mirror of https://github.com/explosion/spaCy.git
151 lines
4.5 KiB
Cython
151 lines
4.5 KiB
Cython
cdef class GoldParse:
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def __init__(self):
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pass
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cdef int heads_correct(self, TokenC* tokens, bint score_punct=False) except -1:
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pass
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"""
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@classmethod
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def from_conll(cls, unicode sent_str):
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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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return cls(text, [words], ids, words, tags, heads, labels)
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@classmethod
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def from_docparse(cls, unicode sent_str):
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words = []
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heads = []
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labels = []
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tags = []
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ids = []
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lines = sent_str.strip().split('\n')
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raw_text = lines.pop(0).strip()
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tok_text = lines.pop(0).strip()
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for i, line in enumerate(lines):
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id_, word, pos_string, head_idx, label = _parse_line(line)
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if label == 'root':
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label = 'ROOT'
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words.append(word)
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if head_idx < 0:
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head_idx = id_
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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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tokenized = [sent_str.replace('<SEP>', ' ').split(' ')
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for sent_str in tok_text.split('<SENT>')]
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return cls(raw_text, tokenized, ids, words, tags, heads, labels)
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cdef int heads_correct(self, TokenC* tokens, bint score_punct=False) except -1:
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pass
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def align_to_non_gold_tokens(self, tokens):
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# TODO
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tags = []
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heads = []
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labels = []
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orig_words = list(words)
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missed = []
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for token in tokens:
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while annot and token.idx > annot[0][0]:
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miss_id, miss_tag, miss_head, miss_label = annot.pop(0)
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miss_w = words.pop(0)
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if not is_punct_label(miss_label):
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missed.append(miss_w)
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loss += 1
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if not annot:
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tags.append(None)
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heads.append(None)
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labels.append(None)
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continue
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id_, tag, head, label = annot[0]
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if token.idx == id_:
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tags.append(tag)
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heads.append(head)
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labels.append(label)
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annot.pop(0)
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words.pop(0)
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elif token.idx < id_:
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tags.append(None)
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heads.append(None)
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labels.append(None)
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else:
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raise StandardError
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return loss, tags, heads, labels
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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 _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 _parse_line(line):
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pieces = line.split()
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if len(pieces) == 4:
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return 0, pieces[0], pieces[1], int(pieces[2]) - 1, pieces[3]
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else:
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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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# TODO
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def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
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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_tokenized_gold(file_)
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paragraphs = read_docparse_gold(file_)
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for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
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gold_preproc=gold_preproc):
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assert len(tokens) == len(labels)
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nlp.tagger(tokens)
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nlp.parser(tokens)
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for i, token in enumerate(tokens):
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pos_corr += token.tag_ == tag_strs[i]
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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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"""
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