Wrote some tests

Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py

@@ -47,6 +47,7 @@ def initConstants(args, X, classificationIndices, labelsNames, name, directory):
                 raise
     return kwargs, t_start, feat, CL_type, X, learningRate, labelsString, outputFileName
 
+
 def initTrainTest(X, Y, classificationIndices):
     trainIndices, testIndices = classificationIndices
     X_train = extractSubset(X, trainIndices)
@@ -55,19 +56,21 @@ def initTrainTest(X, Y, classificationIndices):
     y_test = Y[testIndices]
     return X_train, y_train, X_test, y_test
 
+
 def getKWARGS(classifierModule, hyperParamSearch, nIter, CL_type, X_train, y_train, randomState,
               outputFileName, KFolds, nbCores, metrics, kwargs):
     if hyperParamSearch != "None":
+        logging.debug("Start:\t " + hyperParamSearch + " best settings with " + str(nIter) + " iterations for " + CL_type)
         classifierHPSearch = getattr(classifierModule, hyperParamSearch)
-        logging.debug("Start:\t RandomSearch best settings with " + str(nIter) + " iterations for " + CL_type)
         cl_desc = classifierHPSearch(X_train, y_train, randomState, outputFileName, KFolds=KFolds, nbCores=nbCores,
                                      metric=metrics[0], nIter=nIter)
         clKWARGS = dict((str(index), desc) for index, desc in enumerate(cl_desc))
-        logging.debug("Done:\t RandomSearch best settings")
+        logging.debug("Done:\t " + hyperParamSearch + "RandomSearch best settings")
     else:
         clKWARGS = kwargs[CL_type + "KWARGS"]
     return clKWARGS
 
+
 def saveResults(stringAnalysis, outputFileName, full_labels_pred, y_train_pred, y_train, imagesAnalysis):
     logging.info(stringAnalysis)
     outputTextFile = open(outputFileName + '.txt', 'w')

Code/Tests/Test_MonoView/test_ExecClassifMonoView.py

@@ -53,21 +53,27 @@ class Test_initConstants(unittest.TestCase):
         os.rmdir("Code/Tests/temp_tests/test_dir")
         os.rmdir("Code/Tests/temp_tests")
 
+
 class Test_initTrainTest(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
         cls.random_state = np.random.RandomState(42)
         cls.X = cls.random_state.randint(0,500,(10,5))
-        print(cls.X)
         cls.Y = cls.random_state.randint(0,2,10)
-        print(cls.Y)
         cls.classificationIndices = [np.array([0,2,4,6,8]),np.array([1,3,5,7,9])]
-        import pdb; pdb.set_trace()
 
     def test_simple(cls):
         X_train, y_train, X_test, y_test = ExecClassifMonoView.initTrainTest(cls.X, cls.Y, cls.classificationIndices)
-        np.testing.assert_array_equal(X_train, np.array([np.array([]), np.array([]), np.array([]), np.array([]), np.array([])]))
-        np.testing.assert_array_equal(X_test, np.array([np.array([]), np.array([]), np.array([]), np.array([]), np.array([])]))
-        np.testing.assert_array_equal(y_train, np.array([]))
-        np.testing.assert_array_equal(y_test, np.array([]))
\ No newline at end of file
+        np.testing.assert_array_equal(X_train, np.array([np.array([102,435,348,270,106]),
+                                                         np.array([466,214,330,458,87]),
+                                                         np.array([149,308,257,343,491]),
+                                                         np.array([276,160,459,313,21]),
+                                                         np.array([58,169,475,187,463])]))
+        np.testing.assert_array_equal(X_test, np.array([np.array([71,188,20,102,121]),
+                                                        np.array([372,99,359,151,130]),
+                                                        np.array([413,293,385,191,443]),
+                                                        np.array([252,235,344,48,474]),
+                                                        np.array([270,189,445,174,445])]))
+        np.testing.assert_array_equal(y_train, np.array([0,0,1,0,0]))
+        np.testing.assert_array_equal(y_test, np.array([1,1,0,0,0]))
\ No newline at end of file