multilabel for AveragePrecision

CHANGELOG.md

@@ -17,6 +17,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added Symmetric Mean Absolute Percentage error (SMAPE) ([#375](https://github.com/PyTorchLightning/metrics/issues/375))
 
 
+- Allowed passing labels in (n_samples, n_classes) to `AveragePrecision` ([#386](https://github.com/PyTorchLightning/metrics/issues/386))
+
+
 ### Changed
 
 - Moved `psnr` and `ssim` from `functional.regression.*` to `functional.image.*` ([#382](https://github.com/PyTorchLightning/metrics/pull/382))

tests/classification/test_average_precision.py

@@ -21,6 +21,7 @@
 from tests.classification.inputs import _input_binary_prob
 from tests.classification.inputs import _input_multiclass_prob as _input_mcls_prob
 from tests.classification.inputs import _input_multidim_multiclass_prob as _input_mdmc_prob
+from tests.classification.inputs import _input_multilabel
 from tests.helpers import seed_all
 from tests.helpers.testers import NUM_CLASSES, MetricTester
 from torchmetrics.classification.average_precision import AveragePrecision
@@ -55,6 +56,12 @@ def _sk_avg_prec_multiclass_prob(preds, target, num_classes=1):
     return _sk_average_precision_score(y_true=sk_target, probas_pred=sk_preds, num_classes=num_classes)
 
 
+def _sk_avg_prec_multilabel_prob(preds, target, num_classes):
+    sk_preds = preds.reshape(-1, num_classes).numpy()
+    sk_target = target.view(-1, num_classes).numpy()
+    return sk_average_precision_score(sk_target, sk_preds, average=None)
+
+
 def _sk_avg_prec_multidim_multiclass_prob(preds, target, num_classes=1):
     sk_preds = preds.transpose(0, 1).reshape(num_classes, -1).transpose(0, 1).numpy()
     sk_target = target.view(-1).numpy()
@@ -66,6 +73,7 @@ def _sk_avg_prec_multidim_multiclass_prob(preds, target, num_classes=1):
         (_input_binary_prob.preds, _input_binary_prob.target, _sk_avg_prec_binary_prob, 1),
         (_input_mcls_prob.preds, _input_mcls_prob.target, _sk_avg_prec_multiclass_prob, NUM_CLASSES),
         (_input_mdmc_prob.preds, _input_mdmc_prob.target, _sk_avg_prec_multidim_multiclass_prob, NUM_CLASSES),
+        (_input_multilabel.preds, _input_multilabel.target, _sk_avg_prec_multilabel_prob, NUM_CLASSES),
     ]
 )
 class TestAveragePrecision(MetricTester):

torchmetrics/functional/classification/precision_recall_curve.py

@@ -148,13 +148,20 @@ def _precision_recall_curve_compute_multi_class(
     precision, recall, thresholds = [], [], []
     for cls in range(num_classes):
         preds_cls = preds[:, cls]
-        res = precision_recall_curve(
+
+        prc_args = dict(
             preds=preds_cls,
             target=target,
             num_classes=1,
             pos_label=cls,
             sample_weights=sample_weights,
         )
+        if target.ndim > 1:
+            prc_args.update(dict(
+                target=target[:, cls],
+                pos_label=1,
+            ))
+        res = precision_recall_curve(**prc_args)
         precision.append(res[0])
         recall.append(res[1])
         thresholds.append(res[2])