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ConvNCF.py
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ConvNCF.py
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from __future__ import absolute_import
from __future__ import division
import os
import math
import numpy as np
import tensorflow as tf
from multiprocessing import Pool
from multiprocessing import cpu_count
import argparse
import logging
from time import time
from time import strftime
from time import localtime
from Dataset import Dataset
from saver import GMFSaver
# use conv instead of pooling
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
_user_input = None
_item_input_pos = None
_batch_size = None
_index = None
_model = None
_sess = None
_dataset = None
_K = None
_feed_dict = None
_output = None
TRAIN_KEEP_PROB = 1
TEST_KEEP_PROB = 1
def parse_args():
parser = argparse.ArgumentParser(description="Run MF-BPR.")
parser.add_argument('--path', nargs='?', default='Data/',
help='Input data path.')
parser.add_argument('--dataset', nargs='?', default='yelp',
help='Choose a dataset.')
parser.add_argument('--model', nargs='?', default='ConvNCF',
help='Choose model: ConvNCF')
parser.add_argument('--verbose', type=int, default=100,
help='Interval of evaluation.')
parser.add_argument('--batch_size', type=int, default=512,
help='batch_size')
parser.add_argument('--epochs', type=int, default=1500,
help='Number of epochs.')
parser.add_argument('--embed_size', type=int, default=64,
help='Embedding size.')
parser.add_argument('--hidden_size', type=int, default=128,
help='Number of hidden neurons.')
parser.add_argument('--dns', type=int, default=1,
help='number of negative sample for each positive in dns.')
parser.add_argument('--regs', nargs='?', default='[0.01,10,1]',
help='Regularization for user and item embeddings, fully-connected weights, CNN filter weights.')
parser.add_argument('--task', nargs='?', default='',
help='Add the task name for launching experiments')
parser.add_argument('--num_neg', type=int, default=4,
help='Number of negative instances to pair with a positive instance.')
parser.add_argument('--lr_embed', type=float, default=0.05,
help='Learning rate for embeddings.')
parser.add_argument('--lr_net', type=float, default=0.05,
help='Learning rate for CNN.')
parser.add_argument('--net_channel', nargs='?', default='[32,32,32,32,32,32]',
help='net_channel, should be 6 layers here')
parser.add_argument('--pretrain', type=int, default=0,
help='Use the pretraining weights or not')
parser.add_argument('--ckpt', type=int, default=0,
help='Save the pretraining weights or not')
parser.add_argument('--train_auc', type=int, default=0,
help='Calculate train_auc or not')
parser.add_argument('--keep', type=float, default=1.0,
help='keep probability in training')
return parser.parse_args()
#---------- data preparation -------
# data sampling and shuffling
# input: dataset(Mat, List, Rating, Negatives), batch_choice, num_negatives
# output: [_user_input_list, _item_input_pos_list]
def sampling(dataset):
_user_input, _item_input_pos = [], []
for (u, i) in dataset.trainMatrix.keys():
# positive instance
_user_input.append(u)
_item_input_pos.append(i)
return _user_input, _item_input_pos
def shuffle(samples, batch_size, dataset, model):
global _user_input
global _item_input_pos
global _batch_size
global _index
global _model
global _dataset
_user_input, _item_input_pos = samples
_batch_size = batch_size
_index = range(len(_user_input))
_model = model
_dataset = dataset
np.random.shuffle(_index)
num_batch = len(_user_input) // _batch_size
pool = Pool(cpu_count())
res = pool.map(_get_train_batch, range(num_batch))
pool.close()
pool.join()
user_list = [r[0] for r in res]
item_pos_list = [r[1] for r in res]
user_dns_list = [r[2] for r in res]
item_dns_list = [r[3] for r in res]
return user_list, item_pos_list, user_dns_list, item_dns_list
def _get_train_batch(i):
user_batch, item_batch = [], []
user_neg_batch, item_neg_batch = [], []
begin = i * _batch_size
for idx in range(begin, begin + _batch_size):
