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model.py
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model.py
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#import tensorflow as tf
import numpy as np
import sys
import time
from evaluation import *
class Model():
def __init__(self, args, data):
self.parse_args(args, data)
self.show_config()
self.generate_placeholders()
self.generate_variables()
if self.call_interactive_interface == 1:
self.call_interactive_interface_without_training()
sys.exit()
def parse_args(self, args, data):
self.data = data
self.dataset_name = args.dataset_name
self.num_datapoints = self.data.num_datapoints
self.num_tokens = self.data.num_tokens
self.num_links = len(self.data.total_links)
self.num_labels = self.data.num_labels
self.learning_rate = args.learning_rate
self.num_epoch = args.num_epoch
self.num_neg = args.num_neg
self.num_topics = args.num_topics
self.visualization_dimensions = args.visualization_dimensions
self.minibatch_size = args.minibatch_size
if self.minibatch_size == 0:
self.minibatch_size = self.num_links
self.regularizer = args.regularizer
self.call_interactive_interface = args.call_interactive_interface
self.temperature = 1
self.temperature_min = 0.1
self.anealing_rate = 0.00003
self.label_smoothing = 0
self.labeling_ratio = args.labeling_ratio
self.label_depth = self.data.label_depth
def show_config(self):
print('******************************************************')
print('numpy version:', np.__version__)
print('tensorflow version:', tf.__version__)
print('dataset name:', self.dataset_name)
print('#data points:', self.num_datapoints)
print('#links:', self.num_links)
print('#tokens:', self.data.num_tokens)
print('#labels:', self.num_labels)
print('learning rate:', self.learning_rate)
print('#epoch:', self.num_epoch)
print('#negative samples:', self.num_neg)
print('#topics:', self.num_topics)
print('visualization dimensions:', self.visualization_dimensions)
print('minibatch size:', self.minibatch_size)
print('labeling ratio:', self.labeling_ratio)
# print('label depth:', self.label_depth)
print('******************************************************')
def generate_placeholders(self):
self.sampling_links = tf.placeholder('int32', [None, 2])
self.sampling_neg_links = tf.placeholder('int32', [self.minibatch_size * self.num_neg])
self.sampling_labels = tf.placeholder('int32', [None, self.label_depth])
self.sampling_labels_mask = tf.placeholder('bool', [None, self.label_depth])
self.sampling_attribute = tf.placeholder('float64', [self.minibatch_size, self.num_tokens])
self.attribute = tf.placeholder('float64', [self.num_datapoints, self.num_tokens])
self.alpha = tf.placeholder('float64', [self.minibatch_size])
self.learning_rate_adaptive = tf.placeholder('float64', [])
self.vertex_id_per_label = tf.placeholder('int32', [None])
self.label_id_per_label = tf.placeholder('int32', [None])
def generate_variables(self):
self.visual_coor = tf.Variable(tf.random_normal([self.num_datapoints, self.visualization_dimensions], dtype='float64'), dtype='float64')
self.label_coor = tf.Variable(tf.random_normal([self.num_labels + 1, self.visualization_dimensions], dtype='float64'), dtype='float64')
self.topic_coor = tf.Variable(tf.random_normal([self.num_topics, self.visualization_dimensions], dtype='float64'), dtype='float64')
self.topic_word = tf.Variable(tf.random_normal([self.num_topics, self.num_tokens], dtype='float64'), dtype='float64')
self.bias = tf.Variable(tf.random_normal([self.num_tokens], dtype='float64'), dtype='float64')
def evaluate_diff_numerator(self, a, b, size_a, size_b):
diff = tf.reshape(tf.expand_dims(a, 1) - tf.expand_dims(b, 0), [-1, self.visualization_dimensions]) # this is gaussian distribution
squared_distance = tf.reshape(tf.reduce_sum(tf.square(diff), axis=1), [-1, size_b])
numerator = -0.5 * squared_distance
return numerator
def encoder(self):
self.i_coor = tf.gather(self.visual_coor, self.sampling_links[:, 0])
self.j_coor = tf.gather(self.visual_coor, self.sampling_links[:, 1])
self.j_coor_context = tf.gather(self.visual_coor, self.sampling_links[:, 1])
self.avg_coor = 0.5 * (self.i_coor + self.j_coor)
