main.py

import torch
import numpy as np
import argparse
import time
from util import *
from trainer import Trainer
from HOMGNN import HOMnet
import matplotlib.pyplot as plt
plt.style.use(['ggplot', 'seaborn-paper'])


# print('finished')
def str_to_bool(value):
    if isinstance(value, bool):
        return value
    if value.lower() in {'false', 'f', '0', 'no', 'n'}:
        return False
    elif value.lower() in {'true', 't', '1', 'yes', 'y'}:
        return True
    raise ValueError(f'{value} is not a valid boolean value')


parser = argparse.ArgumentParser()

parser.add_argument('--device',type=str,default='cuda:0',help='')
parser.add_argument('--data',type=str,default='data/METR-LA',help='data path')

parser.add_argument('--adj_data', type=str,default='data/sensor_graph/adj_mx.pkl',help='adj data path')
parser.add_argument('--gcn_true', type=str_to_bool, default=True, help='whether to add graph convolution layer')
parser.add_argument('--buildA_true', type=str_to_bool, default=True,help='whether to construct adaptive adjacency matrix')
parser.add_argument('--load_static_feature', type=str_to_bool, default=False,help='whether to load static feature')
parser.add_argument('--cl', type=str_to_bool, default=True,help='whether to do curriculum learning')

parser.add_argument('--gcn_depth',type=int,default=2,help='graph convolution')
parser.add_argument('--num_nodes',type=int,default=207,help='number of nodes/variables')
parser.add_argument('--dropout',type=float,default=0.3,help='dropout rate')
parser.add_argument('--order',type=int,default=2,help='the order value of the higher-order GNN')
parser.add_argument('--neiaccount',type=int,default=2,help='parameter')
parser.add_argument('--subgraph_size',type=int,default=20,help='k')

parser.add_argument('--node_dim',type=int,default=40,help='dim of nodes')
parser.add_argument('--dilation_exponential',type=int,default=1,help='dilation exponential')

parser.add_argument('--conv_channels',type=int,default=32,help='convolution channels')
parser.add_argument('--residual_channels',type=int,default=32,help='residual channels')
parser.add_argument('--skip_channels',type=int,default=64,help='skip channels')
parser.add_argument('--end_channels',type=int,default=128,help='end channels')


parser.add_argument('--in_dim',type=int,default=2,help='inputs dimension')
parser.add_argument('--seq_in_len',type=int,default=12,help='input sequence length')
parser.add_argument('--seq_out_len',type=int,default=12,help='output sequence length')

parser.add_argument('--layers',type=int,default=3,help='number of layers') # default 3
parser.add_argument('--batch_size',type=int,default=48,help='batch size')
parser.add_argument('--learning_rate',type=float,default=0.001,help='learning rate')
parser.add_argument('--weight_decay',type=float,default=0.0001,help='weight decay rate')
parser.add_argument('--clip',type=int,default=5,help='clip')
parser.add_argument('--step_size1',type=int,default=2500,help='step_size')

parser.add_argument('--epochs',type=int,default=100,help='')
parser.add_argument('--print_every',type=int,default=50,help='')
parser.add_argument('--seed',type=int,default=101,help='random seed')
parser.add_argument('--save',type=str,default='./save/',help='save path')
parser.add_argument('--expid',type=int,default=1,help='experiment id')

parser.add_argument('--propalpha',type=float,default=0.05,help='prop alpha')
parser.add_argument('--tanhalpha',type=float,default=3,help='adj alpha')

parser.add_argument('--num_split',type=int,default=1,help='number of splits for graphs')

parser.add_argument('--runs',type=int,default=1,help='number of runs')



args = parser.parse_args()
torch.set_num_threads(3)


def main(runid):

