parameters: (filename, model_results_path, txn_sample_rate, model_map={}, predict_cache={}, trim=0.2)
return_statement: return _default_get_ou_runner_data(filename)
docstring: Get the training data from the ou runner
  
      :param filename: the input data file
      :param model_results_path: results log directory
      :param txn_sample_rate: sampling rate for the transaction OUs
      :param model_map: the map from OpUnit to the ou model
      :param predict_cache: cache for the ou model prediction
      :param trim: % of too high/too low anomalies to prune
      :return: the list of Data for execution operating units
docstring_summary: Get the training data from the ou runner
docstring_tokens: Get the training data from the ou runner
function: def get_ou_runner_data(filename, model_results_path, txn_sample_rate, model_map={}, predict_cache={}, trim=0.2):
      """Get the training data from the ou runner
  
      :param filename: the input data file
      :param model_results_path: results log directory
      :param txn_sample_rate: sampling rate for the transaction OUs
      :param model_map: the map from OpUnit to the ou model
      :param predict_cache: cache for the ou model prediction
      :param trim: % of too high/too low anomalies to prune
      :return: the list of Data for execution operating units
      """
  
      if "txn" in filename:
          # Cannot handle the transaction manager data yet
          return _txn_get_ou_runner_data(filename, model_results_path, txn_sample_rate)
      if "execution" in filename:
          # Handle the execution data
          return _execution_get_ou_runner_data(filename, model_map, predict_cache, trim)
      if "gc" in filename or "log" in filename:
          # Handle of the gc or log data with interval-based conversion
          return _interval_get_ou_runner_data(filename, model_results_path)
  
      return _default_get_ou_runner_data(filename)
function_tokens: def get_ou_runner_data ( filename , model_results_path , txn_sample_rate , model_map = { } , predict_cache = { } , trim = 0.2 ) : if "txn" in filename : # Cannot handle the transaction manager data yet return _txn_get_ou_runner_data ( filename , model_results_path , txn_sample_rate ) if "execution" in filename : # Handle the execution data return _execution_get_ou_runner_data ( filename , model_map , predict_cache , trim ) if "gc" in filename or "log" in filename : # Handle of the gc or log data with interval-based conversion return _interval_get_ou_runner_data ( filename , model_results_path ) return _default_get_ou_runner_data ( filename )