Precision At Fixed Recall¶
Module Interface¶
- class torchmetrics.PrecisionAtFixedRecall(**kwargs)[source]¶
Compute the highest 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.
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'
ormultilabel
. See the documentation ofBinaryPrecisionAtFixedRecall
,MulticlassPrecisionAtFixedRecall
andMultilabelPrecisionAtFixedRecall
for the specific details of each argument influence and examples.
BinaryPrecisionAtFixedRecall¶
- class torchmetrics.classification.BinaryPrecisionAtFixedRecall(min_recall, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]¶
Compute the highest possible precision value given the minimum recall thresholds provided.
This is done by first calculating the precision-recall curve for different thresholds and the find the precision for a given recall level.
As input to
forward
andupdate
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
andcompute
the metric returns the following output:precision
(Tensor
): A scalar tensor with the maximum precision for the given recall levelthreshold
(Tensor
): A scalar tensor with the corresponding threshold level
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 \(\mathcal{O}(n_{samples})\) 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:
min_recall¶ (
float
) – float value specifying minimum recall 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 calculationIf 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.
Example
>>> from torch import tensor >>> from torchmetrics.classification import BinaryPrecisionAtFixedRecall >>> preds = tensor([0, 0.5, 0.7, 0.8]) >>> target = tensor([0, 1, 1, 0]) >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5, thresholds=None) >>> metric(preds, target) (tensor(0.6667), tensor(0.5000)) >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5, thresholds=5) >>> metric(preds, target) (tensor(0.6667), tensor(0.5000))
- plot(val=None, ax=None)[source]¶
Plot a single or multiple values from the metric.
- Parameters:
val¶ (
Union
[Tensor
,Sequence
[Tensor
],None
]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.ax¶ (
Optional
[Axes
]) – An matplotlib axis object. If provided will add plot to that axis
- Return type:
- Returns:
Figure object and Axes object
- Raises:
ModuleNotFoundError – If matplotlib is not installed
>>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import BinaryPrecisionAtFixedRecall >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5) >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() # the returned plot only shows the maximum recall value by default
>>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import BinaryPrecisionAtFixedRecall >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5) >>> values = [ ] >>> for _ in range(10): ... # we index by 0 such that only the maximum recall value is plotted ... values.append(metric(rand(10), randint(2,(10,)))[0]) >>> fig_, ax_ = metric.plot(values)
MulticlassPrecisionAtFixedRecall¶
- class torchmetrics.classification.MulticlassPrecisionAtFixedRecall(num_classes, min_recall, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]¶
Compute the highest possible precision value given the minimum recall thresholds provided.
This is done by first calculating the precision-recall curve for different thresholds and the find the precision for a given recall level.
As input to
forward
andupdate
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
andcompute
the metric returns a tuple of either 2 tensors or 2 lists containing:precision
(Tensor
): A 1d tensor of size(n_classes, )
with the maximum precision for the given recall level per classthreshold
(Tensor
): A 1d tensor of size(n_classes, )
with the corresponding threshold level per class
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 \(\mathcal{O}(n_{samples})\) 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 classesmin_recall¶ (
float
) – float value specifying minimum recall 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 calculationIf 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.
Example
>>> from torch import tensor >>> from torchmetrics.classification import MulticlassPrecisionAtFixedRecall >>> preds = 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 = tensor([0, 1, 3, 2]) >>> metric = MulticlassPrecisionAtFixedRecall(num_classes=5, min_recall=0.5, thresholds=None) >>> metric(preds, target) (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]), tensor([7.5000e-01, 7.5000e-01, 5.0000e-02, 5.0000e-02, 1.0000e+06])) >>> mcrafp = MulticlassPrecisionAtFixedRecall(num_classes=5, min_recall=0.5, thresholds=5) >>> mcrafp(preds, target) (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]), tensor([7.5000e-01, 7.5000e-01, 0.0000e+00, 0.0000e+00, 1.0000e+06]))
- plot(val=None, ax=None)[source]¶
Plot a single or multiple values from the metric.
- Parameters:
val¶ (
Union
[Tensor
,Sequence
[Tensor
],None
]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.ax¶ (
Optional
[Axes
]) – An matplotlib axis object. If provided will add plot to that axis
- Return type:
- Returns:
Figure object and Axes object
- Raises:
ModuleNotFoundError – If matplotlib is not installed
>>> from torch import rand, randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassPrecisionAtFixedRecall >>> metric = MulticlassPrecisionAtFixedRecall(num_classes=3, min_recall=0.5) >>> metric.update(rand(20, 3).softmax(dim=-1), randint(3, (20,))) >>> fig_, ax_ = metric.plot() # the returned plot only shows the maximum recall value by default
>>> from torch import rand, randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassPrecisionAtFixedRecall >>> metric = MulticlassPrecisionAtFixedRecall(num_classes=3, min_recall=0.5) >>> values = [] >>> for _ in range(20): ... # we index by 0 such that only the maximum recall value is plotted ... values.append(metric(rand(20, 3).softmax(dim=-1), randint(3, (20,)))[0]) >>> fig_, ax_ = metric.plot(values)
MultilabelPrecisionAtFixedRecall¶
- class torchmetrics.classification.MultilabelPrecisionAtFixedRecall(num_labels, min_recall, thresholds=None, ignore_index=None, validate_args=True, **kwargs)[source]¶
Compute the highest possible precision value given the minimum recall thresholds provided.
This is done by first calculating the precision-recall curve for different thresholds and the find the precision for a given recall level.
As input to
forward
andupdate
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, ...)
