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meta_atten.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CosineSimilarity
cos = CosineSimilarity(dim=1,eps=1e-8)
class Hete_average(nn.Module):
def __init__(self, meta_atten, cuda, embed_dim, meta_num, homo_dim, hidden,node_num):
super(Hete_average, self).__init__()
self.meta_atten = meta_atten
self.active = F.tanh
self.cuda = cuda
self.embed_dim = embed_dim
self.meta_num = meta_num
self.hidden=hidden
self.homo_dim=homo_dim
if cuda:
self.attention=torch.ones(1,meta_num).cuda()
else:
self.attention=torch.ones(1,meta_num)
print(self.attention)
def forward(self, nodes,homo_embedding_list):
if self.cuda:
final_embedding = torch.zeros(len(nodes), self.embed_dim).cuda()
else:
final_embedding = torch.zeros(len(nodes),self.embed_dim)
if self.meta_atten:
for i in range(self.meta_num):
final_embedding += self.attention[0,i].repeat(len(nodes), self.homo_dim) * homo_embedding_list[i]
else:
pass
return final_embedding
class Hete_self(nn.Module):
def __init__(self, meta_atten, cuda, embed_dim, meta_num, homo_dim, hidden,node_num):
super(Hete_self, self).__init__()
self.meta_atten = meta_atten
self.meta_num = meta_num
self.cuda = cuda
self.embed_dim = embed_dim
self.homo_dim=homo_dim
self.homo2hidden = nn.Linear(homo_dim, hidden,bias=True)
self.hidden = hidden
if cuda:
self.homo2hidden=self.homo2hidden.cuda()
self.out_embedding = nn.Parameter(torch.cuda.FloatTensor(node_num,hidden))
else:
self.out_embedding = nn.Parameter(torch.FloatTensor(node_num,hidden))
nn.init.uniform_(self.out_embedding)
def forward(self,nodes,homo_embedding_list):
if self.cuda:
final_embedding = torch.zeros(len(nodes), self.homo_dim).cuda()
else:
final_embedding = torch.zeros(len(nodes),self.homo_dim)
if self.meta_atten:
if self.cuda:
attention=torch.cuda.FloatTensor(len(nodes),self.meta_num)
else:
attention=torch.FloatTensor(len(nodes),self.meta_num)
for i in range(self.meta_num):
homo_embedding = homo_embedding_list[i]
hidden_embedding = torch.tanh(self.homo2hidden(homo_embedding))
attention[:, i] = cos(hidden_embedding,self.out_embedding[nodes])
attention = F.softmax(attention, dim=1)
for i in range(self.meta_num):
final_embedding += attention[:, i].view(-1, 1).repeat(1, self.homo_dim) * homo_embedding_list[i]
else:
for i in range(self.meta_num):
homo_embedding = homo_embedding_list[i]
final_embedding +=homo_embedding
final_embedding = final_embedding*1.0/self.meta_num
return final_embedding
# class Hete_self(nn.Module):
# def __init__(self, meta_atten, cuda, embed_dim, meta_num, homo_dim, hidden,node_num):
# super(Hete_self, self).__init__()
# self.meta_atten = meta_atten
# self.meta_num = meta_num
# self.cuda = cuda
# self.embed_dim = embed_dim
# self.homo_dim=homo_dim
# self.homo2hidden = nn.Linear(homo_dim, hidden,bias=False)
# self.hidden = hidden
# if cuda:
# self.homo2hidden=self.homo2hidden.cuda()
# self.self_embed = nn.Parameter(torch.cuda.FloatTensor(1, hidden))
# self.beta = nn.Parameter(torch.cuda.FloatTensor(1,self.meta_num))
# else:
# self.self_embed = nn.Parameter(torch.FloatTensor(1, hidden))
# self.beta = nn.Parameter(torch.FloatTensor(1,self.meta_num))
# nn.init.uniform_(self.self_embed)
# nn.init.uniform_(self.beta)
# def forward(self, nodes,homo_embedding_list):
# if self.cuda:
# final_embedding = torch.zeros(len(nodes), self.homo_dim).cuda()
# else:
# final_embedding = torch.zeros(len(nodes),self.homo_dim)
# if self.meta_atten:
# if self.cuda:
# attention=torch.cuda.FloatTensor(len(nodes),self.meta_num)
# else:
# attention=torch.FloatTensor(len(nodes),self.meta_num)
# self_embedding = self.self_embed.repeat(len(nodes),1)
# #self_embedding = self.self_embed[nodes]
# for i in range(self.meta_num):
# homo_embedding = homo_embedding_list[i]
# #homo_embedding = F.normalize(homo_embedding)
# hidden_embedding = self.homo2hidden(homo_embedding)
# #hidden_embedding = F.tanh(hidden_embedding)
# #hidden_embedding = F.dropout(hidden_embedding,0.5,training=self.training)
# #hidden_embedding = nn.BatchNorm1d(self.hidden).cuda()(hidden_embedding)
# #hidden_embedding = F.normalize(hidden_embedding)
# #self_embedding = F.normalize(self_embedding)
# #attention[:, i] = self.beta[0,i]*torch.diag(self_embedding.mm(hidden_embedding.t()))
# attention[:, i] = self.beta[0, i] * cos(hidden_embedding,self_embedding)
# #attention[:, i] = cos(self_embedding,hidden_embedding)
# #print('Beta',self.beta)
# #print('Before softmax:',torch.mean(attention,dim=0))
# #attention = F.relu(attention)
# attention = F.softmax(attention, dim=1)
# #print('After softmax:',torch.mean(attention,dim=0))
# for i in range(self.meta_num):
# final_embedding += attention[:, i].view(-1, 1).repeat(1, self.homo_dim) * homo_embedding_list[i]
# else:
# for i in range(self.meta_num):
# homo_embedding = homo_embedding_list[i]
# final_embedding +=homo_embedding
# final_embedding = final_embedding*1.0/self.meta_num
# # final_embedding = torch.zeros(self.meta_num,homo_embedding_list[0].shape[0],homo_embedding_list[0].shape[1]).cuda()
# # for i in range(self.meta_num):
# # final_embedding[i]=homo_embedding_list[i]
# # final_embedding = torch.max(final_embedding,dim=0)[0]
# return final_embedding
class Hete_MLP2_atten(nn.Module):
def __init__(self, meta_atten, cuda, embed_dim, meta_num, homo_dim, hidden,node_num):
super(Hete_MLP2_atten, self).__init__()
self.meta_atten = meta_atten
self.cuda = cuda
self.embed_dim = embed_dim
self.meta_num = meta_num
#self.beta = nn.Parameter(torch.FloatTensor(1,self.meta_num))
self.homo_dim=homo_dim
self.homo2hidden=nn.Linear(homo_dim,hidden,bias=True)
self.hidden2atten=nn.Linear(hidden,1,bias=True)
if cuda:
self.homo2hidden=self.homo2hidden.cuda()
self.hidden2atten=self.hidden2atten.cuda()
#self.beta = self.beta.cuda()
def forward(self, nodes,homo_embedding_list):
if self.cuda:
final_embedding = torch.zeros(len(nodes), self.homo_dim).cuda()
else:
final_embedding = torch.zeros(len(nodes),self.homo_dim)
if self.meta_atten:
attention = torch.FloatTensor(len(nodes), self.meta_num)
if self.cuda:
attention = attention.cuda()
for i in range(self.meta_num):
homo_embedding = homo_embedding_list[i]
#homo_embedding = F.normalize(homo_embedding)
attention[:, i] =self.hidden2atten(F.tanh(self.homo2hidden(homo_embedding))).squeeze(dim=1)
attention = F.softmax(attention, dim=1)
#print(torch.mean(attention,dim=0,keepdim=True))
for i in range(self.meta_num):
final_embedding += attention[:, i].view(-1, 1).repeat(1, self.homo_dim) * homo_embedding_list[i]
else:
for i in range(self.meta_num):
final_embedding +=homo_embedding_list[i] * 1.0 / self.meta_num
return final_embedding
