Release 12 (from main)

models/linear_regression/linear_regression_model.py

@@ -73,6 +73,15 @@ class LinearRegressionModel(BaseEstimator, TransformerMixin):
                 + ', '.join(missing_model_features)
             )
 
+    def _apply_clipping(self, predictions: np.ndarray) -> None:
+        """Replace in place predictions above max with Q3 and below min with Q1."""
+        for i in range(len(predictions)):
+            # Each limit is optional on its own (SIENTIAPDE-2219)
+            if 'max' in self.clipping and predictions[i] > self.clipping['max']:
+                predictions[i] = self.q3_target
+            elif 'min' in self.clipping and predictions[i] < self.clipping['min']:
+                predictions[i] = self.q1_target
+
     def create_poly_features(self, input_data: pd.DataFrame, fit: bool = False) -> pd.DataFrame:
         """
         Function to create polynomial features
@@ -289,13 +298,8 @@ class LinearRegressionModel(BaseEstimator, TransformerMixin):
             print(f'X_test shape after removing NaN rows: {x_valid.shape}')
 
         valid_pred = self.regr.predict(x_valid[model_features])
-
         if self.clipping:
-            for i in range(len(valid_pred)):
-                if valid_pred[i] > self.clipping['max']:
-                    valid_pred[i] = self.q3_target
-                elif valid_pred[i] < self.clipping['min']:
-                    valid_pred[i] = self.q1_target
+            self._apply_clipping(valid_pred)
 
         y_pred[row_mask.to_numpy()] = valid_pred
         return y_pred