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nextitrec.py
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nextitrec.py
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import tensorflow as tf
import data_loader_recsys
import generator_recsys
import utils
import shutil
import time
import math
import eval
import numpy as np
import argparse
# You can run it directly, first training and then evaluating
# nextitrec_generate.py can only be run when the model parameters are saved, i.e.,
# save_path = saver.save(sess,
# "Data/Models/generation_model/model_nextitnet.ckpt".format(iter, numIters))
# if you are dealing very huge industry dataset, e.g.,several hundred million items, you may have memory problem during training, but it
# be easily solved by simply changing the last layer, you do not need to calculate the cross entropy loss
# based on the whole item vector. Similarly, you can also change the last layer (use tf.nn.embedding_lookup or gather) in the prediction phrase
# if you want to just rank the recalled items instead of all items. The current code should be okay if the item size < 5 million.
#Strongly suggest running codes on GPU with more than 10G memory!!!
#if your session data is very long e.g, >50, and you find it may not have very strong internal sequence properties, you can consider generate subsequences
def generatesubsequence(train_set):
# create subsession only for training
subseqtrain = []
for i in range(len(train_set)):
# print x_train[i]
seq = train_set[i]
lenseq = len(seq)
# session lens=100 shortest subsession=5 realvalue+95 0
for j in range(lenseq - 2):
subseqend = seq[:len(seq) - j]
subseqbeg = [0] * j
subseq = np.append(subseqbeg, subseqend)
# beginseq=padzero+subseq
# newsubseq=pad+subseq
subseqtrain.append(subseq)
x_train = np.array(subseqtrain) # list to ndarray
del subseqtrain
# Randomly shuffle data
np.random.seed(10)
shuffle_train = np.random.permutation(np.arange(len(x_train)))
x_train = x_train[shuffle_train]
print "generating subsessions is done!"
return x_train
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--top_k', type=int, default=5,
help='Sample from top k predictions')
parser.add_argument('--beta1', type=float, default=0.9,
help='hyperpara-Adam')
#this is a demo dataset, which just let you run this code, suggest dataset link: http://grouplens.org/datasets/.
parser.add_argument('--datapath', type=str, default='Data/Session/user-filter-20000items-session5.csv',
help='data path')
parser.add_argument('--eval_iter', type=int, default=1000,
help='Sample generator output evry x steps')
parser.add_argument('--save_para_every', type=int, default=1000,
help='save model parameters every')
parser.add_argument('--tt_percentage', type=float, default=0.2,
help='0.2 means 80% training 20% testing')
parser.add_argument('--is_generatesubsession', type=bool, default=False,
help='whether generating a subsessions, e.g., 12345-->01234,00123,00012 It may be useful for very some very long sequences')
args = parser.parse_args()
dl = data_loader_recsys.Data_Loader({'model_type': 'generator', 'dir_name': args.datapath})
all_samples = dl.item
items = dl.item_dict
print "len(items)",len(items)
# Randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(all_samples)))
all_samples = all_samples[shuffle_indices]
# Split train/test set
dev_sample_index = -1 * int(args.tt_percentage * float(len(all_samples)))
train_set, valid_set = all_samples[:dev_sample_index], all_samples[dev_sample_index:]
if args.is_generatesubsession:
x_train = generatesubsequence(train_set)
model_para = {
#if you changed the parameters here, also do not forget to change paramters in nextitrec_generate.py
'item_size': len(items),
'dilated_channels': 100,#larger is better until 512 or 1024
# if you use nextitnet_residual_block, you can use [1, 4, 1, 4, 1,4,],
# if you use nextitnet_residual_block_one, you can tune and i suggest [1, 2, 4, ], for a trial
# when you change it do not forget to change it in nextitrec_generate.py
'dilations': [1, 2, 1, 2, 1, 2, ],#YOU should tune this hyper-parameter, refer to the paper.
