Precision Recall Curve¶

Module Interface¶

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

Note

From v0.10 an ‘binary_*’, ‘multiclass_*’, `’multilabel_*’ version now exist of each classification metric. Moving forward we recommend using these versions. This base metric will still work as it did prior to v0.10 until v0.11. From v0.11 the task argument introduced in this metric will be required and the general order of arguments may change, such that this metric will just function as an single entrypoint to calling the three specialized versions.

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 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 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 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

preds¶ (Tensor) – Predictions from model
target¶ (Tensor) – Ground truth values

Return type

None

BinaryPrecisionRecallCurve¶

class torchmetrics.classification.BinaryPrecisionRecallCurve(thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]

Computes the precision-recall curve for binary tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen.

Accepts the following input tensors:

preds (float tensor): (N, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.
target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size $\mathcal{O}(n_{samples})$ whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size $\mathcal{O}(n_{thresholds})$ (constant memory).

Parameters

thresholds¶ (Union[int, List[float], Tensor, None]) –
Can be one of:
- If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.
- If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.
- If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation
- If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns

a tuple of 3 tensors containing:

precision: an 1d tensor of size (n_thresholds+1, ) with precision values
recall: an 1d tensor of size (n_thresholds+1, ) with recall values
thresholds: an 1d tensor of size (n_thresholds, ) with increasing threshold values

Return type

(tuple)

Example

>>> from torchmetrics.classification import BinaryPrecisionRecallCurve
>>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
>>> target = torch.tensor([0, 1, 1, 0])
>>> metric = BinaryPrecisionRecallCurve(thresholds=None)
>>> metric(preds, target)  
(tensor([0.6667, 0.5000, 0.0000, 1.0000]),
 tensor([1.0000, 0.5000, 0.0000, 0.0000]),
 tensor([0.5000, 0.7000, 0.8000]))
>>> metric = BinaryPrecisionRecallCurve(thresholds=5)
>>> metric(preds, target)  
(tensor([0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000]),
 tensor([1., 1., 1., 0., 0., 0.]),
 tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))

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

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type: Tuple[Tensor, Tensor, Tensor]

update(preds, target)[source]

Override this method to update the state variables of your metric class.

Return type: None

MulticlassPrecisionRecallCurve¶

class torchmetrics.classification.MulticlassPrecisionRecallCurve(num_classes, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]

Computes the precision-recall curve for multiclass tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen.

Accepts the following input tensors:

preds (float tensor): (N, C, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply softmax per sample.
target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size $\mathcal{O}(n_{samples})$ whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size $\mathcal{O}(n_{thresholds} \times n_{classes})$ (constant memory).

Parameters

num_classes¶ (int) – Integer specifing the number of classes
thresholds¶ (Union[int, List[float], Tensor, None]) –
Can be one of:
- If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.
- If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.
- If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation
- If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns

a tuple of either 3 tensors or 3 lists containing

precision: if thresholds=None a list for each class is returned with an 1d tensor of size (n_thresholds+1, ) with precision values (length may differ between classes). If thresholds is set to something else, then a single 2d tensor of size (n_classes, n_thresholds+1) with precision values is returned.
recall: if thresholds=None a list for each class is returned with an 1d tensor of size (n_thresholds+1, ) with recall values (length may differ between classes). If thresholds is set to something else, then a single 2d tensor of size (n_classes, n_thresholds+1) with recall values is returned.
thresholds: if thresholds=None a list for each class is returned with an 1d tensor of size (n_thresholds, ) with increasing threshold values (length may differ between classes). If threshold is set to something else, then a single 1d tensor of size (n_thresholds, ) is returned with shared threshold values for all classes.

