import argparse
import torch as th
import torch.optim as optim
import torch.nn.functional as F
from dataloader import GASDataset
from model import GAS
from sklearn.metrics import f1_score, roc_auc_score, precision_recall_curve


def main(args):
    # Step 1: Prepare graph data and retrieve train/validation/test index ============================= #
    # Load dataset
    dataset = GASDataset(args.dataset)
    graph = dataset[0]

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

    # binary classification
    num_classes = dataset.num_classes

    # retrieve labels of ground truth
    labels = graph.edges['forward'].data['label'].to(device).long()

    # Extract node features
    e_feat = graph.edges['forward'].data['feat'].to(device)
    u_feat = graph.nodes['u'].data['feat'].to(device)
    v_feat = graph.nodes['v'].data['feat'].to(device)

    # retrieve masks for train/validation/test
    train_mask = graph.edges['forward'].data['train_mask']
    val_mask = graph.edges['forward'].data['val_mask']
    test_mask = graph.edges['forward'].data['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)

    graph = graph.to(device)

    # Step 2: Create model =================================================================== #
    model = GAS(e_in_dim=e_feat.shape[-1],
                u_in_dim=u_feat.shape[-1],
                v_in_dim=v_feat.shape[-1],
                e_hid_dim=args.e_hid_dim,
                u_hid_dim=args.u_hid_dim,
                v_hid_dim=args.v_hid_dim,
                out_dim=num_classes,
                num_layers=args.num_layers,
                dropout=args.dropout,
                activation=F.relu)

    model = model.to(device)

    # Step 3: Create training components ===================================================== #
    loss_fn = th.nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)

    # Step 4: training epochs =============================================================== #
    for epoch in range(args.max_epoch):
        # Training and validation using a full graph
        model.train()
        logits = model(graph, e_feat, u_feat, v_feat)

        # compute loss
        tr_loss = loss_fn(logits[train_idx], labels[train_idx])
        tr_f1 = f1_score(labels[train_idx].cpu(), logits[train_idx].argmax(dim=1).cpu())
        tr_auc = roc_auc_score(labels[train_idx].cpu(), logits[train_idx][:, 1].detach().cpu())
        tr_pre, tr_re, _ = precision_recall_curve(labels[train_idx].cpu(), logits[train_idx][:, 1].detach().cpu())
        tr_rap = tr_re[tr_pre > args.precision].max()

        # validation
        valid_loss = loss_fn(logits[val_idx], labels[val_idx])
        valid_f1 = f1_score(labels[val_idx].cpu(), logits[val_idx].argmax(dim=1).cpu())
        valid_auc = roc_auc_score(labels[val_idx].cpu(), logits[val_idx][:, 1].detach().cpu())
        valid_pre, valid_re, _ = precision_recall_curve(labels[val_idx].cpu(), logits[val_idx][:, 1].detach().cpu())
        valid_rap = valid_re[valid_pre > args.precision].max()

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

        # Print out performance
        print("In epoch {}, Train R@P: {:.4f} | Train F1: {:.4f} | Train AUC: {:.4f} | Train Loss: {:.4f}; "
              "Valid R@P: {:.4f} | Valid F1: {:.4f} | Valid AUC: {:.4f} | Valid loss: {:.4f}".
              format(epoch, tr_rap, tr_f1, tr_auc, tr_loss.item(), valid_rap, valid_f1, valid_auc, valid_loss.item()))

    # Test after all epoch
    model.eval()

    # forward
    logits = model(graph, e_feat, u_feat, v_feat)

    # compute loss
    test_loss = loss_fn(logits[test_idx], labels[test_idx])
    test_f1 = f1_score(labels[test_idx].cpu(), logits[test_idx].argmax(dim=1).cpu())
    test_auc = roc_auc_score(labels[test_idx].cpu(), logits[test_idx][:, 1].detach().cpu())
    test_pre, test_re, _ = precision_recall_curve(labels[test_idx].cpu(), logits[test_idx][:, 1].detach().cpu())
    test_rap = test_re[test_pre > args.precision].max()

    print("Test R@P: {:.4f} | Test F1: {:.4f} | Test AUC: {:.4f} | Test loss: {:.4f}".
          format(test_rap, test_f1, test_auc, test_loss.item()))


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='GCN-based Anti-Spam Model')
    parser.add_argument("--dataset", type=str, default="pol", help="'pol', or 'gos'")
    parser.add_argument("--gpu", type=int, default=-1, help="GPU Index. Default: -1, using CPU.")
    parser.add_argument("--e_hid_dim", type=int, default=128, help="Hidden layer dimension for edges")
    parser.add_argument("--u_hid_dim", type=int, default=128, help="Hidden layer dimension for source nodes")
    parser.add_argument("--v_hid_dim", type=int, default=128, help="Hidden layer dimension for destination nodes")
    parser.add_argument("--num_layers", type=int, default=2, help="Number of GCN layers")
    parser.add_argument("--max_epoch", type=int, default=100, help="The max number of epochs. Default: 100")
    parser.add_argument("--lr", type=float, default=0.001, help="Learning rate. Default: 1e-3")
    parser.add_argument("--dropout", type=float, default=0.0, help="Dropout rate. Default: 0.0")
    parser.add_argument("--weight_decay", type=float, default=5e-4, help="Weight Decay. Default: 0.0005")
    parser.add_argument("--precision", type=float, default=0.9, help="The value p in recall@p precision. Default: 0.9")

    args = parser.parse_args()
    print(args)
    main(args)

import argparse
import torch as th
import torch.optim as optim
import torch.nn.functional as F
from dataloader import GASDataset
from model import GAS
from sklearn.metrics import f1_score, roc_auc_score, precision_recall_curve


def main(args):
    # Step 1: Prepare graph data and retrieve train/validation/test index ============================= #
    # Load dataset
    dataset = GASDataset(args.dataset)
    graph = dataset[0]

