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RUL-Mamba/Multivariable_RUL_Prediction_RULMambaVAN_optimize.py
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eason 79db6e5c96 feat: 添加RUL-Mamba模型及相关组件 
新增锂电池剩余使用寿命预测模型RUL-Mamba，包含以下主要组件：
1. 添加Mamba模块作为核心时序建模组件
2. 实现特征注意力网络(FAN)和门控残差网络(GRN)
3. 新增数据预处理和归一化层
4. 添加模型训练和评估脚本
5. 补充README文档说明使用方法
6. 添加可视化辅助工具Helper_Plot.py
7. 实现多种时间序列处理层(Embedding、AutoCorrelation等)
8. 添加配置文件requirements.txt
9. 补充测试数据集TJU battery data
2026-01-09 08:53:50 +08:00

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# -*- coding: utf-8 -*-
import os
from assistant import get_gpus_memory_info
id,_ = get_gpus_memory_info()
os.environ["CUDA_VISIBLE_DEVICES"] = str(id)
# os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512"
from shutil import copyfile
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import torch
from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.data.encoders import NaNLabelEncoder,EncoderNormalizer,MultiNormalizer,TorchNormalizer
from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, MultiHorizonMetric, MultiLoss, QuantileLoss
import scipy.io
from sklearn.preprocessing import MinMaxScaler
# from pytorch_forecasting.data import GroupNormalizer
import warnings
warnings.filterwarnings("ignore")
import matplotlib
matplotlib.use('agg')
from datetime import datetime
import matplotlib.colors as mcolors
from matplotlib.font_manager import FontProperties
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='RULMambaVAN',help='Model name.')
parser.add_argument('--seq_len', type=int, default=64, help='input sequence length')
parser.add_argument('--label_len', type=int, default=0, help='start token length')
parser.add_argument('--pred_len', type=int, default=1, help='prediction sequence length')
parser.add_argument('--Battery_list', type=list, default=['CY25_1', 'CY25_2', 'CY25_3'], help='Battery data')
parser.add_argument('--data_dir', type=str, default='data/TJU data/Dataset_3_NCM_NCA_battery/', help='path of the data file')
parser.add_argument('--Rated_Capacity', type=float, default=2.5, help='Rate Capacity')
parser.add_argument('--test_name', type=str, default='CY25_1', help='Battery data used for test')
parser.add_argument('--start_point_list', type=int, default=[200,300,400], help='The cycle when prediction gets started.')
parser.add_argument('--seed', type=int, default=1, help='Random seed.')
parser.add_argument('--root_dir', type=str, default='TJU_RUL_prediction_AutoML', help='root path of the store file')
parser.add_argument('--count', type=int, default=10, help='The number of independent experiment.')
parser.add_argument('--batch_size', type=int, default=128, help='The batch size.')
parser.add_argument('--max_epochs', type=int, default=200, help='max train epochs')
args = parser.parse_args()
def rul_value_error(y_test, y_predict, threshold):
true_re, pred_re = len(y_test), 0
for i in range(len(y_test)-1):
if y_test[i] <= threshold >= y_test[i+1]:
true_re = i - 1
break
for i in range(len(y_predict)-1):
if y_predict[i] <= threshold:
pred_re = i - 1
break
rul_real = true_re + 1
rul_pred = pred_re + 1
ae_error = abs(true_re - pred_re)
re_score = abs(true_re - pred_re)/true_re
if re_score > 1: re_score = 1
return rul_real,rul_pred,ae_error,re_score
# 打印日志到文件
def print_log(print_string, log_file, visible=True):
if visible:
print("{}".format(print_string))
# 写入日志文件
log_file.write('{}\n'.format(print_string))
# 刷新缓存区，将数据写入
log_file.flush()
from DataPreProcess import MultiVariateBatteryDataProcess,BatteryDataProcess
#------------------------------------------------- step 1: 数据准备 ----------------------------------------
