Table.from_table: Obey is_sparse when returning subarrays

When we return subarryas, the flag `is_sparse` wasn't considered, but we simpy returned the subarray in it's original format.

Orange/data/table.py

@@ -268,6 +268,15 @@ def from_table(cls, domain, source, row_indices=...):
 
         def get_columns(row_indices, src_cols, n_rows, dtype=np.float64,
                         is_sparse=False):
+            def match_type(x):
+                """ Assure that matrix and column are both dense or sparse. """
+                if is_sparse == sp.issparse(x):
+                    return x
+                elif is_sparse:
+                    x = np.asarray(x)
+                    return sp.csc_matrix(x.reshape(-1, 1).astype(np.float))
+                else:
+                    return np.ravel(x.toarray())
 
             if not len(src_cols):
                 if is_sparse:
@@ -278,33 +287,23 @@ def get_columns(row_indices, src_cols, n_rows, dtype=np.float64,
             n_src_attrs = len(source.domain.attributes)
             if all(isinstance(x, Integral) and 0 <= x < n_src_attrs
                    for x in src_cols):
-                return _subarray(source.X, row_indices, src_cols)
+                return match_type(_subarray(source.X, row_indices, src_cols))
             if all(isinstance(x, Integral) and x < 0 for x in src_cols):
-                arr = _subarray(source.metas, row_indices,
-                                [-1 - x for x in src_cols])
+                arr = match_type(_subarray(source.metas, row_indices,
+                                [-1 - x for x in src_cols]))
                 if arr.dtype != dtype:
                     return arr.astype(dtype)
                 return arr
             if all(isinstance(x, Integral) and x >= n_src_attrs
                    for x in src_cols):
-                return _subarray(source._Y, row_indices,
-                                 [x - n_src_attrs for x in src_cols])
+                return match_type(_subarray(source._Y, row_indices,
+                                 [x - n_src_attrs for x in src_cols]))
 
             if is_sparse:
                 a = sp.dok_matrix((n_rows, len(src_cols)), dtype=dtype)
             else:
                 a = np.empty((n_rows, len(src_cols)), dtype=dtype)
 
-            def match_type(x):
-                """ Assure that matrix and column are both dense or sparse. """
-                if is_sparse == sp.issparse(x):
-                    return x
-                elif is_sparse:
-                    x = np.asarray(x)
-                    return sp.csc_matrix(x.reshape(-1, 1).astype(np.float))
-                else:
-                    return np.ravel(x.toarray())
-
             shared_cache = _conversion_cache
             for i, col in enumerate(src_cols):
                 if col is None: