Metadata-Version: 2.1
Name: coniferest
Version: 0.0.10
Summary: Coniferous forests for better machine learning
Author: SNAD team
Author-email: Vladimir Korolev <balodja@gmail.com>
License: MIT
Project-URL: Source Code, https://github.com/snad-space/coniferest
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: click
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib

# coniferest

[![PyPI version](https://badge.fury.io/py/coniferest.svg)](https://pypi.org/project/coniferest/)
[![Documentation Status](https://readthedocs.org/projects/coniferest/badge/?version=latest)](https://coniferest.readthedocs.io/en/latest/?badge=latest)
![Test Workflow](https://github.com/snad-space/coniferest/actions/workflows/test.yml/badge.svg)
![Build and publish wheels](https://github.com/snad-space/coniferest/actions/workflows/wheels.yml/badge.svg)


Package for active anomaly detection with isolation forests, made by [SNAD collaboration](https://snad.space/).

It includes:
* `IsolationForest` - reimplementation of scikit-learn's isolation forest with much better scoring performance.
* `AADForest` - reimplementation of Active Anomaly detection algorithm with isolation forests from Shubhomoy Das' [`ad_examples` package](https://github.com/shubhomoydas/ad_examples) with better performance, much less code and more flexible dependencies.
* `PineForest` - our own active learning model based on the idea of tree filtering.

Install the package with `pip install coniferest`.

See the documentation for the [**Tutorial**](https://coniferest.readthedocs.io/en/latest/tutorial.html).
