docstr: :param mode: either 'val_sampling', 'val_patient' or 'test'. handles prediction lists of different forms.
docstr_tok: [":","param","mode",":","either","'val_sampling","'",",","'val_patient","'","or","'test","'",".","handles","prediction","lists","of","different","forms","."]
code: def __init__(self, cf, logger, mode="test"):
      """
      :param mode: either 'val_sampling', 'val_patient' or 'test'. handles prediction lists of different forms.
      """
      self.cf = cf
      self.logger = logger
      self.mode = mode
  
file_path: medicaldetectiontoolkit/evaluator.py
imports: import os
  import numpy
  import pandas
  from sklearn.metrics import roc_auc_score, average_precision_score
  from sklearn.metrics import roc_curve, precision_recall_curve
  import utils.model_utils
  import plotting
  from multiprocessing import Pool
label_desc: Use image node_id 1 to create a new Evaluator object  with example: obj = Evaluator(cf, logger, mode)