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require_relative "setup.rb"
class LazarRegressionTest < MiniTest::Test
def test_default_regression
algorithms = {
:descriptors => {
:method => "fingerprint",
:type => "MP2D"
},
:similarity => {
:method => "Algorithm::Similarity.tanimoto",
:min => 0.5
},
:prediction => {
:method => "Algorithm::Caret.rf",
},
:feature_selection => nil,
}
training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM_log10.csv")
model = Model::Lazar.create training_dataset: training_dataset
assert_kind_of Model::LazarRegression, model
assert_equal algorithms, model.algorithms
substance = training_dataset.substances[145]
prediction = model.predict substance
assert_includes prediction[:prediction_interval][0]..prediction[:prediction_interval][1], prediction[:measurements].median, "This assertion assures that measured values are within the prediction interval. It may fail in 5% of the predictions."
substance = Compound.from_smiles "c1ccc(cc1)Oc1ccccc1"
prediction = model.predict substance
refute_nil prediction[:value]
refute_nil prediction[:prediction_interval]
refute_empty prediction[:neighbors]
end
def test_weighted_average
training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
algorithms = {
:similarity => {
:min => 0
},
:prediction => {
:method => "Algorithm::Regression.weighted_average",
},
}
model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
compound = Compound.from_smiles "CC(C)(C)CN"
prediction = model.predict compound
assert_equal -0.86, prediction[:value].round(2)
assert_equal model.substance_ids.size, prediction[:neighbors].size
end
def test_mpd_fingerprints
training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
algorithms = {
:descriptors => {
:method => "fingerprint",
:type => "MP2D"
},
}
model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
compound = Compound.from_smiles "CCCSCCSCC"
prediction = model.predict compound
assert_equal 3, prediction[:neighbors].size
assert prediction[:value].round(2) > 1.37, "Prediction value (#{prediction[:value].round(2)}) should be larger than 1.37."
end
def test_local_physchem_regression
training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
algorithms = {
:descriptors => {
:method => "calculate_properties",
:features => PhysChem.openbabel_descriptors,
},
:similarity => {
:method => "Algorithm::Similarity.weighted_cosine",
:min => 0.5
},
}
model = Model::Lazar.create(training_dataset:training_dataset, algorithms:algorithms)
compound = Compound.from_smiles "NC(=O)OCCC"
prediction = model.predict compound
refute_nil prediction[:value]
end
def test_local_physchem_regression_with_feature_selection
training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
algorithms = {
:descriptors => {
:method => "calculate_properties",
:features => PhysChem.openbabel_descriptors,
},
:similarity => {
:method => "Algorithm::Similarity.weighted_cosine",
:min => 0.5
},
:feature_selection => {
:method => "Algorithm::FeatureSelection.correlation_filter",
},
}
model = Model::Lazar.create(training_dataset:training_dataset, algorithms:algorithms)
compound = Compound.from_smiles "NC(=O)OCCC"
prediction = model.predict compound
refute_nil prediction[:value]
end
def test_unweighted_cosine_physchem_regression
algorithms = {
:descriptors => {
:method => "calculate_properties",
:features => PhysChem.openbabel_descriptors,
},
:similarity => {
:method => "Algorithm::Similarity.cosine",
}
}
training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv")
model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
assert_kind_of Model::LazarRegression, model
assert_equal "Algorithm::Caret.rf", model.algorithms[:prediction][:method]
assert_equal "Algorithm::Similarity.cosine", model.algorithms[:similarity][:method]
assert_equal 0.5, model.algorithms[:similarity][:min]
algorithms[:descriptors].delete :features
assert_equal algorithms[:descriptors], model.algorithms[:descriptors]
prediction = model.predict training_dataset.substances[10]
refute_nil prediction[:value]
end
def test_regression_with_feature_selection
algorithms = {
:feature_selection => {
:method => "Algorithm::FeatureSelection.correlation_filter",
},
}
training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM_log10.csv")
model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
assert_kind_of Model::LazarRegression, model
assert_equal "Algorithm::Caret.rf", model.algorithms[:prediction][:method]
assert_equal "Algorithm::Similarity.tanimoto", model.algorithms[:similarity][:method]
assert_equal 0.5, model.algorithms[:similarity][:min]
assert_equal algorithms[:feature_selection][:method], model.algorithms[:feature_selection][:method]
prediction = model.predict training_dataset.substances[145]
refute_nil prediction[:value]
end
def test_regression_parameters
algorithms = {
:descriptors => {
:method => "fingerprint",
:type => "MP2D"
},
:similarity => {
:method => "Algorithm::Similarity.tanimoto",
:min => 0.3
},
:prediction => {
:method => "Algorithm::Regression.weighted_average",
},
:feature_selection => nil,
}
training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv")
model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
assert_kind_of Model::LazarRegression, model
assert_equal "Algorithm::Regression.weighted_average", model.algorithms[:prediction][:method]
assert_equal "Algorithm::Similarity.tanimoto", model.algorithms[:similarity][:method]
assert_equal algorithms[:similarity][:min], model.algorithms[:similarity][:min]
assert_equal algorithms[:prediction][:parameters], model.algorithms[:prediction][:parameters]
substance = training_dataset.substances[10]
prediction = model.predict substance
assert_equal 0.83, prediction[:value].round(2)
end
end
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