Scipy

zenml.integrations.scipy special

Initialization of the Scipy integration.

ScipyIntegration (Integration)

Definition of scipy integration for ZenML.

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

class ScipyIntegration(Integration):
    """Definition of scipy integration for ZenML."""

    NAME = SCIPY
    REQUIREMENTS = ["scipy"]

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

activate() classmethod

Activates the integration.

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

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

materializers special

Initialization of the Scipy materializers.

sparse_materializer

Implementation of the Scipy Sparse Materializer.

SparseMaterializer (BaseMaterializer)

Materializer to read and write scipy sparse matrices.

Source code in zenml/integrations/scipy/materializers/sparse_materializer.py

class SparseMaterializer(BaseMaterializer):
    """Materializer to read and write scipy sparse matrices."""

    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (spmatrix,)
    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA

    def load(self, data_type: Type[Any]) -> spmatrix:
        """Reads spmatrix from npz file.

        Args:
            data_type: The type of the spmatrix to load.

        Returns:
            A spmatrix object.
        """
        with fileio.open(os.path.join(self.uri, DATA_FILENAME), "rb") as f:
            mat = load_npz(f)
        return mat

    def save(self, mat: spmatrix) -> None:
        """Writes a spmatrix to the artifact store as a npz file.

        Args:
            mat: The spmatrix to write.
        """
        with fileio.open(os.path.join(self.uri, DATA_FILENAME), "wb") as f:
            save_npz(f, mat)

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

        Args:
            mat: The `spmatrix` object to extract metadata from.

        Returns:
            The extracted metadata as a dictionary.
        """
        return {
            "shape": mat.shape,
            "dtype": DType(mat.dtype),
            "nnz": mat.nnz,
        }

extract_metadata(self, mat)

Extract metadata from the given spmatrix object.

Parameters:

Name	Type	Description	Default
mat	scipy.sparse.spmatrix	The spmatrix object to extract metadata from.	required

Returns:

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

Source code in zenml/integrations/scipy/materializers/sparse_materializer.py

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

    Args:
        mat: The `spmatrix` object to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    return {
        "shape": mat.shape,
        "dtype": DType(mat.dtype),
        "nnz": mat.nnz,
    }

load(self, data_type)

Reads spmatrix from npz file.

Parameters:

Name	Type	Description	Default
data_type	Type[Any]	The type of the spmatrix to load.	required

Returns:

Type	Description
scipy.sparse.spmatrix	A spmatrix object.

Source code in zenml/integrations/scipy/materializers/sparse_materializer.py

def load(self, data_type: Type[Any]) -> spmatrix:
    """Reads spmatrix from npz file.

    Args:
        data_type: The type of the spmatrix to load.

    Returns:
        A spmatrix object.
    """
    with fileio.open(os.path.join(self.uri, DATA_FILENAME), "rb") as f:
        mat = load_npz(f)
    return mat

save(self, mat)

Writes a spmatrix to the artifact store as a npz file.

Parameters:

Name	Type	Description	Default
mat	scipy.sparse.spmatrix	The spmatrix to write.	required

Source code in zenml/integrations/scipy/materializers/sparse_materializer.py

def save(self, mat: spmatrix) -> None:
    """Writes a spmatrix to the artifact store as a npz file.

    Args:
        mat: The spmatrix to write.
    """
    with fileio.open(os.path.join(self.uri, DATA_FILENAME), "wb") as f:
        save_npz(f, mat)