Fix f1 score for macro and ignore index (#495)

* fix
* add testing

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

CHANGELOG.md

@@ -24,6 +24,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- Fixed bug in `F1` with `average='macro'` and `ignore_index!=None` ([#495](https://github.com/PyTorchLightning/metrics/pull/495))
 
 ## [0.5.1] - 2021-08-30
 

tests/classification/test_f_beta.py

@@ -426,22 +426,25 @@ def test_top_k(
     assert torch.isclose(metric_fn(preds, target, top_k=k, average=average, num_classes=3), result)
 
 
+@pytest.mark.parametrize("ignore_index", [None, 2])
 @pytest.mark.parametrize("average", ["micro", "macro", "weighted"])
 @pytest.mark.parametrize(
     "metric_class, metric_functional, sk_fn",
     [(partial(FBeta, beta=2.0), partial(fbeta, beta=2.0), partial(fbeta_score, beta=2.0)), (F1, f1, f1_score)],
 )
-def test_same_input(metric_class, metric_functional, sk_fn, average):
+def test_same_input(metric_class, metric_functional, sk_fn, average, ignore_index):
     preds = _input_miss_class.preds
     target = _input_miss_class.target
     preds_flat = torch.cat([p for p in preds], dim=0)
     target_flat = torch.cat([t for t in target], dim=0)
 
-    mc = metric_class(num_classes=NUM_CLASSES, average=average)
+    mc = metric_class(num_classes=NUM_CLASSES, average=average, ignore_index=ignore_index)
     for i in range(NUM_BATCHES):
         mc.update(preds[i], target[i])
     class_res = mc.compute()
-    func_res = metric_functional(preds_flat, target_flat, num_classes=NUM_CLASSES, average=average)
+    func_res = metric_functional(
+        preds_flat, target_flat, num_classes=NUM_CLASSES, average=average, ignore_index=ignore_index
+    )
     sk_res = sk_fn(target_flat, preds_flat, average=average, zero_division=0)
 
     assert torch.allclose(class_res, torch.tensor(sk_res).float())

torchmetrics/functional/classification/f_beta.py

@@ -64,7 +64,6 @@ def _fbeta_compute(
         >>> _fbeta_compute(tp, fp, tn, fn, beta=0.5, ignore_index=None, average='micro', mdmc_average=None)
         tensor(0.3333)
     """
-
     if average == AvgMethod.MICRO and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
         mask = tp >= 0
         precision = _safe_divide(tp[mask].sum().float(), (tp[mask] + fp[mask]).sum())
@@ -73,11 +72,6 @@ def _fbeta_compute(
         precision = _safe_divide(tp.float(), tp + fp)
         recall = _safe_divide(tp.float(), tp + fn)
 
-    if average == AvgMethod.MACRO and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
-        cond = tp + fp + fn == 0
-        precision = precision[~cond]
-        recall = recall[~cond]
-
     num = (1 + beta ** 2) * precision * recall
     denom = beta ** 2 * precision + recall
     denom[denom == 0.0] = 1.0  # avoid division by 0
@@ -100,6 +94,11 @@ def _fbeta_compute(
             num[ignore_index, ...] = -1
             denom[ignore_index, ...] = -1
 
+    if average == AvgMethod.MACRO and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
+        cond = (tp + fp + fn == 0) | (tp + fp + fn == -3)
+        num = num[~cond]
+        denom = denom[~cond]
+
     return _reduce_stat_scores(
         numerator=num,
         denominator=denom,