$\textit{lucie}$: An Improved Python Package for Loading Datasets from the UCI Machine Learning Repository

TL;DR

lucie is a Python package that significantly improves the importability of datasets from the UCI ML Repository by handling nonstandard formats, achieving a success rate of 95.4%.

Contribution

It introduces an automated utility that enhances dataset import success from UCI, addressing limitations of existing tools.

Findings

95.4% success rate on top datasets

Outperforms ucimlrepo's 73.1% success rate

Available as a Python package with high code coverage

Abstract

The University of California--Irvine (UCI) Machine Learning (ML) Repository (UCIMLR) is consistently cited as one of the most popular dataset repositories, hosting hundreds of high-impact datasets. However, a significant portion, including 28.4% of the top 250, cannot be imported via the $\textit{ucimlrepo}$ package that is provided and recommended by the UCIMLR website. Instead, they are hosted as .zip files, containing nonstandard formats that are difficult to import without additional ad hoc processing. To address this issue, here we present $\textit{lucie}$ -- $\underline{l}oad$ $\underline{U}niversity$ $\underline{C}alifornia$ $\underline{I}rvine$ $\underline{e}xamples$ -- a utility that automatically determines the data format and imports many of these previously non-importable datasets, while preserving as much of a tabular data structure as possible. $\textit{lucie}$ was designed using the top 100 most popular datasets and benchmarked on the next 130, where it resulted in a success rate of 95.4% vs. 73.1% for $\textit{ucimlrepo}$. $\textit{lucie}$ is available as a Python package on PyPI with 98% code coverage.