mirror of https://github.com/explosion/spaCy.git
188 lines
6.7 KiB
Python
Executable File
188 lines
6.7 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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from __future__ import print_function
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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 io
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import random
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import plac
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import re
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import spacy.util
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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.gold import merge_sents
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from spacy.scorer import Scorer
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from spacy.syntax.arc_eager import ArcEager
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from spacy.syntax.ner import BiluoPushDown
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from spacy.tagger import Tagger
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from spacy.syntax.parser import Parser
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from spacy.syntax.nonproj import PseudoProjectivity
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def _corrupt(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 add_noise(orig, noise_level):
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if random.random() >= noise_level:
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return orig
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elif type(orig) == list:
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corrupted = [_corrupt(word, noise_level) for word in orig]
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corrupted = [w for w in corrupted if w]
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return corrupted
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else:
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return ''.join(_corrupt(c, noise_level) for c in orig)
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def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
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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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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=verbose)
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def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, entity_cfg,
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n_iter=15, seed=0, gold_preproc=False, n_sents=0, corruption_level=0):
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print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
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format_str = '{:d}\t{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
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with Language.train(model_dir, train_data,
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tagger_cfg, parser_cfg, entity_cfg) as trainer:
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loss = 0
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for itn, epoch in enumerate(trainer.epochs(n_iter, gold_preproc=gold_preproc,
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augment_data=None)):
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for doc, gold in epoch:
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trainer.update(doc, gold)
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dev_scores = trainer.evaluate(dev_data, gold_preproc=gold_preproc)
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print(format_str.format(itn, trainer.nlp.parser.model.nr_weight,
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trainer.nlp.parser.model.nr_active_feat, **dev_scores.scores))
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def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
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beam_width=None, cand_preproc=None):
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print("Load parser", model_dir)
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nlp = Language(path=model_dir)
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if nlp.lang == 'de':
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nlp.vocab.morphology.lemmatizer = lambda string,pos: set([string])
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if beam_width is not None:
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nlp.parser.cfg.beam_width = beam_width
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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.parser(tokens)
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nlp.entity(tokens)
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else:
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tokens = nlp(raw_text)
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gold = GoldParse.from_annot_tuples(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(data_dir=model_dir)
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gold_tuples = read_json_file(dev_loc)
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scorer = Scorer()
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out_file = io.open(out_loc, 'w', 'utf8')
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for raw_text, sents in gold_tuples:
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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)
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#gold = GoldParse(tokens, annot_tuples)
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#scorer.score(tokens, gold, verbose=False)
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for sent in tokens.sents:
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for t in sent:
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if not t.is_space:
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out_file.write(
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'%d\t%s\t%s\t%s\t%s\n' % (t.i, t.orth_, t.tag_, t.head.orth_, t.dep_)
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)
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out_file.write('\n')
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@plac.annotations(
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language=("The language to train", "positional", None, str, ['en','de', 'zh']),
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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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model_dir=("Location of output model directory",),
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eval_only=("Skip training, and only evaluate", "flag", "e", bool),
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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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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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verbose=("Verbose error reporting", "flag", "v", bool),
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debug=("Debug mode", "flag", "d", bool),
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pseudoprojective=("Use pseudo-projective parsing", "flag", "p", bool),
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L1=("L1 regularization penalty", "option", "L", float),
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)
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def main(language, 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, eval_only=False, pseudoprojective=False,
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L1=1e-6):
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parser_cfg = dict(locals())
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tagger_cfg = dict(locals())
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entity_cfg = dict(locals())
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lang = spacy.util.get_lang_class(language)
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parser_cfg['features'] = lang.Defaults.parser_features
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entity_cfg['features'] = lang.Defaults.entity_features
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if not eval_only:
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gold_train = list(read_json_file(train_loc))
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gold_dev = list(read_json_file(dev_loc))
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if n_sents > 0:
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gold_train = gold_train[:n_sents]
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train(lang, gold_train, gold_dev, model_dir, tagger_cfg, parser_cfg, entity_cfg,
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n_sents=n_sents, gold_preproc=gold_preproc, corruption_level=corruption_level,
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n_iter=n_iter)
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if out_loc:
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write_parses(lang, dev_loc, model_dir, out_loc)
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scorer = evaluate(lang, list(read_json_file(dev_loc)),
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model_dir, gold_preproc=gold_preproc, verbose=verbose)
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print('TOK', 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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