diff --git a/doc-sources/get-started.rst b/doc-sources/get-started.rst index 4225ee63..8904043f 100644 --- a/doc-sources/get-started.rst +++ b/doc-sources/get-started.rst @@ -162,7 +162,7 @@ It should look like:: .. STOP -Note that *lincs* uses `YAML anchors and references `_ to avoid repeating the same sufficient coalitions in all profiles. +Note that *lincs* uses `YAML anchors and references `_ to avoid repeating the same sufficient coalitions again and again. All ``*coalitions`` means is "use the same value as the ``&coalitions`` anchor". The file format is documented in our :ref:`user guide `. @@ -301,9 +301,9 @@ To see how close a trained model is to the original one, you can reclassify a te .. EXTEND command-line-example/run.sh -First, generate a testing set:: +First, generate a testing set from the original model:: - lincs generate classified-alternatives problem.yml model.yml 10000 --output-alternatives testing-set.csv + lincs generate classified-alternatives problem.yml model.yml 3000 --output-alternatives testing-set.csv .. APPEND-TO-LAST-LINE --random-seed 44 .. STOP @@ -312,7 +312,7 @@ First, generate a testing set:: .. EXTEND command-line-example/run.sh -And ask the trained model to classify it:: +Then ask the trained model to classify it:: lincs classify problem.yml trained-model.yml testing-set.csv --output-alternatives reclassified-testing-set.csv @@ -324,9 +324,9 @@ And ask the trained model to classify it:: There are a few differences between the original testing set and the reclassified one:: - diff <(tail -n +2 testing-set.csv) <(tail -n +2 reclassified-testing-set.csv) + diff testing-set.csv reclassified-testing-set.csv -.. APPEND-TO-LAST-LINE >classification-diff.txt || true +.. APPEND-TO-LAST-LINE | tail -n +5 >classification-diff.txt || true .. STOP .. highlight:: diff @@ -335,62 +335,22 @@ There are a few differences between the original testing set and the reclassifie That command should show a few alternatives that are not classified the same way by the original and the trained model:: - 521c521 + 522c522 < "Alternative 520",0.617141366,0.326259822,0.901315808,0.460642993,"Category 3" --- > "Alternative 520",0.617141366,0.326259822,0.901315808,0.460642993,"Category 2" - 614c614 + 615c615 < "Alternative 613",0.547554553,0.0552174859,0.690436542,0.511019647,"Category 2" --- > "Alternative 613",0.547554553,0.0552174859,0.690436542,0.511019647,"Category 1" - 2595c2595 + 2596c2596 < "Alternative 2594",0.234433308,0.780464768,0.162389532,0.622178912,"Category 2" --- > "Alternative 2594",0.234433308,0.780464768,0.162389532,0.622178912,"Category 1" - 2609c2609 + 2610c2610 < "Alternative 2608",0.881479025,0.055544015,0.82936728,0.853676081,"Category 2" --- > "Alternative 2608",0.881479025,0.055544015,0.82936728,0.853676081,"Category 1" - 3128c3128 - < "Alternative 3127",0.146532759,0.324625522,0.926948965,0.817662537,"Category 3" - --- - > "Alternative 3127",0.146532759,0.324625522,0.926948965,0.817662537,"Category 2" - 3691c3691 - < "Alternative 3690",0.157966524,0.326220334,0.925864339,0.844398499,"Category 3" - --- - > "Alternative 3690",0.157966524,0.326220334,0.925864339,0.844398499,"Category 2" - 3774c3774 - < "Alternative 3773",0.484662831,0.325856268,0.966965079,0.980859697,"Category 3" - --- - > "Alternative 3773",0.484662831,0.325856268,0.966965079,0.980859697,"Category 2" - 4214c4214 - < "Alternative 4213",0.254853547,0.32587868,0.809560299,0.554913938,"Category 3" - --- - > "Alternative 4213",0.254853547,0.32587868,0.809560299,0.554913938,"Category 2" - 4265c4265 - < "Alternative 4264",0.533336997,0.0553873181,0.735466599,0.457309902,"Category 2" - --- - > "Alternative 4264",0.533336997,0.0553873181,0.735466599,0.457309902,"Category 1" - 5346c5346 - < "Alternative 