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https://github.com/explosion/spaCy.git
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546 lines
17 KiB
Python
546 lines
17 KiB
Python
# flake8: noqa
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"""Train for CONLL 2017 UD treebank evaluation. Takes .conllu files, writes
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.conllu format for development data, allowing the official scorer to be used.
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"""
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from __future__ import unicode_literals
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import plac
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from pathlib import Path
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import re
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import sys
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import json
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import spacy
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import spacy.util
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from spacy.tokens import Token, Doc
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from spacy.gold import GoldParse
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from spacy.util import compounding, minibatch, minibatch_by_words
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from spacy.syntax.nonproj import projectivize
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from spacy.matcher import Matcher
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from spacy import displacy
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from collections import defaultdict, Counter
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from timeit import default_timer as timer
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import itertools
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import random
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import numpy.random
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from . import conll17_ud_eval
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from spacy import lang
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from spacy.lang import zh
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from spacy.lang import ja
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try:
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import torch
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except ImportError:
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torch = None
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################
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# Data reading #
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################
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space_re = re.compile("\s+")
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def split_text(text):
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return [space_re.sub(" ", par.strip()) for par in text.split("\n\n")]
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def read_data(
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nlp,
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conllu_file,
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text_file,
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raw_text=True,
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oracle_segments=False,
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max_doc_length=None,
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limit=None,
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):
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"""Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,
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include Doc objects created using nlp.make_doc and then aligned against
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the gold-standard sequences. If oracle_segments=True, include Doc objects
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created from the gold-standard segments. At least one must be True."""
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if not raw_text and not oracle_segments:
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raise ValueError("At least one of raw_text or oracle_segments must be True")
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paragraphs = split_text(text_file.read())
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conllu = read_conllu(conllu_file)
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# sd is spacy doc; cd is conllu doc
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# cs is conllu sent, ct is conllu token
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docs = []
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golds = []
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for doc_id, (text, cd) in enumerate(zip(paragraphs, conllu)):
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sent_annots = []
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for cs in cd:
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sent = defaultdict(list)
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for id_, word, lemma, pos, tag, morph, head, dep, _, space_after in cs:
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if "." in id_:
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continue
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if "-" in id_:
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continue
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id_ = int(id_) - 1
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head = int(head) - 1 if head != "0" else id_
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sent["words"].append(word)
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sent["tags"].append(tag)
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sent["heads"].append(head)
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sent["deps"].append("ROOT" if dep == "root" else dep)
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sent["spaces"].append(space_after == "_")
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sent["entities"] = ["-"] * len(sent["words"])
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sent["heads"], sent["deps"] = projectivize(sent["heads"], sent["deps"])
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if oracle_segments:
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docs.append(Doc(nlp.vocab, words=sent["words"], spaces=sent["spaces"]))
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golds.append(GoldParse(docs[-1], **sent))
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sent_annots.append(sent)
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if raw_text and max_doc_length and len(sent_annots) >= max_doc_length:
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doc, gold = _make_gold(nlp, None, sent_annots)
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sent_annots = []
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docs.append(doc)
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golds.append(gold)
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if limit and len(docs) >= limit:
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return docs, golds
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if raw_text and sent_annots:
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doc, gold = _make_gold(nlp, None, sent_annots)
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docs.append(doc)
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golds.append(gold)
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if limit and len(docs) >= limit:
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return docs, golds
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return docs, golds
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def read_conllu(file_):
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docs = []
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sent = []
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doc = []
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for line in file_:
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if line.startswith("# newdoc"):
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if doc:
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docs.append(doc)
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doc = []
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elif line.startswith("#"):
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continue
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elif not line.strip():
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if sent:
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doc.append(sent)
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sent = []
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else:
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sent.append(list(line.strip().split("\t")))
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if len(sent[-1]) != 10:
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print(repr(line))
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raise ValueError
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if sent:
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doc.append(sent)
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if doc:
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docs.append(doc)
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return docs
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def _make_gold(nlp, text, sent_annots, drop_deps=0.0):
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# Flatten the conll annotations, and adjust the head indices
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flat = defaultdict(list)
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sent_starts = []
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for sent in sent_annots:
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flat["heads"].extend(len(flat["words"]) + head for head in sent["heads"])
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for field in ["words", "tags", "deps", "entities", "spaces"]:
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flat[field].extend(sent[field])
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sent_starts.append(True)
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sent_starts.extend([False] * (len(sent["words"]) - 1))
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# Construct text if necessary
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assert len(flat["words"]) == len(flat["spaces"])
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if text is None:
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text = "".join(
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word + " " * space for word, space in zip(flat["words"], flat["spaces"])
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)
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doc = nlp.make_doc(text)
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flat.pop("spaces")
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gold = GoldParse(doc, **flat)
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gold.sent_starts = sent_starts
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for i in range(len(gold.heads)):
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if random.random() < drop_deps:
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gold.heads[i] = None
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gold.labels[i] = None
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return doc, gold
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#############################
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# Data transforms for spaCy #
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#############################
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def golds_to_gold_tuples(docs, golds):
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"""Get out the annoying 'tuples' format used by begin_training, given the
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GoldParse objects."""
