/
main_book.py
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/
main_book.py
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import os
import argparse
import torch
import numpy as np
from train import *
from model import *
import sampler
from preprocess import process
from evaluate import evaluate
import pdb
def main():
parser = argparse.ArgumentParser(description="Pytorch implementation of Adaptive Learning Meta-paths to learn on Heterogeneous graph")
parser.add_argument('--seed', type=int, default=777)
parser.add_argument('--dataset', type=str, default='book')
parser.add_argument('--model_name', type=str, default='GIN')
parser.add_argument('--num_epochs', type=int, default=50)
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--wlr', type=float, default=0.0001)
parser.add_argument('--decay', type=float, default=0.0001)
parser.add_argument('--num_layers', type=int, default=2)
parser.add_argument('--num_hops', type=int, default=3)
parser.add_argument('--neighbor_size',type=int, default=[16,16,16])
parser.add_argument('--emb_dim', type=int, default=64)
parser.add_argument('--weight_emb_dim', type=int, default=1000)
parser.add_argument('--n_meta', type=int, default=5)
parser.add_argument('--metapath', type=str, default=[0,1,2,3,8])
parser.add_argument('--num_hub', type=int, default=5)
parser.add_argument('--n_fold', type=int, default=3)
parser.add_argument('--selar', type=str, default='True')
parser.add_argument('--selarhint', type=str, default='False')
parser.add_argument('--hreg', type=float, default=1)
args = parser.parse_args()
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_dataset, train_fold, meta_fold, valid_target, test_target, kg, hub, num_nodes, n_relation, n_user, n_entity, n_item = process(args)
ui = train_dataset[train_dataset[:,2]==1]
iu = train_dataset[train_dataset[:,2]==1][:, [1,0,2]]
ui[:, 2] = n_relation
iu[:, 2] = n_relation+1
train_all = torch.cat((ui, iu))
edge_index = np.concatenate((train_all, kg)).T
edge_index = torch.from_numpy(edge_index)
if args.model_name == "GCN":
model = MetaNetworkGCN
elif args.model_name == "GAT":
model = MetaNetworkGAT
elif args.model_name == "GIN":
model = MetaNetworkGIN
elif args.model_name == "SGC":
model = MetaNetworkSGC
model = model(args, num_nodes).to(device)
optimizer = torch.optim.Adam(model.params(), lr=args.lr, weight_decay=args.decay)
### Task-specific layers ###
input_dim = args.n_meta + 2
phis, opt_phi = [],[]
for _ in range(args.n_meta+1):
phi = Phi(args.emb_dim, 100, args.emb_dim).to(device)
opt_phi.append(torch.optim.Adam(phi.params(), lr=args.lr))
phis.append(phi)
### Weighting function ###
vnet = Weight(input_dim, args.weight_emb_dim, 1).to(device)
optimizer_v = torch.optim.Adam(vnet.params(), lr=args.wlr)
### For Hint Network ###
if args.selarhint == 'True':
h_nets, h_optimizers = [], []
for _ in range(args.n_meta):
net = MetaNetworkHUB(args).to(device)
opt = torch.optim.Adam(net.params(), lr=args.lr, weight_decay=args.decay)
h_nets.append(net)
h_optimizers.append(opt)
h_vnet = Weight(input_dim+1, args.weight_emb_dim, 1).to(device)
h_optimizer_v = torch.optim.Adam(h_vnet.params(), lr=args.wlr, weight_decay=args.h_decay)
best_valid_auc, best_test_auc, best_valid_auc_epoch = 0,0,0
train_loaders = []
for _ in range(args.n_fold):
train_loaders.append(sampler.Meta_NeighborSampler(args=args, edge_index=edge_index, num_nodes=num_nodes, size=args.neighbor_size, num_hops=args.num_layers, batch_size=args.batch_size))
for epoch in range(1, args.num_epochs + 1):
loaders = []
if args.selar == 'True':
for i, train_loader in enumerate(train_loaders):
loaders.append(train_loader(train_fold[i], meta_fold[i], None))
train_loader = sampler.Meta_NeighborSampler(args=args, edge_index=edge_index, num_nodes=num_nodes, size=args.neighbor_size, num_hops=args.num_layers, batch_size=args.batch_size)
loader = train_loader(train_dataset, None, None)
train_losses, train_auc = selar_train(args, epoch, loaders, loader, model, vnet, phis, optimizer, optimizer_v, opt_phi, num_nodes, device)
if args.selarhint == 'True':
for i, train_loader in enumerate(train_loaders):
loaders.append(train_loader(train_fold[i], meta_fold[i], hub))
train_loader = sampler.Meta_NeighborSampler(args=args, edge_index=edge_index, num_nodes=num_nodes, size=args.neighbor_size, num_hops=args.num_layers, batch_size=args.batch_size)
loader = train_loader(train_dataset, None, hub)
train_losses, train_auc = selarhint_train(args, epoch, loaders, loader, model, h_nets, vnet, h_vnet, phis, optimizer, h_optimizers, optimizer_v, h_optimizer_v, opt_phi, num_nodes, device)
eval_loader = sampler.eval_NeighborSampler(args=args, edge_index=edge_index, num_nodes=num_nodes, size=args.neighbor_size, num_hops=args.num_layers, batch_size=args.batch_size)
valid_losses, valid_auc, valid_f1, valid_acc = evaluate(args, valid_target, eval_loader, model, phis, device)
test_losses, test_auc, test_f1, test_acc = evaluate(args, test_target, eval_loader, model, phis, device)
print('{:2} Epoch - Train Loss: {}'.format(epoch, train_losses))
print('{:2} Epoch - Train auc: {} '.format(epoch, train_auc))
print('{:2} Epoch - Test auc: {} '.format(epoch, test_auc))
if valid_auc > best_valid_auc:
best_valid_auc_epoch = epoch
best_test_auc = test_auc
best_valid_auc = valid_auc
if args.use_wandb == 'True':
log_dict = {"Train Loss": train_losses, "Test Loss": test_losses,'Valid Loss': valid_losses,
"Train auc": train_auc, "Test auc": test_auc, 'Valid auc': valid_auc,
"Best Test auc": best_test_auc}
wandb.log(log_dict)
print('{:2} Best Test auc: {} '.format(epoch, best_test_auc))
if __name__ == "__main__":
main()