Update train_ud for Universal Dependencies 2

bin/parser/train_ud.py

@@ -14,7 +14,7 @@ from spacy.language import Language
 from spacy.gold import GoldParse
 from spacy.vocab import Vocab
 from spacy.tagger import Tagger
-from spacy.pipeline import DependencyParser
+from spacy.pipeline import DependencyParser, BeamDependencyParser
 from spacy.syntax.parser import get_templates
 from spacy.syntax.arc_eager import ArcEager
 from spacy.scorer import Scorer
@@ -35,8 +35,8 @@ def read_conllx(loc, n=0):
                 lines.pop(0)
             tokens = []
             for line in lines:
-                id_, word, lemma, tag, pos, morph, head, dep, _1, _2 = line.split()
+                id_, word, lemma, pos, tag, morph, head, dep, _1, _2 = line.split()
-                if '-' in id_:
+                if '-' in id_ or '.' in id_:
                     continue
                 try:
                     id_ = int(id_) - 1
@@ -66,12 +66,8 @@ def score_model(vocab, tagger, parser, gold_docs, verbose=False):
     return scorer
 
 
-def main(train_loc, dev_loc, model_dir, tag_map_loc=None):
+def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
-    if tag_map_loc:
+    LangClass = spacy.util.get_lang_class(lang_name)
-        with open(tag_map_loc) as file_:
-            tag_map = json.loads(file_.read())
-    else:
-        tag_map = DEFAULT_TAG_MAP
     train_sents = list(read_conllx(train_loc))
     train_sents = PseudoProjectivity.preprocess_training_data(train_sents)
 
@@ -79,13 +75,37 @@ def main(train_loc, dev_loc, model_dir, tag_map_loc=None):
     features = get_templates('basic')
 
     model_dir = pathlib.Path(model_dir)
+    if not model_dir.exists():
+        model_dir.mkdir()
     if not (model_dir / 'deps').exists():
         (model_dir / 'deps').mkdir()
+    if not (model_dir / 'pos').exists():
+        (model_dir / 'pos').mkdir()
     with (model_dir / 'deps' / 'config.json').open('wb') as file_:
         file_.write(
             json.dumps(
                 {'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8'))
-    vocab = Vocab(lex_attr_getters=Language.Defaults.lex_attr_getters, tag_map=tag_map)
+
+    vocab = LangClass.Defaults.create_vocab()
+    if not (model_dir / 'vocab').exists():
+        (model_dir / 'vocab').mkdir()
+    else:
+        if (model_dir / 'vocab' / 'strings.json').exists():
+            with (model_dir / 'vocab' / 'strings.json').open() as file_:
+                vocab.strings.load(file_)
+            if (model_dir / 'vocab' / 'lexemes.bin').exists():
+                vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin')
+
+    if clusters_loc is not None:
+        clusters_loc = pathlib.Path(clusters_loc)
+        with clusters_loc.open() as file_:
+            for line in file_:
+                try:
+                    cluster, word, freq = line.split()
+                except ValueError:
+                    continue
+                lex = vocab[word]
+                lex.cluster = int(cluster[::-1], 2)
     # Populate vocab
     for _, doc_sents in train_sents:
         for (ids, words, tags, heads, deps, ner), _ in doc_sents:
@@ -95,13 +115,13 @@ def main(train_loc, dev_loc, model_dir, tag_map_loc=None):
                 _ = vocab[dep]
             for tag in tags:
                 _ = vocab[tag]
-            if tag_map:
+            if vocab.morphology.tag_map:
                 for tag in tags:
-                    assert tag in tag_map, repr(tag)
+                    assert tag in vocab.morphology.tag_map, repr(tag)
-    tagger = Tagger(vocab, tag_map=tag_map)
+    tagger = Tagger(vocab)
     parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0)
 
-    for itn in range(15):
+    for itn in range(30):
         loss = 0.
         for _, doc_sents in train_sents:
             for (ids, words, tags, heads, deps, ner), _ in doc_sents: