diff options
Diffstat (limited to 'test/nanoparticles.rb')
| -rw-r--r-- | test/nanoparticles.rb | 81 |
1 files changed, 45 insertions, 36 deletions
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 |