self.sampling_labels_coor = {}
for depth in range(self.label_depth):
self.sampling_labels_coor[depth] = tf.gather(self.label_coor, self.sampling_labels[:, depth])
q_numerator_vertex = self.evaluate_diff_numerator(self.avg_coor, self.topic_coor, self.minibatch_size, self.num_topics)
q_numerator_label = 0
for depth in range(self.label_depth):
q_numerator_label_tmp = self.evaluate_diff_numerator(self.sampling_labels_coor[depth], self.topic_coor, self.minibatch_size, self.num_topics)
q_numerator_label += tf.multiply(q_numerator_label_tmp, tf.tile(tf.expand_dims(tf.cast(self.sampling_labels_mask[:, depth], 'float64'), axis=1), [1, self.num_topics]))
q_numerator = q_numerator_vertex + q_numerator_label
self.q = tf.nn.softmax(q_numerator)
return self.q
def sample_topic_from_q(self):
g = tf.constant(np.random.gumbel(loc=0., scale=1., size=[self.minibatch_size, self.num_topics]), dtype='float64')
self.z_soft = tf.nn.softmax((tf.log(self.q + 1e-20) + g) / self.temperature)
self.z_sampled_topic_coor = tf.matmul(self.z_soft, self.topic_coor)
return self.z_sampled_topic_coor
def decoder(self):
numerator = self.evaluate_diff_numerator(self.z_sampled_topic_coor, self.visual_coor, self.minibatch_size, self.num_datapoints)
numerator = tf.log(numerator + 1e-20)
decoding_labels = tf.one_hot(indices=self.sampling_links[:, 1], depth=self.num_datapoints, dtype='float64')
reconstruction_loss = tf.nn.softmax_cross_entropy_with_logits(labels=decoding_labels, logits=numerator)
return reconstruction_loss
def decoder_neg(self):
self.j_coor_neg = tf.gather(self.visual_coor, self.sampling_neg_links)
self.j_coor_neg_context = tf.gather(self.visual_coor, self.sampling_neg_links)
self.pos_term = tf.nn.sigmoid(-0.5 * tf.reduce_sum(tf.square(self.z_sampled_topic_coor - self.j_coor_context), axis=1)) # this is gaussian distribution
neg_term = tf.nn.sigmoid(-0.5 * tf.reduce_sum(tf.square(tf.reshape(tf.tile(self.z_sampled_topic_coor, [1, self.num_neg]), [self.minibatch_size * self.num_neg, self.visualization_dimensions]) - self.j_coor_neg_context), axis=1))
neg_term = tf.reshape(neg_term, [self.minibatch_size, self.num_neg])
reconstruction_loss = - tf.log(self.pos_term + 1e-20) - tf.reduce_sum(tf.log(1 - neg_term + 1e-20), axis=1)
return reconstruction_loss
def evaluate_conditional_prior(self): # evaluate p(t|w, l(w))
i_numerator_vertex = self.evaluate_diff_numerator(self.i_coor, self.topic_coor, self.minibatch_size, self.num_topics)
i_numerator_label = 0
for depth in range(self.label_depth):
i_numerator_label_tmp = self.evaluate_diff_numerator(self.sampling_labels_coor[depth], self.topic_coor, self.minibatch_size, self.num_topics)
i_numerator_label += tf.multiply(i_numerator_label_tmp, tf.tile(tf.expand_dims(tf.cast(self.sampling_labels_mask[:, depth], 'float64'), axis=1), [1, self.num_topics]))
i_numerator = i_numerator_vertex + i_numerator_label
self.conditional_prior_i = tf.nn.softmax(i_numerator)
return self.conditional_prior_i
def evaluate_kl_divergence(self):
self.evaluate_conditional_prior()
kld_loss = tf.reduce_sum(tf.multiply(self.q, tf.log(self.q + 1e-20) - tf.log(self.conditional_prior_i + 1e-20)), axis=1)
return kld_loss
def evaluate_log_label(self): # evaluate log(p(l_w|w)), this is for single-label or multi-label classification
self.i_numerator_vertex = self.evaluate_diff_numerator(self.i_coor, self.label_coor, self.minibatch_size, self.num_labels + 1)[:, :-1]
sampling_labels_one_hot = 0
for depth in range(self.label_depth):
sampling_labels_one_hot_tmp = tf.one_hot(indices=self.sampling_labels[:, depth], depth=(self.num_labels + 1), dtype='float64') + 1e-20
sampling_labels_one_hot += sampling_labels_one_hot_tmp[:, :-1]
label_reconstruction_loss = tf.nn.softmax_cross_entropy_with_logits(labels=tf.boolean_mask(sampling_labels_one_hot, mask=self.sampling_labels_mask[:, 0]),
logits=tf.boolean_mask(self.i_numerator_vertex, mask=self.sampling_labels_mask[:, 0]))
#labels = tf.boolean_mask(sampling_labels_one_hot, mask=self.sampling_labels_mask[:, 0])
#logits = tf.boolean_mask(self.i_numerator_vertex, mask=self.sampling_labels_mask[:, 0])
