def run(pool, expName, name, args):
  """
  Runs :func:`analyzeWeightPruning` in parallel and save the results

  :param pool: multiprocessing pool
  :param expName: Experiment name
  :param name: File/Plot name (i.e. 'weight_prunning')
  :param args: Argument list to be passed to :func:`analyzeWeightPruning`
  :return: panda dataframe with all the results

  """
  tables = pool.map(analyzeWeightPruning, args)
  merged = pd.concat(tables, axis=1).sort_index(axis=1)
  filename = "{}_{}".format(name, expName)
  plotDataframe(merged, filename, "{}.pdf".format(filename))
  print()
  print(filename)
  print(tabulate(merged, headers='keys', tablefmt='fancy_grid',
                 numalign="right"))
  merged.to_csv("{}.csv".format(filename))
  return merged
def run ( pool , expName , name , args ) : tables = pool . map ( analyzeWeightPruning , args ) merged = pd . concat ( tables , axis = 1 ) . sort_index ( axis = 1 ) filename = "{}_{}" . format ( name , expName ) plotDataframe ( merged , filename , "{}.pdf" . format ( filename ) ) print ( ) print ( filename ) print ( tabulate ( merged , headers = 'keys' , tablefmt = 'fancy_grid' , numalign = "right" ) ) merged . to_csv ( "{}.csv" . format ( filename ) ) return merged
Runs :func:`analyzeWeightPruning` in parallel and save the results

  :param pool: multiprocessing pool
  :param expName: Experiment name
  :param name: File/Plot name (i.e. 'weight_prunning')
  :param args: Argument list to be passed to :func:`analyzeWeightPruning`
  :return: panda dataframe with all the results