Merge branch 'main' into remove_defined_tag_update_ft

Author: qiuosier

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

@@ -71,7 +71,7 @@ def generate_report(self):
         try:
             anomaly_output = self._build_model()
         except Exception as e:
-            logger.warn(f"Found exception: {e}")
+            logger.warning(f"Found exception: {e}")
             if self.spec.datetime_column:
                 anomaly_output = self._fallback_build_model()
             raise e
@@ -347,7 +347,7 @@ def _save_report(
                 storage_options=storage_options,
             )
 
-        logger.warn(
+        logger.warning(
             f"The report has been successfully "
             f"generated and placed to the: {unique_output_dir}."
         )
@@ -356,7 +356,7 @@ def _fallback_build_model(self):
         """
         Fallback method for the sub model _build_model method.
         """
-        logger.warn(
+        logger.warning(
             f"The build_model method has failed for the model: {self.spec.model}. "
             "A fallback model will be built."
         )

ads/opctl/operator/lowcode/anomaly/model/randomcutforest.py

@@ -95,7 +95,7 @@ def _build_model(self) -> AnomalyOutput:
 
                 anomaly_output.add_output(target, anomaly, score)
             except Exception as e:
-                logger.warn(f"Encountered Error: {e}. Skipping series {target}.")
+                logger.warning(f"Encountered Error: {e}. Skipping series {target}.")
 
         return anomaly_output
 

ads/opctl/operator/lowcode/anomaly/utils.py

@@ -44,7 +44,7 @@ def _build_metrics_df(y_true, y_pred, column_name):
         # Throws exception if y_true has only one class
         metrics[SupportedMetrics.ROC_AUC] = roc_auc_score(y_true, y_pred)
     except Exception as e:
-        logger.warn(f"An exception occurred: {e}")
+        logger.warning(f"An exception occurred: {e}")
         metrics[SupportedMetrics.ROC_AUC] = None
     precision, recall, thresholds = precision_recall_curve(y_true, y_pred)
     metrics[SupportedMetrics.PRC_AUC] = auc(recall, precision)

ads/opctl/operator/lowcode/common/transformations.py

@@ -98,7 +98,11 @@ def run(self, data):
         return clean_df
 
     def _remove_trailing_whitespace(self, df):
-        return df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)
+        return df.apply(
+            lambda x: x.str.strip()
+            if hasattr(x, "dtype") and x.dtype == "object"
+            else x
+        )
 
     def _clean_column_names(self, df):
         """

ads/opctl/operator/lowcode/common/utils.py

@@ -3,6 +3,7 @@
 # Copyright (c) 2024, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
+import json
 import logging
 import os
 import shutil
@@ -12,7 +13,6 @@
 
 import fsspec
 import oracledb
-import json
 import pandas as pd
 
 from ads.common.object_storage_details import ObjectStorageDetails
@@ -142,6 +142,11 @@ def write_data(data, filename, format, storage_options=None, index=False, **kwar
     )
 
 
+def write_json(json_dict, filename, storage_options=None):
+    with fsspec.open(filename, mode="w", **storage_options) as f:
+        f.write(json.dumps(json_dict))
+
+
 def write_simple_json(data, path):
     if ObjectStorageDetails.is_oci_path(path):
         storage_options = default_signer()
@@ -265,7 +270,7 @@ def find_output_dirname(output_dir: OutputDirectory):
     while os.path.exists(unique_output_dir):
         unique_output_dir = f"{output_dir}_{counter}"
         counter += 1
-    logger.warn(
+    logger.warning(
         f"Since the output directory was not specified, the output will be saved to {unique_output_dir} directory."
     )
     return unique_output_dir

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

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-# Copyright (c) 2023, 2024 Oracle and/or its affiliates.
+# Copyright (c) 2023, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import logging
@@ -132,13 +132,14 @@ def _train_model(self, i, s_id, df, model_kwargs):
 
             logger.debug("===========Done===========")
         except Exception as e:
-            self.errors_dict[s_id] = {
+            new_error = {
                 "model_name": self.spec.model,
                 "error": str(e),
                 "error_trace": traceback.format_exc(),
             }
-            logger.warn(f"Encountered Error: {e}. Skipping.")
-            logger.warn(traceback.format_exc())
+            self.errors_dict[s_id] = new_error
+            logger.warning(f"Encountered Error: {e}. Skipping.")
+            logger.warning(traceback.format_exc())
 
     def _build_model(self) -> pd.DataFrame:
         full_data_dict = self.datasets.get_data_by_series()
@@ -166,7 +167,7 @@ def _generate_report(self):
             sec5_text = rc.Heading("ARIMA Model Parameters", level=2)
             blocks = [
                 rc.Html(
-                    m['model'].summary().as_html(),
+                    m["model"].summary().as_html(),
                     label=s_id if self.target_cat_col else None,
                 )
                 for i, (s_id, m) in enumerate(self.models.items())
@@ -201,11 +202,15 @@ 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(
@@ -235,7 +240,7 @@ def _generate_report(self):
                     local_explanation_section,
                 ]
             except Exception as e:
-                logger.warn(f"Failed to generate Explanations with error: {e}.")
+                logger.warning(f"Failed to generate Explanations with error: {e}.")
                 logger.debug(f"Full Traceback: {traceback.format_exc()}")
 
