doctests fix

src/torchmetrics/classification/stat_scores.py

@@ -233,7 +233,7 @@ class MulticlassStatScores(_AbstractStatScores):
  >>> from torchmetrics import MulticlassStatScores
  >>> target = torch.tensor([2, 1, 0, 0])
  >>> preds = torch.tensor([2, 1, 0, 1])
- >>> metric = MulticlassStatScores(num_classes=3)
+ >>> metric = MulticlassStatScores(num_classes=3, average='micro')
  >>> metric(preds, target)
  tensor([3, 1, 7, 1, 4])
  >>> metric = MulticlassStatScores(num_classes=3, average=None)
@@ -251,7 +251,7 @@ class MulticlassStatScores(_AbstractStatScores):
  ... [0.71, 0.09, 0.20],
  ... [0.05, 0.82, 0.13],
  ... ])
- >>> metric = MulticlassStatScores(num_classes=3)
+ >>> metric = MulticlassStatScores(num_classes=3, average='micro')
  >>> metric(preds, target)
  tensor([3, 1, 7, 1, 4])
  >>> metric = MulticlassStatScores(num_classes=3, average=None)
@@ -264,7 +264,7 @@ class MulticlassStatScores(_AbstractStatScores):
  >>> from torchmetrics import MulticlassStatScores
  >>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
  >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
- >>> metric = MulticlassStatScores(num_classes=3, multidim_average="samplewise")
+ >>> metric = MulticlassStatScores(num_classes=3, multidim_average="samplewise", average='micro')
  >>> metric(preds, target)
  tensor([[3, 3, 9, 3, 6],
  [2, 4, 8, 4, 6]])
@@ -379,40 +379,46 @@ class MultilabelStatScores(_AbstractStatScores):
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
  Example (preds is int tensor):
- >>> from torchmetrics.functional import multilabel_stat_scores
+ >>> from torchmetrics import MultilabelStatScores
  >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
  >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
- >>> multilabel_stat_scores(preds, target, num_labels=3)
+ >>> metric = MultilabelStatScores(num_labels=3, average='micro')
+ >>> metric(preds, target)
  tensor([2, 1, 2, 1, 3])
- >>> multilabel_stat_scores(preds, target, num_labels=3, average=None)
+ >>> metric = MultilabelStatScores(num_labels=3, average=None)
+ >>> metric(preds, target)
  tensor([[1, 0, 1, 0, 1],
  [0, 0, 1, 1, 1],
  [1, 1, 0, 0, 1]])
 
  Example (preds is float tensor):
- >>> from torchmetrics.functional import multilabel_stat_scores
+ >>> from torchmetrics import MultilabelStatScores
  >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
  >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
- >>> multilabel_stat_scores(preds, target, num_labels=3)
+ >>> metric = MultilabelStatScores(num_labels=3, average='micro')
+ >>> metric(preds, target)
  tensor([2, 1, 2, 1, 3])
- >>> multilabel_stat_scores(preds, target, num_labels=3, average=None)
+ >>> metric = MultilabelStatScores(num_labels=3, average=None)
+ >>> metric(preds, target)
  tensor([[1, 0, 1, 0, 1],
  [0, 0, 1, 1, 1],
  [1, 1, 0, 0, 1]])
 
  Example (multidim tensors):
- >>> from torchmetrics.functional import multilabel_stat_scores
+ >>> from torchmetrics import MultilabelStatScores
  >>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
  >>> preds = torch.tensor(
  ... [
  ... [[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
  ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]],
  ... ]
  ... )
- >>> multilabel_stat_scores(preds, target, num_labels=3, multidim_average='samplewise')
+ >>> metric = MultilabelStatScores(num_labels=3, multidim_average='samplewise', average='micro')
+ >>> metric(preds, target)
  tensor([[2, 3, 0, 1, 3],
  [0, 2, 1, 3, 3]])
- >>> multilabel_stat_scores(preds, target, num_labels=3, multidim_average='samplewise', average=None)
+ >>> metric = MultilabelStatScores(num_labels=3, multidim_average='samplewise', average=None)
+ >>> metric(preds, target)
  tensor([[[1, 1, 0, 0, 1],
  [1, 1, 0, 0, 1],
  [0, 1, 0, 1, 1]],

src/torchmetrics/functional/classification/stat_scores.py

@@ -509,7 +509,7 @@ def multiclass_stat_scores(
  >>> from torchmetrics.functional import multiclass_stat_scores
  >>> target = torch.tensor([2, 1, 0, 0])
  >>> preds = torch.tensor([2, 1, 0, 1])
- >>> multiclass_stat_scores(preds, target, num_classes=3)
+ >>> multiclass_stat_scores(preds, target, num_classes=3, average='micro')
  tensor([3, 1, 7, 1, 4])
  >>> multiclass_stat_scores(preds, target, num_classes=3, average=None)
  tensor([[1, 0, 2, 1, 2],
@@ -525,7 +525,7 @@ def multiclass_stat_scores(
  ... [0.71, 0.09, 0.20],
  ... [0.05, 0.82, 0.13],
  ... ])
- >>> multiclass_stat_scores(preds, target, num_classes=3)
+ >>> multiclass_stat_scores(preds, target, num_classes=3, average='micro')
  tensor([3, 1, 7, 1, 4])
  >>> multiclass_stat_scores(preds, target, num_classes=3, average=None)
  tensor([[1, 0, 2, 1, 2],
@@ -536,7 +536,7 @@ def multiclass_stat_scores(
  >>> from torchmetrics.functional import multiclass_stat_scores
  >>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
  >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
- >>> multiclass_stat_scores(preds, target, num_classes=3, multidim_average='samplewise')
+ >>> multiclass_stat_scores(preds, target, num_classes=3, multidim_average='samplewise', average='micro')
  tensor([[3, 3, 9, 3, 6],
  [2, 4, 8, 4, 6]])
  >>> multiclass_stat_scores(preds, target, num_classes=3, multidim_average='samplewise', average=None)
@@ -764,7 +764,7 @@ def multilabel_stat_scores(
  >>> from torchmetrics.functional import multilabel_stat_scores
  >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
  >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
- >>> multilabel_stat_scores(preds, target, num_labels=3)
+ >>> multilabel_stat_scores(preds, target, num_labels=3, average='micro')
  tensor([2, 1, 2, 1, 3])
  >>> multilabel_stat_scores(preds, target, num_labels=3, average=None)
  tensor([[1, 0, 1, 0, 1],
@@ -775,7 +775,7 @@ def multilabel_stat_scores(
  >>> from torchmetrics.functional import multilabel_stat_scores
  >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
  >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
- >>> multilabel_stat_scores(preds, target, num_labels=3)
+ >>> multilabel_stat_scores(preds, target, num_labels=3, average='micro')
  tensor([2, 1, 2, 1, 3])
  >>> multilabel_stat_scores(preds, target, num_labels=3, average=None)
  tensor([[1, 0, 1, 0, 1],
@@ -791,7 +791,7 @@ def multilabel_stat_scores(
  ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]],
  ... ]
  ... )
- >>> multilabel_stat_scores(preds, target, num_labels=3, multidim_average='samplewise')
+ >>> multilabel_stat_scores(preds, target, num_labels=3, multidim_average='samplewise', average='micro')
  tensor([[2, 3, 0, 1, 3],
  [0, 2, 1, 3, 3]])
  >>> multilabel_stat_scores(preds, target, num_labels=3, multidim_average='samplewise', average=None)