mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-22 03:49:17 +03:00
119 lines
4.5 KiB
Cython
119 lines
4.5 KiB
Cython
from __future__ import division
|
|
from __future__ import unicode_literals
|
|
|
|
cimport cython
|
|
import random
|
|
import os
|
|
from os import path
|
|
import shutil
|
|
import json
|
|
|
|
from thinc.features cimport ConjFeat
|
|
|
|
from .context cimport fill_context
|
|
from .context cimport N_FIELDS
|
|
from .moves cimport Move
|
|
from .moves cimport fill_moves, transition, best_accepted
|
|
from .moves cimport set_accept_if_valid, set_accept_if_oracle
|
|
from ._state cimport entity_is_open
|
|
from .moves import get_n_moves
|
|
from ._state cimport State
|
|
from ._state cimport init_state
|
|
|
|
|
|
def setup_model_dir(tag_names, templates, model_dir):
|
|
if path.exists(model_dir):
|
|
shutil.rmtree(model_dir)
|
|
os.mkdir(model_dir)
|
|
config = {
|
|
'templates': templates,
|
|
'tag_names': tag_names,
|
|
}
|
|
with open(path.join(model_dir, 'config.json'), 'w') as file_:
|
|
json.dump(config, file_)
|
|
|
|
|
|
|
|
def train(train_sents, model_dir, nr_iter=10):
|
|
cdef Tokens tokens
|
|
parser = NERParser(model_dir)
|
|
for _ in range(nr_iter):
|
|
n_corr = 0
|
|
total = 0
|
|
for i, (tokens, golds) in enumerate(train_sents):
|
|
if any([g == 0 for g in golds]):
|
|
continue
|
|
n_corr += parser.train(tokens, golds)
|
|
total += len([g for g in golds if g != 0])
|
|
print('%.4f' % ((n_corr / total) * 100))
|
|
random.shuffle(train_sents)
|
|
parser.model.end_training()
|
|
parser.model.dump(path.join(model_dir, 'model'))
|
|
|
|
|
|
cdef class NERParser:
|
|
def __init__(self, model_dir):
|
|
self.mem = Pool()
|
|
cfg = json.load(open(path.join(model_dir, 'config.json')))
|
|
templates = cfg['templates']
|
|
self.tag_names = cfg['tag_names']
|
|
self.extractor = Extractor(templates, [ConjFeat] * len(templates))
|
|
self.n_classes = len(self.tag_names)
|
|
self._moves = <Move*>self.mem.alloc(len(self.tag_names), sizeof(Move))
|
|
fill_moves(self._moves, self.tag_names)
|
|
self.model = LinearModel(self.n_classes)
|
|
if path.exists(path.join(model_dir, 'model')):
|
|
self.model.load(path.join(model_dir, 'model'))
|
|
|
|
self._context = <atom_t*>self.mem.alloc(N_FIELDS, sizeof(atom_t))
|
|
self._feats = <feat_t*>self.mem.alloc(self.extractor.n+1, sizeof(feat_t))
|
|
self._values = <weight_t*>self.mem.alloc(self.extractor.n+1, sizeof(weight_t))
|
|
self._scores = <weight_t*>self.mem.alloc(self.model.nr_class, sizeof(weight_t))
|
|
|
|
cpdef int train(self, Tokens tokens, gold_classes) except -1:
|
|
cdef Pool mem = Pool()
|
|
cdef State* s = init_state(mem, tokens.length)
|
|
cdef Move* golds = <Move*>mem.alloc(len(gold_classes), sizeof(Move))
|
|
for tok_i, clas in enumerate(gold_classes):
|
|
golds[tok_i] = self._moves[clas]
|
|
assert golds[tok_i].clas == clas, '%d vs %d' % (golds[tok_i].clas, clas)
|
|
cdef Move* guess
|
|
n_correct = 0
|
|
cdef int f = 0
|
|
while s.i < tokens.length:
|
|
fill_context(self._context, s.i, tokens)
|
|
self.extractor.extract(self._feats, self._values, self._context, NULL)
|
|
self.model.score(self._scores, self._feats, self._values)
|
|
|
|
set_accept_if_valid(self._moves, self.n_classes, s)
|
|
guess = best_accepted(self._moves, self._scores, self.n_classes)
|
|
assert guess.clas != 0
|
|
assert gold_classes[s.i] != 0
|
|
set_accept_if_oracle(self._moves, golds, self.n_classes, s)
|
|
gold = best_accepted(self._moves, self._scores, self.n_classes)
|
|
if guess.clas == gold.clas:
|
|
counts = {}
|
|
n_correct += 1
|
|
else:
|
|
counts = {guess.clas: {}, gold.clas: {}}
|
|
self.extractor.count(counts[gold.clas], self._feats, 1)
|
|
self.extractor.count(counts[guess.clas], self._feats, -1)
|
|
self.model.update(counts)
|
|
gold_str = self.tag_names[gold.clas]
|
|
transition(s, guess)
|
|
tokens.ner[s.i-1] = s.tags[s.i-1]
|
|
return n_correct
|
|
|
|
cpdef int set_tags(self, Tokens tokens) except -1:
|
|
cdef Pool mem = Pool()
|
|
cdef State* s = init_state(mem, tokens.length)
|
|
cdef Move* move
|
|
while s.i < tokens.length:
|
|
fill_context(self._context, s.i, tokens)
|
|
self.extractor.extract(self._feats, self._values, self._context, NULL)
|
|
self.model.score(self._scores, self._feats, self._values)
|
|
set_accept_if_valid(self._moves, self.n_classes, s)
|
|
move = best_accepted(self._moves, self._scores, self.n_classes)
|
|
transition(s, move)
|
|
tokens.ner[s.i-1] = s.tags[s.i-1]
|