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Whylogs

zenml.integrations.whylogs

Initialization of the whylogs integration.

Attributes

WHYLOGS = 'whylogs' module-attribute

WHYLOGS_DATA_VALIDATOR_FLAVOR = 'whylogs' module-attribute

Classes

Flavor

Class for ZenML Flavors.

Attributes
config_class: Type[StackComponentConfig] abstractmethod property

Returns StackComponentConfig config class.

Returns:

Type Description
Type[StackComponentConfig]

The config class.
config_schema: Dict[str, Any] property

The config schema for a flavor.

Returns:

Type Description
Dict[str, Any]

The config schema.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[StackComponent] abstractmethod property

Implementation class for this flavor.

Returns:

Type Description
Type[StackComponent]

The implementation class for this flavor.
logo_url: Optional[str] property

A url to represent the flavor in the dashboard.

Returns:

Type Description
Optional[str]

The flavor logo.
name: str abstractmethod property

The flavor name.

Returns:

Type Description
str

The flavor name.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
service_connector_requirements: Optional[ServiceConnectorRequirements] property

Service connector resource requirements for service connectors.

Specifies resource requirements that are used to filter the available service connector types that are compatible with this flavor.

Returns:

Type Description
Optional[ServiceConnectorRequirements]

Requirements for compatible service connectors, if a service
Optional[ServiceConnectorRequirements]

connector is required for this flavor.
type: StackComponentType abstractmethod property

The stack component type.

Returns:

Type Description
StackComponentType

The stack component type.
Functions
from_model(flavor_model: FlavorResponse) -> Flavor classmethod

Loads a flavor from a model.

Parameters:

Name Type Description Default
flavor_model FlavorResponse

The model to load from.

 required

Raises:

Type Description
CustomFlavorImportError

If the custom flavor can't be imported.
ImportError

If the flavor can't be imported.

Returns:

Type Description
Flavor

The loaded flavor.
Source code in src/zenml/stack/flavor.py 
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@classmethod
def from_model(cls, flavor_model: FlavorResponse) -> "Flavor":
    """Loads a flavor from a model.

    Args:
        flavor_model: The model to load from.

    Raises:
        CustomFlavorImportError: If the custom flavor can't be imported.
        ImportError: If the flavor can't be imported.

    Returns:
        The loaded flavor.
    """
    try:
        flavor = source_utils.load(flavor_model.source)()
    except (ModuleNotFoundError, ImportError, NotImplementedError) as err:
        if flavor_model.is_custom:
            flavor_module, _ = flavor_model.source.rsplit(".", maxsplit=1)
            expected_file_path = os.path.join(
                source_utils.get_source_root(),
                flavor_module.replace(".", os.path.sep),
            )
            raise CustomFlavorImportError(
                f"Couldn't import custom flavor {flavor_model.name}: "
                f"{err}. Make sure the custom flavor class "
                f"`{flavor_model.source}` is importable. If it is part of "
                "a library, make sure it is installed. If "
                "it is a local code file, make sure it exists at "
                f"`{expected_file_path}.py`."
            )
        else:
            raise ImportError(
                f"Couldn't import flavor {flavor_model.name}: {err}"
            )
    return cast(Flavor, flavor)
generate_default_docs_url() -> str

Generate the doc urls for all inbuilt and integration flavors.

Note that this method is not going to be useful for custom flavors, which do not have any docs in the main zenml docs.

Returns:

Type Description
str

The complete url to the zenml documentation
Source code in src/zenml/stack/flavor.py 
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def generate_default_docs_url(self) -> str:
    """Generate the doc urls for all inbuilt and integration flavors.

    Note that this method is not going to be useful for custom flavors,
    which do not have any docs in the main zenml docs.

    Returns:
        The complete url to the zenml documentation
    """
    from zenml import __version__

    component_type = self.type.plural.replace("_", "-")
    name = self.name.replace("_", "-")

    try:
        is_latest = is_latest_zenml_version()
    except RuntimeError:
        # We assume in error cases that we are on the latest version
        is_latest = True

    if is_latest:
        base = "https://docs.zenml.io"
    else:
        base = f"https://zenml-io.gitbook.io/zenml-legacy-documentation/v/{__version__}"
    return f"{base}/stack-components/{component_type}/{name}"
generate_default_sdk_docs_url() -> str

Generate SDK docs url for a flavor.

Returns:

Type Description
str

The complete url to the zenml SDK docs
Source code in src/zenml/stack/flavor.py 
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def generate_default_sdk_docs_url(self) -> str:
    """Generate SDK docs url for a flavor.

