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61 lines
2.0 KiB
Cython
61 lines
2.0 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 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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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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@cython.cdivision
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@cython.boundscheck(False)
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cdef int regularize(self, Feature* feats, int n, int a=3) except -1:
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# Use the Zipfian corruptions technique from here:
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# http://www.aclweb.org/anthology/N13-1077
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# This seems good for 0.1 - 0.3 % on OOD data.
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cdef int i
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cdef long[:] zipfs = numpy.random.zipf(a, n)
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for i in range(n):
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feats[i].value *= 1 / zipfs[i]
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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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