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('boston') 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': R2_METRIC, 'task': REGRESSION, 'is_sparse': False, 'label_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.name = '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, '.auto-sklearn', 'true_targets_ensemble.npy'))) break
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('boston') 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': R2_METRIC, 'task': REGRESSION, 'is_sparse': False, 'label_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.name = '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, '.auto-sklearn', 'true_targets_ensemble.npy'))) break
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, "label_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=["lda"], include_preprocessors=["pca"]) # 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_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("boston") 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": R2_METRIC, "task": REGRESSION, "is_sparse": False, "label_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.name = "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, ".auto-sklearn", "true_targets_ensemble.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, 'label_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=['lda'], include_preprocessors=['pca']) 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)
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, 'label_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=['lda'], include_preprocessors=['pca']) # 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)
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, 'label_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=['extra_trees'], 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)
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, 'label_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=['extra_trees'], 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)
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, "label_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=["extra_trees"], 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_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, 'label_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=['extra_trees'], 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)
def test_with_abalone(self): dataset = "abalone" dataset_path = os.path.join(os.path.dirname(__file__), ".datasets", dataset) D = CompetitionDataManager(dataset_path) 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)
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, "label_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=["lda"], include_preprocessors=["pca"]) 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_path = os.path.join(os.path.dirname(__file__), '.datasets', dataset) D = CompetitionDataManager(dataset_path) 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)
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, "label_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=["extra_trees"], 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)))