Publish version 0.10.2

doc-sources/changelog.rst

@@ -4,18 +4,16 @@
 Changelog
 =========
 
-Version 0.10.1
-==============
-
-- Fix ``lincs visualize`` to use criteria's min/max values and category correlation
-
-Version 0.10.0
-==============
+Versions 0.10.0 to 0.10.2
+=========================
 
 This is the first release candidate for version 1.0.0.
 
 - **Breaking**: Allow more flexible description of accepted values in the model json schema. See user guide for details.
 - **Breaking**: Rename option ``--ucncs.approach`` to ``--ucncs.strategy``
+- **Breaking**: Rename option ``--output-classified-alternatives`` to ``--output-alternatives``
+- Fix line ends on Windows
+- Fix ``lincs visualize`` to use criteria's min/max values and category correlation
 - Validate consistency with problem when loading alternatives or model files
 - Output "reproduction command" in ``lincs classify``
 - Improve documentation

doc-sources/conceptual-overview.rst

@@ -362,7 +362,7 @@ If yes, they get a partial scholarship.
 .. STOP
 
 .. START concept-example/expected-classified-alternatives.csv
-    # Reproduction command (with lincs version 0.10.2-dev): lincs classify problem.yml model.yml alternatives.csv
+    # Reproduction command (with lincs version 0.10.2): lincs classify problem.yml model.yml alternatives.csv
     name,Math,Physics,Literature,History,category
     A,1,1,1,1,"Full scholarship"
     B,1,1,1,0,"Full scholarship"

doc-sources/get-started.rst

@@ -86,7 +86,7 @@ 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 0.10.2-dev): lincs generate classification-problem 4 3 --random-seed 40
+    # Reproduction command (with lincs version 0.10.2): lincs generate classification-problem 4 3 --random-seed 40
     kind: classification-problem
     format_version: 1
     criteria:
@@ -142,7 +142,7 @@ Then generate an NCS classification model::
 
 It should look like::
 
-    # Reproduction command (with lincs version 0.10.2-dev): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
+    # Reproduction command (with lincs version 0.10.2): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
     kind: ncs-classification-model
     format_version: 1
     accepted_values:
@@ -212,7 +212,7 @@ 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 0.10.2-dev): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0
+    # Reproduction command (with lincs version 0.10.2): 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,"Category 3"
     "Alternative 2",0.731993914,0.779690981,0.598658502,0.596850157,"Category 2"
@@ -268,7 +268,7 @@ 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 0.10.2-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
+    # Reproduction command (with lincs version 0.10.2): 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
     # Termination condition: target accuracy reached
     # Number of iterations: 22
     kind: ncs-classification-model

doc-sources/user-guide.rst

@@ -475,7 +475,7 @@ Here is an example using the `Alglib <https://www.alglib.net/>`_ solver::
 It should produce a very similar model, with slight numerical differences.
 
 .. START alglib-learning/expected-alglib-trained-model.yml
-    # Reproduction command (with lincs version 0.10.2-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
+    # Reproduction command (with lincs version 0.10.2): 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
     # Termination condition: target accuracy reached
     # Number of iterations: 9
     kind: ncs-classification-model
@@ -591,7 +591,7 @@ And::
 
 They should produce a different kind of model, with the sufficient coalitions specified explicitly by their roots::
 
-    # Reproduction command (with lincs version 0.10.2-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions
+    # Reproduction command (with lincs version 0.10.2): 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:
@@ -613,7 +613,7 @@ They should produce a different kind of model, with the sufficient coalitions sp
 .. STOP
 
 .. START sat-learnings/expected-max-sat-by-separation-trained-model.yml
-    # Reproduction command (with lincs version 0.10.2-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy max-sat-by-separation
+    # Reproduction command (with lincs version 0.10.2): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy max-sat-by-separation
     kind: ncs-classification-model
     format_version: 1
     accepted_values:

docs/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 4d21c6d4dfc9ed9c5861a55078162fe0
+config: 3e5c069326af377335200e954a72817b
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/_sources/changelog.rst.txt

@@ -4,18 +4,16 @@
 Changelog
 =========
 
-Version 0.10.1
-==============
-
-- Fix ``lincs visualize`` to use criteria's min/max values and category correlation
-
-Version 0.10.0
-==============
+Versions 0.10.0 to 0.10.2
+=========================
 
 This is the first release candidate for version 1.0.0.
 
 - **Breaking**: Allow more flexible description of accepted values in the model json schema. See user guide for details.
 - **Breaking**: Rename option ``--ucncs.approach`` to ``--ucncs.strategy``
+- **Breaking**: Rename option ``--output-classified-alternatives`` to ``--output-alternatives``
+- Fix line ends on Windows
+- Fix ``lincs visualize`` to use criteria's min/max values and category correlation
 - Validate consistency with problem when loading alternatives or model files
 - Output "reproduction command" in ``lincs classify``
 - Improve documentation

docs/_sources/conceptual-overview.rst.txt

@@ -160,7 +160,7 @@ The profiles for this model look like this:
     lincs visualize classification-model problem.yml model.yml concept-example-model.png
     cp concept-example-model.png ../../../../doc-sources
 
-    lincs classify problem.yml model.yml alternatives.csv --output-classified-alternatives classified-alternatives.csv
+    lincs classify problem.yml model.yml alternatives.csv --output-alternatives classified-alternatives.csv
     diff classified-alternatives.csv expected-classified-alternatives.csv
 .. STOP
 
