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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class NCF(nn.Module):
def __init__(self, user_num, item_num, factor_num, num_layers,
dropout, model, GMF_model=None, MLP_model=None):
super(NCF, self).__init__()
"""
user_num: number of users;
item_num: number of items;
factor_num: number of predictive factors;
num_layers: the number of layers in MLP model;
dropout: dropout rate between fully connected layers;
model: 'MLP', 'GMF', 'NeuMF-end', and 'NeuMF-pre';
GMF_model: pre-trained GMF weights;
MLP_model: pre-trained MLP weights.
"""
self.dropout = dropout
self.model = model
self.GMF_model = GMF_model
self.MLP_model = MLP_model
self.embed_user_GMF = nn.Embedding(user_num, factor_num)
self.embed_item_GMF = nn.Embedding(item_num, factor_num)
self.embed_user_MLP = nn.Embedding(
user_num, factor_num * (2 ** (num_layers - 1)))
self.embed_item_MLP = nn.Embedding(
item_num, factor_num * (2 ** (num_layers - 1)))
MLP_modules = []
for i in range(num_layers):
input_size = factor_num * (2 ** (num_layers - i))
MLP_modules.append(nn.Dropout(p=self.dropout))
MLP_modules.append(nn.Linear(input_size, input_size//2))
MLP_modules.append(nn.ReLU())
self.MLP_layers = nn.Sequential(*MLP_modules)
if self.model in ['MLP', 'GMF']:
predict_size = factor_num
else:
predict_size = factor_num * 2
self.predict_layer = nn.Linear(predict_size, 1)
self._init_weight_()
def _init_weight_(self):
""" We leave the weights initialization here. """
if not self.model == 'NeuMF-pre':
nn.init.normal_(self.embed_user_GMF.weight, std=0.01)
nn.init.normal_(self.embed_user_MLP.weight, std=0.01)
nn.init.normal_(self.embed_item_GMF.weight, std=0.01)
nn.init.normal_(self.embed_item_MLP.weight, std=0.01)
for m in self.MLP_layers:
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
nn.init.kaiming_uniform_(self.predict_layer.weight,
a=1, nonlinearity='sigmoid')
for m in self.modules():
if isinstance(m, nn.Linear) and m.bias is not None:
m.bias.data.zero_()
else:
# embedding layers
self.embed_user_GMF.weight.data.copy_(
self.GMF_model.embed_user_GMF.weight)
self.embed_item_GMF.weight.data.copy_(
self.GMF_model.embed_item_GMF.weight)
self.embed_user_MLP.weight.data.copy_(
self.MLP_model.embed_user_MLP.weight)
self.embed_item_MLP.weight.data.copy_(
self.MLP_model.embed_item_MLP.weight)
# mlp layers
for (m1, m2) in zip(
self.MLP_layers, self.MLP_model.MLP_layers):
if isinstance(m1, nn.Linear) and isinstance(m2, nn.Linear):
m1.weight.data.copy_(m2.weight)
m1.bias.data.copy_(m2.bias)
# predict layers
predict_weight = torch.cat([
self.GMF_model.predict_layer.weight,
self.MLP_model.predict_layer.weight], dim=1)
precit_bias = self.GMF_model.predict_layer.bias + \
self.MLP_model.predict_layer.bias
self.predict_layer.weight.data.copy_(0.5 * predict_weight)
self.predict_layer.bias.data.copy_(0.5 * precit_bias)
def forward(self, user, item):
if not self.model == 'MLP':
embed_user_GMF = self.embed_user_GMF(user)
embed_item_GMF = self.embed_item_GMF(item)
output_GMF = embed_user_GMF * embed_item_GMF
if not self.model == 'GMF':
embed_user_MLP = self.embed_user_MLP(user)
embed_item_MLP = self.embed_item_MLP(item)
interaction = torch.cat((embed_user_MLP, embed_item_MLP), -1)
output_MLP = self.MLP_layers(interaction)
if self.model == 'GMF':
concat = output_GMF
elif self.model == 'MLP':
concat = output_MLP
else:
concat = torch.cat((output_GMF, output_MLP), -1)
prediction = self.predict_layer(concat)
# # 为了适配PLC,在这里加sigmoid
# prediction = torch.sigmoid(prediction)
return prediction.view(-1)
def apply_activation(act_name, x):
"""
Apply activation function
:param act_name: name of the activation function
:param x: input
:return: output after activation
"""
if act_name == 'sigmoid':
return torch.sigmoid(x)
elif act_name == 'tanh':
return torch.tanh(x)
elif act_name == 'relu':
return torch.relu(x)
elif act_name == 'elu':
return F.elu(x)
else:
raise NotImplementedError('Choose appropriate activation function. (current input: %s)' % act_name)
