Merge pull request scipy#9646 from ev-br/stats_mode_obj

BUG: stats: mode for objects w/ndim > 1

scipy/stats/stats.py

@@ -239,13 +239,17 @@ def _contains_nan(a, nan_policy='propagate'):
         with np.errstate(invalid='ignore'):
             contains_nan = np.isnan(np.sum(a))
     except TypeError:
-        # If the check cannot be properly performed we fallback to omitting
-        # nan values and raising a warning. This can happen when attempting to
-        # sum things that are not numbers (e.g. as in the function `mode`).
-        contains_nan = False
-        nan_policy = 'omit'
-        warnings.warn("The input array could not be properly checked for nan "
-                      "values. nan values will be ignored.", RuntimeWarning)
+        # This can happen when attempting to sum things which are not
+        # numbers (e.g. as in the function `mode`). Try an alternative method:
+        try:
+            contains_nan = np.nan in set(a.ravel())
+        except TypeError:
+            # Don't know what to do. Fall back to omitting nan values and
+            # issue a warning.
+            contains_nan = False
+            nan_policy = 'omit'
+            warnings.warn("The input array could not be properly checked for nan "
+                          "values. nan values will be ignored.", RuntimeWarning)
 
     if contains_nan and nan_policy == 'raise':
         raise ValueError("The input contains nan values")
@@ -439,9 +443,8 @@ def mode(a, axis=0, nan_policy='propagate'):
         a = ma.masked_invalid(a)
         return mstats_basic.mode(a, axis)
 
-    if (NumpyVersion(np.__version__) < '1.9.0') or (a.dtype == object and np.nan in set(a)):
+    if a.dtype == object and np.nan in set(a.ravel()):
         # Fall back to a slower method since np.unique does not work with NaN
-        # or for older numpy which does not support return_counts
         scores = set(np.ravel(a))  # get ALL unique values
         testshape = list(a.shape)
         testshape[axis] = 1

scipy/stats/tests/test_stats.py

@@ -1430,23 +1430,15 @@ def test_axes(self):
 
     def test_strings(self):
         data1 = ['rain', 'showers', 'showers']
-
-        with suppress_warnings() as sup:
-            r = sup.record(RuntimeWarning, ".*checked for nan values")
-            vals = stats.mode(data1)
-            assert_equal(len(r), 1)
-
+        vals = stats.mode(data1)
         assert_equal(vals[0][0], 'showers')
         assert_equal(vals[1][0], 2)
 
     def test_mixed_objects(self):
         objects = [10, True, np.nan, 'hello', 10]
         arr = np.empty((5,), dtype=object)
         arr[:] = objects
-        with suppress_warnings() as sup:
-            r = sup.record(RuntimeWarning, ".*checked for nan values")
-            vals = stats.mode(arr)
-            assert_equal(len(r), 1)
+        vals = stats.mode(arr)
         assert_equal(vals[0][0], 10)
         assert_equal(vals[1][0], 2)
 
@@ -1474,10 +1466,7 @@ def __hash__(self):
         arr[:] = points
         assert_(len(set(points)) == 4)
         assert_equal(np.unique(arr).shape, (4,))
-        with suppress_warnings() as sup:
-            r = sup.record(RuntimeWarning, ".*checked for nan values")
-            vals = stats.mode(arr)
-            assert_equal(len(r), 1)
+        vals = stats.mode(arr)
 
         assert_equal(vals[0][0], Point(2))
         assert_equal(vals[1][0], 4)
@@ -1511,6 +1500,20 @@ def test_smallest_equal(self, data):
         result = stats.mode(data, nan_policy='omit')
         assert_equal(result[0][0], 1)
 
+    def test_obj_arrays_ndim(self):
+        # regression test for gh-9645: `mode` fails for object arrays w/ndim > 1
+        data = [['Oxidation'], ['Oxidation'], ['Polymerization'], ['Reduction']]
+        ar = np.array(data, dtype=object)
+        m = stats.mode(ar, axis=0)
+        assert np.all(m.mode == 'Oxidation') and m.mode.shape == (1, 1)
+        assert np.all(m.count == 2) and m.count.shape == (1, 1)
+
+        data1 = data + [[np.nan]]
+        ar1 = np.array(data1, dtype=object)
+        m = stats.mode(ar1, axis=0)
+        assert np.all(m.mode == 'Oxidation') and m.mode.shape == (1, 1)
+        assert np.all(m.count == 2) and m.count.shape == (1, 1)
+
 
 class TestVariability(object):