spaCy/bin/parser/conll_train.py
2015-10-31 00:53:51 +11:00

142 lines
4.3 KiB
Python
Executable File

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