# class Hete_MLP1_atten(nn.Module):
# '''
# single layer MLP
# '''
# def __init__(self, feature_list,num_sample,nbr_atten,meta_atten, cuda, feature_dim, embed_dim, meta_num, homo_dim, hidden,adj_lists_list):
# super(Hete_MLP1_atten, self).__init__()
# self.meta_atten = meta_atten
# self.cuda = cuda
# self.feature_dim = feature_dim
# self.embed_dim = embed_dim
# self.meta_num = meta_num
# self.adj_lists_list=adj_lists_list
# self.encoder = nn.ModuleList()
# self.homo_dim=homo_dim
# for i in range(meta_num):
# self.encoder.append(HomoAttention(features=feature_list[i], num_sample=num_sample, cuda=cuda,
# nbr_atten=nbr_atten, embed_dim=homo_dim, feature_dim=feature_dim))
# self.homo2atten=nn.Linear(homo_dim,1,bias=True)
# if cuda:
# self.homo2atten=self.homo2atten.cuda()
#
# def forward(self, nodes):
# if self.cuda:
# final_embedding = torch.zeros(len(nodes), self.homo_dim).cuda()
# else:
# final_embedding = torch.zeros(len(nodes),self.homo_dim)
#
# if self.meta_atten:
# attention = torch.zeros(len(nodes), self.meta_num)
# if self.cuda:
# attention = attention.cuda()
# homo_embedding_tensor=torch.cuda.FloatTensor(self.meta_num,len(nodes),self.homo_dim)
# else:
# homo_embedding_tensor = torch.FloatTensor(self.meta_num, len(nodes), self.homo_dim)
# for i in range(self.meta_num):
# homo_embedding = self.encoder[i].forward(nodes,[list(self.adj_lists_list[i][int(node)]) for node in nodes])
# homo_embedding=F.normalize(homo_embedding)
# homo_embedding_tensor[i]=homo_embedding
# attention[:, i] =F.tanh(self.homo2atten(homo_embedding)).squeeze(dim=1)
# attention = F.softmax(attention, dim=1)
# print(torch.mean(attention,dim=0,keepdim=True))
# for i in range(self.meta_num):
# homo_embedding=homo_embedding_tensor[i]
# final_embedding += attention[:, i].view(-1, 1).repeat(1, homo_embedding.shape[1]) * homo_embedding
# else:
# pass
# return final_embedding
#
# class Hete_MLP12_atten(nn.Module):
# def __init__(self, feature_list,num_sample,nbr_atten,meta_atten, cuda, feature_dim, embed_dim, meta_num, homo_dim, hidden,adj_lists_list):
# super(Hete_MLP12_atten, self).__init__()
# self.meta_atten = meta_atten
# self.cuda = cuda
# self.embed_dim = embed_dim
# self.meta_num = meta_num
# self.adj_lists_list=adj_lists_list
# self.encoder = nn.ModuleList()
# self.W_list = nn.ParameterList()
# self.homo_dim=homo_dim
# for i in range(meta_num):
# self.encoder.append(HomoAttention(features=feature_list[i], num_sample=num_sample, cuda=cuda,
# nbr_atten=nbr_atten, embed_dim=homo_dim, feature_dim=feature_dim))
# self.homo2embed=nn.Linear(homo_dim,embed_dim,bias=True)
# self.embed2hidden=nn.Linear(embed_dim,hidden,bias=True)
# self.hidden2atten=nn.Linear(hidden,1,bias=False)
# if cuda:
# self.homo2embed = self.homo2embed.cuda()
# self.embed2hidden = self.embed2hidden.cuda()
# self.hidden2atten = self.hidden2atten.cuda()
#
#
# def forward(self, nodes):
# if self.cuda:
# final_embedding = torch.zeros(len(nodes), self.embed_dim).cuda()
# else:
# final_embedding = torch.zeros(len(nodes),self.embed_dim)
#
# if self.meta_atten:
# attention = torch.FloatTensor(len(nodes), self.meta_num)
# if self.cuda:
# attention = attention.cuda()
# homo_embedding_tensor=torch.cuda.FloatTensor(self.meta_num,len(nodes),self.embed_dim)
# for i in range(self.meta_num):
# homo_embedding = self.encoder[i].forward(nodes,[list(self.adj_lists_list[i][int(node)]) for node in nodes])
# homo_embedding = F.normalize(homo_embedding,p=2,dim=1)
# homo_embedding = self.homo2embed(homo_embedding)
# homo_embedding_tensor[i]=homo_embedding
# attention[:, i] = self.hidden2atten(F.tanh(self.embed2hidden(homo_embedding))).squeeze(dim=1)
# attention = F.softmax(attention, dim=1)
# print(torch.mean(attention,dim=0,keepdim=True))
# for i in range(self.meta_num):
# homo_embedding=homo_embedding_tensor[i]
# final_embedding += attention[:, i].view(-1, 1).repeat(1, homo_embedding.shape[1]) * homo_embedding
# else:
# pass
# return final_embedding
#
# class Hete_concate(nn.Module):
# def __init__(self, feature_list,num_sample,nbr_atten,meta_atten, cuda, feature_dim, embed_dim, meta_num, homo_dim, hidden,adj_lists_list):
# super(Hete_concate, self).