mirror of https://github.com/explosion/spaCy.git
280 lines
9.6 KiB
Python
Executable File
280 lines
9.6 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import division
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from __future__ import unicode_literals
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import os
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from os import path
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import shutil
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import codecs
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import random
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import plac
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import cProfile
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import pstats
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import re
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import spacy.util
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from spacy.en import English
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from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir
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from spacy.syntax.util import Config
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from spacy.gold import read_json_file
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from spacy.gold import GoldParse
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from spacy.scorer import Scorer
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def add_noise(c, noise_level):
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if random.random() >= noise_level:
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return c
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elif c == ' ':
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return '\n'
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elif c == '\n':
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return ' '
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elif c in ['.', "'", "!", "?"]:
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return ''
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else:
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return c.lower()
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def score_model(scorer, nlp, raw_text, annot_tuples, train_tags=None):
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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else:
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tokens = nlp.tokenizer(raw_text)
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if train_tags is not None:
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key = hash(tokens.string)
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nlp.tagger.tag_from_strings(tokens, train_tags[key])
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else:
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nlp.tagger(tokens)
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nlp.entity(tokens)
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nlp.parser(tokens)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold, verbose=False)
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def _merge_sents(sents):
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m_deps = [[], [], [], [], [], []]
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m_brackets = []
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i = 0
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for (ids, words, tags, heads, labels, ner), brackets in sents:
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m_deps[0].extend(id_ + i for id_ in ids)
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m_deps[1].extend(words)
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m_deps[2].extend(tags)
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m_deps[3].extend(head + i for head in heads)
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m_deps[4].extend(labels)
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m_deps[5].extend(ner)
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m_brackets.extend((b['first'] + i, b['last'] + i, b['label']) for b in brackets)
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i += len(ids)
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return [(m_deps, m_brackets)]
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def get_train_tags(Language, model_dir, docs, gold_preproc):
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taggings = {}
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for train_part, test_part in get_partitions(docs, 5):
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nlp = _train_tagger(Language, model_dir, train_part, gold_preproc)
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for tokens in _tag_partition(nlp, test_part):
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taggings[hash(tokens.string)] = [w.tag_ for w in tokens]
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return taggings
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def get_partitions(docs, n_parts):
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random.shuffle(docs)
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n_test = len(docs) / n_parts
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n_train = len(docs) - n_test
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for part in range(n_parts):
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start = int(part * n_test)
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end = int(start + n_test)
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yield docs[:start] + docs[end:], docs[start:end]
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def _train_tagger(Language, model_dir, docs, gold_preproc=False, n_iter=5):
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pos_model_dir = path.join(model_dir, 'pos')
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if path.exists(pos_model_dir):
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shutil.rmtree(pos_model_dir)
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os.mkdir(pos_model_dir)
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setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
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nlp = Language(data_dir=model_dir)
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print "Itn.\tTag %"
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for itn in range(n_iter):
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scorer = Scorer()
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correct = 0
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total = 0
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for raw_text, sents in docs:
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if gold_preproc:
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raw_text = None
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else:
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sents = _merge_sents(sents)
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for annot_tuples, ctnt in sents:
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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else:
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tokens = nlp.tokenizer(raw_text)
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gold = GoldParse(tokens, annot_tuples)
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correct += nlp.tagger.train(tokens, gold.tags)
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total += len(tokens)
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random.shuffle(docs)
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print itn, '%.3f' % (correct / total)
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nlp.tagger.model.end_training()
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nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
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return nlp
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def _tag_partition(nlp, docs, gold_preproc=False):
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for raw_text, sents in docs:
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if gold_preproc:
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raw_text = None
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else:
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sents = _merge_sents(sents)
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for annot_tuples, _ in sents:
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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else:
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tokens = nlp.tokenizer(raw_text)
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nlp.tagger(tokens)
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yield tokens
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def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
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seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
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train_tags=None, beam_width=1):
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dep_model_dir = path.join(model_dir, 'deps')
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pos_model_dir = path.join(model_dir, 'pos')
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ner_model_dir = path.join(model_dir, 'ner')
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if path.exists(dep_model_dir):
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shutil.rmtree(dep_model_dir)
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if path.exists(pos_model_dir):
