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* Work on alignment, for evaluation with non-gold preprocessing

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Matthew Honnibal 2015-01-30 10:31:03 +11:00
parent ebf7d2fab1
commit 11ed65b93c
1 changed files with 94 additions and 58 deletions
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152
bin/parser/train.py
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@ -45,7 +45,7 @@ def read_tokenized_gold(file_):
def read_docparse_gold(file_): def read_docparse_gold(file_):
sents = [] paragraphs = []
for sent_str in file_.read().strip().split('\n\n'): for sent_str in file_.read().strip().split('\n\n'):
words = [] words = []
heads = [] heads = []
@ -59,10 +59,6 @@ def read_docparse_gold(file_):
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'
if pos_string == "``":
word = "``"
elif pos_string == "''":
word = "''"
words.append(word) words.append(word)
if head_idx < 0: if head_idx < 0:
head_idx = id_ head_idx = id_
@ -70,30 +66,20 @@ def read_docparse_gold(file_):
heads.append(head_idx) heads.append(head_idx)
labels.append(label) labels.append(label)
tags.append(pos_string) tags.append(pos_string)
heads = _map_indices_to_tokens(ids, heads) tokenized = [sent_str.replace('<SEP>', ' ').split(' ')
words = tok_text.replace('<SENT>', ' ').replace('<SEP>', ' ').split() for sent_str in tok_text.split('<SENT>')]
#print words paragraphs.append((raw_text, tokenized, ids, words, tags, heads, labels))
#print heads return paragraphs
sents.append((words, heads, labels, tags))
#sent_strings = tok_text.split('<SENT>')
#for sent in sent_strings:
# sent_words = sent.replace('<SEP>', ' ').split(' ')
# sent_heads = []
# sent_labels = []
# sent_tags = []
# sent_ids = []
# while len(sent_heads) < len(sent_words):
# sent_heads.append(heads.pop(0))
# sent_labels.append(labels.pop(0))
# sent_tags.append(tags.pop(0))
# sent_ids.append(ids.pop(0))
# sent_heads = _map_indices_to_tokens(sent_ids, sent_heads)
# sents.append((sent_words, sent_heads, sent_labels, sent_tags))
return sents
def _map_indices_to_tokens(ids, heads): def _map_indices_to_tokens(ids, heads):
return [ids.index(head) for head in heads] mapped = []
for head in heads:
if head not in ids:
mapped.append(None)
else:
mapped.append(ids.index(head))
return mapped
def _parse_line(line): def _parse_line(line):
@ -108,10 +94,71 @@ def _parse_line(line):
label = pieces[7] label = pieces[7]
return id_, word, pos, head_idx, label return id_, word, pos, head_idx, label
def _align_annotations_to_non_gold_tokens(tokens, words, annot):
tags = []
heads = []
labels = []
loss = 0
print [t.orth_ for t in tokens]
print words
for token in tokens:
print token.orth_, words[0]
while annot and token.idx > annot[0][0]:
annot.pop(0)
words.pop(0)
loss += 1
if not annot:
tags.append(None)
heads.append(None)
labels.append(None)
continue
id_, tag, head, label = annot[0]
if token.idx == id_:
tags.append(tag)
heads.append(head)
labels.append(label)
annot.pop(0)
words.pop(0)
elif token.idx < id_:
tags.append(None)
heads.append(None)
labels.append(None)
else:
raise StandardError
return loss, tags, heads, labels
def iter_data(paragraphs, tokenizer, gold_preproc=False):
for raw, tokenized, ids, words, tags, heads, labels in paragraphs:
if not gold_preproc:
tokens = tokenizer(raw)
loss, tags, heads, labels = _align_annotations_to_non_gold_tokens(
tokens, words, zip(ids, tags, heads, labels))
ids = [t.idx for t in tokens]
heads = _map_indices_to_tokens(ids, heads)
yield tokens, tags, heads, labels
else:
assert len(words) == len(heads)
for words in tokenized:
sent_ids = ids[:len(words)]
sent_tags = tags[:len(words)]
sent_heads = heads[:len(words)]
sent_labels = labels[:len(words)]
sent_heads = _map_indices_to_tokens(sent_ids, sent_heads)
tokens = tokenizer.tokens_from_list(words)
yield tokens, sent_tags, sent_heads, sent_labels
ids = ids[len(words):]
tags = tags[len(words):]
heads = heads[len(words):]
labels = labels[len(words):]