    # # torch.cuda.manual_seed(0)
    torch.manual_seed(args.seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    np.random.seed(args.seed)
    #load data
    device = torch.device(args.device)
    dataloader = load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size)
    scaler = dataloader['scaler']

    predefined_A = load_adj(args.adj_data)
    predefined_A = torch.tensor(predefined_A)-torch.eye(args.num_nodes)
    predefined_A = predefined_A.to(device)

    model = HOMnet(args.gcn_true, args.buildA_true, args.gcn_depth, args.num_nodes,
                  device, predefined_A=predefined_A,
                  dropout=args.dropout, subgraph_size=args.subgraph_size,
                  node_dim=args.node_dim,
                  dilation_exponential=args.dilation_exponential,
                  conv_channels=args.conv_channels, residual_channels=args.residual_channels,
                  skip_channels=args.skip_channels, end_channels= args.end_channels,
                  seq_length=args.seq_in_len, in_dim=args.in_dim, out_dim=args.seq_out_len,
                  layers=args.layers, propalpha=args.propalpha, tanhalpha=args.tanhalpha, layer_norm_affline=True, order=args.order, neiaccount=args.neiaccount)

    print(args)
    nParams = sum([p.nelement() for p in model.parameters()])
    print('Number of model parameters is', nParams)

    engine = Trainer(model, args.learning_rate, args.weight_decay, args.clip, args.step_size1, args.seq_out_len, scaler, device, args.cl)

    print("start training...",flush=True)
    his_loss =[]
    val_time = []
    train_time = []
    minl = 1e5
    for i in range(1,args.epochs+1):
        train_loss = []
        train_mape = []
        train_rmse = []
        t1 = time.time()
        dataloader['train_loader'].shuffle()
        for iter, (x, y) in enumerate(dataloader['train_loader'].get_iterator()):
            trainx = torch.Tensor(x).to(device)
            trainx = trainx.transpose(1, 3)
            trainy = torch.Tensor(y).to(device)
            trainy = trainy.transpose(1, 3)

            metrics = engine.train(trainx, trainy[:, 0, :, :])
            train_loss.append(metrics[0])
            train_mape.append(metrics[1])
            train_rmse.append(metrics[2])

        t2 = time.time()
        train_time.append(t2-t1)
        #validation
        valid_loss = []
        valid_mape = []
        valid_rmse = []

        s1 = time.time()
        for iter, (x, y) in enumerate(dataloader['val_loader'].get_iterator()):
            testx = torch.Tensor(x).to(device)
            testx = testx.transpose(1, 3)
            testy = torch.Tensor(y).to(device)
            testy = testy.transpose(1, 3)
            metrics = engine.eval(testx, testy[:,0,:,:])
            valid_loss.append(metrics[0])
            valid_mape.append(metrics[1])
            valid_rmse.append(metrics[2])
        s2 = time.time()
        log = 'Epoch: {:03d}, Inference Time: {:.4f} secs'
        print(log.format(i,(s2-s1)))
        val_time.append(s2-s1)
        mtrain_loss = np.mean(train_loss)
        mtrain_mape = np.mean(train_mape)
        mtrain_rmse = np.mean(train_rmse)

        mvalid_loss = np.mean(valid_loss)
        mvalid_mape = np.mean(valid_mape)
        mvalid_rmse = np.mean(valid_rmse)
        his_loss.append(mvalid_loss)

        log = 'Epoch: {:03d}, Train Loss: {:.4f}, Train MAPE: {:.4f}, Train RMSE: {:.4f}, Valid Loss: {:.4f}, Valid MAPE: {:.4f}, Valid RMSE: {:.4f}, Training Time: {:.4f}/epoch'
        print(log.format(i, mtrain_loss, mtrain_mape, mtrain_rmse, mvalid_loss, mvalid_mape, mvalid_rmse, (t2 - t1)),flush=True)
        if i == 1:
            Log.info(log.format(i, mtrain_loss, mtrain_mape, mtrain_rmse, mvalid_loss, mvalid_mape, mvalid_rmse, (t2 - t1)))

        if mvalid_loss<minl:
            torch.save(engine.model.state_dict(), args.save + "exp" + str(args.expid) + "_" + str(runid) +".pth")
            minl = mvalid_loss

    print("Average Training Time: {:.4f} secs/epoch".format(np.mean(train_time)))
    print("Average Inference Time: {:.4f} secs".format(np.mean(val_time)))


    bestid = np.argmin(his_loss)
    engine.model.load_state_dict(torch.load(args.save + "exp" + str(args.expid) + "_" + str(runid) +".pth"))

    print("Training finished")
    Log.info("The valid loss on best model is:{} on epochs:{}".format(str(round(his_loss[bestid], 4)), bestid + 1))

