mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
368 lines
12 KiB
Cython
368 lines
12 KiB
Cython
from __future__ import unicode_literals, print_function
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import numpy
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import io
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import json
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import random
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import re
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import os
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from os import path
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from libc.string cimport memset
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try:
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import ujson as json
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except ImportError:
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import json
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from .syntax import nonproj
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def tags_to_entities(tags):
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entities = []
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start = None
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for i, tag in enumerate(tags):
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if tag.startswith('O'):
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# TODO: We shouldn't be getting these malformed inputs. Fix this.
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if start is not None:
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start = None
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continue
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elif tag == '-':
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continue
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elif tag.startswith('I'):
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assert start is not None, tags[:i]
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continue
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if tag.startswith('U'):
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entities.append((tag[2:], i, i))
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elif tag.startswith('B'):
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start = i
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elif tag.startswith('L'):
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entities.append((tag[2:], start, i))
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start = None
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else:
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raise Exception(tag)
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return entities
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def merge_sents(sents):
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m_deps = [[], [], [], [], [], []]
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m_brackets = []
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i = 0
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for (ids, words, tags, heads, labels, ner), brackets in sents:
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m_deps[0].extend(id_ + i for id_ in ids)
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m_deps[1].extend(words)
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m_deps[2].extend(tags)
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m_deps[3].extend(head + i for head in heads)
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m_deps[4].extend(labels)
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m_deps[5].extend(ner)
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m_brackets.extend((b['first'] + i, b['last'] + i, b['label']) for b in brackets)
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i += len(ids)
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return [(m_deps, m_brackets)]
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def align(cand_words, gold_words):
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cost, edit_path = _min_edit_path(cand_words, gold_words)
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alignment = []
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i_of_gold = 0
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for move in edit_path:
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if move == 'M':
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alignment.append(i_of_gold)
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i_of_gold += 1
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elif move == 'S':
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alignment.append(None)
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i_of_gold += 1
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elif move == 'D':
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alignment.append(None)
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elif move == 'I':
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i_of_gold += 1
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else:
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raise Exception(move)
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return alignment
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punct_re = re.compile(r'\W')
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def _min_edit_path(cand_words, gold_words):
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cdef:
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Pool mem
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int i, j, n_cand, n_gold
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int* curr_costs
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int* prev_costs
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# TODO: Fix this --- just do it properly, make the full edit matrix and
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# then walk back over it...
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# Preprocess inputs
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cand_words = [punct_re.sub('', w) for w in cand_words]
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gold_words = [punct_re.sub('', w) for w in gold_words]
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if cand_words == gold_words:
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return 0, ''.join(['M' for _ in gold_words])
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mem = Pool()
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n_cand = len(cand_words)
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n_gold = len(gold_words)
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# Levenshtein distance, except we need the history, and we may want different
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# costs.
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# Mark operations with a string, and score the history using _edit_cost.
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previous_row = []
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prev_costs = <int*>mem.alloc(n_gold + 1, sizeof(int))
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curr_costs = <int*>mem.alloc(n_gold + 1, sizeof(int))
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for i in range(n_gold + 1):
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cell = ''
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for j in range(i):
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cell += 'I'
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previous_row.append('I' * i)
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prev_costs[i] = i
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for i, cand in enumerate(cand_words):
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current_row = ['D' * (i + 1)]
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curr_costs[0] = i+1
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for j, gold in enumerate(gold_words):
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if gold.lower() == cand.lower():
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s_cost = prev_costs[j]
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i_cost = curr_costs[j] + 1
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d_cost = prev_costs[j + 1] + 1
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else:
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s_cost = prev_costs[j] + 1
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i_cost = curr_costs[j] + 1
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d_cost = prev_costs[j + 1] + (1 if cand else 0)
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if s_cost <= i_cost and s_cost <= d_cost:
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best_cost = s_cost
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best_hist = previous_row[j] + ('M' if gold == cand else 'S')
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elif i_cost <= s_cost and i_cost <= d_cost:
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best_cost = i_cost
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best_hist = current_row[j] + 'I'
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else:
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best_cost = d_cost
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best_hist = previous_row[j + 1] + 'D'
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current_row.append(best_hist)
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curr_costs[j+1] = best_cost
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previous_row = current_row
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for j in range(len(gold_words) + 1):
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prev_costs[j] = curr_costs[j]
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curr_costs[j] = 0
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return prev_costs[n_gold], previous_row[-1]
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def read_json_file(loc, docs_filter=None):
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if path.isdir(loc):
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for filename in os.listdir(loc):
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yield from read_json_file(path.join(loc, filename))
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else:
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with open(loc) as file_:
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docs = json.load(file_)
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for doc in docs:
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if docs_filter is not None and not docs_filter(doc):
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continue
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paragraphs = []
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for paragraph in doc['paragraphs']:
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sents = []
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for sent in paragraph['sentences']:
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words = []
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ids = []
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tags = []
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heads = []
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labels = []
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ner = []
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for i, token in enumerate(sent['tokens']):
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words.append(token['orth'])
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ids.append(i)
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tags.append(token.get('tag','-'))
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heads.append(token.get('head',0) + i)
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labels.append(token.get('dep',''))
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# Ensure ROOT label is case-insensitive
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if labels[-1].lower() == 'root':
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labels[-1] = 'ROOT'
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ner.append(token.get('ner', '-'))
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sents.append((
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(ids, words, tags, heads, labels, ner),
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sent.get('brackets', [])))
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if sents:
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yield (paragraph.get('raw', None), sents)
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def _iob_to_biluo(tags):
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out = []
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curr_label = None
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tags = list(tags)
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while tags:
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out.extend(_consume_os(tags))
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out.extend(_consume_ent(tags))
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return out
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def _consume_os(tags):
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while tags and tags[0] == 'O':
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yield tags.pop(0)
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def _consume_ent(tags):
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if not tags:
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return []
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target = tags.pop(0).replace('B', 'I')
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length = 1
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while tags and tags[0] == target:
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length += 1
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tags.pop(0)
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label = target[2:]
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if length == 1:
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return ['U-' + label]
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else:
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start = 'B-' + label
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end = 'L-' + label
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middle = ['I-%s' % label for _ in range(1, length - 1)]
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return [start] + middle + [end]
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cdef class GoldParse:
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@classmethod
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def from_annot_tuples(cls, doc, annot_tuples, make_projective=False):
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_, words, tags, heads, deps, entities = annot_tuples
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return cls(doc, words=words, tags=tags, heads=heads, deps=deps, entities=entities,
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make_projective=make_projective)
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def __init__(self, doc, annot_tuples=None, words=None, tags=None, heads=None,
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deps=None, entities=None, make_projective=False):
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if words is None:
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words = [token.text for token in doc]
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if tags is None:
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tags = [None for _ in doc]
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if heads is None:
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heads = [None for _ in doc]
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if deps is None:
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deps = [None for _ in doc]
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if entities is None:
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entities = [None for _ in doc]
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elif len(entities) == 0:
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entities = ['O' for _ in doc]
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elif not isinstance(entities[0], basestring):
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# Assume we have entities specified by character offset.
