ator(dataset=df_train)

data_validation_pipeline()As can be seen from the step definition , the step takes in a dataset and it returns a Deepchecks SuiteResult object that contains the test results:

@step

def deepchecks_data_integrity_check_step(

dataset: pd.DataFrame,

check_list: Optional[Sequence[DeepchecksDataIntegrityCheck]] = None,

dataset_kwargs: Optional[Dict[str, Any]] = None,

check_kwargs: Optional[Dict[str, Any]] = None,

run_kwargs: Optional[Dict[str, Any]] = None,

) -> SuiteResult:

...

If needed, you can specify a custom list of data integrity Deepchecks tests to be executed by supplying a check_list argument:

from zenml.integrations.deepchecks.validation_checks import DeepchecksDataIntegrityCheck

from zenml.integrations.deepchecks.steps import deepchecks_data_integrity_check_step

@pipeline

def validation_pipeline():

deepchecks_data_integrity_check_step(

check_list=[

DeepchecksDataIntegrityCheck.TABULAR_MIXED_DATA_TYPES,

DeepchecksDataIntegrityCheck.TABULAR_DATA_DUPLICATES,

DeepchecksDataIntegrityCheck.TABULAR_CONFLICTING_LABELS,

],

dataset=...

You should consult the official Deepchecks documentation for more information on what each test is useful for.

For more customization, the data integrity step also allows for additional keyword arguments to be supplied to be passed transparently to the Deepchecks library:

dataset_kwargs: Additional keyword arguments to be passed to the Deepchecks tabular.Dataset or vision.VisionData constructor. This is used to pass additional information about how the data is structured, e.g.:Copydeepchecks_data_integrity_check_step(
    dataset_kwargs=dict(label='class', cat_features=['country', 'state']),
    ...
)