whyshift 0.1.3

A package of various specified distribution shift patterns of out-of-distributoin generalization problem on tabular data, and tools for diagnosing model performance are integrated.

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 tools to diagnose performance degradation are integrated in it, including performance degradation decomposition and risky region identification. 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}
}

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']