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model_CLUSTER_GCN.py
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model_CLUSTER_GCN.py
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#224 92.22 30epoch
#su 96.05 30epoch
#all 98.98 30epoch
#label 62.38/26.58/37.27 30epoch
import torch
import torch.nn.functional as F
from torch.nn import ModuleList
from tqdm import tqdm
from torch_geometric.datasets import Reddit
from torch_geometric.data import ClusterData, ClusterLoader, NeighborSampler
from torch_geometric.nn import SAGEConv
import torch_geometric.utils as tu
import create_dataset as cd
import data_deal as dd
import sys
epoch_list=[]
value_list=[]
type_list=[]
dataset=cd.MyOwnDataset()
data = dataset[0]
print(data)
cluster_data = ClusterData(data, num_parts=5, recursive=False,save_dir=dataset.processed_dir)#a graph data object into multiple subgraphs, as motivated by the `"Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks"
train_loader = ClusterLoader(cluster_data, batch_size=20, shuffle=True,num_workers=0)
#The data loader scheme from the `"Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" <https:arxiv.org/abs/1905.07953>`_ paper which merges partioned subgraphs and their between-cluster links from a large-scale graph data object to form a mini-batch.
subgraph_loader = NeighborSampler(data.edge_index, sizes=[-1], batch_size=1024,
shuffle=False, num_workers=0)
#Given a GNN with :math:`L` layers and a specific mini-batch of nodes :obj:`node_idx` for which we want to compute embeddings, this module iteratively samples neighbors and constructs bipartite graphs that simulate the actual computation flow of GNNs.
class Net(torch.nn.Module):
def __init__(self, in_channels, out_channels):
super(Net, self).__init__()
self.convs = ModuleList(
[SAGEConv(in_channels, 128),
SAGEConv(128, out_channels)])
def forward(self, x, edge_index):
for i, conv in enumerate(self.convs):
x = conv(x, edge_index)
if i != len(self.convs) - 1:
x = F.relu(x)
x = F.dropout(x, p=0.5, training=self.training)
return F.log_softmax(x, dim=-1)
def inference(self, x_all):
# print(len(x_all))
# print('xxx')
# print(x_all.size(0))
# print(self.convs)
pbar = tqdm(total=x_all.size(0) * len(self.convs))
pbar.set_description('Evaluating')
# Compute representations of nodes layer by layer, using *all*
# available edges. This leads to faster computation in contrast to
# immediately computing the final representations of each batch.
for i, conv in enumerate(self.convs):
xs = []
# print(self.convs)
for batch_size, n_id, adj in subgraph_loader:
edge_index, _, size = adj.to(device)
# print(size[1])#1024 。。。 225
x = x_all[n_id].to(device)
# print(len(x))
# print(len(x_all))
x_target = x[:size[1]]
# print(len(x_target))
x = conv((x, x_target), edge_index)
if i != len(self.convs) - 1:
x = F.relu(x)
# print(len(x))#325时xs为225
xs.append(x.cpu())#Returns a copy of this object in CPU memory.
# print(len(x.cpu()))
pbar.update(batch_size)
x_all = torch.cat(xs, dim=0)
# print(len(x_all))
#333025 - 225 = 332800
#332800 / 1024 = 325
pbar.close()
return x_all
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(dataset.num_features, 2).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.005)
def train():
model.train()
total_loss = total_nodes = 0
for batch in train_loader:
batch = batch.to(device)
optimizer.zero_grad()
out = model(batch.x, batch.edge_index)
loss = F.nll_loss(out[batch.train_mask], batch.y[batch.train_mask])
loss.backward()
optimizer.step()
nodes = batch.train_mask.sum().item()
total_loss += loss.item() * nodes
total_nodes += nodes
return total_loss / total_nodes
@torch.no_grad()
def test(): # Inference should be performed on the full graph.
model.eval()#测试模型
# print('data.x')
# print(data.x)
# print(type(data.x))
out = model.inference(data.x)
added = torch.zeros((len(data.x) - len(out)), 2)
out = torch.cat((out, added), 0)
y_pred = out.argmax(dim=-1)
# print('out')
# print(out)
# print(out.size())
# print(len(out))
# print(len(data.x))
# print(len(y_pred))
# print(len(data.y))
# print(len(data.train_mask))
# print(len(data.val_mask))
# print(len(data.test_mask))
# print(len(data.train_mask)+len(data.val_mask)+len(data.test_mask))
accs = []
for mask in [data.train_mask, data.val_mask, data.test_mask]:
# print(y_pred[mask][0:500])
# print(data.y[mask][0:500])
# print(tu.precision(y_pred[mask], data.y[mask], 2))
# print(tu.recall(y_pred[mask], data.y[mask], 2))
# print(tu.f1_score(y_pred[mask], data.y[mask], 2))
# print(len(mask))
correct = y_pred[mask].eq(data.y[mask]).sum().item()
accs.append(correct / mask.sum().item())
value_list.append(tu.precision(y_pred[mask], data.y[mask], 2)[1].item())
value_list.append(tu.recall(y_pred[mask], data.y[mask], 2)[1].item())
value_list.append(tu.f1_score(y_pred[mask], data.y[mask], 2)[1].item())
type_list.append('precision')
type_list.append('recall')
type_list.append('f1')
return accs
for epoch in range(1,151):
loss = train()
if epoch % 5 == 0:
train_acc, val_acc, test_acc = test()
epoch_list.append(epoch)
epoch_list.append(epoch)
epoch_list.append(epoch)
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, '
f'Val: {val_acc:.4f}, test: {test_acc:.4f}')
else:
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}')
# print(len(epoch_list))
# print(len(value_list))
# print(len(type_list))
dd.plot_solute(epoch_list,value_list,type_list)
# node_df=dd.read_csv(r'small/su_node.csv')
# edge_df=dd.read_csv(r'small/su_edge.csv')
# node_fea,type_list=dd.get_node_fea(node_df)
# train_mask,val_mask,test_mask=dd.split_data(type_list,[6,2,2])
# edge_list=dd.id_to_num(node_df,edge_df)
# edge_fea=dd.get_edge_fea(edge_df)
# data1=dd.make_torch_data1(node_fea,edge_list,type_list,train_mask,val_mask,test_mask,edge_fea)
# print(data1)
# data1 = data1.to(device)
# #model.eval()
# out = model.inference(data1.x)
# pred = out.argmax(dim=-1)
# print(len(pred))
# dd.get_pred(pred,data1.y)
#1pre0.7562 f1 0.6243 recall 0.5316
#2pre 0.7533 f1 0.7433 recall 0.7336
#3pre 0.7667 f1 0.7662 recall 0.7557