incerto.calibration.IdentityCalibrator#
- class incerto.calibration.IdentityCalibrator[source]#
Bases:
BaseCalibratorNo-op calibrator that returns the original softmax probabilities.
- __init__()#
Methods
__init__()fit(logits, labels)Fit the calibrator on a validation set.
load(path)Load a calibrator from a file.
load_state_dict(state)Load state (no-op for identity calibrator).
predict(logits)Apply calibration to logits and return a Categorical distribution.
save(path)Save calibrator state to a file.
Return empty state dict (no parameters to save).
- fit(logits, labels)[source]#
Fit the calibrator on a validation set.
This method should learn any calibration parameters needed to map uncalibrated logits to calibrated probabilities.
- Parameters:
- Returns:
For method chaining
- Return type:
self
Note
The validation set used here should be separate from both training and test sets to avoid overfitting the calibration parameters.
- predict(logits)[source]#
Apply calibration to logits and return a Categorical distribution.
This method applies the learned calibration to transform uncalibrated logits into calibrated probabilities.
- Parameters:
logits (
Tensor) – Uncalibrated model outputs of shape (n_samples, n_classes).- Return type:
- Returns:
A torch.distributions.Categorical distribution over calibrated probabilities. Access probabilities via .probs or sample via .sample().
Example
>>> calibrated_dist = calibrator.predict(test_logits) >>> calibrated_probs = calibrated_dist.probs # shape: (n_samples, n_classes) >>> predictions = calibrated_dist.sample() # shape: (n_samples,) >>> log_probs = calibrated_dist.log_prob(labels) # shape: (n_samples,)