def print_evaluation_stats(test_stats):
for output_field, result in test_stats.items():
if output_field != COMBINED or (output_field == COMBINED and len(test_stats) > 2):
logger.info(f"\n===== {output_field} =====")
for metric in sorted(list(result)):
if metric not in SKIP_EVAL_METRICS:
value = result[metric]
if isinstance(value, OrderedDict):
value_repr = repr_ordered_dict(value)
else:
value_repr = pformat(result[metric], indent=2)
logger.info(f"{metric}: {value_repr}")
def print_test_results(test_stats):
for output_field, result in test_stats.items():
if (output_field != 'combined'
or (output_field == 'combined' and len(test_stats) > 2)):
logger.info('\n===== {} ====='.format(output_field))
for measure in sorted(list(result)):
if measure != 'confusion_matrix' and measure != 'roc_curve':
value = result[measure]
if isinstance(value, OrderedDict):
value_repr = repr_ordered_dict(value)
else:
value_repr = pformat(result[measure], indent=2)
logger.info('{0}: {1}'.format(measure, value_repr))
