Fix metrics in macro average

CHANGELOG.md

@@ -77,6 +77,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- Fixed bug where classification metrics with `average='macro'` would lead to wrong result if a class was missing ([#303](https://github.com/PyTorchLightning/metrics/pull/303))
+
+
 - Fixed `weighted`, `multi-class` AUROC computation to allow for 0 observations of some class, as contribution to final AUROC is 0 ([#348](https://github.com/PyTorchLightning/metrics/issues/348))
 
 
@@ -85,7 +88,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Fixed calculation in `IoU` metric when using `ignore_index` argument ([#328](https://github.com/PyTorchLightning/metrics/pull/328))
 
-
 ## [0.4.1] - 2021-07-05
 
 ### Changed

tests/classification/inputs.py

@@ -116,3 +116,10 @@ def generate_plausible_inputs_binary(num_batches=NUM_BATCHES, batch_size=BATCH_S
 _input_multilabel_prob_plausible = generate_plausible_inputs_multilabel()
 
 _input_binary_prob_plausible = generate_plausible_inputs_binary()
+
+# randomly remove one class from the input
+_temp = torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE))
+_class_remove, _class_replace = torch.multinomial(torch.ones(NUM_CLASSES), num_samples=2, replacement=False)
+_temp[_temp == _class_remove] = _class_replace
+
+_input_multiclass_with_missing_class = Input(_temp.clone(), _temp.clone())

tests/classification/test_accuracy.py

@@ -23,6 +23,7 @@
 from tests.classification.inputs import _input_multiclass as _input_mcls
 from tests.classification.inputs import _input_multiclass_logits as _input_mcls_logits
 from tests.classification.inputs import _input_multiclass_prob as _input_mcls_prob
+from tests.classification.inputs import _input_multiclass_with_missing_class as _input_miss_class
 from tests.classification.inputs import _input_multidim_multiclass as _input_mdmc
 from tests.classification.inputs import _input_multidim_multiclass_prob as _input_mdmc_prob
 from tests.classification.inputs import _input_multilabel as _input_mlb
@@ -31,7 +32,7 @@
 from tests.classification.inputs import _input_multilabel_multidim_prob as _input_mlmd_prob
 from tests.classification.inputs import _input_multilabel_prob as _input_mlb_prob
 from tests.helpers import seed_all
-from tests.helpers.testers import NUM_CLASSES, THRESHOLD, MetricTester
+from tests.helpers.testers import NUM_BATCHES, NUM_CLASSES, THRESHOLD, MetricTester
 from torchmetrics import Accuracy
 from torchmetrics.functional import accuracy
 from torchmetrics.utilities.checks import _input_format_classification
@@ -350,3 +351,21 @@ def test_class_not_present(metric_class, metric_fn, ignore_index, expected):
     cl_metric(preds, target)
     result_cl = cl_metric.compute()
     assert torch.allclose(expected, result_cl, equal_nan=True)
+
+
+@pytest.mark.parametrize('average', ['micro', 'macro', 'weighted'])
+def test_same_input(average):
+    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 = Accuracy(num_classes=NUM_CLASSES, average=average)
+    for i in range(NUM_BATCHES):
+        mc.update(preds[i], target[i])
+    class_res = mc.compute()
+    func_res = accuracy(preds_flat, target_flat, num_classes=NUM_CLASSES, average=average)
+    sk_res = sk_accuracy(target_flat, preds_flat)
+
+    assert torch.allclose(class_res, torch.tensor(sk_res).float())
+    assert torch.allclose(func_res, torch.tensor(sk_res).float())

tests/classification/test_f_beta.py

@@ -24,13 +24,14 @@
 from tests.classification.inputs import _input_multiclass as _input_mcls
 from tests.classification.inputs import _input_multiclass_logits as _input_mcls_logits
 from tests.classification.inputs import _input_multiclass_prob as _input_mcls_prob
+from tests.classification.inputs import _input_multiclass_with_missing_class as _input_miss_class
 from tests.classification.inputs import _input_multidim_multiclass as _input_mdmc
 from tests.classification.inputs import _input_multidim_multiclass_prob as _input_mdmc_prob
 from tests.classification.inputs import _input_multilabel as _input_mlb
 from tests.classification.inputs import _input_multilabel_logits as _input_mlb_logits
 from tests.classification.inputs import _input_multilabel_prob as _input_mlb_prob
 from tests.helpers import seed_all
-from tests.helpers.testers import NUM_CLASSES, THRESHOLD, MetricTester
+from tests.helpers.testers import NUM_BATCHES, NUM_CLASSES, THRESHOLD, MetricTester
 from torchmetrics import F1, FBeta, Metric
 from torchmetrics.functional import f1, fbeta
 from torchmetrics.utilities.checks import _input_format_classification
@@ -55,7 +56,6 @@ def _sk_fbeta_f1(preds, target, sk_fn, num_classes, average, multiclass, ignore_
         preds, target, THRESHOLD, num_classes=num_classes, multiclass=multiclass
     )
     sk_preds, sk_target = sk_preds.numpy(), sk_target.numpy()
-
     sk_scores = sk_fn(sk_target, sk_preds, average=average, zero_division=0, labels=labels)
 
     if len(labels) != num_classes and not average:
@@ -408,3 +408,25 @@ def test_top_k(
 