5345",0.815349102,0.580399215,0.162403136,0.995580792,"Category 2" - --- - > "Alternative 5345",0.815349102,0.580399215,0.162403136,0.995580792,"Category 1" - 5781c5781 - < "Alternative 5780",0.333638728,0.325458288,0.69509089,0.761675119,"Category 3" - --- - > "Alternative 5780",0.333638728,0.325458288,0.69509089,0.761675119,"Category 2" - 8032c8032 - < "Alternative 8031",0.602598071,0.0554222316,0.920983374,0.00566159375,"Category 2" - --- - > "Alternative 8031",0.602598071,0.0554222316,0.920983374,0.00566159375,"Category 1" - 9689c9689 - < "Alternative 9688",0.940304875,0.885046899,0.162586793,0.515185535,"Category 2" - --- - > "Alternative 9688",0.940304875,0.885046899,0.162586793,0.515185535,"Category 1" - 9934c9934 - < "Alternative 9933",0.705289483,0.11529737,0.162508503,0.0438248962,"Category 2" - --- - > "Alternative 9933",0.705289483,0.11529737,0.162508503,0.0438248962,"Category 1" .. STOP @@ -415,7 +375,7 @@ You can also measure the classification accuracy of the trained model on that te It should be close to 100%:: - 9986/10000 + 2996/3000 .. STOP @@ -430,6 +390,6 @@ What now? If you haven't done so yet, we recommend you now read our :doc:`conceptual overview documentation `. Keep in mind that we've only demonstrated the default learning approach in this guide. -See the :ref:`learning documentation ` in our user guide for more details. +See our :doc:`user guide ` for more details. Once you're comfortable with the concepts and tooling, you can use a learning set based on real-world data and train a model that you can use to classify new real-world alternatives. diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle index bbf5f3d9..a3eb13ef 100644 Binary files a/docs/.doctrees/environment.pickle and b/docs/.doctrees/environment.pickle differ diff --git a/docs/.doctrees/get-started.doctree b/docs/.doctrees/get-started.doctree index 357b7653..2a81647e 100644 Binary files a/docs/.doctrees/get-started.doctree and b/docs/.doctrees/get-started.doctree differ diff --git a/docs/_sources/get-started.rst.txt b/docs/_sources/get-started.rst.txt index e738b887..0b81b155 100644 --- a/docs/_sources/get-started.rst.txt +++ b/docs/_sources/get-started.rst.txt @@ -162,7 +162,7 @@ It should look like:: .. STOP -Note that *lincs* uses `YAML anchors and references `_ to avoid repeating the same sufficient coalitions in all profiles. +Note that *lincs* uses `YAML anchors and references `_ to avoid repeating the same sufficient coalitions again and again. All ``*coalitions`` means is "use the same value as the ``&coalitions`` anchor". The file format is documented in our :ref:`user guide `. @@ -301,9 +301,9 @@ To see how close a trained model is to the original one, you can reclassify a te .. EXTEND command-line-example/run.sh -First, generate a testing set:: +First, generate a testing set from the original model:: - lincs generate classified-alternatives problem.yml model.yml 10000 --output-alternatives testing-set.csv + lincs generate classified-alternatives problem.yml model.yml 3000 --output-alternatives testing-set.csv .. APPEND-TO-LAST-LINE --random-seed 44 .. STOP @@ -312,7 +312,7 @@ First, generate a testing set:: .. EXTEND command-line-example/run.sh -And ask the trained model to classify it:: +Then ask the trained model to classify it:: lincs classify problem.yml trained-model.yml testing-set.csv --output-alternatives reclassified-testing-set.csv @@ -324,9 +324,9 @@ And ask the trained model to classify it:: There are a few differences between the original testing set and the reclassified one:: - diff <(tail -n +2 testing-set.csv) <(tail -n +2 reclassified-testing-set.csv) + diff testing-set.csv reclassified-testing-set.csv -.. APPEND-TO-LAST-LINE >classification-diff.txt || true +.. APPEND-TO-LAST-LINE | tail -n +5 >classification-diff.txt || true .. STOP .. highlight:: diff @@ -335,62 +335,22 @@ There are a few differences between the original testing set and the reclassifie That command should show a few alternatives that are not classified the same way by the original and the trained model:: - 521c521 + 522c522 < "Alternative 520",0.617141366,0.326259822,0.901315808,0.460642993,"Category 3" --- > "Alternative 520",0.617141366,0.326259822,0.901315808,0.460642993,"Category 2" - 614c614 + 615c615 < "Alternative 613",0.547554553,0.0552174859,0.690436542,0.511019647,"Category 2" --- > "Alternative 613",0.547554553,0.0552174859,0.690436542,0.511019647,"Category 1" - 2595c2595 + 2596c2596 < "Alternative 2594",0.234433308,0.780464768,0.162389532,0.622178912,"Category 2" --- > "Alternative 2594",0.234433308,0.780464768,0.162389532,0.622178912,"Category 1" - 2609c2609 + 2610c2610 < "Alternative 2608",0.881479025,0.055544015,0.82936728,0.853676081,"Category 2" --- > "Alternative 2608",0.881479025,0.055544015,0.82936728,0.853676081,"Category 1" - 3128c3128 - < "Alternative 3127",0.146532759,0.324625522,0.926948965,0.817662537,"Category 3" - --- - > "Alternative 3127",0.146532759,0.324625522,0.926948965,0.817662537,"Category 2" - 3691c3691 - < "Alternative 3690",0.157966524,0.326220334,0.925864339,0.844398499,"Category 3" - --- - > "Alternative 3690",0.157966524,0.326220334,0.925864339,0.844398499,"Category 2" - 3774c3774 - < "Alternative 3773",0.484662831,0.325856268,0.966965079,0.980859697,"Category 3" - --- - > "Alternative 3773",0.484662831,0.325856268,0.966965079,0.980859697,"Category 2" - 4214c4214 - < "Alternative 4213",0.254853547,0.32587868,0.809560299,0.554913938,"Category 3" - --- - > "Alternative 4213",0.254853547,0.32587868,0.809560299,0.554913938,"Category 2" - 4265c4265 - < "Alternative 4264",0.533336997,0.0553873181,0.735466599,0.457309902,"Category 2" - --- - > "Alternative 4264",0.533336997,0.0553873181,0.735466599,0.457309902,"Category 1" - 5346c5346 - < "Alternative 5345",0.815349102,0.580399215,0.162403136,0.995580792,"Category 2" - --- - > "Alternative 5345",0.815349102,0.580399215,0.162403136,0.995580792,"Category 1" - 5781c5781 - < "Alternative 5780",0.333638728,0.325458288,0.69509089,0.761675119,"Category 3" - --- - > "Alternative 5780",0.333638728,0.325458288,0.69509089,0.761675119,"Category 2" - 8032c8032 - < "Alternative 8031",0.602598071,0.0554222316,0.920983374,0.00566159375,"Category 2" - --- - > "Alternative 8031",0.602598071,0.0554222316,0.920983374,0.00566159375,"Category 1" - 9689c9689 - < "Alternative 9688",0.940304875,0.885046899,0.162586793,0.515185535,"Category 2" - --- - > "Alternative 9688",0.940304875,0.885046899,0.162586793,0.515185535,"Category 1" - 9934c9934 - < "Alternative 9933",0.705289483,0.11529737,0.162508503,0.0438248962,"Category 2" - --- - > "Alternative 9933",0.705289483,0.11529737,0.162508503,0.0438248962,"Category 1" .. STOP @@ -415,7 +375,7 @@ You can also measure the classification accuracy of the trained model on that te It should be close to 100%:: - 9986/10000 + 2996/3000 .. STOP @@ -430,6 +390,6 @@ What now? If you haven't done so yet, we recommend you now read our :doc:`conceptual overview documentation `. Keep in mind that we've only demonstrated the default learning approach in this guide. -See the :ref:`learning documentation ` in our user guide for more details. +See our :doc:`user guide ` for more details. Once you're comfortable with the concepts and tooling, you can use a learning set based on real-world data and train a model that you can use to classify new real-world alternatives. diff --git a/docs/get-started.html b/docs/get-started.html index e6d08e8a..fc7053a8 100644 --- a/docs/get-started.html +++ b/docs/get-started.html @@ -130,7 +130,7 @@

