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require_relative "setup.rb"
class NanoparticleValidationTest < MiniTest::Test
include OpenTox::Validation
def setup
@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_validate_default_nanoparticle_model
model = Model::Lazar.create training_dataset: @training_dataset, prediction_feature: @prediction_feature
cv = CrossValidation.create model
p cv
p cv.rmse
p cv.r_squared
#File.open("tmp.pdf","w+"){|f| f.puts cv.correlation_plot}
refute_nil cv.r_squared
refute_nil cv.rmse
end
def test_validate_pls_nanoparticle_model
algorithms = {
:descriptors => {
:method => "properties",
:categories => ["P-CHEM"]
},
:prediction => {:method => 'Algorithm::Caret.pls' },
}
model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
assert_equal "Algorithm::Caret.pls", model.algorithms[:prediction][:method]
cv = CrossValidation.create model
p cv.rmse
p cv.r_squared
refute_nil cv.r_squared
refute_nil cv.rmse
end
def test_validate_proteomics_pls_nanoparticle_model
algorithms = {
:descriptors => {
:method => "properties",
:categories => ["Proteomics"]
},
:prediction => {:method => 'Algorithm::Caret.pls' },
}
model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
assert_equal "Algorithm::Caret.pls", model.algorithms[:prediction][:method]
cv = CrossValidation.create model
p cv.rmse
p cv.r_squared
refute_nil cv.r_squared
refute_nil cv.rmse
end
def test_validate_all_default_nanoparticle_model
algorithms = {
:descriptors => {
:method => "properties",
:categories => ["Proteomics","P-CHEM"]
},
}
model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
cv = CrossValidation.create model
p cv.rmse
p cv.r_squared
refute_nil cv.r_squared
refute_nil cv.rmse
end
def test_nanoparticle_fingerprint_model
algorithms = {
:descriptors => {
:method => "fingerprint",
:type => "MP2D",
},
:similarity => {
:method => "Algorithm::Similarity.tanimoto",
:min => 0.1
},
:feature_selection => nil
}
model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
cv = CrossValidation.create model
p cv.rmse
p cv.r_squared
refute_nil cv.r_squared
refute_nil cv.rmse
end
def test_nanoparticle_fingerprint_weighted_average_model
algorithms = {
:descriptors => {
:method => "fingerprint",
:type => "MP2D",
},
:similarity => {
:method => "Algorithm::Similarity.tanimoto",
:min => 0.1
},
:prediction => { :method => "OpenTox::Algorithm::Regression.weighted_average" },
:feature_selection => nil
}
model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
cv = CrossValidation.create model
p cv.rmse
p cv.r_squared
refute_nil cv.r_squared
refute_nil cv.rmse
end
def test_nanoparticle_fingerprint_model_with_feature_selection
algorithms = {
:descriptors => {
:method => "fingerprint",
:type => "MP2D",
},
:similarity => {
:method => "Algorithm::Similarity.tanimoto",
:min => 0.1
},
}
model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
cv = CrossValidation.create model
p cv.rmse
p cv.r_squared
refute_nil cv.r_squared
refute_nil cv.rmse
end
end
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