TST check for nan and inf + single sample for metrics cl… (scikit-lea…

…rn#10830)

sklearn/metrics/cluster/supervised.py

@@ -21,7 +21,7 @@
 from scipy import sparse as sp
 
 from .expected_mutual_info_fast import expected_mutual_information
-from ...utils.validation import check_array
+from ...utils.validation import check_array, check_consistent_length
 from ...utils.fixes import comb, _astype_copy_false
 
 
@@ -36,14 +36,18 @@ def check_clusterings(labels_true, labels_pred):
 
     Parameters
     ----------
-    labels_true : int array, shape = [n_samples]
-        The true labels
+    labels_true : array-like of shape (n_samples,)
+        The true labels.
 
-    labels_pred : int array, shape = [n_samples]
-        The predicted labels
+    labels_pred : array-like of shape (n_samples,)
+        The predicted labels.
     """
-    labels_true = np.asarray(labels_true)
-    labels_pred = np.asarray(labels_pred)
+    labels_true = check_array(
+        labels_true, ensure_2d=False, ensure_min_samples=0
+    )
+    labels_pred = check_array(
+        labels_pred, ensure_2d=False, ensure_min_samples=0
+    )
 
     # input checks
     if labels_true.ndim != 1:
@@ -52,10 +56,8 @@ def check_clusterings(labels_true, labels_pred):
     if labels_pred.ndim != 1:
         raise ValueError(
             "labels_pred must be 1D: shape is %r" % (labels_pred.shape,))
-    if labels_true.shape != labels_pred.shape:
-        raise ValueError(
-            "labels_true and labels_pred must have same size, got %d and %d"
-            % (labels_true.shape[0], labels_pred.shape[0]))
+    check_consistent_length(labels_true, labels_pred)
+
     return labels_true, labels_pred
 
 

sklearn/metrics/cluster/tests/test_common.py

@@ -126,8 +126,7 @@ def test_normalized_output(metric_name):
 # 0.22 AMI and NMI changes
 @pytest.mark.filterwarnings('ignore::FutureWarning')
 @pytest.mark.parametrize(
-    "metric_name",
-    dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS)
+    "metric_name", dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS)
 )
 def test_permute_labels(metric_name):
     # All clustering metrics do not change score due to permutations of labels
@@ -150,11 +149,10 @@ def test_permute_labels(metric_name):
 # 0.22 AMI and NMI changes
 @pytest.mark.filterwarnings('ignore::FutureWarning')
 @pytest.mark.parametrize(
-    "metric_name",
-    dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS)
+    "metric_name", dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS)
 )
 # For all clustering metrics Input parameters can be both
-# in the form of arrays lists, positive, negetive or string
+# in the form of arrays lists, positive, negative or string
 def test_format_invariance(metric_name):
     y_true = [0, 0, 0, 0, 1, 1, 1, 1]
     y_pred = [0, 1, 2, 3, 4, 5, 6, 7]
@@ -183,3 +181,29 @@ def generate_formats(y):
         y_true_gen = generate_formats(y_true)
         for (y_true_fmt, fmt_name) in y_true_gen:
             assert score_1 == metric(X, y_true_fmt)
+
+
+@pytest.mark.parametrize("metric", SUPERVISED_METRICS.values())
+def test_single_sample(metric):
+    # only the supervised metrics support single sample
+    for i, j in [(0, 0), (0, 1), (1, 0), (1, 1)]:
+        metric([i], [j])
+
+
+@pytest.mark.parametrize(
+    "metric_name, metric_func",
+    dict(SUPERVISED_METRICS, **UNSUPERVISED_METRICS).items()
+)
+def test_inf_nan_input(metric_name, metric_func):
+    if metric_name in SUPERVISED_METRICS:
+        invalids = [([0, 1], [np.inf, np.inf]),
+                    ([0, 1], [np.nan, np.nan]),
+                    ([0, 1], [np.nan, np.inf])]
+    else:
+        X = np.random.randint(10, size=(2, 10))
+        invalids = [(X, [np.inf, np.inf]),
+                    (X, [np.nan, np.nan]),
+                    (X, [np.nan, np.inf])]
+    with pytest.raises(ValueError, match='contains NaN, infinity'):
+        for args in invalids:
+            metric_func(*args)

sklearn/metrics/cluster/tests/test_supervised.py

@@ -34,8 +34,8 @@
 @ignore_warnings(category=FutureWarning)
 def test_error_messages_on_wrong_input():
     for score_func in score_funcs:
-        expected = ('labels_true and labels_pred must have same size,'
-                    ' got 2 and 3')
+        expected = (r'Found input variables with inconsistent numbers '
+                    r'of samples: \[2, 3\]')
         with pytest.raises(ValueError, match=expected):
             score_func([0, 1], [1, 1, 1])