spaCy/spacy/_ml.pyx
2015-10-13 15:14:38 +11:00

113 lines
3.9 KiB
Cython

# cython: profile=True
from __future__ import unicode_literals
from __future__ import division
from os import path
import tempfile
import os
import shutil
import json
import cython
import numpy.random
from thinc.features cimport Feature, count_feats
from thinc.api cimport Example
cdef int arg_max(const weight_t* scores, const int n_classes) nogil:
cdef int i
cdef int best = 0
cdef weight_t mode = scores[0]
for i in range(1, n_classes):
if scores[i] > mode:
mode = scores[i]
best = i
return best
cdef int arg_max_if_true(const weight_t* scores, const int* is_valid,
const int n_classes) nogil:
cdef int i
cdef int best = 0
cdef weight_t mode = -900000
for i in range(n_classes):
if is_valid[i] and scores[i] > mode:
mode = scores[i]
best = i
return best
cdef int arg_max_if_zero(const weight_t* scores, const int* costs,
const int n_classes) nogil:
cdef int i
cdef int best = 0
cdef weight_t mode = -900000
for i in range(n_classes):
if costs[i] == 0 and scores[i] > mode:
mode = scores[i]
best = i
return best
cdef class Model:
def __init__(self, n_classes, templates, model_loc=None):
if model_loc is not None and path.isdir(model_loc):
model_loc = path.join(model_loc, 'model')
self._templates = templates
self.n_classes = n_classes
self._extractor = Extractor(templates)
self.n_feats = self._extractor.n_templ
self._model = LinearModel(n_classes, self._extractor.n_templ)
self.model_loc = model_loc
if self.model_loc and path.exists(self.model_loc):
self._model.load(self.model_loc, freq_thresh=0)
def __reduce__(self):
_, model_loc = tempfile.mkstemp()
# TODO: This is a potentially buggy implementation. We're not really
# given a good guarantee that all internal state is saved correctly here,
# since there are learning parameters for e.g. the model averaging in
# averaged perceptron, the gradient calculations in AdaGrad, etc
# that aren't necessarily saved. So, if we're part way through training
# the model, and then we pickle it, we won't recover the state correctly.
self._model.dump(model_loc)
return (Model, (self.n_classes, self._templates, model_loc),
None, None)
def predict(self, Example eg):
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.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
feats = self._extractor.get_feats(context, &n_feats)
return self._model.get_scores(feats, n_feats)
cdef int set_scores(self, weight_t* scores, atom_t* context) nogil:
cdef int n_feats
feats = self._extractor.get_feats(context, &n_feats)
self._model.set_scores(scores, feats, n_feats)
cdef int update(self, atom_t* context, class_t guess, class_t gold, int cost) except -1:
cdef int n_feats
if cost == 0:
self._model.update({})
else:
feats = self._extractor.get_feats(context, &n_feats)
counts = {gold: {}, guess: {}}
count_feats(counts[gold], feats, n_feats, cost)
count_feats(counts[guess], feats, n_feats, -cost)
self._model.update(counts)
def end_training(self, model_loc=None):
if model_loc is None:
model_loc = self.model_loc
self._model.end_training()
self._model.dump(model_loc, freq_thresh=0)