tcav/utils.py [158:206]:
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    min_p_val=0.05):
  """Helper function to organize results.
  If you ran TCAV with a random_counterpart, supply it here, otherwise supply random_concepts.
  If you get unexpected output, make sure you are using the correct keywords.

  Args:
    results: dictionary of results from TCAV runs.
    random_counterpart: name of the random_counterpart used, if it was used.
    random_concepts: list of random experiments that were run.
    num_random_exp: number of random experiments that were run.
    min_p_val: minimum p value for statistical significance
  """

  # helper function, returns if this is a random concept
  def is_random_concept(concept):
    if random_counterpart:
      return random_counterpart == concept

    elif random_concepts:
      return concept in random_concepts

    else:
      return 'random500_' in concept

  # print class, it will be the same for all
  print("Class =", results[0]['target_class'])

  # prepare data
  # dict with keys of concepts containing dict with bottlenecks
  result_summary = {}

  # random
  random_i_ups = {}

  for result in results:
    if result['cav_concept'] not in result_summary:
      result_summary[result['cav_concept']] = {}

    if result['bottleneck'] not in result_summary[result['cav_concept']]:
      result_summary[result['cav_concept']][result['bottleneck']] = []

    result_summary[result['cav_concept']][result['bottleneck']].append(result)

    # store random
    if is_random_concept(result['cav_concept']):
      if result['bottleneck'] not in random_i_ups:
        random_i_ups[result['bottleneck']] = []

      random_i_ups[result['bottleneck']].append(result['i_up'])
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tcav/utils_plot.py [27:78]:
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    min_p_val=0.05):
  """Helper function to organize results.
  When run in a notebook, outputs a matplotlib bar plot of the
  TCAV scores for all bottlenecks for each concept, replacing the
  bars with asterisks when the TCAV score is not statistically significant.
  If you ran TCAV with a random_counterpart, supply it here, otherwise supply random_concepts.
  If you get unexpected output, make sure you are using the correct keywords.

  Args:
    results: dictionary of results from TCAV runs.
    random_counterpart: name of the random_counterpart used, if it was used. 
    random_concepts: list of random experiments that were run. 
    num_random_exp: number of random experiments that were run.
    min_p_val: minimum p value for statistical significance
  """

  # helper function, returns if this is a random concept
  def is_random_concept(concept):
    if random_counterpart:
      return random_counterpart == concept
    
    elif random_concepts:
      return concept in random_concepts

    else:
      return 'random500_' in concept

  # print class, it will be the same for all
  print("Class =", results[0]['target_class'])

  # prepare data
  # dict with keys of concepts containing dict with bottlenecks
  result_summary = {}
    
  # random
  random_i_ups = {}
    
  for result in results:
    if result['cav_concept'] not in result_summary:
      result_summary[result['cav_concept']] = {}
    
    if result['bottleneck'] not in result_summary[result['cav_concept']]:
      result_summary[result['cav_concept']][result['bottleneck']] = []
    
    result_summary[result['cav_concept']][result['bottleneck']].append(result)

    # store random
    if is_random_concept(result['cav_concept']):
      if result['bottleneck'] not in random_i_ups:
        random_i_ups[result['bottleneck']] = []
        
      random_i_ups[result['bottleneck']].append(result['i_up'])
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