ROC¶
Module Interface¶
- class torchmetrics.ROC(num_classes=None, pos_label=None, **kwargs)[source]
Computes the Receiver Operating Characteristic (ROC). Works for both binary, multiclass and multilabel problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.
Forward accepts
preds
(float tensor):(N, ...)
(binary) or(N, C, ...)
(multiclass/multilabel) tensor with probabilities, where C is the number of classes/labels.target
(long tensor):(N, ...)
or(N, C, ...)
with integer labels
Note
If either the positive class or negative class is completly missing in the target tensor, the roc values are not well-defined in this case and a tensor of zeros will be returned (either fpr or tpr depending on what class is missing) together with a warning.
- Parameters
num_classes¶ (
Optional
[int
]) – integer with number of classes for multi-label and multiclass problems. Should be set toNone
for binary problemspos_label¶ (
Optional
[int
]) – integer determining the positive class. Default isNone
which for binary problem is translated to 1. For multiclass problems this argument should not be set as we iteratively change it in the range[0,num_classes-1]
kwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
- Example (binary case):
>>> from torchmetrics import ROC >>> pred = torch.tensor([0, 1, 2, 3]) >>> target = torch.tensor([0, 1, 1, 1]) >>> roc = ROC(pos_label=1) >>> fpr, tpr, thresholds = roc(pred, target) >>> fpr tensor([0., 0., 0., 0., 1.]) >>> tpr tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000]) >>> thresholds tensor([4, 3, 2, 1, 0])
- Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05], ... [0.05, 0.05, 0.05, 0.75]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> roc = ROC(num_classes=4) >>> fpr, tpr, thresholds = roc(pred, target) >>> fpr [tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]), tensor([0.0000, 0.3333, 1.0000])] >>> tpr [tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.])] >>> thresholds [tensor([1.7500, 0.7500, 0.0500]), tensor([1.7500, 0.7500, 0.0500]), tensor([1.7500, 0.7500, 0.0500]), tensor([1.7500, 0.7500, 0.0500])]
- Example (multilabel case):
>>> pred = torch.tensor([[0.8191, 0.3680, 0.1138], ... [0.3584, 0.7576, 0.1183], ... [0.2286, 0.3468, 0.1338], ... [0.8603, 0.0745, 0.1837]]) >>> target = torch.tensor([[1, 1, 0], [0, 1, 0], [0, 0, 0], [0, 1, 1]]) >>> roc = ROC(num_classes=3, pos_label=1) >>> fpr, tpr, thresholds = roc(pred, target) >>> fpr [tensor([0.0000, 0.3333, 0.3333, 0.6667, 1.0000]), tensor([0., 0., 0., 1., 1.]), tensor([0.0000, 0.0000, 0.3333, 0.6667, 1.0000])] >>> tpr [tensor([0., 0., 1., 1., 1.]), tensor([0.0000, 0.3333, 0.6667, 0.6667, 1.0000]), tensor([0., 1., 1., 1., 1.])] >>> thresholds [tensor([1.8603, 0.8603, 0.8191, 0.3584, 0.2286]), tensor([1.7576, 0.7576, 0.3680, 0.3468, 0.0745]), tensor([1.1837, 0.1837, 0.1338, 0.1183, 0.1138])]
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- compute()[source]
Compute the receiver operating characteristic.
- Return type
Union
[Tuple
[Tensor
,Tensor
,Tensor
],Tuple
[List
[Tensor
],List
[Tensor
],List
[Tensor
]]]- Returns
3-element tuple containing
- fpr: tensor with false positive rates.
If multiclass, this is a list of such tensors, one for each class.
- tpr: tensor with true positive rates.
If multiclass, this is a list of such tensors, one for each class.
- thresholds:
thresholds used for computing false- and true-positive rates
Functional Interface¶
- torchmetrics.functional.roc(preds, target, num_classes=None, pos_label=None, sample_weights=None)[source]
Computes the Receiver Operating Characteristic (ROC). Works with both binary, multiclass and multilabel input.
Note
If either the positive class or negative class is completly missing in the target tensor, the roc values are not well-defined in this case and a tensor of zeros will be returned (either fpr or tpr depending on what class is missing) together with a warning.
- Parameters
preds¶ (
Tensor
) – predictions from model (logits or probabilities)num_classes¶ (
Optional
[int
]) – integer with number of classes for multi-label and multiclass problems. Should be set toNone
for binary problems.pos_label¶ (
Optional
[int
]) – integer determining the positive class. Default isNone
which for binary problem is translated to 1. For multiclass problems this argument should not be set as we iteratively change it in the range[0, num_classes-1]
sample_weights¶ (
Optional
[Sequence
]) – sample weights for each data point
- Return type
Union
[Tuple
[Tensor
,Tensor
,Tensor
],Tuple
[List
[Tensor
],List
[Tensor
],List
[Tensor
]]]- Returns
3-element tuple containing
- fpr: tensor with false positive rates.
If multiclass or multilabel, this is a list of such tensors, one for each class/label.
- tpr: tensor with true positive rates.
If multiclass or multilabel, this is a list of such tensors, one for each class/label.
- thresholds: tensor with thresholds used for computing false- and true postive rates
If multiclass or multilabel, this is a list of such tensors, one for each class/label.
- Example (binary case):
>>> from torchmetrics.functional import roc >>> pred = torch.tensor([0, 1, 2, 3]) >>> target = torch.tensor([0, 1, 1, 1]) >>> fpr, tpr, thresholds = roc(pred, target, pos_label=1) >>> fpr tensor([0., 0., 0., 0., 1.]) >>> tpr tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000]) >>> thresholds tensor([4, 3, 2, 1, 0])
- Example (multiclass case):
>>> from torchmetrics.functional import roc >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05], ... [0.05, 0.05, 0.05, 0.75]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> fpr, tpr, thresholds = roc(pred, target, num_classes=4) >>> fpr [tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]), tensor([0.0000, 0.3333, 1.0000])] >>> tpr [tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.])] >>> thresholds [tensor([1.7500, 0.7500, 0.0500]), tensor([1.7500, 0.7500, 0.0500]), tensor([1.7500, 0.7500, 0.0500]), tensor([1.7500, 0.7500, 0.0500])]
- Example (multilabel case):
>>> from torchmetrics.functional import roc >>> pred = torch.tensor([[0.8191, 0.3680, 0.1138], ... [0.3584, 0.7576, 0.1183], ... [0.2286, 0.3468, 0.1338], ... [0.8603, 0.0745, 0.1837]]) >>> target = torch.tensor([[1, 1, 0], [0, 1, 0], [0, 0, 0], [0, 1, 1]]) >>> fpr, tpr, thresholds = roc(pred, target, num_classes=3, pos_label=1) >>> fpr [tensor([0.0000, 0.3333, 0.3333, 0.6667, 1.0000]), tensor([0., 0., 0., 1., 1.]), tensor([0.0000, 0.0000, 0.3333, 0.6667, 1.0000])] >>> tpr [tensor([0., 0., 1., 1., 1.]), tensor([0.0000, 0.3333, 0.6667, 0.6667, 1.0000]), tensor([0., 1., 1., 1., 1.])] >>> thresholds [tensor([1.8603, 0.8603, 0.8191, 0.3584, 0.2286]), tensor([1.7576, 0.7576, 0.3680, 0.3468, 0.0745]), tensor([1.1837, 0.1837, 0.1338, 0.1183, 0.1138])]