feat: Add hierarchy and window configs to Vertex Forecasting training job

google/cloud/aiplatform/training_jobs.py

@@ -4037,6 +4037,13 @@ def run(
         model_display_name: Optional[str] = None,
         model_labels: Optional[Dict[str, str]] = None,
         additional_experiments: Optional[List[str]] = None,
+        hierarchy_group_columns: Optional[List[str]] = None,
+        hierarchy_group_total_weight: Optional[float] = None,
+        hierarchy_temporal_total_weight: Optional[float] = None,
+        hierarchy_group_temporal_total_weight: Optional[float] = None,
+        window_column: Optional[str] = None,
+        window_stride_length: Optional[int] = None,
+        window_max_count: Optional[int] = None,
         sync: bool = True,
         create_request_timeout: Optional[float] = None,
     ) -> models.Model:
@@ -4157,7 +4164,7 @@ def run(
                 Applies only if [export_evaluated_data_items] is True and
                 [export_evaluated_data_items_bigquery_destination_uri] is specified.
             quantiles (List[float]):
-                Quantiles to use for the `minimize-quantile-loss`
+                Quantiles to use for the ``minimize-quantile-loss``
                 [AutoMLForecastingTrainingJob.optimization_objective]. This argument is required in
                 this case.
 
@@ -4200,6 +4207,37 @@ def run(
                 Optional. Additional experiment flags for the time series forcasting training.
             create_request_timeout (float):
                 Optional. The timeout for the create request in seconds.
+            hierarchy_group_columns (List[str]):
+                Optional. A list of time series attribute column names that
+                define the time series hierarchy. Only one level of hierarchy is
+                supported, ex. ``region`` for a hierarchy of stores or
+                ``department`` for a hierarchy of products. If multiple columns
+                are specified, time series will be grouped by their combined
+                values, ex. (``blue``, ``large``) for ``color`` and ``size``, up
+                to 5 columns are accepted. If no group columns are specified,
+                all time series are considered to be part of the same group.
+            hierarchy_group_total_weight (float):
+                Optional. The weight of the loss for predictions aggregated over
+                time series in the same hierarchy group.
+            hierarchy_temporal_total_weight (float):
+                Optional. The weight of the loss for predictions aggregated over
+                the horizon for a single time series.
+            hierarchy_group_temporal_total_weight (float):
+                Optional. The weight of the loss for predictions aggregated over
+                both the horizon and time series in the same hierarchy group.
+            window_column (str):
+                Optional. Name of the column that should be used to filter input
+                rows. The column should contain either booleans or string
+                booleans; if the value of the row is True, generate a sliding
+                window from that row.
+            window_stride_length (int):
+                Optional. Step length used to generate input examples. Every
+                ``window_stride_length`` rows will be used to generate a sliding
+                window.
+            window_max_count (int):
+                Optional. Number of rows that should be used to generate input
+                examples. If the total row count is larger than this number, the
+                input data will be randomly sampled to hit the count.
             sync (bool):
                 Whether to execute this method synchronously. If False, this method
                 will be executed in concurrent Future and any downstream object will
@@ -4254,6 +4292,13 @@ def run(
             validation_options=validation_options,
             model_display_name=model_display_name,
             model_labels=model_labels,
+            hierarchy_group_columns=hierarchy_group_columns,
+            hierarchy_group_total_weight=hierarchy_group_total_weight,
+            hierarchy_temporal_total_weight=hierarchy_temporal_total_weight,
+            hierarchy_group_temporal_total_weight=hierarchy_group_temporal_total_weight,
+            window_column=window_column,
+            window_stride_length=window_stride_length,
+            window_max_count=window_max_count,
             sync=sync,
             create_request_timeout=create_request_timeout,
         )
@@ -4286,6 +4331,13 @@ def _run(
         budget_milli_node_hours: int = 1000,
         model_display_name: Optional[str] = None,
         model_labels: Optional[Dict[str, str]] = None,
+        hierarchy_group_columns: Optional[List[str]] = None,
+        hierarchy_group_total_weight: Optional[float] = None,
+        hierarchy_temporal_total_weight: Optional[float] = None,
+        hierarchy_group_temporal_total_weight: Optional[float] = None,
+        window_column: Optional[str] = None,
+        window_stride_length: Optional[int] = None,
+        window_max_count: Optional[int] = None,
         sync: bool = True,
         create_request_timeout: Optional[float] = None,
     ) -> models.Model:
@@ -4453,6 +4505,37 @@ def _run(
                 are allowed.
                 See https://goo.gl/xmQnxf for more information
                 and examples of labels.
+            hierarchy_group_columns (List[str]):
+                Optional. A list of time series attribute column names that
+                define the time series hierarchy. Only one level of hierarchy is
+                supported, ex. ``region`` for a hierarchy of stores or
+                ``department`` for a hierarchy of products. If multiple columns
+                are specified, time series will be grouped by their combined
+                values, ex. (``blue``, ``large``) for ``color`` and ``size``, up
+                to 5 columns are accepted. If no group columns are specified,
+                all time series are considered to be part of the same group.
+            hierarchy_group_total_weight (float):
+                Optional. The weight of the loss for predictions aggregated over
+                time series in the same hierarchy group.
+            hierarchy_temporal_total_weight (float):
+                Optional. The weight of the loss for predictions aggregated over
+                the horizon for a single time series.
+            hierarchy_group_temporal_total_weight (float):
+                Optional. The weight of the loss for predictions aggregated over
+                both the horizon and time series in the same hierarchy group.
+            window_column (str):
+                Optional. Name of the column that should be used to filter input
+                rows. The column should contain either booleans or string
+                booleans; if the value of the row is True, generate a sliding
+                window from that row.
+            window_stride_length (int):
+                Optional. Step length used to generate input examples. Every
+                ``window_stride_length`` rows will be used to generate a sliding
+                window.
+            window_max_count (int):
+                Optional. Number of rows that should be used to generate input
+                examples. If the total row count is larger than this number, the
+                input data will be randomly sampled to hit the count.
             sync (bool):
                 Whether to execute this method synchronously. If False, this method
                 will be executed in concurrent Future and any downstream object will
@@ -4482,6 +4565,12 @@ def _run(
                 % column_names
             )
 
