dBestCost - dEpsilon) + { + dBestCost = dCost; + + // Sauvegarde de la meilleure solution + if (bCleanNonInformativeVariables) + { + // On sauvegarde l'antecedent de la meilleure grille post mergee + // On ne sauvegarde donc pas la meilleure grille post mergee car cela necessiterait de + // modifier la grille initiale qui devrait etre en coherence avec cette grille post + // mergee. La grille initiale est conservee pour une granularite donnee La grille de + // reference est necessaire pour HandleOptimizationStep + dataGridManager.CopyInformativeDataGrid(&neighbourDataGrid, optimizedDataGrid); + } + + else + { + dataGridManager.CopyDataGrid(&neighbourDataGrid, optimizedDataGrid); + } + + // Gestion de la meilleure solution + if (dataGridOptimizer != NULL) + dataGridOptimizer->HandleOptimizationStep(optimizedDataGrid, initialDataGrid, false); + } + + // Sinon: on passe a un niveau de voisinage plus fin + else + nIndex++; + + // Test de fin de tache + if (TaskProgression::IsInterruptionRequested()) + break; + + // Test si depassement de temps + if (IsOptimizationTimeElapsed()) + break; + + // Test cas d'une unique iteration + if (GetSlightOptimizationMode()) + break; + } + + ensure(fabs(dBestCost - dataGridCosts->ComputeDataGridTotalCost(optimizedDataGrid)) < dEpsilon); + return dBestCost; +} + double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* initialDataGrid, double dDecreaseFactor, int nMinIndex, int nMaxIndex, KWDataGrid* optimizedDataGrid, double dOptimizedDataGridCost, @@ -1724,6 +2072,13 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init require(nMinIndex <= nMaxIndex); require(fabs(dOptimizedDataGridCost - dataGridCosts->ComputeDataGridTotalCost(optimizedDataGrid)) < dEpsilon); + /* CH IV Refactoring : DDDDD + // Test du remplacement de la methode actuelle, par son proto + // BUG actuellement (il faut probablement passer la grille de reference en parametre pour regler le probleme...° + return PROTO_VNSOptimizeVarPartDataGrid(initialDataGrid, dDecreaseFactor, nMinIndex, nMaxIndex, + optimizedDataGrid, dOptimizedDataGridCost, dBestMergedDataGridCost); + */ + // Initialisations dataGridManager.SetSourceDataGrid(initialDataGrid); neighbourDataGrid.SetDataGridCosts(dataGridCosts); @@ -1739,10 +2094,14 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init dNeighbourhoodSize = pow(1.0 / dDecreaseFactor, nIndex); dVNSNeighbourhoodSize = dNeighbourhoodSize; - // Generation d'une solution dans un voisinage de la meilleure solution + // Generation d'une solution dans un voisinnage de la meilleure solution + KWDataGridOptimizer::GetProfiler()->BeginMethod("Generate neighbour solution"); + KWDataGridOptimizer::GetProfiler()->WriteKeyString("Neighbourhood size", + DoubleToString(dNeighbourhoodSize)); GenerateNeighbourSolution(initialDataGrid, optimizedDataGrid, dNeighbourhoodSize, &neighbourDataGrid); + KWDataGridOptimizer::GetProfiler()->EndMethod("Generate neighbour solution"); - // Parametrage du profiling + // Parametrage du profiling pour l'optimisation KWDataGridOptimizer::GetProfiler()->BeginMethod("Optimize solution"); KWDataGridOptimizer::GetProfiler()->WriteKeyString("Index", IntToString(nIndex)); KWDataGridOptimizer::GetProfiler()->WriteKeyString("VNS neighbourhood size", @@ -1877,10 +2236,8 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init "Improvement number", IntToString(nImprovementNumber)); // Exploration des deplacements pour tous les attributs - GetDataGridOptimizer()->CCTimerPostIVOptimize.Start(); bImprovement = varPartDataGridPostOptimizer.PostOptimizeLightVarPartDataGrid( &partitionedReferencePostMergedDataGrid, &mergedDataGrid, &ivGroups); - GetDataGridOptimizer()->CCTimerPostIVOptimize.Stop(); KWDataGridOptimizer::GetProfiler()->EndMethod("Post-optimization IV"); if (bImprovement) @@ -2076,11 +2433,9 @@ double KWDataGridVNSOptimizer::OptimizeSolution(const KWDataGrid* initialDataGri // Fin CH RefontePrior2 { KWDataGridOptimizer::GetProfiler()->BeginMethod("Pre-optimization"); - GetDataGridOptimizer()->CCTimerPreOptimize.Start(); dCost = dataGridPostOptimizer.PostOptimizeDataGrid(initialDataGrid, dataGridMerger, false); if (bDisplay) cout << dCost << "\n"; - GetDataGridOptimizer()->CCTimerPreOptimize.Stop(); KWDataGridOptimizer::GetProfiler()->EndMethod("Pre-optimization"); } @@ -2088,11 +2443,9 @@ double KWDataGridVNSOptimizer::OptimizeSolution(const KWDataGrid* initialDataGri if (optimizationParameters.GetOptimize() and not TaskProgression::IsInterruptionRequested()) { KWDataGridOptimizer::GetProfiler()->BeginMethod("Greedy merge optimization"); - GetDataGridOptimizer()->CCTimerMainOptimize.Start(); dCost = dataGridMerger->Merge(); if (bDisplay) cout << dCost << "\n"; - GetDataGridOptimizer()->CCTimerMainOptimize.Stop(); KWDataGridOptimizer::GetProfiler()->EndMethod("Greedy merge optimization"); } // Post-optimisation de la grille @@ -2103,14 +2456,12 @@ double KWDataGridVNSOptimizer::OptimizeSolution(const KWDataGrid* initialDataGri // Fin CH RefontePrior2) { KWDataGridOptimizer::GetProfiler()->BeginMethod("Post-optimization"); - GetDataGridOptimizer()->CCTimerPostOptimize.Start(); // Cas d'une optimisation legere (pour les granularites intermediaires en co-clustering) if (GetSlightOptimizationMode()) dCost = dataGridPostOptimizer.PostOptimizeDataGrid(initialDataGrid, dataGridMerger, false); // Sinon else dCost = dataGridPostOptimizer.PostOptimizeDataGrid(initialDataGrid, dataGridMerger, true); - GetDataGridOptimizer()->CCTimerPostOptimize.Stop(); KWDataGridOptimizer::GetProfiler()->EndMethod("Post-optimization"); if (bDisplay) cout << dCost << "\n\n"; diff --git a/src/Learning/KWDataPreparation/KWDataGridOptimizer.h b/src/Learning/KWDataPreparation/KWDataGridOptimizer.h index d8264aaa2..a4646bc99 100644 --- a/src/Learning/KWDataPreparation/KWDataGridOptimizer.h +++ b/src/Learning/KWDataPreparation/KWDataGridOptimizer.h @@ -93,11 +93,6 @@ class KWDataGridOptimizer : public Object virtual void HandleOptimizationStep(const KWDataGrid* optimizedDataGrid, const KWDataGrid* initialGranularizedDataGrid, boolean bIsLastSaving) const; - mutable Timer CCTimerPreOptimize; - mutable Timer CCTimerMainOptimize; - mutable Timer CCTimerPostOptimize; - mutable Timer CCTimerPostIVOptimize; - ////////////////////////////////////////////////////////////////// // Gestion d'un profiler dedie a l'optimisation des grilles // Ce profiler doit etre demarre depuis le point d'entree de l'optimisation, @@ -244,6 +239,28 @@ class KWDataGridVNSOptimizer : public Object double VNSOptimizeDataGrid(const KWDataGrid* initialDataGrid, double dDecreaseFactor, int nMinIndex, int nMaxIndex, KWDataGrid* optimizedDataGrid, double dOptimizedDataGridCost) const; + // CH IV Refactoring + // Methode de post-optimisation d'un grille optimisee en redecoupant ses parties de variables + // Methode temporaire permettant de reutiliser le code de la methode principale VNSOptimizeDataGrid + // en isolant cette partie de post-optimisation specifique VarPart, et de supprimer l'ancienne + // methode VNSOptimizeVarPartDataGrid + // Parametre (a faire evoluer si necessaire): + // - initialDataGrid: grille initiale + // - neighbourDataGrid: grille courante en cours d'optimisation + // - dNeighbourDataGridCost: cout de la grille courante + // - mergedDataGrid: grille optimisee si amelioration + // En sortie, on rend la nouvelle version de la grille optimisee (ou courante) integrant la post-ptimisation et on renvoie son cout + // + // A terme, il faudra isoler ce service de post-optimisation pour le deplacer en quatrime sous-methode + // en fin de la methode OptimizeSolution + // - Pre-optimization + // - Greedy merge optimization + // - Post-optimization + // - Post-optimization IV + double PROTO_VNSDataGridPostOptimizeVarPart(const KWDataGrid* initialDataGrid, + KWDataGridMerger* neighbourDataGrid, + double dNeighbourDataGridCost) const; + // CH IV Begin // Pilotage de la meta heuristique VNS, avec des voisinages successifs de taille décroissante // selon un facteur geometrique (de DecreaseFactor^MinIndex a Factor^MaxIndex) @@ -251,6 +268,10 @@ class KWDataGridVNSOptimizer : public Object // des grilles de meilleur cout apres post-fusion En sortie : optimizedDataGrid contient la grille antecedent // avant post-fusion de la meilleure grille post-fusionne Le cout renvoye est le cout de cette optimizedDataGrid // dBestMergedDataGridCost contient le cout de la meilleure grille apres post-fusion (meilleur cout) + // CH IV Refactoring: proto en vue de fusionner la methode avec VNSOptimizeDataGrid (plnate actuellement) + double PROTO_VNSOptimizeVarPartDataGrid(const KWDataGrid* initialDataGrid, double dDecreaseFactor, + int nMinIndex, int nMaxIndex, KWDataGrid* optimizedDataGrid, + double dOptimizedDataGridCost, double& dBestMergedDataGridCost) const; double VNSOptimizeVarPartDataGrid(const KWDataGrid* initialDataGrid, double dDecreaseFactor, int nMinIndex, int nMaxIndex, KWDataGrid* optimizedDataGrid, double dOptimizedDataGridCost, double& dBestMergedDataGridCost) const; diff --git a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp index 918bcf0ba..f50a79823 100644 --- a/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp +++ b/src/Learning/MODL_Coclustering/MODL_Coclustering.cpp @@ -36,7 +36,6 @@ int main(int argc, char** argv) // Choix du repertoire de lancement pour le debugage sous Windows (a commenter apres fin du debug) //SetWindowsDebugDir("y_CoclusteringIV_Standard", "IrisLight"); //SetWindowsDebugDir("y_CoclusteringIV_Standard", "Iris"); - SetWindowsDebugDir("y_CoclusteringIV_Standard", "ExtractClustersIris"); // Point d'arret sur l'allocation d'un bloc memoire // MemSetAllocIndexExit(77); From 3074e5713c37282a57ebbea3eda93466e3939db5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Fri, 29 Sep 2023 16:57:58 +0200 Subject: [PATCH 14/17] Diverse corrections (mainly dimensionning) and cleanup of "refactoring" tags MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Prise en compte de corretcion de Carine - nettoyage des balises "CH IV Refactoring" - impacts - KWAttributeSubsetStats - CreateDataGridCells: ajout d'un require - KWDataGrid - KWDataGridManager - BuildUnivariateDataGridFromGranularizedPartition: nouvelle methode - BuildDataGridFromUnivariateStats -> BuildUnivariateDataGridFromAttributeStats - CloneInnerAttributes: correction en recopiant la VarPartGranularity - KWDataGridMerger - KWDGMPartValueNumberCompare: ajout d'un critere de tri secondaire - KWDataGridOptimizer - OptimizeWithBestUnivariatePartitionForCurrentGranularity: utilisation de la nouvelle methode BuildDataGridFromGranularizedPartition - KWDataGridPostOptimizer - KWGrouperMODL - KWGrouperMODLOptimization - KWHierachicalMultinomialStudy - nettoyage massif - KWVersion - CCCoclusteringBuilder - CreateVarPartDataGrid: utilisation directe de la methode dediee CreateVarPartDataGridCells - CreateDataGridCells: supression de cette methode, au profit de la methode dediee VarPart CreateVarPartDataGridCells - CCCoclusteringReport - CCHierarchicalDataGrid - CCLearningProblemView - simplification de la definition de l'interface Nettoyage du code, en supprimant les sections DDD de KWDataGridOptimizer KWDataGridVNSOptimizer - PROTO_VNSDataGridPostOptimizeVarPart - ajout de parametres mergedDataGrid et partitionedReferencePostMergedDataGrid - corrige les probleme, en etant tres porche du code actuel - gestion des grilles "antecedants" - mais la difference avec les entre grille standard et IV reste importante Nom des fichiers de profiling: - ajouter le nombre d'individus et de variables dans le nom du fichier - pour distinguer les variantes de scénario avec un meme dictionnaire - impact dans CCCoclusteringBuilder::ComputeCoclustering: GetProfiler()-> Start(filename) Profiling: - ajout d'une categorie de profiling "Save best solution", pour avoir une stats sur le nombre d'ameliorations - impact dans CCCoclusteringBuilder::HandleOptimizationStep Gestion de typicalites negatives lors des relectures de rapport de coclustering - provoque un bug dans LearningTest\TestCoclustering\Bugs\NegativeTypicalities - ajout d'une tolerance: les typicalites négatives des rapports sont remplacees par 0 - impacts: - CCCoclusteringReport::ReadComposition - CCCoclusteringReport::ReadJSONTypicalities Probleme de dimensionnement: - jeu de test LearningTest\TestCoclustering\y_CoclusteringIV\Isolet 617 variables, 6238 instances - malgré 32 Gb RAM, message "Not enough memory to create initial data grid..." - correction dans CCCoclusteringBuilder - CheckMemoryForVarPartDataGridInitialization: nouvelle methode dediee au dimensionnement dans le cas instances x variables - attention, les cellules sont dimensionnees par rapport aux nombre de dimensions de la grille, pas par rapport au nombre de variables interne - exploite dans ComputeCoclustering, FillVarPartTupleTableFromDatabase - CheckMemoryForDataGridOptimization: corrections en se basnt effectivement sur el nombre de dimensions de la grille - CreateVarPartDataGridCells: arret si erreur dans la boucle de parcours de variables internes (pour n'avoir qu'une seule fois le message d'erreur) - test en cours: optimisation tres longue, mais passe desormais l'etape de dimensionnement Test complets effectues sur LearningTest\TestCoclustering - Standard, SmallInstabilities, Bugs, NewPriorV9 - y_CoclusteringIV_Standard --- .../KWAttributeSubsetStats.cpp | 1 + src/Learning/KWDataPreparation/KWDataGrid.cpp | 4 - .../KWDataPreparation/KWDataGridManager.cpp | 34 +- .../KWDataPreparation/KWDataGridManager.h | 10 +- .../KWDataPreparation/KWDataGridMerger.cpp | 12 +- .../KWDataPreparation/KWDataGridMerger.h | 3 +- .../KWDataPreparation/KWDataGridOptimizer.cpp | 241 ++++------- .../KWDataPreparation/KWDataGridOptimizer.h | 9 +- .../KWDataGridPostOptimizer.cpp | 5 + .../KWDataGridPostOptimizer.h | 54 +-- .../KWDataPreparation/KWGrouperMODL.h | 3 +- .../KWGrouperMODLOptimization.cpp | 1 + .../KWTest/KWHierarchicalMultinomialStudy.cpp | 399 +----------------- .../KWTest/KWHierarchicalMultinomialStudy.h | 24 +- src/Learning/KWUtils/KWVersion.h | 2 + .../CCCoclusteringBuilder.cpp | 223 ++++++++-- .../MODL_Coclustering/CCCoclusteringBuilder.h | 24 +- .../CCCoclusteringReport.cpp | 13 +- .../CCHierarchicalDataGrid.h | 1 + .../CCLearningProblemView.cpp | 34 +- 20 files changed, 399 insertions(+), 698 deletions(-) diff --git a/src/Learning/KWDataPreparation/KWAttributeSubsetStats.cpp b/src/Learning/KWDataPreparation/KWAttributeSubsetStats.cpp index befde8810..814650624 100644 --- a/src/Learning/KWDataPreparation/KWAttributeSubsetStats.cpp +++ b/src/Learning/KWDataPreparation/KWAttributeSubsetStats.cpp @@ -927,6 +927,7 @@ boolean KWAttributeSubsetStats::CreateDataGridCells(const KWTupleTable* tupleTab require(dataGrid->GetAttributeNumber() <= tupleTable->GetAttributeNumber()); require(dataGrid->GetCellNumber() == 0); require(dataGrid->Check()); + require(not dataGrid->IsVarPartDataGrid()); // Passage en mode update Global::ActivateErrorFlowControl(); diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index 5685cf94d..f2eef055f 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -3251,10 +3251,6 @@ void KWDGAttribute::Write(ostream& ost) const // Affichage des caracteristiques du fourre-tout ost << "CatchAllNumber\t" << GetCatchAllValueNumber() << endl; - // CH IV Refactoring: nettoyer les lignes suivantes??? - // ost << "CatchAll\t" << (GetCatchAllValueSet() != NULL) << endl; - // if (GetCatchAllValueSet() != NULL) - // GetCatchAllValueSet()->Write(cout); // Affichage des caracteristiques de la poubelle ost << "Garbage\tPossible\tExist\tSize\n"; diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index e2496b7c7..e0a89dc94 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -295,7 +295,6 @@ void KWDataGridManager::ExportNullDataGrid(KWDataGrid* targetDataGrid) const targetDataGrid->GetVarPartAttribute()->GetInnerAttributes() != sourceDataGrid->GetVarPartAttribute()->GetInnerAttributes()); } -// CH IV End void KWDataGridManager::InitializeQuantileBuilders(ObjectDictionary* odQuantilesBuilders, IntVector* ivMaxPartNumbers) const @@ -1380,8 +1379,8 @@ void KWDataGridManager::AddRandomParts(KWDataGrid* targetDataGrid, const KWDataG ensure(targetDataGrid->GetCellNumber() == 0); } -void KWDataGridManager::BuildDataGridFromUnivariateStats(KWDataGrid* targetDataGrid, - KWAttributeStats* attributeStats) const +void KWDataGridManager::BuildUnivariateDataGridFromAttributeStats(KWDataGrid* targetDataGrid, + KWAttributeStats* attributeStats) const { KWDGAttribute* targetAttribute; @@ -1894,6 +1893,31 @@ void KWDataGridManager::BuildDataGridAttributeFromUnivariateStats(KWDGAttribute* } } +void KWDataGridManager::BuildUnivariateDataGridFromGranularizedPartition(KWDataGrid* univariateTargetDataGrid, + int nAttributeIndex, + KWClassStats* classStats) const +{ + KWDGAttribute* targetAttribute; + KWDGAttribute* sourceAttribute; + + require(0 < nAttributeIndex < sourceDataGrid->GetAttributeNumber()); + + // Initialisation de la grille cible a une variable + InitialiseDataGrid(sourceDataGrid, univariateTargetDataGrid, 1); + + // Initialisation de l'attribut cible + sourceAttribute = sourceDataGrid->GetAttributeAt(nAttributeIndex); + targetAttribute = univariateTargetDataGrid->GetAttributeAt(0); + InitialiseAttribute(sourceAttribute, targetAttribute); + + // Construction de la partition optimale associee a la granularite de l'attribut source selon classStats + BuildDataGridAttributeFromGranularizedPartition(sourceAttribute, targetAttribute, classStats); + + // Export des cellules selon la nouvelle partition + univariateTargetDataGrid->DeleteAllCells(); + ExportCells(univariateTargetDataGrid); +} + void KWDataGridManager::BuildDataGridAttributeFromGranularizedPartition(KWDGAttribute* sourceAttribute, KWDGAttribute* targetAttribute, KWClassStats* classStats) const @@ -3371,6 +3395,7 @@ KWDGInnerAttributes* KWDataGridManager::CloneInnerAttributes(const KWDGInnerAttr // Partage des partitions de la grille source resultInnerAttributes = new KWDGInnerAttributes; + resultInnerAttributes->SetVarPartGranularity(sourceInnerAttributes->GetVarPartGranularity()); // Parcours des attributs internes for (nInnerAttribute = 0; nInnerAttribute < sourceInnerAttributes->GetInnerAttributeNumber(); nInnerAttribute++) @@ -3830,8 +3855,9 @@ void KWDataGridManager::SortAttributePartsByTargetGroups(const KWDGAttribute* so } // Tri des association, apres une randomisation pour avoir un ordre aleatoire par groupe + // CH IV Refactoring: a revoir plus tard apres avoir integre la retokenisation dans le cadre du GenerateVNS // CH IV Refactoring: Code specifique suite a refactoring et unification avec l'ancienne methode SortVarPartAttributeParts - // CH IV Refactoring: Pourquoi le Shuffle est fait dans le cas VarPart et pas VarPart? + // CH IV Refactoring: Pourquoi le Shuffle est fait dans le cas Symbol et pas VarPart? on ne sait pas // CH IV Refactoring: Probleme potentiel supplementaire, faire un shuffle suivi d'un sort entraine des instabilite entre // CH IV Refactoring: windows et linux, car le Sort de windows ne semble pas etre un "stable sort" (qui garantit qu'en // CH IV Refactoring: cas d'egalite, les items sont dans le meme ordre que l'ordre initial) diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.h b/src/Learning/KWDataPreparation/KWDataGridManager.h index e308c2a5b..7bf369827 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.h +++ b/src/Learning/KWDataPreparation/KWDataGridManager.h @@ -200,9 +200,10 @@ class KWDataGridManager : public Object // Service de recuperation des partitions univariees pour initialiser // la grille cible (de facon compatible avec la grille source) - // Creation de la grille cible a partir d'une partition univariee + // Creation d'une grille cible univariee a partir d'une partition univariee // Ajout de la transmission de la granularite a la grille cible - void BuildDataGridFromUnivariateStats(KWDataGrid* targetDataGrid, KWAttributeStats* attributeStats) const; + void BuildUnivariateDataGridFromAttributeStats(KWDataGrid* targetDataGrid, + KWAttributeStats* attributeStats) const; // Creation de la grille cible a partir du produit cartesien des partitions // univariees des attributs de la grille source @@ -223,6 +224,11 @@ class KWDataGridManager : public Object // Dans toutes ces methodes, l egalite entre la granularite de la sourceDataGrid // et celle de la targetDataGrid est assuree + // Creation d'une grille univariee contenant un seul des attributs de la grille initiale ayant pour partition + // la partition optimale a la granularite de la grille initiale + void BuildUnivariateDataGridFromGranularizedPartition(KWDataGrid* univariateTargetDataGrid, int nAttributeIndex, + KWClassStats* classStats) const; + // Creation d'un attribut de grille a partir d'une partition univariee calculee // pour la granularite de l'attribut source void BuildDataGridAttributeFromGranularizedPartition(KWDGAttribute* sourceAttribute, diff --git a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp index 33c3511f7..5a31590bd 100644 --- a/src/Learning/KWDataPreparation/KWDataGridMerger.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridMerger.cpp @@ -1932,17 +1932,21 @@ int KWDGMPartValueNumberCompare(const void* elem1, const void* elem2) { KWDGMPart* partM1; KWDGMPart* partM2; + int nCompare; // Acces aux parties partM1 = cast(KWDGMPart*, *(Object**)elem1); partM2 = cast(KWDGMPart*, *(Object**)elem2); assert(KWType::IsCoclusteringGroupableType(partM1->GetPartType())); - assert(partM1->GetPartType() == partM1->GetPartType()); - - // CH IV Refactoring: faut-il ajouter une fonction de comparaison secondaire sur les valeurs en cas degalite? + assert(partM1->GetPartType() == partM2->GetPartType()); // Comparaison du nombre de modalites par valeurs decroissantes dans le cas groupable - return partM2->GetValueSet()->GetValueNumber() - partM1->GetValueSet()->GetValueNumber(); + nCompare = partM2->GetValueSet()->GetValueNumber() - partM1->GetValueSet()->GetValueNumber(); + + // En cas d'egalite, comparaison des premieres valeurs + if (nCompare == 0) + nCompare = partM1->GetValueSet()->GetHeadValue()->CompareValue(partM2->GetValueSet()->GetHeadValue()); + return nCompare; } ////////////////////////////////////////////////////////////////////////////// // Classe KWDGMCell diff --git a/src/Learning/KWDataPreparation/KWDataGridMerger.h b/src/Learning/KWDataPreparation/KWDataGridMerger.h index a8a148c8a..d85100d17 100644 --- a/src/Learning/KWDataPreparation/KWDataGridMerger.h +++ b/src/Learning/KWDataPreparation/KWDataGridMerger.h @@ -539,7 +539,8 @@ inline void KWDGMAttribute::RemoveAllPartsFromValueNumberList() KWDGMPart* partM; slPartValueNumbers->RemoveAll(); - // CH IV Refactoring: je ne comprend pas ce qui suit dans cette methode + + // Dereferencement des positions dans la liste qui vient d'etre detruite part = GetHeadPart(); while (part != NULL) { diff --git a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp index aea050d04..d9d8c9861 100644 --- a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp @@ -210,16 +210,6 @@ double KWDataGridOptimizer::OptimizeDataGrid(const KWDataGrid* initialDataGrid, } bIsLastGranularity = true; - // CH IV Refactoring: nettoyer lignes suivantes? - /* CH - // Affichage pour grille instances * variables - if (bDisplayGranularities and granularizedDataGrid.GetVarPartDataGrid()) - cout << "Prepartitionnement\t" << - granularizedDataGrid.GetInnerAttributesNames()->GetVarPartGranularity() << "\tGranularite\t" << - nGranularityIndex - << "\tNombre de clusters" << ivCurrentPartNumber << flush; - */ - // Si on n'a pas encore atteint la granularite max if (nGranularityIndex < nGranularityMax) { @@ -995,54 +985,17 @@ KWDataGridOptimizer::OptimizeWithBestUnivariatePartitionForCurrentGranularity(co if (attributeStats->GetPreparedDataGridStats()->GetGranularity() != initialDataGrid->GetGranularity()) { - // CH IV Refactoring: mieux utiliser les possibibilite de dataGridManager??? - // - // Export de la granularite - univariateDataGrid.SetGranularity(initialDataGrid->GetGranularity()); - - // Initialisation de la grille cible avec le nombre d'attributs demandes - univariateDataGrid.Initialize(1, initialDataGrid->GetTargetValueNumber()); - - // Initialisation des valeurs cibles - for (nTarget = 0; nTarget < initialDataGrid->GetTargetValueNumber(); nTarget++) - { - univariateDataGrid.SetTargetValueAt(nTarget, - initialDataGrid->GetTargetValueAt(nTarget)); - } - - // Extraction de l'attribut source associe - assert(initialAttribute != NULL); - - // Initialisation de l'attribut - targetAttribute = univariateDataGrid.GetAttributeAt(0); - // Recuperation des caracteristiques de l'attribute dont cout de selection/construction - // de l'attribut - targetAttribute->SetAttributeName(initialAttribute->GetAttributeName()); - targetAttribute->SetAttributeType(initialAttribute->GetAttributeType()); - targetAttribute->SetAttributeTargetFunction( - initialAttribute->GetAttributeTargetFunction()); - targetAttribute->SetCost(initialAttribute->GetCost()); - targetAttribute->SetInitialValueNumber(initialAttribute->GetInitialValueNumber()); - targetAttribute->SetGranularizedValueNumber( - initialAttribute->GetGranularizedValueNumber()); - - // Transfert du parametrage du fourre-tout - targetAttribute->InitializeCatchAllValueSet(initialAttribute->GetCatchAllValueSet()); - - dataGridManager.BuildDataGridAttributeFromGranularizedPartition( - initialAttribute, targetAttribute, classStats); - bEvaluated = targetAttribute->GetPartNumber() > 1; - - // Export des nouvelles cellules - univariateDataGrid.DeleteAllCells(); - dataGridManager.ExportCells(&univariateDataGrid); + dataGridManager.BuildUnivariateDataGridFromGranularizedPartition( + &univariateDataGrid, nAttribute, classStats); + bEvaluated = univariateDataGrid.GetAttributeAt(0)->GetPartNumber() > 1; } // Cas ou la granularite de la meilleure partition univariee de l'attribut correspond // a la granularite courante de la grille else { - dataGridManager.BuildDataGridFromUnivariateStats(&univariateDataGrid, attributeStats); + dataGridManager.BuildUnivariateDataGridFromAttributeStats(&univariateDataGrid, + attributeStats); // Transfert du parametrage du fourre-tout targetAttribute = univariateDataGrid.GetAttributeAt(0); @@ -1554,7 +1507,6 @@ double KWDataGridVNSOptimizer::IterativeVNSOptimizeDataGrid(const KWDataGrid* in dMergedCost = dCost; dCost = VNSOptimizeVarPartDataGrid(initialDataGrid, dDecreaseFactor, 0, nIndexNumber, ¤tDataGrid, dCost, dMergedCost); - if (dMergedCost < dBestMergedCost - dEpsilon) { dBestCost = dCost; @@ -1613,7 +1565,7 @@ double KWDataGridVNSOptimizer::VNSOptimizeDataGrid(const KWDataGrid* initialData neighbourDataGrid.SetDataGridCosts(dataGridCosts); dBestCost = dOptimizedDataGridCost; - // On optimize tant qu'on ne depasse pas la taille max de voisinnage + // On optimise tant qu'on ne depasse pas la taille max de voisinnage nVNSMaxLevel = nMaxIndex; nIndex = nMinIndex; while (nIndex <= nMaxIndex) @@ -1676,15 +1628,13 @@ double KWDataGridVNSOptimizer::VNSOptimizeDataGrid(const KWDataGrid* initialData } // CH IV Begin -double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWDataGrid* initialDataGrid, - KWDataGridMerger* neighbourDataGrid, - double dNeighbourDataGridCost) const +double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart( + const KWDataGrid* initialDataGrid, KWDataGridMerger* neighbourDataGrid, double dNeighbourDataGridCost, + KWDataGrid* mergedDataGrid, KWDataGrid* partitionedReferencePostMergedDataGrid) const { double dCost; double dMergedCost; double dFusionDeltaCost; - KWDataGrid mergedDataGrid; - KWDataGrid partitionedReferencePostMergedDataGrid; KWDataGridManager dataGridManager; ALString sLabel; boolean bDisplayResults = false; @@ -1714,17 +1664,11 @@ double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWData dataGridManager.SetSourceDataGrid(neighbourDataGrid); // Creation d'une nouvelle grille avec nouvelle description des PV fusionnees dFusionDeltaCost = - dataGridManager.ExportDataGridWithVarPartMergeOptimization(&mergedDataGrid, dataGridCosts); - assert(not mergedDataGrid.GetVarPartsShared()); + dataGridManager.ExportDataGridWithVarPartMergeOptimization(mergedDataGrid, dataGridCosts); + assert(not mergedDataGrid->GetVarPartsShared()); // Calcul et verification du cout dMergedCost = dCost + dFusionDeltaCost; - // CH IV Refactoring: nettoyer lignes suivantes? DDD? - // CH DDD pour debuggage et passage dans methode visualiser calcul pas a pas - dMergedCost = dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid); - // Le cout precedent devra etre correct - assert(dMergedCost * (1 - dEpsilon) < dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid)); - assert(dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid) < dMergedCost * (1 + dEpsilon)); // Cas ou le cout de la grille avec PV voisines fusionnees est plus eleve que le cout avant // fusion @@ -1742,13 +1686,13 @@ double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWData cout << "Grille avant fusion\n"; neighbourDataGrid->Write(cout); cout << "Grille apres fusion\n"; - mergedDataGrid.Write(cout); + mergedDataGrid->Write(cout); debug(false); } // Post-optimisation de l'attribut VarPart uniquement dans le cas d'une optimisation approfondie // (i.e. derniere granularite) - if (mergedDataGrid.GetInformativeAttributeNumber() > 1 and + if (mergedDataGrid->GetInformativeAttributeNumber() > 1 and optimizationParameters.GetVarPartPostOptimize() and not GetSlightOptimizationMode()) { CCVarPartDataGridPostOptimizer varPartDataGridPostOptimizer; @@ -1763,12 +1707,12 @@ double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWData // Parametrage pour l'attribut VarPart varPartDataGridPostOptimizer.SetPostOptimizationAttributeName( - mergedDataGrid.GetVarPartAttribute()->GetAttributeName()); + mergedDataGrid->GetVarPartAttribute()->GetAttributeName()); if (bDisplayResults) { cout << "VNSOptimizeVarPartDataGrid : grille a post-optimiser" << endl; - mergedDataGrid.Write(cout); + mergedDataGrid->Write(cout); cout << "Debut PostOptimisation VarPart" << endl; cout << "VNSOptimizeVarPartDataGrid: grille initiale du DataGridOptimizer" << endl; GetDataGridOptimizer()->GetInitialVarPartDataGrid()->Write(cout); @@ -1780,28 +1724,24 @@ double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWData // Boucle : on continue a post-optimiser tant qu'au moins un deplacement de VarPart // permet d'ameliorer le critere bImprovement = true; - // CH IV Debug - // CH IV Refactoring: nettoyer? int nImprovementNumber = 0; while (bImprovement) { - // CH IV Debug - // CH IV Refactoring: nettoyer? nImprovementNumber++; // Construction d'une grille de reference avec des clusters contenant une seule // PV a partir des PV apres fusion Parametrage par la grille initiale de // l'optimiseur dataGridManager.SetSourceDataGrid(GetDataGridOptimizer()->GetInitialVarPartDataGrid()); - assert(mergedDataGrid.Check()); + assert(mergedDataGrid->Check()); dataGridManager.ExportDataGridWithSingletonVarParts( - &mergedDataGrid, &partitionedReferencePostMergedDataGrid, false); + mergedDataGrid, partitionedReferencePostMergedDataGrid, false); ivGroups.SetSize( - partitionedReferencePostMergedDataGrid.GetVarPartAttribute()->GetPartNumber()); - assert(partitionedReferencePostMergedDataGrid.Check()); - assert(partitionedReferencePostMergedDataGrid.GetVarPartsShared()); - assert(partitionedReferencePostMergedDataGrid.GetInnerAttributes() == - mergedDataGrid.GetInnerAttributes()); + partitionedReferencePostMergedDataGrid->GetVarPartAttribute()->GetPartNumber()); + assert(partitionedReferencePostMergedDataGrid->Check()); + assert(partitionedReferencePostMergedDataGrid->GetVarPartsShared()); + assert(partitionedReferencePostMergedDataGrid->GetInnerAttributes() == + mergedDataGrid->GetInnerAttributes()); // Affichage de la grille courante if (bDisplayResults) @@ -1809,9 +1749,9 @@ double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWData cout << "nImprovementNumber\t" << nImprovementNumber << endl; cout << "VNSOptimizeVarPartDataGrid: grille post-fusionnee cout\t" << dMergedCost << endl; - mergedDataGrid.Write(cout); + mergedDataGrid->Write(cout); cout << "VNSOptimizeVarPartDataGrid : grille de reference" << endl; - partitionedReferencePostMergedDataGrid.Write(cout); + partitionedReferencePostMergedDataGrid->Write(cout); cout << flush; } @@ -1822,31 +1762,31 @@ double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWData // Exploration des deplacements pour tous les attributs bImprovement = varPartDataGridPostOptimizer.PostOptimizeLightVarPartDataGrid( - &partitionedReferencePostMergedDataGrid, &mergedDataGrid, &ivGroups); + partitionedReferencePostMergedDataGrid, mergedDataGrid, &ivGroups); KWDataGridOptimizer::GetProfiler()->EndMethod("Post-optimization IV"); if (bImprovement) { // Mise a jour de la grille pour l'optimisation de cet attribut - nGroupNumber = mergedDataGrid.GetVarPartAttribute()->GetPartNumber(); - dataGridManager.SetSourceDataGrid(&partitionedReferencePostMergedDataGrid); - dataGridManager.UpdateVarPartDataGridFromVarPartGroups(&mergedDataGrid, + nGroupNumber = mergedDataGrid->GetVarPartAttribute()->GetPartNumber(); + dataGridManager.SetSourceDataGrid(partitionedReferencePostMergedDataGrid); + dataGridManager.UpdateVarPartDataGridFromVarPartGroups(mergedDataGrid, &ivGroups, nGroupNumber); if (bDisplayResults) { dNewMergedCost = - dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid); + dataGridCosts->ComputeDataGridTotalCost(mergedDataGrid); cout << "VNSOptimizeVarPartDataGrid: grille mise a jour best " "deplacement\tCout\t" << dNewMergedCost << endl; - mergedDataGrid.Write(cout); - assert(mergedDataGrid.Check()); + mergedDataGrid->Write(cout); + assert(mergedDataGrid->Check()); } // Mise a jour de la grille fusionnee courante par la grille obtenue par // fusion de la grille comportant les deplacements - dataGridManager.SetSourceDataGrid(&mergedDataGrid); + dataGridManager.SetSourceDataGrid(mergedDataGrid); dVarPartFusionDeltaCost = dataGridManager.ExportDataGridWithVarPartMergeOptimization( &mergedMergedDataGrid, dataGridCosts); @@ -1872,7 +1812,7 @@ double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWData neighbourDataGrid->DeleteAll(); dataGridManager.SetSourceDataGrid(initialDataGrid); dataGridManager.ExportDataGridWithReferenceVarPartClusters( - &mergedDataGrid, neighbourDataGrid); + mergedDataGrid, neighbourDataGrid); if (bDisplayResults) { cout << "VNSOptimizeVarPartDataGrid: grille best " @@ -1889,58 +1829,24 @@ double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart(const KWData // cas ou la poste optimisation permet d'ameliorer le cout ? on est // quand meme dans le cas if(bImprovement) donc il y a amelioration du // fait de la post-optimisation - mergedDataGrid.DeleteAll(); + mergedDataGrid->DeleteAll(); dataGridManager.SetSourceDataGrid(&mergedMergedDataGrid); - dataGridManager.ExportDataGrid(&mergedDataGrid); - mergedDataGrid.SetVarPartsShared(false); + dataGridManager.ExportDataGrid(mergedDataGrid); + mergedDataGrid->SetVarPartsShared(false); mergedMergedDataGrid.SetVarPartsShared(true); mergedMergedDataGrid.DeleteAll(); - assert(mergedDataGrid.Check()); + assert(mergedDataGrid->Check()); } // Nettoyage -> peut etre a supprimer pour mutualiser avec utilisation de cette // grille pour HangleOptimizationStep - partitionedReferencePostMergedDataGrid.DeleteAll(); + partitionedReferencePostMergedDataGrid->DeleteAll(); ivGroups.SetSize(0); } if (bDisplayResults) cout << "Fin PostOptimisation VarPart" << endl; } } - - // Si amelioration: on la memorise - if (dMergedCost < dNeighbourDataGridCost - dEpsilon) - { - // Sauvegarde de la meilleure solution - if (bCleanNonInformativeVariables) - { - // On sauvegarde l'antecedent de la meilleure grille post mergee - // On ne sauvegarde donc pas la meilleure grille post mergee car cela necessiterait de - // modifier la grille initiale qui devrait etre en coherence avec cette grille post - // mergee. La grille initiale est conservee pour une granularite donnee La grille de - // reference est necessaire pour HandleOptimizationStep - dataGridManager.CopyInformativeDataGrid(&mergedDataGrid, neighbourDataGrid); - } - - else - { - dataGridManager.CopyDataGrid(&mergedDataGrid, neighbourDataGrid); - } - - // Gestion de la meilleure solution - if (dataGridOptimizer != NULL) - { - assert(mergedDataGrid.GetInformativeAttributeNumber() > 0); - dataGridManager.SetSourceDataGrid(GetDataGridOptimizer()->GetInitialVarPartDataGrid()); - dataGridManager.ExportDataGridWithSingletonVarParts( - &mergedDataGrid, &partitionedReferencePostMergedDataGrid, true); - dataGridOptimizer->HandleOptimizationStep(&mergedDataGrid, - &partitionedReferencePostMergedDataGrid, false); - - // Nettoyage - partitionedReferencePostMergedDataGrid.DeleteAll(); - } - } - ensure(fabs(dMergedCost - dataGridCosts->ComputeDataGridTotalCost(neighbourDataGrid)) < dEpsilon); + ensure(fabs(dCost - dataGridCosts->ComputeDataGridTotalCost(neighbourDataGrid)) < dEpsilon); return dMergedCost; } @@ -1952,6 +1858,10 @@ double KWDataGridVNSOptimizer::PROTO_VNSOptimizeVarPartDataGrid(const KWDataGrid { double dBestCost; double dCost; + double dMergedCost; + double dBestMergedCost; + KWDataGrid mergedDataGrid; + KWDataGrid partitionedReferencePostMergedDataGrid; KWDataGridManager dataGridManager; KWDataGridMerger neighbourDataGrid; int nIndex; @@ -1968,8 +1878,9 @@ double KWDataGridVNSOptimizer::PROTO_VNSOptimizeVarPartDataGrid(const KWDataGrid dataGridManager.SetSourceDataGrid(initialDataGrid); neighbourDataGrid.SetDataGridCosts(dataGridCosts); dBestCost = dOptimizedDataGridCost; + dBestMergedCost = dBestCost; - // On optimize tant qu'on ne depasse pas la taille max de voisinage + // On optimise tant qu'on ne depasse pas la taille max de voisinage nVNSMaxLevel = nMaxIndex; nIndex = nMinIndex; while (nIndex <= nMaxIndex) @@ -1997,13 +1908,17 @@ double KWDataGridVNSOptimizer::PROTO_VNSOptimizeVarPartDataGrid(const KWDataGrid // Post-optimisation des parties de variables de la grille // A terme, a deplacer dans OptimizeSolution + dMergedCost = dCost; if (initialDataGrid->IsVarPartDataGrid()) - dCost = PROTO_VNSDataGridPostOptimizeVarPart(initialDataGrid, &neighbourDataGrid, dCost); + dMergedCost = PROTO_VNSDataGridPostOptimizeVarPart(initialDataGrid, &neighbourDataGrid, dCost, + &mergedDataGrid, + &partitionedReferencePostMergedDataGrid); // Si amelioration: on la memorise - if (dCost < dBestCost - dEpsilon) + if (dMergedCost < dBestMergedCost - dEpsilon) { dBestCost = dCost; + dBestMergedCost = dMergedCost; // Sauvegarde de la meilleure solution if (bCleanNonInformativeVariables) @@ -2023,13 +1938,32 @@ double KWDataGridVNSOptimizer::PROTO_VNSOptimizeVarPartDataGrid(const KWDataGrid // Gestion de la meilleure solution if (dataGridOptimizer != NULL) - dataGridOptimizer->HandleOptimizationStep(optimizedDataGrid, initialDataGrid, false); - } + { + if (mergedDataGrid.GetInformativeAttributeNumber() == 0) + dataGridOptimizer->HandleOptimizationStep(optimizedDataGrid, initialDataGrid, + false); + else + { + dataGridManager.SetSourceDataGrid( + GetDataGridOptimizer()->GetInitialVarPartDataGrid()); + dataGridManager.ExportDataGridWithSingletonVarParts( + &mergedDataGrid, &partitionedReferencePostMergedDataGrid, true); + dataGridOptimizer->HandleOptimizationStep( + &mergedDataGrid, &partitionedReferencePostMergedDataGrid, false); + + // Nettoyage + partitionedReferencePostMergedDataGrid.DeleteAll(); + } + } + } // Sinon: on passe a un niveau de voisinage plus fin else nIndex++; + // Nettoyage + mergedDataGrid.DeleteAll(); + // Test de fin de tache if (TaskProgression::IsInterruptionRequested()) break; @@ -2043,6 +1977,8 @@ double KWDataGridVNSOptimizer::PROTO_VNSOptimizeVarPartDataGrid(const KWDataGrid break; } + // Memorisation du meilleur cout parmi les grilles post-fusionnees + dBestMergedDataGridCost = dBestMergedCost; ensure(fabs(dBestCost - dataGridCosts->ComputeDataGridTotalCost(optimizedDataGrid)) < dEpsilon); return dBestCost; } @@ -2072,12 +2008,14 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init require(nMinIndex <= nMaxIndex); require(fabs(dOptimizedDataGridCost - dataGridCosts->ComputeDataGridTotalCost(optimizedDataGrid)) < dEpsilon); - /* CH IV Refactoring : DDDDD + // CH IV Refactoring : DDDDD // Test du remplacement de la methode actuelle, par son proto - // BUG actuellement (il faut probablement passer la grille de reference en parametre pour regler le probleme...° - return PROTO_VNSOptimizeVarPartDataGrid(initialDataGrid, dDecreaseFactor, nMinIndex, nMaxIndex, - optimizedDataGrid, dOptimizedDataGridCost, dBestMergedDataGridCost); - */ + boolean bProto = false; + //DDD bProto = true; + if (bProto) + return PROTO_VNSOptimizeVarPartDataGrid(initialDataGrid, dDecreaseFactor, nMinIndex, nMaxIndex, + optimizedDataGrid, dOptimizedDataGridCost, + dBestMergedDataGridCost); // Initialisations dataGridManager.SetSourceDataGrid(initialDataGrid); @@ -2085,7 +2023,7 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init dBestCost = dOptimizedDataGridCost; dBestMergedCost = dBestCost; - // On optimize tant qu'on ne depasse pas la taille max de voisinage + // On optimise tant qu'on ne depasse pas la taille max de voisinage nVNSMaxLevel = nMaxIndex; nIndex = nMinIndex; while (nIndex <= nMaxIndex) @@ -2131,16 +2069,10 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init // Calcul et verification du cout dMergedCost = dCost + dFusionDeltaCost; - // CH IV Refactoring: nettoyer lignes suivantes? DDD? - // CH DDD pour debuggage et passage dans methode visualiser calcul pas a pas - dMergedCost = dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid); - // Le cout precedent devra etre correct - assert(dMergedCost * (1 - dEpsilon) < dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid)); - assert(dataGridCosts->ComputeDataGridTotalCost(&mergedDataGrid) < dMergedCost * (1 + dEpsilon)); // Cas ou le cout de la grille avec PV voisines fusionnees est plus eleve que le cout avant // fusion - if (dMergedCost > dCost * (1 + dEpsilon) and bDisplayResults) + if (bDisplayResults and dMergedCost > dCost * (1 + dEpsilon)) { sLabel = "PROBLEME : degradation du cout lors de la fusion des parties de variables " "contigues"; @@ -2194,13 +2126,10 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init // Boucle : on continue a post-optimiser tant qu'au moins un deplacement de VarPart // permet d'ameliorer le critere bImprovement = true; - // CH IV Debug - // CH IV Refactoring: nettoyer? + int nImprovementNumber = 0; while (bImprovement) { - // CH IV Debug - // CH IV Refactoring: nettoyer? nImprovementNumber++; // Construction d'une grille de reference avec des clusters contenant une seule @@ -2343,7 +2272,6 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init // reference est necessaire pour HandleOptimizationStep dataGridManager.CopyInformativeDataGrid(&neighbourDataGrid, optimizedDataGrid); } - else { dataGridManager.CopyDataGrid(&neighbourDataGrid, optimizedDataGrid); @@ -2355,7 +2283,6 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init if (mergedDataGrid.GetInformativeAttributeNumber() == 0) dataGridOptimizer->HandleOptimizationStep(optimizedDataGrid, initialDataGrid, false); - else { dataGridManager.SetSourceDataGrid( diff --git a/src/Learning/KWDataPreparation/KWDataGridOptimizer.h b/src/Learning/KWDataPreparation/KWDataGridOptimizer.h index a4646bc99..208d49053 100644 --- a/src/Learning/KWDataPreparation/KWDataGridOptimizer.h +++ b/src/Learning/KWDataPreparation/KWDataGridOptimizer.h @@ -119,8 +119,8 @@ class KWDataGridOptimizer : public Object double InitializeWithTerminalDataGrid(const KWDataGrid* initialDataGrid, KWDataGrid* optimizedDataGrid) const; // Cette methode calcule pour chaque attribut de la grille initialDataGrid - // le partitionnement univarie optimal associe au partitionnement obtenu par projection univarie de la grille - // La granularite du partitionnement est celui de la grille initiale + // le partitionnement univarie optimal associe au partitionnement obtenu par projection univariee de la grille + // La granularite du partitionnement est celle de la grille initiale double OptimizeWithBestUnivariatePartitionForCurrentGranularity(const KWDataGrid* initialDataGrid, KWDataGrid* optimizedDataGrid) const; @@ -258,8 +258,9 @@ class KWDataGridVNSOptimizer : public Object // - Post-optimization // - Post-optimization IV double PROTO_VNSDataGridPostOptimizeVarPart(const KWDataGrid* initialDataGrid, - KWDataGridMerger* neighbourDataGrid, - double dNeighbourDataGridCost) const; + KWDataGridMerger* neighbourDataGrid, double dNeighbourDataGridCost, + KWDataGrid* mergedDataGrid, + KWDataGrid* partitionedReferencePostMergedDataGrid) const; // CH IV Begin // Pilotage de la meta heuristique VNS, avec des voisinages successifs de taille décroissante diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp index cb61d6c8a..05e0b06c8 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.cpp @@ -4190,6 +4190,7 @@ boolean CCVarPartDataGridPostOptimizer::PostOptimizeLightVarPartDataGrid(const K } // CH IV Refactoring: supprimer les lignes commentees de cette methode? +// CH IV Refactoring : a faire en fonction de la conservation ou non du groupe poubelle pour les attributs internes double CCVarPartDataGridPostOptimizer::ComputeVarPartsSymbolAttributeVariationCost( KWDGAttribute* attribute, int nClusterNumberVariation, int nVarPartsNumberVariation, ALString sInnerAttributeName, KWDGPart* varPartIn, KWDGPart* varPartOut, KWDGPart* innerPart) const @@ -4233,6 +4234,7 @@ double CCVarPartDataGridPostOptimizer::ComputeVarPartsSymbolAttributeVariationCo assert(not GetVarPartAttributeGarbage()); // CH IV Refactoring: nettoyer le code ci-dessous? + // CH IV Refactoring : a faire en fonction de la conservation ou non du groupe poubelle pour les attributs internes //// Cout de structure si attribut parties de variable et poubelle // if (attribute->GetAttributeType() == KWType::VarPart and GetVarPartAttributeGarbage()) //{ @@ -4302,6 +4304,7 @@ double CCVarPartDataGridPostOptimizer::ComputeVarPartsSymbolAttributeVariationCo // Cas d'un attribut interne categoriel avec groupe poubelle a integrer plus tard assert(not GetInnerAttributeGarbage()); // CH IV Refactoring: nettoyer le code ci-dessous? + // CH IV Refactoring : a faire en fonction de la conservation ou non du groupe poubelle pour les attributs internes // if (GetInnerAttributeGarbage()) //{ // // Taille de la poubelle @@ -4449,6 +4452,7 @@ double CCVarPartDataGridPostOptimizer::ComputeVarPartsContinuousAttributeVariati assert(not GetVarPartAttributeGarbage()); // CH IV Refactoring: nettoyer ci dessous? + // CH IV Refactoring : a faire en fonction de la conservation ou non du groupe poubelle pour les attributs internes //// Cout de structure si attribut parties de variable et poubelle // if (attribute->GetAttributeType() == KWType::VarPart and GetVarPartAttributeGarbage()) //{ @@ -4537,6 +4541,7 @@ double CCVarPartDataGridPostOptimizer::ComputeVarPartsContinuousAttributeVariati // A integrer plus tard assert(not GetInnerAttributeGarbage()); // CH IV Refactoring: nettoyer ci dessous? + // CH IV Refactoring : a faire en fonction de la conservation ou non du groupe poubelle pour les attributs internes // if (GetInnerAttributeGarbage()) //{ // // Taille de la poubelle diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h index 51de641b0..6e8855e7a 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h @@ -692,44 +692,7 @@ class CCVarPartDataGridPostOptimizer : public Object void SetPostOptimizationAttributeName(const ALString& sValue); const ALString& GetPostOptimizationAttributeName() const; - // CH IV Refactoring: supprimer tout ce qui est commente dans cette classe? - - // Parametrage de la structure des couts de la grille de donnees - // Memoire: les specifications sont referencees et destinees a etre partagees par plusieurs algorithmes - // void SetDataGridCosts(const KWDataGridCosts* kwdgcCosts); - // const KWDataGridCosts* GetDataGridCosts() const; - - // Optimisation de l'attribut VarPart d'un grille [[[pour une structure de cout donnees ]]] - // La grille de reference en entree contient la description la plus fine possible des - // parties de variable de chaque attribut interne dans l'attribut VarPart - // En entree, la grille optimisee constitue une solution de depart, dont les attributs internes ont ete - // post-fusionnes par rapport a la grille de reference En sortie, la grille optimisee est amelioree par la - // methode de post-optimisation. - // [[[Le mode pousse (DeepPostOptimization) remet en question fortement les partition univariees, alors - // que le mode leger se contente de deplacer les frontieres - // Retourne le cout de codage MODL de la grille post-optimisee]]] - // double PostOptimizeVarPartDataGrid(const KWDataGrid* initialDataGrid, KWDataGrid* optimizedDataGrid,boolean - // bDeepPostOptimization) const; - - // Optimisation legere - // Seuls les deplacements des parties de variables unternes numeriques (intervalles) sont envisagees - // Cette optimisation ne remet donc pas en question les partitions des attributs internes - // Elle teste les deplacements de PV qui conduisent a une ou deux fusions de PV et prend en compte la variation - // de cout du prior lie a ce deplacement Elle ne teste que les deplacements d'intervalles (attribut interne - // numerique) En entree : - // - referenceDataGrid : grille identique a optimizedDataGrid avec pour l'attribut VarPart 1 cluster par PV des - // attributs internes - // - sInnerAttributeName : nom de l'attribut interne dont l'affectation des intervalles dans les clusters de PV - // est optimise - // - grille optimise : amelioree par la post-optimisation, avec la meme partition des attributs internes (les - // fusions d'intervalles restent a effectuer) En sortie : ivGroups : pour chaque cluster mono-PV de la grille de - // reference, donne son cluster dans la grille optimisee - // double PostOptimizeLightVarPartDataGrid(const KWDataGrid* referenceDataGrid, ALString sInnerAttributeName, - // KWDataGrid* optimizedDataGrid, IntVector* ivGroups) const; - - // La variation de cout se calcule en partant de la meme grille initiale pour tous les deplacements - // Renvoie vrai s'il existe au moins une amelioration - // Pour tous les attributs internes numeriques et categoriels + // PostOptimise les attributs internes numeriques et categoriels // En entree: // - referenceDataGrid contient la grille de reference avec des clusters contenant une seule PV // a partir des PV de la grille optimisee @@ -737,25 +700,14 @@ class CCVarPartDataGridPostOptimizer : public Object // - optimizedDataGrid : grille dont on post-optimise les parties de variable // - ivGroups : vecteur d'index qui indique pour chaque PV l'index du cluster de PV // auquel elle appartient, mis a jour au cours de cette post-optimisation + // Renvoie true s'il existe au moins une amelioration + // La variation de cout se calcule en partant de la meme grille de reference pour tous les deplacements boolean PostOptimizeLightVarPartDataGrid(const KWDataGrid* referenceDataGrid, KWDataGrid* optimizedDataGrid, IntVector* ivGroups) const; - ///////////////////////////////////////////////////////////////// - ///// Methode avancees - - // Construction d'une grille initiale pour l'optimisation univariee, ayant les memes partitions - // par attribut que la grille optimisee, sauf sur l'attribut a optimiser (dans ce cas, partition la - // plus fine possible provenant de la grille initiale) - // KWDataGrid* BuildUnivariateInitialDataGrid(const KWDataGrid* optimizedDataGrid, - // const KWDataGrid* initialDataGrid, - // const ALString& sPostOptimizationAttributeName) const; - ///////////////////////////////////////////////////////////////// //// Implementation protected: - // Parametrage de la structure des couts - // const KWDataGridCosts* dataGridCosts; - // Methodes dediees de calcul de variation de cout par attribut, par cluster ... // Variation de cout lors du deplacement d'un intervalle entier d'un attribut interne numerique vers un cluster diff --git a/src/Learning/KWDataPreparation/KWGrouperMODL.h b/src/Learning/KWDataPreparation/KWGrouperMODL.h index d5a905c9d..38e29cfa4 100644 --- a/src/Learning/KWDataPreparation/KWGrouperMODL.h +++ b/src/Learning/KWDataPreparation/KWGrouperMODL.h @@ -336,9 +336,8 @@ class KWGrouperMODL : public KWGrouperMODLFamily SortedList* frequencyList) const; // CH IV Begin - // CH IV Refactoring: completer le commentaire? // Post-optimisation selon un algorithme EM - // On effectue + // On effectue a chaque iteration tous les deplacements elementaires qui ameliorent le cout void EMPostOptimizeGroupsWithGarbage(KWFrequencyTable* kwftSource, KWFrequencyTable* kwftTarget, IntVector* ivGroups, int nMaxStepNumber, SortedList* frequencyList) const; // CH IV End diff --git a/src/Learning/KWDataPreparation/KWGrouperMODLOptimization.cpp b/src/Learning/KWDataPreparation/KWGrouperMODLOptimization.cpp index 379305183..9a7ea7bfa 100644 --- a/src/Learning/KWDataPreparation/KWGrouperMODLOptimization.cpp +++ b/src/Learning/KWDataPreparation/KWGrouperMODLOptimization.cpp @@ -1888,6 +1888,7 @@ void KWGrouperMODL::FastPostOptimizeGroupsWithGarbage(KWFrequencyTable* kwftSour // CH IV Begin // CH IV Refactoring: nettoyer tout le code commente dans cette methode? +// CH Reponse Carine : pas encore car methode non finalisee au moment de l'integration du code void KWGrouperMODL::EMPostOptimizeGroupsWithGarbage(KWFrequencyTable* kwftSource, KWFrequencyTable* kwftTarget, IntVector* ivGroups, int nMaxStepNumber, SortedList* frequencyList) const diff --git a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp index cdccb6f2e..f960c864e 100644 --- a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp +++ b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.cpp @@ -1020,12 +1020,13 @@ boolean KWAttributePairStatsStudy::ComputeStats(const KWTupleTable* tupleTable) ObjectDictionary odQuantilesBuilders; IntVector ivMaxPartNumbers; int nTotalFrequency; - int nPartileNumber; KWDataGrid dgGranularizedDataGrid; double dCurrentCost; - int nBestPartileNumber; double dPreviousPartileSize; double dPartileSize; + int nGranularityMax; + int nGranularity; + int nBestGranularity; // Parametrage de quantile builder dataGridManager.SetSourceDataGrid(dataGrid); @@ -1034,25 +1035,32 @@ boolean KWAttributePairStatsStudy::ComputeStats(const KWTupleTable* tupleTable) // Effectif total nTotalFrequency = dataGrid->GetGridFrequency(); + // Granularite max + nGranularityMax = (int)ceil(log(nTotalFrequency * 1.0) / log(2.0)); + // Parcours des partile possible dGridCost = DBL_MAX; - nBestPartileNumber = -1; + nBestGranularity = -1; dPreviousPartileSize = INT_MAX; - for (nPartileNumber = 1; nPartileNumber < nTotalFrequency; nPartileNumber++) + for (nGranularity = 1; nGranularity < nGranularityMax; nGranularity++) { // On arrete si on va au dela de la limite des entiers - if (nPartileNumber * 1.0 * nPartileNumber > INT_MAX / 2) + //if (nPartileNumber * 1.0 * nPartileNumber > INT_MAX / 2) + if (pow(2, nGranularity) * 1.0 * pow(2, nGranularity) > INT_MAX / 2) break; // On saute si pas de changement significatif de taille de partile - dPartileSize = nTotalFrequency * 1.0 / nPartileNumber; + dPartileSize = nTotalFrequency * 1.0 / pow(2, nGranularity); if (fabs(dPreviousPartileSize - dPartileSize) < 0.1) continue; dPreviousPartileSize = dPartileSize; // Export d'une grille granularisee dgGranularizedDataGrid.DeleteAll(); - ExportGranularizedDataGrid(&dgGranularizedDataGrid, nPartileNumber, &odQuantilesBuilders); + dataGridManager.ExportGranularizedDataGrid(&dgGranularizedDataGrid, nGranularity, + &odQuantilesBuilders); + + //ExportGranularizedDataGrid(&dgGranularizedDataGrid, nPartileNumber, &odQuantilesBuilders); // Memorisation de son cout dCurrentCost = dataGridCosts->ComputeDataGridTotalCost(&dgGranularizedDataGrid); @@ -1060,7 +1068,7 @@ boolean KWAttributePairStatsStudy::ComputeStats(const KWTupleTable* tupleTable) // Affichage if (bDisplay) { - cout << "Partiles\t" << nPartileNumber << "\t" << dCurrentCost << "\t" + cout << "Partiles\t" << nGranularity << "\t" << dCurrentCost << "\t" << dgGranularizedDataGrid.GetAttributeAt(0)->GetPartNumber() << "\t" << dgGranularizedDataGrid.GetAttributeAt(1)->GetPartNumber() << "\t" << dgGranularizedDataGrid.GetCellNumber() << endl; @@ -1071,13 +1079,13 @@ boolean KWAttributePairStatsStudy::ComputeStats(const KWTupleTable* tupleTable) if (dCurrentCost < dGridCost) { dGridCost = dCurrentCost; - nBestPartileNumber = nPartileNumber; + nBestGranularity = nGranularity; } } // On memorise la solution correspondant au meilleurs niveau de partile optimizedDataGrid = new KWDataGrid; - ExportGranularizedDataGrid(optimizedDataGrid, nBestPartileNumber, &odQuantilesBuilders); + dataGridManager.ExportGranularizedDataGrid(optimizedDataGrid, nBestGranularity, &odQuantilesBuilders); // Nettoyage odQuantilesBuilders.DeleteAll(); @@ -1129,377 +1137,6 @@ KWDataGridCosts* KWAttributePairStatsStudy::CreateDataGridCost() const return dataGridCosts; } -boolean KWAttributePairStatsStudy::CreateAttributeIntervals(const KWTupleTable* tupleTable, KWDGAttribute* dgAttribute) -{ - int nTuple; - const KWTuple* tuple; - KWTupleTable attributeTupleTable; - Continuous cSourceValue; - Continuous cSourceRef; - KWDGPart* part; - Continuous cIntervalBound; - double dProgression; - - require(Check()); - require(CheckSpecifications()); - require(tupleTable != NULL); - require(tupleTable->GetSize() > 0); - require(tupleTable->LookupAttributeIndex(dgAttribute->GetAttributeName()) != -1); - require(tupleTable->GetAttributeTypeAt(tupleTable->LookupAttributeIndex(dgAttribute->GetAttributeName())) == - KWType::Continuous); - require(dgAttribute != NULL); - require(dgAttribute->GetPartNumber() == 0); - - // Construction d'une table de tuples univariee dediee a l'attribut - tupleTable->BuildUnivariateTupleTable(dgAttribute->GetAttributeName(), &attributeTupleTable); - if (TaskProgression::IsInterruptionRequested()) - return false; - - // Creation d'une premiere partie, avec sa borne inf (contenant la valeur manquante) - part = dgAttribute->AddPart(); - part->GetInterval()->SetLowerBound(KWDGInterval::GetMinLowerBound()); - part->GetInterval()->SetUpperBound(KWDGInterval::GetMaxUpperBound()); - - // Creation des parties de l'attribut pour chaque tuple - cSourceRef = KWDGInterval::GetMinLowerBound(); - - for (nTuple = 0; nTuple < attributeTupleTable.GetSize(); nTuple++) - { - tuple = attributeTupleTable.GetAt(nTuple); - - // Progression - if (periodicTestDisplay.IsTestAllowed(nTuple)) - { - // Avancement: au prorata de la base pour l'attribut en cours, en reservant 50 pour la creation - // des cellules - dProgression = - dgAttribute->GetAttributeIndex() * 50.0 / dgAttribute->GetDataGrid()->GetAttributeNumber(); - dProgression += (nTuple * 50.0 / attributeTupleTable.GetSize()) / - dgAttribute->GetDataGrid()->GetAttributeNumber(); - TaskProgression::DisplayProgression((int)dProgression); - if (TaskProgression::IsInterruptionRequested()) - return false; - } - - // Valeur du tuple - cSourceValue = tuple->GetContinuousAt(0); - - // Memorisation de la valeur de reference initiale pour le premier tuple - if (nTuple == 0) - cSourceRef = cSourceValue; - // Creation d'un nouvel intervalle sinon - else - { - assert(cSourceValue > cSourceRef); - - // Calcul de la borne sup de l'intervalle courant, comme moyenne de la valeur - // des deux objets de part et d'autre de l'intervalle - cIntervalBound = KWContinuous::GetHumanReadableLowerMeanValue(cSourceRef, cSourceValue); - - // Memorisation de la borne sup de l'intervalle en court - part->GetInterval()->SetUpperBound(cIntervalBound); - - // Creation d'une nouvelle partie avec sa borne inf - part = dgAttribute->AddPart(); - part->GetInterval()->SetLowerBound(cIntervalBound); - part->GetInterval()->SetUpperBound(KWDGInterval::GetMaxUpperBound()); - - // Nouvelle valeur de reference - cSourceRef = cSourceValue; - } - } - - // Parametrage du nombre total de valeurs (= nombre d'instances) - dgAttribute->SetInitialValueNumber(attributeTupleTable.GetTotalFrequency()); - dgAttribute->SetGranularizedValueNumber(attributeTupleTable.GetTotalFrequency()); - assert(dgAttribute->GetPartNumber() == attributeTupleTable.GetSize() or - GetPregranularizedNumericalAttributes()); - assert(dgAttribute->GetInitialValueNumber() + 1 >= dgAttribute->GetPartNumber()); - ensure(dgAttribute->Check()); - - return true; -} - -void KWAttributePairStatsStudy::ExportGranularizedDataGrid(KWDataGrid* targetDataGrid, int nPartileNumber, - ObjectDictionary* odQuantilesBuilders) const -{ - require(Check()); - require(targetDataGrid != NULL and targetDataGrid->IsEmpty()); - require(nPartileNumber > 0); - require(odQuantilesBuilders->GetCount() == dataGridManager.GetSourceDataGrid()->GetAttributeNumber()); - - // Export des attributs - dataGridManager.ExportAttributes(targetDataGrid); - - // Export des parties granularisees des attributs - ExportGranularizedParts(targetDataGrid, nPartileNumber, odQuantilesBuilders); - - // Export des cellules - dataGridManager.ExportCells(targetDataGrid); - ensure(dataGridManager.CheckDataGrid(targetDataGrid)); - - // Memorisation de la granularite - targetDataGrid->SetGranularity(nPartileNumber); -} - -void KWAttributePairStatsStudy::ExportGranularizedParts(KWDataGrid* targetDataGrid, int nPartileNumber, - ObjectDictionary* odQuantileBuilders) const -{ - ObjectDictionary odSourceAttributes; - int nAttribute; - KWDGAttribute* sourceAttribute; - KWDGAttribute* targetAttribute; - KWQuantileGroupBuilder* quantileGroupBuilder; - KWQuantileIntervalBuilder* quantileIntervalBuilder; - boolean bDisplayResults = false; - - require(Check()); - require(targetDataGrid != NULL and dataGridManager.CheckAttributes(targetDataGrid) and - dataGridManager.CheckGranularity(targetDataGrid)); - require(0 < nPartileNumber and nPartileNumber <= dataGridManager.GetSourceDataGrid()->GetGridFrequency()); - require(odQuantileBuilders->GetCount() == dataGridManager.GetSourceDataGrid()->GetAttributeNumber()); - - // Rangement des attributs sources dans un dictionnaire - for (nAttribute = 0; nAttribute < dataGridManager.GetSourceDataGrid()->GetAttributeNumber(); nAttribute++) - { - sourceAttribute = dataGridManager.GetSourceDataGrid()->GetAttributeAt(nAttribute); - odSourceAttributes.SetAt(sourceAttribute->GetAttributeName(), sourceAttribute); - } - - // Initialisation des parties des attributs - for (nAttribute = 0; nAttribute < targetDataGrid->GetAttributeNumber(); nAttribute++) - { - // Recherche des attributs cible et source - targetAttribute = targetDataGrid->GetAttributeAt(nAttribute); - sourceAttribute = cast(KWDGAttribute*, odSourceAttributes.Lookup(targetAttribute->GetAttributeName())); - - targetAttribute->SetInitialValueNumber(sourceAttribute->GetInitialValueNumber()); - - // Cas d'un attribut "cible" (regression, classif avec groupage) : pas de granularisation mais poubelle - // envisageable - if (sourceAttribute->GetAttributeTargetFunction()) - { - targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); - - dataGridManager.ExportAttributeParts(targetDataGrid, sourceAttribute->GetAttributeName()); - if (bDisplayResults) - { - cout << "Attribut cible " << targetAttribute->GetAttributeName() << endl; - cout << "Partile number = " << targetAttribute->GetGranularizedValueNumber() << endl; - } - } - - // Cas des attributs sources - else - { - // Granularisation dans le cas continu - if (sourceAttribute->GetAttributeType() == KWType::Continuous) - { - quantileIntervalBuilder = - cast(KWQuantileIntervalBuilder*, - odQuantileBuilders->Lookup(sourceAttribute->GetAttributeName())); - - ExportGranularizedPartsForContinuousAttribute(targetDataGrid, sourceAttribute, - targetAttribute, nPartileNumber, - quantileIntervalBuilder); - } - // Granularisation dans le cas symbolique - else - { - quantileGroupBuilder = - cast(KWQuantileGroupBuilder*, - odQuantileBuilders->Lookup(sourceAttribute->GetAttributeName())); - - ExportGranularizedPartsForSymbolAttribute(targetDataGrid, sourceAttribute, - targetAttribute, nPartileNumber, - quantileGroupBuilder); - } - } - } - - ensure(dataGridManager.CheckParts(targetDataGrid)); - ensure(targetDataGrid->GetCellNumber() == 0); -} - -void KWAttributePairStatsStudy::ExportGranularizedPartsForContinuousAttribute( - KWDataGrid* targetDataGrid, KWDGAttribute* sourceAttribute, KWDGAttribute* targetAttribute, int nPartileNumber, - KWQuantileIntervalBuilder* quantileBuilder) const -{ - KWDGPart* sourcePart; - KWDGPart* targetPart; - ObjectArray oaSourceParts; - int nValueNumber; - int nPartileIndex; - int nActualPartileNumber; - double dPartileSize; - boolean bDisplayResults = false; - - require(quantileBuilder != NULL); - - nValueNumber = dataGridManager.GetSourceDataGrid()->GetGridFrequency(); - - // Nombre potentiel de partiles associes a cette granularite - if (nPartileNumber > nValueNumber) - nPartileNumber = nValueNumber; - - // Effectif theorique par partile - dPartileSize = (double)nValueNumber / (double)nPartileNumber; - - if (bDisplayResults) - { - cout << "Attribut " << targetAttribute->GetAttributeName() << endl; - cout << "nPartileNumber = " << nPartileNumber << " \t dPartileSize = " << dPartileSize << endl; - } - - // Export des parties de l'attribut source - sourceAttribute->ExportParts(&oaSourceParts); - - // Calcul des quantiles - quantileBuilder->ComputeQuantiles(nPartileNumber); - - // Initialisation du nombre effectif de partiles (peut etre inferieur au nombre theorique du fait de doublons) - nActualPartileNumber = quantileBuilder->GetIntervalNumber(); - - // Creation des partiles - for (nPartileIndex = 0; nPartileIndex < nActualPartileNumber; nPartileIndex++) - { - targetPart = targetAttribute->AddPart(); - - // Extraction du premier l'intervalle du partile - sourcePart = - cast(KWDGPart*, oaSourceParts.GetAt(quantileBuilder->GetIntervalFirstValueIndexAt(nPartileIndex))); - // Memorisation de sa borne inf - targetPart->GetInterval()->SetLowerBound(sourcePart->GetInterval()->GetLowerBound()); - - // Extraction du dernier intervalle du partile - sourcePart = - cast(KWDGPart*, oaSourceParts.GetAt(quantileBuilder->GetIntervalLastValueIndexAt(nPartileIndex))); - // Memorisation de sa borne sup - targetPart->GetInterval()->SetUpperBound(sourcePart->GetInterval()->GetUpperBound()); - } - - // Initialisation du nombre de valeurs apres granularisation - // Cas d'un attribut explicatif dans le cadre d'une analyse supervisee - // Mise a jour du parametrage du nombre de partiles par le nombre effectif de partiles - if ((targetDataGrid->GetTargetValueNumber() > 0 or - (targetDataGrid->GetTargetAttribute() != NULL and not sourceAttribute->GetAttributeTargetFunction()))) - targetAttribute->SetGranularizedValueNumber(nPartileNumber); - // Sinon, la granularisation n'est qu'un procede de construction d'une grille initiale - else - targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); -} - -void KWAttributePairStatsStudy::ExportGranularizedPartsForSymbolAttribute(KWDataGrid* targetDataGrid, - KWDGAttribute* sourceAttribute, - KWDGAttribute* targetAttribute, - int nGranularity, - KWQuantileGroupBuilder* quantileBuilder) const -{ - ObjectArray oaSourceParts; - KWDGPart* sourcePart; - KWDGPart* targetPart; - KWDGValueSet* cleanedValueSet; - int nValueNumber; - int nPartileNumber; - int nActualPartileNumber; - int nPartileIndex; - int nSourceIndex; - - require(quantileBuilder != NULL); - - nValueNumber = dataGridManager.GetSourceDataGrid()->GetGridFrequency(); - - // Nombre potentiel de partiles associes a cette granularite - nPartileNumber = (int)pow(2, nGranularity); - if (nPartileNumber > nValueNumber) - nPartileNumber = nValueNumber; - // Initialisation - nActualPartileNumber = nPartileNumber; - - // Cas ou la granularisation n'est pas appliquee : non prise en compte de la granularite - if (nGranularity == 0) - { - dataGridManager.ExportAttributeParts(targetDataGrid, sourceAttribute->GetAttributeName()); - targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); - } - - // Granularisation - else - { - // Export des parties de l'attribut source - sourceAttribute->ExportParts(&oaSourceParts); - - // Cas du nombre de partiles associe a la granularite maximale - if (nPartileNumber == nValueNumber) - // Seuillage de nPartileNumber au nombre de partiles associe a la granularite precedente - // pour que la granularisation rassemble les eventuelles valeurs sources - // singletons dans le fourre-tout - // Pour G tel que 2^G < N <= 2^(G+1) on aura 1 < N/2^G <= 2 c'est a dire un effectif minimal par - // partile de 2 (donc pas de singleton apres granularisation) - nPartileNumber = (int)pow(2, nGranularity - 1); - - // Calcul des quantiles - quantileBuilder->ComputeQuantiles(nPartileNumber); - - // Initialisation du nombre effectif de partiles (peut etre inferieur au nombre theorique du fait de - // doublons) - nActualPartileNumber = quantileBuilder->GetGroupNumber(); - - // Creation des partiles - for (nPartileIndex = 0; nPartileIndex < nActualPartileNumber; nPartileIndex++) - { - targetPart = targetAttribute->AddPart(); - - // Parcours des instances du partile - for (nSourceIndex = quantileBuilder->GetGroupFirstValueIndexAt(nPartileIndex); - nSourceIndex <= quantileBuilder->GetGroupLastValueIndexAt(nPartileIndex); nSourceIndex++) - { - // Extraction de la partie a ajouter dans le groupe - sourcePart = cast(KWDGPart*, oaSourceParts.GetAt(nSourceIndex)); - - // Ajout de ses valeurs - targetPart->GetValueSet()->UpgradeFrom(sourcePart->GetValueSet()); - - // CH IV Begin - // Cas de la granularisation d'un attribut interne dans un attribut de grille de type - // VarPart - if (sourceAttribute->IsInnerAttribute()) - targetPart->SetPartFrequency(targetPart->GetPartFrequency() + - sourcePart->GetPartFrequency()); - // CH IV End - } - - // Compression et memorisation du fourre-tout si necessaire (mode supervise, attribut non cible) - // La partie qui contient la StarValue est compressee uniquement si elle contient plus d'une - // modalite (cas d'un vrai fourre-tout) - if ((targetDataGrid->GetTargetValueNumber() > 0 or - (targetDataGrid->GetTargetAttribute() != NULL and - not sourceAttribute->GetAttributeTargetFunction())) and - targetPart->GetValueSet()->IsDefaultPart() and - targetPart->GetValueSet()->GetValueNumber() > 1) - { - // Compression du fourre-tout et memorisation de ses valeurs - cleanedValueSet = - cast(KWDGSymbolValueSet*, targetPart->GetValueSet())->ConvertToCleanedValueSet(); - targetAttribute->InitializeCatchAllValueSet(cleanedValueSet); - delete cleanedValueSet; - } - // Tri des valeurs du fourre tout - if (targetPart->GetValueSet()->IsDefaultPart()) - targetPart->GetValueSet()->SortValues(); - } - } - - // Cas d'un attribut explicatif dans le cadre d'une analyse supervisee - // Mise a jour du parametrage du nombre de partiles par le nombre effectif de groupes distincts - if ((targetDataGrid->GetTargetValueNumber() > 0 or - (targetDataGrid->GetTargetAttribute() != NULL and not sourceAttribute->GetAttributeTargetFunction()))) - targetAttribute->SetGranularizedValueNumber(nActualPartileNumber); - // Sinon, la granularisation n'est qu'un procede de construction d'une grille initiale - else - targetAttribute->SetGranularizedValueNumber(sourceAttribute->GetInitialValueNumber()); -} - //////////////////////////////////////////////////////////////////////////////////////// // Classe KWDataGridClusteringCostsBivariate diff --git a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h index 6f7cad3c9..d7920dd0d 100644 --- a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h +++ b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h @@ -115,6 +115,11 @@ class KWAttributePairStatsStudy : public KWAttributePairStats boolean GetHierarchicalMultinomialPrior() const; // Redefinition de la methode d'optimisation + // CH IV Begin + //Le parcours exhaustif des granularites entieres remplace par un parcours des granularites par puissance de 2 + // Les methodes specifiques d'export de la grille granularisee de la classe KWHierarchicalMultinomialStudy sont supprimees au profit de l'utilisation + // des methodes de KWDataGridManager pour simplifier la maintenance du code de KWTest + // CH IV End boolean ComputeStats(const KWTupleTable* tupleTable) override; // Redefinition de la structure des cout pour l'etude des prior @@ -123,25 +128,6 @@ class KWAttributePairStatsStudy : public KWAttributePairStats ///////////////////////////////////////////////// ///// Implementation protected: - // Methode redefinie, pour eviter la pre-granularisation des attributs numeriques - boolean CreateAttributeIntervals(const KWTupleTable* tupleTable, KWDGAttribute* dgAttribute) override; - - // Export d'une grille granularisee pour une granularite commune a tous ses attributs - // (attribut, parties et cellules) - // Methode adaptee de KWDataGridManager - // CH IV Refactoring: maintenir cette classe, ou la supprimer? - // CH IV Refactoring: seule difference avec methode de KWDataGridManager (maintenant refactore...): PartileNumber vs Granularity??? - void ExportGranularizedDataGrid(KWDataGrid* targetDataGrid, int nPartileNumber, - ObjectDictionary* odQuantilesBuilders) const; - void ExportGranularizedParts(KWDataGrid* targetDataGrid, int nPartileNumber, - ObjectDictionary* odQuantilesBuilders) const; - void ExportGranularizedPartsForContinuousAttribute(KWDataGrid* targetDataGrid, KWDGAttribute* sourceAttribute, - KWDGAttribute* targetAttribute, int nPartileNumber, - KWQuantileIntervalBuilder* quantileIntervalBuilder) const; - void ExportGranularizedPartsForSymbolAttribute(KWDataGrid* targetDataGrid, KWDGAttribute* sourceAttribute, - KWDGAttribute* targetAttribute, int nGranularity, - KWQuantileGroupBuilder* quantileGroupBuilder) const; - // Variables boolean bHierarchicalMultinomialPrior; KWDataGridManager dataGridManager; diff --git a/src/Learning/KWUtils/KWVersion.h b/src/Learning/KWUtils/KWVersion.h index a3505826b..62726449f 100644 --- a/src/Learning/KWUtils/KWVersion.h +++ b/src/Learning/KWUtils/KWVersion.h @@ -62,7 +62,9 @@ const ALString GetLearningSystemType(); // CH IV Refactoring: commenter les methodes // CH IV Refactoring: verifier si ces methodes sont utilisees // CH IV Refactoring: envisager d'abandonner la gestion des poubelles +// peut etre utile dans la tokenisation pour reduire le cout d'une partition avec gros groupe poubelle ? // pour les attributs de grille de type VarPart et les attributs internes qu'il contiennent? +// CH IV Refactoring: La methode de postOptimisationVarPart ne prend pas en compte le groupe poubelle const boolean GetVarPartAttributeGarbage(); void SetVarPartAttributeGarbage(const boolean bValue); diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp index 7d9f5e590..cc8d649b4 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.cpp @@ -349,12 +349,13 @@ boolean CCCoclusteringBuilder::CheckVarPartSpecifications() const boolean CCCoclusteringBuilder::ComputeCoclustering() { boolean bOk = true; - boolean bProfileOptimisation = true; + boolean bProfileOptimisation = false; KWTupleTable tupleTable; KWTupleTable tupleFrequencyTable; CCCoclusteringOptimizer dataGridOptimizer; KWDataGrid optimizedDataGrid; KWDataGridManager dataGridManager; + ALString sProfileFileName; ALString sTmp; // CH IV Begin ObjectDictionary odObservationNumbers; @@ -403,8 +404,14 @@ boolean CCCoclusteringBuilder::ComputeCoclustering() // Verification de la memoire necessaire pour construire une grille initiale a partir des tuples nMaxCellNumberConstraint = 0; if (bOk and not TaskProgression::IsInterruptionRequested()) - bOk = - CheckMemoryForDataGridInitialization(GetDatabase(), tupleTable.GetSize(), nMaxCellNumberConstraint); + { + if (not GetVarPartCoclustering()) + bOk = CheckMemoryForDataGridInitialization(GetDatabase(), tupleTable.GetSize(), + nMaxCellNumberConstraint); + else + bOk = CheckMemoryForVarPartDataGridInitialization(GetDatabase(), tupleTable.GetSize(), + nMaxCellNumberConstraint); + } // Creation du DataGrid if (bOk and not TaskProgression::IsInterruptionRequested()) @@ -454,9 +461,29 @@ boolean CCCoclusteringBuilder::ComputeCoclustering() // Choix du fichier de trace a parametrer if (bProfileOptimisation) { + // Nom du fichier de profiling, avec le nombre d'individus et de variables + sProfileFileName = "C:/temp/DataGridOptimizationProfiling_"; + sProfileFileName += GetDatabase()->GetClassName(); + if (initialDataGrid->IsVarPartDataGrid()) + { + sProfileFileName += "_I"; + sProfileFileName += IntToString(initialDataGrid->GetAttributeAt(0)->GetInitialValueNumber()); + sProfileFileName += "_V"; + sProfileFileName += + IntToString(initialDataGrid->GetInnerAttributes()->GetInnerAttributeNumber()); + } + else + { + sProfileFileName += "_F"; + sProfileFileName += IntToString(initialDataGrid->GetGridFrequency()); + sProfileFileName += "_D"; + sProfileFileName += IntToString(initialDataGrid->GetAttributeNumber()); + } + sProfileFileName += ".txt"; + + // Lancement du profiling KWDataGridOptimizer::GetProfiler()->SetTrace(true); - KWDataGridOptimizer::GetProfiler()->Start("d:/temp/DataGridOptimizationProfiling" + - GetDatabase()->GetClassName() + ".txt"); + KWDataGridOptimizer::GetProfiler()->Start(sProfileFileName); } // Trace de debut d'optimisation, avec informations sur la grille initiale @@ -1238,7 +1265,7 @@ KWDataGrid* CCCoclusteringBuilder::CreateVarPartDataGrid(const KWTupleTable* tup { TaskProgression::DisplayLabel("Initialize cells"); - bCellCreationOk = CreateDataGridCells(tupleTable, dataGrid); + bCellCreationOk = CreateVarPartDataGridCells(tupleTable, dataGrid); } // Nettoyage des eventuelles parties vides du fait d'observations manquantes @@ -1317,20 +1344,6 @@ void CCCoclusteringBuilder::CleanVarPartDataGrid(KWDataGrid* dataGrid) dataGrid->UpdateAllStatistics(); } -boolean CCCoclusteringBuilder::CreateDataGridCells(const KWTupleTable* tupleTable, KWDataGrid* dataGrid) -{ - boolean bOk; - - // Cas ou le coclustering se ramene a un coclustering standard, appel de la methode standard de creation de - // cellules - if (not GetVarPartCoclustering()) - bOk = KWAttributeSubsetStats::CreateDataGridCells(tupleTable, dataGrid); - // Cas du coclustering instances x variables - else - bOk = CreateVarPartDataGridCells(tupleTable, dataGrid); - return bOk; -} - boolean CCCoclusteringBuilder::CreateVarPartDataGridCells(const KWTupleTable* tupleTable, KWDataGrid* dataGrid) { boolean bOk = true; @@ -1357,6 +1370,7 @@ boolean CCCoclusteringBuilder::CreateVarPartDataGridCells(const KWTupleTable* tu require(tupleTable != NULL); require(dataGrid != NULL); require(GetVarPartCoclustering()); + require(dataGrid->IsVarPartDataGrid()); require(Check()); require(CheckVarPartSpecifications()); require(tupleTable != NULL); @@ -1555,6 +1569,10 @@ boolean CCCoclusteringBuilder::CreateVarPartDataGridCells(const KWTupleTable* tu cout << *cell; } } + + // Arret si erreur + if (not bOk) + break; } // Fin du mode update @@ -1669,6 +1687,10 @@ void CCCoclusteringBuilder::HandleOptimizationStep(const KWDataGrid* optimizedDa (bIsLastSaving and ((coclusteringDataGrid == NULL) or (coclusteringDataGrid->GetGranularity() < initialGranularizedDataGrid->GetGranularity()))))) { + // Ajout de trace lie au profiling + KWDataGridOptimizer::GetProfiler()->BeginMethod("Save best solution"); + + // Memorisation du meilleur cout dAnyTimeBestCost = dCost; // Sauvegarde de la grille @@ -1779,9 +1801,6 @@ void CCCoclusteringBuilder::HandleOptimizationStep(const KWDataGrid* optimizedDa bWriteOk = coclusteringReport.WriteJSONReport(sReportFileName, coclusteringDataGrid); JSONFile::SetVerboseMode(true); - // Ajout de trace lie au profiling - KWDataGridOptimizer::GetProfiler()->WriteKeyString("NEW BEST LEVEL", DoubleToString(dLevel)); - // Sauvegarde au format Khc necessaire if (bWriteOk and GetExportAsKhc()) { @@ -1797,6 +1816,10 @@ void CCCoclusteringBuilder::HandleOptimizationStep(const KWDataGrid* optimizedDa // Memorisation du nouveau nom du dernier fichier sauvegarde if (bWriteOk) sLastActualAnyTimeReportFileName = sReportFileName; + + // Ajout de trace lie au profiling + KWDataGridOptimizer::GetProfiler()->WriteKeyString("New best level", DoubleToString(dLevel)); + KWDataGridOptimizer::GetProfiler()->EndMethod("Save best solution"); } else { @@ -2291,8 +2314,8 @@ boolean CCCoclusteringBuilder::FillVarPartTupleTableFromDatabase(KWDatabase* dat // Test regulierement si il y a assez de memoire if (tupleTable->GetSize() % 65536 == 0) { - bOk = CheckMemoryForDataGridInitialization(GetDatabase(), tupleTable->GetSize(), - nMaxCellNumberConstraint); + bOk = CheckMemoryForVarPartDataGridInitialization( + GetDatabase(), tupleTable->GetSize(), nMaxCellNumberConstraint); if (not bOk) break; } @@ -2735,6 +2758,7 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridInitialization(KWDatabase* ALString sMessage; ALString sTmp; + require(not GetVarPartCoclustering()); require(database != NULL); require(database->IsOpenedForRead() or odDescriptiveStats.GetCount() > 0); require(nTupleNumber > 0); @@ -2797,7 +2821,136 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridInitialization(KWDatabase* // travail, et finales nMaxCellNumber = 0; if (lNecessaryMemory < lAvailableMemory) - nMaxCellNumber = (int)((lAvailableMemory - lNecessaryMemory) / (3 * lSizeOfCell)); + nMaxCellNumber = + (int)min((longint)INT_MAX, ((lAvailableMemory - lNecessaryMemory) / (3 * lSizeOfCell))); + + // Affichage de stats memoire + if (bDisplayMemoryStats) + { + cout << "CheckMemoryForDataGridInitialization" << endl; + cout << "\tInitial data grid: " << lInitialDataGridSize << endl; + cout << "\tWorking data grid: " << lWorkingDataGridSize << endl; + cout << "\t Size of cell: " << lSizeOfCell << endl; + cout << "\tMax cell number: " << nMaxCellNumber << endl; + cout << "\t Necessary: " << lNecessaryMemory << endl; + cout << "\t Available: " << lAvailableMemory << endl; + cout << "\t OK: " << (lNecessaryMemory <= lAvailableMemory) << endl; + } + + // Test si memoire suffisante + if (lNecessaryMemory > lAvailableMemory) + { + sMessage = "Not enough memory to create initial data grid "; + if (database->IsOpenedForRead() and database->GetReadPercentage() < 0.99) + sMessage += sTmp + "after reading " + IntToString((int)(100 * database->GetReadPercentage())) + + "% of the database"; + else + sMessage += RMResourceManager::BuildMissingMemoryMessage(lNecessaryMemory); + AddError(sMessage); + AddMessage(RMResourceManager::BuildMemoryLimitMessage()); + if (RMResourceConstraints::GetIgnoreMemoryLimit()) + RMResourceManager::DisplayIgnoreMemoryLimitMessage(); + else + bOk = false; + } + return bOk; +} + +boolean CCCoclusteringBuilder::CheckMemoryForVarPartDataGridInitialization(KWDatabase* database, int nTupleNumber, + int& nMaxCellNumber) const +{ + boolean bOk = true; + boolean bDisplayMemoryStats = false; + int nDataGridDimensionNumber; + int nAttributeNumber; + longint lAvailableMemory; + longint lNecessaryMemory; + longint lInitialDataGridSize; + longint lWorkingDataGridSize; + longint lSizeOfCell; + double dMaxCellNumber; + int nInstanceNumber; + int nValueNumber; + int nAttribute; + KWAttribute* attribute; + KWDescriptiveStats* descriptiveStats; + ALString sMessage; + ALString sTmp; + + require(GetVarPartCoclustering()); + require(database != NULL); + require(database->IsOpenedForRead() or odDescriptiveStats.GetCount() > 0); + require(nTupleNumber > 0); + require(odDescriptiveStats.GetCount() == 0 or + odDescriptiveStats.GetCount() == + GetClass()->GetLoadedAttributeNumber() - (GetFrequencyAttributeName() == "" ? 0 : 1)); + + // Calcul des caracteristiques memoire disponibles (le fichier est lu a ce moment) + lAvailableMemory = RMResourceManager::GetRemainingAvailableMemory(); + + // Nombre de dimensions de la grille + nDataGridDimensionNumber = 2; + + // Stats de bases sur les variables et instances + nAttributeNumber = GetClass()->GetLoadedAttributeNumber(); + + // Recherche du nombre d'instances + nInstanceNumber = nTupleNumber; + descriptiveStats = cast(KWDescriptiveStats*, odDescriptiveStats.Lookup(GetIdentifierAttributeName())); + if (descriptiveStats != NULL) + nInstanceNumber = descriptiveStats->GetValueNumber(); + + // Prise en compte d'une grille initiale "minimale" estimee de facon fine en prenant en compte les nombres de + // valeurs par variable + lNecessaryMemory = 0; + lInitialDataGridSize = sizeof(KWDataGrid) + sizeof(void*); + nInstanceNumber = 0; + dMaxCellNumber = 0; + for (nAttribute = 0; nAttribute < GetClass()->GetLoadedAttributeNumber(); nAttribute++) + { + attribute = GetClass()->GetLoadedAttributeAt(nAttribute); + + // Recherche des stats descriptives de l'attribut, sauf si attribut d'effectif ou identifiant + if (attribute->GetName() != GetFrequencyAttributeName() and + attribute->GetName() != GetIdentifierAttributeName()) + { + // Nombre de valeur de l'attribut si ses statistiques descriptives sont disponible + descriptiveStats = cast(KWDescriptiveStats*, odDescriptiveStats.Lookup(attribute->GetName())); + if (descriptiveStats != NULL) + nValueNumber = descriptiveStats->GetValueNumber(); + // Estimation sinon + else + nValueNumber = (int)sqrt(nTupleNumber); + + // Prise en compte de la taille de stockage de l'attribut, de ses parties et valeurs + dMaxCellNumber += nInstanceNumber * nValueNumber; + lInitialDataGridSize += sizeof(KWDGAttribute) + sizeof(void*); + if (attribute->GetType() == KWType::Continuous) + lInitialDataGridSize += + nValueNumber * (sizeof(KWDGMPart) + sizeof(KWDGInterval) + sizeof(void*)); + else if (attribute->GetType() == KWType::Symbol) + lInitialDataGridSize += nValueNumber * (sizeof(KWDGMPart) + sizeof(KWDGValueSet) + + sizeof(KWDGValue) + 2 * sizeof(void*)); + } + } + lSizeOfCell = sizeof(KWDGMCell) + nDataGridDimensionNumber * sizeof(void*); + lInitialDataGridSize += (longint)ceil(dMaxCellNumber) * lSizeOfCell; + lNecessaryMemory += lInitialDataGridSize; + + // Plus une grille de travail, et une pour la meilleure solution (de taille estimee minimale) + lWorkingDataGridSize = sizeof(KWDataGridMerger) + nAttributeNumber * sizeof(KWDGMAttribute) + + (longint)ceil(sqrt(dMaxCellNumber)) * nAttributeNumber * + (lSizeOfCell + sizeof(KWDGMPart) + sizeof(KWDGMPartMerge) + sizeof(KWDGInterval) + + sizeof(KWDGValueSet) + sizeof(KWDGValue)); + lWorkingDataGridSize *= 2; + lNecessaryMemory += lWorkingDataGridSize; + + // Estimation du nombre max de cellules que l'on peut charger en memoire, pour les grilles initiales, de + // travail, et finales + nMaxCellNumber = 0; + if (lNecessaryMemory < lAvailableMemory) + nMaxCellNumber = + (int)min((longint)INT_MAX, ((lAvailableMemory - lNecessaryMemory) / (3 * lSizeOfCell))); // Affichage de stats memoire if (bDisplayMemoryStats) @@ -2805,6 +2958,7 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridInitialization(KWDatabase* cout << "CheckMemoryForDataGridInitialization" << endl; cout << "\tInitial data grid: " << lInitialDataGridSize << endl; cout << "\tWorking data grid: " << lWorkingDataGridSize << endl; + cout << "\t Size of cell: " << lSizeOfCell << endl; cout << "\tMax cell number: " << nMaxCellNumber << endl; cout << "\t Necessary: " << lNecessaryMemory << endl; cout << "\t Available: " << lAvailableMemory << endl; @@ -2918,7 +3072,7 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridOptimization(KWDataGrid* in } // CH IV End } - lSizeOfCell = sizeof(KWDGMCell) + ((longint)2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*); + lSizeOfCell = sizeof(KWDGMCell) + ((longint)2 + inputInitialDataGrid->GetAttributeNumber()) * sizeof(void*); lWorkingDataGridSize += inputInitialDataGrid->GetCellNumber() * lSizeOfCell + lTotalValueNumber * (sizeof(KWDGValue) + sizeof(void*)); lWorkingDataGridSize += inputInitialDataGrid->GetCellNumber() * lSizeOfCell + @@ -2929,7 +3083,7 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridOptimization(KWDataGrid* in // Prise en compte de la memoire de travail pour post-optimisation lDataGridPostOptimizationSize = inputInitialDataGrid->GetCellNumber() * - (sizeof(KWDGMCell) + ((longint)2 + GetClass()->GetLoadedAttributeNumber()) * sizeof(void*)); + (sizeof(KWDGMCell) + ((longint)2 + inputInitialDataGrid->GetAttributeNumber()) * sizeof(void*)); lDataGridPostOptimizationSize += inputInitialDataGrid->GetCellNumber() * sizeof(KWDGPOCellFrequencyVector); lDataGridPostOptimizationSize += lInitialMaxPartNumber * (sizeof(KWMODLLineDeepOptimization) + 2 * sizeof(int) + sizeof(KWDGPOPartFrequencyVector)); @@ -2940,6 +3094,7 @@ boolean CCCoclusteringBuilder::CheckMemoryForDataGridOptimization(KWDataGrid* in { cout << "CheckMemoryForDataGridOptimization" << endl; cout << "\tWorking data grid: " << lWorkingDataGridSize << endl; + cout << "\t Size of cell: " << lSizeOfCell << endl; cout << "\tData grid post-optimization: " << lDataGridPostOptimizationSize << endl; cout << "\t Necessary: " << lNecessaryMemory << endl; cout << "\t Available: " << lAvailableMemory << endl; @@ -3500,7 +3655,8 @@ void CCCoclusteringBuilder::ComputeValueTypicalitiesAt(const KWDataGrid* inputIn } // CH IV Refactoring: nettoyer lignes suivantes? - // CH DDD + // CH IV Refactoring: maintenu pour l'instant afin d'identifier en Release les bases pour lesquelles on obtient un dDeltaCost < 0 + // Affichage en cas d'anomalie de dDeltaCost negatif if (bDisplay and dDeltaCost < 0) cout << "ComputeValueTypicalities :: dDeltaCost < 0\t" << dDeltaCost << endl; @@ -4162,11 +4318,10 @@ void CCCoclusteringBuilder::SortAttributePartsAndValues(CCHierarchicalDataGrid* innerPart = innerAttribute->GetHeadPart(); while (innerPart != NULL) { - // CH IV Refactoring - // CH IV voir avec Marc si le tri a deja ete fait suite au renommage des methodes de tri - // Tri des valeurs de la partie si attribut categoriel - //if (innerAttribute->GetAttributeType() == KWType::Symbol) - //cast(CCHDGValueSet*, innerPart->GetValueSet())->SortValuesByTypicality(); + + // Tri des valeurs de la partie si attribut interne categoriel + if (innerAttribute->GetAttributeType() == KWType::Symbol) + innerPart->GetSymbolValueSet()->SortValuesByDecreasingTypicalities(); // Initialisation du nom de la partie cast(CCHDGPart*, innerPart)->SetPartName(innerPart->GetVarPartLabel()); diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.h b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.h index 3b4fc295a..2ad4c8170 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.h +++ b/src/Learning/MODL_Coclustering/CCCoclusteringBuilder.h @@ -92,23 +92,18 @@ class CCCoclusteringBuilder : public KWAttributeSubsetStats // Memoire: appartient a l'appelant KWDataGridCosts* CreateDataGridCost() const override; - // Traite le cas VarPart ou les attributs sont de type Simple ou de type parties de variables - // L'attribut de grile de type VarPart cree contient un cluster de parties de variable pour chaque partie de - // variable de chaque attribut interne L'effectif de la variable identifiant est alimente par le vecteur - // ivObservationNumbers + // Creation d'une grille avec une dimension de type VarPart + // La dimension VarPart contient un cluster de parties de variable pour chaque partie de + // variable de chaque attribut interne + // L'effectif de la variable identifiant est alimente par le vecteur ivObservationNumbers KWDataGrid* CreateVarPartDataGrid(const KWTupleTable* tupleTable, ObjectDictionary& odObservationNumbers); // Nettoyage des eventuelles parties de variables vides du fait d'observations manquantes void CleanVarPartDataGrid(KWDataGrid* dataGrid); - // Alimentation des cellules d'un DataGrid dans dont les attributs et parties sont correctement initialises, + // Alimentation des cellules d'un VarPartDataGrid dont les attributs et parties sont correctement initialises, // Renvoie true si cellule correctement initialisee, false sinon (sans nettoyage des celulles crees) - // Pour les attributs de type VarPart, on parcourt l'ensemble des attributs internes de l'ensemble de ces - // attributs pour alimenter les cellules associees a chaque observation (l'observation d'un attribut interne par - // attribut de grille de type VarPart) CH IV Refactoring: passer en override de la methode ancetre? OK - // verifier egalement pour les autres methodes CreateVarPartDataGrid, CreateVarPartDataGrid - // qui pourrait etre des redefinitions des methodes de la classe ancetre? - boolean CreateDataGridCells(const KWTupleTable* tupleTable, KWDataGrid* dataGrid) override; + // Pour la dimension VarPart, on parcourt l'ensemble des attributs internes pour alimenter les cellules associees a chaque observation boolean CreateVarPartDataGridCells(const KWTupleTable* tupleTable, KWDataGrid* dataGrid); ///////////////////////////////////////////////////////////////////////////// @@ -226,6 +221,13 @@ class CCCoclusteringBuilder : public KWAttributeSubsetStats // On renvoie en sortie le nombre max de cellules de la grille initiale boolean CheckMemoryForDataGridInitialization(KWDatabase* database, int nTupleNumber, int& nMaxCellNumber) const; + // CH IV Begin + // Verification de la memoire necessaire pour construire une grille initiale de type VarPart a partir d'un nombre de tuples + // On renvoie en sortie le nombre max de cellules de la grille initiale + boolean CheckMemoryForVarPartDataGridInitialization(KWDatabase* database, int nTupleNumber, + int& nMaxCellNumber) const; + // CH IV End + // Verification de la memoire necessaire pour optimiser le coclustering, la grille initiale etant construite boolean CheckMemoryForDataGridOptimization(KWDataGrid* inputInitialDataGrid) const; diff --git a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp index 93c1e871d..7b686cafe 100644 --- a/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp +++ b/src/Learning/MODL_Coclustering/CCCoclusteringReport.cpp @@ -1692,9 +1692,13 @@ boolean CCCoclusteringReport::ReadComposition(CCHierarchicalDataGrid* coclusteri { // Tolerance pour les typicalite negatives if (dTypicality < 0) + { AddWarning(sTmp + "Value specification (" + sValueName + ") with typicality less than 0 for variable " + - dgAttribute->GetAttributeName()); + dgAttribute->GetAttributeName() + + " (replaced by 0)"); + dTypicality = 0; + } // Erreur pour les typicalite supereures a 1 else { @@ -2966,6 +2970,7 @@ boolean CCCoclusteringReport::ReadJSONDimensionSummaries(CCHierarchicalDataGrid* // CH IV Begin // Prise en compte d'un coclustering de type VarPart // CH IV Refactoring: potentiellement inutile apres la fin du refactoring + // CH IV Refactoring: on le conserve pour l'instant dans l'hypothese ou l'on etende le coclustering IV a plusieurs variables hors variable varPart if (bOk and varPartAttribute != NULL) { // Parametrage de l'attribut identifiant, en prenant le premier attribut catgoriel trouve @@ -2979,6 +2984,7 @@ boolean CCCoclusteringReport::ReadJSONDimensionSummaries(CCHierarchicalDataGrid* // Tests d'integrite dans le cas d'un coclustering de type instances * variables // CH IV Refactoring: potentiellement inutile apres la fin du refactoring + // CH IV Refactoring: a voir ulterieurement en fonction de l'extension a plus que deux variables if (bOk and coclusteringDataGrid->IsVarPartDataGrid()) { assert(varPartAttribute == coclusteringDataGrid->GetVarPartAttribute()); @@ -3904,10 +3910,13 @@ boolean CCCoclusteringReport::ReadJSONTypicalities(KWDGAttribute* dgAttribute, i // Tolerance pour les typicalite negatives if (dValueTypicality < 0) + { AddWarning(sTmp + "Typicality (" + DoubleToString(dValueTypicality) + ") less than 0 for variable " + dgAttribute->GetAttributeName() + " in \"valueTypicalities\" line " + - IntToString(JSONTokenizer::GetCurrentLineIndex())); + IntToString(JSONTokenizer::GetCurrentLineIndex()) + " (replaced by 0)"); + dValueTypicality = 0; + } // Erreur pour les typicalite supereures a 1 else if (dValueTypicality > 1) { diff --git a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h index c6bcef04c..9c0fc4d50 100644 --- a/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h +++ b/src/Learning/MODL_Coclustering/CCHierarchicalDataGrid.h @@ -91,6 +91,7 @@ class CCHierarchicalDataGrid : public KWDataGrid KWDatabase databaseSpec; // CH IV Refactoring: le sIdentifierAttributeName est-il toujours utile??? + // CH IV Refactoring: on le conserve pour l'instant dans l'hypothese ou l'on etende le coclustering IV a plusieurs variables hors variable varPart ALString sIdentifierAttributeName; }; diff --git a/src/Learning/MODL_Coclustering/CCLearningProblemView.cpp b/src/Learning/MODL_Coclustering/CCLearningProblemView.cpp index e6d899e99..9d851a69a 100644 --- a/src/Learning/MODL_Coclustering/CCLearningProblemView.cpp +++ b/src/Learning/MODL_Coclustering/CCLearningProblemView.cpp @@ -82,28 +82,6 @@ CCLearningProblemView::CCLearningProblemView() coclusteringSpecView->GetFieldAt("FrequencyAttributeName")->SetStyle("HelpedComboBox"); coclusteringSpecView->GetFieldAt("FrequencyAttributeName")->SetParameters("ContinuousAttributes:Name"); - // CH IV Begin - // CH IV Refactoring: plutot tout declarer et utiliser "normalement" (partout dans ce fichier source), - // et parametrer la visibilite selon GetLearningCoclusteringIVExpertMode() - - // Parametrage de liste d'aide pour le nom de l'attribut identifiant pour le coclustering instances * variables - if (GetLearningCoclusteringIVExpertMode()) - { - varPartCoclusteringSpecView = - cast(CCVarPartCoclusteringSpecView*, analysisSpecView->GetFieldAt("VarPartCoclusteringParameters")); - - // Creation d'une liste cachee des attributs de type Categorical de la classe en cours - categoricalAttributeNameHelpList = new UIList; - categoricalAttributeNameHelpList->AddStringField("Name", "Name", ""); - AddListField("CategoricalAttributes", "Simple variables", categoricalAttributeNameHelpList); - categoricalAttributeNameHelpList->SetVisible(false); - - varPartCoclusteringSpecView->GetFieldAt("IdentifierAttributeName")->SetStyle("HelpedComboBox"); - varPartCoclusteringSpecView->GetFieldAt("IdentifierAttributeName") - ->SetParameters("CategoricalAttributes:Name"); - } - // CH IV End - // Creation d'une liste cachee des attributs de type simple de la classe en cours simpleAttributeNameHelpList = new UIList; simpleAttributeNameHelpList->AddStringField("Name", "Name", ""); @@ -119,6 +97,18 @@ CCLearningProblemView::CCLearningProblemView() ->GetFieldAt("Name") ->SetParameters("SimpleAttributes:Name"); + // Creation d'une liste cachee des attributs de type Categorical de la classe en cours + categoricalAttributeNameHelpList = new UIList; + categoricalAttributeNameHelpList->AddStringField("Name", "Name", ""); + AddListField("CategoricalAttributes", "Simple variables", categoricalAttributeNameHelpList); + categoricalAttributeNameHelpList->SetVisible(false); + + // Parametrage de liste d'aide pour le nom de l'attribut identifiant pour le coclustering instances * variables + varPartCoclusteringSpecView = + cast(CCVarPartCoclusteringSpecView*, analysisSpecView->GetFieldAt("VarPartCoclusteringParameters")); + varPartCoclusteringSpecView->GetFieldAt("IdentifierAttributeName")->SetStyle("HelpedComboBox"); + varPartCoclusteringSpecView->GetFieldAt("IdentifierAttributeName")->SetParameters("CategoricalAttributes:Name"); + // Passage en ergonomie onglets SetStyle("TabbedPanes"); From 6e7220aa258f7e5f7690edc59c906dfba7c438bc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Thu, 5 Oct 2023 17:04:10 +0200 Subject: [PATCH 15/17] Update LearningTest scenarios and results for Khiops V11 Modification des -pre-commit - fichier .pre-commit-config.yaml - on exclu le formatage json pour tous les fichier sous test\LearningTest Micro-correction dans les scripts de test\LearningTest\cmd\python - attention, le "pretty print" du pre-commit est tres different de clui de pycharm (plus joli) Mise a jour globales de tous les tests - supression prealable des arborescence concernees - ex: test\LearningTest\TestKhiops\Standard - copie des arborescences correspondante du LearningTest local a jour avec la version en cours - nettoyage des sous-repertoires results - se placer dans le repertoire local test\LearningTest - lancer la commande suivante sur tous les sous-repertorie concernes python cmd\python\apply_command clean TestKhiops\standard ... - nettoyage des fichiers comparisonResults.log - se placer dans le repertoire local test\LearningTest delete comparisonResults.log /s --- .pre-commit-config.yaml | 1 + .../results.ref/Coclustering.khc | 2 +- .../results.ref/Coclustering.khcj | 2 +- .../Adult2varsTiny/results.ref/err.txt | 12 +- .../Adult2varsTiny/results.ref/time.log | 2 +- .../Standard-unstable/Adult2varsTiny/test.prm | 14 +- .../Adult2vars/results.ref/Coclustering.khc | 2 +- .../Adult2vars/results.ref/Coclustering.khcj | 2 +- .../Standard/Adult2vars/results.ref/err.txt | 12 +- .../Standard/Adult2vars/results.ref/time.log | 2 +- .../Standard/Adult2vars/test.prm | 14 +- .../results.ref/Coclustering.khc | 2 +- .../results.ref/Coclustering.khcj | 2 +- .../Adult2varsFrequency/results.ref/err.txt | 12 +- .../Adult2varsFrequency/results.ref/time.log | 2 +- .../Standard/Adult2varsFrequency/test.prm | 10 +- .../results.ref/Coclustering.khc | 83 + .../results.ref/Coclustering.khcj | 221 + .../Adult2varsTiny/results.ref/err.txt | 8 + .../Adult2varsTiny/results.ref/time.log | 1 + .../Standard/Adult2varsTiny/test.prm | 47 + .../results.ref/err.txt | 2 +- .../results.ref/time.log | 2 +- .../Adult2varsWrongFrequency/test.prm | 10 +- .../AdultMissing/results.ref/Coclustering.khc | 2 +- .../results.ref/Coclustering.khcj | 2 +- .../Standard/AdultMissing/results.ref/err.txt | 16 +- .../AdultMissing/results.ref/time.log | 2 +- .../Standard/AdultMissing/test.prm | 8 +- .../AdultSmall/results.ref/Coclustering.khc | 2 +- .../AdultSmall/results.ref/Coclustering.khcj | 2 +- .../Standard/AdultSmall/results.ref/err.txt | 14 +- .../Standard/AdultSmall/results.ref/time.log | 2 +- .../Standard/AdultSmall/test.prm | 8 +- .../AdultSmall1var/results.ref/err.txt | 2 +- .../AdultSmall1var/results.ref/time.log | 2 +- .../Standard/AdultSmall1var/test.prm | 8 +- .../AllCCResultsApiMode/SubDir/Iris.kdic} | 19 +- .../AllCCResultsApiMode/SubDir/Iris.txt | 151 + .../SubDir/SpliceJunctionDNACoclustering.khc | 3721 + .../results.ref/Clusters.txt | 5 + .../results.ref/Clusters_bad.json | 5 + .../results.ref/Coclustering.khc | 69 + .../results.ref/Coclustering.khcj | 299 + .../results.ref/Coclustering_bad.json | 299 + .../results.ref/Coclustering_bad.json.khc | 69 + .../results.ref/DeploymentCoclustering.kdic | 51 + .../DeploymentCoclustering_bad.json | 51 + .../results.ref/SimplifiedCoclustering.khc | 69 + .../results.ref/SimplifiedCoclustering.khcj | 299 + .../SimplifiedCoclustering_bad.json | 299 + .../SimplifiedCoclustering_bad.json.khc | 69 + .../AllCCResultsApiMode/results.ref/err.txt | 32 + .../AllCCResultsApiMode/results.ref/time.log | 1 + .../Standard/AllCCResultsApiMode/test.prm | 152 + .../AllCCResultsStandardMode/SubDir/Iris.kdic | 9 + .../AllCCResultsStandardMode/SubDir/Iris.txt | 151 + .../SubDir/SpliceJunctionDNACoclustering.khc | 3721 + .../results.ref/Clusters.bad.txt | 5 + .../results.ref/Clusters.txt | 5 + .../results.ref/Coclustering.bad.khc | 69 + .../results.ref/Coclustering.bad.khcj | 299 + .../results.ref/Coclustering.khc | 69 + .../results.ref/Coclustering.khcj | 299 + .../DeploymentCoclustering.bad.kdic | 51 + .../results.ref/DeploymentCoclustering.kdic | 51 + .../SimplifiedCoclustering.bad.khc | 69 + .../SimplifiedCoclustering.bad.khcj | 299 + .../results.ref/SimplifiedCoclustering.khc | 69 + .../results.ref/SimplifiedCoclustering.khcj | 299 + .../results.ref/err.txt | 32 + .../results.ref/time.log | 1 + .../AllCCResultsStandardMode/test.prm | 152 + .../results.ref/DeployOccupation.kdic | 2 +- .../results.ref/DeployOccupation50.kdic | 2 +- .../results.ref/SimplifiedCoclustering50.khc | 2 +- .../results.ref/SimplifiedCoclustering50.khcj | 2 +- .../results.ref/err.txt | 6 +- .../results.ref/time.log | 2 +- .../Standard/DeployDistributionAdult/test.prm | 9 +- .../results.ref/DeployOccupation.kdic | 2 +- .../results.ref/DeployOccupation50.kdic | 2 +- .../results.ref/SimplifiedCoclustering50.khc | 2 +- .../results.ref/SimplifiedCoclustering50.khcj | 2 +- .../results.ref/err.txt | 6 +- .../results.ref/time.log | 2 +- .../DeployDistributionAdult2vars/test.prm | 9 +- .../AdultCoclusteringFrequency.json | 1772 +- .../results.ref/DeployOccupation.kdic | 2 +- .../results.ref/DeployOccupation50.kdic | 2 +- .../results.ref/SimplifiedCoclustering50.khc | 2 +- .../results.ref/SimplifiedCoclustering50.khcj | 2 +- .../results.ref/err.txt | 6 +- .../results.ref/time.log | 2 +- .../test.prm | 9 +- .../results.ref/DeployOccupation.kdic | 2 +- .../results.ref/err.txt | 2 +- .../results.ref/time.log | 2 +- .../DeployDistributionAdultSmall/test.prm | 5 +- .../results.ref/CapColorDeployment.kdic | 2 +- .../results.ref/SimplifiedCoclustering.khc | 2 +- .../results.ref/SimplifiedCoclustering.khcj | 2 +- .../results.ref/err.txt | 4 +- .../results.ref/time.log | 2 +- .../test.prm | 10 +- .../ExtractClustersAdult/results.ref/err.txt | 8 +- .../ExtractClustersAdult/results.ref/time.log | 2 +- .../Standard/ExtractClustersAdult/test.prm | 23 +- .../Iris/results.ref/Coclustering.khc | 2 +- .../Iris/results.ref/Coclustering.khcj | 2 +- .../Standard/Iris/results.ref/err.txt | 12 +- .../Standard/Iris/results.ref/time.log | 2 +- .../TestCoclustering/Standard/Iris/test.prm | 8 +- .../Standard/IrisFastPath/readme.txt | 2 + .../IrisFastPath/results.ref/Coclustering.khc | 50 + .../results.ref/Coclustering.khcj | 206 + .../Standard/IrisFastPath/results.ref/err.txt | 11 + .../IrisFastPath/results.ref/time.log | 1 + .../Standard/IrisFastPath/test.prm | 37 + .../IrisMissing/results.ref/Coclustering.khc | 2 +- .../IrisMissing/results.ref/Coclustering.khcj | 2 +- .../Standard/IrisMissing/results.ref/err.txt | 12 +- .../Standard/IrisMissing/results.ref/time.log | 2 +- .../Standard/IrisMissing/test.prm | 8 +- .../Coclustering.V9.json | 1094 +- .../results.ref/CapColorDeployment.kdic | 2 +- .../results.ref/SimplifiedCoclustering.khc | 2 +- .../results.ref/SimplifiedCoclustering.khcj | 2 +- .../results.ref/err.txt | 4 +- .../results.ref/time.log | 2 +- .../test.prm | 10 +- .../PostProcessedCoclustering_JSON.khc | 2 +- .../PostProcessedCoclustering_JSON.khcj | 2 +- .../PostProcessedCoclustering_KHC.khc | 2 +- .../PostProcessedCoclustering_KHC.khcj | 2 +- .../results.ref/err.txt | 4 +- .../results.ref/time.log | 2 +- .../JSONSimplifyAdultMissing/test.prm | 8 +- .../PostProcessedCoclustering_JSON.khc | 2 +- .../PostProcessedCoclustering_JSON.khcj | 2 +- .../PostProcessedCoclustering_KHC.khc | 2 +- .../PostProcessedCoclustering_KHC.khcj | 2 +- .../JSONSimplifyIris/results.ref/err.txt | 4 +- .../JSONSimplifyIris/results.ref/time.log | 2 +- .../Standard/JSONSimplifyIris/test.prm | 8 +- .../Mushroom/results.ref/Coclustering.khc | 2 +- .../Mushroom/results.ref/Coclustering.khcj | 2 +- .../Standard/Mushroom/results.ref/err.txt | 12 +- .../Standard/Mushroom/results.ref/time.log | 2 +- .../Standard/Mushroom/test.prm | 8 +- .../results.ref/Coclustering.khc | 2 +- .../results.ref/Coclustering.khcj | 2 +- .../MushroomBasic/results.ref/err.txt | 4 +- .../MushroomBasic/results.ref/time.log | 2 +- .../Standard/MushroomBasic/test.prm | 8 +- .../results.ref/Coclustering.khc | 2 +- .../results.ref/Coclustering.khcj | 2 +- .../MushroomNoHeaderLine/results.ref/err.txt | 14 +- .../MushroomNoHeaderLine/results.ref/time.log | 2 +- .../Standard/MushroomNoHeaderLine/test.prm | 10 +- .../results.ref/DeployPowerCurveDate.kdic | 2 +- .../results.ref/err.txt | 2 +- .../results.ref/time.log | 2 +- .../PreparePowerCurveDeployment/test.prm | 5 +- .../results.ref/Coclustering.kdic | 2 +- .../results.ref/err.txt | 2 +- .../results.ref/time.log | 2 +- .../PrepareSpliceJunctionDeployment/test.prm | 8 +- .../PostProcessedCoclustering_10000cells.khc | 2 +- .../PostProcessedCoclustering_10000cells.khcj | 2 +- .../PostProcessedCoclustering_100cells.khc | 2 +- .../PostProcessedCoclustering_100cells.khcj | 2 +- .../PostProcessedCoclustering_50info.khc | 2 +- .../PostProcessedCoclustering_50info.khcj | 2 +- .../PostProcessedCoclustering_max5age.khc | 2 +- .../PostProcessedCoclustering_max5age.khcj | 2 +- ...stProcessedCoclustering_max5occupation.khc | 2 +- ...tProcessedCoclustering_max5occupation.khcj | 2 +- .../SimplifyAdult/results.ref/err.txt | 10 +- .../SimplifyAdult/results.ref/time.log | 2 +- .../Standard/SimplifyAdult/test.prm | 14 +- .../PostProcessedCoclustering_10000cells.khc | 2 +- .../PostProcessedCoclustering_10000cells.khcj | 2 +- .../PostProcessedCoclustering_100cells.khc | 2 +- .../PostProcessedCoclustering_100cells.khcj | 2 +- .../PostProcessedCoclustering_50info.khc | 2 +- .../PostProcessedCoclustering_50info.khcj | 2 +- .../PostProcessedCoclustering_max5age.khc | 2 +- .../PostProcessedCoclustering_max5age.khcj | 2 +- ...stProcessedCoclustering_max5occupation.khc | 2 +- ...tProcessedCoclustering_max5occupation.khcj | 2 +- .../SimplifyAdultV75/results.ref/err.txt | 10 +- .../SimplifyAdultV75/results.ref/time.log | 2 +- .../Standard/SimplifyAdultV75/test.prm | 14 +- ...pliceJunction.txt => D_SpliceJunction.txt} | 6358 +- .../SpliceJunction/results.ref/err.txt | 6 +- .../SpliceJunction/results.ref/time.log | 2 +- .../MultiTables/SpliceJunction/test.prm | 24 +- .../results.ref/{T_Adult.txt => D_Adult.txt} | 97686 +++--- .../Standard/Adult/results.ref/err.txt | 6 +- .../Standard/Adult/results.ref/time.log | 2 +- .../TestKNITransfer/Standard/Adult/test.prm | 20 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 302 +- .../Standard/Iris/results.ref/err.txt | 2 +- .../Standard/Iris/results.ref/time.log | 2 +- .../TestKNITransfer/Standard/Iris/test.prm | 20 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 302 +- .../Standard/Iris2D/results.ref/err.txt | 2 +- .../Standard/Iris2D/results.ref/time.log | 2 +- .../TestKNITransfer/Standard/Iris2D/test.prm | 20 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 302 +- .../Standard/IrisC/results.ref/err.txt | 2 +- .../Standard/IrisC/results.ref/time.log | 2 +- .../TestKNITransfer/Standard/IrisC/test.prm | 20 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 302 +- .../Standard/IrisG/results.ref/err.txt | 4 +- .../Standard/IrisG/results.ref/time.log | 2 +- .../TestKNITransfer/Standard/IrisG/test.prm | 20 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 302 +- .../Standard/IrisR/results.ref/err.txt | 2 +- .../Standard/IrisR/results.ref/time.log | 2 +- .../TestKNITransfer/Standard/IrisR/test.prm | 20 +- .../IrisU/results.ref/time.log | 1 - .../{IrisU => IrisU2D}/IrisExtended.kdic | 34 +- .../AnalysisResults.Preparation2DReport.xls} | 2814 +- .../AnalysisResults.PreparationReport.xls} | 451 +- .../results.ref/AnalysisResults.khj} | 5234 +- .../{IrisU => IrisU2D}/results.ref/err.txt | 12 +- .../IrisU2D/results.ref/time.log | 1 + .../{IrisU => IrisU2D}/test.prm | 112 +- ...xls => AnalysisResults.ModelingReport.xls} | 88 +- ... => AnalysisResults.PreparationReport.xls} | 904 +- ... AnalysisResults.TestEvaluationReport.xls} | 4132 +- ...AnalysisResults.TrainEvaluationReport.xls} | 4132 +- .../{AllReports.khj => AnalysisResults.khj} | 2379 +- ...deling.kdic => AnalysisResults.model.kdic} | 138 +- .../results.ref/{T_Adult.txt => D_Adult.txt} | 68584 ++-- .../Adult/results.ref/EvaluationReport.khj | 2 +- .../Adult/results.ref/EvaluationReport.xls | 2 +- .../Standard/Adult/results.ref/err.txt | 12 +- .../Standard/Adult/results.ref/time.log | 2 +- .../TestKhiops/Standard/Adult/test.prm | 45 +- .../AnalysisResults.PreparationReport.xls | 1854 + .../AdultU/results.ref/AnalysisResults.khj | 2437 + .../Standard/AdultU/results.ref/err.txt | 5 + .../Standard/AdultU/results.ref/time.log | 1 + .../TestKhiops/Standard/AdultU/test.prm | 56 + .../BenchIris/results.ref/benchmark.xls | 70 +- .../Standard/BenchIris/results.ref/time.log | 2 +- .../TestKhiops/Standard/BenchIris/test.prm | 6 +- .../results.ref/benchmark.xls | 1087 +- .../BenchUnivariateIris/results.ref/time.log | 2 +- .../Standard/BenchUnivariateIris/test.prm | 60 +- ...xls => AnalysisResults.ModelingReport.xls} | 66 +- ... => AnalysisResults.PreparationReport.xls} | 208 +- ... AnalysisResults.TestEvaluationReport.xls} | 6140 +- ...AnalysisResults.TrainEvaluationReport.xls} | 6140 +- .../{AllReports.khj => AnalysisResults.khj} | 1140 +- ...deling.kdic => AnalysisResults.model.kdic} | 76 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 212 +- .../Standard/Iris/results.ref/err.txt | 4 +- .../Standard/Iris/results.ref/time.log | 2 +- .../TestKhiops/Standard/Iris/test.prm | 32 +- ...xls => AnalysisResults.ModelingReport.xls} | 70 +- ...> AnalysisResults.Preparation2DReport.xls} | 1405 +- ... => AnalysisResults.PreparationReport.xls} | 404 +- .../AnalysisResults.TestEvaluationReport.xls} | 6138 +- ...AnalysisResults.TrainEvaluationReport.xls} | 6138 +- .../{AllReports.khj => AnalysisResults.khj} | 4905 +- ...deling.kdic => AnalysisResults.model.kdic} | 108 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 212 +- .../Standard/Iris2D/results.ref/err.txt | 6 +- .../Standard/Iris2D/results.ref/time.log | 2 +- .../TestKhiops/Standard/Iris2D/test.prm | 32 +- ...xls => AnalysisResults.ModelingReport.xls} | 70 +- ... => AnalysisResults.PreparationReport.xls} | 404 +- .../AnalysisResults.TestEvaluationReport.xls} | 6138 +- ...AnalysisResults.TrainEvaluationReport.xls} | 6138 +- .../{AllReports.khj => AnalysisResults.khj} | 1698 +- ...deling.kdic => AnalysisResults.model.kdic} | 108 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 212 +- .../Standard/IrisC/results.ref/err.txt | 6 +- .../Standard/IrisC/results.ref/time.log | 2 +- .../TestKhiops/Standard/IrisC/test.prm | 32 +- .../Standard/IrisFastPath/readme.txt | 2 + .../AnalysisResults.ModelingReport.xls | 29 + .../AnalysisResults.PreparationReport.xls | 105 + .../AnalysisResults.TestEvaluationReport.xls} | 6099 +- ...AnalysisResults.TrainEvaluationReport.xls} | 6099 +- .../results.ref/AnalysisResults.khj | 451 + .../results.ref/AnalysisResults.model.kdic | 20 + .../Standard/IrisFastPath/results.ref/err.txt | 13 + .../IrisFastPath/results.ref/time.log | 1 + .../TestKhiops/Standard/IrisFastPath/test.prm | 40 + ...xls => AnalysisResults.ModelingReport.xls} | 66 +- ... => AnalysisResults.PreparationReport.xls} | 454 +- ... AnalysisResults.TestEvaluationReport.xls} | 6138 +- ...AnalysisResults.TrainEvaluationReport.xls} | 6138 +- .../{AllReports.khj => AnalysisResults.khj} | 1792 +- ...deling.kdic => AnalysisResults.model.kdic} | 104 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 212 +- .../Standard/IrisG/results.ref/err.txt | 6 +- .../Standard/IrisG/results.ref/time.log | 2 +- .../TestKhiops/Standard/IrisG/test.prm | 32 +- ...xls => AnalysisResults.ModelingReport.xls} | 58 +- ... => AnalysisResults.PreparationReport.xls} | 194 +- ... AnalysisResults.TestEvaluationReport.xls} | 4088 +- ...AnalysisResults.TrainEvaluationReport.xls} | 4088 +- .../{AllReports.khj => AnalysisResults.khj} | 810 +- ...deling.kdic => AnalysisResults.model.kdic} | 40 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 212 +- .../Standard/IrisLight/results.ref/err.txt | 4 +- .../Standard/IrisLight/results.ref/time.log | 2 +- .../TestKhiops/Standard/IrisLight/test.prm | 34 +- ...xls => AnalysisResults.ModelingReport.xls} | 58 +- ...> AnalysisResults.Preparation2DReport.xls} | 159 +- ... => AnalysisResults.PreparationReport.xls} | 212 +- .../AnalysisResults.TestEvaluationReport.xls | 3049 + .../AnalysisResults.TrainEvaluationReport.xls | 3049 + .../{AllReports.khj => AnalysisResults.khj} | 1220 +- ...deling.kdic => AnalysisResults.model.kdic} | 42 +- .../Standard/IrisLight2D/results.ref/err.txt | 4 +- .../Standard/IrisLight2D/results.ref/time.log | 2 +- .../TestKhiops/Standard/IrisLight2D/test.prm | 16 +- ...xls => AnalysisResults.ModelingReport.xls} | 60 +- ... => AnalysisResults.PreparationReport.xls} | 234 +- ... AnalysisResults.TestEvaluationReport.xls} | 6098 +- ...AnalysisResults.TrainEvaluationReport.xls} | 6098 +- .../AnalysisResults.TreePreparationReport.xls | 102 + .../{AllReports.khj => AnalysisResults.khj} | 1580 +- ...deling.kdic => AnalysisResults.model.kdic} | 44 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 212 +- .../IrisLightWithTrees/results.ref/err.txt | 4 +- .../IrisLightWithTrees/results.ref/time.log | 2 +- .../Standard/IrisLightWithTrees/test.prm | 30 +- ...xls => AnalysisResults.ModelingReport.xls} | 74 +- ... => AnalysisResults.PreparationReport.xls} | 439 +- .../AnalysisResults.TestEvaluationReport.xls | 1067 + .../AnalysisResults.TrainEvaluationReport.xls | 1067 + .../{AllReports.khj => AnalysisResults.khj} | 1663 +- ...deling.kdic => AnalysisResults.model.kdic} | 172 +- .../results.ref/{T_IrisR.txt => D_IrisR.txt} | 212 +- .../results.ref/TestEvaluationReport.xls | 1057 - .../results.ref/TrainEvaluationReport.xls | 1057 - .../Standard/IrisR/results.ref/err.txt | 9 +- .../Standard/IrisR/results.ref/time.log | 2 +- .../TestKhiops/Standard/IrisR/test.prm | 32 +- .../results.ref/{T_Iris.txt => D_Iris.txt} | 212 +- .../Standard/IrisTransfer/results.ref/err.txt | 4 +- .../IrisTransfer/results.ref/time.log | 2 +- .../TestKhiops/Standard/IrisTransfer/test.prm | 30 +- .../Standard/IrisU2D/IrisExtended.kdic | 17 + .../AnalysisResults.Preparation2DReport.xls | 1407 + .../AnalysisResults.PreparationReport.xls | 205 + .../IrisU2D/results.ref/AnalysisResults.khj | 2716 + .../Standard/IrisU2D/results.ref/err.txt | 6 + .../Standard/IrisU2D/results.ref/time.log | 1 + .../TestKhiops/Standard/IrisU2D/test.prm | 58 + ...> C100_AnalysisResults.ModelingReport.xls} | 68 +- ...C100_AnalysisResults.PreparationReport.xls | 275 + ..._AnalysisResults.TestEvaluationReport.xls} | 6098 +- ...AnalysisResults.TrainEvaluationReport.xls} | 6098 +- ...llReports.khj => C100_AnalysisResults.khj} | 4494 +- ...g.kdic => C100_AnalysisResults.model.kdic} | 94 +- .../results.ref/C100_PreparationReport.xls | 273 - .../SpliceJunction/results.ref/err.txt | 8 +- .../SpliceJunction/results.ref/time.log | 2 +- .../Standard/SpliceJunction/test.prm | 22 +- ...0_AnalysisResults.Preparation2DReport.xls} | 726 +- ...C100_AnalysisResults.PreparationReport.xls | 274 + ...llReports.khj => C100_AnalysisResults.khj} | 7915 +- .../results.ref/C100_PreparationReport.xls | 273 - .../SpliceJunction2D/results.ref/err.txt | 6 +- .../SpliceJunction2D/results.ref/time.log | 2 +- .../Standard/SpliceJunction2D/test.prm | 23 +- .../SpliceJunctionExternal.kdic | 2 +- ...C100_AnalysisResults.PreparationReport.xls | 274 + ...llReports.khj => C100_AnalysisResults.khj} | 4023 +- .../results.ref/C100_PreparationReport.xls | 273 - .../results.ref/err.txt | 6 +- .../results.ref/time.log | 2 +- .../Standard/SpliceJunctionExternal/test.prm | 27 +- ...C100_AnalysisResults.PreparationReport.xls | 274 + ...llReports.khj => C100_AnalysisResults.khj} | 4023 +- .../results.ref/C100_PreparationReport.xls | 273 - .../results.ref/err.txt | 6 +- .../results.ref/time.log | 2 +- .../SpliceJunctionPreparation/test.prm | 23 +- ...pliceJunction.txt => D_SpliceJunction.txt} | 4510 +- ...unctionDNA.txt => D_SpliceJunctionDNA.txt} | 270482 +++++++-------- .../results.ref/err.txt | 6 +- .../results.ref/time.log | 2 +- .../Standard/SpliceJunctionTransfer/test.prm | 28 +- ...> C100_AnalysisResults.ModelingReport.xls} | 68 +- ...C100_AnalysisResults.PreparationReport.xls | 275 + ...AnalysisResults.TrainEvaluationReport.xls} | 6098 +- ..._AnalysisResults.TreePreparationReport.xls | 98 + ...llReports.khj => C100_AnalysisResults.khj} | 4815 +- ...g.kdic => C100_AnalysisResults.model.kdic} | 94 +- .../results.ref/C100_PreparationReport.xls | 273 - .../results.ref/err.txt | 6 +- .../results.ref/time.log | 2 +- .../Standard/SpliceJunctionWithTrees/test.prm | 22 +- test/LearningTest/cmd/python/apply_command.py | 4 +- .../cmd/python/apply_command_all.py | 1 - test/LearningTest/cmd/python/check_results.py | 3 +- .../cmd/python/learning_test_env.py | 5 +- test/LearningTest/cmd/python/test_khiops.py | 2 +- test/LearningTest/cmd/python/utils.py | 2 +- 409 files changed, 342976 insertions(+), 314909 deletions(-) create mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khc create mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khcj create mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/err.txt create mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/time.log create mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/test.prm rename test/LearningTest/{TestKhiops/Standard/Iris/IrisStandard.kdic => TestCoclustering/Standard/AllCCResultsApiMode/SubDir/Iris.kdic} (85%) create mode 100644 test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/Iris.txt create mode 100644 test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/SpliceJunctionDNACoclustering.khc create mode 100644 test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Clusters.txt create mode 100644 test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Clusters_bad.json create mode 100644 test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering.khc create mode 100644 test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering.khcj create mode 100644 test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering_bad.json create mode 100644 test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering_bad.json.khc create mode 100644 test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/DeploymentCoclustering.kdic create mode 100644 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(97%) rename test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/{T_Iris.txt => D_Iris.txt} (97%) rename test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/{T_Iris.txt => D_Iris.txt} (97%) rename test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/{T_Iris.txt => D_Iris.txt} (97%) rename test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/{T_Iris.txt => D_Iris.txt} (97%) rename test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/{T_Iris.txt => D_Iris.txt} (94%) delete mode 100644 test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/time.log rename test/LearningTest/TestKhiops/Standard-unstable/{IrisU => IrisU2D}/IrisExtended.kdic (97%) rename test/LearningTest/TestKhiops/Standard-unstable/{IrisU/results.ref/PreparationReport2D.xls => IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls} (96%) rename test/LearningTest/TestKhiops/Standard-unstable/{IrisU/results.ref/PreparationReport.xls => IrisU2D/results.ref/AnalysisResults.PreparationReport.xls} (51%) rename test/LearningTest/TestKhiops/Standard-unstable/{IrisU/results.ref/AllReports.khj => IrisU2D/results.ref/AnalysisResults.khj} (84%) rename test/LearningTest/TestKhiops/Standard-unstable/{IrisU => IrisU2D}/results.ref/err.txt (63%) create mode 100644 test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/time.log rename test/LearningTest/TestKhiops/Standard-unstable/{IrisU => IrisU2D}/test.prm (57%) rename test/LearningTest/TestKhiops/Standard/Adult/results.ref/{ModelingReport.xls => AnalysisResults.ModelingReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/Adult/results.ref/{PreparationReport.xls => AnalysisResults.PreparationReport.xls} (89%) rename test/LearningTest/TestKhiops/Standard/Adult/results.ref/{TestEvaluationReport.xls => AnalysisResults.TestEvaluationReport.xls} (96%) rename test/LearningTest/TestKhiops/Standard/Adult/results.ref/{TrainEvaluationReport.xls => AnalysisResults.TrainEvaluationReport.xls} (96%) rename test/LearningTest/TestKhiops/Standard/Adult/results.ref/{AllReports.khj => AnalysisResults.khj} (98%) rename test/LearningTest/TestKhiops/Standard/Adult/results.ref/{Modeling.kdic => AnalysisResults.model.kdic} (99%) rename test/LearningTest/TestKhiops/Standard/Adult/results.ref/{T_Adult.txt => D_Adult.txt} (96%) create mode 100644 test/LearningTest/TestKhiops/Standard/AdultU/results.ref/AnalysisResults.PreparationReport.xls create mode 100644 test/LearningTest/TestKhiops/Standard/AdultU/results.ref/AnalysisResults.khj create mode 100644 test/LearningTest/TestKhiops/Standard/AdultU/results.ref/err.txt create mode 100644 test/LearningTest/TestKhiops/Standard/AdultU/results.ref/time.log create mode 100644 test/LearningTest/TestKhiops/Standard/AdultU/test.prm rename test/LearningTest/TestKhiops/Standard/Iris/results.ref/{ModelingReport.xls => AnalysisResults.ModelingReport.xls} (93%) rename test/LearningTest/TestKhiops/Standard/Iris/results.ref/{PreparationReport.xls => AnalysisResults.PreparationReport.xls} (85%) rename test/LearningTest/TestKhiops/Standard/Iris/results.ref/{TestEvaluationReport.xls => AnalysisResults.TestEvaluationReport.xls} (96%) rename test/LearningTest/TestKhiops/Standard/Iris/results.ref/{TrainEvaluationReport.xls => AnalysisResults.TrainEvaluationReport.xls} (96%) rename test/LearningTest/TestKhiops/Standard/Iris/results.ref/{AllReports.khj => AnalysisResults.khj} (99%) rename test/LearningTest/TestKhiops/Standard/Iris/results.ref/{Modeling.kdic => AnalysisResults.model.kdic} (98%) rename test/LearningTest/TestKhiops/Standard/Iris/results.ref/{T_Iris.txt => D_Iris.txt} (98%) rename test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/{ModelingReport.xls => AnalysisResults.ModelingReport.xls} (94%) rename test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/{Preparation2DReport.xls => AnalysisResults.Preparation2DReport.xls} (96%) rename test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/{PreparationReport.xls => AnalysisResults.PreparationReport.xls} (81%) rename test/LearningTest/TestKhiops/Standard/{IrisC/results.ref/TestEvaluationReport.xls => Iris2D/results.ref/AnalysisResults.TestEvaluationReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/{TrainEvaluationReport.xls => AnalysisResults.TrainEvaluationReport.xls} (96%) rename test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/{AllReports.khj => AnalysisResults.khj} (98%) rename test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/{Modeling.kdic => AnalysisResults.model.kdic} (98%) rename test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/{T_Iris.txt => D_Iris.txt} (98%) rename test/LearningTest/TestKhiops/Standard/IrisC/results.ref/{ModelingReport.xls => AnalysisResults.ModelingReport.xls} (94%) rename test/LearningTest/TestKhiops/Standard/IrisC/results.ref/{PreparationReport.xls => AnalysisResults.PreparationReport.xls} (81%) rename test/LearningTest/TestKhiops/Standard/{Iris2D/results.ref/TestEvaluationReport.xls => IrisC/results.ref/AnalysisResults.TestEvaluationReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/IrisC/results.ref/{TrainEvaluationReport.xls => AnalysisResults.TrainEvaluationReport.xls} (96%) rename test/LearningTest/TestKhiops/Standard/IrisC/results.ref/{AllReports.khj => AnalysisResults.khj} (99%) rename test/LearningTest/TestKhiops/Standard/IrisC/results.ref/{Modeling.kdic => AnalysisResults.model.kdic} (98%) rename test/LearningTest/TestKhiops/Standard/IrisC/results.ref/{T_Iris.txt => D_Iris.txt} (98%) create mode 100644 test/LearningTest/TestKhiops/Standard/IrisFastPath/readme.txt create mode 100644 test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.ModelingReport.xls create mode 100644 test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.PreparationReport.xls rename test/LearningTest/TestKhiops/Standard/{IrisLight2D/results.ref/TestEvaluationReport.xls => IrisFastPath/results.ref/AnalysisResults.TestEvaluationReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/{IrisLight2D/results.ref/TrainEvaluationReport.xls => IrisFastPath/results.ref/AnalysisResults.TrainEvaluationReport.xls} (95%) create mode 100644 test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.khj create mode 100644 test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.model.kdic create mode 100644 test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/err.txt create mode 100644 test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/time.log create mode 100644 test/LearningTest/TestKhiops/Standard/IrisFastPath/test.prm rename test/LearningTest/TestKhiops/Standard/IrisG/results.ref/{ModelingReport.xls => AnalysisResults.ModelingReport.xls} (93%) rename test/LearningTest/TestKhiops/Standard/IrisG/results.ref/{PreparationReport.xls => AnalysisResults.PreparationReport.xls} (83%) rename test/LearningTest/TestKhiops/Standard/IrisG/results.ref/{TestEvaluationReport.xls => AnalysisResults.TestEvaluationReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/IrisG/results.ref/{TrainEvaluationReport.xls => AnalysisResults.TrainEvaluationReport.xls} (96%) rename test/LearningTest/TestKhiops/Standard/IrisG/results.ref/{AllReports.khj => AnalysisResults.khj} (99%) rename test/LearningTest/TestKhiops/Standard/IrisG/results.ref/{Modeling.kdic => AnalysisResults.model.kdic} (98%) rename test/LearningTest/TestKhiops/Standard/IrisG/results.ref/{T_Iris.txt => D_Iris.txt} (98%) rename test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/{ModelingReport.xls => AnalysisResults.ModelingReport.xls} (93%) rename test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/{PreparationReport.xls => AnalysisResults.PreparationReport.xls} (85%) rename test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/{TestEvaluationReport.xls => AnalysisResults.TestEvaluationReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/{TrainEvaluationReport.xls => AnalysisResults.TrainEvaluationReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/{AllReports.khj => AnalysisResults.khj} (99%) rename test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/{Modeling.kdic => AnalysisResults.model.kdic} (98%) rename test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/{T_Iris.txt => D_Iris.txt} (97%) rename test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/{ModelingReport.xls => AnalysisResults.ModelingReport.xls} (92%) rename test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/{Preparation2DReport.xls => AnalysisResults.Preparation2DReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/{PreparationReport.xls => AnalysisResults.PreparationReport.xls} (84%) create mode 100644 test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.TestEvaluationReport.xls create mode 100644 test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.TrainEvaluationReport.xls rename test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/{AllReports.khj => AnalysisResults.khj} (99%) rename test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/{Modeling.kdic => AnalysisResults.model.kdic} (98%) rename test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/{ModelingReport.xls => AnalysisResults.ModelingReport.xls} (93%) rename test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/{PreparationReport.xls => AnalysisResults.PreparationReport.xls} (83%) rename test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/{TestEvaluationReport.xls => AnalysisResults.TestEvaluationReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/{TrainEvaluationReport.xls => AnalysisResults.TrainEvaluationReport.xls} (95%) create mode 100644 test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TreePreparationReport.xls rename test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/{AllReports.khj => AnalysisResults.khj} (98%) rename test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/{Modeling.kdic => AnalysisResults.model.kdic} (98%) rename test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/{T_Iris.txt => D_Iris.txt} (98%) rename test/LearningTest/TestKhiops/Standard/IrisR/results.ref/{ModelingReport.xls => AnalysisResults.ModelingReport.xls} (89%) rename test/LearningTest/TestKhiops/Standard/IrisR/results.ref/{PreparationReport.xls => AnalysisResults.PreparationReport.xls} (81%) create mode 100644 test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.TestEvaluationReport.xls create mode 100644 test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.TrainEvaluationReport.xls rename test/LearningTest/TestKhiops/Standard/IrisR/results.ref/{AllReports.khj => AnalysisResults.khj} (73%) rename test/LearningTest/TestKhiops/Standard/IrisR/results.ref/{Modeling.kdic => AnalysisResults.model.kdic} (72%) rename test/LearningTest/TestKhiops/Standard/IrisR/results.ref/{T_IrisR.txt => D_IrisR.txt} (92%) delete mode 100644 test/LearningTest/TestKhiops/Standard/IrisR/results.ref/TestEvaluationReport.xls delete mode 100644 test/LearningTest/TestKhiops/Standard/IrisR/results.ref/TrainEvaluationReport.xls rename test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/{T_Iris.txt => D_Iris.txt} (96%) create mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/IrisExtended.kdic create mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls create mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls create mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.khj create mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/err.txt create mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/time.log create mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/test.prm rename test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/{C100_ModelingReport.xls => C100_AnalysisResults.ModelingReport.xls} (95%) create mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.PreparationReport.xls rename test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/{C100_TestEvaluationReport.xls => C100_AnalysisResults.TestEvaluationReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/{C100_TrainEvaluationReport.xls => C100_AnalysisResults.TrainEvaluationReport.xls} (95%) rename test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/{C100_AllReports.khj => C100_AnalysisResults.khj} (97%) rename test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/{C100_Modeling.kdic => C100_AnalysisResults.model.kdic} (98%) delete mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_PreparationReport.xls rename test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/{C100_Preparation2DReport.xls => C100_AnalysisResults.Preparation2DReport.xls} (98%) create mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.PreparationReport.xls rename test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/{C100_AllReports.khj => C100_AnalysisResults.khj} (95%) delete mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_PreparationReport.xls create mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AnalysisResults.PreparationReport.xls rename test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/{C100_AllReports.khj => C100_AnalysisResults.khj} (92%) delete mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_PreparationReport.xls create mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AnalysisResults.PreparationReport.xls rename test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/{C100_AllReports.khj => C100_AnalysisResults.khj} (92%) delete mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_PreparationReport.xls rename test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/{T_SpliceJunction.txt => D_SpliceJunction.txt} (95%) rename test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/{T_SpliceJunctionDNA.txt => D_SpliceJunctionDNA.txt} (96%) rename test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/{C100_ModelingReport.xls => C100_AnalysisResults.ModelingReport.xls} (95%) create mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.PreparationReport.xls rename test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/{C100_TrainEvaluationReport.xls => C100_AnalysisResults.TrainEvaluationReport.xls} (95%) create mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.TreePreparationReport.xls rename test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/{C100_AllReports.khj => C100_AnalysisResults.khj} (96%) rename test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/{C100_Modeling.kdic => C100_AnalysisResults.model.kdic} (98%) delete mode 100644 test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_PreparationReport.xls diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b5a08aab7..fd16f7fbc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -28,6 +28,7 @@ repos: hooks: - id: pretty-format-json args: [--autofix, --no-ensure-ascii, --no-sort-keys] + exclude: test/LearningTest/ - id: trailing-whitespace types_or: [c, c++, java, python, markdown] - repo: https://github.com/python-jsonschema/check-jsonschema diff --git a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/Coclustering.khc index 7f04f6b6c..0d34c62b2 100644 --- a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/Coclustering.khcj index 9e9468817..e08b00cc2 100644 --- a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/err.txt index 049fc2191..464233a51 100644 --- a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/err.txt @@ -1,8 +1,8 @@ Database ../../../datasets/Adult/Adult.txt : Read records: 48,842 Selected records: 240 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.0137204 Size: 3*5 - 0:00:00 Write intermediate coclustering report Coclustering(2).khc Level: 0.0137755 Size: 2*3 - 0:00:00 Write intermediate coclustering report Coclustering(3).khc Level: 0.0139406 Size: 3*4 - 0:00:00 Write intermediate coclustering report Coclustering(4).khc Level: 0.0141152 Size: 3*3 -Write coclustering report ./results\Coclustering.khc -Coclustering analysis time: 0:00:00.06 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.0137204 Size: 3*5 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0137755 Size: 2*3 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.0139406 Size: 3*4 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.0141152 Size: 3*3 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.05 diff --git a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/time.log index 6dc1e53f6..b22d29e3c 100644 --- a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.14558053016662598 +Overal time: 0.13622331619262695 diff --git a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/test.prm b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/test.prm index b3d03a24b..f5a1eea04 100644 --- a/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/test.prm +++ b/test/LearningTest/TestCoclustering/Standard-unstable/Adult2varsTiny/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,11 +23,11 @@ ClassFileName ../../../datasets/Adult/Adult.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key Adult // List item selection -Database.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file -Database.SampleNumberPercentage 0.5 // Sample percentage -Database.SelectionAttribute native_country // Selection variable -Database.SelectionValue United-States // Selection value +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file +Database.DatabaseSpec.Sampling.SampleNumberPercentage 0.5 // Sample percentage +Database.DatabaseSpec.Selection.SelectionAttribute native_country // Selection variable +Database.DatabaseSpec.Selection.SelectionValue United-States // Selection value AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name occupation // Name @@ -33,7 +35,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert AnalysisSpec.CoclusteringParameters.Attributes.Name education // Name AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/Coclustering.khc index e7e81807d..b4f6603f0 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/Coclustering.khcj index dfe62e138..8d8155dc2 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/err.txt index 71b65793c..1b3ece023 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/err.txt @@ -1,8 +1,8 @@ Database ../../../datasets/Adult/Adult.txt : Read records: 48,842 Selected records: 31,389 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.0138226 Size: 4*4 Granularity: 3 - 0:00:00 Write intermediate coclustering report Coclustering(2).khc Level: 0.029615 Size: 8*7 Granularity: 5 - 0:00:00 Write intermediate coclustering report Coclustering(3).khc Level: 0.038056 Size: 8*9 - 0:00:00 Write intermediate coclustering report Coclustering(4).khc Level: 0.0381382 Size: 9*9 -Write coclustering report ./results\Coclustering.khc -Coclustering analysis time: 0:00:00.10 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.0138226 Size: 4*4 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.029615 Size: 8*7 Granularity: 5 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.038056 Size: 8*9 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.0381382 Size: 9*9 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.08 diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/time.log index 48bf33fd4..28e7a14a7 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/Adult2vars/results.ref/time.log @@ -1 +1 @@ -Overal time: 2.5432066917419434 +Overal time: 0.1733393669128418 diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2vars/test.prm b/test/LearningTest/TestCoclustering/Standard/Adult2vars/test.prm index ad31303ea..ef73ba791 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2vars/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/Adult2vars/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,11 +23,11 @@ ClassFileName ../../../datasets/Adult/Adult.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key Adult // List item selection -Database.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file -Database.SampleNumberPercentage 70 // Sample percentage -Database.SelectionAttribute native_country // Selection variable -Database.SelectionValue United-States // Selection value +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file +Database.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +Database.DatabaseSpec.Selection.SelectionAttribute native_country // Selection variable +Database.DatabaseSpec.Selection.SelectionValue United-States // Selection value AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name occupation // Name @@ -33,7 +35,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert AnalysisSpec.CoclusteringParameters.Attributes.Name education // Name AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/Coclustering.khc index 2f876b863..8b9c25e06 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/Coclustering.khcj index dfb716c11..669d89566 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/err.txt index 95533a16f..deb3c22c2 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/err.txt @@ -1,9 +1,9 @@ Database ./OccupationEducationFrequency.txt : Read records: 209 Database ./OccupationEducationFrequency.txt : Total frequency: 48,842 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.008869 Size: 3*4 Granularity: 3 - 0:00:00 Write intermediate coclustering report Coclustering(2).khc Level: 0.0165635 Size: 6*4 Granularity: 4 - 0:00:00 Write intermediate coclustering report Coclustering(3).khc Level: 0.029554 Size: 8*8 Granularity: 5 - 0:00:00 Write intermediate coclustering report Coclustering(4).khc Level: 0.0396542 Size: 9*9 -Write coclustering report ./results\Coclustering.khc -Coclustering analysis time: 0:00:00.07 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.008869 Size: 3*4 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0165635 Size: 6*4 Granularity: 4 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.029554 Size: 8*8 Granularity: 5 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.0396542 Size: 9*9 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.06 diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/time.log index 406aa064f..73ea1d37e 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.15358591079711914 +Overal time: 0.12560343742370605 diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/test.prm b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/test.prm index 8e2e33f9a..f3ea9a07e 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsFrequency/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,19 +23,19 @@ ClassFileName ./OccupationEducationFrequency.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key OccupationEducationFrequency // List item selection -Database.DatabaseFiles.DataTableName ./OccupationEducationFrequency.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key OccupationEducationFrequency // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ./OccupationEducationFrequency.txt // Database file AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name occupation // Name AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name education // Name -AnalysisSpec.CoclusteringParameters.FrequencyAttribute frequency // Frequency variable +AnalysisSpec.CoclusteringParameters.FrequencyAttributeName frequency // Frequency variable AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khc new file mode 100644 index 000000000..187238285 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khc @@ -0,0 +1,83 @@ +#Khiops 10.4.6i +Short description +Dimensions 2 +Name Type Parts Initial parts Values Interest Description +occupation Categorical 3 3 12 1 +education Categorical 3 3 13 1 + +Coclustering stats +Instances 240 +Cells 9 +Null cost 1066.368632 +Cost 1051.316663 +Level 0.01411516443 +Initial dimensions 2 +Frequency variable +Dictionary Adult +Database ../../../datasets/Adult/Adult.txt +Sample percentage 0.5 +Sampling mode Include sample +Selection variable native_country +Selection value United-States + +Hierarchy occupation +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Prof-specialty, Exec-managerial, Protective-serv, ...} A2 90 1 1 1 6 +{Other-service, Craft-repair, Transport-moving} A4 70 0.812391 1 3 6 +{Sales, Farming-fishing, Adm-clerical, ...} A4 80 0.655405 1 5 6 +A2 240 0.830416 -0.727419 2 3 +A4 A2 150 0.728665 0.72783 4 5 + +Hierarchy education +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Bachelors, Masters, Assoc-voc, ...} B1 83 1 1 1 6 +{HS-grad, 11th, 10th, ...} B3 105 0.903801 1 3 6 +{Some-college, 9th} B3 52 0.59125 1 5 6 +B1 240 0.869351 0 2 2 +B3 B1 157 0.800281 0.696343 4 4 + +Composition occupation +Cluster Value Frequency Typicality +{Prof-specialty, Exec-managerial, Protective-serv, ...} Prof-specialty 54 1 +{Prof-specialty, Exec-managerial, Protective-serv, ...} Exec-managerial 23 0.342621 +{Prof-specialty, Exec-managerial, Protective-serv, ...} Protective-serv 6 0.148043 +{Prof-specialty, Exec-managerial, Protective-serv, ...} Tech-support 7 0.0502739 +{Prof-specialty, Exec-managerial, Protective-serv, ...} * 0 0 +{Other-service, Craft-repair, Transport-moving} Other-service 33 1 +{Other-service, Craft-repair, Transport-moving} Craft-repair 27 0.682053 +{Other-service, Craft-repair, Transport-moving} Transport-moving 10 0.398653 +{Sales, Farming-fishing, Adm-clerical, ...} Sales 27 1 +{Sales, Farming-fishing, Adm-clerical, ...} Farming-fishing 11 0.631255 +{Sales, Farming-fishing, Adm-clerical, ...} Adm-clerical 21 0.428212 +{Sales, Farming-fishing, Adm-clerical, ...} Handlers-cleaners 12 0.37083 +{Sales, Farming-fishing, Adm-clerical, ...} Machine-op-inspct 9 0.322156 + +Composition education +Cluster Value Frequency Typicality +{Bachelors, Masters, Assoc-voc, ...} Bachelors 39 1 +{Bachelors, Masters, Assoc-voc, ...} Masters 14 0.756619 +{Bachelors, Masters, Assoc-voc, ...} Assoc-voc 10 0.272011 +{Bachelors, Masters, Assoc-voc, ...} Assoc-acdm 10 0.245697 +{Bachelors, Masters, Assoc-voc, ...} Prof-school 4 0.220568 +{Bachelors, Masters, Assoc-voc, ...} Doctorate 2 0.166163 +{Bachelors, Masters, Assoc-voc, ...} 12th 4 0.143425 +{Bachelors, Masters, Assoc-voc, ...} * 0 0 +{HS-grad, 11th, 10th, ...} HS-grad 82 1 +{HS-grad, 11th, 10th, ...} 11th 12 0.318105 +{HS-grad, 11th, 10th, ...} 10th 8 0.271284 +{HS-grad, 11th, 10th, ...} 7th-8th 3 0.0969771 +{Some-college, 9th} Some-college 47 1 +{Some-college, 9th} 9th 5 0.300942 + +Cells +occupation education Frequency +{Prof-specialty, Exec-managerial, Protective-serv, ...} {Bachelors, Masters, Assoc-voc, ...} 60 +{Sales, Farming-fishing, Adm-clerical, ...} {HS-grad, 11th, 10th, ...} 52 +{Other-service, Craft-repair, Transport-moving} {HS-grad, 11th, 10th, ...} 42 +{Other-service, Craft-repair, Transport-moving} {Some-college, 9th} 25 +{Sales, Farming-fishing, Adm-clerical, ...} {Bachelors, Masters, Assoc-voc, ...} 20 +{Prof-specialty, Exec-managerial, Protective-serv, ...} {Some-college, 9th} 19 +{Prof-specialty, Exec-managerial, Protective-serv, ...} {HS-grad, 11th, 10th, ...} 11 +{Sales, Farming-fishing, Adm-clerical, ...} {Some-college, 9th} 8 +{Other-service, Craft-repair, Transport-moving} {Bachelors, Masters, Assoc-voc, ...} 3 + diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khcj new file mode 100644 index 000000000..b7b94d083 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khcj @@ -0,0 +1,221 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 240, + "cells": 9, + "nullCost": 1066.368632, + "cost": 1051.316663, + "level": 0.01411516443, + "initialDimensions": 2, + "frequencyVariable": "", + "dictionary": "Adult", + "database": "..\/..\/..\/datasets\/Adult\/Adult.txt", + "samplePercentage": 0.5, + "samplingMode": "Include sample", + "selectionVariable": "native_country", + "selectionValue": "United-States" + }, + "dimensionSummaries": [ + { + "name": "occupation", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 12, + "interest": 1, + "description": "" + }, + { + "name": "education", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 13, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "occupation", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Prof-specialty, Exec-managerial, Protective-serv, ...}", + "values": ["Prof-specialty","Exec-managerial","Protective-serv","Tech-support"], + "valueFrequencies": [54,23,6,7], + "valueTypicalities": [1,0.342621,0.148043,0.0502739] + }, + { + "cluster": "{Other-service, Craft-repair, Transport-moving}", + "values": ["Other-service","Craft-repair","Transport-moving"], + "valueFrequencies": [33,27,10], + "valueTypicalities": [1,0.682053,0.398653] + }, + { + "cluster": "{Sales, Farming-fishing, Adm-clerical, ...}", + "values": ["Sales","Farming-fishing","Adm-clerical","Handlers-cleaners","Machine-op-inspct"], + "valueFrequencies": [27,11,21,12,9], + "valueTypicalities": [1,0.631255,0.428212,0.37083,0.322156] + } + ], + "defaultGroupIndex": 0 + }, + { + "name": "education", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Bachelors, Masters, Assoc-voc, ...}", + "values": ["Bachelors","Masters","Assoc-voc","Assoc-acdm","Prof-school","Doctorate","12th"], + "valueFrequencies": [39,14,10,10,4,2,4], + "valueTypicalities": [1,0.756619,0.272011,0.245697,0.220568,0.166163,0.143425] + }, + { + "cluster": "{HS-grad, 11th, 10th, ...}", + "values": ["HS-grad","11th","10th","7th-8th"], + "valueFrequencies": [82,12,8,3], + "valueTypicalities": [1,0.318105,0.271284,0.0969771] + }, + { + "cluster": "{Some-college, 9th}", + "values": ["Some-college","9th"], + "valueFrequencies": [47,5], + "valueTypicalities": [1,0.300942] + } + ], + "defaultGroupIndex": 0 + } + ], + "dimensionHierarchies": [ + { + "name": "occupation", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Prof-specialty, Exec-managerial, Protective-serv, ...}", + "parentCluster": "A2", + "frequency": 90, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "{Other-service, Craft-repair, Transport-moving}", + "parentCluster": "A4", + "frequency": 70, + "interest": 0.812391, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "{Sales, Farming-fishing, Adm-clerical, ...}", + "parentCluster": "A4", + "frequency": 80, + "interest": 0.655405, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "A2", + "parentCluster": "", + "frequency": 240, + "interest": 0.830416, + "hierarchicalLevel": -0.727419, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "A4", + "parentCluster": "A2", + "frequency": 150, + "interest": 0.728665, + "hierarchicalLevel": 0.72783, + "rank": 4, + "hierarchicalRank": 5, + "isLeaf": false + } + ] + }, + { + "name": "education", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Bachelors, Masters, Assoc-voc, ...}", + "parentCluster": "B1", + "frequency": 83, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "{HS-grad, 11th, 10th, ...}", + "parentCluster": "B3", + "frequency": 105, + "interest": 0.903801, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "{Some-college, 9th}", + "parentCluster": "B3", + "frequency": 52, + "interest": 0.59125, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "B1", + "parentCluster": "", + "frequency": 240, + "interest": 0.869351, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 2, + "isLeaf": false + }, + { + "cluster": "B3", + "parentCluster": "B1", + "frequency": 157, + "interest": 0.800281, + "hierarchicalLevel": 0.696343, + "rank": 4, + "hierarchicalRank": 4, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0], + [2,1], + [1,1], + [1,2], + [2,0], + [0,2], + [0,1], + [2,2], + [1,0] + ], + "cellFrequencies": [60,52,42,25,20,19,11,8,3] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/err.txt new file mode 100644 index 000000000..bbcd62c09 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/err.txt @@ -0,0 +1,8 @@ +Database ../../../datasets/Adult/Adult.txt : Read records: 48,842 Selected records: 240 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.0137204 Size: 3*5 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0137755 Size: 2*3 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.0139406 Size: 3*4 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.0141152 Size: 3*3 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.05 + diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/time.log new file mode 100644 index 000000000..5f65e3db4 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/time.log @@ -0,0 +1 @@ +Overal time: 0.13622331619262695 diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/test.prm b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/test.prm new file mode 100644 index 000000000..131282135 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/test.prm @@ -0,0 +1,47 @@ +// Mon Jul 11 10:35:30 2011 +// C:\Users\boullema\Learning\Win32\debug\MODL_Coclustering +// Output command file +// +//This file contains recorded commands, that can be replayed. +//Commands are based on user interactions: +// field update +// list item selection +// menu action +//Every command can be commented, using //. +//For example, commenting the last Exit command will allow other +//user interactions, after the commands have been replayed. +// +// + +// -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB +ClassManagement.OpenFile // Open... + +// -> Open +ClassFileName ../../../datasets/Adult/Adult.kdic // Dictionary file +OK // Open +// <- Open + +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file +Database.DatabaseSpec.Sampling.SampleNumberPercentage 0.5 // Sample percentage +Database.DatabaseSpec.Selection.SelectionAttribute native_country // Selection variable +Database.DatabaseSpec.Selection.SelectionValue United-States // Selection value + +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.Name occupation // Name +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.Name education // Name + +AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report +BuildCoclustering // Build coclustering + +Exit // Close +// <- Khiops Coclustering + + +// -> Khiops Coclustering +OK // Close +// <- Khiops Coclustering diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/results.ref/err.txt index d048ceb78..6f7140737 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/results.ref/err.txt @@ -7,5 +7,5 @@ warning : Database ./OccupationEducationFrequency.txt : Record 7 : Record 6, fre warning : Database ./OccupationEducationFrequency.txt : Record 8 : Record 7, frequency variable (frequency) with non integer value (0.6 -> 1) warning : Database ./OccupationEducationFrequency.txt : Record 9 : Ignored record 8, frequency variable (frequency) with value too large (1e+10) error : Coclustering OccupationEducationFrequency : Read database interrupted after record 11 because total frequency is too large (4,000,000,002) -Coclustering analysis time: 0:00:00.01 +Coclustering analysis time: 0:00:00.02 diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/results.ref/time.log index c3e976075..382261812 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.1096959114074707 +Overal time: 0.10705804824829102 diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/test.prm b/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/test.prm index 8e2e33f9a..f3ea9a07e 100644 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/Adult2varsWrongFrequency/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,19 +23,19 @@ ClassFileName ./OccupationEducationFrequency.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key OccupationEducationFrequency // List item selection -Database.DatabaseFiles.DataTableName ./OccupationEducationFrequency.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key OccupationEducationFrequency // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ./OccupationEducationFrequency.txt // Database file AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name occupation // Name AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name education // Name -AnalysisSpec.CoclusteringParameters.FrequencyAttribute frequency // Frequency variable +AnalysisSpec.CoclusteringParameters.FrequencyAttributeName frequency // Frequency variable AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/Coclustering.khc index 6909f5e04..20e645185 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 3 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/Coclustering.khcj index 71a0967f7..1c8a2660b 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/err.txt index 03cf88e79..8720fb924 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/err.txt @@ -1,10 +1,10 @@ Database ./AdultMissing10000.txt : Read records: 10,000 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.00338501 Size: 2*1*2 Granularity: 1 - 0:00:00 Write intermediate coclustering report Coclustering(2).khc Level: 0.0279628 Size: 2*3*2 Granularity: 2 - 0:00:00 Write intermediate coclustering report Coclustering(3).khc Level: 0.0304703 Size: 3*3*2 Granularity: 3 - 0:00:00 Write intermediate coclustering report Coclustering(4).khc Level: 0.0331267 Size: 6*4*2 Granularity: 5 - 0:00:00 Write intermediate coclustering report Coclustering(5).khc Level: 0.0338808 Size: 7*4*2 - 0:00:00 Write intermediate coclustering report Coclustering(6).khc Level: 0.0339522 Size: 8*4*2 -Write coclustering report ./results\Coclustering.khc -Coclustering analysis time: 0:00:00.12 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.00338501 Size: 2*1*2 Granularity: 1 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0279628 Size: 2*3*2 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.0304703 Size: 3*3*2 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.0331267 Size: 6*4*2 Granularity: 5 + 0:00:00 Write intermediate coclustering report Coclustering(5).khcj Level: 0.0338808 Size: 7*4*2 + 0:00:00 Write intermediate coclustering report Coclustering(6).khcj Level: 0.0339522 Size: 8*4*2 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.14 diff --git a/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/time.log index 95756466c..36d70b3c1 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/AdultMissing/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.21542572975158691 +Overal time: 0.2224133014678955 diff --git a/test/LearningTest/TestCoclustering/Standard/AdultMissing/test.prm b/test/LearningTest/TestCoclustering/Standard/AdultMissing/test.prm index f5d9618de..8a35a7a00 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultMissing/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/AdultMissing/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,8 +23,8 @@ ClassFileName ../../../datasets/Adult/Adult.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key Adult // List item selection -Database.DatabaseFiles.DataTableName ./AdultMissing10000.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ./AdultMissing10000.txt // Database file AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name age // Name @@ -33,7 +35,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.Name class // Name //AnalysisSpec.SystemParameters.OptimizationTime 1 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/Coclustering.khc index 64e7cc7ea..225a6b37a 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 5 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/Coclustering.khcj index a1e26f9cb..44f8f47d1 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/err.txt index 5d4920536..d5feecca1 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/err.txt @@ -1,9 +1,9 @@ Database ./Adult1000.txt : Read records: 1000 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.00132277 Size: 1*2*1*1*2 Granularity: 1 - 0:00:00 Write intermediate coclustering report Coclustering(2).khc Level: 0.0184751 Size: 1*2*3*2*2 Granularity: 2 - 0:00:00 Write intermediate coclustering report Coclustering(3).khc Level: 0.0200085 Size: 3*2*2*2*2 Granularity: 3 - 0:00:00 Write intermediate coclustering report Coclustering(4).khc Level: 0.0278485 Size: 3*2*2*2*2 Granularity: 4 - 0:00:00 Write intermediate coclustering report Coclustering(5).khc Level: 0.029571 Size: 3*2*2*2*2 -Write coclustering report ./results\Coclustering.khc -Coclustering analysis time: 0:00:00.18 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.00132277 Size: 1*2*1*1*2 Granularity: 1 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0184751 Size: 1*2*3*2*2 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.0200085 Size: 3*2*2*2*2 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.0278485 Size: 3*2*2*2*2 Granularity: 4 + 0:00:00 Write intermediate coclustering report Coclustering(5).khcj Level: 0.029571 Size: 3*2*2*2*2 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.17 diff --git a/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/time.log index 1b793fc1e..77b27261d 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/AdultSmall/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.26627659797668457 +Overal time: 0.23691654205322266 diff --git a/test/LearningTest/TestCoclustering/Standard/AdultSmall/test.prm b/test/LearningTest/TestCoclustering/Standard/AdultSmall/test.prm index ce8d7918e..f44731844 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultSmall/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/AdultSmall/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,8 +23,8 @@ ClassFileName ../../../datasets/Adult/Adult.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key Adult // List item selection -Database.DatabaseFiles.DataTableName ./Adult1000.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ./Adult1000.txt // Database file //AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable //AnalysisSpec.CoclusteringParameters.Attributes.Name age // Name @@ -44,7 +46,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert AnalysisSpec.CoclusteringParameters.Attributes.Name class // Name AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering diff --git a/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/results.ref/err.txt index 246760635..a00125c79 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/results.ref/err.txt @@ -1,4 +1,4 @@ Database ./Adult1000.txt : Read records: 1000 No informative coclustering found in data -Coclustering analysis time: 0:00:00.02 +Coclustering analysis time: 0:00:00.05 diff --git a/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/results.ref/time.log index 5c1997ea9..5bcb4c3ba 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.10124421119689941 +Overal time: 0.10941505432128906 diff --git a/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/test.prm b/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/test.prm index 446f19c65..b1f280c48 100644 --- a/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/AdultSmall1var/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,8 +23,8 @@ ClassFileName ../../../datasets/Adult/Adult.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key Adult // List item selection -Database.DatabaseFiles.DataTableName ./Adult1000.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ./Adult1000.txt // Database file AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name native_country // Name @@ -30,7 +32,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert AnalysisSpec.CoclusteringParameters.Attributes.Name sex // Name //AnalysisSpec.SystemParameters.OptimizationTime 1 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering diff --git a/test/LearningTest/TestKhiops/Standard/Iris/IrisStandard.kdic b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/Iris.kdic similarity index 85% rename from test/LearningTest/TestKhiops/Standard/Iris/IrisStandard.kdic rename to test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/Iris.kdic index 69923fe58..5169bce8a 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/IrisStandard.kdic +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/Iris.kdic @@ -1,10 +1,9 @@ -#Khiops 7.7.2i - -Dictionary Iris -{ - Numerical SepalLength ; - Numerical SepalWidth ; - Numerical PetalLength ; - Numerical PetalWidth ; - Categorical Class ; -}; + +Dictionary Iris +{ + Numerical SepalLength ; + Numerical SepalWidth ; + Numerical PetalLength ; + Numerical PetalWidth ; + Categorical Class ; +}; diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/Iris.txt b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/Iris.txt new file mode 100644 index 000000000..ed75374e6 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/Iris.txt @@ -0,0 +1,151 @@ +SepalLength SepalWidth PetalLength PetalWidth Class +5.1 3.5 1.4 0.2 Iris-setosa +4.9 3.0 1.4 0.2 Iris-setosa +4.7 3.2 1.3 0.2 Iris-setosa +4.6 3.1 1.5 0.2 Iris-setosa +5.0 3.6 1.4 0.2 Iris-setosa +5.4 3.9 1.7 0.4 Iris-setosa +4.6 3.4 1.4 0.3 Iris-setosa +5.0 3.4 1.5 0.2 Iris-setosa +4.4 2.9 1.4 0.2 Iris-setosa +4.9 3.1 1.5 0.1 Iris-setosa +5.4 3.7 1.5 0.2 Iris-setosa +4.8 3.4 1.6 0.2 Iris-setosa +4.8 3.0 1.4 0.1 Iris-setosa +4.3 3.0 1.1 0.1 Iris-setosa +5.8 4.0 1.2 0.2 Iris-setosa +5.7 4.4 1.5 0.4 Iris-setosa +5.4 3.9 1.3 0.4 Iris-setosa +5.1 3.5 1.4 0.3 Iris-setosa +5.7 3.8 1.7 0.3 Iris-setosa +5.1 3.8 1.5 0.3 Iris-setosa +5.4 3.4 1.7 0.2 Iris-setosa +5.1 3.7 1.5 0.4 Iris-setosa +4.6 3.6 1.0 0.2 Iris-setosa +5.1 3.3 1.7 0.5 Iris-setosa +4.8 3.4 1.9 0.2 Iris-setosa +5.0 3.0 1.6 0.2 Iris-setosa +5.0 3.4 1.6 0.4 Iris-setosa +5.2 3.5 1.5 0.2 Iris-setosa +5.2 3.4 1.4 0.2 Iris-setosa +4.7 3.2 1.6 0.2 Iris-setosa +4.8 3.1 1.6 0.2 Iris-setosa +5.4 3.4 1.5 0.4 Iris-setosa +5.2 4.1 1.5 0.1 Iris-setosa +5.5 4.2 1.4 0.2 Iris-setosa +4.9 3.1 1.5 0.1 Iris-setosa +5.0 3.2 1.2 0.2 Iris-setosa +5.5 3.5 1.3 0.2 Iris-setosa +4.9 3.1 1.5 0.1 Iris-setosa +4.4 3.0 1.3 0.2 Iris-setosa +5.1 3.4 1.5 0.2 Iris-setosa +5.0 3.5 1.3 0.3 Iris-setosa +4.5 2.3 1.3 0.3 Iris-setosa +4.4 3.2 1.3 0.2 Iris-setosa +5.0 3.5 1.6 0.6 Iris-setosa +5.1 3.8 1.9 0.4 Iris-setosa +4.8 3.0 1.4 0.3 Iris-setosa +5.1 3.8 1.6 0.2 Iris-setosa +4.6 3.2 1.4 0.2 Iris-setosa +5.3 3.7 1.5 0.2 Iris-setosa +5.0 3.3 1.4 0.2 Iris-setosa +7.0 3.2 4.7 1.4 Iris-versicolor +6.4 3.2 4.5 1.5 Iris-versicolor +6.9 3.1 4.9 1.5 Iris-versicolor +5.5 2.3 4.0 1.3 Iris-versicolor +6.5 2.8 4.6 1.5 Iris-versicolor +5.7 2.8 4.5 1.3 Iris-versicolor +6.3 3.3 4.7 1.6 Iris-versicolor +4.9 2.4 3.3 1.0 Iris-versicolor +6.6 2.9 4.6 1.3 Iris-versicolor +5.2 2.7 3.9 1.4 Iris-versicolor +5.0 2.0 3.5 1.0 Iris-versicolor +5.9 3.0 4.2 1.5 Iris-versicolor +6.0 2.2 4.0 1.0 Iris-versicolor +6.1 2.9 4.7 1.4 Iris-versicolor +5.6 2.9 3.6 1.3 Iris-versicolor +6.7 3.1 4.4 1.4 Iris-versicolor +5.6 3.0 4.5 1.5 Iris-versicolor +5.8 2.7 4.1 1.0 Iris-versicolor +6.2 2.2 4.5 1.5 Iris-versicolor +5.6 2.5 3.9 1.1 Iris-versicolor +5.9 3.2 4.8 1.8 Iris-versicolor +6.1 2.8 4.0 1.3 Iris-versicolor +6.3 2.5 4.9 1.5 Iris-versicolor +6.1 2.8 4.7 1.2 Iris-versicolor +6.4 2.9 4.3 1.3 Iris-versicolor +6.6 3.0 4.4 1.4 Iris-versicolor +6.8 2.8 4.8 1.4 Iris-versicolor +6.7 3.0 5.0 1.7 Iris-versicolor +6.0 2.9 4.5 1.5 Iris-versicolor +5.7 2.6 3.5 1.0 Iris-versicolor +5.5 2.4 3.8 1.1 Iris-versicolor +5.5 2.4 3.7 1.0 Iris-versicolor +5.8 2.7 3.9 1.2 Iris-versicolor +6.0 2.7 5.1 1.6 Iris-versicolor +5.4 3.0 4.5 1.5 Iris-versicolor +6.0 3.4 4.5 1.6 Iris-versicolor +6.7 3.1 4.7 1.5 Iris-versicolor +6.3 2.3 4.4 1.3 Iris-versicolor +5.6 3.0 4.1 1.3 Iris-versicolor +5.5 2.5 4.0 1.3 Iris-versicolor +5.5 2.6 4.4 1.2 Iris-versicolor +6.1 3.0 4.6 1.4 Iris-versicolor +5.8 2.6 4.0 1.2 Iris-versicolor +5.0 2.3 3.3 1.0 Iris-versicolor +5.6 2.7 4.2 1.3 Iris-versicolor +5.7 3.0 4.2 1.2 Iris-versicolor +5.7 2.9 4.2 1.3 Iris-versicolor +6.2 2.9 4.3 1.3 Iris-versicolor +5.1 2.5 3.0 1.1 Iris-versicolor +5.7 2.8 4.1 1.3 Iris-versicolor +6.3 3.3 6.0 2.5 Iris-virginica +5.8 2.7 5.1 1.9 Iris-virginica +7.1 3.0 5.9 2.1 Iris-virginica +6.3 2.9 5.6 1.8 Iris-virginica +6.5 3.0 5.8 2.2 Iris-virginica +7.6 3.0 6.6 2.1 Iris-virginica +4.9 2.5 4.5 1.7 Iris-virginica +7.3 2.9 6.3 1.8 Iris-virginica +6.7 2.5 5.8 1.8 Iris-virginica +7.2 3.6 6.1 2.5 Iris-virginica +6.5 3.2 5.1 2.0 Iris-virginica +6.4 2.7 5.3 1.9 Iris-virginica +6.8 3.0 5.5 2.1 Iris-virginica +5.7 2.5 5.0 2.0 Iris-virginica +5.8 2.8 5.1 2.4 Iris-virginica +6.4 3.2 5.3 2.3 Iris-virginica +6.5 3.0 5.5 1.8 Iris-virginica +7.7 3.8 6.7 2.2 Iris-virginica +7.7 2.6 6.9 2.3 Iris-virginica +6.0 2.2 5.0 1.5 Iris-virginica +6.9 3.2 5.7 2.3 Iris-virginica +5.6 2.8 4.9 2.0 Iris-virginica +7.7 2.8 6.7 2.0 Iris-virginica +6.3 2.7 4.9 1.8 Iris-virginica +6.7 3.3 5.7 2.1 Iris-virginica +7.2 3.2 6.0 1.8 Iris-virginica +6.2 2.8 4.8 1.8 Iris-virginica +6.1 3.0 4.9 1.8 Iris-virginica +6.4 2.8 5.6 2.1 Iris-virginica +7.2 3.0 5.8 1.6 Iris-virginica +7.4 2.8 6.1 1.9 Iris-virginica +7.9 3.8 6.4 2.0 Iris-virginica +6.4 2.8 5.6 2.2 Iris-virginica +6.3 2.8 5.1 1.5 Iris-virginica +6.1 2.6 5.6 1.4 Iris-virginica +7.7 3.0 6.1 2.3 Iris-virginica +6.3 3.4 5.6 2.4 Iris-virginica +6.4 3.1 5.5 1.8 Iris-virginica +6.0 3.0 4.8 1.8 Iris-virginica +6.9 3.1 5.4 2.1 Iris-virginica +6.7 3.1 5.6 2.4 Iris-virginica +6.9 3.1 5.1 2.3 Iris-virginica +5.8 2.7 5.1 1.9 Iris-virginica +6.8 3.2 5.9 2.3 Iris-virginica +6.7 3.3 5.7 2.5 Iris-virginica +6.7 3.0 5.2 2.3 Iris-virginica +6.3 2.5 5.0 1.9 Iris-virginica +6.5 3.0 5.2 2.0 Iris-virginica +6.2 3.4 5.4 2.3 Iris-virginica +5.9 3.0 5.1 1.8 Iris-virginica diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/SpliceJunctionDNACoclustering.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/SpliceJunctionDNACoclustering.khc new file mode 100644 index 000000000..059fb7414 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/SubDir/SpliceJunctionDNACoclustering.khc @@ -0,0 +1,3721 @@ +#Khiops 7.7.2i +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +SampleId Categorical 9 9 3178 1 +Pos Numerical 13 13 191400 1 +Char Categorical 4 4 8 1 + +Coclustering stats +Instances 191400 +Cells 446 +Null cost 3.952e+006 +Cost 3.94432e+006 +Level 0.00194418 +Initial dimensions 3 +Frequency variable +Dictionary SpliceJunctionDNA +Database ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt + +Bounds +Name Min Max +Pos 1 60 + +Hierarchy SampleId +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} A17 28320 1 1 1 26 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} A17 18120 0.815517 1 3 26 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} A21 17280 0.768173 1 5 26 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} A21 17220 0.767011 1 7 26 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} A19 25020 0.73237 1 9 26 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} A19 23760 0.690727 1 11 26 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} A18 22380 0.825937 1 13 26 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} A18 24780 0.730234 1 15 26 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} A16 14520 0.661623 1 17 26 +A17 A7 46440 0.928018 0.874132 2 19 +A7 A4 129720 0.804153 0.24448 4 9 +A21 A10 34500 0.767593 0.977008 6 23 +A10 A7 83280 0.735081 0.561493 8 12 +A19 A10 48780 0.712086 0.929093 10 21 +A4 191400 0.786317 0.0549351 12 6 +A18 A16 47160 0.77565 0.905723 14 20 +A16 A4 61680 0.748807 0.856356 16 18 + +Hierarchy Pos +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;15.5] ]-inf;28.5] 47850 0.256594 1 1 26 +]15.5;26.5] ]15.5;27.5] 35090 0.382703 1 3 26 +]26.5;27.5] ]15.5;27.5] 3190 0.287986 1 5 26 +]27.5;28.5] ]15.5;28.5] 3190 0.588203 1 7 26 +]28.5;29.5] ]28.5;+inf[ 3190 1 1 9 26 +]29.5;30.5] ]29.5;31.5] 3190 0.774911 1 11 26 +]30.5;31.5] ]29.5;31.5] 3190 0.674393 1 13 26 +]31.5;32.5] ]31.5;+inf[ 3190 0.870658 1 15 26 +]32.5;33.5] ]32.5;34.5] 3190 0.493713 1 17 26 +]33.5;34.5] ]32.5;34.5] 3190 0.334615 1 19 26 +]34.5;35.5] ]32.5;35.5] 3190 0.409016 1 21 26 +]35.5;36.5] ]35.5;+inf[ 3190 0.26895 1 23 26 +]36.5;+inf[ ]35.5;+inf[ 76560 0.0983381 1 25 26 +]-inf;28.5] ]-inf;+inf[ 89320 0.319101 0.691729 2 14 +]15.5;27.5] ]15.5;28.5] 38280 0.37481 0.960755 4 22 +]15.5;28.5] ]-inf;28.5] 41470 0.391225 0.825343 6 17 +]-inf;+inf[ 191400 0.268687 -0.000688577 8 4 +]28.5;+inf[ ]-inf;+inf[ 102080 0.224574 0.158149 10 8 +]29.5;31.5] ]29.5;+inf[ 6380 0.724652 0.743575 12 15 +]29.5;+inf[ ]28.5;+inf[ 98890 0.19956 0.373977 14 10 +]31.5;+inf[ ]29.5;+inf[ 92510 0.163347 0.509065 16 11 +]32.5;34.5] ]32.5;35.5] 6380 0.414164 0.994249 18 25 +]32.5;35.5] ]32.5;+inf[ 9570 0.412448 0.788492 20 16 +]32.5;+inf[ ]31.5;+inf[ 89320 0.138086 0.614176 22 13 +]35.5;+inf[ ]32.5;+inf[ 79750 0.105163 0.988136 24 24 + +Hierarchy Char +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{G, N, D, ...} C1 50305 1 1 1 26 +{T} C5 46308 0.867258 1 3 26 +{A} C5 44487 0.837853 1 5 26 +{C} C3 50300 0.839347 1 7 26 +C1 191400 0.887977 0 2 3 +C5 C3 90795 0.852851 0.11058 4 7 +C3 C1 141095 0.848037 0.0458795 6 5 + +Composition SampleId +Cluster Value Frequency Typicality +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYLCA-ACCEPTOR-924 120 1 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMVPNP-ACCEPTOR-1688 60 0.807316 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW2B-ACCEPTOR-2660 60 0.742414 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHANTLE-ACCEPTOR-3110 60 0.719859 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGASTA-ACCEPTOR-6600 60 0.710249 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW1B-ACCEPTOR-2658 60 0.704572 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMIL1B-ACCEPTOR-3546 60 0.695147 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHBA4-ACCEPTOR-10696 60 0.691962 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHBA4-ACCEPTOR-6885 60 0.691962 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} MACHBA-ACCEPTOR-238 60 0.691962 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHY1A-ACCEPTOR-1380 60 0.679567 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOCII-ACCEPTOR-3131 60 0.672758 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCS1-ACCEPTOR-1814 60 0.668863 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-51551 60 0.668863 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYLCA-ACCEPTOR-3623 60 0.668316 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHEA-ACCEPTOR-2678 60 0.665954 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFA-ACCEPTOR-2140 60 0.661668 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFAB-ACCEPTOR-5619 60 0.661668 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFX-ACCEPTOR-1914 60 0.661668 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHB27B-ACCEPTOR-2421 60 0.661276 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-28732 60 0.660452 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOCIA-ACCEPTOR-634 60 0.657721 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGH-ACCEPTOR-1572 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-43393 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-6465 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHN-ACCEPTOR-1797 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHVA-ACCEPTOR-741 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-8990 60 0.650823 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-ACCEPTOR-31395 60 0.639565 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKER18-ACCEPTOR-5495 60 0.636296 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFAB-ACCEPTOR-1237 60 0.635085 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMIS-ACCEPTOR-1500 60 0.634371 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGAST2-ACCEPTOR-916 60 0.632934 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-ACCEPTOR-30776 60 0.632888 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHPARS1-ACCEPTOR-2519 60 0.628972 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-42976 60 0.623284 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOC2-ACCEPTOR-3143 60 0.622276 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOC2G-ACCEPTOR-2851 60 0.622276 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMOTNPI-ACCEPTOR-808 60 0.620495 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMUBILP-ACCEPTOR-1488 60 0.61957 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYLCA-ACCEPTOR-2214 120 0.617924 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-6683 60 0.617824 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-2023 60 0.61598 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-28878 60 0.615811 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGH-ACCEPTOR-1154 60 0.614791 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-6047 60 0.614791 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHN-ACCEPTOR-1379 60 0.614791 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCS1-ACCEPTOR-1396 60 0.614421 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-28313 60 0.614421 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-51135 60 0.614421 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-3161 60 0.61142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-3163 60 0.61142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-4089 60 0.61142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-2628 60 0.61142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-13046 60 0.602277 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBA-ACCEPTOR-2893 60 0.600912 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBB-ACCEPTOR-2827 60 0.600912 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMDES-ACCEPTOR-3015 60 0.599847 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAA-ACCEPTOR-2567 60 0.599827 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCYC1A-ACCEPTOR-2399 60 0.599564 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMOTNPI-ACCEPTOR-1091 60 0.597979 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAI1-ACCEPTOR-653 60 0.59795 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAIT-ACCEPTOR-398 60 0.59795 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-13642 60 0.596255 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-1386 60 0.595439 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMLYL1B-ACCEPTOR-3525 60 0.595105 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMH19-ACCEPTOR-2449 60 0.593758 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-21798 60 0.592178 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPPD-ACCEPTOR-1642 60 0.588925 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-2316 60 0.588105 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCS1-ACCEPTOR-800 60 0.58709 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-27717 60 0.58709 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-50539 60 0.58709 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYC3L-ACCEPTOR-766 60 0.583606 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHDRHA-ACCEPTOR-4276 60 0.583557 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMRASH-ACCEPTOR-3201 60 0.579787 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAACT-ACCEPTOR-1638 60 0.578528 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCD8A-ACCEPTOR-733 60 0.577575 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1GLY2-ACCEPTOR-2108 60 0.577502 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPIM1A-ACCEPTOR-1687 60 0.577005 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKAL2-ACCEPTOR-3494 60 0.576779 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCSPB-ACCEPTOR-1874 60 0.575135 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1GLY2-ACCEPTOR-3244 60 0.574888 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-34324 60 0.573464 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-42394 60 0.572879 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-14059 60 0.572476 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-4815 60 0.571718 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRCA-ACCEPTOR-5420 60 0.56965 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMETIF1-ACCEPTOR-1463 60 0.568823 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPPPA-ACCEPTOR-1721 60 0.568234 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMG0S19A-ACCEPTOR-2812 60 0.564955 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW2B-ACCEPTOR-885 60 0.564762 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPOMC-ACCEPTOR-4445 60 0.563651 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGH-ACCEPTOR-571 60 0.563186 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-5465 60 0.563186 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHN-ACCEPTOR-798 60 0.563186 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-883 60 0.563167 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-2851 60 0.561485 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPPD-ACCEPTOR-2473 60 0.561485 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTUBBM-ACCEPTOR-1265 60 0.559167 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} MACHBA-ACCEPTOR-587 60 0.558617 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYCRT-ACCEPTOR-1111 60 0.558265 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMEDHB17-ACCEPTOR-2346 60 0.555887 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1ATP-ACCEPTOR-11880 60 0.555555 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ORAIGECA-ACCEPTOR-583 60 0.554854 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHBA4-ACCEPTOR-11050 60 0.553779 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMANT1-ACCEPTOR-2955 60 0.553601 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKEREP-ACCEPTOR-4597 60 0.552419 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCD8A-ACCEPTOR-1670 60 0.550782 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMIS-ACCEPTOR-1697 60 0.550128 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPIM1A-ACCEPTOR-4496 60 0.549439 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOCIA-ACCEPTOR-1956 60 0.547693 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMRPS14-ACCEPTOR-5473 60 0.547499 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAACT-ACCEPTOR-3116 60 0.544522 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-1387 60 0.543139 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAP-ACCEPTOR-1501 60 0.542389 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMERP-ACCEPTOR-2578 60 0.54156 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMERPA-ACCEPTOR-2587 60 0.54156 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-6969 60 0.537959 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPOMC-ACCEPTOR-7483 60 0.537673 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGCB1-ACCEPTOR-1590 60 0.537557 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMH6-ACCEPTOR-955 60 0.535445 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-7876 60 0.534303 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOE4-ACCEPTOR-3751 60 0.532639 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} GCRHBBA1-ACCEPTOR-1385 60 0.532318 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMBNPA-ACCEPTOR-1629 60 0.532056 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBA-ACCEPTOR-1384 60 0.530608 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBB-ACCEPTOR-1318 60 0.530608 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-13504 60 0.53054 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAK1-ACCEPTOR-5504 60 0.527574 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} CHPIGECA-ACCEPTOR-585 60 0.527566 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-29793 60 0.526709 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHB27B-ACCEPTOR-690 60 0.526366 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMEDHB17-ACCEPTOR-3575 60 0.525136 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-2960 60 0.525128 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-2962 60 0.525128 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-3889 60 0.525128 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-2330 60 0.524874 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYLCA-ACCEPTOR-2864 60 0.524853 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRF1A-ACCEPTOR-4965 60 0.523646 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMVPNP-ACCEPTOR-2057 60 0.522772 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMH19-ACCEPTOR-2224 60 0.522369 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-13878 60 0.522162 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-19200 60 0.520076 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMH6-ACCEPTOR-2724 60 0.518424 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMG0S19B-ACCEPTOR-2948 60 0.518104 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMP45C17-ACCEPTOR-4146 60 0.517318 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRF1A-ACCEPTOR-3240 60 0.516876 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYCC-ACCEPTOR-4476 60 0.515802 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRCA-ACCEPTOR-5180 60 0.515615 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCYC1A-ACCEPTOR-3478 60 0.514893 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHEA-ACCEPTOR-869 60 0.514584 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-22870 60 0.514455 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHEA-ACCEPTOR-2278 60 0.513904 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOA4A-ACCEPTOR-2286 60 0.513796 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMP45C17-ACCEPTOR-7956 60 0.513289 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHPARS1-ACCEPTOR-2888 60 0.513115 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHPARS1-ACCEPTOR-4612 60 0.513115 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTUBAG-ACCEPTOR-2405 60 0.511893 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCAPG-ACCEPTOR-2733 60 0.511787 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMG0S19A-ACCEPTOR-3347 60 0.511271 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHST-ACCEPTOR-4120 60 0.510521 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-2297 60 0.5098 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-2298 60 0.5098 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-3226 60 0.5098 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-1763 60 0.5098 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKEREP-ACCEPTOR-3671 60 0.509423 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSHBGA-ACCEPTOR-3120 60 0.508779 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1GLY2-ACCEPTOR-3502 60 0.507788 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMINCP-ACCEPTOR-3281 60 0.506995 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMG0S19B-ACCEPTOR-3484 60 0.506617 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-2427 60 0.506452 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-1648 60 0.505004 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-1649 60 0.505004 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-2577 60 0.505004 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-1114 60 0.505004 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} BABAPOE-ACCEPTOR-3437 60 0.504555 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACTGA-ACCEPTOR-1621 60 0.498671 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAK1-DONOR-954 60 0.498442 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSPRO-ACCEPTOR-4385 60 0.498078 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMIS-ACCEPTOR-1185 60 0.496597 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCRYGBC-ACCEPTOR-2225 60 0.495759 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-32821 60 0.495575 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHBA4-ACCEPTOR-7232 60 0.494621 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMP3A-ACCEPTOR-2544 60 0.49459 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOCII-ACCEPTOR-3595 60 0.494291 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMEDHB17-ACCEPTOR-3311 60 0.494008 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-32395 60 0.493883 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHST-ACCEPTOR-3478 60 0.492662 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMRP14A-ACCEPTOR-3418 60 0.4923 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-646 60 0.492263 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-851 60 0.491722 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAA-ACCEPTOR-3726 60 0.489703 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKEREP-ACCEPTOR-2070 60 0.488328 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRT1A-ACCEPTOR-209 60 0.486869 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMOPS-ACCEPTOR-4065 60 0.485301 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHANTLE-ACCEPTOR-1363 60 0.48246 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW1B-ACCEPTOR-882 60 0.48104 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-2283 60 0.480726 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} GCRHBBA6-ACCEPTOR-590 60 0.480466 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-4549 60 0.480089 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHMG14A-ACCEPTOR-1361 60 0.47778 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-21144 60 0.47737 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAI1-ACCEPTOR-1647 60 0.476807 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAIT-ACCEPTOR-1394 60 0.476807 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMDES-ACCEPTOR-1920 60 0.476694 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHPARS1-ACCEPTOR-6470 60 0.47495 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} TARHBB-ACCEPTOR-1686 60 0.474423 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAACT-ACCEPTOR-2578 60 0.473247 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} LEMHBB-ACCEPTOR-1437 60 0.473067 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMIL1AG-NEG-4981 60 0.472962 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-ACCEPTOR-19737 60 0.472385 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOC2-ACCEPTOR-3599 60 0.471727 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOC2G-ACCEPTOR-3309 60 0.471727 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} BABAPOE-ACCEPTOR-801 60 0.470295 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOE4-ACCEPTOR-1818 60 0.470295 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-2041 60 0.470285 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-2042 60 0.470285 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-2970 60 0.470285 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-1507 60 0.470285 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCSPB-ACCEPTOR-2217 60 0.469733 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAI1-ACCEPTOR-902 60 0.468478 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAIT-ACCEPTOR-648 60 0.468478 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKEREP-ACCEPTOR-3451 60 0.466907 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAP-ACCEPTOR-2927 60 0.466159 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-2440 60 0.465655 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPPD-ACCEPTOR-2062 60 0.465655 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} LEMHBB-ACCEPTOR-346 60 0.46504 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHB27B-ACCEPTOR-2053 60 0.463333 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-20042 60 0.462236 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCAPG-ACCEPTOR-1733 60 0.461213 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGCB1-ACCEPTOR-6555 60 0.460594 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFAB-ACCEPTOR-1431 60 0.46049 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-20331 60 0.459479 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-35070 60 0.459113 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0.453805 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCFVII-ACCEPTOR-8277 60 0.451886 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMINCP-ACCEPTOR-2049 60 0.4517 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMOPS-ACCEPTOR-5138 60 0.451611 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTUBBM-ACCEPTOR-1078 60 0.451442 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} BABA1AT-NEG-181 60 0.450798 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1ATP-ACCEPTOR-9382 60 0.450062 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKAL2-ACCEPTOR-1745 60 0.448918 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMFOS-ACCEPTOR-1753 60 0.447885 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW2B-ACCEPTOR-2263 60 0.445554 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMETIF1-ACCEPTOR-1065 60 0.444313 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1GLY2-ACCEPTOR-4746 60 0.443999 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRCA-ACCEPTOR-5063 60 0.443687 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSPRO-ACCEPTOR-1827 60 0.442428 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMH19-ACCEPTOR-2643 60 0.441902 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAP-ACCEPTOR-993 60 0.440744 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMRPS17A-ACCEPTOR-3688 60 0.439852 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAA-ACCEPTOR-1929 60 0.439795 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGASTA-ACCEPTOR-6254 60 0.438789 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMRASH-ACCEPTOR-2012 60 0.437795 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-12537 60 0.436487 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSHBGA-ACCEPTOR-4319 60 0.436322 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-ACCEPTOR-25978 60 0.435703 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACTGA-ACCEPTOR-886 60 0.435572 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCKMT-ACCEPTOR-1127 60 0.435355 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMLYL1B-DONOR-2543 60 0.435339 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-22617 60 0.434805 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCD8A-DONOR-1087 60 0.433666 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-1703 60 0.433157 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-11871 60 0.43197 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ORAHBD-ACCEPTOR-596 60 0.429899 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCKMT-ACCEPTOR-4984 60 0.429206 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-2926 60 0.427364 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSHBGA-ACCEPTOR-5188 60 0.42694 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPRTA-ACCEPTOR-774 60 0.426426 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-26314 60 0.424649 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ATRINS-ACCEPTOR-701 60 0.42437 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMH6-ACCEPTOR-3284 60 0.42424 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-15000 60 0.421179 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} CHPIGECA-ACCEPTOR-989 60 0.420891 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAA-ACCEPTOR-606 60 0.419351 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHDRHA-ACCEPTOR-3705 60 0.418727 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACTGA-ACCEPTOR-1104 60 0.418518 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFA-ACCEPTOR-1558 60 0.417381 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFAB-ACCEPTOR-5037 60 0.417381 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0.409674 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPNMTA-ACCEPTOR-3129 60 0.409385 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPPD-ACCEPTOR-421 60 0.407802 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFAB-ACCEPTOR-1784 60 0.407183 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAP-ACCEPTOR-2374 60 0.406872 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPRTA-ACCEPTOR-2558 60 0.406235 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACTGA-ACCEPTOR-2414 60 0.405939 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRCA-ACCEPTOR-8223 60 0.405762 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ORAHBD-ACCEPTOR-1713 60 0.405573 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSPRO-ACCEPTOR-3195 60 0.405136 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKER18-ACCEPTOR-5107 60 0.404959 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-2698 60 0.40241 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOCIA-ACCEPTOR-4946 60 0.401942 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMIL1B-ACCEPTOR-5693 60 0.4015 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-7024 60 0.400632 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} BABAPOE-ACCEPTOR-2624 60 0.40034 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSPRO-ACCEPTOR-2022 60 0.398598 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCFVII-ACCEPTOR-4264 60 0.397687 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMT2A-ACCEPTOR-1047 60 0.397663 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMIRBPG-ACCEPTOR-6987 60 0.397178 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-1861 60 0.396108 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAACT-ACCEPTOR-2337 60 0.39516 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPRTA-ACCEPTOR-2252 60 0.394645 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAK1-ACCEPTOR-6626 60 0.391114 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTCBA-NEG-2161 60 0.390157 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSHBGA-DONOR-2724 60 0.388838 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCACY-ACCEPTOR-1973 60 0.387483 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMT2A-ACCEPTOR-1454 60 0.386905 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-21467 60 0.386186 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPNMTA-ACCEPTOR-3450 60 0.384614 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ORAIGECA-ACCEPTOR-990 60 0.384451 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCKMT-ACCEPTOR-2362 60 0.382668 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGRP78-ACCEPTOR-775 60 0.382554 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCSPB-ACCEPTOR-3118 60 0.380243 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPPPA-ACCEPTOR-2425 60 0.379829 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-2698 60 0.378295 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HUMALPPD-ACCEPTOR-1845 60 0.373728 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-9160 60 0.373266 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHMG14A-ACCEPTOR-1599 60 0.372702 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPIM1A-ACCEPTOR-1335 60 0.371644 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-ACCEPTOR-23573 60 0.37074 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW1B-ACCEPTOR-3217 60 0.36768 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW2B-ACCEPTOR-3219 60 0.36768 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBA-ACCEPTOR-1121 60 0.367045 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBB-ACCEPTOR-1057 60 0.367045 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, 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HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} LEMHBDPS-NEG-1441 60 0.253526 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKER18-ACCEPTOR-4128 60 0.251891 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAA-ACCEPTOR-4156 60 0.24649 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMINHBB2-NEG-301 60 0.24507 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSPRO-DONOR-4152 60 0.244075 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-666 60 0.241636 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPLPSPC-ACCEPTOR-2176 60 0.240586 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPGAMMG-ACCEPTOR-1450 60 0.237702 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMFVII-DONOR-70 60 0.234375 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHSP90B-ACCEPTOR-5391 60 0.224127 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-1369 60 0.223239 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-12318 60 0.220958 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-3090 60 0.217513 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCRYGBC-ACCEPTOR-19693 60 0.215237 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMOPS-ACCEPTOR-2409 60 0.213952 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-DONOR-18038 60 0.20387 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOA4A-NEG-3241 60 0.201438 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCAPG-ACCEPTOR-2044 60 0.200837 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMDAFA-ACCEPTOR-1235 60 0.189343 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMARC6-NEG-61 60 0.18923 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCSPB-ACCEPTOR-1260 60 0.188817 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPGAMMG-ACCEPTOR-3519 60 0.169025 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMUBILP-ACCEPTOR-1158 60 0.152056 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMDES-ACCEPTOR-2164 60 0.144288 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-18496 120 1 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYC3L-ACCEPTOR-4242 120 0.730679 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-27861 60 0.577657 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-6362 60 0.573496 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-9613 60 0.528335 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-31587 60 0.526001 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-9611 60 0.524991 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCSFGMA-ACCEPTOR-891 60 0.508401 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIFNINI-ACCEPTOR-3147 60 0.50808 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5-ACCEPTOR-2088 60 0.503937 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5A-ACCEPTOR-2091 60 0.503937 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYLCA-ACCEPTOR-1831 120 0.503081 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-NEG-841 60 0.49555 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHMG14A-ACCEPTOR-4663 60 0.478026 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2-ACCEPTOR-5027 60 0.470244 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2A-ACCEPTOR-5980 60 0.470244 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2B-ACCEPTOR-4857 60 0.470244 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYLCA-ACCEPTOR-2481 120 0.469061 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMANFA-ACCEPTOR-785 60 0.451328 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-8745 60 0.449656 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHMG14A-ACCEPTOR-6789 60 0.44948 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-41424 60 0.44269 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-23298 60 0.438452 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2-ACCEPTOR-685 60 0.436068 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2A-ACCEPTOR-1623 60 0.436068 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2B-ACCEPTOR-500 60 0.436068 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5-ACCEPTOR-1853 60 0.435726 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5A-ACCEPTOR-1857 60 0.435726 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-6914 60 0.433626 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMP45C17-ACCEPTOR-5038 60 0.432812 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIFNINI-ACCEPTOR-3654 60 0.431178 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYCAA-ACCEPTOR-1188 60 0.429827 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-ACCEPTOR-6540 60 0.425878 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFABP-ACCEPTOR-4068 60 0.424722 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTNFA-ACCEPTOR-1791 60 0.419724 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTNFAB-ACCEPTOR-5270 60 0.419724 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTNFX-ACCEPTOR-1565 60 0.419724 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGG-ACCEPTOR-3631 60 0.418993 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-20604 60 0.418256 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-14589 60 0.417787 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5-ACCEPTOR-875 60 0.416369 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5A-ACCEPTOR-874 60 0.416369 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-19790 60 0.414569 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTUBAG-ACCEPTOR-2034 60 0.41417 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH1-ACCEPTOR-4714 60 0.413492 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-7729 60 0.409175 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-5606 60 0.407047 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-8083 60 0.406823 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-17940 60 0.406505 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-9261 60 0.404366 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHANTLE-ACCEPTOR-4055 60 0.404266 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-16955 60 0.403283 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCSFGMA-ACCEPTOR-2554 60 0.402956 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTHB-ACCEPTOR-20387 60 0.401091 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFABP-ACCEPTOR-2320 60 0.400253 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH2-ACCEPTOR-3802 60 0.396843 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-10045 60 0.396731 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-3526 60 0.396334 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-7965 60 0.395966 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-NEG-44641 60 0.395507 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-13672 60 0.394705 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-5022 60 0.3911 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-16911 60 0.389185 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBGA-ACCEPTOR-425 60 0.38841 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMRPS14-ACCEPTOR-2108 60 0.388082 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-3641 60 0.38449 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPARS1-ACCEPTOR-3750 60 0.383927 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-2326 60 0.383652 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-4464 60 0.380945 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSHBGA-ACCEPTOR-2856 60 0.38037 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-7572 60 0.380092 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSAA1A-ACCEPTOR-5878 60 0.379426 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-6717 60 0.379015 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-18319 60 0.374579 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFABP-ACCEPTOR-3516 60 0.373489 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBE-ACCEPTOR-1474 60 0.371457 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-12451 60 0.36526 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-3391 60 0.363095 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGG-ACCEPTOR-5138 60 0.36303 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHEA-ACCEPTOR-3890 60 0.362827 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCD8A-ACCEPTOR-2860 60 0.362089 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-4046 60 0.361307 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFERP1-NEG-961 60 0.360053 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-16573 60 0.359774 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDRHA-ACCEPTOR-2969 60 0.358973 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBG-ACCEPTOR-364 60 0.356635 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-12016 60 0.356078 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMETN3-ACCEPTOR-391 60 0.354597 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDOB-ACCEPTOR-4486 60 0.348945 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFOS-ACCEPTOR-2658 60 0.348561 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-11925 60 0.346093 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMCR-NEG-5341 60 0.346045 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMKEREP-ACCEPTOR-3986 60 0.345931 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH2-ACCEPTOR-1848 60 0.343595 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-20185 60 0.342948 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGRP78-ACCEPTOR-2145 60 0.342594 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGFP40H-ACCEPTOR-4066 60 0.341784 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMDES-ACCEPTOR-5413 60 0.341457 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHIAPPA-ACCEPTOR-5862 60 0.341092 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-6266 60 0.340191 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-14947 60 0.339526 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMH6-ACCEPTOR-2326 60 0.339413 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMH6-ACCEPTOR-3673 60 0.33908 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-1522 60 0.338644 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMRPS14-ACCEPTOR-4137 60 0.337323 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIFNINI-ACCEPTOR-2785 60 0.336713 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFOS-ACCEPTOR-2436 60 0.336286 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-5816 60 0.335618 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-4173 60 0.333856 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-2605 60 0.333307 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMADAG-ACCEPTOR-32543 60 0.332175 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMADAM2-ACCEPTOR-784 60 0.332175 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMRP14A-ACCEPTOR-1386 60 0.332092 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH1-ACCEPTOR-2738 60 0.331081 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGFP40H-ACCEPTOR-2431 60 0.330853 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-28261 60 0.329334 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-34907 60 0.328365 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYCC-ACCEPTOR-6624 60 0.327883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYCRT-ACCEPTOR-3258 60 0.327883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-13888 60 0.327525 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDOB-ACCEPTOR-3357 60 0.326837 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTUBAG-ACCEPTOR-2858 60 0.324437 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGCB1-ACCEPTOR-3009 60 0.324428 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDOB-ACCEPTOR-4124 60 0.324237 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-33757 60 0.32267 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-9259 60 0.322183 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-1782 60 0.319904 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-6011 60 0.317729 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMLACTA-ACCEPTOR-1512 60 0.317317 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-4093 60 0.3168 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-2160 60 0.316433 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-17658 60 0.316319 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-2534 60 0.315557 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGCB1-ACCEPTOR-5810 60 0.315485 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-ACCEPTOR-1506 60 0.312939 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-39785 60 0.312902 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-2796 60 0.312852 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBG-ACCEPTOR-1452 60 0.311668 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCFVII-ACCEPTOR-9389 60 0.311455 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-6772 60 0.309103 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-5127 60 0.308214 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ORAHBG2F-ACCEPTOR-1492 60 0.307707 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMLACTA-ACCEPTOR-2160 60 0.307494 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYCAA-ACCEPTOR-1466 60 0.306279 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-14750 60 0.304251 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEXCI5R-NEG-1321 60 0.303173 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHANTLE-ACCEPTOR-2733 60 0.303118 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-40003 60 0.302995 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPARS1-ACCEPTOR-5472 60 0.302926 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} MACHBCA2-ACCEPTOR-1693 60 0.301896 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} MACHBGA1-ACCEPTOR-1692 60 0.301896 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGFP40H-ACCEPTOR-992 60 0.301338 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBGA-ACCEPTOR-1498 60 0.299939 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} GIBHBGGL-ACCEPTOR-8438 60 0.298305 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPIM1A-ACCEPTOR-3558 60 0.297954 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW1B-ACCEPTOR-3569 60 0.297524 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW2B-ACCEPTOR-3570 60 0.297524 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHBBAG-ACCEPTOR-2565 60 0.294661 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-3176 60 0.29333 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCRYGBC-ACCEPTOR-5283 60 0.293221 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPMPC-NEG-1 60 0.292416 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCP21OH-ACCEPTOR-1838 60 0.292408 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCP21OHC-ACCEPTOR-1839 60 0.292408 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCP42-ACCEPTOR-2767 60 0.292408 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCP52-ACCEPTOR-1304 60 0.292408 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-9633 60 0.292194 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMRPS17A-ACCEPTOR-2225 60 0.292188 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCD8A-ACCEPTOR-5474 60 0.291103 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCKMT-ACCEPTOR-2011 60 0.290952 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-4265 60 0.290768 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-22075 60 0.290713 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATP1A2-ACCEPTOR-15320 60 0.289713 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGRP78-ACCEPTOR-2347 60 0.289521 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-32398 60 0.289239 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-32974 60 0.287444 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-ACCEPTOR-879 60 0.285857 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHMG14A-ACCEPTOR-1487 60 0.285102 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMKAL2-ACCEPTOR-5424 60 0.281868 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMDES-ACCEPTOR-1763 60 0.281318 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-NEG-241 60 0.280175 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-6511 60 0.27941 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPSAA-ACCEPTOR-5669 60 0.279298 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} GIBHBGGL-ACCEPTOR-3506 60 0.276742 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ORAHBG1F-ACCEPTOR-1486 60 0.276742 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHIAPPA-ACCEPTOR-959 60 0.276304 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} TARHBD-ACCEPTOR-1884 60 0.275875 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW1B-ACCEPTOR-3357 60 0.275761 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW2B-ACCEPTOR-3359 60 0.275761 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW1B-ACCEPTOR-2261 60 0.274893 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATP1A2-ACCEPTOR-13550 60 0.27238 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDOB-ACCEPTOR-2649 60 0.272057 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-4869 60 0.270289 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCRPG-ACCEPTOR-578 60 0.270099 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCRPGA-ACCEPTOR-539 60 0.270099 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMA1GLY2-ACCEPTOR-2469 60 0.269395 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMANFA-ACCEPTOR-2205 60 0.268908 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-10837 60 0.268677 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-ACCEPTOR-4293 60 0.268442 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMKER18-ACCEPTOR-6098 60 0.266796 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-9414 60 0.265728 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-5471 60 0.264984 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGRP78-ACCEPTOR-2848 60 0.262655 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGCB1-ACCEPTOR-3353 60 0.262447 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2-ACCEPTOR-3038 60 0.259094 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2A-ACCEPTOR-3975 60 0.259094 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2B-ACCEPTOR-2852 60 0.259094 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMRP8A-ACCEPTOR-2302 60 0.257776 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-2520 60 0.255664 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGG-ACCEPTOR-1802 60 0.254909 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ORAHBG2F-ACCEPTOR-392 60 0.253766 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-3351 60 0.25349 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-5216 60 0.253095 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-23130 60 0.251929 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHBBAG-ACCEPTOR-1466 60 0.251416 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHBBGG-ACCEPTOR-2363 60 0.251416 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCFXI14-NEG-481 60 0.250649 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-29440 60 0.250365 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGCB1-ACCEPTOR-949 60 0.250084 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMLDL100-NEG-5941 60 0.247957 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-12808 60 0.245251 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-15504 60 0.245117 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTCBYX-NEG-1 60 0.244476 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} GIBHBGGL-ACCEPTOR-2401 60 0.243883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} GIBHBGGL-ACCEPTOR-7321 60 0.243883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} MACHBCA2-ACCEPTOR-586 60 0.243883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} MACHBGA1-ACCEPTOR-588 60 0.243883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCRYGBC-ACCEPTOR-18489 60 0.243771 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-2347 60 0.243234 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-3793 60 0.24098 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-2698 60 0.239165 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSPRO-ACCEPTOR-2841 60 0.233964 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATP1A2-ACCEPTOR-10913 60 0.232816 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-13834 60 0.230933 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMKER18-ACCEPTOR-4857 60 0.228166 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-12121 60 0.228104 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-NEG-12121 60 0.228104 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMDES-ACCEPTOR-2504 60 0.227709 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-7462 60 0.226615 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATP1A2-ACCEPTOR-24266 60 0.225667 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHANTLE-ACCEPTOR-3667 60 0.225532 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHANTLE-ACCEPTOR-3842 60 0.224849 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBE-ACCEPTOR-410 60 0.223502 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-6976 60 0.220991 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HUMSAA-ACCEPTOR-3086 60 0.20519 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-5710 60 0.204474 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ORAHBG1F-ACCEPTOR-391 60 0.202291 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTHB-ACCEPTOR-9825 60 0.201962 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGRP78-ACCEPTOR-3371 60 0.198741 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-2727 60 0.197933 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMA1ATP-ACCEPTOR-10909 60 0.194381 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCACY-ACCEPTOR-2504 60 0.190094 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-4604 60 0.187086 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCKMT-ACCEPTOR-4348 60 0.186354 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-7858 60 0.186149 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHN-ACCEPTOR-1166 60 0.182689 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDRHA-ACCEPTOR-5181 60 0.182387 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-5220 60 0.182051 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSAA1A-ACCEPTOR-6290 60 0.181613 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-2062 60 0.181011 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCS1-ACCEPTOR-1184 60 0.179489 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHCSA-ACCEPTOR-28101 60 0.179489 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHCSA-ACCEPTOR-50922 60 0.179489 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-31083 60 0.179318 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPLAPL-ACCEPTOR-987 60 0.17763 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMINCPPS-ACCEPTOR-4028 60 0.175924 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCATF-ACCEPTOR-1108 60 0.175906 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHCSA-ACCEPTOR-42765 60 0.174844 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIRBPG-ACCEPTOR-4936 60 0.174692 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFBRAA-NEG-1 60 0.174648 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGH-ACCEPTOR-941 60 0.171989 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHCSA-ACCEPTOR-5835 60 0.171989 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPLPSPC-ACCEPTOR-2621 60 0.16798 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCKMT-ACCEPTOR-1529 60 0.167938 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-3649 60 0.167069 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMP45C17-ACCEPTOR-6267 60 0.163697 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTUBBM-ACCEPTOR-1850 60 0.161173 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSHBGA-ACCEPTOR-5640 60 0.159211 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-2965 60 0.157783 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMRPS17A-ACCEPTOR-1611 60 0.154849 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMP45C17-ACCEPTOR-5819 60 0.152551 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-5972 60 0.143691 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTHB-ACCEPTOR-20170 60 0.139823 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMKEREP-ACCEPTOR-2715 60 0.139727 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMP45C17-ACCEPTOR-3774 60 0.139411 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-DONOR-12309 60 0.136031 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMINSRC-NEG-2101 60 0.123606 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-DONOR-3748 60 0.105842 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALPPD-ACCEPTOR-985 60 0.0983941 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGFP40H-ACCEPTOR-1110 60 0.093152 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMADAG-ACCEPTOR-35621 60 0.0629178 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ATRINS-DONOR-905 60 1 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMERP-DONOR-598 60 0.936729 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMERPA-DONOR-608 60 0.936729 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMKEREP-DONOR-3892 60 0.921043 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMSAACT-DONOR-2210 60 0.887319 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMOTNPI-DONOR-508 60 0.877711 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMTUBBM-DONOR-1187 60 0.866743 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMMYLCA-DONOR-952 120 0.863528 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMMYLCA-DONOR-1975 120 0.851513 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMCFVII-DONOR-4425 60 0.839879 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMH19-DONOR-2128 60 0.83566 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMHMG14A-DONOR-1039 60 0.829852 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMSAACT-DONOR-2770 60 0.823351 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMHBA4-DONOR-10579 60 0.819388 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMHBA4-DONOR-6768 60 0.819388 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} MACHBA-DONOR-121 60 0.819388 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMRPS17A-DONOR-615 60 0.816926 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HUMGLTH1-NEG-241, ...} HUMCFXII3-NEG-3061 60 0.638933 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGP-NEG-1321 60 0.638616 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAK1-NEG-10921 60 0.63861 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDPBF-NEG-61 60 0.636501 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMINSR2-NEG-1 60 0.636159 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCOL11A2-NEG-1861 60 0.635547 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMLDLRRL-NEG-11761 60 0.635234 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMERPA-NEG-1021 60 0.632105 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} AGMORS12A-NEG-181 60 0.630794 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCW1B-ACCEPTOR-1398 60 0.627346 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRYR-NEG-14761 60 0.626131 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCW2B-ACCEPTOR-1399 60 0.625866 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTHX-NEG-841 60 0.624685 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCB44A-NEG-361 60 0.623294 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHSP70D-NEG-1441 60 0.622686 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGA7331-NEG-241 60 0.621472 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPRC1-NEG-61 60 0.615076 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRO2-NEG-2461 60 0.611545 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTKRA-NEG-12841 60 0.611373 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSPBAA-NEG-7621 60 0.610747 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMYCF1-NEG-1081 60 0.610166 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMC3A1R-NEG-2941 60 0.609837 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMANK-NEG-1981 60 0.609299 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ORAINVOL-NEG-2161 60 0.607313 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRYR-NEG-10201 60 0.604986 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFESFPS-NEG-6661 60 0.60381 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRBC-NEG-661 60 0.602639 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMERVKA2-NEG-901 60 0.601828 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPROD4-NEG-721 60 0.601453 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGSTPIG-NEG-1381 60 0.600441 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMBHAA-NEG-1261 60 0.597786 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHCKA-NEG-61 60 0.596682 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMINT2-NEG-5041 60 0.596642 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRGE-NEG-1801 60 0.596539 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUM4F2HG1-NEG-1441 60 0.595573 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMYONM-NEG-1501 60 0.595457 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGKAR-NEG-181 60 0.595235 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMME491-NEG-601 60 0.594795 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMET-NEG-301 60 0.594066 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMATP-NEG-1981 60 0.593467 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMLYL1B-NEG-4081 60 0.592491 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHHLA-NEG-241 60 0.59232 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} MACHBB-NEG-4141 60 0.592033 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMK562B-NEG-1 60 0.591461 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPGKA09-NEG-241 60 0.591298 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCG5B-NEG-1261 60 0.591287 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMEAR2-NEG-1381 60 0.590382 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTHMOD-NEG-421 60 0.590178 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} CHPAZGLO-NEG-1 60 0.58832 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMOCS1-NEG-1 60 0.587078 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCRYGBC-NEG-3781 60 0.585827 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMPO1-NEG-1681 60 0.584433 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALPPB-NEG-781 60 0.581389 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPRTA-NEG-1981 60 0.58081 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSERDHY-NEG-241 60 0.580403 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMKER654-NEG-1441 60 0.579821 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNFM-NEG-1921 60 0.578655 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPROD1-NEG-1741 60 0.576028 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSAP1-NEG-121 60 0.575954 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCCK2-NEG-361 60 0.574334 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMETIA-NEG-1921 60 0.57372 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCP21OH-NEG-1981 60 0.573254 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALDC-NEG-61 60 0.572454 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMASP-NEG-541 60 0.572103 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHEA-ACCEPTOR-1381 60 0.570858 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPOVRA-NEG-541 60 0.570383 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCP21OHC-NEG-3061 60 0.569291 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMOTNPI-NEG-481 60 0.567914 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} GCRHBBA1-NEG-61 60 0.566461 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUM5FDX-NEG-2041 60 0.566376 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMATP1A2-NEG-20341 60 0.566069 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMRP8A-NEG-1381 60 0.565139 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHSP90-NEG-1 60 0.564402 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCNRAB-NEG-1741 60 0.563859 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHLA1EA-NEG-121 60 0.56365 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMKERP2-NEG-1381 60 0.563645 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMVWFA04-NEG-301 60 0.563226 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGLYSA-NEG-2761 60 0.562739 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMDB1A-NEG-121 60 0.561872 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMYTEST-NEG-301 60 0.56173 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCP21OHA-NEG-2521 60 0.561605 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCYP450-NEG-7321 60 0.561589 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHBB-NEG-5761 60 0.561095 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCAG-NEG-1 60 0.559351 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGNPAS-NEG-61 60 0.558987 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMBAT2A-NEG-2761 60 0.558829 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMITI2-NEG-481 60 0.555789 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMC1A1-NEG-3901 60 0.554961 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPLAP2A-NEG-481 60 0.554923 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRBK-NEG-1321 60 0.553868 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAK1-NEG-6361 60 0.552713 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMOPS-NEG-181 60 0.55095 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCAPG-NEG-1261 60 0.550645 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMPO-NEG-301 60 0.55 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSPM1-NEG-181 60 0.549277 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMVDR-NEG-1441 60 0.548411 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMLD78R-NEG-1 60 0.548213 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRB4A-NEG-781 60 0.54793 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSAACT-NEG-301 60 0.547872 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRNP70K-NEG-1141 60 0.542581 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTPA-NEG-8761 60 0.542076 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMACROS-NEG-1 60 0.541741 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPOA4C-NEG-2761 60 0.540684 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFVII-ACCEPTOR-136 60 0.540189 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHPA2BR-NEG-1261 60 0.539447 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGLI-NEG-901 60 0.539067 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCSPCP-NEG-1261 60 0.539035 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGHBU-NEG-301 60 0.538728 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMVIL2-NEG-1441 60 0.53867 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGRP78-NEG-421 60 0.537038 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFGRINT-NEG-421 60 0.536192 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMC4A1-NEG-2761 60 0.535468 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHER2A-NEG-3301 60 0.534568 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFNRA-NEG-361 60 0.534468 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMBCRD-NEG-301 60 0.531861 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNMYCA-NEG-4561 60 0.531372 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMLAMP1A-NEG-841 60 0.531161 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMARF1A-NEG-301 60 0.531054 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHC1-NEG-4321 60 0.531018 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMLC2A-NEG-1021 60 0.530411 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} TARHBD-NEG-1981 60 0.528991 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFCERA-NEG-121 60 0.528249 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCP42-NEG-4501 60 0.527965 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRCC1B-NEG-361 60 0.527918 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGCD2-NEG-121 60 0.527869 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNMYCA-NEG-1 60 0.526789 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHM71-NEG-181 60 0.526673 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPOCIP-NEG-301 60 0.526387 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTPO03-NEG-61 60 0.524902 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHANTLE-NEG-3181 60 0.523904 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMH6-ACCEPTOR-1451 60 0.523076 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTRKR-NEG-1201 60 0.521661 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHB27B-ACCEPTOR-1202 60 0.521247 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMACCYBB-NEG-3481 60 0.520913 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHMPFK-NEG-901 60 0.520401 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDC3B-NEG-2461 60 0.520127 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCACA-NEG-181 60 0.518995 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMUPAX-NEG-3541 60 0.517163 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPLA-NEG-1261 60 0.516815 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFNRAS-NEG-601 60 0.514218 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCG2A1-NEG-4561 60 0.512408 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} BABBMYHA-NEG-301 60 0.5123 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGFIIM-NEG-121 60 0.511551 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMP68-NEG-1081 60 0.508347 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCRYGBC-NEG-12901 60 0.505862 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRUN1C2-NEG-241 60 0.503968 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHPRTB-NEG-49201 60 0.503533 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMITF1-NEG-601 60 0.503075 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} GCRHBBA6-ACCEPTOR-920 60 0.502875 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHLACW2-NEG-781 60 0.502454 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPOE3-NEG-1 60 0.502005 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTM-NEG-1441 60 0.500045 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPAIB1-NEG-61 60 0.500011 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCKMT-NEG-1321 60 0.498609 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCAL-NEG-301 60 0.497528 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCY4ARO-NEG-541 60 0.497183 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCKMM1-NEG-1921 60 0.49656 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIDB-NEG-241 60 0.494582 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPMGR-NEG-1621 60 0.493996 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} CHPMHCBAA-NEG-841 60 0.493881 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMELANA-NEG-1261 60 0.493383 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCARN-NEG-1561 60 0.493224 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIRBPG-NEG-3781 60 0.492968 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCSIST-NEG-2221 60 0.492948 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHANTLE-DONOR-2150 60 0.492384 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTHB-NEG-17281 60 0.490249 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALIPOA-NEG-781 60 0.489179 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPIM1A-NEG-1741 60 0.487817 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMELAM1A-NEG-1141 60 0.484116 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCOL1A07-NEG-1 60 0.48399 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMDRD2A-NEG-841 60 0.483173 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTHR-NEG-1501 60 0.482949 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMYHC5-NEG-1141 60 0.479433 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMP42LA-NEG-2161 60 0.479234 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNFH3-NEG-1 60 0.478608 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} MACPEPSG-NEG-2161 60 0.47836 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHANTLE-ACCEPTOR-1874 60 0.478333 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCW3-NEG-2761 60 0.476873 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTGFKA-NEG-1141 60 0.476397 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ATRINS-ACCEPTOR-1678 60 0.47497 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHAWB-NEG-721 60 0.470735 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGGTA-NEG-661 60 0.469272 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMACTGP1-NEG-481 60 0.466362 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMDBHRA-NEG-1321 60 0.465497 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMBAT3B1-NEG-421 60 0.463285 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRETREC-NEG-1321 60 0.462935 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRENA3-NEG-361 60 0.458819 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMA1AR2-NEG-3601 60 0.45875 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHEXB01-NEG-481 60 0.458418 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCCWB-NEG-241 60 0.456771 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMH6-DONOR-1727 60 0.45546 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPOMC-NEG-421 60 0.455367 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHLADP4-NEG-721 60 0.455258 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCW2B-DONOR-1675 60 0.454601 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCYC1A-NEG-4381 60 0.454124 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGHVH-NEG-481 60 0.452907 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGHCSA-NEG-38521 60 0.451735 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMYP-NEG-961 60 0.451107 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPEP7-NEG-1 60 0.450832 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHB27B-DONOR-1478 60 0.45057 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCW1B-DONOR-1674 60 0.45057 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} MACHBA-NEG-421 60 0.45047 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTPOB-NEG-2401 60 0.449725 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFXIIIA-NEG-181 60 0.448107 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMDEF1AA-NEG-121 60 0.447858 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPRCM-NEG-1681 60 0.444207 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMENOG-NEG-121 60 0.444163 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCD7-NEG-241 60 0.443271 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRBS-NEG-601 60 0.442033 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPIGF2A-NEG-301 60 0.440604 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRAFR-NEG-2521 60 0.438858 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} GCRHBBA1-ACCEPTOR-1706 60 0.437573 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIHRP-NEG-541 60 0.437054 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAMYA1-NEG-541 60 0.434972 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHKB3R-NEG-121 60 0.43311 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGFIA1-NEG-841 60 0.432569 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUM4COLA-NEG-1021 60 0.432322 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCKMT-NEG-5881 60 0.431546 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGFII5-NEG-121 60 0.43117 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGHVAQ-NEG-61 60 0.430864 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMVWFR1-NEG-3481 60 0.43037 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGPMSS-NEG-1501 60 0.429191 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRGRSALU-NEG-1981 60 0.427772 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGCBPRC-NEG-1501 60 0.427473 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRAP2-NEG-181 60 0.42676 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMBPA-NEG-661 60 0.425258 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTHBNA-NEG-961 60 0.42383 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHLDPWB-NEG-61 60 0.423603 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMINCP-NEG-3121 60 0.42321 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSPIA04-NEG-61 60 0.423163 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIL2RA-NEG-2161 60 0.422166 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHA44G-NEG-2701 60 0.422101 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGELS3-NEG-121 60 0.421277 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPALC-NEG-2281 60 0.419475 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGCD7-NEG-1921 60 0.41891 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGHVA-NEG-481 60 0.417735 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMIFA-NEG-181 60 0.415465 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMERG11-NEG-181 60 0.414897 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIL2RBC-NEG-1681 60 0.414164 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTGLU-NEG-781 60 0.412198 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHTRP-NEG-661 60 0.408614 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMKER19-NEG-541 60 0.403825 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALDH03-NEG-1 60 0.402658 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCA2VR-NEG-361 60 0.402253 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCOLII2-NEG-781 60 0.400065 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMINIFI-NEG-481 60 0.399472 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFCRHA-NEG-661 60 0.399365 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMASGPR1-NEG-661 60 0.398728 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHA2-NEG-1321 60 0.398551 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDR2A-NEG-61 60 0.398077 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMGI2-NEG-901 60 0.396547 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSYNPR-NEG-61 60 0.395587 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHCR-NEG-661 60 0.395406 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTCBVD-NEG-1 60 0.394137 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPBGDR2-NEG-1 60 0.394096 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTPAR-NEG-361 60 0.386846 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMXCGD-NEG-1201 60 0.385692 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMDBLPRO-NEG-181 60 0.384723 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCNPG3-NEG-121 60 0.381164 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALIPOA-NEG-9901 60 0.378625 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRCD-NEG-601 60 0.378403 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} 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HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGRP78-DONOR-3086 60 0.43625 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMC9M-NEG-841 60 0.435581 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMAFP-DONOR-7047 60 0.434442 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMPRPH1-DONOR-3555 60 0.433545 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHBBAG-DONOR-1689 60 0.433544 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} LEMHBE-DONOR-288 60 0.432079 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSHBGA-DONOR-4189 60 0.431209 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSODA-DONOR-4739 60 0.430693 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMPRP0A-NEG-2161 60 0.420664 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGCRA-NEG-3721 60 0.418251 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMHDOB-DONOR-3639 60 0.414646 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSODA-DONOR-6932 60 0.41316 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGCB1-DONOR-581 60 0.407943 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSAA-DONOR-701 60 0.40641 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMADAG-NEG-9001 60 0.404812 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSODA-DONOR-3510 60 0.403941 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-2752 60 0.402424 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-7352 60 0.401265 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMFXIIIB-NEG-841 60 0.400411 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSHBGA-DONOR-5396 60 0.393882 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTUBAG-DONOR-2257 60 0.391184 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMAPA4R-NEG-2761 60 0.390765 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-4574 60 0.387929 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTFPB-DONOR-6562 60 0.386307 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMINCPPS-DONOR-2825 60 0.385109 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCKMT-DONOR-5110 60 0.383038 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMFIXG-DONOR-20733 60 0.381724 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMAFP-DONOR-19845 60 0.378902 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-5858 60 0.377773 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGCB1-DONOR-3143 60 0.376326 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGG-DONOR-230 60 0.368442 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSAA1A-DONOR-6017 60 0.367947 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMEF1A-ACCEPTOR-3311 60 0.367124 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMRTVLH2-NEG-4681 60 0.363515 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHPRTB-NEG-53761 60 0.359999 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-3558 60 0.359642 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMRTVLH2-NEG-121 60 0.356328 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSAA-DONOR-34 60 0.354715 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMALBGC-DONOR-6905 60 0.350859 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMKTEP2A-NEG-1441 60 0.349786 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMH6-ACCEPTOR-3562 60 0.349135 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCD1B1-NEG-241 60 0.348346 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTNFA-DONOR-1839 60 0.342948 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTNFAB-DONOR-5318 60 0.342948 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTNFX-DONOR-1613 60 0.342948 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-ACCEPTOR-7018 60 0.339502 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMKEREP-DONOR-3368 60 0.337561 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMATPSYB-ACCEPTOR-3096 60 0.328045 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMA1GLY2-DONOR-3352 60 0.324983 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHPARS1-DONOR-3825 60 0.317844 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCYPIIE-DONOR-12950 60 0.314013 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMRP14A-DONOR-1551 60 0.306367 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMYC3L-NEG-3301 60 0.297142 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMHDXAR2-NEG-1 60 0.2917 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMACTGA-DONOR-2060 60 0.287417 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMA1GLY2-DONOR-3606 60 0.283732 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMH6-DONOR-3321 60 0.280272 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMIL1B-NEG-6721 60 0.276992 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} MNKHBD-DONOR-347 60 0.274563 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHPARS1-NEG-4801 60 0.273287 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMVIM06-NEG-1 60 0.265701 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMFIXG-DONOR-23501 60 0.258593 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ORAHBD-DONOR-466 60 0.2582 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMATPCAPM-NEG-661 60 0.249874 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHBB32-NEG-121 60 0.246244 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMDES-DONOR-2016 60 0.238813 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCALLA-NEG-2161 60 0.217972 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMADH1CA-NEG-121 60 0.211778 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSAA1A-DONOR-1815 60 0.207596 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCKMT-ACCEPTOR-5313 60 0.196222 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMRP8A-ACCEPTOR-1988 60 0.193932 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGRP78-DONOR-1007 60 0.188581 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMLACF-NEG-541 60 0.16601 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMFBRG-NEG-7201 60 0.126014 + +Composition Char +Cluster Value Frequency Typicality +{G, N, D, ...} G 50245 1 +{G, N, D, ...} N 56 0.00682706 +{G, N, D, ...} D 2 0.000410479 +{G, N, D, ...} S 1 0.000255295 +{G, N, D, ...} R 1 0.000106161 +{T} T 46308 1 +{A} A 44487 1 +{C} C 50300 1 + +Cells +SampleId Pos Char Frequency +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]36.5;+inf[ {G, N, D, ...} 3633 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]36.5;+inf[ {C} 3462 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]36.5;+inf[ {C} 3374 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]36.5;+inf[ {G, N, D, ...} 3364 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]36.5;+inf[ {G, N, D, ...} 3354 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]36.5;+inf[ {A} 3298 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]36.5;+inf[ {T} 3223 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]-inf;15.5] {C} 2942 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]36.5;+inf[ {C} 2809 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]36.5;+inf[ {C} 2735 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]36.5;+inf[ {G, N, D, ...} 2642 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]15.5;26.5] {C} 2447 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]36.5;+inf[ {A} 2423 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]36.5;+inf[ {A} 2423 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]-inf;15.5] {G, N, D, ...} 2333 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]36.5;+inf[ {T} 2273 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]36.5;+inf[ {A} 2187 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]-inf;15.5] {A} 2125 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]36.5;+inf[ {T} 2111 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]36.5;+inf[ {T} 2095 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]36.5;+inf[ {T} 2079 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]-inf;15.5] {C} 2047 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]36.5;+inf[ {G, N, D, ...} 2018 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]-inf;15.5] {T} 1905 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]36.5;+inf[ {A} 1901 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]15.5;26.5] {T} 1901 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]36.5;+inf[ {C} 1861 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]36.5;+inf[ {A} 1839 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]-inf;15.5] {T} 1837 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]36.5;+inf[ {G, N, D, ...} 1821 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]15.5;26.5] {T} 1792 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]36.5;+inf[ {G, N, D, ...} 1768 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]36.5;+inf[ {T} 1739 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]36.5;+inf[ {C} 1685 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]15.5;26.5] {G, N, D, ...} 1684 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]36.5;+inf[ {T} 1682 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]-inf;15.5] {C} 1676 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]-inf;15.5] {T} 1653 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]36.5;+inf[ {G, N, D, ...} 1621 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]-inf;15.5] {A} 1614 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]36.5;+inf[ {A} 1600 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]15.5;26.5] {C} 1561 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]36.5;+inf[ {C} 1558 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]15.5;26.5] {A} 1535 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]15.5;26.5] {T} 1503 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]-inf;15.5] {G, N, D, ...} 1466 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]-inf;15.5] {C} 1412 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]-inf;15.5] {G, N, D, ...} 1384 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]-inf;15.5] {G, N, D, ...} 1379 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]-inf;15.5] {G, N, D, ...} 1372 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]-inf;15.5] {T} 1357 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]36.5;+inf[ {T} 1347 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]-inf;15.5] {T} 1342 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]15.5;26.5] {G, N, D, ...} 1292 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]36.5;+inf[ {C} 1286 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]-inf;15.5] {C} 1247 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]15.5;26.5] {C} 1206 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]-inf;15.5] {A} 1196 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]36.5;+inf[ {A} 1180 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]-inf;15.5] {A} 1179 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]-inf;15.5] {C} 1174 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]-inf;15.5] {G, N, D, ...} 1171 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]15.5;26.5] {A} 1138 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]-inf;15.5] {A} 1109 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]-inf;15.5] {A} 1106 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]-inf;15.5] {A} 1094 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]15.5;26.5] {T} 1083 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]36.5;+inf[ {C} 1070 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]36.5;+inf[ {T} 1068 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]-inf;15.5] {T} 1050 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]-inf;15.5] {G, N, D, ...} 1050 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]36.5;+inf[ {G, N, D, ...} 1043 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]-inf;15.5] {C} 1026 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]36.5;+inf[ {A} 988 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]-inf;15.5] {C} 964 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]-inf;15.5] {T} 936 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]-inf;15.5] {T} 934 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]15.5;26.5] {C} 931 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]-inf;15.5] {G, N, D, ...} 931 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]-inf;15.5] {A} 930 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]15.5;26.5] {C} 917 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]15.5;26.5] {G, N, D, ...} 915 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]15.5;26.5] {A} 915 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]15.5;26.5] {G, N, D, ...} 902 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]15.5;26.5] {C} 887 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]15.5;26.5] {G, N, D, ...} 874 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]15.5;26.5] {C} 860 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]15.5;26.5] {T} 856 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]-inf;15.5] {A} 813 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]-inf;15.5] {T} 807 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]15.5;26.5] {A} 797 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]15.5;26.5] {T} 782 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]15.5;26.5] {C} 782 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]15.5;26.5] {T} 761 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]15.5;26.5] {A} 759 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]15.5;26.5] {A} 759 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]15.5;26.5] {A} 744 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]15.5;26.5] {G, N, D, ...} 680 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]-inf;15.5] {C} 669 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]15.5;26.5] {T} 622 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]-inf;15.5] {G, N, D, ...} 620 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]15.5;26.5] {C} 584 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]15.5;26.5] {T} 576 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]15.5;26.5] {G, N, D, ...} 558 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]15.5;26.5] {G, N, D, ...} 545 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]28.5;29.5] {A} 472 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]29.5;30.5] {G, N, D, ...} 472 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]27.5;28.5] {C} 441 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]15.5;26.5] {A} 384 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]29.5;30.5] {G, N, D, ...} 302 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]28.5;29.5] {A} 301 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]15.5;26.5] {A} 299 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]31.5;32.5] {T} 287 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]30.5;31.5] {G, N, D, ...} 284 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]30.5;31.5] {G, N, D, ...} 284 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]31.5;32.5] {T} 276 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]34.5;35.5] {G, N, D, ...} 263 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]15.5;26.5] {G, N, D, ...} 259 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]34.5;35.5] {G, N, D, ...} 245 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]29.5;30.5] {G, N, D, ...} 241 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]29.5;30.5] {G, N, D, ...} 239 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]30.5;31.5] {G, N, D, ...} 238 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]33.5;34.5] {A} 230 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]31.5;32.5] {T} 229 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]30.5;31.5] {G, N, D, ...} 219 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]26.5;27.5] {C} 182 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]32.5;33.5] {G, N, D, ...} 181 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]29.5;30.5] {G, N, D, ...} 180 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]33.5;34.5] {A} 179 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]34.5;35.5] {G, N, D, ...} 175 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]33.5;34.5] {A} 173 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]27.5;28.5] {C} 172 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]29.5;30.5] {C} 172 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]30.5;31.5] {T} 170 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]26.5;27.5] {C} 169 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]35.5;36.5] {C} 168 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]29.5;30.5] {G, N, D, ...} 168 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]28.5;29.5] {A} 167 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]26.5;27.5] {G, N, D, ...} 162 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]32.5;33.5] {A} 161 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]34.5;35.5] {C} 160 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]26.5;27.5] {G, N, D, ...} 160 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]29.5;30.5] {T} 158 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]28.5;29.5] {A} 157 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]31.5;32.5] {T} 154 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]33.5;34.5] {C} 154 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]35.5;36.5] {C} 153 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]33.5;34.5] {G, N, D, ...} 153 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]30.5;31.5] {G, N, D, ...} 152 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]26.5;27.5] {A} 150 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]34.5;35.5] {T} 150 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]28.5;29.5] {C} 149 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]28.5;29.5] {A} 148 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]27.5;28.5] {G, N, D, ...} 148 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]30.5;31.5] {G, N, D, ...} 147 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]27.5;28.5] {A} 147 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]32.5;33.5] {C} 145 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]35.5;36.5] {G, N, D, ...} 145 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]30.5;31.5] {A} 145 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]35.5;36.5] {T} 145 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]32.5;33.5] {G, N, D, ...} 145 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]30.5;31.5] {C} 144 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]32.5;33.5] {G, N, D, ...} 143 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]31.5;32.5] {A} 143 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]35.5;36.5] {T} 142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]34.5;35.5] {C} 142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]32.5;33.5] {C} 141 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]34.5;35.5] {T} 141 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]31.5;32.5] {G, N, D, ...} 140 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]31.5;32.5] {C} 137 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]27.5;28.5] {C} 137 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]30.5;31.5] {A} 137 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]33.5;34.5] {C} 136 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]33.5;34.5] {A} 136 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]33.5;34.5] {T} 136 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]29.5;30.5] {A} 135 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]27.5;28.5] {T} 135 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]28.5;29.5] {T} 135 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]32.5;33.5] {T} 135 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]34.5;35.5] {A} 134 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]35.5;36.5] {G, N, D, ...} 134 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]33.5;34.5] {G, N, D, ...} 133 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]28.5;29.5] {G, N, D, ...} 132 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]34.5;35.5] {C} 131 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]28.5;29.5] {A} 131 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]33.5;34.5] {C} 130 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]31.5;32.5] {T} 130 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]26.5;27.5] {T} 129 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]35.5;36.5] {C} 124 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]30.5;31.5] {G, N, D, ...} 124 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]35.5;36.5] {A} 124 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]32.5;33.5] {A} 124 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]27.5;28.5] {C} 123 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]26.5;27.5] {A} 123 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]32.5;33.5] {A} 122 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]32.5;33.5] {G, N, D, ...} 122 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]35.5;36.5] {T} 121 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]31.5;32.5] {G, N, D, ...} 120 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]27.5;28.5] {C} 119 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]27.5;28.5] {G, N, D, ...} 118 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]34.5;35.5] {G, N, D, ...} 117 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]34.5;35.5] {G, N, D, ...} 116 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]32.5;33.5] {A} 116 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]30.5;31.5] {C} 114 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]30.5;31.5] {T} 113 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]27.5;28.5] {T} 112 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]32.5;33.5] {T} 111 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]33.5;34.5] {G, N, D, ...} 110 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]27.5;28.5] {C} 109 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]31.5;32.5] {C} 109 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]32.5;33.5] {C} 109 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]31.5;32.5] {G, N, D, ...} 109 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]29.5;30.5] {A} 108 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]27.5;28.5] {A} 107 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]26.5;27.5] {G, N, D, ...} 107 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]33.5;34.5] {T} 105 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]26.5;27.5] {C} 105 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]35.5;36.5] {G, N, D, ...} 105 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]34.5;35.5] {A} 105 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]34.5;35.5] {A} 104 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]35.5;36.5] {A} 104 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]34.5;35.5] {G, N, D, ...} 104 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]27.5;28.5] {A} 104 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]32.5;33.5] {A} 103 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]26.5;27.5] {A} 103 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]27.5;28.5] {T} 103 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]28.5;29.5] {G, N, D, ...} 103 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]32.5;33.5] {T} 102 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]29.5;30.5] {G, N, D, ...} 102 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]28.5;29.5] {C} 101 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]34.5;35.5] {T} 100 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]26.5;27.5] {G, N, D, ...} 100 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]31.5;32.5] {C} 100 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]35.5;36.5] {T} 100 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]32.5;33.5] {G, N, D, ...} 99 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]33.5;34.5] {A} 99 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]35.5;36.5] {T} 98 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]32.5;33.5] {T} 98 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]31.5;32.5] {A} 98 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]32.5;33.5] {A} 98 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]26.5;27.5] {T} 97 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]32.5;33.5] {A} 97 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]28.5;29.5] {T} 97 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]35.5;36.5] {T} 97 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]31.5;32.5] {T} 95 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]28.5;29.5] {T} 95 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]29.5;30.5] {C} 95 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]28.5;29.5] {C} 95 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]26.5;27.5] {C} 94 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]26.5;27.5] {G, N, D, ...} 94 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]35.5;36.5] {A} 94 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]32.5;33.5] {C} 94 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]31.5;32.5] {A} 94 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]26.5;27.5] {T} 92 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]29.5;30.5] {A} 92 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]35.5;36.5] {C} 92 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]35.5;36.5] {T} 92 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]31.5;32.5] {T} 91 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]33.5;34.5] {A} 91 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]34.5;35.5] {C} 90 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]28.5;29.5] {G, N, D, ...} 90 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]31.5;32.5] {A} 90 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]33.5;34.5] {G, N, D, ...