Deepchecks

`zenml.integrations.deepchecks` `special`

Deepchecks integration for ZenML.

The Deepchecks integration provides a way to validate your data in your pipelines. It includes a way to detect data anomalies and define checks to ensure quality of data.

The integration includes custom materializers to store and visualize Deepchecks SuiteResults.

`DeepchecksIntegration (Integration)`

Definition of Deepchecks integration for ZenML.

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

class DeepchecksIntegration(Integration):
    """Definition of [Deepchecks](https://github.com/deepchecks/deepchecks) integration for ZenML."""

    NAME = DEEPCHECKS
    REQUIREMENTS = [
        "deepchecks[vision]>=0.18.0",
        "torchvision>=0.14.0",
        "opencv-python==4.5.5.64",  # pin to same version
        "opencv-python-headless==4.5.5.64",  # pin to same version
        "tenacity!=8.4.0",  # https://github.com/jd/tenacity/issues/471
        # The deepchecks integrations requires pandas to work.
        # However, their version 0.18.0 is still not compatible with
        # pandas>=2.2.0, so we limit the version here.
        "pandas<2.2.0",
    ]

    APT_PACKAGES = ["ffmpeg", "libsm6", "libxext6"]
    REQUIREMENTS_IGNORED_ON_UNINSTALL = ["pandas", "torchvision", "tenacity"]

    @classmethod
    def activate(cls) -> None:
        """Activate the Deepchecks integration."""
        from zenml.integrations.deepchecks 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)

    @classmethod
    def flavors(cls) -> List[Type[Flavor]]:
        """Declare the stack component flavors for the Deepchecks integration.

        Returns:
            List of stack component flavors for this integration.
        """
        from zenml.integrations.deepchecks.flavors import (
            DeepchecksDataValidatorFlavor,
        )

        return [DeepchecksDataValidatorFlavor]

`activate()` `classmethod`

Activate the Deepchecks integration.

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

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

`flavors()` `classmethod`

Declare the stack component flavors for the Deepchecks integration.

Returns:

Type	Description
`List[Type[zenml.stack.flavor.Flavor]]`	List of stack component flavors for this integration.

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

@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for the Deepchecks integration.

    Returns:
        List of stack component flavors for this integration.
    """
    from zenml.integrations.deepchecks.flavors import (
        DeepchecksDataValidatorFlavor,
    )

    return [DeepchecksDataValidatorFlavor]

`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/deepchecks/__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)

`data_validators` `special`

Initialization of the Deepchecks data validator for ZenML.

`deepchecks_data_validator`

Implementation of the Deepchecks data validator.

`DeepchecksDataValidator (BaseDataValidator)`

Deepchecks data validator stack component.

Source code in zenml/integrations/deepchecks/data_validators/deepchecks_data_validator.py

class DeepchecksDataValidator(BaseDataValidator):
    """Deepchecks data validator stack component."""

    NAME: ClassVar[str] = "Deepchecks"
    FLAVOR: ClassVar[Type[BaseDataValidatorFlavor]] = (
        DeepchecksDataValidatorFlavor
    )

    @staticmethod
    def _split_checks(
        check_list: Sequence[str],
    ) -> Tuple[Sequence[str], Sequence[str]]:
        """Split a list of check identifiers in two lists, one for tabular and one for computer vision checks.

        Args:
            check_list: A list of check identifiers.

        Returns:
            List of tabular check identifiers and list of computer vision
            check identifiers.
        """
        tabular_checks = list(
            filter(
                lambda check: DeepchecksValidationCheck.is_tabular_check(
                    check
                ),
                check_list,
            )
        )
        vision_checks = list(
            filter(
                lambda check: DeepchecksValidationCheck.is_vision_check(check),
                check_list,
            )
        )
        return tabular_checks, vision_checks

    @classmethod
    def _create_and_run_check_suite(
        cls,
        check_enum: Type[DeepchecksValidationCheck],
        reference_dataset: Union[pd.DataFrame, DataLoader[Any]],
        comparison_dataset: Optional[
            Union[pd.DataFrame, DataLoader[Any]]
        ] = None,
        model: Optional[Union[ClassifierMixin, Module]] = None,
        check_list: Optional[Sequence[str]] = None,
        dataset_kwargs: Dict[str, Any] = {},
        check_kwargs: Dict[str, Dict[str, Any]] = {},
        run_kwargs: Dict[str, Any] = {},
    ) -> SuiteResult:
        """Create and run a Deepchecks check suite corresponding to the input parameters.

        This method contains generic logic common to all Deepchecks data
        validator methods that validates the input arguments and uses them to
        generate and run a Deepchecks check suite.

        Args:
            check_enum: ZenML enum type grouping together Deepchecks checks with
                the same characteristics. This is used to generate a default
                list of checks, if a custom list isn't provided via the
                `check_list` argument.
            reference_dataset: Primary (reference) dataset argument used during
                validation.
            comparison_dataset: Optional secondary (comparison) dataset argument
                used during comparison checks.
            model: Optional model argument used during validation.
            check_list: Optional list of ZenML Deepchecks check identifiers
                specifying the list of Deepchecks checks to be performed.
            dataset_kwargs: Additional keyword arguments to be passed to the
                Deepchecks tabular.Dataset or vision.VisionData constructor.
            check_kwargs: Additional keyword arguments to be passed to the
                Deepchecks check object constructors. Arguments are grouped for
                each check and indexed using the full check class name or
                check enum value as dictionary keys.
            run_kwargs: Additional keyword arguments to be passed to the
                Deepchecks Suite `run` method.