Return type

(tuple)

Example

>>> from torchmetrics.classification import MulticlassPrecisionRecallCurve
>>> preds = 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])
>>> metric = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=None)
>>> precision, recall, thresholds = metric(preds, 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)]
>>> metric = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=5)
>>> metric(preds, target)  
(tensor([[0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
         [0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
         [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
         [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]),
 tensor([[1., 1., 1., 1., 0., 0.],
         [1., 1., 1., 1., 0., 0.],
         [1., 0., 0., 0., 0., 0.],
         [1., 0., 0., 0., 0., 0.],
         [0., 0., 0., 0., 0., 0.]]),
 tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))

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

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type: Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]

update(preds, target)[source]

Override this method to update the state variables of your metric class.

Return type: None

MultilabelPrecisionRecallCurve¶

class torchmetrics.classification.MultilabelPrecisionRecallCurve(num_labels, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]

Computes the precision-recall curve for multilabel tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen.

Accepts the following input tensors:

preds (float tensor): (N, C, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.
target (int tensor): (N, C, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size $\mathcal{O}(n_{samples})$ whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size $\mathcal{O}(n_{thresholds} \times n_{labels})$ (constant memory).

Parameters

preds¶ – Tensor with predictions
target¶ – Tensor with true labels
num_labels¶ (int) – Integer specifing the number of labels
thresholds¶ (Union[int, List[float], Tensor, None]) –
Can be one of:
- If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.
- If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.
- If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation
- If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Returns

a tuple of either 3 tensors or 3 lists containing

precision: if thresholds=None a list for each label is returned with an 1d tensor of size (n_thresholds+1, ) with precision values (length may differ between labels). If thresholds is set to something else, then a single 2d tensor of size (n_labels, n_thresholds+1) with precision values is returned.
recall: if thresholds=None a list for each label is returned with an 1d tensor of size (n_thresholds+1, ) with recall values (length may differ between labels). If thresholds is set to something else, then a single 2d tensor of size (n_labels, n_thresholds+1) with recall values is returned.
thresholds: if thresholds=None a list for each label is returned with an 1d tensor of size (n_thresholds, ) with increasing threshold values (length may differ between labels). If threshold is set to something else, then a single 1d tensor of size (n_thresholds, ) is returned with shared threshold values for all labels.

Return type

(tuple)

Example

>>> from torchmetrics.classification import MultilabelPrecisionRecallCurve
>>> preds = torch.tensor([[0.75, 0.05, 0.35],
...                       [0.45, 0.75, 0.05],
...                       [0.05, 0.55, 0.75],
...                       [0.05, 0.65, 0.05]])
>>> target = torch.tensor([[1, 0, 1],
...                        [0, 0, 0],
...                        [0, 1, 1],
...                        [1, 1, 1]])
>>> metric = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=None)
>>> precision, recall, thresholds = metric(preds, target)
>>> precision  
[tensor([0.5000, 0.5000, 1.0000, 1.0000]), tensor([0.6667, 0.5000, 0.0000, 1.0000]),
 tensor([0.7500, 1.0000, 1.0000, 1.0000])]
>>> recall  
[tensor([1.0000, 0.5000, 0.5000, 0.0000]), tensor([1.0000, 0.5000, 0.0000, 0.0000]),
 tensor([1.0000, 0.6667, 0.3333, 0.0000])]
>>> thresholds  
[tensor([0.0500, 0.4500, 0.7500]), tensor([0.5500, 0.6500, 0.7500]),
 tensor([0.0500, 0.3500, 0.7500])]
>>> metric = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=5)
>>> metric(preds, target)  
(tensor([[0.5000, 0.5000, 1.0000, 1.0000, 0.0000, 1.0000],
         [0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000],
         [0.7500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000]]),
 tensor([[1.0000, 0.5000, 0.5000, 0.5000, 0.0000, 0.0000],
         [1.0000, 1.0000, 1.0000, 0.0000, 0.0000, 0.0000],
         [1.0000, 0.6667, 0.3333, 0.3333, 0.0000, 0.0000]]),
 tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))

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

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type: Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]

update(preds, target)[source]

Override this method to update the state variables of your metric class.

