Lightgbm

zenml.integrations.lightgbm special

Initialization of the LightGBM integration.

LightGBMIntegration (Integration)

Definition of lightgbm integration for ZenML.

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

class LightGBMIntegration(Integration):
    """Definition of lightgbm integration for ZenML."""

    NAME = LIGHTGBM
    REQUIREMENTS = ["lightgbm>=1.0.0"]

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

activate() classmethod

Activates the integration.

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

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

materializers special

Initialization of the Neural Prophet materializer.

lightgbm_booster_materializer

Implementation of the LightGBM booster materializer.

LightGBMBoosterMaterializer (BaseMaterializer)

Materializer to read data to and from lightgbm.Booster.

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

class LightGBMBoosterMaterializer(BaseMaterializer):
    """Materializer to read data to and from lightgbm.Booster."""

    ASSOCIATED_TYPES = (lgb.Booster,)
    ASSOCIATED_ARTIFACT_TYPES = (ModelArtifact,)

    def handle_input(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.
        """
        super().handle_input(data_type)
        filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)

        # Create a temporary folder
        temp_dir = tempfile.mkdtemp(prefix="zenml-temp-")
        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)

        # Cleanup and return
        fileio.rmtree(temp_dir)
        return booster

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

        Args:
            booster: A lightgbm Booster model.
        """
        super().handle_return(booster)

        filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)

        # Make a temporary phantom artifact
        with tempfile.NamedTemporaryFile(
            mode="w", suffix=".txt", delete=False
        ) as f:
            booster.save_model(f.name)
            # Copy it into artifact store
            fileio.copy(f.name, filepath)

        # Close and remove the temporary file
        f.close()
        fileio.remove(f.name)

handle_input(self, data_type)

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 zenml/integrations/lightgbm/materializers/lightgbm_booster_materializer.py

def handle_input(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.
    """
    super().handle_input(data_type)
    filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)

    # Create a temporary folder
    temp_dir = tempfile.mkdtemp(prefix="zenml-temp-")
    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)

    # Cleanup and return
    fileio.rmtree(temp_dir)
    return booster

handle_return(self, booster)

Creates a JSON serialization for a lightgbm Booster model.

Parameters:

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

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

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

    Args:
        booster: A lightgbm Booster model.
    """
    super().handle_return(booster)

    filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)

    # Make a temporary phantom artifact
    with tempfile.NamedTemporaryFile(
        mode="w", suffix=".txt", delete=False
    ) as f:
        booster.save_model(f.name)
        # Copy it into artifact store
        fileio.copy(f.name, filepath)

    # Close and remove the temporary file
    f.close()
    fileio.remove(f.name)

lightgbm_dataset_materializer

Implementation of the LightGBM materializer.

LightGBMDatasetMaterializer (BaseMaterializer)

Materializer to read data to and from lightgbm.Dataset.

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

class LightGBMDatasetMaterializer(BaseMaterializer):
    """Materializer to read data to and from lightgbm.Dataset."""

    ASSOCIATED_TYPES = (lgb.Dataset,)
    ASSOCIATED_ARTIFACT_TYPES = (DataArtifact,)

    def handle_input(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.
        """
        super().handle_input(data_type)
        filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)

        # Create a temporary folder
        temp_dir = tempfile.mkdtemp(prefix="zenml-temp-")
        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)

        # No clean up this time because matrix is lazy loaded
        return matrix

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

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

        # Make a temporary phantom artifact
        temp_dir = tempfile.mkdtemp(prefix="zenml-temp-")
        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)
        fileio.rmtree(temp_dir)

handle_input(self, data_type)

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 zenml/integrations/lightgbm/materializers/lightgbm_dataset_materializer.py

def handle_input(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.
    """
    super().handle_input(data_type)
    filepath = os.path.join(self.artifact.uri, DEFAULT_FILENAME)

    # Create a temporary folder
    temp_dir = tempfile.mkdtemp(prefix="zenml-temp-")
    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)

    # No clean up this time because matrix is lazy loaded
    return matrix

handle_return(self, matrix)

Creates a binary serialization for a lightgbm.Dataset object.

Parameters:

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

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

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

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

    # Make a temporary phantom artifact
    temp_dir = tempfile.mkdtemp(prefix="zenml-temp-")
    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)
    fileio.rmtree(temp_dir)