compound tests fixed

1 files changed, 0 insertions, 109 deletions

diff --git a/test/validation.rb b/test/validation.rb
deleted file mode 100644
index 03adf69..0000000
--- a/test/validation.rb
+++ /dev/null
@@ -1,109 +0,0 @@
-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