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test_multi_6domain_release.py
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test_multi_6domain_release.py
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from tempo.data_provider.data_factory import data_provider
from tempo.utils.tools import EarlyStopping, adjust_learning_rate, visual, vali, test
from torch.utils.data import Subset
from tqdm import tqdm
from tempo.models.PatchTST import PatchTST
from tempo.models.GPT4TS import GPT4TS
from tempo.models.DLinear import DLinear
from tempo.models.TEMPO import TEMPO
# from models.T5 import T54TS
from tempo.models.ETSformer import ETSformer
import numpy as np
import torch
import torch.nn as nn
from torch import optim
from numpy.random import choice
import os
import time
import warnings
import matplotlib.pyplot as plt
import numpy as np
import argparse
import random
import sys
from omegaconf import OmegaConf
def get_init_config(config_path=None):
config = OmegaConf.load(config_path)
return config
warnings.filterwarnings('ignore')
fix_seed = 2021
random.seed(fix_seed)
torch.manual_seed(fix_seed)
np.random.seed(fix_seed)
parser = argparse.ArgumentParser(description='GPT4TS')
parser.add_argument('--model_id', type=str, default='weather_GTP4TS_multi-debug')
parser.add_argument('--checkpoints', type=str, default='/l/users/defu.cao/checkpoints_multi_dataset/')
parser.add_argument('--task_name', type=str, default='long_term_forecast')
parser.add_argument('--prompt', type=int, default=0)
parser.add_argument('--num_nodes', type=int, default=1)
parser.add_argument('--seq_len', type=int, default=512)
parser.add_argument('--pred_len', type=int, default=96)
parser.add_argument('--label_len', type=int, default=48)
parser.add_argument('--decay_fac', type=float, default=0.9)
parser.add_argument('--learning_rate', type=float, default=0.001)
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--num_workers', type=int, default=0)
parser.add_argument('--train_epochs', type=int, default=10)
parser.add_argument('--lradj', type=str, default='type3') # for what
parser.add_argument('--patience', type=int, default=5)
parser.add_argument('--gpt_layers', type=int, default=6)
parser.add_argument('--is_gpt', type=int, default=1)
parser.add_argument('--e_layers', type=int, default=3)
parser.add_argument('--d_model', type=int, default=768)
parser.add_argument('--n_heads', type=int, default=4)
parser.add_argument('--d_ff', type=int, default=768)
parser.add_argument('--dropout', type=float, default=0.3)
parser.add_argument('--enc_in', type=int, default=7)
parser.add_argument('--c_out', type=int, default=7)
parser.add_argument('--patch_size', type=int, default=16)
parser.add_argument('--kernel_size', type=int, default=25)
parser.add_argument('--loss_func', type=str, default='mse')
parser.add_argument('--pretrain', type=int, default=1)
parser.add_argument('--freeze', type=int, default=1)
parser.add_argument('--model', type=str, default='GPT4TS_multi')
parser.add_argument('--stride', type=int, default=8)
parser.add_argument('--max_len', type=int, default=-1)
parser.add_argument('--hid_dim', type=int, default=16)
parser.add_argument('--tmax', type=int, default=10)
parser.add_argument('--itr', type=int, default=3)
parser.add_argument('--cos', type=int, default=0)
parser.add_argument('--equal', type=int, default=1, help='1: equal sampling, 0: dont do the equal sampling')
parser.add_argument('--pool', action='store_true', help='whether use prompt pool')
parser.add_argument('--no_stl_loss', action='store_true', help='whether use prompt pool')
parser.add_argument('--stl_weight', type=float, default=0.01)
parser.add_argument('--config_path', type=str, default='./data_config.yml')
parser.add_argument('--datasets', type=str, default='exchange')
parser.add_argument('--target_data', type=str, default='ETTm1')
parser.add_argument('--use_token', type=int, default=0)
parser.add_argument('--electri_multiplier', type=int, default=1)
parser.add_argument('--traffic_multiplier', type=int, default=1)
