def test_default_configuration_iris_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(AdaboostClassifier) self.assertAlmostEqual( 0.22452300738472031, sklearn.metrics.log_loss(targets, predictions))
def test_default_configuration_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(ExtraTreesClassifier) self.assertAlmostEqual( 0.1086791056721286, sklearn.metrics.log_loss(targets, predictions))
def test_default_configuration_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(ExtraTreesClassifier) self.assertAlmostEqual(0.1086791056721286, sklearn.metrics.log_loss( targets, predictions))
def test_default_configuration_predict_proba(self): for i in range(2): predictions, targets = \ _test_classifier_predict_proba(DeepNetIterative) self.assertAlmostEqual( 0.76018262995220975, sklearn.metrics.log_loss(targets, predictions))
def test_default_configuration_predict_proba(self): for i in range(2): predictions, targets = \ _test_classifier_predict_proba(KNearestNeighborsClassifier) self.assertAlmostEqual( 1.381551055796429, sklearn.metrics.log_loss(targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = _test_classifier_predict_proba( DecisionTree, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual( 0.83333333333333337, sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration_predict_proba_individual(self): # Leave this additional test here for i in range(2): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='digits', train_size_maximum=500) self.assertAlmostEqual( 5.4706296711768925, sklearn.metrics.log_loss(targets, predictions)) for i in range(2): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='iris') self.assertAlmostEqual( 0.84336416900751887, sklearn.metrics.log_loss(targets, predictions)) # 2 class for i in range(2): X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') remove_training_data = Y_train == 2 remove_test_data = Y_test == 2 X_train = X_train[~remove_training_data] Y_train = Y_train[~remove_training_data] X_test = X_test[~remove_test_data] Y_test = Y_test[~remove_test_data] ss = sklearn.preprocessing.StandardScaler() X_train = ss.fit_transform(X_train) configuration_space = LibSVM_SVC.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() cls = LibSVM_SVC(random_state=1, **{ hp_name: default[hp_name] for hp_name in default if default[hp_name] is not None }) cls = cls.fit(X_train, Y_train) prediction = cls.predict_proba(X_test) self.assertAlmostEqual(sklearn.metrics.log_loss( Y_test, prediction), 0.6932, places=4)
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = _test_classifier_predict_proba( DecisionTree, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.83333333333333337, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_predict_proba_multilabel(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(LDA, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.96639166748245653, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(classifier=GaussianNB, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.98533237262174234, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_predict_proba_multilabel(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(KNearestNeighborsClassifier, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.97060428849902536, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(2): predictions, targets = \ _test_classifier_predict_proba(classifier=BernoulliNB, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual( 0.66666666666666663, sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration_predict_proba_multilabel(self): for i in range(2): predictions, targets = \ _test_classifier_predict_proba(DeepNetIterative, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual( 0.76835649552496521, sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(classifier=MultinomialNB, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual( 0.76548981051208942, sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration_predict_proba_multilabel(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(ExtraTreesClassifier, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.99401797442008899, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_predict_proba_multilabel(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(RandomForest, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.99252721833266977, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(classifier=MultinomialNB, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.76548981051208942, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(classifier=AdaboostClassifier, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.9722131915406923, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(classifier=PassiveAggressive, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.99703892466326138, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(classifier=GaussianNB, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual( 0.98533237262174234, sklearn.metrics.average_precision_score(targets, predictions))
def test_default_iris_predict_proba(self): for i in range(2): predictions, targets = \ _test_classifier_predict_proba(dataset="iris", classifier=self.module) self.assertAlmostEqual(self.res["default_iris_proba"], sklearn.metrics.log_loss(targets, predictions), places=self.res.get( "default_iris_proba_places", 7))
def test_default_iris_predict_proba(self): for i in range(2): predictions, targets = \ _test_classifier_predict_proba(dataset="iris", classifier=self.module) self.assertAlmostEqual( self.res["default_iris_proba"], sklearn.metrics.log_loss(targets, predictions), places=self.res.get("default_iris_proba_places", 7))
def test_default_configuration_predict_proba_multilabel(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(ExtraTreesClassifier, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual( 0.99401797442008899, sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(classifier=GradientBoostingClassifier, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(0.92926139448174994, sklearn.metrics.average_precision_score( targets, predictions))