user_batch.append(_user_input[_index[idx]])
item_batch.append(_item_input_pos[_index[idx]])
for dns in range(_model.dns):
user = _user_input[_index[idx]]
user_neg_batch.append(user)
# negtive k
gtItem = _dataset.testRatings[user][1]
j = np.random.randint(_dataset.num_items)
while j in _dataset.trainList[_user_input[_index[idx]]]:
j = np.random.randint(_dataset.num_items)
item_neg_batch.append(j)
return np.array(user_batch)[:,None], np.array(item_batch)[:,None], \
np.array(user_neg_batch)[:,None], np.array(item_neg_batch)[:,None]
#---------- model definition -------
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
# prediction model
class ConvNCF:
def __init__(self, num_users, num_items, args):
self.num_items = num_items
self.num_users = num_users
self.embedding_size = args.embed_size
self.lr_embed = args.lr_embed
self.lr_net = args.lr_net
self.hidden_size = args.hidden_size
self.nc = eval(args.net_channel)
regs = eval(args.regs)
self.lambda_bilinear = regs[0]
self.gamma_bilinear = regs[1]
self.lambda_weight = regs[2]
self.dns = args.dns
self.train_auc = args.train_auc
self.prepared = False
def _create_placeholders(self):
with tf.name_scope("input_data"):
self.user_input = tf.placeholder(tf.int32, shape = [None, 1], name = "user_input")
self.item_input_pos = tf.placeholder(tf.int32, shape = [None, 1], name = "item_input_pos")
self.item_input_neg = tf.placeholder(tf.int32, shape = [None, 1], name = "item_input_neg")
self.keep_prob = tf.placeholder(tf.float32, name = "keep_prob")
def _conv_weight(self, isz, osz):
return (weight_variable([2,2,isz,osz]), bias_variable([osz]))
def _conv_layer(self, input, P):
conv = tf.nn.conv2d(input, P[0], strides=[1, 2, 2, 1], padding='SAME')
return tf.nn.relu(conv + P[1])
def _create_variables(self):
with tf.name_scope("embedding"):
self.embedding_P = tf.Variable(tf.truncated_normal(shape=[self.num_users, self.embedding_size], mean=0.0, stddev=0.01),
name='embedding_P', dtype=tf.float32) #(users, embedding_size)
self.embedding_Q = tf.Variable(tf.truncated_normal(shape=[self.num_items, self.embedding_size], mean=0.0, stddev=0.01),
name='embedding_Q', dtype=tf.float32) #(items, embedding_size)
# here should have 6 iszs due to the size of outer products is 64x64
iszs = [1] + self.nc[:-1]
oszs = self.nc
self.P = []
for isz, osz in zip(iszs, oszs):
self.P.append(self._conv_weight(isz, osz))
self.W = weight_variable([self.nc[-1], 1])
self.b = weight_variable([1])
def _create_inference(self, item_input):
with tf.name_scope("inference"):
# embedding look up
self.embedding_p = tf.nn.embedding_lookup(self.embedding_P, self.user_input)
self.embedding_q = tf.nn.embedding_lookup(self.embedding_Q, item_input)
# outer product of P_u and Q_i
self.relation = tf.matmul(tf.transpose(self.embedding_p, perm=[0, 2, 1]), self.embedding_q)
self.net_input = tf.expand_dims(self.relation, -1)
# CNN
self.layer = []
input = self.net_input
for p in self.P:
self.layer.append(self._conv_layer(input, p))
input = self.layer[-1]
# prediction
self.dropout = tf.nn.dropout(self.layer[-1], self.keep_prob)
self.output_layer = tf.matmul(tf.reshape(self.dropout,[-1,self.nc[-1]]), self.W) + self.b
return self.embedding_p, self.embedding_q, self.output_layer
def _regular(self, params):
res = 0
for param in params:
res += tf.reduce_sum(tf.square(param[0])) + tf.reduce_sum(tf.square(param[1]))
return res
def _create_loss(self):
with tf.name_scope("loss"):
# BPR loss for L(Theta)
self.p1, self.q1, self.output = self._create_inference(self.item_input_pos)
self.p2, self.q2, self.output_neg = self._create_inference(self.item_input_neg)
self.result = self.output - self.output_neg
self.loss = tf.reduce_sum(tf.log(1 + tf.exp(-self.result)))
self.opt_loss = self.loss + self.lambda_bilinear * ( tf.reduce_sum(tf.square(self.p1)) \
+ tf.reduce_sum(tf.square(self.q2)) + tf.reduce_sum(tf.square(self.q1)))\
+ self.gamma_bilinear * self._regular([(self.W, self.b)]) \
+ self.lambda_weight * (self._regular(self.P) + self._regular([(self.W, self.b)]))
