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sent_conv.py
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sent_conv.py
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import os
import cPickle as pickle
import numpy as np
from collections import defaultdict, OrderedDict
import theano
import theano.tensor as T
import re
import datetime
import codecs
import logging
import sys
import timeit
import util
from util import sigmoid, make_idx_data_cv, evaluate, get_idx_from_sent, ConfigBase, init_log
from layers import HiddenLayer, LogisticRegression
logger = None
# theano.config.optimizer = 'None'
theano.config.on_unused_input = 'ignore'
class Config(ConfigBase):
def __init__(self, conf_path):
super(Config, self).__init__(conf_path)
self.word_vectors = 'word2vec'
self.adjust_input = 0
self.do_train = 0
self.train_path = ''
self.cv_index = 0
self.do_test = 0
self.test_path = ''
self.test_out_path = ''
self.batch_size = 50
self.n_epochs = 100
self.filter_num = 100
self.filter_hs = [3, 4, 5]
self.L1_reg = 0.0
self.L2_reg = 0.0
self.n_hidden = 100
self.out_root = './outdir'
self.load_conf()
self.auto_conf()
def auto_conf(self):
util.mkdir(self.out_root)
self.out_dir = os.path.join(self.out_root, self.name)
util.mkdir(self.out_dir)
self.model_path = os.path.join(self.out_dir, 'model')
self.log_path = os.path.join(self.out_dir, 'log')
self.test_out_path = os.path.join(self.out_dir, 'test_out')
class ConvLayer(object):
def __init__(self, rng, data, W=None, b=None, filter_h=2, filter_num=50, k=300):
"""
:param data: a 3D tensor (sentence number, sentence length, word vector size).
:param W: a matrix (filter_num, word vector size)
:param filter_h: converlution operation window size.
:param filter_num: the feature map number of each converlution window size.
So the total feature maps are `filter_num`, which is
also the size of the new vector representation of the sentence.
"""
if W is None:
W = np.asarray(rng.uniform(size=(filter_num, k * filter_h)),
dtype=theano.config.floatX
)
self.W = theano.shared(value=W, name='W', borrow=True)
# initialize the biases b as a vector of n_out 0s
if b is None:
b = np.asarray(rng.uniform(
size=(filter_num,)),
dtype=theano.config.floatX
)
self.b = theano.shared(value=b, name='b', borrow=True)
X_h, X_w = data.shape[1], data.shape[2]
idx_range = T.arange(X_h - filter_h + 1)
self.window_results, updates = theano.scan(fn=lambda i, X, filter_h: T.flatten(data[:, i: i + filter_h], outdim=2),
sequences=idx_range,
outputs_info=None,
non_sequences=[data, filter_h]
)
self.window_results = T.transpose(self.window_results, axes=(1, 0, 2))
c = sigmoid(T.dot(self.window_results, self.W.T) + self.b)
# max pooling
c_max = T.max(c, axis=1)
self.c = c
# c_max (sentence number, filter_num)
self.c_max = c_max
self.params = [self.W, self.b]
class SentConv(object):
def __init__(self,
learning_rate=0.1,
L1_reg=0.00,
L2_reg=0.0001,
filter_hs=[3, 4, 5],
filter_num=100,
n_hidden=100,
n_out=2,
word_idx_map=None,
wordvec=None,
k=300,
adjust_input=False):
"""
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient
:type L1_reg: float
:param L1_reg: L1-norm's weight when added to the cost (see
regularization)
:type L2_reg: float
:param L2_reg: L2-norm's weight when added to the cost (see
regularization)
"""
self.learning_rate = learning_rate
self.L1_reg = L1_reg
self.L2_reg = L2_reg
self.word_idx_map = word_idx_map
rng = np.random.RandomState(3435)
