Add example for training parser

examples/training/train_parser.py

@@ -0,0 +1,81 @@
+from __future__ import unicode_literals, print_function
+import json
+import pathlib
+import random
+
+import spacy
+from spacy.pipeline import DependencyParser
+from spacy.gold import GoldParse
+from spacy.tokens import Doc
+
+
+def train_parser(nlp, train_data, left_labels, right_labels):
+    parser = DependencyParser.blank(
+                                nlp.vocab,
+                                left_labels=left_labels,
+                                right_labels=right_labels,
+                                features=nlp.defaults.parser_features)
+    for itn in range(1000):
+        random.shuffle(train_data)
+        loss = 0
+        for words, heads, deps in train_data:
+            doc = nlp.make_doc(words)
+            gold = GoldParse(doc, heads=heads, deps=deps)
+            loss += parser.update(doc, gold)
+    parser.model.end_training()
+    return parser
+
+
+def main(model_dir=None):
+    if model_dir is not None:
+        model_dir = pathlb.Path(model_dir)
+        if not model_dir.exists():
+            model_dir.mkdir()
+        assert model_dir.isdir()
+
+    nlp = spacy.load('en', tagger=False, parser=False, entity=False, vectors=False)
+    nlp.make_doc = lambda words: Doc(nlp.vocab, zip(words, [True]*len(words)))
+
+    train_data = [
+        (
+            ['They', 'trade',  'mortgage', '-', 'backed', 'securities', '.'],
+            [1, 1, 4, 4, 5, 1, 1],
+            ['nsubj', 'ROOT', 'compound', 'punct', 'nmod', 'dobj', 'punct']
+        ),
+        (
+            ['I', 'like', 'London', 'and', 'Berlin', '.'],
+            [1, 1, 1, 2, 2, 1],
+            ['nsubj', 'ROOT', 'dobj', 'cc', 'conj', 'punct']
+        )
+    ]
+    left_labels = set()
+    right_labels = set()
+    for _, heads, deps in train_data:
+        for i, (head, dep) in enumerate(zip(heads, deps)):
+            if i < head:
+                left_labels.add(dep)
+            elif i > head:
+                right_labels.add(dep)
+    parser = train_parser(nlp, train_data, sorted(left_labels), sorted(right_labels))
+
+    doc = nlp.make_doc(['I', 'like', 'securities', '.'])
+    with parser.step_through(doc) as state:
+        while not state.is_final:
+            action = state.predict()
+            state.transition(action)
+    #parser(doc)
+    for word in doc:
+        print(word.text, word.dep_, word.head.text)
+
+    if model_dir is not None:
+        with (model_dir / 'config.json').open('wb') as file_:
+            json.dump(parser.cfg, file_)
+        parser.model.dump(str(model_dir / 'model'))
+
+
+if __name__ == '__main__':
+    main()
+    # I nsubj like
+    # like ROOT like
+    # securities dobj like
+    # . cc securities