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require_relative "setup.rb"
class ValidationTest < MiniTest::Test
include OpenTox::Validation
# defaults
def test_default_classification_crossvalidation
dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
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})."
end
def test_default_regression_crossvalidation
dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
model = Model::Lazar.create training_dataset: dataset
cv = RegressionCrossValidation.create model
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
# parameters
def test_classification_crossvalidation_parameters
dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
algorithms = {
:similarity => { :min => 0.3, },
:descriptors => { :type => "FP3" }
}
model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms
cv = ClassificationCrossValidation.create model
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.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
end
end
def test_regression_crossvalidation_params
dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
algorithms = {
:prediction => { :method => "OpenTox::Algorithm::Regression.weighted_average" },
:descriptors => { :type => "MACCS", },
:similarity => {:min => 0.7}
}
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 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
refute_nil cv.mae
end
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::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
end
# LOO
def test_classification_loo_validation
dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
model = Model::Lazar.create training_dataset: dataset
loo = ClassificationLeaveOneOut.create model
assert_equal 14, loo.nr_unpredicted
refute_empty loo.confusion_matrix
assert loo.accuracy > 0.77
assert loo.weighted_accuracy > loo.accuracy, "Weighted accuracy (#{loo.weighted_accuracy}) should be larger than accuracy (#{loo.accuracy})."
end
def test_regression_loo_validation
dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv")
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
# repeated CV
def test_repeated_crossvalidation
dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
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"
end
end
end
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