def _get_eval_metrics(lst_metrics):
'''
return multiple evaluation metrics
'''
all_metrics = [
'accuracy', 'ce', 'f1', 'mae', 'mse', 'rmse', 'top_k_accuracy',
'hinge_loss'
]
lst_child_metrics = []
eval_metrics = mx.metric.CompositeEvalMetric()
for metric in lst_metrics:
assert metric in all_metrics, logger.error(
'Invalid evaluation metric!')
if metric == 'accuracy':
lst_child_metrics.append(mx.metric.Accuracy())
elif metric == 'ce':
lst_child_metrics.append(mx.metric.CrossEntropy())
elif metric == 'f1':
lst_child_metrics.append(mx.metric.F1())
elif metric == 'mae':
lst_child_metrics.append(mx.metric.MAE())
elif metric == 'mse':
lst_child_metrics.append(mx.metric.MSE())
elif metric == 'rmse':
lst_child_metrics.append(mx.metric.RMSE())
elif metric == 'top_k_accuracy':
lst_child_metrics.append(
mx.metric.TopKAccuracy(top_k=int(args['--top-k'])))
elif metric == 'hinge_loss':
lst_child_metrics.append(svm_metric.HingeLoss())
for child_metric in lst_child_metrics:
eval_metrics.add(child_metric)
return eval_metrics
def inst_eval_metrics(lst_metrics, top_k=5):
'''
'''
all_metrics = {
'acc': mx.metric.Accuracy(),
'ce': mx.metric.CrossEntropy(),
'f1': mx.metric.F1(),
'mae': mx.metric.MAE(),
'mse': mx.metric.MSE(),
'rmse': mx.metric.RMSE(),
'top_k_acc': mx.metric.TopKAccuracy(top_k=top_k),
'hl': svm_metric.HingeLoss()
}
eval_metrics = mx.metric.CompositeEvalMetric()
for metric in lst_metrics:
assert metric in all_metrics, logging.error(
'Invalid evaluation metric!')
eval_metrics.add(all_metrics[metric])
logging.info('{} added into evaluation metric list'.format(metric))
return eval_metrics