def test_default_configuration_iterative_fit(self): regressor = SimpleRegressionPipeline( include={'regressor': ['random_forest'], 'feature_preprocessor': ['no_preprocessing']}) X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston') regressor.fit_transformer(X_train, Y_train) for i in range(1, 11): regressor.iterative_fit(X_train, Y_train) self.assertEqual(regressor.steps[-1][-1].choice.estimator.n_estimators, i)
def test_default_configuration_iterative_fit(self): regressor = SimpleRegressionPipeline( include={'regressor': ['random_forest'], 'preprocessor': ['no_preprocessing']}) X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston') XT = regressor.fit_transformer(X_train, Y_train) for i in range(1, 11): regressor.iterative_fit(X_train, Y_train) self.assertEqual(regressor.steps[-1][-1].choice.estimator.n_estimators, i)