text: from main import run_main
  import numpy as np
  
  #datasets = ['DREAM5_ecoli_expression_data']
  datasets = ['gasch_GSE102475','gasch_GSE102475']
  #ground_truths = ['DREAM5_yeast_ground_truth']
  ground_truths = ['yeast_chipunion_KDUnion_intersect','yeast_chipunion_KDUnion_intersect']
  epochs = [5,5]
  leap_path = 'logs/leap/MAC_symmetric_gasch_example.csv'
  #leap_path = 'logs/leap/MAC_symmetric_dream5_ecoli.csv'
  runs = range(2)
  
  granpy, pearson, random, leap = ([] for i in range(4))
  for dataset, ground_truth, epoch in zip(datasets, ground_truths, epochs):
      for run in runs:
          granpy_scores, pearson_scores, random_scores, leap_scores = run_main(dataset, ground_truth, epoch, leap_path)
          granpy.append(granpy_scores)
          pearson.append(pearson_scores)
          random.append(random_scores)
          leap.append(leap_scores)
  
  scores = [granpy, pearson, random, leap]
  for score in scores:
      print(score)
      acc, ap, roc, f1 = zip(*score)
      for idx, dataset in enumerate(datasets):
          start = idx*(len(score)/len(datasets))
          end = (idx+1)*(len(score)/len(datasets))
          print(start)
          print(end)
          print(acc)
          print(acc[start:end])
          print('mean acc ' + str(np.mean(acc[start:end])) + ' with a sd of ' + str(np.std(acc[start:end])))
          print('mean ap ' + str(np.mean(ap[start:end])) + ' with a sd of ' + str(np.std(ap[start:end])))
          print('mean roc ' + str(np.mean(roc[start:end])) + ' with a sd of ' + str(np.std(roc[start:end])))
          print('mean f1 ' + str(np.mean(f1[start:end])) + ' with sd a of ' + str(np.std(f1[start:end])))
  
  
  if len(granpy) != 0:
      print(pearson)
      print(granpy)
      #acc, ap, roc = zip(*pearson)
      #print('unzipped pearson acc for 2 runs')
      #print(acc)
      #print('average acc of the 2 runs')
      #print(np.mean(acc))
      #print(ap)
      #print(np.mean(granpy_scores))
  