diff --git a/doc-sources/conceptual-overview.rst b/doc-sources/conceptual-overview.rst index 9a02682e..1d7443d2 100644 --- a/doc-sources/conceptual-overview.rst +++ b/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): lincs classify problem.yml model.yml alternatives.csv + # Reproduction command (with lincs version 0.10.3-dev): 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" diff --git a/doc-sources/get-started.rst b/doc-sources/get-started.rst index e738b887..4225ee63 100644 --- a/doc-sources/get-started.rst +++ b/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): lincs generate classification-problem 4 3 --random-seed 40 + # Reproduction command (with lincs version 0.10.3-dev): 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): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort + # Reproduction command (with lincs version 0.10.3-dev): 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): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0 + # Reproduction command (with lincs version 0.10.3-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,"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): 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 0.10.3-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 # Termination condition: target accuracy reached # Number of iterations: 22 kind: ncs-classification-model diff --git a/doc-sources/user-guide.rst b/doc-sources/user-guide.rst index cf7ce4e9..6040ad4f 100644 --- a/doc-sources/user-guide.rst +++ b/doc-sources/user-guide.rst @@ -475,7 +475,7 @@ Here is an example using the `Alglib `_ 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): 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 0.10.3-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 # 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): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions + # Reproduction command (with lincs version 0.10.3-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: @@ -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): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy max-sat-by-separation + # Reproduction command (with lincs version 0.10.3-dev): 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: diff --git a/lincs/__init__.py b/lincs/__init__.py index 02fc8575..2b568a6b 100644 --- a/lincs/__init__.py +++ b/lincs/__init__.py @@ -1,6 +1,6 @@ # Copyright 2023 Vincent Jacques -__version__ = "0.10.2" +__version__ = "0.10.3-dev" # I/O from liblincs import DataValidationException