black format

openstef/exceptions.py

@@ -3,6 +3,8 @@
 # SPDX-License-Identifier: MPL-2.0
 
 """Openstef custom exceptions."""
+
+
 # Define custom exception
 class NoPredictedLoadError(Exception):
     """No predicted load for given datatime range."""

openstef/feature_engineering/holiday_features.py

@@ -173,7 +173,6 @@ def make_holiday_func(requested_date):
     if (is_holiday_in_two_days or is_saturday_in_two_days) and (
         not is_holiday_tommorow and not is_weekend_tommorrow
     ):
-
         # Create feature function for each holiday
         holiday_functions.update(
             {
@@ -195,7 +194,6 @@ def make_holiday_func(requested_date):
     if (is_saturday_two_days_ago or is_holiday_two_days_ago) and (
         not is_holiday_yesterday and not is_weekend_yesterday
     ):
-
         # Create featurefunction for the bridge function
         holiday_functions.update(
             {

openstef/feature_engineering/lag_features.py

@@ -78,7 +78,6 @@ def extract_lag_features(
     days_list = []
 
     for lag_feature in feature_names:
-
         # Select the number of days or the number of minutes by matching with a regular expression
         number_of_minutes = re.search(r"T-(\d+)min", lag_feature)
         number_of_days = re.search(r"T-(\d+)d", lag_feature)

openstef/model/objective.py

@@ -274,7 +274,6 @@ def get_pruning_callback(self, trial: optuna.trial.FrozenTrial):
 
     @classmethod
     def get_default_values(cls) -> dict:
-
         default_parameter_values = super().get_default_values()
         default_parameter_values.update({"gamma": 0.0, "booster": "gbtree"})
         return default_parameter_values

openstef/model/standard_deviation_generator.py

@@ -8,7 +8,6 @@
 
 class StandardDeviationGenerator:
     def __init__(self, validation_data: pd.DataFrame) -> None:
-
         self.validation_data = validation_data
 
     def generate_standard_deviation_data(self, model: RegressorMixin) -> RegressorMixin:

openstef/tasks/create_components_forecast.py

@@ -107,7 +107,6 @@ def create_components_forecast_task(
     if forecasts.index.max() < datetime.utcnow().replace(
         tzinfo=timezone.utc
     ) + timedelta(hours=30):
-
         # Check which input data is missing the most.
         # Do this by counting the NANs for (load)forecast, radiation and windspeed
         max_index = forecasts.index.max()
@@ -135,7 +134,6 @@ def main(config: object = None, database: object = None):
         )
 
     with TaskContext(taskname, config, database) as context:
-
         model_type = [ml.value for ml in MLModelType]
 
         PredictionJobLoop(

openstef/tasks/create_forecast.py

@@ -116,7 +116,6 @@ def main(model_type=None, config=None, database=None):
         )
 
     with TaskContext(taskname, config, database) as context:
-
         if model_type is None:
             model_type = [ml.value for ml in MLModelType]
 

openstef/tasks/run_tracy.py

@@ -56,7 +56,6 @@ def run_tracy(context: TaskContext) -> None:
     context.logger.info("Start processing Tracy jobs", num_jobs=num_jobs)
 
     for i, job in enumerate(tracy_jobs):
-
         # get a new logger with bound job
         logger = context.logger.bind(job=job)
         logger.info("Process job", job_counter=i, total_jobs=num_jobs)

openstef/tasks/split_forecast.py

@@ -203,6 +203,7 @@ def find_components(
             - Dict with the coefficients that result from the fitting
 
