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utils.py
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utils.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import random
import time
def sample_top(a=[], top_k=10):
idx = np.argsort(a)[::-1]
idx = idx[:top_k]
probs = a[idx]
probs = probs / np.sum(probs)
choice = np.random.choice(idx, p=probs)
return choice
# fajie
def sample_top_k(a=[], top_k=10):
idx = np.argsort(a)[::-1]
idx = idx[:top_k]
# probs = a[idx]
# probs = probs / np.sum(probs)
# choice = np.random.choice(idx, p=probs)
return idx
print sample_top_k(np.array([0.02,0.01,0.01,0.16,0.8]),3)
# unit_sequence_lens=3 should be >= 2, otherwise there will be a bug, the default value is 3
# item_batch[1] should be larger than unit_sequence_lens
def regenerate_seq_(itembatch,padtoken='0'):
unit_sequence_lens = 3#定义单位长度，写死了
generated_itembatch=[]
generated_itembatch_padend=[]# 每个sequence最后一个index填写0操作
for seq_num in range(len(itembatch)): #遍历输入的序列数据
# the first sequence is from the target user, and the second sequence is from a random user, e.g., itembatch[0]=[1,2,3,4,5]
#第一个sequence是该用户的sequence，第二个是随机采样了一个大小为5的序列，第三个为随机才养了一个大小为5的序列
sequence=itembatch[seq_num]
lens = len(sequence)
def random_generate(sampled_num):
gen_sequence = random.sample(sequence, lens)
return gen_sequence
if seq_num==0:
# lens=50, split into several small seqs
# if lens<=unit_sequence_lens:
# padcount=unit_sequence_lens-lens
# gen_sequence=[padtoken]*padcount+sequence
# generated_itembatch.append(gen_sequence)
if lens<=2*unit_sequence_lens:
gen_seq_num=random_generate(lens)
for i in xrange(len(gen_seq_num)):
generated_itembatch.append([gen_seq_num[i]])#make sure it is a list
elif lens<=3*unit_sequence_lens:
# randomly select two
gen_seq_num = random_generate(lens)
generated_itembatch.append(gen_seq_num[0:unit_sequence_lens])
generated_itembatch.append(gen_seq_num[1*unit_sequence_lens:2*unit_sequence_lens])
elif lens <= 5 * unit_sequence_lens:
gen_seq_num = random_generate(lens)
generated_itembatch.append(gen_seq_num[0:unit_sequence_lens])
generated_itembatch.append(gen_seq_num[1*unit_sequence_lens:2 * unit_sequence_lens])
generated_itembatch.append(gen_seq_num[2*unit_sequence_lens:3 * unit_sequence_lens])
elif lens <= 9 * unit_sequence_lens:
gen_seq_num = random_generate(lens)
generated_itembatch.append(gen_seq_num[0:unit_sequence_lens])
generated_itembatch.append(gen_seq_num[1 * unit_sequence_lens:2 * unit_sequence_lens])
generated_itembatch.append(gen_seq_num[2 * unit_sequence_lens:3 * unit_sequence_lens])
generated_itembatch.append(gen_seq_num[3 * unit_sequence_lens:4 * unit_sequence_lens])
generated_itembatch.append(gen_seq_num[4 * unit_sequence_lens:5 * unit_sequence_lens])
else:
# >=21 5 group
gen_seq_num = random_generate(lens)
generated_itembatch.append(gen_seq_num[0:unit_sequence_lens])
generated_itembatch.append(gen_seq_num[1 * unit_sequence_lens:2 * unit_sequence_lens])
generated_itembatch.append(gen_seq_num[2 * unit_sequence_lens:3 * unit_sequence_lens])
generated_itembatch.append(gen_seq_num[3 * unit_sequence_lens:4 * unit_sequence_lens])
generated_itembatch.append(gen_seq_num[4 * unit_sequence_lens:5 * unit_sequence_lens])
generated_itembatch.append(gen_seq_num[5 * unit_sequence_lens:6 * unit_sequence_lens])
else:
# the second sequence from a random user
lens_follow=len(generated_itembatch[0]) #make sure they have the same lenghts with the above process
if lens>=unit_sequence_lens:
gen_seq_num = random.sample(sequence, lens_follow)
# print gen_seq_num
generated_itembatch.append(gen_seq_num)
# gen_seq_num = random.sample(sequence, lens_follow)
# generated_itembatch.append(gen_seq_num)
# add padding for the last index
for seq_num in range(len(generated_itembatch)):
gen_sequence=generated_itembatch[seq_num]+ [padtoken]
generated_itembatch_padend.append(gen_sequence)
# print generated_itembatch_padend
return generated_itembatch_padend
def regenerate_seq(itembatch,padtoken='0',unit_sequence_lens=10):
generated_itembatch=[]
generated_itembatch_padend=[]#add padding after the last index
for seq_num in range(len(itembatch)):
# the first sequence is from the target user, and the secodne sequence is from a random user, e.g., itembatch[0]=[1,2,3,4,5]
sequence=itembatch[seq_num]
lens = len(sequence)
if seq_num==0:
# lens=50, split into several small seqs
if lens<=unit_sequence_lens:
padcount=unit_sequence_lens-lens
#['0', '0', '0', '75UhOolrY1GQe4Byi', '72LLb8QF31GPvOe6K', '73bTHTaSE1GNPmJhx', '767NZGg511GX9WhQn', '72mqDJHEf1GQPwgrA']
gen_sequence=[padtoken]*padcount+sequence
generated_itembatch.append(gen_sequence)
else:
# if the sequence is larger than unit_sequence_lens=8
subseq_count = lens / unit_sequence_lens+1
for gen_seq_num in range(subseq_count):
if gen_seq_num>=1:
break
# if len(sequence)==
if gen_seq_num==0:
gen_sequence = sequence[
-unit_sequence_lens * (gen_seq_num + 1):]
generated_itembatch.append(gen_sequence)
else:
gen_sequence= sequence[
-unit_sequence_lens * (gen_seq_num + 1):-unit_sequence_lens * (gen_seq_num)]
if len(gen_sequence)<unit_sequence_lens:
padcount = unit_sequence_lens - len(gen_sequence)
gen_sequence = [padtoken] * padcount + gen_sequence
else:
gen_sequence=gen_sequence
generated_itembatch.append(gen_sequence)
def randome_generate(sampled_num):
# if sampled_num>unit_sequence_lens:
# n=unit_sequence_lens
# else:
# n = random.randint(1, unit_sequence_lens)
padcount = unit_sequence_lens - sampled_num
randomsampled_seq = random.sample(sequence, sampled_num) # no repeated item
gen_sequence = [padtoken] * padcount + randomsampled_seq
generated_itembatch.append(gen_sequence)
# do it twice
randome_generate(random.randint(1, unit_sequence_lens))
# randome_generate(random.randint(1, unit_sequence_lens))
randome_generate(1)
randome_generate(1)
else:
# the second sequence from a random user
if lens<=unit_sequence_lens:
padcount=unit_sequence_lens-lens
#['0', '0', '0', '75UhOolrY1GQe4Byi', '72LLb8QF31GPvOe6K', '73bTHTaSE1GNPmJhx', '767NZGg511GX9WhQn', '72mqDJHEf1GQPwgrA']
gen_sequence=[padtoken]*padcount+sequence
generated_itembatch.append(gen_sequence)
else:
gen_sequence=sequence[
-unit_sequence_lens:]
generated_itembatch.append(gen_sequence)
# add padding for the last index
for seq_num in range(len(generated_itembatch)):
