kwargs support for the transformation

ads/feature_store/common/utils/transformation_utils.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 # -*- coding: utf-8; -*-
-
+import json
 # Copyright (c) 2023 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
@@ -28,6 +28,7 @@ def apply_transformation(
         spark: SparkSession,
         dataframe: Union[DataFrame, pd.DataFrame],
         transformation: Transformation,
+        transformation_kwargs: str,
     ):
         """
         Perform data transformation using either SQL or Pandas, depending on the specified transformation mode.
@@ -36,6 +37,7 @@ def apply_transformation(
             spark: A SparkSession object.
             transformation (Transformation): A transformation object containing details of transformation to be performed.
             dataframe (DataFrame): The input dataframe to be transformed.
+            transformation_kwargs(str): The transformation parameters as json string.
 
         Returns:
             DataFrame: The resulting transformed data.
@@ -54,15 +56,17 @@ def apply_transformation(
         )
         transformed_data = None
 
+        transformation_kwargs_dict = json.loads(transformation_kwargs)
+
         if transformation.transformation_mode == TransformationMode.SQL.value:
             # Register the temporary table
             temporary_table_view = "df_view"
             dataframe.createOrReplaceTempView(temporary_table_view)
 
             transformed_data = spark.sql(
-                transformation_function_caller(temporary_table_view)
+                transformation_function_caller(temporary_table_view, **transformation_kwargs_dict)
             )
         elif transformation.transformation_mode == TransformationMode.PANDAS.value:
-            transformed_data = transformation_function_caller(dataframe)
+            transformed_data = transformation_function_caller(dataframe, **transformation_kwargs_dict)
 
         return transformed_data

ads/feature_store/entity.py

@@ -292,6 +292,7 @@ def _build_feature_group(
         name: str = None,
         description: str = None,
         compartment_id: str = None,
+        transformation_kwargs: Dict = None,
     ):
         feature_group_resource = (
             FeatureGroup()
@@ -304,6 +305,7 @@ def _build_feature_group(
             .with_entity_id(self.id)
             .with_transformation_id(transformation_id)
             .with_partition_keys(partition_keys)
+            .with_transformation_kwargs(transformation_kwargs)
             .with_primary_keys(primary_keys)
             .with_input_feature_details(input_feature_details)
             .with_statistics_config(statistics_config)
@@ -328,6 +330,7 @@ def create_feature_group(
         name: str = None,
         description: str = None,
         compartment_id: str = None,
+        transformation_kwargs: Dict = None,
     ) -> "FeatureGroup":
         """Creates FeatureGroup  resource.
 
@@ -355,6 +358,8 @@ def create_feature_group(
             Description about the Resource.
         compartment_id: str = None
             compartment_id
+        transformation_kwargs: Dict
+            Arguments for the transformation.
 
 
         Returns
@@ -391,6 +396,7 @@ def create_feature_group(
             name,
             description,
             compartment_id,
+            transformation_kwargs,
         )
 
         return self.oci_feature_group.create()

ads/feature_store/execution_strategy/spark/spark_execution.py

@@ -9,6 +9,7 @@
 import pandas as pd
 
 from ads.common.decorator.runtime_dependency import OptionalDependency
+from ads.feature_store.common.utils.base64_encoder_decoder import Base64EncoderDecoder
 from ads.feature_store.common.utils.utility import (
     get_features,
     show_ingestion_summary,
@@ -238,11 +239,20 @@ def _save_offline_dataframe(
             # Apply the transformation
             if feature_group.transformation_id:
                 logger.info("Dataframe is transformation enabled.")
+
+                # Get the Transformation Arguments if exists and pass to the transformation function.
+                transformation_kwargs = Base64EncoderDecoder.decode(
+                    feature_group.transformation_kwargs
+                )
+
                 # Loads the transformation resource
                 transformation = Transformation.from_id(feature_group.transformation_id)
 
                 featured_data = TransformationUtils.apply_transformation(
-                    self._spark_session, data_frame, transformation
+                    self._spark_session,
+                    data_frame,
+                    transformation,
+                    transformation_kwargs,
                 )
             else:
                 logger.info("Transformation not defined.")

ads/feature_store/feature_group.py

@@ -20,6 +20,7 @@
 from ads.feature_store.common.exceptions import (
     NotMaterializedError,
 )
+from ads.feature_store.common.utils.base64_encoder_decoder import Base64EncoderDecoder
 from ads.feature_store.common.utils.utility import (
     get_metastore_id,
     get_execution_engine_type,
@@ -137,6 +138,7 @@ class FeatureGroup(Builder):
     CONST_LIFECYCLE_STATE = "lifecycleState"
     CONST_LAST_JOB_ID = "jobId"
     CONST_INFER_SCHEMA = "isInferSchema"
+    CONST_TRANSFORMATION_KWARGS = "transformationParameters"
 
     attribute_map = {
         CONST_ID: "id",
@@ -157,6 +159,7 @@ class FeatureGroup(Builder):
         CONST_STATISTICS_CONFIG: "statistics_config",
         CONST_INFER_SCHEMA: "is_infer_schema",
         CONST_PARTITION_KEYS: "partition_keys",
+        CONST_TRANSFORMATION_KWARGS: "transformation_parameters",
     }
 
     def __init__(self, spec: Dict = None, **kwargs) -> None:
@@ -325,6 +328,34 @@ def with_primary_keys(self, primary_keys: List[str]) -> "FeatureGroup":
             },
         )
 
+    @property
+    def transformation_kwargs(self) -> str:
+        return self.get_spec(self.CONST_TRANSFORMATION_KWARGS)
+
+    @transformation_kwargs.setter
+    def transformation_kwargs(self, value: Dict):
+        self.with_transformation_kwargs(value)
+
+    def with_transformation_kwargs(
+        self, transformation_kwargs: Dict = {}
+    ) -> "FeatureGroup":
+        """Sets the primary keys of the feature group.
+
+        Parameters
+        ----------
+        transformation_kwargs: Dict
+            Dictionary containing the transformation arguments.
+
+        Returns
+        -------
+        FeatureGroup
+            The FeatureGroup instance (self)
+        """
+        return self.set_spec(
+            self.CONST_TRANSFORMATION_KWARGS,
+            Base64EncoderDecoder.encode(json.dumps(transformation_kwargs)),
+        )
+
     @property
     def partition_keys(self) -> List[str]:
         return self.get_spec(self.CONST_PARTITION_KEYS)