def testGroup(self):
'''
Build a small synthetic data set and labelling for training
on GrCNNBagger.
'''
np.random.seed(42)
train_size, input_dim = 500, 50
data = [np.random.rand(np.random.randint(5, 20), input_dim).astype(np.float32)
for _ in xrange(train_size)]
p = 0.45
labels = np.random.binomial(1, p, train_size)
logger.debug('Building GrCNNBagger...')
start_time = time.time()
grbagger = GrCNNBagger(self.configer, verbose=True)
end_time = time.time()
logger.debug('Time used to build the model: %f seconds.' % (end_time-start_time))
learn_rate = 0.01
# Training using stochastic gradient descent algorithm
epoch = 60
training_cost, training_acc = [], []
for i in xrange(epoch):
costs = 0.0
correct_count = 0
logger.debug('=' * 50)
for j in xrange(train_size):
results = grbagger.compute_gradient_and_cost(data[j], labels[j])
prob = grbagger.show_prob(data[j])
scores = grbagger.show_scores(data[j])
weights = grbagger.show_weights(data[j])
grads, cost, pred = results[:-2], results[-2], results[-1]
if pred == labels[j]: correct_count += 1
if cost == np.nan:
logger.error('Error here, in %dth epoch, %dth instance' % (i, j))
logger.error('Gradients: ')
logger.error(grads)
costs += cost
grbagger.update_params(grads, learn_rate)
logger.debug('Training epoch: %d, total cost: %f, accuracy = %f'
% (i, costs, float(correct_count) / train_size))
training_cost.append(costs)
training_acc.append(float(correct_count) / train_size)
logger.debug('Training finished...')
logger.debug('Number of parameters in the model: %d' % grbagger.num_params)
logger.debug('Output the weighting vector for each input instance: ')
# Record instance scores and instance weights
instance_scores, instance_weights, instance_labels = [], [], labels
for j in xrange(train_size):
weights = grbagger.show_weights(data[j])
scores = grbagger.show_scores(data[j])
weights = weights.ravel()
scores = scores.ravel()
pred = np.sum(scores * weights) >= 0.5
logger.debug('Instance %d, true label: %d, predicted label: %d' % (j, labels[j], pred))
logger.debug('scores = {}'.format(scores))
logger.debug('weights = {}'.format(weights))
logger.debug('-' * 50)
instance_scores.append(scores)
instance_weights.append(weights)
hist_grad += np.square(grad)
## Update the word-embedding matrix
if i > training_threshold_epoch:
tmp = senti_train_sparse_select[j].T.dot(input_grad)
accumu_embedding += tmp
hist_embedding += np.square(tmp)
# Updating model parameters
for accumu_grad, hist_grad in zip(accumu_grads, hist_grads):
accumu_grad /= batch_size
accumu_grad /= fudge_factor + np.sqrt(hist_grad)
# Updating word-embedding matrix
if i > training_threshold_epoch:
accumu_embedding /= batch_size
accumu_embedding /= fudge_factor + np.sqrt(hist_embedding)
word_embedding._embedding -= rate * accumu_embedding
grbagger.update_params(accumu_grads, rate)
# Clear all the cache again
accumu_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
hist_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
if i > training_threshold_epoch:
accumu_embedding[:] = 0.0
hist_embedding[:] = 0.0
if num_batch * batch_size < train_size:
for j in xrange(num_batch*batch_size, train_size):
train_sent_rep = senti_train_sparse_select[j].dot(word_embedding.embedding)
results = grbagger.compute_gradient_and_cost(train_sent_rep, senti_train_label[j])
input_grad = grbagger.compute_input_gradient(train_sent_rep, senti_train_label[j])
grads, cost, pred = results[:-2], results[-2], results[-1]
if pred == senti_train_label[j]: correct_count += 1
costs += cost
for accumu_grad, hist_grad, grad in zip(accumu_grads, hist_grads, grads):
def testActiveAndPassive(self):
np.random.seed(1991)
sp_train_filename = '../data/refined_train_sp.txt'
sp_test_filename = '../data/refined_test_sp.txt'
sp_train_txt, sp_train_label = [], []
sp_test_txt, sp_test_label = [], []
start_time = time.time()
# Read training data set
with file(sp_train_filename, 'r') as fin:
reader = csv.reader(fin, delimiter='|')
for txt, label in reader:
sp_train_txt.append(txt)
sp_train_label.append(int(label))
# Read test data set
with file(sp_test_filename, 'r') as fin:
reader = csv.reader(fin, delimiter='|')
for txt, label in reader:
sp_test_txt.append(txt)
sp_test_label.append(label)
end_time = time.time()
logger.debug('Finished loading training and test data sets...')
