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", "pandas"]

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

activate() staticmethod

Activate the Facets integration.

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

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

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) pydantic-model

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.core.frame.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]]]

    class Config:
        """Pydantic config."""

        arbitrary_types_allowed = True

Config

Pydantic config.

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

class Config:
    """Pydantic config."""

    arbitrary_types_allowed = True

steps special

Facets Standard Steps.

facets_visualization_steps

Facets Standard Steps.