def test_multicollinearity_returns_warning():
col = pd.Series([1, 0, 0, 3, 4])
X = pd.DataFrame({
'col_1': col,
'col_2': col * 3,
'col_3': ~col,
'col_4': col / 2,
'col_5': col + 1,
'not_collinear': [0, 1, 0, 0, 0]
})
multi_check = MulticollinearityDataCheck(threshold=0.95)
assert multi_check.validate(X) == {
"warnings": [
DataCheckWarning(
message=
"Columns are likely to be correlated: [('col_1', 'col_2'), ('col_1', 'col_3'), ('col_1', 'col_4'), ('col_1', 'col_5'), ('col_2', 'col_3'), ('col_2', 'col_4'), ('col_2', 'col_5'), ('col_3', 'col_4'), ('col_3', 'col_5'), ('col_4', 'col_5')]",
data_check_name=multi_data_check_name,
message_code=DataCheckMessageCode.IS_MULTICOLLINEAR,
details={
'columns': [('col_1', 'col_2'), ('col_1', 'col_3'),
('col_1', 'col_4'), ('col_1', 'col_5'),
('col_2', 'col_3'), ('col_2', 'col_4'),
('col_2', 'col_5'), ('col_3', 'col_4'),
('col_3', 'col_5'), ('col_4', 'col_5')]
}).to_dict()
],
"errors": [],
"actions": []
}
def test_multicollinearity_nonnumeric_cols(data_type, make_data_type):
X = pd.DataFrame({
'col_1': ["a", "b", "c", "d", "a"],
'col_2': ["w", "x", "y", "z", "w"],
'col_3': ["a", "a", "c", "d", "a"],
'col_4': ["a", "b", "c", "d", "a"],
'col_5': ["0", "0", "1", "2", "0"],
'col_6': [1, 1, 2, 3, 1]
})
X = make_data_type(data_type, X)
multi_check = MulticollinearityDataCheck(threshold=0.9)
assert multi_check.validate(X) == {
"warnings": [
DataCheckWarning(
message=
"Columns are likely to be correlated: [('col_1', 'col_2'), ('col_1', 'col_4'), ('col_2', 'col_4'), ('col_3', 'col_5'), ('col_3', 'col_6'), ('col_5', 'col_6')]",
data_check_name=multi_data_check_name,
message_code=DataCheckMessageCode.IS_MULTICOLLINEAR,
details={
'columns': [('col_1', 'col_2'), ('col_1', 'col_4'),
('col_2', 'col_4'), ('col_3', 'col_5'),
('col_3', 'col_6'), ('col_5', 'col_6')]
}).to_dict()
],
"errors": [],
"actions": []
}
def test_multicollinearity_data_check_input_formats():
multi_check = MulticollinearityDataCheck(threshold=0.9)
# test empty pd.DataFrame
assert multi_check.validate(pd.DataFrame()) == {
"warnings": [],
"errors": []
}
def test_multicollinearity_data_check_init():
multi_check = MulticollinearityDataCheck()
assert multi_check.threshold == 0.9
multi_check = MulticollinearityDataCheck(threshold=0.0)
assert multi_check.threshold == 0
multi_check = MulticollinearityDataCheck(threshold=0.5)
assert multi_check.threshold == 0.5
multi_check = MulticollinearityDataCheck(threshold=1.0)
assert multi_check.threshold == 1.0
with pytest.raises(ValueError, match="threshold must be a float between 0 and 1, inclusive."):
MulticollinearityDataCheck(threshold=-0.1)
with pytest.raises(ValueError, match="threshold must be a float between 0 and 1, inclusive."):
MulticollinearityDataCheck(threshold=1.1)