    Returns:
        The complete url to the zenml SDK docs
    """
    from zenml import __version__

    base = f"https://sdkdocs.zenml.io/{__version__}"

    component_type = self.type.plural

    if "zenml.integrations" in self.__module__:
        # Get integration name out of module path which will look something
        #  like this "zenml.integrations.<integration>....
        integration = self.__module__.split(
            "zenml.integrations.", maxsplit=1
        )[1].split(".")[0]

        return (
            f"{base}/integration_code_docs"
            f"/integrations-{integration}/#{self.__module__}"
        )

    else:
        return (
            f"{base}/core_code_docs/core-{component_type}/"
            f"#{self.__module__}"
        )
to_model(integration: Optional[str] = None, is_custom: bool = True) -> FlavorRequest

Converts a flavor to a model.

Parameters:

Name Type Description Default
integration Optional[str]

The integration to use for the model.

 None
is_custom bool

Whether the flavor is a custom flavor.

 True

Returns:

Type Description
FlavorRequest

The model.
Source code in src/zenml/stack/flavor.py 
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def to_model(
    self,
    integration: Optional[str] = None,
    is_custom: bool = True,
) -> FlavorRequest:
    """Converts a flavor to a model.

    Args:
        integration: The integration to use for the model.
        is_custom: Whether the flavor is a custom flavor.

    Returns:
        The model.
    """
    connector_requirements = self.service_connector_requirements
    connector_type = (
        connector_requirements.connector_type
        if connector_requirements
        else None
    )
    resource_type = (
        connector_requirements.resource_type
        if connector_requirements
        else None
    )
    resource_id_attr = (
        connector_requirements.resource_id_attr
        if connector_requirements
        else None
    )

    model = FlavorRequest(
        name=self.name,
        type=self.type,
        source=source_utils.resolve(self.__class__).import_path,
        config_schema=self.config_schema,
        connector_type=connector_type,
        connector_resource_type=resource_type,
        connector_resource_id_attr=resource_id_attr,
        integration=integration,
        logo_url=self.logo_url,
        docs_url=self.docs_url,
        sdk_docs_url=self.sdk_docs_url,
        is_custom=is_custom,
    )
    return model

Integration

Base class for integration in ZenML.

Functions
activate() -> None classmethod

Abstract method to activate the integration.

Source code in src/zenml/integrations/integration.py 
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@classmethod
def activate(cls) -> None:
    """Abstract method to activate the integration."""
check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

Type Description
bool

True if all required packages are installed, False otherwise.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def check_installation(cls) -> bool:
    """Method to check whether the required packages are installed.

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for r in cls.get_requirements():
        try:
            # First check if the base package is installed
            dist = pkg_resources.get_distribution(r)

            # Next, check if the dependencies (including extras) are
            # installed
            deps: List[Requirement] = []

            _, extras = parse_requirement(r)
            if extras:
                extra_list = extras[1:-1].split(",")
                for extra in extra_list:
                    try:
                        requirements = dist.requires(extras=[extra])  # type: ignore[arg-type]
                    except pkg_resources.UnknownExtra as e:
                        logger.debug(f"Unknown extra: {str(e)}")
                        return False
                    deps.extend(requirements)
            else:
                deps = dist.requires()

            for ri in deps:
                try:
                    # Remove the "extra == ..." part from the requirement string
                    cleaned_req = re.sub(
                        r"; extra == \"\w+\"", "", str(ri)
                    )
                    pkg_resources.get_distribution(cleaned_req)
                except pkg_resources.DistributionNotFound as e:
                    logger.debug(
                        f"Unable to find required dependency "
                        f"'{e.req}' for requirement '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False
                except pkg_resources.VersionConflict as e:
                    logger.debug(
                        f"Package version '{e.dist}' does not match "
                        f"version '{e.req}' required by '{r}' "
                        f"necessary for integration '{cls.NAME}'."
                    )
                    return False

        except pkg_resources.DistributionNotFound as e:
            logger.debug(
                f"Unable to find required package '{e.req}' for "
                f"integration {cls.NAME}."
            )
            return False
        except pkg_resources.VersionConflict as e:
            logger.debug(
                f"Package version '{e.dist}' does not match version "
                f"'{e.req}' necessary for integration {cls.NAME}."
            )
            return False

    logger.debug(
        f"Integration {cls.NAME} is installed correctly with "
        f"requirements {cls.get_requirements()}."
    )
    return True
flavors() -> List[Type[Flavor]] classmethod

Abstract method to declare new stack component flavors.

Returns:

Type Description
List[Type[Flavor]]

A list of new stack component flavors.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Abstract method to declare new stack component flavors.

    Returns:
        A list of new stack component flavors.
    """
    return []
get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] 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
python_version Optional[str]

The Python version to use for the requirements.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def get_requirements(
    cls,
    target_os: Optional[str] = None,
    python_version: 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.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    return cls.REQUIREMENTS
get_uninstall_requirements(target_os: Optional[str] = None) -> List[str] classmethod

Method to get the uninstall 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 src/zenml/integrations/integration.py 
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@classmethod
def get_uninstall_requirements(
    cls, target_os: Optional[str] = None
) -> List[str]:
    """Method to get the uninstall requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.

    Returns:
        A list of requirements.
    """
    ret = []
    for each in cls.get_requirements(target_os=target_os):
        is_ignored = False
        for ignored in cls.REQUIREMENTS_IGNORED_ON_UNINSTALL:
            if each.startswith(ignored):
                is_ignored = True
                break
        if not is_ignored:
            ret.append(each)
    return ret
plugin_flavors() -> List[Type[BasePluginFlavor]] classmethod

Abstract method to declare new plugin flavors.

Returns:

Type Description
List[Type[BasePluginFlavor]]

A list of new plugin flavors.
Source code in src/zenml/integrations/integration.py 
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@classmethod
def plugin_flavors(cls) -> List[Type["BasePluginFlavor"]]:
    """Abstract method to declare new plugin flavors.

    Returns:
        A list of new plugin flavors.
    """
    return []

WhylogsIntegration

Bases: Integration

Definition of whylogs integration for ZenML.

Functions
activate() -> None classmethod

Activates the integration.

Source code in src/zenml/integrations/whylogs/__init__.py 
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@classmethod
def activate(cls) -> None:
    """Activates the integration."""
    from zenml.integrations.whylogs import materializers  # noqa
    from zenml.integrations.whylogs import secret_schemas  # noqa
flavors() -> List[Type[Flavor]] classmethod

Declare the stack component flavors for the Great Expectations integration.

Returns:

Type Description
List[Type[Flavor]]

List of stack component flavors for this integration.
Source code in src/zenml/integrations/whylogs/__init__.py 
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@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Declare the stack component flavors for the Great Expectations integration.

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

    return [WhylogsDataValidatorFlavor]
get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] 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
python_version Optional[str]

The Python version to use for the requirements.

 None

Returns:

Type Description
List[str]

A list of requirements.
Source code in src/zenml/integrations/whylogs/__init__.py 
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@classmethod
def get_requirements(cls, target_os: Optional[str] = None, python_version: 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.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    from zenml.integrations.pandas import PandasIntegration

    return cls.REQUIREMENTS + \
        PandasIntegration.get_requirements(target_os=target_os, python_version=python_version)

Modules

constants

Whylogs integration constants.

data_validators

Initialization of the whylogs data validator for ZenML.

Classes
WhylogsDataValidator(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], created: datetime, updated: datetime, labels: Optional[Dict[str, Any]] = None, connector_requirements: Optional[ServiceConnectorRequirements] = None, connector: Optional[UUID] = None, connector_resource_id: Optional[str] = None, *args: Any, **kwargs: Any)

Bases: BaseDataValidator, AuthenticationMixin

Whylogs data validator stack component.

Attributes:

Name Type Description
authentication_secret

Optional ZenML secret with Whylabs credentials. If configured, all the data profiles returned by all pipeline steps will automatically be uploaded to Whylabs in addition to being stored in the ZenML Artifact Store.
Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

    Raises:
        ValueError: If a secret reference is passed as name.
    """
    if secret_utils.is_secret_reference(name):
        raise ValueError(
            "Passing the `name` attribute of a stack component as a "
            "secret reference is not allowed."
        )

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None
Attributes
config: WhylogsDataValidatorConfig property

Returns the WhylogsDataValidatorConfig config.

Returns:

Type Description
WhylogsDataValidatorConfig

The configuration.
settings_class: Optional[Type[BaseSettings]] property

Settings class for the Whylogs data validator.

Returns:

Type Description
Optional[Type[BaseSettings]]

The settings class.
Functions
data_profiling(dataset: pd.DataFrame, comparison_dataset: Optional[pd.DataFrame] = None, profile_list: Optional[Sequence[str]] = None, dataset_timestamp: Optional[datetime.datetime] = None, **kwargs: Any) -> DatasetProfileView

Analyze a dataset and generate a data profile with whylogs.

Parameters:

Name Type Description Default
dataset DataFrame

Target dataset to be profiled.

 required
comparison_dataset Optional[DataFrame]

Optional dataset to be used for data profiles that require a baseline for comparison (e.g data drift profiles).

 None
profile_list Optional[Sequence[str]]

Optional list identifying the categories of whylogs data profiles to be generated (unused).

 None
dataset_timestamp Optional[datetime]

timestamp to associate with the generated dataset profile (Optional). The current time is used if not supplied.

 None
**kwargs Any

Extra keyword arguments (unused).

 {}

Returns:

Type Description
DatasetProfileView

A whylogs profile view object.
Source code in src/zenml/integrations/whylogs/data_validators/whylogs_data_validator.py 
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def data_profiling(
    self,
    dataset: pd.DataFrame,
    comparison_dataset: Optional[pd.DataFrame] = None,
    profile_list: Optional[Sequence[str]] = None,
    dataset_timestamp: Optional[datetime.datetime] = None,
    **kwargs: Any,
) -> DatasetProfileView:
    """Analyze a dataset and generate a data profile with whylogs.

