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Calculate area under the ROC and p-values when comparing predictions

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alistairewj/pyroc

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pyroc

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:

@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}
}

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Calculate area under the ROC and p-values when comparing predictions

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