parametrize_with_checks#

sklearn.utils.estimator_checks.parametrize_with_checks(estimators, *, legacy: bool = True, expected_failed_checks: Callable | None = None)[source]#

Pytest specific decorator for parametrizing estimator checks.

Checks are categorised into the following groups:

The id of each check is set to be a pprint version of the estimator and the name of the check with its keyword arguments. This allows to use pytest -k to specify which tests to run:

pytest test_check_estimators.py -k check_estimators_fit_returns_self
Parameters:
estimatorslist of estimators instances

Estimators to generated checks for.

Changed in version 0.24: Passing a class was deprecated in version 0.23, and support for classes was removed in 0.24. Pass an instance instead.

Added in version 0.24.

legacybool, default=True

Whether to include legacy checks. Over time we remove checks from this category and move them into their specific category.

Added in version 1.6.

expected_failed_checkscallable, default=None

A callable that takes an estimator as input and returns a dictionary of the form:

{
    "check_name": "my reason",
}

Where "check_name" is the name of the check, and "my reason" is why the check fails. These tests will be marked as xfail if the check fails.

Added in version 1.6.

Returns:
decoratorpytest.mark.parametrize

See also

check_estimator

Check if estimator adheres to scikit-learn conventions.

Examples

>>> from sklearn.utils.estimator_checks import parametrize_with_checks
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.tree import DecisionTreeRegressor

>>> @parametrize_with_checks([LogisticRegression(),
...                           DecisionTreeRegressor()])
... def test_sklearn_compatible_estimator(estimator, check):
...     check(estimator)