Recall At Fixed Precision¶
Module Interface¶
BinaryRecallAtFixedPrecision¶
- class torchmetrics.classification.BinaryRecallAtFixedPrecision(min_precision, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]
Computes the higest possible recall value given the minimum precision thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level.
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 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
min_precision¶ (
float
) – float value specifying minimum precision threshold.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 toFalse
for faster computations.kwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
- Returns
a tuple of 2 tensors containing:
recall: an scalar tensor with the maximum recall for the given precision level
threshold: an scalar tensor with the corresponding threshold level
- Return type
(tuple)
Example
>>> from torchmetrics.classification import BinaryRecallAtFixedPrecision >>> preds = torch.tensor([0, 0.5, 0.7, 0.8]) >>> target = torch.tensor([0, 1, 1, 0]) >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5, thresholds=None) >>> metric(preds, target) (tensor(1.), tensor(0.5000)) >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5, thresholds=5) >>> metric(preds, target) (tensor(1.), tensor(0.5000))
Initializes internal Module state, shared by both nn.Module and ScriptModule.
MulticlassRecallAtFixedPrecision¶
- class torchmetrics.classification.MulticlassRecallAtFixedPrecision(num_classes, min_precision, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]
Computes the higest possible recall value given the minimum precision thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level.
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
num_classes¶ (
int
) – Integer specifing the number of classesmin_precision¶ (
float
) – float value specifying minimum precision threshold.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 toFalse
for faster computations.kwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
- Returns
a tuple of either 2 tensors or 2 lists containing
recall: an 1d tensor of size (n_classes, ) with the maximum recall for the given precision level per class
thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class
- Return type
(tuple)
Example
>>> from torchmetrics.classification import MulticlassRecallAtFixedPrecision >>> 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 = MulticlassRecallAtFixedPrecision(num_classes=5, min_precision=0.5, thresholds=None) >>> metric(preds, target) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06])) >>> metric = MulticlassRecallAtFixedPrecision(num_classes=5, min_precision=0.5, thresholds=5) >>> metric(preds, target) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06]))
Initializes internal Module state, shared by both nn.Module and ScriptModule.
MultilabelRecallAtFixedPrecision¶
- class torchmetrics.classification.MultilabelRecallAtFixedPrecision(num_labels, min_precision, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]
Computes the higest possible recall value given the minimum precision thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level.
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
min_precision¶ (
float
) – float value specifying minimum precision threshold.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 toFalse
for faster computations.kwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
- Returns
a tuple of either 2 tensors or 2 lists containing
recall: an 1d tensor of size (n_classes, ) with the maximum recall for the given precision level per class
thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class
- Return type
(tuple)
Example
>>> from torchmetrics.classification import MultilabelRecallAtFixedPrecision >>> 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 = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5, thresholds=None) >>> metric(preds, target) (tensor([1., 1., 1.]), tensor([0.0500, 0.5500, 0.0500])) >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5, thresholds=5) >>> metric(preds, target) (tensor([1., 1., 1.]), tensor([0.0000, 0.5000, 0.0000]))
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
binary_recall_at_fixed_precision¶
- torchmetrics.functional.classification.binary_recall_at_fixed_precision(preds, target, min_precision, thresholds=None, ignore_index=None, validate_args=True)[source]
Computes the higest possible recall value given the minimum precision thresholds provided for binary tasks. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level.
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 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
min_precision¶ (
float
) – float value specifying minimum precision threshold.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 toFalse
for faster computations.
- Returns
a tuple of 2 tensors containing:
recall: an scalar tensor with the maximum recall for the given precision level
threshold: an scalar tensor with the corresponding threshold level
- Return type
(tuple)
Example
>>> from torchmetrics.functional.classification import binary_recall_at_fixed_precision >>> preds = torch.tensor([0, 0.5, 0.7, 0.8]) >>> target = torch.tensor([0, 1, 1, 0]) >>> binary_recall_at_fixed_precision(preds, target, min_precision=0.5, thresholds=None) (tensor(1.), tensor(0.5000)) >>> binary_recall_at_fixed_precision(preds, target, min_precision=0.5, thresholds=5) (tensor(1.), tensor(0.5000))
multiclass_recall_at_fixed_precision¶
- torchmetrics.functional.classification.multiclass_recall_at_fixed_precision(preds, target, num_classes, min_precision, thresholds=None, ignore_index=None, validate_args=True)[source]
Computes the higest possible recall value given the minimum precision thresholds provided for multiclass tasks. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level.
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
num_classes¶ (
int
) – Integer specifing the number of classesmin_precision¶ (
float
) – float value specifying minimum precision threshold.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 toFalse
for faster computations.
- Returns
a tuple of either 2 tensors or 2 lists containing
recall: an 1d tensor of size (n_classes, ) with the maximum recall for the given precision level per class
thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class
- Return type
(tuple)
Example
>>> from torchmetrics.functional.classification import multiclass_recall_at_fixed_precision >>> 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]) >>> multiclass_recall_at_fixed_precision(preds, target, num_classes=5, min_precision=0.5, thresholds=None) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06])) >>> multiclass_recall_at_fixed_precision(preds, target, num_classes=5, min_precision=0.5, thresholds=5) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06]))
multilabel_recall_at_fixed_precision¶
- torchmetrics.functional.classification.multilabel_recall_at_fixed_precision(preds, target, num_labels, min_precision, thresholds=None, ignore_index=None, validate_args=True)[source]
Computes the higest possible recall value given the minimum precision thresholds provided for multilabel tasks. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level.
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
min_precision¶ (
float
) – float value specifying minimum precision threshold.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 toFalse
for faster computations.
- Returns
a tuple of either 2 tensors or 2 lists containing
recall: an 1d tensor of size (n_classes, ) with the maximum recall for the given precision level per class
thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class
- Return type
(tuple)
Example
>>> from torchmetrics.functional.classification import multilabel_recall_at_fixed_precision >>> 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]]) >>> multilabel_recall_at_fixed_precision(preds, target, num_labels=3, min_precision=0.5, thresholds=None) (tensor([1., 1., 1.]), tensor([0.0500, 0.5500, 0.0500])) >>> multilabel_recall_at_fixed_precision(preds, target, num_labels=3, min_precision=0.5, thresholds=5) (tensor([1., 1., 1.]), tensor([0.0000, 0.5000, 0.0000]))