Shortcuts

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 to None for binary problems

  • pos_label (Optional[int]) – integer determining the positive class. Default is None 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

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Return type

None

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)

  • target (Tensor) – ground truth values

  • num_classes (Optional[int]) – integer with number of classes for multi-label and multiclass problems. Should be set to None for binary problems.

  • pos_label (Optional[int]) – integer determining the positive class. Default is None 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])]
Read the Docs v: stable
Versions
latest
stable
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
refactor-structure
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.