    Args:
        dataset: Target dataset to be profiled.
        comparison_dataset: Optional dataset to be used for data profiles
            that require a baseline for comparison (e.g data drift profiles).
        profile_list: Optional list identifying the categories of whylogs
            data profiles to be generated (unused).
        dataset_timestamp: timestamp to associate with the generated
            dataset profile (Optional). The current time is used if not
            supplied.
        **kwargs: Extra keyword arguments (unused).

    Returns:
        A whylogs profile view object.
    """
    results = why.log(pandas=dataset)
    profile = results.profile()
    dataset_timestamp = dataset_timestamp or utc_now()
    profile.set_dataset_timestamp(dataset_timestamp=dataset_timestamp)
    return profile.view()
upload_profile_view(profile_view: DatasetProfileView, dataset_id: Optional[str] = None) -> None

Upload a whylogs data profile view to Whylabs, if configured to do so.

Parameters:

Name Type Description Default
profile_view DatasetProfileView

Whylogs profile view to upload.

 required
dataset_id Optional[str]

Optional dataset identifier to use for the uploaded data profile. If omitted, a dataset identifier will be retrieved using other means, in order: * the default dataset identifier configured in the Data Validator secret * a dataset ID will be generated automatically based on the current pipeline/step information.

 None

Raises:

Type Description
ValueError

If the dataset ID was not provided and could not be retrieved or inferred from other sources.
Source code in src/zenml/integrations/whylogs/data_validators/whylogs_data_validator.py 
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def upload_profile_view(
    self,
    profile_view: DatasetProfileView,
    dataset_id: Optional[str] = None,
) -> None:
    """Upload a whylogs data profile view to Whylabs, if configured to do so.

    Args:
        profile_view: Whylogs profile view to upload.
        dataset_id: Optional dataset identifier to use for the uploaded
            data profile. If omitted, a dataset identifier will be retrieved
            using other means, in order:
                * the default dataset identifier configured in the Data
                Validator secret
                * a dataset ID will be generated automatically based on the
                current pipeline/step information.

    Raises:
        ValueError: If the dataset ID was not provided and could not be
            retrieved or inferred from other sources.
    """
    secret = self.get_typed_authentication_secret(
        expected_schema_type=WhylabsSecretSchema
    )
    if not secret:
        return

    dataset_id = dataset_id or secret.whylabs_default_dataset_id

    if not dataset_id:
        # use the current pipeline name and the step name to generate a
        # unique dataset 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
            dataset_id = f"{pipeline_name}_{step_name}"
        except RuntimeError:
            raise ValueError(
                "A dataset ID was not specified and could not be "
                "generated from the current pipeline and step name."
            )

    # Instantiate WhyLabs Writer
    writer = WhyLabsWriter(
        org_id=secret.whylabs_default_org_id,
        api_key=secret.whylabs_api_key,
        dataset_id=dataset_id,
    )

    # pass a profile view to the writer's write method
    writer.write(profile=profile_view)

    logger.info(
        f"Uploaded data profile for dataset {dataset_id} to Whylabs."
    )
Modules
whylogs_data_validator

Implementation of the whylogs data validator.

Classes
WhylogsDataValidator(name: str, id: UUID, config: StackComponentConfig, flavor: str, type: StackComponentType, user: Optional[UUID], created: datetime, updated: datetime, labels: Optional[Dict[str, Any]] = None, connector_requirements: Optional[ServiceConnectorRequirements] = None, connector: Optional[UUID] = None, connector_resource_id: Optional[str] = None, *args: Any, **kwargs: Any)

Bases: BaseDataValidator, AuthenticationMixin

Whylogs data validator stack component.

Attributes:

Name Type Description
authentication_secret

Optional ZenML secret with Whylabs credentials. If configured, all the data profiles returned by all pipeline steps will automatically be uploaded to Whylabs in addition to being stored in the ZenML Artifact Store.
Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self,
    name: str,
    id: UUID,
    config: StackComponentConfig,
    flavor: str,
    type: StackComponentType,
    user: Optional[UUID],
    created: datetime,
    updated: datetime,
    labels: Optional[Dict[str, Any]] = None,
    connector_requirements: Optional[ServiceConnectorRequirements] = None,
    connector: Optional[UUID] = None,
    connector_resource_id: Optional[str] = None,
    *args: Any,
    **kwargs: Any,
):
    """Initializes a StackComponent.

    Args:
        name: The name of the component.
        id: The unique ID of the component.
        config: The config of the component.
        flavor: The flavor of the component.
        type: The type of the component.
        user: The ID of the user who created the component.
        created: The creation time of the component.
        updated: The last update time of the component.
        labels: The labels of the component.
        connector_requirements: The requirements for the connector.
        connector: The ID of a connector linked to the component.
        connector_resource_id: The custom resource ID to access through
            the connector.
        *args: Additional positional arguments.
        **kwargs: Additional keyword arguments.

