# 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