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