    Raises:
        ValueError: If a secret reference is passed as name.
    """
    if secret_utils.is_secret_reference(name):
        raise ValueError(
            "Passing the `name` attribute of a stack component as a "
            "secret reference is not allowed."
        )

    self.id = id
    self.name = name
    self._config = config
    self.flavor = flavor
    self.type = type
    self.user = user
    self.created = created
    self.updated = updated
    self.labels = labels
    self.connector_requirements = connector_requirements
    self.connector = connector
    self.connector_resource_id = connector_resource_id
    self._connector_instance: Optional[ServiceConnector] = None
Attributes
config: WhylogsDataValidatorConfig property

Returns the WhylogsDataValidatorConfig config.

Returns:

Type Description
WhylogsDataValidatorConfig

The configuration.
settings_class: Optional[Type[BaseSettings]] property

Settings class for the Whylogs data validator.

Returns:

Type Description
Optional[Type[BaseSettings]]

The settings class.
Functions
data_profiling(dataset: pd.DataFrame, comparison_dataset: Optional[pd.DataFrame] = None, profile_list: Optional[Sequence[str]] = None, dataset_timestamp: Optional[datetime.datetime] = None, **kwargs: Any) -> DatasetProfileView

Analyze a dataset and generate a data profile with whylogs.

Parameters:

Name Type Description Default
dataset DataFrame

Target dataset to be profiled.

 required
comparison_dataset Optional[DataFrame]

Optional dataset to be used for data profiles that require a baseline for comparison (e.g data drift profiles).

 None
profile_list Optional[Sequence[str]]

Optional list identifying the categories of whylogs data profiles to be generated (unused).

 None
dataset_timestamp Optional[datetime]

timestamp to associate with the generated dataset profile (Optional). The current time is used if not supplied.

 None
**kwargs Any

Extra keyword arguments (unused).

 {}

Returns:

Type Description
DatasetProfileView

A whylogs profile view object.
Source code in src/zenml/integrations/whylogs/data_validators/whylogs_data_validator.py 
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def data_profiling(
    self,
    dataset: pd.DataFrame,
    comparison_dataset: Optional[pd.DataFrame] = None,
    profile_list: Optional[Sequence[str]] = None,
    dataset_timestamp: Optional[datetime.datetime] = None,
    **kwargs: Any,
) -> DatasetProfileView:
    """Analyze a dataset and generate a data profile with whylogs.

    Args:
        dataset: Target dataset to be profiled.
        comparison_dataset: Optional dataset to be used for data profiles
            that require a baseline for comparison (e.g data drift profiles).
        profile_list: Optional list identifying the categories of whylogs
            data profiles to be generated (unused).
        dataset_timestamp: timestamp to associate with the generated
            dataset profile (Optional). The current time is used if not
            supplied.
        **kwargs: Extra keyword arguments (unused).

    Returns:
        A whylogs profile view object.
    """
    results = why.log(pandas=dataset)
    profile = results.profile()
    dataset_timestamp = dataset_timestamp or utc_now()
    profile.set_dataset_timestamp(dataset_timestamp=dataset_timestamp)
    return profile.view()
upload_profile_view(profile_view: DatasetProfileView, dataset_id: Optional[str] = None) -> None

Upload a whylogs data profile view to Whylabs, if configured to do so.

Parameters:

Name Type Description Default
profile_view DatasetProfileView

Whylogs profile view to upload.

 required
dataset_id Optional[str]

Optional dataset identifier to use for the uploaded data profile. If omitted, a dataset identifier will be retrieved using other means, in order: * the default dataset identifier configured in the Data Validator secret * a dataset ID will be generated automatically based on the current pipeline/step information.

 None

Raises:

Type Description
ValueError

If the dataset ID was not provided and could not be retrieved or inferred from other sources.
Source code in src/zenml/integrations/whylogs/data_validators/whylogs_data_validator.py 
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def upload_profile_view(
    self,
    profile_view: DatasetProfileView,
    dataset_id: Optional[str] = None,
) -> None:
    """Upload a whylogs data profile view to Whylabs, if configured to do so.

    Args:
        profile_view: Whylogs profile view to upload.
        dataset_id: Optional dataset identifier to use for the uploaded
            data profile. If omitted, a dataset identifier will be retrieved
            using other means, in order:
                * the default dataset identifier configured in the Data
                Validator secret
                * a dataset ID will be generated automatically based on the
                current pipeline/step information.

