from evalml.data_checks import (
DataCheck,
DataCheckAction,
DataCheckActionCode,
DataCheckError,
DataCheckMessageCode,
DataCheckWarning,
)
from evalml.utils import infer_feature_types
[docs]class NoVarianceDataCheck(DataCheck):
"""Check if the target or any of the features have no variance.
Arguments:
count_nan_as_value (bool): If True, missing values will be counted as their own unique value.
Additionally, if true, will return a DataCheckWarning instead of an error
if the feature has mostly missing data and only one unique value.
Defaults to False.
"""
def __init__(self, count_nan_as_value=False):
self._dropnan = not count_nan_as_value
def _check_for_errors(self, column_name, count_unique, any_nulls):
"""Checks if a column has no variance.
Arguments:
column_name (str): Name of the column we are checking.
count_unique (float): Number of unique values in this column.
any_nulls (bool): Whether this column has any missing data.
Returns:
DataCheckError if the column has no variance or DataCheckWarning if the column has two unique values including NaN.
"""
message = f"{column_name} has {int(count_unique)} unique value."
if count_unique <= 1:
return DataCheckError(
message=message.format(name=column_name),
data_check_name=self.name,
message_code=DataCheckMessageCode.NO_VARIANCE,
details={"column": column_name},
)
elif count_unique == 2 and not self._dropnan and any_nulls:
return DataCheckWarning(
message=f"{column_name} has two unique values including nulls. "
"Consider encoding the nulls for "
"this column to be useful for machine learning.",
data_check_name=self.name,
message_code=DataCheckMessageCode.NO_VARIANCE_WITH_NULL,
details={"column": column_name},
)
[docs] def validate(self, X, y):
"""Check if the target or any of the features have no variance (1 unique value).
Arguments:
X (pd.DataFrame, np.ndarray): The input features.
y (pd.Series, np.ndarray): The target data.
Returns:
dict: dict of warnings/errors corresponding to features or target with no variance.
"""
results = {"warnings": [], "errors": [], "actions": []}
X = infer_feature_types(X)
y = infer_feature_types(y)
unique_counts = X.nunique(dropna=self._dropnan).to_dict()
any_nulls = (X.isnull().any()).to_dict()
for col_name in unique_counts:
message = self._check_for_errors(
col_name, unique_counts[col_name], any_nulls[col_name]
)
if not message:
continue
DataCheck._add_message(message, results)
results["actions"].append(
DataCheckAction(
DataCheckActionCode.DROP_COL, metadata={"column": col_name}
).to_dict()
)
y_name = getattr(y, "name")
if not y_name:
y_name = "Y"
target_message = self._check_for_errors(
y_name, y.nunique(dropna=self._dropnan), y.isnull().any()
)
if target_message:
DataCheck._add_message(target_message, results)
return results