 test suite . It consists of the following fields:column_mapping: This is an EvidentlyColumnMapping object that is the exact equivalent of the ColumnMapping object in Evidently. It is used to describe the columns in the dataset and how they should be treated (e.g. as categorical, numerical, or text features).

tests: This is a list of EvidentlyTestConfig objects that are used to configure the tests that will be run as part of your test suite in a declarative way. This is the same as configuring the tests that go in the Evidently TestSuite.

download_nltk_data: This is a boolean that is used to indicate whether the NLTK data should be downloaded. This is only needed if you are using Evidently tests or test presets that handle text data, which require the NLTK data to be downloaded ahead of time.

There are several ways you can reference the Evidently tests when configuring EvidentlyTestConfig items, similar to how you reference them in an EvidentlyMetricConfig object:

by class name: this is the easiest way to reference an Evidently test. You can use the name of a test or test preset class as it appears in the Evidently documentation (e.g."DataQualityTestPreset", "TestColumnRegExp").

by full class path: you can also use the full Python class path of the test or test preset class ( e.g. "evidently.test_preset.DataQualityTestPreset", "evidently.tests.TestColumnRegExp"). This is useful if you want to use tests or test presets that are not included in Evidently library.

by passing in the class itself: you can also import and pass in an Evidently test or test preset class itself, e.g.:Copyfrom evidently.tests import TestColumnRegExp

...

evidently_test_step.with_options(
    parameters=dict(
        tests=[EvidentlyTestConfig.test(TestColumnRegExp)]
    ),
)

As can be seen in the example, there are two basic ways of adding tests to your Evidently test step configuration: