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model.py
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model.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from layer import MLP, Propagation
from utils import bpr_loss, reg_loss
from gsl_uu import GSL4uu
from utils import _convert_sp_mat_to_sp_tensor
import scipy.sparse as sp
from encoder import Encoder
class Inac_rec(nn.Module):
def __init__(self, args, num_users, num_items, cluster_map):
super(Inac_rec, self).__init__()
self.cluster_map = cluster_map # [act_id, cluster_index]
self.cluster_index = np.sort(list(set(self.cluster_map[:, 1])))
self.cluster_num = len(self.cluster_index)
self.loss_lam = args.loss_lam
self.args = args
self.GSL4uu = GSL4uu(args.hidden_dim, args.m_head, args.dropout, args.min_keep, args.max_add, \
args.pseudo_num, args.pseudo_lam, args.tau, args.edge_emb_flag)
self.uu_graph = None
self.encoder = Encoder(args, num_users, num_items)
self.num_users = num_users
self.num_items = num_items
self.weight = args.add_weight
def train_encoder(self, batch_user_pos_neg, ui_graph, user_feat, item_feat):
user_final_emb, item_emb, user_emb_ego, item_emb_ego = self.encoder(ui_graph, self.uu_graph, user_feat, item_feat)
batch_user, batch_pos, batch_neg = batch_user_pos_neg[:, 0], batch_user_pos_neg[:, 1], batch_user_pos_neg[:, 2]
rec_loss = bpr_loss(user_final_emb[batch_user], item_emb[batch_pos], item_emb[batch_neg])
reg = reg_loss(user_emb_ego[batch_user], item_emb_ego[batch_pos], item_emb_ego[batch_neg])
return rec_loss + reg*self.args.weight_decay
def get_whole_stru(self, unique_user, final_dele_indices, final_dele_sim, final_add_indices, final_add_sim, user_num):
dele_graph = torch.sparse_coo_tensor(final_dele_indices.t(), final_dele_sim, (user_num, user_num)).cuda()
dele_graph = torch.sparse.softmax(dele_graph, 1)
add_graph = torch.sparse_coo_tensor(final_add_indices.t(), final_add_sim, (user_num, user_num)).cuda()
add_graph = torch.sparse.softmax(add_graph, 1)
self_loop = torch.sparse_coo_tensor(torch.cat([unique_user.unsqueeze(0), unique_user.unsqueeze(0)]), torch.ones_like(unique_user), (self.num_users, self.num_users)).cuda()
batch_graph = 1/2 * self_loop + 1/2 * (self.weight*add_graph + (1-self.weight)*dele_graph)
return batch_graph
def train_graph_generator(self, user_emb_ego, batch_user_pos_neg, batch_act_user, batch_inact_user, uu_dict, user_emb, item_emb, add_prob, dele_prob):
batch_user, batch_pos, batch_neg = batch_user_pos_neg[:, 0], batch_user_pos_neg[:, 1], batch_user_pos_neg[:, 2]
final_dele_indices, final_dele_sim, final_add_indices, final_add_sim = self.get_stru(user_emb_ego, batch_user, user_emb, uu_dict, add_prob, dele_prob)
unique_user = torch.LongTensor(np.sort(list(set(batch_user))))
batch_graph = self.get_whole_stru(unique_user, final_dele_indices, final_dele_sim, final_add_indices, final_add_sim, self.num_users)
uu_emb = torch.sparse.mm(batch_graph, user_emb)
user_final_emb = self.encoder.user_emb_map(torch.cat([user_emb_ego[batch_user], user_emb[batch_user], uu_emb[batch_user]], -1))
rec_loss = bpr_loss(user_final_emb, item_emb[batch_pos], item_emb[batch_neg])
mimic_loss = self.GSL4uu.mimic_learning(batch_act_user, batch_inact_user, user_emb, self.cluster_map, self.cluster_index)
return rec_loss + self.args.loss_lam * mimic_loss
def get_stru(self, user_emb_ego, batch_user, user_emb_ori, uu_dict, add_prob, dele_prob, edge_emb=None):
target = np.sort(list(set(batch_user)))
link_target_nei = []
target_nei = []
for u in target:
u_nei = uu_dict[u]
target_nei += u_nei
t = [u]*len(u_nei)
link_target_nei.append(np.array([t, u_nei]).T)
link_target_nei.append(np.array([u_nei, t]).T)
link_target_nei = np.vstack(link_target_nei)
target_nei = np.sort(list(set(target_nei)))
link_nei_nei = []
target_nei_nei = []
for u in target_nei:
u_nei = uu_dict[u]
target_nei_nei += u_nei
t = [u]*len(u_nei)
link_nei_nei.append(np.array([t, u_nei]).T)
link_nei_nei = np.vstack(link_nei_nei)
target_nei_nei = np.sort(list(set(target_nei_nei)))
cluster = self.cluster_index
link_cluster_nei = []
cluster_nei = []
cluster_final_map = {}
for i in range(len(cluster)):
u_nei = uu_dict[cluster[i]]
cluster_nei += u_nei
t = [cluster[i]]*len(u_nei)
