1 files changed, 63 insertions, 80 deletions
diff --git a/test/validation.rb b/test/validation.rb
index 6764a32..d8eea59 100644
--- a/test/validation.rb
+++ b/test/validation.rb
@@ -2,54 +2,52 @@ require_relative "setup.rb"
 
 class ValidationTest < MiniTest::Test
 
-  def test_fminer_crossvalidation
+  # defaults
+  
+  def test_default_classification_crossvalidation
     dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
-    model = Model::LazarFminerClassification.create dataset
-    cv = ClassificationCrossValidation.create model
-    refute_empty cv.validation_ids
-    assert cv.accuracy > 0.8, "Crossvalidation accuracy lower than 0.8"
-    assert cv.weighted_accuracy > cv.accuracy, "Weighted accuracy (#{cv.weighted_accuracy}) larger than unweighted accuracy(#{cv.accuracy}) "
-  end
-
-  def test_classification_crossvalidation
-    dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
-    model = Model::LazarClassification.create dataset#, features
+    model = Model::LazarClassification.create dataset
     cv = ClassificationCrossValidation.create model
-    assert cv.accuracy > 0.7
-    File.open("tmp.svg","w+"){|f| f.puts cv.confidence_plot}
-    `inkview tmp.svg`
-    p cv.nr_unpredicted
-    p cv.accuracy
-    #assert cv.weighted_accuracy > cv.accuracy, "Weighted accuracy should be larger than unweighted accuracy."
+    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
-    #cv = RegressionCrossValidation.find '561503262b72ed54fd000001'
-    p cv.id
-    File.open("tmp.svg","w+"){|f| f.puts cv.correlation_plot}
-    `inkview tmp.svg`
-    File.open("tmp.svg","w+"){|f| f.puts cv.confidence_plot}
-    `inkview tmp.svg`
-    
-    #puts cv.misclassifications.to_yaml
-    p cv.rmse
-    p cv.weighted_rmse 
     assert cv.rmse < 1.5, "RMSE > 1.5"
-    #assert cv.weighted_rmse < cv.rmse, "Weighted RMSE (#{cv.weighted_rmse}) larger than unweighted RMSE(#{cv.rmse}) "
-    p cv.mae 
-    p cv.weighted_mae 
     assert cv.mae < 1
-    #assert cv.weighted_mae < cv.mae
   end
 
-  def test_regression_crossvalidation
+  # 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"
-    #dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.csv"
     params = {
-      :prediction_algorithm => "OpenTox::Algorithm::Regression.weighted_average",
+      :prediction_algorithm => "OpenTox::Algorithm::Regression.local_weighted_average",
       :neighbor_algorithm => "fingerprint_neighbors",
       :neighbor_algorithm_parameters => {
         :type => "MACCS",
@@ -65,61 +63,46 @@ class ValidationTest < MiniTest::Test
       refute_equal params[:neighbor_algorithm_parameters][:training_dataset_id], model[:neighbor_algorithm_parameters][:training_dataset_id]
     end
 
-    assert cv.rmse < 1.5, "RMSE > 30"
-    assert cv.mae < 1
+    refute_nil cv.rmse
+    refute_nil cv.mae 
   end
 
-  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"
-      assert_operator cv.weighted_accuracy, :>, cv.accuracy
-    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
 
-  def test_crossvalidation_parameters
+  # LOO
+
+  def test_classification_loo_validation
     dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
-    params = {
-      :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 = Hash[params.map{ |k, v| [k.to_s, v] }] # convert symbols to string
-    cv.validations.each do |validation|
-      assert_equal params, validation.model.neighbor_algorithm_parameters
-    end
+    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_physchem_regression_crossvalidation
-    skip
+  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
 
-    @descriptors = OpenTox::Algorithm::Descriptor::OBDESCRIPTORS.keys
-    refute_empty @descriptors
+  # repeated CV
 
-    # UPLOAD DATA
-    training_dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi.csv")
-    feature_dataset = Algorithm::Descriptor.physchem training_dataset, @descriptors
-    feature_dataset.save
-    scaled_feature_dataset = feature_dataset.scale
-    scaled_feature_dataset.save
-    model = Model::LazarRegression.create training_dataset
-    model.neighbor_algorithm = "physchem_neighbors"
-    model.neighbor_algorithm_parameters = {
-      :feature_calculation_algorithm => "OpenTox::Algorithm::Descriptor.physchem",
-      :descriptors => @descriptors,
-      :feature_dataset_id => scaled_feature_dataset.id,
-      :min_sim => 0.3
-    }
-    model.save
-    cv = RegressionCrossValidation.create model
-    p 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