def get_labels(sents): def get_labels(sents):
left_labels = set() left_labels = set()
right_labels = set() right_labels = set()
for _, heads, labels, _ in sents: for raw, tokenized, ids, words, tags, heads, labels in sents:
for child, (head, label) in enumerate(zip(heads, labels)): for child, (head, label) in enumerate(zip(heads, labels)):
if head > child: if head > child:
left_labels.add(label) left_labels.add(label)
@ -120,7 +167,8 @@ 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, sents, 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=True):
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):
@ -132,7 +180,7 @@ def train(Language, sents, model_dir, n_iter=15, feat_set=u'basic', seed=0):
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir) pos_model_dir)
left_labels, right_labels = get_labels(sents) left_labels, right_labels = get_labels(paragraphs)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed, Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
left_labels=left_labels, right_labels=right_labels) left_labels=left_labels, right_labels=right_labels)
@ -142,62 +190,50 @@ def train(Language, sents, model_dir, n_iter=15, feat_set=u'basic', seed=0):
heads_corr = 0 heads_corr = 0
pos_corr = 0 pos_corr = 0
n_tokens = 0 n_tokens = 0
for words, heads, labels, tags in sents: for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
tags = [nlp.tagger.tag_names.index(tag) for tag in tags] gold_preproc=gold_preproc):
tokens = nlp.tokenizer.tokens_from_list(words) tags = [nlp.tagger.tag_names.index(tag) for tag in tag_strs]
nlp.tagger(tokens) nlp.tagger(tokens)
try: heads_corr += nlp.parser.train_sent(tokens, heads, labels, force_gold=False)
heads_corr += nlp.parser.train_sent(tokens, heads, labels, force_gold=False)
except:
print heads
raise
pos_corr += nlp.tagger.train(tokens, tags) pos_corr += nlp.tagger.train(tokens, tags)
n_tokens += len(tokens) n_tokens += len(tokens)
acc = float(heads_corr) / n_tokens acc = float(heads_corr) / n_tokens
pos_acc = float(pos_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(sents) random.shuffle(paragraphs)
nlp.parser.model.end_training() nlp.parser.model.end_training()
nlp.tagger.model.end_training() nlp.tagger.model.end_training()
return acc return acc
def evaluate(Language, dev_loc, model_dir): def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
nlp = Language() nlp = Language()
n_corr = 0 n_corr = 0
total = 0 total = 0
skipped = 0
with codecs.open(dev_loc, 'r', 'utf8') as file_: with codecs.open(dev_loc, 'r', 'utf8') as file_:
sents = read_docparse_gold(file_) paragraphs = read_docparse_gold(file_)
for words, heads, labels, tags in sents: for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
tokens = nlp.tokenizer.tokens_from_list(words) gold_preproc=gold_preproc):
assert len(tokens) == len(labels)
nlp.tagger(tokens) nlp.tagger(tokens)
nlp.parser(tokens) nlp.parser(tokens)
for i, token in enumerate(tokens): for i, token in enumerate(tokens):
#print i, token.orth_, token.head.orth_, tokens[heads[i]].orth_, labels[i], token.head.i == heads[i] if heads[i] is None:
skipped += 1
if labels[i] == 'P' or labels[i] == 'punct': if labels[i] == 'P' or labels[i] == 'punct':
continue continue
n_corr += token.head.i == heads[i] n_corr += token.head.i == heads[i]
total += 1 total += 1
print skipped
return float(n_corr) / total return float(n_corr) / total
PROFILE = False
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_sents = train_sents #train(English, train_sents, model_dir, gold_preproc=False)
if PROFILE: print evaluate(English, dev_loc, model_dir, gold_preproc=False)
import cProfile
import pstats
cmd = "train(EN, train_sents, tag_names, model_dir, n_iter=2)"
cProfile.runctx(cmd, globals(), locals(), "Profile.prof")
s = pstats.Stats("Profile.prof")
s.strip_dirs().sort_stats("time").print_stats()
else:
train(English, train_sents, model_dir)
print evaluate(English, dev_loc, model_dir)
if __name__ == '__main__': if __name__ == '__main__':