Deepchecks

How to test the data and models used in your pipelines with Deepchecks test suites

This is an older version of the ZenML documentation. To read and view the latest version please visit this up-to-date URL.

The Deepchecks Data Validator flavor provided with the ZenML integration uses Deepchecks to run data integrity, data drift, model drift and model performance tests on the datasets and models circulated in your ZenML pipelines. The test results can be used to implement automated corrective actions in your pipelines or to render interactive representations for further visual interpretation, evaluation and documentation.

When would you want to use it?

Deepchecks is an open-source library that you can use to run a variety of data and model validation tests, from data integrity tests that work with a single dataset to model evaluation tests to data drift analyses and model performance comparison tests. All this can be done with minimal configuration input from the user, or customized with specialized conditions that the validation tests should perform.

Deepchecks works with both tabular data and computer vision data (currently in beta). For tabular, the supported dataset format is pandas.DataFrame and the supported model format is sklearn.base.ClassifierMixin. For computer vision, the supported dataset format is torch.utils.data.dataloader.DataLoader and supported model format is torch.nn.Module.

You should use the Deepchecks Data Validator when you need the following data and/or model validation features that are possible with Deepchecks:

Data Integrity Checks for tabular or computer vision data: detect data integrity problems within a single dataset (e.g. missing values, conflicting labels, mixed data types etc.).
Data Drift Checks for tabular or computer vision data: detect data skew and data drift problems by comparing a target dataset against a reference dataset (e.g. feature drift, label drift, new labels etc.).
Model Performance Checks for tabular or computer vision data: evaluate a model and detect problems with its performance (e.g. confusion matrix, boosting overfit, model error analysis)

You should consider one of the other Data Validator flavors if you need a different set of data validation features.

How do you deploy it?

The Deepchecks Data Validator flavor is included in the Deepchecks ZenML integration, you need to install it on your local machine to be able to register an Deepchecks Data Validator and add it to your stack:

zenml integration install deepchecks -y

The Data Validator stack component does not have any configuration parameters. Adding it to a stack is as simple as running e.g.:

# Register the Deepchecks data validator
zenml data-validator register deepchecks_data_validator --flavor=deepchecks

# Register and set a stack with the new data validator
zenml stack register custom_stack -dv deepchecks_data_validator ... --set

How do you use it?

The ZenML integration restructures the way Deepchecks validation checks are organized in four categories, based on the type and number of input parameters that they expect as input. This makes it easier to reason about them when you decide which tests to use in your pipeline steps:

data integrity checks expect a single dataset as input. These correspond one-to-one to the set of Deepchecks data integrity checks for tabular and computer vision data
data drift checks require two datasets as input: target and reference. These correspond one-to-one to the set of Deepchecks train-test checks for tabular data and for computer vision.
model validation checks require a single dataset and a mandatory model as input. This list includes a subset of the model evaluation checks provided by Deepchecks for tabular data and and for computer vision that expect a single dataset as input.
model drift checks require two datasets and a mandatory model as input. This list includes a subset of the model evaluation checks provided by Deepchecks for tabular data and and for computer vision that expect two datasets as input: target and reference.

This structure is directly reflected in how Deepchecks can be used with ZenML: there are four different Deepchecks standard steps and four different ZenML enums for Deepchecks checks. The Deepchecks Data Validator API is also modeled to reflect this same structure.

A notable characteristic of Deepchecks is that you don't need to customize the set of Deepchecks tests that are part of a test suite. Both ZenML and Deepchecks provide sane defaults that will run all available Deepchecks tests in a given category with their default conditions if a custom list of tests and conditions are not provided.

There are three ways you can use Deepchecks in your ZenML pipelines that allow different levels of flexibility:

instantiate, configure and insert one or more of the standard Deepchecks steps shipped with ZenML into your pipelines. This is the easiest way and the recommended approach, but can only be customized through the supported step configuration parameters.
call the data validation methods provided by the Deepchecks Data Validator in your custom step implementation. This method allows for more flexibility concerning what can happen in the pipeline step, but you are still limited to the functionality implemented in the Data Validator.
use the Deepchecks library directly in your custom step implementation. This gives you complete freedom in how you are using Deepchecks' features.

Outside of the pipeline workflow, you can use the ZenML Deepchecks visualizer to display and interact with the Deepchecks test results generated by your pipelines.

The Deepchecks standard steps

ZenML wraps the Deepchecks functionality for tabular data in the form of four standard steps:

DeepchecksDataIntegrityCheckStep: use it in your pipelines to run data integrity tests on a single dataset
DeepchecksDataDriftCheckStep: use it in your pipelines to run data drift tests on two datasets as input: target and reference.
DeepchecksModelValidationCheckStep: class DeepchecksModelDriftCheckStep(BaseStep): use it in your pipelines to run model performance tests using a single dataset and a mandatory model artifact as input
DeepchecksModelDriftCheckStep: use it in your pipelines to run model comparison/drift tests using a mandatory model artifact and two datasets as input: target and reference.

The integration doesn't yet include standard steps for computer vision, but you can still write your own custom steps that call the Deepchecks Data Validator API or even call the Deepchecks library directly.

