2015-06-02 16:37:10 +00:00
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# cython: profile=True
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2014-12-30 10:20:15 +00:00
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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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2015-10-12 08:33:11 +00:00
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import tempfile
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2014-12-30 10:20:15 +00:00
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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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2015-05-26 20:17:15 +00:00
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import numpy.random
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2014-12-30 10:20:15 +00:00
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from thinc.features cimport Feature, count_feats
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2015-06-26 04:25:36 +00:00
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from thinc.api cimport Example
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2014-12-30 10:20:15 +00:00
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2015-11-04 16:53:03 +00:00
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from thinc.learner cimport arg_max, arg_max_if_true, arg_max_if_zero
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2015-06-26 04:25:36 +00:00
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2014-12-30 10:20:15 +00:00
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cdef class Model:
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2014-12-30 14:16:47 +00:00
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def __init__(self, n_classes, templates, model_loc=None):
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2014-12-31 08:40:59 +00:00
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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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2015-10-12 08:33:11 +00:00
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self._templates = templates
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2015-11-04 16:53:03 +00:00
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n_atoms = max([max(templ) for templ in templates]) + 1
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2014-12-31 08:40:59 +00:00
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self.n_classes = n_classes
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2014-12-30 10:20:15 +00:00
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self._extractor = Extractor(templates)
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2015-06-26 11:51:39 +00:00
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self.n_feats = self._extractor.n_templ
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2015-11-04 16:53:03 +00:00
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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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2014-12-30 14:16:47 +00:00
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self.model_loc = model_loc
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2014-12-30 10:20:15 +00:00
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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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2015-10-12 08:33:11 +00:00
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def __reduce__(self):
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2015-10-13 04:10:04 +00:00
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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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2015-10-13 04:14:38 +00:00
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return (Model, (self.n_classes, self._templates, model_loc),
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2015-10-12 08:33:11 +00:00
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None, None)
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2015-06-26 04:25:36 +00:00
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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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2015-06-26 04:25:36 +00:00
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2015-06-01 22:27:07 +00:00
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cdef const weight_t* score(self, atom_t* context) except NULL:
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2015-11-04 16:53:03 +00:00
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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.scores
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2015-05-31 16:48:05 +00:00
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2015-07-14 21:47:03 +00:00
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cdef int set_scores(self, weight_t* scores, atom_t* context) nogil:
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2015-11-04 16:53:03 +00:00
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cdef int nr_feat = self._model.extractor.set_feats(self._eg.features, context)
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self._model.set_scores(
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scores,
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self._model.weights.c_map,
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self._eg.c.features,
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nr_feat
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)
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2015-06-02 16:37:10 +00:00
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2014-12-31 08:40:59 +00:00
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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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2014-12-30 10:20:15 +00:00
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2015-08-27 07:16:11 +00:00
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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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2014-12-30 10:20:15 +00:00
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self._model.end_training()
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2015-08-27 07:16:11 +00:00
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self._model.dump(model_loc, freq_thresh=0)
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