fix modular doctests

Author: rittik9

src/torchmetrics/classification/hamming.py

@@ -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])
@@ -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],

src/torchmetrics/classification/negative_predictive_value.py

@@ -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])
@@ -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],
@@ -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])
@@ -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],

src/torchmetrics/classification/precision_recall.py

@@ -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])
@@ -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],
@@ -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])
@@ -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],
@@ -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])
@@ -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],

src/torchmetrics/classification/specificity.py

@@ -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])
@@ -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],

src/torchmetrics/functional/classification/accuracy.py

@@ -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])
 
@@ -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])
 
@@ -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]])