# -------------------------------数据分析和数据预处理【至关重要】---------------------------------------
BatteryData = np.load('data/TJU data/Dataset_3_NCM_NCA_battery_1C.npy', allow_pickle=True)
BatteryData = BatteryData.item()
_,_,df_all = MultiVariateBatteryDataProcess(BatteryData,args.test_name,args.start_point_list[0],args)
real_data = df_all['target'].values*args.Rated_Capacity
all_pred_data_list = []
root_dir = 'results_{}/{}/{}/'.format(args.root_dir,args.test_name,args.model)
if not os.path.exists(root_dir):
os.makedirs(root_dir)
save_figure_dir = os.path.join(root_dir,'figures')
if not os.path.exists(save_figure_dir):
os.makedirs(save_figure_dir)
for start_point in args.start_point_list:
df_train,df_test,df_all = MultiVariateBatteryDataProcess(BatteryData,args.test_name,start_point,args)
mask_len =len(df_train) # 训练集按照80%，20%划分训练集和验证集
# tf_test =len(df_test)
time_varying_known_reals=['voltage mean','voltage std','voltage kurtosis','voltage skewness','CC Q','CC charge time','voltage slope','voltage entropy',
'current mean','current std','current kurtosis','current skewness','CV Q','CV charge time','current slope','current entropy','Capacity']
time_varying_unknown_reals = ['target']
# --------------------- 历史长度和预测长度 -------------------------
max_prediction_length = args.pred_len
max_encoder_length = args.seq_len #24
# ---(5)构建数据集---
training = TimeSeriesDataSet(
df_train[0:int(0.8*mask_len)],
time_idx="time_idx",
target="target",
group_ids=['group_id'],
min_encoder_length=max_encoder_length,
max_encoder_length=max_encoder_length,
min_prediction_length=max_prediction_length,
max_prediction_length=max_prediction_length,
# 特征（除了target外的）
time_varying_known_reals=time_varying_known_reals, # 【随时间变化且在未来已知的连续变量】
# 未来未知
time_varying_unknown_reals=time_varying_unknown_reals, # 【随时间变化且在未来未知的连续变量】
# # use softplus and normalize by group 【如何计算】 Softplus函数可以看作是ReLU函数的平滑。Softplus(x)=log(1+e^x)
target_normalizer=EncoderNormalizer(),#MultiNormalizer([TorchNormalizer(),TorchNormalizer()]), # EncoderNormalizer(), TorchNormalizer()
add_encoder_length=False
)
# 从数据集中获取数据加载器 # train ( bool , optional ) -- 如果数据加载器用于训练或预测，如果为真，将打乱并丢弃最后一批
train_dataloader = training.to_dataloader(train=True, batch_size=args.batch_size,shuffle=True,num_workers=0,drop_last=True)
# --- 保存文件名，设置特征数量【必须】 -------
for x, (y, weight) in iter(train_dataloader):
print("['encoder_cont']:", x['encoder_cont'].shape) # [batch_size, max_encoder_length, 5]
print("['decoder_cont']:", x['decoder_cont'].shape) # [batch_size, max_prediction_length, 15]
en_feats_num = x['encoder_cat'].shape[-1] + x['encoder_cont'].shape[-1]
de_feats_num = x['decoder_cat'].shape[-1] + x['decoder_cont'].shape[-1]
print('y:', y[0].shape) #
# print('weight:',weight.shape)
break
validing = TimeSeriesDataSet(
df_train[int(0.8 * mask_len):],
time_idx="time_idx", # 【时间索引字段，确定样本的顺序】
target='target',
group_ids=['group_id'], # 【无】
min_encoder_length=max_encoder_length,
max_encoder_length=max_encoder_length,
min_prediction_length=max_prediction_length,
max_prediction_length=max_prediction_length,
# 特征（除了target外的）
time_varying_known_reals=time_varying_known_reals, # 【随时间变化且在未来已知的连续变量】
# 未来未知
time_varying_unknown_reals=time_varying_unknown_reals, # 【随时间变化且在未来未知的连续变量】
target_normalizer=EncoderNormalizer(),
add_encoder_length=False
)
val_dataloader = validing.to_dataloader(train=False,batch_size=args.batch_size,shuffle=False,num_workers=0,drop_last=False)
testing= TimeSeriesDataSet(
df_test,
time_idx="time_idx", # 【时间索引字段，确定样本的顺序】
target='target',
group_ids=['group_id'], # 【无】
min_encoder_length=max_encoder_length,
max_encoder_length=max_encoder_length,
