https://github.com/ml-stat-Sustech/TorchCP
A Python toolbox for conformal prediction research on deep learning models, using PyTorch.
https://github.com/ml-stat-Sustech/TorchCP
Last synced: 3 months ago
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A Python toolbox for conformal prediction research on deep learning models, using PyTorch.
- Host: GitHub
- URL: https://github.com/ml-stat-Sustech/TorchCP
- Owner: ml-stat-Sustech
- License: lgpl-3.0
- Created: 2023-12-06T09:08:41.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-12-04T14:10:24.000Z (7 months ago)
- Last Synced: 2024-12-04T14:43:44.805Z (7 months ago)
- Language: Python
- Homepage:
- Size: 8.91 MB
- Stars: 234
- Watchers: 4
- Forks: 33
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Authors: AUTHORS.txt
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- awesome-conformal-prediction - TorchCP - A library for conformal prediction
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TorchCP: A Python toolbox for Conformal Prediction in Deep Learning.
Technical Report
·
Documentation
TorchCP is a Python toolbox for conformal prediction research on deep learning models, built on the PyTorch Library with
strong GPU acceleration. In the toolbox, we implement representative methods (including posthoc and training methods)
for many tasks of conformal prediction, including: Classification, Regression, Graph Node Classification, and LLM. We
build the basic framework of TorchCP based on [`AdverTorch`](https://github.com/BorealisAI/advertorch/tree/master). This
codebase is still under construction and maintained by [`Hongxin Wei`](https://hongxin001.github.io/)'s research group
at SUSTech. Comments, issues, contributions, and collaborations are all welcomed!
## Updates of New Version (1.0.2)
This version includes major refactoring of trainers, new uncertainty-aware classifiers, and important bug fixes ([#45](https://github.com/ml-stat-Sustech/TorchCP/issues/45)).
Detailed changelog can be found in the [Documentation](https://torchcp.readthedocs.io/en/latest/CHANGELOG.html).
# Overview
TorchCP has implemented the following methods:
## Classification
| Year | Title | Venue | Code Link | Implementation |
|------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|-----------------------------------------------------------------------------------|-----------------------------------------------------|
| 2025 | [**C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets**](https://openreview.net/forum?id=8Gqz2opok1) | ICLR'25 | | classification.loss.cd |
| 2024 | [**Delving into temperature scaling for adaptive conformal prediction**](https://arxiv.org/abs/2402.04344) | Arxiv | | classification.loss.confts |
| 2024 | [**Conformal Prediction for Deep Classifier via Label Ranking**](https://arxiv.org/abs/2310.06430) | ICML'24 | [Link](https://github.com/ml-stat-Sustech/conformal_prediction_via_label_ranking) | classification.score.saps |
| 2023 | [**Class-Conditional Conformal Prediction with Many Classes**](https://arxiv.org/abs/2306.09335) | NeurIPS'23 | [Link](https://github.com/tiffanyding/class-conditional-conformal) | classification.predictor.cluster |
| 2023 | [**Conformal Prediction Sets for Ordinal Classification**](https://proceedings.neurips.cc/paper_files/paper/2023/file/029f699912bf3db747fe110948cc6169-Paper-Conference.pdf) | NeurIPS'23 | | classification.trainer.ordinal |
| 2022 | [**Training Uncertainty-Aware Classifiers with Conformalized Deep Learning**](https://arxiv.org/abs/2205.05878) | NeurIPS'22 | [Link](https://github.com/bat-sheva/conformal-learning) | classification.loss.uncertainty_aware |
| 2022 | [**Learning Optimal Conformal Classifiers**](https://arxiv.org/abs/2110.09192) | ICLR'22 | [Link](https://github.com/google-deepmind/conformal_training/tree/main) | classification.loss.conftr |
| 2021 | [**Uncertainty Sets for Image Classifiers using Conformal Prediction**](https://arxiv.org/abs/2009.14193 ) | ICLR'21 | [Link](https://github.com/aangelopoulos/conformal_classification) | classification.score.raps classification.score.topk |
| 2020 | [**Classification with Valid and Adaptive Coverage**](https://proceedings.neurips.cc/paper/2020/file/244edd7e85dc81602b7615cd705545f5-Paper.pdf) | NeurIPS'20 | [Link](https://github.com/msesia/arc) | classification.score.aps |
