feast-dev · feast-ci-bot · Mar 29, 2022 · Mar 29, 2022 · Mar 29, 2022 · Mar 29, 2022
@@ -399,14 +399,14 @@ def delete_feature_view(self, name: str):
     @log_exceptions_and_usage
     def delete_feature_service(self, name: str):
         """
-            Deletes a feature service.
+        Deletes a feature service.
 
-            Args:
-                name: Name of feature service.
+        Args:
+            name: Name of feature service.
 
-            Raises:
-                FeatureServiceNotFoundException: The feature view could not be found.
-            """
+        Raises:
+            FeatureServiceNotFoundException: The feature view could not be found.
+        """
         return self._registry.delete_feature_service(name, self.project)
 
     def _get_features(
@@ -903,17 +903,17 @@ def create_saved_dataset(
         feature_service: Optional[FeatureService] = None,
     ) -> SavedDataset:
         """
-            Execute provided retrieval job and persist its outcome in given storage.
-            Storage type (eg, BigQuery or Redshift) must be the same as globally configured offline store.
-            After data successfully persisted saved dataset object with dataset metadata is committed to the registry.
-            Name for the saved dataset should be unique within project, since it's possible to overwrite previously stored dataset
-            with the same name.
+        Execute provided retrieval job and persist its outcome in given storage.
+        Storage type (eg, BigQuery or Redshift) must be the same as globally configured offline store.
+        After data successfully persisted saved dataset object with dataset metadata is committed to the registry.
+        Name for the saved dataset should be unique within project, since it's possible to overwrite previously stored dataset
+        with the same name.
 
-            Returns:
-                SavedDataset object with attached RetrievalJob
+        Returns:
+            SavedDataset object with attached RetrievalJob
 
-            Raises:
-                ValueError if given retrieval job doesn't have metadata
+        Raises:
+            ValueError if given retrieval job doesn't have metadata
         """
         warnings.warn(
             "Saving dataset is an experimental feature. "
@@ -1589,11 +1589,11 @@ def _get_unique_entities(
         join_key_values: Dict[str, List[Value]],
         entity_name_to_join_key_map: Dict[str, str],
     ) -> Tuple[Tuple[Dict[str, Value], ...], Tuple[List[int], ...]]:
-        """ Return the set of unique composite Entities for a Feature View and the indexes at which they appear.
+        """Return the set of unique composite Entities for a Feature View and the indexes at which they appear.
 
-            This method allows us to query the OnlineStore for data we need only once
-            rather than requesting and processing data for the same combination of
-            Entities multiple times.
+        This method allows us to query the OnlineStore for data we need only once
+        rather than requesting and processing data for the same combination of
+        Entities multiple times.
         """
         # Get the correct set of entity values with the correct join keys.
         table_entity_values = self._get_table_entity_values(
@@ -1629,14 +1629,14 @@ def _read_from_online_store(
         requested_features: List[str],
         table: FeatureView,
     ) -> List[Tuple[List[Timestamp], List["FieldStatus.ValueType"], List[Value]]]:
-        """ Read and process data from the OnlineStore for a given FeatureView.
+        """Read and process data from the OnlineStore for a given FeatureView.
 
-            This method guarantees that the order of the data in each element of the
-            List returned is the same as the order of `requested_features`.
+        This method guarantees that the order of the data in each element of the
+        List returned is the same as the order of `requested_features`.
 