def test_default_configuration_predict_proba_multilabel(self): for i in range(2): predictions, targets = \ _test_classifier_predict_proba(KNearestNeighborsClassifier, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual( 0.97060428849902536, sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration_predict_proba_multilabel(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(RandomForest, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual( 0.9943139211500065, sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration_multilabel_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(classifier=AdaboostClassifier, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual( 0.9722131915406923, sklearn.metrics.average_precision_score(targets, predictions))
def test_default_configuration_predict_proba(self): for i in range(10): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='digits', train_size_maximum=500) self.assertAlmostEqual( 4.6680593525563063, sklearn.metrics.log_loss(targets, predictions)) for i in range(10): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='iris') self.assertAlmostEqual( 0.8649665185853217, sklearn.metrics.log_loss(targets, predictions)) # 2 class for i in range(10): X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') remove_training_data = Y_train == 2 remove_test_data = Y_test == 2 X_train = X_train[~remove_training_data] Y_train = Y_train[~remove_training_data] X_test = X_test[~remove_test_data] Y_test = Y_test[~remove_test_data] ss = sklearn.preprocessing.StandardScaler() X_train = ss.fit_transform(X_train) configuration_space = LibSVM_SVC.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() cls = LibSVM_SVC(random_state=1, **{ hp_name: default[hp_name] for hp_name in default if default[hp_name] is not None }) cls = cls.fit(X_train, Y_train) prediction = cls.predict_proba(X_test) self.assertAlmostEqual( sklearn.metrics.log_loss(Y_test, prediction), 0.69323680119641773)
def test_default_configuration_predict_proba_individual(self): # Leave this additional test here for i in range(2): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='digits', train_size_maximum=500) self.assertAlmostEqual(5.4706296711768925, sklearn.metrics.log_loss(targets, predictions)) for i in range(2): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='iris') self.assertAlmostEqual(0.84336416900751887, sklearn.metrics.log_loss(targets, predictions)) # 2 class for i in range(2): X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') remove_training_data = Y_train == 2 remove_test_data = Y_test == 2 X_train = X_train[~remove_training_data] Y_train = Y_train[~remove_training_data] X_test = X_test[~remove_test_data] Y_test = Y_test[~remove_test_data] ss = sklearn.preprocessing.StandardScaler() X_train = ss.fit_transform(X_train) configuration_space = LibSVM_SVC.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() cls = LibSVM_SVC(random_state=1, **{hp_name: default[hp_name] for hp_name in default if default[hp_name] is not None}) cls = cls.fit(X_train, Y_train) prediction = cls.predict_proba(X_test) self.assertAlmostEqual(sklearn.metrics.log_loss(Y_test, prediction), 0.6932, places=4)
def test_default_configuration_predict_proba(self): for i in range(10): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='digits', train_size_maximum=500) self.assertAlmostEqual(4.6680593525563063, sklearn.metrics.log_loss(targets, predictions)) for i in range(10): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='iris') self.assertAlmostEqual(0.8649665185853217, sklearn.metrics.log_loss(targets, predictions)) # 2 class for i in range(10): X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') remove_training_data = Y_train == 2 remove_test_data = Y_test == 2 X_train = X_train[~remove_training_data] Y_train = Y_train[~remove_training_data] X_test = X_test[~remove_test_data] Y_test = Y_test[~remove_test_data] ss = sklearn.preprocessing.StandardScaler() X_train = ss.fit_transform(X_train) configuration_space = LibSVM_SVC.get_hyperparameter_search_space() default = configuration_space.get_default_configuration() cls = LibSVM_SVC(random_state=1, **{hp_name: default[hp_name] for hp_name in default if default[hp_name] is not None}) cls = cls.fit(X_train, Y_train) prediction = cls.predict_proba(X_test) self.assertAlmostEqual(sklearn.metrics.log_loss(Y_test, prediction), 0.69323680119641773)
def test_default_digits_multilabel_predict_proba(self): if not self.module.get_properties()["handles_multilabel"]: return for i in range(2): predictions, targets = \ _test_classifier_predict_proba(classifier=self.module, make_multilabel=True) self.assertEqual(predictions.shape, ((50, 3))) self.assertAlmostEqual(self.res["default_digits_multilabel_proba"], sklearn.metrics.roc_auc_score( targets, predictions, average='macro'), places=self.res.get( "default_digits_multilabel_proba_places", 7))
def test_default_iris_predict_proba(self): if self.__class__ == BaseClassificationComponentTest: return for _ in range(2): predictions, targets = _test_classifier_predict_proba( dataset="iris", classifier=self.module ) self.assertAlmostEqual( self.res["default_iris_proba"], sklearn.metrics.log_loss(targets, predictions), places=self.res.get("default_iris_proba_places", 7) )
def test_default_configuration_predict_proba(self): for i in range(10): predictions, targets = _test_classifier_predict_proba(DecisionTree) self.assertAlmostEqual( 0.51333963481747835, sklearn.metrics.log_loss(targets, predictions))
def test_default_configuration_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(KNearestNeighborsClassifier) self.assertAlmostEqual(1.381551055796429, sklearn.metrics.log_loss(targets, predictions))
def test_default_configuration_predict_proba(self): for i in range(10): predictions, targets = _test_classifier_predict_proba( DecisionTree, dataset='iris') self.assertAlmostEqual(0.28069887755912964, sklearn.metrics.log_loss(targets, predictions))
def test_default_configuration_iris_predict_proba(self): for i in range(10): predictions, targets = \ _test_classifier_predict_proba(AdaboostClassifier) self.assertAlmostEqual(0.22452300738472031, sklearn.metrics.log_loss(targets, predictions))
def test_default_configuration_predict_proba(self): for i in range(10): predictions, targets = _test_classifier_predict_proba(DecisionTree) self.assertAlmostEqual(0.28069887755912964, sklearn.metrics.log_loss(targets, predictions))