target_distribution_data_check

Data check that checks if the target data contains certain distributions that may need to be transformed prior training to improve model performance.

Module Contents

Classes Summary

TargetDistributionDataCheck

Check if the target data contains certain distributions that may need to be transformed prior training to improve model performance.

Contents

class evalml.data_checks.target_distribution_data_check.TargetDistributionDataCheck

Check if the target data contains certain distributions that may need to be transformed prior training to improve model performance.

Methods

name	Return a name describing the data check.
validate	Check if the target data has a certain distribution.

name(cls)

Return a name describing the data check.

validate(self, X, y)

Check if the target data has a certain distribution.

Parameters

• X (pd.DataFrame, np.ndarray) – Features. Ignored.

• y (pd.Series, np.ndarray) – Target data to check for underlying distributions.

Returns

List with DataCheckErrors if certain distributions are found in the target data.

Return type

dict (DataCheckError)

Example

>>> from scipy.stats import lognorm
>>> X = None
>>> y = [0.946, 0.972, 1.154, 0.954, 0.969, 1.222, 1.038, 0.999, 0.973, 0.897]
>>> target_check = TargetDistributionDataCheck()
>>> assert target_check.validate(X, y) == {
...     "errors": [],
...     "warnings": [{"message": "Target may have a lognormal distribution.",
...                   "data_check_name": "TargetDistributionDataCheck",
...                   "level": "warning",
...                   "code": "TARGET_LOGNORMAL_DISTRIBUTION",
...                   "details": {"shapiro-statistic/pvalue": '0.84/0.045'}}],
...     "actions": [{'code': 'TRANSFORM_TARGET', 'metadata': {'column': None, 'transformation_strategy': 'lognormal', 'is_target': True}}]}

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