fix: Fix default AutoML Forecasting transformations list.

PiperOrigin-RevId: 526734524

google/cloud/aiplatform/training_jobs.py

@@ -2438,7 +2438,9 @@ def _run(
             (
                 self._column_transformations,
                 column_names,
-            ) = dataset._get_default_column_transformations(target_column)
+            ) = column_transformations_utils.get_default_column_transformations(
+                dataset=dataset, target_column=target_column
+            )
 
             _LOGGER.info(
                 "The column transformation of type 'auto' was set for the following columns: %s."

google/cloud/aiplatform/utils/column_transformations_utils.py

@@ -16,7 +16,6 @@
 #
 
 from typing import Dict, List, Optional, Tuple
-import warnings
 
 from google.cloud.aiplatform import datasets
 
@@ -51,9 +50,9 @@ def get_default_column_transformations(
 
 
 def validate_and_get_column_transformations(
-    column_specs: Optional[Dict[str, str]],
-    column_transformations: Optional[List[Dict[str, Dict[str, str]]]],
-) -> List[Dict[str, Dict[str, str]]]:
+    column_specs: Optional[Dict[str, str]] = None,
+    column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None,
+) -> Optional[List[Dict[str, Dict[str, str]]]]:
     """Validates column specs and transformations, then returns processed transformations.
 
     Args:
@@ -91,21 +90,13 @@ def validate_and_get_column_transformations(
     # user populated transformations
     if column_transformations is not None and column_specs is not None:
         raise ValueError(
-            "Both column_transformations and column_specs were passed. Only one is allowed."
+            "Both column_transformations and column_specs were passed. Only "
+            "one is allowed."
         )
-    if column_transformations is not None:
-        warnings.simplefilter("always", DeprecationWarning)
-        warnings.warn(
-            "consider using column_specs instead. column_transformations will be deprecated in the future.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return column_transformations
     elif column_specs is not None:
         return [
             {transformation: {"column_name": column_name}}
             for column_name, transformation in column_specs.items()
         ]
     else:
-        return None
+        return column_transformations

tests/unit/aiplatform/test_automl_forecasting_training_jobs.py

@@ -1294,3 +1294,41 @@ def test_run_call_pipeline_if_set_additional_experiments_probabilistic_inference
             training_pipeline=true_training_pipeline,
             timeout=None,
         )
+
+    def test_automl_forecasting_with_no_transformations(
+        self,
+        mock_pipeline_service_create,
+        mock_pipeline_service_get,
+        mock_dataset_time_series,
+        mock_model_service_get,
+    ):
+        aiplatform.init(project=_TEST_PROJECT)
+        job = training_jobs.AutoMLForecastingTrainingJob(
+            display_name=_TEST_DISPLAY_NAME,
+            optimization_objective=_TEST_TRAINING_OPTIMIZATION_OBJECTIVE_NAME,
+        )
+        mock_dataset_time_series.column_names = [
+            "a",
+            "b",
+            _TEST_TRAINING_TARGET_COLUMN,
+        ]
+        job.run(
+            dataset=mock_dataset_time_series,
+            predefined_split_column_name=_TEST_PREDEFINED_SPLIT_COLUMN_NAME,
+            target_column=_TEST_TRAINING_TARGET_COLUMN,
+            time_column=_TEST_TRAINING_TIME_COLUMN,
+            time_series_identifier_column=_TEST_TRAINING_TIME_SERIES_IDENTIFIER_COLUMN,
+            unavailable_at_forecast_columns=_TEST_TRAINING_UNAVAILABLE_AT_FORECAST_COLUMNS,
+            available_at_forecast_columns=_TEST_TRAINING_AVAILABLE_AT_FORECAST_COLUMNS,
+            forecast_horizon=_TEST_TRAINING_FORECAST_HORIZON,
+            data_granularity_unit=_TEST_TRAINING_DATA_GRANULARITY_UNIT,
+            data_granularity_count=_TEST_TRAINING_DATA_GRANULARITY_COUNT,
+            model_display_name=_TEST_MODEL_DISPLAY_NAME,
+            time_series_attribute_columns=_TEST_TRAINING_TIME_SERIES_ATTRIBUTE_COLUMNS,
+            context_window=_TEST_TRAINING_CONTEXT_WINDOW,
+            budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS,
+        )
+        assert job._column_transformations == [
+            {"auto": {"column_name": "a"}},
+            {"auto": {"column_name": "b"}},
+        ]

tests/unit/aiplatform/test_utils.py

@@ -35,7 +35,9 @@
 from google.cloud import storage
 from google.cloud.aiplatform import compat, utils
 from google.cloud.aiplatform.compat.types import pipeline_failure_policy
+from google.cloud.aiplatform import datasets
 from google.cloud.aiplatform.utils import (
+    column_transformations_utils,
     gcs_utils,
     pipeline_utils,
     prediction_utils,
@@ -485,6 +487,51 @@ def test_timestamped_unique_name():
     assert re.match(r"\d{4}-\d{2}-\d{2}-\d{2}-\d{2}-\d{2}-.{5}", name)
 
 
+class TestColumnTransformationsUtils:
+
+    column_transformations = [
+        {"auto": {"column_name": "a"}},
+        {"auto": {"column_name": "b"}},
+    ]
+    column_specs = {"a": "auto", "b": "auto"}
+
+    def test_get_default_column_transformations(self):
+        ds = mock.MagicMock(datasets.TimeSeriesDataset)
+        ds.column_names = ["a", "b", "target"]
+        (
+            transforms,
+            columns,
+        ) = column_transformations_utils.get_default_column_transformations(
+            dataset=ds, target_column="target"
+        )
+        assert transforms == [
+            {"auto": {"column_name": "a"}},
+            {"auto": {"column_name": "b"}},
+        ]
+        assert columns == ["a", "b"]
+
+    def test_validate_transformations_with_multiple_configs(self):
+        with pytest.raises(ValueError):
+            (
+                column_transformations_utils.validate_and_get_column_transformations(
+                    column_transformations=self.column_transformations,
+                    column_specs=self.column_specs,
+                )
+            )
+
+    def test_validate_transformations_with_column_specs(self):
+        actual = column_transformations_utils.validate_and_get_column_transformations(
+            column_specs=self.column_specs
+        )
+        assert actual == self.column_transformations
+
+    def test_validate_transformations_with_column_transformations(self):
+        actual = column_transformations_utils.validate_and_get_column_transformations(
+            column_transformations=self.column_transformations
+        )
+        assert actual == self.column_transformations
+
+
 @pytest.mark.usefixtures("google_auth_mock")
 class TestGcsUtils:
     def test_upload_to_gcs(self, json_file, mock_storage_blob_upload_from_filename):