    Raises:
        ValueError: If the dataset ID was not provided and could not be
            retrieved or inferred from other sources.
    """
    secret = self.get_typed_authentication_secret(
        expected_schema_type=WhylabsSecretSchema
    )
    if not secret:
        return

    dataset_id = dataset_id or secret.whylabs_default_dataset_id

    if not dataset_id:
        # use the current pipeline name and the step name to generate a
        # unique dataset 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
            dataset_id = f"{pipeline_name}_{step_name}"
        except RuntimeError:
            raise ValueError(
                "A dataset ID was not specified and could not be "
                "generated from the current pipeline and step name."
            )

    # Instantiate WhyLabs Writer
    writer = WhyLabsWriter(
        org_id=secret.whylabs_default_org_id,
        api_key=secret.whylabs_api_key,
        dataset_id=dataset_id,
    )

    # pass a profile view to the writer's write method
    writer.write(profile=profile_view)

    logger.info(
        f"Uploaded data profile for dataset {dataset_id} to Whylabs."
    )
Functions

flavors

WhyLabs whylogs integration flavors.

Classes
WhylogsDataValidatorConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseDataValidatorConfig, AuthenticationConfigMixin, WhylogsDataValidatorSettings

Config for the whylogs data validator.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/getting-started/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)
WhylogsDataValidatorFlavor

Bases: BaseDataValidatorFlavor

Whylogs data validator flavor.

Attributes
config_class: Type[WhylogsDataValidatorConfig] property

Returns WhylogsDataValidatorConfig config class.

Returns:

Type Description
Type[WhylogsDataValidatorConfig]

The config class.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[WhylogsDataValidator] property

Implementation class for this flavor.

Returns:

Type Description
Type[WhylogsDataValidator]

The implementation class.
logo_url: str property

A url to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
Modules
whylogs_data_validator_flavor

WhyLabs whylogs data validator flavor.

Classes
WhylogsDataValidatorConfig(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseDataValidatorConfig, AuthenticationConfigMixin, WhylogsDataValidatorSettings

Config for the whylogs data validator.

Source code in src/zenml/stack/stack_component.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references don't clash with pydantic validation.

    StackComponents allow the specification of all their string attributes
    using secret references of the form `{{secret_name.key}}`. This however
    is only possible when the stack component does not perform any explicit
    validation of this attribute using pydantic validators. If this were
    the case, the validation would run on the secret reference and would
    fail or in the worst case, modify the secret reference and lead to
    unexpected behavior. This method ensures that no attributes that require
    custom pydantic validation are set as secret references.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using
            plain-text secrets.
        **kwargs: Arguments to initialize this stack component.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            is passed as a secret reference, or if the `name` attribute
            was passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}` for a `{self.__class__.__name__}` "
                    "stack component. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure your stack "
                    "components with secrets here: "
                    "https://docs.zenml.io/getting-started/deploying-zenml/secret-management"
                )
            continue

        if pydantic_utils.has_validators(
            pydantic_class=self.__class__, field_name=key
        ):
            raise ValueError(
                f"Passing the stack component attribute `{key}` as a "
                "secret reference is not allowed as additional validation "
                "is required for this attribute."
            )

    super().__init__(**kwargs)
WhylogsDataValidatorFlavor

Bases: BaseDataValidatorFlavor

Whylogs data validator flavor.

Attributes
config_class: Type[WhylogsDataValidatorConfig] property

Returns WhylogsDataValidatorConfig config class.

Returns:

Type Description
Type[WhylogsDataValidatorConfig]

The config class.
docs_url: Optional[str] property

A url to point at docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor docs url.
implementation_class: Type[WhylogsDataValidator] property

Implementation class for this flavor.

Returns:

Type Description
Type[WhylogsDataValidator]

The implementation class.
logo_url: str property

A url to represent the flavor in the dashboard.

Returns:

Type Description
str

The flavor logo.
name: str property

Name of the flavor.

Returns:

Type Description
str

The name of the flavor.
sdk_docs_url: Optional[str] property

A url to point at SDK docs explaining this flavor.

Returns:

Type Description
Optional[str]

A flavor SDK docs url.
WhylogsDataValidatorSettings(warn_about_plain_text_secrets: bool = False, **kwargs: Any)

Bases: BaseSettings

Settings for the Whylogs data validator.

Attributes:

Name Type Description
enable_whylabs bool

If set to True for a step, all the whylogs data profile views returned by the step will automatically be uploaded to the Whylabs platform if Whylabs credentials are configured.
dataset_id Optional[str]

Dataset ID to use when uploading profiles to Whylabs.
Source code in src/zenml/config/secret_reference_mixin.py 
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def __init__(
    self, warn_about_plain_text_secrets: bool = False, **kwargs: Any
) -> None:
    """Ensures that secret references are only passed for valid fields.

    This method ensures that secret references are not passed for fields
    that explicitly prevent them or require pydantic validation.

    Args:
        warn_about_plain_text_secrets: If true, then warns about using plain-text secrets.
        **kwargs: Arguments to initialize this object.

