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Python implementation for "Adapting to Online Label Shift with Provable Guarantees"

This is the python implementation for the experiments in the paper "Adapting to Online Label Shift with Provable Guarantees" [1]. We will first introduce the structure and requirements of the code, followed by a brief instruction for a quick start.

Code Structure

main.py is the entrance of the program. The code mainly contains the following three parts.

  • online: Implementations of the proposed algorithms (ATLAS and ATLAS-ADA).

    • models/ogd.py implements the base-algorithm.
    • models/meta.py implements the meta-algorithm.
    • models/atlas.py implements the ATLAS and ATLAS-ADA algorithms.
    • the subfolder utils contains the codes for the basic components of ATLAS and ATLAS-ADA, including the implementations of the unbiased risk estimator (risk.py), step size tuning (schedule.py,lr.py) and the hint function hint_function.py, etc.

  • dataset: Code for datasets pre-processing.

    • toy_data.py is the code for the simulated experiments on benchmark datasets.
    • table_data.py is the code for the real-life SHL dataset for the locomotion detection task.

  • utils: Useful tools for running the code.

    • argparse.py parses the configurations.
    • multi_thread.py is a tool multi-threaded acceleration.
    • shift_simulate.py is a tool to generate the simulated label shifts.
    • tools.py includes other useful tools, including a timer for testing the program runtime.

Requirements

  • matplotlib==3.3.3
  • numpy==1.19.2
  • pandas==1.4.1
  • prettytable==0.9.2
  • PyYAML==6.0
  • scikit_learn==0.23.2
  • torch==1.7.0
  • tqdm==4.62.3

Quick Start

We provide several demos in the folder ./demos. To get a quick start, the readers can run any demo by bash train.sh in the corresponding path with the parameter specified in config.yaml.

For example, one can use .\demos\locomotion\ATLAS\train.sh to run ATLAS algorithm over the SHL datasets [2], whose parameters are set as .\demos\locomotion\ATLAS\config.yaml

Reference:

[1] Yong Bai*, Yu-Jie Zhang*, Peng Zhao, Masashi Sugiyama, and Zhi-Hua Zhou. Adapting to Online Label Shift with Provable Guarantees. In: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, Louisiana, 2022.