def _wrapper(
recall_metric: Metric,
precision_metric: Metric,
f: Metric,
a_recall: Metric,
a_precision: Metric,
a_f: Metric,
) -> Union[Collection[str], Dict]:
p_tensor, r_tensor, f_tensor = precision_metric, recall_metric, f
if p_tensor.shape != r_tensor.shape:
raise ValueError(
"Internal error: Precision and Recall have mismatched shapes: "
f"{p_tensor.shape} vs {r_tensor.shape}. Please, open an issue "
"with a reference on this error. Thank you!")
dict_obj = {}
for idx, p_label in enumerate(p_tensor):
dict_obj[_get_label_for_class(idx)] = {
"precision": p_label.item(),
"recall": r_tensor[idx].item(),
"f{0}-score".format(beta): f_tensor[idx].item(),
}
dict_obj["macro avg"] = {
"precision": a_precision.item(),
"recall": a_recall.item(),
"f{0}-score".format(beta): a_f.item(),
}
return dict_obj if output_dict else json.dumps(dict_obj)
def test_abstract_class():
with raises(TypeError):
Metric()