From 4348eec89033e6677c9f628646fc67bd03c73fe6 Mon Sep 17 00:00:00 2001 From: Christoph Helma Date: Thu, 6 Oct 2016 19:14:10 +0200 Subject: nano caret regression fixed --- test/all.rb | 2 +- test/model.rb | 31 ++++---------------- test/nanoparticles.rb | 81 ++++++++++++++++++++++++++++----------------------- test/validation.rb | 61 +++++++++++++++++--------------------- 4 files changed, 79 insertions(+), 96 deletions(-) (limited to 'test') diff --git a/test/all.rb b/test/all.rb index a10bcaa..eddf4e6 100644 --- a/test/all.rb +++ b/test/all.rb @@ -1,5 +1,5 @@ # "./default_environment.rb" has to be executed separately -exclude = ["./setup.rb","./all.rb", "./default_environment.rb","./nanoparticles.rb"] +exclude = ["./setup.rb","./all.rb", "./default_environment.rb"] (Dir[File.join(File.dirname(__FILE__),"*.rb")]-exclude).each do |test| require_relative test end diff --git a/test/model.rb b/test/model.rb index 563d081..02b8e73 100644 --- a/test/model.rb +++ b/test/model.rb @@ -13,7 +13,7 @@ class ModelTest < MiniTest::Test :min => 0.1 }, :prediction => { - :method => "Algorithm::Regression.caret", + :method => "Algorithm::Caret.regression", :parameters => "pls", }, :feature_selection => nil, @@ -65,7 +65,7 @@ class ModelTest < MiniTest::Test training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.mini_log10.csv") model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms assert_kind_of Model::LazarRegression, model - assert_equal "Algorithm::Regression.caret", model.algorithms[:prediction][:method] + assert_equal "Algorithm::Caret.regression", model.algorithms[:prediction][:method] assert_equal "Algorithm::Similarity.weighted_cosine", model.algorithms[:similarity][:method] assert_equal 0.1, model.algorithms[:similarity][:min] assert_equal algorithms[:descriptors], model.algorithms[:descriptors] @@ -78,7 +78,7 @@ class ModelTest < MiniTest::Test training_dataset = Dataset.where(name: "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first end model = Model::Lazar.create training_dataset: training_dataset - assert_equal "Algorithm::Regression.caret", model.algorithms[:prediction][:method] + assert_equal "Algorithm::Caret.regression", model.algorithms[:prediction][:method] assert_equal "rf", model.algorithms[:prediction][:parameters] assert_equal "Algorithm::Similarity.weighted_cosine", model.algorithms[:similarity][:method] prediction = model.predict training_dataset.substances[14] @@ -87,6 +87,7 @@ class ModelTest < MiniTest::Test end def test_nanoparticle_parameters + skip end def test_regression_with_feature_selection @@ -98,13 +99,14 @@ class ModelTest < MiniTest::Test training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.mini_log10.csv") model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms assert_kind_of Model::LazarRegression, model - assert_equal "Algorithm::Regression.caret", model.algorithms[:prediction][:method] + assert_equal "Algorithm::Caret.regression", model.algorithms[:prediction][:method] assert_equal "Algorithm::Similarity.tanimoto", model.algorithms[:similarity][:method] assert_equal 0.1, model.algorithms[:similarity][:min] assert_equal algorithms[:feature_selection][:method], model.algorithms[:feature_selection][:method] end def test_caret_parameters + skip end def test_default_classification @@ -153,25 +155,4 @@ class ModelTest < MiniTest::Test assert_equal 4, prediction[:neighbors].size end -=begin - def test_physchem_description - assert_equal 355, PhysChem.descriptors.size - assert_equal 15, PhysChem.openbabel_descriptors.size - assert_equal 295, PhysChem.cdk_descriptors.size - assert_equal 45, PhysChem.joelib_descriptors.size - assert_equal 310, PhysChem.unique_descriptors.size - end - - def test_physchem - assert_equal 355, PhysChem.descriptors.size - c = Compound.from_smiles "CC(=O)CC(C)C" - logP = PhysChem.find_or_create_by :name => "Openbabel.logP" - assert_equal 1.6215, logP.calculate(c) - jlogP = PhysChem.find_or_create_by :name => "Joelib.LogP" - assert_equal 3.5951, jlogP.calculate(c) - alogP = PhysChem.find_or_create_by :name => "Cdk.ALOGP.ALogP" - assert_equal 0.35380000000000034, alogP.calculate(c) - end -=end - end diff --git a/test/nanoparticles.rb b/test/nanoparticles.rb index 23c09e7..9b2d2d9 100644 --- a/test/nanoparticles.rb +++ b/test/nanoparticles.rb @@ -5,29 +5,26 @@ class NanoparticleTest < MiniTest::Test include OpenTox::Validation def setup - # TODO: multiple runs create duplicates - #$mongo.database.drop - #Import::Enanomapper.import File.join(File.dirname(__FILE__),"data","enm") @training_dataset = Dataset.where(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first unless @training_dataset Import::Enanomapper.import File.join(File.dirname(__FILE__),"data","enm") @training_dataset = Dataset.where(name: "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first end + @prediction_feature = @training_dataset.features.select{|f| f["name"] == 'log2(Net cell association)'}.first end def test_create_model - skip - @training_dataset = Dataset.find_or_create_by(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles") - feature = Feature.find_or_create_by(name: "Net cell association", category: "TOX", unit: "mL/ug(Mg)") - model = Model::LazarRegression.create(feature, @training_dataset, {:prediction_algorithm => "OpenTox::Algorithm::Regression.local_weighted_average", :neighbor_algorithm => "physchem_neighbors"}) + model = Model::Lazar.create training_dataset: @training_dataset nanoparticle = @training_dataset.nanoparticles[-34] prediction = model.predict nanoparticle + p prediction refute_nil prediction[:value] assert_includes nanoparticle.dataset_ids, @training_dataset.id model.delete end def test_inspect_cv + skip cv = CrossValidation.all.sort_by{|cv| cv.created_at}.last #p cv #p cv.id @@ -45,6 +42,7 @@ class NanoparticleTest < MiniTest::Test end end def test_inspect_worst_prediction + skip cv = CrossValidation.all.sort_by{|cv| cv.created_at}.last worst_predictions = cv.worst_predictions(n: 3,show_neigbors: false) @@ -64,10 +62,8 @@ class NanoparticleTest < MiniTest::Test end def test_validate_model - #feature = Feature.find_or_create_by(name: "Net cell association", category: "TOX", unit: "mL/ug(Mg)") - feature = Feature.find_or_create_by(name: "Log2 transformed", category: "TOX") - - model = Model::LazarRegression.create(feature, @training_dataset, {:prediction_algorithm => "OpenTox::Algorithm::Regression.local_weighted_average", :feature_selection_algorithm => :correlation_filter, :neighbor_algorithm => "physchem_neighbors", :neighbor_algorithm_parameters => {:min_sim => 0.5}}) + algorithms = { :prediction => {:method => "Algorithm::Regression.weighted_average" } } + model = Model::Lazar.create training_dataset: @training_dataset cv = RegressionCrossValidation.create model p cv.rmse p cv.r_squared @@ -77,17 +73,14 @@ class NanoparticleTest < MiniTest::Test end def test_validate_pls_model - feature = Feature.find_or_create_by(name: "Log2 transformed", category: "TOX") - - model = Model::LazarRegression.create(feature, @training_dataset, { - :prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression", - :feature_selection_algorithm => :correlation_filter, - :prediction_algorithm_parameters => {:method => 'pls'}, - #:feature_selection_algorithm_parameters => {:category => "P-CHEM"}, - #:feature_selection_algorithm_parameters => {:category => "Proteomics"}, - :neighbor_algorithm => "physchem_neighbors", - :neighbor_algorithm_parameters => {:min_sim => 0.5} - }) + algorithms = { + :descriptors => { + :method => "properties", + :types => ["P-CHEM"] + }, + :prediction => {:method => "Algorithm::Caret.regression", :parameters => 'pls' }, + } + model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms cv = RegressionCrossValidation.create model p cv.rmse p cv.r_squared @@ -96,17 +89,14 @@ class NanoparticleTest < MiniTest::Test end def test_validate_random_forest_model - feature = Feature.find_or_create_by(name: "Log2 transformed", category: "TOX") - - model = Model::LazarRegression.create(feature, @training_dataset, { - :prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression", - :prediction_algorithm_parameters => {:method => 'rf'}, - :feature_selection_algorithm => :correlation_filter, - #:feature_selection_algorithm_parameters => {:category => "P-CHEM"}, - #:feature_selection_algorithm_parameters => {:category => "Proteomics"}, - :neighbor_algorithm => "physchem_neighbors", - :neighbor_algorithm_parameters => {:min_sim => 0.5} - }) + algorithms = { + :descriptors => { + :method => "properties", + :types => ["P-CHEM"] + }, + :prediction => {:method => "Algorithm::Caret.regression", :parameters => 'rf' } + } + model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms cv = RegressionCrossValidation.create model p cv.rmse p cv.r_squared @@ -115,9 +105,28 @@ class NanoparticleTest < MiniTest::Test end def test_validate_proteomics_pls_model - feature = Feature.find_or_create_by(name: "Log2 transformed", category: "TOX") - - model = Model::LazarRegression.create(feature, @training_dataset, {:prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression", :neighbor_algorithm => "proteomics_neighbors", :neighbor_algorithm_parameters => {:min_sim => 0.5}}) + algorithms = { + :descriptors => { + :method => "properties", + :types => ["Proteomics"] + }, + :prediction => {:method => "Algorithm::Caret.regression", :parameters => 'rf' } + } + model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms + cv = RegressionCrossValidation.create model + p cv.rmse + p cv.r_squared + refute_nil cv.r_squared + refute_nil cv.rmse + end + + def test_validate_all_default_model + algorithms = { + :descriptors => { + :types => ["Proteomics","P-CHEM"] + }, + } + model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms cv = RegressionCrossValidation.create model p cv.rmse p cv.r_squared diff --git a/test/validation.rb b/test/validation.rb index b4f5a92..03adf69 100644 --- a/test/validation.rb +++ b/test/validation.rb @@ -7,7 +7,7 @@ class ValidationTest < MiniTest::Test def test_default_classification_crossvalidation dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv" - model = Model::LazarClassification.create dataset.features.first, dataset + model = Model::Lazar.create training_dataset: dataset cv = ClassificationCrossValidation.create model assert cv.accuracy > 0.7, "Accuracy (#{cv.accuracy}) should be larger than 0.7, this may occur due to an unfavorable training/test set split" assert cv.weighted_accuracy > cv.accuracy, "Weighted accuracy (#{cv.weighted_accuracy}) should be larger than accuracy (#{cv.accuracy})." @@ -15,9 +15,9 @@ class ValidationTest < MiniTest::Test def test_default_regression_crossvalidation dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv" - model = Model::LazarRegression.create dataset.features.first, dataset + model = Model::Lazar.create training_dataset: dataset cv = RegressionCrossValidation.create model - assert cv.rmse < 1.5, "RMSE #{cv.rmse} should be larger than 1.5, this may occur due to an unfavorable training/test set split" + assert cv.rmse < 1.5, "RMSE #{cv.rmse} should be smaller than 1.5, this may occur due to an unfavorable training/test set split" assert cv.mae < 1, "MAE #{cv.mae} should be smaller than 1, this may occur due to an unfavorable training/test set split" end @@ -25,23 +25,20 @@ class ValidationTest < MiniTest::Test def test_classification_crossvalidation_parameters dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv" - params = { - :neighbor_algorithm_parameters => { - :min_sim => 0.3, - :type => "FP3" - } + algorithms = { + :similarity => { :min => 0.3, }, + :descriptors => { :type => "FP3" } } - model = Model::LazarClassification.create dataset.features.first, dataset, params - model.save + model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms cv = ClassificationCrossValidation.create model - params = model.neighbor_algorithm_parameters + params = model.algorithms params = Hash[params.map{ |k, v| [k.to_s, v] }] # convert symbols to string - + cv.validations.each do |validation| - validation_params = validation.model.neighbor_algorithm_parameters - refute_nil params["dataset_id"] - refute_nil validation_params[:dataset_id] - refute_equal params["dataset_id"], validation_params[:dataset_id] + 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 ["min_sim","type","prediction_feature_id"].each do |k| assert_equal params[k], validation_params[k] end @@ -50,24 +47,20 @@ class ValidationTest < MiniTest::Test def test_regression_crossvalidation_params dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv" - params = { - :prediction_algorithm => "OpenTox::Algorithm::Regression.local_weighted_average", - :neighbor_algorithm => "fingerprint_neighbors", - :neighbor_algorithm_parameters => { - :type => "MACCS", - :min_sim => 0.7, - } + algorithms = { + :prediction => { :method => "OpenTox::Algorithm::Regression.weighted_average" }, + :descriptors => { :type => "MACCS", }, + :similarity => {:min => 0.7} } - model = Model::LazarRegression.create dataset.features.first, dataset, params - assert_equal params[:neighbor_algorithm_parameters][:type], model[:neighbor_algorithm_parameters][:type] + model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms + assert_equal algorithms[:descriptors][:type], model.algorithms[:descriptors][:type] cv = RegressionCrossValidation.create model cv.validation_ids.each do |vid| model = Model::Lazar.find(Validation.find(vid).model_id) - assert_equal params[:neighbor_algorithm_parameters][:type], model[:neighbor_algorithm_parameters][:type] - assert_equal params[:neighbor_algorithm_parameters][:min_sim], model[:neighbor_algorithm_parameters][:min_sim] - refute_nil model[:neighbor_algorithm_parameters][:dataset_id] - refute_equal dataset.id, model[:neighbor_algorithm_parameters][:dataset_id] - assert_equal model.training_dataset_id, model[:neighbor_algorithm_parameters][:dataset_id] + assert_equal algorithms[:descriptors][:type], model.algorithms[:descriptors][:type] + assert_equal algorithms[:similarity][:min], model.algorithms[:similarity][:min] + refute_nil model.training_dataset_id + refute_equal dataset.id, model.training_dataset_id end refute_nil cv.rmse @@ -77,7 +70,7 @@ class ValidationTest < MiniTest::Test def test_physchem_regression_crossvalidation skip # TODO: fix training_dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv") - model = Model::LazarRegression.create(training_dataset.features.first, training_dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression") + model = Model::Lazar.create(training_dataset.features.first, training_dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression") cv = RegressionCrossValidation.create model refute_nil cv.rmse refute_nil cv.mae @@ -87,7 +80,7 @@ class ValidationTest < MiniTest::Test def test_classification_loo_validation dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv" - model = Model::LazarClassification.create dataset.features.first, dataset + model = Model::Lazar.create training_dataset: dataset loo = ClassificationLeaveOneOut.create model assert_equal 14, loo.nr_unpredicted refute_empty loo.confusion_matrix @@ -97,7 +90,7 @@ class ValidationTest < MiniTest::Test def test_regression_loo_validation dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv") - model = Model::LazarRegression.create dataset.features.first, dataset + model = Model::Lazar.create training_dataset: dataset loo = RegressionLeaveOneOut.create model assert loo.r_squared > 0.34, "R^2 (#{loo.r_squared}) should be larger than 0.034" end @@ -106,7 +99,7 @@ class ValidationTest < MiniTest::Test def test_repeated_crossvalidation dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv" - model = Model::LazarClassification.create dataset.features.first, dataset + model = Model::Lazar.create training_dataset: dataset repeated_cv = RepeatedCrossValidation.create model repeated_cv.crossvalidations.each do |cv| assert_operator cv.accuracy, :>, 0.7, "model accuracy < 0.7, this may happen by chance due to an unfavorable training/test set split" -- cgit v1.2.3