ENH / TST better coverage of supervised clustering metrics, slight cleanup

Author: amueller

sklearn/metrics/cluster/supervised.py

@@ -73,13 +73,13 @@ def contingency_matrix(labels_true, labels_pred, eps=None):
     # Using coo_matrix to accelerate simple histogram calculation,
     # i.e. bins are consecutive integers
     # Currently, coo_matrix is faster than histogram2d for simple cases
-    contingency = np.asarray(coo_matrix((np.ones(class_idx.shape[0]),
-                                         (class_idx, cluster_idx)),
-                                        shape=(n_classes, n_clusters),
-                                        dtype=np.int).todense())
+    contingency = coo_matrix((np.ones(class_idx.shape[0]),
+                              (class_idx, cluster_idx)),
+                             shape=(n_classes, n_clusters),
+                             dtype=np.int).toarray()
     if eps is not None:
-        # Must be a float matrix to accept float eps
-        contingency = np.array(contingency, dtype='float') + eps
+        # don't use += as contingency is integer
+        contingency = contingency + eps
     return contingency
 
 

sklearn/metrics/cluster/tests/test_supervised.py

@@ -168,6 +168,19 @@ def test_adjusted_mutual_info_score():
 def test_entropy():
     ent = entropy([0, 0, 42.])
     assert_almost_equal(ent, 0.6365141, 5)
+    assert_almost_equal(entropy([]), 1)
+
+
+def test_contingency_matrix():
+    labels_a = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])
+    labels_b = np.array([1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3, 1, 3, 3, 3, 2, 2])
+    C = contingency_matrix(labels_a, labels_b)
+    C2 = np.histogram2d(labels_a, labels_b,
+                        bins=(np.arange(1, 5),
+                              np.arange(1, 5)))[0]
+    assert_array_almost_equal(C, C2)
+    C = contingency_matrix(labels_a, labels_b, eps=.1)
+    assert_array_almost_equal(C, C2 + .1)
 
 
 def test_exactly_zero_info_score():