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conure_tp_t1.py
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conure_tp_t1.py
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import tensorflow as tf
import data_loader_t1 as data_loader
import generator_prune_t1 as generator_recsys
import math
import numpy as np
import argparse
import config
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')
parser.add_argument('--datapath', type=str, default='Data/Session/original_desen_pretrain.csv',
help='data path')
parser.add_argument('--datapath_index', type=str, default='Data/Session/index.csv',
help='data path')
parser.add_argument('--eval_iter', type=int, default=20000,
help='Sample generator output evry x steps')
parser.add_argument('--save_para_every', type=int, default=20000,
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('--has_positionalembedding', type=bool, default=False,
help='whether contains positional embedding before performing cnnn')
parser.add_argument('--max_position', type=int, default=1000,
help='maximum number of for positional embedding, it has to be larger than the sequence lens')
args = parser.parse_args()
dl = data_loader.Data_Loader({'model_type': 'generator', 'dir_name': args.datapath,'dir_name_index': args.datapath_index})
all_samples = dl.item
items = dl.item_dict
bigemb= dl.embed_len
print "len(source)",len(items)
print "len(allitems)", bigemb
# 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:]
model_para = {
'item_size': len(items),
'bigemb':bigemb,
'dilated_channels': 256,
'dilations': [1,4,1,4,1,4,1,4,],
'kernel_size': 3,
'learning_rate':0.001,
'batch_size':32,
'iterations':4,
'has_positionalembedding': args.has_positionalembedding,
'max_position': args.max_position,
'is_negsample':True,
'taskID': config.taskID_1st #this is the start taskID index from 10001 i.e., ID=1
}
taskID=model_para['taskID']
itemrec = generator_recsys.NextItNet_Decoder(model_para)
itemrec.train_graph(model_para['is_negsample'], ispre=True)
optimizer = tf.train.AdamOptimizer(model_para['learning_rate'], beta1=args.beta1).minimize(itemrec.loss)
itemrec.save_impwei(reuse=True) # save important weight
itemrec.predict_graph(model_para['is_negsample'],reuse=True, ispre=True)
tf.add_to_collection("dilate_input", itemrec.dilate_input)
tf.add_to_collection("context_embedding", itemrec.context_embedding)
sess = tf.Session()
init=tf.global_variables_initializer()
sess.run(init)
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]:
item_batch = train_set[batch_no * batch_size: (batch_no + 1) * batch_size, :]
_, loss = sess.run(
[optimizer, itemrec.loss],
feed_dict={
itemrec.itemseq_input: item_batch
})
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)
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
curr_preds_5=[]
rec_preds_5=[]
ndcg_preds_5=[]
while (batch_no_test + 1) * batch_size_test < valid_set.shape[0]:
if (numIters / (args.eval_iter) < 5):
if (batch_no_test > 5000):
break
else:
if (batch_no_test > 9000):
break
item_batch = valid_set[batch_no_test * batch_size_test: (batch_no_test + 1) * batch_size_test, :]
[top_k_batch] = sess.run(
[itemrec.top_k],
feed_dict={
itemrec.input_predict: item_batch,
})
top_k=np.squeeze(top_k_batch[1])
for bi in range(top_k.shape[0]):
pred_items_5 = top_k[bi][:5]
true_item=item_batch[bi][-1]
predictmap_5={ch : i for i, ch in enumerate(pred_items_5)}
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
batch_no_test += 1
if (numIters / (args.eval_iter) < 5):
if (batch_no_test == 5000):
print "mrr_5:", sum(curr_preds_5) / float(len(curr_preds_5)), "hit_5:", sum(rec_preds_5) / float(
len(rec_preds_5)), "ndcg_5:", sum(ndcg_preds_5) / float(
len(ndcg_preds_5))
else:
if (batch_no_test == 9000):
print "mrr_5:", sum(curr_preds_5) / float(len(curr_preds_5)), "hit_5:", sum(rec_preds_5) / float(
len(rec_preds_5)), "ndcg_5:", sum(ndcg_preds_5) / float(
len(ndcg_preds_5))
numIters += 1
#after training
_mask_val_list = sess.run(itemrec.mask_val_list)
for layer_id, dilation in enumerate(model_para['dilations']):
resblock_type = "decoder"
resblock_name = "nextitnet_residual_block{}_layer_{}_{}".format(resblock_type, layer_id, dilation)
with tf.variable_scope(resblock_name, reuse=tf.AUTO_REUSE):
with tf.variable_scope("dilated_conv1"):
name_conv1 = "mask_filter/{}_mask_val".format(taskID)
init_conv1 = tf.constant(_mask_val_list[2 * layer_id])
mask_val_conv1 = tf.get_variable(name_conv1,
initializer=init_conv1, trainable=False) # there is no optimizer
with tf.variable_scope("dilated_conv2"):
name_conv2 = "mask_filter/{}_mask_val".format(taskID)
init_conv2 = tf.constant(_mask_val_list[2 * layer_id + 1])
mask_val_conv2 = tf.get_variable(name_conv2,
initializer=init_conv2, trainable=False)
unitialized_vars = []
for var in tf.global_variables():
try:
sess.run(var)
except tf.errors.FailedPreconditionError:
unitialized_vars.append(var)
initialize_op = tf.variables_initializer(unitialized_vars)
sess.run(initialize_op)
saver = tf.train.Saver()
save_path = saver.save(sess,
"Data/Models/generation_model_t1/model_nextitnet_transfer_pretrain.ckpt".format(iter,
numIters))
print "Save models done!"
if __name__ == '__main__':
main()