updated the documents

ads/feature_store/common/spark_session_singleton.py

@@ -75,8 +75,9 @@ def __init__(self, metastore_id: str = None):
                 "spark.hadoop.oracle.dcat.metastore.id", metastore_id
             ).config(
                 "spark.sql.warehouse.dir", metastore.default_managed_table_location
-            )\
-                .config("spark.driver.memory", "16G")
+            ).config(
+                "spark.driver.memory", "16G"
+            )
 
         if developer_enabled():
             # Configure spark session with delta jars only in developer mode. In other cases,

ads/feature_store/common/utils/feature_schema_mapper.py

@@ -241,7 +241,6 @@ def map_feature_type_to_pandas(feature_type):
         raise TypeError(f"Feature Type {feature_type} is not supported for pandas")
 
 
-
 def map_spark_type_to_stats_data_type(spark_type):
     """Maps the spark data types to MLM library data types
     args:

ads/feature_store/common/utils/utility.py

@@ -44,7 +44,7 @@
 
 
 def get_execution_engine_type(
-        data_frame: Union[DataFrame, pd.DataFrame]
+    data_frame: Union[DataFrame, pd.DataFrame]
 ) -> ExecutionEngine:
     """
     Determines the execution engine type for a given DataFrame.
@@ -84,7 +84,7 @@ def get_metastore_id(feature_store_id: str):
 
 
 def validate_delta_format_parameters(
-        timestamp: datetime = None, version_number: int = None, is_restore: bool = False
+    timestamp: datetime = None, version_number: int = None, is_restore: bool = False
 ):
     """
     Validate the user input provided as part of preview, restore APIs for ingested data, Ingested data is
@@ -118,9 +118,9 @@ def validate_delta_format_parameters(
 
 
 def get_features(
-        output_columns: List[dict],
-        parent_id: str,
-        entity_type: EntityType = EntityType.FEATURE_GROUP,
+    output_columns: List[dict],
+    parent_id: str,
+    entity_type: EntityType = EntityType.FEATURE_GROUP,
 ) -> List[Feature]:
     """
     Returns a list of features, given a list of output_columns and a feature_group_id.
@@ -155,7 +155,9 @@ def get_features(
 
 
 def get_schema_from_pandas_df(df: pd.DataFrame, feature_store_id: str):
-    spark = SparkSessionSingleton(get_metastore_id(feature_store_id)).get_spark_session()
+    spark = SparkSessionSingleton(
+        get_metastore_id(feature_store_id)
+    ).get_spark_session()
     converted_df = spark.createDataFrame(df)
     return get_schema_from_spark_df(converted_df)
 
@@ -174,7 +176,9 @@ def get_schema_from_spark_df(df: DataFrame):
     return schema_details
 
 
-def get_schema_from_df(data_frame: Union[DataFrame, pd.DataFrame], feature_store_id: str) -> List[dict]:
+def get_schema_from_df(
+    data_frame: Union[DataFrame, pd.DataFrame], feature_store_id: str
+) -> List[dict]:
     """
     Given a DataFrame, returns a list of dictionaries that describe its schema.
     If the DataFrame is a pandas DataFrame, it uses pandas methods to get the schema.
@@ -187,8 +191,7 @@ def get_schema_from_df(data_frame: Union[DataFrame, pd.DataFrame], feature_store
 
 
 def get_input_features_from_df(
-        data_frame: Union[DataFrame, pd.DataFrame],
-        feature_store_id: str
+    data_frame: Union[DataFrame, pd.DataFrame], feature_store_id: str
 ) -> List[FeatureDetail]:
     """
     Given a DataFrame, returns a list of FeatureDetail objects that represent its input features.
@@ -205,7 +208,7 @@ def get_input_features_from_df(
 
 
 def convert_expectation_suite_to_expectation(
-        expectation_suite: ExpectationSuite, expectation_type: ExpectationType
+    expectation_suite: ExpectationSuite, expectation_type: ExpectationType
 ):
     """
     Convert an ExpectationSuite object to an Expectation object with detailed rule information.
@@ -264,7 +267,7 @@ def largest_matching_subset_of_primary_keys(left_feature_group, right_feature_gr
 
