mirror of
https://github.com/explosion/spaCy.git
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82 lines
3.3 KiB
Cython
82 lines
3.3 KiB
Cython
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cimport cython
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import random
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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 json
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from thinc.features cimport ConjFeat
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from ..context cimport fill_context
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from ..context cimport N_FIELDS
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from .moves cimport Move
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from .moves cimport fill_moves, transition, best_accepted
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from .moves cimport set_accept_if_valid, set_accept_if_oracle
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from .moves import get_n_moves
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from ._state cimport State
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from ._state cimport init_state
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cdef class NERParser:
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def __init__(self, model_dir):
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self.mem = Pool()
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cfg = json.load(open(path.join(model_dir, 'config.json')))
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templates = cfg['templates']
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self.entity_types = cfg['entity_types']
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self.extractor = Extractor(templates, [ConjFeat] * len(templates))
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self.n_classes = get_n_moves(len(self.entity_types))
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self._moves = <Move*>self.mem.alloc(self.n_classes, sizeof(Move))
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fill_moves(self._moves, len(self.entity_types))
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self.model = LinearModel(len(self.tag_names))
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if path.exists(path.join(model_dir, 'model')):
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self.model.load(path.join(model_dir, 'model'))
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self._context = <atom_t*>self.mem.alloc(N_FIELDS, sizeof(atom_t))
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self._feats = <feat_t*>self.mem.alloc(self.extractor.n+1, sizeof(feat_t))
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self._values = <weight_t*>self.mem.alloc(self.extractor.n+1, sizeof(weight_t))
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self._scores = <weight_t*>self.mem.alloc(self.model.nr_class, sizeof(weight_t))
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cpdef int train(self, Tokens tokens, gold_classes):
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cdef Pool mem = Pool()
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cdef State* s = init_state(mem, tokens.length)
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cdef Move* golds = <Move*>mem.alloc(len(gold_classes), sizeof(Move))
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for i, clas in enumerate(gold_classes):
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golds[i] = self.moves[clas - 1]
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assert golds[i].id == clas
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cdef Move* guess
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while s.i < tokens.length:
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fill_context(self._context, s.i, tokens)
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self.extractor.extract(self._feats, self._values, self._context, NULL)
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self.model.score(self._scores, self._feats, self._values)
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set_accept_if_valid(self._moves, self.n_classes, s)
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guess = best_accepted(self._moves, self._scores, self.n_classes)
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set_accept_if_oracle(self._moves, golds, self.n_classes, s) # TODO
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gold = best_accepted(self._moves, self._scores, self.n_classes)
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if guess.clas == gold.clas:
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self.model.update({})
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return 0
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counts = {guess.clas: {}, gold.clas: {}}
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self.extractor.count(counts[gold.clas], self._feats, 1)
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self.extractor.count(counts[guess.clas], self._feats, -1)
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self.model.update(counts)
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transition(s, guess)
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tokens.ner[s.i-1] = s.tags[s.i-1]
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cpdef int set_tags(self, Tokens tokens) except -1:
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cdef Pool mem = Pool()
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cdef State* s = init_state(mem, tokens.length)
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cdef Move* move
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while s.i < tokens.length:
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fill_context(self._context, s.i, tokens)
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self.extractor.extract(self._feats, self._values, self._context, NULL)
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self.model.score(self._scores, self._feats, self._values)
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set_accept_if_valid(self._moves, self.n_classes, s)
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move = best_accepted(self._moves, self._scores, self.n_classes)
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transition(s, move)
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tokens.ner[s.i-1] = s.tags[s.i-1]
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