"""Data check that checks if there are any columns in the input that are either too unique for classification problems or not unique enough for regression problems."""
from evalml.data_checks import (
DataCheck,
DataCheckAction,
DataCheckActionCode,
DataCheckMessageCode,
DataCheckWarning,
)
from evalml.problem_types import (
handle_problem_types,
is_multiclass,
is_regression,
)
from evalml.utils.woodwork_utils import infer_feature_types
warning_not_unique_enough = (
"Input columns ({}) for {} problem type are not unique enough."
)
warning_too_unique = "Input columns ({}) for {} problem type are too unique."
[docs]class UniquenessDataCheck(DataCheck):
"""Check if there are any columns in the input that are either too unique for classification problems or not unique enough for regression problems.
Args:
problem_type (str or ProblemTypes): The specific problem type to data check for.
e.g. 'binary', 'multiclass', 'regression, 'time series regression'
threshold(float): The threshold to set as an upper bound on uniqueness for classification type problems
or lower bound on for regression type problems. Defaults to 0.50.
"""
def __init__(self, problem_type, threshold=0.50):
self.problem_type = handle_problem_types(problem_type)
if threshold < 0 or threshold > 1:
raise ValueError("threshold must be a float between 0 and 1, inclusive.")
self.threshold = threshold
[docs] def validate(self, X, y=None):
"""Check if there are any columns in the input that are too unique in the case of classification problems or not unique enough in the case of regression problems.
Args:
X (pd.DataFrame, np.ndarray): Features.
y (pd.Series, np.ndarray): Ignored. Defaults to None.
Returns:
dict: dict with a DataCheckWarning if there are any too unique or not
unique enough columns.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({
... 'regression_unique_enough': [float(x) for x in range(100)],
... 'regression_not_unique_enough': [float(1) for x in range(100)]
... })
>>> uniqueness_check = UniquenessDataCheck(problem_type="regression", threshold=0.8)
>>> assert uniqueness_check.validate(df) == {
... "errors": [],
... "warnings": [{"message": "Input columns (regression_not_unique_enough) for regression problem type are not unique enough.",
... "data_check_name": "UniquenessDataCheck",
... "level": "warning",
... "code": "NOT_UNIQUE_ENOUGH",
... "details": {"column": "regression_not_unique_enough", 'uniqueness_score': 0.0}}],
... "actions": [{"code": "DROP_COL",
... "metadata": {"column": "regression_not_unique_enough"}}]}
"""
results = {"warnings": [], "errors": [], "actions": []}
X = infer_feature_types(X)
res = X.apply(UniquenessDataCheck.uniqueness_score)
if is_regression(self.problem_type):
not_unique_enough_cols = list(res.index[res < self.threshold])
results["warnings"].extend(
[
DataCheckWarning(
message=warning_not_unique_enough.format(
col_name, self.problem_type
),
data_check_name=self.name,
message_code=DataCheckMessageCode.NOT_UNIQUE_ENOUGH,
details={
"column": col_name,
"uniqueness_score": res.loc[col_name],
},
).to_dict()
for col_name in not_unique_enough_cols
]
)
results["actions"].extend(
[
DataCheckAction(
action_code=DataCheckActionCode.DROP_COL,
metadata={"column": col_name},
).to_dict()
for col_name in not_unique_enough_cols
]
)
elif is_multiclass(self.problem_type):
too_unique_cols = list(res.index[res > self.threshold])
results["warnings"].extend(
[
DataCheckWarning(
message=warning_too_unique.format(col_name, self.problem_type),
data_check_name=self.name,
message_code=DataCheckMessageCode.TOO_UNIQUE,
details={
"column": col_name,
"uniqueness_score": res.loc[col_name],
},
).to_dict()
for col_name in too_unique_cols
]
)
results["actions"].extend(
[
DataCheckAction(
action_code=DataCheckActionCode.DROP_COL,
metadata={"column": col_name},
).to_dict()
for col_name in too_unique_cols
]
)
return results
[docs] @staticmethod
def uniqueness_score(col):
"""Calculate a uniqueness score for the provided field. NaN values are not considered as unique values in the calculation.
Based on the Herfindahl–Hirschman Index.
Args:
col (pd.Series): Feature values.
Returns:
(float): Uniqueness score.
"""
norm_counts = col.value_counts() / col.value_counts().sum()
square_counts = norm_counts ** 2
score = 1 - square_counts.sum()
return score