} 89 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]31.5;32.5] {T} 89 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]27.5;28.5] {C} 89 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]29.5;30.5] {C} 89 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]33.5;34.5] {C} 89 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]31.5;32.5] {A} 88 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]30.5;31.5] {C} 88 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]28.5;29.5] {A} 88 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]27.5;28.5] {A} 88 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]27.5;28.5] {A} 87 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]28.5;29.5] {A} 87 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]29.5;30.5] {T} 87 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]33.5;34.5] {T} 86 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]30.5;31.5] {A} 85 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]33.5;34.5] {T} 85 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]29.5;30.5] {T} 85 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]32.5;33.5] {T} 85 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]34.5;35.5] {T} 85 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]34.5;35.5] {A} 85 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]35.5;36.5] {A} 84 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]34.5;35.5] {A} 84 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]28.5;29.5] {T} 84 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]31.5;32.5] {C} 84 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]27.5;28.5] {G, N, D, ...} 83 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]35.5;36.5] {T} 82 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]26.5;27.5] {T} 82 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]34.5;35.5] {C} 82 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]27.5;28.5] {G, N, D, ...} 82 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]30.5;31.5] {A} 82 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]32.5;33.5] {A} 81 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]26.5;27.5] {C} 81 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]34.5;35.5] {T} 81 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]26.5;27.5] {A} 81 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]35.5;36.5] {G, N, D, ...} 80 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]32.5;33.5] {G, N, D, ...} 80 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]32.5;33.5] {A} 80 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]27.5;28.5] {A} 79 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]34.5;35.5] {G, N, D, ...} 79 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]31.5;32.5] {A} 79 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]31.5;32.5] {G, N, D, ...} 78 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]26.5;27.5] {A} 78 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]33.5;34.5] {T} 78 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]33.5;34.5] {G, N, D, ...} 77 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]30.5;31.5] {T} 77 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]31.5;32.5] {T} 77 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]35.5;36.5] {G, N, D, ...} 76 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]26.5;27.5] {G, N, D, ...} 76 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]31.5;32.5] {G, N, D, ...} 76 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]28.5;29.5] {C} 75 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]28.5;29.5] {G, N, D, ...} 75 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]30.5;31.5] {G, N, D, ...} 75 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]35.5;36.5] {C} 75 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]33.5;34.5] {A} 74 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]32.5;33.5] {T} 74 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]30.5;31.5] {A} 74 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]33.5;34.5] {G, N, D, ...} 74 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]26.5;27.5] {G, N, D, ...} 73 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]33.5;34.5] {A} 73 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]31.5;32.5] {C} 73 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]32.5;33.5] {G, N, D, ...} 73 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]26.5;27.5] {C} 73 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]29.5;30.5] {G, N, D, ...} 72 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]26.5;27.5] {T} 72 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]33.5;34.5] {C} 72 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]35.5;36.5] {C} 71 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]30.5;31.5] {C} 71 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]26.5;27.5] {A} 71 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]30.5;31.5] {G, N, D, ...} 70 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]35.5;36.5] {A} 70 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]27.5;28.5] {C} 70 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]26.5;27.5] {C} 69 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]30.5;31.5] {A} 69 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]35.5;36.5] {A} 69 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]27.5;28.5] {T} 68 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]31.5;32.5] {C} 67 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]29.5;30.5] {A} 67 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]35.5;36.5] {C} 67 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]35.5;36.5] {G, N, D, ...} 67 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]26.5;27.5] {A} 66 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]32.5;33.5] {C} 66 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]33.5;34.5] {C} 66 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]33.5;34.5] {A} 66 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]35.5;36.5] {G, N, D, ...} 65 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]26.5;27.5] {C} 65 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]27.5;28.5] {A} 64 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]32.5;33.5] {C} 64 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]34.5;35.5] {T} 63 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]28.5;29.5] {A} 62 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]26.5;27.5] {T} 62 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]28.5;29.5] {C} 61 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]33.5;34.5] {T} 61 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]29.5;30.5] {C} 60 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]35.5;36.5] {A} 60 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]27.5;28.5] {T} 59 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]34.5;35.5] {C} 59 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]34.5;35.5] {G, N, D, ...} 59 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]26.5;27.5] {T} 59 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]32.5;33.5] {G, N, D, ...} 58 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]33.5;34.5] {G, N, D, ...} 58 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]27.5;28.5] {G, N, D, ...} 57 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]34.5;35.5] {A} 57 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]35.5;36.5] {G, N, D, ...} 57 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]30.5;31.5] {T} 56 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]26.5;27.5] {T} 55 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]35.5;36.5] {T} 54 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]32.5;33.5] {G, N, D, ...} 53 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]31.5;32.5] {G, N, D, ...} 52 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]26.5;27.5] {G, N, D, ...} 51 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]29.5;30.5] {T} 50 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]27.5;28.5] {G, N, D, ...} 50 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]26.5;27.5] {A} 49 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]27.5;28.5] {C} 49 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]26.5;27.5] {A} 48 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]35.5;36.5] {A} 48 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]29.5;30.5] {G, N, D, ...} 47 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]30.5;31.5] {C} 46 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]34.5;35.5] {G, N, D, ...} 45 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]26.5;27.5] {C} 45 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]30.5;31.5] {T} 45 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]28.5;29.5] {T} 45 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]28.5;29.5] {G, N, D, ...} 44 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]27.5;28.5] {T} 43 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]30.5;31.5] {C} 43 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]28.5;29.5] {G, N, D, ...} 41 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]28.5;29.5] {G, N, D, ...} 38 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]29.5;30.5] {T} 38 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]35.5;36.5] {G, N, D, ...} 38 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]35.5;36.5] {C} 37 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]28.5;29.5] {T} 35 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]27.5;28.5] {G, N, D, ...} 35 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]26.5;27.5] {T} 34 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]34.5;35.5] {A} 34 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]28.5;29.5] {C} 34 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]26.5;27.5] {G, N, D, ...} 33 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]27.5;28.5] {T} 33 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]33.5;34.5] {G, N, D, ...} 31 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]27.5;28.5] {T} 30 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]33.5;34.5] {C} 30 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]35.5;36.5] {A} 29 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]29.5;30.5] {A} 29 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]33.5;34.5] {G, N, D, ...} 27 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]30.5;31.5] {T} 27 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]33.5;34.5] {T} 27 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]28.5;29.5] {T} 26 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]29.5;30.5] {A} 26 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]27.5;28.5] {T} 25 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]35.5;36.5] {C} 23 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]33.5;34.5] {C} 22 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]34.5;35.5] {T} 22 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]28.5;29.5] {C} 21 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]29.5;30.5] {T} 21 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]34.5;35.5] {C} 21 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]33.5;34.5] {T} 20 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]29.5;30.5] {C} 18 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]27.5;28.5] {A} 18 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]29.5;30.5] {A} 17 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]32.5;33.5] {C} 14 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]34.5;35.5] {T} 13 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]31.5;32.5] {A} 13 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]29.5;30.5] {T} 12 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]34.5;35.5] {C} 12 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]33.5;34.5] {C} 11 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]34.5;35.5] {C} 11 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]34.5;35.5] {A} 11 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]34.5;35.5] {T} 10 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]33.5;34.5] {T} 9 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]31.5;32.5] {C} 9 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]29.5;30.5] {C} 7 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]32.5;33.5] {T} 7 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]27.5;28.5] {G, N, D, ...} 4 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]30.5;31.5] {C} 4 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]32.5;33.5] {T} 4 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]32.5;33.5] {C} 3 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]30.5;31.5] {A} 2 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]30.5;31.5] {C} 2 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]27.5;28.5] {A} 2 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]30.5;31.5] {C} 2 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]31.5;32.5] {G, N, D, ...} 2 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]31.5;32.5] {A} 1 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]32.5;33.5] {T} 1 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]28.5;29.5] {G, N, D, ...} 1 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]32.5;33.5] {C} 1 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]29.5;30.5] {C} 1 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]30.5;31.5] {A} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Clusters.txt b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Clusters.txt new file mode 100644 index 000000000..905e780fe --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Clusters.txt @@ -0,0 +1,5 @@ +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Clusters_bad.json b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Clusters_bad.json new file mode 100644 index 000000000..905e780fe --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Clusters_bad.json @@ -0,0 +1,5 @@ +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering.khc new file mode 100644 index 000000000..628dd01fa --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering.khc @@ -0,0 +1,69 @@ +#Khiops 10.4.6i +Short description +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +PetalLength Numerical 3 3 150 1 +PetalWidth Numerical 3 3 150 1 +Class Categorical 3 3 3 1 + +Coclustering stats +Instances 150 +Cells 7 +Null cost 1379.668177 +Cost 1164.273132 +Level 0.1561209056 +Initial dimensions 3 +Frequency variable +Dictionary Iris +Database ./SubDir/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 +]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 +]-inf;+inf[ 150 0.648091 -0.0145257 2 4 +]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 +]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 +]-inf;+inf[ 150 0.681865 0.374198 2 5 +]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 + +Hierarchy Class +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Iris-setosa} C1 50 1 1 1 9 +{Iris-versicolor} C4 50 0.912663 1 3 9 +{Iris-virginica} C4 50 0.912663 1 5 9 +C1 150 0.941775 0 2 3 +C4 C1 100 0.912663 0.739904 4 6 + +Composition Class +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 + +Cells +PetalLength PetalWidth Class Frequency +]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 +]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 +]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 +]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 +]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 +]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 +]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering.khcj new file mode 100644 index 000000000..d3d6473d0 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering.khcj @@ -0,0 +1,299 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 150, + "cells": 7, + "nullCost": 1379.668177, + "cost": 1164.273132, + "level": 0.1561209056, + "initialDimensions": 3, + "frequencyVariable": "", + "dictionary": "Iris", + "database": ".\/SubDir\/Iris.txt", + "samplePercentage": 100, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "" + }, + "dimensionSummaries": [ + { + "name": "PetalLength", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 1, + "max": 6.9 + }, + { + "name": "PetalWidth", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 0.1, + "max": 2.5 + }, + { + "name": "Class", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 3, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "PetalLength", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;2.4]", + "bounds": [1,2.4] + }, + { + "cluster": "]2.4;4.75]", + "bounds": [2.4,4.75] + }, + { + "cluster": "]4.75;+inf[", + "bounds": [4.75,6.9] + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;0.8]", + "bounds": [0.1,0.8] + }, + { + "cluster": "]0.8;1.75]", + "bounds": [0.8,1.75] + }, + { + "cluster": "]1.75;+inf[", + "bounds": [1.75,2.5] + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Iris-setosa}", + "values": ["Iris-setosa"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-versicolor}", + "values": ["Iris-versicolor"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-virginica}", + "values": ["Iris-virginica"], + "valueFrequencies": [50], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 2 + } + ], + "dimensionHierarchies": [ + { + "name": "PetalLength", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;2.4]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.557285, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]2.4;4.75]", + "parentCluster": "]2.4;+inf[", + "frequency": 45, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]4.75;+inf[", + "parentCluster": "]2.4;+inf[", + "frequency": 55, + "interest": 0.442715, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.648091, + "hierarchicalLevel": -0.0145257, + "rank": 2, + "hierarchicalRank": 4, + "isLeaf": false + }, + { + "cluster": "]2.4;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.693493, + "hierarchicalLevel": 0.856289, + "rank": 4, + "hierarchicalRank": 8, + "isLeaf": false + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;0.8]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.569949, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]0.8;1.75]", + "parentCluster": "]0.8;+inf[", + "frequency": 54, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]1.75;+inf[", + "parentCluster": "]0.8;+inf[", + "frequency": 46, + "interest": 0.430051, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.681865, + "hierarchicalLevel": 0.374198, + "rank": 2, + "hierarchicalRank": 5, + "isLeaf": false + }, + { + "cluster": "]0.8;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.737824, + "hierarchicalLevel": 0.704564, + "rank": 4, + "hierarchicalRank": 7, + "isLeaf": false + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Iris-setosa}", + "parentCluster": "C1", + "frequency": 50, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-versicolor}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-virginica}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "C1", + "parentCluster": "", + "frequency": 150, + "interest": 0.941775, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "C4", + "parentCluster": "C1", + "frequency": 100, + "interest": 0.912663, + "hierarchicalLevel": 0.739904, + "rank": 4, + "hierarchicalRank": 6, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0,0], + [2,2,2], + [1,1,1], + [2,1,1], + [2,1,2], + [1,1,2], + [2,2,1] + ], + "cellFrequencies": [50,45,44,5,4,1,1] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering_bad.json b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering_bad.json new file mode 100644 index 000000000..d3d6473d0 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering_bad.json @@ -0,0 +1,299 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 150, + "cells": 7, + "nullCost": 1379.668177, + "cost": 1164.273132, + "level": 0.1561209056, + "initialDimensions": 3, + "frequencyVariable": "", + "dictionary": "Iris", + "database": ".\/SubDir\/Iris.txt", + "samplePercentage": 100, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "" + }, + "dimensionSummaries": [ + { + "name": "PetalLength", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 1, + "max": 6.9 + }, + { + "name": "PetalWidth", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 0.1, + "max": 2.5 + }, + { + "name": "Class", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 3, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "PetalLength", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;2.4]", + "bounds": [1,2.4] + }, + { + "cluster": "]2.4;4.75]", + "bounds": [2.4,4.75] + }, + { + "cluster": "]4.75;+inf[", + "bounds": [4.75,6.9] + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;0.8]", + "bounds": [0.1,0.8] + }, + { + "cluster": "]0.8;1.75]", + "bounds": [0.8,1.75] + }, + { + "cluster": "]1.75;+inf[", + "bounds": [1.75,2.5] + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Iris-setosa}", + "values": ["Iris-setosa"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-versicolor}", + "values": ["Iris-versicolor"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-virginica}", + "values": ["Iris-virginica"], + "valueFrequencies": [50], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 2 + } + ], + "dimensionHierarchies": [ + { + "name": "PetalLength", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;2.4]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.557285, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]2.4;4.75]", + "parentCluster": "]2.4;+inf[", + "frequency": 45, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]4.75;+inf[", + "parentCluster": "]2.4;+inf[", + "frequency": 55, + "interest": 0.442715, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.648091, + "hierarchicalLevel": -0.0145257, + "rank": 2, + "hierarchicalRank": 4, + "isLeaf": false + }, + { + "cluster": "]2.4;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.693493, + "hierarchicalLevel": 0.856289, + "rank": 4, + "hierarchicalRank": 8, + "isLeaf": false + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;0.8]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.569949, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]0.8;1.75]", + "parentCluster": "]0.8;+inf[", + "frequency": 54, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]1.75;+inf[", + "parentCluster": "]0.8;+inf[", + "frequency": 46, + "interest": 0.430051, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.681865, + "hierarchicalLevel": 0.374198, + "rank": 2, + "hierarchicalRank": 5, + "isLeaf": false + }, + { + "cluster": "]0.8;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.737824, + "hierarchicalLevel": 0.704564, + "rank": 4, + "hierarchicalRank": 7, + "isLeaf": false + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Iris-setosa}", + "parentCluster": "C1", + "frequency": 50, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-versicolor}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-virginica}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "C1", + "parentCluster": "", + "frequency": 150, + "interest": 0.941775, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "C4", + "parentCluster": "C1", + "frequency": 100, + "interest": 0.912663, + "hierarchicalLevel": 0.739904, + "rank": 4, + "hierarchicalRank": 6, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0,0], + [2,2,2], + [1,1,1], + [2,1,1], + [2,1,2], + [1,1,2], + [2,2,1] + ], + "cellFrequencies": [50,45,44,5,4,1,1] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering_bad.json.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering_bad.json.khc new file mode 100644 index 000000000..628dd01fa --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/Coclustering_bad.json.khc @@ -0,0 +1,69 @@ +#Khiops 10.4.6i +Short description +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +PetalLength Numerical 3 3 150 1 +PetalWidth Numerical 3 3 150 1 +Class Categorical 3 3 3 1 + +Coclustering stats +Instances 150 +Cells 7 +Null cost 1379.668177 +Cost 1164.273132 +Level 0.1561209056 +Initial dimensions 3 +Frequency variable +Dictionary Iris +Database ./SubDir/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 +]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 +]-inf;+inf[ 150 0.648091 -0.0145257 2 4 +]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 +]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 +]-inf;+inf[ 150 0.681865 0.374198 2 5 +]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 + +Hierarchy Class +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Iris-setosa} C1 50 1 1 1 9 +{Iris-versicolor} C4 50 0.912663 1 3 9 +{Iris-virginica} C4 50 0.912663 1 5 9 +C1 150 0.941775 0 2 3 +C4 C1 100 0.912663 0.739904 4 6 + +Composition Class +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 + +Cells +PetalLength PetalWidth Class Frequency +]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 +]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 +]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 +]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 +]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 +]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 +]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/DeploymentCoclustering.kdic b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/DeploymentCoclustering.kdic new file mode 100644 index 000000000..a4317f884 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/DeploymentCoclustering.kdic @@ -0,0 +1,51 @@ +#Khiops 10.4.6i + +Root Dictionary SpliceJunction (SampleId) +{ + Categorical SampleId ; + Categorical Class ; + Table(SpliceJunctionDNA) DNA ; +Unused Structure(DataGrid) P_Coclustering = DataGrid(ValueGroups(ValueGroup("HUMMYLCA-ACCEPTOR-924", "HUMVPNP-ACCEPTOR-1688", "HUMMHCW2B-ACCEPTOR-2660", "HUMMHANTLE-ACCEPTOR-3110", "HUMGASTA-ACCEPTOR-6600", "HUMMHCW1B-ACCEPTOR-2658", "HUMIL1B-ACCEPTOR-3546", "HUMHBA4-ACCEPTOR-10696", "HUMHBA4-ACCEPTOR-6885", "MACHBA-ACCEPTOR-238", "HUMTHY1A-ACCEPTOR-1380", "HUMAPOCII-ACCEPTOR-3131", "HUMCS1-ACCEPTOR-1814", "HUMGHCSA-ACCEPTOR-51551", "HUMMYLCA-ACCEPTOR-3623", "HUMMHEA-ACCEPTOR-2678", "HUMTNFA-ACCEPTOR-2140", "HUMTNFAB-ACCEPTOR-5619", "HUMTNFX-ACCEPTOR-1914", "HUMMHB27B-ACCEPTOR-2421", "HUMGHCSA-ACCEPTOR-28732", "HUMAPOCIA-ACCEPTOR-634", "HUMGH-ACCEPTOR-1572", "HUMGHCSA-ACCEPTOR-43393", "HUMGHCSA-ACCEPTOR-6465", "HUMGHN-ACCEPTOR-1797", "HUMGHVA-ACCEPTOR-741", "HUMATP1A2-ACCEPTOR-8990", "HUMTPA-ACCEPTOR-31395", "HUMKER18-ACCEPTOR-5495", "HUMTNFAB-ACCEPTOR-1237", "HUMMIS-ACCEPTOR-1500", "HUMGAST2-ACCEPTOR-916", "HUMTPA-ACCEPTOR-30776", "HUMHPARS1-ACCEPTOR-2519", "HUMGHCSA-ACCEPTOR-42976", "HUMAPOC2-ACCEPTOR-3143", "HUMAPOC2G-ACCEPTOR-2851", "HUMOTNPI-ACCEPTOR-808", "HUMUBILP-ACCEPTOR-1488", "HUMMYLCA-ACCEPTOR-2214", "HUMATP1A2-ACCEPTOR-6683", "HUMALPHA-ACCEPTOR-2023", "HUMADAG-ACCEPTOR-28878", "HUMGH-ACCEPTOR-1154", "HUMGHCSA-ACCEPTOR-6047", "HUMGHN-ACCEPTOR-1379", "HUMCS1-ACCEPTOR-1396", "HUMGHCSA-ACCEPTOR-28313", "HUMGHCSA-ACCEPTOR-51135", "HUMCP21OH-ACCEPTOR-3161", "HUMCP21OHC-ACCEPTOR-3163", "HUMMHCP42-ACCEPTOR-4089", "HUMMHCP52-ACCEPTOR-2628", "HUMGHCSA-ACCEPTOR-13046", "HUMACCYBA-ACCEPTOR-2893", "HUMACCYBB-ACCEPTOR-2827", "HUMDES-ACCEPTOR-3015", "HUMSAA-ACCEPTOR-2567", "HUMCYC1A-ACCEPTOR-2399", "HUMOTNPI-ACCEPTOR-1091", "HUMAPOAI1-ACCEPTOR-653", "HUMAPOAIT-ACCEPTOR-398", "HUMGHCSA-ACCEPTOR-13642", "HUMCP21OH-ACCEPTOR-1386", "HUMLYL1B-ACCEPTOR-3525", "HUMH19-ACCEPTOR-2449", "HUMATP1A2-ACCEPTOR-21798", "HUMALPPD-ACCEPTOR-1642", "HUMMHCP42-ACCEPTOR-2316", "HUMCS1-ACCEPTOR-800", "HUMGHCSA-ACCEPTOR-27717", "HUMGHCSA-ACCEPTOR-50539", "HUMMYC3L-ACCEPTOR-766", "HUMMHDRHA-ACCEPTOR-4276", "HUMRASH-ACCEPTOR-3201", "HUMSAACT-ACCEPTOR-1638", "HUMMHCD8A-ACCEPTOR-733", "HUMA1GLY2-ACCEPTOR-2108", "HUMPIM1A-ACCEPTOR-1687", "HUMKAL2-ACCEPTOR-3494", "HUMCSPB-ACCEPTOR-1874", "HUMA1GLY2-ACCEPTOR-3244", "HUMADAG-ACCEPTOR-34324", "HUMGHCSA-ACCEPTOR-42394", "HUMGHCSA-ACCEPTOR-14059", "HUMTKRA-ACCEPTOR-4815", "HUMPRCA-ACCEPTOR-5420", "HUMMETIF1-ACCEPTOR-1463", "HUMPPPA-ACCEPTOR-1721", "HUMG0S19A-ACCEPTOR-2812", "HUMMHCW2B-ACCEPTOR-885", "HUMPOMC-ACCEPTOR-4445", "HUMGH-ACCEPTOR-571", "HUMGHCSA-ACCEPTOR-5465", "HUMGHN-ACCEPTOR-798", "HUMTHB-ACCEPTOR-883", "HUMALPHA-ACCEPTOR-2851", "HUMALPPD-ACCEPTOR-2473", "HUMTUBBM-ACCEPTOR-1265", "MACHBA-ACCEPTOR-587", "HUMMYCRT-ACCEPTOR-1111", "HUMEDHB17-ACCEPTOR-2346", "HUMA1ATP-ACCEPTOR-11880", "ORAIGECA-ACCEPTOR-583", "HUMHBA4-ACCEPTOR-11050", "HUMANT1-ACCEPTOR-2955", "HUMKEREP-ACCEPTOR-4597", "HUMMHCD8A-ACCEPTOR-1670", "HUMMIS-ACCEPTOR-1697", "HUMPIM1A-ACCEPTOR-4496", "HUMAPOCIA-ACCEPTOR-1956", "HUMRPS14-ACCEPTOR-5473", "HUMSAACT-ACCEPTOR-3116", "HUMCP21OHC-ACCEPTOR-1387", "HUMPSAP-ACCEPTOR-1501", "HUMERP-ACCEPTOR-2578", "HUMERPA-ACCEPTOR-2587", "HUMATP1A2-ACCEPTOR-6969", "HUMPOMC-ACCEPTOR-7483", "HUMGCB1-ACCEPTOR-1590", "HUMMH6-ACCEPTOR-955", "HUMTHB-ACCEPTOR-7876", "HUMAPOE4-ACCEPTOR-3751", "GCRHBBA1-ACCEPTOR-1385", "HUMBNPA-ACCEPTOR-1629", "HUMACCYBA-ACCEPTOR-1384", "HUMACCYBB-ACCEPTOR-1318", "HUMGHCSA-ACCEPTOR-13504", "HUMAK1-ACCEPTOR-5504", "CHPIGECA-ACCEPTOR-585", "HUMADAG-ACCEPTOR-29793", "HUMMHB27B-ACCEPTOR-690", "HUMEDHB17-ACCEPTOR-3575", "HUMCP21OH-ACCEPTOR-2960", "HUMCP21OHC-ACCEPTOR-2962", "HUMMHCP42-ACCEPTOR-3889", "HUMTKRA-ACCEPTOR-2330", "HUMMYLCA-ACCEPTOR-2864", "HUMPRF1A-ACCEPTOR-4965", "HUMVPNP-ACCEPTOR-2057", "HUMH19-ACCEPTOR-2224", "HUMATP1A2-ACCEPTOR-13878", "HUMADAG-ACCEPTOR-19200", "HUMMH6-ACCEPTOR-2724", "HUMG0S19B-ACCEPTOR-2948", "HUMP45C17-ACCEPTOR-4146", "HUMPRF1A-ACCEPTOR-3240", "HUMMYCC-ACCEPTOR-4476", "HUMPRCA-ACCEPTOR-5180", "HUMCYC1A-ACCEPTOR-3478", "HUMMHEA-ACCEPTOR-869", "HUMATP1A2-ACCEPTOR-22870", "HUMMHEA-ACCEPTOR-2278", "HUMAPOA4A-ACCEPTOR-2286", "HUMP45C17-ACCEPTOR-7956", "HUMHPARS1-ACCEPTOR-2888", "HUMHPARS1-ACCEPTOR-4612", "HUMTUBAG-ACCEPTOR-2405", "HUMCAPG-ACCEPTOR-2733", "HUMG0S19A-ACCEPTOR-3347", "HUMHST-ACCEPTOR-4120", "HUMCP21OH-ACCEPTOR-2297", "HUMCP21OHC-ACCEPTOR-2298", "HUMMHCP42-ACCEPTOR-3226", "HUMMHCP52-ACCEPTOR-1763", "HUMKEREP-ACCEPTOR-3671", "HUMSHBGA-ACCEPTOR-3120", "HUMA1GLY2-ACCEPTOR-3502", "HUMINCP-ACCEPTOR-3281", "HUMG0S19B-ACCEPTOR-3484", "HUMMHCP52-ACCEPTOR-2427", "HUMCP21OH-ACCEPTOR-1648", "HUMCP21OHC-ACCEPTOR-1649", "HUMMHCP42-ACCEPTOR-2577", "HUMMHCP52-ACCEPTOR-1114", "BABAPOE-ACCEPTOR-3437", "HUMACTGA-ACCEPTOR-1621", "HUMAK1-DONOR-954", "HUMSPRO-ACCEPTOR-4385", "HUMMIS-ACCEPTOR-1185", "HUMCRYGBC-ACCEPTOR-2225", "HUMADAG-ACCEPTOR-32821", "HUMHBA4-ACCEPTOR-7232", "HUMP3A-ACCEPTOR-2544", "HUMAPOCII-ACCEPTOR-3595", "HUMEDHB17-ACCEPTOR-3311", "HUMADAG-ACCEPTOR-32395", "HUMHST-ACCEPTOR-3478", "HUMMRP14A-ACCEPTOR-3418", "HUMALPI-ACCEPTOR-646", "HUMMHCP52-ACCEPTOR-851", "HUMPSAA-ACCEPTOR-3726", "HUMKEREP-ACCEPTOR-2070", "HUMPRT1A-ACCEPTOR-209", "HUMOPS-ACCEPTOR-4065", "HUMMHANTLE-ACCEPTOR-1363", "HUMMHCW1B-ACCEPTOR-882", "HUMALPI-ACCEPTOR-2283", "GCRHBBA6-ACCEPTOR-590", "HUMTHB-ACCEPTOR-4549", "HUMHMG14A-ACCEPTOR-1361", "HUMATP1A2-ACCEPTOR-21144", "HUMAPOAI1-ACCEPTOR-1647", "HUMAPOAIT-ACCEPTOR-1394", "HUMDES-ACCEPTOR-1920", "HUMHPARS1-ACCEPTOR-6470", "TARHBB-ACCEPTOR-1686", "HUMSAACT-ACCEPTOR-2578", "LEMHBB-ACCEPTOR-1437", "HUMIL1AG-NEG-4981", "HUMTPA-ACCEPTOR-19737", "HUMAPOC2-ACCEPTOR-3599", "HUMAPOC2G-ACCEPTOR-3309", "BABAPOE-ACCEPTOR-801", "HUMAPOE4-ACCEPTOR-1818", "HUMCP21OH-ACCEPTOR-2041", "HUMCP21OHC-ACCEPTOR-2042", "HUMMHCP42-ACCEPTOR-2970", "HUMMHCP52-ACCEPTOR-1507", "HUMCSPB-ACCEPTOR-2217", "HUMAPOAI1-ACCEPTOR-902", "HUMAPOAIT-ACCEPTOR-648", "HUMKEREP-ACCEPTOR-3451", "HUMPSAP-ACCEPTOR-2927", "HUMALPHA-ACCEPTOR-2440", "HUMALPPD-ACCEPTOR-2062", "LEMHBB-ACCEPTOR-346", "HUMMHB27B-ACCEPTOR-2053", "HUMATP1A2-ACCEPTOR-20042", "HUMCAPG-ACCEPTOR-1733", "HUMGCB1-ACCEPTOR-6555", "HUMTNFAB-ACCEPTOR-1431", "HUMATP1A2-ACCEPTOR-20331", "HUMADAG-ACCEPTOR-35070", "HUMALPI-ACCEPTOR-2064", "HUMPPPA-ACCEPTOR-2162", "HUMA1ATP-ACCEPTOR-7282", "MNKHBD-ACCEPTOR-475", "HUMRPS17A-ACCEPTOR-463", "HUMCFVII-ACCEPTOR-10094", "HUMPLPSPC-ACCEPTOR-1326", "HUMMIS-DONOR-1857", "HUMTPA-ACCEPTOR-35307", "HUMCFVII-ACCEPTOR-8277", "HUMINCP-ACCEPTOR-2049", "HUMOPS-ACCEPTOR-5138", "HUMTUBBM-ACCEPTOR-1078", "BABA1AT-NEG-181", "HUMA1ATP-ACCEPTOR-9382", "HUMKAL2-ACCEPTOR-1745", "HUMFOS-ACCEPTOR-1753", "HUMMHCW2B-ACCEPTOR-2263", "HUMMETIF1-ACCEPTOR-1065", "HUMA1GLY2-ACCEPTOR-4746", "HUMPRCA-ACCEPTOR-5063", "HUMSPRO-ACCEPTOR-1827", "HUMH19-ACCEPTOR-2643", "HUMPSAP-ACCEPTOR-993", "HUMRPS17A-ACCEPTOR-3688", "HUMPSAA-ACCEPTOR-1929", "HUMGASTA-ACCEPTOR-6254", "HUMRASH-ACCEPTOR-2012", "HUMTKRA-ACCEPTOR-12537", "HUMSHBGA-ACCEPTOR-4319", "HUMTPA-ACCEPTOR-25978", "HUMACTGA-ACCEPTOR-886", "HUMCKMT-ACCEPTOR-1127", "HUMLYL1B-DONOR-2543", "HUMATP1A2-ACCEPTOR-22617", "HUMMHCD8A-DONOR-1087", "HUMTHB-ACCEPTOR-1703", "HUMTKRA-ACCEPTOR-11871", "ORAHBD-ACCEPTOR-596", "HUMCKMT-ACCEPTOR-4984", "HUMALPI-ACCEPTOR-2926", "HUMSHBGA-ACCEPTOR-5188", "HUMAPRTA-ACCEPTOR-774", "HUMADAG-ACCEPTOR-26314", "ATRINS-ACCEPTOR-701", "HUMMH6-ACCEPTOR-3284", "HUMATP1A2-ACCEPTOR-15000", "CHPIGECA-ACCEPTOR-989", "HUMSAA-ACCEPTOR-606", "HUMMHDRHA-ACCEPTOR-3705", "HUMACTGA-ACCEPTOR-1104", "HUMTNFA-ACCEPTOR-1558", "HUMTNFAB-ACCEPTOR-5037", "HUMTNFX-ACCEPTOR-1332", "TARHBB-ACCEPTOR-2748", "HUMUBILP-ACCEPTOR-806", "HUMCFVII-ACCEPTOR-1624", "HUMGPIBAA-ACCEPTOR-3033", "HUMPRCA-ACCEPTOR-10486", "TARHBD-ACCEPTOR-594", "HUMCFVII-ACCEPTOR-6353", "HUMRASH-ACCEPTOR-2344", "HUMPNMTA-ACCEPTOR-3129", "HUMALPPD-ACCEPTOR-421", "HUMTNFAB-ACCEPTOR-1784", "HUMPSAP-ACCEPTOR-2374", "HUMAPRTA-ACCEPTOR-2558", "HUMACTGA-ACCEPTOR-2414", "HUMPRCA-ACCEPTOR-8223", "ORAHBD-ACCEPTOR-1713", "HUMSPRO-ACCEPTOR-3195", "HUMKER18-ACCEPTOR-5107", "HUMALPI-ACCEPTOR-2698", "HUMAPOCIA-ACCEPTOR-4946", "HUMIL1B-ACCEPTOR-5693", "HUMTHB-ACCEPTOR-7024", "BABAPOE-ACCEPTOR-2624", "HUMSPRO-ACCEPTOR-2022", "HUMCFVII-ACCEPTOR-4264", "HUMMT2A-ACCEPTOR-1047", "HUMIRBPG-ACCEPTOR-6987", "HUMALPI-ACCEPTOR-1861", "HUMSAACT-ACCEPTOR-2337", "HUMAPRTA-ACCEPTOR-2252", "HUMAK1-ACCEPTOR-6626", "HUMTCBA-NEG-2161", "HUMSHBGA-DONOR-2724", "HUMCACY-ACCEPTOR-1973", "HUMMT2A-ACCEPTOR-1454", "HUMATP1A2-ACCEPTOR-21467", "HUMPNMTA-ACCEPTOR-3450", "ORAIGECA-ACCEPTOR-990", "HUMCKMT-ACCEPTOR-2362", "HUMGRP78-ACCEPTOR-775", "HUMCSPB-ACCEPTOR-3118", "HUMPPPA-ACCEPTOR-2425", "HUMCP21OH-ACCEPTOR-2698", "HUMCP21OHC-ACCEPTOR-2699", "HUMMHCP42-ACCEPTOR-3627", "HUMMHCP52-ACCEPTOR-2165", "HUMAPRTA-ACCEPTOR-1848", "HUMPRCA-ACCEPTOR-3434", "MNKHBD-ACCEPTOR-1583", "HUMAPOE4-ACCEPTOR-2977", "HUMALPHA-ACCEPTOR-2224", "HUMALPPD-ACCEPTOR-1845", "HUMTHB-ACCEPTOR-9160", "HUMHMG14A-ACCEPTOR-1599", "HUMPIM1A-ACCEPTOR-1335", "HUMTPA-ACCEPTOR-23573", "HUMMHCW1B-ACCEPTOR-3217", "HUMMHCW2B-ACCEPTOR-3219", "HUMACCYBA-ACCEPTOR-1121", "HUMACCYBB-ACCEPTOR-1057", "HUMLACTA-ACCEPTOR-2735", "HUMALPHA-ACCEPTOR-1789", "HUMSPRO-ACCEPTOR-2445", "HUMEDHB17-ACCEPTOR-2031", "HUMDES-ACCEPTOR-7550", "HUMSHBGA-ACCEPTOR-3640", "HUMALPHA-ACCEPTOR-809", "HUMPLAPL-ACCEPTOR-681", "HUMGCB1-ACCEPTOR-4081", "HUMGSHPXG-ACCEPTOR-968", "HUMSHBGA-ACCEPTOR-4029", "HUMALPPD-ACCEPTOR-1408", "HUMATP1A2-ACCEPTOR-12450", "HUMCKMT-ACCEPTOR-2563", "HUMP45C17-ACCEPTOR-7334", "HUMOPS-ACCEPTOR-3783", "HUMTHY1A-DONOR-34", "HUMKER18-ACCEPTOR-3708", "HUMALPI-ACCEPTOR-415", "HUMCP21OH-ACCEPTOR-1014", "HUMCP21OHC-ACCEPTOR-1015", "HUMMHCP42-ACCEPTOR-1945", "HUMMHCP52-ACCEPTOR-479", "HUMPLPSPC-ACCEPTOR-1830", "HUMTHB-ACCEPTOR-10541", "HUMSAACT-ACCEPTOR-2856", "HUMACCYBA-ACCEPTOR-2599", "HUMACCYBB-ACCEPTOR-2533", "HUMCSFGMA-ACCEPTOR-1620", "HUMMHEA-ACCEPTOR-3682", "CHPIGECA-ACCEPTOR-1392", "HUMERP-ACCEPTOR-1164", "HUMERPA-ACCEPTOR-1171", "HUMBNPA-ACCEPTOR-831", "HUMALPPD-ACCEPTOR-2719", "HUMMIS-ACCEPTOR-1947", "HUMGH-ACCEPTOR-986", "ORAIGECA-ACCEPTOR-1393", "HUMAPOC2-ACCEPTOR-2908", "HUMAPOC2G-ACCEPTOR-2616", "HUMAPOCII-ACCEPTOR-2897", "HUMSPRO-ACCEPTOR-3807", "HUMATP1A2-ACCEPTOR-10073", "HUMSP18A-NEG-361", "HUMALPI-ACCEPTOR-965", "HUMALPHA-ACCEPTOR-581", "HUMALPPD-ACCEPTOR-192", "HUMPLAPL-ACCEPTOR-452", "HUMCYPIIE-ACCEPTOR-3877", "HUMCFVII-ACCEPTOR-11034", "HUMMHB27B-ACCEPTOR-3119", "HUMATP1A2-ACCEPTOR-19381", "HUMTHB-ACCEPTOR-1970", "HUMMHDOB-ACCEPTOR-4706", "HUMATP1A2-ACCEPTOR-9720", "HUMCYPIIE-ACCEPTOR-9142", "HUMAPOA4A-ACCEPTOR-1382", "HUMGCB1-ACCEPTOR-5186", "HUMSAA1A-ACCEPTOR-3145", "HUMKEREP-ACCEPTOR-3206", "HUMMHB27B-ACCEPTOR-2980", "HUMTHB-ACCEPTOR-7663", "HUMKAL2-ACCEPTOR-3894", "HUMP3A-DONOR-1299", "HUMOPS-DONOR-2578", "HUMCYC1A-ACCEPTOR-3281", "HUMSAACT-ACCEPTOR-1885", "HUMFBPA-NEG-421", "HUMRPS14-ACCEPTOR-2872", "HUMANT1-ACCEPTOR-5005", "HUMATP1A2-ACCEPTOR-20736", "HUMALPI-ACCEPTOR-1631", "HUMANT1-ACCEPTOR-3950", "HUMACHRG4-NEG-1", "HUMALPHA-ACCEPTOR-1117", "HUMACCYBA-ACCEPTOR-2065", "HUMACCYBB-ACCEPTOR-1999", "HUMCYPIIE-ACCEPTOR-7515", "HUMIRBPG-ACCEPTOR-8736", "HUMMHCD8A-ACCEPTOR-1987", "HUMERP-ACCEPTOR-1566", "HUMERPA-ACCEPTOR-1575", "HUMAK1-ACCEPTOR-10045", "HUMLYL1B-ACCEPTOR-2630", "HUMALPI-ACCEPTOR-1216", "HUMPRCA-ACCEPTOR-9239", "HUMERP-ACCEPTOR-2264", "HUMERPA-ACCEPTOR-2273", "HUMH19-ACCEPTOR-2846", "HUMCFVII-ACCEPTOR-6448", "HUMMYCE1-NEG-421", "HUMAPRTA-DONOR-618", "HUMCHRA-NEG-121", "HUMGCB1-ACCEPTOR-6344", "HUMGCB1-ACCEPTOR-1905", "HUMCYPIIE-DONOR-7676", "HUMMHEA-ACCEPTOR-3545", "LEMHBDPS-NEG-1441", "HUMKER18-ACCEPTOR-4128", "HUMPSAA-ACCEPTOR-4156", "HUMINHBB2-NEG-301", "HUMSPRO-DONOR-4152", "HUMTKRA-ACCEPTOR-666", "HUMPLPSPC-ACCEPTOR-2176", "HUMPGAMMG-ACCEPTOR-1450", "HUMFVII-DONOR-70", "HUMHSP90B-ACCEPTOR-5391", "HUMALPHA-ACCEPTOR-1369", "HUMTKRA-ACCEPTOR-12318", "HUMALPHA-ACCEPTOR-3090", "HUMCRYGBC-ACCEPTOR-19693", "HUMOPS-ACCEPTOR-2409", "HUMTPA-DONOR-18038", "HUMAPOA4A-NEG-3241", "HUMCAPG-ACCEPTOR-2044", "HUMDAFA-ACCEPTOR-1235", "HUMARC6-NEG-61", "HUMCSPB-ACCEPTOR-1260", "HUMPGAMMG-ACCEPTOR-3519", "HUMUBILP-ACCEPTOR-1158", "HUMDES-ACCEPTOR-2164"), ValueGroup("HUMALBGC-ACCEPTOR-18496", "HUMMYC3L-ACCEPTOR-4242", "HUMHPRTB-ACCEPTOR-27861", "HUMTFPB-ACCEPTOR-6362", "HUMFIXG-ACCEPTOR-9613", "HUMHPRTB-ACCEPTOR-31587", "HUMAFP-ACCEPTOR-9611", "HUMCSFGMA-ACCEPTOR-891", "HUMIFNINI-ACCEPTOR-3147", "HUMIL5-ACCEPTOR-2088", "HUMIL5A-ACCEPTOR-2091", "HUMMYLCA-ACCEPTOR-1831", "HUMATPSYB-NEG-841", "HUMHMG14A-ACCEPTOR-4663", "HUMIL2-ACCEPTOR-5027", "HUMIL2A-ACCEPTOR-5980", "HUMIL2B-ACCEPTOR-4857", "HUMMYLCA-ACCEPTOR-2481", "HUMANFA-ACCEPTOR-785", "HUMATPSYB-ACCEPTOR-8745", "HUMHMG14A-ACCEPTOR-6789", "HUMHPRTB-ACCEPTOR-41424", "HUMFIXG-ACCEPTOR-23298", "HUMIL2-ACCEPTOR-685", "HUMIL2A-ACCEPTOR-1623", "HUMIL2B-ACCEPTOR-500", "HUMIL5-ACCEPTOR-1853", "HUMIL5A-ACCEPTOR-1857", "HUMAFP-ACCEPTOR-6914", "HUMP45C17-ACCEPTOR-5038", "HUMIFNINI-ACCEPTOR-3654", "HUMCYCAA-ACCEPTOR-1188", "HUMIL1B-ACCEPTOR-6540", "HUMFABP-ACCEPTOR-4068", "HUMTNFA-ACCEPTOR-1791", "HUMTNFAB-ACCEPTOR-5270", "HUMTNFX-ACCEPTOR-1565", "HUMGG-ACCEPTOR-3631", "HUMFIXG-ACCEPTOR-20604", "HUMAFP-ACCEPTOR-14589", "HUMIL5-ACCEPTOR-875", "HUMIL5A-ACCEPTOR-874", "HUMAFP-ACCEPTOR-19790", "HUMTUBAG-ACCEPTOR-2034", "HUMPRPH1-ACCEPTOR-4714", "HUMALBGC-ACCEPTOR-7729", "HUMPCNA-ACCEPTOR-5606", "HUMCYPIIE-ACCEPTOR-8083", "HUMTPA-ACCEPTOR-17940", "HUMFIXG-ACCEPTOR-9261", "HUMMHANTLE-ACCEPTOR-4055", "HUMAFP-ACCEPTOR-16955", "HUMCSFGMA-ACCEPTOR-2554", "HUMTHB-ACCEPTOR-20387", "HUMFABP-ACCEPTOR-2320", "HUMPRPH2-ACCEPTOR-3802", "HUMTFPB-ACCEPTOR-10045", "HUMPCNA-ACCEPTOR-3526", "HUMAFP-ACCEPTOR-7965", "HUMHPRTB-NEG-44641", "HUMALBGC-ACCEPTOR-13672", "HUMHSP90B-ACCEPTOR-5022", "HUMALBGC-ACCEPTOR-16911", "LEMHBGA-ACCEPTOR-425", "HUMRPS14-ACCEPTOR-2108", "HUMEF1A-ACCEPTOR-3641", "HUMHPARS1-ACCEPTOR-3750", "HUMPCNA-ACCEPTOR-2326", "HUMATPSYB-ACCEPTOR-4464", "HUMSHBGA-ACCEPTOR-2856", "HUMSODA-ACCEPTOR-7572", "HUMSAA1A-ACCEPTOR-5878", "HUMSODA-ACCEPTOR-6717", "HUMAFP-ACCEPTOR-18319", "HUMFABP-ACCEPTOR-3516", "LEMHBE-ACCEPTOR-1474", "HUMALBGC-ACCEPTOR-12451", "HUMSODA-ACCEPTOR-3391", "HUMGG-ACCEPTOR-5138", "HUMMHEA-ACCEPTOR-3890", "HUMMHCD8A-ACCEPTOR-2860", "HUMALBGC-ACCEPTOR-4046", "HUMFERP1-NEG-961", "HUMHPRTB-ACCEPTOR-16573", "HUMMHDRHA-ACCEPTOR-2969", "LEMHBG-ACCEPTOR-364", "HUMAFP-ACCEPTOR-12016", "HUMETN3-ACCEPTOR-391", "HUMMHDOB-ACCEPTOR-4486", "HUMFOS-ACCEPTOR-2658", "HUMTFPB-ACCEPTOR-11925", "HUMMCR-NEG-5341", "HUMKEREP-ACCEPTOR-3986", "HUMPRPH2-ACCEPTOR-1848", "HUMAFP-ACCEPTOR-20185", "HUMGRP78-ACCEPTOR-2145", "HUMGFP40H-ACCEPTOR-4066", "HUMDES-ACCEPTOR-5413", "HUMHIAPPA-ACCEPTOR-5862", "HUMSODA-ACCEPTOR-6266", "HUMALBGC-ACCEPTOR-14947", "HUMMH6-ACCEPTOR-2326", "HUMMH6-ACCEPTOR-3673", "HUMEF1A-ACCEPTOR-1522", "HUMRPS14-ACCEPTOR-4137", "HUMIFNINI-ACCEPTOR-2785", "HUMFOS-ACCEPTOR-2436", "HUMPCNA-ACCEPTOR-5816", "HUMSODA-ACCEPTOR-4173", "HUMHSP90B-ACCEPTOR-2605", "HUMADAG-ACCEPTOR-32543", "HUMADAM2-ACCEPTOR-784", "HUMMRP14A-ACCEPTOR-1386", "HUMPRPH1-ACCEPTOR-2738", "HUMGFP40H-ACCEPTOR-2431", "HUMTPA-ACCEPTOR-28261", "HUMHPRTB-ACCEPTOR-34907", "HUMMYCC-ACCEPTOR-6624", "HUMMYCRT-ACCEPTOR-3258", "HUMAFP-ACCEPTOR-13888", "HUMMHDOB-ACCEPTOR-3357", "HUMTUBAG-ACCEPTOR-2858", "HUMGCB1-ACCEPTOR-3009", "HUMMHDOB-ACCEPTOR-4124", "HUMFIXG-ACCEPTOR-33757", "HUMTFPB-ACCEPTOR-9259", "HUMAFP-ACCEPTOR-1782", "HUMALBGC-ACCEPTOR-6011", "HUMLACTA-ACCEPTOR-1512", "HUMATPSYB-ACCEPTOR-4093", "HUMTFPB-ACCEPTOR-2160", "HUMALBGC-ACCEPTOR-17658", "HUMALBGC-ACCEPTOR-2534", "HUMGCB1-ACCEPTOR-5810", "HUMIL1B-ACCEPTOR-1506", "HUMHPRTB-ACCEPTOR-39785", "HUMAFP-ACCEPTOR-2796", "LEMHBG-ACCEPTOR-1452", "HUMCFVII-ACCEPTOR-9389", "HUMALBGC-ACCEPTOR-6772", "HUMSODA-ACCEPTOR-5127", "ORAHBG2F-ACCEPTOR-1492", "HUMLACTA-ACCEPTOR-2160", "HUMCYCAA-ACCEPTOR-1466", "HUMHPRTB-ACCEPTOR-14750", "HUMEXCI5R-NEG-1321", "HUMMHANTLE-ACCEPTOR-2733", "HUMHPRTB-ACCEPTOR-40003", "HUMHPARS1-ACCEPTOR-5472", "MACHBCA2-ACCEPTOR-1693", "MACHBGA1-ACCEPTOR-1692", "HUMGFP40H-ACCEPTOR-992", "LEMHBGA-ACCEPTOR-1498", "GIBHBGGL-ACCEPTOR-8438", "HUMPIM1A-ACCEPTOR-3558", "HUMMHCW1B-ACCEPTOR-3569", "HUMMHCW2B-ACCEPTOR-3570", "HUMHBBAG-ACCEPTOR-2565", "HUMSODA-ACCEPTOR-3176", "HUMCRYGBC-ACCEPTOR-5283", "HUMCYPMPC-NEG-1", "HUMCP21OH-ACCEPTOR-1838", "HUMCP21OHC-ACCEPTOR-1839", "HUMMHCP42-ACCEPTOR-2767", "HUMMHCP52-ACCEPTOR-1304", "HUMATPSYB-ACCEPTOR-9633", "HUMRPS17A-ACCEPTOR-2225", "HUMMHCD8A-ACCEPTOR-5474", "HUMCKMT-ACCEPTOR-2011", "HUMHSP90B-ACCEPTOR-4265", "HUMTPA-ACCEPTOR-22075", "HUMATP1A2-ACCEPTOR-15320", "HUMGRP78-ACCEPTOR-2347", "HUMTPA-ACCEPTOR-32398", "HUMFIXG-ACCEPTOR-32974", "HUMIL1B-ACCEPTOR-879", "HUMHMG14A-ACCEPTOR-1487", "HUMKAL2-ACCEPTOR-5424", "HUMDES-ACCEPTOR-1763", "HUMALBGC-NEG-241", "HUMSODA-ACCEPTOR-6511", "HUMPSAA-ACCEPTOR-5669", "GIBHBGGL-ACCEPTOR-3506", "ORAHBG1F-ACCEPTOR-1486", "HUMHIAPPA-ACCEPTOR-959", "TARHBD-ACCEPTOR-1884", "HUMMHCW1B-ACCEPTOR-3357", "HUMMHCW2B-ACCEPTOR-3359", "HUMMHCW1B-ACCEPTOR-2261", "HUMATP1A2-ACCEPTOR-13550", "HUMMHDOB-ACCEPTOR-2649", "HUMSODA-ACCEPTOR-4869", "HUMCRPG-ACCEPTOR-578", "HUMCRPGA-ACCEPTOR-539", "HUMA1GLY2-ACCEPTOR-2469", "HUMANFA-ACCEPTOR-2205", "HUMALBGC-ACCEPTOR-10837", "HUMIL1B-ACCEPTOR-4293", "HUMKER18-ACCEPTOR-6098", "HUMALBGC-ACCEPTOR-9414", "HUMATPSYB-ACCEPTOR-5471", "HUMGRP78-ACCEPTOR-2848", "HUMGCB1-ACCEPTOR-3353", "HUMIL2-ACCEPTOR-3038", "HUMIL2A-ACCEPTOR-3975", "HUMIL2B-ACCEPTOR-2852", "HUMMRP8A-ACCEPTOR-2302", "HUMPCNA-ACCEPTOR-2520", "HUMGG-ACCEPTOR-1802", "ORAHBG2F-ACCEPTOR-392", "HUMHSP90B-ACCEPTOR-3351", "HUMAFP-ACCEPTOR-5216", "HUMTPA-ACCEPTOR-23130", "HUMHBBAG-ACCEPTOR-1466", "HUMHBBGG-ACCEPTOR-2363", "HUMCFXI14-NEG-481", "HUMTPA-ACCEPTOR-29440", "HUMGCB1-ACCEPTOR-949", "HUMLDL100-NEG-5941", "HUMCYPIIE-ACCEPTOR-12808", "HUMALBGC-ACCEPTOR-15504", "HUMTCBYX-NEG-1", "GIBHBGGL-ACCEPTOR-2401", "GIBHBGGL-ACCEPTOR-7321", "MACHBCA2-ACCEPTOR-586", "MACHBGA1-ACCEPTOR-588", "HUMCRYGBC-ACCEPTOR-18489", "HUMEF1A-ACCEPTOR-2347", "HUMSODA-ACCEPTOR-3793", "HUMATPSYB-ACCEPTOR-2698", "HUMSPRO-ACCEPTOR-2841", "HUMATP1A2-ACCEPTOR-10913", "HUMCYPIIE-ACCEPTOR-13834", "HUMKER18-ACCEPTOR-4857", "HUMCYPIIE-ACCEPTOR-12121", "HUMCYPIIE-NEG-12121", "HUMDES-ACCEPTOR-2504", "HUMHSP90B-ACCEPTOR-7462", "HUMATP1A2-ACCEPTOR-24266", "HUMMHANTLE-ACCEPTOR-3667", "HUMMHANTLE-ACCEPTOR-3842", "LEMHBE-ACCEPTOR-410", "HUMCYPIIE-ACCEPTOR-6976", "HUMPRPH1-ACCEPTOR-3136", "HUMPRPH2-ACCEPTOR-2241", "HUMAFP-ACCEPTOR-15169", "HUMGRP78-ACCEPTOR-4506", "HUMBLYM1-ACCEPTOR-505", "HUMFIXG-ACCEPTOR-13327", "HUMPRL1-NEG-61", "HUMALPPD-ACCEPTOR-734", "HUMSAA-ACCEPTOR-3086", "HUMHSP90B-ACCEPTOR-5710", "ORAHBG1F-ACCEPTOR-391", "HUMTHB-ACCEPTOR-9825", "HUMGRP78-ACCEPTOR-3371", "HUMEF1A-ACCEPTOR-2727", "HUMA1ATP-ACCEPTOR-10909", "HUMCACY-ACCEPTOR-2504", "HUMSODA-ACCEPTOR-4604", "HUMCKMT-ACCEPTOR-4348", "HUMATPSYB-ACCEPTOR-7858", "HUMGHN-ACCEPTOR-1166", "HUMMHDRHA-ACCEPTOR-5181", "HUMATPSYB-ACCEPTOR-5220", "HUMSAA1A-ACCEPTOR-6290", "HUMEF1A-ACCEPTOR-2062", "HUMCS1-ACCEPTOR-1184", "HUMGHCSA-ACCEPTOR-28101", "HUMGHCSA-ACCEPTOR-50922", "HUMTPA-ACCEPTOR-31083", "HUMPLAPL-ACCEPTOR-987", "HUMINCPPS-ACCEPTOR-4028", "HUMCATF-ACCEPTOR-1108", "HUMGHCSA-ACCEPTOR-42765", "HUMIRBPG-ACCEPTOR-4936", "HUMFBRAA-NEG-1", "HUMGH-ACCEPTOR-941", "HUMGHCSA-ACCEPTOR-5835", "HUMPLPSPC-ACCEPTOR-2621", "HUMCKMT-ACCEPTOR-1529", "HUMHSP90B-ACCEPTOR-3649", "HUMP45C17-ACCEPTOR-6267", "HUMTUBBM-ACCEPTOR-1850", "HUMSHBGA-ACCEPTOR-5640", "HUMEF1A-ACCEPTOR-2965", "HUMRPS17A-ACCEPTOR-1611", "HUMP45C17-ACCEPTOR-5819", "HUMHSP90B-ACCEPTOR-5972", "HUMTHB-ACCEPTOR-20170", "HUMKEREP-ACCEPTOR-2715", "HUMP45C17-ACCEPTOR-3774", "HUMCYPIIE-DONOR-12309", "HUMINSRC-NEG-2101", "HUMIL1B-DONOR-3748", "HUMALPPD-ACCEPTOR-985", "HUMGFP40H-ACCEPTOR-1110", "HUMADAG-ACCEPTOR-35621"), ValueGroup("ATRINS-DONOR-905", "HUMERP-DONOR-598", "HUMERPA-DONOR-608", "HUMKEREP-DONOR-3892", "HUMSAACT-DONOR-2210", "HUMOTNPI-DONOR-508", "HUMTUBBM-DONOR-1187", "HUMMYLCA-DONOR-952", "HUMMYLCA-DONOR-1975", "HUMCFVII-DONOR-4425", "HUMH19-DONOR-2128", "HUMHMG14A-DONOR-1039", "HUMSAACT-DONOR-2770", "HUMHBA4-DONOR-10579", "HUMHBA4-DONOR-6768", "MACHBA-DONOR-121", "HUMRPS17A-DONOR-615", "GCRHBBA1-DONOR-1260", "BABAPOE-DONOR-2817", "HUMMHCW2B-DONOR-1155", "HUMP45C17-DONOR-2106", "HUMACTGA-DONOR-1344", "HUMMHANTLE-DONOR-1633", "HUMACCYBA-DONOR-1624", "HUMACCYBB-DONOR-1558", "HUMDES-DONOR-2630", "HUMALPPD-DONOR-2590", "HUMMHB27B-DONOR-960", "HUMPIM1A-DONOR-1222", "HUMMHCW1B-DONOR-1152", "HUMCSPB-DONOR-258", "HUMCYC1A-DONOR-1506", "HUMCYPIIE-DONOR-4037", "HUMAPOE4-DONOR-3170", "HUMBNPA-DONOR-1087", "HUMATP1A2-DONOR-21929", "HUMHPRTB-DONOR-1674", "HUMP45C17-DONOR-7438", "HUMTUBAG-DONOR-506", "HUMPCNA-DONOR-1618", "GCRHBBA6-DONOR-461", "HUMPRCA-DONOR-2171", "HUMDES-DONOR-711", "HUMUBILP-DONOR-603", "HUMRASH-DONOR-2504", "HUMAPOAI1-DONOR-457", "HUMACTGA-DONOR-2321", "HUMTNFA-DONOR-952", "HUMTNFAB-DONOR-4431", "HUMTNFX-DONOR-726", "HUMAPOAIT-DONOR-201", "HUMA1GLY2-DONOR-1693", "HUMERP-DONOR-1310", "HUMERPA-DONOR-1317", "HUMCACY-DONOR-2132", "HUMACTGA-DONOR-513", "HUMPIM1A-DONOR-1594", "HUMADAG-DONOR-29909", "HUMADAG-DONOR-32467", "HUMADAM2-DONOR-708", "HUMMHEA-DONOR-1139", "HUMALPHA-DONOR-698", "HUMCAPG-DONOR-375", "HUMFOS-DONOR-1000", "HUMP45C17-DONOR-3913", "HUMA1GLY2-DONOR-2251", "HUMINCP-DONOR-539", "HUMKEREP-DONOR-4033", "HUMEDHB17-DONOR-2795", "HUMGCB1-DONOR-4319", "HUMCS1-DONOR-1304", "HUMGHCSA-DONOR-51042", "HUMKER18-DONOR-5233", "HUMACCYBA-DONOR-289", "HUMACCYBB-DONOR-290", "HUMAFP-DONOR-15308", "HUMGHCSA-DONOR-5955", "HUMGHN-DONOR-1286", "HUMPRCA-DONOR-5088", "HUMPSAA-DONOR-4293", "HUMCFVII-DONOR-10218", "HUMTPA-DONOR-26070", "HUMDES-DONOR-7366", "HUMGHCSA-DONOR-6212", "HUMGHN-DONOR-1544", "HUMPLAPL-DONOR-569", "HUMTKRA-DONOR-556", "HUMRPS14-DONOR-4214", "HUMTFPB-DONOR-10205", "HUMDES-DONOR-1824", "HUMCSFGMA-DONOR-1746", "HUMTFPB-DONOR-992", "HUMTPA-DONOR-29526", "HUMHPARS1-DONOR-1016", "HUMPOMC-DONOR-737", "HUMPPPA-DONOR-2234", "HUMATP1A2-DONOR-19518", "HUMGHCSA-DONOR-51300", "HUMGCB1-DONOR-5411", "HUMTPA-DONOR-31536", "HUMGHCSA-DONOR-28221", "HUMCP21OH-DONOR-917", "HUMCP21OHC-DONOR-918", "HUMMHCP42-DONOR-1848", "HUMMHCP52-DONOR-382", "HUMGH-DONOR-1061", "HUMALPPD-DONOR-309", "HUMCFVII-DONOR-6562", "HUMMYC3L-DONOR-1271", "HUMMHCP52-DONOR-1594", "HUMACCYBA-DONOR-2781", "HUMACCYBB-DONOR-2715", "HUMKER18-DONOR-5719", "HUMATP1A2-DONOR-22962", "HUMMH6-DONOR-1225", "HUMUBILP-DONOR-1367", "CHPIGECA-DONOR-903", "ORAIGECA-DONOR-904", "HUMHST-DONOR-3582", "HUMGHCSA-DONOR-13807", "HUMTKRA-DONOR-2441", "HUMPSAP-DONOR-170", "HUMCRYGBC-DONOR-2468", "HUMGHCSA-DONOR-28478", "HUMAPRTA-DONOR-2331", "HUMCAPG-DONOR-2299", "HUMLPL-NEG-181", "HUMEF1A-DONOR-579", "HUMCP21OH-DONOR-1541", "HUMCP21OHC-DONOR-1542", "HUMMHCP42-DONOR-2471", "HUMMHCP52-DONOR-1006", "HUMCP21OH-DONOR-1750", "HUMCP21OHC-DONOR-1751", "HUMMHCP42-DONOR-2679", "HUMMHCP52-DONOR-1216", "HUMATP1A2-DONOR-15496", "HUMALPPD-DONOR-1715", "HUMERP-DONOR-2444", "HUMERPA-DONOR-2453", "HUMAK1-DONOR-3959", "HUMCP21OH-DONOR-2128", "HUMMHCP42-DONOR-3057", "HUMTACEC-NEG-2341", "HUMA1ATP-DONOR-11057", "HUMATP1A2-DONOR-1663", "HUMH19-DONOR-2562", "HUMH19-DONOR-2359", "HUMKAL2-DONOR-4031", "HUMALPI-DONOR-532", "HUMPSAP-DONOR-2452", "HUMGHCSA-DONOR-42885", "HUMTKRA-DONOR-12438", "HUMA1GLY2-DONOR-2540", "HUMMHCD8A-DONOR-2891", "HUMA1ATP-DONOR-1972", "HUMCP21OHC-DONOR-2129", "HUMNGFR-NEG-2821", "HUMCS1-DONOR-1561", "HUMH19-DONOR-2766", "HUMADAG-DONOR-32886", "HUMGHVA-DONOR-488", "HUMG0S19A-DONOR-2927", "HUMG0S19B-DONOR-3063", "HUMCACY-DONOR-1386", "HUMGHCSA-DONOR-13551", "HUMACCYBA-DONOR-2504", "HUMACCYBB-DONOR-2438", "HUMCYC1A-DONOR-3019", "HUMTHB-DONOR-4453", "HUMALPHA-DONOR-2096", "HUMCSPB-DONOR-2010", "HUMPPPA-DONOR-965", "HUMALPI-DONOR-762", "HUMHPARS1-DONOR-2990", "HUMHPARS1-DONOR-4714", "HUMTKRA-DONOR-4909", "HUMTNFA-DONOR-1604", "HUMTNFAB-DONOR-5083", "HUMTNFX-DONOR-1378", "HUMCSPB-DONOR-1408", "HUMGAST2-DONOR-786", "HUMGASTA-DONOR-6470", "HUMGCB1-DONOR-1782", "HUMNMCY1A-NEG-661", "HUMATP1A2-DONOR-9194", "HUMFOS-DONOR-2005", "HUMCYPIIE-DONOR-7126", "HUMSAACT-DONOR-1779", "HUMMYCC-DONOR-5248", "HUMCFVII-DONOR-9499", "HUMALPI-DONOR-1934", "HUMTPA-DONOR-23748", "HUMLACTA-DONOR-1671", "HUMGHCSA-DONOR-43141", "HUMMIS-DONOR-593", "HUMCRYGBC-DONOR-2131", "HUMRASH-DONOR-1745", "HUMEDHB17-DONOR-2199", "HUMALPHA-DONOR-925", "HUMPLAPL-DONOR-797", "HUMATPSYB-DONOR-2217", "HUMP45C17-DONOR-4376", "HUMMETIF1-DONOR-480", "HUMALPPD-DONOR-100", "HUMPLAPL-DONOR-373", "HUMTPA-DONOR-31220", "HUMHMG14A-DONOR-1517", "HUMTNFAB-DONOR-950", "HUMPOMC-DONOR-4597", "HUMCFVII-DONOR-1690", "HUMPRCA-DONOR-5555", "HUMCYPIIE-DONOR-8260", "HUMMT2A-DONOR-421", "HUMINCP-DONOR-2163", "HUMGCB1-DONOR-5974", "HUMADAG-DONOR-26437", "HUMAPOA4A-DONOR-1025", "HUMALPHA-DONOR-487", "ORAHBG2F-DONOR-615", "HUMCAPG-DONOR-1279", "HUMGH-DONOR-1319", "HUMMT2A-DONOR-1113", "HUMATP1A2-DONOR-21613", "HUMALPPD-DONOR-537", "HUMRPS17A-DONOR-171", "HUMGHCSA-DONOR-12784", "HUMGHCSA-DONOR-42122", "HUMMYCRT-DONOR-1883", "HUMMHCD8A-DONOR-638", "HUMP45C17-DONOR-6437", "HUMALPI-DONOR-1389", "HUMTHB-DONOR-8003", "HUMAK1-DONOR-6743", "HUMMRP14A-DONOR-999", "HUMADAG-DONOR-19262", "HUMPLPSPC-DONOR-1953", "HUMMETIF1-DONOR-1131", "HUMGHCSA-DONOR-5205", "HUMCS1-DONOR-535", "HUMGHCSA-DONOR-27451", "HUMALPPD-DONOR-1158", "HUMMHEA-DONOR-1657", "HUMCFVII-DONOR-6378", "HUMA1ATP-DONOR-9653", "HUMGHCSA-DONOR-50275", "HUMGSA1R-NEG-661", "HUMMHDOB-DONOR-4235", "HUMGH-DONOR-315", "HUMIRBPG-DONOR-3151", "HUMCP21OH-DONOR-1940", "HUMCP21OHC-DONOR-1941", "HUMMHCP42-DONOR-2869", "HUMMHCP52-DONOR-1406", "HUMALPI-DONOR-333", "HUMCKMT-DONOR-1326", "HUMCRYGBC-DONOR-18732", "HUMGHN-DONOR-539", "HUMSHBGA-DONOR-3802", "HUMCRPG-DONOR-300", "HUMCRPGA-DONOR-261", "HUMP45C17-DONOR-6035", "HUMMHDRHA-DONOR-3215", "HUMVPNP-DONOR-315", "HUMHSP27P-NEG-601", "HUMPGAMMG-DONOR-1347", "HUMAPOAIT-DONOR-461", "ATRINS-DONOR-521", "HUMSHBGA-DONOR-2948", "HUMTHY1A-DONOR-853", "HUMG0S19A-DONOR-2124", "HUMIRBPG-DONOR-7130", "HUMG0S19B-DONOR-2263", "HUMTPA-DONOR-30972", "HUMAPOAI1-DONOR-716", "HUMPSAP-DONOR-1188", "HUMATP1A2-DONOR-11031", "HUMADAG-DONOR-35173", "HUMSPBAA-NEG-3061", "HUMCFVII-DONOR-556", "HUMCFVII-DONOR-8418", "HUMALDOA-NEG-4501", "HUMCAPG-DONOR-1869", "HUMAICEB-NEG-2641", "HUMAPOCIA-DONOR-2092", "HUMACTGA-ACCEPTOR-2139"), ValueGroup("HUMMYLCA-DONOR-644", "HUMALPI1-DONOR-42", "HUMMHCD8A-DONOR-1781", "HUMVPNP-DONOR-1890", "HUMPSAP-DONOR-1621", "HUMHST-DONOR-2861", "HUMFGFB-NEG-181", "HUMPRCA-DONOR-5318", "HUMAPRTA-DONOR-1982", "HUMCSF1G1-NEG-361", "HUMDES-DONOR-3236", "HUMALPHA-DONOR-2968", "HUMGRP78-DONOR-685", "HUMADAG-DONOR-4034", "HUMTUBBM-DONOR-775", "HUMMYC3L-DONOR-402", "HUMATP1A2-DONOR-20891", "CHPIGECA-DONOR-1313", "HUMHMG14A-DONOR-1394", "HUMMHB27B-DONOR-2538", "HUMMHB27B-DONOR-3013", "HUMMHEA-DONOR-3578", "HUMMHCW1B-DONOR-2778", "HUMMHCW2B-DONOR-2780", "ORAIGECA-DONOR-1314", "HUMPIM1A-DONOR-1442", "HUMGSHPXG-DONOR-690", "HUMEDHB17-DONOR-3489", "HUMMHB27B-DONOR-561", "HUMMIS-DONOR-1609", "LEMHBGA-DONOR-302", "HUMPRT1A-DONOR-118", "HUMMHCW1B-DONOR-3250", "HUMMHCW1B-DONOR-754", "HUMMHCW2B-DONOR-756", "HUMPNMTA-DONOR-3337", "HUMPSAA-DONOR-2089", "HUMANDREC-NEG-1681", "HUMMH6-DONOR-826", "HUMANT1-DONOR-3442", "HUMOTNPI-DONOR-1007", "HUMMRP8A-DONOR-2152", "HUMMHCW1B-DONOR-2537", "HUMATP1A2-DONOR-6788", "LEMHBG-DONOR-241", "HUMPRF1A-DONOR-3783", "HUMMHANTLE-DONOR-3227", "HUMAK1-DONOR-5876", "HUMALPPD-DONOR-1543", "HUMACTGA-DONOR-1015", "HUMMH6-DONOR-2841", "HUMSAACT-DONOR-769", "HUMMHCW2B-DONOR-3252", "BABAPOE-DONOR-30", "HUMAPOE4-DONOR-1061", "GCRHBBA1-DONOR-1590", "HUMALPI-DONOR-2815", "HUMPLPSPC-DONOR-1485", "HUMSAACT-DONOR-3038", "HUMMHEA-DONOR-2795", "HUMSODA-DONOR-327", "HUMADAG-DONOR-32645", "HUMALPI-DONOR-1766", "MACHBA-DONOR-443", "HUMHBA4-DONOR-10901", "HUMHBA4-DONOR-7090", "HUMMHEA-DONOR-739", "HUMAPOC2-DONOR-522", "HUMAPOC2G-DONOR-225", "HUMAPOCII-DONOR-541", "HUMKAL2-DONOR-539", "HUMMHCW2B-DONOR-2539", "HUMTHB-DONOR-7792", "HUMALPI-DONOR-1140", "HUMALPHA-DONOR-2359", "HUMMHDOB-DONOR-2919", "HUMRPS14-DONOR-225", "HUMPNMTA-DONOR-2130", "HUMCP21OH-DONOR-3064", "HUMCP21OHC-DONOR-3066", "HUMMHCP52-DONOR-2531", "HUMMHANTLE-DONOR-3889", "HUMTHB-DONOR-1044", "HUMOPS-DONOR-3949", "HUMCYCAA-DONOR-115", "HUMTUBBM-DONOR-1376", "GIBHBGGL-DONOR-2278", "HUMHBBAG-DONOR-1344", "HUMHBBGG-DONOR-2241", "ORAHBG2F-DONOR-269", "HUMOPS-DONOR-626", "HUMTNFAB-DONOR-1345", "HUMATP1A2-DONOR-20500", "HUMAPOAI1-DONOR-1059", "HUMAPOAIT-DONOR-805", "HUMALPHA-DONOR-1924", "GIBHBGGL-DONOR-7198", "HUMANT1-DONOR-1688", "HUMPSAA-DONOR-691", "HUMACCYBA-DONOR-1250", "HUMACCYBB-DONOR-1186", "GCRHBBA6-DONOR-795", "ORAHBG1F-DONOR-269", "HUMGPIBAA-DONOR-2800", "HUMBNPA-DONOR-600", "HUMKER18-DONOR-5022", "HUMALPPD-DONOR-1980", "HUMALPPD-DONOR-909", "HUMEDHB17-DONOR-2526", "HUMPRF1A-DONOR-1477", "HUMSNU1P2A-NEG-301", "HUMPIM1A-DONOR-2054", "HUMKAL2-DONOR-3781", "HUMATP1A2-DONOR-12719", "HUMTPA-DONOR-28433", "HUMRPS14-DONOR-3034", "HUMCP21OH-DONOR-2877", "HUMMHCP42-DONOR-3806", "HUMMHCP52-DONOR-2344", "HUMPSAA-DONOR-4013", "HUMTHB-DONOR-7339", "HUMKER18-DONOR-3791", "HUMANFA-DONOR-1112", "MACHBCA2-DONOR-463", "MACHBGA1-DONOR-465", "HUMTHB-DONOR-4686", "HUMGCB1-DONOR-6461", "HUMUBILP-DONOR-912", "HUMCS1-DONOR-961", "HUMGHCSA-DONOR-27878", "HUMGHCSA-DONOR-50700", "HUMALPI-DONOR-2199", "HUMALPHA-DONOR-1292", "HUMMHEA-DONOR-2554", "HUMCKMT-DONOR-523", "HUMSAA-DONOR-2706", "HUMHMG14A-DONOR-4792", "HUMMHANTLE-DONOR-3008", "HUMGCB1-DONOR-1037", "HUMKAL2-DONOR-1905", "HUMSAA1A-DONOR-3240", "HUMTHB-DONOR-497", "HUMDES-DONOR-5457", "HUMSHBGA-DONOR-3310", "HUMSPRO-DONOR-1947", "HUMMHEA-DONOR-3725", "HUMMH6-DONOR-2602", "HUMFOLA1-NEG-541", "HUMGASTA-DONOR-3213", "HUMMHDRHA-DONOR-4442", "HUMLYL1B-DONOR-882", "HUMLYL1B-DONOR-2722", "HUMMHCP42-DONOR-3993", "HUMATP1A2-DONOR-13749", "HUMADAG-DONOR-29022", "HUMCP21OHC-DONOR-2878", "HUMAK1-DONOR-10237", "HUMAPOC2-DONOR-2976", "HUMAPOC2G-DONOR-2684", "HUMAPOCII-DONOR-2965", "HUMATP1A2-DONOR-15190", "HUMMHB27B-DONOR-2329", "HUMAPOCIA-DONOR-712", "HUMALPHA-DONOR-2632", "HUMALPPD-DONOR-2254", "HUMATP1A2-DONOR-7029", "HUMTPA-DONOR-3683", "HUMPLPSPC-DONOR-628", "HUMCYC1A-DONOR-2526", "HUMTKRA-DONOR-11961", "HUMALPI-DONOR-2475", "HUMDES-DONOR-2326", "HUMPGAMMG-DONOR-1631", "HUMATP1A2-DONOR-9834", "HUMSPRO-DONOR-2998", "HUMMIS-DONOR-1328", "HUMMYCRT-DONOR-888", "HUMATP1A2-DONOR-20193", "HUMALPHA-DONOR-1542", "HUMMHANTLE-DONOR-1239", "HUMCSFGMA-DONOR-793", "HUMMHCW1B-NEG-241", "HUMTKRA-DONOR-698", "HUMCYC1A-DONOR-2305", "HUMTPA-DONOR-22213", "HUMGH-DONOR-732", "HUMGHCSA-DONOR-5626", "HUMGHN-DONOR-959", "HUMHMG14A-DONOR-1647", "HUMPRCA-DONOR-8366", "HUMCKMT-DONOR-2233", "HUMTHB-DONOR-2021", "HUMKER18-DONOR-4285", "HUMKEREP-DONOR-3577", "HUMADAG-DONOR-34454", "HUMMHCD8A-DONOR-2098", "HUMGHCSA-DONOR-13226", "HUMCYC1A-DONOR-3382", "HUMTHB-DONOR-9999", "HUMAK1-DONOR-5540", "HUMGFP40H-DONOR-880", "HUMATP1A2-DONOR-22719", "HUMMRP8A-DONOR-1504", "HUMSAACT-DONOR-2499", "HUMGCB1-DONOR-3526", "HUMCKMT-DONOR-4483", "HUMTNFX-NEG-1681", "HUMANFA-DONOR-663", "HUMTPA-DONOR-19780", "HUMATP1A2-DONOR-21268", "HUMPRPH2-DONOR-917", "HUMAPOA4A-DONOR-1509", "HUMTPA-DONOR-32565", "HUMATPSYB-DONOR-5379", "HUMADAG-DONOR-31274", "HUMTHB-ACCEPTOR-4347", "HUMMHDRHA-DONOR-565", "HUMAK1-ACCEPTOR-5712", "HUMAPOE4-DONOR-1884", "ATRINS-NEG-841", "HUMSPRO-DONOR-2585", "HUMTHB-DONOR-10723", "HUMSODA-DONOR-4951", "HUMPRCA-DONOR-9357", "HUMMHCW1B-DONOR-3405", "HUMMHCW2B-DONOR-3407", "HUMEDHB17-DONOR-1938", "HUMOPS-DONOR-4305", "HUMTPA-DONOR-23241", "HUMTHB-DONOR-1728", "HUMAPRTA-DONOR-881", "HUMCYC1A-DONOR-2769", "HUMTHB-DONOR-9328", "CHPIGECA-DONOR-378", "HUMPLPSPC-DONOR-2287", "HUMKEREP-DONOR-814", "HUMGHCSA-DONOR-42555", "HUMMH6-DONOR-3506", "HUMFIL-NEG-841", "HUMPGAMMG-NEG-2221", "HUMIRBPG-DONOR-5127", "HUMSPRO-DONOR-1735", "HUMKER18-DONOR-2967", "ORAIGECA-DONOR-378", "BABAPOE-DONOR-867", "HUMGCB1-DONOR-2052", "HUMCETP6-NEG-481", "HUMSODA-DONOR-3967", "HUMKEREP-DONOR-2153", "HUMCYPIIE-DONOR-9284", "HUMHPARS1-DONOR-2602", "HUMTHB-DONOR-20241", "HUMSPRO-DONOR-2367", "TARHBB-DONOR-1560", "TARHBD-DONOR-468", "HUMCR1R-NEG-2581", "HUMPRCA-NEG-8881", "HUMHPARS1-DONOR-5547", "HUMRASH-DONOR-2191", "HUMPIM1A-DONOR-3735", "LEMHBB-DONOR-216", "HUMAPOCIA-DONOR-496", "HUMPGK1-NEG-181", "HUMRPS14-DONOR-2259", "HUMHLADPB-NEG-8761", "HUMCKMT-DONOR-2687", "HUMSPRO-DONOR-3348", "HUMPIM1A-ACCEPTOR-1543", "HUMSHBGA-DONOR-4456", "HUMATP1A2-DONOR-10208", "HUMMHSXA-NEG-1", "HUMG6PA-NEG-781", "HUMCP21OH-DONOR-2498", "HUMCP21OHC-DONOR-2499", "HUMMHCP42-DONOR-3427", "HUMMHCP52-DONOR-1964", "HUMATP1A2-DONOR-14299", "HUMAPOC2-DONOR-3303", "HUMAPOC2G-DONOR-3011", "HUMAPOCII-DONOR-3291", "HUMA1MICR-NEG-1081", "HUMPPPA-DONOR-1912", "HUMHSP90B-ACCEPTOR-3995", "HUMCAPG-ACCEPTOR-1131", "HUMIL1B-DONOR-941", "HUMADRBRA-NEG-2941"), ValueGroup("HUMFGFG1-NEG-901", "HUMCOLTHA-NEG-481", "HUMGLTH1-NEG-241", "HUMKRASP-NEG-541", "HUMIGF2AP-NEG-421", "HUMPLP1-NEG-301", "HUMRASH-NEG-601", "HUMINT2-NEG-481", "HUMPLG2B-NEG-181", "HUMRASSK2-NEG-1381", "HUMADRA-NEG-961", "ORARGIT-NEG-241", "HUMPDGA1-NEG-481", "HUMSISM-NEG-241", "HUMMUC18B-NEG-61", "HUMNAKATP1-NEG-721", "HUMSRTR2A-NEG-61", "HUMALDAR-NEG-1081", "HUMEDHB17-NEG-3541", "HUMMHAW-NEG-1201", "HUMARSBX-NEG-421", "HUMVWFM-NEG-5101", "HUMCYPB1-NEG-1141", "HUMRYR-NEG-1081", "HUMHBA4-NEG-6601", "HUMUPAA-NEG-1501", "HUMHBA3-NEG-1021", "HUMMG2-NEG-541", "HUMFESFUR-NEG-3481", "HUMIGHAB-NEG-121", "HUMTHYR01-NEG-181", "HUMGSHPXG-NEG-1081", "HUMIFNB3-NEG-5641", "HUMRENT1-NEG-601", "HUMPTCAA-NEG-481", "HUMA2PIG3-NEG-541", "HUMINHA-NEG-1", "HUMAPOBG-NEG-121", "HUMNARIA-NEG-3841", "ORAHBA01-NEG-121", "HUMSISG1-NEG-5401", "HUMCD1A-NEG-1261", "HUMCOL4A2-NEG-2581", "HUMGIR-NEG-121", "HUMPLPSPC-NEG-1801", "HUMTSG14A-NEG-301", "HUMOAT01-NEG-1", "HUMGENX-NEG-2341", "CHPRGITX-NEG-1441", "HUMCG2A1-NEG-1", "HUMHKR42-NEG-181", "HUMKERMII-NEG-181", "HUMCOL4A6-NEG-301", "HUMALLFU-NEG-61", "HUMMHCBWC-NEG-121", "HUMETS2PR-NEG-901", "HUMA1ATP-NEG-301", "HUMSB3B2-NEG-121", "HUMADRA2R-NEG-2581", "HUMEF2AB-NEG-721", "HUMNMYC-NEG-1561", "HUMEL01-NEG-901", "HUMMT2A-NEG-121", "HUMMYHC08-NEG-421", "HUMATP1A2-NEG-2101", "HUMTIR-NEG-901", "HUMMHB27B-NEG-1201", "HUMTPA-NEG-31561", "HUMIGMUTDM-NEG-361", "HUMMHCAR4-NEG-241", "HUMU170SC-NEG-481", "HUMCKBBA-NEG-481", "HUMBCL3AA-NEG-1381", "HUMCOL2A1-NEG-961", "MACAPOE-NEG-181", "HUMMHBW4-NEG-61", "HUMTRK2H-NEG-1081", "HUMERSP2B-NEG-781", "HUMICAM1-NEG-481", "HUMCINHP-NEG-121", "HUMAPOA4-NEG-61", "HUMRGLTIB-NEG-721", "HUMEGFAA-NEG-421", "HUMTFP-NEG-61", "HUMCSFGM-NEG-1", "HUMMHCP52-NEG-781", "HUMPAC-NEG-541", "HUMBCR-NEG-1801", "HUMCFXII3-NEG-3061", "HUMGP-NEG-1321", "HUMAK1-NEG-10921", "HUMMHDPBF-NEG-61", "HUMINSR2-NEG-1", "HUMCOL11A2-NEG-1861", "HUMLDLRRL-NEG-11761", "HUMERPA-NEG-1021", "AGMORS12A-NEG-181", "HUMMHCW1B-ACCEPTOR-1398", "HUMRYR-NEG-14761", "HUMMHCW2B-ACCEPTOR-1399", "HUMTHX-NEG-841", "HUMMHCB44A-NEG-361", "HUMHSP70D-NEG-1441", "HUMGA7331-NEG-241", "HUMPRC1-NEG-61", "HUMMHDRO2-NEG-2461", "HUMTKRA-NEG-12841", "HUMSPBAA-NEG-7621", "HUMMYCF1-NEG-1081", "HUMC3A1R-NEG-2941", "HUMANK-NEG-1981", "ORAINVOL-NEG-2161", "HUMRYR-NEG-10201", "HUMFESFPS-NEG-6661", "HUMMHDRBC-NEG-661", "HUMERVKA2-NEG-901", "HUMPROD4-NEG-721", "HUMGSTPIG-NEG-1381", "HUMBHAA-NEG-1261", "HUMHCKA-NEG-61", "HUMINT2-NEG-5041", "HUMRGE-NEG-1801", "HUM4F2HG1-NEG-1441", "HUMMYONM-NEG-1501", "HUMIGKAR-NEG-181", "HUMME491-NEG-601", "HUMET-NEG-301", "HUMATP-NEG-1981", "HUMLYL1B-NEG-4081", "HUMMHHLA-NEG-241", "MACHBB-NEG-4141", "HUMMK562B-NEG-1", "HUMPGKA09-NEG-241", "HUMCG5B-NEG-1261", "HUMEAR2-NEG-1381", "HUMTHMOD-NEG-421", "CHPAZGLO-NEG-1", "HUMOCS1-NEG-1", "HUMCRYGBC-NEG-3781", "HUMMPO1-NEG-1681", "HUMALPPB-NEG-781", "HUMAPRTA-NEG-1981", "HUMSERDHY-NEG-241", "HUMKER654-NEG-1441", "HUMNFM-NEG-1921", "HUMPROD1-NEG-1741", "HUMSAP1-NEG-121", "HUMCCK2-NEG-361", "HUMMETIA-NEG-1921", "HUMCP21OH-NEG-1981", "HUMALDC-NEG-61", "HUMASP-NEG-541", "HUMMHEA-ACCEPTOR-1381", "HUMPOVRA-NEG-541", "HUMCP21OHC-NEG-3061", "HUMOTNPI-NEG-481", "GCRHBBA1-NEG-61", "HUM5FDX-NEG-2041", "HUMATP1A2-NEG-20341", "HUMMRP8A-NEG-1381", "HUMHSP90-NEG-1", "HUMCNRAB-NEG-1741", "HUMHLA1EA-NEG-121", "HUMKERP2-NEG-1381", "HUMVWFA04-NEG-301", "HUMGLYSA-NEG-2761", "HUMDB1A-NEG-121", "HUMYTEST-NEG-301", "HUMCP21OHA-NEG-2521", "HUMCYP450-NEG-7321", "HUMHBB-NEG-5761", "HUMMHCAG-NEG-1", "HUMGNPAS-NEG-61", "HUMBAT2A-NEG-2761", "HUMITI2-NEG-481", "HUMC1A1-NEG-3901", "HUMPLAP2A-NEG-481", "HUMMHDRBK-NEG-1321", "HUMAK1-NEG-6361", "HUMOPS-NEG-181", "HUMCAPG-NEG-1261", "HUMMPO-NEG-301", "HUMSPM1-NEG-181", "HUMVDR-NEG-1441", "HUMLD78R-NEG-1", "HUMMHDRB4A-NEG-781", "HUMSAACT-NEG-301", "HUMRNP70K-NEG-1141", "HUMTPA-NEG-8761", "HUMACROS-NEG-1", "HUMAPOA4C-NEG-2761", "HUMFVII-ACCEPTOR-136", "HUMHPA2BR-NEG-1261", "HUMGLI-NEG-901", "HUMCSPCP-NEG-1261", "HUMIGHBU-NEG-301", "HUMVIL2-NEG-1441", "HUMGRP78-NEG-421", "HUMFGRINT-NEG-421", "HUMC4A1-NEG-2761", "HUMHER2A-NEG-3301", "HUMFNRA-NEG-361", "HUMBCRD-NEG-301", "HUMNMYCA-NEG-4561", "HUMLAMP1A-NEG-841", "HUMARF1A-NEG-301", "HUMMHC1-NEG-4321", "HUMMLC2A-NEG-1021", "TARHBD-NEG-1981", "HUMFCERA-NEG-121", "HUMMHCP42-NEG-4501", "HUMRCC1B-NEG-361", "HUMIGCD2-NEG-121", "HUMNMYCA-NEG-1", "HUMMHM71-NEG-181", "HUMAPOCIP-NEG-301", "HUMTPO03-NEG-61", "HUMMHANTLE-NEG-3181", "HUMMH6-ACCEPTOR-1451", "HUMTRKR-NEG-1201", "HUMMHB27B-ACCEPTOR-1202", "HUMACCYBB-NEG-3481", "HUMHMPFK-NEG-901", "HUMMHDC3B-NEG-2461", "HUMMHCACA-NEG-181", "HUMUPAX-NEG-3541", "HUMAPLA-NEG-1261", "HUMFNRAS-NEG-601", "HUMCG2A1-NEG-4561", "BABBMYHA-NEG-301", "HUMGFIIM-NEG-121", "HUMP68-NEG-1081", "HUMCRYGBC-NEG-12901", "HUMRUN1C2-NEG-241", "HUMHPRTB-NEG-49201", "HUMITF1-NEG-601", "GCRHBBA6-ACCEPTOR-920", "HUMHLACW2-NEG-781", "HUMAPOE3-NEG-1", "HUMTM-NEG-1441", "HUMPAIB1-NEG-61", "HUMCKMT-NEG-1321", "HUMCAL-NEG-301", "HUMCY4ARO-NEG-541", "HUMCKMM1-NEG-1921", "HUMIDB-NEG-241", "HUMPMGR-NEG-1621", "CHPMHCBAA-NEG-841", "HUMELANA-NEG-1261", "HUMCARN-NEG-1561", "HUMIRBPG-NEG-3781", "HUMCSIST-NEG-2221", "HUMMHANTLE-DONOR-2150", "HUMTHB-NEG-17281", "HUMALIPOA-NEG-781", "HUMPIM1A-NEG-1741", "HUMELAM1A-NEG-1141", "HUMCOL1A07-NEG-1", "HUMDRD2A-NEG-841", "HUMTHR-NEG-1501", "HUMMYHC5-NEG-1141", "HUMP42LA-NEG-2161", "HUMNFH3-NEG-1", "MACPEPSG-NEG-2161", "HUMMHANTLE-ACCEPTOR-1874", "HUMMHCW3-NEG-2761", "HUMTGFKA-NEG-1141", "ATRINS-ACCEPTOR-1678", "HUMMHAWB-NEG-721", "HUMGGTA-NEG-661", "HUMACTGP1-NEG-481", "HUMDBHRA-NEG-1321", "HUMBAT3B1-NEG-421", "HUMRETREC-NEG-1321", "HUMRENA3-NEG-361", "HUMA1AR2-NEG-3601", "HUMHEXB01-NEG-481", "HUMMHCCWB-NEG-241", "HUMMH6-DONOR-1727", "HUMPOMC-NEG-421", "HUMHLADP4-NEG-721", "HUMMHCW2B-DONOR-1675", "HUMCYC1A-NEG-4381", "HUMIGHVH-NEG-481", "HUMGHCSA-NEG-38521", "HUMMYP-NEG-961", "HUMPEP7-NEG-1", "HUMMHB27B-DONOR-1478", "HUMMHCW1B-DONOR-1674", "MACHBA-NEG-421", "HUMTPOB-NEG-2401", "HUMFXIIIA-NEG-181", "HUMDEF1AA-NEG-121", "HUMPRCM-NEG-1681", "HUMENOG-NEG-121", "HUMCD7-NEG-241", "HUMMHDRBS-NEG-601", "HUMPIGF2A-NEG-301", "HUMRAFR-NEG-2521", "GCRHBBA1-ACCEPTOR-1706", "HUMIHRP-NEG-541", "HUMAMYA1-NEG-541", "HUMHKB3R-NEG-121", "HUMGFIA1-NEG-841", "HUM4COLA-NEG-1021", "HUMCKMT-NEG-5881", "HUMIGFII5-NEG-121", "HUMIGHVAQ-NEG-61", "HUMVWFR1-NEG-3481", "HUMGPMSS-NEG-1501", "HUMRGRSALU-NEG-1981", "HUMGCBPRC-NEG-1501", "HUMRAP2-NEG-181", "HUMMBPA-NEG-661", "HUMTHBNA-NEG-961", "HUMHLDPWB-NEG-61", "HUMINCP-NEG-3121", "HUMSPIA04-NEG-61", "HUMIL2RA-NEG-2161", "HUMHA44G-NEG-2701", "HUMGELS3-NEG-121", "HUMPALC-NEG-2281", "HUMIGCD7-NEG-1921", "HUMIGHVA-NEG-481", "HUMMIFA-NEG-181", "HUMERG11-NEG-181", "HUMIL2RBC-NEG-1681", "HUMTGLU-NEG-781", "HUMMHTRP-NEG-661", "HUMKER19-NEG-541", "HUMALDH03-NEG-1", "HUMCA2VR-NEG-361", "HUMCOLII2-NEG-781", "HUMINIFI-NEG-481", "HUMFCRHA-NEG-661", "HUMASGPR1-NEG-661", "HUMMHA2-NEG-1321", "HUMMHDR2A-NEG-61", "HUMMGI2-NEG-901", "HUMSYNPR-NEG-61", "HUMHCR-NEG-661", "HUMTCBVD-NEG-1", "HUMPBGDR2-NEG-1", "HUMTPAR-NEG-361", "HUMXCGD-NEG-1201", "HUMDBLPRO-NEG-181", "HUMCNPG3-NEG-121", "HUMALIPOA-NEG-9901", "HUMMHDRCD-NEG-601", "HUMCG1PA1-NEG-1501", "HUMPRF1A-NEG-3601", "HUMATP1A2-NEG-11221", "HUMCOX4A-NEG-301", "HUMTHY1A-ACCEPTOR-517", "HUMVWFR-NEG-3301", "HUMIGHCH-NEG-121", "HUMPCNA-NEG-301", "HUMDBH01-NEG-1321", "HUMCYP145-NEG-301", "HUMCYP2A4-NEG-1", "HUMMHCP41-NEG-541", "GCRRSAGAB-NEG-121", "HUMALPP-NEG-1981", "HUMALPPD-NEG-841", "HUMACTASK-NEG-1021", "HUMPPPA-NEG-1561", "HUMTBBM40-NEG-841", "HUMXEH-NEG-1441", "HUMRASFA2-NEG-1321", "HUMGCB-NEG-2281", "HUMPAIA-NEG-9781", "HUMTBB5-NEG-7801", "HUMADH21C-NEG-541", "HUMDES-NEG-7201", "HUMINT2-NEG-9601", "HUMVWF01-NEG-2101", "HUMFVII-NEG-721", "HUMSPTA1A-NEG-1861", "HUMADAG-ACCEPTOR-31146", "HUMGCSFR1-NEG-61", "HUMSYN25A-NEG-1", "HUMCG4A11-NEG-961", "HUMAPRT-NEG-421", "HUMCRYGBC-NEG-8341", "HUMRETPON-NEG-1261", "HUMTHBNB-NEG-3541", "HUMTF-NEG-1261", "HUMLDL100-NEG-1381", "HUMTCRDA4-NEG-661", "HUMBCL2A-NEG-4561", "HUMCOL1A25-NEG-121", "HUMMHDQOB-NEG-1", "HUMSLIPG-NEG-961", "HUMMH-NEG-3541", "HUMMHDQBR-NEG-601", "HUMADAG-NEG-4441", "HUMKERC4-NEG-961", "HUMMHC4A-NEG-2761", "HUMBGPG-NEG-1021", "MACHBDPS2-NEG-1", "HUMDES-ACCEPTOR-7283", "HUMADAG-NEG-36361", "HUMFCGRB-NEG-61", "HUMLPLR-NEG-361", "HUMTUBAFB-NEG-1", "HUMER41-NEG-3661", "HUMADPRT01-NEG-61", "HUMKEREP-NEG-3721", "HUMCYC1A-ACCEPTOR-2108", "HUMMYCL2A-NEG-2341", "HUMDMP-NEG-3481", "HUMCYC1A-ACCEPTOR-2611", "HUMC1R-NEG-1801", "HUMFNMC-NEG-3481", "HUMRGM-NEG-4441", "HUMIGKVJ2-NEG-961", "HUMCYC1A-ACCEPTOR-2858"), ValueGroup("HUMBCL2A-NEG-1", "HUMRGNTSA-NEG-2161", "HUMPDGFB1-NEG-1381", "HUMARP450-NEG-2521", "HUMIGCC8-NEG-481", "HUMPDGFA1-NEG-841", "ORAHBPSBD-NEG-2881", "HUMFUR1-NEG-3901", "HUMPYGM09-NEG-1", "HUMRASR2-NEG-1621", "HUMTPO13-NEG-1021", "HUMIIP-NEG-61", "HUMMYCBL1-NEG-1", "HUMSISPDG-NEG-2581", "HUMTRANSB-NEG-181", "HUMANTP53-NEG-121", "CHPCD4A-NEG-961", "HUMG3PD-NEG-481", "HUMATP1A2-NEG-6661", "HUMPGRR-NEG-1681", "HUMCENPBR-NEG-901", "HUMEGR2A-NEG-421", "HUMMHB39W-NEG-1", "HUMCYPBX3-NEG-241", "HUMBCI2-NEG-241", "HUMGFIA2-NEG-3961", "HUMLYL1A-NEG-841", "HUMFOLLI1-NEG-2041", "HUMUG2B-NEG-781", "HUMMHDRHA-NEG-5341", "HUMTFA01-NEG-1321", "HUMMYCLYA-NEG-781", "CHPINVOL-NEG-1", "HUMAK1-NEG-1801", "HUMINSPR-NEG-61", "HUMRENT2-NEG-4321", "HUMHXM-NEG-1321", "HUMHPRTB-NEG-3601", "HUMTUBBM-NEG-2281", "HUMLSZH-NEG-841", "HUMPRCA-NEG-4321", "HUMHMGIB-NEG-721", "HUMTGFBG1-NEG-1801", "HUMIRGT-NEG-1681", "HUMRBP1-NEG-421", "HUMTROPA3-NEG-901", "HUMIGCC4-NEG-781", "HUMMUCC-NEG-121", "HUMANTCE-NEG-841", "HUMIGCH5-NEG-241", "HUMKEREP9-NEG-241", "HUMAGG-NEG-541", "HUMRNPA2A-NEG-1201", "HUMU3AA-NEG-961", "HUMENKPH1-NEG-1141", "HUMMYCNH-NEG-1", "HUMMHCW2B-NEG-3721", "HUMTKR-NEG-2641", "HUMIL2RX6-NEG-1", "HUMMYCBLK-NEG-781", "HUMLAMAR-NEG-1081", "HUMLCK-NEG-181", "HUMHMGYB-NEG-1561", "HUMCRYGA1-NEG-181", "HUMMHADX2-NEG-1201", "HUMALPI-NEG-3901", "CHPCLA108-NEG-841", "HUMAPOBA-NEG-4141", "HUMBHA05-NEG-61", "HUMMHDQBC-NEG-841", "HUMIGLAM1-NEG-121", "HUMPAIA-NEG-14341", "HUMLAMC-NEG-181", "HUMCFVII-NEG-661", "HUMCYP345-NEG-1381", "HUMCN2-NEG-841", "HUMMSOD-NEG-61", "HUMALDCG-NEG-1441", "HUMTCBJD-NEG-361", "HUMIRBPM-NEG-3241", "HUMHSP90B-NEG-6541", "HUMVWFA29-NEG-661", "HUMINT1G-NEG-601", "HUMTGFB-NEG-541", "HUMARAF1R-NEG-1381", "HUMIGFIIR-NEG-6841", "HUMCRYGBC-NEG-22021", "HUMESTR-NEG-2161", "HUMTHMA-NEG-121", "HUMVWFA24-NEG-781", "HUMSGLT1-NEG-1141", "HUMVWFA17-NEG-301", "HUMBCR22I-NEG-1681", "HUMMGSA-NEG-1", "HUMCCG1-NEG-1621", "HUMTCBXF-NEG-541", "HUMBN51-NEG-1261", "HUMASA-NEG-61", "HUMTACEB-NEG-241", "HUMMHCAI-NEG-1", "HUMIL2R-NEG-1", "HUMMYCTR-NEG-2041", "HUMMPOA-NEG-1321", "HUMAPB18-NEG-601", "HUMMYCC-NEG-2761", "HUMTGTPV2-NEG-841", "HUMFERG2-NEG-1", "HUMCPA4-NEG-601", "HUMIGCD6-NEG-901", "HUMC1INHI-NEG-121", "HUMPLAKO-NEG-1861", "HUMPSAP-NEG-2401", "HUMKAL2-NEG-1741", "HUMTPIPSC-NEG-61", "HUMKER56K-NEG-1201", "HUMIL7A-NEG-361", "HUMSTROMR-NEG-661", "HUMAPOCIB-NEG-1441", "HUMMHCGE1-NEG-61", "HUMRENA4-NEG-2461", "HUMPKCB1A-NEG-1321", "HUMPKCB2A-NEG-1321", "HUMCATR-NEG-1081", "HUMSPR1B-NEG-181", "HUMGAA-NEG-781", "HUMPKLR-NEG-1441", "HUMCN4GEL-NEG-2521", "HUMMHDRA2-NEG-241", "BABT1GL-NEG-61", "HUMMHCGE2-NEG-3301", "HUMSECP1-NEG-181", "HUMASFB-NEG-1021", "HUMACCYBA-NEG-2521", "HUMGLCB-NEG-661", "HUMLPHAA-NEG-1861", "HUMPBG2-NEG-841", "HUMCFVII-NEG-5221", "HUMAPC3G5-NEG-481", "ORAHBBE-NEG-2581", "HUMSODA-NEG-6361", "HUMDES-NEG-2641", "HUMVILLR1-NEG-361", "HUMFMSD-NEG-661", "HUMTFPB-NEG-421", "HUMC8GA-NEG-241", "HUMPAIA-NEG-661", "HUMP15095-NEG-4021", "HUMLCATG-NEG-3841", "HUMEPISIB2-NEG-1081", "HUMTHYB10-NEG-181", "HUM8KER1-NEG-61", "HUMMYLCA-NEG-3181", "HUMERB2R-NEG-2521", "HUMCYPD-NEG-361", "HUMEP-NEG-2461", "HUMIL71-NEG-61", "HUMHST-NEG-3961", "HUMBAT3B2-NEG-4141", "HUMPRB1-NEG-601", "HUMMHA3-NEG-1501", "HUMMHB17W-NEG-1681", "HUMPRPG-NEG-421", "HUMTHYRR5-NEG-781", "HUMTRO-NEG-181", "HUMIL1RA-NEG-2821", "HUMMYCST-NEG-1", "HUMALPL01-NEG-721", "HUMIMP-NEG-181", "HUMARA-NEG-1441", "HUMGSTPI-NEG-301", "HUMTPI-NEG-61", "HUMMHDRGB-NEG-121", "HUMSTSB-NEG-1021", "HUMGALAB-NEG-901", "HUMALP5-NEG-781", "HUMMYCH-NEG-901", "HUMDNFA-NEG-2101", "HUMTPA-NEG-27001", "HUMB2M2-NEG-1201", "HUMGAPJR-NEG-961", "HUMCETP2-NEG-661", "HUMIRRA-NEG-1861", "HUMIGLPAV-NEG-301", "HUMTPA-NEG-22441", "HUMMGPH-NEG-61", "HUMGCB1-NEG-4201", "HUMGNAZ-NEG-961", "HUMINV2-NEG-121", "HUMCYPX4-NEG-2101", "HUMUMOD-NEG-1621", "HUMLYL1B-ACCEPTOR-2189", "HUMPEPD-NEG-421", "HUMPS2G3-NEG-421", "HUMBLAST1-NEG-661", "HUMUKI3-NEG-2941", "HUMPRA403-NEG-1", "HUMLOX5-NEG-61", "HUMCR1L04-NEG-1", "HUMFVIIIB-NEG-4681", "HUMGPIBA-NEG-1561", "HUMMHDOAG1-NEG-61", "HUMADAG-NEG-27241", "HUMGLUTRN-NEG-1021", "HUMLECGBB-NEG-61", "HUMAK1-ACCEPTOR-3918", "BABAPOE-NEG-1201", "HUMLDLR18-NEG-1621", "HUMHLAB13-NEG-901", "HUMMHDOB-NEG-4261", "HUMPOMC3-NEG-241", "HUMMHDRB-NEG-901", "HUMNCAMB-NEG-661", "HUMP45C17-NEG-4861", "HUMAPOAI1-NEG-721", "HUMBCL2C-NEG-3061", "HUMAPOE4-NEG-3301", "HUMHAPRA-NEG-481", "HUMAPOB26-NEG-2881", "HUMCYPIIE-NEG-7561", "HUMGFI-NEG-1", "HUMNGFBA2-NEG-4021", "HUMSPARC07-NEG-1", "HUMPC125-NEG-3181", "HUMCGB-NEG-181", "HUMLKHA4-NEG-61", "HUMIFNB3-NEG-1081", "HUMBARR-NEG-361", "HUMCYP2BA-NEG-2761", "HUMA1GP01-NEG-301", "HUMCOL1A18-NEG-241", "HUMPSPBA-NEG-601", "HUMLGTPA-NEG-1261", "HUMSHBG-NEG-1021", "HUMTKRA-NEG-8281", "HUMTOPI-NEG-1261", "HUMGIAA-NEG-901", "HUMIGVKW1-NEG-841", "HUMGASTA-NEG-6241", "HUMINCPPS-DONOR-940", "HUMHPARS2-NEG-6961", "MACHBPEA-NEG-2161", "HUMIGKAG-NEG-481", "HUMLAM1B-NEG-121", "HUMRSA6-NEG-961", "HUMNGFBA1-NEG-2341", "CHPHPA2-NEG-1", "HUMTCRT3D-NEG-1081", "HUMSAA-NEG-2821", "HUMMRCOX2-NEG-481", "HUMTP53B-NEG-1021", "HUMIGFIIR-NEG-2281", "HUMALIPOA-NEG-5341", "HUMMHB7A-NEG-1261", "HUMGHCSA-NEG-24841", "HUMCRYGQ6-NEG-361", "HUMEGFRBB3-NEG-1201", "HUMTPA-NEG-17881", "HUMHBBAG-NEG-1441", "HUMRAF19-NEG-721", "HUMACTSM7-NEG-61", "HUMIGHAE2-NEG-781", "HUMLCAT1-NEG-721", "HUMMYHC04-NEG-1", "HUMBAT2B4-NEG-6001", "HUMGPIBAA-NEG-3661", "HUMIFNATC-NEG-661", "HUMDMHSP-NEG-1741", "HUMDHPRA-NEG-421", "HUMRASH-NEG-5161", "HUMAPOAIT-NEG-661", "HUMATPAR-NEG-3241", "HUMATPSY1-NEG-1", "HUMCYP450-NEG-2761", "HUMBMP1A-NEG-2161", "HUMINCPPS-NEG-3181", "HUMGFIBPA-NEG-1441", "HUMHPARS2-NEG-2401", "HUMTCBCE-NEG-1621", "HUMMHCA1A-NEG-601", "HUMMHBIA-NEG-541", "HUMHPRTB-NEG-17281", "HUMA2PIBB-NEG-421", "HUMPOLP-NEG-2341", "HUMTRGC64-NEG-61", "HUMSATM29B-NEG-181", "HUMAMIPEP-NEG-3361", "HUMNTSXPH-NEG-1081", "HUMMRNAEN-NEG-1681", "HUMHBGF1-NEG-601", "HUMHBB-NEG-1201", "HUMIGHBC-NEG-481", "HUMPFKM23-NEG-1621", "HUMIGKVAB-NEG-1261", "HUMTCDXA-NEG-1", "HUMTSHX-NEG-241", "HUMAPOB2-NEG-7621", "HUMCYPX1-NEG-4201", "HUMTCAYJ-NEG-181", "HUMMHEA-NEG-2041", "HUMGLYPL-NEG-1081", "HUMG0S19B-NEG-2821", "HUMALPHA-NEG-181", "HUMELA301-NEG-1", "HUMD2A-NEG-841", "HUMFESFPS-NEG-2101", "HUMMRP14A-NEG-1561", "HUMCANPR-NEG-841", "HUMIGCB4-NEG-61", "HUMGRFCIG-NEG-1501", "HUMMHDBW1-NEG-1", "HUMTPOA-NEG-841", "HUMMHDRWA-NEG-901", "HUMALPHB-NEG-241", "HUMCAMA-NEG-841", "HUMMET2AB-NEG-61", "HUMMHDCB-NEG-6601", "HUMP45C21-NEG-901", "HUMINSRC-NEG-6661", "HUMSRC2B-NEG-61", "HUMTFAA-NEG-2881", "HUMTPO08-NEG-601", "HUMPNMTA-NEG-421", "HUMCYC1-NEG-961", "HUMNEBB-NEG-241", "HUMCYP45C-NEG-3781", "HUMGGTX-NEG-1981", "HUMHBA4-NEG-11161", "HUMPAIR1-NEG-1081", "HUMRFPA-NEG-841", "HUMPSAA-NEG-2521", "HUMADAG-NEG-18121", "HUMFX-NEG-241", "HUMAK1-ACCEPTOR-10488", "HUMRASAA-NEG-481", "HUMFBRB3-NEG-1", "HUMTCRAI2-NEG-61", "HUMGPP3A08-NEG-3721", "HUMMHBC-NEG-1921", "HUMMYCAWT-NEG-1", "HUMIL4-NEG-301", "HUMGTPBPA-NEG-1081", "HUMALPI1-ACCEPTOR-124", "HUMIGKVM-NEG-421", "HUMTPO-NEG-2401", "HUMGLUD1-NEG-721", "HUMKADA-NEG-601", "HUMALPHLB-NEG-901", "HUMTCBJC-NEG-721", "HUMLDLRRL-NEG-7201", "HUMSISG1-NEG-841", "HUMMCR-NEG-781", "HUMIGKCB-NEG-1081", "HUMAPOBB-NEG-8101", "HUMAE1-NEG-2101", "HUMSNRNP1-NEG-301", "HUMA1ACMB-NEG-421", "HUMSPTA1A-NEG-6421", "HUMATCT4-NEG-1561", "HUMPDGFRAA-NEG-3421", "HUMADHIG-NEG-241", "HUMFVIIIB-NEG-121", "HUMPRP-NEG-1", "HUMUBPSE1-NEG-661", "HUMAPOCIA-NEG-2221", "HUMC1A2-NEG-661", "HUMLAP-NEG-1141", "HUMIGF27-NEG-1", "HUMSLK-NEG-841", "HUMACTGA-NEG-2641", "HUMCR1-NEG-241", "HUMINSRA-NEG-4201", "HUMPDHA-NEG-1141", "HUMHBA1-NEG-841", "HUMTCBYB-NEG-61", "HUMSTS-NEG-3361", "HUMRSKP08-NEG-61", "HUMNGFB-NEG-9361", "HUMGIP2A5-NEG-1", "HUMHTA1-NEG-241", "HUMATPB-NEG-841", "HUMFESFPS-NEG-11221", "HUMAT3X1-NEG-1", "HUMGHCSA-NEG-43081", "HUMEDHB17-ACCEPTOR-2701", "HUMFAV-NEG-3961", "HUMC1INH-NEG-481", "HUMHBA4-NEG-2041", "HUMDAFA-DONOR-1117", "HUMA1ATR-NEG-781", "HUMTNFAB-DONOR-1537", "HUMHLADPB-NEG-13321", "HUMASL1A-NEG-961", "HUMMCM-NEG-481", "HUMUKA-NEG-1501", "HUMC3-NEG-3481", "HUMMHDQAW-NEG-301"), ValueGroup("HUMIGJHI-NEG-61", "HUMALUAGP-NEG-181", "HUMERYA-NEG-661", "HUMMDNCF-NEG-1141", "HUMHTV1A-NEG-121", "HUMGFP40H-NEG-1201", "HUMIGJ2-NEG-181", "HUMDYS-NEG-12961", "HUMHIAPPA-NEG-1861", "HUMCREB-NEG-2101", "HUMF13A15-NEG-1981", "HUMC6AI-NEG-301", "HUMC45B2B-NEG-1021", "HUMGHCSA-NEG-15721", "HUMTHB-NEG-8161", "HUMCBP-NEG-1321", "HUMVTNR-NEG-5641", "HUMHPRTB-NEG-21841", "HUMPTPASE-NEG-1801", "HUMDBPG-NEG-1141", "HUMGNAS6-NEG-1", "HUMALBGC-NEG-9361", "HUMPOMC-NEG-4981", "HUMHLASBA-NEG-9721", "HUMVIPMR5-NEG-121", "HUMAFP-NEG-4141", "HUMLIPCR-NEG-1261", "HUMGHR-NEG-841", "HUMIFNAA-NEG-1621", "HUMGCRB-NEG-3481", "HUMALBGC-NEG-18481", "HUMRASK25-NEG-361", "HUMFNRB-NEG-1081", "HUMGG-NEG-5581", "HUMANT1-NEG-2881", "HUMTM30R-NEG-1441", "HUMMHCD8A-NEG-3781", "HUMPTHLHA-NEG-1381", "HUMFIXG-NEG-8221", "HUMHISH3C-NEG-781", "HUMTFRRA-NEG-3481", "HUMVWFA31-NEG-3961", "HUMFVIII-NEG-2821", "HUMCRYABA-NEG-3301", "HUMAFP-NEG-17821", "HUMHPRTB-NEG-12721", "HUMAFP1-NEG-241", "HUMRAG1-NEG-4141", "HUMHBB-NEG-42241", "HUMAMYA3-NEG-721", "HUMHPRT9-NEG-121", "HUMPLPA-NEG-2041", "HUMPA2I-NEG-541", "HUMNFLG-NEG-1861", "HUMACBPA2-NEG-1", "HUMGYPA06-NEG-421", "HUMCHEBG2-NEG-1", "HUMTHB-NEG-12721", "HUMGLA-NEG-8281", "HUMRA3A-NEG-1", "HUMGFB-NEG-2701", "HUMPALF1-NEG-781", "HUMGLUCG2-NEG-1621", "HUMPYHBASA-NEG-2641", "HUMHPRTB-NEG-40081", "HUMTSHBA1-NEG-961", "HUMADH4C1-NEG-1501", "HUMFBRG-NEG-2641", "HUMCFXI03-NEG-421", "HUMTPA-NEG-36121", "HUMNAKATPA-NEG-3061", "HUMEGFRER-NEG-901", "HUMHMG17G-NEG-3661", "HUMHLADZA-NEG-5281", "TARHBB-NEG-541", "HUMIL2A-NEG-1261", "HUMRGRSAL-NEG-1981", "HUMIFNRG-NEG-1561", "HUMAPOB10-NEG-301", "HUMGDHR-NEG-841", "HUMPGK2P-NEG-361", "HUMIFNAGS-NEG-5281", "CHPRSALPI-NEG-481", "GORHBBPG-NEG-181", "HUMIFNINI-NEG-1201", "HUMKER18-NEG-5941", "HUMIL2-NEG-2401", "HUMLC3-NEG-901", "HUMC7A-NEG-1861", "HUMHPRTB-NEG-30961", "HUMHPARS1-NEG-241", "HUMMFD8A-NEG-181", "HUMABL1B-NEG-1621", "HUMIGKP1-NEG-1441", "HUMAMYB-NEG-2041", "HUMMCAF-NEG-661", "HUMPRPC4B-NEG-541", "HUMAPOB4A-NEG-241", "HUMNAKATP2-NEG-2161", "AGMORS9A-NEG-481", "HUMIFNAGS-NEG-9841", "HUMSTSXG2-NEG-2461", "HUMVWFA-NEG-1", "HUMCOLA2I-NEG-841", "HUMAPOBB-NEG-3541", "HUMIL2A-NEG-5821", "HUMTNFA-NEG-3241", "HUMSIGMG3-NEG-301", "HUMDYZ1-NEG-3301", "CHPHBBPCH-NEG-301", "HUMCOL3A1A-NEG-661", "HUMHBB-NEG-55921", "HUM3ALPH-NEG-1", "HUMCALC1-NEG-1561", "HUMNPY4-NEG-241", "HUMGLA-NEG-3721", "HUMPC64-NEG-1801", "HUMGKAS-NEG-1441", "HUMGNAI1-NEG-1201", "HUMMHSBA-NEG-61", "HUMAG1-NEG-2101", "HUMALBAF1-NEG-301", "HUMCH15M-NEG-121", "MNKHAPSE-NEG-5461", "HUMMYHC-NEG-5941", "HUMFAPYSY-NEG-541", "HUMINCPPS-ACCEPTOR-2712", "HUMADH3G1-NEG-841", "HUMRGNTS-NEG-1981", "HUMCYAR03-NEG-301", "HUMFIXG-NEG-35581", "HUMSPRO-NEG-1321", "HUMFIXG-NEG-3661", "HUMKER18-NEG-1381", "HUMFOLMES-NEG-721", "HUMIGVKA2-NEG-601", "HUMERVKA-NEG-2161", "HUMELIA-NEG-2641", "HUMCAATP3-NEG-61", "HUMIGKREC-NEG-1", "HUMDAFA-NEG-1561", "CHPHBBC-NEG-1321", "HUMTCRDA-NEG-1741", "HUMGBR-NEG-2521", "HUMFIXG-NEG-31021", "HUMPCNA-NEG-4861", "HUMSTATHG2-NEG-1", "HUMPTH2-NEG-841", "HUMHBVINT-NEG-61", "HUMIFNN-NEG-601", "HUMBAT2B3-NEG-1", "HUMPDGFR-NEG-5281", "HUMTCRGE-NEG-121", "HUMHBEG-NEG-721", "HUMGPP3A17-NEG-1081", "HUMMETPOA-NEG-901", "HUMCRPGP-NEG-481", "HUMCDW44A-NEG-1261", "HUMUPIR1-NEG-841", "HUMAFP-NEG-13261", "HUMJUNA-NEG-3301", "GORHBPSBD-NEG-2281", "HUMFN3A-NEG-661", "HUMIFNAGS-NEG-721", "HUMHRGA-NEG-481", "HUMFERHX-NEG-481", "CHPGGGLOG-NEG-961", "HUMFIBEDA-NEG-2641", "HUMAFP-NEG-8701", "HUMARB-NEG-3541", "HUMINSRD-NEG-3601", "HUMRPS14-NEG-3421", "HUMHK1A-NEG-3421", "HUMMHDRW1-NEG-2581", "MACHBPSBD-NEG-1141", "HUMREPAL1-NEG-2821", "HUMALBGC-NEG-4801", "HUMNMYC-NEG-6121", "HUMAACT-NEG-481", "HUMHPS12-NEG-1621", "HUMTHEP2-NEG-121", "HUMMMTVPOL-NEG-421", "HUMMETIF-NEG-1681", "HUMMHDQAR-NEG-1", "HUMHBB-NEG-37681", "HUMPDHE1B-NEG-301", "HUMERSP1A-NEG-241", "HUMFABP-NEG-661", "HUMHBEG-NEG-5281", "HUMFVIIIC-NEG-121", "HUMALPSAT-NEG-541", "HUMFIXG-NEG-12781", "HUMHBEG-NEG-9841", "HUMHLASBA-NEG-5161", "HUMDBLTP-NEG-1081", "HUMCERP-NEG-121", "HUMFIXG-NEG-26461", "HUMPZP-NEG-1741", "HUMRASN2-NEG-1", "HUMSATAX-NEG-1441", "HUMM6PR-NEG-1861", "HUMKUP-NEG-1021", "HUMAREPAP-NEG-61", "HUMAPA-NEG-841", "HUMMGA2PS-NEG-1861", "GIBHBGGL-NEG-7861", "HUMATPSYB-NEG-9961", "HUMHPRTB-NEG-26401", "HUMTPO11-NEG-241", "HUMCACY-NEG-3481", "HUMHPRTB-NEG-35521", "HUMALDA1-NEG-661", "HUMHBB-NEG-10321", "HUMTPA-NEG-13321", "HUMRSCNTF-NEG-61", "HUMMYCL-NEG-1681", "HUMSATAR2-NEG-181", "HUMPIP-NEG-241", "HUMAMY107-NEG-1", "HUMGYPC-NEG-421", "HUMIRBPG-NEG-8341", "HUMABLLA-NEG-1", "ORAHBBPSE-NEG-2101", "HUMHLADPB-NEG-4201", "HUMIL1B-NEG-2161", "HUMS71AA-NEG-3301", "HUMLDLR01-NEG-241", "HUMAPBM-NEG-3841", "HUMBRAFA-NEG-601", "HUMIFNG-NEG-4321", "HUMGCB2-NEG-1141", "HUMEB2CR2-NEG-1141", "HUMERV34A-NEG-2521", "HUMHBB-NEG-28561", "HUMLMWOS1-NEG-301", "HUMADRBR-NEG-1861", "HUMVIP1-NEG-421", "HUMANFA-NEG-2581", "HUMMDR3-NEG-301", "HUMCSYNA-NEG-2461", "HUMPP12R-NEG-1261", "HUMCERPS-NEG-1381", "HUMGG-NEG-1021", "HUMTGC1-NEG-61", "HUMAMY2A-NEG-361", "HUMCYPNO-NEG-961", "HUMLOX5A-NEG-2161", "HUMTBB46P-NEG-421", "HUMLDL100-NEG-10501", "HUMFBRGAB-NEG-1081", "HUMIGKVI1-NEG-541", "HUMMHDQA1-NEG-661", "HUMCNR-NEG-2101", "HUMIL1BX-NEG-5461", "HUMIFNA01-NEG-421", "HUMIGB7-NEG-1381", "HUMATP1A2-NEG-15781", "HUMC1A2-NEG-5221", "CHPRSA-NEG-2161", "HUMMDR1A07-NEG-781", "HUMOAS06-NEG-181", "HUMG3PDP-NEG-1", "HUMLIDNA-NEG-2701", "HUMA1ATP-NEG-4861", "HUMPAI2A4-NEG-121", "HUMGLAA-NEG-421", "HUMRISDAD-NEG-3721", "HUMTKRA-NEG-3721", "HUMGLYCA4-NEG-181", "HUMPKCAMD-NEG-781", "HUMFVIII-NEG-7381", "HUMTCRB22-NEG-241", "HUMHBB-NEG-51361", "HUMLDHB1-NEG-2461", "HUMISG1-NEG-241", "HUMHPRTB-NEG-8161", "HUMPALC-NEG-6841", "HUMNGFB-NEG-4801", "HUMPLA2A1-NEG-1561", "HUMFIXA-NEG-781", "HUMSTATHG2-NEG-4561", "HUMMHDRHA-NEG-781", "HUMIGHVV2-NEG-841", "HUMRPHO2A-NEG-1861", "HUMCD2A-NEG-121", "MNKHBPSBD-NEG-961", "HUMANTNC-NEG-2401", "HUMIL1AG-NEG-421", "HUMDYS-NEG-3841", "HUMACHRA7-NEG-901", "HUMHSP90B-NEG-1981", "HUMIFNAD-NEG-841", "HUMAREPBV-NEG-61", "HUMPLP6-NEG-421", "HUMTHROMPR-NEG-121", "HUMHBBRT-NEG-1141", "HUMRPS17A-NEG-1561", "HUMPRPH1-NEG-4081", "HUMHBBGG-NEG-481", "HUMTCRGAA-NEG-1", "HUMCIX-NEG-2521", "HUMTCAXB-NEG-1321", "HUMUMPS-NEG-1561", "HUMTCRAJ3-NEG-301", "HUMLHRH-NEG-961", "HUMDMDHI41-NEG-481", "HUMGYPCDC-NEG-421", "HUMNRNPEP-NEG-1141", "HUMDONT01-NEG-481", "HUMMYCG2-NEG-1501", "MACHBPEA-NEG-6721", "HUMIGKVS-NEG-421", "HUMIGHHSG4-NEG-601", "HUMBURKA-NEG-1141", "HUMMYCC-NEG-7321", "HUMFXIIIC-NEG-1561", "HUMAPB13-NEG-1", "HUMTCBV81-NEG-541", "HUMGHRA06-NEG-1", "HUMCRPG-NEG-781", "HUMUBIBP-NEG-481", "HUMINS02-NEG-181", "HUMTPO04-NEG-3121", "HUMFIXG6-NEG-1", "HUMCG1A1B-NEG-121", "HUMHP2FS-NEG-1021", "HUMAPB48A-NEG-2041", "HUMHSC70-NEG-1021", "HUMMHDRP1-NEG-481", "HUMTCRB11-NEG-1", "HUMTFRR-NEG-1741", "HUMMHCC6A-NEG-2401", "HUMPROSA-NEG-1021", "HUMTRD117-NEG-301", "HUMLCK2-NEG-841", "HUMIGGFCRA-NEG-1", "HUMHBB-NEG-24001", "HUMANTCD2-NEG-661", "HUMTBG-NEG-301", "HUMHGMCOA-NEG-181", "HUMPSG3A-NEG-481", "HUMSATA-NEG-361", "HUMTCRAC-NEG-1441", "HUMADH2C1-NEG-841", "HUMGHCSA-NEG-20281", "HUMACTCA2-NEG-1381", "HUMT1418-NEG-2161", "HUMGPP3A04-NEG-421", "HUMAPOBF-NEG-5641", "HUMCRBP2-NEG-421", "HUMPUMP1-NEG-661", "HUMATPA2-NEG-781", "HUMB100DI-NEG-121", "HUMTCAYD-NEG-1", "HUMC4BP-NEG-241", "HUMNGFB-NEG-241", "HUMBMP3A-NEG-1021", "HUMTFPC-NEG-241", "HUMVPF-NEG-661", "HUMCR1RS-NEG-1681", "HUMPPS-NEG-1561", "HUMCFVII-NEG-9781", "HUMIGHVQ-NEG-61", "HUMENKB1-NEG-1081", "HUMGASTA-NEG-1681", "HUMTYR-NEG-1141", "HUMODC-NEG-241", "HUMH1FNC1-NEG-1", "HUMAPOB2-NEG-3061", "HUMPMG3BA-NEG-3001", "HUMIAIG4-NEG-421", "HUMHLA1PS-NEG-3061", "HUMFIXG-NEG-21901"), ValueGroup("HUMGHCSA-NEG-11161", "HUMFMSCPO-NEG-61", "HUMCYCPS3-NEG-1081", "HUMHBB-NEG-65041", "HUMADAG-NEG-31801", "HUMLCA-NEG-2521", "HUMH-NEG-3061", "HUMHF10-NEG-241", "HUMDBPA-NEG-1381", "HUMGHCSA-NEG-29401", "HUMMYHC10-NEG-1561", "HUMLDHA7-NEG-361", "HUMGHCSA-NEG-52201", "HUMRSCNTT-NEG-121", "HUMFNI-NEG-121", "HUMFOS-NEG-6061", "HUMP45C17-NEG-301", "HUMPRLR-NEG-1861", "HUMGRP78-NEG-4981", "HUMRSLINE-NEG-2161", "HUMALDB2-NEG-1681", "HUMTROPFB-NEG-421", "HUMATP1A2-NEG-24901", "HUMPMPCA-NEG-1141", "HUMF8L1B-NEG-2041", "HUMP47-NEG-721", "HUMMXB-NEG-1741", "HUMTGFB2A-NEG-2401", "HUMANTLAA-NEG-481", "HUMNCAM-NEG-121", "HUMCHROMB-NEG-421", "HUMCA1XIA-NEG-3421", "HUMERBC-NEG-421", "HUMMAC1A-NEG-4021", "HUMPLASTA-NEG-1561", "HUMALBGC-NEG-13921", "HUMTGL7-NEG-181", "GIBHBGGL-NEG-3301", "HUMCAM-NEG-121", "HUMCARM-NEG-661", "HUMCATF-NEG-721", "AGMKPNRSB-NEG-1", "HUMIL1AG-NEG-9541", "MACRSMB-NEG-1", "HUMRSKPA2-NEG-121", "HUMTCR3G1-NEG-961", "HUMBSF2-NEG-4141", "HUMRPS6A-NEG-601", "HUMUDPGT-NEG-1801", "HUMAPB09-NEG-1", "HUMAFLP2-NEG-61", "HUMRBSA-NEG-1381", "HUMSBLA-NEG-961", "HUMDUG-NEG-2941", "HUM7B2-NEG-901", "HUMGPP3A21-NEG-2521", "HUMRSP1GPB-NEG-181", "HUMTHRAA-NEG-361", "HUMP3A-NEG-2461", "HUMHBB-NEG-19441", "HUMGLUCG2-NEG-6181", "HUMCS5-NEG-2161", "HUMHPBA-NEG-901", "HUMALDC01-NEG-361", "LEMHBGA-NEG-361", "HUMHPARS1-NEG-9361", "HUMDYS-NEG-8401", "HUMIL2R6-NEG-1", "HUMCEAE-NEG-61", "HUMTFPB-NEG-9541", "HUMTNFAB-NEG-2641", "HUM18BPD-NEG-61", "HUMCD20-NEG-721", "HUMIR3EBA-NEG-121", "HUMHBARSA-NEG-1081", "HUMIGGRLA-NEG-841", "HUMETS1A-NEG-481", "GORRGITX-NEG-1", "HUMTBB14P-NEG-1441", "HUMGHCSA-NEG-65881", "HUM7SKP41-NEG-541", "HUMMHSBB1-NEG-1561", "HUMRSKPNA-NEG-2341", "HUMSNRNP9-NEG-121", "HUMGPP3A11-NEG-961", "HUMIL1P-NEG-301", "HUMTOPIIX-NEG-841", "HUMEDF-NEG-841", "HUMTCGCF-NEG-121", "HUMSMCK-NEG-1021", "HUMMHDRS1-NEG-241", "HUMSODG5-NEG-181", "HUMCYAR10-NEG-1381", "HUMPALD-NEG-3841", "GORHBBPG-NEG-4741", "HUMTCBCC-NEG-1981", "HUMERMTV2-NEG-301", "HUMMYC3L-DONOR-2594", "HUMALDOBG-NEG-1", "HUMIGKC1-NEG-1681", "HUMCMYBB-NEG-901", "HUMGMCSFG-NEG-2821", "HUMRNPLA-NEG-181", "HUMMPRCI-NEG-601", "HUMGHCSA-NEG-33961", "HUMADAG-NEG-22681", "HUMRHOC9-NEG-121", "HUMSATA7A-NEG-2461", "HUMANP70-NEG-361", "HUMHELB-NEG-2461", "HUMHBB-NEG-46801", "HUMPHH-NEG-541", "HUMSIGMG4-NEG-961", "HUMREN02-NEG-121", "HUMMHDR3-NEG-1441", "HUMMYH1R-NEG-1621", "HUMHBB-NEG-33121", "HUMJNLTRA-NEG-121", "HUMLAMB2-NEG-961", "ORAHBBPSE-NEG-6661", "HUMZFX-NEG-781", "HUMCYPBX1-NEG-541", "HUMCAIII1-NEG-181", "GIBIL2LTR-NEG-241", "HUMMEHR-NEG-361", "HUMFUMA-NEG-841", "HUMENKPH2-NEG-3781", "HUMPNU-NEG-721", "HUMAS08-NEG-721", "HUMRSH3-NEG-1621", "HUMDBPC-NEG-2821", "HUMIGMUD-NEG-721", "HUMBPGM3-NEG-661", "HUMHMG14A-NEG-61", "HUMGFIAB5-NEG-421", "HUMVWFA37-NEG-661", "HUMTHYRR-NEG-481", "HUMINSR-NEG-541", "HUMPRPH2-NEG-3661", "HUMHMG17-NEG-301", "HUMHBB-NEG-69601", "HUMH38-NEG-661", "HUMALDB1-NEG-2761", "HUMAPOBA-NEG-8701", "HUMAPOC2-NEG-2521", "HUMC5A2B-NEG-181", "CHPHPA3-NEG-2041", "HUMHBB-NEG-14881", "MNKHAPSE-NEG-901", "HUMCYCPSJ-NEG-361", "HUMIGKVI5-NEG-2101", "HUMGCB2-NEG-5701", "HUMF13A05-NEG-121", "HUMHOM4-NEG-841", "HUMATCCD2-NEG-1261", "HUMPPOL-NEG-1081", "HUMIGKVW-NEG-841", "HUMAPB21-NEG-7501", "HUMTGPL1-NEG-1201", "HUMUGU4CA-NEG-121", "HUMTCBVW2-NEG-301", "HUMAPB21-NEG-2941", "HUMKALA-NEG-181", "HUMC8B-NEG-241", "CHPP44-NEG-481", "HUMCINHA-NEG-541", "HUMPRC7-NEG-961", "HUMMHDC3B-NEG-7021", "HUMGHCSA-NEG-2041", "HUMCYPSCC-NEG-1501", "HUMAPOB2-NEG-12181", "HUMGSTC-NEG-421", "HUMMDR1-NEG-2401", "HUMAPOBF-NEG-1081", "HUMCOL4A1-NEG-961", "HUMVWFA01-NEG-3961", "HUMPAPA-NEG-2401", "HUMPDGFARA-NEG-4621", "HUMIL2B-NEG-3241", "HUMPS12-NEG-1321", "HUMMYHC-NEG-1381", "HUMGP3A-NEG-1921", "HUMREPA-NEG-1141", "HUMARMA-NEG-961", "HUMELI-NEG-721", "HUMHOXB-NEG-541", "HUMIGLV71-NEG-721", "HUMBGAL-NEG-781", "HUMIGMUD-NEG-5281", "HUMMHDCB-NEG-2041", "HUMIFNF-NEG-601", "HUMHODB2-NEG-301", "HUMCYPAX-NEG-421", "HUMER41-NEG-8221", "HUMUG3PD-NEG-61", "AGMRSKPNI-NEG-1141", "HUMERG2-NEG-1321", "HUMXYES2-NEG-1741", "HUMLDLRRL-NEG-2641", "HUMCOL1A39-NEG-361", "HUMGLNRS-NEG-361", "HUMMAP2A-NEG-421", "HUMSNRNP3-NEG-3901", "HUMATPSY1-NEG-4561", "HUMHLASBA-NEG-601", "HUMC1SAB-NEG-841", "HUMTOPII-NEG-1081", "HUMTROPSR-NEG-721", "HUMA1GLY2-NEG-1501", "HUMKER7R-NEG-601", "HUMAPOAI1-NEG-5281", "HUMMDRA3-NEG-961", "HUMDMDA-NEG-1201", "HUMCYPHLP-NEG-421", "HUMTCAJA-NEG-61", "HUMGHCSA-NEG-61321", "HUMCSPB-NEG-61", "HUMHSDB-NEG-121", "HUMPAI2-NEG-181", "HUMVTNR-NEG-1081", "CHPHBBPCH-NEG-4861", "HUMGHCSA-NEG-6601", "HUMTDTA-NEG-781", "HUMTKFER-NEG-1081", "HUMTCAXN-NEG-61", "HUMPKB-NEG-1741", "HUMFIGRD-NEG-361", "HUMGPBPS-NEG-961", "HUMCYCB-NEG-181", "HUMCHEF-NEG-181", "HUMSAA1A-NEG-3901", "HUMCEAB-NEG-121", "HUMC5B-NEG-3241", "HUMBCRR-NEG-1261", "HUMGHCSA-NEG-56761", "HUMCGPRA-NEG-1981", "HUMPRLA-NEG-241", "HUMATH3U5-NEG-181", "HUMIFNB3-NEG-10201", "HUMMTLMC2-NEG-781", "HUMRSO5C-NEG-361", "HUMINHBA-NEG-541", "HUMGPP3A07-NEG-1381", "HUMLDHBR-NEG-121", "HUMCSFM-NEG-181", "HUMPC2A-NEG-481", "HUMBDGALA-NEG-1441", "HUMTCBYM-NEG-181", "HUMSHBGA-NEG-4501", "HUMHTV3A-NEG-841", "HUMHMGCOA-NEG-2101", "HUMNAKATP2-NEG-6721", "HUMAPB21-NEG-12061", "HUMRIBIR-NEG-1681", "HUMADAG-NEG-13561", "HUMGDHA-NEG-181", "HUMCYPIIE-NEG-3001", "HUMFOS-NEG-1501", "HUMIFNAPS-NEG-721", "HUMBLYM1-DONOR-127", "HUMGTLPA-NEG-1621", "HUMALDB1-NEG-7321", "HUMTBB5-NEG-3241", "HUMPOLDNAA-NEG-3481", "HUMAPB03-NEG-1441", "HUMHBB-NEG-60481", "HUMTCOBI-NEG-1261", "HUMCOLA1C-NEG-1141", "HUMALDOAP-NEG-661", "HUMCEALV-NEG-721", "HUMG0S19A-NEG-2341", "HUMC21PLA-NEG-121", "HUMKITCR-NEG-1021", "HUMFABPL-NEG-61", "HUMFVIIIC-NEG-4681", "HUMZFY-NEG-2341", "HUMLYRE-NEG-1801", "HUMDLDH-NEG-1021", "HUMTCSM-NEG-241", "HUMVWFM-NEG-541", "HUMCYCPSE-NEG-541", "HUMRSALPI-NEG-181", "HUMAMD-NEG-601", "HUMHPP16C-NEG-541", "HUMGFAP-NEG-2941", "HUMANT61K-NEG-1681", "HUMACHRM2-NEG-1681", "HUMAPOBA-NEG-13261", "HUMPDGFARA-NEG-61", "HUMPDGFR-NEG-721", "HUMREPHY2-NEG-481", "HUMCTHD-NEG-1801", "HUMCYES1-NEG-1501", "HUMIGKVI3-NEG-421", "HUMATPSYB-NEG-5401", "HUMTCBZE-NEG-241", "HUMAGP1A-NEG-421", "HUMTHYRR-NEG-5041", "HUMARGL-NEG-241", "HUMMPRCI-NEG-5161", "HUMRB1RA-NEG-3061", "HUMTCAYS-NEG-121", "HUMPOLBA-NEG-781", "HUMEN1-NEG-1201", "HUMHSPGC-NEG-2821", "HUMMHCW1C-NEG-781", "HUMRSSA1B-NEG-601", "HUMC4A2-NEG-3721", "HUMTUBAG-NEG-3361", "HUMPRCA-DONOR-3601", "HUMTHB-NEG-3601", "HUMMBP17K-NEG-361", "HUMMHDRR2-NEG-121", "HUMCALLA14-NEG-61", "HUMTPA-NEG-4201", "HUMICAM1A-NEG-1981", "HUMCYP4A2-NEG-781", "HUMPAP4-NEG-781", "HUMPGKPX-NEG-961", "HUMLCAR-NEG-2161", "HUMA2M-NEG-4441", "HUMAPOCII-NEG-121", "HUMPEPC1-NEG-901", "HUMNID-NEG-3541", "HUMP120PC-NEG-2101", "HUMALDH2-NEG-1201", "HUMMHDRBX-NEG-61", "HUMZNF8-NEG-661", "HUMCR1-NEG-4801", "HUMTHROMR-NEG-721", "HUMFIXG-NEG-17341", "HUMNOXF-NEG-361", "HUMLAM1B-NEG-4681", "HUMGHCSA-NEG-47641", "HUMDCC-NEG-1561", "HUMIL1BX-NEG-901", "HUMMYOHP-NEG-901", "HUMCFTRM-NEG-2281", "HUMTNFB-NEG-121", "HUMEBUR04-NEG-241", "HUMTRA-NEG-781", "HUMHLS2R-NEG-1201", "HUMMHSXA-NEG-4561", "HUMCMOSAA-NEG-121", "HUMLMYC2-NEG-901", "HUMADH2E2-NEG-1", "MACAPOA-NEG-961", "HUMIL5A-NEG-481", "HUMGPPSBB-NEG-1021", "HUMFVA-NEG-3001", "HUMGFRIL-NEG-1441", "HUMPPARP1-NEG-181", "HUMA1ATP-NEG-9421", "HUMTFPB-NEG-4981", "HUMNAKATP1-NEG-5281", "HUMETN1-NEG-181", "HUMFSH3-NEG-1021", "HUMER141-NEG-121", "HUMAPOB-NEG-1321", "HUMPDGFRA-NEG-4441", "HUMPPH1-NEG-61", "HUMOPS-NEG-4741", "HUMAPOAIA-NEG-1", "HUMRBS-NEG-2221", "HUMMHDRC2-NEG-841", "HUMDYSIN7-NEG-3541", "HUMIFNB2-NEG-361", "HUMIGLC2A-NEG-361", "HUMPTHL2-NEG-1", "HUMAPOC2G-NEG-3001", "HUMMHDRS2-NEG-2461", "HUMHPA1B-NEG-601", "HUMLAPA-NEG-2881", "HUMIL6-NEG-1021", "HUMHIAPPA-NEG-6421", "HUMEBVR-NEG-841", "HUMROSMCF-NEG-121", "HUMRYR-NEG-5641", "HUMFLA1A-NEG-961", "HUMABLA-NEG-2701", "HUMMH6-NEG-1201", "HUMTCGXH-NEG-601", "HUMIFNA04-NEG-961", "HUMCRYGBC-NEG-17461", "HUMIGKPC-NEG-61", "HUMHLADZA-NEG-721", "HUMEF1AB-NEG-481", "HUMMHDRB3-NEG-661", "HUMUKPM-NEG-781", "HUMGHV-NEG-1861", "HUMT28E-NEG-121", "HUMSATOD-NEG-841", "HUMERVKA-NEG-6721", "HUMPAIA-NEG-5221", "HUMVWFR1-NEG-8041", "HUMG6PDA-NEG-601", "HUMCATG12-NEG-241", "HUMIGHAJ-NEG-541", "HUMPSBGA-NEG-781", "HUMFN-NEG-3241", "HUMCYPMP-NEG-181", "HUMCTSE-NEG-661", "HUMIRBP-NEG-3481", "HUMBAT2B4-NEG-1441", "HUMCS3-NEG-361", "HUMRCYP3-NEG-1081", "ORAHBG2F-NEG-181", "HUMC1RX-NEG-1201", "HUMPSAA1-NEG-1", "HUMGPPSB1-NEG-241", "HUMEGFRN-NEG-4381", "HUMKER672-NEG-2401", "HUMMEDA-NEG-2761"), ValueGroup("HUMMYLCA-DONOR-2559", "HUMIL2-DONOR-595", "HUMIL2A-DONOR-1533", "HUMIL2B-DONOR-410", "HUMALBGC-DONOR-2592", "HUMHPRTB-DONOR-16757", "HUMALBGC-DONOR-13770", "HUMAFP-DONOR-970", "HUMALBGC-DONOR-17044", "HUMPCNA-DONOR-3721", "HUMERP-DONOR-1653", "HUMERPA-DONOR-1662", "HUMALBGC-DONOR-6223", "HUMHPRTB-DONOR-40080", "HUMAFP-DONOR-1834", "HUMALBGC-DONOR-15086", "HUMTFPB-DONOR-9438", "HUMGG-DONOR-3769", "HUMFIXG-DONOR-13441", "HUMALBGC-DONOR-7827", "HUMGFP40H-DONOR-2563", "HUMHPRTB-DONOR-39832", "HUMIL5-DONOR-908", "HUMIL5A-DONOR-907", "HUMFABP-DONOR-2493", "HUMFABP-DONOR-3624", "HUMHPRTB-DONOR-31605", "HUMIL2-DONOR-745", "HUMIL2A-DONOR-1683", "HUMIL2B-DONOR-560", "HUMAFP-DONOR-14687", "HUMALBGC-DONOR-15728", "HUMMYLCA-DONOR-2388", "HUMALBGC-DONOR-4179", "HUMHMG14A-NEG-4621", "TARHBB-DONOR-1909", "HUMGRP78-DONOR-1510", "HUMFIXG-DONOR-9425", "HUMALBGC-DONOR-17726", "HUMFABP-DONOR-1126", "HUMIL5-DONOR-1982", "HUMIL5A-DONOR-1986", "HUMHLASBA-NEG-14281", "HUMHPRTB-DONOR-14857", "HUMALBGC-DONOR-11052", "HUMATPSYB-DONOR-3271", "HUMHSP90B-DONOR-6241", "HUMHIAPPA-DONOR-629", "HUMHPRTB-DONOR-34990", "HUMAFP-DONOR-2929", "HUMFIXG-DONOR-9638", "HUMLACTA-DONOR-2236", "HUMTR211-NEG-901", "HUMIL2-DONOR-3182", "HUMIL2A-DONOR-4119", "HUMIL2B-DONOR-2996", "HUMAFP-DONOR-14021", "TARHBD-DONOR-817", "HUMSODA-DONOR-5211", "HUMHSP90B-DONOR-5582", "HUMGRP78-DONOR-2735", "HUMIFNAII-NEG-61", "HUMP53A-NEG-1", "HUMGRP78-DONOR-3539", "HUMAFP-DONOR-17179", "HUMCATF-DONOR-646", "HUMALBGC-DONOR-12584", "HUMPCNA-DONOR-5730", "HUMIFNINI-DONOR-2232", "HUMALBGC-DONOR-1825", "HUMAFP-DONOR-5428", "HUMPCNA-DONOR-2588", "HUMCYCAA-DONOR-1365", "HUMSODA-DONOR-3286", "HUMTUBAG-DONOR-2554", "HUMHMG17P-NEG-1081", "HUMEF1A-DONOR-2242", "LEMHBB-DONOR-569", "HUMMHDOB-DONOR-4518", "HUMATPSYB-DONOR-8947", "HUMHSP90B-DONOR-4129", "HUMHSP90B-DONOR-3809", "HUMCKMT-DONOR-1625", "HUMEF1A-DONOR-1696", "HUMAFP-DONOR-9741", "HUMFIXG-DONOR-3053", "HUMEF1A-DONOR-2644", "HUMSODA-DONOR-6429", "HUMP45C17-DONOR-5125", "HUMATPSYB-DONOR-5594", "HUMFIXG-DONOR-33089", "HUMANT1-DONOR-4091", "HUMIL5-DONOR-667", "HUMIL5A-DONOR-666", "HUMATPSYB-DONOR-2881", "HUMHSP90B-DONOR-5188", "HUMETN3-DONOR-227", "HUMEF1A-DONOR-2878", "HUMEF1A-DONOR-3546", "HUMALBGC-DONOR-9544", "HUMIFNINI-DONOR-2912", "HUMRPS17A-DONOR-1717", "HUMPRPH2-DONOR-1884", "HUMIL1B-DONOR-1558", "ORAHBD-DONOR-819", "HUMHPRTB-DONOR-27927", "HUMGFP40H-DONOR-1143", "HUMEF1A-NEG-1561", "HUMMLCAC-NEG-601", "HUMGRP78-DONOR-2258", "HUMFOS-DONOR-2544", "HUMTPO06-NEG-1681", "HUMCYPIIE-DONOR-2972", "HUMPRPH1-DONOR-2774", "HUMRPS17A-DONOR-2291", "HUMHIS4A2-NEG-301", "HUMHIAPPA-DONOR-1054", "HUMEF1A-DONOR-3222", "HUMCSFGMA-DONOR-933", "HUMGFP40H-DONOR-1028", "HUMTFPB-DONOR-2272", "HUMATPSYB-DONOR-4215", "HUMLACTA-DONOR-865", "HUMKEREP-DONOR-2872", "HUMAFP-DONOR-12231", "HUMSPROTR-NEG-541", "HUMGRP78-ACCEPTOR-1372", "HUMIFNINI-DONOR-3237", "HUMGG-DONOR-1964", "HUMPRPH2-DONOR-2660", "HUMSODA-DONOR-6624", "HUMMYCC-DONOR-2852", "HUMPCNA-DONOR-2424", "HUMATPSYB-DONOR-4649", "HUMSODA-DONOR-4346", "HUMMHDOB-DONOR-1105", "LEMHBG-DONOR-587", "GIBHBGGL-DONOR-2624", "GIBHBGGL-DONOR-7544", "LEMHBE-DONOR-633", "HUMAFP-DONOR-8063", "HUMATPSYB-DONOR-8071", "HUMIL1B-DONOR-5824", "HUMMHDRHA-DONOR-3987", "HUMCRYGBC-DONOR-18389", "MACHBCA2-DONOR-809", "MACHBGA1-DONOR-811", "HUMHSP90B-DONOR-1172", "HUMPRPH1-DONOR-1687", "HUMIL1B-DONOR-416", "HUMCP21OH-DONOR-1104", "HUMCP21OHC-DONOR-1105", "HUMMHCP42-DONOR-2035", "HUMMHCP52-DONOR-569", "MNKHBD-DONOR-698", "HUMIL1B-DONOR-4458", "HUMSODA-NEG-1801", "HUMA1ATP-DONOR-7932", "HUMAPOBB-NEG-12661", "HUMCSPB-DONOR-2478", "ORAHBG1F-DONOR-614", "HUMMHANTLE-DONOR-3700", "LEMHBGA-DONOR-648", "HUMAFP-DONOR-18452", "HUMCKMT-DONOR-2448", "HUMGRP78-DONOR-3086", "HUMC9M-NEG-841", "HUMAFP-DONOR-7047", "HUMPRPH1-DONOR-3555", "HUMHBBAG-DONOR-1689", "LEMHBE-DONOR-288", "HUMSHBGA-DONOR-4189", "HUMSODA-DONOR-4739", "HUMPRP0A-NEG-2161", "HUMGCRA-NEG-3721", "HUMMHDOB-DONOR-3639", "HUMSODA-DONOR-6932", "HUMGCB1-DONOR-581", "HUMSAA-DONOR-701", "HUMADAG-NEG-9001", "HUMSODA-DONOR-3510", "HUMHSP90B-DONOR-2752", "HUMHSP90B-DONOR-7352", "HUMFXIIIB-NEG-841", "HUMSHBGA-DONOR-5396", "HUMTUBAG-DONOR-2257", "HUMAPA4R-NEG-2761", "HUMHSP90B-DONOR-4574", "HUMTFPB-DONOR-6562", "HUMINCPPS-DONOR-2825", "HUMCKMT-DONOR-5110", "HUMFIXG-DONOR-20733", "HUMAFP-DONOR-19845", "HUMHSP90B-DONOR-5858", "HUMGCB1-DONOR-3143", "HUMGG-DONOR-230", "HUMSAA1A-DONOR-6017", "HUMEF1A-ACCEPTOR-3311", "HUMRTVLH2-NEG-4681", "HUMHPRTB-NEG-53761", "HUMHSP90B-DONOR-3558", "HUMRTVLH2-NEG-121", "HUMSAA-DONOR-34", "HUMALBGC-DONOR-6905", "HUMKTEP2A-NEG-1441", "HUMMH6-ACCEPTOR-3562", "HUMCD1B1-NEG-241", "HUMTNFA-DONOR-1839", "HUMTNFAB-DONOR-5318", "HUMTNFX-DONOR-1613", "HUMHSP90B-ACCEPTOR-7018", "HUMKEREP-DONOR-3368", "HUMATPSYB-ACCEPTOR-3096", "HUMA1GLY2-DONOR-3352", "HUMHPARS1-DONOR-3825", "HUMCYPIIE-DONOR-12950", "HUMMRP14A-DONOR-1551", "HUMMYC3L-NEG-3301", "HUMMHDXAR2-NEG-1", "HUMACTGA-DONOR-2060", "HUMA1GLY2-DONOR-3606", "HUMMH6-DONOR-3321", "HUMIL1B-NEG-6721", "MNKHBD-DONOR-347", "HUMHPARS1-NEG-4801", "HUMVIM06-NEG-1", "HUMFIXG-DONOR-23501", "ORAHBD-DONOR-466", "HUMATPCAPM-NEG-661", "HUMHBB32-NEG-121", "HUMDES-DONOR-2016", "HUMCALLA-NEG-2161", "HUMADH1CA-NEG-121", "HUMSAA1A-DONOR-1815", "HUMCKMT-ACCEPTOR-5313", "HUMMRP8A-ACCEPTOR-1988", "HUMGRP78-DONOR-1007", "HUMLACF-NEG-541", "HUMFBRG-NEG-7201", " * ")), IntervalBounds(15.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5), ValueGroups(ValueGroup("G", "N", "D", "S", "R"), ValueGroup("T"), ValueGroup("A"), ValueGroup("C", " * ")), Frequencies(1379, 620, 1171, 1384, 2333, 1372, 1050, 1466, 931, 545, 259, 902, 874, 1684, 915, 680, 1292, 558, 160, 33, 94, 107, 162, 73, 76, 100, 51, 4, 0, 57, 50, 148, 83, 82, 118, 35, 0, 1, 44, 41, 132, 90, 75, 103, 38, 472, 302, 241, 239, 180, 72, 47, 102, 168, 219, 147, 284, 284, 152, 70, 75, 124, 238, 120, 52, 0, 2, 140, 78, 76, 109, 0, 122, 53, 181, 145, 143, 80, 58, 99, 73, 133, 77, 27, 58, 153, 89, 74, 110, 31, 104, 45, 263, 245, 117, 79, 59, 116, 175, 134, 65, 80, 67, 145, 76, 57, 105, 38, 3364, 1821, 1768, 3354, 3633, 2018, 1621, 2642, 1043, 1653, 1837, 807, 934, 1050, 1342, 1905, 1357, 936, 1901, 1792, 576, 622, 782, 1083, 1503, 856, 761, 62, 97, 34, 59, 72, 92, 129, 82, 55, 25, 112, 43, 30, 68, 103, 135, 59, 33, 0, 0, 35, 45, 97, 95, 135, 84, 26, 0, 0, 12, 21, 50, 85, 158, 87, 38, 45, 27, 0, 0, 77, 113, 170, 56, 0, 154, 95, 287, 276, 89, 91, 130, 77, 229, 111, 102, 1, 4, 85, 74, 135, 98, 7, 86, 85, 9, 20, 61, 105, 136, 78, 27, 141, 63, 13, 10, 85, 100, 150, 81, 22, 100, 82, 142, 97, 54, 98, 145, 92, 121, 2079, 1682, 1347, 1068, 1739, 2273, 3223, 2111, 2095, 1106, 1109, 930, 813, 1196, 1179, 1614, 2125, 1094, 299, 384, 759, 744, 915, 797, 1138, 1535, 759, 81, 103, 66, 48, 78, 49, 123, 150, 71, 2, 18, 79, 88, 64, 87, 107, 147, 104, 472, 301, 148, 167, 87, 62, 88, 131, 157, 0, 0, 17, 26, 92, 67, 108, 135, 29, 137, 85, 2, 1, 74, 69, 82, 145, 0, 98, 88, 1, 0, 79, 90, 94, 143, 13, 98, 81, 103, 124, 80, 97, 116, 122, 161, 99, 74, 230, 179, 73, 66, 91, 136, 173, 85, 104, 0, 11, 84, 57, 105, 134, 34, 70, 84, 29, 48, 94, 69, 104, 124, 60, 2423, 2187, 988, 1180, 1901, 1839, 2423, 3298, 1600, 2942, 964, 1412, 1174, 1676, 2047, 1026, 1247, 669, 2447, 887, 931, 917, 1206, 1561, 782, 860, 584, 169, 69, 94, 73, 105, 182, 45, 81, 65, 441, 172, 109, 119, 137, 123, 49, 89, 70, 0, 0, 61, 34, 101, 149, 75, 95, 21, 0, 0, 18, 1, 95, 172, 60, 89, 7, 71, 43, 2, 2, 114, 144, 46, 88, 4, 100, 67, 0, 9, 109, 137, 73, 84, 0, 141, 66, 3, 14, 109, 145, 64, 94, 1, 154, 66, 22, 30, 130, 136, 72, 89, 11, 142, 90, 12, 21, 131, 160, 59, 82, 11, 168, 71, 37, 75, 124, 153, 67, 92, 23, 3462, 1558, 2809, 1286, 2735, 3374, 1685, 1861, 1070), 0, Frequencies(3178, 191400, 8), Frequencies(3178, 191400, 8)) ; // DataGrid(SampleId, Pos, Char) +Unused Structure(VectorC) P_SampleIdLabels = VectorC("{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...}", "{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...}", "{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...}", "{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...}", "{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...}", "{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...}", "{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...}", "{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...}", "{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...}") ; // Cluster labels for variable SampleId +Unused Structure(Vector) P_PosSet = TableVector(DNA, Pos) ; // Value distribution for variable Pos +Unused Structure(VectorC) P_CharSet = TableVectorC(DNA, Char) ; // Value distribution for variable Char +Unused Structure(DataGridDeployment) P_DeployedCoclusteringAtSampleId = DataGridDeployment(P_Coclustering, 1, P_PosSet, P_CharSet) ; // Deployed coclustering for variable SampleId +Unused Numerical P_SampleIdIndex = PredictedPartIndex(P_DeployedCoclusteringAtSampleId) ; // Predicted cluster index for variable SampleId + Categorical P_SampleIdPredictedLabel = ValueAtC(P_SampleIdLabels, P_SampleIdIndex) ; // Predicted label for variable SampleId +Unused Structure(Vector) P_SampleIdPartDistances = PredictedPartDistances(P_DeployedCoclusteringAtSampleId) ; // Vector of cluster distances for variable SampleId + Numerical `P_SampleIdDistance{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...}` = ValueAt(P_SampleIdPartDistances, 1) ; + Numerical `P_SampleIdDistance{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...}` = ValueAt(P_SampleIdPartDistances, 2) ; + Numerical `P_SampleIdDistance{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...}` = ValueAt(P_SampleIdPartDistances, 3) ; + Numerical `P_SampleIdDistance{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...}` = ValueAt(P_SampleIdPartDistances, 4) ; + Numerical `P_SampleIdDistance{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...}` = ValueAt(P_SampleIdPartDistances, 5) ; + Numerical `P_SampleIdDistance{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...}` = ValueAt(P_SampleIdPartDistances, 6) ; + Numerical `P_SampleIdDistance{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...}` = ValueAt(P_SampleIdPartDistances, 7) ; + Numerical `P_SampleIdDistance{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...}` = ValueAt(P_SampleIdPartDistances, 8) ; + Numerical `P_SampleIdDistance{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...}` = ValueAt(P_SampleIdPartDistances, 9) ; +Unused Structure(Vector) P_PosPartFrequencies = PredictedPartFrequenciesAt(P_DeployedCoclusteringAtSampleId, 2) ; // Vector of cluster frequencies for variable Pos + Numerical `P_PosFrequency]-inf;15.5]` = ValueAt(P_PosPartFrequencies, 1) ; + Numerical `P_PosFrequency]15.5;26.5]` = ValueAt(P_PosPartFrequencies, 2) ; + Numerical `P_PosFrequency]26.5;27.5]` = ValueAt(P_PosPartFrequencies, 3) ; + Numerical `P_PosFrequency]27.5;28.5]` = ValueAt(P_PosPartFrequencies, 4) ; + Numerical `P_PosFrequency]28.5;29.5]` = ValueAt(P_PosPartFrequencies, 5) ; + Numerical `P_PosFrequency]29.5;30.5]` = ValueAt(P_PosPartFrequencies, 6) ; + Numerical `P_PosFrequency]30.5;31.5]` = ValueAt(P_PosPartFrequencies, 7) ; + Numerical `P_PosFrequency]31.5;32.5]` = ValueAt(P_PosPartFrequencies, 8) ; + Numerical `P_PosFrequency]32.5;33.5]` = ValueAt(P_PosPartFrequencies, 9) ; + Numerical `P_PosFrequency]33.5;34.5]` = ValueAt(P_PosPartFrequencies, 10) ; + Numerical `P_PosFrequency]34.5;35.5]` = ValueAt(P_PosPartFrequencies, 11) ; + Numerical `P_PosFrequency]35.5;36.5]` = ValueAt(P_PosPartFrequencies, 12) ; + Numerical `P_PosFrequency]36.5;+inf[` = ValueAt(P_PosPartFrequencies, 13) ; +Unused Structure(Vector) P_CharPartFrequencies = PredictedPartFrequenciesAt(P_DeployedCoclusteringAtSampleId, 3) ; // Vector of cluster frequencies for variable Char + Numerical `P_CharFrequency{G, N, D, ...}` = ValueAt(P_CharPartFrequencies, 1) ; + Numerical `P_CharFrequency{T}` = ValueAt(P_CharPartFrequencies, 2) ; + Numerical `P_CharFrequency{A}` = ValueAt(P_CharPartFrequencies, 3) ; + Numerical `P_CharFrequency{C}` = ValueAt(P_CharPartFrequencies, 4) ; +}; + +Dictionary SpliceJunctionDNA (SampleId) +{ + Categorical SampleId ; + Numerical Pos ; + Categorical Char ; +}; diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/DeploymentCoclustering_bad.json b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/DeploymentCoclustering_bad.json new file mode 100644 index 000000000..a4317f884 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/DeploymentCoclustering_bad.json @@ -0,0 +1,51 @@ +#Khiops 10.4.6i + +Root Dictionary SpliceJunction (SampleId) +{ + Categorical SampleId ; + Categorical Class ; + Table(SpliceJunctionDNA) DNA ; +Unused Structure(DataGrid) P_Coclustering = DataGrid(ValueGroups(ValueGroup("HUMMYLCA-ACCEPTOR-924", "HUMVPNP-ACCEPTOR-1688", "HUMMHCW2B-ACCEPTOR-2660", "HUMMHANTLE-ACCEPTOR-3110", "HUMGASTA-ACCEPTOR-6600", "HUMMHCW1B-ACCEPTOR-2658", "HUMIL1B-ACCEPTOR-3546", "HUMHBA4-ACCEPTOR-10696", "HUMHBA4-ACCEPTOR-6885", "MACHBA-ACCEPTOR-238", "HUMTHY1A-ACCEPTOR-1380", "HUMAPOCII-ACCEPTOR-3131", "HUMCS1-ACCEPTOR-1814", "HUMGHCSA-ACCEPTOR-51551", "HUMMYLCA-ACCEPTOR-3623", "HUMMHEA-ACCEPTOR-2678", "HUMTNFA-ACCEPTOR-2140", "HUMTNFAB-ACCEPTOR-5619", "HUMTNFX-ACCEPTOR-1914", "HUMMHB27B-ACCEPTOR-2421", "HUMGHCSA-ACCEPTOR-28732", "HUMAPOCIA-ACCEPTOR-634", "HUMGH-ACCEPTOR-1572", "HUMGHCSA-ACCEPTOR-43393", "HUMGHCSA-ACCEPTOR-6465", "HUMGHN-ACCEPTOR-1797", "HUMGHVA-ACCEPTOR-741", "HUMATP1A2-ACCEPTOR-8990", "HUMTPA-ACCEPTOR-31395", "HUMKER18-ACCEPTOR-5495", "HUMTNFAB-ACCEPTOR-1237", "HUMMIS-ACCEPTOR-1500", "HUMGAST2-ACCEPTOR-916", "HUMTPA-ACCEPTOR-30776", "HUMHPARS1-ACCEPTOR-2519", "HUMGHCSA-ACCEPTOR-42976", "HUMAPOC2-ACCEPTOR-3143", "HUMAPOC2G-ACCEPTOR-2851", "HUMOTNPI-ACCEPTOR-808", "HUMUBILP-ACCEPTOR-1488", "HUMMYLCA-ACCEPTOR-2214", "HUMATP1A2-ACCEPTOR-6683", "HUMALPHA-ACCEPTOR-2023", "HUMADAG-ACCEPTOR-28878", "HUMGH-ACCEPTOR-1154", "HUMGHCSA-ACCEPTOR-6047", "HUMGHN-ACCEPTOR-1379", "HUMCS1-ACCEPTOR-1396", "HUMGHCSA-ACCEPTOR-28313", "HUMGHCSA-ACCEPTOR-51135", "HUMCP21OH-ACCEPTOR-3161", "HUMCP21OHC-ACCEPTOR-3163", "HUMMHCP42-ACCEPTOR-4089", "HUMMHCP52-ACCEPTOR-2628", "HUMGHCSA-ACCEPTOR-13046", "HUMACCYBA-ACCEPTOR-2893", "HUMACCYBB-ACCEPTOR-2827", "HUMDES-ACCEPTOR-3015", "HUMSAA-ACCEPTOR-2567", "HUMCYC1A-ACCEPTOR-2399", "HUMOTNPI-ACCEPTOR-1091", "HUMAPOAI1-ACCEPTOR-653", "HUMAPOAIT-ACCEPTOR-398", "HUMGHCSA-ACCEPTOR-13642", "HUMCP21OH-ACCEPTOR-1386", "HUMLYL1B-ACCEPTOR-3525", "HUMH19-ACCEPTOR-2449", "HUMATP1A2-ACCEPTOR-21798", "HUMALPPD-ACCEPTOR-1642", "HUMMHCP42-ACCEPTOR-2316", "HUMCS1-ACCEPTOR-800", "HUMGHCSA-ACCEPTOR-27717", "HUMGHCSA-ACCEPTOR-50539", "HUMMYC3L-ACCEPTOR-766", "HUMMHDRHA-ACCEPTOR-4276", "HUMRASH-ACCEPTOR-3201", "HUMSAACT-ACCEPTOR-1638", "HUMMHCD8A-ACCEPTOR-733", "HUMA1GLY2-ACCEPTOR-2108", "HUMPIM1A-ACCEPTOR-1687", "HUMKAL2-ACCEPTOR-3494", "HUMCSPB-ACCEPTOR-1874", "HUMA1GLY2-ACCEPTOR-3244", "HUMADAG-ACCEPTOR-34324", "HUMGHCSA-ACCEPTOR-42394", "HUMGHCSA-ACCEPTOR-14059", "HUMTKRA-ACCEPTOR-4815", "HUMPRCA-ACCEPTOR-5420", "HUMMETIF1-ACCEPTOR-1463", "HUMPPPA-ACCEPTOR-1721", "HUMG0S19A-ACCEPTOR-2812", "HUMMHCW2B-ACCEPTOR-885", "HUMPOMC-ACCEPTOR-4445", "HUMGH-ACCEPTOR-571", "HUMGHCSA-ACCEPTOR-5465", "HUMGHN-ACCEPTOR-798", "HUMTHB-ACCEPTOR-883", "HUMALPHA-ACCEPTOR-2851", "HUMALPPD-ACCEPTOR-2473", "HUMTUBBM-ACCEPTOR-1265", "MACHBA-ACCEPTOR-587", "HUMMYCRT-ACCEPTOR-1111", "HUMEDHB17-ACCEPTOR-2346", "HUMA1ATP-ACCEPTOR-11880", "ORAIGECA-ACCEPTOR-583", "HUMHBA4-ACCEPTOR-11050", "HUMANT1-ACCEPTOR-2955", "HUMKEREP-ACCEPTOR-4597", "HUMMHCD8A-ACCEPTOR-1670", "HUMMIS-ACCEPTOR-1697", "HUMPIM1A-ACCEPTOR-4496", "HUMAPOCIA-ACCEPTOR-1956", "HUMRPS14-ACCEPTOR-5473", "HUMSAACT-ACCEPTOR-3116", "HUMCP21OHC-ACCEPTOR-1387", "HUMPSAP-ACCEPTOR-1501", "HUMERP-ACCEPTOR-2578", "HUMERPA-ACCEPTOR-2587", "HUMATP1A2-ACCEPTOR-6969", "HUMPOMC-ACCEPTOR-7483", "HUMGCB1-ACCEPTOR-1590", "HUMMH6-ACCEPTOR-955", "HUMTHB-ACCEPTOR-7876", "HUMAPOE4-ACCEPTOR-3751", "GCRHBBA1-ACCEPTOR-1385", "HUMBNPA-ACCEPTOR-1629", "HUMACCYBA-ACCEPTOR-1384", "HUMACCYBB-ACCEPTOR-1318", "HUMGHCSA-ACCEPTOR-13504", "HUMAK1-ACCEPTOR-5504", "CHPIGECA-ACCEPTOR-585", "HUMADAG-ACCEPTOR-29793", "HUMMHB27B-ACCEPTOR-690", "HUMEDHB17-ACCEPTOR-3575", "HUMCP21OH-ACCEPTOR-2960", "HUMCP21OHC-ACCEPTOR-2962", "HUMMHCP42-ACCEPTOR-3889", "HUMTKRA-ACCEPTOR-2330", "HUMMYLCA-ACCEPTOR-2864", "HUMPRF1A-ACCEPTOR-4965", "HUMVPNP-ACCEPTOR-2057", "HUMH19-ACCEPTOR-2224", "HUMATP1A2-ACCEPTOR-13878", "HUMADAG-ACCEPTOR-19200", "HUMMH6-ACCEPTOR-2724", "HUMG0S19B-ACCEPTOR-2948", "HUMP45C17-ACCEPTOR-4146", "HUMPRF1A-ACCEPTOR-3240", "HUMMYCC-ACCEPTOR-4476", "HUMPRCA-ACCEPTOR-5180", "HUMCYC1A-ACCEPTOR-3478", "HUMMHEA-ACCEPTOR-869", "HUMATP1A2-ACCEPTOR-22870", "HUMMHEA-ACCEPTOR-2278", "HUMAPOA4A-ACCEPTOR-2286", "HUMP45C17-ACCEPTOR-7956", "HUMHPARS1-ACCEPTOR-2888", "HUMHPARS1-ACCEPTOR-4612", "HUMTUBAG-ACCEPTOR-2405", "HUMCAPG-ACCEPTOR-2733", "HUMG0S19A-ACCEPTOR-3347", "HUMHST-ACCEPTOR-4120", "HUMCP21OH-ACCEPTOR-2297", "HUMCP21OHC-ACCEPTOR-2298", "HUMMHCP42-ACCEPTOR-3226", "HUMMHCP52-ACCEPTOR-1763", "HUMKEREP-ACCEPTOR-3671", "HUMSHBGA-ACCEPTOR-3120", "HUMA1GLY2-ACCEPTOR-3502", "HUMINCP-ACCEPTOR-3281", "HUMG0S19B-ACCEPTOR-3484", "HUMMHCP52-ACCEPTOR-2427", "HUMCP21OH-ACCEPTOR-1648", "HUMCP21OHC-ACCEPTOR-1649", "HUMMHCP42-ACCEPTOR-2577", "HUMMHCP52-ACCEPTOR-1114", "BABAPOE-ACCEPTOR-3437", "HUMACTGA-ACCEPTOR-1621", "HUMAK1-DONOR-954", "HUMSPRO-ACCEPTOR-4385", "HUMMIS-ACCEPTOR-1185", "HUMCRYGBC-ACCEPTOR-2225", "HUMADAG-ACCEPTOR-32821", "HUMHBA4-ACCEPTOR-7232", "HUMP3A-ACCEPTOR-2544", "HUMAPOCII-ACCEPTOR-3595", "HUMEDHB17-ACCEPTOR-3311", "HUMADAG-ACCEPTOR-32395", "HUMHST-ACCEPTOR-3478", "HUMMRP14A-ACCEPTOR-3418", "HUMALPI-ACCEPTOR-646", "HUMMHCP52-ACCEPTOR-851", "HUMPSAA-ACCEPTOR-3726", "HUMKEREP-ACCEPTOR-2070", "HUMPRT1A-ACCEPTOR-209", "HUMOPS-ACCEPTOR-4065", "HUMMHANTLE-ACCEPTOR-1363", "HUMMHCW1B-ACCEPTOR-882", "HUMALPI-ACCEPTOR-2283", "GCRHBBA6-ACCEPTOR-590", "HUMTHB-ACCEPTOR-4549", "HUMHMG14A-ACCEPTOR-1361", "HUMATP1A2-ACCEPTOR-21144", "HUMAPOAI1-ACCEPTOR-1647", "HUMAPOAIT-ACCEPTOR-1394", "HUMDES-ACCEPTOR-1920", "HUMHPARS1-ACCEPTOR-6470", "TARHBB-ACCEPTOR-1686", "HUMSAACT-ACCEPTOR-2578", "LEMHBB-ACCEPTOR-1437", "HUMIL1AG-NEG-4981", "HUMTPA-ACCEPTOR-19737", "HUMAPOC2-ACCEPTOR-3599", "HUMAPOC2G-ACCEPTOR-3309", "BABAPOE-ACCEPTOR-801", "HUMAPOE4-ACCEPTOR-1818", "HUMCP21OH-ACCEPTOR-2041", "HUMCP21OHC-ACCEPTOR-2042", "HUMMHCP42-ACCEPTOR-2970", "HUMMHCP52-ACCEPTOR-1507", "HUMCSPB-ACCEPTOR-2217", "HUMAPOAI1-ACCEPTOR-902", "HUMAPOAIT-ACCEPTOR-648", "HUMKEREP-ACCEPTOR-3451", "HUMPSAP-ACCEPTOR-2927", "HUMALPHA-ACCEPTOR-2440", "HUMALPPD-ACCEPTOR-2062", "LEMHBB-ACCEPTOR-346", "HUMMHB27B-ACCEPTOR-2053", "HUMATP1A2-ACCEPTOR-20042", "HUMCAPG-ACCEPTOR-1733", "HUMGCB1-ACCEPTOR-6555", "HUMTNFAB-ACCEPTOR-1431", "HUMATP1A2-ACCEPTOR-20331", "HUMADAG-ACCEPTOR-35070", "HUMALPI-ACCEPTOR-2064", "HUMPPPA-ACCEPTOR-2162", "HUMA1ATP-ACCEPTOR-7282", "MNKHBD-ACCEPTOR-475", "HUMRPS17A-ACCEPTOR-463", "HUMCFVII-ACCEPTOR-10094", "HUMPLPSPC-ACCEPTOR-1326", "HUMMIS-DONOR-1857", "HUMTPA-ACCEPTOR-35307", "HUMCFVII-ACCEPTOR-8277", "HUMINCP-ACCEPTOR-2049", "HUMOPS-ACCEPTOR-5138", "HUMTUBBM-ACCEPTOR-1078", "BABA1AT-NEG-181", "HUMA1ATP-ACCEPTOR-9382", "HUMKAL2-ACCEPTOR-1745", "HUMFOS-ACCEPTOR-1753", "HUMMHCW2B-ACCEPTOR-2263", "HUMMETIF1-ACCEPTOR-1065", "HUMA1GLY2-ACCEPTOR-4746", "HUMPRCA-ACCEPTOR-5063", "HUMSPRO-ACCEPTOR-1827", "HUMH19-ACCEPTOR-2643", "HUMPSAP-ACCEPTOR-993", "HUMRPS17A-ACCEPTOR-3688", "HUMPSAA-ACCEPTOR-1929", "HUMGASTA-ACCEPTOR-6254", "HUMRASH-ACCEPTOR-2012", "HUMTKRA-ACCEPTOR-12537", "HUMSHBGA-ACCEPTOR-4319", "HUMTPA-ACCEPTOR-25978", "HUMACTGA-ACCEPTOR-886", "HUMCKMT-ACCEPTOR-1127", "HUMLYL1B-DONOR-2543", "HUMATP1A2-ACCEPTOR-22617", "HUMMHCD8A-DONOR-1087", "HUMTHB-ACCEPTOR-1703", "HUMTKRA-ACCEPTOR-11871", "ORAHBD-ACCEPTOR-596", "HUMCKMT-ACCEPTOR-4984", "HUMALPI-ACCEPTOR-2926", "HUMSHBGA-ACCEPTOR-5188", "HUMAPRTA-ACCEPTOR-774", "HUMADAG-ACCEPTOR-26314", "ATRINS-ACCEPTOR-701", "HUMMH6-ACCEPTOR-3284", "HUMATP1A2-ACCEPTOR-15000", "CHPIGECA-ACCEPTOR-989", "HUMSAA-ACCEPTOR-606", "HUMMHDRHA-ACCEPTOR-3705", "HUMACTGA-ACCEPTOR-1104", "HUMTNFA-ACCEPTOR-1558", "HUMTNFAB-ACCEPTOR-5037", "HUMTNFX-ACCEPTOR-1332", "TARHBB-ACCEPTOR-2748", "HUMUBILP-ACCEPTOR-806", "HUMCFVII-ACCEPTOR-1624", "HUMGPIBAA-ACCEPTOR-3033", "HUMPRCA-ACCEPTOR-10486", "TARHBD-ACCEPTOR-594", "HUMCFVII-ACCEPTOR-6353", "HUMRASH-ACCEPTOR-2344", "HUMPNMTA-ACCEPTOR-3129", "HUMALPPD-ACCEPTOR-421", "HUMTNFAB-ACCEPTOR-1784", "HUMPSAP-ACCEPTOR-2374", "HUMAPRTA-ACCEPTOR-2558", "HUMACTGA-ACCEPTOR-2414", "HUMPRCA-ACCEPTOR-8223", "ORAHBD-ACCEPTOR-1713", "HUMSPRO-ACCEPTOR-3195", "HUMKER18-ACCEPTOR-5107", "HUMALPI-ACCEPTOR-2698", "HUMAPOCIA-ACCEPTOR-4946", "HUMIL1B-ACCEPTOR-5693", "HUMTHB-ACCEPTOR-7024", "BABAPOE-ACCEPTOR-2624", "HUMSPRO-ACCEPTOR-2022", "HUMCFVII-ACCEPTOR-4264", "HUMMT2A-ACCEPTOR-1047", "HUMIRBPG-ACCEPTOR-6987", "HUMALPI-ACCEPTOR-1861", "HUMSAACT-ACCEPTOR-2337", "HUMAPRTA-ACCEPTOR-2252", "HUMAK1-ACCEPTOR-6626", "HUMTCBA-NEG-2161", "HUMSHBGA-DONOR-2724", "HUMCACY-ACCEPTOR-1973", "HUMMT2A-ACCEPTOR-1454", "HUMATP1A2-ACCEPTOR-21467", "HUMPNMTA-ACCEPTOR-3450", "ORAIGECA-ACCEPTOR-990", "HUMCKMT-ACCEPTOR-2362", "HUMGRP78-ACCEPTOR-775", "HUMCSPB-ACCEPTOR-3118", "HUMPPPA-ACCEPTOR-2425", "HUMCP21OH-ACCEPTOR-2698", "HUMCP21OHC-ACCEPTOR-2699", "HUMMHCP42-ACCEPTOR-3627", "HUMMHCP52-ACCEPTOR-2165", "HUMAPRTA-ACCEPTOR-1848", "HUMPRCA-ACCEPTOR-3434", "MNKHBD-ACCEPTOR-1583", "HUMAPOE4-ACCEPTOR-2977", "HUMALPHA-ACCEPTOR-2224", "HUMALPPD-ACCEPTOR-1845", "HUMTHB-ACCEPTOR-9160", "HUMHMG14A-ACCEPTOR-1599", "HUMPIM1A-ACCEPTOR-1335", "HUMTPA-ACCEPTOR-23573", "HUMMHCW1B-ACCEPTOR-3217", "HUMMHCW2B-ACCEPTOR-3219", "HUMACCYBA-ACCEPTOR-1121", "HUMACCYBB-ACCEPTOR-1057", "HUMLACTA-ACCEPTOR-2735", "HUMALPHA-ACCEPTOR-1789", "HUMSPRO-ACCEPTOR-2445", "HUMEDHB17-ACCEPTOR-2031", "HUMDES-ACCEPTOR-7550", "HUMSHBGA-ACCEPTOR-3640", "HUMALPHA-ACCEPTOR-809", "HUMPLAPL-ACCEPTOR-681", "HUMGCB1-ACCEPTOR-4081", "HUMGSHPXG-ACCEPTOR-968", "HUMSHBGA-ACCEPTOR-4029", "HUMALPPD-ACCEPTOR-1408", "HUMATP1A2-ACCEPTOR-12450", "HUMCKMT-ACCEPTOR-2563", "HUMP45C17-ACCEPTOR-7334", "HUMOPS-ACCEPTOR-3783", "HUMTHY1A-DONOR-34", "HUMKER18-ACCEPTOR-3708", "HUMALPI-ACCEPTOR-415", "HUMCP21OH-ACCEPTOR-1014", "HUMCP21OHC-ACCEPTOR-1015", "HUMMHCP42-ACCEPTOR-1945", "HUMMHCP52-ACCEPTOR-479", "HUMPLPSPC-ACCEPTOR-1830", "HUMTHB-ACCEPTOR-10541", "HUMSAACT-ACCEPTOR-2856", "HUMACCYBA-ACCEPTOR-2599", "HUMACCYBB-ACCEPTOR-2533", "HUMCSFGMA-ACCEPTOR-1620", "HUMMHEA-ACCEPTOR-3682", "CHPIGECA-ACCEPTOR-1392", "HUMERP-ACCEPTOR-1164", "HUMERPA-ACCEPTOR-1171", "HUMBNPA-ACCEPTOR-831", "HUMALPPD-ACCEPTOR-2719", "HUMMIS-ACCEPTOR-1947", "HUMGH-ACCEPTOR-986", "ORAIGECA-ACCEPTOR-1393", "HUMAPOC2-ACCEPTOR-2908", "HUMAPOC2G-ACCEPTOR-2616", "HUMAPOCII-ACCEPTOR-2897", "HUMSPRO-ACCEPTOR-3807", "HUMATP1A2-ACCEPTOR-10073", "HUMSP18A-NEG-361", "HUMALPI-ACCEPTOR-965", "HUMALPHA-ACCEPTOR-581", "HUMALPPD-ACCEPTOR-192", "HUMPLAPL-ACCEPTOR-452", "HUMCYPIIE-ACCEPTOR-3877", "HUMCFVII-ACCEPTOR-11034", "HUMMHB27B-ACCEPTOR-3119", "HUMATP1A2-ACCEPTOR-19381", "HUMTHB-ACCEPTOR-1970", "HUMMHDOB-ACCEPTOR-4706", "HUMATP1A2-ACCEPTOR-9720", "HUMCYPIIE-ACCEPTOR-9142", "HUMAPOA4A-ACCEPTOR-1382", "HUMGCB1-ACCEPTOR-5186", "HUMSAA1A-ACCEPTOR-3145", "HUMKEREP-ACCEPTOR-3206", "HUMMHB27B-ACCEPTOR-2980", "HUMTHB-ACCEPTOR-7663", "HUMKAL2-ACCEPTOR-3894", "HUMP3A-DONOR-1299", "HUMOPS-DONOR-2578", "HUMCYC1A-ACCEPTOR-3281", "HUMSAACT-ACCEPTOR-1885", "HUMFBPA-NEG-421", "HUMRPS14-ACCEPTOR-2872", "HUMANT1-ACCEPTOR-5005", "HUMATP1A2-ACCEPTOR-20736", "HUMALPI-ACCEPTOR-1631", "HUMANT1-ACCEPTOR-3950", "HUMACHRG4-NEG-1", "HUMALPHA-ACCEPTOR-1117", "HUMACCYBA-ACCEPTOR-2065", "HUMACCYBB-ACCEPTOR-1999", "HUMCYPIIE-ACCEPTOR-7515", "HUMIRBPG-ACCEPTOR-8736", "HUMMHCD8A-ACCEPTOR-1987", "HUMERP-ACCEPTOR-1566", "HUMERPA-ACCEPTOR-1575", "HUMAK1-ACCEPTOR-10045", "HUMLYL1B-ACCEPTOR-2630", "HUMALPI-ACCEPTOR-1216", "HUMPRCA-ACCEPTOR-9239", "HUMERP-ACCEPTOR-2264", "HUMERPA-ACCEPTOR-2273", "HUMH19-ACCEPTOR-2846", "HUMCFVII-ACCEPTOR-6448", "HUMMYCE1-NEG-421", "HUMAPRTA-DONOR-618", "HUMCHRA-NEG-121", "HUMGCB1-ACCEPTOR-6344", "HUMGCB1-ACCEPTOR-1905", "HUMCYPIIE-DONOR-7676", "HUMMHEA-ACCEPTOR-3545", "LEMHBDPS-NEG-1441", "HUMKER18-ACCEPTOR-4128", "HUMPSAA-ACCEPTOR-4156", "HUMINHBB2-NEG-301", "HUMSPRO-DONOR-4152", "HUMTKRA-ACCEPTOR-666", "HUMPLPSPC-ACCEPTOR-2176", "HUMPGAMMG-ACCEPTOR-1450", "HUMFVII-DONOR-70", "HUMHSP90B-ACCEPTOR-5391", "HUMALPHA-ACCEPTOR-1369", "HUMTKRA-ACCEPTOR-12318", "HUMALPHA-ACCEPTOR-3090", "HUMCRYGBC-ACCEPTOR-19693", "HUMOPS-ACCEPTOR-2409", "HUMTPA-DONOR-18038", "HUMAPOA4A-NEG-3241", "HUMCAPG-ACCEPTOR-2044", "HUMDAFA-ACCEPTOR-1235", "HUMARC6-NEG-61", "HUMCSPB-ACCEPTOR-1260", "HUMPGAMMG-ACCEPTOR-3519", "HUMUBILP-ACCEPTOR-1158", "HUMDES-ACCEPTOR-2164"), ValueGroup("HUMALBGC-ACCEPTOR-18496", "HUMMYC3L-ACCEPTOR-4242", "HUMHPRTB-ACCEPTOR-27861", "HUMTFPB-ACCEPTOR-6362", "HUMFIXG-ACCEPTOR-9613", "HUMHPRTB-ACCEPTOR-31587", "HUMAFP-ACCEPTOR-9611", "HUMCSFGMA-ACCEPTOR-891", "HUMIFNINI-ACCEPTOR-3147", "HUMIL5-ACCEPTOR-2088", "HUMIL5A-ACCEPTOR-2091", "HUMMYLCA-ACCEPTOR-1831", "HUMATPSYB-NEG-841", "HUMHMG14A-ACCEPTOR-4663", "HUMIL2-ACCEPTOR-5027", "HUMIL2A-ACCEPTOR-5980", "HUMIL2B-ACCEPTOR-4857", "HUMMYLCA-ACCEPTOR-2481", "HUMANFA-ACCEPTOR-785", "HUMATPSYB-ACCEPTOR-8745", "HUMHMG14A-ACCEPTOR-6789", "HUMHPRTB-ACCEPTOR-41424", "HUMFIXG-ACCEPTOR-23298", "HUMIL2-ACCEPTOR-685", "HUMIL2A-ACCEPTOR-1623", "HUMIL2B-ACCEPTOR-500", "HUMIL5-ACCEPTOR-1853", "HUMIL5A-ACCEPTOR-1857", "HUMAFP-ACCEPTOR-6914", "HUMP45C17-ACCEPTOR-5038", "HUMIFNINI-ACCEPTOR-3654", "HUMCYCAA-ACCEPTOR-1188", "HUMIL1B-ACCEPTOR-6540", "HUMFABP-ACCEPTOR-4068", "HUMTNFA-ACCEPTOR-1791", "HUMTNFAB-ACCEPTOR-5270", "HUMTNFX-ACCEPTOR-1565", "HUMGG-ACCEPTOR-3631", "HUMFIXG-ACCEPTOR-20604", "HUMAFP-ACCEPTOR-14589", "HUMIL5-ACCEPTOR-875", "HUMIL5A-ACCEPTOR-874", "HUMAFP-ACCEPTOR-19790", "HUMTUBAG-ACCEPTOR-2034", "HUMPRPH1-ACCEPTOR-4714", "HUMALBGC-ACCEPTOR-7729", "HUMPCNA-ACCEPTOR-5606", "HUMCYPIIE-ACCEPTOR-8083", "HUMTPA-ACCEPTOR-17940", "HUMFIXG-ACCEPTOR-9261", "HUMMHANTLE-ACCEPTOR-4055", "HUMAFP-ACCEPTOR-16955", "HUMCSFGMA-ACCEPTOR-2554", "HUMTHB-ACCEPTOR-20387", "HUMFABP-ACCEPTOR-2320", "HUMPRPH2-ACCEPTOR-3802", "HUMTFPB-ACCEPTOR-10045", "HUMPCNA-ACCEPTOR-3526", "HUMAFP-ACCEPTOR-7965", "HUMHPRTB-NEG-44641", "HUMALBGC-ACCEPTOR-13672", "HUMHSP90B-ACCEPTOR-5022", "HUMALBGC-ACCEPTOR-16911", "LEMHBGA-ACCEPTOR-425", "HUMRPS14-ACCEPTOR-2108", "HUMEF1A-ACCEPTOR-3641", "HUMHPARS1-ACCEPTOR-3750", "HUMPCNA-ACCEPTOR-2326", "HUMATPSYB-ACCEPTOR-4464", "HUMSHBGA-ACCEPTOR-2856", "HUMSODA-ACCEPTOR-7572", "HUMSAA1A-ACCEPTOR-5878", "HUMSODA-ACCEPTOR-6717", "HUMAFP-ACCEPTOR-18319", "HUMFABP-ACCEPTOR-3516", "LEMHBE-ACCEPTOR-1474", "HUMALBGC-ACCEPTOR-12451", "HUMSODA-ACCEPTOR-3391", "HUMGG-ACCEPTOR-5138", "HUMMHEA-ACCEPTOR-3890", "HUMMHCD8A-ACCEPTOR-2860", "HUMALBGC-ACCEPTOR-4046", "HUMFERP1-NEG-961", "HUMHPRTB-ACCEPTOR-16573", "HUMMHDRHA-ACCEPTOR-2969", "LEMHBG-ACCEPTOR-364", "HUMAFP-ACCEPTOR-12016", "HUMETN3-ACCEPTOR-391", "HUMMHDOB-ACCEPTOR-4486", "HUMFOS-ACCEPTOR-2658", "HUMTFPB-ACCEPTOR-11925", "HUMMCR-NEG-5341", "HUMKEREP-ACCEPTOR-3986", "HUMPRPH2-ACCEPTOR-1848", "HUMAFP-ACCEPTOR-20185", "HUMGRP78-ACCEPTOR-2145", "HUMGFP40H-ACCEPTOR-4066", "HUMDES-ACCEPTOR-5413", "HUMHIAPPA-ACCEPTOR-5862", "HUMSODA-ACCEPTOR-6266", "HUMALBGC-ACCEPTOR-14947", "HUMMH6-ACCEPTOR-2326", "HUMMH6-ACCEPTOR-3673", "HUMEF1A-ACCEPTOR-1522", "HUMRPS14-ACCEPTOR-4137", "HUMIFNINI-ACCEPTOR-2785", "HUMFOS-ACCEPTOR-2436", "HUMPCNA-ACCEPTOR-5816", "HUMSODA-ACCEPTOR-4173", "HUMHSP90B-ACCEPTOR-2605", "HUMADAG-ACCEPTOR-32543", "HUMADAM2-ACCEPTOR-784", "HUMMRP14A-ACCEPTOR-1386", "HUMPRPH1-ACCEPTOR-2738", "HUMGFP40H-ACCEPTOR-2431", "HUMTPA-ACCEPTOR-28261", "HUMHPRTB-ACCEPTOR-34907", "HUMMYCC-ACCEPTOR-6624", "HUMMYCRT-ACCEPTOR-3258", "HUMAFP-ACCEPTOR-13888", "HUMMHDOB-ACCEPTOR-3357", "HUMTUBAG-ACCEPTOR-2858", "HUMGCB1-ACCEPTOR-3009", "HUMMHDOB-ACCEPTOR-4124", "HUMFIXG-ACCEPTOR-33757", "HUMTFPB-ACCEPTOR-9259", "HUMAFP-ACCEPTOR-1782", "HUMALBGC-ACCEPTOR-6011", "HUMLACTA-ACCEPTOR-1512", "HUMATPSYB-ACCEPTOR-4093", "HUMTFPB-ACCEPTOR-2160", "HUMALBGC-ACCEPTOR-17658", "HUMALBGC-ACCEPTOR-2534", "HUMGCB1-ACCEPTOR-5810", "HUMIL1B-ACCEPTOR-1506", "HUMHPRTB-ACCEPTOR-39785", "HUMAFP-ACCEPTOR-2796", "LEMHBG-ACCEPTOR-1452", "HUMCFVII-ACCEPTOR-9389", "HUMALBGC-ACCEPTOR-6772", "HUMSODA-ACCEPTOR-5127", "ORAHBG2F-ACCEPTOR-1492", "HUMLACTA-ACCEPTOR-2160", "HUMCYCAA-ACCEPTOR-1466", "HUMHPRTB-ACCEPTOR-14750", "HUMEXCI5R-NEG-1321", "HUMMHANTLE-ACCEPTOR-2733", "HUMHPRTB-ACCEPTOR-40003", "HUMHPARS1-ACCEPTOR-5472", "MACHBCA2-ACCEPTOR-1693", "MACHBGA1-ACCEPTOR-1692", "HUMGFP40H-ACCEPTOR-992", "LEMHBGA-ACCEPTOR-1498", "GIBHBGGL-ACCEPTOR-8438", "HUMPIM1A-ACCEPTOR-3558", "HUMMHCW1B-ACCEPTOR-3569", "HUMMHCW2B-ACCEPTOR-3570", "HUMHBBAG-ACCEPTOR-2565", "HUMSODA-ACCEPTOR-3176", "HUMCRYGBC-ACCEPTOR-5283", "HUMCYPMPC-NEG-1", "HUMCP21OH-ACCEPTOR-1838", "HUMCP21OHC-ACCEPTOR-1839", "HUMMHCP42-ACCEPTOR-2767", "HUMMHCP52-ACCEPTOR-1304", "HUMATPSYB-ACCEPTOR-9633", "HUMRPS17A-ACCEPTOR-2225", "HUMMHCD8A-ACCEPTOR-5474", "HUMCKMT-ACCEPTOR-2011", "HUMHSP90B-ACCEPTOR-4265", "HUMTPA-ACCEPTOR-22075", "HUMATP1A2-ACCEPTOR-15320", "HUMGRP78-ACCEPTOR-2347", "HUMTPA-ACCEPTOR-32398", "HUMFIXG-ACCEPTOR-32974", "HUMIL1B-ACCEPTOR-879", "HUMHMG14A-ACCEPTOR-1487", "HUMKAL2-ACCEPTOR-5424", "HUMDES-ACCEPTOR-1763", "HUMALBGC-NEG-241", "HUMSODA-ACCEPTOR-6511", "HUMPSAA-ACCEPTOR-5669", "GIBHBGGL-ACCEPTOR-3506", "ORAHBG1F-ACCEPTOR-1486", "HUMHIAPPA-ACCEPTOR-959", "TARHBD-ACCEPTOR-1884", "HUMMHCW1B-ACCEPTOR-3357", "HUMMHCW2B-ACCEPTOR-3359", "HUMMHCW1B-ACCEPTOR-2261", "HUMATP1A2-ACCEPTOR-13550", "HUMMHDOB-ACCEPTOR-2649", "HUMSODA-ACCEPTOR-4869", "HUMCRPG-ACCEPTOR-578", "HUMCRPGA-ACCEPTOR-539", "HUMA1GLY2-ACCEPTOR-2469", "HUMANFA-ACCEPTOR-2205", "HUMALBGC-ACCEPTOR-10837", "HUMIL1B-ACCEPTOR-4293", "HUMKER18-ACCEPTOR-6098", "HUMALBGC-ACCEPTOR-9414", "HUMATPSYB-ACCEPTOR-5471", "HUMGRP78-ACCEPTOR-2848", "HUMGCB1-ACCEPTOR-3353", "HUMIL2-ACCEPTOR-3038", "HUMIL2A-ACCEPTOR-3975", "HUMIL2B-ACCEPTOR-2852", "HUMMRP8A-ACCEPTOR-2302", "HUMPCNA-ACCEPTOR-2520", "HUMGG-ACCEPTOR-1802", "ORAHBG2F-ACCEPTOR-392", "HUMHSP90B-ACCEPTOR-3351", "HUMAFP-ACCEPTOR-5216", "HUMTPA-ACCEPTOR-23130", "HUMHBBAG-ACCEPTOR-1466", "HUMHBBGG-ACCEPTOR-2363", "HUMCFXI14-NEG-481", "HUMTPA-ACCEPTOR-29440", "HUMGCB1-ACCEPTOR-949", "HUMLDL100-NEG-5941", "HUMCYPIIE-ACCEPTOR-12808", "HUMALBGC-ACCEPTOR-15504", "HUMTCBYX-NEG-1", "GIBHBGGL-ACCEPTOR-2401", "GIBHBGGL-ACCEPTOR-7321", "MACHBCA2-ACCEPTOR-586", "MACHBGA1-ACCEPTOR-588", "HUMCRYGBC-ACCEPTOR-18489", "HUMEF1A-ACCEPTOR-2347", "HUMSODA-ACCEPTOR-3793", "HUMATPSYB-ACCEPTOR-2698", "HUMSPRO-ACCEPTOR-2841", "HUMATP1A2-ACCEPTOR-10913", "HUMCYPIIE-ACCEPTOR-13834", "HUMKER18-ACCEPTOR-4857", "HUMCYPIIE-ACCEPTOR-12121", "HUMCYPIIE-NEG-12121", "HUMDES-ACCEPTOR-2504", "HUMHSP90B-ACCEPTOR-7462", "HUMATP1A2-ACCEPTOR-24266", "HUMMHANTLE-ACCEPTOR-3667", "HUMMHANTLE-ACCEPTOR-3842", "LEMHBE-ACCEPTOR-410", "HUMCYPIIE-ACCEPTOR-6976", "HUMPRPH1-ACCEPTOR-3136", "HUMPRPH2-ACCEPTOR-2241", "HUMAFP-ACCEPTOR-15169", "HUMGRP78-ACCEPTOR-4506", "HUMBLYM1-ACCEPTOR-505", "HUMFIXG-ACCEPTOR-13327", "HUMPRL1-NEG-61", "HUMALPPD-ACCEPTOR-734", "HUMSAA-ACCEPTOR-3086", "HUMHSP90B-ACCEPTOR-5710", "ORAHBG1F-ACCEPTOR-391", "HUMTHB-ACCEPTOR-9825", "HUMGRP78-ACCEPTOR-3371", "HUMEF1A-ACCEPTOR-2727", "HUMA1ATP-ACCEPTOR-10909", "HUMCACY-ACCEPTOR-2504", "HUMSODA-ACCEPTOR-4604", "HUMCKMT-ACCEPTOR-4348", "HUMATPSYB-ACCEPTOR-7858", "HUMGHN-ACCEPTOR-1166", "HUMMHDRHA-ACCEPTOR-5181", "HUMATPSYB-ACCEPTOR-5220", "HUMSAA1A-ACCEPTOR-6290", "HUMEF1A-ACCEPTOR-2062", "HUMCS1-ACCEPTOR-1184", "HUMGHCSA-ACCEPTOR-28101", "HUMGHCSA-ACCEPTOR-50922", "HUMTPA-ACCEPTOR-31083", "HUMPLAPL-ACCEPTOR-987", "HUMINCPPS-ACCEPTOR-4028", "HUMCATF-ACCEPTOR-1108", "HUMGHCSA-ACCEPTOR-42765", "HUMIRBPG-ACCEPTOR-4936", "HUMFBRAA-NEG-1", "HUMGH-ACCEPTOR-941", "HUMGHCSA-ACCEPTOR-5835", "HUMPLPSPC-ACCEPTOR-2621", "HUMCKMT-ACCEPTOR-1529", "HUMHSP90B-ACCEPTOR-3649", "HUMP45C17-ACCEPTOR-6267", "HUMTUBBM-ACCEPTOR-1850", "HUMSHBGA-ACCEPTOR-5640", "HUMEF1A-ACCEPTOR-2965", "HUMRPS17A-ACCEPTOR-1611", "HUMP45C17-ACCEPTOR-5819", "HUMHSP90B-ACCEPTOR-5972", "HUMTHB-ACCEPTOR-20170", "HUMKEREP-ACCEPTOR-2715", "HUMP45C17-ACCEPTOR-3774", "HUMCYPIIE-DONOR-12309", "HUMINSRC-NEG-2101", "HUMIL1B-DONOR-3748", "HUMALPPD-ACCEPTOR-985", "HUMGFP40H-ACCEPTOR-1110", "HUMADAG-ACCEPTOR-35621"), ValueGroup("ATRINS-DONOR-905", "HUMERP-DONOR-598", "HUMERPA-DONOR-608", "HUMKEREP-DONOR-3892", "HUMSAACT-DONOR-2210", "HUMOTNPI-DONOR-508", "HUMTUBBM-DONOR-1187", "HUMMYLCA-DONOR-952", "HUMMYLCA-DONOR-1975", "HUMCFVII-DONOR-4425", "HUMH19-DONOR-2128", "HUMHMG14A-DONOR-1039", "HUMSAACT-DONOR-2770", "HUMHBA4-DONOR-10579", "HUMHBA4-DONOR-6768", "MACHBA-DONOR-121", "HUMRPS17A-DONOR-615", "GCRHBBA1-DONOR-1260", "BABAPOE-DONOR-2817", "HUMMHCW2B-DONOR-1155", "HUMP45C17-DONOR-2106", "HUMACTGA-DONOR-1344", "HUMMHANTLE-DONOR-1633", "HUMACCYBA-DONOR-1624", "HUMACCYBB-DONOR-1558", "HUMDES-DONOR-2630", "HUMALPPD-DONOR-2590", "HUMMHB27B-DONOR-960", "HUMPIM1A-DONOR-1222", "HUMMHCW1B-DONOR-1152", "HUMCSPB-DONOR-258", "HUMCYC1A-DONOR-1506", "HUMCYPIIE-DONOR-4037", "HUMAPOE4-DONOR-3170", "HUMBNPA-DONOR-1087", "HUMATP1A2-DONOR-21929", "HUMHPRTB-DONOR-1674", "HUMP45C17-DONOR-7438", "HUMTUBAG-DONOR-506", "HUMPCNA-DONOR-1618", "GCRHBBA6-DONOR-461", "HUMPRCA-DONOR-2171", "HUMDES-DONOR-711", "HUMUBILP-DONOR-603", "HUMRASH-DONOR-2504", "HUMAPOAI1-DONOR-457", "HUMACTGA-DONOR-2321", "HUMTNFA-DONOR-952", "HUMTNFAB-DONOR-4431", "HUMTNFX-DONOR-726", "HUMAPOAIT-DONOR-201", "HUMA1GLY2-DONOR-1693", "HUMERP-DONOR-1310", "HUMERPA-DONOR-1317", "HUMCACY-DONOR-2132", "HUMACTGA-DONOR-513", "HUMPIM1A-DONOR-1594", "HUMADAG-DONOR-29909", "HUMADAG-DONOR-32467", "HUMADAM2-DONOR-708", "HUMMHEA-DONOR-1139", "HUMALPHA-DONOR-698", "HUMCAPG-DONOR-375", "HUMFOS-DONOR-1000", "HUMP45C17-DONOR-3913", "HUMA1GLY2-DONOR-2251", "HUMINCP-DONOR-539", "HUMKEREP-DONOR-4033", "HUMEDHB17-DONOR-2795", "HUMGCB1-DONOR-4319", "HUMCS1-DONOR-1304", "HUMGHCSA-DONOR-51042", "HUMKER18-DONOR-5233", "HUMACCYBA-DONOR-289", "HUMACCYBB-DONOR-290", "HUMAFP-DONOR-15308", "HUMGHCSA-DONOR-5955", "HUMGHN-DONOR-1286", "HUMPRCA-DONOR-5088", "HUMPSAA-DONOR-4293", "HUMCFVII-DONOR-10218", "HUMTPA-DONOR-26070", "HUMDES-DONOR-7366", "HUMGHCSA-DONOR-6212", "HUMGHN-DONOR-1544", "HUMPLAPL-DONOR-569", "HUMTKRA-DONOR-556", "HUMRPS14-DONOR-4214", "HUMTFPB-DONOR-10205", "HUMDES-DONOR-1824", "HUMCSFGMA-DONOR-1746", "HUMTFPB-DONOR-992", "HUMTPA-DONOR-29526", "HUMHPARS1-DONOR-1016", "HUMPOMC-DONOR-737", "HUMPPPA-DONOR-2234", "HUMATP1A2-DONOR-19518", "HUMGHCSA-DONOR-51300", "HUMGCB1-DONOR-5411", "HUMTPA-DONOR-31536", "HUMGHCSA-DONOR-28221", "HUMCP21OH-DONOR-917", "HUMCP21OHC-DONOR-918", "HUMMHCP42-DONOR-1848", "HUMMHCP52-DONOR-382", "HUMGH-DONOR-1061", "HUMALPPD-DONOR-309", "HUMCFVII-DONOR-6562", "HUMMYC3L-DONOR-1271", "HUMMHCP52-DONOR-1594", "HUMACCYBA-DONOR-2781", "HUMACCYBB-DONOR-2715", "HUMKER18-DONOR-5719", "HUMATP1A2-DONOR-22962", "HUMMH6-DONOR-1225", "HUMUBILP-DONOR-1367", "CHPIGECA-DONOR-903", "ORAIGECA-DONOR-904", "HUMHST-DONOR-3582", "HUMGHCSA-DONOR-13807", "HUMTKRA-DONOR-2441", "HUMPSAP-DONOR-170", "HUMCRYGBC-DONOR-2468", "HUMGHCSA-DONOR-28478", "HUMAPRTA-DONOR-2331", "HUMCAPG-DONOR-2299", "HUMLPL-NEG-181", "HUMEF1A-DONOR-579", "HUMCP21OH-DONOR-1541", "HUMCP21OHC-DONOR-1542", "HUMMHCP42-DONOR-2471", "HUMMHCP52-DONOR-1006", "HUMCP21OH-DONOR-1750", "HUMCP21OHC-DONOR-1751", "HUMMHCP42-DONOR-2679", "HUMMHCP52-DONOR-1216", "HUMATP1A2-DONOR-15496", "HUMALPPD-DONOR-1715", "HUMERP-DONOR-2444", "HUMERPA-DONOR-2453", "HUMAK1-DONOR-3959", "HUMCP21OH-DONOR-2128", "HUMMHCP42-DONOR-3057", "HUMTACEC-NEG-2341", "HUMA1ATP-DONOR-11057", "HUMATP1A2-DONOR-1663", "HUMH19-DONOR-2562", "HUMH19-DONOR-2359", "HUMKAL2-DONOR-4031", "HUMALPI-DONOR-532", "HUMPSAP-DONOR-2452", "HUMGHCSA-DONOR-42885", "HUMTKRA-DONOR-12438", "HUMA1GLY2-DONOR-2540", "HUMMHCD8A-DONOR-2891", "HUMA1ATP-DONOR-1972", "HUMCP21OHC-DONOR-2129", "HUMNGFR-NEG-2821", "HUMCS1-DONOR-1561", "HUMH19-DONOR-2766", "HUMADAG-DONOR-32886", "HUMGHVA-DONOR-488", "HUMG0S19A-DONOR-2927", "HUMG0S19B-DONOR-3063", "HUMCACY-DONOR-1386", "HUMGHCSA-DONOR-13551", "HUMACCYBA-DONOR-2504", "HUMACCYBB-DONOR-2438", "HUMCYC1A-DONOR-3019", "HUMTHB-DONOR-4453", "HUMALPHA-DONOR-2096", "HUMCSPB-DONOR-2010", "HUMPPPA-DONOR-965", "HUMALPI-DONOR-762", "HUMHPARS1-DONOR-2990", "HUMHPARS1-DONOR-4714", "HUMTKRA-DONOR-4909", "HUMTNFA-DONOR-1604", "HUMTNFAB-DONOR-5083", "HUMTNFX-DONOR-1378", "HUMCSPB-DONOR-1408", "HUMGAST2-DONOR-786", "HUMGASTA-DONOR-6470", "HUMGCB1-DONOR-1782", "HUMNMCY1A-NEG-661", "HUMATP1A2-DONOR-9194", "HUMFOS-DONOR-2005", "HUMCYPIIE-DONOR-7126", "HUMSAACT-DONOR-1779", "HUMMYCC-DONOR-5248", "HUMCFVII-DONOR-9499", "HUMALPI-DONOR-1934", "HUMTPA-DONOR-23748", "HUMLACTA-DONOR-1671", "HUMGHCSA-DONOR-43141", "HUMMIS-DONOR-593", "HUMCRYGBC-DONOR-2131", "HUMRASH-DONOR-1745", "HUMEDHB17-DONOR-2199", "HUMALPHA-DONOR-925", "HUMPLAPL-DONOR-797", "HUMATPSYB-DONOR-2217", "HUMP45C17-DONOR-4376", "HUMMETIF1-DONOR-480", "HUMALPPD-DONOR-100", "HUMPLAPL-DONOR-373", "HUMTPA-DONOR-31220", "HUMHMG14A-DONOR-1517", "HUMTNFAB-DONOR-950", "HUMPOMC-DONOR-4597", "HUMCFVII-DONOR-1690", "HUMPRCA-DONOR-5555", "HUMCYPIIE-DONOR-8260", "HUMMT2A-DONOR-421", "HUMINCP-DONOR-2163", "HUMGCB1-DONOR-5974", "HUMADAG-DONOR-26437", "HUMAPOA4A-DONOR-1025", "HUMALPHA-DONOR-487", "ORAHBG2F-DONOR-615", "HUMCAPG-DONOR-1279", "HUMGH-DONOR-1319", "HUMMT2A-DONOR-1113", "HUMATP1A2-DONOR-21613", "HUMALPPD-DONOR-537", "HUMRPS17A-DONOR-171", "HUMGHCSA-DONOR-12784", "HUMGHCSA-DONOR-42122", "HUMMYCRT-DONOR-1883", "HUMMHCD8A-DONOR-638", "HUMP45C17-DONOR-6437", "HUMALPI-DONOR-1389", "HUMTHB-DONOR-8003", "HUMAK1-DONOR-6743", "HUMMRP14A-DONOR-999", "HUMADAG-DONOR-19262", "HUMPLPSPC-DONOR-1953", "HUMMETIF1-DONOR-1131", "HUMGHCSA-DONOR-5205", "HUMCS1-DONOR-535", "HUMGHCSA-DONOR-27451", "HUMALPPD-DONOR-1158", "HUMMHEA-DONOR-1657", "HUMCFVII-DONOR-6378", "HUMA1ATP-DONOR-9653", "HUMGHCSA-DONOR-50275", "HUMGSA1R-NEG-661", "HUMMHDOB-DONOR-4235", "HUMGH-DONOR-315", "HUMIRBPG-DONOR-3151", "HUMCP21OH-DONOR-1940", "HUMCP21OHC-DONOR-1941", "HUMMHCP42-DONOR-2869", "HUMMHCP52-DONOR-1406", "HUMALPI-DONOR-333", "HUMCKMT-DONOR-1326", "HUMCRYGBC-DONOR-18732", "HUMGHN-DONOR-539", "HUMSHBGA-DONOR-3802", "HUMCRPG-DONOR-300", "HUMCRPGA-DONOR-261", "HUMP45C17-DONOR-6035", "HUMMHDRHA-DONOR-3215", "HUMVPNP-DONOR-315", "HUMHSP27P-NEG-601", "HUMPGAMMG-DONOR-1347", "HUMAPOAIT-DONOR-461", "ATRINS-DONOR-521", "HUMSHBGA-DONOR-2948", "HUMTHY1A-DONOR-853", "HUMG0S19A-DONOR-2124", "HUMIRBPG-DONOR-7130", "HUMG0S19B-DONOR-2263", "HUMTPA-DONOR-30972", "HUMAPOAI1-DONOR-716", "HUMPSAP-DONOR-1188", "HUMATP1A2-DONOR-11031", "HUMADAG-DONOR-35173", "HUMSPBAA-NEG-3061", "HUMCFVII-DONOR-556", "HUMCFVII-DONOR-8418", "HUMALDOA-NEG-4501", "HUMCAPG-DONOR-1869", "HUMAICEB-NEG-2641", "HUMAPOCIA-DONOR-2092", "HUMACTGA-ACCEPTOR-2139"), ValueGroup("HUMMYLCA-DONOR-644", "HUMALPI1-DONOR-42", "HUMMHCD8A-DONOR-1781", "HUMVPNP-DONOR-1890", "HUMPSAP-DONOR-1621", "HUMHST-DONOR-2861", "HUMFGFB-NEG-181", "HUMPRCA-DONOR-5318", "HUMAPRTA-DONOR-1982", "HUMCSF1G1-NEG-361", "HUMDES-DONOR-3236", "HUMALPHA-DONOR-2968", "HUMGRP78-DONOR-685", "HUMADAG-DONOR-4034", "HUMTUBBM-DONOR-775", "HUMMYC3L-DONOR-402", "HUMATP1A2-DONOR-20891", "CHPIGECA-DONOR-1313", "HUMHMG14A-DONOR-1394", "HUMMHB27B-DONOR-2538", "HUMMHB27B-DONOR-3013", "HUMMHEA-DONOR-3578", "HUMMHCW1B-DONOR-2778", "HUMMHCW2B-DONOR-2780", "ORAIGECA-DONOR-1314", "HUMPIM1A-DONOR-1442", "HUMGSHPXG-DONOR-690", "HUMEDHB17-DONOR-3489", "HUMMHB27B-DONOR-561", "HUMMIS-DONOR-1609", "LEMHBGA-DONOR-302", "HUMPRT1A-DONOR-118", "HUMMHCW1B-DONOR-3250", "HUMMHCW1B-DONOR-754", "HUMMHCW2B-DONOR-756", "HUMPNMTA-DONOR-3337", "HUMPSAA-DONOR-2089", "HUMANDREC-NEG-1681", "HUMMH6-DONOR-826", "HUMANT1-DONOR-3442", "HUMOTNPI-DONOR-1007", "HUMMRP8A-DONOR-2152", "HUMMHCW1B-DONOR-2537", "HUMATP1A2-DONOR-6788", "LEMHBG-DONOR-241", "HUMPRF1A-DONOR-3783", "HUMMHANTLE-DONOR-3227", "HUMAK1-DONOR-5876", "HUMALPPD-DONOR-1543", "HUMACTGA-DONOR-1015", "HUMMH6-DONOR-2841", "HUMSAACT-DONOR-769", "HUMMHCW2B-DONOR-3252", "BABAPOE-DONOR-30", "HUMAPOE4-DONOR-1061", "GCRHBBA1-DONOR-1590", "HUMALPI-DONOR-2815", "HUMPLPSPC-DONOR-1485", "HUMSAACT-DONOR-3038", "HUMMHEA-DONOR-2795", "HUMSODA-DONOR-327", "HUMADAG-DONOR-32645", "HUMALPI-DONOR-1766", "MACHBA-DONOR-443", "HUMHBA4-DONOR-10901", "HUMHBA4-DONOR-7090", "HUMMHEA-DONOR-739", "HUMAPOC2-DONOR-522", "HUMAPOC2G-DONOR-225", "HUMAPOCII-DONOR-541", "HUMKAL2-DONOR-539", "HUMMHCW2B-DONOR-2539", "HUMTHB-DONOR-7792", "HUMALPI-DONOR-1140", "HUMALPHA-DONOR-2359", "HUMMHDOB-DONOR-2919", "HUMRPS14-DONOR-225", "HUMPNMTA-DONOR-2130", "HUMCP21OH-DONOR-3064", "HUMCP21OHC-DONOR-3066", "HUMMHCP52-DONOR-2531", "HUMMHANTLE-DONOR-3889", "HUMTHB-DONOR-1044", "HUMOPS-DONOR-3949", "HUMCYCAA-DONOR-115", "HUMTUBBM-DONOR-1376", "GIBHBGGL-DONOR-2278", "HUMHBBAG-DONOR-1344", "HUMHBBGG-DONOR-2241", "ORAHBG2F-DONOR-269", "HUMOPS-DONOR-626", "HUMTNFAB-DONOR-1345", "HUMATP1A2-DONOR-20500", "HUMAPOAI1-DONOR-1059", "HUMAPOAIT-DONOR-805", "HUMALPHA-DONOR-1924", "GIBHBGGL-DONOR-7198", "HUMANT1-DONOR-1688", "HUMPSAA-DONOR-691", "HUMACCYBA-DONOR-1250", "HUMACCYBB-DONOR-1186", "GCRHBBA6-DONOR-795", "ORAHBG1F-DONOR-269", "HUMGPIBAA-DONOR-2800", "HUMBNPA-DONOR-600", "HUMKER18-DONOR-5022", "HUMALPPD-DONOR-1980", "HUMALPPD-DONOR-909", "HUMEDHB17-DONOR-2526", "HUMPRF1A-DONOR-1477", "HUMSNU1P2A-NEG-301", "HUMPIM1A-DONOR-2054", "HUMKAL2-DONOR-3781", "HUMATP1A2-DONOR-12719", "HUMTPA-DONOR-28433", "HUMRPS14-DONOR-3034", "HUMCP21OH-DONOR-2877", "HUMMHCP42-DONOR-3806", "HUMMHCP52-DONOR-2344", "HUMPSAA-DONOR-4013", "HUMTHB-DONOR-7339", "HUMKER18-DONOR-3791", "HUMANFA-DONOR-1112", "MACHBCA2-DONOR-463", "MACHBGA1-DONOR-465", "HUMTHB-DONOR-4686", "HUMGCB1-DONOR-6461", "HUMUBILP-DONOR-912", "HUMCS1-DONOR-961", "HUMGHCSA-DONOR-27878", "HUMGHCSA-DONOR-50700", "HUMALPI-DONOR-2199", "HUMALPHA-DONOR-1292", "HUMMHEA-DONOR-2554", "HUMCKMT-DONOR-523", "HUMSAA-DONOR-2706", "HUMHMG14A-DONOR-4792", "HUMMHANTLE-DONOR-3008", "HUMGCB1-DONOR-1037", "HUMKAL2-DONOR-1905", "HUMSAA1A-DONOR-3240", "HUMTHB-DONOR-497", "HUMDES-DONOR-5457", "HUMSHBGA-DONOR-3310", "HUMSPRO-DONOR-1947", "HUMMHEA-DONOR-3725", "HUMMH6-DONOR-2602", "HUMFOLA1-NEG-541", "HUMGASTA-DONOR-3213", "HUMMHDRHA-DONOR-4442", "HUMLYL1B-DONOR-882", "HUMLYL1B-DONOR-2722", "HUMMHCP42-DONOR-3993", "HUMATP1A2-DONOR-13749", "HUMADAG-DONOR-29022", "HUMCP21OHC-DONOR-2878", "HUMAK1-DONOR-10237", "HUMAPOC2-DONOR-2976", "HUMAPOC2G-DONOR-2684", "HUMAPOCII-DONOR-2965", "HUMATP1A2-DONOR-15190", "HUMMHB27B-DONOR-2329", "HUMAPOCIA-DONOR-712", "HUMALPHA-DONOR-2632", "HUMALPPD-DONOR-2254", "HUMATP1A2-DONOR-7029", "HUMTPA-DONOR-3683", "HUMPLPSPC-DONOR-628", "HUMCYC1A-DONOR-2526", "HUMTKRA-DONOR-11961", "HUMALPI-DONOR-2475", "HUMDES-DONOR-2326", "HUMPGAMMG-DONOR-1631", "HUMATP1A2-DONOR-9834", "HUMSPRO-DONOR-2998", "HUMMIS-DONOR-1328", "HUMMYCRT-DONOR-888", "HUMATP1A2-DONOR-20193", "HUMALPHA-DONOR-1542", "HUMMHANTLE-DONOR-1239", "HUMCSFGMA-DONOR-793", "HUMMHCW1B-NEG-241", "HUMTKRA-DONOR-698", "HUMCYC1A-DONOR-2305", "HUMTPA-DONOR-22213", "HUMGH-DONOR-732", "HUMGHCSA-DONOR-5626", "HUMGHN-DONOR-959", "HUMHMG14A-DONOR-1647", "HUMPRCA-DONOR-8366", "HUMCKMT-DONOR-2233", "HUMTHB-DONOR-2021", "HUMKER18-DONOR-4285", "HUMKEREP-DONOR-3577", "HUMADAG-DONOR-34454", "HUMMHCD8A-DONOR-2098", "HUMGHCSA-DONOR-13226", "HUMCYC1A-DONOR-3382", "HUMTHB-DONOR-9999", "HUMAK1-DONOR-5540", "HUMGFP40H-DONOR-880", "HUMATP1A2-DONOR-22719", "HUMMRP8A-DONOR-1504", "HUMSAACT-DONOR-2499", "HUMGCB1-DONOR-3526", "HUMCKMT-DONOR-4483", "HUMTNFX-NEG-1681", "HUMANFA-DONOR-663", "HUMTPA-DONOR-19780", "HUMATP1A2-DONOR-21268", "HUMPRPH2-DONOR-917", "HUMAPOA4A-DONOR-1509", "HUMTPA-DONOR-32565", "HUMATPSYB-DONOR-5379", "HUMADAG-DONOR-31274", "HUMTHB-ACCEPTOR-4347", "HUMMHDRHA-DONOR-565", "HUMAK1-ACCEPTOR-5712", "HUMAPOE4-DONOR-1884", "ATRINS-NEG-841", "HUMSPRO-DONOR-2585", "HUMTHB-DONOR-10723", "HUMSODA-DONOR-4951", "HUMPRCA-DONOR-9357", "HUMMHCW1B-DONOR-3405", "HUMMHCW2B-DONOR-3407", "HUMEDHB17-DONOR-1938", "HUMOPS-DONOR-4305", "HUMTPA-DONOR-23241", "HUMTHB-DONOR-1728", "HUMAPRTA-DONOR-881", "HUMCYC1A-DONOR-2769", "HUMTHB-DONOR-9328", "CHPIGECA-DONOR-378", "HUMPLPSPC-DONOR-2287", "HUMKEREP-DONOR-814", "HUMGHCSA-DONOR-42555", "HUMMH6-DONOR-3506", "HUMFIL-NEG-841", "HUMPGAMMG-NEG-2221", "HUMIRBPG-DONOR-5127", "HUMSPRO-DONOR-1735", "HUMKER18-DONOR-2967", "ORAIGECA-DONOR-378", "BABAPOE-DONOR-867", "HUMGCB1-DONOR-2052", "HUMCETP6-NEG-481", "HUMSODA-DONOR-3967", "HUMKEREP-DONOR-2153", "HUMCYPIIE-DONOR-9284", "HUMHPARS1-DONOR-2602", "HUMTHB-DONOR-20241", "HUMSPRO-DONOR-2367", "TARHBB-DONOR-1560", "TARHBD-DONOR-468", "HUMCR1R-NEG-2581", "HUMPRCA-NEG-8881", "HUMHPARS1-DONOR-5547", "HUMRASH-DONOR-2191", "HUMPIM1A-DONOR-3735", "LEMHBB-DONOR-216", "HUMAPOCIA-DONOR-496", "HUMPGK1-NEG-181", "HUMRPS14-DONOR-2259", "HUMHLADPB-NEG-8761", "HUMCKMT-DONOR-2687", "HUMSPRO-DONOR-3348", "HUMPIM1A-ACCEPTOR-1543", "HUMSHBGA-DONOR-4456", "HUMATP1A2-DONOR-10208", "HUMMHSXA-NEG-1", "HUMG6PA-NEG-781", "HUMCP21OH-DONOR-2498", "HUMCP21OHC-DONOR-2499", "HUMMHCP42-DONOR-3427", "HUMMHCP52-DONOR-1964", "HUMATP1A2-DONOR-14299", "HUMAPOC2-DONOR-3303", "HUMAPOC2G-DONOR-3011", "HUMAPOCII-DONOR-3291", "HUMA1MICR-NEG-1081", "HUMPPPA-DONOR-1912", "HUMHSP90B-ACCEPTOR-3995", "HUMCAPG-ACCEPTOR-1131", "HUMIL1B-DONOR-941", "HUMADRBRA-NEG-2941"), ValueGroup("HUMFGFG1-NEG-901", "HUMCOLTHA-NEG-481", "HUMGLTH1-NEG-241", "HUMKRASP-NEG-541", "HUMIGF2AP-NEG-421", "HUMPLP1-NEG-301", "HUMRASH-NEG-601", "HUMINT2-NEG-481", "HUMPLG2B-NEG-181", "HUMRASSK2-NEG-1381", "HUMADRA-NEG-961", "ORARGIT-NEG-241", "HUMPDGA1-NEG-481", "HUMSISM-NEG-241", "HUMMUC18B-NEG-61", "HUMNAKATP1-NEG-721", "HUMSRTR2A-NEG-61", "HUMALDAR-NEG-1081", "HUMEDHB17-NEG-3541", "HUMMHAW-NEG-1201", "HUMARSBX-NEG-421", "HUMVWFM-NEG-5101", "HUMCYPB1-NEG-1141", "HUMRYR-NEG-1081", "HUMHBA4-NEG-6601", "HUMUPAA-NEG-1501", "HUMHBA3-NEG-1021", "HUMMG2-NEG-541", "HUMFESFUR-NEG-3481", "HUMIGHAB-NEG-121", "HUMTHYR01-NEG-181", "HUMGSHPXG-NEG-1081", "HUMIFNB3-NEG-5641", "HUMRENT1-NEG-601", "HUMPTCAA-NEG-481", "HUMA2PIG3-NEG-541", "HUMINHA-NEG-1", "HUMAPOBG-NEG-121", "HUMNARIA-NEG-3841", "ORAHBA01-NEG-121", "HUMSISG1-NEG-5401", "HUMCD1A-NEG-1261", "HUMCOL4A2-NEG-2581", "HUMGIR-NEG-121", "HUMPLPSPC-NEG-1801", "HUMTSG14A-NEG-301", "HUMOAT01-NEG-1", "HUMGENX-NEG-2341", "CHPRGITX-NEG-1441", "HUMCG2A1-NEG-1", "HUMHKR42-NEG-181", "HUMKERMII-NEG-181", "HUMCOL4A6-NEG-301", "HUMALLFU-NEG-61", "HUMMHCBWC-NEG-121", "HUMETS2PR-NEG-901", "HUMA1ATP-NEG-301", "HUMSB3B2-NEG-121", "HUMADRA2R-NEG-2581", "HUMEF2AB-NEG-721", "HUMNMYC-NEG-1561", "HUMEL01-NEG-901", "HUMMT2A-NEG-121", "HUMMYHC08-NEG-421", "HUMATP1A2-NEG-2101", "HUMTIR-NEG-901", "HUMMHB27B-NEG-1201", "HUMTPA-NEG-31561", "HUMIGMUTDM-NEG-361", "HUMMHCAR4-NEG-241", "HUMU170SC-NEG-481", "HUMCKBBA-NEG-481", "HUMBCL3AA-NEG-1381", "HUMCOL2A1-NEG-961", "MACAPOE-NEG-181", "HUMMHBW4-NEG-61", "HUMTRK2H-NEG-1081", "HUMERSP2B-NEG-781", "HUMICAM1-NEG-481", "HUMCINHP-NEG-121", "HUMAPOA4-NEG-61", "HUMRGLTIB-NEG-721", "HUMEGFAA-NEG-421", "HUMTFP-NEG-61", "HUMCSFGM-NEG-1", "HUMMHCP52-NEG-781", "HUMPAC-NEG-541", "HUMBCR-NEG-1801", "HUMCFXII3-NEG-3061", "HUMGP-NEG-1321", "HUMAK1-NEG-10921", "HUMMHDPBF-NEG-61", "HUMINSR2-NEG-1", "HUMCOL11A2-NEG-1861", "HUMLDLRRL-NEG-11761", "HUMERPA-NEG-1021", "AGMORS12A-NEG-181", "HUMMHCW1B-ACCEPTOR-1398", "HUMRYR-NEG-14761", "HUMMHCW2B-ACCEPTOR-1399", "HUMTHX-NEG-841", "HUMMHCB44A-NEG-361", "HUMHSP70D-NEG-1441", "HUMGA7331-NEG-241", "HUMPRC1-NEG-61", "HUMMHDRO2-NEG-2461", "HUMTKRA-NEG-12841", "HUMSPBAA-NEG-7621", "HUMMYCF1-NEG-1081", "HUMC3A1R-NEG-2941", "HUMANK-NEG-1981", "ORAINVOL-NEG-2161", "HUMRYR-NEG-10201", "HUMFESFPS-NEG-6661", "HUMMHDRBC-NEG-661", "HUMERVKA2-NEG-901", "HUMPROD4-NEG-721", "HUMGSTPIG-NEG-1381", "HUMBHAA-NEG-1261", "HUMHCKA-NEG-61", "HUMINT2-NEG-5041", "HUMRGE-NEG-1801", "HUM4F2HG1-NEG-1441", "HUMMYONM-NEG-1501", "HUMIGKAR-NEG-181", "HUMME491-NEG-601", "HUMET-NEG-301", "HUMATP-NEG-1981", "HUMLYL1B-NEG-4081", "HUMMHHLA-NEG-241", "MACHBB-NEG-4141", "HUMMK562B-NEG-1", "HUMPGKA09-NEG-241", "HUMCG5B-NEG-1261", "HUMEAR2-NEG-1381", "HUMTHMOD-NEG-421", "CHPAZGLO-NEG-1", "HUMOCS1-NEG-1", "HUMCRYGBC-NEG-3781", "HUMMPO1-NEG-1681", "HUMALPPB-NEG-781", "HUMAPRTA-NEG-1981", "HUMSERDHY-NEG-241", "HUMKER654-NEG-1441", "HUMNFM-NEG-1921", "HUMPROD1-NEG-1741", "HUMSAP1-NEG-121", "HUMCCK2-NEG-361", "HUMMETIA-NEG-1921", "HUMCP21OH-NEG-1981", "HUMALDC-NEG-61", "HUMASP-NEG-541", "HUMMHEA-ACCEPTOR-1381", "HUMPOVRA-NEG-541", "HUMCP21OHC-NEG-3061", "HUMOTNPI-NEG-481", "GCRHBBA1-NEG-61", "HUM5FDX-NEG-2041", "HUMATP1A2-NEG-20341", "HUMMRP8A-NEG-1381", "HUMHSP90-NEG-1", "HUMCNRAB-NEG-1741", "HUMHLA1EA-NEG-121", "HUMKERP2-NEG-1381", "HUMVWFA04-NEG-301", "HUMGLYSA-NEG-2761", "HUMDB1A-NEG-121", "HUMYTEST-NEG-301", "HUMCP21OHA-NEG-2521", "HUMCYP450-NEG-7321", "HUMHBB-NEG-5761", "HUMMHCAG-NEG-1", "HUMGNPAS-NEG-61", "HUMBAT2A-NEG-2761", "HUMITI2-NEG-481", "HUMC1A1-NEG-3901", "HUMPLAP2A-NEG-481", "HUMMHDRBK-NEG-1321", "HUMAK1-NEG-6361", "HUMOPS-NEG-181", "HUMCAPG-NEG-1261", "HUMMPO-NEG-301", "HUMSPM1-NEG-181", "HUMVDR-NEG-1441", "HUMLD78R-NEG-1", "HUMMHDRB4A-NEG-781", "HUMSAACT-NEG-301", "HUMRNP70K-NEG-1141", "HUMTPA-NEG-8761", "HUMACROS-NEG-1", "HUMAPOA4C-NEG-2761", "HUMFVII-ACCEPTOR-136", "HUMHPA2BR-NEG-1261", "HUMGLI-NEG-901", "HUMCSPCP-NEG-1261", "HUMIGHBU-NEG-301", "HUMVIL2-NEG-1441", "HUMGRP78-NEG-421", "HUMFGRINT-NEG-421", "HUMC4A1-NEG-2761", "HUMHER2A-NEG-3301", "HUMFNRA-NEG-361", "HUMBCRD-NEG-301", "HUMNMYCA-NEG-4561", "HUMLAMP1A-NEG-841", "HUMARF1A-NEG-301", "HUMMHC1-NEG-4321", "HUMMLC2A-NEG-1021", "TARHBD-NEG-1981", "HUMFCERA-NEG-121", "HUMMHCP42-NEG-4501", "HUMRCC1B-NEG-361", "HUMIGCD2-NEG-121", "HUMNMYCA-NEG-1", "HUMMHM71-NEG-181", "HUMAPOCIP-NEG-301", "HUMTPO03-NEG-61", "HUMMHANTLE-NEG-3181", "HUMMH6-ACCEPTOR-1451", "HUMTRKR-NEG-1201", "HUMMHB27B-ACCEPTOR-1202", "HUMACCYBB-NEG-3481", "HUMHMPFK-NEG-901", "HUMMHDC3B-NEG-2461", "HUMMHCACA-NEG-181", "HUMUPAX-NEG-3541", "HUMAPLA-NEG-1261", "HUMFNRAS-NEG-601", "HUMCG2A1-NEG-4561", "BABBMYHA-NEG-301", "HUMGFIIM-NEG-121", "HUMP68-NEG-1081", "HUMCRYGBC-NEG-12901", "HUMRUN1C2-NEG-241", "HUMHPRTB-NEG-49201", "HUMITF1-NEG-601", "GCRHBBA6-ACCEPTOR-920", "HUMHLACW2-NEG-781", "HUMAPOE3-NEG-1", "HUMTM-NEG-1441", "HUMPAIB1-NEG-61", "HUMCKMT-NEG-1321", "HUMCAL-NEG-301", "HUMCY4ARO-NEG-541", "HUMCKMM1-NEG-1921", "HUMIDB-NEG-241", "HUMPMGR-NEG-1621", "CHPMHCBAA-NEG-841", "HUMELANA-NEG-1261", "HUMCARN-NEG-1561", "HUMIRBPG-NEG-3781", "HUMCSIST-NEG-2221", "HUMMHANTLE-DONOR-2150", "HUMTHB-NEG-17281", "HUMALIPOA-NEG-781", "HUMPIM1A-NEG-1741", "HUMELAM1A-NEG-1141", "HUMCOL1A07-NEG-1", "HUMDRD2A-NEG-841", "HUMTHR-NEG-1501", "HUMMYHC5-NEG-1141", "HUMP42LA-NEG-2161", "HUMNFH3-NEG-1", "MACPEPSG-NEG-2161", "HUMMHANTLE-ACCEPTOR-1874", "HUMMHCW3-NEG-2761", "HUMTGFKA-NEG-1141", "ATRINS-ACCEPTOR-1678", "HUMMHAWB-NEG-721", "HUMGGTA-NEG-661", "HUMACTGP1-NEG-481", "HUMDBHRA-NEG-1321", "HUMBAT3B1-NEG-421", "HUMRETREC-NEG-1321", "HUMRENA3-NEG-361", "HUMA1AR2-NEG-3601", "HUMHEXB01-NEG-481", "HUMMHCCWB-NEG-241", "HUMMH6-DONOR-1727", "HUMPOMC-NEG-421", "HUMHLADP4-NEG-721", "HUMMHCW2B-DONOR-1675", "HUMCYC1A-NEG-4381", "HUMIGHVH-NEG-481", "HUMGHCSA-NEG-38521", "HUMMYP-NEG-961", "HUMPEP7-NEG-1", "HUMMHB27B-DONOR-1478", "HUMMHCW1B-DONOR-1674", "MACHBA-NEG-421", "HUMTPOB-NEG-2401", "HUMFXIIIA-NEG-181", "HUMDEF1AA-NEG-121", "HUMPRCM-NEG-1681", "HUMENOG-NEG-121", "HUMCD7-NEG-241", "HUMMHDRBS-NEG-601", "HUMPIGF2A-NEG-301", "HUMRAFR-NEG-2521", "GCRHBBA1-ACCEPTOR-1706", "HUMIHRP-NEG-541", "HUMAMYA1-NEG-541", "HUMHKB3R-NEG-121", "HUMGFIA1-NEG-841", "HUM4COLA-NEG-1021", "HUMCKMT-NEG-5881", "HUMIGFII5-NEG-121", "HUMIGHVAQ-NEG-61", "HUMVWFR1-NEG-3481", "HUMGPMSS-NEG-1501", "HUMRGRSALU-NEG-1981", "HUMGCBPRC-NEG-1501", "HUMRAP2-NEG-181", "HUMMBPA-NEG-661", "HUMTHBNA-NEG-961", "HUMHLDPWB-NEG-61", "HUMINCP-NEG-3121", "HUMSPIA04-NEG-61", "HUMIL2RA-NEG-2161", "HUMHA44G-NEG-2701", "HUMGELS3-NEG-121", "HUMPALC-NEG-2281", "HUMIGCD7-NEG-1921", "HUMIGHVA-NEG-481", "HUMMIFA-NEG-181", "HUMERG11-NEG-181", "HUMIL2RBC-NEG-1681", "HUMTGLU-NEG-781", "HUMMHTRP-NEG-661", "HUMKER19-NEG-541", "HUMALDH03-NEG-1", "HUMCA2VR-NEG-361", "HUMCOLII2-NEG-781", "HUMINIFI-NEG-481", "HUMFCRHA-NEG-661", "HUMASGPR1-NEG-661", "HUMMHA2-NEG-1321", "HUMMHDR2A-NEG-61", "HUMMGI2-NEG-901", "HUMSYNPR-NEG-61", "HUMHCR-NEG-661", "HUMTCBVD-NEG-1", "HUMPBGDR2-NEG-1", "HUMTPAR-NEG-361", "HUMXCGD-NEG-1201", "HUMDBLPRO-NEG-181", "HUMCNPG3-NEG-121", "HUMALIPOA-NEG-9901", "HUMMHDRCD-NEG-601", "HUMCG1PA1-NEG-1501", "HUMPRF1A-NEG-3601", "HUMATP1A2-NEG-11221", "HUMCOX4A-NEG-301", "HUMTHY1A-ACCEPTOR-517", "HUMVWFR-NEG-3301", "HUMIGHCH-NEG-121", "HUMPCNA-NEG-301", "HUMDBH01-NEG-1321", "HUMCYP145-NEG-301", "HUMCYP2A4-NEG-1", "HUMMHCP41-NEG-541", "GCRRSAGAB-NEG-121", "HUMALPP-NEG-1981", "HUMALPPD-NEG-841", "HUMACTASK-NEG-1021", "HUMPPPA-NEG-1561", "HUMTBBM40-NEG-841", "HUMXEH-NEG-1441", "HUMRASFA2-NEG-1321", "HUMGCB-NEG-2281", "HUMPAIA-NEG-9781", "HUMTBB5-NEG-7801", "HUMADH21C-NEG-541", "HUMDES-NEG-7201", "HUMINT2-NEG-9601", "HUMVWF01-NEG-2101", "HUMFVII-NEG-721", "HUMSPTA1A-NEG-1861", "HUMADAG-ACCEPTOR-31146", "HUMGCSFR1-NEG-61", "HUMSYN25A-NEG-1", "HUMCG4A11-NEG-961", "HUMAPRT-NEG-421", "HUMCRYGBC-NEG-8341", "HUMRETPON-NEG-1261", "HUMTHBNB-NEG-3541", "HUMTF-NEG-1261", "HUMLDL100-NEG-1381", "HUMTCRDA4-NEG-661", "HUMBCL2A-NEG-4561", "HUMCOL1A25-NEG-121", "HUMMHDQOB-NEG-1", "HUMSLIPG-NEG-961", "HUMMH-NEG-3541", "HUMMHDQBR-NEG-601", "HUMADAG-NEG-4441", "HUMKERC4-NEG-961", "HUMMHC4A-NEG-2761", "HUMBGPG-NEG-1021", "MACHBDPS2-NEG-1", "HUMDES-ACCEPTOR-7283", "HUMADAG-NEG-36361", "HUMFCGRB-NEG-61", "HUMLPLR-NEG-361", "HUMTUBAFB-NEG-1", "HUMER41-NEG-3661", "HUMADPRT01-NEG-61", "HUMKEREP-NEG-3721", "HUMCYC1A-ACCEPTOR-2108", "HUMMYCL2A-NEG-2341", "HUMDMP-NEG-3481", "HUMCYC1A-ACCEPTOR-2611", "HUMC1R-NEG-1801", "HUMFNMC-NEG-3481", "HUMRGM-NEG-4441", "HUMIGKVJ2-NEG-961", "HUMCYC1A-ACCEPTOR-2858"), ValueGroup("HUMBCL2A-NEG-1", "HUMRGNTSA-NEG-2161", "HUMPDGFB1-NEG-1381", "HUMARP450-NEG-2521", "HUMIGCC8-NEG-481", "HUMPDGFA1-NEG-841", "ORAHBPSBD-NEG-2881", "HUMFUR1-NEG-3901", "HUMPYGM09-NEG-1", "HUMRASR2-NEG-1621", "HUMTPO13-NEG-1021", "HUMIIP-NEG-61", "HUMMYCBL1-NEG-1", "HUMSISPDG-NEG-2581", "HUMTRANSB-NEG-181", "HUMANTP53-NEG-121", "CHPCD4A-NEG-961", "HUMG3PD-NEG-481", "HUMATP1A2-NEG-6661", "HUMPGRR-NEG-1681", "HUMCENPBR-NEG-901", "HUMEGR2A-NEG-421", "HUMMHB39W-NEG-1", "HUMCYPBX3-NEG-241", "HUMBCI2-NEG-241", "HUMGFIA2-NEG-3961", "HUMLYL1A-NEG-841", "HUMFOLLI1-NEG-2041", "HUMUG2B-NEG-781", "HUMMHDRHA-NEG-5341", "HUMTFA01-NEG-1321", "HUMMYCLYA-NEG-781", "CHPINVOL-NEG-1", "HUMAK1-NEG-1801", "HUMINSPR-NEG-61", "HUMRENT2-NEG-4321", "HUMHXM-NEG-1321", "HUMHPRTB-NEG-3601", "HUMTUBBM-NEG-2281", "HUMLSZH-NEG-841", "HUMPRCA-NEG-4321", "HUMHMGIB-NEG-721", "HUMTGFBG1-NEG-1801", "HUMIRGT-NEG-1681", "HUMRBP1-NEG-421", "HUMTROPA3-NEG-901", "HUMIGCC4-NEG-781", "HUMMUCC-NEG-121", "HUMANTCE-NEG-841", "HUMIGCH5-NEG-241", "HUMKEREP9-NEG-241", "HUMAGG-NEG-541", "HUMRNPA2A-NEG-1201", "HUMU3AA-NEG-961", "HUMENKPH1-NEG-1141", "HUMMYCNH-NEG-1", "HUMMHCW2B-NEG-3721", "HUMTKR-NEG-2641", "HUMIL2RX6-NEG-1", "HUMMYCBLK-NEG-781", "HUMLAMAR-NEG-1081", "HUMLCK-NEG-181", "HUMHMGYB-NEG-1561", "HUMCRYGA1-NEG-181", "HUMMHADX2-NEG-1201", "HUMALPI-NEG-3901", "CHPCLA108-NEG-841", "HUMAPOBA-NEG-4141", "HUMBHA05-NEG-61", "HUMMHDQBC-NEG-841", "HUMIGLAM1-NEG-121", "HUMPAIA-NEG-14341", "HUMLAMC-NEG-181", "HUMCFVII-NEG-661", "HUMCYP345-NEG-1381", "HUMCN2-NEG-841", "HUMMSOD-NEG-61", "HUMALDCG-NEG-1441", "HUMTCBJD-NEG-361", "HUMIRBPM-NEG-3241", "HUMHSP90B-NEG-6541", "HUMVWFA29-NEG-661", "HUMINT1G-NEG-601", "HUMTGFB-NEG-541", "HUMARAF1R-NEG-1381", "HUMIGFIIR-NEG-6841", "HUMCRYGBC-NEG-22021", "HUMESTR-NEG-2161", "HUMTHMA-NEG-121", "HUMVWFA24-NEG-781", "HUMSGLT1-NEG-1141", "HUMVWFA17-NEG-301", "HUMBCR22I-NEG-1681", "HUMMGSA-NEG-1", "HUMCCG1-NEG-1621", "HUMTCBXF-NEG-541", "HUMBN51-NEG-1261", "HUMASA-NEG-61", "HUMTACEB-NEG-241", "HUMMHCAI-NEG-1", "HUMIL2R-NEG-1", "HUMMYCTR-NEG-2041", "HUMMPOA-NEG-1321", "HUMAPB18-NEG-601", "HUMMYCC-NEG-2761", "HUMTGTPV2-NEG-841", "HUMFERG2-NEG-1", "HUMCPA4-NEG-601", "HUMIGCD6-NEG-901", "HUMC1INHI-NEG-121", "HUMPLAKO-NEG-1861", "HUMPSAP-NEG-2401", "HUMKAL2-NEG-1741", "HUMTPIPSC-NEG-61", "HUMKER56K-NEG-1201", "HUMIL7A-NEG-361", "HUMSTROMR-NEG-661", "HUMAPOCIB-NEG-1441", "HUMMHCGE1-NEG-61", "HUMRENA4-NEG-2461", "HUMPKCB1A-NEG-1321", "HUMPKCB2A-NEG-1321", "HUMCATR-NEG-1081", "HUMSPR1B-NEG-181", "HUMGAA-NEG-781", "HUMPKLR-NEG-1441", "HUMCN4GEL-NEG-2521", "HUMMHDRA2-NEG-241", "BABT1GL-NEG-61", "HUMMHCGE2-NEG-3301", "HUMSECP1-NEG-181", "HUMASFB-NEG-1021", "HUMACCYBA-NEG-2521", "HUMGLCB-NEG-661", "HUMLPHAA-NEG-1861", "HUMPBG2-NEG-841", "HUMCFVII-NEG-5221", "HUMAPC3G5-NEG-481", "ORAHBBE-NEG-2581", "HUMSODA-NEG-6361", "HUMDES-NEG-2641", "HUMVILLR1-NEG-361", "HUMFMSD-NEG-661", "HUMTFPB-NEG-421", "HUMC8GA-NEG-241", "HUMPAIA-NEG-661", "HUMP15095-NEG-4021", "HUMLCATG-NEG-3841", "HUMEPISIB2-NEG-1081", "HUMTHYB10-NEG-181", "HUM8KER1-NEG-61", "HUMMYLCA-NEG-3181", "HUMERB2R-NEG-2521", "HUMCYPD-NEG-361", "HUMEP-NEG-2461", "HUMIL71-NEG-61", "HUMHST-NEG-3961", "HUMBAT3B2-NEG-4141", "HUMPRB1-NEG-601", "HUMMHA3-NEG-1501", "HUMMHB17W-NEG-1681", "HUMPRPG-NEG-421", "HUMTHYRR5-NEG-781", "HUMTRO-NEG-181", "HUMIL1RA-NEG-2821", "HUMMYCST-NEG-1", "HUMALPL01-NEG-721", "HUMIMP-NEG-181", "HUMARA-NEG-1441", "HUMGSTPI-NEG-301", "HUMTPI-NEG-61", "HUMMHDRGB-NEG-121", "HUMSTSB-NEG-1021", "HUMGALAB-NEG-901", "HUMALP5-NEG-781", "HUMMYCH-NEG-901", "HUMDNFA-NEG-2101", "HUMTPA-NEG-27001", "HUMB2M2-NEG-1201", "HUMGAPJR-NEG-961", "HUMCETP2-NEG-661", "HUMIRRA-NEG-1861", "HUMIGLPAV-NEG-301", "HUMTPA-NEG-22441", "HUMMGPH-NEG-61", "HUMGCB1-NEG-4201", "HUMGNAZ-NEG-961", "HUMINV2-NEG-121", "HUMCYPX4-NEG-2101", "HUMUMOD-NEG-1621", "HUMLYL1B-ACCEPTOR-2189", "HUMPEPD-NEG-421", "HUMPS2G3-NEG-421", "HUMBLAST1-NEG-661", "HUMUKI3-NEG-2941", "HUMPRA403-NEG-1", "HUMLOX5-NEG-61", "HUMCR1L04-NEG-1", "HUMFVIIIB-NEG-4681", "HUMGPIBA-NEG-1561", "HUMMHDOAG1-NEG-61", "HUMADAG-NEG-27241", "HUMGLUTRN-NEG-1021", "HUMLECGBB-NEG-61", "HUMAK1-ACCEPTOR-3918", "BABAPOE-NEG-1201", "HUMLDLR18-NEG-1621", "HUMHLAB13-NEG-901", "HUMMHDOB-NEG-4261", "HUMPOMC3-NEG-241", "HUMMHDRB-NEG-901", "HUMNCAMB-NEG-661", "HUMP45C17-NEG-4861", "HUMAPOAI1-NEG-721", "HUMBCL2C-NEG-3061", "HUMAPOE4-NEG-3301", "HUMHAPRA-NEG-481", "HUMAPOB26-NEG-2881", "HUMCYPIIE-NEG-7561", "HUMGFI-NEG-1", "HUMNGFBA2-NEG-4021", "HUMSPARC07-NEG-1", "HUMPC125-NEG-3181", "HUMCGB-NEG-181", "HUMLKHA4-NEG-61", "HUMIFNB3-NEG-1081", "HUMBARR-NEG-361", "HUMCYP2BA-NEG-2761", "HUMA1GP01-NEG-301", "HUMCOL1A18-NEG-241", "HUMPSPBA-NEG-601", "HUMLGTPA-NEG-1261", "HUMSHBG-NEG-1021", "HUMTKRA-NEG-8281", "HUMTOPI-NEG-1261", "HUMGIAA-NEG-901", "HUMIGVKW1-NEG-841", "HUMGASTA-NEG-6241", "HUMINCPPS-DONOR-940", "HUMHPARS2-NEG-6961", "MACHBPEA-NEG-2161", "HUMIGKAG-NEG-481", "HUMLAM1B-NEG-121", "HUMRSA6-NEG-961", "HUMNGFBA1-NEG-2341", "CHPHPA2-NEG-1", "HUMTCRT3D-NEG-1081", "HUMSAA-NEG-2821", "HUMMRCOX2-NEG-481", "HUMTP53B-NEG-1021", "HUMIGFIIR-NEG-2281", "HUMALIPOA-NEG-5341", "HUMMHB7A-NEG-1261", "HUMGHCSA-NEG-24841", "HUMCRYGQ6-NEG-361", "HUMEGFRBB3-NEG-1201", "HUMTPA-NEG-17881", "HUMHBBAG-NEG-1441", "HUMRAF19-NEG-721", "HUMACTSM7-NEG-61", "HUMIGHAE2-NEG-781", "HUMLCAT1-NEG-721", "HUMMYHC04-NEG-1", "HUMBAT2B4-NEG-6001", "HUMGPIBAA-NEG-3661", "HUMIFNATC-NEG-661", "HUMDMHSP-NEG-1741", "HUMDHPRA-NEG-421", "HUMRASH-NEG-5161", "HUMAPOAIT-NEG-661", "HUMATPAR-NEG-3241", "HUMATPSY1-NEG-1", "HUMCYP450-NEG-2761", "HUMBMP1A-NEG-2161", "HUMINCPPS-NEG-3181", "HUMGFIBPA-NEG-1441", "HUMHPARS2-NEG-2401", "HUMTCBCE-NEG-1621", "HUMMHCA1A-NEG-601", "HUMMHBIA-NEG-541", "HUMHPRTB-NEG-17281", "HUMA2PIBB-NEG-421", "HUMPOLP-NEG-2341", "HUMTRGC64-NEG-61", "HUMSATM29B-NEG-181", "HUMAMIPEP-NEG-3361", "HUMNTSXPH-NEG-1081", "HUMMRNAEN-NEG-1681", "HUMHBGF1-NEG-601", "HUMHBB-NEG-1201", "HUMIGHBC-NEG-481", "HUMPFKM23-NEG-1621", "HUMIGKVAB-NEG-1261", "HUMTCDXA-NEG-1", "HUMTSHX-NEG-241", "HUMAPOB2-NEG-7621", "HUMCYPX1-NEG-4201", "HUMTCAYJ-NEG-181", "HUMMHEA-NEG-2041", "HUMGLYPL-NEG-1081", "HUMG0S19B-NEG-2821", "HUMALPHA-NEG-181", "HUMELA301-NEG-1", "HUMD2A-NEG-841", "HUMFESFPS-NEG-2101", "HUMMRP14A-NEG-1561", "HUMCANPR-NEG-841", "HUMIGCB4-NEG-61", "HUMGRFCIG-NEG-1501", "HUMMHDBW1-NEG-1", "HUMTPOA-NEG-841", "HUMMHDRWA-NEG-901", "HUMALPHB-NEG-241", "HUMCAMA-NEG-841", "HUMMET2AB-NEG-61", "HUMMHDCB-NEG-6601", "HUMP45C21-NEG-901", "HUMINSRC-NEG-6661", "HUMSRC2B-NEG-61", "HUMTFAA-NEG-2881", "HUMTPO08-NEG-601", "HUMPNMTA-NEG-421", "HUMCYC1-NEG-961", "HUMNEBB-NEG-241", "HUMCYP45C-NEG-3781", "HUMGGTX-NEG-1981", "HUMHBA4-NEG-11161", "HUMPAIR1-NEG-1081", "HUMRFPA-NEG-841", "HUMPSAA-NEG-2521", "HUMADAG-NEG-18121", "HUMFX-NEG-241", "HUMAK1-ACCEPTOR-10488", "HUMRASAA-NEG-481", "HUMFBRB3-NEG-1", "HUMTCRAI2-NEG-61", "HUMGPP3A08-NEG-3721", "HUMMHBC-NEG-1921", "HUMMYCAWT-NEG-1", "HUMIL4-NEG-301", "HUMGTPBPA-NEG-1081", "HUMALPI1-ACCEPTOR-124", "HUMIGKVM-NEG-421", "HUMTPO-NEG-2401", "HUMGLUD1-NEG-721", "HUMKADA-NEG-601", "HUMALPHLB-NEG-901", "HUMTCBJC-NEG-721", "HUMLDLRRL-NEG-7201", "HUMSISG1-NEG-841", "HUMMCR-NEG-781", "HUMIGKCB-NEG-1081", "HUMAPOBB-NEG-8101", "HUMAE1-NEG-2101", "HUMSNRNP1-NEG-301", "HUMA1ACMB-NEG-421", "HUMSPTA1A-NEG-6421", "HUMATCT4-NEG-1561", "HUMPDGFRAA-NEG-3421", "HUMADHIG-NEG-241", "HUMFVIIIB-NEG-121", "HUMPRP-NEG-1", "HUMUBPSE1-NEG-661", "HUMAPOCIA-NEG-2221", "HUMC1A2-NEG-661", "HUMLAP-NEG-1141", "HUMIGF27-NEG-1", "HUMSLK-NEG-841", "HUMACTGA-NEG-2641", "HUMCR1-NEG-241", "HUMINSRA-NEG-4201", "HUMPDHA-NEG-1141", "HUMHBA1-NEG-841", "HUMTCBYB-NEG-61", "HUMSTS-NEG-3361", "HUMRSKP08-NEG-61", "HUMNGFB-NEG-9361", "HUMGIP2A5-NEG-1", "HUMHTA1-NEG-241", "HUMATPB-NEG-841", "HUMFESFPS-NEG-11221", "HUMAT3X1-NEG-1", "HUMGHCSA-NEG-43081", "HUMEDHB17-ACCEPTOR-2701", "HUMFAV-NEG-3961", "HUMC1INH-NEG-481", "HUMHBA4-NEG-2041", "HUMDAFA-DONOR-1117", "HUMA1ATR-NEG-781", "HUMTNFAB-DONOR-1537", "HUMHLADPB-NEG-13321", "HUMASL1A-NEG-961", "HUMMCM-NEG-481", "HUMUKA-NEG-1501", "HUMC3-NEG-3481", "HUMMHDQAW-NEG-301"), ValueGroup("HUMIGJHI-NEG-61", "HUMALUAGP-NEG-181", "HUMERYA-NEG-661", "HUMMDNCF-NEG-1141", "HUMHTV1A-NEG-121", "HUMGFP40H-NEG-1201", "HUMIGJ2-NEG-181", "HUMDYS-NEG-12961", "HUMHIAPPA-NEG-1861", "HUMCREB-NEG-2101", "HUMF13A15-NEG-1981", "HUMC6AI-NEG-301", "HUMC45B2B-NEG-1021", "HUMGHCSA-NEG-15721", "HUMTHB-NEG-8161", "HUMCBP-NEG-1321", "HUMVTNR-NEG-5641", "HUMHPRTB-NEG-21841", "HUMPTPASE-NEG-1801", "HUMDBPG-NEG-1141", "HUMGNAS6-NEG-1", "HUMALBGC-NEG-9361", "HUMPOMC-NEG-4981", "HUMHLASBA-NEG-9721", "HUMVIPMR5-NEG-121", "HUMAFP-NEG-4141", "HUMLIPCR-NEG-1261", "HUMGHR-NEG-841", "HUMIFNAA-NEG-1621", "HUMGCRB-NEG-3481", "HUMALBGC-NEG-18481", "HUMRASK25-NEG-361", "HUMFNRB-NEG-1081", "HUMGG-NEG-5581", "HUMANT1-NEG-2881", "HUMTM30R-NEG-1441", "HUMMHCD8A-NEG-3781", "HUMPTHLHA-NEG-1381", "HUMFIXG-NEG-8221", "HUMHISH3C-NEG-781", "HUMTFRRA-NEG-3481", "HUMVWFA31-NEG-3961", "HUMFVIII-NEG-2821", "HUMCRYABA-NEG-3301", "HUMAFP-NEG-17821", "HUMHPRTB-NEG-12721", "HUMAFP1-NEG-241", "HUMRAG1-NEG-4141", "HUMHBB-NEG-42241", "HUMAMYA3-NEG-721", "HUMHPRT9-NEG-121", "HUMPLPA-NEG-2041", "HUMPA2I-NEG-541", "HUMNFLG-NEG-1861", "HUMACBPA2-NEG-1", "HUMGYPA06-NEG-421", "HUMCHEBG2-NEG-1", "HUMTHB-NEG-12721", "HUMGLA-NEG-8281", "HUMRA3A-NEG-1", "HUMGFB-NEG-2701", "HUMPALF1-NEG-781", "HUMGLUCG2-NEG-1621", "HUMPYHBASA-NEG-2641", "HUMHPRTB-NEG-40081", "HUMTSHBA1-NEG-961", "HUMADH4C1-NEG-1501", "HUMFBRG-NEG-2641", "HUMCFXI03-NEG-421", "HUMTPA-NEG-36121", "HUMNAKATPA-NEG-3061", "HUMEGFRER-NEG-901", "HUMHMG17G-NEG-3661", "HUMHLADZA-NEG-5281", "TARHBB-NEG-541", "HUMIL2A-NEG-1261", "HUMRGRSAL-NEG-1981", "HUMIFNRG-NEG-1561", "HUMAPOB10-NEG-301", "HUMGDHR-NEG-841", "HUMPGK2P-NEG-361", "HUMIFNAGS-NEG-5281", "CHPRSALPI-NEG-481", "GORHBBPG-NEG-181", "HUMIFNINI-NEG-1201", "HUMKER18-NEG-5941", "HUMIL2-NEG-2401", "HUMLC3-NEG-901", "HUMC7A-NEG-1861", "HUMHPRTB-NEG-30961", "HUMHPARS1-NEG-241", "HUMMFD8A-NEG-181", "HUMABL1B-NEG-1621", "HUMIGKP1-NEG-1441", "HUMAMYB-NEG-2041", "HUMMCAF-NEG-661", "HUMPRPC4B-NEG-541", "HUMAPOB4A-NEG-241", "HUMNAKATP2-NEG-2161", "AGMORS9A-NEG-481", "HUMIFNAGS-NEG-9841", "HUMSTSXG2-NEG-2461", "HUMVWFA-NEG-1", "HUMCOLA2I-NEG-841", "HUMAPOBB-NEG-3541", "HUMIL2A-NEG-5821", "HUMTNFA-NEG-3241", "HUMSIGMG3-NEG-301", "HUMDYZ1-NEG-3301", "CHPHBBPCH-NEG-301", "HUMCOL3A1A-NEG-661", "HUMHBB-NEG-55921", "HUM3ALPH-NEG-1", "HUMCALC1-NEG-1561", "HUMNPY4-NEG-241", "HUMGLA-NEG-3721", "HUMPC64-NEG-1801", "HUMGKAS-NEG-1441", "HUMGNAI1-NEG-1201", "HUMMHSBA-NEG-61", "HUMAG1-NEG-2101", "HUMALBAF1-NEG-301", "HUMCH15M-NEG-121", "MNKHAPSE-NEG-5461", "HUMMYHC-NEG-5941", "HUMFAPYSY-NEG-541", "HUMINCPPS-ACCEPTOR-2712", "HUMADH3G1-NEG-841", "HUMRGNTS-NEG-1981", "HUMCYAR03-NEG-301", "HUMFIXG-NEG-35581", "HUMSPRO-NEG-1321", "HUMFIXG-NEG-3661", "HUMKER18-NEG-1381", "HUMFOLMES-NEG-721", "HUMIGVKA2-NEG-601", "HUMERVKA-NEG-2161", "HUMELIA-NEG-2641", "HUMCAATP3-NEG-61", "HUMIGKREC-NEG-1", "HUMDAFA-NEG-1561", "CHPHBBC-NEG-1321", "HUMTCRDA-NEG-1741", "HUMGBR-NEG-2521", "HUMFIXG-NEG-31021", "HUMPCNA-NEG-4861", "HUMSTATHG2-NEG-1", "HUMPTH2-NEG-841", "HUMHBVINT-NEG-61", "HUMIFNN-NEG-601", "HUMBAT2B3-NEG-1", "HUMPDGFR-NEG-5281", "HUMTCRGE-NEG-121", "HUMHBEG-NEG-721", "HUMGPP3A17-NEG-1081", "HUMMETPOA-NEG-901", "HUMCRPGP-NEG-481", "HUMCDW44A-NEG-1261", "HUMUPIR1-NEG-841", "HUMAFP-NEG-13261", "HUMJUNA-NEG-3301", "GORHBPSBD-NEG-2281", "HUMFN3A-NEG-661", "HUMIFNAGS-NEG-721", "HUMHRGA-NEG-481", "HUMFERHX-NEG-481", "CHPGGGLOG-NEG-961", "HUMFIBEDA-NEG-2641", "HUMAFP-NEG-8701", "HUMARB-NEG-3541", "HUMINSRD-NEG-3601", "HUMRPS14-NEG-3421", "HUMHK1A-NEG-3421", "HUMMHDRW1-NEG-2581", "MACHBPSBD-NEG-1141", "HUMREPAL1-NEG-2821", "HUMALBGC-NEG-4801", "HUMNMYC-NEG-6121", "HUMAACT-NEG-481", "HUMHPS12-NEG-1621", "HUMTHEP2-NEG-121", "HUMMMTVPOL-NEG-421", "HUMMETIF-NEG-1681", "HUMMHDQAR-NEG-1", "HUMHBB-NEG-37681", "HUMPDHE1B-NEG-301", "HUMERSP1A-NEG-241", "HUMFABP-NEG-661", "HUMHBEG-NEG-5281", "HUMFVIIIC-NEG-121", "HUMALPSAT-NEG-541", "HUMFIXG-NEG-12781", "HUMHBEG-NEG-9841", "HUMHLASBA-NEG-5161", "HUMDBLTP-NEG-1081", "HUMCERP-NEG-121", "HUMFIXG-NEG-26461", "HUMPZP-NEG-1741", "HUMRASN2-NEG-1", "HUMSATAX-NEG-1441", "HUMM6PR-NEG-1861", "HUMKUP-NEG-1021", "HUMAREPAP-NEG-61", "HUMAPA-NEG-841", "HUMMGA2PS-NEG-1861", "GIBHBGGL-NEG-7861", "HUMATPSYB-NEG-9961", "HUMHPRTB-NEG-26401", "HUMTPO11-NEG-241", "HUMCACY-NEG-3481", "HUMHPRTB-NEG-35521", "HUMALDA1-NEG-661", "HUMHBB-NEG-10321", "HUMTPA-NEG-13321", "HUMRSCNTF-NEG-61", "HUMMYCL-NEG-1681", "HUMSATAR2-NEG-181", "HUMPIP-NEG-241", "HUMAMY107-NEG-1", "HUMGYPC-NEG-421", "HUMIRBPG-NEG-8341", "HUMABLLA-NEG-1", "ORAHBBPSE-NEG-2101", "HUMHLADPB-NEG-4201", "HUMIL1B-NEG-2161", "HUMS71AA-NEG-3301", "HUMLDLR01-NEG-241", "HUMAPBM-NEG-3841", "HUMBRAFA-NEG-601", "HUMIFNG-NEG-4321", "HUMGCB2-NEG-1141", "HUMEB2CR2-NEG-1141", "HUMERV34A-NEG-2521", "HUMHBB-NEG-28561", "HUMLMWOS1-NEG-301", "HUMADRBR-NEG-1861", "HUMVIP1-NEG-421", "HUMANFA-NEG-2581", "HUMMDR3-NEG-301", "HUMCSYNA-NEG-2461", "HUMPP12R-NEG-1261", "HUMCERPS-NEG-1381", "HUMGG-NEG-1021", "HUMTGC1-NEG-61", "HUMAMY2A-NEG-361", "HUMCYPNO-NEG-961", "HUMLOX5A-NEG-2161", "HUMTBB46P-NEG-421", "HUMLDL100-NEG-10501", "HUMFBRGAB-NEG-1081", "HUMIGKVI1-NEG-541", "HUMMHDQA1-NEG-661", "HUMCNR-NEG-2101", "HUMIL1BX-NEG-5461", "HUMIFNA01-NEG-421", "HUMIGB7-NEG-1381", "HUMATP1A2-NEG-15781", "HUMC1A2-NEG-5221", "CHPRSA-NEG-2161", "HUMMDR1A07-NEG-781", "HUMOAS06-NEG-181", "HUMG3PDP-NEG-1", "HUMLIDNA-NEG-2701", "HUMA1ATP-NEG-4861", "HUMPAI2A4-NEG-121", "HUMGLAA-NEG-421", "HUMRISDAD-NEG-3721", "HUMTKRA-NEG-3721", "HUMGLYCA4-NEG-181", "HUMPKCAMD-NEG-781", "HUMFVIII-NEG-7381", "HUMTCRB22-NEG-241", "HUMHBB-NEG-51361", "HUMLDHB1-NEG-2461", "HUMISG1-NEG-241", "HUMHPRTB-NEG-8161", "HUMPALC-NEG-6841", "HUMNGFB-NEG-4801", "HUMPLA2A1-NEG-1561", "HUMFIXA-NEG-781", "HUMSTATHG2-NEG-4561", "HUMMHDRHA-NEG-781", "HUMIGHVV2-NEG-841", "HUMRPHO2A-NEG-1861", "HUMCD2A-NEG-121", "MNKHBPSBD-NEG-961", "HUMANTNC-NEG-2401", "HUMIL1AG-NEG-421", "HUMDYS-NEG-3841", "HUMACHRA7-NEG-901", "HUMHSP90B-NEG-1981", "HUMIFNAD-NEG-841", "HUMAREPBV-NEG-61", "HUMPLP6-NEG-421", "HUMTHROMPR-NEG-121", "HUMHBBRT-NEG-1141", "HUMRPS17A-NEG-1561", "HUMPRPH1-NEG-4081", "HUMHBBGG-NEG-481", "HUMTCRGAA-NEG-1", "HUMCIX-NEG-2521", "HUMTCAXB-NEG-1321", "HUMUMPS-NEG-1561", "HUMTCRAJ3-NEG-301", "HUMLHRH-NEG-961", "HUMDMDHI41-NEG-481", "HUMGYPCDC-NEG-421", "HUMNRNPEP-NEG-1141", "HUMDONT01-NEG-481", "HUMMYCG2-NEG-1501", "MACHBPEA-NEG-6721", "HUMIGKVS-NEG-421", "HUMIGHHSG4-NEG-601", "HUMBURKA-NEG-1141", "HUMMYCC-NEG-7321", "HUMFXIIIC-NEG-1561", "HUMAPB13-NEG-1", "HUMTCBV81-NEG-541", "HUMGHRA06-NEG-1", "HUMCRPG-NEG-781", "HUMUBIBP-NEG-481", "HUMINS02-NEG-181", "HUMTPO04-NEG-3121", "HUMFIXG6-NEG-1", "HUMCG1A1B-NEG-121", "HUMHP2FS-NEG-1021", "HUMAPB48A-NEG-2041", "HUMHSC70-NEG-1021", "HUMMHDRP1-NEG-481", "HUMTCRB11-NEG-1", "HUMTFRR-NEG-1741", "HUMMHCC6A-NEG-2401", "HUMPROSA-NEG-1021", "HUMTRD117-NEG-301", "HUMLCK2-NEG-841", "HUMIGGFCRA-NEG-1", "HUMHBB-NEG-24001", "HUMANTCD2-NEG-661", "HUMTBG-NEG-301", "HUMHGMCOA-NEG-181", "HUMPSG3A-NEG-481", "HUMSATA-NEG-361", "HUMTCRAC-NEG-1441", "HUMADH2C1-NEG-841", "HUMGHCSA-NEG-20281", "HUMACTCA2-NEG-1381", "HUMT1418-NEG-2161", "HUMGPP3A04-NEG-421", "HUMAPOBF-NEG-5641", "HUMCRBP2-NEG-421", "HUMPUMP1-NEG-661", "HUMATPA2-NEG-781", "HUMB100DI-NEG-121", "HUMTCAYD-NEG-1", "HUMC4BP-NEG-241", "HUMNGFB-NEG-241", "HUMBMP3A-NEG-1021", "HUMTFPC-NEG-241", "HUMVPF-NEG-661", "HUMCR1RS-NEG-1681", "HUMPPS-NEG-1561", "HUMCFVII-NEG-9781", "HUMIGHVQ-NEG-61", "HUMENKB1-NEG-1081", "HUMGASTA-NEG-1681", "HUMTYR-NEG-1141", "HUMODC-NEG-241", "HUMH1FNC1-NEG-1", "HUMAPOB2-NEG-3061", "HUMPMG3BA-NEG-3001", "HUMIAIG4-NEG-421", "HUMHLA1PS-NEG-3061", "HUMFIXG-NEG-21901"), ValueGroup("HUMGHCSA-NEG-11161", "HUMFMSCPO-NEG-61", "HUMCYCPS3-NEG-1081", "HUMHBB-NEG-65041", "HUMADAG-NEG-31801", "HUMLCA-NEG-2521", "HUMH-NEG-3061", "HUMHF10-NEG-241", "HUMDBPA-NEG-1381", "HUMGHCSA-NEG-29401", "HUMMYHC10-NEG-1561", "HUMLDHA7-NEG-361", "HUMGHCSA-NEG-52201", "HUMRSCNTT-NEG-121", "HUMFNI-NEG-121", "HUMFOS-NEG-6061", "HUMP45C17-NEG-301", "HUMPRLR-NEG-1861", "HUMGRP78-NEG-4981", "HUMRSLINE-NEG-2161", "HUMALDB2-NEG-1681", "HUMTROPFB-NEG-421", "HUMATP1A2-NEG-24901", "HUMPMPCA-NEG-1141", "HUMF8L1B-NEG-2041", "HUMP47-NEG-721", "HUMMXB-NEG-1741", "HUMTGFB2A-NEG-2401", "HUMANTLAA-NEG-481", "HUMNCAM-NEG-121", "HUMCHROMB-NEG-421", "HUMCA1XIA-NEG-3421", "HUMERBC-NEG-421", "HUMMAC1A-NEG-4021", "HUMPLASTA-NEG-1561", "HUMALBGC-NEG-13921", "HUMTGL7-NEG-181", "GIBHBGGL-NEG-3301", "HUMCAM-NEG-121", "HUMCARM-NEG-661", "HUMCATF-NEG-721", "AGMKPNRSB-NEG-1", "HUMIL1AG-NEG-9541", "MACRSMB-NEG-1", "HUMRSKPA2-NEG-121", "HUMTCR3G1-NEG-961", "HUMBSF2-NEG-4141", "HUMRPS6A-NEG-601", "HUMUDPGT-NEG-1801", "HUMAPB09-NEG-1", "HUMAFLP2-NEG-61", "HUMRBSA-NEG-1381", "HUMSBLA-NEG-961", "HUMDUG-NEG-2941", "HUM7B2-NEG-901", "HUMGPP3A21-NEG-2521", "HUMRSP1GPB-NEG-181", "HUMTHRAA-NEG-361", "HUMP3A-NEG-2461", "HUMHBB-NEG-19441", "HUMGLUCG2-NEG-6181", "HUMCS5-NEG-2161", "HUMHPBA-NEG-901", "HUMALDC01-NEG-361", "LEMHBGA-NEG-361", "HUMHPARS1-NEG-9361", "HUMDYS-NEG-8401", "HUMIL2R6-NEG-1", "HUMCEAE-NEG-61", "HUMTFPB-NEG-9541", "HUMTNFAB-NEG-2641", "HUM18BPD-NEG-61", "HUMCD20-NEG-721", "HUMIR3EBA-NEG-121", "HUMHBARSA-NEG-1081", "HUMIGGRLA-NEG-841", "HUMETS1A-NEG-481", "GORRGITX-NEG-1", "HUMTBB14P-NEG-1441", "HUMGHCSA-NEG-65881", "HUM7SKP41-NEG-541", "HUMMHSBB1-NEG-1561", "HUMRSKPNA-NEG-2341", "HUMSNRNP9-NEG-121", "HUMGPP3A11-NEG-961", "HUMIL1P-NEG-301", "HUMTOPIIX-NEG-841", "HUMEDF-NEG-841", "HUMTCGCF-NEG-121", "HUMSMCK-NEG-1021", "HUMMHDRS1-NEG-241", "HUMSODG5-NEG-181", "HUMCYAR10-NEG-1381", "HUMPALD-NEG-3841", "GORHBBPG-NEG-4741", "HUMTCBCC-NEG-1981", "HUMERMTV2-NEG-301", "HUMMYC3L-DONOR-2594", "HUMALDOBG-NEG-1", "HUMIGKC1-NEG-1681", "HUMCMYBB-NEG-901", "HUMGMCSFG-NEG-2821", "HUMRNPLA-NEG-181", "HUMMPRCI-NEG-601", "HUMGHCSA-NEG-33961", "HUMADAG-NEG-22681", "HUMRHOC9-NEG-121", "HUMSATA7A-NEG-2461", "HUMANP70-NEG-361", "HUMHELB-NEG-2461", "HUMHBB-NEG-46801", "HUMPHH-NEG-541", "HUMSIGMG4-NEG-961", "HUMREN02-NEG-121", "HUMMHDR3-NEG-1441", "HUMMYH1R-NEG-1621", "HUMHBB-NEG-33121", "HUMJNLTRA-NEG-121", "HUMLAMB2-NEG-961", "ORAHBBPSE-NEG-6661", "HUMZFX-NEG-781", "HUMCYPBX1-NEG-541", "HUMCAIII1-NEG-181", "GIBIL2LTR-NEG-241", "HUMMEHR-NEG-361", "HUMFUMA-NEG-841", "HUMENKPH2-NEG-3781", "HUMPNU-NEG-721", "HUMAS08-NEG-721", "HUMRSH3-NEG-1621", "HUMDBPC-NEG-2821", "HUMIGMUD-NEG-721", "HUMBPGM3-NEG-661", "HUMHMG14A-NEG-61", "HUMGFIAB5-NEG-421", "HUMVWFA37-NEG-661", "HUMTHYRR-NEG-481", "HUMINSR-NEG-541", "HUMPRPH2-NEG-3661", "HUMHMG17-NEG-301", "HUMHBB-NEG-69601", "HUMH38-NEG-661", "HUMALDB1-NEG-2761", "HUMAPOBA-NEG-8701", "HUMAPOC2-NEG-2521", "HUMC5A2B-NEG-181", "CHPHPA3-NEG-2041", "HUMHBB-NEG-14881", "MNKHAPSE-NEG-901", "HUMCYCPSJ-NEG-361", "HUMIGKVI5-NEG-2101", "HUMGCB2-NEG-5701", "HUMF13A05-NEG-121", "HUMHOM4-NEG-841", "HUMATCCD2-NEG-1261", "HUMPPOL-NEG-1081", "HUMIGKVW-NEG-841", "HUMAPB21-NEG-7501", "HUMTGPL1-NEG-1201", "HUMUGU4CA-NEG-121", "HUMTCBVW2-NEG-301", "HUMAPB21-NEG-2941", "HUMKALA-NEG-181", "HUMC8B-NEG-241", "CHPP44-NEG-481", "HUMCINHA-NEG-541", "HUMPRC7-NEG-961", "HUMMHDC3B-NEG-7021", "HUMGHCSA-NEG-2041", "HUMCYPSCC-NEG-1501", "HUMAPOB2-NEG-12181", "HUMGSTC-NEG-421", "HUMMDR1-NEG-2401", "HUMAPOBF-NEG-1081", "HUMCOL4A1-NEG-961", "HUMVWFA01-NEG-3961", "HUMPAPA-NEG-2401", "HUMPDGFARA-NEG-4621", "HUMIL2B-NEG-3241", "HUMPS12-NEG-1321", "HUMMYHC-NEG-1381", "HUMGP3A-NEG-1921", "HUMREPA-NEG-1141", "HUMARMA-NEG-961", "HUMELI-NEG-721", "HUMHOXB-NEG-541", "HUMIGLV71-NEG-721", "HUMBGAL-NEG-781", "HUMIGMUD-NEG-5281", "HUMMHDCB-NEG-2041", "HUMIFNF-NEG-601", "HUMHODB2-NEG-301", "HUMCYPAX-NEG-421", "HUMER41-NEG-8221", "HUMUG3PD-NEG-61", "AGMRSKPNI-NEG-1141", "HUMERG2-NEG-1321", "HUMXYES2-NEG-1741", "HUMLDLRRL-NEG-2641", "HUMCOL1A39-NEG-361", "HUMGLNRS-NEG-361", "HUMMAP2A-NEG-421", "HUMSNRNP3-NEG-3901", "HUMATPSY1-NEG-4561", "HUMHLASBA-NEG-601", "HUMC1SAB-NEG-841", "HUMTOPII-NEG-1081", "HUMTROPSR-NEG-721", "HUMA1GLY2-NEG-1501", "HUMKER7R-NEG-601", "HUMAPOAI1-NEG-5281", "HUMMDRA3-NEG-961", "HUMDMDA-NEG-1201", "HUMCYPHLP-NEG-421", "HUMTCAJA-NEG-61", "HUMGHCSA-NEG-61321", "HUMCSPB-NEG-61", "HUMHSDB-NEG-121", "HUMPAI2-NEG-181", "HUMVTNR-NEG-1081", "CHPHBBPCH-NEG-4861", "HUMGHCSA-NEG-6601", "HUMTDTA-NEG-781", "HUMTKFER-NEG-1081", "HUMTCAXN-NEG-61", "HUMPKB-NEG-1741", "HUMFIGRD-NEG-361", "HUMGPBPS-NEG-961", "HUMCYCB-NEG-181", "HUMCHEF-NEG-181", "HUMSAA1A-NEG-3901", "HUMCEAB-NEG-121", "HUMC5B-NEG-3241", "HUMBCRR-NEG-1261", "HUMGHCSA-NEG-56761", "HUMCGPRA-NEG-1981", "HUMPRLA-NEG-241", "HUMATH3U5-NEG-181", "HUMIFNB3-NEG-10201", "HUMMTLMC2-NEG-781", "HUMRSO5C-NEG-361", "HUMINHBA-NEG-541", "HUMGPP3A07-NEG-1381", "HUMLDHBR-NEG-121", "HUMCSFM-NEG-181", "HUMPC2A-NEG-481", "HUMBDGALA-NEG-1441", "HUMTCBYM-NEG-181", "HUMSHBGA-NEG-4501", "HUMHTV3A-NEG-841", "HUMHMGCOA-NEG-2101", "HUMNAKATP2-NEG-6721", "HUMAPB21-NEG-12061", "HUMRIBIR-NEG-1681", "HUMADAG-NEG-13561", "HUMGDHA-NEG-181", "HUMCYPIIE-NEG-3001", "HUMFOS-NEG-1501", "HUMIFNAPS-NEG-721", "HUMBLYM1-DONOR-127", "HUMGTLPA-NEG-1621", "HUMALDB1-NEG-7321", "HUMTBB5-NEG-3241", "HUMPOLDNAA-NEG-3481", "HUMAPB03-NEG-1441", "HUMHBB-NEG-60481", "HUMTCOBI-NEG-1261", "HUMCOLA1C-NEG-1141", "HUMALDOAP-NEG-661", "HUMCEALV-NEG-721", "HUMG0S19A-NEG-2341", "HUMC21PLA-NEG-121", "HUMKITCR-NEG-1021", "HUMFABPL-NEG-61", "HUMFVIIIC-NEG-4681", "HUMZFY-NEG-2341", "HUMLYRE-NEG-1801", "HUMDLDH-NEG-1021", "HUMTCSM-NEG-241", "HUMVWFM-NEG-541", "HUMCYCPSE-NEG-541", "HUMRSALPI-NEG-181", "HUMAMD-NEG-601", "HUMHPP16C-NEG-541", "HUMGFAP-NEG-2941", "HUMANT61K-NEG-1681", "HUMACHRM2-NEG-1681", "HUMAPOBA-NEG-13261", "HUMPDGFARA-NEG-61", "HUMPDGFR-NEG-721", "HUMREPHY2-NEG-481", "HUMCTHD-NEG-1801", "HUMCYES1-NEG-1501", "HUMIGKVI3-NEG-421", "HUMATPSYB-NEG-5401", "HUMTCBZE-NEG-241", "HUMAGP1A-NEG-421", "HUMTHYRR-NEG-5041", "HUMARGL-NEG-241", "HUMMPRCI-NEG-5161", "HUMRB1RA-NEG-3061", "HUMTCAYS-NEG-121", "HUMPOLBA-NEG-781", "HUMEN1-NEG-1201", "HUMHSPGC-NEG-2821", "HUMMHCW1C-NEG-781", "HUMRSSA1B-NEG-601", "HUMC4A2-NEG-3721", "HUMTUBAG-NEG-3361", "HUMPRCA-DONOR-3601", "HUMTHB-NEG-3601", "HUMMBP17K-NEG-361", "HUMMHDRR2-NEG-121", "HUMCALLA14-NEG-61", "HUMTPA-NEG-4201", "HUMICAM1A-NEG-1981", "HUMCYP4A2-NEG-781", "HUMPAP4-NEG-781", "HUMPGKPX-NEG-961", "HUMLCAR-NEG-2161", "HUMA2M-NEG-4441", "HUMAPOCII-NEG-121", "HUMPEPC1-NEG-901", "HUMNID-NEG-3541", "HUMP120PC-NEG-2101", "HUMALDH2-NEG-1201", "HUMMHDRBX-NEG-61", "HUMZNF8-NEG-661", "HUMCR1-NEG-4801", "HUMTHROMR-NEG-721", "HUMFIXG-NEG-17341", "HUMNOXF-NEG-361", "HUMLAM1B-NEG-4681", "HUMGHCSA-NEG-47641", "HUMDCC-NEG-1561", "HUMIL1BX-NEG-901", "HUMMYOHP-NEG-901", "HUMCFTRM-NEG-2281", "HUMTNFB-NEG-121", "HUMEBUR04-NEG-241", "HUMTRA-NEG-781", "HUMHLS2R-NEG-1201", "HUMMHSXA-NEG-4561", "HUMCMOSAA-NEG-121", "HUMLMYC2-NEG-901", "HUMADH2E2-NEG-1", "MACAPOA-NEG-961", "HUMIL5A-NEG-481", "HUMGPPSBB-NEG-1021", "HUMFVA-NEG-3001", "HUMGFRIL-NEG-1441", "HUMPPARP1-NEG-181", "HUMA1ATP-NEG-9421", "HUMTFPB-NEG-4981", "HUMNAKATP1-NEG-5281", "HUMETN1-NEG-181", "HUMFSH3-NEG-1021", "HUMER141-NEG-121", "HUMAPOB-NEG-1321", "HUMPDGFRA-NEG-4441", "HUMPPH1-NEG-61", "HUMOPS-NEG-4741", "HUMAPOAIA-NEG-1", "HUMRBS-NEG-2221", "HUMMHDRC2-NEG-841", "HUMDYSIN7-NEG-3541", "HUMIFNB2-NEG-361", "HUMIGLC2A-NEG-361", "HUMPTHL2-NEG-1", "HUMAPOC2G-NEG-3001", "HUMMHDRS2-NEG-2461", "HUMHPA1B-NEG-601", "HUMLAPA-NEG-2881", "HUMIL6-NEG-1021", "HUMHIAPPA-NEG-6421", "HUMEBVR-NEG-841", "HUMROSMCF-NEG-121", "HUMRYR-NEG-5641", "HUMFLA1A-NEG-961", "HUMABLA-NEG-2701", "HUMMH6-NEG-1201", "HUMTCGXH-NEG-601", "HUMIFNA04-NEG-961", "HUMCRYGBC-NEG-17461", "HUMIGKPC-NEG-61", "HUMHLADZA-NEG-721", "HUMEF1AB-NEG-481", "HUMMHDRB3-NEG-661", "HUMUKPM-NEG-781", "HUMGHV-NEG-1861", "HUMT28E-NEG-121", "HUMSATOD-NEG-841", "HUMERVKA-NEG-6721", "HUMPAIA-NEG-5221", "HUMVWFR1-NEG-8041", "HUMG6PDA-NEG-601", "HUMCATG12-NEG-241", "HUMIGHAJ-NEG-541", "HUMPSBGA-NEG-781", "HUMFN-NEG-3241", "HUMCYPMP-NEG-181", "HUMCTSE-NEG-661", "HUMIRBP-NEG-3481", "HUMBAT2B4-NEG-1441", "HUMCS3-NEG-361", "HUMRCYP3-NEG-1081", "ORAHBG2F-NEG-181", "HUMC1RX-NEG-1201", "HUMPSAA1-NEG-1", "HUMGPPSB1-NEG-241", "HUMEGFRN-NEG-4381", "HUMKER672-NEG-2401", "HUMMEDA-NEG-2761"), ValueGroup("HUMMYLCA-DONOR-2559", "HUMIL2-DONOR-595", "HUMIL2A-DONOR-1533", "HUMIL2B-DONOR-410", "HUMALBGC-DONOR-2592", "HUMHPRTB-DONOR-16757", "HUMALBGC-DONOR-13770", "HUMAFP-DONOR-970", "HUMALBGC-DONOR-17044", "HUMPCNA-DONOR-3721", "HUMERP-DONOR-1653", "HUMERPA-DONOR-1662", "HUMALBGC-DONOR-6223", "HUMHPRTB-DONOR-40080", "HUMAFP-DONOR-1834", "HUMALBGC-DONOR-15086", "HUMTFPB-DONOR-9438", "HUMGG-DONOR-3769", "HUMFIXG-DONOR-13441", "HUMALBGC-DONOR-7827", "HUMGFP40H-DONOR-2563", "HUMHPRTB-DONOR-39832", "HUMIL5-DONOR-908", "HUMIL5A-DONOR-907", "HUMFABP-DONOR-2493", "HUMFABP-DONOR-3624", "HUMHPRTB-DONOR-31605", "HUMIL2-DONOR-745", "HUMIL2A-DONOR-1683", "HUMIL2B-DONOR-560", "HUMAFP-DONOR-14687", "HUMALBGC-DONOR-15728", "HUMMYLCA-DONOR-2388", "HUMALBGC-DONOR-4179", "HUMHMG14A-NEG-4621", "TARHBB-DONOR-1909", "HUMGRP78-DONOR-1510", "HUMFIXG-DONOR-9425", "HUMALBGC-DONOR-17726", "HUMFABP-DONOR-1126", "HUMIL5-DONOR-1982", "HUMIL5A-DONOR-1986", "HUMHLASBA-NEG-14281", "HUMHPRTB-DONOR-14857", "HUMALBGC-DONOR-11052", "HUMATPSYB-DONOR-3271", "HUMHSP90B-DONOR-6241", "HUMHIAPPA-DONOR-629", "HUMHPRTB-DONOR-34990", "HUMAFP-DONOR-2929", "HUMFIXG-DONOR-9638", "HUMLACTA-DONOR-2236", "HUMTR211-NEG-901", "HUMIL2-DONOR-3182", "HUMIL2A-DONOR-4119", "HUMIL2B-DONOR-2996", "HUMAFP-DONOR-14021", "TARHBD-DONOR-817", "HUMSODA-DONOR-5211", "HUMHSP90B-DONOR-5582", "HUMGRP78-DONOR-2735", "HUMIFNAII-NEG-61", "HUMP53A-NEG-1", "HUMGRP78-DONOR-3539", "HUMAFP-DONOR-17179", "HUMCATF-DONOR-646", "HUMALBGC-DONOR-12584", "HUMPCNA-DONOR-5730", "HUMIFNINI-DONOR-2232", "HUMALBGC-DONOR-1825", "HUMAFP-DONOR-5428", "HUMPCNA-DONOR-2588", "HUMCYCAA-DONOR-1365", "HUMSODA-DONOR-3286", "HUMTUBAG-DONOR-2554", "HUMHMG17P-NEG-1081", "HUMEF1A-DONOR-2242", "LEMHBB-DONOR-569", "HUMMHDOB-DONOR-4518", "HUMATPSYB-DONOR-8947", "HUMHSP90B-DONOR-4129", "HUMHSP90B-DONOR-3809", "HUMCKMT-DONOR-1625", "HUMEF1A-DONOR-1696", "HUMAFP-DONOR-9741", "HUMFIXG-DONOR-3053", "HUMEF1A-DONOR-2644", "HUMSODA-DONOR-6429", "HUMP45C17-DONOR-5125", "HUMATPSYB-DONOR-5594", "HUMFIXG-DONOR-33089", "HUMANT1-DONOR-4091", "HUMIL5-DONOR-667", "HUMIL5A-DONOR-666", "HUMATPSYB-DONOR-2881", "HUMHSP90B-DONOR-5188", "HUMETN3-DONOR-227", "HUMEF1A-DONOR-2878", "HUMEF1A-DONOR-3546", "HUMALBGC-DONOR-9544", "HUMIFNINI-DONOR-2912", "HUMRPS17A-DONOR-1717", "HUMPRPH2-DONOR-1884", "HUMIL1B-DONOR-1558", "ORAHBD-DONOR-819", "HUMHPRTB-DONOR-27927", "HUMGFP40H-DONOR-1143", "HUMEF1A-NEG-1561", "HUMMLCAC-NEG-601", "HUMGRP78-DONOR-2258", "HUMFOS-DONOR-2544", "HUMTPO06-NEG-1681", "HUMCYPIIE-DONOR-2972", "HUMPRPH1-DONOR-2774", "HUMRPS17A-DONOR-2291", "HUMHIS4A2-NEG-301", "HUMHIAPPA-DONOR-1054", "HUMEF1A-DONOR-3222", "HUMCSFGMA-DONOR-933", "HUMGFP40H-DONOR-1028", "HUMTFPB-DONOR-2272", "HUMATPSYB-DONOR-4215", "HUMLACTA-DONOR-865", "HUMKEREP-DONOR-2872", "HUMAFP-DONOR-12231", "HUMSPROTR-NEG-541", "HUMGRP78-ACCEPTOR-1372", "HUMIFNINI-DONOR-3237", "HUMGG-DONOR-1964", "HUMPRPH2-DONOR-2660", "HUMSODA-DONOR-6624", "HUMMYCC-DONOR-2852", "HUMPCNA-DONOR-2424", "HUMATPSYB-DONOR-4649", "HUMSODA-DONOR-4346", "HUMMHDOB-DONOR-1105", "LEMHBG-DONOR-587", "GIBHBGGL-DONOR-2624", "GIBHBGGL-DONOR-7544", "LEMHBE-DONOR-633", "HUMAFP-DONOR-8063", "HUMATPSYB-DONOR-8071", "HUMIL1B-DONOR-5824", "HUMMHDRHA-DONOR-3987", "HUMCRYGBC-DONOR-18389", "MACHBCA2-DONOR-809", "MACHBGA1-DONOR-811", "HUMHSP90B-DONOR-1172", "HUMPRPH1-DONOR-1687", "HUMIL1B-DONOR-416", "HUMCP21OH-DONOR-1104", "HUMCP21OHC-DONOR-1105", "HUMMHCP42-DONOR-2035", "HUMMHCP52-DONOR-569", "MNKHBD-DONOR-698", "HUMIL1B-DONOR-4458", "HUMSODA-NEG-1801", "HUMA1ATP-DONOR-7932", "HUMAPOBB-NEG-12661", "HUMCSPB-DONOR-2478", "ORAHBG1F-DONOR-614", "HUMMHANTLE-DONOR-3700", "LEMHBGA-DONOR-648", "HUMAFP-DONOR-18452", "HUMCKMT-DONOR-2448", "HUMGRP78-DONOR-3086", "HUMC9M-NEG-841", "HUMAFP-DONOR-7047", "HUMPRPH1-DONOR-3555", "HUMHBBAG-DONOR-1689", "LEMHBE-DONOR-288", "HUMSHBGA-DONOR-4189", "HUMSODA-DONOR-4739", "HUMPRP0A-NEG-2161", "HUMGCRA-NEG-3721", "HUMMHDOB-DONOR-3639", "HUMSODA-DONOR-6932", "HUMGCB1-DONOR-581", "HUMSAA-DONOR-701", "HUMADAG-NEG-9001", "HUMSODA-DONOR-3510", "HUMHSP90B-DONOR-2752", "HUMHSP90B-DONOR-7352", "HUMFXIIIB-NEG-841", "HUMSHBGA-DONOR-5396", "HUMTUBAG-DONOR-2257", "HUMAPA4R-NEG-2761", "HUMHSP90B-DONOR-4574", "HUMTFPB-DONOR-6562", "HUMINCPPS-DONOR-2825", "HUMCKMT-DONOR-5110", "HUMFIXG-DONOR-20733", "HUMAFP-DONOR-19845", "HUMHSP90B-DONOR-5858", "HUMGCB1-DONOR-3143", "HUMGG-DONOR-230", "HUMSAA1A-DONOR-6017", "HUMEF1A-ACCEPTOR-3311", "HUMRTVLH2-NEG-4681", "HUMHPRTB-NEG-53761", "HUMHSP90B-DONOR-3558", "HUMRTVLH2-NEG-121", "HUMSAA-DONOR-34", "HUMALBGC-DONOR-6905", "HUMKTEP2A-NEG-1441", "HUMMH6-ACCEPTOR-3562", "HUMCD1B1-NEG-241", "HUMTNFA-DONOR-1839", "HUMTNFAB-DONOR-5318", "HUMTNFX-DONOR-1613", "HUMHSP90B-ACCEPTOR-7018", "HUMKEREP-DONOR-3368", "HUMATPSYB-ACCEPTOR-3096", "HUMA1GLY2-DONOR-3352", "HUMHPARS1-DONOR-3825", "HUMCYPIIE-DONOR-12950", "HUMMRP14A-DONOR-1551", "HUMMYC3L-NEG-3301", "HUMMHDXAR2-NEG-1", "HUMACTGA-DONOR-2060", "HUMA1GLY2-DONOR-3606", "HUMMH6-DONOR-3321", "HUMIL1B-NEG-6721", "MNKHBD-DONOR-347", "HUMHPARS1-NEG-4801", "HUMVIM06-NEG-1", "HUMFIXG-DONOR-23501", "ORAHBD-DONOR-466", "HUMATPCAPM-NEG-661", "HUMHBB32-NEG-121", "HUMDES-DONOR-2016", "HUMCALLA-NEG-2161", "HUMADH1CA-NEG-121", "HUMSAA1A-DONOR-1815", "HUMCKMT-ACCEPTOR-5313", "HUMMRP8A-ACCEPTOR-1988", "HUMGRP78-DONOR-1007", "HUMLACF-NEG-541", "HUMFBRG-NEG-7201", " * ")), IntervalBounds(15.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5), ValueGroups(ValueGroup("G", "N", "D", "S", "R"), ValueGroup("T"), ValueGroup("A"), ValueGroup("C", " * ")), Frequencies(1379, 620, 1171, 1384, 2333, 1372, 1050, 1466, 931, 545, 259, 902, 874, 1684, 915, 680, 1292, 558, 160, 33, 94, 107, 162, 73, 76, 100, 51, 4, 0, 57, 50, 148, 83, 82, 118, 35, 0, 1, 44, 41, 132, 90, 75, 103, 38, 472, 302, 241, 239, 180, 72, 47, 102, 168, 219, 147, 284, 284, 152, 70, 75, 124, 238, 120, 52, 0, 2, 140, 78, 76, 109, 0, 122, 53, 181, 145, 143, 80, 58, 99, 73, 133, 77, 27, 58, 153, 89, 74, 110, 31, 104, 45, 263, 245, 117, 79, 59, 116, 175, 134, 65, 80, 67, 145, 76, 57, 105, 38, 3364, 1821, 1768, 3354, 3633, 2018, 1621, 2642, 1043, 1653, 1837, 807, 934, 1050, 1342, 1905, 1357, 936, 1901, 1792, 576, 622, 782, 1083, 1503, 856, 761, 62, 97, 34, 59, 72, 92, 129, 82, 55, 25, 112, 43, 30, 68, 103, 135, 59, 33, 0, 0, 35, 45, 97, 95, 135, 84, 26, 0, 0, 12, 21, 50, 85, 158, 87, 38, 45, 27, 0, 0, 77, 113, 170, 56, 0, 154, 95, 287, 276, 89, 91, 130, 77, 229, 111, 102, 1, 4, 85, 74, 135, 98, 7, 86, 85, 9, 20, 61, 105, 136, 78, 27, 141, 63, 13, 10, 85, 100, 150, 81, 22, 100, 82, 142, 97, 54, 98, 145, 92, 121, 2079, 1682, 1347, 1068, 1739, 2273, 3223, 2111, 2095, 1106, 1109, 930, 813, 1196, 1179, 1614, 2125, 1094, 299, 384, 759, 744, 915, 797, 1138, 1535, 759, 81, 103, 66, 48, 78, 49, 123, 150, 71, 2, 18, 79, 88, 64, 87, 107, 147, 104, 472, 301, 148, 167, 87, 62, 88, 131, 157, 0, 0, 17, 26, 92, 67, 108, 135, 29, 137, 85, 2, 1, 74, 69, 82, 145, 0, 98, 88, 1, 0, 79, 90, 94, 143, 13, 98, 81, 103, 124, 80, 97, 116, 122, 161, 99, 74, 230, 179, 73, 66, 91, 136, 173, 85, 104, 0, 11, 84, 57, 105, 134, 34, 70, 84, 29, 48, 94, 69, 104, 124, 60, 2423, 2187, 988, 1180, 1901, 1839, 2423, 3298, 1600, 2942, 964, 1412, 1174, 1676, 2047, 1026, 1247, 669, 2447, 887, 931, 917, 1206, 1561, 782, 860, 584, 169, 69, 94, 73, 105, 182, 45, 81, 65, 441, 172, 109, 119, 137, 123, 49, 89, 70, 0, 0, 61, 34, 101, 149, 75, 95, 21, 0, 0, 18, 1, 95, 172, 60, 89, 7, 71, 43, 2, 2, 114, 144, 46, 88, 4, 100, 67, 0, 9, 109, 137, 73, 84, 0, 141, 66, 3, 14, 109, 145, 64, 94, 1, 154, 66, 22, 30, 130, 136, 72, 89, 11, 142, 90, 12, 21, 131, 160, 59, 82, 11, 168, 71, 37, 75, 124, 153, 67, 92, 23, 3462, 1558, 2809, 1286, 2735, 3374, 1685, 1861, 1070), 0, Frequencies(3178, 191400, 8), Frequencies(3178, 191400, 8)) ; // DataGrid(SampleId, Pos, Char) +Unused Structure(VectorC) P_SampleIdLabels = VectorC("{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...}", "{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...}", "{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...}", "{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...}", "{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...}", "{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...}", "{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...}", "{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...}", "{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...}") ; // Cluster labels for variable SampleId +Unused Structure(Vector) P_PosSet = TableVector(DNA, Pos) ; // Value distribution for variable Pos +Unused Structure(VectorC) P_CharSet = TableVectorC(DNA, Char) ; // Value distribution for variable Char +Unused Structure(DataGridDeployment) P_DeployedCoclusteringAtSampleId = DataGridDeployment(P_Coclustering, 1, P_PosSet, P_CharSet) ; // Deployed coclustering for variable SampleId +Unused Numerical P_SampleIdIndex = PredictedPartIndex(P_DeployedCoclusteringAtSampleId) ; // Predicted cluster index for variable SampleId + Categorical P_SampleIdPredictedLabel = ValueAtC(P_SampleIdLabels, P_SampleIdIndex) ; // Predicted label for variable SampleId +Unused Structure(Vector) P_SampleIdPartDistances = PredictedPartDistances(P_DeployedCoclusteringAtSampleId) ; // Vector of cluster distances for variable SampleId + Numerical `P_SampleIdDistance{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...}` = ValueAt(P_SampleIdPartDistances, 1) ; + Numerical `P_SampleIdDistance{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...}` = ValueAt(P_SampleIdPartDistances, 2) ; + Numerical `P_SampleIdDistance{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...}` = ValueAt(P_SampleIdPartDistances, 3) ; + Numerical `P_SampleIdDistance{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...}` = ValueAt(P_SampleIdPartDistances, 4) ; + Numerical `P_SampleIdDistance{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...}` = ValueAt(P_SampleIdPartDistances, 5) ; + Numerical `P_SampleIdDistance{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...}` = ValueAt(P_SampleIdPartDistances, 6) ; + Numerical `P_SampleIdDistance{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...}` = ValueAt(P_SampleIdPartDistances, 7) ; + Numerical `P_SampleIdDistance{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...}` = ValueAt(P_SampleIdPartDistances, 8) ; + Numerical `P_SampleIdDistance{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...}` = ValueAt(P_SampleIdPartDistances, 9) ; +Unused Structure(Vector) P_PosPartFrequencies = PredictedPartFrequenciesAt(P_DeployedCoclusteringAtSampleId, 2) ; // Vector of cluster frequencies for variable Pos + Numerical `P_PosFrequency]-inf;15.5]` = ValueAt(P_PosPartFrequencies, 1) ; + Numerical `P_PosFrequency]15.5;26.5]` = ValueAt(P_PosPartFrequencies, 2) ; + Numerical `P_PosFrequency]26.5;27.5]` = ValueAt(P_PosPartFrequencies, 3) ; + Numerical `P_PosFrequency]27.5;28.5]` = ValueAt(P_PosPartFrequencies, 4) ; + Numerical `P_PosFrequency]28.5;29.5]` = ValueAt(P_PosPartFrequencies, 5) ; + Numerical `P_PosFrequency]29.5;30.5]` = ValueAt(P_PosPartFrequencies, 6) ; + Numerical `P_PosFrequency]30.5;31.5]` = ValueAt(P_PosPartFrequencies, 7) ; + Numerical `P_PosFrequency]31.5;32.5]` = ValueAt(P_PosPartFrequencies, 8) ; + Numerical `P_PosFrequency]32.5;33.5]` = ValueAt(P_PosPartFrequencies, 9) ; + Numerical `P_PosFrequency]33.5;34.5]` = ValueAt(P_PosPartFrequencies, 10) ; + Numerical `P_PosFrequency]34.5;35.5]` = ValueAt(P_PosPartFrequencies, 11) ; + Numerical `P_PosFrequency]35.5;36.5]` = ValueAt(P_PosPartFrequencies, 12) ; + Numerical `P_PosFrequency]36.5;+inf[` = ValueAt(P_PosPartFrequencies, 13) ; +Unused Structure(Vector) P_CharPartFrequencies = PredictedPartFrequenciesAt(P_DeployedCoclusteringAtSampleId, 3) ; // Vector of cluster frequencies for variable Char + Numerical `P_CharFrequency{G, N, D, ...}` = ValueAt(P_CharPartFrequencies, 1) ; + Numerical `P_CharFrequency{T}` = ValueAt(P_CharPartFrequencies, 2) ; + Numerical `P_CharFrequency{A}` = ValueAt(P_CharPartFrequencies, 3) ; + Numerical `P_CharFrequency{C}` = ValueAt(P_CharPartFrequencies, 4) ; +}; + +Dictionary SpliceJunctionDNA (SampleId) +{ + Categorical SampleId ; + Numerical Pos ; + Categorical Char ; +}; diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering.khc new file mode 100644 index 000000000..729e62ea2 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering.khc @@ -0,0 +1,69 @@ +#Khiops 10.4.6i +Short description +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +PetalLength Numerical 3 3 150 1 +PetalWidth Numerical 3 3 150 1 +Class Categorical 3 3 3 1 + +Coclustering stats +Instances 150 +Cells 7 +Null cost 1379.668177 +Cost 1164.273132 +Level 0.1561209054 +Initial dimensions 3 +Frequency variable +Dictionary Iris +Database ./SubDir/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 +]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 +]-inf;+inf[ 150 0.648091 -0.0145257 2 4 +]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 +]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 +]-inf;+inf[ 150 0.681865 0.374198 2 5 +]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 + +Hierarchy Class +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Iris-setosa} C1 50 1 1 1 9 +{Iris-versicolor} C4 50 0.912663 1 3 9 +{Iris-virginica} C4 50 0.912663 1 5 9 +C1 150 0.941775 0 2 3 +C4 C1 100 0.912663 0.739904 4 6 + +Composition Class +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 + +Cells +PetalLength PetalWidth Class Frequency +]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 +]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 +]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 +]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 +]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 +]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 +]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering.khcj b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering.khcj new file mode 100644 index 000000000..7c376b664 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering.khcj @@ -0,0 +1,299 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 150, + "cells": 7, + "nullCost": 1379.668177, + "cost": 1164.273132, + "level": 0.1561209054, + "initialDimensions": 3, + "frequencyVariable": "", + "dictionary": "Iris", + "database": ".\/SubDir\/Iris.txt", + "samplePercentage": 100, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "" + }, + "dimensionSummaries": [ + { + "name": "PetalLength", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 1, + "max": 6.9 + }, + { + "name": "PetalWidth", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 0.1, + "max": 2.5 + }, + { + "name": "Class", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 3, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "PetalLength", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;2.4]", + "bounds": [1,2.4] + }, + { + "cluster": "]2.4;4.75]", + "bounds": [2.4,4.75] + }, + { + "cluster": "]4.75;+inf[", + "bounds": [4.75,6.9] + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;0.8]", + "bounds": [0.1,0.8] + }, + { + "cluster": "]0.8;1.75]", + "bounds": [0.8,1.75] + }, + { + "cluster": "]1.75;+inf[", + "bounds": [1.75,2.5] + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Iris-setosa}", + "values": ["Iris-setosa"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-versicolor}", + "values": ["Iris-versicolor"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-virginica}", + "values": ["Iris-virginica"], + "valueFrequencies": [50], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 2 + } + ], + "dimensionHierarchies": [ + { + "name": "PetalLength", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;2.4]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.557285, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]2.4;4.75]", + "parentCluster": "]2.4;+inf[", + "frequency": 45, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]4.75;+inf[", + "parentCluster": "]2.4;+inf[", + "frequency": 55, + "interest": 0.442715, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.648091, + "hierarchicalLevel": -0.0145257, + "rank": 2, + "hierarchicalRank": 4, + "isLeaf": false + }, + { + "cluster": "]2.4;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.693493, + "hierarchicalLevel": 0.856289, + "rank": 4, + "hierarchicalRank": 8, + "isLeaf": false + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;0.8]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.569949, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]0.8;1.75]", + "parentCluster": "]0.8;+inf[", + "frequency": 54, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]1.75;+inf[", + "parentCluster": "]0.8;+inf[", + "frequency": 46, + "interest": 0.430051, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.681865, + "hierarchicalLevel": 0.374198, + "rank": 2, + "hierarchicalRank": 5, + "isLeaf": false + }, + { + "cluster": "]0.8;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.737824, + "hierarchicalLevel": 0.704564, + "rank": 4, + "hierarchicalRank": 7, + "isLeaf": false + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Iris-setosa}", + "parentCluster": "C1", + "frequency": 50, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-versicolor}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-virginica}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "C1", + "parentCluster": "", + "frequency": 150, + "interest": 0.941775, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "C4", + "parentCluster": "C1", + "frequency": 100, + "interest": 0.912663, + "hierarchicalLevel": 0.739904, + "rank": 4, + "hierarchicalRank": 6, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0,0], + [2,2,2], + [1,1,1], + [2,1,1], + [2,1,2], + [1,1,2], + [2,2,1] + ], + "cellFrequencies": [50,45,44,5,4,1,1] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering_bad.json b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering_bad.json new file mode 100644 index 000000000..7c376b664 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering_bad.json @@ -0,0 +1,299 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 150, + "cells": 7, + "nullCost": 1379.668177, + "cost": 1164.273132, + "level": 0.1561209054, + "initialDimensions": 3, + "frequencyVariable": "", + "dictionary": "Iris", + "database": ".\/SubDir\/Iris.txt", + "samplePercentage": 100, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "" + }, + "dimensionSummaries": [ + { + "name": "PetalLength", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 1, + "max": 6.9 + }, + { + "name": "PetalWidth", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 0.1, + "max": 2.5 + }, + { + "name": "Class", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 3, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "PetalLength", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;2.4]", + "bounds": [1,2.4] + }, + { + "cluster": "]2.4;4.75]", + "bounds": [2.4,4.75] + }, + { + "cluster": "]4.75;+inf[", + "bounds": [4.75,6.9] + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;0.8]", + "bounds": [0.1,0.8] + }, + { + "cluster": "]0.8;1.75]", + "bounds": [0.8,1.75] + }, + { + "cluster": "]1.75;+inf[", + "bounds": [1.75,2.5] + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Iris-setosa}", + "values": ["Iris-setosa"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-versicolor}", + "values": ["Iris-versicolor"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-virginica}", + "values": ["Iris-virginica"], + "valueFrequencies": [50], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 2 + } + ], + "dimensionHierarchies": [ + { + "name": "PetalLength", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;2.4]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.557285, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]2.4;4.75]", + "parentCluster": "]2.4;+inf[", + "frequency": 45, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]4.75;+inf[", + "parentCluster": "]2.4;+inf[", + "frequency": 55, + "interest": 0.442715, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.648091, + "hierarchicalLevel": -0.0145257, + "rank": 2, + "hierarchicalRank": 4, + "isLeaf": false + }, + { + "cluster": "]2.4;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.693493, + "hierarchicalLevel": 0.856289, + "rank": 4, + "hierarchicalRank": 8, + "isLeaf": false + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;0.8]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.569949, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]0.8;1.75]", + "parentCluster": "]0.8;+inf[", + "frequency": 54, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]1.75;+inf[", + "parentCluster": "]0.8;+inf[", + "frequency": 46, + "interest": 0.430051, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.681865, + "hierarchicalLevel": 0.374198, + "rank": 2, + "hierarchicalRank": 5, + "isLeaf": false + }, + { + "cluster": "]0.8;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.737824, + "hierarchicalLevel": 0.704564, + "rank": 4, + "hierarchicalRank": 7, + "isLeaf": false + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Iris-setosa}", + "parentCluster": "C1", + "frequency": 50, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-versicolor}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-virginica}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "C1", + "parentCluster": "", + "frequency": 150, + "interest": 0.941775, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "C4", + "parentCluster": "C1", + "frequency": 100, + "interest": 0.912663, + "hierarchicalLevel": 0.739904, + "rank": 4, + "hierarchicalRank": 6, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0,0], + [2,2,2], + [1,1,1], + [2,1,1], + [2,1,2], + [1,1,2], + [2,2,1] + ], + "cellFrequencies": [50,45,44,5,4,1,1] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering_bad.json.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering_bad.json.khc new file mode 100644 index 000000000..729e62ea2 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/SimplifiedCoclustering_bad.json.khc @@ -0,0 +1,69 @@ +#Khiops 10.4.6i +Short description +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +PetalLength Numerical 3 3 150 1 +PetalWidth Numerical 3 3 150 1 +Class Categorical 3 3 3 1 + +Coclustering stats +Instances 150 +Cells 7 +Null cost 1379.668177 +Cost 1164.273132 +Level 0.1561209054 +Initial dimensions 3 +Frequency variable +Dictionary Iris +Database ./SubDir/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 +]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 +]-inf;+inf[ 150 0.648091 -0.0145257 2 4 +]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 +]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 +]-inf;+inf[ 150 0.681865 0.374198 2 5 +]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 + +Hierarchy Class +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Iris-setosa} C1 50 1 1 1 9 +{Iris-versicolor} C4 50 0.912663 1 3 9 +{Iris-virginica} C4 50 0.912663 1 5 9 +C1 150 0.941775 0 2 3 +C4 C1 100 0.912663 0.739904 4 6 + +Composition Class +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 + +Cells +PetalLength PetalWidth Class Frequency +]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 +]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 +]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 +]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 +]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 +]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 +]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/err.txt new file mode 100644 index 000000000..5203eb07f --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/err.txt @@ -0,0 +1,32 @@ +Database ./SubDir/Iris.txt : Read records: 150 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.043904 Size: 2*2*1 Granularity: 1 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.095524 Size: 3*3*3 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.130686 Size: 3*3*3 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.156121 Size: 3*3*3 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.06 + +Database ./SubDir/Iris.txt : Read records: 150 + 0:00:00 Write intermediate coclustering report Coclustering_bad(1).json Level: 0.043904 Size: 2*2*1 Granularity: 1 + 0:00:00 Write intermediate coclustering report Coclustering_bad(2).json Level: 0.095524 Size: 3*3*3 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering_bad(3).json Level: 0.130686 Size: 3*3*3 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering_bad(4).json Level: 0.156121 Size: 3*3*3 +Write coclustering report ./results/Coclustering_bad.json +Coclustering analysis time: 0:00:00.05 + +error : File coclustering report : Missing file name +Write simplified report ./results/SimplifiedCoclustering.khcj + +Write simplified report ./results/SimplifiedCoclustering_bad.json + +error : File simplified coclustering report : Missing file name +Write cluster table file ./results/Clusters.txt + +Write cluster table file ./results/Clusters_bad.json + +error : File cluster table file : Missing file name +Write deployment dictionary file ./results/DeploymentCoclustering.kdic + +Write deployment dictionary file ./results/DeploymentCoclustering_bad.json + +error : File coclustering dictionary : Missing file name diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/time.log new file mode 100644 index 000000000..4121ef824 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/results.ref/time.log @@ -0,0 +1 @@ +Overal time: 0.27737927436828613 diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/test.prm b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/test.prm new file mode 100644 index 000000000..c0c81c264 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsApiMode/test.prm @@ -0,0 +1,152 @@ +// Mon Jul 04 14:13:56 2011 +// C:\Users\boullema\Learning\Win32\debug\MODL_Coclustering +// Output command file +// +//This file contains recorded commands, that can be replayed. +//Commands are based on user interactions: +// field update +// list item selection +// menu action +//Every command can be commented, using //. +//For example, commenting the last Exit command will allow other +//user interactions, after the commands have been replayed. +// +// + +// -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB + +////////////////////////////////////////////////////////////////////////////////////////////////// +// Build coclustering + +ClassManagement.OpenFile // Open... + +// -> Open +ClassFileName ./SubDir/Iris.kdic // Dictionary file +OK // Open +// <- Open + +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ./SubDir/Iris.txt // Database file +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.Name PetalLength // Name +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.Name PetalWidth // Name +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.Name Class // Name + +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report +BuildCoclustering // Build coclustering + +AnalysisResults.CoclusteringFileName ./results/Coclustering_bad.json // Coclustering report +BuildCoclustering // Build coclustering + +AnalysisResults.CoclusteringFileName // Coclustering report +BuildCoclustering // Build coclustering + + +////////////////////////////////////////////////////////////////////////////////////////////////// +// Simplify coclustering + +LearningTools.PostProcessCoclustering // Simplify coclustering... + +// -> Coclustering post-processing +SelectInputCoclustering // Select input coclustering... + +// -> Open +InputCoclusteringFileName ./results/Coclustering.khcj // Input coclustering file +OK // Open +// <- Open + +PostProcessedCoclusteringFileName ./results/SimplifiedCoclustering.khcj // Simplified coclustering report +PostProcessCoclustering // Simplify coclustering + +PostProcessedCoclusteringFileName ./results/SimplifiedCoclustering_bad.json // Simplified coclustering report +PostProcessCoclustering // Simplify coclustering + +PostProcessedCoclusteringFileName // Simplified coclustering report +PostProcessCoclustering // Simplify coclustering + +Exit // Close +// <- Coclustering post-processing + + +////////////////////////////////////////////////////////////////////////////////////////////////// +// Extract clusters + +LearningTools.ExtractClusters // Extract clusters... + +SelectInputCoclustering // Select input coclustering... +// -> Cluster extraction + +// -> Open +InputCoclusteringFileName ./results/Coclustering.khcj // Input coclustering file +OK // Open +// <- Open + +CoclusteringAttribute Class // Coclustering variable + +ClusterFileName ./results/Clusters.txt // Cluster table file +ExtractClusters // Extract clusters + +ClusterFileName ./results/Clusters_bad.json // Cluster table file +ExtractClusters // Extract clusters + +ClusterFileName // Cluster table file +ExtractClusters // Extract clusters + +Exit // Close +// <- Cluster extraction + + +////////////////////////////////////////////////////////////////////////////////////////////////// +// Prepare deployment + +ClassManagement.OpenFile // Open... + +// -> Open +ClassFileName ../../../MTdatasets/SpliceJunction/SpliceJunction.kdic // Dictionary file +OK // Open +// <- Open + +Database.ClassName SpliceJunction // Analysis dictionary + +LearningTools.PrepareDeployment // Prepare deployment... + +// -> Coclustering deployment preparation +SelectInputCoclustering // Select input coclustering... + +// -> Open +InputCoclusteringFileName ./SubDir/SpliceJunctionDNACoclustering.khc // Input coclustering file +OK // Open +// <- Open + +DeploymentSpec.InputClassName SpliceJunction // Input dictionary +DeploymentSpec.InputObjectArrayAttributeName DNA // Input array variable +DeploymentSpec.DeployedAttributeName SampleId // Coclustering deployed variable +DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster variable +DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables +DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables +DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix + +CoclusteringDictionaryFileName ./results/DeploymentCoclustering.kdic // Coclustering dictionary file +PrepareDeployment // Prepare deployment + +CoclusteringDictionaryFileName ./results/DeploymentCoclustering_bad.json // Coclustering dictionary file +PrepareDeployment // Prepare deployment + +CoclusteringDictionaryFileName // Coclustering dictionary file +PrepareDeployment // Prepare deployment + +Exit // Close +// <- Coclustering deployment preparation + + +Exit // Close +// <- Khiops Coclustering + +// -> Khiops Coclustering +OK // Close +// <- Khiops Coclustering + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/SubDir/Iris.kdic b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/SubDir/Iris.kdic new file mode 100644 index 000000000..5169bce8a --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/SubDir/Iris.kdic @@ -0,0 +1,9 @@ + +Dictionary Iris +{ + Numerical SepalLength ; + Numerical SepalWidth ; + Numerical PetalLength ; + Numerical PetalWidth ; + Categorical Class ; +}; diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/SubDir/Iris.txt b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/SubDir/Iris.txt new file mode 100644 index 000000000..ed75374e6 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/SubDir/Iris.txt @@ -0,0 +1,151 @@ +SepalLength SepalWidth PetalLength PetalWidth Class +5.1 3.5 1.4 0.2 Iris-setosa +4.9 3.0 1.4 0.2 Iris-setosa +4.7 3.2 1.3 0.2 Iris-setosa +4.6 3.1 1.5 0.2 Iris-setosa +5.0 3.6 1.4 0.2 Iris-setosa +5.4 3.9 1.7 0.4 Iris-setosa +4.6 3.4 1.4 0.3 Iris-setosa +5.0 3.4 1.5 0.2 Iris-setosa +4.4 2.9 1.4 0.2 Iris-setosa +4.9 3.1 1.5 0.1 Iris-setosa +5.4 3.7 1.5 0.2 Iris-setosa +4.8 3.4 1.6 0.2 Iris-setosa +4.8 3.0 1.4 0.1 Iris-setosa +4.3 3.0 1.1 0.1 Iris-setosa +5.8 4.0 1.2 0.2 Iris-setosa +5.7 4.4 1.5 0.4 Iris-setosa +5.4 3.9 1.3 0.4 Iris-setosa +5.1 3.5 1.4 0.3 Iris-setosa +5.7 3.8 1.7 0.3 Iris-setosa +5.1 3.8 1.5 0.3 Iris-setosa +5.4 3.4 1.7 0.2 Iris-setosa +5.1 3.7 1.5 0.4 Iris-setosa +4.6 3.6 1.0 0.2 Iris-setosa +5.1 3.3 1.7 0.5 Iris-setosa +4.8 3.4 1.9 0.2 Iris-setosa +5.0 3.0 1.6 0.2 Iris-setosa +5.0 3.4 1.6 0.4 Iris-setosa +5.2 3.5 1.5 0.2 Iris-setosa +5.2 3.4 1.4 0.2 Iris-setosa +4.7 3.2 1.6 0.2 Iris-setosa +4.8 3.1 1.6 0.2 Iris-setosa +5.4 3.4 1.5 0.4 Iris-setosa +5.2 4.1 1.5 0.1 Iris-setosa +5.5 4.2 1.4 0.2 Iris-setosa +4.9 3.1 1.5 0.1 Iris-setosa +5.0 3.2 1.2 0.2 Iris-setosa +5.5 3.5 1.3 0.2 Iris-setosa +4.9 3.1 1.5 0.1 Iris-setosa +4.4 3.0 1.3 0.2 Iris-setosa +5.1 3.4 1.5 0.2 Iris-setosa +5.0 3.5 1.3 0.3 Iris-setosa +4.5 2.3 1.3 0.3 Iris-setosa +4.4 3.2 1.3 0.2 Iris-setosa +5.0 3.5 1.6 0.6 Iris-setosa +5.1 3.8 1.9 0.4 Iris-setosa +4.8 3.0 1.4 0.3 Iris-setosa +5.1 3.8 1.6 0.2 Iris-setosa +4.6 3.2 1.4 0.2 Iris-setosa +5.3 3.7 1.5 0.2 Iris-setosa +5.0 3.3 1.4 0.2 Iris-setosa +7.0 3.2 4.7 1.4 Iris-versicolor +6.4 3.2 4.5 1.5 Iris-versicolor +6.9 3.1 4.9 1.5 Iris-versicolor +5.5 2.3 4.0 1.3 Iris-versicolor +6.5 2.8 4.6 1.5 Iris-versicolor +5.7 2.8 4.5 1.3 Iris-versicolor +6.3 3.3 4.7 1.6 Iris-versicolor +4.9 2.4 3.3 1.0 Iris-versicolor +6.6 2.9 4.6 1.3 Iris-versicolor +5.2 2.7 3.9 1.4 Iris-versicolor +5.0 2.0 3.5 1.0 Iris-versicolor +5.9 3.0 4.2 1.5 Iris-versicolor +6.0 2.2 4.0 1.0 Iris-versicolor +6.1 2.9 4.7 1.4 Iris-versicolor +5.6 2.9 3.6 1.3 Iris-versicolor +6.7 3.1 4.4 1.4 Iris-versicolor +5.6 3.0 4.5 1.5 Iris-versicolor +5.8 2.7 4.1 1.0 Iris-versicolor +6.2 2.2 4.5 1.5 Iris-versicolor +5.6 2.5 3.9 1.1 Iris-versicolor +5.9 3.2 4.8 1.8 Iris-versicolor +6.1 2.8 4.0 1.3 Iris-versicolor +6.3 2.5 4.9 1.5 Iris-versicolor +6.1 2.8 4.7 1.2 Iris-versicolor +6.4 2.9 4.3 1.3 Iris-versicolor +6.6 3.0 4.4 1.4 Iris-versicolor +6.8 2.8 4.8 1.4 Iris-versicolor +6.7 3.0 5.0 1.7 Iris-versicolor +6.0 2.9 4.5 1.5 Iris-versicolor +5.7 2.6 3.5 1.0 Iris-versicolor +5.5 2.4 3.8 1.1 Iris-versicolor +5.5 2.4 3.7 1.0 Iris-versicolor +5.8 2.7 3.9 1.2 Iris-versicolor +6.0 2.7 5.1 1.6 Iris-versicolor +5.4 3.0 4.5 1.5 Iris-versicolor +6.0 3.4 4.5 1.6 Iris-versicolor +6.7 3.1 4.7 1.5 Iris-versicolor +6.3 2.3 4.4 1.3 Iris-versicolor +5.6 3.0 4.1 1.3 Iris-versicolor +5.5 2.5 4.0 1.3 Iris-versicolor +5.5 2.6 4.4 1.2 Iris-versicolor +6.1 3.0 4.6 1.4 Iris-versicolor +5.8 2.6 4.0 1.2 Iris-versicolor +5.0 2.3 3.3 1.0 Iris-versicolor +5.6 2.7 4.2 1.3 Iris-versicolor +5.7 3.0 4.2 1.2 Iris-versicolor +5.7 2.9 4.2 1.3 Iris-versicolor +6.2 2.9 4.3 1.3 Iris-versicolor +5.1 2.5 3.0 1.1 Iris-versicolor +5.7 2.8 4.1 1.3 Iris-versicolor +6.3 3.3 6.0 2.5 Iris-virginica +5.8 2.7 5.1 1.9 Iris-virginica +7.1 3.0 5.9 2.1 Iris-virginica +6.3 2.9 5.6 1.8 Iris-virginica +6.5 3.0 5.8 2.2 Iris-virginica +7.6 3.0 6.6 2.1 Iris-virginica +4.9 2.5 4.5 1.7 Iris-virginica +7.3 2.9 6.3 1.8 Iris-virginica +6.7 2.5 5.8 1.8 Iris-virginica +7.2 3.6 6.1 2.5 Iris-virginica +6.5 3.2 5.1 2.0 Iris-virginica +6.4 2.7 5.3 1.9 Iris-virginica +6.8 3.0 5.5 2.1 Iris-virginica +5.7 2.5 5.0 2.0 Iris-virginica +5.8 2.8 5.1 2.4 Iris-virginica +6.4 3.2 5.3 2.3 Iris-virginica +6.5 3.0 5.5 1.8 Iris-virginica +7.7 3.8 6.7 2.2 Iris-virginica +7.7 2.6 6.9 2.3 Iris-virginica +6.0 2.2 5.0 1.5 Iris-virginica +6.9 3.2 5.7 2.3 Iris-virginica +5.6 2.8 4.9 2.0 Iris-virginica +7.7 2.8 6.7 2.0 Iris-virginica +6.3 2.7 4.9 1.8 Iris-virginica +6.7 3.3 5.7 2.1 Iris-virginica +7.2 3.2 6.0 1.8 Iris-virginica +6.2 2.8 4.8 1.8 Iris-virginica +6.1 3.0 4.9 1.8 Iris-virginica +6.4 2.8 5.6 2.1 Iris-virginica +7.2 3.0 5.8 1.6 Iris-virginica +7.4 2.8 6.1 1.9 Iris-virginica +7.9 3.8 6.4 2.0 Iris-virginica +6.4 2.8 5.6 2.2 Iris-virginica +6.3 2.8 5.1 1.5 Iris-virginica +6.1 2.6 5.6 1.4 Iris-virginica +7.7 3.0 6.1 2.3 Iris-virginica +6.3 3.4 5.6 2.4 Iris-virginica +6.4 3.1 5.5 1.8 Iris-virginica +6.0 3.0 4.8 1.8 Iris-virginica +6.9 3.1 5.4 2.1 Iris-virginica +6.7 3.1 5.6 2.4 Iris-virginica +6.9 3.1 5.1 2.3 Iris-virginica +5.8 2.7 5.1 1.9 Iris-virginica +6.8 3.2 5.9 2.3 Iris-virginica +6.7 3.3 5.7 2.5 Iris-virginica +6.7 3.0 5.2 2.3 Iris-virginica +6.3 2.5 5.0 1.9 Iris-virginica +6.5 3.0 5.2 2.0 Iris-virginica +6.2 3.4 5.4 2.3 Iris-virginica +5.9 3.0 5.1 1.8 Iris-virginica diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/SubDir/SpliceJunctionDNACoclustering.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/SubDir/SpliceJunctionDNACoclustering.khc new file mode 100644 index 000000000..059fb7414 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/SubDir/SpliceJunctionDNACoclustering.khc @@ -0,0 +1,3721 @@ +#Khiops 7.7.2i +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +SampleId Categorical 9 9 3178 1 +Pos Numerical 13 13 191400 1 +Char Categorical 4 4 8 1 + +Coclustering stats +Instances 191400 +Cells 446 +Null cost 3.952e+006 +Cost 3.94432e+006 +Level 0.00194418 +Initial dimensions 3 +Frequency variable +Dictionary SpliceJunctionDNA +Database ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt + +Bounds +Name Min Max +Pos 1 60 + +Hierarchy SampleId +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} A17 28320 1 1 1 26 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} A17 18120 0.815517 1 3 26 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} A21 17280 0.768173 1 5 26 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} A21 17220 0.767011 1 7 26 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} A19 25020 0.73237 1 9 26 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} A19 23760 0.690727 1 11 26 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} A18 22380 0.825937 1 13 26 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} A18 24780 0.730234 1 15 26 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} A16 14520 0.661623 1 17 26 +A17 A7 46440 0.928018 0.874132 2 19 +A7 A4 129720 0.804153 0.24448 4 9 +A21 A10 34500 0.767593 0.977008 6 23 +A10 A7 83280 0.735081 0.561493 8 12 +A19 A10 48780 0.712086 0.929093 10 21 +A4 191400 0.786317 0.0549351 12 6 +A18 A16 47160 0.77565 0.905723 14 20 +A16 A4 61680 0.748807 0.856356 16 18 + +Hierarchy Pos +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;15.5] ]-inf;28.5] 47850 0.256594 1 1 26 +]15.5;26.5] ]15.5;27.5] 35090 0.382703 1 3 26 +]26.5;27.5] ]15.5;27.5] 3190 0.287986 1 5 26 +]27.5;28.5] ]15.5;28.5] 3190 0.588203 1 7 26 +]28.5;29.5] ]28.5;+inf[ 3190 1 1 9 26 +]29.5;30.5] ]29.5;31.5] 3190 0.774911 1 11 26 +]30.5;31.5] ]29.5;31.5] 3190 0.674393 1 13 26 +]31.5;32.5] ]31.5;+inf[ 3190 0.870658 1 15 26 +]32.5;33.5] ]32.5;34.5] 3190 0.493713 1 17 26 +]33.5;34.5] ]32.5;34.5] 3190 0.334615 1 19 26 +]34.5;35.5] ]32.5;35.5] 3190 0.409016 1 21 26 +]35.5;36.5] ]35.5;+inf[ 3190 0.26895 1 23 26 +]36.5;+inf[ ]35.5;+inf[ 76560 0.0983381 1 25 26 +]-inf;28.5] ]-inf;+inf[ 89320 0.319101 0.691729 2 14 +]15.5;27.5] ]15.5;28.5] 38280 0.37481 0.960755 4 22 +]15.5;28.5] ]-inf;28.5] 41470 0.391225 0.825343 6 17 +]-inf;+inf[ 191400 0.268687 -0.000688577 8 4 +]28.5;+inf[ ]-inf;+inf[ 102080 0.224574 0.158149 10 8 +]29.5;31.5] ]29.5;+inf[ 6380 0.724652 0.743575 12 15 +]29.5;+inf[ ]28.5;+inf[ 98890 0.19956 0.373977 14 10 +]31.5;+inf[ ]29.5;+inf[ 92510 0.163347 0.509065 16 11 +]32.5;34.5] ]32.5;35.5] 6380 0.414164 0.994249 18 25 +]32.5;35.5] ]32.5;+inf[ 9570 0.412448 0.788492 20 16 +]32.5;+inf[ ]31.5;+inf[ 89320 0.138086 0.614176 22 13 +]35.5;+inf[ ]32.5;+inf[ 79750 0.105163 0.988136 24 24 + +Hierarchy Char +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{G, N, D, ...} C1 50305 1 1 1 26 +{T} C5 46308 0.867258 1 3 26 +{A} C5 44487 0.837853 1 5 26 +{C} C3 50300 0.839347 1 7 26 +C1 191400 0.887977 0 2 3 +C5 C3 90795 0.852851 0.11058 4 7 +C3 C1 141095 0.848037 0.0458795 6 5 + +Composition SampleId +Cluster Value Frequency Typicality +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYLCA-ACCEPTOR-924 120 1 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMVPNP-ACCEPTOR-1688 60 0.807316 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW2B-ACCEPTOR-2660 60 0.742414 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHANTLE-ACCEPTOR-3110 60 0.719859 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGASTA-ACCEPTOR-6600 60 0.710249 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW1B-ACCEPTOR-2658 60 0.704572 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMIL1B-ACCEPTOR-3546 60 0.695147 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHBA4-ACCEPTOR-10696 60 0.691962 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHBA4-ACCEPTOR-6885 60 0.691962 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} MACHBA-ACCEPTOR-238 60 0.691962 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHY1A-ACCEPTOR-1380 60 0.679567 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOCII-ACCEPTOR-3131 60 0.672758 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCS1-ACCEPTOR-1814 60 0.668863 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-51551 60 0.668863 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYLCA-ACCEPTOR-3623 60 0.668316 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHEA-ACCEPTOR-2678 60 0.665954 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFA-ACCEPTOR-2140 60 0.661668 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFAB-ACCEPTOR-5619 60 0.661668 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFX-ACCEPTOR-1914 60 0.661668 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHB27B-ACCEPTOR-2421 60 0.661276 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-28732 60 0.660452 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOCIA-ACCEPTOR-634 60 0.657721 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGH-ACCEPTOR-1572 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-43393 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-6465 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHN-ACCEPTOR-1797 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHVA-ACCEPTOR-741 60 0.654921 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-8990 60 0.650823 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-ACCEPTOR-31395 60 0.639565 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKER18-ACCEPTOR-5495 60 0.636296 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFAB-ACCEPTOR-1237 60 0.635085 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMIS-ACCEPTOR-1500 60 0.634371 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGAST2-ACCEPTOR-916 60 0.632934 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-ACCEPTOR-30776 60 0.632888 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHPARS1-ACCEPTOR-2519 60 0.628972 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-42976 60 0.623284 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOC2-ACCEPTOR-3143 60 0.622276 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOC2G-ACCEPTOR-2851 60 0.622276 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMOTNPI-ACCEPTOR-808 60 0.620495 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMUBILP-ACCEPTOR-1488 60 0.61957 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYLCA-ACCEPTOR-2214 120 0.617924 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-6683 60 0.617824 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-2023 60 0.61598 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-28878 60 0.615811 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGH-ACCEPTOR-1154 60 0.614791 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-6047 60 0.614791 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHN-ACCEPTOR-1379 60 0.614791 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCS1-ACCEPTOR-1396 60 0.614421 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-28313 60 0.614421 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-51135 60 0.614421 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-3161 60 0.61142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-3163 60 0.61142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-4089 60 0.61142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-2628 60 0.61142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-13046 60 0.602277 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBA-ACCEPTOR-2893 60 0.600912 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBB-ACCEPTOR-2827 60 0.600912 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMDES-ACCEPTOR-3015 60 0.599847 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAA-ACCEPTOR-2567 60 0.599827 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCYC1A-ACCEPTOR-2399 60 0.599564 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMOTNPI-ACCEPTOR-1091 60 0.597979 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAI1-ACCEPTOR-653 60 0.59795 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAIT-ACCEPTOR-398 60 0.59795 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-13642 60 0.596255 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-1386 60 0.595439 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMLYL1B-ACCEPTOR-3525 60 0.595105 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMH19-ACCEPTOR-2449 60 0.593758 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-21798 60 0.592178 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPPD-ACCEPTOR-1642 60 0.588925 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-2316 60 0.588105 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCS1-ACCEPTOR-800 60 0.58709 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-27717 60 0.58709 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-50539 60 0.58709 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYC3L-ACCEPTOR-766 60 0.583606 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHDRHA-ACCEPTOR-4276 60 0.583557 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMRASH-ACCEPTOR-3201 60 0.579787 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAACT-ACCEPTOR-1638 60 0.578528 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCD8A-ACCEPTOR-733 60 0.577575 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1GLY2-ACCEPTOR-2108 60 0.577502 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPIM1A-ACCEPTOR-1687 60 0.577005 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKAL2-ACCEPTOR-3494 60 0.576779 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCSPB-ACCEPTOR-1874 60 0.575135 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1GLY2-ACCEPTOR-3244 60 0.574888 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-34324 60 0.573464 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-42394 60 0.572879 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-14059 60 0.572476 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-4815 60 0.571718 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRCA-ACCEPTOR-5420 60 0.56965 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMETIF1-ACCEPTOR-1463 60 0.568823 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPPPA-ACCEPTOR-1721 60 0.568234 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMG0S19A-ACCEPTOR-2812 60 0.564955 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW2B-ACCEPTOR-885 60 0.564762 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPOMC-ACCEPTOR-4445 60 0.563651 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGH-ACCEPTOR-571 60 0.563186 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-5465 60 0.563186 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHN-ACCEPTOR-798 60 0.563186 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-883 60 0.563167 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-2851 60 0.561485 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPPD-ACCEPTOR-2473 60 0.561485 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTUBBM-ACCEPTOR-1265 60 0.559167 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} MACHBA-ACCEPTOR-587 60 0.558617 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYCRT-ACCEPTOR-1111 60 0.558265 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMEDHB17-ACCEPTOR-2346 60 0.555887 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1ATP-ACCEPTOR-11880 60 0.555555 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ORAIGECA-ACCEPTOR-583 60 0.554854 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHBA4-ACCEPTOR-11050 60 0.553779 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMANT1-ACCEPTOR-2955 60 0.553601 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKEREP-ACCEPTOR-4597 60 0.552419 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCD8A-ACCEPTOR-1670 60 0.550782 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMIS-ACCEPTOR-1697 60 0.550128 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPIM1A-ACCEPTOR-4496 60 0.549439 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOCIA-ACCEPTOR-1956 60 0.547693 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMRPS14-ACCEPTOR-5473 60 0.547499 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAACT-ACCEPTOR-3116 60 0.544522 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-1387 60 0.543139 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAP-ACCEPTOR-1501 60 0.542389 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMERP-ACCEPTOR-2578 60 0.54156 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMERPA-ACCEPTOR-2587 60 0.54156 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-6969 60 0.537959 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPOMC-ACCEPTOR-7483 60 0.537673 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGCB1-ACCEPTOR-1590 60 0.537557 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMH6-ACCEPTOR-955 60 0.535445 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-7876 60 0.534303 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOE4-ACCEPTOR-3751 60 0.532639 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} GCRHBBA1-ACCEPTOR-1385 60 0.532318 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMBNPA-ACCEPTOR-1629 60 0.532056 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBA-ACCEPTOR-1384 60 0.530608 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBB-ACCEPTOR-1318 60 0.530608 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGHCSA-ACCEPTOR-13504 60 0.53054 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAK1-ACCEPTOR-5504 60 0.527574 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} CHPIGECA-ACCEPTOR-585 60 0.527566 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-29793 60 0.526709 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHB27B-ACCEPTOR-690 60 0.526366 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMEDHB17-ACCEPTOR-3575 60 0.525136 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-2960 60 0.525128 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-2962 60 0.525128 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-3889 60 0.525128 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-2330 60 0.524874 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYLCA-ACCEPTOR-2864 60 0.524853 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRF1A-ACCEPTOR-4965 60 0.523646 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMVPNP-ACCEPTOR-2057 60 0.522772 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMH19-ACCEPTOR-2224 60 0.522369 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-13878 60 0.522162 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-19200 60 0.520076 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMH6-ACCEPTOR-2724 60 0.518424 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMG0S19B-ACCEPTOR-2948 60 0.518104 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMP45C17-ACCEPTOR-4146 60 0.517318 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRF1A-ACCEPTOR-3240 60 0.516876 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMYCC-ACCEPTOR-4476 60 0.515802 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRCA-ACCEPTOR-5180 60 0.515615 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCYC1A-ACCEPTOR-3478 60 0.514893 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHEA-ACCEPTOR-869 60 0.514584 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-22870 60 0.514455 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHEA-ACCEPTOR-2278 60 0.513904 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOA4A-ACCEPTOR-2286 60 0.513796 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMP45C17-ACCEPTOR-7956 60 0.513289 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHPARS1-ACCEPTOR-2888 60 0.513115 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHPARS1-ACCEPTOR-4612 60 0.513115 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTUBAG-ACCEPTOR-2405 60 0.511893 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCAPG-ACCEPTOR-2733 60 0.511787 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMG0S19A-ACCEPTOR-3347 60 0.511271 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHST-ACCEPTOR-4120 60 0.510521 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-2297 60 0.5098 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-2298 60 0.5098 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-3226 60 0.5098 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-1763 60 0.5098 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKEREP-ACCEPTOR-3671 60 0.509423 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSHBGA-ACCEPTOR-3120 60 0.508779 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1GLY2-ACCEPTOR-3502 60 0.507788 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMINCP-ACCEPTOR-3281 60 0.506995 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMG0S19B-ACCEPTOR-3484 60 0.506617 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-2427 60 0.506452 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-1648 60 0.505004 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-1649 60 0.505004 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-2577 60 0.505004 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-1114 60 0.505004 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} BABAPOE-ACCEPTOR-3437 60 0.504555 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACTGA-ACCEPTOR-1621 60 0.498671 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAK1-DONOR-954 60 0.498442 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSPRO-ACCEPTOR-4385 60 0.498078 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMIS-ACCEPTOR-1185 60 0.496597 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCRYGBC-ACCEPTOR-2225 60 0.495759 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-32821 60 0.495575 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHBA4-ACCEPTOR-7232 60 0.494621 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMP3A-ACCEPTOR-2544 60 0.49459 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOCII-ACCEPTOR-3595 60 0.494291 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMEDHB17-ACCEPTOR-3311 60 0.494008 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-32395 60 0.493883 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHST-ACCEPTOR-3478 60 0.492662 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMRP14A-ACCEPTOR-3418 60 0.4923 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-646 60 0.492263 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-851 60 0.491722 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAA-ACCEPTOR-3726 60 0.489703 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKEREP-ACCEPTOR-2070 60 0.488328 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRT1A-ACCEPTOR-209 60 0.486869 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMOPS-ACCEPTOR-4065 60 0.485301 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHANTLE-ACCEPTOR-1363 60 0.48246 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW1B-ACCEPTOR-882 60 0.48104 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-2283 60 0.480726 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} GCRHBBA6-ACCEPTOR-590 60 0.480466 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-4549 60 0.480089 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHMG14A-ACCEPTOR-1361 60 0.47778 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-21144 60 0.47737 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAI1-ACCEPTOR-1647 60 0.476807 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAIT-ACCEPTOR-1394 60 0.476807 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMDES-ACCEPTOR-1920 60 0.476694 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHPARS1-ACCEPTOR-6470 60 0.47495 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} TARHBB-ACCEPTOR-1686 60 0.474423 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAACT-ACCEPTOR-2578 60 0.473247 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} LEMHBB-ACCEPTOR-1437 60 0.473067 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMIL1AG-NEG-4981 60 0.472962 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-ACCEPTOR-19737 60 0.472385 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOC2-ACCEPTOR-3599 60 0.471727 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOC2G-ACCEPTOR-3309 60 0.471727 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} BABAPOE-ACCEPTOR-801 60 0.470295 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOE4-ACCEPTOR-1818 60 0.470295 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-2041 60 0.470285 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-2042 60 0.470285 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-2970 60 0.470285 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-1507 60 0.470285 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCSPB-ACCEPTOR-2217 60 0.469733 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAI1-ACCEPTOR-902 60 0.468478 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOAIT-ACCEPTOR-648 60 0.468478 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMKEREP-ACCEPTOR-3451 60 0.466907 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAP-ACCEPTOR-2927 60 0.466159 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-2440 60 0.465655 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPPD-ACCEPTOR-2062 60 0.465655 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} LEMHBB-ACCEPTOR-346 60 0.46504 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHB27B-ACCEPTOR-2053 60 0.463333 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-20042 60 0.462236 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCAPG-ACCEPTOR-1733 60 0.461213 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGCB1-ACCEPTOR-6555 60 0.460594 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFAB-ACCEPTOR-1431 60 0.46049 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-20331 60 0.459479 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-35070 60 0.459113 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-2064 60 0.458756 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPPPA-ACCEPTOR-2162 60 0.458319 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1ATP-ACCEPTOR-7282 60 0.457955 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} MNKHBD-ACCEPTOR-475 60 0.457174 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMRPS17A-ACCEPTOR-463 60 0.456894 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCFVII-ACCEPTOR-10094 60 0.455317 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPLPSPC-ACCEPTOR-1326 60 0.454857 +{HUMMYLCA-ACCEPTOR-924, 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HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMFOS-ACCEPTOR-1753 60 0.447885 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW2B-ACCEPTOR-2263 60 0.445554 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMETIF1-ACCEPTOR-1065 60 0.444313 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMA1GLY2-ACCEPTOR-4746 60 0.443999 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRCA-ACCEPTOR-5063 60 0.443687 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSPRO-ACCEPTOR-1827 60 0.442428 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMH19-ACCEPTOR-2643 60 0.441902 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPSAP-ACCEPTOR-993 60 0.440744 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMRPS17A-ACCEPTOR-3688 60 0.439852 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0.435339 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-22617 60 0.434805 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCD8A-DONOR-1087 60 0.433666 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-1703 60 0.433157 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-11871 60 0.43197 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ORAHBD-ACCEPTOR-596 60 0.429899 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCKMT-ACCEPTOR-4984 60 0.429206 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-2926 60 0.427364 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSHBGA-ACCEPTOR-5188 60 0.42694 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPRTA-ACCEPTOR-774 60 0.426426 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMADAG-ACCEPTOR-26314 60 0.424649 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ATRINS-ACCEPTOR-701 60 0.42437 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMH6-ACCEPTOR-3284 60 0.42424 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-15000 60 0.421179 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} CHPIGECA-ACCEPTOR-989 60 0.420891 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMSAA-ACCEPTOR-606 60 0.419351 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHDRHA-ACCEPTOR-3705 60 0.418727 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACTGA-ACCEPTOR-1104 60 0.418518 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFA-ACCEPTOR-1558 60 0.417381 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFAB-ACCEPTOR-5037 60 0.417381 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTNFX-ACCEPTOR-1332 60 0.417381 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} TARHBB-ACCEPTOR-2748 60 0.416399 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMUBILP-ACCEPTOR-806 60 0.412048 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCFVII-ACCEPTOR-1624 60 0.411799 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMGPIBAA-ACCEPTOR-3033 60 0.411016 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRCA-ACCEPTOR-10486 60 0.410881 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} TARHBD-ACCEPTOR-594 60 0.410105 +{HUMMYLCA-ACCEPTOR-924, 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HUMPPPA-ACCEPTOR-2425 60 0.379829 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OH-ACCEPTOR-2698 60 0.378295 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCP21OHC-ACCEPTOR-2699 60 0.378295 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP42-ACCEPTOR-3627 60 0.378295 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCP52-ACCEPTOR-2165 60 0.378295 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPRTA-ACCEPTOR-1848 60 0.377741 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPRCA-ACCEPTOR-3434 60 0.376818 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} MNKHBD-ACCEPTOR-1583 60 0.375885 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOE4-ACCEPTOR-2977 60 0.374634 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-2224 60 0.373728 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPPD-ACCEPTOR-1845 60 0.373728 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTHB-ACCEPTOR-9160 60 0.373266 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHMG14A-ACCEPTOR-1599 60 0.372702 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPIM1A-ACCEPTOR-1335 60 0.371644 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-ACCEPTOR-23573 60 0.37074 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW1B-ACCEPTOR-3217 60 0.36768 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHCW2B-ACCEPTOR-3219 60 0.36768 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBA-ACCEPTOR-1121 60 0.367045 +{HUMMYLCA-ACCEPTOR-924, 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HUMSAACT-ACCEPTOR-2856 60 0.346287 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBA-ACCEPTOR-2599 60 0.343027 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBB-ACCEPTOR-2533 60 0.343027 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCSFGMA-ACCEPTOR-1620 60 0.342723 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMMHEA-ACCEPTOR-3682 60 0.342581 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} CHPIGECA-ACCEPTOR-1392 60 0.340476 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMERP-ACCEPTOR-1164 60 0.339301 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMERPA-ACCEPTOR-1171 60 0.339301 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMBNPA-ACCEPTOR-831 60 0.33573 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, 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HUMANT1-ACCEPTOR-5005 60 0.304641 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMATP1A2-ACCEPTOR-20736 60 0.303195 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPI-ACCEPTOR-1631 60 0.301845 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMANT1-ACCEPTOR-3950 60 0.300252 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACHRG4-NEG-1 60 0.300103 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-1117 60 0.297866 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBA-ACCEPTOR-2065 60 0.294914 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMACCYBB-ACCEPTOR-1999 60 0.294914 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCYPIIE-ACCEPTOR-7515 60 0.293368 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, 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HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMFVII-DONOR-70 60 0.234375 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMHSP90B-ACCEPTOR-5391 60 0.224127 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-1369 60 0.223239 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTKRA-ACCEPTOR-12318 60 0.220958 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMALPHA-ACCEPTOR-3090 60 0.217513 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCRYGBC-ACCEPTOR-19693 60 0.215237 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMOPS-ACCEPTOR-2409 60 0.213952 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMTPA-DONOR-18038 60 0.20387 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMAPOA4A-NEG-3241 60 0.201438 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCAPG-ACCEPTOR-2044 60 0.200837 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMDAFA-ACCEPTOR-1235 60 0.189343 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMARC6-NEG-61 60 0.18923 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMCSPB-ACCEPTOR-1260 60 0.188817 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMPGAMMG-ACCEPTOR-3519 60 0.169025 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMUBILP-ACCEPTOR-1158 60 0.152056 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} HUMDES-ACCEPTOR-2164 60 0.144288 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-18496 120 1 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYC3L-ACCEPTOR-4242 120 0.730679 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-27861 60 0.577657 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-6362 60 0.573496 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-9613 60 0.528335 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-31587 60 0.526001 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-9611 60 0.524991 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCSFGMA-ACCEPTOR-891 60 0.508401 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIFNINI-ACCEPTOR-3147 60 0.50808 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5-ACCEPTOR-2088 60 0.503937 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5A-ACCEPTOR-2091 60 0.503937 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYLCA-ACCEPTOR-1831 120 0.503081 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-NEG-841 60 0.49555 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHMG14A-ACCEPTOR-4663 60 0.478026 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2-ACCEPTOR-5027 60 0.470244 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2A-ACCEPTOR-5980 60 0.470244 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2B-ACCEPTOR-4857 60 0.470244 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYLCA-ACCEPTOR-2481 120 0.469061 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMANFA-ACCEPTOR-785 60 0.451328 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-8745 60 0.449656 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHMG14A-ACCEPTOR-6789 60 0.44948 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-41424 60 0.44269 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-23298 60 0.438452 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2-ACCEPTOR-685 60 0.436068 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2A-ACCEPTOR-1623 60 0.436068 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2B-ACCEPTOR-500 60 0.436068 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5-ACCEPTOR-1853 60 0.435726 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5A-ACCEPTOR-1857 60 0.435726 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-6914 60 0.433626 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMP45C17-ACCEPTOR-5038 60 0.432812 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIFNINI-ACCEPTOR-3654 60 0.431178 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYCAA-ACCEPTOR-1188 60 0.429827 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-ACCEPTOR-6540 60 0.425878 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFABP-ACCEPTOR-4068 60 0.424722 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTNFA-ACCEPTOR-1791 60 0.419724 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTNFAB-ACCEPTOR-5270 60 0.419724 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTNFX-ACCEPTOR-1565 60 0.419724 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGG-ACCEPTOR-3631 60 0.418993 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-20604 60 0.418256 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-14589 60 0.417787 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5-ACCEPTOR-875 60 0.416369 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL5A-ACCEPTOR-874 60 0.416369 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-19790 60 0.414569 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTUBAG-ACCEPTOR-2034 60 0.41417 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH1-ACCEPTOR-4714 60 0.413492 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-7729 60 0.409175 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-5606 60 0.407047 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-8083 60 0.406823 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-17940 60 0.406505 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-9261 60 0.404366 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHANTLE-ACCEPTOR-4055 60 0.404266 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-16955 60 0.403283 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCSFGMA-ACCEPTOR-2554 60 0.402956 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTHB-ACCEPTOR-20387 60 0.401091 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFABP-ACCEPTOR-2320 60 0.400253 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH2-ACCEPTOR-3802 60 0.396843 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-10045 60 0.396731 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-3526 60 0.396334 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-7965 60 0.395966 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-NEG-44641 60 0.395507 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-13672 60 0.394705 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-5022 60 0.3911 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-16911 60 0.389185 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBGA-ACCEPTOR-425 60 0.38841 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMRPS14-ACCEPTOR-2108 60 0.388082 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-3641 60 0.38449 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPARS1-ACCEPTOR-3750 60 0.383927 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-2326 60 0.383652 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-4464 60 0.380945 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSHBGA-ACCEPTOR-2856 60 0.38037 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-7572 60 0.380092 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSAA1A-ACCEPTOR-5878 60 0.379426 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-6717 60 0.379015 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-18319 60 0.374579 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFABP-ACCEPTOR-3516 60 0.373489 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBE-ACCEPTOR-1474 60 0.371457 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-12451 60 0.36526 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-3391 60 0.363095 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGG-ACCEPTOR-5138 60 0.36303 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHEA-ACCEPTOR-3890 60 0.362827 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCD8A-ACCEPTOR-2860 60 0.362089 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-4046 60 0.361307 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFERP1-NEG-961 60 0.360053 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-16573 60 0.359774 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDRHA-ACCEPTOR-2969 60 0.358973 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBG-ACCEPTOR-364 60 0.356635 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-12016 60 0.356078 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMETN3-ACCEPTOR-391 60 0.354597 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDOB-ACCEPTOR-4486 60 0.348945 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFOS-ACCEPTOR-2658 60 0.348561 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-11925 60 0.346093 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMCR-NEG-5341 60 0.346045 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMKEREP-ACCEPTOR-3986 60 0.345931 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH2-ACCEPTOR-1848 60 0.343595 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-20185 60 0.342948 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGRP78-ACCEPTOR-2145 60 0.342594 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGFP40H-ACCEPTOR-4066 60 0.341784 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMDES-ACCEPTOR-5413 60 0.341457 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHIAPPA-ACCEPTOR-5862 60 0.341092 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-6266 60 0.340191 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-14947 60 0.339526 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMH6-ACCEPTOR-2326 60 0.339413 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMH6-ACCEPTOR-3673 60 0.33908 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-1522 60 0.338644 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMRPS14-ACCEPTOR-4137 60 0.337323 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIFNINI-ACCEPTOR-2785 60 0.336713 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFOS-ACCEPTOR-2436 60 0.336286 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-5816 60 0.335618 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-4173 60 0.333856 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-2605 60 0.333307 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMADAG-ACCEPTOR-32543 60 0.332175 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMADAM2-ACCEPTOR-784 60 0.332175 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMRP14A-ACCEPTOR-1386 60 0.332092 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH1-ACCEPTOR-2738 60 0.331081 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGFP40H-ACCEPTOR-2431 60 0.330853 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-28261 60 0.329334 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-34907 60 0.328365 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYCC-ACCEPTOR-6624 60 0.327883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMYCRT-ACCEPTOR-3258 60 0.327883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-13888 60 0.327525 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDOB-ACCEPTOR-3357 60 0.326837 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTUBAG-ACCEPTOR-2858 60 0.324437 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGCB1-ACCEPTOR-3009 60 0.324428 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDOB-ACCEPTOR-4124 60 0.324237 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-33757 60 0.32267 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-9259 60 0.322183 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-1782 60 0.319904 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-6011 60 0.317729 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMLACTA-ACCEPTOR-1512 60 0.317317 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-4093 60 0.3168 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTFPB-ACCEPTOR-2160 60 0.316433 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-17658 60 0.316319 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-2534 60 0.315557 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGCB1-ACCEPTOR-5810 60 0.315485 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-ACCEPTOR-1506 60 0.312939 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-39785 60 0.312902 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-2796 60 0.312852 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBG-ACCEPTOR-1452 60 0.311668 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCFVII-ACCEPTOR-9389 60 0.311455 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-6772 60 0.309103 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-5127 60 0.308214 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ORAHBG2F-ACCEPTOR-1492 60 0.307707 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMLACTA-ACCEPTOR-2160 60 0.307494 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYCAA-ACCEPTOR-1466 60 0.306279 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-14750 60 0.304251 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEXCI5R-NEG-1321 60 0.303173 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHANTLE-ACCEPTOR-2733 60 0.303118 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPRTB-ACCEPTOR-40003 60 0.302995 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHPARS1-ACCEPTOR-5472 60 0.302926 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} MACHBCA2-ACCEPTOR-1693 60 0.301896 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} MACHBGA1-ACCEPTOR-1692 60 0.301896 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGFP40H-ACCEPTOR-992 60 0.301338 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBGA-ACCEPTOR-1498 60 0.299939 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} GIBHBGGL-ACCEPTOR-8438 60 0.298305 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPIM1A-ACCEPTOR-3558 60 0.297954 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW1B-ACCEPTOR-3569 60 0.297524 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW2B-ACCEPTOR-3570 60 0.297524 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHBBAG-ACCEPTOR-2565 60 0.294661 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-3176 60 0.29333 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCRYGBC-ACCEPTOR-5283 60 0.293221 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPMPC-NEG-1 60 0.292416 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCP21OH-ACCEPTOR-1838 60 0.292408 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCP21OHC-ACCEPTOR-1839 60 0.292408 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCP42-ACCEPTOR-2767 60 0.292408 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCP52-ACCEPTOR-1304 60 0.292408 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-9633 60 0.292194 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMRPS17A-ACCEPTOR-2225 60 0.292188 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCD8A-ACCEPTOR-5474 60 0.291103 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCKMT-ACCEPTOR-2011 60 0.290952 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-4265 60 0.290768 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-22075 60 0.290713 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATP1A2-ACCEPTOR-15320 60 0.289713 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGRP78-ACCEPTOR-2347 60 0.289521 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-32398 60 0.289239 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-32974 60 0.287444 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-ACCEPTOR-879 60 0.285857 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHMG14A-ACCEPTOR-1487 60 0.285102 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMKAL2-ACCEPTOR-5424 60 0.281868 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMDES-ACCEPTOR-1763 60 0.281318 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-NEG-241 60 0.280175 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-6511 60 0.27941 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPSAA-ACCEPTOR-5669 60 0.279298 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} GIBHBGGL-ACCEPTOR-3506 60 0.276742 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ORAHBG1F-ACCEPTOR-1486 60 0.276742 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHIAPPA-ACCEPTOR-959 60 0.276304 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} TARHBD-ACCEPTOR-1884 60 0.275875 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW1B-ACCEPTOR-3357 60 0.275761 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW2B-ACCEPTOR-3359 60 0.275761 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHCW1B-ACCEPTOR-2261 60 0.274893 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATP1A2-ACCEPTOR-13550 60 0.27238 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDOB-ACCEPTOR-2649 60 0.272057 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-4869 60 0.270289 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCRPG-ACCEPTOR-578 60 0.270099 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCRPGA-ACCEPTOR-539 60 0.270099 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMA1GLY2-ACCEPTOR-2469 60 0.269395 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMANFA-ACCEPTOR-2205 60 0.268908 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-10837 60 0.268677 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-ACCEPTOR-4293 60 0.268442 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMKER18-ACCEPTOR-6098 60 0.266796 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-9414 60 0.265728 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-5471 60 0.264984 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGRP78-ACCEPTOR-2848 60 0.262655 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGCB1-ACCEPTOR-3353 60 0.262447 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2-ACCEPTOR-3038 60 0.259094 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2A-ACCEPTOR-3975 60 0.259094 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL2B-ACCEPTOR-2852 60 0.259094 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMRP8A-ACCEPTOR-2302 60 0.257776 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPCNA-ACCEPTOR-2520 60 0.255664 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGG-ACCEPTOR-1802 60 0.254909 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ORAHBG2F-ACCEPTOR-392 60 0.253766 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-3351 60 0.25349 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-5216 60 0.253095 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-23130 60 0.251929 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHBBAG-ACCEPTOR-1466 60 0.251416 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHBBGG-ACCEPTOR-2363 60 0.251416 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCFXI14-NEG-481 60 0.250649 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-29440 60 0.250365 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGCB1-ACCEPTOR-949 60 0.250084 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMLDL100-NEG-5941 60 0.247957 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-12808 60 0.245251 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALBGC-ACCEPTOR-15504 60 0.245117 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTCBYX-NEG-1 60 0.244476 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} GIBHBGGL-ACCEPTOR-2401 60 0.243883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} GIBHBGGL-ACCEPTOR-7321 60 0.243883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} MACHBCA2-ACCEPTOR-586 60 0.243883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} MACHBGA1-ACCEPTOR-588 60 0.243883 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCRYGBC-ACCEPTOR-18489 60 0.243771 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-2347 60 0.243234 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-3793 60 0.24098 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-2698 60 0.239165 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSPRO-ACCEPTOR-2841 60 0.233964 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATP1A2-ACCEPTOR-10913 60 0.232816 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-13834 60 0.230933 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMKER18-ACCEPTOR-4857 60 0.228166 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-12121 60 0.228104 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-NEG-12121 60 0.228104 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMDES-ACCEPTOR-2504 60 0.227709 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-7462 60 0.226615 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATP1A2-ACCEPTOR-24266 60 0.225667 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHANTLE-ACCEPTOR-3667 60 0.225532 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHANTLE-ACCEPTOR-3842 60 0.224849 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} LEMHBE-ACCEPTOR-410 60 0.223502 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCYPIIE-ACCEPTOR-6976 60 0.220991 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH1-ACCEPTOR-3136 60 0.220891 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRPH2-ACCEPTOR-2241 60 0.220891 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMAFP-ACCEPTOR-15169 60 0.219965 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGRP78-ACCEPTOR-4506 60 0.218829 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMBLYM1-ACCEPTOR-505 60 0.214298 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFIXG-ACCEPTOR-13327 60 0.214008 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPRL1-NEG-61 60 0.208497 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALPPD-ACCEPTOR-734 60 0.206912 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSAA-ACCEPTOR-3086 60 0.20519 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-5710 60 0.204474 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ORAHBG1F-ACCEPTOR-391 60 0.202291 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTHB-ACCEPTOR-9825 60 0.201962 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGRP78-ACCEPTOR-3371 60 0.198741 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-2727 60 0.197933 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMA1ATP-ACCEPTOR-10909 60 0.194381 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCACY-ACCEPTOR-2504 60 0.190094 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSODA-ACCEPTOR-4604 60 0.187086 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCKMT-ACCEPTOR-4348 60 0.186354 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-7858 60 0.186149 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHN-ACCEPTOR-1166 60 0.182689 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMMHDRHA-ACCEPTOR-5181 60 0.182387 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMATPSYB-ACCEPTOR-5220 60 0.182051 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSAA1A-ACCEPTOR-6290 60 0.181613 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMEF1A-ACCEPTOR-2062 60 0.181011 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCS1-ACCEPTOR-1184 60 0.179489 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHCSA-ACCEPTOR-28101 60 0.179489 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHCSA-ACCEPTOR-50922 60 0.179489 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTPA-ACCEPTOR-31083 60 0.179318 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPLAPL-ACCEPTOR-987 60 0.17763 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMINCPPS-ACCEPTOR-4028 60 0.175924 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCATF-ACCEPTOR-1108 60 0.175906 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHCSA-ACCEPTOR-42765 60 0.174844 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIRBPG-ACCEPTOR-4936 60 0.174692 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMFBRAA-NEG-1 60 0.174648 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGH-ACCEPTOR-941 60 0.171989 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGHCSA-ACCEPTOR-5835 60 0.171989 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMPLPSPC-ACCEPTOR-2621 60 0.16798 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMCKMT-ACCEPTOR-1529 60 0.167938 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMHSP90B-ACCEPTOR-3649 60 0.167069 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMP45C17-ACCEPTOR-6267 60 0.163697 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMTUBBM-ACCEPTOR-1850 60 0.161173 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMSHBGA-ACCEPTOR-5640 60 0.159211 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HUMHPRTB-ACCEPTOR-27861, ...} HUMINSRC-NEG-2101 60 0.123606 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMIL1B-DONOR-3748 60 0.105842 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMALPPD-ACCEPTOR-985 60 0.0983941 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMGFP40H-ACCEPTOR-1110 60 0.093152 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} HUMADAG-ACCEPTOR-35621 60 0.0629178 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ATRINS-DONOR-905 60 1 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMERP-DONOR-598 60 0.936729 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMERPA-DONOR-608 60 0.936729 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMKEREP-DONOR-3892 60 0.921043 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMSAACT-DONOR-2210 60 0.887319 +{ATRINS-DONOR-905, 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0.819388 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMRPS17A-DONOR-615 60 0.816926 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} GCRHBBA1-DONOR-1260 60 0.814881 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} BABAPOE-DONOR-2817 60 0.798394 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMMHCW2B-DONOR-1155 60 0.795996 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMP45C17-DONOR-2106 60 0.791593 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMACTGA-DONOR-1344 60 0.789597 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMMHANTLE-DONOR-1633 60 0.787597 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMACCYBA-DONOR-1624 60 0.784175 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMACCYBB-DONOR-1558 60 0.784175 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} HUMDES-DONOR-2630 60 0.783627 +{ATRINS-DONOR-905, HUMERP-DONOR-598, 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0.143376 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFGFG1-NEG-901 60 1 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCOLTHA-NEG-481 60 0.985871 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGLTH1-NEG-241 60 0.970848 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMKRASP-NEG-541 60 0.967784 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGF2AP-NEG-421 60 0.941117 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPLP1-NEG-301 60 0.938944 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRASH-NEG-601 60 0.890782 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMINT2-NEG-481 60 0.87614 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPLG2B-NEG-181 60 0.856445 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRASSK2-NEG-1381 60 0.848226 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMADRA-NEG-961 60 0.840202 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ORARGIT-NEG-241 60 0.833693 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPDGA1-NEG-481 60 0.831983 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSISM-NEG-241 60 0.831753 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMUC18B-NEG-61 60 0.826531 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNAKATP1-NEG-721 60 0.823365 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSRTR2A-NEG-61 60 0.820721 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALDAR-NEG-1081 60 0.815346 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMEDHB17-NEG-3541 60 0.810584 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHAW-NEG-1201 60 0.810559 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMARSBX-NEG-421 60 0.806403 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMVWFM-NEG-5101 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HUMIFNB3-NEG-5641 60 0.748537 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRENT1-NEG-601 60 0.745713 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPTCAA-NEG-481 60 0.743174 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMA2PIG3-NEG-541 60 0.735741 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMINHA-NEG-1 60 0.734816 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPOBG-NEG-121 60 0.733641 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNARIA-NEG-3841 60 0.728211 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ORAHBA01-NEG-121 60 0.727392 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSISG1-NEG-5401 60 0.721689 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCD1A-NEG-1261 60 0.719338 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCOL4A2-NEG-2581 60 0.71738 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGIR-NEG-121 60 0.714721 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPLPSPC-NEG-1801 60 0.710652 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTSG14A-NEG-301 60 0.708049 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMOAT01-NEG-1 60 0.703346 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGENX-NEG-2341 60 0.701349 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} CHPRGITX-NEG-1441 60 0.701105 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCG2A1-NEG-1 60 0.699302 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHKR42-NEG-181 60 0.69917 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMKERMII-NEG-181 60 0.695393 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCOL4A6-NEG-301 60 0.694685 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALLFU-NEG-61 60 0.693212 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCBWC-NEG-121 60 0.69307 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMETS2PR-NEG-901 60 0.692303 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMA1ATP-NEG-301 60 0.68913 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSB3B2-NEG-121 60 0.686002 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMADRA2R-NEG-2581 60 0.684881 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMEF2AB-NEG-721 60 0.684481 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNMYC-NEG-1561 60 0.678677 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMEL01-NEG-901 60 0.677919 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMT2A-NEG-121 60 0.676739 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMYHC08-NEG-421 60 0.676689 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMATP1A2-NEG-2101 60 0.675494 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTIR-NEG-901 60 0.672793 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHB27B-NEG-1201 60 0.67263 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTPA-NEG-31561 60 0.671733 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGMUTDM-NEG-361 60 0.669224 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCAR4-NEG-241 60 0.668657 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMU170SC-NEG-481 60 0.667261 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCKBBA-NEG-481 60 0.665578 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMBCL3AA-NEG-1381 60 0.664932 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCOL2A1-NEG-961 60 0.661556 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} MACAPOE-NEG-181 60 0.66121 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHBW4-NEG-61 60 0.660323 +{HUMFGFG1-NEG-901, 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HUMMYCF1-NEG-1081 60 0.610166 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMC3A1R-NEG-2941 60 0.609837 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMANK-NEG-1981 60 0.609299 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ORAINVOL-NEG-2161 60 0.607313 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRYR-NEG-10201 60 0.604986 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFESFPS-NEG-6661 60 0.60381 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRBC-NEG-661 60 0.602639 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMERVKA2-NEG-901 60 0.601828 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPROD4-NEG-721 60 0.601453 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGSTPIG-NEG-1381 60 0.600441 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMBHAA-NEG-1261 60 0.597786 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHCKA-NEG-61 60 0.596682 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMINT2-NEG-5041 60 0.596642 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRGE-NEG-1801 60 0.596539 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUM4F2HG1-NEG-1441 60 0.595573 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMYONM-NEG-1501 60 0.595457 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGKAR-NEG-181 60 0.595235 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMME491-NEG-601 60 0.594795 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMET-NEG-301 60 0.594066 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMATP-NEG-1981 60 0.593467 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMLYL1B-NEG-4081 60 0.592491 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHHLA-NEG-241 60 0.59232 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} MACHBB-NEG-4141 60 0.592033 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMK562B-NEG-1 60 0.591461 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPGKA09-NEG-241 60 0.591298 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCG5B-NEG-1261 60 0.591287 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMEAR2-NEG-1381 60 0.590382 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTHMOD-NEG-421 60 0.590178 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} CHPAZGLO-NEG-1 60 0.58832 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMOCS1-NEG-1 60 0.587078 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCRYGBC-NEG-3781 60 0.585827 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMPO1-NEG-1681 60 0.584433 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALPPB-NEG-781 60 0.581389 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPRTA-NEG-1981 60 0.58081 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSERDHY-NEG-241 60 0.580403 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMKER654-NEG-1441 60 0.579821 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNFM-NEG-1921 60 0.578655 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPROD1-NEG-1741 60 0.576028 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSAP1-NEG-121 60 0.575954 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCCK2-NEG-361 60 0.574334 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMETIA-NEG-1921 60 0.57372 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCP21OH-NEG-1981 60 0.573254 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALDC-NEG-61 60 0.572454 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMASP-NEG-541 60 0.572103 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHEA-ACCEPTOR-1381 60 0.570858 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPOVRA-NEG-541 60 0.570383 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCP21OHC-NEG-3061 60 0.569291 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMOTNPI-NEG-481 60 0.567914 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} GCRHBBA1-NEG-61 60 0.566461 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUM5FDX-NEG-2041 60 0.566376 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMATP1A2-NEG-20341 60 0.566069 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMRP8A-NEG-1381 60 0.565139 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHSP90-NEG-1 60 0.564402 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCNRAB-NEG-1741 60 0.563859 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHLA1EA-NEG-121 60 0.56365 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMKERP2-NEG-1381 60 0.563645 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMVWFA04-NEG-301 60 0.563226 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGLYSA-NEG-2761 60 0.562739 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMDB1A-NEG-121 60 0.561872 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMYTEST-NEG-301 60 0.56173 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCP21OHA-NEG-2521 60 0.561605 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCYP450-NEG-7321 60 0.561589 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHBB-NEG-5761 60 0.561095 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCAG-NEG-1 60 0.559351 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGNPAS-NEG-61 60 0.558987 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMBAT2A-NEG-2761 60 0.558829 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMITI2-NEG-481 60 0.555789 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMC1A1-NEG-3901 60 0.554961 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPLAP2A-NEG-481 60 0.554923 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRBK-NEG-1321 60 0.553868 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAK1-NEG-6361 60 0.552713 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMOPS-NEG-181 60 0.55095 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCAPG-NEG-1261 60 0.550645 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMPO-NEG-301 60 0.55 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSPM1-NEG-181 60 0.549277 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMVDR-NEG-1441 60 0.548411 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMLD78R-NEG-1 60 0.548213 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRB4A-NEG-781 60 0.54793 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSAACT-NEG-301 60 0.547872 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRNP70K-NEG-1141 60 0.542581 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTPA-NEG-8761 60 0.542076 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMACROS-NEG-1 60 0.541741 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPOA4C-NEG-2761 60 0.540684 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFVII-ACCEPTOR-136 60 0.540189 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHPA2BR-NEG-1261 60 0.539447 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGLI-NEG-901 60 0.539067 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCSPCP-NEG-1261 60 0.539035 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGHBU-NEG-301 60 0.538728 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMVIL2-NEG-1441 60 0.53867 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGRP78-NEG-421 60 0.537038 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFGRINT-NEG-421 60 0.536192 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMC4A1-NEG-2761 60 0.535468 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHER2A-NEG-3301 60 0.534568 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFNRA-NEG-361 60 0.534468 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMBCRD-NEG-301 60 0.531861 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNMYCA-NEG-4561 60 0.531372 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMLAMP1A-NEG-841 60 0.531161 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMARF1A-NEG-301 60 0.531054 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHC1-NEG-4321 60 0.531018 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMLC2A-NEG-1021 60 0.530411 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} TARHBD-NEG-1981 60 0.528991 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFCERA-NEG-121 60 0.528249 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCP42-NEG-4501 60 0.527965 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRCC1B-NEG-361 60 0.527918 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGCD2-NEG-121 60 0.527869 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNMYCA-NEG-1 60 0.526789 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHM71-NEG-181 60 0.526673 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPOCIP-NEG-301 60 0.526387 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTPO03-NEG-61 60 0.524902 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHANTLE-NEG-3181 60 0.523904 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMH6-ACCEPTOR-1451 60 0.523076 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTRKR-NEG-1201 60 0.521661 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHB27B-ACCEPTOR-1202 60 0.521247 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMACCYBB-NEG-3481 60 0.520913 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHMPFK-NEG-901 60 0.520401 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDC3B-NEG-2461 60 0.520127 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCACA-NEG-181 60 0.518995 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMUPAX-NEG-3541 60 0.517163 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPLA-NEG-1261 60 0.516815 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFNRAS-NEG-601 60 0.514218 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCG2A1-NEG-4561 60 0.512408 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} BABBMYHA-NEG-301 60 0.5123 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGFIIM-NEG-121 60 0.511551 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMP68-NEG-1081 60 0.508347 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCRYGBC-NEG-12901 60 0.505862 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRUN1C2-NEG-241 60 0.503968 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHPRTB-NEG-49201 60 0.503533 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMITF1-NEG-601 60 0.503075 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} GCRHBBA6-ACCEPTOR-920 60 0.502875 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHLACW2-NEG-781 60 0.502454 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAPOE3-NEG-1 60 0.502005 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTM-NEG-1441 60 0.500045 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPAIB1-NEG-61 60 0.500011 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCKMT-NEG-1321 60 0.498609 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCAL-NEG-301 60 0.497528 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCY4ARO-NEG-541 60 0.497183 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCKMM1-NEG-1921 60 0.49656 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIDB-NEG-241 60 0.494582 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPMGR-NEG-1621 60 0.493996 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} CHPMHCBAA-NEG-841 60 0.493881 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMELANA-NEG-1261 60 0.493383 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCARN-NEG-1561 60 0.493224 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIRBPG-NEG-3781 60 0.492968 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCSIST-NEG-2221 60 0.492948 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHANTLE-DONOR-2150 60 0.492384 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTHB-NEG-17281 60 0.490249 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALIPOA-NEG-781 60 0.489179 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPIM1A-NEG-1741 60 0.487817 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMELAM1A-NEG-1141 60 0.484116 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCOL1A07-NEG-1 60 0.48399 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMDRD2A-NEG-841 60 0.483173 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTHR-NEG-1501 60 0.482949 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMYHC5-NEG-1141 60 0.479433 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMP42LA-NEG-2161 60 0.479234 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMNFH3-NEG-1 60 0.478608 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} MACPEPSG-NEG-2161 60 0.47836 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHANTLE-ACCEPTOR-1874 60 0.478333 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCW3-NEG-2761 60 0.476873 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTGFKA-NEG-1141 60 0.476397 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ATRINS-ACCEPTOR-1678 60 0.47497 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHAWB-NEG-721 60 0.470735 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGGTA-NEG-661 60 0.469272 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMACTGP1-NEG-481 60 0.466362 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMDBHRA-NEG-1321 60 0.465497 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMBAT3B1-NEG-421 60 0.463285 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRETREC-NEG-1321 60 0.462935 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRENA3-NEG-361 60 0.458819 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMA1AR2-NEG-3601 60 0.45875 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHEXB01-NEG-481 60 0.458418 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCCWB-NEG-241 60 0.456771 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMH6-DONOR-1727 60 0.45546 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPOMC-NEG-421 60 0.455367 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHLADP4-NEG-721 60 0.455258 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCW2B-DONOR-1675 60 0.454601 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCYC1A-NEG-4381 60 0.454124 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGHVH-NEG-481 60 0.452907 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGHCSA-NEG-38521 60 0.451735 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMYP-NEG-961 60 0.451107 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPEP7-NEG-1 60 0.450832 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHB27B-DONOR-1478 60 0.45057 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHCW1B-DONOR-1674 60 0.45057 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} MACHBA-NEG-421 60 0.45047 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTPOB-NEG-2401 60 0.449725 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFXIIIA-NEG-181 60 0.448107 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMDEF1AA-NEG-121 60 0.447858 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPRCM-NEG-1681 60 0.444207 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMENOG-NEG-121 60 0.444163 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCD7-NEG-241 60 0.443271 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHDRBS-NEG-601 60 0.442033 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPIGF2A-NEG-301 60 0.440604 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRAFR-NEG-2521 60 0.438858 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} GCRHBBA1-ACCEPTOR-1706 60 0.437573 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIHRP-NEG-541 60 0.437054 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMAMYA1-NEG-541 60 0.434972 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHKB3R-NEG-121 60 0.43311 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGFIA1-NEG-841 60 0.432569 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUM4COLA-NEG-1021 60 0.432322 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCKMT-NEG-5881 60 0.431546 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGFII5-NEG-121 60 0.43117 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGHVAQ-NEG-61 60 0.430864 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMVWFR1-NEG-3481 60 0.43037 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGPMSS-NEG-1501 60 0.429191 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRGRSALU-NEG-1981 60 0.427772 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGCBPRC-NEG-1501 60 0.427473 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMRAP2-NEG-181 60 0.42676 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMBPA-NEG-661 60 0.425258 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTHBNA-NEG-961 60 0.42383 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHLDPWB-NEG-61 60 0.423603 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMINCP-NEG-3121 60 0.42321 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMSPIA04-NEG-61 60 0.423163 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIL2RA-NEG-2161 60 0.422166 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMHA44G-NEG-2701 60 0.422101 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMGELS3-NEG-121 60 0.421277 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMPALC-NEG-2281 60 0.419475 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGCD7-NEG-1921 60 0.41891 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIGHVA-NEG-481 60 0.417735 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMIFA-NEG-181 60 0.415465 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMERG11-NEG-181 60 0.414897 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMIL2RBC-NEG-1681 60 0.414164 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMTGLU-NEG-781 60 0.412198 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHTRP-NEG-661 60 0.408614 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMKER19-NEG-541 60 0.403825 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMALDH03-NEG-1 60 0.402658 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCA2VR-NEG-361 60 0.402253 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMCOLII2-NEG-781 60 0.400065 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMINIFI-NEG-481 60 0.399472 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMFCRHA-NEG-661 60 0.399365 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMASGPR1-NEG-661 60 0.398728 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} HUMMHA2-NEG-1321 60 0.398551 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...