-            This method assumes that `provider.online_read` returns data for each
-            combination of Entities in `entity_rows` in the same order as they
-            are provided.
+        This method assumes that `provider.online_read` returns data for each
+        combination of Entities in `entity_rows` in the same order as they
+        are provided.
         """
         # Instantiate one EntityKeyProto per Entity.
         entity_key_protos = [
@@ -1693,23 +1693,23 @@ def _populate_response_from_feature_data(
         requested_features: Iterable[str],
         table: FeatureView,
     ):
-        """ Populate the GetOnlineFeaturesResponse with feature data.
-
-            This method assumes that `_read_from_online_store` returns data for each
-            combination of Entities in `entity_rows` in the same order as they
-            are provided.
-
-            Args:
-                feature_data: A list of data in Protobuf form which was retrieved from the OnlineStore.
-                indexes: A list of indexes which should be the same length as `feature_data`. Each list
-                    of indexes corresponds to a set of result rows in `online_features_response`.
-                online_features_response: The object to populate.
-                full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names,
-                    changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to
-                    "customer_fv__daily_transactions").
-                requested_features: The names of the features in `feature_data`. This should be ordered in the same way as the
-                    data in `feature_data`.
-                table: The FeatureView that `feature_data` was retrieved from.
+        """Populate the GetOnlineFeaturesResponse with feature data.
+
+        This method assumes that `_read_from_online_store` returns data for each
+        combination of Entities in `entity_rows` in the same order as they
+        are provided.
+
+        Args:
+            feature_data: A list of data in Protobuf form which was retrieved from the OnlineStore.
+            indexes: A list of indexes which should be the same length as `feature_data`. Each list
+                of indexes corresponds to a set of result rows in `online_features_response`.
+            online_features_response: The object to populate.
+            full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names,
+                changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to
+                "customer_fv__daily_transactions").
+            requested_features: The names of the features in `feature_data`. This should be ordered in the same way as the
+                data in `feature_data`.
+            table: The FeatureView that `feature_data` was retrieved from.
         """
         # Add the feature names to the response.
         requested_feature_refs = [
@@ -1973,7 +1973,7 @@ def _group_feature_refs(
     List[Tuple[RequestFeatureView, List[str]]],
     Set[str],
 ]:
-    """ Get list of feature views and corresponding feature names based on feature references"""
+    """Get list of feature views and corresponding feature names based on feature references"""
 
     # view name to view proto
     view_index = {view.projection.name_to_use(): view for view in all_feature_views}
@@ -2046,7 +2046,7 @@ def _print_materialization_log(
 
 
 def _validate_feature_views(feature_views: List[BaseFeatureView]):
-    """ Verify feature views have case-insensitively unique names"""
+    """Verify feature views have case-insensitively unique names"""
     fv_names = set()
     for fv in feature_views:
         case_insensitive_fv_name = fv.name.lower()
@@ -2061,7 +2061,7 @@ def _validate_feature_views(feature_views: List[BaseFeatureView]):
 
 
 def _validate_data_sources(data_sources: List[DataSource]):
-    """ Verify data sources have case-insensitively unique names"""
+    """Verify data sources have case-insensitively unique names"""
     ds_names = set()
     for fv in data_sources:
         case_insensitive_ds_name = fv.name.lower()

@@ -56,7 +56,7 @@
 
 
 class BigQueryOfflineStoreConfig(FeastConfigBaseModel):
-    """ Offline store config for GCP BigQuery """
+    """Offline store config for GCP BigQuery"""
 
     type: Literal["bigquery"] = "bigquery"
     """ Offline store type selector"""

@@ -27,20 +27,20 @@ def __init__(
     ):
         """Create a BigQuerySource from an existing table or query.
 
-         Args:
-             table (optional): The BigQuery table where features can be found.
-             table_ref (optional): (Deprecated) The BigQuery table where features can be found.
-             event_timestamp_column: Event timestamp column used for point in time joins of feature values.
-             created_timestamp_column (optional): Timestamp column when row was created, used for deduplicating rows.
-             field_mapping: A dictionary mapping of column names in this data source to feature names in a feature table
-                 or view. Only used for feature columns, not entities or timestamp columns.
-             date_partition_column (optional): Timestamp column used for partitioning.
-             query (optional): SQL query to execute to generate data for this data source.
-             name (optional): Name for the source. Defaults to the table_ref if not specified.
-         Example:
-             >>> from feast import BigQuerySource
-             >>> my_bigquery_source = BigQuerySource(table="gcp_project:bq_dataset.bq_table")
-         """
+        Args:
+            table (optional): The BigQuery table where features can be found.
+            table_ref (optional): (Deprecated) The BigQuery table where features can be found.
+            event_timestamp_column: Event timestamp column used for point in time joins of feature values.
+            created_timestamp_column (optional): Timestamp column when row was created, used for deduplicating rows.
+            field_mapping: A dictionary mapping of column names in this data source to feature names in a feature table
+                or view. Only used for feature columns, not entities or timestamp columns.
+            date_partition_column (optional): Timestamp column used for partitioning.
+            query (optional): SQL query to execute to generate data for this data source.
+            name (optional): Name for the source. Defaults to the table_ref if not specified.
+        Example:
+            >>> from feast import BigQuerySource
+            >>> my_bigquery_source = BigQuerySource(table="gcp_project:bq_dataset.bq_table")
+        """
         if table is None and table_ref is None and query is None:
             raise ValueError('No "table" or "query" argument provided.')
         if not table and table_ref:

