Add xfail test for #3433. Improve test for add label.

spacy/tests/parser/test_add_label.py

@@ -8,7 +8,8 @@ from spacy.attrs import NORM
 from spacy.gold import GoldParse
 from spacy.vocab import Vocab
 from spacy.tokens import Doc
-from spacy.pipeline import DependencyParser
+from spacy.pipeline import DependencyParser, EntityRecognizer
+from spacy.util import fix_random_seed
 
 
 @pytest.fixture
@@ -19,18 +20,6 @@ def vocab():
 @pytest.fixture
 def parser(vocab):
     parser = DependencyParser(vocab)
-    parser.cfg["token_vector_width"] = 8
-    parser.cfg["hidden_width"] = 30
-    parser.cfg["hist_size"] = 0
-    parser.add_label("left")
-    parser.begin_training([], **parser.cfg)
-    sgd = Adam(NumpyOps(), 0.001)
-
-    for i in range(10):
-        losses = {}
-        doc = Doc(vocab, words=["a", "b", "c", "d"])
-        gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
-        parser.update([doc], [gold], sgd=sgd, losses=losses)
     return parser
 
 
@@ -38,10 +27,22 @@ def test_init_parser(parser):
     pass
 
 
-# TODO: This is flakey, because it depends on what the parser first learns.
-# TODO: This now seems to be implicated in segfaults. Not sure what's up!
-@pytest.mark.skip
+def _train_parser(parser):
+    fix_random_seed(1)
+    parser.add_label("left")
+    parser.begin_training([], **parser.cfg)
+    sgd = Adam(NumpyOps(), 0.001)
+
+    for i in range(10):
+        losses = {}
+        doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
+        gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
+        parser.update([doc], [gold], sgd=sgd, losses=losses)
+    return parser
+
+
 def test_add_label(parser):
+    parser = _train_parser(parser)
     doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
     doc = parser(doc)
     assert doc[0].head.i == 1
@@ -69,3 +70,16 @@ def test_add_label(parser):
     doc = parser(doc)
     assert doc[0].dep_ == "right"
     assert doc[2].dep_ == "left"
+
+
+@pytest.mark.xfail
+def test_add_label_deserializes_correctly():
+    ner1 = EntityRecognizer(Vocab())
+    ner1.add_label("C")
+    ner1.add_label("B")
+    ner1.add_label("A")
+    ner1.begin_training([])
+    ner2 = EntityRecognizer(Vocab()).from_bytes(ner1.to_bytes())
+    assert ner1.moves.n_moves == ner2.moves.n_moves
+    for i in range(ner1.moves.n_moves):
+        assert ner1.moves.get_class_name(i) == ner2.moves.get_class_name(i)