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MNT Raise erorr when normalize is invalid in confusion_matrix #15888

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Merged
merged 4 commits into from
Dec 18, 2019
Merged

MNT Raise erorr when normalize is invalid in confusion_matrix #15888

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MNT Raise erorr when normalize is invalid in confusion_matrix
qinhanmin2014 Dec 14, 2019
465fa64
remove
qinhanmin2014 Dec 14, 2019
39eedac
Update sklearn/metrics/_classification.py
qinhanmin2014 Dec 14, 2019
d531f73
whats new
qinhanmin2014 Dec 15, 2019
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7 changes: 7 additions & 0 deletions doc/whats_new/v0.22.rst
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Expand Up @@ -15,6 +15,13 @@ This is a bug-fix release to primarily resolve some packaging issues in version
Changelog
---------

:mod:`sklearn.metrics`
......................

- |Fix| :func:`metrics.plot_confusion_matrix` now raises error when `normalize`
is invalid. Previously, it runs fine with no normalization.
:pr:`15888` by `Hanmin Qin`_.

:mod:`sklearn.utils`
....................

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4 changes: 4 additions & 0 deletions sklearn/metrics/_classification.py
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Expand Up @@ -283,6 +283,10 @@ def confusion_matrix(y_true, y_pred, labels=None, sample_weight=None,

check_consistent_length(y_true, y_pred, sample_weight)

if normalize not in ['true', 'pred', 'all', None]:
raise ValueError("normalize must be one of {'true', 'pred', "
"'all', None}")

n_labels = labels.size
label_to_ind = {y: x for x, y in enumerate(labels)}
# convert yt, yp into index
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4 changes: 0 additions & 4 deletions sklearn/metrics/_plot/confusion_matrix.py
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Expand Up @@ -184,10 +184,6 @@ def plot_confusion_matrix(estimator, X, y_true, labels=None,
if not is_classifier(estimator):
raise ValueError("plot_confusion_matrix only supports classifiers")

if normalize not in {'true', 'pred', 'all', None}:
raise ValueError("normalize must be one of {'true', 'pred', "
"'all', None}")

y_pred = estimator.predict(X)
cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight,
labels=labels, normalize=normalize)
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7 changes: 7 additions & 0 deletions sklearn/metrics/tests/test_classification.py
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Expand Up @@ -526,6 +526,13 @@ def test_confusion_matrix_normalize(normalize, cm_dtype, expected_results):
assert cm.dtype.kind == cm_dtype


def test_confusion_matrix_normalize_wrong_option():
y_test = [0, 0, 0, 0, 1, 1, 1, 1]
y_pred = [0, 0, 0, 0, 0, 0, 0, 0]
with pytest.raises(ValueError, match='normalize must be one of'):
confusion_matrix(y_test, y_pred, normalize=True)


def test_confusion_matrix_normalize_single_class():
y_test = [0, 0, 0, 0, 1, 1, 1, 1]
y_pred = [0, 0, 0, 0, 0, 0, 0, 0]
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