# -*- coding: utf-8 -*-
# @Time : 2020/10/4 16:55
# @Author : Yujie Lu
# @Email : yujielu1998@gmail.com
r"""
GCSAN
################################################
Reference:
Chengfeng Xu et al. "Graph Contextualized Self-Attention Network for Session-based Recommendation." in IJCAI 2019.
"""
import math
import numpy as np
import torch
from torch import nn
from torch.nn import Parameter
from torch.nn import functional as F
from recbole.model.loss import EmbLoss, BPRLoss
from recbole.model.abstract_recommender import SequentialRecommender
from recbole.model.layers import TransformerEncoder
[docs]class GNN(nn.Module):
r"""Graph neural networks are well-suited for session-based recommendation,
because it can automatically extract features of session graphs with considerations of rich node connections.
"""
def __init__(self, embedding_size, step=1):
super(GNN, self).__init__()
self.step = step
self.embedding_size = embedding_size
self.input_size = embedding_size * 2
self.gate_size = embedding_size * 3
self.w_ih = Parameter(torch.Tensor(self.gate_size, self.input_size))
self.w_hh = Parameter(torch.Tensor(self.gate_size, self.embedding_size))
self.b_ih = Parameter(torch.Tensor(self.gate_size))
self.b_hh = Parameter(torch.Tensor(self.gate_size))
self.linear_edge_in = nn.Linear(self.embedding_size, self.embedding_size, bias=True)
self.linear_edge_out = nn.Linear(self.embedding_size, self.embedding_size, bias=True)
# parameters initialization
self._reset_parameters()
def _reset_parameters(self):
stdv = 1.0 / math.sqrt(self.embedding_size)
for weight in self.parameters():
weight.data.uniform_(-stdv, stdv)
[docs] def GNNCell(self, A, hidden):
r"""Obtain latent vectors of nodes via gated graph neural network.
Args:
A (torch.FloatTensor): The connection matrix,shape of [batch_size, max_session_len, 2 * max_session_len]
hidden (torch.FloatTensor): The item node embedding matrix, shape of
[batch_size, max_session_len, embedding_size]
Returns:
torch.FloatTensor: Latent vectors of nodes,shape of [batch_size, max_session_len, embedding_size]
"""
input_in = torch.matmul(A[:, :, :A.size(1)], self.linear_edge_in(hidden))
input_out = torch.matmul(A[:, :, A.size(1): 2 * A.size(1)], self.linear_edge_out(hidden))
# [batch_size, max_session_len, embedding_size * 2]
inputs = torch.cat([input_in, input_out], 2)
# gi.size equals to gh.size, shape of [batch_size, max_session_len, embdding_size * 3]
gi = F.linear(inputs, self.w_ih, self.b_ih)
gh = F.linear(hidden, self.w_hh, self.b_hh)
# (batch_size, max_session_len, embedding_size)
i_r, i_i, i_n = gi.chunk(3, 2)
h_r, h_i, h_n = gh.chunk(3, 2)
resetgate = torch.sigmoid(i_r + h_r)
inputgate = torch.sigmoid(i_i + h_i)
newgate = torch.tanh(i_n + resetgate * h_n)
hy = (1 - inputgate) * hidden + inputgate * newgate
return hy
[docs] def forward(self, A, hidden):
for i in range(self.step):
hidden = self.GNNCell(A, hidden)
return hidden
[docs]class GCSAN(SequentialRecommender):
r"""GCSAN captures rich local dependencies via graph nerual network,
and learns long-range dependencies by applying the self-attention mechanism.
Note:
In the original paper, the attention mechanism in the self-attention layer is a single head,
for the reusability of the project code, we use a unified transformer component.
According to the experimental results, we only applied regularization to embedding.
"""
def __init__(self, config, dataset):
super(GCSAN, self).__init__(config, dataset)
# load parameters info
self.n_layers = config['n_layers']
self.n_heads = config['n_heads']
self.hidden_size = config['hidden_size'] # same as embedding_size
self.inner_size = config['inner_size'] # the dimensionality in feed-forward layer
self.hidden_dropout_prob = config['hidden_dropout_prob']
self.attn_dropout_prob = config['attn_dropout_prob']
self.hidden_act = config['hidden_act']
self.layer_norm_eps = config['layer_norm_eps']
self.step = config['step']
self.device = config['device']
self.weight = config['weight']
self.reg_weight = config['reg_weight']
self.loss_type = config['loss_type']
self.initializer_range = config['initializer_range']
# define layers and loss
self.item_embedding = nn.Embedding(self.n_items, self.hidden_size, padding_idx=0)
self.gnn = GNN(self.hidden_size, self.step)
self.self_attention = TransformerEncoder(n_layers=self.n_layers, n_heads=self.n_heads,
hidden_size=self.hidden_size, inner_size=self.inner_size,
hidden_dropout_prob=self.hidden_dropout_prob,
attn_dropout_prob=self.attn_dropout_prob,
hidden_act=self.hidden_act, layer_norm_eps=self.layer_norm_eps)
self.reg_loss = EmbLoss()
if self.loss_type == 'BPR':
self.loss_fct = BPRLoss()
elif self.loss_type == 'CE':
self.loss_fct = nn.CrossEntropyLoss()
else:
raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!")