} HUMRSP1GPB-NEG-181 60 0.606565 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} HUMTHRAA-NEG-361 60 0.606135 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} HUMP3A-NEG-2461 60 0.604372 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} HUMHBB-NEG-19441 60 0.6043 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} HUMGLUCG2-NEG-6181 60 0.603826 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} HUMCS5-NEG-2161 60 0.603798 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} HUMHPBA-NEG-901 60 0.603336 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} HUMALDC01-NEG-361 60 0.601585 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} LEMHBGA-NEG-361 60 0.600666 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} HUMHPARS1-NEG-9361 60 0.599268 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} HUMDYS-NEG-8401 60 0.599148 +{HUMGHCSA-NEG-11161, 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...} HUMMHDOB-DONOR-3639 60 0.414646 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSODA-DONOR-6932 60 0.41316 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGCB1-DONOR-581 60 0.407943 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSAA-DONOR-701 60 0.40641 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMADAG-NEG-9001 60 0.404812 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSODA-DONOR-3510 60 0.403941 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-2752 60 0.402424 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-7352 60 0.401265 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMFXIIIB-NEG-841 60 0.400411 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSHBGA-DONOR-5396 60 0.393882 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTUBAG-DONOR-2257 60 0.391184 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMAPA4R-NEG-2761 60 0.390765 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-4574 60 0.387929 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTFPB-DONOR-6562 60 0.386307 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMINCPPS-DONOR-2825 60 0.385109 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCKMT-DONOR-5110 60 0.383038 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMFIXG-DONOR-20733 60 0.381724 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMAFP-DONOR-19845 60 0.378902 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-5858 60 0.377773 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGCB1-DONOR-3143 60 0.376326 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGG-DONOR-230 60 0.368442 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSAA1A-DONOR-6017 60 0.367947 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMEF1A-ACCEPTOR-3311 60 0.367124 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMRTVLH2-NEG-4681 60 0.363515 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHPRTB-NEG-53761 60 0.359999 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-DONOR-3558 60 0.359642 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMRTVLH2-NEG-121 60 0.356328 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSAA-DONOR-34 60 0.354715 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMALBGC-DONOR-6905 60 0.350859 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMKTEP2A-NEG-1441 60 0.349786 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMH6-ACCEPTOR-3562 60 0.349135 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCD1B1-NEG-241 60 0.348346 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTNFA-DONOR-1839 60 0.342948 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTNFAB-DONOR-5318 60 0.342948 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMTNFX-DONOR-1613 60 0.342948 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHSP90B-ACCEPTOR-7018 60 0.339502 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMKEREP-DONOR-3368 60 0.337561 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMATPSYB-ACCEPTOR-3096 60 0.328045 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMA1GLY2-DONOR-3352 60 0.324983 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHPARS1-DONOR-3825 60 0.317844 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCYPIIE-DONOR-12950 60 0.314013 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMRP14A-DONOR-1551 60 0.306367 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMYC3L-NEG-3301 60 0.297142 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMHDXAR2-NEG-1 60 0.2917 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMACTGA-DONOR-2060 60 0.287417 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMA1GLY2-DONOR-3606 60 0.283732 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMH6-DONOR-3321 60 0.280272 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMIL1B-NEG-6721 60 0.276992 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} MNKHBD-DONOR-347 60 0.274563 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHPARS1-NEG-4801 60 0.273287 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMVIM06-NEG-1 60 0.265701 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMFIXG-DONOR-23501 60 0.258593 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ORAHBD-DONOR-466 60 0.2582 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMATPCAPM-NEG-661 60 0.249874 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMHBB32-NEG-121 60 0.246244 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMDES-DONOR-2016 60 0.238813 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCALLA-NEG-2161 60 0.217972 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMADH1CA-NEG-121 60 0.211778 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMSAA1A-DONOR-1815 60 0.207596 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMCKMT-ACCEPTOR-5313 60 0.196222 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMMRP8A-ACCEPTOR-1988 60 0.193932 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMGRP78-DONOR-1007 60 0.188581 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMLACF-NEG-541 60 0.16601 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} HUMFBRG-NEG-7201 60 0.126014 + +Composition Char +Cluster Value Frequency Typicality +{G, N, D, ...} G 50245 1 +{G, N, D, ...} N 56 0.00682706 +{G, N, D, ...} D 2 0.000410479 +{G, N, D, ...} S 1 0.000255295 +{G, N, D, ...} R 1 0.000106161 +{T} T 46308 1 +{A} A 44487 1 +{C} C 50300 1 + +Cells +SampleId Pos Char Frequency +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]36.5;+inf[ {G, N, D, ...} 3633 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]36.5;+inf[ {C} 3462 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]36.5;+inf[ {C} 3374 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]36.5;+inf[ {G, N, D, ...} 3364 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]36.5;+inf[ {G, N, D, ...} 3354 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]36.5;+inf[ {A} 3298 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]36.5;+inf[ {T} 3223 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]-inf;15.5] {C} 2942 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]36.5;+inf[ {C} 2809 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]36.5;+inf[ {C} 2735 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]36.5;+inf[ {G, N, D, ...} 2642 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]15.5;26.5] {C} 2447 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]36.5;+inf[ {A} 2423 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]36.5;+inf[ {A} 2423 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]-inf;15.5] {G, N, D, ...} 2333 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]36.5;+inf[ {T} 2273 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]36.5;+inf[ {A} 2187 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]-inf;15.5] {A} 2125 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]36.5;+inf[ {T} 2111 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]36.5;+inf[ {T} 2095 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]36.5;+inf[ {T} 2079 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]-inf;15.5] {C} 2047 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]36.5;+inf[ {G, N, D, ...} 2018 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]-inf;15.5] {T} 1905 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]36.5;+inf[ {A} 1901 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]15.5;26.5] {T} 1901 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]36.5;+inf[ {C} 1861 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]36.5;+inf[ {A} 1839 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]-inf;15.5] {T} 1837 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]36.5;+inf[ {G, N, D, ...} 1821 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]15.5;26.5] {T} 1792 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]36.5;+inf[ {G, N, D, ...} 1768 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]36.5;+inf[ {T} 1739 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]36.5;+inf[ {C} 1685 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]15.5;26.5] {G, N, D, ...} 1684 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]36.5;+inf[ {T} 1682 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]-inf;15.5] {C} 1676 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]-inf;15.5] {T} 1653 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]36.5;+inf[ {G, N, D, ...} 1621 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]-inf;15.5] {A} 1614 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]36.5;+inf[ {A} 1600 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]15.5;26.5] {C} 1561 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]36.5;+inf[ {C} 1558 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]15.5;26.5] {A} 1535 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]15.5;26.5] {T} 1503 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]-inf;15.5] {G, N, D, ...} 1466 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]-inf;15.5] {C} 1412 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]-inf;15.5] {G, N, D, ...} 1384 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]-inf;15.5] {G, N, D, ...} 1379 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]-inf;15.5] {G, N, D, ...} 1372 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]-inf;15.5] {T} 1357 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]36.5;+inf[ {T} 1347 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]-inf;15.5] {T} 1342 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]15.5;26.5] {G, N, D, ...} 1292 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]36.5;+inf[ {C} 1286 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]-inf;15.5] {C} 1247 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]15.5;26.5] {C} 1206 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]-inf;15.5] {A} 1196 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]36.5;+inf[ {A} 1180 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]-inf;15.5] {A} 1179 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]-inf;15.5] {C} 1174 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]-inf;15.5] {G, N, D, ...} 1171 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]15.5;26.5] {A} 1138 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]-inf;15.5] {A} 1109 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]-inf;15.5] {A} 1106 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]-inf;15.5] {A} 1094 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]15.5;26.5] {T} 1083 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]36.5;+inf[ {C} 1070 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]36.5;+inf[ {T} 1068 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]-inf;15.5] {T} 1050 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]-inf;15.5] {G, N, D, ...} 1050 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]36.5;+inf[ {G, N, D, ...} 1043 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]-inf;15.5] {C} 1026 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]36.5;+inf[ {A} 988 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]-inf;15.5] {C} 964 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]-inf;15.5] {T} 936 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]-inf;15.5] {T} 934 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]15.5;26.5] {C} 931 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]-inf;15.5] {G, N, D, ...} 931 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]-inf;15.5] {A} 930 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]15.5;26.5] {C} 917 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]15.5;26.5] {G, N, D, ...} 915 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]15.5;26.5] {A} 915 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]15.5;26.5] {G, N, D, ...} 902 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]15.5;26.5] {C} 887 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]15.5;26.5] {G, N, D, ...} 874 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]15.5;26.5] {C} 860 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]15.5;26.5] {T} 856 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]-inf;15.5] {A} 813 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]-inf;15.5] {T} 807 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]15.5;26.5] {A} 797 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]15.5;26.5] {T} 782 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]15.5;26.5] {C} 782 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]15.5;26.5] {T} 761 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]15.5;26.5] {A} 759 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]15.5;26.5] {A} 759 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]15.5;26.5] {A} 744 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]15.5;26.5] {G, N, D, ...} 680 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]-inf;15.5] {C} 669 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]15.5;26.5] {T} 622 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]-inf;15.5] {G, N, D, ...} 620 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]15.5;26.5] {C} 584 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]15.5;26.5] {T} 576 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]15.5;26.5] {G, N, D, ...} 558 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]15.5;26.5] {G, N, D, ...} 545 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]28.5;29.5] {A} 472 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]29.5;30.5] {G, N, D, ...} 472 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]27.5;28.5] {C} 441 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]15.5;26.5] {A} 384 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]29.5;30.5] {G, N, D, ...} 302 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]28.5;29.5] {A} 301 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]15.5;26.5] {A} 299 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]31.5;32.5] {T} 287 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]30.5;31.5] {G, N, D, ...} 284 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]30.5;31.5] {G, N, D, ...} 284 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]31.5;32.5] {T} 276 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]34.5;35.5] {G, N, D, ...} 263 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]15.5;26.5] {G, N, D, ...} 259 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]34.5;35.5] {G, N, D, ...} 245 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]29.5;30.5] {G, N, D, ...} 241 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]29.5;30.5] {G, N, D, ...} 239 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]30.5;31.5] {G, N, D, ...} 238 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]33.5;34.5] {A} 230 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]31.5;32.5] {T} 229 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]30.5;31.5] {G, N, D, ...} 219 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]26.5;27.5] {C} 182 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]32.5;33.5] {G, N, D, ...} 181 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]29.5;30.5] {G, N, D, ...} 180 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]33.5;34.5] {A} 179 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]34.5;35.5] {G, N, D, ...} 175 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]33.5;34.5] {A} 173 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]27.5;28.5] {C} 172 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]29.5;30.5] {C} 172 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]30.5;31.5] {T} 170 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]26.5;27.5] {C} 169 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]35.5;36.5] {C} 168 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]29.5;30.5] {G, N, D, ...} 168 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]28.5;29.5] {A} 167 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]26.5;27.5] {G, N, D, ...} 162 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]32.5;33.5] {A} 161 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]34.5;35.5] {C} 160 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]26.5;27.5] {G, N, D, ...} 160 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]29.5;30.5] {T} 158 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]28.5;29.5] {A} 157 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]31.5;32.5] {T} 154 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]33.5;34.5] {C} 154 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]35.5;36.5] {C} 153 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]33.5;34.5] {G, N, D, ...} 153 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]30.5;31.5] {G, N, D, ...} 152 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]26.5;27.5] {A} 150 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]34.5;35.5] {T} 150 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]28.5;29.5] {C} 149 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]28.5;29.5] {A} 148 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]27.5;28.5] {G, N, D, ...} 148 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]30.5;31.5] {G, N, D, ...} 147 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]27.5;28.5] {A} 147 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]32.5;33.5] {C} 145 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]35.5;36.5] {G, N, D, ...} 145 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]30.5;31.5] {A} 145 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]35.5;36.5] {T} 145 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]32.5;33.5] {G, N, D, ...} 145 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]30.5;31.5] {C} 144 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]32.5;33.5] {G, N, D, ...} 143 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]31.5;32.5] {A} 143 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]35.5;36.5] {T} 142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]34.5;35.5] {C} 142 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]32.5;33.5] {C} 141 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]34.5;35.5] {T} 141 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]31.5;32.5] {G, N, D, ...} 140 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]31.5;32.5] {C} 137 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]27.5;28.5] {C} 137 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]30.5;31.5] {A} 137 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]33.5;34.5] {C} 136 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]33.5;34.5] {A} 136 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]33.5;34.5] {T} 136 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]29.5;30.5] {A} 135 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]27.5;28.5] {T} 135 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]28.5;29.5] {T} 135 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]32.5;33.5] {T} 135 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]34.5;35.5] {A} 134 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]35.5;36.5] {G, N, D, ...} 134 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]33.5;34.5] {G, N, D, ...} 133 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]28.5;29.5] {G, N, D, ...} 132 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]34.5;35.5] {C} 131 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]28.5;29.5] {A} 131 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]33.5;34.5] {C} 130 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]31.5;32.5] {T} 130 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]26.5;27.5] {T} 129 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]35.5;36.5] {C} 124 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]30.5;31.5] {G, N, D, ...} 124 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]35.5;36.5] {A} 124 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]32.5;33.5] {A} 124 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]27.5;28.5] {C} 123 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]26.5;27.5] {A} 123 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]32.5;33.5] {A} 122 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]32.5;33.5] {G, N, D, ...} 122 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]35.5;36.5] {T} 121 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]31.5;32.5] {G, N, D, ...} 120 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]27.5;28.5] {C} 119 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]27.5;28.5] {G, N, D, ...} 118 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]34.5;35.5] {G, N, D, ...} 117 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]34.5;35.5] {G, N, D, ...} 116 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]32.5;33.5] {A} 116 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]30.5;31.5] {C} 114 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]30.5;31.5] {T} 113 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]27.5;28.5] {T} 112 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]32.5;33.5] {T} 111 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]33.5;34.5] {G, N, D, ...} 110 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]27.5;28.5] {C} 109 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]31.5;32.5] {C} 109 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]32.5;33.5] {C} 109 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]31.5;32.5] {G, N, D, ...} 109 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]29.5;30.5] {A} 108 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]27.5;28.5] {A} 107 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]26.5;27.5] {G, N, D, ...} 107 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]33.5;34.5] {T} 105 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]26.5;27.5] {C} 105 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]35.5;36.5] {G, N, D, ...} 105 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]34.5;35.5] {A} 105 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]34.5;35.5] {A} 104 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]35.5;36.5] {A} 104 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]34.5;35.5] {G, N, D, ...} 104 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]27.5;28.5] {A} 104 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]32.5;33.5] {A} 103 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]26.5;27.5] {A} 103 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]27.5;28.5] {T} 103 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]28.5;29.5] {G, N, D, ...} 103 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]32.5;33.5] {T} 102 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]29.5;30.5] {G, N, D, ...} 102 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]28.5;29.5] {C} 101 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]34.5;35.5] {T} 100 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]26.5;27.5] {G, N, D, ...} 100 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]31.5;32.5] {C} 100 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]35.5;36.5] {T} 100 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]32.5;33.5] {G, N, D, ...} 99 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]33.5;34.5] {A} 99 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]35.5;36.5] {T} 98 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]32.5;33.5] {T} 98 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]31.5;32.5] {A} 98 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]32.5;33.5] {A} 98 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]26.5;27.5] {T} 97 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]32.5;33.5] {A} 97 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]28.5;29.5] {T} 97 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]35.5;36.5] {T} 97 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]31.5;32.5] {T} 95 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]28.5;29.5] {T} 95 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]29.5;30.5] {C} 95 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]28.5;29.5] {C} 95 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]26.5;27.5] {C} 94 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]26.5;27.5] {G, N, D, ...} 94 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]35.5;36.5] {A} 94 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]32.5;33.5] {C} 94 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]31.5;32.5] {A} 94 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]26.5;27.5] {T} 92 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]29.5;30.5] {A} 92 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]35.5;36.5] {C} 92 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]35.5;36.5] {T} 92 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]31.5;32.5] {T} 91 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]33.5;34.5] {A} 91 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]34.5;35.5] {C} 90 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]28.5;29.5] {G, N, D, ...} 90 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]31.5;32.5] {A} 90 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]33.5;34.5] {G, N, D, ...} 89 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]31.5;32.5] {T} 89 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]27.5;28.5] {C} 89 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]29.5;30.5] {C} 89 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]33.5;34.5] {C} 89 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]31.5;32.5] {A} 88 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]30.5;31.5] {C} 88 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]28.5;29.5] {A} 88 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]27.5;28.5] {A} 88 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]27.5;28.5] {A} 87 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]28.5;29.5] {A} 87 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]29.5;30.5] {T} 87 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]33.5;34.5] {T} 86 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]30.5;31.5] {A} 85 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]33.5;34.5] {T} 85 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]29.5;30.5] {T} 85 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]32.5;33.5] {T} 85 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]34.5;35.5] {T} 85 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]34.5;35.5] {A} 85 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]35.5;36.5] {A} 84 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]34.5;35.5] {A} 84 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]28.5;29.5] {T} 84 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]31.5;32.5] {C} 84 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]27.5;28.5] {G, N, D, ...} 83 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]35.5;36.5] {T} 82 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]26.5;27.5] {T} 82 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]34.5;35.5] {C} 82 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]27.5;28.5] {G, N, D, ...} 82 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]30.5;31.5] {A} 82 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]32.5;33.5] {A} 81 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]26.5;27.5] {C} 81 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]34.5;35.5] {T} 81 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]26.5;27.5] {A} 81 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]35.5;36.5] {G, N, D, ...} 80 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]32.5;33.5] {G, N, D, ...} 80 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]32.5;33.5] {A} 80 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]27.5;28.5] {A} 79 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]34.5;35.5] {G, N, D, ...} 79 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]31.5;32.5] {A} 79 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]31.5;32.5] {G, N, D, ...} 78 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]26.5;27.5] {A} 78 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]33.5;34.5] {T} 78 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]33.5;34.5] {G, N, D, ...} 77 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]30.5;31.5] {T} 77 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]31.5;32.5] {T} 77 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]35.5;36.5] {G, N, D, ...} 76 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]26.5;27.5] {G, N, D, ...} 76 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]31.5;32.5] {G, N, D, ...} 76 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]28.5;29.5] {C} 75 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]28.5;29.5] {G, N, D, ...} 75 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]30.5;31.5] {G, N, D, ...} 75 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]35.5;36.5] {C} 75 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]33.5;34.5] {A} 74 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]32.5;33.5] {T} 74 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]30.5;31.5] {A} 74 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]33.5;34.5] {G, N, D, ...} 74 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]26.5;27.5] {G, N, D, ...} 73 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]33.5;34.5] {A} 73 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]31.5;32.5] {C} 73 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]32.5;33.5] {G, N, D, ...} 73 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]26.5;27.5] {C} 73 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]29.5;30.5] {G, N, D, ...} 72 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]26.5;27.5] {T} 72 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]33.5;34.5] {C} 72 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]35.5;36.5] {C} 71 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]30.5;31.5] {C} 71 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]26.5;27.5] {A} 71 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]30.5;31.5] {G, N, D, ...} 70 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]35.5;36.5] {A} 70 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]27.5;28.5] {C} 70 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]26.5;27.5] {C} 69 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]30.5;31.5] {A} 69 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]35.5;36.5] {A} 69 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]27.5;28.5] {T} 68 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]31.5;32.5] {C} 67 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]29.5;30.5] {A} 67 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]35.5;36.5] {C} 67 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]35.5;36.5] {G, N, D, ...} 67 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]26.5;27.5] {A} 66 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]32.5;33.5] {C} 66 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]33.5;34.5] {C} 66 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]33.5;34.5] {A} 66 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]35.5;36.5] {G, N, D, ...} 65 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]26.5;27.5] {C} 65 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]27.5;28.5] {A} 64 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]32.5;33.5] {C} 64 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]34.5;35.5] {T} 63 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]28.5;29.5] {A} 62 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]26.5;27.5] {T} 62 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]28.5;29.5] {C} 61 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]33.5;34.5] {T} 61 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]29.5;30.5] {C} 60 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]35.5;36.5] {A} 60 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]27.5;28.5] {T} 59 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]34.5;35.5] {C} 59 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]34.5;35.5] {G, N, D, ...} 59 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]26.5;27.5] {T} 59 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]32.5;33.5] {G, N, D, ...} 58 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]33.5;34.5] {G, N, D, ...} 58 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]27.5;28.5] {G, N, D, ...} 57 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]34.5;35.5] {A} 57 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]35.5;36.5] {G, N, D, ...} 57 +{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...} ]30.5;31.5] {T} 56 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]26.5;27.5] {T} 55 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]35.5;36.5] {T} 54 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]32.5;33.5] {G, N, D, ...} 53 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]31.5;32.5] {G, N, D, ...} 52 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]26.5;27.5] {G, N, D, ...} 51 +{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...} ]29.5;30.5] {T} 50 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]27.5;28.5] {G, N, D, ...} 50 +{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...} ]26.5;27.5] {A} 49 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]27.5;28.5] {C} 49 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]26.5;27.5] {A} 48 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]35.5;36.5] {A} 48 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]29.5;30.5] {G, N, D, ...} 47 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]30.5;31.5] {C} 46 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]34.5;35.5] {G, N, D, ...} 45 +{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...} ]26.5;27.5] {C} 45 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]30.5;31.5] {T} 45 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]28.5;29.5] {T} 45 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]28.5;29.5] {G, N, D, ...} 44 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]27.5;28.5] {T} 43 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]30.5;31.5] {C} 43 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]28.5;29.5] {G, N, D, ...} 41 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]28.5;29.5] {G, N, D, ...} 38 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]29.5;30.5] {T} 38 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]35.5;36.5] {G, N, D, ...} 38 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]35.5;36.5] {C} 37 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]28.5;29.5] {T} 35 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]27.5;28.5] {G, N, D, ...} 35 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]26.5;27.5] {T} 34 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]34.5;35.5] {A} 34 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]28.5;29.5] {C} 34 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]26.5;27.5] {G, N, D, ...} 33 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]27.5;28.5] {T} 33 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]33.5;34.5] {G, N, D, ...} 31 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]27.5;28.5] {T} 30 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]33.5;34.5] {C} 30 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]35.5;36.5] {A} 29 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]29.5;30.5] {A} 29 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]33.5;34.5] {G, N, D, ...} 27 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]30.5;31.5] {T} 27 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]33.5;34.5] {T} 27 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]28.5;29.5] {T} 26 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]29.5;30.5] {A} 26 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]27.5;28.5] {T} 25 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]35.5;36.5] {C} 23 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]33.5;34.5] {C} 22 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]34.5;35.5] {T} 22 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]28.5;29.5] {C} 21 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]29.5;30.5] {T} 21 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]34.5;35.5] {C} 21 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]33.5;34.5] {T} 20 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]29.5;30.5] {C} 18 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]27.5;28.5] {A} 18 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]29.5;30.5] {A} 17 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]32.5;33.5] {C} 14 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]34.5;35.5] {T} 13 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]31.5;32.5] {A} 13 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]29.5;30.5] {T} 12 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]34.5;35.5] {C} 12 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]33.5;34.5] {C} 11 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]34.5;35.5] {C} 11 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]34.5;35.5] {A} 11 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]34.5;35.5] {T} 10 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]33.5;34.5] {T} 9 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]31.5;32.5] {C} 9 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]29.5;30.5] {C} 7 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]32.5;33.5] {T} 7 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]27.5;28.5] {G, N, D, ...} 4 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]30.5;31.5] {C} 4 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]32.5;33.5] {T} 4 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]32.5;33.5] {C} 3 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]30.5;31.5] {A} 2 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]30.5;31.5] {C} 2 +{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...} ]27.5;28.5] {A} 2 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]30.5;31.5] {C} 2 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]31.5;32.5] {G, N, D, ...} 2 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]31.5;32.5] {A} 1 +{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...} ]32.5;33.5] {T} 1 +{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...} ]28.5;29.5] {G, N, D, ...} 1 +{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...} ]32.5;33.5] {C} 1 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]29.5;30.5] {C} 1 +{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...} ]30.5;31.5] {A} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Clusters.bad.txt b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Clusters.bad.txt new file mode 100644 index 000000000..905e780fe --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Clusters.bad.txt @@ -0,0 +1,5 @@ +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Clusters.txt b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Clusters.txt new file mode 100644 index 000000000..905e780fe --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Clusters.txt @@ -0,0 +1,5 @@ +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.bad.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.bad.khc new file mode 100644 index 000000000..628dd01fa --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.bad.khc @@ -0,0 +1,69 @@ +#Khiops 10.4.6i +Short description +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +PetalLength Numerical 3 3 150 1 +PetalWidth Numerical 3 3 150 1 +Class Categorical 3 3 3 1 + +Coclustering stats +Instances 150 +Cells 7 +Null cost 1379.668177 +Cost 1164.273132 +Level 0.1561209056 +Initial dimensions 3 +Frequency variable +Dictionary Iris +Database ./SubDir/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 +]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 +]-inf;+inf[ 150 0.648091 -0.0145257 2 4 +]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 +]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 +]-inf;+inf[ 150 0.681865 0.374198 2 5 +]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 + +Hierarchy Class +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Iris-setosa} C1 50 1 1 1 9 +{Iris-versicolor} C4 50 0.912663 1 3 9 +{Iris-virginica} C4 50 0.912663 1 5 9 +C1 150 0.941775 0 2 3 +C4 C1 100 0.912663 0.739904 4 6 + +Composition Class +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 + +Cells +PetalLength PetalWidth Class Frequency +]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 +]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 +]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 +]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 +]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 +]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 +]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.bad.khcj b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.bad.khcj new file mode 100644 index 000000000..d3d6473d0 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.bad.khcj @@ -0,0 +1,299 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 150, + "cells": 7, + "nullCost": 1379.668177, + "cost": 1164.273132, + "level": 0.1561209056, + "initialDimensions": 3, + "frequencyVariable": "", + "dictionary": "Iris", + "database": ".\/SubDir\/Iris.txt", + "samplePercentage": 100, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "" + }, + "dimensionSummaries": [ + { + "name": "PetalLength", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 1, + "max": 6.9 + }, + { + "name": "PetalWidth", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 0.1, + "max": 2.5 + }, + { + "name": "Class", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 3, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "PetalLength", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;2.4]", + "bounds": [1,2.4] + }, + { + "cluster": "]2.4;4.75]", + "bounds": [2.4,4.75] + }, + { + "cluster": "]4.75;+inf[", + "bounds": [4.75,6.9] + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;0.8]", + "bounds": [0.1,0.8] + }, + { + "cluster": "]0.8;1.75]", + "bounds": [0.8,1.75] + }, + { + "cluster": "]1.75;+inf[", + "bounds": [1.75,2.5] + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Iris-setosa}", + "values": ["Iris-setosa"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-versicolor}", + "values": ["Iris-versicolor"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-virginica}", + "values": ["Iris-virginica"], + "valueFrequencies": [50], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 2 + } + ], + "dimensionHierarchies": [ + { + "name": "PetalLength", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;2.4]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.557285, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]2.4;4.75]", + "parentCluster": "]2.4;+inf[", + "frequency": 45, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]4.75;+inf[", + "parentCluster": "]2.4;+inf[", + "frequency": 55, + "interest": 0.442715, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.648091, + "hierarchicalLevel": -0.0145257, + "rank": 2, + "hierarchicalRank": 4, + "isLeaf": false + }, + { + "cluster": "]2.4;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.693493, + "hierarchicalLevel": 0.856289, + "rank": 4, + "hierarchicalRank": 8, + "isLeaf": false + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;0.8]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.569949, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]0.8;1.75]", + "parentCluster": "]0.8;+inf[", + "frequency": 54, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]1.75;+inf[", + "parentCluster": "]0.8;+inf[", + "frequency": 46, + "interest": 0.430051, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.681865, + "hierarchicalLevel": 0.374198, + "rank": 2, + "hierarchicalRank": 5, + "isLeaf": false + }, + { + "cluster": "]0.8;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.737824, + "hierarchicalLevel": 0.704564, + "rank": 4, + "hierarchicalRank": 7, + "isLeaf": false + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Iris-setosa}", + "parentCluster": "C1", + "frequency": 50, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-versicolor}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-virginica}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "C1", + "parentCluster": "", + "frequency": 150, + "interest": 0.941775, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "C4", + "parentCluster": "C1", + "frequency": 100, + "interest": 0.912663, + "hierarchicalLevel": 0.739904, + "rank": 4, + "hierarchicalRank": 6, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0,0], + [2,2,2], + [1,1,1], + [2,1,1], + [2,1,2], + [1,1,2], + [2,2,1] + ], + "cellFrequencies": [50,45,44,5,4,1,1] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.khc new file mode 100644 index 000000000..628dd01fa --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.khc @@ -0,0 +1,69 @@ +#Khiops 10.4.6i +Short description +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +PetalLength Numerical 3 3 150 1 +PetalWidth Numerical 3 3 150 1 +Class Categorical 3 3 3 1 + +Coclustering stats +Instances 150 +Cells 7 +Null cost 1379.668177 +Cost 1164.273132 +Level 0.1561209056 +Initial dimensions 3 +Frequency variable +Dictionary Iris +Database ./SubDir/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 +]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 +]-inf;+inf[ 150 0.648091 -0.0145257 2 4 +]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 +]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 +]-inf;+inf[ 150 0.681865 0.374198 2 5 +]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 + +Hierarchy Class +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Iris-setosa} C1 50 1 1 1 9 +{Iris-versicolor} C4 50 0.912663 1 3 9 +{Iris-virginica} C4 50 0.912663 1 5 9 +C1 150 0.941775 0 2 3 +C4 C1 100 0.912663 0.739904 4 6 + +Composition Class +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 + +Cells +PetalLength PetalWidth Class Frequency +]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 +]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 +]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 +]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 +]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 +]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 +]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.khcj new file mode 100644 index 000000000..d3d6473d0 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/Coclustering.khcj @@ -0,0 +1,299 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 150, + "cells": 7, + "nullCost": 1379.668177, + "cost": 1164.273132, + "level": 0.1561209056, + "initialDimensions": 3, + "frequencyVariable": "", + "dictionary": "Iris", + "database": ".\/SubDir\/Iris.txt", + "samplePercentage": 100, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "" + }, + "dimensionSummaries": [ + { + "name": "PetalLength", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 1, + "max": 6.9 + }, + { + "name": "PetalWidth", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 0.1, + "max": 2.5 + }, + { + "name": "Class", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 3, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "PetalLength", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;2.4]", + "bounds": [1,2.4] + }, + { + "cluster": "]2.4;4.75]", + "bounds": [2.4,4.75] + }, + { + "cluster": "]4.75;+inf[", + "bounds": [4.75,6.9] + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;0.8]", + "bounds": [0.1,0.8] + }, + { + "cluster": "]0.8;1.75]", + "bounds": [0.8,1.75] + }, + { + "cluster": "]1.75;+inf[", + "bounds": [1.75,2.5] + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Iris-setosa}", + "values": ["Iris-setosa"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-versicolor}", + "values": ["Iris-versicolor"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-virginica}", + "values": ["Iris-virginica"], + "valueFrequencies": [50], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 2 + } + ], + "dimensionHierarchies": [ + { + "name": "PetalLength", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;2.4]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.557285, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]2.4;4.75]", + "parentCluster": "]2.4;+inf[", + "frequency": 45, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]4.75;+inf[", + "parentCluster": "]2.4;+inf[", + "frequency": 55, + "interest": 0.442715, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.648091, + "hierarchicalLevel": -0.0145257, + "rank": 2, + "hierarchicalRank": 4, + "isLeaf": false + }, + { + "cluster": "]2.4;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.693493, + "hierarchicalLevel": 0.856289, + "rank": 4, + "hierarchicalRank": 8, + "isLeaf": false + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;0.8]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.569949, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]0.8;1.75]", + "parentCluster": "]0.8;+inf[", + "frequency": 54, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]1.75;+inf[", + "parentCluster": "]0.8;+inf[", + "frequency": 46, + "interest": 0.430051, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.681865, + "hierarchicalLevel": 0.374198, + "rank": 2, + "hierarchicalRank": 5, + "isLeaf": false + }, + { + "cluster": "]0.8;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.737824, + "hierarchicalLevel": 0.704564, + "rank": 4, + "hierarchicalRank": 7, + "isLeaf": false + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Iris-setosa}", + "parentCluster": "C1", + "frequency": 50, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-versicolor}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-virginica}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "C1", + "parentCluster": "", + "frequency": 150, + "interest": 0.941775, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "C4", + "parentCluster": "C1", + "frequency": 100, + "interest": 0.912663, + "hierarchicalLevel": 0.739904, + "rank": 4, + "hierarchicalRank": 6, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0,0], + [2,2,2], + [1,1,1], + [2,1,1], + [2,1,2], + [1,1,2], + [2,2,1] + ], + "cellFrequencies": [50,45,44,5,4,1,1] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/DeploymentCoclustering.bad.kdic b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/DeploymentCoclustering.bad.kdic new file mode 100644 index 000000000..a4317f884 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/DeploymentCoclustering.bad.kdic @@ -0,0 +1,51 @@ +#Khiops 10.4.6i + +Root Dictionary SpliceJunction (SampleId) +{ + Categorical SampleId ; + Categorical Class ; + Table(SpliceJunctionDNA) DNA ; +Unused Structure(DataGrid) P_Coclustering = DataGrid(ValueGroups(ValueGroup("HUMMYLCA-ACCEPTOR-924", "HUMVPNP-ACCEPTOR-1688", "HUMMHCW2B-ACCEPTOR-2660", "HUMMHANTLE-ACCEPTOR-3110", "HUMGASTA-ACCEPTOR-6600", "HUMMHCW1B-ACCEPTOR-2658", "HUMIL1B-ACCEPTOR-3546", "HUMHBA4-ACCEPTOR-10696", "HUMHBA4-ACCEPTOR-6885", "MACHBA-ACCEPTOR-238", "HUMTHY1A-ACCEPTOR-1380", "HUMAPOCII-ACCEPTOR-3131", "HUMCS1-ACCEPTOR-1814", "HUMGHCSA-ACCEPTOR-51551", "HUMMYLCA-ACCEPTOR-3623", "HUMMHEA-ACCEPTOR-2678", "HUMTNFA-ACCEPTOR-2140", "HUMTNFAB-ACCEPTOR-5619", "HUMTNFX-ACCEPTOR-1914", "HUMMHB27B-ACCEPTOR-2421", "HUMGHCSA-ACCEPTOR-28732", "HUMAPOCIA-ACCEPTOR-634", "HUMGH-ACCEPTOR-1572", "HUMGHCSA-ACCEPTOR-43393", "HUMGHCSA-ACCEPTOR-6465", "HUMGHN-ACCEPTOR-1797", "HUMGHVA-ACCEPTOR-741", "HUMATP1A2-ACCEPTOR-8990", "HUMTPA-ACCEPTOR-31395", "HUMKER18-ACCEPTOR-5495", "HUMTNFAB-ACCEPTOR-1237", "HUMMIS-ACCEPTOR-1500", "HUMGAST2-ACCEPTOR-916", "HUMTPA-ACCEPTOR-30776", "HUMHPARS1-ACCEPTOR-2519", "HUMGHCSA-ACCEPTOR-42976", "HUMAPOC2-ACCEPTOR-3143", "HUMAPOC2G-ACCEPTOR-2851", "HUMOTNPI-ACCEPTOR-808", "HUMUBILP-ACCEPTOR-1488", "HUMMYLCA-ACCEPTOR-2214", "HUMATP1A2-ACCEPTOR-6683", "HUMALPHA-ACCEPTOR-2023", "HUMADAG-ACCEPTOR-28878", "HUMGH-ACCEPTOR-1154", "HUMGHCSA-ACCEPTOR-6047", "HUMGHN-ACCEPTOR-1379", "HUMCS1-ACCEPTOR-1396", "HUMGHCSA-ACCEPTOR-28313", "HUMGHCSA-ACCEPTOR-51135", "HUMCP21OH-ACCEPTOR-3161", "HUMCP21OHC-ACCEPTOR-3163", "HUMMHCP42-ACCEPTOR-4089", "HUMMHCP52-ACCEPTOR-2628", "HUMGHCSA-ACCEPTOR-13046", "HUMACCYBA-ACCEPTOR-2893", "HUMACCYBB-ACCEPTOR-2827", "HUMDES-ACCEPTOR-3015", "HUMSAA-ACCEPTOR-2567", "HUMCYC1A-ACCEPTOR-2399", "HUMOTNPI-ACCEPTOR-1091", "HUMAPOAI1-ACCEPTOR-653", "HUMAPOAIT-ACCEPTOR-398", "HUMGHCSA-ACCEPTOR-13642", "HUMCP21OH-ACCEPTOR-1386", "HUMLYL1B-ACCEPTOR-3525", "HUMH19-ACCEPTOR-2449", "HUMATP1A2-ACCEPTOR-21798", "HUMALPPD-ACCEPTOR-1642", "HUMMHCP42-ACCEPTOR-2316", "HUMCS1-ACCEPTOR-800", "HUMGHCSA-ACCEPTOR-27717", "HUMGHCSA-ACCEPTOR-50539", "HUMMYC3L-ACCEPTOR-766", "HUMMHDRHA-ACCEPTOR-4276", "HUMRASH-ACCEPTOR-3201", "HUMSAACT-ACCEPTOR-1638", "HUMMHCD8A-ACCEPTOR-733", "HUMA1GLY2-ACCEPTOR-2108", "HUMPIM1A-ACCEPTOR-1687", "HUMKAL2-ACCEPTOR-3494", "HUMCSPB-ACCEPTOR-1874", "HUMA1GLY2-ACCEPTOR-3244", "HUMADAG-ACCEPTOR-34324", "HUMGHCSA-ACCEPTOR-42394", "HUMGHCSA-ACCEPTOR-14059", "HUMTKRA-ACCEPTOR-4815", "HUMPRCA-ACCEPTOR-5420", "HUMMETIF1-ACCEPTOR-1463", "HUMPPPA-ACCEPTOR-1721", "HUMG0S19A-ACCEPTOR-2812", "HUMMHCW2B-ACCEPTOR-885", "HUMPOMC-ACCEPTOR-4445", "HUMGH-ACCEPTOR-571", "HUMGHCSA-ACCEPTOR-5465", "HUMGHN-ACCEPTOR-798", "HUMTHB-ACCEPTOR-883", "HUMALPHA-ACCEPTOR-2851", "HUMALPPD-ACCEPTOR-2473", "HUMTUBBM-ACCEPTOR-1265", "MACHBA-ACCEPTOR-587", "HUMMYCRT-ACCEPTOR-1111", "HUMEDHB17-ACCEPTOR-2346", "HUMA1ATP-ACCEPTOR-11880", "ORAIGECA-ACCEPTOR-583", "HUMHBA4-ACCEPTOR-11050", "HUMANT1-ACCEPTOR-2955", "HUMKEREP-ACCEPTOR-4597", "HUMMHCD8A-ACCEPTOR-1670", "HUMMIS-ACCEPTOR-1697", "HUMPIM1A-ACCEPTOR-4496", "HUMAPOCIA-ACCEPTOR-1956", "HUMRPS14-ACCEPTOR-5473", "HUMSAACT-ACCEPTOR-3116", "HUMCP21OHC-ACCEPTOR-1387", "HUMPSAP-ACCEPTOR-1501", "HUMERP-ACCEPTOR-2578", "HUMERPA-ACCEPTOR-2587", "HUMATP1A2-ACCEPTOR-6969", "HUMPOMC-ACCEPTOR-7483", "HUMGCB1-ACCEPTOR-1590", "HUMMH6-ACCEPTOR-955", "HUMTHB-ACCEPTOR-7876", "HUMAPOE4-ACCEPTOR-3751", "GCRHBBA1-ACCEPTOR-1385", "HUMBNPA-ACCEPTOR-1629", "HUMACCYBA-ACCEPTOR-1384", "HUMACCYBB-ACCEPTOR-1318", "HUMGHCSA-ACCEPTOR-13504", "HUMAK1-ACCEPTOR-5504", "CHPIGECA-ACCEPTOR-585", "HUMADAG-ACCEPTOR-29793", "HUMMHB27B-ACCEPTOR-690", "HUMEDHB17-ACCEPTOR-3575", "HUMCP21OH-ACCEPTOR-2960", "HUMCP21OHC-ACCEPTOR-2962", "HUMMHCP42-ACCEPTOR-3889", "HUMTKRA-ACCEPTOR-2330", "HUMMYLCA-ACCEPTOR-2864", "HUMPRF1A-ACCEPTOR-4965", "HUMVPNP-ACCEPTOR-2057", "HUMH19-ACCEPTOR-2224", "HUMATP1A2-ACCEPTOR-13878", "HUMADAG-ACCEPTOR-19200", "HUMMH6-ACCEPTOR-2724", "HUMG0S19B-ACCEPTOR-2948", "HUMP45C17-ACCEPTOR-4146", "HUMPRF1A-ACCEPTOR-3240", "HUMMYCC-ACCEPTOR-4476", "HUMPRCA-ACCEPTOR-5180", "HUMCYC1A-ACCEPTOR-3478", "HUMMHEA-ACCEPTOR-869", "HUMATP1A2-ACCEPTOR-22870", "HUMMHEA-ACCEPTOR-2278", "HUMAPOA4A-ACCEPTOR-2286", "HUMP45C17-ACCEPTOR-7956", "HUMHPARS1-ACCEPTOR-2888", "HUMHPARS1-ACCEPTOR-4612", "HUMTUBAG-ACCEPTOR-2405", "HUMCAPG-ACCEPTOR-2733", "HUMG0S19A-ACCEPTOR-3347", "HUMHST-ACCEPTOR-4120", "HUMCP21OH-ACCEPTOR-2297", "HUMCP21OHC-ACCEPTOR-2298", "HUMMHCP42-ACCEPTOR-3226", "HUMMHCP52-ACCEPTOR-1763", "HUMKEREP-ACCEPTOR-3671", "HUMSHBGA-ACCEPTOR-3120", "HUMA1GLY2-ACCEPTOR-3502", "HUMINCP-ACCEPTOR-3281", "HUMG0S19B-ACCEPTOR-3484", "HUMMHCP52-ACCEPTOR-2427", "HUMCP21OH-ACCEPTOR-1648", "HUMCP21OHC-ACCEPTOR-1649", "HUMMHCP42-ACCEPTOR-2577", "HUMMHCP52-ACCEPTOR-1114", "BABAPOE-ACCEPTOR-3437", "HUMACTGA-ACCEPTOR-1621", "HUMAK1-DONOR-954", "HUMSPRO-ACCEPTOR-4385", "HUMMIS-ACCEPTOR-1185", "HUMCRYGBC-ACCEPTOR-2225", "HUMADAG-ACCEPTOR-32821", "HUMHBA4-ACCEPTOR-7232", "HUMP3A-ACCEPTOR-2544", "HUMAPOCII-ACCEPTOR-3595", "HUMEDHB17-ACCEPTOR-3311", "HUMADAG-ACCEPTOR-32395", "HUMHST-ACCEPTOR-3478", "HUMMRP14A-ACCEPTOR-3418", "HUMALPI-ACCEPTOR-646", "HUMMHCP52-ACCEPTOR-851", "HUMPSAA-ACCEPTOR-3726", "HUMKEREP-ACCEPTOR-2070", "HUMPRT1A-ACCEPTOR-209", "HUMOPS-ACCEPTOR-4065", "HUMMHANTLE-ACCEPTOR-1363", "HUMMHCW1B-ACCEPTOR-882", "HUMALPI-ACCEPTOR-2283", "GCRHBBA6-ACCEPTOR-590", "HUMTHB-ACCEPTOR-4549", "HUMHMG14A-ACCEPTOR-1361", "HUMATP1A2-ACCEPTOR-21144", "HUMAPOAI1-ACCEPTOR-1647", "HUMAPOAIT-ACCEPTOR-1394", "HUMDES-ACCEPTOR-1920", "HUMHPARS1-ACCEPTOR-6470", "TARHBB-ACCEPTOR-1686", "HUMSAACT-ACCEPTOR-2578", "LEMHBB-ACCEPTOR-1437", "HUMIL1AG-NEG-4981", "HUMTPA-ACCEPTOR-19737", "HUMAPOC2-ACCEPTOR-3599", "HUMAPOC2G-ACCEPTOR-3309", "BABAPOE-ACCEPTOR-801", "HUMAPOE4-ACCEPTOR-1818", "HUMCP21OH-ACCEPTOR-2041", "HUMCP21OHC-ACCEPTOR-2042", "HUMMHCP42-ACCEPTOR-2970", "HUMMHCP52-ACCEPTOR-1507", "HUMCSPB-ACCEPTOR-2217", "HUMAPOAI1-ACCEPTOR-902", "HUMAPOAIT-ACCEPTOR-648", "HUMKEREP-ACCEPTOR-3451", "HUMPSAP-ACCEPTOR-2927", "HUMALPHA-ACCEPTOR-2440", "HUMALPPD-ACCEPTOR-2062", "LEMHBB-ACCEPTOR-346", "HUMMHB27B-ACCEPTOR-2053", "HUMATP1A2-ACCEPTOR-20042", "HUMCAPG-ACCEPTOR-1733", "HUMGCB1-ACCEPTOR-6555", "HUMTNFAB-ACCEPTOR-1431", "HUMATP1A2-ACCEPTOR-20331", "HUMADAG-ACCEPTOR-35070", "HUMALPI-ACCEPTOR-2064", "HUMPPPA-ACCEPTOR-2162", "HUMA1ATP-ACCEPTOR-7282", "MNKHBD-ACCEPTOR-475", "HUMRPS17A-ACCEPTOR-463", "HUMCFVII-ACCEPTOR-10094", "HUMPLPSPC-ACCEPTOR-1326", "HUMMIS-DONOR-1857", "HUMTPA-ACCEPTOR-35307", "HUMCFVII-ACCEPTOR-8277", "HUMINCP-ACCEPTOR-2049", "HUMOPS-ACCEPTOR-5138", "HUMTUBBM-ACCEPTOR-1078", "BABA1AT-NEG-181", "HUMA1ATP-ACCEPTOR-9382", "HUMKAL2-ACCEPTOR-1745", "HUMFOS-ACCEPTOR-1753", "HUMMHCW2B-ACCEPTOR-2263", "HUMMETIF1-ACCEPTOR-1065", "HUMA1GLY2-ACCEPTOR-4746", "HUMPRCA-ACCEPTOR-5063", "HUMSPRO-ACCEPTOR-1827", "HUMH19-ACCEPTOR-2643", "HUMPSAP-ACCEPTOR-993", "HUMRPS17A-ACCEPTOR-3688", "HUMPSAA-ACCEPTOR-1929", "HUMGASTA-ACCEPTOR-6254", "HUMRASH-ACCEPTOR-2012", "HUMTKRA-ACCEPTOR-12537", "HUMSHBGA-ACCEPTOR-4319", "HUMTPA-ACCEPTOR-25978", "HUMACTGA-ACCEPTOR-886", "HUMCKMT-ACCEPTOR-1127", "HUMLYL1B-DONOR-2543", "HUMATP1A2-ACCEPTOR-22617", "HUMMHCD8A-DONOR-1087", "HUMTHB-ACCEPTOR-1703", "HUMTKRA-ACCEPTOR-11871", "ORAHBD-ACCEPTOR-596", "HUMCKMT-ACCEPTOR-4984", "HUMALPI-ACCEPTOR-2926", "HUMSHBGA-ACCEPTOR-5188", "HUMAPRTA-ACCEPTOR-774", "HUMADAG-ACCEPTOR-26314", "ATRINS-ACCEPTOR-701", "HUMMH6-ACCEPTOR-3284", "HUMATP1A2-ACCEPTOR-15000", "CHPIGECA-ACCEPTOR-989", "HUMSAA-ACCEPTOR-606", "HUMMHDRHA-ACCEPTOR-3705", "HUMACTGA-ACCEPTOR-1104", "HUMTNFA-ACCEPTOR-1558", "HUMTNFAB-ACCEPTOR-5037", "HUMTNFX-ACCEPTOR-1332", "TARHBB-ACCEPTOR-2748", "HUMUBILP-ACCEPTOR-806", "HUMCFVII-ACCEPTOR-1624", "HUMGPIBAA-ACCEPTOR-3033", "HUMPRCA-ACCEPTOR-10486", "TARHBD-ACCEPTOR-594", "HUMCFVII-ACCEPTOR-6353", "HUMRASH-ACCEPTOR-2344", "HUMPNMTA-ACCEPTOR-3129", "HUMALPPD-ACCEPTOR-421", "HUMTNFAB-ACCEPTOR-1784", "HUMPSAP-ACCEPTOR-2374", "HUMAPRTA-ACCEPTOR-2558", "HUMACTGA-ACCEPTOR-2414", "HUMPRCA-ACCEPTOR-8223", "ORAHBD-ACCEPTOR-1713", "HUMSPRO-ACCEPTOR-3195", "HUMKER18-ACCEPTOR-5107", "HUMALPI-ACCEPTOR-2698", "HUMAPOCIA-ACCEPTOR-4946", "HUMIL1B-ACCEPTOR-5693", "HUMTHB-ACCEPTOR-7024", "BABAPOE-ACCEPTOR-2624", "HUMSPRO-ACCEPTOR-2022", "HUMCFVII-ACCEPTOR-4264", "HUMMT2A-ACCEPTOR-1047", "HUMIRBPG-ACCEPTOR-6987", "HUMALPI-ACCEPTOR-1861", "HUMSAACT-ACCEPTOR-2337", "HUMAPRTA-ACCEPTOR-2252", "HUMAK1-ACCEPTOR-6626", "HUMTCBA-NEG-2161", "HUMSHBGA-DONOR-2724", "HUMCACY-ACCEPTOR-1973", "HUMMT2A-ACCEPTOR-1454", "HUMATP1A2-ACCEPTOR-21467", "HUMPNMTA-ACCEPTOR-3450", "ORAIGECA-ACCEPTOR-990", "HUMCKMT-ACCEPTOR-2362", "HUMGRP78-ACCEPTOR-775", "HUMCSPB-ACCEPTOR-3118", "HUMPPPA-ACCEPTOR-2425", "HUMCP21OH-ACCEPTOR-2698", "HUMCP21OHC-ACCEPTOR-2699", "HUMMHCP42-ACCEPTOR-3627", "HUMMHCP52-ACCEPTOR-2165", "HUMAPRTA-ACCEPTOR-1848", "HUMPRCA-ACCEPTOR-3434", "MNKHBD-ACCEPTOR-1583", "HUMAPOE4-ACCEPTOR-2977", "HUMALPHA-ACCEPTOR-2224", "HUMALPPD-ACCEPTOR-1845", "HUMTHB-ACCEPTOR-9160", "HUMHMG14A-ACCEPTOR-1599", "HUMPIM1A-ACCEPTOR-1335", "HUMTPA-ACCEPTOR-23573", "HUMMHCW1B-ACCEPTOR-3217", "HUMMHCW2B-ACCEPTOR-3219", "HUMACCYBA-ACCEPTOR-1121", "HUMACCYBB-ACCEPTOR-1057", "HUMLACTA-ACCEPTOR-2735", "HUMALPHA-ACCEPTOR-1789", "HUMSPRO-ACCEPTOR-2445", "HUMEDHB17-ACCEPTOR-2031", "HUMDES-ACCEPTOR-7550", "HUMSHBGA-ACCEPTOR-3640", "HUMALPHA-ACCEPTOR-809", "HUMPLAPL-ACCEPTOR-681", "HUMGCB1-ACCEPTOR-4081", "HUMGSHPXG-ACCEPTOR-968", "HUMSHBGA-ACCEPTOR-4029", "HUMALPPD-ACCEPTOR-1408", "HUMATP1A2-ACCEPTOR-12450", "HUMCKMT-ACCEPTOR-2563", "HUMP45C17-ACCEPTOR-7334", "HUMOPS-ACCEPTOR-3783", "HUMTHY1A-DONOR-34", "HUMKER18-ACCEPTOR-3708", "HUMALPI-ACCEPTOR-415", "HUMCP21OH-ACCEPTOR-1014", "HUMCP21OHC-ACCEPTOR-1015", "HUMMHCP42-ACCEPTOR-1945", "HUMMHCP52-ACCEPTOR-479", "HUMPLPSPC-ACCEPTOR-1830", "HUMTHB-ACCEPTOR-10541", "HUMSAACT-ACCEPTOR-2856", "HUMACCYBA-ACCEPTOR-2599", "HUMACCYBB-ACCEPTOR-2533", "HUMCSFGMA-ACCEPTOR-1620", "HUMMHEA-ACCEPTOR-3682", "CHPIGECA-ACCEPTOR-1392", "HUMERP-ACCEPTOR-1164", "HUMERPA-ACCEPTOR-1171", "HUMBNPA-ACCEPTOR-831", "HUMALPPD-ACCEPTOR-2719", "HUMMIS-ACCEPTOR-1947", "HUMGH-ACCEPTOR-986", "ORAIGECA-ACCEPTOR-1393", "HUMAPOC2-ACCEPTOR-2908", "HUMAPOC2G-ACCEPTOR-2616", "HUMAPOCII-ACCEPTOR-2897", "HUMSPRO-ACCEPTOR-3807", "HUMATP1A2-ACCEPTOR-10073", "HUMSP18A-NEG-361", "HUMALPI-ACCEPTOR-965", "HUMALPHA-ACCEPTOR-581", "HUMALPPD-ACCEPTOR-192", "HUMPLAPL-ACCEPTOR-452", "HUMCYPIIE-ACCEPTOR-3877", "HUMCFVII-ACCEPTOR-11034", "HUMMHB27B-ACCEPTOR-3119", "HUMATP1A2-ACCEPTOR-19381", "HUMTHB-ACCEPTOR-1970", "HUMMHDOB-ACCEPTOR-4706", "HUMATP1A2-ACCEPTOR-9720", "HUMCYPIIE-ACCEPTOR-9142", "HUMAPOA4A-ACCEPTOR-1382", "HUMGCB1-ACCEPTOR-5186", "HUMSAA1A-ACCEPTOR-3145", "HUMKEREP-ACCEPTOR-3206", "HUMMHB27B-ACCEPTOR-2980", "HUMTHB-ACCEPTOR-7663", "HUMKAL2-ACCEPTOR-3894", "HUMP3A-DONOR-1299", "HUMOPS-DONOR-2578", "HUMCYC1A-ACCEPTOR-3281", "HUMSAACT-ACCEPTOR-1885", "HUMFBPA-NEG-421", "HUMRPS14-ACCEPTOR-2872", "HUMANT1-ACCEPTOR-5005", "HUMATP1A2-ACCEPTOR-20736", "HUMALPI-ACCEPTOR-1631", "HUMANT1-ACCEPTOR-3950", "HUMACHRG4-NEG-1", "HUMALPHA-ACCEPTOR-1117", "HUMACCYBA-ACCEPTOR-2065", "HUMACCYBB-ACCEPTOR-1999", "HUMCYPIIE-ACCEPTOR-7515", "HUMIRBPG-ACCEPTOR-8736", "HUMMHCD8A-ACCEPTOR-1987", "HUMERP-ACCEPTOR-1566", "HUMERPA-ACCEPTOR-1575", "HUMAK1-ACCEPTOR-10045", "HUMLYL1B-ACCEPTOR-2630", "HUMALPI-ACCEPTOR-1216", "HUMPRCA-ACCEPTOR-9239", "HUMERP-ACCEPTOR-2264", "HUMERPA-ACCEPTOR-2273", "HUMH19-ACCEPTOR-2846", "HUMCFVII-ACCEPTOR-6448", "HUMMYCE1-NEG-421", "HUMAPRTA-DONOR-618", "HUMCHRA-NEG-121", "HUMGCB1-ACCEPTOR-6344", "HUMGCB1-ACCEPTOR-1905", "HUMCYPIIE-DONOR-7676", "HUMMHEA-ACCEPTOR-3545", "LEMHBDPS-NEG-1441", "HUMKER18-ACCEPTOR-4128", "HUMPSAA-ACCEPTOR-4156", "HUMINHBB2-NEG-301", "HUMSPRO-DONOR-4152", "HUMTKRA-ACCEPTOR-666", "HUMPLPSPC-ACCEPTOR-2176", "HUMPGAMMG-ACCEPTOR-1450", "HUMFVII-DONOR-70", "HUMHSP90B-ACCEPTOR-5391", "HUMALPHA-ACCEPTOR-1369", "HUMTKRA-ACCEPTOR-12318", "HUMALPHA-ACCEPTOR-3090", "HUMCRYGBC-ACCEPTOR-19693", "HUMOPS-ACCEPTOR-2409", "HUMTPA-DONOR-18038", "HUMAPOA4A-NEG-3241", "HUMCAPG-ACCEPTOR-2044", "HUMDAFA-ACCEPTOR-1235", "HUMARC6-NEG-61", "HUMCSPB-ACCEPTOR-1260", "HUMPGAMMG-ACCEPTOR-3519", "HUMUBILP-ACCEPTOR-1158", "HUMDES-ACCEPTOR-2164"), ValueGroup("HUMALBGC-ACCEPTOR-18496", "HUMMYC3L-ACCEPTOR-4242", "HUMHPRTB-ACCEPTOR-27861", "HUMTFPB-ACCEPTOR-6362", "HUMFIXG-ACCEPTOR-9613", "HUMHPRTB-ACCEPTOR-31587", "HUMAFP-ACCEPTOR-9611", "HUMCSFGMA-ACCEPTOR-891", "HUMIFNINI-ACCEPTOR-3147", "HUMIL5-ACCEPTOR-2088", "HUMIL5A-ACCEPTOR-2091", "HUMMYLCA-ACCEPTOR-1831", "HUMATPSYB-NEG-841", "HUMHMG14A-ACCEPTOR-4663", "HUMIL2-ACCEPTOR-5027", "HUMIL2A-ACCEPTOR-5980", "HUMIL2B-ACCEPTOR-4857", "HUMMYLCA-ACCEPTOR-2481", "HUMANFA-ACCEPTOR-785", "HUMATPSYB-ACCEPTOR-8745", "HUMHMG14A-ACCEPTOR-6789", "HUMHPRTB-ACCEPTOR-41424", "HUMFIXG-ACCEPTOR-23298", "HUMIL2-ACCEPTOR-685", "HUMIL2A-ACCEPTOR-1623", "HUMIL2B-ACCEPTOR-500", "HUMIL5-ACCEPTOR-1853", "HUMIL5A-ACCEPTOR-1857", "HUMAFP-ACCEPTOR-6914", "HUMP45C17-ACCEPTOR-5038", "HUMIFNINI-ACCEPTOR-3654", "HUMCYCAA-ACCEPTOR-1188", "HUMIL1B-ACCEPTOR-6540", "HUMFABP-ACCEPTOR-4068", "HUMTNFA-ACCEPTOR-1791", "HUMTNFAB-ACCEPTOR-5270", "HUMTNFX-ACCEPTOR-1565", "HUMGG-ACCEPTOR-3631", "HUMFIXG-ACCEPTOR-20604", "HUMAFP-ACCEPTOR-14589", "HUMIL5-ACCEPTOR-875", "HUMIL5A-ACCEPTOR-874", "HUMAFP-ACCEPTOR-19790", "HUMTUBAG-ACCEPTOR-2034", "HUMPRPH1-ACCEPTOR-4714", "HUMALBGC-ACCEPTOR-7729", "HUMPCNA-ACCEPTOR-5606", "HUMCYPIIE-ACCEPTOR-8083", "HUMTPA-ACCEPTOR-17940", "HUMFIXG-ACCEPTOR-9261", "HUMMHANTLE-ACCEPTOR-4055", "HUMAFP-ACCEPTOR-16955", "HUMCSFGMA-ACCEPTOR-2554", "HUMTHB-ACCEPTOR-20387", "HUMFABP-ACCEPTOR-2320", "HUMPRPH2-ACCEPTOR-3802", "HUMTFPB-ACCEPTOR-10045", "HUMPCNA-ACCEPTOR-3526", "HUMAFP-ACCEPTOR-7965", "HUMHPRTB-NEG-44641", "HUMALBGC-ACCEPTOR-13672", "HUMHSP90B-ACCEPTOR-5022", "HUMALBGC-ACCEPTOR-16911", "LEMHBGA-ACCEPTOR-425", "HUMRPS14-ACCEPTOR-2108", "HUMEF1A-ACCEPTOR-3641", "HUMHPARS1-ACCEPTOR-3750", "HUMPCNA-ACCEPTOR-2326", "HUMATPSYB-ACCEPTOR-4464", "HUMSHBGA-ACCEPTOR-2856", "HUMSODA-ACCEPTOR-7572", "HUMSAA1A-ACCEPTOR-5878", "HUMSODA-ACCEPTOR-6717", "HUMAFP-ACCEPTOR-18319", "HUMFABP-ACCEPTOR-3516", "LEMHBE-ACCEPTOR-1474", "HUMALBGC-ACCEPTOR-12451", "HUMSODA-ACCEPTOR-3391", "HUMGG-ACCEPTOR-5138", "HUMMHEA-ACCEPTOR-3890", "HUMMHCD8A-ACCEPTOR-2860", "HUMALBGC-ACCEPTOR-4046", "HUMFERP1-NEG-961", "HUMHPRTB-ACCEPTOR-16573", "HUMMHDRHA-ACCEPTOR-2969", "LEMHBG-ACCEPTOR-364", "HUMAFP-ACCEPTOR-12016", "HUMETN3-ACCEPTOR-391", "HUMMHDOB-ACCEPTOR-4486", "HUMFOS-ACCEPTOR-2658", "HUMTFPB-ACCEPTOR-11925", "HUMMCR-NEG-5341", "HUMKEREP-ACCEPTOR-3986", "HUMPRPH2-ACCEPTOR-1848", "HUMAFP-ACCEPTOR-20185", "HUMGRP78-ACCEPTOR-2145", "HUMGFP40H-ACCEPTOR-4066", "HUMDES-ACCEPTOR-5413", "HUMHIAPPA-ACCEPTOR-5862", "HUMSODA-ACCEPTOR-6266", "HUMALBGC-ACCEPTOR-14947", "HUMMH6-ACCEPTOR-2326", "HUMMH6-ACCEPTOR-3673", "HUMEF1A-ACCEPTOR-1522", "HUMRPS14-ACCEPTOR-4137", "HUMIFNINI-ACCEPTOR-2785", "HUMFOS-ACCEPTOR-2436", "HUMPCNA-ACCEPTOR-5816", "HUMSODA-ACCEPTOR-4173", "HUMHSP90B-ACCEPTOR-2605", "HUMADAG-ACCEPTOR-32543", "HUMADAM2-ACCEPTOR-784", "HUMMRP14A-ACCEPTOR-1386", "HUMPRPH1-ACCEPTOR-2738", "HUMGFP40H-ACCEPTOR-2431", "HUMTPA-ACCEPTOR-28261", "HUMHPRTB-ACCEPTOR-34907", "HUMMYCC-ACCEPTOR-6624", "HUMMYCRT-ACCEPTOR-3258", "HUMAFP-ACCEPTOR-13888", "HUMMHDOB-ACCEPTOR-3357", "HUMTUBAG-ACCEPTOR-2858", "HUMGCB1-ACCEPTOR-3009", "HUMMHDOB-ACCEPTOR-4124", "HUMFIXG-ACCEPTOR-33757", "HUMTFPB-ACCEPTOR-9259", "HUMAFP-ACCEPTOR-1782", "HUMALBGC-ACCEPTOR-6011", "HUMLACTA-ACCEPTOR-1512", "HUMATPSYB-ACCEPTOR-4093", "HUMTFPB-ACCEPTOR-2160", "HUMALBGC-ACCEPTOR-17658", "HUMALBGC-ACCEPTOR-2534", "HUMGCB1-ACCEPTOR-5810", "HUMIL1B-ACCEPTOR-1506", "HUMHPRTB-ACCEPTOR-39785", "HUMAFP-ACCEPTOR-2796", "LEMHBG-ACCEPTOR-1452", "HUMCFVII-ACCEPTOR-9389", "HUMALBGC-ACCEPTOR-6772", "HUMSODA-ACCEPTOR-5127", "ORAHBG2F-ACCEPTOR-1492", "HUMLACTA-ACCEPTOR-2160", "HUMCYCAA-ACCEPTOR-1466", "HUMHPRTB-ACCEPTOR-14750", "HUMEXCI5R-NEG-1321", "HUMMHANTLE-ACCEPTOR-2733", "HUMHPRTB-ACCEPTOR-40003", "HUMHPARS1-ACCEPTOR-5472", "MACHBCA2-ACCEPTOR-1693", "MACHBGA1-ACCEPTOR-1692", "HUMGFP40H-ACCEPTOR-992", "LEMHBGA-ACCEPTOR-1498", "GIBHBGGL-ACCEPTOR-8438", "HUMPIM1A-ACCEPTOR-3558", "HUMMHCW1B-ACCEPTOR-3569", "HUMMHCW2B-ACCEPTOR-3570", "HUMHBBAG-ACCEPTOR-2565", "HUMSODA-ACCEPTOR-3176", "HUMCRYGBC-ACCEPTOR-5283", "HUMCYPMPC-NEG-1", "HUMCP21OH-ACCEPTOR-1838", "HUMCP21OHC-ACCEPTOR-1839", "HUMMHCP42-ACCEPTOR-2767", "HUMMHCP52-ACCEPTOR-1304", "HUMATPSYB-ACCEPTOR-9633", "HUMRPS17A-ACCEPTOR-2225", "HUMMHCD8A-ACCEPTOR-5474", "HUMCKMT-ACCEPTOR-2011", "HUMHSP90B-ACCEPTOR-4265", "HUMTPA-ACCEPTOR-22075", "HUMATP1A2-ACCEPTOR-15320", "HUMGRP78-ACCEPTOR-2347", "HUMTPA-ACCEPTOR-32398", "HUMFIXG-ACCEPTOR-32974", "HUMIL1B-ACCEPTOR-879", "HUMHMG14A-ACCEPTOR-1487", "HUMKAL2-ACCEPTOR-5424", "HUMDES-ACCEPTOR-1763", "HUMALBGC-NEG-241", "HUMSODA-ACCEPTOR-6511", "HUMPSAA-ACCEPTOR-5669", "GIBHBGGL-ACCEPTOR-3506", "ORAHBG1F-ACCEPTOR-1486", "HUMHIAPPA-ACCEPTOR-959", "TARHBD-ACCEPTOR-1884", "HUMMHCW1B-ACCEPTOR-3357", "HUMMHCW2B-ACCEPTOR-3359", "HUMMHCW1B-ACCEPTOR-2261", "HUMATP1A2-ACCEPTOR-13550", "HUMMHDOB-ACCEPTOR-2649", "HUMSODA-ACCEPTOR-4869", "HUMCRPG-ACCEPTOR-578", "HUMCRPGA-ACCEPTOR-539", "HUMA1GLY2-ACCEPTOR-2469", "HUMANFA-ACCEPTOR-2205", "HUMALBGC-ACCEPTOR-10837", "HUMIL1B-ACCEPTOR-4293", "HUMKER18-ACCEPTOR-6098", "HUMALBGC-ACCEPTOR-9414", "HUMATPSYB-ACCEPTOR-5471", "HUMGRP78-ACCEPTOR-2848", "HUMGCB1-ACCEPTOR-3353", "HUMIL2-ACCEPTOR-3038", "HUMIL2A-ACCEPTOR-3975", "HUMIL2B-ACCEPTOR-2852", "HUMMRP8A-ACCEPTOR-2302", "HUMPCNA-ACCEPTOR-2520", "HUMGG-ACCEPTOR-1802", "ORAHBG2F-ACCEPTOR-392", "HUMHSP90B-ACCEPTOR-3351", "HUMAFP-ACCEPTOR-5216", "HUMTPA-ACCEPTOR-23130", "HUMHBBAG-ACCEPTOR-1466", "HUMHBBGG-ACCEPTOR-2363", "HUMCFXI14-NEG-481", "HUMTPA-ACCEPTOR-29440", "HUMGCB1-ACCEPTOR-949", "HUMLDL100-NEG-5941", "HUMCYPIIE-ACCEPTOR-12808", "HUMALBGC-ACCEPTOR-15504", "HUMTCBYX-NEG-1", "GIBHBGGL-ACCEPTOR-2401", "GIBHBGGL-ACCEPTOR-7321", "MACHBCA2-ACCEPTOR-586", "MACHBGA1-ACCEPTOR-588", "HUMCRYGBC-ACCEPTOR-18489", "HUMEF1A-ACCEPTOR-2347", "HUMSODA-ACCEPTOR-3793", "HUMATPSYB-ACCEPTOR-2698", "HUMSPRO-ACCEPTOR-2841", "HUMATP1A2-ACCEPTOR-10913", "HUMCYPIIE-ACCEPTOR-13834", "HUMKER18-ACCEPTOR-4857", "HUMCYPIIE-ACCEPTOR-12121", "HUMCYPIIE-NEG-12121", "HUMDES-ACCEPTOR-2504", "HUMHSP90B-ACCEPTOR-7462", "HUMATP1A2-ACCEPTOR-24266", "HUMMHANTLE-ACCEPTOR-3667", "HUMMHANTLE-ACCEPTOR-3842", "LEMHBE-ACCEPTOR-410", "HUMCYPIIE-ACCEPTOR-6976", "HUMPRPH1-ACCEPTOR-3136", "HUMPRPH2-ACCEPTOR-2241", "HUMAFP-ACCEPTOR-15169", "HUMGRP78-ACCEPTOR-4506", "HUMBLYM1-ACCEPTOR-505", "HUMFIXG-ACCEPTOR-13327", "HUMPRL1-NEG-61", "HUMALPPD-ACCEPTOR-734", "HUMSAA-ACCEPTOR-3086", "HUMHSP90B-ACCEPTOR-5710", "ORAHBG1F-ACCEPTOR-391", "HUMTHB-ACCEPTOR-9825", "HUMGRP78-ACCEPTOR-3371", "HUMEF1A-ACCEPTOR-2727", "HUMA1ATP-ACCEPTOR-10909", "HUMCACY-ACCEPTOR-2504", "HUMSODA-ACCEPTOR-4604", "HUMCKMT-ACCEPTOR-4348", "HUMATPSYB-ACCEPTOR-7858", "HUMGHN-ACCEPTOR-1166", "HUMMHDRHA-ACCEPTOR-5181", "HUMATPSYB-ACCEPTOR-5220", "HUMSAA1A-ACCEPTOR-6290", "HUMEF1A-ACCEPTOR-2062", "HUMCS1-ACCEPTOR-1184", "HUMGHCSA-ACCEPTOR-28101", "HUMGHCSA-ACCEPTOR-50922", "HUMTPA-ACCEPTOR-31083", "HUMPLAPL-ACCEPTOR-987", "HUMINCPPS-ACCEPTOR-4028", "HUMCATF-ACCEPTOR-1108", "HUMGHCSA-ACCEPTOR-42765", "HUMIRBPG-ACCEPTOR-4936", "HUMFBRAA-NEG-1", "HUMGH-ACCEPTOR-941", "HUMGHCSA-ACCEPTOR-5835", "HUMPLPSPC-ACCEPTOR-2621", "HUMCKMT-ACCEPTOR-1529", "HUMHSP90B-ACCEPTOR-3649", "HUMP45C17-ACCEPTOR-6267", "HUMTUBBM-ACCEPTOR-1850", "HUMSHBGA-ACCEPTOR-5640", "HUMEF1A-ACCEPTOR-2965", "HUMRPS17A-ACCEPTOR-1611", "HUMP45C17-ACCEPTOR-5819", "HUMHSP90B-ACCEPTOR-5972", "HUMTHB-ACCEPTOR-20170", "HUMKEREP-ACCEPTOR-2715", "HUMP45C17-ACCEPTOR-3774", "HUMCYPIIE-DONOR-12309", "HUMINSRC-NEG-2101", "HUMIL1B-DONOR-3748", "HUMALPPD-ACCEPTOR-985", "HUMGFP40H-ACCEPTOR-1110", "HUMADAG-ACCEPTOR-35621"), ValueGroup("ATRINS-DONOR-905", "HUMERP-DONOR-598", "HUMERPA-DONOR-608", "HUMKEREP-DONOR-3892", "HUMSAACT-DONOR-2210", "HUMOTNPI-DONOR-508", "HUMTUBBM-DONOR-1187", "HUMMYLCA-DONOR-952", "HUMMYLCA-DONOR-1975", "HUMCFVII-DONOR-4425", "HUMH19-DONOR-2128", "HUMHMG14A-DONOR-1039", "HUMSAACT-DONOR-2770", "HUMHBA4-DONOR-10579", "HUMHBA4-DONOR-6768", "MACHBA-DONOR-121", "HUMRPS17A-DONOR-615", "GCRHBBA1-DONOR-1260", "BABAPOE-DONOR-2817", "HUMMHCW2B-DONOR-1155", "HUMP45C17-DONOR-2106", "HUMACTGA-DONOR-1344", "HUMMHANTLE-DONOR-1633", "HUMACCYBA-DONOR-1624", "HUMACCYBB-DONOR-1558", "HUMDES-DONOR-2630", "HUMALPPD-DONOR-2590", "HUMMHB27B-DONOR-960", "HUMPIM1A-DONOR-1222", "HUMMHCW1B-DONOR-1152", "HUMCSPB-DONOR-258", "HUMCYC1A-DONOR-1506", "HUMCYPIIE-DONOR-4037", "HUMAPOE4-DONOR-3170", "HUMBNPA-DONOR-1087", "HUMATP1A2-DONOR-21929", "HUMHPRTB-DONOR-1674", "HUMP45C17-DONOR-7438", "HUMTUBAG-DONOR-506", "HUMPCNA-DONOR-1618", "GCRHBBA6-DONOR-461", "HUMPRCA-DONOR-2171", "HUMDES-DONOR-711", "HUMUBILP-DONOR-603", "HUMRASH-DONOR-2504", "HUMAPOAI1-DONOR-457", "HUMACTGA-DONOR-2321", "HUMTNFA-DONOR-952", "HUMTNFAB-DONOR-4431", "HUMTNFX-DONOR-726", "HUMAPOAIT-DONOR-201", "HUMA1GLY2-DONOR-1693", "HUMERP-DONOR-1310", "HUMERPA-DONOR-1317", "HUMCACY-DONOR-2132", "HUMACTGA-DONOR-513", "HUMPIM1A-DONOR-1594", "HUMADAG-DONOR-29909", "HUMADAG-DONOR-32467", "HUMADAM2-DONOR-708", "HUMMHEA-DONOR-1139", "HUMALPHA-DONOR-698", "HUMCAPG-DONOR-375", "HUMFOS-DONOR-1000", "HUMP45C17-DONOR-3913", "HUMA1GLY2-DONOR-2251", "HUMINCP-DONOR-539", "HUMKEREP-DONOR-4033", "HUMEDHB17-DONOR-2795", "HUMGCB1-DONOR-4319", "HUMCS1-DONOR-1304", "HUMGHCSA-DONOR-51042", "HUMKER18-DONOR-5233", "HUMACCYBA-DONOR-289", "HUMACCYBB-DONOR-290", "HUMAFP-DONOR-15308", "HUMGHCSA-DONOR-5955", "HUMGHN-DONOR-1286", "HUMPRCA-DONOR-5088", "HUMPSAA-DONOR-4293", "HUMCFVII-DONOR-10218", "HUMTPA-DONOR-26070", "HUMDES-DONOR-7366", "HUMGHCSA-DONOR-6212", "HUMGHN-DONOR-1544", "HUMPLAPL-DONOR-569", "HUMTKRA-DONOR-556", "HUMRPS14-DONOR-4214", "HUMTFPB-DONOR-10205", "HUMDES-DONOR-1824", "HUMCSFGMA-DONOR-1746", "HUMTFPB-DONOR-992", "HUMTPA-DONOR-29526", "HUMHPARS1-DONOR-1016", "HUMPOMC-DONOR-737", "HUMPPPA-DONOR-2234", "HUMATP1A2-DONOR-19518", "HUMGHCSA-DONOR-51300", "HUMGCB1-DONOR-5411", "HUMTPA-DONOR-31536", "HUMGHCSA-DONOR-28221", "HUMCP21OH-DONOR-917", "HUMCP21OHC-DONOR-918", "HUMMHCP42-DONOR-1848", "HUMMHCP52-DONOR-382", "HUMGH-DONOR-1061", "HUMALPPD-DONOR-309", "HUMCFVII-DONOR-6562", "HUMMYC3L-DONOR-1271", "HUMMHCP52-DONOR-1594", "HUMACCYBA-DONOR-2781", "HUMACCYBB-DONOR-2715", "HUMKER18-DONOR-5719", "HUMATP1A2-DONOR-22962", "HUMMH6-DONOR-1225", "HUMUBILP-DONOR-1367", "CHPIGECA-DONOR-903", "ORAIGECA-DONOR-904", "HUMHST-DONOR-3582", "HUMGHCSA-DONOR-13807", "HUMTKRA-DONOR-2441", "HUMPSAP-DONOR-170", "HUMCRYGBC-DONOR-2468", "HUMGHCSA-DONOR-28478", "HUMAPRTA-DONOR-2331", "HUMCAPG-DONOR-2299", "HUMLPL-NEG-181", "HUMEF1A-DONOR-579", "HUMCP21OH-DONOR-1541", "HUMCP21OHC-DONOR-1542", "HUMMHCP42-DONOR-2471", "HUMMHCP52-DONOR-1006", "HUMCP21OH-DONOR-1750", "HUMCP21OHC-DONOR-1751", "HUMMHCP42-DONOR-2679", "HUMMHCP52-DONOR-1216", "HUMATP1A2-DONOR-15496", "HUMALPPD-DONOR-1715", "HUMERP-DONOR-2444", "HUMERPA-DONOR-2453", "HUMAK1-DONOR-3959", "HUMCP21OH-DONOR-2128", "HUMMHCP42-DONOR-3057", "HUMTACEC-NEG-2341", "HUMA1ATP-DONOR-11057", "HUMATP1A2-DONOR-1663", "HUMH19-DONOR-2562", "HUMH19-DONOR-2359", "HUMKAL2-DONOR-4031", "HUMALPI-DONOR-532", "HUMPSAP-DONOR-2452", "HUMGHCSA-DONOR-42885", "HUMTKRA-DONOR-12438", "HUMA1GLY2-DONOR-2540", "HUMMHCD8A-DONOR-2891", "HUMA1ATP-DONOR-1972", "HUMCP21OHC-DONOR-2129", "HUMNGFR-NEG-2821", "HUMCS1-DONOR-1561", "HUMH19-DONOR-2766", "HUMADAG-DONOR-32886", "HUMGHVA-DONOR-488", "HUMG0S19A-DONOR-2927", "HUMG0S19B-DONOR-3063", "HUMCACY-DONOR-1386", "HUMGHCSA-DONOR-13551", "HUMACCYBA-DONOR-2504", "HUMACCYBB-DONOR-2438", "HUMCYC1A-DONOR-3019", "HUMTHB-DONOR-4453", "HUMALPHA-DONOR-2096", "HUMCSPB-DONOR-2010", "HUMPPPA-DONOR-965", "HUMALPI-DONOR-762", "HUMHPARS1-DONOR-2990", "HUMHPARS1-DONOR-4714", "HUMTKRA-DONOR-4909", "HUMTNFA-DONOR-1604", "HUMTNFAB-DONOR-5083", "HUMTNFX-DONOR-1378", "HUMCSPB-DONOR-1408", "HUMGAST2-DONOR-786", "HUMGASTA-DONOR-6470", "HUMGCB1-DONOR-1782", "HUMNMCY1A-NEG-661", "HUMATP1A2-DONOR-9194", "HUMFOS-DONOR-2005", "HUMCYPIIE-DONOR-7126", "HUMSAACT-DONOR-1779", "HUMMYCC-DONOR-5248", "HUMCFVII-DONOR-9499", "HUMALPI-DONOR-1934", "HUMTPA-DONOR-23748", "HUMLACTA-DONOR-1671", "HUMGHCSA-DONOR-43141", "HUMMIS-DONOR-593", "HUMCRYGBC-DONOR-2131", "HUMRASH-DONOR-1745", "HUMEDHB17-DONOR-2199", "HUMALPHA-DONOR-925", "HUMPLAPL-DONOR-797", "HUMATPSYB-DONOR-2217", "HUMP45C17-DONOR-4376", "HUMMETIF1-DONOR-480", "HUMALPPD-DONOR-100", "HUMPLAPL-DONOR-373", "HUMTPA-DONOR-31220", "HUMHMG14A-DONOR-1517", "HUMTNFAB-DONOR-950", "HUMPOMC-DONOR-4597", "HUMCFVII-DONOR-1690", "HUMPRCA-DONOR-5555", "HUMCYPIIE-DONOR-8260", "HUMMT2A-DONOR-421", "HUMINCP-DONOR-2163", "HUMGCB1-DONOR-5974", "HUMADAG-DONOR-26437", "HUMAPOA4A-DONOR-1025", "HUMALPHA-DONOR-487", "ORAHBG2F-DONOR-615", "HUMCAPG-DONOR-1279", "HUMGH-DONOR-1319", "HUMMT2A-DONOR-1113", "HUMATP1A2-DONOR-21613", "HUMALPPD-DONOR-537", "HUMRPS17A-DONOR-171", "HUMGHCSA-DONOR-12784", "HUMGHCSA-DONOR-42122", "HUMMYCRT-DONOR-1883", "HUMMHCD8A-DONOR-638", "HUMP45C17-DONOR-6437", "HUMALPI-DONOR-1389", "HUMTHB-DONOR-8003", "HUMAK1-DONOR-6743", "HUMMRP14A-DONOR-999", "HUMADAG-DONOR-19262", "HUMPLPSPC-DONOR-1953", "HUMMETIF1-DONOR-1131", "HUMGHCSA-DONOR-5205", "HUMCS1-DONOR-535", "HUMGHCSA-DONOR-27451", "HUMALPPD-DONOR-1158", "HUMMHEA-DONOR-1657", "HUMCFVII-DONOR-6378", "HUMA1ATP-DONOR-9653", "HUMGHCSA-DONOR-50275", "HUMGSA1R-NEG-661", "HUMMHDOB-DONOR-4235", "HUMGH-DONOR-315", "HUMIRBPG-DONOR-3151", "HUMCP21OH-DONOR-1940", "HUMCP21OHC-DONOR-1941", "HUMMHCP42-DONOR-2869", "HUMMHCP52-DONOR-1406", "HUMALPI-DONOR-333", "HUMCKMT-DONOR-1326", "HUMCRYGBC-DONOR-18732", "HUMGHN-DONOR-539", "HUMSHBGA-DONOR-3802", "HUMCRPG-DONOR-300", "HUMCRPGA-DONOR-261", "HUMP45C17-DONOR-6035", "HUMMHDRHA-DONOR-3215", "HUMVPNP-DONOR-315", "HUMHSP27P-NEG-601", "HUMPGAMMG-DONOR-1347", "HUMAPOAIT-DONOR-461", "ATRINS-DONOR-521", "HUMSHBGA-DONOR-2948", "HUMTHY1A-DONOR-853", "HUMG0S19A-DONOR-2124", "HUMIRBPG-DONOR-7130", "HUMG0S19B-DONOR-2263", "HUMTPA-DONOR-30972", "HUMAPOAI1-DONOR-716", "HUMPSAP-DONOR-1188", "HUMATP1A2-DONOR-11031", "HUMADAG-DONOR-35173", "HUMSPBAA-NEG-3061", "HUMCFVII-DONOR-556", "HUMCFVII-DONOR-8418", "HUMALDOA-NEG-4501", "HUMCAPG-DONOR-1869", "HUMAICEB-NEG-2641", "HUMAPOCIA-DONOR-2092", "HUMACTGA-ACCEPTOR-2139"), ValueGroup("HUMMYLCA-DONOR-644", "HUMALPI1-DONOR-42", "HUMMHCD8A-DONOR-1781", "HUMVPNP-DONOR-1890", "HUMPSAP-DONOR-1621", "HUMHST-DONOR-2861", "HUMFGFB-NEG-181", "HUMPRCA-DONOR-5318", "HUMAPRTA-DONOR-1982", "HUMCSF1G1-NEG-361", "HUMDES-DONOR-3236", "HUMALPHA-DONOR-2968", "HUMGRP78-DONOR-685", "HUMADAG-DONOR-4034", "HUMTUBBM-DONOR-775", "HUMMYC3L-DONOR-402", "HUMATP1A2-DONOR-20891", "CHPIGECA-DONOR-1313", "HUMHMG14A-DONOR-1394", "HUMMHB27B-DONOR-2538", "HUMMHB27B-DONOR-3013", "HUMMHEA-DONOR-3578", "HUMMHCW1B-DONOR-2778", "HUMMHCW2B-DONOR-2780", "ORAIGECA-DONOR-1314", "HUMPIM1A-DONOR-1442", "HUMGSHPXG-DONOR-690", "HUMEDHB17-DONOR-3489", "HUMMHB27B-DONOR-561", "HUMMIS-DONOR-1609", "LEMHBGA-DONOR-302", "HUMPRT1A-DONOR-118", "HUMMHCW1B-DONOR-3250", "HUMMHCW1B-DONOR-754", "HUMMHCW2B-DONOR-756", "HUMPNMTA-DONOR-3337", "HUMPSAA-DONOR-2089", "HUMANDREC-NEG-1681", "HUMMH6-DONOR-826", "HUMANT1-DONOR-3442", "HUMOTNPI-DONOR-1007", "HUMMRP8A-DONOR-2152", "HUMMHCW1B-DONOR-2537", "HUMATP1A2-DONOR-6788", "LEMHBG-DONOR-241", "HUMPRF1A-DONOR-3783", "HUMMHANTLE-DONOR-3227", "HUMAK1-DONOR-5876", "HUMALPPD-DONOR-1543", "HUMACTGA-DONOR-1015", "HUMMH6-DONOR-2841", "HUMSAACT-DONOR-769", "HUMMHCW2B-DONOR-3252", "BABAPOE-DONOR-30", "HUMAPOE4-DONOR-1061", "GCRHBBA1-DONOR-1590", "HUMALPI-DONOR-2815", "HUMPLPSPC-DONOR-1485", "HUMSAACT-DONOR-3038", "HUMMHEA-DONOR-2795", "HUMSODA-DONOR-327", "HUMADAG-DONOR-32645", "HUMALPI-DONOR-1766", "MACHBA-DONOR-443", "HUMHBA4-DONOR-10901", "HUMHBA4-DONOR-7090", "HUMMHEA-DONOR-739", "HUMAPOC2-DONOR-522", "HUMAPOC2G-DONOR-225", "HUMAPOCII-DONOR-541", "HUMKAL2-DONOR-539", "HUMMHCW2B-DONOR-2539", "HUMTHB-DONOR-7792", "HUMALPI-DONOR-1140", "HUMALPHA-DONOR-2359", "HUMMHDOB-DONOR-2919", "HUMRPS14-DONOR-225", "HUMPNMTA-DONOR-2130", "HUMCP21OH-DONOR-3064", "HUMCP21OHC-DONOR-3066", "HUMMHCP52-DONOR-2531", "HUMMHANTLE-DONOR-3889", "HUMTHB-DONOR-1044", "HUMOPS-DONOR-3949", "HUMCYCAA-DONOR-115", "HUMTUBBM-DONOR-1376", "GIBHBGGL-DONOR-2278", "HUMHBBAG-DONOR-1344", "HUMHBBGG-DONOR-2241", "ORAHBG2F-DONOR-269", "HUMOPS-DONOR-626", "HUMTNFAB-DONOR-1345", "HUMATP1A2-DONOR-20500", "HUMAPOAI1-DONOR-1059", "HUMAPOAIT-DONOR-805", "HUMALPHA-DONOR-1924", "GIBHBGGL-DONOR-7198", "HUMANT1-DONOR-1688", "HUMPSAA-DONOR-691", "HUMACCYBA-DONOR-1250", "HUMACCYBB-DONOR-1186", "GCRHBBA6-DONOR-795", "ORAHBG1F-DONOR-269", "HUMGPIBAA-DONOR-2800", "HUMBNPA-DONOR-600", "HUMKER18-DONOR-5022", "HUMALPPD-DONOR-1980", "HUMALPPD-DONOR-909", "HUMEDHB17-DONOR-2526", "HUMPRF1A-DONOR-1477", "HUMSNU1P2A-NEG-301", "HUMPIM1A-DONOR-2054", "HUMKAL2-DONOR-3781", "HUMATP1A2-DONOR-12719", "HUMTPA-DONOR-28433", "HUMRPS14-DONOR-3034", "HUMCP21OH-DONOR-2877", "HUMMHCP42-DONOR-3806", "HUMMHCP52-DONOR-2344", "HUMPSAA-DONOR-4013", "HUMTHB-DONOR-7339", "HUMKER18-DONOR-3791", "HUMANFA-DONOR-1112", "MACHBCA2-DONOR-463", "MACHBGA1-DONOR-465", "HUMTHB-DONOR-4686", "HUMGCB1-DONOR-6461", "HUMUBILP-DONOR-912", "HUMCS1-DONOR-961", "HUMGHCSA-DONOR-27878", "HUMGHCSA-DONOR-50700", "HUMALPI-DONOR-2199", "HUMALPHA-DONOR-1292", "HUMMHEA-DONOR-2554", "HUMCKMT-DONOR-523", "HUMSAA-DONOR-2706", "HUMHMG14A-DONOR-4792", "HUMMHANTLE-DONOR-3008", "HUMGCB1-DONOR-1037", "HUMKAL2-DONOR-1905", "HUMSAA1A-DONOR-3240", "HUMTHB-DONOR-497", "HUMDES-DONOR-5457", "HUMSHBGA-DONOR-3310", "HUMSPRO-DONOR-1947", "HUMMHEA-DONOR-3725", "HUMMH6-DONOR-2602", "HUMFOLA1-NEG-541", "HUMGASTA-DONOR-3213", "HUMMHDRHA-DONOR-4442", "HUMLYL1B-DONOR-882", "HUMLYL1B-DONOR-2722", "HUMMHCP42-DONOR-3993", "HUMATP1A2-DONOR-13749", "HUMADAG-DONOR-29022", "HUMCP21OHC-DONOR-2878", "HUMAK1-DONOR-10237", "HUMAPOC2-DONOR-2976", "HUMAPOC2G-DONOR-2684", "HUMAPOCII-DONOR-2965", "HUMATP1A2-DONOR-15190", "HUMMHB27B-DONOR-2329", "HUMAPOCIA-DONOR-712", "HUMALPHA-DONOR-2632", "HUMALPPD-DONOR-2254", "HUMATP1A2-DONOR-7029", "HUMTPA-DONOR-3683", "HUMPLPSPC-DONOR-628", "HUMCYC1A-DONOR-2526", "HUMTKRA-DONOR-11961", "HUMALPI-DONOR-2475", "HUMDES-DONOR-2326", "HUMPGAMMG-DONOR-1631", "HUMATP1A2-DONOR-9834", "HUMSPRO-DONOR-2998", "HUMMIS-DONOR-1328", "HUMMYCRT-DONOR-888", "HUMATP1A2-DONOR-20193", "HUMALPHA-DONOR-1542", "HUMMHANTLE-DONOR-1239", "HUMCSFGMA-DONOR-793", "HUMMHCW1B-NEG-241", "HUMTKRA-DONOR-698", "HUMCYC1A-DONOR-2305", "HUMTPA-DONOR-22213", "HUMGH-DONOR-732", "HUMGHCSA-DONOR-5626", "HUMGHN-DONOR-959", "HUMHMG14A-DONOR-1647", "HUMPRCA-DONOR-8366", "HUMCKMT-DONOR-2233", "HUMTHB-DONOR-2021", "HUMKER18-DONOR-4285", "HUMKEREP-DONOR-3577", "HUMADAG-DONOR-34454", "HUMMHCD8A-DONOR-2098", "HUMGHCSA-DONOR-13226", "HUMCYC1A-DONOR-3382", "HUMTHB-DONOR-9999", "HUMAK1-DONOR-5540", "HUMGFP40H-DONOR-880", "HUMATP1A2-DONOR-22719", "HUMMRP8A-DONOR-1504", "HUMSAACT-DONOR-2499", "HUMGCB1-DONOR-3526", "HUMCKMT-DONOR-4483", "HUMTNFX-NEG-1681", "HUMANFA-DONOR-663", "HUMTPA-DONOR-19780", "HUMATP1A2-DONOR-21268", "HUMPRPH2-DONOR-917", "HUMAPOA4A-DONOR-1509", "HUMTPA-DONOR-32565", "HUMATPSYB-DONOR-5379", "HUMADAG-DONOR-31274", "HUMTHB-ACCEPTOR-4347", "HUMMHDRHA-DONOR-565", "HUMAK1-ACCEPTOR-5712", "HUMAPOE4-DONOR-1884", "ATRINS-NEG-841", "HUMSPRO-DONOR-2585", "HUMTHB-DONOR-10723", "HUMSODA-DONOR-4951", "HUMPRCA-DONOR-9357", "HUMMHCW1B-DONOR-3405", "HUMMHCW2B-DONOR-3407", "HUMEDHB17-DONOR-1938", "HUMOPS-DONOR-4305", "HUMTPA-DONOR-23241", "HUMTHB-DONOR-1728", "HUMAPRTA-DONOR-881", "HUMCYC1A-DONOR-2769", "HUMTHB-DONOR-9328", "CHPIGECA-DONOR-378", "HUMPLPSPC-DONOR-2287", "HUMKEREP-DONOR-814", "HUMGHCSA-DONOR-42555", "HUMMH6-DONOR-3506", "HUMFIL-NEG-841", "HUMPGAMMG-NEG-2221", "HUMIRBPG-DONOR-5127", "HUMSPRO-DONOR-1735", "HUMKER18-DONOR-2967", "ORAIGECA-DONOR-378", "BABAPOE-DONOR-867", "HUMGCB1-DONOR-2052", "HUMCETP6-NEG-481", "HUMSODA-DONOR-3967", "HUMKEREP-DONOR-2153", "HUMCYPIIE-DONOR-9284", "HUMHPARS1-DONOR-2602", "HUMTHB-DONOR-20241", "HUMSPRO-DONOR-2367", "TARHBB-DONOR-1560", "TARHBD-DONOR-468", "HUMCR1R-NEG-2581", "HUMPRCA-NEG-8881", "HUMHPARS1-DONOR-5547", "HUMRASH-DONOR-2191", "HUMPIM1A-DONOR-3735", "LEMHBB-DONOR-216", "HUMAPOCIA-DONOR-496", "HUMPGK1-NEG-181", "HUMRPS14-DONOR-2259", "HUMHLADPB-NEG-8761", "HUMCKMT-DONOR-2687", "HUMSPRO-DONOR-3348", "HUMPIM1A-ACCEPTOR-1543", "HUMSHBGA-DONOR-4456", "HUMATP1A2-DONOR-10208", "HUMMHSXA-NEG-1", "HUMG6PA-NEG-781", "HUMCP21OH-DONOR-2498", "HUMCP21OHC-DONOR-2499", "HUMMHCP42-DONOR-3427", "HUMMHCP52-DONOR-1964", "HUMATP1A2-DONOR-14299", "HUMAPOC2-DONOR-3303", "HUMAPOC2G-DONOR-3011", "HUMAPOCII-DONOR-3291", "HUMA1MICR-NEG-1081", "HUMPPPA-DONOR-1912", "HUMHSP90B-ACCEPTOR-3995", "HUMCAPG-ACCEPTOR-1131", "HUMIL1B-DONOR-941", "HUMADRBRA-NEG-2941"), ValueGroup("HUMFGFG1-NEG-901", "HUMCOLTHA-NEG-481", "HUMGLTH1-NEG-241", "HUMKRASP-NEG-541", "HUMIGF2AP-NEG-421", "HUMPLP1-NEG-301", "HUMRASH-NEG-601", "HUMINT2-NEG-481", "HUMPLG2B-NEG-181", "HUMRASSK2-NEG-1381", "HUMADRA-NEG-961", "ORARGIT-NEG-241", "HUMPDGA1-NEG-481", "HUMSISM-NEG-241", "HUMMUC18B-NEG-61", "HUMNAKATP1-NEG-721", "HUMSRTR2A-NEG-61", "HUMALDAR-NEG-1081", "HUMEDHB17-NEG-3541", "HUMMHAW-NEG-1201", "HUMARSBX-NEG-421", "HUMVWFM-NEG-5101", "HUMCYPB1-NEG-1141", "HUMRYR-NEG-1081", "HUMHBA4-NEG-6601", "HUMUPAA-NEG-1501", "HUMHBA3-NEG-1021", "HUMMG2-NEG-541", "HUMFESFUR-NEG-3481", "HUMIGHAB-NEG-121", "HUMTHYR01-NEG-181", "HUMGSHPXG-NEG-1081", "HUMIFNB3-NEG-5641", "HUMRENT1-NEG-601", "HUMPTCAA-NEG-481", "HUMA2PIG3-NEG-541", "HUMINHA-NEG-1", "HUMAPOBG-NEG-121", "HUMNARIA-NEG-3841", "ORAHBA01-NEG-121", "HUMSISG1-NEG-5401", "HUMCD1A-NEG-1261", "HUMCOL4A2-NEG-2581", "HUMGIR-NEG-121", "HUMPLPSPC-NEG-1801", "HUMTSG14A-NEG-301", "HUMOAT01-NEG-1", "HUMGENX-NEG-2341", "CHPRGITX-NEG-1441", "HUMCG2A1-NEG-1", "HUMHKR42-NEG-181", "HUMKERMII-NEG-181", "HUMCOL4A6-NEG-301", "HUMALLFU-NEG-61", "HUMMHCBWC-NEG-121", "HUMETS2PR-NEG-901", "HUMA1ATP-NEG-301", "HUMSB3B2-NEG-121", "HUMADRA2R-NEG-2581", "HUMEF2AB-NEG-721", "HUMNMYC-NEG-1561", "HUMEL01-NEG-901", "HUMMT2A-NEG-121", "HUMMYHC08-NEG-421", "HUMATP1A2-NEG-2101", "HUMTIR-NEG-901", "HUMMHB27B-NEG-1201", "HUMTPA-NEG-31561", "HUMIGMUTDM-NEG-361", "HUMMHCAR4-NEG-241", "HUMU170SC-NEG-481", "HUMCKBBA-NEG-481", "HUMBCL3AA-NEG-1381", "HUMCOL2A1-NEG-961", "MACAPOE-NEG-181", "HUMMHBW4-NEG-61", "HUMTRK2H-NEG-1081", "HUMERSP2B-NEG-781", "HUMICAM1-NEG-481", "HUMCINHP-NEG-121", "HUMAPOA4-NEG-61", "HUMRGLTIB-NEG-721", "HUMEGFAA-NEG-421", "HUMTFP-NEG-61", "HUMCSFGM-NEG-1", "HUMMHCP52-NEG-781", "HUMPAC-NEG-541", "HUMBCR-NEG-1801", "HUMCFXII3-NEG-3061", "HUMGP-NEG-1321", "HUMAK1-NEG-10921", "HUMMHDPBF-NEG-61", "HUMINSR2-NEG-1", "HUMCOL11A2-NEG-1861", "HUMLDLRRL-NEG-11761", "HUMERPA-NEG-1021", "AGMORS12A-NEG-181", "HUMMHCW1B-ACCEPTOR-1398", "HUMRYR-NEG-14761", "HUMMHCW2B-ACCEPTOR-1399", "HUMTHX-NEG-841", "HUMMHCB44A-NEG-361", "HUMHSP70D-NEG-1441", "HUMGA7331-NEG-241", "HUMPRC1-NEG-61", "HUMMHDRO2-NEG-2461", "HUMTKRA-NEG-12841", "HUMSPBAA-NEG-7621", "HUMMYCF1-NEG-1081", "HUMC3A1R-NEG-2941", "HUMANK-NEG-1981", "ORAINVOL-NEG-2161", "HUMRYR-NEG-10201", "HUMFESFPS-NEG-6661", "HUMMHDRBC-NEG-661", "HUMERVKA2-NEG-901", "HUMPROD4-NEG-721", "HUMGSTPIG-NEG-1381", "HUMBHAA-NEG-1261", "HUMHCKA-NEG-61", "HUMINT2-NEG-5041", "HUMRGE-NEG-1801", "HUM4F2HG1-NEG-1441", "HUMMYONM-NEG-1501", "HUMIGKAR-NEG-181", "HUMME491-NEG-601", "HUMET-NEG-301", "HUMATP-NEG-1981", "HUMLYL1B-NEG-4081", "HUMMHHLA-NEG-241", "MACHBB-NEG-4141", "HUMMK562B-NEG-1", "HUMPGKA09-NEG-241", "HUMCG5B-NEG-1261", "HUMEAR2-NEG-1381", "HUMTHMOD-NEG-421", "CHPAZGLO-NEG-1", "HUMOCS1-NEG-1", "HUMCRYGBC-NEG-3781", "HUMMPO1-NEG-1681", "HUMALPPB-NEG-781", "HUMAPRTA-NEG-1981", "HUMSERDHY-NEG-241", "HUMKER654-NEG-1441", "HUMNFM-NEG-1921", "HUMPROD1-NEG-1741", "HUMSAP1-NEG-121", "HUMCCK2-NEG-361", "HUMMETIA-NEG-1921", "HUMCP21OH-NEG-1981", "HUMALDC-NEG-61", "HUMASP-NEG-541", "HUMMHEA-ACCEPTOR-1381", "HUMPOVRA-NEG-541", "HUMCP21OHC-NEG-3061", "HUMOTNPI-NEG-481", "GCRHBBA1-NEG-61", "HUM5FDX-NEG-2041", "HUMATP1A2-NEG-20341", "HUMMRP8A-NEG-1381", "HUMHSP90-NEG-1", "HUMCNRAB-NEG-1741", "HUMHLA1EA-NEG-121", "HUMKERP2-NEG-1381", "HUMVWFA04-NEG-301", "HUMGLYSA-NEG-2761", "HUMDB1A-NEG-121", "HUMYTEST-NEG-301", "HUMCP21OHA-NEG-2521", "HUMCYP450-NEG-7321", "HUMHBB-NEG-5761", "HUMMHCAG-NEG-1", "HUMGNPAS-NEG-61", "HUMBAT2A-NEG-2761", "HUMITI2-NEG-481", "HUMC1A1-NEG-3901", "HUMPLAP2A-NEG-481", "HUMMHDRBK-NEG-1321", "HUMAK1-NEG-6361", "HUMOPS-NEG-181", "HUMCAPG-NEG-1261", "HUMMPO-NEG-301", "HUMSPM1-NEG-181", "HUMVDR-NEG-1441", "HUMLD78R-NEG-1", "HUMMHDRB4A-NEG-781", "HUMSAACT-NEG-301", "HUMRNP70K-NEG-1141", "HUMTPA-NEG-8761", "HUMACROS-NEG-1", "HUMAPOA4C-NEG-2761", "HUMFVII-ACCEPTOR-136", "HUMHPA2BR-NEG-1261", "HUMGLI-NEG-901", "HUMCSPCP-NEG-1261", "HUMIGHBU-NEG-301", "HUMVIL2-NEG-1441", "HUMGRP78-NEG-421", "HUMFGRINT-NEG-421", "HUMC4A1-NEG-2761", "HUMHER2A-NEG-3301", "HUMFNRA-NEG-361", "HUMBCRD-NEG-301", "HUMNMYCA-NEG-4561", "HUMLAMP1A-NEG-841", "HUMARF1A-NEG-301", "HUMMHC1-NEG-4321", "HUMMLC2A-NEG-1021", "TARHBD-NEG-1981", "HUMFCERA-NEG-121", "HUMMHCP42-NEG-4501", "HUMRCC1B-NEG-361", "HUMIGCD2-NEG-121", "HUMNMYCA-NEG-1", "HUMMHM71-NEG-181", "HUMAPOCIP-NEG-301", "HUMTPO03-NEG-61", "HUMMHANTLE-NEG-3181", "HUMMH6-ACCEPTOR-1451", "HUMTRKR-NEG-1201", "HUMMHB27B-ACCEPTOR-1202", "HUMACCYBB-NEG-3481", "HUMHMPFK-NEG-901", "HUMMHDC3B-NEG-2461", "HUMMHCACA-NEG-181", "HUMUPAX-NEG-3541", "HUMAPLA-NEG-1261", "HUMFNRAS-NEG-601", "HUMCG2A1-NEG-4561", "BABBMYHA-NEG-301", "HUMGFIIM-NEG-121", "HUMP68-NEG-1081", "HUMCRYGBC-NEG-12901", "HUMRUN1C2-NEG-241", "HUMHPRTB-NEG-49201", "HUMITF1-NEG-601", "GCRHBBA6-ACCEPTOR-920", "HUMHLACW2-NEG-781", "HUMAPOE3-NEG-1", "HUMTM-NEG-1441", "HUMPAIB1-NEG-61", "HUMCKMT-NEG-1321", "HUMCAL-NEG-301", "HUMCY4ARO-NEG-541", "HUMCKMM1-NEG-1921", "HUMIDB-NEG-241", "HUMPMGR-NEG-1621", "CHPMHCBAA-NEG-841", "HUMELANA-NEG-1261", "HUMCARN-NEG-1561", "HUMIRBPG-NEG-3781", "HUMCSIST-NEG-2221", "HUMMHANTLE-DONOR-2150", "HUMTHB-NEG-17281", "HUMALIPOA-NEG-781", "HUMPIM1A-NEG-1741", "HUMELAM1A-NEG-1141", "HUMCOL1A07-NEG-1", "HUMDRD2A-NEG-841", "HUMTHR-NEG-1501", "HUMMYHC5-NEG-1141", "HUMP42LA-NEG-2161", "HUMNFH3-NEG-1", "MACPEPSG-NEG-2161", "HUMMHANTLE-ACCEPTOR-1874", "HUMMHCW3-NEG-2761", "HUMTGFKA-NEG-1141", "ATRINS-ACCEPTOR-1678", "HUMMHAWB-NEG-721", "HUMGGTA-NEG-661", "HUMACTGP1-NEG-481", "HUMDBHRA-NEG-1321", "HUMBAT3B1-NEG-421", "HUMRETREC-NEG-1321", "HUMRENA3-NEG-361", "HUMA1AR2-NEG-3601", "HUMHEXB01-NEG-481", "HUMMHCCWB-NEG-241", "HUMMH6-DONOR-1727", "HUMPOMC-NEG-421", "HUMHLADP4-NEG-721", "HUMMHCW2B-DONOR-1675", "HUMCYC1A-NEG-4381", "HUMIGHVH-NEG-481", "HUMGHCSA-NEG-38521", "HUMMYP-NEG-961", "HUMPEP7-NEG-1", "HUMMHB27B-DONOR-1478", "HUMMHCW1B-DONOR-1674", "MACHBA-NEG-421", "HUMTPOB-NEG-2401", "HUMFXIIIA-NEG-181", "HUMDEF1AA-NEG-121", "HUMPRCM-NEG-1681", "HUMENOG-NEG-121", "HUMCD7-NEG-241", "HUMMHDRBS-NEG-601", "HUMPIGF2A-NEG-301", "HUMRAFR-NEG-2521", "GCRHBBA1-ACCEPTOR-1706", "HUMIHRP-NEG-541", "HUMAMYA1-NEG-541", "HUMHKB3R-NEG-121", "HUMGFIA1-NEG-841", "HUM4COLA-NEG-1021", "HUMCKMT-NEG-5881", "HUMIGFII5-NEG-121", "HUMIGHVAQ-NEG-61", "HUMVWFR1-NEG-3481", "HUMGPMSS-NEG-1501", "HUMRGRSALU-NEG-1981", "HUMGCBPRC-NEG-1501", "HUMRAP2-NEG-181", "HUMMBPA-NEG-661", "HUMTHBNA-NEG-961", "HUMHLDPWB-NEG-61", "HUMINCP-NEG-3121", "HUMSPIA04-NEG-61", "HUMIL2RA-NEG-2161", "HUMHA44G-NEG-2701", "HUMGELS3-NEG-121", "HUMPALC-NEG-2281", "HUMIGCD7-NEG-1921", "HUMIGHVA-NEG-481", "HUMMIFA-NEG-181", "HUMERG11-NEG-181", "HUMIL2RBC-NEG-1681", "HUMTGLU-NEG-781", "HUMMHTRP-NEG-661", "HUMKER19-NEG-541", "HUMALDH03-NEG-1", "HUMCA2VR-NEG-361", "HUMCOLII2-NEG-781", "HUMINIFI-NEG-481", "HUMFCRHA-NEG-661", "HUMASGPR1-NEG-661", "HUMMHA2-NEG-1321", "HUMMHDR2A-NEG-61", "HUMMGI2-NEG-901", "HUMSYNPR-NEG-61", "HUMHCR-NEG-661", "HUMTCBVD-NEG-1", "HUMPBGDR2-NEG-1", "HUMTPAR-NEG-361", "HUMXCGD-NEG-1201", "HUMDBLPRO-NEG-181", "HUMCNPG3-NEG-121", "HUMALIPOA-NEG-9901", "HUMMHDRCD-NEG-601", "HUMCG1PA1-NEG-1501", "HUMPRF1A-NEG-3601", "HUMATP1A2-NEG-11221", "HUMCOX4A-NEG-301", "HUMTHY1A-ACCEPTOR-517", "HUMVWFR-NEG-3301", "HUMIGHCH-NEG-121", "HUMPCNA-NEG-301", "HUMDBH01-NEG-1321", "HUMCYP145-NEG-301", "HUMCYP2A4-NEG-1", "HUMMHCP41-NEG-541", "GCRRSAGAB-NEG-121", "HUMALPP-NEG-1981", "HUMALPPD-NEG-841", "HUMACTASK-NEG-1021", "HUMPPPA-NEG-1561", "HUMTBBM40-NEG-841", "HUMXEH-NEG-1441", "HUMRASFA2-NEG-1321", "HUMGCB-NEG-2281", "HUMPAIA-NEG-9781", "HUMTBB5-NEG-7801", "HUMADH21C-NEG-541", "HUMDES-NEG-7201", "HUMINT2-NEG-9601", "HUMVWF01-NEG-2101", "HUMFVII-NEG-721", "HUMSPTA1A-NEG-1861", "HUMADAG-ACCEPTOR-31146", "HUMGCSFR1-NEG-61", "HUMSYN25A-NEG-1", "HUMCG4A11-NEG-961", "HUMAPRT-NEG-421", "HUMCRYGBC-NEG-8341", "HUMRETPON-NEG-1261", "HUMTHBNB-NEG-3541", "HUMTF-NEG-1261", "HUMLDL100-NEG-1381", "HUMTCRDA4-NEG-661", "HUMBCL2A-NEG-4561", "HUMCOL1A25-NEG-121", "HUMMHDQOB-NEG-1", "HUMSLIPG-NEG-961", "HUMMH-NEG-3541", "HUMMHDQBR-NEG-601", "HUMADAG-NEG-4441", "HUMKERC4-NEG-961", "HUMMHC4A-NEG-2761", "HUMBGPG-NEG-1021", "MACHBDPS2-NEG-1", "HUMDES-ACCEPTOR-7283", "HUMADAG-NEG-36361", "HUMFCGRB-NEG-61", "HUMLPLR-NEG-361", "HUMTUBAFB-NEG-1", "HUMER41-NEG-3661", "HUMADPRT01-NEG-61", "HUMKEREP-NEG-3721", "HUMCYC1A-ACCEPTOR-2108", "HUMMYCL2A-NEG-2341", "HUMDMP-NEG-3481", "HUMCYC1A-ACCEPTOR-2611", "HUMC1R-NEG-1801", "HUMFNMC-NEG-3481", "HUMRGM-NEG-4441", "HUMIGKVJ2-NEG-961", "HUMCYC1A-ACCEPTOR-2858"), ValueGroup("HUMBCL2A-NEG-1", "HUMRGNTSA-NEG-2161", "HUMPDGFB1-NEG-1381", "HUMARP450-NEG-2521", "HUMIGCC8-NEG-481", "HUMPDGFA1-NEG-841", "ORAHBPSBD-NEG-2881", "HUMFUR1-NEG-3901", "HUMPYGM09-NEG-1", "HUMRASR2-NEG-1621", "HUMTPO13-NEG-1021", "HUMIIP-NEG-61", "HUMMYCBL1-NEG-1", "HUMSISPDG-NEG-2581", "HUMTRANSB-NEG-181", "HUMANTP53-NEG-121", "CHPCD4A-NEG-961", "HUMG3PD-NEG-481", "HUMATP1A2-NEG-6661", "HUMPGRR-NEG-1681", "HUMCENPBR-NEG-901", "HUMEGR2A-NEG-421", "HUMMHB39W-NEG-1", "HUMCYPBX3-NEG-241", "HUMBCI2-NEG-241", "HUMGFIA2-NEG-3961", "HUMLYL1A-NEG-841", "HUMFOLLI1-NEG-2041", "HUMUG2B-NEG-781", "HUMMHDRHA-NEG-5341", "HUMTFA01-NEG-1321", "HUMMYCLYA-NEG-781", "CHPINVOL-NEG-1", "HUMAK1-NEG-1801", "HUMINSPR-NEG-61", "HUMRENT2-NEG-4321", "HUMHXM-NEG-1321", "HUMHPRTB-NEG-3601", "HUMTUBBM-NEG-2281", "HUMLSZH-NEG-841", "HUMPRCA-NEG-4321", "HUMHMGIB-NEG-721", "HUMTGFBG1-NEG-1801", "HUMIRGT-NEG-1681", "HUMRBP1-NEG-421", "HUMTROPA3-NEG-901", "HUMIGCC4-NEG-781", "HUMMUCC-NEG-121", "HUMANTCE-NEG-841", "HUMIGCH5-NEG-241", "HUMKEREP9-NEG-241", "HUMAGG-NEG-541", "HUMRNPA2A-NEG-1201", "HUMU3AA-NEG-961", "HUMENKPH1-NEG-1141", "HUMMYCNH-NEG-1", "HUMMHCW2B-NEG-3721", "HUMTKR-NEG-2641", "HUMIL2RX6-NEG-1", "HUMMYCBLK-NEG-781", "HUMLAMAR-NEG-1081", "HUMLCK-NEG-181", "HUMHMGYB-NEG-1561", "HUMCRYGA1-NEG-181", "HUMMHADX2-NEG-1201", "HUMALPI-NEG-3901", "CHPCLA108-NEG-841", "HUMAPOBA-NEG-4141", "HUMBHA05-NEG-61", "HUMMHDQBC-NEG-841", "HUMIGLAM1-NEG-121", "HUMPAIA-NEG-14341", "HUMLAMC-NEG-181", "HUMCFVII-NEG-661", "HUMCYP345-NEG-1381", "HUMCN2-NEG-841", "HUMMSOD-NEG-61", "HUMALDCG-NEG-1441", "HUMTCBJD-NEG-361", "HUMIRBPM-NEG-3241", "HUMHSP90B-NEG-6541", "HUMVWFA29-NEG-661", "HUMINT1G-NEG-601", "HUMTGFB-NEG-541", "HUMARAF1R-NEG-1381", "HUMIGFIIR-NEG-6841", "HUMCRYGBC-NEG-22021", "HUMESTR-NEG-2161", "HUMTHMA-NEG-121", "HUMVWFA24-NEG-781", "HUMSGLT1-NEG-1141", "HUMVWFA17-NEG-301", "HUMBCR22I-NEG-1681", "HUMMGSA-NEG-1", "HUMCCG1-NEG-1621", "HUMTCBXF-NEG-541", "HUMBN51-NEG-1261", "HUMASA-NEG-61", "HUMTACEB-NEG-241", "HUMMHCAI-NEG-1", "HUMIL2R-NEG-1", "HUMMYCTR-NEG-2041", "HUMMPOA-NEG-1321", "HUMAPB18-NEG-601", "HUMMYCC-NEG-2761", "HUMTGTPV2-NEG-841", "HUMFERG2-NEG-1", "HUMCPA4-NEG-601", "HUMIGCD6-NEG-901", "HUMC1INHI-NEG-121", "HUMPLAKO-NEG-1861", "HUMPSAP-NEG-2401", "HUMKAL2-NEG-1741", "HUMTPIPSC-NEG-61", "HUMKER56K-NEG-1201", "HUMIL7A-NEG-361", "HUMSTROMR-NEG-661", "HUMAPOCIB-NEG-1441", "HUMMHCGE1-NEG-61", "HUMRENA4-NEG-2461", "HUMPKCB1A-NEG-1321", "HUMPKCB2A-NEG-1321", "HUMCATR-NEG-1081", "HUMSPR1B-NEG-181", "HUMGAA-NEG-781", "HUMPKLR-NEG-1441", "HUMCN4GEL-NEG-2521", "HUMMHDRA2-NEG-241", "BABT1GL-NEG-61", "HUMMHCGE2-NEG-3301", "HUMSECP1-NEG-181", "HUMASFB-NEG-1021", "HUMACCYBA-NEG-2521", "HUMGLCB-NEG-661", "HUMLPHAA-NEG-1861", "HUMPBG2-NEG-841", "HUMCFVII-NEG-5221", "HUMAPC3G5-NEG-481", "ORAHBBE-NEG-2581", "HUMSODA-NEG-6361", "HUMDES-NEG-2641", "HUMVILLR1-NEG-361", "HUMFMSD-NEG-661", "HUMTFPB-NEG-421", "HUMC8GA-NEG-241", "HUMPAIA-NEG-661", "HUMP15095-NEG-4021", "HUMLCATG-NEG-3841", "HUMEPISIB2-NEG-1081", "HUMTHYB10-NEG-181", "HUM8KER1-NEG-61", "HUMMYLCA-NEG-3181", "HUMERB2R-NEG-2521", "HUMCYPD-NEG-361", "HUMEP-NEG-2461", "HUMIL71-NEG-61", "HUMHST-NEG-3961", "HUMBAT3B2-NEG-4141", "HUMPRB1-NEG-601", "HUMMHA3-NEG-1501", "HUMMHB17W-NEG-1681", "HUMPRPG-NEG-421", "HUMTHYRR5-NEG-781", "HUMTRO-NEG-181", "HUMIL1RA-NEG-2821", "HUMMYCST-NEG-1", "HUMALPL01-NEG-721", "HUMIMP-NEG-181", "HUMARA-NEG-1441", "HUMGSTPI-NEG-301", "HUMTPI-NEG-61", "HUMMHDRGB-NEG-121", "HUMSTSB-NEG-1021", "HUMGALAB-NEG-901", "HUMALP5-NEG-781", "HUMMYCH-NEG-901", "HUMDNFA-NEG-2101", "HUMTPA-NEG-27001", "HUMB2M2-NEG-1201", "HUMGAPJR-NEG-961", "HUMCETP2-NEG-661", "HUMIRRA-NEG-1861", "HUMIGLPAV-NEG-301", "HUMTPA-NEG-22441", "HUMMGPH-NEG-61", "HUMGCB1-NEG-4201", "HUMGNAZ-NEG-961", "HUMINV2-NEG-121", "HUMCYPX4-NEG-2101", "HUMUMOD-NEG-1621", "HUMLYL1B-ACCEPTOR-2189", "HUMPEPD-NEG-421", "HUMPS2G3-NEG-421", "HUMBLAST1-NEG-661", "HUMUKI3-NEG-2941", "HUMPRA403-NEG-1", "HUMLOX5-NEG-61", "HUMCR1L04-NEG-1", "HUMFVIIIB-NEG-4681", "HUMGPIBA-NEG-1561", "HUMMHDOAG1-NEG-61", "HUMADAG-NEG-27241", "HUMGLUTRN-NEG-1021", "HUMLECGBB-NEG-61", "HUMAK1-ACCEPTOR-3918", "BABAPOE-NEG-1201", "HUMLDLR18-NEG-1621", "HUMHLAB13-NEG-901", "HUMMHDOB-NEG-4261", "HUMPOMC3-NEG-241", "HUMMHDRB-NEG-901", "HUMNCAMB-NEG-661", "HUMP45C17-NEG-4861", "HUMAPOAI1-NEG-721", "HUMBCL2C-NEG-3061", "HUMAPOE4-NEG-3301", "HUMHAPRA-NEG-481", "HUMAPOB26-NEG-2881", "HUMCYPIIE-NEG-7561", "HUMGFI-NEG-1", "HUMNGFBA2-NEG-4021", "HUMSPARC07-NEG-1", "HUMPC125-NEG-3181", "HUMCGB-NEG-181", "HUMLKHA4-NEG-61", "HUMIFNB3-NEG-1081", "HUMBARR-NEG-361", "HUMCYP2BA-NEG-2761", "HUMA1GP01-NEG-301", "HUMCOL1A18-NEG-241", "HUMPSPBA-NEG-601", "HUMLGTPA-NEG-1261", "HUMSHBG-NEG-1021", "HUMTKRA-NEG-8281", "HUMTOPI-NEG-1261", "HUMGIAA-NEG-901", "HUMIGVKW1-NEG-841", "HUMGASTA-NEG-6241", "HUMINCPPS-DONOR-940", "HUMHPARS2-NEG-6961", "MACHBPEA-NEG-2161", "HUMIGKAG-NEG-481", "HUMLAM1B-NEG-121", "HUMRSA6-NEG-961", "HUMNGFBA1-NEG-2341", "CHPHPA2-NEG-1", "HUMTCRT3D-NEG-1081", "HUMSAA-NEG-2821", "HUMMRCOX2-NEG-481", "HUMTP53B-NEG-1021", "HUMIGFIIR-NEG-2281", "HUMALIPOA-NEG-5341", "HUMMHB7A-NEG-1261", "HUMGHCSA-NEG-24841", "HUMCRYGQ6-NEG-361", "HUMEGFRBB3-NEG-1201", "HUMTPA-NEG-17881", "HUMHBBAG-NEG-1441", "HUMRAF19-NEG-721", "HUMACTSM7-NEG-61", "HUMIGHAE2-NEG-781", "HUMLCAT1-NEG-721", "HUMMYHC04-NEG-1", "HUMBAT2B4-NEG-6001", "HUMGPIBAA-NEG-3661", "HUMIFNATC-NEG-661", "HUMDMHSP-NEG-1741", "HUMDHPRA-NEG-421", "HUMRASH-NEG-5161", "HUMAPOAIT-NEG-661", "HUMATPAR-NEG-3241", "HUMATPSY1-NEG-1", "HUMCYP450-NEG-2761", "HUMBMP1A-NEG-2161", "HUMINCPPS-NEG-3181", "HUMGFIBPA-NEG-1441", "HUMHPARS2-NEG-2401", "HUMTCBCE-NEG-1621", "HUMMHCA1A-NEG-601", "HUMMHBIA-NEG-541", "HUMHPRTB-NEG-17281", "HUMA2PIBB-NEG-421", "HUMPOLP-NEG-2341", "HUMTRGC64-NEG-61", "HUMSATM29B-NEG-181", "HUMAMIPEP-NEG-3361", "HUMNTSXPH-NEG-1081", "HUMMRNAEN-NEG-1681", "HUMHBGF1-NEG-601", "HUMHBB-NEG-1201", "HUMIGHBC-NEG-481", "HUMPFKM23-NEG-1621", "HUMIGKVAB-NEG-1261", "HUMTCDXA-NEG-1", "HUMTSHX-NEG-241", "HUMAPOB2-NEG-7621", "HUMCYPX1-NEG-4201", "HUMTCAYJ-NEG-181", "HUMMHEA-NEG-2041", "HUMGLYPL-NEG-1081", "HUMG0S19B-NEG-2821", "HUMALPHA-NEG-181", "HUMELA301-NEG-1", "HUMD2A-NEG-841", "HUMFESFPS-NEG-2101", "HUMMRP14A-NEG-1561", "HUMCANPR-NEG-841", "HUMIGCB4-NEG-61", "HUMGRFCIG-NEG-1501", "HUMMHDBW1-NEG-1", "HUMTPOA-NEG-841", "HUMMHDRWA-NEG-901", "HUMALPHB-NEG-241", "HUMCAMA-NEG-841", "HUMMET2AB-NEG-61", "HUMMHDCB-NEG-6601", "HUMP45C21-NEG-901", "HUMINSRC-NEG-6661", "HUMSRC2B-NEG-61", "HUMTFAA-NEG-2881", "HUMTPO08-NEG-601", "HUMPNMTA-NEG-421", "HUMCYC1-NEG-961", "HUMNEBB-NEG-241", "HUMCYP45C-NEG-3781", "HUMGGTX-NEG-1981", "HUMHBA4-NEG-11161", "HUMPAIR1-NEG-1081", "HUMRFPA-NEG-841", "HUMPSAA-NEG-2521", "HUMADAG-NEG-18121", "HUMFX-NEG-241", "HUMAK1-ACCEPTOR-10488", "HUMRASAA-NEG-481", "HUMFBRB3-NEG-1", "HUMTCRAI2-NEG-61", "HUMGPP3A08-NEG-3721", "HUMMHBC-NEG-1921", "HUMMYCAWT-NEG-1", "HUMIL4-NEG-301", "HUMGTPBPA-NEG-1081", "HUMALPI1-ACCEPTOR-124", "HUMIGKVM-NEG-421", "HUMTPO-NEG-2401", "HUMGLUD1-NEG-721", "HUMKADA-NEG-601", "HUMALPHLB-NEG-901", "HUMTCBJC-NEG-721", "HUMLDLRRL-NEG-7201", "HUMSISG1-NEG-841", "HUMMCR-NEG-781", "HUMIGKCB-NEG-1081", "HUMAPOBB-NEG-8101", "HUMAE1-NEG-2101", "HUMSNRNP1-NEG-301", "HUMA1ACMB-NEG-421", "HUMSPTA1A-NEG-6421", "HUMATCT4-NEG-1561", "HUMPDGFRAA-NEG-3421", "HUMADHIG-NEG-241", "HUMFVIIIB-NEG-121", "HUMPRP-NEG-1", "HUMUBPSE1-NEG-661", "HUMAPOCIA-NEG-2221", "HUMC1A2-NEG-661", "HUMLAP-NEG-1141", "HUMIGF27-NEG-1", "HUMSLK-NEG-841", "HUMACTGA-NEG-2641", "HUMCR1-NEG-241", "HUMINSRA-NEG-4201", "HUMPDHA-NEG-1141", "HUMHBA1-NEG-841", "HUMTCBYB-NEG-61", "HUMSTS-NEG-3361", "HUMRSKP08-NEG-61", "HUMNGFB-NEG-9361", "HUMGIP2A5-NEG-1", "HUMHTA1-NEG-241", "HUMATPB-NEG-841", "HUMFESFPS-NEG-11221", "HUMAT3X1-NEG-1", "HUMGHCSA-NEG-43081", "HUMEDHB17-ACCEPTOR-2701", "HUMFAV-NEG-3961", "HUMC1INH-NEG-481", "HUMHBA4-NEG-2041", "HUMDAFA-DONOR-1117", "HUMA1ATR-NEG-781", "HUMTNFAB-DONOR-1537", "HUMHLADPB-NEG-13321", "HUMASL1A-NEG-961", "HUMMCM-NEG-481", "HUMUKA-NEG-1501", "HUMC3-NEG-3481", "HUMMHDQAW-NEG-301"), ValueGroup("HUMIGJHI-NEG-61", "HUMALUAGP-NEG-181", "HUMERYA-NEG-661", "HUMMDNCF-NEG-1141", "HUMHTV1A-NEG-121", "HUMGFP40H-NEG-1201", "HUMIGJ2-NEG-181", "HUMDYS-NEG-12961", "HUMHIAPPA-NEG-1861", "HUMCREB-NEG-2101", "HUMF13A15-NEG-1981", "HUMC6AI-NEG-301", "HUMC45B2B-NEG-1021", "HUMGHCSA-NEG-15721", "HUMTHB-NEG-8161", "HUMCBP-NEG-1321", "HUMVTNR-NEG-5641", "HUMHPRTB-NEG-21841", "HUMPTPASE-NEG-1801", "HUMDBPG-NEG-1141", "HUMGNAS6-NEG-1", "HUMALBGC-NEG-9361", "HUMPOMC-NEG-4981", "HUMHLASBA-NEG-9721", "HUMVIPMR5-NEG-121", "HUMAFP-NEG-4141", "HUMLIPCR-NEG-1261", "HUMGHR-NEG-841", "HUMIFNAA-NEG-1621", "HUMGCRB-NEG-3481", "HUMALBGC-NEG-18481", "HUMRASK25-NEG-361", "HUMFNRB-NEG-1081", "HUMGG-NEG-5581", "HUMANT1-NEG-2881", "HUMTM30R-NEG-1441", "HUMMHCD8A-NEG-3781", "HUMPTHLHA-NEG-1381", "HUMFIXG-NEG-8221", "HUMHISH3C-NEG-781", "HUMTFRRA-NEG-3481", "HUMVWFA31-NEG-3961", "HUMFVIII-NEG-2821", "HUMCRYABA-NEG-3301", "HUMAFP-NEG-17821", "HUMHPRTB-NEG-12721", "HUMAFP1-NEG-241", "HUMRAG1-NEG-4141", "HUMHBB-NEG-42241", "HUMAMYA3-NEG-721", "HUMHPRT9-NEG-121", "HUMPLPA-NEG-2041", "HUMPA2I-NEG-541", "HUMNFLG-NEG-1861", "HUMACBPA2-NEG-1", "HUMGYPA06-NEG-421", "HUMCHEBG2-NEG-1", "HUMTHB-NEG-12721", "HUMGLA-NEG-8281", "HUMRA3A-NEG-1", "HUMGFB-NEG-2701", "HUMPALF1-NEG-781", "HUMGLUCG2-NEG-1621", "HUMPYHBASA-NEG-2641", "HUMHPRTB-NEG-40081", "HUMTSHBA1-NEG-961", "HUMADH4C1-NEG-1501", "HUMFBRG-NEG-2641", "HUMCFXI03-NEG-421", "HUMTPA-NEG-36121", "HUMNAKATPA-NEG-3061", "HUMEGFRER-NEG-901", "HUMHMG17G-NEG-3661", "HUMHLADZA-NEG-5281", "TARHBB-NEG-541", "HUMIL2A-NEG-1261", "HUMRGRSAL-NEG-1981", "HUMIFNRG-NEG-1561", "HUMAPOB10-NEG-301", "HUMGDHR-NEG-841", "HUMPGK2P-NEG-361", "HUMIFNAGS-NEG-5281", "CHPRSALPI-NEG-481", "GORHBBPG-NEG-181", "HUMIFNINI-NEG-1201", "HUMKER18-NEG-5941", "HUMIL2-NEG-2401", "HUMLC3-NEG-901", "HUMC7A-NEG-1861", "HUMHPRTB-NEG-30961", "HUMHPARS1-NEG-241", "HUMMFD8A-NEG-181", "HUMABL1B-NEG-1621", "HUMIGKP1-NEG-1441", "HUMAMYB-NEG-2041", "HUMMCAF-NEG-661", "HUMPRPC4B-NEG-541", "HUMAPOB4A-NEG-241", "HUMNAKATP2-NEG-2161", "AGMORS9A-NEG-481", "HUMIFNAGS-NEG-9841", "HUMSTSXG2-NEG-2461", "HUMVWFA-NEG-1", "HUMCOLA2I-NEG-841", "HUMAPOBB-NEG-3541", "HUMIL2A-NEG-5821", "HUMTNFA-NEG-3241", "HUMSIGMG3-NEG-301", "HUMDYZ1-NEG-3301", "CHPHBBPCH-NEG-301", "HUMCOL3A1A-NEG-661", "HUMHBB-NEG-55921", "HUM3ALPH-NEG-1", "HUMCALC1-NEG-1561", "HUMNPY4-NEG-241", "HUMGLA-NEG-3721", "HUMPC64-NEG-1801", "HUMGKAS-NEG-1441", "HUMGNAI1-NEG-1201", "HUMMHSBA-NEG-61", "HUMAG1-NEG-2101", "HUMALBAF1-NEG-301", "HUMCH15M-NEG-121", "MNKHAPSE-NEG-5461", "HUMMYHC-NEG-5941", "HUMFAPYSY-NEG-541", "HUMINCPPS-ACCEPTOR-2712", "HUMADH3G1-NEG-841", "HUMRGNTS-NEG-1981", "HUMCYAR03-NEG-301", "HUMFIXG-NEG-35581", "HUMSPRO-NEG-1321", "HUMFIXG-NEG-3661", "HUMKER18-NEG-1381", "HUMFOLMES-NEG-721", "HUMIGVKA2-NEG-601", "HUMERVKA-NEG-2161", "HUMELIA-NEG-2641", "HUMCAATP3-NEG-61", "HUMIGKREC-NEG-1", "HUMDAFA-NEG-1561", "CHPHBBC-NEG-1321", "HUMTCRDA-NEG-1741", "HUMGBR-NEG-2521", "HUMFIXG-NEG-31021", "HUMPCNA-NEG-4861", "HUMSTATHG2-NEG-1", "HUMPTH2-NEG-841", "HUMHBVINT-NEG-61", "HUMIFNN-NEG-601", "HUMBAT2B3-NEG-1", "HUMPDGFR-NEG-5281", "HUMTCRGE-NEG-121", "HUMHBEG-NEG-721", "HUMGPP3A17-NEG-1081", "HUMMETPOA-NEG-901", "HUMCRPGP-NEG-481", "HUMCDW44A-NEG-1261", "HUMUPIR1-NEG-841", "HUMAFP-NEG-13261", "HUMJUNA-NEG-3301", "GORHBPSBD-NEG-2281", "HUMFN3A-NEG-661", "HUMIFNAGS-NEG-721", "HUMHRGA-NEG-481", "HUMFERHX-NEG-481", "CHPGGGLOG-NEG-961", "HUMFIBEDA-NEG-2641", "HUMAFP-NEG-8701", "HUMARB-NEG-3541", "HUMINSRD-NEG-3601", "HUMRPS14-NEG-3421", "HUMHK1A-NEG-3421", "HUMMHDRW1-NEG-2581", "MACHBPSBD-NEG-1141", "HUMREPAL1-NEG-2821", "HUMALBGC-NEG-4801", "HUMNMYC-NEG-6121", "HUMAACT-NEG-481", "HUMHPS12-NEG-1621", "HUMTHEP2-NEG-121", "HUMMMTVPOL-NEG-421", "HUMMETIF-NEG-1681", "HUMMHDQAR-NEG-1", "HUMHBB-NEG-37681", "HUMPDHE1B-NEG-301", "HUMERSP1A-NEG-241", "HUMFABP-NEG-661", "HUMHBEG-NEG-5281", "HUMFVIIIC-NEG-121", "HUMALPSAT-NEG-541", "HUMFIXG-NEG-12781", "HUMHBEG-NEG-9841", "HUMHLASBA-NEG-5161", "HUMDBLTP-NEG-1081", "HUMCERP-NEG-121", "HUMFIXG-NEG-26461", "HUMPZP-NEG-1741", "HUMRASN2-NEG-1", "HUMSATAX-NEG-1441", "HUMM6PR-NEG-1861", "HUMKUP-NEG-1021", "HUMAREPAP-NEG-61", "HUMAPA-NEG-841", "HUMMGA2PS-NEG-1861", "GIBHBGGL-NEG-7861", "HUMATPSYB-NEG-9961", "HUMHPRTB-NEG-26401", "HUMTPO11-NEG-241", "HUMCACY-NEG-3481", "HUMHPRTB-NEG-35521", "HUMALDA1-NEG-661", "HUMHBB-NEG-10321", "HUMTPA-NEG-13321", "HUMRSCNTF-NEG-61", "HUMMYCL-NEG-1681", "HUMSATAR2-NEG-181", "HUMPIP-NEG-241", "HUMAMY107-NEG-1", "HUMGYPC-NEG-421", "HUMIRBPG-NEG-8341", "HUMABLLA-NEG-1", "ORAHBBPSE-NEG-2101", "HUMHLADPB-NEG-4201", "HUMIL1B-NEG-2161", "HUMS71AA-NEG-3301", "HUMLDLR01-NEG-241", "HUMAPBM-NEG-3841", "HUMBRAFA-NEG-601", "HUMIFNG-NEG-4321", "HUMGCB2-NEG-1141", "HUMEB2CR2-NEG-1141", "HUMERV34A-NEG-2521", "HUMHBB-NEG-28561", "HUMLMWOS1-NEG-301", "HUMADRBR-NEG-1861", "HUMVIP1-NEG-421", "HUMANFA-NEG-2581", "HUMMDR3-NEG-301", "HUMCSYNA-NEG-2461", "HUMPP12R-NEG-1261", "HUMCERPS-NEG-1381", "HUMGG-NEG-1021", "HUMTGC1-NEG-61", "HUMAMY2A-NEG-361", "HUMCYPNO-NEG-961", "HUMLOX5A-NEG-2161", "HUMTBB46P-NEG-421", "HUMLDL100-NEG-10501", "HUMFBRGAB-NEG-1081", "HUMIGKVI1-NEG-541", "HUMMHDQA1-NEG-661", "HUMCNR-NEG-2101", "HUMIL1BX-NEG-5461", "HUMIFNA01-NEG-421", "HUMIGB7-NEG-1381", "HUMATP1A2-NEG-15781", "HUMC1A2-NEG-5221", "CHPRSA-NEG-2161", "HUMMDR1A07-NEG-781", "HUMOAS06-NEG-181", "HUMG3PDP-NEG-1", "HUMLIDNA-NEG-2701", "HUMA1ATP-NEG-4861", "HUMPAI2A4-NEG-121", "HUMGLAA-NEG-421", "HUMRISDAD-NEG-3721", "HUMTKRA-NEG-3721", "HUMGLYCA4-NEG-181", "HUMPKCAMD-NEG-781", "HUMFVIII-NEG-7381", "HUMTCRB22-NEG-241", "HUMHBB-NEG-51361", "HUMLDHB1-NEG-2461", "HUMISG1-NEG-241", "HUMHPRTB-NEG-8161", "HUMPALC-NEG-6841", "HUMNGFB-NEG-4801", "HUMPLA2A1-NEG-1561", "HUMFIXA-NEG-781", "HUMSTATHG2-NEG-4561", "HUMMHDRHA-NEG-781", "HUMIGHVV2-NEG-841", "HUMRPHO2A-NEG-1861", "HUMCD2A-NEG-121", "MNKHBPSBD-NEG-961", "HUMANTNC-NEG-2401", "HUMIL1AG-NEG-421", "HUMDYS-NEG-3841", "HUMACHRA7-NEG-901", "HUMHSP90B-NEG-1981", "HUMIFNAD-NEG-841", "HUMAREPBV-NEG-61", "HUMPLP6-NEG-421", "HUMTHROMPR-NEG-121", "HUMHBBRT-NEG-1141", "HUMRPS17A-NEG-1561", "HUMPRPH1-NEG-4081", "HUMHBBGG-NEG-481", "HUMTCRGAA-NEG-1", "HUMCIX-NEG-2521", "HUMTCAXB-NEG-1321", "HUMUMPS-NEG-1561", "HUMTCRAJ3-NEG-301", "HUMLHRH-NEG-961", "HUMDMDHI41-NEG-481", "HUMGYPCDC-NEG-421", "HUMNRNPEP-NEG-1141", "HUMDONT01-NEG-481", "HUMMYCG2-NEG-1501", "MACHBPEA-NEG-6721", "HUMIGKVS-NEG-421", "HUMIGHHSG4-NEG-601", "HUMBURKA-NEG-1141", "HUMMYCC-NEG-7321", "HUMFXIIIC-NEG-1561", "HUMAPB13-NEG-1", "HUMTCBV81-NEG-541", "HUMGHRA06-NEG-1", "HUMCRPG-NEG-781", "HUMUBIBP-NEG-481", "HUMINS02-NEG-181", "HUMTPO04-NEG-3121", "HUMFIXG6-NEG-1", "HUMCG1A1B-NEG-121", "HUMHP2FS-NEG-1021", "HUMAPB48A-NEG-2041", "HUMHSC70-NEG-1021", "HUMMHDRP1-NEG-481", "HUMTCRB11-NEG-1", "HUMTFRR-NEG-1741", "HUMMHCC6A-NEG-2401", "HUMPROSA-NEG-1021", "HUMTRD117-NEG-301", "HUMLCK2-NEG-841", "HUMIGGFCRA-NEG-1", "HUMHBB-NEG-24001", "HUMANTCD2-NEG-661", "HUMTBG-NEG-301", "HUMHGMCOA-NEG-181", "HUMPSG3A-NEG-481", "HUMSATA-NEG-361", "HUMTCRAC-NEG-1441", "HUMADH2C1-NEG-841", "HUMGHCSA-NEG-20281", "HUMACTCA2-NEG-1381", "HUMT1418-NEG-2161", "HUMGPP3A04-NEG-421", "HUMAPOBF-NEG-5641", "HUMCRBP2-NEG-421", "HUMPUMP1-NEG-661", "HUMATPA2-NEG-781", "HUMB100DI-NEG-121", "HUMTCAYD-NEG-1", "HUMC4BP-NEG-241", "HUMNGFB-NEG-241", "HUMBMP3A-NEG-1021", "HUMTFPC-NEG-241", "HUMVPF-NEG-661", "HUMCR1RS-NEG-1681", "HUMPPS-NEG-1561", "HUMCFVII-NEG-9781", "HUMIGHVQ-NEG-61", "HUMENKB1-NEG-1081", "HUMGASTA-NEG-1681", "HUMTYR-NEG-1141", "HUMODC-NEG-241", "HUMH1FNC1-NEG-1", "HUMAPOB2-NEG-3061", "HUMPMG3BA-NEG-3001", "HUMIAIG4-NEG-421", "HUMHLA1PS-NEG-3061", "HUMFIXG-NEG-21901"), ValueGroup("HUMGHCSA-NEG-11161", "HUMFMSCPO-NEG-61", "HUMCYCPS3-NEG-1081", "HUMHBB-NEG-65041", "HUMADAG-NEG-31801", "HUMLCA-NEG-2521", "HUMH-NEG-3061", "HUMHF10-NEG-241", "HUMDBPA-NEG-1381", "HUMGHCSA-NEG-29401", "HUMMYHC10-NEG-1561", "HUMLDHA7-NEG-361", "HUMGHCSA-NEG-52201", "HUMRSCNTT-NEG-121", "HUMFNI-NEG-121", "HUMFOS-NEG-6061", "HUMP45C17-NEG-301", "HUMPRLR-NEG-1861", "HUMGRP78-NEG-4981", "HUMRSLINE-NEG-2161", "HUMALDB2-NEG-1681", "HUMTROPFB-NEG-421", "HUMATP1A2-NEG-24901", "HUMPMPCA-NEG-1141", "HUMF8L1B-NEG-2041", "HUMP47-NEG-721", "HUMMXB-NEG-1741", "HUMTGFB2A-NEG-2401", "HUMANTLAA-NEG-481", "HUMNCAM-NEG-121", "HUMCHROMB-NEG-421", "HUMCA1XIA-NEG-3421", "HUMERBC-NEG-421", "HUMMAC1A-NEG-4021", "HUMPLASTA-NEG-1561", "HUMALBGC-NEG-13921", "HUMTGL7-NEG-181", "GIBHBGGL-NEG-3301", "HUMCAM-NEG-121", "HUMCARM-NEG-661", "HUMCATF-NEG-721", "AGMKPNRSB-NEG-1", "HUMIL1AG-NEG-9541", "MACRSMB-NEG-1", "HUMRSKPA2-NEG-121", "HUMTCR3G1-NEG-961", "HUMBSF2-NEG-4141", "HUMRPS6A-NEG-601", "HUMUDPGT-NEG-1801", "HUMAPB09-NEG-1", "HUMAFLP2-NEG-61", "HUMRBSA-NEG-1381", "HUMSBLA-NEG-961", "HUMDUG-NEG-2941", "HUM7B2-NEG-901", "HUMGPP3A21-NEG-2521", "HUMRSP1GPB-NEG-181", "HUMTHRAA-NEG-361", "HUMP3A-NEG-2461", "HUMHBB-NEG-19441", "HUMGLUCG2-NEG-6181", "HUMCS5-NEG-2161", "HUMHPBA-NEG-901", "HUMALDC01-NEG-361", "LEMHBGA-NEG-361", "HUMHPARS1-NEG-9361", "HUMDYS-NEG-8401", "HUMIL2R6-NEG-1", "HUMCEAE-NEG-61", "HUMTFPB-NEG-9541", "HUMTNFAB-NEG-2641", "HUM18BPD-NEG-61", "HUMCD20-NEG-721", "HUMIR3EBA-NEG-121", "HUMHBARSA-NEG-1081", "HUMIGGRLA-NEG-841", "HUMETS1A-NEG-481", "GORRGITX-NEG-1", "HUMTBB14P-NEG-1441", "HUMGHCSA-NEG-65881", "HUM7SKP41-NEG-541", "HUMMHSBB1-NEG-1561", "HUMRSKPNA-NEG-2341", "HUMSNRNP9-NEG-121", "HUMGPP3A11-NEG-961", "HUMIL1P-NEG-301", "HUMTOPIIX-NEG-841", "HUMEDF-NEG-841", "HUMTCGCF-NEG-121", "HUMSMCK-NEG-1021", "HUMMHDRS1-NEG-241", "HUMSODG5-NEG-181", "HUMCYAR10-NEG-1381", "HUMPALD-NEG-3841", "GORHBBPG-NEG-4741", "HUMTCBCC-NEG-1981", "HUMERMTV2-NEG-301", "HUMMYC3L-DONOR-2594", "HUMALDOBG-NEG-1", "HUMIGKC1-NEG-1681", "HUMCMYBB-NEG-901", "HUMGMCSFG-NEG-2821", "HUMRNPLA-NEG-181", "HUMMPRCI-NEG-601", "HUMGHCSA-NEG-33961", "HUMADAG-NEG-22681", "HUMRHOC9-NEG-121", "HUMSATA7A-NEG-2461", "HUMANP70-NEG-361", "HUMHELB-NEG-2461", "HUMHBB-NEG-46801", "HUMPHH-NEG-541", "HUMSIGMG4-NEG-961", "HUMREN02-NEG-121", "HUMMHDR3-NEG-1441", "HUMMYH1R-NEG-1621", "HUMHBB-NEG-33121", "HUMJNLTRA-NEG-121", "HUMLAMB2-NEG-961", "ORAHBBPSE-NEG-6661", "HUMZFX-NEG-781", "HUMCYPBX1-NEG-541", "HUMCAIII1-NEG-181", "GIBIL2LTR-NEG-241", "HUMMEHR-NEG-361", "HUMFUMA-NEG-841", "HUMENKPH2-NEG-3781", "HUMPNU-NEG-721", "HUMAS08-NEG-721", "HUMRSH3-NEG-1621", "HUMDBPC-NEG-2821", "HUMIGMUD-NEG-721", "HUMBPGM3-NEG-661", "HUMHMG14A-NEG-61", "HUMGFIAB5-NEG-421", "HUMVWFA37-NEG-661", "HUMTHYRR-NEG-481", "HUMINSR-NEG-541", "HUMPRPH2-NEG-3661", "HUMHMG17-NEG-301", "HUMHBB-NEG-69601", "HUMH38-NEG-661", "HUMALDB1-NEG-2761", "HUMAPOBA-NEG-8701", "HUMAPOC2-NEG-2521", "HUMC5A2B-NEG-181", "CHPHPA3-NEG-2041", "HUMHBB-NEG-14881", "MNKHAPSE-NEG-901", "HUMCYCPSJ-NEG-361", "HUMIGKVI5-NEG-2101", "HUMGCB2-NEG-5701", "HUMF13A05-NEG-121", "HUMHOM4-NEG-841", "HUMATCCD2-NEG-1261", "HUMPPOL-NEG-1081", "HUMIGKVW-NEG-841", "HUMAPB21-NEG-7501", "HUMTGPL1-NEG-1201", "HUMUGU4CA-NEG-121", "HUMTCBVW2-NEG-301", "HUMAPB21-NEG-2941", "HUMKALA-NEG-181", "HUMC8B-NEG-241", "CHPP44-NEG-481", "HUMCINHA-NEG-541", "HUMPRC7-NEG-961", "HUMMHDC3B-NEG-7021", "HUMGHCSA-NEG-2041", "HUMCYPSCC-NEG-1501", "HUMAPOB2-NEG-12181", "HUMGSTC-NEG-421", "HUMMDR1-NEG-2401", "HUMAPOBF-NEG-1081", "HUMCOL4A1-NEG-961", "HUMVWFA01-NEG-3961", "HUMPAPA-NEG-2401", "HUMPDGFARA-NEG-4621", "HUMIL2B-NEG-3241", "HUMPS12-NEG-1321", "HUMMYHC-NEG-1381", "HUMGP3A-NEG-1921", "HUMREPA-NEG-1141", "HUMARMA-NEG-961", "HUMELI-NEG-721", "HUMHOXB-NEG-541", "HUMIGLV71-NEG-721", "HUMBGAL-NEG-781", "HUMIGMUD-NEG-5281", "HUMMHDCB-NEG-2041", "HUMIFNF-NEG-601", "HUMHODB2-NEG-301", "HUMCYPAX-NEG-421", "HUMER41-NEG-8221", "HUMUG3PD-NEG-61", "AGMRSKPNI-NEG-1141", "HUMERG2-NEG-1321", "HUMXYES2-NEG-1741", "HUMLDLRRL-NEG-2641", "HUMCOL1A39-NEG-361", "HUMGLNRS-NEG-361", "HUMMAP2A-NEG-421", "HUMSNRNP3-NEG-3901", "HUMATPSY1-NEG-4561", "HUMHLASBA-NEG-601", "HUMC1SAB-NEG-841", "HUMTOPII-NEG-1081", "HUMTROPSR-NEG-721", "HUMA1GLY2-NEG-1501", "HUMKER7R-NEG-601", "HUMAPOAI1-NEG-5281", "HUMMDRA3-NEG-961", "HUMDMDA-NEG-1201", "HUMCYPHLP-NEG-421", "HUMTCAJA-NEG-61", "HUMGHCSA-NEG-61321", "HUMCSPB-NEG-61", "HUMHSDB-NEG-121", "HUMPAI2-NEG-181", "HUMVTNR-NEG-1081", "CHPHBBPCH-NEG-4861", "HUMGHCSA-NEG-6601", "HUMTDTA-NEG-781", "HUMTKFER-NEG-1081", "HUMTCAXN-NEG-61", "HUMPKB-NEG-1741", "HUMFIGRD-NEG-361", "HUMGPBPS-NEG-961", "HUMCYCB-NEG-181", "HUMCHEF-NEG-181", "HUMSAA1A-NEG-3901", "HUMCEAB-NEG-121", "HUMC5B-NEG-3241", "HUMBCRR-NEG-1261", "HUMGHCSA-NEG-56761", "HUMCGPRA-NEG-1981", "HUMPRLA-NEG-241", "HUMATH3U5-NEG-181", "HUMIFNB3-NEG-10201", "HUMMTLMC2-NEG-781", "HUMRSO5C-NEG-361", "HUMINHBA-NEG-541", "HUMGPP3A07-NEG-1381", "HUMLDHBR-NEG-121", "HUMCSFM-NEG-181", "HUMPC2A-NEG-481", "HUMBDGALA-NEG-1441", "HUMTCBYM-NEG-181", "HUMSHBGA-NEG-4501", "HUMHTV3A-NEG-841", "HUMHMGCOA-NEG-2101", "HUMNAKATP2-NEG-6721", "HUMAPB21-NEG-12061", "HUMRIBIR-NEG-1681", "HUMADAG-NEG-13561", "HUMGDHA-NEG-181", "HUMCYPIIE-NEG-3001", "HUMFOS-NEG-1501", "HUMIFNAPS-NEG-721", "HUMBLYM1-DONOR-127", "HUMGTLPA-NEG-1621", "HUMALDB1-NEG-7321", "HUMTBB5-NEG-3241", "HUMPOLDNAA-NEG-3481", "HUMAPB03-NEG-1441", "HUMHBB-NEG-60481", "HUMTCOBI-NEG-1261", "HUMCOLA1C-NEG-1141", "HUMALDOAP-NEG-661", "HUMCEALV-NEG-721", "HUMG0S19A-NEG-2341", "HUMC21PLA-NEG-121", "HUMKITCR-NEG-1021", "HUMFABPL-NEG-61", "HUMFVIIIC-NEG-4681", "HUMZFY-NEG-2341", "HUMLYRE-NEG-1801", "HUMDLDH-NEG-1021", "HUMTCSM-NEG-241", "HUMVWFM-NEG-541", "HUMCYCPSE-NEG-541", "HUMRSALPI-NEG-181", "HUMAMD-NEG-601", "HUMHPP16C-NEG-541", "HUMGFAP-NEG-2941", "HUMANT61K-NEG-1681", "HUMACHRM2-NEG-1681", "HUMAPOBA-NEG-13261", "HUMPDGFARA-NEG-61", "HUMPDGFR-NEG-721", "HUMREPHY2-NEG-481", "HUMCTHD-NEG-1801", "HUMCYES1-NEG-1501", "HUMIGKVI3-NEG-421", "HUMATPSYB-NEG-5401", "HUMTCBZE-NEG-241", "HUMAGP1A-NEG-421", "HUMTHYRR-NEG-5041", "HUMARGL-NEG-241", "HUMMPRCI-NEG-5161", "HUMRB1RA-NEG-3061", "HUMTCAYS-NEG-121", "HUMPOLBA-NEG-781", "HUMEN1-NEG-1201", "HUMHSPGC-NEG-2821", "HUMMHCW1C-NEG-781", "HUMRSSA1B-NEG-601", "HUMC4A2-NEG-3721", "HUMTUBAG-NEG-3361", "HUMPRCA-DONOR-3601", "HUMTHB-NEG-3601", "HUMMBP17K-NEG-361", "HUMMHDRR2-NEG-121", "HUMCALLA14-NEG-61", "HUMTPA-NEG-4201", "HUMICAM1A-NEG-1981", "HUMCYP4A2-NEG-781", "HUMPAP4-NEG-781", "HUMPGKPX-NEG-961", "HUMLCAR-NEG-2161", "HUMA2M-NEG-4441", "HUMAPOCII-NEG-121", "HUMPEPC1-NEG-901", "HUMNID-NEG-3541", "HUMP120PC-NEG-2101", "HUMALDH2-NEG-1201", "HUMMHDRBX-NEG-61", "HUMZNF8-NEG-661", "HUMCR1-NEG-4801", "HUMTHROMR-NEG-721", "HUMFIXG-NEG-17341", "HUMNOXF-NEG-361", "HUMLAM1B-NEG-4681", "HUMGHCSA-NEG-47641", "HUMDCC-NEG-1561", "HUMIL1BX-NEG-901", "HUMMYOHP-NEG-901", "HUMCFTRM-NEG-2281", "HUMTNFB-NEG-121", "HUMEBUR04-NEG-241", "HUMTRA-NEG-781", "HUMHLS2R-NEG-1201", "HUMMHSXA-NEG-4561", "HUMCMOSAA-NEG-121", "HUMLMYC2-NEG-901", "HUMADH2E2-NEG-1", "MACAPOA-NEG-961", "HUMIL5A-NEG-481", "HUMGPPSBB-NEG-1021", "HUMFVA-NEG-3001", "HUMGFRIL-NEG-1441", "HUMPPARP1-NEG-181", "HUMA1ATP-NEG-9421", "HUMTFPB-NEG-4981", "HUMNAKATP1-NEG-5281", "HUMETN1-NEG-181", "HUMFSH3-NEG-1021", "HUMER141-NEG-121", "HUMAPOB-NEG-1321", "HUMPDGFRA-NEG-4441", "HUMPPH1-NEG-61", "HUMOPS-NEG-4741", "HUMAPOAIA-NEG-1", "HUMRBS-NEG-2221", "HUMMHDRC2-NEG-841", "HUMDYSIN7-NEG-3541", "HUMIFNB2-NEG-361", "HUMIGLC2A-NEG-361", "HUMPTHL2-NEG-1", "HUMAPOC2G-NEG-3001", "HUMMHDRS2-NEG-2461", "HUMHPA1B-NEG-601", "HUMLAPA-NEG-2881", "HUMIL6-NEG-1021", "HUMHIAPPA-NEG-6421", "HUMEBVR-NEG-841", "HUMROSMCF-NEG-121", "HUMRYR-NEG-5641", "HUMFLA1A-NEG-961", "HUMABLA-NEG-2701", "HUMMH6-NEG-1201", "HUMTCGXH-NEG-601", "HUMIFNA04-NEG-961", "HUMCRYGBC-NEG-17461", "HUMIGKPC-NEG-61", "HUMHLADZA-NEG-721", "HUMEF1AB-NEG-481", "HUMMHDRB3-NEG-661", "HUMUKPM-NEG-781", "HUMGHV-NEG-1861", "HUMT28E-NEG-121", "HUMSATOD-NEG-841", "HUMERVKA-NEG-6721", "HUMPAIA-NEG-5221", "HUMVWFR1-NEG-8041", "HUMG6PDA-NEG-601", "HUMCATG12-NEG-241", "HUMIGHAJ-NEG-541", "HUMPSBGA-NEG-781", "HUMFN-NEG-3241", "HUMCYPMP-NEG-181", "HUMCTSE-NEG-661", "HUMIRBP-NEG-3481", "HUMBAT2B4-NEG-1441", "HUMCS3-NEG-361", "HUMRCYP3-NEG-1081", "ORAHBG2F-NEG-181", "HUMC1RX-NEG-1201", "HUMPSAA1-NEG-1", "HUMGPPSB1-NEG-241", "HUMEGFRN-NEG-4381", "HUMKER672-NEG-2401", "HUMMEDA-NEG-2761"), ValueGroup("HUMMYLCA-DONOR-2559", "HUMIL2-DONOR-595", "HUMIL2A-DONOR-1533", "HUMIL2B-DONOR-410", "HUMALBGC-DONOR-2592", "HUMHPRTB-DONOR-16757", "HUMALBGC-DONOR-13770", "HUMAFP-DONOR-970", "HUMALBGC-DONOR-17044", "HUMPCNA-DONOR-3721", "HUMERP-DONOR-1653", "HUMERPA-DONOR-1662", "HUMALBGC-DONOR-6223", "HUMHPRTB-DONOR-40080", "HUMAFP-DONOR-1834", "HUMALBGC-DONOR-15086", "HUMTFPB-DONOR-9438", "HUMGG-DONOR-3769", "HUMFIXG-DONOR-13441", "HUMALBGC-DONOR-7827", "HUMGFP40H-DONOR-2563", "HUMHPRTB-DONOR-39832", "HUMIL5-DONOR-908", "HUMIL5A-DONOR-907", "HUMFABP-DONOR-2493", "HUMFABP-DONOR-3624", "HUMHPRTB-DONOR-31605", "HUMIL2-DONOR-745", "HUMIL2A-DONOR-1683", "HUMIL2B-DONOR-560", "HUMAFP-DONOR-14687", "HUMALBGC-DONOR-15728", "HUMMYLCA-DONOR-2388", "HUMALBGC-DONOR-4179", "HUMHMG14A-NEG-4621", "TARHBB-DONOR-1909", "HUMGRP78-DONOR-1510", "HUMFIXG-DONOR-9425", "HUMALBGC-DONOR-17726", "HUMFABP-DONOR-1126", "HUMIL5-DONOR-1982", "HUMIL5A-DONOR-1986", "HUMHLASBA-NEG-14281", "HUMHPRTB-DONOR-14857", "HUMALBGC-DONOR-11052", "HUMATPSYB-DONOR-3271", "HUMHSP90B-DONOR-6241", "HUMHIAPPA-DONOR-629", "HUMHPRTB-DONOR-34990", "HUMAFP-DONOR-2929", "HUMFIXG-DONOR-9638", "HUMLACTA-DONOR-2236", "HUMTR211-NEG-901", "HUMIL2-DONOR-3182", "HUMIL2A-DONOR-4119", "HUMIL2B-DONOR-2996", "HUMAFP-DONOR-14021", "TARHBD-DONOR-817", "HUMSODA-DONOR-5211", "HUMHSP90B-DONOR-5582", "HUMGRP78-DONOR-2735", "HUMIFNAII-NEG-61", "HUMP53A-NEG-1", "HUMGRP78-DONOR-3539", "HUMAFP-DONOR-17179", "HUMCATF-DONOR-646", "HUMALBGC-DONOR-12584", "HUMPCNA-DONOR-5730", "HUMIFNINI-DONOR-2232", "HUMALBGC-DONOR-1825", "HUMAFP-DONOR-5428", "HUMPCNA-DONOR-2588", "HUMCYCAA-DONOR-1365", "HUMSODA-DONOR-3286", "HUMTUBAG-DONOR-2554", "HUMHMG17P-NEG-1081", "HUMEF1A-DONOR-2242", "LEMHBB-DONOR-569", "HUMMHDOB-DONOR-4518", "HUMATPSYB-DONOR-8947", "HUMHSP90B-DONOR-4129", "HUMHSP90B-DONOR-3809", "HUMCKMT-DONOR-1625", "HUMEF1A-DONOR-1696", "HUMAFP-DONOR-9741", "HUMFIXG-DONOR-3053", "HUMEF1A-DONOR-2644", "HUMSODA-DONOR-6429", "HUMP45C17-DONOR-5125", "HUMATPSYB-DONOR-5594", "HUMFIXG-DONOR-33089", "HUMANT1-DONOR-4091", "HUMIL5-DONOR-667", "HUMIL5A-DONOR-666", "HUMATPSYB-DONOR-2881", "HUMHSP90B-DONOR-5188", "HUMETN3-DONOR-227", "HUMEF1A-DONOR-2878", "HUMEF1A-DONOR-3546", "HUMALBGC-DONOR-9544", "HUMIFNINI-DONOR-2912", "HUMRPS17A-DONOR-1717", "HUMPRPH2-DONOR-1884", "HUMIL1B-DONOR-1558", "ORAHBD-DONOR-819", "HUMHPRTB-DONOR-27927", "HUMGFP40H-DONOR-1143", "HUMEF1A-NEG-1561", "HUMMLCAC-NEG-601", "HUMGRP78-DONOR-2258", "HUMFOS-DONOR-2544", "HUMTPO06-NEG-1681", "HUMCYPIIE-DONOR-2972", "HUMPRPH1-DONOR-2774", "HUMRPS17A-DONOR-2291", "HUMHIS4A2-NEG-301", "HUMHIAPPA-DONOR-1054", "HUMEF1A-DONOR-3222", "HUMCSFGMA-DONOR-933", "HUMGFP40H-DONOR-1028", "HUMTFPB-DONOR-2272", "HUMATPSYB-DONOR-4215", "HUMLACTA-DONOR-865", "HUMKEREP-DONOR-2872", "HUMAFP-DONOR-12231", "HUMSPROTR-NEG-541", "HUMGRP78-ACCEPTOR-1372", "HUMIFNINI-DONOR-3237", "HUMGG-DONOR-1964", "HUMPRPH2-DONOR-2660", "HUMSODA-DONOR-6624", "HUMMYCC-DONOR-2852", "HUMPCNA-DONOR-2424", "HUMATPSYB-DONOR-4649", "HUMSODA-DONOR-4346", "HUMMHDOB-DONOR-1105", "LEMHBG-DONOR-587", "GIBHBGGL-DONOR-2624", "GIBHBGGL-DONOR-7544", "LEMHBE-DONOR-633", "HUMAFP-DONOR-8063", "HUMATPSYB-DONOR-8071", "HUMIL1B-DONOR-5824", "HUMMHDRHA-DONOR-3987", "HUMCRYGBC-DONOR-18389", "MACHBCA2-DONOR-809", "MACHBGA1-DONOR-811", "HUMHSP90B-DONOR-1172", "HUMPRPH1-DONOR-1687", "HUMIL1B-DONOR-416", "HUMCP21OH-DONOR-1104", "HUMCP21OHC-DONOR-1105", "HUMMHCP42-DONOR-2035", "HUMMHCP52-DONOR-569", "MNKHBD-DONOR-698", "HUMIL1B-DONOR-4458", "HUMSODA-NEG-1801", "HUMA1ATP-DONOR-7932", "HUMAPOBB-NEG-12661", "HUMCSPB-DONOR-2478", "ORAHBG1F-DONOR-614", "HUMMHANTLE-DONOR-3700", "LEMHBGA-DONOR-648", "HUMAFP-DONOR-18452", "HUMCKMT-DONOR-2448", "HUMGRP78-DONOR-3086", "HUMC9M-NEG-841", "HUMAFP-DONOR-7047", "HUMPRPH1-DONOR-3555", "HUMHBBAG-DONOR-1689", "LEMHBE-DONOR-288", "HUMSHBGA-DONOR-4189", "HUMSODA-DONOR-4739", "HUMPRP0A-NEG-2161", "HUMGCRA-NEG-3721", "HUMMHDOB-DONOR-3639", "HUMSODA-DONOR-6932", "HUMGCB1-DONOR-581", "HUMSAA-DONOR-701", "HUMADAG-NEG-9001", "HUMSODA-DONOR-3510", "HUMHSP90B-DONOR-2752", "HUMHSP90B-DONOR-7352", "HUMFXIIIB-NEG-841", "HUMSHBGA-DONOR-5396", "HUMTUBAG-DONOR-2257", "HUMAPA4R-NEG-2761", "HUMHSP90B-DONOR-4574", "HUMTFPB-DONOR-6562", "HUMINCPPS-DONOR-2825", "HUMCKMT-DONOR-5110", "HUMFIXG-DONOR-20733", "HUMAFP-DONOR-19845", "HUMHSP90B-DONOR-5858", "HUMGCB1-DONOR-3143", "HUMGG-DONOR-230", "HUMSAA1A-DONOR-6017", "HUMEF1A-ACCEPTOR-3311", "HUMRTVLH2-NEG-4681", "HUMHPRTB-NEG-53761", "HUMHSP90B-DONOR-3558", "HUMRTVLH2-NEG-121", "HUMSAA-DONOR-34", "HUMALBGC-DONOR-6905", "HUMKTEP2A-NEG-1441", "HUMMH6-ACCEPTOR-3562", "HUMCD1B1-NEG-241", "HUMTNFA-DONOR-1839", "HUMTNFAB-DONOR-5318", "HUMTNFX-DONOR-1613", "HUMHSP90B-ACCEPTOR-7018", "HUMKEREP-DONOR-3368", "HUMATPSYB-ACCEPTOR-3096", "HUMA1GLY2-DONOR-3352", "HUMHPARS1-DONOR-3825", "HUMCYPIIE-DONOR-12950", "HUMMRP14A-DONOR-1551", "HUMMYC3L-NEG-3301", "HUMMHDXAR2-NEG-1", "HUMACTGA-DONOR-2060", "HUMA1GLY2-DONOR-3606", "HUMMH6-DONOR-3321", "HUMIL1B-NEG-6721", "MNKHBD-DONOR-347", "HUMHPARS1-NEG-4801", "HUMVIM06-NEG-1", "HUMFIXG-DONOR-23501", "ORAHBD-DONOR-466", "HUMATPCAPM-NEG-661", "HUMHBB32-NEG-121", "HUMDES-DONOR-2016", "HUMCALLA-NEG-2161", "HUMADH1CA-NEG-121", "HUMSAA1A-DONOR-1815", "HUMCKMT-ACCEPTOR-5313", "HUMMRP8A-ACCEPTOR-1988", "HUMGRP78-DONOR-1007", "HUMLACF-NEG-541", "HUMFBRG-NEG-7201", " * ")), IntervalBounds(15.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5), ValueGroups(ValueGroup("G", "N", "D", "S", "R"), ValueGroup("T"), ValueGroup("A"), ValueGroup("C", " * ")), Frequencies(1379, 620, 1171, 1384, 2333, 1372, 1050, 1466, 931, 545, 259, 902, 874, 1684, 915, 680, 1292, 558, 160, 33, 94, 107, 162, 73, 76, 100, 51, 4, 0, 57, 50, 148, 83, 82, 118, 35, 0, 1, 44, 41, 132, 90, 75, 103, 38, 472, 302, 241, 239, 180, 72, 47, 102, 168, 219, 147, 284, 284, 152, 70, 75, 124, 238, 120, 52, 0, 2, 140, 78, 76, 109, 0, 122, 53, 181, 145, 143, 80, 58, 99, 73, 133, 77, 27, 58, 153, 89, 74, 110, 31, 104, 45, 263, 245, 117, 79, 59, 116, 175, 134, 65, 80, 67, 145, 76, 57, 105, 38, 3364, 1821, 1768, 3354, 3633, 2018, 1621, 2642, 1043, 1653, 1837, 807, 934, 1050, 1342, 1905, 1357, 936, 1901, 1792, 576, 622, 782, 1083, 1503, 856, 761, 62, 97, 34, 59, 72, 92, 129, 82, 55, 25, 112, 43, 30, 68, 103, 135, 59, 33, 0, 0, 35, 45, 97, 95, 135, 84, 26, 0, 0, 12, 21, 50, 85, 158, 87, 38, 45, 27, 0, 0, 77, 113, 170, 56, 0, 154, 95, 287, 276, 89, 91, 130, 77, 229, 111, 102, 1, 4, 85, 74, 135, 98, 7, 86, 85, 9, 20, 61, 105, 136, 78, 27, 141, 63, 13, 10, 85, 100, 150, 81, 22, 100, 82, 142, 97, 54, 98, 145, 92, 121, 2079, 1682, 1347, 1068, 1739, 2273, 3223, 2111, 2095, 1106, 1109, 930, 813, 1196, 1179, 1614, 2125, 1094, 299, 384, 759, 744, 915, 797, 1138, 1535, 759, 81, 103, 66, 48, 78, 49, 123, 150, 71, 2, 18, 79, 88, 64, 87, 107, 147, 104, 472, 301, 148, 167, 87, 62, 88, 131, 157, 0, 0, 17, 26, 92, 67, 108, 135, 29, 137, 85, 2, 1, 74, 69, 82, 145, 0, 98, 88, 1, 0, 79, 90, 94, 143, 13, 98, 81, 103, 124, 80, 97, 116, 122, 161, 99, 74, 230, 179, 73, 66, 91, 136, 173, 85, 104, 0, 11, 84, 57, 105, 134, 34, 70, 84, 29, 48, 94, 69, 104, 124, 60, 2423, 2187, 988, 1180, 1901, 1839, 2423, 3298, 1600, 2942, 964, 1412, 1174, 1676, 2047, 1026, 1247, 669, 2447, 887, 931, 917, 1206, 1561, 782, 860, 584, 169, 69, 94, 73, 105, 182, 45, 81, 65, 441, 172, 109, 119, 137, 123, 49, 89, 70, 0, 0, 61, 34, 101, 149, 75, 95, 21, 0, 0, 18, 1, 95, 172, 60, 89, 7, 71, 43, 2, 2, 114, 144, 46, 88, 4, 100, 67, 0, 9, 109, 137, 73, 84, 0, 141, 66, 3, 14, 109, 145, 64, 94, 1, 154, 66, 22, 30, 130, 136, 72, 89, 11, 142, 90, 12, 21, 131, 160, 59, 82, 11, 168, 71, 37, 75, 124, 153, 67, 92, 23, 3462, 1558, 2809, 1286, 2735, 3374, 1685, 1861, 1070), 0, Frequencies(3178, 191400, 8), Frequencies(3178, 191400, 8)) ; // DataGrid(SampleId, Pos, Char) +Unused Structure(VectorC) P_SampleIdLabels = VectorC("{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...}", "{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...}", "{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...}", "{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...}", "{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...}", "{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...}", "{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...}", "{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...}", "{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...}") ; // Cluster labels for variable SampleId +Unused Structure(Vector) P_PosSet = TableVector(DNA, Pos) ; // Value distribution for variable Pos +Unused Structure(VectorC) P_CharSet = TableVectorC(DNA, Char) ; // Value distribution for variable Char +Unused Structure(DataGridDeployment) P_DeployedCoclusteringAtSampleId = DataGridDeployment(P_Coclustering, 1, P_PosSet, P_CharSet) ; // Deployed coclustering for variable SampleId +Unused Numerical P_SampleIdIndex = PredictedPartIndex(P_DeployedCoclusteringAtSampleId) ; // Predicted cluster index for variable SampleId + Categorical P_SampleIdPredictedLabel = ValueAtC(P_SampleIdLabels, P_SampleIdIndex) ; // Predicted label for variable SampleId +Unused Structure(Vector) P_SampleIdPartDistances = PredictedPartDistances(P_DeployedCoclusteringAtSampleId) ; // Vector of cluster distances for variable SampleId + Numerical `P_SampleIdDistance{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...}` = ValueAt(P_SampleIdPartDistances, 1) ; + Numerical `P_SampleIdDistance{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...}` = ValueAt(P_SampleIdPartDistances, 2) ; + Numerical `P_SampleIdDistance{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...}` = ValueAt(P_SampleIdPartDistances, 3) ; + Numerical `P_SampleIdDistance{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...}` = ValueAt(P_SampleIdPartDistances, 4) ; + Numerical `P_SampleIdDistance{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...}` = ValueAt(P_SampleIdPartDistances, 5) ; + Numerical `P_SampleIdDistance{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...}` = ValueAt(P_SampleIdPartDistances, 6) ; + Numerical `P_SampleIdDistance{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...}` = ValueAt(P_SampleIdPartDistances, 7) ; + Numerical `P_SampleIdDistance{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...}` = ValueAt(P_SampleIdPartDistances, 8) ; + Numerical `P_SampleIdDistance{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...}` = ValueAt(P_SampleIdPartDistances, 9) ; +Unused Structure(Vector) P_PosPartFrequencies = PredictedPartFrequenciesAt(P_DeployedCoclusteringAtSampleId, 2) ; // Vector of cluster frequencies for variable Pos + Numerical `P_PosFrequency]-inf;15.5]` = ValueAt(P_PosPartFrequencies, 1) ; + Numerical `P_PosFrequency]15.5;26.5]` = ValueAt(P_PosPartFrequencies, 2) ; + Numerical `P_PosFrequency]26.5;27.5]` = ValueAt(P_PosPartFrequencies, 3) ; + Numerical `P_PosFrequency]27.5;28.5]` = ValueAt(P_PosPartFrequencies, 4) ; + Numerical `P_PosFrequency]28.5;29.5]` = ValueAt(P_PosPartFrequencies, 5) ; + Numerical `P_PosFrequency]29.5;30.5]` = ValueAt(P_PosPartFrequencies, 6) ; + Numerical `P_PosFrequency]30.5;31.5]` = ValueAt(P_PosPartFrequencies, 7) ; + Numerical `P_PosFrequency]31.5;32.5]` = ValueAt(P_PosPartFrequencies, 8) ; + Numerical `P_PosFrequency]32.5;33.5]` = ValueAt(P_PosPartFrequencies, 9) ; + Numerical `P_PosFrequency]33.5;34.5]` = ValueAt(P_PosPartFrequencies, 10) ; + Numerical `P_PosFrequency]34.5;35.5]` = ValueAt(P_PosPartFrequencies, 11) ; + Numerical `P_PosFrequency]35.5;36.5]` = ValueAt(P_PosPartFrequencies, 12) ; + Numerical `P_PosFrequency]36.5;+inf[` = ValueAt(P_PosPartFrequencies, 13) ; +Unused Structure(Vector) P_CharPartFrequencies = PredictedPartFrequenciesAt(P_DeployedCoclusteringAtSampleId, 3) ; // Vector of cluster frequencies for variable Char + Numerical `P_CharFrequency{G, N, D, ...}` = ValueAt(P_CharPartFrequencies, 1) ; + Numerical `P_CharFrequency{T}` = ValueAt(P_CharPartFrequencies, 2) ; + Numerical `P_CharFrequency{A}` = ValueAt(P_CharPartFrequencies, 3) ; + Numerical `P_CharFrequency{C}` = ValueAt(P_CharPartFrequencies, 4) ; +}; + +Dictionary SpliceJunctionDNA (SampleId) +{ + Categorical SampleId ; + Numerical Pos ; + Categorical Char ; +}; diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/DeploymentCoclustering.kdic b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/DeploymentCoclustering.kdic new file mode 100644 index 000000000..a4317f884 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/DeploymentCoclustering.kdic @@ -0,0 +1,51 @@ +#Khiops 10.4.6i + +Root Dictionary SpliceJunction (SampleId) +{ + Categorical SampleId ; + Categorical Class ; + Table(SpliceJunctionDNA) DNA ; +Unused Structure(DataGrid) P_Coclustering = DataGrid(ValueGroups(ValueGroup("HUMMYLCA-ACCEPTOR-924", "HUMVPNP-ACCEPTOR-1688", "HUMMHCW2B-ACCEPTOR-2660", "HUMMHANTLE-ACCEPTOR-3110", "HUMGASTA-ACCEPTOR-6600", "HUMMHCW1B-ACCEPTOR-2658", "HUMIL1B-ACCEPTOR-3546", "HUMHBA4-ACCEPTOR-10696", "HUMHBA4-ACCEPTOR-6885", "MACHBA-ACCEPTOR-238", "HUMTHY1A-ACCEPTOR-1380", "HUMAPOCII-ACCEPTOR-3131", "HUMCS1-ACCEPTOR-1814", "HUMGHCSA-ACCEPTOR-51551", "HUMMYLCA-ACCEPTOR-3623", "HUMMHEA-ACCEPTOR-2678", "HUMTNFA-ACCEPTOR-2140", "HUMTNFAB-ACCEPTOR-5619", "HUMTNFX-ACCEPTOR-1914", "HUMMHB27B-ACCEPTOR-2421", "HUMGHCSA-ACCEPTOR-28732", "HUMAPOCIA-ACCEPTOR-634", "HUMGH-ACCEPTOR-1572", "HUMGHCSA-ACCEPTOR-43393", "HUMGHCSA-ACCEPTOR-6465", "HUMGHN-ACCEPTOR-1797", "HUMGHVA-ACCEPTOR-741", "HUMATP1A2-ACCEPTOR-8990", "HUMTPA-ACCEPTOR-31395", "HUMKER18-ACCEPTOR-5495", "HUMTNFAB-ACCEPTOR-1237", "HUMMIS-ACCEPTOR-1500", "HUMGAST2-ACCEPTOR-916", "HUMTPA-ACCEPTOR-30776", "HUMHPARS1-ACCEPTOR-2519", "HUMGHCSA-ACCEPTOR-42976", "HUMAPOC2-ACCEPTOR-3143", "HUMAPOC2G-ACCEPTOR-2851", "HUMOTNPI-ACCEPTOR-808", "HUMUBILP-ACCEPTOR-1488", "HUMMYLCA-ACCEPTOR-2214", "HUMATP1A2-ACCEPTOR-6683", "HUMALPHA-ACCEPTOR-2023", "HUMADAG-ACCEPTOR-28878", "HUMGH-ACCEPTOR-1154", "HUMGHCSA-ACCEPTOR-6047", "HUMGHN-ACCEPTOR-1379", "HUMCS1-ACCEPTOR-1396", "HUMGHCSA-ACCEPTOR-28313", "HUMGHCSA-ACCEPTOR-51135", "HUMCP21OH-ACCEPTOR-3161", "HUMCP21OHC-ACCEPTOR-3163", "HUMMHCP42-ACCEPTOR-4089", "HUMMHCP52-ACCEPTOR-2628", "HUMGHCSA-ACCEPTOR-13046", "HUMACCYBA-ACCEPTOR-2893", "HUMACCYBB-ACCEPTOR-2827", "HUMDES-ACCEPTOR-3015", "HUMSAA-ACCEPTOR-2567", "HUMCYC1A-ACCEPTOR-2399", "HUMOTNPI-ACCEPTOR-1091", "HUMAPOAI1-ACCEPTOR-653", "HUMAPOAIT-ACCEPTOR-398", "HUMGHCSA-ACCEPTOR-13642", "HUMCP21OH-ACCEPTOR-1386", "HUMLYL1B-ACCEPTOR-3525", "HUMH19-ACCEPTOR-2449", "HUMATP1A2-ACCEPTOR-21798", "HUMALPPD-ACCEPTOR-1642", "HUMMHCP42-ACCEPTOR-2316", "HUMCS1-ACCEPTOR-800", "HUMGHCSA-ACCEPTOR-27717", "HUMGHCSA-ACCEPTOR-50539", "HUMMYC3L-ACCEPTOR-766", "HUMMHDRHA-ACCEPTOR-4276", "HUMRASH-ACCEPTOR-3201", "HUMSAACT-ACCEPTOR-1638", "HUMMHCD8A-ACCEPTOR-733", "HUMA1GLY2-ACCEPTOR-2108", "HUMPIM1A-ACCEPTOR-1687", "HUMKAL2-ACCEPTOR-3494", "HUMCSPB-ACCEPTOR-1874", "HUMA1GLY2-ACCEPTOR-3244", "HUMADAG-ACCEPTOR-34324", "HUMGHCSA-ACCEPTOR-42394", "HUMGHCSA-ACCEPTOR-14059", "HUMTKRA-ACCEPTOR-4815", "HUMPRCA-ACCEPTOR-5420", "HUMMETIF1-ACCEPTOR-1463", "HUMPPPA-ACCEPTOR-1721", "HUMG0S19A-ACCEPTOR-2812", "HUMMHCW2B-ACCEPTOR-885", "HUMPOMC-ACCEPTOR-4445", "HUMGH-ACCEPTOR-571", "HUMGHCSA-ACCEPTOR-5465", "HUMGHN-ACCEPTOR-798", "HUMTHB-ACCEPTOR-883", "HUMALPHA-ACCEPTOR-2851", "HUMALPPD-ACCEPTOR-2473", "HUMTUBBM-ACCEPTOR-1265", "MACHBA-ACCEPTOR-587", "HUMMYCRT-ACCEPTOR-1111", "HUMEDHB17-ACCEPTOR-2346", "HUMA1ATP-ACCEPTOR-11880", "ORAIGECA-ACCEPTOR-583", "HUMHBA4-ACCEPTOR-11050", "HUMANT1-ACCEPTOR-2955", "HUMKEREP-ACCEPTOR-4597", "HUMMHCD8A-ACCEPTOR-1670", "HUMMIS-ACCEPTOR-1697", "HUMPIM1A-ACCEPTOR-4496", "HUMAPOCIA-ACCEPTOR-1956", "HUMRPS14-ACCEPTOR-5473", "HUMSAACT-ACCEPTOR-3116", "HUMCP21OHC-ACCEPTOR-1387", "HUMPSAP-ACCEPTOR-1501", "HUMERP-ACCEPTOR-2578", "HUMERPA-ACCEPTOR-2587", "HUMATP1A2-ACCEPTOR-6969", "HUMPOMC-ACCEPTOR-7483", "HUMGCB1-ACCEPTOR-1590", "HUMMH6-ACCEPTOR-955", "HUMTHB-ACCEPTOR-7876", "HUMAPOE4-ACCEPTOR-3751", "GCRHBBA1-ACCEPTOR-1385", "HUMBNPA-ACCEPTOR-1629", "HUMACCYBA-ACCEPTOR-1384", "HUMACCYBB-ACCEPTOR-1318", "HUMGHCSA-ACCEPTOR-13504", "HUMAK1-ACCEPTOR-5504", "CHPIGECA-ACCEPTOR-585", "HUMADAG-ACCEPTOR-29793", "HUMMHB27B-ACCEPTOR-690", "HUMEDHB17-ACCEPTOR-3575", "HUMCP21OH-ACCEPTOR-2960", "HUMCP21OHC-ACCEPTOR-2962", "HUMMHCP42-ACCEPTOR-3889", "HUMTKRA-ACCEPTOR-2330", "HUMMYLCA-ACCEPTOR-2864", "HUMPRF1A-ACCEPTOR-4965", "HUMVPNP-ACCEPTOR-2057", "HUMH19-ACCEPTOR-2224", "HUMATP1A2-ACCEPTOR-13878", "HUMADAG-ACCEPTOR-19200", "HUMMH6-ACCEPTOR-2724", "HUMG0S19B-ACCEPTOR-2948", "HUMP45C17-ACCEPTOR-4146", "HUMPRF1A-ACCEPTOR-3240", "HUMMYCC-ACCEPTOR-4476", "HUMPRCA-ACCEPTOR-5180", "HUMCYC1A-ACCEPTOR-3478", "HUMMHEA-ACCEPTOR-869", "HUMATP1A2-ACCEPTOR-22870", "HUMMHEA-ACCEPTOR-2278", "HUMAPOA4A-ACCEPTOR-2286", "HUMP45C17-ACCEPTOR-7956", "HUMHPARS1-ACCEPTOR-2888", "HUMHPARS1-ACCEPTOR-4612", "HUMTUBAG-ACCEPTOR-2405", "HUMCAPG-ACCEPTOR-2733", "HUMG0S19A-ACCEPTOR-3347", "HUMHST-ACCEPTOR-4120", "HUMCP21OH-ACCEPTOR-2297", "HUMCP21OHC-ACCEPTOR-2298", "HUMMHCP42-ACCEPTOR-3226", "HUMMHCP52-ACCEPTOR-1763", "HUMKEREP-ACCEPTOR-3671", "HUMSHBGA-ACCEPTOR-3120", "HUMA1GLY2-ACCEPTOR-3502", "HUMINCP-ACCEPTOR-3281", "HUMG0S19B-ACCEPTOR-3484", "HUMMHCP52-ACCEPTOR-2427", "HUMCP21OH-ACCEPTOR-1648", "HUMCP21OHC-ACCEPTOR-1649", "HUMMHCP42-ACCEPTOR-2577", "HUMMHCP52-ACCEPTOR-1114", "BABAPOE-ACCEPTOR-3437", "HUMACTGA-ACCEPTOR-1621", "HUMAK1-DONOR-954", "HUMSPRO-ACCEPTOR-4385", "HUMMIS-ACCEPTOR-1185", "HUMCRYGBC-ACCEPTOR-2225", "HUMADAG-ACCEPTOR-32821", "HUMHBA4-ACCEPTOR-7232", "HUMP3A-ACCEPTOR-2544", "HUMAPOCII-ACCEPTOR-3595", "HUMEDHB17-ACCEPTOR-3311", "HUMADAG-ACCEPTOR-32395", "HUMHST-ACCEPTOR-3478", "HUMMRP14A-ACCEPTOR-3418", "HUMALPI-ACCEPTOR-646", "HUMMHCP52-ACCEPTOR-851", "HUMPSAA-ACCEPTOR-3726", "HUMKEREP-ACCEPTOR-2070", "HUMPRT1A-ACCEPTOR-209", "HUMOPS-ACCEPTOR-4065", "HUMMHANTLE-ACCEPTOR-1363", "HUMMHCW1B-ACCEPTOR-882", "HUMALPI-ACCEPTOR-2283", "GCRHBBA6-ACCEPTOR-590", "HUMTHB-ACCEPTOR-4549", "HUMHMG14A-ACCEPTOR-1361", "HUMATP1A2-ACCEPTOR-21144", "HUMAPOAI1-ACCEPTOR-1647", "HUMAPOAIT-ACCEPTOR-1394", "HUMDES-ACCEPTOR-1920", "HUMHPARS1-ACCEPTOR-6470", "TARHBB-ACCEPTOR-1686", "HUMSAACT-ACCEPTOR-2578", "LEMHBB-ACCEPTOR-1437", "HUMIL1AG-NEG-4981", "HUMTPA-ACCEPTOR-19737", "HUMAPOC2-ACCEPTOR-3599", "HUMAPOC2G-ACCEPTOR-3309", "BABAPOE-ACCEPTOR-801", "HUMAPOE4-ACCEPTOR-1818", "HUMCP21OH-ACCEPTOR-2041", "HUMCP21OHC-ACCEPTOR-2042", "HUMMHCP42-ACCEPTOR-2970", "HUMMHCP52-ACCEPTOR-1507", "HUMCSPB-ACCEPTOR-2217", "HUMAPOAI1-ACCEPTOR-902", "HUMAPOAIT-ACCEPTOR-648", "HUMKEREP-ACCEPTOR-3451", "HUMPSAP-ACCEPTOR-2927", "HUMALPHA-ACCEPTOR-2440", "HUMALPPD-ACCEPTOR-2062", "LEMHBB-ACCEPTOR-346", "HUMMHB27B-ACCEPTOR-2053", "HUMATP1A2-ACCEPTOR-20042", "HUMCAPG-ACCEPTOR-1733", "HUMGCB1-ACCEPTOR-6555", "HUMTNFAB-ACCEPTOR-1431", "HUMATP1A2-ACCEPTOR-20331", "HUMADAG-ACCEPTOR-35070", "HUMALPI-ACCEPTOR-2064", "HUMPPPA-ACCEPTOR-2162", "HUMA1ATP-ACCEPTOR-7282", "MNKHBD-ACCEPTOR-475", "HUMRPS17A-ACCEPTOR-463", "HUMCFVII-ACCEPTOR-10094", "HUMPLPSPC-ACCEPTOR-1326", "HUMMIS-DONOR-1857", "HUMTPA-ACCEPTOR-35307", "HUMCFVII-ACCEPTOR-8277", "HUMINCP-ACCEPTOR-2049", "HUMOPS-ACCEPTOR-5138", "HUMTUBBM-ACCEPTOR-1078", "BABA1AT-NEG-181", "HUMA1ATP-ACCEPTOR-9382", "HUMKAL2-ACCEPTOR-1745", "HUMFOS-ACCEPTOR-1753", "HUMMHCW2B-ACCEPTOR-2263", "HUMMETIF1-ACCEPTOR-1065", "HUMA1GLY2-ACCEPTOR-4746", "HUMPRCA-ACCEPTOR-5063", "HUMSPRO-ACCEPTOR-1827", "HUMH19-ACCEPTOR-2643", "HUMPSAP-ACCEPTOR-993", "HUMRPS17A-ACCEPTOR-3688", "HUMPSAA-ACCEPTOR-1929", "HUMGASTA-ACCEPTOR-6254", "HUMRASH-ACCEPTOR-2012", "HUMTKRA-ACCEPTOR-12537", "HUMSHBGA-ACCEPTOR-4319", "HUMTPA-ACCEPTOR-25978", "HUMACTGA-ACCEPTOR-886", "HUMCKMT-ACCEPTOR-1127", "HUMLYL1B-DONOR-2543", "HUMATP1A2-ACCEPTOR-22617", "HUMMHCD8A-DONOR-1087", "HUMTHB-ACCEPTOR-1703", "HUMTKRA-ACCEPTOR-11871", "ORAHBD-ACCEPTOR-596", "HUMCKMT-ACCEPTOR-4984", "HUMALPI-ACCEPTOR-2926", "HUMSHBGA-ACCEPTOR-5188", "HUMAPRTA-ACCEPTOR-774", "HUMADAG-ACCEPTOR-26314", "ATRINS-ACCEPTOR-701", "HUMMH6-ACCEPTOR-3284", "HUMATP1A2-ACCEPTOR-15000", "CHPIGECA-ACCEPTOR-989", "HUMSAA-ACCEPTOR-606", "HUMMHDRHA-ACCEPTOR-3705", "HUMACTGA-ACCEPTOR-1104", "HUMTNFA-ACCEPTOR-1558", "HUMTNFAB-ACCEPTOR-5037", "HUMTNFX-ACCEPTOR-1332", "TARHBB-ACCEPTOR-2748", "HUMUBILP-ACCEPTOR-806", "HUMCFVII-ACCEPTOR-1624", "HUMGPIBAA-ACCEPTOR-3033", "HUMPRCA-ACCEPTOR-10486", "TARHBD-ACCEPTOR-594", "HUMCFVII-ACCEPTOR-6353", "HUMRASH-ACCEPTOR-2344", "HUMPNMTA-ACCEPTOR-3129", "HUMALPPD-ACCEPTOR-421", "HUMTNFAB-ACCEPTOR-1784", "HUMPSAP-ACCEPTOR-2374", "HUMAPRTA-ACCEPTOR-2558", "HUMACTGA-ACCEPTOR-2414", "HUMPRCA-ACCEPTOR-8223", "ORAHBD-ACCEPTOR-1713", "HUMSPRO-ACCEPTOR-3195", "HUMKER18-ACCEPTOR-5107", "HUMALPI-ACCEPTOR-2698", "HUMAPOCIA-ACCEPTOR-4946", "HUMIL1B-ACCEPTOR-5693", "HUMTHB-ACCEPTOR-7024", "BABAPOE-ACCEPTOR-2624", "HUMSPRO-ACCEPTOR-2022", "HUMCFVII-ACCEPTOR-4264", "HUMMT2A-ACCEPTOR-1047", "HUMIRBPG-ACCEPTOR-6987", "HUMALPI-ACCEPTOR-1861", "HUMSAACT-ACCEPTOR-2337", "HUMAPRTA-ACCEPTOR-2252", "HUMAK1-ACCEPTOR-6626", "HUMTCBA-NEG-2161", "HUMSHBGA-DONOR-2724", "HUMCACY-ACCEPTOR-1973", "HUMMT2A-ACCEPTOR-1454", "HUMATP1A2-ACCEPTOR-21467", "HUMPNMTA-ACCEPTOR-3450", "ORAIGECA-ACCEPTOR-990", "HUMCKMT-ACCEPTOR-2362", "HUMGRP78-ACCEPTOR-775", "HUMCSPB-ACCEPTOR-3118", "HUMPPPA-ACCEPTOR-2425", "HUMCP21OH-ACCEPTOR-2698", "HUMCP21OHC-ACCEPTOR-2699", "HUMMHCP42-ACCEPTOR-3627", "HUMMHCP52-ACCEPTOR-2165", "HUMAPRTA-ACCEPTOR-1848", "HUMPRCA-ACCEPTOR-3434", "MNKHBD-ACCEPTOR-1583", "HUMAPOE4-ACCEPTOR-2977", "HUMALPHA-ACCEPTOR-2224", "HUMALPPD-ACCEPTOR-1845", "HUMTHB-ACCEPTOR-9160", "HUMHMG14A-ACCEPTOR-1599", "HUMPIM1A-ACCEPTOR-1335", "HUMTPA-ACCEPTOR-23573", "HUMMHCW1B-ACCEPTOR-3217", "HUMMHCW2B-ACCEPTOR-3219", "HUMACCYBA-ACCEPTOR-1121", "HUMACCYBB-ACCEPTOR-1057", "HUMLACTA-ACCEPTOR-2735", "HUMALPHA-ACCEPTOR-1789", "HUMSPRO-ACCEPTOR-2445", "HUMEDHB17-ACCEPTOR-2031", "HUMDES-ACCEPTOR-7550", "HUMSHBGA-ACCEPTOR-3640", "HUMALPHA-ACCEPTOR-809", "HUMPLAPL-ACCEPTOR-681", "HUMGCB1-ACCEPTOR-4081", "HUMGSHPXG-ACCEPTOR-968", "HUMSHBGA-ACCEPTOR-4029", "HUMALPPD-ACCEPTOR-1408", "HUMATP1A2-ACCEPTOR-12450", "HUMCKMT-ACCEPTOR-2563", "HUMP45C17-ACCEPTOR-7334", "HUMOPS-ACCEPTOR-3783", "HUMTHY1A-DONOR-34", "HUMKER18-ACCEPTOR-3708", "HUMALPI-ACCEPTOR-415", "HUMCP21OH-ACCEPTOR-1014", "HUMCP21OHC-ACCEPTOR-1015", "HUMMHCP42-ACCEPTOR-1945", "HUMMHCP52-ACCEPTOR-479", "HUMPLPSPC-ACCEPTOR-1830", "HUMTHB-ACCEPTOR-10541", "HUMSAACT-ACCEPTOR-2856", "HUMACCYBA-ACCEPTOR-2599", "HUMACCYBB-ACCEPTOR-2533", "HUMCSFGMA-ACCEPTOR-1620", "HUMMHEA-ACCEPTOR-3682", "CHPIGECA-ACCEPTOR-1392", "HUMERP-ACCEPTOR-1164", "HUMERPA-ACCEPTOR-1171", "HUMBNPA-ACCEPTOR-831", "HUMALPPD-ACCEPTOR-2719", "HUMMIS-ACCEPTOR-1947", "HUMGH-ACCEPTOR-986", "ORAIGECA-ACCEPTOR-1393", "HUMAPOC2-ACCEPTOR-2908", "HUMAPOC2G-ACCEPTOR-2616", "HUMAPOCII-ACCEPTOR-2897", "HUMSPRO-ACCEPTOR-3807", "HUMATP1A2-ACCEPTOR-10073", "HUMSP18A-NEG-361", "HUMALPI-ACCEPTOR-965", "HUMALPHA-ACCEPTOR-581", "HUMALPPD-ACCEPTOR-192", "HUMPLAPL-ACCEPTOR-452", "HUMCYPIIE-ACCEPTOR-3877", "HUMCFVII-ACCEPTOR-11034", "HUMMHB27B-ACCEPTOR-3119", "HUMATP1A2-ACCEPTOR-19381", "HUMTHB-ACCEPTOR-1970", "HUMMHDOB-ACCEPTOR-4706", "HUMATP1A2-ACCEPTOR-9720", "HUMCYPIIE-ACCEPTOR-9142", "HUMAPOA4A-ACCEPTOR-1382", "HUMGCB1-ACCEPTOR-5186", "HUMSAA1A-ACCEPTOR-3145", "HUMKEREP-ACCEPTOR-3206", "HUMMHB27B-ACCEPTOR-2980", "HUMTHB-ACCEPTOR-7663", "HUMKAL2-ACCEPTOR-3894", "HUMP3A-DONOR-1299", "HUMOPS-DONOR-2578", "HUMCYC1A-ACCEPTOR-3281", "HUMSAACT-ACCEPTOR-1885", "HUMFBPA-NEG-421", "HUMRPS14-ACCEPTOR-2872", "HUMANT1-ACCEPTOR-5005", "HUMATP1A2-ACCEPTOR-20736", "HUMALPI-ACCEPTOR-1631", "HUMANT1-ACCEPTOR-3950", "HUMACHRG4-NEG-1", "HUMALPHA-ACCEPTOR-1117", "HUMACCYBA-ACCEPTOR-2065", "HUMACCYBB-ACCEPTOR-1999", "HUMCYPIIE-ACCEPTOR-7515", "HUMIRBPG-ACCEPTOR-8736", "HUMMHCD8A-ACCEPTOR-1987", "HUMERP-ACCEPTOR-1566", "HUMERPA-ACCEPTOR-1575", "HUMAK1-ACCEPTOR-10045", "HUMLYL1B-ACCEPTOR-2630", "HUMALPI-ACCEPTOR-1216", "HUMPRCA-ACCEPTOR-9239", "HUMERP-ACCEPTOR-2264", "HUMERPA-ACCEPTOR-2273", "HUMH19-ACCEPTOR-2846", "HUMCFVII-ACCEPTOR-6448", "HUMMYCE1-NEG-421", "HUMAPRTA-DONOR-618", "HUMCHRA-NEG-121", "HUMGCB1-ACCEPTOR-6344", "HUMGCB1-ACCEPTOR-1905", "HUMCYPIIE-DONOR-7676", "HUMMHEA-ACCEPTOR-3545", "LEMHBDPS-NEG-1441", "HUMKER18-ACCEPTOR-4128", "HUMPSAA-ACCEPTOR-4156", "HUMINHBB2-NEG-301", "HUMSPRO-DONOR-4152", "HUMTKRA-ACCEPTOR-666", "HUMPLPSPC-ACCEPTOR-2176", "HUMPGAMMG-ACCEPTOR-1450", "HUMFVII-DONOR-70", "HUMHSP90B-ACCEPTOR-5391", "HUMALPHA-ACCEPTOR-1369", "HUMTKRA-ACCEPTOR-12318", "HUMALPHA-ACCEPTOR-3090", "HUMCRYGBC-ACCEPTOR-19693", "HUMOPS-ACCEPTOR-2409", "HUMTPA-DONOR-18038", "HUMAPOA4A-NEG-3241", "HUMCAPG-ACCEPTOR-2044", "HUMDAFA-ACCEPTOR-1235", "HUMARC6-NEG-61", "HUMCSPB-ACCEPTOR-1260", "HUMPGAMMG-ACCEPTOR-3519", "HUMUBILP-ACCEPTOR-1158", "HUMDES-ACCEPTOR-2164"), ValueGroup("HUMALBGC-ACCEPTOR-18496", "HUMMYC3L-ACCEPTOR-4242", "HUMHPRTB-ACCEPTOR-27861", "HUMTFPB-ACCEPTOR-6362", "HUMFIXG-ACCEPTOR-9613", "HUMHPRTB-ACCEPTOR-31587", "HUMAFP-ACCEPTOR-9611", "HUMCSFGMA-ACCEPTOR-891", "HUMIFNINI-ACCEPTOR-3147", "HUMIL5-ACCEPTOR-2088", "HUMIL5A-ACCEPTOR-2091", "HUMMYLCA-ACCEPTOR-1831", "HUMATPSYB-NEG-841", "HUMHMG14A-ACCEPTOR-4663", "HUMIL2-ACCEPTOR-5027", "HUMIL2A-ACCEPTOR-5980", "HUMIL2B-ACCEPTOR-4857", "HUMMYLCA-ACCEPTOR-2481", "HUMANFA-ACCEPTOR-785", "HUMATPSYB-ACCEPTOR-8745", "HUMHMG14A-ACCEPTOR-6789", "HUMHPRTB-ACCEPTOR-41424", "HUMFIXG-ACCEPTOR-23298", "HUMIL2-ACCEPTOR-685", "HUMIL2A-ACCEPTOR-1623", "HUMIL2B-ACCEPTOR-500", "HUMIL5-ACCEPTOR-1853", "HUMIL5A-ACCEPTOR-1857", "HUMAFP-ACCEPTOR-6914", "HUMP45C17-ACCEPTOR-5038", "HUMIFNINI-ACCEPTOR-3654", "HUMCYCAA-ACCEPTOR-1188", "HUMIL1B-ACCEPTOR-6540", "HUMFABP-ACCEPTOR-4068", "HUMTNFA-ACCEPTOR-1791", "HUMTNFAB-ACCEPTOR-5270", "HUMTNFX-ACCEPTOR-1565", "HUMGG-ACCEPTOR-3631", "HUMFIXG-ACCEPTOR-20604", "HUMAFP-ACCEPTOR-14589", "HUMIL5-ACCEPTOR-875", "HUMIL5A-ACCEPTOR-874", "HUMAFP-ACCEPTOR-19790", "HUMTUBAG-ACCEPTOR-2034", "HUMPRPH1-ACCEPTOR-4714", "HUMALBGC-ACCEPTOR-7729", "HUMPCNA-ACCEPTOR-5606", "HUMCYPIIE-ACCEPTOR-8083", "HUMTPA-ACCEPTOR-17940", "HUMFIXG-ACCEPTOR-9261", "HUMMHANTLE-ACCEPTOR-4055", "HUMAFP-ACCEPTOR-16955", "HUMCSFGMA-ACCEPTOR-2554", "HUMTHB-ACCEPTOR-20387", "HUMFABP-ACCEPTOR-2320", "HUMPRPH2-ACCEPTOR-3802", "HUMTFPB-ACCEPTOR-10045", "HUMPCNA-ACCEPTOR-3526", "HUMAFP-ACCEPTOR-7965", "HUMHPRTB-NEG-44641", "HUMALBGC-ACCEPTOR-13672", "HUMHSP90B-ACCEPTOR-5022", "HUMALBGC-ACCEPTOR-16911", "LEMHBGA-ACCEPTOR-425", "HUMRPS14-ACCEPTOR-2108", "HUMEF1A-ACCEPTOR-3641", "HUMHPARS1-ACCEPTOR-3750", "HUMPCNA-ACCEPTOR-2326", "HUMATPSYB-ACCEPTOR-4464", "HUMSHBGA-ACCEPTOR-2856", "HUMSODA-ACCEPTOR-7572", "HUMSAA1A-ACCEPTOR-5878", "HUMSODA-ACCEPTOR-6717", "HUMAFP-ACCEPTOR-18319", "HUMFABP-ACCEPTOR-3516", "LEMHBE-ACCEPTOR-1474", "HUMALBGC-ACCEPTOR-12451", "HUMSODA-ACCEPTOR-3391", "HUMGG-ACCEPTOR-5138", "HUMMHEA-ACCEPTOR-3890", "HUMMHCD8A-ACCEPTOR-2860", "HUMALBGC-ACCEPTOR-4046", "HUMFERP1-NEG-961", "HUMHPRTB-ACCEPTOR-16573", "HUMMHDRHA-ACCEPTOR-2969", "LEMHBG-ACCEPTOR-364", "HUMAFP-ACCEPTOR-12016", "HUMETN3-ACCEPTOR-391", "HUMMHDOB-ACCEPTOR-4486", "HUMFOS-ACCEPTOR-2658", "HUMTFPB-ACCEPTOR-11925", "HUMMCR-NEG-5341", "HUMKEREP-ACCEPTOR-3986", "HUMPRPH2-ACCEPTOR-1848", "HUMAFP-ACCEPTOR-20185", "HUMGRP78-ACCEPTOR-2145", "HUMGFP40H-ACCEPTOR-4066", "HUMDES-ACCEPTOR-5413", "HUMHIAPPA-ACCEPTOR-5862", "HUMSODA-ACCEPTOR-6266", "HUMALBGC-ACCEPTOR-14947", "HUMMH6-ACCEPTOR-2326", "HUMMH6-ACCEPTOR-3673", "HUMEF1A-ACCEPTOR-1522", "HUMRPS14-ACCEPTOR-4137", "HUMIFNINI-ACCEPTOR-2785", "HUMFOS-ACCEPTOR-2436", "HUMPCNA-ACCEPTOR-5816", "HUMSODA-ACCEPTOR-4173", "HUMHSP90B-ACCEPTOR-2605", "HUMADAG-ACCEPTOR-32543", "HUMADAM2-ACCEPTOR-784", "HUMMRP14A-ACCEPTOR-1386", "HUMPRPH1-ACCEPTOR-2738", "HUMGFP40H-ACCEPTOR-2431", "HUMTPA-ACCEPTOR-28261", "HUMHPRTB-ACCEPTOR-34907", "HUMMYCC-ACCEPTOR-6624", "HUMMYCRT-ACCEPTOR-3258", "HUMAFP-ACCEPTOR-13888", "HUMMHDOB-ACCEPTOR-3357", "HUMTUBAG-ACCEPTOR-2858", "HUMGCB1-ACCEPTOR-3009", "HUMMHDOB-ACCEPTOR-4124", "HUMFIXG-ACCEPTOR-33757", "HUMTFPB-ACCEPTOR-9259", "HUMAFP-ACCEPTOR-1782", "HUMALBGC-ACCEPTOR-6011", "HUMLACTA-ACCEPTOR-1512", "HUMATPSYB-ACCEPTOR-4093", "HUMTFPB-ACCEPTOR-2160", "HUMALBGC-ACCEPTOR-17658", "HUMALBGC-ACCEPTOR-2534", "HUMGCB1-ACCEPTOR-5810", "HUMIL1B-ACCEPTOR-1506", "HUMHPRTB-ACCEPTOR-39785", "HUMAFP-ACCEPTOR-2796", "LEMHBG-ACCEPTOR-1452", "HUMCFVII-ACCEPTOR-9389", "HUMALBGC-ACCEPTOR-6772", "HUMSODA-ACCEPTOR-5127", "ORAHBG2F-ACCEPTOR-1492", "HUMLACTA-ACCEPTOR-2160", "HUMCYCAA-ACCEPTOR-1466", "HUMHPRTB-ACCEPTOR-14750", "HUMEXCI5R-NEG-1321", "HUMMHANTLE-ACCEPTOR-2733", "HUMHPRTB-ACCEPTOR-40003", "HUMHPARS1-ACCEPTOR-5472", "MACHBCA2-ACCEPTOR-1693", "MACHBGA1-ACCEPTOR-1692", "HUMGFP40H-ACCEPTOR-992", "LEMHBGA-ACCEPTOR-1498", "GIBHBGGL-ACCEPTOR-8438", "HUMPIM1A-ACCEPTOR-3558", "HUMMHCW1B-ACCEPTOR-3569", "HUMMHCW2B-ACCEPTOR-3570", "HUMHBBAG-ACCEPTOR-2565", "HUMSODA-ACCEPTOR-3176", "HUMCRYGBC-ACCEPTOR-5283", "HUMCYPMPC-NEG-1", "HUMCP21OH-ACCEPTOR-1838", "HUMCP21OHC-ACCEPTOR-1839", "HUMMHCP42-ACCEPTOR-2767", "HUMMHCP52-ACCEPTOR-1304", "HUMATPSYB-ACCEPTOR-9633", "HUMRPS17A-ACCEPTOR-2225", "HUMMHCD8A-ACCEPTOR-5474", "HUMCKMT-ACCEPTOR-2011", "HUMHSP90B-ACCEPTOR-4265", "HUMTPA-ACCEPTOR-22075", "HUMATP1A2-ACCEPTOR-15320", "HUMGRP78-ACCEPTOR-2347", "HUMTPA-ACCEPTOR-32398", "HUMFIXG-ACCEPTOR-32974", "HUMIL1B-ACCEPTOR-879", "HUMHMG14A-ACCEPTOR-1487", "HUMKAL2-ACCEPTOR-5424", "HUMDES-ACCEPTOR-1763", "HUMALBGC-NEG-241", "HUMSODA-ACCEPTOR-6511", "HUMPSAA-ACCEPTOR-5669", "GIBHBGGL-ACCEPTOR-3506", "ORAHBG1F-ACCEPTOR-1486", "HUMHIAPPA-ACCEPTOR-959", "TARHBD-ACCEPTOR-1884", "HUMMHCW1B-ACCEPTOR-3357", "HUMMHCW2B-ACCEPTOR-3359", "HUMMHCW1B-ACCEPTOR-2261", "HUMATP1A2-ACCEPTOR-13550", "HUMMHDOB-ACCEPTOR-2649", "HUMSODA-ACCEPTOR-4869", "HUMCRPG-ACCEPTOR-578", "HUMCRPGA-ACCEPTOR-539", "HUMA1GLY2-ACCEPTOR-2469", "HUMANFA-ACCEPTOR-2205", "HUMALBGC-ACCEPTOR-10837", "HUMIL1B-ACCEPTOR-4293", "HUMKER18-ACCEPTOR-6098", "HUMALBGC-ACCEPTOR-9414", "HUMATPSYB-ACCEPTOR-5471", "HUMGRP78-ACCEPTOR-2848", "HUMGCB1-ACCEPTOR-3353", "HUMIL2-ACCEPTOR-3038", "HUMIL2A-ACCEPTOR-3975", "HUMIL2B-ACCEPTOR-2852", "HUMMRP8A-ACCEPTOR-2302", "HUMPCNA-ACCEPTOR-2520", "HUMGG-ACCEPTOR-1802", "ORAHBG2F-ACCEPTOR-392", "HUMHSP90B-ACCEPTOR-3351", "HUMAFP-ACCEPTOR-5216", "HUMTPA-ACCEPTOR-23130", "HUMHBBAG-ACCEPTOR-1466", "HUMHBBGG-ACCEPTOR-2363", "HUMCFXI14-NEG-481", "HUMTPA-ACCEPTOR-29440", "HUMGCB1-ACCEPTOR-949", "HUMLDL100-NEG-5941", "HUMCYPIIE-ACCEPTOR-12808", "HUMALBGC-ACCEPTOR-15504", "HUMTCBYX-NEG-1", "GIBHBGGL-ACCEPTOR-2401", "GIBHBGGL-ACCEPTOR-7321", "MACHBCA2-ACCEPTOR-586", "MACHBGA1-ACCEPTOR-588", "HUMCRYGBC-ACCEPTOR-18489", "HUMEF1A-ACCEPTOR-2347", "HUMSODA-ACCEPTOR-3793", "HUMATPSYB-ACCEPTOR-2698", "HUMSPRO-ACCEPTOR-2841", "HUMATP1A2-ACCEPTOR-10913", "HUMCYPIIE-ACCEPTOR-13834", "HUMKER18-ACCEPTOR-4857", "HUMCYPIIE-ACCEPTOR-12121", "HUMCYPIIE-NEG-12121", "HUMDES-ACCEPTOR-2504", "HUMHSP90B-ACCEPTOR-7462", "HUMATP1A2-ACCEPTOR-24266", "HUMMHANTLE-ACCEPTOR-3667", "HUMMHANTLE-ACCEPTOR-3842", "LEMHBE-ACCEPTOR-410", "HUMCYPIIE-ACCEPTOR-6976", "HUMPRPH1-ACCEPTOR-3136", "HUMPRPH2-ACCEPTOR-2241", "HUMAFP-ACCEPTOR-15169", "HUMGRP78-ACCEPTOR-4506", "HUMBLYM1-ACCEPTOR-505", "HUMFIXG-ACCEPTOR-13327", "HUMPRL1-NEG-61", "HUMALPPD-ACCEPTOR-734", "HUMSAA-ACCEPTOR-3086", "HUMHSP90B-ACCEPTOR-5710", "ORAHBG1F-ACCEPTOR-391", "HUMTHB-ACCEPTOR-9825", "HUMGRP78-ACCEPTOR-3371", "HUMEF1A-ACCEPTOR-2727", "HUMA1ATP-ACCEPTOR-10909", "HUMCACY-ACCEPTOR-2504", "HUMSODA-ACCEPTOR-4604", "HUMCKMT-ACCEPTOR-4348", "HUMATPSYB-ACCEPTOR-7858", "HUMGHN-ACCEPTOR-1166", "HUMMHDRHA-ACCEPTOR-5181", "HUMATPSYB-ACCEPTOR-5220", "HUMSAA1A-ACCEPTOR-6290", "HUMEF1A-ACCEPTOR-2062", "HUMCS1-ACCEPTOR-1184", "HUMGHCSA-ACCEPTOR-28101", "HUMGHCSA-ACCEPTOR-50922", "HUMTPA-ACCEPTOR-31083", "HUMPLAPL-ACCEPTOR-987", "HUMINCPPS-ACCEPTOR-4028", "HUMCATF-ACCEPTOR-1108", "HUMGHCSA-ACCEPTOR-42765", "HUMIRBPG-ACCEPTOR-4936", "HUMFBRAA-NEG-1", "HUMGH-ACCEPTOR-941", "HUMGHCSA-ACCEPTOR-5835", "HUMPLPSPC-ACCEPTOR-2621", "HUMCKMT-ACCEPTOR-1529", "HUMHSP90B-ACCEPTOR-3649", "HUMP45C17-ACCEPTOR-6267", "HUMTUBBM-ACCEPTOR-1850", "HUMSHBGA-ACCEPTOR-5640", "HUMEF1A-ACCEPTOR-2965", "HUMRPS17A-ACCEPTOR-1611", "HUMP45C17-ACCEPTOR-5819", "HUMHSP90B-ACCEPTOR-5972", "HUMTHB-ACCEPTOR-20170", "HUMKEREP-ACCEPTOR-2715", "HUMP45C17-ACCEPTOR-3774", "HUMCYPIIE-DONOR-12309", "HUMINSRC-NEG-2101", "HUMIL1B-DONOR-3748", "HUMALPPD-ACCEPTOR-985", "HUMGFP40H-ACCEPTOR-1110", "HUMADAG-ACCEPTOR-35621"), ValueGroup("ATRINS-DONOR-905", "HUMERP-DONOR-598", "HUMERPA-DONOR-608", "HUMKEREP-DONOR-3892", "HUMSAACT-DONOR-2210", "HUMOTNPI-DONOR-508", "HUMTUBBM-DONOR-1187", "HUMMYLCA-DONOR-952", "HUMMYLCA-DONOR-1975", "HUMCFVII-DONOR-4425", "HUMH19-DONOR-2128", "HUMHMG14A-DONOR-1039", "HUMSAACT-DONOR-2770", "HUMHBA4-DONOR-10579", "HUMHBA4-DONOR-6768", "MACHBA-DONOR-121", "HUMRPS17A-DONOR-615", "GCRHBBA1-DONOR-1260", "BABAPOE-DONOR-2817", "HUMMHCW2B-DONOR-1155", "HUMP45C17-DONOR-2106", "HUMACTGA-DONOR-1344", "HUMMHANTLE-DONOR-1633", "HUMACCYBA-DONOR-1624", "HUMACCYBB-DONOR-1558", "HUMDES-DONOR-2630", "HUMALPPD-DONOR-2590", "HUMMHB27B-DONOR-960", "HUMPIM1A-DONOR-1222", "HUMMHCW1B-DONOR-1152", "HUMCSPB-DONOR-258", "HUMCYC1A-DONOR-1506", "HUMCYPIIE-DONOR-4037", "HUMAPOE4-DONOR-3170", "HUMBNPA-DONOR-1087", "HUMATP1A2-DONOR-21929", "HUMHPRTB-DONOR-1674", "HUMP45C17-DONOR-7438", "HUMTUBAG-DONOR-506", "HUMPCNA-DONOR-1618", "GCRHBBA6-DONOR-461", "HUMPRCA-DONOR-2171", "HUMDES-DONOR-711", "HUMUBILP-DONOR-603", "HUMRASH-DONOR-2504", "HUMAPOAI1-DONOR-457", "HUMACTGA-DONOR-2321", "HUMTNFA-DONOR-952", "HUMTNFAB-DONOR-4431", "HUMTNFX-DONOR-726", "HUMAPOAIT-DONOR-201", "HUMA1GLY2-DONOR-1693", "HUMERP-DONOR-1310", "HUMERPA-DONOR-1317", "HUMCACY-DONOR-2132", "HUMACTGA-DONOR-513", "HUMPIM1A-DONOR-1594", "HUMADAG-DONOR-29909", "HUMADAG-DONOR-32467", "HUMADAM2-DONOR-708", "HUMMHEA-DONOR-1139", "HUMALPHA-DONOR-698", "HUMCAPG-DONOR-375", "HUMFOS-DONOR-1000", "HUMP45C17-DONOR-3913", "HUMA1GLY2-DONOR-2251", "HUMINCP-DONOR-539", "HUMKEREP-DONOR-4033", "HUMEDHB17-DONOR-2795", "HUMGCB1-DONOR-4319", "HUMCS1-DONOR-1304", "HUMGHCSA-DONOR-51042", "HUMKER18-DONOR-5233", "HUMACCYBA-DONOR-289", "HUMACCYBB-DONOR-290", "HUMAFP-DONOR-15308", "HUMGHCSA-DONOR-5955", "HUMGHN-DONOR-1286", "HUMPRCA-DONOR-5088", "HUMPSAA-DONOR-4293", "HUMCFVII-DONOR-10218", "HUMTPA-DONOR-26070", "HUMDES-DONOR-7366", "HUMGHCSA-DONOR-6212", "HUMGHN-DONOR-1544", "HUMPLAPL-DONOR-569", "HUMTKRA-DONOR-556", "HUMRPS14-DONOR-4214", "HUMTFPB-DONOR-10205", "HUMDES-DONOR-1824", "HUMCSFGMA-DONOR-1746", "HUMTFPB-DONOR-992", "HUMTPA-DONOR-29526", "HUMHPARS1-DONOR-1016", "HUMPOMC-DONOR-737", "HUMPPPA-DONOR-2234", "HUMATP1A2-DONOR-19518", "HUMGHCSA-DONOR-51300", "HUMGCB1-DONOR-5411", "HUMTPA-DONOR-31536", "HUMGHCSA-DONOR-28221", "HUMCP21OH-DONOR-917", "HUMCP21OHC-DONOR-918", "HUMMHCP42-DONOR-1848", "HUMMHCP52-DONOR-382", "HUMGH-DONOR-1061", "HUMALPPD-DONOR-309", "HUMCFVII-DONOR-6562", "HUMMYC3L-DONOR-1271", "HUMMHCP52-DONOR-1594", "HUMACCYBA-DONOR-2781", "HUMACCYBB-DONOR-2715", "HUMKER18-DONOR-5719", "HUMATP1A2-DONOR-22962", "HUMMH6-DONOR-1225", "HUMUBILP-DONOR-1367", "CHPIGECA-DONOR-903", "ORAIGECA-DONOR-904", "HUMHST-DONOR-3582", "HUMGHCSA-DONOR-13807", "HUMTKRA-DONOR-2441", "HUMPSAP-DONOR-170", "HUMCRYGBC-DONOR-2468", "HUMGHCSA-DONOR-28478", "HUMAPRTA-DONOR-2331", "HUMCAPG-DONOR-2299", "HUMLPL-NEG-181", "HUMEF1A-DONOR-579", "HUMCP21OH-DONOR-1541", "HUMCP21OHC-DONOR-1542", "HUMMHCP42-DONOR-2471", "HUMMHCP52-DONOR-1006", "HUMCP21OH-DONOR-1750", "HUMCP21OHC-DONOR-1751", "HUMMHCP42-DONOR-2679", "HUMMHCP52-DONOR-1216", "HUMATP1A2-DONOR-15496", "HUMALPPD-DONOR-1715", "HUMERP-DONOR-2444", "HUMERPA-DONOR-2453", "HUMAK1-DONOR-3959", "HUMCP21OH-DONOR-2128", "HUMMHCP42-DONOR-3057", "HUMTACEC-NEG-2341", "HUMA1ATP-DONOR-11057", "HUMATP1A2-DONOR-1663", "HUMH19-DONOR-2562", "HUMH19-DONOR-2359", "HUMKAL2-DONOR-4031", "HUMALPI-DONOR-532", "HUMPSAP-DONOR-2452", "HUMGHCSA-DONOR-42885", "HUMTKRA-DONOR-12438", "HUMA1GLY2-DONOR-2540", "HUMMHCD8A-DONOR-2891", "HUMA1ATP-DONOR-1972", "HUMCP21OHC-DONOR-2129", "HUMNGFR-NEG-2821", "HUMCS1-DONOR-1561", "HUMH19-DONOR-2766", "HUMADAG-DONOR-32886", "HUMGHVA-DONOR-488", "HUMG0S19A-DONOR-2927", "HUMG0S19B-DONOR-3063", "HUMCACY-DONOR-1386", "HUMGHCSA-DONOR-13551", "HUMACCYBA-DONOR-2504", "HUMACCYBB-DONOR-2438", "HUMCYC1A-DONOR-3019", "HUMTHB-DONOR-4453", "HUMALPHA-DONOR-2096", "HUMCSPB-DONOR-2010", "HUMPPPA-DONOR-965", "HUMALPI-DONOR-762", "HUMHPARS1-DONOR-2990", "HUMHPARS1-DONOR-4714", "HUMTKRA-DONOR-4909", "HUMTNFA-DONOR-1604", "HUMTNFAB-DONOR-5083", "HUMTNFX-DONOR-1378", "HUMCSPB-DONOR-1408", "HUMGAST2-DONOR-786", "HUMGASTA-DONOR-6470", "HUMGCB1-DONOR-1782", "HUMNMCY1A-NEG-661", "HUMATP1A2-DONOR-9194", "HUMFOS-DONOR-2005", "HUMCYPIIE-DONOR-7126", "HUMSAACT-DONOR-1779", "HUMMYCC-DONOR-5248", "HUMCFVII-DONOR-9499", "HUMALPI-DONOR-1934", "HUMTPA-DONOR-23748", "HUMLACTA-DONOR-1671", "HUMGHCSA-DONOR-43141", "HUMMIS-DONOR-593", "HUMCRYGBC-DONOR-2131", "HUMRASH-DONOR-1745", "HUMEDHB17-DONOR-2199", "HUMALPHA-DONOR-925", "HUMPLAPL-DONOR-797", "HUMATPSYB-DONOR-2217", "HUMP45C17-DONOR-4376", "HUMMETIF1-DONOR-480", "HUMALPPD-DONOR-100", "HUMPLAPL-DONOR-373", "HUMTPA-DONOR-31220", "HUMHMG14A-DONOR-1517", "HUMTNFAB-DONOR-950", "HUMPOMC-DONOR-4597", "HUMCFVII-DONOR-1690", "HUMPRCA-DONOR-5555", "HUMCYPIIE-DONOR-8260", "HUMMT2A-DONOR-421", "HUMINCP-DONOR-2163", "HUMGCB1-DONOR-5974", "HUMADAG-DONOR-26437", "HUMAPOA4A-DONOR-1025", "HUMALPHA-DONOR-487", "ORAHBG2F-DONOR-615", "HUMCAPG-DONOR-1279", "HUMGH-DONOR-1319", "HUMMT2A-DONOR-1113", "HUMATP1A2-DONOR-21613", "HUMALPPD-DONOR-537", "HUMRPS17A-DONOR-171", "HUMGHCSA-DONOR-12784", "HUMGHCSA-DONOR-42122", "HUMMYCRT-DONOR-1883", "HUMMHCD8A-DONOR-638", "HUMP45C17-DONOR-6437", "HUMALPI-DONOR-1389", "HUMTHB-DONOR-8003", "HUMAK1-DONOR-6743", "HUMMRP14A-DONOR-999", "HUMADAG-DONOR-19262", "HUMPLPSPC-DONOR-1953", "HUMMETIF1-DONOR-1131", "HUMGHCSA-DONOR-5205", "HUMCS1-DONOR-535", "HUMGHCSA-DONOR-27451", "HUMALPPD-DONOR-1158", "HUMMHEA-DONOR-1657", "HUMCFVII-DONOR-6378", "HUMA1ATP-DONOR-9653", "HUMGHCSA-DONOR-50275", "HUMGSA1R-NEG-661", "HUMMHDOB-DONOR-4235", "HUMGH-DONOR-315", "HUMIRBPG-DONOR-3151", "HUMCP21OH-DONOR-1940", "HUMCP21OHC-DONOR-1941", "HUMMHCP42-DONOR-2869", "HUMMHCP52-DONOR-1406", "HUMALPI-DONOR-333", "HUMCKMT-DONOR-1326", "HUMCRYGBC-DONOR-18732", "HUMGHN-DONOR-539", "HUMSHBGA-DONOR-3802", "HUMCRPG-DONOR-300", "HUMCRPGA-DONOR-261", "HUMP45C17-DONOR-6035", "HUMMHDRHA-DONOR-3215", "HUMVPNP-DONOR-315", "HUMHSP27P-NEG-601", "HUMPGAMMG-DONOR-1347", "HUMAPOAIT-DONOR-461", "ATRINS-DONOR-521", "HUMSHBGA-DONOR-2948", "HUMTHY1A-DONOR-853", "HUMG0S19A-DONOR-2124", "HUMIRBPG-DONOR-7130", "HUMG0S19B-DONOR-2263", "HUMTPA-DONOR-30972", "HUMAPOAI1-DONOR-716", "HUMPSAP-DONOR-1188", "HUMATP1A2-DONOR-11031", "HUMADAG-DONOR-35173", "HUMSPBAA-NEG-3061", "HUMCFVII-DONOR-556", "HUMCFVII-DONOR-8418", "HUMALDOA-NEG-4501", "HUMCAPG-DONOR-1869", "HUMAICEB-NEG-2641", "HUMAPOCIA-DONOR-2092", "HUMACTGA-ACCEPTOR-2139"), ValueGroup("HUMMYLCA-DONOR-644", "HUMALPI1-DONOR-42", "HUMMHCD8A-DONOR-1781", "HUMVPNP-DONOR-1890", "HUMPSAP-DONOR-1621", "HUMHST-DONOR-2861", "HUMFGFB-NEG-181", "HUMPRCA-DONOR-5318", "HUMAPRTA-DONOR-1982", "HUMCSF1G1-NEG-361", "HUMDES-DONOR-3236", "HUMALPHA-DONOR-2968", "HUMGRP78-DONOR-685", "HUMADAG-DONOR-4034", "HUMTUBBM-DONOR-775", "HUMMYC3L-DONOR-402", "HUMATP1A2-DONOR-20891", "CHPIGECA-DONOR-1313", "HUMHMG14A-DONOR-1394", "HUMMHB27B-DONOR-2538", "HUMMHB27B-DONOR-3013", "HUMMHEA-DONOR-3578", "HUMMHCW1B-DONOR-2778", "HUMMHCW2B-DONOR-2780", "ORAIGECA-DONOR-1314", "HUMPIM1A-DONOR-1442", "HUMGSHPXG-DONOR-690", "HUMEDHB17-DONOR-3489", "HUMMHB27B-DONOR-561", "HUMMIS-DONOR-1609", "LEMHBGA-DONOR-302", "HUMPRT1A-DONOR-118", "HUMMHCW1B-DONOR-3250", "HUMMHCW1B-DONOR-754", "HUMMHCW2B-DONOR-756", "HUMPNMTA-DONOR-3337", "HUMPSAA-DONOR-2089", "HUMANDREC-NEG-1681", "HUMMH6-DONOR-826", "HUMANT1-DONOR-3442", "HUMOTNPI-DONOR-1007", "HUMMRP8A-DONOR-2152", "HUMMHCW1B-DONOR-2537", "HUMATP1A2-DONOR-6788", "LEMHBG-DONOR-241", "HUMPRF1A-DONOR-3783", "HUMMHANTLE-DONOR-3227", "HUMAK1-DONOR-5876", "HUMALPPD-DONOR-1543", "HUMACTGA-DONOR-1015", "HUMMH6-DONOR-2841", "HUMSAACT-DONOR-769", "HUMMHCW2B-DONOR-3252", "BABAPOE-DONOR-30", "HUMAPOE4-DONOR-1061", "GCRHBBA1-DONOR-1590", "HUMALPI-DONOR-2815", "HUMPLPSPC-DONOR-1485", "HUMSAACT-DONOR-3038", "HUMMHEA-DONOR-2795", "HUMSODA-DONOR-327", "HUMADAG-DONOR-32645", "HUMALPI-DONOR-1766", "MACHBA-DONOR-443", "HUMHBA4-DONOR-10901", "HUMHBA4-DONOR-7090", "HUMMHEA-DONOR-739", "HUMAPOC2-DONOR-522", "HUMAPOC2G-DONOR-225", "HUMAPOCII-DONOR-541", "HUMKAL2-DONOR-539", "HUMMHCW2B-DONOR-2539", "HUMTHB-DONOR-7792", "HUMALPI-DONOR-1140", "HUMALPHA-DONOR-2359", "HUMMHDOB-DONOR-2919", "HUMRPS14-DONOR-225", "HUMPNMTA-DONOR-2130", "HUMCP21OH-DONOR-3064", "HUMCP21OHC-DONOR-3066", "HUMMHCP52-DONOR-2531", "HUMMHANTLE-DONOR-3889", "HUMTHB-DONOR-1044", "HUMOPS-DONOR-3949", "HUMCYCAA-DONOR-115", "HUMTUBBM-DONOR-1376", "GIBHBGGL-DONOR-2278", "HUMHBBAG-DONOR-1344", "HUMHBBGG-DONOR-2241", "ORAHBG2F-DONOR-269", "HUMOPS-DONOR-626", "HUMTNFAB-DONOR-1345", "HUMATP1A2-DONOR-20500", "HUMAPOAI1-DONOR-1059", "HUMAPOAIT-DONOR-805", "HUMALPHA-DONOR-1924", "GIBHBGGL-DONOR-7198", "HUMANT1-DONOR-1688", "HUMPSAA-DONOR-691", "HUMACCYBA-DONOR-1250", "HUMACCYBB-DONOR-1186", "GCRHBBA6-DONOR-795", "ORAHBG1F-DONOR-269", "HUMGPIBAA-DONOR-2800", "HUMBNPA-DONOR-600", "HUMKER18-DONOR-5022", "HUMALPPD-DONOR-1980", "HUMALPPD-DONOR-909", "HUMEDHB17-DONOR-2526", "HUMPRF1A-DONOR-1477", "HUMSNU1P2A-NEG-301", "HUMPIM1A-DONOR-2054", "HUMKAL2-DONOR-3781", "HUMATP1A2-DONOR-12719", "HUMTPA-DONOR-28433", "HUMRPS14-DONOR-3034", "HUMCP21OH-DONOR-2877", "HUMMHCP42-DONOR-3806", "HUMMHCP52-DONOR-2344", "HUMPSAA-DONOR-4013", "HUMTHB-DONOR-7339", "HUMKER18-DONOR-3791", "HUMANFA-DONOR-1112", "MACHBCA2-DONOR-463", "MACHBGA1-DONOR-465", "HUMTHB-DONOR-4686", "HUMGCB1-DONOR-6461", "HUMUBILP-DONOR-912", "HUMCS1-DONOR-961", "HUMGHCSA-DONOR-27878", "HUMGHCSA-DONOR-50700", "HUMALPI-DONOR-2199", "HUMALPHA-DONOR-1292", "HUMMHEA-DONOR-2554", "HUMCKMT-DONOR-523", "HUMSAA-DONOR-2706", "HUMHMG14A-DONOR-4792", "HUMMHANTLE-DONOR-3008", "HUMGCB1-DONOR-1037", "HUMKAL2-DONOR-1905", "HUMSAA1A-DONOR-3240", "HUMTHB-DONOR-497", "HUMDES-DONOR-5457", "HUMSHBGA-DONOR-3310", "HUMSPRO-DONOR-1947", "HUMMHEA-DONOR-3725", "HUMMH6-DONOR-2602", "HUMFOLA1-NEG-541", "HUMGASTA-DONOR-3213", "HUMMHDRHA-DONOR-4442", "HUMLYL1B-DONOR-882", "HUMLYL1B-DONOR-2722", "HUMMHCP42-DONOR-3993", "HUMATP1A2-DONOR-13749", "HUMADAG-DONOR-29022", "HUMCP21OHC-DONOR-2878", "HUMAK1-DONOR-10237", "HUMAPOC2-DONOR-2976", "HUMAPOC2G-DONOR-2684", "HUMAPOCII-DONOR-2965", "HUMATP1A2-DONOR-15190", "HUMMHB27B-DONOR-2329", "HUMAPOCIA-DONOR-712", "HUMALPHA-DONOR-2632", "HUMALPPD-DONOR-2254", "HUMATP1A2-DONOR-7029", "HUMTPA-DONOR-3683", "HUMPLPSPC-DONOR-628", "HUMCYC1A-DONOR-2526", "HUMTKRA-DONOR-11961", "HUMALPI-DONOR-2475", "HUMDES-DONOR-2326", "HUMPGAMMG-DONOR-1631", "HUMATP1A2-DONOR-9834", "HUMSPRO-DONOR-2998", "HUMMIS-DONOR-1328", "HUMMYCRT-DONOR-888", "HUMATP1A2-DONOR-20193", "HUMALPHA-DONOR-1542", "HUMMHANTLE-DONOR-1239", "HUMCSFGMA-DONOR-793", "HUMMHCW1B-NEG-241", "HUMTKRA-DONOR-698", "HUMCYC1A-DONOR-2305", "HUMTPA-DONOR-22213", "HUMGH-DONOR-732", "HUMGHCSA-DONOR-5626", "HUMGHN-DONOR-959", "HUMHMG14A-DONOR-1647", "HUMPRCA-DONOR-8366", "HUMCKMT-DONOR-2233", "HUMTHB-DONOR-2021", "HUMKER18-DONOR-4285", "HUMKEREP-DONOR-3577", "HUMADAG-DONOR-34454", "HUMMHCD8A-DONOR-2098", "HUMGHCSA-DONOR-13226", "HUMCYC1A-DONOR-3382", "HUMTHB-DONOR-9999", "HUMAK1-DONOR-5540", "HUMGFP40H-DONOR-880", "HUMATP1A2-DONOR-22719", "HUMMRP8A-DONOR-1504", "HUMSAACT-DONOR-2499", "HUMGCB1-DONOR-3526", "HUMCKMT-DONOR-4483", "HUMTNFX-NEG-1681", "HUMANFA-DONOR-663", "HUMTPA-DONOR-19780", "HUMATP1A2-DONOR-21268", "HUMPRPH2-DONOR-917", "HUMAPOA4A-DONOR-1509", "HUMTPA-DONOR-32565", "HUMATPSYB-DONOR-5379", "HUMADAG-DONOR-31274", "HUMTHB-ACCEPTOR-4347", "HUMMHDRHA-DONOR-565", "HUMAK1-ACCEPTOR-5712", "HUMAPOE4-DONOR-1884", "ATRINS-NEG-841", "HUMSPRO-DONOR-2585", "HUMTHB-DONOR-10723", "HUMSODA-DONOR-4951", "HUMPRCA-DONOR-9357", "HUMMHCW1B-DONOR-3405", "HUMMHCW2B-DONOR-3407", "HUMEDHB17-DONOR-1938", "HUMOPS-DONOR-4305", "HUMTPA-DONOR-23241", "HUMTHB-DONOR-1728", "HUMAPRTA-DONOR-881", "HUMCYC1A-DONOR-2769", "HUMTHB-DONOR-9328", "CHPIGECA-DONOR-378", "HUMPLPSPC-DONOR-2287", "HUMKEREP-DONOR-814", "HUMGHCSA-DONOR-42555", "HUMMH6-DONOR-3506", "HUMFIL-NEG-841", "HUMPGAMMG-NEG-2221", "HUMIRBPG-DONOR-5127", "HUMSPRO-DONOR-1735", "HUMKER18-DONOR-2967", "ORAIGECA-DONOR-378", "BABAPOE-DONOR-867", "HUMGCB1-DONOR-2052", "HUMCETP6-NEG-481", "HUMSODA-DONOR-3967", "HUMKEREP-DONOR-2153", "HUMCYPIIE-DONOR-9284", "HUMHPARS1-DONOR-2602", "HUMTHB-DONOR-20241", "HUMSPRO-DONOR-2367", "TARHBB-DONOR-1560", "TARHBD-DONOR-468", "HUMCR1R-NEG-2581", "HUMPRCA-NEG-8881", "HUMHPARS1-DONOR-5547", "HUMRASH-DONOR-2191", "HUMPIM1A-DONOR-3735", "LEMHBB-DONOR-216", "HUMAPOCIA-DONOR-496", "HUMPGK1-NEG-181", "HUMRPS14-DONOR-2259", "HUMHLADPB-NEG-8761", "HUMCKMT-DONOR-2687", "HUMSPRO-DONOR-3348", "HUMPIM1A-ACCEPTOR-1543", "HUMSHBGA-DONOR-4456", "HUMATP1A2-DONOR-10208", "HUMMHSXA-NEG-1", "HUMG6PA-NEG-781", "HUMCP21OH-DONOR-2498", "HUMCP21OHC-DONOR-2499", "HUMMHCP42-DONOR-3427", "HUMMHCP52-DONOR-1964", "HUMATP1A2-DONOR-14299", "HUMAPOC2-DONOR-3303", "HUMAPOC2G-DONOR-3011", "HUMAPOCII-DONOR-3291", "HUMA1MICR-NEG-1081", "HUMPPPA-DONOR-1912", "HUMHSP90B-ACCEPTOR-3995", "HUMCAPG-ACCEPTOR-1131", "HUMIL1B-DONOR-941", "HUMADRBRA-NEG-2941"), ValueGroup("HUMFGFG1-NEG-901", "HUMCOLTHA-NEG-481", "HUMGLTH1-NEG-241", "HUMKRASP-NEG-541", "HUMIGF2AP-NEG-421", "HUMPLP1-NEG-301", "HUMRASH-NEG-601", "HUMINT2-NEG-481", "HUMPLG2B-NEG-181", "HUMRASSK2-NEG-1381", "HUMADRA-NEG-961", "ORARGIT-NEG-241", "HUMPDGA1-NEG-481", "HUMSISM-NEG-241", "HUMMUC18B-NEG-61", "HUMNAKATP1-NEG-721", "HUMSRTR2A-NEG-61", "HUMALDAR-NEG-1081", "HUMEDHB17-NEG-3541", "HUMMHAW-NEG-1201", "HUMARSBX-NEG-421", "HUMVWFM-NEG-5101", "HUMCYPB1-NEG-1141", "HUMRYR-NEG-1081", "HUMHBA4-NEG-6601", "HUMUPAA-NEG-1501", "HUMHBA3-NEG-1021", "HUMMG2-NEG-541", "HUMFESFUR-NEG-3481", "HUMIGHAB-NEG-121", "HUMTHYR01-NEG-181", "HUMGSHPXG-NEG-1081", "HUMIFNB3-NEG-5641", "HUMRENT1-NEG-601", "HUMPTCAA-NEG-481", "HUMA2PIG3-NEG-541", "HUMINHA-NEG-1", "HUMAPOBG-NEG-121", "HUMNARIA-NEG-3841", "ORAHBA01-NEG-121", "HUMSISG1-NEG-5401", "HUMCD1A-NEG-1261", "HUMCOL4A2-NEG-2581", "HUMGIR-NEG-121", "HUMPLPSPC-NEG-1801", "HUMTSG14A-NEG-301", "HUMOAT01-NEG-1", "HUMGENX-NEG-2341", "CHPRGITX-NEG-1441", "HUMCG2A1-NEG-1", "HUMHKR42-NEG-181", "HUMKERMII-NEG-181", "HUMCOL4A6-NEG-301", "HUMALLFU-NEG-61", "HUMMHCBWC-NEG-121", "HUMETS2PR-NEG-901", "HUMA1ATP-NEG-301", "HUMSB3B2-NEG-121", "HUMADRA2R-NEG-2581", "HUMEF2AB-NEG-721", "HUMNMYC-NEG-1561", "HUMEL01-NEG-901", "HUMMT2A-NEG-121", "HUMMYHC08-NEG-421", "HUMATP1A2-NEG-2101", "HUMTIR-NEG-901", "HUMMHB27B-NEG-1201", "HUMTPA-NEG-31561", "HUMIGMUTDM-NEG-361", "HUMMHCAR4-NEG-241", "HUMU170SC-NEG-481", "HUMCKBBA-NEG-481", "HUMBCL3AA-NEG-1381", "HUMCOL2A1-NEG-961", "MACAPOE-NEG-181", "HUMMHBW4-NEG-61", "HUMTRK2H-NEG-1081", "HUMERSP2B-NEG-781", "HUMICAM1-NEG-481", "HUMCINHP-NEG-121", "HUMAPOA4-NEG-61", "HUMRGLTIB-NEG-721", "HUMEGFAA-NEG-421", "HUMTFP-NEG-61", "HUMCSFGM-NEG-1", "HUMMHCP52-NEG-781", "HUMPAC-NEG-541", "HUMBCR-NEG-1801", "HUMCFXII3-NEG-3061", "HUMGP-NEG-1321", "HUMAK1-NEG-10921", "HUMMHDPBF-NEG-61", "HUMINSR2-NEG-1", "HUMCOL11A2-NEG-1861", "HUMLDLRRL-NEG-11761", "HUMERPA-NEG-1021", "AGMORS12A-NEG-181", "HUMMHCW1B-ACCEPTOR-1398", "HUMRYR-NEG-14761", "HUMMHCW2B-ACCEPTOR-1399", "HUMTHX-NEG-841", "HUMMHCB44A-NEG-361", "HUMHSP70D-NEG-1441", "HUMGA7331-NEG-241", "HUMPRC1-NEG-61", "HUMMHDRO2-NEG-2461", "HUMTKRA-NEG-12841", "HUMSPBAA-NEG-7621", "HUMMYCF1-NEG-1081", "HUMC3A1R-NEG-2941", "HUMANK-NEG-1981", "ORAINVOL-NEG-2161", "HUMRYR-NEG-10201", "HUMFESFPS-NEG-6661", "HUMMHDRBC-NEG-661", "HUMERVKA2-NEG-901", "HUMPROD4-NEG-721", "HUMGSTPIG-NEG-1381", "HUMBHAA-NEG-1261", "HUMHCKA-NEG-61", "HUMINT2-NEG-5041", "HUMRGE-NEG-1801", "HUM4F2HG1-NEG-1441", "HUMMYONM-NEG-1501", "HUMIGKAR-NEG-181", "HUMME491-NEG-601", "HUMET-NEG-301", "HUMATP-NEG-1981", "HUMLYL1B-NEG-4081", "HUMMHHLA-NEG-241", "MACHBB-NEG-4141", "HUMMK562B-NEG-1", "HUMPGKA09-NEG-241", "HUMCG5B-NEG-1261", "HUMEAR2-NEG-1381", "HUMTHMOD-NEG-421", "CHPAZGLO-NEG-1", "HUMOCS1-NEG-1", "HUMCRYGBC-NEG-3781", "HUMMPO1-NEG-1681", "HUMALPPB-NEG-781", "HUMAPRTA-NEG-1981", "HUMSERDHY-NEG-241", "HUMKER654-NEG-1441", "HUMNFM-NEG-1921", "HUMPROD1-NEG-1741", "HUMSAP1-NEG-121", "HUMCCK2-NEG-361", "HUMMETIA-NEG-1921", "HUMCP21OH-NEG-1981", "HUMALDC-NEG-61", "HUMASP-NEG-541", "HUMMHEA-ACCEPTOR-1381", "HUMPOVRA-NEG-541", "HUMCP21OHC-NEG-3061", "HUMOTNPI-NEG-481", "GCRHBBA1-NEG-61", "HUM5FDX-NEG-2041", "HUMATP1A2-NEG-20341", "HUMMRP8A-NEG-1381", "HUMHSP90-NEG-1", "HUMCNRAB-NEG-1741", "HUMHLA1EA-NEG-121", "HUMKERP2-NEG-1381", "HUMVWFA04-NEG-301", "HUMGLYSA-NEG-2761", "HUMDB1A-NEG-121", "HUMYTEST-NEG-301", "HUMCP21OHA-NEG-2521", "HUMCYP450-NEG-7321", "HUMHBB-NEG-5761", "HUMMHCAG-NEG-1", "HUMGNPAS-NEG-61", "HUMBAT2A-NEG-2761", "HUMITI2-NEG-481", "HUMC1A1-NEG-3901", "HUMPLAP2A-NEG-481", "HUMMHDRBK-NEG-1321", "HUMAK1-NEG-6361", "HUMOPS-NEG-181", "HUMCAPG-NEG-1261", "HUMMPO-NEG-301", "HUMSPM1-NEG-181", "HUMVDR-NEG-1441", "HUMLD78R-NEG-1", "HUMMHDRB4A-NEG-781", "HUMSAACT-NEG-301", "HUMRNP70K-NEG-1141", "HUMTPA-NEG-8761", "HUMACROS-NEG-1", "HUMAPOA4C-NEG-2761", "HUMFVII-ACCEPTOR-136", "HUMHPA2BR-NEG-1261", "HUMGLI-NEG-901", "HUMCSPCP-NEG-1261", "HUMIGHBU-NEG-301", "HUMVIL2-NEG-1441", "HUMGRP78-NEG-421", "HUMFGRINT-NEG-421", "HUMC4A1-NEG-2761", "HUMHER2A-NEG-3301", "HUMFNRA-NEG-361", "HUMBCRD-NEG-301", "HUMNMYCA-NEG-4561", "HUMLAMP1A-NEG-841", "HUMARF1A-NEG-301", "HUMMHC1-NEG-4321", "HUMMLC2A-NEG-1021", "TARHBD-NEG-1981", "HUMFCERA-NEG-121", "HUMMHCP42-NEG-4501", "HUMRCC1B-NEG-361", "HUMIGCD2-NEG-121", "HUMNMYCA-NEG-1", "HUMMHM71-NEG-181", "HUMAPOCIP-NEG-301", "HUMTPO03-NEG-61", "HUMMHANTLE-NEG-3181", "HUMMH6-ACCEPTOR-1451", "HUMTRKR-NEG-1201", "HUMMHB27B-ACCEPTOR-1202", "HUMACCYBB-NEG-3481", "HUMHMPFK-NEG-901", "HUMMHDC3B-NEG-2461", "HUMMHCACA-NEG-181", "HUMUPAX-NEG-3541", "HUMAPLA-NEG-1261", "HUMFNRAS-NEG-601", "HUMCG2A1-NEG-4561", "BABBMYHA-NEG-301", "HUMGFIIM-NEG-121", "HUMP68-NEG-1081", "HUMCRYGBC-NEG-12901", "HUMRUN1C2-NEG-241", "HUMHPRTB-NEG-49201", "HUMITF1-NEG-601", "GCRHBBA6-ACCEPTOR-920", "HUMHLACW2-NEG-781", "HUMAPOE3-NEG-1", "HUMTM-NEG-1441", "HUMPAIB1-NEG-61", "HUMCKMT-NEG-1321", "HUMCAL-NEG-301", "HUMCY4ARO-NEG-541", "HUMCKMM1-NEG-1921", "HUMIDB-NEG-241", "HUMPMGR-NEG-1621", "CHPMHCBAA-NEG-841", "HUMELANA-NEG-1261", "HUMCARN-NEG-1561", "HUMIRBPG-NEG-3781", "HUMCSIST-NEG-2221", "HUMMHANTLE-DONOR-2150", "HUMTHB-NEG-17281", "HUMALIPOA-NEG-781", "HUMPIM1A-NEG-1741", "HUMELAM1A-NEG-1141", "HUMCOL1A07-NEG-1", "HUMDRD2A-NEG-841", "HUMTHR-NEG-1501", "HUMMYHC5-NEG-1141", "HUMP42LA-NEG-2161", "HUMNFH3-NEG-1", "MACPEPSG-NEG-2161", "HUMMHANTLE-ACCEPTOR-1874", "HUMMHCW3-NEG-2761", "HUMTGFKA-NEG-1141", "ATRINS-ACCEPTOR-1678", "HUMMHAWB-NEG-721", "HUMGGTA-NEG-661", "HUMACTGP1-NEG-481", "HUMDBHRA-NEG-1321", "HUMBAT3B1-NEG-421", "HUMRETREC-NEG-1321", "HUMRENA3-NEG-361", "HUMA1AR2-NEG-3601", "HUMHEXB01-NEG-481", "HUMMHCCWB-NEG-241", "HUMMH6-DONOR-1727", "HUMPOMC-NEG-421", "HUMHLADP4-NEG-721", "HUMMHCW2B-DONOR-1675", "HUMCYC1A-NEG-4381", "HUMIGHVH-NEG-481", "HUMGHCSA-NEG-38521", "HUMMYP-NEG-961", "HUMPEP7-NEG-1", "HUMMHB27B-DONOR-1478", "HUMMHCW1B-DONOR-1674", "MACHBA-NEG-421", "HUMTPOB-NEG-2401", "HUMFXIIIA-NEG-181", "HUMDEF1AA-NEG-121", "HUMPRCM-NEG-1681", "HUMENOG-NEG-121", "HUMCD7-NEG-241", "HUMMHDRBS-NEG-601", "HUMPIGF2A-NEG-301", "HUMRAFR-NEG-2521", "GCRHBBA1-ACCEPTOR-1706", "HUMIHRP-NEG-541", "HUMAMYA1-NEG-541", "HUMHKB3R-NEG-121", "HUMGFIA1-NEG-841", "HUM4COLA-NEG-1021", "HUMCKMT-NEG-5881", "HUMIGFII5-NEG-121", "HUMIGHVAQ-NEG-61", "HUMVWFR1-NEG-3481", "HUMGPMSS-NEG-1501", "HUMRGRSALU-NEG-1981", "HUMGCBPRC-NEG-1501", "HUMRAP2-NEG-181", "HUMMBPA-NEG-661", "HUMTHBNA-NEG-961", "HUMHLDPWB-NEG-61", "HUMINCP-NEG-3121", "HUMSPIA04-NEG-61", "HUMIL2RA-NEG-2161", "HUMHA44G-NEG-2701", "HUMGELS3-NEG-121", "HUMPALC-NEG-2281", "HUMIGCD7-NEG-1921", "HUMIGHVA-NEG-481", "HUMMIFA-NEG-181", "HUMERG11-NEG-181", "HUMIL2RBC-NEG-1681", "HUMTGLU-NEG-781", "HUMMHTRP-NEG-661", "HUMKER19-NEG-541", "HUMALDH03-NEG-1", "HUMCA2VR-NEG-361", "HUMCOLII2-NEG-781", "HUMINIFI-NEG-481", "HUMFCRHA-NEG-661", "HUMASGPR1-NEG-661", "HUMMHA2-NEG-1321", "HUMMHDR2A-NEG-61", "HUMMGI2-NEG-901", "HUMSYNPR-NEG-61", "HUMHCR-NEG-661", "HUMTCBVD-NEG-1", "HUMPBGDR2-NEG-1", "HUMTPAR-NEG-361", "HUMXCGD-NEG-1201", "HUMDBLPRO-NEG-181", "HUMCNPG3-NEG-121", "HUMALIPOA-NEG-9901", "HUMMHDRCD-NEG-601", "HUMCG1PA1-NEG-1501", "HUMPRF1A-NEG-3601", "HUMATP1A2-NEG-11221", "HUMCOX4A-NEG-301", "HUMTHY1A-ACCEPTOR-517", "HUMVWFR-NEG-3301", "HUMIGHCH-NEG-121", "HUMPCNA-NEG-301", "HUMDBH01-NEG-1321", "HUMCYP145-NEG-301", "HUMCYP2A4-NEG-1", "HUMMHCP41-NEG-541", "GCRRSAGAB-NEG-121", "HUMALPP-NEG-1981", "HUMALPPD-NEG-841", "HUMACTASK-NEG-1021", "HUMPPPA-NEG-1561", "HUMTBBM40-NEG-841", "HUMXEH-NEG-1441", "HUMRASFA2-NEG-1321", "HUMGCB-NEG-2281", "HUMPAIA-NEG-9781", "HUMTBB5-NEG-7801", "HUMADH21C-NEG-541", "HUMDES-NEG-7201", "HUMINT2-NEG-9601", "HUMVWF01-NEG-2101", "HUMFVII-NEG-721", "HUMSPTA1A-NEG-1861", "HUMADAG-ACCEPTOR-31146", "HUMGCSFR1-NEG-61", "HUMSYN25A-NEG-1", "HUMCG4A11-NEG-961", "HUMAPRT-NEG-421", "HUMCRYGBC-NEG-8341", "HUMRETPON-NEG-1261", "HUMTHBNB-NEG-3541", "HUMTF-NEG-1261", "HUMLDL100-NEG-1381", "HUMTCRDA4-NEG-661", "HUMBCL2A-NEG-4561", "HUMCOL1A25-NEG-121", "HUMMHDQOB-NEG-1", "HUMSLIPG-NEG-961", "HUMMH-NEG-3541", "HUMMHDQBR-NEG-601", "HUMADAG-NEG-4441", "HUMKERC4-NEG-961", "HUMMHC4A-NEG-2761", "HUMBGPG-NEG-1021", "MACHBDPS2-NEG-1", "HUMDES-ACCEPTOR-7283", "HUMADAG-NEG-36361", "HUMFCGRB-NEG-61", "HUMLPLR-NEG-361", "HUMTUBAFB-NEG-1", "HUMER41-NEG-3661", "HUMADPRT01-NEG-61", "HUMKEREP-NEG-3721", "HUMCYC1A-ACCEPTOR-2108", "HUMMYCL2A-NEG-2341", "HUMDMP-NEG-3481", "HUMCYC1A-ACCEPTOR-2611", "HUMC1R-NEG-1801", "HUMFNMC-NEG-3481", "HUMRGM-NEG-4441", "HUMIGKVJ2-NEG-961", "HUMCYC1A-ACCEPTOR-2858"), ValueGroup("HUMBCL2A-NEG-1", "HUMRGNTSA-NEG-2161", "HUMPDGFB1-NEG-1381", "HUMARP450-NEG-2521", "HUMIGCC8-NEG-481", "HUMPDGFA1-NEG-841", "ORAHBPSBD-NEG-2881", "HUMFUR1-NEG-3901", "HUMPYGM09-NEG-1", "HUMRASR2-NEG-1621", "HUMTPO13-NEG-1021", "HUMIIP-NEG-61", "HUMMYCBL1-NEG-1", "HUMSISPDG-NEG-2581", "HUMTRANSB-NEG-181", "HUMANTP53-NEG-121", "CHPCD4A-NEG-961", "HUMG3PD-NEG-481", "HUMATP1A2-NEG-6661", "HUMPGRR-NEG-1681", "HUMCENPBR-NEG-901", "HUMEGR2A-NEG-421", "HUMMHB39W-NEG-1", "HUMCYPBX3-NEG-241", "HUMBCI2-NEG-241", "HUMGFIA2-NEG-3961", "HUMLYL1A-NEG-841", "HUMFOLLI1-NEG-2041", "HUMUG2B-NEG-781", "HUMMHDRHA-NEG-5341", "HUMTFA01-NEG-1321", "HUMMYCLYA-NEG-781", "CHPINVOL-NEG-1", "HUMAK1-NEG-1801", "HUMINSPR-NEG-61", "HUMRENT2-NEG-4321", "HUMHXM-NEG-1321", "HUMHPRTB-NEG-3601", "HUMTUBBM-NEG-2281", "HUMLSZH-NEG-841", "HUMPRCA-NEG-4321", "HUMHMGIB-NEG-721", "HUMTGFBG1-NEG-1801", "HUMIRGT-NEG-1681", "HUMRBP1-NEG-421", "HUMTROPA3-NEG-901", "HUMIGCC4-NEG-781", "HUMMUCC-NEG-121", "HUMANTCE-NEG-841", "HUMIGCH5-NEG-241", "HUMKEREP9-NEG-241", "HUMAGG-NEG-541", "HUMRNPA2A-NEG-1201", "HUMU3AA-NEG-961", "HUMENKPH1-NEG-1141", "HUMMYCNH-NEG-1", "HUMMHCW2B-NEG-3721", "HUMTKR-NEG-2641", "HUMIL2RX6-NEG-1", "HUMMYCBLK-NEG-781", "HUMLAMAR-NEG-1081", "HUMLCK-NEG-181", "HUMHMGYB-NEG-1561", "HUMCRYGA1-NEG-181", "HUMMHADX2-NEG-1201", "HUMALPI-NEG-3901", "CHPCLA108-NEG-841", "HUMAPOBA-NEG-4141", "HUMBHA05-NEG-61", "HUMMHDQBC-NEG-841", "HUMIGLAM1-NEG-121", "HUMPAIA-NEG-14341", "HUMLAMC-NEG-181", "HUMCFVII-NEG-661", "HUMCYP345-NEG-1381", "HUMCN2-NEG-841", "HUMMSOD-NEG-61", "HUMALDCG-NEG-1441", "HUMTCBJD-NEG-361", "HUMIRBPM-NEG-3241", "HUMHSP90B-NEG-6541", "HUMVWFA29-NEG-661", "HUMINT1G-NEG-601", "HUMTGFB-NEG-541", "HUMARAF1R-NEG-1381", "HUMIGFIIR-NEG-6841", "HUMCRYGBC-NEG-22021", "HUMESTR-NEG-2161", "HUMTHMA-NEG-121", "HUMVWFA24-NEG-781", "HUMSGLT1-NEG-1141", "HUMVWFA17-NEG-301", "HUMBCR22I-NEG-1681", "HUMMGSA-NEG-1", "HUMCCG1-NEG-1621", "HUMTCBXF-NEG-541", "HUMBN51-NEG-1261", "HUMASA-NEG-61", "HUMTACEB-NEG-241", "HUMMHCAI-NEG-1", "HUMIL2R-NEG-1", "HUMMYCTR-NEG-2041", "HUMMPOA-NEG-1321", "HUMAPB18-NEG-601", "HUMMYCC-NEG-2761", "HUMTGTPV2-NEG-841", "HUMFERG2-NEG-1", "HUMCPA4-NEG-601", "HUMIGCD6-NEG-901", "HUMC1INHI-NEG-121", "HUMPLAKO-NEG-1861", "HUMPSAP-NEG-2401", "HUMKAL2-NEG-1741", "HUMTPIPSC-NEG-61", "HUMKER56K-NEG-1201", "HUMIL7A-NEG-361", "HUMSTROMR-NEG-661", "HUMAPOCIB-NEG-1441", "HUMMHCGE1-NEG-61", "HUMRENA4-NEG-2461", "HUMPKCB1A-NEG-1321", "HUMPKCB2A-NEG-1321", "HUMCATR-NEG-1081", "HUMSPR1B-NEG-181", "HUMGAA-NEG-781", "HUMPKLR-NEG-1441", "HUMCN4GEL-NEG-2521", "HUMMHDRA2-NEG-241", "BABT1GL-NEG-61", "HUMMHCGE2-NEG-3301", "HUMSECP1-NEG-181", "HUMASFB-NEG-1021", "HUMACCYBA-NEG-2521", "HUMGLCB-NEG-661", "HUMLPHAA-NEG-1861", "HUMPBG2-NEG-841", "HUMCFVII-NEG-5221", "HUMAPC3G5-NEG-481", "ORAHBBE-NEG-2581", "HUMSODA-NEG-6361", "HUMDES-NEG-2641", "HUMVILLR1-NEG-361", "HUMFMSD-NEG-661", "HUMTFPB-NEG-421", "HUMC8GA-NEG-241", "HUMPAIA-NEG-661", "HUMP15095-NEG-4021", "HUMLCATG-NEG-3841", "HUMEPISIB2-NEG-1081", "HUMTHYB10-NEG-181", "HUM8KER1-NEG-61", "HUMMYLCA-NEG-3181", "HUMERB2R-NEG-2521", "HUMCYPD-NEG-361", "HUMEP-NEG-2461", "HUMIL71-NEG-61", "HUMHST-NEG-3961", "HUMBAT3B2-NEG-4141", "HUMPRB1-NEG-601", "HUMMHA3-NEG-1501", "HUMMHB17W-NEG-1681", "HUMPRPG-NEG-421", "HUMTHYRR5-NEG-781", "HUMTRO-NEG-181", "HUMIL1RA-NEG-2821", "HUMMYCST-NEG-1", "HUMALPL01-NEG-721", "HUMIMP-NEG-181", "HUMARA-NEG-1441", "HUMGSTPI-NEG-301", "HUMTPI-NEG-61", "HUMMHDRGB-NEG-121", "HUMSTSB-NEG-1021", "HUMGALAB-NEG-901", "HUMALP5-NEG-781", "HUMMYCH-NEG-901", "HUMDNFA-NEG-2101", "HUMTPA-NEG-27001", "HUMB2M2-NEG-1201", "HUMGAPJR-NEG-961", "HUMCETP2-NEG-661", "HUMIRRA-NEG-1861", "HUMIGLPAV-NEG-301", "HUMTPA-NEG-22441", "HUMMGPH-NEG-61", "HUMGCB1-NEG-4201", "HUMGNAZ-NEG-961", "HUMINV2-NEG-121", "HUMCYPX4-NEG-2101", "HUMUMOD-NEG-1621", "HUMLYL1B-ACCEPTOR-2189", "HUMPEPD-NEG-421", "HUMPS2G3-NEG-421", "HUMBLAST1-NEG-661", "HUMUKI3-NEG-2941", "HUMPRA403-NEG-1", "HUMLOX5-NEG-61", "HUMCR1L04-NEG-1", "HUMFVIIIB-NEG-4681", "HUMGPIBA-NEG-1561", "HUMMHDOAG1-NEG-61", "HUMADAG-NEG-27241", "HUMGLUTRN-NEG-1021", "HUMLECGBB-NEG-61", "HUMAK1-ACCEPTOR-3918", "BABAPOE-NEG-1201", "HUMLDLR18-NEG-1621", "HUMHLAB13-NEG-901", "HUMMHDOB-NEG-4261", "HUMPOMC3-NEG-241", "HUMMHDRB-NEG-901", "HUMNCAMB-NEG-661", "HUMP45C17-NEG-4861", "HUMAPOAI1-NEG-721", "HUMBCL2C-NEG-3061", "HUMAPOE4-NEG-3301", "HUMHAPRA-NEG-481", "HUMAPOB26-NEG-2881", "HUMCYPIIE-NEG-7561", "HUMGFI-NEG-1", "HUMNGFBA2-NEG-4021", "HUMSPARC07-NEG-1", "HUMPC125-NEG-3181", "HUMCGB-NEG-181", "HUMLKHA4-NEG-61", "HUMIFNB3-NEG-1081", "HUMBARR-NEG-361", "HUMCYP2BA-NEG-2761", "HUMA1GP01-NEG-301", "HUMCOL1A18-NEG-241", "HUMPSPBA-NEG-601", "HUMLGTPA-NEG-1261", "HUMSHBG-NEG-1021", "HUMTKRA-NEG-8281", "HUMTOPI-NEG-1261", "HUMGIAA-NEG-901", "HUMIGVKW1-NEG-841", "HUMGASTA-NEG-6241", "HUMINCPPS-DONOR-940", "HUMHPARS2-NEG-6961", "MACHBPEA-NEG-2161", "HUMIGKAG-NEG-481", "HUMLAM1B-NEG-121", "HUMRSA6-NEG-961", "HUMNGFBA1-NEG-2341", "CHPHPA2-NEG-1", "HUMTCRT3D-NEG-1081", "HUMSAA-NEG-2821", "HUMMRCOX2-NEG-481", "HUMTP53B-NEG-1021", "HUMIGFIIR-NEG-2281", "HUMALIPOA-NEG-5341", "HUMMHB7A-NEG-1261", "HUMGHCSA-NEG-24841", "HUMCRYGQ6-NEG-361", "HUMEGFRBB3-NEG-1201", "HUMTPA-NEG-17881", "HUMHBBAG-NEG-1441", "HUMRAF19-NEG-721", "HUMACTSM7-NEG-61", "HUMIGHAE2-NEG-781", "HUMLCAT1-NEG-721", "HUMMYHC04-NEG-1", "HUMBAT2B4-NEG-6001", "HUMGPIBAA-NEG-3661", "HUMIFNATC-NEG-661", "HUMDMHSP-NEG-1741", "HUMDHPRA-NEG-421", "HUMRASH-NEG-5161", "HUMAPOAIT-NEG-661", "HUMATPAR-NEG-3241", "HUMATPSY1-NEG-1", "HUMCYP450-NEG-2761", "HUMBMP1A-NEG-2161", "HUMINCPPS-NEG-3181", "HUMGFIBPA-NEG-1441", "HUMHPARS2-NEG-2401", "HUMTCBCE-NEG-1621", "HUMMHCA1A-NEG-601", "HUMMHBIA-NEG-541", "HUMHPRTB-NEG-17281", "HUMA2PIBB-NEG-421", "HUMPOLP-NEG-2341", "HUMTRGC64-NEG-61", "HUMSATM29B-NEG-181", "HUMAMIPEP-NEG-3361", "HUMNTSXPH-NEG-1081", "HUMMRNAEN-NEG-1681", "HUMHBGF1-NEG-601", "HUMHBB-NEG-1201", "HUMIGHBC-NEG-481", "HUMPFKM23-NEG-1621", "HUMIGKVAB-NEG-1261", "HUMTCDXA-NEG-1", "HUMTSHX-NEG-241", "HUMAPOB2-NEG-7621", "HUMCYPX1-NEG-4201", "HUMTCAYJ-NEG-181", "HUMMHEA-NEG-2041", "HUMGLYPL-NEG-1081", "HUMG0S19B-NEG-2821", "HUMALPHA-NEG-181", "HUMELA301-NEG-1", "HUMD2A-NEG-841", "HUMFESFPS-NEG-2101", "HUMMRP14A-NEG-1561", "HUMCANPR-NEG-841", "HUMIGCB4-NEG-61", "HUMGRFCIG-NEG-1501", "HUMMHDBW1-NEG-1", "HUMTPOA-NEG-841", "HUMMHDRWA-NEG-901", "HUMALPHB-NEG-241", "HUMCAMA-NEG-841", "HUMMET2AB-NEG-61", "HUMMHDCB-NEG-6601", "HUMP45C21-NEG-901", "HUMINSRC-NEG-6661", "HUMSRC2B-NEG-61", "HUMTFAA-NEG-2881", "HUMTPO08-NEG-601", "HUMPNMTA-NEG-421", "HUMCYC1-NEG-961", "HUMNEBB-NEG-241", "HUMCYP45C-NEG-3781", "HUMGGTX-NEG-1981", "HUMHBA4-NEG-11161", "HUMPAIR1-NEG-1081", "HUMRFPA-NEG-841", "HUMPSAA-NEG-2521", "HUMADAG-NEG-18121", "HUMFX-NEG-241", "HUMAK1-ACCEPTOR-10488", "HUMRASAA-NEG-481", "HUMFBRB3-NEG-1", "HUMTCRAI2-NEG-61", "HUMGPP3A08-NEG-3721", "HUMMHBC-NEG-1921", "HUMMYCAWT-NEG-1", "HUMIL4-NEG-301", "HUMGTPBPA-NEG-1081", "HUMALPI1-ACCEPTOR-124", "HUMIGKVM-NEG-421", "HUMTPO-NEG-2401", "HUMGLUD1-NEG-721", "HUMKADA-NEG-601", "HUMALPHLB-NEG-901", "HUMTCBJC-NEG-721", "HUMLDLRRL-NEG-7201", "HUMSISG1-NEG-841", "HUMMCR-NEG-781", "HUMIGKCB-NEG-1081", "HUMAPOBB-NEG-8101", "HUMAE1-NEG-2101", "HUMSNRNP1-NEG-301", "HUMA1ACMB-NEG-421", "HUMSPTA1A-NEG-6421", "HUMATCT4-NEG-1561", "HUMPDGFRAA-NEG-3421", "HUMADHIG-NEG-241", "HUMFVIIIB-NEG-121", "HUMPRP-NEG-1", "HUMUBPSE1-NEG-661", "HUMAPOCIA-NEG-2221", "HUMC1A2-NEG-661", "HUMLAP-NEG-1141", "HUMIGF27-NEG-1", "HUMSLK-NEG-841", "HUMACTGA-NEG-2641", "HUMCR1-NEG-241", "HUMINSRA-NEG-4201", "HUMPDHA-NEG-1141", "HUMHBA1-NEG-841", "HUMTCBYB-NEG-61", "HUMSTS-NEG-3361", "HUMRSKP08-NEG-61", "HUMNGFB-NEG-9361", "HUMGIP2A5-NEG-1", "HUMHTA1-NEG-241", "HUMATPB-NEG-841", "HUMFESFPS-NEG-11221", "HUMAT3X1-NEG-1", "HUMGHCSA-NEG-43081", "HUMEDHB17-ACCEPTOR-2701", "HUMFAV-NEG-3961", "HUMC1INH-NEG-481", "HUMHBA4-NEG-2041", "HUMDAFA-DONOR-1117", "HUMA1ATR-NEG-781", "HUMTNFAB-DONOR-1537", "HUMHLADPB-NEG-13321", "HUMASL1A-NEG-961", "HUMMCM-NEG-481", "HUMUKA-NEG-1501", "HUMC3-NEG-3481", "HUMMHDQAW-NEG-301"), ValueGroup("HUMIGJHI-NEG-61", "HUMALUAGP-NEG-181", "HUMERYA-NEG-661", "HUMMDNCF-NEG-1141", "HUMHTV1A-NEG-121", "HUMGFP40H-NEG-1201", "HUMIGJ2-NEG-181", "HUMDYS-NEG-12961", "HUMHIAPPA-NEG-1861", "HUMCREB-NEG-2101", "HUMF13A15-NEG-1981", "HUMC6AI-NEG-301", "HUMC45B2B-NEG-1021", "HUMGHCSA-NEG-15721", "HUMTHB-NEG-8161", "HUMCBP-NEG-1321", "HUMVTNR-NEG-5641", "HUMHPRTB-NEG-21841", "HUMPTPASE-NEG-1801", "HUMDBPG-NEG-1141", "HUMGNAS6-NEG-1", "HUMALBGC-NEG-9361", "HUMPOMC-NEG-4981", "HUMHLASBA-NEG-9721", "HUMVIPMR5-NEG-121", "HUMAFP-NEG-4141", "HUMLIPCR-NEG-1261", "HUMGHR-NEG-841", "HUMIFNAA-NEG-1621", "HUMGCRB-NEG-3481", "HUMALBGC-NEG-18481", "HUMRASK25-NEG-361", "HUMFNRB-NEG-1081", "HUMGG-NEG-5581", "HUMANT1-NEG-2881", "HUMTM30R-NEG-1441", "HUMMHCD8A-NEG-3781", "HUMPTHLHA-NEG-1381", "HUMFIXG-NEG-8221", "HUMHISH3C-NEG-781", "HUMTFRRA-NEG-3481", "HUMVWFA31-NEG-3961", "HUMFVIII-NEG-2821", "HUMCRYABA-NEG-3301", "HUMAFP-NEG-17821", "HUMHPRTB-NEG-12721", "HUMAFP1-NEG-241", "HUMRAG1-NEG-4141", "HUMHBB-NEG-42241", "HUMAMYA3-NEG-721", "HUMHPRT9-NEG-121", "HUMPLPA-NEG-2041", "HUMPA2I-NEG-541", "HUMNFLG-NEG-1861", "HUMACBPA2-NEG-1", "HUMGYPA06-NEG-421", "HUMCHEBG2-NEG-1", "HUMTHB-NEG-12721", "HUMGLA-NEG-8281", "HUMRA3A-NEG-1", "HUMGFB-NEG-2701", "HUMPALF1-NEG-781", "HUMGLUCG2-NEG-1621", "HUMPYHBASA-NEG-2641", "HUMHPRTB-NEG-40081", "HUMTSHBA1-NEG-961", "HUMADH4C1-NEG-1501", "HUMFBRG-NEG-2641", "HUMCFXI03-NEG-421", "HUMTPA-NEG-36121", "HUMNAKATPA-NEG-3061", "HUMEGFRER-NEG-901", "HUMHMG17G-NEG-3661", "HUMHLADZA-NEG-5281", "TARHBB-NEG-541", "HUMIL2A-NEG-1261", "HUMRGRSAL-NEG-1981", "HUMIFNRG-NEG-1561", "HUMAPOB10-NEG-301", "HUMGDHR-NEG-841", "HUMPGK2P-NEG-361", "HUMIFNAGS-NEG-5281", "CHPRSALPI-NEG-481", "GORHBBPG-NEG-181", "HUMIFNINI-NEG-1201", "HUMKER18-NEG-5941", "HUMIL2-NEG-2401", "HUMLC3-NEG-901", "HUMC7A-NEG-1861", "HUMHPRTB-NEG-30961", "HUMHPARS1-NEG-241", "HUMMFD8A-NEG-181", "HUMABL1B-NEG-1621", "HUMIGKP1-NEG-1441", "HUMAMYB-NEG-2041", "HUMMCAF-NEG-661", "HUMPRPC4B-NEG-541", "HUMAPOB4A-NEG-241", "HUMNAKATP2-NEG-2161", "AGMORS9A-NEG-481", "HUMIFNAGS-NEG-9841", "HUMSTSXG2-NEG-2461", "HUMVWFA-NEG-1", "HUMCOLA2I-NEG-841", "HUMAPOBB-NEG-3541", "HUMIL2A-NEG-5821", "HUMTNFA-NEG-3241", "HUMSIGMG3-NEG-301", "HUMDYZ1-NEG-3301", "CHPHBBPCH-NEG-301", "HUMCOL3A1A-NEG-661", "HUMHBB-NEG-55921", "HUM3ALPH-NEG-1", "HUMCALC1-NEG-1561", "HUMNPY4-NEG-241", "HUMGLA-NEG-3721", "HUMPC64-NEG-1801", "HUMGKAS-NEG-1441", "HUMGNAI1-NEG-1201", "HUMMHSBA-NEG-61", "HUMAG1-NEG-2101", "HUMALBAF1-NEG-301", "HUMCH15M-NEG-121", "MNKHAPSE-NEG-5461", "HUMMYHC-NEG-5941", "HUMFAPYSY-NEG-541", "HUMINCPPS-ACCEPTOR-2712", "HUMADH3G1-NEG-841", "HUMRGNTS-NEG-1981", "HUMCYAR03-NEG-301", "HUMFIXG-NEG-35581", "HUMSPRO-NEG-1321", "HUMFIXG-NEG-3661", "HUMKER18-NEG-1381", "HUMFOLMES-NEG-721", "HUMIGVKA2-NEG-601", "HUMERVKA-NEG-2161", "HUMELIA-NEG-2641", "HUMCAATP3-NEG-61", "HUMIGKREC-NEG-1", "HUMDAFA-NEG-1561", "CHPHBBC-NEG-1321", "HUMTCRDA-NEG-1741", "HUMGBR-NEG-2521", "HUMFIXG-NEG-31021", "HUMPCNA-NEG-4861", "HUMSTATHG2-NEG-1", "HUMPTH2-NEG-841", "HUMHBVINT-NEG-61", "HUMIFNN-NEG-601", "HUMBAT2B3-NEG-1", "HUMPDGFR-NEG-5281", "HUMTCRGE-NEG-121", "HUMHBEG-NEG-721", "HUMGPP3A17-NEG-1081", "HUMMETPOA-NEG-901", "HUMCRPGP-NEG-481", "HUMCDW44A-NEG-1261", "HUMUPIR1-NEG-841", "HUMAFP-NEG-13261", "HUMJUNA-NEG-3301", "GORHBPSBD-NEG-2281", "HUMFN3A-NEG-661", "HUMIFNAGS-NEG-721", "HUMHRGA-NEG-481", "HUMFERHX-NEG-481", "CHPGGGLOG-NEG-961", "HUMFIBEDA-NEG-2641", "HUMAFP-NEG-8701", "HUMARB-NEG-3541", "HUMINSRD-NEG-3601", "HUMRPS14-NEG-3421", "HUMHK1A-NEG-3421", "HUMMHDRW1-NEG-2581", "MACHBPSBD-NEG-1141", "HUMREPAL1-NEG-2821", "HUMALBGC-NEG-4801", "HUMNMYC-NEG-6121", "HUMAACT-NEG-481", "HUMHPS12-NEG-1621", "HUMTHEP2-NEG-121", "HUMMMTVPOL-NEG-421", "HUMMETIF-NEG-1681", "HUMMHDQAR-NEG-1", "HUMHBB-NEG-37681", "HUMPDHE1B-NEG-301", "HUMERSP1A-NEG-241", "HUMFABP-NEG-661", "HUMHBEG-NEG-5281", "HUMFVIIIC-NEG-121", "HUMALPSAT-NEG-541", "HUMFIXG-NEG-12781", "HUMHBEG-NEG-9841", "HUMHLASBA-NEG-5161", "HUMDBLTP-NEG-1081", "HUMCERP-NEG-121", "HUMFIXG-NEG-26461", "HUMPZP-NEG-1741", "HUMRASN2-NEG-1", "HUMSATAX-NEG-1441", "HUMM6PR-NEG-1861", "HUMKUP-NEG-1021", "HUMAREPAP-NEG-61", "HUMAPA-NEG-841", "HUMMGA2PS-NEG-1861", "GIBHBGGL-NEG-7861", "HUMATPSYB-NEG-9961", "HUMHPRTB-NEG-26401", "HUMTPO11-NEG-241", "HUMCACY-NEG-3481", "HUMHPRTB-NEG-35521", "HUMALDA1-NEG-661", "HUMHBB-NEG-10321", "HUMTPA-NEG-13321", "HUMRSCNTF-NEG-61", "HUMMYCL-NEG-1681", "HUMSATAR2-NEG-181", "HUMPIP-NEG-241", "HUMAMY107-NEG-1", "HUMGYPC-NEG-421", "HUMIRBPG-NEG-8341", "HUMABLLA-NEG-1", "ORAHBBPSE-NEG-2101", "HUMHLADPB-NEG-4201", "HUMIL1B-NEG-2161", "HUMS71AA-NEG-3301", "HUMLDLR01-NEG-241", "HUMAPBM-NEG-3841", "HUMBRAFA-NEG-601", "HUMIFNG-NEG-4321", "HUMGCB2-NEG-1141", "HUMEB2CR2-NEG-1141", "HUMERV34A-NEG-2521", "HUMHBB-NEG-28561", "HUMLMWOS1-NEG-301", "HUMADRBR-NEG-1861", "HUMVIP1-NEG-421", "HUMANFA-NEG-2581", "HUMMDR3-NEG-301", "HUMCSYNA-NEG-2461", "HUMPP12R-NEG-1261", "HUMCERPS-NEG-1381", "HUMGG-NEG-1021", "HUMTGC1-NEG-61", "HUMAMY2A-NEG-361", "HUMCYPNO-NEG-961", "HUMLOX5A-NEG-2161", "HUMTBB46P-NEG-421", "HUMLDL100-NEG-10501", "HUMFBRGAB-NEG-1081", "HUMIGKVI1-NEG-541", "HUMMHDQA1-NEG-661", "HUMCNR-NEG-2101", "HUMIL1BX-NEG-5461", "HUMIFNA01-NEG-421", "HUMIGB7-NEG-1381", "HUMATP1A2-NEG-15781", "HUMC1A2-NEG-5221", "CHPRSA-NEG-2161", "HUMMDR1A07-NEG-781", "HUMOAS06-NEG-181", "HUMG3PDP-NEG-1", "HUMLIDNA-NEG-2701", "HUMA1ATP-NEG-4861", "HUMPAI2A4-NEG-121", "HUMGLAA-NEG-421", "HUMRISDAD-NEG-3721", "HUMTKRA-NEG-3721", "HUMGLYCA4-NEG-181", "HUMPKCAMD-NEG-781", "HUMFVIII-NEG-7381", "HUMTCRB22-NEG-241", "HUMHBB-NEG-51361", "HUMLDHB1-NEG-2461", "HUMISG1-NEG-241", "HUMHPRTB-NEG-8161", "HUMPALC-NEG-6841", "HUMNGFB-NEG-4801", "HUMPLA2A1-NEG-1561", "HUMFIXA-NEG-781", "HUMSTATHG2-NEG-4561", "HUMMHDRHA-NEG-781", "HUMIGHVV2-NEG-841", "HUMRPHO2A-NEG-1861", "HUMCD2A-NEG-121", "MNKHBPSBD-NEG-961", "HUMANTNC-NEG-2401", "HUMIL1AG-NEG-421", "HUMDYS-NEG-3841", "HUMACHRA7-NEG-901", "HUMHSP90B-NEG-1981", "HUMIFNAD-NEG-841", "HUMAREPBV-NEG-61", "HUMPLP6-NEG-421", "HUMTHROMPR-NEG-121", "HUMHBBRT-NEG-1141", "HUMRPS17A-NEG-1561", "HUMPRPH1-NEG-4081", "HUMHBBGG-NEG-481", "HUMTCRGAA-NEG-1", "HUMCIX-NEG-2521", "HUMTCAXB-NEG-1321", "HUMUMPS-NEG-1561", "HUMTCRAJ3-NEG-301", "HUMLHRH-NEG-961", "HUMDMDHI41-NEG-481", "HUMGYPCDC-NEG-421", "HUMNRNPEP-NEG-1141", "HUMDONT01-NEG-481", "HUMMYCG2-NEG-1501", "MACHBPEA-NEG-6721", "HUMIGKVS-NEG-421", "HUMIGHHSG4-NEG-601", "HUMBURKA-NEG-1141", "HUMMYCC-NEG-7321", "HUMFXIIIC-NEG-1561", "HUMAPB13-NEG-1", "HUMTCBV81-NEG-541", "HUMGHRA06-NEG-1", "HUMCRPG-NEG-781", "HUMUBIBP-NEG-481", "HUMINS02-NEG-181", "HUMTPO04-NEG-3121", "HUMFIXG6-NEG-1", "HUMCG1A1B-NEG-121", "HUMHP2FS-NEG-1021", "HUMAPB48A-NEG-2041", "HUMHSC70-NEG-1021", "HUMMHDRP1-NEG-481", "HUMTCRB11-NEG-1", "HUMTFRR-NEG-1741", "HUMMHCC6A-NEG-2401", "HUMPROSA-NEG-1021", "HUMTRD117-NEG-301", "HUMLCK2-NEG-841", "HUMIGGFCRA-NEG-1", "HUMHBB-NEG-24001", "HUMANTCD2-NEG-661", "HUMTBG-NEG-301", "HUMHGMCOA-NEG-181", "HUMPSG3A-NEG-481", "HUMSATA-NEG-361", "HUMTCRAC-NEG-1441", "HUMADH2C1-NEG-841", "HUMGHCSA-NEG-20281", "HUMACTCA2-NEG-1381", "HUMT1418-NEG-2161", "HUMGPP3A04-NEG-421", "HUMAPOBF-NEG-5641", "HUMCRBP2-NEG-421", "HUMPUMP1-NEG-661", "HUMATPA2-NEG-781", "HUMB100DI-NEG-121", "HUMTCAYD-NEG-1", "HUMC4BP-NEG-241", "HUMNGFB-NEG-241", "HUMBMP3A-NEG-1021", "HUMTFPC-NEG-241", "HUMVPF-NEG-661", "HUMCR1RS-NEG-1681", "HUMPPS-NEG-1561", "HUMCFVII-NEG-9781", "HUMIGHVQ-NEG-61", "HUMENKB1-NEG-1081", "HUMGASTA-NEG-1681", "HUMTYR-NEG-1141", "HUMODC-NEG-241", "HUMH1FNC1-NEG-1", "HUMAPOB2-NEG-3061", "HUMPMG3BA-NEG-3001", "HUMIAIG4-NEG-421", "HUMHLA1PS-NEG-3061", "HUMFIXG-NEG-21901"), ValueGroup("HUMGHCSA-NEG-11161", "HUMFMSCPO-NEG-61", "HUMCYCPS3-NEG-1081", "HUMHBB-NEG-65041", "HUMADAG-NEG-31801", "HUMLCA-NEG-2521", "HUMH-NEG-3061", "HUMHF10-NEG-241", "HUMDBPA-NEG-1381", "HUMGHCSA-NEG-29401", "HUMMYHC10-NEG-1561", "HUMLDHA7-NEG-361", "HUMGHCSA-NEG-52201", "HUMRSCNTT-NEG-121", "HUMFNI-NEG-121", "HUMFOS-NEG-6061", "HUMP45C17-NEG-301", "HUMPRLR-NEG-1861", "HUMGRP78-NEG-4981", "HUMRSLINE-NEG-2161", "HUMALDB2-NEG-1681", "HUMTROPFB-NEG-421", "HUMATP1A2-NEG-24901", "HUMPMPCA-NEG-1141", "HUMF8L1B-NEG-2041", "HUMP47-NEG-721", "HUMMXB-NEG-1741", "HUMTGFB2A-NEG-2401", "HUMANTLAA-NEG-481", "HUMNCAM-NEG-121", "HUMCHROMB-NEG-421", "HUMCA1XIA-NEG-3421", "HUMERBC-NEG-421", "HUMMAC1A-NEG-4021", "HUMPLASTA-NEG-1561", "HUMALBGC-NEG-13921", "HUMTGL7-NEG-181", "GIBHBGGL-NEG-3301", "HUMCAM-NEG-121", "HUMCARM-NEG-661", "HUMCATF-NEG-721", "AGMKPNRSB-NEG-1", "HUMIL1AG-NEG-9541", "MACRSMB-NEG-1", "HUMRSKPA2-NEG-121", "HUMTCR3G1-NEG-961", "HUMBSF2-NEG-4141", "HUMRPS6A-NEG-601", "HUMUDPGT-NEG-1801", "HUMAPB09-NEG-1", "HUMAFLP2-NEG-61", "HUMRBSA-NEG-1381", "HUMSBLA-NEG-961", "HUMDUG-NEG-2941", "HUM7B2-NEG-901", "HUMGPP3A21-NEG-2521", "HUMRSP1GPB-NEG-181", "HUMTHRAA-NEG-361", "HUMP3A-NEG-2461", "HUMHBB-NEG-19441", "HUMGLUCG2-NEG-6181", "HUMCS5-NEG-2161", "HUMHPBA-NEG-901", "HUMALDC01-NEG-361", "LEMHBGA-NEG-361", "HUMHPARS1-NEG-9361", "HUMDYS-NEG-8401", "HUMIL2R6-NEG-1", "HUMCEAE-NEG-61", "HUMTFPB-NEG-9541", "HUMTNFAB-NEG-2641", "HUM18BPD-NEG-61", "HUMCD20-NEG-721", "HUMIR3EBA-NEG-121", "HUMHBARSA-NEG-1081", "HUMIGGRLA-NEG-841", "HUMETS1A-NEG-481", "GORRGITX-NEG-1", "HUMTBB14P-NEG-1441", "HUMGHCSA-NEG-65881", "HUM7SKP41-NEG-541", "HUMMHSBB1-NEG-1561", "HUMRSKPNA-NEG-2341", "HUMSNRNP9-NEG-121", "HUMGPP3A11-NEG-961", "HUMIL1P-NEG-301", "HUMTOPIIX-NEG-841", "HUMEDF-NEG-841", "HUMTCGCF-NEG-121", "HUMSMCK-NEG-1021", "HUMMHDRS1-NEG-241", "HUMSODG5-NEG-181", "HUMCYAR10-NEG-1381", "HUMPALD-NEG-3841", "GORHBBPG-NEG-4741", "HUMTCBCC-NEG-1981", "HUMERMTV2-NEG-301", "HUMMYC3L-DONOR-2594", "HUMALDOBG-NEG-1", "HUMIGKC1-NEG-1681", "HUMCMYBB-NEG-901", "HUMGMCSFG-NEG-2821", "HUMRNPLA-NEG-181", "HUMMPRCI-NEG-601", "HUMGHCSA-NEG-33961", "HUMADAG-NEG-22681", "HUMRHOC9-NEG-121", "HUMSATA7A-NEG-2461", "HUMANP70-NEG-361", "HUMHELB-NEG-2461", "HUMHBB-NEG-46801", "HUMPHH-NEG-541", "HUMSIGMG4-NEG-961", "HUMREN02-NEG-121", "HUMMHDR3-NEG-1441", "HUMMYH1R-NEG-1621", "HUMHBB-NEG-33121", "HUMJNLTRA-NEG-121", "HUMLAMB2-NEG-961", "ORAHBBPSE-NEG-6661", "HUMZFX-NEG-781", "HUMCYPBX1-NEG-541", "HUMCAIII1-NEG-181", "GIBIL2LTR-NEG-241", "HUMMEHR-NEG-361", "HUMFUMA-NEG-841", "HUMENKPH2-NEG-3781", "HUMPNU-NEG-721", "HUMAS08-NEG-721", "HUMRSH3-NEG-1621", "HUMDBPC-NEG-2821", "HUMIGMUD-NEG-721", "HUMBPGM3-NEG-661", "HUMHMG14A-NEG-61", "HUMGFIAB5-NEG-421", "HUMVWFA37-NEG-661", "HUMTHYRR-NEG-481", "HUMINSR-NEG-541", "HUMPRPH2-NEG-3661", "HUMHMG17-NEG-301", "HUMHBB-NEG-69601", "HUMH38-NEG-661", "HUMALDB1-NEG-2761", "HUMAPOBA-NEG-8701", "HUMAPOC2-NEG-2521", "HUMC5A2B-NEG-181", "CHPHPA3-NEG-2041", "HUMHBB-NEG-14881", "MNKHAPSE-NEG-901", "HUMCYCPSJ-NEG-361", "HUMIGKVI5-NEG-2101", "HUMGCB2-NEG-5701", "HUMF13A05-NEG-121", "HUMHOM4-NEG-841", "HUMATCCD2-NEG-1261", "HUMPPOL-NEG-1081", "HUMIGKVW-NEG-841", "HUMAPB21-NEG-7501", "HUMTGPL1-NEG-1201", "HUMUGU4CA-NEG-121", "HUMTCBVW2-NEG-301", "HUMAPB21-NEG-2941", "HUMKALA-NEG-181", "HUMC8B-NEG-241", "CHPP44-NEG-481", "HUMCINHA-NEG-541", "HUMPRC7-NEG-961", "HUMMHDC3B-NEG-7021", "HUMGHCSA-NEG-2041", "HUMCYPSCC-NEG-1501", "HUMAPOB2-NEG-12181", "HUMGSTC-NEG-421", "HUMMDR1-NEG-2401", "HUMAPOBF-NEG-1081", "HUMCOL4A1-NEG-961", "HUMVWFA01-NEG-3961", "HUMPAPA-NEG-2401", "HUMPDGFARA-NEG-4621", "HUMIL2B-NEG-3241", "HUMPS12-NEG-1321", "HUMMYHC-NEG-1381", "HUMGP3A-NEG-1921", "HUMREPA-NEG-1141", "HUMARMA-NEG-961", "HUMELI-NEG-721", "HUMHOXB-NEG-541", "HUMIGLV71-NEG-721", "HUMBGAL-NEG-781", "HUMIGMUD-NEG-5281", "HUMMHDCB-NEG-2041", "HUMIFNF-NEG-601", "HUMHODB2-NEG-301", "HUMCYPAX-NEG-421", "HUMER41-NEG-8221", "HUMUG3PD-NEG-61", "AGMRSKPNI-NEG-1141", "HUMERG2-NEG-1321", "HUMXYES2-NEG-1741", "HUMLDLRRL-NEG-2641", "HUMCOL1A39-NEG-361", "HUMGLNRS-NEG-361", "HUMMAP2A-NEG-421", "HUMSNRNP3-NEG-3901", "HUMATPSY1-NEG-4561", "HUMHLASBA-NEG-601", "HUMC1SAB-NEG-841", "HUMTOPII-NEG-1081", "HUMTROPSR-NEG-721", "HUMA1GLY2-NEG-1501", "HUMKER7R-NEG-601", "HUMAPOAI1-NEG-5281", "HUMMDRA3-NEG-961", "HUMDMDA-NEG-1201", "HUMCYPHLP-NEG-421", "HUMTCAJA-NEG-61", "HUMGHCSA-NEG-61321", "HUMCSPB-NEG-61", "HUMHSDB-NEG-121", "HUMPAI2-NEG-181", "HUMVTNR-NEG-1081", "CHPHBBPCH-NEG-4861", "HUMGHCSA-NEG-6601", "HUMTDTA-NEG-781", "HUMTKFER-NEG-1081", "HUMTCAXN-NEG-61", "HUMPKB-NEG-1741", "HUMFIGRD-NEG-361", "HUMGPBPS-NEG-961", "HUMCYCB-NEG-181", "HUMCHEF-NEG-181", "HUMSAA1A-NEG-3901", "HUMCEAB-NEG-121", "HUMC5B-NEG-3241", "HUMBCRR-NEG-1261", "HUMGHCSA-NEG-56761", "HUMCGPRA-NEG-1981", "HUMPRLA-NEG-241", "HUMATH3U5-NEG-181", "HUMIFNB3-NEG-10201", "HUMMTLMC2-NEG-781", "HUMRSO5C-NEG-361", "HUMINHBA-NEG-541", "HUMGPP3A07-NEG-1381", "HUMLDHBR-NEG-121", "HUMCSFM-NEG-181", "HUMPC2A-NEG-481", "HUMBDGALA-NEG-1441", "HUMTCBYM-NEG-181", "HUMSHBGA-NEG-4501", "HUMHTV3A-NEG-841", "HUMHMGCOA-NEG-2101", "HUMNAKATP2-NEG-6721", "HUMAPB21-NEG-12061", "HUMRIBIR-NEG-1681", "HUMADAG-NEG-13561", "HUMGDHA-NEG-181", "HUMCYPIIE-NEG-3001", "HUMFOS-NEG-1501", "HUMIFNAPS-NEG-721", "HUMBLYM1-DONOR-127", "HUMGTLPA-NEG-1621", "HUMALDB1-NEG-7321", "HUMTBB5-NEG-3241", "HUMPOLDNAA-NEG-3481", "HUMAPB03-NEG-1441", "HUMHBB-NEG-60481", "HUMTCOBI-NEG-1261", "HUMCOLA1C-NEG-1141", "HUMALDOAP-NEG-661", "HUMCEALV-NEG-721", "HUMG0S19A-NEG-2341", "HUMC21PLA-NEG-121", "HUMKITCR-NEG-1021", "HUMFABPL-NEG-61", "HUMFVIIIC-NEG-4681", "HUMZFY-NEG-2341", "HUMLYRE-NEG-1801", "HUMDLDH-NEG-1021", "HUMTCSM-NEG-241", "HUMVWFM-NEG-541", "HUMCYCPSE-NEG-541", "HUMRSALPI-NEG-181", "HUMAMD-NEG-601", "HUMHPP16C-NEG-541", "HUMGFAP-NEG-2941", "HUMANT61K-NEG-1681", "HUMACHRM2-NEG-1681", "HUMAPOBA-NEG-13261", "HUMPDGFARA-NEG-61", "HUMPDGFR-NEG-721", "HUMREPHY2-NEG-481", "HUMCTHD-NEG-1801", "HUMCYES1-NEG-1501", "HUMIGKVI3-NEG-421", "HUMATPSYB-NEG-5401", "HUMTCBZE-NEG-241", "HUMAGP1A-NEG-421", "HUMTHYRR-NEG-5041", "HUMARGL-NEG-241", "HUMMPRCI-NEG-5161", "HUMRB1RA-NEG-3061", "HUMTCAYS-NEG-121", "HUMPOLBA-NEG-781", "HUMEN1-NEG-1201", "HUMHSPGC-NEG-2821", "HUMMHCW1C-NEG-781", "HUMRSSA1B-NEG-601", "HUMC4A2-NEG-3721", "HUMTUBAG-NEG-3361", "HUMPRCA-DONOR-3601", "HUMTHB-NEG-3601", "HUMMBP17K-NEG-361", "HUMMHDRR2-NEG-121", "HUMCALLA14-NEG-61", "HUMTPA-NEG-4201", "HUMICAM1A-NEG-1981", "HUMCYP4A2-NEG-781", "HUMPAP4-NEG-781", "HUMPGKPX-NEG-961", "HUMLCAR-NEG-2161", "HUMA2M-NEG-4441", "HUMAPOCII-NEG-121", "HUMPEPC1-NEG-901", "HUMNID-NEG-3541", "HUMP120PC-NEG-2101", "HUMALDH2-NEG-1201", "HUMMHDRBX-NEG-61", "HUMZNF8-NEG-661", "HUMCR1-NEG-4801", "HUMTHROMR-NEG-721", "HUMFIXG-NEG-17341", "HUMNOXF-NEG-361", "HUMLAM1B-NEG-4681", "HUMGHCSA-NEG-47641", "HUMDCC-NEG-1561", "HUMIL1BX-NEG-901", "HUMMYOHP-NEG-901", "HUMCFTRM-NEG-2281", "HUMTNFB-NEG-121", "HUMEBUR04-NEG-241", "HUMTRA-NEG-781", "HUMHLS2R-NEG-1201", "HUMMHSXA-NEG-4561", "HUMCMOSAA-NEG-121", "HUMLMYC2-NEG-901", "HUMADH2E2-NEG-1", "MACAPOA-NEG-961", "HUMIL5A-NEG-481", "HUMGPPSBB-NEG-1021", "HUMFVA-NEG-3001", "HUMGFRIL-NEG-1441", "HUMPPARP1-NEG-181", "HUMA1ATP-NEG-9421", "HUMTFPB-NEG-4981", "HUMNAKATP1-NEG-5281", "HUMETN1-NEG-181", "HUMFSH3-NEG-1021", "HUMER141-NEG-121", "HUMAPOB-NEG-1321", "HUMPDGFRA-NEG-4441", "HUMPPH1-NEG-61", "HUMOPS-NEG-4741", "HUMAPOAIA-NEG-1", "HUMRBS-NEG-2221", "HUMMHDRC2-NEG-841", "HUMDYSIN7-NEG-3541", "HUMIFNB2-NEG-361", "HUMIGLC2A-NEG-361", "HUMPTHL2-NEG-1", "HUMAPOC2G-NEG-3001", "HUMMHDRS2-NEG-2461", "HUMHPA1B-NEG-601", "HUMLAPA-NEG-2881", "HUMIL6-NEG-1021", "HUMHIAPPA-NEG-6421", "HUMEBVR-NEG-841", "HUMROSMCF-NEG-121", "HUMRYR-NEG-5641", "HUMFLA1A-NEG-961", "HUMABLA-NEG-2701", "HUMMH6-NEG-1201", "HUMTCGXH-NEG-601", "HUMIFNA04-NEG-961", "HUMCRYGBC-NEG-17461", "HUMIGKPC-NEG-61", "HUMHLADZA-NEG-721", "HUMEF1AB-NEG-481", "HUMMHDRB3-NEG-661", "HUMUKPM-NEG-781", "HUMGHV-NEG-1861", "HUMT28E-NEG-121", "HUMSATOD-NEG-841", "HUMERVKA-NEG-6721", "HUMPAIA-NEG-5221", "HUMVWFR1-NEG-8041", "HUMG6PDA-NEG-601", "HUMCATG12-NEG-241", "HUMIGHAJ-NEG-541", "HUMPSBGA-NEG-781", "HUMFN-NEG-3241", "HUMCYPMP-NEG-181", "HUMCTSE-NEG-661", "HUMIRBP-NEG-3481", "HUMBAT2B4-NEG-1441", "HUMCS3-NEG-361", "HUMRCYP3-NEG-1081", "ORAHBG2F-NEG-181", "HUMC1RX-NEG-1201", "HUMPSAA1-NEG-1", "HUMGPPSB1-NEG-241", "HUMEGFRN-NEG-4381", "HUMKER672-NEG-2401", "HUMMEDA-NEG-2761"), ValueGroup("HUMMYLCA-DONOR-2559", "HUMIL2-DONOR-595", "HUMIL2A-DONOR-1533", "HUMIL2B-DONOR-410", "HUMALBGC-DONOR-2592", "HUMHPRTB-DONOR-16757", "HUMALBGC-DONOR-13770", "HUMAFP-DONOR-970", "HUMALBGC-DONOR-17044", "HUMPCNA-DONOR-3721", "HUMERP-DONOR-1653", "HUMERPA-DONOR-1662", "HUMALBGC-DONOR-6223", "HUMHPRTB-DONOR-40080", "HUMAFP-DONOR-1834", "HUMALBGC-DONOR-15086", "HUMTFPB-DONOR-9438", "HUMGG-DONOR-3769", "HUMFIXG-DONOR-13441", "HUMALBGC-DONOR-7827", "HUMGFP40H-DONOR-2563", "HUMHPRTB-DONOR-39832", "HUMIL5-DONOR-908", "HUMIL5A-DONOR-907", "HUMFABP-DONOR-2493", "HUMFABP-DONOR-3624", "HUMHPRTB-DONOR-31605", "HUMIL2-DONOR-745", "HUMIL2A-DONOR-1683", "HUMIL2B-DONOR-560", "HUMAFP-DONOR-14687", "HUMALBGC-DONOR-15728", "HUMMYLCA-DONOR-2388", "HUMALBGC-DONOR-4179", "HUMHMG14A-NEG-4621", "TARHBB-DONOR-1909", "HUMGRP78-DONOR-1510", "HUMFIXG-DONOR-9425", "HUMALBGC-DONOR-17726", "HUMFABP-DONOR-1126", "HUMIL5-DONOR-1982", "HUMIL5A-DONOR-1986", "HUMHLASBA-NEG-14281", "HUMHPRTB-DONOR-14857", "HUMALBGC-DONOR-11052", "HUMATPSYB-DONOR-3271", "HUMHSP90B-DONOR-6241", "HUMHIAPPA-DONOR-629", "HUMHPRTB-DONOR-34990", "HUMAFP-DONOR-2929", "HUMFIXG-DONOR-9638", "HUMLACTA-DONOR-2236", "HUMTR211-NEG-901", "HUMIL2-DONOR-3182", "HUMIL2A-DONOR-4119", "HUMIL2B-DONOR-2996", "HUMAFP-DONOR-14021", "TARHBD-DONOR-817", "HUMSODA-DONOR-5211", "HUMHSP90B-DONOR-5582", "HUMGRP78-DONOR-2735", "HUMIFNAII-NEG-61", "HUMP53A-NEG-1", "HUMGRP78-DONOR-3539", "HUMAFP-DONOR-17179", "HUMCATF-DONOR-646", "HUMALBGC-DONOR-12584", "HUMPCNA-DONOR-5730", "HUMIFNINI-DONOR-2232", "HUMALBGC-DONOR-1825", "HUMAFP-DONOR-5428", "HUMPCNA-DONOR-2588", "HUMCYCAA-DONOR-1365", "HUMSODA-DONOR-3286", "HUMTUBAG-DONOR-2554", "HUMHMG17P-NEG-1081", "HUMEF1A-DONOR-2242", "LEMHBB-DONOR-569", "HUMMHDOB-DONOR-4518", "HUMATPSYB-DONOR-8947", "HUMHSP90B-DONOR-4129", "HUMHSP90B-DONOR-3809", "HUMCKMT-DONOR-1625", "HUMEF1A-DONOR-1696", "HUMAFP-DONOR-9741", "HUMFIXG-DONOR-3053", "HUMEF1A-DONOR-2644", "HUMSODA-DONOR-6429", "HUMP45C17-DONOR-5125", "HUMATPSYB-DONOR-5594", "HUMFIXG-DONOR-33089", "HUMANT1-DONOR-4091", "HUMIL5-DONOR-667", "HUMIL5A-DONOR-666", "HUMATPSYB-DONOR-2881", "HUMHSP90B-DONOR-5188", "HUMETN3-DONOR-227", "HUMEF1A-DONOR-2878", "HUMEF1A-DONOR-3546", "HUMALBGC-DONOR-9544", "HUMIFNINI-DONOR-2912", "HUMRPS17A-DONOR-1717", "HUMPRPH2-DONOR-1884", "HUMIL1B-DONOR-1558", "ORAHBD-DONOR-819", "HUMHPRTB-DONOR-27927", "HUMGFP40H-DONOR-1143", "HUMEF1A-NEG-1561", "HUMMLCAC-NEG-601", "HUMGRP78-DONOR-2258", "HUMFOS-DONOR-2544", "HUMTPO06-NEG-1681", "HUMCYPIIE-DONOR-2972", "HUMPRPH1-DONOR-2774", "HUMRPS17A-DONOR-2291", "HUMHIS4A2-NEG-301", "HUMHIAPPA-DONOR-1054", "HUMEF1A-DONOR-3222", "HUMCSFGMA-DONOR-933", "HUMGFP40H-DONOR-1028", "HUMTFPB-DONOR-2272", "HUMATPSYB-DONOR-4215", "HUMLACTA-DONOR-865", "HUMKEREP-DONOR-2872", "HUMAFP-DONOR-12231", "HUMSPROTR-NEG-541", "HUMGRP78-ACCEPTOR-1372", "HUMIFNINI-DONOR-3237", "HUMGG-DONOR-1964", "HUMPRPH2-DONOR-2660", "HUMSODA-DONOR-6624", "HUMMYCC-DONOR-2852", "HUMPCNA-DONOR-2424", "HUMATPSYB-DONOR-4649", "HUMSODA-DONOR-4346", "HUMMHDOB-DONOR-1105", "LEMHBG-DONOR-587", "GIBHBGGL-DONOR-2624", "GIBHBGGL-DONOR-7544", "LEMHBE-DONOR-633", "HUMAFP-DONOR-8063", "HUMATPSYB-DONOR-8071", "HUMIL1B-DONOR-5824", "HUMMHDRHA-DONOR-3987", "HUMCRYGBC-DONOR-18389", "MACHBCA2-DONOR-809", "MACHBGA1-DONOR-811", "HUMHSP90B-DONOR-1172", "HUMPRPH1-DONOR-1687", "HUMIL1B-DONOR-416", "HUMCP21OH-DONOR-1104", "HUMCP21OHC-DONOR-1105", "HUMMHCP42-DONOR-2035", "HUMMHCP52-DONOR-569", "MNKHBD-DONOR-698", "HUMIL1B-DONOR-4458", "HUMSODA-NEG-1801", "HUMA1ATP-DONOR-7932", "HUMAPOBB-NEG-12661", "HUMCSPB-DONOR-2478", "ORAHBG1F-DONOR-614", "HUMMHANTLE-DONOR-3700", "LEMHBGA-DONOR-648", "HUMAFP-DONOR-18452", "HUMCKMT-DONOR-2448", "HUMGRP78-DONOR-3086", "HUMC9M-NEG-841", "HUMAFP-DONOR-7047", "HUMPRPH1-DONOR-3555", "HUMHBBAG-DONOR-1689", "LEMHBE-DONOR-288", "HUMSHBGA-DONOR-4189", "HUMSODA-DONOR-4739", "HUMPRP0A-NEG-2161", "HUMGCRA-NEG-3721", "HUMMHDOB-DONOR-3639", "HUMSODA-DONOR-6932", "HUMGCB1-DONOR-581", "HUMSAA-DONOR-701", "HUMADAG-NEG-9001", "HUMSODA-DONOR-3510", "HUMHSP90B-DONOR-2752", "HUMHSP90B-DONOR-7352", "HUMFXIIIB-NEG-841", "HUMSHBGA-DONOR-5396", "HUMTUBAG-DONOR-2257", "HUMAPA4R-NEG-2761", "HUMHSP90B-DONOR-4574", "HUMTFPB-DONOR-6562", "HUMINCPPS-DONOR-2825", "HUMCKMT-DONOR-5110", "HUMFIXG-DONOR-20733", "HUMAFP-DONOR-19845", "HUMHSP90B-DONOR-5858", "HUMGCB1-DONOR-3143", "HUMGG-DONOR-230", "HUMSAA1A-DONOR-6017", "HUMEF1A-ACCEPTOR-3311", "HUMRTVLH2-NEG-4681", "HUMHPRTB-NEG-53761", "HUMHSP90B-DONOR-3558", "HUMRTVLH2-NEG-121", "HUMSAA-DONOR-34", "HUMALBGC-DONOR-6905", "HUMKTEP2A-NEG-1441", "HUMMH6-ACCEPTOR-3562", "HUMCD1B1-NEG-241", "HUMTNFA-DONOR-1839", "HUMTNFAB-DONOR-5318", "HUMTNFX-DONOR-1613", "HUMHSP90B-ACCEPTOR-7018", "HUMKEREP-DONOR-3368", "HUMATPSYB-ACCEPTOR-3096", "HUMA1GLY2-DONOR-3352", "HUMHPARS1-DONOR-3825", "HUMCYPIIE-DONOR-12950", "HUMMRP14A-DONOR-1551", "HUMMYC3L-NEG-3301", "HUMMHDXAR2-NEG-1", "HUMACTGA-DONOR-2060", "HUMA1GLY2-DONOR-3606", "HUMMH6-DONOR-3321", "HUMIL1B-NEG-6721", "MNKHBD-DONOR-347", "HUMHPARS1-NEG-4801", "HUMVIM06-NEG-1", "HUMFIXG-DONOR-23501", "ORAHBD-DONOR-466", "HUMATPCAPM-NEG-661", "HUMHBB32-NEG-121", "HUMDES-DONOR-2016", "HUMCALLA-NEG-2161", "HUMADH1CA-NEG-121", "HUMSAA1A-DONOR-1815", "HUMCKMT-ACCEPTOR-5313", "HUMMRP8A-ACCEPTOR-1988", "HUMGRP78-DONOR-1007", "HUMLACF-NEG-541", "HUMFBRG-NEG-7201", " * ")), IntervalBounds(15.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5), ValueGroups(ValueGroup("G", "N", "D", "S", "R"), ValueGroup("T"), ValueGroup("A"), ValueGroup("C", " * ")), Frequencies(1379, 620, 1171, 1384, 2333, 1372, 1050, 1466, 931, 545, 259, 902, 874, 1684, 915, 680, 1292, 558, 160, 33, 94, 107, 162, 73, 76, 100, 51, 4, 0, 57, 50, 148, 83, 82, 118, 35, 0, 1, 44, 41, 132, 90, 75, 103, 38, 472, 302, 241, 239, 180, 72, 47, 102, 168, 219, 147, 284, 284, 152, 70, 75, 124, 238, 120, 52, 0, 2, 140, 78, 76, 109, 0, 122, 53, 181, 145, 143, 80, 58, 99, 73, 133, 77, 27, 58, 153, 89, 74, 110, 31, 104, 45, 263, 245, 117, 79, 59, 116, 175, 134, 65, 80, 67, 145, 76, 57, 105, 38, 3364, 1821, 1768, 3354, 3633, 2018, 1621, 2642, 1043, 1653, 1837, 807, 934, 1050, 1342, 1905, 1357, 936, 1901, 1792, 576, 622, 782, 1083, 1503, 856, 761, 62, 97, 34, 59, 72, 92, 129, 82, 55, 25, 112, 43, 30, 68, 103, 135, 59, 33, 0, 0, 35, 45, 97, 95, 135, 84, 26, 0, 0, 12, 21, 50, 85, 158, 87, 38, 45, 27, 0, 0, 77, 113, 170, 56, 0, 154, 95, 287, 276, 89, 91, 130, 77, 229, 111, 102, 1, 4, 85, 74, 135, 98, 7, 86, 85, 9, 20, 61, 105, 136, 78, 27, 141, 63, 13, 10, 85, 100, 150, 81, 22, 100, 82, 142, 97, 54, 98, 145, 92, 121, 2079, 1682, 1347, 1068, 1739, 2273, 3223, 2111, 2095, 1106, 1109, 930, 813, 1196, 1179, 1614, 2125, 1094, 299, 384, 759, 744, 915, 797, 1138, 1535, 759, 81, 103, 66, 48, 78, 49, 123, 150, 71, 2, 18, 79, 88, 64, 87, 107, 147, 104, 472, 301, 148, 167, 87, 62, 88, 131, 157, 0, 0, 17, 26, 92, 67, 108, 135, 29, 137, 85, 2, 1, 74, 69, 82, 145, 0, 98, 88, 1, 0, 79, 90, 94, 143, 13, 98, 81, 103, 124, 80, 97, 116, 122, 161, 99, 74, 230, 179, 73, 66, 91, 136, 173, 85, 104, 0, 11, 84, 57, 105, 134, 34, 70, 84, 29, 48, 94, 69, 104, 124, 60, 2423, 2187, 988, 1180, 1901, 1839, 2423, 3298, 1600, 2942, 964, 1412, 1174, 1676, 2047, 1026, 1247, 669, 2447, 887, 931, 917, 1206, 1561, 782, 860, 584, 169, 69, 94, 73, 105, 182, 45, 81, 65, 441, 172, 109, 119, 137, 123, 49, 89, 70, 0, 0, 61, 34, 101, 149, 75, 95, 21, 0, 0, 18, 1, 95, 172, 60, 89, 7, 71, 43, 2, 2, 114, 144, 46, 88, 4, 100, 67, 0, 9, 109, 137, 73, 84, 0, 141, 66, 3, 14, 109, 145, 64, 94, 1, 154, 66, 22, 30, 130, 136, 72, 89, 11, 142, 90, 12, 21, 131, 160, 59, 82, 11, 168, 71, 37, 75, 124, 153, 67, 92, 23, 3462, 1558, 2809, 1286, 2735, 3374, 1685, 1861, 1070), 0, Frequencies(3178, 191400, 8), Frequencies(3178, 191400, 8)) ; // DataGrid(SampleId, Pos, Char) +Unused Structure(VectorC) P_SampleIdLabels = VectorC("{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...}", "{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...}", "{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...}", "{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...}", "{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...}", "{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...}", "{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...}", "{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...}", "{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...}") ; // Cluster labels for variable SampleId +Unused Structure(Vector) P_PosSet = TableVector(DNA, Pos) ; // Value distribution for variable Pos +Unused Structure(VectorC) P_CharSet = TableVectorC(DNA, Char) ; // Value distribution for variable Char +Unused Structure(DataGridDeployment) P_DeployedCoclusteringAtSampleId = DataGridDeployment(P_Coclustering, 1, P_PosSet, P_CharSet) ; // Deployed coclustering for variable SampleId +Unused Numerical P_SampleIdIndex = PredictedPartIndex(P_DeployedCoclusteringAtSampleId) ; // Predicted cluster index for variable SampleId + Categorical P_SampleIdPredictedLabel = ValueAtC(P_SampleIdLabels, P_SampleIdIndex) ; // Predicted label for variable SampleId +Unused Structure(Vector) P_SampleIdPartDistances = PredictedPartDistances(P_DeployedCoclusteringAtSampleId) ; // Vector of cluster distances for variable SampleId + Numerical `P_SampleIdDistance{HUMMYLCA-ACCEPTOR-924, HUMVPNP-ACCEPTOR-1688, HUMMHCW2B-ACCEPTOR-2660, ...}` = ValueAt(P_SampleIdPartDistances, 1) ; + Numerical `P_SampleIdDistance{HUMALBGC-ACCEPTOR-18496, HUMMYC3L-ACCEPTOR-4242, HUMHPRTB-ACCEPTOR-27861, ...}` = ValueAt(P_SampleIdPartDistances, 2) ; + Numerical `P_SampleIdDistance{ATRINS-DONOR-905, HUMERP-DONOR-598, HUMERPA-DONOR-608, ...}` = ValueAt(P_SampleIdPartDistances, 3) ; + Numerical `P_SampleIdDistance{HUMMYLCA-DONOR-644, HUMALPI1-DONOR-42, HUMMHCD8A-DONOR-1781, ...}` = ValueAt(P_SampleIdPartDistances, 4) ; + Numerical `P_SampleIdDistance{HUMFGFG1-NEG-901, HUMCOLTHA-NEG-481, HUMGLTH1-NEG-241, ...}` = ValueAt(P_SampleIdPartDistances, 5) ; + Numerical `P_SampleIdDistance{HUMBCL2A-NEG-1, HUMRGNTSA-NEG-2161, HUMPDGFB1-NEG-1381, ...}` = ValueAt(P_SampleIdPartDistances, 6) ; + Numerical `P_SampleIdDistance{HUMIGJHI-NEG-61, HUMALUAGP-NEG-181, HUMERYA-NEG-661, ...}` = ValueAt(P_SampleIdPartDistances, 7) ; + Numerical `P_SampleIdDistance{HUMGHCSA-NEG-11161, HUMFMSCPO-NEG-61, HUMCYCPS3-NEG-1081, ...}` = ValueAt(P_SampleIdPartDistances, 8) ; + Numerical `P_SampleIdDistance{HUMMYLCA-DONOR-2559, HUMIL2-DONOR-595, HUMIL2A-DONOR-1533, ...}` = ValueAt(P_SampleIdPartDistances, 9) ; +Unused Structure(Vector) P_PosPartFrequencies = PredictedPartFrequenciesAt(P_DeployedCoclusteringAtSampleId, 2) ; // Vector of cluster frequencies for variable Pos + Numerical `P_PosFrequency]-inf;15.5]` = ValueAt(P_PosPartFrequencies, 1) ; + Numerical `P_PosFrequency]15.5;26.5]` = ValueAt(P_PosPartFrequencies, 2) ; + Numerical `P_PosFrequency]26.5;27.5]` = ValueAt(P_PosPartFrequencies, 3) ; + Numerical `P_PosFrequency]27.5;28.5]` = ValueAt(P_PosPartFrequencies, 4) ; + Numerical `P_PosFrequency]28.5;29.5]` = ValueAt(P_PosPartFrequencies, 5) ; + Numerical `P_PosFrequency]29.5;30.5]` = ValueAt(P_PosPartFrequencies, 6) ; + Numerical `P_PosFrequency]30.5;31.5]` = ValueAt(P_PosPartFrequencies, 7) ; + Numerical `P_PosFrequency]31.5;32.5]` = ValueAt(P_PosPartFrequencies, 8) ; + Numerical `P_PosFrequency]32.5;33.5]` = ValueAt(P_PosPartFrequencies, 9) ; + Numerical `P_PosFrequency]33.5;34.5]` = ValueAt(P_PosPartFrequencies, 10) ; + Numerical `P_PosFrequency]34.5;35.5]` = ValueAt(P_PosPartFrequencies, 11) ; + Numerical `P_PosFrequency]35.5;36.5]` = ValueAt(P_PosPartFrequencies, 12) ; + Numerical `P_PosFrequency]36.5;+inf[` = ValueAt(P_PosPartFrequencies, 13) ; +Unused Structure(Vector) P_CharPartFrequencies = PredictedPartFrequenciesAt(P_DeployedCoclusteringAtSampleId, 3) ; // Vector of cluster frequencies for variable Char + Numerical `P_CharFrequency{G, N, D, ...}` = ValueAt(P_CharPartFrequencies, 1) ; + Numerical `P_CharFrequency{T}` = ValueAt(P_CharPartFrequencies, 2) ; + Numerical `P_CharFrequency{A}` = ValueAt(P_CharPartFrequencies, 3) ; + Numerical `P_CharFrequency{C}` = ValueAt(P_CharPartFrequencies, 4) ; +}; + +Dictionary SpliceJunctionDNA (SampleId) +{ + Categorical SampleId ; + Numerical Pos ; + Categorical Char ; +}; diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.bad.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.bad.khc new file mode 100644 index 000000000..729e62ea2 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.bad.khc @@ -0,0 +1,69 @@ +#Khiops 10.4.6i +Short description +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +PetalLength Numerical 3 3 150 1 +PetalWidth Numerical 3 3 150 1 +Class Categorical 3 3 3 1 + +Coclustering stats +Instances 150 +Cells 7 +Null cost 1379.668177 +Cost 1164.273132 +Level 0.1561209054 +Initial dimensions 3 +Frequency variable +Dictionary Iris +Database ./SubDir/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 +]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 +]-inf;+inf[ 150 0.648091 -0.0145257 2 4 +]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 +]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 +]-inf;+inf[ 150 0.681865 0.374198 2 5 +]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 + +Hierarchy Class +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Iris-setosa} C1 50 1 1 1 9 +{Iris-versicolor} C4 50 0.912663 1 3 9 +{Iris-virginica} C4 50 0.912663 1 5 9 +C1 150 0.941775 0 2 3 +C4 C1 100 0.912663 0.739904 4 6 + +Composition Class +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 + +Cells +PetalLength PetalWidth Class Frequency +]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 +]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 +]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 +]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 +]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 +]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 +]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.bad.khcj b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.bad.khcj new file mode 100644 index 000000000..7c376b664 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.bad.khcj @@ -0,0 +1,299 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 150, + "cells": 7, + "nullCost": 1379.668177, + "cost": 1164.273132, + "level": 0.1561209054, + "initialDimensions": 3, + "frequencyVariable": "", + "dictionary": "Iris", + "database": ".\/SubDir\/Iris.txt", + "samplePercentage": 100, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "" + }, + "dimensionSummaries": [ + { + "name": "PetalLength", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 1, + "max": 6.9 + }, + { + "name": "PetalWidth", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 0.1, + "max": 2.5 + }, + { + "name": "Class", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 3, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "PetalLength", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;2.4]", + "bounds": [1,2.4] + }, + { + "cluster": "]2.4;4.75]", + "bounds": [2.4,4.75] + }, + { + "cluster": "]4.75;+inf[", + "bounds": [4.75,6.9] + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;0.8]", + "bounds": [0.1,0.8] + }, + { + "cluster": "]0.8;1.75]", + "bounds": [0.8,1.75] + }, + { + "cluster": "]1.75;+inf[", + "bounds": [1.75,2.5] + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Iris-setosa}", + "values": ["Iris-setosa"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-versicolor}", + "values": ["Iris-versicolor"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-virginica}", + "values": ["Iris-virginica"], + "valueFrequencies": [50], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 2 + } + ], + "dimensionHierarchies": [ + { + "name": "PetalLength", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;2.4]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.557285, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]2.4;4.75]", + "parentCluster": "]2.4;+inf[", + "frequency": 45, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]4.75;+inf[", + "parentCluster": "]2.4;+inf[", + "frequency": 55, + "interest": 0.442715, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.648091, + "hierarchicalLevel": -0.0145257, + "rank": 2, + "hierarchicalRank": 4, + "isLeaf": false + }, + { + "cluster": "]2.4;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.693493, + "hierarchicalLevel": 0.856289, + "rank": 4, + "hierarchicalRank": 8, + "isLeaf": false + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;0.8]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.569949, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]0.8;1.75]", + "parentCluster": "]0.8;+inf[", + "frequency": 54, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]1.75;+inf[", + "parentCluster": "]0.8;+inf[", + "frequency": 46, + "interest": 0.430051, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.681865, + "hierarchicalLevel": 0.374198, + "rank": 2, + "hierarchicalRank": 5, + "isLeaf": false + }, + { + "cluster": "]0.8;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.737824, + "hierarchicalLevel": 0.704564, + "rank": 4, + "hierarchicalRank": 7, + "isLeaf": false + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Iris-setosa}", + "parentCluster": "C1", + "frequency": 50, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-versicolor}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-virginica}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "C1", + "parentCluster": "", + "frequency": 150, + "interest": 0.941775, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "C4", + "parentCluster": "C1", + "frequency": 100, + "interest": 0.912663, + "hierarchicalLevel": 0.739904, + "rank": 4, + "hierarchicalRank": 6, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0,0], + [2,2,2], + [1,1,1], + [2,1,1], + [2,1,2], + [1,1,2], + [2,2,1] + ], + "cellFrequencies": [50,45,44,5,4,1,1] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.khc b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.khc new file mode 100644 index 000000000..729e62ea2 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.khc @@ -0,0 +1,69 @@ +#Khiops 10.4.6i +Short description +Dimensions 3 +Name Type Parts Initial parts Values Interest Description +PetalLength Numerical 3 3 150 1 +PetalWidth Numerical 3 3 150 1 +Class Categorical 3 3 3 1 + +Coclustering stats +Instances 150 +Cells 7 +Null cost 1379.668177 +Cost 1164.273132 +Level 0.1561209054 +Initial dimensions 3 +Frequency variable +Dictionary Iris +Database ./SubDir/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.557285 1 1 9 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 9 +]4.75;+inf[ ]2.4;+inf[ 55 0.442715 1 5 9 +]-inf;+inf[ 150 0.648091 -0.0145257 2 4 +]2.4;+inf[ ]-inf;+inf[ 100 0.693493 0.856289 4 8 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.569949 1 1 9 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 9 +]1.75;+inf[ ]0.8;+inf[ 46 0.430051 1 5 9 +]-inf;+inf[ 150 0.681865 0.374198 2 5 +]0.8;+inf[ ]-inf;+inf[ 100 0.737824 0.704564 4 7 + +Hierarchy Class +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +{Iris-setosa} C1 50 1 1 1 9 +{Iris-versicolor} C4 50 0.912663 1 3 9 +{Iris-virginica} C4 50 0.912663 1 5 9 +C1 150 0.941775 0 2 3 +C4 C1 100 0.912663 0.739904 4 6 + +Composition Class +Cluster Value Frequency Typicality +{Iris-setosa} Iris-setosa 50 1 +{Iris-versicolor} Iris-versicolor 50 1 +{Iris-virginica} Iris-virginica 50 1 +{Iris-virginica} * 0 0 + +Cells +PetalLength PetalWidth Class Frequency +]-inf;2.4] ]-inf;0.8] {Iris-setosa} 50 +]4.75;+inf[ ]1.75;+inf[ {Iris-virginica} 45 +]2.4;4.75] ]0.8;1.75] {Iris-versicolor} 44 +]4.75;+inf[ ]0.8;1.75] {Iris-versicolor} 5 +]4.75;+inf[ ]0.8;1.75] {Iris-virginica} 4 +]2.4;4.75] ]0.8;1.75] {Iris-virginica} 1 +]4.75;+inf[ ]1.75;+inf[ {Iris-versicolor} 1 + diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.khcj b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.khcj new file mode 100644 index 000000000..7c376b664 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/SimplifiedCoclustering.khcj @@ -0,0 +1,299 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 150, + "cells": 7, + "nullCost": 1379.668177, + "cost": 1164.273132, + "level": 0.1561209054, + "initialDimensions": 3, + "frequencyVariable": "", + "dictionary": "Iris", + "database": ".\/SubDir\/Iris.txt", + "samplePercentage": 100, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "" + }, + "dimensionSummaries": [ + { + "name": "PetalLength", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 1, + "max": 6.9 + }, + { + "name": "PetalWidth", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 0.1, + "max": 2.5 + }, + { + "name": "Class", + "type": "Categorical", + "parts": 3, + "initialParts": 3, + "values": 3, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "PetalLength", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;2.4]", + "bounds": [1,2.4] + }, + { + "cluster": "]2.4;4.75]", + "bounds": [2.4,4.75] + }, + { + "cluster": "]4.75;+inf[", + "bounds": [4.75,6.9] + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;0.8]", + "bounds": [0.1,0.8] + }, + { + "cluster": "]0.8;1.75]", + "bounds": [0.8,1.75] + }, + { + "cluster": "]1.75;+inf[", + "bounds": [1.75,2.5] + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{Iris-setosa}", + "values": ["Iris-setosa"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-versicolor}", + "values": ["Iris-versicolor"], + "valueFrequencies": [50], + "valueTypicalities": [1] + }, + { + "cluster": "{Iris-virginica}", + "values": ["Iris-virginica"], + "valueFrequencies": [50], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 2 + } + ], + "dimensionHierarchies": [ + { + "name": "PetalLength", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;2.4]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.557285, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]2.4;4.75]", + "parentCluster": "]2.4;+inf[", + "frequency": 45, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]4.75;+inf[", + "parentCluster": "]2.4;+inf[", + "frequency": 55, + "interest": 0.442715, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.648091, + "hierarchicalLevel": -0.0145257, + "rank": 2, + "hierarchicalRank": 4, + "isLeaf": false + }, + { + "cluster": "]2.4;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.693493, + "hierarchicalLevel": 0.856289, + "rank": 4, + "hierarchicalRank": 8, + "isLeaf": false + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;0.8]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.569949, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]0.8;1.75]", + "parentCluster": "]0.8;+inf[", + "frequency": 54, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]1.75;+inf[", + "parentCluster": "]0.8;+inf[", + "frequency": 46, + "interest": 0.430051, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.681865, + "hierarchicalLevel": 0.374198, + "rank": 2, + "hierarchicalRank": 5, + "isLeaf": false + }, + { + "cluster": "]0.8;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.737824, + "hierarchicalLevel": 0.704564, + "rank": 4, + "hierarchicalRank": 7, + "isLeaf": false + } + ] + }, + { + "name": "Class", + "type": "Categorical", + "clusters": [ + { + "cluster": "{Iris-setosa}", + "parentCluster": "C1", + "frequency": 50, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-versicolor}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "{Iris-virginica}", + "parentCluster": "C4", + "frequency": 50, + "interest": 0.912663, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 9, + "isLeaf": true + }, + { + "cluster": "C1", + "parentCluster": "", + "frequency": 150, + "interest": 0.941775, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "C4", + "parentCluster": "C1", + "frequency": 100, + "interest": 0.912663, + "hierarchicalLevel": 0.739904, + "rank": 4, + "hierarchicalRank": 6, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0,0], + [2,2,2], + [1,1,1], + [2,1,1], + [2,1,2], + [1,1,2], + [2,2,1] + ], + "cellFrequencies": [50,45,44,5,4,1,1] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/err.txt new file mode 100644 index 000000000..27a4e77c3 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/err.txt @@ -0,0 +1,32 @@ +Database ./SubDir/Iris.txt : Read records: 150 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.043904 Size: 2*2*1 Granularity: 1 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.095524 Size: 3*3*3 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.130686 Size: 3*3*3 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.156121 Size: 3*3*3 +Write coclustering report ./SubDir\../results\Coclustering.khcj +Coclustering analysis time: 0:00:00.03 + +Database ./SubDir/Iris.txt : Read records: 150 + 0:00:00 Write intermediate coclustering report Coclustering.bad(1).khcj Level: 0.043904 Size: 2*2*1 Granularity: 1 + 0:00:00 Write intermediate coclustering report Coclustering.bad(2).khcj Level: 0.095524 Size: 3*3*3 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering.bad(3).khcj Level: 0.130686 Size: 3*3*3 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering.bad(4).khcj Level: 0.156121 Size: 3*3*3 +Write coclustering report ./SubDir\../results\Coclustering.bad.khcj +Coclustering analysis time: 0:00:00.03 + +error : File coclustering report : Missing file name +Write simplified report ./results\SimplifiedCoclustering.khcj + +Write simplified report ./results\SimplifiedCoclustering.bad.khcj + +error : File simplified coclustering report : Missing file name +Write cluster table file ./results\Clusters.txt + +Write cluster table file ./results\Clusters.bad.txt + +error : File cluster table file : Missing file name +Write deployment dictionary file ./SubDir\../results\DeploymentCoclustering.kdic + +Write deployment dictionary file ./SubDir\../results\DeploymentCoclustering.bad.kdic + +error : File coclustering dictionary : Missing file name diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/time.log new file mode 100644 index 000000000..911d84a7d --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/results.ref/time.log @@ -0,0 +1 @@ +Overal time: 0.20289874076843262 diff --git a/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/test.prm b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/test.prm new file mode 100644 index 000000000..ef58c93eb --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/AllCCResultsStandardMode/test.prm @@ -0,0 +1,152 @@ +// Mon Jul 04 14:13:56 2011 +// C:\Users\boullema\Learning\Win32\debug\MODL_Coclustering +// Output command file +// +//This file contains recorded commands, that can be replayed. +//Commands are based on user interactions: +// field update +// list item selection +// menu action +//Every command can be commented, using //. +//For example, commenting the last Exit command will allow other +//user interactions, after the commands have been replayed. +// +// + +// -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode false // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB + +////////////////////////////////////////////////////////////////////////////////////////////////// +// Build coclustering + +ClassManagement.OpenFile // Open... + +// -> Open +ClassFileName ./SubDir/Iris.kdic // Dictionary file +OK // Open +// <- Open + +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ./SubDir/Iris.txt // Database file +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.Name PetalLength // Name +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.Name PetalWidth // Name +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.Name Class // Name + +AnalysisResults.CoclusteringFileName ../results/Coclustering.khcj // Coclustering report +BuildCoclustering // Build coclustering + +AnalysisResults.CoclusteringFileName ../results/Coclustering.bad // Coclustering report +BuildCoclustering // Build coclustering + +AnalysisResults.CoclusteringFileName // Coclustering report +BuildCoclustering // Build coclustering + + +////////////////////////////////////////////////////////////////////////////////////////////////// +// Simplify coclustering + +LearningTools.PostProcessCoclustering // Simplify coclustering... + +// -> Coclustering post-processing +SelectInputCoclustering // Select input coclustering... + +// -> Open +InputCoclusteringFileName ./results/Coclustering.khcj // Input coclustering file +OK // Open +// <- Open + +PostProcessedCoclusteringFileName ./SimplifiedCoclustering.khcj // Simplified coclustering report +PostProcessCoclustering // Simplify coclustering + +PostProcessedCoclusteringFileName ./SimplifiedCoclustering.bad // Simplified coclustering report +PostProcessCoclustering // Simplify coclustering + +PostProcessedCoclusteringFileName // Simplified coclustering report +PostProcessCoclustering // Simplify coclustering + +Exit // Close +// <- Coclustering post-processing + + +////////////////////////////////////////////////////////////////////////////////////////////////// +// Extract clusters + +LearningTools.ExtractClusters // Extract clusters... + +SelectInputCoclustering // Select input coclustering... +// -> Cluster extraction + +// -> Open +InputCoclusteringFileName ./results/Coclustering.khcj // Input coclustering file +OK // Open +// <- Open + +CoclusteringAttribute Class // Coclustering variable + +ClusterFileName ./Clusters.txt // Cluster table file +ExtractClusters // Extract clusters + +ClusterFileName ./Clusters.bad // Cluster table file +ExtractClusters // Extract clusters + +ClusterFileName // Cluster table file +ExtractClusters // Extract clusters + +Exit // Close +// <- Cluster extraction + + +////////////////////////////////////////////////////////////////////////////////////////////////// +// Prepare deployment + +ClassManagement.OpenFile // Open... + +// -> Open +ClassFileName ../../../MTdatasets/SpliceJunction/SpliceJunction.kdic // Dictionary file +OK // Open +// <- Open + +Database.ClassName SpliceJunction // Analysis dictionary + +LearningTools.PrepareDeployment // Prepare deployment... + +// -> Coclustering deployment preparation +SelectInputCoclustering // Select input coclustering... + +// -> Open +InputCoclusteringFileName ./SubDir/SpliceJunctionDNACoclustering.khc // Input coclustering file +OK // Open +// <- Open + +DeploymentSpec.InputClassName SpliceJunction // Input dictionary +DeploymentSpec.InputObjectArrayAttributeName DNA // Input array variable +DeploymentSpec.DeployedAttributeName SampleId // Coclustering deployed variable +DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster variable +DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables +DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables +DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix + +CoclusteringDictionaryFileName ../results/DeploymentCoclustering.kdic // Coclustering dictionary file +PrepareDeployment // Prepare deployment + +CoclusteringDictionaryFileName ../results/DeploymentCoclustering.bad // Coclustering dictionary file +PrepareDeployment // Prepare deployment + +CoclusteringDictionaryFileName // Coclustering dictionary file +PrepareDeployment // Prepare deployment + +Exit // Close +// <- Coclustering deployment preparation + + +Exit // Close +// <- Khiops Coclustering + +// -> Khiops Coclustering +OK // Close +// <- Khiops Coclustering + diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/DeployOccupation.kdic b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/DeployOccupation.kdic index 36119a94d..a7d8d8f5d 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/DeployOccupation.kdic +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/DeployOccupation.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary Occupation (occupation) { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/DeployOccupation50.kdic b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/DeployOccupation50.kdic index a4b084ee2..c04257205 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/DeployOccupation50.kdic +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/DeployOccupation50.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary Occupation (occupation) { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/SimplifiedCoclustering50.khc b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/SimplifiedCoclustering50.khc index 4bfb59639..af118400c 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/SimplifiedCoclustering50.khc +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/SimplifiedCoclustering50.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/SimplifiedCoclustering50.khcj b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/SimplifiedCoclustering50.khcj index 1def5c7f6..0117af98d 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/SimplifiedCoclustering50.khcj +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/SimplifiedCoclustering50.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/err.txt index c583d38f6..ad6e5224f 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/err.txt @@ -1,6 +1,6 @@ -Write deployment dictionary file ./results\DeployOccupation.kdic +Write deployment dictionary file ./results/DeployOccupation.kdic -Write deployment dictionary file ./results\DeployOccupation50.kdic +Write deployment dictionary file ./results/DeployOccupation50.kdic -Write simplified report ./results\SimplifiedCoclustering50.khc +Write simplified report ./results/SimplifiedCoclustering50.khcj diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/time.log index f69135c26..1d2bd988a 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.13095355033874512 +Overal time: 0.13013124465942383 diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/test.prm b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/test.prm index 86fa8e4d2..07dc120a5 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -38,19 +40,18 @@ DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster va DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.CoclusteringDictionaryFileName DeployOccupation.kdic // Coclustering dictionary file +CoclusteringDictionaryFileName ./results/DeployOccupation.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.CoclusteringDictionaryFileName DeployOccupation50.kdic // Coclustering dictionary file +CoclusteringDictionaryFileName ./results/DeployOccupation50.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment Exit // Close // <- Coclustering deployment preparation LearningTools.PostProcessCoclustering // Simplify coclustering... -AnalysisResults.PostProcessedCoclusteringFileName SimplifiedCoclustering50.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/SimplifiedCoclustering50.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering Exit // Close // <- Simplify coclustering diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/DeployOccupation.kdic b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/DeployOccupation.kdic index 494d5388a..c21498385 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/DeployOccupation.kdic +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/DeployOccupation.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary Occupation (occupation) { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/DeployOccupation50.kdic b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/DeployOccupation50.kdic index 8cbf12dc0..417085a6c 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/DeployOccupation50.kdic +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/DeployOccupation50.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary Occupation (occupation) { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/SimplifiedCoclustering50.khc b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/SimplifiedCoclustering50.khc index 4eef6526e..e2861910e 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/SimplifiedCoclustering50.khc +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/SimplifiedCoclustering50.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/SimplifiedCoclustering50.khcj b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/SimplifiedCoclustering50.khcj index 07e78ec2e..11b6a5d96 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/SimplifiedCoclustering50.khcj +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/SimplifiedCoclustering50.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/err.txt index c583d38f6..ad6e5224f 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/err.txt @@ -1,6 +1,6 @@ -Write deployment dictionary file ./results\DeployOccupation.kdic +Write deployment dictionary file ./results/DeployOccupation.kdic -Write deployment dictionary file ./results\DeployOccupation50.kdic +Write deployment dictionary file ./results/DeployOccupation50.kdic -Write simplified report ./results\SimplifiedCoclustering50.khc +Write simplified report ./results/SimplifiedCoclustering50.khcj diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/time.log index c4f1b20e4..f4c082945 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.08186888694763184 +Overal time: 0.09794926643371582 diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/test.prm b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/test.prm index 124c20e29..6165a6410 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2vars/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -38,20 +40,19 @@ DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster va DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.CoclusteringDictionaryFileName DeployOccupation.kdic // Coclustering dictionary file +CoclusteringDictionaryFileName ./results/DeployOccupation.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.CoclusteringDictionaryFileName DeployOccupation50.kdic // Coclustering dictionary file +CoclusteringDictionaryFileName ./results/DeployOccupation50.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment Exit // Close // <- Coclustering deployment preparation LearningTools.PostProcessCoclustering // Simplify coclustering... -AnalysisResults.PostProcessedCoclusteringFileName SimplifiedCoclustering50.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/SimplifiedCoclustering50.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering Exit // Close // <- Simplify coclustering diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/AdultCoclusteringFrequency.json b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/AdultCoclusteringFrequency.json index bb242c260..255dcd44e 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/AdultCoclusteringFrequency.json +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/AdultCoclusteringFrequency.json @@ -1,1142 +1,634 @@ { - "tool": "Khiops Coclustering", - "version": "9.2.3a", - "coclusteringReport": { - "summary": { - "instances": 48842, - "cells": 90, - "nullCost": 213017, - "cost": 204572, - "level": 0.03964472319, - "initialDimensions": 2, - "frequencyVariable": "frequency", - "dictionary": "OccupationEducationFrequency", - "database": "OccupationEducationFrequency.txt" - }, - "dimensionSummaries": [ - { - "name": "occupation", - "type": "Categorical", - "parts": 9, - "initialParts": 9, - "values": 14, - "interest": 1, - "description": "" - }, - { - "name": "education", - "type": "Categorical", - "parts": 10, - "initialParts": 10, - "values": 16, - "interest": 1, - "description": "" - } - ], - "dimensionPartitions": [ - { - "name": "occupation", - "type": "Categorical", - "valueGroups": [ - { - "cluster": "{Prof-specialty}", - "values": [ - "Prof-specialty" - ], - "valueFrequencies": [ - 8981 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{Exec-managerial}", - "values": [ - "Exec-managerial" - ], - "valueFrequencies": [ - 6086 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{Machine-op-inspct, Transport-moving, Handlers-cleaners}", - "values": [ - "Machine-op-inspct", - "Transport-moving", - "Handlers-cleaners" - ], - "valueFrequencies": [ - 3022, - 2355, - 2072 - ], - "valueTypicalities": [ - 1, - 0.781972, - 0.740515 - ] - }, - { - "cluster": "{Other-service}", - "values": [ - "Other-service" - ], - "valueFrequencies": [ - 4923 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{Farming-fishing, Priv-house-serv}", - "values": [ - "Farming-fishing", - "Priv-house-serv" - ], - "valueFrequencies": [ - 1490, - 242 - ], - "valueTypicalities": [ - 1, - 0.350275 - ] - }, - { - "cluster": "{Craft-repair}", - "values": [ - "Craft-repair" - ], - "valueFrequencies": [ - 6112 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{Adm-clerical, Protective-serv}", - "values": [ - "Adm-clerical", - "Protective-serv" - ], - "valueFrequencies": [ - 5611, - 983 - ], - "valueTypicalities": [ - 1, - 0.253264 - ] - }, - { - "cluster": "{Sales}", - "values": [ - "Sales" - ], - "valueFrequencies": [ - 5504 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{Tech-support, Armed-Forces}", - "values": [ - "Tech-support", - "Armed-Forces" - ], - "valueFrequencies": [ - 1446, - 15 - ], - "valueTypicalities": [ - 1, - 0.00765636 - ] - } - ], - "defaultGroupIndex": 8 - }, - { - "name": "education", - "type": "Categorical", - "valueGroups": [ - { - "cluster": "{Bachelors}", - "values": [ - "Bachelors" - ], - "valueFrequencies": [ - 8025 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{Masters}", - "values": [ - "Masters" - ], - "valueFrequencies": [ - 2657 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{Prof-school, Doctorate}", - "values": [ - "Prof-school", - "Doctorate" - ], - "valueFrequencies": [ - 834, - 594 - ], - "valueTypicalities": [ - 1, - 0.756428 - ] - }, - { - "cluster": "{HS-grad}", - "values": [ - "HS-grad" - ], - "valueFrequencies": [ - 15784 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{7th-8th, 5th-6th, 1st-4th, ...}", - "values": [ - "7th-8th", - "5th-6th", - "1st-4th", - "Preschool" - ], - "valueFrequencies": [ - 955, - 509, - 247, - 83 - ], - "valueTypicalities": [ - 1, - 0.836335, - 0.450257, - 0.171295 - ] - }, - { - "cluster": "{10th, 9th}", - "values": [ - "10th", - "9th" - ], - "valueFrequencies": [ - 1389, - 756 - ], - "valueTypicalities": [ - 1, - 0.741127 - ] - }, - { - "cluster": "{11th, 12th}", - "values": [ - "11th", - "12th" - ], - "valueFrequencies": [ - 1812, - 657 - ], - "valueTypicalities": [ - 1, - 0.4954 - ] - }, - { - "cluster": "{Some-college}", - "values": [ - "Some-college" - ], - "valueFrequencies": [ - 10878 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{Assoc-voc}", - "values": [ - "Assoc-voc" - ], - "valueFrequencies": [ - 2061 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{Assoc-acdm}", - "values": [ - "Assoc-acdm" - ], - "valueFrequencies": [ - 1601 - ], - "valueTypicalities": [ - 1 - ] - } - ], - "defaultGroupIndex": 9 - } - ], - "dimensionHierarchies": [ - { - "name": "occupation", - "type": "Categorical", - "clusters": [ - { - "cluster": "{Prof-specialty}", - "parentCluster": "A7", - "frequency": 8981, - "interest": 1, - "hierarchicalLevel": 1, - "rank": 1, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Exec-managerial}", - "parentCluster": "A7", - "frequency": 6086, - "interest": 0.705624, - "hierarchicalLevel": 1, - "rank": 3, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Machine-op-inspct, Transport-moving, Handlers-cleaners}", - "parentCluster": "A13", - "frequency": 7449, - "interest": 0.742846, - "hierarchicalLevel": 1, - "rank": 5, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Other-service}", - "parentCluster": "A15", - "frequency": 4923, - "interest": 0.48503, - "hierarchicalLevel": 1, - "rank": 7, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Farming-fishing, Priv-house-serv}", - "parentCluster": "A15", - "frequency": 1732, - "interest": 0.296401, - "hierarchicalLevel": 1, - "rank": 9, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Craft-repair}", - "parentCluster": "A11", - "frequency": 6112, - "interest": 0.521102, - "hierarchicalLevel": 1, - "rank": 11, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Adm-clerical, Protective-serv}", - "parentCluster": "A8", - "frequency": 6594, - "interest": 0.464219, - "hierarchicalLevel": 1, - "rank": 13, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Sales}", - "parentCluster": "A10", - "frequency": 5504, - "interest": 0.380499, - "hierarchicalLevel": 1, - "rank": 15, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Tech-support, Armed-Forces}", - "parentCluster": "A10", - "frequency": 1461, - "interest": 0.284537, - "hierarchicalLevel": 1, - "rank": 17, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "A7", - "parentCluster": "A1", - "frequency": 15067, - "interest": 0.881093, - "hierarchicalLevel": 0.866834, - "rank": 2, - "hierarchicalRank": 8, - "isLeaf": false - }, - { - "cluster": "A1", - "parentCluster": "", - "frequency": 48842, - "interest": 0.623768, - "hierarchicalLevel": 0, - "rank": 4, - "hierarchicalRank": 2, - "isLeaf": false - }, - { - "cluster": "A13", - "parentCluster": "A11", - "frequency": 14104, - "interest": 0.598031, - "hierarchicalLevel": 0.975657, - "rank": 6, - "hierarchicalRank": 14, - "isLeaf": false - }, - { - "cluster": "A15", - "parentCluster": "A13", - "frequency": 6655, - "interest": 0.435938, - "hierarchicalLevel": 0.989938, - "rank": 8, - "hierarchicalRank": 16, - "isLeaf": false - }, - { - "cluster": "A11", - "parentCluster": "A3", - "frequency": 20216, - "interest": 0.574772, - "hierarchicalLevel": 0.955386, - "rank": 10, - "hierarchicalRank": 12, - "isLeaf": false - }, - { - "cluster": "A3", - "parentCluster": "A1", - "frequency": 33775, - "interest": 0.508975, - "hierarchicalLevel": 0.514611, - "rank": 12, - "hierarchicalRank": 4, - "isLeaf": false - }, - { - "cluster": "A8", - "parentCluster": "A3", - "frequency": 13559, - "interest": 0.410874, - "hierarchicalLevel": 0.902536, - "rank": 14, - "hierarchicalRank": 9, - "isLeaf": false - }, - { - "cluster": "A10", - "parentCluster": "A8", - "frequency": 6965, - "interest": 0.36037, - "hierarchicalLevel": 0.943821, - "rank": 16, - "hierarchicalRank": 11, - "isLeaf": false - } - ] - }, - { - "name": "education", - "type": "Categorical", - "clusters": [ - { - "cluster": "{Bachelors}", - "parentCluster": "B5", - "frequency": 8025, - "interest": 1, - "hierarchicalLevel": 1, - "rank": 1, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Masters}", - "parentCluster": "B9", - "frequency": 2657, - "interest": 0.967688, - "hierarchicalLevel": 1, - "rank": 3, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Prof-school, Doctorate}", - "parentCluster": "B9", - "frequency": 1428, - "interest": 0.916464, - "hierarchicalLevel": 1, - "rank": 5, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{HS-grad}", - "parentCluster": "B6", - "frequency": 15784, - "interest": 0.94096, - "hierarchicalLevel": 1, - "rank": 7, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{7th-8th, 5th-6th, 1st-4th, ...}", - "parentCluster": "B14", - "frequency": 1794, - "interest": 0.638895, - "hierarchicalLevel": 1, - "rank": 9, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{10th, 9th}", - "parentCluster": "B17", - "frequency": 2145, - "interest": 0.57401, - "hierarchicalLevel": 1, - "rank": 11, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{11th, 12th}", - "parentCluster": "B17", - "frequency": 2469, - "interest": 0.552302, - "hierarchicalLevel": 1, - "rank": 13, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Some-college}", - "parentCluster": "B12", - "frequency": 10878, - "interest": 0.651545, - "hierarchicalLevel": 1, - "rank": 15, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Assoc-voc}", - "parentCluster": "B16", - "frequency": 2061, - "interest": 0.386297, - "hierarchicalLevel": 1, - "rank": 17, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "{Assoc-acdm}", - "parentCluster": "B16", - "frequency": 1601, - "interest": 0.340395, - "hierarchicalLevel": 1, - "rank": 19, - "hierarchicalRank": 19, - "isLeaf": true - }, - { - "cluster": "B5", - "parentCluster": "B2", - "frequency": 12110, - "interest": 0.98306, - "hierarchicalLevel": 0.776545, - "rank": 2, - "hierarchicalRank": 6, - "isLeaf": false - }, - { - "cluster": "B9", - "parentCluster": "B5", - "frequency": 4085, - "interest": 0.949782, - "hierarchicalLevel": 0.923966, - "rank": 4, - "hierarchicalRank": 10, - "isLeaf": false - }, - { - "cluster": "B2", - "parentCluster": "", - "frequency": 48842, - "interest": 0.796992, - "hierarchicalLevel": -0.00125728, - "rank": 6, - "hierarchicalRank": 3, - "isLeaf": false - }, - { - "cluster": "B6", - "parentCluster": "B4", - "frequency": 22192, - "interest": 0.837833, - "hierarchicalLevel": 0.837966, - "rank": 8, - "hierarchicalRank": 7, - "isLeaf": false - }, - { - "cluster": "B14", - "parentCluster": "B6", - "frequency": 6408, - "interest": 0.583812, - "hierarchicalLevel": 0.984703, - "rank": 10, - "hierarchicalRank": 15, - "isLeaf": false - }, - { - "cluster": "B17", - 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[3697,2911,2486,2373,2166,2025,1936,1580,1503,1481,1369,1302,1288,1268,1258,1192,1171,1159,1156,912,779,664,636,624,567,497,430,422,383,375,362,347,336,332,331,328,324,302,286,279,279,277,275,271,261,259,240,237,234,209,206,200,193,182,181,167,167,163,160,153,139,125,125,122,121,116,110,100,90,69,67,65,63,50,41,39,35,34,31,28,20,19,15,14,13,9,9,8,7,7] + } } diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/DeployOccupation.kdic b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/DeployOccupation.kdic index 073a7c2df..b3bd1891c 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/DeployOccupation.kdic +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/DeployOccupation.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary Occupation (occupation) { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/DeployOccupation50.kdic b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/DeployOccupation50.kdic index ad73c8bae..b2ace7895 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/DeployOccupation50.kdic +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/DeployOccupation50.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary Occupation (occupation) { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/SimplifiedCoclustering50.khc b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/SimplifiedCoclustering50.khc index f87cffb90..bca49ef0e 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/SimplifiedCoclustering50.khc +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/SimplifiedCoclustering50.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/SimplifiedCoclustering50.khcj b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/SimplifiedCoclustering50.khcj index db022a904..a27f8440e 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/SimplifiedCoclustering50.khcj +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/SimplifiedCoclustering50.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/err.txt index c583d38f6..ad6e5224f 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/err.txt @@ -1,6 +1,6 @@ -Write deployment dictionary file ./results\DeployOccupation.kdic +Write deployment dictionary file ./results/DeployOccupation.kdic -Write deployment dictionary file ./results\DeployOccupation50.kdic +Write deployment dictionary file ./results/DeployOccupation50.kdic -Write simplified report ./results\SimplifiedCoclustering50.khc +Write simplified report ./results/SimplifiedCoclustering50.khcj diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/time.log index 1b1041760..d37c9a588 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.0860586166381836 +Overal time: 0.0961904525756836 diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/test.prm b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/test.prm index 0eea66bcd..ab76f0c78 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdult2varsFrequency/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -38,20 +40,19 @@ DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster va DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.CoclusteringDictionaryFileName DeployOccupation.kdic // Coclustering dictionary file +CoclusteringDictionaryFileName ./results/DeployOccupation.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.CoclusteringDictionaryFileName DeployOccupation50.kdic // Coclustering dictionary file +CoclusteringDictionaryFileName ./results/DeployOccupation50.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment Exit // Close // <- Coclustering deployment preparation LearningTools.PostProcessCoclustering // Simplify coclustering... -AnalysisResults.PostProcessedCoclusteringFileName SimplifiedCoclustering50.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/SimplifiedCoclustering50.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering Exit // Close // <- Simplify coclustering diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/DeployOccupation.kdic b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/DeployOccupation.kdic index a4b084ee2..c04257205 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/DeployOccupation.kdic +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/DeployOccupation.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary Occupation (occupation) { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/err.txt index 162ecefc4..e181f85ea 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/err.txt @@ -1,2 +1,2 @@ -Write deployment dictionary file ./results\DeployOccupation.kdic +Write deployment dictionary file ./results/DeployOccupation.kdic diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/time.log index 571d88cef..7a602df9b 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.08229207992553711 +Overal time: 0.07132720947265625 diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/test.prm b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/test.prm index b5e1961c7..8d3f65590 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAdultSmall/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -38,8 +40,7 @@ DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster va DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.CoclusteringDictionaryFileName DeployOccupation.kdic // Coclustering dictionary file +CoclusteringDictionaryFileName ./results/DeployOccupation.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment Exit // Close // <- Coclustering deployment preparation diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/CapColorDeployment.kdic b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/CapColorDeployment.kdic index 6e99ac161..745d84d5c 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/CapColorDeployment.kdic +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/CapColorDeployment.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary CapColor (CapColor) { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khc b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khc index ac6e3a967..43459fe59 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khcj b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khcj index 700b70916..536219dd5 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/err.txt index 20b731baa..bd676a785 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/err.txt @@ -1,4 +1,4 @@ -Write simplified report ./results\SimplifiedCoclustering.khc +Write simplified report ./results/SimplifiedCoclustering.khcj -Write deployment dictionary file ./results\CapColorDeployment.kdic +Write deployment dictionary file ./results/CapColorDeployment.kdic diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/time.log index 0f29ba916..bbcf90c5d 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.08844447135925293 +Overal time: 0.08846521377563477 diff --git a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/test.prm b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/test.prm index 62c6bf142..71fcc5ae1 100644 --- a/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/DeployDistributionAnnotatedMushroom/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,7 +23,7 @@ ClassFileName ./Bivariate.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName CapColor // Dictionary +Database.ClassName CapColor // Dictionary // -> Khiops Coclustering LearningTools.PostProcessCoclustering // Simplify coclustering... @@ -34,8 +36,7 @@ InputCoclusteringFileName ./Coclustering.khc // Input coclustering file OK // Open // <- Open -AnalysisResults.ResultFilesDirectory ./results // Result files directory - +PostProcessedCoclusteringFileName ./results/SimplifiedCoclustering.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering Exit // Close // <- Coclustering post-processing @@ -58,8 +59,7 @@ DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster va DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix -AnalysisResults.CoclusteringDictionaryFileName CapColorDeployment.kdic // Coclustering dictionary file -AnalysisResults.ResultFilesDirectory ./results // Result files directory +CoclusteringDictionaryFileName ./results/CapColorDeployment.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment Exit // Close // <- Coclustering deployment preparation diff --git a/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/results.ref/err.txt index 3728eefed..3ced9391f 100644 --- a/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/results.ref/err.txt @@ -1,8 +1,8 @@ -Write cluster table file ./results\ClustersAge.txt +Write cluster table file ./results/ClustersAge.txt -Write cluster table file ./results\ClustersOccupation.txt +Write cluster table file ./results/ClustersOccupation.txt -Write cluster table file ./results\I50_ClustersAge.txt +Write cluster table file ./results/I50_ClustersAge.txt -Write cluster table file ./results\I50_ClustersOccupation.txt +Write cluster table file ./results/I50_ClustersOccupation.txt diff --git a/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/results.ref/time.log index 8baa77c41..93dc2da44 100644 --- a/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.13704466819763184 +Overal time: 0.1246347427368164 diff --git a/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/test.prm b/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/test.prm index 6e4e175e8..72e191f0a 100644 --- a/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/ExtractClustersAdult/test.prm @@ -14,6 +14,9 @@ // +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB + LearningTools.ExtractClusters // Extract clusters... SelectInputCoclustering // Select input coclustering... @@ -24,24 +27,22 @@ InputCoclusteringFileName ./AdultCoclustering.khc // Input coclustering file OK // Open // <- Open -AnalysisResults.ResultFilesDirectory ./results // Result files directory -CoclusteringAttributeSpec.CoclusteringAttribute age // Coclustering variable -AnalysisResults.ClusterFileName ClustersAge.txt // Cluster table file + +CoclusteringAttribute age // Coclustering variable +ClusterFileName ./results/ClustersAge.txt // Cluster table file ExtractClusters // Extract clusters -CoclusteringAttributeSpec.CoclusteringAttribute occupation // Coclustering variable -AnalysisResults.ClusterFileName ClustersOccupation.txt // Cluster table file +CoclusteringAttribute occupation // Coclustering variable +ClusterFileName ./results/ClustersOccupation.txt // Cluster table file ExtractClusters // Extract clusters PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.ResultFilesPrefix I50_ // Result files prefix - -CoclusteringAttributeSpec.CoclusteringAttribute age // Coclustering variable -AnalysisResults.ClusterFileName ClustersAge.txt // Cluster table file +CoclusteringAttribute age // Coclustering variable +ClusterFileName ./results/I50_ClustersAge.txt // Cluster table file ExtractClusters // Extract clusters -CoclusteringAttributeSpec.CoclusteringAttribute occupation // Coclustering variable -AnalysisResults.ClusterFileName ClustersOccupation.txt // Cluster table file +CoclusteringAttribute occupation // Coclustering variable +ClusterFileName ./results/I50_ClustersOccupation.txt // Cluster table file ExtractClusters // Extract clusters Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/Coclustering.khc index e050ae4e8..5c8584224 100644 --- a/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 3 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/Coclustering.khcj index b7dd3222c..135a13bf1 100644 --- a/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/err.txt index 636c1dfa5..085b20d4b 100644 --- a/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/err.txt @@ -1,8 +1,8 @@ Database ../../../datasets/Iris/Iris.txt : Read records: 150 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.043904 Size: 2*2*1 Granularity: 1 - 0:00:00 Write intermediate coclustering report Coclustering(2).khc Level: 0.095524 Size: 3*3*3 Granularity: 2 - 0:00:00 Write intermediate coclustering report Coclustering(3).khc Level: 0.130686 Size: 3*3*3 Granularity: 3 - 0:00:00 Write intermediate coclustering report Coclustering(4).khc Level: 0.156121 Size: 3*3*3 -Write coclustering report ./results\Coclustering.khc -Coclustering analysis time: 0:00:00.03 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.043904 Size: 2*2*1 Granularity: 1 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.095524 Size: 3*3*3 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.130686 Size: 3*3*3 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.156121 Size: 3*3*3 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.04 diff --git a/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/time.log index 99744a6da..249439a7a 100644 --- a/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/Iris/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.11186623573303223 +Overal time: 0.13213276863098145 diff --git a/test/LearningTest/TestCoclustering/Standard/Iris/test.prm b/test/LearningTest/TestCoclustering/Standard/Iris/test.prm index 8ce0056f1..d6bc51f97 100644 --- a/test/LearningTest/TestCoclustering/Standard/Iris/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/Iris/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,8 +23,8 @@ ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key Iris // List item selection -Database.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name PetalLength // Name AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable @@ -30,7 +32,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.Name PetalWidth // Name AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name Class // Name -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/IrisFastPath/readme.txt b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/readme.txt new file mode 100644 index 000000000..21127a916 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/readme.txt @@ -0,0 +1,2 @@ +Test de la nouvelle ergonomie V11 de Khiops, avec suppression de l'onglet dictionnaire +et possibilité d'analyser une base en construisant son dictionnaire à la volée. diff --git a/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/Coclustering.khc new file mode 100644 index 000000000..d2c1aecc6 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/Coclustering.khc @@ -0,0 +1,50 @@ +#Khiops 10.4.6i +Short description +Dimensions 2 +Name Type Parts Initial parts Values Interest Description +PetalLength Numerical 3 3 150 1 +PetalWidth Numerical 3 3 150 1 + +Coclustering stats +Instances 150 +Cells 4 +Null cost 1210.733359 +Cost 1107.814123 +Level 0.08500569963 +Initial dimensions 2 +Frequency variable +Dictionary Iris +Database ../../../datasets/Iris/Iris.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value + +Bounds +Name Min Max +PetalLength 1 6.9 +PetalWidth 0.1 2.5 + +Hierarchy PetalLength +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;2.4] ]-inf;+inf[ 50 0.590189 1 1 6 +]2.4;4.75] ]2.4;+inf[ 45 1 1 3 6 +]4.75;+inf[ ]2.4;+inf[ 55 0.409811 1 5 6 +]-inf;+inf[ 150 0.646994 0 2 2 +]2.4;+inf[ ]-inf;+inf[ 100 0.675396 0.746684 4 4 + +Hierarchy PetalWidth +Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank +]-inf;0.8] ]-inf;+inf[ 50 0.612775 1 1 6 +]0.8;1.75] ]0.8;+inf[ 54 1 1 3 6 +]1.75;+inf[ ]0.8;+inf[ 46 0.387225 1 5 6 +]-inf;+inf[ 150 0.683007 -0.0668591 2 3 +]0.8;+inf[ ]-inf;+inf[ 100 0.718124 0.671112 4 5 + +Cells +PetalLength PetalWidth Frequency +]-inf;2.4] ]-inf;0.8] 50 +]4.75;+inf[ ]1.75;+inf[ 46 +]2.4;4.75] ]0.8;1.75] 45 +]4.75;+inf[ ]0.8;1.75] 9 + diff --git a/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/Coclustering.khcj new file mode 100644 index 000000000..3a148926e --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/Coclustering.khcj @@ -0,0 +1,206 @@ +{ + "tool": "Khiops Coclustering", + "version": "10.4.6i", + "shortDescription": "", + "coclusteringReport": { + "summary": { + "instances": 150, + "cells": 4, + "nullCost": 1210.733359, + "cost": 1107.814123, + "level": 0.08500569963, + "initialDimensions": 2, + "frequencyVariable": "", + "dictionary": "Iris", + "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", + "samplePercentage": 100, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "" + }, + "dimensionSummaries": [ + { + "name": "PetalLength", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 1, + "max": 6.9 + }, + { + "name": "PetalWidth", + "type": "Numerical", + "parts": 3, + "initialParts": 3, + "values": 150, + "interest": 1, + "description": "", + "min": 0.1, + "max": 2.5 + } + ], + "dimensionPartitions": [ + { + "name": "PetalLength", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;2.4]", + "bounds": [1,2.4] + }, + { + "cluster": "]2.4;4.75]", + "bounds": [2.4,4.75] + }, + { + "cluster": "]4.75;+inf[", + "bounds": [4.75,6.9] + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "intervals": [ + { + "cluster": "]-inf;0.8]", + "bounds": [0.1,0.8] + }, + { + "cluster": "]0.8;1.75]", + "bounds": [0.8,1.75] + }, + { + "cluster": "]1.75;+inf[", + "bounds": [1.75,2.5] + } + ] + } + ], + "dimensionHierarchies": [ + { + "name": "PetalLength", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;2.4]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.590189, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "]2.4;4.75]", + "parentCluster": "]2.4;+inf[", + "frequency": 45, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "]4.75;+inf[", + "parentCluster": "]2.4;+inf[", + "frequency": 55, + "interest": 0.409811, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.646994, + "hierarchicalLevel": 0, + "rank": 2, + "hierarchicalRank": 2, + "isLeaf": false + }, + { + "cluster": "]2.4;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.675396, + "hierarchicalLevel": 0.746684, + "rank": 4, + "hierarchicalRank": 4, + "isLeaf": false + } + ] + }, + { + "name": "PetalWidth", + "type": "Numerical", + "clusters": [ + { + "cluster": "]-inf;0.8]", + "parentCluster": "]-inf;+inf[", + "frequency": 50, + "interest": 0.612775, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "]0.8;1.75]", + "parentCluster": "]0.8;+inf[", + "frequency": 54, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "]1.75;+inf[", + "parentCluster": "]0.8;+inf[", + "frequency": 46, + "interest": 0.387225, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 6, + "isLeaf": true + }, + { + "cluster": "]-inf;+inf[", + "parentCluster": "", + "frequency": 150, + "interest": 0.683007, + "hierarchicalLevel": -0.0668591, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "]0.8;+inf[", + "parentCluster": "]-inf;+inf[", + "frequency": 100, + "interest": 0.718124, + "hierarchicalLevel": 0.671112, + "rank": 4, + "hierarchicalRank": 5, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [0,0], + [2,2], + [1,1], + [2,1] + ], + "cellFrequencies": [50,46,45,9] + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/err.txt new file mode 100644 index 000000000..c7fa74881 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/err.txt @@ -0,0 +1,11 @@ +File format detected: header line and field separator tabulation +Database ../../../datasets/Iris/Iris.txt : Recognized variable types: 1 Categorical, 4 Numerical +warning : A dictionary has been generated automatically. The field types should be checked and the dictionary saved if necessary. For standard use, refer to the 'Data dictionary/Manage dictionaries...' menu. + +Database ../../../datasets/Iris/Iris.txt : Read records: 150 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.05003 Size: 2*2 Granularity: 1 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0676747 Size: 3*3 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.0850057 Size: 3*3 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.02 + diff --git a/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/time.log new file mode 100644 index 000000000..43a0f7e0b --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/results.ref/time.log @@ -0,0 +1 @@ +Overal time: 0.09683680534362793 diff --git a/test/LearningTest/TestCoclustering/Standard/IrisFastPath/test.prm b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/test.prm new file mode 100644 index 000000000..0a7531494 --- /dev/null +++ b/test/LearningTest/TestCoclustering/Standard/IrisFastPath/test.prm @@ -0,0 +1,37 @@ +// 2023-03-31 10:27:54 +// khiops_coclustering +// Output command file +// +//This file contains recorded commands, that can be replayed. +//Commands are based on user interactions: +// field update +// list item selection +// menu action +//Every command can be commented, using //. +//For example, commenting the last Exit command will allow other +//user interactions, after the commands have been replayed. +// +// + +// -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB +Database.DatabaseSpec.Data.DatabaseFiles.List.Key // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file + +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable +AnalysisSpec.CoclusteringParameters.Attributes.List.Index 0 // List item selection +AnalysisSpec.CoclusteringParameters.Attributes.Name PetalLength // Name +AnalysisSpec.CoclusteringParameters.Attributes.List.Index 1 // List item selection +AnalysisSpec.CoclusteringParameters.Attributes.Name PetalWidth // Name + +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report +BuildCoclustering // Train coclustering +Exit // Close +// <- Khiops Coclustering + + +// -> Khiops Coclustering +OK // Yes +// <- Khiops Coclustering diff --git a/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/Coclustering.khc index 186efa9ea..7b7e422e7 100644 --- a/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 3 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/Coclustering.khcj index cb3c539f2..abdae9731 100644 --- a/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/err.txt index 3eae631f0..7fadd7616 100644 --- a/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/err.txt @@ -1,8 +1,8 @@ Database ./IrisMissing.txt : Read records: 150 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.0043303 Size: 2*2*1 Granularity: 1 - 0:00:00 Write intermediate coclustering report Coclustering(2).khc Level: 0.0876137 Size: 3*3*3 Granularity: 2 - 0:00:00 Write intermediate coclustering report Coclustering(3).khc Level: 0.133932 Size: 3*3*3 Granularity: 3 - 0:00:00 Write intermediate coclustering report Coclustering(4).khc Level: 0.148401 Size: 3*4*3 -Write coclustering report ./results\Coclustering.khc -Coclustering analysis time: 0:00:00.04 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.0043303 Size: 2*2*1 Granularity: 1 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0876137 Size: 3*3*3 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.133932 Size: 3*3*3 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.148401 Size: 3*4*3 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.05 diff --git a/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/time.log index ad30a0edd..0172d2130 100644 --- a/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/IrisMissing/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.10683393478393555 +Overal time: 0.11797618865966797 diff --git a/test/LearningTest/TestCoclustering/Standard/IrisMissing/test.prm b/test/LearningTest/TestCoclustering/Standard/IrisMissing/test.prm index 34d06943a..c3753f3de 100644 --- a/test/LearningTest/TestCoclustering/Standard/IrisMissing/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/IrisMissing/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,8 +23,8 @@ ClassFileName ./IrisMissing.