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

Regression

TODO

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

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, ClusterPredictor, 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

• Hongxin Wei
• Jianguo Huang
• Xuanning Zhou