whyshift 0.1.4

WhyShift: A Benchmark with Specified Distribution Shift Patterns

Jiashuo Liu*, Tianyu Wang*, Peng Cui, Hongseok Namkoong

Tsinghua University, Columbia University

WhyShift is a python package that provides a benchmark with various specified distribution shift patterns on real-world tabular data. And several tools to diagnose performance degradation are integrated in it. Our testbed highlights the importance of future research that builds an understanding of how distributions differ. For more details, please refer to our paper.

If you find this repository useful in your research, please cite the following paper:

@inproceedings{liu2023need,
  title={On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets},
  author={Jiashuo Liu and Tianyu Wang and Peng Cui and Hongseok Namkoong},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2023}
}

This paper as follows is an extended version of the previous conference paper, including more algorithms and distribution shifts on tabular data:

@article{wang2026rethinking,
  title={Rethinking Distribution Shifts: Empirical Analysis and Modeling for Tabular Data},
  author={Wang, Tianyu and Liu, Jiashuo and Cui, Peng and Namkoong, Hongseok},
  journal={Management Science (forthcoming)},
  year={2026}
}

For settings utilizing ACS Income, Public Coverage, Mobility datasets

• get_data(task, state, year, need_preprocess, root_dir) function
  • task values: 'income', 'pubcov', 'mobility'
• examples:

  from whyshift import get_data
  # for ACS Income
  X, y, feature_names = get_data("income", "CA", True, './datasets/acs/', 2018)
  # for ACS Public Coverage
  X, y, feature_names = get_data("pubcov", "CA", True, './datasets/acs/', 2018)
  # for ACS Mobility
  X, y, feature_names = get_data("mobility", "CA", True, './datasets/acs/', 2018)
  

• support state values:
  • ['AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'FL', 'GA', 'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', 'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ', 'NM', 'NY', 'NC', 'ND', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VT', 'VA', 'WA', 'WV', 'WI', 'WY', 'PR']

For settings utilizing US Accident, Taxi datasets

• download data files:

  # US Accident:
  https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents
  # Taxi
  https://www.kaggle.com/competitions/nyc-taxi-trip-duration
  

• put data files in dir ./datasets/
  • accident: ./datasets/Accident/US_Accidents_Dec21_updated.csv
  • taxi: ./datasets/Taxi/{city}_clean.csv
• pass the path to the data file of get_data function
• example:

  from whyshift import get_data
  # for US Accident
  X, y, _ = get_data("accident", "CA", True, './datasets/Accident/US_Accidents_Dec21_updated.csv')
  # for Taxi
  X, y, _ = get_data("taxi", "nyc", True, './datasets/Taxi/train.csv')
  

• support state values:
  • for US Accident: ['CA', 'TX', 'FL', 'OR', 'MN', 'VA', 'SC', 'NY', 'PA', 'NC', 'TN', 'MI', 'MO']
  • for Taxi: ['nyc', 'bog', 'uio', 'mex']