An End-to-End Evaluation Framework for Entity Resolution Systems.
Project description
🔍 ER-Evaluation: An End-to-End Evaluation Framework for Entity Resolution Systems
ER-Evaluation is a Python package for the evaluation of entity resolution (ER) systems. It provides data structure definitions, summary statistics, visualizations, error analysis tools, and statistically principled performance estimators.
💻 Installation
Install the released version from PyPI using:
pip install er-evaluation
Or install the development version using:
pip install git+https://github.com/OlivierBinette/er-evaluation.git
📖 Documentation
Please refer to the documentation website er-evaluation.readthedocs.io.
🖼️ Examples
Coming soon.
💭 Development Philosophy
ER-Evaluation is designed to be a unified source of evaluation tools for entity resolution systems, adhering to the Unix philosophy of simplicity, modularity, and composability. The package contains Python functions that take standard data structures such as pandas Series and DataFrames as input, making it easy to integrate into existing workflows. By importing the necessary functions and calling them on your data, you can easily use ER-Evaluation to evaluate your entity resolution system without worrying about custom data structures or complex architectures.
📜 Citation
Please acknowledge the publications below if you use ER-Evaluation:
Binette, Olivier. (2022). ER-Evaluation: An End-to-End Evaluation Framework for Entity Resolution Systems. Available online at github.com/OlivierBinette/ER-Evaluation
Binette, Olivier, Sokhna A York, Emma Hickerson, Youngsoo Baek, Sarvo Madhavan, Christina Jones. (2022). Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org. arXiv e-prints: arxiv:2210.01230
Upcoming: “A Statistical Evaluation Framework for Black-Box Entity Resolution Systems With Application to Inventor Name Disambiguation”
📝 Public License
Changelog
2.0.0 (March 27, 2022)
Improve documentation
Add handling of NA values
Bug fixes
Add datasets module
Add visualization functions
Performance improvements
BREAKING: error_analysis functions have been renamed.
BREAKING: estimators have been renamed.
Added estimators support for sensitivity analyses
Added fairness plots
Performance improvements
Added compress_memberships() function for performance improvements.
1.2.0 (January 11, 2022)
Refactoring and documentation overhaul.
1.1.0 (January 10, 2022)
Added additional error metrics, performance evaluation metrics, and performance estimators.
Added record-level error metrics and error analysis tools.
1.0.2 (December 5, 2022)
Update setup.py with find_packages()
1.0.1 (November 30, 2022)
Add “normalize” option to plot_cluster_sizes_distribution.
Fix bugs in homonimy_rate and and name_variation_rate.
Fix bug in estimators.
1.0.0
Initial release
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