From 9d17895ab9e8cd31e0f32e8e622e13612ea5ff77 Mon Sep 17 00:00:00 2001 From: "helma@in-silico.ch" Date: Fri, 12 Oct 2018 21:58:36 +0200 Subject: validation statistic fixes --- test/classification-validation.rb | 126 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 126 insertions(+) create mode 100644 test/classification-validation.rb (limited to 'test/classification-validation.rb') diff --git a/test/classification-validation.rb b/test/classification-validation.rb new file mode 100644 index 0000000..6ff8be0 --- /dev/null +++ b/test/classification-validation.rb @@ -0,0 +1,126 @@ +require_relative "setup.rb" + +class ValidationClassificationTest < MiniTest::Test + include OpenTox::Validation + + # defaults + + def test_default_classification_crossvalidation + dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv" + model = Model::Lazar.create training_dataset: dataset + cv = ClassificationCrossValidation.create model + assert cv.accuracy[:without_warnings] > 0.65, "Accuracy (#{cv.accuracy[:without_warnings]}) should be larger than 0.65, this may occur due to an unfavorable training/test set split" + assert cv.weighted_accuracy[:all] > cv.accuracy[:all], "Weighted accuracy (#{cv.weighted_accuracy[:all]}) should be larger than accuracy (#{cv.accuracy[:all]})." + File.open("/tmp/tmp.pdf","w+"){|f| f.puts cv.probability_plot(format:"pdf")} + assert_match "PDF", `file -b /tmp/tmp.pdf` + File.open("/tmp/tmp.png","w+"){|f| f.puts cv.probability_plot(format:"png")} + assert_match "PNG", `file -b /tmp/tmp.png` + end + + # parameters + + def test_classification_crossvalidation_parameters + dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv" + algorithms = { + :similarity => { :min => 0.3, }, + :descriptors => { :type => "FP3" } + } + model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms + cv = ClassificationCrossValidation.create model + params = model.algorithms + params = JSON.parse(params.to_json) # convert symbols to string + + cv.validations.each do |validation| + validation_params = validation.model.algorithms + refute_nil model.training_dataset_id + refute_nil validation.model.training_dataset_id + refute_equal model.training_dataset_id, validation.model.training_dataset_id + assert_equal params, validation_params + end + end + + # LOO + + def test_classification_loo_validation + dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv" + model = Model::Lazar.create training_dataset: dataset + loo = ClassificationLeaveOneOut.create model + assert_equal 77, loo.nr_unpredicted + refute_empty loo.confusion_matrix + assert loo.accuracy[:without_warnings] > 0.650 + assert loo.weighted_accuracy[:all] > loo.accuracy[:all], "Weighted accuracy (#{loo.weighted_accuracy[:all]}) should be larger than accuracy (#{loo.accuracy[:all]})." + end + + # repeated CV + + def test_repeated_crossvalidation + dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv" + model = Model::Lazar.create training_dataset: dataset + repeated_cv = RepeatedCrossValidation.create model + repeated_cv.crossvalidations.each do |cv| + assert_operator cv.accuracy[:without_warnings], :>, 0.65, "model accuracy < 0.65, this may happen by chance due to an unfavorable training/test set split" + end + end + + def test_validation_model + m = Model::Validation.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv" + [:endpoint,:species,:source].each do |p| + refute_empty m[p] + end + assert m.classification? + refute m.regression? + m.crossvalidations.each do |cv| + assert cv.accuracy[:without_warnings] > 0.65, "Crossvalidation accuracy (#{cv.accuracy[:without_warnings]}) should be larger than 0.65. This may happen due to an unfavorable training/test set split." + end + prediction = m.predict Compound.from_smiles("OCC(CN(CC(O)C)N=O)O") + assert_equal "false", prediction[:value] + m.delete + end + + def test_carcinogenicity_rf_classification + skip "Caret rf classification may run into a (endless?) loop for some compounds." + dataset = Dataset.from_csv_file "#{DATA_DIR}/multi_cell_call.csv" + algorithms = { + :prediction => { + :method => "Algorithm::Caret.rf", + }, + } + model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms + cv = ClassificationCrossValidation.create model +# cv = ClassificationCrossValidation.find "5bbc822dca626919731e2822" + puts cv.statistics + puts cv.id + + end + + def test_mutagenicity_classification_algorithms + skip "Caret rf classification may run into a (endless?) loop for some compounds." + source_feature = Feature.where(:name => "Ames test categorisation").first + target_feature = Feature.where(:name => "Mutagenicity").first + kazius = Dataset.from_sdf_file "#{DATA_DIR}/cas_4337.sdf" + hansen = Dataset.from_csv_file "#{DATA_DIR}/hansen.csv" + efsa = Dataset.from_csv_file "#{DATA_DIR}/efsa.csv" + dataset = Dataset.merge [kazius,hansen,efsa], {source_feature => target_feature}, {1 => "mutagen", 0 => "nonmutagen"} + model = Model::Lazar.create training_dataset: dataset + repeated_cv = RepeatedCrossValidation.create model + puts repeated_cv.id + repeated_cv.crossvalidations.each do |cv| + puts cv.accuracy + puts cv.confusion_matrix + end + algorithms = { + :prediction => { + :method => "Algorithm::Caret.rf", + }, + } + model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms + repeated_cv = RepeatedCrossValidation.create model + puts repeated_cv.id + repeated_cv.crossvalidations.each do |cv| + puts cv.accuracy + puts cv.confusion_matrix + end + + end + +end -- cgit v1.2.3