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jscn_beta_s2.py
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import tensorflow as tf
import utils as ut
import numpy as np
from scipy.sparse import csr_matrix
import pickle
import os.path
class jscn_beta_s2(object):
def __init__(self, K, M, n_users_1, n_users_2,n_users_3, n_items_1, n_items_2,n_items_3, commonUser_12, commonUser_13, emb_dim, lr, batch_size, decay, DIR):
self.model_name = 'crossGraphCF-s2'
# self.graph = graph
self.n_users_1 = n_users_1
self.n_items_1 = n_items_1
self.n_users_2 = n_users_2
self.n_items_2 = n_items_2
self.n_users_3 = n_users_3
self.n_items_3 = n_items_3
self.common_user_12_1 = [i for i,j in commonUser_12]
self.common_user_12_2 = [j for i,j in commonUser_12]
self.common_user_13_1 = [i for i,j in commonUser_13]
self.common_user_13_3 = [j for i,j in commonUser_13]
self.emb_dim = emb_dim
self.batch_size = batch_size
self.K = K
self.decay = decay
self.bpr_loss = [0]*M
self.num_common_users_12 = len(commonUser_12)
self.num_common_users_13 = len(commonUser_13)
var_list = []
self.users_1 = tf.placeholder(tf.int32, shape=(self.batch_size,))
self.pos_items_1 = tf.placeholder(tf.int32, shape=(self.batch_size, ))
self.neg_items_1 = tf.placeholder(tf.int32, shape=(self.batch_size,))
self.graphLambda_1 = tf.placeholder(tf.float32, shape=(None,
None), name='graphLambda_1')
self.U_1 = tf.placeholder(tf.float32, shape=(self.n_users_1+self.n_items_1,
self.n_users_1 + self.n_items_1), name="U_mat_1")
self.user_embeddings_1 = tf.Variable(
tf.random_normal([self.n_users_1, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
name='user_embeddings')
self.item_embeddings_1 = tf.Variable(
tf.random_normal([self.n_items_1, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
name='item_embeddings')
var_list.append(self.user_embeddings_1)
var_list.append(self.item_embeddings_1)
self.user_mapping_1 = tf.Variable(
tf.random_normal([self.emb_dim*(self.K+1), self.emb_dim*(self.K+1)], mean=0.01, stddev=0.02, dtype=tf.float32),
name='user_mapping_1')
self.item_mapping_1 = tf.Variable(
tf.random_normal([self.emb_dim*(self.K+1), self.emb_dim*(self.K+1)], mean=0.01, stddev=0.02, dtype=tf.float32),
name='item_mapping_1')
# self.user_mapping_1 = tf.Variable(
# tf.random_normal([self.emb_dim, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
# name='user_mapping_1')
# self.item_mapping_1 = tf.Variable(
# tf.random_normal([self.emb_dim, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
# name='item_mapping_1')
var_list.append(self.user_mapping_1)
var_list.append(self.item_mapping_1)
self.filters_1 = []
for k in range(self.K):
self.filters_1.append(
tf.Variable(
tf.random_normal([self.emb_dim, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32)))
A_hat_1 = tf.matmul(self.U_1, tf.transpose(self.U_1)) + tf.matmul(tf.matmul(self.U_1, self.graphLambda_1),
tf.transpose(self.U_1))
var_list.append(self.filters_1)
embeddings_1 = tf.concat([self.user_embeddings_1, self.item_embeddings_1], axis=0)
all_embeddings_1 = [embeddings_1]
for k in range(0, self.K):
embeddings_1 = tf.matmul(A_hat_1, embeddings_1)
embeddings_1 = tf.nn.sigmoid(tf.matmul(embeddings_1, self.filters_1[k]))
all_embeddings_1 += [embeddings_1]
all_embeddings_1 = tf.concat(all_embeddings_1, 1)
self.u_embeddings_1, self.i_embeddings_1 = tf.split(all_embeddings_1, [self.n_users_1, self.n_items_1], 0)
# last_embeddings_1 = all_embeddings_1[-1]
# self.u_embeddings_1, self.i_embeddings_1 = tf.split(last_embeddings_1, [self.n_users_1, self.n_items_1], 0)
# self.u_embeddings_1 = tf.nn.embedding_lookup(self.u_embeddings_1, self.users_1)
# self.pos_i_embeddings_1 = tf.nn.embedding_lookup(self.i_embeddings_1, self.pos_items_1)
# self.neg_i_embeddings_1 = tf.nn.embedding_lookup(self.i_embeddings_1, self.neg_items_1)
batch_u_embeddings_1 = tf.nn.embedding_lookup(self.u_embeddings_1, self.users_1)
