# Training, Testing and Evaluation

# Please run data preprocessing code before running the following commands.

# OPTIONS
# dataset_path: The path of the processed training or testing dataset
# checkpoints_path: The path to store the trained models
# n: The number of KPIs 
# start_epoch: The model with corresponding training epochs will be resumed for testing, default is 30
#              Set it to 40 please, if you want to test the model which trained 40 epochs 
# llh_path: The path of log-likelihood (anomaly score) file output by testing

# Machine-1-1
python trainer.py --dataset_path ../data_preprocess/data_processed/machine-1-1-train --gpu_id 0 --log_path log_trainer/machine-1-1 --checkpoints_path model/machine-1-1 --n 38  
nohup python tester.py --dataset_path ../data_preprocess/data_processed/machine-1-1-test --gpu_id 0 --log_path log_tester/machine-1-1 --checkpoints_path model/machine-1-1 --n 38 --start_epoch 30 2>&1 &
nohup python evaluation.py --llh_path log_tester/machine-1-1 --log_path log_evaluator/machine-1-1 --n 38 --start_epoch 30 2>&1 &

# Machine-1-5
python trainer.py --dataset_path ../data_preprocess/data_processed/machine-1-5-train --gpu_id 0 --log_path log_trainer/machine-1-5 --checkpoints_path model/machine-1-5 --n 38  
nohup python tester.py --dataset_path ../data_preprocess/data_processed/machine-1-5-test --gpu_id 0 --log_path log_tester/machine-1-5 --checkpoints_path model/machine-1-5 --n 38 --start_epoch 30 2>&1 &
nohup python evaluation.py --llh_path log_tester/machine-1-5 --log_path log_evaluator/machine-1-5 --n 38 --start_epoch 30 2>&1 &
