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( 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(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(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_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))