from __future__ import unicode_literals import plac import json from os import path import shutil import os import random import io import pathlib from spacy.tokens import Doc from spacy.syntax.nonproj import PseudoProjectivity 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.syntax.parser import get_templates from spacy.syntax.arc_eager import ArcEager from spacy.scorer import Scorer import spacy.attrs import io def read_conllx(loc): with io.open(loc, 'r', encoding='utf8') as file_: text = file_.read() for sent in text.strip().split('\n\n'): lines = sent.strip().split('\n') if lines: while lines[0].startswith('#'): lines.pop(0) tokens = [] for line in lines: id_, word, lemma, tag, pos, morph, head, dep, _1, _2 = line.split() if '-' in id_: continue try: id_ = int(id_) - 1 head = (int(head) - 1) if head != '0' else id_ dep = 'ROOT' if dep == 'root' else dep tokens.append((id_, word, tag, head, dep, 'O')) except: print(line) raise tuples = [list(t) for t in zip(*tokens)] yield (None, [[tuples, []]]) def score_model(vocab, tagger, parser, gold_docs, verbose=False): scorer = Scorer() for _, gold_doc in gold_docs: for (ids, words, tags, heads, deps, entities), _ in gold_doc: doc = Doc(vocab, words=words) tagger(doc) parser(doc) PseudoProjectivity.deprojectivize(doc) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) scorer.score(doc, gold, verbose=verbose) return scorer def main(train_loc, dev_loc, model_dir, tag_map_loc): with open(tag_map_loc) as file_: tag_map = json.loads(file_.read()) train_sents = list(read_conllx(train_loc)) train_sents = PseudoProjectivity.preprocess_training_data(train_sents) actions = ArcEager.get_actions(gold_parses=train_sents) features = get_templates('basic') model_dir = pathlib.Path(model_dir) with (model_dir / 'deps' / 'config.json').open('w') as file_: json.dump({'pseudoprojective': True, 'labels': actions, 'features': features}, file_) vocab = Vocab(lex_attr_getters=Language.Defaults.lex_attr_getters, tag_map=tag_map) # Populate vocab for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: for word in words: _ = vocab[word] for dep in deps: _ = vocab[dep] for tag in tags: _ = vocab[tag] for tag in tags: assert tag in tag_map, repr(tag) tagger = Tagger(vocab, tag_map=tag_map) parser = DependencyParser(vocab, actions=actions, features=features) for itn in range(15): for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: doc = Doc(vocab, words=words) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) tagger(doc) parser.update(doc, gold) doc = Doc(vocab, words=words) tagger.update(doc, gold) random.shuffle(train_sents) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc)) nlp = Language(vocab=vocab, tagger=tagger, parser=parser) nlp.end_training(model_dir) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc)) if __name__ == '__main__': plac.call(main)