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ApeGNN_APPNP.py
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ApeGNN_APPNP.py
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import torch
import torch.nn as nn
from scipy.special import factorial, comb
import scipy.sparse as sp
class GraphConv(nn.Module):
"""
Graph Convolutional Network
"""
def __init__(self, n_hops, n_users, n_items, interact_mat, deg,
e=1e-7, edge_dropout_rate=0.5, mess_dropout_rate=0.1,
t_u=2, t_i=2, device=6):
super(GraphConv, self).__init__()
self.interact_mat = interact_mat
self.n_users = n_users
self.n_items = n_items
self.n_hops = n_hops
self.edge_dropout_rate = edge_dropout_rate
self.mess_dropout_rate = mess_dropout_rate
self.dropout = nn.Dropout(p=mess_dropout_rate) # mess dropout
self.dev = device
# set (n_users+n_items) t
initializer = nn.init.xavier_uniform_
self.user_t = initializer(torch.empty(self.n_users, 1))
self.item_t = initializer(torch.empty(self.n_items, 1))
self.user_t = nn.Parameter(self.user_t, requires_grad=True)
self.item_t = nn.Parameter(self.item_t, requires_grad=True)
self.degree = torch.sigmoid(torch.log(torch.from_numpy(deg) + e))
t_u, t_i = torch.split(self.degree, [self.n_users, self.n_items], dim=0)
self.user_t.data = t_u
self.item_t.data = t_i
def _sparse_dropout(self, x, rate=0.5):
noise_shape = x._nnz()
random_tensor = rate
random_tensor += torch.rand(noise_shape).to(x.device)
dropout_mask = torch.floor(random_tensor).type(torch.bool)
i = x._indices()
v = x._values()
i = i[:, dropout_mask]
v = v[dropout_mask]
out = torch.sparse.FloatTensor(i, v, x.shape).to(x.device)
return out * (1. / (1 - rate))
def forward(self, user_embed, item_embed, mess_dropout=True, edge_dropout=True):
all_embed = torch.cat([user_embed, item_embed], dim=0)
agg_embed = all_embed
u_weight= self.user_t.to(self.dev) * torch.pow(1 - self.user_t, 0).to(self.dev)
i_weight = self.item_t.to(self.dev) * torch.pow(1 - self.item_t, 0).to(self.dev)
ego_embeddings = torch.cat([u_weight * user_embed, i_weight * item_embed], dim=0)
embs = [ego_embeddings]
for k in range(1, self.n_hops + 1):
interact_mat = self._sparse_dropout(self.interact_mat,
self.edge_dropout_rate) if edge_dropout \
else self.interact_mat
side_embeddings = torch.sparse.mm(interact_mat, agg_embed)
user_embedds, item_embedds = torch.split(side_embeddings, [self.n_users, self.n_items],
dim=0)
user_embedds = user_embedds * self.user_t.to(self.dev) * torch.pow(1-self.user_t, k).to(self.dev)
item_embedds = item_embedds * self.item_t.to(self.dev) * torch.pow(1-self.item_t, k).to(self.dev)
side_embeddings_cur = torch.cat([user_embedds, item_embedds], dim=0)
agg_embed = side_embeddings
if mess_dropout:
agg_embed = self.dropout(agg_embed)
embs.append(side_embeddings_cur)
embs = torch.stack(embs, dim=1) # [n_entity, n_hops+1, emb_size]
return embs[:self.n_users, :], embs[self.n_users:, :]
class APPNP(nn.Module):
def __init__(self, data_config, args_config, adj_mat, deg):
super(APPNP, self).__init__()
self.n_users = data_config['n_users']
self.n_items = data_config['n_items']
self.adj_mat = adj_mat
self.deg = deg
self.e = args_config.e
self.decay = args_config.l2
self.emb_size = args_config.dim
self.context_hops = args_config.context_hops
self.mess_dropout = args_config.mess_dropout
self.mess_dropout_rate = args_config.mess_dropout_rate
self.edge_dropout = args_config.edge_dropout
self.edge_dropout_rate = args_config.edge_dropout_rate
self.pool = args_config.pool
self.n_negs = args_config.n_negs
self.ns = args_config.ns
self.K = args_config.K
self.heatkernel = args_config.heatkernel
self.device = torch.device("cuda:0") if args_config.cuda else torch.device("cpu")
self.t_u = args_config.t_u
self.t_i = args_config.t_i
self._init_weight()
self.user_embed = nn.Parameter(self.user_embed)
self.item_embed = nn.Parameter(self.item_embed)
self.gcn = self._init_model()
def _init_weight(self):
initializer = nn.init.xavier_uniform_
self.user_embed = initializer(torch.empty(self.n_users, self.emb_size))
self.item_embed = initializer(torch.empty(self.n_items, self.emb_size))
# [n_users+n_items, n_users+n_items]
self.sparse_norm_adj = self._convert_sp_mat_to_sp_tensor(self.adj_mat).to(self.device)
def _init_model(self):
return GraphConv(n_hops=self.context_hops,
n_users=self.n_users,
n_items=self.n_items,
interact_mat=self.sparse_norm_adj,
deg=self.deg,
e=self.e,
edge_dropout_rate=self.edge_dropout_rate,
mess_dropout_rate=self.mess_dropout_rate,
t_u=self.t_u,
t_i=self.t_i,
device=self.device)
def _convert_sp_mat_to_sp_tensor(self, X):
coo = X.tocoo()
i = torch.LongTensor([coo.row, coo.col])
v = torch.from_numpy(coo.data).float()
return torch.sparse.FloatTensor(i, v, coo.shape)
def forward(self, batch=None):
user = batch['users']
pos_item = batch['pos_items']
neg_item = batch['neg_items'] # [batch_size, n_negs * K]
user_gcn_emb, item_gcn_emb = self.gcn(self.user_embed,
self.item_embed,
edge_dropout=self.edge_dropout,
mess_dropout=self.mess_dropout)
if self.ns == 'rns': # n_negs = 1
neg_gcn_embs = item_gcn_emb[neg_item[:, :self.K]]
else:
neg_gcn_embs = []
for k in range(self.K):
neg_gcn_embs.append(self.negative_sampling(user_gcn_emb, item_gcn_emb,
user, neg_item[:, k * self.n_negs: (k + 1) * self.n_negs],
pos_item))
neg_gcn_embs = torch.stack(neg_gcn_embs, dim=1)
return self.create_bpr_loss(user_gcn_emb[user], item_gcn_emb[pos_item], neg_gcn_embs)
def negative_sampling(self, user_gcn_emb, item_gcn_emb, user, neg_candidates, pos_item):
batch_size = user.shape[0]
s_e, p_e = user_gcn_emb[user], item_gcn_emb[pos_item] # [batch_size, n_hops+1, channel]
if self.pool != 'concat':
s_e = self.pooling(s_e).unsqueeze(dim=1)
"""positive mixing"""
seed = torch.rand(batch_size, 1, p_e.shape[1], 1).to(p_e.device) # (0, 1)
n_e = item_gcn_emb[neg_candidates] # [batch_size, n_negs, n_hops, channel]
n_e_ = seed * p_e.unsqueeze(dim=1) + (1 - seed) * n_e # mixing
"""hop mixing"""
scores = (s_e.unsqueeze(dim=1) * n_e_).sum(dim=-1) # [batch_size, n_negs, n_hops+1]
indices = torch.max(scores, dim=1)[1].detach()
neg_items_emb_ = n_e_.permute([0, 2, 1, 3]) # [batch_size, n_hops+1, n_negs, channel]
# [batch_size, n_hops+1, channel]
return neg_items_emb_[[[i] for i in range(batch_size)],
range(neg_items_emb_.shape[1]), indices, :]
def pooling(self, embeddings):
# [-1, n_hops, channel]
if self.pool == 'mean':
return embeddings.mean(dim=1)
elif self.pool == 'sum':
return embeddings.sum(dim=1)
elif self.pool == 'concat':
return embeddings.view(embeddings.shape[0], -1)
else: # final
return embeddings[:, -1, :]
def generate(self, split=True):
user_gcn_emb, item_gcn_emb = self.gcn(self.user_embed,
self.item_embed,
edge_dropout=False,
mess_dropout=False)
user_gcn_emb, item_gcn_emb = self.pooling(user_gcn_emb), self.pooling(item_gcn_emb)
if split:
return user_gcn_emb, item_gcn_emb
else:
return torch.cat([user_gcn_emb, item_gcn_emb], dim=0)
def rating(self, u_g_embeddings=None, i_g_embeddings=None):
return torch.matmul(u_g_embeddings, i_g_embeddings.t())
def create_bpr_loss(self, user_gcn_emb, pos_gcn_embs, neg_gcn_embs):
# user_gcn_emb: [batch_size, n_hops+1, channel]
# pos_gcn_embs: [batch_size, n_hops+1, channel]
# neg_gcn_embs: [batch_size, K, n_hops+1, channel]
batch_size = user_gcn_emb.shape[0]
u_e = self.pooling(user_gcn_emb)
pos_e = self.pooling(pos_gcn_embs)
neg_e = self.pooling(neg_gcn_embs.view(-1, neg_gcn_embs.shape[2], neg_gcn_embs.shape[3])).view(batch_size,
self.K, -1)
pos_scores = torch.sum(torch.mul(u_e, pos_e), axis=1)
neg_scores = torch.sum(torch.mul(u_e.unsqueeze(dim=1), neg_e), axis=-1) # [batch_size, K]
mf_loss = torch.mean(torch.log(1 + torch.exp(neg_scores - pos_scores.unsqueeze(dim=1)).sum(dim=1)))
# cul regularizer
regularize = (torch.norm(user_gcn_emb[:, 0, :]) ** 2
+ torch.norm(pos_gcn_embs[:, 0, :]) ** 2
+ torch.norm(neg_gcn_embs[:, :, 0, :]) ** 2) / 2 # take hop=0
emb_loss = self.decay * regularize / batch_size
return mf_loss + emb_loss, mf_loss, emb_loss