link_cluster_nei.append(np.array([t, u_nei]).T)
cluster_final_map[i] = cluster[i]
link_cluster_nei = np.vstack(link_cluster_nei)
cluster_nei = np.sort(list(set(cluster_nei)))
all_nodes = np.sort(list(set(list(target) + list(target_nei) + list(target_nei_nei) + list(cluster) + list(cluster_nei))))
all_nodes_num = len(all_nodes)
all_nodes_map = {}
for n in range(len(all_nodes)):
all_nodes_map[all_nodes[n]] = n
all_links_ = np.vstack([link_target_nei, link_nei_nei, link_cluster_nei])
all_links = []
for one_link in all_links_:
all_links.append((all_nodes_map[one_link[0]], all_nodes_map[one_link[1]]))
all_links = np.array(list(set(all_links)))
all_links = all_links[np.argsort(all_links[:, 0])].T
batch_subgraph = torch.sparse_coo_tensor(all_links, [1]*all_links.shape[1], (all_nodes_num, all_nodes_num)).cuda()
tn_nodes = np.sort(list(set(list(target) + list(target_nei))))
tn_nodes_ = np.array([all_nodes_map[n] for n in tn_nodes])
tn_nodes_num = len(tn_nodes)
tn_nodes_map = {}
tn_final_map = {}
for n in range(len(tn_nodes_)):
tn_nodes_map[tn_nodes_[n]] = n
tn_final_map[n] = tn_nodes[n]
tn_links = []
for one_link in link_target_nei:
tn_links.append((tn_nodes_map[all_nodes_map[one_link[0]]], tn_nodes_map[all_nodes_map[one_link[1]]]))
tn_links = np.array(list(set(tn_links)))
tn_links = tn_links[np.argsort(tn_links[:, 0])].T
tn_subgraph = torch.sparse_coo_tensor(tn_links, [1]*tn_links.shape[1], (tn_nodes_num, tn_nodes_num)).cuda()
## T=0 ##
prob_dele_edge = dele_prob[all_nodes]
prob_add_edge = add_prob[all_nodes]
dele_final_indices, dele_final_sim, add_final_indices, add_final_sim = self.GSL4uu.new_stru(user_emb_ori, prob_dele_edge, prob_add_edge, all_nodes, cluster, batch_subgraph)
dele_sim_t0 = torch.sparse_coo_tensor(dele_final_indices, dele_final_sim[0], (all_nodes_num, all_nodes_num))
dele_sim_t0 = torch.sparse.softmax(dele_sim_t0, 1)
add_sim_t0 = torch.sparse_coo_tensor(add_final_indices, add_final_sim[0], (all_nodes_num, len(cluster)))
add_sim_t0 = torch.sparse.softmax(add_sim_t0, 1)
ori_feat = user_emb_ori[all_nodes]
cluster_ori_feat = user_emb_ori[cluster]
dele_emb = torch.sparse.mm(dele_sim_t0, ori_feat)
add_emb = torch.sparse.mm(add_sim_t0, cluster_ori_feat)
user_emb_t0 = 1/2 * ori_feat + 1/2 * (self.weight * add_emb + (1-self.weight) * dele_emb)
user_emb_t0 = self.encoder.user_emb_map(torch.cat([user_emb_ego[all_nodes], ori_feat, user_emb_t0], -1))
## T=1 ##
prob_dele_edge = dele_prob[tn_nodes]
prob_add_edge = add_prob[tn_nodes]
# user_emb_ori_ = user_emb_ori[all_nodes]
cluster_ = np.array([all_nodes_map[c] for c in cluster])
dele_final_indices, dele_final_sim, add_final_indices, add_final_sim = self.GSL4uu.new_stru(user_emb_t0, prob_dele_edge, prob_add_edge, tn_nodes_, cluster_, tn_subgraph)
dele_final_indices = dele_final_indices.data.cpu().numpy()
add_final_indices = add_final_indices.data.cpu().numpy()
dele_sele = []
final_dele_indices = []
for i in range(dele_final_indices.shape[1]):
if tn_final_map[dele_final_indices[0, i]] in target:
dele_sele.append(i)
final_dele_indices.append([tn_final_map[dele_final_indices[0, i]], tn_final_map[dele_final_indices[1, i]]])
final_dele_indices = torch.LongTensor(final_dele_indices).cuda()
final_dele_sim = dele_final_sim[0, dele_sele]
add_sele = []
final_add_indices = []
for i in range(add_final_indices.shape[1]):
if tn_final_map[add_final_indices[0, i]] in target:
add_sele.append(i)
final_add_indices.append([tn_final_map[add_final_indices[0, i]], cluster_final_map[add_final_indices[1, i]]])
final_add_indices = torch.LongTensor(final_add_indices).cuda()
final_add_sim = add_final_sim[0, add_sele]
return final_dele_indices, final_dele_sim, final_add_indices, final_add_sim
def get_emb(self, ui_graph, user_feat, item_feat, users, temp_flag=False):
if temp_flag:
user_final_emb, item_emb, user_emb_ego = self.encoder(ui_graph, self.uu_graph, user_feat, item_feat, train=False, temp_flag=temp_flag)
return user_final_emb.detach()[users], item_emb.detach(), user_emb_ego.detach()
else:
user_final_emb, item_emb = self.encoder(ui_graph, self.uu_graph, user_feat, item_feat, train=False, temp_flag=temp_flag)
return user_final_emb.detach()[users], item_emb.detach()