min_prediction_length=max_prediction_length,
max_prediction_length=max_prediction_length,
# 特征（除了target外的）
time_varying_known_reals=time_varying_known_reals, # 【随时间变化且在未来已知的连续变量】
# 未来未知
time_varying_unknown_reals=time_varying_unknown_reals, # 【随时间变化且在未来未知的连续变量】
# # use softplus and normalize by group 【如何计算】 Softplus函数可以看作是ReLU函数的平滑。Softplus(x)=log(1+e^x)
target_normalizer=EncoderNormalizer(),
add_encoder_length=False
)
test_dataloader = testing.to_dataloader(train=False,batch_size=args.batch_size,shuffle=False,num_workers=0,drop_last=False)
#mask_len==
sp_root_dir = os.path.join(root_dir,'SP{}/'.format(start_point))
if not os.path.exists(sp_root_dir):
os.makedirs(sp_root_dir)
# 记录统计日志
after_name = 'in_l_{}_out_l_{}_Pcap'.format(max_encoder_length, max_prediction_length)
stat_log_path = os.path.join(sp_root_dir,
'log_stat_Feas_{}_{}_{}.txt'.format(len(training.reals), en_feats_num, after_name))
stat_log = open(stat_log_path, 'w', encoding='UTF-8')
print_log('model name:{}\n'.format(args.model),stat_log)
print_log('test name:{}, start point:{}\n'.format(args.test_name,start_point),stat_log)
# 10次随机实验的指标平均值:MAE,RMSE,R^2,RE,RUL_real,RUL_pred,AE==|RUL_real-RUL_pred|
MAE_avg = 0.
RMSE_avg = 0.
r2_avg = 0.
RE_avg= 0.
AE_avg = 0.
RUL_real_avg = 0.
RUL_pred_avg = 0.
count=0
stat_pred_data_list = []
from assistant import set_seed
while count<args.count:
count+=1
args.seed = set_seed(count)
# --------------------------------------step 2: 网络构建--------------------------------------------------------
# ----------------------------------------- 创建基线模型 ----------------------------------------------
from ModelsModify.RULMambaVAN import RULMambaVANNetModel
model = RULMambaVANNetModel.from_dataset(
training,
learning_rate=0.001,
lookback=args.seq_len,
predict=args.pred_len,
enc_in=len(time_varying_known_reals),
d_model=16,
d_ff=32,
dropout=0.1,
loss=SMAPE(),
)
# ----------------------------------------- 保存文件名，设置特征数量【必须】 -----------------------------------
after_name = 'in_l_{}_out_l_{}_Pcap'.format(max_encoder_length, max_prediction_length)
import time, datetime
save_dir = sp_root_dir+'Exp{}/'.format(count)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
curr_time = time.strftime('%Y-%m-%d %X', time.localtime()).replace(':', '-')
log_path = os.path.join(save_dir,
'log_Feas_{}_{}_{}.txt'.format(len(training.reals), en_feats_num, after_name))
log = open(log_path, 'w', encoding='UTF-8')
# 保存训练结果
study_model_path = os.path.join(save_dir, str(model).split('\n')[0][:-1])
if not os.path.exists(study_model_path):
os.makedirs(study_model_path)
print_log(('train dataset:{},val:{},test:{}'.format(int(0.8 * mask_len),int(0.1 * mask_len), len(df_test))), log)
print_log(('Input Feature num:{} ,name:{}'.format(len(training.reals), training.reals)), log)
print_log(('time_varying_unknown_reals num:{},decoder num:{} ,name:{}'.format(len(training.time_varying_unknown_reals),
de_feats_num - len(training.time_varying_unknown_reals),
training.time_varying_unknown_reals)),log)
print_log('model name:{}\n'.format(args.model),log)
print_log((f"Number of parameters in network(参数数量(Params)): {model.size()/1e3:.1f}k"),log)
# print_log(('model structure:\n{}'.format(model)),log)
# --------------------------------------step 3: 训练--------------------------------------------------------
# 提前停止的回call函数
early_stop_callback = EarlyStopping(monitor='val_loss',min_delta=1e-5,patience=10,verbose=False,mode='min')
