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spaCy
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* Use new Example class

This commit is contained in:
Matthew Honnibal 2015-06-28 22:36:03 +02:00
parent 735f1af91f
commit f4986d5d3c
4 changed files with 31 additions and 30 deletions
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1
spacy/_ml.pxd
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@ -5,6 +5,7 @@ from cymem.cymem cimport Pool
from thinc.learner cimport LinearModel
from thinc.features cimport Extractor, Feature
from thinc.typedefs cimport atom_t, feat_t, weight_t, class_t
from thinc.api cimport ExampleC
from preshed.maps cimport PreshMapArray

16
spacy/_ml.pyx
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@ -61,18 +61,14 @@ cdef class Model:
self._model.load(self.model_loc, freq_thresh=0)
def predict(self, Example eg):
self.set_scores(&eg.scores[0], &eg.atoms[0])
eg.guess = arg_max_if_true(&eg.scores[0], &eg.is_valid[0],
self.n_classes)
self.set_scores(eg.c.scores, eg.c.atoms)
eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
def train(self, Example eg):
self.set_scores(&eg.scores[0], &eg.atoms[0])
eg.guess = arg_max_if_true(&eg.scores[0],
&eg.is_valid[0], self.n_classes)
eg.best = arg_max_if_zero(&eg.scores[0], &eg.costs[0],
self.n_classes)
eg.cost = eg.costs[eg.guess]
self.update(&eg.atoms[0], eg.guess, eg.best, eg.cost)
self.predict(eg)
eg.c.best = arg_max_if_zero(eg.c.scores, eg.c.costs, self.n_classes)
eg.c.cost = eg.c.costs[eg.c.guess]
self.update(eg.c.atoms, eg.c.guess, eg.c.best, eg.c.cost)
cdef const weight_t* score(self, atom_t* context) except NULL:
cdef int n_feats

15
spacy/_theano.pyx
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@ -1,4 +1,4 @@
from thinc.api cimport Example
from thinc.api cimport Example, ExampleC
from thinc.typedefs cimport weight_t
from ._ml cimport arg_max_if_true
@ -33,20 +33,17 @@ cdef class TheanoModel(Model):
cdef int i
for i in range(self.n_classes):
eg.scores[i] = theano_scores[i]
eg.guess = arg_max_if_true(&eg.scores[0], <int*>eg.is_valid[0],
self.n_classes)
eg.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
def train(self, Example eg):
self.input_layer.fill(eg.embeddings, eg.atoms, use_avg=False)
theano_scores, update, y = self.train_func(eg.embeddings, eg.costs, self.eta)
self.input_layer.update(update, eg.atoms, self.t, self.eta, self.mu)
for i in range(self.n_classes):
eg.scores[i] = theano_scores[i]
eg.guess = arg_max_if_true(&eg.scores[0], <int*>eg.is_valid[0],
self.n_classes)
eg.best = arg_max_if_zero(&eg.scores[0], <int*>eg.costs[0],
self.n_classes)
eg.cost = eg.costs[eg.guess]
eg.c.scores[i] = theano_scores[i]
eg.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
eg.best = arg_max_if_zero(eg.c.scores, eg.c.costs, self.n_classes)
eg.cost = eg.c.costs[eg.guess]
self.t += 1
def end_training(self):

29
spacy/syntax/parser.pyx
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@ -71,14 +71,17 @@ cdef class Parser:
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
self.moves.initialize_state(stcls)
cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE, self.model.n_feats)
cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
self.model.n_feats, self.model.n_feats)
while not stcls.is_final():
eg.wipe()
fill_context(&eg.atoms[0], stcls)
self.moves.set_valid(<bint*>&eg.is_valid[0], stcls)
memset(eg.c.scores, 0, eg.c.nr_class * sizeof(weight_t))
self.moves.set_valid(<bint*>eg.c.is_valid, stcls)
fill_context(eg.c.atoms, stcls)
self.model.predict(eg)
self.moves.c[eg.guess].do(stcls, self.moves.c[eg.guess].label)
self.moves.c[eg.c.guess].do(stcls, self.moves.c[eg.c.guess].label)
self.moves.finalize_state(stcls)
tokens.set_parse(stcls._sent)
@ -86,20 +89,24 @@ cdef class Parser:
self.moves.preprocess_gold(gold)
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
self.moves.initialize_state(stcls)
cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE, self.model.n_feats)
cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
self.model.n_feats, self.model.n_feats)
cdef int cost = 0
while not stcls.is_final():
eg.wipe()
fill_context(&eg.atoms[0], stcls)
self.moves.set_costs(<bint*>&eg.is_valid[0], &eg.costs[0], stcls, gold)
memset(eg.c.scores, 0, eg.c.nr_class * sizeof(weight_t))
self.moves.set_costs(<bint*>eg.c.is_valid, eg.c.costs, stcls, gold)
fill_context(eg.c.atoms, stcls)
self.model.train(eg)
self.moves.c[eg.guess].do(stcls, self.moves.c[eg.guess].label)
cost += eg.cost
self.moves.c[eg.c.guess].do(stcls, self.moves.c[eg.c.guess].label)
cost += eg.c.cost
return cost
# These are passed as callbacks to thinc.search.Beam
"""
cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1: