mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-24 12:59:21 +03:00
8661218fe8
* Work on refactoring greedy parser * Compile updated parser * Fix refactored parser * Update test * Fix refactored parser * Fix refactored parser * Readd beam search after refactor * Fix beam search after refactor * Fix parser * Fix beam parsing * Support oracle segmentation in ud-train CLI command * Avoid relying on final gold check in beam search * Add a keyword argument sink to GoldParse * Bug fixes to beam search after refactor * Avoid importing fused token symbol in ud-run-test, untl that's added * Avoid importing fused token symbol in ud-run-test, untl that's added * Don't modify Token in global scope * Fix error in beam gradient calculation * Default to beam_update_prob 1 * Set a more aggressive threshold on the max violn update * Disable some tests to figure out why CI fails * Disable some tests to figure out why CI fails * Add some diagnostics to travis.yml to try to figure out why build fails * Tell Thinc to link against system blas on Travis * Point thinc to libblas on Travis * Try running sudo=true for travis * Unhack travis.sh * Restore beam_density argument for parser beam * Require thinc 6.11.1.dev16 * Revert hacks to tests * Revert hacks to travis.yml * Update thinc requirement * Fix parser model loading * Fix size limits in training data * Add missing name attribute for parser * Fix appveyor for Windows
412 lines
15 KiB
Python
412 lines
15 KiB
Python
'''Train for CONLL 2017 UD treebank evaluation. Takes .conllu files, writes
|
|
.conllu format for development data, allowing the official scorer to be used.
|
|
'''
|
|
from __future__ import unicode_literals
|
|
import plac
|
|
import tqdm
|
|
from pathlib import Path
|
|
import re
|
|
import sys
|
|
import json
|
|
|
|
import spacy
|
|
import spacy.util
|
|
from ..tokens import Token, Doc
|
|
from ..gold import GoldParse
|
|
from ..util import compounding, minibatch_by_words
|
|
from ..syntax.nonproj import projectivize
|
|
from ..matcher import Matcher
|
|
from .. import displacy
|
|
from collections import defaultdict, Counter
|
|
from timeit import default_timer as timer
|
|
|
|
import itertools
|
|
import random
|
|
import numpy.random
|
|
import cytoolz
|
|
|
|
from . import conll17_ud_eval
|
|
|
|
from .. import lang
|
|
from .. import lang
|
|
from ..lang import zh
|
|
from ..lang import ja
|
|
|
|
|
|
################
|
|
# Data reading #
|
|
################
|
|
|
|
space_re = re.compile('\s+')
|
|
def split_text(text):
|
|
return [space_re.sub(' ', par.strip()) for par in text.split('\n\n')]
|
|
|
|
|
|
def read_data(nlp, conllu_file, text_file, raw_text=True, oracle_segments=False,
|
|
max_doc_length=None, limit=None):
|
|
'''Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,
|
|
include Doc objects created using nlp.make_doc and then aligned against
|
|
the gold-standard sequences. If oracle_segments=True, include Doc objects
|
|
created from the gold-standard segments. At least one must be True.'''
|
|
if not raw_text and not oracle_segments:
|
|
raise ValueError("At least one of raw_text or oracle_segments must be True")
|
|
paragraphs = split_text(text_file.read())
|
|
conllu = read_conllu(conllu_file)
|
|
# sd is spacy doc; cd is conllu doc
|
|
# cs is conllu sent, ct is conllu token
|
|
docs = []
|
|
golds = []
|
|
for doc_id, (text, cd) in enumerate(zip(paragraphs, conllu)):
|
|
sent_annots = []
|
|
for cs in cd:
|
|
sent = defaultdict(list)
|
|
for id_, word, lemma, pos, tag, morph, head, dep, _, space_after in cs:
|
|
if '.' in id_:
|
|
continue
|
|
if '-' in id_:
|
|
continue
|
|
id_ = int(id_)-1
|
|
head = int(head)-1 if head != '0' else id_
|
|
sent['words'].append(word)
|
|
sent['tags'].append(tag)
|
|
sent['heads'].append(head)
|
|
