scores.EntropyScore

Entropy-based confidence score.

Usage

scores.EntropyScore()

Computes the predictive entropy of the output distribution. Higher entropy indicates higher uncertainty (higher score). Supports multiclass, binary, and multilabel tasks.

Parameters

temperature: float or None = None: Optional initial temperature. If None, temperature is fitted if labels are provided to fit.
task: ("multiclass", "binary", "multilabel") = "multiclass": Task type for score computation.

Examples

import torch
from seapig.scores.logits import EntropyScore
logits = torch.randn(2, 3)
EntropyScore().score(logits)

Attributes

Name	Description
ident	str(object=’’) -> str

ident

str(object=’’) -> str

ident: str = "entropy"

str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to sys.getdefaultencoding(). errors defaults to ‘strict’.

Methods

Name	Description
score()	Compute predictive entropy for each sample (task-aware).

score()

Compute predictive entropy for each sample (task-aware).