Feast

zenml.integrations.feast special

Initialization for Feast integration.

The Feast integration offers a way to connect to a Feast Feature Store. ZenML implements a dedicated stack component that you can access as part of your ZenML steps in the usual ways.

FeastIntegration (Integration)

Definition of Feast integration for ZenML.

Source code in zenml/integrations/feast/__init__.py

class FeastIntegration(Integration):
    """Definition of Feast integration for ZenML."""

    NAME = FEAST
    # click is added to keep the feast click version in sync with ZenML's click
    REQUIREMENTS = ["feast", "click>=8.0.1,<8.1.4"]
    REQUIREMENTS_IGNORED_ON_UNINSTALL = ["click", "pandas"]

    @classmethod
    def flavors(cls) -> List[Type[Flavor]]:
        """Declare the stack component flavors for the Feast integration.

        Returns:
            List of stack component flavors for this integration.
        """
        from zenml.integrations.feast.flavors import FeastFeatureStoreFlavor

        return [FeastFeatureStoreFlavor]

    @classmethod
    def get_requirements(cls, target_os: Optional[str] = None) -> List[str]:
        """Method to get the requirements for the integration.

        Args:
            target_os: The target operating system to get the requirements for.

        Returns:
            A list of requirements.
        """
        from zenml.integrations.pandas import PandasIntegration

        return cls.REQUIREMENTS + \
            PandasIntegration.get_requirements(target_os=target_os)

flavors() classmethod

Declare the stack component flavors for the Feast integration.

Returns:

Type	Description
List[Type[zenml.stack.flavor.Flavor]]	List of stack component flavors for this integration.

Source code in zenml/integrations/feast/__init__.py

@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for the Feast integration.

    Returns:
        List of stack component flavors for this integration.
    """
    from zenml.integrations.feast.flavors import FeastFeatureStoreFlavor

    return [FeastFeatureStoreFlavor]

get_requirements(target_os=None) classmethod

Method to get the requirements for the integration.

Parameters:

Name	Type	Description	Default
target_os	Optional[str]	The target operating system to get the requirements for.	None

Returns:

Type	Description
List[str]	A list of requirements.

Source code in zenml/integrations/feast/__init__.py

@classmethod
def get_requirements(cls, target_os: Optional[str] = None) -> List[str]:
    """Method to get the requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.

    Returns:
        A list of requirements.
    """
    from zenml.integrations.pandas import PandasIntegration

    return cls.REQUIREMENTS + \
        PandasIntegration.get_requirements(target_os=target_os)

feature_stores special

Feast Feature Store integration for ZenML.

Feature stores allow data teams to serve data via an offline store and an online low-latency store where data is kept in sync between the two. It also offers a centralized registry where features (and feature schemas) are stored for use within a team or wider organization. Feature stores are a relatively recent addition to commonly-used machine learning stacks. Feast is a leading open-source feature store, first developed by Gojek in collaboration with Google.

feast_feature_store

Implementation of the Feast Feature Store for ZenML.

FeastFeatureStore (BaseFeatureStore)

Class to interact with the Feast feature store.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

class FeastFeatureStore(BaseFeatureStore):
    """Class to interact with the Feast feature store."""

    @property
    def config(self) -> FeastFeatureStoreConfig:
        """Returns the `FeastFeatureStoreConfig` config.

        Returns:
            The configuration.
        """
        return cast(FeastFeatureStoreConfig, self._config)

    def get_historical_features(
        self,
        entity_df: Union[pd.DataFrame, str],
        features: List[str],
        full_feature_names: bool = False,
    ) -> pd.DataFrame:
        """Returns the historical features for training or batch scoring.

        Args:
            entity_df: The entity DataFrame or entity name.
            features: The features to retrieve.
            full_feature_names: Whether to return the full feature names.

        Raise:
            ConnectionError: If the online component (Redis) is not available.

        Returns:
            The historical features as a Pandas DataFrame.
        """
        fs = FeatureStore(repo_path=self.config.feast_repo)

        return fs.get_historical_features(
            entity_df=entity_df,
            features=features,
            full_feature_names=full_feature_names,
        ).to_df()

    def get_online_features(
        self,
        entity_rows: List[Dict[str, Any]],
        features: List[str],
        full_feature_names: bool = False,
    ) -> Dict[str, Any]:
        """Returns the latest online feature data.

        Args:
            entity_rows: The entity rows to retrieve.
            features: The features to retrieve.
            full_feature_names: Whether to return the full feature names.

        Raise:
            ConnectionError: If the online component (Redis) is not available.

        Returns:
            The latest online feature data as a dictionary.
        """
        fs = FeatureStore(repo_path=self.config.feast_repo)

        return fs.get_online_features(  # type: ignore[no-any-return]
            entity_rows=entity_rows,
            features=features,
            full_feature_names=full_feature_names,
        ).to_dict()

    def get_data_sources(self) -> List[str]:
        """Returns the data sources' names.

