🔍 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/Valires/er-evaluation.git

📖 Documentation

Please refer to the documentation website er-evaluation.readthedocs.io.

🖼️ Usage Examples

Please refer to the User Guide or our Visualization Examples for a complete usage guide.

In summary, here's how you might use the package.

1. Import your predicted disambiguations and reference benchmark dataset.

import er_evaluation as ee

predictions, reference = ee.load_pv_disambiguations()

2. Plot summary statistics and compare disambiguations.

ee.plot_summaries(predictions)

ee.plot_comparison(predictions)

3. Define sampling weights and estimate performance metrics.

ee.plot_estimates(predictions, {"sample":reference, "weights":"cluster_size"})

4. Perform error analysis using cluster-level explanatory features and cluster error metrics.

ee.make_dt_regressor_plot(
        y,
        weights,
        features_df,
        numerical_features,
        categorical_features,
        max_depth=3,
        type="sunburst"
)

💭 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/Valires/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

• GNU Affero General Public License v3