Lightgbm

zenml.integrations.lightgbm

Initialization of the LightGBM integration.

Attributes

LIGHTGBM = 'lightgbm' module-attribute

Classes

Integration

Base class for integration in ZenML.

Functions

activate() -> None classmethod

Abstract method to activate the integration.

Source code in src/zenml/integrations/integration.py

@classmethod
def activate(cls) -> None:
    """Abstract method to activate the integration."""

check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

Type	Description
bool	True if all required packages are installed, False otherwise.

Source code in src/zenml/integrations/integration.py

@classmethod
def check_installation(cls) -> bool:
    """Method to check whether the required packages are installed.

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for requirement in cls.get_requirements():
        parsed_requirement = Requirement(requirement)

        if not requirement_installed(parsed_requirement):
            logger.debug(
                "Requirement '%s' for integration '%s' is not installed "
                "or installed with the wrong version.",
                requirement,
                cls.NAME,
            )
            return False

        dependencies = get_dependencies(parsed_requirement)

        for dependency in dependencies:
            if not requirement_installed(dependency):
                logger.debug(
                    "Requirement '%s' for integration '%s' is not "
                    "installed or installed with the wrong version.",
                    dependency,
                    cls.NAME,
                )
                return False

    logger.debug(
        f"Integration '{cls.NAME}' is installed correctly with "
        f"requirements {cls.get_requirements()}."
    )
    return True

flavors() -> List[Type[Flavor]] classmethod

Abstract method to declare new stack component flavors.

Returns:

Type	Description
List[Type[Flavor]]	A list of new stack component flavors.

Source code in src/zenml/integrations/integration.py

@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Abstract method to declare new stack component flavors.

    Returns:
        A list of new stack component flavors.
    """
    return []

get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] 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
python_version	Optional[str]	The Python version to use for the requirements.	None

Returns:

Type	Description
List[str]	A list of requirements.

Source code in src/zenml/integrations/integration.py

@classmethod
def get_requirements(
    cls,
    target_os: Optional[str] = None,
    python_version: 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.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    return cls.REQUIREMENTS

get_uninstall_requirements(target_os: Optional[str] = None) -> List[str] classmethod

Method to get the uninstall 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 src/zenml/integrations/integration.py

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

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

    Returns:
        A list of requirements.
    """
    ret = []
    for each in cls.get_requirements(target_os=target_os):
        is_ignored = False
        for ignored in cls.REQUIREMENTS_IGNORED_ON_UNINSTALL:
            if each.startswith(ignored):
                is_ignored = True
                break
        if not is_ignored:
            ret.append(each)
    return ret

plugin_flavors() -> List[Type[BasePluginFlavor]] classmethod

Abstract method to declare new plugin flavors.

Returns:

Type	Description
List[Type[BasePluginFlavor]]	A list of new plugin flavors.

Source code in src/zenml/integrations/integration.py

@classmethod
def plugin_flavors(cls) -> List[Type["BasePluginFlavor"]]:
    """Abstract method to declare new plugin flavors.

    Returns:
        A list of new plugin flavors.
    """
    return []

LightGBMIntegration

Bases: Integration

Definition of lightgbm integration for ZenML.

Functions

activate() -> None classmethod

Activates the integration.

Source code in src/zenml/integrations/lightgbm/__init__.py

@classmethod
def activate(cls) -> None:
    """Activates the integration."""
    from zenml.integrations.lightgbm import materializers  # noqa

Modules

materializers

Initialization of the Neural Prophet materializer.

Classes

Modules

lightgbm_booster_materializer

Implementation of the LightGBM booster materializer.

Classes

LightGBMBoosterMaterializer(uri: str, artifact_store: Optional[BaseArtifactStore] = None)

Bases: BaseMaterializer

Materializer to read data to and from lightgbm.Booster.

Source code in src/zenml/materializers/base_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Initializes a materializer with the given URI.

    Args:
        uri: The URI where the artifact data will be stored.
        artifact_store: The artifact store used to store this artifact.
    """
    self.uri = uri
    self._artifact_store = artifact_store

Functions

load(data_type: Type[Any]) -> lgb.Booster

Reads a lightgbm Booster model from a serialized JSON file.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	A lightgbm Booster type.	required

Returns:

Type	Description
Booster	A lightgbm Booster object.