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key IrisMissing // List item selection -Database.DatabaseFiles.DataTableName ./IrisMissing.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key IrisMissing // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ./IrisMissing.txt // Database file AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name PetalLength // Name AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable @@ -30,7 +32,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.Name PW_MM40 // Name AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name Class // Name -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/Coclustering.V9.json b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/Coclustering.V9.json index 4ac01445d..2accfb5ed 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/Coclustering.V9.json +++ b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/Coclustering.V9.json @@ -1,649 +1,449 @@ { - "tool": "Khiops Coclustering", - "version": "9.2.3a", - "coclusteringReport": { - "summary": { - "instances": 8416, - "cells": 25, - "nullCost": 27983.3, - "cost": 24901.7, - "level": 0.1101228233, - "initialDimensions": 2, - "frequencyVariable": "", - "dictionary": "Mushroom", - "database": "C:\\Temp\\Mushroom\\Mushroom.txt" - }, - "dimensionSummaries": [ - { - "name": "CapColor", - "type": "Categorical", - "parts": 7, - "initialParts": 7, - "values": 10, - "interest": 1, - "description": "" - }, - { - "name": "Odor", - "type": "Categorical", - "parts": 7, - "initialParts": 7, - "values": 9, - "interest": 1, - "description": "" - } - ], - "dimensionPartitions": [ - { - "name": "CapColor", - "type": "Categorical", - "valueGroups": [ - { - "cluster": "{YELLOW}", - "values": [ - "YELLOW" - ], - "valueFrequencies": [ - 1072 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{GRAY, BUFF}", - "values": [ - "GRAY", - "BUFF" - ], - "valueFrequencies": [ - 2096, - 168 - ], - "valueTypicalities": [ - 1, - 0.186724 - ] - }, - { - "cluster": "{WHITE}", - "values": [ - "WHITE" - ], - "valueFrequencies": [ - 1040 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{PINK}", - "values": [ - "PINK" - ], - "valueFrequencies": [ - 144 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{CINNAMON, GREEN, PURPLE}", - "values": [ - "CINNAMON", - "GREEN", - "PURPLE" - ], - "valueFrequencies": [ - 44, - 16, - 16 - ], - "valueTypicalities": [ - 1, - 0.32908, - 0.286005 - ] - }, - { - "cluster": "{RED}", - "values": [ - "RED" - ], - "valueFrequencies": [ - 1500 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{BROWN}", - "values": [ - "BROWN" - ], - "valueFrequencies": [ - 2320 - ], - "valueTypicalities": [ - 1 - ] - } - ], - "defaultGroupIndex": 6 - }, - { - "name": "Odor", - "type": "Categorical", - "valueGroups": [ - { - "cluster": "{ALMOND, ANISE}", - "values": [ - "ALMOND", - "ANISE" - ], - "valueFrequencies": [ - 400, - 400 - ], - "valueTypicalities": [ - 1, - 1 - ] - }, - { - "cluster": "{FOUL}", - "values": [ - "FOUL" - ], - "valueFrequencies": [ - 2160 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{FISHY, SPICY}", - "values": [ - "FISHY", - "SPICY" - ], - "valueFrequencies": [ - 576, - 576 - ], - "valueTypicalities": [ - 1, - 1 - ] - }, - { - "cluster": "{MUSTY}", - "values": [ - "MUSTY" - ], - "valueFrequencies": [ - 48 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{NONE}", - "values": [ - "NONE" - ], - "valueFrequencies": [ - 3808 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{CREOSOTE}", - "values": [ - "CREOSOTE" - ], - "valueFrequencies": [ - 192 - ], - "valueTypicalities": [ - 1 - ] - }, - { - "cluster": "{PUNGENT}", - "values": [ - "PUNGENT" - ], - "valueFrequencies": [ - 256 - ], - "valueTypicalities": [ - 1 - ] - } - ], - "defaultGroupIndex": 6 - } - ], - "dimensionHierarchies": [ - { - "name": "CapColor", - "type": "Categorical", - "clusters": [ - { - "cluster": "{YELLOW}", - "parentCluster": "A2", - "frequency": 1072, - "interest": 1, - "hierarchicalLevel": 1, - "rank": 1, - "hierarchicalRank": 14, - "isLeaf": true, - "shortDescription": "Jaune" - }, - { - "cluster": "{GRAY, BUFF}", - "parentCluster": "A6", - "frequency": 2264, - "interest": 0.739123, - "hierarchicalLevel": 1, - "rank": 3, - "hierarchicalRank": 14, - "isLeaf": true, - "shortDescription": "Gris", - "description": "Gris¤comme¤Gandalf" - }, - { - "cluster": "{WHITE}", - "parentCluster": "A9", - "frequency": 1040, - "interest": 0.626873, - "hierarchicalLevel": 1, - "rank": 5, - "hierarchicalRank": 14, - "isLeaf": true, - "shortDescription": "Blanc", - "description": "Blanc¤comme¤Saruman" - }, - { - "cluster": "{PINK}", - "parentCluster": "A12", - "frequency": 144, - "interest": 0.276019, - "hierarchicalLevel": 1, - "rank": 7, - "hierarchicalRank": 14, - "isLeaf": true, - "shortDescription": "Rose" - }, - { - "cluster": "{CINNAMON, GREEN, PURPLE}", - "parentCluster": "A12", - "frequency": 76, - "interest": 0.14494, - "hierarchicalLevel": 1, - "rank": 9, - "hierarchicalRank": 14, - "isLeaf": true, - "shortDescription": "Cannelle" - }, - { - "cluster": "{RED}", - "parentCluster": "A10", - "frequency": 1500, - "interest": 0.680039, - "hierarchicalLevel": 1, - "rank": 11, - "hierarchicalRank": 14, - "isLeaf": true, - "shortDescription": "Rouge", - "description": "Rouge!!!°Ah¤la¤la,¤ça¤craint..." - }, - { - "cluster": "{BROWN}", - "parentCluster": "A10", - "frequency": 2320, - "interest": 0.668346, - "hierarchicalLevel": 1, - "rank": 13, - "hierarchicalRank": 14, - "isLeaf": true, - "shortDescription": "Brun" - }, - { - "cluster": "A2", - "parentCluster": "", - "frequency": 8416, - "interest": 0.71515, - "hierarchicalLevel": -0.00326863, - "rank": 2, - "hierarchicalRank": 3, - "isLeaf": false - }, - { - "cluster": "A6", - "parentCluster": "A3", - "frequency": 3524, - "interest": 0.674258, - "hierarchicalLevel": 0.651661, - "rank": 4, - "hierarchicalRank": 7, - "isLeaf": false - }, - { - "cluster": "A9", - "parentCluster": "A6", - "frequency": 1260, - "interest": 0.557706, - "hierarchicalLevel": 0.895619, - "rank": 6, - "hierarchicalRank": 10, - "isLeaf": false - }, - { - "cluster": "A12", - "parentCluster": "A9", - "frequency": 220, - "interest": 0.230737, - "hierarchicalLevel": 0.995743, - "rank": 8, - "hierarchicalRank": 13, - "isLeaf": false - }, - { - "cluster": "A3", - "parentCluster": "A2", - "frequency": 7344, - "interest": 0.673571, - "hierarchicalLevel": 0.358859, - "rank": 10, - "hierarchicalRank": 4, - "isLeaf": false - }, - { - "cluster": "A10", - "parentCluster": "A3", - "frequency": 3820, - "interest": 0.672937, - "hierarchicalLevel": 0.942919, - "rank": 12, - "hierarchicalRank": 11, - "isLeaf": false - } - ] - }, - { - "name": "Odor", - "type": "Categorical", - "clusters": [ - { - "cluster": "{ALMOND, ANISE}", - "parentCluster": "B5", - "frequency": 800, - "interest": 0.91958, - "hierarchicalLevel": 1, - "rank": 1, - "hierarchicalRank": 14, - "isLeaf": true - }, - { - "cluster": "{FOUL}", - "parentCluster": "B5", - "frequency": 2160, - "interest": 0.876044, - "hierarchicalLevel": 1, - "rank": 3, - "hierarchicalRank": 14, - "isLeaf": true - }, - { - "cluster": "{FISHY, SPICY}", - "parentCluster": "B11", - "frequency": 1152, - "interest": 1, - "hierarchicalLevel": 1, - "rank": 5, - "hierarchicalRank": 14, - "isLeaf": true - }, - { - "cluster": "{MUSTY}", - "parentCluster": "B11", - "frequency": 48, - "interest": 0.145564, - "hierarchicalLevel": 1, - "rank": 7, - "hierarchicalRank": 14, - "isLeaf": true - }, - { - "cluster": "{NONE}", - "parentCluster": "B7", - "frequency": 3808, - "interest": 0.865195, - "hierarchicalLevel": 1, - "rank": 9, - "hierarchicalRank": 14, - "isLeaf": true - }, - { - "cluster": "{CREOSOTE}", - "parentCluster": "B8", - "frequency": 192, - "interest": 0.46277, - "hierarchicalLevel": 1, - "rank": 11, - "hierarchicalRank": 14, - "isLeaf": true - }, - { - "cluster": "{PUNGENT}", - "parentCluster": "B8", - "frequency": 256, - "interest": 0.378504, - "hierarchicalLevel": 1, - "rank": 13, - "hierarchicalRank": 14, - "isLeaf": true - }, - { - "cluster": "B5", - "parentCluster": "B1", - "frequency": 2960, - "interest": 0.88781, - "hierarchicalLevel": 0.633019, - "rank": 2, - "hierarchicalRank": 6, - "isLeaf": false - }, - { - "cluster": "B1", - "parentCluster": "", - "frequency": 8416, - "interest": 0.863512, - "hierarchicalLevel": 0, - "rank": 4, - "hierarchicalRank": 2, - "isLeaf": false - }, - { - "cluster": "B11", - "parentCluster": "B4", - "frequency": 1200, - "interest": 0.965823, - "hierarchicalLevel": 0.98921, - "rank": 6, - "hierarchicalRank": 12, - "isLeaf": false - }, - { - "cluster": "B4", - "parentCluster": "B1", - "frequency": 5456, - "interest": 0.85033, - "hierarchicalLevel": 0.405738, - "rank": 8, - "hierarchicalRank": 5, - "isLeaf": false - }, - { - "cluster": "B7", - "parentCluster": "B4", - "frequency": 4256, - "interest": 0.817766, - "hierarchicalLevel": 0.80499, - "rank": 10, - "hierarchicalRank": 8, - "isLeaf": false - }, - { - "cluster": "B8", - "parentCluster": "B7", - "frequency": 448, - "interest": 0.414618, - "hierarchicalLevel": 0.859857, - "rank": 12, - "hierarchicalRank": 9, - "isLeaf": false - } - ] - } - ], - "cellPartIndexes": [ - [ - 1, - 4 - ], - [ - 6, - 4 - ], - [ - 1, - 1 - ], - [ - 0, - 1 - ], - [ - 5, - 4 - ], - [ - 6, - 2 - ], - [ - 5, - 2 - ], - [ - 2, - 4 - ], - [ - 0, - 0 - ], - [ - 2, - 0 - ], - [ - 6, - 1 - ], - [ - 5, - 1 - ], - [ - 6, - 6 - ], - [ - 2, - 6 - ], - [ - 6, - 0 - ], - [ - 2, - 1 - ], - [ - 3, - 4 - ], - [ - 4, - 4 - ], - [ - 1, - 5 - ], - [ - 3, - 5 - ], - [ - 2, - 5 - ], - [ - 6, - 3 - ], - [ - 0, - 4 - ], - [ - 4, - 3 - ], - [ - 5, - 3 - ] - ], - "cellFrequencies": [ - 1360, - 1208, - 840, - 648, - 624, - 576, - 576, - 448, - 400, - 304, - 288, - 288, - 128, - 128, - 96, - 96, - 80, - 64, - 64, - 64, - 64, - 24, - 24, - 12, - 12 - ] - } + "tool": "Khiops Coclustering", + "version": "9.2.3a", + "coclusteringReport": { + "summary": { + "instances": 8416, + "cells": 25, + "nullCost": 27983.3, + "cost": 24901.7, + "level": 0.1101228233, + "initialDimensions": 2, + "frequencyVariable": "", + "dictionary": "Mushroom", + "database": "C:\\Temp\\Mushroom\\Mushroom.txt" + }, + "dimensionSummaries": [ + { + "name": "CapColor", + "type": "Categorical", + "parts": 7, + "initialParts": 7, + "values": 10, + "interest": 1, + "description": "" + }, + { + "name": "Odor", + "type": "Categorical", + "parts": 7, + "initialParts": 7, + "values": 9, + "interest": 1, + "description": "" + } + ], + "dimensionPartitions": [ + { + "name": "CapColor", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{YELLOW}", + "values": ["YELLOW"], + "valueFrequencies": [1072], + "valueTypicalities": [1] + }, + { + "cluster": "{GRAY, BUFF}", + "values": ["GRAY","BUFF"], + "valueFrequencies": [2096,168], + "valueTypicalities": [1,0.186724] + }, + { + "cluster": "{WHITE}", + "values": ["WHITE"], + "valueFrequencies": [1040], + "valueTypicalities": [1] + }, + { + "cluster": "{PINK}", + "values": ["PINK"], + "valueFrequencies": [144], + "valueTypicalities": [1] + }, + { + "cluster": "{CINNAMON, GREEN, PURPLE}", + "values": ["CINNAMON","GREEN","PURPLE"], + "valueFrequencies": [44,16,16], + "valueTypicalities": [1,0.32908,0.286005] + }, + { + "cluster": "{RED}", + "values": ["RED"], + "valueFrequencies": [1500], + "valueTypicalities": [1] + }, + { + "cluster": "{BROWN}", + "values": ["BROWN"], + "valueFrequencies": [2320], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 6 + }, + { + "name": "Odor", + "type": "Categorical", + "valueGroups": [ + { + "cluster": "{ALMOND, ANISE}", + "values": ["ALMOND","ANISE"], + "valueFrequencies": [400,400], + "valueTypicalities": [1,1] + }, + { + "cluster": "{FOUL}", + "values": ["FOUL"], + "valueFrequencies": [2160], + "valueTypicalities": [1] + }, + { + "cluster": "{FISHY, SPICY}", + "values": ["FISHY","SPICY"], + "valueFrequencies": [576,576], + "valueTypicalities": [1,1] + }, + { + "cluster": "{MUSTY}", + "values": ["MUSTY"], + "valueFrequencies": [48], + "valueTypicalities": [1] + }, + { + "cluster": "{NONE}", + "values": ["NONE"], + "valueFrequencies": [3808], + "valueTypicalities": [1] + }, + { + "cluster": "{CREOSOTE}", + "values": ["CREOSOTE"], + "valueFrequencies": [192], + "valueTypicalities": [1] + }, + { + "cluster": "{PUNGENT}", + "values": ["PUNGENT"], + "valueFrequencies": [256], + "valueTypicalities": [1] + } + ], + "defaultGroupIndex": 6 + } + ], + "dimensionHierarchies": [ + { + "name": "CapColor", + "type": "Categorical", + "clusters": [ + { + "cluster": "{YELLOW}", + "parentCluster": "A2", + "frequency": 1072, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 14, + "isLeaf": true, + "shortDescription": "Jaune" + }, + { + "cluster": "{GRAY, BUFF}", + "parentCluster": "A6", + "frequency": 2264, + "interest": 0.739123, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 14, + "isLeaf": true, + "shortDescription": "Gris", + "description": "Gris¤comme¤Gandalf" + }, + { + "cluster": "{WHITE}", + "parentCluster": "A9", + "frequency": 1040, + "interest": 0.626873, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 14, + "isLeaf": true, + "shortDescription": "Blanc", + "description": "Blanc¤comme¤Saruman" + }, + { + "cluster": "{PINK}", + "parentCluster": "A12", + "frequency": 144, + "interest": 0.276019, + "hierarchicalLevel": 1, + "rank": 7, + "hierarchicalRank": 14, + "isLeaf": true, + "shortDescription": "Rose" + }, + { + "cluster": "{CINNAMON, GREEN, PURPLE}", + "parentCluster": "A12", + "frequency": 76, + "interest": 0.14494, + "hierarchicalLevel": 1, + "rank": 9, + "hierarchicalRank": 14, + "isLeaf": true, + "shortDescription": "Cannelle" + }, + { + "cluster": "{RED}", + "parentCluster": "A10", + "frequency": 1500, + "interest": 0.680039, + "hierarchicalLevel": 1, + "rank": 11, + "hierarchicalRank": 14, + "isLeaf": true, + "shortDescription": "Rouge", + "description": "Rouge!!!°Ah¤la¤la,¤ça¤craint..." + }, + { + "cluster": "{BROWN}", + "parentCluster": "A10", + "frequency": 2320, + "interest": 0.668346, + "hierarchicalLevel": 1, + "rank": 13, + "hierarchicalRank": 14, + "isLeaf": true, + "shortDescription": "Brun" + }, + { + "cluster": "A2", + "parentCluster": "", + "frequency": 8416, + "interest": 0.71515, + "hierarchicalLevel": -0.00326863, + "rank": 2, + "hierarchicalRank": 3, + "isLeaf": false + }, + { + "cluster": "A6", + "parentCluster": "A3", + "frequency": 3524, + "interest": 0.674258, + "hierarchicalLevel": 0.651661, + "rank": 4, + "hierarchicalRank": 7, + "isLeaf": false + }, + { + "cluster": "A9", + "parentCluster": "A6", + "frequency": 1260, + "interest": 0.557706, + "hierarchicalLevel": 0.895619, + "rank": 6, + "hierarchicalRank": 10, + "isLeaf": false + }, + { + "cluster": "A12", + "parentCluster": "A9", + "frequency": 220, + "interest": 0.230737, + "hierarchicalLevel": 0.995743, + "rank": 8, + "hierarchicalRank": 13, + "isLeaf": false + }, + { + "cluster": "A3", + "parentCluster": "A2", + "frequency": 7344, + "interest": 0.673571, + "hierarchicalLevel": 0.358859, + "rank": 10, + "hierarchicalRank": 4, + "isLeaf": false + }, + { + "cluster": "A10", + "parentCluster": "A3", + "frequency": 3820, + "interest": 0.672937, + "hierarchicalLevel": 0.942919, + "rank": 12, + "hierarchicalRank": 11, + "isLeaf": false + } + ] + }, + { + "name": "Odor", + "type": "Categorical", + "clusters": [ + { + "cluster": "{ALMOND, ANISE}", + "parentCluster": "B5", + "frequency": 800, + "interest": 0.91958, + "hierarchicalLevel": 1, + "rank": 1, + "hierarchicalRank": 14, + "isLeaf": true + }, + { + "cluster": "{FOUL}", + "parentCluster": "B5", + "frequency": 2160, + "interest": 0.876044, + "hierarchicalLevel": 1, + "rank": 3, + "hierarchicalRank": 14, + "isLeaf": true + }, + { + "cluster": "{FISHY, SPICY}", + "parentCluster": "B11", + "frequency": 1152, + "interest": 1, + "hierarchicalLevel": 1, + "rank": 5, + "hierarchicalRank": 14, + "isLeaf": true + }, + { + "cluster": "{MUSTY}", + "parentCluster": "B11", + "frequency": 48, + "interest": 0.145564, + "hierarchicalLevel": 1, + "rank": 7, + "hierarchicalRank": 14, + "isLeaf": true + }, + { + "cluster": "{NONE}", + "parentCluster": "B7", + "frequency": 3808, + "interest": 0.865195, + "hierarchicalLevel": 1, + "rank": 9, + "hierarchicalRank": 14, + "isLeaf": true + }, + { + "cluster": "{CREOSOTE}", + "parentCluster": "B8", + "frequency": 192, + "interest": 0.46277, + "hierarchicalLevel": 1, + "rank": 11, + "hierarchicalRank": 14, + "isLeaf": true + }, + { + "cluster": "{PUNGENT}", + "parentCluster": "B8", + "frequency": 256, + "interest": 0.378504, + "hierarchicalLevel": 1, + "rank": 13, + "hierarchicalRank": 14, + "isLeaf": true + }, + { + "cluster": "B5", + "parentCluster": "B1", + "frequency": 2960, + "interest": 0.88781, + "hierarchicalLevel": 0.633019, + "rank": 2, + "hierarchicalRank": 6, + "isLeaf": false + }, + { + "cluster": "B1", + "parentCluster": "", + "frequency": 8416, + "interest": 0.863512, + "hierarchicalLevel": 0, + "rank": 4, + "hierarchicalRank": 2, + "isLeaf": false + }, + { + "cluster": "B11", + "parentCluster": "B4", + "frequency": 1200, + "interest": 0.965823, + "hierarchicalLevel": 0.98921, + "rank": 6, + "hierarchicalRank": 12, + "isLeaf": false + }, + { + "cluster": "B4", + "parentCluster": "B1", + "frequency": 5456, + "interest": 0.85033, + "hierarchicalLevel": 0.405738, + "rank": 8, + "hierarchicalRank": 5, + "isLeaf": false + }, + { + "cluster": "B7", + "parentCluster": "B4", + "frequency": 4256, + "interest": 0.817766, + "hierarchicalLevel": 0.80499, + "rank": 10, + "hierarchicalRank": 8, + "isLeaf": false + }, + { + "cluster": "B8", + "parentCluster": "B7", + "frequency": 448, + "interest": 0.414618, + "hierarchicalLevel": 0.859857, + "rank": 12, + "hierarchicalRank": 9, + "isLeaf": false + } + ] + } + ], + "cellPartIndexes": [ + [1,4], + [6,4], + [1,1], + [0,1], + [5,4], + [6,2], + [5,2], + [2,4], + [0,0], + [2,0], + [6,1], + [5,1], + [6,6], + [2,6], + [6,0], + [2,1], + [3,4], + [4,4], + [1,5], + [3,5], + [2,5], + [6,3], + [0,4], + [4,3], + [5,3] + ], + "cellFrequencies": [1360,1208,840,648,624,576,576,448,400,304,288,288,128,128,96,96,80,64,64,64,64,24,24,12,12] + } } diff --git a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/CapColorDeployment.kdic b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/CapColorDeployment.kdic index 6e99ac161..745d84d5c 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/CapColorDeployment.kdic +++ b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/CapColorDeployment.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary CapColor (CapColor) { diff --git a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khc b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khc index edbfbde11..52d59fe0d 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khcj b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khcj index 700b70916..536219dd5 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/SimplifiedCoclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/err.txt index 20b731baa..bd676a785 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/err.txt @@ -1,4 +1,4 @@ -Write simplified report ./results\SimplifiedCoclustering.khc +Write simplified report ./results/SimplifiedCoclustering.khcj -Write deployment dictionary file ./results\CapColorDeployment.kdic +Write deployment dictionary file ./results/CapColorDeployment.kdic diff --git a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/time.log index 3ed6e87e1..cccef528f 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.09000587463378906 +Overal time: 0.09011387825012207 diff --git a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/test.prm b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/test.prm index c3d724b53..92c9424c0 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/JSONDeployDistributionAnnotatedMushroom/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,7 +23,7 @@ ClassFileName ./Bivariate.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName CapColor // Dictionary +Database.ClassName CapColor // Dictionary // -> Khiops Coclustering LearningTools.PostProcessCoclustering // Simplify coclustering... @@ -34,8 +36,7 @@ InputCoclusteringFileName ./Coclustering.khcj // Input coclustering file OK // Open // <- Open -AnalysisResults.ResultFilesDirectory ./results // Result files directory - +PostProcessedCoclusteringFileName ./results/SimplifiedCoclustering.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering Exit // Close // <- Coclustering post-processing @@ -58,8 +59,7 @@ DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster va DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix -AnalysisResults.CoclusteringDictionaryFileName CapColorDeployment.kdic // Coclustering dictionary file -AnalysisResults.ResultFilesDirectory ./results // Result files directory +CoclusteringDictionaryFileName ./results/CapColorDeployment.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment Exit // Close // <- Coclustering deployment preparation diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_JSON.khc b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_JSON.khc index 1238e3083..fad69d8c2 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_JSON.khc +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_JSON.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 3 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_JSON.khcj b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_JSON.khcj index edd0daa90..5b62c6569 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_JSON.khcj +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_JSON.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_KHC.khc b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_KHC.khc index 1238e3083..fad69d8c2 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_KHC.khc +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_KHC.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 3 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_KHC.khcj b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_KHC.khcj index edd0daa90..5b62c6569 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_KHC.khcj +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/PostProcessedCoclustering_KHC.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/err.txt index dae8e21dc..629564c57 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/err.txt @@ -1,4 +1,4 @@ -Write simplified report ./results\PostProcessedCoclustering_KHC.khc +Write simplified report ./results/PostProcessedCoclustering_KHC.khcj -Write simplified report ./results\PostProcessedCoclustering_JSON.khc +Write simplified report ./results/PostProcessedCoclustering_JSON.khcj diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/time.log index 78b403d5a..0a91cb6f7 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.0907132625579834 +Overal time: 0.12277865409851074 diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/test.prm b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/test.prm index 4baed13d8..2f86cd3ea 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyAdultMissing/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB LearningTools.PostProcessCoclustering // Simplify coclustering... // -> Coclustering post-processing @@ -25,8 +27,7 @@ OK // Open PostProcessingSpec.MaxCellNumber 0 // Max cell number PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_KHC.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_KHC.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering @@ -39,8 +40,7 @@ OK // Open PostProcessingSpec.MaxCellNumber 0 // Max cell number PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_JSON.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_JSON.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_JSON.khc b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_JSON.khc index ffc85662d..4a53409df 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_JSON.khc +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_JSON.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 3 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_JSON.khcj b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_JSON.khcj index 2f11cb5df..f86b37dc6 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_JSON.khcj +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_JSON.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_KHC.khc b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_KHC.khc index ffc85662d..4a53409df 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_KHC.khc +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_KHC.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 3 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_KHC.khcj b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_KHC.khcj index 2f11cb5df..f86b37dc6 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_KHC.khcj +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/PostProcessedCoclustering_KHC.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/err.txt index dae8e21dc..629564c57 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/err.txt @@ -1,4 +1,4 @@ -Write simplified report ./results\PostProcessedCoclustering_KHC.khc +Write simplified report ./results/PostProcessedCoclustering_KHC.khcj -Write simplified report ./results\PostProcessedCoclustering_JSON.khc +Write simplified report ./results/PostProcessedCoclustering_JSON.khcj diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/time.log index 5786e5c8e..56f23ca89 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.09060835838317871 +Overal time: 0.10579848289489746 diff --git a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/test.prm b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/test.prm index 4de578b7e..8be8fc0a0 100644 --- a/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/JSONSimplifyIris/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB LearningTools.PostProcessCoclustering // Simplify coclustering... // -> Coclustering post-processing @@ -25,8 +27,7 @@ OK // Open PostProcessingSpec.MaxCellNumber 0 // Max cell number PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_KHC.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_KHC.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering @@ -39,8 +40,7 @@ OK // Open PostProcessingSpec.MaxCellNumber 0 // Max cell number PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_JSON.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_JSON.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/Coclustering.khc index 5de45f908..e3312f1b8 100644 --- a/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/Coclustering.khcj index 718b0fc76..891edfe10 100644 --- a/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/err.txt index 0284808cf..d2a20f7d2 100644 --- a/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/err.txt @@ -1,8 +1,8 @@ Database ../../../datasets/Mushroom/Mushroom.txt : Read records: 8416 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.00515959 Size: 2*2 Granularity: 2 - 0:00:00 Write intermediate coclustering report Coclustering(2).khc Level: 0.0453955 Size: 4*3 Granularity: 3 - 0:00:00 Write intermediate coclustering report Coclustering(3).khc Level: 0.108244 Size: 6*6 - 0:00:00 Write intermediate coclustering report Coclustering(4).khc Level: 0.109924 Size: 7*7 -Write coclustering report ./results\Coclustering.khc -Coclustering analysis time: 0:00:00.04 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.00515959 Size: 2*2 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0453955 Size: 4*3 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.108244 Size: 6*6 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.109924 Size: 7*7 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.03 diff --git a/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/time.log index 8512f4a71..ccbc414f7 100644 --- a/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/Mushroom/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.12003970146179199 +Overal time: 0.10158228874206543 diff --git a/test/LearningTest/TestCoclustering/Standard/Mushroom/test.prm b/test/LearningTest/TestCoclustering/Standard/Mushroom/test.prm index 31832af24..ce2733f22 100644 --- a/test/LearningTest/TestCoclustering/Standard/Mushroom/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/Mushroom/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,8 +23,8 @@ ClassFileName ../../../datasets/Mushroom/Mushroom.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key Mushroom // List item selection -Database.DatabaseFiles.DataTableName ../../../datasets/Mushroom/Mushroom.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Mushroom // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Mushroom/Mushroom.txt // Database file AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name CapColor // Name @@ -30,7 +32,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert AnalysisSpec.CoclusteringParameters.Attributes.Name Odor // Name AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/Coclustering.khc index f6746048c..633cd630f 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/Coclustering.khcj index 06f4335b7..30d86f586 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/err.txt index ec6b6670d..3fd76194d 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/err.txt @@ -1,5 +1,5 @@ Database ../../../datasets/Mushroom/Mushroom.txt : Read records: 8416 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.00794475 Size: 2*2 -Write coclustering report ./results\Coclustering.khc + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.00794475 Size: 2*2 +Write coclustering report ./results/Coclustering.khcj Coclustering analysis time: 0:00:00.02 diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/time.log index 52bee9864..943b8c1af 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.09298229217529297 +Overal time: 0.0918886661529541 diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/test.prm b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/test.prm index e7787992a..c3f1e89f7 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomBasic/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/MushroomBasic/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,8 +23,8 @@ ClassFileName ../../../datasets/Mushroom/Mushroom.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key Mushroom // List item selection -Database.DatabaseFiles.DataTableName ../../../datasets/Mushroom/Mushroom.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Mushroom // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Mushroom/Mushroom.txt // Database file AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name GillSize // Name @@ -30,7 +32,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert AnalysisSpec.CoclusteringParameters.Attributes.Name GillSpacing // Name AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/Coclustering.khc index 65a3b4b54..99968f61d 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/Coclustering.khc +++ b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/Coclustering.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 2 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/Coclustering.khcj index 0fdd2b031..287655b79 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/Coclustering.khcj +++ b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/Coclustering.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/err.txt index 0414618af..c52707b56 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/err.txt @@ -1,9 +1,9 @@ Database ../../../datasets/Mushroom/Mushroom.txt : Read records: 8417 - 0:00:00 Write intermediate coclustering report Coclustering(1).khc Level: 0.00511516 Size: 2*2 Granularity: 2 - 0:00:00 Write intermediate coclustering report Coclustering(2).khc Level: 0.0453148 Size: 4*3 Granularity: 3 - 0:00:00 Write intermediate coclustering report Coclustering(3).khc Level: 0.10933 Size: 6*6 - 0:00:00 Write intermediate coclustering report Coclustering(4).khc Level: 0.109337 Size: 6*7 - 0:00:00 Write intermediate coclustering report Coclustering(5).khc Level: 0.110023 Size: 7*7 -Write coclustering report ./results\Coclustering.khc -Coclustering analysis time: 0:00:00.04 + 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.00511516 Size: 2*2 Granularity: 2 + 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0453148 Size: 4*3 Granularity: 3 + 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.10933 Size: 6*6 + 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.109337 Size: 6*7 + 0:00:00 Write intermediate coclustering report Coclustering(5).khcj Level: 0.110023 Size: 7*7 +Write coclustering report ./results/Coclustering.khcj +Coclustering analysis time: 0:00:00.03 diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/time.log index da6e1634a..90bd2aacf 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.11599516868591309 +Overal time: 0.09532546997070312 diff --git a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/test.prm b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/test.prm index 925160f25..beb168e6c 100644 --- a/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/MushroomNoHeaderLine/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,9 +23,9 @@ ClassFileName ../../../datasets/Mushroom/Mushroom.kdic // Dictionary file OK // Open // <- Open -Database.DatabaseFiles.List.Key Mushroom // List item selection -Database.DatabaseFiles.DataTableName ../../../datasets/Mushroom/Mushroom.txt // Database file -Database.HeaderLineUsed false // Header line used +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Mushroom // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Mushroom/Mushroom.txt // Database file +Database.DatabaseSpec.Data.HeaderLineUsed false // Header line used AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable AnalysisSpec.CoclusteringParameters.Attributes.Name CapColor // Name @@ -31,7 +33,7 @@ AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert AnalysisSpec.CoclusteringParameters.Attributes.Name Odor // Name AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report BuildCoclustering // Build coclustering diff --git a/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/DeployPowerCurveDate.kdic b/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/DeployPowerCurveDate.kdic index 6be3ed322..d0379270e 100644 --- a/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/DeployPowerCurveDate.kdic +++ b/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/DeployPowerCurveDate.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i // Root entity: curve, defined by its identifier (Date) and its array of points Root Dictionary PowerCurve (Date) diff --git a/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/err.txt index 7669106aa..06b913a63 100644 --- a/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/err.txt @@ -1,2 +1,2 @@ -Write deployment dictionary file ./results\DeployPowerCurveDate.kdic +Write deployment dictionary file ./results/DeployPowerCurveDate.kdic diff --git a/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/time.log index 66af5688c..142fd1ef9 100644 --- a/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.1067802906036377 +Overal time: 0.07956528663635254 diff --git a/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/test.prm b/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/test.prm index bfd178c34..5ab94ee64 100644 --- a/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/PreparePowerCurveDeployment/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -38,8 +40,7 @@ DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster va DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.CoclusteringDictionaryFileName DeployPowerCurveDate.kdic // Coclustering dictionary file +CoclusteringDictionaryFileName ./results/DeployPowerCurveDate.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment Exit // Close // <- Coclustering deployment preparation diff --git a/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/Coclustering.kdic b/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/Coclustering.kdic index 84806fa18..3c4e51b91 100644 --- a/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/Coclustering.kdic +++ b/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/Coclustering.kdic @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Root Dictionary SpliceJunction (SampleId) { diff --git a/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/err.txt index 78c265d15..1c7eff9e0 100644 --- a/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/err.txt @@ -1,2 +1,2 @@ -Write deployment dictionary file ./results\Coclustering.kdic +Write deployment dictionary file ./results/Coclustering.kdic diff --git a/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/time.log index dd272a11d..7b31d7acb 100644 --- a/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.10568642616271973 +Overal time: 0.08544278144836426 diff --git a/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/test.prm b/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/test.prm index bb31fdaaf..3b140e1ae 100644 --- a/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/PrepareSpliceJunctionDeployment/test.prm @@ -14,6 +14,9 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB + ClassManagement.OpenFile // Open... // -> Open @@ -21,7 +24,7 @@ ClassFileName ../../../MTdatasets/SpliceJunction/SpliceJunction.kdic // Di OK // Open // <- Open -ClassManagement.ClassName SpliceJunction // Analysis dictionary +Database.ClassName SpliceJunction // Analysis dictionary LearningTools.PrepareDeployment // Prepare deployment... @@ -40,8 +43,7 @@ DeploymentSpec.BuildPredictedClusterAttribute true // Build predicted cluster va DeploymentSpec.BuildClusterDistanceAttributes true // Build inter-cluster distance variables DeploymentSpec.BuildFrequencyRecodingAttributes true // Build frequency recoding variables DeploymentSpec.OutputAttributesPrefix P_ // Output variables prefix -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.CoclusteringDictionaryFileName Coclustering.kdic // Coclustering dictionary file +CoclusteringDictionaryFileName ./results/Coclustering.kdic // Coclustering dictionary file PrepareDeployment // Prepare deployment Exit // Close // <- Coclustering deployment preparation diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_10000cells.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_10000cells.khc index 25b781d85..c0547815b 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_10000cells.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_10000cells.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_10000cells.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_10000cells.khcj index b8097c3e4..fcd4c5d8c 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_10000cells.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_10000cells.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_100cells.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_100cells.khc index ecdb7e6aa..240b2ae22 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_100cells.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_100cells.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_100cells.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_100cells.khcj index 4cfcaaf6d..0851955bd 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_100cells.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_100cells.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_50info.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_50info.khc index 4bfb59639..af118400c 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_50info.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_50info.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_50info.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_50info.khcj index 1def5c7f6..0117af98d 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_50info.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_50info.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5age.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5age.khc index 1722389b9..a463fb3a2 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5age.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5age.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5age.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5age.khcj index a5bc5ea8b..c66b301ef 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5age.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5age.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5occupation.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5occupation.khc index 23a84b3da..3cd3c62d3 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5occupation.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5occupation.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5occupation.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5occupation.khcj index 8f3b8c03b..46044db34 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5occupation.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/PostProcessedCoclustering_max5occupation.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/err.txt index e5a3eb6f6..d4d1dc371 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/err.txt @@ -1,10 +1,10 @@ -Write simplified report ./results\PostProcessedCoclustering_10000cells.khc +Write simplified report ./results/PostProcessedCoclustering_10000cells.khcj -Write simplified report ./results\PostProcessedCoclustering_100cells.khc +Write simplified report ./results/PostProcessedCoclustering_100cells.khcj -Write simplified report ./results\PostProcessedCoclustering_50info.khc +Write simplified report ./results/PostProcessedCoclustering_50info.khcj -Write simplified report ./results\PostProcessedCoclustering_max5age.khc +Write simplified report ./results/PostProcessedCoclustering_max5age.khcj -Write simplified report ./results\PostProcessedCoclustering_max5occupation.khc +Write simplified report ./results/PostProcessedCoclustering_max5occupation.khcj diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/time.log index 36ddbe94d..a312c1c30 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.18829107284545898 +Overal time: 0.1704425811767578 diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/test.prm b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/test.prm index 7f824b4f9..897de4bdb 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdult/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB LearningTools.PostProcessCoclustering // Simplify coclustering... // -> Coclustering post-processing @@ -25,30 +27,28 @@ OK // Open // <- Open PostProcessingSpec.MaxCellNumber 10000 // Max cell number -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_10000cells.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_10000cells.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering PostProcessingSpec.MaxCellNumber 100 // Max cell number -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_100cells.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_100cells.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering PostProcessingSpec.MaxCellNumber 0 // Max cell number PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_50info.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_50info.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering PostProcessingSpec.MaxPreservedInformation 0 // Max preserved information PostProcessingSpec.PostProcessedAttributes.List.Key age // List item selection PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 5 // Max part number -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_max5age.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_max5age.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 0 // Max part number PostProcessingSpec.PostProcessedAttributes.List.Key occupation // List item selection PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 5 // Max part number -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_max5occupation.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_max5occupation.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering Exit // Close diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_10000cells.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_10000cells.khc index 59c6b45c8..27cd51f68 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_10000cells.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_10000cells.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_10000cells.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_10000cells.khcj index 4d16dec87..5654f889f 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_10000cells.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_10000cells.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_100cells.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_100cells.khc index fbf6c5fc7..6375fdff8 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_100cells.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_100cells.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_100cells.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_100cells.khcj index 12bc64d3b..c90cbc945 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_100cells.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_100cells.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_50info.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_50info.khc index 925e97593..381ff387a 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_50info.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_50info.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_50info.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_50info.khcj index bff53c324..491fa46bf 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_50info.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_50info.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5age.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5age.khc index d3229aeef..bfb09556a 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5age.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5age.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5age.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5age.khcj index 0bc6c1e43..1f04b543b 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5age.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5age.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5occupation.khc b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5occupation.khc index 424b5a77e..b9f7927c2 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5occupation.khc +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5occupation.khc @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Short description Dimensions 6 Name Type Parts Initial parts Values Interest Description diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5occupation.khcj b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5occupation.khcj index bec505c42..98daa6a45 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5occupation.khcj +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/PostProcessedCoclustering_max5occupation.khcj @@ -1,6 +1,6 @@ { "tool": "Khiops Coclustering", - "version": "10.1", + "version": "10.4.6i", "shortDescription": "", "coclusteringReport": { "summary": { diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/err.txt index 98c21548a..f944ce11a 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/err.txt +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/err.txt @@ -19,7 +19,7 @@ warning : Coclustering report ./AdultCoclustering.khc (line 50) : Part ({Adm-cle warning : Coclustering report ./AdultCoclustering.khc (line 51) : Part (B18) with missing hierarchical rank for variable occupation warning : Coclustering report ./AdultCoclustering.khc (line 52) : Part (B5) with missing hierarchical rank for variable occupation warning : Coclustering report : ... -Write simplified report ./results\PostProcessedCoclustering_10000cells.khc +Write simplified report ./results/PostProcessedCoclustering_10000cells.khcj warning : Coclustering report ./AdultCoclustering.khc (line 30) : Part (]-inf;18.5]) with missing hierarchical rank for variable age warning : Coclustering report ./AdultCoclustering.khc (line 31) : Part (]18.5;23.5]) with missing hierarchical rank for variable age @@ -42,7 +42,7 @@ warning : Coclustering report ./AdultCoclustering.khc (line 50) : Part ({Adm-cle warning : Coclustering report ./AdultCoclustering.khc (line 51) : Part (B18) with missing hierarchical rank for variable occupation warning : Coclustering report ./AdultCoclustering.khc (line 52) : Part (B5) with missing hierarchical rank for variable occupation warning : Coclustering report : ... -Write simplified report ./results\PostProcessedCoclustering_100cells.khc +Write simplified report ./results/PostProcessedCoclustering_100cells.khcj warning : Coclustering report ./AdultCoclustering.khc (line 30) : Part (]-inf;18.5]) with missing hierarchical rank for variable age warning : Coclustering report ./AdultCoclustering.khc (line 31) : Part (]18.5;23.5]) with missing hierarchical rank for variable age @@ -65,7 +65,7 @@ warning : Coclustering report ./AdultCoclustering.khc (line 50) : Part ({Adm-cle warning : Coclustering report ./AdultCoclustering.khc (line 51) : Part (B18) with missing hierarchical rank for variable occupation warning : Coclustering report ./AdultCoclustering.khc (line 52) : Part (B5) with missing hierarchical rank for variable occupation warning : Coclustering report : ... -Write simplified report ./results\PostProcessedCoclustering_50info.khc +Write simplified report ./results/PostProcessedCoclustering_50info.khcj warning : Coclustering report ./AdultCoclustering.khc (line 30) : Part (]-inf;18.5]) with missing hierarchical rank for variable age warning : Coclustering report ./AdultCoclustering.khc (line 31) : Part (]18.5;23.5]) with missing hierarchical rank for variable age @@ -88,7 +88,7 @@ warning : Coclustering report ./AdultCoclustering.khc (line 50) : Part ({Adm-cle warning : Coclustering report ./AdultCoclustering.khc (line 51) : Part (B18) with missing hierarchical rank for variable occupation warning : Coclustering report ./AdultCoclustering.khc (line 52) : Part (B5) with missing hierarchical rank for variable occupation warning : Coclustering report : ... -Write simplified report ./results\PostProcessedCoclustering_max5age.khc +Write simplified report ./results/PostProcessedCoclustering_max5age.khcj warning : Coclustering report ./AdultCoclustering.khc (line 30) : Part (]-inf;18.5]) with missing hierarchical rank for variable age warning : Coclustering report ./AdultCoclustering.khc (line 31) : Part (]18.5;23.5]) with missing hierarchical rank for variable age @@ -111,5 +111,5 @@ warning : Coclustering report ./AdultCoclustering.khc (line 50) : Part ({Adm-cle warning : Coclustering report ./AdultCoclustering.khc (line 51) : Part (B18) with missing hierarchical rank for variable occupation warning : Coclustering report ./AdultCoclustering.khc (line 52) : Part (B5) with missing hierarchical rank for variable occupation warning : Coclustering report : ... -Write simplified report ./results\PostProcessedCoclustering_max5occupation.khc +Write simplified report ./results/PostProcessedCoclustering_max5occupation.khcj diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/time.log index af59239ad..65ed5236b 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/time.log +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.23333477973937988 +Overal time: 0.15564870834350586 diff --git a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/test.prm b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/test.prm index 7f824b4f9..beed2436a 100644 --- a/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/test.prm +++ b/test/LearningTest/TestCoclustering/Standard/SimplifyAdultV75/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Coclustering +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB LearningTools.PostProcessCoclustering // Simplify coclustering... // -> Coclustering post-processing @@ -25,30 +27,28 @@ OK // Open // <- Open PostProcessingSpec.MaxCellNumber 10000 // Max cell number -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_10000cells.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_10000cells.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering PostProcessingSpec.MaxCellNumber 100 // Max cell number -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_100cells.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_100cells.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering PostProcessingSpec.MaxCellNumber 0 // Max cell number PostProcessingSpec.MaxPreservedInformation 50 // Max preserved information -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_50info.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_50info.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering PostProcessingSpec.MaxPreservedInformation 0 // Max preserved information PostProcessingSpec.PostProcessedAttributes.List.Key age // List item selection PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 5 // Max part number -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_max5age.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_max5age.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 0 // Max part number PostProcessingSpec.PostProcessedAttributes.List.Key occupation // List item selection PostProcessingSpec.PostProcessedAttributes.MaxPartNumber 5 // Max part number -AnalysisResults.PostProcessedCoclusteringFileName PostProcessedCoclustering_max5occupation.khc // Post-processed coclustering report +PostProcessedCoclusteringFileName ./results/PostProcessedCoclustering_max5occupation.khcj // Simplified coclustering report PostProcessCoclustering // Simplify coclustering Exit // Close diff --git a/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/T_SpliceJunction.txt b/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/D_SpliceJunction.txt similarity index 96% rename from test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/T_SpliceJunction.txt rename to test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/D_SpliceJunction.txt index db02ea5ae..fb61ef239 100644 --- a/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/T_SpliceJunction.txt +++ b/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/D_SpliceJunction.txt @@ -1,3179 +1,3179 @@ -SampleId Class CountDNA -AGMKPNRSB-NEG-1 N 60 -AGMORS12A-NEG-181 N 60 -AGMORS9A-NEG-481 N 60 -AGMRSKPNI-NEG-1141 N 60 -ATRINS-ACCEPTOR-1678 IE 60 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EI 60 -HUMALBGC-DONOR-1825 EI 60 -HUMALBGC-DONOR-2592 EI 60 -HUMALBGC-DONOR-4179 EI 60 -HUMALBGC-DONOR-6223 EI 60 -HUMALBGC-DONOR-6905 EI 60 -HUMALBGC-DONOR-7827 EI 60 -HUMALBGC-DONOR-9544 EI 60 -HUMALBGC-NEG-13921 N 60 -HUMALBGC-NEG-18481 N 60 -HUMALBGC-NEG-241 N 60 -HUMALBGC-NEG-4801 N 60 -HUMALBGC-NEG-9361 N 60 -HUMALDA1-NEG-661 N 60 -HUMALDAR-NEG-1081 N 60 -HUMALDB1-NEG-2761 N 60 -HUMALDB1-NEG-7321 N 60 -HUMALDB2-NEG-1681 N 60 -HUMALDC-NEG-61 N 60 -HUMALDC01-NEG-361 N 60 -HUMALDCG-NEG-1441 N 60 -HUMALDH03-NEG-1 N 60 -HUMALDH2-NEG-1201 N 60 -HUMALDOA-NEG-4501 N 60 -HUMALDOAP-NEG-661 N 60 -HUMALDOBG-NEG-1 N 60 -HUMALIPOA-NEG-5341 N 60 -HUMALIPOA-NEG-781 N 60 -HUMALIPOA-NEG-9901 N 60 -HUMALLFU-NEG-61 N 60 -HUMALP5-NEG-781 N 60 -HUMALPHA-ACCEPTOR-1117 IE 60 -HUMALPHA-ACCEPTOR-1369 IE 60 -HUMALPHA-ACCEPTOR-1789 IE 60 -HUMALPHA-ACCEPTOR-2023 IE 60 -HUMALPHA-ACCEPTOR-2224 IE 60 -HUMALPHA-ACCEPTOR-2440 IE 60 -HUMALPHA-ACCEPTOR-2851 IE 60 -HUMALPHA-ACCEPTOR-3090 IE 60 -HUMALPHA-ACCEPTOR-581 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60 -HUMGCB1-DONOR-5411 EI 60 -HUMGCB1-DONOR-581 EI 60 -HUMGCB1-DONOR-5974 EI 60 -HUMGCB1-DONOR-6461 EI 60 -HUMGCB1-NEG-4201 N 60 -HUMGCB2-NEG-1141 N 60 -HUMGCB2-NEG-5701 N 60 -HUMGCBPRC-NEG-1501 N 60 -HUMGCRA-NEG-3721 N 60 -HUMGCRB-NEG-3481 N 60 -HUMGCSFR1-NEG-61 N 60 -HUMGDHA-NEG-181 N 60 -HUMGDHR-NEG-841 N 60 -HUMGELS3-NEG-121 N 60 -HUMGENX-NEG-2341 N 60 -HUMGFAP-NEG-2941 N 60 -HUMGFB-NEG-2701 N 60 -HUMGFI-NEG-1 N 60 -HUMGFIA1-NEG-841 N 60 -HUMGFIA2-NEG-3961 N 60 -HUMGFIAB5-NEG-421 N 60 -HUMGFIBPA-NEG-1441 N 60 -HUMGFIIM-NEG-121 N 60 -HUMGFP40H-ACCEPTOR-1110 IE 60 -HUMGFP40H-ACCEPTOR-2431 IE 60 -HUMGFP40H-ACCEPTOR-4066 IE 60 -HUMGFP40H-ACCEPTOR-992 IE 60 -HUMGFP40H-DONOR-1028 EI 60 -HUMGFP40H-DONOR-1143 EI 60 -HUMGFP40H-DONOR-2563 EI 60 -HUMGFP40H-DONOR-880 EI 60 -HUMGFP40H-NEG-1201 N 60 -HUMGFRIL-NEG-1441 N 60 -HUMGG-ACCEPTOR-1802 IE 60 -HUMGG-ACCEPTOR-3631 IE 60 -HUMGG-ACCEPTOR-5138 IE 60 -HUMGG-DONOR-1964 EI 60 -HUMGG-DONOR-230 EI 60 -HUMGG-DONOR-3769 EI 60 -HUMGG-NEG-1021 N 60 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a/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/err.txt b/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/err.txt index 8bf078ac2..0da0c0f40 100644 --- a/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/err.txt +++ b/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/err.txt @@ -1,9 +1,9 @@ Deploy model SpliceJunction Input database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt: Read records: 3178 Table ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt Records: 190,680 -Output database ./results/T_SpliceJunction.txt: Written records: 3178 -Model deployment time: 0:00:00.13 +Output database ./results/D_SpliceJunction.txt: Written records: 3178 +Model deployment time: 0:00:00.10 Create database file ./results/KNI_SpliceJunction.txt with KNI Deploy model SpliceJunction: Written records : 3178 -KNI deployment time: 0:00:00.14 +KNI deployment time: 0:00:00.11 diff --git a/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/time.log b/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/time.log index f9411151d..4862970c8 100644 --- a/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/time.log +++ b/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.40596461296081543 +Overal time: 0.3371291160583496 diff --git a/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/test.prm b/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/test.prm index b2d2cf0be..ca1755b62 100644 --- a/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/test.prm +++ b/test/LearningTest/TestKNITransfer/MultiTables/SpliceJunction/test.prm @@ -14,6 +14,8 @@ // // -> Khiops Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,19 +23,19 @@ ClassFileName ./SpliceJunction.kdic // Dictionary file OK // Open // <- Open -TransferDatabase // Transfer database... +TransferDatabase // Deploy model... // -> Database transfer -ClassName SpliceJunction // Transfer dictionary -SourceDatabase.DatabaseFiles.List.Key SpliceJunction // List item selection -SourceDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file -SourceDatabase.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection -SourceDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file -TargetDatabase.DatabaseFiles.List.Key SpliceJunction // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_SpliceJunction.txt // Data table file -TransferDatabase // Transfer database - -TargetDatabase.DatabaseFiles.DataTableName ./results/KNI_SpliceJunction.txt // Data table file +ClassName SpliceJunction // Deployment dictionary +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction // List item selection +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_SpliceJunction.txt // Data table file +TransferDatabase // Deploy model + +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/KNI_SpliceJunction.txt // Data table file KNILogFileName results/KNILog.txt // KNI log file name KNIStreamMaxMemory 100 // KNI max stream memory in Mo KNITransferDatabase // KNI transfer database diff --git a/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/T_Adult.txt b/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/D_Adult.txt similarity index 97% rename from test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/T_Adult.txt rename to test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/D_Adult.txt index 83e504d27..466b683ee 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/T_Adult.txt +++ b/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/D_Adult.txt @@ -1,48843 +1,48843 @@ -class Predictedclass Scoreclass Probclassmore -less less 0.9999923333 7.666709107e-06 -less more 0.6767829342 0.6767829342 -less less 0.9329539581 0.06704604187 -less less 0.8953221226 0.1046778774 -less less 0.6497366146 0.3502633854 -less more 0.7869624022 0.7869624022 -less less 0.9948917691 0.005108230879 -more more 0.5313164574 0.5313164574 -more more 0.9955886109 0.9955886109 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less 0.781602269 0.218397731 +less less 0.6468153004 0.3531846996 +less less 0.6789827445 0.3210172555 +less less 0.718100812 0.281899188 +less less 0.997350959 0.002649040952 +less less 0.729930124 0.270069876 +less less 0.9392253143 0.06077468568 +less less 0.7915480129 0.2084519871 +less less 0.9787172399 0.02128276013 +less more 0.7633490378 0.7633490378 +less less 0.7467071828 0.2532928172 +more more 0.7625852072 0.7625852072 diff --git a/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/err.txt b/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/err.txt index 01f983b8e..20807642e 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/err.txt +++ b/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/err.txt @@ -1,8 +1,8 @@ Deploy model SNB_Adult Input database ../../../datasets/Adult/Adult.txt: Read records: 48,842 -Output database ./results/T_Adult.txt: Written records: 48,842 -Model deployment time: 0:00:00.16 +Output database ./results/D_Adult.txt: Written records: 48,842 +Model deployment time: 0:00:00.12 Create database file ./results/KNI_Adult.txt with KNI Deploy model SNB_Adult: Written records : 48,842 -KNI deployment time: 0:00:00.23 +KNI deployment time: 0:00:00.18 diff --git a/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/time.log b/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/time.log index 5d0c89c83..61a10e340 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/time.log +++ b/test/LearningTest/TestKNITransfer/Standard/Adult/results.ref/time.log @@ -1 +1 @@ -Overal time: 1.1103532314300537 +Overal time: 1.1912345886230469 diff --git a/test/LearningTest/TestKNITransfer/Standard/Adult/test.prm b/test/LearningTest/TestKNITransfer/Standard/Adult/test.prm index d8644c74c..844e2cd3a 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Adult/test.prm +++ b/test/LearningTest/TestKNITransfer/Standard/Adult/test.prm @@ -13,21 +13,23 @@ // // +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... ClassFileName ./AdultModeling.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Adult // Transfer dictionary -SourceDatabase.DatabaseFiles.List.Key SNB_Adult // List item selection -SourceDatabase.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file -TargetDatabase.DatabaseFiles.List.Key SNB_Adult // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Adult.txt // Database file -TransferDatabase // Transfer database - -TargetDatabase.DatabaseFiles.DataTableName ./results/KNI_Adult.txt // Database file +ClassName SNB_Adult // Deployment dictionary +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Adult // List item selection +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Adult // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Adult.txt // Database file +TransferDatabase // Deploy model + +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/KNI_Adult.txt // Database file KNILogFileName results/KNILog.txt // KNI log file name KNIStreamMaxMemory 100 // KNI max stream memory in Mo KNITransferDatabase // KNI transfer database diff --git a/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/T_Iris.txt b/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/D_Iris.txt similarity index 97% rename from test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/T_Iris.txt rename to test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/D_Iris.txt index 60d0d2b49..00b05f667 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/D_Iris.txt @@ -1,151 +1,151 @@ -Class PredictedClass ScoreClass -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.99542373 -Iris-setosa Iris-setosa 0.9966405858 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966405858 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966405858 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966405858 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-setosa Iris-setosa 0.9966768514 -Iris-setosa Iris-setosa 0.9966499015 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-virginica 0.7559023645 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965439339 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965439339 -Iris-versicolor Iris-versicolor 0.9965439339 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9921075149 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-virginica 0.7559023645 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-virginica 0.7559023645 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-virginica 0.7559023645 -Iris-versicolor Iris-versicolor 0.9965439339 -Iris-versicolor Iris-versicolor 0.9960008565 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965439339 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-versicolor Iris-versicolor 0.9962134372 -Iris-versicolor Iris-versicolor 0.9965439339 -Iris-versicolor Iris-versicolor 0.9965240356 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-versicolor 0.9965439339 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9746837763 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.8972275552 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9746837763 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.7559023645 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.8972275552 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-versicolor 0.9921075149 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.7559023645 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9746837763 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.7559023645 -Iris-virginica Iris-virginica 0.7559023645 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9746837763 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-versicolor 0.9921075149 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9652837678 -Iris-virginica Iris-virginica 0.9746837763 -Iris-virginica Iris-virginica 0.9652837678 +Class PredictedClass ScoreClass +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.99542373 +Iris-setosa Iris-setosa 0.9966405858 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966405858 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966405858 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966405858 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-setosa Iris-setosa 0.9966768514 +Iris-setosa Iris-setosa 0.9966499015 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-virginica 0.7559023645 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965439339 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965439339 +Iris-versicolor Iris-versicolor 0.9965439339 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9921075149 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-virginica 0.7559023645 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-virginica 0.7559023645 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-virginica 0.7559023645 +Iris-versicolor Iris-versicolor 0.9965439339 +Iris-versicolor Iris-versicolor 0.9960008565 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965439339 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-versicolor Iris-versicolor 0.9962134372 +Iris-versicolor Iris-versicolor 0.9965439339 +Iris-versicolor Iris-versicolor 0.9965240356 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-versicolor 0.9965439339 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9746837763 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.8972275552 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9746837763 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.7559023645 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.8972275552 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-versicolor 0.9921075149 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.7559023645 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9746837763 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.7559023645 +Iris-virginica Iris-virginica 0.7559023645 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9746837763 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-versicolor 0.9921075149 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9652837678 +Iris-virginica Iris-virginica 0.9746837763 +Iris-virginica Iris-virginica 0.9652837678 diff --git a/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/err.txt b/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/err.txt index cbbe72001..f7506f280 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/err.txt +++ b/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/err.txt @@ -1,6 +1,6 @@ Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 150 +Output database ./results/D_Iris.txt: Written records: 150 Model deployment time: 0:00:00.01 Create database file ./results/KNI_Iris.txt with KNI diff --git a/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/time.log b/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/time.log index 71e5404b2..93a339474 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/time.log +++ b/test/LearningTest/TestKNITransfer/Standard/Iris/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.1573195457458496 +Overal time: 0.1663355827331543 diff --git a/test/LearningTest/TestKNITransfer/Standard/Iris/test.prm b/test/LearningTest/TestKNITransfer/Standard/Iris/test.prm index 92a1e63f4..d7ebcd1ef 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Iris/test.prm +++ b/test/LearningTest/TestKNITransfer/Standard/Iris/test.prm @@ -13,21 +13,23 @@ // // +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... ClassFileName ./IrisModeling.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -SourceDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database - -TargetDatabase.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model + +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file KNILogFileName results/KNILog.txt // KNI log file name KNIStreamMaxMemory 100 // KNI max stream memory in Mo KNITransferDatabase // KNI transfer database diff --git a/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/T_Iris.txt b/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/D_Iris.txt similarity index 97% rename from test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/T_Iris.txt rename to test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/D_Iris.txt index e050fb98c..f49ee10ac 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/D_Iris.txt @@ -1,151 +1,151 @@ -Class PredictedClass ScoreClass -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966687333 -Iris-setosa Iris-setosa 0.9966811869 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966811869 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966811869 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966811869 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-setosa Iris-setosa 0.9966827669 -Iris-setosa Iris-setosa 0.9966824401 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9956395399 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966681472 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966681472 -Iris-versicolor Iris-versicolor 0.9966681472 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9897929863 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9956395399 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9904973806 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9904973806 -Iris-versicolor Iris-versicolor 0.9966681472 -Iris-versicolor Iris-versicolor 0.9966199966 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966681472 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-versicolor Iris-versicolor 0.9966283145 -Iris-versicolor Iris-versicolor 0.9966681472 -Iris-versicolor Iris-versicolor 0.9966530868 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-versicolor 0.8515447605 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.996585833 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9964782799 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.996585833 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9827331543 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9954667465 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9960166279 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9841569398 -Iris-virginica Iris-virginica 0.9960166279 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9827331543 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.996585833 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9827331543 -Iris-virginica Iris-virginica 0.9827331543 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.996585833 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9841569398 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.9965712149 -Iris-virginica Iris-virginica 0.996585833 -Iris-virginica Iris-virginica 0.9965712149 +Class PredictedClass ScoreClass +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966687333 +Iris-setosa Iris-setosa 0.9966811869 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966811869 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966811869 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966811869 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-setosa Iris-setosa 0.9966827669 +Iris-setosa Iris-setosa 0.9966824401 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9956395399 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966681472 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966681472 +Iris-versicolor Iris-versicolor 0.9966681472 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9897929863 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9956395399 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9904973806 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9904973806 +Iris-versicolor Iris-versicolor 0.9966681472 +Iris-versicolor Iris-versicolor 0.9966199966 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966681472 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-versicolor Iris-versicolor 0.9966283145 +Iris-versicolor Iris-versicolor 0.9966681472 +Iris-versicolor Iris-versicolor 0.9966530868 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-versicolor 0.8515447605 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.996585833 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9964782799 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.996585833 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9827331543 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9954667465 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9960166279 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9841569398 +Iris-virginica Iris-virginica 0.9960166279 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9827331543 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.996585833 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9827331543 +Iris-virginica Iris-virginica 0.9827331543 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.996585833 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9841569398 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.9965712149 +Iris-virginica Iris-virginica 0.996585833 +Iris-virginica Iris-virginica 0.9965712149 diff --git a/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/err.txt b/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/err.txt index cbbe72001..f7506f280 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/err.txt +++ b/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/err.txt @@ -1,6 +1,6 @@ Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 150 +Output database ./results/D_Iris.txt: Written records: 150 Model deployment time: 0:00:00.01 Create database file ./results/KNI_Iris.txt with KNI diff --git a/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/time.log b/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/time.log index bca41c489..319543253 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/time.log +++ b/test/LearningTest/TestKNITransfer/Standard/Iris2D/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.15861129760742188 +Overal time: 0.1489713191986084 diff --git a/test/LearningTest/TestKNITransfer/Standard/Iris2D/test.prm b/test/LearningTest/TestKNITransfer/Standard/Iris2D/test.prm index 92a1e63f4..d7ebcd1ef 100644 --- a/test/LearningTest/TestKNITransfer/Standard/Iris2D/test.prm +++ b/test/LearningTest/TestKNITransfer/Standard/Iris2D/test.prm @@ -13,21 +13,23 @@ // // +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... ClassFileName ./IrisModeling.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -SourceDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database - -TargetDatabase.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model + +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file KNILogFileName results/KNILog.txt // KNI log file name KNIStreamMaxMemory 100 // KNI max stream memory in Mo KNITransferDatabase // KNI transfer database diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/T_Iris.txt b/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/D_Iris.txt similarity index 97% rename from test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/T_Iris.txt rename to test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/D_Iris.txt index 18da0773c..bd3279f15 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/D_Iris.txt @@ -1,151 +1,151 @@ -Class PredictedClass ScoreClass -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966539181 -Iris-setosa Iris-setosa 0.9966800188 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966800188 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966800188 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966800188 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-setosa Iris-setosa 0.996682622 -Iris-setosa Iris-setosa 0.9966821402 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9959653575 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966688209 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966688209 -Iris-versicolor Iris-versicolor 0.9966688209 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.988409557 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9959653575 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.991081472 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.991081472 -Iris-versicolor Iris-versicolor 0.9966688209 -Iris-versicolor Iris-versicolor 0.996611038 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966688209 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-versicolor Iris-versicolor 0.9966214093 -Iris-versicolor Iris-versicolor 0.9966688209 -Iris-versicolor Iris-versicolor 0.9966518374 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-versicolor 0.8249128019 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9966054901 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965034885 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9966054901 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9846001862 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9952818168 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9959735573 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9885009534 -Iris-virginica Iris-virginica 0.9959735573 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9846001862 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9966054901 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9846001862 -Iris-virginica Iris-virginica 0.9846001862 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9966054901 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9885009534 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9965926025 -Iris-virginica Iris-virginica 0.9966054901 -Iris-virginica Iris-virginica 0.9965926025 +Class PredictedClass ScoreClass +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966539181 +Iris-setosa Iris-setosa 0.9966800188 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966800188 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966800188 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966800188 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-setosa Iris-setosa 0.996682622 +Iris-setosa Iris-setosa 0.9966821402 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9959653575 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966688209 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966688209 +Iris-versicolor Iris-versicolor 0.9966688209 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.988409557 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9959653575 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.991081472 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.991081472 +Iris-versicolor Iris-versicolor 0.9966688209 +Iris-versicolor Iris-versicolor 0.996611038 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966688209 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-versicolor Iris-versicolor 0.9966214093 +Iris-versicolor Iris-versicolor 0.9966688209 +Iris-versicolor Iris-versicolor 0.9966518374 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-versicolor 0.8249128019 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9966054901 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965034885 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9966054901 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9846001862 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9952818168 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9959735573 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9885009534 +Iris-virginica Iris-virginica 0.9959735573 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9846001862 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9966054901 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9846001862 +Iris-virginica Iris-virginica 0.9846001862 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9966054901 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9885009534 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9965926025 +Iris-virginica Iris-virginica 0.9966054901 +Iris-virginica Iris-virginica 0.9965926025 diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/err.txt b/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/err.txt index cbbe72001..f7506f280 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/err.txt +++ b/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/err.txt @@ -1,6 +1,6 @@ Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 150 +Output database ./results/D_Iris.txt: Written records: 150 Model deployment time: 0:00:00.01 Create database file ./results/KNI_Iris.txt with KNI diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/time.log b/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/time.log index 594571f92..69fb67b94 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/time.log +++ b/test/LearningTest/TestKNITransfer/Standard/IrisC/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.20078206062316895 +Overal time: 0.14929533004760742 diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisC/test.prm b/test/LearningTest/TestKNITransfer/Standard/IrisC/test.prm index 92a1e63f4..d7ebcd1ef 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisC/test.prm +++ b/test/LearningTest/TestKNITransfer/Standard/IrisC/test.prm @@ -13,21 +13,23 @@ // // +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... ClassFileName ./IrisModeling.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -SourceDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database - -TargetDatabase.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model + +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file KNILogFileName results/KNILog.txt // KNI log file name KNIStreamMaxMemory 100 // KNI max stream memory in Mo KNITransferDatabase // KNI transfer database diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/T_Iris.txt b/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/D_Iris.txt similarity index 97% rename from test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/T_Iris.txt rename to test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/D_Iris.txt index 5cc6e430d..50c760f3c 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/D_Iris.txt @@ -1,151 +1,151 @@ -Class PredictedClass ScoreClass -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966552225 -Iris-setosa Iris-setosa 0.9966799993 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966799993 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966799993 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966799993 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-setosa Iris-setosa 0.9966825974 -Iris-setosa Iris-setosa 0.9966820339 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9964100087 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966727011 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966727011 -Iris-versicolor Iris-versicolor 0.9966727011 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9926003233 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9964100087 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9938678143 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9938678143 -Iris-versicolor Iris-versicolor 0.9966727011 -Iris-versicolor Iris-versicolor 0.9966395146 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966727011 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-versicolor Iris-versicolor 0.9966395226 -Iris-versicolor Iris-versicolor 0.9966727011 -Iris-versicolor Iris-versicolor 0.9966606708 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-versicolor 0.6045247676 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966209117 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9965585297 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966209117 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9909814571 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9954457047 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9960519582 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9927544041 -Iris-virginica Iris-virginica 0.9960519582 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9909814571 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966209117 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9909814571 -Iris-virginica Iris-virginica 0.9909814571 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966209117 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9927544041 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966215559 -Iris-virginica Iris-virginica 0.9966209117 -Iris-virginica Iris-virginica 0.9966215559 +Class PredictedClass ScoreClass +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966552225 +Iris-setosa Iris-setosa 0.9966799993 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966799993 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966799993 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966799993 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-setosa Iris-setosa 0.9966825974 +Iris-setosa Iris-setosa 0.9966820339 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9964100087 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966727011 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966727011 +Iris-versicolor Iris-versicolor 0.9966727011 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9926003233 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9964100087 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9938678143 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9938678143 +Iris-versicolor Iris-versicolor 0.9966727011 +Iris-versicolor Iris-versicolor 0.9966395146 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966727011 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-versicolor Iris-versicolor 0.9966395226 +Iris-versicolor Iris-versicolor 0.9966727011 +Iris-versicolor Iris-versicolor 0.9966606708 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-versicolor 0.6045247676 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966209117 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9965585297 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966209117 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9909814571 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9954457047 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9960519582 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9927544041 +Iris-virginica Iris-virginica 0.9960519582 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9909814571 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966209117 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9909814571 +Iris-virginica Iris-virginica 0.9909814571 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966209117 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9927544041 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966215559 +Iris-virginica Iris-virginica 0.9966209117 +Iris-virginica Iris-virginica 0.9966215559 diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/err.txt b/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/err.txt index 05a8c666a..f7506f280 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/err.txt +++ b/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/err.txt @@ -1,8 +1,8 @@ Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 150 +Output database ./results/D_Iris.txt: Written records: 150 Model deployment time: 0:00:00.01 Create database file ./results/KNI_Iris.txt with KNI Deploy model SNB_Iris: Written records : 150 -KNI deployment time: 0:00:00.01 +KNI deployment time: 0:00:00 diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/time.log b/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/time.log index 995fc5ad7..5cb6b8134 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/time.log +++ b/test/LearningTest/TestKNITransfer/Standard/IrisG/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.17465472221374512 +Overal time: 0.16084790229797363 diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisG/test.prm b/test/LearningTest/TestKNITransfer/Standard/IrisG/test.prm index 92a1e63f4..d7ebcd1ef 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisG/test.prm +++ b/test/LearningTest/TestKNITransfer/Standard/IrisG/test.prm @@ -13,21 +13,23 @@ // // +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... ClassFileName ./IrisModeling.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -SourceDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database - -TargetDatabase.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model + +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file KNILogFileName results/KNILog.txt // KNI log file name KNIStreamMaxMemory 100 // KNI max stream memory in Mo KNITransferDatabase // KNI transfer database diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/T_Iris.txt b/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/D_Iris.txt similarity index 94% rename from test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/T_Iris.txt rename to test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/D_Iris.txt index ca5d3d9e7..ff5e53880 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/D_Iris.txt @@ -1,151 +1,151 @@ -PetalLength MPetalLength -1.4 1.353772585 -1.4 1.353772607 -1.3 1.353772607 -1.5 1.618305152 -1.4 1.353772585 -1.7 1.618305131 -1.4 1.353772585 -1.5 1.618305131 -1.4 1.353772607 -1.5 1.618305152 -1.5 1.618305131 -1.6 1.618305131 -1.4 1.353772607 -1.1 1.353772607 -1.2 1.353773508 -1.5 1.618305202 -1.3 1.353772585 -1.4 1.353772585 -1.7 1.618305202 -1.5 1.618305131 -1.7 1.618305131 -1.5 1.618305131 -1 1.353772585 -1.7 1.618305152 -1.9 1.618305131 -1.6 1.618305152 -1.6 1.618305131 -1.5 1.618305131 -1.4 1.353772585 -1.6 1.618305152 -1.6 1.618305152 -1.5 1.618305131 -1.5 1.618305131 -1.4 1.353772657 -1.5 1.618305152 -1.2 1.353772607 -1.3 1.353772657 -1.5 1.618305152 -1.3 1.353772607 -1.5 1.618305131 -1.3 1.353772585 -1.3 1.353772607 -1.3 1.353772607 -1.6 1.618305131 -1.9 1.618305131 -1.4 1.353772607 -1.6 1.618305131 -1.4 1.353772607 -1.5 1.618305131 -1.4 1.353772607 -4.7 4.383505564 -4.5 4.383505564 -4.9 4.383505564 -4 4.321965492 -4.6 4.383505564 -4.5 4.321965492 -4.7 4.383505564 -3.3 3.582226028 -4.6 4.383505564 -3.9 3.582226028 -3.5 3.582226028 -4.2 4.383505564 -4 4.383505564 -4.7 4.383505564 -3.6 3.590980828 -4.4 4.383505564 -4.5 4.321965492 -4.1 4.383505564 -4.5 4.383505564 -3.9 3.590980828 -4.8 4.818954421 -4 4.383505564 -4.9 4.383505564 -4.7 4.383505564 -4.3 4.383505564 -4.4 4.383505564 -4.8 4.383505564 -5 5.356772405 -4.5 4.383505564 -3.5 3.590980828 -3.8 3.590980828 -3.7 3.590980828 -3.9 3.656564631 -5.1 5.335446679 -4.5 4.349782974 -4.5 4.383505559 -4.7 4.383505564 -4.4 4.383505564 -4.1 4.321965492 -4 4.321965492 -4.4 4.321965492 -4.6 4.383505564 -4 4.383505564 -3.3 3.582226028 -4.2 4.321965492 -4.2 4.321965492 -4.2 4.321965492 -4.3 4.383505564 -3 3.582226028 -4.1 4.321965492 -6 6.657606251 -5.1 5.357116083 -5.9 5.357116083 -5.6 5.357116083 -5.8 5.357116083 -6.6 6.657606251 -4.5 4.918071814 -6.3 6.657606251 -5.8 5.357116083 -6.1 6.657606242 -5.1 5.357116083 -5.3 5.357116083 -5.5 5.357116083 -5 5.357114271 -5.1 5.357116083 -5.3 5.357116083 -5.5 5.357116083 -6.7 6.657606242 -6.9 6.657606251 -5 5.356899594 -5.7 5.357116083 -4.9 4.917420451 -6.7 6.657606251 -4.9 4.918362111 -5.7 5.357116083 -6 6.657606251 -4.8 4.918362111 -4.9 4.918362111 -5.6 5.357116083 -5.8 5.356899594 -6.1 6.657606251 -6.4 6.657606242 -5.6 5.357116083 -5.1 5.356899594 -5.6 5.356899594 -6.1 6.657606251 -5.6 5.357116081 -5.5 5.357116083 -4.8 4.918362111 -5.4 5.357116083 -5.6 5.357116083 -5.1 5.357116083 -5.1 5.357116083 -5.9 5.357116083 -5.7 5.357116083 -5.2 5.357116083 -5 5.357116083 -5.2 5.357116083 -5.4 5.357116081 -5.1 5.357116083 +PetalLength MPetalLength +1.4 1.353772585 +1.4 1.353772607 +1.3 1.353772607 +1.5 1.618305152 +1.4 1.353772585 +1.7 1.618305131 +1.4 1.353772585 +1.5 1.618305131 +1.4 1.353772607 +1.5 1.618305152 +1.5 1.618305131 +1.6 1.618305131 +1.4 1.353772607 +1.1 1.353772607 +1.2 1.353773508 +1.5 1.618305202 +1.3 1.353772585 +1.4 1.353772585 +1.7 1.618305202 +1.5 1.618305131 +1.7 1.618305131 +1.5 1.618305131 +1 1.353772585 +1.7 1.618305152 +1.9 1.618305131 +1.6 1.618305152 +1.6 1.618305131 +1.5 1.618305131 +1.4 1.353772585 +1.6 1.618305152 +1.6 1.618305152 +1.5 1.618305131 +1.5 1.618305131 +1.4 1.353772657 +1.5 1.618305152 +1.2 1.353772607 +1.3 1.353772657 +1.5 1.618305152 +1.3 1.353772607 +1.5 1.618305131 +1.3 1.353772585 +1.3 1.353772607 +1.3 1.353772607 +1.6 1.618305131 +1.9 1.618305131 +1.4 1.353772607 +1.6 1.618305131 +1.4 1.353772607 +1.5 1.618305131 +1.4 1.353772607 +4.7 4.383505564 +4.5 4.383505564 +4.9 4.383505564 +4 4.321965492 +4.6 4.383505564 +4.5 4.321965492 +4.7 4.383505564 +3.3 3.582226028 +4.6 4.383505564 +3.9 3.582226028 +3.5 3.582226028 +4.2 4.383505564 +4 4.383505564 +4.7 4.383505564 +3.6 3.590980828 +4.4 4.383505564 +4.5 4.321965492 +4.1 4.383505564 +4.5 4.383505564 +3.9 3.590980828 +4.8 4.818954421 +4 4.383505564 +4.9 4.383505564 +4.7 4.383505564 +4.3 4.383505564 +4.4 4.383505564 +4.8 4.383505564 +5 5.356772405 +4.5 4.383505564 +3.5 3.590980828 +3.8 3.590980828 +3.7 3.590980828 +3.9 3.656564631 +5.1 5.335446679 +4.5 4.349782974 +4.5 4.383505559 +4.7 4.383505564 +4.4 4.383505564 +4.1 4.321965492 +4 4.321965492 +4.4 4.321965492 +4.6 4.383505564 +4 4.383505564 +3.3 3.582226028 +4.2 4.321965492 +4.2 4.321965492 +4.2 4.321965492 +4.3 4.383505564 +3 3.582226028 +4.1 4.321965492 +6 6.657606251 +5.1 5.357116083 +5.9 5.357116083 +5.6 5.357116083 +5.8 5.357116083 +6.6 6.657606251 +4.5 4.918071814 +6.3 6.657606251 +5.8 5.357116083 +6.1 6.657606242 +5.1 5.357116083 +5.3 5.357116083 +5.5 5.357116083 +5 5.357114271 +5.1 5.357116083 +5.3 5.357116083 +5.5 5.357116083 +6.7 6.657606242 +6.9 6.657606251 +5 5.356899594 +5.7 5.357116083 +4.9 4.917420451 +6.7 6.657606251 +4.9 4.918362111 +5.7 5.357116083 +6 6.657606251 +4.8 4.918362111 +4.9 4.918362111 +5.6 5.357116083 +5.8 5.356899594 +6.1 6.657606251 +6.4 6.657606242 +5.6 5.357116083 +5.1 5.356899594 +5.6 5.356899594 +6.1 6.657606251 +5.6 5.357116081 +5.5 5.357116083 +4.8 4.918362111 +5.4 5.357116083 +5.6 5.357116083 +5.1 5.357116083 +5.1 5.357116083 +5.9 5.357116083 +5.7 5.357116083 +5.2 5.357116083 +5 5.357116083 +5.2 5.357116083 +5.4 5.357116081 +5.1 5.357116083 diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/err.txt b/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/err.txt index cbbe72001..f7506f280 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/err.txt +++ b/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/err.txt @@ -1,6 +1,6 @@ Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 150 +Output database ./results/D_Iris.txt: Written records: 150 Model deployment time: 0:00:00.01 Create database file ./results/KNI_Iris.txt with KNI diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/time.log b/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/time.log index 9395f5316..e3a73f4d7 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/time.log +++ b/test/LearningTest/TestKNITransfer/Standard/IrisR/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.1569523811340332 +Overal time: 0.1508781909942627 diff --git a/test/LearningTest/TestKNITransfer/Standard/IrisR/test.prm b/test/LearningTest/TestKNITransfer/Standard/IrisR/test.prm index 92a1e63f4..d7ebcd1ef 100644 --- a/test/LearningTest/TestKNITransfer/Standard/IrisR/test.prm +++ b/test/LearningTest/TestKNITransfer/Standard/IrisR/test.prm @@ -13,21 +13,23 @@ // // +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... ClassFileName ./IrisModeling.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -SourceDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database - -TargetDatabase.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model + +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/KNI_Iris.txt // Database file KNILogFileName results/KNILog.txt // KNI log file name KNIStreamMaxMemory 100 // KNI max stream memory in Mo KNITransferDatabase // KNI transfer database diff --git a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/time.log b/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/time.log deleted file mode 100644 index 272a1ce03..000000000 --- a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/time.log +++ /dev/null @@ -1 +0,0 @@ -Overal time: 0.5429747104644775 diff --git a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/IrisExtended.kdic b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/IrisExtended.kdic similarity index 97% rename from test/LearningTest/TestKhiops/Standard-unstable/IrisU/IrisExtended.kdic rename to test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/IrisExtended.kdic index ed8a34633..f50517f00 100644 --- a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/IrisExtended.kdic +++ b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/IrisExtended.kdic @@ -1,17 +1,17 @@ -#Khiops 7.7.2i - -Dictionary Iris -{ - Numerical SepalLength ; - Numerical SepalWidth ; - Numerical PetalLength ; - Numerical PetalWidth ; - Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; - Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; - Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; - Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; - Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; - Numerical Dummy1 = Copy(0) ; - Numerical Dummy2 = Random() ; - Categorical Class ; -}; +#Khiops 7.7.2i + +Dictionary Iris +{ + Numerical SepalLength ; + Numerical SepalWidth ; + Numerical PetalLength ; + Numerical PetalWidth ; + Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; + Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; + Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; + Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; + Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; + Numerical Dummy1 = Copy(0) ; + Numerical Dummy2 = Random() ; + Categorical Class ; +}; diff --git a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/PreparationReport2D.xls b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls similarity index 96% rename from test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/PreparationReport2D.xls rename to test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls index 5bed7be3d..13f7eaabe 100644 --- a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/PreparationReport2D.xls +++ b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls @@ -1,1407 +1,1407 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Unsupervised analysis - -Evaluated variable pairs 55 -Informative variable pairs 38 - - -Variables pairs statistics - -Rank Name 1 Name 2 Level Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost -R01 Class Class1 0.286471 2 2 2 2 6.71557 18.9311 110.25 -R02 Class Class2 0.270234 2 2 2 2 6.71557 19.0156 110.25 -R03 Class SPetalLength 0.258511 2 3 3 5 6.71557 41.7647 157.188 -R04 Class1 SPetalLength 0.231831 2 2 2 2 6.71557 27.2099 142.253 -R05 PetalLength SPetalLength 0.151582 2 5 5 5 6.71557 69.091 386.913 -R06 Class2 SPetalLength 0.142436 2 2 2 4 6.71557 27.7273 158.704 -R07 Class PetalWidth 0.14197 2 3 3 5 6.71557 31.1679 396.708 -R08 Class PetalLength 0.136908 2 3 3 5 6.71557 31.1679 399.272 -R09 Class1 LowerPetalLength 0.111506 2 2 2 2 6.71557 13.7255 386.913 -R10 Class1 PetalLength 0.111506 2 2 2 2 6.71557 13.7255 386.913 -R11 Class1 PetalWidth 0.111506 2 2 2 2 6.71557 13.7255 386.913 -R12 PetalWidth SPetalLength 0.109807 2 3 3 5 6.71557 40.5555 438.232 -R13 Class LowerPetalLength 0.0982915 2 2 2 2 6.71557 19.0436 430.955 -R14 LowerPetalLength SPetalLength 0.0887331 2 2 2 2 6.71557 27.3225 462.959 -R15 PetalLength PetalWidth 0.0785935 2 3 3 4 6.71557 29.9587 676.972 -R16 Class UpperPetalWidth 0.0721164 2 2 2 4 6.71557 19.0868 444.17 -R17 PetalWidth UpperPetalWidth 0.0703191 2 3 3 3 6.71557 29.9587 683.381 -R18 LowerPetalLength PetalLength 0.0701201 2 3 3 3 6.71557 29.9587 683.535 -R19 Class2 PetalWidth 0.0662843 2 2 3 5 6.71557 20.8147 396.708 -R20 SPetalLength SepalLength 0.0654694 2 3 4 5 6.71557 49.4973 453.472 -R21 Class2 PetalLength 0.0606416 2 2 3 5 6.71557 20.8147 399.272 -R22 LowerPetalLength PetalWidth 0.0598398 2 2 2 2 6.71557 13.838 707.618 -R23 Class SepalLength 0.059526 2 3 3 7 6.71557 31.1679 438.466 -R24 Class1 Class2 0.0559199 2 2 2 3 6.71557 13.6129 110.25 -R25 Class1 SepalLength 0.0531576 2 2 2 4 6.71557 13.7255 413.664 -R26 SPetalLength UpperPetalWidth 0.0466723 2 3 2 5 6.71557 31.8478 481.373 -R27 PetalLength SepalLength 0.0407398 2 4 4 8 6.71557 47.7303 688.519 -R28 PetalLength UpperPetalWidth 0.0401281 2 2 2 3 6.71557 13.838 722.885 -R29 PetalWidth SepalLength 0.0303985 2 3 3 8 6.71557 29.9587 714.3 -R30 LowerPetalLength SepalLength 0.0253003 2 2 2 4 6.71557 13.838 734.369 -R31 Class1 UpperPetalWidth 0.0166012 2 2 2 3 6.71557 13.7255 430.424 -R32 SepalLength UpperPetalWidth 0.0164148 2 2 2 4 6.71557 13.838 741.251 -R33 Class1 SepalWidth 0.00749643 2 2 3 5 6.71557 20.8147 427.509 -R34 Class2 LowerPetalLength 0.0065114 2 2 2 3 6.71557 13.7255 430.955 -R35 Class SepalWidth 0.00543684 2 3 2 6 6.71557 22.1365 474.893 -R36 SPetalLength SepalWidth 0.00373995 2 3 3 9 6.71557 38.9735 497.662 -R37 LowerPetalLength UpperPetalWidth 0.00366071 2 2 2 3 6.71557 13.838 751.129 -R38 PetalWidth SepalWidth 0.00221737 2 3 2 6 6.71557 20.9273 745.158 -R39 Class Dummy2 0 0 1 1 1 0.693147 8.64312 497.163 -R40 Class1 Dummy2 0 0 1 1 1 0.693147 4.66344 453.12 -R41 Class2 Dummy2 0 0 1 1 1 0.693147 4.66344 448.998 -R42 Class2 SepalLength 0 0 1 1 1 0.693147 4.66344 448.998 -R43 Class2 SepalWidth 0 0 1 1 1 0.693147 4.66344 448.998 -R44 Class2 UpperPetalWidth 0 0 1 1 1 0.693147 4.66344 448.998 -R45 Dummy2 LowerPetalLength 0 0 1 1 1 0.693147 0 773.825 -R46 Dummy2 PetalLength 0 0 1 1 1 0.693147 0 773.825 -R47 Dummy2 PetalWidth 0 0 1 1 1 0.693147 0 773.825 -R48 Dummy2 SPetalLength 0 0 1 1 1 0.693147 15.5317 529.166 -R49 Dummy2 SepalLength 0 0 1 1 1 0.693147 0 773.825 -R50 Dummy2 SepalWidth 0 0 1 1 1 0.693147 0 773.825 -R51 Dummy2 UpperPetalWidth 0 0 1 1 1 0.693147 0 773.825 -R52 LowerPetalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 -R53 PetalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 -R54 SepalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 -R55 SepalWidth UpperPetalWidth 0 0 1 1 1 0.693147 0 773.825 - - --------------------------------------------------------------------------------- - -Variables pairs detailed statistics -(Pairs with two jointly informative variables) - - -Rank R01 -Variables - Type Name - Categorical Class - Categorical Class1 - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * -{Iris-setosa} 38 0.361905 1 Iris-setosa -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * - -Cell frequencies - Class1 -Class {} {setosa} Total Coverage -{Iris-virginica, Iris-versicolor} 67 0 67 0.638095 -{Iris-setosa} 0 38 38 0.361905 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 2 -Cell Id Class Class1 Frequency Coverage -C1 {Iris-virginica, Iris-versicolor} {} 67 0.638095 -C4 {Iris-setosa} {setosa} 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R02 -Variables - Type Name - Categorical Class - Categorical Class2 - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa, Iris-virginica} 73 0.695238 2 Iris-setosa Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * - -Cell frequencies - Class2 -Class {} {versicolor} Total Coverage -{Iris-setosa, Iris-virginica} 73 0 73 0.695238 -{Iris-versicolor} 0 32 32 0.304762 -Total 73 32 105 -Coverage 0.695238 0.304762 - -Cells 2 -Cell Id Class Class2 Frequency Coverage -C1 {Iris-setosa, Iris-virginica} {} 73 0.695238 -C4 {Iris-versicolor} {versicolor} 32 0.304762 - Total 105 1 - ----------------------------------------------- -Rank R03 -Variables - Type Name - Categorical Class - Categorical SPetalLength - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -SPetalLength -Group Frequency Coverage Size Value list -{1} 38 0.361905 1 1 -{5, 6} 34 0.32381 2 5 6 * -{4, 3} 33 0.314286 2 4 3 - -Cell frequencies - SPetalLength -Class {1} {5, 6} {4, 3} Total Coverage -{Iris-setosa} 38 0 0 38 0.361905 -{Iris-virginica} 0 32 3 35 0.333333 -{Iris-versicolor} 0 2 30 32 0.304762 -Total 38 34 33 105 -Coverage 0.361905 0.32381 0.314286 - -Cells 5 -Cell Id Class SPetalLength Frequency Coverage -C1 {Iris-setosa} {1} 38 0.361905 -C5 {Iris-virginica} {5, 6} 32 0.304762 -C9 {Iris-versicolor} {4, 3} 30 0.285714 -C8 {Iris-virginica} {4, 3} 3 0.0285714 -C6 {Iris-versicolor} {5, 6} 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R04 -Variables - Type Name - Categorical Class1 - Categorical SPetalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -SPetalLength -Group Frequency Coverage Size Value list -{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * -{1} 38 0.361905 1 1 - -Cell frequencies - SPetalLength -Class1 {5, 4, 3, ...} {1} Total Coverage -{} 67 0 67 0.638095 -{setosa} 0 38 38 0.361905 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 2 -Cell Id Class1 SPetalLength Frequency Coverage -C1 {} {5, 4, 3, ...} 67 0.638095 -C4 {setosa} {1} 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R05 -Variables - Type Name - Numerical PetalLength - Categorical SPetalLength - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;3.95] 8 0.0761905 -]3.95;4.95] 25 0.238095 -]4.95;5.95] 27 0.257143 -]5.95;+inf[ 7 0.0666667 -SPetalLength -Group Frequency Coverage Size Value list -{1} 38 0.361905 1 1 -{5} 27 0.257143 1 5 -{4} 25 0.238095 1 4 -{3} 8 0.0761905 1 3 -{6} 7 0.0666667 1 6 * - -Cell frequencies - SPetalLength -PetalLength {1} {5} {4} {3} {6} Total Coverage -]-inf;2.4] 38 0 0 0 0 38 0.361905 -]2.4;3.95] 0 0 0 8 0 8 0.0761905 -]3.95;4.95] 0 0 25 0 0 25 0.238095 -]4.95;5.95] 0 27 0 0 0 27 0.257143 -]5.95;+inf[ 0 0 0 0 7 7 0.0666667 -Total 38 27 25 8 7 105 -Coverage 0.361905 0.257143 0.238095 0.0761905 0.0666667 - -Cells 5 -Cell Id PetalLength SPetalLength Frequency Coverage -C1 ]-inf;2.4] {1} 38 0.361905 -C9 ]4.95;5.95] {5} 27 0.257143 -C13 ]3.95;4.95] {4} 25 0.238095 -C17 ]2.4;3.95] {3} 8 0.0761905 -C25 ]5.95;+inf[ {6} 7 0.0666667 - Total 105 1 - ----------------------------------------------- -Rank R06 -Variables - Type Name - Categorical Class2 - Categorical SPetalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -SPetalLength -Group Frequency Coverage Size Value list -{1, 5, 6} 72 0.685714 3 1 5 6 * -{4, 3} 33 0.314286 2 4 3 - -Cell frequencies - SPetalLength -Class2 {1, 5, 6} {4, 3} Total Coverage -{} 70 3 73 0.695238 -{versicolor} 2 30 32 0.304762 -Total 72 33 105 -Coverage 0.685714 0.314286 - -Cells 4 -Cell Id Class2 SPetalLength Frequency Coverage -C1 {} {1, 5, 6} 70 0.666667 -C4 {versicolor} {4, 3} 30 0.285714 -C3 {} {4, 3} 3 0.0285714 -C2 {versicolor} {1, 5, 6} 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R07 -Variables - Type Name - Categorical Class - Numerical PetalWidth - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.75] 33 0.314286 -]1.75;+inf[ 34 0.32381 - -Cell frequencies - PetalWidth -Class ]-inf;0.75] ]0.75;1.75] ]1.75;+inf[ Total Coverage -{Iris-setosa} 38 0 0 38 0.361905 -{Iris-virginica} 0 2 33 35 0.333333 -{Iris-versicolor} 0 31 1 32 0.304762 -Total 38 33 34 105 -Coverage 0.361905 0.314286 0.32381 - -Cells 5 -Cell Id Class PetalWidth Frequency Coverage -C1 {Iris-setosa} ]-inf;0.75] 38 0.361905 -C8 {Iris-virginica} ]1.75;+inf[ 33 0.314286 -C6 {Iris-versicolor} ]0.75;1.75] 31 0.295238 -C5 {Iris-virginica} ]0.75;1.75] 2 0.0190476 -C9 {Iris-versicolor} ]1.75;+inf[ 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R08 -Variables - Type Name - Categorical Class - Numerical PetalLength - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;4.85] 30 0.285714 -]4.85;+inf[ 37 0.352381 - -Cell frequencies - PetalLength -Class ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Total Coverage -{Iris-setosa} 38 0 0 38 0.361905 -{Iris-virginica} 0 1 34 35 0.333333 -{Iris-versicolor} 0 29 3 32 0.304762 -Total 38 30 37 105 -Coverage 0.361905 0.285714 0.352381 - -Cells 5 -Cell Id Class PetalLength Frequency Coverage -C1 {Iris-setosa} ]-inf;2.4] 38 0.361905 -C8 {Iris-virginica} ]4.85;+inf[ 34 0.32381 -C6 {Iris-versicolor} ]2.4;4.85] 29 0.27619 -C9 {Iris-versicolor} ]4.85;+inf[ 3 0.0285714 -C5 {Iris-virginica} ]2.4;4.85] 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R09 -Variables - Type Name - Categorical Class1 - Numerical LowerPetalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - LowerPetalLength -Class1 ]-inf;2.4] ]2.4;+inf[ Total Coverage -{} 0 67 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id Class1 LowerPetalLength Frequency Coverage -C3 {} ]2.4;+inf[ 67 0.638095 -C2 {setosa} ]-inf;2.4] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R10 -Variables - Type Name - Categorical Class1 - Numerical PetalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - PetalLength -Class1 ]-inf;2.4] ]2.4;+inf[ Total Coverage -{} 0 67 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id Class1 PetalLength Frequency Coverage -C3 {} ]2.4;+inf[ 67 0.638095 -C2 {setosa} ]-inf;2.4] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R11 -Variables - Type Name - Categorical Class1 - Numerical PetalWidth - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;+inf[ 67 0.638095 - -Cell frequencies - PetalWidth -Class1 ]-inf;0.75] ]0.75;+inf[ Total Coverage -{} 0 67 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id Class1 PetalWidth Frequency Coverage -C3 {} ]0.75;+inf[ 67 0.638095 -C2 {setosa} ]-inf;0.75] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R12 -Variables - Type Name - Numerical PetalWidth - Categorical SPetalLength - -Variables stats -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.65] 32 0.304762 -]1.65;+inf[ 35 0.333333 -SPetalLength -Group Frequency Coverage Size Value list -{1} 38 0.361905 1 1 -{5, 6} 34 0.32381 2 5 6 * -{4, 3} 33 0.314286 2 4 3 - -Cell frequencies - SPetalLength -PetalWidth {1} {5, 6} {4, 3} Total Coverage -]-inf;0.75] 38 0 0 38 0.361905 -]0.75;1.65] 0 3 29 32 0.304762 -]1.65;+inf[ 0 31 4 35 0.333333 -Total 38 34 33 105 -Coverage 0.361905 0.32381 0.314286 - -Cells 5 -Cell Id PetalWidth SPetalLength Frequency Coverage -C1 ]-inf;0.75] {1} 38 0.361905 -C6 ]1.65;+inf[ {5, 6} 31 0.295238 -C8 ]0.75;1.65] {4, 3} 29 0.27619 -C9 ]1.65;+inf[ {4, 3} 4 0.0380952 -C5 ]0.75;1.65] {5, 6} 3 0.0285714 - Total 105 1 - ----------------------------------------------- -Rank R13 -Variables - Type Name - Categorical Class - Numerical LowerPetalLength - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * -{Iris-setosa} 38 0.361905 1 Iris-setosa -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - LowerPetalLength -Class ]-inf;2.4] ]2.4;+inf[ Total Coverage -{Iris-virginica, Iris-versicolor} 0 67 67 0.638095 -{Iris-setosa} 38 0 38 0.361905 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id Class LowerPetalLength Frequency Coverage -C3 {Iris-virginica, Iris-versicolor} ]2.4;+inf[ 67 0.638095 -C2 {Iris-setosa} ]-inf;2.4] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R14 -Variables - Type Name - Numerical LowerPetalLength - Categorical SPetalLength - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -SPetalLength -Group Frequency Coverage Size Value list -{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * -{1} 38 0.361905 1 1 - -Cell frequencies - SPetalLength -LowerPetalLength {5, 4, 3, ...} {1} Total Coverage -]-inf;2.4] 0 38 38 0.361905 -]2.4;+inf[ 67 0 67 0.638095 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 2 -Cell Id LowerPetalLength SPetalLength Frequency Coverage -C2 ]2.4;+inf[ {5, 4, 3, ...} 67 0.638095 -C3 ]-inf;2.4] {1} 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R15 -Variables - Type Name - Numerical PetalLength - Numerical PetalWidth - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;4.75] 27 0.257143 -]4.75;+inf[ 40 0.380952 -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.65] 32 0.304762 -]1.65;+inf[ 35 0.333333 - -Cell frequencies - PetalWidth -PetalLength ]-inf;0.75] ]0.75;1.65] ]1.65;+inf[ Total Coverage -]-inf;2.4] 38 0 0 38 0.361905 -]2.4;4.75] 0 27 0 27 0.257143 -]4.75;+inf[ 0 5 35 40 0.380952 -Total 38 32 35 105 -Coverage 0.361905 0.304762 0.333333 - -Cells 4 -Cell Id PetalLength PetalWidth Frequency Coverage -C1 ]-inf;2.4] ]-inf;0.75] 38 0.361905 -C9 ]4.75;+inf[ ]1.65;+inf[ 35 0.333333 -C5 ]2.4;4.75] ]0.75;1.65] 27 0.257143 -C6 ]4.75;+inf[ ]0.75;1.65] 5 0.047619 - Total 105 1 - ----------------------------------------------- -Rank R16 -Variables - Type Name - Categorical Class - Numerical UpperPetalWidth - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa, Iris-versicolor} 70 0.666667 2 Iris-setosa Iris-versicolor * -{Iris-virginica} 35 0.333333 1 Iris-virginica -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.75] 71 0.67619 -]1.75;+inf[ 34 0.32381 - -Cell frequencies - UpperPetalWidth -Class ]-inf;1.75] ]1.75;+inf[ Total Coverage -{Iris-setosa, Iris-versicolor} 69 1 70 0.666667 -{Iris-virginica} 2 33 35 0.333333 -Total 71 34 105 -Coverage 0.67619 0.32381 - -Cells 4 -Cell Id Class UpperPetalWidth Frequency Coverage -C1 {Iris-setosa, Iris-versicolor} ]-inf;1.75] 69 0.657143 -C4 {Iris-virginica} ]1.75;+inf[ 33 0.314286 -C2 {Iris-virginica} ]-inf;1.75] 2 0.0190476 -C3 {Iris-setosa, Iris-versicolor} ]1.75;+inf[ 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R17 -Variables - Type Name - Numerical PetalWidth - Numerical UpperPetalWidth - -Variables stats -PetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;2.05] 20 0.190476 -]2.05;+inf[ 18 0.171429 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;2.05] 20 0.190476 -]2.05;+inf[ 18 0.171429 - -Cell frequencies - UpperPetalWidth -PetalWidth ]-inf;1.55] ]1.55;2.05] ]2.05;+inf[ Total Coverage -]-inf;1.55] 67 0 0 67 0.638095 -]1.55;2.05] 0 20 0 20 0.190476 -]2.05;+inf[ 0 0 18 18 0.171429 -Total 67 20 18 105 -Coverage 0.638095 0.190476 0.171429 - -Cells 3 -Cell Id PetalWidth UpperPetalWidth Frequency Coverage -C1 ]-inf;1.55] ]-inf;1.55] 67 0.638095 -C5 ]1.55;2.05] ]1.55;2.05] 20 0.190476 -C9 ]2.05;+inf[ ]2.05;+inf[ 18 0.171429 - Total 105 1 - ----------------------------------------------- -Rank R18 -Variables - Type Name - Numerical LowerPetalLength - Numerical PetalLength - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;1.45] 17 0.161905 -]1.45;2.4] 21 0.2 -]2.4;+inf[ 67 0.638095 -PetalLength -Interval Frequency Coverage -]-inf;1.45] 17 0.161905 -]1.45;2.4] 21 0.2 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - PetalLength -LowerPetalLength ]-inf;1.45] ]1.45;2.4] ]2.4;+inf[ Total Coverage -]-inf;1.45] 17 0 0 17 0.161905 -]1.45;2.4] 0 21 0 21 0.2 -]2.4;+inf[ 0 0 67 67 0.638095 -Total 17 21 67 105 -Coverage 0.161905 0.2 0.638095 - -Cells 3 -Cell Id LowerPetalLength PetalLength Frequency Coverage -C9 ]2.4;+inf[ ]2.4;+inf[ 67 0.638095 -C5 ]1.45;2.4] ]1.45;2.4] 21 0.2 -C1 ]-inf;1.45] ]-inf;1.45] 17 0.161905 - Total 105 1 - ----------------------------------------------- -Rank R19 -Variables - Type Name - Categorical Class2 - Numerical PetalWidth - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.75] 33 0.314286 -]1.75;+inf[ 34 0.32381 - -Cell frequencies - PetalWidth -Class2 ]-inf;0.75] ]0.75;1.75] ]1.75;+inf[ Total Coverage -{} 38 2 33 73 0.695238 -{versicolor} 0 31 1 32 0.304762 -Total 38 33 34 105 -Coverage 0.361905 0.314286 0.32381 - -Cells 5 -Cell Id Class2 PetalWidth Frequency Coverage -C1 {} ]-inf;0.75] 38 0.361905 -C5 {} ]1.75;+inf[ 33 0.314286 -C4 {versicolor} ]0.75;1.75] 31 0.295238 -C3 {} ]0.75;1.75] 2 0.0190476 -C6 {versicolor} ]1.75;+inf[ 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R20 -Variables - Type Name - Categorical SPetalLength - Numerical SepalLength - -Variables stats -SPetalLength -Group Frequency Coverage Size Value list -{5, 4} 52 0.495238 2 5 4 -{1, 3} 46 0.438095 2 1 3 -{6} 7 0.0666667 1 6 * -SepalLength -Interval Frequency Coverage -]-inf;5.35] 34 0.32381 -]5.35;5.85] 22 0.209524 -]5.85;7.15] 42 0.4 -]7.15;+inf[ 7 0.0666667 - -Cell frequencies - SepalLength -SPetalLength ]-inf;5.35] ]5.35;5.85] ]5.85;7.15] ]7.15;+inf[ Total Coverage -{5, 4} 0 10 42 0 52 0.495238 -{1, 3} 34 12 0 0 46 0.438095 -{6} 0 0 0 7 7 0.0666667 -Total 34 22 42 7 105 -Coverage 0.32381 0.209524 0.4 0.0666667 - -Cells 5 -Cell Id SPetalLength SepalLength Frequency Coverage -C7 {5, 4} ]5.85;7.15] 42 0.4 -C2 {1, 3} ]-inf;5.35] 34 0.32381 -C5 {1, 3} ]5.35;5.85] 12 0.114286 -C4 {5, 4} ]5.35;5.85] 10 0.0952381 -C12 {6} ]7.15;+inf[ 7 0.0666667 - Total 105 1 - ----------------------------------------------- -Rank R21 -Variables - Type Name - Categorical Class2 - Numerical PetalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;4.85] 30 0.285714 -]4.85;+inf[ 37 0.352381 - -Cell frequencies - PetalLength -Class2 ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Total Coverage -{} 38 1 34 73 0.695238 -{versicolor} 0 29 3 32 0.304762 -Total 38 30 37 105 -Coverage 0.361905 0.285714 0.352381 - -Cells 5 -Cell Id Class2 PetalLength Frequency Coverage -C1 {} ]-inf;2.4] 38 0.361905 -C5 {} ]4.85;+inf[ 34 0.32381 -C4 {versicolor} ]2.4;4.85] 29 0.27619 -C6 {versicolor} ]4.85;+inf[ 3 0.0285714 -C3 {} ]2.4;4.85] 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R22 -Variables - Type Name - Numerical LowerPetalLength - Numerical PetalWidth - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;+inf[ 67 0.638095 - -Cell frequencies - PetalWidth -LowerPetalLength ]-inf;0.75] ]0.75;+inf[ Total Coverage -]-inf;2.4] 38 0 38 0.361905 -]2.4;+inf[ 0 67 67 0.638095 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id LowerPetalLength PetalWidth Frequency Coverage -C4 ]2.4;+inf[ ]0.75;+inf[ 67 0.638095 -C1 ]-inf;2.4] ]-inf;0.75] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R23 -Variables - Type Name - Categorical Class - Numerical SepalLength - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;6.15] 28 0.266667 -]6.15;+inf[ 38 0.361905 - -Cell frequencies - SepalLength -Class ]-inf;5.45] ]5.45;6.15] ]6.15;+inf[ Total Coverage -{Iris-setosa} 34 4 0 38 0.361905 -{Iris-virginica} 0 5 30 35 0.333333 -{Iris-versicolor} 5 19 8 32 0.304762 -Total 39 28 38 105 -Coverage 0.371429 0.266667 0.361905 - -Cells 7 -Cell Id Class SepalLength Frequency Coverage -C1 {Iris-setosa} ]-inf;5.45] 34 0.32381 -C8 {Iris-virginica} ]6.15;+inf[ 30 0.285714 -C6 {Iris-versicolor} ]5.45;6.15] 19 0.180952 -C9 {Iris-versicolor} ]6.15;+inf[ 8 0.0761905 -C5 {Iris-virginica} ]5.45;6.15] 5 0.047619 -C3 {Iris-versicolor} ]-inf;5.45] 5 0.047619 -C4 {Iris-setosa} ]5.45;6.15] 4 0.0380952 - Total 105 1 - ----------------------------------------------- -Rank R24 -Variables - Type Name - Categorical Class1 - Categorical Class2 - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * - -Cell frequencies - Class2 -Class1 {} {versicolor} Total Coverage -{} 35 32 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 73 32 105 -Coverage 0.695238 0.304762 - -Cells 3 -Cell Id Class1 Class2 Frequency Coverage -C2 {setosa} {} 38 0.361905 -C1 {} {} 35 0.333333 -C3 {} {versicolor} 32 0.304762 - Total 105 1 - ----------------------------------------------- -Rank R25 -Variables - Type Name - Categorical Class1 - Numerical SepalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;+inf[ 66 0.628571 - -Cell frequencies - SepalLength -Class1 ]-inf;5.45] ]5.45;+inf[ Total Coverage -{} 5 62 67 0.638095 -{setosa} 34 4 38 0.361905 -Total 39 66 105 -Coverage 0.371429 0.628571 - -Cells 4 -Cell Id Class1 SepalLength Frequency Coverage -C3 {} ]5.45;+inf[ 62 0.590476 -C2 {setosa} ]-inf;5.45] 34 0.32381 -C1 {} ]-inf;5.45] 5 0.047619 -C4 {setosa} ]5.45;+inf[ 4 0.0380952 - Total 105 1 - ----------------------------------------------- -Rank R26 -Variables - Type Name - Categorical SPetalLength - Numerical UpperPetalWidth - -Variables stats -SPetalLength -Group Frequency Coverage Size Value list -{1, 3} 46 0.438095 2 1 3 -{5, 6} 34 0.32381 2 5 6 * -{4} 25 0.238095 1 4 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -SPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage -{1, 3} 46 0 46 0.438095 -{5, 6} 2 32 34 0.32381 -{4} 19 6 25 0.238095 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 5 -Cell Id SPetalLength UpperPetalWidth Frequency Coverage -C1 {1, 3} ]-inf;1.55] 46 0.438095 -C5 {5, 6} ]1.55;+inf[ 32 0.304762 -C3 {4} ]-inf;1.55] 19 0.180952 -C6 {4} ]1.55;+inf[ 6 0.0571429 -C2 {5, 6} ]-inf;1.55] 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R27 -Variables - Type Name - Numerical PetalLength - Numerical SepalLength - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;3.55] 42 0.4 -]3.55;4.6] 19 0.180952 -]4.6;5.95] 37 0.352381 -]5.95;+inf[ 7 0.0666667 -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;5.85] 17 0.161905 -]5.85;7.15] 42 0.4 -]7.15;+inf[ 7 0.0666667 - -Cell frequencies - SepalLength -PetalLength ]-inf;5.45] ]5.45;5.85] ]5.85;7.15] ]7.15;+inf[ Total Coverage -]-inf;3.55] 37 5 0 0 42 0.4 -]3.55;4.6] 2 10 7 0 19 0.180952 -]4.6;5.95] 0 2 35 0 37 0.352381 -]5.95;+inf[ 0 0 0 7 7 0.0666667 -Total 39 17 42 7 105 -Coverage 0.371429 0.161905 0.4 0.0666667 - -Cells 8 -Cell Id PetalLength SepalLength Frequency Coverage -C1 ]-inf;3.55] ]-inf;5.45] 37 0.352381 -C11 ]4.6;5.95] ]5.85;7.15] 35 0.333333 -C6 ]3.55;4.6] ]5.45;5.85] 10 0.0952381 -C10 ]3.55;4.6] ]5.85;7.15] 7 0.0666667 -C16 ]5.95;+inf[ ]7.15;+inf[ 7 0.0666667 -C5 ]-inf;3.55] ]5.45;5.85] 5 0.047619 -C2 ]3.55;4.6] ]-inf;5.45] 2 0.0190476 -C7 ]4.6;5.95] ]5.45;5.85] 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R28 -Variables - Type Name - Numerical PetalLength - Numerical UpperPetalWidth - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;4.75] 65 0.619048 -]4.75;+inf[ 40 0.380952 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.65] 70 0.666667 -]1.65;+inf[ 35 0.333333 - -Cell frequencies - UpperPetalWidth -PetalLength ]-inf;1.65] ]1.65;+inf[ Total Coverage -]-inf;4.75] 65 0 65 0.619048 -]4.75;+inf[ 5 35 40 0.380952 -Total 70 35 105 -Coverage 0.666667 0.333333 - -Cells 3 -Cell Id PetalLength UpperPetalWidth Frequency Coverage -C1 ]-inf;4.75] ]-inf;1.65] 65 0.619048 -C4 ]4.75;+inf[ ]1.65;+inf[ 35 0.333333 -C2 ]4.75;+inf[ ]-inf;1.65] 5 0.047619 - Total 105 1 - ----------------------------------------------- -Rank R29 -Variables - Type Name - Numerical PetalWidth - Numerical SepalLength - -Variables stats -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.35] 18 0.171429 -]1.35;+inf[ 49 0.466667 -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;5.85] 17 0.161905 -]5.85;+inf[ 49 0.466667 - -Cell frequencies - SepalLength -PetalWidth ]-inf;5.45] ]5.45;5.85] ]5.85;+inf[ Total Coverage -]-inf;0.75] 34 4 0 38 0.361905 -]0.75;1.35] 3 10 5 18 0.171429 -]1.35;+inf[ 2 3 44 49 0.466667 -Total 39 17 49 105 -Coverage 0.371429 0.161905 0.466667 - -Cells 8 -Cell Id PetalWidth SepalLength Frequency Coverage -C9 ]1.35;+inf[ ]5.85;+inf[ 44 0.419048 -C1 ]-inf;0.75] ]-inf;5.45] 34 0.32381 -C5 ]0.75;1.35] ]5.45;5.85] 10 0.0952381 -C8 ]0.75;1.35] ]5.85;+inf[ 5 0.047619 -C4 ]-inf;0.75] ]5.45;5.85] 4 0.0380952 -C2 ]0.75;1.35] ]-inf;5.45] 3 0.0285714 -C6 ]1.35;+inf[ ]5.45;5.85] 3 0.0285714 -C3 ]1.35;+inf[ ]-inf;5.45] 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R30 -Variables - Type Name - Numerical LowerPetalLength - Numerical SepalLength - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;+inf[ 66 0.628571 - -Cell frequencies - SepalLength -LowerPetalLength ]-inf;5.45] ]5.45;+inf[ Total Coverage -]-inf;2.4] 34 4 38 0.361905 -]2.4;+inf[ 5 62 67 0.638095 -Total 39 66 105 -Coverage 0.371429 0.628571 - -Cells 4 -Cell Id LowerPetalLength SepalLength Frequency Coverage -C4 ]2.4;+inf[ ]5.45;+inf[ 62 0.590476 -C1 ]-inf;2.4] ]-inf;5.45] 34 0.32381 -C2 ]2.4;+inf[ ]-inf;5.45] 5 0.047619 -C3 ]-inf;2.4] ]5.45;+inf[ 4 0.0380952 - Total 105 1 - ----------------------------------------------- -Rank R31 -Variables - Type Name - Categorical Class1 - Numerical UpperPetalWidth - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -Class1 ]-inf;1.55] ]1.55;+inf[ Total Coverage -{} 29 38 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 3 -Cell Id Class1 UpperPetalWidth Frequency Coverage -C3 {} ]1.55;+inf[ 38 0.361905 -C2 {setosa} ]-inf;1.55] 38 0.361905 -C1 {} ]-inf;1.55] 29 0.27619 - Total 105 1 - ----------------------------------------------- -Rank R32 -Variables - Type Name - Numerical SepalLength - Numerical UpperPetalWidth - -Variables stats -SepalLength -Interval Frequency Coverage -]-inf;5.85] 56 0.533333 -]5.85;+inf[ 49 0.466667 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -SepalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage -]-inf;5.85] 54 2 56 0.533333 -]5.85;+inf[ 13 36 49 0.466667 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 4 -Cell Id SepalLength UpperPetalWidth Frequency Coverage -C1 ]-inf;5.85] ]-inf;1.55] 54 0.514286 -C4 ]5.85;+inf[ ]1.55;+inf[ 36 0.342857 -C2 ]5.85;+inf[ ]-inf;1.55] 13 0.12381 -C3 ]-inf;5.85] ]1.55;+inf[ 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R33 -Variables - Type Name - Categorical Class1 - Numerical SepalWidth - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -SepalWidth -Interval Frequency Coverage -]-inf;2.85] 30 0.285714 -]2.85;3.35] 49 0.466667 -]3.35;+inf[ 26 0.247619 - -Cell frequencies - SepalWidth -Class1 ]-inf;2.85] ]2.85;3.35] ]3.35;+inf[ Total Coverage -{} 30 32 5 67 0.638095 -{setosa} 0 17 21 38 0.361905 -Total 30 49 26 105 -Coverage 0.285714 0.466667 0.247619 - -Cells 5 -Cell Id Class1 SepalWidth Frequency Coverage -C3 {} ]2.85;3.35] 32 0.304762 -C1 {} ]-inf;2.85] 30 0.285714 -C6 {setosa} ]3.35;+inf[ 21 0.2 -C4 {setosa} ]2.85;3.35] 17 0.161905 -C5 {} ]3.35;+inf[ 5 0.047619 - Total 105 1 - ----------------------------------------------- -Rank R34 -Variables - Type Name - Categorical Class2 - Numerical LowerPetalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - LowerPetalLength -Class2 ]-inf;2.4] ]2.4;+inf[ Total Coverage -{} 38 35 73 0.695238 -{versicolor} 0 32 32 0.304762 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 3 -Cell Id Class2 LowerPetalLength Frequency Coverage -C1 {} ]-inf;2.4] 38 0.361905 -C3 {} ]2.4;+inf[ 35 0.333333 -C4 {versicolor} ]2.4;+inf[ 32 0.304762 - Total 105 1 - ----------------------------------------------- -Rank R35 -Variables - Type Name - Categorical Class - Numerical SepalWidth - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -SepalWidth -Interval Frequency Coverage -]-inf;2.95] 36 0.342857 -]2.95;+inf[ 69 0.657143 - -Cell frequencies - SepalWidth -Class ]-inf;2.95] ]2.95;+inf[ Total Coverage -{Iris-setosa} 1 37 38 0.361905 -{Iris-virginica} 13 22 35 0.333333 -{Iris-versicolor} 22 10 32 0.304762 -Total 36 69 105 -Coverage 0.342857 0.657143 - -Cells 6 -Cell Id Class SepalWidth Frequency Coverage -C4 {Iris-setosa} ]2.95;+inf[ 37 0.352381 -C5 {Iris-virginica} ]2.95;+inf[ 22 0.209524 -C3 {Iris-versicolor} ]-inf;2.95] 22 0.209524 -C2 {Iris-virginica} ]-inf;2.95] 13 0.12381 -C6 {Iris-versicolor} ]2.95;+inf[ 10 0.0952381 -C1 {Iris-setosa} ]-inf;2.95] 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R36 -Variables - Type Name - Categorical SPetalLength - Numerical SepalWidth - -Variables stats -SPetalLength -Group Frequency Coverage Size Value list -{4} 40 0.380952 3 4 * -{1} 38 0.361905 1 1 -{5} 27 0.257143 1 5 -SepalWidth -Interval Frequency Coverage -]-inf;2.95] 36 0.342857 -]2.95;3.25] 41 0.390476 -]3.25;+inf[ 28 0.266667 - -Cell frequencies - SepalWidth -SPetalLength ]-inf;2.95] ]2.95;3.25] ]3.25;+inf[ Total Coverage -{4} 26 10 4 40 0.380952 -{1} 1 15 22 38 0.361905 -{5} 9 16 2 27 0.257143 -Total 36 41 28 105 -Coverage 0.342857 0.390476 0.266667 - -Cells 9 -Cell Id SPetalLength SepalWidth Frequency Coverage -C1 {4} ]-inf;2.95] 26 0.247619 -C8 {1} ]3.25;+inf[ 22 0.209524 -C6 {5} ]2.95;3.25] 16 0.152381 -C5 {1} ]2.95;3.25] 15 0.142857 -C4 {4} ]2.95;3.25] 10 0.0952381 -C3 {5} ]-inf;2.95] 9 0.0857143 -C7 {4} ]3.25;+inf[ 4 0.0380952 -C9 {5} ]3.25;+inf[ 2 0.0190476 -C2 {1} ]-inf;2.95] 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R37 -Variables - Type Name - Numerical LowerPetalLength - Numerical UpperPetalWidth - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -LowerPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage -]-inf;2.4] 38 0 38 0.361905 -]2.4;+inf[ 29 38 67 0.638095 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 3 -Cell Id LowerPetalLength UpperPetalWidth Frequency Coverage -C1 ]-inf;2.4] ]-inf;1.55] 38 0.361905 -C4 ]2.4;+inf[ ]1.55;+inf[ 38 0.361905 -C2 ]2.4;+inf[ ]-inf;1.55] 29 0.27619 - Total 105 1 - ----------------------------------------------- -Rank R38 -Variables - Type Name - Numerical PetalWidth - Numerical SepalWidth - -Variables stats -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.45] 24 0.228571 -]1.45;+inf[ 43 0.409524 -SepalWidth -Interval Frequency Coverage -]-inf;2.95] 36 0.342857 -]2.95;+inf[ 69 0.657143 - -Cell frequencies - SepalWidth -PetalWidth ]-inf;2.95] ]2.95;+inf[ Total Coverage -]-inf;0.75] 1 37 38 0.361905 -]0.75;1.45] 21 3 24 0.228571 -]1.45;+inf[ 14 29 43 0.409524 -Total 36 69 105 -Coverage 0.342857 0.657143 - -Cells 6 -Cell Id PetalWidth SepalWidth Frequency Coverage -C4 ]-inf;0.75] ]2.95;+inf[ 37 0.352381 -C6 ]1.45;+inf[ ]2.95;+inf[ 29 0.27619 -C2 ]0.75;1.45] ]-inf;2.95] 21 0.2 -C3 ]1.45;+inf[ ]-inf;2.95] 14 0.133333 -C5 ]0.75;1.45] ]2.95;+inf[ 3 0.0285714 -C1 ]-inf;0.75] ]-inf;2.95] 1 0.00952381 - Total 105 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 4 + Numerical 8 + Total 12 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Unsupervised analysis + +Evaluated variable pairs 55 +Informative variable pairs 38 + + +Variables pairs statistics + +Rank Name 1 Name 2 Level Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost +R01 Class Class1 0.286471 2 2 2 2 6.71557 18.9311 110.25 +R02 Class Class2 0.270234 2 2 2 2 6.71557 19.0156 110.25 +R03 Class SPetalLength 0.258511 2 3 3 5 6.71557 41.7647 157.188 +R04 Class1 SPetalLength 0.231831 2 2 2 2 6.71557 27.2099 142.253 +R05 PetalLength SPetalLength 0.151582 2 5 5 5 6.71557 69.091 386.913 +R06 Class2 SPetalLength 0.142436 2 2 2 4 6.71557 27.7273 158.704 +R07 Class PetalWidth 0.14197 2 3 3 5 6.71557 31.1679 396.708 +R08 Class PetalLength 0.136908 2 3 3 5 6.71557 31.1679 399.272 +R09 Class1 LowerPetalLength 0.111506 2 2 2 2 6.71557 13.7255 386.913 +R10 Class1 PetalLength 0.111506 2 2 2 2 6.71557 13.7255 386.913 +R11 Class1 PetalWidth 0.111506 2 2 2 2 6.71557 13.7255 386.913 +R12 PetalWidth SPetalLength 0.109807 2 3 3 5 6.71557 40.5555 438.232 +R13 Class LowerPetalLength 0.0982915 2 2 2 2 6.71557 19.0436 430.955 +R14 LowerPetalLength SPetalLength 0.0887331 2 2 2 2 6.71557 27.3225 462.959 +R15 PetalLength PetalWidth 0.0785935 2 3 3 4 6.71557 29.9587 676.972 +R16 Class UpperPetalWidth 0.0721164 2 2 2 4 6.71557 19.0868 444.17 +R17 PetalWidth UpperPetalWidth 0.0703191 2 3 3 3 6.71557 29.9587 683.381 +R18 LowerPetalLength PetalLength 0.0701201 2 3 3 3 6.71557 29.9587 683.535 +R19 Class2 PetalWidth 0.0662843 2 2 3 5 6.71557 20.8147 396.708 +R20 SPetalLength SepalLength 0.0654694 2 3 4 5 6.71557 49.4973 453.472 +R21 Class2 PetalLength 0.0606416 2 2 3 5 6.71557 20.8147 399.272 +R22 LowerPetalLength PetalWidth 0.0598398 2 2 2 2 6.71557 13.838 707.618 +R23 Class SepalLength 0.059526 2 3 3 7 6.71557 31.1679 438.466 +R24 Class1 Class2 0.0559199 2 2 2 3 6.71557 13.6129 110.25 +R25 Class1 SepalLength 0.0531576 2 2 2 4 6.71557 13.7255 413.664 +R26 SPetalLength UpperPetalWidth 0.0466723 2 3 2 5 6.71557 31.8478 481.373 +R27 PetalLength SepalLength 0.0407398 2 4 4 8 6.71557 47.7303 688.519 +R28 PetalLength UpperPetalWidth 0.0401281 2 2 2 3 6.71557 13.838 722.885 +R29 PetalWidth SepalLength 0.0303985 2 3 3 8 6.71557 29.9587 714.3 +R30 LowerPetalLength SepalLength 0.0253003 2 2 2 4 6.71557 13.838 734.369 +R31 Class1 UpperPetalWidth 0.0166012 2 2 2 3 6.71557 13.7255 430.424 +R32 SepalLength UpperPetalWidth 0.0164148 2 2 2 4 6.71557 13.838 741.251 +R33 Class1 SepalWidth 0.00749643 2 2 3 5 6.71557 20.8147 427.509 +R34 Class2 LowerPetalLength 0.0065114 2 2 2 3 6.71557 13.7255 430.955 +R35 Class SepalWidth 0.00543684 2 3 2 6 6.71557 22.1365 474.893 +R36 SPetalLength SepalWidth 0.00373995 2 3 3 9 6.71557 38.9735 497.662 +R37 LowerPetalLength UpperPetalWidth 0.00366071 2 2 2 3 6.71557 13.838 751.129 +R38 PetalWidth SepalWidth 0.00221737 2 3 2 6 6.71557 20.9273 745.158 +R39 Class Dummy2 0 0 1 1 1 0.693147 8.64312 497.163 +R40 Class1 Dummy2 0 0 1 1 1 0.693147 4.66344 453.12 +R41 Class2 Dummy2 0 0 1 1 1 0.693147 4.66344 448.998 +R42 Class2 SepalLength 0 0 1 1 1 0.693147 4.66344 448.998 +R43 Class2 SepalWidth 0 0 1 1 1 0.693147 4.66344 448.998 +R44 Class2 UpperPetalWidth 0 0 1 1 1 0.693147 4.66344 448.998 +R45 Dummy2 LowerPetalLength 0 0 1 1 1 0.693147 0 773.825 +R46 Dummy2 PetalLength 0 0 1 1 1 0.693147 0 773.825 +R47 Dummy2 PetalWidth 0 0 1 1 1 0.693147 0 773.825 +R48 Dummy2 SPetalLength 0 0 1 1 1 0.693147 15.5317 529.166 +R49 Dummy2 SepalLength 0 0 1 1 1 0.693147 0 773.825 +R50 Dummy2 SepalWidth 0 0 1 1 1 0.693147 0 773.825 +R51 Dummy2 UpperPetalWidth 0 0 1 1 1 0.693147 0 773.825 +R52 LowerPetalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 +R53 PetalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 +R54 SepalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 +R55 SepalWidth UpperPetalWidth 0 0 1 1 1 0.693147 0 773.825 + + +-------------------------------------------------------------------------------- + +Variables pairs detailed statistics +(Pairs with two jointly informative variables) + + +Rank R01 +Variables + Type Name + Categorical Class + Categorical Class1 + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * +{Iris-setosa} 38 0.361905 1 Iris-setosa +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * + +Cell frequencies + Class1 +Class {} {setosa} Total Coverage +{Iris-virginica, Iris-versicolor} 67 0 67 0.638095 +{Iris-setosa} 0 38 38 0.361905 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 2 +Cell Id Class Class1 Frequency Coverage +C1 {Iris-virginica, Iris-versicolor} {} 67 0.638095 +C4 {Iris-setosa} {setosa} 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R02 +Variables + Type Name + Categorical Class + Categorical Class2 + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa, Iris-virginica} 73 0.695238 2 Iris-setosa Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * + +Cell frequencies + Class2 +Class {} {versicolor} Total Coverage +{Iris-setosa, Iris-virginica} 73 0 73 0.695238 +{Iris-versicolor} 0 32 32 0.304762 +Total 73 32 105 +Coverage 0.695238 0.304762 + +Cells 2 +Cell Id Class Class2 Frequency Coverage +C1 {Iris-setosa, Iris-virginica} {} 73 0.695238 +C4 {Iris-versicolor} {versicolor} 32 0.304762 + Total 105 1 + +---------------------------------------------- +Rank R03 +Variables + Type Name + Categorical Class + Categorical SPetalLength + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +SPetalLength +Group Frequency Coverage Size Value list +{1} 38 0.361905 1 1 +{5, 6} 34 0.32381 2 5 6 * +{4, 3} 33 0.314286 2 4 3 + +Cell frequencies + SPetalLength +Class {1} {5, 6} {4, 3} Total Coverage +{Iris-setosa} 38 0 0 38 0.361905 +{Iris-virginica} 0 32 3 35 0.333333 +{Iris-versicolor} 0 2 30 32 0.304762 +Total 38 34 33 105 +Coverage 0.361905 0.32381 0.314286 + +Cells 5 +Cell Id Class SPetalLength Frequency Coverage +C1 {Iris-setosa} {1} 38 0.361905 +C5 {Iris-virginica} {5, 6} 32 0.304762 +C9 {Iris-versicolor} {4, 3} 30 0.285714 +C8 {Iris-virginica} {4, 3} 3 0.0285714 +C6 {Iris-versicolor} {5, 6} 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R04 +Variables + Type Name + Categorical Class1 + Categorical SPetalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +SPetalLength +Group Frequency Coverage Size Value list +{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * +{1} 38 0.361905 1 1 + +Cell frequencies + SPetalLength +Class1 {5, 4, 3, ...} {1} Total Coverage +{} 67 0 67 0.638095 +{setosa} 0 38 38 0.361905 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 2 +Cell Id Class1 SPetalLength Frequency Coverage +C1 {} {5, 4, 3, ...} 67 0.638095 +C4 {setosa} {1} 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R05 +Variables + Type Name + Numerical PetalLength + Categorical SPetalLength + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;3.95] 8 0.0761905 +]3.95;4.95] 25 0.238095 +]4.95;5.95] 27 0.257143 +]5.95;+inf[ 7 0.0666667 +SPetalLength +Group Frequency Coverage Size Value list +{1} 38 0.361905 1 1 +{5} 27 0.257143 1 5 +{4} 25 0.238095 1 4 +{3} 8 0.0761905 1 3 +{6} 7 0.0666667 1 6 * + +Cell frequencies + SPetalLength +PetalLength {1} {5} {4} {3} {6} Total Coverage +]-inf;2.4] 38 0 0 0 0 38 0.361905 +]2.4;3.95] 0 0 0 8 0 8 0.0761905 +]3.95;4.95] 0 0 25 0 0 25 0.238095 +]4.95;5.95] 0 27 0 0 0 27 0.257143 +]5.95;+inf[ 0 0 0 0 7 7 0.0666667 +Total 38 27 25 8 7 105 +Coverage 0.361905 0.257143 0.238095 0.0761905 0.0666667 + +Cells 5 +Cell Id PetalLength SPetalLength Frequency Coverage +C1 ]-inf;2.4] {1} 38 0.361905 +C9 ]4.95;5.95] {5} 27 0.257143 +C13 ]3.95;4.95] {4} 25 0.238095 +C17 ]2.4;3.95] {3} 8 0.0761905 +C25 ]5.95;+inf[ {6} 7 0.0666667 + Total 105 1 + +---------------------------------------------- +Rank R06 +Variables + Type Name + Categorical Class2 + Categorical SPetalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +SPetalLength +Group Frequency Coverage Size Value list +{1, 5, 6} 72 0.685714 3 1 5 6 * +{4, 3} 33 0.314286 2 4 3 + +Cell frequencies + SPetalLength +Class2 {1, 5, 6} {4, 3} Total Coverage +{} 70 3 73 0.695238 +{versicolor} 2 30 32 0.304762 +Total 72 33 105 +Coverage 0.685714 0.314286 + +Cells 4 +Cell Id Class2 SPetalLength Frequency Coverage +C1 {} {1, 5, 6} 70 0.666667 +C4 {versicolor} {4, 3} 30 0.285714 +C3 {} {4, 3} 3 0.0285714 +C2 {versicolor} {1, 5, 6} 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R07 +Variables + Type Name + Categorical Class + Numerical PetalWidth + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.75] 33 0.314286 +]1.75;+inf[ 34 0.32381 + +Cell frequencies + PetalWidth +Class ]-inf;0.75] ]0.75;1.75] ]1.75;+inf[ Total Coverage +{Iris-setosa} 38 0 0 38 0.361905 +{Iris-virginica} 0 2 33 35 0.333333 +{Iris-versicolor} 0 31 1 32 0.304762 +Total 38 33 34 105 +Coverage 0.361905 0.314286 0.32381 + +Cells 5 +Cell Id Class PetalWidth Frequency Coverage +C1 {Iris-setosa} ]-inf;0.75] 38 0.361905 +C8 {Iris-virginica} ]1.75;+inf[ 33 0.314286 +C6 {Iris-versicolor} ]0.75;1.75] 31 0.295238 +C5 {Iris-virginica} ]0.75;1.75] 2 0.0190476 +C9 {Iris-versicolor} ]1.75;+inf[ 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R08 +Variables + Type Name + Categorical Class + Numerical PetalLength + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;4.85] 30 0.285714 +]4.85;+inf[ 37 0.352381 + +Cell frequencies + PetalLength +Class ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Total Coverage +{Iris-setosa} 38 0 0 38 0.361905 +{Iris-virginica} 0 1 34 35 0.333333 +{Iris-versicolor} 0 29 3 32 0.304762 +Total 38 30 37 105 +Coverage 0.361905 0.285714 0.352381 + +Cells 5 +Cell Id Class PetalLength Frequency Coverage +C1 {Iris-setosa} ]-inf;2.4] 38 0.361905 +C8 {Iris-virginica} ]4.85;+inf[ 34 0.32381 +C6 {Iris-versicolor} ]2.4;4.85] 29 0.27619 +C9 {Iris-versicolor} ]4.85;+inf[ 3 0.0285714 +C5 {Iris-virginica} ]2.4;4.85] 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R09 +Variables + Type Name + Categorical Class1 + Numerical LowerPetalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + LowerPetalLength +Class1 ]-inf;2.4] ]2.4;+inf[ Total Coverage +{} 0 67 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id Class1 LowerPetalLength Frequency Coverage +C3 {} ]2.4;+inf[ 67 0.638095 +C2 {setosa} ]-inf;2.4] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R10 +Variables + Type Name + Categorical Class1 + Numerical PetalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + PetalLength +Class1 ]-inf;2.4] ]2.4;+inf[ Total Coverage +{} 0 67 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id Class1 PetalLength Frequency Coverage +C3 {} ]2.4;+inf[ 67 0.638095 +C2 {setosa} ]-inf;2.4] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R11 +Variables + Type Name + Categorical Class1 + Numerical PetalWidth + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;+inf[ 67 0.638095 + +Cell frequencies + PetalWidth +Class1 ]-inf;0.75] ]0.75;+inf[ Total Coverage +{} 0 67 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id Class1 PetalWidth Frequency Coverage +C3 {} ]0.75;+inf[ 67 0.638095 +C2 {setosa} ]-inf;0.75] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R12 +Variables + Type Name + Numerical PetalWidth + Categorical SPetalLength + +Variables stats +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.65] 32 0.304762 +]1.65;+inf[ 35 0.333333 +SPetalLength +Group Frequency Coverage Size Value list +{1} 38 0.361905 1 1 +{5, 6} 34 0.32381 2 5 6 * +{4, 3} 33 0.314286 2 4 3 + +Cell frequencies + SPetalLength +PetalWidth {1} {5, 6} {4, 3} Total Coverage +]-inf;0.75] 38 0 0 38 0.361905 +]0.75;1.65] 0 3 29 32 0.304762 +]1.65;+inf[ 0 31 4 35 0.333333 +Total 38 34 33 105 +Coverage 0.361905 0.32381 0.314286 + +Cells 5 +Cell Id PetalWidth SPetalLength Frequency Coverage +C1 ]-inf;0.75] {1} 38 0.361905 +C6 ]1.65;+inf[ {5, 6} 31 0.295238 +C8 ]0.75;1.65] {4, 3} 29 0.27619 +C9 ]1.65;+inf[ {4, 3} 4 0.0380952 +C5 ]0.75;1.65] {5, 6} 3 0.0285714 + Total 105 1 + +---------------------------------------------- +Rank R13 +Variables + Type Name + Categorical Class + Numerical LowerPetalLength + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * +{Iris-setosa} 38 0.361905 1 Iris-setosa +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + LowerPetalLength +Class ]-inf;2.4] ]2.4;+inf[ Total Coverage +{Iris-virginica, Iris-versicolor} 0 67 67 0.638095 +{Iris-setosa} 38 0 38 0.361905 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id Class LowerPetalLength Frequency Coverage +C3 {Iris-virginica, Iris-versicolor} ]2.4;+inf[ 67 0.638095 +C2 {Iris-setosa} ]-inf;2.4] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R14 +Variables + Type Name + Numerical LowerPetalLength + Categorical SPetalLength + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +SPetalLength +Group Frequency Coverage Size Value list +{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * +{1} 38 0.361905 1 1 + +Cell frequencies + SPetalLength +LowerPetalLength {5, 4, 3, ...} {1} Total Coverage +]-inf;2.4] 0 38 38 0.361905 +]2.4;+inf[ 67 0 67 0.638095 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 2 +Cell Id LowerPetalLength SPetalLength Frequency Coverage +C2 ]2.4;+inf[ {5, 4, 3, ...} 67 0.638095 +C3 ]-inf;2.4] {1} 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R15 +Variables + Type Name + Numerical PetalLength + Numerical PetalWidth + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;4.75] 27 0.257143 +]4.75;+inf[ 40 0.380952 +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.65] 32 0.304762 +]1.65;+inf[ 35 0.333333 + +Cell frequencies + PetalWidth +PetalLength ]-inf;0.75] ]0.75;1.65] ]1.65;+inf[ Total Coverage +]-inf;2.4] 38 0 0 38 0.361905 +]2.4;4.75] 0 27 0 27 0.257143 +]4.75;+inf[ 0 5 35 40 0.380952 +Total 38 32 35 105 +Coverage 0.361905 0.304762 0.333333 + +Cells 4 +Cell Id PetalLength PetalWidth Frequency Coverage +C1 ]-inf;2.4] ]-inf;0.75] 38 0.361905 +C9 ]4.75;+inf[ ]1.65;+inf[ 35 0.333333 +C5 ]2.4;4.75] ]0.75;1.65] 27 0.257143 +C6 ]4.75;+inf[ ]0.75;1.65] 5 0.047619 + Total 105 1 + +---------------------------------------------- +Rank R16 +Variables + Type Name + Categorical Class + Numerical UpperPetalWidth + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa, Iris-versicolor} 70 0.666667 2 Iris-setosa Iris-versicolor * +{Iris-virginica} 35 0.333333 1 Iris-virginica +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.75] 71 0.67619 +]1.75;+inf[ 34 0.32381 + +Cell frequencies + UpperPetalWidth +Class ]-inf;1.75] ]1.75;+inf[ Total Coverage +{Iris-setosa, Iris-versicolor} 69 1 70 0.666667 +{Iris-virginica} 2 33 35 0.333333 +Total 71 34 105 +Coverage 0.67619 0.32381 + +Cells 4 +Cell Id Class UpperPetalWidth Frequency Coverage +C1 {Iris-setosa, Iris-versicolor} ]-inf;1.75] 69 0.657143 +C4 {Iris-virginica} ]1.75;+inf[ 33 0.314286 +C2 {Iris-virginica} ]-inf;1.75] 2 0.0190476 +C3 {Iris-setosa, Iris-versicolor} ]1.75;+inf[ 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R17 +Variables + Type Name + Numerical PetalWidth + Numerical UpperPetalWidth + +Variables stats +PetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;2.05] 20 0.190476 +]2.05;+inf[ 18 0.171429 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;2.05] 20 0.190476 +]2.05;+inf[ 18 0.171429 + +Cell frequencies + UpperPetalWidth +PetalWidth ]-inf;1.55] ]1.55;2.05] ]2.05;+inf[ Total Coverage +]-inf;1.55] 67 0 0 67 0.638095 +]1.55;2.05] 0 20 0 20 0.190476 +]2.05;+inf[ 0 0 18 18 0.171429 +Total 67 20 18 105 +Coverage 0.638095 0.190476 0.171429 + +Cells 3 +Cell Id PetalWidth UpperPetalWidth Frequency Coverage +C1 ]-inf;1.55] ]-inf;1.55] 67 0.638095 +C5 ]1.55;2.05] ]1.55;2.05] 20 0.190476 +C9 ]2.05;+inf[ ]2.05;+inf[ 18 0.171429 + Total 105 1 + +---------------------------------------------- +Rank R18 +Variables + Type Name + Numerical LowerPetalLength + Numerical PetalLength + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;1.45] 17 0.161905 +]1.45;2.4] 21 0.2 +]2.4;+inf[ 67 0.638095 +PetalLength +Interval Frequency Coverage +]-inf;1.45] 17 0.161905 +]1.45;2.4] 21 0.2 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + PetalLength +LowerPetalLength ]-inf;1.45] ]1.45;2.4] ]2.4;+inf[ Total Coverage +]-inf;1.45] 17 0 0 17 0.161905 +]1.45;2.4] 0 21 0 21 0.2 +]2.4;+inf[ 0 0 67 67 0.638095 +Total 17 21 67 105 +Coverage 0.161905 0.2 0.638095 + +Cells 3 +Cell Id LowerPetalLength PetalLength Frequency Coverage +C9 ]2.4;+inf[ ]2.4;+inf[ 67 0.638095 +C5 ]1.45;2.4] ]1.45;2.4] 21 0.2 +C1 ]-inf;1.45] ]-inf;1.45] 17 0.161905 + Total 105 1 + +---------------------------------------------- +Rank R19 +Variables + Type Name + Categorical Class2 + Numerical PetalWidth + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.75] 33 0.314286 +]1.75;+inf[ 34 0.32381 + +Cell frequencies + PetalWidth +Class2 ]-inf;0.75] ]0.75;1.75] ]1.75;+inf[ Total Coverage +{} 38 2 33 73 0.695238 +{versicolor} 0 31 1 32 0.304762 +Total 38 33 34 105 +Coverage 0.361905 0.314286 0.32381 + +Cells 5 +Cell Id Class2 PetalWidth Frequency Coverage +C1 {} ]-inf;0.75] 38 0.361905 +C5 {} ]1.75;+inf[ 33 0.314286 +C4 {versicolor} ]0.75;1.75] 31 0.295238 +C3 {} ]0.75;1.75] 2 0.0190476 +C6 {versicolor} ]1.75;+inf[ 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R20 +Variables + Type Name + Categorical SPetalLength + Numerical SepalLength + +Variables stats +SPetalLength +Group Frequency Coverage Size Value list +{5, 4} 52 0.495238 2 5 4 +{1, 3} 46 0.438095 2 1 3 +{6} 7 0.0666667 1 6 * +SepalLength +Interval Frequency Coverage +]-inf;5.35] 34 0.32381 +]5.35;5.85] 22 0.209524 +]5.85;7.15] 42 0.4 +]7.15;+inf[ 7 0.0666667 + +Cell frequencies + SepalLength +SPetalLength ]-inf;5.35] ]5.35;5.85] ]5.85;7.15] ]7.15;+inf[ Total Coverage +{5, 4} 0 10 42 0 52 0.495238 +{1, 3} 34 12 0 0 46 0.438095 +{6} 0 0 0 7 7 0.0666667 +Total 34 22 42 7 105 +Coverage 0.32381 0.209524 0.4 0.0666667 + +Cells 5 +Cell Id SPetalLength SepalLength Frequency Coverage +C7 {5, 4} ]5.85;7.15] 42 0.4 +C2 {1, 3} ]-inf;5.35] 34 0.32381 +C5 {1, 3} ]5.35;5.85] 12 0.114286 +C4 {5, 4} ]5.35;5.85] 10 0.0952381 +C12 {6} ]7.15;+inf[ 7 0.0666667 + Total 105 1 + +---------------------------------------------- +Rank R21 +Variables + Type Name + Categorical Class2 + Numerical PetalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;4.85] 30 0.285714 +]4.85;+inf[ 37 0.352381 + +Cell frequencies + PetalLength +Class2 ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Total Coverage +{} 38 1 34 73 0.695238 +{versicolor} 0 29 3 32 0.304762 +Total 38 30 37 105 +Coverage 0.361905 0.285714 0.352381 + +Cells 5 +Cell Id Class2 PetalLength Frequency Coverage +C1 {} ]-inf;2.4] 38 0.361905 +C5 {} ]4.85;+inf[ 34 0.32381 +C4 {versicolor} ]2.4;4.85] 29 0.27619 +C6 {versicolor} ]4.85;+inf[ 3 0.0285714 +C3 {} ]2.4;4.85] 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R22 +Variables + Type Name + Numerical LowerPetalLength + Numerical PetalWidth + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;+inf[ 67 0.638095 + +Cell frequencies + PetalWidth +LowerPetalLength ]-inf;0.75] ]0.75;+inf[ Total Coverage +]-inf;2.4] 38 0 38 0.361905 +]2.4;+inf[ 0 67 67 0.638095 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id LowerPetalLength PetalWidth Frequency Coverage +C4 ]2.4;+inf[ ]0.75;+inf[ 67 0.638095 +C1 ]-inf;2.4] ]-inf;0.75] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R23 +Variables + Type Name + Categorical Class + Numerical SepalLength + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;6.15] 28 0.266667 +]6.15;+inf[ 38 0.361905 + +Cell frequencies + SepalLength +Class ]-inf;5.45] ]5.45;6.15] ]6.15;+inf[ Total Coverage +{Iris-setosa} 34 4 0 38 0.361905 +{Iris-virginica} 0 5 30 35 0.333333 +{Iris-versicolor} 5 19 8 32 0.304762 +Total 39 28 38 105 +Coverage 0.371429 0.266667 0.361905 + +Cells 7 +Cell Id Class SepalLength Frequency Coverage +C1 {Iris-setosa} ]-inf;5.45] 34 0.32381 +C8 {Iris-virginica} ]6.15;+inf[ 30 0.285714 +C6 {Iris-versicolor} ]5.45;6.15] 19 0.180952 +C9 {Iris-versicolor} ]6.15;+inf[ 8 0.0761905 +C5 {Iris-virginica} ]5.45;6.15] 5 0.047619 +C3 {Iris-versicolor} ]-inf;5.45] 5 0.047619 +C4 {Iris-setosa} ]5.45;6.15] 4 0.0380952 + Total 105 1 + +---------------------------------------------- +Rank R24 +Variables + Type Name + Categorical Class1 + Categorical Class2 + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * + +Cell frequencies + Class2 +Class1 {} {versicolor} Total Coverage +{} 35 32 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 73 32 105 +Coverage 0.695238 0.304762 + +Cells 3 +Cell Id Class1 Class2 Frequency Coverage +C2 {setosa} {} 38 0.361905 +C1 {} {} 35 0.333333 +C3 {} {versicolor} 32 0.304762 + Total 105 1 + +---------------------------------------------- +Rank R25 +Variables + Type Name + Categorical Class1 + Numerical SepalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;+inf[ 66 0.628571 + +Cell frequencies + SepalLength +Class1 ]-inf;5.45] ]5.45;+inf[ Total Coverage +{} 5 62 67 0.638095 +{setosa} 34 4 38 0.361905 +Total 39 66 105 +Coverage 0.371429 0.628571 + +Cells 4 +Cell Id Class1 SepalLength Frequency Coverage +C3 {} ]5.45;+inf[ 62 0.590476 +C2 {setosa} ]-inf;5.45] 34 0.32381 +C1 {} ]-inf;5.45] 5 0.047619 +C4 {setosa} ]5.45;+inf[ 4 0.0380952 + Total 105 1 + +---------------------------------------------- +Rank R26 +Variables + Type Name + Categorical SPetalLength + Numerical UpperPetalWidth + +Variables stats +SPetalLength +Group Frequency Coverage Size Value list +{1, 3} 46 0.438095 2 1 3 +{5, 6} 34 0.32381 2 5 6 * +{4} 25 0.238095 1 4 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +SPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage +{1, 3} 46 0 46 0.438095 +{5, 6} 2 32 34 0.32381 +{4} 19 6 25 0.238095 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 5 +Cell Id SPetalLength UpperPetalWidth Frequency Coverage +C1 {1, 3} ]-inf;1.55] 46 0.438095 +C5 {5, 6} ]1.55;+inf[ 32 0.304762 +C3 {4} ]-inf;1.55] 19 0.180952 +C6 {4} ]1.55;+inf[ 6 0.0571429 +C2 {5, 6} ]-inf;1.55] 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R27 +Variables + Type Name + Numerical PetalLength + Numerical SepalLength + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;3.55] 42 0.4 +]3.55;4.6] 19 0.180952 +]4.6;5.95] 37 0.352381 +]5.95;+inf[ 7 0.0666667 +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;5.85] 17 0.161905 +]5.85;7.15] 42 0.4 +]7.15;+inf[ 7 0.0666667 + +Cell frequencies + SepalLength +PetalLength ]-inf;5.45] ]5.45;5.85] ]5.85;7.15] ]7.15;+inf[ Total Coverage +]-inf;3.55] 37 5 0 0 42 0.4 +]3.55;4.6] 2 10 7 0 19 0.180952 +]4.6;5.95] 0 2 35 0 37 0.352381 +]5.95;+inf[ 0 0 0 7 7 0.0666667 +Total 39 17 42 7 105 +Coverage 0.371429 0.161905 0.4 0.0666667 + +Cells 8 +Cell Id PetalLength SepalLength Frequency Coverage +C1 ]-inf;3.55] ]-inf;5.45] 37 0.352381 +C11 ]4.6;5.95] ]5.85;7.15] 35 0.333333 +C6 ]3.55;4.6] ]5.45;5.85] 10 0.0952381 +C10 ]3.55;4.6] ]5.85;7.15] 7 0.0666667 +C16 ]5.95;+inf[ ]7.15;+inf[ 7 0.0666667 +C5 ]-inf;3.55] ]5.45;5.85] 5 0.047619 +C2 ]3.55;4.6] ]-inf;5.45] 2 0.0190476 +C7 ]4.6;5.95] ]5.45;5.85] 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R28 +Variables + Type Name + Numerical PetalLength + Numerical UpperPetalWidth + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;4.75] 65 0.619048 +]4.75;+inf[ 40 0.380952 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.65] 70 0.666667 +]1.65;+inf[ 35 0.333333 + +Cell frequencies + UpperPetalWidth +PetalLength ]-inf;1.65] ]1.65;+inf[ Total Coverage +]-inf;4.75] 65 0 65 0.619048 +]4.75;+inf[ 5 35 40 0.380952 +Total 70 35 105 +Coverage 0.666667 0.333333 + +Cells 3 +Cell Id PetalLength UpperPetalWidth Frequency Coverage +C1 ]-inf;4.75] ]-inf;1.65] 65 0.619048 +C4 ]4.75;+inf[ ]1.65;+inf[ 35 0.333333 +C2 ]4.75;+inf[ ]-inf;1.65] 5 0.047619 + Total 105 1 + +---------------------------------------------- +Rank R29 +Variables + Type Name + Numerical PetalWidth + Numerical SepalLength + +Variables stats +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.35] 18 0.171429 +]1.35;+inf[ 49 0.466667 +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;5.85] 17 0.161905 +]5.85;+inf[ 49 0.466667 + +Cell frequencies + SepalLength +PetalWidth ]-inf;5.45] ]5.45;5.85] ]5.85;+inf[ Total Coverage +]-inf;0.75] 34 4 0 38 0.361905 +]0.75;1.35] 3 10 5 18 0.171429 +]1.35;+inf[ 2 3 44 49 0.466667 +Total 39 17 49 105 +Coverage 0.371429 0.161905 0.466667 + +Cells 8 +Cell Id PetalWidth SepalLength Frequency Coverage +C9 ]1.35;+inf[ ]5.85;+inf[ 44 0.419048 +C1 ]-inf;0.75] ]-inf;5.45] 34 0.32381 +C5 ]0.75;1.35] ]5.45;5.85] 10 0.0952381 +C8 ]0.75;1.35] ]5.85;+inf[ 5 0.047619 +C4 ]-inf;0.75] ]5.45;5.85] 4 0.0380952 +C2 ]0.75;1.35] ]-inf;5.45] 3 0.0285714 +C6 ]1.35;+inf[ ]5.45;5.85] 3 0.0285714 +C3 ]1.35;+inf[ ]-inf;5.45] 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R30 +Variables + Type Name + Numerical LowerPetalLength + Numerical SepalLength + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;+inf[ 66 0.628571 + +Cell frequencies + SepalLength +LowerPetalLength ]-inf;5.45] ]5.45;+inf[ Total Coverage +]-inf;2.4] 34 4 38 0.361905 +]2.4;+inf[ 5 62 67 0.638095 +Total 39 66 105 +Coverage 0.371429 0.628571 + +Cells 4 +Cell Id LowerPetalLength SepalLength Frequency Coverage +C4 ]2.4;+inf[ ]5.45;+inf[ 62 0.590476 +C1 ]-inf;2.4] ]-inf;5.45] 34 0.32381 +C2 ]2.4;+inf[ ]-inf;5.45] 5 0.047619 +C3 ]-inf;2.4] ]5.45;+inf[ 4 0.0380952 + Total 105 1 + +---------------------------------------------- +Rank R31 +Variables + Type Name + Categorical Class1 + Numerical UpperPetalWidth + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +Class1 ]-inf;1.55] ]1.55;+inf[ Total Coverage +{} 29 38 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 3 +Cell Id Class1 UpperPetalWidth Frequency Coverage +C3 {} ]1.55;+inf[ 38 0.361905 +C2 {setosa} ]-inf;1.55] 38 0.361905 +C1 {} ]-inf;1.55] 29 0.27619 + Total 105 1 + +---------------------------------------------- +Rank R32 +Variables + Type Name + Numerical SepalLength + Numerical UpperPetalWidth + +Variables stats +SepalLength +Interval Frequency Coverage +]-inf;5.85] 56 0.533333 +]5.85;+inf[ 49 0.466667 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +SepalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage +]-inf;5.85] 54 2 56 0.533333 +]5.85;+inf[ 13 36 49 0.466667 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 4 +Cell Id SepalLength UpperPetalWidth Frequency Coverage +C1 ]-inf;5.85] ]-inf;1.55] 54 0.514286 +C4 ]5.85;+inf[ ]1.55;+inf[ 36 0.342857 +C2 ]5.85;+inf[ ]-inf;1.55] 13 0.12381 +C3 ]-inf;5.85] ]1.55;+inf[ 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R33 +Variables + Type Name + Categorical Class1 + Numerical SepalWidth + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +SepalWidth +Interval Frequency Coverage +]-inf;2.85] 30 0.285714 +]2.85;3.35] 49 0.466667 +]3.35;+inf[ 26 0.247619 + +Cell frequencies + SepalWidth +Class1 ]-inf;2.85] ]2.85;3.35] ]3.35;+inf[ Total Coverage +{} 30 32 5 67 0.638095 +{setosa} 0 17 21 38 0.361905 +Total 30 49 26 105 +Coverage 0.285714 0.466667 0.247619 + +Cells 5 +Cell Id Class1 SepalWidth Frequency Coverage +C3 {} ]2.85;3.35] 32 0.304762 +C1 {} ]-inf;2.85] 30 0.285714 +C6 {setosa} ]3.35;+inf[ 21 0.2 +C4 {setosa} ]2.85;3.35] 17 0.161905 +C5 {} ]3.35;+inf[ 5 0.047619 + Total 105 1 + +---------------------------------------------- +Rank R34 +Variables + Type Name + Categorical Class2 + Numerical LowerPetalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + LowerPetalLength +Class2 ]-inf;2.4] ]2.4;+inf[ Total Coverage +{} 38 35 73 0.695238 +{versicolor} 0 32 32 0.304762 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 3 +Cell Id Class2 LowerPetalLength Frequency Coverage +C1 {} ]-inf;2.4] 38 0.361905 +C3 {} ]2.4;+inf[ 35 0.333333 +C4 {versicolor} ]2.4;+inf[ 32 0.304762 + Total 105 1 + +---------------------------------------------- +Rank R35 +Variables + Type Name + Categorical Class + Numerical SepalWidth + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +SepalWidth +Interval Frequency Coverage +]-inf;2.95] 36 0.342857 +]2.95;+inf[ 69 0.657143 + +Cell frequencies + SepalWidth +Class ]-inf;2.95] ]2.95;+inf[ Total Coverage +{Iris-setosa} 1 37 38 0.361905 +{Iris-virginica} 13 22 35 0.333333 +{Iris-versicolor} 22 10 32 0.304762 +Total 36 69 105 +Coverage 0.342857 0.657143 + +Cells 6 +Cell Id Class SepalWidth Frequency Coverage +C4 {Iris-setosa} ]2.95;+inf[ 37 0.352381 +C5 {Iris-virginica} ]2.95;+inf[ 22 0.209524 +C3 {Iris-versicolor} ]-inf;2.95] 22 0.209524 +C2 {Iris-virginica} ]-inf;2.95] 13 0.12381 +C6 {Iris-versicolor} ]2.95;+inf[ 10 0.0952381 +C1 {Iris-setosa} ]-inf;2.95] 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R36 +Variables + Type Name + Categorical SPetalLength + Numerical SepalWidth + +Variables stats +SPetalLength +Group Frequency Coverage Size Value list +{4} 40 0.380952 3 4 * +{1} 38 0.361905 1 1 +{5} 27 0.257143 1 5 +SepalWidth +Interval Frequency Coverage +]-inf;2.95] 36 0.342857 +]2.95;3.25] 41 0.390476 +]3.25;+inf[ 28 0.266667 + +Cell frequencies + SepalWidth +SPetalLength ]-inf;2.95] ]2.95;3.25] ]3.25;+inf[ Total Coverage +{4} 26 10 4 40 0.380952 +{1} 1 15 22 38 0.361905 +{5} 9 16 2 27 0.257143 +Total 36 41 28 105 +Coverage 0.342857 0.390476 0.266667 + +Cells 9 +Cell Id SPetalLength SepalWidth Frequency Coverage +C1 {4} ]-inf;2.95] 26 0.247619 +C8 {1} ]3.25;+inf[ 22 0.209524 +C6 {5} ]2.95;3.25] 16 0.152381 +C5 {1} ]2.95;3.25] 15 0.142857 +C4 {4} ]2.95;3.25] 10 0.0952381 +C3 {5} ]-inf;2.95] 9 0.0857143 +C7 {4} ]3.25;+inf[ 4 0.0380952 +C9 {5} ]3.25;+inf[ 2 0.0190476 +C2 {1} ]-inf;2.95] 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R37 +Variables + Type Name + Numerical LowerPetalLength + Numerical UpperPetalWidth + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +LowerPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage +]-inf;2.4] 38 0 38 0.361905 +]2.4;+inf[ 29 38 67 0.638095 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 3 +Cell Id LowerPetalLength UpperPetalWidth Frequency Coverage +C1 ]-inf;2.4] ]-inf;1.55] 38 0.361905 +C4 ]2.4;+inf[ ]1.55;+inf[ 38 0.361905 +C2 ]2.4;+inf[ ]-inf;1.55] 29 0.27619 + Total 105 1 + +---------------------------------------------- +Rank R38 +Variables + Type Name + Numerical PetalWidth + Numerical SepalWidth + +Variables stats +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.45] 24 0.228571 +]1.45;+inf[ 43 0.409524 +SepalWidth +Interval Frequency Coverage +]-inf;2.95] 36 0.342857 +]2.95;+inf[ 69 0.657143 + +Cell frequencies + SepalWidth +PetalWidth ]-inf;2.95] ]2.95;+inf[ Total Coverage +]-inf;0.75] 1 37 38 0.361905 +]0.75;1.45] 21 3 24 0.228571 +]1.45;+inf[ 14 29 43 0.409524 +Total 36 69 105 +Coverage 0.342857 0.657143 + +Cells 6 +Cell Id PetalWidth SepalWidth Frequency Coverage +C4 ]-inf;0.75] ]2.95;+inf[ 37 0.352381 +C6 ]1.45;+inf[ ]2.95;+inf[ 29 0.27619 +C2 ]0.75;1.45] ]-inf;2.95] 21 0.2 +C3 ]1.45;+inf[ ]-inf;2.95] 14 0.133333 +C5 ]0.75;1.45] ]2.95;+inf[ 3 0.0285714 +C1 ]-inf;0.75] ]-inf;2.95] 1 0.00952381 + Total 105 1 diff --git a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls similarity index 51% rename from test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls index 5df7a380f..64abcac16 100644 --- a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls @@ -1,246 +1,205 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Unsupervised analysis - -Evaluated variables 12 -Native variables 5 -Constructed variables 7 - -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 100 -Discretization EqualWidth -Value grouping BasicGrouping - - -Categorical variables statistics - -Rank Name Values Mode Mode coverage Constr. cost Derivation rule -R01 Class 3 Iris-setosa 0.361905 3.17805 -R02 Class1 2 0.638095 3.17805 IfC(EQc(Class, "Iris-setosa"), "setosa", "") -R03 Class2 2 0.695238 3.17805 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") -R09 SPetalLength 5 1 0.361905 3.17805 AsCategorical(Floor(PetalLength)) - - -Numerical variables statistics - -Rank Name Values Min Max Mean Std dev Missing number Constr. cost Derivation rule -R04 Dummy1 1 0 0 0 0 0 3.17805 Copy(0) -R05 Dummy2 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 3.17805 Random() -R06 LowerPetalLength 10 1 3 2.446666667 0.7433600251 0 3.17805 If(LE(PetalLength, 3), PetalLength, 3) -R07 PetalLength 36 1 6.9 3.686666667 1.80132579 0 3.17805 -R08 PetalWidth 21 0.1 2.5 1.175238095 0.7880996979 0 3.17805 -R10 SepalLength 31 4.3 7.7 5.827619048 0.8375127846 0 3.17805 -R11 SepalWidth 23 2 4.4 3.081904762 0.4284592446 0 3.17805 -R12 UpperPetalWidth 11 1.5 2.5 1.692380952 0.2962287527 0 3.17805 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R01 -Variable Categorical Class - -Variable stats -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * - - -Values -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-virginica 35 0.333333 -Iris-versicolor 32 0.304762 - ----------------------------------------------- -Rank R02 -Variable Categorical Class1 - -Variable stats -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * - - -Values -Value Frequency Coverage - 67 0.638095 -setosa 38 0.361905 - ----------------------------------------------- -Rank R03 -Variable Categorical Class2 - -Variable stats -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * - - -Values -Value Frequency Coverage - 73 0.695238 -versicolor 32 0.304762 - ----------------------------------------------- -Rank R05 -Variable Numerical Dummy2 - -Variable stats -Interval Frequency Coverage -]-inf;0.102] 7 0.0666667 -]0.102;0.206] 9 0.0857143 -]0.206;0.296] 8 0.0761905 -]0.296;0.403] 14 0.133333 -]0.403;0.4979] 13 0.12381 -]0.4979;0.5879] 14 0.133333 -]0.5879;0.698] 8 0.0761905 -]0.698;0.8] 13 0.12381 -]0.8;0.894] 10 0.0952381 -]0.894;+inf[ 9 0.0857143 - - ----------------------------------------------- -Rank R06 -Variable Numerical LowerPetalLength - -Variable stats -Interval Frequency Coverage -]-inf;1.25] 4 0.0380952 -]1.25;1.45] 13 0.12381 -]1.45;1.65] 15 0.142857 -]1.65;1.8] 4 0.0380952 -]1.8;2.4] 2 0.0190476 -]2.4;+inf[ 67 0.638095 - - ----------------------------------------------- -Rank R07 -Variable Numerical PetalLength - -Variable stats -Interval Frequency Coverage -]-inf;1.55] 27 0.257143 -]1.55;2.4] 11 0.104762 -]2.4;3.4] 2 0.0190476 -]3.4;3.95] 6 0.0571429 -]3.95;4.6] 15 0.142857 -]4.6;5.15] 18 0.171429 -]5.15;5.75] 15 0.142857 -]5.75;6.4] 8 0.0761905 -]6.4;+inf[ 3 0.0285714 - - ----------------------------------------------- -Rank R08 -Variable Numerical PetalWidth - -Variable stats -Interval Frequency Coverage -]-inf;0.35] 31 0.295238 -]0.35;0.75] 7 0.0666667 -]0.75;1.05] 5 0.047619 -]1.05;1.35] 13 0.12381 -]1.35;1.55] 11 0.104762 -]1.55;1.75] 4 0.0380952 -]1.75;2.05] 16 0.152381 -]2.05;2.25] 7 0.0666667 -]2.25;+inf[ 11 0.104762 - - ----------------------------------------------- -Rank R09 -Variable Categorical SPetalLength - -Variable stats -Group Frequency Coverage Size Value list -{1} 38 0.361905 1 1 -{5} 27 0.257143 1 5 -{4} 25 0.238095 1 4 -{3} 8 0.0761905 1 3 -{6} 7 0.0666667 1 6 * - - -Values -Value Frequency Coverage -1 38 0.361905 -5 27 0.257143 -4 25 0.238095 -3 8 0.0761905 -6 7 0.0666667 - ----------------------------------------------- -Rank R10 -Variable Numerical SepalLength - -Variable stats -Interval Frequency Coverage -]-inf;4.65] 7 0.0666667 -]4.65;4.95] 11 0.104762 -]4.95;5.35] 16 0.152381 -]5.35;5.65] 13 0.12381 -]5.65;6.05] 16 0.152381 -]6.05;6.35] 12 0.114286 -]6.35;6.65] 10 0.0952381 -]6.65;7.05] 12 0.114286 -]7.05;7.3] 3 0.0285714 -]7.3;+inf[ 5 0.047619 - - ----------------------------------------------- -Rank R11 -Variable Numerical SepalWidth - -Variable stats -Interval Frequency Coverage -]-inf;2.25] 2 0.0190476 -]2.25;2.45] 3 0.0285714 -]2.45;2.75] 14 0.133333 -]2.75;2.95] 17 0.161905 -]2.95;3.25] 41 0.390476 -]3.25;3.45] 12 0.114286 -]3.45;3.65] 5 0.047619 -]3.65;3.95] 7 0.0666667 -]3.95;4.15] 2 0.0190476 -]4.15;+inf[ 2 0.0190476 - - ----------------------------------------------- -Rank R12 -Variable Numerical UpperPetalWidth - -Variable stats -Interval Frequency Coverage -]-inf;1.65] 70 0.666667 -]1.65;1.75] 1 0.00952381 -]1.75;1.85] 9 0.0857143 -]1.85;1.95] 2 0.0190476 -]1.95;2.05] 5 0.047619 -]2.05;2.15] 4 0.0380952 -]2.15;2.25] 3 0.0285714 -]2.25;2.35] 8 0.0761905 -]2.35;2.45] 2 0.0190476 -]2.45;+inf[ 1 0.00952381 - +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 4 + Numerical 8 + Total 12 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Unsupervised analysis + +Evaluated variables 12 +Native variables 5 +Constructed variables 7 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 100 +Discretization MODL +Value grouping MODL + + +Categorical variables statistics + +Rank Name Values Mode Mode coverage Missing number Sparse missing number Constr. cost Derivation rule +R01 Class 3 Iris-setosa 0.361905 0 0 3.17805 +R02 Class1 2 0.638095 67 0 3.17805 IfC(EQc(Class, "Iris-setosa"), "setosa", "") +R03 Class2 2 0.695238 73 0 3.17805 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") +R09 SPetalLength 5 1 0.361905 0 0 3.17805 AsCategorical(Floor(PetalLength)) + + +Numerical variables statistics + +Rank Name Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Derivation rule +R04 Dummy1 1 0 0 0 0 0 0 3.17805 Copy(0) +R05 Dummy2 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0 3.17805 Random() +R06 LowerPetalLength 10 1 3 2.446666667 0.7433600251 0 0 3.17805 If(LE(PetalLength, 3), PetalLength, 3) +R07 PetalLength 36 1 6.9 3.686666667 1.80132579 0 0 3.17805 +R08 PetalWidth 21 0.1 2.5 1.175238095 0.7880996979 0 0 3.17805 +R10 SepalLength 31 4.3 7.7 5.827619048 0.8375127846 0 0 3.17805 +R11 SepalWidth 23 2 4.4 3.081904762 0.4284592446 0 0 3.17805 +R12 UpperPetalWidth 11 1.5 2.5 1.692380952 0.2962287527 0 0 3.17805 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R01 +Variable Categorical Class + +Variable stats +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * + + +Values +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-virginica 35 0.333333 +Iris-versicolor 32 0.304762 + +---------------------------------------------- +Rank R02 +Variable Categorical Class1 + +Variable stats +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * + + +Values +Value Frequency Coverage + 67 0.638095 +setosa 38 0.361905 + +---------------------------------------------- +Rank R03 +Variable Categorical Class2 + +Variable stats +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * + + +Values +Value Frequency Coverage + 73 0.695238 +versicolor 32 0.304762 + +---------------------------------------------- +Rank R05 +Variable Numerical Dummy2 + +Variable stats +Interval Frequency Coverage +]-inf;+inf[ 105 1 + + +---------------------------------------------- +Rank R06 +Variable Numerical LowerPetalLength + +Variable stats +Interval Frequency Coverage +]-inf;1.25] 4 0.0380952 +]1.25;1.75] 32 0.304762 +]1.75;2.95] 2 0.0190476 +]2.95;+inf[ 67 0.638095 + + +---------------------------------------------- +Rank R07 +Variable Numerical PetalLength + +Variable stats +Interval Frequency Coverage +]-inf;1.25] 4 0.0380952 +]1.25;1.75] 32 0.304762 +]1.75;3.85] 7 0.0666667 +]3.85;6.15] 59 0.561905 +]6.15;+inf[ 3 0.0285714 + + +---------------------------------------------- +Rank R08 +Variable Numerical PetalWidth + +Variable stats +Interval Frequency Coverage +]-inf;0.15] 6 0.0571429 +]0.15;0.25] 20 0.190476 +]0.25;0.45] 11 0.104762 +]0.45;0.85] 1 0.00952381 +]0.85;+inf[ 67 0.638095 + + +---------------------------------------------- +Rank R09 +Variable Categorical SPetalLength + +Variable stats +Group Frequency Coverage Size Value list +{1} 38 0.361905 1 1 +{5} 27 0.257143 1 5 +{4} 25 0.238095 1 4 +{3} 8 0.0761905 1 3 +{6} 7 0.0666667 1 6 * + + +Values +Value Frequency Coverage +1 38 0.361905 +5 27 0.257143 +4 25 0.238095 +3 8 0.0761905 +6 7 0.0666667 + +---------------------------------------------- +Rank R10 +Variable Numerical SepalLength + +Variable stats +Interval Frequency Coverage +]-inf;7] 97 0.92381 +]7;+inf[ 8 0.0761905 + + +---------------------------------------------- +Rank R11 +Variable Numerical SepalWidth + +Variable stats +Interval Frequency Coverage +]-inf;2.45] 5 0.047619 +]2.45;3.25] 72 0.685714 +]3.25;+inf[ 28 0.266667 + + +---------------------------------------------- +Rank R12 +Variable Numerical UpperPetalWidth + +Variable stats +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + diff --git a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/AllReports.khj b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/AnalysisResults.khj similarity index 84% rename from test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/AllReports.khj rename to test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/AnalysisResults.khj index bd938d590..bfc1926d9 100644 --- a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/AllReports.khj +++ b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/AnalysisResults.khj @@ -1,2518 +1,2716 @@ -{ - "tool": "Khiops", - "version": "10.1", - "shortDescription": "", - "preparationReport": { - "reportType": "Preparation", - "summary": { - "dictionary": "Iris", - "variables": { - "types": [ - "Categorical", - "Numerical" - ], - "numbers": [ - 4, - 8 - ] - }, - "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", - "samplePercentage": 70, - "samplingMode": "Include sample", - "selectionVariable": "", - "selectionValue": "", - "instances": 105, - "learningTask": "Unsupervised analysis", - "evaluatedVariables": 12, - "nativeVariables": 5, - "constructedVariables": 7, - "featureEngineering": { - "maxNumberOfConstructedVariables": 0, - "maxNumberOfTrees": 0, - "maxNumberOfVariablePairs": 100 - }, - "discretization": "EqualWidth", - "valueGrouping": "BasicGrouping" - }, - "variablesStatistics": [ - { - "rank": "R01", - "name": "Class", - "type": "Categorical", - "values": 3, - "mode": "Iris-setosa", - "modeFrequency": 38, - "constructionCost": 3.17805 - }, - { - "rank": "R02", - "name": "Class1", - "type": "Categorical", - "values": 2, - "mode": "", - "modeFrequency": 67, - "constructionCost": 3.17805, - "derivationRule": "IfC(EQc(Class, \"Iris-setosa\"), \"setosa\", \"\")" - }, - { - "rank": "R03", - "name": "Class2", - "type": "Categorical", - "values": 2, - "mode": "", - "modeFrequency": 73, - "constructionCost": 3.17805, - "derivationRule": "IfC(EQc(Class, \"Iris-versicolor\"), \"versicolor\", \"\")" - }, - { - "rank": "R04", - "name": "Dummy1", - "type": "Numerical", - "values": 1, - "min": 0, - "max": 0, - "mean": 0, - "stdDev": 0, - "missingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "Copy(0)" - }, - { - "rank": "R05", - "name": "Dummy2", - "type": "Numerical", - "values": 105, - "min": 0.005121241265, - "max": 0.9859650261, - "mean": 0.5173966838, - "stdDev": 0.2650019122, - "missingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "Random()" - }, - { - "rank": "R06", - "name": "LowerPetalLength", - "type": "Numerical", - "values": 10, - "min": 1, - "max": 3, - "mean": 2.446666667, - "stdDev": 0.7433600251, - "missingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "If(LE(PetalLength, 3), PetalLength, 3)" - }, - { - "rank": "R07", - "name": "PetalLength", - "type": "Numerical", - "values": 36, - "min": 1, - "max": 6.9, - "mean": 3.686666667, - "stdDev": 1.80132579, - "missingNumber": 0, - "constructionCost": 3.17805 - }, - { - "rank": "R08", - "name": "PetalWidth", - "type": "Numerical", - "values": 21, - "min": 0.1, - "max": 2.5, - "mean": 1.175238095, - "stdDev": 0.7880996979, - "missingNumber": 0, - "constructionCost": 3.17805 - }, - { - "rank": "R09", - "name": "SPetalLength", - "type": "Categorical", - "values": 5, - "mode": "1", - "modeFrequency": 38, - "constructionCost": 3.17805, - "derivationRule": "AsCategorical(Floor(PetalLength))" - }, - { - "rank": "R10", - "name": "SepalLength", - "type": "Numerical", - "values": 31, - "min": 4.3, - "max": 7.7, - "mean": 5.827619048, - "stdDev": 0.8375127846, - "missingNumber": 0, - "constructionCost": 3.17805 - }, - { - "rank": "R11", - "name": "SepalWidth", - "type": "Numerical", - "values": 23, - "min": 2, - "max": 4.4, - "mean": 3.081904762, - "stdDev": 0.4284592446, - "missingNumber": 0, - "constructionCost": 3.17805 - }, - { - "rank": "R12", - "name": "UpperPetalWidth", - "type": "Numerical", - "values": 11, - "min": 1.5, - "max": 2.5, - "mean": 1.692380952, - "stdDev": 0.2962287527, - "missingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "If(GE(PetalWidth, 1.5), PetalWidth, 1.5)" - } - ], - "variablesDetailedStatistics": { - "R01": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "Class", - "type": "Categorical", - "partitionType": "Value groups", - "partition": [ - ["Iris-setosa"], - ["Iris-virginica"], - ["Iris-versicolor"] - ], - "defaultGroupIndex": 2 - } - ], - "frequencies": [38,35,32] - }, - "inputValues": { - "values": ["Iris-setosa","Iris-virginica","Iris-versicolor"], - "frequencies": [38,35,32] - } - }, - "R02": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "Class1", - "type": "Categorical", - "partitionType": "Value groups", - "partition": [ - [""], - ["setosa"] - ], - "defaultGroupIndex": 1 - } - ], - "frequencies": [67,38] - }, - "inputValues": { - "values": ["","setosa"], - "frequencies": [67,38] - } - }, - "R03": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "Class2", - "type": "Categorical", - "partitionType": "Value groups", - "partition": [ - [""], - ["versicolor"] - ], - "defaultGroupIndex": 1 - } - ], - "frequencies": [73,32] - }, - "inputValues": { - "values": ["","versicolor"], - "frequencies": [73,32] - } - }, - "R05": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "Dummy2", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [0.005121241265,0.102], - [0.102,0.206], - [0.206,0.296], - [0.296,0.403], - [0.403,0.4979], - [0.4979,0.5879], - [0.5879,0.698], - [0.698,0.8], - [0.8,0.894], - [0.894,0.9859650261] - ] - } - ], - "frequencies": [7,9,8,14,13,14,8,13,10,9] - } - }, - "R06": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "LowerPetalLength", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [1,1.25], - [1.25,1.45], - [1.45,1.65], - [1.65,1.8], - [1.8,2.4], - [2.4,3] - ] - } - ], - "frequencies": [4,13,15,4,2,67] - } - }, - "R07": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "PetalLength", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [1,1.55], - [1.55,2.4], - [2.4,3.4], - [3.4,3.95], - [3.95,4.6], - [4.6,5.15], - [5.15,5.75], - [5.75,6.4], - [6.4,6.9] - ] - } - ], - "frequencies": [27,11,2,6,15,18,15,8,3] - } - }, - "R08": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "PetalWidth", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [0.1,0.35], - [0.35,0.75], - [0.75,1.05], - [1.05,1.35], - [1.35,1.55], - [1.55,1.75], - [1.75,2.05], - [2.05,2.25], - [2.25,2.5] - ] - } - ], - "frequencies": [31,7,5,13,11,4,16,7,11] - } - }, - "R09": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "SPetalLength", - "type": "Categorical", - "partitionType": "Value groups", - "partition": [ - ["1"], - ["5"], - ["4"], - ["3"], - ["6"] - ], - "defaultGroupIndex": 4 - } - ], - "frequencies": [38,27,25,8,7] - }, - "inputValues": { - "values": ["1","5","4","3","6"], - "frequencies": [38,27,25,8,7] - } - }, - "R10": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "SepalLength", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [4.3,4.65], - [4.65,4.95], - [4.95,5.35], - [5.35,5.65], - [5.65,6.05], - [6.05,6.35], - [6.35,6.65], - [6.65,7.05], - [7.05,7.3], - [7.3,7.7] - ] - } - ], - "frequencies": [7,11,16,13,16,12,10,12,3,5] - } - }, - "R11": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "SepalWidth", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [2,2.25], - [2.25,2.45], - [2.45,2.75], - [2.75,2.95], - [2.95,3.25], - [3.25,3.45], - [3.45,3.65], - [3.65,3.95], - [3.95,4.15], - [4.15,4.4] - ] - } - ], - "frequencies": [2,3,14,17,41,12,5,7,2,2] - } - }, - "R12": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "UpperPetalWidth", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [1.5,1.65], - [1.65,1.75], - [1.75,1.85], - [1.85,1.95], - [1.95,2.05], - [2.05,2.15], - [2.15,2.25], - [2.25,2.35], - [2.35,2.45], - [2.45,2.5] - ] - } - ], - "frequencies": [70,1,9,2,5,4,3,8,2,1] - } - } - } - }, - "bivariatePreparationReport": { - "reportType": "BivariatePreparation", - "summary": { - "dictionary": "Iris", - "variables": { - "types": [ - "Categorical", - "Numerical" - ], - "numbers": [ - 4, - 8 - ] - }, - "database": 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test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/err.txt index 82ae520c0..0e8a46c9e 100644 --- a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/err.txt @@ -1,6 +1,6 @@ -Train unsupervised model -Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 -Evaluation of variable pairs -Data preparation time: 0:00:00.37 -Write report ./results\AllReports.khj - +Train unsupervised model +Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 +Evaluation of variable pairs +Data preparation time: 0:00:00.31 +Write report ./results/AnalysisResults.khj + diff --git a/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/time.log b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/time.log new file mode 100644 index 000000000..d11750ce1 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/results.ref/time.log @@ -0,0 +1 @@ +Overal time: 0.4437694549560547 diff --git a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/test.prm b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/test.prm similarity index 57% rename from test/LearningTest/TestKhiops/Standard-unstable/IrisU/test.prm rename to test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/test.prm index f5d612729..635c9360e 100644 --- a/test/LearningTest/TestKhiops/Standard-unstable/IrisU/test.prm +++ b/test/LearningTest/TestKhiops/Standard-unstable/IrisU2D/test.prm @@ -1,54 +1,58 @@ -// Thu Jun 12 13:31:06 2008 -// modl -// Output command file -// -//This file contains recorded commands, that can be replayed. -//Commands are based on user interactions: -// field update -// list index selection -// menu action -//Every command can be commented, using //. -//For example, commenting the last Exit command will allow other -//user interactions, after the commands have been replayed. -// -// - -// -> Data preparation and scoring -ClassManagement.OpenFile // Open... - -// -> Open -ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file -ClassFileName ./IrisExtended.kdic // Dictionary file -OK // Open -// <- Open - -ClassManagement.ClassName Iris // Dictionary - -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage - - -AnalysisSpec.TargetAttributeName // Target variable - -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor false // Predicteur Bayesien Naif Selectif - -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 100 // Max number of pairs of variables - -AnalysisResults.ResultFilesDirectory ./results // Result files directory -AnalysisResults.Preparation2DFileName PreparationReport2D.xls // Preparation report - - - -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables -ComputeStats // Analyse database - -Exit // Close -// <- Data preparation and scoring - - -// -> Data preparation and scoring -OK // Close -// <- Data preparation and scoring - +// Thu Jun 12 13:31:06 2008 +// modl +// Output command file +// +//This file contains recorded commands, that can be replayed. +//Commands are based on user interactions: +// field update +// list index selection +// menu action +//Every command can be commented, using //. +//For example, commenting the last Exit command will allow other +//user interactions, after the commands have been replayed. +// +// + +// -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB +ClassManagement.OpenFile // Open... + +// -> Open +ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file +ClassFileName ./IrisExtended.kdic // Dictionary file +OK // Open +// <- Open + +TrainDatabase.ClassName Iris // Dictionary + +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage + + +AnalysisSpec.TargetAttributeName // Target variable + +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true // Do data preparation only + +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 100 // Max number of pairs of variables + +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report + + + +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls +ComputeStats // Analyse database + +Exit // Close +// <- Data preparation and scoring + + +// -> Data preparation and scoring +OK // Close +// <- Data preparation and scoring + diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.ModelingReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/Adult/results.ref/ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.ModelingReport.xls index 2a9ccb454..b7ca9bc60 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.ModelingReport.xls @@ -1,44 +1,44 @@ -#Khiops 10.1 -Modeling report - -Dictionary Adult -Target variable class -Database ../../../datasets/Adult/Adult.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 13 -Univariate relationship 1 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -Pcapital_gain capital_gain 0.139679 0.964874 0.367114 -Pmarital_status marital_status 0.197864 0.479034 0.307869 -Prelationship relationship 0.208145 0.417999 0.294965 -Page age 0.117931 0.562531 0.257565 -Peducation_num education_num 0.112569 0.483429 0.233279 -Poccupation occupation 0.090111 0.501984 0.212683 -Pcapital_loss capital_loss 0.0536587 0.796906 0.206787 -Phours_per_week hours_per_week 0.0717333 0.466827 0.182995 -Peducation education 0.112034 0.246124 0.166055 -Pworkclass workclass 0.0224888 0.125031 0.0530263 -Pnative_country native_country 0.00652175 0.367218 0.0489378 -Prace race 0.0102259 0.222687 0.0477198 -Psex sex 0.0466409 0.023468 0.0330843 - ----------------------------------------------- -Predictor Univariate relationship +#Khiops 10.4.6i +Modeling report + +Dictionary Adult +Target variable class +Database ../../../datasets/Adult/Adult.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 13 +Univariate relationship 1 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +Pcapital_gain capital_gain 0.139679 0.964874 0.367114 +Pmarital_status marital_status 0.197864 0.479034 0.307869 +Prelationship relationship 0.208145 0.417999 0.294965 +Page age 0.117931 0.562531 0.257565 +Peducation_num education_num 0.112569 0.483429 0.233279 +Poccupation occupation 0.090111 0.501984 0.212683 +Pcapital_loss capital_loss 0.0536587 0.796906 0.206787 +Phours_per_week hours_per_week 0.0717333 0.466827 0.182995 +Peducation education 0.112034 0.246124 0.166055 +Pworkclass workclass 0.0224888 0.125031 0.0530263 +Pnative_country native_country 0.00652175 0.367218 0.0489378 +Prace race 0.0102259 0.222687 0.0477198 +Psex sex 0.0466409 0.023468 0.0330843 + +---------------------------------------------- +Predictor Univariate relationship diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.PreparationReport.xls similarity index 89% rename from test/LearningTest/TestKhiops/Standard/Adult/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.PreparationReport.xls index aafb5fd39..ab92616bc 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.PreparationReport.xls @@ -1,451 +1,453 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Adult -Variables - Categorical 9 - Numerical 7 - Total 16 - -Database ../../../datasets/Adult/Adult.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 34291 - -Learning task Classification analysis - -Target variable Categorical class -Main target value more - -Target variable stats -Value Frequency Coverage -less 26117 0.761628 -more 8174 0.238372 - -Evaluated variables 15 -Informative variables 13 - -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 10.4427 - Data cost 18827.2 - - -Categorical variables statistics - -Rank Name Level Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost -R01 relationship 0.208145 5 6 Husband 0.401592 3.4012 53.9185 14859.9 -R02 marital_status 0.197864 3 7 Married-civ-spouse 0.455192 3.4012 39.207 15068.3 -R06 education 0.112034 7 16 HS-grad 0.323758 3.4012 88.9107 16635.5 -R07 occupation 0.090111 8 14 Prof-specialty 0.184567 3.4012 94.4276 17042.9 -R10 sex 0.0466409 2 2 Male 0.66726 3.4012 20.8234 17935.5 -R11 workclass 0.0224888 4 8 Private 0.751276 3.4012 47.5855 18363.7 -R12 race 0.0102259 2 5 White 0.854481 3.4012 25.3683 18616.9 -R13 native_country 0.00652175 3 40 United-States 0.915371 3.4012 73.666 18638.4 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost -R03 capital_gain 0.139679 19 122 0 99999 1129.327783 7735.318813 0 3.4012 188.918 16014.7 -R04 age 0.117931 12 74 17 90 38.67338369 13.77705677 0 3.4012 122.056 16491.2 -R05 education_num 0.112569 7 16 1 16 10.08075005 2.555450221 0 3.4012 78.8343 16635.5 -R08 hours_per_week 0.0717333 6 93 1 99 40.41281969 12.3602687 0 3.4012 69.0858 17414.5 -R09 capital_loss 0.0536587 16 95 0 4356 87.26231373 403.1609014 0 3.4012 169.971 17654.1 -R14 Label 0 1 34291 2 48842 24421.00207 14110.19055 0 0.693147 10.4427 18827.2 -R15 fnlwgt 0 1 22525 12285 1490400 189903.0584 105303.8837 0 0.693147 10.4427 18827.2 - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R01 -Variable Categorical relationship - -Variable stats -Group Size Value list -{Husband, Wife} 2 Husband Wife -{Not-in-family} 1 Not-in-family -{Own-child} 1 Own-child -{Unmarried} 1 Unmarried -{Other-relative} 1 Other-relative * - -% target values -Group less more Interest Frequency Coverage -{Husband, Wife} 0.548238 0.451762 0.422448 15382 0.448573 -{Own-child} 0.985228 0.0147719 0.287464 5348 0.155959 -{Not-in-family} 0.90009 0.0999098 0.142352 8868 0.25861 -{Unmarried} 0.93731 0.0626899 0.101483 3621 0.105596 -{Other-relative} 0.969216 0.0307836 0.0462536 1072 0.0312618 -Total 0.761628 0.238372 1 34291 1 - -Values -Value Frequency Coverage -Husband 13771 0.401592 -Not-in-family 8868 0.25861 -Own-child 5348 0.155959 -Unmarried 3621 0.105596 -Wife 1611 0.0469803 -Other-relative 1072 0.0312618 - ----------------------------------------------- -Rank R02 -Variable Categorical marital_status - -Variable stats -Group Size Value list -{Married-civ-spouse, Married-AF-spouse} 2 Married-civ-spouse Married-AF-spouse * -{Never-married, Separated} 2 Never-married Separated -{Divorced, Widowed, Married-spouse-absent} 3 Divorced Widowed Married-spouse-absent - -% target values -Group less more Interest Frequency Coverage -{Never-married, Separated} 0.953587 0.0464128 0.460279 12475 0.363798 -{Married-civ-spouse, Married-AF-spouse} 0.553211 0.446789 0.4328 15636 0.45598 -{Divorced, Widowed, Married-spouse-absent} 0.901456 0.0985437 0.106921 6180 0.180222 -Total 0.761628 0.238372 1 34291 1 - -Values -Value Frequency Coverage -Married-civ-spouse 15609 0.455192 -Never-married 11400 0.332449 -Divorced 4655 0.13575 -Widowed 1093 0.0318743 -Separated 1075 0.0313493 -Married-spouse-absent 432 0.0125981 -Married-AF-spouse 27 0.000787379 - ----------------------------------------------- -Rank R03 -Variable Numerical capital_gain - -% target values -Interval less more Interest Frequency Coverage -]-inf;57] 0.796308 0.203692 0.0385112 31420 0.916275 -]57;3048] 1 0 0.0478367 492 0.0143478 -]3048;3120] 0.0636364 0.936364 0.0441147 110 0.00320784 -]3120;4240] 1 0 0.0311133 320 0.00933189 -]4240;4400] 0.142857 0.857143 0.0214422 70 0.00204135 -]4400;4668] 1 0 0.00748665 77 0.00224549 -]4668;4826] 0 1 0.0148484 29 0.000845703 -]4826;4970] 0.758621 0.241379 2.57249e-07 29 0.000845703 -]4970;5119] 1 0 0.00777833 80 0.00233297 -]5119;5310] 0 1 0.0532493 104 0.00303287 -]5310;6808] 0.6 0.4 0.00125563 55 0.00160392 -]6808;7070] 1 0 0.00330579 34 0.000991514 -]7070;7436] 0 1 0.137731 269 0.00784462 -]7436;7560] 1 0 0.000388917 4 0.000116649 -]7560;10000] 0.00534759 0.994652 0.186215 374 0.0109067 -]10000;10585] 0.156863 0.843137 0.0148831 51 0.00148727 -]10585;30900] 0.00172414 0.998276 0.293924 580 0.0169141 -]30900;70000] 1 0 0.000680604 7 0.000204135 -]70000;+inf[ 0 1 0.0952344 186 0.00542416 -Total 0.761628 0.238372 1 34291 1 - ----------------------------------------------- -Rank R04 -Variable Numerical age - -% target values -Interval less more Interest Frequency Coverage -]-inf;21.5] 0.997875 0.00212508 0.372056 3294 0.0960602 -]21.5;23.5] 0.98447 0.0155297 0.164954 1803 0.0525794 -]23.5;25.5] 0.952071 0.047929 0.0997211 1690 0.0492841 -]25.5;27.5] 0.913456 0.0865442 0.057524 1687 0.0491966 -]27.5;30.5] 0.836812 0.163188 0.0194256 2635 0.0768423 -]30.5;35.5] 0.768085 0.231915 0.000232794 4631 0.13505 -]35.5;37.5] 0.69452 0.30548 0.00939477 1843 0.0537459 -]37.5;42.5] 0.65368 0.34632 0.0532907 4158 0.121256 -]42.5;55.5] 0.612847 0.387153 0.192212 8095 0.236068 -]55.5;61.5] 0.656179 0.343821 0.0268643 2193 0.0639526 -]61.5;66.5] 0.734199 0.265801 0.00101566 1155 0.0336823 -]66.5;+inf[ 0.810298 0.189702 0.00330892 1107 0.0322825 -Total 0.761628 0.238372 1 34291 1 - ----------------------------------------------- -Rank R05 -Variable Numerical education_num - -% target values -Interval less more Interest Frequency Coverage -]-inf;8.5] 0.943106 0.0568942 0.24816 4482 0.130705 -]8.5;9.5] 0.842191 0.157809 0.100321 11102 0.323758 -]9.5;10.5] 0.811903 0.188097 0.0258925 7645 0.222945 -]10.5;12.5] 0.742073 0.257927 0.00123361 2586 0.0754134 -]12.5;13.5] 0.586005 0.413995 0.195066 5645 0.16462 -]13.5;14.5] 0.457112 0.542888 0.181348 1842 0.0537167 -]14.5;+inf[ 0.266936 0.733064 0.247978 989 0.0288414 -Total 0.761628 0.238372 1 34291 1 - ----------------------------------------------- -Rank R06 -Variable Categorical education - -Variable stats -Group Size Value list -{HS-grad} 1 HS-grad -{Some-college} 1 Some-college -{Bachelors} 1 Bachelors -{11th, 10th, 7th-8th, ...} 8 11th 10th 7th-8th 9th 12th 5th-6th 1st-4th Preschool * -{Assoc-voc, Assoc-acdm} 2 Assoc-voc Assoc-acdm -{Masters} 1 Masters -{Prof-school, Doctorate} 2 Prof-school Doctorate - -% target values -Group less more Interest Frequency Coverage -{11th, 10th, 7th-8th, ...} 0.943106 0.0568942 0.24816 4482 0.130705 -{Prof-school, Doctorate} 0.266936 0.733064 0.247978 989 0.0288414 -{Bachelors} 0.586005 0.413995 0.195066 5645 0.16462 -{Masters} 0.457112 0.542888 0.181348 1842 0.0537167 -{HS-grad} 0.842191 0.157809 0.100321 11102 0.323758 -{Some-college} 0.811903 0.188097 0.0258925 7645 0.222945 -{Assoc-voc, Assoc-acdm} 0.742073 0.257927 0.00123361 2586 0.0754134 -Total 0.761628 0.238372 1 34291 1 - -Values -Value Frequency Coverage -HS-grad 11102 0.323758 -Some-college 7645 0.222945 -Bachelors 5645 0.16462 -Masters 1842 0.0537167 -Assoc-voc 1453 0.0423726 -11th 1277 0.0372401 -Assoc-acdm 1133 0.0330407 -10th 1004 0.0292788 -7th-8th 638 0.0186055 -Prof-school 584 0.0170307 -9th 528 0.0153976 -12th 465 0.0135604 -Doctorate 405 0.0118107 -5th-6th 357 0.0104109 -1st-4th 160 0.00466595 -Preschool 53 0.0015456 - ----------------------------------------------- -Rank R07 -Variable Categorical occupation - -Variable stats -Group Size Value list -{Prof-specialty, Protective-serv, Armed-Forces} 3 Prof-specialty Protective-serv Armed-Forces * -{Craft-repair, Transport-moving} 2 Craft-repair Transport-moving -{Sales, Tech-support} 2 Sales Tech-support -{Exec-managerial} 1 Exec-managerial -{Adm-clerical} 1 Adm-clerical -{Other-service, Priv-house-serv} 2 Other-service Priv-house-serv -{Machine-op-inspct, Farming-fishing} 2 Machine-op-inspct Farming-fishing -{Handlers-cleaners} 1 Handlers-cleaners - -% target values -Group less more Interest Frequency Coverage -{Exec-managerial} 0.524942 0.475058 0.322787 4290 0.125106 -{Other-service, Priv-house-serv} 0.962057 0.0379434 0.320807 3637 0.106063 -{Prof-specialty, Protective-serv, Armed-Forces} 0.663204 0.336796 0.0985361 7028 0.204952 -{Machine-op-inspct, Farming-fishing} 0.886811 0.113189 0.0893676 3101 0.0904319 -{Handlers-cleaners} 0.934586 0.0654141 0.0872203 1437 0.041906 -{Adm-clerical} 0.862104 0.137896 0.0693478 3887 0.113353 -{Sales, Tech-support} 0.728088 0.271912 0.00839068 4906 0.14307 -{Craft-repair, Transport-moving} 0.780849 0.219151 0.00354381 6005 0.175119 -Total 0.761628 0.238372 1 34291 1 - -Values -Value Frequency Coverage -Prof-specialty 6329 0.184567 -Craft-repair 4309 0.12566 -Exec-managerial 4290 0.125106 -Adm-clerical 3887 0.113353 -Sales 3872 0.112916 -Other-service 3457 0.100814 -Machine-op-inspct 2092 0.0610073 -Transport-moving 1696 0.049459 -Handlers-cleaners 1437 0.041906 -Tech-support 1034 0.0301537 -Farming-fishing 1009 0.0294246 -Protective-serv 686 0.0200052 -Priv-house-serv 180 0.00524919 -Armed-Forces 13 0.000379108 - ----------------------------------------------- -Rank R08 -Variable Numerical hours_per_week - -% target values -Interval less more Interest Frequency Coverage -]-inf;34.5] 0.931477 0.0685232 0.434249 5925 0.172786 -]34.5;39.5] 0.844568 0.155432 0.0348336 2329 0.0679187 -]39.5;40.5] 0.7902 0.2098 0.0264835 15939 0.464816 -]40.5;49.5] 0.651227 0.348773 0.0719813 3260 0.0950687 -]49.5;64.5] 0.560007 0.439993 0.399988 5716 0.166691 -]64.5;+inf[ 0.634581 0.365419 0.0324643 1122 0.03272 -Total 0.761628 0.238372 1 34291 1 - ----------------------------------------------- -Rank R09 -Variable Numerical capital_loss - -% target values -Interval less more Interest Frequency Coverage -]-inf;70] 0.77456 0.22544 0.0131577 32696 0.953486 -]70;1457] 0.977528 0.0224719 0.0146582 89 0.00259543 -]1457;1494] 0.413043 0.586957 0.0109609 46 0.00134146 -]1494;1537] 1 0 0.00399388 17 0.000495757 -]1537;1568.5] 0 1 0.0383523 31 0.000904027 -]1568.5;1789] 1 0 0.0878653 374 0.0109067 -]1789;1846] 0.666667 0.333333 0.000178532 9 0.00026246 -]1846;1859] 0 1 0.0606214 49 0.00142895 -]1859;1881] 1 0 0.00986723 42 0.00122481 -]1881;1906.5] 0.0304709 0.969529 0.393118 361 0.0105275 -]1906.5;1975.5] 0.956522 0.0434783 0.0028567 23 0.00067073 -]1975.5;1978.5] 0 1 0.22764 184 0.00536584 -]1978.5;2156] 1 0 0.0277222 118 0.00344114 -]2156;2384] 0.609756 0.390244 0.00602329 123 0.00358695 -]2384;2580] 0.0510204 0.94898 0.0991934 98 0.00285789 -]2580;+inf[ 0.516129 0.483871 0.00379124 31 0.000904027 -Total 0.761628 0.238372 1 34291 1 - ----------------------------------------------- -Rank R10 -Variable Categorical sex - -Variable stats -Group Size Value list -{Male} 1 Male -{Female} 1 Female * - -% target values -Group less more Interest Frequency Coverage -{Female} 0.8922 0.1078 0.71524 11410 0.33274 -{Male} 0.696517 0.303483 0.28476 22881 0.66726 -Total 0.761628 0.238372 1 34291 1 - -Values -Value Frequency Coverage -Male 22881 0.66726 -Female 11410 0.33274 - ----------------------------------------------- -Rank R11 -Variable Categorical workclass - -Variable stats -Group Size Value list -{Private, Without-pay, Never-worked} 3 Private Without-pay Never-worked * -{Self-emp-not-inc, Local-gov, State-gov} 3 Self-emp-not-inc Local-gov State-gov -{Self-emp-inc} 1 Self-emp-inc -{Federal-gov} 1 Federal-gov - -% target values -Group less more Interest Frequency Coverage -{Self-emp-inc} 0.435511 0.564489 0.642787 1194 0.0348196 -{Private, Without-pay, Never-worked} 0.793081 0.206919 0.160564 25783 0.751888 -{Federal-gov} 0.606786 0.393214 0.130634 1002 0.0292205 -{Self-emp-not-inc, Local-gov, State-gov} 0.719423 0.280577 0.0660155 6312 0.184072 -Total 0.761628 0.238372 1 34291 1 - -Values -Value Frequency Coverage -Private 25762 0.751276 -Self-emp-not-inc 2673 0.0779505 -Local-gov 2246 0.0654982 -State-gov 1393 0.0406229 -Self-emp-inc 1194 0.0348196 -Federal-gov 1002 0.0292205 -Without-pay 12 0.000349946 -Never-worked 9 0.00026246 - ----------------------------------------------- -Rank R12 -Variable Categorical race - -Variable stats -Group Size Value list -{White, Asian-Pac-Islander} 2 White Asian-Pac-Islander -{Black, Amer-Indian-Eskimo, Other} 3 Black Amer-Indian-Eskimo Other * - -% target values -Group less more Interest Frequency Coverage -{Black, Amer-Indian-Eskimo, Other} 0.883453 0.116547 0.902111 3904 0.113849 -{White, Asian-Pac-Islander} 0.745977 0.254023 0.0978889 30387 0.886151 -Total 0.761628 0.238372 1 34291 1 - -Values -Value Frequency Coverage -White 29301 0.854481 -Black 3307 0.0964393 -Asian-Pac-Islander 1086 0.0316701 -Amer-Indian-Eskimo 331 0.00965268 -Other 266 0.00775714 - ----------------------------------------------- -Rank R13 -Variable Categorical native_country - -Variable stats -Group Size Value list -{United-States, Philippines, Cuba, ...} 12 United-States Philippines Cuba China Jamaica South Italy Poland Ireland Hong Hungary Scotland -{Mexico, Puerto-Rico, El-Salvador, ...} 17 Mexico Puerto-Rico El-Salvador Dominican-Republic Guatemala Haiti Vietnam Columbia Portugal Nicaragua Peru Ecuador Thailand Trinadad&Tobago Honduras Outlying-US Laos * -{Germany, Canada, India, ...} 11 Germany Canada India England Japan Taiwan Iran Greece France Cambodia Yugoslavia - -% target values -Group less more Interest Frequency Coverage -{Mexico, Puerto-Rico, El-Salvador, ...} 0.936034 0.0639659 0.840639 1407 0.0410312 -{Germany, Canada, India, ...} 0.64011 0.35989 0.147839 728 0.0212301 -{United-States, Philippines, Cuba, ...} 0.756748 0.243252 0.0115219 32156 0.937739 -Total 0.761628 0.238372 1 34291 1 - -Values -Value Frequency Coverage -United-States 31389 0.915371 -Mexico 641 0.018693 -Philippines 211 0.00615322 -Germany 146 0.00425768 -Puerto-Rico 127 0.0037036 -Canada 125 0.00364527 -India 114 0.00332449 -El-Salvador 109 0.00317868 -Cuba 96 0.00279957 -England 91 0.00265376 -China 89 0.00259543 -Jamaica 83 0.00242046 -South 80 0.00233297 -Italy 77 0.00224549 -Dominican-Republic 74 0.002158 -Guatemala 67 0.00195387 -Japan 64 0.00186638 -Haiti 58 0.00169141 -Poland 57 0.00166224 -Vietnam 53 0.0015456 -Columbia 52 0.00151643 -Taiwan 49 0.00142895 -Portugal 44 0.00128314 -Iran 41 0.00119565 -Nicaragua 37 0.001079 -Peru 36 0.00104984 -Greece 34 0.000991514 -Ecuador 30 0.000874865 -France 26 0.000758216 -Ireland 25 0.000729054 -Hong 21 0.000612406 -Thailand 21 0.000612406 -Cambodia 19 0.000554081 -Yugoslavia 19 0.000554081 -Trinadad&Tobago 16 0.000466595 -Honduras 15 0.000437433 -Hungary 15 0.000437433 -Outlying-US 15 0.000437433 -Scotland 13 0.000379108 -Laos 12 0.000349946 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Adult +Variables + Categorical 9 + Numerical 7 + Total 16 + +Database ../../../datasets/Adult/Adult.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 34291 + +Learning task Classification analysis + +Target variable Categorical class +Main target value more + +Target variable stats +Value Frequency Coverage +less 26117 0.761628 +more 8174 0.238372 + +Evaluated variables 15 +Informative variables 13 +Selected variables 13 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 10.4427 + Data cost 18827.2 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost +R01 relationship 0.208145 5 6 Husband 0.401592 0 0 3.4012 53.9185 14859.9 +R02 marital_status 0.197864 3 7 Married-civ-spouse 0.455192 0 0 3.4012 39.207 15068.3 +R06 education 0.112034 7 16 HS-grad 0.323758 0 0 3.4012 88.9107 16635.5 +R07 occupation 0.090111 8 14 Prof-specialty 0.184567 0 0 3.4012 94.4276 17042.9 +R10 sex 0.0466409 2 2 Male 0.66726 0 0 3.4012 20.8234 17935.5 +R11 workclass 0.0224888 4 8 Private 0.751276 0 0 3.4012 47.5855 18363.7 +R12 race 0.0102259 2 5 White 0.854481 0 0 3.4012 25.3683 18616.9 +R13 native_country 0.00652175 3 40 United-States 0.915371 0 0 3.4012 73.666 18638.4 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost +R03 capital_gain 0.139679 19 122 0 99999 1129.327783 7735.318813 0 0 3.4012 188.918 16014.7 +R04 age 0.117931 12 74 17 90 38.67338369 13.77705677 0 0 3.4012 122.056 16491.2 +R05 education_num 0.112569 7 16 1 16 10.08075005 2.555450221 0 0 3.4012 78.8343 16635.5 +R08 hours_per_week 0.0717333 6 93 1 99 40.41281969 12.3602687 0 0 3.4012 69.0858 17414.5 +R09 capital_loss 0.0536587 16 95 0 4356 87.26231373 403.1609014 0 0 3.4012 169.971 17654.1 +R14 Label 0 1 34291 2 48842 24421.00207 14110.19055 0 0 0.693147 10.4427 18827.2 +R15 fnlwgt 0 1 22525 12285 1490400 189903.0584 105303.8837 0 0 0.693147 10.4427 18827.2 + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R01 +Variable Categorical relationship + +Variable stats +Group Size Value list +{Husband, Wife} 2 Husband Wife +{Not-in-family} 1 Not-in-family +{Own-child} 1 Own-child +{Unmarried} 1 Unmarried +{Other-relative} 1 Other-relative * + +% target values +Group less more Interest Frequency Coverage +{Husband, Wife} 0.548238 0.451762 0.422448 15382 0.448573 +{Own-child} 0.985228 0.0147719 0.287464 5348 0.155959 +{Not-in-family} 0.90009 0.0999098 0.142352 8868 0.25861 +{Unmarried} 0.93731 0.0626899 0.101483 3621 0.105596 +{Other-relative} 0.969216 0.0307836 0.0462536 1072 0.0312618 +Total 0.761628 0.238372 1 34291 1 + +Values +Value Frequency Coverage +Husband 13771 0.401592 +Not-in-family 8868 0.25861 +Own-child 5348 0.155959 +Unmarried 3621 0.105596 +Wife 1611 0.0469803 +Other-relative 1072 0.0312618 + +---------------------------------------------- +Rank R02 +Variable Categorical marital_status + +Variable stats +Group Size Value list +{Married-civ-spouse, Married-AF-spouse} 2 Married-civ-spouse Married-AF-spouse * +{Never-married, Separated} 2 Never-married Separated +{Divorced, Widowed, Married-spouse-absent} 3 Divorced Widowed Married-spouse-absent + +% target values +Group less more Interest Frequency Coverage +{Never-married, Separated} 0.953587 0.0464128 0.460279 12475 0.363798 +{Married-civ-spouse, Married-AF-spouse} 0.553211 0.446789 0.4328 15636 0.45598 +{Divorced, Widowed, Married-spouse-absent} 0.901456 0.0985437 0.106921 6180 0.180222 +Total 0.761628 0.238372 1 34291 1 + +Values +Value Frequency Coverage +Married-civ-spouse 15609 0.455192 +Never-married 11400 0.332449 +Divorced 4655 0.13575 +Widowed 1093 0.0318743 +Separated 1075 0.0313493 +Married-spouse-absent 432 0.0125981 +Married-AF-spouse 27 0.000787379 + +---------------------------------------------- +Rank R03 +Variable Numerical capital_gain + +% target values +Interval less more Interest Frequency Coverage +]-inf;57] 0.796308 0.203692 0.0385112 31420 0.916275 +]57;3048] 1 0 0.0478367 492 0.0143478 +]3048;3120] 0.0636364 0.936364 0.0441147 110 0.00320784 +]3120;4240] 1 0 0.0311133 320 0.00933189 +]4240;4400] 0.142857 0.857143 0.0214422 70 0.00204135 +]4400;4668] 1 0 0.00748665 77 0.00224549 +]4668;4826] 0 1 0.0148484 29 0.000845703 +]4826;4970] 0.758621 0.241379 2.57249e-07 29 0.000845703 +]4970;5119] 1 0 0.00777833 80 0.00233297 +]5119;5310] 0 1 0.0532493 104 0.00303287 +]5310;6808] 0.6 0.4 0.00125563 55 0.00160392 +]6808;7070] 1 0 0.00330579 34 0.000991514 +]7070;7436] 0 1 0.137731 269 0.00784462 +]7436;7560] 1 0 0.000388917 4 0.000116649 +]7560;10000] 0.00534759 0.994652 0.186215 374 0.0109067 +]10000;10585] 0.156863 0.843137 0.0148831 51 0.00148727 +]10585;30900] 0.00172414 0.998276 0.293924 580 0.0169141 +]30900;70000] 1 0 0.000680604 7 0.000204135 +]70000;+inf[ 0 1 0.0952344 186 0.00542416 +Total 0.761628 0.238372 1 34291 1 + +---------------------------------------------- +Rank R04 +Variable Numerical age + +% target values +Interval less more Interest Frequency Coverage +]-inf;21.5] 0.997875 0.00212508 0.372056 3294 0.0960602 +]21.5;23.5] 0.98447 0.0155297 0.164954 1803 0.0525794 +]23.5;25.5] 0.952071 0.047929 0.0997211 1690 0.0492841 +]25.5;27.5] 0.913456 0.0865442 0.057524 1687 0.0491966 +]27.5;30.5] 0.836812 0.163188 0.0194256 2635 0.0768423 +]30.5;35.5] 0.768085 0.231915 0.000232794 4631 0.13505 +]35.5;37.5] 0.69452 0.30548 0.00939477 1843 0.0537459 +]37.5;42.5] 0.65368 0.34632 0.0532907 4158 0.121256 +]42.5;55.5] 0.612847 0.387153 0.192212 8095 0.236068 +]55.5;61.5] 0.656179 0.343821 0.0268643 2193 0.0639526 +]61.5;66.5] 0.734199 0.265801 0.00101566 1155 0.0336823 +]66.5;+inf[ 0.810298 0.189702 0.00330892 1107 0.0322825 +Total 0.761628 0.238372 1 34291 1 + +---------------------------------------------- +Rank R05 +Variable Numerical education_num + +% target values +Interval less more Interest Frequency Coverage +]-inf;8.5] 0.943106 0.0568942 0.24816 4482 0.130705 +]8.5;9.5] 0.842191 0.157809 0.100321 11102 0.323758 +]9.5;10.5] 0.811903 0.188097 0.0258925 7645 0.222945 +]10.5;12.5] 0.742073 0.257927 0.00123361 2586 0.0754134 +]12.5;13.5] 0.586005 0.413995 0.195066 5645 0.16462 +]13.5;14.5] 0.457112 0.542888 0.181348 1842 0.0537167 +]14.5;+inf[ 0.266936 0.733064 0.247978 989 0.0288414 +Total 0.761628 0.238372 1 34291 1 + +---------------------------------------------- +Rank R06 +Variable Categorical education + +Variable stats +Group Size Value list +{HS-grad} 1 HS-grad +{Some-college} 1 Some-college +{Bachelors} 1 Bachelors +{11th, 10th, 7th-8th, ...} 8 11th 10th 7th-8th 9th 12th 5th-6th 1st-4th Preschool * +{Assoc-voc, Assoc-acdm} 2 Assoc-voc Assoc-acdm +{Masters} 1 Masters +{Prof-school, Doctorate} 2 Prof-school Doctorate + +% target values +Group less more Interest Frequency Coverage +{11th, 10th, 7th-8th, ...} 0.943106 0.0568942 0.24816 4482 0.130705 +{Prof-school, Doctorate} 0.266936 0.733064 0.247978 989 0.0288414 +{Bachelors} 0.586005 0.413995 0.195066 5645 0.16462 +{Masters} 0.457112 0.542888 0.181348 1842 0.0537167 +{HS-grad} 0.842191 0.157809 0.100321 11102 0.323758 +{Some-college} 0.811903 0.188097 0.0258925 7645 0.222945 +{Assoc-voc, Assoc-acdm} 0.742073 0.257927 0.00123361 2586 0.0754134 +Total 0.761628 0.238372 1 34291 1 + +Values +Value Frequency Coverage +HS-grad 11102 0.323758 +Some-college 7645 0.222945 +Bachelors 5645 0.16462 +Masters 1842 0.0537167 +Assoc-voc 1453 0.0423726 +11th 1277 0.0372401 +Assoc-acdm 1133 0.0330407 +10th 1004 0.0292788 +7th-8th 638 0.0186055 +Prof-school 584 0.0170307 +9th 528 0.0153976 +12th 465 0.0135604 +Doctorate 405 0.0118107 +5th-6th 357 0.0104109 +1st-4th 160 0.00466595 +Preschool 53 0.0015456 + +---------------------------------------------- +Rank R07 +Variable Categorical occupation + +Variable stats +Group Size Value list +{Prof-specialty, Protective-serv, Armed-Forces} 3 Prof-specialty Protective-serv Armed-Forces * +{Craft-repair, Transport-moving} 2 Craft-repair Transport-moving +{Sales, Tech-support} 2 Sales Tech-support +{Exec-managerial} 1 Exec-managerial +{Adm-clerical} 1 Adm-clerical +{Other-service, Priv-house-serv} 2 Other-service Priv-house-serv +{Machine-op-inspct, Farming-fishing} 2 Machine-op-inspct Farming-fishing +{Handlers-cleaners} 1 Handlers-cleaners + +% target values +Group less more Interest Frequency Coverage +{Exec-managerial} 0.524942 0.475058 0.322787 4290 0.125106 +{Other-service, Priv-house-serv} 0.962057 0.0379434 0.320807 3637 0.106063 +{Prof-specialty, Protective-serv, Armed-Forces} 0.663204 0.336796 0.0985361 7028 0.204952 +{Machine-op-inspct, Farming-fishing} 0.886811 0.113189 0.0893676 3101 0.0904319 +{Handlers-cleaners} 0.934586 0.0654141 0.0872203 1437 0.041906 +{Adm-clerical} 0.862104 0.137896 0.0693478 3887 0.113353 +{Sales, Tech-support} 0.728088 0.271912 0.00839068 4906 0.14307 +{Craft-repair, Transport-moving} 0.780849 0.219151 0.00354381 6005 0.175119 +Total 0.761628 0.238372 1 34291 1 + +Values +Value Frequency Coverage +Prof-specialty 6329 0.184567 +Craft-repair 4309 0.12566 +Exec-managerial 4290 0.125106 +Adm-clerical 3887 0.113353 +Sales 3872 0.112916 +Other-service 3457 0.100814 +Machine-op-inspct 2092 0.0610073 +Transport-moving 1696 0.049459 +Handlers-cleaners 1437 0.041906 +Tech-support 1034 0.0301537 +Farming-fishing 1009 0.0294246 +Protective-serv 686 0.0200052 +Priv-house-serv 180 0.00524919 +Armed-Forces 13 0.000379108 + +---------------------------------------------- +Rank R08 +Variable Numerical hours_per_week + +% target values +Interval less more Interest Frequency Coverage +]-inf;34.5] 0.931477 0.0685232 0.434249 5925 0.172786 +]34.5;39.5] 0.844568 0.155432 0.0348336 2329 0.0679187 +]39.5;40.5] 0.7902 0.2098 0.0264835 15939 0.464816 +]40.5;49.5] 0.651227 0.348773 0.0719813 3260 0.0950687 +]49.5;64.5] 0.560007 0.439993 0.399988 5716 0.166691 +]64.5;+inf[ 0.634581 0.365419 0.0324643 1122 0.03272 +Total 0.761628 0.238372 1 34291 1 + +---------------------------------------------- +Rank R09 +Variable Numerical capital_loss + +% target values +Interval less more Interest Frequency Coverage +]-inf;70] 0.77456 0.22544 0.0131577 32696 0.953486 +]70;1457] 0.977528 0.0224719 0.0146582 89 0.00259543 +]1457;1494] 0.413043 0.586957 0.0109609 46 0.00134146 +]1494;1537] 1 0 0.00399388 17 0.000495757 +]1537;1568.5] 0 1 0.0383523 31 0.000904027 +]1568.5;1789] 1 0 0.0878653 374 0.0109067 +]1789;1846] 0.666667 0.333333 0.000178532 9 0.00026246 +]1846;1859] 0 1 0.0606214 49 0.00142895 +]1859;1881] 1 0 0.00986723 42 0.00122481 +]1881;1906.5] 0.0304709 0.969529 0.393118 361 0.0105275 +]1906.5;1975.5] 0.956522 0.0434783 0.0028567 23 0.00067073 +]1975.5;1978.5] 0 1 0.22764 184 0.00536584 +]1978.5;2156] 1 0 0.0277222 118 0.00344114 +]2156;2384] 0.609756 0.390244 0.00602329 123 0.00358695 +]2384;2580] 0.0510204 0.94898 0.0991934 98 0.00285789 +]2580;+inf[ 0.516129 0.483871 0.00379124 31 0.000904027 +Total 0.761628 0.238372 1 34291 1 + +---------------------------------------------- +Rank R10 +Variable Categorical sex + +Variable stats +Group Size Value list +{Male} 1 Male +{Female} 1 Female * + +% target values +Group less more Interest Frequency Coverage +{Female} 0.8922 0.1078 0.71524 11410 0.33274 +{Male} 0.696517 0.303483 0.28476 22881 0.66726 +Total 0.761628 0.238372 1 34291 1 + +Values +Value Frequency Coverage +Male 22881 0.66726 +Female 11410 0.33274 + +---------------------------------------------- +Rank R11 +Variable Categorical workclass + +Variable stats +Group Size Value list +{Private, Without-pay, Never-worked} 3 Private Without-pay Never-worked * +{Self-emp-not-inc, Local-gov, State-gov} 3 Self-emp-not-inc Local-gov State-gov +{Self-emp-inc} 1 Self-emp-inc +{Federal-gov} 1 Federal-gov + +% target values +Group less more Interest Frequency Coverage +{Self-emp-inc} 0.435511 0.564489 0.642787 1194 0.0348196 +{Private, Without-pay, Never-worked} 0.793081 0.206919 0.160564 25783 0.751888 +{Federal-gov} 0.606786 0.393214 0.130634 1002 0.0292205 +{Self-emp-not-inc, Local-gov, State-gov} 0.719423 0.280577 0.0660155 6312 0.184072 +Total 0.761628 0.238372 1 34291 1 + +Values +Value Frequency Coverage +Private 25762 0.751276 +Self-emp-not-inc 2673 0.0779505 +Local-gov 2246 0.0654982 +State-gov 1393 0.0406229 +Self-emp-inc 1194 0.0348196 +Federal-gov 1002 0.0292205 +Without-pay 12 0.000349946 +Never-worked 9 0.00026246 + +---------------------------------------------- +Rank R12 +Variable Categorical race + +Variable stats +Group Size Value list +{White, Asian-Pac-Islander} 2 White Asian-Pac-Islander +{Black, Amer-Indian-Eskimo, Other} 3 Black Amer-Indian-Eskimo Other * + +% target values +Group less more Interest Frequency Coverage +{Black, Amer-Indian-Eskimo, Other} 0.883453 0.116547 0.902111 3904 0.113849 +{White, Asian-Pac-Islander} 0.745977 0.254023 0.0978889 30387 0.886151 +Total 0.761628 0.238372 1 34291 1 + +Values +Value Frequency Coverage +White 29301 0.854481 +Black 3307 0.0964393 +Asian-Pac-Islander 1086 0.0316701 +Amer-Indian-Eskimo 331 0.00965268 +Other 266 0.00775714 + +---------------------------------------------- +Rank R13 +Variable Categorical native_country + +Variable stats +Group Size Value list +{United-States, Philippines, Cuba, ...} 12 United-States Philippines Cuba China Jamaica South Italy Poland Ireland Hong Hungary Scotland +{Mexico, Puerto-Rico, El-Salvador, ...} 17 Mexico Puerto-Rico El-Salvador Dominican-Republic Guatemala Haiti Vietnam Columbia Portugal Nicaragua Peru Ecuador Thailand Trinadad&Tobago Honduras Outlying-US Laos * +{Germany, Canada, India, ...} 11 Germany Canada India England Japan Taiwan Iran Greece France Cambodia Yugoslavia + +% target values +Group less more Interest Frequency Coverage +{Mexico, Puerto-Rico, El-Salvador, ...} 0.936034 0.0639659 0.840639 1407 0.0410312 +{Germany, Canada, India, ...} 0.64011 0.35989 0.147839 728 0.0212301 +{United-States, Philippines, Cuba, ...} 0.756748 0.243252 0.0115219 32156 0.937739 +Total 0.761628 0.238372 1 34291 1 + +Values +Value Frequency Coverage +United-States 31389 0.915371 +Mexico 641 0.018693 +Philippines 211 0.00615322 +Germany 146 0.00425768 +Puerto-Rico 127 0.0037036 +Canada 125 0.00364527 +India 114 0.00332449 +El-Salvador 109 0.00317868 +Cuba 96 0.00279957 +England 91 0.00265376 +China 89 0.00259543 +Jamaica 83 0.00242046 +South 80 0.00233297 +Italy 77 0.00224549 +Dominican-Republic 74 0.002158 +Guatemala 67 0.00195387 +Japan 64 0.00186638 +Haiti 58 0.00169141 +Poland 57 0.00166224 +Vietnam 53 0.0015456 +Columbia 52 0.00151643 +Taiwan 49 0.00142895 +Portugal 44 0.00128314 +Iran 41 0.00119565 +Nicaragua 37 0.001079 +Peru 36 0.00104984 +Greece 34 0.000991514 +Ecuador 30 0.000874865 +France 26 0.000758216 +Ireland 25 0.000729054 +Hong 21 0.000612406 +Thailand 21 0.000612406 +Cambodia 19 0.000554081 +Yugoslavia 19 0.000554081 +Trinadad&Tobago 16 0.000466595 +Honduras 15 0.000437433 +Hungary 15 0.000437433 +Outlying-US 15 0.000437433 +Scotland 13 0.000379108 +Laos 12 0.000349946 diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.TestEvaluationReport.xls similarity index 96% rename from test/LearningTest/TestKhiops/Standard/Adult/results.ref/TestEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.TestEvaluationReport.xls index 32771d77d..910d2b23a 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/TestEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -1,2066 +1,2066 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary Adult -Target variable Categorical class -Main target value more -Database ../../../datasets/Adult/Adult.txt -Sample percentage 70 -Sampling mode Exclude sample -Selection variable -Selection value -Instances 14551 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.865714 0.474223 0.921665 -Univariate relationship 0.758573 0.204368 0.775895 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.865714 -Compression 0.474223 -AUC 0.921665 - -Confusion matrix - less more -$less 10459 1375 -$more 579 2138 - ----------------------------------------------- -Classifier Univariate relationship -Accuracy 0.758573 -Compression 0.204368 -AUC 0.775895 - -Confusion matrix - less more -$less 11038 3513 -$more 0 0 - -% target values -Group less more Interest Frequency Coverage -{Husband, Wife} 0.551388 0.448612 0.4132 6665 0.458044 -{Own-child} 0.98567 0.0143305 0.295224 2233 0.15346 -{Not-in-family} 0.89502 0.10498 0.136783 3715 0.255309 -{Unmarried} 0.945479 0.0545213 0.116129 1504 0.103361 -{Other-relative} 0.956221 0.0437788 0.0386637 434 0.0298261 -Total 0.758573 0.241427 1 14551 1 - - - ----------------------------------------------- -Lift curves less -Size Random Optimal Selective Naive Bayes Univariate relationship -0 0 0 0 0 -0.1 0.1 0.131826 0.131826 0.129937 -0.2 0.2 0.263653 0.263653 0.259875 -0.3 0.3 0.395479 0.395479 0.389812 -0.4 0.4 0.527306 0.527306 0.519749 -0.5 0.5 0.659132 0.659132 0.649686 -0.6 0.6 0.790959 0.790959 0.779624 -0.7 0.7 0.922785 0.922785 0.909561 -0.8 0.8 1.05461 1.05461 1.0395 -0.9 0.9 1.18644 1.18644 1.16944 -1 1 1.31826 1.31826 1.29937 -1.1 1.1 1.45009 1.45009 1.42931 -1.2 1.2 1.58192 1.58192 1.55925 -1.3 1.3 1.71374 1.71374 1.68918 -1.4 1.4 1.84557 1.84557 1.81912 -1.5 1.5 1.9774 1.9774 1.94906 -1.6 1.6 2.10922 2.10922 2.079 -1.7 1.7 2.24105 2.24105 2.20893 -1.8 1.8 2.37288 2.37288 2.33887 -1.9 1.9 2.5047 2.5047 2.46881 -2 2 2.63653 2.63653 2.59875 -2.1 2.1 2.76835 2.76835 2.72868 -2.2 2.2 2.90018 2.90018 2.85862 -2.3 2.3 3.03201 3.03201 2.98856 -2.4 2.4 3.16383 3.16383 3.11849 -2.5 2.5 3.29566 3.29566 3.24843 -2.6 2.6 3.42749 3.42749 3.37837 -2.7 2.7 3.55931 3.55931 3.50831 -2.8 2.8 3.69114 3.69114 3.63824 -2.9 2.9 3.82297 3.82297 3.76818 -3 3 3.95479 3.95479 3.89812 -3.1 3.1 4.08662 4.08662 4.02806 -3.2 3.2 4.21845 4.21845 4.15799 -3.3 3.3 4.35027 4.35027 4.28793 -3.4 3.4 4.4821 4.4821 4.41787 -3.5 3.5 4.61392 4.60487 4.5478 -3.6 3.6 4.74575 4.72763 4.67774 -3.7 3.7 4.87758 4.85946 4.80768 -3.8 3.8 5.0094 4.99128 4.93762 -3.9 3.9 5.14123 5.12311 5.06755 -4 4 5.27306 5.24588 5.19749 -4.1 4.1 5.40488 5.3777 5.32743 -4.2 4.2 5.53671 5.50953 5.45737 -4.3 4.3 5.66854 5.64136 5.5873 -4.4 4.4 5.80036 5.77318 5.71724 -4.5 4.5 5.93219 5.90501 5.84718 -4.6 4.6 6.06402 6.03684 5.97711 -4.7 4.7 6.19584 6.16866 6.10705 -4.8 4.8 6.32767 6.30049 6.23699 -4.9 4.9 6.45949 6.43232 6.36693 -5 5 6.59132 6.56414 6.49686 -5.1 5.1 6.72315 6.69597 6.6268 -5.2 5.2 6.85497 6.82779 6.75674 -5.3 5.3 6.9868 6.95962 6.88668 -5.4 5.4 7.11863 7.09145 7.01661 -5.5 5.5 7.25045 7.22327 7.14655 -5.6 5.6 7.38228 7.3551 7.27649 -5.7 5.7 7.51411 7.48693 7.40642 -5.8 5.8 7.64593 7.61875 7.53636 -5.9 5.9 7.77776 7.75058 7.6663 -6 6 7.90959 7.88241 7.79624 -6.1 6.1 8.04141 8.01423 7.92617 -6.2 6.2 8.17324 8.14606 8.05611 -6.3 6.3 8.30506 8.27789 8.18605 -6.4 6.4 8.43689 8.40971 8.31599 -6.5 6.5 8.56872 8.54154 8.44592 -6.6 6.6 8.70054 8.67336 8.57586 -6.7 6.7 8.83237 8.80519 8.7058 -6.8 6.8 8.9642 8.93702 8.83574 -6.9 6.9 9.09602 9.06884 8.96567 -7 7 9.22785 9.20067 9.09561 -7.1 7.1 9.35968 9.3325 9.22555 -7.2 7.2 9.4915 9.46432 9.35548 -7.3 7.3 9.62333 9.59615 9.48542 -7.4 7.4 9.75515 9.72798 9.61536 -7.5 7.5 9.88698 9.8598 9.7453 -7.6 7.6 10.0188 9.99163 9.87523 -7.7 7.7 10.1506 10.1235 10.0052 -7.8 7.8 10.2825 10.2553 10.1351 -7.9 7.9 10.4143 10.3871 10.265 -8 8 10.5461 10.5189 10.395 -8.1 8.1 10.6779 10.6508 10.5249 -8.2 8.2 10.8098 10.7826 10.6549 -8.3 8.3 10.9416 10.9144 10.7848 -8.4 8.4 11.0734 11.0462 10.9147 -8.5 8.5 11.2052 11.1781 11.0447 -8.6 8.6 11.3371 11.3099 11.1746 -8.7 8.7 11.4689 11.4417 11.3045 -8.8 8.8 11.6007 11.5735 11.4345 -8.9 8.9 11.7326 11.7054 11.5644 -9 9 11.8644 11.8372 11.6944 -9.1 9.1 11.9962 11.969 11.8243 -9.2 9.2 12.128 12.1009 11.9542 -9.3 9.3 12.2599 12.2327 12.0842 -9.4 9.4 12.3917 12.3645 12.2141 -9.5 9.5 12.5235 12.4963 12.344 -9.6 9.6 12.6553 12.6282 12.474 -9.7 9.7 12.7872 12.76 12.6039 -9.8 9.8 12.919 12.8918 12.7339 -9.9 9.9 13.0508 13.0236 12.8638 -10 10 13.1826 13.1555 12.9937 -10.1 10.1 13.3145 13.2873 13.1237 -10.2 10.2 13.4463 13.4191 13.2536 -10.3 10.3 13.5781 13.5509 13.3835 -10.4 10.4 13.7099 13.6828 13.5135 -10.5 10.5 13.8418 13.8146 13.6434 -10.6 10.6 13.9736 13.9464 13.7734 -10.7 10.7 14.1054 14.0782 13.9033 -10.8 10.8 14.2373 14.2101 14.0332 -10.9 10.9 14.3691 14.3419 14.1632 -11 11 14.5009 14.4737 14.2931 -11.1 11.1 14.6327 14.6056 14.423 -11.2 11.2 14.7646 14.7374 14.553 -11.3 11.3 14.8964 14.8692 14.6829 -11.4 11.4 15.0282 15.001 14.8128 -11.5 11.5 15.16 15.1329 14.9428 -11.6 11.6 15.2919 15.2647 15.0727 -11.7 11.7 15.4237 15.3965 15.2027 -11.8 11.8 15.5555 15.5283 15.3326 -11.9 11.9 15.6873 15.6602 15.4625 -12 12 15.8192 15.792 15.5925 -12.1 12.1 15.951 15.9238 15.7224 -12.2 12.2 16.0828 16.0556 15.8523 -12.3 12.3 16.2146 16.1875 15.9823 -12.4 12.4 16.3465 16.3193 16.1122 -12.5 12.5 16.4783 16.4511 16.2422 -12.6 12.6 16.6101 16.5829 16.3721 -12.7 12.7 16.742 16.7148 16.502 -12.8 12.8 16.8738 16.8466 16.632 -12.9 12.9 17.0056 16.9784 16.7619 -13 13 17.1374 17.1103 16.8918 -13.1 13.1 17.2693 17.2421 17.0218 -13.2 13.2 17.4011 17.3739 17.1517 -13.3 13.3 17.5329 17.5057 17.2817 -13.4 13.4 17.6647 17.6376 17.4116 -13.5 13.5 17.7966 17.7694 17.5415 -13.6 13.6 17.9284 17.9012 17.6715 -13.7 13.7 18.0602 18.033 17.8014 -13.8 13.8 18.192 18.1649 17.9313 -13.9 13.9 18.3239 18.2967 18.0613 -14 14 18.4557 18.4285 18.1912 -14.1 14.1 18.5875 18.5603 18.3212 -14.2 14.2 18.7194 18.6922 18.4511 -14.3 14.3 18.8512 18.824 18.581 -14.4 14.4 18.983 18.9558 18.711 -14.5 14.5 19.1148 19.0877 18.8409 -14.6 14.6 19.2467 19.2195 18.9708 -14.7 14.7 19.3785 19.3513 19.1008 -14.8 14.8 19.5103 19.4831 19.2307 -14.9 14.9 19.6421 19.615 19.3607 -15 15 19.774 19.7468 19.4906 -15.1 15.1 19.9058 19.8786 19.6205 -15.2 15.2 20.0376 20.0104 19.7505 -15.3 15.3 20.1694 20.1423 19.8804 -15.4 15.4 20.3013 20.2741 20.0082 -15.5 15.5 20.4331 20.4059 20.1343 -15.6 15.6 20.5649 20.5377 20.2604 -15.7 15.7 20.6967 20.6605 20.3864 -15.8 15.8 20.8286 20.7923 20.5125 -15.9 15.9 20.9604 20.9242 20.6385 -16 16 21.0922 21.056 20.7646 -16.1 16.1 21.2241 21.1878 20.8906 -16.2 16.2 21.3559 21.3196 21.0167 -16.3 16.3 21.4877 21.4515 21.1427 -16.4 16.4 21.6195 21.5833 21.2688 -16.5 16.5 21.7514 21.7151 21.3949 -16.6 16.6 21.8832 21.8469 21.5209 -16.7 16.7 22.015 21.9788 21.647 -16.8 16.8 22.1468 22.1106 21.773 -16.9 16.9 22.2787 22.2424 21.8991 -17 17 22.4105 22.3743 22.0251 -17.1 17.1 22.5423 22.5061 22.1512 -17.2 17.2 22.6741 22.6288 22.2772 -17.3 17.3 22.806 22.7607 22.4033 -17.4 17.4 22.9378 22.8925 22.5294 -17.5 17.5 23.0696 23.0243 22.6554 -17.6 17.6 23.2014 23.1562 22.7815 -17.7 17.7 23.3333 23.288 22.9075 -17.8 17.8 23.4651 23.4198 23.0336 -17.9 17.9 23.5969 23.5516 23.1596 -18 18 23.7288 23.6835 23.2857 -18.1 18.1 23.8606 23.8153 23.4117 -18.2 18.2 23.9924 23.9471 23.5378 -18.3 18.3 24.1242 24.0789 23.6638 -18.4 18.4 24.2561 24.2108 23.7889 -18.5 18.5 24.3879 24.3426 23.9135 -18.6 18.6 24.5197 24.4744 24.0382 -18.7 18.7 24.6515 24.6062 24.1628 -18.8 18.8 24.7834 24.7381 24.2874 -18.9 18.9 24.9152 24.8699 24.4121 -19 19 25.047 25.0017 24.5367 -19.1 19.1 25.1788 25.1335 24.6614 -19.2 19.2 25.3107 25.2654 24.786 -19.3 19.3 25.4425 25.3972 24.9106 -19.4 19.4 25.5743 25.529 25.0353 -19.5 19.5 25.7062 25.6609 25.1599 -19.6 19.6 25.838 25.7927 25.2846 -19.7 19.7 25.9698 25.9245 25.4092 -19.8 19.8 26.1016 26.0563 25.5338 -19.9 19.9 26.2335 26.1791 25.6585 -20 20 26.3653 26.3109 25.7831 -20.1 20.1 26.4971 26.4428 25.9078 -20.2 20.2 26.6289 26.5746 26.0324 -20.3 20.3 26.7608 26.7064 26.157 -20.4 20.4 26.8926 26.8382 26.2817 -20.5 20.5 27.0244 26.9701 26.4063 -20.6 20.6 27.1562 27.1019 26.531 -20.7 20.7 27.2881 27.2337 26.6556 -20.8 20.8 27.4199 27.3655 26.7802 -20.9 20.9 27.5517 27.4974 26.9049 -21 21 27.6835 27.6292 27.0295 -21.1 21.1 27.8154 27.761 27.1541 -21.2 21.2 27.9472 27.8928 27.2788 -21.3 21.3 28.079 28.0247 27.4034 -21.4 21.4 28.2109 28.1474 27.5281 -21.5 21.5 28.3427 28.2793 27.6527 -21.6 21.6 28.4745 28.4111 27.7773 -21.7 21.7 28.6063 28.5429 27.902 -21.8 21.8 28.7382 28.6747 28.0266 -21.9 21.9 28.87 28.8005 28.1513 -22 22 29.0018 28.9293 28.2759 -22.1 22.1 29.1336 29.0612 28.4005 -22.2 22.2 29.2655 29.193 28.5252 -22.3 22.3 29.3973 29.3248 28.6498 -22.4 22.4 29.5291 29.4566 28.7745 -22.5 22.5 29.6609 29.5885 28.8991 -22.6 22.6 29.7928 29.7203 29.0237 -22.7 22.7 29.9246 29.8431 29.1484 -22.8 22.8 30.0564 29.9749 29.273 -22.9 22.9 30.1882 30.0977 29.3976 -23 23 30.3201 30.2295 29.5223 -23.1 23.1 30.4519 30.3613 29.6469 -23.2 23.2 30.5837 30.4931 29.7716 -23.3 23.3 30.7156 30.625 29.8962 -23.4 23.4 30.8474 30.7568 30.0208 -23.5 23.5 30.9792 30.8886 30.1455 -23.6 23.6 31.111 31.0204 30.2701 -23.7 23.7 31.2429 31.1523 30.3948 -23.8 23.8 31.3747 31.2841 30.5194 -23.9 23.9 31.5065 31.4159 30.644 -24 24 31.6383 31.5477 30.7687 -24.1 24.1 31.7702 31.6796 30.8933 -24.2 24.2 31.902 31.8114 31.018 -24.3 24.3 32.0338 31.9432 31.1426 -24.4 24.4 32.1656 32.075 31.2672 -24.5 24.5 32.2975 32.2069 31.3919 -24.6 24.6 32.4293 32.3387 31.5165 -24.7 24.7 32.5611 32.4705 31.6412 -24.8 24.8 32.693 32.5933 31.7658 -24.9 24.9 32.8248 32.7251 31.8904 -25 25 32.9566 32.8569 32.0151 -25.1 25.1 33.0884 32.9888 32.1397 -25.2 25.2 33.2203 33.1206 32.2643 -25.3 25.3 33.3521 33.2434 32.389 -25.4 25.4 33.4839 33.3752 32.5136 -25.5 25.5 33.6157 33.507 32.6383 -25.6 25.6 33.7476 33.6388 32.7629 -25.7 25.7 33.8794 33.7707 32.8875 -25.8 25.8 34.0112 33.9025 33.0122 -25.9 25.9 34.143 34.0253 33.1368 -26 26 34.2749 34.1571 33.2615 -26.1 26.1 34.4067 34.2889 33.3861 -26.2 26.2 34.5385 34.4117 33.5107 -26.3 26.3 34.6703 34.5435 33.6354 -26.4 26.4 34.8022 34.6753 33.76 -26.5 26.5 34.934 34.8072 33.8847 -26.6 26.6 35.0658 34.939 34.0093 -26.7 26.7 35.1977 35.0708 34.1339 -26.8 26.8 35.3295 35.2026 34.2586 -26.9 26.9 35.4613 35.3345 34.3832 -27 27 35.5931 35.4663 34.5079 -27.1 27.1 35.725 35.5981 34.6325 -27.2 27.2 35.8568 35.73 34.7571 -27.3 27.3 35.9886 35.8618 34.8818 -27.4 27.4 36.1204 35.9755 35.0064 -27.5 27.5 36.2523 36.1073 35.131 -27.6 27.6 36.3841 36.2391 35.2557 -27.7 27.7 36.5159 36.371 35.3803 -27.8 27.8 36.6477 36.5028 35.505 -27.9 27.9 36.7796 36.6346 35.6296 -28 28 36.9114 36.7664 35.7542 -28.1 28.1 37.0432 36.8983 35.8789 -28.2 28.2 37.175 37.0301 36.0035 -28.3 28.3 37.3069 37.1529 36.1282 -28.4 28.4 37.4387 37.2756 36.2528 -28.5 28.5 37.5705 37.4075 36.3774 -28.6 28.6 37.7024 37.5302 36.5021 -28.7 28.7 37.8342 37.662 36.6244 -28.8 28.8 37.966 37.7939 36.7424 -28.9 28.9 38.0978 37.9257 36.8603 -29 29 38.2297 38.0575 36.9783 -29.1 29.1 38.3615 38.1894 37.0963 -29.2 29.2 38.4933 38.3212 37.2143 -29.3 29.3 38.6251 38.4439 37.3323 -29.4 29.4 38.757 38.5758 37.4503 -29.5 29.5 38.8888 38.7076 37.5683 -29.6 29.6 39.0206 38.8394 37.6863 -29.7 29.7 39.1524 38.9713 37.8042 -29.8 29.8 39.2843 39.094 37.9222 -29.9 29.9 39.4161 39.2258 38.0402 -30 30 39.5479 39.3577 38.1582 -30.1 30.1 39.6798 39.4895 38.2762 -30.2 30.2 39.8116 39.6123 38.3942 -30.3 30.3 39.9434 39.7441 38.5122 -30.4 30.4 40.0752 39.8759 38.6302 -30.5 30.5 40.2071 40.0077 38.7481 -30.6 30.6 40.3389 40.1396 38.8661 -30.7 30.7 40.4707 40.2714 38.9841 -30.8 30.8 40.6025 40.4032 39.1021 -30.9 30.9 40.7344 40.5351 39.2201 -31 31 40.8662 40.6578 39.3381 -31.1 31.1 40.998 40.7896 39.4561 -31.2 31.2 41.1298 40.9034 39.5741 -31.3 31.3 41.2617 41.0352 39.692 -31.4 31.4 41.3935 41.167 39.81 -31.5 31.5 41.5253 41.2988 39.928 -31.6 31.6 41.6571 41.4307 40.046 -31.7 31.7 41.789 41.5625 40.164 -31.8 31.8 41.9208 41.6943 40.282 -31.9 31.9 42.0526 41.8261 40.4 -32 32 42.1845 41.958 40.5179 -32.1 32.1 42.3163 42.0898 40.6359 -32.2 32.2 42.4481 42.2216 40.7539 -32.3 32.3 42.5799 42.3534 40.8719 -32.4 32.4 42.7118 42.4853 40.9899 -32.5 32.5 42.8436 42.608 41.1079 -32.6 32.6 42.9754 42.7399 41.2259 -32.7 32.7 43.1072 42.8717 41.3439 -32.8 32.8 43.2391 43.0035 41.4618 -32.9 32.9 43.3709 43.1353 41.5798 -33 33 43.5027 43.2672 41.6978 -33.1 33.1 43.6345 43.3899 41.8158 -33.2 33.2 43.7664 43.5127 41.9338 -33.3 33.3 43.8982 43.6445 42.0518 -33.4 33.4 44.03 43.7764 42.1698 -33.5 33.5 44.1618 43.9082 42.2878 -33.6 33.6 44.2937 44.04 42.4057 -33.7 33.7 44.4255 44.1628 42.5237 -33.8 33.8 44.5573 44.2946 42.6417 -33.9 33.9 44.6892 44.4264 42.7597 -34 34 44.821 44.5492 42.8777 -34.1 34.1 44.9528 44.681 42.9957 -34.2 34.2 45.0846 44.8128 43.1137 -34.3 34.3 45.2165 44.9447 43.2317 -34.4 34.4 45.3483 45.0716 43.3496 -34.5 34.5 45.4801 45.1902 43.4676 -34.6 34.6 45.6119 45.313 43.5856 -34.7 34.7 45.7438 45.4448 43.7036 -34.8 34.8 45.8756 45.5766 43.8216 -34.9 34.9 46.0074 45.6994 43.9396 -35 35 46.1392 45.8312 44.0576 -35.1 35.1 46.2711 45.954 44.1756 -35.2 35.2 46.4029 46.0677 44.2935 -35.3 35.3 46.5347 46.1995 44.4115 -35.4 35.4 46.6666 46.3313 44.5295 -35.5 35.5 46.7984 46.4632 44.6475 -35.6 35.6 46.9302 46.595 44.7655 -35.7 35.7 47.062 46.7178 44.8835 -35.8 35.8 47.1939 46.8405 45.0015 -35.9 35.9 47.3257 46.9724 45.1195 -36 36 47.4575 47.1042 45.2374 -36.1 36.1 47.5893 47.2302 45.3554 -36.2 36.2 47.7212 47.3588 45.4734 -36.3 36.3 47.853 47.4906 45.5914 -36.4 36.4 47.9848 47.6224 45.7094 -36.5 36.5 48.1166 47.7543 45.8274 -36.6 36.6 48.2485 47.8861 45.9454 -36.7 36.7 48.3803 48.0089 46.0634 -36.8 36.8 48.5121 48.1407 46.1813 -36.9 36.9 48.6439 48.2725 46.2993 -37 37 48.7758 48.3953 46.4173 -37.1 37.1 48.9076 48.5271 46.5353 -37.2 37.2 49.0394 48.6589 46.6533 -37.3 37.3 49.1713 48.7908 46.7713 -37.4 37.4 49.3031 48.9226 46.8893 -37.5 37.5 49.4349 49.0272 47.0073 -37.6 37.6 49.5667 49.1591 47.1252 -37.7 37.7 49.6986 49.2909 47.2432 -37.8 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89.3876 -85.5 85.5 100 97.1078 89.4603 -85.6 85.6 100 97.1462 89.533 -85.7 85.7 100 97.1952 89.6057 -85.8 85.8 100 97.2618 89.6784 -85.9 85.9 100 97.303 89.7511 -86 86 100 97.3742 89.8238 -86.1 86.1 100 97.4072 89.8964 -86.2 86.2 100 97.445 89.9691 -86.3 86.3 100 97.5177 90.0418 -86.4 86.4 100 97.5783 90.1145 -86.5 86.5 100 97.5992 90.1872 -86.6 86.6 100 97.6354 90.2599 -86.7 86.7 100 97.6735 90.3326 -86.8 86.8 100 97.7318 90.4053 -86.9 86.9 100 97.7853 90.4779 -87 87 100 97.8166 90.5506 -87.1 87.1 100 97.8801 90.6233 -87.2 87.2 100 97.9024 90.696 -87.3 87.3 100 97.9435 90.7687 -87.4 87.4 100 97.9896 90.8414 -87.5 87.5 100 98.025 90.9141 -87.6 87.6 100 98.087 90.9868 -87.7 87.7 100 98.1253 91.0594 -87.8 87.8 100 98.17 91.1321 -87.9 87.9 100 98.1971 91.2048 -88 88 100 98.2474 91.2775 -88.1 88.1 100 98.3136 91.3502 -88.2 88.2 100 98.3691 91.4229 -88.3 88.3 100 98.4382 91.4956 -88.4 88.4 100 98.4887 91.5683 -88.5 88.5 100 98.5142 91.6409 -88.6 88.6 100 98.5325 91.7136 -88.7 88.7 100 98.5457 91.7863 -88.8 88.8 100 98.5703 91.859 -88.9 88.9 100 98.6229 91.9317 -89 89 100 98.654 92.0044 -89.1 89.1 100 98.6816 92.0771 -89.2 89.2 100 98.7226 92.1498 -89.3 89.3 100 98.7407 92.2224 -89.4 89.4 100 98.7751 92.2951 -89.5 89.5 100 98.8257 92.3678 -89.6 89.6 100 98.8585 92.4405 -89.7 89.7 100 98.8857 92.5132 -89.8 89.8 100 98.931 92.5859 -89.9 89.9 100 98.9688 92.6586 -90 90 100 99.0034 92.7313 -90.1 90.1 100 99.0397 92.8039 -90.2 90.2 100 99.0693 92.8766 -90.3 90.3 100 99.1054 92.9493 -90.4 90.4 100 99.1489 93.022 -90.5 90.5 100 99.1634 93.0947 -90.6 90.6 100 99.1937 93.1674 -90.7 90.7 100 99.1985 93.2401 -90.8 90.8 100 99.2299 93.3128 -90.9 90.9 100 99.2571 93.3854 -91 91 100 99.2897 93.4581 -91.1 91.1 100 99.3115 93.5308 -91.2 91.2 100 99.3568 93.6035 -91.3 91.3 100 99.3862 93.6762 -91.4 91.4 100 99.393 93.7489 -91.5 91.5 100 99.4302 93.8216 -91.6 91.6 100 99.5009 93.8943 -91.7 91.7 100 99.538 93.9669 -91.8 91.8 100 99.5678 94.0396 -91.9 91.9 100 99.5923 94.1123 -92 92 100 99.5982 94.185 -92.1 92.1 100 99.6376 94.2577 -92.2 92.2 100 99.6557 94.3304 -92.3 92.3 100 99.6881 94.4031 -92.4 92.4 100 99.7101 94.4758 -92.5 92.5 100 99.7101 94.5484 -92.6 92.6 100 99.7307 94.6211 -92.7 92.7 100 99.7463 94.6938 -92.8 92.8 100 99.7735 94.7665 -92.9 92.9 100 99.8097 94.8392 -93 93 100 99.8208 94.9119 -93.1 93.1 100 99.8414 94.9846 -93.2 93.2 100 99.8508 95.0573 -93.3 93.3 100 99.8624 95.1299 -93.4 93.4 100 99.8913 95.2026 -93.5 93.5 100 99.9003 95.2753 -93.6 93.6 100 99.9094 95.348 -93.7 93.7 100 99.9275 95.4207 -93.8 93.8 100 99.954 95.4934 -93.9 93.9 100 99.9547 95.5661 -94 94 100 99.9547 95.6388 -94.1 94.1 100 99.9728 95.7114 -94.2 94.2 100 99.9728 95.7841 -94.3 94.3 100 99.9728 95.8568 -94.4 94.4 100 99.9728 95.9295 -94.5 94.5 100 99.9819 96.0022 -94.6 94.6 100 99.9819 96.0749 -94.7 94.7 100 99.9819 96.1476 -94.8 94.8 100 99.9819 96.2203 -94.9 94.9 100 99.9819 96.2929 -95 95 100 99.9819 96.3656 -95.1 95.1 100 99.9819 96.4383 -95.2 95.2 100 99.9819 96.511 -95.3 95.3 100 99.9909 96.5837 -95.4 95.4 100 99.9909 96.6564 -95.5 95.5 100 99.9909 96.7291 -95.6 95.6 100 99.9909 96.8018 -95.7 95.7 100 99.9909 96.8744 -95.8 95.8 100 99.9909 96.9471 -95.9 95.9 100 99.9909 97.0198 -96 96 100 99.9909 97.0925 -96.1 96.1 100 99.9909 97.1652 -96.2 96.2 100 99.9909 97.2379 -96.3 96.3 100 99.9909 97.3106 -96.4 96.4 100 99.9909 97.3833 -96.5 96.5 100 99.9909 97.4559 -96.6 96.6 100 99.9909 97.5286 -96.7 96.7 100 99.9909 97.6013 -96.8 96.8 100 99.9909 97.674 -96.9 96.9 100 99.9909 97.7467 -97 97 100 99.9909 97.8194 -97.1 97.1 100 99.9909 97.8921 -97.2 97.2 100 99.9909 97.9648 -97.3 97.3 100 99.9909 98.0374 -97.4 97.4 100 99.9909 98.1101 -97.5 97.5 100 99.9909 98.1828 -97.6 97.6 100 99.9909 98.2555 -97.7 97.7 100 99.9909 98.3282 -97.8 97.8 100 99.9909 98.4009 -97.9 97.9 100 99.9948 98.4736 -98 98 100 100 98.5463 -98.1 98.1 100 100 98.6189 -98.2 98.2 100 100 98.6916 -98.3 98.3 100 100 98.7643 -98.4 98.4 100 100 98.837 -98.5 98.5 100 100 98.9097 -98.6 98.6 100 100 98.9824 -98.7 98.7 100 100 99.0551 -98.8 98.8 100 100 99.1278 -98.9 98.9 100 100 99.2004 -99 99 100 100 99.2731 -99.1 99.1 100 100 99.3458 -99.2 99.2 100 100 99.4185 -99.3 99.3 100 100 99.4912 -99.4 99.4 100 100 99.5639 -99.5 99.5 100 100 99.6366 -99.6 99.6 100 100 99.7093 -99.7 99.7 100 100 99.7819 -99.8 99.8 100 100 99.8546 -99.9 99.9 100 100 99.9273 -100 100 100 100 100 - -Lift curves more -Size Random Optimal Selective Naive Bayes Univariate relationship -0 0 0 0 0 -0.1 0.1 0.414204 0.414204 0.185817 -0.2 0.2 0.828409 0.828409 0.371634 -0.3 0.3 1.24261 1.24261 0.557451 -0.4 0.4 1.65682 1.65682 0.743268 -0.5 0.5 2.07102 2.07102 0.929086 -0.6 0.6 2.48523 2.48523 1.1149 -0.7 0.7 2.89943 2.89943 1.30072 -0.8 0.8 3.31364 3.31364 1.48654 -0.9 0.9 3.72784 3.72784 1.67235 -1 1 4.14204 4.14204 1.85817 -1.1 1.1 4.55625 4.55625 2.04399 -1.2 1.2 4.97045 4.97045 2.22981 -1.3 1.3 5.38466 5.38466 2.41562 -1.4 1.4 5.79886 5.79886 2.60144 -1.5 1.5 6.21307 6.21307 2.78726 -1.6 1.6 6.62727 6.62727 2.97307 -1.7 1.7 7.04147 7.04147 3.15889 -1.8 1.8 7.45568 7.45568 3.34471 -1.9 1.9 7.86988 7.86988 3.53053 -2 2 8.28409 8.28409 3.71634 -2.1 2.1 8.69829 8.68204 3.90216 -2.2 2.2 9.1125 9.08403 4.08798 -2.3 2.3 9.5267 9.49824 4.27379 -2.4 2.4 9.94091 9.91244 4.45961 -2.5 2.5 10.3551 10.3266 4.64543 -2.6 2.6 10.7693 10.7408 4.83125 -2.7 2.7 11.1835 11.1551 5.01706 -2.8 2.8 11.5977 11.5693 5.20288 -2.9 2.9 12.0119 11.9835 5.3887 -3 3 12.4261 12.3977 5.57451 -3.1 3.1 12.8403 12.8119 5.76033 -3.2 3.2 13.2545 13.2261 5.94615 -3.3 3.3 13.6687 13.6403 6.13196 -3.4 3.4 14.0829 14.0545 6.31778 -3.5 3.5 14.4972 14.4687 6.5036 -3.6 3.6 14.9114 14.8829 6.68942 -3.7 3.7 15.3256 15.2971 6.87523 -3.8 3.8 15.7398 15.7113 7.06105 -3.9 3.9 16.154 16.1255 7.24687 -4 4 16.5682 16.5397 7.43268 -4.1 4.1 16.9824 16.9539 7.6185 -4.2 4.2 17.3966 17.3681 7.80432 -4.3 4.3 17.8108 17.7823 7.99014 -4.4 4.4 18.225 18.1965 8.17595 -4.5 4.5 18.6392 18.6107 8.36177 -4.6 4.6 19.0534 19.0249 8.54759 -4.7 4.7 19.4676 19.4391 8.7334 -4.8 4.8 19.8818 19.8249 8.91922 -4.9 4.9 20.296 20.2391 9.10504 -5 5 20.7102 20.6533 9.29086 -5.1 5.1 21.1244 21.0675 9.47667 -5.2 5.2 21.5386 21.4817 9.66249 -5.3 5.3 21.9528 21.8959 9.84831 -5.4 5.4 22.367 22.3101 10.0341 -5.5 5.5 22.7812 22.7243 10.2199 -5.6 5.6 23.1954 23.11 10.4058 -5.7 5.7 23.6096 23.5243 10.5916 -5.8 5.8 24.0239 23.9385 10.7774 -5.9 5.9 24.4381 24.3527 10.9632 -6 6 24.8523 24.7099 11.149 -6.1 6.1 25.2665 25.1241 11.3348 -6.2 6.2 25.6807 25.536 11.5207 -6.3 6.3 26.0949 25.8672 11.7065 -6.4 6.4 26.5091 26.2244 11.8923 -6.5 6.5 26.9233 26.6102 12.0781 -6.6 6.6 27.3375 26.9959 12.2639 -6.7 6.7 27.7517 27.3195 12.4497 -6.8 6.8 28.1659 27.6971 12.6356 -6.9 6.9 28.5801 28.0819 12.8214 -7 7 28.9943 28.4311 13.0072 -7.1 7.1 29.4085 28.8107 13.193 -7.2 7.2 29.8227 29.1111 13.3788 -7.3 7.3 30.2369 29.4399 13.5646 -7.4 7.4 30.6511 29.8051 13.7505 -7.5 7.5 31.0653 30.1544 13.9363 -7.6 7.6 31.4795 30.5686 14.1221 -7.7 7.7 31.8937 30.9137 14.3079 -7.8 7.8 32.3079 31.2262 14.4937 -7.9 7.9 32.7221 31.5835 14.6796 -8 8 33.1364 31.8739 14.8654 -8.1 8.1 33.5506 32.2696 15.0512 -8.2 8.2 33.9648 32.6067 15.237 -8.3 8.3 34.379 32.9272 15.4228 -8.4 8.4 34.7932 33.2249 15.6086 -8.5 8.5 35.2074 33.4172 15.7945 -8.6 8.6 35.6216 33.7144 15.9803 -8.7 8.7 36.0358 34.1073 16.1661 -8.8 8.8 36.45 34.4289 16.3519 -8.9 8.9 36.8642 34.7008 16.5377 -9 9 37.2784 35.0468 16.7235 -9.1 9.1 37.6926 35.3584 16.9094 -9.2 9.2 38.1068 35.6872 17.0952 -9.3 9.3 38.521 36.0026 17.281 -9.4 9.4 38.9352 36.4018 17.4668 -9.5 9.5 39.3494 36.7208 17.6526 -9.6 9.6 39.7636 37.0893 17.8384 -9.7 9.7 40.1778 37.3671 18.0243 -9.8 9.8 40.592 37.6678 18.2101 -9.9 9.9 41.0062 37.9889 18.3959 -10 10 41.4204 38.2892 18.5817 -10.1 10.1 41.8346 38.5945 18.7675 -10.2 10.2 42.2488 38.8899 18.9533 -10.3 10.3 42.6631 39.1618 19.1392 -10.4 10.4 43.0773 39.4906 19.325 -10.5 10.5 43.4915 39.8018 19.5108 -10.6 10.6 43.9057 40.057 19.6966 -10.7 10.7 44.3199 40.3631 19.8824 -10.8 10.8 44.7341 40.7204 20.0682 -10.9 10.9 45.1483 41.0057 20.2541 -11 11 45.5625 41.3332 20.4399 -11.1 11.1 45.9767 41.6499 20.6257 -11.2 11.2 46.3909 41.8988 20.8115 -11.3 11.3 46.8051 42.2356 20.9973 -11.4 11.4 47.2193 42.6084 21.1832 -11.5 11.5 47.6335 42.9651 21.369 -11.6 11.6 48.0477 43.299 21.5548 -11.7 11.7 48.4619 43.5548 21.7406 -11.8 11.8 48.8761 43.7518 21.9264 -11.9 11.9 49.2903 43.9917 22.1122 -12 12 49.7045 44.1979 22.2981 -12.1 12.1 50.1187 44.4541 22.4839 -12.2 12.2 50.5329 44.7829 22.6697 -12.3 12.3 50.9471 45.0569 22.8555 -12.4 12.4 51.3613 45.3505 23.0413 -12.5 12.5 51.7755 45.57 23.2271 -12.6 12.6 52.1898 45.8731 23.413 -12.7 12.7 52.604 46.1422 23.5988 -12.8 12.8 53.0182 46.4276 23.7846 -12.9 12.9 53.4324 46.7714 23.9704 -13 13 53.8466 46.9863 24.1562 -13.1 13.1 54.2608 47.3022 24.342 -13.2 13.2 54.675 47.5481 24.5279 -13.3 13.3 55.0892 47.7793 24.7137 -13.4 13.4 55.5034 48.0738 24.8995 -13.5 13.5 55.9176 48.3742 25.0853 -13.6 13.6 56.3318 48.7226 25.2711 -13.7 13.7 56.746 48.9464 25.4569 -13.8 13.8 57.1602 49.1322 25.6428 -13.9 13.9 57.5744 49.4276 25.8286 -14 14 57.9886 49.7384 26.0144 -14.1 14.1 58.4028 49.9288 26.2002 -14.2 14.2 58.817 50.2135 26.386 -14.3 14.3 59.2312 50.4183 26.5718 -14.4 14.4 59.6454 50.6787 26.7577 -14.5 14.5 60.0596 50.9722 26.9435 -14.6 14.6 60.4738 51.2231 27.1293 -14.7 14.7 60.888 51.5039 27.3151 -14.8 14.8 61.3022 51.761 27.5009 -14.9 14.9 61.7165 51.9812 27.6868 -15 15 62.1307 52.2346 27.8726 -15.1 15.1 62.5449 52.3826 28.0584 -15.2 15.2 62.9591 52.6379 28.2442 -15.3 15.3 63.3733 52.9264 28.43 -15.4 15.4 63.7875 53.2674 28.6158 -15.5 15.5 64.2017 53.5555 28.8017 -15.6 15.6 64.6159 53.8843 28.9875 -15.7 15.7 65.0301 54.0564 29.1733 -15.8 15.8 65.4443 54.3142 29.3591 -15.9 15.9 65.8585 54.4998 29.5449 -16 16 66.2727 54.6872 29.7307 -16.1 16.1 66.6869 54.9388 29.9166 -16.2 16.2 67.1011 55.0646 30.1024 -16.3 16.3 67.5153 55.2466 30.2882 -16.4 16.4 67.9295 55.4518 30.474 -16.5 16.5 68.3437 55.6504 30.6598 -16.6 16.6 68.7579 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99.905 +98.5 98.5 100 100 99.911 +98.6 98.6 100 100 99.9169 +98.7 98.7 100 100 99.9228 +98.8 98.8 100 100 99.9288 +98.9 98.9 100 100 99.9347 +99 99 100 100 99.9406 +99.1 99.1 100 100 99.9466 +99.2 99.2 100 100 99.9525 +99.3 99.3 100 100 99.9584 +99.4 99.4 100 100 99.9644 +99.5 99.5 100 100 99.9703 +99.6 99.6 100 100 99.9763 +99.7 99.7 100 100 99.9822 +99.8 99.8 100 100 99.9881 +99.9 99.9 100 100 99.9941 +100 100 100 100 100 diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/TrainEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.TrainEvaluationReport.xls similarity index 96% rename from test/LearningTest/TestKhiops/Standard/Adult/results.ref/TrainEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.TrainEvaluationReport.xls index ee892ac96..5d817bc8c 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/TrainEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -1,2066 +1,2066 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary Adult -Target variable Categorical class -Main target value more -Database ../../../datasets/Adult/Adult.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 34291 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.869295 0.490475 0.926145 -Univariate relationship 0.761628 0.209952 0.780095 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.869295 -Compression 0.490475 -AUC 0.926145 - -Confusion matrix - less more -$less 24742 3107 -$more 1375 5067 - ----------------------------------------------- -Classifier Univariate relationship -Accuracy 0.761628 -Compression 0.209952 -AUC 0.780095 - -Confusion matrix - less more -$less 26117 8174 -$more 0 0 - -% target values -Group less more Interest Frequency Coverage -{Husband, Wife} 0.548238 0.451762 0.422448 15382 0.448573 -{Own-child} 0.985228 0.0147719 0.287464 5348 0.155959 -{Not-in-family} 0.90009 0.0999098 0.142352 8868 0.25861 -{Unmarried} 0.93731 0.0626899 0.101483 3621 0.105596 -{Other-relative} 0.969216 0.0307836 0.0462536 1072 0.0312618 -Total 0.761628 0.238372 1 34291 1 - - - ----------------------------------------------- -Lift curves less -Size Random Optimal Selective Naive Bayes Univariate relationship -0 0 0 0 0 -0.1 0.1 0.131298 0.131298 0.129358 -0.2 0.2 0.262595 0.262595 0.258716 -0.3 0.3 0.393893 0.393893 0.388074 -0.4 0.4 0.52519 0.52519 0.517432 -0.5 0.5 0.656488 0.656488 0.646791 -0.6 0.6 0.787786 0.787786 0.776149 -0.7 0.7 0.919083 0.919083 0.905507 -0.8 0.8 1.05038 1.05038 1.03486 -0.9 0.9 1.18168 1.18168 1.16422 -1 1 1.31298 1.31298 1.29358 -1.1 1.1 1.44427 1.44427 1.42294 -1.2 1.2 1.57557 1.57557 1.5523 -1.3 1.3 1.70687 1.70687 1.68166 -1.4 1.4 1.83817 1.83817 1.81101 -1.5 1.5 1.96946 1.96946 1.94037 -1.6 1.6 2.10076 2.10076 2.06973 -1.7 1.7 2.23206 2.23206 2.19909 -1.8 1.8 2.36336 2.36336 2.32845 -1.9 1.9 2.49465 2.49465 2.4578 -2 2 2.62595 2.62595 2.58716 -2.1 2.1 2.75725 2.75725 2.71652 -2.2 2.2 2.88855 2.88855 2.84588 -2.3 2.3 3.01985 3.01985 2.97524 -2.4 2.4 3.15114 3.15114 3.10459 -2.5 2.5 3.28244 3.28244 3.23395 -2.6 2.6 3.41374 3.41374 3.36331 -2.7 2.7 3.54504 3.54504 3.49267 -2.8 2.8 3.67633 3.67633 3.62203 -2.9 2.9 3.80763 3.80763 3.75139 -3 3 3.93893 3.93893 3.88074 -3.1 3.1 4.07023 4.07023 4.0101 -3.2 3.2 4.20152 4.20152 4.13946 -3.3 3.3 4.33282 4.33282 4.26882 -3.4 3.4 4.46412 4.46412 4.39818 -3.5 3.5 4.59542 4.59542 4.52753 -3.6 3.6 4.72671 4.72671 4.65689 -3.7 3.7 4.85801 4.85801 4.78625 -3.8 3.8 4.98931 4.98931 4.91561 -3.9 3.9 5.12061 5.12061 5.04497 -4 4 5.2519 5.2519 5.17432 -4.1 4.1 5.3832 5.3832 5.30368 -4.2 4.2 5.5145 5.5145 5.43304 -4.3 4.3 5.6458 5.6458 5.5624 -4.4 4.4 5.7771 5.7771 5.69176 -4.5 4.5 5.90839 5.90839 5.82111 -4.6 4.6 6.03969 6.03969 5.95047 -4.7 4.7 6.17099 6.17099 6.07983 -4.8 4.8 6.30229 6.30229 6.20919 -4.9 4.9 6.43358 6.43358 6.33855 -5 5 6.56488 6.56488 6.46791 -5.1 5.1 6.69618 6.69618 6.59726 -5.2 5.2 6.82748 6.82748 6.72662 -5.3 5.3 6.95877 6.95495 6.85598 -5.4 5.4 7.09007 7.08624 6.98534 -5.5 5.5 7.22137 7.21754 7.1147 -5.6 5.6 7.35267 7.34884 7.24405 -5.7 5.7 7.48396 7.48014 7.37341 -5.8 5.8 7.61526 7.61143 7.50277 -5.9 5.9 7.74656 7.74273 7.63213 -6 6 7.87786 7.87403 7.76149 -6.1 6.1 8.00915 8.00533 7.89084 -6.2 6.2 8.14045 8.13662 8.0202 -6.3 6.3 8.27175 8.26792 8.14956 -6.4 6.4 8.40305 8.39922 8.27892 -6.5 6.5 8.53435 8.53052 8.40828 -6.6 6.6 8.66564 8.66181 8.53764 -6.7 6.7 8.79694 8.79311 8.66699 -6.8 6.8 8.92824 8.92441 8.79635 -6.9 6.9 9.05954 9.05571 8.92571 -7 7 9.19083 9.187 9.05507 -7.1 7.1 9.32213 9.3183 9.18443 -7.2 7.2 9.45343 9.4496 9.31378 -7.3 7.3 9.58473 9.5809 9.44314 -7.4 7.4 9.71602 9.7122 9.5725 -7.5 7.5 9.84732 9.84349 9.70186 -7.6 7.6 9.97862 9.97479 9.83122 -7.7 7.7 10.1099 10.1061 9.96057 -7.8 7.8 10.2412 10.2374 10.0899 -7.9 7.9 10.3725 10.3687 10.2193 -8 8 10.5038 10.5 10.3486 -8.1 8.1 10.6351 10.6313 10.478 -8.2 8.2 10.7664 10.7626 10.6074 -8.3 8.3 10.8977 10.8939 10.7367 -8.4 8.4 11.029 11.0252 10.8661 -8.5 8.5 11.1603 11.1565 10.9954 -8.6 8.6 11.2916 11.2878 11.1248 -8.7 8.7 11.4229 11.4191 11.2542 -8.8 8.8 11.5542 11.5504 11.3835 -8.9 8.9 11.6855 11.6778 11.5129 -9 9 11.8168 11.8091 11.6422 -9.1 9.1 11.9481 11.9404 11.7716 -9.2 9.2 12.0794 12.0717 11.9009 -9.3 9.3 12.2107 12.203 12.0303 -9.4 9.4 12.342 12.3305 12.1597 -9.5 9.5 12.4733 12.4618 12.289 -9.6 9.6 12.6046 12.5931 12.4184 -9.7 9.7 12.7359 12.7244 12.5477 -9.8 9.8 12.8672 12.8557 12.6771 -9.9 9.9 12.9985 12.987 12.8065 -10 10 13.1298 13.1183 12.9358 -10.1 10.1 13.2611 13.2496 13.0652 -10.2 10.2 13.3924 13.3809 13.1945 -10.3 10.3 13.5237 13.5083 13.3239 -10.4 10.4 13.655 13.6396 13.4532 -10.5 10.5 13.7863 13.7709 13.5826 -10.6 10.6 13.9175 13.9022 13.712 -10.7 10.7 14.0488 14.0335 13.8413 -10.8 10.8 14.1801 14.1648 13.9707 -10.9 10.9 14.3114 14.2961 14.1 -11 11 14.4427 14.4274 14.2294 -11.1 11.1 14.574 14.5587 14.3588 -11.2 11.2 14.7053 14.69 14.4881 -11.3 11.3 14.8366 14.8213 14.6175 -11.4 11.4 14.9679 14.9526 14.7468 -11.5 11.5 15.0992 15.0839 14.8762 -11.6 11.6 15.2305 15.2152 15.0055 -11.7 11.7 15.3618 15.3465 15.1349 -11.8 11.8 15.4931 15.4778 15.2643 -11.9 11.9 15.6244 15.6091 15.3936 -12 12 15.7557 15.7404 15.523 -12.1 12.1 15.887 15.8717 15.6523 -12.2 12.2 16.0183 16.003 15.7817 -12.3 12.3 16.1496 16.1343 15.911 -12.4 12.4 16.2809 16.2656 16.0404 -12.5 12.5 16.4122 16.3969 16.1698 -12.6 12.6 16.5435 16.5282 16.2991 -12.7 12.7 16.6748 16.6595 16.4285 -12.8 12.8 16.8061 16.7908 16.5578 -12.9 12.9 16.9374 16.9221 16.6872 -13 13 17.0687 17.0534 16.8166 -13.1 13.1 17.2 17.1847 16.9459 -13.2 13.2 17.3313 17.316 17.0753 -13.3 13.3 17.4626 17.4473 17.2046 -13.4 13.4 17.5939 17.5786 17.334 -13.5 13.5 17.7252 17.7099 17.4633 -13.6 13.6 17.8565 17.8412 17.5927 -13.7 13.7 17.9878 17.9725 17.7221 -13.8 13.8 18.1191 18.1038 17.8514 -13.9 13.9 18.2504 18.2351 17.9808 -14 14 18.3817 18.3664 18.1101 -14.1 14.1 18.513 18.4976 18.2395 -14.2 14.2 18.6443 18.6289 18.3689 -14.3 14.3 18.7756 18.7602 18.4982 -14.4 14.4 18.9069 18.8915 18.6276 -14.5 14.5 19.0382 19.0228 18.7569 -14.6 14.6 19.1695 19.1541 18.8863 -14.7 14.7 19.3008 19.2854 19.0156 -14.8 14.8 19.432 19.4167 19.145 -14.9 14.9 19.5633 19.548 19.2744 -15 15 19.6946 19.6793 19.4037 -15.1 15.1 19.8259 19.8106 19.5331 -15.2 15.2 19.9572 19.9419 19.6624 -15.3 15.3 20.0885 20.0732 19.7918 -15.4 15.4 20.2198 20.2045 19.9211 -15.5 15.5 20.3511 20.3358 20.0505 -15.6 15.6 20.4824 20.4671 20.1798 -15.7 15.7 20.6137 20.5984 20.307 -15.8 15.8 20.745 20.7297 20.4343 -15.9 15.9 20.8763 20.861 20.5615 -16 16 21.0076 20.9885 20.6888 -16.1 16.1 21.1389 21.1198 20.8161 -16.2 16.2 21.2702 21.2511 20.9433 -16.3 16.3 21.4015 21.3824 21.0706 -16.4 16.4 21.5328 21.5137 21.1978 -16.5 16.5 21.6641 21.645 21.3251 -16.6 16.6 21.7954 21.7763 21.4523 -16.7 16.7 21.9267 21.9076 21.5796 -16.8 16.8 22.058 22.035 21.7068 -16.9 16.9 22.1893 22.1625 21.8341 -17 17 22.3206 22.2938 21.9614 -17.1 17.1 22.4519 22.4251 22.0886 -17.2 17.2 22.5832 22.5564 22.2159 -17.3 17.3 22.7145 22.6877 22.3431 -17.4 17.4 22.8458 22.819 22.4704 -17.5 17.5 22.9771 22.9503 22.5976 -17.6 17.6 23.1084 23.0816 22.7249 -17.7 17.7 23.2397 23.2129 22.8522 -17.8 17.8 23.371 23.3442 22.9794 -17.9 17.9 23.5023 23.4755 23.1067 -18 18 23.6336 23.6068 23.2339 -18.1 18.1 23.7649 23.7381 23.3612 -18.2 18.2 23.8962 23.8694 23.4884 -18.3 18.3 24.0275 24.0007 23.6157 -18.4 18.4 24.1588 24.132 23.7429 -18.5 18.5 24.2901 24.2633 23.8702 -18.6 18.6 24.4214 24.3946 23.9975 -18.7 18.7 24.5527 24.5259 24.1247 -18.8 18.8 24.684 24.6533 24.2487 -18.9 18.9 24.8153 24.7846 24.3718 -19 19 24.9465 24.9139 24.4948 -19.1 19.1 25.0778 25.0434 24.6179 -19.2 19.2 25.2091 25.1747 24.741 -19.3 19.3 25.3404 25.3022 24.864 -19.4 19.4 25.4717 25.4334 24.9871 -19.5 19.5 25.603 25.5647 25.1102 -19.6 19.6 25.7343 25.696 25.2332 -19.7 19.7 25.8656 25.8273 25.3563 -19.8 19.8 25.9969 25.9586 25.4794 -19.9 19.9 26.1282 26.0899 25.6024 -20 20 26.2595 26.2212 25.7255 -20.1 20.1 26.3908 26.3525 25.8486 -20.2 20.2 26.5221 26.4838 25.9716 -20.3 20.3 26.6534 26.6151 26.0947 -20.4 20.4 26.7847 26.7464 26.2178 -20.5 20.5 26.916 26.8777 26.3408 -20.6 20.6 27.0473 27.009 26.4639 -20.7 20.7 27.1786 27.1403 26.587 -20.8 20.8 27.3099 27.2716 26.71 -20.9 20.9 27.4412 27.4029 26.8331 -21 21 27.5725 27.5342 26.9562 -21.1 21.1 27.7038 27.6655 27.0792 -21.2 21.2 27.8351 27.7968 27.2023 -21.3 21.3 27.9664 27.9281 27.3254 -21.4 21.4 28.0977 28.0594 27.4484 -21.5 21.5 28.229 28.1907 27.5715 -21.6 21.6 28.3603 28.322 27.6946 -21.7 21.7 28.4916 28.4533 27.8176 -21.8 21.8 28.6229 28.5846 27.9407 -21.9 21.9 28.7542 28.7159 28.0638 -22 22 28.8855 28.8472 28.1868 -22.1 22.1 29.0168 28.9785 28.3099 -22.2 22.2 29.1481 29.1098 28.433 -22.3 22.3 29.2794 29.2411 28.556 -22.4 22.4 29.4107 29.3647 28.6791 -22.5 22.5 29.542 29.496 28.8022 -22.6 22.6 29.6733 29.6197 28.9252 -22.7 22.7 29.8046 29.751 29.0483 -22.8 22.8 29.9359 29.8823 29.1714 -22.9 22.9 30.0672 30.0136 29.2944 -23 23 30.1985 30.1448 29.4175 -23.1 23.1 30.3298 30.2761 29.5406 -23.2 23.2 30.461 30.4074 29.6636 -23.3 23.3 30.5923 30.5387 29.7867 -23.4 23.4 30.7236 30.6662 29.9098 -23.5 23.5 30.8549 30.7975 30.0328 -23.6 23.6 30.9862 30.9288 30.1559 -23.7 23.7 31.1175 31.0601 30.279 -23.8 23.8 31.2488 31.1914 30.402 -23.9 23.9 31.3801 31.3227 30.5251 -24 24 31.5114 31.454 30.6482 -24.1 24.1 31.6427 31.5853 30.7712 -24.2 24.2 31.774 31.7128 30.8943 -24.3 24.3 31.9053 31.8441 31.0174 -24.4 24.4 32.0366 31.9754 31.1404 -24.5 24.5 32.1679 32.1028 31.2635 -24.6 24.6 32.2992 32.2341 31.3866 -24.7 24.7 32.4305 32.3616 31.5096 -24.8 24.8 32.5618 32.4929 31.6327 -24.9 24.9 32.6931 32.6204 31.7558 -25 25 32.8244 32.7517 31.8788 -25.1 25.1 32.9557 32.883 32.0019 -25.2 25.2 33.087 33.0143 32.125 -25.3 25.3 33.2183 33.1455 32.248 -25.4 25.4 33.3496 33.2768 32.3711 -25.5 25.5 33.4809 33.4081 32.4942 -25.6 25.6 33.6122 33.5394 32.6172 -25.7 25.7 33.7435 33.6707 32.7403 -25.8 25.8 33.8748 33.802 32.8634 -25.9 25.9 34.0061 33.9333 32.9864 -26 26 34.1374 34.0608 33.1095 -26.1 26.1 34.2687 34.1921 33.2326 -26.2 26.2 34.4 34.3234 33.3556 -26.3 26.3 34.5313 34.4547 33.4787 -26.4 26.4 34.6626 34.5822 33.6018 -26.5 26.5 34.7939 34.7135 33.7248 -26.6 26.6 34.9252 34.8448 33.8479 -26.7 26.7 35.0565 34.9722 33.971 -26.8 26.8 35.1878 35.1035 34.094 -26.9 26.9 35.3191 35.2348 34.2171 -27 27 35.4504 35.3661 34.3402 -27.1 27.1 35.5817 35.4974 34.4632 -27.2 27.2 35.713 35.6287 34.5863 -27.3 27.3 35.8443 35.76 34.7094 -27.4 27.4 35.9755 35.8913 34.8324 -27.5 27.5 36.1068 36.0226 34.9555 -27.6 27.6 36.2381 36.1539 35.0786 -27.7 27.7 36.3694 36.2852 35.2016 -27.8 27.8 36.5007 36.4165 35.3247 -27.9 27.9 36.632 36.5478 35.4478 -28 28 36.7633 36.6791 35.5708 -28.1 28.1 36.8946 36.8104 35.6939 -28.2 28.2 37.0259 36.9417 35.817 -28.3 28.3 37.1572 37.0692 35.94 -28.4 28.4 37.2885 37.2005 36.0631 -28.5 28.5 37.4198 37.3318 36.1862 -28.6 28.6 37.5511 37.4631 36.3092 -28.7 28.7 37.6824 37.5944 36.4323 -28.8 28.8 37.8137 37.7218 36.5554 -28.9 28.9 37.945 37.8531 36.6784 -29 29 38.0763 37.9844 36.8015 -29.1 29.1 38.2076 38.1119 36.9246 -29.2 29.2 38.3389 38.2432 37.0476 -29.3 29.3 38.4702 38.3745 37.1698 -29.4 29.4 38.6015 38.5058 37.288 -29.5 29.5 38.7328 38.6371 37.4062 -29.6 29.6 38.8641 38.7684 37.5243 -29.7 29.7 38.9954 38.8958 37.6425 -29.8 29.8 39.1267 39.0271 37.7607 -29.9 29.9 39.258 39.1584 37.8789 -30 30 39.3893 39.2897 37.9971 -30.1 30.1 39.5206 39.421 38.1152 -30.2 30.2 39.6519 39.5523 38.2334 -30.3 30.3 39.7832 39.6798 38.3516 -30.4 30.4 39.9145 39.8092 38.4698 -30.5 30.5 40.0458 39.9347 38.588 -30.6 30.6 40.1771 40.066 38.7061 -30.7 30.7 40.3084 40.1973 38.8243 -30.8 30.8 40.4397 40.3286 38.9425 -30.9 30.9 40.571 40.4599 39.0607 -31 31 40.7023 40.5912 39.1789 -31.1 31.1 40.8336 40.7225 39.297 -31.2 31.2 40.9649 40.85 39.4152 -31.3 31.3 41.0962 40.9813 39.5334 -31.4 31.4 41.2275 41.1088 39.6516 -31.5 31.5 41.3588 41.2362 39.7698 -31.6 31.6 41.49 41.3675 39.8879 -31.7 31.7 41.6213 41.4988 40.0061 -31.8 31.8 41.7526 41.6301 40.1243 -31.9 31.9 41.8839 41.7614 40.2425 -32 32 42.0152 41.8927 40.3607 -32.1 32.1 42.1465 42.024 40.4788 -32.2 32.2 42.2778 42.1515 40.597 -32.3 32.3 42.4091 42.2789 40.7152 -32.4 32.4 42.5404 42.4102 40.8334 -32.5 32.5 42.6717 42.5415 40.9516 -32.6 32.6 42.803 42.669 41.0697 -32.7 32.7 42.9343 42.8003 41.1879 -32.8 32.8 43.0656 42.9247 41.3061 -32.9 32.9 43.1969 43.0552 41.4243 -33 33 43.3282 43.1827 41.5425 -33.1 33.1 43.4595 43.3102 41.6606 -33.2 33.2 43.5908 43.4338 41.7788 -33.3 33.3 43.7221 43.5651 41.897 -33.4 33.4 43.8534 43.6888 42.0152 -33.5 33.5 43.9847 43.8201 42.1333 -33.6 33.6 44.116 43.9475 42.2515 -33.7 33.7 44.2473 44.075 42.3697 -33.8 33.8 44.3786 44.2025 42.4879 -33.9 33.9 44.5099 44.3338 42.6061 -34 34 44.6412 44.4612 42.7242 -34.1 34.1 44.7725 44.5925 42.8424 -34.2 34.2 44.9038 44.72 42.9606 -34.3 34.3 45.0351 44.8513 43.0788 -34.4 34.4 45.1664 44.9749 43.197 -34.5 34.5 45.2977 45.1062 43.3151 -34.6 34.6 45.429 45.2337 43.4333 -34.7 34.7 45.5603 45.365 43.5515 -34.8 34.8 45.6916 45.4963 43.6697 -34.9 34.9 45.8229 45.6276 43.7879 -35 35 45.9542 45.7551 43.906 -35.1 35.1 46.0855 45.8825 44.0242 -35.2 35.2 46.2168 46.01 44.1424 -35.3 35.3 46.3481 46.1375 44.2606 -35.4 35.4 46.4794 46.2649 44.3788 -35.5 35.5 46.6107 46.3962 44.4969 -35.6 35.6 46.742 46.5253 44.6151 -35.7 35.7 46.8733 46.655 44.7333 -35.8 35.8 47.0045 46.7863 44.8515 -35.9 35.9 47.1358 46.9138 44.9697 -36 36 47.2671 47.0412 45.0878 -36.1 36.1 47.3984 47.1649 45.206 -36.2 36.2 47.5297 47.2962 45.3242 -36.3 36.3 47.661 47.4198 45.4424 -36.4 36.4 47.7923 47.5511 45.5606 -36.5 36.5 47.9236 47.6824 45.6787 -36.6 36.6 48.0549 47.8137 45.7969 -36.7 36.7 48.1862 47.945 45.9151 -36.8 36.8 48.3175 48.0725 46.0333 -36.9 36.9 48.4488 48.1999 46.1515 -37 37 48.5801 48.3312 46.2696 -37.1 37.1 48.7114 48.4587 46.3878 -37.2 37.2 48.8427 48.5862 46.506 -37.3 37.3 48.974 48.7175 46.6242 -37.4 37.4 49.1053 48.8449 46.7424 -37.5 37.5 49.2366 48.9762 46.8605 -37.6 37.6 49.3679 49.0976 46.9787 -37.7 37.7 49.4992 49.2235 47.0969 -37.8 37.8 49.6305 49.3472 47.2151 -37.9 37.9 49.7618 49.4708 47.3333 -38 38 49.8931 49.5983 47.4514 -38.1 38.1 50.0244 49.7257 47.5696 -38.2 38.2 50.1557 49.8532 47.6878 -38.3 38.3 50.287 49.9845 47.806 -38.4 38.4 50.4183 50.1158 47.9242 -38.5 38.5 50.5496 50.2394 48.0423 -38.6 38.6 50.6809 50.3707 48.1605 -38.7 38.7 50.8122 50.4982 48.2787 -38.8 38.8 50.9435 50.6257 48.3969 -38.9 38.9 51.0748 50.7531 48.5151 -39 39 51.2061 50.8844 48.6332 -39.1 39.1 51.3374 51.0119 48.7514 -39.2 39.2 51.4687 51.1394 48.8696 -39.3 39.3 51.6 51.2668 48.9878 -39.4 39.4 51.7313 51.3981 49.106 -39.5 39.5 51.8626 51.5256 49.2241 -39.6 39.6 51.9939 51.6569 49.3423 -39.7 39.7 52.1252 51.7882 49.4605 -39.8 39.8 52.2565 51.9157 49.5787 -39.9 39.9 52.3878 52.0393 49.6969 -40 40 52.519 52.1706 49.815 -40.1 40.1 52.6503 52.2964 49.9332 -40.2 40.2 52.7816 52.4179 50.0514 -40.3 40.3 52.9129 52.5401 50.1696 -40.4 40.4 53.0442 52.669 50.2877 -40.5 40.5 53.1755 52.7965 50.4059 -40.6 40.6 53.3068 52.9239 50.5241 -40.7 40.7 53.4381 53.0514 50.6423 -40.8 40.8 53.5694 53.1789 50.7605 -40.9 40.9 53.7007 53.3102 50.8786 -41 41 53.832 53.4415 50.9968 -41.1 41.1 53.9633 53.5651 51.115 -41.2 41.2 54.0946 53.6964 51.2332 -41.3 41.3 54.2259 53.8277 51.3514 -41.4 41.4 54.3572 53.959 51.4695 -41.5 41.5 54.4885 54.0788 51.5877 -41.6 41.6 54.6198 54.1986 51.7059 -41.7 41.7 54.7511 54.3184 51.8241 -41.8 41.8 54.8824 54.4497 51.9423 -41.9 41.9 55.0137 54.5772 52.0604 -42 42 55.145 54.6953 52.1786 -42.1 42.1 55.2763 54.8245 52.2968 -42.2 42.2 55.4076 54.9558 52.415 -42.3 42.3 55.5389 55.0756 52.5332 -42.4 42.4 55.6702 55.2031 52.6513 -42.5 42.5 55.8015 55.3305 52.7695 -42.6 42.6 55.9328 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81.1 100 94.6751 86.3953 -81.2 81.2 100 94.73 86.4673 -81.3 81.3 100 94.7884 86.5393 -81.4 81.4 100 94.8578 86.6113 -81.5 81.5 100 94.9311 86.6833 -81.6 81.6 100 95.0064 86.7552 -81.7 81.7 100 95.0645 86.8272 -81.8 81.8 100 95.122 86.8992 -81.9 81.9 100 95.1855 86.9712 -82 82 100 95.2435 87.0432 -82.1 82.1 100 95.3096 87.1152 -82.2 82.2 100 95.3627 87.1871 -82.3 82.3 100 95.4099 87.2591 -82.4 82.4 100 95.4666 87.3311 -82.5 82.5 100 95.527 87.4031 -82.6 82.6 100 95.5929 87.4751 -82.7 82.7 100 95.6536 87.547 -82.8 82.8 100 95.7115 87.619 -82.9 82.9 100 95.7853 87.691 -83 83 100 95.8534 87.763 -83.1 83.1 100 95.9167 87.835 -83.2 83.2 100 95.9894 87.907 -83.3 83.3 100 96.0542 87.9789 -83.4 83.4 100 96.1175 88.0509 -83.5 83.5 100 96.1558 88.1229 -83.6 83.6 100 96.2164 88.1949 -83.7 83.7 100 96.2645 88.2669 -83.8 83.8 100 96.3281 88.3389 -83.9 83.9 100 96.3882 88.4108 -84 84 100 96.4422 88.4828 -84.1 84.1 100 96.4886 88.5548 -84.2 84.2 100 96.5444 88.6268 -84.3 84.3 100 96.5989 88.6988 -84.4 84.4 100 96.6606 88.7707 -84.5 84.5 100 96.7166 88.8427 -84.6 84.6 100 96.7679 88.9147 -84.7 84.7 100 96.8182 88.9867 -84.8 84.8 100 96.8689 89.0587 -84.9 84.9 100 96.9369 89.1307 -85 85 100 96.9762 89.2026 -85.1 85.1 100 97.0317 89.2746 -85.2 85.2 100 97.0822 89.3466 -85.3 85.3 100 97.1283 89.4186 -85.4 85.4 100 97.1767 89.4906 -85.5 85.5 100 97.2348 89.5626 -85.6 85.6 100 97.2829 89.6345 -85.7 85.7 100 97.3283 89.7065 -85.8 85.8 100 97.3913 89.7785 -85.9 85.9 100 97.434 89.8505 -86 86 100 97.4653 89.9225 -86.1 86.1 100 97.5035 89.9945 -86.2 86.2 100 97.5345 90.0664 -86.3 86.3 100 97.5739 90.1384 -86.4 86.4 100 97.6295 90.2104 -86.5 86.5 100 97.6709 90.2824 -86.6 86.6 100 97.7151 90.3544 -86.7 86.7 100 97.7537 90.4263 -86.8 86.8 100 97.7901 90.4983 -86.9 86.9 100 97.8439 90.5703 -87 87 100 97.8981 90.6423 -87.1 87.1 100 97.9394 90.7143 -87.2 87.2 100 98.0085 90.7863 -87.3 87.3 100 98.04 90.8582 -87.4 87.4 100 98.0894 90.9302 -87.5 87.5 100 98.1464 91.0022 -87.6 87.6 100 98.1927 91.0742 -87.7 87.7 100 98.2274 91.1462 -87.8 87.8 100 98.266 91.2182 -87.9 87.9 100 98.311 91.2901 -88 88 100 98.3511 91.3621 -88.1 88.1 100 98.38 91.4341 -88.2 88.2 100 98.4107 91.5061 -88.3 88.3 100 98.4531 91.5781 -88.4 88.4 100 98.4941 91.65 -88.5 88.5 100 98.5323 91.722 -88.6 88.6 100 98.564 91.794 -88.7 88.7 100 98.6152 91.866 -88.8 88.8 100 98.6444 91.938 -88.9 88.9 100 98.6842 92.01 -89 89 100 98.7224 92.0819 -89.1 89.1 100 98.761 92.1539 -89.2 89.2 100 98.7923 92.2259 -89.3 89.3 100 98.819 92.2979 -89.4 89.4 100 98.865 92.3699 -89.5 89.5 100 98.9066 92.4419 -89.6 89.6 100 98.9413 92.5138 -89.7 89.7 100 98.9892 92.5858 -89.8 89.8 100 99.0176 92.6578 -89.9 89.9 100 99.0494 92.7298 -90 90 100 99.0772 92.8018 -90.1 90.1 100 99.1245 92.8737 -90.2 90.2 100 99.1645 92.9457 -90.3 90.3 100 99.1803 93.0177 -90.4 90.4 100 99.2133 93.0897 -90.5 90.5 100 99.2317 93.1617 -90.6 90.6 100 99.2651 93.2337 -90.7 90.7 100 99.3031 93.3056 -90.8 90.8 100 99.3376 93.3776 -90.9 90.9 100 99.3702 93.4496 -91 91 100 99.4064 93.5216 -91.1 91.1 100 99.455 93.5936 -91.2 91.2 100 99.4853 93.6656 -91.3 91.3 100 99.5031 93.7375 -91.4 91.4 100 99.5482 93.8095 -91.5 91.5 100 99.5704 93.8815 -91.6 91.6 100 99.5826 93.9535 -91.7 91.7 100 99.6092 94.0255 -91.8 91.8 100 99.6401 94.0974 -91.9 91.9 100 99.6574 94.1694 -92 92 100 99.6835 94.2414 -92.1 92.1 100 99.6991 94.3134 -92.2 92.2 100 99.7154 94.3854 -92.3 92.3 100 99.7303 94.4574 -92.4 92.4 100 99.7471 94.5293 -92.5 92.5 100 99.7622 94.6013 -92.6 92.6 100 99.7959 94.6733 -92.7 92.7 100 99.8392 94.7453 -92.8 92.8 100 99.8468 94.8173 -92.9 92.9 100 99.8698 94.8893 -93 93 100 99.882 94.9612 -93.1 93.1 100 99.9001 95.0332 -93.2 93.2 100 99.9234 95.1052 -93.3 93.3 100 99.9387 95.1772 -93.4 93.4 100 99.9464 95.2492 -93.5 93.5 100 99.9541 95.3211 -93.6 93.6 100 99.9579 95.3931 -93.7 93.7 100 99.9604 95.4651 -93.8 93.8 100 99.9694 95.5371 -93.9 93.9 100 99.9732 95.6091 -94 94 100 99.9732 95.6811 -94.1 94.1 100 99.9732 95.753 -94.2 94.2 100 99.977 95.825 -94.3 94.3 100 99.977 95.897 -94.4 94.4 100 99.9809 95.969 -94.5 94.5 100 99.9809 96.041 -94.6 94.6 100 99.9847 96.113 -94.7 94.7 100 99.9885 96.1849 -94.8 94.8 100 99.9885 96.2569 -94.9 94.9 100 99.9885 96.3289 -95 95 100 99.9962 96.4009 -95.1 95.1 100 99.9962 96.4729 -95.2 95.2 100 99.9962 96.5448 -95.3 95.3 100 99.9962 96.6168 -95.4 95.4 100 99.9962 96.6888 -95.5 95.5 100 99.9962 96.7608 -95.6 95.6 100 100 96.8328 -95.7 95.7 100 100 96.9048 -95.8 95.8 100 100 96.9767 -95.9 95.9 100 100 97.0487 -96 96 100 100 97.1207 -96.1 96.1 100 100 97.1927 -96.2 96.2 100 100 97.2647 -96.3 96.3 100 100 97.3367 -96.4 96.4 100 100 97.4086 -96.5 96.5 100 100 97.4806 -96.6 96.6 100 100 97.5526 -96.7 96.7 100 100 97.6246 -96.8 96.8 100 100 97.6966 -96.9 96.9 100 100 97.7685 -97 97 100 100 97.8405 -97.1 97.1 100 100 97.9125 -97.2 97.2 100 100 97.9845 -97.3 97.3 100 100 98.0565 -97.4 97.4 100 100 98.1285 -97.5 97.5 100 100 98.2004 -97.6 97.6 100 100 98.2724 -97.7 97.7 100 100 98.3444 -97.8 97.8 100 100 98.4164 -97.9 97.9 100 100 98.4884 -98 98 100 100 98.5604 -98.1 98.1 100 100 98.6323 -98.2 98.2 100 100 98.7043 -98.3 98.3 100 100 98.7763 -98.4 98.4 100 100 98.8483 -98.5 98.5 100 100 98.9203 -98.6 98.6 100 100 98.9922 -98.7 98.7 100 100 99.0642 -98.8 98.8 100 100 99.1362 -98.9 98.9 100 100 99.2082 -99 99 100 100 99.2802 -99.1 99.1 100 100 99.3522 -99.2 99.2 100 100 99.4241 -99.3 99.3 100 100 99.4961 -99.4 99.4 100 100 99.5681 -99.5 99.5 100 100 99.6401 -99.6 99.6 100 100 99.7121 -99.7 99.7 100 100 99.7841 -99.8 99.8 100 100 99.856 -99.9 99.9 100 100 99.928 -100 100 100 100 100 - -Lift curves more -Size Random Optimal Selective Naive Bayes Univariate relationship -0 0 0 0 0 -0.1 0.1 0.419513 0.419513 0.18952 -0.2 0.2 0.839026 0.839026 0.37904 -0.3 0.3 1.25854 1.25854 0.56856 -0.4 0.4 1.67805 1.67805 0.75808 -0.5 0.5 2.09757 2.09757 0.9476 -0.6 0.6 2.51708 2.51708 1.13712 -0.7 0.7 2.93659 2.93659 1.32664 -0.8 0.8 3.3561 3.3561 1.51616 -0.9 0.9 3.77562 3.77562 1.70568 -1 1 4.19513 4.19513 1.8952 -1.1 1.1 4.61464 4.61464 2.08472 -1.2 1.2 5.03416 5.03416 2.27424 -1.3 1.3 5.45367 5.45367 2.46376 -1.4 1.4 5.87318 5.87318 2.65328 -1.5 1.5 6.2927 6.2927 2.8428 -1.6 1.6 6.71221 6.71221 3.03232 -1.7 1.7 7.13172 7.13172 3.22184 -1.8 1.8 7.55124 7.55124 3.41136 -1.9 1.9 7.97075 7.97075 3.60088 -2 2 8.39026 8.39026 3.7904 -2.1 2.1 8.80977 8.80977 3.97992 -2.2 2.2 9.22929 9.22929 4.16944 -2.3 2.3 9.6488 9.6488 4.35896 -2.4 2.4 10.0683 10.0683 4.54848 -2.5 2.5 10.4878 10.4878 4.738 -2.6 2.6 10.9073 10.9073 4.92752 -2.7 2.7 11.3269 11.3269 5.11704 -2.8 2.8 11.7464 11.7464 5.30656 -2.9 2.9 12.1659 12.1659 5.49608 -3 3 12.5854 12.5854 5.6856 -3.1 3.1 13.0049 13.0049 5.87512 -3.2 3.2 13.4244 13.4244 6.06464 -3.3 3.3 13.8439 13.8439 6.25416 -3.4 3.4 14.2634 14.2634 6.44368 -3.5 3.5 14.683 14.683 6.6332 -3.6 3.6 15.1025 15.1025 6.82272 -3.7 3.7 15.522 15.522 7.01224 -3.8 3.8 15.9415 15.9415 7.20176 -3.9 3.9 16.361 16.361 7.39128 -4 4 16.7805 16.7805 7.5808 -4.1 4.1 17.2 17.2 7.77032 -4.2 4.2 17.6195 17.6195 7.95984 -4.3 4.3 18.0391 18.0391 8.14936 -4.4 4.4 18.4586 18.4586 8.33888 -4.5 4.5 18.8781 18.8659 8.5284 -4.6 4.6 19.2976 19.2854 8.71792 -4.7 4.7 19.7171 19.7049 8.90744 -4.8 4.8 20.1366 20.1244 9.09696 -4.9 4.9 20.5561 20.5439 9.28648 -5 5 20.9757 20.9634 9.476 -5.1 5.1 21.3952 21.3585 9.66552 -5.2 5.2 21.8147 21.778 9.85504 -5.3 5.3 22.2342 22.1975 10.0446 -5.4 5.4 22.6537 22.6048 10.2341 -5.5 5.5 23.0732 23.0121 10.4236 -5.6 5.6 23.4927 23.4316 10.6131 -5.7 5.7 23.9122 23.8388 10.8026 -5.8 5.8 24.3318 24.2584 10.9922 -5.9 5.9 24.7513 24.6656 11.1817 -6 6 25.1708 25.0851 11.3712 -6.1 6.1 25.5903 25.5047 11.5607 -6.2 6.2 26.0098 25.9119 11.7502 -6.3 6.3 26.4293 26.3029 11.9398 -6.4 6.4 26.8488 26.7143 12.1293 -6.5 6.5 27.2684 27.1215 12.3188 -6.6 6.6 27.6879 27.5166 12.5083 -6.7 6.7 28.1074 27.9116 12.6978 -6.8 6.8 28.5269 28.2822 12.8874 -6.9 6.9 28.9464 28.6274 13.0769 -7 7 29.3659 28.9888 13.2664 -7.1 7.1 29.7854 29.3695 13.4559 -7.2 7.2 30.2049 29.7156 13.6454 -7.3 7.3 30.6245 30.1106 13.835 -7.4 7.4 31.044 30.392 14.0245 -7.5 7.5 31.4635 30.7037 14.214 -7.6 7.6 31.883 31.0748 14.4035 -7.7 7.7 32.3025 31.4408 14.593 -7.8 7.8 32.722 31.8126 14.7826 -7.9 7.9 33.1415 32.18 14.9721 -8 8 33.561 32.5498 15.1616 -8.1 8.1 33.9806 32.886 15.3511 -8.2 8.2 34.4001 33.2501 15.5406 -8.3 8.3 34.8196 33.5708 15.7302 -8.4 8.4 35.2391 33.9056 15.9197 -8.5 8.5 35.6586 34.2858 16.1092 -8.6 8.6 36.0781 34.6345 16.2987 -8.7 8.7 36.4976 34.91 16.4882 -8.8 8.8 36.9172 35.2725 16.6778 -8.9 8.9 37.3367 35.5953 16.8673 -9 9 37.7562 35.8596 17.0568 -9.1 9.1 38.1757 36.1634 17.2463 -9.2 9.2 38.5952 36.4787 17.4358 -9.3 9.3 39.0147 36.7881 17.6254 -9.4 9.4 39.4342 37.0862 17.8149 -9.5 9.5 39.8537 37.3991 18.0044 -9.6 9.6 40.2733 37.7597 18.1939 -9.7 9.7 40.6928 38.0738 18.3834 -9.8 9.8 41.1123 38.4428 18.573 -9.9 9.9 41.5318 38.7346 18.7625 -10 10 41.9513 39.0029 18.952 -10.1 10.1 42.3708 39.3335 19.1415 -10.2 10.2 42.7903 39.6516 19.331 -10.3 10.3 43.2098 39.9801 19.5206 -10.4 10.4 43.6294 40.2466 19.7101 -10.5 10.5 44.0489 40.5553 19.8996 -10.6 10.6 44.4684 40.842 20.0891 -10.7 10.7 44.8879 41.1144 20.2786 -10.8 10.8 45.3074 41.4485 20.4682 -10.9 10.9 45.7269 41.7683 20.6577 -11 11 46.1464 42.0644 20.8472 -11.1 11.1 46.566 42.3619 21.0367 -11.2 11.2 46.9855 42.6541 21.2262 -11.3 11.3 47.405 42.9802 21.4158 -11.4 11.4 47.8245 43.2364 21.6053 -11.5 11.5 48.244 43.5544 21.7948 -11.6 11.6 48.6635 43.852 21.9843 -11.7 11.7 49.083 44.1405 22.1738 -11.8 11.8 49.5025 44.4246 22.3634 -11.9 11.9 49.9221 44.7461 22.5529 -12 12 50.3416 45.073 22.7424 -12.1 12.1 50.7611 45.3646 22.9319 -12.2 12.2 51.1806 45.6403 23.1214 -12.3 12.3 51.6001 45.9364 23.311 -12.4 12.4 52.0196 46.2452 23.5005 -12.5 12.5 52.4391 46.5168 23.69 -12.6 12.6 52.8586 46.7539 23.8795 -12.7 12.7 53.2782 47.0157 24.069 -12.8 12.8 53.6977 47.3345 24.2586 -12.9 12.9 54.1172 47.5331 24.4481 -13 13 54.5367 47.821 24.6376 -13.1 13.1 54.9562 48.067 24.8271 -13.2 13.2 55.3757 48.3148 25.0166 -13.3 13.3 55.7952 48.6179 25.2062 -13.4 13.4 56.2148 48.9142 25.3957 -13.5 13.5 56.6343 49.1926 25.5852 -13.6 13.6 57.0538 49.4796 25.7747 -13.7 13.7 57.4733 49.7216 25.9642 -13.8 13.8 57.8928 50.0154 26.1538 -13.9 13.9 58.3123 50.3358 26.3433 -14 14 58.7318 50.633 26.5328 -14.1 14.1 59.1513 50.9528 26.7223 -14.2 14.2 59.5709 51.2356 26.9118 -14.3 14.3 59.9904 51.4539 27.1014 -14.4 14.4 60.4099 51.7283 27.2909 -14.5 14.5 60.8294 51.9941 27.4804 -14.6 14.6 61.2489 52.228 27.6699 -14.7 14.7 61.6684 52.493 27.8594 -14.8 14.8 62.0879 52.7651 28.049 -14.9 14.9 62.5075 53.0232 28.2385 -15 15 62.927 53.2655 28.428 -15.1 15.1 63.3465 53.5593 28.6175 -15.2 15.2 63.766 53.7618 28.807 -15.3 15.3 64.1855 54.0191 28.9966 -15.4 15.4 64.605 54.2779 29.1861 -15.5 15.5 65.0245 54.5336 29.3756 -15.6 15.6 65.444 54.7743 29.5651 -15.7 15.7 65.8636 54.9967 29.7546 -15.8 15.8 66.2831 55.2421 29.9442 -15.9 15.9 66.7026 55.4833 30.1337 -16 16 67.1221 55.7545 30.3232 -16.1 16.1 67.5416 56.0016 30.5127 -16.2 16.2 67.9611 56.2288 30.7022 -16.3 16.3 68.3806 56.4451 30.8918 -16.4 16.4 68.8001 56.7109 31.0813 -16.5 16.5 69.2197 56.9368 31.2708 -16.6 16.6 69.6392 57.234 31.4603 -16.7 16.7 70.0587 57.4515 31.6498 -16.8 16.8 70.4782 57.6639 31.8394 -16.9 16.9 70.8977 57.851 32.0289 -17 17 71.3172 58.0681 32.2184 -17.1 17.1 71.7367 58.2701 32.4079 -17.2 17.2 72.1563 58.454 32.5974 -17.3 17.3 72.5758 58.6886 32.787 -17.4 17.4 72.9953 58.914 32.9765 -17.5 17.5 73.4148 59.1231 33.166 -17.6 17.6 73.8343 59.3494 33.3555 -17.7 17.7 74.2538 59.5878 33.545 -17.8 17.8 74.6733 59.8565 33.7346 -17.9 17.9 75.0928 60.1063 33.9241 -18 18 75.5124 60.3147 34.1136 -18.1 18.1 75.9319 60.5488 34.3031 -18.2 18.2 76.3514 60.7656 34.4926 -18.3 18.3 76.7709 61.0014 34.6822 -18.4 18.4 77.1904 61.2351 34.8717 -18.5 18.5 77.6099 61.4142 35.0612 -18.6 18.6 78.0294 61.5993 35.2507 -18.7 18.7 78.4489 61.7973 35.4402 -18.8 18.8 78.8685 62.0303 35.6298 -18.9 18.9 79.288 62.2741 35.8193 -19 19 79.7075 62.4543 36.0088 -19.1 19.1 80.127 62.7045 36.1983 -19.2 19.2 80.5465 62.9576 36.3878 -19.3 19.3 80.966 63.1414 36.5774 -19.4 19.4 81.3855 63.2943 36.7669 -19.5 19.5 81.8051 63.5307 36.9564 -19.6 19.6 82.2246 63.7269 37.1459 -19.7 19.7 82.6441 63.9262 37.3354 -19.8 19.8 83.0636 64.1057 37.525 -19.9 19.9 83.4831 64.252 37.7145 -20 20 83.9026 64.406 37.904 -20.1 20.1 84.3221 64.5961 38.0935 -20.2 20.2 84.7416 64.8568 38.283 -20.3 20.3 85.1612 65.0009 38.4726 -20.4 20.4 85.5807 65.2872 38.6621 -20.5 20.5 86.0002 65.4889 38.8516 -20.6 20.6 86.4197 65.6931 39.0411 -20.7 20.7 86.8392 65.953 39.2306 -20.8 20.8 87.2587 66.1283 39.4202 -20.9 20.9 87.6782 66.336 39.6097 -21 21 88.0977 66.528 39.7992 -21.1 21.1 88.5173 66.7238 39.9887 -21.2 21.2 88.9368 66.879 40.1782 -21.3 21.3 89.3563 67.0907 40.3678 -21.4 21.4 89.7758 67.2774 40.5573 -21.5 21.5 90.1953 67.4414 40.7468 -21.6 21.6 90.6148 67.5724 40.9363 -21.7 21.7 91.0343 67.7759 41.1258 -21.8 21.8 91.4539 67.9596 41.3154 -21.9 21.9 91.8734 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-97.6 97.6 100 100 99.8513 -97.7 97.7 100 100 99.8575 -97.8 97.8 100 100 99.8637 -97.9 97.9 100 100 99.8699 -98 98 100 100 99.8761 -98.1 98.1 100 100 99.8823 -98.2 98.2 100 100 99.8885 -98.3 98.3 100 100 99.8947 -98.4 98.4 100 100 99.9008 -98.5 98.5 100 100 99.907 -98.6 98.6 100 100 99.9132 -98.7 98.7 100 100 99.9194 -98.8 98.8 100 100 99.9256 -98.9 98.9 100 100 99.9318 -99 99 100 100 99.938 -99.1 99.1 100 100 99.9442 -99.2 99.2 100 100 99.9504 -99.3 99.3 100 100 99.9566 -99.4 99.4 100 100 99.9628 -99.5 99.5 100 100 99.969 -99.6 99.6 100 100 99.9752 -99.7 99.7 100 100 99.9814 -99.8 99.8 100 100 99.9876 -99.9 99.9 100 100 99.9938 -100 100 100 100 100 +#Khiops 10.4.6i +Train evaluation report + +Dictionary Adult +Target variable Categorical class +Main target value more +Database ../../../datasets/Adult/Adult.