# used at the first time when emgeddings are pretrained yet network are randomly initialized
# if not, the parameters may be NaN.
def _create_pre_optimizer(self):
self.pre_opt = tf.train.AdagradOptimizer(learning_rate=0.01).minimize(self.loss)
def _create_optimizer(self):
# seperated optimizer
var_list1 = [self.embedding_P, self.embedding_Q]
#[self.W1,self.W2,self.W3,self.W4,self.b1,self.b2,self.b3,self.b4,self.P1,self.P2,self.P3]
var_list2 = list(set(tf.trainable_variables()) - set(var_list1))
opt1 = tf.train.AdagradOptimizer(self.lr_embed)
opt2 = tf.train.AdagradOptimizer(self.lr_net)
grads = tf.gradients(self.opt_loss, var_list1 + var_list2)
grads1 = grads[:len(var_list1)]
grads2 = grads[len(var_list1):]
train_op1 = opt1.apply_gradients(zip(grads1, var_list1))
train_op2 = opt2.apply_gradients(zip(grads2, var_list2))
self.optimizer = tf.group(train_op1, train_op2)
def build_graph(self):
self._create_placeholders()
self._create_variables()
self._create_loss()
self._create_pre_optimizer()
self._create_optimizer()
def load_parameter_MF(self, sess, path):
ps = np.load(path)
ap = tf.assign(self.embedding_P, ps[0])
aq = tf.assign(self.embedding_Q, ps[1])
#ah = tf.assign(self.h, np.diag(ps[2][:,0]).reshape(4096,1))
sess.run([ap,aq])
print "parameter loaded"
def load_parameter_logloss(self, sess, path):
ps = np.load(path).tolist()
ap = tf.assign(self.embedding_P, ps['P'])
aq = tf.assign(self.embedding_Q, ps['Q'])
sess.run([ap,aq])
print "logloss parameter loaded"
def save_net_parameters(self, sess, path):
pass
def get_optimizer(self):
if self.prepared: # common optimize
return self.optimizer
else:
# do a previous optimizing with none regularizations if it is the first time to optimize the neural network.
# if not, the parameters may be NaN.
return self.pre_opt
def dxyeval(sess, model, dataset):
eval_feed_dicts = init_eval_model(model, dataset)
hr, ndcg, auc, train_auc = evaluate(model, sess, dataset, eval_feed_dicts)
res = "Epoch: HR = %.4f, NDCG = %.4f AUC = %.4f train_AUC = %.4f" % (hr, ndcg, auc, train_auc)
print res
#---------- training -------
# training
def training(model, dataset, args, saver = None): # saver is an object to save pq
with tf.Session() as sess:
# initialized the save op
ckpt_save_path = "Pretrain/dropout/%s_embed_%s/" % (args.dataset, "_".join(map(str,model.nc)))
ckpt_save_file = str(TRAIN_KEEP_PROB) + '_'.join(map(str, eval(args.regs)))
if not os.path.exists(ckpt_save_path):
os.makedirs(ckpt_save_path)
# initialize saver
saver_ckpt = tf.train.Saver(tf.trainable_variables())
# pretrain or not
sess.run(tf.global_variables_initializer())
# restore the weights when pretrained
if args.pretrain:
#saver_ckpt.restore(sess, "Pretrain/MF_BPR/embed_32_32_32_32_32_32/1e-06_0_10-1440")
model.load_parameter_MF(sess, "best_%s_MF.npy" % args.dataset)
dxyeval(sess,model,dataset)
# initialize for Evaluate
eval_feed_dicts = init_eval_model(model, dataset)
# sample the data
samples = sampling(dataset)
#initialize the max_ndcg to memorize the best result
max_ndcg = 0
max_res = " "
# train by epoch
for epoch_count in range(args.epochs):
print "start epoch", epoch_count
# initialize for training batches
batch_begin = time()
batches = shuffle(samples, args.batch_size, dataset, model)
batch_time = time() - batch_begin
# compute the accuracy before training
_, prev_acc = 0,0 #training_loss_acc(model, sess, prev_batch)
# training the model
train_begin = time()
train_batches = training_batch(model, sess, batches)
train_time = time() - train_begin
print "batch cost:", batch_time, 'train cost:', train_time