@@ -31,7 +31,7 @@
 
 
 class FileOfflineStoreConfig(FeastConfigBaseModel):
-    """ Offline store config for local (file-based) store """
+    """Offline store config for local (file-based) store"""
 
     type: Literal["file"] = "file"
     """ Offline store type selector"""

@@ -39,7 +39,7 @@
 
 
 class RedshiftOfflineStoreConfig(FeastConfigBaseModel):
-    """ Offline store config for AWS Redshift """
+    """Offline store config for AWS Redshift"""
 
     type: Literal["redshift"] = "redshift"
     """ Offline store type selector"""
@@ -341,7 +341,7 @@ def _to_arrow_internal(self) -> pa.Table:
 
     @log_exceptions_and_usage
     def to_s3(self) -> str:
-        """ Export dataset to S3 in Parquet format and return path """
+        """Export dataset to S3 in Parquet format and return path"""
         if self.on_demand_feature_views:
             transformed_df = self.to_df()
             aws_utils.upload_df_to_s3(self._s3_resource, self._s3_path, transformed_df)
@@ -361,7 +361,7 @@ def to_s3(self) -> str:
 
     @log_exceptions_and_usage
     def to_redshift(self, table_name: str) -> None:
-        """ Save dataset as a new Redshift table """
+        """Save dataset as a new Redshift table"""
         if self.on_demand_feature_views:
             transformed_df = self.to_df()
             aws_utils.upload_df_to_redshift(

@@ -54,7 +54,7 @@
 
 
 class SnowflakeOfflineStoreConfig(FeastConfigBaseModel):
-    """ Offline store config for Snowflake """
+    """Offline store config for Snowflake"""
 
     type: Literal["snowflake.offline"] = "snowflake.offline"
     """ Offline store type selector"""
@@ -336,7 +336,7 @@ def _to_arrow_internal(self) -> pa.Table:
                 )
 
     def to_snowflake(self, table_name: str) -> None:
-        """ Save dataset as a new Snowflake table """
+        """Save dataset as a new Snowflake table"""
         if self.on_demand_feature_views is not None:
             transformed_df = self.to_df()
 

@@ -55,7 +55,7 @@
 
 
 class DatastoreOnlineStoreConfig(FeastConfigBaseModel):
-    """ Online store config for GCP Datastore """
+    """Online store config for GCP Datastore"""
 
     type: Literal["datastore"] = "datastore"
     """ Online store type selector"""

@@ -37,7 +37,7 @@
 
 
 class SqliteOnlineStoreConfig(FeastConfigBaseModel):
-    """ Online store config for local (SQLite-based) store """
+    """Online store config for local (SQLite-based) store"""
 
     type: Literal[
         "sqlite", "feast.infra.online_stores.sqlite.SqliteOnlineStore"

@@ -183,7 +183,7 @@ def retrieve_saved_dataset(
         Returns:
              RetrievalJob object, which is lazy wrapper for actual query performed under the hood.
 
-         """
+        """
         ...
 
     def get_feature_server_endpoint(self) -> Optional[str]:

@@ -151,7 +151,7 @@ def execute_redshift_statement(
 
 
 def get_redshift_statement_result(redshift_data_client, statement_id: str) -> dict:
-    """ Get the Redshift statement result """
+    """Get the Redshift statement result"""
     return redshift_data_client.get_statement_result(Id=statement_id)
 