Return type: None

Functional Interface¶

torchmetrics.functional.precision_recall_curve(preds, target, num_classes=None, pos_label=None, sample_weights=None, task=None, num_labels=None, thresholds=None, ignore_index=None, validate_args=True)[source]

Note

From v0.10 an ‘binary_*’, ‘multiclass_*’, `’multilabel_*’ version now exist of each classification metric. Moving forward we recommend using these versions. This base metric will still work as it did prior to v0.10 until v0.11. From v0.11 the task argument introduced in this metric will be required and the general order of arguments may change, such that this metric will just function as an single entrypoint to calling the three specialized versions.

Computes precision-recall pairs for different thresholds.

Parameters

preds¶ (Tensor) – predictions from model (probabilities).
target¶ (Tensor) – ground truth labels.
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

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])]

binary_precision_recall_curve¶

torchmetrics.functional.classification.binary_precision_recall_curve(preds, target, thresholds=None, ignore_index=None, validate_args=True)[source]

Computes the precision-recall curve for binary tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen.

Accepts the following input tensors:

preds (float tensor): (N, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.
target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size $\mathcal{O}(n_{samples})$ whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size $\mathcal{O}(n_{thresholds})$ (constant memory).

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
thresholds¶ (Union[int, List[float], Tensor, None]) –
Can be one of:
- If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.
- If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.
- If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation
- If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Returns

a tuple of 3 tensors containing:

precision: an 1d tensor of size (n_thresholds+1, ) with precision values
recall: an 1d tensor of size (n_thresholds+1, ) with recall values
thresholds: an 1d tensor of size (n_thresholds, ) with increasing threshold values

Return type

(tuple)

Example

>>> from torchmetrics.functional.classification import binary_precision_recall_curve
>>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
>>> target = torch.tensor([0, 1, 1, 0])
>>> binary_precision_recall_curve(preds, target, thresholds=None)  
(tensor([0.6667, 0.5000, 0.0000, 1.0000]),
 tensor([1.0000, 0.5000, 0.0000, 0.0000]),
 tensor([0.5000, 0.7000, 0.8000]))
>>> binary_precision_recall_curve(preds, target, thresholds=5)  
(tensor([0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000]),
 tensor([1., 1., 1., 0., 0., 0.]),
 tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))

multiclass_precision_recall_curve¶

torchmetrics.functional.classification.multiclass_precision_recall_curve(preds, target, num_classes, thresholds=None, ignore_index=None, validate_args=True)[source]

Computes the precision-recall curve for multiclass tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen.

Accepts the following input tensors:

preds (float tensor): (N, C, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply softmax per sample.
target (int tensor): (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size $\mathcal{O}(n_{samples})$ whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size $\mathcal{O}(n_{thresholds} \times n_{classes})$ (constant memory).

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
num_classes¶ (int) – Integer specifing the number of classes
thresholds¶ (Union[int, List[float], Tensor, None]) –
Can be one of:
- If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.
- If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.
- If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation
- If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Returns

a tuple of either 3 tensors or 3 lists containing

precision: if thresholds=None a list for each class is returned with an 1d tensor of size (n_thresholds+1, ) with precision values (length may differ between classes). If thresholds is set to something else, then a single 2d tensor of size (n_classes, n_thresholds+1) with precision values is returned.
recall: if thresholds=None a list for each class is returned with an 1d tensor of size (n_thresholds+1, ) with recall values (length may differ between classes). If thresholds is set to something else, then a single 2d tensor of size (n_classes, n_thresholds+1) with recall values is returned.
thresholds: if thresholds=None a list for each class is returned with an 1d tensor of size (n_thresholds, ) with increasing threshold values (length may differ between classes). If threshold is set to something else, then a single 1d tensor of size (n_thresholds, ) is returned with shared threshold values for all classes.

