def test_uniqueness_data_check_warnings():
data = 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")
assert uniqueness_check.validate(data) == {
"warnings": [DataCheckWarning(
message="Input columns (regression_not_unique_enough) for regression problem type are not unique enough.",
data_check_name=uniqueness_data_check_name,
message_code=DataCheckMessageCode.NOT_UNIQUE_ENOUGH,
details={"column": "regression_not_unique_enough",
'uniqueness_score': 0.0}).to_dict()],
"errors": [],
"actions": [DataCheckAction(DataCheckActionCode.DROP_COL, details={"column": 'regression_not_unique_enough'}).to_dict()]
}
data = pd.DataFrame({'multiclass_too_unique': ["Cats", "Are", "Absolutely", "The", "Best"] * 20,
'multiclass_not_too_unique': ["Cats", "Cats", "Best", "Best", "Best"] * 20})
uniqueness_check = UniquenessDataCheck(problem_type="multiclass")
assert uniqueness_check.validate(data) == {
"warnings": [DataCheckWarning(
message="Input columns (multiclass_too_unique) for multiclass problem type are too unique.",
data_check_name=uniqueness_data_check_name,
message_code=DataCheckMessageCode.TOO_UNIQUE,
details={"column": "multiclass_too_unique",
'uniqueness_score': 0.7999999999999999}).to_dict()],
"errors": [],
"actions": [DataCheckAction(DataCheckActionCode.DROP_COL, details={"column": 'multiclass_too_unique'}).to_dict()]
}
def test_uniqueness_data_check_init():
uniqueness_check = UniquenessDataCheck("regression")
assert uniqueness_check.threshold == 0.50
uniqueness_check = UniquenessDataCheck("regression", threshold=0.0)
assert uniqueness_check.threshold == 0
uniqueness_check = UniquenessDataCheck("regression", threshold=0.5)
assert uniqueness_check.threshold == 0.5
uniqueness_check = UniquenessDataCheck("regression", threshold=1.0)
assert uniqueness_check.threshold == 1.0
with pytest.raises(ValueError, match="threshold must be a float between 0 and 1, inclusive."):
UniquenessDataCheck("regression", threshold=-0.1)
with pytest.raises(ValueError, match="threshold must be a float between 0 and 1, inclusive."):
UniquenessDataCheck("regression", threshold=1.1)