| 2019 | [**Conformal Prediction Under Covariate Shift**](https://arxiv.org/abs/1904.06019) | NeurIPS'19 | [Link](https://github.com/ryantibs/conformal/) | classification.predictor.weight |
| 2016 | [**Least Ambiguous Set-Valued Classifiers with Bounded Error Levels**](https://arxiv.org/abs/1609.00451) | JASA | | classification.score.thr |
| 2016 | [**Hedging Predictions in Machine Learning**](https://ieeexplore.ieee.org/document/8129828) | The Computer Journal | | classification.score.knn |
| 2015 | [**Bias reduction through conditional conformal prediction**](https://dl.acm.org/doi/abs/10.3233/IDA-150786) | Intell. Data Anal. | | classification.score.margin |
| 2012 | [**Conditional Validity of Inductive Conformal Predictors**](https://proceedings.mlr.press/v25/vovk12.html) | ACML'12 | | classification.predictor.classwise |
| 2007 | [**Hedging Predictions in Machine Learning**](https://ieeexplore.ieee.org/document/8129828) | The Computer Journal | | classification.score.knn |
## Regression
| Year | Title | Venue | Code Link | Implementation | Remark |
|------|-------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|--------------------------------------------------------|--------------------------------------------|---------------------|
| 2023 | [**Conformal Prediction via Regression-as-Classification**](http://etash.me/papers/Bayesian_Conformal_Prediction_through_Memory_Adaptation.pdf) | RegML @ NeurIPS 2023 | [link](https://github.com/EtashGuha/R2CCP/tree/master) | regression.score.r2ccp | |
| 2021 | [**Adaptive Conformal Inference Under Distribution Shift**](https://arxiv.org/abs/2106.00170) | NeurIPS'21 | [Link](https://github.com/isgibbs/AdaptiveConformal) | regression.predictor.aci | support time series |
| 2020 | [**A comparison of some conformal quantile regression methods**](https://onlinelibrary.wiley.com/doi/epdf/10.1002/sta4.261) | Stat | [Link](https://github.com/soroushzargar/DAPS) | regression.score.cqm regression.score.cqrr | |
| 2020 | [**Conformal Prediction Interval for Dynamic Time-Series**](https://proceedings.mlr.press/v139/xu21h.html) | ICML'21 | [Link](https://github.com/hamrel-cxu/EnbPI) | regression.predictor.ensemble | support time series |
| 2019 | [**Adaptive, Distribution-Free Prediction Intervals for Deep Networks**](https://proceedings.mlr.press/v108/kivaranovic20a.html) | AISTATS'19 | [Link](https://github.com/yromano/cqr) | regression.score.cqrfm | |
| 2019 | [**Conformalized Quantile Regression**](https://proceedings.neurips.cc/paper_files/paper/2019/file/5103c3584b063c431bd1268e9b5e76fb-Paper.pdf) | NeurIPS'19 | [Link](https://github.com/yromano/cqr) | regression.score.cqr | |
| 2017 | [**Distribution-Free Predictive Inference For Regression**](https://arxiv.org/abs/1604.04173) | JASA | [Link](https://github.com/ryantibs/conformal) | regression.predictor.split | |
## Graph
| Year | Title | Venue | Code Link | Implementation |
|------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|------------------------------------------------------------|----------------------------------|
| 2024 | [**Similarity-Navigated Conformal Prediction for Graph Neural Networks**](https://arxiv.org/abs/2405.14303) | NeuIPS'24 | [Link](https://github.com/janqsong/SNAPS) | graph.score.snaps |
| 2023 | [**Distribution Free Prediction Sets for Node Classification**](https://proceedings.mlr.press/v202/clarkson23a) | ICML'23 | [Link](https://github.com/jase-clarkson/graph_cp) | graph.predictor.naps |
| 2023 | [**Conformal Prediction Sets for Graph Neural Networks**](https://proceedings.mlr.press/v202/h-zargarbashi23a.html) | ICML'23 | [Link](https://github.com/soroushzargar/DAPS) | graph.score.daps |
| 2023 | [**Uncertainty Quantification over Graph with Conformalized Graph Neural Networks**](https://proceedings.neurips.cc/paper_files/paper/2023/hash/54a1495b06c4ee2f07184afb9a37abda-Abstract-Conference.html) | NeurIPS'23 | [Link](https://github.com/snap-stanford/conformalized-gnn) | graph.trainer.cfgnn |
# Language Models
| Year | Title | Venue | Code Link | Implementation |
|------|-------------------------------------------------------------------------------|---------|---------------------------------------------------------------|-----------------------------|