logger.debug('Time used for loading: %f seconds.' % (end_time-start_time))
embedding_filename = '../data/wiki_embeddings.txt'
word_embedding = WordEmbedding(embedding_filename)
start_time = time.time()
# Starting and Ending token for each sentence
blank_token = word_embedding.wordvec('</s>')
# Word-vector representation
sp_train_label = np.asarray(sp_train_label, dtype=np.int32)
sp_test_label = np.asarray(sp_test_label, dtype=np.int32)
train_size = len(sp_train_txt)
test_size = len(sp_test_txt)
# Check size
logger.debug('Training size: %d' % train_size)
logger.debug('Test size: %d' % test_size)
# Sequential modeling for each sentence
sp_train_set, sp_test_set = [], []
# Embedding for training set
for i, sent in enumerate(sp_train_txt):
words = sent.split()
words = [word.lower() for word in words]
vectors = np.zeros((len(words)+2, word_embedding.embedding_dim()), dtype=floatX)
vectors[1:-1, :] = np.asarray([word_embedding.wordvec(word) for word in words])
sp_train_set.append(vectors)
# Embedding for test set
for i, sent in enumerate(sp_test_txt):
words = sent.split()
words = [word.lower() for word in words]
vectors = np.zeros((len(words)+2, word_embedding.embedding_dim()), dtype=floatX)
vectors[1:-1, :] = np.asarray([word_embedding.wordvec(word) for word in words])
sp_test_set.append(vectors)
end_time = time.time()
logger.debug('Time used to build initial training and test matrix: %f seconds.' % (end_time-start_time))
# Now, start training
start_time = time.time()
grbagger = GrCNNBagger(self.configer, verbose=True)
end_time = time.time()
logger.debug('Time used to build the model: %f seconds.' % (end_time-start_time))
learn_rate = 2e-2
# Training using stochastic gradient descent algorithm
epoch = 200
batch_size = 10
start_time = time.time()
highest_train_accuracy, highest_test_accuracy = 0.0, 0.0
training_acc, training_cost = [], []
test_acc, test_cost = [], []
try:
sample_size = 0
fuedge_factor = 1e-6
for i in xrange(epoch):
costs = 0.0
correct_count = 0
logger.debug('=' * 50)
# rate = learn_rate / (1+i)
rate = learn_rate
# Training
num_batch = train_size / batch_size
for k in xrange(num_batch):
accumu_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
hist_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
for j in xrange(k*batch_size, (k+1)*batch_size):
results = grbagger.compute_gradient_and_cost(sp_train_set[j], sp_train_label[j])
for r in results:
if np.isnan(np.sum(r)):
logger.debug('*' * 50)
logger.debug('Error!!!!!')