# EarlyStopping 被配置为监控验证集上的损失（val_loss），并且在连续10个epoch（patience）中没有改进（即损失没有下降超过最小变化阈值 min_delta）时
# 停止训练。verbose=False 表示在停止训练时不会打印任何信息，mode='min' 表示我们关注的性能指标是最小化的，即损失。
# 学习率日志
# lr_logger = LearningRateMonitor() # log the learning rate
# 记录结果到张量板
# logger = TensorBoardLogger(os.path.join(study_model_path,"lightning_logs")) # logging results to a tensorboard
# 将在trainer.fit过程中产生的训练结果输出到日志，你可以使用Lightning框架提供的回调功能
# import logging
# # 设置日志记录器
# logger = logging.getLogger('lightning_logger')
# logger.setLevel(logging.INFO)
# # 创建一个文件处理器，将日志写入文件
# file_handler = logging.FileHandler(stat_log_path)
# file_handler.setLevel(logging.INFO)
# # 创建一个格式化器，设置日志消息的格式
# formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# file_handler.setFormatter(formatter)
# # 将文件处理器添加到日志记录器
# logger.addHandler(file_handler)
# # 创建一个TensorBoardLogger实例
# tb_logger = TensorBoardLogger(save_dir=os.path.join(study_model_path,"logs/"), name='training_metrics')
# 训练器设置
trainer = pl.Trainer(
max_epochs=args.max_epochs,
gpus=1,
# weights_summary="top",
gradient_clip_val=0.2,
# limit_train_batches=30, # coment in for training, running valiation every 30 batches
# fast_dev_run=True, # comment in to check that networkor dataset has no serious bugs
# callbacks=[lr_logger, early_stop_callback],
# callbacks=[early_stop_callback,CustomLoggerCallback()],
callbacks=[early_stop_callback],
logger=False, # 禁用内置的日志记录器
default_root_dir=study_model_path,
)
#'''
# 训练网络 ckpt_path: Path/URL of the checkpoint from which training is resumed.
trainer.fit(model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)
# *** 加载最优模型(本循环内) ***
best_model_path = trainer.checkpoint_callback.best_model_path
print('best_model_path:',best_model_path)
# print(('best_model_path:{}'.format(best_model_path)),log)
# 加载最佳模型参数
device=torch.device('cuda')
best_model = RULMambaVANNetModel.load_from_checkpoint(best_model_path).to(device=device)
# 最优前推
# 预测值(未经处理的)，返回输入x
predictions = best_model.predict(test_dataloader,batch_size=256)
actuals_df = df_all.loc[df_all['Cycle']>=start_point,['Cycle','target']]
actuals = actuals_df['target'].values
# ---------------------------- 统计常用指标 ------------------
from sklearn.metrics import r2_score
# from scipy.stats import pearsonr
predictions = predictions.detach().cpu().numpy().reshape(-1)
y_true = actuals*args.Rated_Capacity
y_pred = predictions*args.Rated_Capacity
mask = y_true >= 0.
# 指标:MAE,RMSE,R^2,RE,RUL_real,RUL_pred,AE==RUL_err,
# 统计指标：AMAE,ARMSE,AR^2,ARE,AAE
# NMAE
NMAE = np.mean(np.abs(y_true[mask] - y_pred[mask]))
# NRMSE
NRMSE = np.sqrt(np.mean(np.square(y_true[mask] - y_pred[mask])))
# R^2
r2 = r2_score(y_true[mask], y_pred[mask])
# RUL_real,RUL_pred,AE==RUL_err,RE
RUL_real,RUL_pred,AE,RE = rul_value_error(y_true[mask],y_pred[mask], threshold=args.Rated_Capacity*0.7)
print_log("实际运行的Epoch数量: {} \n".format(trainer.current_epoch),log)
print_log((
'第{}次实验的结果: \nMAE:{:.4f}, RMSE:{:.4f}, r2:{:.4f}, RUL_real:{}, RUL_pred:{}, AE:{}, RE:{:.4f} \n'.format(
count,NMAE,NRMSE,r2,RUL_real,RUL_pred,AE,RE)), log)
# 保存模型
trainer.save_checkpoint(
log_path[:-4] + '_MAE_' + str(NMAE)[:6] + '_RMSE_' + str(NRMSE)[:6] + '_AE_' + str(AE) + '.ckpt')
# --------------------------------------- 超参数调优 ---------------------------------
'''
使用 [optuna]( https://optuna.org/ ) 进行的超参数调整直接构建到 pytorch-forecasting 中。
例如，我们可以使用该 optimize_hyperparameters()函数来优化 TFT 的超参数。
'''
#'''
import pickle
# from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
from ModelsModify.optimize import optimize_hyperparameters
# 创建一个study，进行超参数优化； 优化方式：
study = optimize_hyperparameters(
RULMambaVANNetModel,
train_dataloaders=train_dataloader,
val_dataloaders=val_dataloader,
model_path=study_model_path, # 设置保存模型的文件夹
n_trials=200, # n_trials (int, optional): Number of hyperparameter trials to run. Defaults to 100.
