目录
Inductive Graph-based Matrix Completion (IGMC) 模型
Inductive Graph-based Matrix Completion (IGMC) 模型
原版代码：
IGMC/models.py at master · muhanzhang/IGMC · GitHub
GNN推理示例
torch_geometric版本：torch_geometric-2.5.3
原版报错，edge_type找不到，通过删除参数修正的：
import torch
import torch.nn.functional as F
from torch.nn import Linear
from torch_geometric.nn import GCNConv, global_add_pool
from torch_geometric.utils import dropout_adj
from torch_geometric.data import Data, DataLoader
class GNN(torch.nn.Module):
# a base GNN class, GCN message passing + sum_pooling
def __init__(self, dataset, gconv=GCNConv, latent_dim=[32, 32, 32, 1],
regression=False, adj_dropout=0.2, force_undirected=False):
super(GNN, self).__init__()
self.regression = regression
self.adj_dropout = adj_dropout
self.force_undirected = force_undirected
self.convs = torch.nn.ModuleList()
self.convs.append(gconv(dataset.num_features, latent_dim[0]))
for i in range(0, len(latent_dim)-1):
self.convs.append(gconv(latent_dim[i], latent_dim[i+1]))
self.lin1 = Linear(sum(latent_dim), 128)
if self.regression:
self.lin2 = Linear(128, 1)
else:
self.lin2 = Linear(128, dataset.num_classes)
def reset_parameters(self):
for conv in self.convs:
conv.reset_parameters()
self.lin1.reset_parameters()
self.lin2.reset_parameters()
def forward(self, data):
x, edge_index, batch = data.x, data.edge_index, data.batch
if self.adj_dropout > 0:
edge_index, _ = dropout_adj(
edge_index, p=self.adj_dropout,
force_undirected=self.force_undirected, num_nodes=len(x),
training=self.training
)
concat_states = []
for conv in self.convs:
x = torch.tanh(conv(x, edge_index))
concat_states.append(x)
concat_states = torch.cat(concat_states, 1)
x = global_add_pool(concat_states, batch)
x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
if self.regression:
return x[:, 0]
else:
return F.log_softmax(x, dim=-1)
def __repr__(self):
return self.__class__.__name__
# 创建一个简单的数据类，用于模拟数据集属性
class SimpleDataset:
num_features = 2
num_classes = 2
# 创建一个简单的图数据集
edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]], dtype=torch.long)
x = torch.tensor([[1, 0], [0, 1], [1, 0], [0, 1]], dtype=torch.float)
batch = torch.tensor([0, 0, 1, 1], dtype=torch.long)
# 使用 Data 类构建图数据
data = Data(x=x, edge_index=edge_index, batch=batch)
# 构建 DataLoader
loader = DataLoader([data], batch_size=2, shuffle=False)
dataset = SimpleDataset()
# 实例化模型
model = GNN(dataset)
# 模型推理
model.eval()
for data in loader:
out = model(data)
print(out)
igmc推理示例：
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear, Conv1d
from torch_geometric.nn import GCNConv, RGCNConv, global_sort_pool, global_add_pool
from torch_geometric.utils import dropout_adj
from util_functions import *
import pdb
import time
from torch_geometric.data import Data, DataLoader
class GNN(torch.nn.Module):
# a base GNN class, GCN message passing + sum_pooling
def __init__(self, dataset, gconv=GCNConv, latent_dim=[32, 32, 32, 1],
regression=False, adj_dropout=0.2, force_undirected=False):
super(GNN, self).__init__()
self.regression = regression
self.adj_dropout = adj_dropout
self.force_undirected = force_undirected
self.convs = torch.nn.ModuleList()
self.convs.append(gconv(dataset.num_features, latent_dim[0]))
for i in range(0, len(latent_dim)-1):
self.convs.append(gconv(latent_dim[i], latent_dim[i+1]))
self.lin1 = Linear(sum(latent_dim), 128)
