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ltr_gan_d_nn_g_nn.py
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ltr_gan_d_nn_g_nn.py
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import cPickle
import random
import tensorflow as tf
import numpy as np
from eval.precision import precision_at_k
from eval.ndcg import ndcg_at_k
from eval.map import MAP
from eval.mrr import MRR
import utils as ut
from dis_model_pointwise_nn import DIS
from gen_model_nn import GEN
FEATURE_SIZE = 46
HIDDEN_SIZE = 46
BATCH_SIZE = 8
WEIGHT_DECAY = 0.01
D_LEARNING_RATE = 0.001
G_LEARNING_RATE = 0.001
TEMPERATURE = 0.2
LAMBDA = 0.5
workdir = 'MQ2008-semi'
DIS_TRAIN_FILE = workdir + '/run-train-gan.txt'
GAN_MODEL_BEST_FILE = workdir + '/gan_best_nn.model'
query_url_feature, query_url_index, query_index_url =\
ut.load_all_query_url_feature(workdir + '/Large_norm.txt', FEATURE_SIZE)
query_pos_train = ut.get_query_pos(workdir + '/train.txt')
query_pos_test = ut.get_query_pos(workdir + '/test.txt')
def generate_for_d(sess, model, filename):
data = []
print('negative sampling for d using g ...')
for query in query_pos_train:
pos_list = query_pos_train[query]
all_list = query_index_url[query]
candidate_list = all_list
candidate_list_feature = [query_url_feature[query][url] for url in candidate_list]
candidate_list_feature = np.asarray(candidate_list_feature)
candidate_list_score = sess.run(model.pred_score, feed_dict={model.pred_data: candidate_list_feature})
# softmax for candidate
exp_rating = np.exp(candidate_list_score - np.max(candidate_list_score))
prob = exp_rating / np.sum(exp_rating)
neg_list = np.random.choice(candidate_list, size=[len(pos_list)], p=prob)
for i in range(len(pos_list)):
data.append((query, pos_list[i], neg_list[i]))
random.shuffle(data)
with open(filename, 'w') as fout:
for (q, pos, neg) in data:
fout.write(','.join([str(f) for f in query_url_feature[q][pos]])
+ '\t'
+ ','.join([str(f) for f in query_url_feature[q][neg]]) + '\n')
fout.flush()
def main():
discriminator = DIS(FEATURE_SIZE, HIDDEN_SIZE, WEIGHT_DECAY, D_LEARNING_RATE, param=None)
generator = GEN(FEATURE_SIZE, HIDDEN_SIZE, WEIGHT_DECAY, G_LEARNING_RATE, temperature=TEMPERATURE, param=None)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.initialize_all_variables())
print('start adversarial training')
p_best_val = 0.0
ndcg_best_val = 0.0
for epoch in range(30):
if epoch >= 0:
# G generate negative for D, then train D
print('Training D ...')
for d_epoch in range(100):
if d_epoch % 30 == 0:
generate_for_d(sess, generator, DIS_TRAIN_FILE)
train_size = ut.file_len(DIS_TRAIN_FILE)
index = 1
while True:
if index > train_size:
break
if index + BATCH_SIZE <= train_size + 1:
input_pos, input_neg = ut.get_batch_data(DIS_TRAIN_FILE, index, BATCH_SIZE)
else:
input_pos, input_neg = ut.get_batch_data(DIS_TRAIN_FILE, index, train_size - index + 1)
index += BATCH_SIZE
pred_data = []
pred_data.extend(input_pos)
pred_data.extend(input_neg)
pred_data = np.asarray(pred_data)
pred_data_label = [1.0] * len(input_pos)
pred_data_label.extend([0.0] * len(input_neg))
pred_data_label = np.asarray(pred_data_label)
_ = sess.run(discriminator.d_updates,
feed_dict={discriminator.pred_data: pred_data,
discriminator.pred_data_label: pred_data_label})
# Train G
print('Training G ...')