        Returns:
            Deepchecks SuiteResult object with the Suite run results.

        Raises:
            TypeError: If the datasets, model and check list arguments combine
                data types and/or checks from different categories (tabular and
                computer vision).
        """
        # Detect what type of check to perform (tabular or computer vision) from
        # the dataset/model datatypes and the check list. At the same time,
        # validate the combination of data types used for dataset and model
        # arguments and the check list.
        is_tabular = False
        is_vision = False
        for dataset in [reference_dataset, comparison_dataset]:
            if dataset is None:
                continue
            if isinstance(dataset, pd.DataFrame):
                is_tabular = True
            elif isinstance(dataset, DataLoader):
                is_vision = True
            else:
                raise TypeError(
                    f"Unsupported dataset data type found: {type(dataset)}. "
                    f"Supported data types are {str(pd.DataFrame)} for tabular "
                    f"data and {str(DataLoader)} for computer vision data."
                )

        if model:
            if isinstance(model, ClassifierMixin):
                is_tabular = True
            elif isinstance(model, Module):
                is_vision = True
            else:
                raise TypeError(
                    f"Unsupported model data type found: {type(model)}. "
                    f"Supported data types are {str(ClassifierMixin)} for "
                    f"tabular data and {str(Module)} for computer vision "
                    f"data."
                )

        if is_tabular and is_vision:
            raise TypeError(
                f"Tabular and computer vision data types used for datasets and "
                f"models cannot be mixed. They must all belong to the same "
                f"category. Supported data types for tabular data are "
                f"{str(pd.DataFrame)} for datasets and {str(ClassifierMixin)} "
                f"for models. Supported data types for computer vision data "
                f"are {str(pd.DataFrame)} for datasets and and {str(Module)} "
                f"for models."
            )

        if not check_list:
            # default to executing all the checks listed in the supplied
            # checks enum type if a custom check list is not supplied
            tabular_checks, vision_checks = cls._split_checks(
                check_enum.values()
            )
            if is_tabular:
                check_list = tabular_checks
                vision_checks = []
            else:
                check_list = vision_checks
                tabular_checks = []
        else:
            tabular_checks, vision_checks = cls._split_checks(check_list)

        if tabular_checks and vision_checks:
            raise TypeError(
                f"The check list cannot mix tabular checks "
                f"({tabular_checks}) and computer vision checks ("
                f"{vision_checks})."
            )

        if is_tabular and vision_checks:
            raise TypeError(
                f"Tabular data types used for datasets and models can only "
                f"be used with tabular validation checks. The following "
                f"computer vision checks included in the check list are "
                f"not valid: {vision_checks}."
            )

        if is_vision and tabular_checks:
            raise TypeError(
                f"Computer vision data types used for datasets and models "
                f"can only be used with computer vision validation checks. "
                f"The following tabular checks included in the check list "
                f"are not valid: {tabular_checks}."
            )

        check_classes = map(
            lambda check: (
                check,
                check_enum.get_check_class(check),
            ),
            check_list,
        )

        # use the pipeline name and the step name to generate a unique suite
        # name
        try:
            # get pipeline name and step name
            step_context = get_step_context()
            pipeline_name = step_context.pipeline.name
            step_name = step_context.step_run.name
            suite_name = f"{pipeline_name}_{step_name}"
        except RuntimeError:
            # if not running inside a pipeline step, use random values
            suite_name = f"suite_{random_str(5)}"

        if is_tabular:
            dataset_class = TabularData
            suite_class = TabularSuite
            full_suite = full_tabular_suite()
        else:
            dataset_class = VisionData
            suite_class = VisionSuite
            full_suite = full_vision_suite()

        train_dataset = dataset_class(reference_dataset, **dataset_kwargs)
        test_dataset = None
        if comparison_dataset is not None:
            test_dataset = dataset_class(comparison_dataset, **dataset_kwargs)
        suite = suite_class(name=suite_name)

        # Some Deepchecks checks require a minimum configuration such as
        # conditions to be configured (see https://docs.deepchecks.com/stable/user-guide/general/customizations/examples/plot_configure_check_conditions.html#sphx-glr-user-guide-general-customizations-examples-plot-configure-check-conditions-py)
        # for their execution to have meaning. For checks that don't have
        # custom configuration attributes explicitly specified in the
        # `check_kwargs` input parameter, we use the default check
        # instances extracted from the full suite shipped with Deepchecks.
        default_checks = {
            check.__class__: check for check in full_suite.checks.values()
        }
        for check_name, check_class in check_classes:
            extra_kwargs = check_kwargs.get(check_name, {})
            default_check = default_checks.get(check_class)
            check: BaseCheck
            if extra_kwargs or not default_check:
                check = check_class(**check_kwargs)
            else:
                check = default_check

            # extract the condition kwargs from the check kwargs
            for arg_name, condition_kwargs in extra_kwargs.items():
                if not arg_name.startswith("condition_") or not isinstance(
                    condition_kwargs, dict
                ):
                    continue
                condition_method = getattr(check, f"add_{arg_name}", None)
                if not condition_method or not callable(condition_method):
                    logger.warning(
                        f"Deepchecks check type {check.__class__} has no "
                        f"condition named {arg_name}. Ignoring the check "
                        f"argument."
                    )
                    continue
                condition_method(**condition_kwargs)

            suite.add(check)
        return suite.run(
            train_dataset=train_dataset,
            test_dataset=test_dataset,
            model=model,
            **run_kwargs,
        )

    def data_validation(
        self,
        dataset: Union[pd.DataFrame, DataLoader[Any]],
        comparison_dataset: Optional[Any] = None,
        check_list: Optional[Sequence[str]] = None,
        dataset_kwargs: Dict[str, Any] = {},
        check_kwargs: Dict[str, Dict[str, Any]] = {},
        run_kwargs: Dict[str, Any] = {},
        **kwargs: Any,
    ) -> SuiteResult:
        """Run one or more Deepchecks data validation checks on a dataset.

        Call this method to analyze and identify potential integrity problems
        with a single dataset (e.g. missing values, conflicting labels, mixed
        data types etc.) and dataset comparison checks (e.g. data drift
        checks). Dataset comparison checks require that a second dataset be
        supplied via the `comparison_dataset` argument.

        The `check_list` argument may be used to specify a custom set of
        Deepchecks data integrity checks to perform, identified by
        `DeepchecksDataIntegrityCheck` and `DeepchecksDataDriftCheck` enum
        values. If omitted:

        * if the `comparison_dataset` is omitted, a suite with all available
        data integrity checks will be performed on the input data. See
        `DeepchecksDataIntegrityCheck` for a list of Deepchecks builtin
        checks that are compatible with this method.

        * if the `comparison_dataset` is supplied, a suite with all
        available data drift checks will be performed on the input
        data. See `DeepchecksDataDriftCheck` for a list of Deepchecks
        builtin checks that are compatible with this method.

        Args:
            dataset: Target dataset to be validated.
            comparison_dataset: Optional second dataset to be used for data
                comparison checks (e.g data drift checks).
            check_list: Optional list of ZenML Deepchecks check identifiers
                specifying the data validation checks to be performed.
                `DeepchecksDataIntegrityCheck` enum values should be used for
                single data validation checks and `DeepchecksDataDriftCheck`
                enum values for data comparison checks. If not supplied, the
                entire set of checks applicable to the input dataset(s)
                will be performed.
            dataset_kwargs: Additional keyword arguments to be passed to the
                Deepchecks `tabular.Dataset` or `vision.VisionData` constructor.
            check_kwargs: Additional keyword arguments to be passed to the
                Deepchecks check object constructors. Arguments are grouped for
                each check and indexed using the full check class name or
                check enum value as dictionary keys.
            run_kwargs: Additional keyword arguments to be passed to the
                Deepchecks Suite `run` method.
            kwargs: Additional keyword arguments (unused).

        Returns:
            A Deepchecks SuiteResult with the results of the validation.
        """
        check_enum: Type[DeepchecksValidationCheck]
        if comparison_dataset is None:
            check_enum = DeepchecksDataIntegrityCheck
        else:
            check_enum = DeepchecksDataDriftCheck

        return self._create_and_run_check_suite(
            check_enum=check_enum,
            reference_dataset=dataset,
            comparison_dataset=comparison_dataset,
            check_list=check_list,
            dataset_kwargs=dataset_kwargs,
            check_kwargs=check_kwargs,
            run_kwargs=run_kwargs,
        )

    def model_validation(
        self,
        dataset: Union[pd.DataFrame, DataLoader[Any]],
        model: Union[ClassifierMixin, Module],
        comparison_dataset: Optional[Any] = None,
        check_list: Optional[Sequence[str]] = None,
        dataset_kwargs: Dict[str, Any] = {},
        check_kwargs: Dict[str, Dict[str, Any]] = {},
        run_kwargs: Dict[str, Any] = {},
        **kwargs: Any,
    ) -> Any:
        """Run one or more Deepchecks model validation checks.

        Call this method to perform model validation checks (e.g. confusion
        matrix validation, performance reports, model error analyses, etc).
        A second dataset is required for model performance comparison tests
        (i.e. tests that identify changes in a model behavior by comparing how
        it performs on two different datasets).

        The `check_list` argument may be used to specify a custom set of
        Deepchecks model validation checks to perform, identified by
        `DeepchecksModelValidationCheck` and `DeepchecksModelDriftCheck` enum
        values. If omitted:

            * if the `comparison_dataset` is omitted, a suite with all available
            model validation checks will be performed on the input data. See
            `DeepchecksModelValidationCheck` for a list of Deepchecks builtin
            checks that are compatible with this method.

            * if the `comparison_dataset` is supplied, a suite with all
            available model comparison checks will be performed on the input
            data. See `DeepchecksModelValidationCheck` for a list of Deepchecks
            builtin checks that are compatible with this method.

        Args:
            dataset: Target dataset to be validated.
            model: Target model to be validated.
            comparison_dataset: Optional second dataset to be used for model
                comparison checks.
            check_list: Optional list of ZenML Deepchecks check identifiers
                specifying the model validation checks to be performed.
                `DeepchecksModelValidationCheck` enum values should be used for
                model validation checks and `DeepchecksModelDriftCheck` enum
                values for model comparison checks. If not supplied, the
                entire set of checks applicable to the input dataset(s)
                will be performed.
            dataset_kwargs: Additional keyword arguments to be passed to the
                Deepchecks tabular.Dataset or vision.VisionData constructor.
            check_kwargs: Additional keyword arguments to be passed to the
                Deepchecks check object constructors. Arguments are grouped for
                each check and indexed using the full check class name or
                check enum value as dictionary keys.
            run_kwargs: Additional keyword arguments to be passed to the
                Deepchecks Suite `run` method.
            kwargs: Additional keyword arguments (unused).