logger.debug('NaN found at %dth training instance' % j)
logger.debug('*' * 50)
grads, cost, pred = results[:-2], results[-2], results[-1]
if pred == sp_train_label[j]: correct_count += 1
costs += cost
for accumu_grad, hist_grad, grad in zip(accumu_grads, hist_grads, grads):
accumu_grad += grad
hist_grad += np.square(grad)
for accumu_grad, hist_grad in zip(accumu_grads, hist_grads):
accumu_grad /= batch_size
accumu_grad /= fuedge_factor + np.sqrt(hist_grad)
grbagger.update_params(accumu_grads, rate)
accumu_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
hist_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
if num_batch * batch_size < train_size:
for j in xrange(num_batch*batch_size, train_size):
results = grbagger.compute_gradient_and_cost(sp_train_set[j], sp_train_label[j])
grads, cost, pred = results[:-2], results[-2], results[-1]
if pred == sp_train_label[j]: correct_count += 1
costs += cost
for accumu_grad, hist_grad, grad in zip(accumu_grads, hist_grads, grads):
accumu_grad += grad
hist_grad += np.square(grad)
for accumu_grad, hist_grad in zip(accumu_grads, hist_grads):
accumu_grad /= train_size-num_batch*batch_size
accumu_grad /= fuedge_factor + np.sqrt(hist_grad)
grbagger.update_params(accumu_grads, rate)
train_accuracy = float(correct_count) / train_size
logger.debug('Training epoch: %d, total cost: %f, accuracy = %f'
% (i, costs, train_accuracy))
## Adding training accuracy and training cost
training_acc.append(train_accuracy)
training_cost.append(costs)
if train_accuracy > highest_train_accuracy: highest_train_accuracy = train_accuracy
# Testing
correct_count = 0
costs = 0.0
for j in xrange(test_size):
pred = grbagger.predict(sp_test_set[j])
cost = grbagger.show_cost(sp_test_set[j], sp_test_label[j])
if pred == sp_test_label[j]: correct_count += 1
costs += cost
test_accuracy = float(correct_count) / test_size
## Adding test accuracy and test cost
test_acc.append(test_accuracy)
test_cost.append(costs)
logger.debug('Test accuracy: %f' % test_accuracy)
if test_accuracy > highest_test_accuracy: highest_test_accuracy = test_accuracy
# Sampling to show the weights and experts of training and test instances
logger.debug('Training Sampling: ')
for j in xrange(sample_size):
idx = np.random.randint(train_size)
weights = grbagger.show_weights(sp_train_set[idx])
scores = grbagger.show_scores(sp_train_set[idx])
prob = grbagger.show_prob(sp_train_set[idx])
label = sp_train_label[idx]
logger.debug('Training idx: {}'.format(idx))
logger.debug('Training scores: {}'.format(scores))
logger.debug('Training weights: {}'.format(weights))
logger.debug('Training probability: {}'.format(prob))
logger.debug('Training label: {}'.format(label))
logger.debug('-' * 50)
logger.debug('Test Sampling: ')
for j in xrange(sample_size):
idx = np.random.randint(test_size)
weights = grbagger.show_weights(sp_test_set[idx])
scores = grbagger.show_scores(sp_test_set[idx])
prob = grbagger.show_prob(sp_test_set[idx])
label = sp_test_label[idx]
logger.debug('Test idx: {}'.format(idx))
logger.debug('Test scores: {}'.format(scores))
logger.debug('Test weights: {}'.format(weights))
logger.debug('Test probability: {}'.format(prob))
logger.debug('Test label: {}'.format(label))
logger.debug('-' * 50)
# Check norms of the model parameter
for param in grbagger.params:
val = param.get_value(borrow=True)
norm = np.sqrt(np.sum(np.square(val)))
logger.debug('Parameter: {}, L2-norm: {}'.format(param.name, norm))
except:
logger.debug('Error appeared!')
traceback.print_exc(file=sys.stdout)
logger.debug('-' * 50)
finally:
end_time = time.time()
logger.debug('Time used for training: %f seconds.' % (end_time-start_time))
logger.debug('Highest training accuracy: %f' % highest_train_accuracy)
logger.debug('Highest test accuracy: %f' % highest_test_accuracy)
GrCNNBagger.save('sp-grbagger.model', grbagger)
# Save all the training and test records
training_acc = np.asarray(training_acc)
training_cost = np.asarray(training_cost)
test_acc = np.asarray(test_acc)
test_cost = np.asarray(test_cost)
with file('sp-records.npy', 'w') as fout:
np.save(fout, training_acc)
np.save(fout, training_cost)
np.save(fout, test_acc)
np.save(fout, test_cost)
logger.debug('Training and test records saved to sp-records.npy...')
logger.debug('Finished...')
def testSentimentFineTune(self):
'''
Build a small model and use it on sentiment analysis task. With fine-tunning
the word-embedding matrix.