         model_description = rc.Text(

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

@@ -184,13 +184,18 @@ def _build_model(self) -> pd.DataFrame:
                     "selected_model_params": model.selected_model_params_,
                 }
             except Exception as e:
-                self.errors_dict[s_id] = {
+                new_error = {
                     "model_name": self.spec.model,
                     "error": str(e),
                     "error_trace": traceback.format_exc(),
                 }
-                logger.warn(f"Encountered Error: {e}. Skipping.")
-                logger.warn(traceback.format_exc())
+                if s_id in self.errors_dict:
+                    self.errors_dict[s_id]["model_fitting"] = new_error
+                else:
+                    self.errors_dict[s_id] = {"model_fitting": new_error}
+                logger.warning(f"Encountered Error: {e}. Skipping.")
+                logger.warning(f"self.errors_dict[s_id]: {self.errors_dict[s_id]}")
+                logger.warning(traceback.format_exc())
 
         logger.debug("===========Forecast Generated===========")
 
@@ -257,7 +262,9 @@ def _generate_report(self):
                 )
 
                 self.formatted_global_explanation.rename(
-                    columns={self.spec.datetime_column.name: ForecastOutputColumns.DATE},
+                    columns={
+                        self.spec.datetime_column.name: ForecastOutputColumns.DATE
+                    },
                     inplace=True,
                 )
 
@@ -312,7 +319,7 @@ def _generate_report(self):
                     local_explanation_section,
                 ]
             except Exception as e:
-                logger.warn(f"Failed to generate Explanations with error: {e}.")
+                logger.warning(f"Failed to generate Explanations with error: {e}.")
                 logger.debug(f"Full Traceback: {traceback.format_exc()}")
 
         model_description = rc.Text(
@@ -462,14 +469,27 @@ def explain_model(self):
                         index="row", columns="Feature", values="Attribution"
                     )
                     explanations_df = explanations_df.reset_index(drop=True)
-
+                    explanations_df[ForecastOutputColumns.DATE] = (
+                        self.datasets.get_horizon_at_series(
+                            s_id=s_id
+                        )[self.spec.datetime_column.name].reset_index(drop=True)
+                    )
                     # Store the explanations in the local_explanation dictionary
                     self.local_explanation[s_id] = explanations_df
 
                     self.global_explanation[s_id] = dict(
                         zip(
-                            self.local_explanation[s_id].columns,
-                            np.nanmean(np.abs(self.local_explanation[s_id]), axis=0),
+                            self.local_explanation[s_id]
+                            .drop(ForecastOutputColumns.DATE, axis=1)
+                            .columns,
+                            np.nanmean(
+                                np.abs(
+                                    self.local_explanation[s_id].drop(
+                                        ForecastOutputColumns.DATE, axis=1
+                                    )
+                                ),
+                                axis=0,
+                            ),
                         )
                     )
                 else:
@@ -478,7 +498,9 @@ def explain_model(self):
             except Exception as e:
                 if s_id in self.errors_dict:
                     self.errors_dict[s_id]["explainer_error"] = str(e)
-                    self.errors_dict[s_id]["explainer_error_trace"] = traceback.format_exc()
+                    self.errors_dict[s_id]["explainer_error_trace"] = (
+                        traceback.format_exc()
+                    )
                 else:
                     self.errors_dict[s_id] = {
                         "model_name": self.spec.model,

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

@@ -211,8 +211,8 @@ def _build_model(self) -> pd.DataFrame:
                     "error": str(e),
                     "error_trace": traceback.format_exc(),
                 }
-            logger.warn(f"Encountered Error: {e}. Skipping.")
-            logger.warn(traceback.format_exc())
+            logger.warning(f"Encountered Error: {e}. Skipping.")
+            logger.warning(traceback.format_exc())
 
         logger.debug("===========Done===========")
 
@@ -242,7 +242,7 @@ def _generate_report(self) -> tuple:
                     self.models.df_wide_numeric, series=s_id
                 ),
                 self.datasets.list_series_ids(),
-                target_category_column=self.target_cat_col
+                target_category_column=self.target_cat_col,
             )
             section_1 = rc.Block(
                 rc.Heading("Forecast Overview", level=2),
@@ -260,15 +260,17 @@ def _generate_report(self) -> tuple:
                 )
 
             except KeyError:
-                logger.warn("Issue generating Model Parameters Table Section. Skipping")
+                logger.warning(
+                    "Issue generating Model Parameters Table Section. Skipping"
+                )
                 sec2 = rc.Text("Error generating model parameters.")
 
             section_2 = rc.Block(sec2_text, sec2)
 
             all_sections = [section_1, section_2]
 
         if self.spec.generate_explanations:
-            logger.warn("Explanations not yet supported for the AutoTS Module")
+            logger.warning("Explanations not yet supported for the AutoTS Module")
 
         # Model Description
         model_description = rc.Text(