gen_sequence=generated_itembatch[seq_num]+ [padtoken]
generated_itembatch_padend.append(gen_sequence)
# print generated_itembatch_padend
return generated_itembatch_padend
fajieitem1 = '0'
fajieitem2 = '6XmQrjS7b1GTNZCGB' # jingwen
fajieitem3 = '72LLb8QF31GPvOe6K' # zhamazha
fajieitem4 = '6S4kEkrrE1GXxcK0D' # banlangtuan
fajieitem5 = '6ZHN0z1uS1GVe8KVk' # dilireba
fajieitem6 = '72HlpiyUv1GOR38a6' # hugenvyou
fajieitem7 = '75p4rLDZ51GSRAZP4' # yeliangchangge
fajieitem8 = '6YcOkN0RY1GSOm3UK' # ouyangnana
fajieitem9 = '75p4rLDZ51GSHmd6y' # zhangmin
fajieitem10 = '74BX1xeD51GKu5gwW' # gezhongmeinv
fajieitem11 = '6YBjJ5ujA1GS6wtBd' # mao he xishuai
fajieitem12 = '767NZGg511GX9WhQn' # liuluoguo
fajieitem13 = '6YcOkN0RY1GPrAIAf' # sun yaowei
fajieitem14 = '763nKN9qn1GSoF3VV' # zhangshaohan yinxingdechibang
fajieitem15 = '73bTHTaSE1GNPmJhx' # diao sha yu
fajieitem16 = '70fWKDPDV1GPVoqO2' # quanjiafu music hongchenlaiyalai
fajieitem17 = '757HTMXHO1GQAtp4k' # dulante kuli doutui
fajieitem18 = '6YtvyJIQT1GNyM4Im' # juxi
fajieitem19 = '75UhOolrY1GQe4Byi' # dayanglu
fajieitem20 = '72mqDJHEf1GQPwgrA' # xiaohai chifan
fajieitem21 = '6YcOkN0RY1GN4Crjj' # xiaohailianquan
fajieitem22 = '72mqDJHEf1GNAQiL1' # xiaohaiqiche
fajieitem23 = '754pYmxBp1GT89zph' # zhang junning
fajieitem24 = '71sR4fOgm1GOEadPW' # mingxing shandong
fajieitem25 = '73ESNtFhJ1GLWrBCh' # jujingwen
fajieitem26 = '6ZYyJ12oE1GLYoqKY' # chen qiaoen
fajieitem27 = '6YcOkN0RY1GTg2wPv' # pig
fajieitem28 = '74380E8pf1GX0qLLR' # kaoyu
fajieitem29 = '70hww2B2q1GXq5aFf' # zhaiyezi
fajieitem30 = '74mC4BObB1GUZS9pV' # pao mian
fajieitem31 = '73ESNtFhJ1GLWrBCh' # jujingwen
fajieitem32 = '733S2MonS1GIEeO56' # suyanmeinv
fajieitem33 = '72oFjdHde1GM8itPn' # tongliya
fajieitem34 = '71AGPPjav1GS5DjwO' # dilireba
fajieitem35 = '755InKD3v1GX6NQ9V' # dianying
fajieitem36 = '6YqFEZwMt1GWMvKnU' # chehuo
fajieitem37 = '74jvNxTTk1GWEn59J' # dengchao
fajieitem38 = '6YRJm6AAM1GST45sQ' # cat and crabs
item_batch=[[ fajieitem2,fajieitem3,fajieitem4,fajieitem20, fajieitem23, fajieitem27,fajieitem28,fajieitem35,
fajieitem35, fajieitem28, fajieitem27, fajieitem23, fajieitem20, fajieitem12,fajieitem15, fajieitem3, fajieitem19],
[fajieitem12,fajieitem15, fajieitem6,fajieitem3,fajieitem27,fajieitem20, fajieitem24, fajieitem18, fajieitem10,fajieitem20,
fajieitem2,fajieitem23,fajieitem24,fajieitem25,fajieitem26,fajieitem28,fajieitem29,fajieitem30,fajieitem3,fajieitem33],
]
# item_batch = [
# [ fajieitem2,fajieitem3,fajieitem4,fajieitem20, fajieitem23, fajieitem27],
# [fajieitem21, fajieitem22, fajieitem23, fajieitem24, fajieitem25, fajieitem27, fajieitem28, fajieitem29],
# ]
item_batch=[[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]]
# self.input_predict = self.input_predict[:, 0:-1]
a=[1]
a=a[0:-1]
print a
# item_batch=[[1,2,3,4,5,6,7],[1,2,3,4,5]]
# item_batch=[[fajieitem1, fajieitem19, fajieitem3,fajieitem15,fajieitem12,fajieitem20],
# [fajieitem12,fajieitem15, fajieitem6,fajieitem3,fajieitem27,fajieitem20],
# ]
# start = time.time()
# for i in range(1):
# regenerate_seq_(item_batch)
# end=time.time()
# print "time",end-start