def create_percentage_difference_dictionary()

in fairness_indicators/remediation/weight_utils.py [0:0]

• 15 lines of code
• 6 McCabe index (conditional complexity)

def create_percentage_difference_dictionary(
    eval_result: tfma.EvalResult,
    baseline_name: Text, metric_name: Text) -> Dict[Text, Any]:
  """Creates dictionary of a % difference between a baseline and other slices.

  Args:
    eval_result: Loaded eval result from running TensorFlow Model Analysis.
    baseline_name: Name of the baseline slice, 'Overall' or a specified tuple.
    metric_name: Name of the metric on which to perform comparisons.

  Returns:
    Dictionary mapping slices to percentage difference from the baseline slice.
  """
  baseline_value = get_baseline_value(eval_result, baseline_name, metric_name)
  difference = {}
  for metrics_tuple in eval_result.slicing_metrics:
    slice_key = metrics_tuple[0]
    metrics = metrics_tuple[1]
    # Concatenate feature name/values for intersectional features.
    column = '-'.join([elem[0] for elem in slice_key])
    feature_val = '-'.join([elem[1] for elem in slice_key])
    if column not in difference:
      difference[column] = {}
    difference[column][feature_val] = (_get_metric_value(metrics, metric_name)
                                       - baseline_value) / baseline_value
  return difference