Facets

zenml.integrations.facets special

Facets integration for ZenML.

FacetsIntegration (Integration)

Definition of Facets integration for ZenML.

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

class FacetsIntegration(Integration):
    """Definition of Facets integration for ZenML."""

    NAME = FACETS
    REQUIREMENTS = ["facets-overview>=1.0.0"]

    REQUIREMENTS_IGNORED_ON_UNINSTALL = ["pandas"]

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

    @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)

activate() classmethod

Activate the Facets integration.

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

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

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/facets/__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)

materializers special

Facets Materializers.

facets_materializer

Implementation of the FacetsMaterializer.

FacetsMaterializer (BaseMaterializer)

Materializer to save Facets visualizations.

This materializer is used to visualize and compare dataset statistics using Facets. In contrast to other materializers, this materializer only saves the visualization and not the data itself.

Source code in zenml/integrations/facets/materializers/facets_materializer.py

class FacetsMaterializer(BaseMaterializer):
    """Materializer to save Facets visualizations.

    This materializer is used to visualize and compare dataset statistics using
    Facets. In contrast to other materializers, this materializer only saves
    the visualization and not the data itself.
    """

    ASSOCIATED_TYPES = (FacetsComparison,)
    ASSOCIATED_ARTIFACT_TYPE = ArtifactType.DATA_ANALYSIS

    def save_visualizations(
        self, data: FacetsComparison
    ) -> Dict[str, VisualizationType]:
        """Save a Facets visualization of the data.

        Args:
            data: The data to visualize.

        Returns:
            A dictionary of visualization URIs and their types.
        """
        proto = GenericFeatureStatisticsGenerator().ProtoFromDataFrames(
            data.datasets
        )
        protostr = base64.b64encode(proto.SerializeToString()).decode("utf-8")
        template = os.path.join(
            os.path.abspath(os.path.dirname(__file__)),
            "stats.html",
        )
        html = io_utils.read_file_contents_as_string(template)
        html = html.replace("protostr", protostr)
        visualization_path = os.path.join(self.uri, VISUALIZATION_FILENAME)
        visualization_path = visualization_path.replace("\\", "/")
        with fileio.open(visualization_path, "w") as f:
            f.write(html)
        return {visualization_path: VisualizationType.HTML}

save_visualizations(self, data)

Save a Facets visualization of the data.

Parameters:

Name	Type	Description	Default
data	FacetsComparison	The data to visualize.	required

Returns:

Type	Description
Dict[str, zenml.enums.VisualizationType]	A dictionary of visualization URIs and their types.

Source code in zenml/integrations/facets/materializers/facets_materializer.py

def save_visualizations(
    self, data: FacetsComparison
) -> Dict[str, VisualizationType]:
    """Save a Facets visualization of the data.

    Args:
        data: The data to visualize.

    Returns:
        A dictionary of visualization URIs and their types.
    """
    proto = GenericFeatureStatisticsGenerator().ProtoFromDataFrames(
        data.datasets
    )
    protostr = base64.b64encode(proto.SerializeToString()).decode("utf-8")
    template = os.path.join(
        os.path.abspath(os.path.dirname(__file__)),
        "stats.html",
    )
    html = io_utils.read_file_contents_as_string(template)
    html = html.replace("protostr", protostr)
    visualization_path = os.path.join(self.uri, VISUALIZATION_FILENAME)
    visualization_path = visualization_path.replace("\\", "/")
    with fileio.open(visualization_path, "w") as f:
        f.write(html)
    return {visualization_path: VisualizationType.HTML}

models

Models used by the Facets integration.

FacetsComparison (BaseModel)

Facets comparison model.

Returning this from any step will automatically visualize the datasets statistics using Facets.

Attributes:

Name	Type	Description
datasets	List[Dict[str, Union[str, pandas.DataFrame]]]	List of datasets to compare. Should be in the format [{"name": "dataset_name", "table": pd.DataFrame}, ...].

Source code in zenml/integrations/facets/models.py

class FacetsComparison(BaseModel):
    """Facets comparison model.

    Returning this from any step will automatically visualize the datasets
    statistics using Facets.

    Attributes:
        datasets: List of datasets to compare. Should be in the format
            `[{"name": "dataset_name", "table": pd.DataFrame}, ...]`.
    """

    datasets: List[Dict[str, Union[str, pd.DataFrame]]]
    model_config = ConfigDict(arbitrary_types_allowed=True)

steps special

Facets Standard Steps.

facets_visualization_steps

Facets Standard Steps.