        Returns:
            A Deepchecks SuiteResult with the results of the validation.
        """
        check_enum: Type[DeepchecksValidationCheck]
        if comparison_dataset is None:
            check_enum = DeepchecksModelValidationCheck
        else:
            check_enum = DeepchecksModelDriftCheck

        return self._create_and_run_check_suite(
            check_enum=check_enum,
            reference_dataset=dataset,
            comparison_dataset=comparison_dataset,
            model=model,
            check_list=check_list,
            dataset_kwargs=dataset_kwargs,
            check_kwargs=check_kwargs,
            run_kwargs=run_kwargs,
        )

`FLAVOR (BaseDataValidatorFlavor)`

Flavor of the Deepchecks data validator.

Source code in zenml/integrations/deepchecks/data_validators/deepchecks_data_validator.py

class DeepchecksDataValidatorFlavor(BaseDataValidatorFlavor):
    """Flavor of the Deepchecks data validator."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            The name of the flavor.
        """
        return DEEPCHECKS_DATA_VALIDATOR_FLAVOR

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at SDK docs explaining this flavor.

        Returns:
            A flavor SDK docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/data_validator/deepchecks.png"

    @property
    def implementation_class(self) -> Type["DeepchecksDataValidator"]:
        """Implementation class.

        Returns:
            The implementation class.
        """
        from zenml.integrations.deepchecks.data_validators import (
            DeepchecksDataValidator,
        )

        return DeepchecksDataValidator

docs_url: Optional[str] property readonly

A url to point at docs explaining this flavor.

Returns:

Type	Description
`Optional[str]`	A flavor docs url.

implementation_class: Type[DeepchecksDataValidator] property readonly

Implementation class.

Returns:

Type	Description
`Type[DeepchecksDataValidator]`	The implementation class.

logo_url: str property readonly

A url to represent the flavor in the dashboard.

Returns:

Type	Description
`str`	The flavor logo.

name: str property readonly

Name of the flavor.

Returns:

Type	Description
`str`	The name of the flavor.

sdk_docs_url: Optional[str] property readonly

A url to point at SDK docs explaining this flavor.

Returns:

Type	Description
`Optional[str]`	A flavor SDK docs url.

`data_validation(self, dataset, comparison_dataset=None, check_list=None, dataset_kwargs={}, check_kwargs={}, run_kwargs={}, **kwargs)`

Run one or more Deepchecks data validation checks on a dataset.

Call this method to analyze and identify potential integrity problems with a single dataset (e.g. missing values, conflicting labels, mixed data types etc.) and dataset comparison checks (e.g. data drift checks). Dataset comparison checks require that a second dataset be supplied via the comparison_dataset argument.

The check_list argument may be used to specify a custom set of Deepchecks data integrity checks to perform, identified by DeepchecksDataIntegrityCheck and DeepchecksDataDriftCheck enum values. If omitted:

if the comparison_dataset is omitted, a suite with all available data integrity checks will be performed on the input data. See DeepchecksDataIntegrityCheck for a list of Deepchecks builtin checks that are compatible with this method.
if the comparison_dataset is supplied, a suite with all available data drift checks will be performed on the input data. See DeepchecksDataDriftCheck for a list of Deepchecks builtin checks that are compatible with this method.

Parameters:

Name	Type	Description	Default
`dataset`	`Union[pandas.DataFrame, torch.utils.data.dataloader.DataLoader[Any]]`	Target dataset to be validated.	required
`comparison_dataset`	`Optional[Any]`	Optional second dataset to be used for data comparison checks (e.g data drift checks).	`None`
`check_list`	`Optional[Sequence[str]]`	Optional list of ZenML Deepchecks check identifiers specifying the data validation checks to be performed. `DeepchecksDataIntegrityCheck` enum values should be used for single data validation checks and `DeepchecksDataDriftCheck` enum values for data comparison checks. If not supplied, the entire set of checks applicable to the input dataset(s) will be performed.	`None`
`dataset_kwargs`	`Dict[str, Any]`	Additional keyword arguments to be passed to the Deepchecks `tabular.Dataset` or `vision.VisionData` constructor.	`{}`
`check_kwargs`	`Dict[str, Dict[str, Any]]`	Additional keyword arguments to be passed to the Deepchecks check object constructors. Arguments are grouped for each check and indexed using the full check class name or check enum value as dictionary keys.	`{}`
`run_kwargs`	`Dict[str, Any]`	Additional keyword arguments to be passed to the Deepchecks Suite `run` method.	`{}`
`kwargs`	`Any`	Additional keyword arguments (unused).	`{}`

Returns:

Type	Description
`deepchecks.core.suite.SuiteResult`	A Deepchecks SuiteResult with the results of the validation.

Source code in zenml/integrations/deepchecks/data_validators/deepchecks_data_validator.py

def data_validation(
    self,
    dataset: Union[pd.DataFrame, DataLoader[Any]],
    comparison_dataset: Optional[Any] = None,
    check_list: Optional[Sequence[str]] = None,
    dataset_kwargs: Dict[str, Any] = {},
    check_kwargs: Dict[str, Dict[str, Any]] = {},
    run_kwargs: Dict[str, Any] = {},
    **kwargs: Any,
) -> SuiteResult:
    """Run one or more Deepchecks data validation checks on a dataset.

    Call this method to analyze and identify potential integrity problems
    with a single dataset (e.g. missing values, conflicting labels, mixed
    data types etc.) and dataset comparison checks (e.g. data drift
    checks). Dataset comparison checks require that a second dataset be
    supplied via the `comparison_dataset` argument.

