torcheval.metrics.R2Score

class torcheval.metrics.R2Score(*, multioutput: str = 'uniform_average', num_regressors: int = 0, device: Optional[device] = None)

Compute R-squared score, which is the proportion of variance in the dependent variable that can be explained by the independent variable. Its functional version is torcheval.metrics.functional.r2_score().

Parameters:

• multioutput (str, Optional) –
  • 'uniform_average' [default]: Return scores of all outputs are averaged with uniform weight.
  • 'raw_values': Return a full set of scores.
  • variance_weighted: Return scores of all outputs are averaged with weighted by the variances of each individual output.
• num_regressors (int, Optional) – Number of independent variables used, applied to adjusted R-squared score. Defaults to zero (standard R-squared score).

Raises:

ValueError –

• If value of multioutput does not exist in (raw_values, uniform_average, variance_weighted). - If value of num_regressors is not an integer in the range of [0, n_samples - 1].

Examples:

>>> import torch
>>> from torcheval.metrics import R2Score
>>> metric = R2Score()
>>> input = torch.tensor([0, 2, 1, 3])
>>> target = torch.tensor([0, 1, 2, 3])
>>> metric.update(input, target)
>>> metric.compute()
tensor(0.6)

>>> metric = R2Score()
>>> input = torch.tensor([[0, 2], [1, 6]])
>>> target = torch.tensor([[0, 1], [2, 5]])
>>> metric.update(input, target)
>>> metric.compute()
tensor(0.6250)

>>> metric = R2Score(multioutput="raw_values")
>>> input = torch.tensor([[0, 2], [1, 6]])
>>> target = torch.tensor([[0, 1], [2, 5]])
>>> metric.update(input, target)
>>> metric.compute()
tensor([0.5000, 0.7500])

>>> metric = R2Score(multioutput="variance_weighted")
>>> input = torch.tensor([[0, 2], [1, 6]])
>>> target = torch.tensor([[0, 1], [2, 5]])
>>> metric.update(input, target)
>>> metric.compute()
tensor(0.7000)

>>> metric = R2Score(multioutput="raw_values", num_regressors=2)
>>> input = torch.tensor([1.2, 2.5, 3.6, 4.5, 6])
>>> target = torch.tensor([1, 2, 3, 4, 5])
>>> metric.update(input, target)
>>> metric.compute()
tensor(0.6200)

__init__(*, multioutput: str = 'uniform_average', num_regressors: int = 0, device: Optional[device] = None) → None

Initialize a metric object and its internal states.

Use self._add_state() to initialize state variables of your metric class. The state variables should be either torch.Tensor, a list of torch.Tensor, or a dictionary with torch.Tensor as values

Methods

__init__(*[, multioutput, num_regressors, ...])	Initialize a metric object and its internal states.
compute()	Return the R-squared score.
load_state_dict(state_dict[, strict])	Loads metric state variables from state_dict.
merge_state(metrics)	Implement this method to update the current metric's state variables to be the merged states of the current metric and input metrics.
reset()	Reset the metric state variables to their default value.
state_dict()	Save metric state variables in state_dict.
to(device, *args, **kwargs)	Move tensors in metric state variables to device.
update(input, target)	Update states with the ground truth values and predictions.

Attributes

device

The last input device of Metric.to().