#label_reconstruction_loss = - tf.reduce_sum(tf.multiply(labels, tf.log(tf.nn.sigmoid(logits) * 2 + 1e-20)) + tf.multiply(1 - labels, tf.log(1 - tf.nn.sigmoid(logits) * 2 + 1e-20)), axis=1)
label_reconstruction_loss = tf.reduce_mean(label_reconstruction_loss)
return label_reconstruction_loss
def decoder_content(self):
output_logits = tf.add(tf.matmul(self.conditional_prior_i, self.topic_word), self.bias)
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output_logits, labels=self.sampling_attribute))
return loss
def label_smoothness(self):
self.j_numerator_vertex = self.evaluate_diff_numerator(self.j_coor, self.label_coor, self.minibatch_size, self.num_labels + 1)[:, :-1]
#reg = tf.reduce_mean(tf.multiply(tf.reduce_mean(tf.square(self.i_numerator_vertex - self.j_numerator_vertex), axis=1), self.alpha))
#reg = tf.reduce_mean(tf.multiply(tf.reduce_sum(tf.square(tf.nn.softmax(self.i_numerator_vertex) - tf.nn.softmax(self.j_numerator_vertex)), axis=1), self.alpha))
reg = tf.reduce_mean(tf.multiply(tf.reduce_sum(tf.multiply(tf.nn.softmax(self.i_numerator_vertex), tf.log(tf.nn.softmax(self.i_numerator_vertex) + 1e-20) - tf.log(tf.nn.softmax(self.j_numerator_vertex) + 1e-20)), axis=1), self.alpha))
return reg
def construct_model(self):
self.encoder()
self.sample_topic_from_q()
loss = self.decoder_neg()
loss += self.evaluate_kl_divergence()
loss = tf.reduce_mean(loss)
loss += self.decoder_content()
loss += self.evaluate_log_label()
loss += self.regularizer * self.label_smoothness()
return loss
def generate_feed_dict(self):
self.feed_dict = {}
self.feed_dict[self.sampling_links] = self.data.sampling_links
self.feed_dict[self.sampling_neg_links] = self.data.sampling_neg_links
self.feed_dict[self.sampling_labels] = self.data.sampling_labels
self.feed_dict[self.sampling_labels_mask] = self.data.sampling_labels_mask
self.feed_dict[self.sampling_attribute] = self.data.sampling_attribute
self.feed_dict[self.alpha] = self.data.alpha
self.feed_dict[self.attribute] = self.data.attribute
self.feed_dict[self.learning_rate_adaptive] = self.learning_rate
return self.feed_dict
def testing(self):
topic_topic_dist = tf.nn.softmax(self.evaluate_diff_numerator(self.topic_coor, self.topic_coor, self.num_topics, self.num_topics))
self.topic_word_inference = tf.add(tf.matmul(topic_topic_dist, self.topic_word), self.bias)
i_coor = tf.gather(self.visual_coor, self.vertex_id_per_label)
sampling_labels_coor = tf.gather(self.label_coor, self.label_id_per_label)
i_numerator_vertex = self.evaluate_diff_numerator(i_coor, self.topic_coor, -1, self.num_topics)
i_numerator_label = self.evaluate_diff_numerator(sampling_labels_coor, self.topic_coor, -1, self.num_topics)
self.label_topic_dist = tf.expand_dims(tf.reduce_mean(tf.nn.softmax(i_numerator_vertex + i_numerator_label), axis=0), axis=0)
self.label_word_inference = tf.squeeze(tf.add(tf.matmul(self.label_topic_dist, self.topic_word), self.bias))
self.label_dist = tf.nn.softmax(self.evaluate_diff_numerator(self.visual_coor, self.label_coor, -1, self.num_labels + 1)[:, :-1])
def call_interactive_interface_without_training(self):
visual_coor = np.loadtxt('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_vertex_coor.txt')
topic_coor = np.loadtxt('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_topic_coor.txt')
label_coor = np.loadtxt('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_label_coor.txt')
topic_top_words, label_top_words = [], []
with open('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_topic_top_words.txt') as file:
for line in file:
topic_top_words_one_topic = []
line = line.split()
for word in line:
topic_top_words_one_topic.append(word)
topic_top_words.append(topic_top_words_one_topic)
with open('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_label_top_words.txt') as file:
for line in file:
label_top_words_one_label = []
line = line.split()
for word in line:
label_top_words_one_label.append(word)
label_top_words.append(label_top_words_one_label)
topic_top_words, label_top_words = np.array(topic_top_words), np.array(label_top_words)
top_words = np.concatenate([topic_top_words, label_top_words], axis=0)
visualization(self.dataset_name, visual_coor, label_coor, topic_coor,