| 2023 | [**Conformal Language Modeling**](https://openreview.net/forum?id=pzUhfQ74c5) | ICLR'24 | [Link](https://github.com/Varal7/conformal-language-modeling) | llm.predictor.conformal_llm |
## TODO
TorchCP is still under active development. We will add the following features/items down the road:
| Year | Title | Venue | Code |
|------|-----------------------------------------------------------------------------------------------------------------|---------|----------------------------------------------------------------------------|
| 2022 | [**Adaptive Conformal Predictions for Time Series**](https://arxiv.org/abs/2202.07282) | ICML'22 | [Link](https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries) |
| 2022 | [**Conformal Prediction Sets with Limited False Positives**](https://arxiv.org/abs/2202.07650) | ICML'22 | [Link](https://github.com/ajfisch/conformal-fp) |
| 2021 | [**Optimized conformal classification using gradient descent approximation**](https://arxiv.org/abs/2105.11255) | Arxiv | |
## Installation
TorchCP is developed with Python 3.9 and PyTorch 2.0.1. To install TorchCP, simply run
```
pip install torchcp
```
To install from TestPyPI server, run
```
pip install --index-url https://test.pypi.org/simple/ --no-deps torchcp
```
## Unit Test
TorchCP achieves 100% unit test coverage. You can use the following command to test the code implementation:
```
pytest --cov=torchcp tests
```
## Examples
Here, we provide a simple example for a classification task, with THR score and SplitPredictor.
```python
from torchcp.classification.score import THR
from torchcp.classification.predictor import SplitPredictor
# Preparing a calibration data and a test data.
cal_dataloader = ...
test_dataloader = ...
# Preparing a pytorch model
model = ...
model.eval()
# Options of score function: THR, APS, SAPS, RAPS
# Define a conformal prediction algorithm. Optional: SplitPredictor, ClusteredPredictor, ClassWisePredictor
predictor = SplitPredictor(score_function=THR(), model=model)
# Calibrating the predictor with significance level as 0.1
predictor.calibrate(cal_dataloader, alpha=0.1)
#########################################
# Predicting for test instances
########################################
test_instances = ...
predict_sets = predictor.predict(test_instances)
print(predict_sets)
#########################################
# Evaluating the coverage rate and average set size on a given dataset.
########################################
result_dict = predictor.evaluate(test_dataloader)
print(f"Coverage Rate: {result_dict['coverage_rate']:.4f}")
print(f"Average Set Size: {result_dict['average_size']:.4f}")
```
You may find more tutorials in [`examples`](https://github.com/ml-stat-Sustech/TorchCP/tree/master/examples) folder.
## License
This project is licensed under the LGPL. The terms and conditions can be found in the LICENSE and LICENSE.GPL files.
## Citation
If you find our repository useful for your research, please consider citing the
following [technical report](https://arxiv.org/abs/2402.12683):
```
@misc{huang2024torchcp,
title={TorchCP: A Python Library for Conformal Prediction},
author={Jianguo Huang and Jianqing Song and Xuanning Zhou and Bingyi Jing and Hongxin Wei},
year={2024},
eprint={2402.12683},
archivePrefix={arXiv},
primaryClass={cs.LG},
}
```
We welcome you to cite the following works:
```
@inproceedings{huangconformal,
title={Conformal Prediction for Deep Classifier via Label Ranking},
author={Huang, Jianguo and Xi, HuaJun and Zhang, Linjun and Yao, Huaxiu and Qiu, Yue and Wei, Hongxin},
booktitle={Forty-first International Conference on Machine Learning}
}
@article{xi2024does,
title={Does Confidence Calibration Help Conformal Prediction?},
author={Xi, Huajun and Huang, Jianguo and Feng, Lei and Wei, Hongxin},
journal={arXiv preprint arXiv:2402.04344},
year={2024}
}
@inproceedings{
liu2025cadapter,
title={C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets},
author={Kangdao Liu and Hao Zeng and Jianguo Huang and Huiping Zhuang and Chi Man VONG and Hongxin Wei},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=8Gqz2opok1}
}
```
## Contributors
* [Hongxin Wei](https://hongxin001.github.io/)
* [Jianguo Huang](https://jianguo99.github.io/)
* [Xuanning Zhou](https://github.com/Shinning-Zhou)
* [Jianqing Song](https://janqsong.github.io/)
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