[FIX] Corpus - from_table: keep text feature when renamed

orangecontrib/text/corpus.py

@@ -591,6 +591,7 @@ def retain_preprocessing(orig, new, key=...):
         if isinstance(orig, Corpus):
             if isinstance(key, tuple):  # get row selection
                 key = key[0]
+
             if orig._tokens is not None:  # retain preprocessing
                 if isinstance(key, Integral):
                     new._tokens = np.array([orig._tokens[key]])
@@ -606,9 +607,22 @@ def retain_preprocessing(orig, new, key=...):
                 else:
                     raise TypeError('Indexing by type {} not supported.'.format(type(key)))
                 new._dictionary = orig._dictionary
+
+            if isinstance(new, Corpus):
+                # _find_identical_feature returns non when feature not found
+                # filter this Nones from list
+                new.text_features = list(filter(None, [
+                    new._find_identical_feature(tf)
+                    for tf in orig.text_features
+                ]))
+            else:
+                new.text_features = [
+                    tf
+                    for tf in orig.text_features
+                    if tf in set(new.domain.metas)
+                ]
+
             new._titles = orig._titles[key]
-            new_domain_metas = set(new.domain.metas)
-            new.text_features = [tf for tf in orig.text_features if tf in new_domain_metas]
             new.ngram_range = orig.ngram_range
             new.attributes = orig.attributes
             new.used_preprocessor = orig.used_preprocessor

orangecontrib/text/tests/test_corpus.py

@@ -180,6 +180,18 @@ def test_from_table(self):
         np.testing.assert_equal(t.metas, c.metas)
         self.assertEqual(c.text_features, [t.domain.metas[0]])
 
+    def test_from_table_renamed(self):
+        c1 = Corpus.from_file('book-excerpts')
+        new_domain = Domain(c1.domain.attributes, metas=[c1.domain.metas[0].renamed("text1")])
+
+        # when text feature renamed
+        c2 = Corpus.from_table(new_domain, c1)
+        self.assertIsInstance(c2, Corpus)
+        self.assertEqual(len(c1), len(c2))
+        np.testing.assert_equal(c1.metas, c2.metas)
+        self.assertEqual(1, len(c2.text_features))
+        self.assertEqual("text1", c2.text_features[0].name)
+
     def test_infer_text_features(self):
         c = Corpus.from_file('friends-transcripts')
         tf = c.text_features