'kernel_size': 3,
'learning_rate':0.001,#YOU should tune this hyper-parameter
'batch_size':128,#YOU should tune this hyper-parameter
'iterations':400,# if your dataset is small, suggest adding regularization to prevent overfitting
'is_negsample':False #False denotes no negative sampling
}
itemrec = generator_recsys.NextItNet_Decoder(model_para)
itemrec.train_graph(model_para['is_negsample'])
optimizer = tf.train.AdamOptimizer(model_para['learning_rate'], beta1=args.beta1).minimize(itemrec.loss)
itemrec.predict_graph(model_para['is_negsample'],reuse=True)
sess= tf.Session()
init=tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver()
numIters = 1
for iter in range(model_para['iterations']):
batch_no = 0
batch_size = model_para['batch_size']
while (batch_no + 1) * batch_size < train_set.shape[0]:
start = time.clock()
item_batch = train_set[batch_no * batch_size: (batch_no + 1) * batch_size, :]
_, loss, results = sess.run(
[optimizer, itemrec.loss,
itemrec.arg_max_prediction],
feed_dict={
itemrec.itemseq_input: item_batch
})
end = time.clock()
if numIters % args.eval_iter == 0:
print "-------------------------------------------------------train1"
print "LOSS: {}\tITER: {}\tBATCH_NO: {}\t STEP:{}\t total_batches:{}".format(
loss, iter, batch_no, numIters, train_set.shape[0] / batch_size)
print "TIME FOR BATCH", end - start
print "TIME FOR ITER (mins)", (end - start) * (train_set.shape[0] / batch_size) / 60.0
if numIters % args.eval_iter == 0:
print "-------------------------------------------------------test1"
if (batch_no + 1) * batch_size < valid_set.shape[0]:
item_batch = valid_set[(batch_no) * batch_size: (batch_no + 1) * batch_size, :]
loss = sess.run(
[itemrec.loss_test],
feed_dict={
itemrec.input_predict: item_batch
})
print "LOSS: {}\tITER: {}\tBATCH_NO: {}\t STEP:{}\t total_batches:{}".format(
loss, iter, batch_no, numIters, valid_set.shape[0] / batch_size)
batch_no += 1
if numIters % args.eval_iter == 0:
batch_no_test = 0
batch_size_test = batch_size*1
curr_preds_5=[]
rec_preds_5=[] #1
ndcg_preds_5=[] #1
curr_preds_20 = []
rec_preds_20 = [] # 1
ndcg_preds_20 = [] # 1
while (batch_no_test + 1) * batch_size_test < valid_set.shape[0]:
if (numIters / (args.eval_iter) < 10):
if (batch_no_test > 20):
break
else:
if (batch_no_test > 500):
break
item_batch = valid_set[batch_no_test * batch_size_test: (batch_no_test + 1) * batch_size_test, :]
[probs] = sess.run(
[itemrec.g_probs],
feed_dict={
itemrec.input_predict: item_batch
})
for bi in range(probs.shape[0]):
pred_items_5 = utils.sample_top_k(probs[bi][-1], top_k=args.top_k)#top_k=5
pred_items_20 = utils.sample_top_k(probs[bi][-1], top_k=args.top_k+15)
true_item=item_batch[bi][-1]
predictmap_5={ch : i for i, ch in enumerate(pred_items_5)}
pred_items_20 = {ch: i for i, ch in enumerate(pred_items_20)}
rank_5=predictmap_5.get(true_item)
rank_20 = pred_items_20.get(true_item)
if rank_5 ==None:
curr_preds_5.append(0.0)
rec_preds_5.append(0.0)#2
ndcg_preds_5.append(0.0)#2
else:
MRR_5 = 1.0/(rank_5+1)
Rec_5=1.0#3
ndcg_5 = 1.0 / math.log(rank_5 + 2, 2) # 3
curr_preds_5.append(MRR_5)
rec_preds_5.append(Rec_5)#4
ndcg_preds_5.append(ndcg_5) # 4
if rank_20 ==None:
curr_preds_20.append(0.0)
rec_preds_20.append(0.0)#2
ndcg_preds_20.append(0.0)#2
else:
MRR_20 = 1.0/(rank_20+1)
Rec_20=1.0#3
ndcg_20 = 1.0 / math.log(rank_20 + 2, 2) # 3
curr_preds_20.append(MRR_20)
rec_preds_20.append(Rec_20)#4
ndcg_preds_20.append(ndcg_20) # 4
batch_no_test += 1
print "BATCH_NO: {}".format(batch_no_test)
print "Accuracy mrr_5:",sum(curr_preds_5) / float(len(curr_preds_5))#5
print "Accuracy mrr_20:", sum(curr_preds_20) / float(len(curr_preds_20)) # 5
print "Accuracy hit_5:", sum(rec_preds_5) / float(len(rec_preds_5))#5
print "Accuracy hit_20:", sum(rec_preds_20) / float(len(rec_preds_20)) # 5
print "Accuracy ndcg_5:", sum(ndcg_preds_5) / float(len(ndcg_preds_5)) # 5
print "Accuracy ndcg_20:", sum(ndcg_preds_20) / float(len(ndcg_preds_20)) #
#print "curr_preds",curr_preds
# print "---------------------------Test Accuray----------------------------"
numIters += 1
if numIters % args.save_para_every == 0:
save_path = saver.save(sess,
"Data/Models/generation_model/model_nextitnet.ckpt".format(iter, numIters))
if __name__ == '__main__':
main()