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* Fix standard conll file reading. Script needs refactoring.

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This commit is contained in:
Matthew Honnibal 2015-02-02 23:02:48 +11:00
parent c55a33d045
commit 27986d7f5c
1 changed files with 29 additions and 11 deletions
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40
bin/parser/train.py
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@ -19,6 +19,7 @@ from spacy.en import English
from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir 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.util import Config from spacy.syntax.util import Config
@ -36,7 +37,7 @@ def read_tokenized_gold(file_):
labels = [] labels = []
tags = [] tags = []
for i, line in enumerate(sent_str.split('\n')): for i, line in enumerate(sent_str.split('\n')):
word, pos_string, head_idx, label = _parse_line(line) id_, word, pos_string, head_idx, label = _parse_line(line)
words.append(word) words.append(word)
if head_idx == -1: if head_idx == -1:
head_idx = i head_idx = i
@ -44,22 +45,29 @@ def read_tokenized_gold(file_):
heads.append(head_idx) heads.append(head_idx)
labels.append(label) labels.append(label)
tags.append(pos_string) tags.append(pos_string)
sents.append((ids_, words, heads, labels, tags)) text = ' '.join(words)
sents.append((text, [words], ids, words, tags, heads, labels))
return sents return sents
def read_docparse_gold(file_): def read_docparse_gold(file_):
paragraphs = [] paragraphs = []
for sent_str in file_.read().strip().split('\n\n'): for sent_str in file_.read().strip().split('<text>'):
if not sent_str.strip():
continue
words = [] words = []
heads = [] heads = []
labels = [] labels = []
tags = [] tags = []
ids = [] ids = []
try:
raw_text, sent_str = sent_str.strip().split('</text>', 1)
except:
print sent_str
raise
lines = sent_str.strip().split('\n') lines = sent_str.strip().split('\n')
raw_text = lines[0] tok_text = lines.pop(0)
tok_text = lines[1] for i, line in enumerate(lines):
for i, line in enumerate(lines[2:]):
id_, word, pos_string, head_idx, label = _parse_line(line) id_, word, pos_string, head_idx, label = _parse_line(line)
if label == 'root': if label == 'root':
label = 'ROOT' label = 'ROOT'
@ -180,7 +188,7 @@ def get_labels(sents):
def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0, def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False): gold_preproc=False, force_gold=False):
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')
if path.exists(dep_model_dir): if path.exists(dep_model_dir):
@ -205,7 +213,10 @@ def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer, for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
gold_preproc=gold_preproc): gold_preproc=gold_preproc):
nlp.tagger(tokens) nlp.tagger(tokens)
heads_corr += nlp.parser.train_sent(tokens, heads, labels, force_gold=False) try:
heads_corr += nlp.parser.train_sent(tokens, heads, labels, force_gold=force_gold)
except OracleError:
continue
pos_corr += nlp.tagger.train(tokens, tag_strs) pos_corr += nlp.tagger.train(tokens, tag_strs)
n_tokens += len(tokens) n_tokens += len(tokens)
acc = float(heads_corr) / n_tokens acc = float(heads_corr) / n_tokens
@ -221,10 +232,13 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
global loss global loss
nlp = Language() nlp = Language()
n_corr = 0 n_corr = 0
pos_corr = 0
n_tokens = 0
total = 0 total = 0
skipped = 0 skipped = 0
loss = 0 loss = 0
with codecs.open(dev_loc, 'r', 'utf8') as file_: with codecs.open(dev_loc, 'r', 'utf8') as file_:
#paragraphs = read_tokenized_gold(file_)
paragraphs = read_docparse_gold(file_) paragraphs = read_docparse_gold(file_)
for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer, for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
gold_preproc=gold_preproc): gold_preproc=gold_preproc):
@ -232,6 +246,8 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
nlp.tagger(tokens) nlp.tagger(tokens)
nlp.parser(tokens) nlp.parser(tokens)
for i, token in enumerate(tokens): for i, token in enumerate(tokens):
pos_corr += token.tag_ == tag_strs[i]
n_tokens += 1
if heads[i] is None: if heads[i] is None:
skipped += 1 skipped += 1
continue continue
@ -240,14 +256,16 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
n_corr += token.head.i == heads[i] n_corr += token.head.i == heads[i]
total += 1 total += 1
print loss, skipped, (loss+skipped + total) print loss, skipped, (loss+skipped + total)
print pos_corr / n_tokens
return float(n_corr) / (total + loss) return float(n_corr) / (total + loss)
def main(train_loc, dev_loc, model_dir): def main(train_loc, dev_loc, model_dir):
with codecs.open(train_loc, 'r', 'utf8') as file_: with codecs.open(train_loc, 'r', 'utf8') as file_:
train_sents = read_docparse_gold(file_) #train_sents = read_docparse_gold(file_)
train(English, train_sents, model_dir, gold_preproc=False) train_sents = read_tokenized_gold(file_)
print evaluate(English, dev_loc, model_dir, gold_preproc=False) #train(English, train_sents, model_dir, gold_preproc=True, force_gold=False)
print evaluate(English, dev_loc, model_dir, gold_preproc=True)
if __name__ == '__main__': if __name__ == '__main__':