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require_relative "setup.rb"
class NanoparticleTest < 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_create_model
model = Model::Lazar.create training_dataset: @training_dataset, prediction_feature: @prediction_feature
nanoparticle = @training_dataset.nanoparticles[-34]
prediction = model.predict nanoparticle
refute_nil prediction[:value]
assert_includes nanoparticle.dataset_ids, @training_dataset.id
assert true, @prediction_feature.measured
model.delete
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.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 => { :types => ["P-CHEM"] },
:prediction => {:parameters => 'pls' },
}
model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
assert_equal "pls", model.algorithms[:prediction][:parameters]
assert_equal "Algorithm::Caret.regression", 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 => { :types => ["Proteomics"] },
:prediction => { :parameters => 'pls' }
}
model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
assert_equal "pls", model.algorithms[:prediction][:parameters]
assert_equal "Algorithm::Caret.regression", 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 => {
:types => ["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_export
skip
Dataset.all.each do |d|
puts d.to_csv
end
end
def test_import_ld
skip
dataset_ids = Import::Enanomapper.import_ld
end
end
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