batch_pos_i_embeddings_1 = tf.nn.embedding_lookup(self.i_embeddings_1, self.pos_items_1)
batch_neg_i_embeddings_1 = tf.nn.embedding_lookup(self.i_embeddings_1, self.neg_items_1)
commonUserEmbedding_12_1 = tf.nn.embedding_lookup(self.u_embeddings_1, self.common_user_12_1)
commonUserEmbedding_13_1 = tf.nn.embedding_lookup(self.u_embeddings_1, self.common_user_13_1)
# self.all_ratings_1 = tf.matmul(self.u_embeddings_1, self.i_embeddings_1, transpose_a=False, transpose_b=True)
self.all_ratings_1 = tf.matmul(self.u_embeddings_1, self.i_embeddings_1, transpose_a=False, transpose_b=True)
# self.bpr_loss[0] = self.create_bpr_loss(self.u_embeddings_1, self.pos_i_embeddings_1, self.neg_i_embeddings_1)
self.bpr_loss[0] = self.create_bpr_loss(batch_u_embeddings_1, batch_pos_i_embeddings_1, batch_neg_i_embeddings_1)
# second graph
self.users_2 = tf.placeholder(tf.int32, shape=(self.batch_size,))
self.pos_items_2 = tf.placeholder(tf.int32, shape=(self.batch_size, ))
self.neg_items_2 = tf.placeholder(tf.int32, shape=(self.batch_size,))
self.graphLambda_2 = tf.placeholder(tf.float32, shape=(None,
None), name='graphLambda_2')
self.U_2 = tf.placeholder(tf.float32, shape=(self.n_users_2+self.n_items_2,
self.n_users_2 + self.n_items_2), name="U_mat_2")
self.user_embeddings_2 = tf.Variable(
tf.random_normal([self.n_users_2, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
name='user_embeddings_2')
self.item_embeddings_2 = tf.Variable(
tf.random_normal([self.n_items_2, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
name='item_embeddings_2')
var_list.append(self.user_embeddings_2)
var_list.append(self.item_embeddings_2)
self.user_mapping_2 = tf.Variable(
tf.random_normal([self.emb_dim*(self.K+1), self.emb_dim*(self.K+1)], mean=0.01, stddev=0.02, dtype=tf.float32),
name='user_mapping_2')
self.item_mapping_2 = tf.Variable(
tf.random_normal([self.emb_dim*(self.K+1), self.emb_dim*(self.K+1)], mean=0.01, stddev=0.02, dtype=tf.float32),
name='item_mapping_2')
# self.user_mapping_2 = tf.Variable(
# tf.random_normal([self.emb_dim, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
# name='user_mapping_2')
# self.item_mapping_2 = tf.Variable(
# tf.random_normal([self.emb_dim, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
# name='item_mapping_2')
var_list.append(self.user_mapping_2)
var_list.append(self.item_mapping_2)
self.filters_2 = []
for k in range(self.K):
self.filters_2.append(
tf.Variable(
tf.random_normal([self.emb_dim, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32)))
var_list.append(self.filters_2)
A_hat_2 = tf.matmul(self.U_2, tf.transpose(self.U_2)) + tf.matmul(tf.matmul(self.U_2, self.graphLambda_2),
tf.transpose(self.U_2))
embeddings_2 = tf.concat([self.user_embeddings_2, self.item_embeddings_2], axis=0)
all_embeddings_2 = [embeddings_2]
for k in range(0, self.K):
embeddings_2 = tf.matmul(A_hat_2, embeddings_2)
embeddings_2 = tf.nn.sigmoid(tf.matmul(embeddings_2, self.filters_2[k]))
all_embeddings_2 += [embeddings_2]
all_embeddings_2 = tf.concat(all_embeddings_2, 1)
self.u_embeddings_2, self.i_embeddings_2 = tf.split(all_embeddings_2, [self.n_users_2, self.n_items_2], 0)
# last_embeddings_2 = all_embeddings_2[-1]
# self.u_embeddings_2, self.i_embeddings_2 = tf.split(last_embeddings_2, [self.n_users_2, self.n_items_2], 0)
batch_u_embeddings_2 = tf.nn.embedding_lookup(self.u_embeddings_2, self.users_2)
batch_pos_i_embeddings_2 = tf.nn.embedding_lookup(self.i_embeddings_2, self.pos_items_2)
batch_neg_i_embeddings_2 = tf.nn.embedding_lookup(self.i_embeddings_2, self.neg_items_2)
commonUserEmbedding_12_2 = tf.nn.embedding_lookup(self.u_embeddings_2, self.common_user_12_2)
self.all_ratings_2 = tf.matmul(self.u_embeddings_2, self.i_embeddings_2, transpose_a=False, transpose_b=True)