        Raise:
            ConnectionError: If the online component (Redis) is not available.

        Returns:
            The data sources' names.
        """
        fs = FeatureStore(repo_path=self.config.feast_repo)
        return [ds.name for ds in fs.list_data_sources()]

    def get_entities(self) -> List[str]:
        """Returns the entity names.

        Raise:
            ConnectionError: If the online component (Redis) is not available.

        Returns:
            The entity names.
        """
        fs = FeatureStore(repo_path=self.config.feast_repo)
        return [ds.name for ds in fs.list_entities()]

    def get_feature_services(self) -> List[str]:
        """Returns the feature service names.

        Raise:
            ConnectionError: If the online component (Redis) is not available.

        Returns:
            The feature service names.
        """
        fs = FeatureStore(repo_path=self.config.feast_repo)
        return [ds.name for ds in fs.list_feature_services()]

    def get_feature_views(self) -> List[str]:
        """Returns the feature view names.

        Raise:
            ConnectionError: If the online component (Redis) is not available.

        Returns:
            The feature view names.
        """
        fs = FeatureStore(repo_path=self.config.feast_repo)
        return [ds.name for ds in fs.list_feature_views()]

    def get_project(self) -> str:
        """Returns the project name.

        Raise:
            ConnectionError: If the online component (Redis) is not available.

        Returns:
            The project name.
        """
        fs = FeatureStore(repo_path=self.config.feast_repo)
        return str(fs.project)

    def get_registry(self) -> BaseRegistry:
        """Returns the feature store registry.

        Raise:
            ConnectionError: If the online component (Redis) is not available.

        Returns:
            The registry.
        """
        fs: FeatureStore = FeatureStore(repo_path=self.config.feast_repo)
        return fs.registry

    def get_feast_version(self) -> str:
        """Returns the version of Feast used.

        Raise:
            ConnectionError: If the online component (Redis) is not available.

        Returns:
            The version of Feast currently being used.
        """
        fs = FeatureStore(repo_path=self.config.feast_repo)
        return str(fs.version())

config: FeastFeatureStoreConfig property readonly

Returns the FeastFeatureStoreConfig config.

Returns:

Type	Description
FeastFeatureStoreConfig	The configuration.

get_data_sources(self)

Returns the data sources' names.

Exceptions:

Type	Description
ConnectionError	If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The data sources' names.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_data_sources(self) -> List[str]:
    """Returns the data sources' names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The data sources' names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_data_sources()]

get_entities(self)

Returns the entity names.

Exceptions:

Type	Description
ConnectionError	If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The entity names.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_entities(self) -> List[str]:
    """Returns the entity names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The entity names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_entities()]

get_feast_version(self)

Returns the version of Feast used.

Exceptions:

Type	Description
ConnectionError	If the online component (Redis) is not available.

Returns:

Type	Description
str	The version of Feast currently being used.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_feast_version(self) -> str:
    """Returns the version of Feast used.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The version of Feast currently being used.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return str(fs.version())

get_feature_services(self)

Returns the feature service names.

Exceptions:

Type	Description
ConnectionError	If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The feature service names.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_feature_services(self) -> List[str]:
    """Returns the feature service names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The feature service names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_feature_services()]

get_feature_views(self)

Returns the feature view names.

Exceptions:

Type	Description
ConnectionError	If the online component (Redis) is not available.

Returns:

Type	Description
List[str]	The feature view names.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_feature_views(self) -> List[str]:
    """Returns the feature view names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The feature view names.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return [ds.name for ds in fs.list_feature_views()]

get_historical_features(self, entity_df, features, full_feature_names=False)

Returns the historical features for training or batch scoring.

Parameters:

Name	Type	Description	Default
entity_df	Union[pandas.DataFrame, str]	The entity DataFrame or entity name.	required
features	List[str]	The features to retrieve.	required
full_feature_names	bool	Whether to return the full feature names.	False

Exceptions:

Type	Description
ConnectionError	If the online component (Redis) is not available.

Returns:

Type	Description
pandas.DataFrame	The historical features as a Pandas DataFrame.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_historical_features(
    self,
    entity_df: Union[pd.DataFrame, str],
    features: List[str],
    full_feature_names: bool = False,
) -> pd.DataFrame:
    """Returns the historical features for training or batch scoring.

    Args:
        entity_df: The entity DataFrame or entity name.
        features: The features to retrieve.
        full_feature_names: Whether to return the full feature names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The historical features as a Pandas DataFrame.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)

    return fs.get_historical_features(
        entity_df=entity_df,
        features=features,
        full_feature_names=full_feature_names,
    ).to_df()

get_online_features(self, entity_rows, features, full_feature_names=False)

Returns the latest online feature data.

