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* Work on updating train script for named entity recognition

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Matthew Honnibal 2015-03-09 01:46:53 -04:00
parent 357dcdcc01
commit 4539c70542
1 changed files with 46 additions and 42 deletions
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bin/parser/train.py
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@ -21,7 +21,8 @@ from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir
from spacy.syntax.parser import GreedyParser from spacy.syntax.parser import GreedyParser
from spacy.syntax.parser import OracleError from spacy.syntax.parser import OracleError
from spacy.syntax.util import Config from spacy.syntax.util import Config
from spacy.syntax.conll import GoldParse, is_punct_label from spacy.syntax.conll import read_docparse_file
from spacy.syntax.conll import GoldParse
def is_punct_label(label): def is_punct_label(label):
@ -183,47 +184,56 @@ def get_labels(sents):
return list(sorted(left_labels)), list(sorted(right_labels)) return list(sorted(left_labels)), list(sorted(right_labels))
def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0, def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False, force_gold=False): gold_preproc=False, force_gold=False, n_sents=0):
print "Setup model dir" print "Setup model dir"
dep_model_dir = path.join(model_dir, 'deps') dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos') pos_model_dir = path.join(model_dir, 'pos')
ner_model_dir = path.join(model_dir, 'ner')
if path.exists(dep_model_dir): if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir) shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir): if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir) shutil.rmtree(pos_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
os.mkdir(dep_model_dir) os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir) os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, os.mkdir(ner_model_dir)
pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
gold_tuples = read_docparse_file(train_loc)
labels = Language.ParserTransitionSystem.get_labels(gold_sents)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed, Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=labels) labels=Language.ParserTransitionSystem.get_labels(gold_tuples))
Config.write(ner_model_dir, 'config', features=feat_set, seed=seed,
labels=Language.EntityTransitionSystem.get_labels(gold_tuples))
nlp = Language() nlp = Language()
for itn in range(n_iter): for itn in range(n_iter):
heads_corr = 0 dep_corr = 0
pos_corr = 0 pos_corr = 0
n_tokens = 0 n_tokens = 0
n_all_tokens = 0 for raw_text, segmented_text, annot_tuples in gold_tuples:
for gold_sent in gold_sents:
if gold_preproc: if gold_preproc:
#print ' '.join(gold_sent.words) sents = [nlp.tokenizer.tokens_from_list(s) for s in segmented_text]
tokens = nlp.tokenizer.tokens_from_list(gold_sent.words)
gold_sent.map_heads(nlp.parser.moves.label_ids)
else: else:
tokens = nlp.tokenizer(gold_sent.raw_text) sents = [nlp.tokenizer(raw_text)]
gold_sent.align_to_tokens(tokens, nlp.parser.moves.label_ids) for tokens in sents:
nlp.tagger(tokens)
heads_corr += nlp.parser.train(tokens, gold_sent, force_gold=force_gold) gold = GoldParse(tokens, annot_tuples, nlp.tags,
pos_corr += nlp.tagger.train(tokens, gold_sent.tags) nlp.parser.moves.label_ids,
n_tokens += gold_sent.n_non_punct nlp.entity.moves.label_ids)
n_all_tokens += len(tokens)
acc = float(heads_corr) / n_tokens nlp.tagger(tokens)
pos_acc = float(pos_corr) / n_all_tokens dep_corr += nlp.parser.train(tokens, gold, force_gold=force_gold)
pos_corr += nlp.tagger.train(tokens, gold.tags_)
n_tokens += len(tokens)
acc = float(dep_corr) / n_tokens
pos_acc = float(pos_corr) / n_tokens
print '%d: ' % itn, '%.3f' % acc, '%.3f' % pos_acc print '%d: ' % itn, '%.3f' % acc, '%.3f' % pos_acc
random.shuffle(gold_sents) random.shuffle(gold_tuples)
nlp.parser.model.end_training() nlp.parser.model.end_training()
nlp.tagger.model.end_training() nlp.tagger.model.end_training()
return acc return acc
@ -239,22 +249,22 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
total = 0 total = 0
skipped = 0 skipped = 0
loss = 0 loss = 0
with codecs.open(dev_loc, 'r', 'utf8') as file_: gold_tuples = read_docparse_file(train_loc)
#paragraphs = read_tokenized_gold(file_) for raw_text, segmented_text, annot_tuples in gold_tuples:
paragraphs = read_docparse_gold(file_) if gold_preproc:
for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer, tokens = nlp.tokenizer.tokens_from_list(gold_sent.words)
gold_preproc=gold_preproc): nlp.tagger(tokens)
assert len(tokens) == len(labels) nlp.parser(tokens)
nlp.tagger(tokens) gold_sent.map_heads(nlp.parser.moves.label_ids)
nlp.parser(tokens) else:
tokens = nlp(gold_sent.raw_text)
loss += gold_sent.align_to_tokens(tokens, nlp.parser.moves.label_ids)
for i, token in enumerate(tokens): for i, token in enumerate(tokens):
pos_corr += token.tag_ == gold_sent.tags[i] pos_corr += token.tag_ == gold_sent.tags[i]
n_tokens += 1 n_tokens += 1
if gold_sent.heads[i] is None: if gold_sent.heads[i] is None:
skipped += 1 skipped += 1
continue continue
#print i, token.orth_, token.head.i, gold_sent.py_heads[i], gold_sent.labels[i],
#print gold_sent.is_correct(i, token.head.i)
if gold_sent.labels[i] != 'P': if gold_sent.labels[i] != 'P':
n_corr += gold_sent.is_correct(i, token.head.i) n_corr += gold_sent.is_correct(i, token.head.i)
total += 1 total += 1
@ -263,12 +273,6 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
return float(n_corr) / (total + loss) return float(n_corr) / (total + loss)
def read_gold(loc, n=0):
sent_strs = open(loc).read().strip().split('\n\n')
if n == 0:
n = len(sent_strs)
return [GoldParse.from_docparse(sent) for sent in sent_strs[:n]]
@plac.annotations( @plac.annotations(
train_loc=("Training file location",), train_loc=("Training file location",),
@ -277,9 +281,9 @@ def read_gold(loc, n=0):
n_sents=("Number of training sentences", "option", "n", int) n_sents=("Number of training sentences", "option", "n", int)
) )
def main(train_loc, dev_loc, model_dir, n_sents=0): def main(train_loc, dev_loc, model_dir, n_sents=0):
#train(English, read_gold(train_loc, n=n_sents), model_dir, train(English, train_loc, model_dir,
# gold_preproc=False, force_gold=False) gold_preproc=False, force_gold=False, n_sents=n_sents)
print evaluate(English, read_gold(dev_loc), model_dir, gold_preproc=False) print evaluate(English, dev_loc, model_dir, gold_preproc=False)
if __name__ == '__main__': if __name__ == '__main__':