    Raises:
        ValueError: If an attribute that requires custom pydantic validation
            or an attribute which explicitly disallows secret references
            is passed as a secret reference.
    """
    for key, value in kwargs.items():
        try:
            field = self.__class__.model_fields[key]
        except KeyError:
            # Value for a private attribute or non-existing field, this
            # will fail during the upcoming pydantic validation
            continue

        if value is None:
            continue

        if not secret_utils.is_secret_reference(value):
            if (
                secret_utils.is_secret_field(field)
                and warn_about_plain_text_secrets
            ):
                logger.warning(
                    "You specified a plain-text value for the sensitive "
                    f"attribute `{key}`. This is currently only a warning, "
                    "but future versions of ZenML will require you to pass "
                    "in sensitive information as secrets. Check out the "
                    "documentation on how to configure values with secrets "
                    "here: https://docs.zenml.io/getting-started/deploying-zenml/secret-management"
                )
            continue

        if secret_utils.is_clear_text_field(field):
            raise ValueError(
                f"Passing the `{key}` attribute as a secret reference is "
                "not allowed."
            )

        requires_validation = has_validators(
            pydantic_class=self.__class__, field_name=key
        )
        if requires_validation:
            raise ValueError(
                f"Passing the attribute `{key}` as a secret reference is "
                "not allowed as additional validation is required for "
                "this attribute."
            )

    super().__init__(**kwargs)

materializers

Initialization of the whylogs materializer.

Classes
Modules
whylogs_materializer

Implementation of the whylogs materializer.

Classes
WhylogsMaterializer(uri: str, artifact_store: Optional[BaseArtifactStore] = None)

Bases: BaseMaterializer

Materializer to read/write whylogs dataset profile views.

Source code in src/zenml/materializers/base_materializer.py 
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def __init__(
    self, uri: str, artifact_store: Optional[BaseArtifactStore] = None
):
    """Initializes a materializer with the given URI.

    Args:
        uri: The URI where the artifact data will be stored.
        artifact_store: The artifact store used to store this artifact.
    """
    self.uri = uri
    self._artifact_store = artifact_store
Functions
load(data_type: Type[Any]) -> DatasetProfileView

Reads and returns a whylogs dataset profile view.

Parameters:

Name Type Description Default
data_type Type[Any]

The type of the data to read.

 required

Returns:

Type Description
DatasetProfileView

A loaded whylogs dataset profile view object.
Source code in src/zenml/integrations/whylogs/materializers/whylogs_materializer.py 
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def load(self, data_type: Type[Any]) -> DatasetProfileView:
    """Reads and returns a whylogs dataset profile view.

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

    Returns:
        A loaded whylogs dataset profile view object.
    """
    filepath = os.path.join(self.uri, PROFILE_FILENAME)

    with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
        temp_file = os.path.join(str(temp_dir), PROFILE_FILENAME)

        # Copy from artifact store to temporary file
        fileio.copy(filepath, temp_file)
        profile_view = DatasetProfileView.read(temp_file)

        return profile_view
save(profile_view: DatasetProfileView) -> None

Writes a whylogs dataset profile view.

Parameters:

Name Type Description Default
profile_view DatasetProfileView

A whylogs dataset profile view object.

 required
Source code in src/zenml/integrations/whylogs/materializers/whylogs_materializer.py 
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def save(self, profile_view: DatasetProfileView) -> None:
    """Writes a whylogs dataset profile view.

    Args:
        profile_view: A whylogs dataset profile view object.
    """
    filepath = os.path.join(self.uri, PROFILE_FILENAME)

    with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
        temp_file = os.path.join(str(temp_dir), PROFILE_FILENAME)

        profile_view.write(temp_file)

        # Copy it into artifact store
        fileio.copy(temp_file, filepath)

    try:
        self._upload_to_whylabs(profile_view)
    except Exception as e:
        logger.error(
            "Failed to upload whylogs profile view to Whylabs: %s", e
        )
save_visualizations(profile_view: DatasetProfileView) -> Dict[str, VisualizationType]

Saves visualizations for the given whylogs dataset profile view.

Parameters:

Name Type Description Default
profile_view DatasetProfileView

The whylogs dataset profile view to visualize.

 required

Returns:

Type Description
Dict[str, VisualizationType]

A dictionary of visualization URIs and their types.
Source code in src/zenml/integrations/whylogs/materializers/whylogs_materializer.py 
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def save_visualizations(
    self,
    profile_view: DatasetProfileView,
) -> Dict[str, VisualizationType]:
    """Saves visualizations for the given whylogs dataset profile view.

    Args:
        profile_view: The whylogs dataset profile view to visualize.