 
 def convert_pandas_datatype_with_schema(
-        raw_feature_details: List[dict], input_df: pd.DataFrame
+    raw_feature_details: List[dict], input_df: pd.DataFrame
 ) -> pd.DataFrame:
     feature_detail_map = {}
     columns_to_remove = []
@@ -281,21 +284,25 @@ def convert_pandas_datatype_with_schema(
                 .where(pd.notnull(input_df[column]), None)
             )
         else:
-            logger.warning("column" + column + "doesn't exist in the input feature details")
+            logger.warning(
+                "column" + column + "doesn't exist in the input feature details"
+            )
             columns_to_remove.append(column)
     return input_df.drop(columns=columns_to_remove)
 
 
 def convert_spark_dataframe_with_schema(
-        raw_feature_details: List[dict], input_df: DataFrame
+    raw_feature_details: List[dict], input_df: DataFrame
 ) -> DataFrame:
     feature_detail_map = {}
     columns_to_remove = []
     for feature_details in raw_feature_details:
         feature_detail_map[feature_details.get("name")] = feature_details
     for column in input_df.columns:
         if column not in feature_detail_map.keys():
-            logger.warning("column" + column + "doesn't exist in the input feature details")
+            logger.warning(
+                "column" + column + "doesn't exist in the input feature details"
+            )
             columns_to_remove.append(column)
 
     return input_df.drop(*columns_to_remove)

ads/feature_store/entity.py

@@ -370,7 +370,9 @@ def create_feature_group(
         raw_feature_details = (
             input_feature_details
             if input_feature_details
-            else get_input_features_from_df(schema_details_dataframe, self.feature_store_id)
+            else get_input_features_from_df(
+                schema_details_dataframe, self.feature_store_id
+            )
         )
 
         self.oci_feature_group = self._build_feature_group(

ads/feature_store/execution_strategy/spark/spark_execution.py

@@ -38,9 +38,7 @@
 from ads.feature_store.transformation import Transformation
 
 from ads.feature_store.feature_statistics.statistics_service import StatisticsService
-from ads.feature_store.common.utils.utility import (
-    validate_input_feature_details
-)
+from ads.feature_store.common.utils.utility import validate_input_feature_details
 
 logger = logging.getLogger(__name__)
 
@@ -178,7 +176,9 @@ def _save_offline_dataframe(
             self.spark_engine.create_database(database)
 
             if not feature_group.is_infer_schema:
-                data_frame = validate_input_feature_details(feature_group.input_feature_details, data_frame)
+                data_frame = validate_input_feature_details(
+                    feature_group.input_feature_details, data_frame
+                )
 
             # TODO: Get event timestamp column and apply filtering basis from and to timestamp
 

ads/feature_store/feature_group.py

@@ -476,9 +476,10 @@ def with_input_feature_details(
     def with_schema_details_from_dataframe(
         self, data_frame: Union[DataFrame, pd.DataFrame]
     ) -> "FeatureGroup":
-
         if not self.feature_store_id:
-            raise ValueError("FeatureStore id must be set before calling `with_schema_details_from_dataframe`")
+            raise ValueError(
+                "FeatureStore id must be set before calling `with_schema_details_from_dataframe`"
+            )
 
         schema_details = get_schema_from_df(data_frame, self.feature_store_id)
         feature_details = []

ads/feature_store/feature_store_registrar.py

@@ -73,11 +73,11 @@ def __init__(self):
 
 class _ModelBuilderHashDict(Generic[_ModelBuilderT]):
     def __init__(
-            self,
-            builders: Optional[List[_ModelBuilderT]],
-            hash_fn: Callable = lambda model: model.name
-            if model.attribute_map.get("name")
-            else model.display_name,
+        self,
+        builders: Optional[List[_ModelBuilderT]],
+        hash_fn: Callable = lambda model: model.name
+        if model.attribute_map.get("name")
+        else model.display_name,
     ):
         self.__hash_fn = hash_fn
         self._dict: Dict[str, _ModelBuilderT] = {}
@@ -125,10 +125,10 @@ def get(self, key: str) -> Union[None, _ModelBuilderT]:
 
 class _ElementMap(Generic[_ModelBuilderT]):
     def __init__(
-            self,
-            element_type: _ModelBuilderT,
-            element_dict: _ModelBuilderHashDict[_ModelBuilderT],
-            parent_child_map: Dict[str, "_ParentChildMap"] = None,
+        self,
+        element_type: _ModelBuilderT,
+        element_dict: _ModelBuilderHashDict[_ModelBuilderT],
+        parent_child_map: Dict[str, "_ParentChildMap"] = None,
     ):
         self.element_type = element_type
         self.element_dict = element_dict
@@ -140,9 +140,9 @@ def add_element_from_dict(self, config: dict) -> str:
 
