Stats: Fix counting nans for non-numeric data

Stats returned negative numbers of nans on non-numberic data (metas). Hence, for metas, table widget always showed 'no missing values'. The problem was in priorities. We should first negate binary array and only then sum. Without brackets binary array was first summed and only then ~ was applied. On integers ~ returns -(x+1) which lead to negative values.

Author: nikicc

Orange/statistics/util.py

@@ -195,7 +195,7 @@ def stats(X, weights=None, compute_variance=False):
             X.shape[1] - non_zero,
             non_zero))
     else:
-        nans = ~X.astype(bool).sum(axis=0) if X.size else np.zeros(X.shape[1])
+        nans = (~X.astype(bool)).sum(axis=0) if X.size else np.zeros(X.shape[1])
         return np.column_stack((
             np.tile(np.inf, X.shape[1]),
             np.tile(-np.inf, X.shape[1]),

Orange/tests/test_statistics.py

@@ -69,3 +69,13 @@ def test_stats_weights_sparse(self):
         weights = np.array([1, 3])
         np.testing.assert_equal(stats(X, weights), [[0, 2, 1.5, 0, 1, 1],
                                                     [1, 3, 2.5, 0, 0, 2]])
+
+    def test_stats_non_numeric(self):
+        X = np.array([
+            ['', 'a', 'b'],
+            ['a', '', 'b'],
+            ['a', 'b', ''],
+        ], dtype=object)
+        np.testing.assert_equal(stats(X), [[np.inf, -np.inf, 0, 0, 1, 2],
+                                           [np.inf, -np.inf, 0, 0, 1, 2],
+                                           [np.inf, -np.inf, 0, 0, 1, 2]])