sent['deps'].append('ROOT' if dep == 'root' else dep)
|
|
sent['spaces'].append(space_after == '_')
|
|
sent['entities'] = ['-'] * len(sent['words'])
|
|
sent['heads'], sent['deps'] = projectivize(sent['heads'],
|
|
sent['deps'])
|
|
if oracle_segments:
|
|
docs.append(Doc(nlp.vocab, words=sent['words'], spaces=sent['spaces']))
|
|
golds.append(GoldParse(docs[-1], **sent))
|
|
|
|
sent_annots.append(sent)
|
|
if raw_text and max_doc_length and len(sent_annots) >= max_doc_length:
|
|
doc, gold = _make_gold(nlp, None, sent_annots)
|
|
sent_annots = []
|
|
docs.append(doc)
|
|
golds.append(gold)
|
|
if limit and len(docs) >= limit:
|
|
return docs, golds
|
|
|
|
if raw_text and sent_annots:
|
|
doc, gold = _make_gold(nlp, None, sent_annots)
|
|
docs.append(doc)
|
|
golds.append(gold)
|
|
if limit and len(docs) >= limit:
|
|
return docs, golds
|
|
return docs, golds
|
|
|
|
|
|
def read_conllu(file_):
|
|
docs = []
|
|
sent = []
|
|
doc = []
|
|
for line in file_:
|
|
if line.startswith('# newdoc'):
|
|
if doc:
|
|
docs.append(doc)
|
|
doc = []
|
|
elif line.startswith('#'):
|
|
continue
|
|
elif not line.strip():
|
|
if sent:
|
|
doc.append(sent)
|
|
sent = []
|
|
else:
|
|
sent.append(list(line.strip().split('\t')))
|
|
if len(sent[-1]) != 10:
|
|
print(repr(line))
|
|
raise ValueError
|
|
if sent:
|
|
doc.append(sent)
|
|
if doc:
|
|
docs.append(doc)
|
|
return docs
|
|
|
|
|
|
def _make_gold(nlp, text, sent_annots, drop_deps=0.0):
|
|
# Flatten the conll annotations, and adjust the head indices
|
|
flat = defaultdict(list)
|
|
sent_starts = []
|
|
for sent in sent_annots:
|
|
flat['heads'].extend(len(flat['words'])+head for head in sent['heads'])
|
|
for field in ['words', 'tags', 'deps', 'entities', 'spaces']:
|
|
flat[field].extend(sent[field])
|
|
sent_starts.append(True)
|
|
sent_starts.extend([False] * (len(sent['words'])-1))
|
|
# Construct text if necessary
|
|
assert len(flat['words']) == len(flat['spaces'])
|
|
if text is None:
|
|
text = ''.join(word+' '*space for word, space in zip(flat['words'], flat['spaces']))
|
|
doc = nlp.make_doc(text)
|
|
flat.pop('spaces')
|
|
gold = GoldParse(doc, **flat)
|
|
gold.sent_starts = sent_starts
|
|
for i in range(len(gold.heads)):
|
|
if random.random() < drop_deps:
|
|
gold.heads[i] = None
|
|
gold.labels[i] = None
|
|
|
|
return doc, gold
|
|
|
|
#############################
|
|
# Data transforms for spaCy #
|
|
#############################
|
|
|
|
def golds_to_gold_tuples(docs, golds):
|
|
'''Get out the annoying 'tuples' format used by begin_training, given the
|
|
GoldParse objects.'''
|
|
tuples = []
|
|
for doc, gold in zip(docs, golds):
|
|
text = doc.text
|
|
ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
|
|
sents = [((ids, words, tags, heads, labels, iob), [])]
|
|
tuples.append((text, sents))
|
|
return tuples
|
|
|
|
|
|
##############
|
|
# Evaluation #
|
|
##############
|
|
|
|
def evaluate(nlp, text_loc, gold_loc, sys_loc, limit=None):
|
|
if text_loc.parts[-1].endswith('.conllu'):
|
|
docs = []
|
|
with text_loc.open() as file_:
|
|
for conllu_doc in read_conllu(file_):
|
|
for conllu_sent in conllu_doc:
|
|
words = [line[1] for line in conllu_sent]
|
|
docs.append(Doc(nlp.vocab, words=words))
|
|
for name, component in nlp.pipeline:
|
|
docs = list(component.pipe(docs))
|
|
else:
|
|
with text_loc.open('r', encoding='utf8') as text_file:
|
|
texts = split_text(text_file.read())
|
|
docs = list(nlp.pipe(texts))
|
|
with sys_loc.open('w', encoding='utf8') as out_file:
|
|
write_conllu(docs, out_file)
|
|
with gold_loc.open('r', encoding='utf8') as gold_file:
|
|
gold_ud = conll17_ud_eval.load_conllu(gold_file)
|
|
with sys_loc.open('r', encoding='utf8') as sys_file:
|
|
sys_ud = conll17_ud_eval.load_conllu(sys_file)
|
|
scores = conll17_ud_eval.evaluate(gold_ud, sys_ud)
