RBEIS implementations using Pandas and PySpark for SML
RBEIS is a method originally developed for imputing categorical data in relatively small social surveys with the intention of minimising conditional imputation variance. It is derived from CANCEIS, which is better suited to large datasets such as the Census. This implementation of RBEIS works with Pandas DataFrames.
- Python 3.6.8
- pandas 0.20.3
- numpy 1.13.1
- wheel 0.29.0
(untested on newer versions; this is the environment in which RBEIS originally had to be developed)
To install, simply call (awaiting approval to publish to PyPI)pip install rbeis.
Whilst still in the pre-release stage, the latest RBEIS wheel can be manually downloaded from GitHub. Download the latest *.whl file, and run pip install path/to/wheel.
Our tests make use of Lemus and Stam's Art History Textbook Data dataset, for which we are grateful to the authors for publishing and to Tidy Tuesday for publicising. We have included the original dataset at tests/artists_original.csv, a modified version at tests/artists_unique_count.csv and the same modified version with some data removed (for the purpose of testing imputation) at tests/artists_unique_count_missing.csv.
Lemus S, Stam H (2022). arthistory: Art History Textbook Data. https://github.com/saralemus7/arthistory, https://saralemus7.github.io/arthistory/.