3 files changed, 31 insertions, 2 deletions
diff --git a/test/lazar-physchem-short.rb b/test/lazar-physchem-short.rb
index ecf8aff..e74a4b9 100644
--- a/test/lazar-physchem-short.rb
+++ b/test/lazar-physchem-short.rb
@@ -2,6 +2,7 @@ require_relative "setup.rb"
 
 class LazarPhyschemDescriptorTest < MiniTest::Test
   def test_epafhm
+    skip "Physchem Regression not yet implemented."
     # check available descriptors
     @descriptors = OpenTox::Algorithm::Descriptor::DESCRIPTORS.keys
     assert_equal 111,@descriptors.size,"wrong number of physchem descriptors"
diff --git a/test/lazar-regression.rb b/test/lazar-regression.rb
new file mode 100644
index 0000000..c36f521
--- /dev/null
+++ b/test/lazar-regression.rb
@@ -0,0 +1,27 @@
+require_relative "setup.rb"
+
+class LazarRegressionTest < MiniTest::Test
+
+  def test_weighted_average
+    training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
+    model = Model::LazarRegression.create training_dataset
+    compound = Compound.from_smiles "CC(C)(C)CN"
+    prediction = model.predict compound
+    assert_equal 13.6, prediction[:value].round(1)
+    assert_equal 0.83, prediction[:confidence].round(2)
+    assert_equal 1, prediction[:neighbors].size
+  end
+
+  def test_weighted_average_with_relevant_fingerprints
+    training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
+    model = Model::LazarRegression.create training_dataset
+    model.update(:prediction_algorithm => "OpenTox::Algorithm::Regression.weighted_average_with_relevant_fingerprints")
+    compound = Compound.from_smiles "CC(C)(C)CN"
+    prediction = model.predict compound
+    p prediction
+    #assert_equal 13.6, prediction[:value].round(1)
+    #assert_equal 0.83, prediction[:confidence].round(2)
+    #assert_equal 1, prediction[:neighbors].size
+  end
+
+end
diff --git a/test/validation.rb b/test/validation.rb
index d98feb5..485769c 100644
--- a/test/validation.rb
+++ b/test/validation.rb
@@ -4,10 +4,11 @@ class ValidationTest < MiniTest::Test
 
   def test_fminer_crossvalidation
     dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
-    model = Model::LazarFminerClassification.create dataset#, features
+    model = Model::LazarFminerClassification.create dataset
     cv = ClassificationCrossValidation.create model
     p cv.accuracy
     p cv.weighted_accuracy
+    refute_empty cv.validation_ids
     assert cv.accuracy > 0.8
     assert cv.weighted_accuracy > cv.accuracy, "Weighted accuracy (#{cv.weighted_accuracy}) larger than unweighted accuracy(#{cv.accuracy}) "
   end
@@ -31,7 +32,7 @@ class ValidationTest < MiniTest::Test
     p cv.weighted_rmse
     p cv.mae
     p cv.weighted_mae
-    `inkview #{cv.plot}`
+    #`inkview #{cv.plot}`
     assert cv.rmse < 30, "RMSE > 30"
     assert cv.weighted_rmse < cv.rmse, "Weighted RMSE (#{cv.weighted_rmse}) larger than unweighted RMSE(#{cv.rmse}) "
     assert cv.mae < 12