table: Ensure correct dtype in _compute_distributions

Fix an 'ValueError: cannot convert float NaN to integer' in bincount
when the column data comes from a object array and contains NaN values.

Author: ales-erjavec

Orange/data/table.py

@@ -1309,6 +1309,8 @@ def _get_matrix(M, cachedM, col):
                 m, W, Xcsc = _get_matrix(self.X, Xcsc, col)
             elif col < 0:
                 m, W, Xcsc = _get_matrix(self.metas, Xcsc, col * (-1) - 1)
+                if np.issubdtype(m.dtype, np.dtype(object)):
+                    m = m.astype(float)
             else:
                 m, W, Ycsc = _get_matrix(self._Y, Ycsc, col - self.X.shape[1])
             if var.is_discrete:

Orange/tests/test_distribution.py

@@ -430,7 +430,13 @@ def test_compute_distributions_metas(self):
         variable = d.domain[-2]
         dist, _ = d._compute_distributions([variable])[0]
         np.testing.assert_almost_equal(dist, [3, 3, 2])
-
+        # repeat with nan values
+        assert d.metas.dtype.kind == "O"
+        assert d.metas[0, 1] == 0
+        d.metas[0, 1] = np.nan
+        dist, nanc = d._compute_distributions([variable])[0]
+        np.testing.assert_almost_equal(dist, [2, 3, 2])
+        self.assertEqual(nanc, 1)
 
 if __name__ == "__main__":
     unittest.main()