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entities = biluo_tags_from_offsets(doc, entities)
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self.mem = Pool()
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self.loss = 0
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self.length = len(doc)
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# These are filled by the tagger/parser/entity recogniser
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self.c.tags = <int*>self.mem.alloc(len(doc), sizeof(int))
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self.c.heads = <int*>self.mem.alloc(len(doc), sizeof(int))
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self.c.labels = <int*>self.mem.alloc(len(doc), sizeof(int))
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self.c.ner = <Transition*>self.mem.alloc(len(doc), sizeof(Transition))
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self.tags = [None] * len(doc)
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self.heads = [None] * len(doc)
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self.labels = [''] * len(doc)
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self.ner = ['-'] * len(doc)
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self.cand_to_gold = align([t.orth_ for t in doc], words)
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self.gold_to_cand = align(words, [t.orth_ for t in doc])
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annot_tuples = (range(len(words)), words, tags, heads, deps, entities)
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self.orig_annot = list(zip(*annot_tuples))
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for i, gold_i in enumerate(self.cand_to_gold):
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if doc[i].isspace():
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self.tags[i] = 'SP'
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self.heads[i] = None
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self.labels[i] = None
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self.ner[i] = 'O'
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elif gold_i is None:
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pass
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else:
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self.tags[i] = tags[gold_i]
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self.heads[i] = self.gold_to_cand[heads[gold_i]]
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self.labels[i] = deps[gold_i]
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self.ner[i] = entities[gold_i]
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cycle = nonproj.contains_cycle(self.heads)
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if cycle != None:
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raise Exception("Cycle found: %s" % cycle)
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if make_projective:
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proj_heads,_ = nonproj.PseudoProjectivity.projectivize(self.heads, self.labels)
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self.heads = proj_heads
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def __len__(self):
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return self.length
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@property
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def is_projective(self):
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return not nonproj.is_nonproj_tree(self.heads)
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def biluo_tags_from_offsets(doc, entities):
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'''Encode labelled spans into per-token tags, using the Begin/In/Last/Unit/Out
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scheme (biluo).
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Arguments:
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doc (Doc):
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The document that the entity offsets refer to. The output tags will
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refer to the token boundaries within the document.
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entities (sequence):
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A sequence of (start, end, label) triples. start and end should be
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character-offset integers denoting the slice into the original string.
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Returns:
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tags (list):
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A list of unicode strings, describing the tags. Each tag string will
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be of the form either "", "O" or "{action}-{label}", where action is one
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of "B", "I", "L", "U". The empty string "" is used where the entity
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offsets don't align with the tokenization in the Doc object. The
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training algorithm will view these as missing values. "O" denotes
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a non-entity token. "B" denotes the beginning of a multi-token entity,
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"I" the inside of an entity of three or more tokens, and "L" the end
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of an entity of two or more tokens. "U" denotes a single-token entity.
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Example:
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text = 'I like London.'
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entities = [(len('I like '), len('I like London'), 'LOC')]
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doc = nlp.tokenizer(text)
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ['O', 'O', 'U-LOC', 'O']
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'''
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starts = {token.idx: token.i for token in doc}
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ends = {token.idx+len(token): token.i for token in doc}
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biluo = ['' for _ in doc]
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# Handle entity cases
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for start_char, end_char, label in entities:
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start_token = starts.get(start_char)
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end_token = ends.get(end_char)
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# Only interested if the tokenization is correct
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if start_token is not None and end_token is not None:
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if start_token == end_token:
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biluo[start_token] = 'U-%s' % label
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else:
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biluo[start_token] = 'B-%s' % label
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for i in range(start_token+1, end_token):
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biluo[i] = 'I-%s' % label
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biluo[end_token] = 'L-%s' % label
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# Now distinguish the O cases from ones where we miss the tokenization
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entity_chars = set()
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for start_char, end_char, label in entities:
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for i in range(start_char, end_char):
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entity_chars.add(i)
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for token in doc:
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for i in range(token.idx, token.idx+len(token)):
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if i in entity_chars:
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break
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else:
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biluo[token.i] = 'O'
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return biluo
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def is_punct_label(label):
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return label == 'P' or label.lower() == 'punct'
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