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

    # binary classification
    num_classes = dataset.num_classes

    # retrieve labels of ground truth
    labels = graph.edges['forward'].data['label'].to(device).long()

    # Extract node features
    e_feat = graph.edges['forward'].data['feat'].to(device)
    u_feat = graph.nodes['u'].data['feat'].to(device)
    v_feat = graph.nodes['v'].data['feat'].to(device)

    # retrieve masks for train/validation/test
    train_mask = graph.edges['forward'].data['train_mask']
    val_mask = graph.edges['forward'].data['val_mask']
    test_mask = graph.edges['forward'].data['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)

    graph = graph.to(device)

    # Step 2: Create model =================================================================== #
    model = GAS(e_in_dim=e_feat.shape[-1],
                u_in_dim=u_feat.shape[-1],
                v_in_dim=v_feat.shape[-1],
                e_hid_dim=args.e_hid_dim,
                u_hid_dim=args.u_hid_dim,
                v_hid_dim=args.v_hid_dim,
                out_dim=num_classes,
                num_layers=args.num_layers,
                dropout=args.dropout,
                activation=F.relu)

    model = model.to(device)

    # Step 3: Create training components ===================================================== #
    loss_fn = th.nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)

    # Step 4: training epochs =============================================================== #
    for epoch in range(args.max_epoch):
        # Training and validation using a full graph
        model.train()
        logits = model(graph, e_feat, u_feat, v_feat)

        # compute loss
        tr_loss = loss_fn(logits[train_idx], labels[train_idx])
        tr_f1 = f1_score(labels[train_idx].cpu(), logits[train_idx].argmax(dim=1).cpu())
        tr_auc = roc_auc_score(labels[train_idx].cpu(), logits[train_idx][:, 1].detach().cpu())
        tr_pre, tr_re, _ = precision_recall_curve(labels[train_idx].cpu(), logits[train_idx][:, 1].detach().cpu())
        tr_rap = tr_re[tr_pre > args.precision].max()

        # validation
        valid_loss = loss_fn(logits[val_idx], labels[val_idx])
        valid_f1 = f1_score(labels[val_idx].cpu(), logits[val_idx].argmax(dim=1).cpu())
        valid_auc = roc_auc_score(labels[val_idx].cpu(), logits[val_idx][:, 1].detach().cpu())
        valid_pre, valid_re, _ = precision_recall_curve(labels[val_idx].cpu(), logits[val_idx][:, 1].detach().cpu())
        valid_rap = valid_re[valid_pre > args.precision].max()

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

        # Print out performance
        print("In epoch {}, Train R@P: {:.4f} | Train F1: {:.4f} | Train AUC: {:.4f} | Train Loss: {:.4f}; "
              "Valid R@P: {:.4f} | Valid F1: {:.4f} | Valid AUC: {:.4f} | Valid loss: {:.4f}".
              format(epoch, tr_rap, tr_f1, tr_auc, tr_loss.item(), valid_rap, valid_f1, valid_auc, valid_loss.item()))

    # Test after all epoch
    model.eval()

    # forward
    logits = model(graph, e_feat, u_feat, v_feat)

    # compute loss
    test_loss = loss_fn(logits[test_idx], labels[test_idx])
    test_f1 = f1_score(labels[test_idx].cpu(), logits[test_idx].argmax(dim=1).cpu())
    test_auc = roc_auc_score(labels[test_idx].cpu(), logits[test_idx][:, 1].detach().cpu())
    test_pre, test_re, _ = precision_recall_curve(labels[test_idx].cpu(), logits[test_idx][:, 1].detach().cpu())
    test_rap = test_re[test_pre > args.precision].max()

    print("Test R@P: {:.4f} | Test F1: {:.4f} | Test AUC: {:.4f} | Test loss: {:.4f}".
          format(test_rap, test_f1, test_auc, test_loss.item()))


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='GCN-based Anti-Spam Model')
    parser.add_argument("--dataset", type=str, default="pol", help="'pol', or 'gos'")
    parser.add_argument("--gpu", type=int, default=-1, help="GPU Index. Default: -1, using CPU.")
    parser.add_argument("--e_hid_dim", type=int, default=128, help="Hidden layer dimension for edges")
    parser.add_argument("--u_hid_dim", type=int, default=128, help="Hidden layer dimension for source nodes")
    parser.add_argument("--v_hid_dim", type=int, default=128, help="Hidden layer dimension for destination nodes")
    parser.add_argument("--num_layers", type=int, default=2, help="Number of GCN layers")
    parser.add_argument("--max_epoch", type=int, default=100, help="The max number of epochs. Default: 100")
    parser.add_argument("--lr", type=float, default=0.001, help="Learning rate. Default: 1e-3")
    parser.add_argument("--dropout", type=float, default=0.0, help="Dropout rate. Default: 0.0")
    parser.add_argument("--weight_decay", type=float, default=5e-4, help="Weight Decay. Default: 0.0005")
    parser.add_argument("--precision", type=float, default=0.9, help="The value p in recall@p precision. Default: 0.9")

    args = parser.parse_args()
    print(args)
    main(args)

# Step 1: Prepare graph data and retrieve train/validation/test index ============================= # # Load dataset # check cuda # binary classification # retrieve labels of ground truth # Extract node features # retrieve masks for train/validation/test # Step 2: Create model =================================================================== # # Step 3: Create training components ===================================================== # # Step 4: training epochs =============================================================== # # Training and validation using a full graph # compute loss # validation # backward # Print out performance # Test after all epoch # forward # compute loss