for g_epoch in range(30):
for query in query_pos_train.keys():
pos_list = query_pos_train[query]
pos_set = set(pos_list)
all_list = query_index_url[query]
all_list_feature = [query_url_feature[query][url] for url in all_list]
all_list_feature = np.asarray(all_list_feature)
all_list_score = sess.run(generator.pred_score, {generator.pred_data: all_list_feature})
# softmax for all
exp_rating = np.exp(all_list_score - np.max(all_list_score))
prob = exp_rating / np.sum(exp_rating)
prob_IS = prob * (1.0 - LAMBDA)
for i in range(len(all_list)):
if all_list[i] in pos_set:
prob_IS[i] += (LAMBDA / (1.0 * len(pos_list)))
choose_index = np.random.choice(np.arange(len(all_list)), [5 * len(pos_list)], p=prob_IS)
choose_list = np.array(all_list)[choose_index]
choose_feature = [query_url_feature[query][url] for url in choose_list]
choose_IS = np.array(prob)[choose_index] / np.array(prob_IS)[choose_index]
choose_index = np.asarray(choose_index)
choose_feature = np.asarray(choose_feature)
choose_IS = np.asarray(choose_IS)
choose_reward = sess.run(discriminator.reward, feed_dict={discriminator.pred_data: choose_feature})
_ = sess.run(generator.g_updates,
feed_dict={generator.pred_data: all_list_feature,
generator.sample_index: choose_index,
generator.reward: choose_reward,
generator.important_sampling: choose_IS})
p_5 = precision_at_k(sess, generator, query_pos_test, query_pos_train, query_url_feature, k=5)
ndcg_5 = ndcg_at_k(sess, generator, query_pos_test, query_pos_train, query_url_feature, k=5)
if p_5 > p_best_val:
p_best_val = p_5
ndcg_best_val = ndcg_5
generator.save_model(sess, GAN_MODEL_BEST_FILE)
print("Best:", "gen p@5 ", p_5, "gen ndcg@5 ", ndcg_5)
elif p_5 == p_best_val:
if ndcg_5 > ndcg_best_val:
ndcg_best_val = ndcg_5
generator.save_model(sess, GAN_MODEL_BEST_FILE)
print("Best:", "gen p@5 ", p_5, "gen ndcg@5 ", ndcg_5)
sess.close()
param_best = cPickle.load(open(GAN_MODEL_BEST_FILE))
assert param_best is not None
generator_best = GEN(FEATURE_SIZE, HIDDEN_SIZE, WEIGHT_DECAY, G_LEARNING_RATE, temperature=TEMPERATURE, param=param_best)
sess = tf.Session(config=config)
sess.run(tf.initialize_all_variables())
p_1_best = precision_at_k(sess, generator_best, query_pos_test, query_pos_train, query_url_feature, k=1)
p_3_best = precision_at_k(sess, generator_best, query_pos_test, query_pos_train, query_url_feature, k=3)
p_5_best = precision_at_k(sess, generator_best, query_pos_test, query_pos_train, query_url_feature, k=5)
p_10_best = precision_at_k(sess, generator_best, query_pos_test, query_pos_train, query_url_feature, k=10)
ndcg_1_best = ndcg_at_k(sess, generator_best, query_pos_test, query_pos_train, query_url_feature, k=1)
ndcg_3_best = ndcg_at_k(sess, generator_best, query_pos_test, query_pos_train, query_url_feature, k=3)
ndcg_5_best = ndcg_at_k(sess, generator_best, query_pos_test, query_pos_train, query_url_feature, k=5)
ndcg_10_best = ndcg_at_k(sess, generator_best, query_pos_test, query_pos_train, query_url_feature, k=10)
map_best = MAP(sess, generator_best, query_pos_test, query_pos_train, query_url_feature)
mrr_best = MRR(sess, generator_best, query_pos_test, query_pos_train, query_url_feature)
print("Best ", "p@1 ", p_1_best, "p@3 ", p_3_best, "p@5 ", p_5_best, "p@10 ", p_10_best)
print("Best ", "ndcg@1 ", ndcg_1_best, "ndcg@3 ", ndcg_3_best, "ndcg@5 ", ndcg_5_best, "p@10 ", ndcg_10_best)
print("Best MAP ", map_best)
print("Best MRR ", mrr_best)
if __name__ == '__main__':
main()