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tuples = []
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for doc, gold in zip(docs, golds):
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text = doc.text
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ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
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sents = [((ids, words, tags, heads, labels, iob), [])]
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tuples.append((text, sents))
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return tuples
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##############
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# Evaluation #
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##############
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def evaluate(nlp, text_loc, gold_loc, sys_loc, limit=None):
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if text_loc.parts[-1].endswith(".conllu"):
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docs = []
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with text_loc.open() as file_:
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for conllu_doc in read_conllu(file_):
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for conllu_sent in conllu_doc:
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words = [line[1] for line in conllu_sent]
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docs.append(Doc(nlp.vocab, words=words))
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for name, component in nlp.pipeline:
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docs = list(component.pipe(docs))
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else:
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with text_loc.open("r", encoding="utf8") as text_file:
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texts = split_text(text_file.read())
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docs = list(nlp.pipe(texts))
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with sys_loc.open("w", encoding="utf8") as out_file:
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write_conllu(docs, out_file)
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with gold_loc.open("r", encoding="utf8") as gold_file:
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gold_ud = conll17_ud_eval.load_conllu(gold_file)
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with sys_loc.open("r", encoding="utf8") as sys_file:
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sys_ud = conll17_ud_eval.load_conllu(sys_file)
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scores = conll17_ud_eval.evaluate(gold_ud, sys_ud)
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return docs, scores
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def write_conllu(docs, file_):
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merger = Matcher(docs[0].vocab)
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merger.add("SUBTOK", None, [{"DEP": "subtok", "op": "+"}])
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for i, doc in enumerate(docs):
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matches = merger(doc)
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spans = [doc[start : end + 1] for _, start, end in matches]
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with doc.retokenize() as retokenizer:
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for span in spans:
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retokenizer.merge(span)
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file_.write("# newdoc id = {i}\n".format(i=i))
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for j, sent in enumerate(doc.sents):
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file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))
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file_.write("# text = {text}\n".format(text=sent.text))
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for k, token in enumerate(sent):
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if token.head.i > sent[-1].i or token.head.i < sent[0].i:
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for word in doc[sent[0].i - 10 : sent[0].i]:
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print(word.i, word.head.i, word.text, word.dep_)
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for word in sent:
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print(word.i, word.head.i, word.text, word.dep_)
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for word in doc[sent[-1].i : sent[-1].i + 10]:
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print(word.i, word.head.i, word.text, word.dep_)
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raise ValueError(
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"Invalid parse: head outside sentence (%s)" % token.text
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)
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file_.write(token._.get_conllu_lines(k) + "\n")
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file_.write("\n")
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def print_progress(itn, losses, ud_scores):
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fields = {
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"dep_loss": losses.get("parser", 0.0),
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"tag_loss": losses.get("tagger", 0.0),
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"words": ud_scores["Words"].f1 * 100,
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"sents": ud_scores["Sentences"].f1 * 100,
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"tags": ud_scores["XPOS"].f1 * 100,
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"uas": ud_scores["UAS"].f1 * 100,
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"las": ud_scores["LAS"].f1 * 100,
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}
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header = ["Epoch", "Loss", "LAS", "UAS", "TAG", "SENT", "WORD"]
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if itn == 0:
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print("\t".join(header))
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tpl = "\t".join(
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(
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"{:d}",
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"{dep_loss:.1f}",