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shutil.rmtree(pos_model_dir)
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if path.exists(ner_model_dir):
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shutil.rmtree(ner_model_dir)
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os.mkdir(dep_model_dir)
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os.mkdir(pos_model_dir)
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os.mkdir(ner_model_dir)
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setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
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Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
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labels=Language.ParserTransitionSystem.get_labels(gold_tuples),
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beam_width=beam_width)
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Config.write(ner_model_dir, 'config', features='ner', seed=seed,
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labels=Language.EntityTransitionSystem.get_labels(gold_tuples),
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beam_width=1)
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if n_sents > 0:
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gold_tuples = gold_tuples[:n_sents]
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nlp = Language(data_dir=model_dir)
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print "Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %"
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for itn in range(n_iter):
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scorer = Scorer()
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loss = 0
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for raw_text, sents in gold_tuples:
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if gold_preproc:
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raw_text = None
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else:
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sents = _merge_sents(sents)
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for annot_tuples, ctnt in sents:
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score_model(scorer, nlp, raw_text, annot_tuples, train_tags)
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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else:
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tokens = nlp.tokenizer(raw_text)
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if train_tags is not None:
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sent_id = hash(tokens.string)
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nlp.tagger.tag_from_strings(tokens, train_tags[sent_id])
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else:
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nlp.tagger(tokens)
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gold = GoldParse(tokens, annot_tuples, make_projective=True)
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loss += nlp.parser.train(tokens, gold)
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nlp.entity.train(tokens, gold)
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nlp.tagger.train(tokens, gold.tags)
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random.shuffle(gold_tuples)
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print '%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
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scorer.tags_acc,
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scorer.token_acc)
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nlp.parser.model.end_training()
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nlp.entity.model.end_training()
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nlp.tagger.model.end_training()
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nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
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def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False):
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nlp = Language(data_dir=model_dir)
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scorer = Scorer()
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for raw_text, sents in gold_tuples:
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if gold_preproc:
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raw_text = None
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else:
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sents = _merge_sents(sents)
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for annot_tuples, brackets in sents:
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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nlp.tagger(tokens)
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nlp.entity(tokens)
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nlp.parser(tokens)
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else:
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tokens = nlp(raw_text, merge_mwes=False)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold, verbose=verbose)
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return scorer
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def write_parses(Language, dev_loc, model_dir, out_loc):
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nlp = Language()
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gold_tuples = read_docparse_file(dev_loc)
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scorer = Scorer()
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out_file = codecs.open(out_loc, 'w', 'utf8')
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for raw_text, segmented_text, annot_tuples in gold_tuples:
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tokens = nlp(raw_text)
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for t in tokens:
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out_file.write(
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'%s\t%s\t%s\t%s\n' % (t.orth_, t.tag_, t.head.orth_, t.dep_)
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)
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return scorer
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@plac.annotations(
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train_loc=("Location of training file or directory"),
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dev_loc=("Location of development file or directory"),
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corruption_level=("Amount of noise to add to training data", "option", "c", float),
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gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
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model_dir=("Location of output model directory",),
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out_loc=("Out location", "option", "o", str),
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n_sents=("Number of training sentences", "option", "n", int),
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n_iter=("Number of training iterations", "option", "i", int),
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beam_width=("Number of candidates to maintain in the beam", "option", "k", int),
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verbose=("Verbose error reporting", "flag", "v", bool),
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debug=("Debug mode", "flag", "d", bool)
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)
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def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
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debug=False, corruption_level=0.0, gold_preproc=False, beam_width=1):
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gold_train = list(read_json_file(train_loc))
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#taggings = get_train_tags(English, model_dir, gold_train, gold_preproc)
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taggings = None
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train(English, gold_train, model_dir,
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feat_set='basic' if not debug else 'debug',
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gold_preproc=gold_preproc, n_sents=n_sents,
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corruption_level=corruption_level, n_iter=n_iter,
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train_tags=taggings, beam_width=beam_width)
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if out_loc:
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write_parses(English, dev_loc, model_dir, out_loc)
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scorer = evaluate(English, list(read_json_file(dev_loc)),
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model_dir, gold_preproc=gold_preproc, verbose=verbose)
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print 'TOK', 100-scorer.token_acc
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print 'POS', scorer.tags_acc
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print 'UAS', scorer.uas
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print 'LAS', scorer.las
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print 'NER P', scorer.ents_p
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print 'NER R', scorer.ents_r
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print 'NER F', scorer.ents_f
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if __name__ == '__main__':
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plac.call(main)
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