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Version: 3.2

Feature Store

Initialize Feature Store​

Initialize a Feature Store.

from katonic.fs import FeatureStore
feature_store = FeatureStore(
    user_name = "your-name",
    project_name = "new-project",
    description = "project-description",
)

Define Entity Key​

Entity keys (Unique Id) will act as Primary keys to retrieve features.

from katonic.fs import Entity
entity = Entity(name="id", value_type=ValueType.INT64)

Define Data Source​

File Source - CSV​

from katonic.fs import FileSource
data_source = FileSource(
    path = "path/to/your/csv/data/source/file",
    file_format = "csv",
    event_timestamp_column = "event-timestamp-column"
)

File Source - Parquet​

from katonic.fs import FileSource
data_source = FileSource(
    path = "path/to/your/parquet/data/source/file",
    file_format = "parquet",
    event_timestamp_column = "event-timestamp-column"
)

DataFrame Source - Pandas​

from katonic.fs import DataFrameSource
batch_source = DataFrameSource(
        df=pandas_dataframe,
        event_timestamp_column="event-timestamp-column",
        created_timestamp_column="created-timestamp-column",
    )

DataFrame Source - Spark​

from katonic.fs import DataFrameSource
batch_source = DataFrameSource(
        df=spark_dataframe,
        mode="append",
        event_timestamp_column="event-timestamp-column",
        created_timestamp_column="created-timestamp-column",
    )

Feature View​

A feature view is a group of features.

from katonic.fs import FeatureView
feature_view  = FeatureView(
    name="feature-view-name",
    entities=["entity-key"],
    ttl="2d", # no of days/months/years/hours
    features=features_list,
    batch_source=batch_source,
)

Write Data to Offline Store​

Store data to Offline store.

from katonic.fs import Entity, FeatureView
entity = Entity(name="id", value_type=ValueType.INT64)
feature_view  = FeatureView(
    name="feature-view-name",
    entities=["entity-key"],
    ttl="2d", # no of days/months/years/hours
    features=features_list,
    batch_source=batch_source,
)
feature_store.write_table([entity, feature_view])

Historical Data Retrieval​

Retrieve training data from Offline store.

training_df = feature_store.get_historical_features(
    entity_df=entity-df,
    feature_view=["feature-view-name"],
    features=features_list,
).to_df()

Publish Data to Online Store​

It materializes the latest features from the Offline feature store to an Online store.

feature_store.publish_table(
    start_ts = start_date_as_datetime_object,
    end_ts = end_date_as_datetime_object
)

Online Features Retrieval​

It will used to get the latest features at low latency and also for the online serving.

feature_store.get_online_features(
    entity_rows=[{"entity-key": entity-value}],
    feature_view=["feature-view-name"],
    features=features_list,
).to_df()

Feature Store Registry​

Feature Store Registy is a tracking engine for the feature definitions and their related metadata.

List Entities​

It will list all the entities present in the Feature Registry from all the project.

from katonic.fs import FeatureStore
feature_store = FeatureStore(
    user_name = "your-name",
    project_name = "new-project",
    description = "project-description",
)
feature_store.list_entities()

List Feature View​

It will list all the Feature Views present in the Feature Registry from all the project.

feature_store.list_feature_views()

Get Registry Info - Given User Name​

It will Get all the Meta present in the Feature Registry related with given user name.

feature_store.get_registry_info(user_name='user')

Get Registry Info - Given Project Name​

It will Get all the Meta present in the Feature Registry related with given project name.

feature_store.get_registry_info(project_name='housing_price')

Get Registry Info - Given User Name, Project Name​

It will Get all the Meta present in the Feature Registry related with given project name and user name.

feature_store.get_registry_info(user_name='user', project_name='housing_price')