checking for the test

hoi/core/tests/test_entropies.py

@@ -40,7 +40,7 @@ def test_entropy_gc(self, x, biascorrect, copnorm):
 
     @pytest.mark.parametrize("x", [x1, x2, j1, j2])
     def test_entropy_bin(self, x):
-        x_bin = digitize(x, n_bins=3)
+        x_bin, _ = digitize(x, n_bins=3)
         hx = entropy_bin(x_bin)
         hx = np.asarray(hx)
         assert hx.dtype == np.float32

hoi/core/tests/test_mi.py

@@ -34,8 +34,8 @@ def test_mi_gc(self, xy, biascorrect, copnorm):
     @pytest.mark.parametrize("xy", [(x1, x2), (j1, j2)])
     def test_mi_bin(self, xy):
         mi_fcn = get_mi(method="binning")
-        x_binned = digitize(xy[0], n_bins=3)
-        y_binned = digitize(xy[1], n_bins=3)
+        x_binned, _ = digitize(xy[0], n_bins=3)
+        y_binned, _ = digitize(xy[1], n_bins=3)
         mi = mi_fcn(x_binned, y_binned)
         assert mi.dtype == np.float32
         assert mi.shape == ()

hoi/utils/tests/test_stats.py

@@ -31,11 +31,18 @@ class TestStats(object):
     @pytest.mark.parametrize("bins", [n + 2 for n in range(5)])
     @pytest.mark.parametrize("sklearn", [True, False])
     def test_digitize(self, arr, bins, sklearn):
-        x_binned = digitize(x=arr, n_bins=bins, axis=0, use_sklearn=sklearn)
-        assert arr.shape == x_binned.shape
-        for row in x_binned:
-            for val in row:
-                assert isinstance(val, np.int64)
+        if sklearn:
+            x_binned = digitize(x=arr, n_bins=bins, axis=0, use_sklearn=sklearn)
+            assert arr.shape == x_binned.shape
+            for row in x_binned:
+                for val in row:
+                    assert isinstance(val, np.int64)
+        else:
+            x_binned, _ = digitize(x=arr, n_bins=bins, axis=0, use_sklearn=sklearn)
+            assert arr.shape == x_binned.shape
+            for row in x_binned:
+                for val in row:
+                    assert isinstance(val, np.int64)
 
     @pytest.mark.parametrize("x", [x1, x2, j2])
     @pytest.mark.parametrize("minmax", [(-10.0, 10.0), (-1.0, 1.0)])