def test_own_regression_reports(): """ testing regressor.test_on """ X, y, sample_weight = generate_regression_data() regressor = SklearnRegressor(RandomForestRegressor()) regressor.fit(X, y, sample_weight=sample_weight) report = regressor.test_on(X, y, sample_weight=sample_weight) mse1 = report.compute_metric(mean_squared_error) lds = LabeledDataStorage(X, y, sample_weight=sample_weight) mse2 = regressor.test_on_lds(lds=lds).compute_metric(mean_squared_error) assert mse1 == mse2, 'Something wrong with test_on'
def check_grid(estimator, check_instance=True, has_staged_pp=True, has_importances=True, use_weights=False, classification=True): if classification: X, y, sample_weight = generate_classification_data() else: X, y, sample_weight = generate_regression_data() assert len(sample_weight) == len(X), 'somehow lengths are different' if use_weights: assert estimator == estimator.fit(X, y, sample_weight=sample_weight) estimator = estimator.fit_best_estimator(X, y, sample_weight=sample_weight) else: assert estimator == estimator.fit(X, y) estimator = estimator.fit_best_estimator(X, y) if classification: check_classification_model(estimator, X, y, check_instance=check_instance, has_staged_pp=has_staged_pp, has_importances=has_importances) else: check_regression_model(estimator, X, y, check_instance=check_instance, has_stages=has_staged_pp, has_importances=has_importances) return estimator
def test_Exception_reg_feature_importances(): X, _, _ = generate_regression_data() cl = MatrixNetRegressor(api_config_file=CONFIG_FILE_WRONG_URL, iterations=50) print(cl.feature_importances_)
def test_Exception_reg_staged_predict(): X, _, _ = generate_regression_data() cl = MatrixNetRegressor(api_config_file=CONFIG_FILE_WRONG_URL, iterations=50) for _ in cl.staged_predict(X): pass
def test_Exception_reg_synchronized(): X, _, _ = generate_regression_data() cl = MatrixNetRegressor(api_config_file=CONFIG_FILE_WRONG_URL, iterations=50) cl.synchronize()
def test_Exception_reg_trained_status(): X, _, _ = generate_regression_data() cl = MatrixNetRegressor(api_config_file=CONFIG_FILE_WRONG_URL, iterations=50) cl.training_status()