max_epochs=args.max_epochs, # max_epochs (int, optional): Maximum number of epochs to run training. Defaults to 20.
enc_in = len(time_varying_known_reals),
max_encoder_length = args.seq_len,
max_prediction_length = args.pred_len,
gradient_clip_val_range=(0.1, 0.5),
learning_rate_range=(0.0001, 0.01),
trainer_kwargs=dict(limit_train_batches=128),
# reduce_on_plateau_patience=4,
use_learning_rate_finder=False,) # use Optuna to find ideal learning rate or use in-built learning rate finder
# 保存训练结果，ckpt格式，用pickle保存
with open(study_model_path+".pkl", "wb") as fout:
pickle.dump(study, fout)
# show best hyperparameters
print('study.best_trial.params:\n', study.best_trial.params)
# ----------------------------------------------- 评估性能 ----------------------------------------------------------
'''
# PyTorch Lightning 自动检查点训练，因此，我们可以轻松检索最佳模型并加载它。
'''
# load the best model according to the validation loss
# (given that we use early stopping, this is not necessarily the last epoch)
# 获取最佳模型路径 'lightning_logs\\default\\version_12\\checkpoints\\epoch=9-step=299.ckpt'
with open(study_model_path+".pkl", 'rb') as f:
study = pickle.load(f)
print('study:', study)
# 打印最佳模型
print('best_trial:{}\n\n'.format(study.best_trial))
# 打印最佳模型的关键参数
print('study.best_trial number:{} value:{} params:{}:\n'.format(study.best_trial.number, study.best_trial.value,
study.best_trial.params))
# 打印所有尝试的结果
print('study.trials:{}\n'.format(study.trials))
# *** ---------------------------打印最佳模型，并写入log *** ------------------------------------
# 打印最佳模型
print_log(('best_trial:{}\n\n'.format(study.best_trial)), log)
# 打印最佳模型的关键参数
print_log(('study.best_trial number:{} value:{} params:{}:\n'.format(study.best_trial.number, study.best_trial.value,
study.best_trial.params)), log)
# 打印所有尝试的结果
print_log(('study.trials:{}\n'.format(study.trials)), log)
# ---- 获取study的最优模型保存结果---
# 获取study最优模型的文件夹
study_best_model_dir = os.path.join(study_model_path, 'trial_{}'.format(study.best_trial.number))
# 获取study最优模型的路径
study_best_model_path=os.path.join(study_best_model_dir, os.listdir(study_best_model_dir)[0])
# 加载最佳模型参数
best_study_model = RULMambaVANNetModel.load_from_checkpoint(study_best_model_path)
predictions = best_study_model.predict(test_dataloader,batch_size=256)
actuals_df = df_all.loc[df_all['Cycle']>=start_point,['Cycle','target']]
actuals = actuals_df['target'].values
predictions = predictions.detach().cpu().numpy().reshape(-1)
y_true = actuals*args.Rated_Capacity
y_pred = predictions*args.Rated_Capacity
mask = y_true >= 0.