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 34291 + + +Predictors performance + +Classifier Accuracy Compression AUC +Selective Naive Bayes 0.869295 0.490475 0.926145 +Univariate relationship 0.761628 0.209952 0.780095 + + +-------------------------------------------------------------------------------- + +Predictors detailed performance + + +Classifier Selective Naive Bayes +Accuracy 0.869295 +Compression 0.490475 +AUC 0.926145 + +Confusion matrix + less more +$less 24742 3107 +$more 1375 5067 + +---------------------------------------------- +Classifier Univariate relationship +Accuracy 0.761628 +Compression 0.209952 +AUC 0.780095 + +Confusion matrix + less more +$less 26117 8174 +$more 0 0 + +% target values +Group less more Interest Frequency Coverage +{Husband, Wife} 0.548238 0.451762 0.422448 15382 0.448573 +{Own-child} 0.985228 0.0147719 0.287464 5348 0.155959 +{Not-in-family} 0.90009 0.0999098 0.142352 8868 0.25861 +{Unmarried} 0.93731 0.0626899 0.101483 3621 0.105596 +{Other-relative} 0.969216 0.0307836 0.0462536 1072 0.0312618 +Total 0.761628 0.238372 1 34291 1 + + + 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["Self-emp-inc"], + ["Federal-gov"] + ], + "defaultGroupIndex": 0 + }, + { + "variable": "class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["less","more"] + } + ], + "partTargetFrequencies": [ + [20448,5335], + [4541,1771], + [520,674], + [608,394] + ], + "partInterests": [0.160564,0.0660155,0.642787,0.130634] + }, + "inputValues": { + "values": ["Private","Self-emp-not-inc","Local-gov","State-gov","Self-emp-inc","Federal-gov","Without-pay","Never-worked"], + "frequencies": [25762,2673,2246,1393,1194,1002,12,9] + } + }, + "R12": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "race", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["White","Asian-Pac-Islander"], + ["Black","Amer-Indian-Eskimo","Other"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["less","more"] + } + ], + "partTargetFrequencies": [ + 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], + "partTargetFrequencies": [ + [24334,7822], + [1317,90], + [466,262] + ], + "partInterests": [0.0115219,0.840639,0.147839] + }, + "inputValues": { + "values": ["United-States","Mexico","Philippines","Germany","Puerto-Rico","Canada","India","El-Salvador","Cuba","England","China","Jamaica","South","Italy","Dominican-Republic","Guatemala","Japan","Haiti","Poland","Vietnam","Columbia","Taiwan","Portugal","Iran","Nicaragua","Peru","Greece","Ecuador","France","Ireland","Hong","Thailand","Cambodia","Yugoslavia","Trinadad&Tobago","Honduras","Hungary","Outlying-US","Scotland","Laos"], + "frequencies": [31389,641,211,146,127,125,114,109,96,91,89,83,80,77,74,67,64,58,57,53,52,49,44,41,37,36,34,30,26,25,21,21,19,19,16,15,15,15,13,12] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/Modeling.kdic b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.model.kdic similarity index 99% rename from test/LearningTest/TestKhiops/Standard/Adult/results.ref/Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.model.kdic index 285e63ae5..ef6b36b63 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/AnalysisResults.model.kdic @@ -1,69 +1,69 @@ -#Khiops 10.1 - -Dictionary BU1_Adult - -{ -Unused Numerical Label ; -Unused Numerical age ; -Unused Categorical workclass ; -Unused Numerical fnlwgt ; -Unused Categorical education ; -Unused Numerical education_num ; -Unused Categorical marital_status ; -Unused Categorical occupation ; -Unused Categorical relationship ; -Unused Categorical race ; -Unused Categorical sex ; -Unused Numerical capital_gain ; -Unused Numerical capital_loss ; -Unused Numerical hours_per_week ; -Unused Categorical native_country ; -Unused Categorical class ; -Unused Structure(DataGrid) Vclass = DataGrid(ValueSetC("less", "more"), Frequencies(26117, 8174)) ; -Unused Structure(DataGrid) Prelationship = DataGrid(ValueGroups(ValueGroup("Husband", "Wife"), ValueGroup("Not-in-family"), ValueGroup("Own-child"), ValueGroup("Unmarried"), ValueGroup("Other-relative", " * ")), ValueSetC("less", "more"), Frequencies(8433, 7982, 5269, 3394, 1039, 6949, 886, 79, 227, 33)) ; // DataGrid(relationship, class) -Unused Structure(Classifier) BU1class = NBClassifier(DataGridStats(Prelationship, relationship), Vclass) ; - Categorical Predictedclass = TargetValue(BU1class) ; -Unused Numerical Scoreclass = TargetProb(BU1class) ; - Numerical Probclassless = TargetProbAt(BU1class, "less") ; - Numerical Probclassmore = TargetProbAt(BU1class, "more") ; -}; - -Dictionary SNB_Adult - -{ -Unused Numerical Label ; -Unused Numerical age ; -Unused Categorical workclass ; -Unused Numerical fnlwgt ; -Unused Categorical education ; -Unused Numerical education_num ; -Unused Categorical marital_status ; -Unused Categorical occupation ; -Unused Categorical relationship ; -Unused Categorical race ; -Unused Categorical sex ; -Unused Numerical capital_gain ; -Unused Numerical capital_loss ; -Unused Numerical hours_per_week ; -Unused Categorical native_country ; -Unused Categorical class ; -Unused Structure(DataGrid) Vclass = DataGrid(ValueSetC("less", "more"), Frequencies(26117, 8174)) ; -Unused Structure(DataGrid) Page = DataGrid(IntervalBounds(21.5, 23.5, 25.5, 27.5, 30.5, 35.5, 37.5, 42.5, 55.5, 61.5, 66.5), ValueSetC("less", "more"), Frequencies(3287, 1775, 1609, 1541, 2205, 3557, 1280, 2718, 4961, 1439, 848, 897, 7, 28, 81, 146, 430, 1074, 563, 1440, 3134, 754, 307, 210)) ; // DataGrid(age, class) -Unused Structure(DataGrid) Pworkclass = DataGrid(ValueGroups(ValueGroup("Private", "Without-pay", "Never-worked", " * "), ValueGroup("Self-emp-not-inc", "Local-gov", "State-gov"), ValueGroup("Self-emp-inc"), ValueGroup("Federal-gov")), ValueSetC("less", "more"), Frequencies(20448, 4541, 520, 608, 5335, 1771, 674, 394)) ; // DataGrid(workclass, class) -Unused Structure(DataGrid) Peducation = DataGrid(ValueGroups(ValueGroup("HS-grad"), ValueGroup("Some-college"), ValueGroup("Bachelors"), ValueGroup("11th", "10th", "7th-8th", "9th", "12th", "5th-6th", "1st-4th", "Preschool", " * "), ValueGroup("Assoc-voc", "Assoc-acdm"), ValueGroup("Masters"), ValueGroup("Prof-school", "Doctorate")), ValueSetC("less", "more"), Frequencies(9350, 6207, 3308, 4227, 1919, 842, 264, 1752, 1438, 2337, 255, 667, 1000, 725)) ; // DataGrid(education, class) -Unused Structure(DataGrid) Peducation_num = DataGrid(IntervalBounds(8.5, 9.5, 10.5, 12.5, 13.5, 14.5), ValueSetC("less", "more"), Frequencies(4227, 9350, 6207, 1919, 3308, 842, 264, 255, 1752, 1438, 667, 2337, 1000, 725)) ; // DataGrid(education_num, class) -Unused Structure(DataGrid) Pmarital_status = DataGrid(ValueGroups(ValueGroup("Married-civ-spouse", "Married-AF-spouse", " * "), ValueGroup("Never-married", "Separated"), ValueGroup("Divorced", "Widowed", "Married-spouse-absent")), ValueSetC("less", "more"), Frequencies(8650, 11896, 5571, 6986, 579, 609)) ; // DataGrid(marital_status, class) -Unused Structure(DataGrid) Poccupation = DataGrid(ValueGroups(ValueGroup("Prof-specialty", "Protective-serv", "Armed-Forces", " * "), ValueGroup("Craft-repair", "Transport-moving"), ValueGroup("Sales", "Tech-support"), ValueGroup("Exec-managerial"), ValueGroup("Adm-clerical"), ValueGroup("Other-service", "Priv-house-serv"), ValueGroup("Machine-op-inspct", "Farming-fishing"), ValueGroup("Handlers-cleaners")), ValueSetC("less", "more"), Frequencies(4661, 4689, 3572, 2252, 3351, 3499, 2750, 1343, 2367, 1316, 1334, 2038, 536, 138, 351, 94)) ; // DataGrid(occupation, class) -Unused Structure(DataGrid) Prelationship = DataGrid(ValueGroups(ValueGroup("Husband", "Wife"), ValueGroup("Not-in-family"), ValueGroup("Own-child"), ValueGroup("Unmarried"), ValueGroup("Other-relative", " * ")), ValueSetC("less", "more"), Frequencies(8433, 7982, 5269, 3394, 1039, 6949, 886, 79, 227, 33)) ; // DataGrid(relationship, class) -Unused Structure(DataGrid) Prace = DataGrid(ValueGroups(ValueGroup("White", "Asian-Pac-Islander"), ValueGroup("Black", "Amer-Indian-Eskimo", "Other", " * ")), ValueSetC("less", "more"), Frequencies(22668, 3449, 7719, 455)) ; // DataGrid(race, class) -Unused Structure(DataGrid) Psex = DataGrid(ValueGroups(ValueGroup("Male"), ValueGroup("Female", " * ")), ValueSetC("less", "more"), Frequencies(15937, 10180, 6944, 1230)) ; // DataGrid(sex, class) -Unused Structure(DataGrid) Pcapital_gain = DataGrid(IntervalBounds(57, 3048, 3120, 4240, 4400, 4668, 4826, 4970, 5119, 5310, 6808, 7070, 7436, 7560, 10000, 10585, 30900, 70000), ValueSetC("less", "more"), Frequencies(25020, 492, 7, 320, 10, 77, 0, 22, 80, 0, 33, 34, 0, 4, 2, 8, 1, 7, 0, 6400, 0, 103, 0, 60, 0, 29, 7, 0, 104, 22, 0, 269, 0, 372, 43, 579, 0, 186)) ; // DataGrid(capital_gain, class) -Unused Structure(DataGrid) Pcapital_loss = DataGrid(IntervalBounds(70, 1457, 1494, 1537, 1568.5, 1789, 1846, 1859, 1881, 1906.5, 1975.5, 1978.5, 2156, 2384, 2580), ValueSetC("less", "more"), Frequencies(25325, 87, 19, 17, 0, 374, 6, 0, 42, 11, 22, 0, 118, 75, 5, 16, 7371, 2, 27, 0, 31, 0, 3, 49, 0, 350, 1, 184, 0, 48, 93, 15)) ; // DataGrid(capital_loss, class) -Unused Structure(DataGrid) Phours_per_week = DataGrid(IntervalBounds(34.5, 39.5, 40.5, 49.5, 64.5), ValueSetC("less", "more"), Frequencies(5519, 1967, 12595, 2123, 3201, 712, 406, 362, 3344, 1137, 2515, 410)) ; // DataGrid(hours_per_week, class) -Unused Structure(DataGrid) Pnative_country = DataGrid(ValueGroups(ValueGroup("United-States", "Philippines", "Cuba", "China", "Jamaica", "South", "Italy", "Poland", "Ireland", "Hong", "Hungary", "Scotland"), ValueGroup("Mexico", "Puerto-Rico", "El-Salvador", "Dominican-Republic", "Guatemala", "Haiti", "Vietnam", "Columbia", "Portugal", "Nicaragua", "Peru", "Ecuador", "Thailand", "Trinadad&Tobago", "Honduras", "Outlying-US", "Laos", " * "), ValueGroup("Germany", "Canada", "India", "England", "Japan", "Taiwan", "Iran", "Greece", "France", "Cambodia", "Yugoslavia")), ValueSetC("less", "more"), Frequencies(24334, 1317, 466, 7822, 90, 262)) ; // DataGrid(native_country, class) -Unused Structure(Classifier) SNBclass = SNBClassifier(Vector(0.5625305176, 0.1250305176, 0.2461242676, 0.4834289551, 0.4790344238, 0.5019836426, 0.4179992676, 0.2226867676, 0.02346801758, 0.9648742676, 0.7969055176, 0.4668273926, 0.3672180176), DataGridStats(Page, age), DataGridStats(Pworkclass, workclass), DataGridStats(Peducation, education), DataGridStats(Peducation_num, education_num), DataGridStats(Pmarital_status, marital_status), DataGridStats(Poccupation, occupation), DataGridStats(Prelationship, relationship), DataGridStats(Prace, race), DataGridStats(Psex, sex), DataGridStats(Pcapital_gain, capital_gain), DataGridStats(Pcapital_loss, capital_loss), DataGridStats(Phours_per_week, hours_per_week), DataGridStats(Pnative_country, native_country), Vclass) ; - Categorical Predictedclass = TargetValue(SNBclass) ; -Unused Numerical Scoreclass = TargetProb(SNBclass) ; - Numerical Probclassless = TargetProbAt(SNBclass, "less") ; - Numerical Probclassmore = TargetProbAt(SNBclass, "more") ; -}; +#Khiops 10.4.6i + +Dictionary BU1_Adult + +{ +Unused Numerical Label ; +Unused Numerical age ; +Unused Categorical workclass ; +Unused Numerical fnlwgt ; +Unused Categorical education ; +Unused Numerical education_num ; +Unused Categorical marital_status ; +Unused Categorical occupation ; +Unused Categorical relationship ; +Unused Categorical race ; +Unused Categorical sex ; +Unused Numerical capital_gain ; +Unused Numerical capital_loss ; +Unused Numerical hours_per_week ; +Unused Categorical native_country ; +Unused Categorical class ; +Unused Structure(DataGrid) Vclass = DataGrid(ValueSetC("less", "more"), Frequencies(26117, 8174)) ; +Unused Structure(DataGrid) Prelationship = DataGrid(ValueGroups(ValueGroup("Husband", "Wife"), ValueGroup("Not-in-family"), ValueGroup("Own-child"), ValueGroup("Unmarried"), ValueGroup("Other-relative", " * ")), ValueSetC("less", "more"), Frequencies(8433, 7982, 5269, 3394, 1039, 6949, 886, 79, 227, 33)) ; // DataGrid(relationship, class) +Unused Structure(Classifier) BU1class = NBClassifier(DataGridStats(Prelationship, relationship), Vclass) ; + Categorical Predictedclass = TargetValue(BU1class) ; +Unused Numerical Scoreclass = TargetProb(BU1class) ; + Numerical Probclassless = TargetProbAt(BU1class, "less") ; + Numerical Probclassmore = TargetProbAt(BU1class, "more") ; +}; + +Dictionary SNB_Adult + +{ +Unused Numerical Label ; +Unused Numerical age ; +Unused Categorical workclass ; +Unused Numerical fnlwgt ; +Unused Categorical education ; +Unused Numerical education_num ; +Unused Categorical marital_status ; +Unused Categorical occupation ; +Unused Categorical relationship ; +Unused Categorical race ; +Unused Categorical sex ; +Unused Numerical capital_gain ; +Unused Numerical capital_loss ; +Unused Numerical hours_per_week ; +Unused Categorical native_country ; +Unused Categorical class ; +Unused Structure(DataGrid) Vclass = DataGrid(ValueSetC("less", "more"), Frequencies(26117, 8174)) ; +Unused Structure(DataGrid) Page = DataGrid(IntervalBounds(21.5, 23.5, 25.5, 27.5, 30.5, 35.5, 37.5, 42.5, 55.5, 61.5, 66.5), ValueSetC("less", "more"), Frequencies(3287, 1775, 1609, 1541, 2205, 3557, 1280, 2718, 4961, 1439, 848, 897, 7, 28, 81, 146, 430, 1074, 563, 1440, 3134, 754, 307, 210)) ; // DataGrid(age, class) +Unused Structure(DataGrid) Pworkclass = DataGrid(ValueGroups(ValueGroup("Private", "Without-pay", "Never-worked", " * "), ValueGroup("Self-emp-not-inc", "Local-gov", "State-gov"), ValueGroup("Self-emp-inc"), ValueGroup("Federal-gov")), ValueSetC("less", "more"), Frequencies(20448, 4541, 520, 608, 5335, 1771, 674, 394)) ; // DataGrid(workclass, class) +Unused Structure(DataGrid) Peducation = DataGrid(ValueGroups(ValueGroup("HS-grad"), ValueGroup("Some-college"), ValueGroup("Bachelors"), ValueGroup("11th", "10th", "7th-8th", "9th", "12th", "5th-6th", "1st-4th", "Preschool", " * "), ValueGroup("Assoc-voc", "Assoc-acdm"), ValueGroup("Masters"), ValueGroup("Prof-school", "Doctorate")), ValueSetC("less", "more"), Frequencies(9350, 6207, 3308, 4227, 1919, 842, 264, 1752, 1438, 2337, 255, 667, 1000, 725)) ; // DataGrid(education, class) +Unused Structure(DataGrid) Peducation_num = DataGrid(IntervalBounds(8.5, 9.5, 10.5, 12.5, 13.5, 14.5), ValueSetC("less", "more"), Frequencies(4227, 9350, 6207, 1919, 3308, 842, 264, 255, 1752, 1438, 667, 2337, 1000, 725)) ; // DataGrid(education_num, class) +Unused Structure(DataGrid) Pmarital_status = DataGrid(ValueGroups(ValueGroup("Married-civ-spouse", "Married-AF-spouse", " * "), ValueGroup("Never-married", "Separated"), ValueGroup("Divorced", "Widowed", "Married-spouse-absent")), ValueSetC("less", "more"), Frequencies(8650, 11896, 5571, 6986, 579, 609)) ; // DataGrid(marital_status, class) +Unused Structure(DataGrid) Poccupation = DataGrid(ValueGroups(ValueGroup("Prof-specialty", "Protective-serv", "Armed-Forces", " * "), ValueGroup("Craft-repair", "Transport-moving"), ValueGroup("Sales", "Tech-support"), ValueGroup("Exec-managerial"), ValueGroup("Adm-clerical"), ValueGroup("Other-service", "Priv-house-serv"), ValueGroup("Machine-op-inspct", "Farming-fishing"), ValueGroup("Handlers-cleaners")), ValueSetC("less", "more"), Frequencies(4661, 4689, 3572, 2252, 3351, 3499, 2750, 1343, 2367, 1316, 1334, 2038, 536, 138, 351, 94)) ; // DataGrid(occupation, class) +Unused Structure(DataGrid) Prelationship = DataGrid(ValueGroups(ValueGroup("Husband", "Wife"), ValueGroup("Not-in-family"), ValueGroup("Own-child"), ValueGroup("Unmarried"), ValueGroup("Other-relative", " * ")), ValueSetC("less", "more"), Frequencies(8433, 7982, 5269, 3394, 1039, 6949, 886, 79, 227, 33)) ; // DataGrid(relationship, class) +Unused Structure(DataGrid) Prace = DataGrid(ValueGroups(ValueGroup("White", "Asian-Pac-Islander"), ValueGroup("Black", "Amer-Indian-Eskimo", "Other", " * ")), ValueSetC("less", "more"), Frequencies(22668, 3449, 7719, 455)) ; // DataGrid(race, class) +Unused Structure(DataGrid) Psex = DataGrid(ValueGroups(ValueGroup("Male"), ValueGroup("Female", " * ")), ValueSetC("less", "more"), Frequencies(15937, 10180, 6944, 1230)) ; // DataGrid(sex, class) +Unused Structure(DataGrid) Pcapital_gain = DataGrid(IntervalBounds(57, 3048, 3120, 4240, 4400, 4668, 4826, 4970, 5119, 5310, 6808, 7070, 7436, 7560, 10000, 10585, 30900, 70000), ValueSetC("less", "more"), Frequencies(25020, 492, 7, 320, 10, 77, 0, 22, 80, 0, 33, 34, 0, 4, 2, 8, 1, 7, 0, 6400, 0, 103, 0, 60, 0, 29, 7, 0, 104, 22, 0, 269, 0, 372, 43, 579, 0, 186)) ; // DataGrid(capital_gain, class) +Unused Structure(DataGrid) Pcapital_loss = DataGrid(IntervalBounds(70, 1457, 1494, 1537, 1568.5, 1789, 1846, 1859, 1881, 1906.5, 1975.5, 1978.5, 2156, 2384, 2580), ValueSetC("less", "more"), Frequencies(25325, 87, 19, 17, 0, 374, 6, 0, 42, 11, 22, 0, 118, 75, 5, 16, 7371, 2, 27, 0, 31, 0, 3, 49, 0, 350, 1, 184, 0, 48, 93, 15)) ; // DataGrid(capital_loss, class) +Unused Structure(DataGrid) Phours_per_week = DataGrid(IntervalBounds(34.5, 39.5, 40.5, 49.5, 64.5), ValueSetC("less", "more"), Frequencies(5519, 1967, 12595, 2123, 3201, 712, 406, 362, 3344, 1137, 2515, 410)) ; // DataGrid(hours_per_week, class) +Unused Structure(DataGrid) Pnative_country = DataGrid(ValueGroups(ValueGroup("United-States", "Philippines", "Cuba", "China", "Jamaica", "South", "Italy", "Poland", "Ireland", "Hong", "Hungary", "Scotland"), ValueGroup("Mexico", "Puerto-Rico", "El-Salvador", "Dominican-Republic", "Guatemala", "Haiti", "Vietnam", "Columbia", "Portugal", "Nicaragua", "Peru", "Ecuador", "Thailand", "Trinadad&Tobago", "Honduras", "Outlying-US", "Laos", " * "), ValueGroup("Germany", "Canada", "India", "England", "Japan", "Taiwan", "Iran", "Greece", "France", "Cambodia", "Yugoslavia")), ValueSetC("less", "more"), Frequencies(24334, 1317, 466, 7822, 90, 262)) ; // DataGrid(native_country, class) +Unused Structure(Classifier) SNBclass = SNBClassifier(Vector(0.5625305176, 0.1250305176, 0.2461242676, 0.4834289551, 0.4790344238, 0.5019836426, 0.4179992676, 0.2226867676, 0.02346801758, 0.9648742676, 0.7969055176, 0.4668273926, 0.3672180176), DataGridStats(Page, age), DataGridStats(Pworkclass, workclass), DataGridStats(Peducation, education), DataGridStats(Peducation_num, education_num), DataGridStats(Pmarital_status, marital_status), DataGridStats(Poccupation, occupation), DataGridStats(Prelationship, relationship), DataGridStats(Prace, race), DataGridStats(Psex, sex), DataGridStats(Pcapital_gain, capital_gain), DataGridStats(Pcapital_loss, capital_loss), DataGridStats(Phours_per_week, hours_per_week), DataGridStats(Pnative_country, native_country), Vclass) ; + Categorical Predictedclass = TargetValue(SNBclass) ; +Unused Numerical Scoreclass = TargetProb(SNBclass) ; + Numerical Probclassless = TargetProbAt(SNBclass, "less") ; + Numerical Probclassmore = TargetProbAt(SNBclass, "more") ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/T_Adult.txt b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/D_Adult.txt similarity index 96% rename from test/LearningTest/TestKhiops/Standard/Adult/results.ref/T_Adult.txt rename to test/LearningTest/TestKhiops/Standard/Adult/results.ref/D_Adult.txt index 975b43480..b15c6ef6e 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/T_Adult.txt +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/D_Adult.txt @@ -1,34292 +1,34292 @@ -Predictedclass Probclassless Probclassmore -more 0.4122763795 0.5877236205 -less 0.9624691952 0.03753080482 -less 0.9158716208 0.08412837917 -more 0.2594836411 0.7405163589 -less 0.9939376151 0.006062384897 -more 0.4223655109 0.5776344891 -more 0.001798128 0.998201872 -more 7.347401589e-06 0.9999926526 -less 0.996630844 0.003369156022 -less 0.9138640497 0.08613595033 -less 0.5746107925 0.4253892075 -less 0.9126718707 0.08732812927 -less 0.9963529611 0.003647038923 -less 0.9793312789 0.02066872108 -less 0.7167495561 0.2832504439 -more 0.1180112844 0.8819887156 -less 0.9158668357 0.08413316428 -less 0.9999894024 1.059756089e-05 -less 0.9341320494 0.06586795061 -more 0.400299924 0.599700076 -less 0.9989605391 0.001039460861 -more 0.4239690102 0.5760309898 -less 0.8317713687 0.1682286313 -less 0.6575636329 0.3424363671 -less 0.9735668152 0.0264331848 -less 0.9984310497 0.00156895034 -less 0.9770659485 0.02293405149 -less 0.9526625457 0.04733745431 -less 0.9868489293 0.01315107068 -less 0.9921381115 0.007861888493 -less 0.9915518866 0.008448113424 -less 0.7192210574 0.2807789426 -less 0.8768042186 0.1231957814 -less 0.9940948144 0.005905185575 -more 0.398717025 0.601282975 -less 0.7718992951 0.2281007049 -less 0.6803464646 0.3196535354 -less 0.5622817761 0.4377182239 -less 0.8283779592 0.1716220408 -more 0.005213667301 0.9947863327 -less 0.5676465947 0.4323534053 -less 0.5876472732 0.4123527268 -less 0.9200744059 0.07992559412 -less 0.8975767903 0.1024232097 -less 0.9999922959 7.70410189e-06 -less 0.999992386 7.614011464e-06 -less 0.6575636329 0.3424363671 -more 0.1379409397 0.8620590603 -less 0.9092781102 0.09072188977 -less 0.9632779802 0.03672201979 -less 0.724080755 0.275919245 -less 0.9904589939 0.009541006117 -less 0.9991012202 0.0008987798152 -less 0.9614899652 0.03851003477 -less 0.9942412817 0.005758718317 -less 0.938103912 0.06189608801 -less 0.9981483582 0.001851641754 -less 0.6417408307 0.3582591693 -less 0.9120648317 0.08793516832 -less 0.9999027662 9.723377116e-05 -less 0.9037671717 0.09623282833 -less 0.8412556359 0.1587443641 -less 0.571184659 0.428815341 -more 0.006869697553 0.9931303024 -less 0.9658681548 0.03413184521 -more 0.2528270478 0.7471729522 -less 0.9849348826 0.01506511736 -more 0.4973700242 0.5026299758 -less 0.8094550092 0.1905449908 -less 0.9943436954 0.005656304576 -less 0.6602712554 0.3397287446 -less 0.9986435819 0.001356418088 -less 0.9706018642 0.02939813578 -less 0.9710594341 0.02894056587 -less 0.8097742193 0.1902257807 -less 0.9751597601 0.02484023986 -more 0.0595192479 0.9404807521 -less 0.9999925939 7.406093166e-06 -less 0.8415658584 0.1584341416 -more 0.4384521559 0.5615478441 -less 0.5896728629 0.4103271371 -more 0.4620170057 0.5379829943 -less 0.9995558335 0.0004441664708 -more 0.2123297807 0.7876702193 -less 0.9417961904 0.05820380963 -more 0.3754337993 0.6245662007 -more 0.2945580315 0.7054419685 -less 0.6835309205 0.3164690795 -less 0.9188336674 0.08116633258 -more 0.1022956976 0.8977043024 -more 0.3182182848 0.6817817152 -less 0.9060682254 0.09393177461 -more 0.2292657045 0.7707342955 -less 0.5239123772 0.4760876228 -less 0.8522531014 0.1477468986 -less 0.9992285759 0.0007714241482 -less 0.621907283 0.378092717 -less 0.6358125068 0.3641874932 -less 0.9886974303 0.01130256974 -more 5.625897352e-05 0.999943741 -less 0.9866519158 0.01334808417 -less 0.9845896981 0.0154103019 -less 0.9887660527 0.01123394727 -less 0.7621590614 0.2378409386 -less 0.8834974559 0.1165025441 -less 0.9708829433 0.02911705672 -less 0.9724386407 0.02756135929 -less 0.9045159245 0.09548407549 -less 0.9991227084 0.0008772916157 -less 0.8609401677 0.1390598323 -less 0.5805156412 0.4194843588 -less 0.9904341586 0.009565841432 -less 0.9743330279 0.02566697206 -less 0.9310569468 0.06894305319 -less 0.960051772 0.03994822799 -less 0.6352819767 0.3647180233 -less 0.9865103419 0.01348965809 -less 0.7241261941 0.2758738059 -less 0.9998690921 0.0001309079275 -less 0.9303489902 0.06965100985 -less 0.9999926004 7.399605247e-06 -less 0.7242620689 0.2757379311 -more 9.321257648e-06 0.9999906787 -less 0.985411301 0.01458869896 -less 0.9991568608 0.0008431392437 -less 0.9846191516 0.01538084843 -less 0.9312377478 0.06876225217 -less 0.9617949217 0.03820507834 -less 0.927056561 0.07294343898 -less 0.6323316467 0.3676683533 -less 0.7253772235 0.2746227765 -more 0.2585553717 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0.0003414035115 0.9996585965 +less 0.9858733414 0.01412665855 +less 0.7976394933 0.2023605067 +less 0.998230111 0.001769888979 +more 0.1766571459 0.8233428541 +less 0.9755191781 0.02448082189 +less 0.8120292293 0.1879707707 +less 0.9999925425 7.457467254e-06 +less 0.9504028198 0.04959718021 +less 0.9991290168 0.0008709831917 +less 0.8327044421 0.1672955579 +more 0.2286520663 0.7713479337 +less 0.9147906974 0.0852093026 +less 0.9991276323 0.0008723676854 +less 0.9932618376 0.006738162367 +less 0.9969860989 0.003013901106 +more 0.3781184881 0.6218815119 +less 0.9990025052 0.0009974947994 +less 0.9999915016 8.498448226e-06 +less 0.9676106535 0.03238934654 +less 0.7461187732 0.2538812268 +less 0.9999927086 7.29141453e-06 +less 0.990830822 0.009169177988 +more 0.004396368504 0.9956036315 +less 0.7218094996 0.2781905004 +more 0.3225966533 0.6774033467 +less 0.7453233994 0.2546766006 +less 0.728312373 0.271687627 +less 0.8144647455 0.1855352545 +less 0.7279166367 0.2720833633 +less 0.636638145 0.363361855 +less 0.9869092119 0.01309078806 +less 0.9999926627 7.337273524e-06 +less 0.829838578 0.170161422 +more 0.2134670207 0.7865329793 +less 0.9997185276 0.0002814723852 +less 0.7870109545 0.2129890455 +more 0.001422612829 0.9985773872 +less 0.9999911888 8.811245678e-06 +less 0.8225015342 0.1774984658 +more 7.413910462e-06 0.9999925861 +more 0.3753306636 0.6246693364 +less 0.9887655411 0.01123445889 +more 0.4155737527 0.5844262473 +less 0.9999925727 7.427300901e-06 +more 0.001753695429 0.9982463046 +less 0.9978512996 0.002148700383 +less 0.8903108029 0.1096891971 +less 0.9646826687 0.03531733134 +more 8.481899142e-05 0.999915181 +less 0.6575636329 0.3424363671 +less 0.9760076746 0.02399232535 +more 0.2664812015 0.7335187985 +less 0.9884531891 0.01154681095 +less 0.8538133295 0.1461866705 +less 0.7180266662 0.2819733338 +less 0.571184659 0.428815341 +less 0.7441638922 0.2558361078 +less 0.9977792973 0.00222070272 +less 0.8149728418 0.1850271582 +less 0.9607566453 0.03924335472 +more 0.2648478807 0.7351521193 +more 0.2323535354 0.7676464646 diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/EvaluationReport.khj b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/EvaluationReport.khj index 4beb95c77..f40a4373f 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/EvaluationReport.khj +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/EvaluationReport.khj @@ -1,6 +1,6 @@ { "tool": "Khiops", - "version": "10.1", + "version": "10.4.6i", "evaluationReport": { "reportType": "Evaluation", "evaluationType": "", diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/EvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/EvaluationReport.xls index 96c72dfd7..8b6cfc910 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/EvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/EvaluationReport.xls @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Predictor evaluation report Dictionary Adult diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/err.txt index 8a1ddf1aa..b687a2e59 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/err.txt @@ -1,21 +1,21 @@ Train supervised model for classification of target variable class Database ../../../datasets/Adult/Adult.txt: Read records: 48,842 Selected records: 34,291 -Data preparation time: 0:00:00.39 -SNB train time: 0:00:00.89 +Data preparation time: 0:00:00.30 +SNB train time: 0:00:00.68 Build Selective Naive Bayes SNB_Adult Build Univariate BU1_Adult Train evaluation of Selective Naive Bayes on database ../../../datasets/Adult/Adult.txt Train evaluation of Univariate relationship on database ../../../datasets/Adult/Adult.txt Test evaluation of Selective Naive Bayes on database ../../../datasets/Adult/Adult.txt Test evaluation of Univariate relationship on database ../../../datasets/Adult/Adult.txt -Write report ./results\AllReports.khj +Write report ./results/AnalysisResults.khj Deploy model SNB_Adult Input database ../../../datasets/Adult/Adult.txt: Read records: 48,842 -Output database ./results/T_Adult.txt: Written records: 34,291 -Model deployment time: 0:00:00.12 +Output database ./results/D_Adult.txt: Written records: 34,291 +Model deployment time: 0:00:00.09 Predictor evaluation of Univariate relationship on database ../../../datasets/Adult/Adult.txt Predictor evaluation of Selective Naive Bayes on database ../../../datasets/Adult/Adult.txt -Write predictor evaluation report ./results\EvaluationReport.khj +Write predictor evaluation report ./results/EvaluationReport.khj diff --git a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/time.log index c773a1de1..93bbbe0a1 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/Adult/results.ref/time.log @@ -1 +1 @@ -Overal time: 3.197479248046875 +Overal time: 1.987966775894165 diff --git a/test/LearningTest/TestKhiops/Standard/Adult/test.prm b/test/LearningTest/TestKhiops/Standard/Adult/test.prm index bf73b4312..7ac7a645c 100644 --- a/test/LearningTest/TestKhiops/Standard/Adult/test.prm +++ b/test/LearningTest/TestKhiops/Standard/Adult/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,40 +23,40 @@ ClassFileName ../../../datasets/Adult/Adult.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Adult // Dictionary +TrainDatabase.ClassName Adult // Dictionary -TrainDatabase.DatabaseFiles.List.Key Adult // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage AnalysisSpec.TargetAttributeName class // Target variable AnalysisSpec.MainTargetModality more // Main target modality -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries +AnalysisSpec.PredictorsSpec.ExpertSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report -//AnalysisSpec.SystemParameters.MaxItemNumberInReports 0 // Max item number in reports - +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database ClassManagement.OpenFile // Open... -ClassFileName ./results/Modeling.kdic // Dictionary file +ClassFileName ./results/AnalysisResults.model.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Adult // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key SNB_Adult // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Adult.txt // Database file -TransferDatabase // Transfer database +ClassName SNB_Adult // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Adult // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Adult.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer @@ -63,7 +65,7 @@ Exit // Close ClassManagement.OpenFile // Open... // -> Open -ClassFileName ./results/Modeling.kdic // Dictionary file +ClassFileName ./results/AnalysisResults.model.kdic // Dictionary file OK // Open // <- Open @@ -71,12 +73,13 @@ OK // Open LearningTools.EvaluatePredictors // Evaluate predictors... // -> Evaluate predictors -EvaluationDatabase.DatabaseFiles.List.Key Adult // List item selection -EvaluationDatabase.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file -EvaluationDatabase.SampleNumberPercentage 70 // Sample percentage +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection +EvaluationDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file +EvaluationDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage EvaluatedPredictors.List.Key SNB_Adult // List item selection EvaluatedPredictors.Evaluated true // Evaluated -EvaluationFileName ./results/EvaluationReport.xls // Evaluation report +EvaluationFileName ./results/EvaluationReport.khj // Evaluation report +ExportAsXls true // Export as xls EvaluatePredictors // Evaluate predictors Exit // Close // <- Evaluate predictors diff --git a/test/LearningTest/TestKhiops/Standard/AdultU/results.ref/AnalysisResults.PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/AdultU/results.ref/AnalysisResults.PreparationReport.xls new file mode 100644 index 000000000..69a0d0141 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/AdultU/results.ref/AnalysisResults.PreparationReport.xls @@ -0,0 +1,1854 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Adult +Variables + Categorical 9 + Numerical 7 + Total 16 + +Database ../../../datasets/Adult/Adult.txt +Sample percentage 100 +Sampling mode Include sample +Selection variable +Selection value +Instances 48842 + +Learning task Unsupervised analysis + +Evaluated variables 16 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + + +Categorical variables statistics + +Rank Name Values Mode Mode coverage Missing number Sparse missing number Constr. cost +R05 class 2 less 0.760718 0 0 3.46574 +R06 education 16 HS-grad 0.323164 0 0 3.46574 +R10 marital_status 7 Married-civ-spouse 0.458192 0 0 3.46574 +R11 native_country 41 United-States 0.914971 0 0 3.46574 +R12 occupation 14 Prof-specialty 0.183879 0 0 3.46574 +R13 race 5 White 0.855043 0 0 3.46574 +R14 relationship 6 Husband 0.403669 0 0 3.46574 +R15 sex 2 Male 0.668482 0 0 3.46574 +R16 workclass 8 Private 0.751505 0 0 3.46574 + + +Numerical variables statistics + +Rank Name Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost +R01 Label 48842 1 48842 24421.5 14099.47092 0 0 3.46574 +R02 age 74 17 90 38.64358544 13.71036958 0 0 3.46574 +R03 capital_gain 123 0 99999 1079.067626 7451.94277 0 0 3.46574 +R04 capital_loss 99 0 4356 87.50231358 403.0004265 0 0 3.46574 +R07 education_num 16 1 16 10.07808853 2.570946436 0 0 3.46574 +R08 fnlwgt 28523 12285 1490400 189664.1346 105602.9443 0 0 3.46574 +R09 hours_per_week 96 1 99 40.42238238 12.39131717 0 0 3.46574 + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R01 +Variable Numerical Label + +Variable stats +Interval Frequency Coverage +]-inf;+inf[ 48842 1 + + +---------------------------------------------- +Rank R02 +Variable Numerical age + +Variable stats +Interval Frequency Coverage +]-inf;17.5] 595 0.0121821 +]17.5;18.5] 862 0.0176487 +]18.5;21.5] 3262 0.0667868 +]21.5;29.5] 9796 0.200565 +]29.5;38.5] 11723 0.240019 +]38.5;42.5] 4793 0.0981328 +]42.5;47.5] 5445 0.111482 +]47.5;51.5] 3435 0.0703288 +]51.5;53.5] 1449 0.0296671 +]53.5;55.5] 1234 0.0252651 +]55.5;59.5] 2193 0.0448999 +]59.5;62.5] 1293 0.0264731 +]62.5;64.5] 675 0.0138201 +]64.5;67.5] 759 0.0155399 +]67.5;69.5] 327 0.00669506 +]69.5;73.5] 479 0.00980713 +]73.5;77.5] 272 0.00556898 +]77.5;81.5] 139 0.00284591 +]81.5;84.5] 39 0.000798493 +]84.5;89.5] 17 0.000348061 +]89.5;+inf[ 55 0.00112608 + + +---------------------------------------------- +Rank R03 +Variable Numerical capital_gain + +Variable stats +Interval Frequency Coverage +]-inf;0] 44807 0.917387 +]0;112] 0 0 +]112;120] 8 0.000163793 +]120;576] 5 0.000102371 +]576;640] 52 0.00106466 +]640;896] 0 0 +]896;1024] 16 0.000327587 +]1024;1152] 59 0.00120798 +]1152;1408] 7 0.000143319 +]1408;1536] 51 0.00104418 +]1536;1792] 2 4.09484e-05 +]1792;2048] 36 0.000737071 +]2048;2304] 171 0.00350109 +]2304;2816] 168 0.00343966 +]2816;3072] 126 0.00257975 +]3072;3328] 291 0.00595799 +]3328;3584] 96 0.00196552 +]3584;3840] 49 0.00100323 +]3840;5120] 566 0.0115884 +]5120;5632] 170 0.00348061 +]5632;6144] 9 0.000184268 +]6144;7168] 98 0.00200647 +]7168;8192] 803 0.0164408 +]8192;9216] 82 0.00167888 +]9216;10240] 36 0.000737071 +]10240;11264] 91 0.00186315 +]11264;13312] 4 8.18967e-05 +]13312;14336] 91 0.00186315 +]14336;15360] 557 0.0114041 +]15360;16384] 8 0.000163793 +]16384;18432] 0 0 +]18432;20480] 51 0.00104418 +]20480;24576] 1 2.04742e-05 +]24576;26624] 20 0.000409484 +]26624;28672] 58 0.0011875 +]28672;45056] 9 0.000184268 +]45056;98304] 0 0 +]98304;+inf[ 244 0.0049957 + + +---------------------------------------------- +Rank R04 +Variable Numerical capital_loss + +Variable stats +Interval Frequency Coverage +]-inf;0.5] 46560 0.953278 +]0.5;624.5] 14 0.000286639 +]624.5;656.5] 21 0.000429958 +]656.5;864.5] 2 4.09484e-05 +]864.5;880.5] 6 0.000122845 +]880.5;1088.5] 2 4.09484e-05 +]1088.5;1104.5] 11 0.000225216 +]1104.5;1328.5] 10 0.000204742 +]1328.5;1392.5] 21 0.000429958 +]1392.5;1408.5] 35 0.000716596 +]1408.5;1472.5] 8 0.000163793 +]1472.5;1488.5] 71 0.00145367 +]1488.5;1504.5] 26 0.000532329 +]1504.5;1552.5] 4 8.18967e-05 +]1552.5;1632.5] 253 0.00517997 +]1632.5;1664.5] 14 0.000286639 +]1664.5;1680.5] 94 0.00192457 +]1680.5;1712.5] 0 0 +]1712.5;1744.5] 180 0.00368535 +]1744.5;1760.5] 2 4.09484e-05 +]1760.5;1776.5] 20 0.000409484 +]1776.5;1840.5] 9 0.000184268 +]1840.5;1856.5] 70 0.00143319 +]1856.5;1872.5] 1 2.04742e-05 +]1872.5;1904.5] 596 0.0122026 +]1904.5;1968.5] 4 8.18967e-05 +]1968.5;1984.5] 317 0.00649032 +]1984.5;2000.5] 0 0 +]2000.5;2016.5] 68 0.00139224 +]2016.5;2048.5] 12 0.00024569 +]2048.5;2064.5] 45 0.000921338 +]2064.5;2128.5] 1 2.04742e-05 +]2128.5;2176.5] 24 0.00049138 +]2176.5;2208.5] 46 0.000941812 +]2208.5;2256.5] 19 0.000389009 +]2256.5;2272.5] 42 0.000859916 +]2272.5;2336.5] 2 4.09484e-05 +]2336.5;2352.5] 29 0.000593751 +]2352.5;2368.5] 0 0 +]2368.5;2400.5] 36 0.000737071 +]2400.5;2416.5] 72 0.00147414 +]2416.5;2432.5] 0 0 +]2432.5;2480.5] 31 0.0006347 +]2480.5;2544.5] 1 2.04742e-05 +]2544.5;2560.5] 22 0.000450432 +]2560.5;2816.5] 9 0.000184268 +]2816.5;2832.5] 14 0.000286639 +]2832.5;2992.5] 0 0 +]2992.5;3008.5] 5 0.000102371 +]3008.5;4352.5] 10 0.000204742 +]4352.5;+inf[ 3 6.14225e-05 + + +---------------------------------------------- +Rank R05 +Variable Categorical class + +Variable stats +Group Frequency Coverage Size Value list +{less} 37155 0.760718 1 less +{more} 11687 0.239282 1 more * + + +Values +Value Frequency Coverage +less 37155 0.760718 +more 11687 0.239282 + +---------------------------------------------- +Rank R06 +Variable Categorical education + +Variable stats +Group Frequency Coverage Size Value list +{HS-grad} 15784 0.323164 1 HS-grad +{Some-college} 10878 0.222718 1 Some-college +{Bachelors} 8025 0.164305 1 Bachelors +{Masters} 2657 0.0543999 1 Masters +{Assoc-voc} 2061 0.0421973 1 Assoc-voc +{11th} 1812 0.0370992 1 11th +{Assoc-acdm} 1601 0.0327792 1 Assoc-acdm +{10th} 1389 0.0284386 1 10th +{7th-8th} 955 0.0195528 1 7th-8th +{Prof-school} 834 0.0170755 1 Prof-school +{9th} 756 0.0154785 1 9th +{12th} 657 0.0134515 1 12th +{Doctorate} 594 0.0121617 1 Doctorate +{5th-6th} 509 0.0104214 1 5th-6th +{1st-4th} 247 0.00505712 1 1st-4th +{Preschool} 83 0.00169936 1 Preschool * + + +Values +Value Frequency Coverage +HS-grad 15784 0.323164 +Some-college 10878 0.222718 +Bachelors 8025 0.164305 +Masters 2657 0.0543999 +Assoc-voc 2061 0.0421973 +11th 1812 0.0370992 +Assoc-acdm 1601 0.0327792 +10th 1389 0.0284386 +7th-8th 955 0.0195528 +Prof-school 834 0.0170755 +9th 756 0.0154785 +12th 657 0.0134515 +Doctorate 594 0.0121617 +5th-6th 509 0.0104214 +1st-4th 247 0.00505712 +Preschool 83 0.00169936 + +---------------------------------------------- +Rank R07 +Variable Numerical education_num + +Variable stats +Interval Frequency Coverage +]-inf;1.5] 83 0.00169936 +]1.5;2.5] 247 0.00505712 +]2.5;3.5] 509 0.0104214 +]3.5;4.5] 955 0.0195528 +]4.5;5.5] 756 0.0154785 +]5.5;6.5] 1389 0.0284386 +]6.5;7.5] 1812 0.0370992 +]7.5;8.5] 657 0.0134515 +]8.5;9.5] 15784 0.323164 +]9.5;10.5] 10878 0.222718 +]10.5;11.5] 2061 0.0421973 +]11.5;12.5] 1601 0.0327792 +]12.5;13.5] 8025 0.164305 +]13.5;14.5] 2657 0.0543999 +]14.5;15.5] 834 0.0170755 +]15.5;+inf[ 594 0.0121617 + + +---------------------------------------------- +Rank R08 +Variable Numerical fnlwgt + +Variable stats +Interval Frequency Coverage +]-inf;13768.5] 2 4.09484e-05 +]13768.5;13770.5] 3 6.14225e-05 +]13770.5;19300.5] 4 8.18967e-05 +]19300.5;19302.5] 6 0.000122845 +]19302.5;19912.5] 19 0.000389009 +]19912.5;19914.5] 5 0.000102371 +]19914.5;20056.5] 0 0 +]20056.5;20058.5] 4 8.18967e-05 +]20058.5;20306.5] 5 0.000102371 +]20306.5;20308.5] 5 0.000102371 +]20308.5;20532.5] 8 0.000163793 +]20532.5;20534.5] 4 8.18967e-05 +]20534.5;20794.5] 2 4.09484e-05 +]20794.5;20796.5] 5 0.000102371 +]20796.5;20952.5] 3 6.14225e-05 +]20952.5;20956.5] 5 0.000102371 +]20956.5;22148.5] 30 0.000614225 +]22148.5;22244.5] 16 0.000327587 +]22244.5;22246.5] 10 0.000204742 +]22246.5;22404.5] 3 6.14225e-05 +]22404.5;22492.5] 15 0.000307113 +]22492.5;22494.5] 5 0.000102371 +]22494.5;22742.5] 6 0.000122845 +]22742.5;22744.5] 4 8.18967e-05 +]22744.5;22964.5] 4 8.18967e-05 +]22964.5;22966.5] 5 0.000102371 +]22966.5;23774.5] 32 0.000655174 +]23774.5;23780.5] 8 0.000163793 +]23780.5;24006.5] 15 0.000307113 +]24006.5;24008.5] 6 0.000122845 +]24008.5;24502.5] 44 0.000900864 +]24502.5;24504.5] 5 0.000102371 +]24504.5;24762.5] 15 0.000307113 +]24762.5;24764.5] 5 0.000102371 +]24764.5;25954.5] 67 0.00137177 +]25954.5;25956.5] 5 0.000102371 +]25956.5;26246.5] 6 0.000122845 +]26246.5;26254.5] 7 0.000143319 +]26254.5;26500.5] 7 0.000143319 +]26500.5;26502.5] 4 8.18967e-05 +]26502.5;27406.5] 112 0.00229311 +]27406.5;27444.5] 20 0.000409484 +]27444.5;28570.5] 115 0.00235453 +]28570.5;28572.5] 6 0.000122845 +]28572.5;28976.5] 25 0.000511855 +]28976.5;29700.5] 112 0.00229311 +]29700.5;29702.5] 8 0.000163793 +]29702.5;29808.5] 12 0.00024569 +]29808.5;29814.5] 11 0.000225216 +]29814.5;30288.5] 81 0.00165841 +]30288.5;30290.5] 5 0.000102371 +]30290.5;30410.5] 2 4.09484e-05 +]30410.5;31738.5] 217 0.0044429 +]31738.5;31740.5] 6 0.000122845 +]31740.5;32182.5] 54 0.00110561 +]32182.5;32186.5] 8 0.000163793 +]32186.5;32526.5] 52 0.00106466 +]32526.5;32528.5] 7 0.000143319 +]32528.5;32730.5] 34 0.000696122 +]32730.5;32732.5] 9 0.000184268 +]32732.5;32948.5] 34 0.000696122 +]32948.5;32950.5] 8 0.000163793 +]32950.5;33104.5] 30 0.000614225 +]33104.5;33126.5] 27 0.000552803 +]33126.5;33732.5] 126 0.00257975 +]33732.5;33792.5] 0 0 +]33792.5;33798.5] 12 0.00024569 +]33798.5;33974.5] 24 0.00049138 +]33974.5;33976.5] 9 0.000184268 +]33976.5;34376.5] 101 0.00206789 +]34376.5;34378.5] 10 0.000204742 +]34378.5;34566.5] 31 0.0006347 +]34566.5;34574.5] 12 0.00024569 +]34574.5;34614.5] 1 2.04742e-05 +]34614.5;34616.5] 6 0.000122845 +]34616.5;34844.5] 25 0.000511855 +]34844.5;34848.5] 7 0.000143319 +]34848.5;35632.5] 116 0.00237501 +]35632.5;35634.5] 7 0.000143319 +]35634.5;35722.5] 11 0.000225216 +]35722.5;35724.5] 7 0.000143319 +]35724.5;36490.5] 115 0.00235453 +]36490.5;36988.5] 33 0.000675648 +]36988.5;36990.5] 7 0.000143319 +]36990.5;37776.5] 116 0.00237501 +]37776.5;37778.5] 8 0.000163793 +]37778.5;37868.5] 9 0.000184268 +]37868.5;37870.5] 6 0.000122845 +]37870.5;37930.5] 6 0.000122845 +]37930.5;37940.5] 21 0.000429958 +]37940.5;38222.5] 17 0.000348061 +]38222.5;38454.5] 40 0.000818967 +]38454.5;38456.5] 7 0.000143319 +]38456.5;38946.5] 52 0.00106466 +]38946.5;38950.5] 10 0.000204742 +]38950.5;39476.5] 57 0.00116703 +]39476.5;39478.5] 7 0.000143319 +]39478.5;39644.5] 35 0.000716596 +]39644.5;39984.5] 17 0.000348061 +]39984.5;39986.5] 5 0.000102371 +]39986.5;40020.5] 1 2.04742e-05 +]40020.5;40024.5] 8 0.000163793 +]40024.5;40664.5] 42 0.000859916 +]40664.5;40666.5] 9 0.000184268 +]40666.5;40914.5] 13 0.000266164 +]40914.5;40916.5] 5 0.000102371 +]40916.5;40954.5] 1 2.04742e-05 +]40954.5;40956.5] 7 0.000143319 +]40956.5;41354.5] 30 0.000614225 +]41354.5;41356.5] 7 0.000143319 +]41356.5;42042.5] 61 0.00124893 +]42042.5;42044.5] 5 0.000102371 +]42044.5;42702.5] 36 0.000737071 +]42702.5;42706.5] 9 0.000184268 +]42706.5;42898.5] 13 0.000266164 +]42898.5;42902.5] 7 0.000143319 +]42902.5;43710.5] 71 0.00145367 +]43710.5;43712.5] 11 0.000225216 +]43712.5;45598.5] 141 0.00288686 +]45598.5;45614.5] 12 0.00024569 +]45614.5;45794.5] 11 0.000225216 +]45794.5;45796.5] 6 0.000122845 +]45796.5;46398.5] 46 0.000941812 +]46398.5;46402.5] 8 0.000163793 +]46402.5;47540.5] 103 0.00210884 +]47540.5;47542.5] 5 0.000102371 +]47542.5;47900.5] 20 0.000409484 +]47900.5;47908.5] 9 0.000184268 +]47908.5;48086.5] 13 0.000266164 +]48086.5;48124.5] 22 0.000450432 +]48124.5;48342.5] 17 0.000348061 +]48342.5;48344.5] 6 0.000122845 +]48344.5;48518.5] 17 0.000348061 +]48518.5;48520.5] 10 0.000204742 +]48520.5;50176.5] 183 0.00374678 +]50176.5;50178.5] 6 0.000122845 +]50178.5;52260.5] 219 0.00448385 +]52260.5;52268.5] 10 0.000204742 +]52268.5;52868.5] 62 0.0012694 +]52868.5;52870.5] 5 0.000102371 +]52870.5;53364.5] 49 0.00100323 +]53364.5;53374.5] 9 0.000184268 +]53374.5;53480.5] 5 0.000102371 +]53480.5;53482.5] 6 0.000122845 +]53482.5;53832.5] 33 0.000675648 +]53832.5;53834.5] 7 0.000143319 +]53834.5;53954.5] 13 0.000266164 +]53954.5;53956.5] 8 0.000163793 +]53956.5;54258.5] 28 0.000573277 +]54258.5;54260.5] 8 0.000163793 +]54260.5;54606.5] 41 0.000839441 +]54606.5;54612.5] 10 0.000204742 +]54612.5;55358.5] 70 0.00143319 +]55358.5;55396.5] 23 0.000470906 +]55396.5;55464.5] 2 4.09484e-05 +]55464.5;55466.5] 5 0.000102371 +]55466.5;56480.5] 100 0.00204742 +]56480.5;56484.5] 7 0.000143319 +]56484.5;56650.5] 14 0.000286639 +]56650.5;56652.5] 9 0.000184268 +]56652.5;56840.5] 10 0.000204742 +]56840.5;56842.5] 5 0.000102371 +]56842.5;57232.5] 27 0.000552803 +]57232.5;57234.5] 7 0.000143319 +]57234.5;59082.5] 132 0.00270259 +]59082.5;59084.5] 5 0.000102371 +]59084.5;59378.5] 20 0.000409484 +]59378.5;59380.5] 5 0.000102371 +]59380.5;59468.5] 3 6.14225e-05 +]59468.5;59470.5] 6 0.000122845 +]59470.5;60330.5] 60 0.00122845 +]60330.5;60332.5] 6 0.000122845 +]60332.5;60666.5] 29 0.000593751 +]60666.5;60668.5] 6 0.000122845 +]60668.5;60720.5] 1 2.04742e-05 +]60720.5;60726.5] 10 0.000204742 +]60726.5;61268.5] 23 0.000470906 +]61268.5;61342.5] 13 0.000266164 +]61342.5;61344.5] 8 0.000163793 +]61344.5;61776.5] 25 0.000511855 +]61776.5;61778.5] 7 0.000143319 +]61778.5;61884.5] 6 0.000122845 +]61884.5;61886.5] 10 0.000204742 +]61886.5;63502.5] 92 0.00188362 +]63502.5;63510.5] 7 0.000143319 +]63510.5;64290.5] 44 0.000900864 +]64290.5;64292.5] 7 0.000143319 +]64292.5;64518.5] 11 0.000225216 +]64518.5;64520.5] 5 0.000102371 +]64520.5;64630.5] 4 8.18967e-05 +]64630.5;64632.5] 6 0.000122845 +]64632.5;64872.5] 9 0.000184268 +]64872.5;64876.5] 7 0.000143319 +]64876.5;65622.5] 67 0.00137177 +]65622.5;65624.5] 6 0.000122845 +]65624.5;66472.5] 56 0.00114655 +]66472.5;66474.5] 5 0.000102371 +]66474.5;66684.5] 12 0.00024569 +]66684.5;66692.5] 8 0.000163793 +]66692.5;67216.5] 42 0.000859916 +]67216.5;67244.5] 17 0.000348061 +]67244.5;67316.5] 3 6.14225e-05 +]67316.5;67320.5] 6 0.000122845 +]67320.5;67432.5] 5 0.000102371 +]67432.5;67434.5] 5 0.000102371 +]67434.5;67714.5] 13 0.000266164 +]67714.5;67728.5] 9 0.000184268 +]67728.5;68728.5] 65 0.00133082 +]68728.5;68730.5] 5 0.000102371 +]68730.5;68894.5] 12 0.00024569 +]68894.5;68900.5] 11 0.000225216 +]68900.5;69248.5] 17 0.000348061 +]69248.5;69252.5] 8 0.000163793 +]69252.5;69332.5] 5 0.000102371 +]69332.5;69334.5] 6 0.000122845 +]69334.5;70238.5] 70 0.00143319 +]70238.5;70240.5] 9 0.000184268 +]70240.5;72336.5] 160 0.00327587 +]72336.5;72338.5] 10 0.000204742 +]72338.5;72884.5] 40 0.000818967 +]72884.5;72896.5] 20 0.000409484 +]72896.5;73198.5] 28 0.000573277 +]73198.5;73200.5] 6 0.000122845 +]73200.5;73512.5] 32 0.000655174 +]73512.5;73514.5] 7 0.000143319 +]73514.5;73926.5] 30 0.000614225 +]73926.5;73928.5] 5 0.000102371 +]73928.5;74782.5] 56 0.00114655 +]74782.5;74784.5] 5 0.000102371 +]74784.5;76480.5] 134 0.00274354 +]76480.5;76494.5] 10 0.000204742 +]76494.5;76624.5] 3 6.14225e-05 +]76624.5;76626.5] 4 8.18967e-05 +]76626.5;76844.5] 8 0.000163793 +]76844.5;76860.5] 11 0.000225216 +]76860.5;77142.5] 24 0.00049138 +]77142.5;77146.5] 10 0.000204742 +]77146.5;77818.5] 64 0.00131035 +]77818.5;77820.5] 5 0.000102371 +]77820.5;77882.5] 2 4.09484e-05 +]77882.5;77884.5] 6 0.000122845 +]77884.5;78372.5] 40 0.000818967 +]78372.5;78374.5] 6 0.000122845 +]78374.5;78528.5] 9 0.000184268 +]78528.5;78530.5] 11 0.000225216 +]78530.5;79584.5] 71 0.00145367 +]79584.5;79586.5] 11 0.000225216 +]79586.5;80056.5] 31 0.0006347 +]80056.5;80058.5] 7 0.000143319 +]80058.5;80678.5] 51 0.00104418 +]80678.5;80680.5] 7 0.000143319 +]80680.5;80932.5] 11 0.000225216 +]80932.5;80934.5] 6 0.000122845 +]80934.5;81130.5] 11 0.000225216 +]81130.5;81146.5] 12 0.00024569 +]81146.5;81532.5] 29 0.000593751 +]81532.5;81534.5] 6 0.000122845 +]81534.5;82392.5] 65 0.00133082 +]82392.5;82394.5] 11 0.000225216 +]82394.5;82486.5] 2 4.09484e-05 +]82486.5;82488.5] 5 0.000102371 +]82488.5;82620.5] 26 0.000532329 +]82620.5;82628.5] 10 0.000204742 +]82628.5;82774.5] 8 0.000163793 +]82774.5;82784.5] 9 0.000184268 +]82784.5;83062.5] 24 0.00049138 +]83062.5;83066.5] 7 0.000143319 +]83066.5;83140.5] 3 6.14225e-05 +]83140.5;83142.5] 6 0.000122845 +]83142.5;83410.5] 18 0.000368535 +]83410.5;83414.5] 10 0.000204742 +]83414.5;84014.5] 74 0.00151509 +]84014.5;84118.5] 1 2.04742e-05 +]84118.5;84120.5] 6 0.000122845 +]84120.5;85994.5] 175 0.00358298 +]85994.5;85996.5] 6 0.000122845 +]85996.5;86142.5] 8 0.000163793 +]86142.5;86154.5] 15 0.000307113 +]86154.5;86550.5] 22 0.000450432 +]86550.5;86552.5] 6 0.000122845 +]86552.5;86642.5] 7 0.000143319 +]86642.5;86648.5] 11 0.000225216 +]86648.5;87204.5] 48 0.000982761 +]87204.5;87210.5] 9 0.000184268 +]87210.5;87486.5] 28 0.000573277 +]87486.5;87584.5] 32 0.000655174 +]87584.5;88054.5] 27 0.000552803 +]88054.5;88056.5] 5 0.000102371 +]88056.5;88562.5] 36 0.000737071 +]88562.5;88564.5] 5 0.000102371 +]88564.5;89038.5] 45 0.000921338 +]89038.5;89042.5] 10 0.000204742 +]89042.5;89506.5] 52 0.00106466 +]89506.5;89508.5] 6 0.000122845 +]89508.5;91188.5] 177 0.00362393 +]91188.5;91190.5] 7 0.000143319 +]91190.5;91664.5] 42 0.000859916 +]91664.5;91734.5] 24 0.00049138 +]91734.5;92966.5] 102 0.00208837 +]92966.5;92968.5] 5 0.000102371 +]92968.5;93204.5] 20 0.000409484 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+]99198.5;99200.5] 8 0.000163793 +]99200.5;99392.5] 60 0.00122845 +]99392.5;100134.5] 117 0.00239548 +]100134.5;100136.5] 7 0.000143319 +]100136.5;100344.5] 32 0.000655174 +]100344.5;100346.5] 8 0.000163793 +]100346.5;100668.5] 32 0.000655174 +]100668.5;100670.5] 6 0.000122845 +]100670.5;100732.5] 4 8.18967e-05 +]100732.5;100734.5] 8 0.000163793 +]100734.5;101264.5] 79 0.00161746 +]101264.5;101266.5] 7 0.000143319 +]101266.5;101344.5] 19 0.000389009 +]101344.5;101346.5] 10 0.000204742 +]101346.5;101560.5] 31 0.0006347 +]101560.5;101562.5] 8 0.000163793 +]101562.5;101592.5] 6 0.000122845 +]101592.5;101594.5] 9 0.000184268 +]101594.5;101708.5] 19 0.000389009 +]101708.5;101710.5] 8 0.000163793 +]101710.5;102306.5] 97 0.001986 +]102306.5;102308.5] 12 0.00024569 +]102308.5;102358.5] 17 0.000348061 +]102358.5;102360.5] 9 0.000184268 +]102360.5;102474.5] 16 0.000327587 +]102474.5;102476.5] 9 0.000184268 +]102476.5;102770.5] 57 0.00116703 +]102770.5;102772.5] 8 0.000163793 +]102772.5;103062.5] 46 0.000941812 +]103062.5;103064.5] 8 0.000163793 +]103064.5;103276.5] 22 0.000450432 +]103276.5;103278.5] 8 0.000163793 +]103278.5;103322.5] 1 2.04742e-05 +]103322.5;103324.5] 10 0.000204742 +]103324.5;103640.5] 82 0.00167888 +]103640.5;103652.5] 16 0.000327587 +]103652.5;103924.5] 41 0.000839441 +]103924.5;103926.5] 9 0.000184268 +]103926.5;104194.5] 50 0.00102371 +]104194.5;104196.5] 13 0.000266164 +]104196.5;104500.5] 57 0.00116703 +]104500.5;104510.5] 18 0.000368535 +]104510.5;105802.5] 220 0.00450432 +]105802.5;105804.5] 8 0.000163793 +]105804.5;105934.5] 28 0.000573277 +]105934.5;105938.5] 9 0.000184268 +]105938.5;106012.5] 6 0.000122845 +]106012.5;106014.5] 6 0.000122845 +]106014.5;106346.5] 52 0.00106466 +]106346.5;106348.5] 6 0.000122845 +]106348.5;106898.5] 93 0.0019041 +]106898.5;106900.5] 11 0.000225216 +]106900.5;107230.5] 61 0.00124893 +]107230.5;107236.5] 16 0.000327587 +]107236.5;107300.5] 7 0.000143319 +]107300.5;107302.5] 11 0.000225216 +]107302.5;107800.5] 96 0.00196552 +]107800.5;107802.5] 7 0.000143319 +]107802.5;107880.5] 13 0.000266164 +]107880.5;107882.5] 9 0.000184268 +]107882.5;108138.5] 36 0.000737071 +]108138.5;108140.5] 14 0.000286639 +]108140.5;108292.5] 20 0.000409484 +]108292.5;108294.5] 9 0.000184268 +]108294.5;108434.5] 21 0.000429958 +]108434.5;108438.5] 14 0.000286639 +]108438.5;108944.5] 93 0.0019041 +]108944.5;108948.5] 8 0.000163793 +]108948.5;108992.5] 2 4.09484e-05 +]108992.5;109014.5] 13 0.000266164 +]109014.5;109016.5] 8 0.000163793 +]109016.5;109132.5] 19 0.000389009 +]109132.5;109134.5] 11 0.000225216 +]109134.5;109412.5] 41 0.000839441 +]109412.5;109422.5] 11 0.000225216 +]109422.5;109910.5] 75 0.00153556 +]109910.5;109912.5] 7 0.000143319 +]109912.5;110170.5] 62 0.0012694 +]110170.5;110172.5] 7 0.000143319 +]110172.5;110456.5] 46 0.000941812 +]110456.5;110458.5] 7 0.000143319 +]110458.5;110620.5] 19 0.000389009 +]110620.5;110622.5] 10 0.000204742 +]110622.5;110996.5] 67 0.00137177 +]110996.5;110998.5] 6 0.000122845 +]110998.5;111126.5] 11 0.000225216 +]111126.5;111130.5] 15 0.000307113 +]111130.5;111482.5] 70 0.00143319 +]111482.5;111484.5] 14 0.000286639 +]111484.5;111498.5] 0 0 +]111498.5;111500.5] 9 0.000184268 +]111500.5;111566.5] 12 0.00024569 +]111566.5;111568.5] 16 0.000327587 +]111568.5;112114.5] 82 0.00167888 +]112114.5;112116.5] 13 0.000266164 +]112116.5;112136.5] 2 4.09484e-05 +]112136.5;112140.5] 10 0.000204742 +]112140.5;112260.5] 14 0.000286639 +]112260.5;112272.5] 15 0.000307113 +]112272.5;112496.5] 38 0.000778019 +]112496.5;112498.5] 12 0.00024569 +]112498.5;112760.5] 42 0.000859916 +]112760.5;112764.5] 10 0.000204742 +]112764.5;112846.5] 22 0.000450432 +]112846.5;112848.5] 10 0.000204742 +]112848.5;113322.5] 73 0.00149462 +]113322.5;113324.5] 13 0.000266164 +]113324.5;113362.5] 2 4.09484e-05 +]113362.5;113364.5] 17 0.000348061 +]113364.5;113836.5] 99 0.00202694 +]113836.5;113838.5] 10 0.000204742 +]113838.5;114156.5] 54 0.00110561 +]114156.5;114158.5] 10 0.000204742 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+]139264.5;139268.5] 10 0.000204742 +]139268.5;139768.5] 71 0.00145367 +]139768.5;139772.5] 7 0.000143319 +]139772.5;140000.5] 27 0.000552803 +]140000.5;140004.5] 11 0.000225216 +]140004.5;140640.5] 84 0.00171983 +]140640.5;140644.5] 8 0.000163793 +]140644.5;140848.5] 31 0.0006347 +]140848.5;140856.5] 14 0.000286639 +]140856.5;141580.5] 115 0.00235453 +]141580.5;141584.5] 10 0.000204742 +]141584.5;142708.5] 166 0.00339871 +]142708.5;142720.5] 20 0.000409484 +]142720.5;143044.5] 62 0.0012694 +]143044.5;143072.5] 31 0.0006347 +]143072.5;143580.5] 92 0.00188362 +]143580.5;143584.5] 12 0.00024569 +]143584.5;143764.5] 24 0.00049138 +]143764.5;143776.5] 13 0.000266164 +]143776.5;144060.5] 53 0.00108513 +]144060.5;144072.5] 17 0.000348061 +]144072.5;144608.5] 94 0.00192457 +]144608.5;144776.5] 10 0.000204742 +]144776.5;144780.5] 13 0.000266164 +]144780.5;144948.5] 27 0.000552803 +]144948.5;144952.5] 13 0.000266164 +]144952.5;145408.5] 76 0.00155604 +]145408.5;145444.5] 32 0.000655174 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0.000225216 +]200472.5;201120.5] 145 0.00296876 +]201120.5;201128.5] 13 0.000266164 +]201128.5;201696.5] 124 0.0025388 +]201696.5;201700.5] 11 0.000225216 +]201700.5;202024.5] 65 0.00133082 +]202024.5;202028.5] 9 0.000184268 +]202028.5;202556.5] 113 0.00231358 +]202556.5;202560.5] 11 0.000225216 +]202560.5;202680.5] 17 0.000348061 +]202680.5;202684.5] 8 0.000163793 +]202684.5;202868.5] 26 0.000532329 +]202868.5;202872.5] 13 0.000266164 +]202872.5;203484.5] 140 0.00286639 +]203484.5;203488.5] 21 0.000429958 +]203488.5;203760.5] 40 0.000818967 +]203760.5;203788.5] 24 0.00049138 +]203788.5;204224.5] 89 0.0018222 +]204224.5;204236.5] 15 0.000307113 +]204236.5;204840.5] 148 0.00303018 +]204840.5;205096.5] 29 0.000593751 +]205096.5;205100.5] 10 0.000204742 +]205100.5;205336.5] 34 0.000696122 +]205336.5;205340.5] 10 0.000204742 +]205340.5;205936.5] 107 0.00219074 +]205936.5;205956.5] 18 0.000368535 +]205956.5;206044.5] 14 0.000286639 +]206044.5;206052.5] 11 0.000225216 +]206052.5;206596.5] 81 0.00165841 +]206596.5;206612.5] 19 0.000389009 +]206612.5;206860.5] 29 0.000593751 +]206860.5;206892.5] 21 0.000429958 +]206892.5;206944.5] 8 0.000163793 +]206944.5;206952.5] 12 0.00024569 +]206952.5;207300.5] 59 0.00120798 +]207300.5;207304.5] 8 0.000163793 +]207304.5;207664.5] 58 0.0011875 +]207664.5;207688.5] 24 0.00049138 +]207688.5;207936.5] 27 0.000552803 +]207936.5;207940.5] 8 0.000163793 +]207940.5;208356.5] 68 0.00139224 +]208356.5;208360.5] 8 0.000163793 +]208360.5;208944.5] 101 0.00206789 +]208944.5;208948.5] 9 0.000184268 +]208948.5;209100.5] 22 0.000450432 +]209100.5;209104.5] 8 0.000163793 +]209104.5;209316.5] 41 0.000839441 +]209316.5;209320.5] 9 0.000184268 +]209320.5;210032.5] 149 0.00305065 +]210032.5;210436.5] 46 0.000941812 +]210436.5;210564.5] 47 0.000962287 +]210564.5;210732.5] 15 0.000307113 +]210732.5;210736.5] 10 0.000204742 +]210736.5;210944.5] 48 0.000982761 +]210944.5;210948.5] 10 0.000204742 +]210948.5;211604.5] 140 0.00286639 +]211604.5;211964.5] 42 0.000859916 +]211964.5;211968.5] 10 0.000204742 +]211968.5;212892.5] 143 0.00292781 +]212892.5;212896.5] 14 0.000286639 +]212896.5;213136.5] 40 0.000818967 +]213136.5;213140.5] 11 0.000225216 +]213140.5;213432.5] 78 0.00159699 +]213432.5;213608.5] 6 0.000122845 +]213608.5;214232.5] 94 0.00192457 +]214232.5;214244.5] 14 0.000286639 +]214244.5;214540.5] 37 0.000757545 +]214540.5;214544.5] 17 0.000348061 +]214544.5;214896.5] 77 0.00157651 +]214896.5;215380.5] 55 0.00112608 +]215380.5;215504.5] 46 0.000941812 +]215504.5;215612.5] 6 0.000122845 +]215612.5;215624.5] 11 0.000225216 +]215624.5;215852.5] 14 0.000286639 +]215852.5;216128.5] 57 0.00116703 +]216128.5;216132.5] 13 0.000266164 +]216132.5;216408.5] 39 0.000798493 +]216408.5;216416.5] 14 0.000286639 +]216416.5;216468.5] 3 6.14225e-05 +]216468.5;216484.5] 17 0.000348061 +]216484.5;216844.5] 62 0.0012694 +]216844.5;216868.5] 20 0.000409484 +]216868.5;217456.5] 85 0.00174031 +]217456.5;217460.5] 8 0.000163793 +]217460.5;217960.5] 54 0.00110561 +]217960.5;217964.5] 8 0.000163793 +]217964.5;218488.5] 68 0.00139224 +]218488.5;218492.5] 12 0.00024569 +]218492.5;219152.5] 91 0.00186315 +]219152.5;219156.5] 8 0.000163793 +]219156.5;220688.5] 234 0.00479096 +]220688.5;220696.5] 10 0.000204742 +]220696.5;221164.5] 59 0.00120798 +]221164.5;221172.5] 16 0.000327587 +]221172.5;222432.5] 151 0.0030916 +]222432.5;222452.5] 15 0.000307113 +]222452.5;223016.5] 82 0.00167888 +]223016.5;223020.5] 8 0.000163793 +]223020.5;223208.5] 11 0.000225216 +]223208.5;223216.5] 13 0.000266164 +]223216.5;224556.5] 194 0.00397199 +]224556.5;224568.5] 15 0.000307113 +]224568.5;225452.5] 123 0.00251832 +]225452.5;225456.5] 7 0.000143319 +]225456.5;226292.5] 104 0.00212931 +]226292.5;226296.5] 10 0.000204742 +]226296.5;226440.5] 14 0.000286639 +]226440.5;226444.5] 12 0.00024569 +]226444.5;226736.5] 47 0.000962287 +]226736.5;226868.5] 4 8.18967e-05 +]226868.5;226992.5] 44 0.000900864 +]226992.5;227064.5] 3 6.14225e-05 +]227064.5;227068.5] 9 0.000184268 +]227068.5;227592.5] 80 0.00163793 +]227592.5;227616.5] 17 0.000348061 +]227616.5;227884.5] 30 0.000614225 +]227884.5;227892.5] 9 0.000184268 +]227892.5;228648.5] 104 0.00212931 +]228648.5;228652.5] 7 0.000143319 +]228652.5;229144.5] 54 0.00110561 +]229144.5;229148.5] 7 0.000143319 +]229148.5;229728.5] 69 0.00141272 +]229728.5;229744.5] 16 0.000327587 +]229744.5;230244.5] 56 0.00114655 +]230244.5;230248.5] 7 0.000143319 +]230248.5;230680.5] 43 0.00088039 +]230680.5;230684.5] 9 0.000184268 +]230684.5;231864.5] 178 0.0036444 +]231864.5;231868.5] 6 0.000122845 +]231868.5;233128.5] 117 0.00239548 +]233128.5;233152.5] 14 0.000286639 +]233152.5;233508.5] 37 0.000757545 +]233508.5;233512.5] 7 0.000143319 +]233512.5;233776.5] 24 0.00049138 +]233776.5;233780.5] 8 0.000163793 +]233780.5;234104.5] 35 0.000716596 +]234104.5;234108.5] 7 0.000143319 +]234108.5;234632.5] 52 0.00106466 +]234632.5;234664.5] 18 0.000368535 +]234664.5;235644.5] 115 0.00235453 +]235644.5;235648.5] 7 0.000143319 +]235648.5;236388.5] 94 0.00192457 +]236388.5;236396.5] 13 0.000266164 +]236396.5;236768.5] 62 0.0012694 +]236768.5;236772.5] 8 0.000163793 +]236772.5;236860.5] 15 0.000307113 +]236860.5;236864.5] 8 0.000163793 +]236864.5;237712.5] 81 0.00165841 +]237712.5;237736.5] 13 0.000266164 +]237736.5;237864.5] 7 0.000143319 +]237864.5;237868.5] 8 0.000163793 +]237868.5;237940.5] 8 0.000163793 +]237940.5;237944.5] 9 0.000184268 +]237944.5;238184.5] 18 0.000368535 +]238184.5;238188.5] 7 0.000143319 +]238188.5;238380.5] 20 0.000409484 +]238380.5;238388.5] 9 0.000184268 +]238388.5;238908.5] 53 0.00108513 +]238908.5;238920.5] 14 0.000286639 +]238920.5;239536.5] 71 0.00145367 +]239536.5;239540.5] 7 0.000143319 +]239540.5;239752.5] 18 0.000368535 +]239752.5;239756.5] 9 0.000184268 +]239756.5;241884.5] 242 0.00495475 +]241884.5;241896.5] 12 0.00024569 +]241896.5;241996.5] 9 0.000184268 +]241996.5;242000.5] 13 0.000266164 +]242000.5;243188.5] 128 0.0026207 +]243188.5;243192.5] 7 0.000143319 +]243192.5;245360.5] 264 0.00540518 +]245360.5;245380.5] 14 0.000286639 +]245380.5;246436.5] 70 0.00143319 +]246436.5;246440.5] 8 0.000163793 +]246440.5;246888.5] 20 0.000409484 +]246888.5;246892.5] 11 0.000225216 +]246892.5;247324.5] 60 0.00122845 +]247324.5;247328.5] 8 0.000163793 +]247328.5;248988.5] 190 0.00389009 +]248988.5;248992.5] 7 0.000143319 +]248992.5;249036.5] 1 2.04742e-05 +]249036.5;249048.5] 12 0.00024569 +]249048.5;250036.5] 92 0.00188362 +]250036.5;250040.5] 9 0.000184268 +]250040.5;251852.5] 159 0.00325539 +]251852.5;251856.5] 6 0.000122845 +]251856.5;251904.5] 1 2.04742e-05 +]251904.5;251908.5] 6 0.000122845 +]251908.5;252748.5] 72 0.00147414 +]252748.5;252752.5] 7 0.000143319 +]252752.5;253188.5] 32 0.000655174 +]253188.5;253192.5] 6 0.000122845 +]253192.5;254284.5] 89 0.0018222 +]254284.5;254304.5] 13 0.000266164 +]254304.5;255000.5] 55 0.00112608 +]255000.5;255004.5] 8 0.000163793 +]255004.5;257248.5] 239 0.00489333 +]257248.5;257252.5] 8 0.000163793 +]257252.5;259296.5] 193 0.00395152 +]259296.5;259324.5] 15 0.000307113 +]259324.5;260780.5] 120 0.0024569 +]260780.5;260784.5] 7 0.000143319 +]260784.5;263560.5] 222 0.00454527 +]263560.5;263568.5] 9 0.000184268 +]263568.5;265072.5] 128 0.0026207 +]265072.5;265104.5] 15 0.000307113 +]265104.5;269720.5] 429 0.00878342 +]269720.5;269736.5] 12 0.00024569 +]269736.5;271928.5] 169 0.00346014 +]271928.5;271936.5] 8 0.000163793 +]271936.5;272936.5] 73 0.00149462 +]272936.5;272952.5] 12 0.00024569 +]272952.5;277480.5] 336 0.00687933 +]277480.5;277488.5] 8 0.000163793 +]277488.5;287984.5] 861 0.0176283 +]287984.5;287992.5] 8 0.000163793 +]287992.5;289400.5] 83 0.00169936 +]289400.5;289448.5] 18 0.000368535 +]289448.5;292464.5] 220 0.00450432 +]292464.5;292472.5] 7 0.000143319 +]292472.5;295584.5] 187 0.00382867 +]295584.5;295592.5] 7 0.000143319 +]295592.5;296448.5] 48 0.000982761 +]296448.5;296488.5] 15 0.000307113 +]296488.5;299808.5] 201 0.00411531 +]299808.5;299832.5] 8 0.000163793 +]299832.5;300680.5] 27 0.000552803 +]300680.5;300688.5] 7 0.000143319 +]300688.5;301608.5] 63 0.00128987 +]301608.5;301616.5] 8 0.000163793 +]301616.5;306776.5] 353 0.00722739 +]306776.5;306792.5] 9 0.000184268 +]306792.5;315968.5] 566 0.0115884 +]315968.5;315984.5] 9 0.000184268 +]315984.5;318640.5] 162 0.00331682 +]318640.5;318648.5] 7 0.000143319 +]318648.5;320448.5] 104 0.00212931 +]320448.5;320456.5] 10 0.000204742 +]320456.5;321752.5] 61 0.00124893 +]321752.5;321880.5] 23 0.000470906 +]321880.5;323048.5] 51 0.00104418 +]323048.5;323072.5] 9 0.000184268 +]323072.5;323784.5] 29 0.000593751 +]323784.5;323816.5] 12 0.00024569 +]323816.5;328192.5] 221 0.00452479 +]328192.5;328240.5] 14 0.000286639 +]328240.5;329424.5] 66 0.0013513 +]329424.5;329432.5] 9 0.000184268 +]329432.5;329976.5] 23 0.000470906 +]329976.5;329984.5] 10 0.000204742 +]329984.5;330904.5] 48 0.000982761 +]330904.5;331368.5] 5 0.000102371 +]331368.5;331648.5] 27 0.000552803 +]331648.5;331656.5] 7 0.000143319 +]331656.5;334264.5] 113 0.00231358 +]334264.5;334296.5] 17 0.000348061 +]334296.5;340912.5] 393 0.00804635 +]340912.5;340920.5] 10 0.000204742 +]340920.5;341624.5] 35 0.000716596 +]341624.5;341672.5] 19 0.000389009 +]341672.5;344616.5] 184 0.00376725 +]344616.5;344624.5] 7 0.000143319 +]344624.5;347488.5] 142 0.00290733 +]347488.5;347496.5] 7 0.000143319 +]347496.5;349144.5] 97 0.001986 +]349144.5;349152.5] 10 0.000204742 +]349152.5;353008.5] 174 0.00356251 +]353008.5;353016.5] 7 0.000143319 +]353016.5;353816.5] 34 0.000696122 +]353816.5;353824.5] 6 0.000122845 +]353824.5;355848.5] 79 0.00161746 +]355848.5;355856.5] 7 0.000143319 +]355856.5;360888.5] 230 0.00470906 +]360888.5;361272.5] 3 6.14225e-05 +]361272.5;361280.5] 7 0.000143319 +]361280.5;361480.5] 5 0.000102371 +]361480.5;361504.5] 10 0.000204742 +]361504.5;361880.5] 11 0.000225216 +]361880.5;361888.5] 6 0.000122845 +]361888.5;365736.5] 124 0.0025388 +]365736.5;365752.5] 8 0.000163793 +]365752.5;376392.5] 384 0.00786209 +]376392.5;376488.5] 18 0.000368535 +]376488.5;377016.5] 17 0.000348061 +]377016.5;377024.5] 7 0.000143319 +]377024.5;381152.5] 140 0.00286639 +]381152.5;381160.5] 7 0.000143319 +]381160.5;389712.5] 214 0.00438147 +]389712.5;389728.5] 7 0.000143319 +]389728.5;393672.5] 111 0.00227263 +]393672.5;393728.5] 11 0.000225216 +]393728.5;394920.5] 28 0.000573277 +]394920.5;394928.5] 6 0.000122845 +]394928.5;398872.5] 73 0.00149462 +]398872.5;399128.5] 28 0.000573277 +]399128.5;403464.5] 106 0.00217026 +]403464.5;403520.5] 10 0.000204742 +]403520.5;406640.5] 72 0.00147414 +]406640.5;406664.5] 7 0.000143319 +]406664.5;409168.5] 54 0.00110561 +]409168.5;409232.5] 17 0.000348061 +]409232.5;410032.5] 19 0.000389009 +]410032.5;410040.5] 6 0.000122845 +]410040.5;410632.5] 29 0.000593751 +]410632.5;421832.5] 205 0.00419721 +]421832.5;421840.5] 5 0.000102371 +]421840.5;423216.5] 20 0.000409484 +]423216.5;423224.5] 6 0.000122845 +]423224.5;428424.5] 102 0.00208837 +]428424.5;443504.5] 191 0.00391057 +]443504.5;443552.5] 9 0.000184268 +]443552.5;445376.5] 21 0.000429958 +]445376.5;445384.5] 7 0.000143319 +]445384.5;452400.5] 83 0.00169936 +]452400.5;452408.5] 5 0.000102371 +]452408.5;455360.5] 30 0.000614225 +]455360.5;455400.5] 7 0.000143319 +]455400.5;455992.5] 3 6.14225e-05 +]455992.5;456000.5] 5 0.000102371 +]456000.5;456736.5] 20 0.000409484 +]456736.5;467104.5] 83 0.00169936 +]467104.5;467112.5] 4 8.18967e-05 +]467112.5;473128.5] 44 0.000900864 +]473128.5;473136.5] 5 0.000102371 +]473136.5;475320.5] 14 0.000286639 +]475320.5;475328.5] 5 0.000102371 +]475328.5;481984.5] 47 0.000962287 +]481984.5;481992.5] 5 0.000102371 +]481992.5;488704.5] 43 0.00088039 +]488704.5;488720.5] 5 0.000102371 +]488720.5;509360.5] 114 0.00233406 +]509360.5;509368.5] 4 8.18967e-05 +]509368.5;510064.5] 5 0.000102371 +]510064.5;510072.5] 4 8.18967e-05 +]510072.5;548240.5] 124 0.0025388 +]548240.5;548672.5] 11 0.000225216 +]548672.5;557344.5] 25 0.000511855 +]557344.5;557360.5] 4 8.18967e-05 +]557360.5;594176.5] 83 0.00169936 +]594176.5;594208.5] 4 8.18967e-05 +]594208.5;609776.5] 36 0.000737071 +]609776.5;609792.5] 4 8.18967e-05 +]609792.5;772928.5] 137 0.00280496 +]772928.5;857520.5] 15 0.000307113 +]857520.5;857536.5] 2 4.09484e-05 +]857536.5;953584.5] 14 0.000286639 +]953584.5;953600.5] 2 4.09484e-05 +]953600.5;1033216.5] 3 6.14225e-05 +]1033216.5;1033232.5] 2 4.09484e-05 +]1033232.5;+inf[ 15 0.000307113 + + +---------------------------------------------- +Rank R09 +Variable Numerical hours_per_week + +Variable stats +Interval Frequency Coverage +]-inf;1.5] 27 0.000552803 +]1.5;3.5] 112 0.00229311 +]3.5;6.5] 271 0.0055485 +]6.5;7.5] 45 0.000921338 +]7.5;8.5] 218 0.00446337 +]8.5;9.5] 27 0.000552803 +]9.5;10.5] 425 0.00870153 +]10.5;11.5] 20 0.000409484 +]11.5;12.5] 247 0.00505712 +]12.5;14.5] 83 0.00169936 +]14.5;15.5] 623 0.0127554 +]15.5;16.5] 303 0.00620368 +]16.5;17.5] 42 0.000859916 +]17.5;18.5] 129 0.00264117 +]18.5;19.5] 19 0.000389009 +]19.5;20.5] 1862 0.0381229 +]20.5;23.5] 148 0.00303018 +]23.5;24.5] 354 0.00724786 +]24.5;25.5] 958 0.0196143 +]25.5;27.5] 83 0.00169936 +]27.5;28.5] 140 0.00286639 +]28.5;29.5] 15 0.000307113 +]29.5;30.5] 1700 0.0348061 +]30.5;31.5] 12 0.00024569 +]31.5;32.5] 423 0.00866058 +]32.5;34.5] 109 0.00223169 +]34.5;35.5] 1937 0.0396585 +]35.5;36.5] 336 0.00687933 +]36.5;37.5] 242 0.00495475 +]37.5;38.5] 714 0.0146186 +]38.5;39.5] 63 0.00128987 +]39.5;40.5] 22803 0.466873 +]40.5;41.5] 59 0.00120798 +]41.5;42.5] 338 0.00692027 +]42.5;43.5] 227 0.00464764 +]43.5;44.5] 310 0.006347 +]44.5;45.5] 2717 0.0556284 +]45.5;46.5] 129 0.00264117 +]46.5;47.5] 82 0.00167888 +]47.5;48.5] 770 0.0157651 +]48.5;49.5] 39 0.000798493 +]49.5;50.5] 4246 0.0869334 +]50.5;51.5] 20 0.000409484 +]51.5;52.5] 205 0.00419721 +]52.5;54.5] 101 0.00206789 +]54.5;55.5] 1051 0.0215184 +]55.5;56.5] 141 0.00288686 +]56.5;58.5] 57 0.00116703 +]58.5;59.5] 7 0.000143319 +]59.5;60.5] 2177 0.0445723 +]60.5;61.5] 4 8.18967e-05 +]61.5;64.5] 60 0.00122845 +]64.5;65.5] 355 0.00726833 +]65.5;68.5] 45 0.000921338 +]68.5;69.5] 1 2.04742e-05 +]69.5;70.5] 437 0.00894722 +]70.5;71.5] 0 0 +]71.5;72.5] 107 0.00219074 +]72.5;74.5] 7 0.000143319 +]74.5;75.5] 105 0.00214979 +]75.5;79.5] 27 0.000552803 +]79.5;80.5] 210 0.00429958 +]80.5;83.5] 4 8.18967e-05 +]83.5;84.5] 72 0.00147414 +]84.5;85.5] 17 0.000348061 +]85.5;89.5] 12 0.00024569 +]89.5;90.5] 42 0.000859916 +]90.5;95.5] 9 0.000184268 +]95.5;98.5] 25 0.000511855 +]98.5;+inf[ 137 0.00280496 + + +---------------------------------------------- +Rank R10 +Variable Categorical marital_status + +Variable stats +Group Frequency Coverage Size Value list +{Married-civ-spouse} 22379 0.458192 1 Married-civ-spouse +{Never-married} 16117 0.329982 1 Never-married +{Divorced} 6633 0.135805 1 Divorced +{Separated} 1530 0.0313255 1 Separated +{Widowed} 1518 0.0310798 1 Widowed +{Married-spouse-absent} 628 0.0128578 1 Married-spouse-absent +{Married-AF-spouse} 37 0.000757545 1 Married-AF-spouse * + + +Values +Value Frequency Coverage +Married-civ-spouse 22379 0.458192 +Never-married 16117 0.329982 +Divorced 6633 0.135805 +Separated 1530 0.0313255 +Widowed 1518 0.0310798 +Married-spouse-absent 628 0.0128578 +Married-AF-spouse 37 0.000757545 + +---------------------------------------------- +Rank R11 +Variable Categorical native_country + +Variable stats +Group Frequency Coverage Size Value list +{United-States} 44689 0.914971 1 United-States +{Mexico} 951 0.0194709 1 Mexico +{Philippines} 295 0.00603988 1 Philippines +{Germany} 206 0.00421768 1 Germany +{Puerto-Rico} 184 0.00376725 1 Puerto-Rico +{Canada} 182 0.0037263 1 Canada +{El-Salvador} 155 0.0031735 1 El-Salvador +{India} 151 0.0030916 1 India +{Cuba} 138 0.00282544 1 Cuba +{England} 127 0.00260022 1 England +{China} 122 0.00249785 1 China +{South} 115 0.00235453 1 South +{Jamaica} 106 0.00217026 1 Jamaica +{Italy} 105 0.00214979 1 Italy +{Dominican-Republic} 103 0.00210884 1 Dominican-Republic +{Japan} 92 0.00188362 1 Japan +{Guatemala} 88 0.00180173 1 Guatemala +{Poland} 87 0.00178125 1 Poland +{Vietnam} 86 0.00176078 1 Vietnam +{Columbia} 85 0.00174031 1 Columbia +{Haiti} 75 0.00153556 1 Haiti +{Portugal} 67 0.00137177 1 Portugal +{Taiwan} 65 0.00133082 1 Taiwan +{Iran} 59 0.00120798 1 Iran +{Greece} 49 0.00100323 1 Greece +{Nicaragua} 49 0.00100323 1 Nicaragua +{Peru} 46 0.000941812 1 Peru +{Ecuador} 45 0.000921338 1 Ecuador +{France} 38 0.000778019 1 France +{Ireland} 37 0.000757545 1 Ireland +{Hong} 30 0.000614225 1 Hong +{Thailand} 30 0.000614225 1 Thailand +{Cambodia} 28 0.000573277 1 Cambodia +{Trinadad&Tobago} 27 0.000552803 1 Trinadad&Tobago +{Laos} 23 0.000470906 1 Laos +{Outlying-US} 23 0.000470906 1 Outlying-US +{Yugoslavia} 23 0.000470906 1 Yugoslavia +{Scotland} 21 0.000429958 1 Scotland +{Honduras} 20 0.000409484 1 Honduras +{Hungary} 20 0.000409484 2 Hungary * + + +Values +Value Frequency Coverage +United-States 44689 0.914971 +Mexico 951 0.0194709 +Philippines 295 0.00603988 +Germany 206 0.00421768 +Puerto-Rico 184 0.00376725 +Canada 182 0.0037263 +El-Salvador 155 0.0031735 +India 151 0.0030916 +Cuba 138 0.00282544 +England 127 0.00260022 +China 122 0.00249785 +South 115 0.00235453 +Jamaica 106 0.00217026 +Italy 105 0.00214979 +Dominican-Republic 103 0.00210884 +Japan 92 0.00188362 +Guatemala 88 0.00180173 +Poland 87 0.00178125 +Vietnam 86 0.00176078 +Columbia 85 0.00174031 +Haiti 75 0.00153556 +Portugal 67 0.00137177 +Taiwan 65 0.00133082 +Iran 59 0.00120798 +Greece 49 0.00100323 +Nicaragua 49 0.00100323 +Peru 46 0.000941812 +Ecuador 45 0.000921338 +France 38 0.000778019 +Ireland 37 0.000757545 +Hong 30 0.000614225 +Thailand 30 0.000614225 +Cambodia 28 0.000573277 +Trinadad&Tobago 27 0.000552803 +Laos 23 0.000470906 +Outlying-US 23 0.000470906 +Yugoslavia 23 0.000470906 +Scotland 21 0.000429958 +Honduras 20 0.000409484 +Hungary 19 0.000389009 +Holand-Netherlands 1 2.04742e-05 + +---------------------------------------------- +Rank R12 +Variable Categorical occupation 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["Married-civ-spouse","Never-married","Divorced","Separated","Widowed","Married-spouse-absent","Married-AF-spouse"], + "frequencies": [22379,16117,6633,1530,1518,628,37] + } + }, + "R11": { + "dataGrid": { + "isSupervised": false, + "dimensions": [ + { + "variable": "native_country", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["United-States"], + ["Mexico"], + ["Philippines"], + ["Germany"], + ["Puerto-Rico"], + ["Canada"], + ["El-Salvador"], + ["India"], + ["Cuba"], + ["England"], + ["China"], + ["South"], + ["Jamaica"], + ["Italy"], + ["Dominican-Republic"], + ["Japan"], + ["Guatemala"], + ["Poland"], + ["Vietnam"], + ["Columbia"], + ["Haiti"], + ["Portugal"], + ["Taiwan"], + ["Iran"], + ["Greece"], + ["Nicaragua"], + ["Peru"], + ["Ecuador"], + ["France"], + ["Ireland"], + ["Hong"], + ["Thailand"], + ["Cambodia"], + ["Trinadad&Tobago"], + ["Laos"], + ["Outlying-US"], + ["Yugoslavia"], + ["Scotland"], + ["Honduras"], + ["Hungary"] + ], + "defaultGroupIndex": 39 + } + ], + "frequencies": [44689,951,295,206,184,182,155,151,138,127,122,115,106,105,103,92,88,87,86,85,75,67,65,59,49,49,46,45,38,37,30,30,28,27,23,23,23,21,20,20] + }, + "inputValues": { + "values": ["United-States","Mexico","Philippines","Germany","Puerto-Rico","Canada","El-Salvador","India","Cuba","England","China","South","Jamaica","Italy","Dominican-Republic","Japan","Guatemala","Poland","Vietnam","Columbia","Haiti","Portugal","Taiwan","Iran","Greece","Nicaragua","Peru","Ecuador","France","Ireland","Hong","Thailand","Cambodia","Trinadad&Tobago","Laos","Outlying-US","Yugoslavia","Scotland","Honduras","Hungary","Holand-Netherlands"], + "frequencies": [44689,951,295,206,184,182,155,151,138,127,122,115,106,105,103,92,88,87,86,85,75,67,65,59,49,49,46,45,38,37,30,30,28,27,23,23,23,21,20,19,1] + } + }, + "R12": { + "dataGrid": { + "isSupervised": false, + "dimensions": [ + { + "variable": "occupation", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Prof-specialty"], + ["Craft-repair"], + ["Exec-managerial"], + ["Adm-clerical"], + ["Sales"], + ["Other-service"], + ["Machine-op-inspct"], + ["Transport-moving"], + ["Handlers-cleaners"], + ["Farming-fishing"], + ["Tech-support"], + ["Protective-serv"], + ["Priv-house-serv"], + ["Armed-Forces"] + ], + "defaultGroupIndex": 13 + } + ], + "frequencies": [8981,6112,6086,5611,5504,4923,3022,2355,2072,1490,1446,983,242,15] + }, + "inputValues": { + "values": ["Prof-specialty","Craft-repair","Exec-managerial","Adm-clerical","Sales","Other-service","Machine-op-inspct","Transport-moving","Handlers-cleaners","Farming-fishing","Tech-support","Protective-serv","Priv-house-serv","Armed-Forces"], + "frequencies": [8981,6112,6086,5611,5504,4923,3022,2355,2072,1490,1446,983,242,15] + } + }, + "R13": { + "dataGrid": { + "isSupervised": false, + "dimensions": [ + { + "variable": "race", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["White"], + ["Black"], + ["Asian-Pac-Islander"], + ["Amer-Indian-Eskimo"], + ["Other"] + ], + "defaultGroupIndex": 4 + } + ], + "frequencies": [41762,4685,1519,470,406] + }, + "inputValues": { + "values": ["White","Black","Asian-Pac-Islander","Amer-Indian-Eskimo","Other"], + "frequencies": [41762,4685,1519,470,406] + } + }, + "R14": { + "dataGrid": { + "isSupervised": false, + "dimensions": [ + { + "variable": "relationship", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Husband"], + ["Not-in-family"], + ["Own-child"], + ["Unmarried"], + ["Wife"], + ["Other-relative"] + ], + "defaultGroupIndex": 5 + } + ], + "frequencies": [19716,12583,7581,5125,2331,1506] + }, + "inputValues": { + "values": ["Husband","Not-in-family","Own-child","Unmarried","Wife","Other-relative"], + "frequencies": [19716,12583,7581,5125,2331,1506] + } + }, + "R15": { + "dataGrid": { + "isSupervised": false, + "dimensions": [ + { + "variable": "sex", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Male"], + ["Female"] + ], + "defaultGroupIndex": 1 + } + ], + "frequencies": [32650,16192] + }, + "inputValues": { + "values": ["Male","Female"], + "frequencies": [32650,16192] + } + }, + "R16": { + "dataGrid": { + "isSupervised": false, + "dimensions": [ + { + "variable": "workclass", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Private"], + ["Self-emp-not-inc"], + ["Local-gov"], + ["State-gov"], + ["Self-emp-inc"], + ["Federal-gov"], + ["Without-pay"], + ["Never-worked"] + ], + "defaultGroupIndex": 7 + } + ], + "frequencies": [36705,3862,3136,1981,1695,1432,21,10] + }, + "inputValues": { + "values": ["Private","Self-emp-not-inc","Local-gov","State-gov","Self-emp-inc","Federal-gov","Without-pay","Never-worked"], + "frequencies": [36705,3862,3136,1981,1695,1432,21,10] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/AdultU/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/AdultU/results.ref/err.txt new file mode 100644 index 000000000..358e8dfe8 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/AdultU/results.ref/err.txt @@ -0,0 +1,5 @@ +Train unsupervised model +Database ../../../datasets/Adult/Adult.txt: Read records: 48,842 +Data preparation time: 0:00:02.49 +Write report ./results/AnalysisResults.khj + diff --git a/test/LearningTest/TestKhiops/Standard/AdultU/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/AdultU/results.ref/time.log new file mode 100644 index 000000000..2e8118737 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/AdultU/results.ref/time.log @@ -0,0 +1 @@ +Overal time: 2.631314754486084 diff --git a/test/LearningTest/TestKhiops/Standard/AdultU/test.prm b/test/LearningTest/TestKhiops/Standard/AdultU/test.prm new file mode 100644 index 000000000..69234ff03 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/AdultU/test.prm @@ -0,0 +1,56 @@ +// Thu Jun 12 13:31:06 2008 +// modl +// Output command file +// +//This file contains recorded commands, that can be replayed. +//Commands are based on user interactions: +// field update +// list index selection +// menu action +//Every command can be commented, using //. +//For example, commenting the last Exit command will allow other +//user interactions, after the commands have been replayed. +// +// + +// -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB +ClassManagement.OpenFile // Open... + +// -> Open +ClassFileName ../../../datasets/Adult/Adult.kdic // Dictionary file +OK // Open +// <- Open + +TrainDatabase.ClassName Adult // Dictionary + +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100 // Sample percentage + + +AnalysisSpec.TargetAttributeName // Target variable + +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true // Do data preparation only + +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 0 // Max number of pairs of variables + +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report + + +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls +ComputeStats // Analyse database + +Exit // Close +// <- Data preparation and scoring + + +// -> Data preparation and scoring +OK // Close +// <- Data preparation and scoring + diff --git a/test/LearningTest/TestKhiops/Standard/BenchIris/results.ref/benchmark.xls b/test/LearningTest/TestKhiops/Standard/BenchIris/results.ref/benchmark.xls index f0176a69f..d364f4127 100644 --- a/test/LearningTest/TestKhiops/Standard/BenchIris/results.ref/benchmark.xls +++ b/test/LearningTest/TestKhiops/Standard/BenchIris/results.ref/benchmark.xls @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Predictor evaluation report @@ -13,11 +13,11 @@ Database Dictionary Class Variables Cont. var. Cat. var. Instances Values Maj. a -Predictor Label Constr. vars Tree vars Var pairs Grouped target Discretization Min freq. Max nb Param. D. Value grouping Min freq. Max nb Param. G. DG algorithm Level Univariate initialization Pre-optimize Optimize Post-optimize -Selective Naive Bayes SNB 0 0 0 false MODL 0 0 0 MODL 0 0 0 VNS 4 1 1 1 1 -Naive Bayes NB 0 0 0 false MODL 0 0 0 MODL 0 0 0 VNS 4 1 1 1 1 -Data Grid DG 0 0 0 false MODL 0 0 0 MODL 0 0 0 VNS 4 1 1 1 1 -Univariate BU 0 0 0 false MODL 0 0 0 MODL 0 0 0 VNS 4 1 1 1 1 +Predictor Label Constr. vars Text vars Tree vars Var pairs Grouped target Max parts Min freq Discretization Param. D. Value grouping Param. G. DG algorithm Level Univariate initialization Pre-optimize Optimize Post-optimize +Selective Naive Bayes SNB 0 0 0 0 false 0 0 MODL 0 MODL 0 VNS 4 1 1 1 1 +Naive Bayes NB 0 0 0 0 false 0 0 MODL 0 MODL 0 VNS 4 1 1 1 1 +Data Grid DG 0 0 0 0 false 0 0 MODL 0 MODL 0 VNS 4 1 1 1 1 +Univariate BU 0 0 0 0 false 0 0 MODL 0 MODL 0 VNS 4 1 1 1 1 Train acc SNB NB DG BU @@ -117,16 +117,16 @@ SignificantTests 0/1/0 1/0/0 0/0/1 0/0/1 Rank 3 4 2 1 Total Time SNB NB DG BU -Mean 0.0317 0.0215 0.0584 0.0212 -GeometricMean 0.0298481 0.0214942 0.0583663 0.0211963 -SignificantTests 0/1/0 0/1/0 1/0/0 0/0/1 -Rank 3 2 4 1 +Mean 0.0285 0.0188 0.0487 0.0191 +GeometricMean 0.0269697 0.0187957 0.0486658 0.0190549 +SignificantTests 0/1/0 0/0/1 1/0/0 0/0/1 +Rank 3 1 4 2 Pre Time SNB NB DG BU -Mean 0.027 0.0213 0.0216 0.0211 -GeometricMean 0.0249806 0.0212951 0.0215899 0.0210979 +Mean 0.0239 0.0188 0.0184 0.0191 +GeometricMean 0.0222341 0.0187957 0.0183884 0.0190549 SignificantTests 0/1/0 0/1/0 0/1/0 0/1/0 -Rank 4 2 3 1 +Rank 4 2 1 3 Experiment details @@ -214,12 +214,12 @@ Iris 2 0 0 4 0 1 1.3 0.458258 -1 1 0 -1 Total Time Dataset SNB NB DG BU Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris 0.0317 0.0141283 0 0.0215 0.0005 0 0.0584 0.00200998 1 0.0212 0.0004 -1 +Iris 0.0285 0.0119436 0 0.0188 0.0004 -1 0.0487 0.00184662 1 0.0191 0.00137477 -1 Pre Time Dataset SNB NB DG BU Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris 0.027 0.0140285 0 0.0213 0.000458258 0 0.0216 0.000663325 0 0.0211 0.0003 0 +Iris 0.0239 0.0117852 0 0.0188 0.0004 0 0.0184 0.000663325 0 0.0191 0.00137477 0 Run details @@ -434,26 +434,26 @@ Iris 10 2 4 1 1 Total Time Dataset Run SNB NB DG BU -Iris 1 0.074 0.021 0.057 0.022 -Iris 2 0.026 0.022 0.056 0.021 -Iris 3 0.026 0.022 0.061 0.021 -Iris 4 0.027 0.021 0.057 0.021 -Iris 5 0.027 0.022 0.058 0.021 -Iris 6 0.027 0.022 0.058 0.021 -Iris 7 0.029 0.022 0.063 0.022 -Iris 8 0.028 0.021 0.058 0.021 -Iris 9 0.027 0.021 0.059 0.021 -Iris 10 0.026 0.021 0.057 0.021 +Iris 1 0.064 0.019 0.047 0.018 +Iris 2 0.024 0.019 0.047 0.019 +Iris 3 0.025 0.019 0.052 0.023 +Iris 4 0.029 0.019 0.048 0.018 +Iris 5 0.023 0.019 0.048 0.019 +Iris 6 0.025 0.019 0.047 0.019 +Iris 7 0.024 0.018 0.052 0.019 +Iris 8 0.024 0.019 0.048 0.019 +Iris 9 0.024 0.019 0.05 0.019 +Iris 10 0.023 0.018 0.048 0.018 Pre Time Dataset Run SNB NB DG BU -Iris 1 0.069 0.021 0.022 0.021 -Iris 2 0.022 0.021 0.021 0.021 -Iris 3 0.022 0.022 0.021 0.021 -Iris 4 0.022 0.021 0.021 0.021 -Iris 5 0.022 0.021 0.022 0.021 -Iris 6 0.022 0.022 0.022 0.021 -Iris 7 0.024 0.022 0.023 0.022 -Iris 8 0.024 0.021 0.022 0.021 -Iris 9 0.022 0.021 0.021 0.021 -Iris 10 0.021 0.021 0.021 0.021 +Iris 1 0.059 0.019 0.018 0.018 +Iris 2 0.02 0.019 0.018 0.019 +Iris 3 0.02 0.019 0.02 0.023 +Iris 4 0.024 0.019 0.018 0.018 +Iris 5 0.019 0.019 0.018 0.019 +Iris 6 0.02 0.019 0.019 0.019 +Iris 7 0.019 0.018 0.019 0.019 +Iris 8 0.02 0.019 0.018 0.019 +Iris 9 0.019 0.019 0.018 0.019 +Iris 10 0.019 0.018 0.018 0.018 diff --git a/test/LearningTest/TestKhiops/Standard/BenchIris/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/BenchIris/results.ref/time.log index d799e5962..439531260 100644 --- a/test/LearningTest/TestKhiops/Standard/BenchIris/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/BenchIris/results.ref/time.log @@ -1 +1 @@ -Overal time: 2.1618406772613525 +Overal time: 1.8771882057189941 diff --git a/test/LearningTest/TestKhiops/Standard/BenchIris/test.prm b/test/LearningTest/TestKhiops/Standard/BenchIris/test.prm index bf4b8eb6e..f20f2d2a7 100644 --- a/test/LearningTest/TestKhiops/Standard/BenchIris/test.prm +++ b/test/LearningTest/TestKhiops/Standard/BenchIris/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB LearningProblemStudy.ClassifierBenchmark // Evaluate classifiers... // -> Learning benchmark @@ -65,8 +67,8 @@ BenchmarkSpecs.InsertItemAfter // Insert after BenchmarkClassSpec.ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file BenchmarkClassSpec.ClassName Iris // Dictionary BenchmarkClassSpec.TargetAttributeName Class // Target variable -Database.DatabaseFiles.List.Key Iris // List item selection -Database.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file Exit // OK // <- Benchmark diff --git a/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/results.ref/benchmark.xls b/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/results.ref/benchmark.xls index ff0bd4023..4165b3c2b 100644 --- a/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/results.ref/benchmark.xls +++ b/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/results.ref/benchmark.xls @@ -1,4 +1,4 @@ -#Khiops 10.1 +#Khiops 10.4.6i Predictor evaluation report @@ -13,643 +13,640 @@ Database Dictionary Class Variables Cont. var. Cat. var. Instances Values Maj. a -Predictor Label Constr. vars Tree vars Var pairs Grouped target Discretization Min freq. Max nb Param. D. Value grouping Min freq. Max nb Param. G. DG algorithm Level Univariate initialization Pre-optimize Optimize Post-optimize -Univariate MODL 0 0 0 false MODL 0 0 0 MODL 0 0 0 VNS 4 1 1 1 1 -Univariate MEF 0 0 0 false MODLEqualFrequency 0 0 0 MODLBasic 0 0 0 VNS 4 1 1 1 1 -Univariate MEW 0 0 0 false MODLEqualWidth 0 0 0 MODLBasic 0 0 0 VNS 4 1 1 1 1 -Univariate EF 0 0 0 false EqualFrequency 0 0 0 BasicGrouping 0 0 0 VNS 4 1 1 1 1 -Univariate EW 0 0 0 false EqualWidth 0 0 0 BasicGrouping 0 0 0 VNS 4 1 1 1 1 +Predictor Label Constr. vars Text vars Tree vars Var pairs Grouped target Max parts Min freq Discretization Param. D. Value grouping Param. G. DG algorithm Level Univariate initialization Pre-optimize Optimize Post-optimize +Univariate MODL 0 0 0 0 false 0 0 MODL 0 MODL 0 VNS 4 1 1 1 1 +Univariate MODL_2 0 0 0 0 false 2 0 MODL 0 MODL 0 VNS 4 1 1 1 1 Dataset Iris -Train acc MODL MEF MEW EF EW -Mean 0.8 0.813333 0.806667 0.806667 0.815 -GeometricMean 0.78059 0.797281 0.791361 0.796486 0.80238 -SignificantTests 0/4/0 0/4/0 1/3/0 2/2/0 1/3/0 -Rank 3.25 2.25 3.25 3 2.5 - -Test acc MODL MEF MEW EF EW -Mean 0.755 0.785 0.751667 0.741667 0.731667 -GeometricMean 0.731329 0.762794 0.71459 0.724302 0.702664 -SignificantTests 0/4/0 0/4/0 0/2/2 0/4/0 0/4/0 -Rank 2.75 2.25 2.25 3.25 3.75 - -Ratio acc MODL MEF MEW EF EW -Mean 0.939944 0.96038 0.913299 0.91165 0.883948 -GeometricMean 0.936893 0.956744 0.902989 0.909372 0.875725 -SignificantTests 0/4/0 0/4/0 0/3/1 1/3/0 0/4/0 -Rank 2.5 2.25 2.25 3.75 4 - -Train Bacc MODL MEF MEW EF EW -Mean 0.8 0.813333 0.806667 0.806667 0.815 -GeometricMean 0.78059 0.797281 0.791361 0.796486 0.80238 -SignificantTests 0/4/0 0/4/0 1/3/0 2/2/0 1/3/0 -Rank 3.25 2.25 3.25 3 2.75 - -Test Bacc MODL MEF MEW EF EW -Mean 0.755 0.785 0.751667 0.741667 0.731667 -GeometricMean 0.731329 0.762794 0.71459 0.724302 0.702664 -SignificantTests 0/4/0 0/4/0 0/2/2 0/4/0 0/4/0 -Rank 3 2.25 2.25 3.5 3.75 - -Ratio Bacc MODL MEF MEW EF EW -Mean 0.939944 0.96038 0.913299 0.91165 0.883948 -GeometricMean 0.936893 0.956744 0.902989 0.909372 0.875725 -SignificantTests 0/4/0 0/4/0 0/3/1 1/3/0 0/4/0 -Rank 2.5 2.25 2.25 3.75 4 - -Train AUC MODL MEF MEW EF EW -Mean 0.871667 0.8947 0.896867 0.919367 0.915867 -GeometricMean 0.863002 0.890064 0.892789 0.916463 0.912328 -SignificantTests 0/4/0 0/4/0 1/2/1 0/3/1 0/3/1 -Rank 4.5 3.5 3.5 1.5 1.5 - -Test AUC MODL MEF MEW EF EW -Mean 0.837333 0.8685 0.851833 0.8677 0.858333 -GeometricMean 0.827194 0.860696 0.840717 0.860435 0.847255 -SignificantTests 0/4/0 0/3/1 1/2/1 0/3/1 0/4/0 -Rank 4.25 2.5 3 2.5 2.5 - -Ratio AUC MODL MEF MEW EF EW -Mean 0.960381 0.968501 0.944546 0.940373 0.931327 -GeometricMean 0.958508 0.967005 0.941675 0.938865 0.928674 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 3.75 2.25 2 3.25 3.5 - -Train CompR MODL MEF MEW EF EW -Mean 0.597912 0.61346 0.616551 0.63546 0.65022 -GeometricMean 0.513074 0.548712 0.561645 0.591274 0.597076 -SignificantTests 0/4/0 0/4/0 0/4/0 0/3/1 0/4/0 -Rank 3.5 3.5 3.25 2.75 1.75 - -Test CompR MODL MEF MEW EF EW -Mean 0.434351 0.484354 0.490759 0.461643 0.478126 -GeometricMean 0.270533 0.347834 0.298717 0.311803 0 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 4.25 2 2.25 3.25 3 - -Ratio CompR MODL MEF MEW EF EW -Mean 0.632915 0.710545 0.673523 0.628311 0.611041 -GeometricMean 0.527278 0.63391 0.53186 0.527342 0 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 3.5 2.25 2 3.5 3.5 - -Features MODL MEF MEW EF EW -Mean 4 4 4 4 4 -GeometricMean 4 4 4 4 4 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 - -Constr. att. MODL MEF MEW EF EW -Mean 0 0 0 0 0 -GeometricMean 0 0 0 0 0 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 - -Inf. features MODL MEF MEW EF EW -Mean 4 4 4 4 4 -GeometricMean 4 4 4 4 4 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 - -Used attributes MODL MEF MEW EF EW -Mean 1 1 1 1 1 -GeometricMean 1 1 1 1 1 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 - -Total Time MODL MEF MEW EF EW -Mean 0.00583791 0.00528388 0.00521033 0.00525252 0.00522034 -GeometricMean 0.00582689 0.00528365 0.00521028 0.00525236 0.00522033 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 5 4 1 3 2 - -Pre Time MODL MEF MEW EF EW -Mean 0.00583791 0.00528388 0.00521033 0.00525252 0.00522034 -GeometricMean 0.00582689 0.00528365 0.00521028 0.00525236 0.00522033 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 5 4 1 3 2 - -Clusters MODL MEF MEW EF EW -Mean 2.625 3.375 3.625 9.875 9.375 -GeometricMean 2.57684 3.31489 3.53346 9.86916 9.33368 -SignificantTests 0/4/0 0/4/0 2/2/0 4/0/0 4/0/0 -Rank 1 1.5 2 4.5 4.25 - -Mono MODL MEF MEW EF EW -Mean 0 0 0 0 0 -GeometricMean 0 0 0 0 0 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 +Train acc MODL MODL_2 +Mean 0.8 0.638333 +GeometricMean 0.78059 0.636799 +SignificantTests 0/4/0 2/2/0 +Rank 1 1.75 + +Test acc MODL MODL_2 +Mean 0.755 0.621667 +GeometricMean 0.731329 0.618855 +SignificantTests 0/4/0 1/3/0 +Rank 1.25 1.5 + +Ratio acc MODL MODL_2 +Mean 0.939944 0.972335 +GeometricMean 0.936893 0.971821 +SignificantTests 0/4/0 0/4/0 +Rank 1.75 1 + +Train Bacc MODL MODL_2 +Mean 0.8 0.638333 +GeometricMean 0.78059 0.636799 +SignificantTests 0/4/0 2/2/0 +Rank 1 1.75 + +Test Bacc MODL MODL_2 +Mean 0.755 0.621667 +GeometricMean 0.731329 0.618855 +SignificantTests 0/4/0 1/3/0 +Rank 1.25 1.5 + +Ratio Bacc MODL MODL_2 +Mean 0.939944 0.972335 +GeometricMean 0.936893 0.971821 +SignificantTests 0/4/0 0/4/0 +Rank 1.75 1 + +Train AUC MODL MODL_2 +Mean 0.871667 0.783333 +GeometricMean 0.863002 0.781199 +SignificantTests 0/4/0 2/2/0 +Rank 1 1.75 + +Test AUC MODL MODL_2 +Mean 0.837333 0.774167 +GeometricMean 0.827194 0.771339 +SignificantTests 0/4/0 1/3/0 +Rank 1.25 1.5 + +Ratio AUC MODL MODL_2 +Mean 0.960381 0.987609 +GeometricMean 0.958508 0.987378 +SignificantTests 0/4/0 0/4/0 +Rank 1.75 1 + +Train CompR MODL MODL_2 +Mean 0.597912 0.436922 +GeometricMean 0.513074 0.404207 +SignificantTests 0/4/0 1/3/0 +Rank 1 1.75 + +Test CompR MODL MODL_2 +Mean 0.434351 0.374987 +GeometricMean 0.270533 0.273493 +SignificantTests 0/4/0 0/4/0 +Rank 1.25 1.5 + +Ratio CompR MODL MODL_2 +Mean 0.632915 0.766192 +GeometricMean 0.527278 0.676616 +SignificantTests 0/4/0 0/4/0 +Rank 1.75 1 + +Features MODL MODL_2 +Mean 4 4 +GeometricMean 4 4 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 + +Constr. att. MODL MODL_2 +Mean 0 0 +GeometricMean 0 0 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 + +Inf. features MODL MODL_2 +Mean 4 4 +GeometricMean 4 4 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 + +Used attributes MODL MODL_2 +Mean 1 1 +GeometricMean 1 1 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 + +Total Time MODL MODL_2 +Mean 0.00480153 0.00449023 +GeometricMean 0.00479343 0.00449016 +SignificantTests 0/4/0 0/4/0 +Rank 2 1 + +Pre Time MODL MODL_2 +Mean 0.00480153 0.00449023 +GeometricMean 0.00479343 0.00449016 +SignificantTests 0/4/0 0/4/0 +Rank 2 1 + +Clusters MODL MODL_2 +Mean 2.625 2 +GeometricMean 2.57684 2 +SignificantTests 0/4/0 0/2/2 +Rank 1.75 1 + +Mono MODL MODL_2 +Mean 0 0 +GeometricMean 0 0 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 All datasets -Train acc MODL MEF MEW EF EW -Mean 0.8 0.813333 0.806667 0.806667 0.815 -GeometricMean 0.78059 0.797281 0.791361 0.796486 0.80238 -SignificantTests 0/4/0 0/4/0 1/3/0 2/2/0 1/3/0 -Rank 3.25 2.25 3.25 3 2.5 - -Test acc MODL MEF MEW EF EW -Mean 0.755 0.785 0.751667 0.741667 0.731667 -GeometricMean 0.731329 0.762794 0.71459 0.724302 0.702664 -SignificantTests 0/4/0 0/4/0 0/2/2 0/4/0 0/4/0 -Rank 2.75 2.25 2.25 3.25 3.75 - -Ratio acc MODL MEF MEW EF EW -Mean 0.939944 0.96038 0.913299 0.91165 0.883948 -GeometricMean 0.936893 0.956744 0.902989 0.909372 0.875725 -SignificantTests 0/4/0 0/4/0 0/3/1 1/3/0 0/4/0 -Rank 2.5 2.25 2.25 3.75 4 - -Train Bacc MODL MEF MEW EF EW -Mean 0.8 0.813333 0.806667 0.806667 0.815 -GeometricMean 0.78059 0.797281 0.791361 0.796486 0.80238 -SignificantTests 0/4/0 0/4/0 1/3/0 2/2/0 1/3/0 -Rank 3.25 2.25 3.25 3 2.75 - -Test Bacc MODL MEF MEW EF EW -Mean 0.755 0.785 0.751667 0.741667 0.731667 -GeometricMean 0.731329 0.762794 0.71459 0.724302 0.702664 -SignificantTests 0/4/0 0/4/0 0/2/2 0/4/0 0/4/0 -Rank 3 2.25 2.25 3.5 3.75 - -Ratio Bacc MODL MEF MEW EF EW -Mean 0.939944 0.96038 0.913299 0.91165 0.883948 -GeometricMean 0.936893 0.956744 0.902989 0.909372 0.875725 -SignificantTests 0/4/0 0/4/0 0/3/1 1/3/0 0/4/0 -Rank 2.5 2.25 2.25 3.75 4 - -Train AUC MODL MEF MEW EF EW -Mean 0.871667 0.8947 0.896867 0.919367 0.915867 -GeometricMean 0.863002 0.890064 0.892789 0.916463 0.912328 -SignificantTests 0/4/0 0/4/0 1/2/1 0/3/1 0/3/1 -Rank 4.5 3.5 3.5 1.5 1.5 - -Test AUC MODL MEF MEW EF EW -Mean 0.837333 0.8685 0.851833 0.8677 0.858333 -GeometricMean 0.827194 0.860696 0.840717 0.860435 0.847255 -SignificantTests 0/4/0 0/3/1 1/2/1 0/3/1 0/4/0 -Rank 4.25 2.5 3 2.5 2.5 - -Ratio AUC MODL MEF MEW EF EW -Mean 0.960381 0.968501 0.944546 0.940373 0.931327 -GeometricMean 0.958508 0.967005 0.941675 0.938865 0.928674 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 3.75 2.25 2 3.25 3.5 - -Train CompR MODL MEF MEW EF EW -Mean 0.597912 0.61346 0.616551 0.63546 0.65022 -GeometricMean 0.513074 0.548712 0.561645 0.591274 0.597076 -SignificantTests 0/4/0 0/4/0 0/4/0 0/3/1 0/4/0 -Rank 3.5 3.5 3.25 2.75 1.75 - -Test CompR MODL MEF MEW EF EW -Mean 0.434351 0.484354 0.490759 0.461643 0.478126 -GeometricMean 0.270533 0.347834 0.298717 0.311803 0 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 4.25 2 2.25 3.25 3 - -Ratio CompR MODL MEF MEW EF EW -Mean 0.632915 0.710545 0.673523 0.628311 0.611041 -GeometricMean 0.527278 0.63391 0.53186 0.527342 0 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 3.5 2.25 2 3.5 3.5 - -Features MODL MEF MEW EF EW -Mean 4 4 4 4 4 -GeometricMean 4 4 4 4 4 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 - -Constr. att. MODL MEF MEW EF EW -Mean 0 0 0 0 0 -GeometricMean 0 0 0 0 0 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 - -Inf. features MODL MEF MEW EF EW -Mean 4 4 4 4 4 -GeometricMean 4 4 4 4 4 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 - -Used attributes MODL MEF MEW EF EW -Mean 1 1 1 1 1 -GeometricMean 1 1 1 1 1 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 - -Total Time MODL MEF MEW EF EW -Mean 0.00583791 0.00528388 0.00521033 0.00525252 0.00522034 -GeometricMean 0.00582689 0.00528365 0.00521028 0.00525236 0.00522033 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 5 4 1 3 2 - -Pre Time MODL MEF MEW EF EW -Mean 0.00583791 0.00528388 0.00521033 0.00525252 0.00522034 -GeometricMean 0.00582689 0.00528365 0.00521028 0.00525236 0.00522033 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 5 4 1 3 2 - -Clusters MODL MEF MEW EF EW -Mean 2.625 3.375 3.625 9.875 9.375 -GeometricMean 2.57684 3.31489 3.53346 9.86916 9.33368 -SignificantTests 0/4/0 0/4/0 2/2/0 4/0/0 4/0/0 -Rank 1 1.5 2 4.5 4.25 - -Mono MODL MEF MEW EF EW -Mean 0 0 0 0 0 -GeometricMean 0 0 0 0 0 -SignificantTests 0/4/0 0/4/0 0/4/0 0/4/0 0/4/0 -Rank 1 1 1 1 1 +Train acc MODL MODL_2 +Mean 0.8 0.638333 +GeometricMean 0.78059 0.636799 +SignificantTests 0/4/0 2/2/0 +Rank 1 1.75 + +Test acc MODL MODL_2 +Mean 0.755 0.621667 +GeometricMean 0.731329 0.618855 +SignificantTests 0/4/0 1/3/0 +Rank 1.25 1.5 + +Ratio acc MODL MODL_2 +Mean 0.939944 0.972335 +GeometricMean 0.936893 0.971821 +SignificantTests 0/4/0 0/4/0 +Rank 1.75 1 + +Train Bacc MODL MODL_2 +Mean 0.8 0.638333 +GeometricMean 0.78059 0.636799 +SignificantTests 0/4/0 2/2/0 +Rank 1 1.75 + +Test Bacc MODL MODL_2 +Mean 0.755 0.621667 +GeometricMean 0.731329 0.618855 +SignificantTests 0/4/0 1/3/0 +Rank 1.25 1.5 + +Ratio Bacc MODL MODL_2 +Mean 0.939944 0.972335 +GeometricMean 0.936893 0.971821 +SignificantTests 0/4/0 0/4/0 +Rank 1.75 1 + +Train AUC MODL MODL_2 +Mean 0.871667 0.783333 +GeometricMean 0.863002 0.781199 +SignificantTests 0/4/0 2/2/0 +Rank 1 1.75 + +Test AUC MODL MODL_2 +Mean 0.837333 0.774167 +GeometricMean 0.827194 0.771339 +SignificantTests 0/4/0 1/3/0 +Rank 1.25 1.5 + +Ratio AUC MODL MODL_2 +Mean 0.960381 0.987609 +GeometricMean 0.958508 0.987378 +SignificantTests 0/4/0 0/4/0 +Rank 1.75 1 + +Train CompR MODL MODL_2 +Mean 0.597912 0.436922 +GeometricMean 0.513074 0.404207 +SignificantTests 0/4/0 1/3/0 +Rank 1 1.75 + +Test CompR MODL MODL_2 +Mean 0.434351 0.374987 +GeometricMean 0.270533 0.273493 +SignificantTests 0/4/0 0/4/0 +Rank 1.25 1.5 + +Ratio CompR MODL MODL_2 +Mean 0.632915 0.766192 +GeometricMean 0.527278 0.676616 +SignificantTests 0/4/0 0/4/0 +Rank 1.75 1 + +Features MODL MODL_2 +Mean 4 4 +GeometricMean 4 4 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 + +Constr. att. MODL MODL_2 +Mean 0 0 +GeometricMean 0 0 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 + +Inf. features MODL MODL_2 +Mean 4 4 +GeometricMean 4 4 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 + +Used attributes MODL MODL_2 +Mean 1 1 +GeometricMean 1 1 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 + +Total Time MODL MODL_2 +Mean 0.00480153 0.00449023 +GeometricMean 0.00479343 0.00449016 +SignificantTests 0/4/0 0/4/0 +Rank 2 1 + +Pre Time MODL MODL_2 +Mean 0.00480153 0.00449023 +GeometricMean 0.00479343 0.00449016 +SignificantTests 0/4/0 0/4/0 +Rank 2 1 + +Clusters MODL MODL_2 +Mean 2.625 2 +GeometricMean 2.57684 2 +SignificantTests 0/4/0 0/2/2 +Rank 1.75 1 + +Mono MODL MODL_2 +Mean 0 0 +GeometricMean 0 0 +SignificantTests 0/4/0 0/4/0 +Rank 1 1 Experiment details Train acc -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.72 0.0666667 0 0.733333 0.0533333 0 0.713333 0.0466667 0 0.746667 0.0266667 0 0.746667 0.0133333 0 -Iris SepalWidth 0.566667 0.02 0 0.6 0.0133333 0 0.606667 0.00666667 0 0.633333 0.00666667 0 0.62 0.00666667 0 -Iris PetalLength 0.946667 0.0133333 0 0.953333 0.00666667 0 0.953333 0.00666667 0 0.906667 0.0133333 1 0.933333 0.0133333 1 -Iris PetalWidth 0.966667 0.0333333 0 0.966667 0.0333333 0 0.953333 0.0333333 1 0.94 0.0333333 1 0.96 0.0266667 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.72 0.0666667 0 0.653333 0 0 +Iris SepalWidth 0.566667 0.02 0 0.566667 0.02 0 +Iris PetalLength 0.946667 0.0133333 0 0.666667 0 1 +Iris PetalWidth 0.966667 0.0333333 0 0.666667 0 1 Test acc -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.62 0.00666667 0 0.733333 0.0266667 0 0.66 0.00666667 -1 0.62 0.02 0 0.593333 0.06 0 -Iris SepalWidth 0.526667 0.00666667 0 0.52 0.0266667 0 0.433333 0.0333333 0 0.553333 0.00666667 0 0.493333 0.0533333 0 -Iris PetalLength 0.92 0.0266667 0 0.946667 0.0133333 0 0.946667 0.0133333 0 0.88 0.04 0 0.88 0.0666667 0 -Iris PetalWidth 0.953333 0.0333333 0 0.94 0.02 0 0.966667 0.0333333 -1 0.913333 0.02 0 0.96 0.0266667 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.62 0.00666667 0 0.626667 0 0 +Iris SepalWidth 0.526667 0.00666667 0 0.526667 0.00666667 0 +Iris PetalLength 0.92 0.0266667 0 0.666667 0 1 +Iris PetalWidth 0.953333 0.0333333 0 0.666667 0 0 Ratio acc -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.869422 0.0897613 0 1.00798 0.109671 0 0.929825 0.0701754 0 0.83046 0.00287356 0 0.793461 0.0661882 0 -Iris SepalWidth 0.930155 0.0210643 0 0.866107 0.0251976 0 0.713768 0.0471014 0 0.873892 0.0197252 1 0.796716 0.0945883 0 -Iris PetalLength 0.971627 0.0144841 0 0.993153 0.0209311 0 0.993153 0.0209311 0 0.970149 0.0298507 0 0.94407 0.0849153 0 -Iris PetalWidth 0.988571 0.0685714 0 0.974286 0.0542857 0 1.01645 0.0705053 -1 0.972099 0.013195 0 1.00154 0.0555985 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.869422 0.0897613 0 0.959184 0 0 +Iris SepalWidth 0.930155 0.0210643 0 0.930155 0.0210643 0 +Iris PetalLength 0.971627 0.0144841 0 1 0 0 +Iris PetalWidth 0.988571 0.0685714 0 1 0 0 Train Bacc -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.72 0.0666667 0 0.733333 0.0533333 0 0.713333 0.0466667 0 0.746667 0.0266667 0 0.746667 0.0133333 0 -Iris SepalWidth 0.566667 0.02 0 0.6 0.0133333 0 0.606667 0.00666667 0 0.633333 0.00666667 0 0.62 0.00666667 0 -Iris PetalLength 0.946667 0.0133333 0 0.953333 0.00666667 0 0.953333 0.00666667 0 0.906667 0.0133333 1 0.933333 0.0133333 1 -Iris PetalWidth 0.966667 0.0333333 0 0.966667 0.0333333 0 0.953333 0.0333333 1 0.94 0.0333333 1 0.96 0.0266667 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.72 0.0666667 0 0.653333 0 0 +Iris SepalWidth 0.566667 0.02 0 0.566667 0.02 0 +Iris PetalLength 0.946667 0.0133333 0 0.666667 0 1 +Iris PetalWidth 0.966667 0.0333333 0 0.666667 0 1 Test Bacc -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.62 0.00666667 0 0.733333 0.0266667 0 0.66 0.00666667 -1 0.62 0.02 0 0.593333 0.06 0 -Iris SepalWidth 0.526667 0.00666667 0 0.52 0.0266667 0 0.433333 0.0333333 0 0.553333 0.00666667 0 0.493333 0.0533333 0 -Iris PetalLength 0.92 0.0266667 0 0.946667 0.0133333 0 0.946667 0.0133333 0 0.88 0.04 0 0.88 0.0666667 0 -Iris PetalWidth 0.953333 0.0333333 0 0.94 0.02 0 0.966667 0.0333333 -1 0.913333 0.02 0 0.96 0.0266667 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.62 0.00666667 0 0.626667 0 0 +Iris SepalWidth 0.526667 0.00666667 0 0.526667 0.00666667 0 +Iris PetalLength 0.92 0.0266667 0 0.666667 0 1 +Iris PetalWidth 0.953333 0.0333333 0 0.666667 0 0 Ratio Bacc -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.869422 0.0897613 0 1.00798 0.109671 0 0.929825 0.0701754 0 0.83046 0.00287356 0 0.793461 0.0661882 0 -Iris SepalWidth 0.930155 0.0210643 0 0.866107 0.0251976 0 0.713768 0.0471014 0 0.873892 0.0197252 1 0.796716 0.0945883 0 -Iris PetalLength 0.971627 0.0144841 0 0.993153 0.0209311 0 0.993153 0.0209311 0 0.970149 0.0298507 0 0.94407 0.0849153 0 -Iris PetalWidth 0.988571 0.0685714 0 0.974286 0.0542857 0 1.01645 0.0705053 -1 0.972099 0.013195 0 1.00154 0.0555985 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.869422 0.0897613 0 0.959184 0 0 +Iris SepalWidth 0.930155 0.0210643 0 0.930155 0.0210643 0 +Iris PetalLength 0.971627 0.0144841 0 1 0 0 +Iris PetalWidth 0.988571 0.0685714 0 1 0 0 Train AUC -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.833333 0.0466667 0 0.843467 0.0234667 0 0.848667 0.0305333 0 0.893067 0.0269333 0 0.889067 0.012 0 -Iris SepalWidth 0.696667 0.00333333 0 0.775333 0.0153333 0 0.785467 0.00653333 -1 0.815467 0.00613333 -1 0.7984 0.00213333 -1 -Iris PetalLength 0.973333 0.00666667 0 0.976667 0.00333333 0 0.976667 0.00333333 0 0.982933 0.00106667 0 0.985467 0.00466667 0 -Iris PetalWidth 0.983333 0.0166667 0 0.983333 0.0166667 0 0.976667 0.0166667 1 0.986 0.00973333 0 0.990533 0.00866667 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.833333 0.0466667 0 0.77 0.0166667 0 +Iris SepalWidth 0.696667 0.00333333 0 0.696667 0.00333333 0 +Iris PetalLength 0.973333 0.00666667 0 0.833333 0 1 +Iris PetalWidth 0.983333 0.0166667 0 0.833333 0 1 Test AUC -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.743333 0.0166667 0 0.847067 0.00893333 -1 0.786267 0.0177333 -1 0.811333 0.00346667 0 0.810933 0.0141333 0 -Iris SepalWidth 0.68 0.02 0 0.6936 0.00826667 0 0.6644 0.0190667 1 0.7144 0.008 0 0.660667 0.00333333 0 -Iris PetalLength 0.959333 0.014 0 0.973333 0.00666667 0 0.973333 0.00666667 0 0.971867 0.0137333 -1 0.973467 0.00706667 0 -Iris PetalWidth 0.966667 0.0266667 0 0.96 0.02 0 0.983333 0.0166667 0 0.9732 0.00386667 0 0.988267 0.0101333 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.743333 0.0166667 0 0.75 0.01 0 +Iris SepalWidth 0.68 0.02 0 0.68 0.02 0 +Iris PetalLength 0.959333 0.014 0 0.833333 0 1 +Iris PetalWidth 0.966667 0.0266667 0 0.833333 0 0 Ratio AUC -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.89593 0.0701721 0 1.00534 0.0385615 0 0.928426 0.0542985 0 0.909424 0.0313084 0 0.912069 0.00358635 0 -Iris SepalWidth 0.976236 0.0333791 0 0.895144 0.0283648 0 0.845723 0.0172398 0 0.876186 0.0164004 0 0.827483 0.00196397 0 -Iris PetalLength 0.985564 0.00763312 0 0.996622 0.0102274 0 0.996622 0.0102274 0 0.988727 0.0128988 0 0.987879 0.011849 0 -Iris PetalWidth 0.983793 0.0437931 0 0.976897 0.0368966 0 1.00741 0.0342562 0 0.987153 0.0136663 0 0.997878 0.0189611 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.89593 0.0701721 0 0.974201 0.0080996 0 +Iris SepalWidth 0.976236 0.0333791 0 0.976236 0.0333791 0 +Iris PetalLength 0.985564 0.00763312 0 1 0 0 +Iris PetalWidth 0.983793 0.0437931 0 1 0 0 Train CompR -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.448621 0.045059 0 0.423446 0.0266485 0 0.454071 0.0277103 0 0.528201 0.0455595 -1 0.508274 0.0326261 0 -Iris SepalWidth 0.208115 0.0100166 0 0.28615 0.0351603 0 0.304961 0.0359773 0 0.328781 0.0171052 0 0.320961 0.0204998 0 -Iris PetalLength 0.833401 0.0272568 0 0.846558 0.0141001 0 0.846558 0.0141001 0 0.820918 0.00470445 0 0.871206 0.0161271 0 -Iris PetalWidth 0.901512 0.0935581 0 0.897686 0.0973842 0 0.860613 0.0838389 0 0.863942 0.0619163 0 0.900442 0.0653808 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.448621 0.045059 0 0.387062 0.0164998 0 +Iris SepalWidth 0.208115 0.0100166 0 0.208115 0.0100166 0 +Iris PetalLength 0.833401 0.0272568 0 0.576254 0 1 +Iris PetalWidth 0.901512 0.0935581 0 0.576254 0 0 Test CompR -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.191904 0.0929808 0 0.329283 0.017601 0 0.252579 0.0752033 0 0.219499 0.0633579 0 0.26244 0.0330194 0 -Iris SepalWidth 0.0709269 0.0362578 0 0.0781413 0.000797759 0 0.0481642 0.00331166 0 0.0771549 0.0177334 0 0.0241257 0.0241257 0 -Iris PetalLength 0.73643 0.100636 0 0.824243 0.0240392 0 0.824243 0.0240392 0 0.773412 0.0554454 0 0.804382 0.0400478 0 -Iris PetalWidth 0.738143 0.173348 0 0.70575 0.140956 0 0.838049 0.0809864 0 0.776506 0.0097849 0 0.821558 0.112925 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.191904 0.0929808 0 0.276513 0.00837166 0 +Iris SepalWidth 0.0709269 0.0362578 0 0.0709269 0.0362578 0 +Iris PetalLength 0.73643 0.100636 0 0.576254 0 0 +Iris PetalWidth 0.738143 0.173348 0 0.576254 0 0 Ratio CompR -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.453152 0.252773 0 0.778093 0.0074011 0 0.568479 0.200312 0 0.429098 0.156962 0 0.522659 0.0985134 0 -Iris SepalWidth 0.350002 0.191065 0 0.276917 0.0312379 0 0.158865 0.00788261 0 0.238121 0.0663253 0 0.0706543 0.0706543 0 -Iris PetalLength 0.880637 0.0919512 0 0.974383 0.0446255 0 0.974383 0.0446255 0 0.941775 0.0621437 0 0.924465 0.0630813 0 -Iris PetalWidth 0.84787 0.280277 0 0.812788 0.245195 0 0.992366 0.190777 0 0.90425 0.0761309 0 0.926385 0.192676 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.453152 0.252773 0 0.714766 0.00884048 0 +Iris SepalWidth 0.350002 0.191065 0 0.350002 0.191065 0 +Iris PetalLength 0.880637 0.0919512 0 1 0 0 +Iris PetalWidth 0.84787 0.280277 0 1 0 0 Features -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 4 0 0 4 0 0 4 0 0 4 0 0 4 0 0 -Iris SepalWidth 4 0 0 4 0 0 4 0 0 4 0 0 4 0 0 -Iris PetalLength 4 0 0 4 0 0 4 0 0 4 0 0 4 0 0 -Iris PetalWidth 4 0 0 4 0 0 4 0 0 4 0 0 4 0 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 4 0 0 4 0 0 +Iris SepalWidth 4 0 0 4 0 0 +Iris PetalLength 4 0 0 4 0 0 +Iris PetalWidth 4 0 0 4 0 0 Constr. att. -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -Iris SepalWidth 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -Iris PetalLength 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -Iris PetalWidth 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0 0 0 0 0 0 +Iris SepalWidth 0 0 0 0 0 0 +Iris PetalLength 0 0 0 0 0 0 +Iris PetalWidth 0 0 0 0 0 0 Inf. features -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 4 0 0 4 0 0 4 0 0 4 0 0 4 0 0 -Iris SepalWidth 4 0 0 4 0 0 4 0 0 4 0 0 4 0 0 -Iris PetalLength 4 0 0 4 0 0 4 0 0 4 0 0 4 0 0 -Iris PetalWidth 4 0 0 4 0 0 4 0 0 4 0 0 4 0 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 4 0 0 4 0 0 +Iris SepalWidth 4 0 0 4 0 0 +Iris PetalLength 4 0 0 4 0 0 +Iris PetalWidth 4 0 0 4 0 0 Used attributes -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 -Iris SepalWidth 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 -Iris PetalLength 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 -Iris PetalWidth 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 1 0 0 1 0 0 +Iris SepalWidth 1 0 0 1 0 0 +Iris PetalLength 1 0 0 1 0 0 +Iris PetalWidth 1 0 0 1 0 0 Total Time -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.00583791 0.000358537 0 0.00528388 4.855e-05 0 0.00521033 2.255e-05 0 0.00525253 4.1625e-05 0 0.00522034 1.04125e-05 0 -Iris SepalWidth 0.00583791 0.000358537 0 0.00528388 4.855e-05 0 0.00521033 2.255e-05 0 0.00525253 4.1625e-05 0 0.00522034 1.04125e-05 0 -Iris PetalLength 0.00583791 0.000358537 0 0.00528388 4.855e-05 0 0.00521033 2.255e-05 0 0.00525253 4.1625e-05 0 0.00522034 1.04125e-05 0 -Iris PetalWidth 0.00583791 0.000358537 0 0.00528388 4.855e-05 0 0.00521033 2.255e-05 0 0.00525253 4.1625e-05 0 0.00522034 1.04125e-05 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.00480153 0.00027875 0 0.00449023 2.42e-05 0 +Iris SepalWidth 0.00480153 0.00027875 0 0.00449023 2.42e-05 0 +Iris PetalLength 0.00480153 0.00027875 0 0.00449023 2.42e-05 0 +Iris PetalWidth 0.00480153 0.00027875 0 0.00449023 2.42e-05 0 Pre Time -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0.00583791 0.000358537 0 0.00528388 4.855e-05 0 0.00521033 2.255e-05 0 0.00525253 4.1625e-05 0 0.00522034 1.04125e-05 0 -Iris SepalWidth 0.00583791 0.000358537 0 0.00528388 4.855e-05 0 0.00521033 2.255e-05 0 0.00525253 4.1625e-05 0 0.00522034 1.04125e-05 0 -Iris PetalLength 0.00583791 0.000358537 0 0.00528388 4.855e-05 0 0.00521033 2.255e-05 0 0.00525253 4.1625e-05 0 0.00522034 1.04125e-05 0 -Iris PetalWidth 0.00583791 0.000358537 0 0.00528388 4.855e-05 0 0.00521033 2.255e-05 0 0.00525253 4.1625e-05 0 0.00522034 1.04125e-05 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0.00480153 0.00027875 0 0.00449023 2.42e-05 0 +Iris SepalWidth 0.00480153 0.00027875 0 0.00449023 2.42e-05 0 +Iris PetalLength 0.00480153 0.00027875 0 0.00449023 2.42e-05 0 +Iris PetalWidth 0.00480153 0.00027875 0 0.00449023 2.42e-05 0 Clusters -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 2.5 0.5 0 3 0 0 3.5 0.5 1 10 0 1 10 0 1 -Iris SepalWidth 2 0 0 4.5 0.5 0 5 0 1 9.5 0.5 1 10 0 1 -Iris PetalLength 3 0 0 3 0 0 3 0 0 10 0 1 8 0 1 -Iris PetalWidth 3 0 0 3 0 0 3 0 0 10 0 1 9.5 0.5 1 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 2.5 0.5 0 2 0 0 +Iris SepalWidth 2 0 0 2 0 0 +Iris PetalLength 3 0 0 2 0 -1 +Iris PetalWidth 3 0 0 2 0 -1 Mono -Dataset Attribute MODL MEF MEW EF EW - Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests -Iris SepalLength 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -Iris SepalWidth 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -Iris PetalLength 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -Iris PetalWidth 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Dataset Attribute MODL MODL_2 + Mean StandardDeviation SignificantTests Mean StandardDeviation SignificantTests +Iris SepalLength 0 0 0 0 0 0 +Iris SepalWidth 0 0 0 0 0 0 +Iris PetalLength 0 0 0 0 0 0 +Iris PetalWidth 0 0 0 0 0 0 Run details Train acc -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.653333 0.68 0.666667 0.72 0.733333 -Iris SepalLength 2 0.786667 0.786667 0.76 0.773333 0.76 -Iris SepalWidth 1 0.546667 0.586667 0.6 0.626667 0.626667 -Iris SepalWidth 2 0.586667 0.613333 0.613333 0.64 0.613333 -Iris PetalLength 1 0.933333 0.946667 0.946667 0.893333 0.92 -Iris PetalLength 2 0.96 0.96 0.96 0.92 0.946667 -Iris PetalWidth 1 0.933333 0.933333 0.92 0.906667 0.933333 -Iris PetalWidth 2 1 1 0.986667 0.973333 0.986667 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.653333 0.653333 +Iris SepalLength 2 0.786667 0.653333 +Iris SepalWidth 1 0.546667 0.546667 +Iris SepalWidth 2 0.586667 0.586667 +Iris PetalLength 1 0.933333 0.666667 +Iris PetalLength 2 0.96 0.666667 +Iris PetalWidth 1 0.933333 0.666667 +Iris PetalWidth 2 1 0.666667 Test acc -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.626667 0.76 0.666667 0.6 0.533333 -Iris SepalLength 2 0.613333 0.706667 0.653333 0.64 0.653333 -Iris SepalWidth 1 0.52 0.493333 0.4 0.56 0.44 -Iris SepalWidth 2 0.533333 0.546667 0.466667 0.546667 0.546667 -Iris PetalLength 1 0.893333 0.96 0.96 0.84 0.946667 -Iris PetalLength 2 0.946667 0.933333 0.933333 0.92 0.813333 -Iris PetalWidth 1 0.986667 0.96 1 0.893333 0.986667 -Iris PetalWidth 2 0.92 0.92 0.933333 0.933333 0.933333 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.626667 0.626667 +Iris SepalLength 2 0.613333 0.626667 +Iris SepalWidth 1 0.52 0.52 +Iris SepalWidth 2 0.533333 0.533333 +Iris PetalLength 1 0.893333 0.666667 +Iris PetalLength 2 0.946667 0.666667 +Iris PetalWidth 1 0.986667 0.666667 +Iris PetalWidth 2 0.92 0.666667 Ratio acc -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.959184 1.11765 1 0.833333 0.727273 -Iris SepalLength 2 0.779661 0.898305 0.859649 0.827586 0.859649 -Iris SepalWidth 1 0.95122 0.840909 0.666667 0.893617 0.702128 -Iris SepalWidth 2 0.909091 0.891304 0.76087 0.854167 0.891304 -Iris PetalLength 1 0.957143 1.01408 1.01408 0.940299 1.02899 -Iris PetalLength 2 0.986111 0.972222 0.972222 1 0.859155 -Iris PetalWidth 1 1.05714 1.02857 1.08696 0.985294 1.05714 -Iris PetalWidth 2 0.92 0.92 0.945946 0.958904 0.945946 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.959184 0.959184 +Iris SepalLength 2 0.779661 0.959184 +Iris SepalWidth 1 0.95122 0.95122 +Iris SepalWidth 2 0.909091 0.909091 +Iris PetalLength 1 0.957143 1 +Iris PetalLength 2 0.986111 1 +Iris PetalWidth 1 1.05714 1 +Iris PetalWidth 2 0.92 1 Train Bacc -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.653333 0.68 0.666667 0.72 0.733333 -Iris SepalLength 2 0.786667 0.786667 0.76 0.773333 0.76 -Iris SepalWidth 1 0.546667 0.586667 0.6 0.626667 0.626667 -Iris SepalWidth 2 0.586667 0.613333 0.613333 0.64 0.613333 -Iris PetalLength 1 0.933333 0.946667 0.946667 0.893333 0.92 -Iris PetalLength 2 0.96 0.96 0.96 0.92 0.946667 -Iris PetalWidth 1 0.933333 0.933333 0.92 0.906667 0.933333 -Iris PetalWidth 2 1 1 0.986667 0.973333 0.986667 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.653333 0.653333 +Iris SepalLength 2 0.786667 0.653333 +Iris SepalWidth 1 0.546667 0.546667 +Iris SepalWidth 2 0.586667 0.586667 +Iris PetalLength 1 0.933333 0.666667 +Iris PetalLength 2 0.96 0.666667 +Iris PetalWidth 1 0.933333 0.666667 +Iris PetalWidth 2 1 0.666667 Test Bacc -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.626667 0.76 0.666667 0.6 0.533333 -Iris SepalLength 2 0.613333 0.706667 0.653333 0.64 0.653333 -Iris SepalWidth 1 0.52 0.493333 0.4 0.56 0.44 -Iris SepalWidth 2 0.533333 0.546667 0.466667 0.546667 0.546667 -Iris PetalLength 1 0.893333 0.96 0.96 0.84 0.946667 -Iris PetalLength 2 0.946667 0.933333 0.933333 0.92 0.813333 -Iris PetalWidth 1 0.986667 0.96 1 0.893333 0.986667 -Iris PetalWidth 2 0.92 0.92 0.933333 0.933333 0.933333 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.626667 0.626667 +Iris SepalLength 2 0.613333 0.626667 +Iris SepalWidth 1 0.52 0.52 +Iris SepalWidth 2 0.533333 0.533333 +Iris PetalLength 1 0.893333 0.666667 +Iris PetalLength 2 0.946667 0.666667 +Iris PetalWidth 1 0.986667 0.666667 +Iris PetalWidth 2 0.92 0.666667 Ratio Bacc -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.959184 1.11765 1 0.833333 0.727273 -Iris SepalLength 2 0.779661 0.898305 0.859649 0.827586 0.859649 -Iris SepalWidth 1 0.95122 0.840909 0.666667 0.893617 0.702128 -Iris SepalWidth 2 0.909091 0.891304 0.76087 0.854167 0.891304 -Iris PetalLength 1 0.957143 1.01408 1.01408 0.940299 1.02899 -Iris PetalLength 2 0.986111 0.972222 0.972222 1 0.859155 -Iris PetalWidth 1 1.05714 1.02857 1.08696 0.985294 1.05714 -Iris PetalWidth 2 0.92 0.92 0.945946 0.958904 0.945946 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.959184 0.959184 +Iris SepalLength 2 0.779661 0.959184 +Iris SepalWidth 1 0.95122 0.95122 +Iris SepalWidth 2 0.909091 0.909091 +Iris PetalLength 1 0.957143 1 +Iris PetalLength 2 0.986111 1 +Iris PetalWidth 1 1.05714 1 +Iris PetalWidth 2 0.92 1 Train AUC -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.786667 0.82 0.818133 0.866133 0.877067 -Iris SepalLength 2 0.88 0.866933 0.8792 0.92 0.901067 -Iris SepalWidth 1 0.7 0.76 0.778933 0.809333 0.800533 -Iris SepalWidth 2 0.693333 0.790667 0.792 0.8216 0.796267 -Iris PetalLength 1 0.966667 0.973333 0.973333 0.981867 0.9808 -Iris PetalLength 2 0.98 0.98 0.98 0.984 0.990133 -Iris PetalWidth 1 0.966667 0.966667 0.96 0.976267 0.981867 -Iris PetalWidth 2 1 1 0.993333 0.995733 0.9992 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.786667 0.786667 +Iris SepalLength 2 0.88 0.753333 +Iris SepalWidth 1 0.7 0.7 +Iris SepalWidth 2 0.693333 0.693333 +Iris PetalLength 1 0.966667 0.833333 +Iris PetalLength 2 0.98 0.833333 +Iris PetalWidth 1 0.966667 0.833333 +Iris PetalWidth 2 1 0.833333 Test AUC -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.76 0.856 0.804 0.8148 0.7968 -Iris SepalLength 2 0.726667 0.838133 0.768533 0.807867 0.825067 -Iris SepalWidth 1 0.66 0.701867 0.645333 0.7224 0.664 -Iris SepalWidth 2 0.7 0.685333 0.683467 0.7064 0.657333 -Iris PetalLength 1 0.945333 0.98 0.98 0.958133 0.980533 -Iris PetalLength 2 0.973333 0.966667 0.966667 0.9856 0.9664 -Iris PetalWidth 1 0.993333 0.98 1 0.977067 0.9984 -Iris PetalWidth 2 0.94 0.94 0.966667 0.969333 0.978133 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.76 0.76 +Iris SepalLength 2 0.726667 0.74 +Iris SepalWidth 1 0.66 0.66 +Iris SepalWidth 2 0.7 0.7 +Iris PetalLength 1 0.945333 0.833333 +Iris PetalLength 2 0.973333 0.833333 +Iris PetalWidth 1 0.993333 0.833333 +Iris PetalWidth 2 0.94 0.833333 Ratio AUC -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.966102 1.0439 0.982725 0.940733 0.908483 -Iris SepalLength 2 0.825758 0.966779 0.874128 0.878116 0.915656 -Iris SepalWidth 1 0.942857 0.923509 0.828483 0.892586 0.829447 -Iris SepalWidth 2 1.00962 0.866779 0.862963 0.859786 0.825519 -Iris PetalLength 1 0.977931 1.00685 1.00685 0.975828 0.999728 -Iris PetalLength 2 0.993197 0.986395 0.986395 1.00163 0.97603 -Iris PetalWidth 1 1.02759 1.01379 1.04167 1.00082 1.01684 -Iris PetalWidth 2 0.94 0.94 0.973154 0.973487 0.978916 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.966102 0.966102 +Iris SepalLength 2 0.825758 0.982301 +Iris SepalWidth 1 0.942857 0.942857 +Iris SepalWidth 2 1.00962 1.00962 +Iris PetalLength 1 0.977931 1 +Iris PetalLength 2 0.993197 1 +Iris PetalWidth 1 1.02759 1 +Iris PetalWidth 2 0.94 1 Train CompR -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.403562 0.396798 0.42636 0.482642 0.475647 -Iris SepalLength 2 0.49368 0.450095 0.481781 0.573761 0.5409 -Iris SepalWidth 1 0.218132 0.25099 0.268984 0.311675 0.300461 -Iris SepalWidth 2 0.198099 0.32131 0.340939 0.345886 0.341461 -Iris PetalLength 1 0.806144 0.832458 0.832458 0.816213 0.855079 -Iris PetalLength 2 0.860658 0.860658 0.860658 0.825622 0.887333 -Iris PetalWidth 1 0.807954 0.800302 0.776774 0.802026 0.835061 -Iris PetalWidth 2 0.99507 0.99507 0.944452 0.925858 0.965822 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.403562 0.403562 +Iris SepalLength 2 0.49368 0.370562 +Iris SepalWidth 1 0.218132 0.218132 +Iris SepalWidth 2 0.198099 0.198099 +Iris PetalLength 1 0.806144 0.576254 +Iris PetalLength 2 0.860658 0.576254 +Iris PetalWidth 1 0.807954 0.576254 +Iris PetalWidth 2 0.99507 0.576254 Test CompR -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.284885 0.311682 0.327782 0.282857 0.295459 -Iris SepalLength 2 0.0989229 0.346884 0.177376 0.156141 0.22942 -Iris SepalWidth 1 0.0346691 0.0773436 0.0448525 0.0948883 0 -Iris SepalWidth 2 0.107185 0.0789391 0.0514758 0.0594215 0.0482513 -Iris PetalLength 1 0.635794 0.848282 0.848282 0.717967 0.84443 -Iris PetalLength 2 0.837066 0.800204 0.800204 0.828858 0.764334 -Iris PetalWidth 1 0.911491 0.846705 0.919035 0.786291 0.934483 -Iris PetalWidth 2 0.564794 0.564794 0.757062 0.766721 0.708632 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.284885 0.284885 +Iris SepalLength 2 0.0989229 0.268141 +Iris SepalWidth 1 0.0346691 0.0346691 +Iris SepalWidth 2 0.107185 0.107185 +Iris PetalLength 1 0.635794 0.576254 +Iris PetalLength 2 0.837066 0.576254 +Iris PetalWidth 1 0.911491 0.576254 +Iris PetalWidth 2 0.564794 0.576254 Ratio CompR -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.705926 0.785494 0.768792 0.58606 0.621173 -Iris SepalLength 2 0.200379 0.770692 0.368167 0.272137 0.424146 -Iris SepalWidth 1 0.158936 0.308155 0.166748 0.304446 0 -Iris SepalWidth 2 0.541067 0.245679 0.150983 0.171795 0.141309 -Iris PetalLength 1 0.788685 1.01901 1.01901 0.879631 0.987546 -Iris PetalLength 2 0.972588 0.929758 0.929758 1.00392 0.861384 -Iris PetalWidth 1 1.12815 1.05798 1.18314 0.980381 1.11906 -Iris PetalWidth 2 0.567593 0.567593 0.801589 0.828119 0.733709 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.705926 0.705926 +Iris SepalLength 2 0.200379 0.723606 +Iris SepalWidth 1 0.158936 0.158936 +Iris SepalWidth 2 0.541067 0.541067 +Iris PetalLength 1 0.788685 1 +Iris PetalLength 2 0.972588 1 +Iris PetalWidth 1 1.12815 1 +Iris PetalWidth 2 0.567593 1 Features -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 4 4 4 4 4 -Iris SepalLength 2 4 4 4 4 4 -Iris SepalWidth 1 4 4 4 4 4 -Iris SepalWidth 2 4 4 4 4 4 -Iris PetalLength 1 4 4 4 4 4 -Iris PetalLength 2 4 4 4 4 4 -Iris PetalWidth 1 4 4 4 4 4 -Iris PetalWidth 2 4 4 4 4 4 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 4 4 +Iris SepalLength 2 4 4 +Iris SepalWidth 1 4 4 +Iris SepalWidth 2 4 4 +Iris PetalLength 1 4 4 +Iris PetalLength 2 4 4 +Iris PetalWidth 1 4 4 +Iris PetalWidth 2 4 4 Constr. att. -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0 0 0 0 0 -Iris SepalLength 2 0 0 0 0 0 -Iris SepalWidth 1 0 0 0 0 0 -Iris SepalWidth 2 0 0 0 0 0 -Iris PetalLength 1 0 0 0 0 0 -Iris PetalLength 2 0 0 0 0 0 -Iris PetalWidth 1 0 0 0 0 0 -Iris PetalWidth 2 0 0 0 0 0 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0 0 +Iris SepalLength 2 0 0 +Iris SepalWidth 1 0 0 +Iris SepalWidth 2 0 0 +Iris PetalLength 1 0 0 +Iris PetalLength 2 0 0 +Iris PetalWidth 1 0 0 +Iris PetalWidth 2 0 0 Inf. features -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 4 4 4 4 4 -Iris SepalLength 2 4 4 4 4 4 -Iris SepalWidth 1 4 4 4 4 4 -Iris SepalWidth 2 4 4 4 4 4 -Iris PetalLength 1 4 4 4 4 4 -Iris PetalLength 2 4 4 4 4 4 -Iris PetalWidth 1 4 4 4 4 4 -Iris PetalWidth 2 4 4 4 4 4 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 4 4 +Iris SepalLength 2 4 4 +Iris SepalWidth 1 4 4 +Iris SepalWidth 2 4 4 +Iris PetalLength 1 4 4 +Iris PetalLength 2 4 4 +Iris PetalWidth 1 4 4 +Iris PetalWidth 2 4 4 Used attributes -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 1 1 1 1 1 -Iris SepalLength 2 1 1 1 1 1 -Iris SepalWidth 1 1 1 1 1 1 -Iris SepalWidth 2 1 1 1 1 1 -Iris PetalLength 1 1 1 1 1 1 -Iris PetalLength 2 1 1 1 1 1 -Iris PetalWidth 1 1 1 1 1 1 -Iris PetalWidth 2 1 1 1 1 1 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 1 1 +Iris SepalLength 2 1 1 +Iris SepalWidth 1 1 1 +Iris SepalWidth 2 1 1 +Iris PetalLength 1 1 1 +Iris PetalLength 2 1 1 +Iris PetalWidth 1 1 1 +Iris PetalWidth 2 1 1 Total Time -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.00619645 0.00533243 0.00523288 0.0052109 0.00523075 -Iris SepalLength 2 0.00547938 0.00523533 0.00518778 0.00529415 0.00520993 -Iris SepalWidth 1 0.00619645 0.00533243 0.00523288 0.0052109 0.00523075 -Iris SepalWidth 2 0.00547938 0.00523533 0.00518778 0.00529415 0.00520993 -Iris PetalLength 1 0.00619645 0.00533243 0.00523288 0.0052109 0.00523075 -Iris PetalLength 2 0.00547938 0.00523533 0.00518778 0.00529415 0.00520993 -Iris PetalWidth 1 0.00619645 0.00533243 0.00523288 0.0052109 0.00523075 -Iris PetalWidth 2 0.00547938 0.00523533 0.00518778 0.00529415 0.00520993 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.00508028 0.00446603 +Iris SepalLength 2 0.00452278 0.00451442 +Iris SepalWidth 1 0.00508028 0.00446603 +Iris SepalWidth 2 0.00452278 0.00451442 +Iris PetalLength 1 0.00508028 0.00446603 +Iris PetalLength 2 0.00452278 0.00451442 +Iris PetalWidth 1 0.00508028 0.00446603 +Iris PetalWidth 2 0.00452278 0.00451442 Pre Time -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0.00619645 0.00533243 0.00523288 0.0052109 0.00523075 -Iris SepalLength 2 0.00547938 0.00523533 0.00518778 0.00529415 0.00520993 -Iris SepalWidth 1 0.00619645 0.00533243 0.00523288 0.0052109 0.00523075 -Iris SepalWidth 2 0.00547938 0.00523533 0.00518778 0.00529415 0.00520993 -Iris PetalLength 1 0.00619645 0.00533243 0.00523288 0.0052109 0.00523075 -Iris PetalLength 2 0.00547938 0.00523533 0.00518778 0.00529415 0.00520993 -Iris PetalWidth 1 0.00619645 0.00533243 0.00523288 0.0052109 0.00523075 -Iris PetalWidth 2 0.00547938 0.00523533 0.00518778 0.00529415 0.00520993 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0.00508028 0.00446603 +Iris SepalLength 2 0.00452278 0.00451442 +Iris SepalWidth 1 0.00508028 0.00446603 +Iris SepalWidth 2 0.00452278 0.00451442 +Iris PetalLength 1 0.00508028 0.00446603 +Iris PetalLength 2 0.00452278 0.00451442 +Iris PetalWidth 1 0.00508028 0.00446603 +Iris PetalWidth 2 0.00452278 0.00451442 Clusters -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 2 3 3 10 10 -Iris SepalLength 2 3 3 4 10 10 -Iris SepalWidth 1 2 4 5 9 10 -Iris SepalWidth 2 2 5 5 10 10 -Iris PetalLength 1 3 3 3 10 8 -Iris PetalLength 2 3 3 3 10 8 -Iris PetalWidth 1 3 3 3 10 9 -Iris PetalWidth 2 3 3 3 10 10 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 2 2 +Iris SepalLength 2 3 2 +Iris SepalWidth 1 2 2 +Iris SepalWidth 2 2 2 +Iris PetalLength 1 3 2 +Iris PetalLength 2 3 2 +Iris PetalWidth 1 3 2 +Iris PetalWidth 2 3 2 Mono -Dataset Attribute Run MODL MEF MEW EF EW -Iris SepalLength 1 0 0 0 0 0 -Iris SepalLength 2 0 0 0 0 0 -Iris SepalWidth 1 0 0 0 0 0 -Iris SepalWidth 2 0 0 0 0 0 -Iris PetalLength 1 0 0 0 0 0 -Iris PetalLength 2 0 0 0 0 0 -Iris PetalWidth 1 0 0 0 0 0 -Iris PetalWidth 2 0 0 0 0 0 +Dataset Attribute Run MODL MODL_2 +Iris SepalLength 1 0 0 +Iris SepalLength 2 0 0 +Iris SepalWidth 1 0 0 +Iris SepalWidth 2 0 0 +Iris PetalLength 1 0 0 +Iris PetalLength 2 0 0 +Iris PetalWidth 1 0 0 +Iris PetalWidth 2 0 0 diff --git a/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/results.ref/time.log index e3e7b6092..0146be70f 100644 --- a/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/results.ref/time.log @@ -1 +1 @@ -Overal time: 1.1892411708831787 +Overal time: 0.49838709831237793 diff --git a/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/test.prm b/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/test.prm index fbc6c1b87..dfa62ba65 100644 --- a/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/test.prm +++ b/test/LearningTest/TestKhiops/Standard/BenchUnivariateIris/test.prm @@ -13,6 +13,8 @@ // // +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -20,19 +22,19 @@ ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName Iris // Transfer dictionary -SourceDatabase.SampleNumberPercentage 100 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/Iris.txt // Database file -TransferDatabase // Transfer database +ClassName Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 100 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/Iris.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer @@ -55,44 +57,14 @@ PredictorSpecs.InsertItemAfter // Insert after PredictorSpecs.InspectItem // Inspect // -> Predictor PredictorLabel MODL // Label -PreprocessingSpec.DiscretizerSpec.SupervisedMethodName MODL // Name -PreprocessingSpec.GrouperSpec.SupervisedMethodName MODL // Name Exit // OK // <- Predictor PredictorSpecs.InsertItemAfter // Insert after PredictorSpecs.InspectItem // Inspect // -> Predictor -PredictorLabel MEF // Label -PreprocessingSpec.DiscretizerSpec.SupervisedMethodName MODLEqualFrequency // Name -PreprocessingSpec.GrouperSpec.SupervisedMethodName MODLBasic // Name -Exit // OK -// <- Predictor - -PredictorSpecs.InsertItemAfter // Insert after -PredictorSpecs.InspectItem // Inspect -// -> Predictor -PredictorLabel MEW // Label -PreprocessingSpec.DiscretizerSpec.SupervisedMethodName MODLEqualWidth // Name -PreprocessingSpec.GrouperSpec.SupervisedMethodName MODLBasic // Name -Exit // OK -// <- Predictor - -PredictorSpecs.InsertItemAfter // Insert after -PredictorSpecs.InspectItem // Inspect -// -> Predictor -PredictorLabel EF // Label -PreprocessingSpec.DiscretizerSpec.SupervisedMethodName EqualFrequency // Name -PreprocessingSpec.GrouperSpec.SupervisedMethodName BasicGrouping // Name -Exit // OK -// <- Predictor - -PredictorSpecs.InsertItemAfter // Insert after -PredictorSpecs.InspectItem // Inspect -// -> Predictor -PredictorLabel EW // Label -PreprocessingSpec.DiscretizerSpec.SupervisedMethodName EqualWidth // Name -PreprocessingSpec.GrouperSpec.SupervisedMethodName BasicGrouping // Name +PredictorLabel MODL_2 // Label +PreprocessingSpec.MaxPartNumber 2 // Max part number Exit // OK // <- Predictor @@ -103,9 +75,9 @@ BenchmarkSpecs.InsertItemAfter // Insert after BenchmarkClassSpec.ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file BenchmarkClassSpec.ClassName Iris // Dictionary BenchmarkClassSpec.TargetAttributeName Class // Target variable -Database.DatabaseFiles.List.Key Iris // List item selection -Database.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -Database.DatabaseFiles.DataTableName ./results/Iris.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/Iris.txt // Database file Exit // OK // <- Benchmark diff --git a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.ModelingReport.xls similarity index 93% rename from test/LearningTest/TestKhiops/Standard/Iris/results.ref/ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.ModelingReport.xls index a88d00358..6011b56a4 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.ModelingReport.xls @@ -1,33 +1,33 @@ -#Khiops 10.1 -Modeling report - -Dictionary Iris -Target variable Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 2 -Univariate PetalWidth 1 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PPetalWidth PetalWidth 0.663031 0.4375 0.538587 -PPetalLength PetalLength 0.621617 0.351562 0.46748 - ----------------------------------------------- -Predictor Univariate PetalWidth +#Khiops 10.4.6i +Modeling report + +Dictionary Iris +Target variable Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 2 +Univariate PetalWidth 1 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PPetalWidth PetalWidth 0.663031 0.4375 0.538587 +PPetalLength PetalLength 0.621617 0.351562 0.46748 + +---------------------------------------------- +Predictor Univariate PetalWidth diff --git a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.PreparationReport.xls similarity index 85% rename from test/LearningTest/TestKhiops/Standard/Iris/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.PreparationReport.xls index 5576e1ee0..3eb85b621 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.PreparationReport.xls @@ -1,103 +1,105 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 1 - Numerical 4 - Total 5 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Classification analysis - -Target variable Categorical Class -Main target value Iris-versicolor - -Target variable stats -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-versicolor 32 0.304762 -Iris-virginica 35 0.333333 - -Evaluated variables 4 -Informative variables 4 - -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 8.64312 - Data cost 110.25 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost -R1 PetalWidth 0.663031 3 21 0.1 2.5 1.175238095 0.7880996979 0 2.07944 28.4221 9.79546 -R2 PetalLength 0.621617 4 36 1 6.9 3.686666667 1.80132579 0 2.07944 31.9046 11.2655 -R3 SepalLength 0.331855 3 31 4.3 7.7 5.827619048 0.8375127846 0 2.07944 26.2686 51.5531 -R4 SepalWidth 0.116679 3 23 2 4.4 3.081904762 0.4284592446 0 2.07944 23.4061 80.1477 - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R1 -Variable Numerical PetalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 -]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R2 -Variable Numerical PetalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 -]3.15;4.75] 0 1 0 0.304909 26 0.247619 -]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 -]5.15;+inf[ 0 0 1 0.281913 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R3 -Variable Numerical SepalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 -]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 -]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R4 -Variable Numerical SepalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 -]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 -]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 1 + Numerical 4 + Total 5 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class +Main target value Iris-versicolor + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variables 4 +Informative variables 4 +Selected variables 2 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 8.64312 + Data cost 110.25 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost +R1 PetalWidth 0.663031 3 21 0.1 2.5 1.175238095 0.7880996979 0 0 2.07944 28.4221 9.79546 +R2 PetalLength 0.621617 4 36 1 6.9 3.686666667 1.80132579 0 0 2.07944 31.9046 11.2655 +R3 SepalLength 0.331855 3 31 4.3 7.7 5.827619048 0.8375127846 0 0 2.07944 26.2686 51.5531 +R4 SepalWidth 0.116679 3 23 2 4.4 3.081904762 0.4284592446 0 0 2.07944 23.4061 80.1477 + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R1 +Variable Numerical PetalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;0.75] 1 0 0 0.374826 38 0.361905 +]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 +]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R2 +Variable Numerical PetalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 +]3.15;4.75] 0 1 0 0.304909 26 0.247619 +]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 +]5.15;+inf[ 0 0 1 0.281913 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R3 +Variable Numerical SepalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 +]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 +]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R4 +Variable Numerical SepalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 +]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 +]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.TestEvaluationReport.xls similarity index 96% rename from test/LearningTest/TestKhiops/Standard/Iris/results.ref/TestEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.TestEvaluationReport.xls index 6d3d55a12..3d3474e0a 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/TestEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -1,3070 +1,3070 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary Iris -Target variable Categorical Class -Main target value Iris-versicolor -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Exclude sample -Selection variable -Selection value -Instances 45 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.955556 0.85231 0.984362 -Univariate PetalWidth 0.933333 0.795811 0.957778 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.955556 -Compression 0.85231 -AUC 0.984362 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 12 0 0 -$Iris-versicolor 0 18 2 -$Iris-virginica 0 0 13 - 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test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.TrainEvaluationReport.xls index d3c1e4b3e..7def1d54c 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/TrainEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -1,3070 +1,3070 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary Iris -Target variable Categorical Class -Main target value Iris-versicolor -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.980952 0.933266 0.997868 -Univariate PetalWidth 0.971429 0.892955 0.986807 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.980952 -Compression 0.933266 -AUC 0.997868 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 31 1 -$Iris-virginica 0 1 34 - ----------------------------------------------- -Classifier Univariate PetalWidth -Accuracy 0.971429 -Compression 0.892955 -AUC 0.986807 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 31 2 -$Iris-virginica 0 1 33 - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 -]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - - - ----------------------------------------------- -Lift curves Iris-setosa -Size Random Optimal Selective Naive Bayes Univariate PetalWidth -0 0 0 0 0 -0.1 0.1 0.276316 0.276316 0.276316 -0.2 0.2 0.552632 0.552632 0.552632 -0.3 0.3 0.828947 0.828947 0.828947 -0.4 0.4 1.10526 1.10526 1.10526 -0.5 0.5 1.38158 1.38158 1.38158 -0.6 0.6 1.65789 1.65789 1.65789 -0.7 0.7 1.93421 1.93421 1.93421 -0.8 0.8 2.21053 2.21053 2.21053 -0.9 0.9 2.48684 2.48684 2.48684 -1 1 2.76316 2.76316 2.76316 -1.1 1.1 3.03947 3.03947 3.03947 -1.2 1.2 3.31579 3.31579 3.31579 -1.3 1.3 3.59211 3.59211 3.59211 -1.4 1.4 3.86842 3.86842 3.86842 -1.5 1.5 4.14474 4.14474 4.14474 -1.6 1.6 4.42105 4.42105 4.42105 -1.7 1.7 4.69737 4.69737 4.69737 -1.8 1.8 4.97368 4.97368 4.97368 -1.9 1.9 5.25 5.25 5.25 -2 2 5.52632 5.52632 5.52632 -2.1 2.1 5.80263 5.80263 5.80263 -2.2 2.2 6.07895 6.07895 6.07895 -2.3 2.3 6.35526 6.35526 6.35526 -2.4 2.4 6.63158 6.63158 6.63158 -2.5 2.5 6.90789 6.90789 6.90789 -2.6 2.6 7.18421 7.18421 7.18421 -2.7 2.7 7.46053 7.46053 7.46053 -2.8 2.8 7.73684 7.73684 7.73684 -2.9 2.9 8.01316 8.01316 8.01316 -3 3 8.28947 8.28947 8.28947 -3.1 3.1 8.56579 8.56579 8.56579 -3.2 3.2 8.84211 8.84211 8.84211 -3.3 3.3 9.11842 9.11842 9.11842 -3.4 3.4 9.39474 9.39474 9.39474 -3.5 3.5 9.67105 9.67105 9.67105 -3.6 3.6 9.94737 9.94737 9.94737 -3.7 3.7 10.2237 10.2237 10.2237 -3.8 3.8 10.5 10.5 10.5 -3.9 3.9 10.7763 10.7763 10.7763 -4 4 11.0526 11.0526 11.0526 -4.1 4.1 11.3289 11.3289 11.3289 -4.2 4.2 11.6053 11.6053 11.6053 -4.3 4.3 11.8816 11.8816 11.8816 -4.4 4.4 12.1579 12.1579 12.1579 -4.5 4.5 12.4342 12.4342 12.4342 -4.6 4.6 12.7105 12.7105 12.7105 -4.7 4.7 12.9868 12.9868 12.9868 -4.8 4.8 13.2632 13.2632 13.2632 -4.9 4.9 13.5395 13.5395 13.5395 -5 5 13.8158 13.8158 13.8158 -5.1 5.1 14.0921 14.0921 14.0921 -5.2 5.2 14.3684 14.3684 14.3684 -5.3 5.3 14.6447 14.6447 14.6447 -5.4 5.4 14.9211 14.9211 14.9211 -5.5 5.5 15.1974 15.1974 15.1974 -5.6 5.6 15.4737 15.4737 15.4737 -5.7 5.7 15.75 15.75 15.75 -5.8 5.8 16.0263 16.0263 16.0263 -5.9 5.9 16.3026 16.3026 16.3026 -6 6 16.5789 16.5789 16.5789 -6.1 6.1 16.8553 16.8553 16.8553 -6.2 6.2 17.1316 17.1316 17.1316 -6.3 6.3 17.4079 17.4079 17.4079 -6.4 6.4 17.6842 17.6842 17.6842 -6.5 6.5 17.9605 17.9605 17.9605 -6.6 6.6 18.2368 18.2368 18.2368 -6.7 6.7 18.5132 18.5132 18.5132 -6.8 6.8 18.7895 18.7895 18.7895 -6.9 6.9 19.0658 19.0658 19.0658 -7 7 19.3421 19.3421 19.3421 -7.1 7.1 19.6184 19.6184 19.6184 -7.2 7.2 19.8947 19.8947 19.8947 -7.3 7.3 20.1711 20.1711 20.1711 -7.4 7.4 20.4474 20.4474 20.4474 -7.5 7.5 20.7237 20.7237 20.7237 -7.6 7.6 21 21 21 -7.7 7.7 21.2763 21.2763 21.2763 -7.8 7.8 21.5526 21.5526 21.5526 -7.9 7.9 21.8289 21.8289 21.8289 -8 8 22.1053 22.1053 22.1053 -8.1 8.1 22.3816 22.3816 22.3816 -8.2 8.2 22.6579 22.6579 22.6579 -8.3 8.3 22.9342 22.9342 22.9342 -8.4 8.4 23.2105 23.2105 23.2105 -8.5 8.5 23.4868 23.4868 23.4868 -8.6 8.6 23.7632 23.7632 23.7632 -8.7 8.7 24.0395 24.0395 24.0395 -8.8 8.8 24.3158 24.3158 24.3158 -8.9 8.9 24.5921 24.5921 24.5921 -9 9 24.8684 24.8684 24.8684 -9.1 9.1 25.1447 25.1447 25.1447 -9.2 9.2 25.4211 25.4211 25.4211 -9.3 9.3 25.6974 25.6974 25.6974 -9.4 9.4 25.9737 25.9737 25.9737 -9.5 9.5 26.25 26.25 26.25 -9.6 9.6 26.5263 26.5263 26.5263 -9.7 9.7 26.8026 26.8026 26.8026 -9.8 9.8 27.0789 27.0789 27.0789 -9.9 9.9 27.3553 27.3553 27.3553 -10 10 27.6316 27.6316 27.6316 -10.1 10.1 27.9079 27.9079 27.9079 -10.2 10.2 28.1842 28.1842 28.1842 -10.3 10.3 28.4605 28.4605 28.4605 -10.4 10.4 28.7368 28.7368 28.7368 -10.5 10.5 29.0132 29.0132 29.0132 -10.6 10.6 29.2895 29.2895 29.2895 -10.7 10.7 29.5658 29.5658 29.5658 -10.8 10.8 29.8421 29.8421 29.8421 -10.9 10.9 30.1184 30.1184 30.1184 -11 11 30.3947 30.3947 30.3947 -11.1 11.1 30.6711 30.6711 30.6711 -11.2 11.2 30.9474 30.9474 30.9474 -11.3 11.3 31.2237 31.2237 31.2237 -11.4 11.4 31.5 31.5 31.5 -11.5 11.5 31.7763 31.7763 31.7763 -11.6 11.6 32.0526 32.0526 32.0526 -11.7 11.7 32.3289 32.3289 32.3289 -11.8 11.8 32.6053 32.6053 32.6053 -11.9 11.9 32.8816 32.8816 32.8816 -12 12 33.1579 33.1579 33.1579 -12.1 12.1 33.4342 33.4342 33.4342 -12.2 12.2 33.7105 33.7105 33.7105 -12.3 12.3 33.9868 33.9868 33.9868 -12.4 12.4 34.2632 34.2632 34.2632 -12.5 12.5 34.5395 34.5395 34.5395 -12.6 12.6 34.8158 34.8158 34.8158 -12.7 12.7 35.0921 35.0921 35.0921 -12.8 12.8 35.3684 35.3684 35.3684 -12.9 12.9 35.6447 35.6447 35.6447 -13 13 35.9211 35.9211 35.9211 -13.1 13.1 36.1974 36.1974 36.1974 -13.2 13.2 36.4737 36.4737 36.4737 -13.3 13.3 36.75 36.75 36.75 -13.4 13.4 37.0263 37.0263 37.0263 -13.5 13.5 37.3026 37.3026 37.3026 -13.6 13.6 37.5789 37.5789 37.5789 -13.7 13.7 37.8553 37.8553 37.8553 -13.8 13.8 38.1316 38.1316 38.1316 -13.9 13.9 38.4079 38.4079 38.4079 -14 14 38.6842 38.6842 38.6842 -14.1 14.1 38.9605 38.9605 38.9605 -14.2 14.2 39.2368 39.2368 39.2368 -14.3 14.3 39.5132 39.5132 39.5132 -14.4 14.4 39.7895 39.7895 39.7895 -14.5 14.5 40.0658 40.0658 40.0658 -14.6 14.6 40.3421 40.3421 40.3421 -14.7 14.7 40.6184 40.6184 40.6184 -14.8 14.8 40.8947 40.8947 40.8947 -14.9 14.9 41.1711 41.1711 41.1711 -15 15 41.4474 41.4474 41.4474 -15.1 15.1 41.7237 41.7237 41.7237 -15.2 15.2 42 42 42 -15.3 15.3 42.2763 42.2763 42.2763 -15.4 15.4 42.5526 42.5526 42.5526 -15.5 15.5 42.8289 42.8289 42.8289 -15.6 15.6 43.1053 43.1053 43.1053 -15.7 15.7 43.3816 43.3816 43.3816 -15.8 15.8 43.6579 43.6579 43.6579 -15.9 15.9 43.9342 43.9342 43.9342 -16 16 44.2105 44.2105 44.2105 -16.1 16.1 44.4868 44.4868 44.4868 -16.2 16.2 44.7632 44.7632 44.7632 -16.3 16.3 45.0395 45.0395 45.0395 -16.4 16.4 45.3158 45.3158 45.3158 -16.5 16.5 45.5921 45.5921 45.5921 -16.6 16.6 45.8684 45.8684 45.8684 -16.7 16.7 46.1447 46.1447 46.1447 -16.8 16.8 46.4211 46.4211 46.4211 -16.9 16.9 46.6974 46.6974 46.6974 -17 17 46.9737 46.9737 46.9737 -17.1 17.1 47.25 47.25 47.25 -17.2 17.2 47.5263 47.5263 47.5263 -17.3 17.3 47.8026 47.8026 47.8026 -17.4 17.4 48.0789 48.0789 48.0789 -17.5 17.5 48.3553 48.3553 48.3553 -17.6 17.6 48.6316 48.6316 48.6316 -17.7 17.7 48.9079 48.9079 48.9079 -17.8 17.8 49.1842 49.1842 49.1842 -17.9 17.9 49.4605 49.4605 49.4605 -18 18 49.7368 49.7368 49.7368 -18.1 18.1 50.0132 50.0132 50.0132 -18.2 18.2 50.2895 50.2895 50.2895 -18.3 18.3 50.5658 50.5658 50.5658 -18.4 18.4 50.8421 50.8421 50.8421 -18.5 18.5 51.1184 51.1184 51.1184 -18.6 18.6 51.3947 51.3947 51.3947 -18.7 18.7 51.6711 51.6711 51.6711 -18.8 18.8 51.9474 51.9474 51.9474 -18.9 18.9 52.2237 52.2237 52.2237 -19 19 52.5 52.5 52.5 -19.1 19.1 52.7763 52.7763 52.7763 -19.2 19.2 53.0526 53.0526 53.0526 -19.3 19.3 53.3289 53.3289 53.3289 -19.4 19.4 53.6053 53.6053 53.6053 -19.5 19.5 53.8816 53.8816 53.8816 -19.6 19.6 54.1579 54.1579 54.1579 -19.7 19.7 54.4342 54.4342 54.4342 -19.8 19.8 54.7105 54.7105 54.7105 -19.9 19.9 54.9868 54.9868 54.9868 -20 20 55.2632 55.2632 55.2632 -20.1 20.1 55.5395 55.5395 55.5395 -20.2 20.2 55.8158 55.8158 55.8158 -20.3 20.3 56.0921 56.0921 56.0921 -20.4 20.4 56.3684 56.3684 56.3684 -20.5 20.5 56.6447 56.6447 56.6447 -20.6 20.6 56.9211 56.9211 56.9211 -20.7 20.7 57.1974 57.1974 57.1974 -20.8 20.8 57.4737 57.4737 57.4737 -20.9 20.9 57.75 57.75 57.75 -21 21 58.0263 58.0263 58.0263 -21.1 21.1 58.3026 58.3026 58.3026 -21.2 21.2 58.5789 58.5789 58.5789 -21.3 21.3 58.8553 58.8553 58.8553 -21.4 21.4 59.1316 59.1316 59.1316 -21.5 21.5 59.4079 59.4079 59.4079 -21.6 21.6 59.6842 59.6842 59.6842 -21.7 21.7 59.9605 59.9605 59.9605 -21.8 21.8 60.2368 60.2368 60.2368 -21.9 21.9 60.5132 60.5132 60.5132 -22 22 60.7895 60.7895 60.7895 -22.1 22.1 61.0658 61.0658 61.0658 -22.2 22.2 61.3421 61.3421 61.3421 -22.3 22.3 61.6184 61.6184 61.6184 -22.4 22.4 61.8947 61.8947 61.8947 -22.5 22.5 62.1711 62.1711 62.1711 -22.6 22.6 62.4474 62.4474 62.4474 -22.7 22.7 62.7237 62.7237 62.7237 -22.8 22.8 63 63 63 -22.9 22.9 63.2763 63.2763 63.2763 -23 23 63.5526 63.5526 63.5526 -23.1 23.1 63.8289 63.8289 63.8289 -23.2 23.2 64.1053 64.1053 64.1053 -23.3 23.3 64.3816 64.3816 64.3816 -23.4 23.4 64.6579 64.6579 64.6579 -23.5 23.5 64.9342 64.9342 64.9342 -23.6 23.6 65.2105 65.2105 65.2105 -23.7 23.7 65.4868 65.4868 65.4868 -23.8 23.8 65.7632 65.7632 65.7632 -23.9 23.9 66.0395 66.0395 66.0395 -24 24 66.3158 66.3158 66.3158 -24.1 24.1 66.5921 66.5921 66.5921 -24.2 24.2 66.8684 66.8684 66.8684 -24.3 24.3 67.1447 67.1447 67.1447 -24.4 24.4 67.4211 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-99.7 99.7 100 100 100 -99.8 99.8 100 100 100 -99.9 99.9 100 100 100 -100 100 100 100 100 +#Khiops 10.4.6i +Train evaluation report + +Dictionary Iris +Target variable Categorical Class +Main target value Iris-versicolor +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + + +Predictors performance + +Classifier Accuracy Compression AUC +Selective Naive Bayes 0.980952 0.933266 0.997868 +Univariate PetalWidth 0.971429 0.892955 0.986807 + + +-------------------------------------------------------------------------------- + +Predictors detailed performance + + +Classifier Selective Naive Bayes +Accuracy 0.980952 +Compression 0.933266 +AUC 0.997868 + +Confusion matrix + Iris-setosa Iris-versicolor Iris-virginica +$Iris-setosa 38 0 0 +$Iris-versicolor 0 31 1 +$Iris-virginica 0 1 34 + +---------------------------------------------- +Classifier Univariate PetalWidth +Accuracy 0.971429 +Compression 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similarity index 99% rename from test/LearningTest/TestKhiops/Standard/Iris/results.ref/AllReports.khj rename to test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.khj index 18bdd0441..38701dbe0 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AllReports.khj +++ b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.khj @@ -1,566 +1,574 @@ -{ - "tool": "Khiops", - "version": "10.1", - "shortDescription": "", - "modelingReport": { - "reportType": "Modeling", - "summary": { - "dictionary": "Iris", - "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", - "samplePercentage": 70, - "samplingMode": "Include sample", - "selectionVariable": "", - "selectionValue": "", - "learningTask": "Classification analysis", - "targetVariable": "Class", - "mainTargetValue": "Iris-versicolor" - }, - "trainedPredictors": [ - { - "rank": "R1", - "type": "Classifier", - "family": "Selective Naive Bayes", - "name": "Selective Naive Bayes", - "variables": 2 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+ } + ] + } + ] + }, + "preparationReport": { + "reportType": "Preparation", + "summary": { + "dictionary": "Iris", + "variables": { + "types": [ + "Categorical", + "Numerical" + ], + "numbers": [ + 1, + 4 + ] + }, + "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", + "samplePercentage": 70, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "", + "instances": 105, + "learningTask": "Classification analysis", + "targetVariable": "Class", + "mainTargetValue": "Iris-versicolor", + "targetDescriptiveStats": { + "values": 3, + "mode": "Iris-setosa", + "modeFrequency": 38, + "missingNumber": 0, + "sparseMissingNumber": 0 + }, + "targetValues": { + "values": ["Iris-setosa","Iris-versicolor","Iris-virginica"], + "frequencies": [38,32,35] + }, + "evaluatedVariables": 4, + "informativeVariables": 4, + "selectedVariables": 2, + "featureEngineering": { + "maxNumberOfConstructedVariables": 0, + "maxNumberOfTextFeatures": 0, + "maxNumberOfTrees": 0, + "maxNumberOfVariablePairs": 0 + }, + "discretization": "MODL", + "valueGrouping": "MODL", + "nullModel": { + "constructionCost": 0.693147, + "preparationCost": 8.64312, + "dataCost": 110.25 + } + }, + "variablesStatistics": [ + { + "rank": "R1", + "name": "PetalWidth", + "type": "Numerical", + "level": 0.663031, + "parts": 3, + "values": 21, + "min": 0.1, + "max": 2.5, + "mean": 1.175238095, + "stdDev": 0.7880996979, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 2.07944, + "preparationCost": 28.4221, + "dataCost": 9.79546 + }, + { + "rank": "R2", + "name": "PetalLength", + "type": "Numerical", + "level": 0.621617, + "parts": 4, + "values": 36, + "min": 1, + "max": 6.9, + "mean": 3.686666667, + "stdDev": 1.80132579, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 2.07944, + "preparationCost": 31.9046, + "dataCost": 11.2655 + }, + { + "rank": "R3", + "name": "SepalLength", + "type": "Numerical", + "level": 0.331855, + "parts": 3, + "values": 31, + "min": 4.3, + "max": 7.7, + "mean": 5.827619048, + "stdDev": 0.8375127846, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 2.07944, + "preparationCost": 26.2686, + "dataCost": 51.5531 + }, + { + "rank": "R4", + "name": "SepalWidth", + "type": "Numerical", + "level": 0.116679, + "parts": 3, + "values": 23, + "min": 2, + "max": 4.4, + "mean": 3.081904762, + "stdDev": 0.4284592446, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 2.07944, + "preparationCost": 23.4061, + "dataCost": 80.1477 + } + ], + "variablesDetailedStatistics": { + "R1": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0.1,0.75], + [0.75,1.75], + [1.75,2.5] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,0,0], + [0,31,2], + [0,1,33] + ], + "partInterests": [0.374826,0.305582,0.319592] + } + }, + "R2": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,3.15], + [3.15,4.75], + [4.75,5.15], + [5.15,6.9] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,1,0], + [0,26,0], + [0,5,9], + [0,0,26] + ], + "partInterests": [0.347012,0.304909,0.066166,0.281913] + } + }, + "R3": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [4.3,5.45], + [5.45,6.15], + [6.15,7.7] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [34,5,0], + [4,19,5], + [0,8,30] + ], + "partInterests": [0.449729,0.147253,0.403018] + } + }, + "R4": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,2.85], + [2.85,3.35], + [3.35,4.4] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [0,18,12], + [17,13,19], + [21,1,4] + ], + "partInterests": [0.544113,0.0133569,0.44253] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/Modeling.kdic b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.model.kdic similarity index 98% rename from test/LearningTest/TestKhiops/Standard/Iris/results.ref/Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.model.kdic index 539530771..bde64d900 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/AnalysisResults.model.kdic @@ -1,38 +1,38 @@ -#Khiops 10.1 - -Dictionary BU1_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) -Unused Structure(Classifier) BU1Class = NBClassifier(DataGridStats(PPetalWidth, PetalWidth), VClass) ; - Categorical PredictedClass = TargetValue(BU1Class) ; -Unused Numerical ScoreClass = TargetProb(BU1Class) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(BU1Class, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(BU1Class, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(BU1Class, "Iris-virginica") ; -}; - -Dictionary SNB_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PPetalLength = DataGrid(IntervalBounds(3.15, 4.75, 5.15), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 1, 26, 5, 0, 0, 0, 9, 26)) ; // DataGrid(PetalLength, Class) -Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) -Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.3515625, 0.4375), DataGridStats(PPetalLength, PetalLength), DataGridStats(PPetalWidth, PetalWidth), VClass) ; - Categorical PredictedClass = TargetValue(SNBClass) ; -Unused Numerical ScoreClass = TargetProb(SNBClass) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; -}; +#Khiops 10.4.6i + +Dictionary BU1_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) +Unused Structure(Classifier) BU1Class = NBClassifier(DataGridStats(PPetalWidth, PetalWidth), VClass) ; + Categorical PredictedClass = TargetValue(BU1Class) ; +Unused Numerical ScoreClass = TargetProb(BU1Class) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(BU1Class, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(BU1Class, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(BU1Class, "Iris-virginica") ; +}; + +Dictionary SNB_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PPetalLength = DataGrid(IntervalBounds(3.15, 4.75, 5.15), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 1, 26, 5, 0, 0, 0, 9, 26)) ; // DataGrid(PetalLength, Class) +Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.3515625, 0.4375), DataGridStats(PPetalLength, PetalLength), DataGridStats(PPetalWidth, PetalWidth), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/T_Iris.txt b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/D_Iris.txt similarity index 98% rename from test/LearningTest/TestKhiops/Standard/Iris/results.ref/T_Iris.txt rename to test/LearningTest/TestKhiops/Standard/Iris/results.ref/D_Iris.txt index 04c0265c1..9a4b21bc3 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/D_Iris.txt @@ -1,106 +1,106 @@ -PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-setosa 0.9884494887 0.008598869265 0.002951642068 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-virginica 0.003737230907 0.1484627973 0.8477999718 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 -Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-versicolor 0.09320452082 0.8544452214 0.05235025774 -Iris-versicolor 0.003370715539 0.976538848 0.02009043647 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003737230907 0.1484627973 0.8477999718 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003737230907 0.1484627973 0.8477999718 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003737230907 0.1484627973 0.8477999718 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003737230907 0.1484627973 0.8477999718 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 -Iris-virginica 0.006528336473 0.1669820602 0.8264896033 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003737230907 0.1484627973 0.8477999718 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003737230907 0.1484627973 0.8477999718 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003737230907 0.1484627973 0.8477999718 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003299033098 0.01448096093 0.982220006 -Iris-virginica 0.003737230907 0.1484627973 0.8477999718 +PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-setosa 0.9884494887 0.008598869265 0.002951642068 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-virginica 0.003737230907 0.1484627973 0.8477999718 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 +Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-versicolor 0.09320452082 0.8544452214 0.05235025774 +Iris-versicolor 0.003370715539 0.976538848 0.02009043647 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003737230907 0.1484627973 0.8477999718 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003737230907 0.1484627973 0.8477999718 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003737230907 0.1484627973 0.8477999718 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003737230907 0.1484627973 0.8477999718 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-versicolor 0.003945652789 0.7219240105 0.2741303367 +Iris-virginica 0.006528336473 0.1669820602 0.8264896033 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003737230907 0.1484627973 0.8477999718 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003737230907 0.1484627973 0.8477999718 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003737230907 0.1484627973 0.8477999718 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003299033098 0.01448096093 0.982220006 +Iris-virginica 0.003737230907 0.1484627973 0.8477999718 diff --git a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/err.txt index f7345c2e4..4d421a4f5 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/err.txt @@ -8,10 +8,10 @@ Train evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iri Train evaluation of Univariate PetalWidth on database ../../../datasets/Iris/Iris.txt Test evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt Test evaluation of Univariate PetalWidth on database ../../../datasets/Iris/Iris.txt -Write report ./results\AllReports.khj +Write report ./results/AnalysisResults.khj Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 105 +Output database ./results/D_Iris.txt: Written records: 105 Model deployment time: 0:00:00.01 diff --git a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/time.log index 1fa4b4dba..fa3f9e231 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/Iris/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.335557222366333 +Overal time: 0.23929762840270996 diff --git a/test/LearningTest/TestKhiops/Standard/Iris/test.prm b/test/LearningTest/TestKhiops/Standard/Iris/test.prm index a183b9c4e..145441e65 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris/test.prm +++ b/test/LearningTest/TestKhiops/Standard/Iris/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,39 +23,41 @@ ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable AnalysisSpec.MainTargetModality Iris-versicolor // Main target modality -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries +AnalysisSpec.PredictorsSpec.ExpertSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database ClassManagement.OpenFile // Open... -ClassFileName ./results/Modeling.kdic // Dictionary file +ClassFileName ./results/AnalysisResults.model.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer diff --git a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.ModelingReport.xls similarity index 94% rename from test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.ModelingReport.xls index 081065fc5..317eaf70c 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.ModelingReport.xls @@ -1,35 +1,35 @@ -#Khiops 10.1 -Modeling report - -Dictionary Iris -Target variable Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 4 -Univariate PetalWidth 1 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PClass2 Class2 0.439898 0.523438 0.479853 -PLowerPetalLength LowerPetalLength 0.478809 0.234375 0.334994 -PSPetalLength SPetalLength 0.620475 0.148438 0.303483 -PClass1 Class1 0.473012 0.164062 0.278574 - ----------------------------------------------- -Predictor Univariate PetalWidth +#Khiops 10.4.6i +Modeling report + +Dictionary Iris +Target variable Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 4 +Univariate PetalWidth 1 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PClass2 Class2 0.439898 0.523438 0.479853 +PLowerPetalLength LowerPetalLength 0.478809 0.234375 0.334994 +PSPetalLength SPetalLength 0.620475 0.148438 0.303483 +PClass1 Class1 0.473012 0.164062 0.278574 + +---------------------------------------------- +Predictor Univariate PetalWidth diff --git a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/Preparation2DReport.xls b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.Preparation2DReport.xls similarity index 96% rename from test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/Preparation2DReport.xls rename to test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.Preparation2DReport.xls index e38b01803..8fb594bcf 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/Preparation2DReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.Preparation2DReport.xls @@ -1,702 +1,703 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-versicolor 32 0.304762 -Iris-virginica 35 0.333333 - -Evaluated variable pairs 45 -Informative variable pairs 0 - - -Variables pairs statistics - -Rank Name 1 Name 2 DeltaLevel Level Level 1 Level 2 Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost -R01 Class1 Dummy2 -0.0288536 0.444159 0.473012 0 1 2 1 2 6.62856 15.7997 44.0428 -R02 Class2 Dummy2 -0.0288536 0.411044 0.439898 0 1 2 1 2 6.62856 15.6381 48.1645 -R03 Dummy2 LowerPetalLength -0.0288536 0.449955 0 0.478809 1 1 2 2 6.62856 15.1066 44.0428 -R04 Dummy2 PetalLength -0.0288536 0.583577 0 0.61243 1 1 4 4 6.62856 31.9046 11.2655 -R05 Dummy2 PetalWidth -0.0288536 0.62499 0 0.653844 1 1 3 3 6.62856 28.4221 9.79546 -R06 Dummy2 SPetalLength -0.0288536 0.591622 0 0.620475 1 1 3 3 6.62856 27.2738 14.9342 -R07 Dummy2 SepalLength -0.0288536 0.293814 0 0.322668 1 1 3 3 6.62856 26.2686 51.5531 -R08 Dummy2 SepalWidth -0.0288536 0.0786391 0 0.107493 1 1 3 3 6.62856 23.4061 80.1477 -R09 Dummy2 UpperPetalWidth -0.0288536 0.300563 0 0.329416 1 1 2 2 6.62856 20.5402 56.4745 -R10 SepalWidth UpperPetalWidth -0.0439741 0.392935 0.107493 0.329416 2 2 2 4 9.02645 24.172 39.3984 -R11 Class2 UpperPetalWidth -0.0976395 0.671675 0.439898 0.329416 2 2 2 4 9.02645 23.5775 6.65929 -R12 SepalLength SepalWidth -0.121906 0.308255 0.322668 0.107493 2 2 2 4 9.02645 24.3329 49.3641 -R13 Class2 SepalLength -0.128245 0.63432 0.439898 0.322668 2 2 2 4 9.02645 24.5695 10.1344 -R14 Class1 SepalWidth -0.136346 0.444159 0.473012 0.107493 1 2 1 2 6.62856 15.7997 44.0428 -R15 Class2 SepalWidth -0.136346 0.411044 0.439898 0.107493 1 2 1 2 6.62856 15.6381 48.1645 -R16 LowerPetalLength SepalWidth -0.136346 0.449955 0.478809 0.107493 1 2 1 2 6.62856 15.1066 44.0428 -R17 PetalLength SepalWidth -0.136346 0.583577 0.61243 0.107493 1 4 1 4 6.62856 31.9046 11.2655 -R18 PetalWidth SepalWidth -0.136346 0.62499 0.653844 0.107493 1 3 1 3 6.62856 28.4221 9.79546 -R19 SPetalLength SepalWidth -0.136346 0.591622 0.620475 0.107493 1 3 1 3 6.62856 27.2738 14.9342 -R20 Class2 LowerPetalLength -0.168707 0.749999 0.439898 0.478809 2 2 2 3 9.02645 20.8703 0 -R21 Class1 Class2 -0.196941 0.715969 0.473012 0.439898 2 2 2 3 9.02645 24.9398 0 -R22 Class1 UpperPetalWidth -0.203907 0.598522 0.473012 0.329416 2 2 2 3 9.02645 29.1894 9.79546 -R23 LowerPetalLength UpperPetalWidth -0.212171 0.596054 0.478809 0.329416 2 2 2 3 9.02645 20.1475 19.1326 -R24 SepalLength UpperPetalWidth -0.249148 0.402937 0.322668 0.329416 2 2 2 3 9.02645 29.1594 33.2149 -R25 Class1 SepalLength -0.326251 0.469429 0.473012 0.322668 2 2 2 3 9.02645 25.0325 29.3902 -R26 Class2 PetalLength -0.333475 0.718853 0.439898 0.61243 2 2 2 4 9.02645 24.5949 0 -R27 LowerPetalLength SepalLength -0.348267 0.45321 0.478809 0.322668 2 2 2 3 9.02645 26.9721 29.3902 -R28 PetalLength SepalLength -0.351521 0.583577 0.61243 0.322668 1 4 1 4 6.62856 31.9046 11.2655 -R29 PetalWidth SepalLength -0.351521 0.62499 0.653844 0.322668 1 3 1 3 6.62856 28.4221 9.79546 -R30 SPetalLength SepalLength -0.351521 0.591622 0.620475 0.322668 1 3 1 3 6.62856 27.2738 14.9342 -R31 PetalLength UpperPetalWidth -0.35827 0.583577 0.61243 0.329416 1 4 1 4 6.62856 31.9046 11.2655 -R32 PetalWidth UpperPetalWidth -0.35827 0.62499 0.653844 0.329416 1 3 1 3 6.62856 28.4221 9.79546 -R33 SPetalLength UpperPetalWidth -0.35827 0.591622 0.620475 0.329416 1 3 1 3 6.62856 27.2738 14.9342 -R34 Class2 SPetalLength -0.366871 0.693503 0.439898 0.620475 2 2 2 3 9.02645 27.6265 0 -R35 Class2 PetalWidth -0.374723 0.719019 0.439898 0.653844 2 2 2 4 9.02645 24.5751 0 -R36 Class1 PetalLength -0.481968 0.603475 0.473012 0.61243 2 2 2 3 9.02645 24.9956 13.397 -R37 Class1 LowerPetalLength -0.501866 0.449955 0.473012 0.478809 1 1 2 2 6.62856 15.1066 44.0428 -R38 LowerPetalLength PetalLength -0.503983 0.587256 0.478809 0.61243 2 2 2 3 9.02645 26.9352 13.397 -R39 LowerPetalLength PetalWidth -0.507662 0.62499 0.478809 0.653844 1 1 3 3 6.62856 28.4221 9.79546 -R40 LowerPetalLength SPetalLength -0.507662 0.591622 0.478809 0.620475 1 1 3 3 6.62856 27.2738 14.9342 -R41 Class1 SPetalLength -0.512711 0.580776 0.473012 0.620475 1 1 3 3 6.62856 28.5707 14.9342 -R42 Class1 PetalWidth -0.528334 0.598522 0.473012 0.653844 2 2 2 3 9.02645 29.1894 9.79546 -R43 PetalLength SPetalLength -0.645511 0.587395 0.61243 0.620475 2 2 2 3 9.02645 26.9186 13.397 -R44 PetalWidth SPetalLength -0.649329 0.62499 0.653844 0.620475 1 3 1 3 6.62856 28.4221 9.79546 -R45 PetalLength PetalWidth -0.679019 0.587256 0.61243 0.653844 2 2 2 3 9.02645 26.9352 13.397 - - --------------------------------------------------------------------------------- - -Variables pairs detailed statistics -(Pairs with two jointly informative variables) - - -Rank R10 -Variables - Type Name - Numerical SepalWidth - Numerical UpperPetalWidth - -Variables stats -SepalWidth -Interval Frequency Coverage -]-inf;3.05] 56 0.533333 -]3.05;+inf[ 49 0.466667 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -SepalWidth ]-inf;1.55] ]1.55;+inf[ Total Coverage -]-inf;3.05] 34 22 56 0.533333 -]3.05;+inf[ 33 16 49 0.466667 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 4 -Cell Id SepalWidth UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C2 ]3.05;+inf[ ]-inf;1.55] 0.939394 0.0606061 0 0.383327 33 0.314286 -C3 ]-inf;3.05] ]1.55;+inf[ 0 0.0909091 0.909091 0.256821 22 0.209524 -C1 ]-inf;3.05] ]-inf;1.55] 0.205882 0.735294 0.0588235 0.212492 34 0.32381 -C4 ]3.05;+inf[ ]1.55;+inf[ 0 0.1875 0.8125 0.14736 16 0.152381 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R11 -Variables - Type Name - Categorical Class2 - Numerical UpperPetalWidth - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -Class2 ]-inf;1.55] ]1.55;+inf[ Total Coverage -{} 40 33 73 0.695238 -{versicolor} 27 5 32 0.304762 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 4 -Cell Id Class2 UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C3 {} ]1.55;+inf[ 0 0 1 0.33833 33 0.314286 -C1 {} ]-inf;1.55] 0.95 0 0.05 0.306831 40 0.380952 -C2 {versicolor} ]-inf;1.55] 0 1 0 0.299395 27 0.257143 -C4 {versicolor} ]1.55;+inf[ 0 1 0 0.0554436 5 0.047619 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R12 -Variables - Type Name - Numerical SepalLength - Numerical SepalWidth - -Variables stats -SepalLength -Interval Frequency Coverage -]-inf;5.75] 53 0.504762 -]5.75;+inf[ 52 0.495238 -SepalWidth -Interval Frequency Coverage -]-inf;3.05] 56 0.533333 -]3.05;+inf[ 49 0.466667 - -Cell frequencies - SepalWidth -SepalLength ]-inf;3.05] ]3.05;+inf[ Total Coverage -]-inf;5.75] 23 30 53 0.504762 -]5.75;+inf[ 33 19 52 0.495238 -Total 56 49 105 -Coverage 0.533333 0.466667 - -Cells 4 -Cell Id SepalLength SepalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C3 ]-inf;5.75] ]3.05;+inf[ 1 0 0 0.523397 30 0.285714 -C2 ]5.75;+inf[ ]-inf;3.05] 0 0.393939 0.606061 0.262519 33 0.314286 -C4 ]5.75;+inf[ ]3.05;+inf[ 0.0526316 0.263158 0.684211 0.11478 19 0.180952 -C1 ]-inf;5.75] ]-inf;3.05] 0.304348 0.608696 0.0869565 0.099304 23 0.219048 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R13 -Variables - Type Name - Categorical Class2 - Numerical SepalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -SepalLength -Interval Frequency Coverage -]-inf;5.75] 53 0.504762 -]5.75;+inf[ 52 0.495238 - -Cell frequencies - SepalLength -Class2 ]-inf;5.75] ]5.75;+inf[ Total Coverage -{} 39 34 73 0.695238 -{versicolor} 14 18 32 0.304762 -Total 53 52 105 -Coverage 0.504762 0.495238 - -Cells 4 -Cell Id Class2 SepalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C3 {} ]5.75;+inf[ 0.0294118 0 0.970588 0.318989 34 0.32381 -C1 {} ]-inf;5.75] 0.948718 0 0.0512821 0.310764 39 0.371429 -C4 {versicolor} ]5.75;+inf[ 0 1 0 0.208264 18 0.171429 -C2 {versicolor} ]-inf;5.75] 0 1 0 0.161983 14 0.133333 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R20 -Variables - Type Name - Categorical Class2 - Numerical LowerPetalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - LowerPetalLength -Class2 ]-inf;2.4] ]2.4;+inf[ Total Coverage -{} 38 35 73 0.695238 -{versicolor} 0 32 32 0.304762 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 3 -Cell Id Class2 LowerPetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C1 {} ]-inf;2.4] 1 0 0 0.335563 38 0.361905 -C3 {} ]2.4;+inf[ 0 0 1 0.334079 35 0.333333 -C4 {versicolor} ]2.4;+inf[ 0 1 0 0.330358 32 0.304762 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R21 -Variables - Type Name - Categorical Class1 - Categorical Class2 - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * - -Cell frequencies - Class2 -Class1 {} {versicolor} Total Coverage -{} 35 32 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 73 32 105 -Coverage 0.695238 0.304762 - -Cells 3 -Cell Id Class1 Class2 Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C2 {setosa} {} 1 0 0 0.335563 38 0.361905 -C1 {} {} 0 0 1 0.334079 35 0.333333 -C3 {} {versicolor} 0 1 0 0.330358 32 0.304762 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R22 -Variables - Type Name - Categorical Class1 - Numerical UpperPetalWidth - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.75] 71 0.67619 -]1.75;+inf[ 34 0.32381 - -Cell frequencies - UpperPetalWidth -Class1 ]-inf;1.75] ]1.75;+inf[ Total Coverage -{} 33 34 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 71 34 105 -Coverage 0.67619 0.32381 - -Cells 3 -Cell Id Class1 UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C2 {setosa} ]-inf;1.75] 1 0 0 0.374826 38 0.361905 -C3 {} ]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -C1 {} ]-inf;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R23 -Variables - Type Name - Numerical LowerPetalLength - Numerical UpperPetalWidth - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -LowerPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage -]-inf;2.4] 38 0 38 0.361905 -]2.4;+inf[ 29 38 67 0.638095 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 3 -Cell Id LowerPetalLength UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C1 ]-inf;2.4] ]-inf;1.55] 1 0 0 0.415191 38 0.361905 -C4 ]2.4;+inf[ ]1.55;+inf[ 0 0.131579 0.868421 0.294541 38 0.361905 -C2 ]2.4;+inf[ ]-inf;1.55] 0 0.931034 0.0689655 0.290269 29 0.27619 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R24 -Variables - Type Name - Numerical SepalLength - Numerical UpperPetalWidth - -Variables stats -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;+inf[ 66 0.628571 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.75] 71 0.67619 -]1.75;+inf[ 34 0.32381 - -Cell frequencies - UpperPetalWidth -SepalLength ]-inf;1.75] ]1.75;+inf[ Total Coverage -]-inf;5.45] 39 0 39 0.371429 -]5.45;+inf[ 32 34 66 0.628571 -Total 71 34 105 -Coverage 0.67619 0.32381 - -Cells 3 -Cell Id SepalLength UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C4 ]5.45;+inf[ ]1.75;+inf[ 0 0.0294118 0.970588 0.431099 34 0.32381 -C1 ]-inf;5.45] ]-inf;1.75] 0.871795 0.128205 0 0.334637 39 0.371429 -C2 ]5.45;+inf[ ]-inf;1.75] 0.125 0.8125 0.0625 0.234263 32 0.304762 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R25 -Variables - Type Name - Categorical Class1 - Numerical SepalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -SepalLength -Interval Frequency Coverage -]-inf;6.15] 67 0.638095 -]6.15;+inf[ 38 0.361905 - -Cell frequencies - SepalLength -Class1 ]-inf;6.15] ]6.15;+inf[ Total Coverage -{} 29 38 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 3 -Cell Id Class1 SepalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C2 {setosa} ]-inf;6.15] 1 0 0 0.469806 38 0.361905 -C3 {} ]6.15;+inf[ 0 0.210526 0.789474 0.278648 38 0.361905 -C1 {} ]-inf;6.15] 0 0.827586 0.172414 0.251546 29 0.27619 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R26 -Variables - Type Name - Categorical Class2 - Numerical PetalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -PetalLength -Interval Frequency Coverage -]-inf;4.2] 53 0.504762 -]4.2;+inf[ 52 0.495238 - -Cell frequencies - PetalLength -Class2 ]-inf;4.2] ]4.2;+inf[ Total Coverage -{} 38 35 73 0.695238 -{versicolor} 15 17 32 0.304762 -Total 53 52 105 -Coverage 0.504762 0.495238 - -Cells 4 -Cell Id Class2 PetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C1 {} ]-inf;4.2] 1 0 0 0.335563 38 0.361905 -C3 {} ]4.2;+inf[ 0 0 1 0.334079 35 0.333333 -C4 {versicolor} ]4.2;+inf[ 0 1 0 0.175503 17 0.161905 -C2 {versicolor} ]-inf;4.2] 0 1 0 0.154855 15 0.142857 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R27 -Variables - Type Name - Numerical LowerPetalLength - Numerical SepalLength - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -SepalLength -Interval Frequency Coverage -]-inf;6.15] 67 0.638095 -]6.15;+inf[ 38 0.361905 - -Cell frequencies - SepalLength -LowerPetalLength ]-inf;6.15] ]6.15;+inf[ Total Coverage -]-inf;2.4] 38 0 38 0.361905 -]2.4;+inf[ 29 38 67 0.638095 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 3 -Cell Id LowerPetalLength SepalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C1 ]-inf;2.4] ]-inf;6.15] 1 0 0 0.469806 38 0.361905 -C4 ]2.4;+inf[ ]6.15;+inf[ 0 0.210526 0.789474 0.278648 38 0.361905 -C2 ]2.4;+inf[ ]-inf;6.15] 0 0.827586 0.172414 0.251546 29 0.27619 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R34 -Variables - Type Name - Categorical Class2 - Categorical SPetalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -SPetalLength -Group Frequency Coverage Size Value list -{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * -{1} 38 0.361905 1 1 - -Cell frequencies - SPetalLength -Class2 {5, 4, 3, ...} {1} Total Coverage -{} 35 38 73 0.695238 -{versicolor} 32 0 32 0.304762 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 3 -Cell Id Class2 SPetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C3 {} {1} 1 0 0 0.335563 38 0.361905 -C1 {} {5, 4, 3, ...} 0 0 1 0.334079 35 0.333333 -C2 {versicolor} {5, 4, 3, ...} 0 1 0 0.330358 32 0.304762 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R35 -Variables - Type Name - Categorical Class2 - Numerical PetalWidth - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -PetalWidth -Interval Frequency Coverage -]-inf;1.35] 56 0.533333 -]1.35;+inf[ 49 0.466667 - -Cell frequencies - PetalWidth -Class2 ]-inf;1.35] ]1.35;+inf[ Total Coverage -{} 38 35 73 0.695238 -{versicolor} 18 14 32 0.304762 -Total 56 49 105 -Coverage 0.533333 0.466667 - -Cells 4 -Cell Id Class2 PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C1 {} ]-inf;1.35] 1 0 0 0.335563 38 0.361905 -C3 {} ]1.35;+inf[ 0 0 1 0.334079 35 0.333333 -C2 {versicolor} ]-inf;1.35] 0 1 0 0.185826 18 0.171429 -C4 {versicolor} ]1.35;+inf[ 0 1 0 0.144532 14 0.133333 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R36 -Variables - Type Name - Categorical Class1 - Numerical PetalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -PetalLength -Interval Frequency Coverage -]-inf;4.75] 65 0.619048 -]4.75;+inf[ 40 0.380952 - -Cell frequencies - PetalLength -Class1 ]-inf;4.75] ]4.75;+inf[ Total Coverage -{} 27 40 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 65 40 105 -Coverage 0.619048 0.380952 - -Cells 3 -Cell Id Class1 PetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C2 {setosa} ]-inf;4.75] 1 0 0 0.386122 38 0.361905 -C1 {} ]-inf;4.75] 0 1 0 0.320737 27 0.257143 -C3 {} ]4.75;+inf[ 0 0.125 0.875 0.293141 40 0.380952 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R38 -Variables - Type Name - Numerical LowerPetalLength - Numerical PetalLength - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -PetalLength -Interval Frequency Coverage -]-inf;4.75] 65 0.619048 -]4.75;+inf[ 40 0.380952 - -Cell frequencies - PetalLength -LowerPetalLength ]-inf;4.75] ]4.75;+inf[ Total Coverage -]-inf;2.4] 38 0 38 0.361905 -]2.4;+inf[ 27 40 67 0.638095 -Total 65 40 105 -Coverage 0.619048 0.380952 - -Cells 3 -Cell Id LowerPetalLength PetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C1 ]-inf;2.4] ]-inf;4.75] 1 0 0 0.386122 38 0.361905 -C2 ]2.4;+inf[ ]-inf;4.75] 0 1 0 0.320737 27 0.257143 -C4 ]2.4;+inf[ ]4.75;+inf[ 0 0.125 0.875 0.293141 40 0.380952 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R42 -Variables - Type Name - Categorical Class1 - Numerical PetalWidth - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -PetalWidth -Interval Frequency Coverage -]-inf;1.75] 71 0.67619 -]1.75;+inf[ 34 0.32381 - -Cell frequencies - PetalWidth -Class1 ]-inf;1.75] ]1.75;+inf[ Total Coverage -{} 33 34 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 71 34 105 -Coverage 0.67619 0.32381 - -Cells 3 -Cell Id Class1 PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C2 {setosa} ]-inf;1.75] 1 0 0 0.374826 38 0.361905 -C3 {} ]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -C1 {} ]-inf;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R43 -Variables - Type Name - Numerical PetalLength - Categorical SPetalLength - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;4.75] 65 0.619048 -]4.75;+inf[ 40 0.380952 -SPetalLength -Group Frequency Coverage Size Value list -{5, 4, 3} 67 0.638095 4 5 4 3 * -{1} 38 0.361905 1 1 - -Cell frequencies - SPetalLength -PetalLength {5, 4, 3} {1} Total Coverage -]-inf;4.75] 27 38 65 0.619048 -]4.75;+inf[ 40 0 40 0.380952 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 3 -Cell Id PetalLength SPetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C3 ]-inf;4.75] {1} 1 0 0 0.386122 38 0.361905 -C1 ]-inf;4.75] {5, 4, 3} 0 1 0 0.320737 27 0.257143 -C2 ]4.75;+inf[ {5, 4, 3} 0 0.125 0.875 0.293141 40 0.380952 - Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R45 -Variables - Type Name - Numerical PetalLength - Numerical PetalWidth - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;4.75] 65 0.619048 -]4.75;+inf[ 40 0.380952 -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;+inf[ 67 0.638095 - -Cell frequencies - PetalWidth -PetalLength ]-inf;0.75] ]0.75;+inf[ Total Coverage -]-inf;4.75] 38 27 65 0.619048 -]4.75;+inf[ 0 40 40 0.380952 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 3 -Cell Id PetalLength PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C1 ]-inf;4.75] ]-inf;0.75] 1 0 0 0.386122 38 0.361905 -C3 ]-inf;4.75] ]0.75;+inf[ 0 1 0 0.320737 27 0.257143 -C4 ]4.75;+inf[ ]0.75;+inf[ 0 0.125 0.875 0.293141 40 0.380952 - Total 0.361905 0.304762 0.333333 1 105 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 4 + Numerical 8 + Total 12 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variable pairs 45 +Informative variable pairs 0 +Selected variable pairs 0 + + +Variables pairs statistics + +Rank Name 1 Name 2 DeltaLevel Level Level 1 Level 2 Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost +R01 Class1 Dummy2 -0.0288536 0.444159 0.473012 0 1 2 1 2 6.62856 15.7997 44.0428 +R02 Class2 Dummy2 -0.0288536 0.411044 0.439898 0 1 2 1 2 6.62856 15.6381 48.1645 +R03 Dummy2 LowerPetalLength -0.0288536 0.449955 0 0.478809 1 1 2 2 6.62856 15.1066 44.0428 +R04 Dummy2 PetalLength -0.0288536 0.583577 0 0.61243 1 1 4 4 6.62856 31.9046 11.2655 +R05 Dummy2 PetalWidth -0.0288536 0.62499 0 0.653844 1 1 3 3 6.62856 28.4221 9.79546 +R06 Dummy2 SPetalLength -0.0288536 0.591622 0 0.620475 1 1 3 3 6.62856 27.2738 14.9342 +R07 Dummy2 SepalLength -0.0288536 0.293814 0 0.322668 1 1 3 3 6.62856 26.2686 51.5531 +R08 Dummy2 SepalWidth -0.0288536 0.0786391 0 0.107493 1 1 3 3 6.62856 23.4061 80.1477 +R09 Dummy2 UpperPetalWidth -0.0288536 0.300563 0 0.329416 1 1 2 2 6.62856 20.5402 56.4745 +R10 SepalWidth UpperPetalWidth -0.0439741 0.392935 0.107493 0.329416 2 2 2 4 9.02645 24.172 39.3984 +R11 Class2 UpperPetalWidth -0.0976395 0.671675 0.439898 0.329416 2 2 2 4 9.02645 23.5775 6.65929 +R12 SepalLength SepalWidth -0.121906 0.308255 0.322668 0.107493 2 2 2 4 9.02645 24.3329 49.3641 +R13 Class2 SepalLength -0.128245 0.63432 0.439898 0.322668 2 2 2 4 9.02645 24.5695 10.1344 +R14 Class1 SepalWidth -0.136346 0.444159 0.473012 0.107493 1 2 1 2 6.62856 15.7997 44.0428 +R15 Class2 SepalWidth -0.136346 0.411044 0.439898 0.107493 1 2 1 2 6.62856 15.6381 48.1645 +R16 LowerPetalLength SepalWidth -0.136346 0.449955 0.478809 0.107493 1 2 1 2 6.62856 15.1066 44.0428 +R17 PetalLength SepalWidth -0.136346 0.583577 0.61243 0.107493 1 4 1 4 6.62856 31.9046 11.2655 +R18 PetalWidth SepalWidth -0.136346 0.62499 0.653844 0.107493 1 3 1 3 6.62856 28.4221 9.79546 +R19 SPetalLength SepalWidth -0.136346 0.591622 0.620475 0.107493 1 3 1 3 6.62856 27.2738 14.9342 +R20 Class2 LowerPetalLength -0.168707 0.749999 0.439898 0.478809 2 2 2 3 9.02645 20.8703 0 +R21 Class1 Class2 -0.196941 0.715969 0.473012 0.439898 2 2 2 3 9.02645 24.9398 0 +R22 Class1 UpperPetalWidth -0.203907 0.598522 0.473012 0.329416 2 2 2 3 9.02645 29.1894 9.79546 +R23 LowerPetalLength UpperPetalWidth -0.212171 0.596054 0.478809 0.329416 2 2 2 3 9.02645 20.1475 19.1326 +R24 SepalLength UpperPetalWidth -0.249148 0.402937 0.322668 0.329416 2 2 2 3 9.02645 29.1594 33.2149 +R25 Class1 SepalLength -0.326251 0.469429 0.473012 0.322668 2 2 2 3 9.02645 25.0325 29.3902 +R26 Class2 PetalLength -0.333475 0.718853 0.439898 0.61243 2 2 2 4 9.02645 24.5949 0 +R27 LowerPetalLength SepalLength -0.348267 0.45321 0.478809 0.322668 2 2 2 3 9.02645 26.9721 29.3902 +R28 PetalLength SepalLength -0.351521 0.583577 0.61243 0.322668 1 4 1 4 6.62856 31.9046 11.2655 +R29 PetalWidth SepalLength -0.351521 0.62499 0.653844 0.322668 1 3 1 3 6.62856 28.4221 9.79546 +R30 SPetalLength SepalLength -0.351521 0.591622 0.620475 0.322668 1 3 1 3 6.62856 27.2738 14.9342 +R31 PetalLength UpperPetalWidth -0.35827 0.583577 0.61243 0.329416 1 4 1 4 6.62856 31.9046 11.2655 +R32 PetalWidth UpperPetalWidth -0.35827 0.62499 0.653844 0.329416 1 3 1 3 6.62856 28.4221 9.79546 +R33 SPetalLength UpperPetalWidth -0.35827 0.591622 0.620475 0.329416 1 3 1 3 6.62856 27.2738 14.9342 +R34 Class2 SPetalLength -0.366871 0.693503 0.439898 0.620475 2 2 2 3 9.02645 27.6265 0 +R35 Class2 PetalWidth -0.374723 0.719019 0.439898 0.653844 2 2 2 4 9.02645 24.5751 0 +R36 Class1 PetalLength -0.481968 0.603475 0.473012 0.61243 2 2 2 3 9.02645 24.9956 13.397 +R37 Class1 LowerPetalLength -0.501866 0.449955 0.473012 0.478809 1 1 2 2 6.62856 15.1066 44.0428 +R38 LowerPetalLength PetalLength -0.503983 0.587256 0.478809 0.61243 2 2 2 3 9.02645 26.9352 13.397 +R39 LowerPetalLength PetalWidth -0.507662 0.62499 0.478809 0.653844 1 1 3 3 6.62856 28.4221 9.79546 +R40 LowerPetalLength SPetalLength -0.507662 0.591622 0.478809 0.620475 1 1 3 3 6.62856 27.2738 14.9342 +R41 Class1 SPetalLength -0.512711 0.580776 0.473012 0.620475 1 1 3 3 6.62856 28.5707 14.9342 +R42 Class1 PetalWidth -0.528334 0.598522 0.473012 0.653844 2 2 2 3 9.02645 29.1894 9.79546 +R43 PetalLength SPetalLength -0.645511 0.587395 0.61243 0.620475 2 2 2 3 9.02645 26.9186 13.397 +R44 PetalWidth SPetalLength -0.649329 0.62499 0.653844 0.620475 1 3 1 3 6.62856 28.4221 9.79546 +R45 PetalLength PetalWidth -0.679019 0.587256 0.61243 0.653844 2 2 2 3 9.02645 26.9352 13.397 + + +-------------------------------------------------------------------------------- + +Variables pairs detailed statistics +(Pairs with two jointly informative variables) + + +Rank R10 +Variables + Type Name + Numerical SepalWidth + Numerical UpperPetalWidth + +Variables stats +SepalWidth +Interval Frequency Coverage +]-inf;3.05] 56 0.533333 +]3.05;+inf[ 49 0.466667 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +SepalWidth ]-inf;1.55] ]1.55;+inf[ Total Coverage +]-inf;3.05] 34 22 56 0.533333 +]3.05;+inf[ 33 16 49 0.466667 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 4 +Cell Id SepalWidth UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C2 ]3.05;+inf[ ]-inf;1.55] 0.939394 0.0606061 0 0.383327 33 0.314286 +C3 ]-inf;3.05] ]1.55;+inf[ 0 0.0909091 0.909091 0.256821 22 0.209524 +C1 ]-inf;3.05] ]-inf;1.55] 0.205882 0.735294 0.0588235 0.212492 34 0.32381 +C4 ]3.05;+inf[ ]1.55;+inf[ 0 0.1875 0.8125 0.14736 16 0.152381 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R11 +Variables + Type Name + Categorical Class2 + Numerical UpperPetalWidth + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +Class2 ]-inf;1.55] ]1.55;+inf[ Total Coverage +{} 40 33 73 0.695238 +{versicolor} 27 5 32 0.304762 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 4 +Cell Id Class2 UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C3 {} ]1.55;+inf[ 0 0 1 0.33833 33 0.314286 +C1 {} ]-inf;1.55] 0.95 0 0.05 0.306831 40 0.380952 +C2 {versicolor} ]-inf;1.55] 0 1 0 0.299395 27 0.257143 +C4 {versicolor} ]1.55;+inf[ 0 1 0 0.0554436 5 0.047619 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R12 +Variables + Type Name + Numerical SepalLength + Numerical SepalWidth + +Variables stats +SepalLength +Interval Frequency Coverage +]-inf;5.75] 53 0.504762 +]5.75;+inf[ 52 0.495238 +SepalWidth +Interval Frequency Coverage +]-inf;3.05] 56 0.533333 +]3.05;+inf[ 49 0.466667 + +Cell frequencies + SepalWidth +SepalLength ]-inf;3.05] ]3.05;+inf[ Total Coverage +]-inf;5.75] 23 30 53 0.504762 +]5.75;+inf[ 33 19 52 0.495238 +Total 56 49 105 +Coverage 0.533333 0.466667 + +Cells 4 +Cell Id SepalLength SepalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C3 ]-inf;5.75] ]3.05;+inf[ 1 0 0 0.523397 30 0.285714 +C2 ]5.75;+inf[ ]-inf;3.05] 0 0.393939 0.606061 0.262519 33 0.314286 +C4 ]5.75;+inf[ ]3.05;+inf[ 0.0526316 0.263158 0.684211 0.11478 19 0.180952 +C1 ]-inf;5.75] ]-inf;3.05] 0.304348 0.608696 0.0869565 0.099304 23 0.219048 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R13 +Variables + Type Name + Categorical Class2 + Numerical SepalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +SepalLength +Interval Frequency Coverage +]-inf;5.75] 53 0.504762 +]5.75;+inf[ 52 0.495238 + +Cell frequencies + SepalLength +Class2 ]-inf;5.75] ]5.75;+inf[ Total Coverage +{} 39 34 73 0.695238 +{versicolor} 14 18 32 0.304762 +Total 53 52 105 +Coverage 0.504762 0.495238 + +Cells 4 +Cell Id Class2 SepalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C3 {} ]5.75;+inf[ 0.0294118 0 0.970588 0.318989 34 0.32381 +C1 {} ]-inf;5.75] 0.948718 0 0.0512821 0.310764 39 0.371429 +C4 {versicolor} ]5.75;+inf[ 0 1 0 0.208264 18 0.171429 +C2 {versicolor} ]-inf;5.75] 0 1 0 0.161983 14 0.133333 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R20 +Variables + Type Name + Categorical Class2 + Numerical LowerPetalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + LowerPetalLength +Class2 ]-inf;2.4] ]2.4;+inf[ Total Coverage +{} 38 35 73 0.695238 +{versicolor} 0 32 32 0.304762 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 3 +Cell Id Class2 LowerPetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C1 {} ]-inf;2.4] 1 0 0 0.335563 38 0.361905 +C3 {} ]2.4;+inf[ 0 0 1 0.334079 35 0.333333 +C4 {versicolor} ]2.4;+inf[ 0 1 0 0.330358 32 0.304762 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R21 +Variables + Type Name + Categorical Class1 + Categorical Class2 + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * + +Cell frequencies + Class2 +Class1 {} {versicolor} Total Coverage +{} 35 32 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 73 32 105 +Coverage 0.695238 0.304762 + +Cells 3 +Cell Id Class1 Class2 Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C2 {setosa} {} 1 0 0 0.335563 38 0.361905 +C1 {} {} 0 0 1 0.334079 35 0.333333 +C3 {} {versicolor} 0 1 0 0.330358 32 0.304762 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R22 +Variables + Type Name + Categorical Class1 + Numerical UpperPetalWidth + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.75] 71 0.67619 +]1.75;+inf[ 34 0.32381 + +Cell frequencies + UpperPetalWidth +Class1 ]-inf;1.75] ]1.75;+inf[ Total Coverage +{} 33 34 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 71 34 105 +Coverage 0.67619 0.32381 + +Cells 3 +Cell Id Class1 UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C2 {setosa} ]-inf;1.75] 1 0 0 0.374826 38 0.361905 +C3 {} ]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 +C1 {} ]-inf;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R23 +Variables + Type Name + Numerical LowerPetalLength + Numerical UpperPetalWidth + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +LowerPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage +]-inf;2.4] 38 0 38 0.361905 +]2.4;+inf[ 29 38 67 0.638095 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 3 +Cell Id LowerPetalLength UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C1 ]-inf;2.4] ]-inf;1.55] 1 0 0 0.415191 38 0.361905 +C4 ]2.4;+inf[ ]1.55;+inf[ 0 0.131579 0.868421 0.294541 38 0.361905 +C2 ]2.4;+inf[ ]-inf;1.55] 0 0.931034 0.0689655 0.290269 29 0.27619 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R24 +Variables + Type Name + Numerical SepalLength + Numerical UpperPetalWidth + +Variables stats +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;+inf[ 66 0.628571 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.75] 71 0.67619 +]1.75;+inf[ 34 0.32381 + +Cell frequencies + UpperPetalWidth +SepalLength ]-inf;1.75] ]1.75;+inf[ Total Coverage +]-inf;5.45] 39 0 39 0.371429 +]5.45;+inf[ 32 34 66 0.628571 +Total 71 34 105 +Coverage 0.67619 0.32381 + +Cells 3 +Cell Id SepalLength UpperPetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C4 ]5.45;+inf[ ]1.75;+inf[ 0 0.0294118 0.970588 0.431099 34 0.32381 +C1 ]-inf;5.45] ]-inf;1.75] 0.871795 0.128205 0 0.334637 39 0.371429 +C2 ]5.45;+inf[ ]-inf;1.75] 0.125 0.8125 0.0625 0.234263 32 0.304762 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R25 +Variables + Type Name + Categorical Class1 + Numerical SepalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +SepalLength +Interval Frequency Coverage +]-inf;6.15] 67 0.638095 +]6.15;+inf[ 38 0.361905 + +Cell frequencies + SepalLength +Class1 ]-inf;6.15] ]6.15;+inf[ Total Coverage +{} 29 38 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 3 +Cell Id Class1 SepalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C2 {setosa} ]-inf;6.15] 1 0 0 0.469806 38 0.361905 +C3 {} ]6.15;+inf[ 0 0.210526 0.789474 0.278648 38 0.361905 +C1 {} ]-inf;6.15] 0 0.827586 0.172414 0.251546 29 0.27619 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R26 +Variables + Type Name + Categorical Class2 + Numerical PetalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +PetalLength +Interval Frequency Coverage +]-inf;4.2] 53 0.504762 +]4.2;+inf[ 52 0.495238 + +Cell frequencies + PetalLength +Class2 ]-inf;4.2] ]4.2;+inf[ Total Coverage +{} 38 35 73 0.695238 +{versicolor} 15 17 32 0.304762 +Total 53 52 105 +Coverage 0.504762 0.495238 + +Cells 4 +Cell Id Class2 PetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C1 {} ]-inf;4.2] 1 0 0 0.335563 38 0.361905 +C3 {} ]4.2;+inf[ 0 0 1 0.334079 35 0.333333 +C4 {versicolor} ]4.2;+inf[ 0 1 0 0.175503 17 0.161905 +C2 {versicolor} ]-inf;4.2] 0 1 0 0.154855 15 0.142857 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R27 +Variables + Type Name + Numerical LowerPetalLength + Numerical SepalLength + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +SepalLength +Interval Frequency Coverage +]-inf;6.15] 67 0.638095 +]6.15;+inf[ 38 0.361905 + +Cell frequencies + SepalLength +LowerPetalLength ]-inf;6.15] ]6.15;+inf[ Total Coverage +]-inf;2.4] 38 0 38 0.361905 +]2.4;+inf[ 29 38 67 0.638095 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 3 +Cell Id LowerPetalLength SepalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C1 ]-inf;2.4] ]-inf;6.15] 1 0 0 0.469806 38 0.361905 +C4 ]2.4;+inf[ ]6.15;+inf[ 0 0.210526 0.789474 0.278648 38 0.361905 +C2 ]2.4;+inf[ ]-inf;6.15] 0 0.827586 0.172414 0.251546 29 0.27619 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R34 +Variables + Type Name + Categorical Class2 + Categorical SPetalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +SPetalLength +Group Frequency Coverage Size Value list +{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * +{1} 38 0.361905 1 1 + +Cell frequencies + SPetalLength +Class2 {5, 4, 3, ...} {1} Total Coverage +{} 35 38 73 0.695238 +{versicolor} 32 0 32 0.304762 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 3 +Cell Id Class2 SPetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C3 {} {1} 1 0 0 0.335563 38 0.361905 +C1 {} {5, 4, 3, ...} 0 0 1 0.334079 35 0.333333 +C2 {versicolor} {5, 4, 3, ...} 0 1 0 0.330358 32 0.304762 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R35 +Variables + Type Name + Categorical Class2 + Numerical PetalWidth + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +PetalWidth +Interval Frequency Coverage +]-inf;1.35] 56 0.533333 +]1.35;+inf[ 49 0.466667 + +Cell frequencies + PetalWidth +Class2 ]-inf;1.35] ]1.35;+inf[ Total Coverage +{} 38 35 73 0.695238 +{versicolor} 18 14 32 0.304762 +Total 56 49 105 +Coverage 0.533333 0.466667 + +Cells 4 +Cell Id Class2 PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C1 {} ]-inf;1.35] 1 0 0 0.335563 38 0.361905 +C3 {} ]1.35;+inf[ 0 0 1 0.334079 35 0.333333 +C2 {versicolor} ]-inf;1.35] 0 1 0 0.185826 18 0.171429 +C4 {versicolor} ]1.35;+inf[ 0 1 0 0.144532 14 0.133333 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R36 +Variables + Type Name + Categorical Class1 + Numerical PetalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +PetalLength +Interval Frequency Coverage +]-inf;4.75] 65 0.619048 +]4.75;+inf[ 40 0.380952 + +Cell frequencies + PetalLength +Class1 ]-inf;4.75] ]4.75;+inf[ Total Coverage +{} 27 40 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 65 40 105 +Coverage 0.619048 0.380952 + +Cells 3 +Cell Id Class1 PetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C2 {setosa} ]-inf;4.75] 1 0 0 0.386122 38 0.361905 +C1 {} ]-inf;4.75] 0 1 0 0.320737 27 0.257143 +C3 {} ]4.75;+inf[ 0 0.125 0.875 0.293141 40 0.380952 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R38 +Variables + Type Name + Numerical LowerPetalLength + Numerical PetalLength + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +PetalLength +Interval Frequency Coverage +]-inf;4.75] 65 0.619048 +]4.75;+inf[ 40 0.380952 + +Cell frequencies + PetalLength +LowerPetalLength ]-inf;4.75] ]4.75;+inf[ Total Coverage +]-inf;2.4] 38 0 38 0.361905 +]2.4;+inf[ 27 40 67 0.638095 +Total 65 40 105 +Coverage 0.619048 0.380952 + +Cells 3 +Cell Id LowerPetalLength PetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C1 ]-inf;2.4] ]-inf;4.75] 1 0 0 0.386122 38 0.361905 +C2 ]2.4;+inf[ ]-inf;4.75] 0 1 0 0.320737 27 0.257143 +C4 ]2.4;+inf[ ]4.75;+inf[ 0 0.125 0.875 0.293141 40 0.380952 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R42 +Variables + Type Name + Categorical Class1 + Numerical PetalWidth + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +PetalWidth +Interval Frequency Coverage +]-inf;1.75] 71 0.67619 +]1.75;+inf[ 34 0.32381 + +Cell frequencies + PetalWidth +Class1 ]-inf;1.75] ]1.75;+inf[ Total Coverage +{} 33 34 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 71 34 105 +Coverage 0.67619 0.32381 + +Cells 3 +Cell Id Class1 PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C2 {setosa} ]-inf;1.75] 1 0 0 0.374826 38 0.361905 +C3 {} ]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 +C1 {} ]-inf;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R43 +Variables + Type Name + Numerical PetalLength + Categorical SPetalLength + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;4.75] 65 0.619048 +]4.75;+inf[ 40 0.380952 +SPetalLength +Group Frequency Coverage Size Value list +{5, 4, 3} 67 0.638095 4 5 4 3 * +{1} 38 0.361905 1 1 + +Cell frequencies + SPetalLength +PetalLength {5, 4, 3} {1} Total Coverage +]-inf;4.75] 27 38 65 0.619048 +]4.75;+inf[ 40 0 40 0.380952 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 3 +Cell Id PetalLength SPetalLength Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C3 ]-inf;4.75] {1} 1 0 0 0.386122 38 0.361905 +C1 ]-inf;4.75] {5, 4, 3} 0 1 0 0.320737 27 0.257143 +C2 ]4.75;+inf[ {5, 4, 3} 0 0.125 0.875 0.293141 40 0.380952 + Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R45 +Variables + Type Name + Numerical PetalLength + Numerical PetalWidth + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;4.75] 65 0.619048 +]4.75;+inf[ 40 0.380952 +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;+inf[ 67 0.638095 + +Cell frequencies + PetalWidth +PetalLength ]-inf;0.75] ]0.75;+inf[ Total Coverage +]-inf;4.75] 38 27 65 0.619048 +]4.75;+inf[ 0 40 40 0.380952 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 3 +Cell Id PetalLength PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C1 ]-inf;4.75] ]-inf;0.75] 1 0 0 0.386122 38 0.361905 +C3 ]-inf;4.75] ]0.75;+inf[ 0 1 0 0.320737 27 0.257143 +C4 ]4.75;+inf[ ]0.75;+inf[ 0 0.125 0.875 0.293141 40 0.380952 + Total 0.361905 0.304762 0.333333 1 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.PreparationReport.xls similarity index 81% rename from test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.PreparationReport.xls index 43e0a7c63..03f296d2f 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.PreparationReport.xls @@ -1,201 +1,203 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-versicolor 32 0.304762 -Iris-virginica 35 0.333333 - -Evaluated variables 11 -Native variables 4 -Constructed variables 7 -Informative variables 9 - -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 100 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 8.64312 - Data cost 110.25 - - -Categorical variables statistics - -Rank Name Level Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost Derivation rule -R02 SPetalLength 0.620475 3 5 1 0.361905 3.17805 27.2738 14.9342 AsCategorical(Floor(PetalLength)) -R05 Class1 0.473012 2 2 0.638095 3.17805 15.7997 44.0428 IfC(EQc(Class, "Iris-setosa"), "setosa", "") -R06 Class2 0.439898 2 2 0.695238 3.17805 15.6381 48.1645 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R01 PetalWidth 0.653844 3 21 0.1 2.5 1.175238095 0.7880996979 0 3.17805 28.4221 9.79546 -R03 PetalLength 0.61243 4 36 1 6.9 3.686666667 1.80132579 0 3.17805 31.9046 11.2655 -R04 LowerPetalLength 0.478809 2 10 1 3 2.446666667 0.7433600251 0 3.17805 15.1066 44.0428 If(LE(PetalLength, 3), PetalLength, 3) -R07 UpperPetalWidth 0.329416 2 11 1.5 2.5 1.692380952 0.2962287527 0 3.17805 20.5402 56.4745 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) -R08 SepalLength 0.322668 3 31 4.3 7.7 5.827619048 0.8375127846 0 3.17805 26.2686 51.5531 -R09 SepalWidth 0.107493 3 23 2 4.4 3.081904762 0.4284592446 0 3.17805 23.4061 80.1477 -R10 Dummy1 0 1 1 0 0 0 0 0 0.693147 8.64312 110.25 Copy(0) -R11 Dummy2 0 1 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0.693147 8.64312 110.25 Random() - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R01 -Variable Numerical PetalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 -]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R02 -Variable Categorical SPetalLength - -Variable stats -Group Size Value list -{1} 1 1 -{5, 6} 2 5 6 * -{4, 3} 2 4 3 - -% target values -Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -{1} 1 0 0 0.39638 38 0.361905 -{5, 6} 0 0.0588235 0.941176 0.307127 34 0.32381 -{4, 3} 0 0.909091 0.0909091 0.296494 33 0.314286 -Total 0.361905 0.304762 0.333333 1 105 1 - -Values -Value Frequency Coverage -1 38 0.361905 -5 27 0.257143 -4 25 0.238095 -3 8 0.0761905 -6 7 0.0666667 - ----------------------------------------------- -Rank R03 -Variable Numerical PetalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 -]3.15;4.75] 0 1 0 0.304909 26 0.247619 -]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 -]5.15;+inf[ 0 0 1 0.281913 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R04 -Variable Numerical LowerPetalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;2.4] 1 0 0 0.561997 38 0.361905 -]2.4;+inf[ 0 0.477612 0.522388 0.438003 67 0.638095 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R05 -Variable Categorical Class1 - -Variable stats -Group Size Value list -{} 1 -{setosa} 1 setosa * - -% target values -Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -{setosa} 1 0 0 0.561997 38 0.361905 -{} 0 0.477612 0.522388 0.438003 67 0.638095 -Total 0.361905 0.304762 0.333333 1 105 1 - -Values -Value Frequency Coverage - 67 0.638095 -setosa 38 0.361905 - ----------------------------------------------- -Rank R06 -Variable Categorical Class2 - -Variable stats -Group Size Value list -{} 1 -{versicolor} 1 versicolor * - -% target values -Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -{versicolor} 0 1 0 0.58897 32 0.304762 -{} 0.520548 0 0.479452 0.41103 73 0.695238 -Total 0.361905 0.304762 0.333333 1 105 1 - -Values -Value Frequency Coverage - 73 0.695238 -versicolor 32 0.304762 - ----------------------------------------------- -Rank R07 -Variable Numerical UpperPetalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;1.75] 0.535211 0.43662 0.028169 0.390205 71 0.67619 -]1.75;+inf[ 0 0.0294118 0.970588 0.609795 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R08 -Variable Numerical SepalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 -]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 -]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R09 -Variable Numerical SepalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 -]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 -]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 4 + Numerical 8 + Total 12 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variables 11 +Native variables 4 +Constructed variables 7 +Informative variables 9 +Selected variables 5 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 100 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 8.64312 + Data cost 110.25 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R02 SPetalLength 0.620475 3 5 1 0.361905 0 0 3.17805 27.2738 14.9342 AsCategorical(Floor(PetalLength)) +R05 Class1 0.473012 2 2 0.638095 67 0 3.17805 15.7997 44.0428 IfC(EQc(Class, "Iris-setosa"), "setosa", "") +R06 Class2 0.439898 2 2 0.695238 73 0 3.17805 15.6381 48.1645 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R01 PetalWidth 0.653844 3 21 0.1 2.5 1.175238095 0.7880996979 0 0 3.17805 28.4221 9.79546 +R03 PetalLength 0.61243 4 36 1 6.9 3.686666667 1.80132579 0 0 3.17805 31.9046 11.2655 +R04 LowerPetalLength 0.478809 2 10 1 3 2.446666667 0.7433600251 0 0 3.17805 15.1066 44.0428 If(LE(PetalLength, 3), PetalLength, 3) +R07 UpperPetalWidth 0.329416 2 11 1.5 2.5 1.692380952 0.2962287527 0 0 3.17805 20.5402 56.4745 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) +R08 SepalLength 0.322668 3 31 4.3 7.7 5.827619048 0.8375127846 0 0 3.17805 26.2686 51.5531 +R09 SepalWidth 0.107493 3 23 2 4.4 3.081904762 0.4284592446 0 0 3.17805 23.4061 80.1477 +R10 Dummy1 0 1 1 0 0 0 0 0 0 0.693147 8.64312 110.25 Copy(0) +R11 Dummy2 0 1 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0 0.693147 8.64312 110.25 Random() + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R01 +Variable Numerical PetalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;0.75] 1 0 0 0.374826 38 0.361905 +]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 +]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R02 +Variable Categorical SPetalLength + +Variable stats +Group Size Value list +{1} 1 1 +{5, 6} 2 5 6 * +{4, 3} 2 4 3 + +% target values +Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +{1} 1 0 0 0.39638 38 0.361905 +{5, 6} 0 0.0588235 0.941176 0.307127 34 0.32381 +{4, 3} 0 0.909091 0.0909091 0.296494 33 0.314286 +Total 0.361905 0.304762 0.333333 1 105 1 + +Values +Value Frequency Coverage +1 38 0.361905 +5 27 0.257143 +4 25 0.238095 +3 8 0.0761905 +6 7 0.0666667 + +---------------------------------------------- +Rank R03 +Variable Numerical PetalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 +]3.15;4.75] 0 1 0 0.304909 26 0.247619 +]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 +]5.15;+inf[ 0 0 1 0.281913 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R04 +Variable Numerical LowerPetalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2.4] 1 0 0 0.561997 38 0.361905 +]2.4;+inf[ 0 0.477612 0.522388 0.438003 67 0.638095 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R05 +Variable Categorical Class1 + +Variable stats +Group Size Value list +{} 1 +{setosa} 1 setosa * + +% target values +Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +{setosa} 1 0 0 0.561997 38 0.361905 +{} 0 0.477612 0.522388 0.438003 67 0.638095 +Total 0.361905 0.304762 0.333333 1 105 1 + +Values +Value Frequency Coverage + 67 0.638095 +setosa 38 0.361905 + +---------------------------------------------- +Rank R06 +Variable Categorical Class2 + +Variable stats +Group Size Value list +{} 1 +{versicolor} 1 versicolor * + +% target values +Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +{versicolor} 0 1 0 0.58897 32 0.304762 +{} 0.520548 0 0.479452 0.41103 73 0.695238 +Total 0.361905 0.304762 0.333333 1 105 1 + +Values +Value Frequency Coverage + 73 0.695238 +versicolor 32 0.304762 + +---------------------------------------------- +Rank R07 +Variable Numerical UpperPetalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;1.75] 0.535211 0.43662 0.028169 0.390205 71 0.67619 +]1.75;+inf[ 0 0.0294118 0.970588 0.609795 34 0.32381 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R08 +Variable Numerical SepalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 +]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 +]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R09 +Variable Numerical SepalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 +]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 +]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.TestEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/IrisC/results.ref/TestEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.TestEvaluationReport.xls index e3c9f88ab..e6c639005 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/TestEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -1,3069 +1,3069 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Exclude sample -Selection variable -Selection value -Instances 45 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 1 0.983613 1 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-4.7 4.7 14.1 14.1 14.1 -4.8 4.8 14.4 14.4 14.4 -4.9 4.9 14.7 14.7 14.7 -5 5 15 15 15 -5.1 5.1 15.3 15.3 15.3 -5.2 5.2 15.6 15.6 15.6 -5.3 5.3 15.9 15.9 15.9 -5.4 5.4 16.2 16.2 16.2 -5.5 5.5 16.5 16.5 16.5 -5.6 5.6 16.8 16.8 16.8 -5.7 5.7 17.1 17.1 17.1 -5.8 5.8 17.4 17.4 17.4 -5.9 5.9 17.7 17.7 17.7 -6 6 18 18 18 -6.1 6.1 18.3 18.3 18.3 -6.2 6.2 18.6 18.6 18.6 -6.3 6.3 18.9 18.9 18.9 -6.4 6.4 19.2 19.2 19.2 -6.5 6.5 19.5 19.5 19.5 -6.6 6.6 19.8 19.8 19.8 -6.7 6.7 20.1 20.1 20.1 -6.8 6.8 20.4 20.4 20.4 -6.9 6.9 20.7 20.7 20.7 -7 7 21 21 21 -7.1 7.1 21.3 21.3 21.3 -7.2 7.2 21.6 21.6 21.6 -7.3 7.3 21.9 21.9 21.9 -7.4 7.4 22.2 22.2 22.2 -7.5 7.5 22.5 22.5 22.5 -7.6 7.6 22.8 22.8 22.8 -7.7 7.7 23.1 23.1 23.1 -7.8 7.8 23.4 23.4 23.4 -7.9 7.9 23.7 23.7 23.7 -8 8 24 24 24 -8.1 8.1 24.3 24.3 24.3 -8.2 8.2 24.6 24.6 24.6 -8.3 8.3 24.9 24.9 24.9 -8.4 8.4 25.2 25.2 25.2 -8.5 8.5 25.5 25.5 25.5 -8.6 8.6 25.8 25.8 25.8 -8.7 8.7 26.1 26.1 26.1 -8.8 8.8 26.4 26.4 26.4 -8.9 8.9 26.7 26.7 26.7 -9 9 27 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b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.TrainEvaluationReport.xls similarity index 96% rename from test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/TrainEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.TrainEvaluationReport.xls index d768f2708..1ee2ef7a3 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/TrainEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -1,3069 +1,3069 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 1 0.984084 1 -Univariate PetalWidth 0.971429 0.892955 0.986807 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 1 -Compression 0.984084 -AUC 1 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 32 0 -$Iris-virginica 0 0 35 - ----------------------------------------------- -Classifier Univariate PetalWidth -Accuracy 0.971429 -Compression 0.892955 -AUC 0.986807 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 31 2 -$Iris-virginica 0 1 33 - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 -]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - - - ----------------------------------------------- -Lift curves Iris-setosa -Size Random Optimal Selective Naive Bayes Univariate PetalWidth -0 0 0 0 0 -0.1 0.1 0.276316 0.276316 0.276316 -0.2 0.2 0.552632 0.552632 0.552632 -0.3 0.3 0.828947 0.828947 0.828947 -0.4 0.4 1.10526 1.10526 1.10526 -0.5 0.5 1.38158 1.38158 1.38158 -0.6 0.6 1.65789 1.65789 1.65789 -0.7 0.7 1.93421 1.93421 1.93421 -0.8 0.8 2.21053 2.21053 2.21053 -0.9 0.9 2.48684 2.48684 2.48684 -1 1 2.76316 2.76316 2.76316 -1.1 1.1 3.03947 3.03947 3.03947 -1.2 1.2 3.31579 3.31579 3.31579 -1.3 1.3 3.59211 3.59211 3.59211 -1.4 1.4 3.86842 3.86842 3.86842 -1.5 1.5 4.14474 4.14474 4.14474 -1.6 1.6 4.42105 4.42105 4.42105 -1.7 1.7 4.69737 4.69737 4.69737 -1.8 1.8 4.97368 4.97368 4.97368 -1.9 1.9 5.25 5.25 5.25 -2 2 5.52632 5.52632 5.52632 -2.1 2.1 5.80263 5.80263 5.80263 -2.2 2.2 6.07895 6.07895 6.07895 -2.3 2.3 6.35526 6.35526 6.35526 -2.4 2.4 6.63158 6.63158 6.63158 -2.5 2.5 6.90789 6.90789 6.90789 -2.6 2.6 7.18421 7.18421 7.18421 -2.7 2.7 7.46053 7.46053 7.46053 -2.8 2.8 7.73684 7.73684 7.73684 -2.9 2.9 8.01316 8.01316 8.01316 -3 3 8.28947 8.28947 8.28947 -3.1 3.1 8.56579 8.56579 8.56579 -3.2 3.2 8.84211 8.84211 8.84211 -3.3 3.3 9.11842 9.11842 9.11842 -3.4 3.4 9.39474 9.39474 9.39474 -3.5 3.5 9.67105 9.67105 9.67105 -3.6 3.6 9.94737 9.94737 9.94737 -3.7 3.7 10.2237 10.2237 10.2237 -3.8 3.8 10.5 10.5 10.5 -3.9 3.9 10.7763 10.7763 10.7763 -4 4 11.0526 11.0526 11.0526 -4.1 4.1 11.3289 11.3289 11.3289 -4.2 4.2 11.6053 11.6053 11.6053 -4.3 4.3 11.8816 11.8816 11.8816 -4.4 4.4 12.1579 12.1579 12.1579 -4.5 4.5 12.4342 12.4342 12.4342 -4.6 4.6 12.7105 12.7105 12.7105 -4.7 4.7 12.9868 12.9868 12.9868 -4.8 4.8 13.2632 13.2632 13.2632 -4.9 4.9 13.5395 13.5395 13.5395 -5 5 13.8158 13.8158 13.8158 -5.1 5.1 14.0921 14.0921 14.0921 -5.2 5.2 14.3684 14.3684 14.3684 -5.3 5.3 14.6447 14.6447 14.6447 -5.4 5.4 14.9211 14.9211 14.9211 -5.5 5.5 15.1974 15.1974 15.1974 -5.6 5.6 15.4737 15.4737 15.4737 -5.7 5.7 15.75 15.75 15.75 -5.8 5.8 16.0263 16.0263 16.0263 -5.9 5.9 16.3026 16.3026 16.3026 -6 6 16.5789 16.5789 16.5789 -6.1 6.1 16.8553 16.8553 16.8553 -6.2 6.2 17.1316 17.1316 17.1316 -6.3 6.3 17.4079 17.4079 17.4079 -6.4 6.4 17.6842 17.6842 17.6842 -6.5 6.5 17.9605 17.9605 17.9605 -6.6 6.6 18.2368 18.2368 18.2368 -6.7 6.7 18.5132 18.5132 18.5132 -6.8 6.8 18.7895 18.7895 18.7895 -6.9 6.9 19.0658 19.0658 19.0658 -7 7 19.3421 19.3421 19.3421 -7.1 7.1 19.6184 19.6184 19.6184 -7.2 7.2 19.8947 19.8947 19.8947 -7.3 7.3 20.1711 20.1711 20.1711 -7.4 7.4 20.4474 20.4474 20.4474 -7.5 7.5 20.7237 20.7237 20.7237 -7.6 7.6 21 21 21 -7.7 7.7 21.2763 21.2763 21.2763 -7.8 7.8 21.5526 21.5526 21.5526 -7.9 7.9 21.8289 21.8289 21.8289 -8 8 22.1053 22.1053 22.1053 -8.1 8.1 22.3816 22.3816 22.3816 -8.2 8.2 22.6579 22.6579 22.6579 -8.3 8.3 22.9342 22.9342 22.9342 -8.4 8.4 23.2105 23.2105 23.2105 -8.5 8.5 23.4868 23.4868 23.4868 -8.6 8.6 23.7632 23.7632 23.7632 -8.7 8.7 24.0395 24.0395 24.0395 -8.8 8.8 24.3158 24.3158 24.3158 -8.9 8.9 24.5921 24.5921 24.5921 -9 9 24.8684 24.8684 24.8684 -9.1 9.1 25.1447 25.1447 25.1447 -9.2 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+ { + "variable": "SPetalLength", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["5","4","3"], + ["1"] + ], + "defaultGroupIndex": 0 + }, + { + "variable": "Target", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "cellIds": ["C1","C2","C3"], + "cellPartIndexes": [ + [0,0], + [1,0], + [0,1] + ], + "cellTargetFrequencies": [ + [0,27,0], + [0,5,35], + [38,0,0] + ], + "cellInterests": [0.320737,0.293141,0.386122] + } + }, + "R45": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,4.75], + [4.75,6.9] + ] + }, + { + "variable": "PetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0.1,0.75], + [0.75,2.5] + ] + }, + { + "variable": "Target", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "cellIds": ["C1","C3","C4"], + "cellPartIndexes": [ + [0,0], + [0,1], + [1,1] + ], + "cellTargetFrequencies": [ + [38,0,0], + [0,27,0], + [0,5,35] + ], + "cellInterests": [0.386122,0.320737,0.293141] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/Modeling.kdic b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.model.kdic similarity index 98% rename from test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.model.kdic index 84f84d2db..dd03a3e8c 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/AnalysisResults.model.kdic @@ -1,54 +1,54 @@ -#Khiops 10.1 - -Dictionary BU1_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; -Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; -Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; -Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; -Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; -Unused Numerical Dummy1 = Copy(0) ; -Unused Numerical Dummy2 = Random() ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) -Unused Structure(Classifier) BU1Class = NBClassifier(DataGridStats(PPetalWidth, PetalWidth), VClass) ; - Categorical PredictedClass = TargetValue(BU1Class) ; -Unused Numerical ScoreClass = TargetProb(BU1Class) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(BU1Class, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(BU1Class, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(BU1Class, "Iris-virginica") ; -}; - -Dictionary SNB_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; -Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; -Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; -Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; -Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; -Unused Numerical Dummy1 = Copy(0) ; -Unused Numerical Dummy2 = Random() ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PSPetalLength = DataGrid(ValueGroups(ValueGroup("1"), ValueGroup("5", "6", " * "), ValueGroup("4", "3")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 2, 30, 0, 32, 3)) ; // DataGrid(SPetalLength, Class) -Unused Structure(DataGrid) PLowerPetalLength = DataGrid(IntervalBounds(2.4), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 32, 0, 35)) ; // DataGrid(LowerPetalLength, Class) -Unused Structure(DataGrid) PClass1 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("setosa", " * ")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(0, 38, 32, 0, 35, 0)) ; // DataGrid(Class1, Class) -Unused Structure(DataGrid) PClass2 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("versicolor", " * ")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 32, 35, 0)) ; // DataGrid(Class2, Class) -Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.1484375, 0.234375, 0.1640625, 0.5234375), DataGridStats(PSPetalLength, SPetalLength), DataGridStats(PLowerPetalLength, LowerPetalLength), DataGridStats(PClass1, Class1), DataGridStats(PClass2, Class2), VClass) ; - Categorical PredictedClass = TargetValue(SNBClass) ; -Unused Numerical ScoreClass = TargetProb(SNBClass) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; -}; +#Khiops 10.4.6i + +Dictionary BU1_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; +Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; +Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; +Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; +Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; +Unused Numerical Dummy1 = Copy(0) ; +Unused Numerical Dummy2 = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) +Unused Structure(Classifier) BU1Class = NBClassifier(DataGridStats(PPetalWidth, PetalWidth), VClass) ; + Categorical PredictedClass = TargetValue(BU1Class) ; +Unused Numerical ScoreClass = TargetProb(BU1Class) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(BU1Class, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(BU1Class, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(BU1Class, "Iris-virginica") ; +}; + +Dictionary SNB_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; +Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; +Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; +Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; +Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; +Unused Numerical Dummy1 = Copy(0) ; +Unused Numerical Dummy2 = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PSPetalLength = DataGrid(ValueGroups(ValueGroup("1"), ValueGroup("5", "6", " * "), ValueGroup("4", "3")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 2, 30, 0, 32, 3)) ; // DataGrid(SPetalLength, Class) +Unused Structure(DataGrid) PLowerPetalLength = DataGrid(IntervalBounds(2.4), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 32, 0, 35)) ; // DataGrid(LowerPetalLength, Class) +Unused Structure(DataGrid) PClass1 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("setosa", " * ")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(0, 38, 32, 0, 35, 0)) ; // DataGrid(Class1, Class) +Unused Structure(DataGrid) PClass2 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("versicolor", " * ")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 32, 35, 0)) ; // DataGrid(Class2, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.1484375, 0.234375, 0.1640625, 0.5234375), DataGridStats(PSPetalLength, SPetalLength), DataGridStats(PLowerPetalLength, LowerPetalLength), DataGridStats(PClass1, Class1), DataGridStats(PClass2, Class2), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/T_Iris.txt b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/D_Iris.txt similarity index 98% rename from test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/T_Iris.txt rename to test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/D_Iris.txt index c15c9dd30..791286cbf 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/D_Iris.txt @@ -1,106 +1,106 @@ -PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001806383389 0.9751708257 0.02302279093 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001806383389 0.9751708257 0.02302279093 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01760717604 0.01934772071 0.9630451033 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01760717604 0.01934772071 0.9630451033 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01760717604 0.01934772071 0.9630451033 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001806383389 0.9751708257 0.02302279093 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001806383389 0.9751708257 0.02302279093 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01760717604 0.01934772071 0.9630451033 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01760717604 0.01934772071 0.9630451033 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01760717604 0.01934772071 0.9630451033 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 diff --git a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/err.txt index c17dc4fe7..f4f455f14 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/err.txt @@ -1,7 +1,7 @@ Train supervised model for classification of target variable Class Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 Evaluation of variable pairs -Data preparation time: 0:00:00.64 +Data preparation time: 0:00:00.52 SNB train time: 0:00:00 Build Selective Naive Bayes SNB_Iris Build Univariate BU1_Iris @@ -9,10 +9,10 @@ Train evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iri Train evaluation of Univariate PetalWidth on database ../../../datasets/Iris/Iris.txt Test evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt Test evaluation of Univariate PetalWidth on database ../../../datasets/Iris/Iris.txt -Write report ./results\AllReports.khj +Write report ./results/AnalysisResults.khj Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 105 +Output database ./results/D_Iris.txt: Written records: 105 Model deployment time: 0:00:00.01 diff --git a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/time.log index 6188edfbc..76c13ef54 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.9163053035736084 +Overal time: 0.7472364902496338 diff --git a/test/LearningTest/TestKhiops/Standard/Iris2D/test.prm b/test/LearningTest/TestKhiops/Standard/Iris2D/test.prm index 0e5220bef..b807565dd 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/test.prm +++ b/test/LearningTest/TestKhiops/Standard/Iris2D/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -22,40 +24,42 @@ ClassFileName ./IrisExtended.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable AnalysisSpec.MainTargetModality // Main target modality -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries +AnalysisSpec.PredictorsSpec.ExpertSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 100 // Max number of pairs of variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database ClassManagement.OpenFile // Open... -ClassFileName ./results/Modeling.kdic // Dictionary file +ClassFileName ./results/AnalysisResults.model.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer diff --git a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.ModelingReport.xls similarity index 94% rename from test/LearningTest/TestKhiops/Standard/IrisC/results.ref/ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.ModelingReport.xls index 9c2d301f4..f8686be76 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.ModelingReport.xls @@ -1,35 +1,35 @@ -#Khiops 10.1 -Modeling report - -Dictionary Iris -Target variable Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 4 -Univariate PetalWidth 1 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PClass2 Class2 0.440626 0.523438 0.48025 -PLowerPetalLength LowerPetalLength 0.479536 0.234375 0.335248 -PSPetalLength SPetalLength 0.621203 0.148438 0.303661 -PClass1 Class1 0.47374 0.164062 0.278788 - ----------------------------------------------- -Predictor Univariate PetalWidth +#Khiops 10.4.6i +Modeling report + +Dictionary Iris +Target variable Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 4 +Univariate PetalWidth 1 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PClass2 Class2 0.440626 0.523438 0.48025 +PLowerPetalLength LowerPetalLength 0.479536 0.234375 0.335248 +PSPetalLength SPetalLength 0.621203 0.148438 0.303661 +PClass1 Class1 0.47374 0.164062 0.278788 + +---------------------------------------------- +Predictor Univariate PetalWidth diff --git a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.PreparationReport.xls similarity index 81% rename from test/LearningTest/TestKhiops/Standard/IrisC/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.PreparationReport.xls index 37cf4c8fe..b47a8438f 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.PreparationReport.xls @@ -1,201 +1,203 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-versicolor 32 0.304762 -Iris-virginica 35 0.333333 - -Evaluated variables 11 -Native variables 4 -Constructed variables 7 -Informative variables 9 - -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 8.64312 - Data cost 110.25 - - -Categorical variables statistics - -Rank Name Level Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost Derivation rule -R02 SPetalLength 0.621203 3 5 1 0.361905 3.09104 27.2738 14.9342 AsCategorical(Floor(PetalLength)) -R05 Class1 0.47374 2 2 0.638095 3.09104 15.7997 44.0428 IfC(EQc(Class, "Iris-setosa"), "setosa", "") -R06 Class2 0.440626 2 2 0.695238 3.09104 15.6381 48.1645 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R01 PetalWidth 0.654571 3 21 0.1 2.5 1.175238095 0.7880996979 0 3.09104 28.4221 9.79546 -R03 PetalLength 0.613158 4 36 1 6.9 3.686666667 1.80132579 0 3.09104 31.9046 11.2655 -R04 LowerPetalLength 0.479536 2 10 1 3 2.446666667 0.7433600251 0 3.09104 15.1066 44.0428 If(LE(PetalLength, 3), PetalLength, 3) -R07 UpperPetalWidth 0.330144 2 11 1.5 2.5 1.692380952 0.2962287527 0 3.09104 20.5402 56.4745 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) -R08 SepalLength 0.323395 3 31 4.3 7.7 5.827619048 0.8375127846 0 3.09104 26.2686 51.5531 -R09 SepalWidth 0.10822 3 23 2 4.4 3.081904762 0.4284592446 0 3.09104 23.4061 80.1477 -R10 Dummy1 0 1 1 0 0 0 0 0 0.693147 8.64312 110.25 Copy(0) -R11 Dummy2 0 1 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0.693147 8.64312 110.25 Random() - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R01 -Variable Numerical PetalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 -]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R02 -Variable Categorical SPetalLength - -Variable stats -Group Size Value list -{1} 1 1 -{5, 6} 2 5 6 * -{4, 3} 2 4 3 - -% target values -Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -{1} 1 0 0 0.39638 38 0.361905 -{5, 6} 0 0.0588235 0.941176 0.307127 34 0.32381 -{4, 3} 0 0.909091 0.0909091 0.296494 33 0.314286 -Total 0.361905 0.304762 0.333333 1 105 1 - -Values -Value Frequency Coverage -1 38 0.361905 -5 27 0.257143 -4 25 0.238095 -3 8 0.0761905 -6 7 0.0666667 - ----------------------------------------------- -Rank R03 -Variable Numerical PetalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 -]3.15;4.75] 0 1 0 0.304909 26 0.247619 -]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 -]5.15;+inf[ 0 0 1 0.281913 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R04 -Variable Numerical LowerPetalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;2.4] 1 0 0 0.561997 38 0.361905 -]2.4;+inf[ 0 0.477612 0.522388 0.438003 67 0.638095 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R05 -Variable Categorical Class1 - -Variable stats -Group Size Value list -{} 1 -{setosa} 1 setosa * - -% target values -Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -{setosa} 1 0 0 0.561997 38 0.361905 -{} 0 0.477612 0.522388 0.438003 67 0.638095 -Total 0.361905 0.304762 0.333333 1 105 1 - -Values -Value Frequency Coverage - 67 0.638095 -setosa 38 0.361905 - ----------------------------------------------- -Rank R06 -Variable Categorical Class2 - -Variable stats -Group Size Value list -{} 1 -{versicolor} 1 versicolor * - -% target values -Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -{versicolor} 0 1 0 0.58897 32 0.304762 -{} 0.520548 0 0.479452 0.41103 73 0.695238 -Total 0.361905 0.304762 0.333333 1 105 1 - -Values -Value Frequency Coverage - 73 0.695238 -versicolor 32 0.304762 - ----------------------------------------------- -Rank R07 -Variable Numerical UpperPetalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;1.75] 0.535211 0.43662 0.028169 0.390205 71 0.67619 -]1.75;+inf[ 0 0.0294118 0.970588 0.609795 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R08 -Variable Numerical SepalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 -]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 -]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R09 -Variable Numerical SepalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 -]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 -]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 4 + Numerical 8 + Total 12 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variables 11 +Native variables 4 +Constructed variables 7 +Informative variables 9 +Selected variables 5 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 8.64312 + Data cost 110.25 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R02 SPetalLength 0.621203 3 5 1 0.361905 0 0 3.09104 27.2738 14.9342 AsCategorical(Floor(PetalLength)) +R05 Class1 0.47374 2 2 0.638095 67 0 3.09104 15.7997 44.0428 IfC(EQc(Class, "Iris-setosa"), "setosa", "") +R06 Class2 0.440626 2 2 0.695238 73 0 3.09104 15.6381 48.1645 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R01 PetalWidth 0.654571 3 21 0.1 2.5 1.175238095 0.7880996979 0 0 3.09104 28.4221 9.79546 +R03 PetalLength 0.613158 4 36 1 6.9 3.686666667 1.80132579 0 0 3.09104 31.9046 11.2655 +R04 LowerPetalLength 0.479536 2 10 1 3 2.446666667 0.7433600251 0 0 3.09104 15.1066 44.0428 If(LE(PetalLength, 3), PetalLength, 3) +R07 UpperPetalWidth 0.330144 2 11 1.5 2.5 1.692380952 0.2962287527 0 0 3.09104 20.5402 56.4745 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) +R08 SepalLength 0.323395 3 31 4.3 7.7 5.827619048 0.8375127846 0 0 3.09104 26.2686 51.5531 +R09 SepalWidth 0.10822 3 23 2 4.4 3.081904762 0.4284592446 0 0 3.09104 23.4061 80.1477 +R10 Dummy1 0 1 1 0 0 0 0 0 0 0.693147 8.64312 110.25 Copy(0) +R11 Dummy2 0 1 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0 0.693147 8.64312 110.25 Random() + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R01 +Variable Numerical PetalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;0.75] 1 0 0 0.374826 38 0.361905 +]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 +]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R02 +Variable Categorical SPetalLength + +Variable stats +Group Size Value list +{1} 1 1 +{5, 6} 2 5 6 * +{4, 3} 2 4 3 + +% target values +Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +{1} 1 0 0 0.39638 38 0.361905 +{5, 6} 0 0.0588235 0.941176 0.307127 34 0.32381 +{4, 3} 0 0.909091 0.0909091 0.296494 33 0.314286 +Total 0.361905 0.304762 0.333333 1 105 1 + +Values +Value Frequency Coverage +1 38 0.361905 +5 27 0.257143 +4 25 0.238095 +3 8 0.0761905 +6 7 0.0666667 + +---------------------------------------------- +Rank R03 +Variable Numerical PetalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 +]3.15;4.75] 0 1 0 0.304909 26 0.247619 +]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 +]5.15;+inf[ 0 0 1 0.281913 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R04 +Variable Numerical LowerPetalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2.4] 1 0 0 0.561997 38 0.361905 +]2.4;+inf[ 0 0.477612 0.522388 0.438003 67 0.638095 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R05 +Variable Categorical Class1 + +Variable stats +Group Size Value list +{} 1 +{setosa} 1 setosa * + +% target values +Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +{setosa} 1 0 0 0.561997 38 0.361905 +{} 0 0.477612 0.522388 0.438003 67 0.638095 +Total 0.361905 0.304762 0.333333 1 105 1 + +Values +Value Frequency Coverage + 67 0.638095 +setosa 38 0.361905 + +---------------------------------------------- +Rank R06 +Variable Categorical Class2 + +Variable stats +Group Size Value list +{} 1 +{versicolor} 1 versicolor * + +% target values +Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +{versicolor} 0 1 0 0.58897 32 0.304762 +{} 0.520548 0 0.479452 0.41103 73 0.695238 +Total 0.361905 0.304762 0.333333 1 105 1 + +Values +Value Frequency Coverage + 73 0.695238 +versicolor 32 0.304762 + +---------------------------------------------- +Rank R07 +Variable Numerical UpperPetalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;1.75] 0.535211 0.43662 0.028169 0.390205 71 0.67619 +]1.75;+inf[ 0 0.0294118 0.970588 0.609795 34 0.32381 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R08 +Variable Numerical SepalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 +]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 +]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R09 +Variable Numerical SepalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 +]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 +]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.TestEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/TestEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.TestEvaluationReport.xls index e3c9f88ab..e6c639005 100644 --- a/test/LearningTest/TestKhiops/Standard/Iris2D/results.ref/TestEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -1,3069 +1,3069 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Exclude sample -Selection variable -Selection value -Instances 45 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 1 0.983613 1 -Univariate PetalWidth 0.933333 0.795811 0.957778 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 1 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100 100 100 +97.5 97.5 100 100 100 +97.6 97.6 100 100 100 +97.7 97.7 100 100 100 +97.8 97.8 100 100 100 +97.9 97.9 100 100 100 +98 98 100 100 100 +98.1 98.1 100 100 100 +98.2 98.2 100 100 100 +98.3 98.3 100 100 100 +98.4 98.4 100 100 100 +98.5 98.5 100 100 100 +98.6 98.6 100 100 100 +98.7 98.7 100 100 100 +98.8 98.8 100 100 100 +98.9 98.9 100 100 100 +99 99 100 100 100 +99.1 99.1 100 100 100 +99.2 99.2 100 100 100 +99.3 99.3 100 100 100 +99.4 99.4 100 100 100 +99.5 99.5 100 100 100 +99.6 99.6 100 100 100 +99.7 99.7 100 100 100 +99.8 99.8 100 100 100 +99.9 99.9 100 100 100 +100 100 100 100 100 diff --git a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/TrainEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.TrainEvaluationReport.xls similarity index 96% rename from test/LearningTest/TestKhiops/Standard/IrisC/results.ref/TrainEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.TrainEvaluationReport.xls index d768f2708..1ee2ef7a3 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/TrainEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -1,3069 +1,3069 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 1 0.984084 1 -Univariate PetalWidth 0.971429 0.892955 0.986807 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 1 -Compression 0.984084 -AUC 1 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 32 0 -$Iris-virginica 0 0 35 - ----------------------------------------------- -Classifier Univariate PetalWidth -Accuracy 0.971429 -Compression 0.892955 -AUC 0.986807 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 31 2 -$Iris-virginica 0 1 33 - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 -]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - - - ----------------------------------------------- -Lift curves Iris-setosa -Size Random Optimal Selective Naive Bayes Univariate PetalWidth -0 0 0 0 0 -0.1 0.1 0.276316 0.276316 0.276316 -0.2 0.2 0.552632 0.552632 0.552632 -0.3 0.3 0.828947 0.828947 0.828947 -0.4 0.4 1.10526 1.10526 1.10526 -0.5 0.5 1.38158 1.38158 1.38158 -0.6 0.6 1.65789 1.65789 1.65789 -0.7 0.7 1.93421 1.93421 1.93421 -0.8 0.8 2.21053 2.21053 2.21053 -0.9 0.9 2.48684 2.48684 2.48684 -1 1 2.76316 2.76316 2.76316 -1.1 1.1 3.03947 3.03947 3.03947 -1.2 1.2 3.31579 3.31579 3.31579 -1.3 1.3 3.59211 3.59211 3.59211 -1.4 1.4 3.86842 3.86842 3.86842 -1.5 1.5 4.14474 4.14474 4.14474 -1.6 1.6 4.42105 4.42105 4.42105 -1.7 1.7 4.69737 4.69737 4.69737 -1.8 1.8 4.97368 4.97368 4.97368 -1.9 1.9 5.25 5.25 5.25 -2 2 5.52632 5.52632 5.52632 -2.1 2.1 5.80263 5.80263 5.80263 -2.2 2.2 6.07895 6.07895 6.07895 -2.3 2.3 6.35526 6.35526 6.35526 -2.4 2.4 6.63158 6.63158 6.63158 -2.5 2.5 6.90789 6.90789 6.90789 -2.6 2.6 7.18421 7.18421 7.18421 -2.7 2.7 7.46053 7.46053 7.46053 -2.8 2.8 7.73684 7.73684 7.73684 -2.9 2.9 8.01316 8.01316 8.01316 -3 3 8.28947 8.28947 8.28947 -3.1 3.1 8.56579 8.56579 8.56579 -3.2 3.2 8.84211 8.84211 8.84211 -3.3 3.3 9.11842 9.11842 9.11842 -3.4 3.4 9.39474 9.39474 9.39474 -3.5 3.5 9.67105 9.67105 9.67105 -3.6 3.6 9.94737 9.94737 9.94737 -3.7 3.7 10.2237 10.2237 10.2237 -3.8 3.8 10.5 10.5 10.5 -3.9 3.9 10.7763 10.7763 10.7763 -4 4 11.0526 11.0526 11.0526 -4.1 4.1 11.3289 11.3289 11.3289 -4.2 4.2 11.6053 11.6053 11.6053 -4.3 4.3 11.8816 11.8816 11.8816 -4.4 4.4 12.1579 12.1579 12.1579 -4.5 4.5 12.4342 12.4342 12.4342 -4.6 4.6 12.7105 12.7105 12.7105 -4.7 4.7 12.9868 12.9868 12.9868 -4.8 4.8 13.2632 13.2632 13.2632 -4.9 4.9 13.5395 13.5395 13.5395 -5 5 13.8158 13.8158 13.8158 -5.1 5.1 14.0921 14.0921 14.0921 -5.2 5.2 14.3684 14.3684 14.3684 -5.3 5.3 14.6447 14.6447 14.6447 -5.4 5.4 14.9211 14.9211 14.9211 -5.5 5.5 15.1974 15.1974 15.1974 -5.6 5.6 15.4737 15.4737 15.4737 -5.7 5.7 15.75 15.75 15.75 -5.8 5.8 16.0263 16.0263 16.0263 -5.9 5.9 16.3026 16.3026 16.3026 -6 6 16.5789 16.5789 16.5789 -6.1 6.1 16.8553 16.8553 16.8553 -6.2 6.2 17.1316 17.1316 17.1316 -6.3 6.3 17.4079 17.4079 17.4079 -6.4 6.4 17.6842 17.6842 17.6842 -6.5 6.5 17.9605 17.9605 17.9605 -6.6 6.6 18.2368 18.2368 18.2368 -6.7 6.7 18.5132 18.5132 18.5132 -6.8 6.8 18.7895 18.7895 18.7895 -6.9 6.9 19.0658 19.0658 19.0658 -7 7 19.3421 19.3421 19.3421 -7.1 7.1 19.6184 19.6184 19.6184 -7.2 7.2 19.8947 19.8947 19.8947 -7.3 7.3 20.1711 20.1711 20.1711 -7.4 7.4 20.4474 20.4474 20.4474 -7.5 7.5 20.7237 20.7237 20.7237 -7.6 7.6 21 21 21 -7.7 7.7 21.2763 21.2763 21.2763 -7.8 7.8 21.5526 21.5526 21.5526 -7.9 7.9 21.8289 21.8289 21.8289 -8 8 22.1053 22.1053 22.1053 -8.1 8.1 22.3816 22.3816 22.3816 -8.2 8.2 22.6579 22.6579 22.6579 -8.3 8.3 22.9342 22.9342 22.9342 -8.4 8.4 23.2105 23.2105 23.2105 -8.5 8.5 23.4868 23.4868 23.4868 -8.6 8.6 23.7632 23.7632 23.7632 -8.7 8.7 24.0395 24.0395 24.0395 -8.8 8.8 24.3158 24.3158 24.3158 -8.9 8.9 24.5921 24.5921 24.5921 -9 9 24.8684 24.8684 24.8684 -9.1 9.1 25.1447 25.1447 25.1447 -9.2 9.2 25.4211 25.4211 25.4211 -9.3 9.3 25.6974 25.6974 25.6974 -9.4 9.4 25.9737 25.9737 25.9737 -9.5 9.5 26.25 26.25 26.25 -9.6 9.6 26.5263 26.5263 26.5263 -9.7 9.7 26.8026 26.8026 26.8026 -9.8 9.8 27.0789 27.0789 27.0789 -9.9 9.9 27.3553 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"missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 3.09104, + "preparationCost": 26.2686, + "dataCost": 51.5531 + }, + { + "rank": "R09", + "name": "SepalWidth", + "type": "Numerical", + "level": 0.10822, + "parts": 3, + "values": 23, + "min": 2, + "max": 4.4, + "mean": 3.081904762, + "stdDev": 0.4284592446, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 3.09104, + "preparationCost": 23.4061, + "dataCost": 80.1477 + }, + { + "rank": "R10", + "name": "Dummy1", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 0, + "max": 0, + "mean": 0, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 8.64312, + "dataCost": 110.25, + "derivationRule": "Copy(0)" + }, + { + "rank": "R11", + "name": "Dummy2", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 105, + "min": 0.005121241265, + "max": 0.9859650261, + "mean": 0.5173966838, + "stdDev": 0.2650019122, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 8.64312, + "dataCost": 110.25, + "derivationRule": "Random()" + } + ], + "variablesDetailedStatistics": { + "R01": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0.1,0.75], + [0.75,1.75], + [1.75,2.5] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,0,0], + [0,31,2], + [0,1,33] + ], + "partInterests": [0.374826,0.305582,0.319592] + } + }, + "R02": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SPetalLength", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["1"], + ["5","6"], + ["4","3"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,0,0], + [0,2,32], + [0,30,3] + ], + "partInterests": [0.39638,0.307127,0.296494] + }, + "inputValues": { + "values": ["1","5","4","3","6"], + "frequencies": [38,27,25,8,7] + } + }, + "R03": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,3.15], + [3.15,4.75], + [4.75,5.15], + [5.15,6.9] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,1,0], + [0,26,0], + [0,5,9], + [0,0,26] + ], + "partInterests": [0.347012,0.304909,0.066166,0.281913] + } + }, + "R04": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "LowerPetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,2.4], + [2.4,3] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,0,0], + [0,32,35] + ], + "partInterests": [0.561997,0.438003] + } + }, + "R05": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Class1", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + [""], + ["setosa"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [0,32,35], + [38,0,0] + ], + "partInterests": [0.438003,0.561997] + }, + "inputValues": { + "values": ["","setosa"], + "frequencies": [67,38] + } + }, + "R06": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Class2", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + [""], + ["versicolor"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,0,35], + [0,32,0] + ], + "partInterests": [0.41103,0.58897] + }, + "inputValues": { + "values": ["","versicolor"], + "frequencies": [73,32] + } + }, + "R07": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "UpperPetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1.5,1.75], + [1.75,2.5] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,31,2], + [0,1,33] + ], + "partInterests": [0.390205,0.609795] + } + }, + "R08": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [4.3,5.45], + [5.45,6.15], + [6.15,7.7] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [34,5,0], + [4,19,5], + [0,8,30] + ], + "partInterests": [0.449729,0.147253,0.403018] + } + }, + "R09": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,2.85], + [2.85,3.35], + [3.35,4.4] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [0,18,12], + [17,13,19], + [21,1,4] + ], + "partInterests": [0.544113,0.0133569,0.44253] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/Modeling.kdic b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.model.kdic similarity index 98% rename from test/LearningTest/TestKhiops/Standard/IrisC/results.ref/Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.model.kdic index dc7481912..1392bf778 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/AnalysisResults.model.kdic @@ -1,54 +1,54 @@ -#Khiops 10.1 - -Dictionary BU1_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; -Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; -Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; -Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; -Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; -Unused Numerical Dummy1 = Copy(0) ; -Unused Numerical Dummy2 = Random() ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) -Unused Structure(Classifier) BU1Class = NBClassifier(DataGridStats(PPetalWidth, PetalWidth), VClass) ; - Categorical PredictedClass = TargetValue(BU1Class) ; -Unused Numerical ScoreClass = TargetProb(BU1Class) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(BU1Class, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(BU1Class, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(BU1Class, "Iris-virginica") ; -}; - -Dictionary SNB_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; -Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; -Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; -Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; -Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; -Unused Numerical Dummy1 = Copy(0) ; -Unused Numerical Dummy2 = Random() ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PSPetalLength = DataGrid(ValueGroups(ValueGroup("1"), ValueGroup("5", "6", " * "), ValueGroup("4", "3")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 2, 30, 0, 32, 3)) ; // DataGrid(SPetalLength, Class) -Unused Structure(DataGrid) PLowerPetalLength = DataGrid(IntervalBounds(2.4), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 32, 0, 35)) ; // DataGrid(LowerPetalLength, Class) -Unused Structure(DataGrid) PClass1 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("setosa", " * ")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(0, 38, 32, 0, 35, 0)) ; // DataGrid(Class1, Class) -Unused Structure(DataGrid) PClass2 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("versicolor", " * ")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 32, 35, 0)) ; // DataGrid(Class2, Class) -Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.1484375, 0.234375, 0.1640625, 0.5234375), DataGridStats(PSPetalLength, SPetalLength), DataGridStats(PLowerPetalLength, LowerPetalLength), DataGridStats(PClass1, Class1), DataGridStats(PClass2, Class2), VClass) ; - Categorical PredictedClass = TargetValue(SNBClass) ; -Unused Numerical ScoreClass = TargetProb(SNBClass) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; -}; +#Khiops 10.4.6i + +Dictionary BU1_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; +Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; +Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; +Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; +Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; +Unused Numerical Dummy1 = Copy(0) ; +Unused Numerical Dummy2 = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) +Unused Structure(Classifier) BU1Class = NBClassifier(DataGridStats(PPetalWidth, PetalWidth), VClass) ; + Categorical PredictedClass = TargetValue(BU1Class) ; +Unused Numerical ScoreClass = TargetProb(BU1Class) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(BU1Class, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(BU1Class, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(BU1Class, "Iris-virginica") ; +}; + +Dictionary SNB_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; +Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; +Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; +Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; +Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; +Unused Numerical Dummy1 = Copy(0) ; +Unused Numerical Dummy2 = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PSPetalLength = DataGrid(ValueGroups(ValueGroup("1"), ValueGroup("5", "6", " * "), ValueGroup("4", "3")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 2, 30, 0, 32, 3)) ; // DataGrid(SPetalLength, Class) +Unused Structure(DataGrid) PLowerPetalLength = DataGrid(IntervalBounds(2.4), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 32, 0, 35)) ; // DataGrid(LowerPetalLength, Class) +Unused Structure(DataGrid) PClass1 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("setosa", " * ")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(0, 38, 32, 0, 35, 0)) ; // DataGrid(Class1, Class) +Unused Structure(DataGrid) PClass2 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("versicolor", " * ")), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 32, 35, 0)) ; // DataGrid(Class2, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.1484375, 0.234375, 0.1640625, 0.5234375), DataGridStats(PSPetalLength, SPetalLength), DataGridStats(PLowerPetalLength, LowerPetalLength), DataGridStats(PClass1, Class1), DataGridStats(PClass2, Class2), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/T_Iris.txt b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/D_Iris.txt similarity index 98% rename from test/LearningTest/TestKhiops/Standard/IrisC/results.ref/T_Iris.txt rename to test/LearningTest/TestKhiops/Standard/IrisC/results.ref/D_Iris.txt index c15c9dd30..791286cbf 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/D_Iris.txt @@ -1,106 +1,106 @@ -PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-setosa 0.9866009047 0.001702972788 0.01169612247 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001806383389 0.9751708257 0.02302279093 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001806383389 0.9751708257 0.02302279093 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01760717604 0.01934772071 0.9630451033 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01760717604 0.01934772071 0.9630451033 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01760717604 0.01934772071 0.9630451033 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 -Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-setosa 0.9866009047 0.001702972788 0.01169612247 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001806383389 0.9751708257 0.02302279093 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001806383389 0.9751708257 0.02302279093 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-versicolor 0.001728448664 0.9864762094 0.01179534194 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01760717604 0.01934772071 0.9630451033 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01760717604 0.01934772071 0.9630451033 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01760717604 0.01934772071 0.9630451033 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 +Iris-virginica 0.01302188738 0.01006673884 0.9769113738 diff --git a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/err.txt index dcdef36a5..4d421a4f5 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/err.txt @@ -1,6 +1,6 @@ Train supervised model for classification of target variable Class Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 -Data preparation time: 0:00:00.04 +Data preparation time: 0:00:00.02 SNB train time: 0:00:00 Build Selective Naive Bayes SNB_Iris Build Univariate BU1_Iris @@ -8,10 +8,10 @@ Train evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iri Train evaluation of Univariate PetalWidth on database ../../../datasets/Iris/Iris.txt Test evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt Test evaluation of Univariate PetalWidth on database ../../../datasets/Iris/Iris.txt -Write report ./results\AllReports.khj +Write report ./results/AnalysisResults.khj Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 105 +Output database ./results/D_Iris.txt: Written records: 105 Model deployment time: 0:00:00.01 diff --git a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/time.log index 468e10ab2..ccf3e4dc2 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/IrisC/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.32671570777893066 +Overal time: 0.26490068435668945 diff --git a/test/LearningTest/TestKhiops/Standard/IrisC/test.prm b/test/LearningTest/TestKhiops/Standard/IrisC/test.prm index 0656f2ea8..c295cf924 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisC/test.prm +++ b/test/LearningTest/TestKhiops/Standard/IrisC/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -22,38 +24,40 @@ ClassFileName ./IrisExtended.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable AnalysisSpec.MainTargetModality // Main target modality -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries +AnalysisSpec.PredictorsSpec.ExpertSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database ClassManagement.OpenFile // Open... -ClassFileName ./results/Modeling.kdic // Dictionary file +ClassFileName ./results/AnalysisResults.model.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer diff --git a/test/LearningTest/TestKhiops/Standard/IrisFastPath/readme.txt b/test/LearningTest/TestKhiops/Standard/IrisFastPath/readme.txt new file mode 100644 index 000000000..21127a916 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisFastPath/readme.txt @@ -0,0 +1,2 @@ +Test de la nouvelle ergonomie V11 de Khiops, avec suppression de l'onglet dictionnaire +et possibilité d'analyser une base en construisant son dictionnaire à la volée. diff --git a/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.ModelingReport.xls new file mode 100644 index 000000000..50fa09577 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.ModelingReport.xls @@ -0,0 +1,29 @@ +#Khiops 10.4.6i +Modeling report + +Dictionary Iris +Target variable Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 2 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PPetalWidth PetalWidth 0.663031 0.4375 0.538587 +PPetalLength PetalLength 0.621617 0.351562 0.46748 diff --git a/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.PreparationReport.xls new file mode 100644 index 000000000..3eb85b621 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.PreparationReport.xls @@ -0,0 +1,105 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 1 + Numerical 4 + Total 5 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class +Main target value Iris-versicolor + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variables 4 +Informative variables 4 +Selected variables 2 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 8.64312 + Data cost 110.25 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost +R1 PetalWidth 0.663031 3 21 0.1 2.5 1.175238095 0.7880996979 0 0 2.07944 28.4221 9.79546 +R2 PetalLength 0.621617 4 36 1 6.9 3.686666667 1.80132579 0 0 2.07944 31.9046 11.2655 +R3 SepalLength 0.331855 3 31 4.3 7.7 5.827619048 0.8375127846 0 0 2.07944 26.2686 51.5531 +R4 SepalWidth 0.116679 3 23 2 4.4 3.081904762 0.4284592446 0 0 2.07944 23.4061 80.1477 + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R1 +Variable Numerical PetalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;0.75] 1 0 0 0.374826 38 0.361905 +]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 +]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R2 +Variable Numerical PetalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 +]3.15;4.75] 0 1 0 0.304909 26 0.247619 +]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 +]5.15;+inf[ 0 0 1 0.281913 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R3 +Variable Numerical SepalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 +]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 +]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R4 +Variable Numerical SepalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 +]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 +]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.TestEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/TestEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.TestEvaluationReport.xls index 60f83cf8e..5951af85f 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/TestEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -1,3049 +1,3050 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary Iris -Target variable 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100 +99.4 99.4 100 100 +99.5 99.5 100 100 +99.6 99.6 100 100 +99.7 99.7 100 100 +99.8 99.8 100 100 +99.9 99.9 100 100 +100 100 100 100 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/TrainEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.TrainEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/TrainEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.TrainEvaluationReport.xls index a0a8d0aed..6b67f9cf6 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/TrainEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -1,3049 +1,3050 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.980952 0.933266 0.997868 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.980952 -Compression 0.933266 -AUC 0.997868 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 31 1 -$Iris-virginica 0 1 34 - - - ----------------------------------------------- -Lift curves Iris-setosa -Size Random Optimal Selective Naive Bayes -0 0 0 0 -0.1 0.1 0.276316 0.276316 -0.2 0.2 0.552632 0.552632 -0.3 0.3 0.828947 0.828947 -0.4 0.4 1.10526 1.10526 -0.5 0.5 1.38158 1.38158 -0.6 0.6 1.65789 1.65789 -0.7 0.7 1.93421 1.93421 -0.8 0.8 2.21053 2.21053 -0.9 0.9 2.48684 2.48684 -1 1 2.76316 2.76316 -1.1 1.1 3.03947 3.03947 -1.2 1.2 3.31579 3.31579 -1.3 1.3 3.59211 3.59211 -1.4 1.4 3.86842 3.86842 -1.5 1.5 4.14474 4.14474 -1.6 1.6 4.42105 4.42105 -1.7 1.7 4.69737 4.69737 -1.8 1.8 4.97368 4.97368 -1.9 1.9 5.25 5.25 -2 2 5.52632 5.52632 -2.1 2.1 5.80263 5.80263 -2.2 2.2 6.07895 6.07895 -2.3 2.3 6.35526 6.35526 -2.4 2.4 6.63158 6.63158 -2.5 2.5 6.90789 6.90789 -2.6 2.6 7.18421 7.18421 -2.7 2.7 7.46053 7.46053 -2.8 2.8 7.73684 7.73684 -2.9 2.9 8.01316 8.01316 -3 3 8.28947 8.28947 -3.1 3.1 8.56579 8.56579 -3.2 3.2 8.84211 8.84211 -3.3 3.3 9.11842 9.11842 -3.4 3.4 9.39474 9.39474 -3.5 3.5 9.67105 9.67105 -3.6 3.6 9.94737 9.94737 -3.7 3.7 10.2237 10.2237 -3.8 3.8 10.5 10.5 -3.9 3.9 10.7763 10.7763 -4 4 11.0526 11.0526 -4.1 4.1 11.3289 11.3289 -4.2 4.2 11.6053 11.6053 -4.3 4.3 11.8816 11.8816 -4.4 4.4 12.1579 12.1579 -4.5 4.5 12.4342 12.4342 -4.6 4.6 12.7105 12.7105 -4.7 4.7 12.9868 12.9868 -4.8 4.8 13.2632 13.2632 -4.9 4.9 13.5395 13.5395 -5 5 13.8158 13.8158 -5.1 5.1 14.0921 14.0921 -5.2 5.2 14.3684 14.3684 -5.3 5.3 14.6447 14.6447 -5.4 5.4 14.9211 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"partTargetFrequencies": [ + [38,0,0], + [0,31,2], + [0,1,33] + ], + "partInterests": [0.374826,0.305582,0.319592] + } + }, + "R2": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,3.15], + [3.15,4.75], + [4.75,5.15], + [5.15,6.9] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,1,0], + [0,26,0], + [0,5,9], + [0,0,26] + ], + "partInterests": [0.347012,0.304909,0.066166,0.281913] + } + }, + "R3": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [4.3,5.45], + [5.45,6.15], + [6.15,7.7] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [34,5,0], + [4,19,5], + [0,8,30] + ], + "partInterests": [0.449729,0.147253,0.403018] + } + }, + "R4": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,2.85], + [2.85,3.35], + [3.35,4.4] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [0,18,12], + [17,13,19], + [21,1,4] + ], + "partInterests": [0.544113,0.0133569,0.44253] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.model.kdic b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.model.kdic new file mode 100644 index 000000000..bc9e7a4cd --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/AnalysisResults.model.kdic @@ -0,0 +1,20 @@ +#Khiops 10.4.6i + +Dictionary SNB_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PPetalLength = DataGrid(IntervalBounds(3.15, 4.75, 5.15), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 1, 26, 5, 0, 0, 0, 9, 26)) ; // DataGrid(PetalLength, Class) +Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.3515625, 0.4375), DataGridStats(PPetalLength, PetalLength), DataGridStats(PPetalWidth, PetalWidth), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/err.txt new file mode 100644 index 000000000..1456c150e --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/err.txt @@ -0,0 +1,13 @@ +File format detected: header line and field separator tabulation +Database ../../../datasets/Iris/Iris.txt : Recognized variable types: 1 Categorical, 4 Numerical +warning : A dictionary has been generated automatically. The field types should be checked and the dictionary saved if necessary. For standard use, refer to the 'Data dictionary/Manage dictionaries...' menu. + +Train supervised model for classification of target variable Class +Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 +Data preparation time: 0:00:00.02 +SNB train time: 0:00:00 +Build Selective Naive Bayes SNB_Iris +Train evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt +Test evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt +Write report ./results/AnalysisResults.khj + diff --git a/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/time.log new file mode 100644 index 000000000..82a5087fb --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisFastPath/results.ref/time.log @@ -0,0 +1 @@ +Overal time: 0.21149301528930664 diff --git a/test/LearningTest/TestKhiops/Standard/IrisFastPath/test.prm b/test/LearningTest/TestKhiops/Standard/IrisFastPath/test.prm new file mode 100644 index 000000000..7f7696d88 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisFastPath/test.prm @@ -0,0 +1,40 @@ +// Thu Jun 12 13:31:06 2008 +// modl +// Output command file +// +//This file contains recorded commands, that can be replayed. +//Commands are based on user interactions: +// field update +// list index selection +// menu action +//Every command can be commented, using //. +//For example, commenting the last Exit command will allow other +//user interactions, after the commands have been replayed. +// +// + +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file + +AnalysisSpec.TargetAttributeName Class // Target variable +AnalysisSpec.MainTargetModality Iris-versicolor // Main target modality + +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report + +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls +ComputeStats // Analyse database + +Exit // Close +// <- Data preparation and scoring + + +// -> Data preparation and scoring +OK // Close +// <- Data preparation and scoring + diff --git a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.ModelingReport.xls similarity index 93% rename from test/LearningTest/TestKhiops/Standard/IrisG/results.ref/ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.ModelingReport.xls index 72b1e07fe..541061ea7 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.ModelingReport.xls @@ -1,33 +1,33 @@ -#Khiops 10.1 -Modeling report - -Dictionary Iris -Target variable Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 2 -Univariate PetalWidth 1 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PLowerPetalLength LowerPetalLength 0.48393 0.554688 0.518102 -PClass2 Class2 0.415416 0.523438 0.466309 - ----------------------------------------------- -Predictor Univariate PetalWidth +#Khiops 10.4.6i +Modeling report + +Dictionary Iris +Target variable Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 2 +Univariate PetalWidth 1 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PLowerPetalLength LowerPetalLength 0.48393 0.554688 0.518102 +PClass2 Class2 0.415416 0.523438 0.466309 + +---------------------------------------------- +Predictor Univariate PetalWidth diff --git a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.PreparationReport.xls similarity index 83% rename from test/LearningTest/TestKhiops/Standard/IrisG/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.PreparationReport.xls index cb877ff00..522b1ccde 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.PreparationReport.xls @@ -1,226 +1,228 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-versicolor 32 0.304762 -Iris-virginica 35 0.333333 - -Evaluated variables 11 -Native variables 4 -Constructed variables 7 -Informative variables 9 - -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 8.64312 - Data cost 110.25 - - -Categorical variables statistics - -Rank Name Level Target groups Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost Derivation rule -R03 SPetalLength 0.587712 3 3 5 1 0.361905 3.09104 31.2788 14.9342 AsCategorical(Floor(PetalLength)) -R05 Class1 0.4499 2 2 2 0.638095 3.09104 18.6507 44.0428 IfC(EQc(Class, "Iris-setosa"), "setosa", "") -R06 Class2 0.415416 2 2 2 0.695238 3.09104 18.6528 48.1645 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") - - -Numerical variables statistics - -Rank Name Level Target groups Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R01 PetalWidth 0.631926 3 3 21 0.1 2.5 1.175238095 0.7880996979 0 3.09104 31.1301 9.79546 -R02 PetalLength 0.590513 3 4 36 1 6.9 3.686666667 1.80132579 0 3.09104 34.6126 11.2655 -R04 LowerPetalLength 0.48393 2 2 10 1 3 2.446666667 0.7433600251 0 3.09104 14.5812 44.0428 If(LE(PetalLength, 3), PetalLength, 3) -R07 UpperPetalWidth 0.327221 2 2 11 1.5 2.5 1.692380952 0.2962287527 0 3.09104 20.1069 57.2573 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) -R08 SepalLength 0.30075 3 3 31 4.3 7.7 5.827619048 0.8375127846 0 3.09104 28.9767 51.5531 -R09 SepalWidth 0.0927267 2 3 23 2 4.4 3.081904762 0.4284592446 0 3.09104 20.767 84.6396 -R10 Dummy1 0 1 1 1 0 0 0 0 0 0.693147 8.64312 110.25 Copy(0) -R11 Dummy2 0 1 1 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0.693147 8.64312 110.25 Random() - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R01 -Variable Numerical PetalWidth - -% target values -Interval {Iris-setosa} {Iris-virginica} {Iris-versicolor} Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.0606061 0.939394 0.305582 33 0.314286 -]1.75;+inf[ 0 0.970588 0.0294118 0.319592 34 0.32381 -Total 0.361905 0.333333 0.304762 1 105 1 - ----------------------------------------------- -Rank R02 -Variable Numerical PetalLength - -% target values -Interval {Iris-setosa} {Iris-virginica} {Iris-versicolor} Interest Frequency Coverage -]-inf;3.15] 0.974359 0 0.025641 0.347012 39 0.371429 -]3.15;4.75] 0 0 1 0.304909 26 0.247619 -]4.75;5.15] 0 0.642857 0.357143 0.066166 14 0.133333 -]5.15;+inf[ 0 1 0 0.281913 26 0.247619 -Total 0.361905 0.333333 0.304762 1 105 1 - ----------------------------------------------- -Rank R03 -Variable Categorical SPetalLength - -Variable stats -Group Size Value list -{1} 1 1 -{5, 6} 2 5 6 * -{4, 3} 2 4 3 - -% target values -Group {Iris-setosa} {Iris-virginica} {Iris-versicolor} Interest Frequency Coverage -{1} 1 0 0 0.39638 38 0.361905 -{5, 6} 0 0.941176 0.0588235 0.307127 34 0.32381 -{4, 3} 0 0.0909091 0.909091 0.296494 33 0.314286 -Total 0.361905 0.333333 0.304762 1 105 1 - -Values -Value Frequency Coverage -1 38 0.361905 -5 27 0.257143 -4 25 0.238095 -3 8 0.0761905 -6 7 0.0666667 - ----------------------------------------------- -Rank R04 -Variable Numerical LowerPetalLength - -% target values -Interval {Iris-virginica, Iris-versicolor} {Iris-setosa} Interest Frequency Coverage -]-inf;2.4] 0 1 0.561997 38 0.361905 -]2.4;+inf[ 1 0 0.438003 67 0.638095 -Total 0.638095 0.361905 1 105 1 - -Target variable stats -Group Frequency Coverage Size Value list -{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * -{Iris-setosa} 38 0.361905 1 Iris-setosa - ----------------------------------------------- -Rank R05 -Variable Categorical Class1 - -Variable stats -Group Size Value list -{} 1 -{setosa} 1 setosa * - -% target values -Group {Iris-virginica, Iris-versicolor} {Iris-setosa} Interest Frequency Coverage -{setosa} 0 1 0.561997 38 0.361905 -{} 1 0 0.438003 67 0.638095 -Total 0.638095 0.361905 1 105 1 - -Target variable stats -Group Frequency Coverage Size Value list -{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * -{Iris-setosa} 38 0.361905 1 Iris-setosa - -Values -Value Frequency Coverage - 67 0.638095 -setosa 38 0.361905 - ----------------------------------------------- -Rank R06 -Variable Categorical Class2 - -Variable stats -Group Size Value list -{} 1 -{versicolor} 1 versicolor * - -% target values -Group {Iris-setosa, Iris-virginica} {Iris-versicolor} Interest Frequency Coverage -{versicolor} 0 1 0.58897 32 0.304762 -{} 1 0 0.41103 73 0.695238 -Total 0.695238 0.304762 1 105 1 - -Target variable stats -Group Frequency Coverage Size Value list -{Iris-setosa, Iris-virginica} 73 0.695238 2 Iris-setosa Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * - -Values -Value Frequency Coverage - 73 0.695238 -versicolor 32 0.304762 - ----------------------------------------------- -Rank R07 -Variable Numerical UpperPetalWidth - -% target values -Interval {Iris-setosa, Iris-versicolor} {Iris-virginica} Interest Frequency Coverage -]-inf;1.75] 0.971831 0.028169 0.395846 71 0.67619 -]1.75;+inf[ 0.0294118 0.970588 0.604154 34 0.32381 -Total 0.666667 0.333333 1 105 1 - -Target variable stats -Group Frequency Coverage Size Value list -{Iris-setosa, Iris-versicolor} 70 0.666667 2 Iris-setosa Iris-versicolor * -{Iris-virginica} 35 0.333333 1 Iris-virginica - ----------------------------------------------- -Rank R08 -Variable Numerical SepalLength - -% target values -Interval {Iris-setosa} {Iris-virginica} {Iris-versicolor} Interest Frequency Coverage -]-inf;5.45] 0.871795 0 0.128205 0.449729 39 0.371429 -]5.45;6.15] 0.142857 0.178571 0.678571 0.147253 28 0.266667 -]6.15;+inf[ 0 0.789474 0.210526 0.403018 38 0.361905 -Total 0.361905 0.333333 0.304762 1 105 1 - ----------------------------------------------- -Rank R09 -Variable Numerical SepalWidth - -% target values -Interval {Iris-virginica, Iris-versicolor} {Iris-setosa} Interest Frequency Coverage -]-inf;2.85] 1 0 0.553199 30 0.285714 -]2.85;3.35] 0.653061 0.346939 0.000981364 49 0.466667 -]3.35;+inf[ 0.192308 0.807692 0.445819 26 0.247619 -Total 0.638095 0.361905 1 105 1 - -Target variable stats -Group Frequency Coverage Size Value list -{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * -{Iris-setosa} 38 0.361905 1 Iris-setosa +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 4 + Numerical 8 + Total 12 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variables 11 +Native variables 4 +Constructed variables 7 +Informative variables 9 +Selected variables 3 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 8.64312 + Data cost 110.25 + + +Categorical variables statistics + +Rank Name Level Target groups Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R03 SPetalLength 0.587712 3 3 5 1 0.361905 0 0 3.09104 31.2788 14.9342 AsCategorical(Floor(PetalLength)) +R05 Class1 0.4499 2 2 2 0.638095 67 0 3.09104 18.6507 44.0428 IfC(EQc(Class, "Iris-setosa"), "setosa", "") +R06 Class2 0.415416 2 2 2 0.695238 73 0 3.09104 18.6528 48.1645 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") + + +Numerical variables statistics + +Rank Name Level Target groups Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R01 PetalWidth 0.631926 3 3 21 0.1 2.5 1.175238095 0.7880996979 0 0 3.09104 31.1301 9.79546 +R02 PetalLength 0.590513 3 4 36 1 6.9 3.686666667 1.80132579 0 0 3.09104 34.6126 11.2655 +R04 LowerPetalLength 0.48393 2 2 10 1 3 2.446666667 0.7433600251 0 0 3.09104 14.5812 44.0428 If(LE(PetalLength, 3), PetalLength, 3) +R07 UpperPetalWidth 0.327221 2 2 11 1.5 2.5 1.692380952 0.2962287527 0 0 3.09104 20.1069 57.2573 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) +R08 SepalLength 0.30075 3 3 31 4.3 7.7 5.827619048 0.8375127846 0 0 3.09104 28.9767 51.5531 +R09 SepalWidth 0.0927267 2 3 23 2 4.4 3.081904762 0.4284592446 0 0 3.09104 20.767 84.6396 +R10 Dummy1 0 1 1 1 0 0 0 0 0 0 0.693147 8.64312 110.25 Copy(0) +R11 Dummy2 0 1 1 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0 0.693147 8.64312 110.25 Random() + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R01 +Variable Numerical PetalWidth + +% target values +Interval {Iris-setosa} {Iris-virginica} {Iris-versicolor} Interest Frequency Coverage +]-inf;0.75] 1 0 0 0.374826 38 0.361905 +]0.75;1.75] 0 0.0606061 0.939394 0.305582 33 0.314286 +]1.75;+inf[ 0 0.970588 0.0294118 0.319592 34 0.32381 +Total 0.361905 0.333333 0.304762 1 105 1 + +---------------------------------------------- +Rank R02 +Variable Numerical PetalLength + +% target values +Interval {Iris-setosa} {Iris-virginica} {Iris-versicolor} Interest Frequency Coverage +]-inf;3.15] 0.974359 0 0.025641 0.347012 39 0.371429 +]3.15;4.75] 0 0 1 0.304909 26 0.247619 +]4.75;5.15] 0 0.642857 0.357143 0.066166 14 0.133333 +]5.15;+inf[ 0 1 0 0.281913 26 0.247619 +Total 0.361905 0.333333 0.304762 1 105 1 + +---------------------------------------------- +Rank R03 +Variable Categorical SPetalLength + +Variable stats +Group Size Value list +{1} 1 1 +{5, 6} 2 5 6 * +{4, 3} 2 4 3 + +% target values +Group {Iris-setosa} {Iris-virginica} {Iris-versicolor} Interest Frequency Coverage +{1} 1 0 0 0.39638 38 0.361905 +{5, 6} 0 0.941176 0.0588235 0.307127 34 0.32381 +{4, 3} 0 0.0909091 0.909091 0.296494 33 0.314286 +Total 0.361905 0.333333 0.304762 1 105 1 + +Values +Value Frequency Coverage +1 38 0.361905 +5 27 0.257143 +4 25 0.238095 +3 8 0.0761905 +6 7 0.0666667 + +---------------------------------------------- +Rank R04 +Variable Numerical LowerPetalLength + +% target values +Interval {Iris-virginica, Iris-versicolor} {Iris-setosa} Interest Frequency Coverage +]-inf;2.4] 0 1 0.561997 38 0.361905 +]2.4;+inf[ 1 0 0.438003 67 0.638095 +Total 0.638095 0.361905 1 105 1 + +Target variable stats +Group Frequency Coverage Size Value list +{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * +{Iris-setosa} 38 0.361905 1 Iris-setosa + +---------------------------------------------- +Rank R05 +Variable Categorical Class1 + +Variable stats +Group Size Value list +{} 1 +{setosa} 1 setosa * + +% target values +Group {Iris-virginica, Iris-versicolor} {Iris-setosa} Interest Frequency Coverage +{setosa} 0 1 0.561997 38 0.361905 +{} 1 0 0.438003 67 0.638095 +Total 0.638095 0.361905 1 105 1 + +Target variable stats +Group Frequency Coverage Size Value list +{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * +{Iris-setosa} 38 0.361905 1 Iris-setosa + +Values +Value Frequency Coverage + 67 0.638095 +setosa 38 0.361905 + +---------------------------------------------- +Rank R06 +Variable Categorical Class2 + +Variable stats +Group Size Value list +{} 1 +{versicolor} 1 versicolor * + +% target values +Group {Iris-setosa, Iris-virginica} {Iris-versicolor} Interest Frequency Coverage +{versicolor} 0 1 0.58897 32 0.304762 +{} 1 0 0.41103 73 0.695238 +Total 0.695238 0.304762 1 105 1 + +Target variable stats +Group Frequency Coverage Size Value list +{Iris-setosa, Iris-virginica} 73 0.695238 2 Iris-setosa Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * + +Values +Value Frequency Coverage + 73 0.695238 +versicolor 32 0.304762 + +---------------------------------------------- +Rank R07 +Variable Numerical UpperPetalWidth + +% target values +Interval {Iris-setosa, Iris-versicolor} {Iris-virginica} Interest Frequency Coverage +]-inf;1.75] 0.971831 0.028169 0.395846 71 0.67619 +]1.75;+inf[ 0.0294118 0.970588 0.604154 34 0.32381 +Total 0.666667 0.333333 1 105 1 + +Target variable stats +Group Frequency Coverage Size Value list +{Iris-setosa, Iris-versicolor} 70 0.666667 2 Iris-setosa Iris-versicolor * +{Iris-virginica} 35 0.333333 1 Iris-virginica + +---------------------------------------------- +Rank R08 +Variable Numerical SepalLength + +% target values +Interval {Iris-setosa} {Iris-virginica} {Iris-versicolor} Interest Frequency Coverage +]-inf;5.45] 0.871795 0 0.128205 0.449729 39 0.371429 +]5.45;6.15] 0.142857 0.178571 0.678571 0.147253 28 0.266667 +]6.15;+inf[ 0 0.789474 0.210526 0.403018 38 0.361905 +Total 0.361905 0.333333 0.304762 1 105 1 + +---------------------------------------------- +Rank R09 +Variable Numerical SepalWidth + +% target values +Interval {Iris-virginica, Iris-versicolor} {Iris-setosa} Interest Frequency Coverage +]-inf;2.85] 1 0 0.553199 30 0.285714 +]2.85;3.35] 0.653061 0.346939 0.000981364 49 0.466667 +]3.35;+inf[ 0.192308 0.807692 0.445819 26 0.247619 +Total 0.638095 0.361905 1 105 1 + +Target variable stats +Group Frequency Coverage Size Value list +{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * +{Iris-setosa} 38 0.361905 1 Iris-setosa diff --git a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.TestEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/IrisG/results.ref/TestEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.TestEvaluationReport.xls index 07440decd..0410c2652 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/TestEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -1,3069 +1,3069 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Exclude sample -Selection variable -Selection value -Instances 45 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 1 0.98449 1 -Univariate PetalWidth 0.933333 0.795811 0.957778 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 1 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100 100 100 +97.5 97.5 100 100 100 +97.6 97.6 100 100 100 +97.7 97.7 100 100 100 +97.8 97.8 100 100 100 +97.9 97.9 100 100 100 +98 98 100 100 100 +98.1 98.1 100 100 100 +98.2 98.2 100 100 100 +98.3 98.3 100 100 100 +98.4 98.4 100 100 100 +98.5 98.5 100 100 100 +98.6 98.6 100 100 100 +98.7 98.7 100 100 100 +98.8 98.8 100 100 100 +98.9 98.9 100 100 100 +99 99 100 100 100 +99.1 99.1 100 100 100 +99.2 99.2 100 100 100 +99.3 99.3 100 100 100 +99.4 99.4 100 100 100 +99.5 99.5 100 100 100 +99.6 99.6 100 100 100 +99.7 99.7 100 100 100 +99.8 99.8 100 100 100 +99.9 99.9 100 100 100 +100 100 100 100 100 diff --git a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/TrainEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.TrainEvaluationReport.xls similarity index 96% rename from test/LearningTest/TestKhiops/Standard/IrisG/results.ref/TrainEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.TrainEvaluationReport.xls index b464133b0..5fdb154be 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/TrainEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -1,3069 +1,3069 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 1 0.984935 1 -Univariate PetalWidth 0.971429 0.892955 0.986807 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 1 -Compression 0.984935 -AUC 1 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 32 0 -$Iris-virginica 0 0 35 - ----------------------------------------------- -Classifier Univariate PetalWidth -Accuracy 0.971429 -Compression 0.892955 -AUC 0.986807 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 31 2 -$Iris-virginica 0 1 33 - -% target values -Interval {Iris-setosa} {Iris-virginica} {Iris-versicolor} Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.0606061 0.939394 0.305582 33 0.314286 -]1.75;+inf[ 0 0.970588 0.0294118 0.319592 34 0.32381 -Total 0.361905 0.333333 0.304762 1 105 1 - - - ----------------------------------------------- -Lift curves Iris-setosa -Size Random Optimal Selective Naive Bayes Univariate PetalWidth -0 0 0 0 0 -0.1 0.1 0.276316 0.276316 0.276316 -0.2 0.2 0.552632 0.552632 0.552632 -0.3 0.3 0.828947 0.828947 0.828947 -0.4 0.4 1.10526 1.10526 1.10526 -0.5 0.5 1.38158 1.38158 1.38158 -0.6 0.6 1.65789 1.65789 1.65789 -0.7 0.7 1.93421 1.93421 1.93421 -0.8 0.8 2.21053 2.21053 2.21053 -0.9 0.9 2.48684 2.48684 2.48684 -1 1 2.76316 2.76316 2.76316 -1.1 1.1 3.03947 3.03947 3.03947 -1.2 1.2 3.31579 3.31579 3.31579 -1.3 1.3 3.59211 3.59211 3.59211 -1.4 1.4 3.86842 3.86842 3.86842 -1.5 1.5 4.14474 4.14474 4.14474 -1.6 1.6 4.42105 4.42105 4.42105 -1.7 1.7 4.69737 4.69737 4.69737 -1.8 1.8 4.97368 4.97368 4.97368 -1.9 1.9 5.25 5.25 5.25 -2 2 5.52632 5.52632 5.52632 -2.1 2.1 5.80263 5.80263 5.80263 -2.2 2.2 6.07895 6.07895 6.07895 -2.3 2.3 6.35526 6.35526 6.35526 -2.4 2.4 6.63158 6.63158 6.63158 -2.5 2.5 6.90789 6.90789 6.90789 -2.6 2.6 7.18421 7.18421 7.18421 -2.7 2.7 7.46053 7.46053 7.46053 -2.8 2.8 7.73684 7.73684 7.73684 -2.9 2.9 8.01316 8.01316 8.01316 -3 3 8.28947 8.28947 8.28947 -3.1 3.1 8.56579 8.56579 8.56579 -3.2 3.2 8.84211 8.84211 8.84211 -3.3 3.3 9.11842 9.11842 9.11842 -3.4 3.4 9.39474 9.39474 9.39474 -3.5 3.5 9.67105 9.67105 9.67105 -3.6 3.6 9.94737 9.94737 9.94737 -3.7 3.7 10.2237 10.2237 10.2237 -3.8 3.8 10.5 10.5 10.5 -3.9 3.9 10.7763 10.7763 10.7763 -4 4 11.0526 11.0526 11.0526 -4.1 4.1 11.3289 11.3289 11.3289 -4.2 4.2 11.6053 11.6053 11.6053 -4.3 4.3 11.8816 11.8816 11.8816 -4.4 4.4 12.1579 12.1579 12.1579 -4.5 4.5 12.4342 12.4342 12.4342 -4.6 4.6 12.7105 12.7105 12.7105 -4.7 4.7 12.9868 12.9868 12.9868 -4.8 4.8 13.2632 13.2632 13.2632 -4.9 4.9 13.5395 13.5395 13.5395 -5 5 13.8158 13.8158 13.8158 -5.1 5.1 14.0921 14.0921 14.0921 -5.2 5.2 14.3684 14.3684 14.3684 -5.3 5.3 14.6447 14.6447 14.6447 -5.4 5.4 14.9211 14.9211 14.9211 -5.5 5.5 15.1974 15.1974 15.1974 -5.6 5.6 15.4737 15.4737 15.4737 -5.7 5.7 15.75 15.75 15.75 -5.8 5.8 16.0263 16.0263 16.0263 -5.9 5.9 16.3026 16.3026 16.3026 -6 6 16.5789 16.5789 16.5789 -6.1 6.1 16.8553 16.8553 16.8553 -6.2 6.2 17.1316 17.1316 17.1316 -6.3 6.3 17.4079 17.4079 17.4079 -6.4 6.4 17.6842 17.6842 17.6842 -6.5 6.5 17.9605 17.9605 17.9605 -6.6 6.6 18.2368 18.2368 18.2368 -6.7 6.7 18.5132 18.5132 18.5132 -6.8 6.8 18.7895 18.7895 18.7895 -6.9 6.9 19.0658 19.0658 19.0658 -7 7 19.3421 19.3421 19.3421 -7.1 7.1 19.6184 19.6184 19.6184 -7.2 7.2 19.8947 19.8947 19.8947 -7.3 7.3 20.1711 20.1711 20.1711 -7.4 7.4 20.4474 20.4474 20.4474 -7.5 7.5 20.7237 20.7237 20.7237 -7.6 7.6 21 21 21 -7.7 7.7 21.2763 21.2763 21.2763 -7.8 7.8 21.5526 21.5526 21.5526 -7.9 7.9 21.8289 21.8289 21.8289 -8 8 22.1053 22.1053 22.1053 -8.1 8.1 22.3816 22.3816 22.3816 -8.2 8.2 22.6579 22.6579 22.6579 -8.3 8.3 22.9342 22.9342 22.9342 -8.4 8.4 23.2105 23.2105 23.2105 -8.5 8.5 23.4868 23.4868 23.4868 -8.6 8.6 23.7632 23.7632 23.7632 -8.7 8.7 24.0395 24.0395 24.0395 -8.8 8.8 24.3158 24.3158 24.3158 -8.9 8.9 24.5921 24.5921 24.5921 -9 9 24.8684 24.8684 24.8684 -9.1 9.1 25.1447 25.1447 25.1447 -9.2 9.2 25.4211 25.4211 25.4211 -9.3 9.3 25.6974 25.6974 25.6974 -9.4 9.4 25.9737 25.9737 25.9737 -9.5 9.5 26.25 26.25 26.25 -9.6 9.6 26.5263 26.5263 26.5263 -9.7 9.7 26.8026 26.8026 26.8026 -9.8 9.8 27.0789 27.0789 27.0789 -9.9 9.9 27.3553 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"type": "Numerical", + "level": 0.30075, + "targetParts": 3, + "parts": 3, + "values": 31, + "min": 4.3, + "max": 7.7, + "mean": 5.827619048, + "stdDev": 0.8375127846, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 3.09104, + "preparationCost": 28.9767, + "dataCost": 51.5531 + }, + { + "rank": "R09", + "name": "SepalWidth", + "type": "Numerical", + "level": 0.0927267, + "targetParts": 2, + "parts": 3, + "values": 23, + "min": 2, + "max": 4.4, + "mean": 3.081904762, + "stdDev": 0.4284592446, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 3.09104, + "preparationCost": 20.767, + "dataCost": 84.6396 + }, + { + "rank": "R10", + "name": "Dummy1", + "type": "Numerical", + "level": 0, + "targetParts": 1, + "parts": 1, + "values": 1, + "min": 0, + "max": 0, + "mean": 0, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 8.64312, + "dataCost": 110.25, + "derivationRule": "Copy(0)" + }, + { + "rank": "R11", + "name": "Dummy2", + "type": "Numerical", + "level": 0, + "targetParts": 1, + "parts": 1, + "values": 105, + "min": 0.005121241265, + "max": 0.9859650261, + "mean": 0.5173966838, + "stdDev": 0.2650019122, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 8.64312, + "dataCost": 110.25, + "derivationRule": "Random()" + } + ], + "variablesDetailedStatistics": { + "R01": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0.1,0.75], + [0.75,1.75], + [1.75,2.5] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Iris-setosa"], + ["Iris-virginica"], + ["Iris-versicolor"] + ], + "defaultGroupIndex": 2 + } + ], + "partTargetFrequencies": [ + [38,0,0], + [0,2,31], + [0,33,1] + ], + "partInterests": [0.374826,0.305582,0.319592] + } + }, + "R02": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,3.15], + [3.15,4.75], + [4.75,5.15], + [5.15,6.9] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Iris-setosa"], + ["Iris-virginica"], + ["Iris-versicolor"] + ], + "defaultGroupIndex": 2 + } + ], + "partTargetFrequencies": [ + [38,0,1], + [0,0,26], + [0,9,5], + [0,26,0] + ], + "partInterests": [0.347012,0.304909,0.066166,0.281913] + } + }, + "R03": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SPetalLength", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["1"], + ["5","6"], + ["4","3"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Iris-setosa"], + ["Iris-virginica"], + ["Iris-versicolor"] + ], + "defaultGroupIndex": 2 + } + ], + "partTargetFrequencies": [ + [38,0,0], + [0,32,2], + [0,3,30] + ], + "partInterests": [0.39638,0.307127,0.296494] + }, + "inputValues": { + "values": ["1","5","4","3","6"], + "frequencies": [38,27,25,8,7] + } + }, + "R04": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "LowerPetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,2.4], + [2.4,3] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Iris-virginica","Iris-versicolor"], + ["Iris-setosa"] + ], + "defaultGroupIndex": 0 + } + ], + "partTargetFrequencies": [ + [0,38], + [67,0] + ], + "partInterests": [0.561997,0.438003] + } + }, + "R05": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Class1", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + [""], + ["setosa"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Iris-virginica","Iris-versicolor"], + ["Iris-setosa"] + ], + "defaultGroupIndex": 0 + } + ], + "partTargetFrequencies": [ + [67,0], + [0,38] + ], + "partInterests": [0.438003,0.561997] + }, + "inputValues": { + "values": ["","setosa"], + "frequencies": [67,38] + } + }, + "R06": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Class2", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + [""], + ["versicolor"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Iris-setosa","Iris-virginica"], + ["Iris-versicolor"] + ], + "defaultGroupIndex": 1 + } + ], + "partTargetFrequencies": [ + [73,0], + [0,32] + ], + "partInterests": [0.41103,0.58897] + }, + "inputValues": { + "values": ["","versicolor"], + "frequencies": [73,32] + } + }, + "R07": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "UpperPetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1.5,1.75], + [1.75,2.5] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Iris-setosa","Iris-versicolor"], + ["Iris-virginica"] + ], + "defaultGroupIndex": 0 + } + ], + "partTargetFrequencies": [ + [69,2], + [1,33] + ], + "partInterests": [0.395846,0.604154] + } + }, + "R08": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [4.3,5.45], + [5.45,6.15], + [6.15,7.7] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Iris-setosa"], + ["Iris-virginica"], + ["Iris-versicolor"] + ], + "defaultGroupIndex": 2 + } + ], + "partTargetFrequencies": [ + [34,0,5], + [4,5,19], + [0,30,8] + ], + "partInterests": [0.449729,0.147253,0.403018] + } + }, + "R09": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,2.85], + [2.85,3.35], + [3.35,4.4] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["Iris-virginica","Iris-versicolor"], + ["Iris-setosa"] + ], + "defaultGroupIndex": 0 + } + ], + "partTargetFrequencies": [ + [30,0], + [32,17], + [5,21] + ], + "partInterests": [0.553199,0.000981364,0.445819] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/Modeling.kdic b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.model.kdic similarity index 98% rename from test/LearningTest/TestKhiops/Standard/IrisG/results.ref/Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.model.kdic index e40d48f8e..2d75ee2fb 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/AnalysisResults.model.kdic @@ -1,52 +1,52 @@ -#Khiops 10.1 - -Dictionary BU1_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; -Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; -Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; -Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; -Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; -Unused Numerical Dummy1 = Copy(0) ; -Unused Numerical Dummy2 = Random() ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueGroups(ValueGroup("Iris-setosa"), ValueGroup("Iris-virginica"), ValueGroup("Iris-versicolor", " * ")), Frequencies(38, 0, 0, 0, 2, 33, 0, 31, 1)) ; // DataGrid(PetalWidth, Class) -Unused Structure(Classifier) BU1Class = NBClassifier(DataGridStats(PPetalWidth, PetalWidth), VClass) ; - Categorical PredictedClass = TargetValue(BU1Class) ; -Unused Numerical ScoreClass = TargetProb(BU1Class) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(BU1Class, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(BU1Class, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(BU1Class, "Iris-virginica") ; -}; - -Dictionary SNB_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; -Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; -Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; -Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; -Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; -Unused Numerical Dummy1 = Copy(0) ; -Unused Numerical Dummy2 = Random() ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PLowerPetalLength = DataGrid(IntervalBounds(2.4), ValueGroups(ValueGroup("Iris-virginica", "Iris-versicolor", " * "), ValueGroup("Iris-setosa")), Frequencies(0, 67, 38, 0)) ; // DataGrid(LowerPetalLength, Class) -Unused Structure(DataGrid) PClass2 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("versicolor", " * ")), ValueGroups(ValueGroup("Iris-setosa", "Iris-virginica"), ValueGroup("Iris-versicolor", " * ")), Frequencies(73, 0, 0, 32)) ; // DataGrid(Class2, Class) -Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.5546875, 0.5234375), DataGridStats(PLowerPetalLength, LowerPetalLength), DataGridStats(PClass2, Class2), VClass) ; - Categorical PredictedClass = TargetValue(SNBClass) ; -Unused Numerical ScoreClass = TargetProb(SNBClass) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; -}; +#Khiops 10.4.6i + +Dictionary BU1_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; +Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; +Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; +Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; +Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; +Unused Numerical Dummy1 = Copy(0) ; +Unused Numerical Dummy2 = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueGroups(ValueGroup("Iris-setosa"), ValueGroup("Iris-virginica"), ValueGroup("Iris-versicolor", " * ")), Frequencies(38, 0, 0, 0, 2, 33, 0, 31, 1)) ; // DataGrid(PetalWidth, Class) +Unused Structure(Classifier) BU1Class = NBClassifier(DataGridStats(PPetalWidth, PetalWidth), VClass) ; + Categorical PredictedClass = TargetValue(BU1Class) ; +Unused Numerical ScoreClass = TargetProb(BU1Class) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(BU1Class, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(BU1Class, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(BU1Class, "Iris-virginica") ; +}; + +Dictionary SNB_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; +Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; +Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; +Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; +Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; +Unused Numerical Dummy1 = Copy(0) ; +Unused Numerical Dummy2 = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PLowerPetalLength = DataGrid(IntervalBounds(2.4), ValueGroups(ValueGroup("Iris-virginica", "Iris-versicolor", " * "), ValueGroup("Iris-setosa")), Frequencies(0, 67, 38, 0)) ; // DataGrid(LowerPetalLength, Class) +Unused Structure(DataGrid) PClass2 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("versicolor", " * ")), ValueGroups(ValueGroup("Iris-setosa", "Iris-virginica"), ValueGroup("Iris-versicolor", " * ")), Frequencies(73, 0, 0, 32)) ; // DataGrid(Class2, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.5546875, 0.5234375), DataGridStats(PLowerPetalLength, LowerPetalLength), DataGridStats(PClass2, Class2), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/T_Iris.txt b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/D_Iris.txt similarity index 98% rename from test/LearningTest/TestKhiops/Standard/IrisG/results.ref/T_Iris.txt rename to test/LearningTest/TestKhiops/Standard/IrisG/results.ref/D_Iris.txt index e1ba9c063..b9037858a 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/D_Iris.txt @@ -1,106 +1,106 @@ -PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-setosa 0.9901315506 0.001651237906 0.008217211471 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 -Iris-virginica 0.01212480938 0.01417540258 0.973699788 +PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-setosa 0.9901315506 0.001651237906 0.008217211471 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-versicolor 0.001672829297 0.9868304819 0.01149668885 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 +Iris-virginica 0.01212480938 0.01417540258 0.973699788 diff --git a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/err.txt index 6a6fa4c64..adb1063bd 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/err.txt @@ -1,6 +1,6 @@ Train supervised model for classification of target variable Class Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 -Data preparation time: 0:00:00.10 +Data preparation time: 0:00:00.08 SNB train time: 0:00:00 Build Selective Naive Bayes SNB_Iris Build Univariate BU1_Iris @@ -8,10 +8,10 @@ Train evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iri Train evaluation of Univariate PetalWidth on database ../../../datasets/Iris/Iris.txt Test evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt Test evaluation of Univariate PetalWidth on database ../../../datasets/Iris/Iris.txt -Write report ./results\AllReports.khj +Write report ./results/AnalysisResults.khj Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 105 +Output database ./results/D_Iris.txt: Written records: 105 Model deployment time: 0:00:00.01 diff --git a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/time.log index bf4fb73d9..964211e56 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/IrisG/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.3753371238708496 +Overal time: 0.31251096725463867 diff --git a/test/LearningTest/TestKhiops/Standard/IrisG/test.prm b/test/LearningTest/TestKhiops/Standard/IrisG/test.prm index 6fd48b858..430ad6183 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisG/test.prm +++ b/test/LearningTest/TestKhiops/Standard/IrisG/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -22,39 +24,41 @@ ClassFileName ./IrisExtended.