spaCy/bin/tagger/train.py

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2015-08-27 10:15:41 +03:00
#!/usr/bin/env python
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import os
from os import path
import shutil
import codecs
import random
import plac
import re
import spacy.util
from spacy.en import English
from spacy.tagger import Tagger
from spacy.syntax.util import Config
from spacy.gold import read_json_file
from spacy.gold import GoldParse
from spacy.scorer import Scorer
def score_model(scorer, nlp, raw_text, annot_tuples):
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold)
def _merge_sents(sents):
m_deps = [[], [], [], [], [], []]
m_brackets = []
i = 0
for (ids, words, tags, heads, labels, ner), brackets in sents:
m_deps[0].extend(id_ + i for id_ in ids)
m_deps[1].extend(words)
m_deps[2].extend(tags)
m_deps[3].extend(head + i for head in heads)
m_deps[4].extend(labels)
m_deps[5].extend(ner)
m_brackets.extend((b['first'] + i, b['last'] + i, b['label']) for b in brackets)
i += len(ids)
return [(m_deps, m_brackets)]
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
beam_width=1, verbose=False,
use_orig_arc_eager=False):
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
templates = Tagger.default_templates()
nlp = Language(data_dir=model_dir, tagger=False)
nlp.tagger = Tagger.blank(nlp.vocab, templates)
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for raw_text, sents in gold_tuples:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
words = annot_tuples[1]
gold_tags = annot_tuples[2]
score_model(scorer, nlp, raw_text, annot_tuples)
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(words)
else:
tokens = nlp.tokenizer(raw_text)
loss += nlp.tagger.train(tokens, gold_tags)
random.shuffle(gold_tuples)
print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
scorer.tags_acc,
scorer.token_acc))
nlp.end_training(model_dir)
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
beam_width=None):
nlp = Language(data_dir=model_dir)
if beam_width is not None:
nlp.parser.cfg.beam_width = beam_width
scorer = Scorer()
for raw_text, sents in gold_tuples:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, brackets in sents:
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
else:
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
return scorer
def write_parses(Language, dev_loc, model_dir, out_loc, beam_width=None):
nlp = Language(data_dir=model_dir)
if beam_width is not None:
nlp.parser.cfg.beam_width = beam_width
gold_tuples = read_json_file(dev_loc)
scorer = Scorer()
out_file = codecs.open(out_loc, 'w', 'utf8')
for raw_text, sents in gold_tuples:
sents = _merge_sents(sents)
for annot_tuples, brackets in sents:
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
else:
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=False)
for t in tokens:
out_file.write(
'%s\t%s\t%s\t%s\n' % (t.orth_, t.tag_, t.head.orth_, t.dep_)
)
return scorer
@plac.annotations(
train_loc=("Location of training file or directory"),
dev_loc=("Location of development file or directory"),
model_dir=("Location of output model directory",),
eval_only=("Skip training, and only evaluate", "flag", "e", bool),
corruption_level=("Amount of noise to add to training data", "option", "c", float),
gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
out_loc=("Out location", "option", "o", str),
n_sents=("Number of training sentences", "option", "n", int),
n_iter=("Number of training iterations", "option", "i", int),
verbose=("Verbose error reporting", "flag", "v", bool),
debug=("Debug mode", "flag", "d", bool),
)
def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False):
if not eval_only:
gold_train = list(read_json_file(train_loc))
train(English, gold_train, model_dir,
feat_set='basic' if not debug else 'debug',
gold_preproc=gold_preproc, n_sents=n_sents,
corruption_level=corruption_level, n_iter=n_iter,
verbose=verbose)
#if out_loc:
# write_parses(English, dev_loc, model_dir, out_loc, beam_width=beam_width)
scorer = evaluate(English, list(read_json_file(dev_loc)),
model_dir, gold_preproc=gold_preproc, verbose=verbose)
print('TOK', scorer.token_acc)
print('POS', scorer.tags_acc)
print('UAS', scorer.uas)
print('LAS', scorer.las)
print('NER P', scorer.ents_p)
print('NER R', scorer.ents_r)
print('NER F', scorer.ents_f)
if __name__ == '__main__':
plac.call(main)