# pyroc **Repository Path**: acezqy/pyroc ## Basic Information - **Project Name**: pyroc - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-10-27 - **Last Updated**: 2023-10-27 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README pyroc ========= .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.6819206.svg :target: https://doi.org/10.5281/zenodo.6819206 pyroc is a package for analyzing receiver operator characteristic (ROC) curves. It includes the ability to statistically compare the area under the ROC (AUROC) for two or more classifiers. Quick start ----------- Install: pip install pyroc Use: import pyroc import numpy as np pred = np.random.rand(100) target = np.round(pred) # flip 10% of labels target[0:10] = 1 - target[0:10] W = pyroc.auroc(target, pred) # second prediction pred2 = pred pred2[10:20] = 1 - pred2[10:20] auroc, ci = pyroc.auroc_ci(target, [pred, pred2]) print(auroc) print(ci) A usage.ipynb notebook is provided demonstrating common usage of the package (requires Jupyter: `pip install jupyter`). Documentation ------------- Documentation is available on `readthedocs `_. An executable demonstration of the package is available on `GitHub as a Jupyter Notebook `_. Installation ------------ To install the package with pip, run:: pip install pyroc To install this package with conda, run:: conda install -c conda-forge pyroc Acknowledgement --------------- Please use the latest DOI on `Zenodo`_. Example BibTeX: .. code-block:: latex @software{pyroc, author = {Alistair Johnson and Lucas Bulgarelli and Tom Pollard}, title = {alistairewj/pyroc: pyroc v0.2.0}, month = jul, year = 2022, publisher = {Zenodo}, version = {v0.2.0}, doi = {10.5281/zenodo.6819206}, url = {https://doi.org/10.5281/zenodo.6819206} } .. _Zenodo: https://doi.org/10.5281/zenodo.6819205