Fix missing attributes in some metric (#2028)

CHANGELOG.md

@@ -32,6 +32,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed support for pixelwise MSE ([#2017](https://github.com/Lightning-AI/torchmetrics/pull/2017)
 
 
+- Fixed missing attributes `higher_is_better`, `is_differentiable` for some metrics ([#2028](https://github.com/Lightning-AI/torchmetrics/pull/2028)
+
 ## [1.1.0] - 2023-08-22
 
 ### Added

src/torchmetrics/classification/accuracy.py

@@ -92,8 +92,8 @@ class BinaryAccuracy(BinaryStatScores):
         tensor([0.3333, 0.1667])
 
     """
-    is_differentiable = False
-    higher_is_better = True
+    is_differentiable: bool = False
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0
@@ -240,8 +240,8 @@ class MulticlassAccuracy(MulticlassStatScores):
                 [0.0000, 0.3333, 0.5000]])
 
     """
-    is_differentiable = False
-    higher_is_better = True
+    is_differentiable: bool = False
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0
@@ -389,8 +389,8 @@ class MultilabelAccuracy(MultilabelStatScores):
                 [0.0000, 0.0000, 0.5000]])
 
     """
-    is_differentiable = False
-    higher_is_better = True
+    is_differentiable: bool = False
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0

src/torchmetrics/classification/auroc.py

@@ -99,7 +99,7 @@ class BinaryAUROC(BinaryPrecisionRecallCurve):
 
     """
     is_differentiable: bool = False
-    higher_is_better: Optional[bool] = None
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0
@@ -241,7 +241,7 @@ class MulticlassAUROC(MulticlassPrecisionRecallCurve):
     """
 
     is_differentiable: bool = False
-    higher_is_better: Optional[bool] = None
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0
@@ -390,7 +390,7 @@ class MultilabelAUROC(MultilabelPrecisionRecallCurve):
 
     """
     is_differentiable: bool = False
-    higher_is_better: Optional[bool] = None
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0

src/torchmetrics/classification/average_precision.py

@@ -106,7 +106,7 @@ class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
 
     """
     is_differentiable: bool = False
-    higher_is_better: Optional[bool] = None
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0
@@ -239,7 +239,7 @@ class MulticlassAveragePrecision(MulticlassPrecisionRecallCurve):
     """
 
     is_differentiable: bool = False
-    higher_is_better: Optional[bool] = None
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0
@@ -393,7 +393,7 @@ class MultilabelAveragePrecision(MultilabelPrecisionRecallCurve):
 
     """
     is_differentiable: bool = False
-    higher_is_better: Optional[bool] = None
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0

src/torchmetrics/classification/exact_match.py

@@ -94,8 +94,8 @@ class MulticlassExactMatch(Metric):
         tensor([1., 0.])
 
     """
-    is_differentiable = False
-    higher_is_better = True
+    is_differentiable: bool = False
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0
@@ -257,8 +257,8 @@ class MultilabelExactMatch(Metric):
 
     """
 
-    is_differentiable = False
-    higher_is_better = True
+    is_differentiable: bool = False
+    higher_is_better: bool = True
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0

src/torchmetrics/classification/group_fairness.py

@@ -101,8 +101,8 @@ class BinaryGroupStatRates(_AbstractGroupStatScores):
         {'group_0': tensor([0., 0., 1., 0.]), 'group_1': tensor([1., 0., 0., 0.])}
 
     """
-    is_differentiable = False
-    higher_is_better = False
+    is_differentiable: bool = False
+    higher_is_better: bool = False
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0
@@ -207,8 +207,8 @@ class BinaryFairness(_AbstractGroupStatScores):
         {'DP_0_1': tensor(0.), 'EO_0_1': tensor(0.)}
 
     """
-    is_differentiable = False
-    higher_is_better = False
+    is_differentiable: bool = False
+    higher_is_better: bool = False
     full_state_update: bool = False
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0

src/torchmetrics/clustering/mutual_info_score.py

@@ -62,8 +62,8 @@ class MutualInfoScore(Metric):
 
     """
 
-    is_differentiable = True
-    higher_is_better = None
+    is_differentiable: bool = True
+    higher_is_better: bool = True
     full_state_update: bool = True
     plot_lower_bound: float = 0.0
     preds: List[Tensor]

src/torchmetrics/detection/ciou.py

@@ -93,6 +93,10 @@ class CompleteIntersectionOverUnion(IntersectionOverUnion):
             If torchvision is not installed with version 0.13.0 or newer.
 
     """
+    is_differentiable: bool = False
+    higher_is_better: Optional[bool] = True
+    full_state_update: bool = True
+
     _iou_type: str = "ciou"
     _invalid_val: float = -2.0  # unsure, min val could be just -1.5 as well
 

src/torchmetrics/detection/diou.py

@@ -93,6 +93,10 @@ class DistanceIntersectionOverUnion(IntersectionOverUnion):
             If torchvision is not installed with version 0.13.0 or newer.
 
     """
+    is_differentiable: bool = False
+    higher_is_better: Optional[bool] = True
+    full_state_update: bool = True
+
     _iou_type: str = "diou"
     _invalid_val: float = -1.0
 

src/torchmetrics/detection/giou.py

@@ -93,6 +93,10 @@ class GeneralizedIntersectionOverUnion(IntersectionOverUnion):
             If torchvision is not installed with version 0.8.0 or newer.
 
     """
+    is_differentiable: bool = False
+    higher_is_better: Optional[bool] = True
+    full_state_update: bool = True
+
     _iou_type: str = "giou"
     _invalid_val: float = -1.0
 

src/torchmetrics/image/perceptual_path_length.py

@@ -118,6 +118,9 @@ class PerceptualPathLength(Metric):
         tensor([0.3502, 0.1362, 0.2535, 0.0902, 0.1784, 0.0769, 0.5871, 0.0691, 0.3921]))
 
     """
+    is_differentiable: bool = False
+    higher_is_better: Optional[bool] = True
+    full_state_update: bool = True
 
     def __init__(
         self,

src/torchmetrics/regression/concordance.py

@@ -67,6 +67,10 @@ class ConcordanceCorrCoef(PearsonCorrCoef):
         tensor([0.7273, 0.9887])
 
     """
+    is_differentiable: bool = True
+    higher_is_better: bool = True
+    full_state_update: bool = True
+
     plot_lower_bound: float = -1.0
     plot_upper_bound: float = 1.0
 

src/torchmetrics/regression/pearson.py

@@ -110,8 +110,8 @@ class PearsonCorrCoef(Metric):
         tensor([1., 1.])
 
     """
-    is_differentiable = True
-    higher_is_better = None  # both -1 and 1 are optimal
+    is_differentiable: bool = True
+    higher_is_better: Optional[bool] = None  # both -1 and 1 are optimal
     full_state_update: bool = True
     plot_lower_bound: float = -1.0
     plot_upper_bound: float = 1.0