|
|
return docs, scores
|
|
|
|
|
|
def write_conllu(docs, file_):
|
|
merger = Matcher(docs[0].vocab)
|
|
merger.add('SUBTOK', None, [{'DEP': 'subtok', 'op': '+'}])
|
|
for i, doc in enumerate(docs):
|
|
matches = merger(doc)
|
|
spans = [doc[start:end+1] for _, start, end in matches]
|
|
offsets = [(span.start_char, span.end_char) for span in spans]
|
|
for start_char, end_char in offsets:
|
|
doc.merge(start_char, end_char)
|
|
file_.write("# newdoc id = {i}\n".format(i=i))
|
|
for j, sent in enumerate(doc.sents):
|
|
file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))
|
|
file_.write("# text = {text}\n".format(text=sent.text))
|
|
for k, token in enumerate(sent):
|
|
file_.write(token._.get_conllu_lines(k) + '\n')
|
|
file_.write('\n')
|
|
|
|
|
|
def print_progress(itn, losses, ud_scores):
|
|
fields = {
|
|
'dep_loss': losses.get('parser', 0.0),
|
|
'tag_loss': losses.get('tagger', 0.0),
|
|
'words': ud_scores['Words'].f1 * 100,
|
|
'sents': ud_scores['Sentences'].f1 * 100,
|
|
'tags': ud_scores['XPOS'].f1 * 100,
|
|
'uas': ud_scores['UAS'].f1 * 100,
|
|
'las': ud_scores['LAS'].f1 * 100,
|
|
}
|
|
header = ['Epoch', 'Loss', 'LAS', 'UAS', 'TAG', 'SENT', 'WORD']
|
|
if itn == 0:
|
|
print('\t'.join(header))
|
|
tpl = '\t'.join((
|
|
'{:d}',
|
|
'{dep_loss:.1f}',
|
|
'{las:.1f}',
|
|
'{uas:.1f}',
|
|
'{tags:.1f}',
|
|
'{sents:.1f}',
|
|
'{words:.1f}',
|
|
))
|
|
print(tpl.format(itn, **fields))
|
|
|
|
#def get_sent_conllu(sent, sent_id):
|
|
# lines = ["# sent_id = {sent_id}".format(sent_id=sent_id)]
|
|
|
|
def get_token_conllu(token, i):
|
|
if token._.begins_fused:
|
|
n = 1
|
|
while token.nbor(n)._.inside_fused:
|
|
n += 1
|
|
id_ = '%d-%d' % (i, i+n)
|
|
lines = [id_, token.text, '_', '_', '_', '_', '_', '_', '_', '_']
|
|
else:
|
|
lines = []
|
|
if token.head.i == token.i:
|
|
head = 0
|
|
else:
|
|
head = i + (token.head.i - token.i) + 1
|
|
fields = [str(i+1), token.text, token.lemma_, token.pos_, token.tag_, '_',
|
|
str(head), token.dep_.lower(), '_', '_']
|
|
lines.append('\t'.join(fields))
|
|
return '\n'.join(lines)
|
|
|
|
Token.set_extension('get_conllu_lines', method=get_token_conllu)
|
|
Token.set_extension('begins_fused', default=False)
|
|
Token.set_extension('inside_fused', default=False)
|
|
|
|
|
|
##################
|
|
# Initialization #
|
|
##################
|
|
|
|
|
|
def load_nlp(corpus, config, vectors=None):
|
|
lang = corpus.split('_')[0]
|
|
nlp = spacy.blank(lang)
|
|
if config.vectors:
|
|
if not vectors:
|
|
raise ValueError("config asks for vectors, but no vectors "
|
|
"directory set on command line (use -v)")
|
|
if (Path(vectors) / corpus).exists():
|
|
nlp.vocab.from_disk(Path(vectors) / corpus / 'vocab')
|
|
nlp.meta['treebank'] = corpus
|
|
return nlp
|
|
|
|
|
|
def initialize_pipeline(nlp, docs, golds, config, device):
|
|
nlp.add_pipe(nlp.create_pipe('tagger'))
|
|
nlp.add_pipe(nlp.create_pipe('parser'))
|
|
if config.multitask_tag:
|
|
nlp.parser.add_multitask_objective('tag')
|
|
if config.multitask_sent:
|
|
nlp.parser.add_multitask_objective('sent_start')
|
|
for gold in golds:
|
|
for tag in gold.tags:
|
|
if tag is not None:
|
|
nlp.tagger.add_label(tag)
|
|
return nlp.begin_training(lambda: golds_to_gold_tuples(docs, golds), device=device)
|
|
|
|
|
|
########################
|
|
# Command line helpers #
|
|
########################
|
|
|
|
class Config(object):
|
|
def __init__(self, vectors=None, max_doc_length=10, multitask_tag=True,
|
|
multitask_sent=True, multitask_dep=True, multitask_vectors=False,
|
|
nr_epoch=30, batch_size=1000, dropout=0.2):
|
|
for key, value in locals().items():
|
|
setattr(self, key, value)
|
|
|
|
|
|
@classmethod
|
|
def load(cls, loc):
|
|
with Path(loc).open('r', encoding='utf8') as file_:
|
|
cfg = json.load(file_)
|
|