class CDAE(nn.Module):
"""
Collaborative Denoising Autoencoder model class
"""
def __init__(self, num_users, num_items, hidden_dim=32, device="cuda",
corruption_ratio=0.5, act='tanh'):
"""
:param model_conf: model configuration
:param num_users: number of users
:param num_items: number of items
:param device: choice of device
"""
super(CDAE, self).__init__()
self.hidden_dim = hidden_dim
self.corruption_ratio = corruption_ratio
self.num_users = num_users
self.num_items = num_items
self.device = device
self.act = act
self.user_embedding = nn.Embedding(self.num_users, self.hidden_dim)
self.encoder = nn.Linear(self.num_items, self.hidden_dim)
self.decoder = nn.Linear(self.hidden_dim, self.num_items)
def forward(self, user_id, rating_matrix):
"""
Forward pass
:param rating_matrix: rating matrix
"""
# normalize the rating matrix
user_degree = torch.norm(rating_matrix, 2, 1).reshape(-1, 1) # user, 1
item_degree = torch.norm(rating_matrix, 2, 0).reshape(1, -1) # 1, item
normalize = torch.sqrt(user_degree @ item_degree)
zero_mask = normalize == 0
normalize = torch.masked_fill(normalize, zero_mask.bool(), 1e-10)
normalized_rating_matrix = rating_matrix / normalize
# corrupt the rating matrix
normalized_rating_matrix = F.dropout(normalized_rating_matrix, self.corruption_ratio, training=self.training)
# build the collaborative denoising autoencoder
enc = self.encoder(normalized_rating_matrix) + self.user_embedding(user_id)
enc = apply_activation(self.act, enc)
dec = self.decoder(enc)
return torch.sigmoid(dec)
class BasicModel(nn.Module):
def __init__(self):
super(BasicModel, self).__init__()
def getUsersRating(self, users):
raise NotImplementedError
class PairWiseModel(BasicModel):
def __init__(self):
super(PairWiseModel, self).__init__()
def bpr_loss(self, users, pos, neg):
"""
Parameters:
users: users list
pos: positive items for corresponding users
neg: negative items for corresponding users
Return:
(log-loss, l2-loss)
"""
raise NotImplementedError
class LightGCN(BasicModel):
def __init__(self, user_num, item_num,
norm_adj,
latent_dim=64,
n_layers=3,
keep_prob=0.6,
A_split=False,
dropout=0,
pretrain=0,
device='cuda'
):
super(LightGCN, self).__init__()
self.latent_dim = latent_dim
self.n_layers = n_layers
self.keep_prob = keep_prob
self.A_split = A_split
self.num_users = user_num
self.num_items = item_num
self.dropout = dropout
self.pretrain = pretrain
self.Graph = self._convert_sp_mat_to_sp_tensor(norm_adj)
self.Graph = self.Graph.coalesce().to(device)
self._init_weight_()
def _init_weight_(self):
self.embedding_user = torch.nn.Embedding(
num_embeddings=self.num_users, embedding_dim=self.latent_dim)
self.embedding_item = torch.nn.Embedding(
num_embeddings=self.num_items, embedding_dim=self.latent_dim)
if self.pretrain == 0:
# nn.init.xavier_uniform_(self.embedding_user.weight, gain=1)
# nn.init.xavier_uniform_(self.embedding_item.weight, gain=1)
# print('use xavier initilizer')
# random normal init seems to be a better choice when lightGCN actually don't use any non-linear activation function
nn.init.normal_(self.embedding_user.weight, std=0.1)
nn.init.normal_(self.embedding_item.weight, std=0.1)
# world.cprint('use NORMAL distribution initilizer')
else:
self.embedding_user.weight.data.copy_(torch.from_numpy(self.config['user_emb']))
self.embedding_item.weight.data.copy_(torch.from_numpy(self.config['item_emb']))
print('use pretarined data')
self.f = nn.Sigmoid()
print(f"lgn is ready to go(dropout:{self.dropout})")
# print("save_txt")
def _convert_sp_mat_to_sp_tensor(self, X):
coo = X.tocoo().astype(np.float32)
row = torch.Tensor(coo.row).long()
col = torch.Tensor(coo.col).long()
index = torch.stack([row, col])
data = torch.FloatTensor(coo.data)
return torch.sparse.FloatTensor(index, data, torch.Size(coo.shape))
def __dropout_x(self, x, keep_prob):
size = x.size()
index = x.indices().t()
values = x.values()
random_index = torch.rand(len(values)) + keep_prob
random_index = random_index.int().bool()
index = index[random_index]
