This is an authorized fork from PYLLR.
Python toolkit for likelihood-ratio calibration of binary classifiers.
The emphasis is on binary classifiers (for example speaker verification), where the output of the classifier is in the form of a well-calibrated log-likelihood-ratio (LLR). The tools include:
- PAV and ROCCH score analysis.
- DET curves and EER
- DCF and minDCF
- Bayes error-rate plots
- Cllr
Most of the algorithms in LLR-Evaluation are Python translations of the older MATLAB BOSARIS Tookit. Descriptions of the algorithms are available in:
Niko Brümmer and Edward de Villiers, The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF, 2013.
Install using pip
pip install llreval
import llreval
We have included in the examples directory, some code that reproduces the plots in our paper:
Niko Brümmer, Luciana Ferrer and Albert Swart, "Out of a hundred trials, how many errors does your speaker verifier make?", 2011, https://arxiv.org/abs/2104.00732.
For instructions, go to the readme