     assert torch.isclose(class_metric.compute(), result)
     assert torch.isclose(metric_fn(preds, target, top_k=k, average=average, num_classes=3), result)
+
+
+@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):
+    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)
+    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)
+    sk_res = sk_fn(target_flat, preds_flat, average=average, zero_division=0)
+
+    assert torch.allclose(class_res, torch.tensor(sk_res).float())
+    assert torch.allclose(func_res, torch.tensor(sk_res).float())

tests/classification/test_precision_recall.py

@@ -24,13 +24,14 @@
 from tests.classification.inputs import _input_multiclass as _input_mcls
 from tests.classification.inputs import _input_multiclass_logits as _input_mcls_logits
 from tests.classification.inputs import _input_multiclass_prob as _input_mcls_prob
+from tests.classification.inputs import _input_multiclass_with_missing_class as _input_miss_class
 from tests.classification.inputs import _input_multidim_multiclass as _input_mdmc
 from tests.classification.inputs import _input_multidim_multiclass_prob as _input_mdmc_prob
 from tests.classification.inputs import _input_multilabel as _input_mlb
 from tests.classification.inputs import _input_multilabel_logits as _input_mlb_logits
 from tests.classification.inputs import _input_multilabel_prob as _input_mlb_prob
 from tests.helpers import seed_all
-from tests.helpers.testers import NUM_CLASSES, THRESHOLD, MetricTester
+from tests.helpers.testers import NUM_BATCHES, NUM_CLASSES, THRESHOLD, MetricTester
 from torchmetrics import Metric, Precision, Recall
 from torchmetrics.functional import precision, precision_recall, recall
 from torchmetrics.utilities.checks import _input_format_classification
@@ -202,7 +203,7 @@ def test_no_support(metric_class, metric_fn):
 class TestPrecisionRecall(MetricTester):
 
     @pytest.mark.parametrize("ddp", [False, True])
-    @pytest.mark.parametrize("dist_sync_on_step", [True, False])
+    @pytest.mark.parametrize("dist_sync_on_step", [False])
     def test_precision_recall_class(
         self,
         ddp: bool,
@@ -430,3 +431,24 @@ def test_class_not_present(metric_class, metric_fn, ignore_index, expected):
     cl_metric(preds, target)
     result_cl = cl_metric.compute()
     assert torch.allclose(expected, result_cl, equal_nan=True)
+
+
+@pytest.mark.parametrize('average', ['micro', 'macro', 'weighted'])
+@pytest.mark.parametrize(
+    'metric_class, metric_functional, sk_fn', [(Precision, precision, precision_score), (Recall, recall, recall_score)]
+)
+def test_same_input(metric_class, metric_functional, sk_fn, average):
+    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)
+    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)
+    sk_res = sk_fn(target_flat, preds_flat, average=average, zero_division=1)
+
+    assert torch.allclose(class_res, torch.tensor(sk_res).float())
+    assert torch.allclose(func_res, torch.tensor(sk_res).float())

torchmetrics/functional/classification/accuracy.py

@@ -85,6 +85,12 @@ def _accuracy_compute(
     else:
         numerator = tp
         denominator = tp + fn
+
+    if average == AverageMethod.MACRO and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
+        cond = tp + fp + fn == 0
+        numerator = numerator[~cond]
+        denominator = denominator[~cond]
+
     if average == AverageMethod.NONE and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
         # a class is not present if there exists no TPs, no FPs, and no FNs
         meaningless_indeces = torch.nonzero((tp | fn | fp) == 0).cpu()

torchmetrics/functional/classification/f_beta.py

@@ -46,9 +46,14 @@ 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.] = 1  # avoid division by 0
+    denom[denom == 0.] = 1.0  # avoid division by 0
     # if classes matter and a given class is not present in both the preds and the target,
     # computing the score for this class is meaningless, thus they should be ignored
     if average == AvgMethod.NONE and mdmc_average != MDMCAverageMethod.SAMPLEWISE:

torchmetrics/functional/classification/precision_recall.py

@@ -29,6 +29,12 @@ def _precision_compute(
 ) -> Tensor:
     numerator = tp
     denominator = tp + fp
+
+    if average == AverageMethod.MACRO and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
+        cond = tp + fp + fn == 0
+        numerator = numerator[~cond]
+        denominator = denominator[~cond]
+
     if average == AverageMethod.NONE and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
         # a class is not present if there exists no TPs, no FPs, and no FNs
         meaningless_indeces = torch.nonzero((tp | fn | fp) == 0).cpu()
@@ -199,11 +205,18 @@ def _recall_compute(
 ) -> Tensor:
     numerator = tp
     denominator = tp + fn
+
+    if average == AverageMethod.MACRO and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
+        cond = tp + fp + fn == 0
+        numerator = numerator[~cond]
+        denominator = denominator[~cond]
+
     if average == AverageMethod.NONE and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
         # a class is not present if there exists no TPs, no FPs, and no FNs
         meaningless_indeces = ((tp | fn | fp) == 0).nonzero().cpu()
         numerator[meaningless_indeces, ...] = -1
         denominator[meaningless_indeces, ...] = -1
+
     return _reduce_stat_scores(
         numerator=numerator,
         denominator=denominator,