__init__()
# self.meta_atten = meta_atten
# self.cuda = cuda
# self.feature_dim = feature_dim
# self.embed_dim = embed_dim
# self.meta_num = meta_num
# self.adj_lists_list=adj_lists_list
# self.encoder = nn.ModuleList()
# self.homo_dim=homo_dim
# for i in range(meta_num):
# self.encoder.append(HomoAttention(features=feature_list[i], num_sample=num_sample, cuda=cuda,
# nbr_atten=nbr_atten, embed_dim=homo_dim, feature_dim=feature_dim))
#
# self.concate2hidden=nn.Linear(meta_num*homo_dim,hidden,bias=True)
# self.hidden2embed = nn.Linear(hidden,embed_dim,bias=True)
# if cuda:
# self.concate2hidden=self.concate2hidden.cuda()
# self.hidden2embed=self.hidden2embed.cuda()
#
#
# def forward(self, nodes):
# if self.cuda:
# final_embedding = torch.zeros(len(nodes), self.homo_dim).cuda()
# else:
# final_embedding = torch.zeros(len(nodes),self.homo_dim)
#
# if self.meta_atten:
# cat_embedding = torch.zeros(len(nodes),self.homo_dim)
# for i in range(self.meta_num):
# homo_embedding = self.encoder[i].forward(nodes,[list(self.adj_lists_list[i][int(node)]) for node in nodes])
# homo_embedding=F.normalize(homo_embedding)
# if i==0:
# cat_embedding =homo_embedding
# else:
# cat_embedding=torch.cat((cat_embedding,homo_embedding),dim=1)
# final_embedding = self.concate2hidden(cat_embedding)
# #final_embedding = F.tanh(final_embedding)
# final_embedding = self.hidden2embed(final_embedding)
# #final_embedding = F.tanh(final_embedding)
#
# else:
# pass
# return final_embedding
#
# class Hete_MLP3_atten(nn.Module):
# '''
# MLP to transform and then learn embedding
# '''
# def __init__(self, feature_list,num_sample,nbr_atten,meta_atten, cuda, feature_dim, embed_dim, meta_num, homo_dim, hidden,adj_lists_list):
# super(Hete_MLP3_atten, self).__init__()
# self.meta_atten = meta_atten
# self.active = F.tanh
# self.cuda = cuda
# self.feature_dim = feature_dim
# self.embed_dim = embed_dim
# self.meta_num = meta_num
# self.adj_lists_list=adj_lists_list
# self.encoder = nn.ModuleList()
# self.W_list = nn.ModuleList()
# self.homo_dim=homo_dim
# for i in range(meta_num):
# self.encoder.append(HomoAttention(features=feature_list[i], num_sample=num_sample, cuda=cuda,
# nbr_atten=nbr_atten, embed_dim=homo_dim, feature_dim=feature_dim))
# self.W_list.append(nn.Linear(self.homo_dim,1,bias=True))
#
# if cuda:
# self.W_list=self.W_list.cuda()
#
# def forward(self, nodes):
# if self.cuda:
# final_embedding = torch.zeros(len(nodes), self.homo_dim).cuda()
# else:
# final_embedding = torch.zeros(len(nodes),self.homo_dim)
#
# if self.meta_atten:
# attention = torch.zeros(len(nodes), self.meta_num)
# if self.cuda:
# attention = attention.cuda()
# homo_embedding_tensor=torch.cuda.FloatTensor(self.meta_num,len(nodes),self.homo_dim)
# for i in range(self.meta_num):
# homo_embedding = self.encoder[i].forward(nodes,[list(self.adj_lists_list[i][int(node)]) for node in nodes])
# homo_embedding = F.normalize(homo_embedding,p=2,dim=1)
# homo_embedding_tensor[i]=homo_embedding
# attention[:, i] = F.tanh(self.W_list[i](homo_embedding)).squeeze(dim=1)
# attention = F.softmax(attention, dim=1)
# print(torch.mean(attention,dim=0,keepdim=True))
# for i in range(self.meta_num):
# homo_embedding=homo_embedding_tensor[i]
# final_embedding += attention[:, i].view(-1, 1).repeat(1, homo_embedding.shape[1]) * homo_embedding
# else:
# pass
# return final_embedding