Added documentation

openstef/model/regressors/custom_regressor.py

@@ -23,13 +23,11 @@ class CustomOpenstfRegressor(OpenstfRegressor):
 
     @staticmethod
     @abstractmethod
-    def valid_kwargs() -> list[str]:
-        ...
+    def valid_kwargs() -> list[str]: ...
 
     @classmethod
     @abstractmethod
-    def objective(self) -> Type[RegressorObjective]:
-        ...
+    def objective(self) -> Type[RegressorObjective]: ...
 
 
 def load_custom_model(custom_model_path) -> CustomOpenstfRegressor:

openstef/model/regressors/linear_quantile.py

@@ -73,6 +73,9 @@ def __init__(
             missing_values: Value to be considered as missing value
             imputation_strategy: Imputation strategy
             fill_value: Fill value
+            weight_scale_percentile: Percentile used in scaling of the samples
+            weight_exponent: Exponent used in sample weighing
+            weight_floor: Minimum weight for samples
             no_fill_future_values_features: The features for which it does not make sense
                 to fill future values. Rows that contain trailing null values for these
                 features will be removed from the data.
@@ -203,8 +206,8 @@ def fit(self, x: pd.DataFrame, y: pd.Series, **kwargs) -> RegressorMixin:
         return self
 
     def _calculate_sample_weights(self, y: np.array):
-        """Calculate sample weights based on the y values of arbitrary scale. The resulting weights are in the range [0,
-        1] and are used to put more emphasis on certain samples.
+        """Calculate sample weights based on the y values of arbitrary scale.
+        The resulting weights are in the range [0, 1] and are used to put more emphasis on certain samples.
 
         The sample weighting function does:
         * Rescale data to a [-1, 1] range using quantile scaling. 90% of the data will

openstef/model/standard_deviation_generator.py

@@ -40,9 +40,9 @@ def generate_standard_deviation_data(self, model: RegressorMixin) -> RegressorMi
             confidence_interval_horizon = self._calculate_standard_deviation(
                 sub_val.iloc[:, 0], predicted
             )
-            confidence_interval_horizon[
-                "horizon"
-            ] = horizon  # Label with respective horizon
+            confidence_interval_horizon["horizon"] = (
+                horizon  # Label with respective horizon
+            )
             self.standard_deviation = pd.concat(
                 [self.standard_deviation, confidence_interval_horizon]
             )

test/unit/data_classes/test_split_function.py

@@ -62,9 +62,9 @@ def test_load(self):
 
         # Non Callable object
         split_func_dc = copy.deepcopy(self.split_func_with_strings)
-        split_func_dc[
-            "function"
-        ] = "test.unit.data_classes.test_split_function.dummy_not_func"
+        split_func_dc["function"] = (
+            "test.unit.data_classes.test_split_function.dummy_not_func"
+        )
         with self.assertRaises(ValueError):
             _ = split_func_dc.load()
 

test/unit/model/test_custom_models.py

@@ -16,8 +16,7 @@
 )
 
 
-class DummyObjective(RegressorObjective):
-    ...
+class DummyObjective(RegressorObjective): ...
 
 
 class DummyRegressor(CustomOpenstfRegressor):

test/unit/pipeline/test_create_basecase.py

@@ -19,9 +19,9 @@ def setUp(self) -> None:
         self.PJ = TestData.get_prediction_job(pid=307)
         forecast_input = TestData.load("reference_sets/307-test-data.csv")
         # Set last 7 days to nan, just like operationally
-        forecast_input.loc[
-            forecast_input.index.max() - timedelta(days=7) :, "load"
-        ] = np.nan
+        forecast_input.loc[forecast_input.index.max() - timedelta(days=7) :, "load"] = (
+            np.nan
+        )
         # Shift so the input matches 'now'
         offset_seconds = (
             pd.to_datetime(datetime.utcnow(), utc=True)
@@ -80,9 +80,9 @@ def test_create_basecase_forecast_pipeline_constant_load(self):
         forecast_input.loc[
             forecast_input.index.max() - timedelta(days=21) :, "load"
         ] = forecast_input.loc[forecast_input.index.max() - timedelta(days=14), "load"]
-        forecast_input.loc[
-            forecast_input.index.max() - timedelta(days=7) :, "load"
-        ] = np.nan
+        forecast_input.loc[forecast_input.index.max() - timedelta(days=7) :, "load"] = (
+            np.nan
+        )
 
         base_case_forecast = create_basecase_forecast_pipeline(self.PJ, forecast_input)
 

test/unit/pipeline/test_pipeline_train_model.py

@@ -39,8 +39,7 @@
 from openstef.validation import validation
 
 
-class DummyObjective(RegressorObjective):
-    ...
+class DummyObjective(RegressorObjective): ...
 
 
 class DummyRegressor(CustomOpenstfRegressor):