# self.bpr_loss[1] = self.create_bpr_loss(self.u_embeddings_2, self.pos_i_embeddings_2, self.neg_i_embeddings_2)
self.bpr_loss[1] = self.create_bpr_loss(batch_u_embeddings_2, batch_pos_i_embeddings_2, batch_neg_i_embeddings_2)
# print("---------------loss is set----------------")
# third graph
self.users_3 = tf.placeholder(tf.int32, shape=(self.batch_size,))
self.pos_items_3 = tf.placeholder(tf.int32, shape=(self.batch_size, ))
self.neg_items_3 = tf.placeholder(tf.int32, shape=(self.batch_size,))
self.graphLambda_3 = tf.placeholder(tf.float32, shape=(None,
None), name='graphLambda_3')
self.U_3 = tf.placeholder(tf.float32, shape=(self.n_users_3+self.n_items_3,
self.n_users_3 + self.n_items_3), name="U_mat_3")
self.user_embeddings_3 = tf.Variable(
tf.random_normal([self.n_users_3, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
name='user_embeddings_3')
self.item_embeddings_3 = tf.Variable(
tf.random_normal([self.n_items_3, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
name='item_embeddings_3')
var_list.append(self.user_embeddings_3)
var_list.append(self.item_embeddings_3)
self.user_mapping_3 = tf.Variable(
tf.random_normal([self.emb_dim*(self.K+1), self.emb_dim*(self.K+1)], mean=0.01, stddev=0.02, dtype=tf.float32),
name='user_mapping_3')
self.item_mapping_3 = tf.Variable(
tf.random_normal([self.emb_dim*(self.K+1), self.emb_dim*(self.K+1)], mean=0.01, stddev=0.02, dtype=tf.float32),
name='item_mapping_3')
# self.user_mapping_2 = tf.Variable(
# tf.random_normal([self.emb_dim, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
# name='user_mapping_2')
# self.item_mapping_2 = tf.Variable(
# tf.random_normal([self.emb_dim, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32),
# name='item_mapping_2')
var_list.append(self.user_mapping_3)
var_list.append(self.item_mapping_3)
self.filters_3 = []
for k in range(self.K):
self.filters_3.append(
tf.Variable(
tf.random_normal([self.emb_dim, self.emb_dim], mean=0.01, stddev=0.02, dtype=tf.float32)))
var_list.append(self.filters_3)
A_hat_3 = tf.matmul(self.U_3, tf.transpose(self.U_3)) + tf.matmul(tf.matmul(self.U_3, self.graphLambda_3),
tf.transpose(self.U_3))
embeddings_3 = tf.concat([self.user_embeddings_3, self.item_embeddings_3], axis=0)
all_embeddings_3 = [embeddings_3]
for k in range(0, self.K):
embeddings_3 = tf.matmul(A_hat_3, embeddings_3)
embeddings_3 = tf.nn.sigmoid(tf.matmul(embeddings_3, self.filters_3[k]))
all_embeddings_3 += [embeddings_3]
all_embeddings_3 = tf.concat(all_embeddings_3, 1)
self.u_embeddings_3, self.i_embeddings_3 = tf.split(all_embeddings_3, [self.n_users_3, self.n_items_3], 0)
# last_embeddings_2 = all_embeddings_2[-1]
# self.u_embeddings_2, self.i_embeddings_2 = tf.split(last_embeddings_2, [self.n_users_2, self.n_items_2], 0)
batch_u_embeddings_3 = tf.nn.embedding_lookup(self.u_embeddings_3, self.users_3)
batch_pos_i_embeddings_3 = tf.nn.embedding_lookup(self.i_embeddings_3, self.pos_items_3)
batch_neg_i_embeddings_3 = tf.nn.embedding_lookup(self.i_embeddings_3, self.neg_items_3)
commonUserEmbedding_13_3 = tf.nn.embedding_lookup(self.u_embeddings_3, self.common_user_13_3)
self.all_ratings_3 = tf.matmul(self.u_embeddings_3, self.i_embeddings_3, transpose_a=False, transpose_b=True)
# self.bpr_loss[1] = self.create_bpr_loss(self.u_embeddings_3, self.pos_i_embeddings_3, self.neg_i_embeddings_3)
self.bpr_loss[2] = self.create_bpr_loss(batch_u_embeddings_3, batch_pos_i_embeddings_3, batch_neg_i_embeddings_3)
# Loss
self.bpr_loss_all = sum(self.bpr_loss)
self.common_user_loss_12 = self.createCommonUserloss(commonUserEmbedding_12_1, self.user_mapping_1,
commonUserEmbedding_12_2, self.user_mapping_2, self.num_common_users_12)
self.common_user_loss_13 = self.createCommonUserloss(commonUserEmbedding_13_1, self.user_mapping_1,
