Fix/multiclass recall macro avg ignore index

src/torchmetrics/classification/precision_recall.py

@@ -746,6 +746,7 @@ def compute(self) -> Tensor:
             fn,
             average=self.average,
             multidim_average=self.multidim_average,
+            ignore_index=self.ignore_index,
             top_k=self.top_k,
             zero_division=self.zero_division,
         )

src/torchmetrics/functional/classification/precision_recall.py

@@ -43,6 +43,7 @@ def _precision_recall_reduce(
     average: Optional[Literal["binary", "micro", "macro", "weighted", "none"]],
     multidim_average: Literal["global", "samplewise"] = "global",
     multilabel: bool = False,
+    ignore_index: Optional[int] = None,
     top_k: int = 1,
     zero_division: float = 0,
 ) -> Tensor:
@@ -56,7 +57,7 @@ def _precision_recall_reduce(
         return _safe_divide(tp, tp + different_stat, zero_division)
 
     score = _safe_divide(tp, tp + different_stat, zero_division)
-    return _adjust_weights_safe_divide(score, average, multilabel, tp, fp, fn, top_k=top_k)
+    return _adjust_weights_safe_divide(score, average, multilabel, tp, fp, fn, ignore_index=ignore_index, top_k=top_k)
 
 
 def binary_precision(

src/torchmetrics/utilities/compute.py

@@ -61,7 +61,14 @@ def _safe_divide(num: Tensor, denom: Tensor, zero_division: float = 0.0) -> Tens
 
 
 def _adjust_weights_safe_divide(
-    score: Tensor, average: Optional[str], multilabel: bool, tp: Tensor, fp: Tensor, fn: Tensor, top_k: int = 1
+    score: Tensor,
+    average: Optional[str],
+    multilabel: bool,
+    tp: Tensor,
+    fp: Tensor,
+    fn: Tensor,
+    ignore_index: Optional[int] = None,
+    top_k: int = 1,
 ) -> Tensor:
     if average is None or average == "none":
         return score
@@ -71,6 +78,10 @@ def _adjust_weights_safe_divide(
         weights = torch.ones_like(score)
         if not multilabel:
             weights[tp + fp + fn == 0 if top_k == 1 else tp + fn == 0] = 0.0
+
+    if ignore_index is not None and 0 <= ignore_index < len(score):
+        weights[ignore_index] = 0.0
+
     return _safe_divide(weights * score, weights.sum(-1, keepdim=True)).sum(-1)