Removed unnecessary argument from apply

Author: Katharine Hurst

improver/calibration/load_and_apply_quantile_regression_random_forest.py

@@ -28,7 +28,6 @@ def process(
         file_paths: pathlib.Path,
         feature_config: dict,
         target_cube_name: str,
-        n_estimators: int = 100,
         transformation: str = None,
         pre_transform_addition: float = None,
     ):
@@ -66,8 +65,6 @@ def process(
                 A string containing the cube name of the forecast to be
                 calibrated. This will be used to separate it from the rest of the
                 dynamic predictors, if present.
-            n_estimators (int):
-                Number of trees in the forest.
             transformation (str):
                 Transformation to be applied to the data before fitting.
             pre_transform_addition (float):
@@ -155,7 +152,6 @@ def process(
         result = ApplyQuantileRegressionRandomForests(
             feature_config=feature_config,
             quantiles=percentiles,
-            n_estimators=n_estimators,
             transformation=transformation,
             pre_transform_addition=pre_transform_addition,
         )(forecast_cube, template_forecast_cube, qrf_model, cube_inputs)

improver/calibration/quantile_regression_random_forest.py

@@ -222,7 +222,6 @@ def __init__(
         self,
         feature_config,
         quantiles,
-        n_estimators,
         transformation,
         pre_transform_addition,
     ):
@@ -246,8 +245,6 @@ def __init__(
                 }
             quantiles (float):
                 Quantiles used for prediction (values ranging from 0 to 1)
-            n_estimators (int):
-                Number of trees in the forest.
             transformation (str):
                 Transformation to be applied to the data before fitting.
             pre_transform_addition (float):
@@ -256,7 +253,6 @@ def __init__(
 
         self.feature_config = feature_config
         self.quantiles = quantiles
-        self.n_estimators = n_estimators
         self.transformation = transformation
         if self.transformation not in ["log", "log10", "sqrt", "cbrt", None]:
             msg = (

improver/cli/apply_quantile_regression_random_forest.py

@@ -14,7 +14,6 @@ def process(
     *file_paths: cli.inputpath,
     feature_config: cli.inputjson,
     target_cube_name: str,
-    n_estimators: int = 100,
     transformation: str = None,
     pre_transform_addition: float = 0,
 ):
@@ -52,8 +51,6 @@ def process(
             A string containing the cube name of the forecast to be
             calibrated. This will be used to separate it from the rest of the
             feature cubes, if present.
-        n_estimators (int):
-            Number of trees in the forest.
         transformation (str):
             Transformation to be applied to the data before fitting.
         pre_transform_addition (float):
@@ -71,7 +68,6 @@ def process(
         file_paths,
         feature_config=feature_config,
         target_cube_name=target_cube_name,
-        n_estimators=n_estimators,
         transformation=transformation,
         pre_transform_addition=pre_transform_addition,
     )