PredictiveCO Benchmark
Predictive combinatorial optimization benchmark dataset, used to evaluate predictive combinatorial optimization (PtO and PnO) methods under uncertain coefficients, covering real‑world scenarios such as energy‑cost‑aware scheduling and advertising budget allocation.
Dataset description and usage context
PredictiveCO Benchmark
Overview
PredictiveCO Benchmark is a benchmark testing framework for Predictive Combinatorial Optimization (Predictive‑CO). The framework aims to evaluate methods under two design principles: “Predict‑then‑Optimize (PtO)” and “Predict‑and‑Optimize (PnO)”.
Datasets
- Problem Types: Covers 8 problems, including a new industrial dataset for combinatorial advertising.
- Methods: Benchmark includes 11 existing PtO/PnO methods.
Modular Framework
- User Customization: Users can plug in their own problems, predictors, solvers, loss functions, and evaluation methods.
Usage
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Installation: Install locally with:
pip install -e .
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Running Algorithms: Refer to the shell scripts in the “shells/benchmarks” directory.
Citation
bibtex @inproceedings{geng2024predictive, title={Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime}, author={Geng, Haoyu and Ruan, Hang and Wang, Runzhong and Li, Yang and Wang, Yang and Chen, Lei and Yan, Junchi}, booktitle={NeurIPS 2024 Datasets and Benchmarks Track}, year={2024} }
Terms of Use
By using this benchmark dataset, you agree to the terms of use specified in Appendix C.
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