'''
np.random.seed(1991)
fname = './grCNN.conf'
configer = GrCNNConfiger(fname)
senti_train_filename = '../data/sentiment-train.txt'
# senti_train_filename = '../data/sentiment-train-phrases.txt'
senti_test_filename = '../data/sentiment-test.txt'
senti_train_txt, senti_train_label = [], []
senti_test_txt, senti_test_label = [], []
start_time = time.time()
# Read training data set
with file(senti_train_filename, 'r') as fin:
reader = csv.reader(fin, delimiter='|')
for txt, label in reader:
senti_train_txt.append(txt)
senti_train_label.append(int(label))
# Read test data set
with file(senti_test_filename, 'r') as fin:
reader = csv.reader(fin, delimiter='|')
for txt, label in reader:
senti_test_txt.append(txt)
senti_test_label.append(int(label))
end_time = time.time()
logger.debug('Time used to load training and test data set: %f seconds.' % (end_time-start_time))
embedding_filename = '../data/wiki_embeddings.txt'
# Load training/test data sets and wiki-embeddings
word_embedding = WordEmbedding(embedding_filename)
embed_dim = word_embedding.embedding_dim()
start_time = time.time()
blank_index = word_embedding.word2index('</s>')
logger.debug('Blank index: {}'.format(word_embedding.index2word(blank_index)))
# Word-vector representation
senti_train_label = np.asarray(senti_train_label, dtype=np.int32)
senti_test_label = np.asarray(senti_test_label, dtype=np.int32)
train_size = len(senti_train_txt)
test_size = len(senti_test_txt)
# Check size
logger.debug('Training size: %d' % train_size)
logger.debug('Test size: %d' % test_size)
# Shuffling for all the instances
start_time = time.time()
rindex = np.arange(train_size)
tindex = np.arange(test_size)
np.random.shuffle(rindex)
np.random.shuffle(tindex)
# Shuffle label
senti_train_label = senti_train_label[rindex]
senti_test_label = senti_test_label[tindex]
# Shuffle text
senti_train_txt = list(np.asarray(senti_train_txt)[rindex])
senti_test_txt = list(np.asarray(senti_test_txt)[tindex])
end_time = time.time()
logger.debug('Time used to shuffle all the data: %f seconds.' % (end_time-start_time))
# Compute word embedding
senti_train_set = []
senti_test_set = []
# Record the index of each word in each sentence for only once
senti_train_word_index = []
senti_test_word_index = []
# Record the sparse input indicator matrix only once for fast computation
senti_train_sparse_select = []
senti_test_sparse_select = []
# Embedding for training set
for i, sent in enumerate(senti_train_txt):
words = sent.split()
words = [word.lower() for word in words]
vectors = np.zeros((len(words)+2, embed_dim), dtype=np.float32)
vectors[1:-1] = np.asarray([word_embedding.wordvec(word) for word in words])
indices = [blank_index]
indices += [word_embedding.word2index(word) for word in words]
indices += [blank_index]
sparse_select = lil_matrix((len(words)+2, word_embedding.dict_size()), dtype=floatX)
sparse_select[range(len(words)+2), indices] = 1.0
sparse_select = csc_matrix(sparse_select)
senti_train_set.append(vectors)
senti_train_word_index.append(indices)
senti_train_sparse_select.append(sparse_select)
# Embedding for test set
for i, sent in enumerate(senti_test_txt):
words = sent.split()
words = [word.lower() for word in words]
vectors = np.zeros((len(words)+2, embed_dim), dtype=np.float32)
vectors[1:-1] = np.asarray([word_embedding.wordvec(word) for word in words])
indices = [blank_index]
indices += [word_embedding.word2index(word) for word in words]
indices += [blank_index]
sparse_select = lil_matrix((len(words)+2, word_embedding.dict_size()), dtype=floatX)
sparse_select[range(len(words)+2), indices] = 1.0
sparse_select = csc_matrix(sparse_select)
senti_test_set.append(vectors)
senti_test_word_index.append(indices)
senti_test_sparse_select.append(sparse_select)
end_time = time.time()
logger.debug('Time used to build initial matrices: %f seconds.' % (end_time-start_time))
p_count = np.sum(senti_train_label)
