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Fix: Set default average='micro' consistently across classification metrics (some of the subclasses of MulticlassStatScores, MultilabelStatScores) #2882

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Fix: Set default average='micro' consistently across classification metrics (some of the subclasses of MulticlassStatScores, MultilabelStatScores) #2882

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fb80698
fix: Ensure consistent default values('micro') for average argument i…
rittik9 Dec 24, 2024
5c1db37
fix modular doctests
rittik9 Dec 24, 2024
954be86
Update accuracy.py
rittik9 Dec 24, 2024
d50999f
fix default vals for functional forms
rittik9 Dec 24, 2024
64c10c8
Merge branch 'master' into rittik/avg
rittik9 Jan 13, 2025
9489cad
Merge branch 'master' into rittik/avg
rittik9 Jan 25, 2025
d19a1eb
Merge branch 'master' into rittik/avg
rittik9 Jan 31, 2025
fcb51ee
Merge branch 'master' into rittik/avg
rittik9 Feb 26, 2025
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2 changes: 1 addition & 1 deletion docs/source/pages/overview.rst
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Expand Up @@ -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.

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6 changes: 3 additions & 3 deletions src/torchmetrics/classification/accuracy.py
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Expand Up @@ -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])
Expand All @@ -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])
Expand All @@ -239,7 +239,7 @@ class MulticlassAccuracy(MulticlassStatScores):
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassAccuracy(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.5000, 0.2778])
tensor([0.5000, 0.3333])
>>> mca = MulticlassAccuracy(num_classes=3, multidim_average='samplewise', average=None)
>>> mca(preds, target)
tensor([[1.0000, 0.0000, 0.5000],
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6 changes: 3 additions & 3 deletions src/torchmetrics/classification/hamming.py
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Expand Up @@ -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])
Expand All @@ -238,7 +238,7 @@ class MulticlassHammingDistance(MulticlassStatScores):
... [0.05, 0.82, 0.13]])
>>> 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])
Expand All @@ -249,7 +249,7 @@ class MulticlassHammingDistance(MulticlassStatScores):
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassHammingDistance(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.5000, 0.7222])
tensor([0.5000, 0.6667])
>>> mchd = MulticlassHammingDistance(num_classes=3, multidim_average='samplewise', average=None)
>>> mchd(preds, target)
tensor([[0.0000, 1.0000, 0.5000],
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12 changes: 6 additions & 6 deletions src/torchmetrics/classification/negative_predictive_value.py
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Expand Up @@ -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])
Expand All @@ -234,7 +234,7 @@ class MulticlassNegativePredictiveValue(MulticlassStatScores):
... [0.05, 0.82, 0.13]])
>>> 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])
Expand All @@ -245,7 +245,7 @@ class MulticlassNegativePredictiveValue(MulticlassStatScores):
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassNegativePredictiveValue(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.7833, 0.6556])
tensor([0.7500, 0.6667])
>>> metric = MulticlassNegativePredictiveValue(num_classes=3, multidim_average='samplewise', average=None)
>>> metric(preds, target)
tensor([[1.0000, 0.6000, 0.7500],
Expand Down Expand Up @@ -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])
Expand All @@ -382,7 +382,7 @@ class MultilabelNegativePredictiveValue(MultilabelStatScores):
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> 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])
Expand All @@ -394,7 +394,7 @@ class MultilabelNegativePredictiveValue(MultilabelStatScores):
... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> metric = MultilabelNegativePredictiveValue(num_labels=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.0000, 0.1667])
tensor([0.0000, 0.2500])
>>> mls = MultilabelNegativePredictiveValue(num_labels=3, multidim_average='samplewise', average=None)
>>> mls(preds, target)
tensor([[0.0000, 0.0000, 0.0000],
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18 changes: 9 additions & 9 deletions src/torchmetrics/classification/precision_recall.py
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Expand Up @@ -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])
Expand All @@ -251,7 +251,7 @@ class MulticlassPrecision(MulticlassStatScores):
... [0.05, 0.82, 0.13]])
>>> 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])
Expand All @@ -262,7 +262,7 @@ class MulticlassPrecision(MulticlassStatScores):
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassPrecision(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.3889, 0.2778])
tensor([0.5000, 0.3333])
>>> mcp = MulticlassPrecision(num_classes=3, multidim_average='samplewise', average=None)
>>> mcp(preds, target)
tensor([[0.6667, 0.0000, 0.5000],
Expand Down Expand Up @@ -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])
Expand All @@ -413,7 +413,7 @@ class MultilabelPrecision(MultilabelStatScores):
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> 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])
Expand All @@ -425,7 +425,7 @@ class MultilabelPrecision(MultilabelStatScores):
... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> metric = MultilabelPrecision(num_labels=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.3333, 0.0000])
tensor([0.4000, 0.0000])
>>> mlp = MultilabelPrecision(num_labels=3, multidim_average='samplewise', average=None)
>>> mlp(preds, target)
tensor([[0.5000, 0.5000, 0.0000],
Expand Down Expand Up @@ -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])
Expand All @@ -710,7 +710,7 @@ class MulticlassRecall(MulticlassStatScores):
... [0.05, 0.82, 0.13]])
>>> 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])
Expand All @@ -721,7 +721,7 @@ class MulticlassRecall(MulticlassStatScores):
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassRecall(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.5000, 0.2778])
tensor([0.5000, 0.3333])
>>> mcr = MulticlassRecall(num_classes=3, multidim_average='samplewise', average=None)
>>> mcr(preds, target)
tensor([[1.0000, 0.0000, 0.5000],
Expand Down
6 changes: 3 additions & 3 deletions src/torchmetrics/classification/specificity.py
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Expand Up @@ -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])
Expand All @@ -228,7 +228,7 @@ class MulticlassSpecificity(MulticlassStatScores):
... [0.05, 0.82, 0.13]])
>>> 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])
Expand All @@ -239,7 +239,7 @@ class MulticlassSpecificity(MulticlassStatScores):
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassSpecificity(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.7500, 0.6556])
tensor([0.7500, 0.6667])
>>> mcs = MulticlassSpecificity(num_classes=3, multidim_average='samplewise', average=None)
>>> mcs(preds, target)
tensor([[0.7500, 0.7500, 0.7500],
Expand Down
4 changes: 2 additions & 2 deletions src/torchmetrics/classification/stat_scores.py
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Expand Up @@ -309,7 +309,7 @@ def __init__(
self,
num_classes: Optional[int] = None,
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,
Expand Down Expand Up @@ -462,7 +462,7 @@ 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,
Expand Down
10 changes: 5 additions & 5 deletions src/torchmetrics/functional/classification/accuracy.py
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Expand Up @@ -167,7 +167,7 @@ def multiclass_accuracy(
preds: Tensor,
target: Tensor,
num_classes: Optional[int] = None,
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,
Expand Down Expand Up @@ -234,7 +234,7 @@ def multiclass_accuracy(
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([2, 1, 0, 1])
>>> multiclass_accuracy(preds, target, num_classes=3)
tensor(0.8333)
tensor(0.7500)
>>> multiclass_accuracy(preds, target, num_classes=3, average=None)
tensor([0.5000, 1.0000, 1.0000])

