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iTransformer_LSTM_optuna_parallel_MAE.py
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iTransformer_LSTM_optuna_parallel_MAE.py
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import argparse
import csv
import time
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
from torch.utils.data import DataLoader
import optuna
from data import split_data_cnn,data_detime
from models import iTransformer_LSTM
from utils.tools import metrics_of_pv, EarlyStopping, same_seeds,train,evaluate
import warnings
warnings.filterwarnings('ignore')
if __name__ == "__main__":
seeds = 42
same_seeds(seeds)
# site='1B'
site = '7-First-Solar'
# dataset = 'Spring'
# dataset = 'Summer'
dataset = 'Autumn'
# dataset = 'Winter'
parser = argparse.ArgumentParser(description="Hyperparameters")
parser.add_argument("--batch_size", type=int, default=128)
parser.add_argument("--learning_rate", type=float, default=0.0001)
parser.add_argument("--epochs", type=int, default=150)
# parser.add_argument('--data_dir', type=str, default='./dataset', help='数据集的路径')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
args = parser.parse_args()
batch_size = args.batch_size
learning_rate = args.learning_rate
epochs = args.epochs
# file_path = f'./data/{site}/{dataset}_{site}_2017_2020_h.csv'
file_path = f'./data/{site}/{dataset}_{site}_2019_2022_h.csv'
num_nodes = 5
batch = 300
time_length = 24
# predict_length = [1,4]
predict_length = 1
device = torch.device('cuda:0')
df_all = pd.read_csv(file_path, header=0)
multi_steps = False
epoch=0
data_train, data_valid, data_test, timestamp_train, timestamp_valid, timestamp_test, scalar = split_data_cnn(df_all, 0.8, 0.1, time_length)
dataset_train = data_detime(data=data_train, lookback_length=time_length, multi_steps=multi_steps, lookforward_length=predict_length)
dataset_valid = data_detime(data=data_valid, lookback_length=time_length, multi_steps=multi_steps, lookforward_length=predict_length)
dataset_test = data_detime(data=data_test, lookback_length=time_length, multi_steps=multi_steps, lookforward_length=predict_length)
train_loader = DataLoader(dataset_train, batch_size=batch_size, shuffle=True)
valid_loader = DataLoader(dataset_valid, batch_size=batch_size, shuffle=False)
test_loader = DataLoader(dataset_test, batch_size=batch_size, shuffle=False)
def objective(trial):
# model = Model_Torder(input_size=5).to(device)
dim_embed = trial.suggest_categorical('hidden_dim', [16, 32, 64, 128, 256])
layer_L = trial.suggest_categorical('layer_L', [1, 2, 3, 4])
layer_I = trial.suggest_categorical('layer_I', [1, 2, 3, 4, 5, 6])
heads = trial.suggest_categorical('heads', [2, 4, 6, 8, 12, 16])
dim_lstm = trial.suggest_categorical('dim_lstm', [16, 32, 64, 128, 256])
# num_channels = [32] * 1, kernel_size = 3, length_pre = 1,
# length_input = 48, dim_embed = 128, depth = 4, heads = 6, dim_mlp = 10,
model = iTransformer_LSTM(input_size=5, length_input=time_length,dim_lstm=dim_lstm,depth_lstm=layer_L,
dim_embed=dim_embed, depth=layer_I, heads=heads,).to(device)
criterion_MAE = nn.L1Loss(reduction='sum').to(device) # MAE
criterion_MSE = nn.MSELoss(reduction='sum').to(device) # MSE
optm = optim.Adam(model.parameters(), lr=learning_rate)
optm_schedule = torch.optim.lr_scheduler.ReduceLROnPlateau(
optm, mode="min", factor=0.5, patience=5, verbose=True
)
model_name = f"iTransformer_lstm_optuna_{dataset}"
model_save = f"model_save/{dataset}/{model_name}.pt"
train_losses, valid_losses = [], []
earlystopping = EarlyStopping(model_save, patience=10, delta=0.0001)
# if not os.path.exists(f'data_record/{dataset}/{model_name}.csv'):
# os.makedirs(f'data_record/{dataset}/{model_name}.csv'')
need_train = True
# need_train = False
if need_train:
try:
for epoch in range(epochs):
time_start = time.time()
train_loss = train(data=train_loader, model=model, criterion=criterion_MAE,optm=optm, )
valid_loss, ms = evaluate(data=valid_loader, model=model, criterion=criterion_MAE, )
train_losses.append(train_loss)
valid_losses.append(valid_loss)
optm_schedule.step(valid_loss)
earlystopping(valid_loss, model)
# torch.save(model, model_save, pickle_module=dill)
print('')
print(f'{model_name}|time:{(time.time() - time_start):.2f}|Loss_train:{train_loss:.4f}|Learning_rate:{optm.state_dict()["param_groups"][0]["lr"]:.4f}\n'
f'Loss_valid:{valid_loss:.4f}|MAE:{ms[0]:.4f}|RMSE:{ms[1]:.4f}|R2:{ms[2]:.4f}|MBE:{ms[3]:.4f}', flush=True, )
if earlystopping.early_stop:
print("Early stopping")
break # 跳出迭代，结束训练
except KeyboardInterrupt:
print("Training interrupted by user")
# plt.plot(np.arange(len(train_losses)), train_losses, label="train loss")
# plt.plot(np.arange(len(valid_losses)), valid_losses, label="valid rmse")
# plt.legend() # 显示图例
# plt.xlabel("epoches")
# # plt.ylabel("epoch")
# plt.title("Train_loss&Valid_loss")
# plt.show()
test_loss, ms_test = evaluate(
data=test_loader, model=model, criterion=criterion_MAE)
print(f'Test_valid:{test_loss:.4f}|MAE:{ms_test[0]:.4f}|RMSE:{ms_test[1]:.4f}|R2:{ms_test[2]:.4f}|MBE:{ms_test[3]:.4f}', )
with open(f'data_record/{dataset}/Metrics_{model_name}.csv', 'a', encoding='utf-8', newline='') as f:
csv_write = csv.writer(f)
csv_write.writerow([f'{site}_pred1_{model_name}', ms_test[0], ms_test[1], ms_test[2], ms_test[3]])
return test_loss
# with open(model_save, "rb") as f:
# model = torch.load(f, pickle_module=dill)
# model = model.to(device)
# test_loss,ms_test = evaluate(
# data=test_loader, model=model,criterion=criterion_MAE, batch_size=batch_size
# )
# print(f'Test_valid:{test_loss:.4f}|MAE:{ms_test[0]:.4f}|RMSE:{ms_test[1]:.4f}|R2:{ms_test[2]:.4f}|MBE:{ms_test[3]:.4f}',)
# with open(f'data_record/{dataset}/Metrics_{model_name}_{dataset}.csv', 'a', encoding='utf-8',newline='') as f:
# csv_write = csv.writer(f)
# csv_write.writerow([f'{site}_pred1_{model_name}', ms_test[0],ms_test[1],ms_test[2],ms_test[3]])
study = optuna.create_study(direction='minimize', sampler=optuna.samplers.TPESampler(seed=42), load_if_exists=True,
storage=f'sqlite:///db.sqlite3', study_name=f'{site}_{dataset}_LIA_new')
study.optimize(objective, n_trials=100)
print(study.best_params, '\n', study.best_value)
# optuna-dashboard sqlite:///db.sqlite3