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gcn.py
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gcn.py
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import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
import pickle
import gzip
import numpy as np
import time
class GraphConvolution(Module):
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
if bias:
self.bias = Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1. / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input, adj):
support = torch.mm(input, self.weight)
output = torch.sparse.mm(adj, support)
if self.bias is not None:
return output + self.bias
else:
return output
class GraphEncoder(Module):
def __init__(self, device, entity, emb_size, kg, embeddings=None, fix_emb=True, seq='rnn', gcn=True, hidden_size=100, layers=1, rnn_layer=1):
super(GraphEncoder, self).__init__()
self.embedding = nn.Embedding(entity, emb_size, padding_idx=entity-1)
if embeddings is not None:
print("pre-trained embeddings")
self.embedding.from_pretrained(embeddings,freeze=fix_emb)
self.layers = layers
self.user_num = len(kg.G['user'])
self.item_num = len(kg.G['item'])
self.PADDING_ID = entity-1
self.device = device
self.seq = seq
self.gcn = gcn
self.fc1 = nn.Linear(hidden_size, hidden_size)
if self.seq == 'rnn':
self.rnn = nn.GRU(hidden_size, hidden_size, rnn_layer, batch_first=True)
elif self.seq == 'transformer':
self.transformer = nn.TransformerEncoder(encoder_layer=nn.TransformerEncoderLayer(d_model=hidden_size, nhead=4, dim_feedforward=400), num_layers=rnn_layer)
if self.gcn:
indim, outdim = emb_size, hidden_size
self.gnns = nn.ModuleList()
for l in range(layers):
self.gnns.append(GraphConvolution(indim, outdim))
indim = outdim
else:
self.fc2 = nn.Linear(emb_size, hidden_size)
def forward(self, b_state):
"""
:param b_state [N]
:return: [N x L x d]
"""
batch_output = []
for s in b_state:
#neighbors, adj = self.get_state_graph(s)
neighbors, adj = s['neighbors'].to(self.device), s['adj'].to(self.device)
input_state = self.embedding(neighbors)
if self.gcn:
for gnn in self.gnns:
output_state = gnn(input_state, adj)
input_state = output_state
batch_output.append(output_state)
else:
output_state = F.relu(self.fc2(input_state))
batch_output.append(output_state)
seq_embeddings = []
for s, o in zip(b_state, batch_output):
seq_embeddings.append(o[:len(s['cur_node']),:][None,:])
if len(batch_output) > 1:
seq_embeddings = self.padding_seq(seq_embeddings)
seq_embeddings = torch.cat(seq_embeddings, dim=0) # [N x L x d]
if self.seq == 'rnn':
_, h = self.rnn(seq_embeddings)
seq_embeddings = h.permute(1,0,2) #[N*1*D]
elif self.seq == 'transformer':
seq_embeddings = torch.mean(self.transformer(seq_embeddings), dim=1, keepdim=True)
elif self.seq == 'mean':
seq_embeddings = torch.mean(seq_embeddings, dim=1, keepdim=True)
seq_embeddings = F.relu(self.fc1(seq_embeddings))
return seq_embeddings
def padding_seq(self, seq):
padding_size = max([len(x[0]) for x in seq])
padded_seq = []
for s in seq:
cur_size = len(s[0])
emb_size = len(s[0][0])
new_s = torch.zeros((padding_size, emb_size)).to(self.device)
new_s[:cur_size,:] = s[0]
padded_seq.append(new_s[None,:])
return padded_seq