text: import matplotlib.pyplot as plt
  import pandas as pd
  import os
  root_path = os.path.dirname(os.path.abspath('__file__'))
  import sys
  sys.path.append(root_path)
  from tools.models import esvr,multi_optimizer_esvr,esvr_multi_seed
  from tools.LSTM import BuildLSTM
  
  station = 'Zhangjiashan'
  predict_pattern = '7_ahead'
  train_samples = pd.read_csv(root_path+'/'+station+'/data/'+predict_pattern+'_pacf_lag12/minmax_unsample_train.csv')
  dev_samples = pd.read_csv(root_path+'/'+station+'/data/'+predict_pattern+'_pacf_lag12//minmax_unsample_dev.csv')
  test_samples = pd.read_csv(root_path+'/'+station+'/data/'+predict_pattern+'_pacf_lag12//minmax_unsample_test.csv')
  norm_id = pd.read_csv(root_path+'/'+station+'/data/'+predict_pattern+'_pacf_lag12//norm_unsample_id.csv')
  BuildLSTM(
      train_samples = train_samples,
      dev_samples = dev_samples,
      test_samples = test_samples,
      norm_id = norm_id,
      # model_path = root_path+'/'+station+'/projects/lstm/'+predict_pattern+'/',
      model_path = root_path+'\\'+station+'\\projects\\lstm\\'+predict_pattern+'\\',
      lags={"ORIG":12},
      seed = 1,
      batch_size=256,
      n_epochs = 500,
      max_trials = 50,
      executions_per_trial=3,
      max_hidden_layers = 2,
      min_units = 8,
      max_units = 32,
      unit_step = 8,
      min_droprate = 0.0,
      max_droprate = 0.5,
      droprate_step=0.1,
      min_learnrate=1e-4,
      max_learnrate=1e-1,
      n_tune_epochs = 500,
      measurement_time = 'month',
      measurement_unit = '$10^8m^3$',
  )
  
  