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import argparse
import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import Linear
from torch_geometric.datasets import UPFD
from torch_geometric.loader import DataLoader
from torch_geometric.nn import GATConv, GCNConv, SAGEConv, global_max_pool
from torch_geometric.transforms import ToUndirected
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='politifact',
choices=['politifact', 'gossipcop'])
parser.add_argument('--feature', type=str, default='spacy',
choices=['profile', 'spacy', 'bert', 'content'])
parser.add_argument('--model', type=str, default='GCN',
choices=['GCN', 'GAT', 'SAGE'])
args = parser.parse_args()
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'UPFD')
train_dataset = UPFD(path, args.dataset, args.feature, 'train', ToUndirected())
val_dataset = UPFD(path, args.dataset, args.feature, 'val', ToUndirected())
test_dataset = UPFD(path, args.dataset, args.feature, 'test', ToUndirected())
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=128, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)
class Net(torch.nn.Module):
def __init__(self, model, in_channels, hidden_channels, out_channels,
concat=False):
super().__init__()
self.concat = concat
if model == 'GCN':
self.conv1 = GCNConv(in_channels, hidden_channels)
elif model == 'SAGE':
self.conv1 = SAGEConv(in_channels, hidden_channels)
elif model == 'GAT':
self.conv1 = GATConv(in_channels, hidden_channels)
if self.concat:
self.lin0 = Linear(in_channels, hidden_channels)
self.lin1 = Linear(2 * hidden_channels, hidden_channels)
self.lin2 = Linear(hidden_channels, out_channels)
def forward(self, x, edge_index, batch):
h = self.conv1(x, edge_index).relu()
h = global_max_pool(h, batch)
if self.concat:
# Get the root node (tweet) features of each graph:
root = (batch[1:] - batch[:-1]).nonzero(as_tuple=False).view(-1)
root = torch.cat([root.new_zeros(1), root + 1], dim=0)
news = x[root]
news = self.lin0(news).relu()
h = self.lin1(torch.cat([news, h], dim=-1)).relu()
h = self.lin2(h)
return h.log_softmax(dim=-1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(args.model, train_dataset.num_features, 128,
train_dataset.num_classes, concat=True).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=0.01)
def train():
model.train()
total_loss = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
out = model(data.x, data.edge_index, data.batch)
loss = F.nll_loss(out, data.y)
loss.backward()
optimizer.step()
total_loss += float(loss) * data.num_graphs
return total_loss / len(train_loader.dataset)
@torch.no_grad()
def test(loader):
model.eval()
total_correct = total_examples = 0
for data in loader:
data = data.to(device)
pred = model(data.x, data.edge_index, data.batch).argmax(dim=-1)
total_correct += int((pred == data.y).sum())
total_examples += data.num_graphs
return total_correct / total_examples
for epoch in range(1, 61):
loss = train()
train_acc = test(train_loader)
val_acc = test(val_loader)
test_acc = test(test_loader)
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, '
f'Val: {val_acc:.4f}, Test: {test_acc:.4f}')
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