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# Precision Recall Curve¶

## Module Interface¶

class torchmetrics.PrecisionRecallCurve(num_classes=None, pos_label=None, compute_on_step=None, **kwargs)[source]

Computes precision-recall pairs for different thresholds. Works for both binary and multiclass 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) tensor with probabilities, where C is the number of classes.

• target (long tensor): (N, ...) or (N, C, ...) with integer labels

Parameters
Example (binary case):
>>> from torchmetrics import PrecisionRecallCurve
>>> pred = torch.tensor([0, 0.1, 0.8, 0.4])
>>> target = torch.tensor([0, 1, 1, 0])
>>> pr_curve = PrecisionRecallCurve(pos_label=1)
>>> precision, recall, thresholds = pr_curve(pred, target)
>>> precision
tensor([0.6667, 0.5000, 1.0000, 1.0000])
>>> recall
tensor([1.0000, 0.5000, 0.5000, 0.0000])
>>> thresholds
tensor([0.1000, 0.4000, 0.8000])

Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> pr_curve = PrecisionRecallCurve(num_classes=5)
>>> precision, recall, thresholds = pr_curve(pred, target)
>>> precision
[tensor([1., 1.]), tensor([1., 1.]), tensor([0.2500, 0.0000, 1.0000]),
tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])]
>>> recall
[tensor([1., 0.]), tensor([1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])]
>>> thresholds
[tensor(0.7500), tensor(0.7500), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor(0.0500)]


Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Compute the precision-recall curve.

Return type
Returns

3-element tuple containing

precision:

tensor where element i is the precision of predictions with score >= thresholds[i] and the last element is 1. If multiclass, this is a list of such tensors, one for each class.

recall:

tensor where element i is the recall of predictions with score >= thresholds[i] and the last element is 0. If multiclass, this is a list of such tensors, one for each class.

thresholds:

Thresholds used for computing precision/recall scores

update(preds, target)[source]

Update state with predictions and targets.

Parameters
Return type

None

## Functional Interface¶

torchmetrics.functional.precision_recall_curve(preds, target, num_classes=None, pos_label=None, sample_weights=None)[source]

Computes precision-recall pairs for different thresholds.

Parameters
Return type
Returns

3-element tuple containing

precision:

tensor where element i is the precision of predictions with score >= thresholds[i] and the last element is 1. If multiclass, this is a list of such tensors, one for each class.

recall:

tensor where element i is the recall of predictions with score >= thresholds[i] and the last element is 0. If multiclass, this is a list of such tensors, one for each class.

thresholds:

Thresholds used for computing precision/recall scores

Raises
• ValueError – If preds and target don’t have the same number of dimensions, or one additional dimension for preds.

• ValueError – If the number of classes deduced from preds is not the same as the num_classes provided.

Example (binary case):
>>> from torchmetrics.functional import precision_recall_curve
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 0])
>>> precision, recall, thresholds = precision_recall_curve(pred, target, pos_label=1)
>>> precision
tensor([0.6667, 0.5000, 0.0000, 1.0000])
>>> recall
tensor([1.0000, 0.5000, 0.0000, 0.0000])
>>> thresholds
tensor([1, 2, 3])

Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> precision, recall, thresholds = precision_recall_curve(pred, target, num_classes=5)
>>> precision
[tensor([1., 1.]), tensor([1., 1.]), tensor([0.2500, 0.0000, 1.0000]),
tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])]
>>> recall
[tensor([1., 0.]), tensor([1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])]
>>> thresholds
[tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])]


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