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable AnalysisSpec.MainTargetModality // Main target modality -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries +AnalysisSpec.PredictorsSpec.ExpertSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report AnalysisSpec.PreprocessingSpec.TargetGrouped true // Group target values +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database ClassManagement.OpenFile // Open... -ClassFileName ./results/Modeling.kdic // Dictionary file +ClassFileName ./results/AnalysisResults.model.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.ModelingReport.xls similarity index 93% rename from test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.ModelingReport.xls index b5e368f21..e4446585e 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.ModelingReport.xls @@ -1,29 +1,29 @@ -#Khiops 10.1 -Modeling report - -Dictionary Iris -Target variable Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable NoSetosa -Selection value 1 - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 2 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PPetalWidth PetalWidth 0.598749 0.773438 0.680511 -PPetalLength PetalLength 0.493781 0.726562 0.598968 +#Khiops 10.4.6i +Modeling report + +Dictionary Iris +Target variable Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable NoSetosa +Selection value 1 + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 2 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PPetalWidth PetalWidth 0.598749 0.773438 0.680511 +PPetalLength PetalLength 0.493781 0.726562 0.598968 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.PreparationReport.xls similarity index 85% rename from test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.PreparationReport.xls index d5b1a982b..8857f9cb7 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.PreparationReport.xls @@ -1,96 +1,98 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description Training without setosa - -Dictionary Iris -Variables - Categorical 1 - Numerical 4 - Total 5 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable NoSetosa -Selection value 1 -Instances 67 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -Iris-versicolor 32 0.477612 -Iris-virginica 35 0.522388 - -Evaluated variables 4 -Informative variables 4 - -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 4.21951 - Data cost 44.0428 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost -R1 PetalWidth 0.598749 2 16 1 2.5 1.707462687 0.4299647444 0 2.07944 7.76854 9.79546 -R2 PetalLength 0.493781 2 27 3 6.9 4.943283582 0.8377695834 0 2.07944 7.76854 14.9342 -R3 SepalLength 0.170653 2 25 4.9 7.7 6.292537313 0.6538778545 0 2.07944 7.75841 30.7632 -R4 SepalWidth 0.000285089 2 16 2 3.8 2.889552239 0.3228299901 0 2.07944 7.76686 39.0952 - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R1 -Variable Numerical PetalWidth - -% target values -Interval Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;1.75] 0.939394 0.0606061 0.485513 33 0.492537 -]1.75;+inf[ 0.0294118 0.970588 0.514487 34 0.507463 -Total 0.477612 0.522388 1 67 1 - ----------------------------------------------- -Rank R2 -Variable Numerical PetalLength - -% target values -Interval Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;4.95] 0.909091 0.0909091 0.489782 33 0.492537 -]4.95;+inf[ 0.0588235 0.941176 0.510218 34 0.507463 -Total 0.477612 0.522388 1 67 1 - ----------------------------------------------- -Rank R3 -Variable Numerical SepalLength - -% target values -Interval Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;6.25] 0.8 0.2 0.54953 30 0.447761 -]6.25;+inf[ 0.216216 0.783784 0.45047 37 0.552239 -Total 0.477612 0.522388 1 67 1 - ----------------------------------------------- -Rank R4 -Variable Numerical SepalWidth - -% target values -Interval Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;2.95] 0.628571 0.371429 0.471874 35 0.522388 -]2.95;+inf[ 0.3125 0.6875 0.528126 32 0.477612 -Total 0.477612 0.522388 1 67 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description Training without setosa + +Dictionary Iris +Variables + Categorical 1 + Numerical 4 + Total 5 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable NoSetosa +Selection value 1 +Instances 67 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +Iris-versicolor 32 0.477612 +Iris-virginica 35 0.522388 + +Evaluated variables 4 +Informative variables 4 +Selected variables 2 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 4.21951 + Data cost 44.0428 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost +R1 PetalWidth 0.598749 2 16 1 2.5 1.707462687 0.4299647444 0 0 2.07944 7.76854 9.79546 +R2 PetalLength 0.493781 2 27 3 6.9 4.943283582 0.8377695834 0 0 2.07944 7.76854 14.9342 +R3 SepalLength 0.170653 2 25 4.9 7.7 6.292537313 0.6538778545 0 0 2.07944 7.75841 30.7632 +R4 SepalWidth 0.000285089 2 16 2 3.8 2.889552239 0.3228299901 0 0 2.07944 7.76686 39.0952 + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R1 +Variable Numerical PetalWidth + +% target values +Interval Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;1.75] 0.939394 0.0606061 0.485513 33 0.492537 +]1.75;+inf[ 0.0294118 0.970588 0.514487 34 0.507463 +Total 0.477612 0.522388 1 67 1 + +---------------------------------------------- +Rank R2 +Variable Numerical PetalLength + +% target values +Interval Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;4.95] 0.909091 0.0909091 0.489782 33 0.492537 +]4.95;+inf[ 0.0588235 0.941176 0.510218 34 0.507463 +Total 0.477612 0.522388 1 67 1 + +---------------------------------------------- +Rank R3 +Variable Numerical SepalLength + +% target values +Interval Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;6.25] 0.8 0.2 0.54953 30 0.447761 +]6.25;+inf[ 0.216216 0.783784 0.45047 37 0.552239 +Total 0.477612 0.522388 1 67 1 + +---------------------------------------------- +Rank R4 +Variable Numerical SepalWidth + +% target values +Interval Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2.95] 0.628571 0.371429 0.471874 35 0.522388 +]2.95;+inf[ 0.3125 0.6875 0.528126 32 0.477612 +Total 0.477612 0.522388 1 67 1 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.TestEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/TestEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.TestEvaluationReport.xls index d042ce8ef..a04a66836 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/TestEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -1,2044 +1,2044 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Exclude sample -Selection variable NoSetosa -Selection value 1 -Instances 33 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.909091 0.715737 0.966667 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.909091 -Compression 0.715737 -AUC 0.966667 - -Confusion matrix - Iris-versicolor Iris-virginica -$Iris-versicolor 18 3 -$Iris-virginica 0 12 - - - ----------------------------------------------- -Lift curves Iris-versicolor -Size Random Optimal Selective Naive Bayes -0 0 0 0 -0.1 0.1 0.183333 0.173684 -0.2 0.2 0.366667 0.347368 -0.3 0.3 0.55 0.521053 -0.4 0.4 0.733333 0.694737 -0.5 0.5 0.916667 0.868421 -0.6 0.6 1.1 1.04211 -0.7 0.7 1.28333 1.21579 -0.8 0.8 1.46667 1.38947 -0.9 0.9 1.65 1.56316 -1 1 1.83333 1.73684 -1.1 1.1 2.01667 1.91053 -1.2 1.2 2.2 2.08421 -1.3 1.3 2.38333 2.25789 -1.4 1.4 2.56667 2.43158 -1.5 1.5 2.75 2.60526 -1.6 1.6 2.93333 2.77895 -1.7 1.7 3.11667 2.95263 -1.8 1.8 3.3 3.12632 -1.9 1.9 3.48333 3.3 -2 2 3.66667 3.47368 -2.1 2.1 3.85 3.64737 -2.2 2.2 4.03333 3.82105 -2.3 2.3 4.21667 3.99474 -2.4 2.4 4.4 4.16842 -2.5 2.5 4.58333 4.34211 -2.6 2.6 4.76667 4.51579 -2.7 2.7 4.95 4.68947 -2.8 2.8 5.13333 4.86316 -2.9 2.9 5.31667 5.03684 -3 3 5.5 5.21053 -3.1 3.1 5.68333 5.38421 -3.2 3.2 5.86667 5.55789 -3.3 3.3 6.05 5.73158 -3.4 3.4 6.23333 5.90526 -3.5 3.5 6.41667 6.07895 -3.6 3.6 6.6 6.25263 -3.7 3.7 6.78333 6.42632 -3.8 3.8 6.96667 6.6 -3.9 3.9 7.15 6.77368 -4 4 7.33333 6.94737 -4.1 4.1 7.51667 7.12105 -4.2 4.2 7.7 7.29474 -4.3 4.3 7.88333 7.46842 -4.4 4.4 8.06667 7.64211 -4.5 4.5 8.25 7.81579 -4.6 4.6 8.43333 7.98947 -4.7 4.7 8.61667 8.16316 -4.8 4.8 8.8 8.33684 -4.9 4.9 8.98333 8.51053 -5 5 9.16667 8.68421 -5.1 5.1 9.35 8.85789 -5.2 5.2 9.53333 9.03158 -5.3 5.3 9.71667 9.20526 -5.4 5.4 9.9 9.37895 -5.5 5.5 10.0833 9.55263 -5.6 5.6 10.2667 9.72632 -5.7 5.7 10.45 9.9 -5.8 5.8 10.6333 10.0737 -5.9 5.9 10.8167 10.2474 -6 6 11 10.4211 -6.1 6.1 11.1833 10.5947 -6.2 6.2 11.3667 10.7684 -6.3 6.3 11.55 10.9421 -6.4 6.4 11.7333 11.1158 -6.5 6.5 11.9167 11.2895 -6.6 6.6 12.1 11.4632 -6.7 6.7 12.2833 11.6368 -6.8 6.8 12.4667 11.8105 -6.9 6.9 12.65 11.9842 -7 7 12.8333 12.1579 -7.1 7.1 13.0167 12.3316 -7.2 7.2 13.2 12.5053 -7.3 7.3 13.3833 12.6789 -7.4 7.4 13.5667 12.8526 -7.5 7.5 13.75 13.0263 -7.6 7.6 13.9333 13.2 -7.7 7.7 14.1167 13.3737 -7.8 7.8 14.3 13.5474 -7.9 7.9 14.4833 13.7211 -8 8 14.6667 13.8947 -8.1 8.1 14.85 14.0684 -8.2 8.2 15.0333 14.2421 -8.3 8.3 15.2167 14.4158 -8.4 8.4 15.4 14.5895 -8.5 8.5 15.5833 14.7632 -8.6 8.6 15.7667 14.9368 -8.7 8.7 15.95 15.1105 -8.8 8.8 16.1333 15.2842 -8.9 8.9 16.3167 15.4579 -9 9 16.5 15.6316 -9.1 9.1 16.6833 15.8053 -9.2 9.2 16.8667 15.9789 -9.3 9.3 17.05 16.1526 -9.4 9.4 17.2333 16.3263 -9.5 9.5 17.4167 16.5 -9.6 9.6 17.6 16.6737 -9.7 9.7 17.7833 16.8474 -9.8 9.8 17.9667 17.0211 -9.9 9.9 18.15 17.1947 -10 10 18.3333 17.3684 -10.1 10.1 18.5167 17.5421 -10.2 10.2 18.7 17.7158 -10.3 10.3 18.8833 17.8895 -10.4 10.4 19.0667 18.0632 -10.5 10.5 19.25 18.2368 -10.6 10.6 19.4333 18.4105 -10.7 10.7 19.6167 18.5842 -10.8 10.8 19.8 18.7579 -10.9 10.9 19.9833 18.9316 -11 11 20.1667 19.1053 -11.1 11.1 20.35 19.2789 -11.2 11.2 20.5333 19.4526 -11.3 11.3 20.7167 19.6263 -11.4 11.4 20.9 19.8 -11.5 11.5 21.0833 19.9737 -11.6 11.6 21.2667 20.1474 -11.7 11.7 21.45 20.3211 -11.8 11.8 21.6333 20.4947 -11.9 11.9 21.8167 20.6684 -12 12 22 20.8421 -12.1 12.1 22.1833 21.0158 -12.2 12.2 22.3667 21.1895 -12.3 12.3 22.55 21.3632 -12.4 12.4 22.7333 21.5368 -12.5 12.5 22.9167 21.7105 -12.6 12.6 23.1 21.8842 -12.7 12.7 23.2833 22.0579 -12.8 12.8 23.4667 22.2316 -12.9 12.9 23.65 22.4053 -13 13 23.8333 22.5789 -13.1 13.1 24.0167 22.7526 -13.2 13.2 24.2 22.9263 -13.3 13.3 24.3833 23.1 -13.4 13.4 24.5667 23.2737 -13.5 13.5 24.75 23.4474 -13.6 13.6 24.9333 23.6211 -13.7 13.7 25.1167 23.7947 -13.8 13.8 25.3 23.9684 -13.9 13.9 25.4833 24.1421 -14 14 25.6667 24.3158 -14.1 14.1 25.85 24.4895 -14.2 14.2 26.0333 24.6632 -14.3 14.3 26.2167 24.8368 -14.4 14.4 26.4 25.0105 -14.5 14.5 26.5833 25.1842 -14.6 14.6 26.7667 25.3579 -14.7 14.7 26.95 25.5316 -14.8 14.8 27.1333 25.7053 -14.9 14.9 27.3167 25.8789 -15 15 27.5 26.0526 -15.1 15.1 27.6833 26.2263 -15.2 15.2 27.8667 26.4 -15.3 15.3 28.05 26.5737 -15.4 15.4 28.2333 26.7474 -15.5 15.5 28.4167 26.9211 -15.6 15.6 28.6 27.0947 -15.7 15.7 28.7833 27.2684 -15.8 15.8 28.9667 27.4421 -15.9 15.9 29.15 27.6158 -16 16 29.3333 27.7895 -16.1 16.1 29.5167 27.9632 -16.2 16.2 29.7 28.1368 -16.3 16.3 29.8833 28.3105 -16.4 16.4 30.0667 28.4842 -16.5 16.5 30.25 28.6579 -16.6 16.6 30.4333 28.8316 -16.7 16.7 30.6167 29.0053 -16.8 16.8 30.8 29.1789 -16.9 16.9 30.9833 29.3526 -17 17 31.1667 29.5263 -17.1 17.1 31.35 29.7 -17.2 17.2 31.5333 29.8737 -17.3 17.3 31.7167 30.0474 -17.4 17.4 31.9 30.2211 -17.5 17.5 32.0833 30.3947 -17.6 17.6 32.2667 30.5684 -17.7 17.7 32.45 30.7421 -17.8 17.8 32.6333 30.9158 -17.9 17.9 32.8167 31.0895 -18 18 33 31.2632 -18.1 18.1 33.1833 31.4368 -18.2 18.2 33.3667 31.6105 -18.3 18.3 33.55 31.7842 -18.4 18.4 33.7333 31.9579 -18.5 18.5 33.9167 32.1316 -18.6 18.6 34.1 32.3053 -18.7 18.7 34.2833 32.4789 -18.8 18.8 34.4667 32.6526 -18.9 18.9 34.65 32.8263 -19 19 34.8333 33 -19.1 19.1 35.0167 33.1737 -19.2 19.2 35.2 33.3474 -19.3 19.3 35.3833 33.5211 -19.4 19.4 35.5667 33.6947 -19.5 19.5 35.75 33.8684 -19.6 19.6 35.9333 34.0421 -19.7 19.7 36.1167 34.2158 -19.8 19.8 36.3 34.3895 -19.9 19.9 36.4833 34.5632 -20 20 36.6667 34.7368 -20.1 20.1 36.85 34.9105 -20.2 20.2 37.0333 35.0842 -20.3 20.3 37.2167 35.2579 -20.4 20.4 37.4 35.4316 -20.5 20.5 37.5833 35.6053 -20.6 20.6 37.7667 35.7789 -20.7 20.7 37.95 35.9526 -20.8 20.8 38.1333 36.1263 -20.9 20.9 38.3167 36.3 -21 21 38.5 36.4737 -21.1 21.1 38.6833 36.6474 -21.2 21.2 38.8667 36.8211 -21.3 21.3 39.05 36.9947 -21.4 21.4 39.2333 37.1684 -21.5 21.5 39.4167 37.3421 -21.6 21.6 39.6 37.5158 -21.7 21.7 39.7833 37.6895 -21.8 21.8 39.9667 37.8632 -21.9 21.9 40.15 38.0368 -22 22 40.3333 38.2105 -22.1 22.1 40.5167 38.3842 -22.2 22.2 40.7 38.5579 -22.3 22.3 40.8833 38.7316 -22.4 22.4 41.0667 38.9053 -22.5 22.5 41.25 39.0789 -22.6 22.6 41.4333 39.2526 -22.7 22.7 41.6167 39.4263 -22.8 22.8 41.8 39.6 -22.9 22.9 41.9833 39.7737 -23 23 42.1667 39.9474 -23.1 23.1 42.35 40.1211 -23.2 23.2 42.5333 40.2947 -23.3 23.3 42.7167 40.4684 -23.4 23.4 42.9 40.6421 -23.5 23.5 43.0833 40.8158 -23.6 23.6 43.2667 40.9895 -23.7 23.7 43.45 41.1632 -23.8 23.8 43.6333 41.3368 -23.9 23.9 43.8167 41.5105 -24 24 44 41.6842 -24.1 24.1 44.1833 41.8579 -24.2 24.2 44.3667 42.0316 -24.3 24.3 44.55 42.2053 -24.4 24.4 44.7333 42.3789 -24.5 24.5 44.9167 42.5526 -24.6 24.6 45.1 42.7263 -24.7 24.7 45.2833 42.9 -24.8 24.8 45.4667 43.0737 -24.9 24.9 45.65 43.2474 -25 25 45.8333 43.4211 -25.1 25.1 46.0167 43.5947 -25.2 25.2 46.2 43.7684 -25.3 25.3 46.3833 43.9421 -25.4 25.4 46.5667 44.1158 -25.5 25.5 46.75 44.2895 -25.6 25.6 46.9333 44.4632 -25.7 25.7 47.1167 44.6368 -25.8 25.8 47.3 44.8105 -25.9 25.9 47.4833 44.9842 -26 26 47.6667 45.1579 -26.1 26.1 47.85 45.3316 -26.2 26.2 48.0333 45.5053 -26.3 26.3 48.2167 45.6789 -26.4 26.4 48.4 45.8526 -26.5 26.5 48.5833 46.0263 -26.6 26.6 48.7667 46.2 -26.7 26.7 48.95 46.3737 -26.8 26.8 49.1333 46.5474 -26.9 26.9 49.3167 46.7211 -27 27 49.5 46.8947 -27.1 27.1 49.6833 47.0684 -27.2 27.2 49.8667 47.2421 -27.3 27.3 50.05 47.4158 -27.4 27.4 50.2333 47.5895 -27.5 27.5 50.4167 47.7632 -27.6 27.6 50.6 47.9368 -27.7 27.7 50.7833 48.1105 -27.8 27.8 50.9667 48.2842 -27.9 27.9 51.15 48.4579 -28 28 51.3333 48.6316 -28.1 28.1 51.5167 48.8053 -28.2 28.2 51.7 48.9789 -28.3 28.3 51.8833 49.1526 -28.4 28.4 52.0667 49.3263 -28.5 28.5 52.25 49.5 -28.6 28.6 52.4333 49.6737 -28.7 28.7 52.6167 49.8474 -28.8 28.8 52.8 50.0211 -28.9 28.9 52.9833 50.1947 -29 29 53.1667 50.3684 -29.1 29.1 53.35 50.5421 -29.2 29.2 53.5333 50.7158 -29.3 29.3 53.7167 50.8895 -29.4 29.4 53.9 51.0632 -29.5 29.5 54.0833 51.2368 -29.6 29.6 54.2667 51.4105 -29.7 29.7 54.45 51.5842 -29.8 29.8 54.6333 51.7579 -29.9 29.9 54.8167 51.9316 -30 30 55 52.1053 -30.1 30.1 55.1833 52.2789 -30.2 30.2 55.3667 52.4526 -30.3 30.3 55.55 52.6263 -30.4 30.4 55.7333 52.8 -30.5 30.5 55.9167 52.9737 -30.6 30.6 56.1 53.1474 -30.7 30.7 56.2833 53.3211 -30.8 30.8 56.4667 53.4947 -30.9 30.9 56.65 53.6684 -31 31 56.8333 53.8421 -31.1 31.1 57.0167 54.0158 -31.2 31.2 57.2 54.1895 -31.3 31.3 57.3833 54.3632 -31.4 31.4 57.5667 54.5368 -31.5 31.5 57.75 54.7105 -31.6 31.6 57.9333 54.8842 -31.7 31.7 58.1167 55.0579 -31.8 31.8 58.3 55.2316 -31.9 31.9 58.4833 55.4053 -32 32 58.6667 55.5789 -32.1 32.1 58.85 55.7526 -32.2 32.2 59.0333 55.9263 -32.3 32.3 59.2167 56.1 -32.4 32.4 59.4 56.2737 -32.5 32.5 59.5833 56.4474 -32.6 32.6 59.7667 56.6211 -32.7 32.7 59.95 56.7947 -32.8 32.8 60.1333 56.9684 -32.9 32.9 60.3167 57.1421 -33 33 60.5 57.3158 -33.1 33.1 60.6833 57.4895 -33.2 33.2 60.8667 57.6632 -33.3 33.3 61.05 57.8368 -33.4 33.4 61.2333 58.0105 -33.5 33.5 61.4167 58.1842 -33.6 33.6 61.6 58.3579 -33.7 33.7 61.7833 58.5316 -33.8 33.8 61.9667 58.7053 -33.9 33.9 62.15 58.8789 -34 34 62.3333 59.0526 -34.1 34.1 62.5167 59.2263 -34.2 34.2 62.7 59.4 -34.3 34.3 62.8833 59.5737 -34.4 34.4 63.0667 59.7474 -34.5 34.5 63.25 59.9211 -34.6 34.6 63.4333 60.0947 -34.7 34.7 63.6167 60.2684 -34.8 34.8 63.8 60.4421 -34.9 34.9 63.9833 60.6158 -35 35 64.1667 60.7895 -35.1 35.1 64.35 60.9632 -35.2 35.2 64.5333 61.1368 -35.3 35.3 64.7167 61.3105 -35.4 35.4 64.9 61.4842 -35.5 35.5 65.0833 61.6579 -35.6 35.6 65.2667 61.8316 -35.7 35.7 65.45 62.0053 -35.8 35.8 65.6333 62.1789 -35.9 35.9 65.8167 62.3526 -36 36 66 62.5263 -36.1 36.1 66.1833 62.7 -36.2 36.2 66.3667 62.8737 -36.3 36.3 66.55 63.0474 -36.4 36.4 66.7333 63.2211 -36.5 36.5 66.9167 63.3947 -36.6 36.6 67.1 63.5684 -36.7 36.7 67.2833 63.7421 -36.8 36.8 67.4667 63.9158 -36.9 36.9 67.65 64.0895 -37 37 67.8333 64.2632 -37.1 37.1 68.0167 64.4368 -37.2 37.2 68.2 64.6105 -37.3 37.3 68.3833 64.7842 -37.4 37.4 68.5667 64.9579 -37.5 37.5 68.75 65.1316 -37.6 37.6 68.9333 65.3053 -37.7 37.7 69.1167 65.4789 -37.8 37.8 69.3 65.6526 -37.9 37.9 69.4833 65.8263 -38 38 69.6667 66 -38.1 38.1 69.85 66.1737 -38.2 38.2 70.0333 66.3474 -38.3 38.3 70.2167 66.5211 -38.4 38.4 70.4 66.6947 -38.5 38.5 70.5833 66.8684 -38.6 38.6 70.7667 67.0421 -38.7 38.7 70.95 67.2158 -38.8 38.8 71.1333 67.3895 -38.9 38.9 71.3167 67.5632 -39 39 71.5 67.7368 -39.1 39.1 71.6833 67.9105 -39.2 39.2 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99.9421 +99.6 99.6 100 99.9537 +99.7 99.7 100 99.9653 +99.8 99.8 100 99.9768 +99.9 99.9 100 99.9884 +100 100 100 100 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/TrainEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.TrainEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/TrainEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.TrainEvaluationReport.xls index b9418e7c2..231b46551 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/TrainEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -1,2044 +1,2044 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable NoSetosa -Selection value 1 -Instances 67 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.955224 0.867204 0.995089 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.955224 -Compression 0.867204 -AUC 0.995089 - -Confusion matrix - Iris-versicolor Iris-virginica -$Iris-versicolor 31 2 -$Iris-virginica 1 33 - - - ----------------------------------------------- -Lift curves Iris-versicolor -Size Random Optimal Selective Naive Bayes -0 0 0 0 -0.1 0.1 0.209375 0.209375 -0.2 0.2 0.41875 0.41875 -0.3 0.3 0.628125 0.628125 -0.4 0.4 0.8375 0.8375 -0.5 0.5 1.04688 1.04688 -0.6 0.6 1.25625 1.25625 -0.7 0.7 1.46563 1.46563 -0.8 0.8 1.675 1.675 -0.9 0.9 1.88437 1.88438 -1 1 2.09375 2.09375 -1.1 1.1 2.30313 2.30313 -1.2 1.2 2.5125 2.5125 -1.3 1.3 2.72187 2.72188 -1.4 1.4 2.93125 2.93125 -1.5 1.5 3.14062 3.14062 -1.6 1.6 3.35 3.35 -1.7 1.7 3.55938 3.55938 -1.8 1.8 3.76875 3.76875 -1.9 1.9 3.97812 3.97812 -2 2 4.1875 4.1875 -2.1 2.1 4.39687 4.39687 -2.2 2.2 4.60625 4.60625 -2.3 2.3 4.81562 4.81562 -2.4 2.4 5.025 5.025 -2.5 2.5 5.23438 5.23438 -2.6 2.6 5.44375 5.44375 -2.7 2.7 5.65313 5.65313 -2.8 2.8 5.8625 5.8625 -2.9 2.9 6.07188 6.07188 -3 3 6.28125 6.28125 -3.1 3.1 6.49062 6.49062 -3.2 3.2 6.7 6.7 -3.3 3.3 6.90938 6.90938 -3.4 3.4 7.11875 7.11875 -3.5 3.5 7.32813 7.32813 -3.6 3.6 7.5375 7.5375 -3.7 3.7 7.74688 7.74688 -3.8 3.8 7.95625 7.95625 -3.9 3.9 8.16563 8.16563 -4 4 8.375 8.375 -4.1 4.1 8.58438 8.58438 -4.2 4.2 8.79375 8.79375 -4.3 4.3 9.00312 9.00313 -4.4 4.4 9.2125 9.2125 -4.5 4.5 9.42188 9.42187 -4.6 4.6 9.63125 9.63125 -4.7 4.7 9.84062 9.84062 -4.8 4.8 10.05 10.05 -4.9 4.9 10.2594 10.2594 -5 5 10.4688 10.4688 -5.1 5.1 10.6781 10.6781 -5.2 5.2 10.8875 10.8875 -5.3 5.3 11.0969 11.0969 -5.4 5.4 11.3063 11.3063 -5.5 5.5 11.5156 11.5156 -5.6 5.6 11.725 11.725 -5.7 5.7 11.9344 11.9344 -5.8 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84.5875 -40.5 40.5 84.7969 84.7969 -40.6 40.6 85.0063 85.0063 -40.7 40.7 85.2156 85.2156 -40.8 40.8 85.425 85.425 -40.9 40.9 85.6344 85.6344 -41 41 85.8438 85.8438 -41.1 41.1 86.0531 86.0531 -41.2 41.2 86.2625 86.2625 -41.3 41.3 86.4719 86.4719 -41.4 41.4 86.6813 86.6813 -41.5 41.5 86.8906 86.8906 -41.6 41.6 87.1 87.1 -41.7 41.7 87.3094 87.3094 -41.8 41.8 87.5187 87.5187 -41.9 41.9 87.7281 87.7281 -42 42 87.9375 87.9375 -42.1 42.1 88.1469 88.1469 -42.2 42.2 88.3563 88.3562 -42.3 42.3 88.5656 88.5656 -42.4 42.4 88.775 88.775 -42.5 42.5 88.9844 88.9844 -42.6 42.6 89.1938 89.1937 -42.7 42.7 89.4031 89.4031 -42.8 42.8 89.6125 89.6125 -42.9 42.9 89.8219 89.8219 -43 43 90.0312 90.0312 -43.1 43.1 90.2406 90.2406 -43.2 43.2 90.45 90.45 -43.3 43.3 90.6594 90.6422 -43.4 43.4 90.8687 90.7469 -43.5 43.5 91.0781 90.8516 -43.6 43.6 91.2875 90.9563 -43.7 43.7 91.4969 91.0609 -43.8 43.8 91.7062 91.1656 -43.9 43.9 91.9156 91.2703 -44 44 92.125 91.375 -44.1 44.1 92.3344 91.4797 -44.2 44.2 92.5438 91.5844 -44.3 44.3 92.7531 91.6891 -44.4 44.4 92.9625 91.7938 -44.5 44.5 93.1719 91.8984 -44.6 44.6 93.3813 92.0031 -44.7 44.7 93.5906 92.1078 -44.8 44.8 93.8 92.2125 -44.9 44.9 94.0094 92.3172 -45 45 94.2188 92.4219 -45.1 45.1 94.4281 92.5266 -45.2 45.2 94.6375 92.6313 -45.3 45.3 94.8469 92.7359 -45.4 45.4 95.0563 92.8406 -45.5 45.5 95.2656 92.9453 -45.6 45.6 95.475 93.05 -45.7 45.7 95.6844 93.1547 -45.8 45.8 95.8938 93.2594 -45.9 45.9 96.1031 93.3641 -46 46 96.3125 93.4688 -46.1 46.1 96.5219 93.5734 -46.2 46.2 96.7313 93.6781 -46.3 46.3 96.9406 93.7828 -46.4 46.4 97.15 93.8875 -46.5 46.5 97.3594 93.9922 -46.6 46.6 97.5688 94.0969 -46.7 46.7 97.7781 94.2016 -46.8 46.8 97.9875 94.3063 -46.9 46.9 98.1969 94.4109 -47 47 98.4062 94.5156 -47.1 47.1 98.6156 94.6203 -47.2 47.2 98.825 94.725 -47.3 47.3 99.0344 94.8297 -47.4 47.4 99.2437 94.9344 -47.5 47.5 99.4531 95.0391 -47.6 47.6 99.6625 95.1438 -47.7 47.7 99.8719 95.2484 -47.8 47.8 100 95.3531 -47.9 47.9 100 95.4578 -48 48 100 95.5625 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100 98.5742 -52.6 52.6 100 98.6266 -52.7 52.7 100 98.6789 -52.8 52.8 100 98.7313 -52.9 52.9 100 98.7836 -53 53 100 98.8359 -53.1 53.1 100 98.8883 -53.2 53.2 100 98.9406 -53.3 53.3 100 98.993 -53.4 53.4 100 99.0453 -53.5 53.5 100 99.0977 -53.6 53.6 100 99.15 -53.7 53.7 100 99.2023 -53.8 53.8 100 99.2547 -53.9 53.9 100 99.307 -54 54 100 99.3594 -54.1 54.1 100 99.4117 -54.2 54.2 100 99.4641 -54.3 54.3 100 99.5164 -54.4 54.4 100 99.5688 -54.5 54.5 100 99.6211 -54.6 54.6 100 99.6734 -54.7 54.7 100 99.7258 -54.8 54.8 100 99.7781 -54.9 54.9 100 99.8305 -55 55 100 99.8828 -55.1 55.1 100 99.9352 -55.2 55.2 100 99.9875 -55.3 55.3 100 100 -55.4 55.4 100 100 -55.5 55.5 100 100 -55.6 55.6 100 100 -55.7 55.7 100 100 -55.8 55.8 100 100 -55.9 55.9 100 100 -56 56 100 100 -56.1 56.1 100 100 -56.2 56.2 100 100 -56.3 56.3 100 100 -56.4 56.4 100 100 -56.5 56.5 100 100 -56.6 56.6 100 100 -56.7 56.7 100 100 -56.8 56.8 100 100 -56.9 56.9 100 100 -57 57 100 100 -57.1 57.1 100 100 -57.2 57.2 100 100 -57.3 57.3 100 100 -57.4 57.4 100 100 -57.5 57.5 100 100 -57.6 57.6 100 100 -57.7 57.7 100 100 -57.8 57.8 100 100 -57.9 57.9 100 100 -58 58 100 100 -58.1 58.1 100 100 -58.2 58.2 100 100 -58.3 58.3 100 100 -58.4 58.4 100 100 -58.5 58.5 100 100 -58.6 58.6 100 100 -58.7 58.7 100 100 -58.8 58.8 100 100 -58.9 58.9 100 100 -59 59 100 100 -59.1 59.1 100 100 -59.2 59.2 100 100 -59.3 59.3 100 100 -59.4 59.4 100 100 -59.5 59.5 100 100 -59.6 59.6 100 100 -59.7 59.7 100 100 -59.8 59.8 100 100 -59.9 59.9 100 100 -60 60 100 100 -60.1 60.1 100 100 -60.2 60.2 100 100 -60.3 60.3 100 100 -60.4 60.4 100 100 -60.5 60.5 100 100 -60.6 60.6 100 100 -60.7 60.7 100 100 -60.8 60.8 100 100 -60.9 60.9 100 100 -61 61 100 100 -61.1 61.1 100 100 -61.2 61.2 100 100 -61.3 61.3 100 100 -61.4 61.4 100 100 -61.5 61.5 100 100 -61.6 61.6 100 100 -61.7 61.7 100 100 -61.8 61.8 100 100 -61.9 61.9 100 100 -62 62 100 100 -62.1 62.1 100 100 -62.2 62.2 100 100 -62.3 62.3 100 100 -62.4 62.4 100 100 -62.5 62.5 100 100 -62.6 62.6 100 100 -62.7 62.7 100 100 -62.8 62.8 100 100 -62.9 62.9 100 100 -63 63 100 100 -63.1 63.1 100 100 -63.2 63.2 100 100 -63.3 63.3 100 100 -63.4 63.4 100 100 -63.5 63.5 100 100 -63.6 63.6 100 100 -63.7 63.7 100 100 -63.8 63.8 100 100 -63.9 63.9 100 100 -64 64 100 100 -64.1 64.1 100 100 -64.2 64.2 100 100 -64.3 64.3 100 100 -64.4 64.4 100 100 -64.5 64.5 100 100 -64.6 64.6 100 100 -64.7 64.7 100 100 -64.8 64.8 100 100 -64.9 64.9 100 100 -65 65 100 100 -65.1 65.1 100 100 -65.2 65.2 100 100 -65.3 65.3 100 100 -65.4 65.4 100 100 -65.5 65.5 100 100 -65.6 65.6 100 100 -65.7 65.7 100 100 -65.8 65.8 100 100 -65.9 65.9 100 100 -66 66 100 100 -66.1 66.1 100 100 -66.2 66.2 100 100 -66.3 66.3 100 100 -66.4 66.4 100 100 -66.5 66.5 100 100 -66.6 66.6 100 100 -66.7 66.7 100 100 -66.8 66.8 100 100 -66.9 66.9 100 100 -67 67 100 100 -67.1 67.1 100 100 -67.2 67.2 100 100 -67.3 67.3 100 100 -67.4 67.4 100 100 -67.5 67.5 100 100 -67.6 67.6 100 100 -67.7 67.7 100 100 -67.8 67.8 100 100 -67.9 67.9 100 100 -68 68 100 100 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100 100 -73.5 73.5 100 100 -73.6 73.6 100 100 -73.7 73.7 100 100 -73.8 73.8 100 100 -73.9 73.9 100 100 -74 74 100 100 -74.1 74.1 100 100 -74.2 74.2 100 100 -74.3 74.3 100 100 -74.4 74.4 100 100 -74.5 74.5 100 100 -74.6 74.6 100 100 -74.7 74.7 100 100 -74.8 74.8 100 100 -74.9 74.9 100 100 -75 75 100 100 -75.1 75.1 100 100 -75.2 75.2 100 100 -75.3 75.3 100 100 -75.4 75.4 100 100 -75.5 75.5 100 100 -75.6 75.6 100 100 -75.7 75.7 100 100 -75.8 75.8 100 100 -75.9 75.9 100 100 -76 76 100 100 -76.1 76.1 100 100 -76.2 76.2 100 100 -76.3 76.3 100 100 -76.4 76.4 100 100 -76.5 76.5 100 100 -76.6 76.6 100 100 -76.7 76.7 100 100 -76.8 76.8 100 100 -76.9 76.9 100 100 -77 77 100 100 -77.1 77.1 100 100 -77.2 77.2 100 100 -77.3 77.3 100 100 -77.4 77.4 100 100 -77.5 77.5 100 100 -77.6 77.6 100 100 -77.7 77.7 100 100 -77.8 77.8 100 100 -77.9 77.9 100 100 -78 78 100 100 -78.1 78.1 100 100 -78.2 78.2 100 100 -78.3 78.3 100 100 -78.4 78.4 100 100 -78.5 78.5 100 100 -78.6 78.6 100 100 -78.7 78.7 100 100 -78.8 78.8 100 100 -78.9 78.9 100 100 -79 79 100 100 -79.1 79.1 100 100 -79.2 79.2 100 100 -79.3 79.3 100 100 -79.4 79.4 100 100 -79.5 79.5 100 100 -79.6 79.6 100 100 -79.7 79.7 100 100 -79.8 79.8 100 100 -79.9 79.9 100 100 -80 80 100 100 -80.1 80.1 100 100 -80.2 80.2 100 100 -80.3 80.3 100 100 -80.4 80.4 100 100 -80.5 80.5 100 100 -80.6 80.6 100 100 -80.7 80.7 100 100 -80.8 80.8 100 100 -80.9 80.9 100 100 -81 81 100 100 -81.1 81.1 100 100 -81.2 81.2 100 100 -81.3 81.3 100 100 -81.4 81.4 100 100 -81.5 81.5 100 100 -81.6 81.6 100 100 -81.7 81.7 100 100 -81.8 81.8 100 100 -81.9 81.9 100 100 -82 82 100 100 -82.1 82.1 100 100 -82.2 82.2 100 100 -82.3 82.3 100 100 -82.4 82.4 100 100 -82.5 82.5 100 100 -82.6 82.6 100 100 -82.7 82.7 100 100 -82.8 82.8 100 100 -82.9 82.9 100 100 -83 83 100 100 -83.1 83.1 100 100 -83.2 83.2 100 100 -83.3 83.3 100 100 -83.4 83.4 100 100 -83.5 83.5 100 100 -83.6 83.6 100 100 -83.7 83.7 100 100 -83.8 83.8 100 100 -83.9 83.9 100 100 -84 84 100 100 -84.1 84.1 100 100 -84.2 84.2 100 100 -84.3 84.3 100 100 -84.4 84.4 100 100 -84.5 84.5 100 100 -84.6 84.6 100 100 -84.7 84.7 100 100 -84.8 84.8 100 100 -84.9 84.9 100 100 -85 85 100 100 -85.1 85.1 100 100 -85.2 85.2 100 100 -85.3 85.3 100 100 -85.4 85.4 100 100 -85.5 85.5 100 100 -85.6 85.6 100 100 -85.7 85.7 100 100 -85.8 85.8 100 100 -85.9 85.9 100 100 -86 86 100 100 -86.1 86.1 100 100 -86.2 86.2 100 100 -86.3 86.3 100 100 -86.4 86.4 100 100 -86.5 86.5 100 100 -86.6 86.6 100 100 -86.7 86.7 100 100 -86.8 86.8 100 100 -86.9 86.9 100 100 -87 87 100 100 -87.1 87.1 100 100 -87.2 87.2 100 100 -87.3 87.3 100 100 -87.4 87.4 100 100 -87.5 87.5 100 100 -87.6 87.6 100 100 -87.7 87.7 100 100 -87.8 87.8 100 100 -87.9 87.9 100 100 -88 88 100 100 -88.1 88.1 100 100 -88.2 88.2 100 100 -88.3 88.3 100 100 -88.4 88.4 100 100 -88.5 88.5 100 100 -88.6 88.6 100 100 -88.7 88.7 100 100 -88.8 88.8 100 100 -88.9 88.9 100 100 -89 89 100 100 -89.1 89.1 100 100 -89.2 89.2 100 100 -89.3 89.3 100 100 -89.4 89.4 100 100 -89.5 89.5 100 100 -89.6 89.6 100 100 -89.7 89.7 100 100 -89.8 89.8 100 100 -89.9 89.9 100 100 -90 90 100 100 -90.1 90.1 100 100 -90.2 90.2 100 100 -90.3 90.3 100 100 -90.4 90.4 100 100 -90.5 90.5 100 100 -90.6 90.6 100 100 -90.7 90.7 100 100 -90.8 90.8 100 100 -90.9 90.9 100 100 -91 91 100 100 -91.1 91.1 100 100 -91.2 91.2 100 100 -91.3 91.3 100 100 -91.4 91.4 100 100 -91.5 91.5 100 100 -91.6 91.6 100 100 -91.7 91.7 100 100 -91.8 91.8 100 100 -91.9 91.9 100 100 -92 92 100 100 -92.1 92.1 100 100 -92.2 92.2 100 100 -92.3 92.3 100 100 -92.4 92.4 100 100 -92.5 92.5 100 100 -92.6 92.6 100 100 -92.7 92.7 100 100 -92.8 92.8 100 100 -92.9 92.9 100 100 -93 93 100 100 -93.1 93.1 100 100 -93.2 93.2 100 100 -93.3 93.3 100 100 -93.4 93.4 100 100 -93.5 93.5 100 100 -93.6 93.6 100 100 -93.7 93.7 100 100 -93.8 93.8 100 100 -93.9 93.9 100 100 -94 94 100 100 -94.1 94.1 100 100 -94.2 94.2 100 100 -94.3 94.3 100 100 -94.4 94.4 100 100 -94.5 94.5 100 100 -94.6 94.6 100 100 -94.7 94.7 100 100 -94.8 94.8 100 100 -94.9 94.9 100 100 -95 95 100 100 -95.1 95.1 100 100 -95.2 95.2 100 100 -95.3 95.3 100 100 -95.4 95.4 100 100 -95.5 95.5 100 100 -95.6 95.6 100 100 -95.7 95.7 100 100 -95.8 95.8 100 100 -95.9 95.9 100 100 -96 96 100 100 -96.1 96.1 100 100 -96.2 96.2 100 100 -96.3 96.3 100 100 -96.4 96.4 100 100 -96.5 96.5 100 100 -96.6 96.6 100 100 -96.7 96.7 100 100 -96.8 96.8 100 100 -96.9 96.9 100 100 -97 97 100 100 -97.1 97.1 100 100 -97.2 97.2 100 100 -97.3 97.3 100 100 -97.4 97.4 100 100 -97.5 97.5 100 100 -97.6 97.6 100 100 -97.7 97.7 100 100 -97.8 97.8 100 100 -97.9 97.9 100 100 -98 98 100 100 -98.1 98.1 100 100 -98.2 98.2 100 100 -98.3 98.3 100 100 -98.4 98.4 100 100 -98.5 98.5 100 100 -98.6 98.6 100 100 -98.7 98.7 100 100 -98.8 98.8 100 100 -98.9 98.9 100 100 -99 99 100 100 -99.1 99.1 100 100 -99.2 99.2 100 100 -99.3 99.3 100 100 -99.4 99.4 100 100 -99.5 99.5 100 100 -99.6 99.6 100 100 -99.7 99.7 100 100 -99.8 99.8 100 100 -99.9 99.9 100 100 -100 100 100 100 - -Lift curves Iris-virginica -Size Random Optimal Selective Naive Bayes -0 0 0 0 -0.1 0.1 0.191429 0.191429 -0.2 0.2 0.382857 0.382857 -0.3 0.3 0.574286 0.574286 -0.4 0.4 0.765714 0.765714 -0.5 0.5 0.957143 0.957143 -0.6 0.6 1.14857 1.14857 -0.7 0.7 1.34 1.34 -0.8 0.8 1.53143 1.53143 -0.9 0.9 1.72286 1.72286 -1 1 1.91429 1.91429 -1.1 1.1 2.10571 2.10571 -1.2 1.2 2.29714 2.29714 -1.3 1.3 2.48857 2.48857 -1.4 1.4 2.68 2.68 -1.5 1.5 2.87143 2.87143 -1.6 1.6 3.06286 3.06286 -1.7 1.7 3.25429 3.25429 -1.8 1.8 3.44571 3.44571 -1.9 1.9 3.63714 3.63714 -2 2 3.82857 3.82857 -2.1 2.1 4.02 4.02 -2.2 2.2 4.21143 4.21143 -2.3 2.3 4.40286 4.40286 -2.4 2.4 4.59429 4.59429 -2.5 2.5 4.78571 4.78571 -2.6 2.6 4.97714 4.97714 -2.7 2.7 5.16857 5.16857 -2.8 2.8 5.36 5.36 -2.9 2.9 5.55143 5.55143 -3 3 5.74286 5.74286 -3.1 3.1 5.93429 5.93429 -3.2 3.2 6.12571 6.12571 -3.3 3.3 6.31714 6.31714 -3.4 3.4 6.50857 6.50857 -3.5 3.5 6.7 6.7 -3.6 3.6 6.89143 6.89143 -3.7 3.7 7.08286 7.08286 -3.8 3.8 7.27429 7.27429 -3.9 3.9 7.46571 7.46571 -4 4 7.65714 7.65714 -4.1 4.1 7.84857 7.84857 -4.2 4.2 8.04 8.04 -4.3 4.3 8.23143 8.23143 -4.4 4.4 8.42286 8.42286 -4.5 4.5 8.61429 8.61429 -4.6 4.6 8.80571 8.80571 -4.7 4.7 8.99714 8.99714 -4.8 4.8 9.18857 9.18857 -4.9 4.9 9.38 9.38 -5 5 9.57143 9.57143 -5.1 5.1 9.76286 9.76286 -5.2 5.2 9.95429 9.95429 -5.3 5.3 10.1457 10.1457 -5.4 5.4 10.3371 10.3371 -5.5 5.5 10.5286 10.5286 -5.6 5.6 10.72 10.72 -5.7 5.7 10.9114 10.9114 -5.8 5.8 11.1029 11.1029 -5.9 5.9 11.2943 11.2943 -6 6 11.4857 11.4857 -6.1 6.1 11.6771 11.6771 -6.2 6.2 11.8686 11.8686 -6.3 6.3 12.06 12.06 -6.4 6.4 12.2514 12.2514 -6.5 6.5 12.4429 12.4429 -6.6 6.6 12.6343 12.6343 -6.7 6.7 12.8257 12.8257 -6.8 6.8 13.0171 13.0171 -6.9 6.9 13.2086 13.2086 -7 7 13.4 13.4 -7.1 7.1 13.5914 13.5914 -7.2 7.2 13.7829 13.7829 -7.3 7.3 13.9743 13.9743 -7.4 7.4 14.1657 14.1657 -7.5 7.5 14.3571 14.3571 -7.6 7.6 14.5486 14.5486 -7.7 7.7 14.74 14.74 -7.8 7.8 14.9314 14.9314 -7.9 7.9 15.1229 15.1229 -8 8 15.3143 15.3143 -8.1 8.1 15.5057 15.5057 -8.2 8.2 15.6971 15.6971 -8.3 8.3 15.8886 15.8886 -8.4 8.4 16.08 16.08 -8.5 8.5 16.2714 16.2714 -8.6 8.6 16.4629 16.4629 -8.7 8.7 16.6543 16.6543 -8.8 8.8 16.8457 16.8457 -8.9 8.9 17.0371 17.0371 -9 9 17.2286 17.2286 -9.1 9.1 17.42 17.42 -9.2 9.2 17.6114 17.6114 -9.3 9.3 17.8029 17.8029 -9.4 9.4 17.9943 17.9943 -9.5 9.5 18.1857 18.1857 -9.6 9.6 18.3771 18.3771 -9.7 9.7 18.5686 18.5686 -9.8 9.8 18.76 18.76 -9.9 9.9 18.9514 18.9514 -10 10 19.1429 19.1429 -10.1 10.1 19.3343 19.3343 -10.2 10.2 19.5257 19.5257 -10.3 10.3 19.7171 19.7171 -10.4 10.4 19.9086 19.9086 -10.5 10.5 20.1 20.1 -10.6 10.6 20.2914 20.2914 -10.7 10.7 20.4829 20.4829 -10.8 10.8 20.6743 20.6743 -10.9 10.9 20.8657 20.8657 -11 11 21.0571 21.0571 -11.1 11.1 21.2486 21.2486 -11.2 11.2 21.44 21.44 -11.3 11.3 21.6314 21.6314 -11.4 11.4 21.8229 21.8229 -11.5 11.5 22.0143 22.0143 -11.6 11.6 22.2057 22.2057 -11.7 11.7 22.3971 22.3971 -11.8 11.8 22.5886 22.5886 -11.9 11.9 22.78 22.78 -12 12 22.9714 22.9714 -12.1 12.1 23.1629 23.1629 -12.2 12.2 23.3543 23.3543 -12.3 12.3 23.5457 23.5457 -12.4 12.4 23.7371 23.7371 -12.5 12.5 23.9286 23.9286 -12.6 12.6 24.12 24.12 -12.7 12.7 24.3114 24.3114 -12.8 12.8 24.5029 24.5029 -12.9 12.9 24.6943 24.6943 -13 13 24.8857 24.8857 -13.1 13.1 25.0771 25.0771 -13.2 13.2 25.2686 25.2686 -13.3 13.3 25.46 25.46 -13.4 13.4 25.6514 25.6514 -13.5 13.5 25.8429 25.8429 -13.6 13.6 26.0343 26.0343 -13.7 13.7 26.2257 26.2257 -13.8 13.8 26.4171 26.4171 -13.9 13.9 26.6086 26.6086 -14 14 26.8 26.8 -14.1 14.1 26.9914 26.9914 -14.2 14.2 27.1829 27.1829 -14.3 14.3 27.3743 27.3743 -14.4 14.4 27.5657 27.5657 -14.5 14.5 27.7571 27.7571 -14.6 14.6 27.9486 27.9486 -14.7 14.7 28.14 28.14 -14.8 14.8 28.3314 28.3314 -14.9 14.9 28.5229 28.5229 -15 15 28.7143 28.7143 -15.1 15.1 28.9057 28.9057 -15.2 15.2 29.0971 29.0971 -15.3 15.3 29.2886 29.2886 -15.4 15.4 29.48 29.48 -15.5 15.5 29.6714 29.6714 -15.6 15.6 29.8629 29.8629 -15.7 15.7 30.0543 30.0543 -15.8 15.8 30.2457 30.2457 -15.9 15.9 30.4371 30.4371 -16 16 30.6286 30.6286 -16.1 16.1 30.82 30.82 -16.2 16.2 31.0114 31.0114 -16.3 16.3 31.2029 31.2029 -16.4 16.4 31.3943 31.3943 -16.5 16.5 31.5857 31.5857 -16.6 16.6 31.7771 31.7771 -16.7 16.7 31.9686 31.9686 -16.8 16.8 32.16 32.16 -16.9 16.9 32.3514 32.3514 -17 17 32.5429 32.5429 -17.1 17.1 32.7343 32.7343 -17.2 17.2 32.9257 32.9257 -17.3 17.3 33.1171 33.1171 -17.4 17.4 33.3086 33.3086 -17.5 17.5 33.5 33.5 -17.6 17.6 33.6914 33.6914 -17.7 17.7 33.8829 33.8829 -17.8 17.8 34.0743 34.0743 -17.9 17.9 34.2657 34.2657 -18 18 34.4571 34.4571 -18.1 18.1 34.6486 34.6486 -18.2 18.2 34.84 34.84 -18.3 18.3 35.0314 35.0314 -18.4 18.4 35.2229 35.2229 -18.5 18.5 35.4143 35.4143 -18.6 18.6 35.6057 35.6057 -18.7 18.7 35.7971 35.7971 -18.8 18.8 35.9886 35.9886 -18.9 18.9 36.18 36.18 -19 19 36.3714 36.3714 -19.1 19.1 36.5629 36.5629 -19.2 19.2 36.7543 36.7543 -19.3 19.3 36.9457 36.9457 -19.4 19.4 37.1371 37.1371 -19.5 19.5 37.3286 37.3286 -19.6 19.6 37.52 37.52 -19.7 19.7 37.7114 37.7114 -19.8 19.8 37.9029 37.9029 -19.9 19.9 38.0943 38.0943 -20 20 38.2857 38.2857 -20.1 20.1 38.4771 38.4771 -20.2 20.2 38.6686 38.6686 -20.3 20.3 38.86 38.86 -20.4 20.4 39.0514 39.0514 -20.5 20.5 39.2429 39.2429 -20.6 20.6 39.4343 39.4343 -20.7 20.7 39.6257 39.6257 -20.8 20.8 39.8171 39.8171 -20.9 20.9 40.0086 40.0086 -21 21 40.2 40.2 -21.1 21.1 40.3914 40.3914 -21.2 21.2 40.5829 40.5829 -21.3 21.3 40.7743 40.7743 -21.4 21.4 40.9657 40.9657 -21.5 21.5 41.1571 41.1571 -21.6 21.6 41.3486 41.3486 -21.7 21.7 41.54 41.54 -21.8 21.8 41.7314 41.7314 -21.9 21.9 41.9229 41.9229 -22 22 42.1143 42.1143 -22.1 22.1 42.3057 42.3057 -22.2 22.2 42.4971 42.4971 -22.3 22.3 42.6886 42.6886 -22.4 22.4 42.88 42.88 -22.5 22.5 43.0714 43.0714 -22.6 22.6 43.2629 43.2629 -22.7 22.7 43.4543 43.4543 -22.8 22.8 43.6457 43.6457 -22.9 22.9 43.8371 43.8371 -23 23 44.0286 44.0286 -23.1 23.1 44.22 44.22 -23.2 23.2 44.4114 44.4114 -23.3 23.3 44.6029 44.6029 -23.4 23.4 44.7943 44.7943 -23.5 23.5 44.9857 44.9857 -23.6 23.6 45.1771 45.1771 -23.7 23.7 45.3686 45.3686 -23.8 23.8 45.56 45.56 -23.9 23.9 45.7514 45.7514 -24 24 45.9429 45.9429 -24.1 24.1 46.1343 46.1343 -24.2 24.2 46.3257 46.3257 -24.3 24.3 46.5171 46.5171 -24.4 24.4 46.7086 46.7086 -24.5 24.5 46.9 46.9 -24.6 24.6 47.0914 47.0914 -24.7 24.7 47.2829 47.2829 -24.8 24.8 47.4743 47.4743 -24.9 24.9 47.6657 47.6657 -25 25 47.8571 47.8571 -25.1 25.1 48.0486 48.0486 -25.2 25.2 48.24 48.24 -25.3 25.3 48.4314 48.4314 -25.4 25.4 48.6229 48.6229 -25.5 25.5 48.8143 48.8143 -25.6 25.6 49.0057 49.0057 -25.7 25.7 49.1971 49.1971 -25.8 25.8 49.3886 49.3886 -25.9 25.9 49.58 49.58 -26 26 49.7714 49.7714 -26.1 26.1 49.9629 49.9629 -26.2 26.2 50.1543 50.1543 -26.3 26.3 50.3457 50.3457 -26.4 26.4 50.5371 50.5371 -26.5 26.5 50.7286 50.7286 -26.6 26.6 50.92 50.92 -26.7 26.7 51.1114 51.1114 -26.8 26.8 51.3029 51.3029 -26.9 26.9 51.4943 51.4943 -27 27 51.6857 51.6857 -27.1 27.1 51.8771 51.8771 -27.2 27.2 52.0686 52.0686 -27.3 27.3 52.26 52.26 -27.4 27.4 52.4514 52.4514 -27.5 27.5 52.6429 52.6429 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60.1086 60.1086 -31.5 31.5 60.3 60.3 -31.6 31.6 60.4914 60.4914 -31.7 31.7 60.6829 60.6829 -31.8 31.8 60.8743 60.8743 -31.9 31.9 61.0657 61.0657 -32 32 61.2571 61.2571 -32.1 32.1 61.4486 61.4486 -32.2 32.2 61.64 61.64 -32.3 32.3 61.8314 61.8314 -32.4 32.4 62.0229 62.0229 -32.5 32.5 62.2143 62.2143 -32.6 32.6 62.4057 62.4057 -32.7 32.7 62.5971 62.5971 -32.8 32.8 62.7886 62.7886 -32.9 32.9 62.98 62.98 -33 33 63.1714 63.1714 -33.1 33.1 63.3629 63.3629 -33.2 33.2 63.5543 63.5543 -33.3 33.3 63.7457 63.7457 -33.4 33.4 63.9371 63.9371 -33.5 33.5 64.1286 64.1286 -33.6 33.6 64.32 64.32 -33.7 33.7 64.5114 64.5114 -33.8 33.8 64.7029 64.7029 -33.9 33.9 64.8943 64.8943 -34 34 65.0857 65.0857 -34.1 34.1 65.2771 65.2771 -34.2 34.2 65.4686 65.4686 -34.3 34.3 65.66 65.66 -34.4 34.4 65.8514 65.8514 -34.5 34.5 66.0429 66.0429 -34.6 34.6 66.2343 66.2343 -34.7 34.7 66.4257 66.4257 -34.8 34.8 66.6171 66.6171 -34.9 34.9 66.8086 66.8086 -35 35 67 67 -35.1 35.1 67.1914 67.1914 -35.2 35.2 67.3829 67.3829 -35.3 35.3 67.5743 67.5743 -35.4 35.4 67.7657 67.7657 -35.5 35.5 67.9571 67.9571 -35.6 35.6 68.1486 68.1486 -35.7 35.7 68.34 68.34 -35.8 35.8 68.5314 68.5314 -35.9 35.9 68.7229 68.7229 -36 36 68.9143 68.9143 -36.1 36.1 69.1057 69.1057 -36.2 36.2 69.2971 69.2971 -36.3 36.3 69.4886 69.4886 -36.4 36.4 69.68 69.68 -36.5 36.5 69.8714 69.8714 -36.6 36.6 70.0629 70.0629 -36.7 36.7 70.2543 70.2543 -36.8 36.8 70.4457 70.4457 -36.9 36.9 70.6371 70.6371 -37 37 70.8286 70.8286 -37.1 37.1 71.02 71.02 -37.2 37.2 71.2114 71.2114 -37.3 37.3 71.4029 71.4029 -37.4 37.4 71.5943 71.5943 -37.5 37.5 71.7857 71.7857 -37.6 37.6 71.9771 71.9771 -37.7 37.7 72.1686 72.1686 -37.8 37.8 72.36 72.36 -37.9 37.9 72.5514 72.5514 -38 38 72.7429 72.7429 -38.1 38.1 72.9343 72.9343 -38.2 38.2 73.1257 73.1257 -38.3 38.3 73.3171 73.3171 -38.4 38.4 73.5086 73.5086 -38.5 38.5 73.7 73.7 -38.6 38.6 73.8914 73.8914 -38.7 38.7 74.0829 74.0829 -38.8 38.8 74.2743 74.2743 -38.9 38.9 74.4657 74.4657 -39 39 74.6571 74.6571 -39.1 39.1 74.8486 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+ } + ] + } + ] + }, + "preparationReport": { + "reportType": "Preparation", + "summary": { + "dictionary": "Iris", + "variables": { + "types": [ + "Categorical", + "Numerical" + ], + "numbers": [ + 1, + 4 + ] + }, + "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", + "samplePercentage": 70, + "samplingMode": "Include sample", + "selectionVariable": "NoSetosa", + "selectionValue": "1", + "instances": 67, + "learningTask": "Classification analysis", + "targetVariable": "Class", + "targetDescriptiveStats": { + "values": 2, + "mode": "Iris-virginica", + "modeFrequency": 35, + "missingNumber": 0, + "sparseMissingNumber": 0 + }, + "targetValues": { + "values": ["Iris-versicolor","Iris-virginica"], + "frequencies": [32,35] + }, + "evaluatedVariables": 4, + "informativeVariables": 4, + "selectedVariables": 2, + "featureEngineering": { + "maxNumberOfConstructedVariables": 0, + "maxNumberOfTextFeatures": 0, + "maxNumberOfTrees": 0, + "maxNumberOfVariablePairs": 0 + }, + "discretization": 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6.292537313, + "stdDev": 0.6538778545, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 2.07944, + "preparationCost": 7.75841, + "dataCost": 30.7632 + }, + { + "rank": "R4", + "name": "SepalWidth", + "type": "Numerical", + "level": 0.000285089, + "parts": 2, + "values": 16, + "min": 2, + "max": 3.8, + "mean": 2.889552239, + "stdDev": 0.3228299901, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 2.07944, + "preparationCost": 7.76686, + "dataCost": 39.0952 + } + ], + "variablesDetailedStatistics": { + "R1": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,1.75], + [1.75,2.5] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [31,2], + [1,33] + ], + "partInterests": [0.485513,0.514487] + } + }, + "R2": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [3,4.95], + [4.95,6.9] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [30,3], + [2,32] + ], + "partInterests": [0.489782,0.510218] + } + }, + "R3": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [4.9,6.25], + [6.25,7.7] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [24,6], + [8,29] + ], + "partInterests": [0.54953,0.45047] + } + }, + "R4": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,2.95], + [2.95,3.8] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [22,13], + [10,22] + ], + "partInterests": [0.471874,0.528126] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/Modeling.kdic b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.model.kdic similarity index 98% rename from test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.model.kdic index 486dd38be..d700461da 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/AnalysisResults.model.kdic @@ -1,20 +1,20 @@ -#Khiops 10.1 - -Dictionary SNB_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical Class ; -Unused Numerical NoSetosa = NEQc(Class, "Iris-setosa") ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-versicolor", "Iris-virginica"), Frequencies(32, 35)) ; -Unused Structure(DataGrid) PPetalLength = DataGrid(IntervalBounds(4.95), ValueSetC("Iris-versicolor", "Iris-virginica"), Frequencies(30, 2, 3, 32)) ; // DataGrid(PetalLength, Class) -Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(1.75), ValueSetC("Iris-versicolor", "Iris-virginica"), Frequencies(31, 1, 2, 33)) ; // DataGrid(PetalWidth, Class) -Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.7265625, 0.7734375), DataGridStats(PPetalLength, PetalLength), DataGridStats(PPetalWidth, PetalWidth), VClass) ; - Categorical PredictedClass = TargetValue(SNBClass) ; -Unused Numerical ScoreClass = TargetProb(SNBClass) ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; -}; +#Khiops 10.4.6i + +Dictionary SNB_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical Class ; +Unused Numerical NoSetosa = NEQc(Class, "Iris-setosa") ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-versicolor", "Iris-virginica"), Frequencies(32, 35)) ; +Unused Structure(DataGrid) PPetalLength = DataGrid(IntervalBounds(4.95), ValueSetC("Iris-versicolor", "Iris-virginica"), Frequencies(30, 2, 3, 32)) ; // DataGrid(PetalLength, Class) +Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(1.75), ValueSetC("Iris-versicolor", "Iris-virginica"), Frequencies(31, 1, 2, 33)) ; // DataGrid(PetalWidth, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.7265625, 0.7734375), DataGridStats(PPetalLength, PetalLength), DataGridStats(PPetalWidth, PetalWidth), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/T_Iris.txt b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/D_Iris.txt similarity index 97% rename from test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/T_Iris.txt rename to test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/D_Iris.txt index 41c1d0ac6..f437972b2 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/D_Iris.txt @@ -1,106 +1,106 @@ -PredictedClass ProbClassIris-versicolor ProbClassIris-virginica -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-virginica 0.2747622487 0.7252377513 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.5371040848 0.4628959152 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.5371040848 0.4628959152 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-versicolor 0.9752371505 0.02476284952 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.2747622487 0.7252377513 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.2747622487 0.7252377513 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-versicolor 0.5371040848 0.4628959152 -Iris-versicolor 0.5371040848 0.4628959152 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.2747622487 0.7252377513 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 -Iris-virginica 0.01297580762 0.9870241924 +PredictedClass ProbClassIris-versicolor ProbClassIris-virginica +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-virginica 0.2747622487 0.7252377513 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.5371040848 0.4628959152 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.5371040848 0.4628959152 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-versicolor 0.9752371505 0.02476284952 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.2747622487 0.7252377513 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.2747622487 0.7252377513 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-versicolor 0.5371040848 0.4628959152 +Iris-versicolor 0.5371040848 0.4628959152 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.2747622487 0.7252377513 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 +Iris-virginica 0.01297580762 0.9870241924 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/err.txt index fd836f6aa..e05eddf4a 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/err.txt @@ -5,10 +5,10 @@ SNB train time: 0:00:00 Build Selective Naive Bayes SNB_Iris Train evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt Test evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt -Write report ./results\AllReports.khj +Write report ./results/AnalysisResults.khj Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 105 +Output database ./results/D_Iris.txt: Written records: 105 Model deployment time: 0:00:00.01 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/time.log index 5de920156..570ef9d88 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/IrisLight/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.24448823928833008 +Overal time: 0.2018754482269287 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight/test.prm b/test/LearningTest/TestKhiops/Standard/IrisLight/test.prm index 377f806a5..8fbfa39e3 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight/test.prm +++ b/test/LearningTest/TestKhiops/Standard/IrisLight/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,38 +23,40 @@ ClassFileName ./IrisSelection.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage -TrainDatabase.SelectionAttribute NoSetosa // Selection variable -TrainDatabase.SelectionValue 1 // Selection value +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Selection.SelectionAttribute NoSetosa // Selection variable +TrainDatabase.DatabaseSpec.Selection.SelectionValue 1 // Selection value AnalysisSpec.TargetAttributeName Class // Target variable -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif AnalysisResults.ShortDescription Training without setosa // Short description -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database ClassManagement.OpenFile // Open... -ClassFileName ./results/Modeling.kdic // Dictionary file +ClassFileName ./results/AnalysisResults.model.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.ModelingReport.xls similarity index 92% rename from test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.ModelingReport.xls index fefd1e82f..75808843b 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.ModelingReport.xls @@ -1,29 +1,29 @@ -#Khiops 10.1 -Modeling report - -Dictionary Iris -Target variable Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 2 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PPetalWidth PetalWidth 0.65964 0.4375 0.537208 -PPetalLength PetalLength 0.618227 0.351562 0.466203 +#Khiops 10.4.6i +Modeling report + +Dictionary Iris +Target variable Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 2 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PPetalWidth PetalWidth 0.65964 0.4375 0.537208 +PPetalLength PetalLength 0.618227 0.351562 0.466203 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/Preparation2DReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.Preparation2DReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/Preparation2DReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.Preparation2DReport.xls index bb4944b0d..49614b144 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/Preparation2DReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.Preparation2DReport.xls @@ -1,79 +1,80 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 1 - Numerical 5 - Total 6 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-versicolor 32 0.304762 -Iris-virginica 35 0.333333 - -Evaluated variable pairs 4 -Informative variable pairs 0 - - -Variables pairs statistics - -Rank Name 1 Name 2 DeltaLevel Level Level 1 Level 2 Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost -R1 PetalWidth Random -0.0222604 0.63738 0.65964 0 1 3 1 3 5.14695 28.4221 9.79546 -R2 PetalWidth SepalWidth -0.135549 0.63738 0.65964 0.113289 1 3 1 3 5.14695 28.4221 9.79546 -R3 PetalWidth SepalLength -0.350724 0.63738 0.65964 0.328464 1 3 1 3 5.14695 28.4221 9.79546 -R4 PetalLength PetalWidth -0.671628 0.606238 0.618227 0.65964 2 2 2 3 6.75639 26.9352 13.397 - - --------------------------------------------------------------------------------- - -Variables pairs detailed statistics -(Pairs with two jointly informative variables) - - -Rank R4 -Variables - Type Name - Numerical PetalLength - Numerical PetalWidth - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;4.75] 65 0.619048 -]4.75;+inf[ 40 0.380952 -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;+inf[ 67 0.638095 - -Cell frequencies - PetalWidth -PetalLength ]-inf;0.75] ]0.75;+inf[ Total Coverage -]-inf;4.75] 38 27 65 0.619048 -]4.75;+inf[ 0 40 40 0.380952 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 3 -Cell Id PetalLength PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -C1 ]-inf;4.75] ]-inf;0.75] 1 0 0 0.386122 38 0.361905 -C3 ]-inf;4.75] ]0.75;+inf[ 0 1 0 0.320737 27 0.257143 -C4 ]4.75;+inf[ ]0.75;+inf[ 0 0.125 0.875 0.293141 40 0.380952 - Total 0.361905 0.304762 0.333333 1 105 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 1 + Numerical 5 + Total 6 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variable pairs 4 +Informative variable pairs 0 +Selected variable pairs 0 + + +Variables pairs statistics + +Rank Name 1 Name 2 DeltaLevel Level Level 1 Level 2 Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost +R1 PetalWidth Random -0.0222604 0.63738 0.65964 0 1 3 1 3 5.14695 28.4221 9.79546 +R2 PetalWidth SepalWidth -0.135549 0.63738 0.65964 0.113289 1 3 1 3 5.14695 28.4221 9.79546 +R3 PetalWidth SepalLength -0.350724 0.63738 0.65964 0.328464 1 3 1 3 5.14695 28.4221 9.79546 +R4 PetalLength PetalWidth -0.671628 0.606238 0.618227 0.65964 2 2 2 3 6.75639 26.9352 13.397 + + +-------------------------------------------------------------------------------- + +Variables pairs detailed statistics +(Pairs with two jointly informative variables) + + +Rank R4 +Variables + Type Name + Numerical PetalLength + Numerical PetalWidth + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;4.75] 65 0.619048 +]4.75;+inf[ 40 0.380952 +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;+inf[ 67 0.638095 + +Cell frequencies + PetalWidth +PetalLength ]-inf;0.75] ]0.75;+inf[ Total Coverage +]-inf;4.75] 38 27 65 0.619048 +]4.75;+inf[ 0 40 40 0.380952 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 3 +Cell Id PetalLength PetalWidth Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +C1 ]-inf;4.75] ]-inf;0.75] 1 0 0 0.386122 38 0.361905 +C3 ]-inf;4.75] ]0.75;+inf[ 0 1 0 0.320737 27 0.257143 +C4 ]4.75;+inf[ ]0.75;+inf[ 0 0.125 0.875 0.293141 40 0.380952 + Total 0.361905 0.304762 0.333333 1 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.PreparationReport.xls similarity index 84% rename from test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.PreparationReport.xls index e447fa478..266ba9703 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.PreparationReport.xls @@ -1,105 +1,107 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 1 - Numerical 5 - Total 6 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-versicolor 32 0.304762 -Iris-virginica 35 0.333333 - -Evaluated variables 5 -Native variables 4 -Constructed variables 1 -Informative variables 4 - -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 100 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 8.64312 - Data cost 110.25 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R1 PetalWidth 0.65964 3 21 0.1 2.5 1.175238095 0.7880996979 0 2.48491 28.4221 9.79546 -R2 PetalLength 0.618227 4 36 1 6.9 3.686666667 1.80132579 0 2.48491 31.9046 11.2655 -R3 SepalLength 0.328464 3 31 4.3 7.7 5.827619048 0.8375127846 0 2.48491 26.2686 51.5531 -R4 SepalWidth 0.113289 3 23 2 4.4 3.081904762 0.4284592446 0 2.48491 23.4061 80.1477 -R5 Random 0 1 105 0.004570158167 0.98929961 0.4791206614 0.274475005 0 0.693147 8.64312 110.25 Random() - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R1 -Variable Numerical PetalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 -]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R2 -Variable Numerical PetalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 -]3.15;4.75] 0 1 0 0.304909 26 0.247619 -]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 -]5.15;+inf[ 0 0 1 0.281913 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R3 -Variable Numerical SepalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 -]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 -]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R4 -Variable Numerical SepalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 -]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 -]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 1 + Numerical 5 + Total 6 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variables 5 +Native variables 4 +Constructed variables 1 +Informative variables 4 +Selected variables 2 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 100 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 8.64312 + Data cost 110.25 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R1 PetalWidth 0.65964 3 21 0.1 2.5 1.175238095 0.7880996979 0 0 2.48491 28.4221 9.79546 +R2 PetalLength 0.618227 4 36 1 6.9 3.686666667 1.80132579 0 0 2.48491 31.9046 11.2655 +R3 SepalLength 0.328464 3 31 4.3 7.7 5.827619048 0.8375127846 0 0 2.48491 26.2686 51.5531 +R4 SepalWidth 0.113289 3 23 2 4.4 3.081904762 0.4284592446 0 0 2.48491 23.4061 80.1477 +R5 Random 0 1 105 0.004570158167 0.98929961 0.4791206614 0.274475005 0 0 0.693147 8.64312 110.25 Random() + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R1 +Variable Numerical PetalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;0.75] 1 0 0 0.374826 38 0.361905 +]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 +]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R2 +Variable Numerical PetalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 +]3.15;4.75] 0 1 0 0.304909 26 0.247619 +]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 +]5.15;+inf[ 0 0 1 0.281913 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R3 +Variable Numerical SepalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 +]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 +]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R4 +Variable Numerical SepalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 +]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 +]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.TestEvaluationReport.xls new file mode 100644 index 000000000..44df75962 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -0,0 +1,3049 @@ +#Khiops 10.4.6i +Test evaluation report + +Dictionary Iris +Target variable Categorical Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Exclude sample +Selection variable +Selection value +Instances 45 + + +Predictors performance + +Classifier Accuracy Compression AUC +Selective Naive Bayes 0.955556 0.85231 0.984362 + + +-------------------------------------------------------------------------------- + +Predictors detailed performance + + +Classifier Selective Naive Bayes +Accuracy 0.955556 +Compression 0.85231 +AUC 0.984362 + +Confusion matrix + 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diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.TrainEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.TrainEvaluationReport.xls new file mode 100644 index 000000000..25ab1a5c5 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -0,0 +1,3049 @@ +#Khiops 10.4.6i +Train evaluation report + +Dictionary Iris +Target variable Categorical Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + + +Predictors performance + +Classifier Accuracy Compression AUC +Selective Naive Bayes 0.980952 0.933266 0.997868 + + +-------------------------------------------------------------------------------- + +Predictors detailed performance + + +Classifier Selective Naive Bayes +Accuracy 0.980952 +Compression 0.933266 +AUC 0.997868 + +Confusion matrix + Iris-setosa Iris-versicolor Iris-virginica +$Iris-setosa 38 0 0 +$Iris-versicolor 0 31 1 +$Iris-virginica 0 1 34 + + + +---------------------------------------------- +Lift curves Iris-setosa +Size Random Optimal Selective Naive Bayes +0 0 0 0 +0.1 0.1 0.276316 0.276316 +0.2 0.2 0.552632 0.552632 +0.3 0.3 0.828947 0.828947 +0.4 0.4 1.10526 1.10526 +0.5 0.5 1.38158 1.38158 +0.6 0.6 1.65789 1.65789 +0.7 0.7 1.93421 1.93421 +0.8 0.8 2.21053 2.21053 +0.9 0.9 2.48684 2.48684 +1 1 2.76316 2.76316 +1.1 1.1 3.03947 3.03947 +1.2 1.2 3.31579 3.31579 +1.3 1.3 3.59211 3.59211 +1.4 1.4 3.86842 3.86842 +1.5 1.5 4.14474 4.14474 +1.6 1.6 4.42105 4.42105 +1.7 1.7 4.69737 4.69737 +1.8 1.8 4.97368 4.97368 +1.9 1.9 5.25 5.25 +2 2 5.52632 5.52632 +2.1 2.1 5.80263 5.80263 +2.2 2.2 6.07895 6.07895 +2.3 2.3 6.35526 6.35526 +2.4 2.4 6.63158 6.63158 +2.5 2.5 6.90789 6.90789 +2.6 2.6 7.18421 7.18421 +2.7 2.7 7.46053 7.46053 +2.8 2.8 7.73684 7.73684 +2.9 2.9 8.01316 8.01316 +3 3 8.28947 8.28947 +3.1 3.1 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[0.347012,0.304909,0.066166,0.281913] + } + }, + "R3": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [4.3,5.45], + [5.45,6.15], + [6.15,7.7] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [34,5,0], + [4,19,5], + [0,8,30] + ], + "partInterests": [0.449729,0.147253,0.403018] + } + }, + "R4": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,2.85], + [2.85,3.35], + [3.35,4.4] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [0,18,12], + [17,13,19], + [21,1,4] + ], + "partInterests": [0.544113,0.0133569,0.44253] + } + } + } + }, + "bivariatePreparationReport": { + "reportType": "BivariatePreparation", + "summary": { + "dictionary": "Iris", + "variables": { + "types": [ + "Categorical", + "Numerical" + ], + "numbers": [ + 1, + 5 + ] + }, + "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", + "samplePercentage": 70, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "", + "instances": 105, + "learningTask": "Classification analysis", + "targetVariable": "Class", + "targetDescriptiveStats": { + "values": 3, + "mode": "Iris-setosa", + "modeFrequency": 38, + "missingNumber": 0, + "sparseMissingNumber": 0 + }, + "targetValues": { + "values": ["Iris-setosa","Iris-versicolor","Iris-virginica"], + "frequencies": [38,32,35] + }, + "evaluatedVariablePairs": 4, + "informativeVariablePairs": 0, + "selectedVariablePairs": 0 + }, + "variablesPairsStatistics": [ + { + "rank": "R1", + "name1": "PetalWidth", + "name2": "Random", + "deltaLevel": -0.0222604, + "level": 0.63738, + "level1": 0.65964, + "level2": 0, + "variables": 1, + "parts1": 3, + "parts2": 1, + "cells": 3, + "constructionCost": 5.14695, + "preparationCost": 28.4221, + "dataCost": 9.79546 + }, + { + "rank": "R2", + "name1": "PetalWidth", + "name2": "SepalWidth", + "deltaLevel": -0.135549, + "level": 0.63738, + "level1": 0.65964, + "level2": 0.113289, + "variables": 1, + "parts1": 3, + "parts2": 1, + "cells": 3, + "constructionCost": 5.14695, + "preparationCost": 28.4221, + "dataCost": 9.79546 + }, + { + "rank": "R3", + "name1": "PetalWidth", + "name2": "SepalLength", + "deltaLevel": -0.350724, + "level": 0.63738, + "level1": 0.65964, + "level2": 0.328464, + "variables": 1, + "parts1": 3, + "parts2": 1, + "cells": 3, + "constructionCost": 5.14695, + "preparationCost": 28.4221, + "dataCost": 9.79546 + }, + { + "rank": "R4", + "name1": "PetalLength", + "name2": "PetalWidth", + "deltaLevel": -0.671628, + "level": 0.606238, + "level1": 0.618227, + "level2": 0.65964, + "variables": 2, + "parts1": 2, + "parts2": 2, + "cells": 3, + "constructionCost": 6.75639, + "preparationCost": 26.9352, + "dataCost": 13.397 + } + ], + "variablesPairsDetailedStatistics": { + "R4": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,4.75], + [4.75,6.9] + ] + }, + { + "variable": "PetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0.1,0.75], + [0.75,2.5] + ] + }, + { + "variable": "Target", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "cellIds": ["C1","C3","C4"], + "cellPartIndexes": [ + [0,0], + [0,1], + [1,1] + ], + "cellTargetFrequencies": [ + [38,0,0], + [0,27,0], + [0,5,35] + ], + "cellInterests": [0.386122,0.320737,0.293141] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/Modeling.kdic b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.model.kdic similarity index 98% rename from test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.model.kdic index aafec65c3..6734dd778 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/AnalysisResults.model.kdic @@ -1,21 +1,21 @@ -#Khiops 10.1 - -Dictionary SNB_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Numerical Random = Random() ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PPetalLength = DataGrid(IntervalBounds(3.15, 4.75, 5.15), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 1, 26, 5, 0, 0, 0, 9, 26)) ; // DataGrid(PetalLength, Class) -Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) -Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.3515625, 0.4375), DataGridStats(PPetalLength, PetalLength), DataGridStats(PPetalWidth, PetalWidth), VClass) ; - Categorical PredictedClass = TargetValue(SNBClass) ; -Unused Numerical ScoreClass = TargetProb(SNBClass) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; -}; +#Khiops 10.4.6i + +Dictionary SNB_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Numerical Random = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PPetalLength = DataGrid(IntervalBounds(3.15, 4.75, 5.15), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 1, 26, 5, 0, 0, 0, 9, 26)) ; // DataGrid(PetalLength, Class) +Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.3515625, 0.4375), DataGridStats(PPetalLength, PetalLength), DataGridStats(PPetalWidth, PetalWidth), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/err.txt index cb913acfa..c37283be3 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/err.txt @@ -1,10 +1,10 @@ Train supervised model for classification of target variable Class Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 Evaluation of variable pairs -Data preparation time: 0:00:00.11 +Data preparation time: 0:00:00.09 SNB train time: 0:00:00 Build Selective Naive Bayes SNB_Iris Train evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt Test evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt -Write report ./results\AllReports.khj +Write report ./results/AnalysisResults.khj diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/time.log index dffedb471..5cae79a1e 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/IrisLight2D/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.32964205741882324 +Overal time: 0.2889559268951416 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLight2D/test.prm b/test/LearningTest/TestKhiops/Standard/IrisLight2D/test.prm index 19af37126..4d5b2a744 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLight2D/test.prm +++ b/test/LearningTest/TestKhiops/Standard/IrisLight2D/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -22,18 +24,17 @@ ClassFileName ./IrisExtended.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable AnalysisSpec.MainTargetModality // Main target modality -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 100 // Max number of pairs of variables @@ -45,8 +46,11 @@ SpecificAttributePairs.FirstName PetalWidth // First name Exit // Close // <- Variable pairs parameters +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database Exit // Close diff --git a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.ModelingReport.xls similarity index 93% rename from test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.ModelingReport.xls index 51025c962..632710dac 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.ModelingReport.xls @@ -1,30 +1,30 @@ -#Khiops 10.1 -Modeling report - -Dictionary Iris -Target variable Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 3 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PPetalArea PetalArea 0.635515 0.351562 0.472677 -PPetalLength PetalLength 0.618227 0.335938 0.455725 -PPetalWidth PetalWidth 0.65964 0.0703125 0.215362 +#Khiops 10.4.6i +Modeling report + +Dictionary Iris +Target variable Class +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 3 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PPetalArea PetalArea 0.635515 0.351562 0.472677 +PPetalLength PetalLength 0.618227 0.335938 0.455725 +PPetalWidth PetalWidth 0.65964 0.0703125 0.215362 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.PreparationReport.xls similarity index 83% rename from test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.PreparationReport.xls index 7b260537f..e1bda14d3 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.PreparationReport.xls @@ -1,116 +1,118 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 1 - Numerical 5 - Total 6 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-versicolor 32 0.304762 -Iris-virginica 35 0.333333 - -Evaluated variables 5 -Native variables 4 -Constructed variables 1 -Informative variables 5 - -Max number of constructed variables 0 -Max number of trees 2 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 8.64312 - Data cost 110.25 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R1 PetalWidth 0.65964 3 21 0.1 2.5 1.175238095 0.7880996979 0 2.48491 28.4221 9.79546 -R2 PetalArea 0.635515 3 75 0.11 15.87 5.704761905 4.85029407 0 2.48491 32.3161 8.78646 Product(PetalLength, PetalWidth) -R3 PetalLength 0.618227 4 36 1 6.9 3.686666667 1.80132579 0 2.48491 31.9046 11.2655 -R4 SepalLength 0.328464 3 31 4.3 7.7 5.827619048 0.8375127846 0 2.48491 26.2686 51.5531 -R5 SepalWidth 0.113289 3 23 2 4.4 3.081904762 0.4284592446 0 2.48491 23.4061 80.1477 - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R1 -Variable Numerical PetalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.374826 38 0.361905 -]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 -]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R2 -Variable Numerical PetalArea - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;2] 1 0 0 0.368325 38 0.361905 -]2;8.73] 0 0.914286 0.0857143 0.29641 35 0.333333 -]8.73;+inf[ 0 0 1 0.335265 32 0.304762 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R3 -Variable Numerical PetalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 -]3.15;4.75] 0 1 0 0.304909 26 0.247619 -]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 -]5.15;+inf[ 0 0 1 0.281913 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R4 -Variable Numerical SepalLength - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 -]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 -]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 -Total 0.361905 0.304762 0.333333 1 105 1 - ----------------------------------------------- -Rank R5 -Variable Numerical SepalWidth - -% target values -Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage -]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 -]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 -]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 -Total 0.361905 0.304762 0.333333 1 105 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 1 + Numerical 5 + Total 6 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variables 5 +Native variables 4 +Constructed variables 1 +Informative variables 5 +Selected variables 3 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 2 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 8.64312 + Data cost 110.25 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R1 PetalWidth 0.65964 3 21 0.1 2.5 1.175238095 0.7880996979 0 0 2.48491 28.4221 9.79546 +R2 PetalArea 0.635515 3 75 0.11 15.87 5.704761905 4.85029407 0 0 2.48491 32.3161 8.78646 Product(PetalLength, PetalWidth) +R3 PetalLength 0.618227 4 36 1 6.9 3.686666667 1.80132579 0 0 2.48491 31.9046 11.2655 +R4 SepalLength 0.328464 3 31 4.3 7.7 5.827619048 0.8375127846 0 0 2.48491 26.2686 51.5531 +R5 SepalWidth 0.113289 3 23 2 4.4 3.081904762 0.4284592446 0 0 2.48491 23.4061 80.1477 + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R1 +Variable Numerical PetalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;0.75] 1 0 0 0.374826 38 0.361905 +]0.75;1.75] 0 0.939394 0.0606061 0.305582 33 0.314286 +]1.75;+inf[ 0 0.0294118 0.970588 0.319592 34 0.32381 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R2 +Variable Numerical PetalArea + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2] 1 0 0 0.368325 38 0.361905 +]2;8.73] 0 0.914286 0.0857143 0.29641 35 0.333333 +]8.73;+inf[ 0 0 1 0.335265 32 0.304762 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R3 +Variable Numerical PetalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;3.15] 0.974359 0.025641 0 0.347012 39 0.371429 +]3.15;4.75] 0 1 0 0.304909 26 0.247619 +]4.75;5.15] 0 0.357143 0.642857 0.066166 14 0.133333 +]5.15;+inf[ 0 0 1 0.281913 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R4 +Variable Numerical SepalLength + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;5.45] 0.871795 0.128205 0 0.449729 39 0.371429 +]5.45;6.15] 0.142857 0.678571 0.178571 0.147253 28 0.266667 +]6.15;+inf[ 0 0.210526 0.789474 0.403018 38 0.361905 +Total 0.361905 0.304762 0.333333 1 105 1 + +---------------------------------------------- +Rank R5 +Variable Numerical SepalWidth + +% target values +Interval Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +]-inf;2.85] 0 0.6 0.4 0.544113 30 0.285714 +]2.85;3.35] 0.346939 0.265306 0.387755 0.0133569 49 0.466667 +]3.35;+inf[ 0.807692 0.0384615 0.153846 0.44253 26 0.247619 +Total 0.361905 0.304762 0.333333 1 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TestEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/TestEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TestEvaluationReport.xls index 51edd7f60..cdfcf6dd5 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/TestEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -1,3049 +1,3049 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Exclude sample -Selection variable -Selection value -Instances 45 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.933333 0.856583 0.984362 - - 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b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TrainEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/TrainEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TrainEvaluationReport.xls index 4bc3af987..fe252bb02 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/TrainEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -1,3049 +1,3049 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary Iris -Target variable Categorical Class -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.980952 0.945453 0.999067 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.980952 -Compression 0.945453 -AUC 0.999067 - -Confusion matrix - Iris-setosa Iris-versicolor Iris-virginica -$Iris-setosa 38 0 0 -$Iris-versicolor 0 32 2 -$Iris-virginica 0 0 33 - - - ----------------------------------------------- -Lift curves Iris-setosa -Size Random Optimal Selective Naive Bayes -0 0 0 0 -0.1 0.1 0.276316 0.276316 -0.2 0.2 0.552632 0.552632 -0.3 0.3 0.828947 0.828947 -0.4 0.4 1.10526 1.10526 -0.5 0.5 1.38158 1.38158 -0.6 0.6 1.65789 1.65789 -0.7 0.7 1.93421 1.93421 -0.8 0.8 2.21053 2.21053 -0.9 0.9 2.48684 2.48684 -1 1 2.76316 2.76316 -1.1 1.1 3.03947 3.03947 -1.2 1.2 3.31579 3.31579 -1.3 1.3 3.59211 3.59211 -1.4 1.4 3.86842 3.86842 -1.5 1.5 4.14474 4.14474 -1.6 1.6 4.42105 4.42105 -1.7 1.7 4.69737 4.69737 -1.8 1.8 4.97368 4.97368 -1.9 1.9 5.25 5.25 -2 2 5.52632 5.52632 -2.1 2.1 5.80263 5.80263 -2.2 2.2 6.07895 6.07895 -2.3 2.3 6.35526 6.35526 -2.4 2.4 6.63158 6.63158 -2.5 2.5 6.90789 6.90789 -2.6 2.6 7.18421 7.18421 -2.7 2.7 7.46053 7.46053 -2.8 2.8 7.73684 7.73684 -2.9 2.9 8.01316 8.01316 -3 3 8.28947 8.28947 -3.1 3.1 8.56579 8.56579 -3.2 3.2 8.84211 8.84211 -3.3 3.3 9.11842 9.11842 -3.4 3.4 9.39474 9.39474 -3.5 3.5 9.67105 9.67105 -3.6 3.6 9.94737 9.94737 -3.7 3.7 10.2237 10.2237 -3.8 3.8 10.5 10.5 -3.9 3.9 10.7763 10.7763 -4 4 11.0526 11.0526 -4.1 4.1 11.3289 11.3289 -4.2 4.2 11.6053 11.6053 -4.3 4.3 11.8816 11.8816 -4.4 4.4 12.1579 12.1579 -4.5 4.5 12.4342 12.4342 -4.6 4.6 12.7105 12.7105 -4.7 4.7 12.9868 12.9868 -4.8 4.8 13.2632 13.2632 -4.9 4.9 13.5395 13.5395 -5 5 13.8158 13.8158 -5.1 5.1 14.0921 14.0921 -5.2 5.2 14.3684 14.3684 -5.3 5.3 14.6447 14.6447 -5.4 5.4 14.9211 14.9211 -5.5 5.5 15.1974 15.1974 -5.6 5.6 15.4737 15.4737 -5.7 5.7 15.75 15.75 -5.8 5.8 16.0263 16.0263 -5.9 5.9 16.3026 16.3026 -6 6 16.5789 16.5789 -6.1 6.1 16.8553 16.8553 -6.2 6.2 17.1316 17.1316 -6.3 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a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TreePreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TreePreparationReport.xls new file mode 100644 index 000000000..a4272895d --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.TreePreparationReport.xls @@ -0,0 +1,102 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 1 + Numerical 5 + Total 6 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-versicolor 32 0.304762 +Iris-virginica 35 0.333333 + +Evaluated variables 2 +Informative variables 2 +Selected variables 0 + +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 8.64312 + Data cost 110.25 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R1 Tree_2 0.46019 3 5 L1 0.361905 0 0 27.4103 27.264 9.87971 SwitchC(IntervalIndex(IntervalBounds(0.75), PetalWidth), "L0", "L1", SwitchC(IntervalIndex(IntervalBounds(6.25), SepalLength), "L0", SwitchC(IntervalIndex(IntervalBounds(4.75), PetalLength), "L2", "L7", "L8"), SwitchC(IntervalIndex(IntervalBounds(8.66), PetalArea), "L2", "L5", "L6"))) +R2 Tree_1 0.399978 3 4 L5 0.352381 0 0 29.0975 24.7419 17.9152 SwitchC(IntervalIndex(IntervalBounds(5.75), SepalLength), "L0", SwitchC(IntervalIndex(IntervalBounds(2), PetalArea), "L0", "L5", "L6"), SwitchC(IntervalIndex(IntervalBounds(1.75), PetalWidth), "L0", "L3", "L4")) + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R1 +Variable Categorical Tree_2 + +Variable stats +Group Size Value list +{L1} 1 L1 +{L6, L8} 2 L6 L8 * +{L7, L5} 2 L7 L5 + +% target values +Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +{L1} 1 0 0 0.375137 38 0.361905 +{L7, L5} 0 0.967742 0.0322581 0.313998 31 0.295238 +{L6, L8} 0 0.0555556 0.944444 0.310865 36 0.342857 +Total 0.361905 0.304762 0.333333 1 105 1 + +Values +Value Frequency Coverage +L1 38 0.361905 +L6 28 0.266667 +L7 22 0.209524 +L5 9 0.0857143 +L8 8 0.0761905 + +---------------------------------------------- +Rank R2 +Variable Categorical Tree_1 + +Variable stats +Group Size Value list +{L5} 1 L5 +{L3, L6} 2 L3 L6 * +{L4} 1 L4 + +% target values +Group Iris-setosa Iris-versicolor Iris-virginica Interest Frequency Coverage +{L5} 1 0 0 0.401605 37 0.352381 +{L4} 0 0.03125 0.96875 0.328872 32 0.304762 +{L3, L6} 0.0277778 0.861111 0.111111 0.269523 36 0.342857 +Total 0.361905 0.304762 0.333333 1 105 1 + +Values +Value Frequency Coverage +L5 37 0.352381 +L4 32 0.304762 +L3 20 0.190476 +L6 16 0.152381 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AllReports.khj b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.khj similarity index 98% rename from test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AllReports.khj rename to test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.khj index 7b52e8e5a..60895e1f2 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AllReports.khj +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.khj @@ -1,783 +1,797 @@ -{ - "tool": "Khiops", - "version": "10.1", - "shortDescription": "", - "modelingReport": { - "reportType": "Modeling", - "summary": { - "dictionary": "Iris", - "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", - "samplePercentage": 70, - "samplingMode": "Include sample", - "selectionVariable": "", - "selectionValue": "", - "learningTask": "Classification analysis", - "targetVariable": "Class" - }, - "trainedPredictors": [ - { - "rank": "R1", - "type": "Classifier", - "family": "Selective Naive Bayes", - "name": "Selective Naive Bayes", - "variables": 3 - } - ], - "trainedPredictorsDetails": { - "R1": { - "selectedVariables": [ - { - "preparedName": "PPetalArea", - "name": "PetalArea", - "level": 0.635515, - "weight": 0.351562, - "importance": 0.472677 - }, - { - "preparedName": "PPetalLength", - "name": "PetalLength", - "level": 0.618227, - "weight": 0.335938, - "importance": 0.455725 - }, - { - "preparedName": "PPetalWidth", - "name": "PetalWidth", - "level": 0.65964, - "weight": 0.0703125, - "importance": 0.215362 - } - ] - } - } - }, - "trainEvaluationReport": { - "reportType": "Evaluation", - "evaluationType": "Train", - "summary": { - "dictionary": "Iris", - "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", - "samplePercentage": 70, - "samplingMode": "Include sample", - "selectionVariable": "", - "selectionValue": "", - "instances": 105, - "learningTask": "Classification analysis", - "targetVariable": "Class" - }, - "predictorsPerformance": [ - { - "rank": "R1", - "type": "Classifier", - "family": "Selective Naive Bayes", - "name": "Selective Naive Bayes", - "accuracy": 0.980952, - "compression": 0.945453, - "auc": 0.999067 - } - ], - "predictorsDetailedPerformance": { - "R1": { - "confusionMatrix": { - "values": ["Iris-setosa","Iris-versicolor","Iris-virginica"], - "matrix": [ - [38,0,0], - [0,32,2], - [0,0,33] - ] - } - } - }, - "liftCurves": [ - { - "targetValue": "Iris-setosa", - "curves": [ - { - "classifier": "Optimal", - "values": 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{ + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,3.15], + [3.15,4.75], + [4.75,5.15], + [5.15,6.9] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,1,0], + [0,26,0], + [0,5,9], + [0,0,26] + ], + "partInterests": [0.347012,0.304909,0.066166,0.281913] + } + }, + "R4": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [4.3,5.45], + [5.45,6.15], + [6.15,7.7] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [34,5,0], + [4,19,5], + [0,8,30] + ], + "partInterests": [0.449729,0.147253,0.403018] + } + }, + "R5": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,2.85], + [2.85,3.35], + [3.35,4.4] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [0,18,12], + [17,13,19], + [21,1,4] + ], + "partInterests": [0.544113,0.0133569,0.44253] + } + } + } + }, + "treePreparationReport": { + "reportType": "Preparation", + "summary": { + "dictionary": "Iris", + "variables": { + "types": [ + "Categorical", + "Numerical" + ], + "numbers": [ + 1, + 5 + ] + }, + "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", + "samplePercentage": 70, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "", + "instances": 105, + "learningTask": "Classification analysis", + "targetVariable": "Class", + "targetDescriptiveStats": { + "values": 3, + "mode": "Iris-setosa", + "modeFrequency": 38, + "missingNumber": 0, + "sparseMissingNumber": 0 + }, + "targetValues": { + "values": ["Iris-setosa","Iris-versicolor","Iris-virginica"], + "frequencies": [38,32,35] + }, + "evaluatedVariables": 2, + "informativeVariables": 2, + "selectedVariables": 0 + }, + "variablesStatistics": [ + { + "rank": "R1", + "name": "Tree_2", + "type": "Categorical", + "level": 0.46019, + "parts": 3, + "values": 5, + "mode": "L1", + "modeFrequency": 38, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 27.4103, + "preparationCost": 27.264, + "dataCost": 9.87971, + "derivationRule": "SwitchC(IntervalIndex(IntervalBounds(0.75), PetalWidth), \"L0\", \"L1\", SwitchC(IntervalIndex(IntervalBounds(6.25), SepalLength), \"L0\", SwitchC(IntervalIndex(IntervalBounds(4.75), PetalLength), \"L2\", \"L7\", \"L8\"), SwitchC(IntervalIndex(IntervalBounds(8.66), PetalArea), \"L2\", \"L5\", \"L6\")))" + }, + { + "rank": "R2", + "name": "Tree_1", + "type": "Categorical", + "level": 0.399978, + "parts": 3, + "values": 4, + "mode": "L5", + "modeFrequency": 37, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 29.0975, + "preparationCost": 24.7419, + "dataCost": 17.9152, + "derivationRule": "SwitchC(IntervalIndex(IntervalBounds(5.75), SepalLength), \"L0\", SwitchC(IntervalIndex(IntervalBounds(2), PetalArea), \"L0\", \"L5\", \"L6\"), SwitchC(IntervalIndex(IntervalBounds(1.75), PetalWidth), \"L0\", \"L3\", \"L4\"))" + } + ], + "variablesDetailedStatistics": { + "R1": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Tree_2", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["L1"], + ["L6","L8"], + ["L7","L5"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [38,0,0], + [0,2,34], + [0,30,1] + ], + "partInterests": [0.375137,0.310865,0.313998] + }, + "inputValues": { + "values": ["L1","L6","L7","L5","L8"], + "frequencies": [38,28,22,9,8] + } + }, + "R2": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Tree_1", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["L5"], + ["L3","L6"], + ["L4"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["Iris-setosa","Iris-versicolor","Iris-virginica"] + } + ], + "partTargetFrequencies": [ + [37,0,0], + [1,31,4], + [0,1,31] + ], + "partInterests": [0.401605,0.269523,0.328872] + }, + "inputValues": { + "values": ["L5","L4","L3","L6"], + "frequencies": [37,32,20,16] + } + } + }, + "treeDetails": { + "R1": { + "name": "Tree_2", + "variableNumber": 4, + "depth": 4, + "treeNodes": { + "nodeId": "L0", + "variable": "PetalWidth", + "type": "Numerical", + "partition": [ + [0.1,0.75], + [0.75,2.5] + ], + "childNodes": [ + { + "nodeId": "L1", + "targetValues": { + "values": ["Iris-setosa"], + "frequencies": [38] + } + }, + { + "nodeId": "L2", + "variable": "SepalLength", + "type": "Numerical", + "partition": [ + [4.9,6.25], + [6.25,7.7] + ], + "childNodes": [ + { + "nodeId": "L3", + "variable": "PetalLength", + "type": "Numerical", + "partition": [ + [3,4.75], + [4.75,5.6] + ], + "childNodes": [ + { + "nodeId": "L7", + "targetValues": { + "values": ["Iris-versicolor"], + "frequencies": [22] + } + }, + { + "nodeId": "L8", + "targetValues": { + "values": ["Iris-versicolor","Iris-virginica"], + "frequencies": [2,6] + } + } + ] + }, + { + "nodeId": "L4", + "variable": "PetalArea", + "type": "Numerical", + "partition": [ + [5.59,8.66], + [8.66,15.87] + ], + "childNodes": [ + { + "nodeId": "L5", + "targetValues": { + "values": ["Iris-versicolor","Iris-virginica"], + "frequencies": [8,1] + } + }, + { + "nodeId": "L6", + "targetValues": { + "values": ["Iris-virginica"], + "frequencies": [28] + } + } + ] + } + ] + } + ] + } + }, + "R2": { + "name": "Tree_1", + "variableNumber": 3, + "depth": 3, + "treeNodes": { + "nodeId": "L0", + "variable": "SepalLength", + "type": "Numerical", + "partition": [ + [4.3,5.75], + [5.75,7.7] + ], + "childNodes": [ + { + "nodeId": "L1", + "variable": "PetalArea", + "type": "Numerical", + "partition": [ + [0.11,2], + [2,10] + ], + "childNodes": [ + { + "nodeId": "L5", + "targetValues": { + "values": ["Iris-setosa"], + "frequencies": [37] + } + }, + { + "nodeId": "L6", + "targetValues": { + "values": ["Iris-versicolor","Iris-virginica"], + "frequencies": [14,2] + } + } + ] + }, + { + "nodeId": "L2", + "variable": "PetalWidth", + "type": "Numerical", + "partition": [ + [0.2,1.75], + [1.75,2.5] + ], + "childNodes": [ + { + "nodeId": "L3", + "targetValues": { + "values": ["Iris-setosa","Iris-versicolor","Iris-virginica"], + "frequencies": [1,17,2] + } + }, + { + "nodeId": "L4", + "targetValues": { + "values": ["Iris-versicolor","Iris-virginica"], + "frequencies": [1,31] + } + } + ] + } + ] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/Modeling.kdic b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.model.kdic similarity index 98% rename from test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.model.kdic index 984acadfa..bf9204138 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/AnalysisResults.model.kdic @@ -1,22 +1,22 @@ -#Khiops 10.1 - -Dictionary SNB_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Numerical PetalArea = Product(PetalLength, PetalWidth) ; -Unused Categorical Class ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; -Unused Structure(DataGrid) PPetalLength = DataGrid(IntervalBounds(3.15, 4.75, 5.15), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 1, 26, 5, 0, 0, 0, 9, 26)) ; // DataGrid(PetalLength, Class) -Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) -Unused Structure(DataGrid) PPetalArea = DataGrid(IntervalBounds(2, 8.73), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 32, 0, 0, 3, 32)) ; // DataGrid(PetalArea, Class) -Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.3359375, 0.0703125, 0.3515625), DataGridStats(PPetalLength, PetalLength), DataGridStats(PPetalWidth, PetalWidth), DataGridStats(PPetalArea, PetalArea), VClass) ; - Categorical PredictedClass = TargetValue(SNBClass) ; -Unused Numerical ScoreClass = TargetProb(SNBClass) ; - Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; - Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; - Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; -}; +#Khiops 10.4.6i + +Dictionary SNB_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Numerical PetalArea = Product(PetalLength, PetalWidth) ; +Unused Categorical Class ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 32, 35)) ; +Unused Structure(DataGrid) PPetalLength = DataGrid(IntervalBounds(3.15, 4.75, 5.15), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 1, 26, 5, 0, 0, 0, 9, 26)) ; // DataGrid(PetalLength, Class) +Unused Structure(DataGrid) PPetalWidth = DataGrid(IntervalBounds(0.75, 1.75), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 31, 1, 0, 2, 33)) ; // DataGrid(PetalWidth, Class) +Unused Structure(DataGrid) PPetalArea = DataGrid(IntervalBounds(2, 8.73), ValueSetC("Iris-setosa", "Iris-versicolor", "Iris-virginica"), Frequencies(38, 0, 0, 0, 32, 0, 0, 3, 32)) ; // DataGrid(PetalArea, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.3359375, 0.0703125, 0.3515625), DataGridStats(PPetalLength, PetalLength), DataGridStats(PPetalWidth, PetalWidth), DataGridStats(PPetalArea, PetalArea), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical `ProbClassIris-setosa` = TargetProbAt(SNBClass, "Iris-setosa") ; + Numerical `ProbClassIris-versicolor` = TargetProbAt(SNBClass, "Iris-versicolor") ; + Numerical `ProbClassIris-virginica` = TargetProbAt(SNBClass, "Iris-virginica") ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/T_Iris.txt b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/D_Iris.txt similarity index 98% rename from test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/T_Iris.txt rename to test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/D_Iris.txt index 22d7ac58d..0f83be398 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/D_Iris.txt @@ -1,106 +1,106 @@ -PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-setosa 0.986665927 0.009979580183 0.003354492785 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.004632333707 0.5877221771 0.4076454892 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 -Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-versicolor 0.09729845495 0.837277591 0.06542395408 -Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.004715326467 0.03611013397 0.9591745396 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.004715326467 0.03611013397 0.9591745396 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.004715326467 0.03611013397 0.9591745396 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.004715326467 0.03611013397 0.9591745396 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 -Iris-virginica 0.006921981712 0.1597962616 0.8332817567 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-versicolor 0.004632333707 0.5877221771 0.4076454892 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.004715326467 0.03611013397 0.9591745396 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.004715326467 0.03611013397 0.9591745396 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.00384595208 0.004603657686 0.9915503902 -Iris-virginica 0.004715326467 0.03611013397 0.9591745396 +PredictedClass ProbClassIris-setosa ProbClassIris-versicolor ProbClassIris-virginica +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-setosa 0.986665927 0.009979580183 0.003354492785 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.004632333707 0.5877221771 0.4076454892 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 +Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-versicolor 0.09729845495 0.837277591 0.06542395408 +Iris-versicolor 0.003854841828 0.9697434483 0.02640170988 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.004715326467 0.03611013397 0.9591745396 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.004715326467 0.03611013397 0.9591745396 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.004715326467 0.03611013397 0.9591745396 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.004715326467 0.03611013397 0.9591745396 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-versicolor 0.004385617455 0.6873403421 0.3082740404 +Iris-virginica 0.006921981712 0.1597962616 0.8332817567 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-versicolor 0.004632333707 0.5877221771 0.4076454892 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.004715326467 0.03611013397 0.9591745396 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.004715326467 0.03611013397 0.9591745396 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.00384595208 0.004603657686 0.9915503902 +Iris-virginica 0.004715326467 0.03611013397 0.9591745396 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/err.txt index 0bd8c04dd..b0d4e9e3d 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/err.txt @@ -6,10 +6,10 @@ SNB train time: 0:00:00 Build Selective Naive Bayes SNB_Iris Train evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt Test evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt -Write report ./results\AllReports.khj +Write report ./results/AnalysisResults.khj Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 105 +Output database ./results/D_Iris.txt: Written records: 105 Model deployment time: 0:00:00.01 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/time.log index 61f8ee9d4..5e83f422b 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.2812159061431885 +Overal time: 0.23284912109375 diff --git a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/test.prm b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/test.prm index ed80f7d1b..e50366b8d 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/test.prm +++ b/test/LearningTest/TestKhiops/Standard/IrisLightWithTrees/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,37 +23,39 @@ ClassFileName ./IrisCustom.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 2 -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database ClassManagement.OpenFile // Open... -ClassFileName ./results/Modeling.kdic // Dictionary file +ClassFileName ./results/AnalysisResults.model.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.ModelingReport.xls similarity index 89% rename from test/LearningTest/TestKhiops/Standard/IrisR/results.ref/ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.ModelingReport.xls index 80fe527fd..8053ba9b0 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.ModelingReport.xls @@ -1,35 +1,39 @@ -#Khiops 10.1 -Modeling report - -Dictionary Iris -Target variable PetalLength -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 4 -Univariate SPetalLength 1 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PSPetalLength SPetalLength 0.212092 0.523438 0.333192 -PLowerPetalLength LowerPetalLength 0.157721 0.414062 0.255551 -PUpperPetalWidth UpperPetalWidth 0.0912549 0.320312 0.170968 -PClass2 Class2 0.0691819 0.0078125 0.0232483 - ----------------------------------------------- -Predictor Univariate SPetalLength +#Khiops 10.4.6i +Modeling report + +Dictionary Iris +Target variable PetalLength +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Baseline 0 +Selective Naive Bayes 4 +Univariate SPetalLength 1 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Baseline + +---------------------------------------------- +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PSPetalLength SPetalLength 0.212092 0.523438 0.333192 +PLowerPetalLength LowerPetalLength 0.157721 0.414062 0.255551 +PUpperPetalWidth UpperPetalWidth 0.0912549 0.320312 0.170968 +PClass2 Class2 0.0691819 0.0078125 0.0232483 + +---------------------------------------------- +Predictor Univariate SPetalLength diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.PreparationReport.xls similarity index 81% rename from test/LearningTest/TestKhiops/Standard/IrisR/results.ref/PreparationReport.xls rename to test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.PreparationReport.xls index 7cb4fbce7..429358d2e 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/PreparationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.PreparationReport.xls @@ -1,218 +1,221 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Regression analysis - -Target variable Numerical PetalLength - -Values 36 -Min 1 -Max 6.9 -Mean 3.686666667 -Std dev 1.80132579 -Missing number 0 - -Evaluated variables 11 -Native variables 4 -Constructed variables 7 -Informative variables 9 - -Max number of constructed variables 0 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 0 - Data cost 386.913 - - -Categorical variables statistics - -Rank Name Level Target intervals Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost Derivation rule -R01 SPetalLength 0.212092 5 5 5 1 0.361905 3.09104 57.6475 244.659 AsCategorical(Floor(PetalLength)) -R02 Class 0.174746 3 3 3 Iris-setosa 0.361905 3.09104 27.7604 289.022 -R05 Class1 0.129937 2 2 2 0.638095 3.09104 13.4451 320.705 IfC(EQc(Class, "Iris-setosa"), "setosa", "") -R08 Class2 0.0691819 3 2 2 0.695238 3.09104 20.5133 337.186 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") - - -Numerical variables statistics - -Rank Name Level Target intervals Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R03 PetalWidth 0.16061 3 3 21 0.1 2.5 1.175238095 0.7880996979 0 3.09104 27.7841 294.477 -R04 LowerPetalLength 0.157721 3 3 10 1 3 2.446666667 0.7433600251 0 3.09104 26.759 296.622 If(LE(PetalLength, 3), PetalLength, 3) -R06 UpperPetalWidth 0.0912549 2 2 11 1.5 2.5 1.692380952 0.2962287527 0 3.09104 9.37559 339.768 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) -R07 SepalLength 0.0903617 3 3 31 4.3 7.7 5.827619048 0.8375127846 0 3.09104 27.7674 321.722 -R09 SepalWidth 0.01974 2 3 23 2 4.4 3.081904762 0.4284592446 0 3.09104 15.5614 361.302 -R10 Dummy1 0 1 1 1 0 0 0 0 0 0.693147 0 386.913 Copy(0) -R11 Dummy2 0 1 1 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0.693147 0 386.913 Random() - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R01 -Variable Categorical SPetalLength - -Variable stats -Group Size Value list -{1} 1 1 -{5} 1 5 -{4} 1 4 -{3} 1 3 -{6} 1 6 * - -% target values -Group ]-inf;2.4] ]2.4;3.95] ]3.95;4.95] ]4.95;5.95] ]5.95;+inf[ Interest Frequency Coverage -{1} 1 0 0 0 0 0.256249 38 0.361905 -{5} 0 0 0 1 0 0.243293 27 0.257143 -{4} 0 0 1 0 0 0.238036 25 0.238095 -{3} 0 1 0 0 0 0.136651 8 0.0761905 -{6} 0 0 0 0 1 0.125771 7 0.0666667 -Total 0.361905 0.0761905 0.238095 0.257143 0.0666667 1 105 1 - -Values -Value Frequency Coverage -1 38 0.361905 -5 27 0.257143 -4 25 0.238095 -3 8 0.0761905 -6 7 0.0666667 - ----------------------------------------------- -Rank R02 -Variable Categorical Class - -Variable stats -Group Size Value list -{Iris-setosa} 1 Iris-setosa -{Iris-virginica} 1 Iris-virginica -{Iris-versicolor} 1 Iris-versicolor * - -% target values -Group ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Interest Frequency Coverage -{Iris-setosa} 1 0 0 0.385065 38 0.361905 -{Iris-virginica} 0 0.0285714 0.971429 0.320788 35 0.333333 -{Iris-versicolor} 0 0.90625 0.09375 0.294147 32 0.304762 -Total 0.361905 0.285714 0.352381 1 105 1 - -Values -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-virginica 35 0.333333 -Iris-versicolor 32 0.304762 - ----------------------------------------------- -Rank R03 -Variable Numerical PetalWidth - -% target values -Interval ]-inf;2.4] ]2.4;4.75] ]4.75;+inf[ Interest Frequency Coverage -]-inf;0.75] 1 0 0 0.409025 38 0.361905 -]0.75;1.55] 0 0.862069 0.137931 0.277246 29 0.27619 -]1.55;+inf[ 0 0.0526316 0.947368 0.313729 38 0.361905 -Total 0.361905 0.257143 0.380952 1 105 1 - ----------------------------------------------- -Rank R04 -Variable Numerical LowerPetalLength - -% target values -Interval ]-inf;1.45] ]1.45;2.4] ]2.4;+inf[ Interest Frequency Coverage -]-inf;1.45] 1 0 0 0.326327 17 0.161905 -]1.45;2.4] 0 1 0 0.356326 21 0.2 -]2.4;+inf[ 0 0 1 0.317347 67 0.638095 -Total 0.161905 0.2 0.638095 1 105 1 - ----------------------------------------------- -Rank R05 -Variable Categorical Class1 - -Variable stats -Group Size Value list -{} 1 -{setosa} 1 setosa * - -% target values -Group ]-inf;2.4] ]2.4;+inf[ Interest Frequency Coverage -{setosa} 1 0 0.561997 38 0.361905 -{} 0 1 0.438003 67 0.638095 -Total 0.361905 0.638095 1 105 1 - -Values -Value Frequency Coverage - 67 0.638095 -setosa 38 0.361905 - ----------------------------------------------- -Rank R06 -Variable Numerical UpperPetalWidth - -% target values -Interval ]-inf;4.75] ]4.75;+inf[ Interest Frequency Coverage -]-inf;1.55] 0.940299 0.0597015 0.404409 67 0.638095 -]1.55;+inf[ 0.0526316 0.947368 0.595591 38 0.361905 -Total 0.619048 0.380952 1 105 1 - ----------------------------------------------- -Rank R07 -Variable Numerical SepalLength - -% target values -Interval ]-inf;3.55] ]3.55;4.85] ]4.85;+inf[ Interest Frequency Coverage -]-inf;5.45] 0.948718 0.0512821 0 0.452028 39 0.371429 -]5.45;6.15] 0.178571 0.642857 0.178571 0.152867 28 0.266667 -]6.15;+inf[ 0 0.157895 0.842105 0.395105 38 0.361905 -Total 0.4 0.247619 0.352381 1 105 1 - ----------------------------------------------- -Rank R08 -Variable Categorical Class2 - -Variable stats -Group Size Value list -{} 1 -{versicolor} 1 versicolor * - -% target values -Group ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Interest Frequency Coverage -{versicolor} 0 0.90625 0.09375 0.592878 32 0.304762 -{} 0.520548 0.0136986 0.465753 0.407122 73 0.695238 -Total 0.361905 0.285714 0.352381 1 105 1 - -Values -Value Frequency Coverage - 73 0.695238 -versicolor 32 0.304762 - ----------------------------------------------- -Rank R09 -Variable Numerical SepalWidth - -% target values -Interval ]-inf;2.4] ]2.4;+inf[ Interest Frequency Coverage -]-inf;2.85] 0 1 0.553199 30 0.285714 -]2.85;3.35] 0.346939 0.653061 0.000981364 49 0.466667 -]3.35;+inf[ 0.807692 0.192308 0.445819 26 0.247619 -Total 0.361905 0.638095 1 105 1 +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 4 + Numerical 8 + Total 12 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Regression analysis + +Target variable Numerical PetalLength + +Values 36 +Min 1 +Max 6.9 +Mean 3.686666667 +Std dev 1.80132579 +Missing number 0 +Sparse missing number 0 + +Evaluated variables 11 +Native variables 4 +Constructed variables 7 +Informative variables 9 +Selected variables 4 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 0 + Data cost 386.913 + + +Categorical variables statistics + +Rank Name Level Target intervals Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R01 SPetalLength 0.212092 5 5 5 1 0.361905 0 0 3.09104 57.6475 244.659 AsCategorical(Floor(PetalLength)) +R02 Class 0.174746 3 3 3 Iris-setosa 0.361905 0 0 3.09104 27.7604 289.022 +R05 Class1 0.129937 2 2 2 0.638095 67 0 3.09104 13.4451 320.705 IfC(EQc(Class, "Iris-setosa"), "setosa", "") +R08 Class2 0.0691819 3 2 2 0.695238 73 0 3.09104 20.5133 337.186 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") + + +Numerical variables statistics + +Rank Name Level Target intervals Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R03 PetalWidth 0.16061 3 3 21 0.1 2.5 1.175238095 0.7880996979 0 0 3.09104 27.7841 294.477 +R04 LowerPetalLength 0.157721 3 3 10 1 3 2.446666667 0.7433600251 0 0 3.09104 26.759 296.622 If(LE(PetalLength, 3), PetalLength, 3) +R06 UpperPetalWidth 0.0912549 2 2 11 1.5 2.5 1.692380952 0.2962287527 0 0 3.09104 9.37559 339.768 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) +R07 SepalLength 0.0903617 3 3 31 4.3 7.7 5.827619048 0.8375127846 0 0 3.09104 27.7674 321.722 +R09 SepalWidth 0.01974 2 3 23 2 4.4 3.081904762 0.4284592446 0 0 3.09104 15.5614 361.302 +R10 Dummy1 0 1 1 1 0 0 0 0 0 0 0.693147 0 386.913 Copy(0) +R11 Dummy2 0 1 1 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0 0.693147 0 386.913 Random() + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R01 +Variable Categorical SPetalLength + +Variable stats +Group Size Value list +{1} 1 1 +{5} 1 5 +{4} 1 4 +{3} 1 3 +{6} 1 6 * + +% target values +Group ]-inf;2.4] ]2.4;3.95] ]3.95;4.95] ]4.95;5.95] ]5.95;+inf[ Interest Frequency Coverage +{1} 1 0 0 0 0 0.256249 38 0.361905 +{5} 0 0 0 1 0 0.243293 27 0.257143 +{4} 0 0 1 0 0 0.238036 25 0.238095 +{3} 0 1 0 0 0 0.136651 8 0.0761905 +{6} 0 0 0 0 1 0.125771 7 0.0666667 +Total 0.361905 0.0761905 0.238095 0.257143 0.0666667 1 105 1 + +Values +Value Frequency Coverage +1 38 0.361905 +5 27 0.257143 +4 25 0.238095 +3 8 0.0761905 +6 7 0.0666667 + +---------------------------------------------- +Rank R02 +Variable Categorical Class + +Variable stats +Group Size Value list +{Iris-setosa} 1 Iris-setosa +{Iris-virginica} 1 Iris-virginica +{Iris-versicolor} 1 Iris-versicolor * + +% target values +Group ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Interest Frequency Coverage +{Iris-setosa} 1 0 0 0.385065 38 0.361905 +{Iris-virginica} 0 0.0285714 0.971429 0.320788 35 0.333333 +{Iris-versicolor} 0 0.90625 0.09375 0.294147 32 0.304762 +Total 0.361905 0.285714 0.352381 1 105 1 + +Values +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-virginica 35 0.333333 +Iris-versicolor 32 0.304762 + +---------------------------------------------- +Rank R03 +Variable Numerical PetalWidth + +% target values +Interval ]-inf;2.4] ]2.4;4.75] ]4.75;+inf[ Interest Frequency Coverage +]-inf;0.75] 1 0 0 0.409025 38 0.361905 +]0.75;1.55] 0 0.862069 0.137931 0.277246 29 0.27619 +]1.55;+inf[ 0 0.0526316 0.947368 0.313729 38 0.361905 +Total 0.361905 0.257143 0.380952 1 105 1 + +---------------------------------------------- +Rank R04 +Variable Numerical LowerPetalLength + +% target values +Interval ]-inf;1.45] ]1.45;2.4] ]2.4;+inf[ Interest Frequency Coverage +]-inf;1.45] 1 0 0 0.326327 17 0.161905 +]1.45;2.4] 0 1 0 0.356326 21 0.2 +]2.4;+inf[ 0 0 1 0.317347 67 0.638095 +Total 0.161905 0.2 0.638095 1 105 1 + +---------------------------------------------- +Rank R05 +Variable Categorical Class1 + +Variable stats +Group Size Value list +{} 1 +{setosa} 1 setosa * + +% target values +Group ]-inf;2.4] ]2.4;+inf[ Interest Frequency Coverage +{setosa} 1 0 0.561997 38 0.361905 +{} 0 1 0.438003 67 0.638095 +Total 0.361905 0.638095 1 105 1 + +Values +Value Frequency Coverage + 67 0.638095 +setosa 38 0.361905 + +---------------------------------------------- +Rank R06 +Variable Numerical UpperPetalWidth + +% target values +Interval ]-inf;4.75] ]4.75;+inf[ Interest Frequency Coverage +]-inf;1.55] 0.940299 0.0597015 0.404409 67 0.638095 +]1.55;+inf[ 0.0526316 0.947368 0.595591 38 0.361905 +Total 0.619048 0.380952 1 105 1 + +---------------------------------------------- +Rank R07 +Variable Numerical SepalLength + +% target values +Interval ]-inf;3.55] ]3.55;4.85] ]4.85;+inf[ Interest Frequency Coverage +]-inf;5.45] 0.948718 0.0512821 0 0.452028 39 0.371429 +]5.45;6.15] 0.178571 0.642857 0.178571 0.152867 28 0.266667 +]6.15;+inf[ 0 0.157895 0.842105 0.395105 38 0.361905 +Total 0.4 0.247619 0.352381 1 105 1 + +---------------------------------------------- +Rank R08 +Variable Categorical Class2 + +Variable stats +Group Size Value list +{} 1 +{versicolor} 1 versicolor * + +% target values +Group ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Interest Frequency Coverage +{versicolor} 0 0.90625 0.09375 0.592878 32 0.304762 +{} 0.520548 0.0136986 0.465753 0.407122 73 0.695238 +Total 0.361905 0.285714 0.352381 1 105 1 + +Values +Value Frequency Coverage + 73 0.695238 +versicolor 32 0.304762 + +---------------------------------------------- +Rank R09 +Variable Numerical SepalWidth + +% target values +Interval ]-inf;2.4] ]2.4;+inf[ Interest Frequency Coverage +]-inf;2.85] 0 1 0.553199 30 0.285714 +]2.85;3.35] 0.346939 0.653061 0.000981364 49 0.466667 +]3.35;+inf[ 0.807692 0.192308 0.445819 26 0.247619 +Total 0.361905 0.638095 1 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.TestEvaluationReport.xls new file mode 100644 index 000000000..e619c8c04 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.TestEvaluationReport.xls @@ -0,0 +1,1067 @@ +#Khiops 10.4.6i +Test evaluation report + +Dictionary Iris +Target variable Numerical PetalLength +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Exclude sample +Selection variable +Selection value +Instances 45 + + +Predictors performance + +Regressor RMSE MAE NLPD RankRMSE RankMAE RankNLPD +Baseline 1.65697 1.44682 1.45393 0.26541 0.212804 0 +Selective Naive Bayes 0.217591 0.176186 -0.236273 0.0517376 0.043327 -1.6902 +Univariate SPetalLength 0.244656 0.199712 -0.0394947 0.0668878 0.0538632 -1.49342 + + +-------------------------------------------------------------------------------- + +Predictors detailed performance + + +Regressor Baseline +RMSE 1.65697 +MAE 1.44682 +NLPD 1.45393 +RankRMSE 0.26541 +RankMAE 0.212804 +RankNLPD 0 + +---------------------------------------------- +Regressor Selective Naive Bayes +RMSE 0.217591 +MAE 0.176186 +NLPD -0.236273 +RankRMSE 0.0517376 +RankMAE 0.043327 +RankNLPD -1.6902 + +---------------------------------------------- +Regressor Univariate SPetalLength +RMSE 0.244656 +MAE 0.199712 +NLPD -0.0394947 +RankRMSE 0.0668878 +RankMAE 0.0538632 +RankNLPD -1.49342 + +% target values +Group ]-inf;2.4] ]2.4;3.95] ]3.95;4.95] ]4.95;5.95] ]5.95;+inf[ Interest Frequency Coverage +{1} 1 0 0 0 0 0.247916 12 0.266667 +{5} 0 0 0 1 0 0.215978 8 0.177778 +{4} 0 0 1 0 0 0.257797 18 0.4 +{3} 0 1 0 0 0 0.126984 3 0.0666667 +{6} 0 0 0 0 1 0.151326 4 0.0888889 +Total 0.266667 0.0666667 0.4 0.177778 0.0888889 1 45 1 + + + +---------------------------------------------- +REC curves +Size Baseline Selective Naive Bayes Univariate SPetalLength +0 0 0 0 +0.1 0 0 13.3333 +0.2 0 4.44444 13.3333 +0.3 0 4.44444 13.3333 +0.4 0 4.44444 15.5556 +0.5 0 4.44444 20 +0.6 0 4.44444 20 +0.7 0 4.44444 20 +0.8 0 4.44444 20 +0.9 0 4.44444 20 +1 11.1111 4.44444 20 +1.1 11.1111 15.5556 20 +1.2 11.1111 15.5556 20 +1.3 11.1111 15.5556 20 +1.4 11.1111 15.5556 20 +1.5 11.1111 15.5556 20 +1.6 11.1111 15.5556 20 +1.7 11.1111 15.5556 20 +1.8 11.1111 22.2222 20 +1.9 11.1111 22.2222 20 +2 11.1111 22.2222 22.2222 +2.1 11.1111 22.2222 22.2222 +2.2 11.1111 22.2222 22.2222 +2.3 11.1111 26.6667 24.4444 +2.4 15.5556 31.1111 26.6667 +2.5 15.5556 31.1111 26.6667 +2.6 15.5556 33.3333 28.8889 +2.7 15.5556 40 40 +2.8 15.5556 40 40 +2.9 15.5556 40 40 +3 15.5556 40 40 +3.1 15.5556 40 40 +3.2 15.5556 40 40 +3.3 15.5556 40 40 +3.4 15.5556 40 44.4444 +3.5 15.5556 40 44.4444 +3.6 15.5556 48.8889 44.4444 +3.7 15.5556 51.1111 44.4444 +3.8 15.5556 51.1111 44.4444 +3.9 15.5556 51.1111 44.4444 +4 15.5556 51.1111 44.4444 +4.1 15.5556 53.3333 44.4444 +4.2 15.5556 55.5556 44.4444 +4.3 17.7778 55.5556 44.4444 +4.4 17.7778 55.5556 46.6667 +4.5 17.7778 55.5556 46.6667 +4.6 17.7778 57.7778 46.6667 +4.7 17.7778 57.7778 46.6667 +4.8 17.7778 66.6667 48.8889 +4.9 17.7778 66.6667 48.8889 +5 17.7778 66.6667 48.8889 +5.1 17.7778 66.6667 48.8889 +5.2 17.7778 66.6667 48.8889 +5.3 17.7778 66.6667 48.8889 +5.4 17.7778 68.8889 48.8889 +5.5 17.7778 68.8889 48.8889 +5.6 17.7778 77.7778 48.8889 +5.7 17.7778 77.7778 48.8889 +5.8 31.1111 77.7778 48.8889 +5.9 31.1111 77.7778 48.8889 +6 31.1111 77.7778 48.8889 +6.1 31.1111 77.7778 48.8889 +6.2 31.1111 77.7778 48.8889 +6.3 31.1111 77.7778 48.8889 +6.4 31.1111 80 55.5556 +6.5 31.1111 80 55.5556 +6.6 31.1111 80 55.5556 +6.7 31.1111 80 64.4444 +6.8 31.1111 80 64.4444 +6.9 31.1111 80 64.4444 +7 31.1111 80 64.4444 +7.1 31.1111 80 64.4444 +7.2 31.1111 80 64.4444 +7.3 31.1111 80 64.4444 +7.4 31.1111 86.6667 64.4444 +7.5 31.1111 86.6667 71.1111 +7.6 31.1111 86.6667 71.1111 +7.7 33.3333 86.6667 73.3333 +7.8 33.3333 86.6667 73.3333 +7.9 33.3333 86.6667 73.3333 +8 33.3333 86.6667 73.3333 +8.1 33.3333 86.6667 73.3333 +8.2 33.3333 86.6667 73.3333 +8.3 33.3333 86.6667 73.3333 +8.4 33.3333 86.6667 73.3333 +8.5 33.3333 86.6667 73.3333 +8.6 33.3333 86.6667 73.3333 +8.7 33.3333 86.6667 73.3333 +8.8 33.3333 86.6667 77.7778 +8.9 33.3333 91.1111 77.7778 +9 33.3333 93.3333 77.7778 +9.1 33.3333 93.3333 77.7778 +9.2 33.3333 93.3333 77.7778 +9.3 33.3333 93.3333 77.7778 +9.4 33.3333 93.3333 77.7778 +9.5 33.3333 93.3333 77.7778 +9.6 35.5556 93.3333 77.7778 +9.7 35.5556 93.3333 77.7778 +9.8 35.5556 93.3333 77.7778 +9.9 35.5556 93.3333 82.2222 +10 37.7778 95.5556 84.4444 +10.1 37.7778 95.5556 84.4444 +10.2 37.7778 95.5556 86.6667 +10.3 37.7778 95.5556 86.6667 +10.4 37.7778 97.7778 86.6667 +10.5 37.7778 97.7778 91.1111 +10.6 37.7778 97.7778 91.1111 +10.7 37.7778 97.7778 91.1111 +10.8 37.7778 97.7778 91.1111 +10.9 37.7778 97.7778 91.1111 +11 37.7778 97.7778 91.1111 +11.1 37.7778 97.7778 91.1111 +11.2 37.7778 97.7778 91.1111 +11.3 37.7778 97.7778 93.3333 +11.4 37.7778 97.7778 93.3333 +11.5 37.7778 97.7778 93.3333 +11.6 37.7778 100 93.3333 +11.7 37.7778 100 93.3333 +11.8 37.7778 100 93.3333 +11.9 37.7778 100 93.3333 +12 37.7778 100 93.3333 +12.1 37.7778 100 93.3333 +12.2 37.7778 100 93.3333 +12.3 37.7778 100 93.3333 +12.4 40 100 93.3333 +12.5 40 100 93.3333 +12.6 40 100 100 +12.7 40 100 100 +12.8 40 100 100 +12.9 40 100 100 +13 40 100 100 +13.1 40 100 100 +13.2 40 100 100 +13.3 40 100 100 +13.4 42.2222 100 100 +13.5 42.2222 100 100 +13.6 42.2222 100 100 +13.7 42.2222 100 100 +13.8 42.2222 100 100 +13.9 42.2222 100 100 +14 42.2222 100 100 +14.1 42.2222 100 100 +14.2 42.2222 100 100 +14.3 42.2222 100 100 +14.4 42.2222 100 100 +14.5 42.2222 100 100 +14.6 42.2222 100 100 +14.7 42.2222 100 100 +14.8 42.2222 100 100 +14.9 42.2222 100 100 +15 42.2222 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48.8889 100 100 +19.7 48.8889 100 100 +19.8 48.8889 100 100 +19.9 48.8889 100 100 +20 48.8889 100 100 +20.1 48.8889 100 100 +20.2 48.8889 100 100 +20.3 48.8889 100 100 +20.4 48.8889 100 100 +20.5 48.8889 100 100 +20.6 48.8889 100 100 +20.7 48.8889 100 100 +20.8 48.8889 100 100 +20.9 48.8889 100 100 +21 48.8889 100 100 +21.1 48.8889 100 100 +21.2 48.8889 100 100 +21.3 48.8889 100 100 +21.4 48.8889 100 100 +21.5 48.8889 100 100 +21.6 48.8889 100 100 +21.7 48.8889 100 100 +21.8 48.8889 100 100 +21.9 48.8889 100 100 +22 53.3333 100 100 +22.1 53.3333 100 100 +22.2 53.3333 100 100 +22.3 53.3333 100 100 +22.4 53.3333 100 100 +22.5 53.3333 100 100 +22.6 53.3333 100 100 +22.7 53.3333 100 100 +22.8 53.3333 100 100 +22.9 60 100 100 +23 60 100 100 +23.1 60 100 100 +23.2 60 100 100 +23.3 60 100 100 +23.4 60 100 100 +23.5 60 100 100 +23.6 60 100 100 +23.7 60 100 100 +23.8 60 100 100 +23.9 60 100 100 +24 60 100 100 +24.1 60 100 100 +24.2 60 100 100 +24.3 60 100 100 +24.4 60 100 100 +24.5 60 100 100 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+98.7 100 100 100 +98.8 100 100 100 +98.9 100 100 100 +99 100 100 100 +99.1 100 100 100 +99.2 100 100 100 +99.3 100 100 100 +99.4 100 100 100 +99.5 100 100 100 +99.6 100 100 100 +99.7 100 100 100 +99.8 100 100 100 +99.9 100 100 100 +100 100 100 100 diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.TrainEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.TrainEvaluationReport.xls new file mode 100644 index 000000000..12ba1bf96 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.TrainEvaluationReport.xls @@ -0,0 +1,1067 @@ +#Khiops 10.4.6i +Train evaluation report + +Dictionary Iris +Target variable Numerical PetalLength +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + + +Predictors performance + +Regressor RMSE MAE NLPD RankRMSE RankMAE RankNLPD +Baseline 1.80142 1.62949 1.22759 0.28832 0.249977 0 +Selective Naive Bayes 0.246126 0.198752 -0.441506 0.0569905 0.0485448 -1.6691 +Univariate SPetalLength 0.274444 0.222518 -0.202594 0.0797015 0.0649407 -1.43019 + + +-------------------------------------------------------------------------------- + +Predictors detailed performance + + +Regressor Baseline +RMSE 1.80142 +MAE 1.62949 +NLPD 1.22759 +RankRMSE 0.28832 +RankMAE 0.249977 +RankNLPD 0 + +---------------------------------------------- +Regressor Selective Naive Bayes +RMSE 0.246126 +MAE 0.198752 +NLPD -0.441506 +RankRMSE 0.0569905 +RankMAE 0.0485448 +RankNLPD -1.6691 + +---------------------------------------------- +Regressor Univariate SPetalLength +RMSE 0.274444 +MAE 0.222518 +NLPD -0.202594 +RankRMSE 0.0797015 +RankMAE 0.0649407 +RankNLPD -1.43019 + +% target values +Group ]-inf;2.4] ]2.4;3.95] ]3.95;4.95] ]4.95;5.95] ]5.95;+inf[ Interest Frequency Coverage +{1} 1 0 0 0 0 0.256249 38 0.361905 +{5} 0 0 0 1 0 0.243293 27 0.257143 +{4} 0 0 1 0 0 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"partitionType": "Intervals", + "partition": [ + [1,2.4], + [2.4,6.9] + ] + } + ], + "partTargetFrequencies": [ + [0,67], + [38,0] + ], + "partInterests": [0.438003,0.561997] + }, + "inputValues": { + "values": ["","setosa"], + "frequencies": [67,38] + } + }, + "R06": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "UpperPetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1.5,1.55], + [1.55,2.5] + ] + }, + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,4.75], + [4.75,6.9] + ] + } + ], + "partTargetFrequencies": [ + [63,4], + [2,36] + ], + "partInterests": [0.404409,0.595591] + } + }, + "R07": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [4.3,5.45], + [5.45,6.15], + [6.15,7.7] + ] + }, + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,3.55], + [3.55,4.85], + [4.85,6.9] + ] + } + ], + "partTargetFrequencies": [ + [37,2,0], + [5,18,5], + [0,6,32] + ], + "partInterests": [0.452028,0.152867,0.395105] + } + }, + "R08": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Class2", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + [""], + ["versicolor"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,2.4], + [2.4,4.85], + [4.85,6.9] + ] + } + ], + "partTargetFrequencies": [ + [38,1,34], + [0,29,3] + ], + "partInterests": [0.407122,0.592878] + }, + "inputValues": { + "values": ["","versicolor"], + "frequencies": [73,32] + } + }, + "R09": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "SepalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,2.85], + [2.85,3.35], + [3.35,4.4] + ] + }, + { + "variable": "PetalLength", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [1,2.4], + [2.4,6.9] + ] + } + ], + "partTargetFrequencies": [ + [0,30], + [17,32], + [21,5] + ], + "partInterests": [0.553199,0.000981364,0.445819] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/Modeling.kdic b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.model.kdic similarity index 72% rename from test/LearningTest/TestKhiops/Standard/IrisR/results.ref/Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.model.kdic index 547677b59..75edaca48 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/AnalysisResults.model.kdic @@ -1,70 +1,102 @@ -#Khiops 10.1 - -Dictionary BU1_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; -Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; -Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; -Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; -Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; -Unused Numerical Dummy1 = Copy(0) ; -Unused Numerical Dummy2 = Random() ; -Unused Categorical Class ; -Unused Structure(DataGrid) VPetalLength = DataGrid(ValueSet(1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.9, 3, 3.3, 3.5, 3.6, 3.9, 4, 4.1, 4.3, 4.4, 4.5, 4.7, 4.8, 4.9, 5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6, 6.1, 6.7, 6.9), Frequencies(1, 1, 2, 4, 9, 10, 5, 4, 2, 1, 1, 2, 1, 3, 4, 3, 1, 2, 5, 4, 3, 3, 3, 5, 2, 1, 2, 3, 6, 1, 2, 2, 1, 3, 2, 1)) ; -Unused Structure(DataGrid) PSPetalLength = DataGrid(ValueGroups(ValueGroup("1"), ValueGroup("5"), ValueGroup("4"), ValueGroup("3"), ValueGroup("6", " * ")), IntervalBounds(2.4, 3.95, 4.95, 5.95), Frequencies(38, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 25, 0, 0, 0, 27, 0, 0, 0, 0, 0, 0, 0, 7)) ; // DataGrid(SPetalLength, PetalLength) -Unused Structure(RankRegressor) BU1RPetalLength = NBRankRegressor(DataGridStats(PSPetalLength, SPetalLength)) ; -Unused Numerical MRPetalLength = TargetRankMean(BU1RPetalLength) ; -Unused Numerical SDRPetalLength = TargetRankStandardDeviation(BU1RPetalLength) ; -Unused Numerical CPR1PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 0.2) ; // Partile 1 on 5 -Unused Numerical CPR2PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 0.4) ; // Partile 2 on 5 -Unused Numerical CPR3PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 0.6) ; // Partile 3 on 5 -Unused Numerical CPR4PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 0.8) ; // Partile 4 on 5 -Unused Numerical CPR5PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 1) ; // Partile 5 on 5 -Unused Numerical RankPetalLength = ValueRank(VPetalLength, PetalLength) ; -Unused Numerical DRPetalLength = TargetRankDensityAt(BU1RPetalLength, RankPetalLength) ; -Unused Structure(Regressor) BU1PetalLength = NBRegressor(BU1RPetalLength, VPetalLength) ; - Numerical MPetalLength = TargetMean(BU1PetalLength) ; -Unused Numerical SDPetalLength = TargetStandardDeviation(BU1PetalLength) ; -Unused Numerical DPetalLength = TargetDensityAt(BU1PetalLength, PetalLength) ; -}; - -Dictionary SNB_Iris - -{ -Unused Numerical SepalLength ; -Unused Numerical SepalWidth ; -Unused Numerical PetalLength ; -Unused Numerical PetalWidth ; -Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; -Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; -Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; -Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; -Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; -Unused Numerical Dummy1 = Copy(0) ; -Unused Numerical Dummy2 = Random() ; -Unused Categorical Class ; -Unused Structure(DataGrid) VPetalLength = DataGrid(ValueSet(1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.9, 3, 3.3, 3.5, 3.6, 3.9, 4, 4.1, 4.3, 4.4, 4.5, 4.7, 4.8, 4.9, 5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6, 6.1, 6.7, 6.9), Frequencies(1, 1, 2, 4, 9, 10, 5, 4, 2, 1, 1, 2, 1, 3, 4, 3, 1, 2, 5, 4, 3, 3, 3, 5, 2, 1, 2, 3, 6, 1, 2, 2, 1, 3, 2, 1)) ; -Unused Structure(DataGrid) PSPetalLength = DataGrid(ValueGroups(ValueGroup("1"), ValueGroup("5"), ValueGroup("4"), ValueGroup("3"), ValueGroup("6", " * ")), IntervalBounds(2.4, 3.95, 4.95, 5.95), Frequencies(38, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 25, 0, 0, 0, 27, 0, 0, 0, 0, 0, 0, 0, 7)) ; // DataGrid(SPetalLength, PetalLength) -Unused Structure(DataGrid) PLowerPetalLength = DataGrid(IntervalBounds(1.45, 2.4), IntervalBounds(1.45, 2.4), Frequencies(17, 0, 0, 0, 21, 0, 0, 0, 67)) ; // DataGrid(LowerPetalLength, PetalLength) -Unused Structure(DataGrid) PUpperPetalWidth = DataGrid(IntervalBounds(1.55), IntervalBounds(4.75), Frequencies(63, 2, 4, 36)) ; // DataGrid(UpperPetalWidth, PetalLength) -Unused Structure(DataGrid) PClass2 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("versicolor", " * ")), IntervalBounds(2.4, 4.85), Frequencies(38, 0, 1, 29, 34, 3)) ; // DataGrid(Class2, PetalLength) -Unused Structure(RankRegressor) SNBRPetalLength = SNBRankRegressor(Vector(0.5234375, 0.4140625, 0.3203125, 0.0078125), DataGridStats(PSPetalLength, SPetalLength), DataGridStats(PLowerPetalLength, LowerPetalLength), DataGridStats(PUpperPetalWidth, UpperPetalWidth), DataGridStats(PClass2, Class2)) ; -Unused Numerical MRPetalLength = TargetRankMean(SNBRPetalLength) ; -Unused Numerical SDRPetalLength = TargetRankStandardDeviation(SNBRPetalLength) ; -Unused Numerical CPR1PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 0.2) ; // Partile 1 on 5 -Unused Numerical CPR2PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 0.4) ; // Partile 2 on 5 -Unused Numerical CPR3PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 0.6) ; // Partile 3 on 5 -Unused Numerical CPR4PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 0.8) ; // Partile 4 on 5 -Unused Numerical CPR5PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 1) ; // Partile 5 on 5 -Unused Numerical RankPetalLength = ValueRank(VPetalLength, PetalLength) ; -Unused Numerical DRPetalLength = TargetRankDensityAt(SNBRPetalLength, RankPetalLength) ; -Unused Structure(Regressor) SNBPetalLength = SNBRegressor(SNBRPetalLength, VPetalLength) ; - Numerical MPetalLength = TargetMean(SNBPetalLength) ; -Unused Numerical SDPetalLength = TargetStandardDeviation(SNBPetalLength) ; -Unused Numerical DPetalLength = TargetDensityAt(SNBPetalLength, PetalLength) ; -}; +#Khiops 10.4.6i + +Dictionary BU1_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; +Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; +Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; +Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; +Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; +Unused Numerical Dummy1 = Copy(0) ; +Unused Numerical Dummy2 = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VPetalLength = DataGrid(ValueSet(1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.9, 3, 3.3, 3.5, 3.6, 3.9, 4, 4.1, 4.3, 4.4, 4.5, 4.7, 4.8, 4.9, 5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6, 6.1, 6.7, 6.9), Frequencies(1, 1, 2, 4, 9, 10, 5, 4, 2, 1, 1, 2, 1, 3, 4, 3, 1, 2, 5, 4, 3, 3, 3, 5, 2, 1, 2, 3, 6, 1, 2, 2, 1, 3, 2, 1)) ; +Unused Structure(DataGrid) PSPetalLength = DataGrid(ValueGroups(ValueGroup("1"), ValueGroup("5"), ValueGroup("4"), ValueGroup("3"), ValueGroup("6", " * ")), IntervalBounds(2.4, 3.95, 4.95, 5.95), Frequencies(38, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 25, 0, 0, 0, 27, 0, 0, 0, 0, 0, 0, 0, 7)) ; // DataGrid(SPetalLength, PetalLength) +Unused Structure(RankRegressor) BU1RPetalLength = NBRankRegressor(DataGridStats(PSPetalLength, SPetalLength)) ; +Unused Numerical MRPetalLength = TargetRankMean(BU1RPetalLength) ; +Unused Numerical SDRPetalLength = TargetRankStandardDeviation(BU1RPetalLength) ; +Unused Numerical CPR1PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 0.2) ; // Partile 1 on 5 +Unused Numerical CPR2PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 0.4) ; // Partile 2 on 5 +Unused Numerical CPR3PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 0.6) ; // Partile 3 on 5 +Unused Numerical CPR4PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 0.8) ; // Partile 4 on 5 +Unused Numerical CPR5PetalLength = TargetRankCumulativeProbAt(BU1RPetalLength, 1) ; // Partile 5 on 5 +Unused Numerical RankPetalLength = ValueRank(VPetalLength, PetalLength) ; +Unused Numerical DRPetalLength = TargetRankDensityAt(BU1RPetalLength, RankPetalLength) ; +Unused Structure(Regressor) BU1PetalLength = NBRegressor(BU1RPetalLength, VPetalLength) ; + Numerical MPetalLength = TargetMean(BU1PetalLength) ; +Unused Numerical SDPetalLength = TargetStandardDeviation(BU1PetalLength) ; +Unused Numerical DPetalLength = TargetDensityAt(BU1PetalLength, PetalLength) ; +}; + +Dictionary B_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; +Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; +Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; +Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; +Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; +Unused Numerical Dummy1 = Copy(0) ; +Unused Numerical Dummy2 = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VPetalLength = DataGrid(ValueSet(1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.9, 3, 3.3, 3.5, 3.6, 3.9, 4, 4.1, 4.3, 4.4, 4.5, 4.7, 4.8, 4.9, 5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6, 6.1, 6.7, 6.9), Frequencies(1, 1, 2, 4, 9, 10, 5, 4, 2, 1, 1, 2, 1, 3, 4, 3, 1, 2, 5, 4, 3, 3, 3, 5, 2, 1, 2, 3, 6, 1, 2, 2, 1, 3, 2, 1)) ; +Unused Structure(RankRegressor) BRPetalLength = NBRankRegressor(DataGridStats(DataGrid(IntervalBounds(), Frequencies(105)))) ; +Unused Numerical MRPetalLength = TargetRankMean(BRPetalLength) ; +Unused Numerical SDRPetalLength = TargetRankStandardDeviation(BRPetalLength) ; +Unused Numerical CPR1PetalLength = TargetRankCumulativeProbAt(BRPetalLength, 0.2) ; // Partile 1 on 5 +Unused Numerical CPR2PetalLength = TargetRankCumulativeProbAt(BRPetalLength, 0.4) ; // Partile 2 on 5 +Unused Numerical CPR3PetalLength = TargetRankCumulativeProbAt(BRPetalLength, 0.6) ; // Partile 3 on 5 +Unused Numerical CPR4PetalLength = TargetRankCumulativeProbAt(BRPetalLength, 0.8) ; // Partile 4 on 5 +Unused Numerical CPR5PetalLength = TargetRankCumulativeProbAt(BRPetalLength, 1) ; // Partile 5 on 5 +Unused Numerical RankPetalLength = ValueRank(VPetalLength, PetalLength) ; +Unused Numerical DRPetalLength = TargetRankDensityAt(BRPetalLength, RankPetalLength) ; +Unused Structure(Regressor) BPetalLength = NBRegressor(BRPetalLength, VPetalLength) ; + Numerical MPetalLength = TargetMean(BPetalLength) ; +Unused Numerical SDPetalLength = TargetStandardDeviation(BPetalLength) ; +Unused Numerical DPetalLength = TargetDensityAt(BPetalLength, PetalLength) ; +}; + +Dictionary SNB_Iris + +{ +Unused Numerical SepalLength ; +Unused Numerical SepalWidth ; +Unused Numerical PetalLength ; +Unused Numerical PetalWidth ; +Unused Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; +Unused Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; +Unused Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; +Unused Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; +Unused Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; +Unused Numerical Dummy1 = Copy(0) ; +Unused Numerical Dummy2 = Random() ; +Unused Categorical Class ; +Unused Structure(DataGrid) VPetalLength = DataGrid(ValueSet(1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.9, 3, 3.3, 3.5, 3.6, 3.9, 4, 4.1, 4.3, 4.4, 4.5, 4.7, 4.8, 4.9, 5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6, 6.1, 6.7, 6.9), Frequencies(1, 1, 2, 4, 9, 10, 5, 4, 2, 1, 1, 2, 1, 3, 4, 3, 1, 2, 5, 4, 3, 3, 3, 5, 2, 1, 2, 3, 6, 1, 2, 2, 1, 3, 2, 1)) ; +Unused Structure(DataGrid) PSPetalLength = DataGrid(ValueGroups(ValueGroup("1"), ValueGroup("5"), ValueGroup("4"), ValueGroup("3"), ValueGroup("6", " * ")), IntervalBounds(2.4, 3.95, 4.95, 5.95), Frequencies(38, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 25, 0, 0, 0, 27, 0, 0, 0, 0, 0, 0, 0, 7)) ; // DataGrid(SPetalLength, PetalLength) +Unused Structure(DataGrid) PLowerPetalLength = DataGrid(IntervalBounds(1.45, 2.4), IntervalBounds(1.45, 2.4), Frequencies(17, 0, 0, 0, 21, 0, 0, 0, 67)) ; // DataGrid(LowerPetalLength, PetalLength) +Unused Structure(DataGrid) PUpperPetalWidth = DataGrid(IntervalBounds(1.55), IntervalBounds(4.75), Frequencies(63, 2, 4, 36)) ; // DataGrid(UpperPetalWidth, PetalLength) +Unused Structure(DataGrid) PClass2 = DataGrid(ValueGroups(ValueGroup(""), ValueGroup("versicolor", " * ")), IntervalBounds(2.4, 4.85), Frequencies(38, 0, 1, 29, 34, 3)) ; // DataGrid(Class2, PetalLength) +Unused Structure(RankRegressor) SNBRPetalLength = SNBRankRegressor(Vector(0.5234375, 0.4140625, 0.3203125, 0.0078125), DataGridStats(PSPetalLength, SPetalLength), DataGridStats(PLowerPetalLength, LowerPetalLength), DataGridStats(PUpperPetalWidth, UpperPetalWidth), DataGridStats(PClass2, Class2)) ; +Unused Numerical MRPetalLength = TargetRankMean(SNBRPetalLength) ; +Unused Numerical SDRPetalLength = TargetRankStandardDeviation(SNBRPetalLength) ; +Unused Numerical CPR1PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 0.2) ; // Partile 1 on 5 +Unused Numerical CPR2PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 0.4) ; // Partile 2 on 5 +Unused Numerical CPR3PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 0.6) ; // Partile 3 on 5 +Unused Numerical CPR4PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 0.8) ; // Partile 4 on 5 +Unused Numerical CPR5PetalLength = TargetRankCumulativeProbAt(SNBRPetalLength, 1) ; // Partile 5 on 5 +Unused Numerical RankPetalLength = ValueRank(VPetalLength, PetalLength) ; +Unused Numerical DRPetalLength = TargetRankDensityAt(SNBRPetalLength, RankPetalLength) ; +Unused Structure(Regressor) SNBPetalLength = SNBRegressor(SNBRPetalLength, VPetalLength) ; + Numerical MPetalLength = TargetMean(SNBPetalLength) ; +Unused Numerical SDPetalLength = TargetStandardDeviation(SNBPetalLength) ; +Unused Numerical DPetalLength = TargetDensityAt(SNBPetalLength, PetalLength) ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/T_IrisR.txt b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/D_IrisR.txt similarity index 92% rename from test/LearningTest/TestKhiops/Standard/IrisR/results.ref/T_IrisR.txt rename to test/LearningTest/TestKhiops/Standard/IrisR/results.ref/D_IrisR.txt index 61ec7628b..36c10f050 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/T_IrisR.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/D_IrisR.txt @@ -1,106 +1,106 @@ -MPetalLength -1.355171932 -1.355171932 -1.609226997 -1.609226997 -1.355171932 -1.609226997 -1.355171932 -1.609226997 -1.355171932 -1.355171932 -1.355171932 -1.609226997 -1.355171932 -1.355171932 -1.609226997 -1.609226997 -1.355171932 -1.609226997 -1.609226997 -1.609226997 -1.609226997 -1.609226997 -1.355171932 -1.609226997 -1.609226997 -1.609226997 -1.609226997 -1.355171932 -1.609226997 -1.355171932 -1.609226997 -1.355171932 -1.355171932 -1.609226997 -1.355171932 -1.609226997 -1.355171932 -1.609226997 -4.42199468 -4.42199468 -4.42199468 -4.42199468 -4.637706388 -3.669072416 -3.669072416 -3.669072416 -4.42199468 -4.42199468 -3.669072416 -4.42199468 -4.42199468 -3.669072416 -4.637706388 -4.42199468 -4.42199468 -4.42199468 -4.42199468 -4.42199468 -5.40384729 -4.42199468 -3.669072416 -3.669072416 -5.40384729 -4.42199468 -4.637706388 -4.42199468 -4.42199468 -4.42199468 -3.669072416 -4.42199468 -5.404269659 -5.404269659 -5.404269659 -5.404269659 -6.452034245 -5.404269659 -5.404269659 -5.404269659 -5.404269659 -5.404269659 -6.452034245 -6.452034245 -5.404269659 -4.642758411 -6.452034245 -4.642758411 -6.452034245 -5.404269659 -6.452034245 -5.404269659 -5.354525143 -5.354525143 -6.452034245 -5.404269659 -5.404269659 -4.642758411 -5.404269659 -5.404269659 -5.404269659 -5.404269659 -5.404269659 -5.404269659 -5.404269659 -5.404269659 -5.404269659 +MPetalLength +1.355171932 +1.355171932 +1.609226997 +1.609226997 +1.355171932 +1.609226997 +1.355171932 +1.609226997 +1.355171932 +1.355171932 +1.355171932 +1.609226997 +1.355171932 +1.355171932 +1.609226997 +1.609226997 +1.355171932 +1.609226997 +1.609226997 +1.609226997 +1.609226997 +1.609226997 +1.355171932 +1.609226997 +1.609226997 +1.609226997 +1.609226997 +1.355171932 +1.609226997 +1.355171932 +1.609226997 +1.355171932 +1.355171932 +1.609226997 +1.355171932 +1.609226997 +1.355171932 +1.609226997 +4.42199468 +4.42199468 +4.42199468 +4.42199468 +4.637706388 +3.669072416 +3.669072416 +3.669072416 +4.42199468 +4.42199468 +3.669072416 +4.42199468 +4.42199468 +3.669072416 +4.637706388 +4.42199468 +4.42199468 +4.42199468 +4.42199468 +4.42199468 +5.40384729 +4.42199468 +3.669072416 +3.669072416 +5.40384729 +4.42199468 +4.637706388 +4.42199468 +4.42199468 +4.42199468 +3.669072416 +4.42199468 +5.404269659 +5.404269659 +5.404269659 +5.404269659 +6.452034245 +5.404269659 +5.404269659 +5.404269659 +5.404269659 +5.404269659 +6.452034245 +6.452034245 +5.404269659 +4.642758411 +6.452034245 +4.642758411 +6.452034245 +5.404269659 +6.452034245 +5.404269659 +5.354525143 +5.354525143 +6.452034245 +5.404269659 +5.404269659 +4.642758411 +5.404269659 +5.404269659 +5.404269659 +5.404269659 +5.404269659 +5.404269659 +5.404269659 +5.404269659 +5.404269659 diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/TestEvaluationReport.xls deleted file mode 100644 index f7672b821..000000000 --- a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/TestEvaluationReport.xls +++ /dev/null @@ -1,1057 +0,0 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary Iris -Target variable Numerical PetalLength -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Exclude sample -Selection variable -Selection value -Instances 45 - - -Predictors performance - -Regressor RMSE MAE NLPD RankRMSE RankMAE RankNLPD -Selective Naive Bayes 0.217591 0.176186 -0.236273 0.0517376 0.043327 -1.6902 -Univariate SPetalLength 0.244656 0.199712 -0.0394947 0.0668878 0.0538632 -1.49342 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Regressor Selective Naive Bayes -RMSE 0.217591 -MAE 0.176186 -NLPD -0.236273 -RankRMSE 0.0517376 -RankMAE 0.043327 -RankNLPD -1.6902 - ----------------------------------------------- -Regressor Univariate SPetalLength -RMSE 0.244656 -MAE 0.199712 -NLPD -0.0394947 -RankRMSE 0.0668878 -RankMAE 0.0538632 -RankNLPD -1.49342 - -% target values -Group ]-inf;2.4] ]2.4;3.95] ]3.95;4.95] ]4.95;5.95] ]5.95;+inf[ Interest Frequency Coverage -{1} 1 0 0 0 0 0.247916 12 0.266667 -{5} 0 0 0 1 0 0.215978 8 0.177778 -{4} 0 0 1 0 0 0.257797 18 0.4 -{3} 0 1 0 0 0 0.126984 3 0.0666667 -{6} 0 0 0 0 1 0.151326 4 0.0888889 -Total 0.266667 0.0666667 0.4 0.177778 0.0888889 1 45 1 - - - ----------------------------------------------- -REC curves -Size Selective Naive Bayes Univariate SPetalLength -0 0 0 -0.1 0 13.3333 -0.2 4.44444 13.3333 -0.3 4.44444 13.3333 -0.4 4.44444 15.5556 -0.5 4.44444 20 -0.6 4.44444 20 -0.7 4.44444 20 -0.8 4.44444 20 -0.9 4.44444 20 -1 4.44444 20 -1.1 15.5556 20 -1.2 15.5556 20 -1.3 15.5556 20 -1.4 15.5556 20 -1.5 15.5556 20 -1.6 15.5556 20 -1.7 15.5556 20 -1.8 22.2222 20 -1.9 22.2222 20 -2 22.2222 22.2222 -2.1 22.2222 22.2222 -2.2 22.2222 22.2222 -2.3 26.6667 24.4444 -2.4 31.1111 26.6667 -2.5 31.1111 26.6667 -2.6 33.3333 28.8889 -2.7 40 40 -2.8 40 40 -2.9 40 40 -3 40 40 -3.1 40 40 -3.2 40 40 -3.3 40 40 -3.4 40 44.4444 -3.5 40 44.4444 -3.6 48.8889 44.4444 -3.7 51.1111 44.4444 -3.8 51.1111 44.4444 -3.9 51.1111 44.4444 -4 51.1111 44.4444 -4.1 53.3333 44.4444 -4.2 55.5556 44.4444 -4.3 55.5556 44.4444 -4.4 55.5556 46.6667 -4.5 55.5556 46.6667 -4.6 57.7778 46.6667 -4.7 57.7778 46.6667 -4.8 66.6667 48.8889 -4.9 66.6667 48.8889 -5 66.6667 48.8889 -5.1 66.6667 48.8889 -5.2 66.6667 48.8889 -5.3 66.6667 48.8889 -5.4 68.8889 48.8889 -5.5 68.8889 48.8889 -5.6 77.7778 48.8889 -5.7 77.7778 48.8889 -5.8 77.7778 48.8889 -5.9 77.7778 48.8889 -6 77.7778 48.8889 -6.1 77.7778 48.8889 -6.2 77.7778 48.8889 -6.3 77.7778 48.8889 -6.4 80 55.5556 -6.5 80 55.5556 -6.6 80 55.5556 -6.7 80 64.4444 -6.8 80 64.4444 -6.9 80 64.4444 -7 80 64.4444 -7.1 80 64.4444 -7.2 80 64.4444 -7.3 80 64.4444 -7.4 86.6667 64.4444 -7.5 86.6667 71.1111 -7.6 86.6667 71.1111 -7.7 86.6667 73.3333 -7.8 86.6667 73.3333 -7.9 86.6667 73.3333 -8 86.6667 73.3333 -8.1 86.6667 73.3333 -8.2 86.6667 73.3333 -8.3 86.6667 73.3333 -8.4 86.6667 73.3333 -8.5 86.6667 73.3333 -8.6 86.6667 73.3333 -8.7 86.6667 73.3333 -8.8 86.6667 77.7778 -8.9 91.1111 77.7778 -9 93.3333 77.7778 -9.1 93.3333 77.7778 -9.2 93.3333 77.7778 -9.3 93.3333 77.7778 -9.4 93.3333 77.7778 -9.5 93.3333 77.7778 -9.6 93.3333 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a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/TrainEvaluationReport.xls +++ /dev/null @@ -1,1057 +0,0 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary Iris -Target variable Numerical PetalLength -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - - -Predictors performance - -Regressor RMSE MAE NLPD RankRMSE RankMAE RankNLPD -Selective Naive Bayes 0.246126 0.198752 -0.441506 0.0569905 0.0485448 -1.6691 -Univariate SPetalLength 0.274444 0.222518 -0.202594 0.0797015 0.0649407 -1.43019 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Regressor Selective Naive Bayes -RMSE 0.246126 -MAE 0.198752 -NLPD -0.441506 -RankRMSE 0.0569905 -RankMAE 0.0485448 -RankNLPD -1.6691 - ----------------------------------------------- -Regressor Univariate SPetalLength -RMSE 0.274444 -MAE 0.222518 -NLPD -0.202594 -RankRMSE 0.0797015 -RankMAE 0.0649407 -RankNLPD -1.43019 - -% target values -Group ]-inf;2.4] ]2.4;3.95] ]3.95;4.95] ]4.95;5.95] ]5.95;+inf[ Interest Frequency Coverage -{1} 1 0 0 0 0 0.256249 38 0.361905 -{5} 0 0 0 1 0 0.243293 27 0.257143 -{4} 0 0 1 0 0 0.238036 25 0.238095 -{3} 0 1 0 0 0 0.136651 8 0.0761905 -{6} 0 0 0 0 1 0.125771 7 0.0666667 -Total 0.361905 0.0761905 0.238095 0.257143 0.0666667 1 105 1 - - - ----------------------------------------------- -REC curves -Size Selective Naive Bayes Univariate SPetalLength -0 0 0 -0.1 0 4.7619 -0.2 3.80952 4.7619 -0.3 3.80952 4.7619 -0.4 4.7619 5.71429 -0.5 4.7619 8.57143 -0.6 4.7619 8.57143 -0.7 4.7619 8.57143 -0.8 4.7619 8.57143 -0.9 4.7619 8.57143 -1 4.7619 8.57143 -1.1 8.57143 8.57143 -1.2 10.4762 13.3333 -1.3 13.3333 15.2381 -1.4 13.3333 15.2381 -1.5 13.3333 15.2381 -1.6 13.3333 15.2381 -1.7 13.3333 15.2381 -1.8 17.1429 15.2381 -1.9 17.1429 15.2381 -2 17.1429 17.1429 -2.1 17.1429 17.1429 -2.2 17.1429 17.1429 -2.3 19.0476 20 -2.4 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100 100 -83.1 100 100 -83.2 100 100 -83.3 100 100 -83.4 100 100 -83.5 100 100 -83.6 100 100 -83.7 100 100 -83.8 100 100 -83.9 100 100 -84 100 100 -84.1 100 100 -84.2 100 100 -84.3 100 100 -84.4 100 100 -84.5 100 100 -84.6 100 100 -84.7 100 100 -84.8 100 100 -84.9 100 100 -85 100 100 -85.1 100 100 -85.2 100 100 -85.3 100 100 -85.4 100 100 -85.5 100 100 -85.6 100 100 -85.7 100 100 -85.8 100 100 -85.9 100 100 -86 100 100 -86.1 100 100 -86.2 100 100 -86.3 100 100 -86.4 100 100 -86.5 100 100 -86.6 100 100 -86.7 100 100 -86.8 100 100 -86.9 100 100 -87 100 100 -87.1 100 100 -87.2 100 100 -87.3 100 100 -87.4 100 100 -87.5 100 100 -87.6 100 100 -87.7 100 100 -87.8 100 100 -87.9 100 100 -88 100 100 -88.1 100 100 -88.2 100 100 -88.3 100 100 -88.4 100 100 -88.5 100 100 -88.6 100 100 -88.7 100 100 -88.8 100 100 -88.9 100 100 -89 100 100 -89.1 100 100 -89.2 100 100 -89.3 100 100 -89.4 100 100 -89.5 100 100 -89.6 100 100 -89.7 100 100 -89.8 100 100 -89.9 100 100 -90 100 100 -90.1 100 100 -90.2 100 100 -90.3 100 100 -90.4 100 100 -90.5 100 100 -90.6 100 100 -90.7 100 100 -90.8 100 100 -90.9 100 100 -91 100 100 -91.1 100 100 -91.2 100 100 -91.3 100 100 -91.4 100 100 -91.5 100 100 -91.6 100 100 -91.7 100 100 -91.8 100 100 -91.9 100 100 -92 100 100 -92.1 100 100 -92.2 100 100 -92.3 100 100 -92.4 100 100 -92.5 100 100 -92.6 100 100 -92.7 100 100 -92.8 100 100 -92.9 100 100 -93 100 100 -93.1 100 100 -93.2 100 100 -93.3 100 100 -93.4 100 100 -93.5 100 100 -93.6 100 100 -93.7 100 100 -93.8 100 100 -93.9 100 100 -94 100 100 -94.1 100 100 -94.2 100 100 -94.3 100 100 -94.4 100 100 -94.5 100 100 -94.6 100 100 -94.7 100 100 -94.8 100 100 -94.9 100 100 -95 100 100 -95.1 100 100 -95.2 100 100 -95.3 100 100 -95.4 100 100 -95.5 100 100 -95.6 100 100 -95.7 100 100 -95.8 100 100 -95.9 100 100 -96 100 100 -96.1 100 100 -96.2 100 100 -96.3 100 100 -96.4 100 100 -96.5 100 100 -96.6 100 100 -96.7 100 100 -96.8 100 100 -96.9 100 100 -97 100 100 -97.1 100 100 -97.2 100 100 -97.3 100 100 -97.4 100 100 -97.5 100 100 -97.6 100 100 -97.7 100 100 -97.8 100 100 -97.9 100 100 -98 100 100 -98.1 100 100 -98.2 100 100 -98.3 100 100 -98.4 100 100 -98.5 100 100 -98.6 100 100 -98.7 100 100 -98.8 100 100 -98.9 100 100 -99 100 100 -99.1 100 100 -99.2 100 100 -99.3 100 100 -99.4 100 100 -99.5 100 100 -99.6 100 100 -99.7 100 100 -99.8 100 100 -99.9 100 100 -100 100 100 diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/err.txt index e3526621a..d38708661 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/err.txt @@ -1,17 +1,20 @@ Train supervised model for regression of target variable PetalLength Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 -Data preparation time: 0:00:00.23 +Data preparation time: 0:00:00.19 SNB train time: 0:00:00 Build Selective Naive Bayes SNB_Iris +Build Baseline B_Iris Build Univariate BU1_Iris Train evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt +Train evaluation of Baseline on database ../../../datasets/Iris/Iris.txt Train evaluation of Univariate SPetalLength on database ../../../datasets/Iris/Iris.txt Test evaluation of Selective Naive Bayes on database ../../../datasets/Iris/Iris.txt +Test evaluation of Baseline on database ../../../datasets/Iris/Iris.txt Test evaluation of Univariate SPetalLength on database ../../../datasets/Iris/Iris.txt -Write report ./results\AllReports.khj +Write report ./results/AnalysisResults.khj Deploy model SNB_Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_IrisR.txt: Written records: 105 +Output database ./results/D_IrisR.txt: Written records: 105 Model deployment time: 0:00:00.01 diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/time.log index 84b492444..daec461b5 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/IrisR/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.4652562141418457 +Overal time: 0.41715097427368164 diff --git a/test/LearningTest/TestKhiops/Standard/IrisR/test.prm b/test/LearningTest/TestKhiops/Standard/IrisR/test.prm index 36aa9ef64..a8305a909 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisR/test.prm +++ b/test/LearningTest/TestKhiops/Standard/IrisR/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -22,35 +24,37 @@ ClassFileName ./IrisExtended.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage AnalysisSpec.TargetAttributeName PetalLength // Target variable -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif -AnalysisSpec.PredictorsSpec.AdvancedSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries +AnalysisSpec.PredictorsSpec.ExpertSpec.UnivariatePredictorNumber 1 // Nombre de predicteurs univaries +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database ClassManagement.OpenFile // Open... -ClassFileName ./results/Modeling.kdic // Dictionary file +ClassFileName ./results/AnalysisResults.model.kdic // Dictionary file OK // Open -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SNB_Iris // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key SNB_Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_IrisR.txt // Database file -TransferDatabase // Transfer database +ClassName SNB_Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SNB_Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_IrisR.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer diff --git a/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/T_Iris.txt b/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/D_Iris.txt similarity index 96% rename from test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/T_Iris.txt rename to test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/D_Iris.txt index 73f08f1ac..f2765cfdc 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/T_Iris.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/D_Iris.txt @@ -1,106 +1,106 @@ -SepalLength SepalWidth PetalLength PetalWidth Class -4.9 3 1.4 0.2 Iris-setosa -4.7 3.2 1.3 0.2 Iris-setosa -4.6 3.1 1.5 0.2 Iris-setosa -5.4 3.9 1.7 0.4 Iris-setosa -4.6 3.4 1.4 0.3 Iris-setosa -5 3.4 1.5 0.2 Iris-setosa -4.4 2.9 1.4 0.2 Iris-setosa -4.9 3.1 1.5 0.1 Iris-setosa -4.8 3 1.4 0.1 Iris-setosa -4.3 3 1.1 0.1 Iris-setosa -5.8 4 1.2 0.2 Iris-setosa -5.7 4.4 1.5 0.4 Iris-setosa -5.4 3.9 1.3 0.4 Iris-setosa -5.1 3.5 1.4 0.3 Iris-setosa -5.7 3.8 1.7 0.3 Iris-setosa -5.4 3.4 1.7 0.2 Iris-setosa -4.6 3.6 1 0.2 Iris-setosa -5.1 3.3 1.7 0.5 Iris-setosa -4.8 3.4 1.9 0.2 Iris-setosa -5 3 1.6 0.2 Iris-setosa -5 3.4 1.6 0.4 Iris-setosa -5.2 3.5 1.5 0.2 Iris-setosa -5.2 3.4 1.4 0.2 Iris-setosa -4.7 3.2 1.6 0.2 Iris-setosa -4.8 3.1 1.6 0.2 Iris-setosa -5.4 3.4 1.5 0.4 Iris-setosa -5.2 4.1 1.5 0.1 Iris-setosa -5.5 4.2 1.4 0.2 Iris-setosa -4.9 3.1 1.5 0.1 Iris-setosa -5 3.2 1.2 0.2 Iris-setosa -4.9 3.1 1.5 0.1 Iris-setosa -4.4 3 1.3 0.2 Iris-setosa -5 3.5 1.3 0.3 Iris-setosa -5.1 3.8 1.9 0.4 Iris-setosa -4.8 3 1.4 0.3 Iris-setosa -5.1 3.8 1.6 0.2 Iris-setosa -4.6 3.2 1.4 0.2 Iris-setosa -5.3 3.7 1.5 0.2 Iris-setosa -7 3.2 4.7 1.4 Iris-versicolor -6.9 3.1 4.9 1.5 Iris-versicolor -5.5 2.3 4 1.3 Iris-versicolor -5.7 2.8 4.5 1.3 Iris-versicolor -6.3 3.3 4.7 1.6 Iris-versicolor -4.9 2.4 3.3 1 Iris-versicolor -5.2 2.7 3.9 1.4 Iris-versicolor -5 2 3.5 1 Iris-versicolor -6 2.2 4 1 Iris-versicolor -6.1 2.9 4.7 1.4 Iris-versicolor -5.6 2.9 3.6 1.3 Iris-versicolor -5.6 3 4.5 1.5 Iris-versicolor -5.8 2.7 4.1 1 Iris-versicolor -5.6 2.5 3.9 1.1 Iris-versicolor -5.9 3.2 4.8 1.8 Iris-versicolor -6.1 2.8 4 1.3 Iris-versicolor -6.1 2.8 4.7 1.2 Iris-versicolor -6.4 2.9 4.3 1.3 Iris-versicolor -6.6 3 4.4 1.4 Iris-versicolor -6.8 2.8 4.8 1.4 Iris-versicolor -6.7 3 5 1.7 Iris-versicolor -6 2.9 4.5 1.5 Iris-versicolor -5.7 2.6 3.5 1 Iris-versicolor -5.8 2.7 3.9 1.2 Iris-versicolor -6 2.7 5.1 1.6 Iris-versicolor -5.4 3 4.5 1.5 Iris-versicolor -6 3.4 4.5 1.6 Iris-versicolor -6.3 2.3 4.4 1.3 Iris-versicolor -5.6 3 4.1 1.3 Iris-versicolor -5.5 2.5 4 1.3 Iris-versicolor -5.1 2.5 3 1.1 Iris-versicolor -5.7 2.8 4.1 1.3 Iris-versicolor -7.1 3 5.9 2.1 Iris-virginica -6.3 2.9 5.6 1.8 Iris-virginica -6.5 3 5.8 2.2 Iris-virginica -6.7 2.5 5.8 1.8 Iris-virginica -7.2 3.6 6.1 2.5 Iris-virginica -6.5 3.2 5.1 2 Iris-virginica -6.8 3 5.5 2.1 Iris-virginica -5.7 2.5 5 2 Iris-virginica -6.4 3.2 5.3 2.3 Iris-virginica -6.5 3 5.5 1.8 Iris-virginica -7.7 3.8 6.7 2.2 Iris-virginica -7.7 2.6 6.9 2.3 Iris-virginica -6.9 3.2 5.7 2.3 Iris-virginica -5.6 2.8 4.9 2 Iris-virginica -7.7 2.8 6.7 2 Iris-virginica -6.3 2.7 4.9 1.8 Iris-virginica -7.2 3.2 6 1.8 Iris-virginica -6.4 2.8 5.6 2.1 Iris-virginica -7.4 2.8 6.1 1.9 Iris-virginica -6.4 2.8 5.6 2.2 Iris-virginica -6.3 2.8 5.1 1.5 Iris-virginica -6.1 2.6 5.6 1.4 Iris-virginica -7.7 3 6.1 2.3 Iris-virginica -6.3 3.4 5.6 2.4 Iris-virginica -6.4 3.1 5.5 1.8 Iris-virginica -6 3 4.8 1.8 Iris-virginica -6.9 3.1 5.4 2.1 Iris-virginica -6.7 3.1 5.6 2.4 Iris-virginica -6.9 3.1 5.1 2.3 Iris-virginica -6.8 3.2 5.9 2.3 Iris-virginica -6.7 3 5.2 2.3 Iris-virginica -6.3 2.5 5 1.9 Iris-virginica -6.5 3 5.2 2 Iris-virginica -6.2 3.4 5.4 2.3 Iris-virginica -5.9 3 5.1 1.8 Iris-virginica +SepalLength SepalWidth PetalLength PetalWidth Class +4.9 3 1.4 0.2 Iris-setosa +4.7 3.2 1.3 0.2 Iris-setosa +4.6 3.1 1.5 0.2 Iris-setosa +5.4 3.9 1.7 0.4 Iris-setosa +4.6 3.4 1.4 0.3 Iris-setosa +5 3.4 1.5 0.2 Iris-setosa +4.4 2.9 1.4 0.2 Iris-setosa +4.9 3.1 1.5 0.1 Iris-setosa +4.8 3 1.4 0.1 Iris-setosa +4.3 3 1.1 0.1 Iris-setosa +5.8 4 1.2 0.2 Iris-setosa +5.7 4.4 1.5 0.4 Iris-setosa +5.4 3.9 1.3 0.4 Iris-setosa +5.1 3.5 1.4 0.3 Iris-setosa +5.7 3.8 1.7 0.3 Iris-setosa +5.4 3.4 1.7 0.2 Iris-setosa +4.6 3.6 1 0.2 Iris-setosa +5.1 3.3 1.7 0.5 Iris-setosa +4.8 3.4 1.9 0.2 Iris-setosa +5 3 1.6 0.2 Iris-setosa +5 3.4 1.6 0.4 Iris-setosa +5.2 3.5 1.5 0.2 Iris-setosa +5.2 3.4 1.4 0.2 Iris-setosa +4.7 3.2 1.6 0.2 Iris-setosa +4.8 3.1 1.6 0.2 Iris-setosa +5.4 3.4 1.5 0.4 Iris-setosa +5.2 4.1 1.5 0.1 Iris-setosa +5.5 4.2 1.4 0.2 Iris-setosa +4.9 3.1 1.5 0.1 Iris-setosa +5 3.2 1.2 0.2 Iris-setosa +4.9 3.1 1.5 0.1 Iris-setosa +4.4 3 1.3 0.2 Iris-setosa +5 3.5 1.3 0.3 Iris-setosa +5.1 3.8 1.9 0.4 Iris-setosa +4.8 3 1.4 0.3 Iris-setosa +5.1 3.8 1.6 0.2 Iris-setosa +4.6 3.2 1.4 0.2 Iris-setosa +5.3 3.7 1.5 0.2 Iris-setosa +7 3.2 4.7 1.4 Iris-versicolor +6.9 3.1 4.9 1.5 Iris-versicolor +5.5 2.3 4 1.3 Iris-versicolor +5.7 2.8 4.5 1.3 Iris-versicolor +6.3 3.3 4.7 1.6 Iris-versicolor +4.9 2.4 3.3 1 Iris-versicolor +5.2 2.7 3.9 1.4 Iris-versicolor +5 2 3.5 1 Iris-versicolor +6 2.2 4 1 Iris-versicolor +6.1 2.9 4.7 1.4 Iris-versicolor +5.6 2.9 3.6 1.3 Iris-versicolor +5.6 3 4.5 1.5 Iris-versicolor +5.8 2.7 4.1 1 Iris-versicolor +5.6 2.5 3.9 1.1 Iris-versicolor +5.9 3.2 4.8 1.8 Iris-versicolor +6.1 2.8 4 1.3 Iris-versicolor +6.1 2.8 4.7 1.2 Iris-versicolor +6.4 2.9 4.3 1.3 Iris-versicolor +6.6 3 4.4 1.4 Iris-versicolor +6.8 2.8 4.8 1.4 Iris-versicolor +6.7 3 5 1.7 Iris-versicolor +6 2.9 4.5 1.5 Iris-versicolor +5.7 2.6 3.5 1 Iris-versicolor +5.8 2.7 3.9 1.2 Iris-versicolor +6 2.7 5.1 1.6 Iris-versicolor +5.4 3 4.5 1.5 Iris-versicolor +6 3.4 4.5 1.6 Iris-versicolor +6.3 2.3 4.4 1.3 Iris-versicolor +5.6 3 4.1 1.3 Iris-versicolor +5.5 2.5 4 1.3 Iris-versicolor +5.1 2.5 3 1.1 Iris-versicolor +5.7 2.8 4.1 1.3 Iris-versicolor +7.1 3 5.9 2.1 Iris-virginica +6.3 2.9 5.6 1.8 Iris-virginica +6.5 3 5.8 2.2 Iris-virginica +6.7 2.5 5.8 1.8 Iris-virginica +7.2 3.6 6.1 2.5 Iris-virginica +6.5 3.2 5.1 2 Iris-virginica +6.8 3 5.5 2.1 Iris-virginica +5.7 2.5 5 2 Iris-virginica +6.4 3.2 5.3 2.3 Iris-virginica +6.5 3 5.5 1.8 Iris-virginica +7.7 3.8 6.7 2.2 Iris-virginica +7.7 2.6 6.9 2.3 Iris-virginica +6.9 3.2 5.7 2.3 Iris-virginica +5.6 2.8 4.9 2 Iris-virginica +7.7 2.8 6.7 2 Iris-virginica +6.3 2.7 4.9 1.8 Iris-virginica +7.2 3.2 6 1.8 Iris-virginica +6.4 2.8 5.6 2.1 Iris-virginica +7.4 2.8 6.1 1.9 Iris-virginica +6.4 2.8 5.6 2.2 Iris-virginica +6.3 2.8 5.1 1.5 Iris-virginica +6.1 2.6 5.6 1.4 Iris-virginica +7.7 3 6.1 2.3 Iris-virginica +6.3 3.4 5.6 2.4 Iris-virginica +6.4 3.1 5.5 1.8 Iris-virginica +6 3 4.8 1.8 Iris-virginica +6.9 3.1 5.4 2.1 Iris-virginica +6.7 3.1 5.6 2.4 Iris-virginica +6.9 3.1 5.1 2.3 Iris-virginica +6.8 3.2 5.9 2.3 Iris-virginica +6.7 3 5.2 2.3 Iris-virginica +6.3 2.5 5 1.9 Iris-virginica +6.5 3 5.2 2 Iris-virginica +6.2 3.4 5.4 2.3 Iris-virginica +5.9 3 5.1 1.8 Iris-virginica diff --git a/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/err.txt index bb50d636d..b68b5d1da 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/err.txt @@ -1,6 +1,6 @@ Deploy model Iris Input database ../../../datasets/Iris/Iris.txt: Read records: 150 -Output database ./results/T_Iris.txt: Written records: 105 +Output database ./results/D_Iris.txt: Written records: 105 Model deployment time: 0:00:00.01 -error : The path of the target database should be different from that of the source database (./results/T_Iris.txt) +error : The path of the target database should be different from that of the source database (./results/D_Iris.txt) diff --git a/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/time.log index 8645889b8..f5eff3874 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/IrisTransfer/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.18329977989196777 +Overal time: 0.14530396461486816 diff --git a/test/LearningTest/TestKhiops/Standard/IrisTransfer/test.prm b/test/LearningTest/TestKhiops/Standard/IrisTransfer/test.prm index 8a6e07d8b..91fcc4521 100644 --- a/test/LearningTest/TestKhiops/Standard/IrisTransfer/test.prm +++ b/test/LearningTest/TestKhiops/Standard/IrisTransfer/test.prm @@ -14,6 +14,8 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open @@ -21,26 +23,26 @@ ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file OK // Open // <- Open -ClassManagement.ClassName Iris // Dictionary +TrainDatabase.ClassName Iris // Dictionary -TrainDatabase.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.SampleNumberPercentage 70 // Sample percentage +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage //AnalysisSpec.SystemParameters.ParallelSimulated true // Parallel simulated mode -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName Iris // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key Iris // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database - -SourceDatabase.DatabaseFiles.List.Key Iris // List item selection -SourceDatabase.DatabaseFiles.DataTableName ./results/T_Iris.txt // Database file -TransferDatabase // Transfer database +ClassName Iris // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model + +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +SourceDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_Iris.txt // Database file +TransferDatabase // Deploy model Exit // Close // <- Database transfer diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/IrisExtended.kdic b/test/LearningTest/TestKhiops/Standard/IrisU2D/IrisExtended.kdic new file mode 100644 index 000000000..f50517f00 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisU2D/IrisExtended.kdic @@ -0,0 +1,17 @@ +#Khiops 7.7.2i + +Dictionary Iris +{ + Numerical SepalLength ; + Numerical SepalWidth ; + Numerical PetalLength ; + Numerical PetalWidth ; + Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; + Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; + Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; + Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; + Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; + Numerical Dummy1 = Copy(0) ; + Numerical Dummy2 = Random() ; + Categorical Class ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls new file mode 100644 index 000000000..13f7eaabe --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls @@ -0,0 +1,1407 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 4 + Numerical 8 + Total 12 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Unsupervised analysis + +Evaluated variable pairs 55 +Informative variable pairs 38 + + +Variables pairs statistics + +Rank Name 1 Name 2 Level Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost +R01 Class Class1 0.286471 2 2 2 2 6.71557 18.9311 110.25 +R02 Class Class2 0.270234 2 2 2 2 6.71557 19.0156 110.25 +R03 Class SPetalLength 0.258511 2 3 3 5 6.71557 41.7647 157.188 +R04 Class1 SPetalLength 0.231831 2 2 2 2 6.71557 27.2099 142.253 +R05 PetalLength SPetalLength 0.151582 2 5 5 5 6.71557 69.091 386.913 +R06 Class2 SPetalLength 0.142436 2 2 2 4 6.71557 27.7273 158.704 +R07 Class PetalWidth 0.14197 2 3 3 5 6.71557 31.1679 396.708 +R08 Class PetalLength 0.136908 2 3 3 5 6.71557 31.1679 399.272 +R09 Class1 LowerPetalLength 0.111506 2 2 2 2 6.71557 13.7255 386.913 +R10 Class1 PetalLength 0.111506 2 2 2 2 6.71557 13.7255 386.913 +R11 Class1 PetalWidth 0.111506 2 2 2 2 6.71557 13.7255 386.913 +R12 PetalWidth SPetalLength 0.109807 2 3 3 5 6.71557 40.5555 438.232 +R13 Class LowerPetalLength 0.0982915 2 2 2 2 6.71557 19.0436 430.955 +R14 LowerPetalLength SPetalLength 0.0887331 2 2 2 2 6.71557 27.3225 462.959 +R15 PetalLength PetalWidth 0.0785935 2 3 3 4 6.71557 29.9587 676.972 +R16 Class UpperPetalWidth 0.0721164 2 2 2 4 6.71557 19.0868 444.17 +R17 PetalWidth UpperPetalWidth 0.0703191 2 3 3 3 6.71557 29.9587 683.381 +R18 LowerPetalLength PetalLength 0.0701201 2 3 3 3 6.71557 29.9587 683.535 +R19 Class2 PetalWidth 0.0662843 2 2 3 5 6.71557 20.8147 396.708 +R20 SPetalLength SepalLength 0.0654694 2 3 4 5 6.71557 49.4973 453.472 +R21 Class2 PetalLength 0.0606416 2 2 3 5 6.71557 20.8147 399.272 +R22 LowerPetalLength PetalWidth 0.0598398 2 2 2 2 6.71557 13.838 707.618 +R23 Class SepalLength 0.059526 2 3 3 7 6.71557 31.1679 438.466 +R24 Class1 Class2 0.0559199 2 2 2 3 6.71557 13.6129 110.25 +R25 Class1 SepalLength 0.0531576 2 2 2 4 6.71557 13.7255 413.664 +R26 SPetalLength UpperPetalWidth 0.0466723 2 3 2 5 6.71557 31.8478 481.373 +R27 PetalLength SepalLength 0.0407398 2 4 4 8 6.71557 47.7303 688.519 +R28 PetalLength UpperPetalWidth 0.0401281 2 2 2 3 6.71557 13.838 722.885 +R29 PetalWidth SepalLength 0.0303985 2 3 3 8 6.71557 29.9587 714.3 +R30 LowerPetalLength SepalLength 0.0253003 2 2 2 4 6.71557 13.838 734.369 +R31 Class1 UpperPetalWidth 0.0166012 2 2 2 3 6.71557 13.7255 430.424 +R32 SepalLength UpperPetalWidth 0.0164148 2 2 2 4 6.71557 13.838 741.251 +R33 Class1 SepalWidth 0.00749643 2 2 3 5 6.71557 20.8147 427.509 +R34 Class2 LowerPetalLength 0.0065114 2 2 2 3 6.71557 13.7255 430.955 +R35 Class SepalWidth 0.00543684 2 3 2 6 6.71557 22.1365 474.893 +R36 SPetalLength SepalWidth 0.00373995 2 3 3 9 6.71557 38.9735 497.662 +R37 LowerPetalLength UpperPetalWidth 0.00366071 2 2 2 3 6.71557 13.838 751.129 +R38 PetalWidth SepalWidth 0.00221737 2 3 2 6 6.71557 20.9273 745.158 +R39 Class Dummy2 0 0 1 1 1 0.693147 8.64312 497.163 +R40 Class1 Dummy2 0 0 1 1 1 0.693147 4.66344 453.12 +R41 Class2 Dummy2 0 0 1 1 1 0.693147 4.66344 448.998 +R42 Class2 SepalLength 0 0 1 1 1 0.693147 4.66344 448.998 +R43 Class2 SepalWidth 0 0 1 1 1 0.693147 4.66344 448.998 +R44 Class2 UpperPetalWidth 0 0 1 1 1 0.693147 4.66344 448.998 +R45 Dummy2 LowerPetalLength 0 0 1 1 1 0.693147 0 773.825 +R46 Dummy2 PetalLength 0 0 1 1 1 0.693147 0 773.825 +R47 Dummy2 PetalWidth 0 0 1 1 1 0.693147 0 773.825 +R48 Dummy2 SPetalLength 0 0 1 1 1 0.693147 15.5317 529.166 +R49 Dummy2 SepalLength 0 0 1 1 1 0.693147 0 773.825 +R50 Dummy2 SepalWidth 0 0 1 1 1 0.693147 0 773.825 +R51 Dummy2 UpperPetalWidth 0 0 1 1 1 0.693147 0 773.825 +R52 LowerPetalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 +R53 PetalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 +R54 SepalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 +R55 SepalWidth UpperPetalWidth 0 0 1 1 1 0.693147 0 773.825 + + +-------------------------------------------------------------------------------- + +Variables pairs detailed statistics +(Pairs with two jointly informative variables) + + +Rank R01 +Variables + Type Name + Categorical Class + Categorical Class1 + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * +{Iris-setosa} 38 0.361905 1 Iris-setosa +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * + +Cell frequencies + Class1 +Class {} {setosa} Total Coverage +{Iris-virginica, Iris-versicolor} 67 0 67 0.638095 +{Iris-setosa} 0 38 38 0.361905 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 2 +Cell Id Class Class1 Frequency Coverage +C1 {Iris-virginica, Iris-versicolor} {} 67 0.638095 +C4 {Iris-setosa} {setosa} 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R02 +Variables + Type Name + Categorical Class + Categorical Class2 + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa, Iris-virginica} 73 0.695238 2 Iris-setosa Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * + +Cell frequencies + Class2 +Class {} {versicolor} Total Coverage +{Iris-setosa, Iris-virginica} 73 0 73 0.695238 +{Iris-versicolor} 0 32 32 0.304762 +Total 73 32 105 +Coverage 0.695238 0.304762 + +Cells 2 +Cell Id Class Class2 Frequency Coverage +C1 {Iris-setosa, Iris-virginica} {} 73 0.695238 +C4 {Iris-versicolor} {versicolor} 32 0.304762 + Total 105 1 + +---------------------------------------------- +Rank R03 +Variables + Type Name + Categorical Class + Categorical SPetalLength + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +SPetalLength +Group Frequency Coverage Size Value list +{1} 38 0.361905 1 1 +{5, 6} 34 0.32381 2 5 6 * +{4, 3} 33 0.314286 2 4 3 + +Cell frequencies + SPetalLength +Class {1} {5, 6} {4, 3} Total Coverage +{Iris-setosa} 38 0 0 38 0.361905 +{Iris-virginica} 0 32 3 35 0.333333 +{Iris-versicolor} 0 2 30 32 0.304762 +Total 38 34 33 105 +Coverage 0.361905 0.32381 0.314286 + +Cells 5 +Cell Id Class SPetalLength Frequency Coverage +C1 {Iris-setosa} {1} 38 0.361905 +C5 {Iris-virginica} {5, 6} 32 0.304762 +C9 {Iris-versicolor} {4, 3} 30 0.285714 +C8 {Iris-virginica} {4, 3} 3 0.0285714 +C6 {Iris-versicolor} {5, 6} 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R04 +Variables + Type Name + Categorical Class1 + Categorical SPetalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +SPetalLength +Group Frequency Coverage Size Value list +{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * +{1} 38 0.361905 1 1 + +Cell frequencies + SPetalLength +Class1 {5, 4, 3, ...} {1} Total Coverage +{} 67 0 67 0.638095 +{setosa} 0 38 38 0.361905 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 2 +Cell Id Class1 SPetalLength Frequency Coverage +C1 {} {5, 4, 3, ...} 67 0.638095 +C4 {setosa} {1} 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R05 +Variables + Type Name + Numerical PetalLength + Categorical SPetalLength + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;3.95] 8 0.0761905 +]3.95;4.95] 25 0.238095 +]4.95;5.95] 27 0.257143 +]5.95;+inf[ 7 0.0666667 +SPetalLength +Group Frequency Coverage Size Value list +{1} 38 0.361905 1 1 +{5} 27 0.257143 1 5 +{4} 25 0.238095 1 4 +{3} 8 0.0761905 1 3 +{6} 7 0.0666667 1 6 * + +Cell frequencies + SPetalLength +PetalLength {1} {5} {4} {3} {6} Total Coverage +]-inf;2.4] 38 0 0 0 0 38 0.361905 +]2.4;3.95] 0 0 0 8 0 8 0.0761905 +]3.95;4.95] 0 0 25 0 0 25 0.238095 +]4.95;5.95] 0 27 0 0 0 27 0.257143 +]5.95;+inf[ 0 0 0 0 7 7 0.0666667 +Total 38 27 25 8 7 105 +Coverage 0.361905 0.257143 0.238095 0.0761905 0.0666667 + +Cells 5 +Cell Id PetalLength SPetalLength Frequency Coverage +C1 ]-inf;2.4] {1} 38 0.361905 +C9 ]4.95;5.95] {5} 27 0.257143 +C13 ]3.95;4.95] {4} 25 0.238095 +C17 ]2.4;3.95] {3} 8 0.0761905 +C25 ]5.95;+inf[ {6} 7 0.0666667 + Total 105 1 + +---------------------------------------------- +Rank R06 +Variables + Type Name + Categorical Class2 + Categorical SPetalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +SPetalLength +Group Frequency Coverage Size Value list +{1, 5, 6} 72 0.685714 3 1 5 6 * +{4, 3} 33 0.314286 2 4 3 + +Cell frequencies + SPetalLength +Class2 {1, 5, 6} {4, 3} Total Coverage +{} 70 3 73 0.695238 +{versicolor} 2 30 32 0.304762 +Total 72 33 105 +Coverage 0.685714 0.314286 + +Cells 4 +Cell Id Class2 SPetalLength Frequency Coverage +C1 {} {1, 5, 6} 70 0.666667 +C4 {versicolor} {4, 3} 30 0.285714 +C3 {} {4, 3} 3 0.0285714 +C2 {versicolor} {1, 5, 6} 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R07 +Variables + Type Name + Categorical Class + Numerical PetalWidth + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.75] 33 0.314286 +]1.75;+inf[ 34 0.32381 + +Cell frequencies + PetalWidth +Class ]-inf;0.75] ]0.75;1.75] ]1.75;+inf[ Total Coverage +{Iris-setosa} 38 0 0 38 0.361905 +{Iris-virginica} 0 2 33 35 0.333333 +{Iris-versicolor} 0 31 1 32 0.304762 +Total 38 33 34 105 +Coverage 0.361905 0.314286 0.32381 + +Cells 5 +Cell Id Class PetalWidth Frequency Coverage +C1 {Iris-setosa} ]-inf;0.75] 38 0.361905 +C8 {Iris-virginica} ]1.75;+inf[ 33 0.314286 +C6 {Iris-versicolor} ]0.75;1.75] 31 0.295238 +C5 {Iris-virginica} ]0.75;1.75] 2 0.0190476 +C9 {Iris-versicolor} ]1.75;+inf[ 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R08 +Variables + Type Name + Categorical Class + Numerical PetalLength + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;4.85] 30 0.285714 +]4.85;+inf[ 37 0.352381 + +Cell frequencies + PetalLength +Class ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Total Coverage +{Iris-setosa} 38 0 0 38 0.361905 +{Iris-virginica} 0 1 34 35 0.333333 +{Iris-versicolor} 0 29 3 32 0.304762 +Total 38 30 37 105 +Coverage 0.361905 0.285714 0.352381 + +Cells 5 +Cell Id Class PetalLength Frequency Coverage +C1 {Iris-setosa} ]-inf;2.4] 38 0.361905 +C8 {Iris-virginica} ]4.85;+inf[ 34 0.32381 +C6 {Iris-versicolor} ]2.4;4.85] 29 0.27619 +C9 {Iris-versicolor} ]4.85;+inf[ 3 0.0285714 +C5 {Iris-virginica} ]2.4;4.85] 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R09 +Variables + Type Name + Categorical Class1 + Numerical LowerPetalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + LowerPetalLength +Class1 ]-inf;2.4] ]2.4;+inf[ Total Coverage +{} 0 67 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id Class1 LowerPetalLength Frequency Coverage +C3 {} ]2.4;+inf[ 67 0.638095 +C2 {setosa} ]-inf;2.4] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R10 +Variables + Type Name + Categorical Class1 + Numerical PetalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + PetalLength +Class1 ]-inf;2.4] ]2.4;+inf[ Total Coverage +{} 0 67 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id Class1 PetalLength Frequency Coverage +C3 {} ]2.4;+inf[ 67 0.638095 +C2 {setosa} ]-inf;2.4] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R11 +Variables + Type Name + Categorical Class1 + Numerical PetalWidth + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;+inf[ 67 0.638095 + +Cell frequencies + PetalWidth +Class1 ]-inf;0.75] ]0.75;+inf[ Total Coverage +{} 0 67 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id Class1 PetalWidth Frequency Coverage +C3 {} ]0.75;+inf[ 67 0.638095 +C2 {setosa} ]-inf;0.75] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R12 +Variables + Type Name + Numerical PetalWidth + Categorical SPetalLength + +Variables stats +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.65] 32 0.304762 +]1.65;+inf[ 35 0.333333 +SPetalLength +Group Frequency Coverage Size Value list +{1} 38 0.361905 1 1 +{5, 6} 34 0.32381 2 5 6 * +{4, 3} 33 0.314286 2 4 3 + +Cell frequencies + SPetalLength +PetalWidth {1} {5, 6} {4, 3} Total Coverage +]-inf;0.75] 38 0 0 38 0.361905 +]0.75;1.65] 0 3 29 32 0.304762 +]1.65;+inf[ 0 31 4 35 0.333333 +Total 38 34 33 105 +Coverage 0.361905 0.32381 0.314286 + +Cells 5 +Cell Id PetalWidth SPetalLength Frequency Coverage +C1 ]-inf;0.75] {1} 38 0.361905 +C6 ]1.65;+inf[ {5, 6} 31 0.295238 +C8 ]0.75;1.65] {4, 3} 29 0.27619 +C9 ]1.65;+inf[ {4, 3} 4 0.0380952 +C5 ]0.75;1.65] {5, 6} 3 0.0285714 + Total 105 1 + +---------------------------------------------- +Rank R13 +Variables + Type Name + Categorical Class + Numerical LowerPetalLength + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * +{Iris-setosa} 38 0.361905 1 Iris-setosa +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + LowerPetalLength +Class ]-inf;2.4] ]2.4;+inf[ Total Coverage +{Iris-virginica, Iris-versicolor} 0 67 67 0.638095 +{Iris-setosa} 38 0 38 0.361905 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id Class LowerPetalLength Frequency Coverage +C3 {Iris-virginica, Iris-versicolor} ]2.4;+inf[ 67 0.638095 +C2 {Iris-setosa} ]-inf;2.4] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R14 +Variables + Type Name + Numerical LowerPetalLength + Categorical SPetalLength + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +SPetalLength +Group Frequency Coverage Size Value list +{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * +{1} 38 0.361905 1 1 + +Cell frequencies + SPetalLength +LowerPetalLength {5, 4, 3, ...} {1} Total Coverage +]-inf;2.4] 0 38 38 0.361905 +]2.4;+inf[ 67 0 67 0.638095 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 2 +Cell Id LowerPetalLength SPetalLength Frequency Coverage +C2 ]2.4;+inf[ {5, 4, 3, ...} 67 0.638095 +C3 ]-inf;2.4] {1} 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R15 +Variables + Type Name + Numerical PetalLength + Numerical PetalWidth + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;4.75] 27 0.257143 +]4.75;+inf[ 40 0.380952 +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.65] 32 0.304762 +]1.65;+inf[ 35 0.333333 + +Cell frequencies + PetalWidth +PetalLength ]-inf;0.75] ]0.75;1.65] ]1.65;+inf[ Total Coverage +]-inf;2.4] 38 0 0 38 0.361905 +]2.4;4.75] 0 27 0 27 0.257143 +]4.75;+inf[ 0 5 35 40 0.380952 +Total 38 32 35 105 +Coverage 0.361905 0.304762 0.333333 + +Cells 4 +Cell Id PetalLength PetalWidth Frequency Coverage +C1 ]-inf;2.4] ]-inf;0.75] 38 0.361905 +C9 ]4.75;+inf[ ]1.65;+inf[ 35 0.333333 +C5 ]2.4;4.75] ]0.75;1.65] 27 0.257143 +C6 ]4.75;+inf[ ]0.75;1.65] 5 0.047619 + Total 105 1 + +---------------------------------------------- +Rank R16 +Variables + Type Name + Categorical Class + Numerical UpperPetalWidth + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa, Iris-versicolor} 70 0.666667 2 Iris-setosa Iris-versicolor * +{Iris-virginica} 35 0.333333 1 Iris-virginica +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.75] 71 0.67619 +]1.75;+inf[ 34 0.32381 + +Cell frequencies + UpperPetalWidth +Class ]-inf;1.75] ]1.75;+inf[ Total Coverage +{Iris-setosa, Iris-versicolor} 69 1 70 0.666667 +{Iris-virginica} 2 33 35 0.333333 +Total 71 34 105 +Coverage 0.67619 0.32381 + +Cells 4 +Cell Id Class UpperPetalWidth Frequency Coverage +C1 {Iris-setosa, Iris-versicolor} ]-inf;1.75] 69 0.657143 +C4 {Iris-virginica} ]1.75;+inf[ 33 0.314286 +C2 {Iris-virginica} ]-inf;1.75] 2 0.0190476 +C3 {Iris-setosa, Iris-versicolor} ]1.75;+inf[ 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R17 +Variables + Type Name + Numerical PetalWidth + Numerical UpperPetalWidth + +Variables stats +PetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;2.05] 20 0.190476 +]2.05;+inf[ 18 0.171429 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;2.05] 20 0.190476 +]2.05;+inf[ 18 0.171429 + +Cell frequencies + UpperPetalWidth +PetalWidth ]-inf;1.55] ]1.55;2.05] ]2.05;+inf[ Total Coverage +]-inf;1.55] 67 0 0 67 0.638095 +]1.55;2.05] 0 20 0 20 0.190476 +]2.05;+inf[ 0 0 18 18 0.171429 +Total 67 20 18 105 +Coverage 0.638095 0.190476 0.171429 + +Cells 3 +Cell Id PetalWidth UpperPetalWidth Frequency Coverage +C1 ]-inf;1.55] ]-inf;1.55] 67 0.638095 +C5 ]1.55;2.05] ]1.55;2.05] 20 0.190476 +C9 ]2.05;+inf[ ]2.05;+inf[ 18 0.171429 + Total 105 1 + +---------------------------------------------- +Rank R18 +Variables + Type Name + Numerical LowerPetalLength + Numerical PetalLength + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;1.45] 17 0.161905 +]1.45;2.4] 21 0.2 +]2.4;+inf[ 67 0.638095 +PetalLength +Interval Frequency Coverage +]-inf;1.45] 17 0.161905 +]1.45;2.4] 21 0.2 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + PetalLength +LowerPetalLength ]-inf;1.45] ]1.45;2.4] ]2.4;+inf[ Total Coverage +]-inf;1.45] 17 0 0 17 0.161905 +]1.45;2.4] 0 21 0 21 0.2 +]2.4;+inf[ 0 0 67 67 0.638095 +Total 17 21 67 105 +Coverage 0.161905 0.2 0.638095 + +Cells 3 +Cell Id LowerPetalLength PetalLength Frequency Coverage +C9 ]2.4;+inf[ ]2.4;+inf[ 67 0.638095 +C5 ]1.45;2.4] ]1.45;2.4] 21 0.2 +C1 ]-inf;1.45] ]-inf;1.45] 17 0.161905 + Total 105 1 + +---------------------------------------------- +Rank R19 +Variables + Type Name + Categorical Class2 + Numerical PetalWidth + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.75] 33 0.314286 +]1.75;+inf[ 34 0.32381 + +Cell frequencies + PetalWidth +Class2 ]-inf;0.75] ]0.75;1.75] ]1.75;+inf[ Total Coverage +{} 38 2 33 73 0.695238 +{versicolor} 0 31 1 32 0.304762 +Total 38 33 34 105 +Coverage 0.361905 0.314286 0.32381 + +Cells 5 +Cell Id Class2 PetalWidth Frequency Coverage +C1 {} ]-inf;0.75] 38 0.361905 +C5 {} ]1.75;+inf[ 33 0.314286 +C4 {versicolor} ]0.75;1.75] 31 0.295238 +C3 {} ]0.75;1.75] 2 0.0190476 +C6 {versicolor} ]1.75;+inf[ 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R20 +Variables + Type Name + Categorical SPetalLength + Numerical SepalLength + +Variables stats +SPetalLength +Group Frequency Coverage Size Value list +{5, 4} 52 0.495238 2 5 4 +{1, 3} 46 0.438095 2 1 3 +{6} 7 0.0666667 1 6 * +SepalLength +Interval Frequency Coverage +]-inf;5.35] 34 0.32381 +]5.35;5.85] 22 0.209524 +]5.85;7.15] 42 0.4 +]7.15;+inf[ 7 0.0666667 + +Cell frequencies + SepalLength +SPetalLength ]-inf;5.35] ]5.35;5.85] ]5.85;7.15] ]7.15;+inf[ Total Coverage +{5, 4} 0 10 42 0 52 0.495238 +{1, 3} 34 12 0 0 46 0.438095 +{6} 0 0 0 7 7 0.0666667 +Total 34 22 42 7 105 +Coverage 0.32381 0.209524 0.4 0.0666667 + +Cells 5 +Cell Id SPetalLength SepalLength Frequency Coverage +C7 {5, 4} ]5.85;7.15] 42 0.4 +C2 {1, 3} ]-inf;5.35] 34 0.32381 +C5 {1, 3} ]5.35;5.85] 12 0.114286 +C4 {5, 4} ]5.35;5.85] 10 0.0952381 +C12 {6} ]7.15;+inf[ 7 0.0666667 + Total 105 1 + +---------------------------------------------- +Rank R21 +Variables + Type Name + Categorical Class2 + Numerical PetalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +PetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;4.85] 30 0.285714 +]4.85;+inf[ 37 0.352381 + +Cell frequencies + PetalLength +Class2 ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Total Coverage +{} 38 1 34 73 0.695238 +{versicolor} 0 29 3 32 0.304762 +Total 38 30 37 105 +Coverage 0.361905 0.285714 0.352381 + +Cells 5 +Cell Id Class2 PetalLength Frequency Coverage +C1 {} ]-inf;2.4] 38 0.361905 +C5 {} ]4.85;+inf[ 34 0.32381 +C4 {versicolor} ]2.4;4.85] 29 0.27619 +C6 {versicolor} ]4.85;+inf[ 3 0.0285714 +C3 {} ]2.4;4.85] 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R22 +Variables + Type Name + Numerical LowerPetalLength + Numerical PetalWidth + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;+inf[ 67 0.638095 + +Cell frequencies + PetalWidth +LowerPetalLength ]-inf;0.75] ]0.75;+inf[ Total Coverage +]-inf;2.4] 38 0 38 0.361905 +]2.4;+inf[ 0 67 67 0.638095 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 2 +Cell Id LowerPetalLength PetalWidth Frequency Coverage +C4 ]2.4;+inf[ ]0.75;+inf[ 67 0.638095 +C1 ]-inf;2.4] ]-inf;0.75] 38 0.361905 + Total 105 1 + +---------------------------------------------- +Rank R23 +Variables + Type Name + Categorical Class + Numerical SepalLength + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;6.15] 28 0.266667 +]6.15;+inf[ 38 0.361905 + +Cell frequencies + SepalLength +Class ]-inf;5.45] ]5.45;6.15] ]6.15;+inf[ Total Coverage +{Iris-setosa} 34 4 0 38 0.361905 +{Iris-virginica} 0 5 30 35 0.333333 +{Iris-versicolor} 5 19 8 32 0.304762 +Total 39 28 38 105 +Coverage 0.371429 0.266667 0.361905 + +Cells 7 +Cell Id Class SepalLength Frequency Coverage +C1 {Iris-setosa} ]-inf;5.45] 34 0.32381 +C8 {Iris-virginica} ]6.15;+inf[ 30 0.285714 +C6 {Iris-versicolor} ]5.45;6.15] 19 0.180952 +C9 {Iris-versicolor} ]6.15;+inf[ 8 0.0761905 +C5 {Iris-virginica} ]5.45;6.15] 5 0.047619 +C3 {Iris-versicolor} ]-inf;5.45] 5 0.047619 +C4 {Iris-setosa} ]5.45;6.15] 4 0.0380952 + Total 105 1 + +---------------------------------------------- +Rank R24 +Variables + Type Name + Categorical Class1 + Categorical Class2 + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * + +Cell frequencies + Class2 +Class1 {} {versicolor} Total Coverage +{} 35 32 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 73 32 105 +Coverage 0.695238 0.304762 + +Cells 3 +Cell Id Class1 Class2 Frequency Coverage +C2 {setosa} {} 38 0.361905 +C1 {} {} 35 0.333333 +C3 {} {versicolor} 32 0.304762 + Total 105 1 + +---------------------------------------------- +Rank R25 +Variables + Type Name + Categorical Class1 + Numerical SepalLength + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;+inf[ 66 0.628571 + +Cell frequencies + SepalLength +Class1 ]-inf;5.45] ]5.45;+inf[ Total Coverage +{} 5 62 67 0.638095 +{setosa} 34 4 38 0.361905 +Total 39 66 105 +Coverage 0.371429 0.628571 + +Cells 4 +Cell Id Class1 SepalLength Frequency Coverage +C3 {} ]5.45;+inf[ 62 0.590476 +C2 {setosa} ]-inf;5.45] 34 0.32381 +C1 {} ]-inf;5.45] 5 0.047619 +C4 {setosa} ]5.45;+inf[ 4 0.0380952 + Total 105 1 + +---------------------------------------------- +Rank R26 +Variables + Type Name + Categorical SPetalLength + Numerical UpperPetalWidth + +Variables stats +SPetalLength +Group Frequency Coverage Size Value list +{1, 3} 46 0.438095 2 1 3 +{5, 6} 34 0.32381 2 5 6 * +{4} 25 0.238095 1 4 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +SPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage +{1, 3} 46 0 46 0.438095 +{5, 6} 2 32 34 0.32381 +{4} 19 6 25 0.238095 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 5 +Cell Id SPetalLength UpperPetalWidth Frequency Coverage +C1 {1, 3} ]-inf;1.55] 46 0.438095 +C5 {5, 6} ]1.55;+inf[ 32 0.304762 +C3 {4} ]-inf;1.55] 19 0.180952 +C6 {4} ]1.55;+inf[ 6 0.0571429 +C2 {5, 6} ]-inf;1.55] 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R27 +Variables + Type Name + Numerical PetalLength + Numerical SepalLength + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;3.55] 42 0.4 +]3.55;4.6] 19 0.180952 +]4.6;5.95] 37 0.352381 +]5.95;+inf[ 7 0.0666667 +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;5.85] 17 0.161905 +]5.85;7.15] 42 0.4 +]7.15;+inf[ 7 0.0666667 + +Cell frequencies + SepalLength +PetalLength ]-inf;5.45] ]5.45;5.85] ]5.85;7.15] ]7.15;+inf[ Total Coverage +]-inf;3.55] 37 5 0 0 42 0.4 +]3.55;4.6] 2 10 7 0 19 0.180952 +]4.6;5.95] 0 2 35 0 37 0.352381 +]5.95;+inf[ 0 0 0 7 7 0.0666667 +Total 39 17 42 7 105 +Coverage 0.371429 0.161905 0.4 0.0666667 + +Cells 8 +Cell Id PetalLength SepalLength Frequency Coverage +C1 ]-inf;3.55] ]-inf;5.45] 37 0.352381 +C11 ]4.6;5.95] ]5.85;7.15] 35 0.333333 +C6 ]3.55;4.6] ]5.45;5.85] 10 0.0952381 +C10 ]3.55;4.6] ]5.85;7.15] 7 0.0666667 +C16 ]5.95;+inf[ ]7.15;+inf[ 7 0.0666667 +C5 ]-inf;3.55] ]5.45;5.85] 5 0.047619 +C2 ]3.55;4.6] ]-inf;5.45] 2 0.0190476 +C7 ]4.6;5.95] ]5.45;5.85] 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R28 +Variables + Type Name + Numerical PetalLength + Numerical UpperPetalWidth + +Variables stats +PetalLength +Interval Frequency Coverage +]-inf;4.75] 65 0.619048 +]4.75;+inf[ 40 0.380952 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.65] 70 0.666667 +]1.65;+inf[ 35 0.333333 + +Cell frequencies + UpperPetalWidth +PetalLength ]-inf;1.65] ]1.65;+inf[ Total Coverage +]-inf;4.75] 65 0 65 0.619048 +]4.75;+inf[ 5 35 40 0.380952 +Total 70 35 105 +Coverage 0.666667 0.333333 + +Cells 3 +Cell Id PetalLength UpperPetalWidth Frequency Coverage +C1 ]-inf;4.75] ]-inf;1.65] 65 0.619048 +C4 ]4.75;+inf[ ]1.65;+inf[ 35 0.333333 +C2 ]4.75;+inf[ ]-inf;1.65] 5 0.047619 + Total 105 1 + +---------------------------------------------- +Rank R29 +Variables + Type Name + Numerical PetalWidth + Numerical SepalLength + +Variables stats +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.35] 18 0.171429 +]1.35;+inf[ 49 0.466667 +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;5.85] 17 0.161905 +]5.85;+inf[ 49 0.466667 + +Cell frequencies + SepalLength +PetalWidth ]-inf;5.45] ]5.45;5.85] ]5.85;+inf[ Total Coverage +]-inf;0.75] 34 4 0 38 0.361905 +]0.75;1.35] 3 10 5 18 0.171429 +]1.35;+inf[ 2 3 44 49 0.466667 +Total 39 17 49 105 +Coverage 0.371429 0.161905 0.466667 + +Cells 8 +Cell Id PetalWidth SepalLength Frequency Coverage +C9 ]1.35;+inf[ ]5.85;+inf[ 44 0.419048 +C1 ]-inf;0.75] ]-inf;5.45] 34 0.32381 +C5 ]0.75;1.35] ]5.45;5.85] 10 0.0952381 +C8 ]0.75;1.35] ]5.85;+inf[ 5 0.047619 +C4 ]-inf;0.75] ]5.45;5.85] 4 0.0380952 +C2 ]0.75;1.35] ]-inf;5.45] 3 0.0285714 +C6 ]1.35;+inf[ ]5.45;5.85] 3 0.0285714 +C3 ]1.35;+inf[ ]-inf;5.45] 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R30 +Variables + Type Name + Numerical LowerPetalLength + Numerical SepalLength + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +SepalLength +Interval Frequency Coverage +]-inf;5.45] 39 0.371429 +]5.45;+inf[ 66 0.628571 + +Cell frequencies + SepalLength +LowerPetalLength ]-inf;5.45] ]5.45;+inf[ Total Coverage +]-inf;2.4] 34 4 38 0.361905 +]2.4;+inf[ 5 62 67 0.638095 +Total 39 66 105 +Coverage 0.371429 0.628571 + +Cells 4 +Cell Id LowerPetalLength SepalLength Frequency Coverage +C4 ]2.4;+inf[ ]5.45;+inf[ 62 0.590476 +C1 ]-inf;2.4] ]-inf;5.45] 34 0.32381 +C2 ]2.4;+inf[ ]-inf;5.45] 5 0.047619 +C3 ]-inf;2.4] ]5.45;+inf[ 4 0.0380952 + Total 105 1 + +---------------------------------------------- +Rank R31 +Variables + Type Name + Categorical Class1 + Numerical UpperPetalWidth + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +Class1 ]-inf;1.55] ]1.55;+inf[ Total Coverage +{} 29 38 67 0.638095 +{setosa} 38 0 38 0.361905 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 3 +Cell Id Class1 UpperPetalWidth Frequency Coverage +C3 {} ]1.55;+inf[ 38 0.361905 +C2 {setosa} ]-inf;1.55] 38 0.361905 +C1 {} ]-inf;1.55] 29 0.27619 + Total 105 1 + +---------------------------------------------- +Rank R32 +Variables + Type Name + Numerical SepalLength + Numerical UpperPetalWidth + +Variables stats +SepalLength +Interval Frequency Coverage +]-inf;5.85] 56 0.533333 +]5.85;+inf[ 49 0.466667 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +SepalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage +]-inf;5.85] 54 2 56 0.533333 +]5.85;+inf[ 13 36 49 0.466667 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 4 +Cell Id SepalLength UpperPetalWidth Frequency Coverage +C1 ]-inf;5.85] ]-inf;1.55] 54 0.514286 +C4 ]5.85;+inf[ ]1.55;+inf[ 36 0.342857 +C2 ]5.85;+inf[ ]-inf;1.55] 13 0.12381 +C3 ]-inf;5.85] ]1.55;+inf[ 2 0.0190476 + Total 105 1 + +---------------------------------------------- +Rank R33 +Variables + Type Name + Categorical Class1 + Numerical SepalWidth + +Variables stats +Class1 +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * +SepalWidth +Interval Frequency Coverage +]-inf;2.85] 30 0.285714 +]2.85;3.35] 49 0.466667 +]3.35;+inf[ 26 0.247619 + +Cell frequencies + SepalWidth +Class1 ]-inf;2.85] ]2.85;3.35] ]3.35;+inf[ Total Coverage +{} 30 32 5 67 0.638095 +{setosa} 0 17 21 38 0.361905 +Total 30 49 26 105 +Coverage 0.285714 0.466667 0.247619 + +Cells 5 +Cell Id Class1 SepalWidth Frequency Coverage +C3 {} ]2.85;3.35] 32 0.304762 +C1 {} ]-inf;2.85] 30 0.285714 +C6 {setosa} ]3.35;+inf[ 21 0.2 +C4 {setosa} ]2.85;3.35] 17 0.161905 +C5 {} ]3.35;+inf[ 5 0.047619 + Total 105 1 + +---------------------------------------------- +Rank R34 +Variables + Type Name + Categorical Class2 + Numerical LowerPetalLength + +Variables stats +Class2 +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 + +Cell frequencies + LowerPetalLength +Class2 ]-inf;2.4] ]2.4;+inf[ Total Coverage +{} 38 35 73 0.695238 +{versicolor} 0 32 32 0.304762 +Total 38 67 105 +Coverage 0.361905 0.638095 + +Cells 3 +Cell Id Class2 LowerPetalLength Frequency Coverage +C1 {} ]-inf;2.4] 38 0.361905 +C3 {} ]2.4;+inf[ 35 0.333333 +C4 {versicolor} ]2.4;+inf[ 32 0.304762 + Total 105 1 + +---------------------------------------------- +Rank R35 +Variables + Type Name + Categorical Class + Numerical SepalWidth + +Variables stats +Class +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * +SepalWidth +Interval Frequency Coverage +]-inf;2.95] 36 0.342857 +]2.95;+inf[ 69 0.657143 + +Cell frequencies + SepalWidth +Class ]-inf;2.95] ]2.95;+inf[ Total Coverage +{Iris-setosa} 1 37 38 0.361905 +{Iris-virginica} 13 22 35 0.333333 +{Iris-versicolor} 22 10 32 0.304762 +Total 36 69 105 +Coverage 0.342857 0.657143 + +Cells 6 +Cell Id Class SepalWidth Frequency Coverage +C4 {Iris-setosa} ]2.95;+inf[ 37 0.352381 +C5 {Iris-virginica} ]2.95;+inf[ 22 0.209524 +C3 {Iris-versicolor} ]-inf;2.95] 22 0.209524 +C2 {Iris-virginica} ]-inf;2.95] 13 0.12381 +C6 {Iris-versicolor} ]2.95;+inf[ 10 0.0952381 +C1 {Iris-setosa} ]-inf;2.95] 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R36 +Variables + Type Name + Categorical SPetalLength + Numerical SepalWidth + +Variables stats +SPetalLength +Group Frequency Coverage Size Value list +{4} 40 0.380952 3 4 * +{1} 38 0.361905 1 1 +{5} 27 0.257143 1 5 +SepalWidth +Interval Frequency Coverage +]-inf;2.95] 36 0.342857 +]2.95;3.25] 41 0.390476 +]3.25;+inf[ 28 0.266667 + +Cell frequencies + SepalWidth +SPetalLength ]-inf;2.95] ]2.95;3.25] ]3.25;+inf[ Total Coverage +{4} 26 10 4 40 0.380952 +{1} 1 15 22 38 0.361905 +{5} 9 16 2 27 0.257143 +Total 36 41 28 105 +Coverage 0.342857 0.390476 0.266667 + +Cells 9 +Cell Id SPetalLength SepalWidth Frequency Coverage +C1 {4} ]-inf;2.95] 26 0.247619 +C8 {1} ]3.25;+inf[ 22 0.209524 +C6 {5} ]2.95;3.25] 16 0.152381 +C5 {1} ]2.95;3.25] 15 0.142857 +C4 {4} ]2.95;3.25] 10 0.0952381 +C3 {5} ]-inf;2.95] 9 0.0857143 +C7 {4} ]3.25;+inf[ 4 0.0380952 +C9 {5} ]3.25;+inf[ 2 0.0190476 +C2 {1} ]-inf;2.95] 1 0.00952381 + Total 105 1 + +---------------------------------------------- +Rank R37 +Variables + Type Name + Numerical LowerPetalLength + Numerical UpperPetalWidth + +Variables stats +LowerPetalLength +Interval Frequency Coverage +]-inf;2.4] 38 0.361905 +]2.4;+inf[ 67 0.638095 +UpperPetalWidth +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + +Cell frequencies + UpperPetalWidth +LowerPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage +]-inf;2.4] 38 0 38 0.361905 +]2.4;+inf[ 29 38 67 0.638095 +Total 67 38 105 +Coverage 0.638095 0.361905 + +Cells 3 +Cell Id LowerPetalLength UpperPetalWidth Frequency Coverage +C1 ]-inf;2.4] ]-inf;1.55] 38 0.361905 +C4 ]2.4;+inf[ ]1.55;+inf[ 38 0.361905 +C2 ]2.4;+inf[ ]-inf;1.55] 29 0.27619 + Total 105 1 + +---------------------------------------------- +Rank R38 +Variables + Type Name + Numerical PetalWidth + Numerical SepalWidth + +Variables stats +PetalWidth +Interval Frequency Coverage +]-inf;0.75] 38 0.361905 +]0.75;1.45] 24 0.228571 +]1.45;+inf[ 43 0.409524 +SepalWidth +Interval Frequency Coverage +]-inf;2.95] 36 0.342857 +]2.95;+inf[ 69 0.657143 + +Cell frequencies + SepalWidth +PetalWidth ]-inf;2.95] ]2.95;+inf[ Total Coverage +]-inf;0.75] 1 37 38 0.361905 +]0.75;1.45] 21 3 24 0.228571 +]1.45;+inf[ 14 29 43 0.409524 +Total 36 69 105 +Coverage 0.342857 0.657143 + +Cells 6 +Cell Id PetalWidth SepalWidth Frequency Coverage +C4 ]-inf;0.75] ]2.95;+inf[ 37 0.352381 +C6 ]1.45;+inf[ ]2.95;+inf[ 29 0.27619 +C2 ]0.75;1.45] ]-inf;2.95] 21 0.2 +C3 ]1.45;+inf[ ]-inf;2.95] 14 0.133333 +C5 ]0.75;1.45] ]2.95;+inf[ 3 0.0285714 +C1 ]-inf;0.75] ]-inf;2.95] 1 0.00952381 + Total 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls new file mode 100644 index 000000000..64abcac16 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls @@ -0,0 +1,205 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary Iris +Variables + Categorical 4 + Numerical 8 + Total 12 + +Database ../../../datasets/Iris/Iris.txt +Sample percentage 70 +Sampling mode Include sample +Selection variable +Selection value +Instances 105 + +Learning task Unsupervised analysis + +Evaluated variables 12 +Native variables 5 +Constructed variables 7 + +Max number of constructed variables 0 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 100 +Discretization MODL +Value grouping MODL + + +Categorical variables statistics + +Rank Name Values Mode Mode coverage Missing number Sparse missing number Constr. cost Derivation rule +R01 Class 3 Iris-setosa 0.361905 0 0 3.17805 +R02 Class1 2 0.638095 67 0 3.17805 IfC(EQc(Class, "Iris-setosa"), "setosa", "") +R03 Class2 2 0.695238 73 0 3.17805 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") +R09 SPetalLength 5 1 0.361905 0 0 3.17805 AsCategorical(Floor(PetalLength)) + + +Numerical variables statistics + +Rank Name Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Derivation rule +R04 Dummy1 1 0 0 0 0 0 0 3.17805 Copy(0) +R05 Dummy2 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0 3.17805 Random() +R06 LowerPetalLength 10 1 3 2.446666667 0.7433600251 0 0 3.17805 If(LE(PetalLength, 3), PetalLength, 3) +R07 PetalLength 36 1 6.9 3.686666667 1.80132579 0 0 3.17805 +R08 PetalWidth 21 0.1 2.5 1.175238095 0.7880996979 0 0 3.17805 +R10 SepalLength 31 4.3 7.7 5.827619048 0.8375127846 0 0 3.17805 +R11 SepalWidth 23 2 4.4 3.081904762 0.4284592446 0 0 3.17805 +R12 UpperPetalWidth 11 1.5 2.5 1.692380952 0.2962287527 0 0 3.17805 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R01 +Variable Categorical Class + +Variable stats +Group Frequency Coverage Size Value list +{Iris-setosa} 38 0.361905 1 Iris-setosa +{Iris-virginica} 35 0.333333 1 Iris-virginica +{Iris-versicolor} 32 0.304762 1 Iris-versicolor * + + +Values +Value Frequency Coverage +Iris-setosa 38 0.361905 +Iris-virginica 35 0.333333 +Iris-versicolor 32 0.304762 + +---------------------------------------------- +Rank R02 +Variable Categorical Class1 + +Variable stats +Group Frequency Coverage Size Value list +{} 67 0.638095 1 +{setosa} 38 0.361905 1 setosa * + + +Values +Value Frequency Coverage + 67 0.638095 +setosa 38 0.361905 + +---------------------------------------------- +Rank R03 +Variable Categorical Class2 + +Variable stats +Group Frequency Coverage Size Value list +{} 73 0.695238 1 +{versicolor} 32 0.304762 1 versicolor * + + +Values +Value Frequency Coverage + 73 0.695238 +versicolor 32 0.304762 + +---------------------------------------------- +Rank R05 +Variable Numerical Dummy2 + +Variable stats +Interval Frequency Coverage +]-inf;+inf[ 105 1 + + +---------------------------------------------- +Rank R06 +Variable Numerical LowerPetalLength + +Variable stats +Interval Frequency Coverage +]-inf;1.25] 4 0.0380952 +]1.25;1.75] 32 0.304762 +]1.75;2.95] 2 0.0190476 +]2.95;+inf[ 67 0.638095 + + +---------------------------------------------- +Rank R07 +Variable Numerical PetalLength + +Variable stats +Interval Frequency Coverage +]-inf;1.25] 4 0.0380952 +]1.25;1.75] 32 0.304762 +]1.75;3.85] 7 0.0666667 +]3.85;6.15] 59 0.561905 +]6.15;+inf[ 3 0.0285714 + + +---------------------------------------------- +Rank R08 +Variable Numerical PetalWidth + +Variable stats +Interval Frequency Coverage +]-inf;0.15] 6 0.0571429 +]0.15;0.25] 20 0.190476 +]0.25;0.45] 11 0.104762 +]0.45;0.85] 1 0.00952381 +]0.85;+inf[ 67 0.638095 + + +---------------------------------------------- +Rank R09 +Variable Categorical SPetalLength + +Variable stats +Group Frequency Coverage Size Value list +{1} 38 0.361905 1 1 +{5} 27 0.257143 1 5 +{4} 25 0.238095 1 4 +{3} 8 0.0761905 1 3 +{6} 7 0.0666667 1 6 * + + +Values +Value Frequency Coverage +1 38 0.361905 +5 27 0.257143 +4 25 0.238095 +3 8 0.0761905 +6 7 0.0666667 + +---------------------------------------------- +Rank R10 +Variable Numerical SepalLength + +Variable stats +Interval Frequency Coverage +]-inf;7] 97 0.92381 +]7;+inf[ 8 0.0761905 + + +---------------------------------------------- +Rank R11 +Variable Numerical SepalWidth + +Variable stats +Interval Frequency Coverage +]-inf;2.45] 5 0.047619 +]2.45;3.25] 72 0.685714 +]3.25;+inf[ 28 0.266667 + + +---------------------------------------------- +Rank R12 +Variable Numerical UpperPetalWidth + +Variable stats +Interval Frequency Coverage +]-inf;1.55] 67 0.638095 +]1.55;+inf[ 38 0.361905 + diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.khj b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.khj new file mode 100644 index 000000000..bfc1926d9 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.khj @@ -0,0 +1,2716 @@ +{ + "tool": "Khiops", + "version": "10.4.6i", + "shortDescription": "", + "preparationReport": { + "reportType": "Preparation", + "summary": { + "dictionary": "Iris", + "variables": { + "types": [ + "Categorical", + "Numerical" + ], + "numbers": [ + 4, + 8 + ] + }, + "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", + "samplePercentage": 70, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "", + "instances": 105, + "learningTask": "Unsupervised analysis", + "evaluatedVariables": 12, + "nativeVariables": 5, + "constructedVariables": 7, + "featureEngineering": { + "maxNumberOfConstructedVariables": 0, + "maxNumberOfTextFeatures": 0, + "maxNumberOfTrees": 0, + "maxNumberOfVariablePairs": 100 + }, + "discretization": "MODL", + "valueGrouping": "MODL" + }, + "variablesStatistics": [ + { + "rank": 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"cellIds": ["C1","C2","C4"], + "cellPartIndexes": [ + [0,0], + [1,0], + [1,1] + ], + "cellFrequencies": [38,29,38] + } + }, + "R38": { + "dataGrid": { + "isSupervised": false, + "dimensions": [ + { + "variable": "PetalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0.1,0.75], + [0.75,1.45], + [1.45,2.5] + ] + }, + { + "variable": "SepalWidth", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,2.95], + [2.95,4.4] + ] + } + ], + "cellIds": ["C1","C2","C3","C4","C5","C6"], + "cellPartIndexes": [ + [0,0], + [1,0], + [2,0], + [0,1], + [1,1], + [2,1] + ], + "cellFrequencies": [1,21,14,37,3,29] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/err.txt new file mode 100644 index 000000000..0e8a46c9e --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/err.txt @@ -0,0 +1,6 @@ +Train unsupervised model +Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 +Evaluation of variable pairs +Data preparation time: 0:00:00.31 +Write report ./results/AnalysisResults.khj + diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/time.log new file mode 100644 index 000000000..d11750ce1 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/time.log @@ -0,0 +1 @@ +Overal time: 0.4437694549560547 diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/test.prm b/test/LearningTest/TestKhiops/Standard/IrisU2D/test.prm new file mode 100644 index 000000000..635c9360e --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/IrisU2D/test.prm @@ -0,0 +1,58 @@ +// Thu Jun 12 13:31:06 2008 +// modl +// Output command file +// +//This file contains recorded commands, that can be replayed. +//Commands are based on user interactions: +// field update +// list index selection +// menu action +//Every command can be commented, using //. +//For example, commenting the last Exit command will allow other +//user interactions, after the commands have been replayed. +// +// + +// -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB +ClassManagement.OpenFile // Open... + +// -> Open +ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file +ClassFileName ./IrisExtended.kdic // Dictionary file +OK // Open +// <- Open + +TrainDatabase.ClassName Iris // Dictionary + +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage + + +AnalysisSpec.TargetAttributeName // Target variable + +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true // Do data preparation only + +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 100 // Max number of pairs of variables + +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report + + + +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls +ComputeStats // Analyse database + +Exit // Close +// <- Data preparation and scoring + + +// -> Data preparation and scoring +OK // Close +// <- Data preparation and scoring + diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.ModelingReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.ModelingReport.xls index 8b51218d9..a9c96e3a6 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.ModelingReport.xls @@ -1,34 +1,34 @@ -#Khiops 10.1 -Modeling report - -Dictionary SpliceJunction -Target variable Class -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 7 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PMode(DNA.Char) where Pos <= 30.5 Mode(DNA.Char) where Pos <= 30.5 0.0744365 0.316406 0.153467 -PMode(DNA.Char) where Pos in ]15.5, 30.5] Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0508826 0.394531 0.141686 -PMode(DNA.Char) Mode(DNA.Char) 0.0461059 0.417969 0.138819 -PMedian(DNA.Pos) where Char = G Median(DNA.Pos) where Char = G 0.0225808 0.363281 0.0905713 -PMean(DNA.Pos) where Char = G Mean(DNA.Pos) where Char = G 0.0274408 0.238281 0.0808618 -PCount(DNA) where Char = G Count(DNA) where Char = G 0.0160697 0.238281 0.0618798 -PMedian(DNA.Pos) where Char = C Median(DNA.Pos) where Char = C 0.0101423 0.191406 0.0440603 +#Khiops 10.4.6i +Modeling report + +Dictionary SpliceJunction +Target variable Class +Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt +Sample percentage 10 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 7 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PMode(DNA.Char) where Pos <= 30.5 Mode(DNA.Char) where Pos <= 30.5 0.0744365 0.316406 0.153467 +PMode(DNA.Char) where Pos in ]15.5, 30.5] Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0508826 0.394531 0.141686 +PMode(DNA.Char) Mode(DNA.Char) 0.0461059 0.417969 0.138819 +PMedian(DNA.Pos) where Char = G Median(DNA.Pos) where Char = G 0.0225808 0.363281 0.0905713 +PMean(DNA.Pos) where Char = G Mean(DNA.Pos) where Char = G 0.0274408 0.238281 0.0808618 +PCount(DNA) where Char = G Count(DNA) where Char = G 0.0160697 0.238281 0.0618798 +PMedian(DNA.Pos) where Char = C Median(DNA.Pos) where Char = C 0.0101423 0.191406 0.0440603 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.PreparationReport.xls new file mode 100644 index 000000000..e6e020db7 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.PreparationReport.xls @@ -0,0 +1,275 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary SpliceJunction +Variables + Categorical 12 + Numerical 90 + Table 1 + Total 103 + +Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt +Sample percentage 10 +Sampling mode Include sample +Selection variable +Selection value +Instances 303 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +EI 67 0.221122 +IE 69 0.227723 +N 167 0.551155 + +Evaluated variables 101 +Native variables 1 +Constructed variables 100 +Informative variables 7 +Selected variables 7 + +Max number of constructed variables 100 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 10.7442 + Data cost 296.926 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R001 Mode(DNA.Char) where Pos <= 30.5 0.0744365 2 4 C 0.40264 0 0 7.76817 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) +R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0508826 3 4 C 0.376238 0 0 9.15447 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R003 Mode(DNA.Char) 0.0461059 3 4 C 0.392739 0 0 4.27667 30.8759 258.994 TableMode(DNA, Char) +R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) +R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 1 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) +R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) +R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) +R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0 0 0.693147 10.7442 296.926 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R004 Mean(DNA.Pos) where Char = G 0.0274408 2 281 16 46.55555556 31.84768883 4.848828493 1 0 9.15447 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) +R005 Median(DNA.Pos) where Char = G 0.0225808 3 59 8 48 32.44701987 6.725618787 1 0 9.15447 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) +R006 Count(DNA) where Char = G 0.0160697 2 26 0 32 15.81188119 5.174919597 0 0 9.15447 21.0465 273.207 TableCount(`DNA where Char = G`) +R007 Median(DNA.Pos) where Char = C 0.0101423 2 63 9 56.5 29.24587459 7.66919868 0 0 9.15447 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) +R008 Count(DNA) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableCount(DNA) +R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) +R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) +R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) +R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) +R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) +R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) +R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) +R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R022 Count(DNA) where Pos in ]22.5, 30.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R023 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R024 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) +R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) +R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) +R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) +R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) +R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) +R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) +R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) +R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) +R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) +R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) +R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) +R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) +R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) +R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) +R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) +R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) +R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) +R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) +R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) +R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) +R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) +R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) +R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) +R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) +R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) +R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) +R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) +R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) +R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) +R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) +R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) +R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) +R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) +R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) +R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) +R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) +R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) +R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) +R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R001 +Variable Categorical Mode(DNA.Char) where Pos <= 30.5 + +Variable stats +Group Size Value list +{C, T} 2 C T +{G, A} 2 G A * + +% target values +Group EI IE N Interest Frequency Coverage +{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 +{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 122 0.40264 +T 64 0.211221 +G 61 0.20132 +A 56 0.184818 + +---------------------------------------------- +Rank R002 +Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] + +Variable stats +Group Size Value list +{A, G} 2 A G * +{C} 1 C +{T} 1 T + +% target values +Group EI IE N Interest Frequency Coverage +{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 +{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 +{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 114 0.376238 +A 69 0.227723 +T 61 0.20132 +G 59 0.194719 + +---------------------------------------------- +Rank R003 +Variable Categorical Mode(DNA.Char) + +Variable stats +Group Size Value list +{C} 1 C +{T, A} 2 T A * +{G} 1 G + +% target values +Group EI IE N Interest Frequency Coverage +{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 +{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 +{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 119 0.392739 +G 77 0.254125 +T 57 0.188119 +A 50 0.165017 + +---------------------------------------------- +Rank R004 +Variable Numerical Mean(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 +]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R005 +Variable Numerical Median(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 +]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 +]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R006 +Variable Numerical Count(DNA) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 +]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R007 +Variable Numerical Median(DNA.Pos) where Char = C + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 +]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 +Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_TestEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.TestEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_TestEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.TestEvaluationReport.xls index 3a1b35d4c..611abe35e 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_TestEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.TestEvaluationReport.xls @@ -1,3049 +1,3049 @@ -#Khiops 10.1 -Test evaluation report - -Dictionary SpliceJunction -Target variable Categorical Class -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Exclude sample -Selection variable -Selection value -Instances 2875 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.58887 0.165677 0.75466 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.58887 -Compression 0.165677 -AUC 0.75466 - -Confusion matrix - EI IE N -$EI 0 0 0 -$IE 23 305 100 -$N 671 388 1388 - - - ----------------------------------------------- -Lift curves EI -Size Random Optimal Selective Naive Bayes -0 0 0 0 -0.1 0.1 0.414265 0.310699 -0.2 0.2 0.82853 0.621398 -0.3 0.3 1.2428 0.923091 -0.4 0.4 1.65706 1.19236 -0.5 0.5 2.07133 1.46164 -0.6 0.6 2.48559 1.73091 -0.7 0.7 2.89986 2.00018 -0.8 0.8 3.31412 2.26945 -0.9 0.9 3.72839 2.53872 -1 1 4.14265 2.82421 -1.1 1.1 4.55692 3.15562 -1.2 1.2 4.97118 3.48703 -1.3 1.3 5.38545 3.81844 -1.4 1.4 5.79971 4.14986 -1.5 1.5 6.21398 4.48127 -1.6 1.6 6.62824 4.81268 -1.7 1.7 7.04251 5.12644 -1.8 1.8 7.45677 5.39986 -1.9 1.9 7.87104 5.67327 -2 2 8.2853 5.94669 -2.1 2.1 8.69957 6.2201 -2.2 2.2 9.11383 6.49352 -2.3 2.3 9.5281 6.76693 -2.4 2.4 9.94236 7.04035 -2.5 2.5 10.3566 7.31376 -2.6 2.6 10.7709 7.58718 -2.7 2.7 11.1852 7.86059 -2.8 2.8 11.5994 8.13401 -2.9 2.9 12.0137 8.40742 -3 3 12.428 8.68084 -3.1 3.1 12.8422 8.95425 -3.2 3.2 13.2565 9.22767 -3.3 3.3 13.6707 9.50108 -3.4 3.4 14.085 9.7745 -3.5 3.5 14.4993 9.79827 -3.6 3.6 14.9135 9.91835 -3.7 3.7 15.3278 10.0564 -3.8 3.8 15.7421 10.1945 -3.9 3.9 16.1563 10.3326 -4 4 16.5706 10.4707 -4.1 4.1 16.9849 10.5187 -4.2 4.2 17.3991 10.7205 -4.3 4.3 17.8134 11.0519 -4.4 4.4 18.2277 11.2559 -4.5 4.5 18.6419 11.4409 -4.6 4.6 19.0562 11.6258 -4.7 4.7 19.4705 11.8107 -4.8 4.8 19.8847 11.9957 -4.9 4.9 20.299 12.1806 -5 5 20.7133 12.3656 -5.1 5.1 21.1275 12.5505 -5.2 5.2 21.5418 12.7354 -5.3 5.3 21.9561 12.9204 -5.4 5.4 22.3703 13.1053 -5.5 5.5 22.7846 13.2903 -5.6 5.6 23.1988 13.4752 -5.7 5.7 23.6131 13.6601 -5.8 5.8 24.0274 13.8451 -5.9 5.9 24.4416 14.03 -6 6 24.8559 14.215 -6.1 6.1 25.2702 14.3999 -6.2 6.2 25.6844 14.5848 -6.3 6.3 26.0987 14.7559 -6.4 6.4 26.513 14.9055 -6.5 6.5 26.9272 15.0551 -6.6 6.6 27.3415 15.2047 -6.7 6.7 27.7558 15.3543 -6.8 6.8 28.17 15.5039 -6.9 6.9 28.5843 15.6535 -7 7 28.9986 15.8031 -7.1 7.1 29.4128 15.9527 -7.2 7.2 29.8271 16.1023 -7.3 7.3 30.2414 16.2519 -7.4 7.4 30.6556 16.4015 -7.5 7.5 31.0699 16.5511 -7.6 7.6 31.4841 16.6907 -7.7 7.7 31.8984 16.8288 -7.8 7.8 32.3127 16.9669 -7.9 7.9 32.7269 17.1049 -8 8 33.1412 17.243 -8.1 8.1 33.5555 17.3811 -8.2 8.2 33.9697 17.6153 -8.3 8.3 34.384 17.7834 -8.4 8.4 34.7983 18.0596 -8.5 8.5 35.2125 18.3357 -8.6 8.6 35.6268 18.6119 -8.7 8.7 36.0411 18.8881 -8.8 8.8 36.4553 19.1643 -8.9 8.9 36.8696 19.4404 -9 9 37.2839 19.7798 -9.1 9.1 37.6981 20.122 -9.2 9.2 38.1124 20.4642 -9.3 9.3 38.5267 20.8064 -9.4 9.4 38.9409 21.1487 -9.5 9.5 39.3552 21.4909 -9.6 9.6 39.7695 21.8331 -9.7 9.7 40.1837 22.1753 -9.8 9.8 40.598 22.3883 -9.9 9.9 41.0122 22.5955 -10 10 41.4265 22.8026 -10.1 10.1 41.8408 23.0097 -10.2 10.2 42.255 23.2169 -10.3 10.3 42.6693 23.424 -10.4 10.4 43.0836 23.6311 -10.5 10.5 43.4978 23.7704 -10.6 10.6 43.9121 23.9839 -10.7 10.7 44.3264 24.1975 -10.8 10.8 44.7406 24.411 -10.9 10.9 45.1549 24.6245 -11 11 45.5692 24.8381 -11.1 11.1 45.9834 25.0516 -11.2 11.2 46.3977 25.2652 -11.3 11.3 46.812 25.4787 -11.4 11.4 47.2262 25.6922 -11.5 11.5 47.6405 25.9058 -11.6 11.6 48.0548 26.1193 -11.7 11.7 48.469 26.3329 -11.8 11.8 48.8833 26.5464 -11.9 11.9 49.2976 26.7599 -12 12 49.7118 26.9735 -12.1 12.1 50.1261 27.187 -12.2 12.2 50.5403 27.4005 -12.3 12.3 50.9546 27.6141 -12.4 12.4 51.3689 27.8276 -12.5 12.5 51.7831 28.0412 -12.6 12.6 52.1974 28.2547 -12.7 12.7 52.6117 28.4682 -12.8 12.8 53.0259 28.6818 -12.9 12.9 53.4402 28.8953 -13 13 53.8545 29.1089 -13.1 13.1 54.2687 29.3224 -13.2 13.2 54.683 29.5359 -13.3 13.3 55.0973 29.7495 -13.4 13.4 55.5115 29.963 -13.5 13.5 55.9258 30.1766 -13.6 13.6 56.3401 30.3901 -13.7 13.7 56.7543 30.6036 -13.8 13.8 57.1686 30.8172 -13.9 13.9 57.5829 31.214 -14 14 57.9971 31.4252 -14.1 14.1 58.4114 31.5005 -14.2 14.2 58.8256 31.5758 -14.3 14.3 59.2399 31.6512 -14.4 14.4 59.6542 31.8156 -14.5 14.5 60.0684 32.147 -14.6 14.6 60.4827 32.3307 -14.7 14.7 60.897 32.5378 -14.8 14.8 61.3112 32.7089 -14.9 14.9 61.7255 32.871 -15 15 62.1398 33.018 -15.1 15.1 62.554 33.2586 -15.2 15.2 62.9683 33.4991 -15.3 15.3 63.3826 33.7397 -15.4 15.4 63.7968 33.9802 -15.5 15.5 64.2111 34.2207 -15.6 15.6 64.6254 34.4613 -15.7 15.7 65.0396 34.7018 -15.8 15.8 65.4539 34.9424 -15.9 15.9 65.8682 35.1829 -16 16 66.2824 35.4234 -16.1 16.1 66.6967 35.6328 -16.2 16.2 67.111 35.8036 -16.3 16.3 67.5252 36.0673 -16.4 16.4 67.9395 36.3309 -16.5 16.5 68.3537 36.5945 -16.6 16.6 68.768 36.8396 -16.7 16.7 69.1823 37.0605 -16.8 16.8 69.5965 37.2815 -16.9 16.9 70.0108 37.5024 -17 17 70.4251 37.7233 -17.1 17.1 70.8393 37.8963 -17.2 17.2 71.2536 37.8963 -17.3 17.3 71.6679 37.8963 -17.4 17.4 72.0821 38.0043 -17.5 17.5 72.4964 38.2529 -17.6 17.6 72.9107 38.5014 -17.7 17.7 73.3249 38.75 -17.8 17.8 73.7392 38.9986 -17.9 17.9 74.1535 39.2471 -18 18 74.5677 39.4957 -18.1 18.1 74.982 39.7442 -18.2 18.2 75.3963 39.9928 -18.3 18.3 75.8105 40.1087 -18.4 18.4 76.2248 40.1777 -18.5 18.5 76.639 40.2317 -18.6 18.6 77.0533 40.2778 -18.7 18.7 77.4676 40.3238 -18.8 18.8 77.8818 40.3767 -18.9 18.9 78.2961 40.4359 -19 19 78.7104 40.4951 -19.1 19.1 79.1246 40.5542 -19.2 19.2 79.5389 40.6134 -19.3 19.3 79.9532 40.7691 -19.4 19.4 80.3674 40.9762 -19.5 19.5 80.7817 41.1248 -19.6 19.6 81.196 41.2464 -19.7 19.7 81.6102 41.3545 -19.8 19.8 82.0245 41.3621 -19.9 19.9 82.4388 41.4495 -20 20 82.853 41.537 -20.1 20.1 83.2673 41.6244 -20.2 20.2 83.6816 41.7119 -20.3 20.3 84.0958 41.7994 -20.4 20.4 84.5101 41.8868 -20.5 20.5 84.9244 41.9743 -20.6 20.6 85.3386 42.0617 -20.7 20.7 85.7529 42.1492 -20.8 20.8 86.1671 42.2366 -20.9 20.9 86.5814 42.3241 -21 21 86.9957 42.4115 -21.1 21.1 87.4099 42.499 -21.2 21.2 87.8242 42.5865 -21.3 21.3 88.2385 42.6739 -21.4 21.4 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b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.TrainEvaluationReport.xls @@ -1,3049 +1,3049 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary SpliceJunction -Target variable Categorical Class -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value -Instances 303 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.630363 0.254824 0.787031 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.630363 -Compression 0.254824 -AUC 0.787031 - -Confusion matrix - EI IE N -$EI 0 0 0 -$IE 5 42 18 -$N 62 27 149 - - - ----------------------------------------------- -Lift curves EI -Size Random Optimal Selective Naive Bayes -0 0 0 0 -0.1 0.1 0.452239 0.452239 -0.2 0.2 0.904478 0.904478 -0.3 0.3 1.35672 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38.4403 19.0373 -8.6 8.6 38.8925 19.4896 -8.7 8.7 39.3448 19.9418 -8.8 8.8 39.797 20.394 -8.9 8.9 40.2493 20.8463 -9 9 40.7015 21.1642 -9.1 9.1 41.1537 21.4657 -9.2 9.2 41.606 21.7672 -9.3 9.3 42.0582 22.0687 -9.4 9.4 42.5104 22.3701 -9.5 9.5 42.9627 22.6716 -9.6 9.6 43.4149 22.9731 -9.7 9.7 43.8672 23.2746 -9.8 9.8 44.3194 23.5761 -9.9 9.9 44.7716 23.8776 -10 10 45.2239 24.0672 -10.1 10.1 45.6761 24.2556 -10.2 10.2 46.1284 24.444 -10.3 10.3 46.5806 24.6325 -10.4 10.4 47.0328 24.8209 -10.5 10.5 47.4851 25.0093 -10.6 10.6 47.9373 25.1978 -10.7 10.7 48.3896 25.3862 -10.8 10.8 48.8418 25.5746 -10.9 10.9 49.294 25.7631 -11 11 49.7463 25.9515 -11.1 11.1 50.1985 26.1399 -11.2 11.2 50.6507 26.3284 -11.3 11.3 51.103 26.5168 -11.4 11.4 51.5552 26.7052 -11.5 11.5 52.0075 26.8937 -11.6 11.6 52.4597 27.0821 -11.7 11.7 52.9119 27.2705 -11.8 11.8 53.3642 27.459 -11.9 11.9 53.8164 27.6474 -12 12 54.2687 27.8358 -12.1 12.1 54.7209 28.0243 -12.2 12.2 55.1731 28.2127 -12.3 12.3 55.6254 28.4011 -12.4 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+ "partInterests": [0.33703,0.144491,0.518478] + }, + "inputValues": { + "values": ["C","G","T","A"], + "frequencies": [119,77,57,50] + } + }, + "R004": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mean(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [16,31.698], + [31.698,46.55555556] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [34,12,105], + [33,57,62] + ], + "partInterests": [0.57478,0.42522] + } + }, + "R005": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Median(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [8,27.75], + [27.75,37.25], + [37.25,48] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [11,3,58], + [44,31,86], + [12,35,23] + ], + "partInterests": [0.478566,0.0530889,0.468345] + } + }, + "R006": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Count(DNA) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0,19.5], + [19.5,32] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [38,68,130], + [29,1,37] + ], + "partInterests": [0.187162,0.812838] + } + }, + "R007": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Median(DNA.Pos) where Char = C", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [9,27.75], + [27.75,56.5] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [35,53,62], + [32,16,105] + ], + "partInterests": [0.454456,0.545544] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_Modeling.kdic b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.model.kdic similarity index 98% rename from test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.model.kdic index d85129993..c99453876 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_AnalysisResults.model.kdic @@ -1,47 +1,47 @@ -#Khiops 10.1 - -Root Dictionary SNB_SpliceJunction (SampleId) - -{ - Categorical SampleId ; -Unused Categorical Class ; -Unused Table(SNB_SpliceJunctionDNA) DNA ; -Unused Categorical `Mode(DNA.Char)` = TableMode(DNA, Char) ; -Unused Categorical `Mode(DNA.Char) where Pos <= 30.5` = TableMode(`DNA where Pos <= 30.5`, Char) ; -Unused Numerical `Count(DNA) where Char = G` = TableCount(`DNA where Char = G`) ; -Unused Numerical `Mean(DNA.Pos) where Char = G` = TableMean(`DNA where Char = G`, Pos) ; -Unused Numerical `Median(DNA.Pos) where Char = C` = TableMedian(`DNA where Char = C`, Pos) ; -Unused Numerical `Median(DNA.Pos) where Char = G` = TableMedian(`DNA where Char = G`, Pos) ; -Unused Table(SNB_SpliceJunctionDNA) `DNA where Pos <= 30.5` = TableSelection(DNA, LE(Pos, 30.5)) ; -Unused Table(SNB_SpliceJunctionDNA) `DNA where Char = C` = TableSelection(DNA, EQc(Char, "C")) ; -Unused Table(SNB_SpliceJunctionDNA) `DNA where Char = G` = TableSelection(DNA, EQc(Char, "G")) ; - { -Unused Categorical `Mode(DNA.Char) where Pos in ]15.5, 30.5]` ; - } `Mode(DNA.Char) per Pos(4)` = TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) ; -Unused Structure(Partition) `Partition(SpliceJunctionDNA) per Pos(4)` = Partition(IntervalBounds(15.5, 30.5, 45.5)) ; - { -Unused Table(SNB_SpliceJunctionDNA) `DNA where Pos in ]15.5, 30.5]` ; - } `TablePartition(DNA) per Pos(4)` = TablePartition(DNA, `Partition(SpliceJunctionDNA) per Pos(4)`, Pos) ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("EI", "IE", "N"), Frequencies(67, 69, 167)) ; -Unused Structure(DataGrid) `PMode(DNA.Char)` = DataGrid(ValueGroups(ValueGroup("C"), ValueGroup("T", "A", " * "), ValueGroup("G")), ValueSetC("EI", "IE", "N"), Frequencies(17, 16, 34, 49, 17, 3, 53, 74, 40)) ; // DataGrid(`Mode(DNA.Char)`, Class) -Unused Structure(DataGrid) `PMode(DNA.Char) where Pos <= 30.5` = DataGrid(ValueGroups(ValueGroup("C", "T"), ValueGroup("G", "A", " * ")), ValueSetC("EI", "IE", "N"), Frequencies(29, 38, 69, 0, 88, 79)) ; // DataGrid(`Mode(DNA.Char) where Pos <= 30.5`, Class) -Unused Structure(DataGrid) `PCount(DNA) where Char = G` = DataGrid(IntervalBounds(19.5), ValueSetC("EI", "IE", "N"), Frequencies(38, 29, 68, 1, 130, 37)) ; // DataGrid(`Count(DNA) where Char = G`, Class) -Unused Structure(DataGrid) `PMean(DNA.Pos) where Char = G` = DataGrid(IntervalBounds(31.698), ValueSetC("EI", "IE", "N"), Frequencies(34, 33, 12, 57, 105, 62)) ; // DataGrid(`Mean(DNA.Pos) where Char = G`, Class) -Unused Structure(DataGrid) `PMedian(DNA.Pos) where Char = C` = DataGrid(IntervalBounds(27.75), ValueSetC("EI", "IE", "N"), Frequencies(35, 32, 53, 16, 62, 105)) ; // DataGrid(`Median(DNA.Pos) where Char = C`, Class) -Unused Structure(DataGrid) `PMedian(DNA.Pos) where Char = G` = DataGrid(IntervalBounds(27.75, 37.25), ValueSetC("EI", "IE", "N"), Frequencies(11, 44, 12, 3, 31, 35, 58, 86, 23)) ; // DataGrid(`Median(DNA.Pos) where Char = G`, Class) -Unused Structure(DataGrid) `PMode(DNA.Char) where Pos in ]15.5, 30.5]` = DataGrid(ValueGroups(ValueGroup("A", "G", " * "), ValueGroup("C"), ValueGroup("T")), ValueSetC("EI", "IE", "N"), Frequencies(38, 26, 3, 3, 41, 25, 87, 47, 33)) ; // DataGrid(`Mode(DNA.Char) where Pos in ]15.5, 30.5]`, Class) -Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.41796875, 0.31640625, 0.23828125, 0.23828125, 0.19140625, 0.36328125, 0.39453125), DataGridStats(`PMode(DNA.Char)`, `Mode(DNA.Char)`), DataGridStats(`PMode(DNA.Char) where Pos <= 30.5`, `Mode(DNA.Char) where Pos <= 30.5`), DataGridStats(`PCount(DNA) where Char = G`, `Count(DNA) where Char = G`), DataGridStats(`PMean(DNA.Pos) where Char = G`, `Mean(DNA.Pos) where Char = G`), DataGridStats(`PMedian(DNA.Pos) where Char = C`, `Median(DNA.Pos) where Char = C`), DataGridStats(`PMedian(DNA.Pos) where Char = G`, `Median(DNA.Pos) where Char = G`), DataGridStats(`PMode(DNA.Char) where Pos in ]15.5, 30.5]`, `Mode(DNA.Char) where Pos in ]15.5, 30.5]`), VClass) ; - Categorical PredictedClass = TargetValue(SNBClass) ; -Unused Numerical ScoreClass = TargetProb(SNBClass) ; - Numerical ProbClassEI = TargetProbAt(SNBClass, "EI") ; - Numerical ProbClassIE = TargetProbAt(SNBClass, "IE") ; - Numerical ProbClassN = TargetProbAt(SNBClass, "N") ; -}; - -Dictionary SNB_SpliceJunctionDNA (SampleId) - -{ - Categorical SampleId ; - Numerical Pos ; - Categorical Char ; -}; +#Khiops 10.4.6i + +Root Dictionary SNB_SpliceJunction (SampleId) + +{ + Categorical SampleId ; +Unused Categorical Class ; +Unused Table(SNB_SpliceJunctionDNA) DNA ; +Unused Categorical `Mode(DNA.Char)` = TableMode(DNA, Char) ; +Unused Categorical `Mode(DNA.Char) where Pos <= 30.5` = TableMode(`DNA where Pos <= 30.5`, Char) ; +Unused Numerical `Count(DNA) where Char = G` = TableCount(`DNA where Char = G`) ; +Unused Numerical `Mean(DNA.Pos) where Char = G` = TableMean(`DNA where Char = G`, Pos) ; +Unused Numerical `Median(DNA.Pos) where Char = C` = TableMedian(`DNA where Char = C`, Pos) ; +Unused Numerical `Median(DNA.Pos) where Char = G` = TableMedian(`DNA where Char = G`, Pos) ; +Unused Table(SNB_SpliceJunctionDNA) `DNA where Pos <= 30.5` = TableSelection(DNA, LE(Pos, 30.5)) ; +Unused Table(SNB_SpliceJunctionDNA) `DNA where Char = C` = TableSelection(DNA, EQc(Char, "C")) ; +Unused Table(SNB_SpliceJunctionDNA) `DNA where Char = G` = TableSelection(DNA, EQc(Char, "G")) ; + { +Unused Categorical `Mode(DNA.Char) where Pos in ]15.5, 30.5]` ; + } `Mode(DNA.Char) per Pos(4)` = TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) ; +Unused Structure(Partition) `Partition(SpliceJunctionDNA) per Pos(4)` = Partition(IntervalBounds(15.5, 30.5, 45.5)) ; + { +Unused Table(SNB_SpliceJunctionDNA) `DNA where Pos in ]15.5, 30.5]` ; + } `TablePartition(DNA) per Pos(4)` = TablePartition(DNA, `Partition(SpliceJunctionDNA) per Pos(4)`, Pos) ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("EI", "IE", "N"), Frequencies(67, 69, 167)) ; +Unused Structure(DataGrid) `PMode(DNA.Char)` = DataGrid(ValueGroups(ValueGroup("C"), ValueGroup("T", "A", " * "), ValueGroup("G")), ValueSetC("EI", "IE", "N"), Frequencies(17, 16, 34, 49, 17, 3, 53, 74, 40)) ; // DataGrid(`Mode(DNA.Char)`, Class) +Unused Structure(DataGrid) `PMode(DNA.Char) where Pos <= 30.5` = DataGrid(ValueGroups(ValueGroup("C", "T"), ValueGroup("G", "A", " * ")), ValueSetC("EI", "IE", "N"), Frequencies(29, 38, 69, 0, 88, 79)) ; // DataGrid(`Mode(DNA.Char) where Pos <= 30.5`, Class) +Unused Structure(DataGrid) `PCount(DNA) where Char = G` = DataGrid(IntervalBounds(19.5), ValueSetC("EI", "IE", "N"), Frequencies(38, 29, 68, 1, 130, 37)) ; // DataGrid(`Count(DNA) where Char = G`, Class) +Unused Structure(DataGrid) `PMean(DNA.Pos) where Char = G` = DataGrid(IntervalBounds(31.698), ValueSetC("EI", "IE", "N"), Frequencies(34, 33, 12, 57, 105, 62)) ; // DataGrid(`Mean(DNA.Pos) where Char = G`, Class) +Unused Structure(DataGrid) `PMedian(DNA.Pos) where Char = C` = DataGrid(IntervalBounds(27.75), ValueSetC("EI", "IE", "N"), Frequencies(35, 32, 53, 16, 62, 105)) ; // DataGrid(`Median(DNA.Pos) where Char = C`, Class) +Unused Structure(DataGrid) `PMedian(DNA.Pos) where Char = G` = DataGrid(IntervalBounds(27.75, 37.25), ValueSetC("EI", "IE", "N"), Frequencies(11, 44, 12, 3, 31, 35, 58, 86, 23)) ; // DataGrid(`Median(DNA.Pos) where Char = G`, Class) +Unused Structure(DataGrid) `PMode(DNA.Char) where Pos in ]15.5, 30.5]` = DataGrid(ValueGroups(ValueGroup("A", "G", " * "), ValueGroup("C"), ValueGroup("T")), ValueSetC("EI", "IE", "N"), Frequencies(38, 26, 3, 3, 41, 25, 87, 47, 33)) ; // DataGrid(`Mode(DNA.Char) where Pos in ]15.5, 30.5]`, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.41796875, 0.31640625, 0.23828125, 0.23828125, 0.19140625, 0.36328125, 0.39453125), DataGridStats(`PMode(DNA.Char)`, `Mode(DNA.Char)`), DataGridStats(`PMode(DNA.Char) where Pos <= 30.5`, `Mode(DNA.Char) where Pos <= 30.5`), DataGridStats(`PCount(DNA) where Char = G`, `Count(DNA) where Char = G`), DataGridStats(`PMean(DNA.Pos) where Char = G`, `Mean(DNA.Pos) where Char = G`), DataGridStats(`PMedian(DNA.Pos) where Char = C`, `Median(DNA.Pos) where Char = C`), DataGridStats(`PMedian(DNA.Pos) where Char = G`, `Median(DNA.Pos) where Char = G`), DataGridStats(`PMode(DNA.Char) where Pos in ]15.5, 30.5]`, `Mode(DNA.Char) where Pos in ]15.5, 30.5]`), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical ProbClassEI = TargetProbAt(SNBClass, "EI") ; + Numerical ProbClassIE = TargetProbAt(SNBClass, "IE") ; + Numerical ProbClassN = TargetProbAt(SNBClass, "N") ; +}; + +Dictionary SNB_SpliceJunctionDNA (SampleId) + +{ + Categorical SampleId ; + Numerical Pos ; + Categorical Char ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_PreparationReport.xls deleted file mode 100644 index ed87bb8b6..000000000 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/C100_PreparationReport.xls +++ /dev/null @@ -1,273 +0,0 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary SpliceJunction -Variables - Categorical 12 - Numerical 90 - Table 1 - Total 103 - -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value -Instances 303 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -EI 67 0.221122 -IE 69 0.227723 -N 167 0.551155 - -Evaluated variables 101 -Native variables 1 -Constructed variables 100 -Informative variables 7 - -Max number of constructed variables 100 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 10.7442 - Data cost 296.926 - - -Categorical variables statistics - -Rank Name Level Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost Derivation rule -R001 Mode(DNA.Char) where Pos <= 30.5 0.0744365 2 4 C 0.40264 7.76817 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) -R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0508826 3 4 C 0.376238 9.15447 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R003 Mode(DNA.Char) 0.0461059 3 4 C 0.392739 4.27667 30.8759 258.994 TableMode(DNA, Char) -R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) -R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) -R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) -R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) -R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0.693147 10.7442 296.926 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R004 Mean(DNA.Pos) where Char = G 0.0274408 2 281 16 46.55555556 31.84768883 4.848828493 1 9.15447 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) -R005 Median(DNA.Pos) where Char = G 0.0225808 3 59 8 48 32.44701987 6.725618787 1 9.15447 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) -R006 Count(DNA) where Char = G 0.0160697 2 26 0 32 15.81188119 5.174919597 0 9.15447 21.0465 273.207 TableCount(`DNA where Char = G`) -R007 Median(DNA.Pos) where Char = C 0.0101423 2 63 9 56.5 29.24587459 7.66919868 0 9.15447 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) -R008 Count(DNA) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableCount(DNA) -R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) -R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) -R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) -R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) -R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) -R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) -R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) -R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R022 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R023 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R024 Count(DNA) where Pos in ]38.5, 45.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) -R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) -R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) -R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) -R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) -R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) -R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) -R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) -R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) -R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) -R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) -R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) -R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) -R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) -R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) -R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) -R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) -R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) -R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) -R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) -R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) -R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) -R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) -R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) -R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) -R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) -R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) -R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) -R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) -R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) -R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) -R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) -R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) -R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) -R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) -R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) -R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) -R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) -R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) -R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R001 -Variable Categorical Mode(DNA.Char) where Pos <= 30.5 - -Variable stats -Group Size Value list -{C, T} 2 C T -{G, A} 2 G A * - -% target values -Group EI IE N Interest Frequency Coverage -{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 -{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 122 0.40264 -T 64 0.211221 -G 61 0.20132 -A 56 0.184818 - ----------------------------------------------- -Rank R002 -Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] - -Variable stats -Group Size Value list -{A, G} 2 A G * -{C} 1 C -{T} 1 T - -% target values -Group EI IE N Interest Frequency Coverage -{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 -{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 -{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 114 0.376238 -A 69 0.227723 -T 61 0.20132 -G 59 0.194719 - ----------------------------------------------- -Rank R003 -Variable Categorical Mode(DNA.Char) - -Variable stats -Group Size Value list -{C} 1 C -{T, A} 2 T A * -{G} 1 G - -% target values -Group EI IE N Interest Frequency Coverage -{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 -{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 -{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 119 0.392739 -G 77 0.254125 -T 57 0.188119 -A 50 0.165017 - ----------------------------------------------- -Rank R004 -Variable Numerical Mean(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 -]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R005 -Variable Numerical Median(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 -]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 -]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R006 -Variable Numerical Count(DNA) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 -]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R007 -Variable Numerical Median(DNA.Pos) where Char = C - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 -]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 -Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/err.txt index c82520151..2f0ce4d27 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/err.txt @@ -1,11 +1,11 @@ -Variable construction time: 0:00:00.11 (100 constructed variables) +Variable construction time: 0:00:00.08 (100 constructed variables) Train supervised model for classification of target variable Class Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt: Read records: 3178 Selected records: 303 Table ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt Records: 18,180 -Data preparation time: 0:00:00.15 -SNB train time: 0:00:00.01 +Data preparation time: 0:00:00.13 +SNB train time: 0:00:00 Build Selective Naive Bayes SNB_SpliceJunction Train evaluation of Selective Naive Bayes on database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt Test evaluation of Selective Naive Bayes on database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Write report ./results\C100_AllReports.khj +Write report ./results/C100_AnalysisResults.khj diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/time.log index 145dac1d1..177ffc274 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.7093141078948975 +Overal time: 0.5552172660827637 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction/test.prm b/test/LearningTest/TestKhiops/Standard/SpliceJunction/test.prm index 04ce2fafb..1be21c2ac 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction/test.prm +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction/test.prm @@ -14,29 +14,33 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open ClassFileName ../../../MTdatasets/SpliceJunction/SpliceJunction.kdic // Dictionary file OK // Open -ClassManagement.ClassName SpliceJunction // Dictionary +TrainDatabase.ClassName SpliceJunction // Dictionary -TrainDatabase.DatabaseFiles.List.Key SpliceJunction // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file -TrainDatabase.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file -TrainDatabase.SampleNumberPercentage 10 // Sample percentage +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 10 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report -AnalysisResults.ResultFilesPrefix C100_ // Result files directory +AnalysisResults.ReportFileName ./results/C100_AnalysisResults.khj // Analysis report AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database Exit // Close diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_Preparation2DReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.Preparation2DReport.xls similarity index 98% rename from test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_Preparation2DReport.xls rename to test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.Preparation2DReport.xls index 6ba139502..328a61cc5 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_Preparation2DReport.xls +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.Preparation2DReport.xls @@ -1,363 +1,363 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary SpliceJunction -Variables - Categorical 12 - Numerical 90 - Table 1 - Total 103 - -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value -Instances 303 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -EI 67 0.221122 -IE 69 0.227723 -N 167 0.551155 - -Evaluated variable pairs 100 -Informative variable pairs 1 - - -Variables pairs statistics - -Rank Name 1 Name 2 DeltaLevel Level Level 1 Level 2 Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost -R001 Count(DNA) where Char = C Mean(DNA.Pos) where Char = G 0.00199714 0.0281231 0 0.0261259 2 2 3 6 19.767 51.0062 228.918 -R002 Count(DNA) where Char = A Count(DNA) where Char = C 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R003 Count(DNA) where Char = A Count(DNA) where Char = T 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R004 Count(DNA) where Char = A Count(DNA) where Char not in {C, G, T, A} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R005 Count(DNA) where Char = A Count(DNA) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R006 Count(DNA) where Char = A CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R007 Count(DNA) where Char = A CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R008 Count(DNA) where Char = A CountDistinct(DNA.Char) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R009 Count(DNA) where Char = C Count(DNA) where Char = T 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R010 Count(DNA) where Char = C Count(DNA) where Char not in {C, G, T, A} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R011 Count(DNA) where Char = C Count(DNA) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R012 Count(DNA) where Char = C CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R013 Count(DNA) where Char = C CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R014 Count(DNA) where Char = C CountDistinct(DNA.Char) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R015 Count(DNA) where Char = T Count(DNA) where Char not in {C, G, T, A} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R016 Count(DNA) where Char = T Count(DNA) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R017 Count(DNA) where Char = T CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R018 Count(DNA) where Char = T CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R019 Count(DNA) where Char not in {C, G, T, A} Count(DNA) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R020 Count(DNA) where Char not in {C, G, T, A} CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R021 Count(DNA) where Char not in {C, G, T, A} CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R022 Count(DNA) where Char not in {C, G} CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R023 Count(DNA) where Char not in {C, G} CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R024 CountDistinct(DNA.Char) CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 -R025 Count(DNA) where Char = A Mode(DNA.Char) -0.00189687 0.0428942 0 0.0447911 1 1 3 3 6.42789 29.7151 258.994 -R026 Count(DNA) where Char = C Mode(DNA.Char) -0.00189687 0.0428942 0 0.0447911 1 1 3 3 6.42789 29.7151 258.994 -R027 Count(DNA) where Char = T Mode(DNA.Char) -0.00189687 0.0428942 0 0.0447911 1 1 3 3 6.42789 29.7151 258.994 -R028 Count(DNA) where Char not in {C, G} Mode(DNA.Char) -0.00189687 0.0428942 0 0.0447911 1 1 3 3 6.42789 29.7151 258.994 -R029 Count(DNA) where Char not in {C, G} Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00510825 0.0444595 0 0.0495677 1 1 3 3 11.3057 31.3846 251.964 -R030 Count(DNA) where Char = A Count(DNA) where Char = G -0.00566135 0.00909347 0 0.0147548 1 1 2 2 11.3057 21.0465 273.207 -R031 Count(DNA) where Char = A Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 -R032 Count(DNA) where Char = A Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 -R033 Count(DNA) where Char = A Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 -R034 Count(DNA) where Char = A Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R035 Count(DNA) where Char = A Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R036 Count(DNA) where Char = C Count(DNA) where Char = G -0.00566135 0.00909347 0 0.0147548 1 1 2 2 11.3057 21.0465 273.207 -R037 Count(DNA) where Char = C Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 -R038 Count(DNA) where Char = C Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 -R039 Count(DNA) where Char = C Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R040 Count(DNA) where Char = C Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R041 Count(DNA) where Char = G Count(DNA) where Char = T -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 -R042 Count(DNA) where Char = G Count(DNA) where Char not in {C, G, T, A} -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 -R043 Count(DNA) where Char = G Count(DNA) where Char not in {C, G} -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 -R044 Count(DNA) where Char = G CountDistinct(DNA.Char) -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 -R045 Count(DNA) where Char = G CountDistinct(DNA.Char) where Char = G -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 -R046 Count(DNA) where Char = G CountDistinct(DNA.Char) where Char not in {C, G} -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 -R047 Count(DNA) where Char = T Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 -R048 Count(DNA) where Char = T Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 -R049 Count(DNA) where Char = T Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 -R050 Count(DNA) where Char = T Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R051 Count(DNA) where Char = T Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R052 Count(DNA) where Char not in {C, G, T, A} Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 -R053 Count(DNA) where Char not in {C, G, T, A} Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 -R054 Count(DNA) where Char not in {C, G, T, A} Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 -R055 Count(DNA) where Char not in {C, G, T, A} Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R056 Count(DNA) where Char not in {C, G, T, A} Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R057 Count(DNA) where Char not in {C, G} Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 -R058 Count(DNA) where Char not in {C, G} Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 -R059 Count(DNA) where Char not in {C, G} Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 -R060 Count(DNA) where Char not in {C, G} Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R061 CountDistinct(DNA.Char) Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 -R062 CountDistinct(DNA.Char) Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 -R063 CountDistinct(DNA.Char) Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 -R064 CountDistinct(DNA.Char) Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R065 CountDistinct(DNA.Char) Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R066 CountDistinct(DNA.Char) where Char = G Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 -R067 CountDistinct(DNA.Char) where Char = G Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 -R068 CountDistinct(DNA.Char) where Char = G Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 -R069 CountDistinct(DNA.Char) where Char = G Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R070 CountDistinct(DNA.Char) where Char = G Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R071 CountDistinct(DNA.Char) where Char not in {C, G} Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 -R072 CountDistinct(DNA.Char) where Char not in {C, G} Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 -R073 CountDistinct(DNA.Char) where Char not in {C, G} Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 -R074 CountDistinct(DNA.Char) where Char not in {C, G} Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R075 CountDistinct(DNA.Char) where Char not in {C, G} Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R076 Count(DNA) where Char = G Median(DNA.Pos) where Char = C -0.0081426 0.0154397 0.0147548 0.00882745 2 3 2 6 19.767 48.0934 235.742 -R077 Count(DNA) where Char = G Median(DNA.Pos) where Char = G -0.00875321 0.0272675 0.0147548 0.0212659 2 2 3 6 19.767 45.3883 234.8 -R078 Median(DNA.Pos) where Char = C Mode(DNA.Char) -0.0107243 0.0428942 0.00882745 0.0447911 1 1 3 3 6.42789 29.7151 258.994 -R079 Mean(DNA.Pos) where Char = G Median(DNA.Pos) where Char = C -0.0144888 0.0204646 0.0261259 0.00882745 1 2 1 2 11.3057 19.4458 271.302 -R080 Median(DNA.Pos) where Char = C Median(DNA.Pos) where Char = G -0.0144888 0.0156045 0.00882745 0.0212659 1 1 3 3 11.3057 29.3732 262.873 -R081 Median(DNA.Pos) where Char = C Mode(DNA.Char) where Pos <= 30.5 -0.0144888 0.0674603 0.00882745 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R082 Median(DNA.Pos) where Char = C Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0144888 0.0439064 0.00882745 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R083 Median(DNA.Pos) where Char = G Mode(DNA.Char) -0.0145649 0.051492 0.0212659 0.0447911 2 3 3 9 14.8892 68.663 208.933 -R084 Count(DNA) where Char not in {C, G, T, A} Mode(DNA.Char) -0.0159326 0.0288585 0 0.0447911 1 1 3 3 6.42789 34.0432 258.994 -R085 CountDistinct(DNA.Char) Mode(DNA.Char) -0.0159326 0.0288585 0 0.0447911 1 1 3 3 6.42789 34.0432 258.994 -R086 CountDistinct(DNA.Char) where Char = G Mode(DNA.Char) -0.0159326 0.0288585 0 0.0447911 1 1 3 3 6.42789 34.0432 258.994 -R087 CountDistinct(DNA.Char) where Char not in {C, G} Mode(DNA.Char) -0.0159326 0.0288585 0 0.0447911 1 1 3 3 6.42789 34.0432 258.994 -R088 Count(DNA) where Char = G Mode(DNA.Char) -0.0166517 0.0428942 0.0147548 0.0447911 1 1 3 3 6.42789 29.7151 258.994 -R089 Count(DNA) where Char = G Mean(DNA.Pos) where Char = G -0.017638 0.0232427 0.0147548 0.0261259 2 2 2 4 19.767 35.2334 246.196 -R090 Mean(DNA.Pos) where Char = G Mode(DNA.Char) -0.0202285 0.0506885 0.0261259 0.0447911 2 2 3 6 14.8892 45.9195 231.924 -R091 Count(DNA) where Char = G Mode(DNA.Char) where Pos <= 30.5 -0.0204162 0.0674603 0.0147548 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R092 Count(DNA) where Char = G Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0204162 0.0439064 0.0147548 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R093 Mean(DNA.Pos) where Char = G Median(DNA.Pos) where Char = G -0.0269272 0.0204646 0.0261259 0.0212659 1 2 1 2 11.3057 19.4458 271.302 -R094 Median(DNA.Pos) where Char = G Mode(DNA.Char) where Pos <= 30.5 -0.0269272 0.0674603 0.0212659 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R095 Median(DNA.Pos) where Char = G Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0269272 0.0439064 0.0212659 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R096 Mean(DNA.Pos) where Char = G Mode(DNA.Char) where Pos <= 30.5 -0.0317873 0.0674603 0.0261259 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R097 Mean(DNA.Pos) where Char = G Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0317873 0.0439064 0.0261259 0.0495677 1 1 3 3 11.3057 31.5551 251.964 -R098 Mode(DNA.Char) Mode(DNA.Char) where Pos <= 30.5 -0.0504524 0.0674603 0.0447911 0.0731216 1 1 2 2 9.91939 23.5167 254.125 -R099 Mode(DNA.Char) Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0514646 0.0428942 0.0447911 0.0495677 1 3 1 3 6.42789 29.7151 258.994 -R100 Mode(DNA.Char) where Pos <= 30.5 Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0552291 0.0674603 0.0731216 0.0495677 1 2 1 2 9.91939 23.5167 254.125 - - --------------------------------------------------------------------------------- - -Variables pairs detailed statistics -(Pairs with two jointly informative variables) - - -Rank R001 -Variables - Type Name - Numerical Count(DNA) where Char = C - Numerical Mean(DNA.Pos) where Char = G - -Variables stats -Count(DNA) where Char = C -Interval Frequency Coverage -]-inf;16.5] 152 0.50165 -]16.5;+inf[ 151 0.49835 -Mean(DNA.Pos) where Char = G -Interval Frequency Coverage -]-inf;31.698] 151 0.49835 -]31.698;37.6] 114 0.376238 -]37.6;+inf[ 38 0.125413 - -Cell frequencies - Mean(DNA.Pos) where Char = G -Count(DNA) where Char = C ]-inf;31.698] ]31.698;37.6] ]37.6;+inf[ Total Coverage -]-inf;16.5] 79 57 16 152 0.50165 -]16.5;+inf[ 72 57 22 151 0.49835 -Total 151 114 38 303 -Coverage 0.49835 0.376238 0.125413 - -Cells 6 -Cell Id Count(DNA) where Char = C Mean(DNA.Pos) where Char = G EI IE N Interest Frequency Coverage -C1 ]-inf;16.5] ]-inf;31.698] 0.202532 0.0253165 0.772152 0.274659 79 0.260726 -C5 ]-inf;16.5] ]37.6;+inf[ 0 0.75 0.25 0.207197 16 0.0528053 -C4 ]16.5;+inf[ ]31.698;37.6] 0.0877193 0.491228 0.421053 0.194172 57 0.188119 -C3 ]-inf;16.5] ]31.698;37.6] 0.421053 0.0701754 0.508772 0.15674 57 0.188119 -C6 ]16.5;+inf[ ]37.6;+inf[ 0.181818 0.590909 0.227273 0.1336 22 0.0726073 -C2 ]16.5;+inf[ ]-inf;31.698] 0.25 0.138889 0.611111 0.0336322 72 0.237624 - Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R076 -Variables - Type Name - Numerical Count(DNA) where Char = G - Numerical Median(DNA.Pos) where Char = C - -Variables stats -Count(DNA) where Char = G -Interval Frequency Coverage -]-inf;12.5] 75 0.247525 -]12.5;19.5] 161 0.531353 -]19.5;+inf[ 67 0.221122 -Median(DNA.Pos) where Char = C -Interval Frequency Coverage -]-inf;27.75] 150 0.49505 -]27.75;+inf[ 153 0.50495 - -Cell frequencies - Median(DNA.Pos) where Char = C -Count(DNA) where Char = G ]-inf;27.75] ]27.75;+inf[ Total Coverage -]-inf;12.5] 35 40 75 0.247525 -]12.5;19.5] 84 77 161 0.531353 -]19.5;+inf[ 31 36 67 0.221122 -Total 150 153 303 -Coverage 0.49505 0.50495 - -Cells 6 -Cell Id Count(DNA) where Char = G Median(DNA.Pos) where Char = C EI IE N Interest Frequency Coverage -C6 ]19.5;+inf[ ]27.75;+inf[ 0.388889 0 0.611111 0.215965 36 0.118812 -C5 ]12.5;19.5] ]27.75;+inf[ 0.220779 0.0519481 0.727273 0.203532 77 0.254125 -C2 ]12.5;19.5] ]-inf;27.75] 0.178571 0.440476 0.380952 0.199181 84 0.277228 -C3 ]19.5;+inf[ ]-inf;27.75] 0.483871 0.0322581 0.483871 0.166373 31 0.10231 -C4 ]-inf;12.5] ]27.75;+inf[ 0.025 0.3 0.675 0.14009 40 0.132013 -C1 ]-inf;12.5] ]-inf;27.75] 0.142857 0.428571 0.428571 0.0748584 35 0.115512 - Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R077 -Variables - Type Name - Numerical Count(DNA) where Char = G - Numerical Median(DNA.Pos) where Char = G - -Variables stats -Count(DNA) where Char = G -Interval Frequency Coverage -]-inf;19.5] 236 0.778878 -]19.5;+inf[ 67 0.221122 -Median(DNA.Pos) where Char = G -Interval Frequency Coverage -]-inf;27.75] 72 0.237624 -]27.75;37.25] 161 0.531353 -]37.25;+inf[ 70 0.231023 - -Cell frequencies - Median(DNA.Pos) where Char = G -Count(DNA) where Char = G ]-inf;27.75] ]27.75;37.25] ]37.25;+inf[ Total Coverage -]-inf;19.5] 60 116 60 236 0.778878 -]19.5;+inf[ 12 45 10 67 0.221122 -Total 72 161 70 303 -Coverage 0.237624 0.531353 0.231023 - -Cells 6 -Cell Id Count(DNA) where Char = G Median(DNA.Pos) where Char = G EI IE N Interest Frequency Coverage -C5 ]-inf;19.5] ]37.25;+inf[ 0.0833333 0.566667 0.35 0.330661 60 0.19802 -C4 ]19.5;+inf[ ]27.75;37.25] 0.4 0 0.6 0.258497 45 0.148515 -C1 ]-inf;19.5] ]-inf;27.75] 0.116667 0.05 0.833333 0.232266 60 0.19802 -C6 ]19.5;+inf[ ]37.25;+inf[ 0.7 0.1 0.2 0.104024 10 0.0330033 -C2 ]19.5;+inf[ ]-inf;27.75] 0.333333 0 0.666667 0.063096 12 0.039604 -C3 ]-inf;19.5] ]27.75;37.25] 0.224138 0.267241 0.508621 0.0114554 116 0.382838 - Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R083 -Variables - Type Name - Numerical Median(DNA.Pos) where Char = G - Categorical Mode(DNA.Char) - -Variables stats -Median(DNA.Pos) where Char = G -Interval Frequency Coverage -]-inf;30.25] 109 0.359736 -]30.25;38.25] 136 0.448845 -]38.25;+inf[ 58 0.191419 -Mode(DNA.Char) -Group Frequency Coverage Size Value list -{C} 119 0.392739 1 C -{T, A} 107 0.353135 2 T A * -{G} 77 0.254125 1 G - -Cell frequencies - Mode(DNA.Char) -Median(DNA.Pos) where Char = G {C} {T, A} {G} Total Coverage -]-inf;30.25] 41 43 25 109 0.359736 -]30.25;38.25] 44 48 44 136 0.448845 -]38.25;+inf[ 34 16 8 58 0.191419 -Total 119 107 77 303 -Coverage 0.392739 0.353135 0.254125 - -Cells 9 -Cell Id Median(DNA.Pos) where Char = G Mode(DNA.Char) EI IE N Interest Frequency Coverage -C8 ]30.25;38.25] {G} 0.5 0 0.5 0.228745 44 0.145215 -C4 ]-inf;30.25] {T, A} 0.0930233 0.0232558 0.883721 0.176489 43 0.141914 -C2 ]30.25;38.25] {C} 0.113636 0.545455 0.340909 0.150925 44 0.145215 -C3 ]38.25;+inf[ {C} 0.117647 0.588235 0.294118 0.147254 34 0.112211 -C9 ]38.25;+inf[ {G} 0.75 0.25 0 0.10875 8 0.0264026 -C6 ]38.25;+inf[ {T, A} 0 0.5625 0.4375 0.0943793 16 0.0528053 -C7 ]-inf;30.25] {G} 0.24 0.04 0.72 0.0515547 25 0.0825083 -C1 ]-inf;30.25] {C} 0.195122 0.121951 0.682927 0.0271938 41 0.135314 -C5 ]30.25;38.25] {T, A} 0.25 0.145833 0.604167 0.0147099 48 0.158416 - Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R089 -Variables - Type Name - Numerical Count(DNA) where Char = G - Numerical Mean(DNA.Pos) where Char = G - -Variables stats -Count(DNA) where Char = G -Interval Frequency Coverage -]-inf;19.5] 236 0.778878 -]19.5;+inf[ 67 0.221122 -Mean(DNA.Pos) where Char = G -Interval Frequency Coverage -]-inf;31.698] 151 0.49835 -]31.698;+inf[ 152 0.50165 - -Cell frequencies - Mean(DNA.Pos) where Char = G -Count(DNA) where Char = G ]-inf;31.698] ]31.698;+inf[ Total Coverage -]-inf;19.5] 114 122 236 0.778878 -]19.5;+inf[ 37 30 67 0.221122 -Total 151 152 303 -Coverage 0.49835 0.50165 - -Cells 4 -Cell Id Count(DNA) where Char = G Mean(DNA.Pos) where Char = G EI IE N Interest Frequency Coverage -C3 ]-inf;19.5] ]31.698;+inf[ 0.139344 0.459016 0.401639 0.375437 122 0.40264 -C2 ]19.5;+inf[ ]-inf;31.698] 0.351351 0 0.648649 0.234491 37 0.122112 -C4 ]19.5;+inf[ ]31.698;+inf[ 0.533333 0.0333333 0.433333 0.213467 30 0.0990099 -C1 ]-inf;19.5] ]-inf;31.698] 0.184211 0.105263 0.710526 0.176604 114 0.376238 - Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R090 -Variables - Type Name - Numerical Mean(DNA.Pos) where Char = G - Categorical Mode(DNA.Char) - -Variables stats -Mean(DNA.Pos) where Char = G -Interval Frequency Coverage -]-inf;37.6] 265 0.874587 -]37.6;+inf[ 38 0.125413 -Mode(DNA.Char) -Group Frequency Coverage Size Value list -{C} 119 0.392739 1 C -{T, A} 107 0.353135 2 T A * -{G} 77 0.254125 1 G - -Cell frequencies - Mode(DNA.Char) -Mean(DNA.Pos) where Char = G {C} {T, A} {G} Total Coverage -]-inf;37.6] 100 90 75 265 0.874587 -]37.6;+inf[ 19 17 2 38 0.125413 -Total 119 107 77 303 -Coverage 0.392739 0.353135 0.254125 - -Cells 6 -Cell Id Mean(DNA.Pos) where Char = G Mode(DNA.Char) EI IE N Interest Frequency Coverage -C5 ]-inf;37.6] {G} 0.426667 0.04 0.533333 0.268268 75 0.247525 -C3 ]-inf;37.6] {T, A} 0.177778 0.0555556 0.766667 0.22622 90 0.29703 -C4 ]37.6;+inf[ {T, A} 0 0.705882 0.294118 0.193064 17 0.0561056 -C2 ]37.6;+inf[ {C} 0.105263 0.684211 0.210526 0.165905 19 0.0627063 -C1 ]-inf;37.6] {C} 0.15 0.36 0.49 0.0907069 100 0.330033 -C6 ]37.6;+inf[ {G} 1 0 0 0.0558357 2 0.00660066 - Total 0.221122 0.227723 0.551155 1 303 1 +#Khiops 10.4.7i +Descriptive statistics + + +Problem description +Short description + +Dictionary SpliceJunction +Variables + Categorical 12 + Numerical 90 + Table 1 + Total 103 + +Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt +Sample percentage 10 +Sampling mode Include sample +Selection variable +Selection value +Instances 303 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +EI 67 0.221122 +IE 69 0.227723 +N 167 0.551155 + +Evaluated variable pairs 100 +Informative variable pairs 1 + + +Variables pairs statistics + +Rank Name 1 Name 2 DeltaLevel Level Level 1 Level 2 Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost +R001 Count(DNA) where Char = C Mean(DNA.Pos) where Char = G 0.00199714 0.0281231 0 0.0261259 2 2 3 6 19.767 51.0062 228.918 +R002 Count(DNA) where Char = A Count(DNA) where Char = C 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R003 Count(DNA) where Char = A Count(DNA) where Char = T 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R004 Count(DNA) where Char = A Count(DNA) where Char not in {C, G, T, A} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R005 Count(DNA) where Char = A Count(DNA) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R006 Count(DNA) where Char = A CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R007 Count(DNA) where Char = A CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R008 Count(DNA) where Char = A CountDistinct(DNA.Char) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R009 Count(DNA) where Char = C Count(DNA) where Char = T 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R010 Count(DNA) where Char = C Count(DNA) where Char not in {C, G, T, A} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R011 Count(DNA) where Char = C Count(DNA) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R012 Count(DNA) where Char = C CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R013 Count(DNA) where Char = C CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R014 Count(DNA) where Char = C CountDistinct(DNA.Char) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R015 Count(DNA) where Char = T Count(DNA) where Char not in {C, G, T, A} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R016 Count(DNA) where Char = T Count(DNA) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R017 Count(DNA) where Char = T CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R018 Count(DNA) where Char = T CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R019 Count(DNA) where Char not in {C, G, T, A} Count(DNA) where Char not in {C, G} 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R020 Count(DNA) where Char not in {C, G, T, A} CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R021 Count(DNA) where Char not in {C, G, T, A} CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R022 Count(DNA) where Char not in {C, G} CountDistinct(DNA.Char) 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R023 Count(DNA) where Char not in {C, G} CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R024 CountDistinct(DNA.Char) CountDistinct(DNA.Char) where Char = G 0 0 0 0 0 1 1 1 0.693147 10.7442 296.926 +R025 Count(DNA) where Char = A Mode(DNA.Char) -0.00189687 0.0428942 0 0.0447911 1 1 3 3 6.42789 29.7151 258.994 +R026 Count(DNA) where Char = C Mode(DNA.Char) -0.00189687 0.0428942 0 0.0447911 1 1 3 3 6.42789 29.7151 258.994 +R027 Count(DNA) where Char = T Mode(DNA.Char) -0.00189687 0.0428942 0 0.0447911 1 1 3 3 6.42789 29.7151 258.994 +R028 Count(DNA) where Char not in {C, G} Mode(DNA.Char) -0.00189687 0.0428942 0 0.0447911 1 1 3 3 6.42789 29.7151 258.994 +R029 Count(DNA) where Char not in {C, G} Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00510825 0.0444595 0 0.0495677 1 1 3 3 11.3057 31.3846 251.964 +R030 Count(DNA) where Char = A Count(DNA) where Char = G -0.00566135 0.00909347 0 0.0147548 1 1 2 2 11.3057 21.0465 273.207 +R031 Count(DNA) where Char = A Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 +R032 Count(DNA) where Char = A Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 +R033 Count(DNA) where Char = A Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 +R034 Count(DNA) where Char = A Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R035 Count(DNA) where Char = A Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R036 Count(DNA) where Char = C Count(DNA) where Char = G -0.00566135 0.00909347 0 0.0147548 1 1 2 2 11.3057 21.0465 273.207 +R037 Count(DNA) where Char = C Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 +R038 Count(DNA) where Char = C Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 +R039 Count(DNA) where Char = C Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R040 Count(DNA) where Char = C Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R041 Count(DNA) where Char = G Count(DNA) where Char = T -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 +R042 Count(DNA) where Char = G Count(DNA) where Char not in {C, G, T, A} -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 +R043 Count(DNA) where Char = G Count(DNA) where Char not in {C, G} -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 +R044 Count(DNA) where Char = G CountDistinct(DNA.Char) -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 +R045 Count(DNA) where Char = G CountDistinct(DNA.Char) where Char = G -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 +R046 Count(DNA) where Char = G CountDistinct(DNA.Char) where Char not in {C, G} -0.00566135 0.00909347 0.0147548 0 1 2 1 2 11.3057 21.0465 273.207 +R047 Count(DNA) where Char = T Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 +R048 Count(DNA) where Char = T Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 +R049 Count(DNA) where Char = T Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 +R050 Count(DNA) where Char = T Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R051 Count(DNA) where Char = T Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R052 Count(DNA) where Char not in {C, G, T, A} Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 +R053 Count(DNA) where Char not in {C, G, T, A} Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 +R054 Count(DNA) where Char not in {C, G, T, A} Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 +R055 Count(DNA) where Char not in {C, G, T, A} Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R056 Count(DNA) where Char not in {C, G, T, A} Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R057 Count(DNA) where Char not in {C, G} Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 +R058 Count(DNA) where Char not in {C, G} Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 +R059 Count(DNA) where Char not in {C, G} Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 +R060 Count(DNA) where Char not in {C, G} Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R061 CountDistinct(DNA.Char) Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 +R062 CountDistinct(DNA.Char) Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 +R063 CountDistinct(DNA.Char) Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 +R064 CountDistinct(DNA.Char) Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R065 CountDistinct(DNA.Char) Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R066 CountDistinct(DNA.Char) where Char = G Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 +R067 CountDistinct(DNA.Char) where Char = G Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 +R068 CountDistinct(DNA.Char) where Char = G Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 +R069 CountDistinct(DNA.Char) where Char = G Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R070 CountDistinct(DNA.Char) where Char = G Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R071 CountDistinct(DNA.Char) where Char not in {C, G} Mean(DNA.Pos) where Char = G -0.00566135 0.0204646 0 0.0261259 1 1 2 2 11.3057 19.4458 271.302 +R072 CountDistinct(DNA.Char) where Char not in {C, G} Median(DNA.Pos) where Char = C -0.00566135 0.0031661 0 0.00882745 1 1 2 2 11.3057 19.4456 276.636 +R073 CountDistinct(DNA.Char) where Char not in {C, G} Median(DNA.Pos) where Char = G -0.00566135 0.0156045 0 0.0212659 1 1 3 3 11.3057 29.3732 262.873 +R074 CountDistinct(DNA.Char) where Char not in {C, G} Mode(DNA.Char) where Pos <= 30.5 -0.00566135 0.0674603 0 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R075 CountDistinct(DNA.Char) where Char not in {C, G} Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.00566135 0.0439064 0 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R076 Count(DNA) where Char = G Median(DNA.Pos) where Char = C -0.0081426 0.0154397 0.0147548 0.00882745 2 3 2 6 19.767 48.0934 235.742 +R077 Count(DNA) where Char = G Median(DNA.Pos) where Char = G -0.00875321 0.0272675 0.0147548 0.0212659 2 2 3 6 19.767 45.3883 234.8 +R078 Median(DNA.Pos) where Char = C Mode(DNA.Char) -0.0107243 0.0428942 0.00882745 0.0447911 1 1 3 3 6.42789 29.7151 258.994 +R079 Mean(DNA.Pos) where Char = G Median(DNA.Pos) where Char = C -0.0144888 0.0204646 0.0261259 0.00882745 1 2 1 2 11.3057 19.4458 271.302 +R080 Median(DNA.Pos) where Char = C Median(DNA.Pos) where Char = G -0.0144888 0.0156045 0.00882745 0.0212659 1 1 3 3 11.3057 29.3732 262.873 +R081 Median(DNA.Pos) where Char = C Mode(DNA.Char) where Pos <= 30.5 -0.0144888 0.0674603 0.00882745 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R082 Median(DNA.Pos) where Char = C Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0144888 0.0439064 0.00882745 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R083 Median(DNA.Pos) where Char = G Mode(DNA.Char) -0.0145649 0.051492 0.0212659 0.0447911 2 3 3 9 14.8892 68.663 208.933 +R084 Count(DNA) where Char not in {C, G, T, A} Mode(DNA.Char) -0.0159326 0.0288585 0 0.0447911 1 1 3 3 6.42789 34.0432 258.994 +R085 CountDistinct(DNA.Char) Mode(DNA.Char) -0.0159326 0.0288585 0 0.0447911 1 1 3 3 6.42789 34.0432 258.994 +R086 CountDistinct(DNA.Char) where Char = G Mode(DNA.Char) -0.0159326 0.0288585 0 0.0447911 1 1 3 3 6.42789 34.0432 258.994 +R087 CountDistinct(DNA.Char) where Char not in {C, G} Mode(DNA.Char) -0.0159326 0.0288585 0 0.0447911 1 1 3 3 6.42789 34.0432 258.994 +R088 Count(DNA) where Char = G Mode(DNA.Char) -0.0166517 0.0428942 0.0147548 0.0447911 1 1 3 3 6.42789 29.7151 258.994 +R089 Count(DNA) where Char = G Mean(DNA.Pos) where Char = G -0.017638 0.0232427 0.0147548 0.0261259 2 2 2 4 19.767 35.2334 246.196 +R090 Mean(DNA.Pos) where Char = G Mode(DNA.Char) -0.0202285 0.0506885 0.0261259 0.0447911 2 2 3 6 14.8892 45.9195 231.924 +R091 Count(DNA) where Char = G Mode(DNA.Char) where Pos <= 30.5 -0.0204162 0.0674603 0.0147548 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R092 Count(DNA) where Char = G Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0204162 0.0439064 0.0147548 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R093 Mean(DNA.Pos) where Char = G Median(DNA.Pos) where Char = G -0.0269272 0.0204646 0.0261259 0.0212659 1 2 1 2 11.3057 19.4458 271.302 +R094 Median(DNA.Pos) where Char = G Mode(DNA.Char) where Pos <= 30.5 -0.0269272 0.0674603 0.0212659 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R095 Median(DNA.Pos) where Char = G Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0269272 0.0439064 0.0212659 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R096 Mean(DNA.Pos) where Char = G Mode(DNA.Char) where Pos <= 30.5 -0.0317873 0.0674603 0.0261259 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R097 Mean(DNA.Pos) where Char = G Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0317873 0.0439064 0.0261259 0.0495677 1 1 3 3 11.3057 31.5551 251.964 +R098 Mode(DNA.Char) Mode(DNA.Char) where Pos <= 30.5 -0.0504524 0.0674603 0.0447911 0.0731216 1 1 2 2 9.91939 23.5167 254.125 +R099 Mode(DNA.Char) Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0514646 0.0428942 0.0447911 0.0495677 1 3 1 3 6.42789 29.7151 258.994 +R100 Mode(DNA.Char) where Pos <= 30.5 Mode(DNA.Char) where Pos in ]15.5, 30.5] -0.0552291 0.0674603 0.0731216 0.0495677 1 2 1 2 9.91939 23.5167 254.125 + + +-------------------------------------------------------------------------------- + +Variables pairs detailed statistics +(Pairs with two jointly informative variables) + + +Rank R001 +Variables + Type Name + Numerical Count(DNA) where Char = C + Numerical Mean(DNA.Pos) where Char = G + +Variables stats +Count(DNA) where Char = C +Interval Frequency Coverage +]-inf;16.5] 152 0.50165 +]16.5;+inf[ 151 0.49835 +Mean(DNA.Pos) where Char = G +Interval Frequency Coverage +]-inf;31.698] 151 0.49835 +]31.698;37.6] 114 0.376238 +]37.6;+inf[ 38 0.125413 + +Cell frequencies + Mean(DNA.Pos) where Char = G +Count(DNA) where Char = C ]-inf;31.698] ]31.698;37.6] ]37.6;+inf[ Total Coverage +]-inf;16.5] 79 57 16 152 0.50165 +]16.5;+inf[ 72 57 22 151 0.49835 +Total 151 114 38 303 +Coverage 0.49835 0.376238 0.125413 + +Cells 6 +Cell Id Count(DNA) where Char = C Mean(DNA.Pos) where Char = G EI IE N Interest Frequency Coverage +C1 ]-inf;16.5] ]-inf;31.698] 0.202532 0.0253165 0.772152 0.274659 79 0.260726 +C5 ]-inf;16.5] ]37.6;+inf[ 0 0.75 0.25 0.207197 16 0.0528053 +C4 ]16.5;+inf[ ]31.698;37.6] 0.0877193 0.491228 0.421053 0.194172 57 0.188119 +C3 ]-inf;16.5] ]31.698;37.6] 0.421053 0.0701754 0.508772 0.15674 57 0.188119 +C6 ]16.5;+inf[ ]37.6;+inf[ 0.181818 0.590909 0.227273 0.1336 22 0.0726073 +C2 ]16.5;+inf[ ]-inf;31.698] 0.25 0.138889 0.611111 0.0336322 72 0.237624 + Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R076 +Variables + Type Name + Numerical Count(DNA) where Char = G + Numerical Median(DNA.Pos) where Char = C + +Variables stats +Count(DNA) where Char = G +Interval Frequency Coverage +]-inf;12.5] 75 0.247525 +]12.5;19.5] 161 0.531353 +]19.5;+inf[ 67 0.221122 +Median(DNA.Pos) where Char = C +Interval Frequency Coverage +]-inf;27.75] 150 0.49505 +]27.75;+inf[ 153 0.50495 + +Cell frequencies + Median(DNA.Pos) where Char = C +Count(DNA) where Char = G ]-inf;27.75] ]27.75;+inf[ Total Coverage +]-inf;12.5] 35 40 75 0.247525 +]12.5;19.5] 84 77 161 0.531353 +]19.5;+inf[ 31 36 67 0.221122 +Total 150 153 303 +Coverage 0.49505 0.50495 + +Cells 6 +Cell Id Count(DNA) where Char = G Median(DNA.Pos) where Char = C EI IE N Interest Frequency Coverage +C6 ]19.5;+inf[ ]27.75;+inf[ 0.388889 0 0.611111 0.215965 36 0.118812 +C5 ]12.5;19.5] ]27.75;+inf[ 0.220779 0.0519481 0.727273 0.203532 77 0.254125 +C2 ]12.5;19.5] ]-inf;27.75] 0.178571 0.440476 0.380952 0.199181 84 0.277228 +C3 ]19.5;+inf[ ]-inf;27.75] 0.483871 0.0322581 0.483871 0.166373 31 0.10231 +C4 ]-inf;12.5] ]27.75;+inf[ 0.025 0.3 0.675 0.14009 40 0.132013 +C1 ]-inf;12.5] ]-inf;27.75] 0.142857 0.428571 0.428571 0.0748584 35 0.115512 + Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R077 +Variables + Type Name + Numerical Count(DNA) where Char = G + Numerical Median(DNA.Pos) where Char = G + +Variables stats +Count(DNA) where Char = G +Interval Frequency Coverage +]-inf;19.5] 236 0.778878 +]19.5;+inf[ 67 0.221122 +Median(DNA.Pos) where Char = G +Interval Frequency Coverage +]-inf;27.75] 72 0.237624 +]27.75;37.25] 161 0.531353 +]37.25;+inf[ 70 0.231023 + +Cell frequencies + Median(DNA.Pos) where Char = G +Count(DNA) where Char = G ]-inf;27.75] ]27.75;37.25] ]37.25;+inf[ Total Coverage +]-inf;19.5] 60 116 60 236 0.778878 +]19.5;+inf[ 12 45 10 67 0.221122 +Total 72 161 70 303 +Coverage 0.237624 0.531353 0.231023 + +Cells 6 +Cell Id Count(DNA) where Char = G Median(DNA.Pos) where Char = G EI IE N Interest Frequency Coverage +C5 ]-inf;19.5] ]37.25;+inf[ 0.0833333 0.566667 0.35 0.330661 60 0.19802 +C4 ]19.5;+inf[ ]27.75;37.25] 0.4 0 0.6 0.258497 45 0.148515 +C1 ]-inf;19.5] ]-inf;27.75] 0.116667 0.05 0.833333 0.232266 60 0.19802 +C6 ]19.5;+inf[ ]37.25;+inf[ 0.7 0.1 0.2 0.104024 10 0.0330033 +C2 ]19.5;+inf[ ]-inf;27.75] 0.333333 0 0.666667 0.063096 12 0.039604 +C3 ]-inf;19.5] ]27.75;37.25] 0.224138 0.267241 0.508621 0.0114554 116 0.382838 + Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R083 +Variables + Type Name + Numerical Median(DNA.Pos) where Char = G + Categorical Mode(DNA.Char) + +Variables stats +Median(DNA.Pos) where Char = G +Interval Frequency Coverage +]-inf;30.25] 109 0.359736 +]30.25;38.25] 136 0.448845 +]38.25;+inf[ 58 0.191419 +Mode(DNA.Char) +Group Frequency Coverage Size Value list +{C} 119 0.392739 1 C +{T, A} 107 0.353135 2 T A * +{G} 77 0.254125 1 G + +Cell frequencies + Mode(DNA.Char) +Median(DNA.Pos) where Char = G {C} {T, A} {G} Total Coverage +]-inf;30.25] 41 43 25 109 0.359736 +]30.25;38.25] 44 48 44 136 0.448845 +]38.25;+inf[ 34 16 8 58 0.191419 +Total 119 107 77 303 +Coverage 0.392739 0.353135 0.254125 + +Cells 9 +Cell Id Median(DNA.Pos) where Char = G Mode(DNA.Char) EI IE N Interest Frequency Coverage +C8 ]30.25;38.25] {G} 0.5 0 0.5 0.228745 44 0.145215 +C4 ]-inf;30.25] {T, A} 0.0930233 0.0232558 0.883721 0.176489 43 0.141914 +C2 ]30.25;38.25] {C} 0.113636 0.545455 0.340909 0.150925 44 0.145215 +C3 ]38.25;+inf[ {C} 0.117647 0.588235 0.294118 0.147254 34 0.112211 +C9 ]38.25;+inf[ {G} 0.75 0.25 0 0.10875 8 0.0264026 +C6 ]38.25;+inf[ {T, A} 0 0.5625 0.4375 0.0943793 16 0.0528053 +C7 ]-inf;30.25] {G} 0.24 0.04 0.72 0.0515547 25 0.0825083 +C1 ]-inf;30.25] {C} 0.195122 0.121951 0.682927 0.0271938 41 0.135314 +C5 ]30.25;38.25] {T, A} 0.25 0.145833 0.604167 0.0147099 48 0.158416 + Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R089 +Variables + Type Name + Numerical Count(DNA) where Char = G + Numerical Mean(DNA.Pos) where Char = G + +Variables stats +Count(DNA) where Char = G +Interval Frequency Coverage +]-inf;19.5] 236 0.778878 +]19.5;+inf[ 67 0.221122 +Mean(DNA.Pos) where Char = G +Interval Frequency Coverage +]-inf;31.698] 151 0.49835 +]31.698;+inf[ 152 0.50165 + +Cell frequencies + Mean(DNA.Pos) where Char = G +Count(DNA) where Char = G ]-inf;31.698] ]31.698;+inf[ Total Coverage +]-inf;19.5] 114 122 236 0.778878 +]19.5;+inf[ 37 30 67 0.221122 +Total 151 152 303 +Coverage 0.49835 0.50165 + +Cells 4 +Cell Id Count(DNA) where Char = G Mean(DNA.Pos) where Char = G EI IE N Interest Frequency Coverage +C3 ]-inf;19.5] ]31.698;+inf[ 0.139344 0.459016 0.401639 0.375437 122 0.40264 +C2 ]19.5;+inf[ ]-inf;31.698] 0.351351 0 0.648649 0.234491 37 0.122112 +C4 ]19.5;+inf[ ]31.698;+inf[ 0.533333 0.0333333 0.433333 0.213467 30 0.0990099 +C1 ]-inf;19.5] ]-inf;31.698] 0.184211 0.105263 0.710526 0.176604 114 0.376238 + Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R090 +Variables + Type Name + Numerical Mean(DNA.Pos) where Char = G + Categorical Mode(DNA.Char) + +Variables stats +Mean(DNA.Pos) where Char = G +Interval Frequency Coverage +]-inf;37.6] 265 0.874587 +]37.6;+inf[ 38 0.125413 +Mode(DNA.Char) +Group Frequency Coverage Size Value list +{C} 119 0.392739 1 C +{T, A} 107 0.353135 2 T A * +{G} 77 0.254125 1 G + +Cell frequencies + Mode(DNA.Char) +Mean(DNA.Pos) where Char = G {C} {T, A} {G} Total Coverage +]-inf;37.6] 100 90 75 265 0.874587 +]37.6;+inf[ 19 17 2 38 0.125413 +Total 119 107 77 303 +Coverage 0.392739 0.353135 0.254125 + +Cells 6 +Cell Id Mean(DNA.Pos) where Char = G Mode(DNA.Char) EI IE N Interest Frequency Coverage +C5 ]-inf;37.6] {G} 0.426667 0.04 0.533333 0.268268 75 0.247525 +C3 ]-inf;37.6] {T, A} 0.177778 0.0555556 0.766667 0.22622 90 0.29703 +C4 ]37.6;+inf[ {T, A} 0 0.705882 0.294118 0.193064 17 0.0561056 +C2 ]37.6;+inf[ {C} 0.105263 0.684211 0.210526 0.165905 19 0.0627063 +C1 ]-inf;37.6] {C} 0.15 0.36 0.49 0.0907069 100 0.330033 +C6 ]37.6;+inf[ {G} 1 0 0 0.0558357 2 0.00660066 + Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.PreparationReport.xls new file mode 100644 index 000000000..be242e8e0 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.PreparationReport.xls @@ -0,0 +1,274 @@ +#Khiops 10.4.7i +Descriptive statistics + + +Problem description +Short description + +Dictionary SpliceJunction +Variables + Categorical 12 + Numerical 90 + Table 1 + Total 103 + +Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt +Sample percentage 10 +Sampling mode Include sample +Selection variable +Selection value +Instances 303 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +EI 67 0.221122 +IE 69 0.227723 +N 167 0.551155 + +Evaluated variables 101 +Native variables 1 +Constructed variables 100 +Informative variables 7 + +Max number of constructed variables 100 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 100 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 10.7442 + Data cost 296.926 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R001 Mode(DNA.Char) where Pos <= 30.5 0.0731216 2 4 C 0.40264 0 0 8.17364 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) +R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0495677 3 4 C 0.376238 0 0 9.55993 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R003 Mode(DNA.Char) 0.0447911 3 4 C 0.392739 0 0 4.68213 30.8759 258.994 TableMode(DNA, Char) +R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) +R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 1 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) +R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) +R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) +R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0 0 0.693147 10.7442 296.926 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R004 Mean(DNA.Pos) where Char = G 0.0261259 2 281 16 46.55555556 31.84768883 4.848828493 1 0 9.55993 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) +R005 Median(DNA.Pos) where Char = G 0.0212659 3 59 8 48 32.44701987 6.725618787 1 0 9.55993 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) +R006 Count(DNA) where Char = G 0.0147548 2 26 0 32 15.81188119 5.174919597 0 0 9.55993 21.0465 273.207 TableCount(`DNA where Char = G`) +R007 Median(DNA.Pos) where Char = C 0.00882745 2 63 9 56.5 29.24587459 7.66919868 0 0 9.55993 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) +R008 Count(DNA) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableCount(DNA) +R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) +R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) +R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) +R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) +R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) +R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) +R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) +R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R022 Count(DNA) where Pos in ]22.5, 30.