Table: Fix ensure_copy for sparse matrices

`ensure_copy` is checking whether an array is a view through the `.base` argument which doesn't exist on `scipy.sparse` matrices. Since `scipy.sparse` don't work with views (e.g. indexing returns a copy of the matrix) the check should only be performed for dense matrices.

Orange/data/table.py

@@ -912,15 +912,19 @@ def is_copy(self):
 
     def ensure_copy(self):
         """
-        Ensure that the table owns its data; copy arrays when necessary
+        Ensure that the table owns its data; copy arrays when necessary.
+        The check is skipped for sparse matrices since they don't have views like numpy arrays.
         """
-        if self.X.base is not None:
+        def is_view(x):
+            return not sp.issparse(x) and x.base is not None
+
+        if is_view(self.X):
             self.X = self.X.copy()
-        if self._Y.base is not None:
+        if is_view(self._Y):
             self._Y = self._Y.copy()
-        if self.metas.base is not None:
+        if is_view(self.metas):
             self.metas = self.metas.copy()
-        if self.W.base is not None:
+        if is_view(self.W):
             self.W = self.W.copy()
 
     def copy(self):

Orange/tests/test_table.py

@@ -649,6 +649,19 @@ def test_copy(self):
         self.assertFalse(np.all(t.Y == copy.Y))
         self.assertFalse(np.all(t.metas == copy.metas))
 
+    def test_copy_sparse(self):
+        t = data.Table('iris')
+        t.X = csr_matrix(t.X)
+        copy = t.copy()
+
+        self.assertEqual((t.X != copy.X).nnz, 0)      # sparse matrices match by content
+        np.testing.assert_equal(t.Y, copy.Y)
+        np.testing.assert_equal(t.metas, copy.metas)
+
+        self.assertNotEqual(id(t.X), id(copy.X))
+        self.assertNotEqual(id(t._Y), id(copy._Y))
+        self.assertNotEqual(id(t.metas), id(copy.metas))
+
     def test_concatenate(self):
         d1 = data.Domain([data.ContinuousVariable('a1')])
         t1 = data.Table.from_numpy(d1, [[1],