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import numpy as np
import pytest
from sklearn.inspection._pd_utils import _check_feature_names, _get_feature_index
from sklearn.utils._testing import _convert_container
@pytest.mark.parametrize(
"feature_names, array_type, expected_feature_names",
[
(None, "array", ["x0", "x1", "x2"]),
(None, "dataframe", ["a", "b", "c"]),
(np.array(["a", "b", "c"]), "array", ["a", "b", "c"]),
],
)
def test_check_feature_names(feature_names, array_type, expected_feature_names):
X = np.random.randn(10, 3)
column_names = ["a", "b", "c"]
X = _convert_container(X, constructor_name=array_type, columns_name=column_names)
feature_names_validated = _check_feature_names(X, feature_names)
assert feature_names_validated == expected_feature_names
def test_check_feature_names_error():
X = np.random.randn(10, 3)
feature_names = ["a", "b", "c", "a"]
msg = "feature_names should not contain duplicates."
with pytest.raises(ValueError, match=msg):
_check_feature_names(X, feature_names)
@pytest.mark.parametrize("fx, idx", [(0, 0), (1, 1), ("a", 0), ("b", 1), ("c", 2)])
def test_get_feature_index(fx, idx):
feature_names = ["a", "b", "c"]
assert _get_feature_index(fx, feature_names) == idx
@pytest.mark.parametrize(
"fx, feature_names, err_msg",
[
("a", None, "Cannot plot partial dependence for feature 'a'"),
("d", ["a", "b", "c"], "Feature 'd' not in feature_names"),
],
)
def test_get_feature_names_error(fx, feature_names, err_msg):
with pytest.raises(ValueError, match=err_msg):
_get_feature_index(fx, feature_names)
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