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. Uses the Shapiro-Wilks test when the dataset is <=5000 samples, otherwise uses Jarque-Bera.

Contents#

class evalml.data_checks.target_distribution_data_check.TargetDistributionDataCheck[source]#

Check if the target data contains certain distributions that may need to be transformed prior training to improve model performance. Uses the Shapiro-Wilks test when the dataset is <=5000 samples, otherwise uses Jarque-Bera.

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)[source]#

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)

Examples

>>> import pandas as pd

Targets that exhibit a lognormal distribution will raise a warning for the user to transform the target.

>>> 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(None, y) == [
...     {
...         "message": "Target may have a lognormal distribution.",
...         "data_check_name": "TargetDistributionDataCheck",
...         "level": "warning",
...         "code": "TARGET_LOGNORMAL_DISTRIBUTION",
...         "details": {"normalization_method": "shapiro", "statistic": 0.8, "p-value": 0.045, "columns": None, "rows": None},
...         "action_options": [
...             {
...                 "code": "TRANSFORM_TARGET",
...                 "data_check_name": "TargetDistributionDataCheck",
...                 "parameters": {},
...                 "metadata": {
...                     "transformation_strategy": "lognormal",
...                     "is_target": True,
...                     "columns": None,
...                     "rows": None
...                 }
...             }
...         ]
...     }
... ]
...
>>> y = pd.Series([1, 1, 1, 2, 2, 3, 4, 4, 5, 5, 5])
>>> assert target_check.validate(None, y) == []
...
...
>>> y = pd.Series(pd.date_range("1/1/21", periods=10))
>>> assert target_check.validate(None, y) == [
...     {
...         "message": "Target is unsupported datetime type. Valid Woodwork logical types include: integer, double, age, age_fractional",
...         "data_check_name": "TargetDistributionDataCheck",
...         "level": "error",
...         "details": {"columns": None, "rows": None, "unsupported_type": "datetime"},
...         "code": "TARGET_UNSUPPORTED_TYPE",
...         "action_options": []
...     }
... ]