Precision¶
Module Interface¶
- class torchmetrics.Precision(num_classes=None, threshold=0.5, average='micro', mdmc_average=None, ignore_index=None, top_k=None, multiclass=None, compute_on_step=None, **kwargs)[source]
Computes Precision:
Where and represent the number of true positives and false positives respecitively. With the use of
top_k
parameter, this metric can generalize to Precision@K.The reduction method (how the precision scores are aggregated) is controlled by the
average
parameter, and additionally by themdmc_average
parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.- Parameters
num_classes¶ (
Optional
[int
]) – Number of classes. Necessary for'macro'
,'weighted'
andNone
average methods.threshold¶ (
float
) – Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.Defines the reduction that is applied. Should be one of the following:
'micro'
[default]: Calculate the metric globally, across all samples and classes.'macro'
: Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).'weighted'
: Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn
).'none'
orNone
: Calculate the metric for each class separately, and return the metric for every class.'samples'
: Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).
Note
What is considered a sample in the multi-dimensional multi-class case depends on the value of
mdmc_average
.mdmc_average¶ (
Optional
[str
]) –Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
average
parameter). Should be one of the following:None
[default]: Should be left unchanged if your data is not multi-dimensional multi-class.'samplewise'
: In this case, the statistics are computed separately for each sample on theN
axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes...
(see Input types) as theN
dimension within the sample, and computing the metric for the sample based on that.'global'
: In this case theN
and...
dimensions of the inputs (see Input types) are flattened into a newN_X
sample axis, i.e. the inputs are treated as if they were(N_X, C)
. From here on theaverage
parameter applies as usual.
ignore_index¶ (
Optional
[int
]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, andaverage=None
or'none'
, the score for the ignored class will be returned asnan
.top_k¶ (
Optional
[int
]) – Number of the highest probability or logit score predictions considered finding the correct label, relevant only for (multi-dimensional) multi-class inputs. The default value (None
) will be interpreted as 1 for these inputs. Should be left at default (None
) for all other types of inputs.multiclass¶ (
Optional
[bool
]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.compute_on_step¶ (
Optional
[bool
]) –Forward only calls
update()
and returns None if this is set to False.Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (
Dict
[str
,Any
]) – Additional keyword arguments, see Advanced metric settings for more info.
- Raises
ValueError – If
average
is none of"micro"
,"macro"
,"weighted"
,"samples"
,"none"
,None
.
Example
>>> import torch >>> from torchmetrics import Precision >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> precision = Precision(average='macro', num_classes=3) >>> precision(preds, target) tensor(0.1667) >>> precision = Precision(average='micro') >>> precision(preds, target) tensor(0.2500)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- compute()[source]
Computes the precision score based on inputs passed in to
update
previously.- Returns
If
average in ['micro', 'macro', 'weighted', 'samples']
, a one-element tensor will be returnedIf
average in ['none', None]
, the shape will be(C,)
, whereC
stands for the number of classes
- Return type
The shape of the returned tensor depends on the
average
parameter
Functional Interface¶
- torchmetrics.functional.precision(preds, target, average='micro', mdmc_average=None, ignore_index=None, num_classes=None, threshold=0.5, top_k=None, multiclass=None)[source]
Computes Precision
Where and represent the number of true positives and false positives respecitively. With the use of
top_k
parameter, this metric can generalize to Precision@K.The reduction method (how the precision scores are aggregated) is controlled by the
average
parameter, and additionally by themdmc_average
parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.- Parameters
preds¶ (
Tensor
) – Predictions from model (probabilities, logits or labels)Defines the reduction that is applied. Should be one of the following:
'micro'
[default]: Calculate the metric globally, across all samples and classes.'macro'
: Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).'weighted'
: Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn
).'none'
orNone
: Calculate the metric for each class separately, and return the metric for every class.'samples'
: Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).
Note
What is considered a sample in the multi-dimensional multi-class case depends on the value of
mdmc_average
.Note
If
'none'
and a given class doesn’t occur in thepreds
ortarget
, the value for the class will benan
.mdmc_average¶ (
Optional
[str
]) –Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
average
parameter). Should be one of the following:None
[default]: Should be left unchanged if your data is not multi-dimensional multi-class.'samplewise'
: In this case, the statistics are computed separately for each sample on theN
axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes...
(see Input types) as theN
dimension within the sample, and computing the metric for the sample based on that.'global'
: In this case theN
and...
dimensions of the inputs (see Input types) are flattened into a newN_X
sample axis, i.e. the inputs are treated as if they were(N_X, C)
. From here on theaverage
parameter applies as usual.
ignore_index¶ (
Optional
[int
]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, andaverage=None
or'none'
, the score for the ignored class will be returned asnan
.num_classes¶ (
Optional
[int
]) – Number of classes. Necessary for'macro'
,'weighted'
andNone
average methods.threshold¶ (
float
) – Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.Number of highest probability or logit score predictions considered to find the correct label, relevant only for (multi-dimensional) multi-class inputs. The default value (
None
) will be interpreted as 1 for these inputs.Should be left at default (
None
) for all other types of inputs.multiclass¶ (
Optional
[bool
]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.
- Return type
- Returns
The shape of the returned tensor depends on the
average
parameterIf
average in ['micro', 'macro', 'weighted', 'samples']
, a one-element tensor will be returnedIf
average in ['none', None]
, the shape will be(C,)
, whereC
stands for the number of classes
- Raises
ValueError – If
average
is not one of"micro"
,"macro"
,"weighted"
,"samples"
,"none"
orNone
ValueError – If
mdmc_average
is not one ofNone
,"samplewise"
,"global"
.ValueError – If
average
is set butnum_classes
is not provided.ValueError – If
num_classes
is set andignore_index
is not in the range[0, num_classes)
.
Example
>>> from torchmetrics.functional import precision >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> precision(preds, target, average='macro', num_classes=3) tensor(0.1667) >>> precision(preds, target, average='micro') tensor(0.2500)