Source code in src/zenml/integrations/lightgbm/materializers/lightgbm_booster_materializer.py

def load(self, data_type: Type[Any]) -> lgb.Booster:
    """Reads a lightgbm Booster model from a serialized JSON file.

    Args:
        data_type: A lightgbm Booster type.

    Returns:
        A lightgbm Booster object.
    """
    filepath = os.path.join(self.uri, DEFAULT_FILENAME)
    with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
        temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME)

        # Copy from artifact store to temporary file
        fileio.copy(filepath, temp_file)
        booster = lgb.Booster(model_file=temp_file)
        return booster

save(booster: lgb.Booster) -> None

Creates a JSON serialization for a lightgbm Booster model.

Parameters:

Name	Type	Description	Default
booster	Booster	A lightgbm Booster model.	required

Source code in src/zenml/integrations/lightgbm/materializers/lightgbm_booster_materializer.py

def save(self, booster: lgb.Booster) -> None:
    """Creates a JSON serialization for a lightgbm Booster model.

    Args:
        booster: A lightgbm Booster model.
    """
    filepath = os.path.join(self.uri, DEFAULT_FILENAME)
    with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
        tmp_path = os.path.join(temp_dir, "model.txt")
        booster.save_model(tmp_path)
        fileio.copy(tmp_path, filepath)

Modules

lightgbm_dataset_materializer

Implementation of the LightGBM materializer.

Classes

LightGBMDatasetMaterializer(uri: str, artifact_store: Optional[BaseArtifactStore] = None)

Bases: BaseMaterializer

Materializer to read data to and from lightgbm.Dataset.

Source code in src/zenml/materializers/base_materializer.py

def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Initializes a materializer with the given URI.

    Args:
        uri: The URI where the artifact data will be stored.
        artifact_store: The artifact store used to store this artifact.
    """
    self.uri = uri
    self._artifact_store = artifact_store

Functions

extract_metadata(matrix: lgb.Dataset) -> Dict[str, MetadataType]

Extract metadata from the given Dataset object.

Parameters:

Name	Type	Description	Default
matrix	Dataset	The Dataset object to extract metadata from.	required

Returns:

Type	Description
Dict[str, MetadataType]	The extracted metadata as a dictionary.

Source code in src/zenml/integrations/lightgbm/materializers/lightgbm_dataset_materializer.py

def extract_metadata(
    self, matrix: lgb.Dataset
) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given `Dataset` object.

    Args:
        matrix: The `Dataset` object to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    return {"shape": (matrix.num_data(), matrix.num_feature())}

load(data_type: Type[Any]) -> lgb.Dataset

Reads a lightgbm.Dataset binary file and loads it.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	A lightgbm.Dataset type.	required

Returns:

Type	Description
Dataset	A lightgbm.Dataset object.

Source code in src/zenml/integrations/lightgbm/materializers/lightgbm_dataset_materializer.py

def load(self, data_type: Type[Any]) -> lgb.Dataset:
    """Reads a lightgbm.Dataset binary file and loads it.

    Args:
        data_type: A lightgbm.Dataset type.

    Returns:
        A lightgbm.Dataset object.
    """
    filepath = os.path.join(self.uri, DEFAULT_FILENAME)

    with self.get_temporary_directory(delete_at_exit=False) as temp_dir:
        temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME)

        # Copy from artifact store to temporary file
        fileio.copy(filepath, temp_file)
        matrix = lgb.Dataset(temp_file, free_raw_data=False)

        return matrix

save(matrix: lgb.Dataset) -> None

Creates a binary serialization for a lightgbm.Dataset object.

Parameters:

Name	Type	Description	Default
matrix	Dataset	A lightgbm.Dataset object.	required

Source code in src/zenml/integrations/lightgbm/materializers/lightgbm_dataset_materializer.py

def save(self, matrix: lgb.Dataset) -> None:
    """Creates a binary serialization for a lightgbm.Dataset object.

    Args:
        matrix: A lightgbm.Dataset object.
    """
    filepath = os.path.join(self.uri, DEFAULT_FILENAME)

    with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
        temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME)
        matrix.save_binary(temp_file)

        # Copy it into artifact store
        fileio.copy(temp_file, filepath)

Modules