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R023 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R024 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) +R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) +R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) +R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) +R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) +R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) +R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) +R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) +R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) +R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) +R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) +R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) +R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) +R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) +R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) +R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) +R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) +R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) +R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) +R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) +R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) +R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) +R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) +R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) +R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) +R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) +R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) +R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) +R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) +R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) +R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) +R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) +R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) +R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) +R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) +R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) +R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) +R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) +R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) +R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R001 +Variable Categorical Mode(DNA.Char) where Pos <= 30.5 + +Variable stats +Group Size Value list +{C, T} 2 C T +{G, A} 2 G A * + +% target values +Group EI IE N Interest Frequency Coverage +{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 +{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 122 0.40264 +T 64 0.211221 +G 61 0.20132 +A 56 0.184818 + +---------------------------------------------- +Rank R002 +Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] + +Variable stats +Group Size Value list +{A, G} 2 A G * +{C} 1 C +{T} 1 T + +% target values +Group EI IE N Interest Frequency Coverage +{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 +{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 +{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 114 0.376238 +A 69 0.227723 +T 61 0.20132 +G 59 0.194719 + +---------------------------------------------- +Rank R003 +Variable Categorical Mode(DNA.Char) + +Variable stats +Group Size Value list +{C} 1 C +{T, A} 2 T A * +{G} 1 G + +% target values +Group EI IE N Interest Frequency Coverage +{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 +{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 +{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 119 0.392739 +G 77 0.254125 +T 57 0.188119 +A 50 0.165017 + +---------------------------------------------- +Rank R004 +Variable Numerical Mean(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 +]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R005 +Variable Numerical Median(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 +]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 +]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R006 +Variable Numerical Count(DNA) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 +]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R007 +Variable Numerical Median(DNA.Pos) where Char = C + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 +]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 +Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AllReports.khj b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.khj similarity index 95% rename from test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AllReports.khj rename to test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.khj index 218622059..c6d1387d3 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AllReports.khj +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_AnalysisResults.khj @@ -1,3899 +1,4016 @@ -{ - "tool": "Khiops", - "version": "10.1", - "shortDescription": "", - "preparationReport": { - "reportType": "Preparation", - "summary": { - "dictionary": "SpliceJunction", - "variables": { - "types": [ - "Categorical", - "Numerical", - "Table" - ], - "numbers": [ - 12, - 90, - 1 - ] - }, - "database": "..\/..\/..\/MTdatasets\/SpliceJunction\/SpliceJunction.txt", - "samplePercentage": 10, - "samplingMode": "Include sample", - "selectionVariable": "", - "selectionValue": "", - "instances": 303, - "learningTask": "Classification analysis", - "targetVariable": "Class", - "targetDescriptiveStats": { - "values": 3, - "mode": "N", - "modeFrequency": 167 - }, - "targetValues": { - "values": ["EI","IE","N"], - "frequencies": [67,69,167] - }, - "evaluatedVariables": 101, - "nativeVariables": 1, - "constructedVariables": 100, - "informativeVariables": 7, - "featureEngineering": { - "maxNumberOfConstructedVariables": 100, - "maxNumberOfTrees": 0, - "maxNumberOfVariablePairs": 100 - }, - "discretization": "MODL", - "valueGrouping": "MODL", - "nullModel": { - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926 - } - }, - "variablesStatistics": [ - { - "rank": "R001", - "name": "Mode(DNA.Char) where Pos <= 30.5", - "type": "Categorical", - "level": 0.0731216, - "parts": 2, - "values": 4, - "mode": "C", - "modeFrequency": 122, - "constructionCost": 8.17364, - "preparationCost": 23.5167, - "dataCost": 254.125, - "derivationRule": "TableMode(`DNA where Pos <= 30.5`, Char)" - }, - { - "rank": "R002", - "name": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", - "type": "Categorical", - "level": 0.0495677, - "parts": 3, - "values": 4, - "mode": "C", - "modeFrequency": 114, - "constructionCost": 9.55993, - "preparationCost": 31.5551, - "dataCost": 251.964, - "derivationRule": "TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char)" - }, - { - "rank": "R003", - "name": "Mode(DNA.Char)", - "type": "Categorical", - "level": 0.0447911, - "parts": 3, - "values": 4, - "mode": "C", - "modeFrequency": 119, - "constructionCost": 4.68213, - "preparationCost": 30.8759, - "dataCost": 258.994, - "derivationRule": "TableMode(DNA, Char)" - }, - { - "rank": "R004", - "name": "Mean(DNA.Pos) where Char = G", - "type": "Numerical", - "level": 0.0261259, - "parts": 2, - "values": 281, - "min": 16, - "max": 46.55555556, - "mean": 31.84768883, - "stdDev": 4.848828493, - "missingNumber": 1, - "constructionCost": 9.55993, - "preparationCost": 19.4458, - "dataCost": 271.302, - "derivationRule": "TableMean(`DNA where Char = G`, Pos)" - }, - { - "rank": "R005", - "name": "Median(DNA.Pos) where Char = G", - "type": "Numerical", - "level": 0.0212659, - "parts": 3, - "values": 59, - "min": 8, - "max": 48, - "mean": 32.44701987, - "stdDev": 6.725618787, - "missingNumber": 1, - "constructionCost": 9.55993, - "preparationCost": 29.3732, - "dataCost": 262.873, - "derivationRule": "TableMedian(`DNA where Char = G`, Pos)" - }, - { - "rank": "R006", - "name": "Count(DNA) where Char = G", - "type": "Numerical", - "level": 0.0147548, - "parts": 2, - "values": 26, - "min": 0, - "max": 32, - "mean": 15.81188119, - "stdDev": 5.174919597, - "missingNumber": 0, - "constructionCost": 9.55993, - "preparationCost": 21.0465, - "dataCost": 273.207, - "derivationRule": "TableCount(`DNA where Char = G`)" - }, - { - "rank": "R007", - "name": "Median(DNA.Pos) where Char = C", - "type": "Numerical", - "level": 0.00882745, - "parts": 2, - "values": 63, - "min": 9, - "max": 56.5, - "mean": 29.24587459, - "stdDev": 7.66919868, - "missingNumber": 0, - "constructionCost": 9.55993, - "preparationCost": 19.4456, - "dataCost": 276.636, - "derivationRule": "TableMedian(`DNA where Char = C`, Pos)" - }, - { - "rank": "R008", - "name": "Count(DNA)", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 1, - "min": 60, - "max": 60, - "mean": 60, - "stdDev": 0, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(DNA)" - }, - { - "rank": "R009", - "name": "Count(DNA) where Char = A", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 25, - "min": 0, - "max": 27, - "mean": 13.53135314, - "stdDev": 4.652779483, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(`DNA where Char = A`)" - }, - { - "rank": "R010", - "name": "Count(DNA) where Char = C", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 29, - "min": 2, - "max": 34, - "mean": 16.48844884, - "stdDev": 5.649179844, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(`DNA where Char = C`)" - }, - { - "rank": "R011", - "name": "Count(DNA) where Char = T", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 29, - "min": 2, - "max": 34, - "mean": 14.16171617, - "stdDev": 5.235407476, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(`DNA where Char = T`)" - }, - { - "rank": "R012", - "name": "Count(DNA) where Char not in {C, G, T, A}", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 2, - "min": 0, - "max": 1, - "mean": 0.006600660066, - "stdDev": 0.08097586895, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(`DNA where Char not in {C, G, T, A}`)" - }, - { - "rank": "R013", - "name": "Count(DNA) where Char not in {C, G}", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 33, - "min": 10, - "max": 47, - "mean": 27.69966997, - "stdDev": 6.838946411, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(`DNA where Char not in {C, G}`)" - }, - { - "rank": "R014", - "name": "Count(DNA) where Pos <= 15.5", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 1, - "min": 15, - "max": 15, - "mean": 15, - "stdDev": 0, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TablePartitionCount(`TablePartition(DNA) per Pos(4)`)" - }, - { - "rank": "R015", - "name": "Count(DNA) where Pos <= 30.5", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 1, - "min": 30, - "max": 30, - "mean": 30, - "stdDev": 0, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(`DNA where Pos <= 30.5`)" - }, - { - "rank": "R016", - "name": "Count(DNA) where Pos <= 7.5", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 1, - "min": 7, - "max": 7, - "mean": 7, - "stdDev": 0, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TablePartitionCount(`TablePartition(DNA) per Pos(8)`)" - }, - { - "rank": "R017", - "name": "Count(DNA) where Pos > 30.5", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 1, - "min": 30, - "max": 30, - "mean": 30, - "stdDev": 0, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(`DNA where Pos > 30.5`)" - }, - { - "rank": "R018", - "name": "Count(DNA) where Pos > 45.5", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 1, - "min": 15, - "max": 15, - "mean": 15, - "stdDev": 0, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TablePartitionCount(`TablePartition(DNA) per Pos(4)`)" - }, - { - "rank": "R019", - "name": "Count(DNA) where Pos > 53.5", - "type": "Numerical", - "level": 0, - "parts": 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"dataCost": 231.924 + }, + { + "rank": "R091", + "name1": "Count(DNA) where Char = G", + "name2": "Mode(DNA.Char) where Pos <= 30.5", + "deltaLevel": -0.0204162, + "level": 0.0674603, + "level1": 0.0147548, + "level2": 0.0731216, + "variables": 1, + "parts1": 1, + "parts2": 2, + "cells": 2, + "constructionCost": 9.91939, + "preparationCost": 23.5167, + "dataCost": 254.125 + }, + { + "rank": "R092", + "name1": "Count(DNA) where Char = G", + "name2": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", + "deltaLevel": -0.0204162, + "level": 0.0439064, + "level1": 0.0147548, + "level2": 0.0495677, + "variables": 1, + "parts1": 1, + "parts2": 3, + "cells": 3, + "constructionCost": 11.3057, + "preparationCost": 31.5551, + "dataCost": 251.964 + }, + { + "rank": "R093", + "name1": "Mean(DNA.Pos) where Char = G", + "name2": "Median(DNA.Pos) where Char = G", + "deltaLevel": -0.0269272, + "level": 0.0204646, + "level1": 0.0261259, + "level2": 0.0212659, + "variables": 1, + "parts1": 2, + "parts2": 1, + "cells": 2, + "constructionCost": 11.3057, + "preparationCost": 19.4458, + "dataCost": 271.302 + }, + { + "rank": "R094", + "name1": "Median(DNA.Pos) where Char = G", + "name2": "Mode(DNA.Char) where Pos <= 30.5", + "deltaLevel": -0.0269272, + "level": 0.0674603, + "level1": 0.0212659, + "level2": 0.0731216, + "variables": 1, + "parts1": 1, + "parts2": 2, + "cells": 2, + "constructionCost": 9.91939, + "preparationCost": 23.5167, + "dataCost": 254.125 + }, + { + "rank": "R095", + "name1": "Median(DNA.Pos) where Char = G", + "name2": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", + "deltaLevel": -0.0269272, + "level": 0.0439064, + "level1": 0.0212659, + "level2": 0.0495677, + "variables": 1, + "parts1": 1, + "parts2": 3, + "cells": 3, + "constructionCost": 11.3057, + "preparationCost": 31.5551, + "dataCost": 251.964 + }, + { + "rank": "R096", + "name1": "Mean(DNA.Pos) where Char = G", + "name2": "Mode(DNA.Char) where Pos <= 30.5", + "deltaLevel": -0.0317873, + "level": 0.0674603, + "level1": 0.0261259, + "level2": 0.0731216, + "variables": 1, + "parts1": 1, + "parts2": 2, + "cells": 2, + "constructionCost": 9.91939, + "preparationCost": 23.5167, + "dataCost": 254.125 + }, + { + "rank": "R097", + "name1": "Mean(DNA.Pos) where Char = G", + "name2": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", + "deltaLevel": -0.0317873, + "level": 0.0439064, + "level1": 0.0261259, + "level2": 0.0495677, + "variables": 1, + "parts1": 1, + "parts2": 3, + "cells": 3, + "constructionCost": 11.3057, + "preparationCost": 31.5551, + "dataCost": 251.964 + }, + { + "rank": "R098", + "name1": "Mode(DNA.Char)", + "name2": "Mode(DNA.Char) where Pos <= 30.5", + "deltaLevel": -0.0504524, + "level": 0.0674603, + "level1": 0.0447911, + "level2": 0.0731216, + "variables": 1, + "parts1": 1, + "parts2": 2, + "cells": 2, + "constructionCost": 9.91939, + "preparationCost": 23.5167, + "dataCost": 254.125 + }, + { + "rank": "R099", + "name1": "Mode(DNA.Char)", + "name2": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", + "deltaLevel": -0.0514646, + "level": 0.0428942, + "level1": 0.0447911, + "level2": 0.0495677, + "variables": 1, + "parts1": 3, + "parts2": 1, + "cells": 3, + "constructionCost": 6.42789, + "preparationCost": 29.7151, + "dataCost": 258.994 + }, + { + "rank": "R100", + "name1": "Mode(DNA.Char) where Pos <= 30.5", + "name2": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", + "deltaLevel": -0.0552291, + "level": 0.0674603, + "level1": 0.0731216, + "level2": 0.0495677, + "variables": 1, + "parts1": 2, + "parts2": 1, + "cells": 2, + "constructionCost": 9.91939, + "preparationCost": 23.5167, + "dataCost": 254.125 + } + ], + "variablesPairsDetailedStatistics": { + "R001": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Count(DNA) where Char = C", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [2,16.5], + [16.5,34] + ] + }, + { + "variable": "Mean(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [16,31.698], + [31.698,37.6], + [37.6,46.55555556] + ] + }, + { + "variable": "Target", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "cellIds": ["C1","C2","C3","C4","C5","C6"], + "cellPartIndexes": [ + [0,0], + [1,0], + [0,1], + [1,1], + [0,2], + [1,2] + ], + "cellTargetFrequencies": [ + [16,2,61], + [18,10,44], + [24,4,29], + [5,28,24], + [0,12,4], + [4,13,5] + ], + "cellInterests": [0.274659,0.0336322,0.15674,0.194172,0.207197,0.1336] + } + }, + "R076": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Count(DNA) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0,12.5], + [12.5,19.5], + [19.5,32] + ] + }, + { + "variable": "Median(DNA.Pos) where Char = C", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [9,27.75], + [27.75,56.5] + ] + }, + { + "variable": "Target", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "cellIds": ["C1","C2","C3","C4","C5","C6"], + "cellPartIndexes": [ + [0,0], + [1,0], + [2,0], + [0,1], + [1,1], + [2,1] + ], + "cellTargetFrequencies": [ + [5,15,15], + [15,37,32], + [15,1,15], + [1,12,27], + [17,4,56], + [14,0,22] + ], + "cellInterests": [0.0748584,0.199181,0.166373,0.14009,0.203532,0.215965] + } + }, + "R077": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Count(DNA) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0,19.5], + [19.5,32] + ] + }, + { + "variable": "Median(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [8,27.75], + [27.75,37.25], + [37.25,48] + ] + }, + { + "variable": "Target", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "cellIds": ["C1","C2","C3","C4","C5","C6"], + "cellPartIndexes": [ + [0,0], + [1,0], + [0,1], + [1,1], + [0,2], + [1,2] + ], + "cellTargetFrequencies": [ + [7,3,50], + [4,0,8], + [26,31,59], + [18,0,27], + [5,34,21], + [7,1,2] + ], + "cellInterests": [0.232266,0.063096,0.0114554,0.258497,0.330661,0.104024] + } + }, + "R083": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Median(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [8,30.25], + [30.25,38.25], + [38.25,48] + ] + }, + { + "variable": "Mode(DNA.Char)", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["C"], + ["T","A"], + ["G"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Target", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "cellIds": ["C1","C2","C3","C4","C5","C6","C7","C8","C9"], + "cellPartIndexes": [ + [0,0], + [1,0], + [2,0], + [0,1], + [1,1], + [2,1], + [0,2], + [1,2], + [2,2] + ], + "cellTargetFrequencies": [ + [8,5,28], + [5,24,15], + [4,20,10], + [4,1,38], + [12,7,29], + [0,9,7], + [6,1,18], + [22,0,22], + [6,2,0] + ], + "cellInterests": [0.0271938,0.150925,0.147254,0.176489,0.0147099,0.0943793,0.0515547,0.228745,0.10875] + } + }, + "R089": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Count(DNA) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0,19.5], + [19.5,32] + ] + }, + { + "variable": "Mean(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [16,31.698], + [31.698,46.55555556] + ] + }, + { + "variable": "Target", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "cellIds": ["C1","C2","C3","C4"], + "cellPartIndexes": [ + [0,0], + [1,0], + [0,1], + [1,1] + ], + "cellTargetFrequencies": [ + [21,12,81], + [13,0,24], + [17,56,49], + [16,1,13] + ], + "cellInterests": [0.176604,0.234491,0.375437,0.213467] + } + }, + "R090": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mean(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [16,37.6], + [37.6,46.55555556] + ] + }, + { + "variable": "Mode(DNA.Char)", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["C"], + ["T","A"], + ["G"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Target", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "cellIds": ["C1","C2","C3","C4","C5","C6"], + "cellPartIndexes": [ + [0,0], + [1,0], + [0,1], + [1,1], + [0,2], + [1,2] + ], + "cellTargetFrequencies": [ + [15,36,49], + [2,13,4], + [16,5,69], + [0,12,5], + [32,3,40], + [2,0,0] + ], + "cellInterests": [0.0907069,0.165905,0.22622,0.193064,0.268268,0.0558357] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_PreparationReport.xls deleted file mode 100644 index c142cd27d..000000000 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/C100_PreparationReport.xls +++ /dev/null @@ -1,273 +0,0 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary SpliceJunction -Variables - Categorical 12 - Numerical 90 - Table 1 - Total 103 - -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value -Instances 303 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -EI 67 0.221122 -IE 69 0.227723 -N 167 0.551155 - -Evaluated variables 101 -Native variables 1 -Constructed variables 100 -Informative variables 7 - -Max number of constructed variables 100 -Max number of trees 0 -Max number of variable pairs 100 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 10.7442 - Data cost 296.926 - - -Categorical variables statistics - -Rank Name Level Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost Derivation rule -R001 Mode(DNA.Char) where Pos <= 30.5 0.0731216 2 4 C 0.40264 8.17364 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) -R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0495677 3 4 C 0.376238 9.55993 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R003 Mode(DNA.Char) 0.0447911 3 4 C 0.392739 4.68213 30.8759 258.994 TableMode(DNA, Char) -R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) -R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) -R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) -R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) -R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0.693147 10.7442 296.926 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R004 Mean(DNA.Pos) where Char = G 0.0261259 2 281 16 46.55555556 31.84768883 4.848828493 1 9.55993 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) -R005 Median(DNA.Pos) where Char = G 0.0212659 3 59 8 48 32.44701987 6.725618787 1 9.55993 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) -R006 Count(DNA) where Char = G 0.0147548 2 26 0 32 15.81188119 5.174919597 0 9.55993 21.0465 273.207 TableCount(`DNA where Char = G`) -R007 Median(DNA.Pos) where Char = C 0.00882745 2 63 9 56.5 29.24587459 7.66919868 0 9.55993 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) -R008 Count(DNA) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableCount(DNA) -R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) -R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) -R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) -R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) -R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) -R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) -R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) -R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R022 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R023 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R024 Count(DNA) where Pos in ]38.5, 45.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) -R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) -R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) -R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) -R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) -R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) -R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) -R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) -R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) -R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) -R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) -R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) -R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) -R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) -R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) -R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) -R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) -R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) -R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) -R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) -R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) -R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) -R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) -R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) -R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) -R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) -R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) -R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) -R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) -R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) -R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) -R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) -R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) -R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) -R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) -R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) -R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) -R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) -R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) -R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R001 -Variable Categorical Mode(DNA.Char) where Pos <= 30.5 - -Variable stats -Group Size Value list -{C, T} 2 C T -{G, A} 2 G A * - -% target values -Group EI IE N Interest Frequency Coverage -{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 -{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 122 0.40264 -T 64 0.211221 -G 61 0.20132 -A 56 0.184818 - ----------------------------------------------- -Rank R002 -Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] - -Variable stats -Group Size Value list -{A, G} 2 A G * -{C} 1 C -{T} 1 T - -% target values -Group EI IE N Interest Frequency Coverage -{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 -{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 -{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 114 0.376238 -A 69 0.227723 -T 61 0.20132 -G 59 0.194719 - ----------------------------------------------- -Rank R003 -Variable Categorical Mode(DNA.Char) - -Variable stats -Group Size Value list -{C} 1 C -{T, A} 2 T A * -{G} 1 G - -% target values -Group EI IE N Interest Frequency Coverage -{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 -{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 -{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 119 0.392739 -G 77 0.254125 -T 57 0.188119 -A 50 0.165017 - ----------------------------------------------- -Rank R004 -Variable Numerical Mean(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 -]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R005 -Variable Numerical Median(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 -]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 -]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R006 -Variable Numerical Count(DNA) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 -]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R007 -Variable Numerical Median(DNA.Pos) where Char = C - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 -]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 -Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/err.txt index 5033320d1..c20f97aa1 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/err.txt @@ -1,8 +1,8 @@ -Variable construction time: 0:00:00.12 (100 constructed variables) +Variable construction time: 0:00:00.09 (100 constructed variables) Train supervised model for classification of target variable Class Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt: Read records: 3178 Selected records: 303 Table ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt Records: 18,180 Evaluation of variable pairs -Data preparation time: 0:00:02.88 -Write report ./results\C100_AllReports.khj +Data preparation time: 0:00:02.48 +Write report ./results/C100_AnalysisResults.khj diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/time.log index 9f763d659..75be0ad84 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/results.ref/time.log @@ -1 +1 @@ -Overal time: 3.171583890914917 +Overal time: 2.715700626373291 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/test.prm b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/test.prm index fe8db6657..9b287566e 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/test.prm +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunction2D/test.prm @@ -14,32 +14,37 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open ClassFileName ../../../MTdatasets/SpliceJunction/SpliceJunction.kdic // Dictionary file OK // Open -ClassManagement.ClassName SpliceJunction // Dictionary +TrainDatabase.ClassName SpliceJunction // Dictionary -TrainDatabase.DatabaseFiles.List.Key SpliceJunction // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file -TrainDatabase.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file -TrainDatabase.SampleNumberPercentage 10 // Sample percentage +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 10 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor false // Predicteur Bayesien Naif Selectif +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true // Do data preparation only AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 100 // Max number of pairs of variables -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report -AnalysisResults.ResultFilesPrefix C100_ // Result files directory +AnalysisResults.ReportFileName ./results/C100_AnalysisResults.khj // Analysis report AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database Exit // Close diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/SpliceJunctionExternal.kdic b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/SpliceJunctionExternal.kdic index 7ba4877d8..83c887656 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/SpliceJunctionExternal.kdic +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/SpliceJunctionExternal.kdic @@ -1,4 +1,4 @@ -#Khiops 10.0.1i +#Khiops 10.2.9i Root Dictionary SpliceJunction (SampleId) { diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AnalysisResults.PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AnalysisResults.PreparationReport.xls new file mode 100644 index 000000000..4bca2e8be --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AnalysisResults.PreparationReport.xls @@ -0,0 +1,274 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary SpliceJunctionExternal +Variables + Categorical 12 + Numerical 90 + Table 1 + Total 103 + +Database ./SpliceJunctionExternal.txt +Sample percentage 10 +Sampling mode Include sample +Selection variable +Selection value +Instances 303 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +EI 67 0.221122 +IE 69 0.227723 +N 167 0.551155 + +Evaluated variables 101 +Native variables 1 +Constructed variables 100 +Informative variables 7 + +Max number of constructed variables 100 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 10.7442 + Data cost 296.926 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R001 Mode(DNA.Char) where Pos <= 30.5 0.0744365 2 4 C 0.40264 0 0 7.76817 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) +R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0508826 3 4 C 0.376238 0 0 9.15447 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R003 Mode(DNA.Char) 0.0461059 3 4 C 0.392739 0 0 4.27667 30.8759 258.994 TableMode(DNA, Char) +R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) +R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 1 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) +R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) +R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) +R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0 0 0.693147 10.7442 296.926 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R004 Mean(DNA.Pos) where Char = G 0.0274408 2 281 16 46.55555556 31.84768883 4.848828493 1 0 9.15447 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) +R005 Median(DNA.Pos) where Char = G 0.0225808 3 59 8 48 32.44701987 6.725618787 1 0 9.15447 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) +R006 Count(DNA) where Char = G 0.0160697 2 26 0 32 15.81188119 5.174919597 0 0 9.15447 21.0465 273.207 TableCount(`DNA where Char = G`) +R007 Median(DNA.Pos) where Char = C 0.0101423 2 63 9 56.5 29.24587459 7.66919868 0 0 9.15447 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) +R008 Count(DNA) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableCount(DNA) +R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) +R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) +R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) +R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) +R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) +R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) +R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) +R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R022 Count(DNA) where Pos in ]22.5, 30.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R023 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R024 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) +R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) +R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) +R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) +R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) +R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) +R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) +R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) +R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) +R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) +R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) +R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) +R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) +R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) +R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) +R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) +R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) +R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) +R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) +R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) +R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) +R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) +R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) +R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) +R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) +R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) +R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) +R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) +R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) +R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) +R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) +R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) +R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) +R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) +R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) +R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) +R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) +R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) +R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) +R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R001 +Variable Categorical Mode(DNA.Char) where Pos <= 30.5 + +Variable stats +Group Size Value list +{C, T} 2 C T +{G, A} 2 G A * + +% target values +Group EI IE N Interest Frequency Coverage +{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 +{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 122 0.40264 +T 64 0.211221 +G 61 0.20132 +A 56 0.184818 + +---------------------------------------------- +Rank R002 +Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] + +Variable stats +Group Size Value list +{A, G} 2 A G * +{C} 1 C +{T} 1 T + +% target values +Group EI IE N Interest Frequency Coverage +{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 +{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 +{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 114 0.376238 +A 69 0.227723 +T 61 0.20132 +G 59 0.194719 + +---------------------------------------------- +Rank R003 +Variable Categorical Mode(DNA.Char) + +Variable stats +Group Size Value list +{C} 1 C +{T, A} 2 T A * +{G} 1 G + +% target values +Group EI IE N Interest Frequency Coverage +{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 +{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 +{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 119 0.392739 +G 77 0.254125 +T 57 0.188119 +A 50 0.165017 + +---------------------------------------------- +Rank R004 +Variable Numerical Mean(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 +]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R005 +Variable Numerical Median(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 +]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 +]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R006 +Variable Numerical Count(DNA) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 +]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R007 +Variable Numerical Median(DNA.Pos) where Char = C + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 +]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 +Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AllReports.khj b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AnalysisResults.khj similarity index 92% rename from test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AllReports.khj rename to test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AnalysisResults.khj index 271e9a792..e9acbf38f 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AllReports.khj +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_AnalysisResults.khj @@ -1,1954 +1,2069 @@ -{ - "tool": "Khiops", - "version": "10.1", - "shortDescription": "", - "preparationReport": { - "reportType": "Preparation", - "summary": { - "dictionary": "SpliceJunctionExternal", - "variables": { - "types": [ - "Categorical", - "Numerical", - "Table" - ], - "numbers": [ - 12, - 90, - 1 - ] - }, - "database": ".\/SpliceJunctionExternal.txt", - "samplePercentage": 10, - "samplingMode": "Include sample", - "selectionVariable": "", - "selectionValue": "", - "instances": 303, - "learningTask": "Classification analysis", - "targetVariable": "Class", - "targetDescriptiveStats": { - "values": 3, - "mode": "N", - "modeFrequency": 167 - }, - "targetValues": { - "values": ["EI","IE","N"], - "frequencies": [67,69,167] - }, - "evaluatedVariables": 101, - "nativeVariables": 1, - "constructedVariables": 100, - "informativeVariables": 7, - "featureEngineering": { - "maxNumberOfConstructedVariables": 100, - "maxNumberOfTrees": 0, - "maxNumberOfVariablePairs": 0 - }, - "discretization": "MODL", - "valueGrouping": "MODL", - "nullModel": { - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926 - } - }, - "variablesStatistics": [ - { - "rank": "R001", - "name": "Mode(DNA.Char) where Pos <= 30.5", - "type": "Categorical", - "level": 0.0744365, - "parts": 2, - "values": 4, - "mode": "C", - "modeFrequency": 122, - "constructionCost": 7.76817, - "preparationCost": 23.5167, - "dataCost": 254.125, - "derivationRule": "TableMode(`DNA where Pos <= 30.5`, Char)" - }, - { - "rank": "R002", - "name": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", - "type": "Categorical", - "level": 0.0508826, - "parts": 3, - "values": 4, - "mode": "C", - "modeFrequency": 114, - "constructionCost": 9.15447, - "preparationCost": 31.5551, - "dataCost": 251.964, - "derivationRule": "TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char)" - }, - { - "rank": "R003", - "name": "Mode(DNA.Char)", - "type": "Categorical", - "level": 0.0461059, - "parts": 3, - "values": 4, - "mode": "C", - "modeFrequency": 119, - "constructionCost": 4.27667, - "preparationCost": 30.8759, - "dataCost": 258.994, - "derivationRule": "TableMode(DNA, Char)" - }, - { - "rank": "R004", - "name": "Mean(DNA.Pos) where Char = G", - "type": "Numerical", - "level": 0.0274408, - "parts": 2, - "values": 281, - "min": 16, - "max": 46.55555556, - "mean": 31.84768883, - "stdDev": 4.848828493, - "missingNumber": 1, - "constructionCost": 9.15447, - "preparationCost": 19.4458, - "dataCost": 271.302, - "derivationRule": "TableMean(`DNA where Char = G`, Pos)" - }, - { - "rank": "R005", - "name": "Median(DNA.Pos) where Char = G", - "type": "Numerical", - "level": 0.0225808, - "parts": 3, - "values": 59, - "min": 8, - "max": 48, - "mean": 32.44701987, - "stdDev": 6.725618787, - 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"StdDev(DNA.Pos) where Pos in ]30.5, 45.5]", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 4.320493799, + "max": 4.320493799, + "mean": 4.320493799, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos)" + }, + { + "rank": "R092", + "name": "Sum(DNA.Pos)", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 1830, + "max": 1830, + "mean": 1830, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TableSum(DNA, Pos)" + }, + { + "rank": "R093", + "name": "Sum(DNA.Pos) where Char = C", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 233, + "min": 54, + "max": 1063, + "mean": 488.0660066, + "stdDev": 178.1871738, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TableSum(`DNA where Char = C`, Pos)" + }, + { + "rank": "R094", + "name": "Sum(DNA.Pos) where Char = G", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 250, + "min": 111, + "max": 938, + "mean": 503.2649007, + "stdDev": 169.8386146, + "missingNumber": 1, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TableSum(`DNA where Char = G`, Pos)" + }, + { + "rank": "R095", + "name": "Sum(DNA.Pos) where Char not in {C, G}", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 255, + "min": 309, + "max": 1474, + "mean": 840.330033, + "stdDev": 216.9412705, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TableSum(`DNA where Char not in {C, G}`, Pos)" + }, + { + "rank": "R096", + "name": "Sum(DNA.Pos) where Pos <= 15.5", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 120, + "max": 120, + "mean": 120, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos)" + }, + { + "rank": "R097", + "name": "Sum(DNA.Pos) where Pos <= 30.5", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 465, + "max": 465, + "mean": 465, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TableSum(`DNA where Pos <= 30.5`, Pos)" + }, + { + "rank": "R098", + "name": "Sum(DNA.Pos) where Pos > 30.5", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 1365, + "max": 1365, + "mean": 1365, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TableSum(`DNA where Pos > 30.5`, Pos)" + }, + { + "rank": "R099", + "name": "Sum(DNA.Pos) where Pos > 45.5", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 795, + "max": 795, + "mean": 795, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos)" + }, + { + "rank": "R100", + "name": "Sum(DNA.Pos) where Pos in ]15.5, 30.5]", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 345, + "max": 345, + "mean": 345, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos)" + }, + { + "rank": "R101", + "name": "Sum(DNA.Pos) where Pos in ]30.5, 45.5]", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 570, + "max": 570, + "mean": 570, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos)" + } + ], + "variablesDetailedStatistics": { + "R001": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mode(DNA.Char) where Pos <= 30.5", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["C","T"], + ["G","A"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [29,69,88], + [38,0,79] + ], + "partInterests": [0.247927,0.752073] + }, + "inputValues": { + "values": ["C","T","G","A"], + "frequencies": [122,64,61,56] + } + }, + "R002": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["A","G"], + ["C"], + ["T"] + ], + "defaultGroupIndex": 0 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [38,3,87], + [26,41,47], + [3,25,33] + ], + "partInterests": [0.593857,0.15488,0.251263] + }, + "inputValues": { + "values": ["C","A","T","G"], + "frequencies": [114,69,61,59] + } + }, + "R003": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mode(DNA.Char)", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["C"], + ["T","A"], + ["G"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [17,49,53], + [16,17,74], + [34,3,40] + ], + "partInterests": [0.33703,0.144491,0.518478] + }, + "inputValues": { + "values": ["C","G","T","A"], + "frequencies": [119,77,57,50] + } + }, + "R004": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mean(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [16,31.698], + [31.698,46.55555556] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [34,12,105], + [33,57,62] + ], + "partInterests": [0.57478,0.42522] + } + }, + "R005": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Median(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [8,27.75], + [27.75,37.25], + [37.25,48] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [11,3,58], + [44,31,86], + [12,35,23] + ], + "partInterests": [0.478566,0.0530889,0.468345] + } + }, + "R006": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Count(DNA) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0,19.5], + [19.5,32] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [38,68,130], + [29,1,37] + ], + "partInterests": [0.187162,0.812838] + } + }, + "R007": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Median(DNA.Pos) where Char = C", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [9,27.75], + [27.75,56.5] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [35,53,62], + [32,16,105] + ], + "partInterests": [0.454456,0.545544] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_PreparationReport.xls deleted file mode 100644 index 0543507e5..000000000 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/C100_PreparationReport.xls +++ /dev/null @@ -1,273 +0,0 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary SpliceJunctionExternal -Variables - Categorical 12 - Numerical 90 - Table 1 - Total 103 - -Database ./SpliceJunctionExternal.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value -Instances 303 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -EI 67 0.221122 -IE 69 0.227723 -N 167 0.551155 - -Evaluated variables 101 -Native variables 1 -Constructed variables 100 -Informative variables 7 - -Max number of constructed variables 100 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 10.7442 - Data cost 296.926 - - -Categorical variables statistics - -Rank Name Level Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost Derivation rule -R001 Mode(DNA.Char) where Pos <= 30.5 0.0744365 2 4 C 0.40264 7.76817 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) -R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0508826 3 4 C 0.376238 9.15447 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R003 Mode(DNA.Char) 0.0461059 3 4 C 0.392739 4.27667 30.8759 258.994 TableMode(DNA, Char) -R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) -R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) -R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) -R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) -R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0.693147 10.7442 296.926 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R004 Mean(DNA.Pos) where Char = G 0.0274408 2 281 16 46.55555556 31.84768883 4.848828493 1 9.15447 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) -R005 Median(DNA.Pos) where Char = G 0.0225808 3 59 8 48 32.44701987 6.725618787 1 9.15447 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) -R006 Count(DNA) where Char = G 0.0160697 2 26 0 32 15.81188119 5.174919597 0 9.15447 21.0465 273.207 TableCount(`DNA where Char = G`) -R007 Median(DNA.Pos) where Char = C 0.0101423 2 63 9 56.5 29.24587459 7.66919868 0 9.15447 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) -R008 Count(DNA) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableCount(DNA) -R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) -R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) -R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) -R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) -R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) -R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) -R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) -R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R022 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R023 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R024 Count(DNA) where Pos in ]38.5, 45.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) -R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) -R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) -R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) -R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) -R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) -R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) -R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) -R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) -R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) -R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) -R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) -R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) -R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) -R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) -R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) -R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) -R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) -R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) -R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) -R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) -R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) -R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) -R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) -R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) -R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) -R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) -R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) -R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) -R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) -R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) -R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) -R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) -R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) -R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) -R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) -R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) -R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) -R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) -R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R001 -Variable Categorical Mode(DNA.Char) where Pos <= 30.5 - -Variable stats -Group Size Value list -{C, T} 2 C T -{G, A} 2 G A * - -% target values -Group EI IE N Interest Frequency Coverage -{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 -{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 122 0.40264 -T 64 0.211221 -G 61 0.20132 -A 56 0.184818 - ----------------------------------------------- -Rank R002 -Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] - -Variable stats -Group Size Value list -{A, G} 2 A G * -{C} 1 C -{T} 1 T - -% target values -Group EI IE N Interest Frequency Coverage -{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 -{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 -{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 114 0.376238 -A 69 0.227723 -T 61 0.20132 -G 59 0.194719 - ----------------------------------------------- -Rank R003 -Variable Categorical Mode(DNA.Char) - -Variable stats -Group Size Value list -{C} 1 C -{T, A} 2 T A * -{G} 1 G - -% target values -Group EI IE N Interest Frequency Coverage -{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 -{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 -{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 119 0.392739 -G 77 0.254125 -T 57 0.188119 -A 50 0.165017 - ----------------------------------------------- -Rank R004 -Variable Numerical Mean(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 -]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R005 -Variable Numerical Median(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 -]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 -]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R006 -Variable Numerical Count(DNA) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 -]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R007 -Variable Numerical Median(DNA.Pos) where Char = C - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 -]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 -Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/err.txt index 4411d98bb..1cdd9b468 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/err.txt @@ -1,6 +1,6 @@ -Variable construction time: 0:00:00.16 (100 constructed variables) +Variable construction time: 0:00:00.17 (100 constructed variables) Train supervised model for classification of target variable Class Database ./SpliceJunctionExternal.txt: Read records: 3178 Selected records: 303 -Data preparation time: 0:00:00.18 -Write report ./results\C100_AllReports.khj +Data preparation time: 0:00:00.15 +Write report ./results/C100_AnalysisResults.khj diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/time.log index 10d6aa5bb..32d78802a 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.5097329616546631 +Overal time: 0.4532008171081543 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/test.prm b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/test.prm index 3f054afba..ecde824ea 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/test.prm +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionExternal/test.prm @@ -14,31 +14,36 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open ClassFileName ./SpliceJunctionExternal.kdic // Dictionary file OK // Open -ClassManagement.ClassName SpliceJunctionExternal // Dictionary +TrainDatabase.ClassName SpliceJunctionExternal // Dictionary -TrainDatabase.DatabaseFiles.List.Key SpliceJunctionExternal // List item selection -TrainDatabase.DatabaseFiles.DataTableName ./SpliceJunctionExternal.txt // Data table file -TrainDatabase.DatabaseFiles.List.Key SpliceJunction // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file -TrainDatabase.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunctionExternal // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./SpliceJunctionExternal.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file -TrainDatabase.SampleNumberPercentage 10 // Sample percentage +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 10 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor false // Predicteur Bayesien Naif Selectif +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true // Do data preparation only -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report -AnalysisResults.ResultFilesPrefix C100_ // Result files directory +AnalysisResults.ReportFileName ./results/C100_AnalysisResults.khj // Analysis report AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database Exit // Close diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AnalysisResults.PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AnalysisResults.PreparationReport.xls new file mode 100644 index 000000000..09ab1890d --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AnalysisResults.PreparationReport.xls @@ -0,0 +1,274 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary SpliceJunction +Variables + Categorical 12 + Numerical 90 + Table 1 + Total 103 + +Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt +Sample percentage 10 +Sampling mode Include sample +Selection variable +Selection value +Instances 303 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +EI 67 0.221122 +IE 69 0.227723 +N 167 0.551155 + +Evaluated variables 101 +Native variables 1 +Constructed variables 100 +Informative variables 7 + +Max number of constructed variables 100 +Max number of text features 0 +Max number of trees 0 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 10.7442 + Data cost 296.926 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R001 Mode(DNA.Char) where Pos <= 30.5 0.0744365 2 4 C 0.40264 0 0 7.76817 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) +R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0508826 3 4 C 0.376238 0 0 9.15447 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R003 Mode(DNA.Char) 0.0461059 3 4 C 0.392739 0 0 4.27667 30.8759 258.994 TableMode(DNA, Char) +R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) +R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 1 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) +R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) +R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) +R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0 0 0.693147 10.7442 296.926 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R004 Mean(DNA.Pos) where Char = G 0.0274408 2 281 16 46.55555556 31.84768883 4.848828493 1 0 9.15447 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) +R005 Median(DNA.Pos) where Char = G 0.0225808 3 59 8 48 32.44701987 6.725618787 1 0 9.15447 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) +R006 Count(DNA) where Char = G 0.0160697 2 26 0 32 15.81188119 5.174919597 0 0 9.15447 21.0465 273.207 TableCount(`DNA where Char = G`) +R007 Median(DNA.Pos) where Char = C 0.0101423 2 63 9 56.5 29.24587459 7.66919868 0 0 9.15447 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) +R008 Count(DNA) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableCount(DNA) +R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) +R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) +R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) +R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) +R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) +R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) +R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) +R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R022 Count(DNA) where Pos in ]22.5, 30.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R023 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R024 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) +R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) +R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) +R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) +R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) +R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) +R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) +R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) +R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) +R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) +R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) +R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) +R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) +R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) +R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) +R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) +R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) +R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) +R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) +R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) +R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) +R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) +R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) +R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) +R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) +R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) +R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) +R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) +R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) +R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) +R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) +R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) +R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) +R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) +R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) +R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) +R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) +R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) +R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) +R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R001 +Variable Categorical Mode(DNA.Char) where Pos <= 30.5 + +Variable stats +Group Size Value list +{C, T} 2 C T +{G, A} 2 G A * + +% target values +Group EI IE N Interest Frequency Coverage +{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 +{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 122 0.40264 +T 64 0.211221 +G 61 0.20132 +A 56 0.184818 + +---------------------------------------------- +Rank R002 +Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] + +Variable stats +Group Size Value list +{A, G} 2 A G * +{C} 1 C +{T} 1 T + +% target values +Group EI IE N Interest Frequency Coverage +{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 +{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 +{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 114 0.376238 +A 69 0.227723 +T 61 0.20132 +G 59 0.194719 + +---------------------------------------------- +Rank R003 +Variable Categorical Mode(DNA.Char) + +Variable stats +Group Size Value list +{C} 1 C +{T, A} 2 T A * +{G} 1 G + +% target values +Group EI IE N Interest Frequency Coverage +{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 +{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 +{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 119 0.392739 +G 77 0.254125 +T 57 0.188119 +A 50 0.165017 + +---------------------------------------------- +Rank R004 +Variable Numerical Mean(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 +]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R005 +Variable Numerical Median(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 +]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 +]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R006 +Variable Numerical Count(DNA) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 +]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R007 +Variable Numerical Median(DNA.Pos) where Char = C + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 +]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 +Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AllReports.khj b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AnalysisResults.khj similarity index 92% rename from test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AllReports.khj rename to test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AnalysisResults.khj index 3a88b886c..bd09be57a 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AllReports.khj +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_AnalysisResults.khj @@ -1,1954 +1,2069 @@ -{ - "tool": "Khiops", - "version": "10.1", - "shortDescription": "", - "preparationReport": { - "reportType": "Preparation", - "summary": { - "dictionary": "SpliceJunction", - "variables": { - "types": [ - "Categorical", - "Numerical", - "Table" - ], - "numbers": [ - 12, - 90, - 1 - ] - }, - "database": "..\/..\/..\/MTdatasets\/SpliceJunction\/SpliceJunction.txt", - "samplePercentage": 10, - "samplingMode": "Include sample", - "selectionVariable": "", - "selectionValue": "", - "instances": 303, - "learningTask": "Classification analysis", - "targetVariable": "Class", - "targetDescriptiveStats": { - "values": 3, - "mode": "N", - "modeFrequency": 167 - }, - "targetValues": { - "values": ["EI","IE","N"], - "frequencies": [67,69,167] - }, - "evaluatedVariables": 101, - "nativeVariables": 1, - "constructedVariables": 100, - "informativeVariables": 7, - "featureEngineering": { - "maxNumberOfConstructedVariables": 100, - "maxNumberOfTrees": 0, - "maxNumberOfVariablePairs": 0 - }, - "discretization": "MODL", - "valueGrouping": "MODL", - "nullModel": { - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926 - } - }, - "variablesStatistics": [ - { - "rank": "R001", - "name": "Mode(DNA.Char) where Pos <= 30.5", - "type": "Categorical", - "level": 0.0744365, - "parts": 2, - "values": 4, - "mode": "C", - "modeFrequency": 122, - "constructionCost": 7.76817, - "preparationCost": 23.5167, - "dataCost": 254.125, - "derivationRule": "TableMode(`DNA where Pos <= 30.5`, Char)" - }, - { - "rank": "R002", - "name": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", - "type": "Categorical", - "level": 0.0508826, - "parts": 3, - "values": 4, - "mode": "C", - "modeFrequency": 114, - "constructionCost": 9.15447, - "preparationCost": 31.5551, - "dataCost": 251.964, - "derivationRule": "TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char)" - }, - { - "rank": "R003", - "name": "Mode(DNA.Char)", - "type": "Categorical", - "level": 0.0461059, - "parts": 3, - "values": 4, - "mode": "C", - "modeFrequency": 119, - "constructionCost": 4.27667, - "preparationCost": 30.8759, - "dataCost": 258.994, - "derivationRule": "TableMode(DNA, Char)" - }, - { - "rank": "R004", - "name": "Mean(DNA.Pos) where Char = G", - "type": "Numerical", - "level": 0.0274408, - "parts": 2, - "values": 281, - "min": 16, - "max": 46.55555556, - "mean": 31.84768883, - "stdDev": 4.848828493, - "missingNumber": 1, - "constructionCost": 9.15447, - "preparationCost": 19.4458, - "dataCost": 271.302, - "derivationRule": "TableMean(`DNA where Char = G`, Pos)" - }, - { - "rank": "R005", - "name": "Median(DNA.Pos) where Char = G", - "type": "Numerical", - "level": 0.0225808, - "parts": 3, - "values": 59, - "min": 8, - "max": 48, - "mean": 32.44701987, - "stdDev": 6.725618787, - "missingNumber": 1, - "constructionCost": 9.15447, - "preparationCost": 29.3732, - "dataCost": 262.873, - "derivationRule": "TableMedian(`DNA where Char = G`, Pos)" - }, - { - "rank": "R006", - "name": "Count(DNA) where Char = G", - "type": "Numerical", - "level": 0.0160697, - "parts": 2, - "values": 26, - "min": 0, - "max": 32, - "mean": 15.81188119, - "stdDev": 5.174919597, - "missingNumber": 0, - "constructionCost": 9.15447, - "preparationCost": 21.0465, - "dataCost": 273.207, - "derivationRule": "TableCount(`DNA where Char = G`)" - }, - { - "rank": "R007", - "name": "Median(DNA.Pos) where Char = C", - "type": "Numerical", - "level": 0.0101423, - "parts": 2, - "values": 63, - "min": 9, - "max": 56.5, - "mean": 29.24587459, - "stdDev": 7.66919868, - "missingNumber": 0, - "constructionCost": 9.15447, - "preparationCost": 19.4456, - "dataCost": 276.636, - "derivationRule": "TableMedian(`DNA where Char = C`, Pos)" - }, - { - "rank": "R008", - "name": "Count(DNA)", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 1, - "min": 60, - "max": 60, - "mean": 60, - "stdDev": 0, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(DNA)" - }, - { - "rank": "R009", - "name": "Count(DNA) where Char = A", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 25, - "min": 0, - "max": 27, - "mean": 13.53135314, - "stdDev": 4.652779483, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": "TableCount(`DNA where Char = A`)" - }, - { - "rank": "R010", - "name": "Count(DNA) where Char = C", - "type": "Numerical", - "level": 0, - "parts": 1, - "values": 29, - "min": 2, - "max": 34, - "mean": 16.48844884, - "stdDev": 5.649179844, - "missingNumber": 0, - "constructionCost": 0.693147, - "preparationCost": 10.7442, - "dataCost": 296.926, - "derivationRule": 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10.7442, + "dataCost": 296.926, + "derivationRule": "TableSum(`DNA where Char not in {C, G}`, Pos)" + }, + { + "rank": "R096", + "name": "Sum(DNA.Pos) where Pos <= 15.5", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 120, + "max": 120, + "mean": 120, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos)" + }, + { + "rank": "R097", + "name": "Sum(DNA.Pos) where Pos <= 30.5", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 465, + "max": 465, + "mean": 465, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TableSum(`DNA where Pos <= 30.5`, Pos)" + }, + { + "rank": "R098", + "name": "Sum(DNA.Pos) where Pos > 30.5", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 1365, + "max": 1365, + "mean": 1365, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TableSum(`DNA where Pos > 30.5`, Pos)" + }, + { + "rank": "R099", + "name": "Sum(DNA.Pos) where Pos > 45.5", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 795, + "max": 795, + "mean": 795, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos)" + }, + { + "rank": "R100", + "name": "Sum(DNA.Pos) where Pos in ]15.5, 30.5]", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 345, + "max": 345, + "mean": 345, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos)" + }, + { + "rank": "R101", + "name": "Sum(DNA.Pos) where Pos in ]30.5, 45.5]", + "type": "Numerical", + "level": 0, + "parts": 1, + "values": 1, + "min": 570, + "max": 570, + "mean": 570, + "stdDev": 0, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 0.693147, + "preparationCost": 10.7442, + "dataCost": 296.926, + "derivationRule": "TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos)" + } + ], + "variablesDetailedStatistics": { + "R001": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mode(DNA.Char) where Pos <= 30.5", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["C","T"], + ["G","A"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [29,69,88], + [38,0,79] + ], + "partInterests": [0.247927,0.752073] + }, + "inputValues": { + "values": ["C","T","G","A"], + "frequencies": [122,64,61,56] + } + }, + "R002": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["A","G"], + ["C"], + ["T"] + ], + "defaultGroupIndex": 0 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [38,3,87], + [26,41,47], + [3,25,33] + ], + "partInterests": [0.593857,0.15488,0.251263] + }, + "inputValues": { + "values": ["C","A","T","G"], + "frequencies": [114,69,61,59] + } + }, + "R003": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mode(DNA.Char)", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["C"], + ["T","A"], + ["G"] + ], + "defaultGroupIndex": 1 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [17,49,53], + [16,17,74], + [34,3,40] + ], + "partInterests": [0.33703,0.144491,0.518478] + }, + "inputValues": { + "values": ["C","G","T","A"], + "frequencies": [119,77,57,50] + } + }, + "R004": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Mean(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [16,31.698], + [31.698,46.55555556] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [34,12,105], + [33,57,62] + ], + "partInterests": [0.57478,0.42522] + } + }, + "R005": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Median(DNA.Pos) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [8,27.75], + [27.75,37.25], + [37.25,48] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [11,3,58], + [44,31,86], + [12,35,23] + ], + "partInterests": [0.478566,0.0530889,0.468345] + } + }, + "R006": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Count(DNA) where Char = G", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [0,19.5], + [19.5,32] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [38,68,130], + [29,1,37] + ], + "partInterests": [0.187162,0.812838] + } + }, + "R007": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Median(DNA.Pos) where Char = C", + "type": "Numerical", + "partitionType": "Intervals", + "partition": [ + [9,27.75], + [27.75,56.5] + ] + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [35,53,62], + [32,16,105] + ], + "partInterests": [0.454456,0.545544] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_PreparationReport.xls deleted file mode 100644 index ed87bb8b6..000000000 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/C100_PreparationReport.xls +++ /dev/null @@ -1,273 +0,0 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary SpliceJunction -Variables - Categorical 12 - Numerical 90 - Table 1 - Total 103 - -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value -Instances 303 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -EI 67 0.221122 -IE 69 0.227723 -N 167 0.551155 - -Evaluated variables 101 -Native variables 1 -Constructed variables 100 -Informative variables 7 - -Max number of constructed variables 100 -Max number of trees 0 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 10.7442 - Data cost 296.926 - - -Categorical variables statistics - -Rank Name Level Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost Derivation rule -R001 Mode(DNA.Char) where Pos <= 30.5 0.0744365 2 4 C 0.40264 7.76817 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) -R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0508826 3 4 C 0.376238 9.15447 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R003 Mode(DNA.Char) 0.0461059 3 4 C 0.392739 4.27667 30.8759 258.994 TableMode(DNA, Char) -R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) -R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) -R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) -R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) -R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0.693147 10.7442 296.926 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R004 Mean(DNA.Pos) where Char = G 0.0274408 2 281 16 46.55555556 31.84768883 4.848828493 1 9.15447 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) -R005 Median(DNA.Pos) where Char = G 0.0225808 3 59 8 48 32.44701987 6.725618787 1 9.15447 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) -R006 Count(DNA) where Char = G 0.0160697 2 26 0 32 15.81188119 5.174919597 0 9.15447 21.0465 273.207 TableCount(`DNA where Char = G`) -R007 Median(DNA.Pos) where Char = C 0.0101423 2 63 9 56.5 29.24587459 7.66919868 0 9.15447 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) -R008 Count(DNA) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableCount(DNA) -R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) -R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) -R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) -R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) -R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) -R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) -R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) -R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R022 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R023 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R024 Count(DNA) where Pos in ]38.5, 45.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) -R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) -R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) -R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) -R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) -R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) -R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) -R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) -R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) -R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) -R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) -R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) -R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) -R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) -R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) -R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) -R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) -R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) -R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) -R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) -R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) -R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) -R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) -R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) -R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) -R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) -R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) -R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) -R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) -R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) -R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) -R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) -R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) -R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) -R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) -R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) -R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) -R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) -R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) -R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R001 -Variable Categorical Mode(DNA.Char) where Pos <= 30.5 - -Variable stats -Group Size Value list -{C, T} 2 C T -{G, A} 2 G A * - -% target values -Group EI IE N Interest Frequency Coverage -{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 -{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 122 0.40264 -T 64 0.211221 -G 61 0.20132 -A 56 0.184818 - ----------------------------------------------- -Rank R002 -Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] - -Variable stats -Group Size Value list -{A, G} 2 A G * -{C} 1 C -{T} 1 T - -% target values -Group EI IE N Interest Frequency Coverage -{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 -{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 -{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 114 0.376238 -A 69 0.227723 -T 61 0.20132 -G 59 0.194719 - ----------------------------------------------- -Rank R003 -Variable Categorical Mode(DNA.Char) - -Variable stats -Group Size Value list -{C} 1 C -{T, A} 2 T A * -{G} 1 G - -% target values -Group EI IE N Interest Frequency Coverage -{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 -{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 -{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 119 0.392739 -G 77 0.254125 -T 57 0.188119 -A 50 0.165017 - ----------------------------------------------- -Rank R004 -Variable Numerical Mean(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 -]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R005 -Variable Numerical Median(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 -]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 -]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R006 -Variable Numerical Count(DNA) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 -]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R007 -Variable Numerical Median(DNA.Pos) where Char = C - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 -]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 -Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/err.txt index e084e6137..16bfebe7a 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/err.txt @@ -1,7 +1,7 @@ -Variable construction time: 0:00:00.11 (100 constructed variables) +Variable construction time: 0:00:00.08 (100 constructed variables) Train supervised model for classification of target variable Class Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt: Read records: 3178 Selected records: 303 Table ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt Records: 18,180 -Data preparation time: 0:00:00.15 -Write report ./results\C100_AllReports.khj +Data preparation time: 0:00:00.11 +Write report ./results/C100_AnalysisResults.khj diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/time.log index f21c79b33..fdc02d7c6 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.44769883155822754 +Overal time: 0.3499460220336914 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/test.prm b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/test.prm index 7d4998715..3db60d8f5 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/test.prm +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionPreparation/test.prm @@ -14,29 +14,34 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open ClassFileName ../../../MTdatasets/SpliceJunction/SpliceJunction.kdic // Dictionary file OK // Open -ClassManagement.ClassName SpliceJunction // Dictionary +TrainDatabase.ClassName SpliceJunction // Dictionary -TrainDatabase.DatabaseFiles.List.Key SpliceJunction // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file -TrainDatabase.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file -TrainDatabase.SampleNumberPercentage 10 // Sample percentage +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 10 // Sample percentage AnalysisSpec.TargetAttributeName Class // Target variable -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor false // Predicteur Bayesien Naif Selectif +AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true // Do data preparation only -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report -AnalysisResults.ResultFilesPrefix C100_ // Result files directory +AnalysisResults.ReportFileName ./results/C100_AnalysisResults.khj // Analysis report AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database Exit // Close diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/T_SpliceJunction.txt b/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/D_SpliceJunction.txt similarity index 95% rename from test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/T_SpliceJunction.txt rename to test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/D_SpliceJunction.txt index fadb20792..348623eb1 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/T_SpliceJunction.txt +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/D_SpliceJunction.txt @@ -1,2255 +1,2255 @@ -SampleId Class -AGMORS12A-NEG-181 N -AGMORS9A-NEG-481 N -AGMRSKPNI-NEG-1141 N -ATRINS-ACCEPTOR-701 IE -ATRINS-DONOR-521 EI -ATRINS-DONOR-905 EI -ATRINS-NEG-841 N -BABA1AT-NEG-181 N -BABAPOE-ACCEPTOR-801 IE -BABAPOE-DONOR-2817 EI -BABAPOE-DONOR-30 EI -BABAPOE-DONOR-867 EI -BABAPOE-NEG-1201 N -BABBMYHA-NEG-301 N -BABT1GL-NEG-61 N -CHPCD4A-NEG-961 N -CHPGGGLOG-NEG-961 N -CHPHBBC-NEG-1321 N -CHPHBBPCH-NEG-301 N -CHPHBBPCH-NEG-4861 N -CHPHPA2-NEG-1 N -CHPHPA3-NEG-2041 N -CHPIGECA-ACCEPTOR-1392 IE -CHPIGECA-ACCEPTOR-585 IE -CHPIGECA-ACCEPTOR-989 IE -CHPIGECA-DONOR-1313 EI -CHPIGECA-DONOR-378 EI -CHPIGECA-DONOR-903 EI -CHPINVOL-NEG-1 N -CHPMHCBAA-NEG-841 N -CHPRGITX-NEG-1441 N -CHPRSA-NEG-2161 N -GCRHBBA1-ACCEPTOR-1385 IE -GCRHBBA1-NEG-61 N -GCRHBBA6-ACCEPTOR-590 IE -GCRHBBA6-ACCEPTOR-920 IE -GCRHBBA6-DONOR-461 EI -GCRHBBA6-DONOR-795 EI -GIBHBGGL-ACCEPTOR-2401 IE -GIBHBGGL-ACCEPTOR-7321 IE -GIBHBGGL-ACCEPTOR-8438 IE -GIBHBGGL-DONOR-2624 EI -GIBHBGGL-DONOR-7198 EI -GIBHBGGL-DONOR-7544 EI -GIBHBGGL-NEG-7861 N -GIBIL2LTR-NEG-241 N -GORHBBPG-NEG-4741 N -GORHBPSBD-NEG-2281 N -GORRGITX-NEG-1 N -HUM3ALPH-NEG-1 N -HUM4COLA-NEG-1021 N -HUM5FDX-NEG-2041 N -HUM7B2-NEG-901 N -HUM7SKP41-NEG-541 N -HUMA1ACMB-NEG-421 N -HUMA1AR2-NEG-3601 N -HUMA1ATP-ACCEPTOR-10909 IE -HUMA1ATP-ACCEPTOR-11880 IE -HUMA1ATP-ACCEPTOR-7282 IE -HUMA1ATP-ACCEPTOR-9382 IE -HUMA1ATP-DONOR-11057 EI -HUMA1ATP-DONOR-9653 EI -HUMA1ATP-NEG-301 N -HUMA1ATP-NEG-4861 N -HUMA1ATP-NEG-9421 N -HUMA1GLY2-ACCEPTOR-2108 IE -HUMA1GLY2-ACCEPTOR-2469 IE -HUMA1GLY2-ACCEPTOR-3244 IE -HUMA1GP01-NEG-301 N -HUMA1MICR-NEG-1081 N -HUMA2PIG3-NEG-541 N -HUMAACT-NEG-481 N -HUMABL1B-NEG-1621 N -HUMACCYBA-ACCEPTOR-1121 IE -HUMACCYBA-ACCEPTOR-1384 IE -HUMACCYBA-ACCEPTOR-2065 IE -HUMACCYBA-ACCEPTOR-2893 IE -HUMACCYBA-DONOR-1250 EI -HUMACCYBA-DONOR-2504 EI -HUMACCYBA-DONOR-2781 EI -HUMACCYBA-DONOR-289 EI -HUMACCYBA-NEG-2521 N -HUMACCYBB-ACCEPTOR-1318 IE -HUMACCYBB-ACCEPTOR-1999 IE -HUMACCYBB-ACCEPTOR-2533 IE -HUMACCYBB-ACCEPTOR-2827 IE -HUMACCYBB-DONOR-1558 EI -HUMACCYBB-DONOR-290 EI -HUMACHRA7-NEG-901 N -HUMACHRM2-NEG-1681 N -HUMACROS-NEG-1 N -HUMACTASK-NEG-1021 N -HUMACTCA2-NEG-1381 N -HUMACTGA-ACCEPTOR-1104 IE -HUMACTGA-ACCEPTOR-1621 IE -HUMACTGA-ACCEPTOR-2139 IE -HUMACTGA-ACCEPTOR-2414 IE -HUMACTGA-ACCEPTOR-886 IE -HUMACTGA-DONOR-1015 EI -HUMACTGA-DONOR-2060 EI -HUMACTGA-DONOR-513 EI -HUMACTGA-NEG-2641 N -HUMACTGP1-NEG-481 N -HUMACTSM7-NEG-61 N -HUMADAG-ACCEPTOR-19200 IE -HUMADAG-ACCEPTOR-26314 IE -HUMADAG-ACCEPTOR-28878 IE -HUMADAG-ACCEPTOR-29793 IE -HUMADAG-ACCEPTOR-31146 IE -HUMADAG-ACCEPTOR-32395 IE -HUMADAG-ACCEPTOR-32543 IE -HUMADAG-ACCEPTOR-32821 IE -HUMADAG-ACCEPTOR-34324 IE -HUMADAG-ACCEPTOR-35070 IE -HUMADAG-ACCEPTOR-35621 IE -HUMADAG-DONOR-29022 EI -HUMADAG-DONOR-32467 EI -HUMADAG-DONOR-32886 EI -HUMADAG-DONOR-34454 EI -HUMADAG-DONOR-35173 EI -HUMADAG-DONOR-4034 EI -HUMADAG-NEG-13561 N -HUMADAG-NEG-18121 N -HUMADAG-NEG-31801 N -HUMADAG-NEG-36361 N -HUMADAG-NEG-4441 N -HUMADH1CA-NEG-121 N -HUMADH21C-NEG-541 N -HUMADH2C1-NEG-841 N -HUMADH2E2-NEG-1 N -HUMADH4C1-NEG-1501 N -HUMADPRT01-NEG-61 N -HUMADRA-NEG-961 N -HUMADRA2R-NEG-2581 N -HUMADRBR-NEG-1861 N -HUMADRBRA-NEG-2941 N -HUMAFLP2-NEG-61 N -HUMAFP-ACCEPTOR-12016 IE -HUMAFP-ACCEPTOR-15169 IE -HUMAFP-ACCEPTOR-16955 IE -HUMAFP-ACCEPTOR-18319 IE -HUMAFP-ACCEPTOR-2796 IE -HUMAFP-ACCEPTOR-5216 IE -HUMAFP-ACCEPTOR-7965 IE -HUMAFP-DONOR-12231 EI -HUMAFP-DONOR-14021 EI -HUMAFP-DONOR-14687 EI -HUMAFP-DONOR-15308 EI -HUMAFP-DONOR-17179 EI -HUMAFP-DONOR-1834 EI -HUMAFP-DONOR-18452 EI -HUMAFP-DONOR-5428 EI -HUMAFP-DONOR-970 EI -HUMAFP-DONOR-9741 EI -HUMAFP-NEG-13261 N -HUMAFP-NEG-17821 N -HUMAFP-NEG-4141 N -HUMAFP-NEG-8701 N -HUMAFP1-NEG-241 N -HUMAG1-NEG-2101 N -HUMAGG-NEG-541 N -HUMAGP1A-NEG-421 N -HUMAICEB-NEG-2641 N -HUMAK1-ACCEPTOR-10045 IE -HUMAK1-ACCEPTOR-10488 IE -HUMAK1-ACCEPTOR-3918 IE -HUMAK1-ACCEPTOR-5504 IE -HUMAK1-ACCEPTOR-5712 IE -HUMAK1-ACCEPTOR-6626 IE -HUMAK1-DONOR-10237 EI -HUMAK1-DONOR-3959 EI -HUMAK1-DONOR-5540 EI -HUMAK1-DONOR-6743 EI -HUMAK1-DONOR-954 EI -HUMAK1-NEG-10921 N -HUMAK1-NEG-6361 N -HUMALBGC-ACCEPTOR-10837 IE -HUMALBGC-ACCEPTOR-12451 IE -HUMALBGC-ACCEPTOR-13672 IE -HUMALBGC-ACCEPTOR-14947 IE -HUMALBGC-ACCEPTOR-16911 IE -HUMALBGC-ACCEPTOR-17658 IE -HUMALBGC-ACCEPTOR-18496 IE -HUMALBGC-ACCEPTOR-4046 IE -HUMALBGC-ACCEPTOR-6011 IE -HUMALBGC-ACCEPTOR-6772 IE -HUMALBGC-ACCEPTOR-7729 IE -HUMALBGC-ACCEPTOR-9414 IE -HUMALBGC-DONOR-11052 EI -HUMALBGC-DONOR-13770 EI -HUMALBGC-DONOR-17726 EI -HUMALBGC-DONOR-1825 EI -HUMALBGC-DONOR-2592 EI -HUMALBGC-DONOR-6905 EI -HUMALBGC-DONOR-7827 EI -HUMALBGC-NEG-13921 N -HUMALBGC-NEG-18481 N -HUMALBGC-NEG-241 N -HUMALBGC-NEG-9361 N -HUMALDA1-NEG-661 N -HUMALDAR-NEG-1081 N -HUMALDB1-NEG-2761 N -HUMALDB1-NEG-7321 N -HUMALDC-NEG-61 N -HUMALDH03-NEG-1 N -HUMALDH2-NEG-1201 N -HUMALDOAP-NEG-661 N -HUMALDOBG-NEG-1 N -HUMALIPOA-NEG-5341 N -HUMALIPOA-NEG-781 N -HUMALIPOA-NEG-9901 N -HUMALLFU-NEG-61 N -HUMALP5-NEG-781 N -HUMALPHA-ACCEPTOR-1369 IE -HUMALPHA-ACCEPTOR-2224 IE -HUMALPHA-ACCEPTOR-2440 IE -HUMALPHA-ACCEPTOR-3090 IE -HUMALPHA-ACCEPTOR-581 IE -HUMALPHA-ACCEPTOR-809 IE -HUMALPHA-DONOR-1292 EI -HUMALPHA-DONOR-1924 EI -HUMALPHA-DONOR-698 EI -HUMALPHA-NEG-181 N -HUMALPHB-NEG-241 N -HUMALPHLB-NEG-901 N -HUMALPI-ACCEPTOR-1216 IE -HUMALPI-ACCEPTOR-1631 IE -HUMALPI-ACCEPTOR-2064 IE -HUMALPI-ACCEPTOR-2283 IE -HUMALPI-ACCEPTOR-2698 IE -HUMALPI-ACCEPTOR-2926 IE -HUMALPI-ACCEPTOR-415 IE -HUMALPI-ACCEPTOR-646 IE -HUMALPI-ACCEPTOR-965 IE -HUMALPI-DONOR-2199 EI -HUMALPI-DONOR-2475 EI -HUMALPI-DONOR-2815 EI -HUMALPI-DONOR-333 EI -HUMALPI-DONOR-532 EI -HUMALPI-DONOR-762 EI -HUMALPI-NEG-3901 N -HUMALPI1-ACCEPTOR-124 IE -HUMALPI1-DONOR-42 EI -HUMALPPD-ACCEPTOR-1408 IE -HUMALPPD-ACCEPTOR-1642 IE -HUMALPPD-ACCEPTOR-1845 IE -HUMALPPD-ACCEPTOR-2062 IE -HUMALPPD-ACCEPTOR-2473 IE -HUMALPPD-ACCEPTOR-421 IE 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IE -HUMATP1A2-ACCEPTOR-12450 IE -HUMATP1A2-ACCEPTOR-13550 IE -HUMATP1A2-ACCEPTOR-15000 IE -HUMATP1A2-ACCEPTOR-15320 IE -HUMATP1A2-ACCEPTOR-20042 IE -HUMATP1A2-ACCEPTOR-21144 IE -HUMATP1A2-ACCEPTOR-22617 IE -HUMATP1A2-ACCEPTOR-22870 IE -HUMATP1A2-ACCEPTOR-24266 IE -HUMATP1A2-ACCEPTOR-6969 IE -HUMATP1A2-ACCEPTOR-8990 IE -HUMATP1A2-DONOR-10208 EI -HUMATP1A2-DONOR-11031 EI -HUMATP1A2-DONOR-12719 EI -HUMATP1A2-DONOR-13749 EI -HUMATP1A2-DONOR-14299 EI -HUMATP1A2-DONOR-15190 EI -HUMATP1A2-DONOR-15496 EI -HUMATP1A2-DONOR-19518 EI -HUMATP1A2-DONOR-20193 EI -HUMATP1A2-DONOR-20500 EI -HUMATP1A2-DONOR-21268 EI -HUMATP1A2-DONOR-21613 EI -HUMATP1A2-DONOR-21929 EI -HUMATP1A2-DONOR-22962 EI -HUMATP1A2-DONOR-7029 EI -HUMATP1A2-DONOR-9194 EI -HUMATP1A2-DONOR-9834 EI -HUMATP1A2-NEG-11221 N -HUMATP1A2-NEG-15781 N -HUMATP1A2-NEG-20341 N -HUMATP1A2-NEG-2101 N -HUMATP1A2-NEG-6661 N -HUMATPA2-NEG-781 N -HUMATPB-NEG-841 N -HUMATPCAPM-NEG-661 N -HUMATPSYB-ACCEPTOR-3096 IE -HUMATPSYB-ACCEPTOR-4464 IE 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EI -HUMCYC1A-DONOR-3019 EI -HUMCYC1A-DONOR-3382 EI -HUMCYC1A-NEG-4381 N -HUMCYCAA-ACCEPTOR-1466 IE -HUMCYCAA-DONOR-1365 EI -HUMCYCB-NEG-181 N -HUMCYCPSE-NEG-541 N -HUMCYES1-NEG-1501 N -HUMCYP145-NEG-301 N -HUMCYP2BA-NEG-2761 N -HUMCYP345-NEG-1381 N -HUMCYP450-NEG-2761 N -HUMCYP450-NEG-7321 N -HUMCYP4A2-NEG-781 N -HUMCYPAX-NEG-421 N -HUMCYPB1-NEG-1141 N -HUMCYPBX3-NEG-241 N -HUMCYPD-NEG-361 N -HUMCYPHLP-NEG-421 N -HUMCYPIIE-ACCEPTOR-12121 IE -HUMCYPIIE-ACCEPTOR-3877 IE -HUMCYPIIE-ACCEPTOR-6976 IE -HUMCYPIIE-ACCEPTOR-7515 IE -HUMCYPIIE-ACCEPTOR-8083 IE -HUMCYPIIE-DONOR-2972 EI -HUMCYPIIE-DONOR-7126 EI -HUMCYPIIE-DONOR-9284 EI -HUMCYPIIE-NEG-12121 N -HUMCYPIIE-NEG-3001 N -HUMCYPIIE-NEG-7561 N -HUMCYPMP-NEG-181 N -HUMCYPMPC-NEG-1 N -HUMCYPNO-NEG-961 N -HUMCYPX1-NEG-4201 N -HUMDAFA-ACCEPTOR-1235 IE -HUMDAFA-DONOR-1117 EI -HUMDAFA-NEG-1561 N -HUMDB1A-NEG-121 N -HUMDBH01-NEG-1321 N -HUMDBPC-NEG-2821 N -HUMDBPG-NEG-1141 N -HUMDES-ACCEPTOR-1920 IE -HUMDES-ACCEPTOR-2164 IE -HUMDES-ACCEPTOR-5413 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-HUMALBGC-ACCEPTOR-16911 58 G -HUMALBGC-ACCEPTOR-16911 59 C -HUMALBGC-ACCEPTOR-16911 60 C -HUMALBGC-ACCEPTOR-17658 1 T -HUMALBGC-ACCEPTOR-17658 2 C -HUMALBGC-ACCEPTOR-17658 3 C -HUMALBGC-ACCEPTOR-17658 4 T -HUMALBGC-ACCEPTOR-17658 5 A -HUMALBGC-ACCEPTOR-17658 6 A -HUMALBGC-ACCEPTOR-17658 7 C -HUMALBGC-ACCEPTOR-17658 8 A -HUMALBGC-ACCEPTOR-17658 9 T -HUMALBGC-ACCEPTOR-17658 10 C -HUMALBGC-ACCEPTOR-17658 11 T -HUMALBGC-ACCEPTOR-17658 12 G -HUMALBGC-ACCEPTOR-17658 13 T -HUMALBGC-ACCEPTOR-17658 14 C -HUMALBGC-ACCEPTOR-17658 15 A -HUMALBGC-ACCEPTOR-17658 16 T -HUMALBGC-ACCEPTOR-17658 17 G -HUMALBGC-ACCEPTOR-17658 18 T -HUMALBGC-ACCEPTOR-17658 19 C -HUMALBGC-ACCEPTOR-17658 20 T -HUMALBGC-ACCEPTOR-17658 21 T -HUMALBGC-ACCEPTOR-17658 22 T -HUMALBGC-ACCEPTOR-17658 23 G -HUMALBGC-ACCEPTOR-17658 24 T -HUMALBGC-ACCEPTOR-17658 25 G -HUMALBGC-ACCEPTOR-17658 26 T -HUMALBGC-ACCEPTOR-17658 27 T -HUMALBGC-ACCEPTOR-17658 28 C -HUMALBGC-ACCEPTOR-17658 29 A -HUMALBGC-ACCEPTOR-17658 30 G -HUMALBGC-ACCEPTOR-17658 31 G -HUMALBGC-ACCEPTOR-17658 32 G -HUMALBGC-ACCEPTOR-17658 33 T -HUMALBGC-ACCEPTOR-17658 34 A -HUMALBGC-ACCEPTOR-17658 35 A -HUMALBGC-ACCEPTOR-17658 36 A -HUMALBGC-ACCEPTOR-17658 37 A -HUMALBGC-ACCEPTOR-17658 38 A -HUMALBGC-ACCEPTOR-17658 39 A -HUMALBGC-ACCEPTOR-17658 40 C -HUMALBGC-ACCEPTOR-17658 41 T -HUMALBGC-ACCEPTOR-17658 42 T -HUMALBGC-ACCEPTOR-17658 43 G -HUMALBGC-ACCEPTOR-17658 44 T -HUMALBGC-ACCEPTOR-17658 45 T -HUMALBGC-ACCEPTOR-17658 46 G -HUMALBGC-ACCEPTOR-17658 47 C -HUMALBGC-ACCEPTOR-17658 48 T -HUMALBGC-ACCEPTOR-17658 49 G -HUMALBGC-ACCEPTOR-17658 50 C -HUMALBGC-ACCEPTOR-17658 51 A -HUMALBGC-ACCEPTOR-17658 52 A -HUMALBGC-ACCEPTOR-17658 53 G -HUMALBGC-ACCEPTOR-17658 54 T -HUMALBGC-ACCEPTOR-17658 55 C -HUMALBGC-ACCEPTOR-17658 56 A -HUMALBGC-ACCEPTOR-17658 57 A -HUMALBGC-ACCEPTOR-17658 58 G -HUMALBGC-ACCEPTOR-17658 59 C -HUMALBGC-ACCEPTOR-17658 60 T -HUMALBGC-ACCEPTOR-18496 1 A -HUMALBGC-ACCEPTOR-18496 2 A -HUMALBGC-ACCEPTOR-18496 3 G -HUMALBGC-ACCEPTOR-18496 4 A -HUMALBGC-ACCEPTOR-18496 5 C -HUMALBGC-ACCEPTOR-18496 6 T -HUMALBGC-ACCEPTOR-18496 7 T -HUMALBGC-ACCEPTOR-18496 8 A -HUMALBGC-ACCEPTOR-18496 9 T -HUMALBGC-ACCEPTOR-18496 10 A -HUMALBGC-ACCEPTOR-18496 11 T -HUMALBGC-ACCEPTOR-18496 12 T -HUMALBGC-ACCEPTOR-18496 13 T -HUMALBGC-ACCEPTOR-18496 14 G -HUMALBGC-ACCEPTOR-18496 15 T -HUMALBGC-ACCEPTOR-18496 16 C -HUMALBGC-ACCEPTOR-18496 17 C -HUMALBGC-ACCEPTOR-18496 18 T -HUMALBGC-ACCEPTOR-18496 19 T -HUMALBGC-ACCEPTOR-18496 20 T -HUMALBGC-ACCEPTOR-18496 21 T -HUMALBGC-ACCEPTOR-18496 22 G -HUMALBGC-ACCEPTOR-18496 23 T -HUMALBGC-ACCEPTOR-18496 24 T -HUMALBGC-ACCEPTOR-18496 25 T -HUMALBGC-ACCEPTOR-18496 26 T -HUMALBGC-ACCEPTOR-18496 27 T -HUMALBGC-ACCEPTOR-18496 28 C -HUMALBGC-ACCEPTOR-18496 29 A -HUMALBGC-ACCEPTOR-18496 30 G -HUMALBGC-ACCEPTOR-18496 31 C -HUMALBGC-ACCEPTOR-18496 32 C -HUMALBGC-ACCEPTOR-18496 33 T -HUMALBGC-ACCEPTOR-18496 34 A -HUMALBGC-ACCEPTOR-18496 35 C -HUMALBGC-ACCEPTOR-18496 36 C -HUMALBGC-ACCEPTOR-18496 37 A -HUMALBGC-ACCEPTOR-18496 38 T -HUMALBGC-ACCEPTOR-18496 39 G -HUMALBGC-ACCEPTOR-18496 40 A -HUMALBGC-ACCEPTOR-18496 41 G -HUMALBGC-ACCEPTOR-18496 42 A -HUMALBGC-ACCEPTOR-18496 43 A -HUMALBGC-ACCEPTOR-18496 44 T -HUMALBGC-ACCEPTOR-18496 45 A -HUMALBGC-ACCEPTOR-18496 46 A -HUMALBGC-ACCEPTOR-18496 47 G -HUMALBGC-ACCEPTOR-18496 48 A -HUMALBGC-ACCEPTOR-18496 49 G -HUMALBGC-ACCEPTOR-18496 50 A -HUMALBGC-ACCEPTOR-18496 51 A -HUMALBGC-ACCEPTOR-18496 52 A -HUMALBGC-ACCEPTOR-18496 53 G -HUMALBGC-ACCEPTOR-18496 54 A -HUMALBGC-ACCEPTOR-18496 55 A -HUMALBGC-ACCEPTOR-18496 56 A -HUMALBGC-ACCEPTOR-18496 57 A -HUMALBGC-ACCEPTOR-18496 58 T -HUMALBGC-ACCEPTOR-18496 59 G -HUMALBGC-ACCEPTOR-18496 60 A -HUMALBGC-ACCEPTOR-4046 1 T -HUMALBGC-ACCEPTOR-4046 2 A -HUMALBGC-ACCEPTOR-4046 3 C -HUMALBGC-ACCEPTOR-4046 4 T -HUMALBGC-ACCEPTOR-4046 5 A -HUMALBGC-ACCEPTOR-4046 6 C -HUMALBGC-ACCEPTOR-4046 7 A -HUMALBGC-ACCEPTOR-4046 8 T -HUMALBGC-ACCEPTOR-4046 9 T -HUMALBGC-ACCEPTOR-4046 10 T -HUMALBGC-ACCEPTOR-4046 11 T -HUMALBGC-ACCEPTOR-4046 12 T -HUMALBGC-ACCEPTOR-4046 13 C -HUMALBGC-ACCEPTOR-4046 14 T -HUMALBGC-ACCEPTOR-4046 15 A -HUMALBGC-ACCEPTOR-4046 16 C -HUMALBGC-ACCEPTOR-4046 17 A -HUMALBGC-ACCEPTOR-4046 18 T -HUMALBGC-ACCEPTOR-4046 19 C -HUMALBGC-ACCEPTOR-4046 20 C -HUMALBGC-ACCEPTOR-4046 21 T -HUMALBGC-ACCEPTOR-4046 22 T -HUMALBGC-ACCEPTOR-4046 23 T -HUMALBGC-ACCEPTOR-4046 24 G -HUMALBGC-ACCEPTOR-4046 25 T -HUMALBGC-ACCEPTOR-4046 26 T -HUMALBGC-ACCEPTOR-4046 27 T -HUMALBGC-ACCEPTOR-4046 28 C -HUMALBGC-ACCEPTOR-4046 29 A -HUMALBGC-ACCEPTOR-4046 30 G -HUMALBGC-ACCEPTOR-4046 31 G -HUMALBGC-ACCEPTOR-4046 32 G -HUMALBGC-ACCEPTOR-4046 33 T -HUMALBGC-ACCEPTOR-4046 34 G -HUMALBGC-ACCEPTOR-4046 35 T -HUMALBGC-ACCEPTOR-4046 36 T -HUMALBGC-ACCEPTOR-4046 37 G -HUMALBGC-ACCEPTOR-4046 38 A -HUMALBGC-ACCEPTOR-4046 39 T -HUMALBGC-ACCEPTOR-4046 40 T -HUMALBGC-ACCEPTOR-4046 41 G -HUMALBGC-ACCEPTOR-4046 42 C -HUMALBGC-ACCEPTOR-4046 43 C -HUMALBGC-ACCEPTOR-4046 44 T -HUMALBGC-ACCEPTOR-4046 45 T -HUMALBGC-ACCEPTOR-4046 46 T -HUMALBGC-ACCEPTOR-4046 47 G -HUMALBGC-ACCEPTOR-4046 48 C -HUMALBGC-ACCEPTOR-4046 49 T -HUMALBGC-ACCEPTOR-4046 50 C -HUMALBGC-ACCEPTOR-4046 51 A -HUMALBGC-ACCEPTOR-4046 52 G -HUMALBGC-ACCEPTOR-4046 53 T -HUMALBGC-ACCEPTOR-4046 54 A -HUMALBGC-ACCEPTOR-4046 55 T -HUMALBGC-ACCEPTOR-4046 56 C -HUMALBGC-ACCEPTOR-4046 57 T -HUMALBGC-ACCEPTOR-4046 58 T -HUMALBGC-ACCEPTOR-4046 59 C -HUMALBGC-ACCEPTOR-4046 60 A -HUMALBGC-ACCEPTOR-6011 1 A -HUMALBGC-ACCEPTOR-6011 2 G -HUMALBGC-ACCEPTOR-6011 3 C -HUMALBGC-ACCEPTOR-6011 4 A -HUMALBGC-ACCEPTOR-6011 5 A -HUMALBGC-ACCEPTOR-6011 6 C -HUMALBGC-ACCEPTOR-6011 7 T -HUMALBGC-ACCEPTOR-6011 8 G -HUMALBGC-ACCEPTOR-6011 9 A -HUMALBGC-ACCEPTOR-6011 10 A -HUMALBGC-ACCEPTOR-6011 11 A -HUMALBGC-ACCEPTOR-6011 12 C -HUMALBGC-ACCEPTOR-6011 13 C -HUMALBGC-ACCEPTOR-6011 14 T -HUMALBGC-ACCEPTOR-6011 15 G -HUMALBGC-ACCEPTOR-6011 16 C -HUMALBGC-ACCEPTOR-6011 17 T -HUMALBGC-ACCEPTOR-6011 18 T -HUMALBGC-ACCEPTOR-6011 19 T -HUMALBGC-ACCEPTOR-6011 20 C -HUMALBGC-ACCEPTOR-6011 21 T -HUMALBGC-ACCEPTOR-6011 22 T -HUMALBGC-ACCEPTOR-6011 23 C -HUMALBGC-ACCEPTOR-6011 24 C -HUMALBGC-ACCEPTOR-6011 25 A -HUMALBGC-ACCEPTOR-6011 26 T -HUMALBGC-ACCEPTOR-6011 27 T -HUMALBGC-ACCEPTOR-6011 28 T -HUMALBGC-ACCEPTOR-6011 29 A -HUMALBGC-ACCEPTOR-6011 30 G -HUMALBGC-ACCEPTOR-6011 31 C -HUMALBGC-ACCEPTOR-6011 32 A -HUMALBGC-ACCEPTOR-6011 33 T -HUMALBGC-ACCEPTOR-6011 34 A -HUMALBGC-ACCEPTOR-6011 35 C -HUMALBGC-ACCEPTOR-6011 36 C -HUMALBGC-ACCEPTOR-6011 37 C -HUMALBGC-ACCEPTOR-6011 38 T -HUMALBGC-ACCEPTOR-6011 39 T -HUMALBGC-ACCEPTOR-6011 40 T -HUMALBGC-ACCEPTOR-6011 41 T -HUMALBGC-ACCEPTOR-6011 42 T -HUMALBGC-ACCEPTOR-6011 43 G -HUMALBGC-ACCEPTOR-6011 44 G -HUMALBGC-ACCEPTOR-6011 45 A -HUMALBGC-ACCEPTOR-6011 46 G -HUMALBGC-ACCEPTOR-6011 47 A -HUMALBGC-ACCEPTOR-6011 48 C -HUMALBGC-ACCEPTOR-6011 49 A -HUMALBGC-ACCEPTOR-6011 50 A -HUMALBGC-ACCEPTOR-6011 51 A -HUMALBGC-ACCEPTOR-6011 52 T -HUMALBGC-ACCEPTOR-6011 53 T 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28 T -HUMALBGC-ACCEPTOR-6772 29 A -HUMALBGC-ACCEPTOR-6772 30 G -HUMALBGC-ACCEPTOR-6772 31 A -HUMALBGC-ACCEPTOR-6772 32 T -HUMALBGC-ACCEPTOR-6772 33 A -HUMALBGC-ACCEPTOR-6772 34 C -HUMALBGC-ACCEPTOR-6772 35 T -HUMALBGC-ACCEPTOR-6772 36 T -HUMALBGC-ACCEPTOR-6772 37 A -HUMALBGC-ACCEPTOR-6772 38 T -HUMALBGC-ACCEPTOR-6772 39 A -HUMALBGC-ACCEPTOR-6772 40 T -HUMALBGC-ACCEPTOR-6772 41 G -HUMALBGC-ACCEPTOR-6772 42 A -HUMALBGC-ACCEPTOR-6772 43 A -HUMALBGC-ACCEPTOR-6772 44 A -HUMALBGC-ACCEPTOR-6772 45 T -HUMALBGC-ACCEPTOR-6772 46 T -HUMALBGC-ACCEPTOR-6772 47 G -HUMALBGC-ACCEPTOR-6772 48 C -HUMALBGC-ACCEPTOR-6772 49 C -HUMALBGC-ACCEPTOR-6772 50 A -HUMALBGC-ACCEPTOR-6772 51 G -HUMALBGC-ACCEPTOR-6772 52 A -HUMALBGC-ACCEPTOR-6772 53 A -HUMALBGC-ACCEPTOR-6772 54 G -HUMALBGC-ACCEPTOR-6772 55 A -HUMALBGC-ACCEPTOR-6772 56 C -HUMALBGC-ACCEPTOR-6772 57 A -HUMALBGC-ACCEPTOR-6772 58 T -HUMALBGC-ACCEPTOR-6772 59 C -HUMALBGC-ACCEPTOR-6772 60 C -HUMALBGC-ACCEPTOR-7729 1 T -HUMALBGC-ACCEPTOR-7729 2 A -HUMALBGC-ACCEPTOR-7729 3 A -HUMALBGC-ACCEPTOR-7729 4 T -HUMALBGC-ACCEPTOR-7729 5 T -HUMALBGC-ACCEPTOR-7729 6 T -HUMALBGC-ACCEPTOR-7729 7 T -HUMALBGC-ACCEPTOR-7729 8 C -HUMALBGC-ACCEPTOR-7729 9 A -HUMALBGC-ACCEPTOR-7729 10 T -HUMALBGC-ACCEPTOR-7729 11 C -HUMALBGC-ACCEPTOR-7729 12 A -HUMALBGC-ACCEPTOR-7729 13 A -HUMALBGC-ACCEPTOR-7729 14 A -HUMALBGC-ACCEPTOR-7729 15 T -HUMALBGC-ACCEPTOR-7729 16 T -HUMALBGC-ACCEPTOR-7729 17 A -HUMALBGC-ACCEPTOR-7729 18 T -HUMALBGC-ACCEPTOR-7729 19 T -HUMALBGC-ACCEPTOR-7729 20 C -HUMALBGC-ACCEPTOR-7729 21 C -HUMALBGC-ACCEPTOR-7729 22 T -HUMALBGC-ACCEPTOR-7729 23 T -HUMALBGC-ACCEPTOR-7729 24 T -HUMALBGC-ACCEPTOR-7729 25 T -HUMALBGC-ACCEPTOR-7729 26 T -HUMALBGC-ACCEPTOR-7729 27 G -HUMALBGC-ACCEPTOR-7729 28 T -HUMALBGC-ACCEPTOR-7729 29 A -HUMALBGC-ACCEPTOR-7729 30 G -HUMALBGC-ACCEPTOR-7729 31 C -HUMALBGC-ACCEPTOR-7729 32 T -HUMALBGC-ACCEPTOR-7729 33 C -HUMALBGC-ACCEPTOR-7729 34 G -HUMALBGC-ACCEPTOR-7729 35 A -HUMALBGC-ACCEPTOR-7729 36 T -HUMALBGC-ACCEPTOR-7729 37 G -HUMALBGC-ACCEPTOR-7729 38 A -HUMALBGC-ACCEPTOR-7729 39 A -HUMALBGC-ACCEPTOR-7729 40 C -HUMALBGC-ACCEPTOR-7729 41 T -HUMALBGC-ACCEPTOR-7729 42 T -HUMALBGC-ACCEPTOR-7729 43 C -HUMALBGC-ACCEPTOR-7729 44 G -HUMALBGC-ACCEPTOR-7729 45 G -HUMALBGC-ACCEPTOR-7729 46 G -HUMALBGC-ACCEPTOR-7729 47 A -HUMALBGC-ACCEPTOR-7729 48 T -HUMALBGC-ACCEPTOR-7729 49 G -HUMALBGC-ACCEPTOR-7729 50 A -HUMALBGC-ACCEPTOR-7729 51 A -HUMALBGC-ACCEPTOR-7729 52 G -HUMALBGC-ACCEPTOR-7729 53 G -HUMALBGC-ACCEPTOR-7729 54 G -HUMALBGC-ACCEPTOR-7729 55 A -HUMALBGC-ACCEPTOR-7729 56 A -HUMALBGC-ACCEPTOR-7729 57 G -HUMALBGC-ACCEPTOR-7729 58 G -HUMALBGC-ACCEPTOR-7729 59 C -HUMALBGC-ACCEPTOR-7729 60 T -HUMALBGC-ACCEPTOR-9414 1 T -HUMALBGC-ACCEPTOR-9414 2 A -HUMALBGC-ACCEPTOR-9414 3 C -HUMALBGC-ACCEPTOR-9414 4 C -HUMALBGC-ACCEPTOR-9414 5 A -HUMALBGC-ACCEPTOR-9414 6 T -HUMALBGC-ACCEPTOR-9414 7 T -HUMALBGC-ACCEPTOR-9414 8 T -HUMALBGC-ACCEPTOR-9414 9 T -HUMALBGC-ACCEPTOR-9414 10 G -HUMALBGC-ACCEPTOR-9414 11 A -HUMALBGC-ACCEPTOR-9414 12 T -HUMALBGC-ACCEPTOR-9414 13 T -HUMALBGC-ACCEPTOR-9414 14 G -HUMALBGC-ACCEPTOR-9414 15 G -HUMALBGC-ACCEPTOR-9414 16 C -HUMALBGC-ACCEPTOR-9414 17 G -HUMALBGC-ACCEPTOR-9414 18 A -HUMALBGC-ACCEPTOR-9414 19 T -HUMALBGC-ACCEPTOR-9414 20 T -HUMALBGC-ACCEPTOR-9414 21 T -HUMALBGC-ACCEPTOR-9414 22 T -HUMALBGC-ACCEPTOR-9414 23 C -HUMALBGC-ACCEPTOR-9414 24 T -HUMALBGC-ACCEPTOR-9414 25 T -HUMALBGC-ACCEPTOR-9414 26 T -HUMALBGC-ACCEPTOR-9414 27 T -HUMALBGC-ACCEPTOR-9414 28 T -HUMALBGC-ACCEPTOR-9414 29 A -HUMALBGC-ACCEPTOR-9414 30 G -HUMALBGC-ACCEPTOR-9414 31 G -HUMALBGC-ACCEPTOR-9414 32 G -HUMALBGC-ACCEPTOR-9414 33 C -HUMALBGC-ACCEPTOR-9414 34 A -HUMALBGC-ACCEPTOR-9414 35 G -HUMALBGC-ACCEPTOR-9414 36 T -HUMALBGC-ACCEPTOR-9414 37 A -HUMALBGC-ACCEPTOR-9414 38 G -HUMALBGC-ACCEPTOR-9414 39 C -HUMALBGC-ACCEPTOR-9414 40 T -HUMALBGC-ACCEPTOR-9414 41 C -HUMALBGC-ACCEPTOR-9414 42 G -HUMALBGC-ACCEPTOR-9414 43 C -HUMALBGC-ACCEPTOR-9414 44 C -HUMALBGC-ACCEPTOR-9414 45 T -HUMALBGC-ACCEPTOR-9414 46 G -HUMALBGC-ACCEPTOR-9414 47 A -HUMALBGC-ACCEPTOR-9414 48 G -HUMALBGC-ACCEPTOR-9414 49 C -HUMALBGC-ACCEPTOR-9414 50 C -HUMALBGC-ACCEPTOR-9414 51 A -HUMALBGC-ACCEPTOR-9414 52 G -HUMALBGC-ACCEPTOR-9414 53 A -HUMALBGC-ACCEPTOR-9414 54 G -HUMALBGC-ACCEPTOR-9414 55 A -HUMALBGC-ACCEPTOR-9414 56 T -HUMALBGC-ACCEPTOR-9414 57 T -HUMALBGC-ACCEPTOR-9414 58 T -HUMALBGC-ACCEPTOR-9414 59 C -HUMALBGC-ACCEPTOR-9414 60 C -HUMALBGC-DONOR-11052 1 T -HUMALBGC-DONOR-11052 2 G -HUMALBGC-DONOR-11052 3 C -HUMALBGC-DONOR-11052 4 T -HUMALBGC-DONOR-11052 5 G -HUMALBGC-DONOR-11052 6 A -HUMALBGC-DONOR-11052 7 G -HUMALBGC-DONOR-11052 8 G -HUMALBGC-DONOR-11052 9 C -HUMALBGC-DONOR-11052 10 A -HUMALBGC-DONOR-11052 11 A -HUMALBGC-DONOR-11052 12 A -HUMALBGC-DONOR-11052 13 G -HUMALBGC-DONOR-11052 14 G -HUMALBGC-DONOR-11052 15 A -HUMALBGC-DONOR-11052 16 T -HUMALBGC-DONOR-11052 17 G -HUMALBGC-DONOR-11052 18 T -HUMALBGC-DONOR-11052 19 C -HUMALBGC-DONOR-11052 20 T -HUMALBGC-DONOR-11052 21 T 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-HUMALBGC-DONOR-11052 59 A -HUMALBGC-DONOR-11052 60 G -HUMALBGC-DONOR-13770 1 G -HUMALBGC-DONOR-13770 2 C -HUMALBGC-DONOR-13770 3 A -HUMALBGC-DONOR-13770 4 G -HUMALBGC-DONOR-13770 5 C -HUMALBGC-DONOR-13770 6 T -HUMALBGC-DONOR-13770 7 T -HUMALBGC-DONOR-13770 8 G -HUMALBGC-DONOR-13770 9 G -HUMALBGC-DONOR-13770 10 A -HUMALBGC-DONOR-13770 11 G -HUMALBGC-DONOR-13770 12 A -HUMALBGC-DONOR-13770 13 G -HUMALBGC-DONOR-13770 14 T -HUMALBGC-DONOR-13770 15 A -HUMALBGC-DONOR-13770 16 C -HUMALBGC-DONOR-13770 17 A -HUMALBGC-DONOR-13770 18 A -HUMALBGC-DONOR-13770 19 A -HUMALBGC-DONOR-13770 20 T -HUMALBGC-DONOR-13770 21 T -HUMALBGC-DONOR-13770 22 C -HUMALBGC-DONOR-13770 23 C -HUMALBGC-DONOR-13770 24 A -HUMALBGC-DONOR-13770 25 G -HUMALBGC-DONOR-13770 26 A -HUMALBGC-DONOR-13770 27 A -HUMALBGC-DONOR-13770 28 T -HUMALBGC-DONOR-13770 29 G -HUMALBGC-DONOR-13770 30 C -HUMALBGC-DONOR-13770 31 G -HUMALBGC-DONOR-13770 32 T -HUMALBGC-DONOR-13770 33 A -HUMALBGC-DONOR-13770 34 A -HUMALBGC-DONOR-13770 35 G -HUMALBGC-DONOR-13770 36 T -HUMALBGC-DONOR-13770 37 A -HUMALBGC-DONOR-13770 38 A -HUMALBGC-DONOR-13770 39 T -HUMALBGC-DONOR-13770 40 T -HUMALBGC-DONOR-13770 41 T -HUMALBGC-DONOR-13770 42 T -HUMALBGC-DONOR-13770 43 T -HUMALBGC-DONOR-13770 44 A -HUMALBGC-DONOR-13770 45 T -HUMALBGC-DONOR-13770 46 T -HUMALBGC-DONOR-13770 47 G -HUMALBGC-DONOR-13770 48 A -HUMALBGC-DONOR-13770 49 C -HUMALBGC-DONOR-13770 50 T -HUMALBGC-DONOR-13770 51 G -HUMALBGC-DONOR-13770 52 A -HUMALBGC-DONOR-13770 53 T -HUMALBGC-DONOR-13770 54 T -HUMALBGC-DONOR-13770 55 T -HUMALBGC-DONOR-13770 56 T -HUMALBGC-DONOR-13770 57 T -HUMALBGC-DONOR-13770 58 T -HUMALBGC-DONOR-13770 59 T -HUMALBGC-DONOR-13770 60 T -HUMALBGC-DONOR-17726 1 C -HUMALBGC-DONOR-17726 2 T -HUMALBGC-DONOR-17726 3 T -HUMALBGC-DONOR-17726 4 A -HUMALBGC-DONOR-17726 5 T -HUMALBGC-DONOR-17726 6 A -HUMALBGC-DONOR-17726 7 A -HUMALBGC-DONOR-17726 8 C -HUMALBGC-DONOR-17726 9 A -HUMALBGC-DONOR-17726 10 T -HUMALBGC-DONOR-17726 11 C -HUMALBGC-DONOR-17726 12 A -HUMALBGC-DONOR-17726 13 C -HUMALBGC-DONOR-17726 14 A -HUMALBGC-DONOR-17726 15 T -HUMALBGC-DONOR-17726 16 T -HUMALBGC-DONOR-17726 17 T -HUMALBGC-DONOR-17726 18 A -HUMALBGC-DONOR-17726 19 A -HUMALBGC-DONOR-17726 20 A -HUMALBGC-DONOR-17726 21 A -HUMALBGC-DONOR-17726 22 G -HUMALBGC-DONOR-17726 23 C -HUMALBGC-DONOR-17726 24 A -HUMALBGC-DONOR-17726 25 T -HUMALBGC-DONOR-17726 26 C -HUMALBGC-DONOR-17726 27 T -HUMALBGC-DONOR-17726 28 C -HUMALBGC-DONOR-17726 29 A -HUMALBGC-DONOR-17726 30 G -HUMALBGC-DONOR-17726 31 G -HUMALBGC-DONOR-17726 32 T -HUMALBGC-DONOR-17726 33 A -HUMALBGC-DONOR-17726 34 A -HUMALBGC-DONOR-17726 35 C -HUMALBGC-DONOR-17726 36 T -HUMALBGC-DONOR-17726 37 A -HUMALBGC-DONOR-17726 38 T -HUMALBGC-DONOR-17726 39 A -HUMALBGC-DONOR-17726 40 T -HUMALBGC-DONOR-17726 41 T -HUMALBGC-DONOR-17726 42 T -HUMALBGC-DONOR-17726 43 T -HUMALBGC-DONOR-17726 44 G -HUMALBGC-DONOR-17726 45 A -HUMALBGC-DONOR-17726 46 A -HUMALBGC-DONOR-17726 47 T -HUMALBGC-DONOR-17726 48 T -HUMALBGC-DONOR-17726 49 T -HUMALBGC-DONOR-17726 50 T -HUMALBGC-DONOR-17726 51 T -HUMALBGC-DONOR-17726 52 T -HUMALBGC-DONOR-17726 53 A -HUMALBGC-DONOR-17726 54 A -HUMALBGC-DONOR-17726 55 A -HUMALBGC-DONOR-17726 56 A -HUMALBGC-DONOR-17726 57 A -HUMALBGC-DONOR-17726 58 A -HUMALBGC-DONOR-17726 59 G -HUMALBGC-DONOR-17726 60 T -HUMALBGC-DONOR-1825 1 A -HUMALBGC-DONOR-1825 2 T -HUMALBGC-DONOR-1825 3 T -HUMALBGC-DONOR-1825 4 C -HUMALBGC-DONOR-1825 5 C -HUMALBGC-DONOR-1825 6 A -HUMALBGC-DONOR-1825 7 G -HUMALBGC-DONOR-1825 8 G -HUMALBGC-DONOR-1825 9 G -HUMALBGC-DONOR-1825 10 G -HUMALBGC-DONOR-1825 11 T -HUMALBGC-DONOR-1825 12 G -HUMALBGC-DONOR-1825 13 T -HUMALBGC-DONOR-1825 14 G -HUMALBGC-DONOR-1825 15 T -HUMALBGC-DONOR-1825 16 T -HUMALBGC-DONOR-1825 17 T -HUMALBGC-DONOR-1825 18 C -HUMALBGC-DONOR-1825 19 G -HUMALBGC-DONOR-1825 20 T -HUMALBGC-DONOR-1825 21 C -HUMALBGC-DONOR-1825 22 G -HUMALBGC-DONOR-1825 23 A -HUMALBGC-DONOR-1825 24 G -HUMALBGC-DONOR-1825 25 A -HUMALBGC-DONOR-1825 26 T -HUMALBGC-DONOR-1825 27 G -HUMALBGC-DONOR-1825 28 C -HUMALBGC-DONOR-1825 29 A -HUMALBGC-DONOR-1825 30 C -HUMALBGC-DONOR-1825 31 G -HUMALBGC-DONOR-1825 32 T -HUMALBGC-DONOR-1825 33 A -HUMALBGC-DONOR-1825 34 A -HUMALBGC-DONOR-1825 35 G -HUMALBGC-DONOR-1825 36 A -HUMALBGC-DONOR-1825 37 A -HUMALBGC-DONOR-1825 38 A -HUMALBGC-DONOR-1825 39 T -HUMALBGC-DONOR-1825 40 C -HUMALBGC-DONOR-1825 41 C -HUMALBGC-DONOR-1825 42 A -HUMALBGC-DONOR-1825 43 T -HUMALBGC-DONOR-1825 44 T -HUMALBGC-DONOR-1825 45 T -HUMALBGC-DONOR-1825 46 T -HUMALBGC-DONOR-1825 47 T -HUMALBGC-DONOR-1825 48 C -HUMALBGC-DONOR-1825 49 T -HUMALBGC-DONOR-1825 50 A -HUMALBGC-DONOR-1825 51 T -HUMALBGC-DONOR-1825 52 T -HUMALBGC-DONOR-1825 53 G -HUMALBGC-DONOR-1825 54 T -HUMALBGC-DONOR-1825 55 T -HUMALBGC-DONOR-1825 56 C -HUMALBGC-DONOR-1825 57 A -HUMALBGC-DONOR-1825 58 A -HUMALBGC-DONOR-1825 59 C -HUMALBGC-DONOR-1825 60 T -HUMALBGC-DONOR-2592 1 A -HUMALBGC-DONOR-2592 2 G -HUMALBGC-DONOR-2592 3 A -HUMALBGC-DONOR-2592 4 T -HUMALBGC-DONOR-2592 5 T -HUMALBGC-DONOR-2592 6 T -HUMALBGC-DONOR-2592 7 G -HUMALBGC-DONOR-2592 8 G -HUMALBGC-DONOR-2592 9 G -HUMALBGC-DONOR-2592 10 A -HUMALBGC-DONOR-2592 11 G -HUMALBGC-DONOR-2592 12 A -HUMALBGC-DONOR-2592 13 A -HUMALBGC-DONOR-2592 14 G -HUMALBGC-DONOR-2592 15 A -HUMALBGC-DONOR-2592 16 A -HUMALBGC-DONOR-2592 17 A -HUMALBGC-DONOR-2592 18 A -HUMALBGC-DONOR-2592 19 T -HUMALBGC-DONOR-2592 20 T -HUMALBGC-DONOR-2592 21 T -HUMALBGC-DONOR-2592 22 C -HUMALBGC-DONOR-2592 23 A -HUMALBGC-DONOR-2592 24 A -HUMALBGC-DONOR-2592 25 A -HUMALBGC-DONOR-2592 26 G -HUMALBGC-DONOR-2592 27 C -HUMALBGC-DONOR-2592 28 C -HUMALBGC-DONOR-2592 29 T -HUMALBGC-DONOR-2592 30 T -HUMALBGC-DONOR-2592 31 G -HUMALBGC-DONOR-2592 32 T -HUMALBGC-DONOR-2592 33 A -HUMALBGC-DONOR-2592 34 A -HUMALBGC-DONOR-2592 35 G -HUMALBGC-DONOR-2592 36 T -HUMALBGC-DONOR-2592 37 T -HUMALBGC-DONOR-2592 38 A -HUMALBGC-DONOR-2592 39 A -HUMALBGC-DONOR-2592 40 A -HUMALBGC-DONOR-2592 41 A -HUMALBGC-DONOR-2592 42 T -HUMALBGC-DONOR-2592 43 A -HUMALBGC-DONOR-2592 44 T -HUMALBGC-DONOR-2592 45 T -HUMALBGC-DONOR-2592 46 G -HUMALBGC-DONOR-2592 47 A -HUMALBGC-DONOR-2592 48 T -HUMALBGC-DONOR-2592 49 G -HUMALBGC-DONOR-2592 50 A -HUMALBGC-DONOR-2592 51 A -HUMALBGC-DONOR-2592 52 T -HUMALBGC-DONOR-2592 53 C -HUMALBGC-DONOR-2592 54 A -HUMALBGC-DONOR-2592 55 A -HUMALBGC-DONOR-2592 56 A -HUMALBGC-DONOR-2592 57 T -HUMALBGC-DONOR-2592 58 T -HUMALBGC-DONOR-2592 59 T -HUMALBGC-DONOR-2592 60 A -HUMALBGC-DONOR-6905 2 C -HUMALBGC-DONOR-6905 3 T -HUMALBGC-DONOR-6905 4 G -HUMALBGC-DONOR-6905 1 G -HUMALBGC-DONOR-6905 5 A -HUMALBGC-DONOR-6905 6 T -HUMALBGC-DONOR-6905 7 A -HUMALBGC-DONOR-6905 8 A -HUMALBGC-DONOR-6905 9 A -HUMALBGC-DONOR-6905 10 G -HUMALBGC-DONOR-6905 11 C -HUMALBGC-DONOR-6905 12 T -HUMALBGC-DONOR-6905 13 G -HUMALBGC-DONOR-6905 14 C -HUMALBGC-DONOR-6905 15 C -HUMALBGC-DONOR-6905 16 T -HUMALBGC-DONOR-6905 17 G -HUMALBGC-DONOR-6905 18 C -HUMALBGC-DONOR-6905 19 C -HUMALBGC-DONOR-6905 20 T -HUMALBGC-DONOR-6905 21 G -HUMALBGC-DONOR-6905 22 T -HUMALBGC-DONOR-6905 23 T -HUMALBGC-DONOR-6905 24 G -HUMALBGC-DONOR-6905 25 C -HUMALBGC-DONOR-6905 26 C -HUMALBGC-DONOR-6905 27 A -HUMALBGC-DONOR-6905 28 A -HUMALBGC-DONOR-6905 29 A -HUMALBGC-DONOR-6905 30 G -HUMALBGC-DONOR-6905 31 G -HUMALBGC-DONOR-6905 32 T -HUMALBGC-DONOR-6905 33 A -HUMALBGC-DONOR-6905 34 T -HUMALBGC-DONOR-6905 35 T -HUMALBGC-DONOR-6905 36 A -HUMALBGC-DONOR-6905 37 T -HUMALBGC-DONOR-6905 38 G -HUMALBGC-DONOR-6905 39 C -HUMALBGC-DONOR-6905 40 A -HUMALBGC-DONOR-6905 41 A -HUMALBGC-DONOR-6905 42 A -HUMALBGC-DONOR-6905 43 A -HUMALBGC-DONOR-6905 44 G -HUMALBGC-DONOR-6905 45 A -HUMALBGC-DONOR-6905 46 A -HUMALBGC-DONOR-6905 47 T -HUMALBGC-DONOR-6905 48 A -HUMALBGC-DONOR-6905 49 G -HUMALBGC-DONOR-6905 50 A -HUMALBGC-DONOR-6905 51 A -HUMALBGC-DONOR-6905 52 A -HUMALBGC-DONOR-6905 53 A -HUMALBGC-DONOR-6905 54 A -HUMALBGC-DONOR-6905 55 A -HUMALBGC-DONOR-6905 56 A -HUMALBGC-DONOR-6905 57 G -HUMALBGC-DONOR-6905 58 A -HUMALBGC-DONOR-6905 59 G -HUMALBGC-DONOR-6905 60 T -HUMALBGC-DONOR-7827 1 A -HUMALBGC-DONOR-7827 2 A -HUMALBGC-DONOR-7827 3 A -HUMALBGC-DONOR-7827 4 A -HUMALBGC-DONOR-7827 5 T -HUMALBGC-DONOR-7827 6 T -HUMALBGC-DONOR-7827 7 T -HUMALBGC-DONOR-7827 8 G -HUMALBGC-DONOR-7827 9 G -HUMALBGC-DONOR-7827 10 A -HUMALBGC-DONOR-7827 11 G -HUMALBGC-DONOR-7827 12 A -HUMALBGC-DONOR-7827 13 A -HUMALBGC-DONOR-7827 14 A -HUMALBGC-DONOR-7827 15 G -HUMALBGC-DONOR-7827 16 A -HUMALBGC-DONOR-7827 17 G -HUMALBGC-DONOR-7827 18 C -HUMALBGC-DONOR-7827 19 T -HUMALBGC-DONOR-7827 20 T -HUMALBGC-DONOR-7827 21 T -HUMALBGC-DONOR-7827 22 C -HUMALBGC-DONOR-7827 23 A -HUMALBGC-DONOR-7827 24 A -HUMALBGC-DONOR-7827 25 A -HUMALBGC-DONOR-7827 26 G -HUMALBGC-DONOR-7827 27 C -HUMALBGC-DONOR-7827 28 A -HUMALBGC-DONOR-7827 29 T -HUMALBGC-DONOR-7827 30 G -HUMALBGC-DONOR-7827 31 G -HUMALBGC-DONOR-7827 32 T -HUMALBGC-DONOR-7827 33 A -HUMALBGC-DONOR-7827 34 A -HUMALBGC-DONOR-7827 35 A -HUMALBGC-DONOR-7827 36 T -HUMALBGC-DONOR-7827 37 A -HUMALBGC-DONOR-7827 38 C -HUMALBGC-DONOR-7827 39 T -HUMALBGC-DONOR-7827 40 T -HUMALBGC-DONOR-7827 41 T -HUMALBGC-DONOR-7827 42 T -HUMALBGC-DONOR-7827 43 A -HUMALBGC-DONOR-7827 44 A -HUMALBGC-DONOR-7827 45 A -HUMALBGC-DONOR-7827 46 C -HUMALBGC-DONOR-7827 47 A -HUMALBGC-DONOR-7827 48 T -HUMALBGC-DONOR-7827 49 A -HUMALBGC-DONOR-7827 50 G -HUMALBGC-DONOR-7827 51 T -HUMALBGC-DONOR-7827 52 T -HUMALBGC-DONOR-7827 53 G -HUMALBGC-DONOR-7827 54 G -HUMALBGC-DONOR-7827 55 C -HUMALBGC-DONOR-7827 56 A -HUMALBGC-DONOR-7827 57 T -HUMALBGC-DONOR-7827 58 C -HUMALBGC-DONOR-7827 59 T -HUMALBGC-DONOR-7827 60 T -HUMALBGC-NEG-13921 1 T -HUMALBGC-NEG-13921 2 T -HUMALBGC-NEG-13921 3 G -HUMALBGC-NEG-13921 4 T -HUMALBGC-NEG-13921 5 C -HUMALBGC-NEG-13921 6 A -HUMALBGC-NEG-13921 7 A -HUMALBGC-NEG-13921 8 G -HUMALBGC-NEG-13921 9 A -HUMALBGC-NEG-13921 10 A -HUMALBGC-NEG-13921 11 A -HUMALBGC-NEG-13921 12 G -HUMALBGC-NEG-13921 13 A -HUMALBGC-NEG-13921 14 T -HUMALBGC-NEG-13921 15 A -HUMALBGC-NEG-13921 16 G -HUMALBGC-NEG-13921 17 G -HUMALBGC-NEG-13921 18 A -HUMALBGC-NEG-13921 19 G -HUMALBGC-NEG-13921 20 A -HUMALBGC-NEG-13921 21 G -HUMALBGC-NEG-13921 22 G -HUMALBGC-NEG-13921 23 A -HUMALBGC-NEG-13921 24 G -HUMALBGC-NEG-13921 25 A -HUMALBGC-NEG-13921 26 G -HUMALBGC-NEG-13921 27 A -HUMALBGC-NEG-13921 28 T -HUMALBGC-NEG-13921 29 A -HUMALBGC-NEG-13921 30 A -HUMALBGC-NEG-13921 31 A -HUMALBGC-NEG-13921 32 A -HUMALBGC-NEG-13921 33 T -HUMALBGC-NEG-13921 34 A -HUMALBGC-NEG-13921 35 G -HUMALBGC-NEG-13921 36 T -HUMALBGC-NEG-13921 37 T -HUMALBGC-NEG-13921 38 G -HUMALBGC-NEG-13921 39 A -HUMALBGC-NEG-13921 40 T -HUMALBGC-NEG-13921 41 G -HUMALBGC-NEG-13921 42 G -HUMALBGC-NEG-13921 43 G -HUMALBGC-NEG-13921 44 G -HUMALBGC-NEG-13921 45 T -HUMALBGC-NEG-13921 46 G -HUMALBGC-NEG-13921 47 G -HUMALBGC-NEG-13921 48 A -HUMALBGC-NEG-13921 49 G -HUMALBGC-NEG-13921 50 A -HUMALBGC-NEG-13921 51 G -HUMALBGC-NEG-13921 52 G -HUMALBGC-NEG-13921 53 T -HUMALBGC-NEG-13921 54 C -HUMALBGC-NEG-13921 55 T -HUMALBGC-NEG-13921 56 A -HUMALBGC-NEG-13921 57 T -HUMALBGC-NEG-13921 58 A -HUMALBGC-NEG-13921 59 T -HUMALBGC-NEG-13921 60 T -HUMALBGC-NEG-18481 1 T -HUMALBGC-NEG-18481 2 G -HUMALBGC-NEG-18481 3 C -HUMALBGC-NEG-18481 4 C -HUMALBGC-NEG-18481 5 A -HUMALBGC-NEG-18481 6 T -HUMALBGC-NEG-18481 7 T -HUMALBGC-NEG-18481 8 T -HUMALBGC-NEG-18481 9 T -HUMALBGC-NEG-18481 10 A -HUMALBGC-NEG-18481 11 C -HUMALBGC-NEG-18481 12 A -HUMALBGC-NEG-18481 13 A -HUMALBGC-NEG-18481 14 A -HUMALBGC-NEG-18481 15 T -HUMALBGC-NEG-18481 16 A -HUMALBGC-NEG-18481 17 A -HUMALBGC-NEG-18481 18 G -HUMALBGC-NEG-18481 19 A -HUMALBGC-NEG-18481 20 C -HUMALBGC-NEG-18481 21 T -HUMALBGC-NEG-18481 22 T -HUMALBGC-NEG-18481 23 A -HUMALBGC-NEG-18481 24 T -HUMALBGC-NEG-18481 25 A -HUMALBGC-NEG-18481 26 T -HUMALBGC-NEG-18481 27 T -HUMALBGC-NEG-18481 28 T -HUMALBGC-NEG-18481 29 G -HUMALBGC-NEG-18481 30 T -HUMALBGC-NEG-18481 31 C -HUMALBGC-NEG-18481 32 C -HUMALBGC-NEG-18481 33 T -HUMALBGC-NEG-18481 34 T -HUMALBGC-NEG-18481 35 T -HUMALBGC-NEG-18481 36 T -HUMALBGC-NEG-18481 37 G -HUMALBGC-NEG-18481 38 T -HUMALBGC-NEG-18481 39 T -HUMALBGC-NEG-18481 40 T -HUMALBGC-NEG-18481 41 T -HUMALBGC-NEG-18481 42 T -HUMALBGC-NEG-18481 43 C -HUMALBGC-NEG-18481 44 A -HUMALBGC-NEG-18481 45 G -HUMALBGC-NEG-18481 46 C -HUMALBGC-NEG-18481 47 C -HUMALBGC-NEG-18481 48 T -HUMALBGC-NEG-18481 49 A -HUMALBGC-NEG-18481 50 C -HUMALBGC-NEG-18481 51 C -HUMALBGC-NEG-18481 52 A -HUMALBGC-NEG-18481 53 T -HUMALBGC-NEG-18481 54 G -HUMALBGC-NEG-18481 55 A -HUMALBGC-NEG-18481 56 G -HUMALBGC-NEG-18481 57 A -HUMALBGC-NEG-18481 58 A -HUMALBGC-NEG-18481 59 T -HUMALBGC-NEG-18481 60 A -HUMALBGC-NEG-241 1 C -HUMALBGC-NEG-241 2 A -HUMALBGC-NEG-241 3 G -HUMALBGC-NEG-241 4 A -HUMALBGC-NEG-241 5 A -HUMALBGC-NEG-241 6 A -HUMALBGC-NEG-241 7 G -HUMALBGC-NEG-241 8 A -HUMALBGC-NEG-241 9 C -HUMALBGC-NEG-241 10 A -HUMALBGC-NEG-241 11 A -HUMALBGC-NEG-241 12 G -HUMALBGC-NEG-241 13 T -HUMALBGC-NEG-241 14 C -HUMALBGC-NEG-241 15 T -HUMALBGC-NEG-241 16 G -HUMALBGC-NEG-241 17 G -HUMALBGC-NEG-241 18 T -HUMALBGC-NEG-241 19 A -HUMALBGC-NEG-241 20 T -HUMALBGC-NEG-241 21 T -HUMALBGC-NEG-241 22 T -HUMALBGC-NEG-241 23 C -HUMALBGC-NEG-241 24 C -HUMALBGC-NEG-241 25 T -HUMALBGC-NEG-241 26 T -HUMALBGC-NEG-241 27 T -HUMALBGC-NEG-241 28 C -HUMALBGC-NEG-241 29 A -HUMALBGC-NEG-241 30 G -HUMALBGC-NEG-241 31 G -HUMALBGC-NEG-241 32 A -HUMALBGC-NEG-241 33 C -HUMALBGC-NEG-241 34 T -HUMALBGC-NEG-241 35 C -HUMALBGC-NEG-241 36 C -HUMALBGC-NEG-241 37 C -HUMALBGC-NEG-241 38 T -HUMALBGC-NEG-241 39 T -HUMALBGC-NEG-241 40 G -HUMALBGC-NEG-241 41 A -HUMALBGC-NEG-241 42 G -HUMALBGC-NEG-241 43 T -HUMALBGC-NEG-241 44 C -HUMALBGC-NEG-241 45 A -HUMALBGC-NEG-241 46 T -HUMALBGC-NEG-241 47 T -HUMALBGC-NEG-241 48 A -HUMALBGC-NEG-241 49 A -HUMALBGC-NEG-241 50 A -HUMALBGC-NEG-241 51 A -HUMALBGC-NEG-241 52 A -HUMALBGC-NEG-241 53 A -HUMALBGC-NEG-241 54 A -HUMALBGC-NEG-241 55 A -HUMALBGC-NEG-241 56 T -HUMALBGC-NEG-241 57 C -HUMALBGC-NEG-241 58 T -HUMALBGC-NEG-241 59 T -HUMALBGC-NEG-241 60 C -HUMALBGC-NEG-9361 1 C -HUMALBGC-NEG-9361 2 T -HUMALBGC-NEG-9361 3 T -HUMALBGC-NEG-9361 4 A -HUMALBGC-NEG-9361 5 T -HUMALBGC-NEG-9361 6 G -HUMALBGC-NEG-9361 7 A -HUMALBGC-NEG-9361 8 G -HUMALBGC-NEG-9361 9 A -HUMALBGC-NEG-9361 10 A -HUMALBGC-NEG-9361 11 A -HUMALBGC-NEG-9361 12 T -HUMALBGC-NEG-9361 13 A -HUMALBGC-NEG-9361 14 G -HUMALBGC-NEG-9361 15 T -HUMALBGC-NEG-9361 16 A -HUMALBGC-NEG-9361 17 T -HUMALBGC-NEG-9361 18 T -HUMALBGC-NEG-9361 19 T -HUMALBGC-NEG-9361 20 G -HUMALBGC-NEG-9361 21 C -HUMALBGC-NEG-9361 22 C -HUMALBGC-NEG-9361 23 T -HUMALBGC-NEG-9361 24 A -HUMALBGC-NEG-9361 25 G -HUMALBGC-NEG-9361 26 T -HUMALBGC-NEG-9361 27 G -HUMALBGC-NEG-9361 28 T -HUMALBGC-NEG-9361 29 T -HUMALBGC-NEG-9361 30 T -HUMALBGC-NEG-9361 31 T -HUMALBGC-NEG-9361 32 C -HUMALBGC-NEG-9361 33 A -HUMALBGC-NEG-9361 34 T -HUMALBGC-NEG-9361 35 A -HUMALBGC-NEG-9361 36 T -HUMALBGC-NEG-9361 37 A -HUMALBGC-NEG-9361 38 A -HUMALBGC-NEG-9361 39 A -HUMALBGC-NEG-9361 40 A -HUMALBGC-NEG-9361 41 T -HUMALBGC-NEG-9361 42 A -HUMALBGC-NEG-9361 43 T -HUMALBGC-NEG-9361 44 C -HUMALBGC-NEG-9361 45 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-HUMALDA1-NEG-661 29 C -HUMALDA1-NEG-661 30 A -HUMALDA1-NEG-661 31 T -HUMALDA1-NEG-661 32 G -HUMALDA1-NEG-661 33 A -HUMALDA1-NEG-661 34 T -HUMALDA1-NEG-661 35 G -HUMALDA1-NEG-661 36 G -HUMALDA1-NEG-661 37 A -HUMALDA1-NEG-661 38 C -HUMALDA1-NEG-661 39 A -HUMALDA1-NEG-661 40 G -HUMALDA1-NEG-661 41 A -HUMALDA1-NEG-661 42 A -HUMALDA1-NEG-661 43 A -HUMALDA1-NEG-661 44 A -HUMALDA1-NEG-661 45 A -HUMALDA1-NEG-661 46 T -HUMALDA1-NEG-661 47 G -HUMALDA1-NEG-661 48 T -HUMALDA1-NEG-661 49 G -HUMALDA1-NEG-661 50 T -HUMALDA1-NEG-661 51 G -HUMALDA1-NEG-661 52 T -HUMALDA1-NEG-661 53 G -HUMALDA1-NEG-661 54 C -HUMALDA1-NEG-661 55 T -HUMALDA1-NEG-661 56 T -HUMALDA1-NEG-661 57 T -HUMALDA1-NEG-661 58 C -HUMALDA1-NEG-661 59 T -HUMALDA1-NEG-661 60 C -HUMALDAR-NEG-1081 1 A -HUMALDAR-NEG-1081 2 G -HUMALDAR-NEG-1081 3 G -HUMALDAR-NEG-1081 4 C -HUMALDAR-NEG-1081 5 C -HUMALDAR-NEG-1081 6 T -HUMALDAR-NEG-1081 7 G -HUMALDAR-NEG-1081 8 G -HUMALDAR-NEG-1081 9 G -HUMALDAR-NEG-1081 10 G -HUMALDAR-NEG-1081 11 C 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C -HUMASGPR1-NEG-661 19 T -HUMASGPR1-NEG-661 20 C -HUMASGPR1-NEG-661 21 G -HUMASGPR1-NEG-661 22 C -HUMASGPR1-NEG-661 23 T -HUMASGPR1-NEG-661 24 C -HUMASGPR1-NEG-661 25 C -HUMASGPR1-NEG-661 26 G -HUMASGPR1-NEG-661 27 G -HUMASGPR1-NEG-661 28 G -HUMASGPR1-NEG-661 29 A -HUMASGPR1-NEG-661 30 A -HUMASGPR1-NEG-661 31 G -HUMASGPR1-NEG-661 32 G -HUMASGPR1-NEG-661 33 C -HUMASGPR1-NEG-661 34 C -HUMASGPR1-NEG-661 35 T -HUMASGPR1-NEG-661 36 G -HUMASGPR1-NEG-661 37 G -HUMASGPR1-NEG-661 38 G -HUMASGPR1-NEG-661 39 C -HUMASGPR1-NEG-661 40 T -HUMASGPR1-NEG-661 41 G -HUMASGPR1-NEG-661 42 A -HUMASGPR1-NEG-661 43 C -HUMASGPR1-NEG-661 44 G -HUMASGPR1-NEG-661 45 C -HUMASGPR1-NEG-661 46 C -HUMASGPR1-NEG-661 47 G -HUMASGPR1-NEG-661 48 A -HUMASGPR1-NEG-661 49 C -HUMASGPR1-NEG-661 50 A -HUMASGPR1-NEG-661 51 A -HUMASGPR1-NEG-661 52 C -HUMASGPR1-NEG-661 53 T -HUMASGPR1-NEG-661 54 A -HUMASGPR1-NEG-661 55 C -HUMASGPR1-NEG-661 56 T -HUMASGPR1-NEG-661 57 G -HUMASGPR1-NEG-661 58 C -HUMASGPR1-NEG-661 59 C 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G -HUMASP-NEG-541 48 A -HUMASP-NEG-541 49 G -HUMASP-NEG-541 50 A -HUMASP-NEG-541 51 A -HUMASP-NEG-541 52 G -HUMASP-NEG-541 53 A -HUMASP-NEG-541 54 G -HUMASP-NEG-541 55 G -HUMASP-NEG-541 56 A -HUMASP-NEG-541 57 A -HUMASP-NEG-541 58 G -HUMASP-NEG-541 59 G -HUMASP-NEG-541 60 A -HUMATCT4-NEG-1561 1 C -HUMATCT4-NEG-1561 2 G -HUMATCT4-NEG-1561 3 T -HUMATCT4-NEG-1561 4 C -HUMATCT4-NEG-1561 5 C -HUMATCT4-NEG-1561 6 T -HUMATCT4-NEG-1561 7 C -HUMATCT4-NEG-1561 8 C -HUMATCT4-NEG-1561 9 C -HUMATCT4-NEG-1561 10 T -HUMATCT4-NEG-1561 11 A -HUMATCT4-NEG-1561 12 C -HUMATCT4-NEG-1561 13 A -HUMATCT4-NEG-1561 14 A -HUMATCT4-NEG-1561 15 T -HUMATCT4-NEG-1561 16 T -HUMATCT4-NEG-1561 17 T -HUMATCT4-NEG-1561 18 G -HUMATCT4-NEG-1561 19 C -HUMATCT4-NEG-1561 20 C -HUMATCT4-NEG-1561 21 A -HUMATCT4-NEG-1561 22 T -HUMATCT4-NEG-1561 23 T -HUMATCT4-NEG-1561 24 G -HUMATCT4-NEG-1561 25 T -HUMATCT4-NEG-1561 26 T -HUMATCT4-NEG-1561 27 T -HUMATCT4-NEG-1561 28 C -HUMATCT4-NEG-1561 29 T -HUMATCT4-NEG-1561 30 C -HUMATCT4-NEG-1561 31 C -HUMATCT4-NEG-1561 32 T -HUMATCT4-NEG-1561 33 G -HUMATCT4-NEG-1561 34 G -HUMATCT4-NEG-1561 35 G -HUMATCT4-NEG-1561 36 T -HUMATCT4-NEG-1561 37 T -HUMATCT4-NEG-1561 38 A -HUMATCT4-NEG-1561 39 G -HUMATCT4-NEG-1561 40 G -HUMATCT4-NEG-1561 41 C -HUMATCT4-NEG-1561 42 C -HUMATCT4-NEG-1561 43 C -HUMATCT4-NEG-1561 44 C -HUMATCT4-NEG-1561 45 G -HUMATCT4-NEG-1561 46 G -HUMATCT4-NEG-1561 47 C -HUMATCT4-NEG-1561 48 T -HUMATCT4-NEG-1561 49 T -HUMATCT4-NEG-1561 50 C -HUMATCT4-NEG-1561 51 A -HUMATCT4-NEG-1561 52 C -HUMATCT4-NEG-1561 53 T -HUMATCT4-NEG-1561 54 G -HUMATCT4-NEG-1561 55 G -HUMATCT4-NEG-1561 56 T -HUMATCT4-NEG-1561 57 T -HUMATCT4-NEG-1561 58 G -HUMATCT4-NEG-1561 59 A -HUMATCT4-NEG-1561 60 G -HUMATP-NEG-1981 1 T -HUMATP-NEG-1981 59 C -HUMATP-NEG-1981 58 G -HUMATP-NEG-1981 57 A -HUMATP-NEG-1981 56 A -HUMATP-NEG-1981 55 G -HUMATP-NEG-1981 54 T -HUMATP-NEG-1981 53 C -HUMATP-NEG-1981 52 T -HUMATP-NEG-1981 51 A -HUMATP-NEG-1981 50 G -HUMATP-NEG-1981 49 A -HUMATP-NEG-1981 48 C -HUMATP-NEG-1981 47 A -HUMATP-NEG-1981 46 C -HUMATP-NEG-1981 45 T -HUMATP-NEG-1981 44 G -HUMATP-NEG-1981 43 T -HUMATP-NEG-1981 42 T -HUMATP-NEG-1981 41 T -HUMATP-NEG-1981 40 C -HUMATP-NEG-1981 39 G -HUMATP-NEG-1981 38 G -HUMATP-NEG-1981 37 A -HUMATP-NEG-1981 36 T -HUMATP-NEG-1981 35 C -HUMATP-NEG-1981 34 G -HUMATP-NEG-1981 33 T -HUMATP-NEG-1981 32 C -HUMATP-NEG-1981 31 T -HUMATP-NEG-1981 30 C -HUMATP-NEG-1981 29 G -HUMATP-NEG-1981 28 T -HUMATP-NEG-1981 27 G -HUMATP-NEG-1981 26 T -HUMATP-NEG-1981 25 G -HUMATP-NEG-1981 24 A -HUMATP-NEG-1981 23 G -HUMATP-NEG-1981 22 C -HUMATP-NEG-1981 21 C -HUMATP-NEG-1981 20 A -HUMATP-NEG-1981 19 G -HUMATP-NEG-1981 18 A -HUMATP-NEG-1981 17 A -HUMATP-NEG-1981 16 C -HUMATP-NEG-1981 15 A -HUMATP-NEG-1981 14 A -HUMATP-NEG-1981 13 C -HUMATP-NEG-1981 12 G -HUMATP-NEG-1981 11 G -HUMATP-NEG-1981 10 G -HUMATP-NEG-1981 9 T -HUMATP-NEG-1981 2 C -HUMATP-NEG-1981 3 G -HUMATP-NEG-1981 4 G -HUMATP-NEG-1981 5 C -HUMATP-NEG-1981 6 A -HUMATP-NEG-1981 7 G -HUMATP-NEG-1981 8 G -HUMATP-NEG-1981 60 A -HUMATP1A2-ACCEPTOR-10073 1 A -HUMATP1A2-ACCEPTOR-10073 2 T -HUMATP1A2-ACCEPTOR-10073 3 T -HUMATP1A2-ACCEPTOR-10073 4 T -HUMATP1A2-ACCEPTOR-10073 5 C -HUMATP1A2-ACCEPTOR-10073 6 A -HUMATP1A2-ACCEPTOR-10073 7 T -HUMATP1A2-ACCEPTOR-10073 8 G -HUMATP1A2-ACCEPTOR-10073 9 A -HUMATP1A2-ACCEPTOR-10073 10 G -HUMATP1A2-ACCEPTOR-10073 11 C -HUMATP1A2-ACCEPTOR-10073 12 T -HUMATP1A2-ACCEPTOR-10073 13 G -HUMATP1A2-ACCEPTOR-10073 14 C -HUMATP1A2-ACCEPTOR-10073 15 C -HUMATP1A2-ACCEPTOR-10073 16 T -HUMATP1A2-ACCEPTOR-10073 17 G -HUMATP1A2-ACCEPTOR-10073 18 T -HUMATP1A2-ACCEPTOR-10073 19 G -HUMATP1A2-ACCEPTOR-10073 20 G -HUMATP1A2-ACCEPTOR-10073 21 C -HUMATP1A2-ACCEPTOR-10073 22 T -HUMATP1A2-ACCEPTOR-10073 23 C -HUMATP1A2-ACCEPTOR-10073 24 C -HUMATP1A2-ACCEPTOR-10073 25 C -HUMATP1A2-ACCEPTOR-10073 26 C -HUMATP1A2-ACCEPTOR-10073 27 A -HUMATP1A2-ACCEPTOR-10073 28 C -HUMATP1A2-ACCEPTOR-10073 29 A -HUMATP1A2-ACCEPTOR-10073 30 G -HUMATP1A2-ACCEPTOR-10073 31 C -HUMATP1A2-ACCEPTOR-10073 32 A -HUMATP1A2-ACCEPTOR-10073 33 A -HUMATP1A2-ACCEPTOR-10073 34 G -HUMATP1A2-ACCEPTOR-10073 35 C -HUMATP1A2-ACCEPTOR-10073 36 C -HUMATP1A2-ACCEPTOR-10073 37 C -HUMATP1A2-ACCEPTOR-10073 38 T -HUMATP1A2-ACCEPTOR-10073 39 T -HUMATP1A2-ACCEPTOR-10073 40 G -HUMATP1A2-ACCEPTOR-10073 41 T -HUMATP1A2-ACCEPTOR-10073 42 G -HUMATP1A2-ACCEPTOR-10073 43 A -HUMATP1A2-ACCEPTOR-10073 44 T -HUMATP1A2-ACCEPTOR-10073 45 C -HUMATP1A2-ACCEPTOR-10073 46 C -HUMATP1A2-ACCEPTOR-10073 47 G -HUMATP1A2-ACCEPTOR-10073 48 G -HUMATP1A2-ACCEPTOR-10073 49 G -HUMATP1A2-ACCEPTOR-10073 50 A -HUMATP1A2-ACCEPTOR-10073 51 G -HUMATP1A2-ACCEPTOR-10073 52 G -HUMATP1A2-ACCEPTOR-10073 53 G -HUMATP1A2-ACCEPTOR-10073 54 A -HUMATP1A2-ACCEPTOR-10073 55 G -HUMATP1A2-ACCEPTOR-10073 56 A -HUMATP1A2-ACCEPTOR-10073 57 G -HUMATP1A2-ACCEPTOR-10073 58 A -HUMATP1A2-ACCEPTOR-10073 59 A -HUMATP1A2-ACCEPTOR-10073 60 G -HUMATP1A2-ACCEPTOR-10913 1 T -HUMATP1A2-ACCEPTOR-10913 2 C -HUMATP1A2-ACCEPTOR-10913 3 T -HUMATP1A2-ACCEPTOR-10913 4 G -HUMATP1A2-ACCEPTOR-10913 5 A -HUMATP1A2-ACCEPTOR-10913 6 T -HUMATP1A2-ACCEPTOR-10913 7 G -HUMATP1A2-ACCEPTOR-10913 8 A -HUMATP1A2-ACCEPTOR-10913 9 C -HUMATP1A2-ACCEPTOR-10913 10 T -HUMATP1A2-ACCEPTOR-10913 11 A -HUMATP1A2-ACCEPTOR-10913 12 T -HUMATP1A2-ACCEPTOR-10913 13 G -HUMATP1A2-ACCEPTOR-10913 14 C -HUMATP1A2-ACCEPTOR-10913 15 A -HUMATP1A2-ACCEPTOR-10913 16 C -HUMATP1A2-ACCEPTOR-10913 17 T -HUMATP1A2-ACCEPTOR-10913 18 C -HUMATP1A2-ACCEPTOR-10913 19 C -HUMATP1A2-ACCEPTOR-10913 20 T -HUMATP1A2-ACCEPTOR-10913 21 T -HUMATP1A2-ACCEPTOR-10913 22 C -HUMATP1A2-ACCEPTOR-10913 23 C -HUMATP1A2-ACCEPTOR-10913 24 T -HUMATP1A2-ACCEPTOR-10913 25 C -HUMATP1A2-ACCEPTOR-10913 26 C -HUMATP1A2-ACCEPTOR-10913 27 T -HUMATP1A2-ACCEPTOR-10913 28 C -HUMATP1A2-ACCEPTOR-10913 29 A -HUMATP1A2-ACCEPTOR-10913 30 G -HUMATP1A2-ACCEPTOR-10913 31 G -HUMATP1A2-ACCEPTOR-10913 32 T -HUMATP1A2-ACCEPTOR-10913 33 G -HUMATP1A2-ACCEPTOR-10913 34 G -HUMATP1A2-ACCEPTOR-10913 35 A -HUMATP1A2-ACCEPTOR-10913 36 T -HUMATP1A2-ACCEPTOR-10913 37 A -HUMATP1A2-ACCEPTOR-10913 38 A -HUMATP1A2-ACCEPTOR-10913 39 C -HUMATP1A2-ACCEPTOR-10913 40 T -HUMATP1A2-ACCEPTOR-10913 41 C -HUMATP1A2-ACCEPTOR-10913 42 A -HUMATP1A2-ACCEPTOR-10913 43 T -HUMATP1A2-ACCEPTOR-10913 44 C -HUMATP1A2-ACCEPTOR-10913 45 C -HUMATP1A2-ACCEPTOR-10913 46 T -HUMATP1A2-ACCEPTOR-10913 47 T -HUMATP1A2-ACCEPTOR-10913 48 A -HUMATP1A2-ACCEPTOR-10913 49 A -HUMATP1A2-ACCEPTOR-10913 50 C -HUMATP1A2-ACCEPTOR-10913 51 A -HUMATP1A2-ACCEPTOR-10913 52 G -HUMATP1A2-ACCEPTOR-10913 53 G -HUMATP1A2-ACCEPTOR-10913 54 A -HUMATP1A2-ACCEPTOR-10913 55 G -HUMATP1A2-ACCEPTOR-10913 56 A -HUMATP1A2-ACCEPTOR-10913 57 G -HUMATP1A2-ACCEPTOR-10913 58 T -HUMATP1A2-ACCEPTOR-10913 59 C -HUMATP1A2-ACCEPTOR-10913 60 G -HUMATP1A2-ACCEPTOR-12450 1 C -HUMATP1A2-ACCEPTOR-12450 2 A -HUMATP1A2-ACCEPTOR-12450 3 C -HUMATP1A2-ACCEPTOR-12450 4 C -HUMATP1A2-ACCEPTOR-12450 5 C -HUMATP1A2-ACCEPTOR-12450 6 A -HUMATP1A2-ACCEPTOR-12450 7 A -HUMATP1A2-ACCEPTOR-12450 8 C -HUMATP1A2-ACCEPTOR-12450 9 C -HUMATP1A2-ACCEPTOR-12450 10 T -HUMATP1A2-ACCEPTOR-12450 11 G -HUMATP1A2-ACCEPTOR-12450 12 A -HUMATP1A2-ACCEPTOR-12450 13 T -HUMATP1A2-ACCEPTOR-12450 14 G -HUMATP1A2-ACCEPTOR-12450 15 C -HUMATP1A2-ACCEPTOR-12450 16 C -HUMATP1A2-ACCEPTOR-12450 17 C -HUMATP1A2-ACCEPTOR-12450 18 C -HUMATP1A2-ACCEPTOR-12450 19 A -HUMATP1A2-ACCEPTOR-12450 20 C -HUMATP1A2-ACCEPTOR-12450 21 C -HUMATP1A2-ACCEPTOR-12450 22 A -HUMATP1A2-ACCEPTOR-12450 23 T -HUMATP1A2-ACCEPTOR-12450 24 G -HUMATP1A2-ACCEPTOR-12450 25 T -HUMATP1A2-ACCEPTOR-12450 26 T -HUMATP1A2-ACCEPTOR-12450 27 G -HUMATP1A2-ACCEPTOR-12450 28 C -HUMATP1A2-ACCEPTOR-12450 29 A -HUMATP1A2-ACCEPTOR-12450 30 G -HUMATP1A2-ACCEPTOR-12450 31 G -HUMATP1A2-ACCEPTOR-12450 32 C -HUMATP1A2-ACCEPTOR-12450 33 A -HUMATP1A2-ACCEPTOR-12450 34 C -HUMATP1A2-ACCEPTOR-12450 35 T -HUMATP1A2-ACCEPTOR-12450 36 G -HUMATP1A2-ACCEPTOR-12450 37 C -HUMATP1A2-ACCEPTOR-12450 38 C -HUMATP1A2-ACCEPTOR-12450 39 A -HUMATP1A2-ACCEPTOR-12450 40 G -HUMATP1A2-ACCEPTOR-12450 41 G -HUMATP1A2-ACCEPTOR-12450 42 G -HUMATP1A2-ACCEPTOR-12450 43 G -HUMATP1A2-ACCEPTOR-12450 44 C -HUMATP1A2-ACCEPTOR-12450 45 A -HUMATP1A2-ACCEPTOR-12450 46 T -HUMATP1A2-ACCEPTOR-12450 47 T -HUMATP1A2-ACCEPTOR-12450 48 G -HUMATP1A2-ACCEPTOR-12450 49 T -HUMATP1A2-ACCEPTOR-12450 50 G -HUMATP1A2-ACCEPTOR-12450 51 A -HUMATP1A2-ACCEPTOR-12450 52 T -HUMATP1A2-ACCEPTOR-12450 53 T -HUMATP1A2-ACCEPTOR-12450 54 G -HUMATP1A2-ACCEPTOR-12450 55 C -HUMATP1A2-ACCEPTOR-12450 56 C -HUMATP1A2-ACCEPTOR-12450 57 A -HUMATP1A2-ACCEPTOR-12450 58 C -HUMATP1A2-ACCEPTOR-12450 59 A -HUMATP1A2-ACCEPTOR-12450 60 G -HUMATP1A2-ACCEPTOR-13550 1 A -HUMATP1A2-ACCEPTOR-13550 2 C -HUMATP1A2-ACCEPTOR-13550 3 C -HUMATP1A2-ACCEPTOR-13550 4 T -HUMATP1A2-ACCEPTOR-13550 5 T -HUMATP1A2-ACCEPTOR-13550 6 T -HUMATP1A2-ACCEPTOR-13550 7 T -HUMATP1A2-ACCEPTOR-13550 8 T -HUMATP1A2-ACCEPTOR-13550 9 T -HUMATP1A2-ACCEPTOR-13550 10 A -HUMATP1A2-ACCEPTOR-13550 11 T -HUMATP1A2-ACCEPTOR-13550 12 T -HUMATP1A2-ACCEPTOR-13550 13 C -HUMATP1A2-ACCEPTOR-13550 14 T -HUMATP1A2-ACCEPTOR-13550 15 C -HUMATP1A2-ACCEPTOR-13550 16 C -HUMATP1A2-ACCEPTOR-13550 17 T -HUMATP1A2-ACCEPTOR-13550 18 C -HUMATP1A2-ACCEPTOR-13550 19 T -HUMATP1A2-ACCEPTOR-13550 20 T -HUMATP1A2-ACCEPTOR-13550 21 T -HUMATP1A2-ACCEPTOR-13550 22 C -HUMATP1A2-ACCEPTOR-13550 23 T -HUMATP1A2-ACCEPTOR-13550 24 C -HUMATP1A2-ACCEPTOR-13550 25 T -HUMATP1A2-ACCEPTOR-13550 26 A -HUMATP1A2-ACCEPTOR-13550 27 C -HUMATP1A2-ACCEPTOR-13550 28 C -HUMATP1A2-ACCEPTOR-13550 29 A -HUMATP1A2-ACCEPTOR-13550 30 G -HUMATP1A2-ACCEPTOR-13550 31 G -HUMATP1A2-ACCEPTOR-13550 32 T -HUMATP1A2-ACCEPTOR-13550 33 G -HUMATP1A2-ACCEPTOR-13550 34 T -HUMATP1A2-ACCEPTOR-13550 35 G -HUMATP1A2-ACCEPTOR-13550 36 C -HUMATP1A2-ACCEPTOR-13550 37 C -HUMATP1A2-ACCEPTOR-13550 38 T -HUMATP1A2-ACCEPTOR-13550 39 G -HUMATP1A2-ACCEPTOR-13550 40 A -HUMATP1A2-ACCEPTOR-13550 41 C -HUMATP1A2-ACCEPTOR-13550 42 C -HUMATP1A2-ACCEPTOR-13550 43 C -HUMATP1A2-ACCEPTOR-13550 44 T -HUMATP1A2-ACCEPTOR-13550 45 G -HUMATP1A2-ACCEPTOR-13550 46 A -HUMATP1A2-ACCEPTOR-13550 47 C -HUMATP1A2-ACCEPTOR-13550 48 A -HUMATP1A2-ACCEPTOR-13550 49 G -HUMATP1A2-ACCEPTOR-13550 50 C -HUMATP1A2-ACCEPTOR-13550 51 C -HUMATP1A2-ACCEPTOR-13550 52 A -HUMATP1A2-ACCEPTOR-13550 53 A -HUMATP1A2-ACCEPTOR-13550 54 G -HUMATP1A2-ACCEPTOR-13550 55 C -HUMATP1A2-ACCEPTOR-13550 56 G -HUMATP1A2-ACCEPTOR-13550 57 C -HUMATP1A2-ACCEPTOR-13550 58 A -HUMATP1A2-ACCEPTOR-13550 59 T -HUMATP1A2-ACCEPTOR-13550 60 G -HUMATP1A2-ACCEPTOR-15000 1 C -HUMATP1A2-ACCEPTOR-15000 2 C -HUMATP1A2-ACCEPTOR-15000 3 T -HUMATP1A2-ACCEPTOR-15000 4 C -HUMATP1A2-ACCEPTOR-15000 5 T -HUMATP1A2-ACCEPTOR-15000 6 C -HUMATP1A2-ACCEPTOR-15000 7 T -HUMATP1A2-ACCEPTOR-15000 8 G -HUMATP1A2-ACCEPTOR-15000 9 T -HUMATP1A2-ACCEPTOR-15000 10 A -HUMATP1A2-ACCEPTOR-15000 11 A -HUMATP1A2-ACCEPTOR-15000 12 C -HUMATP1A2-ACCEPTOR-15000 13 T -HUMATP1A2-ACCEPTOR-15000 14 A -HUMATP1A2-ACCEPTOR-15000 15 C 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-HUMATP1A2-ACCEPTOR-15320 20 C -HUMATP1A2-ACCEPTOR-15320 21 C -HUMATP1A2-ACCEPTOR-15320 22 C -HUMATP1A2-ACCEPTOR-15320 23 C -HUMATP1A2-ACCEPTOR-15320 24 C -HUMATP1A2-ACCEPTOR-15320 25 C -HUMATP1A2-ACCEPTOR-15320 26 T -HUMATP1A2-ACCEPTOR-15320 27 T -HUMATP1A2-ACCEPTOR-15320 28 T -HUMATP1A2-ACCEPTOR-15320 29 A -HUMATP1A2-ACCEPTOR-15320 30 G -HUMATP1A2-ACCEPTOR-15320 31 G -HUMATP1A2-ACCEPTOR-15320 32 A -HUMATP1A2-ACCEPTOR-15320 33 T -HUMATP1A2-ACCEPTOR-15320 34 T -HUMATP1A2-ACCEPTOR-15320 35 C -HUMATP1A2-ACCEPTOR-15320 36 T -HUMATP1A2-ACCEPTOR-15320 37 G -HUMATP1A2-ACCEPTOR-15320 38 T -HUMATP1A2-ACCEPTOR-15320 39 C -HUMATP1A2-ACCEPTOR-15320 40 A -HUMATP1A2-ACCEPTOR-15320 41 A -HUMATP1A2-ACCEPTOR-15320 42 C -HUMATP1A2-ACCEPTOR-15320 43 T -HUMATP1A2-ACCEPTOR-15320 44 G -HUMATP1A2-ACCEPTOR-15320 45 A -HUMATP1A2-ACCEPTOR-15320 46 A -HUMATP1A2-ACCEPTOR-15320 47 T -HUMATP1A2-ACCEPTOR-15320 48 C -HUMATP1A2-ACCEPTOR-15320 49 T -HUMATP1A2-ACCEPTOR-15320 50 G -HUMATP1A2-ACCEPTOR-15320 51 C -HUMATP1A2-ACCEPTOR-15320 52 C -HUMATP1A2-ACCEPTOR-15320 53 A -HUMATP1A2-ACCEPTOR-15320 54 T -HUMATP1A2-ACCEPTOR-15320 55 C -HUMATP1A2-ACCEPTOR-15320 56 T -HUMATP1A2-ACCEPTOR-15320 57 G -HUMATP1A2-ACCEPTOR-15320 58 G -HUMATP1A2-ACCEPTOR-15320 59 A -HUMATP1A2-ACCEPTOR-15320 60 A -HUMATP1A2-ACCEPTOR-20042 1 C -HUMATP1A2-ACCEPTOR-20042 2 A -HUMATP1A2-ACCEPTOR-20042 3 G -HUMATP1A2-ACCEPTOR-20042 4 C -HUMATP1A2-ACCEPTOR-20042 5 T -HUMATP1A2-ACCEPTOR-20042 6 G -HUMATP1A2-ACCEPTOR-20042 7 T -HUMATP1A2-ACCEPTOR-20042 8 C -HUMATP1A2-ACCEPTOR-20042 9 T -HUMATP1A2-ACCEPTOR-20042 10 C -HUMATP1A2-ACCEPTOR-20042 11 T -HUMATP1A2-ACCEPTOR-20042 12 G -HUMATP1A2-ACCEPTOR-20042 13 T -HUMATP1A2-ACCEPTOR-20042 14 C -HUMATP1A2-ACCEPTOR-20042 15 C -HUMATP1A2-ACCEPTOR-20042 16 C -HUMATP1A2-ACCEPTOR-20042 17 C -HUMATP1A2-ACCEPTOR-20042 18 A -HUMATP1A2-ACCEPTOR-20042 19 C -HUMATP1A2-ACCEPTOR-20042 20 C -HUMATP1A2-ACCEPTOR-20042 21 C -HUMATP1A2-ACCEPTOR-20042 22 A -HUMATP1A2-ACCEPTOR-20042 23 C 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-HUMATP1A2-ACCEPTOR-20042 56 G -HUMATP1A2-ACCEPTOR-20042 57 G -HUMATP1A2-ACCEPTOR-20042 58 C -HUMATP1A2-ACCEPTOR-20042 59 T -HUMATP1A2-ACCEPTOR-20042 60 C -HUMATP1A2-ACCEPTOR-21144 1 C -HUMATP1A2-ACCEPTOR-21144 2 C -HUMATP1A2-ACCEPTOR-21144 3 T -HUMATP1A2-ACCEPTOR-21144 4 C -HUMATP1A2-ACCEPTOR-21144 5 C -HUMATP1A2-ACCEPTOR-21144 6 C -HUMATP1A2-ACCEPTOR-21144 7 T -HUMATP1A2-ACCEPTOR-21144 8 G -HUMATP1A2-ACCEPTOR-21144 9 C -HUMATP1A2-ACCEPTOR-21144 10 C -HUMATP1A2-ACCEPTOR-21144 11 C -HUMATP1A2-ACCEPTOR-21144 12 C -HUMATP1A2-ACCEPTOR-21144 13 A -HUMATP1A2-ACCEPTOR-21144 14 T -HUMATP1A2-ACCEPTOR-21144 15 T -HUMATP1A2-ACCEPTOR-21144 16 T -HUMATP1A2-ACCEPTOR-21144 17 C -HUMATP1A2-ACCEPTOR-21144 18 C -HUMATP1A2-ACCEPTOR-21144 19 T -HUMATP1A2-ACCEPTOR-21144 20 A -HUMATP1A2-ACCEPTOR-21144 21 C -HUMATP1A2-ACCEPTOR-21144 22 C -HUMATP1A2-ACCEPTOR-21144 23 C -HUMATP1A2-ACCEPTOR-21144 24 C -HUMATP1A2-ACCEPTOR-21144 25 A -HUMATP1A2-ACCEPTOR-21144 26 C -HUMATP1A2-ACCEPTOR-21144 27 A -HUMATP1A2-ACCEPTOR-21144 28 C -HUMATP1A2-ACCEPTOR-21144 29 A -HUMATP1A2-ACCEPTOR-21144 30 G -HUMATP1A2-ACCEPTOR-21144 31 G -HUMATP1A2-ACCEPTOR-21144 32 T -HUMATP1A2-ACCEPTOR-21144 33 C -HUMATP1A2-ACCEPTOR-21144 34 C -HUMATP1A2-ACCEPTOR-21144 35 C -HUMATP1A2-ACCEPTOR-21144 36 T -HUMATP1A2-ACCEPTOR-21144 37 G -HUMATP1A2-ACCEPTOR-21144 38 C -HUMATP1A2-ACCEPTOR-21144 39 C -HUMATP1A2-ACCEPTOR-21144 40 A -HUMATP1A2-ACCEPTOR-21144 41 T -HUMATP1A2-ACCEPTOR-21144 42 C -HUMATP1A2-ACCEPTOR-21144 43 T -HUMATP1A2-ACCEPTOR-21144 44 C -HUMATP1A2-ACCEPTOR-21144 45 C -HUMATP1A2-ACCEPTOR-21144 46 T -HUMATP1A2-ACCEPTOR-21144 47 T -HUMATP1A2-ACCEPTOR-21144 48 G -HUMATP1A2-ACCEPTOR-21144 49 G -HUMATP1A2-ACCEPTOR-21144 50 C -HUMATP1A2-ACCEPTOR-21144 51 C -HUMATP1A2-ACCEPTOR-21144 52 T -HUMATP1A2-ACCEPTOR-21144 53 A -HUMATP1A2-ACCEPTOR-21144 54 T -HUMATP1A2-ACCEPTOR-21144 55 G -HUMATP1A2-ACCEPTOR-21144 56 A -HUMATP1A2-ACCEPTOR-21144 57 G -HUMATP1A2-ACCEPTOR-21144 58 G -HUMATP1A2-ACCEPTOR-21144 59 C -HUMATP1A2-ACCEPTOR-21144 60 A -HUMATP1A2-ACCEPTOR-22617 1 C -HUMATP1A2-ACCEPTOR-22617 2 A -HUMATP1A2-ACCEPTOR-22617 3 C -HUMATP1A2-ACCEPTOR-22617 4 C -HUMATP1A2-ACCEPTOR-22617 5 T -HUMATP1A2-ACCEPTOR-22617 6 G -HUMATP1A2-ACCEPTOR-22617 7 C -HUMATP1A2-ACCEPTOR-22617 8 C -HUMATP1A2-ACCEPTOR-22617 9 A -HUMATP1A2-ACCEPTOR-22617 10 C -HUMATP1A2-ACCEPTOR-22617 11 C -HUMATP1A2-ACCEPTOR-22617 12 T -HUMATP1A2-ACCEPTOR-22617 13 C -HUMATP1A2-ACCEPTOR-22617 14 C -HUMATP1A2-ACCEPTOR-22617 15 T -HUMATP1A2-ACCEPTOR-22617 16 T -HUMATP1A2-ACCEPTOR-22617 17 T -HUMATP1A2-ACCEPTOR-22617 18 C -HUMATP1A2-ACCEPTOR-22617 19 T -HUMATP1A2-ACCEPTOR-22617 20 T -HUMATP1A2-ACCEPTOR-22617 21 T -HUMATP1A2-ACCEPTOR-22617 22 G -HUMATP1A2-ACCEPTOR-22617 23 C -HUMATP1A2-ACCEPTOR-22617 24 C -HUMATP1A2-ACCEPTOR-22617 25 T -HUMATP1A2-ACCEPTOR-22617 26 T -HUMATP1A2-ACCEPTOR-22617 27 T -HUMATP1A2-ACCEPTOR-22617 28 C -HUMATP1A2-ACCEPTOR-22617 29 A -HUMATP1A2-ACCEPTOR-22617 30 G -HUMATP1A2-ACCEPTOR-22617 31 G -HUMATP1A2-ACCEPTOR-22617 32 A -HUMATP1A2-ACCEPTOR-22617 33 A -HUMATP1A2-ACCEPTOR-22617 34 C -HUMATP1A2-ACCEPTOR-22617 35 A -HUMATP1A2-ACCEPTOR-22617 36 A -HUMATP1A2-ACCEPTOR-22617 37 G -HUMATP1A2-ACCEPTOR-22617 38 A -HUMATP1A2-ACCEPTOR-22617 39 T -HUMATP1A2-ACCEPTOR-22617 40 C -HUMATP1A2-ACCEPTOR-22617 41 C -HUMATP1A2-ACCEPTOR-22617 42 T -HUMATP1A2-ACCEPTOR-22617 43 G -HUMATP1A2-ACCEPTOR-22617 44 A -HUMATP1A2-ACCEPTOR-22617 45 T -HUMATP1A2-ACCEPTOR-22617 46 T -HUMATP1A2-ACCEPTOR-22617 47 T -HUMATP1A2-ACCEPTOR-22617 48 T -HUMATP1A2-ACCEPTOR-22617 49 T -HUMATP1A2-ACCEPTOR-22617 50 G -HUMATP1A2-ACCEPTOR-22617 51 G -HUMATP1A2-ACCEPTOR-22617 52 G -HUMATP1A2-ACCEPTOR-22617 53 C -HUMATP1A2-ACCEPTOR-22617 54 T -HUMATP1A2-ACCEPTOR-22617 55 C -HUMATP1A2-ACCEPTOR-22617 56 C -HUMATP1A2-ACCEPTOR-22617 57 T -HUMATP1A2-ACCEPTOR-22617 58 G -HUMATP1A2-ACCEPTOR-22617 59 G -HUMATP1A2-ACCEPTOR-22617 60 A -HUMATP1A2-ACCEPTOR-22870 1 G -HUMATP1A2-ACCEPTOR-22870 2 C -HUMATP1A2-ACCEPTOR-22870 3 T -HUMATP1A2-ACCEPTOR-22870 4 G -HUMATP1A2-ACCEPTOR-22870 5 C -HUMATP1A2-ACCEPTOR-22870 6 T -HUMATP1A2-ACCEPTOR-22870 7 G -HUMATP1A2-ACCEPTOR-22870 8 A -HUMATP1A2-ACCEPTOR-22870 9 C -HUMATP1A2-ACCEPTOR-22870 10 A -HUMATP1A2-ACCEPTOR-22870 11 C -HUMATP1A2-ACCEPTOR-22870 12 T -HUMATP1A2-ACCEPTOR-22870 13 C -HUMATP1A2-ACCEPTOR-22870 14 T -HUMATP1A2-ACCEPTOR-22870 15 C -HUMATP1A2-ACCEPTOR-22870 16 C -HUMATP1A2-ACCEPTOR-22870 17 T -HUMATP1A2-ACCEPTOR-22870 18 C -HUMATP1A2-ACCEPTOR-22870 19 C -HUMATP1A2-ACCEPTOR-22870 20 A -HUMATP1A2-ACCEPTOR-22870 21 T -HUMATP1A2-ACCEPTOR-22870 22 T -HUMATP1A2-ACCEPTOR-22870 23 G -HUMATP1A2-ACCEPTOR-22870 24 C -HUMATP1A2-ACCEPTOR-22870 25 T -HUMATP1A2-ACCEPTOR-22870 26 T -HUMATP1A2-ACCEPTOR-22870 27 T -HUMATP1A2-ACCEPTOR-22870 28 C -HUMATP1A2-ACCEPTOR-22870 29 A -HUMATP1A2-ACCEPTOR-22870 30 G -HUMATP1A2-ACCEPTOR-22870 31 A -HUMATP1A2-ACCEPTOR-22870 32 G -HUMATP1A2-ACCEPTOR-22870 33 T -HUMATP1A2-ACCEPTOR-22870 34 C -HUMATP1A2-ACCEPTOR-22870 35 A -HUMATP1A2-ACCEPTOR-22870 36 C -HUMATP1A2-ACCEPTOR-22870 37 C -HUMATP1A2-ACCEPTOR-22870 38 T -HUMATP1A2-ACCEPTOR-22870 39 G -HUMATP1A2-ACCEPTOR-22870 40 G -HUMATP1A2-ACCEPTOR-22870 41 T -HUMATP1A2-ACCEPTOR-22870 42 G -HUMATP1A2-ACCEPTOR-22870 43 G -HUMATP1A2-ACCEPTOR-22870 44 T -HUMATP1A2-ACCEPTOR-22870 45 T -HUMATP1A2-ACCEPTOR-22870 46 C -HUMATP1A2-ACCEPTOR-22870 47 T -HUMATP1A2-ACCEPTOR-22870 48 G -HUMATP1A2-ACCEPTOR-22870 49 C -HUMATP1A2-ACCEPTOR-22870 50 G -HUMATP1A2-ACCEPTOR-22870 51 C -HUMATP1A2-ACCEPTOR-22870 52 C -HUMATP1A2-ACCEPTOR-22870 53 T -HUMATP1A2-ACCEPTOR-22870 54 T -HUMATP1A2-ACCEPTOR-22870 55 C -HUMATP1A2-ACCEPTOR-22870 56 C -HUMATP1A2-ACCEPTOR-22870 57 C -HUMATP1A2-ACCEPTOR-22870 58 C -HUMATP1A2-ACCEPTOR-22870 59 T -HUMATP1A2-ACCEPTOR-22870 60 A -HUMATP1A2-ACCEPTOR-24266 1 T -HUMATP1A2-ACCEPTOR-24266 2 G -HUMATP1A2-ACCEPTOR-24266 3 C -HUMATP1A2-ACCEPTOR-24266 4 A -HUMATP1A2-ACCEPTOR-24266 5 A -HUMATP1A2-ACCEPTOR-24266 6 T -HUMATP1A2-ACCEPTOR-24266 7 C -HUMATP1A2-ACCEPTOR-24266 8 T -HUMATP1A2-ACCEPTOR-24266 9 C -HUMATP1A2-ACCEPTOR-24266 10 C -HUMATP1A2-ACCEPTOR-24266 11 A -HUMATP1A2-ACCEPTOR-24266 12 C -HUMATP1A2-ACCEPTOR-24266 13 T -HUMATP1A2-ACCEPTOR-24266 14 C -HUMATP1A2-ACCEPTOR-24266 15 C -HUMATP1A2-ACCEPTOR-24266 16 C -HUMATP1A2-ACCEPTOR-24266 17 A -HUMATP1A2-ACCEPTOR-24266 18 A -HUMATP1A2-ACCEPTOR-24266 19 T -HUMATP1A2-ACCEPTOR-24266 20 C -HUMATP1A2-ACCEPTOR-24266 21 T -HUMATP1A2-ACCEPTOR-24266 22 C -HUMATP1A2-ACCEPTOR-24266 23 T -HUMATP1A2-ACCEPTOR-24266 24 C -HUMATP1A2-ACCEPTOR-24266 25 T -HUMATP1A2-ACCEPTOR-24266 26 A -HUMATP1A2-ACCEPTOR-24266 27 A -HUMATP1A2-ACCEPTOR-24266 28 C -HUMATP1A2-ACCEPTOR-24266 29 A -HUMATP1A2-ACCEPTOR-24266 30 G -HUMATP1A2-ACCEPTOR-24266 31 G -HUMATP1A2-ACCEPTOR-24266 32 C -HUMATP1A2-ACCEPTOR-24266 33 T -HUMATP1A2-ACCEPTOR-24266 34 G -HUMATP1A2-ACCEPTOR-24266 35 G -HUMATP1A2-ACCEPTOR-24266 36 G -HUMATP1A2-ACCEPTOR-24266 37 T -HUMATP1A2-ACCEPTOR-24266 38 G -HUMATP1A2-ACCEPTOR-24266 39 G -HUMATP1A2-ACCEPTOR-24266 40 A -HUMATP1A2-ACCEPTOR-24266 41 G -HUMATP1A2-ACCEPTOR-24266 42 A -HUMATP1A2-ACCEPTOR-24266 43 A -HUMATP1A2-ACCEPTOR-24266 44 G -HUMATP1A2-ACCEPTOR-24266 45 G -HUMATP1A2-ACCEPTOR-24266 46 A -HUMATP1A2-ACCEPTOR-24266 47 G -HUMATP1A2-ACCEPTOR-24266 48 A -HUMATP1A2-ACCEPTOR-24266 49 C -HUMATP1A2-ACCEPTOR-24266 50 A -HUMATP1A2-ACCEPTOR-24266 51 T -HUMATP1A2-ACCEPTOR-24266 52 A -HUMATP1A2-ACCEPTOR-24266 53 C -HUMATP1A2-ACCEPTOR-24266 54 T -HUMATP1A2-ACCEPTOR-24266 55 A -HUMATP1A2-ACCEPTOR-24266 56 C -HUMATP1A2-ACCEPTOR-24266 57 T -HUMATP1A2-ACCEPTOR-24266 58 G -HUMATP1A2-ACCEPTOR-24266 59 A -HUMATP1A2-ACCEPTOR-24266 60 C -HUMATP1A2-ACCEPTOR-6969 1 C -HUMATP1A2-ACCEPTOR-6969 2 C -HUMATP1A2-ACCEPTOR-6969 3 C -HUMATP1A2-ACCEPTOR-6969 4 C -HUMATP1A2-ACCEPTOR-6969 5 A -HUMATP1A2-ACCEPTOR-6969 6 A -HUMATP1A2-ACCEPTOR-6969 7 A -HUMATP1A2-ACCEPTOR-6969 8 G -HUMATP1A2-ACCEPTOR-6969 9 T -HUMATP1A2-ACCEPTOR-6969 10 A -HUMATP1A2-ACCEPTOR-6969 11 A -HUMATP1A2-ACCEPTOR-6969 12 C -HUMATP1A2-ACCEPTOR-6969 13 T -HUMATP1A2-ACCEPTOR-6969 14 C -HUMATP1A2-ACCEPTOR-6969 15 A -HUMATP1A2-ACCEPTOR-6969 16 C -HUMATP1A2-ACCEPTOR-6969 17 C -HUMATP1A2-ACCEPTOR-6969 18 C -HUMATP1A2-ACCEPTOR-6969 19 T -HUMATP1A2-ACCEPTOR-6969 20 C -HUMATP1A2-ACCEPTOR-6969 21 C -HUMATP1A2-ACCEPTOR-6969 22 C -HUMATP1A2-ACCEPTOR-6969 23 T -HUMATP1A2-ACCEPTOR-6969 24 T -HUMATP1A2-ACCEPTOR-6969 25 C -HUMATP1A2-ACCEPTOR-6969 26 C -HUMATP1A2-ACCEPTOR-6969 27 C -HUMATP1A2-ACCEPTOR-6969 28 C -HUMATP1A2-ACCEPTOR-6969 29 A -HUMATP1A2-ACCEPTOR-6969 30 G -HUMATP1A2-ACCEPTOR-6969 31 G -HUMATP1A2-ACCEPTOR-6969 32 A -HUMATP1A2-ACCEPTOR-6969 33 T -HUMATP1A2-ACCEPTOR-6969 34 G -HUMATP1A2-ACCEPTOR-6969 35 A -HUMATP1A2-ACCEPTOR-6969 36 C -HUMATP1A2-ACCEPTOR-6969 37 C -HUMATP1A2-ACCEPTOR-6969 38 A -HUMATP1A2-ACCEPTOR-6969 39 C -HUMATP1A2-ACCEPTOR-6969 40 A -HUMATP1A2-ACCEPTOR-6969 41 A -HUMATP1A2-ACCEPTOR-6969 42 G -HUMATP1A2-ACCEPTOR-6969 43 C -HUMATP1A2-ACCEPTOR-6969 44 T -HUMATP1A2-ACCEPTOR-6969 45 G -HUMATP1A2-ACCEPTOR-6969 46 T -HUMATP1A2-ACCEPTOR-6969 47 C -HUMATP1A2-ACCEPTOR-6969 48 C -HUMATP1A2-ACCEPTOR-6969 49 T -HUMATP1A2-ACCEPTOR-6969 50 T -HUMATP1A2-ACCEPTOR-6969 51 G -HUMATP1A2-ACCEPTOR-6969 52 G -HUMATP1A2-ACCEPTOR-6969 53 A -HUMATP1A2-ACCEPTOR-6969 54 T -HUMATP1A2-ACCEPTOR-6969 55 G -HUMATP1A2-ACCEPTOR-6969 56 A -HUMATP1A2-ACCEPTOR-6969 57 G -HUMATP1A2-ACCEPTOR-6969 58 C -HUMATP1A2-ACCEPTOR-6969 59 T -HUMATP1A2-ACCEPTOR-6969 60 G -HUMATP1A2-ACCEPTOR-8990 1 G -HUMATP1A2-ACCEPTOR-8990 2 T -HUMATP1A2-ACCEPTOR-8990 3 G -HUMATP1A2-ACCEPTOR-8990 4 C -HUMATP1A2-ACCEPTOR-8990 5 C -HUMATP1A2-ACCEPTOR-8990 6 C -HUMATP1A2-ACCEPTOR-8990 7 C -HUMATP1A2-ACCEPTOR-8990 8 T -HUMATP1A2-ACCEPTOR-8990 9 A -HUMATP1A2-ACCEPTOR-8990 10 C -HUMATP1A2-ACCEPTOR-8990 11 G -HUMATP1A2-ACCEPTOR-8990 12 C -HUMATP1A2-ACCEPTOR-8990 13 C -HUMATP1A2-ACCEPTOR-8990 14 T -HUMATP1A2-ACCEPTOR-8990 15 C -HUMATP1A2-ACCEPTOR-8990 16 T -HUMATP1A2-ACCEPTOR-8990 17 C -HUMATP1A2-ACCEPTOR-8990 18 C -HUMATP1A2-ACCEPTOR-8990 19 T -HUMATP1A2-ACCEPTOR-8990 20 T -HUMATP1A2-ACCEPTOR-8990 21 G -HUMATP1A2-ACCEPTOR-8990 22 C -HUMATP1A2-ACCEPTOR-8990 23 T -HUMATP1A2-ACCEPTOR-8990 24 C -HUMATP1A2-ACCEPTOR-8990 25 C -HUMATP1A2-ACCEPTOR-8990 26 C -HUMATP1A2-ACCEPTOR-8990 27 T -HUMATP1A2-ACCEPTOR-8990 28 C -HUMATP1A2-ACCEPTOR-8990 29 A -HUMATP1A2-ACCEPTOR-8990 30 G -HUMATP1A2-ACCEPTOR-8990 31 G -HUMATP1A2-ACCEPTOR-8990 32 G -HUMATP1A2-ACCEPTOR-8990 33 C -HUMATP1A2-ACCEPTOR-8990 34 C -HUMATP1A2-ACCEPTOR-8990 35 T -HUMATP1A2-ACCEPTOR-8990 36 C -HUMATP1A2-ACCEPTOR-8990 37 A -HUMATP1A2-ACCEPTOR-8990 38 C -HUMATP1A2-ACCEPTOR-8990 39 C -HUMATP1A2-ACCEPTOR-8990 40 A -HUMATP1A2-ACCEPTOR-8990 41 A -HUMATP1A2-ACCEPTOR-8990 42 C -HUMATP1A2-ACCEPTOR-8990 43 C -HUMATP1A2-ACCEPTOR-8990 44 A -HUMATP1A2-ACCEPTOR-8990 45 G -HUMATP1A2-ACCEPTOR-8990 46 C -HUMATP1A2-ACCEPTOR-8990 47 G -HUMATP1A2-ACCEPTOR-8990 48 G -HUMATP1A2-ACCEPTOR-8990 49 G -HUMATP1A2-ACCEPTOR-8990 50 C -HUMATP1A2-ACCEPTOR-8990 51 T -HUMATP1A2-ACCEPTOR-8990 52 C -HUMATP1A2-ACCEPTOR-8990 53 A -HUMATP1A2-ACCEPTOR-8990 54 G -HUMATP1A2-ACCEPTOR-8990 55 G -HUMATP1A2-ACCEPTOR-8990 56 A -HUMATP1A2-ACCEPTOR-8990 57 C -HUMATP1A2-ACCEPTOR-8990 58 G -HUMATP1A2-ACCEPTOR-8990 59 T -HUMATP1A2-ACCEPTOR-8990 60 T -HUMATP1A2-DONOR-10208 1 C -HUMATP1A2-DONOR-10208 2 T -HUMATP1A2-DONOR-10208 3 C -HUMATP1A2-DONOR-10208 4 C -HUMATP1A2-DONOR-10208 5 G -HUMATP1A2-DONOR-10208 6 G -HUMATP1A2-DONOR-10208 7 A -HUMATP1A2-DONOR-10208 8 T -HUMATP1A2-DONOR-10208 9 C -HUMATP1A2-DONOR-10208 10 A -HUMATP1A2-DONOR-10208 11 T -HUMATP1A2-DONOR-10208 12 C -HUMATP1A2-DONOR-10208 13 T -HUMATP1A2-DONOR-10208 14 C -HUMATP1A2-DONOR-10208 15 T -HUMATP1A2-DONOR-10208 16 T -HUMATP1A2-DONOR-10208 17 C -HUMATP1A2-DONOR-10208 18 T -HUMATP1A2-DONOR-10208 19 C -HUMATP1A2-DONOR-10208 20 A -HUMATP1A2-DONOR-10208 21 T -HUMATP1A2-DONOR-10208 22 G -HUMATP1A2-DONOR-10208 23 G -HUMATP1A2-DONOR-10208 24 C -HUMATP1A2-DONOR-10208 25 T -HUMATP1A2-DONOR-10208 26 G -HUMATP1A2-DONOR-10208 27 T -HUMATP1A2-DONOR-10208 28 A -HUMATP1A2-DONOR-10208 29 A -HUMATP1A2-DONOR-10208 30 G -HUMATP1A2-DONOR-10208 31 G -HUMATP1A2-DONOR-10208 32 T -HUMATP1A2-DONOR-10208 33 G -HUMATP1A2-DONOR-10208 34 A -HUMATP1A2-DONOR-10208 35 G -HUMATP1A2-DONOR-10208 36 G -HUMATP1A2-DONOR-10208 37 A -HUMATP1A2-DONOR-10208 38 G -HUMATP1A2-DONOR-10208 39 G -HUMATP1A2-DONOR-10208 40 T -HUMATP1A2-DONOR-10208 41 C -HUMATP1A2-DONOR-10208 42 A -HUMATP1A2-DONOR-10208 43 T -HUMATP1A2-DONOR-10208 44 A -HUMATP1A2-DONOR-10208 45 C -HUMATP1A2-DONOR-10208 46 C -HUMATP1A2-DONOR-10208 47 A -HUMATP1A2-DONOR-10208 48 G -HUMATP1A2-DONOR-10208 49 A -HUMATP1A2-DONOR-10208 50 G -HUMATP1A2-DONOR-10208 51 C -HUMATP1A2-DONOR-10208 52 A -HUMATP1A2-DONOR-10208 53 A -HUMATP1A2-DONOR-10208 54 G -HUMATP1A2-DONOR-10208 55 C -HUMATP1A2-DONOR-10208 56 A -HUMATP1A2-DONOR-10208 57 G -HUMATP1A2-DONOR-10208 58 T -HUMATP1A2-DONOR-10208 59 T -HUMATP1A2-DONOR-10208 60 G -HUMATP1A2-DONOR-11031 1 T -HUMATP1A2-DONOR-11031 2 C -HUMATP1A2-DONOR-11031 3 T -HUMATP1A2-DONOR-11031 4 G -HUMATP1A2-DONOR-11031 5 T -HUMATP1A2-DONOR-11031 6 T -HUMATP1A2-DONOR-11031 7 T -HUMATP1A2-DONOR-11031 8 C -HUMATP1A2-DONOR-11031 9 T -HUMATP1A2-DONOR-11031 10 T -HUMATP1A2-DONOR-11031 11 C -HUMATP1A2-DONOR-11031 12 T -HUMATP1A2-DONOR-11031 13 C -HUMATP1A2-DONOR-11031 14 C -HUMATP1A2-DONOR-11031 15 A -HUMATP1A2-DONOR-11031 16 C -HUMATP1A2-DONOR-11031 17 C -HUMATP1A2-DONOR-11031 18 A -HUMATP1A2-DONOR-11031 19 A -HUMATP1A2-DONOR-11031 20 C -HUMATP1A2-DONOR-11031 21 T -HUMATP1A2-DONOR-11031 22 G -HUMATP1A2-DONOR-11031 23 T -HUMATP1A2-DONOR-11031 24 G -HUMATP1A2-DONOR-11031 25 T -HUMATP1A2-DONOR-11031 26 T -HUMATP1A2-DONOR-11031 27 G -HUMATP1A2-DONOR-11031 28 A -HUMATP1A2-DONOR-11031 29 A -HUMATP1A2-DONOR-11031 30 G -HUMATP1A2-DONOR-11031 31 G -HUMATP1A2-DONOR-11031 32 T -HUMATP1A2-DONOR-11031 33 G -HUMATP1A2-DONOR-11031 34 A -HUMATP1A2-DONOR-11031 35 G -HUMATP1A2-DONOR-11031 36 A -HUMATP1A2-DONOR-11031 37 A -HUMATP1A2-DONOR-11031 38 G -HUMATP1A2-DONOR-11031 39 C -HUMATP1A2-DONOR-11031 40 C -HUMATP1A2-DONOR-11031 41 A -HUMATP1A2-DONOR-11031 42 G -HUMATP1A2-DONOR-11031 43 G -HUMATP1A2-DONOR-11031 44 C -HUMATP1A2-DONOR-11031 45 T -HUMATP1A2-DONOR-11031 46 G -HUMATP1A2-DONOR-11031 47 C -HUMATP1A2-DONOR-11031 48 C -HUMATP1A2-DONOR-11031 49 C -HUMATP1A2-DONOR-11031 50 C -HUMATP1A2-DONOR-11031 51 C -HUMATP1A2-DONOR-11031 52 T -HUMATP1A2-DONOR-11031 53 G -HUMATP1A2-DONOR-11031 54 T -HUMATP1A2-DONOR-11031 55 A -HUMATP1A2-DONOR-11031 56 G -HUMATP1A2-DONOR-11031 57 G -HUMATP1A2-DONOR-11031 58 A -HUMATP1A2-DONOR-11031 59 A -HUMATP1A2-DONOR-11031 60 A -HUMATP1A2-DONOR-12719 1 G -HUMATP1A2-DONOR-12719 2 T -HUMATP1A2-DONOR-12719 3 G -HUMATP1A2-DONOR-12719 4 C -HUMATP1A2-DONOR-12719 5 C -HUMATP1A2-DONOR-12719 6 T -HUMATP1A2-DONOR-12719 7 G -HUMATP1A2-DONOR-12719 8 A -HUMATP1A2-DONOR-12719 9 G -HUMATP1A2-DONOR-12719 10 G -HUMATP1A2-DONOR-12719 11 G -HUMATP1A2-DONOR-12719 12 G -HUMATP1A2-DONOR-12719 13 C -HUMATP1A2-DONOR-12719 14 T -HUMATP1A2-DONOR-12719 15 T -HUMATP1A2-DONOR-12719 16 C -HUMATP1A2-DONOR-12719 17 T -HUMATP1A2-DONOR-12719 18 G -HUMATP1A2-DONOR-12719 19 G -HUMATP1A2-DONOR-12719 20 C -HUMATP1A2-DONOR-12719 21 C -HUMATP1A2-DONOR-12719 22 A -HUMATP1A2-DONOR-12719 23 C -HUMATP1A2-DONOR-12719 24 T -HUMATP1A2-DONOR-12719 25 G -HUMATP1A2-DONOR-12719 26 T -HUMATP1A2-DONOR-12719 27 C -HUMATP1A2-DONOR-12719 28 A -HUMATP1A2-DONOR-12719 29 C -HUMATP1A2-DONOR-12719 30 T -HUMATP1A2-DONOR-12719 31 G -HUMATP1A2-DONOR-12719 32 T -HUMATP1A2-DONOR-12719 33 G -HUMATP1A2-DONOR-12719 34 A -HUMATP1A2-DONOR-12719 35 G -HUMATP1A2-DONOR-12719 36 T -HUMATP1A2-DONOR-12719 37 G -HUMATP1A2-DONOR-12719 38 G -HUMATP1A2-DONOR-12719 39 G -HUMATP1A2-DONOR-12719 40 T -HUMATP1A2-DONOR-12719 41 C -HUMATP1A2-DONOR-12719 42 A -HUMATP1A2-DONOR-12719 43 G -HUMATP1A2-DONOR-12719 44 G -HUMATP1A2-DONOR-12719 45 C -HUMATP1A2-DONOR-12719 46 T -HUMATP1A2-DONOR-12719 47 G -HUMATP1A2-DONOR-12719 48 A -HUMATP1A2-DONOR-12719 49 G -HUMATP1A2-DONOR-12719 50 G -HUMATP1A2-DONOR-12719 51 T -HUMATP1A2-DONOR-12719 52 G -HUMATP1A2-DONOR-12719 53 C -HUMATP1A2-DONOR-12719 54 C -HUMATP1A2-DONOR-12719 55 A -HUMATP1A2-DONOR-12719 56 C -HUMATP1A2-DONOR-12719 57 C -HUMATP1A2-DONOR-12719 58 A -HUMATP1A2-DONOR-12719 59 G -HUMATP1A2-DONOR-12719 60 G -HUMATP1A2-DONOR-13749 1 A -HUMATP1A2-DONOR-13749 2 T -HUMATP1A2-DONOR-13749 3 G -HUMATP1A2-DONOR-13749 4 A -HUMATP1A2-DONOR-13749 5 G -HUMATP1A2-DONOR-13749 6 G -HUMATP1A2-DONOR-13749 7 C -HUMATP1A2-DONOR-13749 8 T -HUMATP1A2-DONOR-13749 9 G -HUMATP1A2-DONOR-13749 10 A -HUMATP1A2-DONOR-13749 11 C -HUMATP1A2-DONOR-13749 12 A -HUMATP1A2-DONOR-13749 13 C -HUMATP1A2-DONOR-13749 14 C -HUMATP1A2-DONOR-13749 15 A -HUMATP1A2-DONOR-13749 16 C -HUMATP1A2-DONOR-13749 17 C -HUMATP1A2-DONOR-13749 18 G -HUMATP1A2-DONOR-13749 19 A -HUMATP1A2-DONOR-13749 20 A -HUMATP1A2-DONOR-13749 21 G -HUMATP1A2-DONOR-13749 22 A -HUMATP1A2-DONOR-13749 23 T -HUMATP1A2-DONOR-13749 24 C -HUMATP1A2-DONOR-13749 25 A -HUMATP1A2-DONOR-13749 26 G -HUMATP1A2-DONOR-13749 27 T -HUMATP1A2-DONOR-13749 28 C -HUMATP1A2-DONOR-13749 29 T -HUMATP1A2-DONOR-13749 30 G -HUMATP1A2-DONOR-13749 31 G -HUMATP1A2-DONOR-13749 32 T -HUMATP1A2-DONOR-13749 33 G -HUMATP1A2-DONOR-13749 34 A -HUMATP1A2-DONOR-13749 35 T -HUMATP1A2-DONOR-13749 36 T -HUMATP1A2-DONOR-13749 37 G -HUMATP1A2-DONOR-13749 38 G -HUMATP1A2-DONOR-13749 39 G -HUMATP1A2-DONOR-13749 40 T -HUMATP1A2-DONOR-13749 41 G -HUMATP1A2-DONOR-13749 42 C -HUMATP1A2-DONOR-13749 43 T -HUMATP1A2-DONOR-13749 44 C -HUMATP1A2-DONOR-13749 45 C -HUMATP1A2-DONOR-13749 46 A -HUMATP1A2-DONOR-13749 47 G -HUMATP1A2-DONOR-13749 48 A -HUMATP1A2-DONOR-13749 49 G -HUMATP1A2-DONOR-13749 50 G -HUMATP1A2-DONOR-13749 51 G -HUMATP1A2-DONOR-13749 52 G -HUMATP1A2-DONOR-13749 53 G -HUMATP1A2-DONOR-13749 54 T -HUMATP1A2-DONOR-13749 55 G -HUMATP1A2-DONOR-13749 56 G -HUMATP1A2-DONOR-13749 57 A -HUMATP1A2-DONOR-13749 58 T -HUMATP1A2-DONOR-13749 59 A -HUMATP1A2-DONOR-13749 60 G -HUMATP1A2-DONOR-14299 1 A -HUMATP1A2-DONOR-14299 2 T -HUMATP1A2-DONOR-14299 3 T -HUMATP1A2-DONOR-14299 4 C -HUMATP1A2-DONOR-14299 5 C -HUMATP1A2-DONOR-14299 6 T -HUMATP1A2-DONOR-14299 7 T -HUMATP1A2-DONOR-14299 8 T -HUMATP1A2-DONOR-14299 9 C -HUMATP1A2-DONOR-14299 10 A -HUMATP1A2-DONOR-14299 11 A -HUMATP1A2-DONOR-14299 12 C -HUMATP1A2-DONOR-14299 13 T -HUMATP1A2-DONOR-14299 14 C -HUMATP1A2-DONOR-14299 15 T -HUMATP1A2-DONOR-14299 16 A -HUMATP1A2-DONOR-14299 17 C -HUMATP1A2-DONOR-14299 18 C -HUMATP1A2-DONOR-14299 19 A -HUMATP1A2-DONOR-14299 20 A -HUMATP1A2-DONOR-14299 21 C -HUMATP1A2-DONOR-14299 22 A -HUMATP1A2-DONOR-14299 23 A -HUMATP1A2-DONOR-14299 24 G -HUMATP1A2-DONOR-14299 25 T -HUMATP1A2-DONOR-14299 26 A -HUMATP1A2-DONOR-14299 27 C -HUMATP1A2-DONOR-14299 28 C -HUMATP1A2-DONOR-14299 29 A -HUMATP1A2-DONOR-14299 30 G -HUMATP1A2-DONOR-14299 31 G -HUMATP1A2-DONOR-14299 32 T -HUMATP1A2-DONOR-14299 33 C -HUMATP1A2-DONOR-14299 34 T -HUMATP1A2-DONOR-14299 35 G -HUMATP1A2-DONOR-14299 36 C -HUMATP1A2-DONOR-14299 37 T -HUMATP1A2-DONOR-14299 38 T -HUMATP1A2-DONOR-14299 39 G -HUMATP1A2-DONOR-14299 40 G -HUMATP1A2-DONOR-14299 41 G -HUMATP1A2-DONOR-14299 42 T -HUMATP1A2-DONOR-14299 43 T -HUMATP1A2-DONOR-14299 44 G -HUMATP1A2-DONOR-14299 45 C -HUMATP1A2-DONOR-14299 46 C -HUMATP1A2-DONOR-14299 47 A -HUMATP1A2-DONOR-14299 48 G -HUMATP1A2-DONOR-14299 49 G -HUMATP1A2-DONOR-14299 50 A -HUMATP1A2-DONOR-14299 51 C -HUMATP1A2-DONOR-14299 52 A -HUMATP1A2-DONOR-14299 53 G -HUMATP1A2-DONOR-14299 54 A -HUMATP1A2-DONOR-14299 55 G -HUMATP1A2-DONOR-14299 56 G -HUMATP1A2-DONOR-14299 57 A -HUMATP1A2-DONOR-14299 58 A -HUMATP1A2-DONOR-14299 59 G -HUMATP1A2-DONOR-14299 60 A -HUMATP1A2-DONOR-15190 2 G -HUMATP1A2-DONOR-15190 3 C -HUMATP1A2-DONOR-15190 4 T -HUMATP1A2-DONOR-15190 5 G -HUMATP1A2-DONOR-15190 6 G -HUMATP1A2-DONOR-15190 7 G -HUMATP1A2-DONOR-15190 8 G -HUMATP1A2-DONOR-15190 1 A -HUMATP1A2-DONOR-15190 59 A -HUMATP1A2-DONOR-15190 58 A -HUMATP1A2-DONOR-15190 57 G -HUMATP1A2-DONOR-15190 56 A -HUMATP1A2-DONOR-15190 55 C -HUMATP1A2-DONOR-15190 54 T -HUMATP1A2-DONOR-15190 53 C -HUMATP1A2-DONOR-15190 52 G -HUMATP1A2-DONOR-15190 51 G -HUMATP1A2-DONOR-15190 50 A -HUMATP1A2-DONOR-15190 49 G -HUMATP1A2-DONOR-15190 48 G -HUMATP1A2-DONOR-15190 47 A -HUMATP1A2-DONOR-15190 46 C -HUMATP1A2-DONOR-15190 45 A -HUMATP1A2-DONOR-15190 44 A -HUMATP1A2-DONOR-15190 43 A -HUMATP1A2-DONOR-15190 42 G -HUMATP1A2-DONOR-15190 41 A -HUMATP1A2-DONOR-15190 40 C -HUMATP1A2-DONOR-15190 39 C -HUMATP1A2-DONOR-15190 38 G -HUMATP1A2-DONOR-15190 37 G -HUMATP1A2-DONOR-15190 36 A -HUMATP1A2-DONOR-15190 35 G -HUMATP1A2-DONOR-15190 34 A -HUMATP1A2-DONOR-15190 33 G -HUMATP1A2-DONOR-15190 32 T -HUMATP1A2-DONOR-15190 31 G -HUMATP1A2-DONOR-15190 30 G -HUMATP1A2-DONOR-15190 29 G -HUMATP1A2-DONOR-15190 28 T -HUMATP1A2-DONOR-15190 27 C -HUMATP1A2-DONOR-15190 26 G -HUMATP1A2-DONOR-15190 25 T -HUMATP1A2-DONOR-15190 24 G -HUMATP1A2-DONOR-15190 23 T -HUMATP1A2-DONOR-15190 22 G -HUMATP1A2-DONOR-15190 21 C -HUMATP1A2-DONOR-15190 20 G -HUMATP1A2-DONOR-15190 19 A -HUMATP1A2-DONOR-15190 18 G -HUMATP1A2-DONOR-15190 17 G -HUMATP1A2-DONOR-15190 16 G -HUMATP1A2-DONOR-15190 15 G -HUMATP1A2-DONOR-15190 14 T -HUMATP1A2-DONOR-15190 13 T -HUMATP1A2-DONOR-15190 12 C -HUMATP1A2-DONOR-15190 11 A -HUMATP1A2-DONOR-15190 10 G -HUMATP1A2-DONOR-15190 60 G -HUMATP1A2-DONOR-15190 9 G -HUMATP1A2-DONOR-15496 59 C -HUMATP1A2-DONOR-15496 1 G -HUMATP1A2-DONOR-15496 2 T -HUMATP1A2-DONOR-15496 3 G -HUMATP1A2-DONOR-15496 4 G -HUMATP1A2-DONOR-15496 5 G -HUMATP1A2-DONOR-15496 6 C -HUMATP1A2-DONOR-15496 7 A -HUMATP1A2-DONOR-15496 8 A -HUMATP1A2-DONOR-15496 9 G -HUMATP1A2-DONOR-15496 10 T -HUMATP1A2-DONOR-15496 11 G -HUMATP1A2-DONOR-15496 12 C -HUMATP1A2-DONOR-15496 13 C -HUMATP1A2-DONOR-15496 14 G -HUMATP1A2-DONOR-15496 15 A -HUMATP1A2-DONOR-15496 16 A -HUMATP1A2-DONOR-15496 17 G -HUMATP1A2-DONOR-15496 18 C -HUMATP1A2-DONOR-15496 19 G -HUMATP1A2-DONOR-15496 20 C -HUMATP1A2-DONOR-15496 21 A -HUMATP1A2-DONOR-15496 22 G -HUMATP1A2-DONOR-15496 23 G -HUMATP1A2-DONOR-15496 24 C -HUMATP1A2-DONOR-15496 25 A -HUMATP1A2-DONOR-15496 26 T -HUMATP1A2-DONOR-15496 27 C -HUMATP1A2-DONOR-15496 28 A -HUMATP1A2-DONOR-15496 29 A -HUMATP1A2-DONOR-15496 30 G -HUMATP1A2-DONOR-15496 31 G -HUMATP1A2-DONOR-15496 32 T -HUMATP1A2-DONOR-15496 33 A -HUMATP1A2-DONOR-15496 34 C -HUMATP1A2-DONOR-15496 35 T -HUMATP1A2-DONOR-15496 36 G -HUMATP1A2-DONOR-15496 37 G -HUMATP1A2-DONOR-15496 38 C -HUMATP1A2-DONOR-15496 39 C -HUMATP1A2-DONOR-15496 40 T -HUMATP1A2-DONOR-15496 41 C -HUMATP1A2-DONOR-15496 42 C -HUMATP1A2-DONOR-15496 43 C -HUMATP1A2-DONOR-15496 44 A -HUMATP1A2-DONOR-15496 45 T -HUMATP1A2-DONOR-15496 46 C -HUMATP1A2-DONOR-15496 47 C -HUMATP1A2-DONOR-15496 48 T -HUMATP1A2-DONOR-15496 49 C -HUMATP1A2-DONOR-15496 50 C -HUMATP1A2-DONOR-15496 51 C -HUMATP1A2-DONOR-15496 52 C -HUMATP1A2-DONOR-15496 53 T -HUMATP1A2-DONOR-15496 54 C -HUMATP1A2-DONOR-15496 55 C -HUMATP1A2-DONOR-15496 56 A -HUMATP1A2-DONOR-15496 57 T -HUMATP1A2-DONOR-15496 58 T -HUMATP1A2-DONOR-15496 60 T -HUMATP1A2-DONOR-19518 59 C -HUMATP1A2-DONOR-19518 1 C -HUMATP1A2-DONOR-19518 2 A -HUMATP1A2-DONOR-19518 3 A -HUMATP1A2-DONOR-19518 4 C -HUMATP1A2-DONOR-19518 5 A -HUMATP1A2-DONOR-19518 6 T -HUMATP1A2-DONOR-19518 7 T -HUMATP1A2-DONOR-19518 8 C -HUMATP1A2-DONOR-19518 9 C -HUMATP1A2-DONOR-19518 10 C -HUMATP1A2-DONOR-19518 11 A -HUMATP1A2-DONOR-19518 12 T -HUMATP1A2-DONOR-19518 13 G -HUMATP1A2-DONOR-19518 14 A -HUMATP1A2-DONOR-19518 15 G -HUMATP1A2-DONOR-19518 16 T -HUMATP1A2-DONOR-19518 17 C -HUMATP1A2-DONOR-19518 18 A -HUMATP1A2-DONOR-19518 19 A -HUMATP1A2-DONOR-19518 20 G -HUMATP1A2-DONOR-19518 21 T -HUMATP1A2-DONOR-19518 22 C -HUMATP1A2-DONOR-19518 23 A -HUMATP1A2-DONOR-19518 24 A -HUMATP1A2-DONOR-19518 25 C -HUMATP1A2-DONOR-19518 26 C -HUMATP1A2-DONOR-19518 27 C -HUMATP1A2-DONOR-19518 28 C -HUMATP1A2-DONOR-19518 29 A -HUMATP1A2-DONOR-19518 30 G -HUMATP1A2-DONOR-19518 31 G -HUMATP1A2-DONOR-19518 32 T -HUMATP1A2-DONOR-19518 33 G -HUMATP1A2-DONOR-19518 34 A -HUMATP1A2-DONOR-19518 35 G -HUMATP1A2-DONOR-19518 36 G -HUMATP1A2-DONOR-19518 37 C -HUMATP1A2-DONOR-19518 38 C -HUMATP1A2-DONOR-19518 39 T -HUMATP1A2-DONOR-19518 40 C -HUMATP1A2-DONOR-19518 41 T -HUMATP1A2-DONOR-19518 42 G -HUMATP1A2-DONOR-19518 43 C -HUMATP1A2-DONOR-19518 44 A -HUMATP1A2-DONOR-19518 45 G -HUMATP1A2-DONOR-19518 46 G -HUMATP1A2-DONOR-19518 47 A -HUMATP1A2-DONOR-19518 48 A -HUMATP1A2-DONOR-19518 49 G -HUMATP1A2-DONOR-19518 50 C -HUMATP1A2-DONOR-19518 51 C -HUMATP1A2-DONOR-19518 52 C -HUMATP1A2-DONOR-19518 53 C -HUMATP1A2-DONOR-19518 54 T -HUMATP1A2-DONOR-19518 55 G -HUMATP1A2-DONOR-19518 56 T -HUMATP1A2-DONOR-19518 57 G -HUMATP1A2-DONOR-19518 58 C -HUMATP1A2-DONOR-19518 60 C -HUMATP1A2-DONOR-20193 59 A -HUMATP1A2-DONOR-20193 58 C -HUMATP1A2-DONOR-20193 57 A -HUMATP1A2-DONOR-20193 56 G -HUMATP1A2-DONOR-20193 55 T -HUMATP1A2-DONOR-20193 54 A -HUMATP1A2-DONOR-20193 53 G -HUMATP1A2-DONOR-20193 52 A -HUMATP1A2-DONOR-20193 51 C -HUMATP1A2-DONOR-20193 50 G -HUMATP1A2-DONOR-20193 49 G -HUMATP1A2-DONOR-20193 48 A -HUMATP1A2-DONOR-20193 47 G -HUMATP1A2-DONOR-20193 46 G -HUMATP1A2-DONOR-20193 45 G -HUMATP1A2-DONOR-20193 44 C -HUMATP1A2-DONOR-20193 43 A -HUMATP1A2-DONOR-20193 42 C -HUMATP1A2-DONOR-20193 41 C -HUMATP1A2-DONOR-20193 40 G -HUMATP1A2-DONOR-20193 39 A -HUMATP1A2-DONOR-20193 38 C -HUMATP1A2-DONOR-20193 37 A -HUMATP1A2-DONOR-20193 36 C -HUMATP1A2-DONOR-20193 35 G -HUMATP1A2-DONOR-20193 34 A -HUMATP1A2-DONOR-20193 33 G -HUMATP1A2-DONOR-20193 32 T -HUMATP1A2-DONOR-20193 31 G -HUMATP1A2-DONOR-20193 30 G -HUMATP1A2-DONOR-20193 29 A -HUMATP1A2-DONOR-20193 28 C -HUMATP1A2-DONOR-20193 27 G -HUMATP1A2-DONOR-20193 26 G -HUMATP1A2-DONOR-20193 25 A -HUMATP1A2-DONOR-20193 24 G -HUMATP1A2-DONOR-20193 23 A -HUMATP1A2-DONOR-20193 22 C -HUMATP1A2-DONOR-20193 21 T -HUMATP1A2-DONOR-20193 20 G -HUMATP1A2-DONOR-20193 19 T -HUMATP1A2-DONOR-20193 18 A -HUMATP1A2-DONOR-20193 17 G -HUMATP1A2-DONOR-20193 16 G -HUMATP1A2-DONOR-20193 15 G -HUMATP1A2-DONOR-20193 14 A -HUMATP1A2-DONOR-20193 13 G -HUMATP1A2-DONOR-20193 12 G -HUMATP1A2-DONOR-20193 11 T -HUMATP1A2-DONOR-20193 10 G -HUMATP1A2-DONOR-20193 9 T -HUMATP1A2-DONOR-20193 8 T -HUMATP1A2-DONOR-20193 7 A -HUMATP1A2-DONOR-20193 6 C -HUMATP1A2-DONOR-20193 5 T -HUMATP1A2-DONOR-20193 4 A -HUMATP1A2-DONOR-20193 3 C -HUMATP1A2-DONOR-20193 2 T -HUMATP1A2-DONOR-20193 60 G -HUMATP1A2-DONOR-20193 1 C -HUMATP1A2-DONOR-20500 60 T -HUMATP1A2-DONOR-20500 1 T -HUMATP1A2-DONOR-20500 2 T -HUMATP1A2-DONOR-20500 3 G -HUMATP1A2-DONOR-20500 4 C -HUMATP1A2-DONOR-20500 5 C -HUMATP1A2-DONOR-20500 6 T -HUMATP1A2-DONOR-20500 7 C -HUMATP1A2-DONOR-20500 8 C -HUMATP1A2-DONOR-20500 9 A -HUMATP1A2-DONOR-20500 10 T -HUMATP1A2-DONOR-20500 11 C -HUMATP1A2-DONOR-20500 12 G -HUMATP1A2-DONOR-20500 13 T -HUMATP1A2-DONOR-20500 14 C -HUMATP1A2-DONOR-20500 15 A -HUMATP1A2-DONOR-20500 16 C -HUMATP1A2-DONOR-20500 17 G -HUMATP1A2-DONOR-20500 18 G -HUMATP1A2-DONOR-20500 19 G -HUMATP1A2-DONOR-20500 20 G -HUMATP1A2-DONOR-20500 21 G -HUMATP1A2-DONOR-20500 22 T -HUMATP1A2-DONOR-20500 23 G -HUMATP1A2-DONOR-20500 24 G -HUMATP1A2-DONOR-20500 25 A -HUMATP1A2-DONOR-20500 26 G -HUMATP1A2-DONOR-20500 27 G -HUMATP1A2-DONOR-20500 28 A -HUMATP1A2-DONOR-20500 29 G -HUMATP1A2-DONOR-20500 59 T -HUMATP1A2-DONOR-20500 30 G -HUMATP1A2-DONOR-20500 31 G -HUMATP1A2-DONOR-20500 32 T -HUMATP1A2-DONOR-20500 33 G -HUMATP1A2-DONOR-20500 34 A -HUMATP1A2-DONOR-20500 35 G -HUMATP1A2-DONOR-20500 36 G -HUMATP1A2-DONOR-20500 37 A -HUMATP1A2-DONOR-20500 38 G -HUMATP1A2-DONOR-20500 39 G -HUMATP1A2-DONOR-20500 40 C -HUMATP1A2-DONOR-20500 41 T -HUMATP1A2-DONOR-20500 42 G -HUMATP1A2-DONOR-20500 43 C -HUMATP1A2-DONOR-20500 44 A -HUMATP1A2-DONOR-20500 45 T -HUMATP1A2-DONOR-20500 46 G -HUMATP1A2-DONOR-20500 47 G -HUMATP1A2-DONOR-20500 48 G -HUMATP1A2-DONOR-20500 49 T -HUMATP1A2-DONOR-20500 50 T -HUMATP1A2-DONOR-20500 51 G -HUMATP1A2-DONOR-20500 52 G -HUMATP1A2-DONOR-20500 53 G -HUMATP1A2-DONOR-20500 54 A -HUMATP1A2-DONOR-20500 55 T -HUMATP1A2-DONOR-20500 56 G -HUMATP1A2-DONOR-20500 57 G -HUMATP1A2-DONOR-20500 58 T -HUMATP1A2-DONOR-21268 31 G -HUMATP1A2-DONOR-21268 58 G -HUMATP1A2-DONOR-21268 57 G -HUMATP1A2-DONOR-21268 56 G -HUMATP1A2-DONOR-21268 55 A -HUMATP1A2-DONOR-21268 54 G -HUMATP1A2-DONOR-21268 53 G -HUMATP1A2-DONOR-21268 52 G -HUMATP1A2-DONOR-21268 51 C -HUMATP1A2-DONOR-21268 50 T -HUMATP1A2-DONOR-21268 49 C -HUMATP1A2-DONOR-21268 48 C -HUMATP1A2-DONOR-21268 47 G -HUMATP1A2-DONOR-21268 46 G -HUMATP1A2-DONOR-21268 45 G -HUMATP1A2-DONOR-21268 44 C -HUMATP1A2-DONOR-21268 43 C -HUMATP1A2-DONOR-21268 42 C -HUMATP1A2-DONOR-21268 41 C -HUMATP1A2-DONOR-21268 40 G -HUMATP1A2-DONOR-21268 39 A -HUMATP1A2-DONOR-21268 38 A -HUMATP1A2-DONOR-21268 37 C -HUMATP1A2-DONOR-21268 36 C -HUMATP1A2-DONOR-21268 35 G -HUMATP1A2-DONOR-21268 60 A -HUMATP1A2-DONOR-21268 34 C -HUMATP1A2-DONOR-21268 33 G -HUMATP1A2-DONOR-21268 32 T -HUMATP1A2-DONOR-21268 30 G -HUMATP1A2-DONOR-21268 29 C -HUMATP1A2-DONOR-21268 28 T -HUMATP1A2-DONOR-21268 27 A -HUMATP1A2-DONOR-21268 26 G -HUMATP1A2-DONOR-21268 25 A -HUMATP1A2-DONOR-21268 24 C -HUMATP1A2-DONOR-21268 23 A -HUMATP1A2-DONOR-21268 22 G -HUMATP1A2-DONOR-21268 21 G -HUMATP1A2-DONOR-21268 20 C -HUMATP1A2-DONOR-21268 19 A -HUMATP1A2-DONOR-21268 18 T -HUMATP1A2-DONOR-21268 17 C -HUMATP1A2-DONOR-21268 16 C -HUMATP1A2-DONOR-21268 15 G -HUMATP1A2-DONOR-21268 14 G -HUMATP1A2-DONOR-21268 13 T -HUMATP1A2-DONOR-21268 12 A -HUMATP1A2-DONOR-21268 11 C -HUMATP1A2-DONOR-21268 10 G -HUMATP1A2-DONOR-21268 9 A -HUMATP1A2-DONOR-21268 8 C -HUMATP1A2-DONOR-21268 7 T -HUMATP1A2-DONOR-21268 6 A -HUMATP1A2-DONOR-21268 5 C -HUMATP1A2-DONOR-21268 4 T -HUMATP1A2-DONOR-21268 3 C -HUMATP1A2-DONOR-21268 2 G -HUMATP1A2-DONOR-21268 1 G -HUMATP1A2-DONOR-21268 59 A -HUMATP1A2-DONOR-21613 59 C -HUMATP1A2-DONOR-21613 58 A -HUMATP1A2-DONOR-21613 57 C -HUMATP1A2-DONOR-21613 56 G -HUMATP1A2-DONOR-21613 55 C -HUMATP1A2-DONOR-21613 54 G -HUMATP1A2-DONOR-21613 53 A -HUMATP1A2-DONOR-21613 52 C -HUMATP1A2-DONOR-21613 51 A -HUMATP1A2-DONOR-21613 50 C -HUMATP1A2-DONOR-21613 49 A -HUMATP1A2-DONOR-21613 48 A -HUMATP1A2-DONOR-21613 47 A -HUMATP1A2-DONOR-21613 46 T -HUMATP1A2-DONOR-21613 45 G -HUMATP1A2-DONOR-21613 44 T -HUMATP1A2-DONOR-21613 43 G -HUMATP1A2-DONOR-21613 42 T -HUMATP1A2-DONOR-21613 41 C -HUMATP1A2-DONOR-21613 40 G -HUMATP1A2-DONOR-21613 39 T -HUMATP1A2-DONOR-21613 38 G -HUMATP1A2-DONOR-21613 37 G -HUMATP1A2-DONOR-21613 36 T -HUMATP1A2-DONOR-21613 35 G -HUMATP1A2-DONOR-21613 34 A -HUMATP1A2-DONOR-21613 33 G -HUMATP1A2-DONOR-21613 32 T -HUMATP1A2-DONOR-21613 31 G -HUMATP1A2-DONOR-21613 30 G -HUMATP1A2-DONOR-21613 1 G -HUMATP1A2-DONOR-21613 29 G -HUMATP1A2-DONOR-21613 28 T -HUMATP1A2-DONOR-21613 27 G -HUMATP1A2-DONOR-21613 26 A -HUMATP1A2-DONOR-21613 25 G -HUMATP1A2-DONOR-21613 24 G -HUMATP1A2-DONOR-21613 23 A -HUMATP1A2-DONOR-21613 22 C -HUMATP1A2-DONOR-21613 21 A -HUMATP1A2-DONOR-21613 20 G -HUMATP1A2-DONOR-21613 19 G -HUMATP1A2-DONOR-21613 18 T -HUMATP1A2-DONOR-21613 17 A -HUMATP1A2-DONOR-21613 16 T -HUMATP1A2-DONOR-21613 15 C -HUMATP1A2-DONOR-21613 14 G -HUMATP1A2-DONOR-21613 13 A -HUMATP1A2-DONOR-21613 12 C -HUMATP1A2-DONOR-21613 11 A -HUMATP1A2-DONOR-21613 10 G -HUMATP1A2-DONOR-21613 9 G -HUMATP1A2-DONOR-21613 8 A -HUMATP1A2-DONOR-21613 7 G -HUMATP1A2-DONOR-21613 6 G -HUMATP1A2-DONOR-21613 5 T -HUMATP1A2-DONOR-21613 4 C -HUMATP1A2-DONOR-21613 3 T -HUMATP1A2-DONOR-21613 2 A -HUMATP1A2-DONOR-21613 60 A -HUMATP1A2-DONOR-21929 60 A -HUMATP1A2-DONOR-21929 59 C -HUMATP1A2-DONOR-21929 58 C -HUMATP1A2-DONOR-21929 57 C -HUMATP1A2-DONOR-21929 56 A -HUMATP1A2-DONOR-21929 55 T -HUMATP1A2-DONOR-21929 54 C -HUMATP1A2-DONOR-21929 53 C -HUMATP1A2-DONOR-21929 52 A -HUMATP1A2-DONOR-21929 51 C -HUMATP1A2-DONOR-21929 50 G -HUMATP1A2-DONOR-21929 49 G -HUMATP1A2-DONOR-21929 48 T -HUMATP1A2-DONOR-21929 47 A -HUMATP1A2-DONOR-21929 46 C -HUMATP1A2-DONOR-21929 45 C -HUMATP1A2-DONOR-21929 44 C -HUMATP1A2-DONOR-21929 43 C -HUMATP1A2-DONOR-21929 42 C -HUMATP1A2-DONOR-21929 41 A -HUMATP1A2-DONOR-21929 40 C -HUMATP1A2-DONOR-21929 39 C -HUMATP1A2-DONOR-21929 38 C -HUMATP1A2-DONOR-21929 37 G -HUMATP1A2-DONOR-21929 36 T -HUMATP1A2-DONOR-21929 34 A -HUMATP1A2-DONOR-21929 33 G -HUMATP1A2-DONOR-21929 32 T -HUMATP1A2-DONOR-21929 30 A -HUMATP1A2-DONOR-21929 29 A -HUMATP1A2-DONOR-21929 35 G -HUMATP1A2-DONOR-21929 28 G -HUMATP1A2-DONOR-21929 27 T -HUMATP1A2-DONOR-21929 26 A -HUMATP1A2-DONOR-21929 25 C -HUMATP1A2-DONOR-21929 24 G -HUMATP1A2-DONOR-21929 23 G -HUMATP1A2-DONOR-21929 22 G -HUMATP1A2-DONOR-21929 21 A -HUMATP1A2-DONOR-21929 20 C -HUMATP1A2-DONOR-21929 19 G -HUMATP1A2-DONOR-21929 18 A -HUMATP1A2-DONOR-21929 17 C -HUMATP1A2-DONOR-21929 16 C -HUMATP1A2-DONOR-21929 15 T -HUMATP1A2-DONOR-21929 14 T -HUMATP1A2-DONOR-21929 13 C -HUMATP1A2-DONOR-21929 12 T -HUMATP1A2-DONOR-21929 11 G -HUMATP1A2-DONOR-21929 10 A -HUMATP1A2-DONOR-21929 9 C -HUMATP1A2-DONOR-21929 8 T -HUMATP1A2-DONOR-21929 7 C -HUMATP1A2-DONOR-21929 6 A -HUMATP1A2-DONOR-21929 5 A -HUMATP1A2-DONOR-21929 4 C -HUMATP1A2-DONOR-21929 3 G -HUMATP1A2-DONOR-21929 2 C -HUMATP1A2-DONOR-21929 1 C -HUMATP1A2-DONOR-21929 31 G -HUMATP1A2-DONOR-22962 59 A -HUMATP1A2-DONOR-22962 58 A -HUMATP1A2-DONOR-22962 57 A -HUMATP1A2-DONOR-22962 56 C -HUMATP1A2-DONOR-22962 55 G -HUMATP1A2-DONOR-22962 54 A -HUMATP1A2-DONOR-22962 53 C -HUMATP1A2-DONOR-22962 52 C -HUMATP1A2-DONOR-22962 51 C -HUMATP1A2-DONOR-22962 50 C -HUMATP1A2-DONOR-22962 49 C -HUMATP1A2-DONOR-22962 48 C -HUMATP1A2-DONOR-22962 47 T -HUMATP1A2-DONOR-22962 46 T -HUMATP1A2-DONOR-22962 45 A -HUMATP1A2-DONOR-22962 44 C -HUMATP1A2-DONOR-22962 43 A -HUMATP1A2-DONOR-22962 42 C -HUMATP1A2-DONOR-22962 41 C -HUMATP1A2-DONOR-22962 40 T -HUMATP1A2-DONOR-22962 39 C -HUMATP1A2-DONOR-22962 38 C -HUMATP1A2-DONOR-22962 37 C -HUMATP1A2-DONOR-22962 36 C -HUMATP1A2-DONOR-22962 35 G -HUMATP1A2-DONOR-22962 34 A -HUMATP1A2-DONOR-22962 33 A -HUMATP1A2-DONOR-22962 32 T -HUMATP1A2-DONOR-22962 31 G -HUMATP1A2-DONOR-22962 30 G -HUMATP1A2-DONOR-22962 29 T -HUMATP1A2-DONOR-22962 28 G -HUMATP1A2-DONOR-22962 27 G -HUMATP1A2-DONOR-22962 26 T -HUMATP1A2-DONOR-22962 25 C -HUMATP1A2-DONOR-22962 24 C -HUMATP1A2-DONOR-22962 23 T -HUMATP1A2-DONOR-22962 22 A -HUMATP1A2-DONOR-22962 21 T -HUMATP1A2-DONOR-22962 20 G -HUMATP1A2-DONOR-22962 19 G -HUMATP1A2-DONOR-22962 18 C -HUMATP1A2-DONOR-22962 17 G -HUMATP1A2-DONOR-22962 16 G -HUMATP1A2-DONOR-22962 15 C -HUMATP1A2-DONOR-22962 14 G -HUMATP1A2-DONOR-22962 13 T -HUMATP1A2-DONOR-22962 12 C -HUMATP1A2-DONOR-22962 11 C -HUMATP1A2-DONOR-22962 10 T -HUMATP1A2-DONOR-22962 9 A -HUMATP1A2-DONOR-22962 8 C -HUMATP1A2-DONOR-22962 7 T -HUMATP1A2-DONOR-22962 6 C -HUMATP1A2-DONOR-22962 5 G -HUMATP1A2-DONOR-22962 4 A -HUMATP1A2-DONOR-22962 3 A -HUMATP1A2-DONOR-22962 2 A -HUMATP1A2-DONOR-22962 60 G -HUMATP1A2-DONOR-22962 1 G -HUMATP1A2-DONOR-7029 1 G -HUMATP1A2-DONOR-7029 58 A -HUMATP1A2-DONOR-7029 57 A -HUMATP1A2-DONOR-7029 56 G -HUMATP1A2-DONOR-7029 55 G -HUMATP1A2-DONOR-7029 54 A -HUMATP1A2-DONOR-7029 53 A -HUMATP1A2-DONOR-7029 52 G -HUMATP1A2-DONOR-7029 51 G -HUMATP1A2-DONOR-7029 50 G -HUMATP1A2-DONOR-7029 49 A -HUMATP1A2-DONOR-7029 48 T -HUMATP1A2-DONOR-7029 47 C -HUMATP1A2-DONOR-7029 46 T -HUMATP1A2-DONOR-7029 45 T -HUMATP1A2-DONOR-7029 44 C -HUMATP1A2-DONOR-7029 43 G -HUMATP1A2-DONOR-7029 42 G -HUMATP1A2-DONOR-7029 41 G -HUMATP1A2-DONOR-7029 40 G -HUMATP1A2-DONOR-7029 39 A -HUMATP1A2-DONOR-7029 38 G -HUMATP1A2-DONOR-7029 37 G -HUMATP1A2-DONOR-7029 36 T -HUMATP1A2-DONOR-7029 35 G -HUMATP1A2-DONOR-7029 34 A -HUMATP1A2-DONOR-7029 33 G -HUMATP1A2-DONOR-7029 32 T -HUMATP1A2-DONOR-7029 31 G -HUMATP1A2-DONOR-7029 30 G -HUMATP1A2-DONOR-7029 29 A -HUMATP1A2-DONOR-7029 28 A -HUMATP1A2-DONOR-7029 27 C -HUMATP1A2-DONOR-7029 26 C -HUMATP1A2-DONOR-7029 25 T -HUMATP1A2-DONOR-7029 24 G -HUMATP1A2-DONOR-7029 23 T -HUMATP1A2-DONOR-7029 22 C -HUMATP1A2-DONOR-7029 21 C -HUMATP1A2-DONOR-7029 20 A -HUMATP1A2-DONOR-7029 19 G -HUMATP1A2-DONOR-7029 18 G -HUMATP1A2-DONOR-7029 17 T -HUMATP1A2-DONOR-7029 16 G -HUMATP1A2-DONOR-7029 15 A -HUMATP1A2-DONOR-7029 14 A -HUMATP1A2-DONOR-7029 13 C -HUMATP1A2-DONOR-7029 12 C -HUMATP1A2-DONOR-7029 11 A -HUMATP1A2-DONOR-7029 10 T -HUMATP1A2-DONOR-7029 9 A -HUMATP1A2-DONOR-7029 8 A -HUMATP1A2-DONOR-7029 7 A -HUMATP1A2-DONOR-7029 6 C -HUMATP1A2-DONOR-7029 5 G -HUMATP1A2-DONOR-7029 4 C -HUMATP1A2-DONOR-7029 3 C -HUMATP1A2-DONOR-7029 2 G -HUMATP1A2-DONOR-7029 60 A -HUMATP1A2-DONOR-7029 59 C -HUMATP1A2-DONOR-9194 59 G -HUMATP1A2-DONOR-9194 58 A -HUMATP1A2-DONOR-9194 57 C -HUMATP1A2-DONOR-9194 56 A -HUMATP1A2-DONOR-9194 55 A -HUMATP1A2-DONOR-9194 54 G -HUMATP1A2-DONOR-9194 53 G -HUMATP1A2-DONOR-9194 52 G -HUMATP1A2-DONOR-9194 51 C -HUMATP1A2-DONOR-9194 50 C -HUMATP1A2-DONOR-9194 49 C -HUMATP1A2-DONOR-9194 48 A -HUMATP1A2-DONOR-9194 47 G -HUMATP1A2-DONOR-9194 46 T -HUMATP1A2-DONOR-9194 45 C -HUMATP1A2-DONOR-9194 44 C -HUMATP1A2-DONOR-9194 43 G -HUMATP1A2-DONOR-9194 42 C -HUMATP1A2-DONOR-9194 41 A -HUMATP1A2-DONOR-9194 40 C -HUMATP1A2-DONOR-9194 39 A -HUMATP1A2-DONOR-9194 38 C -HUMATP1A2-DONOR-9194 37 C -HUMATP1A2-DONOR-9194 36 C -HUMATP1A2-DONOR-9194 35 G -HUMATP1A2-DONOR-9194 34 A -HUMATP1A2-DONOR-9194 33 G -HUMATP1A2-DONOR-9194 32 T -HUMATP1A2-DONOR-9194 31 G -HUMATP1A2-DONOR-9194 30 T -HUMATP1A2-DONOR-9194 1 G -HUMATP1A2-DONOR-9194 29 A -HUMATP1A2-DONOR-9194 28 A -HUMATP1A2-DONOR-9194 27 C -HUMATP1A2-DONOR-9194 26 A -HUMATP1A2-DONOR-9194 25 G -HUMATP1A2-DONOR-9194 24 C -HUMATP1A2-DONOR-9194 23 A -HUMATP1A2-DONOR-9194 22 A -HUMATP1A2-DONOR-9194 21 C -HUMATP1A2-DONOR-9194 20 C -HUMATP1A2-DONOR-9194 19 T -HUMATP1A2-DONOR-9194 18 A -HUMATP1A2-DONOR-9194 17 C -HUMATP1A2-DONOR-9194 16 C -HUMATP1A2-DONOR-9194 15 A -HUMATP1A2-DONOR-9194 14 A -HUMATP1A2-DONOR-9194 13 G -HUMATP1A2-DONOR-9194 12 T -HUMATP1A2-DONOR-9194 11 A -HUMATP1A2-DONOR-9194 10 G -HUMATP1A2-DONOR-9194 9 G -HUMATP1A2-DONOR-9194 8 A -HUMATP1A2-DONOR-9194 7 G -HUMATP1A2-DONOR-9194 6 G -HUMATP1A2-DONOR-9194 5 T -HUMATP1A2-DONOR-9194 4 A -HUMATP1A2-DONOR-9194 3 C -HUMATP1A2-DONOR-9194 2 C -HUMATP1A2-DONOR-9194 60 C -HUMATP1A2-DONOR-9834 1 A -HUMATP1A2-DONOR-9834 60 G -HUMATP1A2-DONOR-9834 59 A -HUMATP1A2-DONOR-9834 58 T -HUMATP1A2-DONOR-9834 57 C -HUMATP1A2-DONOR-9834 56 G -HUMATP1A2-DONOR-9834 55 G -HUMATP1A2-DONOR-9834 54 G -HUMATP1A2-DONOR-9834 53 T -HUMATP1A2-DONOR-9834 52 C -HUMATP1A2-DONOR-9834 51 T -HUMATP1A2-DONOR-9834 50 C -HUMATP1A2-DONOR-9834 49 G -HUMATP1A2-DONOR-9834 48 G -HUMATP1A2-DONOR-9834 47 G -HUMATP1A2-DONOR-9834 46 T -HUMATP1A2-DONOR-9834 45 C -HUMATP1A2-DONOR-9834 44 G -HUMATP1A2-DONOR-9834 43 G -HUMATP1A2-DONOR-9834 42 G -HUMATP1A2-DONOR-9834 41 A -HUMATP1A2-DONOR-9834 40 C -HUMATP1A2-DONOR-9834 39 G -HUMATP1A2-DONOR-9834 38 G -HUMATP1A2-DONOR-9834 37 T -HUMATP1A2-DONOR-9834 36 A -HUMATP1A2-DONOR-9834 35 G -HUMATP1A2-DONOR-9834 34 A -HUMATP1A2-DONOR-9834 33 A -HUMATP1A2-DONOR-9834 32 T -HUMATP1A2-DONOR-9834 30 G -HUMATP1A2-DONOR-9834 29 A -HUMATP1A2-DONOR-9834 28 C -HUMATP1A2-DONOR-9834 27 T -HUMATP1A2-DONOR-9834 26 C -HUMATP1A2-DONOR-9834 25 C -HUMATP1A2-DONOR-9834 24 A -HUMATP1A2-DONOR-9834 23 T -HUMATP1A2-DONOR-9834 22 G -HUMATP1A2-DONOR-9834 21 G -HUMATP1A2-DONOR-9834 20 T -HUMATP1A2-DONOR-9834 19 A -HUMATP1A2-DONOR-9834 18 C -HUMATP1A2-DONOR-9834 17 A -HUMATP1A2-DONOR-9834 16 A -HUMATP1A2-DONOR-9834 15 G -HUMATP1A2-DONOR-9834 14 A -HUMATP1A2-DONOR-9834 13 A -HUMATP1A2-DONOR-9834 12 C -HUMATP1A2-DONOR-9834 11 T -HUMATP1A2-DONOR-9834 10 T -HUMATP1A2-DONOR-9834 9 C -HUMATP1A2-DONOR-9834 8 C -HUMATP1A2-DONOR-9834 7 T -HUMATP1A2-DONOR-9834 6 T -HUMATP1A2-DONOR-9834 5 A -HUMATP1A2-DONOR-9834 4 G -HUMATP1A2-DONOR-9834 3 G -HUMATP1A2-DONOR-9834 2 T -HUMATP1A2-DONOR-9834 31 G -HUMATP1A2-NEG-11221 60 G -HUMATP1A2-NEG-11221 2 A -HUMATP1A2-NEG-11221 3 G -HUMATP1A2-NEG-11221 4 G -HUMATP1A2-NEG-11221 5 G -HUMATP1A2-NEG-11221 6 T -HUMATP1A2-NEG-11221 7 A -HUMATP1A2-NEG-11221 8 T -HUMATP1A2-NEG-11221 9 T -HUMATP1A2-NEG-11221 10 G -HUMATP1A2-NEG-11221 11 A -HUMATP1A2-NEG-11221 12 C -HUMATP1A2-NEG-11221 13 T -HUMATP1A2-NEG-11221 14 G -HUMATP1A2-NEG-11221 15 A -HUMATP1A2-NEG-11221 16 G -HUMATP1A2-NEG-11221 17 G -HUMATP1A2-NEG-11221 18 G -HUMATP1A2-NEG-11221 19 C -HUMATP1A2-NEG-11221 20 A -HUMATP1A2-NEG-11221 21 G -HUMATP1A2-NEG-11221 22 C -HUMATP1A2-NEG-11221 23 C -HUMATP1A2-NEG-11221 24 T -HUMATP1A2-NEG-11221 25 G -HUMATP1A2-NEG-11221 26 A -HUMATP1A2-NEG-11221 27 C -HUMATP1A2-NEG-11221 28 T -HUMATP1A2-NEG-11221 29 C -HUMATP1A2-NEG-11221 30 C -HUMATP1A2-NEG-11221 31 A -HUMATP1A2-NEG-11221 32 T -HUMATP1A2-NEG-11221 33 G -HUMATP1A2-NEG-11221 34 G -HUMATP1A2-NEG-11221 35 G -HUMATP1A2-NEG-11221 36 A -HUMATP1A2-NEG-11221 37 A -HUMATP1A2-NEG-11221 38 A -HUMATP1A2-NEG-11221 39 T -HUMATP1A2-NEG-11221 40 G -HUMATP1A2-NEG-11221 41 T -HUMATP1A2-NEG-11221 42 A -HUMATP1A2-NEG-11221 43 T -HUMATP1A2-NEG-11221 44 C -HUMATP1A2-NEG-11221 45 T -HUMATP1A2-NEG-11221 46 C -HUMATP1A2-NEG-11221 47 C -HUMATP1A2-NEG-11221 48 C -HUMATP1A2-NEG-11221 49 T -HUMATP1A2-NEG-11221 50 T -HUMATP1A2-NEG-11221 51 C -HUMATP1A2-NEG-11221 52 C -HUMATP1A2-NEG-11221 53 C -HUMATP1A2-NEG-11221 54 C -HUMATP1A2-NEG-11221 55 A -HUMATP1A2-NEG-11221 56 G -HUMATP1A2-NEG-11221 57 G -HUMATP1A2-NEG-11221 58 G -HUMATP1A2-NEG-11221 59 A -HUMATP1A2-NEG-11221 1 C -HUMATP1A2-NEG-15781 60 G -HUMATP1A2-NEG-15781 59 T -HUMATP1A2-NEG-15781 58 T -HUMATP1A2-NEG-15781 57 G -HUMATP1A2-NEG-15781 56 A -HUMATP1A2-NEG-15781 55 T -HUMATP1A2-NEG-15781 54 C -HUMATP1A2-NEG-15781 53 C -HUMATP1A2-NEG-15781 52 A -HUMATP1A2-NEG-15781 51 G -HUMATP1A2-NEG-15781 50 A -HUMATP1A2-NEG-15781 49 A -HUMATP1A2-NEG-15781 48 G -HUMATP1A2-NEG-15781 47 T -HUMATP1A2-NEG-15781 46 T -HUMATP1A2-NEG-15781 45 C -HUMATP1A2-NEG-15781 44 T -HUMATP1A2-NEG-15781 43 A -HUMATP1A2-NEG-15781 42 T -HUMATP1A2-NEG-15781 41 C -HUMATP1A2-NEG-15781 40 C -HUMATP1A2-NEG-15781 39 T -HUMATP1A2-NEG-15781 38 C -HUMATP1A2-NEG-15781 37 A -HUMATP1A2-NEG-15781 36 C -HUMATP1A2-NEG-15781 35 G -HUMATP1A2-NEG-15781 34 G -HUMATP1A2-NEG-15781 33 T -HUMATP1A2-NEG-15781 32 C -HUMATP1A2-NEG-15781 31 T -HUMATP1A2-NEG-15781 30 A -HUMATP1A2-NEG-15781 29 A -HUMATP1A2-NEG-15781 28 T -HUMATP1A2-NEG-15781 27 A -HUMATP1A2-NEG-15781 26 C -HUMATP1A2-NEG-15781 25 G -HUMATP1A2-NEG-15781 24 A -HUMATP1A2-NEG-15781 23 A -HUMATP1A2-NEG-15781 22 G -HUMATP1A2-NEG-15781 21 T -HUMATP1A2-NEG-15781 20 T -HUMATP1A2-NEG-15781 19 T -HUMATP1A2-NEG-15781 18 A -HUMATP1A2-NEG-15781 17 G -HUMATP1A2-NEG-15781 16 T -HUMATP1A2-NEG-15781 15 C -HUMATP1A2-NEG-15781 14 T -HUMATP1A2-NEG-15781 13 T -HUMATP1A2-NEG-15781 12 A -HUMATP1A2-NEG-15781 11 C -HUMATP1A2-NEG-15781 10 C -HUMATP1A2-NEG-15781 9 T -HUMATP1A2-NEG-15781 8 T -HUMATP1A2-NEG-15781 7 C -HUMATP1A2-NEG-15781 6 C -HUMATP1A2-NEG-15781 5 C -HUMATP1A2-NEG-15781 4 A -HUMATP1A2-NEG-15781 3 T -HUMATP1A2-NEG-15781 2 G -HUMATP1A2-NEG-15781 1 T -HUMATP1A2-NEG-20341 30 G -HUMATP1A2-NEG-20341 59 C -HUMATP1A2-NEG-20341 58 A -HUMATP1A2-NEG-20341 57 G -HUMATP1A2-NEG-20341 56 C -HUMATP1A2-NEG-20341 55 A -HUMATP1A2-NEG-20341 54 A -HUMATP1A2-NEG-20341 53 G -HUMATP1A2-NEG-20341 52 T -HUMATP1A2-NEG-20341 51 G -HUMATP1A2-NEG-20341 50 G -HUMATP1A2-NEG-20341 49 G -HUMATP1A2-NEG-20341 48 G -HUMATP1A2-NEG-20341 47 C -HUMATP1A2-NEG-20341 46 A -HUMATP1A2-NEG-20341 45 G -HUMATP1A2-NEG-20341 44 T -HUMATP1A2-NEG-20341 43 G -HUMATP1A2-NEG-20341 42 G -HUMATP1A2-NEG-20341 41 G -HUMATP1A2-NEG-20341 40 C -HUMATP1A2-NEG-20341 39 A -HUMATP1A2-NEG-20341 38 G -HUMATP1A2-NEG-20341 37 T -HUMATP1A2-NEG-20341 36 G -HUMATP1A2-NEG-20341 35 C -HUMATP1A2-NEG-20341 34 C -HUMATP1A2-NEG-20341 33 G -HUMATP1A2-NEG-20341 32 G -HUMATP1A2-NEG-20341 31 T -HUMATP1A2-NEG-20341 29 T -HUMATP1A2-NEG-20341 28 T -HUMATP1A2-NEG-20341 27 A -HUMATP1A2-NEG-20341 26 C -HUMATP1A2-NEG-20341 25 C -HUMATP1A2-NEG-20341 24 G -HUMATP1A2-NEG-20341 23 A -HUMATP1A2-NEG-20341 22 G -HUMATP1A2-NEG-20341 21 G -HUMATP1A2-NEG-20341 20 G -HUMATP1A2-NEG-20341 19 A -HUMATP1A2-NEG-20341 18 C 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-HUMBCRR-NEG-1261 55 A -HUMBCRR-NEG-1261 56 C -HUMBCRR-NEG-1261 57 G -HUMBCRR-NEG-1261 58 T -HUMBCRR-NEG-1261 59 G -HUMBCRR-NEG-1261 60 A -HUMBDGALA-NEG-1441 1 A -HUMBDGALA-NEG-1441 2 C -HUMBDGALA-NEG-1441 3 A -HUMBDGALA-NEG-1441 4 T -HUMBDGALA-NEG-1441 5 A -HUMBDGALA-NEG-1441 6 A -HUMBDGALA-NEG-1441 7 C -HUMBDGALA-NEG-1441 8 A -HUMBDGALA-NEG-1441 9 G -HUMBDGALA-NEG-1441 10 G -HUMBDGALA-NEG-1441 11 G -HUMBDGALA-NEG-1441 12 A -HUMBDGALA-NEG-1441 13 A -HUMBDGALA-NEG-1441 14 A -HUMBDGALA-NEG-1441 15 G -HUMBDGALA-NEG-1441 16 C -HUMBDGALA-NEG-1441 17 T -HUMBDGALA-NEG-1441 18 G -HUMBDGALA-NEG-1441 19 G -HUMBDGALA-NEG-1441 20 A -HUMBDGALA-NEG-1441 21 G -HUMBDGALA-NEG-1441 22 C -HUMBDGALA-NEG-1441 23 C -HUMBDGALA-NEG-1441 24 A -HUMBDGALA-NEG-1441 25 C -HUMBDGALA-NEG-1441 26 T -HUMBDGALA-NEG-1441 27 C -HUMBDGALA-NEG-1441 28 T -HUMBDGALA-NEG-1441 29 G -HUMBDGALA-NEG-1441 30 G -HUMBDGALA-NEG-1441 31 A -HUMBDGALA-NEG-1441 32 C -HUMBDGALA-NEG-1441 33 C -HUMBDGALA-NEG-1441 34 T -HUMBDGALA-NEG-1441 35 T -HUMBDGALA-NEG-1441 36 C -HUMBDGALA-NEG-1441 37 T -HUMBDGALA-NEG-1441 38 G -HUMBDGALA-NEG-1441 39 G -HUMBDGALA-NEG-1441 40 T -HUMBDGALA-NEG-1441 41 A -HUMBDGALA-NEG-1441 42 G -HUMBDGALA-NEG-1441 43 A -HUMBDGALA-NEG-1441 44 G -HUMBDGALA-NEG-1441 45 A -HUMBDGALA-NEG-1441 46 A -HUMBDGALA-NEG-1441 47 C -HUMBDGALA-NEG-1441 48 A -HUMBDGALA-NEG-1441 49 T -HUMBDGALA-NEG-1441 50 G -HUMBDGALA-NEG-1441 51 G -HUMBDGALA-NEG-1441 52 G -HUMBDGALA-NEG-1441 53 A -HUMBDGALA-NEG-1441 54 C -HUMBDGALA-NEG-1441 55 G -HUMBDGALA-NEG-1441 56 T -HUMBDGALA-NEG-1441 57 G -HUMBDGALA-NEG-1441 58 T -HUMBDGALA-NEG-1441 59 G -HUMBDGALA-NEG-1441 60 A -HUMBGAL-NEG-781 1 A -HUMBGAL-NEG-781 2 G -HUMBGAL-NEG-781 3 A -HUMBGAL-NEG-781 4 T -HUMBGAL-NEG-781 5 G -HUMBGAL-NEG-781 6 C -HUMBGAL-NEG-781 7 T -HUMBGAL-NEG-781 8 T -HUMBGAL-NEG-781 9 T -HUMBGAL-NEG-781 10 C -HUMBGAL-NEG-781 11 C -HUMBGAL-NEG-781 12 T -HUMBGAL-NEG-781 13 A -HUMBGAL-NEG-781 14 A -HUMBGAL-NEG-781 15 G -HUMBGAL-NEG-781 16 C -HUMBGAL-NEG-781 17 C 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46 A -HUMBGPG-NEG-1021 47 C -HUMBGPG-NEG-1021 48 C -HUMBGPG-NEG-1021 49 C -HUMBGPG-NEG-1021 50 A -HUMBGPG-NEG-1021 51 G -HUMBGPG-NEG-1021 52 G -HUMBGPG-NEG-1021 53 C -HUMBGPG-NEG-1021 54 G -HUMBGPG-NEG-1021 55 C -HUMBGPG-NEG-1021 56 T -HUMBGPG-NEG-1021 57 A -HUMBGPG-NEG-1021 58 C -HUMBGPG-NEG-1021 59 C -HUMBGPG-NEG-1021 60 T -HUMBHA05-NEG-61 1 C -HUMBHA05-NEG-61 2 A -HUMBHA05-NEG-61 3 C -HUMBHA05-NEG-61 4 C -HUMBHA05-NEG-61 5 G -HUMBHA05-NEG-61 6 G -HUMBHA05-NEG-61 7 G -HUMBHA05-NEG-61 8 G -HUMBHA05-NEG-61 9 C -HUMBHA05-NEG-61 10 T -HUMBHA05-NEG-61 11 T -HUMBHA05-NEG-61 12 G -HUMBHA05-NEG-61 13 C -HUMBHA05-NEG-61 14 T -HUMBHA05-NEG-61 15 G -HUMBHA05-NEG-61 16 T -HUMBHA05-NEG-61 17 T -HUMBHA05-NEG-61 18 G -HUMBHA05-NEG-61 19 G -HUMBHA05-NEG-61 20 A -HUMBHA05-NEG-61 21 T -HUMBHA05-NEG-61 22 A -HUMBHA05-NEG-61 23 C -HUMBHA05-NEG-61 24 A -HUMBHA05-NEG-61 25 T -HUMBHA05-NEG-61 26 C -HUMBHA05-NEG-61 27 T -HUMBHA05-NEG-61 28 C -HUMBHA05-NEG-61 29 G -HUMBHA05-NEG-61 30 C -HUMBHA05-NEG-61 31 C 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-HUMBLAST1-NEG-661 42 A -HUMBLAST1-NEG-661 43 G -HUMBLAST1-NEG-661 44 T -HUMBLAST1-NEG-661 45 A -HUMBLAST1-NEG-661 46 G -HUMBLAST1-NEG-661 47 A -HUMBLAST1-NEG-661 48 A -HUMBLAST1-NEG-661 49 T -HUMBLAST1-NEG-661 50 G -HUMBLAST1-NEG-661 51 G -HUMBLAST1-NEG-661 52 A -HUMBLAST1-NEG-661 53 T -HUMBLAST1-NEG-661 54 T -HUMBLAST1-NEG-661 55 G -HUMBLAST1-NEG-661 56 C -HUMBLAST1-NEG-661 57 A -HUMBLAST1-NEG-661 58 A -HUMBLAST1-NEG-661 59 G -HUMBLAST1-NEG-661 60 T -HUMBLYM1-ACCEPTOR-505 1 C -HUMBLYM1-ACCEPTOR-505 2 T -HUMBLYM1-ACCEPTOR-505 3 T -HUMBLYM1-ACCEPTOR-505 4 G -HUMBLYM1-ACCEPTOR-505 5 C -HUMBLYM1-ACCEPTOR-505 6 C -HUMBLYM1-ACCEPTOR-505 7 A -HUMBLYM1-ACCEPTOR-505 8 C -HUMBLYM1-ACCEPTOR-505 9 T -HUMBLYM1-ACCEPTOR-505 10 G -HUMBLYM1-ACCEPTOR-505 11 C -HUMBLYM1-ACCEPTOR-505 12 A -HUMBLYM1-ACCEPTOR-505 13 C -HUMBLYM1-ACCEPTOR-505 14 T -HUMBLYM1-ACCEPTOR-505 15 C -HUMBLYM1-ACCEPTOR-505 16 C -HUMBLYM1-ACCEPTOR-505 17 A -HUMBLYM1-ACCEPTOR-505 18 T -HUMBLYM1-ACCEPTOR-505 19 A -HUMBLYM1-ACCEPTOR-505 20 C -HUMBLYM1-ACCEPTOR-505 21 T -HUMBLYM1-ACCEPTOR-505 22 G -HUMBLYM1-ACCEPTOR-505 23 C -HUMBLYM1-ACCEPTOR-505 24 A -HUMBLYM1-ACCEPTOR-505 25 T -HUMBLYM1-ACCEPTOR-505 26 G -HUMBLYM1-ACCEPTOR-505 27 A -HUMBLYM1-ACCEPTOR-505 28 C -HUMBLYM1-ACCEPTOR-505 29 A -HUMBLYM1-ACCEPTOR-505 30 G -HUMBLYM1-ACCEPTOR-505 31 A -HUMBLYM1-ACCEPTOR-505 32 G -HUMBLYM1-ACCEPTOR-505 33 C -HUMBLYM1-ACCEPTOR-505 34 A -HUMBLYM1-ACCEPTOR-505 35 A -HUMBLYM1-ACCEPTOR-505 36 G -HUMBLYM1-ACCEPTOR-505 37 A -HUMBLYM1-ACCEPTOR-505 38 C -HUMBLYM1-ACCEPTOR-505 39 C -HUMBLYM1-ACCEPTOR-505 40 C -HUMBLYM1-ACCEPTOR-505 41 T -HUMBLYM1-ACCEPTOR-505 42 G -HUMBLYM1-ACCEPTOR-505 43 T -HUMBLYM1-ACCEPTOR-505 44 C -HUMBLYM1-ACCEPTOR-505 45 T -HUMBLYM1-ACCEPTOR-505 46 C -HUMBLYM1-ACCEPTOR-505 47 T -HUMBLYM1-ACCEPTOR-505 48 T -HUMBLYM1-ACCEPTOR-505 49 A -HUMBLYM1-ACCEPTOR-505 50 G -HUMBLYM1-ACCEPTOR-505 51 A -HUMBLYM1-ACCEPTOR-505 52 A -HUMBLYM1-ACCEPTOR-505 53 A -HUMBLYM1-ACCEPTOR-505 54 A 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47 C -HUMGG-DONOR-230 48 T -HUMGG-DONOR-230 49 C -HUMGG-DONOR-230 50 G -HUMGG-DONOR-230 51 A -HUMGG-DONOR-230 52 A -HUMGG-DONOR-230 53 C -HUMGG-DONOR-230 54 T -HUMGG-DONOR-230 55 A -HUMGG-DONOR-230 56 A -HUMGG-DONOR-230 57 C -HUMGG-DONOR-230 58 A -HUMGG-DONOR-230 59 T -HUMGG-DONOR-230 60 A -HUMGG-DONOR-3769 1 T -HUMGG-DONOR-3769 2 T -HUMGG-DONOR-3769 3 G -HUMGG-DONOR-3769 4 G -HUMGG-DONOR-3769 5 C -HUMGG-DONOR-3769 6 T -HUMGG-DONOR-3769 7 G -HUMGG-DONOR-3769 8 G -HUMGG-DONOR-3769 9 T -HUMGG-DONOR-3769 10 G -HUMGG-DONOR-3769 11 A -HUMGG-DONOR-3769 12 A -HUMGG-DONOR-3769 13 A -HUMGG-DONOR-3769 14 G -HUMGG-DONOR-3769 15 G -HUMGG-DONOR-3769 16 C -HUMGG-DONOR-3769 17 C -HUMGG-DONOR-3769 18 G -HUMGG-DONOR-3769 19 A -HUMGG-DONOR-3769 20 G -HUMGG-DONOR-3769 21 G -HUMGG-DONOR-3769 22 A -HUMGG-DONOR-3769 23 A -HUMGG-DONOR-3769 24 G -HUMGG-DONOR-3769 25 G -HUMGG-DONOR-3769 26 C -HUMGG-DONOR-3769 27 G -HUMGG-DONOR-3769 28 A -HUMGG-DONOR-3769 29 G -HUMGG-DONOR-3769 30 A -HUMGG-DONOR-3769 31 G 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49 G -HUMGGTA-NEG-661 50 T -HUMGGTA-NEG-661 51 C -HUMGGTA-NEG-661 52 C -HUMGGTA-NEG-661 53 T -HUMGGTA-NEG-661 54 G -HUMGGTA-NEG-661 55 G -HUMGGTA-NEG-661 56 T -HUMGGTA-NEG-661 57 G -HUMGGTA-NEG-661 58 C -HUMGGTA-NEG-661 59 T -HUMGGTA-NEG-661 60 G -HUMGGTX-NEG-1981 1 C -HUMGGTX-NEG-1981 2 T -HUMGGTX-NEG-1981 3 C -HUMGGTX-NEG-1981 4 C -HUMGGTX-NEG-1981 5 A -HUMGGTX-NEG-1981 6 A -HUMGGTX-NEG-1981 7 G -HUMGGTX-NEG-1981 8 G -HUMGGTX-NEG-1981 9 T -HUMGGTX-NEG-1981 10 C -HUMGGTX-NEG-1981 11 C -HUMGGTX-NEG-1981 12 G -HUMGGTX-NEG-1981 13 C -HUMGGTX-NEG-1981 14 T -HUMGGTX-NEG-1981 15 C -HUMGGTX-NEG-1981 16 C -HUMGGTX-NEG-1981 17 C -HUMGGTX-NEG-1981 18 C -HUMGGTX-NEG-1981 19 G -HUMGGTX-NEG-1981 20 G -HUMGGTX-NEG-1981 21 T -HUMGGTX-NEG-1981 22 C -HUMGGTX-NEG-1981 23 A -HUMGGTX-NEG-1981 24 G -HUMGGTX-NEG-1981 25 C -HUMGGTX-NEG-1981 26 G -HUMGGTX-NEG-1981 27 G -HUMGGTX-NEG-1981 28 G -HUMGGTX-NEG-1981 29 A -HUMGGTX-NEG-1981 30 T -HUMGGTX-NEG-1981 31 C -HUMGGTX-NEG-1981 32 C -HUMGGTX-NEG-1981 33 T -HUMGGTX-NEG-1981 34 G -HUMGGTX-NEG-1981 35 T -HUMGGTX-NEG-1981 36 T -HUMGGTX-NEG-1981 37 C -HUMGGTX-NEG-1981 38 A -HUMGGTX-NEG-1981 39 A -HUMGGTX-NEG-1981 40 T -HUMGGTX-NEG-1981 41 A -HUMGGTX-NEG-1981 42 A -HUMGGTX-NEG-1981 43 T -HUMGGTX-NEG-1981 44 G -HUMGGTX-NEG-1981 45 A -HUMGGTX-NEG-1981 46 A -HUMGGTX-NEG-1981 47 A -HUMGGTX-NEG-1981 48 T -HUMGGTX-NEG-1981 49 G -HUMGGTX-NEG-1981 50 G -HUMGGTX-NEG-1981 51 A -HUMGGTX-NEG-1981 52 C -HUMGGTX-NEG-1981 53 G -HUMGGTX-NEG-1981 54 A -HUMGGTX-NEG-1981 55 C -HUMGGTX-NEG-1981 56 T -HUMGGTX-NEG-1981 57 T -HUMGGTX-NEG-1981 58 C -HUMGGTX-NEG-1981 59 A -HUMGGTX-NEG-1981 60 G -HUMGH-ACCEPTOR-1154 1 C -HUMGH-ACCEPTOR-1154 2 A -HUMGH-ACCEPTOR-1154 3 C -HUMGH-ACCEPTOR-1154 4 A -HUMGH-ACCEPTOR-1154 5 G -HUMGH-ACCEPTOR-1154 6 G -HUMGH-ACCEPTOR-1154 7 C -HUMGH-ACCEPTOR-1154 8 C -HUMGH-ACCEPTOR-1154 9 A -HUMGH-ACCEPTOR-1154 10 A -HUMGH-ACCEPTOR-1154 11 T -HUMGH-ACCEPTOR-1154 12 G -HUMGH-ACCEPTOR-1154 13 C -HUMGH-ACCEPTOR-1154 14 C -HUMGH-ACCEPTOR-1154 15 C -HUMGH-ACCEPTOR-1154 16 G -HUMGH-ACCEPTOR-1154 17 T -HUMGH-ACCEPTOR-1154 18 C -HUMGH-ACCEPTOR-1154 19 C -HUMGH-ACCEPTOR-1154 20 T -HUMGH-ACCEPTOR-1154 21 T -HUMGH-ACCEPTOR-1154 22 C -HUMGH-ACCEPTOR-1154 23 C -HUMGH-ACCEPTOR-1154 24 C -HUMGH-ACCEPTOR-1154 25 C -HUMGH-ACCEPTOR-1154 26 T -HUMGH-ACCEPTOR-1154 27 G -HUMGH-ACCEPTOR-1154 28 C -HUMGH-ACCEPTOR-1154 29 A -HUMGH-ACCEPTOR-1154 30 G -HUMGH-ACCEPTOR-1154 31 A -HUMGH-ACCEPTOR-1154 32 A -HUMGH-ACCEPTOR-1154 33 C -HUMGH-ACCEPTOR-1154 34 C -HUMGH-ACCEPTOR-1154 35 T -HUMGH-ACCEPTOR-1154 36 A -HUMGH-ACCEPTOR-1154 37 G -HUMGH-ACCEPTOR-1154 38 A -HUMGH-ACCEPTOR-1154 39 G -HUMGH-ACCEPTOR-1154 40 C -HUMGH-ACCEPTOR-1154 41 T -HUMGH-ACCEPTOR-1154 42 G -HUMGH-ACCEPTOR-1154 43 C -HUMGH-ACCEPTOR-1154 44 T -HUMGH-ACCEPTOR-1154 45 C -HUMGH-ACCEPTOR-1154 46 C -HUMGH-ACCEPTOR-1154 47 G -HUMGH-ACCEPTOR-1154 48 C -HUMGH-ACCEPTOR-1154 49 A -HUMGH-ACCEPTOR-1154 50 T -HUMGH-ACCEPTOR-1154 51 C -HUMGH-ACCEPTOR-1154 52 T -HUMGH-ACCEPTOR-1154 53 C 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-HUMGH-ACCEPTOR-571 34 C -HUMGH-ACCEPTOR-571 35 C -HUMGH-ACCEPTOR-571 36 C -HUMGH-ACCEPTOR-571 37 G -HUMGH-ACCEPTOR-571 38 G -HUMGH-ACCEPTOR-571 39 A -HUMGH-ACCEPTOR-571 40 C -HUMGH-ACCEPTOR-571 41 G -HUMGH-ACCEPTOR-571 42 T -HUMGH-ACCEPTOR-571 43 C -HUMGH-ACCEPTOR-571 44 C -HUMGH-ACCEPTOR-571 45 C -HUMGH-ACCEPTOR-571 46 T -HUMGH-ACCEPTOR-571 47 G -HUMGH-ACCEPTOR-571 48 C -HUMGH-ACCEPTOR-571 49 T -HUMGH-ACCEPTOR-571 50 C -HUMGH-ACCEPTOR-571 51 C -HUMGH-ACCEPTOR-571 52 T -HUMGH-ACCEPTOR-571 53 G -HUMGH-ACCEPTOR-571 54 G -HUMGH-ACCEPTOR-571 55 C -HUMGH-ACCEPTOR-571 56 T -HUMGH-ACCEPTOR-571 57 T -HUMGH-ACCEPTOR-571 58 T -HUMGH-ACCEPTOR-571 59 T -HUMGH-ACCEPTOR-571 60 G -HUMGH-ACCEPTOR-941 1 G -HUMGH-ACCEPTOR-941 2 T -HUMGH-ACCEPTOR-941 3 A -HUMGH-ACCEPTOR-941 4 G -HUMGH-ACCEPTOR-941 5 A -HUMGH-ACCEPTOR-941 6 C -HUMGH-ACCEPTOR-941 7 C -HUMGH-ACCEPTOR-941 8 T -HUMGH-ACCEPTOR-941 9 T -HUMGH-ACCEPTOR-941 10 G -HUMGH-ACCEPTOR-941 11 G -HUMGH-ACCEPTOR-941 12 T -HUMGH-ACCEPTOR-941 13 G 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47 T -HUMGHN-DONOR-1286 48 C -HUMGHN-DONOR-1286 49 C -HUMGHN-DONOR-1286 50 C -HUMGHN-DONOR-1286 51 C -HUMGHN-DONOR-1286 52 A -HUMGHN-DONOR-1286 53 G -HUMGHN-DONOR-1286 54 G -HUMGHN-DONOR-1286 55 C -HUMGHN-DONOR-1286 56 G -HUMGHN-DONOR-1286 57 G -HUMGHN-DONOR-1286 58 G -HUMGHN-DONOR-1286 59 G -HUMGHN-DONOR-1286 60 A -HUMGHN-DONOR-1544 1 C -HUMGHN-DONOR-1544 2 T -HUMGHN-DONOR-1544 3 A -HUMGHN-DONOR-1544 4 G -HUMGHN-DONOR-1544 5 A -HUMGHN-DONOR-1544 6 G -HUMGHN-DONOR-1544 7 G -HUMGHN-DONOR-1544 8 A -HUMGHN-DONOR-1544 9 A -HUMGHN-DONOR-1544 10 G -HUMGHN-DONOR-1544 11 G -HUMGHN-DONOR-1544 12 C -HUMGHN-DONOR-1544 13 A -HUMGHN-DONOR-1544 14 T -HUMGHN-DONOR-1544 15 C -HUMGHN-DONOR-1544 16 C -HUMGHN-DONOR-1544 17 A -HUMGHN-DONOR-1544 18 A -HUMGHN-DONOR-1544 19 A -HUMGHN-DONOR-1544 20 C -HUMGHN-DONOR-1544 21 G -HUMGHN-DONOR-1544 22 C -HUMGHN-DONOR-1544 23 T -HUMGHN-DONOR-1544 24 G -HUMGHN-DONOR-1544 25 A -HUMGHN-DONOR-1544 26 T -HUMGHN-DONOR-1544 27 G -HUMGHN-DONOR-1544 28 G -HUMGHN-DONOR-1544 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-HUMGPP3A08-NEG-3721 54 A -HUMGPP3A08-NEG-3721 55 A -HUMGPP3A08-NEG-3721 56 C -HUMGPP3A08-NEG-3721 57 T -HUMGPP3A08-NEG-3721 58 C -HUMGPP3A08-NEG-3721 59 C -HUMGPP3A08-NEG-3721 60 T -HUMGPP3A11-NEG-961 1 T -HUMGPP3A11-NEG-961 2 T -HUMGPP3A11-NEG-961 3 T -HUMGPP3A11-NEG-961 4 T -HUMGPP3A11-NEG-961 5 A -HUMGPP3A11-NEG-961 6 G -HUMGPP3A11-NEG-961 7 A -HUMGPP3A11-NEG-961 8 G -HUMGPP3A11-NEG-961 9 A -HUMGPP3A11-NEG-961 10 G -HUMGPP3A11-NEG-961 11 A -HUMGPP3A11-NEG-961 12 G -HUMGPP3A11-NEG-961 13 T -HUMGPP3A11-NEG-961 14 T -HUMGPP3A11-NEG-961 15 T -HUMGPP3A11-NEG-961 16 G -HUMGPP3A11-NEG-961 17 G -HUMGPP3A11-NEG-961 18 A -HUMGPP3A11-NEG-961 19 A -HUMGPP3A11-NEG-961 20 G -HUMGPP3A11-NEG-961 21 G -HUMGPP3A11-NEG-961 22 A -HUMGPP3A11-NEG-961 23 A -HUMGPP3A11-NEG-961 24 G -HUMGPP3A11-NEG-961 25 T -HUMGPP3A11-NEG-961 26 G -HUMGPP3A11-NEG-961 27 T -HUMGPP3A11-NEG-961 28 T -HUMGPP3A11-NEG-961 29 A -HUMGPP3A11-NEG-961 30 T -HUMGPP3A11-NEG-961 31 C -HUMGPP3A11-NEG-961 32 A -HUMGPP3A11-NEG-961 33 G -HUMGPP3A11-NEG-961 34 A -HUMGPP3A11-NEG-961 35 A -HUMGPP3A11-NEG-961 36 T -HUMGPP3A11-NEG-961 37 G -HUMGPP3A11-NEG-961 38 A -HUMGPP3A11-NEG-961 39 T -HUMGPP3A11-NEG-961 40 T -HUMGPP3A11-NEG-961 41 A -HUMGPP3A11-NEG-961 42 G -HUMGPP3A11-NEG-961 43 G -HUMGPP3A11-NEG-961 44 G -HUMGPP3A11-NEG-961 45 G -HUMGPP3A11-NEG-961 46 A -HUMGPP3A11-NEG-961 47 G -HUMGPP3A11-NEG-961 48 A -HUMGPP3A11-NEG-961 49 G -HUMGPP3A11-NEG-961 50 C -HUMGPP3A11-NEG-961 51 A -HUMGPP3A11-NEG-961 52 A -HUMGPP3A11-NEG-961 53 G -HUMGPP3A11-NEG-961 54 A -HUMGPP3A11-NEG-961 55 G -HUMGPP3A11-NEG-961 56 A -HUMGPP3A11-NEG-961 57 T -HUMGPP3A11-NEG-961 58 C -HUMGPP3A11-NEG-961 59 A -HUMGPP3A11-NEG-961 60 G -HUMGPP3A17-NEG-1081 1 G -HUMGPP3A17-NEG-1081 2 C -HUMGPP3A17-NEG-1081 3 A -HUMGPP3A17-NEG-1081 4 T -HUMGPP3A17-NEG-1081 5 T -HUMGPP3A17-NEG-1081 6 T -HUMGPP3A17-NEG-1081 7 T -HUMGPP3A17-NEG-1081 8 T -HUMGPP3A17-NEG-1081 9 T -HUMGPP3A17-NEG-1081 10 T -HUMGPP3A17-NEG-1081 11 G -HUMGPP3A17-NEG-1081 12 A -HUMGPP3A17-NEG-1081 13 G -HUMGPP3A17-NEG-1081 14 G -HUMGPP3A17-NEG-1081 15 C -HUMGPP3A17-NEG-1081 16 A -HUMGPP3A17-NEG-1081 17 A -HUMGPP3A17-NEG-1081 18 T -HUMGPP3A17-NEG-1081 19 G -HUMGPP3A17-NEG-1081 20 A -HUMGPP3A17-NEG-1081 21 T -HUMGPP3A17-NEG-1081 22 G -HUMGPP3A17-NEG-1081 23 T -HUMGPP3A17-NEG-1081 24 C -HUMGPP3A17-NEG-1081 25 A -HUMGPP3A17-NEG-1081 26 A -HUMGPP3A17-NEG-1081 27 G -HUMGPP3A17-NEG-1081 28 A -HUMGPP3A17-NEG-1081 29 A -HUMGPP3A17-NEG-1081 30 A -HUMGPP3A17-NEG-1081 31 T -HUMGPP3A17-NEG-1081 32 A -HUMGPP3A17-NEG-1081 33 A -HUMGPP3A17-NEG-1081 34 A -HUMGPP3A17-NEG-1081 35 G -HUMGPP3A17-NEG-1081 36 A -HUMGPP3A17-NEG-1081 37 A -HUMGPP3A17-NEG-1081 38 T -HUMGPP3A17-NEG-1081 39 T -HUMGPP3A17-NEG-1081 40 T -HUMGPP3A17-NEG-1081 41 A -HUMGPP3A17-NEG-1081 42 A -HUMGPP3A17-NEG-1081 43 A -HUMGPP3A17-NEG-1081 44 T -HUMGPP3A17-NEG-1081 45 T -HUMGPP3A17-NEG-1081 46 T -HUMGPP3A17-NEG-1081 47 T -HUMGPP3A17-NEG-1081 48 A -HUMGPP3A17-NEG-1081 49 G -HUMGPP3A17-NEG-1081 50 G -HUMGPP3A17-NEG-1081 51 A -HUMGPP3A17-NEG-1081 52 C -HUMGPP3A17-NEG-1081 53 T -HUMGPP3A17-NEG-1081 54 T -HUMGPP3A17-NEG-1081 55 T -HUMGPP3A17-NEG-1081 56 A -HUMGPP3A17-NEG-1081 57 C -HUMGPP3A17-NEG-1081 58 A -HUMGPP3A17-NEG-1081 59 G -HUMGPP3A17-NEG-1081 60 C -HUMGPP3A21-NEG-2521 1 T -HUMGPP3A21-NEG-2521 2 T -HUMGPP3A21-NEG-2521 3 G -HUMGPP3A21-NEG-2521 4 A -HUMGPP3A21-NEG-2521 5 C -HUMGPP3A21-NEG-2521 6 A -HUMGPP3A21-NEG-2521 7 T -HUMGPP3A21-NEG-2521 8 A -HUMGPP3A21-NEG-2521 9 C -HUMGPP3A21-NEG-2521 10 A -HUMGPP3A21-NEG-2521 11 T -HUMGPP3A21-NEG-2521 12 C -HUMGPP3A21-NEG-2521 13 C -HUMGPP3A21-NEG-2521 14 A -HUMGPP3A21-NEG-2521 15 A -HUMGPP3A21-NEG-2521 16 C -HUMGPP3A21-NEG-2521 17 A -HUMGPP3A21-NEG-2521 18 T -HUMGPP3A21-NEG-2521 19 T -HUMGPP3A21-NEG-2521 20 A -HUMGPP3A21-NEG-2521 21 A -HUMGPP3A21-NEG-2521 22 A -HUMGPP3A21-NEG-2521 23 A -HUMGPP3A21-NEG-2521 24 G -HUMGPP3A21-NEG-2521 25 C -HUMGPP3A21-NEG-2521 26 C -HUMGPP3A21-NEG-2521 27 A -HUMGPP3A21-NEG-2521 28 C -HUMGPP3A21-NEG-2521 29 C -HUMGPP3A21-NEG-2521 30 C -HUMGPP3A21-NEG-2521 31 C -HUMGPP3A21-NEG-2521 32 C -HUMGPP3A21-NEG-2521 33 A -HUMGPP3A21-NEG-2521 34 A -HUMGPP3A21-NEG-2521 35 A -HUMGPP3A21-NEG-2521 36 T -HUMGPP3A21-NEG-2521 37 G -HUMGPP3A21-NEG-2521 38 C -HUMGPP3A21-NEG-2521 39 C -HUMGPP3A21-NEG-2521 40 C -HUMGPP3A21-NEG-2521 41 A -HUMGPP3A21-NEG-2521 42 A -HUMGPP3A21-NEG-2521 43 G -HUMGPP3A21-NEG-2521 44 A -HUMGPP3A21-NEG-2521 45 A -HUMGPP3A21-NEG-2521 46 A -HUMGPP3A21-NEG-2521 47 A -HUMGPP3A21-NEG-2521 48 A -HUMGPP3A21-NEG-2521 49 A -HUMGPP3A21-NEG-2521 50 A -HUMGPP3A21-NEG-2521 51 G -HUMGPP3A21-NEG-2521 52 A -HUMGPP3A21-NEG-2521 53 A -HUMGPP3A21-NEG-2521 54 A -HUMGPP3A21-NEG-2521 55 G -HUMGPP3A21-NEG-2521 56 A -HUMGPP3A21-NEG-2521 57 C -HUMGPP3A21-NEG-2521 58 T -HUMGPP3A21-NEG-2521 59 T -HUMGPP3A21-NEG-2521 60 A -HUMGPPSB1-NEG-241 1 C -HUMGPPSB1-NEG-241 2 A -HUMGPPSB1-NEG-241 3 A -HUMGPPSB1-NEG-241 4 T -HUMGPPSB1-NEG-241 5 T -HUMGPPSB1-NEG-241 6 T -HUMGPPSB1-NEG-241 7 G -HUMGPPSB1-NEG-241 8 C -HUMGPPSB1-NEG-241 9 C -HUMGPPSB1-NEG-241 10 C -HUMGPPSB1-NEG-241 11 C -HUMGPPSB1-NEG-241 12 A -HUMGPPSB1-NEG-241 13 G -HUMGPPSB1-NEG-241 14 A -HUMGPPSB1-NEG-241 15 A -HUMGPPSB1-NEG-241 16 T -HUMGPPSB1-NEG-241 17 C -HUMGPPSB1-NEG-241 18 T -HUMGPPSB1-NEG-241 19 T -HUMGPPSB1-NEG-241 20 A -HUMGPPSB1-NEG-241 21 C -HUMGPPSB1-NEG-241 22 C -HUMGPPSB1-NEG-241 23 G -HUMGPPSB1-NEG-241 24 G -HUMGPPSB1-NEG-241 25 C -HUMGPPSB1-NEG-241 26 T -HUMGPPSB1-NEG-241 27 A -HUMGPPSB1-NEG-241 28 C -HUMGPPSB1-NEG-241 29 A -HUMGPPSB1-NEG-241 30 T -HUMGPPSB1-NEG-241 31 C -HUMGPPSB1-NEG-241 32 T -HUMGPPSB1-NEG-241 33 G -HUMGPPSB1-NEG-241 34 G -HUMGPPSB1-NEG-241 35 T -HUMGPPSB1-NEG-241 36 A -HUMGPPSB1-NEG-241 37 C -HUMGPPSB1-NEG-241 38 A -HUMGPPSB1-NEG-241 39 A -HUMGPPSB1-NEG-241 40 A -HUMGPPSB1-NEG-241 41 G -HUMGPPSB1-NEG-241 42 G -HUMGPPSB1-NEG-241 43 G -HUMGPPSB1-NEG-241 44 C -HUMGPPSB1-NEG-241 45 A -HUMGPPSB1-NEG-241 46 A -HUMGPPSB1-NEG-241 47 A -HUMGPPSB1-NEG-241 48 T -HUMGPPSB1-NEG-241 49 G -HUMGPPSB1-NEG-241 50 A -HUMGPPSB1-NEG-241 51 G -HUMGPPSB1-NEG-241 52 G -HUMGPPSB1-NEG-241 53 G -HUMGPPSB1-NEG-241 54 A -HUMGPPSB1-NEG-241 55 C -HUMGPPSB1-NEG-241 56 C -HUMGPPSB1-NEG-241 57 T -HUMGPPSB1-NEG-241 58 C -HUMGPPSB1-NEG-241 59 T -HUMGPPSB1-NEG-241 60 A -HUMGRP78-ACCEPTOR-1372 1 G -HUMGRP78-ACCEPTOR-1372 2 T -HUMGRP78-ACCEPTOR-1372 3 G -HUMGRP78-ACCEPTOR-1372 4 T -HUMGRP78-ACCEPTOR-1372 5 C -HUMGRP78-ACCEPTOR-1372 6 T -HUMGRP78-ACCEPTOR-1372 7 G -HUMGRP78-ACCEPTOR-1372 8 A -HUMGRP78-ACCEPTOR-1372 9 A -HUMGRP78-ACCEPTOR-1372 10 A -HUMGRP78-ACCEPTOR-1372 11 A -HUMGRP78-ACCEPTOR-1372 12 C -HUMGRP78-ACCEPTOR-1372 13 A -HUMGRP78-ACCEPTOR-1372 14 A -HUMGRP78-ACCEPTOR-1372 15 T -HUMGRP78-ACCEPTOR-1372 16 A -HUMGRP78-ACCEPTOR-1372 17 A -HUMGRP78-ACCEPTOR-1372 18 T -HUMGRP78-ACCEPTOR-1372 19 T -HUMGRP78-ACCEPTOR-1372 20 C -HUMGRP78-ACCEPTOR-1372 21 T -HUMGRP78-ACCEPTOR-1372 22 T -HUMGRP78-ACCEPTOR-1372 23 T -HUMGRP78-ACCEPTOR-1372 24 A -HUMGRP78-ACCEPTOR-1372 25 A -HUMGRP78-ACCEPTOR-1372 26 A -HUMGRP78-ACCEPTOR-1372 27 A -HUMGRP78-ACCEPTOR-1372 28 T -HUMGRP78-ACCEPTOR-1372 29 A -HUMGRP78-ACCEPTOR-1372 30 G -HUMGRP78-ACCEPTOR-1372 31 G -HUMGRP78-ACCEPTOR-1372 32 T -HUMGRP78-ACCEPTOR-1372 33 G -HUMGRP78-ACCEPTOR-1372 34 G -HUMGRP78-ACCEPTOR-1372 35 T -HUMGRP78-ACCEPTOR-1372 36 T -HUMGRP78-ACCEPTOR-1372 37 G -HUMGRP78-ACCEPTOR-1372 38 A -HUMGRP78-ACCEPTOR-1372 39 A -HUMGRP78-ACCEPTOR-1372 40 A -HUMGRP78-ACCEPTOR-1372 41 A -HUMGRP78-ACCEPTOR-1372 42 G -HUMGRP78-ACCEPTOR-1372 43 A -HUMGRP78-ACCEPTOR-1372 44 A -HUMGRP78-ACCEPTOR-1372 45 A -HUMGRP78-ACCEPTOR-1372 46 A -HUMGRP78-ACCEPTOR-1372 47 C -HUMGRP78-ACCEPTOR-1372 48 T -HUMGRP78-ACCEPTOR-1372 49 A -HUMGRP78-ACCEPTOR-1372 50 A -HUMGRP78-ACCEPTOR-1372 51 A -HUMGRP78-ACCEPTOR-1372 52 C -HUMGRP78-ACCEPTOR-1372 53 C -HUMGRP78-ACCEPTOR-1372 54 A -HUMGRP78-ACCEPTOR-1372 55 T -HUMGRP78-ACCEPTOR-1372 56 A -HUMGRP78-ACCEPTOR-1372 57 C -HUMGRP78-ACCEPTOR-1372 58 A -HUMGRP78-ACCEPTOR-1372 59 T -HUMGRP78-ACCEPTOR-1372 60 T -HUMGRP78-ACCEPTOR-2145 1 C -HUMGRP78-ACCEPTOR-2145 2 A -HUMGRP78-ACCEPTOR-2145 3 A -HUMGRP78-ACCEPTOR-2145 4 T -HUMGRP78-ACCEPTOR-2145 5 A -HUMGRP78-ACCEPTOR-2145 6 C -HUMGRP78-ACCEPTOR-2145 7 A -HUMGRP78-ACCEPTOR-2145 8 C -HUMGRP78-ACCEPTOR-2145 9 T -HUMGRP78-ACCEPTOR-2145 10 T -HUMGRP78-ACCEPTOR-2145 11 A -HUMGRP78-ACCEPTOR-2145 12 A -HUMGRP78-ACCEPTOR-2145 13 T -HUMGRP78-ACCEPTOR-2145 14 T -HUMGRP78-ACCEPTOR-2145 15 A -HUMGRP78-ACCEPTOR-2145 16 T -HUMGRP78-ACCEPTOR-2145 17 C -HUMGRP78-ACCEPTOR-2145 18 T -HUMGRP78-ACCEPTOR-2145 19 T -HUMGRP78-ACCEPTOR-2145 20 T -HUMGRP78-ACCEPTOR-2145 21 A -HUMGRP78-ACCEPTOR-2145 22 T -HUMGRP78-ACCEPTOR-2145 23 C -HUMGRP78-ACCEPTOR-2145 24 C -HUMGRP78-ACCEPTOR-2145 25 T -HUMGRP78-ACCEPTOR-2145 26 T -HUMGRP78-ACCEPTOR-2145 27 T -HUMGRP78-ACCEPTOR-2145 28 T -HUMGRP78-ACCEPTOR-2145 29 A -HUMGRP78-ACCEPTOR-2145 30 G -HUMGRP78-ACCEPTOR-2145 31 G -HUMGRP78-ACCEPTOR-2145 32 T -HUMGRP78-ACCEPTOR-2145 33 T -HUMGRP78-ACCEPTOR-2145 34 A -HUMGRP78-ACCEPTOR-2145 35 C -HUMGRP78-ACCEPTOR-2145 36 C -HUMGRP78-ACCEPTOR-2145 37 C -HUMGRP78-ACCEPTOR-2145 38 A -HUMGRP78-ACCEPTOR-2145 39 T -HUMGRP78-ACCEPTOR-2145 40 G -HUMGRP78-ACCEPTOR-2145 41 C -HUMGRP78-ACCEPTOR-2145 42 A -HUMGRP78-ACCEPTOR-2145 43 G -HUMGRP78-ACCEPTOR-2145 44 T -HUMGRP78-ACCEPTOR-2145 45 T -HUMGRP78-ACCEPTOR-2145 46 G -HUMGRP78-ACCEPTOR-2145 47 T -HUMGRP78-ACCEPTOR-2145 48 T -HUMGRP78-ACCEPTOR-2145 49 A -HUMGRP78-ACCEPTOR-2145 50 C -HUMGRP78-ACCEPTOR-2145 51 T -HUMGRP78-ACCEPTOR-2145 52 G -HUMGRP78-ACCEPTOR-2145 53 T -HUMGRP78-ACCEPTOR-2145 54 A -HUMGRP78-ACCEPTOR-2145 55 C -HUMGRP78-ACCEPTOR-2145 56 C -HUMGRP78-ACCEPTOR-2145 57 A -HUMGRP78-ACCEPTOR-2145 58 G -HUMGRP78-ACCEPTOR-2145 59 C -HUMGRP78-ACCEPTOR-2145 60 C -HUMGRP78-ACCEPTOR-2848 1 G -HUMGRP78-ACCEPTOR-2848 2 A -HUMGRP78-ACCEPTOR-2848 3 C -HUMGRP78-ACCEPTOR-2848 4 A -HUMGRP78-ACCEPTOR-2848 5 T -HUMGRP78-ACCEPTOR-2848 6 C -HUMGRP78-ACCEPTOR-2848 7 A -HUMGRP78-ACCEPTOR-2848 8 C -HUMGRP78-ACCEPTOR-2848 9 A -HUMGRP78-ACCEPTOR-2848 10 G -HUMGRP78-ACCEPTOR-2848 11 T -HUMGRP78-ACCEPTOR-2848 12 A -HUMGRP78-ACCEPTOR-2848 13 A -HUMGRP78-ACCEPTOR-2848 14 C -HUMGRP78-ACCEPTOR-2848 15 C -HUMGRP78-ACCEPTOR-2848 16 A -HUMGRP78-ACCEPTOR-2848 17 T -HUMGRP78-ACCEPTOR-2848 18 G -HUMGRP78-ACCEPTOR-2848 19 T -HUMGRP78-ACCEPTOR-2848 20 C -HUMGRP78-ACCEPTOR-2848 21 T -HUMGRP78-ACCEPTOR-2848 22 T -HUMGRP78-ACCEPTOR-2848 23 T -HUMGRP78-ACCEPTOR-2848 24 T -HUMGRP78-ACCEPTOR-2848 25 T -HUMGRP78-ACCEPTOR-2848 26 T -HUMGRP78-ACCEPTOR-2848 27 C -HUMGRP78-ACCEPTOR-2848 28 T -HUMGRP78-ACCEPTOR-2848 29 A -HUMGRP78-ACCEPTOR-2848 30 G -HUMGRP78-ACCEPTOR-2848 31 G -HUMGRP78-ACCEPTOR-2848 32 A -HUMGRP78-ACCEPTOR-2848 33 T -HUMGRP78-ACCEPTOR-2848 34 C -HUMGRP78-ACCEPTOR-2848 35 T -HUMGRP78-ACCEPTOR-2848 36 G -HUMGRP78-ACCEPTOR-2848 37 T -HUMGRP78-ACCEPTOR-2848 38 T -HUMGRP78-ACCEPTOR-2848 39 C -HUMGRP78-ACCEPTOR-2848 40 C -HUMGRP78-ACCEPTOR-2848 41 G -HUMGRP78-ACCEPTOR-2848 42 G -HUMGRP78-ACCEPTOR-2848 43 T -HUMGRP78-ACCEPTOR-2848 44 C -HUMGRP78-ACCEPTOR-2848 45 T -HUMGRP78-ACCEPTOR-2848 46 A -HUMGRP78-ACCEPTOR-2848 47 C -HUMGRP78-ACCEPTOR-2848 48 T -HUMGRP78-ACCEPTOR-2848 49 A -HUMGRP78-ACCEPTOR-2848 50 T -HUMGRP78-ACCEPTOR-2848 51 G -HUMGRP78-ACCEPTOR-2848 52 A -HUMGRP78-ACCEPTOR-2848 53 A -HUMGRP78-ACCEPTOR-2848 54 G -HUMGRP78-ACCEPTOR-2848 55 C -HUMGRP78-ACCEPTOR-2848 56 C -HUMGRP78-ACCEPTOR-2848 57 C -HUMGRP78-ACCEPTOR-2848 58 G -HUMGRP78-ACCEPTOR-2848 59 T -HUMGRP78-ACCEPTOR-2848 60 C -HUMGRP78-ACCEPTOR-3371 1 A -HUMGRP78-ACCEPTOR-3371 2 A -HUMGRP78-ACCEPTOR-3371 3 T -HUMGRP78-ACCEPTOR-3371 4 T -HUMGRP78-ACCEPTOR-3371 5 A -HUMGRP78-ACCEPTOR-3371 6 A -HUMGRP78-ACCEPTOR-3371 7 A -HUMGRP78-ACCEPTOR-3371 8 A -HUMGRP78-ACCEPTOR-3371 9 G -HUMGRP78-ACCEPTOR-3371 10 T -HUMGRP78-ACCEPTOR-3371 11 T -HUMGRP78-ACCEPTOR-3371 12 A -HUMGRP78-ACCEPTOR-3371 13 A -HUMGRP78-ACCEPTOR-3371 14 A -HUMGRP78-ACCEPTOR-3371 15 T -HUMGRP78-ACCEPTOR-3371 16 A -HUMGRP78-ACCEPTOR-3371 17 A -HUMGRP78-ACCEPTOR-3371 18 C -HUMGRP78-ACCEPTOR-3371 19 T -HUMGRP78-ACCEPTOR-3371 20 T -HUMGRP78-ACCEPTOR-3371 21 A -HUMGRP78-ACCEPTOR-3371 22 T -HUMGRP78-ACCEPTOR-3371 23 C -HUMGRP78-ACCEPTOR-3371 24 C -HUMGRP78-ACCEPTOR-3371 25 T -HUMGRP78-ACCEPTOR-3371 26 T -HUMGRP78-ACCEPTOR-3371 27 A -HUMGRP78-ACCEPTOR-3371 28 C -HUMGRP78-ACCEPTOR-3371 29 A -HUMGRP78-ACCEPTOR-3371 30 G -HUMGRP78-ACCEPTOR-3371 31 G -HUMGRP78-ACCEPTOR-3371 32 T -HUMGRP78-ACCEPTOR-3371 33 G -HUMGRP78-ACCEPTOR-3371 34 A -HUMGRP78-ACCEPTOR-3371 35 C -HUMGRP78-ACCEPTOR-3371 36 C -HUMGRP78-ACCEPTOR-3371 37 T -HUMGRP78-ACCEPTOR-3371 38 G -HUMGRP78-ACCEPTOR-3371 39 G -HUMGRP78-ACCEPTOR-3371 40 T -HUMGRP78-ACCEPTOR-3371 41 A -HUMGRP78-ACCEPTOR-3371 42 C -HUMGRP78-ACCEPTOR-3371 43 T -HUMGRP78-ACCEPTOR-3371 44 G -HUMGRP78-ACCEPTOR-3371 45 C -HUMGRP78-ACCEPTOR-3371 46 T -HUMGRP78-ACCEPTOR-3371 47 T -HUMGRP78-ACCEPTOR-3371 48 C -HUMGRP78-ACCEPTOR-3371 49 A -HUMGRP78-ACCEPTOR-3371 50 T -HUMGRP78-ACCEPTOR-3371 51 G -HUMGRP78-ACCEPTOR-3371 52 T -HUMGRP78-ACCEPTOR-3371 53 A -HUMGRP78-ACCEPTOR-3371 54 T -HUMGRP78-ACCEPTOR-3371 55 G -HUMGRP78-ACCEPTOR-3371 56 T -HUMGRP78-ACCEPTOR-3371 57 C -HUMGRP78-ACCEPTOR-3371 58 C -HUMGRP78-ACCEPTOR-3371 59 C -HUMGRP78-ACCEPTOR-3371 60 C -HUMGRP78-ACCEPTOR-4506 1 C -HUMGRP78-ACCEPTOR-4506 2 T -HUMGRP78-ACCEPTOR-4506 3 A -HUMGRP78-ACCEPTOR-4506 4 A -HUMGRP78-ACCEPTOR-4506 5 C -HUMGRP78-ACCEPTOR-4506 6 A -HUMGRP78-ACCEPTOR-4506 7 A -HUMGRP78-ACCEPTOR-4506 8 G -HUMGRP78-ACCEPTOR-4506 9 T -HUMGRP78-ACCEPTOR-4506 10 T -HUMGRP78-ACCEPTOR-4506 11 T -HUMGRP78-ACCEPTOR-4506 12 T -HUMGRP78-ACCEPTOR-4506 13 T -HUMGRP78-ACCEPTOR-4506 14 C -HUMGRP78-ACCEPTOR-4506 15 T -HUMGRP78-ACCEPTOR-4506 16 G -HUMGRP78-ACCEPTOR-4506 17 T -HUMGRP78-ACCEPTOR-4506 18 A -HUMGRP78-ACCEPTOR-4506 19 T -HUMGRP78-ACCEPTOR-4506 20 G -HUMGRP78-ACCEPTOR-4506 21 T -HUMGRP78-ACCEPTOR-4506 22 A -HUMGRP78-ACCEPTOR-4506 23 A -HUMGRP78-ACCEPTOR-4506 24 C -HUMGRP78-ACCEPTOR-4506 25 T -HUMGRP78-ACCEPTOR-4506 26 T -HUMGRP78-ACCEPTOR-4506 27 C -HUMGRP78-ACCEPTOR-4506 28 T -HUMGRP78-ACCEPTOR-4506 29 A -HUMGRP78-ACCEPTOR-4506 30 G -HUMGRP78-ACCEPTOR-4506 31 G -HUMGRP78-ACCEPTOR-4506 32 T -HUMGRP78-ACCEPTOR-4506 33 G -HUMGRP78-ACCEPTOR-4506 34 A -HUMGRP78-ACCEPTOR-4506 35 A -HUMGRP78-ACCEPTOR-4506 36 A -HUMGRP78-ACCEPTOR-4506 37 G -HUMGRP78-ACCEPTOR-4506 38 A -HUMGRP78-ACCEPTOR-4506 39 C -HUMGRP78-ACCEPTOR-4506 40 C -HUMGRP78-ACCEPTOR-4506 41 C -HUMGRP78-ACCEPTOR-4506 42 C -HUMGRP78-ACCEPTOR-4506 43 T -HUMGRP78-ACCEPTOR-4506 44 G -HUMGRP78-ACCEPTOR-4506 45 A -HUMGRP78-ACCEPTOR-4506 46 C -HUMGRP78-ACCEPTOR-4506 47 A -HUMGRP78-ACCEPTOR-4506 48 A -HUMGRP78-ACCEPTOR-4506 49 A -HUMGRP78-ACCEPTOR-4506 50 A -HUMGRP78-ACCEPTOR-4506 51 G -HUMGRP78-ACCEPTOR-4506 52 A -HUMGRP78-ACCEPTOR-4506 53 C -HUMGRP78-ACCEPTOR-4506 54 A -HUMGRP78-ACCEPTOR-4506 55 A -HUMGRP78-ACCEPTOR-4506 56 T -HUMGRP78-ACCEPTOR-4506 57 C -HUMGRP78-ACCEPTOR-4506 58 A -HUMGRP78-ACCEPTOR-4506 59 T -HUMGRP78-ACCEPTOR-4506 60 C -HUMGRP78-ACCEPTOR-775 1 T -HUMGRP78-ACCEPTOR-775 2 G -HUMGRP78-ACCEPTOR-775 3 C -HUMGRP78-ACCEPTOR-775 4 T -HUMGRP78-ACCEPTOR-775 5 G -HUMGRP78-ACCEPTOR-775 6 A -HUMGRP78-ACCEPTOR-775 7 T -HUMGRP78-ACCEPTOR-775 8 G -HUMGRP78-ACCEPTOR-775 9 T -HUMGRP78-ACCEPTOR-775 10 C -HUMGRP78-ACCEPTOR-775 11 C -HUMGRP78-ACCEPTOR-775 12 C -HUMGRP78-ACCEPTOR-775 13 T -HUMGRP78-ACCEPTOR-775 14 C -HUMGRP78-ACCEPTOR-775 15 T -HUMGRP78-ACCEPTOR-775 16 G -HUMGRP78-ACCEPTOR-775 17 T -HUMGRP78-ACCEPTOR-775 18 C -HUMGRP78-ACCEPTOR-775 19 G -HUMGRP78-ACCEPTOR-775 20 G -HUMGRP78-ACCEPTOR-775 21 G -HUMGRP78-ACCEPTOR-775 22 T -HUMGRP78-ACCEPTOR-775 23 T -HUMGRP78-ACCEPTOR-775 24 T -HUMGRP78-ACCEPTOR-775 25 T -HUMGRP78-ACCEPTOR-775 26 T -HUMGRP78-ACCEPTOR-775 27 G -HUMGRP78-ACCEPTOR-775 28 C -HUMGRP78-ACCEPTOR-775 29 A -HUMGRP78-ACCEPTOR-775 30 G -HUMGRP78-ACCEPTOR-775 31 C -HUMGRP78-ACCEPTOR-775 32 G -HUMGRP78-ACCEPTOR-775 33 T -HUMGRP78-ACCEPTOR-775 34 C -HUMGRP78-ACCEPTOR-775 35 G -HUMGRP78-ACCEPTOR-775 36 G -HUMGRP78-ACCEPTOR-775 37 C -HUMGRP78-ACCEPTOR-775 38 G -HUMGRP78-ACCEPTOR-775 39 T -HUMGRP78-ACCEPTOR-775 40 G -HUMGRP78-ACCEPTOR-775 41 T -HUMGRP78-ACCEPTOR-775 42 T -HUMGRP78-ACCEPTOR-775 43 C -HUMGRP78-ACCEPTOR-775 44 A -HUMGRP78-ACCEPTOR-775 45 A -HUMGRP78-ACCEPTOR-775 46 G -HUMGRP78-ACCEPTOR-775 47 A -HUMGRP78-ACCEPTOR-775 48 A -HUMGRP78-ACCEPTOR-775 49 C -HUMGRP78-ACCEPTOR-775 50 G -HUMGRP78-ACCEPTOR-775 51 G -HUMGRP78-ACCEPTOR-775 52 C -HUMGRP78-ACCEPTOR-775 53 C -HUMGRP78-ACCEPTOR-775 54 G -HUMGRP78-ACCEPTOR-775 55 C -HUMGRP78-ACCEPTOR-775 56 G -HUMGRP78-ACCEPTOR-775 57 T -HUMGRP78-ACCEPTOR-775 58 G -HUMGRP78-ACCEPTOR-775 59 G -HUMGRP78-ACCEPTOR-775 60 A -HUMGRP78-DONOR-1510 1 G -HUMGRP78-DONOR-1510 2 A -HUMGRP78-DONOR-1510 3 A -HUMGRP78-DONOR-1510 4 A -HUMGRP78-DONOR-1510 5 C -HUMGRP78-DONOR-1510 6 C -HUMGRP78-DONOR-1510 7 G -HUMGRP78-DONOR-1510 8 C -HUMGRP78-DONOR-1510 9 T -HUMGRP78-DONOR-1510 10 G -HUMGRP78-DONOR-1510 11 A -HUMGRP78-DONOR-1510 12 G -HUMGRP78-DONOR-1510 13 G -HUMGRP78-DONOR-1510 14 C -HUMGRP78-DONOR-1510 15 T -HUMGRP78-DONOR-1510 16 T -HUMGRP78-DONOR-1510 17 A -HUMGRP78-DONOR-1510 18 T -HUMGRP78-DONOR-1510 19 T -HUMGRP78-DONOR-1510 20 T -HUMGRP78-DONOR-1510 21 G -HUMGRP78-DONOR-1510 22 G -HUMGRP78-DONOR-1510 23 G -HUMGRP78-DONOR-1510 24 A -HUMGRP78-DONOR-1510 25 A -HUMGRP78-DONOR-1510 26 A -HUMGRP78-DONOR-1510 27 G -HUMGRP78-DONOR-1510 28 A -HUMGRP78-DONOR-1510 29 A -HUMGRP78-DONOR-1510 30 G -HUMGRP78-DONOR-1510 31 G -HUMGRP78-DONOR-1510 32 T -HUMGRP78-DONOR-1510 33 A -HUMGRP78-DONOR-1510 34 A -HUMGRP78-DONOR-1510 35 A -HUMGRP78-DONOR-1510 36 T -HUMGRP78-DONOR-1510 37 A -HUMGRP78-DONOR-1510 38 T -HUMGRP78-DONOR-1510 39 T -HUMGRP78-DONOR-1510 40 T -HUMGRP78-DONOR-1510 41 C -HUMGRP78-DONOR-1510 42 T -HUMGRP78-DONOR-1510 43 A -HUMGRP78-DONOR-1510 44 G -HUMGRP78-DONOR-1510 45 A -HUMGRP78-DONOR-1510 46 A -HUMGRP78-DONOR-1510 47 C -HUMGRP78-DONOR-1510 48 A -HUMGRP78-DONOR-1510 49 A -HUMGRP78-DONOR-1510 50 T -HUMGRP78-DONOR-1510 51 G -HUMGRP78-DONOR-1510 52 T -HUMGRP78-DONOR-1510 53 T -HUMGRP78-DONOR-1510 54 A -HUMGRP78-DONOR-1510 55 A -HUMGRP78-DONOR-1510 56 G -HUMGRP78-DONOR-1510 57 T -HUMGRP78-DONOR-1510 58 A -HUMGRP78-DONOR-1510 59 T -HUMGRP78-DONOR-1510 60 T -HUMGRP78-DONOR-3086 1 C -HUMGRP78-DONOR-3086 2 T -HUMGRP78-DONOR-3086 3 G -HUMGRP78-DONOR-3086 4 G -HUMGRP78-DONOR-3086 5 T -HUMGRP78-DONOR-3086 6 G -HUMGRP78-DONOR-3086 7 T -HUMGRP78-DONOR-3086 8 G -HUMGRP78-DONOR-3086 9 C -HUMGRP78-DONOR-3086 10 T -HUMGRP78-DONOR-3086 11 C -HUMGRP78-DONOR-3086 12 T -HUMGRP78-DONOR-3086 13 C -HUMGRP78-DONOR-3086 14 T -HUMGRP78-DONOR-3086 15 G -HUMGRP78-DONOR-3086 16 G -HUMGRP78-DONOR-3086 17 T -HUMGRP78-DONOR-3086 18 G -HUMGRP78-DONOR-3086 19 A -HUMGRP78-DONOR-3086 20 T -HUMGRP78-DONOR-3086 21 C -HUMGRP78-DONOR-3086 22 A -HUMGRP78-DONOR-3086 23 A -HUMGRP78-DONOR-3086 24 G -HUMGRP78-DONOR-3086 25 A -HUMGRP78-DONOR-3086 26 T -HUMGRP78-DONOR-3086 27 A -HUMGRP78-DONOR-3086 28 C -HUMGRP78-DONOR-3086 29 A -HUMGRP78-DONOR-3086 30 G -HUMGRP78-DONOR-3086 31 G -HUMGRP78-DONOR-3086 32 T -HUMGRP78-DONOR-3086 33 A -HUMGRP78-DONOR-3086 34 G -HUMGRP78-DONOR-3086 35 G -HUMGRP78-DONOR-3086 36 T -HUMGRP78-DONOR-3086 37 C -HUMGRP78-DONOR-3086 38 A -HUMGRP78-DONOR-3086 39 T -HUMGRP78-DONOR-3086 40 C -HUMGRP78-DONOR-3086 41 A -HUMGRP78-DONOR-3086 42 T -HUMGRP78-DONOR-3086 43 C -HUMGRP78-DONOR-3086 44 G -HUMGRP78-DONOR-3086 45 C -HUMGRP78-DONOR-3086 46 A -HUMGRP78-DONOR-3086 47 G -HUMGRP78-DONOR-3086 48 C -HUMGRP78-DONOR-3086 49 A -HUMGRP78-DONOR-3086 50 T -HUMGRP78-DONOR-3086 51 C -HUMGRP78-DONOR-3086 52 T -HUMGRP78-DONOR-3086 53 T -HUMGRP78-DONOR-3086 54 T -HUMGRP78-DONOR-3086 55 C -HUMGRP78-DONOR-3086 56 T -HUMGRP78-DONOR-3086 57 T -HUMGRP78-DONOR-3086 58 A -HUMGRP78-DONOR-3086 59 G -HUMGRP78-DONOR-3086 60 T -HUMGRP78-DONOR-3539 1 A -HUMGRP78-DONOR-3539 2 A -HUMGRP78-DONOR-3539 3 C -HUMGRP78-DONOR-3539 4 C -HUMGRP78-DONOR-3539 5 A -HUMGRP78-DONOR-3539 6 A -HUMGRP78-DONOR-3539 7 C -HUMGRP78-DONOR-3539 8 T -HUMGRP78-DONOR-3539 9 G -HUMGRP78-DONOR-3539 10 T -HUMGRP78-DONOR-3539 11 T -HUMGRP78-DONOR-3539 12 A -HUMGRP78-DONOR-3539 13 C -HUMGRP78-DONOR-3539 14 A -HUMGRP78-DONOR-3539 15 A -HUMGRP78-DONOR-3539 16 T -HUMGRP78-DONOR-3539 17 C -HUMGRP78-DONOR-3539 18 A -HUMGRP78-DONOR-3539 19 A -HUMGRP78-DONOR-3539 20 G -HUMGRP78-DONOR-3539 21 G -HUMGRP78-DONOR-3539 22 T -HUMGRP78-DONOR-3539 23 C -HUMGRP78-DONOR-3539 24 T -HUMGRP78-DONOR-3539 25 A -HUMGRP78-DONOR-3539 26 T -HUMGRP78-DONOR-3539 27 G -HUMGRP78-DONOR-3539 28 A -HUMGRP78-DONOR-3539 29 A -HUMGRP78-DONOR-3539 30 G -HUMGRP78-DONOR-3539 31 G -HUMGRP78-DONOR-3539 32 T -HUMGRP78-DONOR-3539 33 A -HUMGRP78-DONOR-3539 34 A -HUMGRP78-DONOR-3539 35 T -HUMGRP78-DONOR-3539 36 T -HUMGRP78-DONOR-3539 37 A -HUMGRP78-DONOR-3539 38 C -HUMGRP78-DONOR-3539 39 C -HUMGRP78-DONOR-3539 40 T -HUMGRP78-DONOR-3539 41 T -HUMGRP78-DONOR-3539 42 A -HUMGRP78-DONOR-3539 43 A -HUMGRP78-DONOR-3539 44 G -HUMGRP78-DONOR-3539 45 T -HUMGRP78-DONOR-3539 46 T -HUMGRP78-DONOR-3539 47 T -HUMGRP78-DONOR-3539 48 G -HUMGRP78-DONOR-3539 49 G -HUMGRP78-DONOR-3539 50 T -HUMGRP78-DONOR-3539 51 T -HUMGRP78-DONOR-3539 52 A -HUMGRP78-DONOR-3539 53 A -HUMGRP78-DONOR-3539 54 T -HUMGRP78-DONOR-3539 55 A -HUMGRP78-DONOR-3539 56 T -HUMGRP78-DONOR-3539 57 C -HUMGRP78-DONOR-3539 58 A -HUMGRP78-DONOR-3539 59 T -HUMGRP78-DONOR-3539 60 G -HUMGRP78-DONOR-685 1 C -HUMGRP78-DONOR-685 2 G -HUMGRP78-DONOR-685 3 G -HUMGRP78-DONOR-685 4 C -HUMGRP78-DONOR-685 5 A -HUMGRP78-DONOR-685 6 T -HUMGRP78-DONOR-685 7 C -HUMGRP78-DONOR-685 8 G -HUMGRP78-DONOR-685 9 A -HUMGRP78-DONOR-685 10 C -HUMGRP78-DONOR-685 11 T -HUMGRP78-DONOR-685 12 T -HUMGRP78-DONOR-685 13 G -HUMGRP78-DONOR-685 14 G -HUMGRP78-DONOR-685 15 G -HUMGRP78-DONOR-685 16 G -HUMGRP78-DONOR-685 17 A -HUMGRP78-DONOR-685 18 C -HUMGRP78-DONOR-685 19 C -HUMGRP78-DONOR-685 20 A -HUMGRP78-DONOR-685 21 C -HUMGRP78-DONOR-685 22 C -HUMGRP78-DONOR-685 23 T -HUMGRP78-DONOR-685 24 A -HUMGRP78-DONOR-685 25 C -HUMGRP78-DONOR-685 26 T -HUMGRP78-DONOR-685 27 C -HUMGRP78-DONOR-685 28 C -HUMGRP78-DONOR-685 29 T -HUMGRP78-DONOR-685 30 G -HUMGRP78-DONOR-685 31 G -HUMGRP78-DONOR-685 32 T -HUMGRP78-DONOR-685 33 A -HUMGRP78-DONOR-685 34 A -HUMGRP78-DONOR-685 35 G -HUMGRP78-DONOR-685 36 T -HUMGRP78-DONOR-685 37 G -HUMGRP78-DONOR-685 38 G -HUMGRP78-DONOR-685 39 G -HUMGRP78-DONOR-685 40 G -HUMGRP78-DONOR-685 41 T -HUMGRP78-DONOR-685 42 T -HUMGRP78-DONOR-685 43 G -HUMGRP78-DONOR-685 44 C -HUMGRP78-DONOR-685 45 G -HUMGRP78-DONOR-685 46 G -HUMGRP78-DONOR-685 47 A -HUMGRP78-DONOR-685 48 T -HUMGRP78-DONOR-685 49 G -HUMGRP78-DONOR-685 50 A -HUMGRP78-DONOR-685 51 G -HUMGRP78-DONOR-685 52 G -HUMGRP78-DONOR-685 53 G -HUMGRP78-DONOR-685 54 G -HUMGRP78-DONOR-685 55 G -HUMGRP78-DONOR-685 56 A -HUMGRP78-DONOR-685 57 C -HUMGRP78-DONOR-685 58 G -HUMGRP78-DONOR-685 59 G -HUMGRP78-DONOR-685 60 G -HUMGRP78-NEG-421 1 T -HUMGRP78-NEG-421 2 T -HUMGRP78-NEG-421 3 C -HUMGRP78-NEG-421 4 G -HUMGRP78-NEG-421 5 C -HUMGRP78-NEG-421 6 G -HUMGRP78-NEG-421 7 A -HUMGRP78-NEG-421 8 G -HUMGRP78-NEG-421 9 T -HUMGRP78-NEG-421 10 G -HUMGRP78-NEG-421 11 T -HUMGRP78-NEG-421 12 G -HUMGRP78-NEG-421 13 A -HUMGRP78-NEG-421 14 G -HUMGRP78-NEG-421 15 A -HUMGRP78-NEG-421 16 G -HUMGRP78-NEG-421 17 G -HUMGRP78-NEG-421 18 G -HUMGRP78-NEG-421 19 A -HUMGRP78-NEG-421 20 A -HUMGRP78-NEG-421 21 G -HUMGRP78-NEG-421 22 C -HUMGRP78-NEG-421 23 G -HUMGRP78-NEG-421 24 C -HUMGRP78-NEG-421 25 C -HUMGRP78-NEG-421 26 G -HUMGRP78-NEG-421 27 C -HUMGRP78-NEG-421 28 G -HUMGRP78-NEG-421 29 G -HUMGRP78-NEG-421 30 C -HUMGRP78-NEG-421 31 C -HUMGRP78-NEG-421 32 T -HUMGRP78-NEG-421 33 G -HUMGRP78-NEG-421 34 T -HUMGRP78-NEG-421 35 A -HUMGRP78-NEG-421 36 T -HUMGRP78-NEG-421 37 T -HUMGRP78-NEG-421 38 T -HUMGRP78-NEG-421 39 C -HUMGRP78-NEG-421 40 T -HUMGRP78-NEG-421 41 A -HUMGRP78-NEG-421 42 G -HUMGRP78-NEG-421 43 A -HUMGRP78-NEG-421 44 C -HUMGRP78-NEG-421 45 C -HUMGRP78-NEG-421 46 T -HUMGRP78-NEG-421 47 G -HUMGRP78-NEG-421 48 C -HUMGRP78-NEG-421 49 C -HUMGRP78-NEG-421 50 C -HUMGRP78-NEG-421 51 T -HUMGRP78-NEG-421 52 T -HUMGRP78-NEG-421 53 C -HUMGRP78-NEG-421 54 G -HUMGRP78-NEG-421 55 C -HUMGRP78-NEG-421 56 C -HUMGRP78-NEG-421 57 T -HUMGRP78-NEG-421 58 G -HUMGRP78-NEG-421 59 G -HUMGRP78-NEG-421 60 T -HUMGRP78-NEG-4981 1 C -HUMGRP78-NEG-4981 2 A -HUMGRP78-NEG-4981 3 A -HUMGRP78-NEG-4981 4 A -HUMGRP78-NEG-4981 5 G -HUMGRP78-NEG-4981 6 C -HUMGRP78-NEG-4981 7 T -HUMGRP78-NEG-4981 8 A -HUMGRP78-NEG-4981 9 A -HUMGRP78-NEG-4981 10 G -HUMGRP78-NEG-4981 11 A -HUMGRP78-NEG-4981 12 A -HUMGRP78-NEG-4981 13 G -HUMGRP78-NEG-4981 14 A -HUMGRP78-NEG-4981 15 A -HUMGRP78-NEG-4981 16 G -HUMGRP78-NEG-4981 17 G -HUMGRP78-NEG-4981 18 A -HUMGRP78-NEG-4981 19 A -HUMGRP78-NEG-4981 20 C -HUMGRP78-NEG-4981 21 T -HUMGRP78-NEG-4981 22 G -HUMGRP78-NEG-4981 23 G -HUMGRP78-NEG-4981 24 A -HUMGRP78-NEG-4981 25 A -HUMGRP78-NEG-4981 26 G -HUMGRP78-NEG-4981 27 A -HUMGRP78-NEG-4981 28 A -HUMGRP78-NEG-4981 29 A -HUMGRP78-NEG-4981 30 T -HUMGRP78-NEG-4981 31 T -HUMGRP78-NEG-4981 32 G -HUMGRP78-NEG-4981 33 T -HUMGRP78-NEG-4981 34 T -HUMGRP78-NEG-4981 35 C -HUMGRP78-NEG-4981 36 A -HUMGRP78-NEG-4981 37 A -HUMGRP78-NEG-4981 38 C -HUMGRP78-NEG-4981 39 C -HUMGRP78-NEG-4981 40 A -HUMGRP78-NEG-4981 41 A -HUMGRP78-NEG-4981 42 T -HUMGRP78-NEG-4981 43 T -HUMGRP78-NEG-4981 44 A -HUMGRP78-NEG-4981 45 T -HUMGRP78-NEG-4981 46 C -HUMGRP78-NEG-4981 47 A -HUMGRP78-NEG-4981 48 G -HUMGRP78-NEG-4981 49 C -HUMGRP78-NEG-4981 50 A -HUMGRP78-NEG-4981 51 A -HUMGRP78-NEG-4981 52 A -HUMGRP78-NEG-4981 53 C -HUMGRP78-NEG-4981 54 T -HUMGRP78-NEG-4981 55 C -HUMGRP78-NEG-4981 56 T -HUMGRP78-NEG-4981 57 A -HUMGRP78-NEG-4981 58 T -HUMGRP78-NEG-4981 59 G -HUMGRP78-NEG-4981 60 G -HUMGSA1R-NEG-661 1 C -HUMGSA1R-NEG-661 2 G -HUMGSA1R-NEG-661 3 C -HUMGSA1R-NEG-661 4 C -HUMGSA1R-NEG-661 5 G -HUMGSA1R-NEG-661 6 C -HUMGSA1R-NEG-661 7 A -HUMGSA1R-NEG-661 8 A -HUMGSA1R-NEG-661 9 G -HUMGSA1R-NEG-661 10 T -HUMGSA1R-NEG-661 11 G -HUMGSA1R-NEG-661 12 G -HUMGSA1R-NEG-661 13 A -HUMGSA1R-NEG-661 14 T 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24 C -HUMHTA1-NEG-241 25 A -HUMHTA1-NEG-241 26 T -HUMHTA1-NEG-241 27 C -HUMHTA1-NEG-241 28 T -HUMHTA1-NEG-241 29 T -HUMHTA1-NEG-241 30 G -HUMHTA1-NEG-241 31 G -HUMHTA1-NEG-241 32 G -HUMHTA1-NEG-241 33 T -HUMHTA1-NEG-241 34 C -HUMHTA1-NEG-241 35 T -HUMHTA1-NEG-241 36 T -HUMHTA1-NEG-241 37 C -HUMHTA1-NEG-241 38 T -HUMHTA1-NEG-241 39 T -HUMHTA1-NEG-241 40 G -HUMHTA1-NEG-241 41 A -HUMHTA1-NEG-241 42 T -HUMHTA1-NEG-241 43 G -HUMHTA1-NEG-241 44 C -HUMHTA1-NEG-241 45 A -HUMHTA1-NEG-241 46 G -HUMHTA1-NEG-241 47 G -HUMHTA1-NEG-241 48 A -HUMHTA1-NEG-241 49 A -HUMHTA1-NEG-241 50 A -HUMHTA1-NEG-241 51 G -HUMHTA1-NEG-241 52 G -HUMHTA1-NEG-241 53 C -HUMHTA1-NEG-241 54 A -HUMHTA1-NEG-241 55 C -HUMHTA1-NEG-241 56 A -HUMHTA1-NEG-241 57 T -HUMHTA1-NEG-241 58 C -HUMHTA1-NEG-241 59 T -HUMHTA1-NEG-241 60 C -HUMHTV1A-NEG-121 1 C -HUMHTV1A-NEG-121 2 A -HUMHTV1A-NEG-121 3 T -HUMHTV1A-NEG-121 4 T -HUMHTV1A-NEG-121 5 A -HUMHTV1A-NEG-121 6 A -HUMHTV1A-NEG-121 7 A -HUMHTV1A-NEG-121 8 A -HUMHTV1A-NEG-121 9 C 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-HUMIFNINI-DONOR-2912 35 G -HUMIFNINI-DONOR-2912 36 T -HUMIFNINI-DONOR-2912 37 G -HUMIFNINI-DONOR-2912 38 A -HUMIFNINI-DONOR-2912 39 A -HUMIFNINI-DONOR-2912 40 A -HUMIFNINI-DONOR-2912 41 T -HUMIFNINI-DONOR-2912 42 C -HUMIFNINI-DONOR-2912 43 C -HUMIFNINI-DONOR-2912 44 C -HUMIFNINI-DONOR-2912 45 A -HUMIFNINI-DONOR-2912 46 T -HUMIFNINI-DONOR-2912 47 C -HUMIFNINI-DONOR-2912 48 T -HUMIFNINI-DONOR-2912 49 G -HUMIFNINI-DONOR-2912 50 A -HUMIFNINI-DONOR-2912 51 T -HUMIFNINI-DONOR-2912 52 T -HUMIFNINI-DONOR-2912 53 A -HUMIFNINI-DONOR-2912 54 T -HUMIFNINI-DONOR-2912 55 C -HUMIFNINI-DONOR-2912 56 A -HUMIFNINI-DONOR-2912 57 C -HUMIFNINI-DONOR-2912 58 T -HUMIFNINI-DONOR-2912 59 T -HUMIFNINI-DONOR-2912 60 C -HUMIFNINI-DONOR-3237 1 G -HUMIFNINI-DONOR-3237 2 A -HUMIFNINI-DONOR-3237 3 A -HUMIFNINI-DONOR-3237 4 T -HUMIFNINI-DONOR-3237 5 T -HUMIFNINI-DONOR-3237 6 T -HUMIFNINI-DONOR-3237 7 A -HUMIFNINI-DONOR-3237 8 C -HUMIFNINI-DONOR-3237 9 T -HUMIFNINI-DONOR-3237 10 G -HUMIFNINI-DONOR-3237 11 A -HUMIFNINI-DONOR-3237 12 A -HUMIFNINI-DONOR-3237 13 A -HUMIFNINI-DONOR-3237 14 G -HUMIFNINI-DONOR-3237 15 C -HUMIFNINI-DONOR-3237 16 A -HUMIFNINI-DONOR-3237 17 G -HUMIFNINI-DONOR-3237 18 T -HUMIFNINI-DONOR-3237 19 T -HUMIFNINI-DONOR-3237 20 A -HUMIFNINI-DONOR-3237 21 G -HUMIFNINI-DONOR-3237 22 C -HUMIFNINI-DONOR-3237 23 A -HUMIFNINI-DONOR-3237 24 A -HUMIFNINI-DONOR-3237 25 G -HUMIFNINI-DONOR-3237 26 G -HUMIFNINI-DONOR-3237 27 A -HUMIFNINI-DONOR-3237 28 A -HUMIFNINI-DONOR-3237 29 A -HUMIFNINI-DONOR-3237 30 G -HUMIFNINI-DONOR-3237 31 G -HUMIFNINI-DONOR-3237 32 T -HUMIFNINI-DONOR-3237 33 A -HUMIFNINI-DONOR-3237 34 G -HUMIFNINI-DONOR-3237 35 G -HUMIFNINI-DONOR-3237 36 T -HUMIFNINI-DONOR-3237 37 T -HUMIFNINI-DONOR-3237 38 T -HUMIFNINI-DONOR-3237 39 G -HUMIFNINI-DONOR-3237 40 C -HUMIFNINI-DONOR-3237 41 T -HUMIFNINI-DONOR-3237 42 G -HUMIFNINI-DONOR-3237 43 T -HUMIFNINI-DONOR-3237 44 T -HUMIFNINI-DONOR-3237 45 G -HUMIFNINI-DONOR-3237 46 C -HUMIFNINI-DONOR-3237 47 C -HUMIFNINI-DONOR-3237 48 T -HUMIFNINI-DONOR-3237 49 G -HUMIFNINI-DONOR-3237 50 C -HUMIFNINI-DONOR-3237 51 A -HUMIFNINI-DONOR-3237 52 G -HUMIFNINI-DONOR-3237 53 C -HUMIFNINI-DONOR-3237 54 C -HUMIFNINI-DONOR-3237 55 G -HUMIFNINI-DONOR-3237 56 A -HUMIFNINI-DONOR-3237 57 A -HUMIFNINI-DONOR-3237 58 T -HUMIFNINI-DONOR-3237 59 T -HUMIFNINI-DONOR-3237 60 G -HUMIFNN-NEG-601 1 C -HUMIFNN-NEG-601 2 C -HUMIFNN-NEG-601 3 T -HUMIFNN-NEG-601 4 C -HUMIFNN-NEG-601 5 A -HUMIFNN-NEG-601 6 T -HUMIFNN-NEG-601 7 T -HUMIFNN-NEG-601 8 G -HUMIFNN-NEG-601 9 A -HUMIFNN-NEG-601 10 T -HUMIFNN-NEG-601 11 T -HUMIFNN-NEG-601 12 A -HUMIFNN-NEG-601 13 A -HUMIFNN-NEG-601 14 T -HUMIFNN-NEG-601 15 A -HUMIFNN-NEG-601 16 C -HUMIFNN-NEG-601 17 A -HUMIFNN-NEG-601 18 T -HUMIFNN-NEG-601 19 C -HUMIFNN-NEG-601 20 A -HUMIFNN-NEG-601 21 T -HUMIFNN-NEG-601 22 C -HUMIFNN-NEG-601 23 T -HUMIFNN-NEG-601 24 C -HUMIFNN-NEG-601 25 A -HUMIFNN-NEG-601 26 C -HUMIFNN-NEG-601 27 A -HUMIFNN-NEG-601 28 C -HUMIFNN-NEG-601 29 T -HUMIFNN-NEG-601 30 T -HUMIFNN-NEG-601 31 T -HUMIFNN-NEG-601 32 C -HUMIFNN-NEG-601 33 A -HUMIFNN-NEG-601 34 T -HUMIFNN-NEG-601 35 G -HUMIFNN-NEG-601 36 A -HUMIFNN-NEG-601 37 G -HUMIFNN-NEG-601 38 T -HUMIFNN-NEG-601 39 T -HUMIFNN-NEG-601 40 C -HUMIFNN-NEG-601 41 T -HUMIFNN-NEG-601 42 T -HUMIFNN-NEG-601 43 C -HUMIFNN-NEG-601 44 C -HUMIFNN-NEG-601 45 A -HUMIFNN-NEG-601 46 T -HUMIFNN-NEG-601 47 T -HUMIFNN-NEG-601 48 T -HUMIFNN-NEG-601 49 C -HUMIFNN-NEG-601 50 A -HUMIFNN-NEG-601 51 A -HUMIFNN-NEG-601 52 A -HUMIFNN-NEG-601 53 G -HUMIFNN-NEG-601 54 A -HUMIFNN-NEG-601 55 C -HUMIFNN-NEG-601 56 T -HUMIFNN-NEG-601 57 C -HUMIFNN-NEG-601 58 A -HUMIFNN-NEG-601 59 C -HUMIFNN-NEG-601 60 T -HUMIGB7-NEG-1381 1 T -HUMIGB7-NEG-1381 2 C -HUMIGB7-NEG-1381 3 T -HUMIGB7-NEG-1381 4 C -HUMIGB7-NEG-1381 5 C -HUMIGB7-NEG-1381 6 C -HUMIGB7-NEG-1381 7 C -HUMIGB7-NEG-1381 8 A -HUMIGB7-NEG-1381 9 T -HUMIGB7-NEG-1381 10 A -HUMIGB7-NEG-1381 11 T -HUMIGB7-NEG-1381 12 G -HUMIGB7-NEG-1381 13 C -HUMIGB7-NEG-1381 14 A -HUMIGB7-NEG-1381 15 A -HUMIGB7-NEG-1381 16 T -HUMIGB7-NEG-1381 17 T -HUMIGB7-NEG-1381 18 T -HUMIGB7-NEG-1381 19 G -HUMIGB7-NEG-1381 20 C -HUMIGB7-NEG-1381 21 T -HUMIGB7-NEG-1381 22 T -HUMIGB7-NEG-1381 23 A -HUMIGB7-NEG-1381 24 A -HUMIGB7-NEG-1381 25 T -HUMIGB7-NEG-1381 26 G -HUMIGB7-NEG-1381 27 T -HUMIGB7-NEG-1381 28 A -HUMIGB7-NEG-1381 29 A -HUMIGB7-NEG-1381 30 C -HUMIGB7-NEG-1381 31 C -HUMIGB7-NEG-1381 32 T -HUMIGB7-NEG-1381 33 C -HUMIGB7-NEG-1381 34 T -HUMIGB7-NEG-1381 35 T -HUMIGB7-NEG-1381 36 C -HUMIGB7-NEG-1381 37 T -HUMIGB7-NEG-1381 38 T -HUMIGB7-NEG-1381 39 T -HUMIGB7-NEG-1381 40 T -HUMIGB7-NEG-1381 41 G -HUMIGB7-NEG-1381 42 C -HUMIGB7-NEG-1381 43 C -HUMIGB7-NEG-1381 44 A -HUMIGB7-NEG-1381 45 T -HUMIGB7-NEG-1381 46 G -HUMIGB7-NEG-1381 47 T -HUMIGB7-NEG-1381 48 T -HUMIGB7-NEG-1381 49 T -HUMIGB7-NEG-1381 50 C -HUMIGB7-NEG-1381 51 C -HUMIGB7-NEG-1381 52 A -HUMIGB7-NEG-1381 53 T -HUMIGB7-NEG-1381 54 T -HUMIGB7-NEG-1381 55 C -HUMIGB7-NEG-1381 56 T -HUMIGB7-NEG-1381 57 G -HUMIGB7-NEG-1381 58 C -HUMIGB7-NEG-1381 59 C -HUMIGB7-NEG-1381 60 A -HUMIGCB4-NEG-61 1 T -HUMIGCB4-NEG-61 2 C -HUMIGCB4-NEG-61 3 G -HUMIGCB4-NEG-61 4 G -HUMIGCB4-NEG-61 5 C -HUMIGCB4-NEG-61 6 A -HUMIGCB4-NEG-61 7 A -HUMIGCB4-NEG-61 8 C -HUMIGCB4-NEG-61 9 C -HUMIGCB4-NEG-61 10 C -HUMIGCB4-NEG-61 11 C -HUMIGCB4-NEG-61 12 C -HUMIGCB4-NEG-61 13 G -HUMIGCB4-NEG-61 14 C -HUMIGCB4-NEG-61 15 A -HUMIGCB4-NEG-61 16 A -HUMIGCB4-NEG-61 17 G -HUMIGCB4-NEG-61 18 T -HUMIGCB4-NEG-61 19 C -HUMIGCB4-NEG-61 20 C -HUMIGCB4-NEG-61 21 A -HUMIGCB4-NEG-61 22 A -HUMIGCB4-NEG-61 23 G -HUMIGCB4-NEG-61 24 C -HUMIGCB4-NEG-61 25 T -HUMIGCB4-NEG-61 26 C -HUMIGCB4-NEG-61 27 A -HUMIGCB4-NEG-61 28 T -HUMIGCB4-NEG-61 29 C -HUMIGCB4-NEG-61 30 T -HUMIGCB4-NEG-61 31 G -HUMIGCB4-NEG-61 32 C -HUMIGCB4-NEG-61 33 C -HUMIGCB4-NEG-61 34 A -HUMIGCB4-NEG-61 35 G -HUMIGCB4-NEG-61 36 G -HUMIGCB4-NEG-61 37 C -HUMIGCB4-NEG-61 38 C -HUMIGCB4-NEG-61 39 A -HUMIGCB4-NEG-61 40 C -HUMIGCB4-NEG-61 41 G -HUMIGCB4-NEG-61 42 G -HUMIGCB4-NEG-61 43 G -HUMIGCB4-NEG-61 44 T -HUMIGCB4-NEG-61 45 T -HUMIGCB4-NEG-61 46 T -HUMIGCB4-NEG-61 47 C -HUMIGCB4-NEG-61 48 A -HUMIGCB4-NEG-61 49 G -HUMIGCB4-NEG-61 50 T -HUMIGCB4-NEG-61 51 C -HUMIGCB4-NEG-61 52 C -HUMIGCB4-NEG-61 53 C -HUMIGCB4-NEG-61 54 C -HUMIGCB4-NEG-61 55 G -HUMIGCB4-NEG-61 56 G -HUMIGCB4-NEG-61 57 C -HUMIGCB4-NEG-61 58 A -HUMIGCB4-NEG-61 59 G -HUMIGCB4-NEG-61 60 A -HUMIGCC4-NEG-781 1 A -HUMIGCC4-NEG-781 2 G -HUMIGCC4-NEG-781 3 G -HUMIGCC4-NEG-781 4 A -HUMIGCC4-NEG-781 5 C -HUMIGCC4-NEG-781 6 C -HUMIGCC4-NEG-781 7 C -HUMIGCC4-NEG-781 8 T -HUMIGCC4-NEG-781 9 G -HUMIGCC4-NEG-781 10 C -HUMIGCC4-NEG-781 11 C -HUMIGCC4-NEG-781 12 C -HUMIGCC4-NEG-781 13 C -HUMIGCC4-NEG-781 14 T -HUMIGCC4-NEG-781 15 G -HUMIGCC4-NEG-781 16 A -HUMIGCC4-NEG-781 17 C -HUMIGCC4-NEG-781 18 C -HUMIGCC4-NEG-781 19 T -HUMIGCC4-NEG-781 20 A -HUMIGCC4-NEG-781 21 A -HUMIGCC4-NEG-781 22 G -HUMIGCC4-NEG-781 23 C -HUMIGCC4-NEG-781 24 C -HUMIGCC4-NEG-781 25 C -HUMIGCC4-NEG-781 26 A -HUMIGCC4-NEG-781 27 C -HUMIGCC4-NEG-781 28 C -HUMIGCC4-NEG-781 29 C -HUMIGCC4-NEG-781 30 C -HUMIGCC4-NEG-781 31 A -HUMIGCC4-NEG-781 32 A -HUMIGCC4-NEG-781 33 A -HUMIGCC4-NEG-781 34 G -HUMIGCC4-NEG-781 35 G -HUMIGCC4-NEG-781 36 C -HUMIGCC4-NEG-781 37 C -HUMIGCC4-NEG-781 38 A -HUMIGCC4-NEG-781 39 A -HUMIGCC4-NEG-781 40 A -HUMIGCC4-NEG-781 41 C -HUMIGCC4-NEG-781 42 T -HUMIGCC4-NEG-781 43 C -HUMIGCC4-NEG-781 44 T -HUMIGCC4-NEG-781 45 C -HUMIGCC4-NEG-781 46 C -HUMIGCC4-NEG-781 47 A -HUMIGCC4-NEG-781 48 C -HUMIGCC4-NEG-781 49 T -HUMIGCC4-NEG-781 50 C -HUMIGCC4-NEG-781 51 C -HUMIGCC4-NEG-781 52 C -HUMIGCC4-NEG-781 53 T -HUMIGCC4-NEG-781 54 C -HUMIGCC4-NEG-781 55 A -HUMIGCC4-NEG-781 56 G -HUMIGCC4-NEG-781 57 C -HUMIGCC4-NEG-781 58 T -HUMIGCC4-NEG-781 59 C -HUMIGCC4-NEG-781 60 G -HUMIGCC8-NEG-481 1 A -HUMIGCC8-NEG-481 2 C -HUMIGCC8-NEG-481 3 C -HUMIGCC8-NEG-481 4 C -HUMIGCC8-NEG-481 5 C -HUMIGCC8-NEG-481 6 G -HUMIGCC8-NEG-481 7 T -HUMIGCC8-NEG-481 8 C -HUMIGCC8-NEG-481 9 G -HUMIGCC8-NEG-481 10 T -HUMIGCC8-NEG-481 11 G -HUMIGCC8-NEG-481 12 C -HUMIGCC8-NEG-481 13 C -HUMIGCC8-NEG-481 14 C -HUMIGCC8-NEG-481 15 T -HUMIGCC8-NEG-481 16 G -HUMIGCC8-NEG-481 17 A -HUMIGCC8-NEG-481 18 C -HUMIGCC8-NEG-481 19 A -HUMIGCC8-NEG-481 20 C -HUMIGCC8-NEG-481 21 T -HUMIGCC8-NEG-481 22 G -HUMIGCC8-NEG-481 23 C -HUMIGCC8-NEG-481 24 G -HUMIGCC8-NEG-481 25 C -HUMIGCC8-NEG-481 26 C -HUMIGCC8-NEG-481 27 T -HUMIGCC8-NEG-481 28 G -HUMIGCC8-NEG-481 29 C -HUMIGCC8-NEG-481 30 A -HUMIGCC8-NEG-481 31 C -HUMIGCC8-NEG-481 32 C -HUMIGCC8-NEG-481 33 C -HUMIGCC8-NEG-481 34 G -HUMIGCC8-NEG-481 35 T -HUMIGCC8-NEG-481 36 G -HUMIGCC8-NEG-481 37 T -HUMIGCC8-NEG-481 38 T -HUMIGCC8-NEG-481 39 C -HUMIGCC8-NEG-481 40 C -HUMIGCC8-NEG-481 41 C -HUMIGCC8-NEG-481 42 C -HUMIGCC8-NEG-481 43 A -HUMIGCC8-NEG-481 44 C -HUMIGCC8-NEG-481 45 A -HUMIGCC8-NEG-481 46 G -HUMIGCC8-NEG-481 47 G -HUMIGCC8-NEG-481 48 G -HUMIGCC8-NEG-481 49 A -HUMIGCC8-NEG-481 50 G -HUMIGCC8-NEG-481 51 C -HUMIGCC8-NEG-481 52 C -HUMIGCC8-NEG-481 53 G -HUMIGCC8-NEG-481 54 C -HUMIGCC8-NEG-481 55 C -HUMIGCC8-NEG-481 56 C -HUMIGCC8-NEG-481 57 C -HUMIGCC8-NEG-481 58 T -HUMIGCC8-NEG-481 59 T -HUMIGCC8-NEG-481 60 C -HUMIGCD2-NEG-121 1 T -HUMIGCD2-NEG-121 2 G -HUMIGCD2-NEG-121 3 G -HUMIGCD2-NEG-121 4 A -HUMIGCD2-NEG-121 5 C -HUMIGCD2-NEG-121 6 C -HUMIGCD2-NEG-121 7 A -HUMIGCD2-NEG-121 8 T -HUMIGCD2-NEG-121 9 C -HUMIGCD2-NEG-121 10 G -HUMIGCD2-NEG-121 11 C -HUMIGCD2-NEG-121 12 G -HUMIGCD2-NEG-121 13 G -HUMIGCD2-NEG-121 14 A -HUMIGCD2-NEG-121 15 T -HUMIGCD2-NEG-121 16 A -HUMIGCD2-NEG-121 17 G -HUMIGCD2-NEG-121 18 A -HUMIGCD2-NEG-121 19 C -HUMIGCD2-NEG-121 20 A -HUMIGCD2-NEG-121 21 A -HUMIGCD2-NEG-121 22 G -HUMIGCD2-NEG-121 23 A -HUMIGCD2-NEG-121 24 A -HUMIGCD2-NEG-121 25 C -HUMIGCD2-NEG-121 26 C -HUMIGCD2-NEG-121 27 G -HUMIGCD2-NEG-121 28 A -HUMIGCD2-NEG-121 29 G -HUMIGCD2-NEG-121 30 G -HUMIGCD2-NEG-121 31 G -HUMIGCD2-NEG-121 32 G -HUMIGCD2-NEG-121 33 C -HUMIGCD2-NEG-121 34 C -HUMIGCD2-NEG-121 35 T -HUMIGCD2-NEG-121 36 C -HUMIGCD2-NEG-121 37 T -HUMIGCD2-NEG-121 38 G -HUMIGCD2-NEG-121 39 C -HUMIGCD2-NEG-121 40 G -HUMIGCD2-NEG-121 41 C -HUMIGCD2-NEG-121 42 C -HUMIGCD2-NEG-121 43 C -HUMIGCD2-NEG-121 44 T -HUMIGCD2-NEG-121 45 G -HUMIGCD2-NEG-121 46 G -HUMIGCD2-NEG-121 47 G -HUMIGCD2-NEG-121 48 C -HUMIGCD2-NEG-121 49 C -HUMIGCD2-NEG-121 50 C -HUMIGCD2-NEG-121 51 A -HUMIGCD2-NEG-121 52 G -HUMIGCD2-NEG-121 53 C -HUMIGCD2-NEG-121 54 T -HUMIGCD2-NEG-121 55 C -HUMIGCD2-NEG-121 56 T -HUMIGCD2-NEG-121 57 G -HUMIGCD2-NEG-121 58 T -HUMIGCD2-NEG-121 59 C -HUMIGCD2-NEG-121 60 C -HUMIGCD7-NEG-1921 1 G -HUMIGCD7-NEG-1921 2 C -HUMIGCD7-NEG-1921 3 A -HUMIGCD7-NEG-1921 4 C -HUMIGCD7-NEG-1921 5 A -HUMIGCD7-NEG-1921 6 T -HUMIGCD7-NEG-1921 7 G -HUMIGCD7-NEG-1921 8 C -HUMIGCD7-NEG-1921 9 C -HUMIGCD7-NEG-1921 10 C -HUMIGCD7-NEG-1921 11 C -HUMIGCD7-NEG-1921 12 G -HUMIGCD7-NEG-1921 13 G -HUMIGCD7-NEG-1921 14 A -HUMIGCD7-NEG-1921 15 C -HUMIGCD7-NEG-1921 16 A -HUMIGCD7-NEG-1921 17 G -HUMIGCD7-NEG-1921 18 T -HUMIGCD7-NEG-1921 19 T -HUMIGCD7-NEG-1921 20 C -HUMIGCD7-NEG-1921 21 T -HUMIGCD7-NEG-1921 22 G -HUMIGCD7-NEG-1921 23 A -HUMIGCD7-NEG-1921 24 G -HUMIGCD7-NEG-1921 25 T -HUMIGCD7-NEG-1921 26 G -HUMIGCD7-NEG-1921 27 A -HUMIGCD7-NEG-1921 28 G -HUMIGCD7-NEG-1921 29 C -HUMIGCD7-NEG-1921 30 A -HUMIGCD7-NEG-1921 31 G -HUMIGCD7-NEG-1921 32 T -HUMIGCD7-NEG-1921 33 G -HUMIGCD7-NEG-1921 34 G -HUMIGCD7-NEG-1921 35 C -HUMIGCD7-NEG-1921 36 A -HUMIGCD7-NEG-1921 37 T -HUMIGCD7-NEG-1921 38 G -HUMIGCD7-NEG-1921 39 G -HUMIGCD7-NEG-1921 40 C -HUMIGCD7-NEG-1921 41 C -HUMIGCD7-NEG-1921 42 G -HUMIGCD7-NEG-1921 43 T -HUMIGCD7-NEG-1921 44 T -HUMIGCD7-NEG-1921 45 T -HUMIGCD7-NEG-1921 46 G -HUMIGCD7-NEG-1921 47 T -HUMIGCD7-NEG-1921 48 C -HUMIGCD7-NEG-1921 49 C -HUMIGCD7-NEG-1921 50 C -HUMIGCD7-NEG-1921 51 T -HUMIGCD7-NEG-1921 52 G -HUMIGCD7-NEG-1921 53 A -HUMIGCD7-NEG-1921 54 G -HUMIGCD7-NEG-1921 55 A -HUMIGCD7-NEG-1921 56 G -HUMIGCD7-NEG-1921 57 A -HUMIGCD7-NEG-1921 58 G -HUMIGCD7-NEG-1921 59 C -HUMIGCD7-NEG-1921 60 C -HUMIGCH5-NEG-241 1 G -HUMIGCH5-NEG-241 2 T -HUMIGCH5-NEG-241 3 G -HUMIGCH5-NEG-241 4 A -HUMIGCH5-NEG-241 5 A -HUMIGCH5-NEG-241 6 C -HUMIGCH5-NEG-241 7 A -HUMIGCH5-NEG-241 8 C -HUMIGCH5-NEG-241 9 T -HUMIGCH5-NEG-241 10 T -HUMIGCH5-NEG-241 11 C -HUMIGCH5-NEG-241 12 T -HUMIGCH5-NEG-241 13 G -HUMIGCH5-NEG-241 14 G -HUMIGCH5-NEG-241 15 G -HUMIGCH5-NEG-241 16 T -HUMIGCH5-NEG-241 17 T -HUMIGCH5-NEG-241 18 T -HUMIGCH5-NEG-241 19 G -HUMIGCH5-NEG-241 20 C -HUMIGCH5-NEG-241 21 C -HUMIGCH5-NEG-241 22 C -HUMIGCH5-NEG-241 23 C -HUMIGCH5-NEG-241 24 C -HUMIGCH5-NEG-241 25 G -HUMIGCH5-NEG-241 26 C -HUMIGCH5-NEG-241 27 A -HUMIGCH5-NEG-241 28 C -HUMIGCH5-NEG-241 29 G -HUMIGCH5-NEG-241 30 C -HUMIGCH5-NEG-241 31 C -HUMIGCH5-NEG-241 32 C -HUMIGCH5-NEG-241 33 C -HUMIGCH5-NEG-241 34 C -HUMIGCH5-NEG-241 35 C -HUMIGCH5-NEG-241 36 T -HUMIGCH5-NEG-241 37 C -HUMIGCH5-NEG-241 38 C -HUMIGCH5-NEG-241 39 A -HUMIGCH5-NEG-241 40 C -HUMIGCH5-NEG-241 41 A -HUMIGCH5-NEG-241 42 G -HUMIGCH5-NEG-241 43 C -HUMIGCH5-NEG-241 44 C -HUMIGCH5-NEG-241 45 C -HUMIGCH5-NEG-241 46 A -HUMIGCH5-NEG-241 47 G -HUMIGCH5-NEG-241 48 G -HUMIGCH5-NEG-241 49 A -HUMIGCH5-NEG-241 50 G -HUMIGCH5-NEG-241 51 C -HUMIGCH5-NEG-241 52 A -HUMIGCH5-NEG-241 53 C -HUMIGCH5-NEG-241 54 C -HUMIGCH5-NEG-241 55 A -HUMIGCH5-NEG-241 56 C -HUMIGCH5-NEG-241 57 G -HUMIGCH5-NEG-241 58 T -HUMIGCH5-NEG-241 59 T -HUMIGCH5-NEG-241 60 C -HUMIGF27-NEG-1 1 G -HUMIGF27-NEG-1 2 A -HUMIGF27-NEG-1 3 C -HUMIGF27-NEG-1 4 A -HUMIGF27-NEG-1 5 A -HUMIGF27-NEG-1 6 C -HUMIGF27-NEG-1 7 T -HUMIGF27-NEG-1 8 T -HUMIGF27-NEG-1 9 C -HUMIGF27-NEG-1 10 C -HUMIGF27-NEG-1 11 C -HUMIGF27-NEG-1 12 C -HUMIGF27-NEG-1 13 A -HUMIGF27-NEG-1 14 G -HUMIGF27-NEG-1 15 A -HUMIGF27-NEG-1 16 T -HUMIGF27-NEG-1 17 A -HUMIGF27-NEG-1 18 C -HUMIGF27-NEG-1 19 C -HUMIGF27-NEG-1 20 C -HUMIGF27-NEG-1 21 C -HUMIGF27-NEG-1 22 G -HUMIGF27-NEG-1 23 T -HUMIGF27-NEG-1 24 G -HUMIGF27-NEG-1 25 G -HUMIGF27-NEG-1 26 G -HUMIGF27-NEG-1 27 C -HUMIGF27-NEG-1 28 A -HUMIGF27-NEG-1 29 A -HUMIGF27-NEG-1 30 G -HUMIGF27-NEG-1 31 T -HUMIGF27-NEG-1 32 T -HUMIGF27-NEG-1 33 C -HUMIGF27-NEG-1 34 T -HUMIGF27-NEG-1 35 T -HUMIGF27-NEG-1 36 C -HUMIGF27-NEG-1 37 C -HUMIGF27-NEG-1 38 A -HUMIGF27-NEG-1 39 A -HUMIGF27-NEG-1 40 T -HUMIGF27-NEG-1 41 A -HUMIGF27-NEG-1 42 T -HUMIGF27-NEG-1 43 G -HUMIGF27-NEG-1 44 A -HUMIGF27-NEG-1 45 C -HUMIGF27-NEG-1 46 A -HUMIGF27-NEG-1 47 C -HUMIGF27-NEG-1 48 C -HUMIGF27-NEG-1 49 T -HUMIGF27-NEG-1 50 G -HUMIGF27-NEG-1 51 G -HUMIGF27-NEG-1 52 A -HUMIGF27-NEG-1 53 A -HUMIGF27-NEG-1 54 G -HUMIGF27-NEG-1 55 C -HUMIGF27-NEG-1 56 A -HUMIGF27-NEG-1 57 G -HUMIGF27-NEG-1 58 T -HUMIGF27-NEG-1 59 C -HUMIGF27-NEG-1 60 C -HUMIGF2AP-NEG-421 1 G -HUMIGF2AP-NEG-421 2 G -HUMIGF2AP-NEG-421 3 A -HUMIGF2AP-NEG-421 4 G -HUMIGF2AP-NEG-421 5 G -HUMIGF2AP-NEG-421 6 G -HUMIGF2AP-NEG-421 7 G -HUMIGF2AP-NEG-421 8 G -HUMIGF2AP-NEG-421 9 T -HUMIGF2AP-NEG-421 10 C -HUMIGF2AP-NEG-421 11 C -HUMIGF2AP-NEG-421 12 T -HUMIGF2AP-NEG-421 13 G -HUMIGF2AP-NEG-421 14 C -HUMIGF2AP-NEG-421 15 A -HUMIGF2AP-NEG-421 16 G -HUMIGF2AP-NEG-421 17 G -HUMIGF2AP-NEG-421 18 G -HUMIGF2AP-NEG-421 19 C -HUMIGF2AP-NEG-421 20 G -HUMIGF2AP-NEG-421 21 G -HUMIGF2AP-NEG-421 22 G -HUMIGF2AP-NEG-421 23 G -HUMIGF2AP-NEG-421 24 G -HUMIGF2AP-NEG-421 25 G -HUMIGF2AP-NEG-421 26 C -HUMIGF2AP-NEG-421 27 T -HUMIGF2AP-NEG-421 28 G -HUMIGF2AP-NEG-421 29 G -HUMIGF2AP-NEG-421 30 G -HUMIGF2AP-NEG-421 31 A -HUMIGF2AP-NEG-421 32 A -HUMIGF2AP-NEG-421 33 G -HUMIGF2AP-NEG-421 34 G -HUMIGF2AP-NEG-421 35 T -HUMIGF2AP-NEG-421 36 G -HUMIGF2AP-NEG-421 37 G -HUMIGF2AP-NEG-421 38 G -HUMIGF2AP-NEG-421 39 G -HUMIGF2AP-NEG-421 40 A -HUMIGF2AP-NEG-421 41 G -HUMIGF2AP-NEG-421 42 A -HUMIGF2AP-NEG-421 43 G -HUMIGF2AP-NEG-421 44 G -HUMIGF2AP-NEG-421 45 C -HUMIGF2AP-NEG-421 46 T -HUMIGF2AP-NEG-421 47 G -HUMIGF2AP-NEG-421 48 C -HUMIGF2AP-NEG-421 49 C -HUMIGF2AP-NEG-421 50 G -HUMIGF2AP-NEG-421 51 A -HUMIGF2AP-NEG-421 52 G -HUMIGF2AP-NEG-421 53 A -HUMIGF2AP-NEG-421 54 G -HUMIGF2AP-NEG-421 55 C -HUMIGF2AP-NEG-421 56 C -HUMIGF2AP-NEG-421 57 A -HUMIGF2AP-NEG-421 58 C -HUMIGF2AP-NEG-421 59 C -HUMIGF2AP-NEG-421 60 C -HUMIGFII5-NEG-121 1 C -HUMIGFII5-NEG-121 2 G -HUMIGFII5-NEG-121 3 G -HUMIGFII5-NEG-121 4 G -HUMIGFII5-NEG-121 5 G -HUMIGFII5-NEG-121 6 T -HUMIGFII5-NEG-121 7 C -HUMIGFII5-NEG-121 8 A -HUMIGFII5-NEG-121 9 C -HUMIGFII5-NEG-121 10 G -HUMIGFII5-NEG-121 11 T -HUMIGFII5-NEG-121 12 G -HUMIGFII5-NEG-121 13 C -HUMIGFII5-NEG-121 14 T -HUMIGFII5-NEG-121 15 C -HUMIGFII5-NEG-121 16 G -HUMIGFII5-NEG-121 17 C -HUMIGFII5-NEG-121 18 C -HUMIGFII5-NEG-121 19 A -HUMIGFII5-NEG-121 20 A -HUMIGFII5-NEG-121 21 G -HUMIGFII5-NEG-121 22 G -HUMIGFII5-NEG-121 23 A -HUMIGFII5-NEG-121 24 G -HUMIGFII5-NEG-121 25 C -HUMIGFII5-NEG-121 26 T -HUMIGFII5-NEG-121 27 C -HUMIGFII5-NEG-121 28 G -HUMIGFII5-NEG-121 29 A -HUMIGFII5-NEG-121 30 G -HUMIGFII5-NEG-121 31 G -HUMIGFII5-NEG-121 32 C -HUMIGFII5-NEG-121 33 G -HUMIGFII5-NEG-121 34 T -HUMIGFII5-NEG-121 35 T -HUMIGFII5-NEG-121 36 C -HUMIGFII5-NEG-121 37 A -HUMIGFII5-NEG-121 38 G -HUMIGFII5-NEG-121 39 G -HUMIGFII5-NEG-121 40 G -HUMIGFII5-NEG-121 41 A -HUMIGFII5-NEG-121 42 G -HUMIGFII5-NEG-121 43 G -HUMIGFII5-NEG-121 44 C -HUMIGFII5-NEG-121 45 C -HUMIGFII5-NEG-121 46 A -HUMIGFII5-NEG-121 47 A -HUMIGFII5-NEG-121 48 A -HUMIGFII5-NEG-121 49 C -HUMIGFII5-NEG-121 50 G -HUMIGFII5-NEG-121 51 T -HUMIGFII5-NEG-121 52 C -HUMIGFII5-NEG-121 53 A -HUMIGFII5-NEG-121 54 C -HUMIGFII5-NEG-121 55 C -HUMIGFII5-NEG-121 56 G -HUMIGFII5-NEG-121 57 T -HUMIGFII5-NEG-121 58 C -HUMIGFII5-NEG-121 59 C -HUMIGFII5-NEG-121 60 C -HUMIGFIIR-NEG-2281 1 G -HUMIGFIIR-NEG-2281 2 G -HUMIGFIIR-NEG-2281 3 C -HUMIGFIIR-NEG-2281 4 A -HUMIGFIIR-NEG-2281 5 C -HUMIGFIIR-NEG-2281 6 A -HUMIGFIIR-NEG-2281 7 C -HUMIGFIIR-NEG-2281 8 C -HUMIGFIIR-NEG-2281 9 C -HUMIGFIIR-NEG-2281 10 T -HUMIGFIIR-NEG-2281 11 A -HUMIGFIIR-NEG-2281 12 T -HUMIGFIIR-NEG-2281 13 A -HUMIGFIIR-NEG-2281 14 A -HUMIGFIIR-NEG-2281 15 C -HUMIGFIIR-NEG-2281 16 A -HUMIGFIIR-NEG-2281 17 A -HUMIGFIIR-NEG-2281 18 T -HUMIGFIIR-NEG-2281 19 G -HUMIGFIIR-NEG-2281 20 A -HUMIGFIIR-NEG-2281 21 A -HUMIGFIIR-NEG-2281 22 A -HUMIGFIIR-NEG-2281 23 G -HUMIGFIIR-NEG-2281 24 A -HUMIGFIIR-NEG-2281 25 C -HUMIGFIIR-NEG-2281 26 A -HUMIGFIIR-NEG-2281 27 C -HUMIGFIIR-NEG-2281 28 A -HUMIGFIIR-NEG-2281 29 C -HUMIGFIIR-NEG-2281 30 A -HUMIGFIIR-NEG-2281 31 C -HUMIGFIIR-NEG-2281 32 C -HUMIGFIIR-NEG-2281 33 G -HUMIGFIIR-NEG-2281 34 A -HUMIGFIIR-NEG-2281 35 G -HUMIGFIIR-NEG-2281 36 A -HUMIGFIIR-NEG-2281 37 G -HUMIGFIIR-NEG-2281 38 C -HUMIGFIIR-NEG-2281 39 T -HUMIGFIIR-NEG-2281 40 A -HUMIGFIIR-NEG-2281 41 C -HUMIGFIIR-NEG-2281 42 G -HUMIGFIIR-NEG-2281 43 C -HUMIGFIIR-NEG-2281 44 T -HUMIGFIIR-NEG-2281 45 C -HUMIGFIIR-NEG-2281 46 A -HUMIGFIIR-NEG-2281 47 T -HUMIGFIIR-NEG-2281 48 C -HUMIGFIIR-NEG-2281 49 A -HUMIGFIIR-NEG-2281 50 C -HUMIGFIIR-NEG-2281 51 C -HUMIGFIIR-NEG-2281 52 T -HUMIGFIIR-NEG-2281 53 T -HUMIGFIIR-NEG-2281 54 T -HUMIGFIIR-NEG-2281 55 C -HUMIGFIIR-NEG-2281 56 T -HUMIGFIIR-NEG-2281 57 C -HUMIGFIIR-NEG-2281 58 T -HUMIGFIIR-NEG-2281 59 G -HUMIGFIIR-NEG-2281 60 T -HUMIGFIIR-NEG-6841 1 T -HUMIGFIIR-NEG-6841 2 G -HUMIGFIIR-NEG-6841 3 C -HUMIGFIIR-NEG-6841 4 G 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49 G -HUMIGKC1-NEG-1681 50 G -HUMIGKC1-NEG-1681 51 T -HUMIGKC1-NEG-1681 52 T -HUMIGKC1-NEG-1681 53 G -HUMIGKC1-NEG-1681 54 C -HUMIGKC1-NEG-1681 55 A -HUMIGKC1-NEG-1681 56 A -HUMIGKC1-NEG-1681 57 A -HUMIGKC1-NEG-1681 58 A -HUMIGKC1-NEG-1681 59 G -HUMIGKC1-NEG-1681 60 T -HUMIGKP1-NEG-1441 1 A -HUMIGKP1-NEG-1441 2 A -HUMIGKP1-NEG-1441 3 T -HUMIGKP1-NEG-1441 4 A -HUMIGKP1-NEG-1441 5 T -HUMIGKP1-NEG-1441 6 C -HUMIGKP1-NEG-1441 7 T -HUMIGKP1-NEG-1441 8 C -HUMIGKP1-NEG-1441 9 A -HUMIGKP1-NEG-1441 10 A -HUMIGKP1-NEG-1441 11 A -HUMIGKP1-NEG-1441 12 G -HUMIGKP1-NEG-1441 13 A -HUMIGKP1-NEG-1441 14 A -HUMIGKP1-NEG-1441 15 T -HUMIGKP1-NEG-1441 16 A -HUMIGKP1-NEG-1441 17 A -HUMIGKP1-NEG-1441 18 G -HUMIGKP1-NEG-1441 19 C -HUMIGKP1-NEG-1441 20 C -HUMIGKP1-NEG-1441 21 A -HUMIGKP1-NEG-1441 22 C -HUMIGKP1-NEG-1441 23 T -HUMIGKP1-NEG-1441 24 C -HUMIGKP1-NEG-1441 25 T -HUMIGKP1-NEG-1441 26 T -HUMIGKP1-NEG-1441 27 G -HUMIGKP1-NEG-1441 28 A -HUMIGKP1-NEG-1441 29 A -HUMIGKP1-NEG-1441 30 A -HUMIGKP1-NEG-1441 31 T -HUMIGKP1-NEG-1441 32 T -HUMIGKP1-NEG-1441 33 C -HUMIGKP1-NEG-1441 34 C -HUMIGKP1-NEG-1441 35 A -HUMIGKP1-NEG-1441 36 A -HUMIGKP1-NEG-1441 37 G -HUMIGKP1-NEG-1441 38 T -HUMIGKP1-NEG-1441 39 A -HUMIGKP1-NEG-1441 40 G -HUMIGKP1-NEG-1441 41 G -HUMIGKP1-NEG-1441 42 A -HUMIGKP1-NEG-1441 43 T -HUMIGKP1-NEG-1441 44 T -HUMIGKP1-NEG-1441 45 A -HUMIGKP1-NEG-1441 46 T -HUMIGKP1-NEG-1441 47 T -HUMIGKP1-NEG-1441 48 T -HUMIGKP1-NEG-1441 49 T -HUMIGKP1-NEG-1441 50 T -HUMIGKP1-NEG-1441 51 C -HUMIGKP1-NEG-1441 52 T -HUMIGKP1-NEG-1441 53 A -HUMIGKP1-NEG-1441 54 A -HUMIGKP1-NEG-1441 55 A -HUMIGKP1-NEG-1441 56 T -HUMIGKP1-NEG-1441 57 A -HUMIGKP1-NEG-1441 58 T -HUMIGKP1-NEG-1441 59 A -HUMIGKP1-NEG-1441 60 G -HUMIGKVAB-NEG-1261 1 T -HUMIGKVAB-NEG-1261 2 C -HUMIGKVAB-NEG-1261 3 C -HUMIGKVAB-NEG-1261 4 T -HUMIGKVAB-NEG-1261 5 T -HUMIGKVAB-NEG-1261 6 T -HUMIGKVAB-NEG-1261 7 A -HUMIGKVAB-NEG-1261 8 C -HUMIGKVAB-NEG-1261 9 A -HUMIGKVAB-NEG-1261 10 G -HUMIGKVAB-NEG-1261 11 A -HUMIGKVAB-NEG-1261 12 C 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-HUMIL1B-ACCEPTOR-879 37 G -HUMIL1B-ACCEPTOR-879 38 A -HUMIL1B-ACCEPTOR-879 39 A -HUMIL1B-ACCEPTOR-879 40 G -HUMIL1B-ACCEPTOR-879 41 C -HUMIL1B-ACCEPTOR-879 42 A -HUMIL1B-ACCEPTOR-879 43 G -HUMIL1B-ACCEPTOR-879 44 C -HUMIL1B-ACCEPTOR-879 45 C -HUMIL1B-ACCEPTOR-879 46 A -HUMIL1B-ACCEPTOR-879 47 T -HUMIL1B-ACCEPTOR-879 48 G -HUMIL1B-ACCEPTOR-879 49 G -HUMIL1B-ACCEPTOR-879 50 C -HUMIL1B-ACCEPTOR-879 51 A -HUMIL1B-ACCEPTOR-879 52 G -HUMIL1B-ACCEPTOR-879 53 A -HUMIL1B-ACCEPTOR-879 54 A -HUMIL1B-ACCEPTOR-879 55 G -HUMIL1B-ACCEPTOR-879 56 T -HUMIL1B-ACCEPTOR-879 57 A -HUMIL1B-ACCEPTOR-879 58 C -HUMIL1B-ACCEPTOR-879 59 C -HUMIL1B-ACCEPTOR-879 60 T -HUMIL1B-DONOR-4458 1 A -HUMIL1B-DONOR-4458 2 C -HUMIL1B-DONOR-4458 3 C -HUMIL1B-DONOR-4458 4 T -HUMIL1B-DONOR-4458 5 C -HUMIL1B-DONOR-4458 6 C -HUMIL1B-DONOR-4458 7 A -HUMIL1B-DONOR-4458 8 G -HUMIL1B-DONOR-4458 9 G -HUMIL1B-DONOR-4458 10 G -HUMIL1B-DONOR-4458 11 A -HUMIL1B-DONOR-4458 12 C -HUMIL1B-DONOR-4458 13 A -HUMIL1B-DONOR-4458 14 G 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38 G -HUMKER18-DONOR-5719 39 G -HUMKER18-DONOR-5719 40 G -HUMKER18-DONOR-5719 41 C -HUMKER18-DONOR-5719 42 T -HUMKER18-DONOR-5719 43 C -HUMKER18-DONOR-5719 44 T -HUMKER18-DONOR-5719 45 C -HUMKER18-DONOR-5719 46 C -HUMKER18-DONOR-5719 47 T -HUMKER18-DONOR-5719 48 A -HUMKER18-DONOR-5719 49 C -HUMKER18-DONOR-5719 50 C -HUMKER18-DONOR-5719 51 C -HUMKER18-DONOR-5719 52 A -HUMKER18-DONOR-5719 53 C -HUMKER18-DONOR-5719 54 A -HUMKER18-DONOR-5719 55 C -HUMKER18-DONOR-5719 56 G -HUMKER18-DONOR-5719 57 T -HUMKER18-DONOR-5719 58 G -HUMKER18-DONOR-5719 59 C -HUMKER18-DONOR-5719 60 T -HUMKER18-NEG-1381 1 G -HUMKER18-NEG-1381 2 G -HUMKER18-NEG-1381 3 T -HUMKER18-NEG-1381 4 T -HUMKER18-NEG-1381 5 G -HUMKER18-NEG-1381 6 A -HUMKER18-NEG-1381 7 A -HUMKER18-NEG-1381 8 T -HUMKER18-NEG-1381 9 T -HUMKER18-NEG-1381 10 G -HUMKER18-NEG-1381 11 T -HUMKER18-NEG-1381 12 A -HUMKER18-NEG-1381 13 A -HUMKER18-NEG-1381 14 A -HUMKER18-NEG-1381 15 T -HUMKER18-NEG-1381 16 G -HUMKER18-NEG-1381 17 C -HUMKER18-NEG-1381 18 T 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4 C -HUMMH6-DONOR-2841 5 T -HUMMH6-DONOR-2841 6 G -HUMMH6-DONOR-2841 7 T -HUMMH6-DONOR-2841 8 G -HUMMH6-DONOR-2841 9 C -HUMMH6-DONOR-2841 10 T -HUMMH6-DONOR-2841 11 G -HUMMH6-DONOR-2841 12 T -HUMMH6-DONOR-2841 13 G -HUMMH6-DONOR-2841 14 G -HUMMH6-DONOR-2841 15 A -HUMMH6-DONOR-2841 16 G -HUMMH6-DONOR-2841 17 A -HUMMH6-DONOR-2841 18 A -HUMMH6-DONOR-2841 19 A -HUMMH6-DONOR-2841 20 G -HUMMH6-DONOR-2841 21 A -HUMMH6-DONOR-2841 22 A -HUMMH6-DONOR-2841 23 G -HUMMH6-DONOR-2841 24 A -HUMMH6-DONOR-2841 25 G -HUMMH6-DONOR-2841 26 C -HUMMH6-DONOR-2841 27 T -HUMMH6-DONOR-2841 28 C -HUMMH6-DONOR-2841 29 A -HUMMH6-DONOR-2841 30 G -HUMMH6-DONOR-2841 31 G -HUMMH6-DONOR-2841 32 T -HUMMH6-DONOR-2841 33 A -HUMMH6-DONOR-2841 34 A -HUMMH6-DONOR-2841 35 G -HUMMH6-DONOR-2841 36 G -HUMMH6-DONOR-2841 37 A -HUMMH6-DONOR-2841 38 A -HUMMH6-DONOR-2841 39 G -HUMMH6-DONOR-2841 40 G -HUMMH6-DONOR-2841 41 G -HUMMH6-DONOR-2841 42 G -HUMMH6-DONOR-2841 43 T -HUMMH6-DONOR-2841 44 G -HUMMH6-DONOR-2841 45 A -HUMMH6-DONOR-2841 46 C -HUMMH6-DONOR-2841 47 A -HUMMH6-DONOR-2841 48 A -HUMMH6-DONOR-2841 49 G -HUMMH6-DONOR-2841 50 T -HUMMH6-DONOR-2841 51 G -HUMMH6-DONOR-2841 52 G -HUMMH6-DONOR-2841 53 G -HUMMH6-DONOR-2841 54 G -HUMMH6-DONOR-2841 55 T -HUMMH6-DONOR-2841 56 C -HUMMH6-DONOR-2841 57 T -HUMMH6-DONOR-2841 58 G -HUMMH6-DONOR-2841 59 A -HUMMH6-DONOR-2841 60 G -HUMMH6-DONOR-3321 1 A -HUMMH6-DONOR-3321 2 G -HUMMH6-DONOR-3321 3 G -HUMMH6-DONOR-3321 4 A -HUMMH6-DONOR-3321 5 G -HUMMH6-DONOR-3321 6 G -HUMMH6-DONOR-3321 7 G -HUMMH6-DONOR-3321 8 A -HUMMH6-DONOR-3321 9 G -HUMMH6-DONOR-3321 10 C -HUMMH6-DONOR-3321 11 T -HUMMH6-DONOR-3321 12 A -HUMMH6-DONOR-3321 13 C -HUMMH6-DONOR-3321 14 T -HUMMH6-DONOR-3321 15 C -HUMMH6-DONOR-3321 16 T -HUMMH6-DONOR-3321 17 C -HUMMH6-DONOR-3321 18 A -HUMMH6-DONOR-3321 19 G -HUMMH6-DONOR-3321 20 G -HUMMH6-DONOR-3321 21 C -HUMMH6-DONOR-3321 22 T -HUMMH6-DONOR-3321 23 G -HUMMH6-DONOR-3321 24 C -HUMMH6-DONOR-3321 25 A -HUMMH6-DONOR-3321 26 A -HUMMH6-DONOR-3321 27 G -HUMMH6-DONOR-3321 28 T -HUMMH6-DONOR-3321 29 A -HUMMH6-DONOR-3321 30 A -HUMMH6-DONOR-3321 31 G -HUMMH6-DONOR-3321 32 T -HUMMH6-DONOR-3321 33 A -HUMMH6-DONOR-3321 34 T -HUMMH6-DONOR-3321 35 G -HUMMH6-DONOR-3321 36 A -HUMMH6-DONOR-3321 37 A -HUMMH6-DONOR-3321 38 G -HUMMH6-DONOR-3321 39 G -HUMMH6-DONOR-3321 40 A -HUMMH6-DONOR-3321 41 G -HUMMH6-DONOR-3321 42 G -HUMMH6-DONOR-3321 43 C -HUMMH6-DONOR-3321 44 T -HUMMH6-DONOR-3321 45 G -HUMMH6-DONOR-3321 46 A -HUMMH6-DONOR-3321 47 T -HUMMH6-DONOR-3321 48 C -HUMMH6-DONOR-3321 49 C -HUMMH6-DONOR-3321 50 C -HUMMH6-DONOR-3321 51 T -HUMMH6-DONOR-3321 52 G -HUMMH6-DONOR-3321 53 A -HUMMH6-DONOR-3321 54 G -HUMMH6-DONOR-3321 55 A -HUMMH6-DONOR-3321 56 T -HUMMH6-DONOR-3321 57 C -HUMMH6-DONOR-3321 58 C -HUMMH6-DONOR-3321 59 T -HUMMH6-DONOR-3321 60 T -HUMMH6-DONOR-3506 1 G -HUMMH6-DONOR-3506 2 C -HUMMH6-DONOR-3506 3 T -HUMMH6-DONOR-3506 4 C -HUMMH6-DONOR-3506 5 T -HUMMH6-DONOR-3506 6 A -HUMMH6-DONOR-3506 7 A -HUMMH6-DONOR-3506 8 T -HUMMH6-DONOR-3506 9 G 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-HUMMH6-DONOR-3506 51 A -HUMMH6-DONOR-3506 52 T -HUMMH6-DONOR-3506 53 G -HUMMH6-DONOR-3506 54 T -HUMMH6-DONOR-3506 55 G -HUMMH6-DONOR-3506 56 T -HUMMH6-DONOR-3506 57 G -HUMMH6-DONOR-3506 58 T -HUMMH6-DONOR-3506 59 G -HUMMH6-DONOR-3506 60 G -HUMMH6-DONOR-826 1 G -HUMMH6-DONOR-826 2 G -HUMMH6-DONOR-826 3 G -HUMMH6-DONOR-826 4 C -HUMMH6-DONOR-826 5 C -HUMMH6-DONOR-826 6 C -HUMMH6-DONOR-826 7 T -HUMMH6-DONOR-826 8 G -HUMMH6-DONOR-826 9 A -HUMMH6-DONOR-826 10 C -HUMMH6-DONOR-826 11 C -HUMMH6-DONOR-826 12 C -HUMMH6-DONOR-826 13 T -HUMMH6-DONOR-826 14 G -HUMMH6-DONOR-826 15 A -HUMMH6-DONOR-826 16 C -HUMMH6-DONOR-826 17 C -HUMMH6-DONOR-826 18 G -HUMMH6-DONOR-826 19 A -HUMMH6-DONOR-826 20 G -HUMMH6-DONOR-826 21 A -HUMMH6-DONOR-826 22 C -HUMMH6-DONOR-826 23 C -HUMMH6-DONOR-826 24 T -HUMMH6-DONOR-826 25 G -HUMMH6-DONOR-826 26 G -HUMMH6-DONOR-826 27 G -HUMMH6-DONOR-826 28 C -HUMMH6-DONOR-826 29 G -HUMMH6-DONOR-826 30 G -HUMMH6-DONOR-826 31 G -HUMMH6-DONOR-826 32 T -HUMMH6-DONOR-826 33 G -HUMMH6-DONOR-826 34 A -HUMMH6-DONOR-826 35 G -HUMMH6-DONOR-826 36 T -HUMMH6-DONOR-826 37 G -HUMMH6-DONOR-826 38 C -HUMMH6-DONOR-826 39 G -HUMMH6-DONOR-826 40 G -HUMMH6-DONOR-826 41 G -HUMMH6-DONOR-826 42 G -HUMMH6-DONOR-826 43 T -HUMMH6-DONOR-826 44 C -HUMMH6-DONOR-826 45 A -HUMMH6-DONOR-826 46 G -HUMMH6-DONOR-826 47 G -HUMMH6-DONOR-826 48 A -HUMMH6-DONOR-826 49 G -HUMMH6-DONOR-826 50 G -HUMMH6-DONOR-826 51 G -HUMMH6-DONOR-826 52 A -HUMMH6-DONOR-826 53 A -HUMMH6-DONOR-826 54 A -HUMMH6-DONOR-826 55 C -HUMMH6-DONOR-826 56 G -HUMMH6-DONOR-826 57 G -HUMMH6-DONOR-826 58 C -HUMMH6-DONOR-826 59 C -HUMMH6-DONOR-826 60 C -HUMMH6-NEG-1201 1 C -HUMMH6-NEG-1201 2 T -HUMMH6-NEG-1201 3 G -HUMMH6-NEG-1201 4 A -HUMMH6-NEG-1201 5 C -HUMMH6-NEG-1201 6 A -HUMMH6-NEG-1201 7 G -HUMMH6-NEG-1201 8 A -HUMMH6-NEG-1201 9 A -HUMMH6-NEG-1201 10 T -HUMMH6-NEG-1201 11 G -HUMMH6-NEG-1201 12 A -HUMMH6-NEG-1201 13 A -HUMMH6-NEG-1201 14 C -HUMMH6-NEG-1201 15 C -HUMMH6-NEG-1201 16 T -HUMMH6-NEG-1201 17 G -HUMMH6-NEG-1201 18 C -HUMMH6-NEG-1201 19 A -HUMMH6-NEG-1201 20 G -HUMMH6-NEG-1201 21 A -HUMMH6-NEG-1201 22 C -HUMMH6-NEG-1201 23 C -HUMMH6-NEG-1201 24 C -HUMMH6-NEG-1201 25 T -HUMMH6-NEG-1201 26 G -HUMMH6-NEG-1201 27 C -HUMMH6-NEG-1201 28 G -HUMMH6-NEG-1201 29 C -HUMMH6-NEG-1201 30 G -HUMMH6-NEG-1201 31 G -HUMMH6-NEG-1201 32 C -HUMMH6-NEG-1201 33 T -HUMMH6-NEG-1201 34 A -HUMMH6-NEG-1201 35 C -HUMMH6-NEG-1201 36 T -HUMMH6-NEG-1201 37 A -HUMMH6-NEG-1201 38 C -HUMMH6-NEG-1201 39 A -HUMMH6-NEG-1201 40 A -HUMMH6-NEG-1201 41 C -HUMMH6-NEG-1201 42 C -HUMMH6-NEG-1201 43 A -HUMMH6-NEG-1201 44 G -HUMMH6-NEG-1201 45 A -HUMMH6-NEG-1201 46 G -HUMMH6-NEG-1201 47 C -HUMMH6-NEG-1201 48 G -HUMMH6-NEG-1201 49 A -HUMMH6-NEG-1201 50 G -HUMMH6-NEG-1201 51 G -HUMMH6-NEG-1201 52 C -HUMMH6-NEG-1201 53 C -HUMMH6-NEG-1201 54 A -HUMMH6-NEG-1201 55 G -HUMMH6-NEG-1201 56 T -HUMMH6-NEG-1201 57 G -HUMMH6-NEG-1201 58 A -HUMMH6-NEG-1201 59 G -HUMMH6-NEG-1201 60 T -HUMMHA3-NEG-1501 1 C -HUMMHA3-NEG-1501 2 T 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46 G -HUMMHA3-NEG-1501 47 C -HUMMHA3-NEG-1501 48 C -HUMMHA3-NEG-1501 49 T -HUMMHA3-NEG-1501 50 T -HUMMHA3-NEG-1501 51 G -HUMMHA3-NEG-1501 52 T -HUMMHA3-NEG-1501 53 T -HUMMHA3-NEG-1501 54 C -HUMMHA3-NEG-1501 55 T -HUMMHA3-NEG-1501 56 C -HUMMHA3-NEG-1501 57 T -HUMMHA3-NEG-1501 58 G -HUMMHA3-NEG-1501 59 C -HUMMHA3-NEG-1501 60 T -HUMMHADX2-NEG-1201 1 C -HUMMHADX2-NEG-1201 2 T -HUMMHADX2-NEG-1201 3 C -HUMMHADX2-NEG-1201 4 C -HUMMHADX2-NEG-1201 5 C -HUMMHADX2-NEG-1201 6 T -HUMMHADX2-NEG-1201 7 A -HUMMHADX2-NEG-1201 8 A -HUMMHADX2-NEG-1201 9 G -HUMMHADX2-NEG-1201 10 C -HUMMHADX2-NEG-1201 11 C -HUMMHADX2-NEG-1201 12 G -HUMMHADX2-NEG-1201 13 G -HUMMHADX2-NEG-1201 14 G -HUMMHADX2-NEG-1201 15 G -HUMMHADX2-NEG-1201 16 G -HUMMHADX2-NEG-1201 17 C -HUMMHADX2-NEG-1201 18 C -HUMMHADX2-NEG-1201 19 T -HUMMHADX2-NEG-1201 20 T -HUMMHADX2-NEG-1201 21 G -HUMMHADX2-NEG-1201 22 A -HUMMHADX2-NEG-1201 23 G -HUMMHADX2-NEG-1201 24 T -HUMMHADX2-NEG-1201 25 C -HUMMHADX2-NEG-1201 26 T -HUMMHADX2-NEG-1201 27 T 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6 C -HUMMHANTLE-ACCEPTOR-1874 7 G -HUMMHANTLE-ACCEPTOR-1874 8 G -HUMMHANTLE-ACCEPTOR-1874 9 G -HUMMHANTLE-ACCEPTOR-1874 10 C -HUMMHANTLE-ACCEPTOR-1874 11 T -HUMMHANTLE-ACCEPTOR-1874 12 G -HUMMHANTLE-ACCEPTOR-1874 13 A -HUMMHANTLE-ACCEPTOR-1874 14 C -HUMMHANTLE-ACCEPTOR-1874 15 C -HUMMHANTLE-ACCEPTOR-1874 16 G -HUMMHANTLE-ACCEPTOR-1874 17 C -HUMMHANTLE-ACCEPTOR-1874 18 G -HUMMHANTLE-ACCEPTOR-1874 19 G -HUMMHANTLE-ACCEPTOR-1874 20 G -HUMMHANTLE-ACCEPTOR-1874 21 G -HUMMHANTLE-ACCEPTOR-1874 22 G -HUMMHANTLE-ACCEPTOR-1874 23 C -HUMMHANTLE-ACCEPTOR-1874 24 G -HUMMHANTLE-ACCEPTOR-1874 25 G -HUMMHANTLE-ACCEPTOR-1874 26 G -HUMMHANTLE-ACCEPTOR-1874 27 C -HUMMHANTLE-ACCEPTOR-1874 28 C -HUMMHANTLE-ACCEPTOR-1874 29 A -HUMMHANTLE-ACCEPTOR-1874 30 G -HUMMHANTLE-ACCEPTOR-1874 31 G -HUMMHANTLE-ACCEPTOR-1874 32 T -HUMMHANTLE-ACCEPTOR-1874 33 T -HUMMHANTLE-ACCEPTOR-1874 34 C -HUMMHANTLE-ACCEPTOR-1874 35 T -HUMMHANTLE-ACCEPTOR-1874 36 C -HUMMHANTLE-ACCEPTOR-1874 37 A -HUMMHANTLE-ACCEPTOR-1874 38 C 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-HUMMHB27B-DONOR-3013 39 T -HUMMHB27B-DONOR-3013 40 G -HUMMHB27B-DONOR-3013 41 G -HUMMHB27B-DONOR-3013 42 G -HUMMHB27B-DONOR-3013 43 G -HUMMHB27B-DONOR-3013 44 G -HUMMHB27B-DONOR-3013 45 T -HUMMHB27B-DONOR-3013 46 G -HUMMHB27B-DONOR-3013 47 G -HUMMHB27B-DONOR-3013 48 G -HUMMHB27B-DONOR-3013 49 A -HUMMHB27B-DONOR-3013 50 G -HUMMHB27B-DONOR-3013 51 T -HUMMHB27B-DONOR-3013 52 G -HUMMHB27B-DONOR-3013 53 T -HUMMHB27B-DONOR-3013 54 G -HUMMHB27B-DONOR-3013 55 G -HUMMHB27B-DONOR-3013 56 A -HUMMHB27B-DONOR-3013 57 G -HUMMHB27B-DONOR-3013 58 G -HUMMHB27B-DONOR-3013 59 A -HUMMHB27B-DONOR-3013 60 G -HUMMHB27B-DONOR-561 1 G -HUMMHB27B-DONOR-561 2 G -HUMMHB27B-DONOR-561 3 G -HUMMHB27B-DONOR-561 4 C -HUMMHB27B-DONOR-561 5 A -HUMMHB27B-DONOR-561 6 G -HUMMHB27B-DONOR-561 7 T -HUMMHB27B-DONOR-561 8 G -HUMMHB27B-DONOR-561 9 G -HUMMHB27B-DONOR-561 10 C -HUMMHB27B-DONOR-561 11 C -HUMMHB27B-DONOR-561 12 C -HUMMHB27B-DONOR-561 13 T -HUMMHB27B-DONOR-561 14 G -HUMMHB27B-DONOR-561 15 A -HUMMHB27B-DONOR-561 16 C 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-HUMMHB27B-DONOR-561 55 A -HUMMHB27B-DONOR-561 56 T -HUMMHB27B-DONOR-561 57 G -HUMMHB27B-DONOR-561 58 G -HUMMHB27B-DONOR-561 59 C -HUMMHB27B-DONOR-561 60 C -HUMMHB27B-DONOR-960 1 T -HUMMHB27B-DONOR-960 2 G -HUMMHB27B-DONOR-960 3 C -HUMMHB27B-DONOR-960 4 T -HUMMHB27B-DONOR-960 5 C -HUMMHB27B-DONOR-960 6 C -HUMMHB27B-DONOR-960 7 G -HUMMHB27B-DONOR-960 8 C -HUMMHB27B-DONOR-960 9 T -HUMMHB27B-DONOR-960 10 A -HUMMHB27B-DONOR-960 11 C -HUMMHB27B-DONOR-960 12 T -HUMMHB27B-DONOR-960 13 A -HUMMHB27B-DONOR-960 14 C -HUMMHB27B-DONOR-960 15 A -HUMMHB27B-DONOR-960 16 A -HUMMHB27B-DONOR-960 17 C -HUMMHB27B-DONOR-960 18 C -HUMMHB27B-DONOR-960 19 A -HUMMHB27B-DONOR-960 20 G -HUMMHB27B-DONOR-960 21 A -HUMMHB27B-DONOR-960 22 G -HUMMHB27B-DONOR-960 23 C -HUMMHB27B-DONOR-960 24 G -HUMMHB27B-DONOR-960 25 A -HUMMHB27B-DONOR-960 26 G -HUMMHB27B-DONOR-960 27 G -HUMMHB27B-DONOR-960 28 C -HUMMHB27B-DONOR-960 29 C -HUMMHB27B-DONOR-960 30 G -HUMMHB27B-DONOR-960 31 G -HUMMHB27B-DONOR-960 32 T -HUMMHB27B-DONOR-960 33 G -HUMMHB27B-DONOR-960 34 A -HUMMHB27B-DONOR-960 35 G -HUMMHB27B-DONOR-960 36 T -HUMMHB27B-DONOR-960 37 G -HUMMHB27B-DONOR-960 38 A -HUMMHB27B-DONOR-960 39 C -HUMMHB27B-DONOR-960 40 C -HUMMHB27B-DONOR-960 41 C -HUMMHB27B-DONOR-960 42 C -HUMMHB27B-DONOR-960 43 G -HUMMHB27B-DONOR-960 44 G -HUMMHB27B-DONOR-960 45 C -HUMMHB27B-DONOR-960 46 C -HUMMHB27B-DONOR-960 47 C -HUMMHB27B-DONOR-960 48 G -HUMMHB27B-DONOR-960 49 G -HUMMHB27B-DONOR-960 50 G -HUMMHB27B-DONOR-960 51 C -HUMMHB27B-DONOR-960 52 G -HUMMHB27B-DONOR-960 53 C -HUMMHB27B-DONOR-960 54 A -HUMMHB27B-DONOR-960 55 G -HUMMHB27B-DONOR-960 56 G -HUMMHB27B-DONOR-960 57 T -HUMMHB27B-DONOR-960 58 C -HUMMHB27B-DONOR-960 59 A -HUMMHB27B-DONOR-960 60 C -HUMMHB27B-NEG-1201 1 G -HUMMHB27B-NEG-1201 2 D -HUMMHB27B-NEG-1201 3 G -HUMMHB27B-NEG-1201 4 A -HUMMHB27B-NEG-1201 5 C -HUMMHB27B-NEG-1201 6 G -HUMMHB27B-NEG-1201 7 G -HUMMHB27B-NEG-1201 8 G -HUMMHB27B-NEG-1201 9 G -HUMMHB27B-NEG-1201 10 C -HUMMHB27B-NEG-1201 11 T -HUMMHB27B-NEG-1201 12 G -HUMMHB27B-NEG-1201 13 A -HUMMHB27B-NEG-1201 14 C -HUMMHB27B-NEG-1201 15 C -HUMMHB27B-NEG-1201 16 G -HUMMHB27B-NEG-1201 17 C -HUMMHB27B-NEG-1201 18 G -HUMMHB27B-NEG-1201 19 G -HUMMHB27B-NEG-1201 20 G -HUMMHB27B-NEG-1201 21 G -HUMMHB27B-NEG-1201 22 G -HUMMHB27B-NEG-1201 23 C -HUMMHB27B-NEG-1201 24 G -HUMMHB27B-NEG-1201 25 G -HUMMHB27B-NEG-1201 26 G -HUMMHB27B-NEG-1201 27 T -HUMMHB27B-NEG-1201 28 C -HUMMHB27B-NEG-1201 29 C -HUMMHB27B-NEG-1201 30 A -HUMMHB27B-NEG-1201 31 G -HUMMHB27B-NEG-1201 32 G -HUMMHB27B-NEG-1201 33 G -HUMMHB27B-NEG-1201 34 T -HUMMHB27B-NEG-1201 35 C -HUMMHB27B-NEG-1201 36 T -HUMMHB27B-NEG-1201 37 C -HUMMHB27B-NEG-1201 38 A -HUMMHB27B-NEG-1201 39 C -HUMMHB27B-NEG-1201 40 A -HUMMHB27B-NEG-1201 41 C -HUMMHB27B-NEG-1201 42 C -HUMMHB27B-NEG-1201 43 C -HUMMHB27B-NEG-1201 44 T -HUMMHB27B-NEG-1201 45 C -HUMMHB27B-NEG-1201 46 C -HUMMHB27B-NEG-1201 47 A -HUMMHB27B-NEG-1201 48 G -HUMMHB27B-NEG-1201 49 A -HUMMHB27B-NEG-1201 50 A -HUMMHB27B-NEG-1201 51 T -HUMMHB27B-NEG-1201 52 A 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37 T -HUMMHB39W-NEG-1 38 C -HUMMHB39W-NEG-1 39 C -HUMMHB39W-NEG-1 40 C -HUMMHB39W-NEG-1 41 G -HUMMHB39W-NEG-1 42 G -HUMMHB39W-NEG-1 43 C -HUMMHB39W-NEG-1 44 C -HUMMHB39W-NEG-1 45 C -HUMMHB39W-NEG-1 46 G -HUMMHB39W-NEG-1 47 G -HUMMHB39W-NEG-1 48 C -HUMMHB39W-NEG-1 49 C -HUMMHB39W-NEG-1 50 G -HUMMHB39W-NEG-1 51 C -HUMMHB39W-NEG-1 52 G -HUMMHB39W-NEG-1 53 G -HUMMHB39W-NEG-1 54 G -HUMMHB39W-NEG-1 55 G -HUMMHB39W-NEG-1 56 A -HUMMHB39W-NEG-1 57 G -HUMMHB39W-NEG-1 58 C -HUMMHB39W-NEG-1 59 C -HUMMHB39W-NEG-1 60 C -HUMMHB7A-NEG-1261 1 G -HUMMHB7A-NEG-1261 2 T -HUMMHB7A-NEG-1261 3 C -HUMMHB7A-NEG-1261 4 C -HUMMHB7A-NEG-1261 5 A -HUMMHB7A-NEG-1261 6 C -HUMMHB7A-NEG-1261 7 T -HUMMHB7A-NEG-1261 8 G -HUMMHB7A-NEG-1261 9 T -HUMMHB7A-NEG-1261 10 G -HUMMHB7A-NEG-1261 11 A -HUMMHB7A-NEG-1261 12 C -HUMMHB7A-NEG-1261 13 C -HUMMHB7A-NEG-1261 14 C -HUMMHB7A-NEG-1261 15 C -HUMMHB7A-NEG-1261 16 T -HUMMHB7A-NEG-1261 17 G -HUMMHB7A-NEG-1261 18 T -HUMMHB7A-NEG-1261 19 T -HUMMHB7A-NEG-1261 20 C -HUMMHB7A-NEG-1261 21 C -HUMMHB7A-NEG-1261 22 C -HUMMHB7A-NEG-1261 23 A -HUMMHB7A-NEG-1261 24 T -HUMMHB7A-NEG-1261 25 G -HUMMHB7A-NEG-1261 26 C -HUMMHB7A-NEG-1261 27 T -HUMMHB7A-NEG-1261 28 G -HUMMHB7A-NEG-1261 29 A -HUMMHB7A-NEG-1261 30 C -HUMMHB7A-NEG-1261 31 C -HUMMHB7A-NEG-1261 32 T -HUMMHB7A-NEG-1261 33 G -HUMMHB7A-NEG-1261 34 T -HUMMHB7A-NEG-1261 35 G -HUMMHB7A-NEG-1261 36 T -HUMMHB7A-NEG-1261 37 T -HUMMHB7A-NEG-1261 38 T -HUMMHB7A-NEG-1261 39 C -HUMMHB7A-NEG-1261 40 C -HUMMHB7A-NEG-1261 41 T -HUMMHB7A-NEG-1261 42 C -HUMMHB7A-NEG-1261 43 C -HUMMHB7A-NEG-1261 44 C -HUMMHB7A-NEG-1261 45 C -HUMMHB7A-NEG-1261 46 A -HUMMHB7A-NEG-1261 47 G -HUMMHB7A-NEG-1261 48 T -HUMMHB7A-NEG-1261 49 C -HUMMHB7A-NEG-1261 50 A -HUMMHB7A-NEG-1261 51 T -HUMMHB7A-NEG-1261 52 C -HUMMHB7A-NEG-1261 53 T -HUMMHB7A-NEG-1261 54 T -HUMMHB7A-NEG-1261 55 T -HUMMHB7A-NEG-1261 56 C -HUMMHB7A-NEG-1261 57 T -HUMMHB7A-NEG-1261 58 T -HUMMHB7A-NEG-1261 59 G -HUMMHB7A-NEG-1261 60 T -HUMMHBC-NEG-1921 1 C -HUMMHBC-NEG-1921 2 A 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46 A -HUMMHBC-NEG-1921 47 T -HUMMHBC-NEG-1921 48 C -HUMMHBC-NEG-1921 49 A -HUMMHBC-NEG-1921 50 A -HUMMHBC-NEG-1921 51 C -HUMMHBC-NEG-1921 52 C -HUMMHBC-NEG-1921 53 T -HUMMHBC-NEG-1921 54 C -HUMMHBC-NEG-1921 55 T -HUMMHBC-NEG-1921 56 T -HUMMHBC-NEG-1921 57 T -HUMMHBC-NEG-1921 58 C -HUMMHBC-NEG-1921 59 A -HUMMHBC-NEG-1921 60 A -HUMMHBIA-NEG-541 1 C -HUMMHBIA-NEG-541 2 C -HUMMHBIA-NEG-541 3 G -HUMMHBIA-NEG-541 4 T -HUMMHBIA-NEG-541 5 C -HUMMHBIA-NEG-541 6 T -HUMMHBIA-NEG-541 7 T -HUMMHBIA-NEG-541 8 C -HUMMHBIA-NEG-541 9 C -HUMMHBIA-NEG-541 10 C -HUMMHBIA-NEG-541 11 A -HUMMHBIA-NEG-541 12 G -HUMMHBIA-NEG-541 13 C -HUMMHBIA-NEG-541 14 C -HUMMHBIA-NEG-541 15 C -HUMMHBIA-NEG-541 16 A -HUMMHBIA-NEG-541 17 C -HUMMHBIA-NEG-541 18 C -HUMMHBIA-NEG-541 19 A -HUMMHBIA-NEG-541 20 T -HUMMHBIA-NEG-541 21 C -HUMMHBIA-NEG-541 22 C -HUMMHBIA-NEG-541 23 C -HUMMHBIA-NEG-541 24 C -HUMMHBIA-NEG-541 25 A -HUMMHBIA-NEG-541 26 T -HUMMHBIA-NEG-541 27 C -HUMMHBIA-NEG-541 28 G -HUMMHBIA-NEG-541 29 T -HUMMHBIA-NEG-541 30 G -HUMMHBIA-NEG-541 31 G -HUMMHBIA-NEG-541 32 G -HUMMHBIA-NEG-541 33 C -HUMMHBIA-NEG-541 34 A -HUMMHBIA-NEG-541 35 T -HUMMHBIA-NEG-541 36 C -HUMMHBIA-NEG-541 37 A -HUMMHBIA-NEG-541 38 A -HUMMHBIA-NEG-541 39 T -HUMMHBIA-NEG-541 40 G -HUMMHBIA-NEG-541 41 C -HUMMHBIA-NEG-541 42 T -HUMMHBIA-NEG-541 43 G -HUMMHBIA-NEG-541 44 G -HUMMHBIA-NEG-541 45 C -HUMMHBIA-NEG-541 46 C -HUMMHBIA-NEG-541 47 T -HUMMHBIA-NEG-541 48 G -HUMMHBIA-NEG-541 49 G -HUMMHBIA-NEG-541 50 T -HUMMHBIA-NEG-541 51 T -HUMMHBIA-NEG-541 52 C -HUMMHBIA-NEG-541 53 T -HUMMHBIA-NEG-541 54 C -HUMMHBIA-NEG-541 55 T -HUMMHBIA-NEG-541 56 T -HUMMHBIA-NEG-541 57 T -HUMMHBIA-NEG-541 58 G -HUMMHBIA-NEG-541 59 C -HUMMHBIA-NEG-541 60 A -HUMMHC1-NEG-4321 1 A -HUMMHC1-NEG-4321 2 A -HUMMHC1-NEG-4321 3 T -HUMMHC1-NEG-4321 4 A -HUMMHC1-NEG-4321 5 G -HUMMHC1-NEG-4321 6 A -HUMMHC1-NEG-4321 7 T -HUMMHC1-NEG-4321 8 G -HUMMHC1-NEG-4321 9 G -HUMMHC1-NEG-4321 10 G -HUMMHC1-NEG-4321 11 G -HUMMHC1-NEG-4321 12 C -HUMMHC1-NEG-4321 13 T -HUMMHC1-NEG-4321 14 G -HUMMHC1-NEG-4321 15 T -HUMMHC1-NEG-4321 16 G -HUMMHC1-NEG-4321 17 C -HUMMHC1-NEG-4321 18 C -HUMMHC1-NEG-4321 19 T -HUMMHC1-NEG-4321 20 A -HUMMHC1-NEG-4321 21 G -HUMMHC1-NEG-4321 22 T -HUMMHC1-NEG-4321 23 G -HUMMHC1-NEG-4321 24 G -HUMMHC1-NEG-4321 25 G -HUMMHC1-NEG-4321 26 T -HUMMHC1-NEG-4321 27 G -HUMMHC1-NEG-4321 28 C -HUMMHC1-NEG-4321 29 T -HUMMHC1-NEG-4321 30 C -HUMMHC1-NEG-4321 31 A -HUMMHC1-NEG-4321 32 G -HUMMHC1-NEG-4321 33 G -HUMMHC1-NEG-4321 34 C -HUMMHC1-NEG-4321 35 C -HUMMHC1-NEG-4321 36 A -HUMMHC1-NEG-4321 37 G -HUMMHC1-NEG-4321 38 T -HUMMHC1-NEG-4321 39 A -HUMMHC1-NEG-4321 40 T -HUMMHC1-NEG-4321 41 G -HUMMHC1-NEG-4321 42 G -HUMMHC1-NEG-4321 43 A -HUMMHC1-NEG-4321 44 C -HUMMHC1-NEG-4321 45 T -HUMMHC1-NEG-4321 46 T -HUMMHC1-NEG-4321 47 T -HUMMHC1-NEG-4321 48 A -HUMMHC1-NEG-4321 49 T -HUMMHC1-NEG-4321 50 G -HUMMHC1-NEG-4321 51 T -HUMMHC1-NEG-4321 52 G -HUMMHC1-NEG-4321 53 G -HUMMHC1-NEG-4321 54 T -HUMMHC1-NEG-4321 55 C -HUMMHC1-NEG-4321 56 A -HUMMHC1-NEG-4321 57 C -HUMMHC1-NEG-4321 58 T -HUMMHC1-NEG-4321 59 G -HUMMHC1-NEG-4321 60 C -HUMMHCAI-NEG-1 1 A -HUMMHCAI-NEG-1 2 T -HUMMHCAI-NEG-1 3 G -HUMMHCAI-NEG-1 4 G -HUMMHCAI-NEG-1 5 C -HUMMHCAI-NEG-1 6 C -HUMMHCAI-NEG-1 7 G -HUMMHCAI-NEG-1 8 T -HUMMHCAI-NEG-1 9 C -HUMMHCAI-NEG-1 10 A -HUMMHCAI-NEG-1 11 T -HUMMHCAI-NEG-1 12 G -HUMMHCAI-NEG-1 13 G -HUMMHCAI-NEG-1 14 C -HUMMHCAI-NEG-1 15 G -HUMMHCAI-NEG-1 16 C -HUMMHCAI-NEG-1 17 C -HUMMHCAI-NEG-1 18 C -HUMMHCAI-NEG-1 19 C -HUMMHCAI-NEG-1 20 G -HUMMHCAI-NEG-1 21 A -HUMMHCAI-NEG-1 22 A -HUMMHCAI-NEG-1 23 C -HUMMHCAI-NEG-1 24 C -HUMMHCAI-NEG-1 25 C -HUMMHCAI-NEG-1 26 T -HUMMHCAI-NEG-1 27 C -HUMMHCAI-NEG-1 28 G -HUMMHCAI-NEG-1 29 T -HUMMHCAI-NEG-1 30 C -HUMMHCAI-NEG-1 31 C -HUMMHCAI-NEG-1 32 T -HUMMHCAI-NEG-1 33 G -HUMMHCAI-NEG-1 34 C -HUMMHCAI-NEG-1 35 T -HUMMHCAI-NEG-1 36 A -HUMMHCAI-NEG-1 37 C -HUMMHCAI-NEG-1 38 T -HUMMHCAI-NEG-1 39 C -HUMMHCAI-NEG-1 40 T -HUMMHCAI-NEG-1 41 C -HUMMHCAI-NEG-1 42 G -HUMMHCAI-NEG-1 43 G -HUMMHCAI-NEG-1 44 G -HUMMHCAI-NEG-1 45 G -HUMMHCAI-NEG-1 46 G -HUMMHCAI-NEG-1 47 C -HUMMHCAI-NEG-1 48 T -HUMMHCAI-NEG-1 49 C -HUMMHCAI-NEG-1 50 T -HUMMHCAI-NEG-1 51 G -HUMMHCAI-NEG-1 52 G -HUMMHCAI-NEG-1 53 C -HUMMHCAI-NEG-1 54 C -HUMMHCAI-NEG-1 55 C -HUMMHCAI-NEG-1 56 T -HUMMHCAI-NEG-1 57 G -HUMMHCAI-NEG-1 58 A -HUMMHCAI-NEG-1 59 C -HUMMHCAI-NEG-1 60 C -HUMMHCAR4-NEG-241 1 C -HUMMHCAR4-NEG-241 2 C -HUMMHCAR4-NEG-241 3 G -HUMMHCAR4-NEG-241 4 G -HUMMHCAR4-NEG-241 5 A -HUMMHCAR4-NEG-241 6 G -HUMMHCAR4-NEG-241 7 T -HUMMHCAR4-NEG-241 8 A -HUMMHCAR4-NEG-241 9 T -HUMMHCAR4-NEG-241 10 T -HUMMHCAR4-NEG-241 11 G -HUMMHCAR4-NEG-241 12 G -HUMMHCAR4-NEG-241 13 G -HUMMHCAR4-NEG-241 14 A -HUMMHCAR4-NEG-241 15 C -HUMMHCAR4-NEG-241 16 C -HUMMHCAR4-NEG-241 17 G -HUMMHCAR4-NEG-241 18 G -HUMMHCAR4-NEG-241 19 A -HUMMHCAR4-NEG-241 20 A -HUMMHCAR4-NEG-241 21 C -HUMMHCAR4-NEG-241 22 A -HUMMHCAR4-NEG-241 23 C -HUMMHCAR4-NEG-241 24 A -HUMMHCAR4-NEG-241 25 C -HUMMHCAR4-NEG-241 26 A -HUMMHCAR4-NEG-241 27 G -HUMMHCAR4-NEG-241 28 A -HUMMHCAR4-NEG-241 29 T -HUMMHCAR4-NEG-241 30 C -HUMMHCAR4-NEG-241 31 T -HUMMHCAR4-NEG-241 32 G -HUMMHCAR4-NEG-241 33 C -HUMMHCAR4-NEG-241 34 A -HUMMHCAR4-NEG-241 35 A -HUMMHCAR4-NEG-241 36 G -HUMMHCAR4-NEG-241 37 G -HUMMHCAR4-NEG-241 38 C -HUMMHCAR4-NEG-241 39 C -HUMMHCAR4-NEG-241 40 C -HUMMHCAR4-NEG-241 41 A -HUMMHCAR4-NEG-241 42 A -HUMMHCAR4-NEG-241 43 G -HUMMHCAR4-NEG-241 44 C -HUMMHCAR4-NEG-241 45 A -HUMMHCAR4-NEG-241 46 C -HUMMHCAR4-NEG-241 47 G -HUMMHCAR4-NEG-241 48 G -HUMMHCAR4-NEG-241 49 A -HUMMHCAR4-NEG-241 50 C -HUMMHCAR4-NEG-241 51 T -HUMMHCAR4-NEG-241 52 G -HUMMHCAR4-NEG-241 53 A -HUMMHCAR4-NEG-241 54 A -HUMMHCAR4-NEG-241 55 C -HUMMHCAR4-NEG-241 56 G -HUMMHCAR4-NEG-241 57 A -HUMMHCAR4-NEG-241 58 G -HUMMHCAR4-NEG-241 59 A -HUMMHCAR4-NEG-241 60 G -HUMMHCBWC-NEG-121 1 C -HUMMHCBWC-NEG-121 2 G -HUMMHCBWC-NEG-121 3 C -HUMMHCBWC-NEG-121 4 G -HUMMHCBWC-NEG-121 5 G -HUMMHCBWC-NEG-121 6 G -HUMMHCBWC-NEG-121 7 G -HUMMHCBWC-NEG-121 8 A -HUMMHCBWC-NEG-121 9 G -HUMMHCBWC-NEG-121 10 C -HUMMHCBWC-NEG-121 11 C -HUMMHCBWC-NEG-121 12 C -HUMMHCBWC-NEG-121 13 C -HUMMHCBWC-NEG-121 14 G -HUMMHCBWC-NEG-121 15 C -HUMMHCBWC-NEG-121 16 T -HUMMHCBWC-NEG-121 17 T -HUMMHCBWC-NEG-121 18 C -HUMMHCBWC-NEG-121 19 A -HUMMHCBWC-NEG-121 20 T -HUMMHCBWC-NEG-121 21 C -HUMMHCBWC-NEG-121 22 A -HUMMHCBWC-NEG-121 23 C -HUMMHCBWC-NEG-121 24 C -HUMMHCBWC-NEG-121 25 G -HUMMHCBWC-NEG-121 26 T -HUMMHCBWC-NEG-121 27 G -HUMMHCBWC-NEG-121 28 G -HUMMHCBWC-NEG-121 29 G -HUMMHCBWC-NEG-121 30 C -HUMMHCBWC-NEG-121 31 T -HUMMHCBWC-NEG-121 32 A -HUMMHCBWC-NEG-121 33 C -HUMMHCBWC-NEG-121 34 G -HUMMHCBWC-NEG-121 35 T -HUMMHCBWC-NEG-121 36 G -HUMMHCBWC-NEG-121 37 G -HUMMHCBWC-NEG-121 38 A -HUMMHCBWC-NEG-121 39 C -HUMMHCBWC-NEG-121 40 G -HUMMHCBWC-NEG-121 41 A -HUMMHCBWC-NEG-121 42 C -HUMMHCBWC-NEG-121 43 A -HUMMHCBWC-NEG-121 44 C -HUMMHCBWC-NEG-121 45 G -HUMMHCBWC-NEG-121 46 C -HUMMHCBWC-NEG-121 47 T -HUMMHCBWC-NEG-121 48 G -HUMMHCBWC-NEG-121 49 T -HUMMHCBWC-NEG-121 50 T -HUMMHCBWC-NEG-121 51 C -HUMMHCBWC-NEG-121 52 G -HUMMHCBWC-NEG-121 53 T -HUMMHCBWC-NEG-121 54 G -HUMMHCBWC-NEG-121 55 A -HUMMHCBWC-NEG-121 56 G -HUMMHCBWC-NEG-121 57 G -HUMMHCBWC-NEG-121 58 T -HUMMHCBWC-NEG-121 59 T -HUMMHCBWC-NEG-121 60 C -HUMMHCC6A-NEG-2401 1 T -HUMMHCC6A-NEG-2401 2 C -HUMMHCC6A-NEG-2401 3 T -HUMMHCC6A-NEG-2401 4 G -HUMMHCC6A-NEG-2401 5 A -HUMMHCC6A-NEG-2401 6 A -HUMMHCC6A-NEG-2401 7 T -HUMMHCC6A-NEG-2401 8 G -HUMMHCC6A-NEG-2401 9 C -HUMMHCC6A-NEG-2401 10 A -HUMMHCC6A-NEG-2401 11 T -HUMMHCC6A-NEG-2401 12 T -HUMMHCC6A-NEG-2401 13 T -HUMMHCC6A-NEG-2401 14 G -HUMMHCC6A-NEG-2401 15 T -HUMMHCC6A-NEG-2401 16 A -HUMMHCC6A-NEG-2401 17 T -HUMMHCC6A-NEG-2401 18 G -HUMMHCC6A-NEG-2401 19 T -HUMMHCC6A-NEG-2401 20 C -HUMMHCC6A-NEG-2401 21 T -HUMMHCC6A-NEG-2401 22 C -HUMMHCC6A-NEG-2401 23 C -HUMMHCC6A-NEG-2401 24 A -HUMMHCC6A-NEG-2401 25 G -HUMMHCC6A-NEG-2401 26 A -HUMMHCC6A-NEG-2401 27 A -HUMMHCC6A-NEG-2401 28 G -HUMMHCC6A-NEG-2401 29 A -HUMMHCC6A-NEG-2401 30 A -HUMMHCC6A-NEG-2401 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-HUMMHCP42-ACCEPTOR-1945 44 G -HUMMHCP42-ACCEPTOR-1945 45 A -HUMMHCP42-ACCEPTOR-1945 46 A -HUMMHCP42-ACCEPTOR-1945 47 C -HUMMHCP42-ACCEPTOR-1945 48 T -HUMMHCP42-ACCEPTOR-1945 49 C -HUMMHCP42-ACCEPTOR-1945 50 C -HUMMHCP42-ACCEPTOR-1945 51 A -HUMMHCP42-ACCEPTOR-1945 52 A -HUMMHCP42-ACCEPTOR-1945 53 G -HUMMHCP42-ACCEPTOR-1945 54 A -HUMMHCP42-ACCEPTOR-1945 55 G -HUMMHCP42-ACCEPTOR-1945 56 G -HUMMHCP42-ACCEPTOR-1945 57 A -HUMMHCP42-ACCEPTOR-1945 58 C -HUMMHCP42-ACCEPTOR-1945 59 C -HUMMHCP42-ACCEPTOR-1945 60 A -HUMMHCP42-ACCEPTOR-2316 1 C -HUMMHCP42-ACCEPTOR-2316 2 C -HUMMHCP42-ACCEPTOR-2316 3 A -HUMMHCP42-ACCEPTOR-2316 4 C -HUMMHCP42-ACCEPTOR-2316 5 C -HUMMHCP42-ACCEPTOR-2316 6 C -HUMMHCP42-ACCEPTOR-2316 7 T -HUMMHCP42-ACCEPTOR-2316 8 C -HUMMHCP42-ACCEPTOR-2316 9 C -HUMMHCP42-ACCEPTOR-2316 10 A -HUMMHCP42-ACCEPTOR-2316 11 G -HUMMHCP42-ACCEPTOR-2316 12 C -HUMMHCP42-ACCEPTOR-2316 13 C -HUMMHCP42-ACCEPTOR-2316 14 C -HUMMHCP42-ACCEPTOR-2316 15 C -HUMMHCP42-ACCEPTOR-2316 16 C 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38 C -HUMMHCP42-DONOR-2035 37 G -HUMMHCP42-DONOR-2035 36 G -HUMMHCP42-DONOR-2035 35 G -HUMMHCP42-DONOR-2035 34 A -HUMMHCP42-DONOR-2035 33 A -HUMMHCP42-DONOR-2035 32 T -HUMMHCP42-DONOR-2035 31 G -HUMMHCP42-DONOR-2035 30 T -HUMMHCP42-DONOR-2035 29 C -HUMMHCP42-DONOR-2035 28 C -HUMMHCP42-DONOR-2035 27 A -HUMMHCP42-DONOR-2035 26 T -HUMMHCP42-DONOR-2035 25 T -HUMMHCP42-DONOR-2035 24 C -HUMMHCP42-DONOR-2035 23 A -HUMMHCP42-DONOR-2035 22 C -HUMMHCP42-DONOR-2035 21 C -HUMMHCP42-DONOR-2035 20 G -HUMMHCP42-DONOR-2035 19 A -HUMMHCP42-DONOR-2035 18 G -HUMMHCP42-DONOR-2035 17 T -HUMMHCP42-DONOR-2035 16 C -HUMMHCP42-DONOR-2035 15 C -HUMMHCP42-DONOR-2035 14 A -HUMMHCP42-DONOR-2035 13 G -HUMMHCP42-DONOR-2035 12 A -HUMMHCP42-DONOR-2035 11 C -HUMMHCP42-DONOR-2035 10 G -HUMMHCP42-DONOR-2035 9 G -HUMMHCP42-DONOR-2035 8 T -HUMMHCP42-DONOR-2035 7 C -HUMMHCP42-DONOR-2035 6 G -HUMMHCP42-DONOR-2035 5 T -HUMMHCP42-DONOR-2035 4 T -HUMMHCP42-DONOR-2035 3 T -HUMMHCP42-DONOR-2035 2 C -HUMMHCP42-DONOR-2035 60 A 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-HUMMIS-ACCEPTOR-1500 3 C -HUMMIS-ACCEPTOR-1500 4 A -HUMMIS-ACCEPTOR-1500 5 G -HUMMIS-ACCEPTOR-1500 6 C -HUMMIS-ACCEPTOR-1500 7 C -HUMMIS-ACCEPTOR-1500 8 G -HUMMIS-ACCEPTOR-1500 9 G -HUMMIS-ACCEPTOR-1500 10 G -HUMMIS-ACCEPTOR-1500 