R2 Score¶
Module Interface¶
- class torchmetrics.R2Score(num_outputs=1, adjusted=0, multioutput='uniform_average', **kwargs)[source]
Compute r2 score also known as R2 Score_Coefficient Determination.
where
is the sum of residual squares, and
is total sum of squares. Can also calculate adjusted r2 score given by
where the parameter
(the number of independent regressors) should be provided as the adjusted argument. The score is only proper defined when
, which can happen for near constant targets. In this case a score of 0 is returned. By definition the score is bounded between 0 and 1, where 1 corresponds to the predictions exactly matching the targets.
As input to
forward
andupdate
the metric accepts the following input:preds
(Tensor
): Predictions from model in float tensor with shape(N,)
or(N, M)
(multioutput)target
(Tensor
): Ground truth values in float tensor with shape(N,)
or(N, M)
(multioutput)
As output of
forward
andcompute
the metric returns the following output:r2score
(Tensor
): A tensor with the r2 score(s)
In the case of multioutput, as default the variances will be uniformly averaged over the additional dimensions. Please see argument
multioutput
for changing this behavior.- Parameters:
num_outputs¶ (
int
) – Number of outputs in multioutput settingadjusted¶ (
int
) – number of independent regressors for calculating adjusted r2 score.Defines aggregation in the case of multiple output scores. Can be one of the following strings:
'raw_values'
returns full set of scores'uniform_average'
scores are uniformly averaged'variance_weighted'
scores are weighted by their individual variances
kwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
- Raises:
ValueError – If
adjusted
parameter is not an integer larger or equal to 0.ValueError – If
multioutput
is not one of"raw_values"
,"uniform_average"
or"variance_weighted"
.
Example
>>> from torchmetrics.regression import R2Score >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> r2score = R2Score() >>> r2score(preds, target) tensor(0.9486)
>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]]) >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]]) >>> r2score = R2Score(num_outputs=2, multioutput='raw_values') >>> r2score(preds, target) tensor([0.9654, 0.9082])
- 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 and Axes object
- Raises:
ModuleNotFoundError – If matplotlib is not installed
>>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import R2Score >>> metric = R2Score() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot()
>>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import R2Score >>> metric = R2Score() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values)
Functional Interface¶
- torchmetrics.functional.r2_score(preds, target, adjusted=0, multioutput='uniform_average')[source]
Compute r2 score also known as R2 Score_Coefficient Determination.
where
is the sum of residual squares, and
is total sum of squares. Can also calculate adjusted r2 score given by
where the parameter
(the number of independent regressors) should be provided as the
adjusted
argument.- Parameters:
adjusted¶ (
int
) – number of independent regressors for calculating adjusted r2 score.Defines aggregation in the case of multiple output scores. Can be one of the following strings:
'raw_values'
returns full set of scores'uniform_average'
scores are uniformly averaged'variance_weighted'
scores are weighted by their individual variances
- Raises:
ValueError – If both
preds
andtargets
are not1D
or2D
tensors.ValueError – If
len(preds)
is less than2
since at least2
sampels are needed to calculate r2 score.ValueError – If
multioutput
is not one ofraw_values
,uniform_average
orvariance_weighted
.ValueError – If
adjusted
is not aninteger
greater than0
.
- Return type:
Example
>>> from torchmetrics.functional.regression import r2_score >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> r2_score(preds, target) tensor(0.9486)
>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]]) >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]]) >>> r2_score(preds, target, multioutput='raw_values') tensor([0.9654, 0.9082])