removed AutoTs explainability

Author: prasankh

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

@@ -328,41 +328,7 @@ def _generate_report(self) -> tuple:
         )
 
     def _custom_predict_autots(self, data):
-        """
-        Predicts the future values of a time series using the AutoTS model.
-
-        Parameters
-        ----------
-            data (numpy.ndarray): The input data to be used for prediction.
-
-        Returns
-        -------
-            numpy.ndarray: The predicted future values of the time series.
-        """
-
-        data.index = pd.to_datetime(data.index)
-        temp_model = copy.deepcopy(self.models)
-
-        if data.shape[0] > 1:
-            temp_model.fit_data(
-                data[~data.index.duplicated()],
-                future_regressor=self.future_regressor_train.head(-self.spec.horizon),
-            )
-            dedup_shape = data.shape[0] - data[~data.index.duplicated()].shape[0] + 1
-            return pd.Series(0, index=np.arange(dedup_shape)).append(
-                temp_model.back_forecast(
-                    tail=data[~data.index.duplicated()].shape[0] - 1
-                )
-                .forecast[self.spec.target_column]
-                .fillna(0)
-            )
-
-        return temp_model.predict(
-            future_regressor=self.future_regressor_train.loc[
-                self.future_regressor_train.index.isin(data.index)
-            ],
-            forecast_length=1,
-        ).forecast[self.series_id]
+        raise NotImplementedError("Autots does not yet support explanations.")
 
     def _generate_train_metrics(self) -> pd.DataFrame:
         """

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

@@ -578,36 +578,22 @@ def explain_model(self, datetime_col_name, explain_predict_fn) -> dict:
         exp_start_time = time.time()
         global_ex_time = 0
         local_ex_time = 0
+        logger.info(f"Calculating explanations using {self.spec.explanations_accuracy_mode} mode")
         for series_id in self.target_columns:
             self.series_id = series_id
-            if self.spec.model == SupportedModels.AutoTS:
-                self.dataset_cols = (
-                    self.full_data_long.loc[
-                        self.full_data_long.series_id == self.category_mapping[self.series_id]
-                    ]
-                    .set_index(datetime_col_name)
-                    .columns
-                )
-
-                self.bg_data = self.full_data_long.loc[
-                    self.full_data_long.series_id == self.category_mapping[self.series_id]
-                ].set_index(datetime_col_name)
-
-            else:
-                self.dataset_cols = (
-                    self.full_data_dict.get(series_id)
-                    .set_index(datetime_col_name)
-                    .drop(series_id, axis=1)
-                    .columns
-                )
+            self.dataset_cols = (
+                self.full_data_dict.get(series_id)
+                .set_index(datetime_col_name)
+                .drop(series_id, axis=1)
+                .columns
+            )
 
-                self.bg_data = self.full_data_dict.get(series_id).set_index(
-                    datetime_col_name
-                )
+            self.bg_data = self.full_data_dict.get(series_id).set_index(
+                datetime_col_name
+            )
             data = self.bg_data[list(self.dataset_cols)][: -self.spec.horizon][
                 list(self.dataset_cols)]
             ratio = SpeedAccuracyMode.ratio[self.spec.explanations_accuracy_mode]
-            logger.info(f"Calculating explanations using {self.spec.explanations_accuracy_mode} mode")
             data_trimmed = data.tail(max(int(len(data) * ratio), 100)).reset_index()
             data_trimmed[datetime_col_name] = data_trimmed[datetime_col_name].apply(lambda x: x.timestamp())
             kernel_explnr = PermutationExplainer(