Metadata-Version: 2.1
Name: menelaus
Version: 0.1.1
Summary: This library implements algorithms for detecting data drift and concept drift for ML and statistics applications.
Home-page: https://github.com/mitre/menelaus
Author: Leigh Nicholl, Thomas Schill, India Lindsay, Anmol Srivastava, Kodie P McNamara, Austin Downing
Author-email: tschill@mitre.org
License: Apache 2.0
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Description-Content-Type: text/x-rst
License-File: LICENSE.txt
Requires-Dist: pandas
Requires-Dist: numpy (>=1.22.0)
Requires-Dist: sklearn
Requires-Dist: scipy
Requires-Dist: joblib
Provides-Extra: dev
Requires-Dist: matplotlib ; extra == 'dev'
Requires-Dist: seaborn ; extra == 'dev'
Requires-Dist: plotly ; extra == 'dev'
Requires-Dist: black ; extra == 'dev'
Requires-Dist: pylint ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: coverage ; extra == 'dev'
Requires-Dist: sphinx ; extra == 'dev'
Requires-Dist: sphinx-rtd-theme ; extra == 'dev'
Requires-Dist: sphinx-autoapi ; extra == 'dev'

Menelaus implements algorithms for the purposes of drift detection. Drift
detection is a branch of machine learning focused on the detection of unforeseen
shifts in data. The relationships between variables in a dataset are rarely
static and can be affected by changes in both internal and external factors,
e.g. changes in data collection techniques, external protocols, and/or
population demographics. Both undetected changes in data and undetected model
underperformance pose risks to the users thereof. The aim of this package is to
enable monitoring of data and machine learning model performance.

For full documentation, see:
GitHub: https://github.com/mitre/menelaus
ReadTheDocs: https://menelaus.readthedocs.io/en/latest/

