torcheval.metrics.MulticlassConfusionMatrix¶
-
class
torcheval.metrics.
MulticlassConfusionMatrix
(num_classes: int, *, normalize: Optional[str] = None, device: Optional[device] = None)[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
BinaryConfusionMatrix
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 (str) –
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.
- device (torch.device) – Device for internal tensors
Examples:
>>> import torch >>> from torcheval.metrics import MulticlassConfusionMatrix >>> input = torch.tensor([0, 2, 1, 3]) >>> target = torch.tensor([0, 1, 2, 3]) >>> metric = MulticlassConfusionMatrix(4) >>> metric.update(input, target) >>> metric.compute() 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]) >>> metric = MulticlassConfusionMatrix(2) >>> metric.update(input, target) >>> metric.compute() 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]) >>> metric = MulticlassConfusionMatrix(3) >>> metric.update(input, target) >>> metric.compute() 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]) >>> metric = MulticlassConfusionMatrix(3) >>> metric.update(input, target) >>> metric.compute() tensor([[1., 1., 1.], [0., 2., 0.], [1., 1., 1.]]) >>> metric.normalized("pred") tensor([[0.5000, 0.2500, 0.5000], [0.0000, 0.5000, 0.0000], [0.5000, 0.2500, 0.5000]]) >>> metric.normalized("true") tensor([[0.3333, 0.3333, 0.3333], [0.0000, 1.0000, 0.0000], [0.3333, 0.3333, 0.3333]]) >>> metric.normalized("all") tensor([[0.1250, 0.1250, 0.1250], [0.0000, 0.2500, 0.0000], [0.1250, 0.1250, 0.1250]]) >>> input = torch.tensor([0, 0, 1, 1, 1, 2, 1, 2]) >>> target = torch.tensor([2, 0, 2, 0, 1, 2, 1, 0]) >>> metric = MulticlassConfusionMatrix(3, normalize="true") >>> metric.update(input, target) >>> metric.compute() tensor([[0.3333, 0.3333, 0.3333], [0.0000, 1.0000, 0.0000], [0.3333, 0.3333, 0.3333]]) >>> metric.normalized(None) tensor([[1., 1., 1.], [0., 2., 0.], [1., 1., 1.]]) >>> input = torch.tensor([0, 0, 1, 1, 1]) >>> target = torch.tensor([0, 0, 0, 0, 1]) >>> metric = MulticlassConfusionMatrix(4) >>> metric.update(input, target) >>> metric.compute() 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]) >>> metric = MulticlassConfusionMatrix(4) >>> metric.update(input, target) >>> metric.compute() tensor([[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
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__init__
(num_classes: int, *, normalize: Optional[str] = None, device: Optional[device] = None) None [source]¶ Initialize a metric object and its internal states.
Use
self._add_state()
to initialize state variables of your metric class. The state variables should be eithertorch.Tensor
, a list oftorch.Tensor
, or a dictionary withtorch.Tensor
as values
Methods
__init__
(num_classes, *[, normalize, device])Initialize a metric object and its internal states. compute
()Return the confusion matrix. load_state_dict
(state_dict[, strict])Loads metric state variables from state_dict. merge_state
(metrics)Implement this method to update the current metric's state variables to be the merged states of the current metric and input metrics. normalized
([normalize])Return the normalized confusion matrix reset
()Reset the metric state variables to their default value. state_dict
()Save metric state variables in state_dict. to
(device, *args, **kwargs)Move tensors in metric state variables to device. update
(input, target)Update Confusion Matrix. Attributes
device
The last input device of Metric.to()
.- 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).