@@ -362,7 +362,7 @@ If yes, they get a partial scholarship.
 .. STOP
 
 .. START concept-example/expected-classified-alternatives.csv
-    # Reproduction command (with lincs version 0.10.1): lincs classify problem.yml model.yml alternatives.csv
+    # Reproduction command (with lincs version 0.10.2): lincs classify problem.yml model.yml alternatives.csv
     name,Math,Physics,Literature,History,category
     A,1,1,1,1,"Full scholarship"
     B,1,1,1,0,"Full scholarship"

docs/_sources/get-started.rst.txt

@@ -86,7 +86,7 @@ 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 0.10.1): lincs generate classification-problem 4 3 --random-seed 40
+    # Reproduction command (with lincs version 0.10.2): lincs generate classification-problem 4 3 --random-seed 40
     kind: classification-problem
     format_version: 1
     criteria:
@@ -142,7 +142,7 @@ Then generate an NCS classification model::
 
 It should look like::
 
-    # Reproduction command (with lincs version 0.10.1): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
+    # Reproduction command (with lincs version 0.10.2): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
     kind: ncs-classification-model
     format_version: 1
     accepted_values:
@@ -195,7 +195,7 @@ It should output something like:
 
 And finally generate a set of classified alternatives::
 
-    lincs generate classified-alternatives problem.yml model.yml 1000 --output-classified-alternatives learning-set.csv
+    lincs generate classified-alternatives problem.yml model.yml 1000 --output-alternatives learning-set.csv
 
 .. APPEND-TO-LAST-LINE --random-seed 42
 .. STOP
@@ -212,7 +212,7 @@ 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 0.10.1): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0
+    # Reproduction command (with lincs version 0.10.2): 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,"Category 3"
     "Alternative 2",0.731993914,0.779690981,0.598658502,0.596850157,"Category 2"
@@ -268,7 +268,7 @@ 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 0.10.1): 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 0.10.2): 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
     # Termination condition: target accuracy reached
     # Number of iterations: 22
     kind: ncs-classification-model
@@ -303,7 +303,7 @@ To see how close a trained model is to the original one, you can reclassify a te
 
 First, generate a testing set::
 
-    lincs generate classified-alternatives problem.yml model.yml 10000 --output-classified-alternatives testing-set.csv
+    lincs generate classified-alternatives problem.yml model.yml 10000 --output-alternatives testing-set.csv
 
 .. APPEND-TO-LAST-LINE --random-seed 44
 .. STOP
@@ -314,7 +314,7 @@ First, generate a testing set::
 
 And ask the trained model to classify it::
 
-    lincs classify problem.yml trained-model.yml testing-set.csv --output-classified-alternatives reclassified-testing-set.csv
+    lincs classify problem.yml trained-model.yml testing-set.csv --output-alternatives reclassified-testing-set.csv
 
 .. STOP
 

docs/_sources/user-guide.rst.txt

@@ -33,7 +33,7 @@ To describe problems, *lincs* uses YAML files conforming to the `JSON schema <ht
     # Check that the NCS model is correct (we don't have explicit commands for that, so we use generate classified-alternatives)
     lincs generate classified-alternatives problem.yml ncs-model.yml 1 >/dev/null
 
-    lincs classify problem.yml mrsort-model.yml unclassified-alternatives.csv --output-classified-alternatives classified-alternatives.csv
+    lincs classify problem.yml mrsort-model.yml unclassified-alternatives.csv --output-alternatives classified-alternatives.csv
     diff <(tail -n +2 classified-alternatives.csv) expected-classified-alternatives.csv
 .. STOP
 
@@ -336,7 +336,7 @@ With its default settings, ``lincs generate classified-alternatives`` requires o
 
     lincs generate classified-alternatives problem.yml model.yml 100
 
-.. APPEND-TO-LAST-LINE --output-classified-alternatives learning-set.csv
+.. APPEND-TO-LAST-LINE --output-alternatives learning-set.csv
 .. STOP
 
 This generates 100 random alternatives, and then classifies them according to the model.
@@ -475,7 +475,7 @@ Here is an example using the `Alglib <https://www.alglib.net/>`_ solver::
 It should produce a very similar model, with slight numerical differences.
 
 .. START alglib-learning/expected-alglib-trained-model.yml
-    # Reproduction command (with lincs version 0.10.1): 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
+    # Reproduction command (with lincs version 0.10.2): 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
     # Termination condition: target accuracy reached
     # Number of iterations: 9
     kind: ncs-classification-model
@@ -591,7 +591,7 @@ And::
 
 They should produce a different kind of model, with the sufficient coalitions specified explicitly by their roots::
 
-    # Reproduction command (with lincs version 0.10.1): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions
+    # Reproduction command (with lincs version 0.10.2): 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:
@@ -613,7 +613,7 @@ They should produce a different kind of model, with the sufficient coalitions sp
 .. STOP
 
 .. START sat-learnings/expected-max-sat-by-separation-trained-model.yml
-    # Reproduction command (with lincs version 0.10.1): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy max-sat-by-separation
+    # Reproduction command (with lincs version 0.10.2): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy max-sat-by-separation
     kind: ncs-classification-model
     format_version: 1
     accepted_values:
@@ -646,7 +646,7 @@ you can use it to classify alternatives with ``lincs classify problem.yml model.
 The ``category`` column in the input alternatives file is ignored and may be empty.
 
 Note that the input files will not be modified: the classified alternatives will be printed on the standard output
-or written in the file specified by ``--output-classified-alternatives``.
+or written in the file specified by ``--output-alternatives``.
 
 Computing a classification accuracy
 -----------------------------------

docs/_static/documentation_options.js

@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.10.1',
+    VERSION: '0.10.2',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',