    The `check_list` argument may be used to specify a custom set of
    Deepchecks data integrity checks to perform, identified by
    `DeepchecksDataIntegrityCheck` and `DeepchecksDataDriftCheck` enum
    values. If omitted:

    * if the `comparison_dataset` is omitted, a suite with all available
    data integrity checks will be performed on the input data. See
    `DeepchecksDataIntegrityCheck` for a list of Deepchecks builtin
    checks that are compatible with this method.

    * if the `comparison_dataset` is supplied, a suite with all
    available data drift checks will be performed on the input
    data. See `DeepchecksDataDriftCheck` for a list of Deepchecks
    builtin checks that are compatible with this method.

    Args:
        dataset: Target dataset to be validated.
        comparison_dataset: Optional second dataset to be used for data
            comparison checks (e.g data drift checks).
        check_list: Optional list of ZenML Deepchecks check identifiers
            specifying the data validation checks to be performed.
            `DeepchecksDataIntegrityCheck` enum values should be used for
            single data validation checks and `DeepchecksDataDriftCheck`
            enum values for data comparison checks. If not supplied, the
            entire set of checks applicable to the input dataset(s)
            will be performed.
        dataset_kwargs: Additional keyword arguments to be passed to the
            Deepchecks `tabular.Dataset` or `vision.VisionData` constructor.
        check_kwargs: Additional keyword arguments to be passed to the
            Deepchecks check object constructors. Arguments are grouped for
            each check and indexed using the full check class name or
            check enum value as dictionary keys.
        run_kwargs: Additional keyword arguments to be passed to the
            Deepchecks Suite `run` method.
        kwargs: Additional keyword arguments (unused).

    Returns:
        A Deepchecks SuiteResult with the results of the validation.
    """
    check_enum: Type[DeepchecksValidationCheck]
    if comparison_dataset is None:
        check_enum = DeepchecksDataIntegrityCheck
    else:
        check_enum = DeepchecksDataDriftCheck

    return self._create_and_run_check_suite(
        check_enum=check_enum,
        reference_dataset=dataset,
        comparison_dataset=comparison_dataset,
        check_list=check_list,
        dataset_kwargs=dataset_kwargs,
        check_kwargs=check_kwargs,
        run_kwargs=run_kwargs,
    )

`model_validation(self, dataset, model, comparison_dataset=None, check_list=None, dataset_kwargs={}, check_kwargs={}, run_kwargs={}, **kwargs)`

Run one or more Deepchecks model validation checks.

Call this method to perform model validation checks (e.g. confusion matrix validation, performance reports, model error analyses, etc). A second dataset is required for model performance comparison tests (i.e. tests that identify changes in a model behavior by comparing how it performs on two different datasets).

The check_list argument may be used to specify a custom set of Deepchecks model validation checks to perform, identified by DeepchecksModelValidationCheck and DeepchecksModelDriftCheck enum values. If omitted:

* if the `comparison_dataset` is omitted, a suite with all available
model validation checks will be performed on the input data. See
`DeepchecksModelValidationCheck` for a list of Deepchecks builtin
checks that are compatible with this method.

* if the `comparison_dataset` is supplied, a suite with all
available model comparison checks will be performed on the input
data. See `DeepchecksModelValidationCheck` for a list of Deepchecks
builtin checks that are compatible with this method.

Parameters:

Name	Type	Description	Default
`dataset`	`Union[pandas.DataFrame, torch.utils.data.dataloader.DataLoader[Any]]`	Target dataset to be validated.	required
`model`	`Union[sklearn.base.ClassifierMixin, torch.nn.Module]`	Target model to be validated.	required
`comparison_dataset`	`Optional[Any]`	Optional second dataset to be used for model comparison checks.	`None`
`check_list`	`Optional[Sequence[str]]`	Optional list of ZenML Deepchecks check identifiers specifying the model validation checks to be performed. `DeepchecksModelValidationCheck` enum values should be used for model validation checks and `DeepchecksModelDriftCheck` enum values for model comparison checks. If not supplied, the entire set of checks applicable to the input dataset(s) will be performed.	`None`
`dataset_kwargs`	`Dict[str, Any]`	Additional keyword arguments to be passed to the Deepchecks tabular.Dataset or vision.VisionData constructor.	`{}`
`check_kwargs`	`Dict[str, Dict[str, Any]]`	Additional keyword arguments to be passed to the Deepchecks check object constructors. Arguments are grouped for each check and indexed using the full check class name or check enum value as dictionary keys.	`{}`
`run_kwargs`	`Dict[str, Any]`	Additional keyword arguments to be passed to the Deepchecks Suite `run` method.	`{}`
`kwargs`	`Any`	Additional keyword arguments (unused).	`{}`

Returns:

Type	Description
`Any`	A Deepchecks SuiteResult with the results of the validation.

Source code in zenml/integrations/deepchecks/data_validators/deepchecks_data_validator.py

def model_validation(
    self,
    dataset: Union[pd.DataFrame, DataLoader[Any]],
    model: Union[ClassifierMixin, Module],
    comparison_dataset: Optional[Any] = None,
    check_list: Optional[Sequence[str]] = None,
    dataset_kwargs: Dict[str, Any] = {},
    check_kwargs: Dict[str, Dict[str, Any]] = {},
    run_kwargs: Dict[str, Any] = {},
    **kwargs: Any,
) -> Any:
    """Run one or more Deepchecks model validation checks.

