tests added and updated, unnecessary memory usage removed

niaaml/pipeline.py

@@ -313,12 +313,9 @@ def evaluate(D, sol):
                     if i[1] is not None:
                         i[1].set_parameters(**args)
 
-                x = copy.deepcopy(self.__x)
-                y = copy.deepcopy(self.__y)
-
                 selected_features_mask = None
 
-                x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+                x_train, x_test, y_train, y_test = train_test_split(self.__x, self.__y, test_size=0.2)
 
                 if feature_selection_algorithm is None:
                     selected_features_mask = np.ones(x.shape[1], dtype=bool)

niaaml/tests/test_feature_selection.py

@@ -0,0 +1,18 @@
+from unittest import TestCase
+from niaaml.preprocessing.feature_selection import ParticleSwarmOptimization, SelectKBest
+from niaaml.data import CSVDataReader
+import os
+
+class FeatureSelectionTestCase(TestCase):
+    def setUp(self):
+        self.__algo1 = ParticleSwarmOptimization()
+        self.__algo2 = SelectKBest()
+
+    def test_select_features_works_fine(self):
+        data_reader = CSVDataReader(src=os.path.dirname(os.path.abspath(__file__)) + '/tests_files/dataset_header_classes.csv', has_header=True, contains_classes=True)
+
+        selected_features_mask = self.__algo1.select_features(data_reader.get_x(), data_reader.get_y())
+        self.assertEqual(data_reader.get_x().shape[1], len(selected_features_mask))
+
+        selected_features_mask = self.__algo2.select_features(data_reader.get_x(), data_reader.get_y())
+        self.assertEqual(data_reader.get_x().shape[1], len(selected_features_mask))

niaaml/tests/test_pipeline_optimizer.py

@@ -19,7 +19,7 @@ def setUp(self):
         )
 
     def test_pipeline_optimizeer_run_works_fine(self):
-        pipeline = self.__pipeline_optimizer.run('Accuracy', 10, 10, 10, 10, 'ParticleSwarmAlgorithm')
+        pipeline = self.__pipeline_optimizer.run('Accuracy', 10, 10, 20, 20, 'ParticleSwarmAlgorithm')
         self.assertIsInstance(pipeline, Pipeline)
         self.assertTrue(isinstance(pipeline.get_classifier(), AdaBoost) or isinstance(pipeline.get_classifier(), Bagging))
         self.assertTrue(isinstance(pipeline.get_feature_selection_algorithm(), SelectKBest) or isinstance(pipeline.get_feature_selection_algorithm(), SelectPercentile))