23241: Removes old unused react_aggregate parameter, "residuals_hyperparameter_feature". MAJOR (#385)

Author: cademack

howso/client/base.py

@@ -3546,7 +3546,6 @@ def react_aggregate(  # noqa: C901
         num_robust_influence_samples_per_case: t.Optional[int] = None,
         num_samples: t.Optional[int] = None,
         prediction_stats_action_feature: t.Optional[str] = None,
-        residuals_hyperparameter_feature: t.Optional[str] = None,
         robust_hyperparameters: t.Optional[bool] = None,
         sample_model_fraction: t.Optional[float] = None,
         sub_model_size: t.Optional[int] = None,
@@ -3748,12 +3747,6 @@ def react_aggregate(  # noqa: C901
             Total sample size of model to use (using sampling with replacement)
             for all non-robust computation. Defaults to 1000.
             If specified overrides sample_model_fraction.```
-        residuals_hyperparameter_feature : str, optional
-            When calculating residuals and prediction stats, uses this target
-            features's hyperparameters. The trainee must have been analyzed with
-            this feature as the action feature first. If not provided, by default
-            residuals and prediction stats uses targetless hyperparameters. Targetless
-            hyperparameters may also be selected using an empty string: "".
         robust_hyperparameters : bool, optional
             When specified, will attempt to return residuals that were
             computed using hyperparameters with the specified robust or
@@ -3832,7 +3825,6 @@ def react_aggregate(  # noqa: C901
         stats = self.execute(trainee_id, "react_aggregate", {
             "action_feature": action_feature,
             "action_features": action_features,
-            "residuals_hyperparameter_feature": residuals_hyperparameter_feature,
             "context_features": context_features,
             "confusion_matrix_min_count": confusion_matrix_min_count,
             "details": details,

howso/engine/trainee.py

@@ -3151,7 +3151,6 @@ def react_aggregate(
         num_robust_influence_samples_per_case: t.Optional[int] = None,
         num_samples: t.Optional[int] = None,
         prediction_stats_action_feature: t.Optional[str] = None,
-        residuals_hyperparameter_feature: t.Optional[str] = None,
         robust_hyperparameters: t.Optional[bool] = None,
         sample_model_fraction: t.Optional[float] = None,
         sub_model_size: t.Optional[int] = None,
@@ -3349,12 +3348,6 @@ def react_aggregate(
             Total sample size of model to use (using sampling with replacement)
             for all non-robust computation. Defaults to 1000.
             If specified overrides sample_model_fraction.```
-        residuals_hyperparameter_feature : str, optional
-            When calculating residuals and prediction stats, uses this target
-            features's hyperparameters. The trainee must have been analyzed with
-            this feature as the action feature first. If not provided, by default
-            residuals and prediction stats uses targetless hyperparameters. Targetless
-            hyperparameters can also be selected using an empty string: "".
         robust_hyperparameters : bool, optional
             When specified, will attempt to return residuals that were
             computed using hyperparameters with the specified robust or
@@ -3404,7 +3397,6 @@ def react_aggregate(
                 num_robust_influence_samples_per_case=num_robust_influence_samples_per_case,
                 num_samples=num_samples,
                 prediction_stats_action_feature=prediction_stats_action_feature,
-                residuals_hyperparameter_feature=residuals_hyperparameter_feature,
                 robust_hyperparameters=robust_hyperparameters,
                 sample_model_fraction=sample_model_fraction,
                 sub_model_size=sub_model_size,