8000 MNT Raise erorr when normalize is invalid in confusion_matrix (#15888) · scikit-learn/scikit-learn@4ad4cc6 · GitHub
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qinhanmin2014qinhanmin2014
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MNT Raise erorr when normalize is invalid in confusion_matrix (#15888)
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‎doc/whats_new/v0.22.rst

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@@ -15,6 +15,13 @@ This is a bug-fix release to primarily resolve some packaging issues in version
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Changelog
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---------
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:mod:`sklearn.metrics`
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......................
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- |Fix| :func:`metrics.plot_confusion_matrix` now raises error when `normalize`
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is invalid. Previously, it runs fine with no normalization.
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:pr:`15888` by `Hanmin Qin`_.
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:mod:`sklearn.utils`
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....................
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‎sklearn/metrics/_classification.py

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@@ -283,6 +283,10 @@ def confusion_matrix(y_true, y_pred, labels=None, sample_weight=None,
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check_consistent_length(y_true, y_pred, sample_weight)
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if normalize not in ['true', 'pred', 'all', None]:
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raise ValueError("normalize must be one of {'true', 'pred', "
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"'all', None}")
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n_labels = labels.size
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label_to_ind = {y: x for x, y in enumerate(labels)}
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# convert yt, yp into index

‎sklearn/metrics/_plot/confusion_matrix.py

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@@ -184,10 +184,6 @@ def plot_confusion_matrix(estimator, X, y_true, labels=None,
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if not is_classifier(estimator):
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raise ValueError("plot_confusion_matrix only supports classifiers")
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if normalize not in {'true', 'pred', 'all', None}:
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raise ValueError("normalize must be one of {'true', 'pred', "
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"'all', None}")
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y_pred = estimator.predict(X)
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cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight,
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labels=labels, normalize=normalize)

‎sklearn/metrics/tests/test_classification.py

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@@ -526,6 +526,13 @@ def test_confusion_matrix_normalize(normalize, cm_dtype, expected_results):
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assert cm.dtype.kind == cm_dtype
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def test_confusion_matrix_normalize_wrong_option():
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y_test = [0, 0, 0, 0, 1, 1, 1, 1]
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y_pred = [0, 0, 0, 0, 0, 0, 0, 0]
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with pytest.raises(ValueError, match='normalize must be one of'):
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confusion_matrix(y_test, y_pred, normalize=True)
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def test_confusion_matrix_normalize_single_class():
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y_test = [0, 0, 0, 0, 1, 1, 1, 1]
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y_pred = [0, 0, 0, 0, 0, 0, 0, 0]

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