logger.debug('Default positive percentage in Train: %f' % (float(p_count) / train_size))
logger.debug('Default negative percentage in Train: %f' % (float(train_size-p_count) / train_size))
p_count = np.sum(senti_test_label)
logger.debug('Default positive percentage in Test: %f' % (float(p_count) / test_size))
logger.debug('Default negative percentage in Test: %f' % (float(test_size-p_count) / test_size))
# Now, start training
start_time = time.time()
grbagger = GrCNNBagger(configer, verbose=True)
end_time = time.time()
logger.debug('Time used to build the model: %f seconds.' % (end_time-start_time))
learn_rate = 0.02
# Training using stochastic gradient descent algorithm
epoch = 200
batch_size = 20
start_time = time.time()
highest_train_accuracy, highest_test_accuracy = 0.0, 0.0
track_training_acc, track_training_cost = [], []
track_test_acc, track_test_cost = [], []
training_threshold_epoch = 30
try:
sample_size = 0
fuedge_factor = 1e-6
# accumu matrix for word-embedding matrix
# hist matrix for word-embedding matrix
accumu_embedding = np.zeros((word_embedding.dict_size(), configer.num_input), dtype=floatX)
hist_embedding = np.zeros((word_embedding.dict_size(), configer.num_input), dtype=floatX)
for i in xrange(epoch):
costs = 0.0
correct_count = 0
logger.debug('=' * 50)
# rate = learn_rate / (1+i)
rate = learn_rate
# Training
num_batch = train_size / batch_size
for k in xrange(num_batch):
# Clear all the cache
accumu_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
hist_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
if i > training_threshold_epoch:
accumu_embedding[:] = 0.0
hist_embedding[:] = 0.0
for j in xrange(k*batch_size, (k+1)*batch_size):
train_sent_rep = senti_train_sparse_select[j].dot(word_embedding.embedding)
results = grbagger.compute_gradient_and_cost(train_sent_rep, senti_train_label[j])
input_grad = grbagger.compute_input_gradient(train_sent_rep, senti_train_label[j])
grads, cost, pred = results[:-2], results[-2], results[-1]
if pred == senti_train_label[j]: correct_count += 1
costs += cost
for accumu_grad, hist_grad, grad in zip(accumu_grads, hist_grads, grads):
accumu_grad += grad
hist_grad += np.square(grad)
## Update the word-embedding matrix
if i > training_threshold_epoch:
tmp = senti_train_sparse_select[j].T.dot(input_grad)
accumu_embedding += tmp
hist_embedding += np.square(tmp)
# Updating model parameters
for accumu_grad, hist_grad in zip(accumu_grads, hist_grads):
accumu_grad /= batch_size
accumu_grad /= fuedge_factor + np.sqrt(hist_grad)
# Updating word-embedding matrix
if i > training_threshold_epoch:
accumu_embedding /= batch_size
accumu_embedding /= fuedge_factor + np.sqrt(hist_embedding)
word_embedding._embedding -= rate * accumu_embedding
grbagger.update_params(accumu_grads, rate)
# Clear all the cache again
accumu_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
hist_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
if i > training_threshold_epoch:
accumu_embedding[:] = 0.0
hist_embedding[:] = 0.0
if num_batch * batch_size < train_size:
for j in xrange(num_batch*batch_size, train_size):
train_sent_rep = senti_train_sparse_select[j].dot(word_embedding.embedding)
results = grbagger.compute_gradient_and_cost(train_sent_rep, senti_train_label[j])
input_grad = grbagger.compute_input_gradient(train_sent_rep, senti_train_label[j])
grads, cost, pred = results[:-2], results[-2], results[-1]
if pred == senti_train_label[j]: correct_count += 1
costs += cost
for accumu_grad, hist_grad, grad in zip(accumu_grads, hist_grads, grads):
accumu_grad += grad
hist_grad += np.square(grad)
## Update the word-embedding matrix
if i > training_threshold_epoch:
tmp = senti_train_sparse_select[j].T.dot(input_grad)
accumu_embedding += tmp
hist_embedding += np.square(tmp)
# Normalizing model parameters
for accumu_grad, hist_grad in zip(accumu_grads, hist_grads):
accumu_grad /= train_size-num_batch*batch_size
