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Implementation for Probabilistic Conformal Prediction

Probabilistic Conformal Prediction Using Conditional Random Samples<br> Zhendong Wang*, Ruijiang Gao*, Mingzhang Yin*, Mingyuan Zhou and David M. Blei <br> https://arxiv.org/abs/2206.06584 <br>

Abstract: This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP construct the predictive set based on random samples from an estimated generative model. It is efficient and compatible with either explicit or implicit conditional generative models. Theoretically, we show that PCP guarantees correct marginal coverage with finite samples. Empirically, we study PCP on a variety of simulated and real datasets. Compared to existing methods for conformal inference, PCP provides sharper predictive sets.

Reproduce Our Results

Requirements

Please see package_install.txt to find how to set up the python environment. In short words,

• 64-bit Python 3.6.
• PyTorch 1.8.0. See https://pytorch.org/ for PyTorch install instructions.
• scikit-learn=0.21.3 and scikit-garden.
• Python libraries: pip install sympy cde tqdm pandas shapely matplotlib seaborn.

Data Preparation

In our paper, following CQR repo and CHR repo, we trained our PCP and HD-PCP model on

1. Publicly Available Datasets: Bike, Bio, Blogdata, facebook1, facebook2 and temperature.
2. Data subject to copyright/usage rules: MEPS_19, MEPS_20 and MEPS_21.

You can download the datasets we used in our paper at their respective websites and get more information about these datasets there. For simple use and reproduction, we provide our downloaded datasets in data folder.

Run Experiments

The running command could be as simple as:

python main.py --dataset bike --n_runs 10 --n_parallel 1 --K 50 --fr 0.2

Now, the code is run only on cpu since the hidden models are all simple and small but could be easily modified to be run on gpu. The running configuration can be customized with command line options:

• --n_runs number of running with different seeds.
• --n_parallel number of parallelly jobs for each serial running.
• --K the sample size of PCP.
• --fr the filtering ratio of PCP. We usually recommend 0.2.

To run multi-dimensional tasks,

python main_md.py --dataset energy --n_runs 10 --K 1000 --method pcp --caltype uniform 

Currently, main.py will include all baselines and our methods for running. If you just simply want the results of our methods, such as PCP or HD-PCP, comment out the other methods and the corresponding backbone model training lines in main.py file.

Citation

@article{wang2022pcp,
  title     = {Probabilistic Conformal Prediction Using Conditional Random Samples},
  author    = {Wang, Zhendong and Gao, Ruijiang and Yin, Mingzhang and Zhou, Mingyuan and Blei, David M},
  journal   = {arXiv preprint arXiv:2206.06584},
  year      = {2022},
  url       = {https://arxiv.org/abs/2206.06584}
}

Acknowledgements

We thank CQR repo and CHR repo for providing good code examples.