mirror of https://github.com/explosion/spaCy.git
570 lines
19 KiB
Python
570 lines
19 KiB
Python
# flake8: noqa
|
|
"""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
|
|
from pathlib import Path
|
|
import re
|
|
import json
|
|
import tqdm
|
|
|
|
import spacy
|
|
import spacy.util
|
|
from bin.ud import conll17_ud_eval
|
|
from spacy.tokens import Token, Doc
|
|
from spacy.gold import Example
|
|
from spacy.util import compounding, minibatch, minibatch_by_words
|
|
from spacy.syntax.nonproj import projectivize
|
|
from spacy.matcher import Matcher
|
|
from spacy import displacy
|
|
from collections import defaultdict
|
|
|
|
import random
|
|
|
|
from spacy import lang
|
|
from spacy.lang import zh
|
|
from spacy.lang import ja
|
|
|
|
try:
|
|
import torch
|
|
except ImportError:
|
|
torch = None
|
|
|
|
|
|
################
|
|
# 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 Example objects. 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["morphology"].append(_parse_morph_string(morph))
|
|
sent["morphology"][-1].add("POS_%s" % pos)
|
|
sent["heads"].append(head)
|
|
sent["deps"].append("ROOT" if dep == "root" else dep)
|
|
sent["spaces"].append(space_after == "_")
|
|
sent["entities"] = ["-"] * len(sent["words"]) # TODO: doc-level format
|
|
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(sent)
|
|
assert golds[-1].morphology is not None
|
|
|
|
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)
|
|
assert gold.morphology is not None
|
|
sent_annots = []
|
|
docs.append(doc)
|
|
golds.append(gold)
|
|
if limit and len(docs) >= limit:
|
|
return golds_to_gold_data(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 golds_to_gold_data(docs, golds)
|
|
return golds_to_gold_data(docs, golds)
|
|
|
|
|
|
def _parse_morph_string(morph_string):
|
|
if morph_string == '_':
|
|
return set()
|
|
output = []
|
|
replacements = {'1': 'one', '2': 'two', '3': 'three'}
|
|
for feature in morph_string.split('|'):
|
|
key, value = feature.split('=')
|
|
value = replacements.get(value, value)
|
|
value = value.split(',')[0]
|
|
output.append('%s_%s' % (key, value.lower()))
|
|
return set(output)
|
|
|
|
|
|
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
|
|
gold = defaultdict(list)
|
|
sent_starts = []
|
|
for sent in sent_annots:
|
|
gold["heads"].extend(len(gold["words"])+head for head in sent["heads"])
|
|
for field in ["words", "tags", "deps", "morphology", "entities", "spaces"]:
|
|
gold[field].extend(sent[field])
|
|
sent_starts.append(True)
|
|
sent_starts.extend([False] * (len(sent["words"]) - 1))
|
|
# Construct text if necessary
|
|
assert len(gold["words"]) == len(gold["spaces"])
|
|
if text is None:
|
|
text = "".join(
|
|
word + " " * space for word, space in zip(gold["words"], gold["spaces"])
|
|
)
|
|
doc = nlp.make_doc(text)
|
|
gold.pop("spaces")
|
|
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_data(docs, golds):
|
|
"""Get out the training data format used by begin_training"""
|
|
data = []
|
|
for doc, gold in zip(docs, golds):
|
|
example = Example.from_dict(doc, gold)
|
|
data.append(example)
|
|
return data
|
|
|
|
|
|
##############
|
|
# Evaluation #
|
|
##############
|
|
|
|
|
|
def evaluate(nlp, text_loc, gold_loc, sys_loc, limit=None):
|
|
if text_loc.parts[-1].endswith(".conllu"):
|
|
docs = []
|
|
with text_loc.open(encoding="utf8") 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_):
|
|
if not Token.has_extension("get_conllu_lines"):
|
|