    Call this method to perform model validation checks (e.g. confusion
    matrix validation, performance reports, model error analyses, etc).
    A second dataset is required for model performance comparison tests
    (i.e. tests that identify changes in a model behavior by comparing how
    it performs on two different datasets).

    The `check_list` argument may be used to specify a custom set of
    Deepchecks model validation checks to perform, identified by
    `DeepchecksModelValidationCheck` and `DeepchecksModelDriftCheck` enum
    values. If omitted:

        * if the `comparison_dataset` is omitted, a suite with all available
        model validation checks will be performed on the input data. See
        `DeepchecksModelValidationCheck` for a list of Deepchecks builtin
        checks that are compatible with this method.

        * if the `comparison_dataset` is supplied, a suite with all
        available model comparison checks will be performed on the input
        data. See `DeepchecksModelValidationCheck` for a list of Deepchecks
        builtin checks that are compatible with this method.

    Args:
        dataset: Target dataset to be validated.
        model: Target model to be validated.
        comparison_dataset: Optional second dataset to be used for model
            comparison checks.
        check_list: Optional list of ZenML Deepchecks check identifiers
            specifying the model validation checks to be performed.
            `DeepchecksModelValidationCheck` enum values should be used for
            model validation checks and `DeepchecksModelDriftCheck` enum
            values for model comparison checks. If not supplied, the
            entire set of checks applicable to the input dataset(s)
            will be performed.
        dataset_kwargs: Additional keyword arguments to be passed to the
            Deepchecks tabular.Dataset or vision.VisionData constructor.
        check_kwargs: Additional keyword arguments to be passed to the
            Deepchecks check object constructors. Arguments are grouped for
            each check and indexed using the full check class name or
            check enum value as dictionary keys.
        run_kwargs: Additional keyword arguments to be passed to the
            Deepchecks Suite `run` method.
        kwargs: Additional keyword arguments (unused).

    Returns:
        A Deepchecks SuiteResult with the results of the validation.
    """
    check_enum: Type[DeepchecksValidationCheck]
    if comparison_dataset is None:
        check_enum = DeepchecksModelValidationCheck
    else:
        check_enum = DeepchecksModelDriftCheck

    return self._create_and_run_check_suite(
        check_enum=check_enum,
        reference_dataset=dataset,
        comparison_dataset=comparison_dataset,
        model=model,
        check_list=check_list,
        dataset_kwargs=dataset_kwargs,
        check_kwargs=check_kwargs,
        run_kwargs=run_kwargs,
    )

`flavors` `special`

Deepchecks integration flavors.

`deepchecks_data_validator_flavor`

Deepchecks data validator flavor.

`DeepchecksDataValidatorFlavor (BaseDataValidatorFlavor)`

Flavor of the Deepchecks data validator.

Source code in zenml/integrations/deepchecks/flavors/deepchecks_data_validator_flavor.py

class DeepchecksDataValidatorFlavor(BaseDataValidatorFlavor):
    """Flavor of the Deepchecks data validator."""

    @property
    def name(self) -> str:
        """Name of the flavor.

        Returns:
            The name of the flavor.
        """
        return DEEPCHECKS_DATA_VALIDATOR_FLAVOR

    @property
    def docs_url(self) -> Optional[str]:
        """A url to point at docs explaining this flavor.

        Returns:
            A flavor docs url.
        """
        return self.generate_default_docs_url()

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """A url to point at SDK docs explaining this flavor.

        Returns:
            A flavor SDK docs url.
        """
        return self.generate_default_sdk_docs_url()

    @property
    def logo_url(self) -> str:
        """A url to represent the flavor in the dashboard.

        Returns:
            The flavor logo.
        """
        return "https://public-flavor-logos.s3.eu-central-1.amazonaws.com/data_validator/deepchecks.png"

    @property
    def implementation_class(self) -> Type["DeepchecksDataValidator"]:
        """Implementation class.

        Returns:
            The implementation class.
        """
        from zenml.integrations.deepchecks.data_validators import (
            DeepchecksDataValidator,
        )

        return DeepchecksDataValidator

`docs_url: Optional[str]` `property` `readonly`

A url to point at docs explaining this flavor.

Returns:

Type	Description
`Optional[str]`	A flavor docs url.

`implementation_class: Type[DeepchecksDataValidator]` `property` `readonly`

Implementation class.

Returns:

Type	Description
`Type[DeepchecksDataValidator]`	The implementation class.

`logo_url: str` `property` `readonly`

A url to represent the flavor in the dashboard.

Returns:

Type	Description
`str`	The flavor logo.

`name: str` `property` `readonly`

Name of the flavor.

Returns:

Type	Description
`str`	The name of the flavor.

`sdk_docs_url: Optional[str]` `property` `readonly`

A url to point at SDK docs explaining this flavor.

Returns:

Type	Description
`Optional[str]`	A flavor SDK docs url.

`materializers` `special`

Deepchecks materializers.

`deepchecks_dataset_materializer`

Implementation of Deepchecks dataset materializer.

`DeepchecksDatasetMaterializer (PandasMaterializer)`

Materializer to read data to and from Deepchecks dataset.

Source code in zenml/integrations/deepchecks/materializers/deepchecks_dataset_materializer.py

class DeepchecksDatasetMaterializer(PandasMaterializer):
    """Materializer to read data to and from Deepchecks dataset."""

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

    def load(self, data_type: Type[Any]) -> Dataset:
        """Reads pandas dataframes and creates `deepchecks.Dataset` from it.

        Args:
            data_type: The type of the data to read.

