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 <http://pyroc.readthedocs.io/en/latest/>. An executable demonstration of the package is available on GitHub as a Jupyter Notebook <https://github.com/alistairewj/pyroc/blob/master/usage.ipynb>.

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