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113 lines
3.9 KiB
Cython
113 lines
3.9 KiB
Cython
# cython: profile=True
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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 tempfile
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import os
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import shutil
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import json
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import cython
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import numpy.random
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from thinc.features cimport Feature, count_feats
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from thinc.api cimport Example
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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 int arg_max_if_true(const weight_t* scores, const int* is_valid,
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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 = -900000
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for i in range(n_classes):
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if is_valid[i] and 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 int arg_max_if_zero(const weight_t* scores, const int* costs,
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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 = -900000
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for i in range(n_classes):
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if costs[i] == 0 and 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._templates = templates
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self.n_classes = n_classes
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self._extractor = Extractor(templates)
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self.n_feats = self._extractor.n_templ
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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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def __reduce__(self):
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_, model_loc = tempfile.mkstemp()
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# TODO: This is a potentially buggy implementation. We're not really
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# given a good guarantee that all internal state is saved correctly here,
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# since there are learning parameters for e.g. the model averaging in
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# averaged perceptron, the gradient calculations in AdaGrad, etc
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# that aren't necessarily saved. So, if we're part way through training
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# the model, and then we pickle it, we won't recover the state correctly.
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self._model.dump(model_loc)
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return (Model, (self.n_classes, self._templates, model_loc),
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None, None)
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def predict(self, Example eg):
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self.set_scores(eg.c.scores, eg.c.atoms)
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eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
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def train(self, Example eg):
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self.predict(eg)
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eg.c.best = arg_max_if_zero(eg.c.scores, eg.c.costs, self.n_classes)
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eg.c.cost = eg.c.costs[eg.c.guess]
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self.update(eg.c.atoms, eg.c.guess, eg.c.best, eg.c.cost)
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cdef const weight_t* score(self, atom_t* context) except NULL:
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cdef int n_feats
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feats = self._extractor.get_feats(context, &n_feats)
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return self._model.get_scores(feats, n_feats)
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cdef int set_scores(self, weight_t* scores, atom_t* context) nogil:
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cdef int n_feats
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feats = self._extractor.get_feats(context, &n_feats)
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self._model.set_scores(scores, feats, n_feats)
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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, model_loc=None):
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if model_loc is None:
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model_loc = self.model_loc
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self._model.end_training()
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self._model.dump(model_loc, freq_thresh=0)
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