Expand All @@ -246,7 +246,7 @@ def multiclass_accuracy(
... [0.71, 0.09, 0.20],
... [0.05, 0.82, 0.13]])
>>> multiclass_accuracy(preds, target, num_classes=3)
tensor(0.8333)
tensor(0.7500)
>>> multiclass_accuracy(preds, target, num_classes=3, average=None)
tensor([0.5000, 1.0000, 1.0000])

Expand All @@ -255,7 +255,7 @@ def multiclass_accuracy(
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> multiclass_accuracy(preds, target, num_classes=3, multidim_average='samplewise')
tensor([0.5000, 0.2778])
tensor([0.5000, 0.3333])
>>> multiclass_accuracy(preds, target, num_classes=3, multidim_average='samplewise', average=None)
tensor([[1.0000, 0.0000, 0.5000],
[0.0000, 0.3333, 0.5000]])
Expand All @@ -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,
Expand Down
10 changes: 5 additions & 5 deletions src/torchmetrics/functional/classification/hamming.py
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Expand Up @@ -163,7 +163,7 @@ def multiclass_hamming_distance(
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,
Expand Down Expand Up @@ -231,7 +231,7 @@ def multiclass_hamming_distance(
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([2, 1, 0, 1])
>>> multiclass_hamming_distance(preds, target, num_classes=3)
tensor(0.1667)
tensor(0.2500)
>>> multiclass_hamming_distance(preds, target, num_classes=3, average=None)
tensor([0.5000, 0.0000, 0.0000])

Expand All @@ -243,7 +243,7 @@ def multiclass_hamming_distance(
... [0.71, 0.09, 0.20],
... [0.05, 0.82, 0.13]])
>>> multiclass_hamming_distance(preds, target, num_classes=3)
tensor(0.1667)
tensor(0.2500)
>>> multiclass_hamming_distance(preds, target, num_classes=3, average=None)
tensor([0.5000, 0.0000, 0.0000])

Expand All @@ -252,7 +252,7 @@ def multiclass_hamming_distance(
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> multiclass_hamming_distance(preds, target, num_classes=3, multidim_average='samplewise')
tensor([0.5000, 0.7222])
tensor([0.5000, 0.6667])
>>> multiclass_hamming_distance(preds, target, num_classes=3, multidim_average='samplewise', average=None)
tensor([[0.0000, 1.0000, 0.5000],
[1.0000, 0.6667, 0.5000]])
Expand All @@ -273,7 +273,7 @@ def multilabel_hamming_distance(
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,
Expand Down
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