function_call_tree.yaml

run_pipeline

  validate_config
    make_dir

  preprocess

  if alg_type in ['prediction', 'anomaly']:
    if not config['train_models']:
      load_models()
        load_pickle_object_as_data()
    else:
      if alg_type == 'prediction':
        train_save_pred_models()
          if alg == 'lstm':
            compile_lstm()
            train_lstm_batch()
          elif alg == 'arima':
            train_arima()
          save_data_as_pickle()
      else:  #alg_type == 'anomaly':
        train_save_htm_models()
          reset_htm_config()
          htm_source.run_batch()
          save_data_as_pickle()

  if alg_type == 'prediction':
    get_models_preds()
      if test_mode == 'online':
        get_preds_online()
    get_models_dists_pred()
      if test_mode == 'batch':
        get_diff()
      else:
        get_diff_online()

  elif alg_type == 'anomaly':
    get_models_anomscores()
      get_htm_dist()
  else:
    get_models_dists_dist()
    if test_mode == 'batch':
      get_dist()
        if alg == 'dtw':
          get_dtw_dist()
        else:  # alg = 'edr'
          get_edr_dist()
    else:
      get_dist_online()
        get_dist()

  write_rankscores()
    make_dir()
    get_rankscore()