parser.add_argument('--embed', type=str, default='timeF')
#args = parser.parse_args([])
args = parser.parse_args()
config = get_init_config(args.config_path)
args.itr = 1
print(args)
SEASONALITY_MAP = {
"minutely": 1440,
"10_minutes": 144,
"half_hourly": 48,
"hourly": 24,
"daily": 7,
"weekly": 1,
"monthly": 12,
"quarterly": 4,
"yearly": 1
}
mses = []
maes = []
for ii in range(args.itr):
setting = '{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_gl{}_df{}_eb{}_itr{}'.format(args.model_id, 336, args.label_len, args.pred_len,
args.d_model, args.n_heads, args.e_layers, args.gpt_layers,
args.d_ff, args.embed, ii)
path = os.path.join(args.checkpoints, setting)
if not os.path.exists(path):
os.makedirs(path)
# if args.freq == 0:
# args.freq = 'h'
device = torch.device('cuda:0')
train_data_name = args.datasets.split(',')
print(train_data_name)
train_datas = []
val_datas = []
min_sample_num = sys.maxsize
for dataset_singe in args.datasets.split(','):
print(dataset_singe)
args.data = config['datasets'][dataset_singe].data
args.root_path = config['datasets'][dataset_singe].root_path
args.data_path = config['datasets'][dataset_singe].data_path
args.data_name = config['datasets'][dataset_singe].data_name
args.features = config['datasets'][dataset_singe].features
args.freq = config['datasets'][dataset_singe].freq
args.target = config['datasets'][dataset_singe].target
args.embed = config['datasets'][dataset_singe].embed
args.percent = config['datasets'][dataset_singe].percent
args.lradj = config['datasets'][dataset_singe].lradj
if args.freq == 0:
args.freq = 'h'
print("dataset: ", args.data)
train_data, train_loader = data_provider(args, 'train')
if dataset_singe not in ['ETTh1', 'ETTh2', 'ILI', 'exchange']:
min_sample_num = min(min_sample_num, len(train_data))
# args.percent = 20
vali_data, vali_loader = data_provider(args, 'val')
# args.percent = 100
# train_datas.append(train_data)
val_datas.append(vali_data)
for dataset_singe in args.datasets.split(','):
print(dataset_singe)
args.data = config['datasets'][dataset_singe].data
args.root_path = config['datasets'][dataset_singe].root_path
args.data_path = config['datasets'][dataset_singe].data_path
args.data_name = config['datasets'][dataset_singe].data_name
args.features = config['datasets'][dataset_singe].features
args.freq = config['datasets'][dataset_singe].freq
args.target = config['datasets'][dataset_singe].target
args.embed = config['datasets'][dataset_singe].embed
args.percent = config['datasets'][dataset_singe].percent
args.lradj = config['datasets'][dataset_singe].lradj
if args.freq == 0:
args.freq = 'h'
# if args.freq != 'h':
# args.freq = SEASONALITY_MAP[test_data.freq]
# print("freq = {}".format(args.freq))
print("dataset: ", args.data)
train_data, train_loader = data_provider(args, 'train')
if dataset_singe not in ['ETTh1', 'ETTh2', 'ILI', 'exchange'] and args.equal == 1:
train_data = Subset(train_data, choice(len(train_data), min_sample_num))
if args.electri_multiplier>1 and args.equal == 1 and dataset_singe in ['electricity']:
train_data = Subset(train_data, choice(len(train_data), int(min_sample_num*args.electri_multiplier)))
if args.traffic_multiplier>1 and args.equal == 1 and dataset_singe in ['traffic']:
train_data = Subset(train_data, choice(len(train_data), int(min_sample_num*args.traffic_multiplier)))
train_datas.append(train_data)
if len(train_datas) > 1:
train_data = torch.utils.data.ConcatDataset([train_datas[0], train_datas[1]])
vali_data = torch.utils.data.ConcatDataset([val_datas[0], val_datas[1]])
for i in range(2,len(train_datas)):
train_data = torch.utils.data.ConcatDataset([train_data, train_datas[i]])
vali_data = torch.utils.data.ConcatDataset([vali_data, val_datas[i]])
# import pdb; pdb.set_trace()
print("Way1",len(train_data))
train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)
vali_loader = torch.utils.data.DataLoader(vali_data, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)
args.data = config['datasets'][args.target_data].data