+        window_config = self._create_window_config(
+            column=window_column,
+            stride_length=window_stride_length,
+            max_count=window_max_count,
+        )
+
         training_task_inputs_dict = {
             # required inputs
             "targetColumn": target_column,
@@ -4505,6 +4594,24 @@ def _run(
             "optimizationObjective": self._optimization_objective,
         }
 
+        # TODO(TheMichaelHu): Remove the ifs once the API supports these inputs.
+        if any(
+            [
+                hierarchy_group_columns,
+                hierarchy_group_total_weight,
+                hierarchy_temporal_total_weight,
+                hierarchy_group_temporal_total_weight,
+            ]
+        ):
+            training_task_inputs_dict["hierarchyConfig"] = {
+                "groupColumns": hierarchy_group_columns,
+                "groupTotalWeight": hierarchy_group_total_weight,
+                "temporalTotalWeight": hierarchy_temporal_total_weight,
+                "groupTemporalTotalWeight": hierarchy_group_temporal_total_weight,
+            }
+        if window_config:
+            training_task_inputs_dict["windowConfig"] = window_config
+
         final_export_eval_bq_uri = export_evaluated_data_items_bigquery_destination_uri
         if final_export_eval_bq_uri and not final_export_eval_bq_uri.startswith(
             "bq://"
@@ -4582,6 +4689,29 @@ def _add_additional_experiments(self, additional_experiments: List[str]):
         """
         self._additional_experiments.extend(additional_experiments)
 
+    @staticmethod
+    def _create_window_config(
+        column: Optional[str] = None,
+        stride_length: Optional[int] = None,
+        max_count: Optional[int] = None,
+    ) -> Optional[Dict[str, Union[int, str]]]:
+        """Creates a window config from training job arguments."""
+        configs = {
+            "column": column,
+            "strideLength": stride_length,
+            "maxCount": max_count,
+        }
+        present_configs = {k: v for k, v in configs.items() if v is not None}
+        if not present_configs:
+            return None
+        if len(present_configs) > 1:
+            raise ValueError(
+                "More than one windowing strategy provided. Make sure only one "
+                "of window_column, window_stride_length, or window_max_count "
+                "is specified."
+            )
+        return present_configs
+
 
 class AutoMLImageTrainingJob(_TrainingJob):
     _supported_training_schemas = (