top_k for MulticlassRecall is not working correctly

[cz23773]

The MulticlassRecall function with a top_k value greater than 1 and the average parameter set to "macro" is not behaving as anticipated. Ideally, as top_k increases, the results should improve. However, on certain occasions, this isn't the case.

To Reproduce

import torch
from torchmetrics.classification import MulticlassRecall

num_classes = 200
preds = torch.randn(5, num_classes).softmax(dim=-1)
target = torch.randint(num_classes, (5,))

f1_val_top1=MulticlassRecall(num_classes=num_classes, top_k=1, average="macro")
f1_val_top5=MulticlassRecall(num_classes=num_classes, top_k=5, average="macro")
f1_val_top10=MulticlassRecall(num_classes=num_classes, top_k=10, average="macro")
f1_val_top100=MulticlassRecall(num_classes=num_classes, top_k=100, average="macro")

print(f1_val_top1(preds, target), f1_val_top5(preds, target), f1_val_top10(preds, target),f1_val_top100(preds, target)) 

it returns tensor(0.) tensor(0.0357) tensor(0.0213) tensor(0.0154)

Expected behavior

The results should rise as k grows.

Environment

• TorchMetrics version: 1.2.1 (and how you installed: pip):
• Python & PyTorch Version: 3.10.0 & 1.12.1
• Any other relevant information such as OS (e.g., Linux): OS

Additional context

I checked the function _adjust_weights_safe_divide where it calculates the recall for function _precision_recall_reduce
and am unsure of this snippets:

      weights = torch.ones_like(score)
        if not multilabel:
            weights[tp + fp + fn == 0] = 0.0
      return _safe_divide(weights * score, weights.sum(-1, keepdim=True)).sum(-1)

In a multiclass scenario, when calculating top-k results, the number of false positives (fp) tends to increase with a higher value of k. This, in turn, augments the value of weights.sum(-1, keepdim=True) and consequently reduces the final recall@k. Also when we calculate recall, should it be weights[tp + fn == 0] = 0.0 ?