        Returns:
            A Deepchecks Dataset.
        """
        df = super().load(data_type)
        return Dataset(df)

    def save(self, dataset: Dataset) -> None:
        """Serializes pandas dataframe within a `Dataset` object.

        Args:
            dataset: A deepchecks.Dataset object.
        """
        super().save(dataset.data)

    def save_visualizations(
        self, dataset: Dataset
    ) -> Dict[str, VisualizationType]:
        """Saves visualizations for the given Deepchecks dataset.

        Args:
            dataset: The Deepchecks dataset to save visualizations for.

        Returns:
            A dictionary of visualization URIs and their types.
        """
        return super().save_visualizations(dataset.data)

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

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

        Returns:
            The extracted metadata as a dictionary.
        """
        return super().extract_metadata(dataset.data)

`extract_metadata(self, dataset)`

Extract metadata from the given Dataset object.

Parameters:

Name	Type	Description	Default
`dataset`	`deepchecks.tabular.Dataset`	The `Dataset` object to extract metadata from.	required

Returns:

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

Source code in zenml/integrations/deepchecks/materializers/deepchecks_dataset_materializer.py

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

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

    Returns:
        The extracted metadata as a dictionary.
    """
    return super().extract_metadata(dataset.data)

`load(self, data_type)`

Reads pandas dataframes and creates deepchecks.Dataset from it.

Parameters:

Name	Type	Description	Default
`data_type`	`Type[Any]`	The type of the data to read.	required

Returns:

Type	Description
`deepchecks.tabular.Dataset`	A Deepchecks Dataset.

Source code in zenml/integrations/deepchecks/materializers/deepchecks_dataset_materializer.py

def load(self, data_type: Type[Any]) -> Dataset:
    """Reads pandas dataframes and creates `deepchecks.Dataset` from it.

    Args:
        data_type: The type of the data to read.

    Returns:
        A Deepchecks Dataset.
    """
    df = super().load(data_type)
    return Dataset(df)

`save(self, dataset)`

Serializes pandas dataframe within a Dataset object.

Parameters:

Name	Type	Description	Default
`dataset`	`deepchecks.tabular.Dataset`	A deepchecks.Dataset object.	required

Source code in zenml/integrations/deepchecks/materializers/deepchecks_dataset_materializer.py

def save(self, dataset: Dataset) -> None:
    """Serializes pandas dataframe within a `Dataset` object.

    Args:
        dataset: A deepchecks.Dataset object.
    """
    super().save(dataset.data)

`save_visualizations(self, dataset)`

Saves visualizations for the given Deepchecks dataset.

Parameters:

Name	Type	Description	Default
`dataset`	`deepchecks.tabular.Dataset`	The Deepchecks dataset to save visualizations for.	required

Returns:

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

Source code in zenml/integrations/deepchecks/materializers/deepchecks_dataset_materializer.py

def save_visualizations(
    self, dataset: Dataset
) -> Dict[str, VisualizationType]:
    """Saves visualizations for the given Deepchecks dataset.

    Args:
        dataset: The Deepchecks dataset to save visualizations for.

    Returns:
        A dictionary of visualization URIs and their types.
    """
    return super().save_visualizations(dataset.data)

`deepchecks_results_materializer`

Implementation of Deepchecks suite results materializer.

`DeepchecksResultMaterializer (BaseMaterializer)`

Materializer to read data to and from CheckResult and SuiteResult objects.

Source code in zenml/integrations/deepchecks/materializers/deepchecks_results_materializer.py

class DeepchecksResultMaterializer(BaseMaterializer):
    """Materializer to read data to and from CheckResult and SuiteResult objects."""

    ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (
        CheckResult,
        SuiteResult,
    )
    ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = (
        ArtifactType.DATA_ANALYSIS
    )

    def load(self, data_type: Type[Any]) -> Union[CheckResult, SuiteResult]:
        """Reads a Deepchecks check or suite result from a serialized JSON file.

        Args:
            data_type: The type of the data to read.

        Returns:
            A Deepchecks CheckResult or SuiteResult.

        Raises:
            RuntimeError: if the input data type is not supported.
        """
        filepath = os.path.join(self.uri, RESULTS_FILENAME)

        json_res = io_utils.read_file_contents_as_string(filepath)
        if data_type == SuiteResult:
            res = SuiteResult.from_json(json_res)
        elif data_type == CheckResult:
            res = CheckResult.from_json(json_res)
        else:
            raise RuntimeError(f"Unknown data type: {data_type}")
        return res

    def save(self, result: Union[CheckResult, SuiteResult]) -> None:
        """Creates a JSON serialization for a CheckResult or SuiteResult.

        Args:
            result: A Deepchecks CheckResult or SuiteResult.
        """
        filepath = os.path.join(self.uri, RESULTS_FILENAME)
        serialized_json = result.to_json(True)
        io_utils.write_file_contents_as_string(filepath, serialized_json)

    def save_visualizations(
        self, result: Union[CheckResult, SuiteResult]
    ) -> Dict[str, VisualizationType]:
        """Saves visualizations for the given Deepchecks result.

        Args:
            result: The Deepchecks result to save visualizations for.

        Returns:
            A dictionary of visualization URIs and their types.
        """
        visualization_path = os.path.join(self.uri, HTML_FILENAME)
        visualization_path = visualization_path.replace("\\", "/")
        with fileio.open(visualization_path, "w") as f:
            result.save_as_html(f)
        return {visualization_path: VisualizationType.HTML}

    def extract_metadata(
        self, result: Union[CheckResult, SuiteResult]
    ) -> Dict[str, "MetadataType"]:
        """Extract metadata from the given Deepchecks result.

