scikit-learn
diff --git a/‎sklearn/utils/tests/test_class_weight.py‎
Lines changed: 15 additions & 10 deletions b/‎sklearn/utils/tests/test_class_weight.py‎
Lines changed: 15 additions & 10 deletions
@@ -18,7 +18,9 @@ def test_compute_class_weight():
     y = np.asarray([2, 2, 2, 3, 3, 4])
     classes = np.unique(y)
     cw = compute_class_weight("auto", classes, y)
-    assert_almost_equal(cw.sum(), classes.shape)
+    class_counts = np.bincount(y)[2:]
+    # total effect of samples is preserved
+    assert_almost_equal(np.dot(cw, class_counts), y.shape[0])
     assert_true(cw[0] < cw[1] < cw[2])
 
 
@@ -63,19 +65,21 @@ def test_compute_class_weight_auto_negative():
     # Test with unbalanced class labels.
     y = np.asarray([-1, 0, 0, -2, -2, -2])
     cw = compute_class_weight("auto", classes, y)
-    assert_almost_equal(cw.sum(), classes.shape)
+    class_counts = np.bincount(y + 2)
+    assert_almost_equal(np.dot(cw, class_counts), y.shape[0])
     assert_equal(len(cw), len(classes))
-    assert_array_almost_equal(cw, np.array([0.545, 1.636, 0.818]), decimal=3)
+    assert_array_almost_equal(cw, [2. / 3, 2., 1.])
 
 
 def test_compute_class_weight_auto_unordered():
     """Test compute_class_weight when classes are unordered"""
     classes = np.array([1, 0, 3])
     y = np.asarray([1, 0, 0, 3, 3, 3])
     cw = compute_class_weight("auto", classes, y)
-    assert_almost_equal(cw.sum(), classes.shape)
+    class_counts = np.bincount(y)[classes]
+    assert_almost_equal(np.dot(cw, class_counts), y.shape[0])
     assert_equal(len(cw), len(classes))
-    assert_array_almost_equal(cw, np.array([1.636, 0.818, 0.545]), decimal=3)
+    assert_array_almost_equal(cw, [2., 1., 2. / 3])
 
 
 def test_compute_sample_weight():
@@ -97,8 +101,8 @@ def test_compute_sample_weight():
     # Test with unbalanced classes
     y = np.asarray([1, 1, 1, 2, 2, 2, 3])
     sample_weight = compute_sample_weight("auto", y)
-    expected = np.asarray([.6, .6, .6, .6, .6, .6, 1.8])
-    assert_array_almost_equal(sample_weight, expected)
+    expected = np.array([0.7777, 0.7777, 0.7777, 0.7777, 0.7777, 0.7777, 2.3333])
+    assert_array_almost_equal(sample_weight, expected, decimal=4)
 
     # Test with `None` weights
     sample_weight = compute_sample_weight(None, y)
@@ -117,7 +121,7 @@ def test_compute_sample_weight():
     # Test with multi-output of unbalanced classes
     y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1], [3, -1]])
     sample_weight = compute_sample_weight("auto", y)
-    assert_array_almost_equal(sample_weight, expected ** 2)
+    assert_array_almost_equal(sample_weight, expected ** 2, decimal=3)
 
 
 def test_compute_sample_weight_with_subsample():
@@ -135,12 +139,13 @@ def test_compute_sample_weight_with_subsample():
     # Test with a subsample
     y = np.asarray([1, 1, 1, 2, 2, 2])
     sample_weight = compute_sample_weight("auto", y, range(4))
-    assert_array_almost_equal(sample_weight, [.5, .5, .5, 1.5, 1.5, 1.5])
+    assert_array_almost_equal(sample_weight, [2. / 3, 2. / 3,
+                                              2. / 3, 2., 2., 2.])
 
     # Test with a bootstrap subsample
     y = np.asarray([1, 1, 1, 2, 2, 2])
     sample_weight = compute_sample_weight("auto", y, [0, 1, 1, 2, 2, 3])
-    expected = np.asarray([1 / 3., 1 / 3., 1 / 3., 5 / 3., 5 / 3., 5 / 3.])
+    expected = np.asarray([0.6, 0.6, 0.6, 3., 3., 3.])
     assert_array_almost_equal(sample_weight, expected)
 
     # Test with a bootstrap subsample for multi-output