Token.set_extension("get_conllu_lines", method=get_token_conllu)
|
|
if not Token.has_extension("begins_fused"):
|
|
Token.set_extension("begins_fused", default=False)
|
|
if not Token.has_extension("inside_fused"):
|
|
Token.set_extension("inside_fused", default=False)
|
|
|
|
merger = Matcher(docs[0].vocab)
|
|
merger.add("SUBTOK", None, [{"DEP": "subtok", "op": "+"}])
|
|
for i, doc in enumerate(docs):
|
|
matches = []
|
|
if doc.is_parsed:
|
|
matches = merger(doc)
|
|
spans = [doc[start : end + 1] for _, start, end in matches]
|
|
seen_tokens = set()
|
|
with doc.retokenize() as retokenizer:
|
|
for span in spans:
|
|
span_tokens = set(range(span.start, span.end))
|
|
if not span_tokens.intersection(seen_tokens):
|
|
retokenizer.merge(span)
|
|
seen_tokens.update(span_tokens)
|
|
|
|
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):
|
|
if token.head.i > sent[-1].i or token.head.i < sent[0].i:
|
|
for word in doc[sent[0].i - 10 : sent[0].i]:
|
|
print(word.i, word.head.i, word.text, word.dep_)
|
|
for word in sent:
|
|
print(word.i, word.head.i, word.text, word.dep_)
|
|
for word in doc[sent[-1].i : sent[-1].i + 10]:
|
|
print(word.i, word.head.i, word.text, word.dep_)
|
|
raise ValueError(
|
|
"Invalid parse: head outside sentence (%s)" % token.text
|
|
)
|
|
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),
|
|
"morph_loss": losses.get("morphologizer", 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,
|
|
"morph": ud_scores["Feats"].f1 * 100,
|
|
}
|
|
header = ["Epoch", "P.Loss", "M.Loss", "LAS", "UAS", "TAG", "MORPH", "SENT", "WORD"]
|
|
if itn == 0:
|
|
print("\t".join(header))
|
|
tpl = "\t".join((
|
|
"{:d}",
|
|
"{dep_loss:.1f}",
|
|
"{morph_loss:.1f}",
|
|
"{las:.1f}",
|
|
"{uas:.1f}",
|
|
"{tags:.1f}",
|
|
"{morph:.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
|
|
features = list(token.morph)
|
|
feat_str = []
|
|
replacements = {"one": "1", "two": "2", "three": "3"}
|
|
for feat in features:
|
|
if not feat.startswith("begin") and not feat.startswith("end"):
|
|
key, value = feat.split("_", 1)
|
|
value = replacements.get(value, value)
|
|
feat_str.append("%s=%s" % (key, value.title()))
|
|
if not feat_str:
|
|
feat_str = "_"
|
|
else:
|
|
feat_str = "|".join(feat_str)
|
|
fields = [str(i+1), token.text, token.lemma_, token.pos_, token.tag_, feat_str,
|
|
str(head), token.dep_.lower(), "_", "_"]
|
|
lines.append("\t".join(fields))
|
|
return "\n".join(lines)
|
|
|
|
|
|
##################
|
|
# 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, examples, config, device):
|
|
nlp.add_pipe(nlp.create_pipe("tagger", config={"set_morphology": False}))
|
|
nlp.add_pipe(nlp.create_pipe("morphologizer"))
|
|
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 eg in examples:
|
|
gold = eg.gold
|
|
for tag in gold.tags:
|
|
if tag is not None:
|
|
nlp.tagger.add_label(tag)
|
|
if torch is not None and device != -1:
|
|
torch.set_default_tensor_type("torch.cuda.FloatTensor")
|
|
optimizer = nlp.begin_training(
|
|
lambda: examples,
|
|
device=device,
|
|
subword_features=config.subword_features,
|
|
conv_depth=config.conv_depth,
|
|
bilstm_depth=config.bilstm_depth,
|
|
)
|
|
if config.pretrained_tok2vec:
|
|
_load_pretrained_tok2vec(nlp, config.pretrained_tok2vec)
|
|
return optimizer
|
|
|
|
|
|
def _load_pretrained_tok2vec(nlp, loc):
|
|
"""Load pretrained weights for the 'token-to-vector' part of the component
|
|
models, which is typically a CNN. See 'spacy pretrain'. Experimental.
