Python package for the log-likelihood-ratio
Project description
LLR-Evaluation (llreval)
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
Install using pip
pip install llreval
Usage
import llreval
Out of a hundred trials, how many errors does your speaker verifier make?
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
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