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https://github.com/uncertainty-toolbox/uncertainty-toolbox

Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
https://github.com/uncertainty-toolbox/uncertainty-toolbox

bayesian-deep-learning bayesian-neural-networks calibration metrics predictive-uncertainty recalibration scoring-rules sharpness toolbox uncertainty uncertainty-calibration uncertainty-estimation uncertainty-metrics uncertainty-quantification uncertainty-toolbox visualization visualizations

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Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

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README 

        





    Website,

    Tutorials, and

    Docs

        




 \

**Uncertainty Toolbox**

> A Python toolbox for predictive uncertainty quantification, calibration,

> [metrics, and visualization](#metrics).\

> Also: a [glossary of useful terms](docs/glossary.md) and a collection

> of [relevant papers and references](docs/paper_list.md).



 \

Many machine learning methods return predictions along with uncertainties of some form,

such as distributions or confidence intervals. This begs the questions: How do we

determine which predictive uncertanties are best? What does it mean to produce a _best_

or _ideal_ uncertainty? Are our uncertainties accurate and _well calibrated_?



Uncertainty Toolbox provides standard metrics to quantify and compare predictive

uncertainty estimates, gives intuition for these metrics, produces visualizations of

these metrics/uncertainties, and implements simple "re-calibration" procedures to

improve these uncertainties.  This toolbox currently focuses on regression tasks.



## Toolbox Contents



Uncertainty Toolbox contains:

* [Glossary](docs/glossary.md) of terms related to predictive uncertainty

  quantification.

* [Metrics](#metrics) for assessing quality of predictive uncertainty estimates.

* [Visualizations](#visualizations) for predictive uncertainty estimates and metrics.

* [Recalibration](#recalibration) methods for improving the calibration of a predictor.

* [Paper list](docs/paper_list.md): publications and references on relevant methods and metrics.



## Installation



Uncertainty Toolbox requires Python 3.6+. For a lightweight installation of the package

only, run:

```bash

pip install uncertainty-toolbox

```



For a full installation with examples, tests, and the latest updates, run:

```bash

git clone https://github.com/uncertainty-toolbox/uncertainty-toolbox.git

cd uncertainty-toolbox

pip install -e . -r requirements/requirements_dev.txt

```

Note that the previous command requires pip ≥ 21.3.



To verify correct installation, you can run the [test suite](tests/) via:

```bash

source shell/run_all_tests.sh

```



## Quick Start



```python

import uncertainty_toolbox as uct



# Load an example dataset of 100 predictions, uncertainties, and ground truth values

predictions, predictions_std, y, x = uct.data.synthetic_sine_heteroscedastic(100)



# Compute all uncertainty metrics

metrics = uct.metrics.get_all_metrics(predictions, predictions_std, y)

```

This example computes [metrics](#metrics) for a vector of predicted values

(`predictions`) and associated uncertainties (`predictions_std`, a vector of standard

deviations), taken with respect to a corresponding set of ground truth values `y`.



**Colab notebook:**

You can also take a look at [this Colab

notebook](https://colab.research.google.com/drive/1lbhwb6MP8FvQh9Q7Jldt4PuPEeSldsG5?usp=sharing),

which walks through a use case of Uncertainty Toolbox.



## Metrics



Uncertainty Toolbox provides a number of [metrics](uncertainty_toolbox/metrics.py) to

quantify and compare predictive uncertainty estimates. For example, the

[`get_all_metrics`](uncertainty_toolbox/metrics.py#L242) function will return:

1. __average calibration__: _mean absolute calibration error, root mean squared calibration error, miscalibration area._

2. __adversarial group calibration__: _mean absolute adversarial group calibration error, root mean squared adversarial group calibration error._

3. __sharpness__: _expected standard deviation._

4. __proper scoring rules__: _negative log-likelihood, continuous ranked probability score, check score, interval score._

5. __accuracy__: _mean absolute error, root mean squared error, median absolute error, coefficient of determination, correlation._



## Visualizations



The following plots are a few of the [visualizations](uncertainty_toolbox/viz.py)

provided by Uncertainty Toolbox. See [this example](examples/viz_readme_figures.py) for code

to reproduce these plots.



**Overconfident** (_too little uncertainty_)



    




**Underconfident** (_too much uncertainty_)



    




**Well calibrated**



    




And here are a few of the calibration metrics for the above three cases:



|              | Mean absolute calibration error (MACE) | Root mean squared calibration error (RMSCE) | Miscalibration area (MA) |

| :----- | :-----: | :-----: | :-----: |

| Overconfident | 0.19429 | 0.21753 | 0.19625 |

| Underconfident | 0.20692 | 0.23003 | 0.20901 |

| Well calibrated | 0.00862 | 0.01040 | 0.00865 |



## Recalibration



The following plots show the results of a

[recalibration](uncertainty_toolbox/recalibration.py) procedure provided by Uncertainty

Toolbox, which transforms a set of predictive uncertainties to improve average

calibration. The algorithm is based on isotonic regression, as proposed by [Kuleshov et

al](docs/paper_list.md#calibration-sharpness-and-recalibration-in-deep-learning).



See [this example](examples/viz_recalibrate_readme.py) for code to reproduce these plots.



**Recalibrating overconfident predictions**












|              | Mean absolute calibration error (MACE) | Root mean squared calibration error (RMSCE) | Miscalibration area (MA) |

| :----- | :-----: | :-----: | :-----: |

| Before Recalibration | 0.19429 | 0.21753 | 0.19625 |

| After Recalibration | 0.01124 | 0.02591 | 0.01117 |



**Recalibrating underconfident predictions**












|              | Mean absolute calibration error (MACE) | Root mean squared calibration error (RMSCE) | Miscalibration area (MA) |

| :----- | :-----: | :-----: | :-----: |

| Before Recalibration | 0.20692 | 0.23003 | 0.20901 |

| After Recalibration | 0.00157 | 0.00205 | 0.00132 |



## Contributing



We welcome and greatly appreciate contributions from the community! Please see

our [contributing guidelines](CONTRIBUTING.md) for details on how to help out.



## Citation



If you found this toolbox helpful, please cite the [following

paper](https://arxiv.org/abs/2109.10254):

```

@article{chung2021uncertainty,

  title={Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification},

  author={Chung, Youngseog and Char, Ian and Guo, Han and Schneider, Jeff and Neiswanger, Willie},

  journal={arXiv preprint arXiv:2109.10254},

  year={2021}

}

```



Additionally, here are papers that led to the development of the toolbox:

```

@article{chung2020beyond,

  title={Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification},

  author={Chung, Youngseog and Neiswanger, Willie and Char, Ian and Schneider, Jeff},

  journal={arXiv preprint arXiv:2011.09588},

  year={2020}

}



@article{tran2020methods,

  title={Methods for comparing uncertainty quantifications for material property predictions},

  author={Tran, Kevin and Neiswanger, Willie and Yoon, Junwoong and Zhang, Qingyang and Xing, Eric and Ulissi, Zachary W},

  journal={Machine Learning: Science and Technology},

  volume={1},

  number={2},

  pages={025006},

  year={2020},

  publisher={IOP Publishing}

}

```



## Acknowledgments



Development of Uncertainty Toolbox is [supported by](docs/acknowledgments.md) the following organizations.