    #valid data
    outputs = []
    realy = torch.Tensor(dataloader['y_val']).to(device)
    realy = realy.transpose(1,3)[:,0,:,:]

    for iter, (x, y) in enumerate(dataloader['val_loader'].get_iterator()):
        testx = torch.Tensor(x).to(device)
        testx = testx.transpose(1,3)
        with torch.no_grad():
            preds = engine.model(testx)
            preds = preds.transpose(1,3)
        outputs.append(preds.squeeze())

    yhat = torch.cat(outputs,dim=0)
    yhat = yhat[:realy.size(0),...]


    pred = scaler.inverse_transform(yhat)
    vmae, vmape, vrmse = metric(pred,realy)

    #test data
    outputs = []
    realy = torch.Tensor(dataloader['y_test']).to(device)
    realy = realy.transpose(1, 3)[:, 0, :, :]

    predictiontimelist = []
    for iter, (x, y) in enumerate(dataloader['test_loader'].get_iterator()):
        testx = torch.Tensor(x).to(device)
        testx = testx.transpose(1, 3)
        with torch.no_grad():
            time1 = time.time()
            preds = engine.model(testx)
            time2 = time.time()
            predictiontimelist.append(time2-time1)
            preds = preds.transpose(1, 3)
        outputs.append(preds.squeeze())
    predictiontime = np.array(predictiontimelist)
    np.save('./predictiontime.npy', predictiontime)

    yhat = torch.cat(outputs, dim=0)
    yhat = yhat[:realy.size(0), ...]


    mae = []
    mape = []
    rmse = []
    for i in range(args.seq_out_len):
        pred = scaler.inverse_transform(yhat[:, :, i])
        real = realy[:, :, i]
        metrics = metric(pred, real)
        log = 'Evaluate best model on test data for horizon {:d}, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}'
        print(log.format(i + 1, metrics[0], metrics[1], metrics[2]))
        mae.append(metrics[0])
        mape.append(metrics[1])
        rmse.append(metrics[2])
    return vmae, vmape, vrmse, mae, mape, rmse

if __name__ == "__main__":
    vmae = []
    vmape = []
    vrmse = []
    mae = []
    mape = []
    rmse = []
    for i in range(args.runs):
        vm1, vm2, vm3, m1, m2, m3 = main(i)
        vmae.append(vm1)
        vmape.append(vm2)
        vrmse.append(vm3)
        mae.append(m1)
        mape.append(m2)
        rmse.append(m3)

    mae = np.array(mae)
    mape = np.array(mape)
    rmse = np.array(rmse)

    amae = np.mean(mae,0)
    amape = np.mean(mape,0)
    armse = np.mean(rmse,0)

    smae = np.std(mae,0)
    smape = np.std(mape,0)
    srmse = np.std(rmse,0)

    #valid data
    print('valid\tAVal-MAE\tAVal-RMSE\tAVal-MAPE')
    log = 'mean:\t{:.4f}\t{:.4f}\t{:.4f}'
    print(log.format(np.mean(vmae),np.mean(vrmse),np.mean(vmape)))
    print('\n')
    Log.info('test\tATest-MAE\tATest-RMSE\tATest-MAPE')
    log = 'mean:\t{:.4f}\t{:.4f}\t{:.4f}'
    Log.info(log.format(np.mean(amae), np.mean(armse), np.mean(amape)))
    print('\n')
    #test data
    Log.info('test|horizon\tTest-MAE\tTest-RMSE\tTest-MAPE')
    for i in [2,5,11]: # [2,5,11]
        log = '{:d}\t{:.4f}\t{:.4f}\t{:.4f}'
        Log.info(log.format(i+1, amae[i], armse[i], amape[i]))