Get lincs - *coalitions -

Note that lincs uses YAML anchors and references to avoid repeating the same sufficient coalitions in all profiles. +

Note that lincs uses YAML anchors and references to avoid repeating the same sufficient coalitions again and again. All *coalitions means is “use the same value as the &coalitions anchor”.

The file format is documented in our user guide.

You can visualize it using:

@@ -195,75 +195,35 @@

Get lincs
lincs generate classified-alternatives problem.yml model.yml 10000 --output-alternatives testing-set.csv
+
First, generate a testing set from the original model:

+
lincs generate classified-alternatives problem.yml model.yml 3000 --output-alternatives testing-set.csv
 

 

-
And ask the trained model to classify it:

+
Then ask the trained model to classify it:

 
lincs classify problem.yml trained-model.yml testing-set.csv --output-alternatives reclassified-testing-set.csv
 

 

 
There are a few differences between the original testing set and the reclassified one:

-
diff <(tail -n +2 testing-set.csv) <(tail -n +2 reclassified-testing-set.csv)
+
diff testing-set.csv reclassified-testing-set.csv
 

 

 
That command should show a few alternatives that are not classified the same way by the original and the trained model:

-
521c521
+
522c522
 < "Alternative 520",0.617141366,0.326259822,0.901315808,0.460642993,"Category 3"
 ---
 > "Alternative 520",0.617141366,0.326259822,0.901315808,0.460642993,"Category 2"
-614c614
+615c615
 < "Alternative 613",0.547554553,0.0552174859,0.690436542,0.511019647,"Category 2"
 ---
 > "Alternative 613",0.547554553,0.0552174859,0.690436542,0.511019647,"Category 1"
-2595c2595
+2596c2596
 < "Alternative 2594",0.234433308,0.780464768,0.162389532,0.622178912,"Category 2"
 ---
 > "Alternative 2594",0.234433308,0.780464768,0.162389532,0.622178912,"Category 1"
-2609c2609
+2610c2610
 < "Alternative 2608",0.881479025,0.055544015,0.82936728,0.853676081,"Category 2"
 ---
 > "Alternative 2608",0.881479025,0.055544015,0.82936728,0.853676081,"Category 1"
-3128c3128
-< "Alternative 3127",0.146532759,0.324625522,0.926948965,0.817662537,"Category 3"
----
-> "Alternative 3127",0.146532759,0.324625522,0.926948965,0.817662537,"Category 2"
-3691c3691
-< "Alternative 3690",0.157966524,0.326220334,0.925864339,0.844398499,"Category 3"
----
-> "Alternative 3690",0.157966524,0.326220334,0.925864339,0.844398499,"Category 2"
-3774c3774
-< "Alternative 3773",0.484662831,0.325856268,0.966965079,0.980859697,"Category 3"
----
-> "Alternative 3773",0.484662831,0.325856268,0.966965079,0.980859697,"Category 2"
-4214c4214
-< "Alternative 4213",0.254853547,0.32587868,0.809560299,0.554913938,"Category 3"
----
-> "Alternative 4213",0.254853547,0.32587868,0.809560299,0.554913938,"Category 2"
-4265c4265
-< "Alternative 4264",0.533336997,0.0553873181,0.735466599,0.457309902,"Category 2"
----
-> "Alternative 4264",0.533336997,0.0553873181,0.735466599,0.457309902,"Category 1"
-5346c5346
-< "Alternative 5345",0.815349102,0.580399215,0.162403136,0.995580792,"Category 2"
----
-> "Alternative 5345",0.815349102,0.580399215,0.162403136,0.995580792,"Category 1"
-5781c5781
-< "Alternative 5780",0.333638728,0.325458288,0.69509089,0.761675119,"Category 3"
----
-> "Alternative 5780",0.333638728,0.325458288,0.69509089,0.761675119,"Category 2"
-8032c8032
-< "Alternative 8031",0.602598071,0.0554222316,0.920983374,0.00566159375,"Category 2"
----
-> "Alternative 8031",0.602598071,0.0554222316,0.920983374,0.00566159375,"Category 1"
-9689c9689
-< "Alternative 9688",0.940304875,0.885046899,0.162586793,0.515185535,"Category 2"
----
-> "Alternative 9688",0.940304875,0.885046899,0.162586793,0.515185535,"Category 1"
-9934c9934
-< "Alternative 9933",0.705289483,0.11529737,0.162508503,0.0438248962,"Category 2"
----
-> "Alternative 9933",0.705289483,0.11529737,0.162508503,0.0438248962,"Category 1"
 

 

 
You can also measure the classification accuracy of the trained model on that testing set:

@@ -271,7 +231,7 @@ 
Get lincs
9986/10000
+
2996/3000
 

 

 
@@ -279,7 +239,7 @@ 
Get lincs

 
If you haven’t done so yet, we recommend you now read our conceptual overview documentation.

