text: from sklearn.model_selection import ParameterGrid
  from model.main.traditional_rnn import Lstm1HL
  from utils.IOUtil import read_dataset_file
  from utils.SettingPaper import lstm1hl_giang1_paras as param_grid
  from utils.SettingPaper import giang1
  
  rv_data = [giang1]
  data_file = ["final"]
  test_type = "normal"                ### normal: for normal test, stability: for n_times test
  run_times = None
  
  if test_type == "normal":           ### For normal test
      run_times = 1
      pathsave = "paper/results/final/"
      all_model_file_name = "log_models"
  elif test_type == "stability":      ### For stability test (n times run with the same parameters)
      run_times = 15
      pathsave = "paper/results/stability/"
      all_model_file_name = "stability_lstm1hl"
  else:
      pass
  
  def train_model(item):
      root_base_paras = {
          "dataset": dataset,
          "data_idx": (0.66, 0, 0.33),
          "sliding": item["sliding_window"],
          "expand_function": item["expand_function"],
          "multi_output": requirement_variables[2],
          "output_idx": requirement_variables[3],
          "method_statistic": 0,  # 0: sliding window, 1: mean, 2: min-mean-max, 3: min-median-max
          "log_filename": all_model_file_name,
          "path_save_result": pathsave + requirement_variables[4],
          "test_type": test_type,
          "draw": True,
          "print_train": 0  # 0: nothing, else : full detail
      }
      root_rnn_paras = {
          "hidden_sizes": item["hidden_sizes"], "epoch": item["epoch"], "batch_size": item["batch_size"],
          "learning_rate": item["learning_rate"], "activations": item["activations"],
          "optimizer": item["optimizer"], "loss": item["loss"], "dropouts": item["dropouts"]
      }
      md = Lstm1HL(root_base_paras=root_base_paras, root_rnn_paras=root_rnn_paras)
      md._running__()
  
  
  for _ in range(run_times):
      for loop in range(len(rv_data)):
          requirement_variables = rv_data[loop]
          filename = requirement_variables[0] + data_file[loop] + ".csv"
          dataset = read_dataset_file(filename, requirement_variables[1])
          # Create combination of params.
          for item in list(ParameterGrid(param_grid)):
              train_model(item)
  
  
  