def test_file_output(self): self.output_dir = os.path.join(os.getcwd(), '.test_file_output') D = get_regression_datamanager() D.name = 'test' configuration_space = get_configuration_space(D.info) configuration = configuration_space.sample_configuration() backend_api = backend.create(self.output_dir, self.output_dir) evaluator = HoldoutEvaluator(D, backend_api, configuration, with_predictions=True, all_scoring_functions=True, output_y_test=True) loss, Y_optimization_pred, Y_valid_pred, Y_test_pred = \ evaluator.fit_predict_and_loss() evaluator.file_output(loss, Y_optimization_pred, Y_valid_pred, Y_test_pred) self.assertTrue( os.path.exists( os.path.join(self.output_dir, '.auto-sklearn', 'true_targets_ensemble.npy')))
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_file_output(self): self.output_dir = os.path.join(os.getcwd(), '.test') D = get_regression_datamanager() D.name = 'test' configuration_space = get_configuration_space(D.info) configuration = configuration_space.sample_configuration() evaluator = HoldoutEvaluator(D, self.output_dir, configuration, with_predictions=True, all_scoring_functions=True, output_y_test=True) loss, Y_optimization_pred, Y_valid_pred, Y_test_pred = \ evaluator.fit_predict_and_loss() evaluator.file_output(loss, Y_optimization_pred, Y_valid_pred, Y_test_pred) self.assertTrue(os.path.exists(os.path.join( self.output_dir, '.auto-sklearn', 'true_targets_ensemble.npy')))
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