MNT Raise erorr when normalize is invalid in confusion_matrix (scikit-learn#15888)

Author: qinhanmin2014

doc/whats_new/v0.22.rst

@@ -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`
 ....................
 

sklearn/metrics/_classification.py

@@ -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

sklearn/metrics/_plot/confusion_matrix.py

@@ -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)

sklearn/metrics/tests/test_classification.py

@@ -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]