Metadata-Version: 2.1
Name: llreval
Version: 0.0.3
Summary: Python package for the log-likelihood-ratio
Author-email: Niko Brummer <niko.brummer@gmail.com>, "David A. van Leeuwen" <david.vanleeuwen@gmail.com>
License: MIT License
        
        Copyright (c) 2021 bsxfan, 2023 David van Leeuwen
        
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Project-URL: Homepage, https://github.com/davidavdav/llreval
Project-URL: Bug Tracker, https://github.com/davidavdav/llreval/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# LLR-Evaluation (llreval)

This is an authorized fork from [PYLLR](https://github.com/bsxfan/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](https://sites.google.com/site/bosaristoolkit/). 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](https://arxiv.org/abs/1304.2865), 2013.

## Install

Install using `pip`
```sh
pip install llreval
```

## Usage

```python
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](https://github.com/davidavdav/llreval/tree/main/examples/interspeech2021/README.md)