All four standard steps behave similarly regarding the configuration parameters and returned artifacts, with the following differences:

the type and number of input artifacts are different, as mentioned above
each step expects a different enum data type to be used when explicitly listing the checks to be performed via the check_list configuration attribute. See the zenml.integrations.deepchecks.validation_checks module for more details about these enums (e.g. the data integrity step expects a list of DeepchecksDataIntegrityCheck values).

This section will only cover how you can use the data integrity step, with a similar usage to be easily inferred for the other three steps.

To instantiate a data integrity step that will run all available Deepchecks data integrity tests with their default configuration, e.g.:

from zenml.integrations.deepchecks.steps import (
    DeepchecksDataIntegrityCheckStepConfig,
    deepchecks_data_integrity_check_step,
)

data_validator = deepchecks_data_integrity_check_step(
    step_name="data_validator",
    config=DeepchecksDataIntegrityCheckStepConfig(),
)

The step can then be inserted into your pipeline where it can take in a dataset, e.g.:

@pipeline(required_integrations=[DEEPCHECKS, SKLEARN])
def data_validation_pipeline(
    data_loader,
    data_validator,
):
    df_train, df_test = data_loader()
    data_validator(dataset=df_train)

pipeline = data_validation_pipeline(
    data_loader=data_loader(),
    data_validator=data_validator,
)
pipeline.run()

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:

class DeepchecksDataIntegrityCheckStep(BaseStep):
    """Deepchecks data integrity validator step."""

    def entrypoint(  # type: ignore[override]
        self,
        dataset: pd.DataFrame,
        config: DeepchecksDataIntegrityCheckStepConfig,
    ) -> SuiteResult:
        ...

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

from zenml.integrations.deepchecks.validation_checks import DeepchecksDataIntegrityCheck
from zenml.integrations.deepchecks.steps import (
    DeepchecksDataIntegrityCheckStepConfig,
    deepchecks_data_integrity_check_step,
)

data_validator = deepchecks_data_integrity_check_step(
    step_name="data_validator",
    config=DeepchecksDataIntegrityCheckStepConfig(
        check_list=[
            DeepchecksDataIntegrityCheck.TABULAR_MIXED_DATA_TYPES,
            DeepchecksDataIntegrityCheck.TABULAR_DATA_DUPLICATES,
            DeepchecksDataIntegrityCheck.TABULAR_CONFLICTING_LABELS,
        ],
    ),
)

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

For more customization, the DeepchecksDataIntegrityCheckStepConfig step configuration 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.:
```
config=DeepchecksDataIntegrityCheckStepConfig(
    dataset_kwargs=dict(label='class', cat_features=['country', 'state']),
),
```

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, e.g.:

config=DeepchecksDataIntegrityCheckStepConfig(
    check_list=[
        DeepchecksDataIntegrityCheck.TABULAR_OUTLIER_SAMPLE_DETECTION,
        DeepchecksDataIntegrityCheck.TABULAR_STRING_LENGTH_OUT_OF_BOUNDS,
        DeepchecksDataIntegrityCheck.TABULAR_STRING_MISMATCH,
    ],
    check_kwargs={
        DeepchecksDataIntegrityCheck.TABULAR_OUTLIER_SAMPLE_DETECTION: dict(
            nearest_neighbors_percent=0.01,
            extent_parameter=3,
        ),
        DeepchecksDataIntegrityCheck.TABULAR_STRING_LENGTH_OUT_OF_BOUNDS: dict(
            num_percentiles=1000,
            min_unique_values=3,
        ),
    },
)

run_kwargs: Additional keyword arguments to be passed to the Deepchecks Suite run method.

The check_kwargs attribute can also be used to customize the conditions configured for each Deepchecks test. ZenML attaches a special meaning to all check arguments that start with condition_ and have a dictionary as value. This is required because there is no declarative way to specify conditions for Deepchecks checks. For example, the following step configuration:

config=DeepchecksDataIntegrityCheckStepConfig(
    check_list=[
        DeepchecksDataIntegrityCheck.TABULAR_OUTLIER_SAMPLE_DETECTION,
        DeepchecksDataIntegrityCheck.TABULAR_STRING_LENGTH_OUT_OF_BOUNDS,
    ],
    dataset_kwargs=dict(label='class', cat_features=['country', 'state']),
    check_kwargs={
        DeepchecksDataIntegrityCheck.TABULAR_OUTLIER_SAMPLE_DETECTION: dict(
            nearest_neighbors_percent=0.01,
            extent_parameter=3,
            condition_outlier_ratio_less_or_equal=dict(
                max_outliers_ratio=0.007,
                outlier_score_threshold= 0.5,
            ),
            condition_no_outliers=dict(
                outlier_score_threshold=0.6,
            )
        ),
        DeepchecksDataIntegrityCheck.TABULAR_STRING_LENGTH_OUT_OF_BOUNDS: dict(
            num_percentiles=1000,
            min_unique_values=3,
            condition_number_of_outliers_less_or_equal=dict(
                max_outliers=3,
            )
        ),
    },
)

is equivalent to running the following Deepchecks tests:

import deepchecks.tabular.checks as tabular_checks
from deepchecks.tabular import Suite
from deepchecks.tabular import Dataset

train_dataset = Dataset(
    reference_dataset,
    label='class',
    cat_features=['country', 'state']
)

suite = Suite(name="custom")
check = tabular_checks.OutlierSampleDetection(
    nearest_neighbors_percent=0.01,
    extent_parameter=3,
)
check.add_condition_outlier_ratio_less_or_equal(
    max_outliers_ratio=0.007,
    outlier_score_threshold= 0.5,
)
check.add_condition_no_outliers(
    outlier_score_threshold=0.6,
)
suite.add(check)
check = tabular_checks.StringLengthOutOfBounds(
    num_percentiles=1000,
    min_unique_values=3,
)
check.add_condition_number_of_outliers_less_or_equal(
    max_outliers=3,
)
suite.run(train_dataset=train_dataset)

You can view the complete list of configuration parameters in the API docs.

You can also check out our examples pages for working examples that use the Deepchecks standard steps:

Data and model validation with Deepchecks

The Deepchecks Data Validator

The Deepchecks Data Validator implements the same interface as do all Data Validators, so this method forces you to maintain some level of compatibility with the overall Data Validator abstraction, which guarantees an easier migration in case you decide to switch to another Data Validator.

All you have to do is call the Deepchecks Data Validator methods when you need to interact with Deepchecks to run tests, e.g.:


import pandas as pd
from deepchecks.core.suite import SuiteResult
from zenml.integrations.deepchecks.data_validators import DeepchecksDataValidator
from zenml.integrations.deepchecks.validation_checks import DeepchecksDataIntegrityCheck
from zenml.steps import step

@step
def data_integrity_check(
    dataset: pd.DataFrame,
) -> SuiteResult:
    """Custom data integrity check step with Deepchecks

    Args:
        dataset: input Pandas DataFrame

    Returns:
        Deepchecks test suite execution result
    """

    # validation pre-processing (e.g. dataset preparation) can take place here

    data_validator = DeepchecksDataValidator.get_active_data_validator()
    suite = data_validator.data_validation(
        dataset=dataset,
        check_list=[
            DeepchecksDataIntegrityCheck.TABULAR_OUTLIER_SAMPLE_DETECTION,
            DeepchecksDataIntegrityCheck.TABULAR_STRING_LENGTH_OUT_OF_BOUNDS,
        ],
    )

    # validation post-processing (e.g. interpret results, take actions) can happen here

    return suite

The arguments that the Deepchecks Data Validator methods can take in are the same as those used for the Deepchecks standard steps.

Have a look at the complete list of methods and parameters available in the DeepchecksDataValidator API in the API docs.

Call Deepchecks directly

You can use the Deepchecks library directly in your custom pipeline steps, and only leverage ZenML's capability of serializing, versioning and storing the SuiteResult objects in its Artifact Store, e.g.:

import pandas as pd
import deepchecks.tabular.checks as tabular_checks

from deepchecks.core.suite import SuiteResult
from deepchecks.tabular import Suite
from deepchecks.tabular import Dataset
from zenml.steps import step

@step
def data_integrity_check(
    dataset: pd.DataFrame,
) -> SuiteResult:
    """Custom data integrity check step with Deepchecks

    Args:
        dataset: a Pandas DataFrame

    Returns:
        Deepchecks test suite execution result
    """

    # validation pre-processing (e.g. dataset preparation) can take place here

    train_dataset = Dataset(
        dataset,
        label='class',
        cat_features=['country', 'state']
    )

    suite = Suite(name="custom")
    check = tabular_checks.OutlierSampleDetection(
        nearest_neighbors_percent=0.01,
        extent_parameter=3,
    )
    check.add_condition_outlier_ratio_less_or_equal(
        max_outliers_ratio=0.007,
        outlier_score_threshold= 0.5,
    )
    suite.add(check)
    check = tabular_checks.StringLengthOutOfBounds(
        num_percentiles=1000,
        min_unique_values=3,
    )
    check.add_condition_number_of_outliers_less_or_equal(
        max_outliers=3,
    )
    results = suite.run(train_dataset=train_dataset)

    # validation post-processing (e.g. interpret results, take actions) can happen here

    return results

The Deepchecks ZenML Visualizer

In the post-execution workflow, you can load and render the Deepchecks test suite results generated and returned by your pipeline steps by means of the ZenML Deepchecks Visualizer, e.g.:

from zenml.integrations.deepchecks.visualizers import DeepchecksVisualizer
from zenml.repository import Repository

def visualize_results(pipeline_name: str, step_name: str) -> None:
    repo = Repository()
    pipeline = repo.get_pipeline(pipeline=pipeline_name)
    last_run = pipeline.runs[-1]
    step = last_run.get_step(step=step_name)
    DeepchecksVisualizer().visualize(step)

if __name__ == "__main__":
    visualize_results("data_validation_pipeline", "data_integrity_check")

The Deepchecks test suite results will be opened as tabs in your browser, or displayed inline in your Jupyter notebook, depending on where you are running the code:

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Last updated 6 months ago