torcheval.metrics.functional.multiclass_confusion_matrix¶
-
torcheval.metrics.functional.
multiclass_confusion_matrix
(input: Tensor, target: Tensor, num_classes: int, *, normalize: Optional[str] = None) Tensor [source]¶ Compute multi-class confusion matrix, a matrix of dimension num_classes x num_classes where each element at position (i,j) is the number of examples with true class i that were predicted to be class j. See also
binary_confusion_matrix
Parameters: - input (Tensor) – Tensor of label predictions.
It could be the predicted labels, with shape of (n_sample, ).
It could also be probabilities or logits with shape of (n_sample, n_class).
torch.argmax
will be used to convert input into predicted labels. - target (Tensor) – Tensor of ground truth labels with shape of (n_sample, ).
- num_classes (int) – Number of classes.
- normalize –
None
[default]:- Give raw counts (‘none’ also defaults to this)
'pred'
:- Normalize across the prediction class, i.e. such that the rows add to one.
'true'
:- Normalize across the condition positive, i.e. such that the columns add to one.
'all'
”- Normalize across all examples, i.e. such that all matrix entries add to one.
Examples:
>>> import torch >>> from torcheval.metrics.functional import multiclass_confusion_matrix >>> input = torch.tensor([0, 2, 1, 3]) >>> target = torch.tensor([0, 1, 2, 3]) >>> multiclass_confusion_matrix(input, target, 4) tensor([[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]]) >>> input = torch.tensor([0, 0, 1, 1, 1]) >>> target = torch.tensor([0, 0, 0, 0, 1]) >>> multiclass_confusion_matrix(input, target, 2) tensor([[2, 2], [0, 1]]) >>> input = torch.tensor([0, 0, 1, 1, 1, 2, 1, 2]) >>> target = torch.tensor([2, 0, 2, 0, 1, 2, 1, 0]) >>> multiclass_confusion_matrix(input, target, 3) tensor([[1, 1, 1], [0, 2, 0], [1, 1, 1]]) >>> input = torch.tensor([0, 0, 1, 1, 1, 2, 1, 2]) >>> target = torch.tensor([2, 0, 2, 0, 1, 2, 1, 0]) >>> multiclass_confusion_matrix(input, target, 3, normalize="pred") tensor([[0.5000, 0.2500, 0.5000], [0.0000, 0.5000, 0.0000], [0.5000, 0.2500, 0.5000]]) >>> input = torch.tensor([0, 0, 1, 1, 1]) >>> target = torch.tensor([0, 0, 0, 0, 1]) >>> multiclass_confusion_matrix(input, target, 4) tensor([[2, 2, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]) >>> input = torch.tensor([[0.9, 0.1, 0, 0], [0.1, 0.2, 0.4, 0.3], [0, 1.0, 0, 0], [0, 0, 0.2, 0.8]]) >>> target = torch.tensor([0, 1, 2, 3]) >>> multiclass_confusion_matrix(input, target, 4) tensor([[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
- input (Tensor) – Tensor of label predictions.
It could be the predicted labels, with shape of (n_sample, ).
It could also be probabilities or logits with shape of (n_sample, n_class).