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spaCy/spacy/cli/ud/ud_train.py
Matthew Honnibal 3993f41cc4 Update morphology branch from develop
2019-03-07 00:14:43 +01:00

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# 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
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, 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
from . import conll17_ud_eval
from ... import lang
from ...lang import zh
from ...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 (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["morphology"].append(_parse_morph_string(morph))
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))
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 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 _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
flat = defaultdict(list)
sent_starts = []
for sent in sent_annots:
<<<<<<< HEAD:spacy/cli/ud_train.py
flat['heads'].extend(len(flat['words'])+head for head in sent['heads'])
for field in ['words', 'tags', 'deps', 'morphology', 'entities', 'spaces']:
=======
flat["heads"].extend(len(flat["words"]) + head for head in sent["heads"])
for field in ["words", "tags", "deps", "entities", "spaces"]:
>>>>>>> develop:spacy/cli/ud/ud_train.py
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]
with doc.retokenize() as retokenizer:
for span in spans:
retokenizer.merge(span)
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 = {
<<<<<<< HEAD:spacy/cli/ud_train.py
'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}',
))
=======
"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}",
)
)
>>>>>>> develop:spacy/cli/ud/ud_train.py
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
<<<<<<< HEAD:spacy/cli/ud_train.py
features = token.vocab.morphology.get(token.morph_key)
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('_')
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)
Token.set_extension('get_conllu_lines', method=get_token_conllu)
Token.set_extension('begins_fused', default=False)
Token.set_extension('inside_fused', default=False)
=======
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)
>>>>>>> develop:spacy/cli/ud/ud_train.py
##################
# 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):
<<<<<<< HEAD:spacy/cli/ud_train.py
nlp.add_pipe(nlp.create_pipe('tagger'))
nlp.add_pipe(nlp.create_pipe('morphologizer'))
nlp.add_pipe(nlp.create_pipe('parser'))
=======
nlp.add_pipe(nlp.create_pipe("tagger"))
nlp.add_pipe(nlp.create_pipe("parser"))
>>>>>>> develop:spacy/cli/ud/ud_train.py
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)
if torch is not None and device != -1:
torch.set_default_tensor_type("torch.cuda.FloatTensor")
optimizer = nlp.begin_training(
lambda: golds_to_gold_tuples(docs, golds),
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 pre-trained 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") as file_:
weights_data = file_.read()
loaded = []
for name, component in nlp.pipeline:
if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
component.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),
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", "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 pre-trained 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,
):
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)
docs, golds = read_data(
nlp,
paths.train.conllu.open(),
paths.train.text.open(),
max_doc_length=config.max_doc_length,
limit=limit,
)
optimizer = initialize_pipeline(nlp, docs, golds, 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):
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)
if config.batch_by_words:
batches = minibatch_by_words(Xs, size=batch_sizes)
else:
batches = minibatch(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.parser.cfg["beam_update_prob"] = next(beam_prob)
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:
<<<<<<< HEAD:spacy/cli/ud_train.py
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)
=======
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)
>>>>>>> develop:spacy/cli/ud/ud_train.py
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)
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