main.py

import dgl
import argparse
import torch as th
from model import CAREGNN
import torch.optim as optim
from utils import EarlyStopping
from sklearn.metrics import recall_score, roc_auc_score


def main(args):
    # Step 1: Prepare graph data and retrieve train/validation/test index ============================= #
    # Load dataset
    dataset = dgl.data.FraudDataset(args.dataset, train_size=0.4)
    graph = dataset[0]
    num_classes = dataset.num_classes

    # check cuda
    if args.gpu >= 0 and th.cuda.is_available():
        device = 'cuda:{}'.format(args.gpu)
    else:
        device = 'cpu'

    # retrieve labels of ground truth
    labels = graph.ndata['label'].to(device).squeeze().long()

    # Extract node features
    feat = graph.ndata['feature'].to(device).float()

    # retrieve masks for train/validation/test
    train_mask = graph.ndata['train_mask']
    val_mask = graph.ndata['val_mask']
    test_mask = graph.ndata['test_mask']

    train_idx = th.nonzero(train_mask, as_tuple=False).squeeze(1).to(device)
    val_idx = th.nonzero(val_mask, as_tuple=False).squeeze(1).to(device)
    test_idx = th.nonzero(test_mask, as_tuple=False).squeeze(1).to(device)

    # Reinforcement learning module only for positive training nodes
    rl_idx = th.nonzero(train_mask.to(device) & labels.bool(), as_tuple=False).squeeze(1)

    graph = graph.to(device)

    # Step 2: Create model =================================================================== #
    model = CAREGNN(in_dim=feat.shape[-1],
                    num_classes=num_classes,
                    hid_dim=args.hid_dim,
                    num_layers=args.num_layers,
                    activation=th.tanh,
                    step_size=args.step_size,
                    edges=graph.canonical_etypes)

    model = model.to(device)

    # Step 3: Create training components ===================================================== #
    _, cnt = th.unique(labels, return_counts=True)
    loss_fn = th.nn.CrossEntropyLoss(weight=1 / cnt)
    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
    if args.early_stop:
        stopper = EarlyStopping(patience=100)

    # Step 4: training epochs =============================================================== #
    for epoch in range(args.max_epoch):
        # Training and validation using a full graph
        model.train()
        logits_gnn, logits_sim = model(graph, feat)

        # compute loss
        tr_loss = loss_fn(logits_gnn[train_idx], labels[train_idx]) + \
                  args.sim_weight * loss_fn(logits_sim[train_idx], labels[train_idx])

        tr_recall = recall_score(labels[train_idx].cpu(), logits_gnn.data[train_idx].argmax(dim=1).cpu())
        tr_auc = roc_auc_score(labels[train_idx].cpu(), logits_gnn.data[train_idx][:, 1].cpu())

        # validation
        val_loss = loss_fn(logits_gnn[val_idx], labels[val_idx]) + \
                   args.sim_weight * loss_fn(logits_sim[val_idx], labels[val_idx])
        val_recall = recall_score(labels[val_idx].cpu(), logits_gnn.data[val_idx].argmax(dim=1).cpu())
        val_auc = roc_auc_score(labels[val_idx].cpu(), logits_gnn.data[val_idx][:, 1].cpu())

        # backward
        optimizer.zero_grad()
        tr_loss.backward()
        optimizer.step()

        # Print out performance
        print("Epoch {}, Train: Recall: {:.4f} AUC: {:.4f} Loss: {:.4f} | Val: Recall: {:.4f} AUC: {:.4f} Loss: {:.4f}"
              .format(epoch, tr_recall, tr_auc, tr_loss.item(), val_recall, val_auc, val_loss.item()))

        # Adjust p value with reinforcement learning module
        model.RLModule(graph, epoch, rl_idx)

        if args.early_stop:
            if stopper.step(val_auc, model):
                break

    # Test after all epoch
    model.eval()
    if args.early_stop:
        model.load_state_dict(th.load('es_checkpoint.pt'))

    # forward
    logits_gnn, logits_sim = model.forward(graph, feat)

    # compute loss
    test_loss = loss_fn(logits_gnn[test_idx], labels[test_idx]) + \
                args.sim_weight * loss_fn(logits_sim[test_idx], labels[test_idx])
    test_recall = recall_score(labels[test_idx].cpu(), logits_gnn[test_idx].argmax(dim=1).cpu())
    test_auc = roc_auc_score(labels[test_idx].cpu(), logits_gnn.data[test_idx][:, 1].cpu())

    print("Test Recall: {:.4f} AUC: {:.4f} Loss: {:.4f}".format(test_recall, test_auc, test_loss.item()))


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='GCN-based Anti-Spam Model')
    parser.add_argument("--dataset", type=str, default="amazon", help="DGL dataset for this model (yelp, or amazon)")
    parser.add_argument("--gpu", type=int, default=-1, help="GPU index. Default: -1, using CPU.")
    parser.add_argument("--hid_dim", type=int, default=64, help="Hidden layer dimension")
    parser.add_argument("--num_layers", type=int, default=1, help="Number of layers")
    parser.add_argument("--max_epoch", type=int, default=30, help="The max number of epochs. Default: 30")
    parser.add_argument("--lr", type=float, default=0.01, help="Learning rate. Default: 0.01")
    parser.add_argument("--weight_decay", type=float, default=0.001, help="Weight decay. Default: 0.001")
    parser.add_argument("--step_size", type=float, default=0.02, help="RL action step size (lambda 2). Default: 0.02")
    parser.add_argument("--sim_weight", type=float, default=2, help="Similarity loss weight (lambda 1). Default: 2")
    parser.add_argument('--early-stop', action='store_true', default=True, help="indicates whether to use early stop")

    args = parser.parse_args()
    print(args)
    th.manual_seed(717)
    main(args)