ppi.py

import argparse

import numpy as np
import torch as th
import torch.optim as optim
from model import GeniePath, GeniePathLazy
from sklearn.metrics import f1_score

from dgl.data import PPIDataset
from dgl.dataloading import GraphDataLoader


def evaluate(model, loss_fn, dataloader, device="cpu"):
    loss = 0
    f1 = 0
    num_blocks = 0
    for subgraph in dataloader:
        subgraph = subgraph.to(device)
        label = subgraph.ndata["label"].to(device)
        feat = subgraph.ndata["feat"]
        logits = model(subgraph, feat)

        # compute loss
        loss += loss_fn(logits, label).item()
        predict = np.where(logits.data.cpu().numpy() >= 0.0, 1, 0)
        f1 += f1_score(label.cpu(), predict, average="micro")
        num_blocks += 1

    return f1 / num_blocks, loss / num_blocks


def main(args):
    # Step 1: Prepare graph data and retrieve train/validation/test index ============================= #
    # Load dataset
    train_dataset = PPIDataset(mode="train")
    valid_dataset = PPIDataset(mode="valid")
    test_dataset = PPIDataset(mode="test")
    train_dataloader = GraphDataLoader(
        train_dataset, batch_size=args.batch_size
    )
    valid_dataloader = GraphDataLoader(
        valid_dataset, batch_size=args.batch_size
    )
    test_dataloader = GraphDataLoader(test_dataset, batch_size=args.batch_size)

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

    num_classes = train_dataset.num_labels

    # Extract node features
    graph = train_dataset[0]
    feat = graph.ndata["feat"]

    # Step 2: Create model =================================================================== #
    if args.lazy:
        model = GeniePathLazy(
            in_dim=feat.shape[-1],
            out_dim=num_classes,
            hid_dim=args.hid_dim,
            num_layers=args.num_layers,
            num_heads=args.num_heads,
            residual=args.residual,
        )
    else:
        model = GeniePath(
            in_dim=feat.shape[-1],
            out_dim=num_classes,
            hid_dim=args.hid_dim,
            num_layers=args.num_layers,
            num_heads=args.num_heads,
            residual=args.residual,
        )

    model = model.to(device)

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

    # Step 4: training epochs =============================================================== #
    for epoch in range(args.max_epoch):
        model.train()
        tr_loss = 0
        tr_f1 = 0
        num_blocks = 0
        for subgraph in train_dataloader:
            subgraph = subgraph.to(device)
            label = subgraph.ndata["label"]
            feat = subgraph.ndata["feat"]
            logits = model(subgraph, feat)

            # compute loss
            batch_loss = loss_fn(logits, label)
            tr_loss += batch_loss.item()
            tr_predict = np.where(logits.data.cpu().numpy() >= 0.0, 1, 0)
            tr_f1 += f1_score(label.cpu(), tr_predict, average="micro")
            num_blocks += 1

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

        # validation
        model.eval()
        val_f1, val_loss = evaluate(model, loss_fn, valid_dataloader, device)

        print(
            "In epoch {}, Train F1: {:.4f} | Train Loss: {:.4f}; Valid F1: {:.4f} | Valid loss: {:.4f}".format(
                epoch,
                tr_f1 / num_blocks,
                tr_loss / num_blocks,
                val_f1,
                val_loss,
            )
        )

    # Test after all epoch
    model.eval()
    test_f1, test_loss = evaluate(model, loss_fn, test_dataloader, device)

    print("Test F1: {:.4f} | Test loss: {:.4f}".format(test_f1, test_loss))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="GeniePath")
    parser.add_argument(
        "--gpu", type=int, default=-1, help="GPU Index. Default: -1, using CPU."
    )
    parser.add_argument(
        "--hid_dim", type=int, default=256, help="Hidden layer dimension"
    )
    parser.add_argument(
        "--num_layers", type=int, default=3, help="Number of GeniePath layers"
    )
    parser.add_argument(
        "--max_epoch",
        type=int,
        default=1000,
        help="The max number of epochs. Default: 1000",
    )
    parser.add_argument(
        "--lr",
        type=float,
        default=0.0004,
        help="Learning rate. Default: 0.0004",
    )
    parser.add_argument(
        "--num_heads",
        type=int,
        default=1,
        help="Number of head in breadth function. Default: 1",
    )
    parser.add_argument(
        "--residual", type=bool, default=False, help="Residual in GAT or not"
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        default=2,
        help="Batch size of graph dataloader",
    )
    parser.add_argument(
        "--lazy", type=bool, default=False, help="Variant GeniePath-Lazy"
    )

    args = parser.parse_args()
    print(args)
    th.manual_seed(16)
    main(args)