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LoadData.py
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LoadData.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 23 12:09:35 2018
@author: LuoJiacheng
"""
import numpy as np
import pandas as pd
class LoadData(object):
def __init__(self,ratio):
file = './datasets/ratings.txt'
f_file = './datasets/trust.txt'
data = np.loadtxt(file)
fdata = np.loadtxt(f_file).astype(int)
data[:,2] = data[:,2]/5
indices = np.arange(len(data[:,2]))
num_train_samples = int(len(indices)*ratio)
np.random.seed(615)
np.random.shuffle(indices)
user = data[:,0].copy().astype(int)
item = data[:,1].copy().astype(int)
rating = data[:,2].copy()
self.user = user[indices]
self.item = item[indices]
self.rating = rating[indices]
self.train_user = self.user[:num_train_samples]
self.train_item = self.item[:num_train_samples]
self.train_rating = self.rating[:num_train_samples]
self.test_user = self.user[num_train_samples:]
self.test_item = self.item[num_train_samples:]
self.test_rating = self.rating[num_train_samples:]
#find friends
friends_dic = {}
for elem in fdata[:,0]:
friends_dic.setdefault(elem,[])
for i,elem in enumerate(fdata[:,0]):
friends_dic[elem].append(fdata[i,1])
self.friends_dic = friends_dic
friends = []
for usr in self.user:
try:
friends.append(self.friends_dic[usr][0])
except:
friends.append(0)
friends = np.array(friends)
self.friends = friends
self.train_friends =self.friends[:num_train_samples]
self.test_friends = self.friends[num_train_samples:]
#sim matrix
matrix = np.zeros([max(fdata[:,1]),max(self.item)])
matrix[self.train_user,self.train_item]=self.train_rating
self.matrix = matrix
#sim
self.train_sim = self.comput_sim(self.train_friends,self.train_user)
self.test_sim = self.comput_sim(self.test_friends,self.test_user)
def comput_sim(self,user,friends):
sim = []
for i,j in zip(user,friends):
sim.append(self.sim_pearson(self.matrix[i,:],self.matrix[j,:]))
sim = ((np.array(sim)+1)/2)
return sim
def sim_pearson(self,user1,user2):
# a=np.isfinite(user1)
# b=np.isfinite(user2)
a_=np.nan_to_num(user1)
b_=np.nan_to_num(user2)
user1[user1 == 0] = np.nan
user2[user2 == 0] = np.nan
a=a_>0
b=b_>0
n=0
k = np.logical_and(a,b)
n = k[k==True].size
if n==0:
return 0
user1_k = user1[k]
user2_k = user2[k]
num = np.sum((user1_k-np.nanmean(user1))*(user2_k-np.nanmean(user2)))
den = np.sqrt(np.sum(pow((user1_k-np.nanmean(user1)),2))*np.sum(pow((user2_k-np.nanmean(user2)),2)))
if den ==0 : return 0
sim = num/den
sim_ac = sim*(2*user1_k.size)/(user1[pd.notnull(user1)].size+user2[pd.notnull(user2)].size)
return sim_ac
def get_batches(self,user,item, Y,batch_size):
'''
数据batch迭代器
user_batch, item_batch,y_batch
return 1 friends ,you can revise
'''
if batch_size is None:
batch_size = 128
n_batches = int(len(Y) / batch_size)
# 这里我们仅保留完整的batch,对于不能整出的部分进行舍弃
while (True):
# inputs
for count in range(n_batches):
friends =[]
user_batch = user[count*batch_size:(count+1)*batch_size]
item_batch = item[count*batch_size:(count+1)*batch_size]
# targets
y_batch = Y[count*batch_size:(count+1)*batch_size]
for usr in user_batch:
try:
friends.append(self.friends_dic[usr][0])
except:
friends.append(0)
friends = np.array(friends)
sim = self.comput_sim(user_batch,friends)
sim = np.reshape(sim,(batch_size,1))
user_batch = np.reshape(user_batch,(batch_size,1))
item_batch = np.reshape(item_batch,(batch_size,1))
friends = np.reshape(friends,(batch_size,1))
y_batch = np.reshape(y_batch,(batch_size,1))
yield user_batch, item_batch,friends,sim,y_batch