if self.regression:
self.lin2 = Linear(128, 1)
else:
self.lin2 = Linear(128, dataset.num_classes)
def reset_parameters(self):
for conv in self.convs:
conv.reset_parameters()
self.lin1.reset_parameters()
self.lin2.reset_parameters()
def forward(self, data):
x, edge_index, batch = data.x, data.edge_index, data.batch
if self.adj_dropout > 0:
# edge_index, edge_type = dropout_adj(
# edge_index, edge_type, p=self.adj_dropout,
# force_undirected=self.force_undirected, num_nodes=len(x),
# training=self.training
# )
edge_index, edge_type = dropout_adj(edge_index, p=self.adj_dropout, force_undirected=self.force_undirected, num_nodes=len(x), training=self.training)
concat_states = []
for conv in self.convs:
x = torch.tanh(conv(x, edge_index))
concat_states.append(x)
concat_states = torch.cat(concat_states, 1)
x = global_add_pool(concat_states, batch)
x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
if self.regression:
return x[:, 0]
else:
return F.log_softmax(x, dim=-1)
def __repr__(self):
return self.__class__.__name__
class IGMC(GNN):
# The GNN model of Inductive Graph-based Matrix Completion.
# Use RGCN convolution + center-nodes readout.
def __init__(self, dataset, gconv=RGCNConv, latent_dim=[32, 32, 32, 32],
num_relations=5, num_bases=2, regression=False, adj_dropout=0.2,
force_undirected=False, side_features=False, n_side_features=0,
multiply_by=1):
super(IGMC, self).__init__(
dataset, GCNConv, latent_dim, regression, adj_dropout, force_undirected
)
self.multiply_by = multiply_by
self.convs = torch.nn.ModuleList()
self.convs.append(gconv(dataset.num_features, latent_dim[0], num_relations, num_bases))
for i in range(0, len(latent_dim)-1):
self.convs.append(gconv(latent_dim[i], latent_dim[i+1], num_relations, num_bases))
self.lin1 = Linear(2*sum(latent_dim), 128)
self.side_features = side_features
if side_features:
self.lin1 = Linear(2*sum(latent_dim)+n_side_features, 128)
def forward(self, data):
start = time.time()
x, edge_index, edge_type, batch = data.x, data.edge_index, data.edge_type, data.batch
if self.adj_dropout > 0:
edge_index, edge_type = dropout_adj(
edge_index, edge_type, p=self.adj_dropout,
force_undirected=self.force_undirected, num_nodes=len(x),
training=self.training
)
concat_states = []
for conv in self.convs:
x = torch.tanh(conv(x, edge_index, edge_type))
concat_states.append(x)
concat_states = torch.cat(concat_states, 1)
users = data.x[:, 0] == 1
items = data.x[:, 1] == 1
x = torch.cat([concat_states[users], concat_states[items]], 1)
if self.side_features:
x = torch.cat([x, data.u_feature, data.v_feature], 1)
x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
if self.regression:
return x[:, 0] * self.multiply_by
else:
return F.log_softmax(x, dim=-1)
class SimpleDataset:
num_features = 2
num_classes = 2
# 创建一个简单的图数据集
edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]], dtype=torch.long)
edge_type = torch.tensor([0, 1, 2, 3], dtype=torch.long)
x = torch.tensor([[1, 0], [0, 1], [1, 0], [0, 1]], dtype=torch.float)
batch = torch.tensor([0, 0, 1, 1], dtype=torch.long)
# 使用 Data 类构建图数据
data = Data(x=x, edge_index=edge_index,edge_type=edge_type, batch=batch)
# 构建 DataLoader
loader = DataLoader([data], batch_size=2, shuffle=False)
dataset = SimpleDataset()
# 实例化模型
model = IGMC(dataset)
# 模型推理
model.eval()
for data in loader:
out = model(data)
print(out)
