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69 lines
2.5 KiB
Cython
69 lines
2.5 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 libc.string cimport memcpy
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from thinc.features cimport Feature, count_feats
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from thinc.api cimport Example
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from thinc.learner cimport arg_max, arg_max_if_true, arg_max_if_zero
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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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n_atoms = max([max(templ) for templ in templates]) + 1
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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)
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self._eg = Example(n_classes, n_atoms, self._extractor.n_templ, 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._model(eg)
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def train(self, Example eg):
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self._model.train(eg)
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cdef const weight_t* score(self, atom_t* context) except NULL:
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memcpy(self._eg.c.atoms, context, self._eg.c.nr_atom * sizeof(context[0]))
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self._model(self._eg)
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return self._eg.c.scores
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cdef int set_scores(self, weight_t* scores, atom_t* context) nogil:
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cdef int nr_feat = self._extractor.set_feats(self._eg.c.features, context)
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self._model.set_scores(scores, self._eg.c.features, nr_feat)
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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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