#!/usr/bin/env python
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import os
from os import path
import shutil
import io
import random
import plac
import re
import spacy.util
from spacy.syntax.util import Config
from spacy.gold import read_json_file
from spacy.gold import GoldParse
from spacy.gold import merge_sents
from spacy.scorer import Scorer
from spacy.syntax.arc_eager import ArcEager
from spacy.syntax.ner import BiluoPushDown
from spacy.tagger import Tagger
from spacy.syntax.parser import Parser
from spacy.syntax.nonproj import PseudoProjectivity
def _corrupt(c, noise_level):
if random.random() >= noise_level:
return c
elif c == ' ':
return '\n'
elif c == '\n':
return ' '
elif c in ['.', "'", "!", "?"]:
return ''
else:
return c.lower()
def add_noise(orig, noise_level):
if random.random() >= noise_level:
return orig
elif type(orig) == list:
corrupted = [_corrupt(word, noise_level) for word in orig]
corrupted = [w for w in corrupted if w]
return corrupted
else:
return ''.join(_corrupt(c, noise_level) for c in orig)
def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
else:
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, entity_cfg,
n_iter=15, seed=0, gold_preproc=False, n_sents=0, corruption_level=0):
print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
format_str = '{:d}\t{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
with Language.train(model_dir, train_data,
tagger_cfg, parser_cfg, entity_cfg) as trainer:
loss = 0
for itn, epoch in enumerate(trainer.epochs(n_iter, gold_preproc=gold_preproc,
augment_data=None)):
for doc, gold in epoch:
trainer.update(doc, gold)
dev_scores = trainer.evaluate(dev_data, gold_preproc=gold_preproc)
print(format_str.format(itn, trainer.nlp.parser.model.nr_weight,
trainer.nlp.parser.model.nr_active_feat, **dev_scores.scores))
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
beam_width=None, cand_preproc=None):
print("Load parser", model_dir)
nlp = Language(path=model_dir)
if nlp.lang == 'de':
nlp.vocab.morphology.lemmatizer = lambda string,pos: set([string])
if beam_width is not None:
nlp.parser.cfg.beam_width = beam_width
scorer = Scorer()
for raw_text, sents in gold_tuples:
if gold_preproc:
raw_text = None
else:
sents = merge_sents(sents)
for annot_tuples, brackets in sents:
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
nlp.parser(tokens)
nlp.entity(tokens)
else:
tokens = nlp(raw_text)
gold = GoldParse.from_annot_tuples(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
return scorer
def write_parses(Language, dev_loc, model_dir, out_loc):
nlp = Language(data_dir=model_dir)
gold_tuples = read_json_file(dev_loc)
scorer = Scorer()
out_file = io.open(out_loc, 'w', 'utf8')
for raw_text, sents in gold_tuples:
sents = _merge_sents(sents)
for annot_tuples, brackets in sents:
if raw_text is None:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
nlp.entity(tokens)
nlp.parser(tokens)
else:
tokens = nlp(raw_text)
#gold = GoldParse(tokens, annot_tuples)
#scorer.score(tokens, gold, verbose=False)
for sent in tokens.sents:
for t in sent:
if not t.is_space:
out_file.write(
'%d\t%s\t%s\t%s\t%s\n' % (t.i, t.orth_, t.tag_, t.head.orth_, t.dep_)
)
out_file.write('\n')
@plac.annotations(
language=("The language to train", "positional", None, str, ['en','de', 'zh']),
train_loc=("Location of training file or directory"),
dev_loc=("Location of development file or directory"),
model_dir=("Location of output model directory",),
eval_only=("Skip training, and only evaluate", "flag", "e", bool),
corruption_level=("Amount of noise to add to training data", "option", "c", float),
gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
out_loc=("Out location", "option", "o", str),
n_sents=("Number of training sentences", "option", "n", int),
n_iter=("Number of training iterations", "option", "i", int),
verbose=("Verbose error reporting", "flag", "v", bool),
debug=("Debug mode", "flag", "d", bool),
pseudoprojective=("Use pseudo-projective parsing", "flag", "p", bool),
L1=("L1 regularization penalty", "option", "L", float),
)
def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False, pseudoprojective=False,
L1=1e-6):
parser_cfg = dict(locals())
tagger_cfg = dict(locals())
entity_cfg = dict(locals())
lang = spacy.util.get_lang_class(language)
parser_cfg['features'] = lang.Defaults.parser_features
entity_cfg['features'] = lang.Defaults.entity_features
if not eval_only:
gold_train = list(read_json_file(train_loc))
gold_dev = list(read_json_file(dev_loc))
if n_sents > 0:
gold_train = gold_train[:n_sents]
train(lang, gold_train, gold_dev, model_dir, tagger_cfg, parser_cfg, entity_cfg,
n_sents=n_sents, gold_preproc=gold_preproc, corruption_level=corruption_level,
n_iter=n_iter)
if out_loc:
write_parses(lang, dev_loc, model_dir, out_loc)
scorer = evaluate(lang, list(read_json_file(dev_loc)),
model_dir, gold_preproc=gold_preproc, verbose=verbose)
print('TOK', scorer.token_acc)
print('POS', scorer.tags_acc)
print('UAS', scorer.uas)
print('LAS', scorer.las)
print('NER P', scorer.ents_p)
print('NER R', scorer.ents_r)
print('NER F', scorer.ents_f)
if __name__ == '__main__':
plac.call(main)