if epoch_count % args.verbose == 0:
_, ndcg, cur_res = output_evaluate(model, sess, dataset, train_batches, eval_feed_dicts,
epoch_count, batch_time, train_time, prev_acc)
# print and log the best result
if max_ndcg < ndcg:
max_ndcg = ndcg
max_res = cur_res
saver_ckpt.save(sess, ckpt_save_path+ckpt_save_file, global_step=epoch_count)
print "saved best", epoch_count
print "best:" + max_res
logging.info("best:" + max_res)
def output_evaluate(model, sess, dataset, train_batches, eval_feed_dicts, epoch_count, batch_time, train_time, prev_acc):
loss_begin = time()
train_loss, post_acc = training_loss_acc(model, sess, train_batches)
loss_time = time() - loss_begin
eval_begin = time()
hr, ndcg, auc, train_auc = evaluate(model, sess, dataset, eval_feed_dicts)
eval_time = time() - eval_begin
res = "Epoch %d [%.1fs + %.1fs]: HR = %.4f, NDCG = %.4f AUC = %.4f train_AUC = %.4f [%.1fs]" \
" ACC = %.4f train_loss = %.4f ACC = %.4f [%.1fs]" % ( epoch_count, batch_time, train_time,
hr, ndcg, auc, train_auc, eval_time, prev_acc, train_loss, post_acc, loss_time)
logging.info(res)
print res
return post_acc, ndcg, res
# input: batch_index (shuffled), model, sess, batches
# do: train the model optimizer
def training_batch(model, sess, batches):
user_input, item_input_pos, user_dns_list, item_dns_list = batches
# dns for every mini-batch
# dns = 1, i.e., BPR
if model.dns == 1:
item_input_neg = item_dns_list
# for BPR training
for i in range(len(user_input)):
feed_dict = {model.user_input: user_input[i],
model.item_input_pos: item_input_pos[i],
model.item_input_neg: item_input_neg[i],
model.keep_prob: TRAIN_KEEP_PROB}
sess.run(model.get_optimizer(), feed_dict)
model.prepared = True
# dns > 1, i.e., BPR-dns
elif model.dns > 1:
item_input_neg = []
for i in range(len(user_input)):
# get the output of negtive sample
feed_dict = {model.user_input: user_dns_list[i],
model.item_input_neg: item_dns_list[i],
model.keep_prob: TRAIN_KEEP_PROB}
output_neg = sess.run(model.output_neg, feed_dict)
# select the best negtive sample as for item_input_neg
item_neg_batch = []
for j in range(0, len(output_neg), model.dns):
item_index = np.argmax(output_neg[j : j + model.dns])
item_neg_batch.append(item_dns_list[i][j : j + model.dns][item_index][0])
item_neg_batch = np.array(item_neg_batch)[:,None]
# for mini-batch BPR training
feed_dict = {model.user_input: user_input[i],
model.item_input_pos: item_input_pos[i],
model.item_input_neg: item_neg_batch,
model.keep_prob: TRAIN_KEEP_PROB}
sess.run(model.get_optimizer(), feed_dict)
item_input_neg.append(item_neg_batch)
return user_input, item_input_pos, item_input_neg
# input: model, sess, batches
# output: training_loss
def training_loss_acc(model, sess, train_batches):
train_loss = 0.0
acc = 0
num_batch = len(train_batches[1])
user_input, item_input_pos, item_input_neg = train_batches
for i in range(len(user_input)):
# print user_input[i][0]. item_input_pos[i][0], item_input_neg[i][0]
feed_dict = {model.user_input: user_input[i],
model.item_input_pos: item_input_pos[i],
model.item_input_neg: item_input_neg[i],
model.keep_prob: TEST_KEEP_PROB}
loss, output_pos, output_neg = sess.run([model.loss, model.output, model.output_neg], feed_dict)
train_loss += loss
acc += ((output_pos - output_neg) > 0).sum() / len(output_pos)
return train_loss / num_batch, acc / num_batch
def init_eval_model(model, dataset):
global _dataset
global _model
_dataset = dataset
_model = model
pool = Pool(cpu_count())
feed_dicts = pool.map(_evaluate_input, range(_dataset.num_users))
pool.close()
pool.join()
print("already load the evaluate model...")