        Args:
            result: The Deepchecks result to extract metadata from.

        Returns:
            The extracted metadata as a dictionary.
        """
        if isinstance(result, CheckResult):
            return {
                "deepchecks_check_name": result.get_header(),
                "deepchecks_check_passed": result.passed_conditions(),
            }
        elif isinstance(result, SuiteResult):
            return {
                "deepchecks_suite_name": result.name,
                "deepchecks_suite_passed": result.passed(),
            }
        return {}

`extract_metadata(self, result)`

Extract metadata from the given Deepchecks result.

Parameters:

Name	Type	Description	Default
`result`	`Union[deepchecks.core.check_result.CheckResult, deepchecks.core.suite.SuiteResult]`	The Deepchecks result to extract metadata from.	required

Returns:

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

Source code in zenml/integrations/deepchecks/materializers/deepchecks_results_materializer.py

def extract_metadata(
    self, result: Union[CheckResult, SuiteResult]
) -> Dict[str, "MetadataType"]:
    """Extract metadata from the given Deepchecks result.

    Args:
        result: The Deepchecks result to extract metadata from.

    Returns:
        The extracted metadata as a dictionary.
    """
    if isinstance(result, CheckResult):
        return {
            "deepchecks_check_name": result.get_header(),
            "deepchecks_check_passed": result.passed_conditions(),
        }
    elif isinstance(result, SuiteResult):
        return {
            "deepchecks_suite_name": result.name,
            "deepchecks_suite_passed": result.passed(),
        }
    return {}

`load(self, data_type)`

Reads a Deepchecks check or suite result from a serialized JSON file.

Parameters:

Name	Type	Description	Default
`data_type`	`Type[Any]`	The type of the data to read.	required

Returns:

Type	Description
`Union[deepchecks.core.check_result.CheckResult, deepchecks.core.suite.SuiteResult]`	A Deepchecks CheckResult or SuiteResult.

Exceptions:

Type	Description
`RuntimeError`	if the input data type is not supported.

Source code in zenml/integrations/deepchecks/materializers/deepchecks_results_materializer.py

def load(self, data_type: Type[Any]) -> Union[CheckResult, SuiteResult]:
    """Reads a Deepchecks check or suite result from a serialized JSON file.

    Args:
        data_type: The type of the data to read.

    Returns:
        A Deepchecks CheckResult or SuiteResult.

    Raises:
        RuntimeError: if the input data type is not supported.
    """
    filepath = os.path.join(self.uri, RESULTS_FILENAME)

    json_res = io_utils.read_file_contents_as_string(filepath)
    if data_type == SuiteResult:
        res = SuiteResult.from_json(json_res)
    elif data_type == CheckResult:
        res = CheckResult.from_json(json_res)
    else:
        raise RuntimeError(f"Unknown data type: {data_type}")
    return res

`save(self, result)`

Creates a JSON serialization for a CheckResult or SuiteResult.

Parameters:

Name	Type	Description	Default
`result`	`Union[deepchecks.core.check_result.CheckResult, deepchecks.core.suite.SuiteResult]`	A Deepchecks CheckResult or SuiteResult.	required

Source code in zenml/integrations/deepchecks/materializers/deepchecks_results_materializer.py

def save(self, result: Union[CheckResult, SuiteResult]) -> None:
    """Creates a JSON serialization for a CheckResult or SuiteResult.

    Args:
        result: A Deepchecks CheckResult or SuiteResult.
    """
    filepath = os.path.join(self.uri, RESULTS_FILENAME)
    serialized_json = result.to_json(True)
    io_utils.write_file_contents_as_string(filepath, serialized_json)

`save_visualizations(self, result)`

Saves visualizations for the given Deepchecks result.

Parameters:

Name	Type	Description	Default
`result`	`Union[deepchecks.core.check_result.CheckResult, deepchecks.core.suite.SuiteResult]`	The Deepchecks result to save visualizations for.	required

Returns:

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

Source code in zenml/integrations/deepchecks/materializers/deepchecks_results_materializer.py

def save_visualizations(
    self, result: Union[CheckResult, SuiteResult]
) -> Dict[str, VisualizationType]:
    """Saves visualizations for the given Deepchecks result.

    Args:
        result: The Deepchecks result to save visualizations for.

    Returns:
        A dictionary of visualization URIs and their types.
    """
    visualization_path = os.path.join(self.uri, HTML_FILENAME)
    visualization_path = visualization_path.replace("\\", "/")
    with fileio.open(visualization_path, "w") as f:
        result.save_as_html(f)
    return {visualization_path: VisualizationType.HTML}

`steps` `special`

Initialization of the Deepchecks Standard Steps.

`deepchecks_data_drift`

Implementation of the Deepchecks data drift validation step.

`deepchecks_data_integrity`

Implementation of the Deepchecks data integrity validation step.

`deepchecks_model_drift`

Implementation of the Deepchecks model drift validation step.

`deepchecks_model_validation`

Implementation of the Deepchecks model validation validation step.

`validation_checks`

Definition of the Deepchecks validation check types.

`DeepchecksDataDriftCheck (DeepchecksValidationCheck)`

Categories of Deepchecks data drift checks.

This list reflects the set of train-test validation checks provided by Deepchecks:

All these checks inherit from deepchecks.tabular.TrainTestCheck or deepchecks.vision.TrainTestCheck and require two datasets as input.