def test_file_output(self): output_dir = os.path.join(os.getcwd(), '.test') try: shutil.rmtree(output_dir) except Exception: pass X_train, Y_train, X_test, Y_test = get_dataset('iris') X_valid = X_test[:25, ] Y_valid = Y_test[:25, ] X_test = X_test[25:, ] Y_test = Y_test[25:, ] D = Dummy() D.info = { 'metric': 'bac_metric', 'task': MULTICLASS_CLASSIFICATION, 'is_sparse': False, 'target_num': 3 } D.data = { 'X_train': X_train, 'Y_train': Y_train, 'X_valid': X_valid, 'X_test': X_test } D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical'] D.basename = 'test' configuration_space = get_configuration_space(D.info) while True: configuration = configuration_space.sample_configuration() evaluator = HoldoutEvaluator(D, configuration, with_predictions=True, all_scoring_functions=True, output_dir=output_dir, output_y_test=True) if not self._fit(evaluator): continue evaluator.predict() evaluator.file_output() self.assertTrue(os.path.exists(os.path.join(output_dir, 'y_optimization.npy'))) break
def test_evaluate_binary_classification(self): X_train, Y_train, X_test, Y_test = get_dataset('iris') eliminate_class_two = Y_train != 2 X_train = X_train[eliminate_class_two] Y_train = Y_train[eliminate_class_two] eliminate_class_two = Y_test != 2 X_test = X_test[eliminate_class_two] Y_test = Y_test[eliminate_class_two] X_valid = X_test[:25, ] Y_valid = Y_test[:25, ] X_test = X_test[25:, ] Y_test = Y_test[25:, ] D = Dummy() D.info = { 'metric': 'auc_metric', 'task': BINARY_CLASSIFICATION, 'is_sparse': False, 'target_num': 2 } D.data = { 'X_train': X_train, 'Y_train': Y_train, 'X_valid': X_valid, 'X_test': X_test } D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical'] configuration_space = get_configuration_space( D.info, include_estimators=['ridge'], include_preprocessors=['select_rates']) err = np.zeros([N_TEST_RUNS]) for i in range(N_TEST_RUNS): print('Evaluate configuration: %d; result:' % i) configuration = configuration_space.sample_configuration() D_ = copy.deepcopy(D) evaluator = HoldoutEvaluator(D_, configuration) if not self._fit(evaluator): continue err[i] = evaluator.predict() self.assertTrue(np.isfinite(err[i])) print(err[i]) self.assertGreaterEqual(err[i], 0.0) print('Number of times it was worse than random guessing:' + str(np.sum(err > 1)))
def test_evaluate_multiclass_classification_all_metrics(self): X_train, Y_train, X_test, Y_test = get_dataset('iris') X_valid = X_test[:25, ] Y_valid = Y_test[:25, ] X_test = X_test[25:, ] Y_test = Y_test[25:, ] D = Dummy() D.info = { 'metric': 'bac_metric', 'task': MULTICLASS_CLASSIFICATION, 'is_sparse': False, 'target_num': 3 } D.data = { 'X_train': X_train, 'Y_train': Y_train, 'X_valid': X_valid, 'X_test': X_test } D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical'] configuration_space = get_configuration_space( D.info, include_estimators=['ridge'], include_preprocessors=['select_rates']) # Test all scoring functions err = [] for i in range(N_TEST_RUNS): print('Evaluate configuration: %d; result:' % i) configuration = configuration_space.sample_configuration() D_ = copy.deepcopy(D) evaluator = HoldoutEvaluator(D_, configuration, all_scoring_functions=True) if not self._fit(evaluator): continue err.append(evaluator.predict()) print(err[-1]) self.assertIsInstance(err[-1], dict) for key in err[-1]: self.assertEqual(len(err[-1]), 5) self.assertTrue(np.isfinite(err[-1][key])) self.assertGreaterEqual(err[-1][key], 0.0) print('Number of times it was worse than random guessing:' + str(np.sum(err > 1)))
def test_evaluate_multilabel_classification(self): X_train, Y_train, X_test, Y_test = get_dataset('iris') Y_train = np.array(convert_to_bin(Y_train, 3)) Y_train[:, -1] = 1 Y_test = np.array(convert_to_bin(Y_test, 3)) Y_test[:, -1] = 1 X_valid = X_test[:25, ] Y_valid = Y_test[:25, ] X_test = X_test[25:, ] Y_test = Y_test[25:, ] D = Dummy() D.info = { 'metric': 'f1_metric', 'task': MULTILABEL_CLASSIFICATION, 'is_sparse': False, 'target_num': 3 } D.data = { 'X_train': X_train, 'Y_train': Y_train, 'X_valid': X_valid, 'X_test': X_test } D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical'] configuration_space = get_configuration_space( D.info, include_estimators=['random_forest'], include_preprocessors=['no_preprocessing']) err = np.zeros([N_TEST_RUNS]) for i in range(N_TEST_RUNS): print('Evaluate configuration: %d; result:' % i) configuration = configuration_space.sample_configuration() D_ = copy.deepcopy(D) evaluator = HoldoutEvaluator(D_, configuration) if not self._fit(evaluator): continue err[i] = evaluator.predict() print(err[i]) self.assertTrue(np.isfinite(err[i])) self.assertGreaterEqual(err[i], 0.0) print('Number of times it was worse than random guessing:' + str(np.sum(err > 1)))
def test_evaluate_regression(self): X_train, Y_train, X_test, Y_test = get_dataset('boston') X_valid = X_test[:200, ] Y_valid = Y_test[:200, ] X_test = X_test[200:, ] Y_test = Y_test[200:, ] D = Dummy() D.info = { 'metric': 'r2_metric', 'task': REGRESSION, 'is_sparse': False, 'target_num': 1 } D.data = { 'X_train': X_train, 'Y_train': Y_train, 'X_valid': X_valid, 'X_test': X_test } D.feat_type = ['numerical', 'Numerical', 'numerical', 'numerical', 'numerical', 'numerical', 'numerical', 'numerical', 'numerical', 'numerical', 'numerical'] configuration_space = get_configuration_space( D.info, include_estimators=['random_forest'], include_preprocessors=['no_preprocessing']) err = np.zeros([N_TEST_RUNS]) for i in range(N_TEST_RUNS): print('Evaluate configuration: %d; result:' % i) configuration = configuration_space.sample_configuration() D_ = copy.deepcopy(D) evaluator = HoldoutEvaluator(D_, configuration) if not self._fit(evaluator): continue err[i] = evaluator.predict() self.assertTrue(np.isfinite(err[i])) print(err[i]) self.assertGreaterEqual(err[i], 0.0) print('Number of times it was worse than random guessing:' + str(np.sum(err > 1)))
def test_with_abalone(self): dataset = 'abalone' dataset_dir = os.path.join(os.path.dirname(__file__), '.datasets') D = CompetitionDataManager(dataset, dataset_dir) configuration_space = get_configuration_space( D.info, include_estimators=['extra_trees'], include_preprocessors=['no_preprocessing']) errors = [] for i in range(N_TEST_RUNS): configuration = configuration_space.sample_configuration() D_ = copy.deepcopy(D) evaluator = HoldoutEvaluator(D_, configuration) if not self._fit(evaluator): continue err = evaluator.predict() self.assertLess(err, 0.99) self.assertTrue(np.isfinite(err)) errors.append(err) # This is a reasonable bound self.assertEqual(10, len(errors)) self.assertLess(min(errors), 0.77)