Precision Recall Curve¶
Module Interface¶
- class torchmetrics.PrecisionRecallCurve(num_classes=None, pos_label=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
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¶ (
Dict
[str
,Any
]) – Additional keyword arguments, see Advanced metric settings for more info.
- 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
Union
[Tuple
[Tensor
,Tensor
,Tensor
],Tuple
[List
[Tensor
],List
[Tensor
],List
[Tensor
]]]- Returns
3-element tuple containing
- precision:
tensor where element
i
is the precision of predictions withscore >= 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 withscore >= 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
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
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
- precision:
tensor where element
i
is the precision of predictions withscore >= 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 withscore >= 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
andtarget
don’t have the same number of dimensions, or one additional dimension forpreds
.ValueError – If the number of classes deduced from
preds
is not the same as thenum_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])]