pauc 0.2.2

A Python library for ROC curve analysis, comparison, and visualization.

pAUC: A simple Python package to calculate ROC AUC confidence intervals using DeLong’s method

pAUC is an intuitive Python library for creating, comparing, and visualizing Receiver Operating Characteristic (ROC) curves. It provides a clean, object-oriented interface designed for rigorous statistical analysis of binary and multi-class classifiers.

The library is built for researchers and data scientists who need reliable statistical tests and publication-quality plots with minimal effort. It implements several key methods for comparing models, and calculating confidence intervals.

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Key Features 🔬

• ROC Curve Generation: Easily create ROC objects from true labels and prediction scores.
• Statistical Comparison: Compare two ROC curves using multiple methods:
  • DeLong's test for correlated or uncorrelated curves.
  • Bootstrap-based tests for flexible comparisons.
  • Venkatraman's test for a non-parametric alternative.
• Confidence Intervals: Calculate CIs for AUC, partial AUC, and coordinates (sensitivity/specificity) using bootstrapping or DeLong's method.
• Partial AUC (pAUC): Analyze specific regions of the ROC curve, focusing on high specificity or high sensitivity.
• Multi-Class Analysis: Native support for one-vs-one multi-class ROC analysis using Hand & Till's method.
• Curve Smoothing: Smooth ROC curves using polynomial or binormal methods.
• Plotting: A simple but powerful plotting function to visualize and annotate one or more curves.

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Installation 💻

To install the package, clone the repository and use pip to install it in your local environment.

git clone https://github.com/srijitseal/pauc.git
cd pauc
pip install .

For development, you can install it in "editable" mode, which links the installation to your source files:

pip install -e .

pAUC requires the following packages:

• numpy
• scipy
• matplotlib

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Contributing

Contributions are welcome! Whether it's bug reports, feature requests, or code contributions, please feel free to open an issue or pull request on our GitHub repository.

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License

This project is licensed under the MIT License. See the LICENSE file for details.