 
@@ -306,7 +306,7 @@ def temporarily_upload_df_to_redshift(
 
 
 def download_s3_directory(s3_resource, bucket: str, key: str, local_dir: str):
-    """ Download the S3 directory to a local disk """
+    """Download the S3 directory to a local disk"""
     bucket_obj = s3_resource.Bucket(bucket)
     if key != "" and not key.endswith("/"):
         key = key + "/"
@@ -318,7 +318,7 @@ def download_s3_directory(s3_resource, bucket: str, key: str, local_dir: str):
 
 
 def delete_s3_directory(s3_resource, bucket: str, key: str):
-    """ Delete S3 directory recursively """
+    """Delete S3 directory recursively"""
     bucket_obj = s3_resource.Bucket(bucket)
     if key != "" and not key.endswith("/"):
         key = key + "/"
@@ -365,7 +365,7 @@ def unload_redshift_query_to_pa(
     iam_role: str,
     query: str,
 ) -> pa.Table:
-    """ Unload Redshift Query results to S3 and get the results in PyArrow Table format """
+    """Unload Redshift Query results to S3 and get the results in PyArrow Table format"""
     bucket, key = get_bucket_and_key(s3_path)
 
     execute_redshift_query_and_unload_to_s3(
@@ -388,7 +388,7 @@ def unload_redshift_query_to_df(
     iam_role: str,
     query: str,
 ) -> pd.DataFrame:
-    """ Unload Redshift Query results to S3 and get the results in Pandas DataFrame format """
+    """Unload Redshift Query results to S3 and get the results in Pandas DataFrame format"""
     table = unload_redshift_query_to_pa(
         redshift_data_client,
         cluster_id,

@@ -57,23 +57,23 @@
 
 
 class FeastBaseModel(BaseModel):
-    """ Feast Pydantic Configuration Class """
+    """Feast Pydantic Configuration Class"""
 
     class Config:
         arbitrary_types_allowed = True
         extra = "allow"
 
 
 class FeastConfigBaseModel(BaseModel):
-    """ Feast Pydantic Configuration Class """
+    """Feast Pydantic Configuration Class"""
 
     class Config:
         arbitrary_types_allowed = True
         extra = "forbid"
 
 
 class RegistryConfig(FeastBaseModel):
-    """ Metadata Store Configuration. Configuration that relates to reading from and writing to the Feast registry."""
+    """Metadata Store Configuration. Configuration that relates to reading from and writing to the Feast registry."""
 
     registry_store_type: Optional[StrictStr]
     """ str: Provider name or a class name that implements RegistryStore. """
@@ -89,7 +89,7 @@ class RegistryConfig(FeastBaseModel):
 
 
 class RepoConfig(FeastBaseModel):
-    """ Repo config. Typically loaded from `feature_store.yaml` """
+    """Repo config. Typically loaded from `feature_store.yaml`"""
 
     registry: Union[StrictStr, RegistryConfig] = "data/registry.db"
     """ str: Path to metadata store. Can be a local path, or remote object storage path, e.g. a GCS URI """

@@ -94,7 +94,7 @@ def get_repo_files(repo_root: Path) -> List[Path]:
 
 
 def parse_repo(repo_root: Path) -> RepoContents:
-    """ Collect feature table definitions from feature repo """
+    """Collect feature table definitions from feature repo"""
     res = RepoContents(
         data_sources=set(),
         entities=set(),
@@ -264,7 +264,7 @@ def teardown(repo_config: RepoConfig, repo_path: Path):
 
 @log_exceptions_and_usage
 def registry_dump(repo_config: RepoConfig, repo_path: Path):
-    """ For debugging only: output contents of the metadata registry """
+    """For debugging only: output contents of the metadata registry"""
     registry_config = repo_config.get_registry_config()
     project = repo_config.project
     registry = Registry(registry_config=registry_config, repo_path=repo_path)

@@ -580,10 +580,10 @@ def pa_to_redshift_value_type(pa_type: pyarrow.DataType) -> str:
 
 def _non_empty_value(value: Any) -> bool:
     """
-        Check that there's enough data we can use for type inference.
-        If primitive type - just checking that it's not None
-        If iterable - checking that there's some elements (len > 0)
-        String is special case: "" - empty string is considered non empty
+    Check that there's enough data we can use for type inference.
+    If primitive type - just checking that it's not None
+    If iterable - checking that there's some elements (len > 0)
+    String is special case: "" - empty string is considered non empty
     """
     return value is not None and (
         not isinstance(value, Sized) or len(value) > 0 or isinstance(value, str)