     """
+
     # Define function to fit
     def weighted_sum(x, *args):
         if len(x) != len(args):

openstef/tasks/utils/predictionjobloop.py

@@ -228,7 +228,6 @@ def _handle_exception_during_iteration(self, prediction_job, e):
             )
 
     def _handle_finished_last_iteration(self, prediction_job, successful):
-
         self.context.perf_meter.complete_level(successful)
 
         if self.on_end_callback is not None:

openstef/validation/validation.py

@@ -252,7 +252,6 @@ def calc_completeness_dataframe(
     logger = structlog.get_logger(__name__)
 
     if homogenise and isinstance(df.index, pd.DatetimeIndex) and len(df) > 0:
-
         median_timediff = int(
             df.reset_index().iloc[:, 0].diff().median().total_seconds() / 60.0
         )

test/unit/feature_engineering/test_apply_features.py

@@ -125,7 +125,6 @@ def test_apply_features_no_pj(self):
         )
 
     def test_train_feature_applicator(self):
-
         input_data_with_features = TrainFeatureApplicator(horizons=[0.25]).add_features(
             TestData.load("input_data.csv"),
             pj={"model": "proleaf", "lat": 52.132633, "lon": 5.291266},
@@ -164,18 +163,14 @@ def test_train_feature_applicator_with_latency(self):
         # Skip first row, since T-30min not available for first row
         self.assertTrue(
             input_data_with_features.loc[horizon == 47, ["APX", "T-30min"]]
-            .iloc[
-                1:,
-            ]
+            .iloc[1:,]
             .isna()
             .all()
             .all()
         )
         self.assertFalse(
             input_data_with_features.loc[horizon == 0.25, ["APX", "T-30min"]]
-            .iloc[
-                1:,
-            ]
+            .iloc[1:,]
             .isna()
             .any()
             .any()

test/unit/feature_engineering/test_general.py

@@ -11,7 +11,6 @@
 
 class TestGeneral(TestCase):
     def test_enforce_feature_order_with_horizon_columns(self):
-
         df = pd.DataFrame(
             np.arange(12).reshape(3, 4), columns=["load", "horizon", "A", "E"]
         )
@@ -21,7 +20,6 @@ def test_enforce_feature_order_with_horizon_columns(self):
         self.assertEqual(result.columns.to_list(), ["load", "A", "E", "horizon"])
 
     def test_enforce_feature_order(self):
-
         df = pd.DataFrame(np.arange(9).reshape(3, 3), columns=["load", "A", "E"])
         result = enforce_feature_order(df)
         self.assertEqual(result.columns.to_list(), ["load", "A", "E"])

test/unit/feature_engineering/test_general_extract_minute_features.py

@@ -152,7 +152,6 @@ def setUp(self):
         ]
 
     def test_extract_minute_features_short_horizon(self):
-
         testlist_minutes, testlist_days = extract_lag_features(
             self.feature_names, horizon=0.25
         )

test/unit/model/regressors/test_arima.py

@@ -94,7 +94,6 @@ def test_set_feature_importance_from_arima(self):
         self.assertTrue(np.allclose(pvalues, importances["gain"]))
 
     def test_score_backtest(self):
-
         model = ARIMAOpenstfRegressor(backtest_max_horizon=180)
         model.fit(self.train_input.iloc[:150, 1:], self.train_input.iloc[:150, 0])
 

test/unit/model/regressors/test_xgb_quantile.py

@@ -23,7 +23,6 @@ def predict(self, input, quantile):
 
 class MockScore:
     def get(self, a, b):
-
         book = {"a": 12, "b": 23, "c": 36}
 
         return book[a] + b

test/unit/model/test_serializer.py

@@ -27,7 +27,8 @@ def _rewrite_absolute_artifact_path(
         metadata_file: str, new_path: str, artifact_path_key: str
     ) -> None:
         """Helper function to rewrite the absolute path of the artifacts in meta.yaml files.
-        This is required since generating new models takes too long for a unit test and relative paths are not supported."""
+        This is required since generating new models takes too long for a unit test and relative paths are not supported.
+        """
         with open(metadata_file, "r") as f:
             metadata = yaml.safe_load(f)
 

