update the column names and enable the global explainer in the reports

Author: codeloop

ads/opctl/operator/lowcode/forecast/model/automlx.py

@@ -247,17 +247,19 @@ def _generate_report(self):
                 self.explain_model()
 
                 global_explanation_section = None
-                if self.spec.explanations_accuracy_mode != SpeedAccuracyMode.AUTOMLX:
-                    # Convert the global explanation data to a DataFrame
-                    global_explanation_df = pd.DataFrame(self.global_explanation)
 
-                    self.formatted_global_explanation = (
-                        global_explanation_df / global_explanation_df.sum(axis=0) * 100
-                    )
-                    self.formatted_global_explanation = self.formatted_global_explanation.rename(
+                # Convert the global explanation data to a DataFrame
+                global_explanation_df = pd.DataFrame(self.global_explanation)
+
+                self.formatted_global_explanation = (
+                    global_explanation_df / global_explanation_df.sum(axis=0) * 100
+                )
+                self.formatted_global_explanation = (
+                    self.formatted_global_explanation.rename(
                         {self.spec.datetime_column.name: ForecastOutputColumns.DATE},
                         axis=1,
                     )
+                )
 
                 aggregate_local_explanations = pd.DataFrame()
                 for s_id, local_ex_df in self.local_explanation.items():
@@ -269,11 +271,15 @@ def _generate_report(self):
                 self.formatted_local_explanation = aggregate_local_explanations
 
                 if not self.target_cat_col:
-                    self.formatted_global_explanation = self.formatted_global_explanation.rename(
-                        {"Series 1": self.original_target_column},
-                        axis=1,
+                    self.formatted_global_explanation = (
+                        self.formatted_global_explanation.rename(
+                            {"Series 1": self.original_target_column},
+                            axis=1,
+                        )
+                    )
+                    self.formatted_local_explanation.drop(
+                        "Series", axis=1, inplace=True
                     )
-                    self.formatted_local_explanation.drop("Series", axis=1, inplace=True)
 
                 # Create a markdown section for the global explainability
                 global_explanation_section = rc.Block(
@@ -422,7 +428,9 @@ def explain_model(self):
                     # Use the MLExplainer class from AutoMLx to generate explanations
                     explainer = automlx.MLExplainer(
                         self.models[s_id],
-                        self.datasets.additional_data.get_data_for_series(series_id=s_id)
+                        self.datasets.additional_data.get_data_for_series(
+                            series_id=s_id
+                        )
                         .drop(self.spec.datetime_column.name, axis=1)
                         .head(-self.spec.horizon)
                         if self.spec.additional_data
@@ -433,7 +441,9 @@ def explain_model(self):
 
                     # Generate explanations for the forecast
                     explanations = explainer.explain_prediction(
-                        X=self.datasets.additional_data.get_data_for_series(series_id=s_id)
+                        X=self.datasets.additional_data.get_data_for_series(
+                            series_id=s_id
+                        )
                         .drop(self.spec.datetime_column.name, axis=1)
                         .tail(self.spec.horizon)
                         if self.spec.additional_data
@@ -445,7 +455,9 @@ def explain_model(self):
                     explanations_df = pd.concat(
                         [exp.to_dataframe() for exp in explanations]
                     )
-                    explanations_df["row"] = explanations_df.groupby("Feature").cumcount()
+                    explanations_df["row"] = explanations_df.groupby(
+                        "Feature"
+                    ).cumcount()
                     explanations_df = explanations_df.pivot(
                         index="row", columns="Feature", values="Attribution"
                     )
@@ -454,14 +466,17 @@ def explain_model(self):
                     # Store the explanations in the local_explanation dictionary
                     self.local_explanation[s_id] = explanations_df
 
-                    self.global_explanation[s_id] = dict(zip(
-                            data_i.columns[1:],
-                            np.average(np.absolute(explanations_df[:, 1:]), axis=0),
+                    self.global_explanation[s_id] = dict(
+                        zip(
+                            self.local_explanation[s_id].columns,
+                            np.nanmean((self.local_explanation[s_id]), axis=0),
                         )
                     )
                 else:
                     # Fall back to the default explanation generation method
                     super().explain_model()
             except Exception as e:
-                logger.warning(f"Failed to generate explanations for series {s_id} with error: {e}.")
+                logger.warning(
+                    f"Failed to generate explanations for series {s_id} with error: {e}."
+                )
                 logger.debug(f"Full Traceback: {traceback.format_exc()}")

ads/opctl/operator/lowcode/forecast/model/base_model.py

@@ -754,14 +754,6 @@ def explain_model(self):
                     logger.warn(
                         "No explanations generated. Ensure that additional data has been provided."
                     )
-                # elif (
-                #     self.spec.model == SupportedModels.AutoMLX
-                #     and self.spec.explanations_accuracy_mode
-                #     == SpeedAccuracyMode.AUTOMLX
-                # ):
-                #     logger.warning(
-                #         "Global explanations not available for AutoMLX models with inherent explainability"
-                #     )
                 else:
                     self.global_explanation[s_id] = dict(
                         zip(