ConformalClsTHR

class torch_uncertainty.post_processing.ConformalClsTHR(alpha, model=None, ts_init_val=1.0, ts_lr=0.1, ts_max_iter=100, enable_ts=True, device=None)

Conformal prediction post-processing for calibrated models.

Parameters:

• alpha (float) – The confidence level, meaning we allow \(1-\alpha\) error.

• model (Module | None) – Model to be calibrated. Defaults to None.

• ts_init_val (float) – Initial value for the temperature. Defaults to 1.0.

• ts_lr (float) – Learning rate for the temperature scaling optimizer. Defaults to 0.1.

• ts_max_iter (int) – Maximum number of iterations for the temperature scaling optimizer. Defaults to 100.

• enable_ts (bool) – Whether to scale the logits. Defaults to True.

• device (Union[Literal['cpu', 'cuda'], device, None]) – device. Defaults to None.

Warning

This implementation only works in the multiclass setting. Raise an issue if binary is needed.

Reference:

• Least ambiguous set-valued classifiers with bounded error levels, Sadinle, M. et al., (2016).

Code inspired by TorchCP.

conformal(inputs)

Perform conformal prediction on the test set.

Return type:

Tensor

model_forward(inputs)

Apply the model and return the scores.

Return type:

Tensor