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"{las:.1f}",
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"{uas:.1f}",
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"{tags:.1f}",
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"{sents:.1f}",
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"{words:.1f}",
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)
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)
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print(tpl.format(itn, **fields))
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# def get_sent_conllu(sent, sent_id):
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# lines = ["# sent_id = {sent_id}".format(sent_id=sent_id)]
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def get_token_conllu(token, i):
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if token._.begins_fused:
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n = 1
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while token.nbor(n)._.inside_fused:
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n += 1
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id_ = "%d-%d" % (i, i + n)
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lines = [id_, token.text, "_", "_", "_", "_", "_", "_", "_", "_"]
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else:
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lines = []
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if token.head.i == token.i:
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head = 0
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else:
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head = i + (token.head.i - token.i) + 1
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fields = [
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str(i + 1),
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token.text,
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token.lemma_,
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token.pos_,
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token.tag_,
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"_",
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str(head),
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token.dep_.lower(),
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"_",
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"_",
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]
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lines.append("\t".join(fields))
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return "\n".join(lines)
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Token.set_extension("get_conllu_lines", method=get_token_conllu)
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Token.set_extension("begins_fused", default=False)
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Token.set_extension("inside_fused", default=False)
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##################
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# Initialization #
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##################
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def load_nlp(corpus, config, vectors=None):
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lang = corpus.split("_")[0]
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nlp = spacy.blank(lang)
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if config.vectors:
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if not vectors:
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raise ValueError(
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"config asks for vectors, but no vectors "
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"directory set on command line (use -v)"
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)
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if (Path(vectors) / corpus).exists():
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nlp.vocab.from_disk(Path(vectors) / corpus / "vocab")
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nlp.meta["treebank"] = corpus
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return nlp
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def initialize_pipeline(nlp, docs, golds, config, device):
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nlp.add_pipe(nlp.create_pipe("tagger"))
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nlp.add_pipe(nlp.create_pipe("parser"))
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if config.multitask_tag:
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nlp.parser.add_multitask_objective("tag")
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if config.multitask_sent:
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nlp.parser.add_multitask_objective("sent_start")
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for gold in golds:
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for tag in gold.tags:
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if tag is not None:
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nlp.tagger.add_label(tag)
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if torch is not None and device != -1:
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torch.set_default_tensor_type("torch.cuda.FloatTensor")
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optimizer = nlp.begin_training(
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lambda: golds_to_gold_tuples(docs, golds),
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device=device,
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subword_features=config.subword_features,
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conv_depth=config.conv_depth,
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bilstm_depth=config.bilstm_depth,
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)
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if config.pretrained_tok2vec:
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_load_pretrained_tok2vec(nlp, config.pretrained_tok2vec)
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return optimizer
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def _load_pretrained_tok2vec(nlp, loc):
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"""Load pre-trained weights for the 'token-to-vector' part of the component
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models, which is typically a CNN. See 'spacy pretrain'. Experimental.