Return type

(tuple)

Example

>>> from torchmetrics.functional.classification import multiclass_precision_recall_curve
>>> preds = 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 = multiclass_precision_recall_curve(
...    preds, target, num_classes=5, thresholds=None
... )
>>> 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])]
>>> multiclass_precision_recall_curve(
...     preds, target, num_classes=5, thresholds=5
... )  
(tensor([[0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
         [0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
         [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
         [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]),
 tensor([[1., 1., 1., 1., 0., 0.],
         [1., 1., 1., 1., 0., 0.],
         [1., 0., 0., 0., 0., 0.],
         [1., 0., 0., 0., 0., 0.],
         [0., 0., 0., 0., 0., 0.]]),
 tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))

multilabel_precision_recall_curve¶

torchmetrics.functional.classification.multilabel_precision_recall_curve(preds, target, num_labels, thresholds=None, ignore_index=None, validate_args=True)[source]

Computes the precision-recall curve for multilabel tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen.

Accepts the following input tensors:

preds (float tensor): (N, C, ...). Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.
target (int tensor): (N, C, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Additional dimension ... will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the thresholds argument to None will activate the non-binned version that uses memory of size $\mathcal{O}(n_{samples})$ whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size $\mathcal{O}(n_{thresholds} \times n_{labels})$ (constant memory).

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
num_labels¶ (int) – Integer specifing the number of labels
thresholds¶ (Union[int, List[float], Tensor, None]) –
Can be one of:
- If set to None, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach.
- If set to an int (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation.
- If set to an list of floats, will use the indicated thresholds in the list as bins for the calculation
- If set to an 1d tensor of floats, will use the indicated thresholds in the tensor as bins for the calculation.
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Returns

a tuple of either 3 tensors or 3 lists containing

precision: if thresholds=None a list for each label is returned with an 1d tensor of size (n_thresholds+1, ) with precision values (length may differ between labels). If thresholds is set to something else, then a single 2d tensor of size (n_labels, n_thresholds+1) with precision values is returned.
recall: if thresholds=None a list for each label is returned with an 1d tensor of size (n_thresholds+1, ) with recall values (length may differ between labels). If thresholds is set to something else, then a single 2d tensor of size (n_labels, n_thresholds+1) with recall values is returned.
thresholds: if thresholds=None a list for each label is returned with an 1d tensor of size (n_thresholds, ) with increasing threshold values (length may differ between labels). If threshold is set to something else, then a single 1d tensor of size (n_thresholds, ) is returned with shared threshold values for all labels.

Return type

(tuple)

Example

>>> from torchmetrics.functional.classification import multilabel_precision_recall_curve
>>> preds = torch.tensor([[0.75, 0.05, 0.35],
...                       [0.45, 0.75, 0.05],
...                       [0.05, 0.55, 0.75],
...                       [0.05, 0.65, 0.05]])
>>> target = torch.tensor([[1, 0, 1],
...                        [0, 0, 0],
...                        [0, 1, 1],
...                        [1, 1, 1]])
>>> precision, recall, thresholds = multilabel_precision_recall_curve(
...    preds, target, num_labels=3, thresholds=None
... )
>>> precision  
[tensor([0.5000, 0.5000, 1.0000, 1.0000]), tensor([0.6667, 0.5000, 0.0000, 1.0000]),
 tensor([0.7500, 1.0000, 1.0000, 1.0000])]
>>> recall  
[tensor([1.0000, 0.5000, 0.5000, 0.0000]), tensor([1.0000, 0.5000, 0.0000, 0.0000]),
 tensor([1.0000, 0.6667, 0.3333, 0.0000])]
>>> thresholds  
[tensor([0.0500, 0.4500, 0.7500]), tensor([0.5500, 0.6500, 0.7500]),
 tensor([0.0500, 0.3500, 0.7500])]
>>> multilabel_precision_recall_curve(
...     preds, target, num_labels=3, thresholds=5
... )  
(tensor([[0.5000, 0.5000, 1.0000, 1.0000, 0.0000, 1.0000],
         [0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000],
         [0.7500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000]]),
 tensor([[1.0000, 0.5000, 0.5000, 0.5000, 0.0000, 0.0000],
         [1.0000, 1.0000, 1.0000, 0.0000, 0.0000, 0.0000],
         [1.0000, 0.6667, 0.3333, 0.3333, 0.0000, 0.0000]]),
 tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))