mirror of
https://github.com/explosion/spaCy.git
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142 lines
4.3 KiB
Python
Executable File
142 lines
4.3 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import division
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from __future__ import unicode_literals
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import os
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from os import path
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import shutil
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import codecs
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import random
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import time
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import gzip
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import plac
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import cProfile
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import pstats
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import spacy.util
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from spacy.en import English
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from spacy.gold import GoldParse
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from spacy.syntax.util import Config
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from spacy.syntax.arc_eager import ArcEager
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from spacy.syntax.parser import Parser
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from spacy.scorer import Scorer
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from spacy.tagger import Tagger
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# Last updated for spaCy v0.97
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def read_conll(file_):
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"""Read a standard CoNLL/MALT-style format"""
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sents = []
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for sent_str in file_.read().strip().split('\n\n'):
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ids = []
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words = []
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heads = []
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labels = []
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tags = []
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for i, line in enumerate(sent_str.split('\n')):
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word, pos_string, head_idx, label = _parse_line(line)
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words.append(word)
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if head_idx < 0:
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head_idx = i
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ids.append(i)
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heads.append(head_idx)
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labels.append(label)
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tags.append(pos_string)
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text = ' '.join(words)
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annot = (ids, words, tags, heads, labels, ['O'] * len(ids))
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sents.append((None, [(annot, [])]))
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return sents
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def _parse_line(line):
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pieces = line.split()
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if len(pieces) == 4:
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word, pos, head_idx, label = pieces
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head_idx = int(head_idx)
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else:
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id_ = int(pieces[0])
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word = pieces[1]
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pos = pieces[4]
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head_idx = int(pieces[6])-1
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label = pieces[7]
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return word, pos, head_idx, label
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def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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nlp.tagger(tokens)
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nlp.parser(tokens)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold, verbose=verbose)
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def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0,
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gold_preproc=False, force_gold=False):
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dep_model_dir = path.join(model_dir, 'deps')
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pos_model_dir = path.join(model_dir, 'pos')
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if path.exists(dep_model_dir):
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shutil.rmtree(dep_model_dir)
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if path.exists(pos_model_dir):
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shutil.rmtree(pos_model_dir)
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os.mkdir(dep_model_dir)
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os.mkdir(pos_model_dir)
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Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
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labels=ArcEager.get_labels(gold_tuples))
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nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
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nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
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nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
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print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
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for itn in range(n_iter):
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scorer = Scorer()
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loss = 0
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for _, sents in gold_tuples:
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for annot_tuples, _ in sents:
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if len(annot_tuples[1]) == 1:
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continue
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score_model(scorer, nlp, None, annot_tuples, verbose=False)
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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nlp.tagger(tokens)
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gold = GoldParse(tokens, annot_tuples, make_projective=True)
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if not gold.is_projective:
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raise Exception(
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"Non-projective sentence in training, after we should "
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"have enforced projectivity: %s" % annot_tuples
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)
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loss += nlp.parser.train(tokens, gold)
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nlp.tagger.train(tokens, gold.tags)
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random.shuffle(gold_tuples)
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print('%d:\t%d\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas,
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scorer.tags_acc, scorer.token_acc))
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print('end training')
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nlp.end_training(model_dir)
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print('done')
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def main(train_loc, dev_loc, model_dir):
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with codecs.open(train_loc, 'r', 'utf8') as file_:
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train_sents = read_conll(file_)
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train(English, train_sents, model_dir)
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nlp = English(data_dir=model_dir)
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dev_sents = read_conll(open(dev_loc))
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scorer = Scorer()
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for _, sents in dev_sents:
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for annot_tuples, _ in sents:
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score_model(scorer, nlp, None, annot_tuples)
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print('TOK', 100-scorer.token_acc)
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print('POS', scorer.tags_acc)
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print('UAS', scorer.uas)
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print('LAS', scorer.las)
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if __name__ == '__main__':
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plac.call(main)
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