return feed_dicts
def _evaluate_input(user):
# generate items_list
item_input = dataset.testNegatives[user] # read negative samples from files
test_item = _dataset.testRatings[user][1]
item_input.append(test_item)
user_input = np.full(len(item_input), user, dtype='int32')[:, None]
item_input = np.array(item_input)[:,None]
return user_input, item_input
def evaluate(model, sess, dataset, feed_dicts):
global _model
global _K
global _sess
global _dataset
global _feed_dicts
global _output
_dataset = dataset
_model = model
_sess = sess
_K = 10
_feed_dicts = feed_dicts
res = []
for user in range(_dataset.num_users):
res.append(_eval_by_user(user))
res = np.array(res)
result = (res.mean(axis = 0)).tolist()
print result
hr, ndcg, auc, train_auc = result[3:6] + [0]
return hr, ndcg, auc, train_auc
def scoreK(K, position, negs):
hr = position < K
if hr:
ndcg = math.log(2) / math.log(position + 2)
else:
ndcg = 0
auc = 1 - (position * 1. / negs) # formula: [#(Xui>Xuj) / #(Items)] = [1 - #(Xui<=Xuj) / #(Items)]
return hr, ndcg, auc
def _eval_by_user(user):
if _model.train_auc:
# get predictions of positive samples in training set
train_item_input = _dataset.trainList[user]
train_user_input = np.full(len(train_item_input), user, dtype='int32')[:, None]
train_item_input = np.array(train_item_input)[:, None]
feed_dict = {_model.user_input: train_user_input, _model.item_input_pos: train_item_input,
model.keep_prob: TEST_KEEP_PROB}
train_predict = _sess.run(_model.output, feed_dict)
# get prredictions of data in testing set
user_input, item_input = _feed_dicts[user]
feed_dict = {_model.user_input: user_input, _model.item_input_pos: item_input,
model.keep_prob: TEST_KEEP_PROB}
predictions = _sess.run(_model.output, feed_dict)
nan_pos = np.argwhere(np.isnan(predictions))
if len(nan_pos) > 0:
print "contain nan", nan_pos
exit()
neg_predict, pos_predict = predictions[:-1], predictions[-1]
position = (neg_predict >= pos_predict).sum()
ret = []
ret += scoreK(5, position, len(predictions))
ret += scoreK(10, position, len(predictions))
ret += scoreK(20, position, len(predictions))
return ret
def init_logging(args):
regs = eval(args.regs)
path = "Log/%s_%s/" % (strftime('%Y-%m-%d_%H', localtime()), args.task)
if not os.path.exists(path):
os.makedirs(path)
fpath = path + "%s_embed_size%.4f_lambda1%.7f_reg2%.7f%s" % (
args.dataset, args.embed_size, regs[0], regs[1],strftime('%Y_%m_%d_%H_%M_%S', localtime()))
logging.basicConfig(filename=fpath,
level=logging.INFO)
print "log to", fpath
logging.info("begin training %s model ......" % args.model)
logging.info("dataset:%s embedding_size:%d dns:%d batch_size:%d"
% (args.dataset, args.embed_size, args.dns, args.batch_size))
print "dataset:%s embedding_size:%d dns:%d batch_szie:%d" \
% (args.dataset, args.embed_size, args.dns, args.batch_size)
logging.info("regs:%.8f, %.8f learning_rate:(%.4f, %.4f)"
% (regs[0], regs[1], args.lr_embed, args.lr_net))
print "regs:%.8f, %.8f learning_rate:(%.4f, %.4f)" \
% (regs[0], regs[1], args.lr_embed, args.lr_net)
print str(args)
logging.info(str(args))
if __name__ == '__main__':
# initialize logging
args = parse_args()
init_logging(args)
TRAIN_KEEP_PROB = args.keep
#initialize dataset
dataset = Dataset(args.path + args.dataset)
#initialize models
model = ConvNCF(dataset.num_users, dataset.num_items, args)
model.build_graph()
#start trainging
#saver = GMFSaver()
#saver.setPrefix("./param")
training(model, dataset, args)