values = values[random_index] / keep_prob
g = torch.sparse.FloatTensor(index.t(), values, size)
return g
def __dropout(self, keep_prob):
if self.A_split:
graph = []
for g in self.Graph:
graph.append(self.__dropout_x(g, keep_prob))
else:
graph = self.__dropout_x(self.Graph, keep_prob)
return graph
def computer(self):
"""
propagate methods for lightGCN
"""
users_emb = self.embedding_user.weight
items_emb = self.embedding_item.weight
all_emb = torch.cat([users_emb, items_emb])
# torch.split(all_emb , [self.num_users, self.num_items])
embs = [all_emb]
if self.dropout:
if self.training:
g_droped = self.__dropout(self.keep_prob)
else:
g_droped = self.Graph
else:
g_droped = self.Graph
for layer in range(self.n_layers):
if self.A_split:
temp_emb = []
for f in range(len(g_droped)):
temp_emb.append(torch.sparse.mm(g_droped[f], all_emb))
side_emb = torch.cat(temp_emb, dim=0)
all_emb = side_emb
else:
all_emb = torch.sparse.mm(g_droped, all_emb)
embs.append(all_emb)
embs = torch.stack(embs, dim=1)
# print(embs.size())
light_out = torch.mean(embs, dim=1)
users, items = torch.split(light_out, [self.num_users, self.num_items])
return users, items
def getUsersRating(self, users):
all_users, all_items = self.computer()
users_emb = all_users[users.long()]
items_emb = all_items
rating = self.f(torch.matmul(users_emb, items_emb.t()))
return rating
def getEmbedding(self, users, pos_items, neg_items):
all_users, all_items = self.computer()
users_emb = all_users[users]
pos_emb = all_items[pos_items]
neg_emb = all_items[neg_items]
users_emb_ego = self.embedding_user(users)
pos_emb_ego = self.embedding_item(pos_items)
neg_emb_ego = self.embedding_item(neg_items)
return users_emb, pos_emb, neg_emb, users_emb_ego, pos_emb_ego, neg_emb_ego
def bpr_loss(self, users, pos, neg):
(users_emb, pos_emb, neg_emb,
userEmb0, posEmb0, negEmb0) = self.getEmbedding(users.long(), pos.long(), neg.long())
reg_loss = (1 / 2) * (userEmb0.norm(2).pow(2) +
posEmb0.norm(2).pow(2) +
negEmb0.norm(2).pow(2)) / float(len(users))
pos_scores = torch.mul(users_emb, pos_emb)
pos_scores = torch.sum(pos_scores, dim=1)
neg_scores = torch.mul(users_emb, neg_emb)
neg_scores = torch.sum(neg_scores, dim=1)
loss = torch.mean(torch.nn.functional.softplus(neg_scores - pos_scores))
return loss, reg_loss
def reg_loss(self, users, pos, neg):
(users_emb, pos_emb, neg_emb,
userEmb0, posEmb0, negEmb0) = self.getEmbedding(users.long(), pos.long(), neg.long())
reg_loss = (1 / 2) * (userEmb0.norm(2).pow(2) +
posEmb0.norm(2).pow(2) +
negEmb0.norm(2).pow(2)) / float(len(users))
return reg_loss
def forward(self, users, items):
# compute embedding
all_users, all_items = self.computer()
# print('forward')
# all_users, all_items = self.computer()
users_emb = all_users[users]
items_emb = all_items[items]
inner_pro = torch.mul(users_emb, items_emb)
gamma = torch.sum(inner_pro, dim=1)
# return torch.sigmoid(gamma)
return gamma
class NGCF(BasicModel):
def __init__(self, user_num, item_num,
norm_adj,
latent_dim=64,
n_layers=3,
keep_prob=0.6,
A_split=False,
dropout=0,
pretrain=0,
device='cuda'
):
super(NGCF, self).__init__()
self.latent_dim = latent_dim
self.n_layers = n_layers
self.keep_prob = keep_prob
self.A_split = A_split
self.num_users = user_num
self.num_items = item_num
self.dropout = dropout
self.pretrain = pretrain
self.dropout = torch.nn.Dropout(0.2)
self.Graph = self._convert_sp_mat_to_sp_tensor(norm_adj)
self.Graph = self.Graph.coalesce().to(device)
self._init_weight_()
def _init_weight_(self):
self.embedding_user = torch.nn.Embedding(
num_embeddings=self.num_users, embedding_dim=self.latent_dim)
self.embedding_item = torch.nn.Embedding(
num_embeddings=self.num_items, embedding_dim=self.latent_dim)
if self.pretrain == 0:
# nn.init.xavier_uniform_(self.embedding_user.weight, gain=1)
# nn.init.xavier_uniform_(self.embedding_item.weight, gain=1)
# print('use xavier initilizer')
# random normal init seems to be a better choice when lightGCN actually don't use any non-linear activation function
nn.init.normal_(self.embedding_user.weight, std=0.1)
