Shortcuts

# Precision Recall Curve¶

## Module Interface¶

class torchmetrics.PrecisionRecallCurve(task: Literal['binary', 'multiclass', 'multilabel'], thresholds: = None, num_classes: = None, num_labels: = None, ignore_index: = None, validate_args: bool = True, **kwargs: Any)[source]

Computes the precision-recall curve. 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.

This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the task argument to either 'binary', 'multiclass' or multilabel. See the documentation of BinaryPrecisionRecallCurve, MulticlassPrecisionRecallCurve and MultilabelPrecisionRecallCurve for the specific details of each argument influence and examples.

Legacy Example:
>>> pred = torch.tensor([0, 0.1, 0.8, 0.4])
>>> target = torch.tensor([0, 1, 1, 0])
>>> 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])

>>> 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 = 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)]


### 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.

As input to forward and update the metric accepts the following input:

• preds (Tensor): A float tensor of shape (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 (Tensor): An int tensor of shape (N, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified). The value 1 always encodes the positive class.

Note

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

As output to forward and compute the metric returns the following output:

• precision (Tensor): 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 (Tensor): 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 (Tensor): 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.

Note

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 whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size (constant memory).

Parameters
• 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.

Example

>>> from torchmetrics.classification import BinaryPrecisionRecallCurve
>>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
>>> target = torch.tensor([0, 1, 1, 0])
>>> bprc = BinaryPrecisionRecallCurve(thresholds=None)
>>> bprc(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]))
>>> bprc = BinaryPrecisionRecallCurve(thresholds=5)
>>> bprc(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.

### 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.

As input to forward and update the metric accepts the following input:

• preds (Tensor): A float tensor of shape (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 (Tensor): An int tensor of shape (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).

Note

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

As output to forward and compute the metric returns the following output:

• precision (Tensor): A 1d tensor of size (n_thresholds+1, ) with precision values

• recall (Tensor): A 1d tensor of size (n_thresholds+1, ) with recall values

• thresholds (Tensor): A 1d tensor of size (n_thresholds, ) with increasing threshold values

Note

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 whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size (constant memory).

Parameters
• num_classes (int) – Integer specifing the number of classes

• 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.

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])
>>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=None)
>>> precision, recall, thresholds = mcprc(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)]
>>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=5)
>>> mcprc(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.

### 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.

As input to forward and update the metric accepts the following input:

• preds (Tensor): A float tensor of shape (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 (Tensor): An int tensor of shape (N, C, ...). Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).

Note

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

As output to forward and compute the metric returns the following a tuple of either 3 tensors or 3 lists containing:

• precision (Tensor or List): 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 (Tensor or List): 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 (Tensor or List): 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.

Note

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 whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size (constant memory).

Parameters
• preds – Tensor with predictions

• target – Tensor with true labels

• num_labels (int) – Integer specifing the number of labels

• 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.

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]])
>>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=None)
>>> precision, recall, thresholds = mlprc(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])]
>>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=5)
>>> mlprc(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.

## Functional Interface¶

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

Computes the precision-recall curve. 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.

This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the task argument to either 'binary', 'multiclass' or multilabel. See the documentation of binary_precision_recall_curve(), multiclass_precision_recall_curve() and multilabel_precision_recall_curve() for the specific details of each argument influence and examples.

Legacy Example:
>>> pred = torch.tensor([0.0, 1.0, 2.0, 3.0])
>>> target = torch.tensor([0, 1, 1, 0])
>>> precision, recall, thresholds = precision_recall_curve(pred, target, task='binary')
>>> precision
tensor([0.6667, 0.5000, 0.0000, 1.0000])
>>> recall
tensor([1.0000, 0.5000, 0.0000, 0.0000])
>>> thresholds
tensor([0.7311, 0.8808, 0.9526])

>>> 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, task='multiclass', 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])]

Return type

### 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). The value 1 always encodes the positive class.

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 whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size (constant memory).

Parameters
• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

• 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 whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size (constant memory).

Parameters
• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

• num_classes (int) – Integer specifing the number of classes

• 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 whereas setting the thresholds argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size (constant memory).

Parameters
• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

• num_labels (int) – Integer specifing the number of labels

• 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]))


© Copyright Copyright (c) 2020-2023, Lightning-AI et al... Revision 6853c553.

Built with Sphinx using a theme provided by Read the Docs.
Versions
latest
stable
v0.11.2
v0.11.1
v0.11.0
v0.10.3
v0.10.2
v0.10.1
v0.10.0
v0.9.3
v0.9.2
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