def test_nn_regressor_complex_no_exceptions(self): encoding = create_test_encoding( value_encoding=ValueEncodings.COMPLEX.value, prefix_length=2, padding=True) labelling = create_test_labelling( label_type=LabelTypes.REMAINING_TIME.value) train_df = complex(self.train_log, labelling, encoding, self.train_add_col) test_df = complex(self.test_log, labelling, encoding, self.test_add_col) train_df, targets_df = self._drop_columns_and_split(train_df) targets_df = targets_df.values.ravel() test_df, _ = self._drop_columns_and_split(test_df) config = self._get_nn_default_config( encoding=ValueEncodings.COMPLEX.value) nn_regressor = NNRegressor(**config) # with HidePrints(): nn_regressor.fit(train_df, targets_df) nn_regressor.predict(test_df)
def _choose_regressor(job: Job) -> RegressorMixin: method, config = get_method_config(job) config.pop('regression_method', None) print("Using method {} with config {}".format(method, config)) if method == RegressionMethods.LINEAR.value: regressor = LinearRegression(**config) elif method == RegressionMethods.RANDOM_FOREST.value: regressor = RandomForestRegressor(**config) elif method == RegressionMethods.LASSO.value: regressor = Lasso(**config) elif method == RegressionMethods.XGBOOST.value: regressor = XGBRegressor(**config) elif method == RegressionMethods.NN.value: config['encoding'] = job.encoding.value_encoding regressor = NNRegressor(**config) else: raise ValueError("Unexpected regression method {}".format(method)) return regressor