Parameters:

Name	Type	Description	Default
entity_rows	List[Dict[str, Any]]	The entity rows to retrieve.	required
features	List[str]	The features to retrieve.	required
full_feature_names	bool	Whether to return the full feature names.	False

Exceptions:

Type	Description
ConnectionError	If the online component (Redis) is not available.

Returns:

Type	Description
Dict[str, Any]	The latest online feature data as a dictionary.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_online_features(
    self,
    entity_rows: List[Dict[str, Any]],
    features: List[str],
    full_feature_names: bool = False,
) -> Dict[str, Any]:
    """Returns the latest online feature data.

    Args:
        entity_rows: The entity rows to retrieve.
        features: The features to retrieve.
        full_feature_names: Whether to return the full feature names.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The latest online feature data as a dictionary.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)

    return fs.get_online_features(  # type: ignore[no-any-return]
        entity_rows=entity_rows,
        features=features,
        full_feature_names=full_feature_names,
    ).to_dict()

get_project(self)

Returns the project name.

Exceptions:

Type	Description
ConnectionError	If the online component (Redis) is not available.

Returns:

Type	Description
str	The project name.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_project(self) -> str:
    """Returns the project name.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The project name.
    """
    fs = FeatureStore(repo_path=self.config.feast_repo)
    return str(fs.project)

get_registry(self)

Returns the feature store registry.

Exceptions:

Type	Description
ConnectionError	If the online component (Redis) is not available.

Returns:

Type	Description
feast.infra.registry.base_registry.BaseRegistry	The registry.

Source code in zenml/integrations/feast/feature_stores/feast_feature_store.py

def get_registry(self) -> BaseRegistry:
    """Returns the feature store registry.

    Raise:
        ConnectionError: If the online component (Redis) is not available.

    Returns:
        The registry.
    """
    fs: FeatureStore = FeatureStore(repo_path=self.config.feast_repo)
    return fs.registry

flavors special

Feast integration flavors.

feast_feature_store_flavor

Feast feature store flavor.

FeastFeatureStoreConfig (BaseFeatureStoreConfig)

Config for Feast feature store.

Source code in zenml/integrations/feast/flavors/feast_feature_store_flavor.py

class FeastFeatureStoreConfig(BaseFeatureStoreConfig):
    """Config for Feast feature store."""

    online_host: str = "localhost"
    online_port: int = 6379
    feast_repo: str

    @property
    def is_local(self) -> bool:
        """Checks if this stack component is running locally.

        Returns:
            True if this config is for a local component, False otherwise.
        """
        return (
            self.online_host == "localhost" or self.online_host == "127.0.0.1"
        )

is_local: bool property readonly

Checks if this stack component is running locally.

Returns:

Type	Description
bool	True if this config is for a local component, False otherwise.

FeastFeatureStoreFlavor (BaseFeatureStoreFlavor)

Feast Feature store flavor.

Source code in zenml/integrations/feast/flavors/feast_feature_store_flavor.py

class FeastFeatureStoreFlavor(BaseFeatureStoreFlavor):
    """Feast Feature store flavor."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            The name of the flavor.
        """
        return FEAST_FEATURE_STORE_FLAVOR

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at SDK docs explaining this flavor.

        Returns:
            A flavor SDK docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/feature_store/feast.png"

    @property
    def config_class(self) -> Type[FeastFeatureStoreConfig]:
        """Returns FeastFeatureStoreConfig config class.

        Returns:
                The config class.
        """
        """Config class for this flavor."""
        return FeastFeatureStoreConfig

    @property
    def implementation_class(self) -> Type["FeastFeatureStore"]:
        """Implementation class for this flavor.

        Returns:
            The implementation class.
        """
        from zenml.integrations.feast.feature_stores import FeastFeatureStore

        return FeastFeatureStore

config_class: Type[zenml.integrations.feast.flavors.feast_feature_store_flavor.FeastFeatureStoreConfig] property readonly

Returns FeastFeatureStoreConfig config class.

Returns:

Type	Description
Type[zenml.integrations.feast.flavors.feast_feature_store_flavor.FeastFeatureStoreConfig]	The config class.

docs_url: Optional[str] property readonly

A url to point at docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor docs url.

implementation_class: Type[FeastFeatureStore] property readonly

Implementation class for this flavor.

Returns:

Type	Description
Type[FeastFeatureStore]	The implementation class.

logo_url: str property readonly

A url to represent the flavor in the dashboard.

Returns:

Type	Description
str	The flavor logo.

name: str property readonly

Name of the flavor.

Returns:

Type	Description
str	The name of the flavor.

sdk_docs_url: Optional[str] property readonly

A url to point at SDK docs explaining this flavor.

Returns:

Type	Description
Optional[str]	A flavor SDK docs url.