11 C -HUMMIS-ACCEPTOR-1500 12 T -HUMMIS-ACCEPTOR-1500 13 G -HUMMIS-ACCEPTOR-1500 14 A -HUMMIS-ACCEPTOR-1500 15 G -HUMMIS-ACCEPTOR-1500 16 C -HUMMIS-ACCEPTOR-1500 17 C -HUMMIS-ACCEPTOR-1500 18 C -HUMMIS-ACCEPTOR-1500 19 T -HUMMIS-ACCEPTOR-1500 20 G -HUMMIS-ACCEPTOR-1500 21 G -HUMMIS-ACCEPTOR-1500 22 T -HUMMIS-ACCEPTOR-1500 23 C -HUMMIS-ACCEPTOR-1500 24 T -HUMMIS-ACCEPTOR-1500 25 C -HUMMIS-ACCEPTOR-1500 26 C -HUMMIS-ACCEPTOR-1500 27 G -HUMMIS-ACCEPTOR-1500 28 C -HUMMIS-ACCEPTOR-1500 29 A -HUMMIS-ACCEPTOR-1500 30 G -HUMMIS-ACCEPTOR-1500 31 A -HUMMIS-ACCEPTOR-1500 32 G -HUMMIS-ACCEPTOR-1500 33 C -HUMMIS-ACCEPTOR-1500 34 C -HUMMIS-ACCEPTOR-1500 35 T -HUMMIS-ACCEPTOR-1500 36 C -HUMMIS-ACCEPTOR-1500 37 T -HUMMIS-ACCEPTOR-1500 38 G -HUMMIS-ACCEPTOR-1500 39 C -HUMMIS-ACCEPTOR-1500 40 C -HUMMIS-ACCEPTOR-1500 41 C -HUMMIS-ACCEPTOR-1500 42 C -HUMMIS-ACCEPTOR-1500 43 T -HUMMIS-ACCEPTOR-1500 44 C -HUMMIS-ACCEPTOR-1500 45 C -HUMMIS-ACCEPTOR-1500 46 C -HUMMIS-ACCEPTOR-1500 47 G -HUMMIS-ACCEPTOR-1500 48 A -HUMMIS-ACCEPTOR-1500 49 G -HUMMIS-ACCEPTOR-1500 50 A -HUMMIS-ACCEPTOR-1500 51 C -HUMMIS-ACCEPTOR-1500 52 A -HUMMIS-ACCEPTOR-1500 53 C -HUMMIS-ACCEPTOR-1500 54 C -HUMMIS-ACCEPTOR-1500 55 C -HUMMIS-ACCEPTOR-1500 56 G -HUMMIS-ACCEPTOR-1500 57 C -HUMMIS-ACCEPTOR-1500 58 T -HUMMIS-ACCEPTOR-1500 59 A -HUMMIS-ACCEPTOR-1500 60 C -HUMMIS-ACCEPTOR-1697 1 C -HUMMIS-ACCEPTOR-1697 2 T -HUMMIS-ACCEPTOR-1697 3 G -HUMMIS-ACCEPTOR-1697 4 A -HUMMIS-ACCEPTOR-1697 5 G -HUMMIS-ACCEPTOR-1697 6 C -HUMMIS-ACCEPTOR-1697 7 C -HUMMIS-ACCEPTOR-1697 8 A -HUMMIS-ACCEPTOR-1697 9 G -HUMMIS-ACCEPTOR-1697 10 C -HUMMIS-ACCEPTOR-1697 11 C -HUMMIS-ACCEPTOR-1697 12 G -HUMMIS-ACCEPTOR-1697 13 C -HUMMIS-ACCEPTOR-1697 14 G -HUMMIS-ACCEPTOR-1697 15 T -HUMMIS-ACCEPTOR-1697 16 G -HUMMIS-ACCEPTOR-1697 17 C -HUMMIS-ACCEPTOR-1697 18 C -HUMMIS-ACCEPTOR-1697 19 C -HUMMIS-ACCEPTOR-1697 20 A -HUMMIS-ACCEPTOR-1697 21 C -HUMMIS-ACCEPTOR-1697 22 C -HUMMIS-ACCEPTOR-1697 23 C -HUMMIS-ACCEPTOR-1697 24 A -HUMMIS-ACCEPTOR-1697 25 C -HUMMIS-ACCEPTOR-1697 26 C -HUMMIS-ACCEPTOR-1697 27 G -HUMMIS-ACCEPTOR-1697 28 C -HUMMIS-ACCEPTOR-1697 29 A -HUMMIS-ACCEPTOR-1697 30 G -HUMMIS-ACCEPTOR-1697 31 A -HUMMIS-ACCEPTOR-1697 32 C -HUMMIS-ACCEPTOR-1697 33 T -HUMMIS-ACCEPTOR-1697 34 C -HUMMIS-ACCEPTOR-1697 35 C -HUMMIS-ACCEPTOR-1697 36 C -HUMMIS-ACCEPTOR-1697 37 G -HUMMIS-ACCEPTOR-1697 38 G -HUMMIS-ACCEPTOR-1697 39 C -HUMMIS-ACCEPTOR-1697 40 T -HUMMIS-ACCEPTOR-1697 41 G -HUMMIS-ACCEPTOR-1697 42 A -HUMMIS-ACCEPTOR-1697 43 G -HUMMIS-ACCEPTOR-1697 44 T -HUMMIS-ACCEPTOR-1697 45 A -HUMMIS-ACCEPTOR-1697 46 C -HUMMIS-ACCEPTOR-1697 47 C -HUMMIS-ACCEPTOR-1697 48 G -HUMMIS-ACCEPTOR-1697 49 C -HUMMIS-ACCEPTOR-1697 50 C -HUMMIS-ACCEPTOR-1697 51 C -HUMMIS-ACCEPTOR-1697 52 G -HUMMIS-ACCEPTOR-1697 53 G 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19 G -HUMMLCAC-NEG-601 20 A -HUMMLCAC-NEG-601 21 C -HUMMLCAC-NEG-601 22 G -HUMMLCAC-NEG-601 23 G -HUMMLCAC-NEG-601 24 T -HUMMLCAC-NEG-601 25 T -HUMMLCAC-NEG-601 26 C -HUMMLCAC-NEG-601 27 A -HUMMLCAC-NEG-601 28 A -HUMMLCAC-NEG-601 29 G -HUMMLCAC-NEG-601 30 A -HUMMLCAC-NEG-601 31 C -HUMMLCAC-NEG-601 32 T -HUMMLCAC-NEG-601 33 G -HUMMLCAC-NEG-601 34 A -HUMMLCAC-NEG-601 35 G -HUMMLCAC-NEG-601 36 C -HUMMLCAC-NEG-601 37 A -HUMMLCAC-NEG-601 38 A -HUMMLCAC-NEG-601 39 C -HUMMLCAC-NEG-601 40 T -HUMMLCAC-NEG-601 41 C -HUMMLCAC-NEG-601 42 C -HUMMLCAC-NEG-601 43 C -HUMMLCAC-NEG-601 44 T -HUMMLCAC-NEG-601 45 G -HUMMLCAC-NEG-601 46 A -HUMMLCAC-NEG-601 47 A -HUMMLCAC-NEG-601 48 T -HUMMLCAC-NEG-601 49 T -HUMMLCAC-NEG-601 50 T -HUMMLCAC-NEG-601 51 T -HUMMLCAC-NEG-601 52 T -HUMMLCAC-NEG-601 53 A -HUMMLCAC-NEG-601 54 T -HUMMLCAC-NEG-601 55 A -HUMMLCAC-NEG-601 56 C -HUMMLCAC-NEG-601 57 A -HUMMLCAC-NEG-601 58 T -HUMMLCAC-NEG-601 59 C -HUMMLCAC-NEG-601 60 T -HUMMPO-NEG-301 1 A -HUMMPO-NEG-301 2 C 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35 C -HUMMYCRT-ACCEPTOR-1111 36 C -HUMMYCRT-ACCEPTOR-1111 37 G -HUMMYCRT-ACCEPTOR-1111 38 C -HUMMYCRT-ACCEPTOR-1111 39 G -HUMMYCRT-ACCEPTOR-1111 40 A -HUMMYCRT-ACCEPTOR-1111 41 C -HUMMYCRT-ACCEPTOR-1111 42 G -HUMMYCRT-ACCEPTOR-1111 43 A -HUMMYCRT-ACCEPTOR-1111 44 T -HUMMYCRT-ACCEPTOR-1111 45 G -HUMMYCRT-ACCEPTOR-1111 46 C -HUMMYCRT-ACCEPTOR-1111 47 C -HUMMYCRT-ACCEPTOR-1111 48 C -HUMMYCRT-ACCEPTOR-1111 49 C -HUMMYCRT-ACCEPTOR-1111 50 T -HUMMYCRT-ACCEPTOR-1111 51 C -HUMMYCRT-ACCEPTOR-1111 52 A -HUMMYCRT-ACCEPTOR-1111 53 A -HUMMYCRT-ACCEPTOR-1111 54 C -HUMMYCRT-ACCEPTOR-1111 55 G -HUMMYCRT-ACCEPTOR-1111 56 T -HUMMYCRT-ACCEPTOR-1111 57 T -HUMMYCRT-ACCEPTOR-1111 58 A -HUMMYCRT-ACCEPTOR-1111 59 C -HUMMYCRT-ACCEPTOR-1111 60 C -HUMMYCRT-ACCEPTOR-3258 1 A -HUMMYCRT-ACCEPTOR-3258 2 A -HUMMYCRT-ACCEPTOR-3258 3 G -HUMMYCRT-ACCEPTOR-3258 4 G -HUMMYCRT-ACCEPTOR-3258 5 A -HUMMYCRT-ACCEPTOR-3258 6 G -HUMMYCRT-ACCEPTOR-3258 7 T -HUMMYCRT-ACCEPTOR-3258 8 G -HUMMYCRT-ACCEPTOR-3258 9 A 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-HUMMYLCA-DONOR-1975 54 C -HUMMYLCA-DONOR-1975 55 T -HUMMYLCA-DONOR-1975 56 C -HUMMYLCA-DONOR-1975 57 A -HUMMYLCA-DONOR-1975 58 T -HUMMYLCA-DONOR-1975 59 T -HUMMYLCA-DONOR-1975 60 G -HUMMYLCA-DONOR-2388 1 C -HUMMYLCA-DONOR-2388 2 T -HUMMYLCA-DONOR-2388 3 G -HUMMYLCA-DONOR-2388 4 A -HUMMYLCA-DONOR-2388 5 A -HUMMYLCA-DONOR-2388 6 A -HUMMYLCA-DONOR-2388 7 T -HUMMYLCA-DONOR-2388 8 C -HUMMYLCA-DONOR-2388 9 C -HUMMYLCA-DONOR-2388 10 G -HUMMYLCA-DONOR-2388 11 G -HUMMYLCA-DONOR-2388 12 C -HUMMYLCA-DONOR-2388 13 A -HUMMYLCA-DONOR-2388 14 T -HUMMYLCA-DONOR-2388 15 G -HUMMYLCA-DONOR-2388 16 T -HUMMYLCA-DONOR-2388 17 T -HUMMYLCA-DONOR-2388 18 C -HUMMYLCA-DONOR-2388 19 T -HUMMYLCA-DONOR-2388 20 T -HUMMYLCA-DONOR-2388 21 G -HUMMYLCA-DONOR-2388 22 T -HUMMYLCA-DONOR-2388 23 C -HUMMYLCA-DONOR-2388 24 A -HUMMYLCA-DONOR-2388 25 C -HUMMYLCA-DONOR-2388 26 A -HUMMYLCA-DONOR-2388 27 C -HUMMYLCA-DONOR-2388 28 T -HUMMYLCA-DONOR-2388 29 G -HUMMYLCA-DONOR-2388 30 G -HUMMYLCA-DONOR-2388 31 G -HUMMYLCA-DONOR-2388 32 T -HUMMYLCA-DONOR-2388 33 A -HUMMYLCA-DONOR-2388 34 A -HUMMYLCA-DONOR-2388 35 G -HUMMYLCA-DONOR-2388 36 G -HUMMYLCA-DONOR-2388 37 T -HUMMYLCA-DONOR-2388 38 T -HUMMYLCA-DONOR-2388 39 C -HUMMYLCA-DONOR-2388 40 T -HUMMYLCA-DONOR-2388 41 G -HUMMYLCA-DONOR-2388 42 T -HUMMYLCA-DONOR-2388 43 G -HUMMYLCA-DONOR-2388 44 T -HUMMYLCA-DONOR-2388 45 C -HUMMYLCA-DONOR-2388 46 C -HUMMYLCA-DONOR-2388 47 T -HUMMYLCA-DONOR-2388 48 T -HUMMYLCA-DONOR-2388 49 G -HUMMYLCA-DONOR-2388 50 T -HUMMYLCA-DONOR-2388 51 C -HUMMYLCA-DONOR-2388 52 C -HUMMYLCA-DONOR-2388 53 T -HUMMYLCA-DONOR-2388 54 T -HUMMYLCA-DONOR-2388 55 G -HUMMYLCA-DONOR-2388 56 A -HUMMYLCA-DONOR-2388 57 G -HUMMYLCA-DONOR-2388 58 C -HUMMYLCA-DONOR-2388 59 T -HUMMYLCA-DONOR-2388 60 G -HUMMYLCA-DONOR-644 1 A -HUMMYLCA-DONOR-644 2 A -HUMMYLCA-DONOR-644 3 G -HUMMYLCA-DONOR-644 4 G -HUMMYLCA-DONOR-644 5 T -HUMMYLCA-DONOR-644 6 C -HUMMYLCA-DONOR-644 7 C -HUMMYLCA-DONOR-644 8 C -HUMMYLCA-DONOR-644 9 G -HUMMYLCA-DONOR-644 10 G -HUMMYLCA-DONOR-644 11 A -HUMMYLCA-DONOR-644 12 G -HUMMYLCA-DONOR-644 13 A -HUMMYLCA-DONOR-644 14 G -HUMMYLCA-DONOR-644 15 C -HUMMYLCA-DONOR-644 16 T -HUMMYLCA-DONOR-644 17 G -HUMMYLCA-DONOR-644 18 A -HUMMYLCA-DONOR-644 19 G -HUMMYLCA-DONOR-644 20 C -HUMMYLCA-DONOR-644 21 A -HUMMYLCA-DONOR-644 22 G -HUMMYLCA-DONOR-644 23 T -HUMMYLCA-DONOR-644 24 C -HUMMYLCA-DONOR-644 25 A -HUMMYLCA-DONOR-644 26 A -HUMMYLCA-DONOR-644 27 G -HUMMYLCA-DONOR-644 28 A -HUMMYLCA-DONOR-644 29 T -HUMMYLCA-DONOR-644 30 G -HUMMYLCA-DONOR-644 31 G -HUMMYLCA-DONOR-644 32 T -HUMMYLCA-DONOR-644 33 G -HUMMYLCA-DONOR-644 34 G -HUMMYLCA-DONOR-644 35 G -HUMMYLCA-DONOR-644 36 G -HUMMYLCA-DONOR-644 37 C -HUMMYLCA-DONOR-644 38 C -HUMMYLCA-DONOR-644 39 C -HUMMYLCA-DONOR-644 40 A -HUMMYLCA-DONOR-644 41 G -HUMMYLCA-DONOR-644 42 G -HUMMYLCA-DONOR-644 43 T -HUMMYLCA-DONOR-644 44 C -HUMMYLCA-DONOR-644 45 T -HUMMYLCA-DONOR-644 46 T -HUMMYLCA-DONOR-644 47 G -HUMMYLCA-DONOR-644 48 G -HUMMYLCA-DONOR-644 49 G -HUMMYLCA-DONOR-644 50 A -HUMMYLCA-DONOR-644 51 G -HUMMYLCA-DONOR-644 52 A -HUMMYLCA-DONOR-644 53 C -HUMMYLCA-DONOR-644 54 G -HUMMYLCA-DONOR-644 55 G -HUMMYLCA-DONOR-644 56 G -HUMMYLCA-DONOR-644 57 C -HUMMYLCA-DONOR-644 58 A -HUMMYLCA-DONOR-644 59 G -HUMMYLCA-DONOR-644 60 G -HUMMYLCA-DONOR-952 1 A -HUMMYLCA-DONOR-952 2 G -HUMMYLCA-DONOR-952 3 T -HUMMYLCA-DONOR-952 4 G -HUMMYLCA-DONOR-952 5 T -HUMMYLCA-DONOR-952 6 G -HUMMYLCA-DONOR-952 7 A -HUMMYLCA-DONOR-952 8 C -HUMMYLCA-DONOR-952 9 T -HUMMYLCA-DONOR-952 10 T -HUMMYLCA-DONOR-952 11 C -HUMMYLCA-DONOR-952 12 A -HUMMYLCA-DONOR-952 13 C -HUMMYLCA-DONOR-952 14 C -HUMMYLCA-DONOR-952 15 G -HUMMYLCA-DONOR-952 16 A -HUMMYLCA-DONOR-952 17 A -HUMMYLCA-DONOR-952 18 G -HUMMYLCA-DONOR-952 19 A -HUMMYLCA-DONOR-952 20 C -HUMMYLCA-DONOR-952 21 C -HUMMYLCA-DONOR-952 22 A -HUMMYLCA-DONOR-952 23 G -HUMMYLCA-DONOR-952 24 A -HUMMYLCA-DONOR-952 25 C -HUMMYLCA-DONOR-952 26 C -HUMMYLCA-DONOR-952 27 G -HUMMYLCA-DONOR-952 28 C -HUMMYLCA-DONOR-952 29 A -HUMMYLCA-DONOR-952 30 G -HUMMYLCA-DONOR-952 31 G -HUMMYLCA-DONOR-952 32 T -HUMMYLCA-DONOR-952 33 A -HUMMYLCA-DONOR-952 34 G -HUMMYLCA-DONOR-952 35 G -HUMMYLCA-DONOR-952 36 T -HUMMYLCA-DONOR-952 37 T -HUMMYLCA-DONOR-952 38 A -HUMMYLCA-DONOR-952 39 T -HUMMYLCA-DONOR-952 40 C -HUMMYLCA-DONOR-952 41 T -HUMMYLCA-DONOR-952 42 C -HUMMYLCA-DONOR-952 43 T -HUMMYLCA-DONOR-952 44 G -HUMMYLCA-DONOR-952 45 A -HUMMYLCA-DONOR-952 46 T -HUMMYLCA-DONOR-952 47 C -HUMMYLCA-DONOR-952 48 C -HUMMYLCA-DONOR-952 49 C -HUMMYLCA-DONOR-952 50 T -HUMMYLCA-DONOR-952 51 A -HUMMYLCA-DONOR-952 52 C -HUMMYLCA-DONOR-952 53 C -HUMMYLCA-DONOR-952 54 G -HUMMYLCA-DONOR-952 55 A -HUMMYLCA-DONOR-952 56 G -HUMMYLCA-DONOR-952 57 G -HUMMYLCA-DONOR-952 58 T -HUMMYLCA-DONOR-952 59 C -HUMMYLCA-DONOR-952 60 C -HUMMYLCA-NEG-3181 1 T -HUMMYLCA-NEG-3181 2 G -HUMMYLCA-NEG-3181 3 G -HUMMYLCA-NEG-3181 4 G -HUMMYLCA-NEG-3181 5 A -HUMMYLCA-NEG-3181 6 A -HUMMYLCA-NEG-3181 7 C -HUMMYLCA-NEG-3181 8 C -HUMMYLCA-NEG-3181 9 T -HUMMYLCA-NEG-3181 10 C -HUMMYLCA-NEG-3181 11 A -HUMMYLCA-NEG-3181 12 C -HUMMYLCA-NEG-3181 13 C -HUMMYLCA-NEG-3181 14 A -HUMMYLCA-NEG-3181 15 G -HUMMYLCA-NEG-3181 16 C -HUMMYLCA-NEG-3181 17 C -HUMMYLCA-NEG-3181 18 T -HUMMYLCA-NEG-3181 19 G -HUMMYLCA-NEG-3181 20 T -HUMMYLCA-NEG-3181 21 A -HUMMYLCA-NEG-3181 22 T -HUMMYLCA-NEG-3181 23 A -HUMMYLCA-NEG-3181 24 C -HUMMYLCA-NEG-3181 25 C -HUMMYLCA-NEG-3181 26 A -HUMMYLCA-NEG-3181 27 G -HUMMYLCA-NEG-3181 28 T -HUMMYLCA-NEG-3181 29 C -HUMMYLCA-NEG-3181 30 T -HUMMYLCA-NEG-3181 31 T -HUMMYLCA-NEG-3181 32 G -HUMMYLCA-NEG-3181 33 C -HUMMYLCA-NEG-3181 34 T -HUMMYLCA-NEG-3181 35 T -HUMMYLCA-NEG-3181 36 T -HUMMYLCA-NEG-3181 37 A -HUMMYLCA-NEG-3181 38 G -HUMMYLCA-NEG-3181 39 G -HUMMYLCA-NEG-3181 40 G -HUMMYLCA-NEG-3181 41 T -HUMMYLCA-NEG-3181 42 G -HUMMYLCA-NEG-3181 43 A -HUMMYLCA-NEG-3181 44 C -HUMMYLCA-NEG-3181 45 C -HUMMYLCA-NEG-3181 46 T -HUMMYLCA-NEG-3181 47 T -HUMMYLCA-NEG-3181 48 C -HUMMYLCA-NEG-3181 49 T -HUMMYLCA-NEG-3181 50 G -HUMMYLCA-NEG-3181 51 G -HUMMYLCA-NEG-3181 52 C -HUMMYLCA-NEG-3181 53 C -HUMMYLCA-NEG-3181 54 C -HUMMYLCA-NEG-3181 55 T -HUMMYLCA-NEG-3181 56 G -HUMMYLCA-NEG-3181 57 G -HUMMYLCA-NEG-3181 58 A -HUMMYLCA-NEG-3181 59 G -HUMMYLCA-NEG-3181 60 C -HUMMYOHP-NEG-901 1 A -HUMMYOHP-NEG-901 2 C -HUMMYOHP-NEG-901 3 T -HUMMYOHP-NEG-901 4 T -HUMMYOHP-NEG-901 5 C -HUMMYOHP-NEG-901 6 T -HUMMYOHP-NEG-901 7 G -HUMMYOHP-NEG-901 8 C -HUMMYOHP-NEG-901 9 T -HUMMYOHP-NEG-901 10 C -HUMMYOHP-NEG-901 11 A -HUMMYOHP-NEG-901 12 G -HUMMYOHP-NEG-901 13 G -HUMMYOHP-NEG-901 14 T -HUMMYOHP-NEG-901 15 G -HUMMYOHP-NEG-901 16 G -HUMMYOHP-NEG-901 17 A -HUMMYOHP-NEG-901 18 A -HUMMYOHP-NEG-901 19 T -HUMMYOHP-NEG-901 20 T -HUMMYOHP-NEG-901 21 G -HUMMYOHP-NEG-901 22 A -HUMMYOHP-NEG-901 23 A -HUMMYOHP-NEG-901 24 C -HUMMYOHP-NEG-901 25 A -HUMMYOHP-NEG-901 26 A -HUMMYOHP-NEG-901 27 G -HUMMYOHP-NEG-901 28 A -HUMMYOHP-NEG-901 29 A -HUMMYOHP-NEG-901 30 G -HUMMYOHP-NEG-901 31 C -HUMMYOHP-NEG-901 32 G -HUMMYOHP-NEG-901 33 G -HUMMYOHP-NEG-901 34 G -HUMMYOHP-NEG-901 35 A -HUMMYOHP-NEG-901 36 G -HUMMYOHP-NEG-901 37 G -HUMMYOHP-NEG-901 38 C -HUMMYOHP-NEG-901 39 T -HUMMYOHP-NEG-901 40 G -HUMMYOHP-NEG-901 41 A -HUMMYOHP-NEG-901 42 G -HUMMYOHP-NEG-901 43 T -HUMMYOHP-NEG-901 44 T -HUMMYOHP-NEG-901 45 T -HUMMYOHP-NEG-901 46 C -HUMMYOHP-NEG-901 47 A -HUMMYOHP-NEG-901 48 G -HUMMYOHP-NEG-901 49 A -HUMMYOHP-NEG-901 50 A -HUMMYOHP-NEG-901 51 A -HUMMYOHP-NEG-901 52 C -HUMMYOHP-NEG-901 53 T -HUMMYOHP-NEG-901 54 G -HUMMYOHP-NEG-901 55 C -HUMMYOHP-NEG-901 56 G -HUMMYOHP-NEG-901 57 C -HUMMYOHP-NEG-901 58 A -HUMMYOHP-NEG-901 59 G -HUMMYOHP-NEG-901 60 G -HUMMYONM-NEG-1501 1 C -HUMMYONM-NEG-1501 2 C -HUMMYONM-NEG-1501 3 T -HUMMYONM-NEG-1501 4 C -HUMMYONM-NEG-1501 5 G -HUMMYONM-NEG-1501 6 A -HUMMYONM-NEG-1501 7 G -HUMMYONM-NEG-1501 8 A -HUMMYONM-NEG-1501 9 A -HUMMYONM-NEG-1501 10 G -HUMMYONM-NEG-1501 11 G -HUMMYONM-NEG-1501 12 C -HUMMYONM-NEG-1501 13 A -HUMMYONM-NEG-1501 14 A -HUMMYONM-NEG-1501 15 A -HUMMYONM-NEG-1501 16 G -HUMMYONM-NEG-1501 17 C -HUMMYONM-NEG-1501 18 A -HUMMYONM-NEG-1501 19 G -HUMMYONM-NEG-1501 20 A -HUMMYONM-NEG-1501 21 C -HUMMYONM-NEG-1501 22 T -HUMMYONM-NEG-1501 23 C -HUMMYONM-NEG-1501 24 T -HUMMYONM-NEG-1501 25 G -HUMMYONM-NEG-1501 26 G -HUMMYONM-NEG-1501 27 A -HUMMYONM-NEG-1501 28 G -HUMMYONM-NEG-1501 29 A -HUMMYONM-NEG-1501 30 A -HUMMYONM-NEG-1501 31 C -HUMMYONM-NEG-1501 32 G -HUMMYONM-NEG-1501 33 A -HUMMYONM-NEG-1501 34 G -HUMMYONM-NEG-1501 35 C -HUMMYONM-NEG-1501 36 G -HUMMYONM-NEG-1501 37 G -HUMMYONM-NEG-1501 38 G -HUMMYONM-NEG-1501 39 G -HUMMYONM-NEG-1501 40 G -HUMMYONM-NEG-1501 41 G -HUMMYONM-NEG-1501 42 A -HUMMYONM-NEG-1501 43 G -HUMMYONM-NEG-1501 44 C -HUMMYONM-NEG-1501 45 T -HUMMYONM-NEG-1501 46 G -HUMMYONM-NEG-1501 47 G -HUMMYONM-NEG-1501 48 C -HUMMYONM-NEG-1501 49 C -HUMMYONM-NEG-1501 50 A -HUMMYONM-NEG-1501 51 A -HUMMYONM-NEG-1501 52 C -HUMMYONM-NEG-1501 53 G -HUMMYONM-NEG-1501 54 A -HUMMYONM-NEG-1501 55 G -HUMMYONM-NEG-1501 56 G -HUMMYONM-NEG-1501 57 T -HUMMYONM-NEG-1501 58 G -HUMMYONM-NEG-1501 59 A -HUMMYONM-NEG-1501 60 A -HUMNAKATP1-NEG-5281 1 T -HUMNAKATP1-NEG-5281 2 G -HUMNAKATP1-NEG-5281 3 C -HUMNAKATP1-NEG-5281 4 C -HUMNAKATP1-NEG-5281 5 T -HUMNAKATP1-NEG-5281 6 A -HUMNAKATP1-NEG-5281 7 G -HUMNAKATP1-NEG-5281 8 T -HUMNAKATP1-NEG-5281 9 A -HUMNAKATP1-NEG-5281 10 C -HUMNAKATP1-NEG-5281 11 A -HUMNAKATP1-NEG-5281 12 G -HUMNAKATP1-NEG-5281 13 C -HUMNAKATP1-NEG-5281 14 C -HUMNAKATP1-NEG-5281 15 T -HUMNAKATP1-NEG-5281 16 G -HUMNAKATP1-NEG-5281 17 T -HUMNAKATP1-NEG-5281 18 T -HUMNAKATP1-NEG-5281 19 A -HUMNAKATP1-NEG-5281 20 C -HUMNAKATP1-NEG-5281 21 T -HUMNAKATP1-NEG-5281 22 A -HUMNAKATP1-NEG-5281 23 T -HUMNAKATP1-NEG-5281 24 G -HUMNAKATP1-NEG-5281 25 C -HUMNAKATP1-NEG-5281 26 A -HUMNAKATP1-NEG-5281 27 G -HUMNAKATP1-NEG-5281 28 C -HUMNAKATP1-NEG-5281 29 T -HUMNAKATP1-NEG-5281 30 A -HUMNAKATP1-NEG-5281 31 A -HUMNAKATP1-NEG-5281 32 T -HUMNAKATP1-NEG-5281 33 C -HUMNAKATP1-NEG-5281 34 A -HUMNAKATP1-NEG-5281 35 G -HUMNAKATP1-NEG-5281 36 G -HUMNAKATP1-NEG-5281 37 G -HUMNAKATP1-NEG-5281 38 A -HUMNAKATP1-NEG-5281 39 G -HUMNAKATP1-NEG-5281 40 A -HUMNAKATP1-NEG-5281 41 T -HUMNAKATP1-NEG-5281 42 A -HUMNAKATP1-NEG-5281 43 A -HUMNAKATP1-NEG-5281 44 T -HUMNAKATP1-NEG-5281 45 C -HUMNAKATP1-NEG-5281 46 C -HUMNAKATP1-NEG-5281 47 T -HUMNAKATP1-NEG-5281 48 T -HUMNAKATP1-NEG-5281 49 G -HUMNAKATP1-NEG-5281 50 T -HUMNAKATP1-NEG-5281 51 A -HUMNAKATP1-NEG-5281 52 A -HUMNAKATP1-NEG-5281 53 A -HUMNAKATP1-NEG-5281 54 C -HUMNAKATP1-NEG-5281 55 C -HUMNAKATP1-NEG-5281 56 A -HUMNAKATP1-NEG-5281 57 G -HUMNAKATP1-NEG-5281 58 T -HUMNAKATP1-NEG-5281 59 A -HUMNAKATP1-NEG-5281 60 A -HUMNAKATP1-NEG-721 1 C -HUMNAKATP1-NEG-721 2 C -HUMNAKATP1-NEG-721 3 C -HUMNAKATP1-NEG-721 4 A -HUMNAKATP1-NEG-721 5 G -HUMNAKATP1-NEG-721 6 G -HUMNAKATP1-NEG-721 7 A -HUMNAKATP1-NEG-721 8 G -HUMNAKATP1-NEG-721 9 G -HUMNAKATP1-NEG-721 10 G -HUMNAKATP1-NEG-721 11 G -HUMNAKATP1-NEG-721 12 T -HUMNAKATP1-NEG-721 13 G -HUMNAKATP1-NEG-721 14 G -HUMNAKATP1-NEG-721 15 A -HUMNAKATP1-NEG-721 16 C -HUMNAKATP1-NEG-721 17 C -HUMNAKATP1-NEG-721 18 C -HUMNAKATP1-NEG-721 19 A -HUMNAKATP1-NEG-721 20 C -HUMNAKATP1-NEG-721 21 A -HUMNAKATP1-NEG-721 22 G -HUMNAKATP1-NEG-721 23 C -HUMNAKATP1-NEG-721 24 C -HUMNAKATP1-NEG-721 25 C -HUMNAKATP1-NEG-721 26 A -HUMNAKATP1-NEG-721 27 G -HUMNAKATP1-NEG-721 28 G -HUMNAKATP1-NEG-721 29 G -HUMNAKATP1-NEG-721 30 A -HUMNAKATP1-NEG-721 31 G -HUMNAKATP1-NEG-721 32 G -HUMNAKATP1-NEG-721 33 C -HUMNAKATP1-NEG-721 34 C -HUMNAKATP1-NEG-721 35 G -HUMNAKATP1-NEG-721 36 A -HUMNAKATP1-NEG-721 37 A -HUMNAKATP1-NEG-721 38 A -HUMNAKATP1-NEG-721 39 G -HUMNAKATP1-NEG-721 40 C -HUMNAKATP1-NEG-721 41 G -HUMNAKATP1-NEG-721 42 C -HUMNAKATP1-NEG-721 43 G -HUMNAKATP1-NEG-721 44 G -HUMNAKATP1-NEG-721 45 G -HUMNAKATP1-NEG-721 46 C -HUMNAKATP1-NEG-721 47 G -HUMNAKATP1-NEG-721 48 G -HUMNAKATP1-NEG-721 49 G -HUMNAKATP1-NEG-721 50 C -HUMNAKATP1-NEG-721 51 A -HUMNAKATP1-NEG-721 52 G -HUMNAKATP1-NEG-721 53 G -HUMNAKATP1-NEG-721 54 C -HUMNAKATP1-NEG-721 55 A -HUMNAKATP1-NEG-721 56 G -HUMNAKATP1-NEG-721 57 A -HUMNAKATP1-NEG-721 58 G -HUMNAKATP1-NEG-721 59 G -HUMNAKATP1-NEG-721 60 C -HUMNAKATP2-NEG-6721 1 A -HUMNAKATP2-NEG-6721 2 G -HUMNAKATP2-NEG-6721 3 C -HUMNAKATP2-NEG-6721 4 G -HUMNAKATP2-NEG-6721 5 A -HUMNAKATP2-NEG-6721 6 G -HUMNAKATP2-NEG-6721 7 A -HUMNAKATP2-NEG-6721 8 T -HUMNAKATP2-NEG-6721 9 G -HUMNAKATP2-NEG-6721 10 A -HUMNAKATP2-NEG-6721 11 A -HUMNAKATP2-NEG-6721 12 G -HUMNAKATP2-NEG-6721 13 A -HUMNAKATP2-NEG-6721 14 T -HUMNAKATP2-NEG-6721 15 A -HUMNAKATP2-NEG-6721 16 A -HUMNAKATP2-NEG-6721 17 G -HUMNAKATP2-NEG-6721 18 G -HUMNAKATP2-NEG-6721 19 A -HUMNAKATP2-NEG-6721 20 T -HUMNAKATP2-NEG-6721 21 A -HUMNAKATP2-NEG-6721 22 A -HUMNAKATP2-NEG-6721 23 A -HUMNAKATP2-NEG-6721 24 G -HUMNAKATP2-NEG-6721 25 T -HUMNAKATP2-NEG-6721 26 T -HUMNAKATP2-NEG-6721 27 G -HUMNAKATP2-NEG-6721 28 G -HUMNAKATP2-NEG-6721 29 A -HUMNAKATP2-NEG-6721 30 A -HUMNAKATP2-NEG-6721 31 A -HUMNAKATP2-NEG-6721 32 T -HUMNAKATP2-NEG-6721 33 G -HUMNAKATP2-NEG-6721 34 T -HUMNAKATP2-NEG-6721 35 G -HUMNAKATP2-NEG-6721 36 G -HUMNAKATP2-NEG-6721 37 A -HUMNAKATP2-NEG-6721 38 G -HUMNAKATP2-NEG-6721 39 T -HUMNAKATP2-NEG-6721 40 A -HUMNAKATP2-NEG-6721 41 T -HUMNAKATP2-NEG-6721 42 T -HUMNAKATP2-NEG-6721 43 T -HUMNAKATP2-NEG-6721 44 T -HUMNAKATP2-NEG-6721 45 G -HUMNAKATP2-NEG-6721 46 G -HUMNAKATP2-NEG-6721 47 A -HUMNAKATP2-NEG-6721 48 C -HUMNAKATP2-NEG-6721 49 T -HUMNAKATP2-NEG-6721 50 G -HUMNAKATP2-NEG-6721 51 G -HUMNAKATP2-NEG-6721 52 G -HUMNAKATP2-NEG-6721 53 C -HUMNAKATP2-NEG-6721 54 A -HUMNAKATP2-NEG-6721 55 A -HUMNAKATP2-NEG-6721 56 C -HUMNAKATP2-NEG-6721 57 T -HUMNAKATP2-NEG-6721 58 C -HUMNAKATP2-NEG-6721 59 C -HUMNAKATP2-NEG-6721 60 C -HUMNAKATPA-NEG-3061 1 C -HUMNAKATPA-NEG-3061 2 T -HUMNAKATPA-NEG-3061 3 A -HUMNAKATPA-NEG-3061 4 A -HUMNAKATPA-NEG-3061 5 T -HUMNAKATPA-NEG-3061 6 A -HUMNAKATPA-NEG-3061 7 A -HUMNAKATPA-NEG-3061 8 T -HUMNAKATPA-NEG-3061 9 T -HUMNAKATPA-NEG-3061 10 G -HUMNAKATPA-NEG-3061 11 C -HUMNAKATPA-NEG-3061 12 C -HUMNAKATPA-NEG-3061 13 A -HUMNAKATPA-NEG-3061 14 G -HUMNAKATPA-NEG-3061 15 A -HUMNAKATPA-NEG-3061 16 G -HUMNAKATPA-NEG-3061 17 T -HUMNAKATPA-NEG-3061 18 A -HUMNAKATPA-NEG-3061 19 C -HUMNAKATPA-NEG-3061 20 A -HUMNAKATPA-NEG-3061 21 G -HUMNAKATPA-NEG-3061 22 T -HUMNAKATPA-NEG-3061 23 G -HUMNAKATPA-NEG-3061 24 C -HUMNAKATPA-NEG-3061 25 T -HUMNAKATPA-NEG-3061 26 C -HUMNAKATPA-NEG-3061 27 T -HUMNAKATPA-NEG-3061 28 T -HUMNAKATPA-NEG-3061 29 G -HUMNAKATPA-NEG-3061 30 T -HUMNAKATPA-NEG-3061 31 T -HUMNAKATPA-NEG-3061 32 G -HUMNAKATPA-NEG-3061 33 A -HUMNAKATPA-NEG-3061 34 T -HUMNAKATPA-NEG-3061 35 C -HUMNAKATPA-NEG-3061 36 A -HUMNAKATPA-NEG-3061 37 T -HUMNAKATPA-NEG-3061 38 G -HUMNAKATPA-NEG-3061 39 T -HUMNAKATPA-NEG-3061 40 A -HUMNAKATPA-NEG-3061 41 T -HUMNAKATPA-NEG-3061 42 T -HUMNAKATPA-NEG-3061 43 C -HUMNAKATPA-NEG-3061 44 A -HUMNAKATPA-NEG-3061 45 G -HUMNAKATPA-NEG-3061 46 T -HUMNAKATPA-NEG-3061 47 C -HUMNAKATPA-NEG-3061 48 A -HUMNAKATPA-NEG-3061 49 A -HUMNAKATPA-NEG-3061 50 G -HUMNAKATPA-NEG-3061 51 T -HUMNAKATPA-NEG-3061 52 T -HUMNAKATPA-NEG-3061 53 A -HUMNAKATPA-NEG-3061 54 A -HUMNAKATPA-NEG-3061 55 A -HUMNAKATPA-NEG-3061 56 A -HUMNAKATPA-NEG-3061 57 C -HUMNAKATPA-NEG-3061 58 A -HUMNAKATPA-NEG-3061 59 A -HUMNAKATPA-NEG-3061 60 T -HUMNCAM-NEG-121 59 A -HUMNCAM-NEG-121 58 T -HUMNCAM-NEG-121 57 G -HUMNCAM-NEG-121 56 G -HUMNCAM-NEG-121 55 T -HUMNCAM-NEG-121 54 G -HUMNCAM-NEG-121 53 A -HUMNCAM-NEG-121 52 A -HUMNCAM-NEG-121 51 C -HUMNCAM-NEG-121 50 C -HUMNCAM-NEG-121 49 T -HUMNCAM-NEG-121 48 T -HUMNCAM-NEG-121 47 A -HUMNCAM-NEG-121 46 C -HUMNCAM-NEG-121 45 G -HUMNCAM-NEG-121 44 G -HUMNCAM-NEG-121 43 T -HUMNCAM-NEG-121 42 A -HUMNCAM-NEG-121 41 T -HUMNCAM-NEG-121 40 G -HUMNCAM-NEG-121 39 A -HUMNCAM-NEG-121 38 A -HUMNCAM-NEG-121 37 G -HUMNCAM-NEG-121 36 A -HUMNCAM-NEG-121 35 A -HUMNCAM-NEG-121 34 G -HUMNCAM-NEG-121 33 T -HUMNCAM-NEG-121 32 G -HUMNCAM-NEG-121 31 G -HUMNCAM-NEG-121 30 T -HUMNCAM-NEG-121 60 T -HUMNCAM-NEG-121 29 T -HUMNCAM-NEG-121 28 G -HUMNCAM-NEG-121 27 A -HUMNCAM-NEG-121 26 C -HUMNCAM-NEG-121 25 G -HUMNCAM-NEG-121 24 A -HUMNCAM-NEG-121 23 G -HUMNCAM-NEG-121 22 A -HUMNCAM-NEG-121 21 G -HUMNCAM-NEG-121 20 G -HUMNCAM-NEG-121 19 T -HUMNCAM-NEG-121 18 G -HUMNCAM-NEG-121 17 A -HUMNCAM-NEG-121 16 G -HUMNCAM-NEG-121 15 T -HUMNCAM-NEG-121 14 C -HUMNCAM-NEG-121 13 G -HUMNCAM-NEG-121 12 A -HUMNCAM-NEG-121 11 A -HUMNCAM-NEG-121 10 A -HUMNCAM-NEG-121 9 C -HUMNCAM-NEG-121 8 A -HUMNCAM-NEG-121 7 T -HUMNCAM-NEG-121 6 A -HUMNCAM-NEG-121 5 A -HUMNCAM-NEG-121 4 A -HUMNCAM-NEG-121 3 C -HUMNCAM-NEG-121 2 T -HUMNCAM-NEG-121 1 C -HUMNEBB-NEG-241 1 G -HUMNEBB-NEG-241 2 C -HUMNEBB-NEG-241 3 C -HUMNEBB-NEG-241 4 A -HUMNEBB-NEG-241 5 A -HUMNEBB-NEG-241 6 C -HUMNEBB-NEG-241 7 T -HUMNEBB-NEG-241 8 A -HUMNEBB-NEG-241 9 C -HUMNEBB-NEG-241 10 A -HUMNEBB-NEG-241 11 G -HUMNEBB-NEG-241 12 G -HUMNEBB-NEG-241 13 A -HUMNEBB-NEG-241 14 A -HUMNEBB-NEG-241 15 C -HUMNEBB-NEG-241 16 G -HUMNEBB-NEG-241 17 T -HUMNEBB-NEG-241 18 G -HUMNEBB-NEG-241 19 A -HUMNEBB-NEG-241 20 T -HUMNEBB-NEG-241 21 C -HUMNEBB-NEG-241 22 C -HUMNEBB-NEG-241 23 A -HUMNEBB-NEG-241 24 T -HUMNEBB-NEG-241 25 A -HUMNEBB-NEG-241 26 C -HUMNEBB-NEG-241 27 C -HUMNEBB-NEG-241 28 T -HUMNEBB-NEG-241 29 A -HUMNEBB-NEG-241 30 C -HUMNEBB-NEG-241 31 A -HUMNEBB-NEG-241 32 A -HUMNEBB-NEG-241 33 C -HUMNEBB-NEG-241 34 A -HUMNEBB-NEG-241 35 T -HUMNEBB-NEG-241 36 G -HUMNEBB-NEG-241 37 C -HUMNEBB-NEG-241 38 T -HUMNEBB-NEG-241 39 T -HUMNEBB-NEG-241 40 C -HUMNEBB-NEG-241 41 C -HUMNEBB-NEG-241 42 T -HUMNEBB-NEG-241 43 G -HUMNEBB-NEG-241 44 A -HUMNEBB-NEG-241 45 T -HUMNEBB-NEG-241 46 G -HUMNEBB-NEG-241 47 C -HUMNEBB-NEG-241 48 C -HUMNEBB-NEG-241 49 A -HUMNEBB-NEG-241 50 T -HUMNEBB-NEG-241 51 G -HUMNEBB-NEG-241 52 A -HUMNEBB-NEG-241 53 G -HUMNEBB-NEG-241 54 C -HUMNEBB-NEG-241 55 T -HUMNEBB-NEG-241 56 T -HUMNEBB-NEG-241 57 T -HUMNEBB-NEG-241 58 G -HUMNEBB-NEG-241 59 A -HUMNEBB-NEG-241 60 A -HUMNFH3-NEG-1 1 G -HUMNFH3-NEG-1 2 A -HUMNFH3-NEG-1 3 A -HUMNFH3-NEG-1 4 G -HUMNFH3-NEG-1 5 C -HUMNFH3-NEG-1 6 C -HUMNFH3-NEG-1 7 A -HUMNFH3-NEG-1 8 T -HUMNFH3-NEG-1 9 T -HUMNFH3-NEG-1 10 C -HUMNFH3-NEG-1 11 A -HUMNFH3-NEG-1 12 G -HUMNFH3-NEG-1 13 C -HUMNFH3-NEG-1 14 A -HUMNFH3-NEG-1 15 G -HUMNFH3-NEG-1 16 C -HUMNFH3-NEG-1 17 T -HUMNFH3-NEG-1 18 G -HUMNFH3-NEG-1 19 G -HUMNFH3-NEG-1 20 A -HUMNFH3-NEG-1 21 C -HUMNFH3-NEG-1 22 G -HUMNFH3-NEG-1 23 C -HUMNFH3-NEG-1 24 T -HUMNFH3-NEG-1 25 G -HUMNFH3-NEG-1 26 A -HUMNFH3-NEG-1 27 G -HUMNFH3-NEG-1 28 C -HUMNFH3-NEG-1 29 T -HUMNFH3-NEG-1 30 G -HUMNFH3-NEG-1 31 A -HUMNFH3-NEG-1 32 G -HUMNFH3-NEG-1 33 G -HUMNFH3-NEG-1 34 A -HUMNFH3-NEG-1 35 A -HUMNFH3-NEG-1 36 C -HUMNFH3-NEG-1 37 A -HUMNFH3-NEG-1 38 C -HUMNFH3-NEG-1 39 C -HUMNFH3-NEG-1 40 A -HUMNFH3-NEG-1 41 A -HUMNFH3-NEG-1 42 G -HUMNFH3-NEG-1 43 T -HUMNFH3-NEG-1 44 G -HUMNFH3-NEG-1 45 G -HUMNFH3-NEG-1 46 G -HUMNFH3-NEG-1 47 A -HUMNFH3-NEG-1 48 G -HUMNFH3-NEG-1 49 A -HUMNFH3-NEG-1 50 T -HUMNFH3-NEG-1 51 G -HUMNFH3-NEG-1 52 G -HUMNFH3-NEG-1 53 C -HUMNFH3-NEG-1 54 C -HUMNFH3-NEG-1 55 G -HUMNFH3-NEG-1 56 C -HUMNFH3-NEG-1 57 C -HUMNFH3-NEG-1 58 C -HUMNFH3-NEG-1 59 A -HUMNFH3-NEG-1 60 G -HUMNFLG-NEG-1861 1 T -HUMNFLG-NEG-1861 2 T -HUMNFLG-NEG-1861 3 C -HUMNFLG-NEG-1861 4 T -HUMNFLG-NEG-1861 5 T -HUMNFLG-NEG-1861 6 T -HUMNFLG-NEG-1861 7 A -HUMNFLG-NEG-1861 8 T -HUMNFLG-NEG-1861 9 G -HUMNFLG-NEG-1861 10 T -HUMNFLG-NEG-1861 11 G -HUMNFLG-NEG-1861 12 T -HUMNFLG-NEG-1861 13 A -HUMNFLG-NEG-1861 14 A -HUMNFLG-NEG-1861 15 A -HUMNFLG-NEG-1861 16 A -HUMNFLG-NEG-1861 17 T -HUMNFLG-NEG-1861 18 C -HUMNFLG-NEG-1861 19 T -HUMNFLG-NEG-1861 20 T -HUMNFLG-NEG-1861 21 C -HUMNFLG-NEG-1861 22 T -HUMNFLG-NEG-1861 23 A -HUMNFLG-NEG-1861 24 C -HUMNFLG-NEG-1861 25 A -HUMNFLG-NEG-1861 26 A -HUMNFLG-NEG-1861 27 C -HUMNFLG-NEG-1861 28 A -HUMNFLG-NEG-1861 29 T -HUMNFLG-NEG-1861 30 T -HUMNFLG-NEG-1861 31 T -HUMNFLG-NEG-1861 32 C -HUMNFLG-NEG-1861 33 T -HUMNFLG-NEG-1861 34 G -HUMNFLG-NEG-1861 35 T -HUMNFLG-NEG-1861 36 T -HUMNFLG-NEG-1861 37 T -HUMNFLG-NEG-1861 38 A -HUMNFLG-NEG-1861 39 A -HUMNFLG-NEG-1861 40 A -HUMNFLG-NEG-1861 41 C -HUMNFLG-NEG-1861 42 A -HUMNFLG-NEG-1861 43 T -HUMNFLG-NEG-1861 44 C -HUMNFLG-NEG-1861 45 T -HUMNFLG-NEG-1861 46 C -HUMNFLG-NEG-1861 47 C -HUMNFLG-NEG-1861 48 A -HUMNFLG-NEG-1861 49 T -HUMNFLG-NEG-1861 50 C -HUMNFLG-NEG-1861 51 T -HUMNFLG-NEG-1861 52 T -HUMNFLG-NEG-1861 53 C -HUMNFLG-NEG-1861 54 T -HUMNFLG-NEG-1861 55 G -HUMNFLG-NEG-1861 56 G -HUMNFLG-NEG-1861 57 G -HUMNFLG-NEG-1861 58 G -HUMNFLG-NEG-1861 59 A -HUMNFLG-NEG-1861 60 G -HUMNFM-NEG-1921 1 G -HUMNFM-NEG-1921 2 G -HUMNFM-NEG-1921 3 C -HUMNFM-NEG-1921 4 C -HUMNFM-NEG-1921 5 G -HUMNFM-NEG-1921 6 C -HUMNFM-NEG-1921 7 T -HUMNFM-NEG-1921 8 C -HUMNFM-NEG-1921 9 G -HUMNFM-NEG-1921 10 C -HUMNFM-NEG-1921 11 T -HUMNFM-NEG-1921 12 A -HUMNFM-NEG-1921 13 G -HUMNFM-NEG-1921 14 A -HUMNFM-NEG-1921 15 G -HUMNFM-NEG-1921 16 C -HUMNFM-NEG-1921 17 A -HUMNFM-NEG-1921 18 C -HUMNFM-NEG-1921 19 G -HUMNFM-NEG-1921 20 C -HUMNFM-NEG-1921 21 G -HUMNFM-NEG-1921 22 C -HUMNFM-NEG-1921 23 G -HUMNFM-NEG-1921 24 C -HUMNFM-NEG-1921 25 C -HUMNFM-NEG-1921 26 G -HUMNFM-NEG-1921 27 C -HUMNFM-NEG-1921 28 A -HUMNFM-NEG-1921 29 G -HUMNFM-NEG-1921 30 A -HUMNFM-NEG-1921 31 C -HUMNFM-NEG-1921 32 C -HUMNFM-NEG-1921 33 T -HUMNFM-NEG-1921 34 A -HUMNFM-NEG-1921 35 G -HUMNFM-NEG-1921 36 G -HUMNFM-NEG-1921 37 G -HUMNFM-NEG-1921 38 T -HUMNFM-NEG-1921 39 A -HUMNFM-NEG-1921 40 T -HUMNFM-NEG-1921 41 T -HUMNFM-NEG-1921 42 T -HUMNFM-NEG-1921 43 G -HUMNFM-NEG-1921 44 C -HUMNFM-NEG-1921 45 G -HUMNFM-NEG-1921 46 G -HUMNFM-NEG-1921 47 A -HUMNFM-NEG-1921 48 T -HUMNFM-NEG-1921 49 C -HUMNFM-NEG-1921 50 A -HUMNFM-NEG-1921 51 G -HUMNFM-NEG-1921 52 C -HUMNFM-NEG-1921 53 G -HUMNFM-NEG-1921 54 T -HUMNFM-NEG-1921 55 C -HUMNFM-NEG-1921 56 C -HUMNFM-NEG-1921 57 T -HUMNFM-NEG-1921 58 C -HUMNFM-NEG-1921 59 G -HUMNFM-NEG-1921 60 C -HUMNGFB-NEG-241 60 T -HUMNGFB-NEG-241 59 C -HUMNGFB-NEG-241 58 T -HUMNGFB-NEG-241 57 T -HUMNGFB-NEG-241 56 C -HUMNGFB-NEG-241 55 C -HUMNGFB-NEG-241 54 T -HUMNGFB-NEG-241 53 C -HUMNGFB-NEG-241 52 T -HUMNGFB-NEG-241 51 A -HUMNGFB-NEG-241 50 A -HUMNGFB-NEG-241 49 G -HUMNGFB-NEG-241 48 T -HUMNGFB-NEG-241 47 A -HUMNGFB-NEG-241 46 A -HUMNGFB-NEG-241 45 A -HUMNGFB-NEG-241 44 G -HUMNGFB-NEG-241 43 T -HUMNGFB-NEG-241 42 A -HUMNGFB-NEG-241 41 G -HUMNGFB-NEG-241 40 A -HUMNGFB-NEG-241 39 A -HUMNGFB-NEG-241 38 A -HUMNGFB-NEG-241 37 C -HUMNGFB-NEG-241 36 T -HUMNGFB-NEG-241 35 C -HUMNGFB-NEG-241 34 G -HUMNGFB-NEG-241 33 A -HUMNGFB-NEG-241 32 A -HUMNGFB-NEG-241 31 A -HUMNGFB-NEG-241 30 G -HUMNGFB-NEG-241 29 A -HUMNGFB-NEG-241 28 G -HUMNGFB-NEG-241 27 G -HUMNGFB-NEG-241 26 A -HUMNGFB-NEG-241 25 G -HUMNGFB-NEG-241 24 A -HUMNGFB-NEG-241 23 T -HUMNGFB-NEG-241 22 A -HUMNGFB-NEG-241 21 T -HUMNGFB-NEG-241 20 C -HUMNGFB-NEG-241 19 C -HUMNGFB-NEG-241 18 T -HUMNGFB-NEG-241 17 A -HUMNGFB-NEG-241 16 T -HUMNGFB-NEG-241 15 T -HUMNGFB-NEG-241 14 C -HUMNGFB-NEG-241 13 T -HUMNGFB-NEG-241 12 T -HUMNGFB-NEG-241 11 C -HUMNGFB-NEG-241 10 G -HUMNGFB-NEG-241 9 A -HUMNGFB-NEG-241 8 C -HUMNGFB-NEG-241 7 C -HUMNGFB-NEG-241 6 G -HUMNGFB-NEG-241 5 T -HUMNGFB-NEG-241 4 G -HUMNGFB-NEG-241 3 T -HUMNGFB-NEG-241 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-HUMRPS17A-ACCEPTOR-3688 52 C -HUMRPS17A-ACCEPTOR-3688 53 T -HUMRPS17A-ACCEPTOR-3688 54 T -HUMRPS17A-ACCEPTOR-3688 55 C -HUMRPS17A-ACCEPTOR-3688 56 A -HUMRPS17A-ACCEPTOR-3688 57 G -HUMRPS17A-ACCEPTOR-3688 58 G -HUMRPS17A-ACCEPTOR-3688 59 T -HUMRPS17A-ACCEPTOR-3688 60 C -HUMRPS17A-ACCEPTOR-463 1 C -HUMRPS17A-ACCEPTOR-463 2 G -HUMRPS17A-ACCEPTOR-463 3 T -HUMRPS17A-ACCEPTOR-463 4 G -HUMRPS17A-ACCEPTOR-463 5 A -HUMRPS17A-ACCEPTOR-463 6 G -HUMRPS17A-ACCEPTOR-463 7 T -HUMRPS17A-ACCEPTOR-463 8 G -HUMRPS17A-ACCEPTOR-463 9 A -HUMRPS17A-ACCEPTOR-463 10 T -HUMRPS17A-ACCEPTOR-463 11 C -HUMRPS17A-ACCEPTOR-463 12 C -HUMRPS17A-ACCEPTOR-463 13 T -HUMRPS17A-ACCEPTOR-463 14 G -HUMRPS17A-ACCEPTOR-463 15 T -HUMRPS17A-ACCEPTOR-463 16 C -HUMRPS17A-ACCEPTOR-463 17 C -HUMRPS17A-ACCEPTOR-463 18 T -HUMRPS17A-ACCEPTOR-463 19 C -HUMRPS17A-ACCEPTOR-463 20 T -HUMRPS17A-ACCEPTOR-463 21 C -HUMRPS17A-ACCEPTOR-463 22 C -HUMRPS17A-ACCEPTOR-463 23 A -HUMRPS17A-ACCEPTOR-463 24 C -HUMRPS17A-ACCEPTOR-463 25 C -HUMRPS17A-ACCEPTOR-463 26 C -HUMRPS17A-ACCEPTOR-463 27 G -HUMRPS17A-ACCEPTOR-463 28 C -HUMRPS17A-ACCEPTOR-463 29 A -HUMRPS17A-ACCEPTOR-463 30 G -HUMRPS17A-ACCEPTOR-463 31 G -HUMRPS17A-ACCEPTOR-463 32 G -HUMRPS17A-ACCEPTOR-463 33 C -HUMRPS17A-ACCEPTOR-463 34 C -HUMRPS17A-ACCEPTOR-463 35 G -HUMRPS17A-ACCEPTOR-463 36 C -HUMRPS17A-ACCEPTOR-463 37 G -HUMRPS17A-ACCEPTOR-463 38 T -HUMRPS17A-ACCEPTOR-463 39 T -HUMRPS17A-ACCEPTOR-463 40 C -HUMRPS17A-ACCEPTOR-463 41 G -HUMRPS17A-ACCEPTOR-463 42 C -HUMRPS17A-ACCEPTOR-463 43 A -HUMRPS17A-ACCEPTOR-463 44 C -HUMRPS17A-ACCEPTOR-463 45 C -HUMRPS17A-ACCEPTOR-463 46 A -HUMRPS17A-ACCEPTOR-463 47 A -HUMRPS17A-ACCEPTOR-463 48 A -HUMRPS17A-ACCEPTOR-463 49 A -HUMRPS17A-ACCEPTOR-463 50 C -HUMRPS17A-ACCEPTOR-463 51 C -HUMRPS17A-ACCEPTOR-463 52 G -HUMRPS17A-ACCEPTOR-463 53 T -HUMRPS17A-ACCEPTOR-463 54 G -HUMRPS17A-ACCEPTOR-463 55 A -HUMRPS17A-ACCEPTOR-463 56 A -HUMRPS17A-ACCEPTOR-463 57 G -HUMRPS17A-ACCEPTOR-463 58 A -HUMRPS17A-ACCEPTOR-463 59 A -HUMRPS17A-ACCEPTOR-463 60 G -HUMRPS17A-DONOR-171 1 T -HUMRPS17A-DONOR-171 2 T -HUMRPS17A-DONOR-171 3 C -HUMRPS17A-DONOR-171 4 C -HUMRPS17A-DONOR-171 5 T -HUMRPS17A-DONOR-171 6 C -HUMRPS17A-DONOR-171 7 T -HUMRPS17A-DONOR-171 8 T -HUMRPS17A-DONOR-171 9 T -HUMRPS17A-DONOR-171 10 T -HUMRPS17A-DONOR-171 11 A -HUMRPS17A-DONOR-171 12 C -HUMRPS17A-DONOR-171 13 C -HUMRPS17A-DONOR-171 14 A -HUMRPS17A-DONOR-171 15 A -HUMRPS17A-DONOR-171 16 G -HUMRPS17A-DONOR-171 17 G -HUMRPS17A-DONOR-171 18 A -HUMRPS17A-DONOR-171 19 C -HUMRPS17A-DONOR-171 20 C -HUMRPS17A-DONOR-171 21 C -HUMRPS17A-DONOR-171 22 G -HUMRPS17A-DONOR-171 23 C -HUMRPS17A-DONOR-171 24 C -HUMRPS17A-DONOR-171 25 A -HUMRPS17A-DONOR-171 26 A -HUMRPS17A-DONOR-171 27 C -HUMRPS17A-DONOR-171 28 A -HUMRPS17A-DONOR-171 29 T -HUMRPS17A-DONOR-171 30 G -HUMRPS17A-DONOR-171 31 G -HUMRPS17A-DONOR-171 32 T -HUMRPS17A-DONOR-171 33 A -HUMRPS17A-DONOR-171 34 G -HUMRPS17A-DONOR-171 35 G -HUMRPS17A-DONOR-171 36 T -HUMRPS17A-DONOR-171 37 G 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-HUMTCBYX-NEG-1 2 G -HUMTCBYX-NEG-1 3 C -HUMTCBYX-NEG-1 4 T -HUMTCBYX-NEG-1 5 T -HUMTCBYX-NEG-1 6 C -HUMTCBYX-NEG-1 7 A -HUMTCBYX-NEG-1 8 T -HUMTCBYX-NEG-1 9 T -HUMTCBYX-NEG-1 10 C -HUMTCBYX-NEG-1 11 C -HUMTCBYX-NEG-1 12 T -HUMTCBYX-NEG-1 13 G -HUMTCBYX-NEG-1 14 C -HUMTCBYX-NEG-1 15 C -HUMTCBYX-NEG-1 16 T -HUMTCBYX-NEG-1 17 C -HUMTCBYX-NEG-1 18 T -HUMTCBYX-NEG-1 19 T -HUMTCBYX-NEG-1 20 G -HUMTCBYX-NEG-1 21 T -HUMTCBYX-NEG-1 22 C -HUMTCBYX-NEG-1 23 T -HUMTCBYX-NEG-1 24 G -HUMTCBYX-NEG-1 25 G -HUMTCBYX-NEG-1 26 C -HUMTCBYX-NEG-1 27 T -HUMTCBYX-NEG-1 28 C -HUMTCBYX-NEG-1 29 A -HUMTCBYX-NEG-1 30 G -HUMTCBYX-NEG-1 31 C -HUMTCBYX-NEG-1 32 A -HUMTCBYX-NEG-1 33 C -HUMTCBYX-NEG-1 34 A -HUMTCBYX-NEG-1 35 T -HUMTCBYX-NEG-1 36 A -HUMTCBYX-NEG-1 37 T -HUMTCBYX-NEG-1 38 T -HUMTCBYX-NEG-1 39 G -HUMTCBYX-NEG-1 40 G -HUMTCBYX-NEG-1 41 T -HUMTCBYX-NEG-1 42 C -HUMTCBYX-NEG-1 43 A -HUMTCBYX-NEG-1 44 T -HUMTCBYX-NEG-1 45 A -HUMTCBYX-NEG-1 46 A -HUMTCBYX-NEG-1 47 A -HUMTCBYX-NEG-1 48 A -HUMTCBYX-NEG-1 49 C 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34 C -HUMTCBZE-NEG-241 35 G -HUMTCBZE-NEG-241 36 A -HUMTCBZE-NEG-241 37 C -HUMTCBZE-NEG-241 38 A -HUMTCBZE-NEG-241 39 A -HUMTCBZE-NEG-241 40 G -HUMTCBZE-NEG-241 41 A -HUMTCBZE-NEG-241 42 C -HUMTCBZE-NEG-241 43 C -HUMTCBZE-NEG-241 44 C -HUMTCBZE-NEG-241 45 A -HUMTCBZE-NEG-241 46 G -HUMTCBZE-NEG-241 47 G -HUMTCBZE-NEG-241 48 C -HUMTCBZE-NEG-241 49 A -HUMTCBZE-NEG-241 50 T -HUMTCBZE-NEG-241 51 G -HUMTCBZE-NEG-241 52 G -HUMTCBZE-NEG-241 53 G -HUMTCBZE-NEG-241 54 C -HUMTCBZE-NEG-241 55 T -HUMTCBZE-NEG-241 56 G -HUMTCBZE-NEG-241 57 A -HUMTCBZE-NEG-241 58 G -HUMTCBZE-NEG-241 59 G -HUMTCBZE-NEG-241 60 C -HUMTCDXA-NEG-1 1 A -HUMTCDXA-NEG-1 2 G -HUMTCDXA-NEG-1 3 A -HUMTCDXA-NEG-1 4 G -HUMTCDXA-NEG-1 5 G -HUMTCDXA-NEG-1 6 C -HUMTCDXA-NEG-1 7 A -HUMTCDXA-NEG-1 8 G -HUMTCDXA-NEG-1 9 A -HUMTCDXA-NEG-1 10 G -HUMTCDXA-NEG-1 11 C -HUMTCDXA-NEG-1 12 T -HUMTCDXA-NEG-1 13 G -HUMTCDXA-NEG-1 14 C -HUMTCDXA-NEG-1 15 C -HUMTCDXA-NEG-1 16 C -HUMTCDXA-NEG-1 17 C -HUMTCDXA-NEG-1 18 T -HUMTCDXA-NEG-1 19 G 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48 G -HUMTCOBI-NEG-1261 49 A -HUMTCOBI-NEG-1261 50 G -HUMTCOBI-NEG-1261 51 C -HUMTCOBI-NEG-1261 52 C -HUMTCOBI-NEG-1261 53 A -HUMTCOBI-NEG-1261 54 A -HUMTCOBI-NEG-1261 55 G -HUMTCOBI-NEG-1261 56 G -HUMTCOBI-NEG-1261 57 A -HUMTCOBI-NEG-1261 58 G -HUMTCOBI-NEG-1261 59 C -HUMTCOBI-NEG-1261 60 T -HUMTCRAC-NEG-1441 1 T -HUMTCRAC-NEG-1441 2 T -HUMTCRAC-NEG-1441 3 T -HUMTCRAC-NEG-1441 4 T -HUMTCRAC-NEG-1441 5 T -HUMTCRAC-NEG-1441 6 T -HUMTCRAC-NEG-1441 7 G -HUMTCRAC-NEG-1441 8 A -HUMTCRAC-NEG-1441 9 G -HUMTCRAC-NEG-1441 10 A -HUMTCRAC-NEG-1441 11 T -HUMTCRAC-NEG-1441 12 G -HUMTCRAC-NEG-1441 13 G -HUMTCRAC-NEG-1441 14 A -HUMTCRAC-NEG-1441 15 G -HUMTCRAC-NEG-1441 16 T -HUMTCRAC-NEG-1441 17 T -HUMTCRAC-NEG-1441 18 T -HUMTCRAC-NEG-1441 19 T -HUMTCRAC-NEG-1441 20 G -HUMTCRAC-NEG-1441 21 C -HUMTCRAC-NEG-1441 22 T -HUMTCRAC-NEG-1441 23 C -HUMTCRAC-NEG-1441 24 T -HUMTCRAC-NEG-1441 25 T -HUMTCRAC-NEG-1441 26 G -HUMTCRAC-NEG-1441 27 T -HUMTCRAC-NEG-1441 28 T -HUMTCRAC-NEG-1441 29 G -HUMTCRAC-NEG-1441 30 C -HUMTCRAC-NEG-1441 31 C -HUMTCRAC-NEG-1441 32 C -HUMTCRAC-NEG-1441 33 A -HUMTCRAC-NEG-1441 34 G -HUMTCRAC-NEG-1441 35 G -HUMTCRAC-NEG-1441 36 C -HUMTCRAC-NEG-1441 37 T -HUMTCRAC-NEG-1441 38 G -HUMTCRAC-NEG-1441 39 G -HUMTCRAC-NEG-1441 40 A -HUMTCRAC-NEG-1441 41 G -HUMTCRAC-NEG-1441 42 T -HUMTCRAC-NEG-1441 43 G -HUMTCRAC-NEG-1441 44 C -HUMTCRAC-NEG-1441 45 A -HUMTCRAC-NEG-1441 46 A -HUMTCRAC-NEG-1441 47 T -HUMTCRAC-NEG-1441 48 G -HUMTCRAC-NEG-1441 49 G -HUMTCRAC-NEG-1441 50 T -HUMTCRAC-NEG-1441 51 G -HUMTCRAC-NEG-1441 52 C -HUMTCRAC-NEG-1441 53 A -HUMTCRAC-NEG-1441 54 T -HUMTCRAC-NEG-1441 55 C -HUMTCRAC-NEG-1441 56 T -HUMTCRAC-NEG-1441 57 T -HUMTCRAC-NEG-1441 58 G -HUMTCRAC-NEG-1441 59 G -HUMTCRAC-NEG-1441 60 C -HUMTCRAI2-NEG-61 1 T -HUMTCRAI2-NEG-61 2 C -HUMTCRAI2-NEG-61 3 A -HUMTCRAI2-NEG-61 4 G -HUMTCRAI2-NEG-61 5 T -HUMTCRAI2-NEG-61 6 G -HUMTCRAI2-NEG-61 7 C -HUMTCRAI2-NEG-61 8 C -HUMTCRAI2-NEG-61 9 C -HUMTCRAI2-NEG-61 10 T -HUMTCRAI2-NEG-61 11 T -HUMTCRAI2-NEG-61 12 C 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-HUMTCRAJ3-NEG-301 38 C -HUMTCRAJ3-NEG-301 39 T -HUMTCRAJ3-NEG-301 40 C -HUMTCRAJ3-NEG-301 41 T -HUMTCRAJ3-NEG-301 42 T -HUMTCRAJ3-NEG-301 43 T -HUMTCRAJ3-NEG-301 44 A -HUMTCRAJ3-NEG-301 45 T -HUMTCRAJ3-NEG-301 46 T -HUMTCRAJ3-NEG-301 47 C -HUMTCRAJ3-NEG-301 48 A -HUMTCRAJ3-NEG-301 49 G -HUMTCRAJ3-NEG-301 50 T -HUMTCRAJ3-NEG-301 51 G -HUMTCRAJ3-NEG-301 52 C -HUMTCRAJ3-NEG-301 53 T -HUMTCRAJ3-NEG-301 54 T -HUMTCRAJ3-NEG-301 55 T -HUMTCRAJ3-NEG-301 56 G -HUMTCRAJ3-NEG-301 57 C -HUMTCRAJ3-NEG-301 58 C -HUMTCRAJ3-NEG-301 59 A -HUMTCRAJ3-NEG-301 60 A -HUMTCRB11-NEG-1 1 G -HUMTCRB11-NEG-1 2 G -HUMTCRB11-NEG-1 3 A -HUMTCRB11-NEG-1 4 C -HUMTCRB11-NEG-1 5 A -HUMTCRB11-NEG-1 6 G -HUMTCRB11-NEG-1 7 G -HUMTCRB11-NEG-1 8 A -HUMTCRB11-NEG-1 9 T -HUMTCRB11-NEG-1 10 G -HUMTCRB11-NEG-1 11 T -HUMTCRB11-NEG-1 12 A -HUMTCRB11-NEG-1 13 G -HUMTCRB11-NEG-1 14 C -HUMTCRB11-NEG-1 15 T -HUMTCRB11-NEG-1 16 C -HUMTCRB11-NEG-1 17 T -HUMTCRB11-NEG-1 18 C -HUMTCRB11-NEG-1 19 A -HUMTCRB11-NEG-1 20 G -HUMTCRB11-NEG-1 21 G -HUMTCRB11-NEG-1 22 T -HUMTCRB11-NEG-1 23 G -HUMTCRB11-NEG-1 24 T -HUMTCRB11-NEG-1 25 G -HUMTCRB11-NEG-1 26 A -HUMTCRB11-NEG-1 27 T -HUMTCRB11-NEG-1 28 C -HUMTCRB11-NEG-1 29 C -HUMTCRB11-NEG-1 30 A -HUMTCRB11-NEG-1 31 A -HUMTCRB11-NEG-1 32 T -HUMTCRB11-NEG-1 33 T -HUMTCRB11-NEG-1 34 T -HUMTCRB11-NEG-1 35 C -HUMTCRB11-NEG-1 36 G -HUMTCRB11-NEG-1 37 G -HUMTCRB11-NEG-1 38 G -HUMTCRB11-NEG-1 39 T -HUMTCRB11-NEG-1 40 C -HUMTCRB11-NEG-1 41 A -HUMTCRB11-NEG-1 42 T -HUMTCRB11-NEG-1 43 G -HUMTCRB11-NEG-1 44 T -HUMTCRB11-NEG-1 45 A -HUMTCRB11-NEG-1 46 T -HUMTCRB11-NEG-1 47 C -HUMTCRB11-NEG-1 48 C -HUMTCRB11-NEG-1 49 C -HUMTCRB11-NEG-1 50 T -HUMTCRB11-NEG-1 51 T -HUMTCRB11-NEG-1 52 T -HUMTCRB11-NEG-1 53 A -HUMTCRB11-NEG-1 54 T -HUMTCRB11-NEG-1 55 T -HUMTCRB11-NEG-1 56 G -HUMTCRB11-NEG-1 57 G -HUMTCRB11-NEG-1 58 T -HUMTCRB11-NEG-1 59 A -HUMTCRB11-NEG-1 60 C -HUMTCRB22-NEG-241 1 A -HUMTCRB22-NEG-241 2 C -HUMTCRB22-NEG-241 3 A -HUMTCRB22-NEG-241 4 A -HUMTCRB22-NEG-241 5 A -HUMTCRB22-NEG-241 6 A 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-HUMTHB-ACCEPTOR-883 33 T -HUMTHB-ACCEPTOR-883 34 T -HUMTHB-ACCEPTOR-883 35 C -HUMTHB-ACCEPTOR-883 36 C -HUMTHB-ACCEPTOR-883 37 T -HUMTHB-ACCEPTOR-883 38 G -HUMTHB-ACCEPTOR-883 39 G -HUMTHB-ACCEPTOR-883 40 C -HUMTHB-ACCEPTOR-883 41 T -HUMTHB-ACCEPTOR-883 42 C -HUMTHB-ACCEPTOR-883 43 C -HUMTHB-ACCEPTOR-883 44 T -HUMTHB-ACCEPTOR-883 45 C -HUMTHB-ACCEPTOR-883 46 A -HUMTHB-ACCEPTOR-883 47 G -HUMTHB-ACCEPTOR-883 48 C -HUMTHB-ACCEPTOR-883 49 A -HUMTHB-ACCEPTOR-883 50 A -HUMTHB-ACCEPTOR-883 51 G -HUMTHB-ACCEPTOR-883 52 C -HUMTHB-ACCEPTOR-883 53 A -HUMTHB-ACCEPTOR-883 54 C -HUMTHB-ACCEPTOR-883 55 G -HUMTHB-ACCEPTOR-883 56 G -HUMTHB-ACCEPTOR-883 57 T -HUMTHB-ACCEPTOR-883 58 C -HUMTHB-ACCEPTOR-883 59 G -HUMTHB-ACCEPTOR-883 60 C -HUMTHB-ACCEPTOR-9160 1 G -HUMTHB-ACCEPTOR-9160 2 C -HUMTHB-ACCEPTOR-9160 3 T -HUMTHB-ACCEPTOR-9160 4 C -HUMTHB-ACCEPTOR-9160 5 C -HUMTHB-ACCEPTOR-9160 6 T -HUMTHB-ACCEPTOR-9160 7 A -HUMTHB-ACCEPTOR-9160 8 A -HUMTHB-ACCEPTOR-9160 9 T -HUMTHB-ACCEPTOR-9160 10 G 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-HUMTHB-ACCEPTOR-9160 48 G -HUMTHB-ACCEPTOR-9160 49 G -HUMTHB-ACCEPTOR-9160 50 A -HUMTHB-ACCEPTOR-9160 51 A -HUMTHB-ACCEPTOR-9160 52 G -HUMTHB-ACCEPTOR-9160 53 A -HUMTHB-ACCEPTOR-9160 54 G -HUMTHB-ACCEPTOR-9160 55 T -HUMTHB-ACCEPTOR-9160 56 C -HUMTHB-ACCEPTOR-9160 57 C -HUMTHB-ACCEPTOR-9160 58 C -HUMTHB-ACCEPTOR-9160 59 C -HUMTHB-ACCEPTOR-9160 60 A -HUMTHB-DONOR-1044 1 T -HUMTHB-DONOR-1044 2 T -HUMTHB-DONOR-1044 3 C -HUMTHB-DONOR-1044 4 G -HUMTHB-DONOR-1044 5 A -HUMTHB-DONOR-1044 6 G -HUMTHB-DONOR-1044 7 G -HUMTHB-DONOR-1044 8 C -HUMTHB-DONOR-1044 9 T -HUMTHB-DONOR-1044 10 C -HUMTHB-DONOR-1044 11 T -HUMTHB-DONOR-1044 12 G -HUMTHB-DONOR-1044 13 G -HUMTHB-DONOR-1044 14 A -HUMTHB-DONOR-1044 15 G -HUMTHB-DONOR-1044 16 T -HUMTHB-DONOR-1044 17 C -HUMTHB-DONOR-1044 18 C -HUMTHB-DONOR-1044 19 T -HUMTHB-DONOR-1044 20 C -HUMTHB-DONOR-1044 21 C -HUMTHB-DONOR-1044 22 A -HUMTHB-DONOR-1044 23 C -HUMTHB-DONOR-1044 24 G -HUMTHB-DONOR-1044 25 G -HUMTHB-DONOR-1044 26 C -HUMTHB-DONOR-1044 27 T -HUMTHB-DONOR-1044 28 A -HUMTHB-DONOR-1044 29 C -HUMTHB-DONOR-1044 30 G -HUMTHB-DONOR-1044 31 G -HUMTHB-DONOR-1044 32 T -HUMTHB-DONOR-1044 33 G -HUMTHB-DONOR-1044 34 A -HUMTHB-DONOR-1044 35 G -HUMTHB-DONOR-1044 36 C -HUMTHB-DONOR-1044 37 C -HUMTHB-DONOR-1044 38 T -HUMTHB-DONOR-1044 39 G -HUMTHB-DONOR-1044 40 G -HUMTHB-DONOR-1044 41 G -HUMTHB-DONOR-1044 42 C -HUMTHB-DONOR-1044 43 T -HUMTHB-DONOR-1044 44 G -HUMTHB-DONOR-1044 45 C -HUMTHB-DONOR-1044 46 T -HUMTHB-DONOR-1044 47 C -HUMTHB-DONOR-1044 48 G -HUMTHB-DONOR-1044 49 G -HUMTHB-DONOR-1044 50 A -HUMTHB-DONOR-1044 51 C -HUMTHB-DONOR-1044 52 G -HUMTHB-DONOR-1044 53 G -HUMTHB-DONOR-1044 54 T -HUMTHB-DONOR-1044 55 G -HUMTHB-DONOR-1044 56 C -HUMTHB-DONOR-1044 57 C -HUMTHB-DONOR-1044 58 G -HUMTHB-DONOR-1044 59 G -HUMTHB-DONOR-1044 60 G -HUMTHB-DONOR-1728 1 T -HUMTHB-DONOR-1728 2 T -HUMTHB-DONOR-1728 3 C -HUMTHB-DONOR-1728 4 A -HUMTHB-DONOR-1728 5 G -HUMTHB-DONOR-1728 6 G -HUMTHB-DONOR-1728 7 A -HUMTHB-DONOR-1728 8 T -HUMTHB-DONOR-1728 9 G 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-HUMTHB-DONOR-2021 33 G -HUMTHB-DONOR-2021 34 A -HUMTHB-DONOR-2021 35 G -HUMTHB-DONOR-2021 36 C -HUMTHB-DONOR-2021 37 A -HUMTHB-DONOR-2021 38 A -HUMTHB-DONOR-2021 39 C -HUMTHB-DONOR-2021 40 T -HUMTHB-DONOR-2021 41 G -HUMTHB-DONOR-2021 42 A -HUMTHB-DONOR-2021 43 C -HUMTHB-DONOR-2021 44 A -HUMTHB-DONOR-2021 45 C -HUMTHB-DONOR-2021 46 G -HUMTHB-DONOR-2021 47 G -HUMTHB-DONOR-2021 48 G -HUMTHB-DONOR-2021 49 T -HUMTHB-DONOR-2021 50 T -HUMTHB-DONOR-2021 51 T -HUMTHB-DONOR-2021 52 G -HUMTHB-DONOR-2021 53 G -HUMTHB-DONOR-2021 54 G -HUMTHB-DONOR-2021 55 G -HUMTHB-DONOR-2021 56 A -HUMTHB-DONOR-2021 57 G -HUMTHB-DONOR-2021 58 C -HUMTHB-DONOR-2021 59 A -HUMTHB-DONOR-2021 60 G -HUMTHB-DONOR-497 1 C -HUMTHB-DONOR-497 2 C -HUMTHB-DONOR-497 3 C -HUMTHB-DONOR-497 4 T -HUMTHB-DONOR-497 5 G -HUMTHB-DONOR-497 6 T -HUMTHB-DONOR-497 7 G -HUMTHB-DONOR-497 8 T -HUMTHB-DONOR-497 9 A -HUMTHB-DONOR-497 10 G -HUMTHB-DONOR-497 11 C -HUMTHB-DONOR-497 12 C -HUMTHB-DONOR-497 13 T -HUMTHB-DONOR-497 14 T 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C +HUMATP1A2-DONOR-12719 6 T +HUMATP1A2-DONOR-12719 7 G +HUMATP1A2-DONOR-12719 8 A +HUMATP1A2-DONOR-12719 9 G +HUMATP1A2-DONOR-12719 10 G +HUMATP1A2-DONOR-12719 11 G +HUMATP1A2-DONOR-12719 12 G +HUMATP1A2-DONOR-12719 13 C +HUMATP1A2-DONOR-12719 14 T +HUMATP1A2-DONOR-12719 15 T +HUMATP1A2-DONOR-12719 16 C +HUMATP1A2-DONOR-12719 17 T +HUMATP1A2-DONOR-12719 18 G +HUMATP1A2-DONOR-12719 19 G +HUMATP1A2-DONOR-12719 20 C +HUMATP1A2-DONOR-12719 21 C +HUMATP1A2-DONOR-12719 22 A +HUMATP1A2-DONOR-12719 23 C +HUMATP1A2-DONOR-12719 24 T +HUMATP1A2-DONOR-12719 25 G +HUMATP1A2-DONOR-12719 26 T +HUMATP1A2-DONOR-12719 27 C +HUMATP1A2-DONOR-12719 28 A +HUMATP1A2-DONOR-12719 29 C +HUMATP1A2-DONOR-12719 30 T +HUMATP1A2-DONOR-12719 31 G +HUMATP1A2-DONOR-12719 32 T +HUMATP1A2-DONOR-12719 33 G +HUMATP1A2-DONOR-12719 34 A +HUMATP1A2-DONOR-12719 35 G +HUMATP1A2-DONOR-12719 36 T +HUMATP1A2-DONOR-12719 37 G +HUMATP1A2-DONOR-12719 38 G +HUMATP1A2-DONOR-12719 39 G +HUMATP1A2-DONOR-12719 40 T +HUMATP1A2-DONOR-12719 41 C +HUMATP1A2-DONOR-12719 42 A +HUMATP1A2-DONOR-12719 43 G +HUMATP1A2-DONOR-12719 44 G +HUMATP1A2-DONOR-12719 45 C +HUMATP1A2-DONOR-12719 46 T +HUMATP1A2-DONOR-12719 47 G +HUMATP1A2-DONOR-12719 48 A +HUMATP1A2-DONOR-12719 49 G +HUMATP1A2-DONOR-12719 50 G +HUMATP1A2-DONOR-12719 51 T +HUMATP1A2-DONOR-12719 52 G +HUMATP1A2-DONOR-12719 53 C +HUMATP1A2-DONOR-12719 54 C +HUMATP1A2-DONOR-12719 55 A +HUMATP1A2-DONOR-12719 56 C +HUMATP1A2-DONOR-12719 57 C +HUMATP1A2-DONOR-12719 58 A +HUMATP1A2-DONOR-12719 59 G +HUMATP1A2-DONOR-12719 60 G +HUMATP1A2-DONOR-13749 1 A +HUMATP1A2-DONOR-13749 2 T +HUMATP1A2-DONOR-13749 3 G +HUMATP1A2-DONOR-13749 4 A +HUMATP1A2-DONOR-13749 5 G +HUMATP1A2-DONOR-13749 6 G +HUMATP1A2-DONOR-13749 7 C +HUMATP1A2-DONOR-13749 8 T +HUMATP1A2-DONOR-13749 9 G +HUMATP1A2-DONOR-13749 10 A +HUMATP1A2-DONOR-13749 11 C +HUMATP1A2-DONOR-13749 12 A +HUMATP1A2-DONOR-13749 13 C +HUMATP1A2-DONOR-13749 14 C +HUMATP1A2-DONOR-13749 15 A +HUMATP1A2-DONOR-13749 16 C +HUMATP1A2-DONOR-13749 17 C +HUMATP1A2-DONOR-13749 18 G +HUMATP1A2-DONOR-13749 19 A +HUMATP1A2-DONOR-13749 20 A +HUMATP1A2-DONOR-13749 21 G +HUMATP1A2-DONOR-13749 22 A +HUMATP1A2-DONOR-13749 23 T +HUMATP1A2-DONOR-13749 24 C +HUMATP1A2-DONOR-13749 25 A +HUMATP1A2-DONOR-13749 26 G +HUMATP1A2-DONOR-13749 27 T +HUMATP1A2-DONOR-13749 28 C +HUMATP1A2-DONOR-13749 29 T +HUMATP1A2-DONOR-13749 30 G +HUMATP1A2-DONOR-13749 31 G +HUMATP1A2-DONOR-13749 32 T +HUMATP1A2-DONOR-13749 33 G +HUMATP1A2-DONOR-13749 34 A +HUMATP1A2-DONOR-13749 35 T +HUMATP1A2-DONOR-13749 36 T +HUMATP1A2-DONOR-13749 37 G +HUMATP1A2-DONOR-13749 38 G +HUMATP1A2-DONOR-13749 39 G +HUMATP1A2-DONOR-13749 40 T +HUMATP1A2-DONOR-13749 41 G +HUMATP1A2-DONOR-13749 42 C +HUMATP1A2-DONOR-13749 43 T +HUMATP1A2-DONOR-13749 44 C +HUMATP1A2-DONOR-13749 45 C +HUMATP1A2-DONOR-13749 46 A +HUMATP1A2-DONOR-13749 47 G +HUMATP1A2-DONOR-13749 48 A +HUMATP1A2-DONOR-13749 49 G +HUMATP1A2-DONOR-13749 50 G +HUMATP1A2-DONOR-13749 51 G +HUMATP1A2-DONOR-13749 52 G 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5 G +HUMATP1A2-DONOR-15190 6 G +HUMATP1A2-DONOR-15190 7 G +HUMATP1A2-DONOR-15190 8 G +HUMATP1A2-DONOR-15190 1 A +HUMATP1A2-DONOR-15190 59 A +HUMATP1A2-DONOR-15190 58 A +HUMATP1A2-DONOR-15190 57 G +HUMATP1A2-DONOR-15190 56 A +HUMATP1A2-DONOR-15190 55 C +HUMATP1A2-DONOR-15190 54 T +HUMATP1A2-DONOR-15190 53 C +HUMATP1A2-DONOR-15190 52 G +HUMATP1A2-DONOR-15190 51 G +HUMATP1A2-DONOR-15190 50 A +HUMATP1A2-DONOR-15190 49 G +HUMATP1A2-DONOR-15190 48 G +HUMATP1A2-DONOR-15190 47 A +HUMATP1A2-DONOR-15190 46 C +HUMATP1A2-DONOR-15190 45 A +HUMATP1A2-DONOR-15190 44 A +HUMATP1A2-DONOR-15190 43 A +HUMATP1A2-DONOR-15190 42 G +HUMATP1A2-DONOR-15190 41 A +HUMATP1A2-DONOR-15190 40 C +HUMATP1A2-DONOR-15190 39 C +HUMATP1A2-DONOR-15190 38 G +HUMATP1A2-DONOR-15190 37 G +HUMATP1A2-DONOR-15190 36 A +HUMATP1A2-DONOR-15190 35 G +HUMATP1A2-DONOR-15190 34 A +HUMATP1A2-DONOR-15190 33 G +HUMATP1A2-DONOR-15190 32 T +HUMATP1A2-DONOR-15190 31 G +HUMATP1A2-DONOR-15190 30 G +HUMATP1A2-DONOR-15190 29 G +HUMATP1A2-DONOR-15190 28 T +HUMATP1A2-DONOR-15190 27 C +HUMATP1A2-DONOR-15190 26 G +HUMATP1A2-DONOR-15190 25 T +HUMATP1A2-DONOR-15190 24 G +HUMATP1A2-DONOR-15190 23 T +HUMATP1A2-DONOR-15190 22 G +HUMATP1A2-DONOR-15190 21 C +HUMATP1A2-DONOR-15190 20 G +HUMATP1A2-DONOR-15190 19 A +HUMATP1A2-DONOR-15190 18 G +HUMATP1A2-DONOR-15190 17 G +HUMATP1A2-DONOR-15190 16 G +HUMATP1A2-DONOR-15190 15 G +HUMATP1A2-DONOR-15190 14 T +HUMATP1A2-DONOR-15190 13 T +HUMATP1A2-DONOR-15190 12 C +HUMATP1A2-DONOR-15190 11 A +HUMATP1A2-DONOR-15190 10 G +HUMATP1A2-DONOR-15190 60 G +HUMATP1A2-DONOR-15190 9 G +HUMATP1A2-DONOR-15496 59 C +HUMATP1A2-DONOR-15496 1 G +HUMATP1A2-DONOR-15496 2 T +HUMATP1A2-DONOR-15496 3 G +HUMATP1A2-DONOR-15496 4 G +HUMATP1A2-DONOR-15496 5 G +HUMATP1A2-DONOR-15496 6 C +HUMATP1A2-DONOR-15496 7 A +HUMATP1A2-DONOR-15496 8 A +HUMATP1A2-DONOR-15496 9 G +HUMATP1A2-DONOR-15496 10 T +HUMATP1A2-DONOR-15496 11 G +HUMATP1A2-DONOR-15496 12 C +HUMATP1A2-DONOR-15496 13 C +HUMATP1A2-DONOR-15496 14 G +HUMATP1A2-DONOR-15496 15 A +HUMATP1A2-DONOR-15496 16 A +HUMATP1A2-DONOR-15496 17 G +HUMATP1A2-DONOR-15496 18 C +HUMATP1A2-DONOR-15496 19 G +HUMATP1A2-DONOR-15496 20 C +HUMATP1A2-DONOR-15496 21 A +HUMATP1A2-DONOR-15496 22 G +HUMATP1A2-DONOR-15496 23 G +HUMATP1A2-DONOR-15496 24 C +HUMATP1A2-DONOR-15496 25 A +HUMATP1A2-DONOR-15496 26 T +HUMATP1A2-DONOR-15496 27 C +HUMATP1A2-DONOR-15496 28 A +HUMATP1A2-DONOR-15496 29 A +HUMATP1A2-DONOR-15496 30 G +HUMATP1A2-DONOR-15496 31 G +HUMATP1A2-DONOR-15496 32 T +HUMATP1A2-DONOR-15496 33 A +HUMATP1A2-DONOR-15496 34 C +HUMATP1A2-DONOR-15496 35 T +HUMATP1A2-DONOR-15496 36 G +HUMATP1A2-DONOR-15496 37 G +HUMATP1A2-DONOR-15496 38 C +HUMATP1A2-DONOR-15496 39 C +HUMATP1A2-DONOR-15496 40 T +HUMATP1A2-DONOR-15496 41 C +HUMATP1A2-DONOR-15496 42 C +HUMATP1A2-DONOR-15496 43 C +HUMATP1A2-DONOR-15496 44 A +HUMATP1A2-DONOR-15496 45 T +HUMATP1A2-DONOR-15496 46 C +HUMATP1A2-DONOR-15496 47 C +HUMATP1A2-DONOR-15496 48 T +HUMATP1A2-DONOR-15496 49 C 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11 A +HUMGRP78-ACCEPTOR-1372 12 C +HUMGRP78-ACCEPTOR-1372 13 A +HUMGRP78-ACCEPTOR-1372 14 A +HUMGRP78-ACCEPTOR-1372 15 T +HUMGRP78-ACCEPTOR-1372 16 A +HUMGRP78-ACCEPTOR-1372 17 A +HUMGRP78-ACCEPTOR-1372 18 T +HUMGRP78-ACCEPTOR-1372 19 T +HUMGRP78-ACCEPTOR-1372 20 C +HUMGRP78-ACCEPTOR-1372 21 T +HUMGRP78-ACCEPTOR-1372 22 T +HUMGRP78-ACCEPTOR-1372 23 T +HUMGRP78-ACCEPTOR-1372 24 A +HUMGRP78-ACCEPTOR-1372 25 A +HUMGRP78-ACCEPTOR-1372 26 A +HUMGRP78-ACCEPTOR-1372 27 A +HUMGRP78-ACCEPTOR-1372 28 T +HUMGRP78-ACCEPTOR-1372 29 A +HUMGRP78-ACCEPTOR-1372 30 G +HUMGRP78-ACCEPTOR-1372 31 G +HUMGRP78-ACCEPTOR-1372 32 T +HUMGRP78-ACCEPTOR-1372 33 G +HUMGRP78-ACCEPTOR-1372 34 G +HUMGRP78-ACCEPTOR-1372 35 T +HUMGRP78-ACCEPTOR-1372 36 T +HUMGRP78-ACCEPTOR-1372 37 G +HUMGRP78-ACCEPTOR-1372 38 A +HUMGRP78-ACCEPTOR-1372 39 A +HUMGRP78-ACCEPTOR-1372 40 A +HUMGRP78-ACCEPTOR-1372 41 A +HUMGRP78-ACCEPTOR-1372 42 G +HUMGRP78-ACCEPTOR-1372 43 A +HUMGRP78-ACCEPTOR-1372 44 A +HUMGRP78-ACCEPTOR-1372 45 A 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20 T +HUMGRP78-ACCEPTOR-2145 21 A +HUMGRP78-ACCEPTOR-2145 22 T +HUMGRP78-ACCEPTOR-2145 23 C +HUMGRP78-ACCEPTOR-2145 24 C +HUMGRP78-ACCEPTOR-2145 25 T +HUMGRP78-ACCEPTOR-2145 26 T +HUMGRP78-ACCEPTOR-2145 27 T +HUMGRP78-ACCEPTOR-2145 28 T +HUMGRP78-ACCEPTOR-2145 29 A +HUMGRP78-ACCEPTOR-2145 30 G +HUMGRP78-ACCEPTOR-2145 31 G +HUMGRP78-ACCEPTOR-2145 32 T +HUMGRP78-ACCEPTOR-2145 33 T +HUMGRP78-ACCEPTOR-2145 34 A +HUMGRP78-ACCEPTOR-2145 35 C +HUMGRP78-ACCEPTOR-2145 36 C +HUMGRP78-ACCEPTOR-2145 37 C +HUMGRP78-ACCEPTOR-2145 38 A +HUMGRP78-ACCEPTOR-2145 39 T +HUMGRP78-ACCEPTOR-2145 40 G +HUMGRP78-ACCEPTOR-2145 41 C +HUMGRP78-ACCEPTOR-2145 42 A +HUMGRP78-ACCEPTOR-2145 43 G +HUMGRP78-ACCEPTOR-2145 44 T +HUMGRP78-ACCEPTOR-2145 45 T +HUMGRP78-ACCEPTOR-2145 46 G +HUMGRP78-ACCEPTOR-2145 47 T +HUMGRP78-ACCEPTOR-2145 48 T +HUMGRP78-ACCEPTOR-2145 49 A +HUMGRP78-ACCEPTOR-2145 50 C +HUMGRP78-ACCEPTOR-2145 51 T +HUMGRP78-ACCEPTOR-2145 52 G +HUMGRP78-ACCEPTOR-2145 53 T +HUMGRP78-ACCEPTOR-2145 54 A 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29 A +HUMGRP78-ACCEPTOR-2848 30 G +HUMGRP78-ACCEPTOR-2848 31 G +HUMGRP78-ACCEPTOR-2848 32 A +HUMGRP78-ACCEPTOR-2848 33 T +HUMGRP78-ACCEPTOR-2848 34 C +HUMGRP78-ACCEPTOR-2848 35 T +HUMGRP78-ACCEPTOR-2848 36 G +HUMGRP78-ACCEPTOR-2848 37 T +HUMGRP78-ACCEPTOR-2848 38 T +HUMGRP78-ACCEPTOR-2848 39 C +HUMGRP78-ACCEPTOR-2848 40 C +HUMGRP78-ACCEPTOR-2848 41 G +HUMGRP78-ACCEPTOR-2848 42 G +HUMGRP78-ACCEPTOR-2848 43 T +HUMGRP78-ACCEPTOR-2848 44 C +HUMGRP78-ACCEPTOR-2848 45 T +HUMGRP78-ACCEPTOR-2848 46 A +HUMGRP78-ACCEPTOR-2848 47 C +HUMGRP78-ACCEPTOR-2848 48 T +HUMGRP78-ACCEPTOR-2848 49 A +HUMGRP78-ACCEPTOR-2848 50 T +HUMGRP78-ACCEPTOR-2848 51 G +HUMGRP78-ACCEPTOR-2848 52 A +HUMGRP78-ACCEPTOR-2848 53 A +HUMGRP78-ACCEPTOR-2848 54 G +HUMGRP78-ACCEPTOR-2848 55 C +HUMGRP78-ACCEPTOR-2848 56 C +HUMGRP78-ACCEPTOR-2848 57 C +HUMGRP78-ACCEPTOR-2848 58 G +HUMGRP78-ACCEPTOR-2848 59 T +HUMGRP78-ACCEPTOR-2848 60 C +HUMGRP78-ACCEPTOR-3371 1 A +HUMGRP78-ACCEPTOR-3371 2 A +HUMGRP78-ACCEPTOR-3371 3 T 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12 T +HUMGRP78-ACCEPTOR-4506 13 T +HUMGRP78-ACCEPTOR-4506 14 C +HUMGRP78-ACCEPTOR-4506 15 T +HUMGRP78-ACCEPTOR-4506 16 G +HUMGRP78-ACCEPTOR-4506 17 T +HUMGRP78-ACCEPTOR-4506 18 A +HUMGRP78-ACCEPTOR-4506 19 T +HUMGRP78-ACCEPTOR-4506 20 G +HUMGRP78-ACCEPTOR-4506 21 T +HUMGRP78-ACCEPTOR-4506 22 A +HUMGRP78-ACCEPTOR-4506 23 A +HUMGRP78-ACCEPTOR-4506 24 C +HUMGRP78-ACCEPTOR-4506 25 T +HUMGRP78-ACCEPTOR-4506 26 T +HUMGRP78-ACCEPTOR-4506 27 C +HUMGRP78-ACCEPTOR-4506 28 T +HUMGRP78-ACCEPTOR-4506 29 A +HUMGRP78-ACCEPTOR-4506 30 G +HUMGRP78-ACCEPTOR-4506 31 G +HUMGRP78-ACCEPTOR-4506 32 T +HUMGRP78-ACCEPTOR-4506 33 G +HUMGRP78-ACCEPTOR-4506 34 A +HUMGRP78-ACCEPTOR-4506 35 A +HUMGRP78-ACCEPTOR-4506 36 A +HUMGRP78-ACCEPTOR-4506 37 G +HUMGRP78-ACCEPTOR-4506 38 A +HUMGRP78-ACCEPTOR-4506 39 C +HUMGRP78-ACCEPTOR-4506 40 C +HUMGRP78-ACCEPTOR-4506 41 C +HUMGRP78-ACCEPTOR-4506 42 C +HUMGRP78-ACCEPTOR-4506 43 T +HUMGRP78-ACCEPTOR-4506 44 G +HUMGRP78-ACCEPTOR-4506 45 A +HUMGRP78-ACCEPTOR-4506 46 C 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13 C +HUMGRP78-DONOR-3086 14 T +HUMGRP78-DONOR-3086 15 G +HUMGRP78-DONOR-3086 16 G +HUMGRP78-DONOR-3086 17 T +HUMGRP78-DONOR-3086 18 G +HUMGRP78-DONOR-3086 19 A +HUMGRP78-DONOR-3086 20 T +HUMGRP78-DONOR-3086 21 C +HUMGRP78-DONOR-3086 22 A +HUMGRP78-DONOR-3086 23 A +HUMGRP78-DONOR-3086 24 G +HUMGRP78-DONOR-3086 25 A +HUMGRP78-DONOR-3086 26 T +HUMGRP78-DONOR-3086 27 A +HUMGRP78-DONOR-3086 28 C +HUMGRP78-DONOR-3086 29 A +HUMGRP78-DONOR-3086 30 G +HUMGRP78-DONOR-3086 31 G +HUMGRP78-DONOR-3086 32 T +HUMGRP78-DONOR-3086 33 A +HUMGRP78-DONOR-3086 34 G +HUMGRP78-DONOR-3086 35 G +HUMGRP78-DONOR-3086 36 T +HUMGRP78-DONOR-3086 37 C +HUMGRP78-DONOR-3086 38 A +HUMGRP78-DONOR-3086 39 T +HUMGRP78-DONOR-3086 40 C +HUMGRP78-DONOR-3086 41 A +HUMGRP78-DONOR-3086 42 T +HUMGRP78-DONOR-3086 43 C +HUMGRP78-DONOR-3086 44 G +HUMGRP78-DONOR-3086 45 C +HUMGRP78-DONOR-3086 46 A +HUMGRP78-DONOR-3086 47 G +HUMGRP78-DONOR-3086 48 C +HUMGRP78-DONOR-3086 49 A +HUMGRP78-DONOR-3086 50 T +HUMGRP78-DONOR-3086 51 C 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47 G +HUMMYONM-NEG-1501 48 C +HUMMYONM-NEG-1501 49 C +HUMMYONM-NEG-1501 50 A +HUMMYONM-NEG-1501 51 A +HUMMYONM-NEG-1501 52 C +HUMMYONM-NEG-1501 53 G +HUMMYONM-NEG-1501 54 A +HUMMYONM-NEG-1501 55 G +HUMMYONM-NEG-1501 56 G +HUMMYONM-NEG-1501 57 T +HUMMYONM-NEG-1501 58 G +HUMMYONM-NEG-1501 59 A +HUMMYONM-NEG-1501 60 A +HUMNAKATP1-NEG-5281 1 T +HUMNAKATP1-NEG-5281 2 G +HUMNAKATP1-NEG-5281 3 C +HUMNAKATP1-NEG-5281 4 C +HUMNAKATP1-NEG-5281 5 T +HUMNAKATP1-NEG-5281 6 A +HUMNAKATP1-NEG-5281 7 G +HUMNAKATP1-NEG-5281 8 T +HUMNAKATP1-NEG-5281 9 A +HUMNAKATP1-NEG-5281 10 C +HUMNAKATP1-NEG-5281 11 A +HUMNAKATP1-NEG-5281 12 G +HUMNAKATP1-NEG-5281 13 C +HUMNAKATP1-NEG-5281 14 C +HUMNAKATP1-NEG-5281 15 T +HUMNAKATP1-NEG-5281 16 G +HUMNAKATP1-NEG-5281 17 T +HUMNAKATP1-NEG-5281 18 T +HUMNAKATP1-NEG-5281 19 A +HUMNAKATP1-NEG-5281 20 C +HUMNAKATP1-NEG-5281 21 T +HUMNAKATP1-NEG-5281 22 A +HUMNAKATP1-NEG-5281 23 T +HUMNAKATP1-NEG-5281 24 G +HUMNAKATP1-NEG-5281 25 C +HUMNAKATP1-NEG-5281 26 A 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5 G +HUMNAKATP1-NEG-721 6 G +HUMNAKATP1-NEG-721 7 A +HUMNAKATP1-NEG-721 8 G +HUMNAKATP1-NEG-721 9 G +HUMNAKATP1-NEG-721 10 G +HUMNAKATP1-NEG-721 11 G +HUMNAKATP1-NEG-721 12 T +HUMNAKATP1-NEG-721 13 G +HUMNAKATP1-NEG-721 14 G +HUMNAKATP1-NEG-721 15 A +HUMNAKATP1-NEG-721 16 C +HUMNAKATP1-NEG-721 17 C +HUMNAKATP1-NEG-721 18 C +HUMNAKATP1-NEG-721 19 A +HUMNAKATP1-NEG-721 20 C +HUMNAKATP1-NEG-721 21 A +HUMNAKATP1-NEG-721 22 G +HUMNAKATP1-NEG-721 23 C +HUMNAKATP1-NEG-721 24 C +HUMNAKATP1-NEG-721 25 C +HUMNAKATP1-NEG-721 26 A +HUMNAKATP1-NEG-721 27 G +HUMNAKATP1-NEG-721 28 G +HUMNAKATP1-NEG-721 29 G +HUMNAKATP1-NEG-721 30 A +HUMNAKATP1-NEG-721 31 G +HUMNAKATP1-NEG-721 32 G +HUMNAKATP1-NEG-721 33 C +HUMNAKATP1-NEG-721 34 C +HUMNAKATP1-NEG-721 35 G +HUMNAKATP1-NEG-721 36 A +HUMNAKATP1-NEG-721 37 A +HUMNAKATP1-NEG-721 38 A +HUMNAKATP1-NEG-721 39 G +HUMNAKATP1-NEG-721 40 C +HUMNAKATP1-NEG-721 41 G +HUMNAKATP1-NEG-721 42 C +HUMNAKATP1-NEG-721 43 G +HUMNAKATP1-NEG-721 44 G +HUMNAKATP1-NEG-721 45 G 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38 A +HUMNCAM-NEG-121 37 G +HUMNCAM-NEG-121 36 A +HUMNCAM-NEG-121 35 A +HUMNCAM-NEG-121 34 G +HUMNCAM-NEG-121 33 T +HUMNCAM-NEG-121 32 G +HUMNCAM-NEG-121 31 G +HUMNCAM-NEG-121 30 T +HUMNCAM-NEG-121 60 T +HUMNCAM-NEG-121 29 T +HUMNCAM-NEG-121 28 G +HUMNCAM-NEG-121 27 A +HUMNCAM-NEG-121 26 C +HUMNCAM-NEG-121 25 G +HUMNCAM-NEG-121 24 A +HUMNCAM-NEG-121 23 G +HUMNCAM-NEG-121 22 A +HUMNCAM-NEG-121 21 G +HUMNCAM-NEG-121 20 G +HUMNCAM-NEG-121 19 T +HUMNCAM-NEG-121 18 G +HUMNCAM-NEG-121 17 A +HUMNCAM-NEG-121 16 G +HUMNCAM-NEG-121 15 T +HUMNCAM-NEG-121 14 C +HUMNCAM-NEG-121 13 G +HUMNCAM-NEG-121 12 A +HUMNCAM-NEG-121 11 A +HUMNCAM-NEG-121 10 A +HUMNCAM-NEG-121 9 C +HUMNCAM-NEG-121 8 A +HUMNCAM-NEG-121 7 T +HUMNCAM-NEG-121 6 A +HUMNCAM-NEG-121 5 A +HUMNCAM-NEG-121 4 A +HUMNCAM-NEG-121 3 C +HUMNCAM-NEG-121 2 T +HUMNCAM-NEG-121 1 C +HUMNEBB-NEG-241 1 G +HUMNEBB-NEG-241 2 C +HUMNEBB-NEG-241 3 C +HUMNEBB-NEG-241 4 A +HUMNEBB-NEG-241 5 A +HUMNEBB-NEG-241 6 C +HUMNEBB-NEG-241 7 T +HUMNEBB-NEG-241 8 A 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+TARHBB-DONOR-1909 10 G +TARHBB-DONOR-1909 11 T +TARHBB-DONOR-1909 12 G +TARHBB-DONOR-1909 13 G +TARHBB-DONOR-1909 14 A +TARHBB-DONOR-1909 15 T +TARHBB-DONOR-1909 16 C +TARHBB-DONOR-1909 17 C +TARHBB-DONOR-1909 18 T +TARHBB-DONOR-1909 19 G +TARHBB-DONOR-1909 20 A +TARHBB-DONOR-1909 21 G +TARHBB-DONOR-1909 22 A +TARHBB-DONOR-1909 23 A +TARHBB-DONOR-1909 24 T +TARHBB-DONOR-1909 25 T +TARHBB-DONOR-1909 26 T +TARHBB-DONOR-1909 27 C +TARHBB-DONOR-1909 28 A +TARHBB-DONOR-1909 29 G +TARHBB-DONOR-1909 30 G +TARHBB-DONOR-1909 31 G +TARHBB-DONOR-1909 32 T +TARHBB-DONOR-1909 33 A +TARHBB-DONOR-1909 34 A +TARHBB-DONOR-1909 35 G +TARHBB-DONOR-1909 36 T +TARHBB-DONOR-1909 37 C +TARHBB-DONOR-1909 38 T +TARHBB-DONOR-1909 39 A +TARHBB-DONOR-1909 40 T +TARHBB-DONOR-1909 41 G +TARHBB-DONOR-1909 42 G +TARHBB-DONOR-1909 43 A +TARHBB-DONOR-1909 44 A +TARHBB-DONOR-1909 45 T +TARHBB-DONOR-1909 46 C +TARHBB-DONOR-1909 47 T +TARHBB-DONOR-1909 48 T +TARHBB-DONOR-1909 49 T +TARHBB-DONOR-1909 50 G +TARHBB-DONOR-1909 51 A +TARHBB-DONOR-1909 52 T +TARHBB-DONOR-1909 53 G +TARHBB-DONOR-1909 54 T +TARHBB-DONOR-1909 55 T +TARHBB-DONOR-1909 56 T +TARHBB-DONOR-1909 57 T +TARHBB-DONOR-1909 58 C +TARHBB-DONOR-1909 59 C +TARHBB-DONOR-1909 60 T +TARHBB-NEG-541 1 A +TARHBB-NEG-541 2 T +TARHBB-NEG-541 3 T +TARHBB-NEG-541 4 C +TARHBB-NEG-541 5 T +TARHBB-NEG-541 6 A +TARHBB-NEG-541 7 C +TARHBB-NEG-541 8 T +TARHBB-NEG-541 9 T +TARHBB-NEG-541 10 A +TARHBB-NEG-541 11 G +TARHBB-NEG-541 12 T +TARHBB-NEG-541 13 A +TARHBB-NEG-541 14 A +TARHBB-NEG-541 15 A +TARHBB-NEG-541 16 C +TARHBB-NEG-541 17 A +TARHBB-NEG-541 18 T +TARHBB-NEG-541 19 A +TARHBB-NEG-541 20 A +TARHBB-NEG-541 21 T +TARHBB-NEG-541 22 T +TARHBB-NEG-541 23 T +TARHBB-NEG-541 24 C +TARHBB-NEG-541 25 T +TARHBB-NEG-541 26 T +TARHBB-NEG-541 27 G +TARHBB-NEG-541 28 T +TARHBB-NEG-541 29 G +TARHBB-NEG-541 30 C +TARHBB-NEG-541 31 T +TARHBB-NEG-541 32 A +TARHBB-NEG-541 33 G +TARHBB-NEG-541 34 A +TARHBB-NEG-541 35 T +TARHBB-NEG-541 36 A +TARHBB-NEG-541 37 A +TARHBB-NEG-541 38 C +TARHBB-NEG-541 39 C +TARHBB-NEG-541 40 A +TARHBB-NEG-541 41 A +TARHBB-NEG-541 42 A +TARHBB-NEG-541 43 T +TARHBB-NEG-541 44 T +TARHBB-NEG-541 45 A +TARHBB-NEG-541 46 A +TARHBB-NEG-541 47 G +TARHBB-NEG-541 48 A +TARHBB-NEG-541 49 A +TARHBB-NEG-541 50 A +TARHBB-NEG-541 51 A +TARHBB-NEG-541 52 C +TARHBB-NEG-541 53 C +TARHBB-NEG-541 54 A +TARHBB-NEG-541 55 A +TARHBB-NEG-541 56 A +TARHBB-NEG-541 57 A +TARHBB-NEG-541 58 C +TARHBB-NEG-541 59 A +TARHBB-NEG-541 60 A +TARHBD-ACCEPTOR-1884 1 C +TARHBD-ACCEPTOR-1884 2 A +TARHBD-ACCEPTOR-1884 3 T +TARHBD-ACCEPTOR-1884 4 A +TARHBD-ACCEPTOR-1884 5 T +TARHBD-ACCEPTOR-1884 6 G +TARHBD-ACCEPTOR-1884 7 T +TARHBD-ACCEPTOR-1884 8 A +TARHBD-ACCEPTOR-1884 9 T +TARHBD-ACCEPTOR-1884 10 C +TARHBD-ACCEPTOR-1884 11 T +TARHBD-ACCEPTOR-1884 12 T +TARHBD-ACCEPTOR-1884 13 T +TARHBD-ACCEPTOR-1884 14 T +TARHBD-ACCEPTOR-1884 15 T +TARHBD-ACCEPTOR-1884 16 A +TARHBD-ACCEPTOR-1884 17 C +TARHBD-ACCEPTOR-1884 18 C +TARHBD-ACCEPTOR-1884 19 T +TARHBD-ACCEPTOR-1884 20 T +TARHBD-ACCEPTOR-1884 21 T +TARHBD-ACCEPTOR-1884 22 T +TARHBD-ACCEPTOR-1884 23 C +TARHBD-ACCEPTOR-1884 24 C +TARHBD-ACCEPTOR-1884 25 C +TARHBD-ACCEPTOR-1884 26 A +TARHBD-ACCEPTOR-1884 27 A +TARHBD-ACCEPTOR-1884 28 C +TARHBD-ACCEPTOR-1884 29 A +TARHBD-ACCEPTOR-1884 30 G +TARHBD-ACCEPTOR-1884 31 C +TARHBD-ACCEPTOR-1884 32 T +TARHBD-ACCEPTOR-1884 33 C +TARHBD-ACCEPTOR-1884 34 C +TARHBD-ACCEPTOR-1884 35 T +TARHBD-ACCEPTOR-1884 36 G +TARHBD-ACCEPTOR-1884 37 G +TARHBD-ACCEPTOR-1884 38 G +TARHBD-ACCEPTOR-1884 39 C +TARHBD-ACCEPTOR-1884 40 A +TARHBD-ACCEPTOR-1884 41 A +TARHBD-ACCEPTOR-1884 42 C +TARHBD-ACCEPTOR-1884 43 G +TARHBD-ACCEPTOR-1884 44 T +TARHBD-ACCEPTOR-1884 45 G +TARHBD-ACCEPTOR-1884 46 C +TARHBD-ACCEPTOR-1884 47 T +TARHBD-ACCEPTOR-1884 48 G +TARHBD-ACCEPTOR-1884 49 G +TARHBD-ACCEPTOR-1884 50 T +TARHBD-ACCEPTOR-1884 51 G +TARHBD-ACCEPTOR-1884 52 T +TARHBD-ACCEPTOR-1884 53 G +TARHBD-ACCEPTOR-1884 54 T +TARHBD-ACCEPTOR-1884 55 G +TARHBD-ACCEPTOR-1884 56 T +TARHBD-ACCEPTOR-1884 57 G +TARHBD-ACCEPTOR-1884 58 C +TARHBD-ACCEPTOR-1884 59 T +TARHBD-ACCEPTOR-1884 60 G +TARHBD-DONOR-468 1 G +TARHBD-DONOR-468 2 G +TARHBD-DONOR-468 3 A +TARHBD-DONOR-468 4 A +TARHBD-DONOR-468 5 G +TARHBD-DONOR-468 6 A +TARHBD-DONOR-468 7 T +TARHBD-DONOR-468 8 G +TARHBD-DONOR-468 9 T +TARHBD-DONOR-468 10 T +TARHBD-DONOR-468 11 G +TARHBD-DONOR-468 12 G +TARHBD-DONOR-468 13 T +TARHBD-DONOR-468 14 G +TARHBD-DONOR-468 15 G +TARHBD-DONOR-468 16 T +TARHBD-DONOR-468 17 G +TARHBD-DONOR-468 18 A +TARHBD-DONOR-468 19 G +TARHBD-DONOR-468 20 G +TARHBD-DONOR-468 21 C +TARHBD-DONOR-468 22 C +TARHBD-DONOR-468 23 C +TARHBD-DONOR-468 24 T +TARHBD-DONOR-468 25 G +TARHBD-DONOR-468 26 G +TARHBD-DONOR-468 27 G +TARHBD-DONOR-468 28 C +TARHBD-DONOR-468 29 A +TARHBD-DONOR-468 30 G +TARHBD-DONOR-468 31 G +TARHBD-DONOR-468 32 T +TARHBD-DONOR-468 33 C +TARHBD-DONOR-468 34 A +TARHBD-DONOR-468 35 G +TARHBD-DONOR-468 36 T +TARHBD-DONOR-468 37 A +TARHBD-DONOR-468 38 T +TARHBD-DONOR-468 39 C +TARHBD-DONOR-468 40 A +TARHBD-DONOR-468 41 T +TARHBD-DONOR-468 42 G +TARHBD-DONOR-468 43 G +TARHBD-DONOR-468 44 C +TARHBD-DONOR-468 45 T +TARHBD-DONOR-468 46 A +TARHBD-DONOR-468 47 T +TARHBD-DONOR-468 48 G +TARHBD-DONOR-468 49 A +TARHBD-DONOR-468 50 G +TARHBD-DONOR-468 51 G +TARHBD-DONOR-468 52 C +TARHBD-DONOR-468 53 A +TARHBD-DONOR-468 54 G +TARHBD-DONOR-468 55 G +TARHBD-DONOR-468 56 C +TARHBD-DONOR-468 57 T +TARHBD-DONOR-468 58 T +TARHBD-DONOR-468 59 A +TARHBD-DONOR-468 60 A +TARHBD-DONOR-817 1 A +TARHBD-DONOR-817 2 A +TARHBD-DONOR-817 3 G +TARHBD-DONOR-817 4 C +TARHBD-DONOR-817 5 T +TARHBD-DONOR-817 6 G +TARHBD-DONOR-817 7 C +TARHBD-DONOR-817 8 A +TARHBD-DONOR-817 9 T +TARHBD-DONOR-817 10 G +TARHBD-DONOR-817 11 T +TARHBD-DONOR-817 12 G +TARHBD-DONOR-817 13 G +TARHBD-DONOR-817 14 A +TARHBD-DONOR-817 15 T +TARHBD-DONOR-817 16 C +TARHBD-DONOR-817 17 C +TARHBD-DONOR-817 18 T +TARHBD-DONOR-817 19 G +TARHBD-DONOR-817 20 A +TARHBD-DONOR-817 21 G +TARHBD-DONOR-817 22 A +TARHBD-DONOR-817 23 A +TARHBD-DONOR-817 24 C +TARHBD-DONOR-817 25 T +TARHBD-DONOR-817 26 T +TARHBD-DONOR-817 27 C +TARHBD-DONOR-817 28 A +TARHBD-DONOR-817 29 G +TARHBD-DONOR-817 30 G +TARHBD-DONOR-817 31 G +TARHBD-DONOR-817 32 T +TARHBD-DONOR-817 33 A +TARHBD-DONOR-817 34 A +TARHBD-DONOR-817 35 G +TARHBD-DONOR-817 36 T +TARHBD-DONOR-817 37 C +TARHBD-DONOR-817 38 T +TARHBD-DONOR-817 39 A +TARHBD-DONOR-817 40 G +TARHBD-DONOR-817 41 G +TARHBD-DONOR-817 42 A +TARHBD-DONOR-817 43 G +TARHBD-DONOR-817 44 A +TARHBD-DONOR-817 45 T +TARHBD-DONOR-817 46 G +TARHBD-DONOR-817 47 T +TARHBD-DONOR-817 48 T +TARHBD-DONOR-817 49 C +TARHBD-DONOR-817 50 T +TARHBD-DONOR-817 51 T +TARHBD-DONOR-817 52 C +TARHBD-DONOR-817 53 T +TARHBD-DONOR-817 54 T +TARHBD-DONOR-817 55 T +TARHBD-DONOR-817 56 G +TARHBD-DONOR-817 57 T +TARHBD-DONOR-817 58 C +TARHBD-DONOR-817 59 C +TARHBD-DONOR-817 60 T +TARHBD-NEG-1981 1 A +TARHBD-NEG-1981 2 G +TARHBD-NEG-1981 3 G +TARHBD-NEG-1981 4 C +TARHBD-NEG-1981 5 T +TARHBD-NEG-1981 6 G +TARHBD-NEG-1981 7 C +TARHBD-NEG-1981 8 C +TARHBD-NEG-1981 9 T +TARHBD-NEG-1981 10 A +TARHBD-NEG-1981 11 T +TARHBD-NEG-1981 12 C +TARHBD-NEG-1981 13 A +TARHBD-NEG-1981 14 G +TARHBD-NEG-1981 15 A +TARHBD-NEG-1981 16 A +TARHBD-NEG-1981 17 G +TARHBD-NEG-1981 18 G +TARHBD-NEG-1981 19 T +TARHBD-NEG-1981 20 G +TARHBD-NEG-1981 21 G +TARHBD-NEG-1981 22 T +TARHBD-NEG-1981 23 G +TARHBD-NEG-1981 24 G +TARHBD-NEG-1981 25 C +TARHBD-NEG-1981 26 T +TARHBD-NEG-1981 27 G +TARHBD-NEG-1981 28 G +TARHBD-NEG-1981 29 T +TARHBD-NEG-1981 30 G +TARHBD-NEG-1981 31 T +TARHBD-NEG-1981 32 G +TARHBD-NEG-1981 33 G +TARHBD-NEG-1981 34 C +TARHBD-NEG-1981 35 T +TARHBD-NEG-1981 36 G +TARHBD-NEG-1981 37 C +TARHBD-NEG-1981 38 T +TARHBD-NEG-1981 39 G +TARHBD-NEG-1981 40 C +TARHBD-NEG-1981 41 T +TARHBD-NEG-1981 42 C +TARHBD-NEG-1981 43 T +TARHBD-NEG-1981 44 G +TARHBD-NEG-1981 45 G +TARHBD-NEG-1981 46 C +TARHBD-NEG-1981 47 T +TARHBD-NEG-1981 48 C +TARHBD-NEG-1981 49 A +TARHBD-NEG-1981 50 C +TARHBD-NEG-1981 51 A +TARHBD-NEG-1981 52 A +TARHBD-NEG-1981 53 G +TARHBD-NEG-1981 54 T +TARHBD-NEG-1981 55 A +TARHBD-NEG-1981 56 C +TARHBD-NEG-1981 57 C +TARHBD-NEG-1981 58 A +TARHBD-NEG-1981 59 T +TARHBD-NEG-1981 60 T diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/err.txt index 9fde4e74f..303d613d1 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/err.txt @@ -1,7 +1,7 @@ Deploy model SpliceJunction Input database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt: Read records: 3178 Table ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt Records: 135,240 -Output database ./results/T_SpliceJunction.txt: Written records: 2254 - Table ./results/T_SpliceJunctionDNA.txt Records: 135,240 -Model deployment time: 0:00:00.19 +Output database ./results/D_SpliceJunction.txt: Written records: 2254 + Table ./results/D_SpliceJunctionDNA.txt Records: 135,240 +Model deployment time: 0:00:00.16 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/time.log index a130686a6..eccf20468 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.3558022975921631 +Overal time: 0.29662609100341797 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/test.prm b/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/test.prm index ffb3f88ac..aad4cd450 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/test.prm +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionTransfer/test.prm @@ -14,28 +14,30 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open ClassFileName ../../../MTdatasets/SpliceJunction/SpliceJunction.kdic // Dictionary file OK // Open -ClassManagement.ClassName SpliceJunction // Dictionary +TrainDatabase.ClassName SpliceJunction // Dictionary -TrainDatabase.DatabaseFiles.List.Key SpliceJunction // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file -TrainDatabase.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file -TransferDatabase // Transfer database +TransferDatabase // Deploy model // -> Database transfer -ClassName SpliceJunction // Transfer dictionary -SourceDatabase.SampleNumberPercentage 70 // Sample percentage -TargetDatabase.DatabaseFiles.List.Key SpliceJunction // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_SpliceJunction.txt // Data table file -TargetDatabase.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection -TargetDatabase.DatabaseFiles.DataTableName ./results/T_SpliceJunctionDNA.txt // Data table file -TransferDatabase // Transfer database +ClassName SpliceJunction // Deployment dictionary +SourceDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_SpliceJunction.txt // Data table file +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection +TargetDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ./results/D_SpliceJunctionDNA.txt // Data table file +TransferDatabase // Deploy model Exit // Close // <- Database transfer diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_ModelingReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.ModelingReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_ModelingReport.xls rename to test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.ModelingReport.xls index 16e57f592..312f75ab7 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_ModelingReport.xls +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.ModelingReport.xls @@ -1,34 +1,34 @@ -#Khiops 10.1 -Modeling report - -Dictionary SpliceJunction -Target variable Class -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value - - -Trained predictors - -Predictor Variables -Selective Naive Bayes 7 - - --------------------------------------------------------------------------------- - -Trained predictors details - - -Predictor Selective Naive Bayes - -Selected variables -Prepared name Name Level Weight Importance -PMode(DNA.Char) where Pos <= 30.5 Mode(DNA.Char) where Pos <= 30.5 0.0731216 0.316406 0.152106 -PMode(DNA.Char) where Pos in ]15.5, 30.5] Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0495677 0.394531 0.139843 -PMode(DNA.Char) Mode(DNA.Char) 0.0447911 0.425781 0.138098 -PMedian(DNA.Pos) where Char = G Median(DNA.Pos) where Char = G 0.0212659 0.378906 0.0897651 -PMean(DNA.Pos) where Char = G Mean(DNA.Pos) where Char = G 0.0261259 0.222656 0.0762699 -PCount(DNA) where Char = G Count(DNA) where Char = G 0.0147548 0.222656 0.0573171 -PMedian(DNA.Pos) where Char = C Median(DNA.Pos) where Char = C 0.00882745 0.160156 0.0376002 +#Khiops 10.4.6i +Modeling report + +Dictionary SpliceJunction +Target variable Class +Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt +Sample percentage 10 +Sampling mode Include sample +Selection variable +Selection value + + +Trained predictors + +Predictor Variables +Selective Naive Bayes 7 + + +-------------------------------------------------------------------------------- + +Trained predictors details + + +Predictor Selective Naive Bayes + +Selected variables +Prepared name Name Level Weight Importance +PMode(DNA.Char) where Pos <= 30.5 Mode(DNA.Char) where Pos <= 30.5 0.0731216 0.316406 0.152106 +PMode(DNA.Char) where Pos in ]15.5, 30.5] Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0495677 0.394531 0.139843 +PMode(DNA.Char) Mode(DNA.Char) 0.0447911 0.425781 0.138098 +PMedian(DNA.Pos) where Char = G Median(DNA.Pos) where Char = G 0.0212659 0.378906 0.0897651 +PMean(DNA.Pos) where Char = G Mean(DNA.Pos) where Char = G 0.0261259 0.222656 0.0762699 +PCount(DNA) where Char = G Count(DNA) where Char = G 0.0147548 0.222656 0.0573171 +PMedian(DNA.Pos) where Char = C Median(DNA.Pos) where Char = C 0.00882745 0.160156 0.0376002 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.PreparationReport.xls new file mode 100644 index 000000000..085e22ce8 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.PreparationReport.xls @@ -0,0 +1,275 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary SpliceJunction +Variables + Categorical 12 + Numerical 90 + Table 1 + Total 103 + +Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt +Sample percentage 10 +Sampling mode Include sample +Selection variable +Selection value +Instances 303 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +EI 67 0.221122 +IE 69 0.227723 +N 167 0.551155 + +Evaluated variables 101 +Native variables 1 +Constructed variables 100 +Informative variables 7 +Selected variables 7 + +Max number of constructed variables 100 +Max number of text features 0 +Max number of trees 5 +Max number of variable pairs 0 +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 10.7442 + Data cost 296.926 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R001 Mode(DNA.Char) where Pos <= 30.5 0.0731216 2 4 C 0.40264 0 0 8.17364 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) +R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0495677 3 4 C 0.376238 0 0 9.55993 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R003 Mode(DNA.Char) 0.0447911 3 4 C 0.392739 0 0 4.68213 30.8759 258.994 TableMode(DNA, Char) +R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) +R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 1 0 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) +R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) +R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0 0 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) +R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0 0 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) +R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0 0 0.693147 10.7442 296.926 + + +Numerical variables statistics + +Rank Name Level Intervals Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R004 Mean(DNA.Pos) where Char = G 0.0261259 2 281 16 46.55555556 31.84768883 4.848828493 1 0 9.55993 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) +R005 Median(DNA.Pos) where Char = G 0.0212659 3 59 8 48 32.44701987 6.725618787 1 0 9.55993 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) +R006 Count(DNA) where Char = G 0.0147548 2 26 0 32 15.81188119 5.174919597 0 0 9.55993 21.0465 273.207 TableCount(`DNA where Char = G`) +R007 Median(DNA.Pos) where Char = C 0.00882745 2 63 9 56.5 29.24587459 7.66919868 0 0 9.55993 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) +R008 Count(DNA) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableCount(DNA) +R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) +R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) +R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) +R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) +R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) +R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) +R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) +R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R022 Count(DNA) where Pos in ]22.5, 30.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R023 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R024 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) +R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) +R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) +R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) +R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) +R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) +R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) +R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) +R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) +R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) +R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) +R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) +R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) +R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) +R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) +R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) +R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) +R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) +R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) +R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) +R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) +R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) +R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) +R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) +R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) +R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) +R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) +R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) +R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) +R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) +R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) +R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) +R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) +R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) +R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) +R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) +R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) +R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) +R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) +R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) +R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) +R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) +R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) +R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) +R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) +R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) +R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) +R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) +R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R001 +Variable Categorical Mode(DNA.Char) where Pos <= 30.5 + +Variable stats +Group Size Value list +{C, T} 2 C T +{G, A} 2 G A * + +% target values +Group EI IE N Interest Frequency Coverage +{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 +{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 122 0.40264 +T 64 0.211221 +G 61 0.20132 +A 56 0.184818 + +---------------------------------------------- +Rank R002 +Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] + +Variable stats +Group Size Value list +{A, G} 2 A G * +{C} 1 C +{T} 1 T + +% target values +Group EI IE N Interest Frequency Coverage +{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 +{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 +{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 114 0.376238 +A 69 0.227723 +T 61 0.20132 +G 59 0.194719 + +---------------------------------------------- +Rank R003 +Variable Categorical Mode(DNA.Char) + +Variable stats +Group Size Value list +{C} 1 C +{T, A} 2 T A * +{G} 1 G + +% target values +Group EI IE N Interest Frequency Coverage +{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 +{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 +{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +C 119 0.392739 +G 77 0.254125 +T 57 0.188119 +A 50 0.165017 + +---------------------------------------------- +Rank R004 +Variable Numerical Mean(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 +]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R005 +Variable Numerical Median(DNA.Pos) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 +]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 +]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R006 +Variable Numerical Count(DNA) where Char = G + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 +]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 +Total 0.221122 0.227723 0.551155 1 303 1 + +---------------------------------------------- +Rank R007 +Variable Numerical Median(DNA.Pos) where Char = C + +% target values +Interval EI IE N Interest Frequency Coverage +]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 +]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 +Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_TrainEvaluationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.TrainEvaluationReport.xls similarity index 95% rename from test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_TrainEvaluationReport.xls rename to test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.TrainEvaluationReport.xls index 0ebce935e..ed18af0ab 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_TrainEvaluationReport.xls +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.TrainEvaluationReport.xls @@ -1,3049 +1,3049 @@ -#Khiops 10.1 -Train evaluation report - -Dictionary SpliceJunction -Target variable Categorical Class -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value -Instances 303 - - -Predictors performance - -Classifier Accuracy Compression AUC -Selective Naive Bayes 0.630363 0.253889 0.786128 - - --------------------------------------------------------------------------------- - -Predictors detailed performance - - -Classifier Selective Naive Bayes -Accuracy 0.630363 -Compression 0.253889 -AUC 0.786128 - -Confusion matrix - EI IE N -$EI 0 0 0 -$IE 5 42 18 -$N 62 27 149 - - - ----------------------------------------------- -Lift curves EI -Size Random Optimal Selective Naive Bayes -0 0 0 0 -0.1 0.1 0.452239 0.452239 -0.2 0.2 0.904478 0.904478 -0.3 0.3 1.35672 1.35672 -0.4 0.4 1.80896 1.80896 -0.5 0.5 2.26119 2.26119 -0.6 0.6 2.71343 2.71343 -0.7 0.7 3.16567 3.16567 -0.8 0.8 3.61791 3.61791 -0.9 0.9 4.07015 4.07015 -1 1 4.52239 4.49254 -1.1 1.1 4.97463 4.64328 -1.2 1.2 5.42687 4.79403 -1.3 1.3 5.8791 4.94478 -1.4 1.4 6.33134 5.09552 -1.5 1.5 6.78358 5.24627 -1.6 1.6 7.23582 5.39701 -1.7 1.7 7.68806 5.54776 -1.8 1.8 8.1403 5.69851 -1.9 1.9 8.59254 5.84925 -2 2 9.04478 6.01493 -2.1 2.1 9.49701 6.24104 -2.2 2.2 9.94925 6.46716 -2.3 2.3 10.4015 6.69328 -2.4 2.4 10.8537 6.9194 -2.5 2.5 11.306 7.14552 -2.6 2.6 11.7582 7.37164 -2.7 2.7 12.2104 7.62478 -2.8 2.8 12.6627 7.89612 -2.9 2.9 13.1149 8.16746 -3 3 13.5672 8.43881 -3.1 3.1 14.0194 8.71015 -3.2 3.2 14.4716 8.98149 -3.3 3.3 14.9239 9.25284 -3.4 3.4 15.3761 9.52418 -3.5 3.5 15.8284 9.79552 -3.6 3.6 16.2806 10.0669 -3.7 3.7 16.7328 10.3382 -3.8 3.8 17.1851 10.6096 -3.9 3.9 17.6373 10.8809 -4 4 18.0896 11.1522 -4.1 4.1 18.5418 11.4236 -4.2 4.2 18.994 11.6949 -4.3 4.3 19.4463 11.9475 -4.4 4.4 19.8985 12.0229 -4.5 4.5 20.3507 12.0983 -4.6 4.6 20.803 12.1736 -4.7 4.7 21.2552 12.249 -4.8 4.8 21.7075 12.3244 -4.9 4.9 22.1597 12.3998 -5 5 22.6119 12.4751 -5.1 5.1 23.0642 12.5505 -5.2 5.2 23.5164 12.6259 -5.3 5.3 23.9687 12.7012 -5.4 5.4 24.4209 12.7766 -5.5 5.5 24.8731 12.852 -5.6 5.6 25.3254 12.9274 -5.7 5.7 25.7776 13.0027 -5.8 5.8 26.2299 13.0781 -5.9 5.9 26.6821 13.1535 -6 6 27.1343 13.2289 -6.1 6.1 27.5866 13.3042 -6.2 6.2 28.0388 13.3796 -6.3 6.3 28.491 13.4993 -6.4 6.4 28.9433 13.7254 -6.5 6.5 29.3955 13.9515 -6.6 6.6 29.8478 14.1776 -6.7 6.7 30.3 14.4037 -6.8 6.8 30.7522 14.6299 -6.9 6.9 31.2045 14.856 -7 7 31.6567 15.0821 -7.1 7.1 32.109 15.3082 -7.2 7.2 32.5612 15.5343 -7.3 7.3 33.0134 15.7604 -7.4 7.4 33.4657 15.9866 -7.5 7.5 33.9179 16.2127 -7.6 7.6 34.3701 16.4388 -7.7 7.7 34.8224 16.6649 -7.8 7.8 35.2746 16.891 -7.9 7.9 35.7269 17.1172 -8 8 36.1791 17.3433 -8.1 8.1 36.6313 17.5694 -8.2 8.2 37.0836 17.7955 -8.3 8.3 37.5358 18.0031 -8.4 8.4 37.9881 18.1915 -8.5 8.5 38.4403 18.38 -8.6 8.6 38.8925 18.5684 -8.7 8.7 39.3448 18.7568 -8.8 8.8 39.797 18.9453 -8.9 8.9 40.2493 19.1337 -9 9 40.7015 19.3221 -9.1 9.1 41.1537 19.5106 -9.2 9.2 41.606 19.699 -9.3 9.3 42.0582 19.8874 -9.4 9.4 42.5104 20.0759 -9.5 9.5 42.9627 20.2643 -9.6 9.6 43.4149 20.4527 -9.7 9.7 43.8672 20.6412 -9.8 9.8 44.3194 20.8296 -9.9 9.9 44.7716 21.018 -10 10 45.2239 21.2065 -10.1 10.1 45.6761 21.3949 -10.2 10.2 46.1284 21.5833 -10.3 10.3 46.5806 21.7718 -10.4 10.4 47.0328 21.9602 -10.5 10.5 47.4851 22.1486 -10.6 10.6 47.9373 22.3371 -10.7 10.7 48.3896 22.5255 -10.8 10.8 48.8418 22.7139 -10.9 10.9 49.294 22.9024 -11 11 49.7463 23.0908 -11.1 11.1 50.1985 23.2792 -11.2 11.2 50.6507 23.4677 -11.3 11.3 51.103 23.6561 -11.4 11.4 51.5552 23.8445 -11.5 11.5 52.0075 24.033 -11.6 11.6 52.4597 24.2214 -11.7 11.7 52.9119 24.4098 -11.8 11.8 53.3642 24.5983 -11.9 11.9 53.8164 24.7867 -12 12 54.2687 24.9751 -12.1 12.1 54.7209 25.1636 -12.2 12.2 55.1731 25.352 -12.3 12.3 55.6254 25.7746 -12.4 12.4 56.0776 26.2269 -12.5 12.5 56.5299 26.6791 -12.6 12.6 56.9821 27.1313 -12.7 12.7 57.4343 27.5836 -12.8 12.8 57.8866 28.0358 -12.9 12.9 58.3388 28.4448 -13 13 58.791 28.7463 -13.1 13.1 59.2433 29.0478 -13.2 13.2 59.6955 29.3493 -13.3 13.3 60.1478 29.6507 -13.4 13.4 60.6 29.9522 -13.5 13.5 61.0522 30.2537 -13.6 13.6 61.5045 30.5552 -13.7 13.7 61.9567 30.8567 -13.8 13.8 62.409 31.1582 -13.9 13.9 62.8612 31.3433 -14 14 63.3134 31.3433 -14.1 14.1 63.7657 31.3433 -14.2 14.2 64.2179 31.3433 -14.3 14.3 64.6701 31.3433 -14.4 14.4 65.1224 31.3433 -14.5 14.5 65.5746 31.3433 -14.6 14.6 66.0269 31.5801 -14.7 14.7 66.4791 31.8816 -14.8 14.8 66.9313 32.1831 -14.9 14.9 67.3836 32.4846 -15 15 67.8358 32.7861 -15.1 15.1 68.2881 33.0876 -15.2 15.2 68.7403 33.3891 -15.3 15.3 69.1925 33.6905 -15.4 15.4 69.6448 33.992 -15.5 15.5 70.097 34.2935 -15.6 15.6 70.5493 34.7284 -15.7 15.7 71.0015 35.1806 -15.8 15.8 71.4537 35.6328 -15.9 15.9 71.906 35.953 -16 16 72.3582 36.1791 -16.1 16.1 72.8104 36.4052 -16.2 16.2 73.2627 36.6313 -16.3 16.3 73.7149 36.8575 -16.4 16.4 74.1672 37.0836 -16.5 16.5 74.6194 37.3097 -16.6 16.6 75.0716 37.3134 -16.7 16.7 75.5239 37.3134 -16.8 16.8 75.9761 37.3134 -16.9 16.9 76.4284 37.3134 -17 17 76.8806 37.3134 -17.1 17.1 77.3328 37.3134 -17.2 17.2 77.7851 37.4866 -17.3 17.3 78.2373 37.9388 -17.4 17.4 78.6896 38.391 -17.5 17.5 79.1418 38.806 -17.6 17.6 79.594 38.806 -17.7 17.7 80.0463 38.806 -17.8 17.8 80.4985 38.806 -17.9 17.9 80.9507 38.806 -18 18 81.403 38.806 -18.1 18.1 81.8552 38.806 -18.2 18.2 82.3075 39.0239 -18.3 18.3 82.7597 39.4761 -18.4 18.4 83.2119 39.9284 -18.5 18.5 83.6642 40.2985 -18.6 18.6 84.1164 40.2985 -18.7 18.7 84.5687 40.2985 -18.8 18.8 85.0209 40.2985 -18.9 18.9 85.4731 40.4479 -19 19 85.9254 40.6175 -19.1 19.1 86.3776 40.7871 -19.2 19.2 86.8299 40.9567 -19.3 19.3 87.2821 41.1263 -19.4 19.4 87.7343 41.2959 -19.5 19.5 88.1866 41.4655 -19.6 19.6 88.6388 41.6351 -19.7 19.7 89.091 41.8047 -19.8 19.8 89.5433 41.9743 -19.9 19.9 89.9955 42.1438 -20 20 90.4478 42.3134 -20.1 20.1 90.9 42.483 -20.2 20.2 91.3522 42.6526 -20.3 20.3 91.8045 42.8222 -20.4 20.4 92.2567 42.9918 -20.5 20.5 92.709 43.1614 -20.6 20.6 93.1612 43.331 -20.7 20.7 93.6134 43.5006 -20.8 20.8 94.0657 43.6701 -20.9 20.9 94.5179 43.8397 -21 21 94.9701 44.0093 -21.1 21.1 95.4224 44.1789 -21.2 21.2 95.8746 44.3485 -21.3 21.3 96.3269 44.5181 -21.4 21.4 96.7791 44.6877 -21.5 21.5 97.2313 44.7761 -21.6 21.6 97.6836 44.7761 -21.7 21.7 98.1358 44.7761 -21.8 21.8 98.5881 44.7761 -21.9 21.9 99.0403 44.7761 -22 22 99.4925 44.7761 -22.1 22.1 99.9448 44.7761 -22.2 22.2 100 45.1731 -22.3 22.3 100 45.6254 -22.4 22.4 100 46.0776 -22.5 22.5 100 46.2687 -22.6 22.6 100 46.2687 -22.7 22.7 100 46.2687 -22.8 22.8 100 46.2687 -22.9 22.9 100 46.2687 -23 23 100 46.2687 -23.1 23.1 100 46.2687 -23.2 23.2 100 46.2687 -23.3 23.3 100 46.2687 -23.4 23.4 100 46.2687 -23.5 23.5 100 46.2687 -23.6 23.6 100 46.2687 -23.7 23.7 100 46.2687 -23.8 23.8 100 46.2687 -23.9 23.9 100 46.2687 -24 24 100 46.2687 -24.1 24.1 100 46.2687 -24.2 24.2 100 46.2687 -24.3 24.3 100 46.2687 -24.4 24.4 100 46.2687 -24.5 24.5 100 46.2687 -24.6 24.6 100 46.2687 -24.7 24.7 100 46.2687 -24.8 24.8 100 46.3761 -24.9 24.9 100 46.6022 -25 25 100 46.8284 -25.1 25.1 100 47.0545 -25.2 25.2 100 47.2806 -25.3 25.3 100 47.5067 -25.4 25.4 100 47.7328 -25.5 25.5 100 47.9307 -25.6 25.6 100 48.1245 -25.7 25.7 100 48.3183 -25.8 25.8 100 48.5122 -25.9 25.9 100 48.706 -26 26 100 48.8998 -26.1 26.1 100 49.0936 -26.2 26.2 100 49.2874 -26.3 26.3 100 49.4812 -26.4 26.4 100 49.6751 -26.5 26.5 100 49.8689 -26.6 26.6 100 50.0627 -26.7 26.7 100 50.2565 -26.8 26.8 100 50.4503 -26.9 26.9 100 50.6441 -27 27 100 50.838 -27.1 27.1 100 51.0318 -27.2 27.2 100 51.2256 -27.3 27.3 100 51.4194 -27.4 27.4 100 51.6132 -27.5 27.5 100 51.807 -27.6 27.6 100 52.0009 -27.7 27.7 100 52.1947 -27.8 27.8 100 52.2388 -27.9 27.9 100 52.2388 -28 28 100 52.2388 -28.1 28.1 100 52.3811 -28.2 28.2 100 52.6826 -28.3 28.3 100 52.9841 -28.4 28.4 100 53.2856 -28.5 28.5 100 53.5871 -28.6 28.6 100 53.8886 -28.7 28.7 100 54.19 -28.8 28.8 100 54.4915 -28.9 28.9 100 54.793 -29 29 100 55.0945 -29.1 29.1 100 55.2239 -29.2 29.2 100 55.2239 -29.3 29.3 100 55.2239 -29.4 29.4 100 55.2239 -29.5 29.5 100 55.2239 -29.6 29.6 100 55.2239 -29.7 29.7 100 55.2239 -29.8 29.8 100 55.4433 -29.9 29.9 100 55.6694 -30 30 100 55.8955 -30.1 30.1 100 56.1216 -30.2 30.2 100 56.3478 -30.3 30.3 100 56.5739 -30.4 30.4 100 56.8 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-Lift curves IE -Size Random Optimal Selective Naive Bayes -0 0 0 0 -0.1 0.1 0.43913 0.351304 -0.2 0.2 0.878261 0.702609 -0.3 0.3 1.31739 1.05391 -0.4 0.4 1.75652 1.40522 -0.5 0.5 2.19565 1.75652 -0.6 0.6 2.63478 2.10783 -0.7 0.7 3.07391 2.45913 -0.8 0.8 3.51304 2.81043 -0.9 0.9 3.95217 3.16174 -1 1 4.3913 3.51304 -1.1 1.1 4.83043 3.86435 -1.2 1.2 5.26957 4.21565 -1.3 1.3 5.7087 4.56696 -1.4 1.4 6.14783 4.91826 -1.5 1.5 6.58696 5.26957 -1.6 1.6 7.02609 5.62087 -1.7 1.7 7.46522 5.93716 -1.8 1.8 7.90435 6.2182 -1.9 1.9 8.34348 6.49925 -2 2 8.78261 6.78029 -2.1 2.1 9.22174 7.06133 -2.2 2.2 9.66087 7.34238 -2.3 2.3 10.1 7.62342 -2.4 2.4 10.5391 7.90446 -2.5 2.5 10.9783 8.18551 -2.6 2.6 11.4174 8.46655 -2.7 2.7 11.8565 8.74759 -2.8 2.8 12.2957 9.02864 -2.9 2.9 12.7348 9.30968 -3 3 13.1739 9.59072 -3.1 3.1 13.613 9.87177 -3.2 3.2 14.0522 10.1528 -3.3 3.3 14.4913 10.4339 -3.4 3.4 14.9304 10.7149 -3.5 3.5 15.3696 10.9959 -3.6 3.6 15.8087 11.277 -3.7 3.7 16.2478 11.558 -3.8 3.8 16.687 11.8391 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000000000..f4deeb9d3 --- /dev/null +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.TreePreparationReport.xls @@ -0,0 +1,98 @@ +#Khiops 10.4.6i +Descriptive statistics + + +Problem description +Short description + +Dictionary SpliceJunction +Variables + Categorical 12 + Numerical 90 + Table 1 + Total 103 + +Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt +Sample percentage 10 +Sampling mode Include sample +Selection variable +Selection value +Instances 303 + +Learning task Classification analysis + +Target variable Categorical Class + +Target variable stats +Value Frequency Coverage +EI 67 0.221122 +IE 69 0.227723 +N 167 0.551155 + +Evaluated variables 2 +Informative variables 2 +Selected variables 0 + +Discretization MODL +Value grouping MODL + +Null model + Constr. cost 0.693147 + Prep. cost 10.7442 + Data cost 296.926 + + +Categorical variables statistics + +Rank Name Level Groups Values Mode Mode coverage Missing number Sparse missing number Constr. cost Prep. cost Data cost Derivation rule +R1 Tree_1 0.0257056 3 3 L2 0.422442 0 0 29.2106 29.7139 241.512 SwitchC(GroupIndex(ValueGroups(ValueGroup("C", "T"), ValueGroup("A", "G", " * ")), `Mode(DNA.Char) where Pos in ]15.5, 30.5]`), "L0", SwitchC(IntervalIndex(IntervalBounds(34.75), `Median(DNA.Pos) where Char = G`), "L0", "L3", "L4"), "L2") +R2 Tree_4 0.020723 2 3 L1 0.49835 0 0 29.2106 21.7369 251.026 SwitchC(IntervalIndex(IntervalBounds(31.698), `Mean(DNA.Pos) where Char = G`), "L0", "L1", SwitchC(GroupIndex(ValueGroups(ValueGroup("C", "T"), ValueGroup("A", "G", " * ")), `Mode(DNA.Char) where Pos in ]15.5, 30.5]`), "L0", "L3", "L4")) + + +-------------------------------------------------------------------------------- + +Variables detailed statistics + + +Rank R1 +Variable Categorical Tree_1 + +Variable stats +Group Size Value list +{L2} 1 L2 +{L4} 1 L4 +{L3} 1 L3 * + +% target values +Group EI IE N Interest Frequency Coverage +{L4} 0.181818 0.556818 0.261364 0.487083 88 0.290429 +{L2} 0.296875 0.0234375 0.679688 0.468243 128 0.422442 +{L3} 0.149425 0.195402 0.655172 0.0446743 87 0.287129 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +L2 128 0.422442 +L4 88 0.290429 +L3 87 0.287129 + +---------------------------------------------- +Rank R2 +Variable Categorical Tree_4 + +Variable stats +Group Size Value list +{L1, L4} 2 L1 L4 * +{L3} 1 L3 + +% target values +Group EI IE N Interest Frequency Coverage +{L3} 0.142857 0.533333 0.32381 0.546708 105 0.346535 +{L1, L4} 0.262626 0.0656566 0.671717 0.453292 198 0.653465 +Total 0.221122 0.227723 0.551155 1 303 1 + +Values +Value Frequency Coverage +L1 151 0.49835 +L3 105 0.346535 +L4 47 0.155116 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AllReports.khj b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.khj similarity index 96% rename from test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AllReports.khj rename to test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.khj index 5c017b2af..840908e91 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AllReports.khj +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.khj @@ -1,2347 +1,2468 @@ -{ - "tool": "Khiops", - "version": "10.1", - "shortDescription": "", - "modelingReport": { - "reportType": "Modeling", - "summary": { - "dictionary": "SpliceJunction", - "database": "..\/..\/..\/MTdatasets\/SpliceJunction\/SpliceJunction.txt", - "samplePercentage": 10, - "samplingMode": "Include sample", - "selectionVariable": "", - "selectionValue": "", - "learningTask": "Classification analysis", - "targetVariable": "Class" - }, - "trainedPredictors": [ - { - "rank": "R1", - "type": "Classifier", - "family": "Selective Naive Bayes", - "name": "Selective Naive Bayes", - "variables": 7 - } - ], - "trainedPredictorsDetails": { - "R1": { - "selectedVariables": [ - { - "preparedName": "PMode(DNA.Char) where Pos <= 30.5", - "name": "Mode(DNA.Char) where Pos <= 30.5", - "level": 0.0731216, - "weight": 0.316406, - "importance": 0.152106 - }, - { - "preparedName": "PMode(DNA.Char) where Pos in ]15.5, 30.5]", - "name": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", - "level": 0.0495677, - "weight": 0.394531, - "importance": 0.139843 - }, - { - "preparedName": "PMode(DNA.Char)", - "name": "Mode(DNA.Char)", - "level": 0.0447911, - "weight": 0.425781, - "importance": 0.138098 - }, - { - "preparedName": "PMedian(DNA.Pos) where Char = G", - "name": "Median(DNA.Pos) where Char = G", - "level": 0.0212659, - "weight": 0.378906, - "importance": 0.0897651 - }, - { - "preparedName": "PMean(DNA.Pos) where Char = G", - "name": "Mean(DNA.Pos) where Char = G", - "level": 0.0261259, - "weight": 0.222656, - "importance": 0.0762699 - }, 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"partInterests": [0.454456,0.545544] + } + } + } + }, + "treePreparationReport": { + "reportType": "Preparation", + "summary": { + "dictionary": "SpliceJunction", + "variables": { + "types": [ + "Categorical", + "Numerical", + "Table" + ], + "numbers": [ + 12, + 90, + 1 + ] + }, + "database": "..\/..\/..\/MTdatasets\/SpliceJunction\/SpliceJunction.txt", + "samplePercentage": 10, + "samplingMode": "Include sample", + "selectionVariable": "", + "selectionValue": "", + "instances": 303, + "learningTask": "Classification analysis", + "targetVariable": "Class", + "targetDescriptiveStats": { + "values": 3, + "mode": "N", + "modeFrequency": 167, + "missingNumber": 0, + "sparseMissingNumber": 0 + }, + "targetValues": { + "values": ["EI","IE","N"], + "frequencies": [67,69,167] + }, + "evaluatedVariables": 2, + "informativeVariables": 2, + "selectedVariables": 0 + }, + "variablesStatistics": [ + { + "rank": "R1", + "name": "Tree_1", + "type": "Categorical", + "level": 0.0257056, + "parts": 3, + "values": 3, + "mode": "L2", + "modeFrequency": 128, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 29.2106, + "preparationCost": 29.7139, + "dataCost": 241.512, + "derivationRule": "SwitchC(GroupIndex(ValueGroups(ValueGroup(\"C\", \"T\"), ValueGroup(\"A\", \"G\", \" * \")), `Mode(DNA.Char) where Pos in ]15.5, 30.5]`), \"L0\", SwitchC(IntervalIndex(IntervalBounds(34.75), `Median(DNA.Pos) where Char = G`), \"L0\", \"L3\", \"L4\"), \"L2\")" + }, + { + "rank": "R2", + "name": "Tree_4", + "type": "Categorical", + "level": 0.020723, + "parts": 2, + "values": 3, + "mode": "L1", + "modeFrequency": 151, + "missingNumber": 0, + "sparseMissingNumber": 0, + "constructionCost": 29.2106, + "preparationCost": 21.7369, + "dataCost": 251.026, + "derivationRule": "SwitchC(IntervalIndex(IntervalBounds(31.698), `Mean(DNA.Pos) where Char = G`), \"L0\", \"L1\", SwitchC(GroupIndex(ValueGroups(ValueGroup(\"C\", \"T\"), ValueGroup(\"A\", \"G\", \" * \")), `Mode(DNA.Char) where Pos in ]15.5, 30.5]`), \"L0\", \"L3\", \"L4\"))" + } + ], + "variablesDetailedStatistics": { + "R1": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Tree_1", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["L2"], + ["L4"], + ["L3"] + ], + "defaultGroupIndex": 2 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [38,3,87], + [16,49,23], + [13,17,57] + ], + "partInterests": [0.468243,0.487083,0.0446743] + }, + "inputValues": { + "values": ["L2","L4","L3"], + "frequencies": [128,88,87] + } + }, + "R2": { + "dataGrid": { + "isSupervised": true, + "dimensions": [ + { + "variable": "Tree_4", + "type": "Categorical", + "partitionType": "Value groups", + "partition": [ + ["L1","L4"], + ["L3"] + ], + "defaultGroupIndex": 0 + }, + { + "variable": "Class", + "type": "Categorical", + "partitionType": "Values", + "partition": ["EI","IE","N"] + } + ], + "partTargetFrequencies": [ + [52,13,133], + [15,56,34] + ], + "partInterests": [0.453292,0.546708] + }, + "inputValues": { + "values": ["L1","L3","L4"], + "frequencies": [151,105,47] + } + } + }, + "treeDetails": { + "R1": { + "name": "Tree_1", + "variableNumber": 2, + "depth": 3, + "treeNodes": { + "nodeId": "L0", + "variable": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", + "type": "Categorical", + "partition": [ + ["C","T"], + ["A","G"] + ], + "defaultGroupIndex": 1, + "childNodes": [ + { + "nodeId": "L1", + "variable": "Median(DNA.Pos) where Char = G", + "type": "Numerical", + "partition": [ + [8,34.75], + [34.75,48] + ], + "childNodes": [ + { + "nodeId": "L3", + "targetValues": { + "values": ["EI","IE","N"], + "frequencies": [13,17,57] + } + }, + { + "nodeId": "L4", + "targetValues": { + "values": ["EI","IE","N"], + "frequencies": [16,49,23] + } + } + ] + }, + { + "nodeId": "L2", + "targetValues": { + "values": ["EI","IE","N"], + "frequencies": [38,3,87] + } + } + ] + } + }, + "R2": { + "name": "Tree_4", + "variableNumber": 2, + "depth": 3, + "treeNodes": { + "nodeId": "L0", + "variable": "Mean(DNA.Pos) where Char = G", + "type": "Numerical", + "partition": [ + [16,31.698], + [31.698,46.55555556] + ], + "childNodes": [ + { + "nodeId": "L1", + "targetValues": { + "values": ["EI","IE","N"], + "frequencies": [34,12,105] + } + }, + { + "nodeId": "L2", + "variable": "Mode(DNA.Char) where Pos in ]15.5, 30.5]", + "type": "Categorical", + "partition": [ + ["C","T"], + ["A","G"] + ], + "defaultGroupIndex": 1, + "childNodes": [ + { + "nodeId": "L3", + "targetValues": { + "values": ["EI","IE","N"], + "frequencies": [15,56,34] + } + }, + { + "nodeId": "L4", + "targetValues": { + "values": ["EI","IE","N"], + "frequencies": [18,1,28] + } + } + ] + } + ] + } + } + } + }, + "khiops_encoding": "ascii" +} diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_Modeling.kdic b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.model.kdic similarity index 98% rename from test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_Modeling.kdic rename to test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.model.kdic index c8c29273b..3e3113ae7 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_Modeling.kdic +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_AnalysisResults.model.kdic @@ -1,47 +1,47 @@ -#Khiops 10.1 - -Root Dictionary SNB_SpliceJunction (SampleId) - -{ - Categorical SampleId ; -Unused Categorical Class ; -Unused Table(SNB_SpliceJunctionDNA) DNA ; -Unused Categorical `Mode(DNA.Char)` = TableMode(DNA, Char) ; -Unused Categorical `Mode(DNA.Char) where Pos <= 30.5` = TableMode(`DNA where Pos <= 30.5`, Char) ; -Unused Numerical `Count(DNA) where Char = G` = TableCount(`DNA where Char = G`) ; -Unused Numerical `Mean(DNA.Pos) where Char = G` = TableMean(`DNA where Char = G`, Pos) ; -Unused Numerical `Median(DNA.Pos) where Char = C` = TableMedian(`DNA where Char = C`, Pos) ; -Unused Numerical `Median(DNA.Pos) where Char = G` = TableMedian(`DNA where Char = G`, Pos) ; -Unused Table(SNB_SpliceJunctionDNA) `DNA where Pos <= 30.5` = TableSelection(DNA, LE(Pos, 30.5)) ; -Unused Table(SNB_SpliceJunctionDNA) `DNA where Char = C` = TableSelection(DNA, EQc(Char, "C")) ; -Unused Table(SNB_SpliceJunctionDNA) `DNA where Char = G` = TableSelection(DNA, EQc(Char, "G")) ; - { -Unused Categorical `Mode(DNA.Char) where Pos in ]15.5, 30.5]` ; - } `Mode(DNA.Char) per Pos(4)` = TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) ; -Unused Structure(Partition) `Partition(SpliceJunctionDNA) per Pos(4)` = Partition(IntervalBounds(15.5, 30.5, 45.5)) ; - { -Unused Table(SNB_SpliceJunctionDNA) `DNA where Pos in ]15.5, 30.5]` ; - } `TablePartition(DNA) per Pos(4)` = TablePartition(DNA, `Partition(SpliceJunctionDNA) per Pos(4)`, Pos) ; -Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("EI", "IE", "N"), Frequencies(67, 69, 167)) ; -Unused Structure(DataGrid) `PMode(DNA.Char)` = DataGrid(ValueGroups(ValueGroup("C"), ValueGroup("T", "A", " * "), ValueGroup("G")), ValueSetC("EI", "IE", "N"), Frequencies(17, 16, 34, 49, 17, 3, 53, 74, 40)) ; // DataGrid(`Mode(DNA.Char)`, Class) -Unused Structure(DataGrid) `PMode(DNA.Char) where Pos <= 30.5` = DataGrid(ValueGroups(ValueGroup("C", "T"), ValueGroup("G", "A", " * ")), ValueSetC("EI", "IE", "N"), Frequencies(29, 38, 69, 0, 88, 79)) ; // DataGrid(`Mode(DNA.Char) where Pos <= 30.5`, Class) -Unused Structure(DataGrid) `PCount(DNA) where Char = G` = DataGrid(IntervalBounds(19.5), ValueSetC("EI", "IE", "N"), Frequencies(38, 29, 68, 1, 130, 37)) ; // DataGrid(`Count(DNA) where Char = G`, Class) -Unused Structure(DataGrid) `PMean(DNA.Pos) where Char = G` = DataGrid(IntervalBounds(31.698), ValueSetC("EI", "IE", "N"), Frequencies(34, 33, 12, 57, 105, 62)) ; // DataGrid(`Mean(DNA.Pos) where Char = G`, Class) -Unused Structure(DataGrid) `PMedian(DNA.Pos) where Char = C` = DataGrid(IntervalBounds(27.75), ValueSetC("EI", "IE", "N"), Frequencies(35, 32, 53, 16, 62, 105)) ; // DataGrid(`Median(DNA.Pos) where Char = C`, Class) -Unused Structure(DataGrid) `PMedian(DNA.Pos) where Char = G` = DataGrid(IntervalBounds(27.75, 37.25), ValueSetC("EI", "IE", "N"), Frequencies(11, 44, 12, 3, 31, 35, 58, 86, 23)) ; // DataGrid(`Median(DNA.Pos) where Char = G`, Class) -Unused Structure(DataGrid) `PMode(DNA.Char) where Pos in ]15.5, 30.5]` = DataGrid(ValueGroups(ValueGroup("A", "G", " * "), ValueGroup("C"), ValueGroup("T")), ValueSetC("EI", "IE", "N"), Frequencies(38, 26, 3, 3, 41, 25, 87, 47, 33)) ; // DataGrid(`Mode(DNA.Char) where Pos in ]15.5, 30.5]`, Class) -Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.42578125, 0.31640625, 0.22265625, 0.22265625, 0.16015625, 0.37890625, 0.39453125), DataGridStats(`PMode(DNA.Char)`, `Mode(DNA.Char)`), DataGridStats(`PMode(DNA.Char) where Pos <= 30.5`, `Mode(DNA.Char) where Pos <= 30.5`), DataGridStats(`PCount(DNA) where Char = G`, `Count(DNA) where Char = G`), DataGridStats(`PMean(DNA.Pos) where Char = G`, `Mean(DNA.Pos) where Char = G`), DataGridStats(`PMedian(DNA.Pos) where Char = C`, `Median(DNA.Pos) where Char = C`), DataGridStats(`PMedian(DNA.Pos) where Char = G`, `Median(DNA.Pos) where Char = G`), DataGridStats(`PMode(DNA.Char) where Pos in ]15.5, 30.5]`, `Mode(DNA.Char) where Pos in ]15.5, 30.5]`), VClass) ; - Categorical PredictedClass = TargetValue(SNBClass) ; -Unused Numerical ScoreClass = TargetProb(SNBClass) ; - Numerical ProbClassEI = TargetProbAt(SNBClass, "EI") ; - Numerical ProbClassIE = TargetProbAt(SNBClass, "IE") ; - Numerical ProbClassN = TargetProbAt(SNBClass, "N") ; -}; - -Dictionary SNB_SpliceJunctionDNA (SampleId) - -{ - Categorical SampleId ; - Numerical Pos ; - Categorical Char ; -}; +#Khiops 10.4.6i + +Root Dictionary SNB_SpliceJunction (SampleId) + +{ + Categorical SampleId ; +Unused Categorical Class ; +Unused Table(SNB_SpliceJunctionDNA) DNA ; +Unused Categorical `Mode(DNA.Char)` = TableMode(DNA, Char) ; +Unused Categorical `Mode(DNA.Char) where Pos <= 30.5` = TableMode(`DNA where Pos <= 30.5`, Char) ; +Unused Numerical `Count(DNA) where Char = G` = TableCount(`DNA where Char = G`) ; +Unused Numerical `Mean(DNA.Pos) where Char = G` = TableMean(`DNA where Char = G`, Pos) ; +Unused Numerical `Median(DNA.Pos) where Char = C` = TableMedian(`DNA where Char = C`, Pos) ; +Unused Numerical `Median(DNA.Pos) where Char = G` = TableMedian(`DNA where Char = G`, Pos) ; +Unused Table(SNB_SpliceJunctionDNA) `DNA where Pos <= 30.5` = TableSelection(DNA, LE(Pos, 30.5)) ; +Unused Table(SNB_SpliceJunctionDNA) `DNA where Char = C` = TableSelection(DNA, EQc(Char, "C")) ; +Unused Table(SNB_SpliceJunctionDNA) `DNA where Char = G` = TableSelection(DNA, EQc(Char, "G")) ; + { +Unused Categorical `Mode(DNA.Char) where Pos in ]15.5, 30.5]` ; + } `Mode(DNA.Char) per Pos(4)` = TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) ; +Unused Structure(Partition) `Partition(SpliceJunctionDNA) per Pos(4)` = Partition(IntervalBounds(15.5, 30.5, 45.5)) ; + { +Unused Table(SNB_SpliceJunctionDNA) `DNA where Pos in ]15.5, 30.5]` ; + } `TablePartition(DNA) per Pos(4)` = TablePartition(DNA, `Partition(SpliceJunctionDNA) per Pos(4)`, Pos) ; +Unused Structure(DataGrid) VClass = DataGrid(ValueSetC("EI", "IE", "N"), Frequencies(67, 69, 167)) ; +Unused Structure(DataGrid) `PMode(DNA.Char)` = DataGrid(ValueGroups(ValueGroup("C"), ValueGroup("T", "A", " * "), ValueGroup("G")), ValueSetC("EI", "IE", "N"), Frequencies(17, 16, 34, 49, 17, 3, 53, 74, 40)) ; // DataGrid(`Mode(DNA.Char)`, Class) +Unused Structure(DataGrid) `PMode(DNA.Char) where Pos <= 30.5` = DataGrid(ValueGroups(ValueGroup("C", "T"), ValueGroup("G", "A", " * ")), ValueSetC("EI", "IE", "N"), Frequencies(29, 38, 69, 0, 88, 79)) ; // DataGrid(`Mode(DNA.Char) where Pos <= 30.5`, Class) +Unused Structure(DataGrid) `PCount(DNA) where Char = G` = DataGrid(IntervalBounds(19.5), ValueSetC("EI", "IE", "N"), Frequencies(38, 29, 68, 1, 130, 37)) ; // DataGrid(`Count(DNA) where Char = G`, Class) +Unused Structure(DataGrid) `PMean(DNA.Pos) where Char = G` = DataGrid(IntervalBounds(31.698), ValueSetC("EI", "IE", "N"), Frequencies(34, 33, 12, 57, 105, 62)) ; // DataGrid(`Mean(DNA.Pos) where Char = G`, Class) +Unused Structure(DataGrid) `PMedian(DNA.Pos) where Char = C` = DataGrid(IntervalBounds(27.75), ValueSetC("EI", "IE", "N"), Frequencies(35, 32, 53, 16, 62, 105)) ; // DataGrid(`Median(DNA.Pos) where Char = C`, Class) +Unused Structure(DataGrid) `PMedian(DNA.Pos) where Char = G` = DataGrid(IntervalBounds(27.75, 37.25), ValueSetC("EI", "IE", "N"), Frequencies(11, 44, 12, 3, 31, 35, 58, 86, 23)) ; // DataGrid(`Median(DNA.Pos) where Char = G`, Class) +Unused Structure(DataGrid) `PMode(DNA.Char) where Pos in ]15.5, 30.5]` = DataGrid(ValueGroups(ValueGroup("A", "G", " * "), ValueGroup("C"), ValueGroup("T")), ValueSetC("EI", "IE", "N"), Frequencies(38, 26, 3, 3, 41, 25, 87, 47, 33)) ; // DataGrid(`Mode(DNA.Char) where Pos in ]15.5, 30.5]`, Class) +Unused Structure(Classifier) SNBClass = SNBClassifier(Vector(0.42578125, 0.31640625, 0.22265625, 0.22265625, 0.16015625, 0.37890625, 0.39453125), DataGridStats(`PMode(DNA.Char)`, `Mode(DNA.Char)`), DataGridStats(`PMode(DNA.Char) where Pos <= 30.5`, `Mode(DNA.Char) where Pos <= 30.5`), DataGridStats(`PCount(DNA) where Char = G`, `Count(DNA) where Char = G`), DataGridStats(`PMean(DNA.Pos) where Char = G`, `Mean(DNA.Pos) where Char = G`), DataGridStats(`PMedian(DNA.Pos) where Char = C`, `Median(DNA.Pos) where Char = C`), DataGridStats(`PMedian(DNA.Pos) where Char = G`, `Median(DNA.Pos) where Char = G`), DataGridStats(`PMode(DNA.Char) where Pos in ]15.5, 30.5]`, `Mode(DNA.Char) where Pos in ]15.5, 30.5]`), VClass) ; + Categorical PredictedClass = TargetValue(SNBClass) ; +Unused Numerical ScoreClass = TargetProb(SNBClass) ; + Numerical ProbClassEI = TargetProbAt(SNBClass, "EI") ; + Numerical ProbClassIE = TargetProbAt(SNBClass, "IE") ; + Numerical ProbClassN = TargetProbAt(SNBClass, "N") ; +}; + +Dictionary SNB_SpliceJunctionDNA (SampleId) + +{ + Categorical SampleId ; + Numerical Pos ; + Categorical Char ; +}; diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_PreparationReport.xls deleted file mode 100644 index f6d2777ac..000000000 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/C100_PreparationReport.xls +++ /dev/null @@ -1,273 +0,0 @@ -#Khiops 10.1 -Descriptive statistics - - -Problem description -Short description - -Dictionary SpliceJunction -Variables - Categorical 12 - Numerical 90 - Table 1 - Total 103 - -Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Sample percentage 10 -Sampling mode Include sample -Selection variable -Selection value -Instances 303 - -Learning task Classification analysis - -Target variable Categorical Class - -Target variable stats -Value Frequency Coverage -EI 67 0.221122 -IE 69 0.227723 -N 167 0.551155 - -Evaluated variables 101 -Native variables 1 -Constructed variables 100 -Informative variables 7 - -Max number of constructed variables 100 -Max number of trees 5 -Max number of variable pairs 0 -Discretization MODL -Value grouping MODL - -Null model - Constr. cost 0.693147 - Prep. cost 10.7442 - Data cost 296.926 - - -Categorical variables statistics - -Rank Name Level Groups Values Mode Mode coverage Constr. cost Prep. cost Data cost Derivation rule -R001 Mode(DNA.Char) where Pos <= 30.5 0.0731216 2 4 C 0.40264 8.17364 23.5167 254.125 TableMode(`DNA where Pos <= 30.5`, Char) -R002 Mode(DNA.Char) where Pos in ]15.5, 30.5] 0.0495677 3 4 C 0.376238 9.55993 31.5551 251.964 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R003 Mode(DNA.Char) 0.0447911 3 4 C 0.392739 4.68213 30.8759 258.994 TableMode(DNA, Char) -R074 Mode(DNA.Char) where Char = C 0 1 1 C 1 0.693147 10.7442 296.926 TableMode(`DNA where Char = C`, Char) -R075 Mode(DNA.Char) where Char = G 0 1 2 G 0.9967 0.693147 10.7442 296.926 TableMode(`DNA where Char = G`, Char) -R076 Mode(DNA.Char) where Char not in {C, G} 0 1 2 T 0.50495 0.693147 10.7442 296.926 TableMode(`DNA where Char not in {C, G}`, Char) -R077 Mode(DNA.Char) where Pos <= 15.5 0 1 4 C 0.425743 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R078 Mode(DNA.Char) where Pos > 30.5 0 1 4 G 0.363036 0.693147 10.7442 296.926 TableMode(`DNA where Pos > 30.5`, Char) -R079 Mode(DNA.Char) where Pos > 45.5 0 1 4 C 0.320132 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R080 Mode(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 G 0.336634 0.693147 10.7442 296.926 TablePartitionMode(`TablePartition(DNA) per Pos(4)`, Char) -R081 SampleId 0 1 303 AGMRSKPNI-NEG-1141 0.00330033 0.693147 10.7442 296.926 - - -Numerical variables statistics - -Rank Name Level Intervals Values Min Max Mean Std dev Missing number Constr. cost Prep. cost Data cost Derivation rule -R004 Mean(DNA.Pos) where Char = G 0.0261259 2 281 16 46.55555556 31.84768883 4.848828493 1 9.55993 19.4458 271.302 TableMean(`DNA where Char = G`, Pos) -R005 Median(DNA.Pos) where Char = G 0.0212659 3 59 8 48 32.44701987 6.725618787 1 9.55993 29.3732 262.873 TableMedian(`DNA where Char = G`, Pos) -R006 Count(DNA) where Char = G 0.0147548 2 26 0 32 15.81188119 5.174919597 0 9.55993 21.0465 273.207 TableCount(`DNA where Char = G`) -R007 Median(DNA.Pos) where Char = C 0.00882745 2 63 9 56.5 29.24587459 7.66919868 0 9.55993 19.4456 276.636 TableMedian(`DNA where Char = C`, Pos) -R008 Count(DNA) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableCount(DNA) -R009 Count(DNA) where Char = A 0 1 25 0 27 13.53135314 4.652779483 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = A`) -R010 Count(DNA) where Char = C 0 1 29 2 34 16.48844884 5.649179844 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = C`) -R011 Count(DNA) where Char = T 0 1 29 2 34 14.16171617 5.235407476 0 0.693147 10.7442 296.926 TableCount(`DNA where Char = T`) -R012 Count(DNA) where Char not in {C, G, T, A} 0 1 2 0 1 0.006600660066 0.08097586895 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G, T, A}`) -R013 Count(DNA) where Char not in {C, G} 0 1 33 10 47 27.69966997 6.838946411 0 0.693147 10.7442 296.926 TableCount(`DNA where Char not in {C, G}`) -R014 Count(DNA) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R015 Count(DNA) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos <= 30.5`) -R016 Count(DNA) where Pos <= 7.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R017 Count(DNA) where Pos > 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableCount(`DNA where Pos > 30.5`) -R018 Count(DNA) where Pos > 45.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R019 Count(DNA) where Pos > 53.5 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R020 Count(DNA) where Pos in ]15.5, 22.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R021 Count(DNA) where Pos in ]15.5, 30.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R022 Count(DNA) where Pos in ]30.5, 38.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R023 Count(DNA) where Pos in ]30.5, 45.5] 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(4)`) -R024 Count(DNA) where Pos in ]38.5, 45.5] 0 1 1 7 7 7 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R025 Count(DNA) where Pos in ]45.5, 53.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R026 Count(DNA) where Pos in ]7.5, 15.5] 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionCount(`TablePartition(DNA) per Pos(8)`) -R027 CountDistinct(DNA.Char) 0 1 4 2 5 3.99669967 0.1519586064 0 0.693147 10.7442 296.926 TableCountDistinct(DNA, Char) -R028 CountDistinct(DNA.Char) where Char = C 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = C`, Char) -R029 CountDistinct(DNA.Char) where Char = G 0 1 2 0 1 0.99669967 0.05735362111 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char = G`, Char) -R030 CountDistinct(DNA.Char) where Char not in {C, G} 0 1 3 1 3 2 0.1148969979 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Char not in {C, G}`, Char) -R031 CountDistinct(DNA.Char) where Pos <= 15.5 0 1 4 2 5 3.848184818 0.3854468996 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R032 CountDistinct(DNA.Char) where Pos <= 30.5 0 1 4 2 5 3.97029703 0.2049927413 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos <= 30.5`, Char) -R033 CountDistinct(DNA.Char) where Pos > 30.5 0 1 4 2 5 3.98019802 0.1805856636 0 0.693147 10.7442 296.926 TableCountDistinct(`DNA where Pos > 30.5`, Char) -R034 CountDistinct(DNA.Char) where Pos > 45.5 0 1 3 2 4 3.821782178 0.42362664 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R035 CountDistinct(DNA.Char) where Pos in ]15.5, 30.5] 0 1 3 2 4 3.884488449 0.3492429139 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R036 CountDistinct(DNA.Char) where Pos in ]30.5, 45.5] 0 1 4 2 5 3.914191419 0.3027322058 0 0.693147 10.7442 296.926 TablePartitionCountDistinct(`TablePartition(DNA) per Pos(4)`, Char) -R037 Max(DNA.Pos) 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(DNA, Pos) -R038 Max(DNA.Pos) where Char = C 0 1 24 27 60 56.38283828 4.743448064 0 0.693147 10.7442 296.926 TableMax(`DNA where Char = C`, Pos) -R039 Max(DNA.Pos) where Char = G 0 1 22 35 60 56.96357616 4.098376087 1 0.693147 10.7442 296.926 TableMax(`DNA where Char = G`, Pos) -R040 Max(DNA.Pos) where Char not in {C, G} 0 1 11 43 60 58.88118812 1.816429214 0 0.693147 10.7442 296.926 TableMax(`DNA where Char not in {C, G}`, Pos) -R041 Max(DNA.Pos) where Pos <= 15.5 0 1 1 15 15 15 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R042 Max(DNA.Pos) where Pos <= 30.5 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos <= 30.5`, Pos) -R043 Max(DNA.Pos) where Pos > 30.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TableMax(`DNA where Pos > 30.5`, Pos) -R044 Max(DNA.Pos) where Pos > 45.5 0 1 1 60 60 60 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R045 Max(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 30 30 30 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R046 Max(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 45 45 45 0 0 0.693147 10.7442 296.926 TablePartitionMax(`TablePartition(DNA) per Pos(4)`, Pos) -R047 Mean(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMean(DNA, Pos) -R048 Mean(DNA.Pos) where Char = C 0 1 274 14.25 46.33333333 29.71814213 5.029068147 0 0.693147 10.7442 296.926 TableMean(`DNA where Char = C`, Pos) -R049 Mean(DNA.Pos) where Char not in {C, G} 0 1 283 20.6 39.91304348 30.33646503 2.577046027 0 0.693147 10.7442 296.926 TableMean(`DNA where Char not in {C, G}`, Pos) -R050 Mean(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R051 Mean(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos <= 30.5`, Pos) -R052 Mean(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMean(`DNA where Pos > 30.5`, Pos) -R053 Mean(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R054 Mean(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R055 Mean(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMean(`TablePartition(DNA) per Pos(4)`, Pos) -R056 Median(DNA.Pos) 0 1 1 30.5 30.5 30.5 0 0 0.693147 10.7442 296.926 TableMedian(DNA, Pos) -R057 Median(DNA.Pos) where Char not in {C, G} 0 1 42 17 45 30.31023102 4.073920227 0 0.693147 10.7442 296.926 TableMedian(`DNA where Char not in {C, G}`, Pos) -R058 Median(DNA.Pos) where Pos <= 15.5 0 1 1 8 8 8 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R059 Median(DNA.Pos) where Pos <= 30.5 0 1 1 15.5 15.5 15.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos <= 30.5`, Pos) -R060 Median(DNA.Pos) where Pos > 30.5 0 1 1 45.5 45.5 45.5 0 0 0.693147 10.7442 296.926 TableMedian(`DNA where Pos > 30.5`, Pos) -R061 Median(DNA.Pos) where Pos > 45.5 0 1 1 53 53 53 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R062 Median(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 23 23 23 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R063 Median(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 38 38 38 0 0 0.693147 10.7442 296.926 TablePartitionMedian(`TablePartition(DNA) per Pos(4)`, Pos) -R064 Min(DNA.Pos) 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(DNA, Pos) -R065 Min(DNA.Pos) where Char = C 0 1 21 1 36 4.151815182 4.732102465 0 0.693147 10.7442 296.926 TableMin(`DNA where Char = C`, Pos) -R066 Min(DNA.Pos) where Char = G 0 1 26 1 33 4.715231788 5.556903091 1 0.693147 10.7442 296.926 TableMin(`DNA where Char = G`, Pos) -R067 Min(DNA.Pos) where Char not in {C, G} 0 1 8 1 8 2.254125413 1.488606169 0 0.693147 10.7442 296.926 TableMin(`DNA where Char not in {C, G}`, Pos) -R068 Min(DNA.Pos) where Pos <= 15.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R069 Min(DNA.Pos) where Pos <= 30.5 0 1 1 1 1 1 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos <= 30.5`, Pos) -R070 Min(DNA.Pos) where Pos > 30.5 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TableMin(`DNA where Pos > 30.5`, Pos) -R071 Min(DNA.Pos) where Pos > 45.5 0 1 1 46 46 46 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R072 Min(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 16 16 16 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R073 Min(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 31 31 31 0 0 0.693147 10.7442 296.926 TablePartitionMin(`TablePartition(DNA) per Pos(4)`, Pos) -R082 StdDev(DNA.Pos) 0 1 1 17.31810228 17.31810228 17.31810228 0 0 0.693147 10.7442 296.926 TableStdDev(DNA, Pos) -R083 StdDev(DNA.Pos) where Char = C 0 1 300 5.465943945 21.52579848 16.51728209 2.350365912 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = C`, Pos) -R084 StdDev(DNA.Pos) where Char = G 0 1 301 8.314794193 21.35025371 16.21388635 2.414371221 1 0.693147 10.7442 296.926 TableStdDev(`DNA where Char = G`, Pos) -R085 StdDev(DNA.Pos) where Char not in {C, G} 0 1 301 11.07066199 19.76587969 17.01671662 1.194228217 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Char not in {C, G}`, Pos) -R086 StdDev(DNA.Pos) where Pos <= 15.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R087 StdDev(DNA.Pos) where Pos <= 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos <= 30.5`, Pos) -R088 StdDev(DNA.Pos) where Pos > 30.5 0 1 1 8.655441448 8.655441448 8.655441448 0 0 0.693147 10.7442 296.926 TableStdDev(`DNA where Pos > 30.5`, Pos) -R089 StdDev(DNA.Pos) where Pos > 45.5 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R090 StdDev(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R091 StdDev(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 4.320493799 4.320493799 4.320493799 0 0 0.693147 10.7442 296.926 TablePartitionStdDev(`TablePartition(DNA) per Pos(4)`, Pos) -R092 Sum(DNA.Pos) 0 1 1 1830 1830 1830 0 0 0.693147 10.7442 296.926 TableSum(DNA, Pos) -R093 Sum(DNA.Pos) where Char = C 0 1 233 54 1063 488.0660066 178.1871738 0 0.693147 10.7442 296.926 TableSum(`DNA where Char = C`, Pos) -R094 Sum(DNA.Pos) where Char = G 0 1 250 111 938 503.2649007 169.8386146 1 0.693147 10.7442 296.926 TableSum(`DNA where Char = G`, Pos) -R095 Sum(DNA.Pos) where Char not in {C, G} 0 1 255 309 1474 840.330033 216.9412705 0 0.693147 10.7442 296.926 TableSum(`DNA where Char not in {C, G}`, Pos) -R096 Sum(DNA.Pos) where Pos <= 15.5 0 1 1 120 120 120 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R097 Sum(DNA.Pos) where Pos <= 30.5 0 1 1 465 465 465 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos <= 30.5`, Pos) -R098 Sum(DNA.Pos) where Pos > 30.5 0 1 1 1365 1365 1365 0 0 0.693147 10.7442 296.926 TableSum(`DNA where Pos > 30.5`, Pos) -R099 Sum(DNA.Pos) where Pos > 45.5 0 1 1 795 795 795 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R100 Sum(DNA.Pos) where Pos in ]15.5, 30.5] 0 1 1 345 345 345 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) -R101 Sum(DNA.Pos) where Pos in ]30.5, 45.5] 0 1 1 570 570 570 0 0 0.693147 10.7442 296.926 TablePartitionSum(`TablePartition(DNA) per Pos(4)`, Pos) - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R001 -Variable Categorical Mode(DNA.Char) where Pos <= 30.5 - -Variable stats -Group Size Value list -{C, T} 2 C T -{G, A} 2 G A * - -% target values -Group EI IE N Interest Frequency Coverage -{G, A} 0.324786 0 0.675214 0.752073 117 0.386139 -{C, T} 0.155914 0.370968 0.473118 0.247927 186 0.613861 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 122 0.40264 -T 64 0.211221 -G 61 0.20132 -A 56 0.184818 - ----------------------------------------------- -Rank R002 -Variable Categorical Mode(DNA.Char) where Pos in ]15.5, 30.5] - -Variable stats -Group Size Value list -{A, G} 2 A G * -{C} 1 C -{T} 1 T - -% target values -Group EI IE N Interest Frequency Coverage -{A, G} 0.296875 0.0234375 0.679688 0.593857 128 0.422442 -{T} 0.0491803 0.409836 0.540984 0.251263 61 0.20132 -{C} 0.22807 0.359649 0.412281 0.15488 114 0.376238 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 114 0.376238 -A 69 0.227723 -T 61 0.20132 -G 59 0.194719 - ----------------------------------------------- -Rank R003 -Variable Categorical Mode(DNA.Char) - -Variable stats -Group Size Value list -{C} 1 C -{T, A} 2 T A * -{G} 1 G - -% target values -Group EI IE N Interest Frequency Coverage -{G} 0.441558 0.038961 0.519481 0.518478 77 0.254125 -{C} 0.142857 0.411765 0.445378 0.33703 119 0.392739 -{T, A} 0.149533 0.158879 0.691589 0.144491 107 0.353135 -Total 0.221122 0.227723 0.551155 1 303 1 - -Values -Value Frequency Coverage -C 119 0.392739 -G 77 0.254125 -T 57 0.188119 -A 50 0.165017 - ----------------------------------------------- -Rank R004 -Variable Numerical Mean(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;31.698] 0.225166 0.0794702 0.695364 0.57478 151 0.49835 -]31.698;+inf[ 0.217105 0.375 0.407895 0.42522 152 0.50165 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R005 -Variable Numerical Median(DNA.Pos) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.152778 0.0416667 0.805556 0.478566 72 0.237624 -]27.75;37.25] 0.273292 0.192547 0.534161 0.0530889 161 0.531353 -]37.25;+inf[ 0.171429 0.5 0.328571 0.468345 70 0.231023 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R006 -Variable Numerical Count(DNA) where Char = G - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;19.5] 0.161017 0.288136 0.550847 0.187162 236 0.778878 -]19.5;+inf[ 0.432836 0.0149254 0.552239 0.812838 67 0.221122 -Total 0.221122 0.227723 0.551155 1 303 1 - ----------------------------------------------- -Rank R007 -Variable Numerical Median(DNA.Pos) where Char = C - -% target values -Interval EI IE N Interest Frequency Coverage -]-inf;27.75] 0.233333 0.353333 0.413333 0.454456 150 0.49505 -]27.75;+inf[ 0.20915 0.104575 0.686275 0.545544 153 0.50495 -Total 0.221122 0.227723 0.551155 1 303 1 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/err.txt index a30c4a176..cbbdffda8 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/err.txt +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/err.txt @@ -1,11 +1,11 @@ -Variable construction time: 0:00:00.11 (100 constructed variables) +Variable construction time: 0:00:00.08 (100 constructed variables) Train supervised model for classification of target variable Class Database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt: Read records: 3178 Selected records: 303 Table ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt Records: 18,180 Computation of 2 distinct decision trees (out of 5 planned) -Data preparation time: 0:00:00.19 +Data preparation time: 0:00:00.14 SNB train time: 0:00:00.01 Build Selective Naive Bayes SNB_SpliceJunction Train evaluation of Selective Naive Bayes on database ../../../MTdatasets/SpliceJunction/SpliceJunction.txt -Write report ./results\C100_AllReports.khj +Write report ./results/C100_AnalysisResults.khj diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/time.log index 048ccd91d..2d6da76a9 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/time.log +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/results.ref/time.log @@ -1 +1 @@ -Overal time: 0.6131529808044434 +Overal time: 0.4563112258911133 diff --git a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/test.prm b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/test.prm index e4cb75972..2fe33dab8 100644 --- a/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/test.prm +++ b/test/LearningTest/TestKhiops/Standard/SpliceJunctionWithTrees/test.prm @@ -14,31 +14,35 @@ // // -> Data preparation and scoring +AnalysisSpec.SystemParameters.APIMode true // API mode +AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB ClassManagement.OpenFile // Open... // -> Open ClassFileName ../../../MTdatasets/SpliceJunction/SpliceJunction.kdic // Dictionary file OK // Open -ClassManagement.ClassName SpliceJunction // Dictionary +TrainDatabase.ClassName SpliceJunction // Dictionary -TrainDatabase.DatabaseFiles.List.Key SpliceJunction // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file -TrainDatabase.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection -TrainDatabase.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunction.txt // Data table file +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key SpliceJunction`DNA // List item selection +TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../MTdatasets/SpliceJunction/SpliceJunctionDNA.txt // Data table file -TrainDatabase.SampleNumberPercentage 10 // Sample percentage +TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 10 // Sample percentage TrainDatabase.TestDatabaseSpecificationMode None // Test database AnalysisSpec.TargetAttributeName Class // Target variable -AnalysisSpec.PredictorsSpec.SelectiveNaiveBayesPredictor true // Predicteur Bayesien Naif Selectif -AnalysisResults.ResultFilesDirectory ./results // Result files directory +AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report -AnalysisResults.ResultFilesPrefix C100_ // Result files directory +AnalysisResults.ReportFileName ./results/C100_AnalysisResults.khj // Analysis report AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 100 // Max number of constructed variables +AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 5 +AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only +AnalysisResults.ExportAsXls true // Export as xls ComputeStats // Analyse database Exit // Close diff --git a/test/LearningTest/cmd/python/apply_command.py b/test/LearningTest/cmd/python/apply_command.py index 77d67fe99..15714be0e 100644 --- a/test/LearningTest/cmd/python/apply_command.py +++ b/test/LearningTest/cmd/python/apply_command.py @@ -363,7 +363,7 @@ def apply_command_performance(work_dir): dir_name = os.path.basename(work_dir) root_name = os.path.basename(os.path.dirname(work_dir)) results_dir = os.path.join(work_dir, "results") - if os.path.isdir((results_dir)): + if os.path.isdir(results_dir): test_pattern = "TestEvaluationReport.xls" for file_name in os.listdir(results_dir): if test_pattern in file_name: @@ -389,7 +389,7 @@ def apply_command_performance_ref(work_dir): dir_name = os.path.basename(work_dir) root_name = os.path.basename(os.path.dirname(work_dir)) results_dir = os.path.join(work_dir, "results.ref") - if os.path.isdir((results_dir)): + if os.path.isdir(results_dir): test_pattern = "TestEvaluationReport.xls" for file_name in os.listdir(results_dir): if test_pattern in file_name: diff --git a/test/LearningTest/cmd/python/apply_command_all.py b/test/LearningTest/cmd/python/apply_command_all.py index 6447899ba..9f938d758 100644 --- a/test/LearningTest/cmd/python/apply_command_all.py +++ b/test/LearningTest/cmd/python/apply_command_all.py @@ -4,7 +4,6 @@ import apply_command import test_khiops - if __name__ == "__main__": all_commands, standard_command_number = apply_command.register_all_commands() diff --git a/test/LearningTest/cmd/python/check_results.py b/test/LearningTest/cmd/python/check_results.py index e8cfdcc8d..10646dfe5 100644 --- a/test/LearningTest/cmd/python/check_results.py +++ b/test/LearningTest/cmd/python/check_results.py @@ -229,7 +229,8 @@ def filter_khiops_temp_dir(value): is_json_file = is_json_file or ".khvj" in os.path.basename(path_ref) is_json_file = is_json_file or ".khcj" in os.path.basename(path_ref) is_json_file = is_json_file or ".kdicj" in os.path.basename(path_ref) - # Cas particulier des fichier .bad qui sont en fait des fichier json (ex: LearningTest\TestKhiops\Advanced\AllResultsApiMode + # Cas particulier des fichier .bad qui sont en fait des fichiers json + # (ex: LearningTest\TestKhiops\Advanced\AllResultsApiMode if ".bad" in os.path.basename(path_ref): if ( os.path.isfile(path_ref.replace(".bad", ".khj")) diff --git a/test/LearningTest/cmd/python/learning_test_env.py b/test/LearningTest/cmd/python/learning_test_env.py index d1b832e12..70194cbc1 100644 --- a/test/LearningTest/cmd/python/learning_test_env.py +++ b/test/LearningTest/cmd/python/learning_test_env.py @@ -106,9 +106,8 @@ def load_learning_test_config(): # Fill missing keys with empty values, the as when the config file is missing if ok: if len(learning_test_config_keys) != len(config_dic): - missing_keys = "" for key in learning_test_config_keys: - if not key in config_dic: + if key not in config_dic: config_dic[key] = "" # Return if ok if ok: @@ -119,7 +118,7 @@ def load_learning_test_config(): print( "The config file " + learning_test_config_file_name - + " must be in directory LearningTest\cmd\python" + + " must be in directory LearningTest\\cmd\\python" ) print("It is optional, in which case all keys are set to empty") print( diff --git a/test/LearningTest/cmd/python/test_khiops.py b/test/LearningTest/cmd/python/test_khiops.py index f352c3ee7..3b86f31f6 100644 --- a/test/LearningTest/cmd/python/test_khiops.py +++ b/test/LearningTest/cmd/python/test_khiops.py @@ -184,7 +184,7 @@ def test(modl_path, samples_path, sample_test): time_file_name = os.path.join( os.getcwd(), os.path.join(test_dir, "results.ref", "time.log") ) - print(time_file_name) ## + print(time_file_name) if os.path.isfile(time_file_name): file_time = open(time_file_name, "r") lines = file_time.readlines() diff --git a/test/LearningTest/cmd/python/utils.py b/test/LearningTest/cmd/python/utils.py index bb8b5e35b..867400fe1 100644 --- a/test/LearningTest/cmd/python/utils.py +++ b/test/LearningTest/cmd/python/utils.py @@ -39,7 +39,7 @@ def copyFilesWithoutExtension(src, dest, tabExt): for fileName in os.listdir(src): if not os.path.isdir(os.path.join(src, fileName)): (root, extension) = os.path.splitext(fileName) - if not extension in tabExt: + if extension not in tabExt: copy(os.path.join(src, fileName), dest) From 27761a5f21d27f097548fa5a9dbe1d2c65ccad14 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Thu, 5 Oct 2023 18:15:01 +0200 Subject: [PATCH 16/17] Corrections for compiler errors and warnings on MAC and Ubuntu Principalement, ajout de override pour une bonne vingtaine de methodes Compilation de KWTest (bac a sable dans Learning) - CreateLargeFiles.cpp: fseeko|ftello pour MAC - nettoyage de quelques lignes de code pour ne plus avoir d'erreurs sur Ubuntu - notamment, ce qui concerne chrono Remplacement systematique de tous les sprintf par des snprintf - sinon, nombreux warning DEPRECATED sur MAC - supression de la methode SecureStrcpy de standard, desormais inutile - ancien usage remplace par snprint Quelques correction de warning de compilation MAC ou Ubuntu impliquant de vraies erreurs Confirmation de la gestion en unstable de quelques jeux de test sous LearningTest Toutes les compilations passent sous Windows, Unutun et MAC, avec un nombre minimal de warning residuels (sur Ubuntu, Release) Tous les jeux de test LearningTest sont valide sur toutes les plateformes --- src/Learning/DTForest/DTDecisionTree.h | 4 +- .../DTForest/DTDecisionTreeDatabaseObject.h | 2 +- src/Learning/DTForest/DTGrouperMODLInternal.h | 12 +- src/Learning/KWDRRuleLibrary/KWDRMath.cpp | 2 +- src/Learning/KWData/KWCYac.cpp | 2 +- src/Learning/KWData/KWCYac.yac | 2 +- src/Learning/KWData/KWContinuous.cpp | 14 +- src/Learning/KWData/KWDate.cpp | 26 +- src/Learning/KWData/KWTime.cpp | 23 +- src/Learning/KWData/KWTimestamp.cpp | 2 +- src/Learning/KWData/KWTimestampTZ.cpp | 4 +- src/Learning/KWData/Profiler.cpp | 2 +- src/Learning/KWDataPreparation/KWDataGrid.cpp | 8 +- src/Learning/KWDataPreparation/KWDataGrid.h | 6 +- .../KWDataPreparation/KWDataGridCosts.h | 2 +- .../KWDataPreparation/KWDataGridManager.cpp | 2 +- .../KWDataPreparation/KWDataGridOptimizer.cpp | 3 - .../KWDataGridPostOptimizer.h | 6 +- .../KWDataPreparation/KWDataGridStats.h | 1 + .../KWDiscretizerUnsupervised.h | 12 +- .../KWDataPreparation/KWGrouperMODLInternal.h | 12 +- src/Learning/KWModeling/KWPredictorBaseline.h | 8 +- src/Learning/KWTest/CreateLargeFiles.cpp | 23 +- src/Learning/KWTest/KWDataGridTest.h | 2 +- .../KWTest/KWHierarchicalMultinomialStudy.h | 18 +- src/Learning/KWTest/KWTextParser.cpp | 4 + src/Learning/KWTest/SparseStudy.cpp | 21 +- src/Learning/KWTest/SparseStudy.h | 9 +- src/Learning/KWTest/StreamTest.cpp | 5 +- src/Learning/KWTest/SystemDivers.cpp | 6 +- .../KWUserInterface/KWPredictorEvaluator.h | 10 +- src/Learning/KWUtils/KWVersion.cpp | 2 +- .../MHHistograms/MHDiscretizerMODLHistogram.h | 4 +- .../MHDiscretizerTruncationMODLHistogram.h | 2 +- .../MHFloatingPointFrequencyTableBuilder.h | 2 +- src/Learning/MHHistograms/MHHistogram.h | 2 +- .../MHHistograms/MHMODLHistogramCost.h | 8 +- .../genum/MHDiscretizerGenumHistogram.h | 4 +- src/Learning/genum/MHEnumHistogramCost.h | 10 +- src/Learning/genum/MHGenumHistogram.cpp | 5 - src/Learning/genum/MHGenumHistogram.h | 7 +- src/Learning/genum/MHGenumHistogramCosts.h | 10 +- src/Learning/genum/MHKMHistogramCost.h | 10 +- .../sample1/SampleOneLearningProblemView.h | 2 +- .../samples/sample2/CMLearningProblemView.h | 1 - .../samples/sample2/CMMajorityClassifier.h | 10 +- src/Learning/samples/sample2/CMModelingSpec.h | 2 +- .../samples/sample2/CMModelingSpecView.h | 8 +- .../samples/sample3/MYLearningProblemView.h | 6 +- src/Norm/base/ALString.cpp | 2 +- src/Norm/base/Longint.cpp | 12 +- src/Norm/base/MemoryManager.cpp | 10 +- src/Norm/base/Portability.cpp | 48 +- src/Norm/base/Portability.h | 8 +- src/Norm/base/Standard.cpp | 16 +- src/Norm/base/SystemResource.cpp | 66 +- src/Norm/genere/Attribute.h | 24 +- src/Norm/samples/sample3/PRWorkerView.h | 2 +- src/Parallel/PLSamples/PEPi.cpp | 6 +- test/KNITest/KNITest.cpp | 8 +- .../results.ref/Coclustering.khc | 83 - .../results.ref/Coclustering.khcj | 221 -- .../Adult2varsTiny/results.ref/err.txt | 8 - .../Adult2varsTiny/results.ref/time.log | 1 - .../Standard/Adult2varsTiny/test.prm | 47 - .../Standard/IrisU2D/IrisExtended.kdic | 17 - .../AnalysisResults.Preparation2DReport.xls | 1407 --------- .../AnalysisResults.PreparationReport.xls | 205 -- .../IrisU2D/results.ref/AnalysisResults.khj | 2716 ----------------- .../Standard/IrisU2D/results.ref/err.txt | 6 - .../Standard/IrisU2D/results.ref/time.log | 1 - .../TestKhiops/Standard/IrisU2D/test.prm | 58 - test/Utils/TestServices.cpp | 1 - 73 files changed, 259 insertions(+), 5062 deletions(-) delete mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khc delete mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khcj delete mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/err.txt delete mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/time.log delete mode 100644 test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/test.prm delete mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/IrisExtended.kdic delete mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls delete mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls delete mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.khj delete mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/err.txt delete mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/time.log delete mode 100644 test/LearningTest/TestKhiops/Standard/IrisU2D/test.prm diff --git a/src/Learning/DTForest/DTDecisionTree.h b/src/Learning/DTForest/DTDecisionTree.h index ce9328f38..284f01196 100644 --- a/src/Learning/DTForest/DTDecisionTree.h +++ b/src/Learning/DTForest/DTDecisionTree.h @@ -66,7 +66,7 @@ class DTDecisionTree : public KWLearningReport WithReplacementAdaBoost, // tirage avec remise, choix pondere (algo AdaBoost) }; - boolean ComputeStats(); + boolean ComputeStats() override; /// Duplication de l'arbre DTDecisionTree* Clone(); @@ -167,7 +167,7 @@ class DTDecisionTree : public KWLearningReport void WriteNodes(ostream&, const ObjectDictionary*); // Ecriture d'un rapport (accessible uniquement si statistiques calculees) - void WriteReport(ostream& ost); + void WriteReport(ostream& ost) override; void WriteDTArrayLineReport(ostream& ost, const ALString& sTitle, ObjectArray* oaLearningReports, DTDecisionTree* tree); diff --git a/src/Learning/DTForest/DTDecisionTreeDatabaseObject.h b/src/Learning/DTForest/DTDecisionTreeDatabaseObject.h index 883d5dcdc..db1b159c7 100644 --- a/src/Learning/DTForest/DTDecisionTreeDatabaseObject.h +++ b/src/Learning/DTForest/DTDecisionTreeDatabaseObject.h @@ -42,7 +42,7 @@ class DTDecisionTreeDatabaseObject : public Object void SetTargetCorrectlyPredicted(boolean); static void WriteHeaderLine(ostream& ost); - void Write(ostream& ost) const; + void Write(ostream& ost) const override; // probas correspondant au noeud auquel appartient l'instance const ContinuousVector* GetTrainNodeProbs() const; diff --git a/src/Learning/DTForest/DTGrouperMODLInternal.h b/src/Learning/DTForest/DTGrouperMODLInternal.h index bd10b51c9..23ae2d837 100644 --- a/src/Learning/DTForest/DTGrouperMODLInternal.h +++ b/src/Learning/DTForest/DTGrouperMODLInternal.h @@ -55,7 +55,7 @@ class DTGrouperMODLTwoClasses : public KWDiscretizerMODL // public DTDiscretizer IntVector*& ivGroups) const; // Nom de l'algorithme - const ALString GetName() const; + const ALString GetName() const override; ///////////////////////////////////////////////////////////////// //// Implementation @@ -65,8 +65,8 @@ class DTGrouperMODLTwoClasses : public KWDiscretizerMODL // public DTDiscretizer // Initialisation des variables de travail // Reimplementation vide de ces methodes virtuelle pour les inhiber // (le parametrage est fait par l'appelant) - void InitializeWorkingData(const KWFrequencyTable* kwftSource) const; - void CleanWorkingData() const; + void InitializeWorkingData(const KWFrequencyTable* kwftSource) const override; + void CleanWorkingData() const override; // Discretisation d'une table granularisee avec recherche d'une table optimale avec groupe poubelle void DiscretizeFrequencyTable(KWFrequencyTable* kwftSource, KWFrequencyTable*& kwftTarget) const; @@ -85,8 +85,8 @@ class DTGrouperMODLTwoClasses : public KWDiscretizerMODL // public DTDiscretizer // Ajout et retrait d'un intervalle de la liste de travail triee par nombre de modalites de l'intervalle // Necessite que la liste soit initialisee - virtual void AddIntervalToWorkingFrequencyList(KWMODLLine* interval) const; - virtual void RemoveIntervalFromWorkingFrequencyList(KWMODLLine* interval) const; + void AddIntervalToWorkingFrequencyList(KWMODLLine* interval) const override; + void RemoveIntervalFromWorkingFrequencyList(KWMODLLine* interval) const override; // Rangement des intervalles dans une liste triee ordonnee par effectif decroissant du nombre de modalites // En entree : frequencyList est NULL @@ -96,7 +96,7 @@ class DTGrouperMODLTwoClasses : public KWDiscretizerMODL // public DTDiscretizer // Calcul du nombre de modalites d'une ligne de contingence decrite par ses index de debut et de fin par rapport // a une table kwftSource En entree, la table kwftSource est initialisee En sortie, le nombre de modalites - virtual int ComputeModalityNumber(const KWFrequencyTable* kwftSource, int nFirstIndex, int LastIndex) const; + int ComputeModalityNumber(const KWFrequencyTable* kwftSource, int nFirstIndex, int LastIndex) const override; // Calcul du nombre de modalites du groupe poubelle d'une partition // En entree, la table kwftTargetWithGarbage dont les KWFrequencyVector contiennent le nombre de modalites par diff --git a/src/Learning/KWDRRuleLibrary/KWDRMath.cpp b/src/Learning/KWDRRuleLibrary/KWDRMath.cpp index 823a54dda..648318547 100644 --- a/src/Learning/KWDRRuleLibrary/KWDRMath.cpp +++ b/src/Learning/KWDRRuleLibrary/KWDRMath.cpp @@ -98,7 +98,7 @@ Symbol KWDRFormatContinuous::ComputeSymbolResult(const KWObject* kwoObject) cons nLength--; if (cValue < 0) nLength++; - sprintf(sBuffer, "%0*.*f", nLength, nPrecision, (double)cValue); + snprintf(sBuffer, sizeof(sBuffer), "%0*.*f", nLength, nPrecision, (double)cValue); return (Symbol)sBuffer; } } diff --git a/src/Learning/KWData/KWCYac.cpp b/src/Learning/KWData/KWCYac.cpp index 1d3934f9a..d722fea70 100644 --- a/src/Learning/KWData/KWCYac.cpp +++ b/src/Learning/KWData/KWCYac.cpp @@ -3215,7 +3215,7 @@ void yyerrorWithLineCorrection(char const* fmt, int nDeltaLineNumber) nLineNumber = yylineno + nDeltaLineNumber; if (nLineNumber <= 0) nLineNumber = 1; - sprintf(sErrorLine, "Line %d", nLineNumber); + snprintf(sErrorLine, sizeof(sErrorLine), "Line %d", nLineNumber); sLabel = fmt; Global::AddError("Read dictionary file", sErrorLine, sLabel); } diff --git a/src/Learning/KWData/KWCYac.yac b/src/Learning/KWData/KWCYac.yac index d1124c72a..13bd7ddec 100644 --- a/src/Learning/KWData/KWCYac.yac +++ b/src/Learning/KWData/KWCYac.yac @@ -1405,7 +1405,7 @@ void yyerrorWithLineCorrection(char const *fmt, int nDeltaLineNumber) nLineNumber = yylineno+nDeltaLineNumber; if (nLineNumber <= 0) nLineNumber = 1; - sprintf(sErrorLine, "Line %d", nLineNumber); + snprintf(sErrorLine, sErrorLine, "Line %d", nLineNumber); sLabel = fmt; Global::AddError("Read dictionary file", sErrorLine, diff --git a/src/Learning/KWData/KWContinuous.cpp b/src/Learning/KWData/KWContinuous.cpp index a9ed654ea..0819a1919 100644 --- a/src/Learning/KWData/KWContinuous.cpp +++ b/src/Learning/KWData/KWContinuous.cpp @@ -1567,7 +1567,7 @@ const char* const KWContinuous::StandardContinuousToString(Continuous cValue) if (cValue == GetMissingValue()) sBuffer[0] = '\0'; else - sprintf(sBuffer, "%.10g", cValue); + snprintf(sBuffer, BUFFER_LENGTH, "%.10g", cValue); return sBuffer; } @@ -1860,7 +1860,7 @@ void KWContinuous::CompareStringToContinuous(Continuous cValue, boolean bShow) return; // Conversion - sprintf(sValue, "%.15g", cValue); + snprintf(sValue, sizeof(sValue), "%.15g", cValue); dRefValue = StandardStringToContinuous(sValue); dNewValue = StringToContinuous(sValue); @@ -1884,7 +1884,7 @@ const char* const KWContinuous::MaxPrecisionDoubleToString(double dValue) { char* sBuffer = StandardGetBuffer(); - sprintf(sBuffer, "%.15g", dValue); + snprintf(sBuffer, BUFFER_LENGTH, "%.15g", dValue); return sBuffer; } @@ -1939,7 +1939,7 @@ void KWContinuous::TestPerformanceStringToContinuous(int nMaxLowerBaseValue, dou { // Nombre positif avec exposant positif cValue = (cUpperBaseValue + nLowerBaseValue) * dPositivePower10[nExponent]; - sprintf(sValue, "%.15g", cValue); + snprintf(sValue, sizeof(sValue), "%.15g", cValue); if (bRefConversion) StandardStringToContinuous(sValue); if (bNewConversion) @@ -1949,7 +1949,7 @@ void KWContinuous::TestPerformanceStringToContinuous(int nMaxLowerBaseValue, dou // Nombre positif avec exposant negatif cValue = (cUpperBaseValue + nLowerBaseValue) * dNegativePower10[nExponent]; - sprintf(sValue, "%.15g", cValue); + snprintf(sValue, sizeof(sValue), "%.15g", cValue); if (bRefConversion) StandardStringToContinuous(sValue); if (bNewConversion) @@ -1959,7 +1959,7 @@ void KWContinuous::TestPerformanceStringToContinuous(int nMaxLowerBaseValue, dou // Nombre negatif avec exposant positif cValue = -(cUpperBaseValue + nLowerBaseValue) * dPositivePower10[nExponent]; - sprintf(sValue, "%.15g", cValue); + snprintf(sValue, sizeof(sValue), "%.15g", cValue); if (bRefConversion) StandardStringToContinuous(sValue); if (bNewConversion) @@ -1969,7 +1969,7 @@ void KWContinuous::TestPerformanceStringToContinuous(int nMaxLowerBaseValue, dou // Nombre negatif avec exposant negatif cValue = -(cUpperBaseValue + nLowerBaseValue) * dNegativePower10[nExponent]; - sprintf(sValue, "%.15g", cValue); + snprintf(sValue, sizeof(sValue), "%.15g", cValue); if (bRefConversion) StandardStringToContinuous(sValue); if (bNewConversion) diff --git a/src/Learning/KWData/KWDate.cpp b/src/Learning/KWData/KWDate.cpp index 640212430..dc148e299 100644 --- a/src/Learning/KWData/KWDate.cpp +++ b/src/Learning/KWData/KWDate.cpp @@ -227,7 +227,7 @@ const char* const Date::ToString() const if (not Check()) sDate[0] = '\0'; else - sprintf(sDate, "%04d-%02d-%02d", GetYear(), GetMonth(), GetDay()); + snprintf(sDate, BUFFER_LENGTH, "%04d-%02d-%02d", GetYear(), GetMonth(), GetDay()); return sDate; } @@ -320,9 +320,11 @@ const char* const Date::TimeZoneToString(boolean bExtended) const else cSign = '-'; if (bExtended) - sprintf(sTimeZone, "%c%02d:%02d", cSign, GetTimeZoneHour(), GetTimeZoneMinute()); + snprintf(sTimeZone, BUFFER_LENGTH, "%c%02d:%02d", cSign, GetTimeZoneHour(), + GetTimeZoneMinute()); else - sprintf(sTimeZone, "%c%02d%02d", cSign, GetTimeZoneHour(), GetTimeZoneMinute()); + snprintf(sTimeZone, BUFFER_LENGTH, "%c%02d%02d", cSign, GetTimeZoneHour(), + GetTimeZoneMinute()); } } return sTimeZone; @@ -618,11 +620,11 @@ const char* const KWDateFormat::DateToString(Date dtValue) const nOffset = 0; assert(nOffset == nYearOffset or nOffset == nMonthOffset or nOffset == nDayOffset); if (nYearOffset == 0) - nOffset += sprintf(sBuffer, "%04d", dtValue.GetYear()); + nOffset += snprintf(sBuffer, BUFFER_LENGTH, "%04d", dtValue.GetYear()); else if (nMonthOffset == 0) - nOffset += sprintf(sBuffer, "%02d", dtValue.GetMonth()); + nOffset += snprintf(sBuffer, BUFFER_LENGTH, "%02d", dtValue.GetMonth()); else - nOffset += sprintf(sBuffer, "%02d", dtValue.GetDay()); + nOffset += snprintf(sBuffer, BUFFER_LENGTH, "%02d", dtValue.GetDay()); // Ecriture du premier separateur optionnel if (nSeparatorOffset1 == nOffset) @@ -634,11 +636,11 @@ const char* const KWDateFormat::DateToString(Date dtValue) const // Ecriture du deuxieme champ assert(nOffset == nYearOffset or nOffset == nMonthOffset or nOffset == nDayOffset); if (nYearOffset == nOffset) - nOffset += sprintf(sBuffer + nOffset, "%04d", dtValue.GetYear()); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%04d", dtValue.GetYear()); else if (nMonthOffset == nOffset) - nOffset += sprintf(sBuffer + nOffset, "%02d", dtValue.GetMonth()); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%02d", dtValue.GetMonth()); else - nOffset += sprintf(sBuffer + nOffset, "%02d", dtValue.GetDay()); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%02d", dtValue.GetDay()); // Ecriture du deuxiemme separateur optionnel if (nSeparatorOffset2 == nOffset) @@ -650,11 +652,11 @@ const char* const KWDateFormat::DateToString(Date dtValue) const // Ecriture du troisieme champ assert(nOffset == nYearOffset or nOffset == nMonthOffset or nOffset == nDayOffset); if (nYearOffset == nOffset) - nOffset += sprintf(sBuffer + nOffset, "%04d", dtValue.GetYear()); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%04d", dtValue.GetYear()); else if (nMonthOffset == nOffset) - nOffset += sprintf(sBuffer + nOffset, "%02d", dtValue.GetMonth()); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%02d", dtValue.GetMonth()); else - nOffset += sprintf(sBuffer + nOffset, "%02d", dtValue.GetDay()); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%02d", dtValue.GetDay()); } return sBuffer; } diff --git a/src/Learning/KWData/KWTime.cpp b/src/Learning/KWData/KWTime.cpp index e6490bbce..44f44c3c8 100644 --- a/src/Learning/KWData/KWTime.cpp +++ b/src/Learning/KWData/KWTime.cpp @@ -62,12 +62,13 @@ const char* const Time::ToString() const // Cas ou il n'y a pas de fraction de secondes if (nSecondFrac == 0) - sprintf(sTime, "%02d:%02d:%02d", GetHour(), GetMinute(), (int)timeValue.timeFields.nSecond); + snprintf(sTime, BUFFER_LENGTH, "%02d:%02d:%02d", GetHour(), GetMinute(), + (int)timeValue.timeFields.nSecond); // Cas avec fraction de secondes else { - nLength = sprintf(sTime, "%02d:%02d:%02d.%04d", GetHour(), GetMinute(), - (int)timeValue.timeFields.nSecond, nSecondFrac); + nLength = snprintf(sTime, BUFFER_LENGTH, "%02d:%02d:%02d.%04d", GetHour(), GetMinute(), + (int)timeValue.timeFields.nSecond, nSecondFrac); // Supression des zero en fin pour ne garder que la partie utile des decimales de secondes for (i = 0; i < 4; i++) @@ -660,9 +661,9 @@ const char* const KWTimeFormat::TimeToString(Time tmValue) const nOffset = 0; assert(nOffset == nHourOffset); if (bMandatoryFirstDigit) - nOffset += sprintf(sBuffer, "%02d", tmValue.GetHour()); + nOffset += snprintf(sBuffer, BUFFER_LENGTH, "%02d", tmValue.GetHour()); else - nOffset += sprintf(sBuffer, "%d", tmValue.GetHour()); + nOffset += snprintf(sBuffer, BUFFER_LENGTH, "%d", tmValue.GetHour()); // Ecriture du premier separateur optionnel if (nSeparatorOffset1 != -1) @@ -673,9 +674,9 @@ const char* const KWTimeFormat::TimeToString(Time tmValue) const // Ecriture du champ minute if (bMandatoryFirstDigit) - nOffset += sprintf(sBuffer + nOffset, "%02d", tmValue.GetMinute()); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%02d", tmValue.GetMinute()); else - nOffset += sprintf(sBuffer + nOffset, "%d", tmValue.GetMinute()); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%d", tmValue.GetMinute()); // Ecriture du deuxieme separateur optionnel if (nSeparatorOffset2 != -1) @@ -693,9 +694,11 @@ const char* const KWTimeFormat::TimeToString(Time tmValue) const if (nSecondOffset != -1) { if (bMandatoryFirstDigit) - nOffset += sprintf(sBuffer + nOffset, "%02d", (int)floor(tmValue.GetSecond())); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%02d", + (int)floor(tmValue.GetSecond())); else - nOffset += sprintf(sBuffer + nOffset, "%d", (int)floor(tmValue.GetSecond())); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, "%d", + (int)floor(tmValue.GetSecond())); } // Ecriture de la partie decimale des secondes @@ -709,7 +712,7 @@ const char* const KWTimeFormat::TimeToString(Time tmValue) const // Ecriture des fractions de secondes if (nSecondFrac > 0) { - nOffset += sprintf(sBuffer + nOffset, ".%04d", nSecondFrac); + nOffset += snprintf(sBuffer + nOffset, BUFFER_LENGTH - nOffset, ".%04d", nSecondFrac); // Supression des zero en fin pour ne garder que la partie utile des decimales de // secondes diff --git a/src/Learning/KWData/KWTimestamp.cpp b/src/Learning/KWData/KWTimestamp.cpp index 27df33bd8..c126f943f 100644 --- a/src/Learning/KWData/KWTimestamp.cpp +++ b/src/Learning/KWData/KWTimestamp.cpp @@ -120,7 +120,7 @@ const char* const Timestamp::ToString() const if (not Check()) sTimestamp[0] = '\0'; else - sprintf(sTimestamp, "%s %s", GetDate().ToString(), GetTime().ToString()); + snprintf(sTimestamp, BUFFER_LENGTH, "%s %s", GetDate().ToString(), GetTime().ToString()); return sTimestamp; } diff --git a/src/Learning/KWData/KWTimestampTZ.cpp b/src/Learning/KWData/KWTimestampTZ.cpp index 7d2f82022..449f7b68b 100644 --- a/src/Learning/KWData/KWTimestampTZ.cpp +++ b/src/Learning/KWData/KWTimestampTZ.cpp @@ -51,8 +51,8 @@ const char* const TimestampTZ::ToString() const if (not Check()) sTimestampTZ[0] = '\0'; else - sprintf(sTimestampTZ, "%s %s%s", GetInternalDate().ToString(), GetInternalTime().ToString(), - GetInternalDate().TimeZoneToString(true)); + snprintf(sTimestampTZ, BUFFER_LENGTH, "%s %s%s", GetInternalDate().ToString(), + GetInternalTime().ToString(), GetInternalDate().TimeZoneToString(true)); return sTimestampTZ; } diff --git a/src/Learning/KWData/Profiler.cpp b/src/Learning/KWData/Profiler.cpp index 36663f0a4..72143865a 100644 --- a/src/Learning/KWData/Profiler.cpp +++ b/src/Learning/KWData/Profiler.cpp @@ -129,7 +129,7 @@ void Profiler::BeginMethod(const ALString& sMethodName) // potentiellement dans les meme sections sJsonKey = sMethodName; sJsonKey += "["; - sJsonKey += IntToString(methodTimer->GetStartNumber()); + sJsonKey += LongintToString(methodTimer->GetStartNumber()); sJsonKey += "]"; fJsonTraceFile->BeginKeyObject(sJsonKey); } diff --git a/src/Learning/KWDataPreparation/KWDataGrid.cpp b/src/Learning/KWDataPreparation/KWDataGrid.cpp index f2eef055f..71facb3fd 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.cpp +++ b/src/Learning/KWDataPreparation/KWDataGrid.cpp @@ -3105,16 +3105,16 @@ boolean KWDGAttribute::Check() const usedVarPart = innerAttribute->GetHeadPart(); while (usedVarPart != NULL) { - // Recherche de la VarPart parmi les VarPart des partie de la variable de type VarPart + // Recherche de la VarPart parmi les VarPart des parties de la variable de type VarPart searchedPart = cast(KWDGPart*, nkdCheckParts.Lookup((NUMERIC)usedVarPart)); // Erreur si VarPart non trouvee if (searchedPart == NULL) { - part->AddError(sTmp + "Inner variable VarPart " + - usedVarPart->GetObjectLabel() + - " not found amond parts of variables"); + searchedPart->AddError(sTmp + "Inner variable VarPart " + + usedVarPart->GetObjectLabel() + + " not found amond parts of variables"); bOk = false; break; } diff --git a/src/Learning/KWDataPreparation/KWDataGrid.h b/src/Learning/KWDataPreparation/KWDataGrid.h index ff3776621..cdccad4e3 100644 --- a/src/Learning/KWDataPreparation/KWDataGrid.h +++ b/src/Learning/KWDataPreparation/KWDataGrid.h @@ -942,7 +942,7 @@ class KWDGValueSet : public KWDGPartValues ~KWDGValueSet(); // Type de valeur gere - virtual int GetValueType() const = 0; + int GetValueType() const override = 0; // Creation virtual KWDGValueSet* Create() const = 0; @@ -1252,7 +1252,7 @@ class KWDGInterval : public KWDGPartValues ~KWDGInterval(); // Type de valeur gere par la partie - int GetValueType() const; + int GetValueType() const override; // Borne inf de l'intervalle // (doit etre egal a KWDGInterval::GetMinLowerBound pour le premier intervalle) @@ -1348,7 +1348,7 @@ class KWDGVarPartValue : public KWDGValue int GetValueType() const override; // Partie de variable - KWDGPart* GetVarPart() const; + KWDGPart* GetVarPart() const override; // Valeur en tant que cle numerique NUMERIC GetNumericKeyValue() const override; diff --git a/src/Learning/KWDataPreparation/KWDataGridCosts.h b/src/Learning/KWDataPreparation/KWDataGridCosts.h index 88df4bea9..abce44c24 100644 --- a/src/Learning/KWDataPreparation/KWDataGridCosts.h +++ b/src/Learning/KWDataPreparation/KWDataGridCosts.h @@ -379,7 +379,7 @@ class KWVarPartDataGridClusteringCosts : public KWDataGridCosts ~KWVarPartDataGridClusteringCosts(); // Duplication - KWDataGridCosts* Clone() const; + KWDataGridCosts* Clone() const override; ///////////////////////////////////////////////////////////////////////// // Calcul des couts locaux par entite de la grille des donnees diff --git a/src/Learning/KWDataPreparation/KWDataGridManager.cpp b/src/Learning/KWDataPreparation/KWDataGridManager.cpp index e0a89dc94..d479a231e 100644 --- a/src/Learning/KWDataPreparation/KWDataGridManager.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridManager.cpp @@ -1900,7 +1900,7 @@ void KWDataGridManager::BuildUnivariateDataGridFromGranularizedPartition(KWDataG KWDGAttribute* targetAttribute; KWDGAttribute* sourceAttribute; - require(0 < nAttributeIndex < sourceDataGrid->GetAttributeNumber()); + require(0 < nAttributeIndex and nAttributeIndex < sourceDataGrid->GetAttributeNumber()); // Initialisation de la grille cible a une variable InitialiseDataGrid(sourceDataGrid, univariateTargetDataGrid, 1); diff --git a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp index d9d8c9861..bdc456b7e 100644 --- a/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp +++ b/src/Learning/KWDataPreparation/KWDataGridOptimizer.cpp @@ -941,7 +941,6 @@ KWDataGridOptimizer::OptimizeWithBestUnivariatePartitionForCurrentGranularity(co double dBestCost; double dCost; int nAttribute; - int nTarget; boolean bEvaluated; boolean bImproved; boolean bDisplayResults = false; @@ -1687,7 +1686,6 @@ double KWDataGridVNSOptimizer::PROTO_VNSDataGridPostOptimizeVarPart( neighbourDataGrid->Write(cout); cout << "Grille apres fusion\n"; mergedDataGrid->Write(cout); - debug(false); } // Post-optimisation de l'attribut VarPart uniquement dans le cas d'une optimisation approfondie @@ -2087,7 +2085,6 @@ double KWDataGridVNSOptimizer::VNSOptimizeVarPartDataGrid(const KWDataGrid* init neighbourDataGrid.Write(cout); cout << "Grille apres fusion\n"; mergedDataGrid.Write(cout); - debug(false); } // Post-optimisation de l'attribut VarPart uniquement dans le cas d'une optimisation approfondie diff --git a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h index 6e8855e7a..bf1c55980 100644 --- a/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h +++ b/src/Learning/KWDataPreparation/KWDataGridPostOptimizer.h @@ -440,10 +440,10 @@ class KWDGAttributeCostParameter : public KWDGAttribute // Redefinition pour acces direct au nombre de modalites du groupe poubelle sans passer // par la garbagePart non accessible en mode "emule" void SetGarbageModalityNumber(int nValue); - int GetGarbageModalityNumber() const; + int GetGarbageModalityNumber() const override; protected: - KWDGPart* NewPart() const; + KWDGPart* NewPart() const override; friend class KWDataGridUnivariateCosts; boolean GetEmulated() const override; int nGarbageModalityNumber; @@ -455,7 +455,7 @@ class KWDGPartCostParameter : public KWDGPart { protected: friend class KWDataGridUnivariateCosts; - void SetPartType(int nValue); + void SetPartType(int nValue) override; boolean GetEmulated() const override; }; diff --git a/src/Learning/KWDataPreparation/KWDataGridStats.h b/src/Learning/KWDataPreparation/KWDataGridStats.h index 5efa7495e..d4cf0f608 100644 --- a/src/Learning/KWDataPreparation/KWDataGridStats.h +++ b/src/Learning/KWDataPreparation/KWDataGridStats.h @@ -661,6 +661,7 @@ class KWDGSAttributeGrouping : public KWDGSAttributePartition // nGranularizedValueNumber + nCatchAllValueNumber = nInitialValueNumber void SetCatchAllValueNumber(int nValue); int GetCatchAllValueNumber() const; + /////////////////////////////////////// // Methodes de test de la classe diff --git a/src/Learning/KWDataPreparation/KWDiscretizerUnsupervised.h b/src/Learning/KWDataPreparation/KWDiscretizerUnsupervised.h index b2812a7e3..c314118b1 100644 --- a/src/Learning/KWDataPreparation/KWDiscretizerUnsupervised.h +++ b/src/Learning/KWDataPreparation/KWDiscretizerUnsupervised.h @@ -101,10 +101,10 @@ class KWDiscretizerEqualFrequency : public KWDiscretizerUsingSourceValues ~KWDiscretizerEqualFrequency(); // Nom de l'algorithme - const ALString GetName() const; + const ALString GetName() const override; // Constructeur generique - KWDiscretizer* Create() const; + KWDiscretizer* Create() const override; // Discretisation // Chaque intervalle etant defini par ses bornes, il ne peut y avoir @@ -162,10 +162,10 @@ class KWDiscretizerMODLEqualWidth : public KWDiscretizerMODLEqualBins ~KWDiscretizerMODLEqualWidth(); // Nom de l'algorithme - const ALString GetName() const; + const ALString GetName() const override; // Constructeur generique - KWDiscretizer* Create() const; + KWDiscretizer* Create() const override; // Discretisation par minimisation d'un critere de cout // Chaque intervalle etant defini par ses bornes, il ne peut y avoir @@ -189,10 +189,10 @@ class KWDiscretizerMODLEqualFrequency : public KWDiscretizerMODLEqualBins ~KWDiscretizerMODLEqualFrequency(); // Nom de l'algorithme - const ALString GetName() const; + const ALString GetName() const override; // Constructeur generique - KWDiscretizer* Create() const; + KWDiscretizer* Create() const override; // Discretisation par minimisation d'un critere de cout // Chaque intervalle etant defini par ses bornes, il peut y avoir diff --git a/src/Learning/KWDataPreparation/KWGrouperMODLInternal.h b/src/Learning/KWDataPreparation/KWGrouperMODLInternal.h index 03f7279f6..76b52e39b 100644 --- a/src/Learning/KWDataPreparation/KWGrouperMODLInternal.h +++ b/src/Learning/KWDataPreparation/KWGrouperMODLInternal.h @@ -58,7 +58,7 @@ class KWGrouperMODLTwoClasses : public KWDiscretizerMODL IntVector*& ivGroups) const; // Nom de l'algorithme - const ALString GetName() const; + const ALString GetName() const override; ///////////////////////////////////////////////////////////////// //// Implementation @@ -68,8 +68,8 @@ class KWGrouperMODLTwoClasses : public KWDiscretizerMODL // Initialisation des variables de travail // Reimplementation vide de ces methodes virtuelle pour les inhiber // (le parametrage est fait par l'appelant) - void InitializeWorkingData(const KWFrequencyTable* kwftSource) const; - void CleanWorkingData() const; + void InitializeWorkingData(const KWFrequencyTable* kwftSource) const override; + void CleanWorkingData() const override; // Discretisation d'une table granularisee avec recherche d'une table optimale avec groupe poubelle void DiscretizeFrequencyTable(KWFrequencyTable* kwftSource, KWFrequencyTable*& kwftTarget) const; @@ -88,8 +88,8 @@ class KWGrouperMODLTwoClasses : public KWDiscretizerMODL // Ajout et retrait d'un intervalle de la liste de travail triee par nombre de modalites de l'intervalle // Necessite que la liste soit initialisee - virtual void AddIntervalToWorkingFrequencyList(KWMODLLine* interval) const; - virtual void RemoveIntervalFromWorkingFrequencyList(KWMODLLine* interval) const; + void AddIntervalToWorkingFrequencyList(KWMODLLine* interval) const override; + void RemoveIntervalFromWorkingFrequencyList(KWMODLLine* interval) const override; // Rangement des intervalles dans une liste triee ordonnee par effectif decroissant du nombre de modalites // En entree : frequencyList est NULL @@ -99,7 +99,7 @@ class KWGrouperMODLTwoClasses : public KWDiscretizerMODL // Calcul du nombre de modalites d'une ligne de contingence decrite par ses index de debut et de fin par rapport // a une table kwftSource En entree, la table kwftSource est initialisee En sortie, le nombre de modalites - virtual int ComputeModalityNumber(const KWFrequencyTable* kwftSource, int nFirstIndex, int LastIndex) const; + int ComputeModalityNumber(const KWFrequencyTable* kwftSource, int nFirstIndex, int LastIndex) const override; // Calcul du nombre de modalites du groupe poubelle d'une partition // En entree, la table kwftTargetWithGarbage dont les KWFrequencyVector contiennent le nombre de modalites par diff --git a/src/Learning/KWModeling/KWPredictorBaseline.h b/src/Learning/KWModeling/KWPredictorBaseline.h index bdbd7918d..e4a9ee3b5 100644 --- a/src/Learning/KWModeling/KWPredictorBaseline.h +++ b/src/Learning/KWModeling/KWPredictorBaseline.h @@ -19,17 +19,17 @@ class KWPredictorBaseline : public KWPredictorNaiveBayes ~KWPredictorBaseline(); // Constructeur generique - KWPredictor* Create() const; + KWPredictor* Create() const override; // Nom du classifier - const ALString GetName() const; + const ALString GetName() const override; // Prefixe du predicteur - const ALString GetPrefix() const; + const ALString GetPrefix() const override; ///////////////////////////////////////////////////////// //// Implementation protected: // Redefinition de la methode d'apprentissage - boolean InternalTrain(); + boolean InternalTrain() override; }; diff --git a/src/Learning/KWTest/CreateLargeFiles.cpp b/src/Learning/KWTest/CreateLargeFiles.cpp index 79f09def6..9a5bb6e5a 100644 --- a/src/Learning/KWTest/CreateLargeFiles.cpp +++ b/src/Learning/KWTest/CreateLargeFiles.cpp @@ -9,8 +9,10 @@ boolean MovePositionInBinaryFile(FILE* fFile, longint lOffset) boolean bOk; // Pour les fichiers de plus de 4 Go, il existe une API speciale (stat64...) -#if defined __MSC__ +#ifdef _WIN32 _fseeki64(fFile, lOffset, SEEK_CUR); +#elif defined(__APPLE__) + fseeko(fFile, lOffset, SEEK_CUR); #else fseeko64(fFile, lOffset, SEEK_CUR); #endif @@ -23,10 +25,12 @@ longint TellPositionInBinaryFile(FILE* fFile) longint lCurrentPosition; // Pour les fichiers de plus de 4 Go, il existe une API speciale (stat64...) -#if defined __MSC__ +#ifdef _WIN32 lCurrentPosition = _ftelli64(fFile); +#elif defined(__APPLE__) + lCurrentPosition = ftello(fFile); #else - lCurrentPosition = fseeko64(fFile); + lCurrentPosition = ftello64(fFile); #endif return lCurrentPosition; } @@ -225,6 +229,7 @@ void CopyFileTwiceThenConcatenate(const ALString& sPathName, boolean bFast) longint lPosition; const int nBufferLength = MemSegmentByteSize; char sBuffer[nBufferLength]; + int nByteReadNumber; FILE* fFile; FILE* fCopy1; FILE* fCopy2; @@ -280,14 +285,14 @@ void CopyFileTwiceThenConcatenate(const ALString& sPathName, boolean bFast) { if (lPosition + nBufferLength < lFileSize) { - fread(sBuffer, sizeof(char), nBufferLength, fFile); + nByteReadNumber = (int)fread(sBuffer, sizeof(char), nBufferLength, fFile); fwrite(sBuffer, sizeof(char), nBufferLength, fCopy1); fwrite(sBuffer, sizeof(char), nBufferLength, fCopy2); lPosition += nBufferLength; } else { - fread(sBuffer, sizeof(char), int(lFileSize - lPosition), fFile); + nByteReadNumber = (int)fread(sBuffer, sizeof(char), int(lFileSize - lPosition), fFile); fwrite(sBuffer, sizeof(char), int(lFileSize - lPosition), fCopy1); fwrite(sBuffer, sizeof(char), int(lFileSize - lPosition), fCopy2); lPosition = lFileSize; @@ -333,13 +338,13 @@ void CopyFileTwiceThenConcatenate(const ALString& sPathName, boolean bFast) { if (lPosition + nBufferLength < lFileSize) { - fread(sBuffer, sizeof(char), nBufferLength, fCopy1); + nByteReadNumber = (int)fread(sBuffer, sizeof(char), nBufferLength, fCopy1); fwrite(sBuffer, sizeof(char), nBufferLength, fConcat); lPosition += nBufferLength; } else { - fread(sBuffer, sizeof(char), int(lFileSize - lPosition), fCopy1); + nByteReadNumber = (int)fread(sBuffer, sizeof(char), int(lFileSize - lPosition), fCopy1); fwrite(sBuffer, sizeof(char), int(lFileSize - lPosition), fConcat); lPosition = lFileSize; } @@ -353,13 +358,13 @@ void CopyFileTwiceThenConcatenate(const ALString& sPathName, boolean bFast) { if (lPosition + nBufferLength < lFileSize) { - fread(sBuffer, sizeof(char), nBufferLength, fCopy2); + nByteReadNumber = (int)fread(sBuffer, sizeof(char), nBufferLength, fCopy2); fwrite(sBuffer, sizeof(char), nBufferLength, fConcat); lPosition += nBufferLength; } else { - fread(sBuffer, sizeof(char), int(lFileSize - lPosition), fCopy2); + nByteReadNumber = (int)fread(sBuffer, sizeof(char), int(lFileSize - lPosition), fCopy2); fwrite(sBuffer, sizeof(char), int(lFileSize - lPosition), fConcat); lPosition = lFileSize; } diff --git a/src/Learning/KWTest/KWDataGridTest.h b/src/Learning/KWTest/KWDataGridTest.h index 26bf47aed..5777b7031 100644 --- a/src/Learning/KWTest/KWDataGridTest.h +++ b/src/Learning/KWTest/KWDataGridTest.h @@ -286,7 +286,7 @@ class KWSGGaussianMixture : public KWSampleGenerator ~KWSGGaussianMixture(); // Redefinition des methodes virtuelles - const ALString GetName() const; + const ALString GetName() const override; void GenerateObjectValues(KWObject* kwoObject) override; // Parametres de la Gaussienne pour la classe '-' (par defaut: moyenne = (-1 -1) et ecart type = (1 1)) diff --git a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h index d7920dd0d..154eed74b 100644 --- a/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h +++ b/src/Learning/KWTest/KWHierarchicalMultinomialStudy.h @@ -144,7 +144,7 @@ class KWDataGridClusteringCostsBivariate : public KWDataGridCosts ~KWDataGridClusteringCostsBivariate(); // Duplication - KWDataGridCosts* Clone() const; + KWDataGridCosts* Clone() const override; ///////////////////////////////////////////////////////////////////////// // Calcul des couts locaux par entite de la grille des donnees @@ -154,24 +154,24 @@ class KWDataGridClusteringCostsBivariate : public KWDataGridCosts // La taille de la grille est donnee par son log, pour ne pas depasser les limites informatiques // Les attributs informatifs sont ceux ayant strictement plus de une partie double ComputeDataGridCost(const KWDataGrid* dataGrid, double dLnGridSize, - int nInformativeAttributeNumber) const; + int nInformativeAttributeNumber) const override; // Calcul du cout local d'un attribut, pour un nombre de parties donnees // Le nombre de parties est le nombre total de parties. // En cas de presence d'une poubelle il s'agit du nombre de parties informatives de l'attribut + 1 - double ComputeAttributeCost(const KWDGAttribute* attribute, int nPartitionSize) const; + double ComputeAttributeCost(const KWDGAttribute* attribute, int nPartitionSize) const override; // Calcul du cout local d'une partie - double ComputePartCost(const KWDGPart* part) const; + double ComputePartCost(const KWDGPart* part) const override; // Calcul du cout local de l'union de deux parties - double ComputePartUnionCost(const KWDGPart* part1, const KWDGPart* part2) const; + double ComputePartUnionCost(const KWDGPart* part1, const KWDGPart* part2) const override; // Calcul du cout local d'une cellule - double ComputeCellCost(const KWDGCell* cell) const; + double ComputeCellCost(const KWDGCell* cell) const override; // Calcul du cout local d'une valeur d'un attribut symbolique - double ComputeValueCost(const KWDGValue* value) const; + double ComputeValueCost(const KWDGValue* value) const override; // Libelle de la classe const ALString GetClassLabel() const override; @@ -188,11 +188,11 @@ class KWDataGridClusteringCostsBivariateH : public KWDataGridClusteringCostsBiva ~KWDataGridClusteringCostsBivariateH(); // Duplication - KWDataGridCosts* Clone() const; + KWDataGridCosts* Clone() const override; // Calcul du cout local d'un DataGrid, selon le prior hierarchique double ComputeDataGridCost(const KWDataGrid* dataGrid, double dLnGridSize, - int nInformativeAttributeNumber) const; + int nInformativeAttributeNumber) const override; // Libelle de la classe const ALString GetClassLabel() const override; diff --git a/src/Learning/KWTest/KWTextParser.cpp b/src/Learning/KWTest/KWTextParser.cpp index 9989095cc..29577a971 100644 --- a/src/Learning/KWTest/KWTextParser.cpp +++ b/src/Learning/KWTest/KWTextParser.cpp @@ -391,6 +391,9 @@ void KWTextParser::BuildLineWordDictionary(char* sLine, ObjectDictionary* odWord // Transformation des caracteres accentues if (bFilterAccentuation) { + // Compilation uniquement sous windows pour cette methode prototype, + // pour eviter les warnings de type illegal character encoding +#ifdef _WIN32 if (cLineChar == 'é') cLineChar = 'e'; else if (cLineChar == 'è') @@ -421,6 +424,7 @@ void KWTextParser::BuildLineWordDictionary(char* sLine, ObjectDictionary* odWord cLineChar = 'u'; else if (cLineChar == 'ç') cLineChar = 'c'; +#endif // _WIN32 } // Traitement du caractere en fonction de l'etat courant diff --git a/src/Learning/KWTest/SparseStudy.cpp b/src/Learning/KWTest/SparseStudy.cpp index ca460a80f..f143e2166 100644 --- a/src/Learning/KWTest/SparseStudy.cpp +++ b/src/Learning/KWTest/SparseStudy.cpp @@ -252,9 +252,6 @@ void BasicVectorAccessTest() } } -#include -using namespace std::chrono; - void DoubleVectorAccessTest() { const int nSize = 1000; @@ -264,16 +261,14 @@ void DoubleVectorAccessTest() int i; double dTotal; char* dvVectorAdress; - std::chrono::time_point start; - std::chrono::time_point stop; - double dDuration; + Timer timer; // Initialisation for (i = 0; i < nSize; i++) dvVector[i] = i; // Lecture - start = high_resolution_clock::now(); + timer.Start(); dTotal = 0; for (nStep = 0; nStep < nStepNumber; nStep++) { @@ -282,14 +277,13 @@ void DoubleVectorAccessTest() dTotal += dvVector[i]; } } - stop = high_resolution_clock::now(); - dDuration = duration_cast(stop - start).count() / 1000000000.0; + timer.Stop(); cout << "Total: " << dTotal << endl; - cout << "Duration: " << dDuration << endl; + cout << "Duration: " << timer.GetElapsedTime() << endl; // Lecture dvVectorAdress = (char*)(void*)&dvVector; - start = high_resolution_clock::now(); + timer.Start(); dTotal = 0; for (nStep = 0; nStep < nStepNumber; nStep++) { @@ -298,10 +292,9 @@ void DoubleVectorAccessTest() dTotal += *((double*)&dvVectorAdress[i * 8]); } } - stop = high_resolution_clock::now(); - dDuration = duration_cast(stop - start).count() / 1000000000.0; + timer.Stop(); cout << "Total: " << dTotal << endl; - cout << "Duration: " << dDuration << endl; + cout << "Duration: " << timer.GetElapsedTime() << endl; } ///////////////////////////////////////////////////////////////////// diff --git a/src/Learning/KWTest/SparseStudy.h b/src/Learning/KWTest/SparseStudy.h index ae24283be..f33291b70 100644 --- a/src/Learning/KWTest/SparseStudy.h +++ b/src/Learning/KWTest/SparseStudy.h @@ -107,7 +107,7 @@ void DoubleVectorAccessTest(); // Classe KWValueIndexPair // Class technique, utilisee uniquement pour l'implementation des blocs sparse // Pas de "packing" des attribut pour optimiser la place memoire -#ifdef _WIN32 +// #ifdef _WIN32 #pragma pack(push) #pragma pack(1) class KWValueIndexPairTest : public SystemObject @@ -117,6 +117,11 @@ class KWValueIndexPairTest : public SystemObject friend class KWContinuousValueBlockTest; }; #pragma pack(pop) +// Sous linux on doit utiliser __attribute__((packed)); mais pragma pack fonctionne sur clang et gcc, on privilegie donc +// la version portable. ATTENTION il faut declarer nIndex avant value sinon le packing n'est pas fait sur linux (on ne +// sait pas pourquoi) + +/* #else // Linux class KWValueIndexPairTest : public SystemObject { @@ -124,7 +129,7 @@ class KWValueIndexPairTest : public SystemObject int nIndex; friend class KWContinuousValueBlockTest; } __attribute__((packed)); -#endif +#endif*/ ///////////////////////////////////////////////////////////////////// // Classe KWContinuousValueBlock: gestion d'un bloc de valeurs sparse diff --git a/src/Learning/KWTest/StreamTest.cpp b/src/Learning/KWTest/StreamTest.cpp index 17ec1caf5..fcfa11217 100644 --- a/src/Learning/KWTest/StreamTest.cpp +++ b/src/Learning/KWTest/StreamTest.cpp @@ -3,15 +3,16 @@ // at https://spdx.org/licenses/BSD-3-Clause-Clear.html or see the "LICENSE" file for more details. #include "StreamTest.h" + #ifdef _WIN32 #include +#endif // _WIN32 // Visual C++: supression des Warning sur les assignation au sein d'un expression conditionnelle (cf. // TestStreamLineCount7 et TestStreamLineCount8) #ifdef __MSC__ #pragma warning(disable : 4706) // disable C4706 warning -#endif -#endif __MSC__ +#endif // __MSC__ void StreamTest(int argc, char** argv) { diff --git a/src/Learning/KWTest/SystemDivers.cpp b/src/Learning/KWTest/SystemDivers.cpp index 533090b43..4fdf1dcc6 100644 --- a/src/Learning/KWTest/SystemDivers.cpp +++ b/src/Learning/KWTest/SystemDivers.cpp @@ -26,7 +26,9 @@ int OpenPdfFile(char* sPdfFilePathName) return ((int)nRet>32); */ char sCommand[1000]; - sprintf(sCommand, "start %s", sPdfFilePathName); - system(sCommand); + int nResult; + + snprintf(sCommand, sizeof(sCommand), "start %s", sPdfFilePathName); + nResult = system(sCommand); return 0; } diff --git a/src/Learning/KWUserInterface/KWPredictorEvaluator.h b/src/Learning/KWUserInterface/KWPredictorEvaluator.h index 56af2d78f..4b71d2c01 100644 --- a/src/Learning/KWUserInterface/KWPredictorEvaluator.h +++ b/src/Learning/KWUserInterface/KWPredictorEvaluator.h @@ -189,16 +189,16 @@ class KWPredictorExternal : public KWPredictor ~KWPredictorExternal(); // Type de predicteur disponible: classification et regression - boolean IsTargetTypeManaged(int nType) const; + boolean IsTargetTypeManaged(int nType) const override; // Constructeur generique - KWPredictor* Create() const; + KWPredictor* Create() const override; // Nom du predicteur - const ALString GetName() const; + const ALString GetName() const override; // Prefixe du predicteur - const ALString GetPrefix() const; + const ALString GetPrefix() const override; // Parametrage du predicteur par un objet KWTrainedPredictor externe // Memoire: le KWTrainedPredictor appartient a l'appele @@ -214,7 +214,7 @@ class KWPredictorExternal : public KWPredictor //// Implementation protected: // Redefinition de la methode d'apprentissage - boolean InternalTrain(); + boolean InternalTrain() override; // Memorisation KWTrainedPredictor* externalTrainedPredictor; diff --git a/src/Learning/KWUtils/KWVersion.cpp b/src/Learning/KWUtils/KWVersion.cpp index 10972662b..d94393165 100644 --- a/src/Learning/KWUtils/KWVersion.cpp +++ b/src/Learning/KWUtils/KWVersion.cpp @@ -504,7 +504,7 @@ boolean GetLearningCoclusteringIVExpertMode() bIsInitialized = true; } // DDD - return true; + bLearningCoclusteringIVExpertMode = true; return bLearningCoclusteringIVExpertMode; } // CH IV End diff --git a/src/Learning/MHHistograms/MHDiscretizerMODLHistogram.h b/src/Learning/MHHistograms/MHDiscretizerMODLHistogram.h index f81bfadd4..1e7ee8724 100644 --- a/src/Learning/MHHistograms/MHDiscretizerMODLHistogram.h +++ b/src/Learning/MHHistograms/MHDiscretizerMODLHistogram.h @@ -23,10 +23,10 @@ class MHDiscretizerMODLHistogram : public KWDiscretizerMODL ~MHDiscretizerMODLHistogram(); // Nom de l'algorithme - const ALString GetName() const; + const ALString GetName() const override; // Constructeur generique - KWDiscretizer* Create() const; + KWDiscretizer* Create() const override; // Utilisation des valeurs sources pour la discretisation. Ici: true boolean IsUsingSourceValues() const override; diff --git a/src/Learning/MHHistograms/MHDiscretizerTruncationMODLHistogram.h b/src/Learning/MHHistograms/MHDiscretizerTruncationMODLHistogram.h index 56e3b0e71..8829c79ca 100644 --- a/src/Learning/MHHistograms/MHDiscretizerTruncationMODLHistogram.h +++ b/src/Learning/MHHistograms/MHDiscretizerTruncationMODLHistogram.h @@ -19,7 +19,7 @@ class MHDiscretizerTruncationMODLHistogram : public MHDiscretizerMODLHistogram ~MHDiscretizerTruncationMODLHistogram(); // Constructeur generique - KWDiscretizer* Create() const; + KWDiscretizer* Create() const override; ///////////////////////////////////////////////////////////////// ///// Implementation diff --git a/src/Learning/MHHistograms/MHFloatingPointFrequencyTableBuilder.h b/src/Learning/MHHistograms/MHFloatingPointFrequencyTableBuilder.h index d461239de..f8d5ee339 100644 --- a/src/Learning/MHHistograms/MHFloatingPointFrequencyTableBuilder.h +++ b/src/Learning/MHHistograms/MHFloatingPointFrequencyTableBuilder.h @@ -270,7 +270,7 @@ class MHFloatingPointFrequencyTableBuilder : public Object // Services divers // Affichage - void Write(ostream& ost) const; + void Write(ostream& ost) const override; // Ecriture des bornes des bins principaux void WriteMainBins(ostream& ost) const; diff --git a/src/Learning/MHHistograms/MHHistogram.h b/src/Learning/MHHistograms/MHHistogram.h index 900468369..9b0c11726 100644 --- a/src/Learning/MHHistograms/MHHistogram.h +++ b/src/Learning/MHHistograms/MHHistogram.h @@ -187,7 +187,7 @@ class MHHistogram : public Object virtual void CopyFrom(const MHHistogram* sourceHistogram); // Duplication - virtual MHHistogram* Clone() const; + MHHistogram* Clone() const; // Ecriture sous forme d'un tableau de bins avec leurs caracteristiques virtual void WriteSummary(ostream& ost) const; diff --git a/src/Learning/MHHistograms/MHMODLHistogramCost.h b/src/Learning/MHHistograms/MHMODLHistogramCost.h index 6084ea99f..a055346bc 100644 --- a/src/Learning/MHHistograms/MHMODLHistogramCost.h +++ b/src/Learning/MHHistograms/MHMODLHistogramCost.h @@ -89,12 +89,12 @@ class MHMODLHistogramCosts : public KWUnivariatePartitionCosts double ComputePartitionGlobalCost(const KWFrequencyTable* partTable) const override; // Affichage du cout de la partition - void WritePartitionCost(int nPartNumber, int nGarbageModalityNumber, ostream& ost) const; + void WritePartitionCost(int nPartNumber, int nGarbageModalityNumber, ostream& ost) const override; // Cout de modele par entite - double ComputePartitionConstructionCost(int nPartNumber) const; - double ComputePartitionModelCost(int nPartNumber, int nGarbageModalityNumber) const; - double ComputePartModelCost(const KWFrequencyVector* part) const; + double ComputePartitionConstructionCost(int nPartNumber) const override; + double ComputePartitionModelCost(int nPartNumber, int nGarbageModalityNumber) const override; + double ComputePartModelCost(const KWFrequencyVector* part) const override; // Libelle de la classe const ALString GetClassLabel() const override; diff --git a/src/Learning/genum/MHDiscretizerGenumHistogram.h b/src/Learning/genum/MHDiscretizerGenumHistogram.h index ced897c69..aeb12b0b7 100644 --- a/src/Learning/genum/MHDiscretizerGenumHistogram.h +++ b/src/Learning/genum/MHDiscretizerGenumHistogram.h @@ -24,10 +24,10 @@ class MHDiscretizerGenumHistogram : public KWDiscretizerMODL ~MHDiscretizerGenumHistogram(); // Nom de l'algorithme - const ALString GetName() const; + const ALString GetName() const override; // Constructeur generique - KWDiscretizer* Create() const; + KWDiscretizer* Create() const override; // Utilisation des valeurs sources pour la discretisation. Ici: true boolean IsUsingSourceValues() const override; diff --git a/src/Learning/genum/MHEnumHistogramCost.h b/src/Learning/genum/MHEnumHistogramCost.h index afd754676..d68552bcb 100644 --- a/src/Learning/genum/MHEnumHistogramCost.h +++ b/src/Learning/genum/MHEnumHistogramCost.h @@ -33,15 +33,15 @@ class MHEnumHistogramCosts : public MHGenumHistogramCosts double ComputePartCost(const KWFrequencyVector* part) const override; // Calcul du cout global de la partition, definie par le tableau de ses parties - double ComputePartitionGlobalCost(const KWFrequencyTable* partTable) const; + double ComputePartitionGlobalCost(const KWFrequencyTable* partTable) const override; // Affichage du cout de la partition - void WritePartitionCost(int nPartNumber, int nGarbageModalityNumber, ostream& ost) const; + void WritePartitionCost(int nPartNumber, int nGarbageModalityNumber, ostream& ost) const override; // Cout de modele par entite - double ComputePartitionConstructionCost(int nPartNumber) const; - double ComputePartitionModelCost(int nPartNumber, int nGarbageModalityNumber) const; - double ComputePartModelCost(const KWFrequencyVector* part) const; + double ComputePartitionConstructionCost(int nPartNumber) const override; + double ComputePartitionModelCost(int nPartNumber, int nGarbageModalityNumber) const override; + double ComputePartModelCost(const KWFrequencyVector* part) const override; // Libelle de la classe const ALString GetClassLabel() const override; diff --git a/src/Learning/genum/MHGenumHistogram.cpp b/src/Learning/genum/MHGenumHistogram.cpp index a1f0b72af..f7301be56 100644 --- a/src/Learning/genum/MHGenumHistogram.cpp +++ b/src/Learning/genum/MHGenumHistogram.cpp @@ -166,11 +166,6 @@ void MHGenumHistogram::CopyFrom(const MHHistogram* sourceHistogram) MHHistogram::CopyFrom(sourceHistogram); } -MHHistogram* MHGenumHistogram::Clone() const -{ - return MHHistogram::Clone(); -} - void MHGenumHistogram::WriteSummary(ostream& ost) const { // Appel de la methode ancetre diff --git a/src/Learning/genum/MHGenumHistogram.h b/src/Learning/genum/MHGenumHistogram.h index 7d0b0db25..cb294c183 100644 --- a/src/Learning/genum/MHGenumHistogram.h +++ b/src/Learning/genum/MHGenumHistogram.h @@ -60,16 +60,13 @@ class MHGenumHistogram : public MHHistogram // Divers // Nettoyage - void Clean(); + void Clean() override; // Creation virtuelle d'un histogramme dans le bon type MHHistogram* Create() const override; // Copie - void CopyFrom(const MHHistogram* sourceHistogram); - - // Duplication - MHHistogram* Clone() const; + void CopyFrom(const MHHistogram* sourceHistogram) override; // Ecriture sous forme d'un tableau de bins avec leurs caracteristiques void WriteSummary(ostream& ost) const override; diff --git a/src/Learning/genum/MHGenumHistogramCosts.h b/src/Learning/genum/MHGenumHistogramCosts.h index 65cb28fe0..d05fa832a 100644 --- a/src/Learning/genum/MHGenumHistogramCosts.h +++ b/src/Learning/genum/MHGenumHistogramCosts.h @@ -41,15 +41,15 @@ class MHGenumHistogramCosts : public KWUnivariatePartitionCosts double ComputePartCost(const KWFrequencyVector* part) const override; // Calcul du cout global de la partition, definie par le tableau de ses parties - double ComputePartitionGlobalCost(const KWFrequencyTable* partTable) const; + double ComputePartitionGlobalCost(const KWFrequencyTable* partTable) const override; // Affichage du cout de la partition - void WritePartitionCost(int nPartNumber, int nGarbageModalityNumber, ostream& ost) const; + void WritePartitionCost(int nPartNumber, int nGarbageModalityNumber, ostream& ost) const override; // Cout de modele par entite - double ComputePartitionConstructionCost(int nPartNumber) const; - double ComputePartitionModelCost(int nPartNumber, int nGarbageModalityNumber) const; - double ComputePartModelCost(const KWFrequencyVector* part) const; + double ComputePartitionConstructionCost(int nPartNumber) const override; + double ComputePartitionModelCost(int nPartNumber, int nGarbageModalityNumber) const override; + double ComputePartModelCost(const KWFrequencyVector* part) const override; // Libelle de la classe const ALString GetClassLabel() const override; diff --git a/src/Learning/genum/MHKMHistogramCost.h b/src/Learning/genum/MHKMHistogramCost.h index a4b85b85d..4d6dcedab 100644 --- a/src/Learning/genum/MHKMHistogramCost.h +++ b/src/Learning/genum/MHKMHistogramCost.h @@ -33,15 +33,15 @@ class MHKMHistogramCosts : public MHGenumHistogramCosts double ComputePartCost(const KWFrequencyVector* part) const override; // Calcul du cout global de la partition, definie par le tableau de ses parties - double ComputePartitionGlobalCost(const KWFrequencyTable* partTable) const; + double ComputePartitionGlobalCost(const KWFrequencyTable* partTable) const override; // Affichage du cout de la partition - void WritePartitionCost(int nPartNumber, int nGarbageModalityNumber, ostream& ost) const; + void WritePartitionCost(int nPartNumber, int nGarbageModalityNumber, ostream& ost) const override; // Cout de modele par entite - double ComputePartitionConstructionCost(int nPartNumber) const; - double ComputePartitionModelCost(int nPartNumber, int nGarbageModalityNumber) const; - double ComputePartModelCost(const KWFrequencyVector* part) const; + double ComputePartitionConstructionCost(int nPartNumber) const override; + double ComputePartitionModelCost(int nPartNumber, int nGarbageModalityNumber) const override; + double ComputePartModelCost(const KWFrequencyVector* part) const override; // Libelle de la classe const ALString GetClassLabel() const override; diff --git a/src/Learning/samples/sample1/SampleOneLearningProblemView.h b/src/Learning/samples/sample1/SampleOneLearningProblemView.h index 21bbf3d34..346fb3ff2 100644 --- a/src/Learning/samples/sample1/SampleOneLearningProblemView.h +++ b/src/Learning/samples/sample1/SampleOneLearningProblemView.h @@ -32,6 +32,6 @@ class SampleOneLearningProblemView : public KWLearningProblemView void ExportStats(); // Acces au probleme d'apprentissage - void SetObject(Object* object); + void SetObject(Object* object) override; SampleOneLearningProblem* GetSampleOneLearningProblem(); }; diff --git a/src/Learning/samples/sample2/CMLearningProblemView.h b/src/Learning/samples/sample2/CMLearningProblemView.h index 83dbe1fe3..b6b7e0e0d 100644 --- a/src/Learning/samples/sample2/CMLearningProblemView.h +++ b/src/Learning/samples/sample2/CMLearningProblemView.h @@ -21,7 +21,6 @@ class CMLearningProblemView : public KWLearningProblemView // Rien pour l'instant // Acces au probleme d'apprentissage - // void SetObject(Object* object); CMLearningProblem* GetCMLearningProblem(); }; diff --git a/src/Learning/samples/sample2/CMMajorityClassifier.h b/src/Learning/samples/sample2/CMMajorityClassifier.h index 12d37a3b3..e209b0287 100644 --- a/src/Learning/samples/sample2/CMMajorityClassifier.h +++ b/src/Learning/samples/sample2/CMMajorityClassifier.h @@ -28,7 +28,7 @@ class CMMajorityClassifier : public KWPredictorNaiveBayes // KWType::Symbol: classification // KWType::Continuous: regression // KWType::None: non supervise - boolean IsTargetTypeManaged(int nType) const; + boolean IsTargetTypeManaged(int nType) const override; // Creation pour renvoyer une instance du meme type dynamique // Doit etre reimplementer dans les sous-classes @@ -37,20 +37,20 @@ class CMMajorityClassifier : public KWPredictorNaiveBayes // { // return new KWSpecificPredictor; // } - KWPredictor* Create() const; + KWPredictor* Create() const override; // Nom du predicteur // Doit etre reimplementer dans les sous-classes - const ALString GetName() const; + const ALString GetName() const override; // Prefixe du predicteur, utilisable pour le nommage de la classe en deploiement (par defaut: "P_") - const ALString GetPrefix() const; + const ALString GetPrefix() const override; /////////////////////////////////////////////////////// //// Implementation protected: // Redefinition de la methode d'apprentissage - boolean InternalTrain(); + boolean InternalTrain() override; // Construction d'un predicteur bayesien naif a partir d'un tableau d'attributs prepares // La methode filtre les attributs inutiles (partition source singleton ou poids nul) diff --git a/src/Learning/samples/sample2/CMModelingSpec.h b/src/Learning/samples/sample2/CMModelingSpec.h index 069f564db..3fee27454 100644 --- a/src/Learning/samples/sample2/CMModelingSpec.h +++ b/src/Learning/samples/sample2/CMModelingSpec.h @@ -30,7 +30,7 @@ class CMModelingSpec : public KWModelingSpec // Divers /// Ecriture - void Write(ostream& ost) const; + void Write(ostream& ost) const override; /// Libelles utilisateur const ALString GetClassLabel() const; diff --git a/src/Learning/samples/sample2/CMModelingSpecView.h b/src/Learning/samples/sample2/CMModelingSpecView.h index 41d050d3a..53264d21a 100644 --- a/src/Learning/samples/sample2/CMModelingSpecView.h +++ b/src/Learning/samples/sample2/CMModelingSpecView.h @@ -23,16 +23,16 @@ class CMModelingSpecView : public KWModelingSpecView // Redefinition des methodes a reimplementer obligatoirement /// Mise a jour de l'objet par les valeurs de l'interface - void EventUpdate(Object* object); + void EventUpdate(Object* object) override; /// Mise a jour des valeurs de l'interface par l'objet - void EventRefresh(Object* object); + void EventRefresh(Object* object) override; /// Libelles utilisateur - const ALString GetClassLabel() const; + const ALString GetClassLabel() const override; /// Parametrage de l'objet edite - void SetObject(Object* object); + void SetObject(Object* object) override; //////////////////////////////////////////////////////// //// Implementation diff --git a/src/Learning/samples/sample3/MYLearningProblemView.h b/src/Learning/samples/sample3/MYLearningProblemView.h index 5b973969d..f06b6bda3 100644 --- a/src/Learning/samples/sample3/MYLearningProblemView.h +++ b/src/Learning/samples/sample3/MYLearningProblemView.h @@ -28,7 +28,7 @@ class MYLearningProblemView : public KWLearningProblemView void RegressorBenchmark(); // Acces au probleme d'apprentissage - void SetObject(Object* object); + void SetObject(Object* object) override; MYLearningProblem* GetMyLearningProblem(); }; @@ -48,10 +48,10 @@ class MYLearningProblemExtendedActionView : public UIObjectView // Redefinition des methodes a reimplementer obligatoirement // Mise a jour de l'objet par les valeurs de l'interface - void EventUpdate(Object* object); + void EventUpdate(Object* object) override; // Mise a jour des valeurs de l'interface par l'objet - void EventRefresh(Object* object); + void EventRefresh(Object* object) override; // Actions de menu void ClassifierBenchmark(); diff --git a/src/Norm/base/ALString.cpp b/src/Norm/base/ALString.cpp index d8fb1074b..c31aaf859 100644 --- a/src/Norm/base/ALString.cpp +++ b/src/Norm/base/ALString.cpp @@ -5,7 +5,7 @@ #include "ALString.h" // Pour une chaine vide, les donnees vont pointer ici -// Cela permet d'eviter de nombreuxtest de pointeur à NULL lors de +// Cela permet d'eviter de nombreux tests de pointeur à NULL lors de // l'appel des fonction de la librairie C standard char ALSCHARNIL = '\0'; diff --git a/src/Norm/base/Longint.cpp b/src/Norm/base/Longint.cpp index 17580db36..b7b172d9d 100644 --- a/src/Norm/base/Longint.cpp +++ b/src/Norm/base/Longint.cpp @@ -25,10 +25,10 @@ const char* const LongintToHumanReadableString(longint lValue) // Affichage exact si des bytes plus petit que 1 KB if (i == 0) - sprintf(sBuffer, "%lld %s", lValue, units[i]); + snprintf(sBuffer, BUFFER_LENGTH, "%lld %s", lValue, units[i]); // Sinon, affichage avec 1 digit de precision else - sprintf(sBuffer, "%.1f %s", lValue / pow(1024.0, i), units[i]); + snprintf(sBuffer, BUFFER_LENGTH, "%.1f %s", lValue / pow(1024.0, i), units[i]); return sBuffer; } @@ -46,7 +46,7 @@ const char* const LongintToReadableString(longint lValue) // Cas des nombre inferieur a 10000 if (lAbsoluteValue < 10000) - sprintf(sBuffer, "%lld", lValue); + snprintf(sBuffer, BUFFER_LENGTH, "%lld", lValue); // Sinon, on insere des separateurs de millier else { @@ -64,14 +64,14 @@ const char* const LongintToReadableString(longint lValue) // Ecriture nPos = 0; if (lValue < 0) - nPos += sprintf(&sBuffer[nPos], "-"); - nPos += sprintf(&sBuffer[nPos], "%d", (int)lTmp); + nPos += snprintf(&sBuffer[nPos], BUFFER_LENGTH - nPos, "-"); + nPos += snprintf(&sBuffer[nPos], BUFFER_LENGTH - nPos, "%d", (int)lTmp); while (lScale > 1) { lScale /= 1000; lTmp = lTmp2 / lScale; lTmp2 = lTmp2 % lScale; - nPos += sprintf(&sBuffer[nPos], ",%03d", (int)lTmp); + nPos += snprintf(&sBuffer[nPos], BUFFER_LENGTH - nPos, ",%03d", (int)lTmp); } } return sBuffer; diff --git a/src/Norm/base/MemoryManager.cpp b/src/Norm/base/MemoryManager.cpp index af4298dce..557d74def 100644 --- a/src/Norm/base/MemoryManager.cpp +++ b/src/Norm/base/MemoryManager.cpp @@ -54,7 +54,8 @@ inline void MemHeapUpdateAlloc(size_t nSize) if (lMemMaxHeapSize > 0 && MemHeapMemory > lMemMaxHeapSize) { char sMessage[100]; - sprintf(sMessage, "Memory user overflow: heap size beyond user limit (%lld)\n", lMemMaxHeapSize); + snprintf(sMessage, sizeof(sMessage), "Memory user overflow: heap size beyond user limit (%lld)\n", + lMemMaxHeapSize); MemFatalError(sMessage); GlobalExit(); } @@ -1528,7 +1529,8 @@ inline void* MemAlloc(size_t nSize) if (MemGetAllocErrorHandler() != NULL) { char sMessage[100]; - sprintf(sMessage, "Memory overflow (malloc allocation error (%lld))\n", (longint)nSize); + snprintf(sMessage, sizeof(sMessage), + "Memory overflow (malloc allocation error (%lld))\n", (longint)nSize); MemFatalError(sMessage); } return NULL; @@ -2028,8 +2030,8 @@ void* DebugMemAlloc(size_t nSize) if (MemGetAllocErrorHandler() != NULL) { char sMessage[100]; - sprintf(sMessage, "Memory overflow (unable to allocate memory (%lld))\n", - (longint)(nSize + MemControlOverhead * sizeof(void*))); + snprintf(sMessage, sizeof(sMessage), "Memory overflow (unable to allocate memory (%lld))\n", + (longint)(nSize + MemControlOverhead * sizeof(void*))); MemFatalError(sMessage); } return NULL; diff --git a/src/Norm/base/Portability.cpp b/src/Norm/base/Portability.cpp index adec8dfa9..53b83fc2b 100644 --- a/src/Norm/base/Portability.cpp +++ b/src/Norm/base/Portability.cpp @@ -143,7 +143,7 @@ void* p_FindFirstFile(const char* dirname, void* pFileData) char sPath[2048]; // On veut tous les fichiers et repertoires - sprintf(sPath, "%s\\*.*", dirname); + snprintf(sPath, sizeof(sPath), "%s\\*.*", dirname); // Recherche la premiere entite du repertoire fdFile = (WIN32_FIND_DATAA*)pFileData; @@ -234,24 +234,6 @@ void StandardGetInputString(char* sBuffer, FILE* fInput) assert(strlen(sBuffer) < BUFFER_LENGTH); } -int SecureStrcpy(char* sDest, const char* sSource, int nMaxLength) -{ - int bOk = 0; - int nLenDest; - int nLengthSource; - - assert(nMaxLength > 0); - - nLengthSource = (int)strlen(sSource); - nLenDest = (int)strlen(sDest); - if (nLenDest + nLengthSource < nMaxLength) - { - strcpy(&sDest[nLenDest], sSource); - bOk = 1; - } - return bOk; -} - // Implementation Windows du lancement d'excutable #ifdef _WIN32 @@ -425,33 +407,26 @@ int OpenApplication(const char* sApplicationExeName, const char* sApplicationLab // Erreur si pas d'association if (!ok) { - sprintf(sErrorMessage, "%s tool not found", sApplicationLabel); + snprintf(sErrorMessage, SYSTEM_MESSAGE_LENGTH, "%s tool not found", sApplicationLabel); } // Lancement de l'application else { // Preparation des arguments: commande de longueur inconnue, et message d'erreur - sCommandLine = new char[strlen(sFileAssociation) + strlen(sFileToOpen) + 10]; - sprintf(sCommandLine, "\"%s\" \"%s\"", sFileAssociation, sFileToOpen); + sCommandLine = StandardGetBuffer(); + snprintf(sCommandLine, BUFFER_LENGTH, "\"%s\" \"%s\"", sFileAssociation, sFileToOpen); + assert(SYSTEM_MESSAGE_LENGTH <= BUFFER_LENGTH); sSystemErrorMessage = StandardGetBuffer(); // Lancement de la commande ok = StartProcess(sCommandLine, sSystemErrorMessage); - delete[] sCommandLine; // Message d'erreur if (!ok) { - // On passe par SecureStrcpy plutot que par sprintf en raison de l'incertitude sur les tailles - // de parametres assert(sErrorMessage[0] == '\0'); - SecureStrcpy(sErrorMessage, "unable to launch ", SYSTEM_MESSAGE_LENGTH); - SecureStrcpy(sErrorMessage, sApplicationLabel, SYSTEM_MESSAGE_LENGTH); - SecureStrcpy(sErrorMessage, " tool using \"", SYSTEM_MESSAGE_LENGTH); - SecureStrcpy(sErrorMessage, sFileAssociation, SYSTEM_MESSAGE_LENGTH); - SecureStrcpy(sErrorMessage, "\" (", SYSTEM_MESSAGE_LENGTH); - SecureStrcpy(sErrorMessage, sSystemErrorMessage, SYSTEM_MESSAGE_LENGTH); - SecureStrcpy(sErrorMessage, ")", SYSTEM_MESSAGE_LENGTH); + snprintf(sErrorMessage, SYSTEM_MESSAGE_LENGTH, "unable to launch %s tool using \"%s\" (%s)", + sApplicationLabel, sFileAssociation, sSystemErrorMessage); } } return ok; @@ -487,10 +462,10 @@ int OpenApplication(const char* sApplicationExeName, const char* sApplicationLab if (sApplicationExeName[0] != '\0') { sCommandLine = StandardGetBuffer(); - sprintf(sCommandLine, "command -v %s > /dev/null", sApplicationExeName); + snprintf(sCommandLine, BUFFER_LENGTH, "command -v %s > /dev/null", sApplicationExeName); ok = system(sCommandLine) == 0; if (!ok) - sprintf(sErrorMessage, "%s tool not installed", sApplicationLabel); + snprintf(sErrorMessage, SYSTEM_MESSAGE_LENGTH, "%s tool not installed", sApplicationLabel); } // Lancement si ok @@ -518,7 +493,8 @@ int OpenApplication(const char* sApplicationExeName, const char* sApplicationLab { ok = 0; assert(strlen(strerror(status)) < SYSTEM_MESSAGE_LENGTH / 2 - 30); - sprintf(sErrorMessage, "unable to launch %s tool (%s)", sApplicationLabel, strerror(status)); + snprintf(sErrorMessage, SYSTEM_MESSAGE_LENGTH, "unable to launch %s tool (%s)", + sApplicationLabel, strerror(status)); } } return ok; @@ -541,7 +517,7 @@ int OpenApplication(const char* sApplicationExeName, const char* sApplicationLab // Initialisations ok = 0; - sprintf(sErrorMessage, "unable to launch %s application on this OS", sApplicationLabel); + snprintf(sErrorMessage, SYSTEM_MESSAGE_LENGTH, "unable to launch %s application on this OS", sApplicationLabel); return ok; } #endif diff --git a/src/Norm/base/Portability.h b/src/Norm/base/Portability.h index 5566c1cad..feb837c60 100644 --- a/src/Norm/base/Portability.h +++ b/src/Norm/base/Portability.h @@ -191,7 +191,7 @@ using namespace std; // Encapsulation de quelques fonctions standards C, en les prefixant par p_ // Les methodes sont identiques dans le cas gcc, et reimplementees pour MS Visual C++ 2008 // (qui a "declasse" ces fonctions en DEPRECATED) -// Il manque les methodes open, sscanf, sprintf +// Il manque les methodes open, sscanf // Declaration de la structure stat #ifdef _WIN32 @@ -256,10 +256,6 @@ char* StandardGetBuffer(); // Renvoie une chaine lue depuis un fichier d'entree, et rangee dans le buffer en parametre(fonction "privee") void StandardGetInputString(char* sBuffer, FILE* fInput); -// Copie sSource vers sDest seulement si il y a la place -// Renvoie 0 si erreur, 1 sinon -int SecureStrcpy(char* sDest, const char* sSource, int nMaxLength); - // Taille maximale des messages systeme // Il faut donc prevoir un buffer de cette taille, plus 1 pour le caractere fin de chaine #define SYSTEM_MESSAGE_LENGTH 512 @@ -276,6 +272,7 @@ int SecureStrcpy(char* sDest, const char* sSource, int nMaxLength); // sApplicationLabel : libelle associe a l'application, pour fabriquer les message d'erreurs // sFileToOpen : nom du fichier a ouvrir, ayant une extension // sErrorMessage : sortie qui vaut vide si le chargement a reussi, le message d'erreur sinon +// (longueur max a prevoir: SYSTEM_MESSAGE_LENGTH) // valeur de retour : true si lancement effectue, false sinon int OpenApplication(const char* sApplicationExeName, const char* sApplicationLabel, const char* sFileToOpen, char* sErrorMessage); @@ -286,6 +283,7 @@ int OpenApplication(const char* sApplicationExeName, const char* sApplicationLab // Chargement d'une librairie partagee. // sLibraryPath : nom et chemin complet de la librairie (avec son extension). // sErrorMessage : sortie qui vaut vide si le chargement a reussi, le message d'erreur sinon +// (longueur max a prevoir: SYSTEM_MESSAGE_LENGTH) // valeur de retour : handle sur la librairie ouverte, ou NULL si echec du chargement void* LoadSharedLibrary(const char* sLibraryPath, char* sErrorMessage); diff --git a/src/Norm/base/Standard.cpp b/src/Norm/base/Standard.cpp index 58f172ec4..0dc33a8e8 100644 --- a/src/Norm/base/Standard.cpp +++ b/src/Norm/base/Standard.cpp @@ -31,9 +31,9 @@ const char* const SecondsToString(double dValue) // Formatage du resultat if (nHundredth == 0) - sprintf(sBuffer, "%d:%2.2d:%2.2d", nHours, nMinutes, nSeconds); + snprintf(sBuffer, BUFFER_LENGTH, "%d:%2.2d:%2.2d", nHours, nMinutes, nSeconds); else - sprintf(sBuffer, "%d:%2.2d:%2.2d.%2.2d", nHours, nMinutes, nSeconds, nHundredth); + snprintf(sBuffer, BUFFER_LENGTH, "%d:%2.2d:%2.2d.%2.2d", nHours, nMinutes, nSeconds, nHundredth); return sBuffer; } @@ -41,7 +41,7 @@ const char* const SecondsToString(double dValue) const char* const IntToString(int nValue) { char* sBuffer = StandardGetBuffer(); - sprintf(sBuffer, "%d", nValue); + snprintf(sBuffer, BUFFER_LENGTH, "%d", nValue); return sBuffer; } @@ -49,7 +49,7 @@ const char* const IntToString(int nValue) const char* const LongintToString(longint lValue) { char* sBuffer = StandardGetBuffer(); - sprintf(sBuffer, "%lld", lValue); + snprintf(sBuffer, BUFFER_LENGTH, "%lld", lValue); return sBuffer; } @@ -58,7 +58,7 @@ const char* const DoubleToString(double dValue) { char* sBuffer = StandardGetBuffer(); - sprintf(sBuffer, "%g", dValue); + snprintf(sBuffer, BUFFER_LENGTH, "%g", dValue); return sBuffer; } @@ -109,9 +109,9 @@ const char* const BooleanToString(boolean bValue) char* sBuffer = StandardGetBuffer(); if (bValue) - sprintf(sBuffer, "%s", "true"); + snprintf(sBuffer, BUFFER_LENGTH, "%s", "true"); else - sprintf(sBuffer, "%s", "false"); + snprintf(sBuffer, BUFFER_LENGTH, "%s", "false"); return sBuffer; } @@ -119,7 +119,7 @@ const char* const BooleanToString(boolean bValue) const char* const PointerToString(const void* pValue) { char* sBuffer = StandardGetBuffer(); - sprintf(sBuffer, "%p", pValue); + snprintf(sBuffer, BUFFER_LENGTH, "%p", pValue); return sBuffer; } diff --git a/src/Norm/base/SystemResource.cpp b/src/Norm/base/SystemResource.cpp index 2e4bc4e75..ee0057021 100644 --- a/src/Norm/base/SystemResource.cpp +++ b/src/Norm/base/SystemResource.cpp @@ -6,7 +6,7 @@ #include "Portability.h" #include "MemoryManager.h" -// Pour eviter les warning sur strcpy et sprintf +// Pour eviter les warning sur strcpy #ifdef _MSC_VER #define _CRT_SECURE_NO_WARNINGS #endif @@ -216,8 +216,8 @@ const char* CurrentPreciseTimestamp() SYSTEMTIME st; GetLocalTime(&st); - sprintf_s(sBuffer, 30, "%04d-%02d-%02d %02d:%02d:%02d.%03d%c", st.wYear, st.wMonth, st.wDay, st.wHour, - st.wMinute, st.wSecond, st.wMilliseconds, '\0'); + snprintf(sBuffer, BUFFER_LENGTH, "%04d-%02d-%02d %02d:%02d:%02d.%03d%c", st.wYear, st.wMonth, st.wDay, st.wHour, + st.wMinute, st.wSecond, st.wMilliseconds, '\0'); return sBuffer; } @@ -227,8 +227,8 @@ const char* CurrentTimestamp() SYSTEMTIME st; GetLocalTime(&st); - sprintf_s(sBuffer, 30, "%04d-%02d-%02d %02d:%02d:%02d%c", st.wYear, st.wMonth, st.wDay, st.wHour, st.wMinute, - st.wSecond, '\0'); + snprintf(sBuffer, BUFFER_LENGTH, "%04d-%02d-%02d %02d:%02d:%02d%c", st.wYear, st.wMonth, st.wDay, st.wHour, + st.wMinute, st.wSecond, '\0'); return sBuffer; } @@ -386,13 +386,13 @@ const char* GetMACAddress() // Memorisation de la nouvelle adresse que si elle est plus grande que la precedente // (ou d'une priorite plus forte, pour favoriser les cates reseau persistantes) // Pour garantir que l'on ne depend pas de l'ordre dans la liste - sprintf(sNewMACAddress, "%02x-%02x-%02x-%02x-%02x-%02x", - (unsigned char)pAdapterAddresses->PhysicalAddress[0], - (unsigned char)pAdapterAddresses->PhysicalAddress[1], - (unsigned char)pAdapterAddresses->PhysicalAddress[2], - (unsigned char)pAdapterAddresses->PhysicalAddress[3], - (unsigned char)pAdapterAddresses->PhysicalAddress[4], - (unsigned char)pAdapterAddresses->PhysicalAddress[5]); + snprintf(sNewMACAddress, sizeof(sNewMACAddress), "%02x-%02x-%02x-%02x-%02x-%02x", + (unsigned char)pAdapterAddresses->PhysicalAddress[0], + (unsigned char)pAdapterAddresses->PhysicalAddress[1], + (unsigned char)pAdapterAddresses->PhysicalAddress[2], + (unsigned char)pAdapterAddresses->PhysicalAddress[3], + (unsigned char)pAdapterAddresses->PhysicalAddress[4], + (unsigned char)pAdapterAddresses->PhysicalAddress[5]); if (nPriority > nBestPriority || strcmp(sNewMACAddress, sMACAddress) > 0) strcpy(sMACAddress, sNewMACAddress); if (bDisplayDetails) @@ -535,8 +535,8 @@ static char* GetOsVersion() osInfo.dwOSVersionInfoSize = sizeof(osInfo); if (fxPtr(&osInfo) == 0) { - sprintf_s(sWindowsVersion, "windows %d.%d (%d)", osInfo.dwMajorVersion, - osInfo.dwMinorVersion, osInfo.dwBuildNumber); + snprintf(sWindowsVersion, sizeof(sWindowsVersion), "windows %d.%d (%d)", + osInfo.dwMajorVersion, osInfo.dwMinorVersion, osInfo.dwBuildNumber); } } } @@ -547,16 +547,8 @@ const char* GetSystemInfos() { char* sInfo = StandardGetBuffer(); - // Nom du processeur - sInfo[0] = '\0'; - SecureStrcpy(sInfo, "cpu=", BUFFER_LENGTH); - SecureStrcpy(sInfo, GetProcessorName(), BUFFER_LENGTH); - SecureStrcpy(sInfo, "\n", BUFFER_LENGTH); - - // OS - SecureStrcpy(sInfo, "os=", BUFFER_LENGTH); - SecureStrcpy(sInfo, GetOsVersion(), BUFFER_LENGTH); - SecureStrcpy(sInfo, "\n", BUFFER_LENGTH); + // Nom du processeur et de l'OS + snprintf(sInfo, BUFFER_LENGTH, "cpu=%s\nos=%s\n", GetProcessorName(), GetOsVersion()); return sInfo; } @@ -834,7 +826,7 @@ const char* CurrentPreciseTimestamp() milliseconds = tv.tv_usec / 1000; /* Print the formatted time, in seconds, followed by a decimal point and the milliseconds. */ - sprintf(sBuffer, "%s.%03ld", time_string, milliseconds); + snprintf(sBuffer, BUFFER_LENGTH, "%s.%03ld", time_string, milliseconds); return sBuffer; } @@ -1130,7 +1122,7 @@ int IsVirtual(const char* sInterfaceName) // L'heuristique ne fonctionne pas, on ne sait pas si c'est virtuel ou non return 0; - sprintf(sDeviceDirectory, "%s%s/device/", sRep, sInterfaceName); + snprintf(sDeviceDirectory, sizeof(sDeviceDirectory), "%s%s/device/", sRep, sInterfaceName); if (IsDirectory(sDeviceDirectory)) // C'est une interface physique return 0; @@ -1226,10 +1218,11 @@ const char* GetMACAddress() // Si l'interface est prioritaire if (nPriority >= nBestPriority) { - sprintf(sNewMACAddress, "%02x-%02x-%02x-%02x-%02x-%02x", - (unsigned char)s->sll_addr[0], (unsigned char)s->sll_addr[1], - (unsigned char)s->sll_addr[2], (unsigned char)s->sll_addr[3], - (unsigned char)s->sll_addr[4], (unsigned char)s->sll_addr[5]); + snprintf(sNewMACAddress, sizeof(sNewMACAddress), + "%02x-%02x-%02x-%02x-%02x-%02x", (unsigned char)s->sll_addr[0], + (unsigned char)s->sll_addr[1], (unsigned char)s->sll_addr[2], + (unsigned char)s->sll_addr[3], (unsigned char)s->sll_addr[4], + (unsigned char)s->sll_addr[5]); // On garde la prioritaire // En cas d'egalite, on garde l'adresse qui est la plus grande suivant @@ -1366,6 +1359,7 @@ const char* GetSystemInfos() int i = 0; int nLineCount; char* sInfo = StandardGetBuffer(); + int nPos; char c; struct utsname buffer; bool bOk; @@ -1401,15 +1395,9 @@ const char* GetSystemInfos() if (uname(&buffer) >= 0) { bOk = true; - bOk = bOk and SecureStrcpy(sInfo, "system=", BUFFER_LENGTH); - bOk = bOk and SecureStrcpy(sInfo, buffer.sysname, BUFFER_LENGTH); - bOk = bOk and SecureStrcpy(sInfo, "\n", BUFFER_LENGTH); - bOk = bOk and SecureStrcpy(sInfo, "release=", BUFFER_LENGTH); - bOk = bOk and SecureStrcpy(sInfo, buffer.release, BUFFER_LENGTH); - bOk = bOk and SecureStrcpy(sInfo, "\n", BUFFER_LENGTH); - bOk = bOk and SecureStrcpy(sInfo, "version=", BUFFER_LENGTH); - bOk = bOk and SecureStrcpy(sInfo, buffer.version, BUFFER_LENGTH); - bOk = bOk and SecureStrcpy(sInfo, "\n", BUFFER_LENGTH); + nPos = (int)strlen(sInfo); + snprintf(&sInfo[nPos], BUFFER_LENGTH - nPos, "system=%s\nrelease=%s\nversion=%s\n", buffer.sysname, + buffer.release, buffer.version); } return sInfo; } diff --git a/src/Norm/genere/Attribute.h b/src/Norm/genere/Attribute.h index 33eced422..39d349873 100644 --- a/src/Norm/genere/Attribute.h +++ b/src/Norm/genere/Attribute.h @@ -53,7 +53,7 @@ class Attribute : public ManagedObject // Champs de la cle // Name - const ALString& GetKey() const; + const ALString& GetKey() const override; //////////////////////////////////////////////////////// // Fonctionnalites generiques @@ -77,22 +77,22 @@ class Attribute : public ManagedObject }; // Nombre de champs - int GetFieldNumber() const; + int GetFieldNumber() const override; // Acces generique aux champs, par une valeur de // type chaine de caracteres // (les champs derives sont ignores en ecriture) - void SetFieldAt(int nFieldIndex, const char* sValue); - const char* GetFieldAt(int nFieldIndex) const; + void SetFieldAt(int nFieldIndex, const char* sValue) override; + const char* GetFieldAt(int nFieldIndex) const override; // Nom d'un champ d'apres son index - const ALString GetFieldNameAt(int nFieldIndex) const; + const ALString GetFieldNameAt(int nFieldIndex) const override; // Libelle d'un champ d'apres son index - const ALString GetFieldLabelAt(int nFieldIndex) const; + const ALString GetFieldLabelAt(int nFieldIndex) const override; // Nom de stockage d'un champ d'apres son index - const ALString GetFieldStorageNameAt(int nFieldIndex) const; + const ALString GetFieldStorageNameAt(int nFieldIndex) const override; // Parametrage du separateur de champs (par defaut: '\t') static void SetFieldSeparator(char cValue); @@ -171,11 +171,11 @@ class Attribute : public ManagedObject //// Implementation protected: // Implementation des methodes virtuelles de ManagedObject - ManagedObject* CloneManagedObject() const; - boolean GetFieldStored(int nFieldIndex) const; - IntVector* GetStoredFieldIndexes() const; - CompareFunction GetCompareKeyFunction() const; - char GetSeparator() const; + ManagedObject* CloneManagedObject() const override; + boolean GetFieldStored(int nFieldIndex) const override; + IntVector* GetStoredFieldIndexes() const override; + CompareFunction GetCompareKeyFunction() const override; + char GetSeparator() const override; // Exemplaire d'objet gere static Attribute managedObjectClass; diff --git a/src/Norm/samples/sample3/PRWorkerView.h b/src/Norm/samples/sample3/PRWorkerView.h index 408afcb0f..8333f2b8b 100644 --- a/src/Norm/samples/sample3/PRWorkerView.h +++ b/src/Norm/samples/sample3/PRWorkerView.h @@ -54,7 +54,7 @@ class PRWorkerView : public UIObjectView void ActionInspectProfessionnalAddress(); // Acces a l'objet edite par la vue - void SetObject(Object* object); + void SetObject(Object* object) override; PRWorker* GetWorker(); // ## diff --git a/src/Parallel/PLSamples/PEPi.cpp b/src/Parallel/PLSamples/PEPi.cpp index e5004228e..3e9248bc4 100644 --- a/src/Parallel/PLSamples/PEPi.cpp +++ b/src/Parallel/PLSamples/PEPi.cpp @@ -78,7 +78,7 @@ const ALString PEPi::GetObjectLabel() const void PEPi::ComputePi() { PEPiTask piTask; - char cPi[29]; + char sPiResult[30]; ALString sTmp; // Parametrage et lancement de la tache de calcul de Pi @@ -86,8 +86,8 @@ void PEPi::ComputePi() piTask.ComputePi(); // Collecte des resultats, avec le maximum de precision - sprintf(cPi, "%.15g", piTask.GetComputedPi()); - SetPi(cPi); + snprintf(sPiResult, sizeof(sPiResult), "%.15g", piTask.GetComputedPi()); + SetPi(sPiResult); SetElapsedTime(piTask.GetJobElapsedTime()); } diff --git a/test/KNITest/KNITest.cpp b/test/KNITest/KNITest.cpp index d5aaea44e..05aae3b6e 100644 --- a/test/KNITest/KNITest.cpp +++ b/test/KNITest/KNITest.cpp @@ -246,20 +246,20 @@ void TestAdult() // Saut du header FileService::OpenInputBinaryFile(sDataPath, fRef); - ch = getc(fRef); + ch = (char)getc(fRef); while (ch != '\n' and ch != EOF) { - ch = getc(fRef); + ch = (char)getc(fRef); } // Copie du fichier tel quel sRefFilePath = sTestPath + "results" + FileService::GetFileSeparator() + "ref_Adult.txt"; FileService::OpenOutputBinaryFile(sRefFilePath, fWithoutHeader); - ch = getc(fRef); + ch = (char)getc(fRef); while (ch != EOF) { putc(ch, fWithoutHeader); - ch = getc(fRef); + ch = (char)getc(fRef); } FileService::CloseInputBinaryFile(sDataPath, fRef); FileService::CloseOutputBinaryFile(sRefFilePath, fWithoutHeader); diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khc b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khc deleted file mode 100644 index 187238285..000000000 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khc +++ /dev/null @@ -1,83 +0,0 @@ -#Khiops 10.4.6i -Short description -Dimensions 2 -Name Type Parts Initial parts Values Interest Description -occupation Categorical 3 3 12 1 -education Categorical 3 3 13 1 - -Coclustering stats -Instances 240 -Cells 9 -Null cost 1066.368632 -Cost 1051.316663 -Level 0.01411516443 -Initial dimensions 2 -Frequency variable -Dictionary Adult -Database ../../../datasets/Adult/Adult.txt -Sample percentage 0.5 -Sampling mode Include sample -Selection variable native_country -Selection value United-States - -Hierarchy occupation -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -{Prof-specialty, Exec-managerial, Protective-serv, ...} A2 90 1 1 1 6 -{Other-service, Craft-repair, Transport-moving} A4 70 0.812391 1 3 6 -{Sales, Farming-fishing, Adm-clerical, ...} A4 80 0.655405 1 5 6 -A2 240 0.830416 -0.727419 2 3 -A4 A2 150 0.728665 0.72783 4 5 - -Hierarchy education -Cluster ParentCluster Frequency Interest HierarchicalLevel Rank HierarchicalRank -{Bachelors, Masters, Assoc-voc, ...} B1 83 1 1 1 6 -{HS-grad, 11th, 10th, ...} B3 105 0.903801 1 3 6 -{Some-college, 9th} B3 52 0.59125 1 5 6 -B1 240 0.869351 0 2 2 -B3 B1 157 0.800281 0.696343 4 4 - -Composition occupation -Cluster Value Frequency Typicality -{Prof-specialty, Exec-managerial, Protective-serv, ...} Prof-specialty 54 1 -{Prof-specialty, Exec-managerial, Protective-serv, ...} Exec-managerial 23 0.342621 -{Prof-specialty, Exec-managerial, Protective-serv, ...} Protective-serv 6 0.148043 -{Prof-specialty, Exec-managerial, Protective-serv, ...} Tech-support 7 0.0502739 -{Prof-specialty, Exec-managerial, Protective-serv, ...} * 0 0 -{Other-service, Craft-repair, Transport-moving} Other-service 33 1 -{Other-service, Craft-repair, Transport-moving} Craft-repair 27 0.682053 -{Other-service, Craft-repair, Transport-moving} Transport-moving 10 0.398653 -{Sales, Farming-fishing, Adm-clerical, ...} Sales 27 1 -{Sales, Farming-fishing, Adm-clerical, ...} Farming-fishing 11 0.631255 -{Sales, Farming-fishing, Adm-clerical, ...} Adm-clerical 21 0.428212 -{Sales, Farming-fishing, Adm-clerical, ...} Handlers-cleaners 12 0.37083 -{Sales, Farming-fishing, Adm-clerical, ...} Machine-op-inspct 9 0.322156 - -Composition education -Cluster Value Frequency Typicality -{Bachelors, Masters, Assoc-voc, ...} Bachelors 39 1 -{Bachelors, Masters, Assoc-voc, ...} Masters 14 0.756619 -{Bachelors, Masters, Assoc-voc, ...} Assoc-voc 10 0.272011 -{Bachelors, Masters, Assoc-voc, ...} Assoc-acdm 10 0.245697 -{Bachelors, Masters, Assoc-voc, ...} Prof-school 4 0.220568 -{Bachelors, Masters, Assoc-voc, ...} Doctorate 2 0.166163 -{Bachelors, Masters, Assoc-voc, ...} 12th 4 0.143425 -{Bachelors, Masters, Assoc-voc, ...} * 0 0 -{HS-grad, 11th, 10th, ...} HS-grad 82 1 -{HS-grad, 11th, 10th, ...} 11th 12 0.318105 -{HS-grad, 11th, 10th, ...} 10th 8 0.271284 -{HS-grad, 11th, 10th, ...} 7th-8th 3 0.0969771 -{Some-college, 9th} Some-college 47 1 -{Some-college, 9th} 9th 5 0.300942 - -Cells -occupation education Frequency -{Prof-specialty, Exec-managerial, Protective-serv, ...} {Bachelors, Masters, Assoc-voc, ...} 60 -{Sales, Farming-fishing, Adm-clerical, ...} {HS-grad, 11th, 10th, ...} 52 -{Other-service, Craft-repair, Transport-moving} {HS-grad, 11th, 10th, ...} 42 -{Other-service, Craft-repair, Transport-moving} {Some-college, 9th} 25 -{Sales, Farming-fishing, Adm-clerical, ...} {Bachelors, Masters, Assoc-voc, ...} 20 -{Prof-specialty, Exec-managerial, Protective-serv, ...} {Some-college, 9th} 19 -{Prof-specialty, Exec-managerial, Protective-serv, ...} {HS-grad, 11th, 10th, ...} 11 -{Sales, Farming-fishing, Adm-clerical, ...} {Some-college, 9th} 8 -{Other-service, Craft-repair, Transport-moving} {Bachelors, Masters, Assoc-voc, ...} 3 - diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khcj b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khcj deleted file mode 100644 index b7b94d083..000000000 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/Coclustering.khcj +++ /dev/null @@ -1,221 +0,0 @@ -{ - "tool": "Khiops Coclustering", - "version": "10.4.6i", - "shortDescription": "", - "coclusteringReport": { - "summary": { - "instances": 240, - "cells": 9, - "nullCost": 1066.368632, - "cost": 1051.316663, - "level": 0.01411516443, - "initialDimensions": 2, - "frequencyVariable": "", - "dictionary": "Adult", - "database": "..\/..\/..\/datasets\/Adult\/Adult.txt", - "samplePercentage": 0.5, - "samplingMode": "Include sample", - "selectionVariable": "native_country", - "selectionValue": "United-States" - }, - "dimensionSummaries": [ - { - "name": "occupation", - "type": "Categorical", - "parts": 3, - "initialParts": 3, - "values": 12, - "interest": 1, - "description": "" - }, - { - "name": "education", - "type": "Categorical", - "parts": 3, - "initialParts": 3, - "values": 13, - "interest": 1, - "description": "" - } - ], - "dimensionPartitions": [ - { - "name": "occupation", - "type": "Categorical", - "valueGroups": [ - { - "cluster": "{Prof-specialty, Exec-managerial, Protective-serv, ...}", - "values": ["Prof-specialty","Exec-managerial","Protective-serv","Tech-support"], - "valueFrequencies": [54,23,6,7], - "valueTypicalities": [1,0.342621,0.148043,0.0502739] - }, - { - "cluster": "{Other-service, Craft-repair, Transport-moving}", - "values": ["Other-service","Craft-repair","Transport-moving"], - "valueFrequencies": [33,27,10], - "valueTypicalities": [1,0.682053,0.398653] - }, - { - "cluster": "{Sales, Farming-fishing, Adm-clerical, ...}", - "values": ["Sales","Farming-fishing","Adm-clerical","Handlers-cleaners","Machine-op-inspct"], - "valueFrequencies": [27,11,21,12,9], - "valueTypicalities": [1,0.631255,0.428212,0.37083,0.322156] - } - ], - "defaultGroupIndex": 0 - }, - { - "name": "education", - "type": "Categorical", - "valueGroups": [ - { - "cluster": "{Bachelors, Masters, Assoc-voc, ...}", - "values": ["Bachelors","Masters","Assoc-voc","Assoc-acdm","Prof-school","Doctorate","12th"], - "valueFrequencies": [39,14,10,10,4,2,4], - "valueTypicalities": [1,0.756619,0.272011,0.245697,0.220568,0.166163,0.143425] - }, - { - "cluster": "{HS-grad, 11th, 10th, ...}", - "values": ["HS-grad","11th","10th","7th-8th"], - "valueFrequencies": [82,12,8,3], - "valueTypicalities": [1,0.318105,0.271284,0.0969771] - }, - { - "cluster": "{Some-college, 9th}", - "values": ["Some-college","9th"], - "valueFrequencies": [47,5], - "valueTypicalities": [1,0.300942] - } - ], - "defaultGroupIndex": 0 - } - ], - "dimensionHierarchies": [ - { - "name": "occupation", - "type": "Categorical", - "clusters": [ - { - "cluster": "{Prof-specialty, Exec-managerial, Protective-serv, ...}", - "parentCluster": "A2", - "frequency": 90, - "interest": 1, - "hierarchicalLevel": 1, - "rank": 1, - "hierarchicalRank": 6, - "isLeaf": true - }, - { - "cluster": "{Other-service, Craft-repair, Transport-moving}", - "parentCluster": "A4", - "frequency": 70, - "interest": 0.812391, - "hierarchicalLevel": 1, - "rank": 3, - "hierarchicalRank": 6, - "isLeaf": true - }, - { - "cluster": "{Sales, Farming-fishing, Adm-clerical, ...}", - "parentCluster": "A4", - "frequency": 80, - "interest": 0.655405, - "hierarchicalLevel": 1, - "rank": 5, - "hierarchicalRank": 6, - "isLeaf": true - }, - { - "cluster": "A2", - "parentCluster": "", - "frequency": 240, - "interest": 0.830416, - "hierarchicalLevel": -0.727419, - "rank": 2, - "hierarchicalRank": 3, - "isLeaf": false - }, - { - "cluster": "A4", - "parentCluster": "A2", - "frequency": 150, - "interest": 0.728665, - "hierarchicalLevel": 0.72783, - "rank": 4, - "hierarchicalRank": 5, - "isLeaf": false - } - ] - }, - { - "name": "education", - "type": "Categorical", - "clusters": [ - { - "cluster": "{Bachelors, Masters, Assoc-voc, ...}", - "parentCluster": "B1", - "frequency": 83, - "interest": 1, - "hierarchicalLevel": 1, - "rank": 1, - "hierarchicalRank": 6, - "isLeaf": true - }, - { - "cluster": "{HS-grad, 11th, 10th, ...}", - "parentCluster": "B3", - "frequency": 105, - "interest": 0.903801, - "hierarchicalLevel": 1, - "rank": 3, - "hierarchicalRank": 6, - "isLeaf": true - }, - { - "cluster": "{Some-college, 9th}", - "parentCluster": "B3", - "frequency": 52, - "interest": 0.59125, - "hierarchicalLevel": 1, - "rank": 5, - "hierarchicalRank": 6, - "isLeaf": true - }, - { - "cluster": "B1", - "parentCluster": "", - "frequency": 240, - "interest": 0.869351, - "hierarchicalLevel": 0, - "rank": 2, - "hierarchicalRank": 2, - "isLeaf": false - }, - { - "cluster": "B3", - "parentCluster": "B1", - "frequency": 157, - "interest": 0.800281, - "hierarchicalLevel": 0.696343, - "rank": 4, - "hierarchicalRank": 4, - "isLeaf": false - } - ] - } - ], - "cellPartIndexes": [ - [0,0], - [2,1], - [1,1], - [1,2], - [2,0], - [0,2], - [0,1], - [2,2], - [1,0] - ], - "cellFrequencies": [60,52,42,25,20,19,11,8,3] - }, - "khiops_encoding": "ascii" -} diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/err.txt b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/err.txt deleted file mode 100644 index bbcd62c09..000000000 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/err.txt +++ /dev/null @@ -1,8 +0,0 @@ -Database ../../../datasets/Adult/Adult.txt : Read records: 48,842 Selected records: 240 - 0:00:00 Write intermediate coclustering report Coclustering(1).khcj Level: 0.0137204 Size: 3*5 - 0:00:00 Write intermediate coclustering report Coclustering(2).khcj Level: 0.0137755 Size: 2*3 - 0:00:00 Write intermediate coclustering report Coclustering(3).khcj Level: 0.0139406 Size: 3*4 - 0:00:00 Write intermediate coclustering report Coclustering(4).khcj Level: 0.0141152 Size: 3*3 -Write coclustering report ./results/Coclustering.khcj -Coclustering analysis time: 0:00:00.05 - diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/time.log b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/time.log deleted file mode 100644 index 5f65e3db4..000000000 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/results.ref/time.log +++ /dev/null @@ -1 +0,0 @@ -Overal time: 0.13622331619262695 diff --git a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/test.prm b/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/test.prm deleted file mode 100644 index 131282135..000000000 --- a/test/LearningTest/TestCoclustering/Standard/Adult2varsTiny/test.prm +++ /dev/null @@ -1,47 +0,0 @@ -// Mon Jul 11 10:35:30 2011 -// C:\Users\boullema\Learning\Win32\debug\MODL_Coclustering -// Output command file -// -//This file contains recorded commands, that can be replayed. -//Commands are based on user interactions: -// field update -// list item selection -// menu action -//Every command can be commented, using //. -//For example, commenting the last Exit command will allow other -//user interactions, after the commands have been replayed. -// -// - -// -> Khiops Coclustering -AnalysisSpec.SystemParameters.APIMode true // API mode -AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB -ClassManagement.OpenFile // Open... - -// -> Open -ClassFileName ../../../datasets/Adult/Adult.kdic // Dictionary file -OK // Open -// <- Open - -Database.DatabaseSpec.Data.DatabaseFiles.List.Key Adult // List item selection -Database.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Adult/Adult.txt // Database file -Database.DatabaseSpec.Sampling.SampleNumberPercentage 0.5 // Sample percentage -Database.DatabaseSpec.Selection.SelectionAttribute native_country // Selection variable -Database.DatabaseSpec.Selection.SelectionValue United-States // Selection value - -AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable -AnalysisSpec.CoclusteringParameters.Attributes.Name occupation // Name -AnalysisSpec.CoclusteringParameters.Attributes.InsertItemAfter // Insert variable -AnalysisSpec.CoclusteringParameters.Attributes.Name education // Name - -AnalysisSpec.SystemParameters.OptimizationTime 0 // Optimization time in seconds -AnalysisResults.CoclusteringFileName ./results/Coclustering.khcj // Coclustering report -BuildCoclustering // Build coclustering - -Exit // Close -// <- Khiops Coclustering - - -// -> Khiops Coclustering -OK // Close -// <- Khiops Coclustering diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/IrisExtended.kdic b/test/LearningTest/TestKhiops/Standard/IrisU2D/IrisExtended.kdic deleted file mode 100644 index f50517f00..000000000 --- a/test/LearningTest/TestKhiops/Standard/IrisU2D/IrisExtended.kdic +++ /dev/null @@ -1,17 +0,0 @@ -#Khiops 7.7.2i - -Dictionary Iris -{ - Numerical SepalLength ; - Numerical SepalWidth ; - Numerical PetalLength ; - Numerical PetalWidth ; - Categorical SPetalLength = AsCategorical(Floor(PetalLength)) ; - Numerical LowerPetalLength = If(LE(PetalLength, 3), PetalLength, 3) ; - Numerical UpperPetalWidth = If(GE(PetalWidth, 1.5), PetalWidth, 1.5) ; - Categorical Class1 = IfC(EQc(Class, "Iris-setosa"), "setosa", "") ; - Categorical Class2 = IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") ; - Numerical Dummy1 = Copy(0) ; - Numerical Dummy2 = Random() ; - Categorical Class ; -}; diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls deleted file mode 100644 index 13f7eaabe..000000000 --- a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.Preparation2DReport.xls +++ /dev/null @@ -1,1407 +0,0 @@ -#Khiops 10.4.6i -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Unsupervised analysis - -Evaluated variable pairs 55 -Informative variable pairs 38 - - -Variables pairs statistics - -Rank Name 1 Name 2 Level Variables Parts 1 Parts 2 Cells Constr. cost Prep. cost Data cost -R01 Class Class1 0.286471 2 2 2 2 6.71557 18.9311 110.25 -R02 Class Class2 0.270234 2 2 2 2 6.71557 19.0156 110.25 -R03 Class SPetalLength 0.258511 2 3 3 5 6.71557 41.7647 157.188 -R04 Class1 SPetalLength 0.231831 2 2 2 2 6.71557 27.2099 142.253 -R05 PetalLength SPetalLength 0.151582 2 5 5 5 6.71557 69.091 386.913 -R06 Class2 SPetalLength 0.142436 2 2 2 4 6.71557 27.7273 158.704 -R07 Class PetalWidth 0.14197 2 3 3 5 6.71557 31.1679 396.708 -R08 Class PetalLength 0.136908 2 3 3 5 6.71557 31.1679 399.272 -R09 Class1 LowerPetalLength 0.111506 2 2 2 2 6.71557 13.7255 386.913 -R10 Class1 PetalLength 0.111506 2 2 2 2 6.71557 13.7255 386.913 -R11 Class1 PetalWidth 0.111506 2 2 2 2 6.71557 13.7255 386.913 -R12 PetalWidth SPetalLength 0.109807 2 3 3 5 6.71557 40.5555 438.232 -R13 Class LowerPetalLength 0.0982915 2 2 2 2 6.71557 19.0436 430.955 -R14 LowerPetalLength SPetalLength 0.0887331 2 2 2 2 6.71557 27.3225 462.959 -R15 PetalLength PetalWidth 0.0785935 2 3 3 4 6.71557 29.9587 676.972 -R16 Class UpperPetalWidth 0.0721164 2 2 2 4 6.71557 19.0868 444.17 -R17 PetalWidth UpperPetalWidth 0.0703191 2 3 3 3 6.71557 29.9587 683.381 -R18 LowerPetalLength PetalLength 0.0701201 2 3 3 3 6.71557 29.9587 683.535 -R19 Class2 PetalWidth 0.0662843 2 2 3 5 6.71557 20.8147 396.708 -R20 SPetalLength SepalLength 0.0654694 2 3 4 5 6.71557 49.4973 453.472 -R21 Class2 PetalLength 0.0606416 2 2 3 5 6.71557 20.8147 399.272 -R22 LowerPetalLength PetalWidth 0.0598398 2 2 2 2 6.71557 13.838 707.618 -R23 Class SepalLength 0.059526 2 3 3 7 6.71557 31.1679 438.466 -R24 Class1 Class2 0.0559199 2 2 2 3 6.71557 13.6129 110.25 -R25 Class1 SepalLength 0.0531576 2 2 2 4 6.71557 13.7255 413.664 -R26 SPetalLength UpperPetalWidth 0.0466723 2 3 2 5 6.71557 31.8478 481.373 -R27 PetalLength SepalLength 0.0407398 2 4 4 8 6.71557 47.7303 688.519 -R28 PetalLength UpperPetalWidth 0.0401281 2 2 2 3 6.71557 13.838 722.885 -R29 PetalWidth SepalLength 0.0303985 2 3 3 8 6.71557 29.9587 714.3 -R30 LowerPetalLength SepalLength 0.0253003 2 2 2 4 6.71557 13.838 734.369 -R31 Class1 UpperPetalWidth 0.0166012 2 2 2 3 6.71557 13.7255 430.424 -R32 SepalLength UpperPetalWidth 0.0164148 2 2 2 4 6.71557 13.838 741.251 -R33 Class1 SepalWidth 0.00749643 2 2 3 5 6.71557 20.8147 427.509 -R34 Class2 LowerPetalLength 0.0065114 2 2 2 3 6.71557 13.7255 430.955 -R35 Class SepalWidth 0.00543684 2 3 2 6 6.71557 22.1365 474.893 -R36 SPetalLength SepalWidth 0.00373995 2 3 3 9 6.71557 38.9735 497.662 -R37 LowerPetalLength UpperPetalWidth 0.00366071 2 2 2 3 6.71557 13.838 751.129 -R38 PetalWidth SepalWidth 0.00221737 2 3 2 6 6.71557 20.9273 745.158 -R39 Class Dummy2 0 0 1 1 1 0.693147 8.64312 497.163 -R40 Class1 Dummy2 0 0 1 1 1 0.693147 4.66344 453.12 -R41 Class2 Dummy2 0 0 1 1 1 0.693147 4.66344 448.998 -R42 Class2 SepalLength 0 0 1 1 1 0.693147 4.66344 448.998 -R43 Class2 SepalWidth 0 0 1 1 1 0.693147 4.66344 448.998 -R44 Class2 UpperPetalWidth 0 0 1 1 1 0.693147 4.66344 448.998 -R45 Dummy2 LowerPetalLength 0 0 1 1 1 0.693147 0 773.825 -R46 Dummy2 PetalLength 0 0 1 1 1 0.693147 0 773.825 -R47 Dummy2 PetalWidth 0 0 1 1 1 0.693147 0 773.825 -R48 Dummy2 SPetalLength 0 0 1 1 1 0.693147 15.5317 529.166 -R49 Dummy2 SepalLength 0 0 1 1 1 0.693147 0 773.825 -R50 Dummy2 SepalWidth 0 0 1 1 1 0.693147 0 773.825 -R51 Dummy2 UpperPetalWidth 0 0 1 1 1 0.693147 0 773.825 -R52 LowerPetalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 -R53 PetalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 -R54 SepalLength SepalWidth 0 0 1 1 1 0.693147 0 773.825 -R55 SepalWidth UpperPetalWidth 0 0 1 1 1 0.693147 0 773.825 - - --------------------------------------------------------------------------------- - -Variables pairs detailed statistics -(Pairs with two jointly informative variables) - - -Rank R01 -Variables - Type Name - Categorical Class - Categorical Class1 - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * -{Iris-setosa} 38 0.361905 1 Iris-setosa -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * - -Cell frequencies - Class1 -Class {} {setosa} Total Coverage -{Iris-virginica, Iris-versicolor} 67 0 67 0.638095 -{Iris-setosa} 0 38 38 0.361905 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 2 -Cell Id Class Class1 Frequency Coverage -C1 {Iris-virginica, Iris-versicolor} {} 67 0.638095 -C4 {Iris-setosa} {setosa} 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R02 -Variables - Type Name - Categorical Class - Categorical Class2 - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa, Iris-virginica} 73 0.695238 2 Iris-setosa Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * - -Cell frequencies - Class2 -Class {} {versicolor} Total Coverage -{Iris-setosa, Iris-virginica} 73 0 73 0.695238 -{Iris-versicolor} 0 32 32 0.304762 -Total 73 32 105 -Coverage 0.695238 0.304762 - -Cells 2 -Cell Id Class Class2 Frequency Coverage -C1 {Iris-setosa, Iris-virginica} {} 73 0.695238 -C4 {Iris-versicolor} {versicolor} 32 0.304762 - Total 105 1 - ----------------------------------------------- -Rank R03 -Variables - Type Name - Categorical Class - Categorical SPetalLength - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -SPetalLength -Group Frequency Coverage Size Value list -{1} 38 0.361905 1 1 -{5, 6} 34 0.32381 2 5 6 * -{4, 3} 33 0.314286 2 4 3 - -Cell frequencies - SPetalLength -Class {1} {5, 6} {4, 3} Total Coverage -{Iris-setosa} 38 0 0 38 0.361905 -{Iris-virginica} 0 32 3 35 0.333333 -{Iris-versicolor} 0 2 30 32 0.304762 -Total 38 34 33 105 -Coverage 0.361905 0.32381 0.314286 - -Cells 5 -Cell Id Class SPetalLength Frequency Coverage -C1 {Iris-setosa} {1} 38 0.361905 -C5 {Iris-virginica} {5, 6} 32 0.304762 -C9 {Iris-versicolor} {4, 3} 30 0.285714 -C8 {Iris-virginica} {4, 3} 3 0.0285714 -C6 {Iris-versicolor} {5, 6} 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R04 -Variables - Type Name - Categorical Class1 - Categorical SPetalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -SPetalLength -Group Frequency Coverage Size Value list -{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * -{1} 38 0.361905 1 1 - -Cell frequencies - SPetalLength -Class1 {5, 4, 3, ...} {1} Total Coverage -{} 67 0 67 0.638095 -{setosa} 0 38 38 0.361905 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 2 -Cell Id Class1 SPetalLength Frequency Coverage -C1 {} {5, 4, 3, ...} 67 0.638095 -C4 {setosa} {1} 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R05 -Variables - Type Name - Numerical PetalLength - Categorical SPetalLength - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;3.95] 8 0.0761905 -]3.95;4.95] 25 0.238095 -]4.95;5.95] 27 0.257143 -]5.95;+inf[ 7 0.0666667 -SPetalLength -Group Frequency Coverage Size Value list -{1} 38 0.361905 1 1 -{5} 27 0.257143 1 5 -{4} 25 0.238095 1 4 -{3} 8 0.0761905 1 3 -{6} 7 0.0666667 1 6 * - -Cell frequencies - SPetalLength -PetalLength {1} {5} {4} {3} {6} Total Coverage -]-inf;2.4] 38 0 0 0 0 38 0.361905 -]2.4;3.95] 0 0 0 8 0 8 0.0761905 -]3.95;4.95] 0 0 25 0 0 25 0.238095 -]4.95;5.95] 0 27 0 0 0 27 0.257143 -]5.95;+inf[ 0 0 0 0 7 7 0.0666667 -Total 38 27 25 8 7 105 -Coverage 0.361905 0.257143 0.238095 0.0761905 0.0666667 - -Cells 5 -Cell Id PetalLength SPetalLength Frequency Coverage -C1 ]-inf;2.4] {1} 38 0.361905 -C9 ]4.95;5.95] {5} 27 0.257143 -C13 ]3.95;4.95] {4} 25 0.238095 -C17 ]2.4;3.95] {3} 8 0.0761905 -C25 ]5.95;+inf[ {6} 7 0.0666667 - Total 105 1 - ----------------------------------------------- -Rank R06 -Variables - Type Name - Categorical Class2 - Categorical SPetalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -SPetalLength -Group Frequency Coverage Size Value list -{1, 5, 6} 72 0.685714 3 1 5 6 * -{4, 3} 33 0.314286 2 4 3 - -Cell frequencies - SPetalLength -Class2 {1, 5, 6} {4, 3} Total Coverage -{} 70 3 73 0.695238 -{versicolor} 2 30 32 0.304762 -Total 72 33 105 -Coverage 0.685714 0.314286 - -Cells 4 -Cell Id Class2 SPetalLength Frequency Coverage -C1 {} {1, 5, 6} 70 0.666667 -C4 {versicolor} {4, 3} 30 0.285714 -C3 {} {4, 3} 3 0.0285714 -C2 {versicolor} {1, 5, 6} 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R07 -Variables - Type Name - Categorical Class - Numerical PetalWidth - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.75] 33 0.314286 -]1.75;+inf[ 34 0.32381 - -Cell frequencies - PetalWidth -Class ]-inf;0.75] ]0.75;1.75] ]1.75;+inf[ Total Coverage -{Iris-setosa} 38 0 0 38 0.361905 -{Iris-virginica} 0 2 33 35 0.333333 -{Iris-versicolor} 0 31 1 32 0.304762 -Total 38 33 34 105 -Coverage 0.361905 0.314286 0.32381 - -Cells 5 -Cell Id Class PetalWidth Frequency Coverage -C1 {Iris-setosa} ]-inf;0.75] 38 0.361905 -C8 {Iris-virginica} ]1.75;+inf[ 33 0.314286 -C6 {Iris-versicolor} ]0.75;1.75] 31 0.295238 -C5 {Iris-virginica} ]0.75;1.75] 2 0.0190476 -C9 {Iris-versicolor} ]1.75;+inf[ 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R08 -Variables - Type Name - Categorical Class - Numerical PetalLength - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;4.85] 30 0.285714 -]4.85;+inf[ 37 0.352381 - -Cell frequencies - PetalLength -Class ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Total Coverage -{Iris-setosa} 38 0 0 38 0.361905 -{Iris-virginica} 0 1 34 35 0.333333 -{Iris-versicolor} 0 29 3 32 0.304762 -Total 38 30 37 105 -Coverage 0.361905 0.285714 0.352381 - -Cells 5 -Cell Id Class PetalLength Frequency Coverage -C1 {Iris-setosa} ]-inf;2.4] 38 0.361905 -C8 {Iris-virginica} ]4.85;+inf[ 34 0.32381 -C6 {Iris-versicolor} ]2.4;4.85] 29 0.27619 -C9 {Iris-versicolor} ]4.85;+inf[ 3 0.0285714 -C5 {Iris-virginica} ]2.4;4.85] 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R09 -Variables - Type Name - Categorical Class1 - Numerical LowerPetalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - LowerPetalLength -Class1 ]-inf;2.4] ]2.4;+inf[ Total Coverage -{} 0 67 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id Class1 LowerPetalLength Frequency Coverage -C3 {} ]2.4;+inf[ 67 0.638095 -C2 {setosa} ]-inf;2.4] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R10 -Variables - Type Name - Categorical Class1 - Numerical PetalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - PetalLength -Class1 ]-inf;2.4] ]2.4;+inf[ Total Coverage -{} 0 67 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id Class1 PetalLength Frequency Coverage -C3 {} ]2.4;+inf[ 67 0.638095 -C2 {setosa} ]-inf;2.4] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R11 -Variables - Type Name - Categorical Class1 - Numerical PetalWidth - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;+inf[ 67 0.638095 - -Cell frequencies - PetalWidth -Class1 ]-inf;0.75] ]0.75;+inf[ Total Coverage -{} 0 67 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id Class1 PetalWidth Frequency Coverage -C3 {} ]0.75;+inf[ 67 0.638095 -C2 {setosa} ]-inf;0.75] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R12 -Variables - Type Name - Numerical PetalWidth - Categorical SPetalLength - -Variables stats -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.65] 32 0.304762 -]1.65;+inf[ 35 0.333333 -SPetalLength -Group Frequency Coverage Size Value list -{1} 38 0.361905 1 1 -{5, 6} 34 0.32381 2 5 6 * -{4, 3} 33 0.314286 2 4 3 - -Cell frequencies - SPetalLength -PetalWidth {1} {5, 6} {4, 3} Total Coverage -]-inf;0.75] 38 0 0 38 0.361905 -]0.75;1.65] 0 3 29 32 0.304762 -]1.65;+inf[ 0 31 4 35 0.333333 -Total 38 34 33 105 -Coverage 0.361905 0.32381 0.314286 - -Cells 5 -Cell Id PetalWidth SPetalLength Frequency Coverage -C1 ]-inf;0.75] {1} 38 0.361905 -C6 ]1.65;+inf[ {5, 6} 31 0.295238 -C8 ]0.75;1.65] {4, 3} 29 0.27619 -C9 ]1.65;+inf[ {4, 3} 4 0.0380952 -C5 ]0.75;1.65] {5, 6} 3 0.0285714 - Total 105 1 - ----------------------------------------------- -Rank R13 -Variables - Type Name - Categorical Class - Numerical LowerPetalLength - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-virginica, Iris-versicolor} 67 0.638095 2 Iris-virginica Iris-versicolor * -{Iris-setosa} 38 0.361905 1 Iris-setosa -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - LowerPetalLength -Class ]-inf;2.4] ]2.4;+inf[ Total Coverage -{Iris-virginica, Iris-versicolor} 0 67 67 0.638095 -{Iris-setosa} 38 0 38 0.361905 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id Class LowerPetalLength Frequency Coverage -C3 {Iris-virginica, Iris-versicolor} ]2.4;+inf[ 67 0.638095 -C2 {Iris-setosa} ]-inf;2.4] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R14 -Variables - Type Name - Numerical LowerPetalLength - Categorical SPetalLength - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -SPetalLength -Group Frequency Coverage Size Value list -{5, 4, 3, ...} 67 0.638095 4 5 4 3 6 * -{1} 38 0.361905 1 1 - -Cell frequencies - SPetalLength -LowerPetalLength {5, 4, 3, ...} {1} Total Coverage -]-inf;2.4] 0 38 38 0.361905 -]2.4;+inf[ 67 0 67 0.638095 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 2 -Cell Id LowerPetalLength SPetalLength Frequency Coverage -C2 ]2.4;+inf[ {5, 4, 3, ...} 67 0.638095 -C3 ]-inf;2.4] {1} 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R15 -Variables - Type Name - Numerical PetalLength - Numerical PetalWidth - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;4.75] 27 0.257143 -]4.75;+inf[ 40 0.380952 -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.65] 32 0.304762 -]1.65;+inf[ 35 0.333333 - -Cell frequencies - PetalWidth -PetalLength ]-inf;0.75] ]0.75;1.65] ]1.65;+inf[ Total Coverage -]-inf;2.4] 38 0 0 38 0.361905 -]2.4;4.75] 0 27 0 27 0.257143 -]4.75;+inf[ 0 5 35 40 0.380952 -Total 38 32 35 105 -Coverage 0.361905 0.304762 0.333333 - -Cells 4 -Cell Id PetalLength PetalWidth Frequency Coverage -C1 ]-inf;2.4] ]-inf;0.75] 38 0.361905 -C9 ]4.75;+inf[ ]1.65;+inf[ 35 0.333333 -C5 ]2.4;4.75] ]0.75;1.65] 27 0.257143 -C6 ]4.75;+inf[ ]0.75;1.65] 5 0.047619 - Total 105 1 - ----------------------------------------------- -Rank R16 -Variables - Type Name - Categorical Class - Numerical UpperPetalWidth - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa, Iris-versicolor} 70 0.666667 2 Iris-setosa Iris-versicolor * -{Iris-virginica} 35 0.333333 1 Iris-virginica -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.75] 71 0.67619 -]1.75;+inf[ 34 0.32381 - -Cell frequencies - UpperPetalWidth -Class ]-inf;1.75] ]1.75;+inf[ Total Coverage -{Iris-setosa, Iris-versicolor} 69 1 70 0.666667 -{Iris-virginica} 2 33 35 0.333333 -Total 71 34 105 -Coverage 0.67619 0.32381 - -Cells 4 -Cell Id Class UpperPetalWidth Frequency Coverage -C1 {Iris-setosa, Iris-versicolor} ]-inf;1.75] 69 0.657143 -C4 {Iris-virginica} ]1.75;+inf[ 33 0.314286 -C2 {Iris-virginica} ]-inf;1.75] 2 0.0190476 -C3 {Iris-setosa, Iris-versicolor} ]1.75;+inf[ 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R17 -Variables - Type Name - Numerical PetalWidth - Numerical UpperPetalWidth - -Variables stats -PetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;2.05] 20 0.190476 -]2.05;+inf[ 18 0.171429 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;2.05] 20 0.190476 -]2.05;+inf[ 18 0.171429 - -Cell frequencies - UpperPetalWidth -PetalWidth ]-inf;1.55] ]1.55;2.05] ]2.05;+inf[ Total Coverage -]-inf;1.55] 67 0 0 67 0.638095 -]1.55;2.05] 0 20 0 20 0.190476 -]2.05;+inf[ 0 0 18 18 0.171429 -Total 67 20 18 105 -Coverage 0.638095 0.190476 0.171429 - -Cells 3 -Cell Id PetalWidth UpperPetalWidth Frequency Coverage -C1 ]-inf;1.55] ]-inf;1.55] 67 0.638095 -C5 ]1.55;2.05] ]1.55;2.05] 20 0.190476 -C9 ]2.05;+inf[ ]2.05;+inf[ 18 0.171429 - Total 105 1 - ----------------------------------------------- -Rank R18 -Variables - Type Name - Numerical LowerPetalLength - Numerical PetalLength - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;1.45] 17 0.161905 -]1.45;2.4] 21 0.2 -]2.4;+inf[ 67 0.638095 -PetalLength -Interval Frequency Coverage -]-inf;1.45] 17 0.161905 -]1.45;2.4] 21 0.2 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - PetalLength -LowerPetalLength ]-inf;1.45] ]1.45;2.4] ]2.4;+inf[ Total Coverage -]-inf;1.45] 17 0 0 17 0.161905 -]1.45;2.4] 0 21 0 21 0.2 -]2.4;+inf[ 0 0 67 67 0.638095 -Total 17 21 67 105 -Coverage 0.161905 0.2 0.638095 - -Cells 3 -Cell Id LowerPetalLength PetalLength Frequency Coverage -C9 ]2.4;+inf[ ]2.4;+inf[ 67 0.638095 -C5 ]1.45;2.4] ]1.45;2.4] 21 0.2 -C1 ]-inf;1.45] ]-inf;1.45] 17 0.161905 - Total 105 1 - ----------------------------------------------- -Rank R19 -Variables - Type Name - Categorical Class2 - Numerical PetalWidth - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.75] 33 0.314286 -]1.75;+inf[ 34 0.32381 - -Cell frequencies - PetalWidth -Class2 ]-inf;0.75] ]0.75;1.75] ]1.75;+inf[ Total Coverage -{} 38 2 33 73 0.695238 -{versicolor} 0 31 1 32 0.304762 -Total 38 33 34 105 -Coverage 0.361905 0.314286 0.32381 - -Cells 5 -Cell Id Class2 PetalWidth Frequency Coverage -C1 {} ]-inf;0.75] 38 0.361905 -C5 {} ]1.75;+inf[ 33 0.314286 -C4 {versicolor} ]0.75;1.75] 31 0.295238 -C3 {} ]0.75;1.75] 2 0.0190476 -C6 {versicolor} ]1.75;+inf[ 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R20 -Variables - Type Name - Categorical SPetalLength - Numerical SepalLength - -Variables stats -SPetalLength -Group Frequency Coverage Size Value list -{5, 4} 52 0.495238 2 5 4 -{1, 3} 46 0.438095 2 1 3 -{6} 7 0.0666667 1 6 * -SepalLength -Interval Frequency Coverage -]-inf;5.35] 34 0.32381 -]5.35;5.85] 22 0.209524 -]5.85;7.15] 42 0.4 -]7.15;+inf[ 7 0.0666667 - -Cell frequencies - SepalLength -SPetalLength ]-inf;5.35] ]5.35;5.85] ]5.85;7.15] ]7.15;+inf[ Total Coverage -{5, 4} 0 10 42 0 52 0.495238 -{1, 3} 34 12 0 0 46 0.438095 -{6} 0 0 0 7 7 0.0666667 -Total 34 22 42 7 105 -Coverage 0.32381 0.209524 0.4 0.0666667 - -Cells 5 -Cell Id SPetalLength SepalLength Frequency Coverage -C7 {5, 4} ]5.85;7.15] 42 0.4 -C2 {1, 3} ]-inf;5.35] 34 0.32381 -C5 {1, 3} ]5.35;5.85] 12 0.114286 -C4 {5, 4} ]5.35;5.85] 10 0.0952381 -C12 {6} ]7.15;+inf[ 7 0.0666667 - Total 105 1 - ----------------------------------------------- -Rank R21 -Variables - Type Name - Categorical Class2 - Numerical PetalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -PetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;4.85] 30 0.285714 -]4.85;+inf[ 37 0.352381 - -Cell frequencies - PetalLength -Class2 ]-inf;2.4] ]2.4;4.85] ]4.85;+inf[ Total Coverage -{} 38 1 34 73 0.695238 -{versicolor} 0 29 3 32 0.304762 -Total 38 30 37 105 -Coverage 0.361905 0.285714 0.352381 - -Cells 5 -Cell Id Class2 PetalLength Frequency Coverage -C1 {} ]-inf;2.4] 38 0.361905 -C5 {} ]4.85;+inf[ 34 0.32381 -C4 {versicolor} ]2.4;4.85] 29 0.27619 -C6 {versicolor} ]4.85;+inf[ 3 0.0285714 -C3 {} ]2.4;4.85] 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R22 -Variables - Type Name - Numerical LowerPetalLength - Numerical PetalWidth - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;+inf[ 67 0.638095 - -Cell frequencies - PetalWidth -LowerPetalLength ]-inf;0.75] ]0.75;+inf[ Total Coverage -]-inf;2.4] 38 0 38 0.361905 -]2.4;+inf[ 0 67 67 0.638095 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 2 -Cell Id LowerPetalLength PetalWidth Frequency Coverage -C4 ]2.4;+inf[ ]0.75;+inf[ 67 0.638095 -C1 ]-inf;2.4] ]-inf;0.75] 38 0.361905 - Total 105 1 - ----------------------------------------------- -Rank R23 -Variables - Type Name - Categorical Class - Numerical SepalLength - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;6.15] 28 0.266667 -]6.15;+inf[ 38 0.361905 - -Cell frequencies - SepalLength -Class ]-inf;5.45] ]5.45;6.15] ]6.15;+inf[ Total Coverage -{Iris-setosa} 34 4 0 38 0.361905 -{Iris-virginica} 0 5 30 35 0.333333 -{Iris-versicolor} 5 19 8 32 0.304762 -Total 39 28 38 105 -Coverage 0.371429 0.266667 0.361905 - -Cells 7 -Cell Id Class SepalLength Frequency Coverage -C1 {Iris-setosa} ]-inf;5.45] 34 0.32381 -C8 {Iris-virginica} ]6.15;+inf[ 30 0.285714 -C6 {Iris-versicolor} ]5.45;6.15] 19 0.180952 -C9 {Iris-versicolor} ]6.15;+inf[ 8 0.0761905 -C5 {Iris-virginica} ]5.45;6.15] 5 0.047619 -C3 {Iris-versicolor} ]-inf;5.45] 5 0.047619 -C4 {Iris-setosa} ]5.45;6.15] 4 0.0380952 - Total 105 1 - ----------------------------------------------- -Rank R24 -Variables - Type Name - Categorical Class1 - Categorical Class2 - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * - -Cell frequencies - Class2 -Class1 {} {versicolor} Total Coverage -{} 35 32 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 73 32 105 -Coverage 0.695238 0.304762 - -Cells 3 -Cell Id Class1 Class2 Frequency Coverage -C2 {setosa} {} 38 0.361905 -C1 {} {} 35 0.333333 -C3 {} {versicolor} 32 0.304762 - Total 105 1 - ----------------------------------------------- -Rank R25 -Variables - Type Name - Categorical Class1 - Numerical SepalLength - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;+inf[ 66 0.628571 - -Cell frequencies - SepalLength -Class1 ]-inf;5.45] ]5.45;+inf[ Total Coverage -{} 5 62 67 0.638095 -{setosa} 34 4 38 0.361905 -Total 39 66 105 -Coverage 0.371429 0.628571 - -Cells 4 -Cell Id Class1 SepalLength Frequency Coverage -C3 {} ]5.45;+inf[ 62 0.590476 -C2 {setosa} ]-inf;5.45] 34 0.32381 -C1 {} ]-inf;5.45] 5 0.047619 -C4 {setosa} ]5.45;+inf[ 4 0.0380952 - Total 105 1 - ----------------------------------------------- -Rank R26 -Variables - Type Name - Categorical SPetalLength - Numerical UpperPetalWidth - -Variables stats -SPetalLength -Group Frequency Coverage Size Value list -{1, 3} 46 0.438095 2 1 3 -{5, 6} 34 0.32381 2 5 6 * -{4} 25 0.238095 1 4 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -SPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage -{1, 3} 46 0 46 0.438095 -{5, 6} 2 32 34 0.32381 -{4} 19 6 25 0.238095 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 5 -Cell Id SPetalLength UpperPetalWidth Frequency Coverage -C1 {1, 3} ]-inf;1.55] 46 0.438095 -C5 {5, 6} ]1.55;+inf[ 32 0.304762 -C3 {4} ]-inf;1.55] 19 0.180952 -C6 {4} ]1.55;+inf[ 6 0.0571429 -C2 {5, 6} ]-inf;1.55] 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R27 -Variables - Type Name - Numerical PetalLength - Numerical SepalLength - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;3.55] 42 0.4 -]3.55;4.6] 19 0.180952 -]4.6;5.95] 37 0.352381 -]5.95;+inf[ 7 0.0666667 -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;5.85] 17 0.161905 -]5.85;7.15] 42 0.4 -]7.15;+inf[ 7 0.0666667 - -Cell frequencies - SepalLength -PetalLength ]-inf;5.45] ]5.45;5.85] ]5.85;7.15] ]7.15;+inf[ Total Coverage -]-inf;3.55] 37 5 0 0 42 0.4 -]3.55;4.6] 2 10 7 0 19 0.180952 -]4.6;5.95] 0 2 35 0 37 0.352381 -]5.95;+inf[ 0 0 0 7 7 0.0666667 -Total 39 17 42 7 105 -Coverage 0.371429 0.161905 0.4 0.0666667 - -Cells 8 -Cell Id PetalLength SepalLength Frequency Coverage -C1 ]-inf;3.55] ]-inf;5.45] 37 0.352381 -C11 ]4.6;5.95] ]5.85;7.15] 35 0.333333 -C6 ]3.55;4.6] ]5.45;5.85] 10 0.0952381 -C10 ]3.55;4.6] ]5.85;7.15] 7 0.0666667 -C16 ]5.95;+inf[ ]7.15;+inf[ 7 0.0666667 -C5 ]-inf;3.55] ]5.45;5.85] 5 0.047619 -C2 ]3.55;4.6] ]-inf;5.45] 2 0.0190476 -C7 ]4.6;5.95] ]5.45;5.85] 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R28 -Variables - Type Name - Numerical PetalLength - Numerical UpperPetalWidth - -Variables stats -PetalLength -Interval Frequency Coverage -]-inf;4.75] 65 0.619048 -]4.75;+inf[ 40 0.380952 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.65] 70 0.666667 -]1.65;+inf[ 35 0.333333 - -Cell frequencies - UpperPetalWidth -PetalLength ]-inf;1.65] ]1.65;+inf[ Total Coverage -]-inf;4.75] 65 0 65 0.619048 -]4.75;+inf[ 5 35 40 0.380952 -Total 70 35 105 -Coverage 0.666667 0.333333 - -Cells 3 -Cell Id PetalLength UpperPetalWidth Frequency Coverage -C1 ]-inf;4.75] ]-inf;1.65] 65 0.619048 -C4 ]4.75;+inf[ ]1.65;+inf[ 35 0.333333 -C2 ]4.75;+inf[ ]-inf;1.65] 5 0.047619 - Total 105 1 - ----------------------------------------------- -Rank R29 -Variables - Type Name - Numerical PetalWidth - Numerical SepalLength - -Variables stats -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.35] 18 0.171429 -]1.35;+inf[ 49 0.466667 -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;5.85] 17 0.161905 -]5.85;+inf[ 49 0.466667 - -Cell frequencies - SepalLength -PetalWidth ]-inf;5.45] ]5.45;5.85] ]5.85;+inf[ Total Coverage -]-inf;0.75] 34 4 0 38 0.361905 -]0.75;1.35] 3 10 5 18 0.171429 -]1.35;+inf[ 2 3 44 49 0.466667 -Total 39 17 49 105 -Coverage 0.371429 0.161905 0.466667 - -Cells 8 -Cell Id PetalWidth SepalLength Frequency Coverage -C9 ]1.35;+inf[ ]5.85;+inf[ 44 0.419048 -C1 ]-inf;0.75] ]-inf;5.45] 34 0.32381 -C5 ]0.75;1.35] ]5.45;5.85] 10 0.0952381 -C8 ]0.75;1.35] ]5.85;+inf[ 5 0.047619 -C4 ]-inf;0.75] ]5.45;5.85] 4 0.0380952 -C2 ]0.75;1.35] ]-inf;5.45] 3 0.0285714 -C6 ]1.35;+inf[ ]5.45;5.85] 3 0.0285714 -C3 ]1.35;+inf[ ]-inf;5.45] 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R30 -Variables - Type Name - Numerical LowerPetalLength - Numerical SepalLength - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -SepalLength -Interval Frequency Coverage -]-inf;5.45] 39 0.371429 -]5.45;+inf[ 66 0.628571 - -Cell frequencies - SepalLength -LowerPetalLength ]-inf;5.45] ]5.45;+inf[ Total Coverage -]-inf;2.4] 34 4 38 0.361905 -]2.4;+inf[ 5 62 67 0.638095 -Total 39 66 105 -Coverage 0.371429 0.628571 - -Cells 4 -Cell Id LowerPetalLength SepalLength Frequency Coverage -C4 ]2.4;+inf[ ]5.45;+inf[ 62 0.590476 -C1 ]-inf;2.4] ]-inf;5.45] 34 0.32381 -C2 ]2.4;+inf[ ]-inf;5.45] 5 0.047619 -C3 ]-inf;2.4] ]5.45;+inf[ 4 0.0380952 - Total 105 1 - ----------------------------------------------- -Rank R31 -Variables - Type Name - Categorical Class1 - Numerical UpperPetalWidth - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -Class1 ]-inf;1.55] ]1.55;+inf[ Total Coverage -{} 29 38 67 0.638095 -{setosa} 38 0 38 0.361905 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 3 -Cell Id Class1 UpperPetalWidth Frequency Coverage -C3 {} ]1.55;+inf[ 38 0.361905 -C2 {setosa} ]-inf;1.55] 38 0.361905 -C1 {} ]-inf;1.55] 29 0.27619 - Total 105 1 - ----------------------------------------------- -Rank R32 -Variables - Type Name - Numerical SepalLength - Numerical UpperPetalWidth - -Variables stats -SepalLength -Interval Frequency Coverage -]-inf;5.85] 56 0.533333 -]5.85;+inf[ 49 0.466667 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -SepalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage -]-inf;5.85] 54 2 56 0.533333 -]5.85;+inf[ 13 36 49 0.466667 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 4 -Cell Id SepalLength UpperPetalWidth Frequency Coverage -C1 ]-inf;5.85] ]-inf;1.55] 54 0.514286 -C4 ]5.85;+inf[ ]1.55;+inf[ 36 0.342857 -C2 ]5.85;+inf[ ]-inf;1.55] 13 0.12381 -C3 ]-inf;5.85] ]1.55;+inf[ 2 0.0190476 - Total 105 1 - ----------------------------------------------- -Rank R33 -Variables - Type Name - Categorical Class1 - Numerical SepalWidth - -Variables stats -Class1 -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * -SepalWidth -Interval Frequency Coverage -]-inf;2.85] 30 0.285714 -]2.85;3.35] 49 0.466667 -]3.35;+inf[ 26 0.247619 - -Cell frequencies - SepalWidth -Class1 ]-inf;2.85] ]2.85;3.35] ]3.35;+inf[ Total Coverage -{} 30 32 5 67 0.638095 -{setosa} 0 17 21 38 0.361905 -Total 30 49 26 105 -Coverage 0.285714 0.466667 0.247619 - -Cells 5 -Cell Id Class1 SepalWidth Frequency Coverage -C3 {} ]2.85;3.35] 32 0.304762 -C1 {} ]-inf;2.85] 30 0.285714 -C6 {setosa} ]3.35;+inf[ 21 0.2 -C4 {setosa} ]2.85;3.35] 17 0.161905 -C5 {} ]3.35;+inf[ 5 0.047619 - Total 105 1 - ----------------------------------------------- -Rank R34 -Variables - Type Name - Categorical Class2 - Numerical LowerPetalLength - -Variables stats -Class2 -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 - -Cell frequencies - LowerPetalLength -Class2 ]-inf;2.4] ]2.4;+inf[ Total Coverage -{} 38 35 73 0.695238 -{versicolor} 0 32 32 0.304762 -Total 38 67 105 -Coverage 0.361905 0.638095 - -Cells 3 -Cell Id Class2 LowerPetalLength Frequency Coverage -C1 {} ]-inf;2.4] 38 0.361905 -C3 {} ]2.4;+inf[ 35 0.333333 -C4 {versicolor} ]2.4;+inf[ 32 0.304762 - Total 105 1 - ----------------------------------------------- -Rank R35 -Variables - Type Name - Categorical Class - Numerical SepalWidth - -Variables stats -Class -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * -SepalWidth -Interval Frequency Coverage -]-inf;2.95] 36 0.342857 -]2.95;+inf[ 69 0.657143 - -Cell frequencies - SepalWidth -Class ]-inf;2.95] ]2.95;+inf[ Total Coverage -{Iris-setosa} 1 37 38 0.361905 -{Iris-virginica} 13 22 35 0.333333 -{Iris-versicolor} 22 10 32 0.304762 -Total 36 69 105 -Coverage 0.342857 0.657143 - -Cells 6 -Cell Id Class SepalWidth Frequency Coverage -C4 {Iris-setosa} ]2.95;+inf[ 37 0.352381 -C5 {Iris-virginica} ]2.95;+inf[ 22 0.209524 -C3 {Iris-versicolor} ]-inf;2.95] 22 0.209524 -C2 {Iris-virginica} ]-inf;2.95] 13 0.12381 -C6 {Iris-versicolor} ]2.95;+inf[ 10 0.0952381 -C1 {Iris-setosa} ]-inf;2.95] 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R36 -Variables - Type Name - Categorical SPetalLength - Numerical SepalWidth - -Variables stats -SPetalLength -Group Frequency Coverage Size Value list -{4} 40 0.380952 3 4 * -{1} 38 0.361905 1 1 -{5} 27 0.257143 1 5 -SepalWidth -Interval Frequency Coverage -]-inf;2.95] 36 0.342857 -]2.95;3.25] 41 0.390476 -]3.25;+inf[ 28 0.266667 - -Cell frequencies - SepalWidth -SPetalLength ]-inf;2.95] ]2.95;3.25] ]3.25;+inf[ Total Coverage -{4} 26 10 4 40 0.380952 -{1} 1 15 22 38 0.361905 -{5} 9 16 2 27 0.257143 -Total 36 41 28 105 -Coverage 0.342857 0.390476 0.266667 - -Cells 9 -Cell Id SPetalLength SepalWidth Frequency Coverage -C1 {4} ]-inf;2.95] 26 0.247619 -C8 {1} ]3.25;+inf[ 22 0.209524 -C6 {5} ]2.95;3.25] 16 0.152381 -C5 {1} ]2.95;3.25] 15 0.142857 -C4 {4} ]2.95;3.25] 10 0.0952381 -C3 {5} ]-inf;2.95] 9 0.0857143 -C7 {4} ]3.25;+inf[ 4 0.0380952 -C9 {5} ]3.25;+inf[ 2 0.0190476 -C2 {1} ]-inf;2.95] 1 0.00952381 - Total 105 1 - ----------------------------------------------- -Rank R37 -Variables - Type Name - Numerical LowerPetalLength - Numerical UpperPetalWidth - -Variables stats -LowerPetalLength -Interval Frequency Coverage -]-inf;2.4] 38 0.361905 -]2.4;+inf[ 67 0.638095 -UpperPetalWidth -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - -Cell frequencies - UpperPetalWidth -LowerPetalLength ]-inf;1.55] ]1.55;+inf[ Total Coverage -]-inf;2.4] 38 0 38 0.361905 -]2.4;+inf[ 29 38 67 0.638095 -Total 67 38 105 -Coverage 0.638095 0.361905 - -Cells 3 -Cell Id LowerPetalLength UpperPetalWidth Frequency Coverage -C1 ]-inf;2.4] ]-inf;1.55] 38 0.361905 -C4 ]2.4;+inf[ ]1.55;+inf[ 38 0.361905 -C2 ]2.4;+inf[ ]-inf;1.55] 29 0.27619 - Total 105 1 - ----------------------------------------------- -Rank R38 -Variables - Type Name - Numerical PetalWidth - Numerical SepalWidth - -Variables stats -PetalWidth -Interval Frequency Coverage -]-inf;0.75] 38 0.361905 -]0.75;1.45] 24 0.228571 -]1.45;+inf[ 43 0.409524 -SepalWidth -Interval Frequency Coverage -]-inf;2.95] 36 0.342857 -]2.95;+inf[ 69 0.657143 - -Cell frequencies - SepalWidth -PetalWidth ]-inf;2.95] ]2.95;+inf[ Total Coverage -]-inf;0.75] 1 37 38 0.361905 -]0.75;1.45] 21 3 24 0.228571 -]1.45;+inf[ 14 29 43 0.409524 -Total 36 69 105 -Coverage 0.342857 0.657143 - -Cells 6 -Cell Id PetalWidth SepalWidth Frequency Coverage -C4 ]-inf;0.75] ]2.95;+inf[ 37 0.352381 -C6 ]1.45;+inf[ ]2.95;+inf[ 29 0.27619 -C2 ]0.75;1.45] ]-inf;2.95] 21 0.2 -C3 ]1.45;+inf[ ]-inf;2.95] 14 0.133333 -C5 ]0.75;1.45] ]2.95;+inf[ 3 0.0285714 -C1 ]-inf;0.75] ]-inf;2.95] 1 0.00952381 - Total 105 1 diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls deleted file mode 100644 index 64abcac16..000000000 --- a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.PreparationReport.xls +++ /dev/null @@ -1,205 +0,0 @@ -#Khiops 10.4.6i -Descriptive statistics - - -Problem description -Short description - -Dictionary Iris -Variables - Categorical 4 - Numerical 8 - Total 12 - -Database ../../../datasets/Iris/Iris.txt -Sample percentage 70 -Sampling mode Include sample -Selection variable -Selection value -Instances 105 - -Learning task Unsupervised analysis - -Evaluated variables 12 -Native variables 5 -Constructed variables 7 - -Max number of constructed variables 0 -Max number of text features 0 -Max number of trees 0 -Max number of variable pairs 100 -Discretization MODL -Value grouping MODL - - -Categorical variables statistics - -Rank Name Values Mode Mode coverage Missing number Sparse missing number Constr. cost Derivation rule -R01 Class 3 Iris-setosa 0.361905 0 0 3.17805 -R02 Class1 2 0.638095 67 0 3.17805 IfC(EQc(Class, "Iris-setosa"), "setosa", "") -R03 Class2 2 0.695238 73 0 3.17805 IfC(EQc(Class, "Iris-versicolor"), "versicolor", "") -R09 SPetalLength 5 1 0.361905 0 0 3.17805 AsCategorical(Floor(PetalLength)) - - -Numerical variables statistics - -Rank Name Values Min Max Mean Std dev Missing number Sparse missing number Constr. cost Derivation rule -R04 Dummy1 1 0 0 0 0 0 0 3.17805 Copy(0) -R05 Dummy2 105 0.005121241265 0.9859650261 0.5173966838 0.2650019122 0 0 3.17805 Random() -R06 LowerPetalLength 10 1 3 2.446666667 0.7433600251 0 0 3.17805 If(LE(PetalLength, 3), PetalLength, 3) -R07 PetalLength 36 1 6.9 3.686666667 1.80132579 0 0 3.17805 -R08 PetalWidth 21 0.1 2.5 1.175238095 0.7880996979 0 0 3.17805 -R10 SepalLength 31 4.3 7.7 5.827619048 0.8375127846 0 0 3.17805 -R11 SepalWidth 23 2 4.4 3.081904762 0.4284592446 0 0 3.17805 -R12 UpperPetalWidth 11 1.5 2.5 1.692380952 0.2962287527 0 0 3.17805 If(GE(PetalWidth, 1.5), PetalWidth, 1.5) - - --------------------------------------------------------------------------------- - -Variables detailed statistics - - -Rank R01 -Variable Categorical Class - -Variable stats -Group Frequency Coverage Size Value list -{Iris-setosa} 38 0.361905 1 Iris-setosa -{Iris-virginica} 35 0.333333 1 Iris-virginica -{Iris-versicolor} 32 0.304762 1 Iris-versicolor * - - -Values -Value Frequency Coverage -Iris-setosa 38 0.361905 -Iris-virginica 35 0.333333 -Iris-versicolor 32 0.304762 - ----------------------------------------------- -Rank R02 -Variable Categorical Class1 - -Variable stats -Group Frequency Coverage Size Value list -{} 67 0.638095 1 -{setosa} 38 0.361905 1 setosa * - - -Values -Value Frequency Coverage - 67 0.638095 -setosa 38 0.361905 - ----------------------------------------------- -Rank R03 -Variable Categorical Class2 - -Variable stats -Group Frequency Coverage Size Value list -{} 73 0.695238 1 -{versicolor} 32 0.304762 1 versicolor * - - -Values -Value Frequency Coverage - 73 0.695238 -versicolor 32 0.304762 - ----------------------------------------------- -Rank R05 -Variable Numerical Dummy2 - -Variable stats -Interval Frequency Coverage -]-inf;+inf[ 105 1 - - ----------------------------------------------- -Rank R06 -Variable Numerical LowerPetalLength - -Variable stats -Interval Frequency Coverage -]-inf;1.25] 4 0.0380952 -]1.25;1.75] 32 0.304762 -]1.75;2.95] 2 0.0190476 -]2.95;+inf[ 67 0.638095 - - ----------------------------------------------- -Rank R07 -Variable Numerical PetalLength - -Variable stats -Interval Frequency Coverage -]-inf;1.25] 4 0.0380952 -]1.25;1.75] 32 0.304762 -]1.75;3.85] 7 0.0666667 -]3.85;6.15] 59 0.561905 -]6.15;+inf[ 3 0.0285714 - - ----------------------------------------------- -Rank R08 -Variable Numerical PetalWidth - -Variable stats -Interval Frequency Coverage -]-inf;0.15] 6 0.0571429 -]0.15;0.25] 20 0.190476 -]0.25;0.45] 11 0.104762 -]0.45;0.85] 1 0.00952381 -]0.85;+inf[ 67 0.638095 - - ----------------------------------------------- -Rank R09 -Variable Categorical SPetalLength - -Variable stats -Group Frequency Coverage Size Value list -{1} 38 0.361905 1 1 -{5} 27 0.257143 1 5 -{4} 25 0.238095 1 4 -{3} 8 0.0761905 1 3 -{6} 7 0.0666667 1 6 * - - -Values -Value Frequency Coverage -1 38 0.361905 -5 27 0.257143 -4 25 0.238095 -3 8 0.0761905 -6 7 0.0666667 - ----------------------------------------------- -Rank R10 -Variable Numerical SepalLength - -Variable stats -Interval Frequency Coverage -]-inf;7] 97 0.92381 -]7;+inf[ 8 0.0761905 - - ----------------------------------------------- -Rank R11 -Variable Numerical SepalWidth - -Variable stats -Interval Frequency Coverage -]-inf;2.45] 5 0.047619 -]2.45;3.25] 72 0.685714 -]3.25;+inf[ 28 0.266667 - - ----------------------------------------------- -Rank R12 -Variable Numerical UpperPetalWidth - -Variable stats -Interval Frequency Coverage -]-inf;1.55] 67 0.638095 -]1.55;+inf[ 38 0.361905 - diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.khj b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.khj deleted file mode 100644 index bfc1926d9..000000000 --- a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/AnalysisResults.khj +++ /dev/null @@ -1,2716 +0,0 @@ -{ - "tool": "Khiops", - "version": "10.4.6i", - "shortDescription": "", - "preparationReport": { - "reportType": "Preparation", - "summary": { - "dictionary": "Iris", - "variables": { - "types": [ - "Categorical", - "Numerical" - ], - "numbers": [ - 4, - 8 - ] - }, - "database": "..\/..\/..\/datasets\/Iris\/Iris.txt", - "samplePercentage": 70, - "samplingMode": "Include sample", - "selectionVariable": "", - "selectionValue": "", - "instances": 105, - "learningTask": "Unsupervised analysis", - "evaluatedVariables": 12, - "nativeVariables": 5, - "constructedVariables": 7, - "featureEngineering": { - "maxNumberOfConstructedVariables": 0, - "maxNumberOfTextFeatures": 0, - "maxNumberOfTrees": 0, - "maxNumberOfVariablePairs": 100 - }, - "discretization": "MODL", - "valueGrouping": "MODL" - }, - "variablesStatistics": [ - { - "rank": "R01", - "name": "Class", - "type": "Categorical", - "values": 3, - "mode": "Iris-setosa", - "modeFrequency": 38, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805 - }, - { - "rank": "R02", - "name": "Class1", - "type": "Categorical", - "values": 2, - "mode": "", - "modeFrequency": 67, - "missingNumber": 67, - "sparseMissingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "IfC(EQc(Class, \"Iris-setosa\"), \"setosa\", \"\")" - }, - { - "rank": "R03", - "name": "Class2", - "type": "Categorical", - "values": 2, - "mode": "", - "modeFrequency": 73, - "missingNumber": 73, - "sparseMissingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "IfC(EQc(Class, \"Iris-versicolor\"), \"versicolor\", \"\")" - }, - { - "rank": "R04", - "name": "Dummy1", - "type": "Numerical", - "values": 1, - "min": 0, - "max": 0, - "mean": 0, - "stdDev": 0, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "Copy(0)" - }, - { - "rank": "R05", - "name": "Dummy2", - "type": "Numerical", - "values": 105, - "min": 0.005121241265, - "max": 0.9859650261, - "mean": 0.5173966838, - "stdDev": 0.2650019122, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "Random()" - }, - { - "rank": "R06", - "name": "LowerPetalLength", - "type": "Numerical", - "values": 10, - "min": 1, - "max": 3, - "mean": 2.446666667, - "stdDev": 0.7433600251, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "If(LE(PetalLength, 3), PetalLength, 3)" - }, - { - "rank": "R07", - "name": "PetalLength", - "type": "Numerical", - "values": 36, - "min": 1, - "max": 6.9, - "mean": 3.686666667, - "stdDev": 1.80132579, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805 - }, - { - "rank": "R08", - "name": "PetalWidth", - "type": "Numerical", - "values": 21, - "min": 0.1, - "max": 2.5, - "mean": 1.175238095, - "stdDev": 0.7880996979, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805 - }, - { - "rank": "R09", - "name": "SPetalLength", - "type": "Categorical", - "values": 5, - "mode": "1", - "modeFrequency": 38, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "AsCategorical(Floor(PetalLength))" - }, - { - "rank": "R10", - "name": "SepalLength", - "type": "Numerical", - "values": 31, - "min": 4.3, - "max": 7.7, - "mean": 5.827619048, - "stdDev": 0.8375127846, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805 - }, - { - "rank": "R11", - "name": "SepalWidth", - "type": "Numerical", - "values": 23, - "min": 2, - "max": 4.4, - "mean": 3.081904762, - "stdDev": 0.4284592446, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805 - }, - { - "rank": "R12", - "name": "UpperPetalWidth", - "type": "Numerical", - "values": 11, - "min": 1.5, - "max": 2.5, - "mean": 1.692380952, - "stdDev": 0.2962287527, - "missingNumber": 0, - "sparseMissingNumber": 0, - "constructionCost": 3.17805, - "derivationRule": "If(GE(PetalWidth, 1.5), PetalWidth, 1.5)" - } - ], - "variablesDetailedStatistics": { - "R01": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "Class", - "type": "Categorical", - "partitionType": "Value groups", - "partition": [ - ["Iris-setosa"], - ["Iris-virginica"], - ["Iris-versicolor"] - ], - "defaultGroupIndex": 2 - } - ], - "frequencies": [38,35,32] - }, - "inputValues": { - "values": ["Iris-setosa","Iris-virginica","Iris-versicolor"], - "frequencies": [38,35,32] - } - }, - "R02": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "Class1", - "type": "Categorical", - "partitionType": "Value groups", - "partition": [ - [""], - ["setosa"] - ], - "defaultGroupIndex": 1 - } - ], - "frequencies": [67,38] - }, - "inputValues": { - "values": ["","setosa"], - "frequencies": [67,38] - } - }, - "R03": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "Class2", - "type": "Categorical", - "partitionType": "Value groups", - "partition": [ - [""], - ["versicolor"] - ], - "defaultGroupIndex": 1 - 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"dimensions": [ - { - "variable": "SPetalLength", - "type": "Categorical", - "partitionType": "Value groups", - "partition": [ - ["4"], - ["1"], - ["5"] - ], - "defaultGroupIndex": 0 - }, - { - "variable": "SepalWidth", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [2,2.95], - [2.95,3.25], - [3.25,4.4] - ] - } - ], - "cellIds": ["C1","C2","C3","C4","C5","C6","C7","C8","C9"], - "cellPartIndexes": [ - [0,0], - [1,0], - [2,0], - [0,1], - [1,1], - [2,1], - [0,2], - [1,2], - [2,2] - ], - "cellFrequencies": [26,1,9,10,15,16,4,22,2] - } - }, - "R37": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "LowerPetalLength", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [1,2.4], - [2.4,3] - ] - }, - { - "variable": "UpperPetalWidth", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [1.5,1.55], - [1.55,2.5] - ] - } - ], - "cellIds": ["C1","C2","C4"], - "cellPartIndexes": [ - [0,0], - [1,0], - [1,1] - ], - "cellFrequencies": [38,29,38] - } - }, - "R38": { - "dataGrid": { - "isSupervised": false, - "dimensions": [ - { - "variable": "PetalWidth", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [0.1,0.75], - [0.75,1.45], - [1.45,2.5] - ] - }, - { - "variable": "SepalWidth", - "type": "Numerical", - "partitionType": "Intervals", - "partition": [ - [2,2.95], - [2.95,4.4] - ] - } - ], - "cellIds": ["C1","C2","C3","C4","C5","C6"], - "cellPartIndexes": [ - [0,0], - [1,0], - [2,0], - [0,1], - [1,1], - [2,1] - ], - "cellFrequencies": [1,21,14,37,3,29] - } - } - } - }, - "khiops_encoding": "ascii" -} diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/err.txt b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/err.txt deleted file mode 100644 index 0e8a46c9e..000000000 --- a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/err.txt +++ /dev/null @@ -1,6 +0,0 @@ -Train unsupervised model -Database ../../../datasets/Iris/Iris.txt: Read records: 150 Selected records: 105 -Evaluation of variable pairs -Data preparation time: 0:00:00.31 -Write report ./results/AnalysisResults.khj - diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/time.log b/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/time.log deleted file mode 100644 index d11750ce1..000000000 --- a/test/LearningTest/TestKhiops/Standard/IrisU2D/results.ref/time.log +++ /dev/null @@ -1 +0,0 @@ -Overal time: 0.4437694549560547 diff --git a/test/LearningTest/TestKhiops/Standard/IrisU2D/test.prm b/test/LearningTest/TestKhiops/Standard/IrisU2D/test.prm deleted file mode 100644 index 635c9360e..000000000 --- a/test/LearningTest/TestKhiops/Standard/IrisU2D/test.prm +++ /dev/null @@ -1,58 +0,0 @@ -// Thu Jun 12 13:31:06 2008 -// modl -// Output command file -// -//This file contains recorded commands, that can be replayed. -//Commands are based on user interactions: -// field update -// list index selection -// menu action -//Every command can be commented, using //. -//For example, commenting the last Exit command will allow other -//user interactions, after the commands have been replayed. -// -// - -// -> Data preparation and scoring -AnalysisSpec.SystemParameters.APIMode true // API mode -AnalysisSpec.SystemParameters.MemoryLimit 2000 // Memory limit in MB -ClassManagement.OpenFile // Open... - -// -> Open -ClassFileName ../../../datasets/Iris/Iris.kdic // Dictionary file -ClassFileName ./IrisExtended.kdic // Dictionary file -OK // Open -// <- Open - -TrainDatabase.ClassName Iris // Dictionary - -TrainDatabase.DatabaseSpec.Data.DatabaseFiles.List.Key Iris // List item selection -TrainDatabase.DatabaseSpec.Data.DatabaseFiles.DataTableName ../../../datasets/Iris/Iris.txt // Database file -TrainDatabase.DatabaseSpec.Sampling.SampleNumberPercentage 70 // Sample percentage - - -AnalysisSpec.TargetAttributeName // Target variable - -AnalysisSpec.PredictorsSpec.AdvancedSpec.DataPreparationOnly true // Do data preparation only - -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxAttributePairNumber 100 // Max number of pairs of variables - -AnalysisResults.ReportFileName ./results/AnalysisResults.khj // Analysis report - - - -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTextFeatureNumber 0 // Max number of text features -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxTreeNumber 0 // Max Tree number -AnalysisSpec.PredictorsSpec.ConstructionSpec.MaxConstructedAttributeNumber 0 // Max number of constructed variables -AnalysisSpec.PredictorsSpec.ConstructionSpec.KeepSelectedAttributesOnly false // Keep selected variables only -AnalysisResults.ExportAsXls true // Export as xls -ComputeStats // Analyse database - -Exit // Close -// <- Data preparation and scoring - - -// -> Data preparation and scoring -OK // Close -// <- Data preparation and scoring - diff --git a/test/Utils/TestServices.cpp b/test/Utils/TestServices.cpp index dcdbbd32b..4c03a6615 100644 --- a/test/Utils/TestServices.cpp +++ b/test/Utils/TestServices.cpp @@ -184,7 +184,6 @@ boolean TestAndCompareResults(const char* sTestPath, const char* test_suite, con ALString sFileName; FILE* stream; int fdInit; - int fd; // Nommmage du ficher de sortie d'apres test suit et test name de googleTest sFileName = sTmp + test_suite + "_" + test_name + ".txt"; From 4154648bb6d83092892aaacbef3a849e2394ce4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marc=20Boull=C3=A9?= Date: Thu, 5 Oct 2023 10:28:33 +0200 Subject: [PATCH 17/17] V10.4.8i, Khiops V11, cc IV, refactoring optimization finalization Changements principaux - nouvelle hierarchie de classes KWDGValueSet, avec specialisation Symbol ou VarPart - simplification importante du code - prise en compte correcte des valeurs manquantes - nouveau format des rapport json, complet - mise en place de profiling - correction de la gestion des typivalites negatives - correction de problemes de dimensionnement dans le cas de tres nombreuses variables internes Mise en phase avec les branches dev-v11 et dev - correction des errors et warnings de compilation sur toutes les plateformes - mis en place de jeux de test de LearningTest specifique V11, avec nouveaux scenarios et resultats de reference - supression de la section specifique clang du CMakeLists.txt principal, desormais inutile --- CMakeLists.txt | 12 ------------ src/Learning/KWUtils/KWKhiopsVersion.h | 2 +- 2 files changed, 1 insertion(+), 13 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 2f4923264..08e1a4947 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -225,18 +225,6 @@ function(set_unix_khiops_options target) # target_compile_options(${target} PRIVATE $<$:-fno-rtti> -fno-exceptions) - # Special options for clang, we remove the following too verbose warnings: - # - # - unsequenced modification - # - overloaded virtual functions - # - inconsistent missing override - # - # These warnings should be add back, but we have a lot of work in Khiops - if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") - target_compile_options(${target} PRIVATE -Wno-unsequenced -Wno-overloaded-virtual - -Wno-inconsistent-missing-override) - endif() - endfunction(set_unix_khiops_options) # Sets the compiling options for MSVC diff --git a/src/Learning/KWUtils/KWKhiopsVersion.h b/src/Learning/KWUtils/KWKhiopsVersion.h index 140de861b..46b8ffbbb 100644 --- a/src/Learning/KWUtils/KWKhiopsVersion.h +++ b/src/Learning/KWUtils/KWKhiopsVersion.h @@ -10,7 +10,7 @@ // dans le TaskManager de Windows (par exemple) // Version de Khiops -#define KHIOPS_VERSION str(10.4.7i) +#define KHIOPS_VERSION str(10.4.8i) // Copyright #define KHIOPS_COPYRIGHT_LABEL str((c)2023 Orange Labs - All rights reserved.)