accumu_grad /= fuedge_factor + np.sqrt(hist_grad)
# Normalizing word-embedding matrix
if i > training_threshold_epoch:
accumu_embedding /= train_size-num_batch*batch_size
accumu_embedding /= fuedge_factor + np.sqrt(hist_embedding)
word_embedding._embedding -= rate * accumu_embedding
# Updating all the parameters
grbagger.update_params(accumu_grads, rate)
train_accuracy = float(correct_count) / train_size
logger.debug('Training epoch: %d, total cost: %f, accuracy = %f'
% (i, costs, train_accuracy))
# Append all the numbers
track_training_cost.append(costs)
track_training_acc.append(train_accuracy)
if train_accuracy > highest_train_accuracy: highest_train_accuracy = train_accuracy
# Testing
correct_count = 0
costs = 0.0
for j in xrange(test_size):
test_sent_rep = senti_test_sparse_select[j].dot(word_embedding.embedding)
pred = grbagger.predict(test_sent_rep)
cost = grbagger.show_cost(test_sent_rep, senti_test_label[j])
if pred == senti_test_label[j]: correct_count += 1
costs += cost
test_accuracy = float(correct_count) / test_size
logger.debug('Test accuracy: %f' % test_accuracy)
# Append all the numbers
track_test_cost.append(costs)
track_test_acc.append(test_accuracy)
if test_accuracy > highest_test_accuracy: highest_test_accuracy = test_accuracy
# Sampling to show the weights and experts of training and test instances
logger.debug('Training Sampling: ')
for j in xrange(sample_size):
idx = np.random.randint(train_size)
weights = grbagger.show_weights(senti_train_set[idx])
scores = grbagger.show_scores(senti_train_set[idx])
prob = grbagger.show_prob(senti_train_set[idx])
label = senti_train_label[idx]
logger.debug('Training idx: {}'.format(idx))
logger.debug('Training scores: {}'.format(scores))
logger.debug('Training weights: {}'.format(weights))
logger.debug('Training probability: {}'.format(prob))
logger.debug('Training label: {}'.format(label))
logger.debug('-' * 50)
logger.debug('Test Sampling: ')
for j in xrange(sample_size):
idx = np.random.randint(test_size)
weights = grbagger.show_weights(senti_test_set[idx])
scores = grbagger.show_scores(senti_test_set[idx])
prob = grbagger.show_prob(senti_test_set[idx])
label = senti_test_label[idx]
logger.debug('Test idx: {}'.format(idx))
logger.debug('Test scores: {}'.format(scores))
logger.debug('Test weights: {}'.format(weights))
logger.debug('Test probability: {}'.format(prob))
logger.debug('Test label: {}'.format(label))
logger.debug('-' * 50)
# Check norms of the model parameter
for param in grbagger.params:
val = param.get_value(borrow=True)
norm = np.sqrt(np.sum(np.square(val)))
logger.debug('Parameter: {}, L2-norm: {}'.format(param.name, norm))
wnorm = np.sqrt(np.sum(np.square(word_embedding._embedding)))
logger.debug('Parameter: {}, L2-norm: {}'.format('Word-Embedding', wnorm))
except:
logger.debug('Error appeared!')
traceback.print_exc(file=sys.stdout)
logger.debug('-' * 50)
finally:
end_time = time.time()
logger.debug('Time used for training: %f seconds.' % (end_time-start_time))
logger.debug('Highest training accuracy: %f' % highest_train_accuracy)
logger.debug('Highest test accuracy: %f' % highest_test_accuracy)
GrCNNBagger.save('fine-grbagger.model', grbagger)
# Save all the tracking numbers
track_training_acc = np.asarray(track_training_acc)
track_training_cost = np.asarray(track_training_cost)
track_test_acc = np.asarray(track_test_acc)
track_test_cost = np.asarray(track_test_cost)
with file('fine-senti-records.npy', 'w') as fout:
np.save(fout, track_training_acc)
np.save(fout, track_training_cost)
np.save(fout, track_test_acc)
np.save(fout, track_test_cost)
logger.debug('Training and test records saved to fine-senti-records.npy...')
logger.debug('Finished...')
def testSentiment(self):
'''
Build a small model and use it on sentiment analysis task.
'''
'''
Load training and test texts and labels
in sentiment analysis task, preprocessing.