    Returns:
        A dictionary of visualization URIs and their types.
    """
    # currently, whylogs doesn't support visualizing a single profile, so
    # we trick it by using the same profile twice, both as reference and
    # target, in a drift report
    visualization = NotebookProfileVisualizer()
    visualization.set_profiles(
        target_profile_view=profile_view,
        reference_profile_view=profile_view,
    )
    rendered_html = visualization.summary_drift_report()
    filepath = os.path.join(self.uri, HTML_FILENAME)
    filepath = filepath.replace("\\", "/")
    with fileio.open(filepath, "w") as f:
        f.write(rendered_html.data)
    return {filepath: VisualizationType.HTML}
Functions Modules

secret_schemas

Initialization for the Whylabs secret schema.

This schema can be used to configure a ZenML secret to authenticate ZenML to use the Whylabs platform to automatically log all whylogs data profiles generated and by pipeline steps.

Classes
WhylabsSecretSchema

Bases: BaseSecretSchema

Whylabs credentials.

Attributes:

Name Type Description
whylabs_default_org_id str

the Whylabs organization ID.
whylabs_api_key str

Whylabs API key.
whylabs_default_dataset_id Optional[str]

default Whylabs dataset ID to use when logging data profiles.
Modules
whylabs_secret_schema

Implementation for Seldon secret schemas.

Classes
WhylabsSecretSchema

Bases: BaseSecretSchema

Whylabs credentials.

Attributes:

Name Type Description
whylabs_default_org_id str

the Whylabs organization ID.
whylabs_api_key str

Whylabs API key.
whylabs_default_dataset_id Optional[str]

default Whylabs dataset ID to use when logging data profiles.

steps

Initialization of the whylogs steps.

Functions
Modules
whylogs_profiler

Implementation of the whylogs profiler step.

Classes Functions
get_whylogs_profiler_step(dataset_timestamp: Optional[datetime.datetime] = None, dataset_id: Optional[str] = None, enable_whylabs: bool = True) -> BaseStep

Shortcut function to create a new instance of the WhylogsProfilerStep step.

The returned WhylogsProfilerStep can be used in a pipeline to generate a whylogs DatasetProfileView from a given pd.DataFrame and save it as an artifact.

Parameters:

Name Type Description Default
dataset_timestamp Optional[datetime]

The timestamp of the dataset.

 None
dataset_id Optional[str]

Optional dataset ID to use to upload the profile to Whylabs.

 None
enable_whylabs bool

Whether to upload the generated profile to Whylabs.

 True

Returns:

Type Description
BaseStep

a WhylogsProfilerStep step instance
Source code in src/zenml/integrations/whylogs/steps/whylogs_profiler.py 
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def get_whylogs_profiler_step(
    dataset_timestamp: Optional[datetime.datetime] = None,
    dataset_id: Optional[str] = None,
    enable_whylabs: bool = True,
) -> BaseStep:
    """Shortcut function to create a new instance of the WhylogsProfilerStep step.

    The returned WhylogsProfilerStep can be used in a pipeline to generate a
    whylogs DatasetProfileView from a given pd.DataFrame and save it as an
    artifact.

    Args:
        dataset_timestamp: The timestamp of the dataset.
        dataset_id: Optional dataset ID to use to upload the profile to Whylabs.
        enable_whylabs: Whether to upload the generated profile to Whylabs.

    Returns:
        a WhylogsProfilerStep step instance
    """
    key = settings_utils.get_flavor_setting_key(WhylogsDataValidatorFlavor())
    settings = WhylogsDataValidatorSettings(
        enable_whylabs=enable_whylabs, dataset_id=dataset_id
    )
    step_instance = whylogs_profiler_step.with_options(
        parameters={"dataset_timestamp": dataset_timestamp},
        settings={key: settings},
    )
    return step_instance
whylogs_profiler_step(dataset: pd.DataFrame, dataset_timestamp: Optional[datetime.datetime] = None) -> DatasetProfileView

Generate a whylogs DatasetProfileView from a given pd.DataFrame.

Parameters:

Name Type Description Default
dataset DataFrame

The dataset to generate the profile for.

 required
dataset_timestamp Optional[datetime]

The timestamp of the dataset.

 None

Returns:

Type Description
DatasetProfileView

whylogs profile with statistics generated for the input dataset.
Source code in src/zenml/integrations/whylogs/steps/whylogs_profiler.py 
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@step
def whylogs_profiler_step(
    dataset: pd.DataFrame,
    dataset_timestamp: Optional[datetime.datetime] = None,
) -> DatasetProfileView:
    """Generate a whylogs `DatasetProfileView` from a given `pd.DataFrame`.

    Args:
        dataset: The dataset to generate the profile for.
        dataset_timestamp: The timestamp of the dataset.

    Returns:
        whylogs profile with statistics generated for the input dataset.
    """
    data_validator = cast(
        WhylogsDataValidator,
        WhylogsDataValidator.get_active_data_validator(),
    )
    return data_validator.data_profiling(
        dataset, dataset_timestamp=dataset_timestamp
    )
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