mirror of
https://github.com/explosion/spaCy.git
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131 lines
3.5 KiB
Python
131 lines
3.5 KiB
Python
#!/usr/bin/env python
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from __future__ import division
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from __future__ import unicode_literals
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import os
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from os import path
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import shutil
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import codecs
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import random
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import time
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import gzip
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import plac
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import cProfile
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import pstats
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import spacy.util
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from spacy.en import English
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from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir
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from spacy.syntax.parser import GreedyParser
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from spacy.syntax.parser import OracleError
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from spacy.syntax.util import Config
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def is_punct_label(label):
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return label == 'P' or label.lower() == 'punct'
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def read_gold(file_):
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"""Read a standard CoNLL/MALT-style format"""
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sents = []
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for sent_str in file_.read().strip().split('\n\n'):
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ids = []
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words = []
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heads = []
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labels = []
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tags = []
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for i, line in enumerate(sent_str.split('\n')):
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id_, word, pos_string, head_idx, label = _parse_line(line)
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words.append(word)
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if head_idx == -1:
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head_idx = i
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ids.append(id_)
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heads.append(head_idx)
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labels.append(label)
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tags.append(pos_string)
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text = ' '.join(words)
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sents.append((text, [words], ids, words, tags, heads, labels))
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return sents
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def _parse_line(line):
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pieces = line.split()
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id_ = int(pieces[0])
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word = pieces[1]
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pos = pieces[3]
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head_idx = int(pieces[6])
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label = pieces[7]
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return id_, word, pos, head_idx, label
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def iter_data(paragraphs, tokenizer, gold_preproc=False):
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for raw, tokenized, ids, words, tags, heads, labels in paragraphs:
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assert len(words) == len(heads)
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for words in tokenized:
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sent_ids = ids[:len(words)]
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sent_tags = tags[:len(words)]
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sent_heads = heads[:len(words)]
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sent_labels = labels[:len(words)]
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sent_heads = _map_indices_to_tokens(sent_ids, sent_heads)
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tokens = tokenizer.tokens_from_list(words)
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yield tokens, sent_tags, sent_heads, sent_labels
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ids = ids[len(words):]
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tags = tags[len(words):]
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heads = heads[len(words):]
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labels = labels[len(words):]
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def _map_indices_to_tokens(ids, heads):
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mapped = []
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for head in heads:
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if head not in ids:
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mapped.append(None)
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else:
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mapped.append(ids.index(head))
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return mapped
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def evaluate(Language, dev_loc, model_dir):
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global loss
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nlp = Language()
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n_corr = 0
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pos_corr = 0
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n_tokens = 0
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total = 0
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skipped = 0
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loss = 0
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with codecs.open(dev_loc, 'r', 'utf8') as file_:
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paragraphs = read_gold(file_)
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for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer):
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assert len(tokens) == len(labels)
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nlp.tagger.tag_from_strings(tokens, tag_strs)
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nlp.parser(tokens)
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for i, token in enumerate(tokens):
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try:
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pos_corr += token.tag_ == tag_strs[i]
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except:
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print i, token.orth_, token.tag
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raise
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n_tokens += 1
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if heads[i] is None:
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skipped += 1
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continue
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if is_punct_label(labels[i]):
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continue
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n_corr += token.head.i == heads[i]
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total += 1
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print loss, skipped, (loss+skipped + total)
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print pos_corr / n_tokens
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return float(n_corr) / (total + loss)
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def main(dev_loc, model_dir):
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print evaluate(English, dev_loc, model_dir)
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if __name__ == '__main__':
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plac.call(main)
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