 class _ParentChildMap:
     def __init__(
-            self,
-            child_mapping: "_ElementMap",
-            parent_child_hash_map: DefaultDict[str, List[str]],
+        self,
+        child_mapping: "_ElementMap",
+        parent_child_hash_map: DefaultDict[str, List[str]],
     ):
         self.child_mapping = child_mapping
         self.parent_child_hash_map = parent_child_hash_map
@@ -158,12 +158,12 @@ class FeatureStoreRegistrar:
     DATASET_SPEC = "dataset"
 
     def __init__(
-            self,
-            feature_store: FeatureStore = None,
-            entities: List[Entity] = None,
-            datasets: List[Dataset] = None,
-            feature_groups: List[FeatureGroup] = None,
-            transformations: List[Transformation] = None,
+        self,
+        feature_store: FeatureStore = None,
+        entities: List[Entity] = None,
+        datasets: List[Dataset] = None,
+        feature_groups: List[FeatureGroup] = None,
+        transformations: List[Transformation] = None,
     ):
         """
         Initialised feature registrar resource
@@ -189,7 +189,7 @@ def __init__(
         self._entity_map = {}
 
     def create(
-            self,
+        self,
     ) -> Tuple[
         FeatureStore,
         List[Entity],
@@ -248,12 +248,10 @@ def _create_transformations(self) -> List[Transformation]:
         for transformation in self._transformations.get_dict().values():
             transformation.feature_store_id = self._feature_store_id
             transformation.compartment_id = (
-                    transformation.compartment_id or self._root_compartment_id
+                transformation.compartment_id or self._root_compartment_id
             )
             # to encode to base64
-            transformation.source_code_function = (
-                transformation.source_code_function
-            )
+            transformation.source_code_function = transformation.source_code_function
         return self._transformations.create_models(self._progress)
 
     def _create_feature_groups(self) -> List[FeatureGroup]:
@@ -288,7 +286,7 @@ def _create_feature_groups(self) -> List[FeatureGroup]:
             feature_group.primary_keys = feature_group.primary_keys
             feature_group.feature_store_id = self._feature_store_id
             feature_group.compartment_id = (
-                    feature_group.compartment_id or self._root_compartment_id
+                feature_group.compartment_id or self._root_compartment_id
             )
 
         return self._feature_groups.create_models(self._progress)
@@ -394,11 +392,11 @@ def generate_yaml(cls, uri: str = "feature_store.yaml"):
 
     @classmethod
     def from_yaml(
-            cls,
-            yaml_string: str = None,
-            uri: str = None,
-            loader: callable = Loader,
-            **kwargs,
+        cls,
+        yaml_string: str = None,
+        uri: str = None,
+        loader: callable = Loader,
+        **kwargs,
     ) -> "FeatureStoreRegistrar":
         """Creates an object from YAML string provided or from URI location containing YAML string
 
@@ -445,11 +443,11 @@ def _find_yaml_definition_file() -> str:
 
     def _get_progress_steps_count(self) -> int:
         return (
-                self._feature_store.get_count()
-                + self._entities.get_count()
-                + self._transformations.get_count()
-                + self._feature_groups.get_count()
-                + self._datasets.get_count()
+            self._feature_store.get_count()
+            + self._entities.get_count()
+            + self._transformations.get_count()
+            + self._feature_groups.get_count()
+            + self._datasets.get_count()
         )
 
     @staticmethod
@@ -470,7 +468,7 @@ def _read_from_file(uri: str, **kwargs) -> str:
 
     @staticmethod
     def _populate_child_mappings(
-            parent_dict: dict, parent_map: _ElementMap, parent_hash: str
+        parent_dict: dict, parent_map: _ElementMap, parent_hash: str
     ):
         for key, elements in parent_dict.get("spec").items():
             if key in parent_map.parent_child_map:

ads/feature_store/transformation.py

@@ -498,4 +498,4 @@ def to_dict(self) -> Dict:
 
     def __repr__(self) -> str:
         """Displays the object as YAML."""
-        return self.to_yaml()
+        return self.to_yaml()