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