self.rng = rng
self.k = k
self.filter_num = filter_num
self.filter_hs = filter_hs
# Can be assigned at the fit step.
self.batch_size = None
self.epoch = 0
self.Words = theano.shared(value=wordvec, name="Words")
X = T.matrix('X')
Y = T.ivector('Y')
self.X = X
self.Y = Y
layer0_input = self.Words[T.cast(X.flatten(), dtype='int32')].reshape((X.shape[0], X.shape[1], self.Words.shape[1]))
self.layer0_input = layer0_input
c_max_list = []
self.conv_layer_s = []
test_case = []
for filter_h in filter_hs:
conv_layer = ConvLayer(rng, layer0_input, filter_h=filter_h, filter_num=filter_num, k=k)
self.conv_layer_s.append(conv_layer)
c_max_list.append(conv_layer.c_max)
max_pooling_out = T.concatenate(c_max_list, axis=1)
max_pooling_out_size = filter_num * len(filter_hs)
self.hidden_layer = HiddenLayer(rng, max_pooling_out, max_pooling_out_size, n_hidden)
self.lr_layer = LogisticRegression(
input=self.hidden_layer.output,
n_in=n_hidden,
n_out=n_out,
)
# L1 norm ; one regularization option is to enforce L1 norm to
# be small
self.L1 = (
sum([abs(conv_layer.W).sum() for conv_layer in self.conv_layer_s])
+ abs(self.hidden_layer.W).sum()
+ abs(self.lr_layer.W).sum()
)
# square of L2 norm ; one regularization option is to enforce
# square of L2 norm to be small
self.L2_sqr = (
sum([(conv_layer.W ** 2).sum() for conv_layer in self.conv_layer_s])
+ (self.hidden_layer.W ** 2).sum()
+ (self.lr_layer.W ** 2).sum()
)
# the cost we minimize during training is the negative log likelihood of
# the model plus the regularization terms (L1 and L2); cost is expressed
# here symbolically
self.cost = (
self.negative_log_likelihood(Y)
+ self.L1_reg * self.L1
+ self.L2_reg * self.L2_sqr
)
# the parameters of the model are the parameters of the two layer it is
# made out of
self.params = []
# also adjust the input word vectors
if adjust_input:
self.params.append(self.Words)
for conv_layer in self.conv_layer_s:
self.params += conv_layer.params
self.params += self.hidden_layer.params
self.params += self.lr_layer.params
# negative log likelihood of the MLP is given by the negative
# log likelihood of the output of the model, computed in the
# logistic regression layer
def negative_log_likelihood(self, Y):
return self.lr_layer.negative_log_likelihood(Y)
# same holds for the function computing the number of errors
def errors(self, Y):
return self.lr_layer.errors(Y)
def fit(self, datasets, batch_size=50, n_epochs=400):
train_x, train_y, valid_x, valid_y = datasets
self.batch_size = batch_size
# compute number of minibatches for training, validation and testing
train_len = train_x.get_value(borrow=True).shape[0]
valid_len = valid_x.get_value(borrow=True).shape[0]
n_train_batches = train_len / batch_size
if train_len % batch_size != 0:
n_train_batches += 1
n_valid_batches = valid_len / batch_size
if valid_len % batch_size != 0:
n_valid_batches += 1
print 'number of train mini batch: %s' % n_train_batches
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
X = self.X
Y = self.Y
learn_rate = T.scalar('Learning Rate')
# compute the gradient of cost with respect to theta (sotred in params)
# the resulting gradients will be stored in a list gparams
gparams = [T.grad(self.cost, param) for param in self.params]
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs
# given two lists of the same length, A = [a1, a2, a3, a4] and
# B = [b1, b2, b3, b4], zip generates a list C of same size, where each
# element is a pair formed from the two lists :
# C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
updates = [
(param, param - learn_rate * gparam)
for param, gparam in zip(self.params, gparams)
]
# compiling a Theano function `train_model` that returns the cost, but
# in the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(
inputs=[index, learn_rate],
outputs=self.cost,
updates=updates,
givens={
X: train_x[index * batch_size: (index + 1) * batch_size],
Y: train_y[index * batch_size: (index + 1) * batch_size]
}
)
test_train_model = theano.function(
inputs=[index],
outputs=self.errors(Y),
givens={
X: train_x[index * batch_size: (index + 1) * batch_size],
Y: train_y[index * batch_size: (index + 1) * batch_size]
}
)
validate_model = theano.function(
inputs=[index],
outputs=self.errors(Y),
givens={
X: valid_x[index * batch_size:(index + 1) * batch_size],
Y: valid_y[index * batch_size:(index + 1) * batch_size]
}
)
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 1000000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.9999 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
done_looping = False
last_cost = np.inf
sys.stdout.flush()
logger.info('already traned number of epochs: %s' % self.epoch)
epoch = self.epoch
while (epoch < n_epochs) and (not done_looping):
epoch += 1
avg_cost_list = []
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index, self.learning_rate)
avg_cost_list.append(minibatch_avg_cost)
# print self.lr_layer.W.get_value()
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
# print self.lr_layer.W.get_value()
# print self.lr_layer.b.get_value()
# train_losses = [test_train_model(i) for i in xrange(n_train_batches)]
# this_train_loss = np.mean(train_losses)
# # compute zero-one loss on validation set
# validation_losses = [validate_model(i) for i
# in xrange(n_valid_batches)]
# this_validation_loss = np.mean(validation_losses)
# train_all_precison, train_label_precision, train_label_recall = \
# self.test(train_x, train_y.eval())
# this_train_loss = 1 - train_all_precison
valid_all_precison, valid_label_precision, valid_label_recall = \
self.test(valid_x, valid_y.eval())
this_validation_loss = 1 - valid_all_precison
avg_cost = np.mean(avg_cost_list)
if avg_cost >= last_cost:
self.learning_rate *= 0.95
last_cost = avg_cost
logger.info(
'epoch %i, learning rate: %f, avg_cost: %f, valid P: %f %%, valid_1_P: %s, valid_1_R: %s' %
(
epoch,
self.learning_rate,
avg_cost,
# (1 - this_train_loss) * 100,
(1 - this_validation_loss) * 100.,
# train_label_precision[1],
# train_label_recall[1],
valid_label_precision[1],
valid_label_recall[1]
)
)
sys.stdout.flush()
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if (
this_validation_loss < best_validation_loss *
improvement_threshold
):
