garrus 0.3.0
A Python package for machine learning and data visualization
License: MIT
Requires Python: <4.0,>=3.12
Keywords: machine-learning, data-visualization, pytorch

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**In the middle of some calibrations...**

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Garrus is a python framework for better confidence estimate of deep neural networks. Modern networks are overconfident estimators, that makes themselves unreliable and therefore limits the deployment of them in safety-critical applications.

Garrus provides tools for high quality confidence estimation such as confidence calibration and ordinal ranking methods, helping networks to **know correctly what they do not know**. 

----

## Installation:
```bash
pip install -U garrus
```

## Documentation:
  - [master v0.2.0](https://github.com/sleep3r/garrus/wiki/0.2.0@master-documentation)
  - [develop v0.2.0](https://github.com/sleep3r/garrus/wiki/0.2.0@develop-documentation)

## Roadmap:
- Core:
  - Calibration metrics:
    - [x] ECE
    - [x] NLL
    - [x] Brier
  - Ordinal Ranking Metrics:
    - [x] AURC
    - [x] E-AURC
    - [x] AUPRE
    - [x] FPR-n%-TPR
  - Visualizations:
    - [x] Reliability Diagram
    - [x] Confidence Histogram
  - [ ] Garrus Profiling
- Confidence Calibration:
    - Scaling:
      - [x] Platt
      - [x] Temperature
    - Binning: 
      - [ ] Histogram
      - [ ] Isotonic Regression
- Confidence Regularization:
  - Losses:
    - [ ] Correctness Ranking Loss
    - [ ] Focal Entropy Penalized Loss
  - [ ] Language Model Beam Search
- Confidence Networks:
  - [ ] ConfidNet
  - [ ] GarrusNet

---

### Citation:
Please use this bibtex if you want to cite this repository in your publications:

    @misc{garrus,
        author = {Kalashnikov, Alexander},
        title = {Deep neural networks calibration framework},
        year = {2021},
        publisher = {GitHub},
        journal = {GitHub repository},
        howpublished = {\url{https://github.com/sleep3r/garrus}},
    }
 
### References:
|Papers|
|---|
| [[1]](https://arxiv.org/pdf/1706.04599.pdf) Guo, Chuan, et al. "On calibration of modern neural networks." International Conference on Machine Learning. PMLR, 2017. APA |
| [[2]](https://arxiv.org/pdf/2007.01458.pdf) Moon, Jooyoung, et al. "Confidence-aware learning for deep neural networks." international conference on machine learning. PMLR, 2020. |
| [[3]](https://arxiv.org/pdf/1909.10155.pdf) Kumar, Ananya, Percy Liang, and Tengyu Ma. "Verified uncertainty calibration." arXiv preprint arXiv:1909.10155 (2019). |