/TorchCP

A Python toolbox for conformal prediction research on deep learning models, using PyTorch.

Primary LanguagePythonGNU Lesser General Public License v3.0LGPL-3.0

TorchCP is a Python toolbox for conformal prediction research on deep learning models, using PyTorch. Specifically, this toolbox has implemented some representative methods (including posthoc and training methods) for classification and regression tasks. We build the framework of TorchCP based on AdverTorch. This codebase is still under construction and maintained by Hongxin Wei's research group at SUSTech. Comments, issues, contributions, and collaborations are all welcomed!

Overview

TorchCP has implemented the following methods:

Classification

Year Title Venue Code Link Implementation
2023 Class-Conditional Conformal Prediction with Many Classes NeurIPS Link classification.predictors.cluster
2023 Conformal Prediction for Deep Classifier via Label Ranking Arxiv Link classification.scores.saps
2021 Learning Optimal Conformal Classifiers ICLR Link classification.loss.conftr
2020 Uncertainty Sets for Image Classifiers using Conformal Prediction ICLR Link classification.scores.raps
2020 Classification with Valid and Adaptive Coverage NeurIPS Link classification.scores.aps
2019 Conformal Prediction Under Covariate Shift NeurIPS Link classification.predictors.weight
2016 Least Ambiguous Set-Valued Classifiers with Bounded Error Levels JASA classification.scores.thr
2015 Bias reduction through conditional conformal prediction Intell. Data Anal. classification.scores.margin
2013 Applications of Class-Conditional Conformal Predictor in Multi-Class Classification ICMLA classification.predictors.classwise

Regression

Year Title Venue Code Link Implementation
2023 Conformal Prediction via Regression-as-Classification RegML @ NeurIPS 2023 link regression.predictors.r2ccp
2021 Adaptive Conformal Inference Under Distribution Shift NeurIPS Link regression.predictors.aci
2019 Conformalized Quantile Regression NeurIPS Link regression.predictors.cqr
2016 Distribution-Free Predictive Inference For Regression JASA Link regression.predictors.split

TODO

TorchCP is still under active development. We will add the following features/items down the road:

Year Title Venue Code Link
2022 Training Uncertainty-Aware Classifiers with Conformalized Deep Learning NeurIPS Link
2022 Adaptive Conformal Predictions for Time Series ICML Link
2022 Predictive Inference with Feature Conformal Prediction ICLR Link
2022 Conformal Prediction Sets with Limited False Positives ICML Link
2021 Optimized conformal classification using gradient descent approximation 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

Examples

Here, we provide a simple example for a classification task, with THR score and SplitPredictor.

from torchcp.classification.scores import THR
from torchcp.classification.predictors 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(result_dict["Coverage_rate"], result_dict["Average_size"])

You may find more tutorials in examples folder.

Documentation

The documentation webpage is on readthedocs https://torchcp.readthedocs.io/en/latest/index.html.

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:

@misc{wei2024torchcp,
      title={TorchCP: A Library for Conformal Prediction based on PyTorch}, 
      author={Hongxin Wei and Jianguo Huang},
      year={2024},
      eprint={2402.12683},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

We welcome you to cite the following works:

@article{huang2023conformal,
  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},
  journal={arXiv preprint arXiv:2310.06430},
  year={2023}
}

@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}
}

Contributors