# Increase patience_increase times based on the current iteration.
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
if patience <= iter:
done_looping = True
break
self.epoch = epoch
end_time = timeit.default_timer()
logger.info(('Optimization complete. Best validation score of %f %% '
'obtained at iteration %i') %
(( 1 - best_validation_loss) * 100., best_iter + 1))
logger.info('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def save(self, path):
with open(path, 'wb') as f:
pickle.dump(self, f, -1)
logger.info('save model to path %s' % path)
return None
@classmethod
def load(self, path):
with open(path, 'rb') as f:
return pickle.load(f)
def predict(self, shared_x, batch_size=None):
if not batch_size:
batch_size = self.batch_size
shared_x_len = shared_x.get_value(borrow=True).shape[0]
n_batches = shared_x_len / batch_size
if shared_x_len % batch_size != 0:
n_batches += 1
index = T.lscalar() # index to a [mini]batch
X = self.X
predict_model = theano.function(
inputs=[index],
outputs=self.lr_layer.y_pred,
givens={
X: shared_x[index * batch_size:(index + 1) * batch_size]
}
)
pred_y = np.concatenate([predict_model(i) for i in range(n_batches)])
return pred_y
def test(self, shared_x, data_y, out_path=None):
pred_y = self.predict(shared_x)
if out_path:
with codecs.open(out_path, 'wb') as f:
f.writelines(['%s\t%s\n' % (x, y) for x, y in zip(data_y, pred_y)])
return evaluate(data_y, pred_y)
def test_from_file(self, path, out_path=None, encoding='utf-8'):
data_x = []
data_y = []
with codecs.open(path, 'rb', encoding=encoding) as f:
for i, line in enumerate(f):
tokens = line.strip('\n').split('\t')
if len(tokens) != 2:
raise ValueError('invalid line %s' % (i+1))
label = int(tokens[0])
sent = tokens[1]
s = get_idx_from_sent(sent, self.word_idx_map)
data_x.append(s)
data_y.append(label)
shared_x = theano.shared(
value=np.asarray(data_x, dtype=theano.config.floatX),
borrow='True'
)
return self.test(shared_x, data_y, out_path=out_path)
def main():
conf_path = sys.argv[1]
conf = Config(conf_path)
global logger
logger = init_log(__file__, conf.log_path)
word_vectors = conf.word_vectors
adjust_input = True if conf.adjust_input else False
logger.info("loading data...")
if word_vectors not in ('rand', 'word2vec'):
raise ValueError('invalid parameter word_vectors %s' % word_vectors)
with open(conf.train_path, 'rb') as f:
x = pickle.load(f)
revs, W, W2, word_idx_map, vocab = x[0], x[1], x[2], x[3], x[4]
logger.info("data loaded!")
if adjust_input:
print "model architecture: CNN-non-static"
else:
print "model architecture: CNN-static"
if word_vectors == "rand":
print "using: random vectors"
U = W2
elif word_vectors == "word2vec":
print "using: word2vec vectors"
U = W
sys.stdout.flush()
results = []
datasets = make_idx_data_cv(revs, word_idx_map, conf.cv_index, max_l=15, k=300)
if os.path.exists(conf.model_path):
sc = SentConv.load(conf.model_path)
logger.info('Load existing model from %s' % conf.model_path)
else:
conf.log(logger)
sc = SentConv(filter_hs=conf.filter_hs, filter_num=conf.filter_num, n_hidden=conf.filter_num, n_out=2, word_idx_map=word_idx_map, wordvec=U, adjust_input=adjust_input)
logger.info('Initiate a model')
if conf.do_train:
try:
sc.fit(datasets, batch_size=conf.batch_size, n_epochs=conf.n_epochs)
except KeyboardInterrupt:
logger.warning('Got control C. Quit.')
return
finally:
sc.save(conf.model_path)
else:
logger.info('config says do not execute train process')
if conf.do_test:
test_result = sc.test_from_file(conf.test_path, encoding='gb18030', out_path=conf.test_out_path)
logger.info('test result of %s' % conf.test_path)
logger.info(test_result)
logger.info('test out path is %s' % conf.test_out_path)
else:
logger.info('config says do not execute test process')
return None
if __name__ == '__main__':
main()