# parameters initialization
self.apply(self._init_weights)
def _init_weights(self, module):
""" Initialize the weights """
if isinstance(module, (nn.Linear, nn.Embedding)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.initializer_range)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
[docs] def get_attention_mask(self, item_seq):
"""Generate left-to-right uni-directional attention mask for multi-head attention."""
attention_mask = (item_seq > 0).long()
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) # torch.int64
# mask for left-to-right unidirectional
max_len = attention_mask.size(-1)
attn_shape = (1, max_len, max_len)
subsequent_mask = torch.triu(torch.ones(attn_shape), diagonal=1) # torch.uint8
subsequent_mask = (subsequent_mask == 0).unsqueeze(1)
subsequent_mask = subsequent_mask.long().to(item_seq.device)
extended_attention_mask = extended_attention_mask * subsequent_mask
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
return extended_attention_mask
def _get_slice(self, item_seq):
items, n_node, A, alias_inputs = [], [], [], []
max_n_node = item_seq.size(1)
item_seq = item_seq.cpu().numpy()
for u_input in item_seq:
node = np.unique(u_input)
items.append(node.tolist() + (max_n_node - len(node)) * [0])
u_A = np.zeros((max_n_node, max_n_node))
for i in np.arange(len(u_input) - 1):
if u_input[i + 1] == 0:
break
u = np.where(node == u_input[i])[0][0]
v = np.where(node == u_input[i + 1])[0][0]
u_A[u][v] = 1
u_sum_in = np.sum(u_A, 0)
u_sum_in[np.where(u_sum_in == 0)] = 1
u_A_in = np.divide(u_A, u_sum_in)
u_sum_out = np.sum(u_A, 1)
u_sum_out[np.where(u_sum_out == 0)] = 1
u_A_out = np.divide(u_A.transpose(), u_sum_out)
u_A = np.concatenate([u_A_in, u_A_out]).transpose()
A.append(u_A)
alias_inputs.append([np.where(node == i)[0][0] for i in u_input])
# The relative coordinates of the item node, shape of [batch_size, max_session_len]
alias_inputs = torch.LongTensor(alias_inputs).to(self.device)
# The connecting matrix, shape of [batch_size, max_session_len, 2 * max_session_len]
A = torch.FloatTensor(A).to(self.device)
# The unique item nodes, shape of [batch_size, max_session_len]
items = torch.LongTensor(items).to(self.device)
return alias_inputs, A, items
[docs] def forward(self, item_seq, item_seq_len):
assert 0 <= self.weight <= 1
alias_inputs, A, items = self._get_slice(item_seq)
hidden = self.item_embedding(items)
hidden = self.gnn(A, hidden)
alias_inputs = alias_inputs.view(-1, alias_inputs.size(1), 1).expand(-1, -1, self.hidden_size)
seq_hidden = torch.gather(hidden, dim=1, index=alias_inputs)
# fetch the last hidden state of last timestamp
ht = self.gather_indexes(seq_hidden, item_seq_len - 1)
a = seq_hidden
attention_mask = self.get_attention_mask(item_seq)
outputs = self.self_attention(a, attention_mask, output_all_encoded_layers=True)
output = outputs[-1]
at = self.gather_indexes(output, item_seq_len - 1)
seq_output = self.weight * at + (1 - self.weight) * ht
return seq_output
[docs] def calculate_loss(self, interaction):
item_seq = interaction[self.ITEM_SEQ]
item_seq_len = interaction[self.ITEM_SEQ_LEN]
seq_output = self.forward(item_seq, item_seq_len)
pos_items = interaction[self.POS_ITEM_ID]
if self.loss_type == 'BPR':
neg_items = interaction[self.NEG_ITEM_ID]
pos_items_emb = self.item_embedding(pos_items)
neg_items_emb = self.item_embedding(neg_items)
pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B]
neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B]
loss = self.loss_fct(pos_score, neg_score)
else: # self.loss_type = 'CE'
test_item_emb = self.item_embedding.weight
logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1))
loss = self.loss_fct(logits, pos_items)
reg_loss = self.reg_loss(self.item_embedding.weight)
total_loss = loss + self.reg_weight * reg_loss
return total_loss
[docs] def predict(self, interaction):
item_seq = interaction[self.ITEM_SEQ]
item_seq_len = interaction[self.ITEM_SEQ_LEN]
test_item = interaction[self.ITEM_ID]
seq_output = self.forward(item_seq, item_seq_len)
test_item_emb = self.item_embedding(test_item)
scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B]
return scores
[docs] def full_sort_predict(self, interaction):
item_seq = interaction[self.ITEM_SEQ]
item_seq_len = interaction[self.ITEM_SEQ_LEN]
seq_output = self.forward(item_seq, item_seq_len)
test_items_emb = self.item_embedding.weight
scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items]
return scores