/examples/pytorch/rgcn-hetero/entity_classify.py

"""Modeling Relational Data with Graph Convolutional Networks
Paper: https://arxiv.org/abs/1703.06103
Reference Code: https://github.com/tkipf/relational-gcn
"""
import argparse
import numpy as np
import time
import torch as th
import torch.nn as nn
import torch.nn.functional as F

from dgl.data.rdf import AIFBDataset, MUTAGDataset, BGSDataset, AMDataset
from model import EntityClassify

def main(args):
    # load graph data
    if args.dataset == 'aifb':
        dataset = AIFBDataset()
    elif args.dataset == 'mutag':
        dataset = MUTAGDataset()
    elif args.dataset == 'bgs':
        dataset = BGSDataset()
    elif args.dataset == 'am':
        dataset = AMDataset()
    else:
        raise ValueError()

    g = dataset[0]
    category = dataset.predict_category
    num_classes = dataset.num_classes
    train_mask = g.nodes[category].data.pop('train_mask')
    test_mask = g.nodes[category].data.pop('test_mask')
    train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
    test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
    labels = g.nodes[category].data.pop('labels')
    category_id = len(g.ntypes)
    for i, ntype in enumerate(g.ntypes):
        if ntype == category:
            category_id = i

    # split dataset into train, validate, test
    if args.validation:
        val_idx = train_idx[:len(train_idx) // 5]
        train_idx = train_idx[len(train_idx) // 5:]
    else:
        val_idx = train_idx

    # check cuda
    use_cuda = args.gpu >= 0 and th.cuda.is_available()
    if use_cuda:
        th.cuda.set_device(args.gpu)
        g = g.to('cuda:%d' % args.gpu)
        labels = labels.cuda()
        train_idx = train_idx.cuda()
        test_idx = test_idx.cuda()

    # create model
    model = EntityClassify(g,
                           args.n_hidden,
                           num_classes,
                           num_bases=args.n_bases,
                           num_hidden_layers=args.n_layers - 2,
                           dropout=args.dropout,
                           use_self_loop=args.use_self_loop)

    if use_cuda:
        model.cuda()

    # optimizer
    optimizer = th.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.l2norm)

    # training loop
    print("start training...")
    dur = []
    model.train()
    for epoch in range(args.n_epochs):
        optimizer.zero_grad()
        if epoch > 5:
            t0 = time.time()
        logits = model()[category]
        loss = F.cross_entropy(logits[train_idx], labels[train_idx])
        loss.backward()
        optimizer.step()
        t1 = time.time()

        if epoch > 5:
            dur.append(t1 - t0)
        train_acc = th.sum(logits[train_idx].argmax(dim=1) == labels[train_idx]).item() / len(train_idx)
        val_loss = F.cross_entropy(logits[val_idx], labels[val_idx])
        val_acc = th.sum(logits[val_idx].argmax(dim=1) == labels[val_idx]).item() / len(val_idx)
        print("Epoch {:05d} | Train Acc: {:.4f} | Train Loss: {:.4f} | Valid Acc: {:.4f} | Valid loss: {:.4f} | Time: {:.4f}".
              format(epoch, train_acc, loss.item(), val_acc, val_loss.item(), np.average(dur)))
    print()
    if args.model_path is not None:
        th.save(model.state_dict(), args.model_path)

    model.eval()
    logits = model.forward()[category]
    test_loss = F.cross_entropy(logits[test_idx], labels[test_idx])
    test_acc = th.sum(logits[test_idx].argmax(dim=1) == labels[test_idx]).item() / len(test_idx)
    print("Test Acc: {:.4f} | Test loss: {:.4f}".format(test_acc, test_loss.item()))
    print()

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='RGCN')
    parser.add_argument("--dropout", type=float, default=0,
            help="dropout probability")
    parser.add_argument("--n-hidden", type=int, default=16,
            help="number of hidden units")
    parser.add_argument("--gpu", type=int, default=-1,
            help="gpu")
    parser.add_argument("--lr", type=float, default=1e-2,
            help="learning rate")
    parser.add_argument("--n-bases", type=int, default=-1,
            help="number of filter weight matrices, default: -1 [use all]")
    parser.add_argument("--n-layers", type=int, default=2,
            help="number of propagation rounds")
    parser.add_argument("-e", "--n-epochs", type=int, default=50,
            help="number of training epochs")
    parser.add_argument("-d", "--dataset", type=str, required=True,
            help="dataset to use")
    parser.add_argument("--model_path", type=str, default=None,
            help='path for save the model')
    parser.add_argument("--l2norm", type=float, default=0,
            help="l2 norm coef")
    parser.add_argument("--use-self-loop", default=False, action='store_true',
            help="include self feature as a special relation")
    fp = parser.add_mutually_exclusive_group(required=False)
    fp.add_argument('--validation', dest='validation', action='store_true')
    fp.add_argument('--testing', dest='validation', action='store_false')
    parser.set_defaults(validation=True)

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