nn.init.normal_(self.embedding_item.weight, std=0.1)
# world.cprint('use NORMAL distribution initilizer')
else:
self.embedding_user.weight.data.copy_(torch.from_numpy(self.config['user_emb']))
self.embedding_item.weight.data.copy_(torch.from_numpy(self.config['item_emb']))
print('use pretarined data')
self.f = nn.Sigmoid()
print(f"lgn is ready to go(dropout:{self.dropout})")
# print("save_txt")
def _convert_sp_mat_to_sp_tensor(self, X):
coo = X.tocoo().astype(np.float32)
row = torch.Tensor(coo.row).long()
col = torch.Tensor(coo.col).long()
index = torch.stack([row, col])
data = torch.FloatTensor(coo.data)
return torch.sparse.FloatTensor(index, data, torch.Size(coo.shape))
def __dropout_x(self, x, keep_prob):
size = x.size()
index = x.indices().t()
values = x.values()
random_index = torch.rand(len(values)) + keep_prob
random_index = random_index.int().bool()
index = index[random_index]
values = values[random_index] / keep_prob
g = torch.sparse.FloatTensor(index.t(), values, size)
return g
def __dropout(self, keep_prob):
if self.A_split:
graph = []
for g in self.Graph:
graph.append(self.__dropout_x(g, keep_prob))
else:
graph = self.__dropout_x(self.Graph, keep_prob)
return graph
def computer(self):
"""
propagate methods for lightGCN
"""
users_emb = self.embedding_user.weight
items_emb = self.embedding_item.weight
all_emb = torch.cat([users_emb, items_emb])
# torch.split(all_emb , [self.num_users, self.num_items])
embs = [all_emb]
if self.dropout:
if self.training:
g_droped = self.__dropout(self.keep_prob)
else:
g_droped = self.Graph
else:
g_droped = self.Graph
for layer in range(self.n_layers):
if self.A_split:
temp_emb = []
for f in range(len(g_droped)):
# 应用 leaky relu 激活函数
activated = F.leaky_relu(torch.sparse.mm(g_droped[f], all_emb), negative_slope=0.2)
# 应用 dropout
dropped_out = self.dropout(activated)
# 归一化
normalized = F.normalize(dropped_out, p=2, dim=1)
temp_emb.append(normalized)
side_emb = torch.cat(temp_emb, dim=0)
all_emb = side_emb
else:
all_emb = torch.sparse.mm(g_droped, all_emb)
embs.append(all_emb)
embs = torch.stack(embs, dim=1)
# print(embs.size())
light_out = torch.mean(embs, dim=1)
users, items = torch.split(light_out, [self.num_users, self.num_items])
return users, items
def getUsersRating(self, users):
all_users, all_items = self.computer()
users_emb = all_users[users.long()]
items_emb = all_items
rating = self.f(torch.matmul(users_emb, items_emb.t()))
return rating
def getEmbedding(self, users, pos_items, neg_items):
all_users, all_items = self.computer()
users_emb = all_users[users]
pos_emb = all_items[pos_items]
neg_emb = all_items[neg_items]
users_emb_ego = self.embedding_user(users)
pos_emb_ego = self.embedding_item(pos_items)
neg_emb_ego = self.embedding_item(neg_items)
return users_emb, pos_emb, neg_emb, users_emb_ego, pos_emb_ego, neg_emb_ego
def bpr_loss(self, users, pos, neg):
(users_emb, pos_emb, neg_emb,
userEmb0, posEmb0, negEmb0) = self.getEmbedding(users.long(), pos.long(), neg.long())
reg_loss = (1 / 2) * (userEmb0.norm(2).pow(2) +
posEmb0.norm(2).pow(2) +
negEmb0.norm(2).pow(2)) / float(len(users))
pos_scores = torch.mul(users_emb, pos_emb)
pos_scores = torch.sum(pos_scores, dim=1)
neg_scores = torch.mul(users_emb, neg_emb)
neg_scores = torch.sum(neg_scores, dim=1)
loss = torch.mean(torch.nn.functional.softplus(neg_scores - pos_scores))
return loss, reg_loss
def reg_loss(self, users, pos, neg):
(users_emb, pos_emb, neg_emb,
userEmb0, posEmb0, negEmb0) = self.getEmbedding(users.long(), pos.long(), neg.long())
reg_loss = (1 / 2) * (userEmb0.norm(2).pow(2) +
posEmb0.norm(2).pow(2) +
negEmb0.norm(2).pow(2)) / float(len(users))
return reg_loss
def forward(self, users, items):
# compute embedding
all_users, all_items = self.computer()
# print('forward')
# all_users, all_items = self.computer()
users_emb = all_users[users]
items_emb = all_items[items]
inner_pro = torch.mul(users_emb, items_emb)
gamma = torch.sum(inner_pro, dim=1)
# return torch.sigmoid(gamma)
return gamma