1 files changed, 0 insertions, 108 deletions
diff --git a/test/validation.rb b/test/validation.rb
deleted file mode 100644
index d8eea59..0000000
--- a/test/validation.rb
+++ /dev/null
@@ -1,108 +0,0 @@
-require_relative "setup.rb"
-
-class ValidationTest < MiniTest::Test
-
-  # defaults
-  
-  def test_default_classification_crossvalidation
-    dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
-    model = Model::LazarClassification.create dataset
-    cv = ClassificationCrossValidation.create model
-    assert cv.accuracy > 0.7, "Accuracy (#{cv.accuracy}) should be larger than 0.7"
-  end
-
-  def test_default_regression_crossvalidation
-    dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
-    model = Model::LazarRegression.create dataset
-    cv = RegressionCrossValidation.create model
-    assert cv.rmse < 1.5, "RMSE > 1.5"
-    assert cv.mae < 1
-  end
-
-  # parameters
-
-  def test_classification_crossvalidation_parameters
-    dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
-    params = {
-        :training_dataset_id => dataset.id,
-      :neighbor_algorithm_parameters => {
-        :min_sim => 0.3,
-        :type => "FP3"
-      }
-    }
-    model = Model::LazarClassification.create dataset, params
-    model.save
-    cv = ClassificationCrossValidation.create model
-    params = model.neighbor_algorithm_parameters
-    params.delete :training_dataset_id
-    params = Hash[params.map{ |k, v| [k.to_s, v] }] # convert symbols to string
-
-    cv.validations.each do |validation|
-      validation_params = validation.model.neighbor_algorithm_parameters
-      validation_params.delete "training_dataset_id"
-      assert_equal params, validation_params
-    end
-  end
-  
-  def test_regression_crossvalidation_params
-    dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
-    params = {
-      :prediction_algorithm => "OpenTox::Algorithm::Regression.local_weighted_average",
-      :neighbor_algorithm => "fingerprint_neighbors",
-      :neighbor_algorithm_parameters => {
-        :type => "MACCS",
-        :min_sim => 0.7,
-      }
-    }
-    model = Model::LazarRegression.create dataset, params
-    cv = RegressionCrossValidation.create model
-    cv.validation_ids.each do |vid|
-      model = Model::Lazar.find(Validation.find(vid).model_id)
-      assert_equal params[:neighbor_algorithm_parameters][:type], model[:neighbor_algorithm_parameters][:type]
-      assert_equal params[:neighbor_algorithm_parameters][:min_sim], model[:neighbor_algorithm_parameters][:min_sim]
-      refute_equal params[:neighbor_algorithm_parameters][:training_dataset_id], model[:neighbor_algorithm_parameters][:training_dataset_id]
-    end
-
-    refute_nil cv.rmse
-    refute_nil cv.mae 
-  end
-
-  def test_physchem_regression_crossvalidation
-
-    training_dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi.csv")
-    model = Model::LazarRegression.create(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::LazarClassification.create dataset
-    loo = ClassificationLeaveOneOutValidation.create model
-    assert_equal 14, loo.nr_unpredicted
-    refute_empty loo.confusion_matrix
-    assert loo.accuracy > 0.77
-  end
-
-  def test_regression_loo_validation
-    dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi.csv")
-    model = Model::LazarRegression.create dataset
-    loo = RegressionLeaveOneOutValidation.create model
-    assert loo.r_squared > 0.34
-  end
-
-  # repeated CV
-
-  def test_repeated_crossvalidation
-    dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
-    model = Model::LazarClassification.create 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