args.root_path = config['datasets'][args.target_data].root_path
args.data_path = config['datasets'][args.target_data].data_path
args.data_name = config['datasets'][args.target_data].data_name
args.features = config['datasets'][dataset_singe].features
args.freq = config['datasets'][args.target_data].freq
args.target = config['datasets'][args.target_data].target
args.embed = config['datasets'][args.target_data].embed
args.percent = config['datasets'][args.target_data].percent
args.lradj = config['datasets'][args.target_data].lradj
if args.freq == 0:
args.freq = 'h'
test_data, test_loader = data_provider(args, 'test')
time_now = time.time()
train_steps = len(train_loader) #190470 -52696
if args.model == 'PatchTST':
model = PatchTST(args, device)
model.to(device)
elif args.model == 'DLinear':
model = DLinear(args, device)
model.to(device)
elif args.model == 'TEMPO':
model = TEMPO(args, device)
model.to(device)
elif args.model == 'T5':
model = T54TS(args, device)
model.to(device)
elif 'ETSformer' in args.model:
model = ETSformer(args, device)
model.to(device)
else:
model = GPT4TS(args, device)
# mse, mae = test(model, test_data, test_loader, args, device, ii)
params = model.parameters()
model_optim = torch.optim.Adam(params, lr=args.learning_rate)
early_stopping = EarlyStopping(patience=args.patience, verbose=True)
if args.loss_func == 'mse':
criterion = nn.MSELoss()
elif args.loss_func == 'smape':
class SMAPE(nn.Module):
def __init__(self):
super(SMAPE, self).__init__()
def forward(self, pred, true):
return torch.mean(200 * torch.abs(pred - true) / (torch.abs(pred) + torch.abs(true) + 1e-8))
criterion = SMAPE()
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(model_optim, T_max=args.tmax, eta_min=1e-8)
best_model_path = path + '/' + 'checkpoint.pth'
print(best_model_path)
model.load_state_dict(torch.load(best_model_path), strict=False)
print("------------------------------------")
mse, mae = test(model, test_data, test_loader, args, device, ii)
torch.cuda.empty_cache()
print('test on the ' + str(args.target_data) + ' dataset: mse:' + str(mse) + ' mae:' + str(mae))
# mse, mae = test(model, test_data, test_loader, args, device, ii)
# torch.cuda.empty_cache()
# mse_s, mae_s = test(model, test_data_s, test_loader_s, args, device, ii)
# torch.cuda.empty_cache()
# mse_t, mae_t = test(model, test_data_t, test_loader_t, args, device, ii)
# torch.cuda.empty_cache()
# mse_f, mae_f = test(model, test_data_f, test_loader_f, args, device, ii)
# torch.cuda.empty_cache()
# mse_5, mae_5 = test(model, test_data_5, test_loader_5, args, device, ii)
# torch.cuda.empty_cache()
# mse_6, mae_6 = test(model, test_data_6, test_loader_6, args, device, ii)
# torch.cuda.empty_cache()
mses.append(mse)
maes.append(mae)
print("mse_mean = {:.4f}, mse_std = {:.4f}".format(np.mean(mses), np.std(mses)))
print("mae_mean = {:.4f}, mae_std = {:.4f}".format(np.mean(maes), np.std(maes)))
# mses_s.append(mse_s)
# maes_s.append(mae_s)
# mses_t.append(mse_t)
# maes_t.append(mae_t)
# mses_f.append(mse_f)
# maes_f.append(mae_f)
# mses_5.append(mse_5)
# maes_5.append(mae_5)
# mses_6.append(mse_6)
# maes_6.append(mae_6)
# mses_7.append(mse_7)
# maes_7.append(mae_7)
# mses = np.array(mses)
# maes = np.array(maes)
# mses_s = np.array(mses_s)
# maes_s = np.array(maes_s)
# mses_t = np.array(mses_t)
# maes_t = np.array(maes_t)
# mses_f = np.array(mses_f)
# maes_f = np.array(maes_f)
# mses_5 = np.array(mses_5)
# maes_5 = np.array(maes_5)
# mses_6 = np.array(mses_6)
# maes_6 = np.array(maes_6)
# mses_7 = np.array(mses_7)
# maes_7 = np.array(maes_7)
# # names = #['weather', 'weather_s', 'weather_t', 'weather_f', 'weather_5', 'ettm2', 'traffic']
# # names = [args.data_name, args.data_name_s, args.data_name_t, args.data_name_f, args.data_name_5, args.data_name_6, args.data_name_7]
# print("mse_mean = {:.4f}, mse_std = {:.4f}".format(np.mean(mses), np.std(mses)))
# print("mae_mean = {:.4f}, mae_std = {:.4f}".format(np.mean(maes), np.std(maes)))
# print("mse_s_mean = {:.4f}, mse_s_std = {:.4f}".format(np.mean(mses_s), np.std(mses_s)))