return cls(**cfg)
|
|
|
|
|
|
class Dataset(object):
|
|
def __init__(self, path, section):
|
|
self.path = path
|
|
self.section = section
|
|
self.conllu = None
|
|
self.text = None
|
|
for file_path in self.path.iterdir():
|
|
name = file_path.parts[-1]
|
|
if section in name and name.endswith('conllu'):
|
|
self.conllu = file_path
|
|
elif section in name and name.endswith('txt'):
|
|
self.text = file_path
|
|
if self.conllu is None:
|
|
msg = "Could not find .txt file in {path} for {section}"
|
|
raise IOError(msg.format(section=section, path=path))
|
|
if self.text is None:
|
|
msg = "Could not find .txt file in {path} for {section}"
|
|
self.lang = self.conllu.parts[-1].split('-')[0].split('_')[0]
|
|
|
|
|
|
class TreebankPaths(object):
|
|
def __init__(self, ud_path, treebank, **cfg):
|
|
self.train = Dataset(ud_path / treebank, 'train')
|
|
self.dev = Dataset(ud_path / treebank, 'dev')
|
|
self.lang = self.train.lang
|
|
|
|
|
|
@plac.annotations(
|
|
ud_dir=("Path to Universal Dependencies corpus", "positional", None, Path),
|
|
corpus=("UD corpus to train and evaluate on, e.g. en, es_ancora, etc",
|
|
"positional", None, str),
|
|
parses_dir=("Directory to write the development parses", "positional", None, Path),
|
|
config=("Path to json formatted config file", "positional"),
|
|
limit=("Size limit", "option", "n", int),
|
|
use_gpu=("Use GPU", "option", "g", int),
|
|
use_oracle_segments=("Use oracle segments", "flag", "G", int),
|
|
vectors_dir=("Path to directory with pre-trained vectors, named e.g. en/",
|
|
"option", "v", Path),
|
|
)
|
|
def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=None,
|
|
use_oracle_segments=False):
|
|
spacy.util.fix_random_seed()
|
|
lang.zh.Chinese.Defaults.use_jieba = False
|
|
lang.ja.Japanese.Defaults.use_janome = False
|
|
|
|
config = Config.load(config)
|
|
paths = TreebankPaths(ud_dir, corpus)
|
|
if not (parses_dir / corpus).exists():
|
|
(parses_dir / corpus).mkdir()
|
|
print("Train and evaluate", corpus, "using lang", paths.lang)
|
|
nlp = load_nlp(paths.lang, config, vectors=vectors_dir)
|
|
|
|
docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
|
|
max_doc_length=None, limit=limit)
|
|
|
|
optimizer = initialize_pipeline(nlp, docs, golds, config, use_gpu)
|
|
|
|
batch_sizes = compounding(config.batch_size//10, config.batch_size, 1.001)
|
|
nlp.parser.cfg['beam_update_prob'] = 1.0
|
|
for i in range(config.nr_epoch):
|
|
docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
|
|
max_doc_length=config.max_doc_length, limit=limit,
|
|
oracle_segments=use_oracle_segments,
|
|
raw_text=not use_oracle_segments)
|
|
Xs = list(zip(docs, golds))
|
|
random.shuffle(Xs)
|
|
batches = minibatch_by_words(Xs, size=batch_sizes)
|
|
losses = {}
|
|
n_train_words = sum(len(doc) for doc in docs)
|
|
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
|
|
for batch in batches:
|
|
batch_docs, batch_gold = zip(*batch)
|
|
pbar.update(sum(len(doc) for doc in batch_docs))
|
|
nlp.update(batch_docs, batch_gold, sgd=optimizer,
|
|
drop=config.dropout, losses=losses)
|
|
|
|
out_path = parses_dir / corpus / 'epoch-{i}.conllu'.format(i=i)
|
|
with nlp.use_params(optimizer.averages):
|
|
if use_oracle_segments:
|
|
parsed_docs, scores = evaluate(nlp, paths.dev.conllu,
|
|
paths.dev.conllu, out_path)
|
|
else:
|
|
parsed_docs, scores = evaluate(nlp, paths.dev.text,
|
|
paths.dev.conllu, out_path)
|
|
print_progress(i, losses, scores)
|
|
_render_parses(i, parsed_docs[:50])
|
|
|
|
|
|
def _render_parses(i, to_render):
|
|
to_render[0].user_data['title'] = "Batch %d" % i
|
|
with Path('/tmp/parses.html').open('w') as file_:
|
|
html = displacy.render(to_render[:5], style='dep', page=True)
|
|
file_.write(html)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
plac.call(main)
|