 
Keep in mind that we’ve only demonstrated the default learning approach in this guide.
-See the learning documentation in our user guide for more details.

+See our user guide for more details.

 
Once you’re comfortable with the concepts and tooling, you can use a learning set based on real-world data and train a model that you can use to classify new real-world alternatives.

 
 
diff --git a/docs/searchindex.js b/docs/searchindex.js
index cc785e77..27114c2d 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
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MR-Sort": [[1, "textsf-u-c-textsf-ncs-a-k-a-mr-sort"]], "Classification accuracy": [[1, "classification-accuracy"]], "Synthetic data": [[1, "synthetic-data"]], "Next": [[1, "next"]], "Contributor guide": [[2, "contributor-guide"]], "Do contribute!": [[2, "do-contribute"]], "Development dependencies": [[2, "development-dependencies"]], "Development cycle": [[2, "development-cycle"]], "./run-development-cycle.sh": [[2, "run-development-cycle-sh"]], "./publish.sh": [[2, "publish-sh"]], "Directory structure": [[2, "directory-structure"]], "General design": [[2, "general-design"]], "Strategies": [[2, "strategies"], [6, "strategies"]], "But beware of virtual function calls": [[2, "but-beware-of-virtual-function-calls"]], "So, why not all templates?": [[2, "so-why-not-all-templates"]], "How-tos": [[2, "how-tos"]], "Update the documentation": [[2, "update-the-documentation"]], "Choose Python or C++ for your change": [[2, "choose-python-or-c-for-your-change"]], "Tweak an existing strategy": 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generated files": [[6, "comments-in-generated-files"]], "Generating synthetic data": [[6, "generating-synthetic-data"]], "About randomness": [[6, "about-randomness"]], "Generating a problem": [[6, "generating-a-problem"]], "Generating a model": [[6, "generating-a-model"]], "Generating alternatives": [[6, "generating-alternatives"]], "Learning a model": [[6, "learning-a-model"]], "An whole tree of options": [[6, "an-whole-tree-of-options"]], "Available learning (sub-)strategies": [[6, "available-learning-sub-strategies"]], "Weights, profiles, breed": [[6, "weights-profiles-breed"]], "General options": [[6, "general-options"]], "Termination": [[6, "termination"]], "\u201cWeights\u201d step": [[6, "weights-step"]], "\u201cProfiles\u201d step": [[6, "profiles-step"]], "\u201cBreed\u201d step": [[6, "breed-step"]], "SAT-based strategies": [[6, "sat-based-strategies"]], "Using a model": [[6, "using-a-model"]], "Classifying alternatives": [[6, "classifying-alternatives"]], "Computing a classification accuracy": [[6, "computing-a-classification-accuracy"]], "Visualizing a model and alternatives": [[6, "visualizing-a-model-and-alternatives"]], "What\u2019s next?": [[6, "what-s-next"]], "Reference": [[5, "reference"]], "The problem file": [[5, "the-problem-file"]], "The NCS model file": [[5, "the-ncs-model-file"]], "The alternatives file": [[5, "the-alternatives-file"]], "Command-line interface": [[5, "command-line-interface"]], "lincs": [[5, "lincs"]], "classification-accuracy": [[5, "lincs-classification-accuracy"]], "classify": [[5, "lincs-classify"]], "generate": [[5, "lincs-generate"]], "classification-model": [[5, "lincs-generate-classification-model"], [5, "lincs-learn-classification-model"], [5, "lincs-visualize-classification-model"]], "classification-problem": [[5, "lincs-generate-classification-problem"]], "classified-alternatives": [[5, "lincs-generate-classified-alternatives"]], "info": [[5, "lincs-info"]], "has-gpu": [[5, "lincs-info-has-gpu"]], "learn": 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