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doc-sources/conceptual-overview/conceptual-overview.ipynb

@@ -21,7 +21,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs classify problem.yml model.yml alternatives.csv\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs classify problem.yml model.yml alternatives.csv\n",
       "name,Math,Physics,Literature,History,category\n",
       "A,1,1,1,1,\"Full scholarship\"\n",
       "B,1,1,1,0,\"Full scholarship\"\n",

doc-sources/get-started.rst

@@ -68,7 +68,7 @@ So, start by generating a classification problem with 4 criteria and 3 categorie
 
 The generated ``problem.yml`` should look like::
 
-    # Reproduction command (with lincs version 0.11.0): lincs generate classification-problem 4 3 --random-seed 40
+    # Reproduction command (with lincs version 0.11.1-dev): lincs generate classification-problem 4 3 --random-seed 40
     kind: classification-problem
     format_version: 1
     criteria:
@@ -132,7 +132,7 @@ Then generate an NCS classification model::
 
 It should look like::
 
-    # Reproduction command (with lincs version 0.11.0): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
+    # Reproduction command (with lincs version 0.11.1-dev): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
     kind: ncs-classification-model
     format_version: 1
     accepted_values:
@@ -199,7 +199,7 @@ Then we'll need to think about the how the ``--max-imbalance`` option interacts
 
 It should start with something like this, and contain 1000 alternatives::
 
-    # Reproduction command (with lincs version 0.11.0): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0
+    # Reproduction command (with lincs version 0.11.1-dev): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0
     name,"Criterion 1","Criterion 2","Criterion 3","Criterion 4",category
     "Alternative 1",0.37454012,0.796543002,0.95071429,0.183434784,"Best category"
     "Alternative 2",0.731993914,0.779690981,0.598658502,0.596850157,"Intermediate category 1"
@@ -237,7 +237,7 @@ The learning set doesn't contain all the information from the original model,
 and the trained model was reconstituted from this partial information,
 so it is numerically different::
 
-    # Reproduction command (with lincs version 0.11.0): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0
+    # Reproduction command (with lincs version 0.11.1-dev): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0
     kind: ncs-classification-model
     format_version: 1
     accepted_values:

doc-sources/get-started/get-started.ipynb

@@ -48,7 +48,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs generate classification-problem 4 3 --random-seed 40\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs generate classification-problem 4 3 --random-seed 40\n",
       "kind: classification-problem\n",
       "format_version: 1\n",
       "criteria:\n",
@@ -126,7 +126,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort\n",
       "kind: ncs-classification-model\n",
       "format_version: 1\n",
       "accepted_values:\n",
@@ -216,7 +216,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0\n",
       "name,\"Criterion 1\",\"Criterion 2\",\"Criterion 3\",\"Criterion 4\",category\n",
       "\"Alternative 1\",0.37454012,0.796543002,0.95071429,0.183434784,\"Best category\"\n",
       "\"Alternative 2\",0.731993914,0.779690981,0.598658502,0.596850157,\"Intermediate category 1\"\n",
@@ -270,7 +270,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0\n",
       "kind: ncs-classification-model\n",
       "format_version: 1\n",
       "accepted_values:\n",

doc-sources/user-guide.rst

@@ -473,7 +473,7 @@ And::
 
 They produce a different kind of model, with the sufficient coalitions specified explicitly by their roots::
 
-    # Reproduction command (with lincs version 0.11.0): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions
+    # Reproduction command (with lincs version 0.11.1-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions
     kind: ncs-classification-model
     format_version: 1
     accepted_values:

doc-sources/user-guide/alglib-learning/alglib-learning.ipynb

@@ -27,7 +27,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver alglib --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver alglib --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0\n",
       "kind: ncs-classification-model\n",
       "format_version: 1\n",
       "accepted_values:\n",

doc-sources/user-guide/gpu-learning/gpu-learning.ipynb

@@ -27,7 +27,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor gpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor gpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0\n",
       "kind: ncs-classification-model\n",
       "format_version: 1\n",
       "accepted_values:\n",

doc-sources/user-guide/sat-learning/sat-learning.ipynb

@@ -22,7 +22,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions\n",
       "kind: ncs-classification-model\n",
       "format_version: 1\n",
       "accepted_values:\n",
@@ -58,7 +58,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy max-sat-by-separation\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy max-sat-by-separation\n",
       "kind: ncs-classification-model\n",
       "format_version: 1\n",
       "accepted_values:\n",

doc-sources/user-guide/synthetic-data/synthetic-data.ipynb

@@ -14,7 +14,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs generate classification-problem 4 3 --random-seed 57\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs generate classification-problem 4 3 --random-seed 57\n",
       "kind: classification-problem\n",
       "format_version: 1\n",
       "criteria:\n",
@@ -63,7 +63,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs generate classification-model problem.yml --random-seed 58 --model-type mrsort\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs generate classification-model problem.yml --random-seed 58 --model-type mrsort\n",
       "kind: ncs-classification-model\n",
       "format_version: 1\n",
       "accepted_values:\n",
@@ -101,7 +101,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs generate classified-alternatives problem.yml model.yml 100 --random-seed 59 --misclassified-count 0\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs generate classified-alternatives problem.yml model.yml 100 --random-seed 59 --misclassified-count 0\n",
       "name,\"Criterion 1\",\"Criterion 2\",\"Criterion 3\",\"Criterion 4\",category\n",
       "\"Alternative 1\",0.924035132,0.804616809,0.157870576,0.637420833,\"Best category\"\n",
       "\"Alternative 2\",0.866915047,0.979161799,0.0841569453,0.397855282,\"Best category\"\n",
@@ -224,7 +224,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 60 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 60 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0\n",
       "kind: ncs-classification-model\n",
       "format_version: 1\n",
       "accepted_values:\n",

integration-tests/all-options-of-problem-generation/all-options-of-problem-generation.ipynb

@@ -10,7 +10,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Reproduction command (with lincs version 0.11.0): lincs generate classification-problem 4 3 --random-seed 42 --denormalized-min-max --allow-decreasing-criteria\n",
+      "# Reproduction command (with lincs version 0.11.1-dev): lincs generate classification-problem 4 3 --random-seed 42 --denormalized-min-max --allow-decreasing-criteria\n",
       "kind: classification-problem\n",
       "format_version: 1\n",
       "criteria:\n",

integration-tests/fail-generating-balanced-alternatives/fail-generating-balanced-alternatives.ipynb

@@ -22,7 +22,7 @@
      "output_type": "stream",
      "text": [
       "ERROR: lincs is unable to generate a balanced set of classified alternatives. Try to increase the allowed imbalance, or use a more lenient model?\n",
-      "Reproduction command (with lincs version 0.11.0): lincs generate classified-alternatives problem.yml model.yml 100 --random-seed 42 --max-imbalance 0.0 --misclassified-count 0\n"
+      "Reproduction command (with lincs version 0.11.1-dev): lincs generate classified-alternatives problem.yml model.yml 100 --random-seed 42 --max-imbalance 0.0 --misclassified-count 0\n"
      ]
     }
    ],

integration-tests/learning-failure-exception/learning-failure-exception.ipynb

@@ -23,7 +23,7 @@
      "output_type": "stream",
      "text": [
       "ERROR: lincs is unable to learn from this learning set using this algorithm and these parameters.\n",
-      "Reproduction command (with lincs version 0.11.0): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions\n"
+      "Reproduction command (with lincs version 0.11.1-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions\n"
      ]
     }
    ],