. 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
andcompute
the metric returns a tuple of either 2 tensors or 2 lists containing:precision
(Tensor
): A 1d tensor of size(n_classes, )
with the maximum precision for the given recall level per classthreshold
(Tensor
): A 1d tensor of size(n_classes, )
with the corresponding threshold level per class
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 \(\mathcal{O}(n_{samples})\) 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:
min_recall¶ (
float
) – float value specifying minimum recall 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 calculationIf 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.
Example
>>> from torch import tensor >>> from torchmetrics.classification import MultilabelPrecisionAtFixedRecall >>> preds = 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 = tensor([[1, 0, 1], ... [0, 0, 0], ... [0, 1, 1], ... [1, 1, 1]]) >>> metric = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5, thresholds=None) >>> metric(preds, target) (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5500, 0.3500])) >>> mlrafp = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5, thresholds=5) >>> mlrafp(preds, target) (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5000, 0.2500]))
- plot(val=None, ax=None)[source]¶
Plot a single or multiple values from the metric.
- Parameters:
val¶ (
Union
[Tensor
,Sequence
[Tensor
],None
]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.ax¶ (
Optional
[Axes
]) – An matplotlib axis object. If provided will add plot to that axis
- Return type:
- Returns:
Figure object and Axes object
- Raises:
ModuleNotFoundError – If matplotlib is not installed
>>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import MultilabelPrecisionAtFixedRecall >>> metric = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5) >>> metric.update(rand(20, 3), randint(2, (20, 3))) >>> fig_, ax_ = metric.plot() # the returned plot only shows the maximum recall value by default
>>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import MultilabelPrecisionAtFixedRecall >>> metric = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5) >>> values = [ ] >>> for _ in range(10): ... # we index by 0 such that only the maximum recall value is plotted ... values.append(metric(rand(20, 3), randint(2, (20, 3)))[0]) >>> fig_, ax_ = metric.plot(values)
Functional Interface¶
binary_precision_at_fixed_recall¶
- torchmetrics.functional.classification.binary_precision_at_fixed_recall(preds, target, min_recall, thresholds=None, ignore_index=None, validate_args=True)[source]¶
Compute the highest possible precision value given the minimum recall thresholds provided for binary tasks.
This is done by first calculating the precision-recall curve for different thresholds and the find the precision for a given recall 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). 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 \(\mathcal{O}(n_{samples})\) 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:
min_recall¶ (
float
) – float value specifying minimum recall 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 calculationIf set to an 1d
Tensor
of floats, will use the indicated thresholds in the tensor as bins for the calculation.
ignore_index¶ (
Optional
[int
]) – Specifies a target value that is ignored and does not contribute to the metric calculationvalidate_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:
precision: an scalar tensor with the maximum precision for the given precision level
threshold: an scalar tensor with the corresponding threshold level
- Return type:
(tuple)
Example
>>> from torchmetrics.functional.classification import binary_precision_at_fixed_recall >>> preds = torch.tensor([0, 0.5, 0.7, 0.8]) >>> target = torch.tensor([0, 1, 1, 0]) >>> binary_precision_at_fixed_recall(preds, target, min_recall=0.5, thresholds=None) (tensor(0.6667), tensor(0.5000)) >>> binary_precision_at_fixed_recall(preds, target, min_recall=0.5, thresholds=5) (tensor(0.6667), tensor(0.5000))
multiclass_precision_at_fixed_recall¶
- torchmetrics.functional.classification.multiclass_precision_at_fixed_recall(preds, target, num_classes, min_recall, thresholds=None, ignore_index=None, validate_args=True)[source]¶
Compute the highest possible precision value given the minimum recall thresholds provided for multiclass tasks.
This is done by first calculating the precision-recall curve for different thresholds and the find the precision for a given recall 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 \(\mathcal{O}(n_{samples})\) 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 classesmin_recall¶ (
float
) – float value specifying minimum recall 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 calculationIf set to an 1d
Tensor
of floats, will use the indicated thresholds in the tensor as bins for the calculation.
ignore_index¶ (
Optional
[int
]) – Specifies a target value that is ignored and does not contribute to the metric calculationvalidate_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
precision: an 1d tensor of size (n_classes, ) with the maximum precision for the given recall 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_precision_at_fixed_recall >>> 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_precision_at_fixed_recall( ... preds, target, num_classes=5, min_recall=0.5, thresholds=None) (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]), tensor([7.5000e-01, 7.5000e-01, 5.0000e-02, 5.0000e-02, 1.0000e+06])) >>> multiclass_precision_at_fixed_recall( ... preds, target, num_classes=5, min_recall=0.5, thresholds=5) (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]), tensor([7.5000e-01, 7.5000e-01, 0.0000e+00, 0.0000e+00, 1.0000e+06]))
multilabel_precision_at_fixed_recall¶
- torchmetrics.functional.classification.multilabel_precision_at_fixed_recall(preds, target, num_labels, min_recall, thresholds=None, ignore_index=None, validate_args=True)[source]¶
Compute the highest possible precision value given the minimum recall thresholds provided for multilabel tasks.
This is done by first calculating the precision-recall curve for different thresholds and the find the precision for a given recall 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 \(\mathcal{O}(n_{samples})\) 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:
min_recall¶ (
float
) – float value specifying minimum recall 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 calculationIf set to an 1d
Tensor
of floats, will use the indicated thresholds in the tensor as bins for the calculation.
ignore_index¶ (
Optional
[int
]) – Specifies a target value that is ignored and does not contribute to the metric calculationvalidate_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
precision: an 1d tensor of size (n_classes, ) with the maximum precision for the given recall 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_precision_at_fixed_recall >>> 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_precision_at_fixed_recall(preds, target, num_labels=3, min_recall=0.5, thresholds=None) (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5500, 0.3500])) >>> multilabel_precision_at_fixed_recall(preds, target, num_labels=3, min_recall=0.5, thresholds=5) (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5000, 0.2500]))