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"""
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with Path(loc).open("rb") as file_:
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weights_data = file_.read()
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loaded = []
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for name, component in nlp.pipeline:
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if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
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component.tok2vec.from_bytes(weights_data)
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loaded.append(name)
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return loaded
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########################
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# Command line helpers #
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########################
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class Config(object):
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def __init__(
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self,
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vectors=None,
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max_doc_length=10,
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multitask_tag=False,
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multitask_sent=False,
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multitask_dep=False,
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multitask_vectors=None,
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bilstm_depth=0,
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nr_epoch=30,
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min_batch_size=100,
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max_batch_size=1000,
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batch_by_words=True,
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dropout=0.2,
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conv_depth=4,
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subword_features=True,
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vectors_dir=None,
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pretrained_tok2vec=None,
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):
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if vectors_dir is not None:
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if vectors is None:
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vectors = True
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if multitask_vectors is None:
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multitask_vectors = True
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for key, value in locals().items():
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setattr(self, key, value)
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@classmethod
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def load(cls, loc, vectors_dir=None):
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with Path(loc).open("r", encoding="utf8") as file_:
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cfg = json.load(file_)
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if vectors_dir is not None:
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cfg["vectors_dir"] = vectors_dir
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return cls(**cfg)
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class Dataset(object):
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def __init__(self, path, section):
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self.path = path
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self.section = section
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self.conllu = None
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self.text = None
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for file_path in self.path.iterdir():
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name = file_path.parts[-1]
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if section in name and name.endswith("conllu"):
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self.conllu = file_path
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elif section in name and name.endswith("txt"):
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self.text = file_path
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if self.conllu is None:
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msg = "Could not find .txt file in {path} for {section}"
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raise IOError(msg.format(section=section, path=path))
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if self.text is None:
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msg = "Could not find .txt file in {path} for {section}"
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self.lang = self.conllu.parts[-1].split("-")[0].split("_")[0]
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class TreebankPaths(object):
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def __init__(self, ud_path, treebank, **cfg):
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self.train = Dataset(ud_path / treebank, "train")
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self.dev = Dataset(ud_path / treebank, "dev")
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self.lang = self.train.lang
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@plac.annotations(
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ud_dir=("Path to Universal Dependencies corpus", "positional", None, Path),
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corpus=(
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"UD corpus to train and evaluate on, e.g. en, es_ancora, etc",
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"positional",
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None,
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str,
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),
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parses_dir=("Directory to write the development parses", "positional", None, Path),
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config=("Path to json formatted config file", "option", "C", Path),
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limit=("Size limit", "option", "n", int),
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gpu_device=("Use GPU", "option", "g", int),
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use_oracle_segments=("Use oracle segments", "flag", "G", int),
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vectors_dir=(
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"Path to directory with pre-trained vectors, named e.g. en/",
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"option",
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"v",
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Path,
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),
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)
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def main(
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ud_dir,
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parses_dir,
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corpus,
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config=None,
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limit=0,
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gpu_device=-1,
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vectors_dir=None,
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use_oracle_segments=False,
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):
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# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
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import tqdm
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spacy.util.fix_random_seed()
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lang.zh.Chinese.Defaults.use_jieba = False
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lang.ja.Japanese.Defaults.use_janome = False
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if config is not None:
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config = Config.load(config, vectors_dir=vectors_dir)
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else:
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config = Config(vectors_dir=vectors_dir)
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paths = TreebankPaths(ud_dir, corpus)
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if not (parses_dir / corpus).exists():
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(parses_dir / corpus).mkdir()
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print("Train and evaluate", corpus, "using lang", paths.lang)
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nlp = load_nlp(paths.lang, config, vectors=vectors_dir)
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docs, golds = read_data(
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nlp,
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paths.train.conllu.open(),
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paths.train.text.open(),
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max_doc_length=config.max_doc_length,
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limit=limit,
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)
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optimizer = initialize_pipeline(nlp, docs, golds, config, gpu_device)
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batch_sizes = compounding(config.min_batch_size, config.max_batch_size, 1.001)
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beam_prob = compounding(0.2, 0.8, 1.001)
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for i in range(config.nr_epoch):
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docs, golds = read_data(
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nlp,
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paths.train.conllu.open(),
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paths.train.text.open(),
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max_doc_length=config.max_doc_length,
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limit=limit,
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oracle_segments=use_oracle_segments,
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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:
|
|
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") as file_:
|
|
html = displacy.render(to_render[:5], style="dep", page=True)
|
|
file_.write(html)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
plac.call(main)
|