import numpy as np
import woodwork as ww
from scipy.stats import shapiro
from evalml.data_checks import (
DataCheck,
DataCheckAction,
DataCheckActionCode,
DataCheckError,
DataCheckMessageCode,
DataCheckWarning,
)
from evalml.utils import infer_feature_types
[docs]class TargetDistributionDataCheck(DataCheck):
"""Checks if the target data contains certain distributions that may need to be transformed prior training to
improve model performance."""
[docs] def validate(self, X, y):
"""Checks if the target data has a certain distribution.
Arguments:
X (pd.DataFrame, np.ndarray): Features. Ignored.
y (pd.Series, np.ndarray): Target data to check for underlying distributions.
Returns:
dict (DataCheckError): List with DataCheckErrors if certain distributions are found in the target data.
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}}]}
"""
results = {"warnings": [], "errors": [], "actions": []}
if y is None:
results["errors"].append(
DataCheckError(
message="Target is None",
data_check_name=self.name,
message_code=DataCheckMessageCode.TARGET_IS_NONE,
details={},
).to_dict()
)
return results
y = infer_feature_types(y)
allowed_types = [
ww.logical_types.Integer.type_string,
ww.logical_types.Double.type_string,
]
is_supported_type = y.ww.logical_type.type_string in allowed_types
if not is_supported_type:
results["errors"].append(
DataCheckError(
message="Target is unsupported {} type. Valid Woodwork logical types include: {}".format(
y.ww.logical_type.type_string,
", ".join([ltype for ltype in allowed_types]),
),
data_check_name=self.name,
message_code=DataCheckMessageCode.TARGET_UNSUPPORTED_TYPE,
details={"unsupported_type": y.ww.logical_type.type_string},
).to_dict()
)
return results
# Check if a normal distribution is detected with p-value above 0.05
if shapiro(y).pvalue >= 0.05:
return results
y_new = round(y, 6)
if any(y <= 0):
y_new = y + abs(y.min()) + 1
y_new = y_new[
y_new < (y_new.mean() + 3 * round(y.std(), 3))
] # Drop values greater than 3 standard deviations
shapiro_test_og = shapiro(y_new)
shapiro_test_log = shapiro(np.log(y_new))
log_detected = False
# If the p-value of the log transformed target is greater than or equal to the p-value of the original target
# with outliers dropped, then it would imply that the log transformed target has more of a normal distribution
if shapiro_test_log.pvalue >= shapiro_test_og.pvalue:
log_detected = True
if log_detected:
details = {
"shapiro-statistic/pvalue": f"{round(shapiro_test_og.statistic, 2)}/{round(shapiro_test_og.pvalue, 3)}"
}
results["warnings"].append(
DataCheckWarning(
message="Target may have a lognormal distribution.",
data_check_name=self.name,
message_code=DataCheckMessageCode.TARGET_LOGNORMAL_DISTRIBUTION,
details=details,
).to_dict()
)
results["actions"].append(
DataCheckAction(
DataCheckActionCode.TRANSFORM_TARGET,
metadata={
"column": None,
"is_target": True,
"transformation_strategy": "lognormal",
},
).to_dict()
)
return results