commonUserEmbedding_13_3, self.user_mapping_3, self.num_common_users_13)
self.loss = self.bpr_loss_all + self.common_user_loss_12 + self.common_user_loss_13
self.opt = tf.train.RMSPropOptimizer(learning_rate=lr)
self.updates = self.opt.minimize(self.loss, var_list=var_list)
# self.updates = self.opt.minimize(self.bpr_loss_all, var_list=var_list)
def create_bpr_loss(self, users, pos_items, neg_items):
pos_scores = tf.reduce_sum(tf.multiply(users, pos_items), axis=1)
neg_scores = tf.reduce_sum(tf.multiply(users, neg_items), axis=1)
regularizer = tf.nn.l2_loss(users) + tf.nn.l2_loss(pos_items) + tf.nn.l2_loss(neg_items)
regularizer = regularizer/self.batch_size
maxi = tf.log_sigmoid(pos_scores - neg_scores)
# maxi = tf.log_sigmoid(neg_scores)
loss = tf.negative(tf.reduce_mean(maxi)) + self.decay * regularizer
# loss = tf.negative(tf.reduce_sum(maxi)) + self.decay * regularizer
return loss
def createCommonUserloss(self, commonUserEmbedding_1, userMapping_1, commonUserEmbedding_2, userMapping_2, numCommonUser):
commonUserLoss = 0.0
regularizer = 0.0
commonUserMapping_1 = tf.matmul(commonUserEmbedding_1, userMapping_1)
commonUserMapping_2 = tf.matmul(commonUserEmbedding_2, userMapping_2)
# regularizer = tf.nn.l2_loss(commonUserMapping_1) + tf.nn.l2_loss(commonUserMapping_2)
# regularizer = regularizer/self.batch_size
# commonUserLoss = commonUserLoss + tf.nn.l2_loss(commonUserEmbedding_1 - commonUserEmbedding_2)/self.num_common_users
commonUserLoss = commonUserLoss + tf.nn.l2_loss(commonUserMapping_1 - commonUserMapping_2)/numCommonUser
# commonUserLoss = regularizer + commonUserLoss
# commonUserLoss = commonUserLoss + tf.losses.absolute_difference(commonUserEmbedding_1, commonUserEmbedding_2)/self.num_common_users
# cosine common user loss
# commonUserLoss = commonUserLoss + tf.losses.cosine_distance(tf.nn.l2_normalize(commonUserEmbedding_1, 0), tf.nn.l2_normalize(commonUserEmbedding_2, 0), dim=0)
return commonUserLoss
def embeddingSaving(self, loss, u_embeddings, i_embeddings,userOpenFile, itemOpenFile):
userOpenFile.write(str(loss))
userOpenFile.write("\n")
# userNum = self.n_users
for uidx in range(self.n_users):
userOpenFile.write(str(u_embeddings[uidx, :]))
userOpenFile.write("\n")
itemOpenFile.write(str(loss))
itemOpenFile.write("\n")
for iidx in range(self.n_items):
itemOpenFile.write(str(i_embeddings[iidx, :]))
itemOpenFile.write("\n")
# def genEigenvector(self, eigenPickleFileName):
# print("----------computing eigen vectors-------------")
# graphLambda, U = np.linalg.eigh(self.L)
# with open(eigenPickleFileName, 'wb') as pf:
# pickle.dump([graphLambda,U], pf)
# print("------------eigen vectors settle----------------")
# return graphLambda, U
# def loadEigenvector(self, eigenPickleFileName):
# print("-------------loading the eigen vectors------------")
# with open(eigenPickleFileName, 'rb') as pf:
# eigPara = pickle.load(pf)
# if len(eigPara)>1:
# graphLambda = eigPara[0]
# U = eigPara[1]
# else:
# self.genEigenvector(eigenPickleFileName)
# print("-----------loading eigen vectors successfully!-----------")
# return graphLambda, U
# def adjacient_matrix(self, self_connection=False):
# A = np.zeros([self.n_users+self.n_items, self.n_users+self.n_items], dtype=np.float32)
# A[:self.n_users, self.n_users:] = self.graph
# A[self.n_users:, :self.n_users] = self.graph.T
# if self_connection == True:
# return np.identity(self.n_users+self.n_items,dtype=np.float32) + A
# return A
# def degree_matrix(self):
# degree = np.sum(self.A, axis=1, keepdims=False)
# #degree = np.diag(degree)
# return degree
# def laplacian_matrix(self, normalized=False):
# if normalized == False:
# return self.D - self.A
# temp = np.dot(np.diag(np.power(self.D, -1)), self.A)
# #temp = np.dot(temp, np.power(self.D, -0.5))
# return np.identity(self.n_users+self.n_items,dtype=np.float32) - temp