|
|
"""
|
|
with Path(loc).open("rb", encoding="utf8") as file_:
|
|
weights_data = file_.read()
|
|
loaded = []
|
|
for name, component in nlp.pipeline:
|
|
if hasattr(component, "model") and component.model.has_ref("tok2vec"):
|
|
component.get_ref("tok2vec").from_bytes(weights_data)
|
|
loaded.append(name)
|
|
return loaded
|
|
|
|
|
|
########################
|
|
# Command line helpers #
|
|
########################
|
|
|
|
|
|
class Config(object):
|
|
def __init__(
|
|
self,
|
|
vectors=None,
|
|
max_doc_length=10,
|
|
multitask_tag=False,
|
|
multitask_sent=False,
|
|
multitask_dep=False,
|
|
multitask_vectors=None,
|
|
bilstm_depth=0,
|
|
nr_epoch=30,
|
|
min_batch_size=100,
|
|
max_batch_size=1000,
|
|
batch_by_words=True,
|
|
dropout=0.2,
|
|
conv_depth=4,
|
|
subword_features=True,
|
|
vectors_dir=None,
|
|
pretrained_tok2vec=None,
|
|
):
|
|
if vectors_dir is not None:
|
|
if vectors is None:
|
|
vectors = True
|
|
if multitask_vectors is None:
|
|
multitask_vectors = True
|
|
for key, value in locals().items():
|
|
setattr(self, key, value)
|
|
|
|
@classmethod
|
|
def load(cls, loc, vectors_dir=None):
|
|
with Path(loc).open("r", encoding="utf8") as file_:
|
|
cfg = json.load(file_)
|
|
if vectors_dir is not None:
|
|
cfg["vectors_dir"] = vectors_dir
|
|
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),
|
|
parses_dir=("Directory to write the development parses", "positional", None, Path),
|
|
corpus=(
|
|
"UD corpus to train and evaluate on, e.g. UD_Spanish-AnCora",
|
|
"positional",
|
|
None,
|
|
str,
|
|
),
|
|
config=("Path to json formatted config file", "option", "C", Path),
|
|
limit=("Size limit", "option", "n", int),
|
|
gpu_device=("Use GPU", "option", "g", int),
|
|
use_oracle_segments=("Use oracle segments", "flag", "G", int),
|
|
vectors_dir=(
|
|
"Path to directory with pretrained vectors, named e.g. en/",
|
|
"option",
|
|
"v",
|
|
Path,
|
|
),
|
|
)
|
|
def main(
|
|
ud_dir,
|
|
parses_dir,
|
|
corpus,
|
|
config=None,
|
|
limit=0,
|
|
gpu_device=-1,
|
|
vectors_dir=None,
|
|
use_oracle_segments=False,
|
|
):
|
|
Token.set_extension("get_conllu_lines", method=get_token_conllu)
|
|
Token.set_extension("begins_fused", default=False)
|
|
Token.set_extension("inside_fused", default=False)
|
|
|
|
Token.set_extension("get_conllu_lines", method=get_token_conllu)
|
|
Token.set_extension("begins_fused", default=False)
|
|
Token.set_extension("inside_fused", default=False)
|
|
|
|
spacy.util.fix_random_seed()
|
|
lang.zh.Chinese.Defaults.use_jieba = False
|
|
lang.ja.Japanese.Defaults.use_janome = False
|
|
|
|
if config is not None:
|
|
config = Config.load(config, vectors_dir=vectors_dir)
|
|
else:
|
|
config = Config(vectors_dir=vectors_dir)
|
|
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)
|
|
|
|
examples = read_data(
|
|
nlp,
|
|
paths.train.conllu.open(encoding="utf8"),
|
|
paths.train.text.open(encoding="utf8"),
|
|
max_doc_length=config.max_doc_length,
|
|
limit=limit,
|
|
)
|
|
|
|
optimizer = initialize_pipeline(nlp, examples, config, gpu_device)
|
|
|
|
batch_sizes = compounding(config.min_batch_size, config.max_batch_size, 1.001)
|
|
beam_prob = compounding(0.2, 0.8, 1.001)
|
|
for i in range(config.nr_epoch):
|
|
examples = read_data(
|
|
nlp,
|
|
paths.train.conllu.open(encoding="utf8"),
|
|
paths.train.text.open(encoding="utf8"),
|
|
max_doc_length=config.max_doc_length,
|
|
limit=limit,
|
|
oracle_segments=use_oracle_segments,
|
|
raw_text=not use_oracle_segments,
|
|
)
|
|
random.shuffle(examples)
|
|
if config.batch_by_words:
|
|
batches = minibatch_by_words(examples, size=batch_sizes)
|
|
else:
|
|
batches = minibatch(examples, size=batch_sizes)
|
|
losses = {}
|
|
n_train_words = sum(len(eg.doc) for eg in examples)
|
|
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
|
|
for batch in batches:
|
|
pbar.update(sum(len(ex.doc) for ex in batch))
|
|
nlp.parser.cfg["beam_update_prob"] = next(beam_prob)
|
|
nlp.update(
|
|
batch,
|
|
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)
|
|
|
|
|
|
def _render_parses(i, to_render):
|
|
to_render[0].user_data["title"] = "Batch %d" % i
|
|
with Path("/tmp/parses.html").open("w", encoding="utf8") as file_:
|
|
html = displacy.render(to_render[:5], style="dep", page=True)
|
|
file_.write(html)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
plac.call(main)
|