def get_eval_results()

in fairness_indicators/tutorial_utils/util.py [0:0]

• 21 lines of code
• 3 McCabe index (conditional complexity)

def get_eval_results(model_location,
                     eval_result_path,
                     validate_tfrecord_file,
                     slice_selection='religion',
                     thresholds=None,
                     compute_confidence_intervals=True):
  """Get Fairness Indicators eval results."""
  if thresholds is None:
    thresholds = [0.4, 0.4125, 0.425, 0.4375, 0.45, 0.4675, 0.475, 0.4875, 0.5]

  # Define slices that you want the evaluation to run on.
  eval_config = text_format.Parse(
      """
    model_specs {
     label_key: '%s'
   }
   metrics_specs {
     metrics {class_name: "AUC"}
     metrics {class_name: "ExampleCount"}
     metrics {class_name: "Accuracy"}
     metrics {
        class_name: "FairnessIndicators"
        config: '{"thresholds": %s}'
     }
   }
   slicing_specs {
     feature_keys: '%s'
   }
   slicing_specs {}
   options {
       compute_confidence_intervals { value: %s }
       disabled_outputs{values: "analysis"}
   }
   """ % (LABEL, thresholds,
          slice_selection, 'true' if compute_confidence_intervals else 'false'),
      tfma.EvalConfig())

  eval_shared_model = tfma.default_eval_shared_model(
      eval_saved_model_path=model_location, tags=[tf.saved_model.SERVING])

  # Run the fairness evaluation.
  return tfma.run_model_analysis(
      eval_shared_model=eval_shared_model,
      data_location=validate_tfrecord_file,
      file_format='tfrecords',
      eval_config=eval_config,
      output_path=eval_result_path,
      extractors=None)