Cleanup

google/cloud/aiplatform/datasets/column_names_dataset.py

@@ -29,6 +29,7 @@
 
 
 class _ColumnNamesDataset(_Dataset):
+
     @property
     def column_names(self) -> List[str]:
         """Retrieve the columns for the dataset by extracting it from the Google Cloud Storage or

google/cloud/aiplatform/training_jobs.py

@@ -3070,7 +3070,7 @@ def __init__(
                 ignored by the training, except for the targetColumn, which should have
                 no transformations defined on.
                 Only one of column_transformations or column_specs should be passed.
-            column_transformations (Union[Dict, List[Dict]]):
+            column_transformations (List[Dict[str, Dict[str, str]]]):
                 Optional. Transformations to apply to the input columns (i.e. columns other
                 than the targetColumn). Each transformation may produce multiple
                 result values from the column's value, and all are used for training.
@@ -3668,7 +3668,7 @@ def __init__(
                 ignored by the training, except for the targetColumn, which should have
                 no transformations defined on.
                 Only one of column_transformations or column_specs should be passed.
-            column_transformations (Union[Dict, List[Dict]]):
+            column_transformations (List[Dict[str, Dict[str, str]]]):
                 Optional. Transformations to apply to the input columns (i.e. columns other
                 than the targetColumn). Each transformation may produce multiple
                 result values from the column's value, and all are used for training.

google/cloud/aiplatform/utils/column_transformations_utils.py

@@ -15,26 +15,26 @@
 # limitations under the License.
 #
 
-from google.cloud.aiplatform import base
-
 from google.cloud.aiplatform.datasets.column_names_dataset import _ColumnNamesDataset
-from typing import Dict, List, Optional, Union, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import warnings
 
 
 def get_default_column_transformations(
     dataset: _ColumnNamesDataset, target_column: str,
-) -> Tuple[Dict, List[str]]:
+) -> Tuple[List[Dict[str, Dict[str, str]]], List[str]]:
     """Get default column transformations from the column names, while omitting the target column.
 
     Args:
+        dataset (_ColumnNamesDataset):
+            Required. The dataset 
         target_column (str):
-            Required. The name of the column values of which the Model is to predict. 
+            Required. The name of the column values of which the Model is to predict.
 
     Returns:
-        Dict
-            The default column transformations.
+        Tuple[Dict[str, Dict[str, Union[bool, str]]], List[str]]:
+            The default column transformations and the default column names.
     """
 
     column_names = [
@@ -51,7 +51,7 @@ def get_default_column_transformations(
 
 def validate_and_get_column_transformations(
     column_specs: Optional[Dict[str, str]],
-    column_transformations: Optional[Union[Dict, List[Dict]]],
+    column_transformations: Optional[List[Dict[str, Dict[str, str]]]],
 ) -> Dict:
     """Validates column specs and transformations, then returns processed transformations.
 
@@ -67,7 +67,7 @@ def validate_and_get_column_transformations(
             ignored by the training, except for the targetColumn, which should have
             no transformations defined on.
             Only one of column_transformations or column_specs should be passed.
-        column_transformations (Union[Dict, List[Dict]]):
+        column_transformations (List[Dict[str, Dict[str, str]]]):
             Optional. Transformations to apply to the input columns (i.e. columns other
             than the targetColumn). Each transformation may produce multiple
             result values from the column's value, and all are used for training.
@@ -81,7 +81,7 @@ def validate_and_get_column_transformations(
             Consider using column_specs as column_transformations will be deprecated eventually.
 
     Returns:
-        Dict
+        List[Dict[str, Dict[str, str]]]:
             The column transformations.
     """
     # user populated transformations