'''
np.random.seed(1991)
senti_train_filename = '../data/sentiment-train.txt'
# senti_train_filename = '../data/sentiment-train-phrases.txt'
senti_test_filename = '../data/sentiment-test.txt'
senti_train_txt, senti_train_label = [], []
senti_test_txt, senti_test_label = [], []
start_time = time.time()
# Read training data set
with file(senti_train_filename, 'r') as fin:
reader = csv.reader(fin, delimiter='|')
for txt, label in reader:
senti_train_txt.append(txt)
senti_train_label.append(int(label))
# Read test data set
with file(senti_test_filename, 'r') as fin:
reader = csv.reader(fin, delimiter='|')
for txt, label in reader:
senti_test_txt.append(txt)
senti_test_label.append(int(label))
end_time = time.time()
logger.debug('Time used to load training and test data set: %f seconds.' % (end_time-start_time))
embedding_filename = '../data/wiki_embeddings.txt'
# Load training/test data sets and wiki-embeddings
word_embedding = WordEmbedding(embedding_filename)
embed_dim = word_embedding.embedding_dim()
start_time = time.time()
# Store original text representation
self.senti_train_txt = senti_train_txt
self.senti_test_txt = senti_test_txt
# Word-vector representation
self.senti_train_label = np.asarray(senti_train_label, dtype=np.int32)
self.senti_test_label = np.asarray(senti_test_label, dtype=np.int32)
train_size = len(senti_train_txt)
test_size = len(senti_test_txt)
# Check size
assert train_size == self.senti_train_label.shape[0]
assert test_size == self.senti_test_label.shape[0]
logger.debug('Training size: %d' % train_size)
logger.debug('Test size: %d' % test_size)
# Compute word embedding
self.senti_train_set = []
self.senti_test_set = []
# Embedding for training set
for i, sent in enumerate(senti_train_txt):
words = sent.split()
words = [word.lower() for word in words]
vectors = np.zeros((len(words)+2, embed_dim), dtype=np.float32)
vectors[1:-1] = np.asarray([word_embedding.wordvec(word) for word in words])
self.senti_train_set.append(vectors)
# Embedding for test set
for i, sent in enumerate(senti_test_txt):
words = sent.split()
words = [word.lower() for word in words]
vectors = np.zeros((len(words)+2, embed_dim), dtype=np.float32)
vectors[1:-1] = np.asarray([word_embedding.wordvec(word) for word in words])
self.senti_test_set.append(vectors)
end_time = time.time()
logger.debug('Time used to build initial training and test matrix: %f seconds.' % (end_time-start_time))
# Store data
self.train_size = train_size
self.test_size = test_size
self.word_embedding = word_embedding
# Shuffling
rindex = np.arange(train_size)
tindex = np.arange(test_size)
np.random.shuffle(rindex)
np.random.shuffle(tindex)
self.senti_train_set = list(np.asarray(self.senti_train_set)[rindex])
self.senti_test_set = list(np.asarray(self.senti_test_set)[tindex])
self.senti_train_label = self.senti_train_label[rindex]
self.senti_test_label = self.senti_test_label[tindex]
senti_train_set, senti_test_set = self.senti_train_set, self.senti_test_set
senti_train_label, senti_test_label = self.senti_train_label, self.senti_test_label
p_count = np.sum(senti_train_label)
logger.debug('Default positive percentage in Train: %f' % (float(p_count) / train_size))
logger.debug('Default negative percentage in Train: %f' % (float(train_size-p_count) / train_size))
p_count = np.sum(senti_test_label)
logger.debug('Default positive percentage in Test: %f' % (float(p_count) / test_size))
logger.debug('Default negative percentage in Test: %f' % (float(test_size-p_count) / test_size))
# Now, start training
start_time = time.time()
grbagger = GrCNNBagger(self.configer, verbose=True)
end_time = time.time()
logger.debug('Time used to build the model: %f seconds.' % (end_time-start_time))
learn_rate = 0.02
# Training using stochastic gradient descent algorithm
epoch = 200
batch_size = 20
start_time = time.time()
highest_train_accuracy, highest_test_accuracy = 0.0, 0.0
track_training_acc, track_training_cost = [], []
track_test_acc, track_test_cost = [], []
try:
sample_size = 0
fuedge_factor = 1e-6
for i in xrange(epoch):
costs = 0.0
correct_count = 0
logger.debug('=' * 50)
# rate = learn_rate / (1+i)
rate = learn_rate
# Training
num_batch = train_size / batch_size
for k in xrange(num_batch):
accumu_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
hist_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
for j in xrange(k*batch_size, (k+1)*batch_size):