test/unit/model/test_standard_deviation_generator.py

@@ -10,7 +10,6 @@
 
 class MockModel:
     def predict(self, *args):
-
         # Prepare mock_forecast -
         # it should include mutliple observations of the
         # same time of day for the same horizon,

test/unit/monitoring/test_teams.py

@@ -18,15 +18,13 @@ def setUp(self):
         self.pj = TestData.get_prediction_job(pid=307)
 
     def test_post_teams(self, teamsmock):
-
         msg = "test"
 
         teams.post_teams(msg, url="MOCK_URL")
         card_mock = teamsmock.connectorcard.return_value
         self.assertTrue(card_mock.send.called)
 
     def test_post_teams_invalid_keys(self, teamsmock):
-
         msg = "test"
         invalid_coefs = pd.DataFrame(
             {

test/unit/pipeline/test_create_component_forecast.py

@@ -25,7 +25,8 @@ def setUp(self) -> None:
     def test_load_dazls_model(self):
         """Version updates of sklearn could break pickle.load
         Let's test for that explicitly
-        Assert that loading the old model generates an exception and the new model does not"""
+        Assert that loading the old model generates an exception and the new model does not
+        """
 
         old_model_file = PROJECT_ROOT / "openstef/data/dazls_stored_3.2.49.sav"
         new_model_file = PROJECT_ROOT / "openstef/data/dazls_stored.sav"

test/unit/pipeline/test_optimize_hyperparameters.py

@@ -127,7 +127,6 @@ def test_optimize_hyperparameters_pipeline_with_custom_split(self):
 
     @patch("openstef.validation.validation.is_data_sufficient", return_value=False)
     def test_optimize_hyperparameters_pipeline_insufficient_data(self, mock):
-
         # if data is not sufficient a InputDataInsufficientError should be raised
         with self.assertRaises(InputDataInsufficientError):
             optimize_hyperparameters_pipeline_core(
@@ -148,7 +147,6 @@ def test_optimize_hyperparameters_pipeline_no_data(self):
             )
 
     def test_optimize_hyperparameters_pipeline_no_load_data(self):
-
         input_data = self.input_data.drop("load", axis=1)
         # if there is no data a InputDataWrongColumnOrderError should be raised
         with self.assertRaises(InputDataWrongColumnOrderError):

test/unit/pipeline/test_train_predict_backtest.py

@@ -92,7 +92,6 @@ def test_train_model_pipeline_core_happy_flow_nfold(self):
         self.assertEqual(len(forecast), len(data_with_features))
 
     def test_train_model_pipeline_core_custom_split(self):
-
         pj = self.pj
         # test wrong custom backtest split
         pj.backtest_split_func = SplitFuncDataClass(

test/unit/postprocessing/test_postprocessing.py

@@ -13,7 +13,6 @@
 
 class TestPostProcess(BaseTestCase):
     def test_post_process_wind_solar(self):
-
         forecast = pd.DataFrame({"forecast": [-10, -15, -33, 1, 1.3806e-23]})
 
         forecast_positive_removed = pd.DataFrame()

test/unit/tasks/test_calculate_kpi.py

@@ -75,7 +75,6 @@ def get_database_mock_predicted_empty():
 
 
 class TestPerformanceCalcKpiForSpecificPid(BaseTestCase):
-
     # Test whether correct kpis are calculated for specific test data
     def test_calc_kpi_for_specific_pid(self):
         kpis = calc_kpi_for_specific_pid(

test/unit/tasks/test_solar.py

@@ -30,7 +30,6 @@ def setUp(self) -> None:
         }
 
     def test_make_solar_predicion_pj(self):
-
         context = MagicMock()
         context.database.get_solar_input = MagicMock(return_value=self.test_solar_input)
 

test/unit/utils/data.py

@@ -15,7 +15,6 @@
 
 
 class TestData:
-
     DATA_FILES_FOLDER = Path(__file__).parent.parent / "data"
     TRAINED_MODELS_FOLDER = Path(__file__).parent.parent / "trained_models"