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spaCy
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* Upd train script, moving lots of functionality to new GoldParse class

This commit is contained in:
Matthew Honnibal 2015-02-21 20:06:29 -05:00
parent f0159ab4b6
commit 34215de61b
1 changed files with 10 additions and 18 deletions
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28
bin/parser/train.py
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@ -61,13 +61,8 @@ def read_docparse_gold(file_):
tags = []
ids = []
lines = sent_str.strip().split('\n')
<<<<<<< HEAD
raw_text = lines.pop(0).strip()
tok_text = lines.pop(0).strip()
=======
raw_text = lines.pop(0)
tok_text = lines.pop(0)
>>>>>>> master
for i, line in enumerate(lines):
id_, word, pos_string, head_idx, label = _parse_line(line)
if label == 'root':
@ -200,9 +195,9 @@ def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir)
left_labels, right_labels = get_labels(paragraphs)
labels = Language.ParserTransitionSystem.get_labels(gold_sents)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
left_labels=left_labels, right_labels=right_labels)
labels=labels)
nlp = Language()
@ -210,14 +205,12 @@ def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
heads_corr = 0
pos_corr = 0
n_tokens = 0
for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
gold_preproc=gold_preproc):
for gold_sent in gold_sents:
tokens = nlp.tokenizer(gold_sent.raw)
gold_sent.align_to_tokens(tokens)
nlp.tagger(tokens)
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)
heads_corr += nlp.parser.train(tokens, gold_sent, force_gold=force_gold)
pos_corr += nlp.tagger.train(tokens, gold_parse.tags)
n_tokens += len(tokens)
acc = float(heads_corr) / n_tokens
pos_acc = float(pos_corr) / n_tokens
@ -265,10 +258,9 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
def main(train_loc, dev_loc, model_dir):
with codecs.open(train_loc, 'r', 'utf8') as file_:
train_sents = read_docparse_gold(file_)
train(English, train_sents, model_dir, gold_preproc=False, force_gold=False)
print evaluate(English, dev_loc, model_dir, gold_preproc=False)
train(English, read_docparse_gold(train_loc), model_dir,
gold_preproc=False, force_gold=False)
print evaluate(English, read_docparse_gold(dev_loc), model_dir, gold_preproc=False)
if __name__ == '__main__':