results = grbagger.compute_gradient_and_cost(senti_train_set[j], senti_train_label[j])
grads, cost, pred = results[:-2], results[-2], results[-1]
if pred == senti_train_label[j]: correct_count += 1
costs += cost
for accumu_grad, hist_grad, grad in zip(accumu_grads, hist_grads, grads):
accumu_grad += grad
hist_grad += np.square(grad)
for accumu_grad, hist_grad in zip(accumu_grads, hist_grads):
accumu_grad /= batch_size
accumu_grad /= fuedge_factor + np.sqrt(hist_grad)
grbagger.update_params(accumu_grads, rate)
accumu_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
hist_grads = [np.zeros(param.get_value().shape, dtype=np.float32) for param in grbagger.params]
if num_batch * batch_size < train_size:
for j in xrange(num_batch*batch_size, train_size):
results = grbagger.compute_gradient_and_cost(senti_train_set[j], senti_train_label[j])
grads, cost, pred = results[:-2], results[-2], results[-1]
if pred == senti_train_label[j]: correct_count += 1
costs += cost
for accumu_grad, hist_grad, grad in zip(accumu_grads, hist_grads, grads):
accumu_grad += grad
hist_grad += np.square(grad)
for accumu_grad, hist_grad in zip(accumu_grads, hist_grads):
accumu_grad /= train_size-num_batch*batch_size
accumu_grad /= fuedge_factor + np.sqrt(hist_grad)
grbagger.update_params(accumu_grads, rate)
train_accuracy = float(correct_count) / train_size
logger.debug('Training epoch: %d, total cost: %f, accuracy = %f'
% (i, costs, train_accuracy))
# Append all the numbers
track_training_cost.append(costs)
track_training_acc.append(train_accuracy)
if train_accuracy > highest_train_accuracy: highest_train_accuracy = train_accuracy
# Testing
correct_count = 0
costs = 0.0
for j in xrange(test_size):
pred = grbagger.predict(senti_test_set[j])
cost = grbagger.show_cost(senti_test_set[j], senti_test_label[j])
if pred == senti_test_label[j]: correct_count += 1
costs += cost
test_accuracy = float(correct_count) / test_size
logger.debug('Test accuracy: %f' % test_accuracy)
# Append all the numbers
track_test_cost.append(costs)
track_test_acc.append(test_accuracy)
if test_accuracy > highest_test_accuracy: highest_test_accuracy = test_accuracy
# Sampling to show the weights and experts of training and test instances
logger.debug('Training Sampling: ')
for j in xrange(sample_size):
idx = np.random.randint(train_size)
weights = grbagger.show_weights(senti_train_set[idx])
scores = grbagger.show_scores(senti_train_set[idx])
prob = grbagger.show_prob(senti_train_set[idx])
label = senti_train_label[idx]
logger.debug('Training idx: {}'.format(idx))
logger.debug('Training scores: {}'.format(scores))
logger.debug('Training weights: {}'.format(weights))
logger.debug('Training probability: {}'.format(prob))
logger.debug('Training label: {}'.format(label))
logger.debug('-' * 50)
logger.debug('Test Sampling: ')
for j in xrange(sample_size):
idx = np.random.randint(test_size)
weights = grbagger.show_weights(senti_test_set[idx])
scores = grbagger.show_scores(senti_test_set[idx])
prob = grbagger.show_prob(senti_test_set[idx])
label = senti_test_label[idx]
logger.debug('Test idx: {}'.format(idx))
logger.debug('Test scores: {}'.format(scores))
logger.debug('Test weights: {}'.format(weights))
logger.debug('Test probability: {}'.format(prob))
logger.debug('Test label: {}'.format(label))
logger.debug('-' * 50)
# Check norms of the model parameter
for param in grbagger.params:
val = param.get_value(borrow=True)
norm = np.sqrt(np.sum(np.square(val)))
logger.debug('Parameter: {}, L2-norm: {}'.format(param.name, norm))
except:
logger.debug('Error appeared!')
traceback.print_exc(file=sys.stdout)
logger.debug('-' * 50)
finally:
end_time = time.time()
logger.debug('Time used for training: %f seconds.' % (end_time-start_time))
logger.debug('Highest training accuracy: %f' % highest_train_accuracy)
logger.debug('Highest test accuracy: %f' % highest_test_accuracy)
GrCNNBagger.save('grbagger.model', grbagger)
# Save all the tracking numbers
track_training_acc = np.asarray(track_training_acc)
track_training_cost = np.asarray(track_training_cost)
track_test_acc = np.asarray(track_test_acc)
track_test_cost = np.asarray(track_test_cost)
with file('senti-records.npy', 'w') as fout:
np.save(fout, track_training_acc)
np.save(fout, track_training_cost)
np.save(fout, track_test_acc)
np.save(fout, track_test_cost)
logger.debug('Training and test records saved to senti-records.npy...')
logger.debug('Finished...')