mirror of
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114 lines
3.7 KiB
Cython
114 lines
3.7 KiB
Cython
from __future__ import unicode_literals
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from __future__ import division
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from os import path
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import os
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import shutil
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import random
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import json
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import cython
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from thinc.features cimport Feature, count_feats
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cdef int arg_max(const weight_t* scores, const int n_classes) nogil:
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cdef int i
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cdef int best = 0
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cdef weight_t mode = scores[0]
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for i in range(1, n_classes):
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if scores[i] > mode:
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mode = scores[i]
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best = i
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return best
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cdef class Model:
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def __init__(self, n_classes, templates, model_loc=None):
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if model_loc is not None and path.isdir(model_loc):
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model_loc = path.join(model_loc, 'model')
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self.n_classes = n_classes
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self._extractor = Extractor(templates)
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self._model = LinearModel(n_classes, self._extractor.n_templ)
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self.model_loc = model_loc
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if self.model_loc and path.exists(self.model_loc):
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self._model.load(self.model_loc, freq_thresh=0)
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cdef int update(self, atom_t* context, class_t guess, class_t gold, int cost) except -1:
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cdef int n_feats
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if cost == 0:
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self._model.update({})
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else:
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feats = self._extractor.get_feats(context, &n_feats)
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counts = {gold: {}, guess: {}}
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count_feats(counts[gold], feats, n_feats, cost)
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count_feats(counts[guess], feats, n_feats, -cost)
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self._model.update(counts)
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def end_training(self):
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self._model.end_training()
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self._model.dump(self.model_loc, freq_thresh=0)
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cdef class HastyModel:
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def __init__(self, n_classes, hasty_templates, full_templates, model_dir):
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full_templates = tuple([t for t in full_templates if t not in hasty_templates])
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self.mem = Pool()
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self.n_classes = n_classes
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self._scores = <weight_t*>self.mem.alloc(self.n_classes, sizeof(weight_t))
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assert path.exists(model_dir)
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assert path.isdir(model_dir)
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self._hasty = Model(n_classes, hasty_templates, path.join(model_dir, 'hasty_model'))
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self._full = Model(n_classes, full_templates, path.join(model_dir, 'full_model'))
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self.hasty_cnt = 0
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self.full_cnt = 0
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cdef const weight_t* score(self, atom_t* context) except NULL:
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cdef int i
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hasty_scores = self._hasty.score(context)
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if will_use_hasty(hasty_scores, self._hasty.n_classes):
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self.hasty_cnt += 1
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return hasty_scores
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else:
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self.full_cnt += 1
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full_scores = self._full.score(context)
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for i in range(self.n_classes):
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self._scores[i] = full_scores[i] + hasty_scores[i]
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return self._scores
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cdef int update(self, atom_t* context, class_t guess, class_t gold, int cost) except -1:
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self._hasty.update(context, guess, gold, cost)
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self._full.update(context, guess, gold, cost)
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def end_training(self):
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self._hasty.end_training()
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self._full.end_training()
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@cython.cdivision(True)
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cdef bint will_use_hasty(const weight_t* scores, int n_classes) nogil:
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cdef:
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weight_t best_score, second_score
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int best, second
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if scores[0] >= scores[1]:
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best = 0
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best_score = scores[0]
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second = 1
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second_score = scores[1]
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else:
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best = 1
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best_score = scores[1]
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second = 0
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second_score = scores[0]
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cdef int i
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for i in range(2, n_classes):
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if scores[i] > best_score:
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second_score = best_score
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second = best
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best = i
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best_score = scores[i]
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elif scores[i] > second_score:
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second_score = scores[i]
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second = i
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return best_score > 0 and second_score < (best_score / 2)
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