# print("mae_s_mean = {:.4f}, mae_s_std = {:.4f}".format(np.mean(maes_s), np.std(maes_s)))
# print("mse_t_mean = {:.4f}, mse_t_std = {:.4f}".format(np.mean(mses_t), np.std(mses_t)))
# print("mae_t_mean = {:.4f}, mae_t_std = {:.4f}".format(np.mean(maes_t), np.std(maes_t)))
# print("mse_f_mean = {:.4f}, mse_f_std = {:.4f}".format(np.mean(mses_f), np.std(mses_f)))
# print("mae_f_mean = {:.4f}, mae_f_std = {:.4f}".format(np.mean(maes_f), np.std(maes_f)))
# print("mse_5_mean = {:.4f}, mse_5_std = {:.4f}".format(np.mean(mses_5), np.std(mses_5)))
# print("mae_5_mean = {:.4f}, mae_5_std = {:.4f}".format(np.mean(maes_5), np.std(maes_5)))
# print("mse_6_mean = {:.4f}, mse_6_std = {:.4f}".format(np.mean(mses_6), np.std(mses_6)))
# print("mae_6_mean = {:.4f}, mae_6_std = {:.4f}".format(np.mean(maes_6), np.std(maes_6)))
# print("mse_7_mean = {:.4f}, mse_7_std = {:.4f}".format(np.mean(mses_7), np.std(mses_7)))
# print("mae_7_mean = {:.4f}, mae_7_std = {:.4f}".format(np.mean(maes_7), np.std(maes_7)))
# import pandas as pd
# import numpy as np
# # # Create a DataFrame
# # data = {
# # 'Metric': ['MSE', 'MAE'] * 7,
# # 'Mean': [
# # np.mean(mses), np.mean(maes),
# # np.mean(mses_s), np.mean(maes_s),
# # np.mean(mses_t), np.mean(maes_t),
# # np.mean(mses_f), np.mean(maes_f),
# # np.mean(mses_5), np.mean(maes_5),
# # np.mean(mses_6), np.mean(maes_6),
# # np.mean(mses_7), np.mean(maes_7)
# # ],
# # 'Standard Deviation': [
# # np.std(mses), np.std(maes),
# # np.std(mses_s), np.std(maes_s),
# # np.std(mses_t), np.std(maes_t),
# # np.std(mses_f), np.std(maes_f),
# # np.std(mses_5), np.std(maes_5),
# # np.std(mses_6), np.std(maes_6),
# # np.std(mses_7), np.std(maes_7)
# # ],
# # 'Model': ['weather', 'weather', 'weather_s', 'weather_s', 'weather_t', 'weather_t',
# # 'weather_f', 'weather_f', 'weather_5', 'weather_5', 'ettm2', 'ettm2', 'traffic', 'traffic']
# # }
# # df = pd.DataFrame(data)
# # # Group by the 'Model' column to make the LaTeX table clearer
# # grouped = df.groupby('Model')
# # # Output the DataFrame to a LaTeX table
# # latex_table = grouped.apply(lambda x: x[['Metric', 'Mean', 'Standard Deviation']].to_latex(index=False, float_format="%.4f"))
# # # Print the LaTeX table
# # print(latex_table)
# # LaTeX table header
# latex_table = """
# \\begin{table}[ht]
# \\centering
# \\begin{tabular}{lrr}
# \\toprule
# Model & MSE (Mean ± Std) & MAE (Mean ± Std) \\\\
# \\midrule
# """
# # Collecting data and creating table rows
# metrics = [(mses, maes), (mses_s, maes_s), (mses_t, maes_t), (mses_f, maes_f), (mses_5, maes_5), (mses_6, maes_6), (mses_7, maes_7)]
# for name, (mse_values, mae_values) in zip(names, metrics):
# mse_mean = np.mean(mse_values)
# mse_std = np.std(mse_values)
# mae_mean = np.mean(mae_values)
# mae_std = np.std(mae_values)
# latex_table += "{} & {:.4f} ± {:.4f} & {:.4f} ± {:.4f} \\\\\n".format(name, mse_mean, mse_std, mae_mean, mae_std)
# # LaTeX table footer
# latex_table += """
# \\bottomrule
# \\end{tabular}
# \\caption{Summary of model performance.}
# \\label{tab:model_performance}
# \\end{table}
# """
# print(latex_table)
# # Create a DataFrame for the data
# data = {
# 'Model': names,
# 'MSE Mean': [np.mean(mses), np.mean(mses_s), np.mean(mses_t), np.mean(mses_f), np.mean(mses_5), np.mean(mses_6), np.mean(mses_7)],
# 'MSE Std': [np.std(mses), np.std(mses_s), np.std(mses_t), np.std(mses_f), np.std(mses_5), np.std(mses_6), np.std(mses_7)],
# 'MAE Mean': [np.mean(maes), np.mean(maes_s), np.mean(maes_t), np.mean(maes_f), np.mean(maes_5), np.mean(maes_6), np.mean(maes_7)],
# 'MAE Std': [np.std(maes), np.std(maes_s), np.std(maes_t), np.std(maes_f), np.std(maes_5), np.std(maes_6), np.std(maes_7)]
# }
# df = pd.DataFrame(data)
# print(df)
# # Write the DataFrame to an Excel file
# excel_file_path = os.path.join(args.checkpoints, args.model_id + '.xlsx')
# with pd.ExcelWriter(excel_file_path, engine='xlsxwriter') as writer:
# df.to_excel(writer, index=False, sheet_name='Performance')
# print(f"Data has been written to {excel_file_path}")