fix: Ensure consistent default values('micro') for average argument in classification metrics

Author: rittik9

docs/source/pages/overview.rst

@@ -453,7 +453,7 @@ of metrics e.g. computation of confidence intervals by resampling of input data.
 .. testoutput::
     :options: +NORMALIZE_WHITESPACE
 
-    {'mean': tensor(0.1333), 'std': tensor(0.1554)}
+    {'mean': tensor(0.1069), 'std': tensor(0.1180)}
 
 You can see all implemented wrappers under the wrapper section of the API docs.
 

src/torchmetrics/classification/accuracy.py

@@ -214,7 +214,7 @@ class MulticlassAccuracy(MulticlassStatScores):
         >>> preds = tensor([2, 1, 0, 1])
         >>> metric = MulticlassAccuracy(num_classes=3)
         >>> metric(preds, target)
-        tensor(0.8333)
+        tensor(0.7500)
         >>> mca = MulticlassAccuracy(num_classes=3, average=None)
         >>> mca(preds, target)
         tensor([0.5000, 1.0000, 1.0000])
@@ -228,7 +228,7 @@ class MulticlassAccuracy(MulticlassStatScores):
         ...                 [0.05, 0.82, 0.13]])
         >>> metric = MulticlassAccuracy(num_classes=3)
         >>> metric(preds, target)
-        tensor(0.8333)
+        tensor(0.7500)
         >>> mca = MulticlassAccuracy(num_classes=3, average=None)
         >>> mca(preds, target)
         tensor([0.5000, 1.0000, 1.0000])

src/torchmetrics/classification/hamming.py

@@ -224,7 +224,7 @@ class MulticlassHammingDistance(MulticlassStatScores):
         >>> preds = tensor([2, 1, 0, 1])
         >>> metric = MulticlassHammingDistance(num_classes=3)
         >>> metric(preds, target)
-        tensor(0.1667)
+        tensor(0.2500)
         >>> mchd = MulticlassHammingDistance(num_classes=3, average=None)
         >>> mchd(preds, target)
         tensor([0.5000, 0.0000, 0.0000])

src/torchmetrics/classification/negative_predictive_value.py

@@ -220,7 +220,7 @@ class MulticlassNegativePredictiveValue(MulticlassStatScores):
         >>> preds = tensor([2, 1, 0, 1])
         >>> metric = MulticlassNegativePredictiveValue(num_classes=3)
         >>> metric(preds, target)
-        tensor(0.8889)
+        tensor(0.8750)
         >>> metric = MulticlassNegativePredictiveValue(num_classes=3, average=None)
         >>> metric(preds, target)
         tensor([0.6667, 1.0000, 1.0000])
@@ -371,7 +371,7 @@ class MultilabelNegativePredictiveValue(MultilabelStatScores):
         >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
         >>> metric = MultilabelNegativePredictiveValue(num_labels=3)
         >>> metric(preds, target)
-        tensor(0.5000)
+        tensor(0.6667)
         >>> mls = MultilabelNegativePredictiveValue(num_labels=3, average=None)
         >>> mls(preds, target)
         tensor([1.0000, 0.5000, 0.0000])

src/torchmetrics/classification/precision_recall.py

@@ -237,7 +237,7 @@ class MulticlassPrecision(MulticlassStatScores):
         >>> preds = tensor([2, 1, 0, 1])
         >>> metric = MulticlassPrecision(num_classes=3)
         >>> metric(preds, target)
-        tensor(0.8333)
+        tensor(0.7500)
         >>> mcp = MulticlassPrecision(num_classes=3, average=None)
         >>> mcp(preds, target)
         tensor([1.0000, 0.5000, 1.0000])
@@ -402,7 +402,7 @@ class MultilabelPrecision(MultilabelStatScores):
         >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
         >>> metric = MultilabelPrecision(num_labels=3)
         >>> metric(preds, target)
-        tensor(0.5000)
+        tensor(0.6667)
         >>> mlp = MultilabelPrecision(num_labels=3, average=None)
         >>> mlp(preds, target)
         tensor([1.0000, 0.0000, 0.5000])
@@ -696,7 +696,7 @@ class MulticlassRecall(MulticlassStatScores):
         >>> preds = tensor([2, 1, 0, 1])
         >>> metric = MulticlassRecall(num_classes=3)
         >>> metric(preds, target)
-        tensor(0.8333)
+        tensor(0.7500)
         >>> mcr = MulticlassRecall(num_classes=3, average=None)
         >>> mcr(preds, target)
         tensor([0.5000, 1.0000, 1.0000])

src/torchmetrics/classification/specificity.py

@@ -214,7 +214,7 @@ class MulticlassSpecificity(MulticlassStatScores):
         >>> preds = tensor([2, 1, 0, 1])
         >>> metric = MulticlassSpecificity(num_classes=3)
         >>> metric(preds, target)
-        tensor(0.8889)
+        tensor(0.8750)
         >>> mcs = MulticlassSpecificity(num_classes=3, average=None)
         >>> mcs(preds, target)
         tensor([1.0000, 0.6667, 1.0000])

src/torchmetrics/classification/stat_scores.py

@@ -309,7 +309,7 @@ def __init__(
         self,
         num_classes: int,
         top_k: int = 1,
-        average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
+        average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
         multidim_average: Literal["global", "samplewise"] = "global",
         ignore_index: Optional[int] = None,
         validate_args: bool = True,
@@ -461,7 +461,7 @@ def __init__(
         self,
         num_labels: int,
         threshold: float = 0.5,
-        average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
+        average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
         multidim_average: Literal["global", "samplewise"] = "global",
         ignore_index: Optional[int] = None,
         validate_args: bool = True,

src/torchmetrics/functional/classification/accuracy.py

@@ -167,7 +167,7 @@ def multiclass_accuracy(
     preds: Tensor,
     target: Tensor,
     num_classes: int,
-    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     top_k: int = 1,
     multidim_average: Literal["global", "samplewise"] = "global",
     ignore_index: Optional[int] = None,
@@ -276,7 +276,7 @@ def multilabel_accuracy(
     target: Tensor,
     num_labels: int,
     threshold: float = 0.5,
-    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     multidim_average: Literal["global", "samplewise"] = "global",
     ignore_index: Optional[int] = None,
     validate_args: bool = True,