# 指标:MAE,RMSE,R^2,RE,RUL_real,RUL_pred,AE==RUL_err,
# 统计指标：AMAE,ARMSE,AR^2,ARE,AAE
# NMAE
NMAE = np.mean(np.abs(y_true[mask] - y_pred[mask]))
# NRMSE
NRMSE = np.sqrt(np.mean(np.square(y_true[mask] - y_pred[mask])))
# R^2
r2 = r2_score(y_true[mask], y_pred[mask])
# RUL_real,RUL_pred,AE==RUL_err,RE
RUL_real,RUL_pred,AE,RE = rul_value_error(y_true[mask],y_pred[mask], threshold=args.Rated_Capacity*0.7)
print_log((
'第{}次实验最佳模型的指标: \nMAE:{:.4f}, RMSE:{:.4f}, r2:{:.4f}, RUL_real:{}, RUL_pred:{}, AE:{}, RE:{:.4f} \n'.format(
count,NMAE,NRMSE,r2,RUL_real,RUL_pred,AE,RE)), log)
print_log((
'第{}次实验最佳模型的指标: \nMAE:{:.4f}, RMSE:{:.4f}, r2:{:.4f}, RUL_real:{}, RUL_pred:{}, AE:{}, RE:{:.4f} \n'.format(
count,NMAE,NRMSE,r2,RUL_real,RUL_pred,AE,RE)), stat_log)
# 记录最佳模型统计结果
stat_pred_data_list.append(y_pred)
MAE_avg += NMAE
RMSE_avg += NRMSE
r2_avg += r2
RE_avg += RE
AE_avg += AE
RUL_real_avg += RUL_real
RUL_pred_avg += RUL_pred
f=open(study_model_path+'.txt','w',encoding='UTF-8')
# 打印实验结果
print_log((
'第{}次实验的最佳结果: \nMAE:{:.4f}, RMSE:{:.4f}, r2:{:.4f}, RUL_real:{}, RUL_pred:{}, AE:{}, RE:{:.4f} \n'.format(
count,NMAE,NRMSE,r2,RUL_real,RUL_pred,AE,RE)), f)
print_log(('study_best_model_path:{}'.format(study_best_model_path)),f)
copyfile(study_best_model_path,log_path[:-4]+'_Best_Study_MAE_'+ str(NMAE)[:6] + '_RMSE_' + str(NRMSE)[:6] + '_AE_' + str(AE) + '.ckpt')
f.close()
del model
log.close()
all_pred_data_list.append(stat_pred_data_list)
# 记录结果之和
MAE_avg /= args.count
RMSE_avg /= args.count
r2_avg /= args.count
RE_avg /= args.count
AE_avg /= args.count
RUL_real_avg /= args.count
RUL_pred_avg /= args.count
print_log((
'{}次实验最佳模型指标的统计平均值: \nMAE:{:.4f}, RMSE:{:.4f}, r2:{:.4f}, RUL_real:{}, RUL_pred:{}, AE:{}, RE:{:.4f} \n'.format(
args.count,MAE_avg,RMSE_avg,r2_avg,RUL_real_avg,RUL_pred_avg,AE_avg,RE_avg)),stat_log)
setting = '{}_sl{}_ll{}_pl{}_bs{}_ct{}_tn{}_me{}'.format(
args.model,
args.seq_len,
args.label_len,
args.pred_len,
args.batch_size,
args.count,
int(args.test_name[-1:]),
args.max_epochs)
print_log('args setting : {}\n'.format(setting),stat_log)
stat_log.close()
results = dict()
results['SP200'] = all_pred_data_list[0]
results['SP300'] = all_pred_data_list[1]
results['SP400'] = all_pred_data_list[2]
if not os.path.exists('results'):
os.makedirs('results')
torch.save(results, 'results/RUL_{}_{}.pth'.format(args.test_name,args.model))
from Helper_Plot import *
if args.test_name == 'CY25_3':
for i in range(args.count):
single_model_draw_test_CY25_3_plt(real_data,all_pred_data_list[0][i],all_pred_data_list[1][i],all_pred_data_list[2][i],
save_filename='best_model_{}'.format(i)+'_RUL_Prediction',save_figure_dir=save_figure_dir,Rated_Capacity=args.Rated_Capacity,model=args.model)
elif args.test_name == 'CY25_1':
for i in range(args.count):
single_model_draw_test_CY25_1_plt(real_data,all_pred_data_list[0][i],all_pred_data_list[1][i],all_pred_data_list[2][i],
save_filename='best_model_{}'.format(i)+'_RUL_Prediction',save_figure_dir=save_figure_dir,Rated_Capacity=args.Rated_Capacity,model=args.model)
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