np.concatenate([np.squeeze(self.data.label), [np.amax(self.data.label) + 1] * len(label_coor), [np.amax(self.data.label) + 2] * len(topic_coor)], axis=0),
self.data.total_links,
self.data.test_indices,
top_words)
def train(self):
loss = self.construct_model()
self.testing()
self.label_topic_words, self.topic_top_words = [], []
optimizer = tf.train.AdamOptimizer(self.learning_rate_adaptive).minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
num_minibatch = int(np.ceil(self.num_links / self.minibatch_size))
t = time.time()
one_epoch_loss = 0
for epoch_index in range(1, self.num_epoch + 1):
for minibatch_index in range(1, num_minibatch + 1):
self.data.prepare_minibatch(num_minibatch, minibatch_index)
self.generate_feed_dict()
_, one_epoch_loss = sess.run([optimizer, loss], feed_dict=self.feed_dict)
if epoch_index % 20 == 0:
self.temperature = np.maximum(self.temperature * np.exp(-self.anealing_rate * epoch_index), self.temperature_min)
self.learning_rate = self.learning_rate * 0.95
if epoch_index % 25 == 0 or epoch_index == 1:
print('******************************************************')
print('Time: %ds' % (time.time() - t), '\tEpoch: %d/%d' % (epoch_index, self.num_epoch), '\tLoss: %f' % one_epoch_loss)
visual_coor = sess.run(self.visual_coor)
topic_coor = sess.run(self.topic_coor)
label_coor = sess.run(self.label_coor)[:-1]
X_train = visual_coor[self.data.label_mask[:, 0]]
X_test = visual_coor[self.data.test_indices]
topic_word = sess.run(self.topic_word_inference)
label_word = []
for idx in range(self.num_labels):
label_word.append(sess.run(self.label_word_inference, feed_dict={self.vertex_id_per_label: self.data.vertex_id_per_label[idx],
self.label_id_per_label: self.data.label_id_per_label[idx]}))
label_word = np.array(label_word)
label_dist = sess.run(self.label_dist)
self.topic_top_words = output_top_words(topic_word, 20, self.data.voc)
self.label_top_words = output_top_words(label_word, 20, self.data.voc)
self.top_words = np.concatenate([self.topic_top_words, self.label_top_words], axis=0)
classification_knn(X_train=X_train, X_test=X_test, Y_train=np.squeeze(self.data.training_label), Y_test=np.squeeze(self.data.test_label))
np.savetxt('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_topic_word.txt', topic_word, delimiter='\t')
np.savetxt('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_label_word.txt', label_word, delimiter='\t')
np.savetxt('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_vertex_coor.txt', visual_coor, delimiter='\t')
np.savetxt('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_topic_coor.txt', topic_coor, delimiter='\t')
np.savetxt('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(self.num_topics) + '_' + str(self.labeling_ratio) + '_label_coor.txt', label_coor, delimiter='\t')
with open('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(
self.num_topics) + '_' + str(self.labeling_ratio) + '_topic_top_words.txt', 'w') as file:
for line in self.topic_top_words:
for word in line:
word = word.replace('\n', '')
if len(word) > 0:
file.write(word)
file.write(' ')
file.write('\n')
with open('./results/' + self.dataset_name + '/' + self.dataset_name + '_' + str(
self.num_topics) + '_' + str(self.labeling_ratio) + '_label_top_words.txt', 'w') as file:
for line in self.label_top_words:
for word in line:
word = word.replace('\n', '')
if len(word) > 0:
file.write(word)
file.write(' ')
file.write('\n')
print('Finish training! Training time:', time.time() - t)
print('******************************************************')
print('Keywords of each label:')
print(self.label_top_words)
print('******************************************************')
print('Keywords of each topic:')
print(self.topic_top_words)
self.top_words = np.concatenate([self.topic_top_words, self.label_top_words], axis=0)
visual_coor = sess.run(self.visual_coor)
topic_coor = sess.run(self.topic_coor)
label_coor = sess.run(self.label_coor)[:-1]
visualization(self.dataset_name, visual_coor, label_coor, topic_coor,
np.concatenate([np.squeeze(self.data.label), [np.amax(self.data.label) + 1] * len(label_coor), [np.amax(self.data.label) + 2] * len(topic_coor)], axis=0),
self.data.total_links,
self.data.test_indices,
self.top_words)