Metadata-Version: 2.1
Name: giants
Version: 1.0.0
Summary: Statistical and geospatial modeling tools for mapping big trees.
Home-page: https://the.forestobservatory.com/giants
Author: Salo Sciences
Author-email: cba@salo.ai
License: MIT
Keywords: ecology,conservation,remote sensing,machine learning
Platform: any
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.21.2)
Requires-Dist: scikit-learn (>=0.24.2)

# giants

<img src="img/redwoods.jpg" alt="giant forests"/>

<p align="center">
  <em>Basic machine learning optimization support, developed to identify big trees.</em>
</p>

![License](https://img.shields.io/github/license/forestobservatory/giants)
![PyPI package](https://img.shields.io/pypi/v/giants)
![PyPI downloads](https://img.shields.io/pypi/dm/giants)
![Last commit](https://img.shields.io/github/last-commit/earth-chris/giants)
![Lines of code](https://img.shields.io/tokei/lines/github/earth-chris/giants)
![Forest Observatory](https://img.shields.io/twitter/follow/forestobs)

---

**Documentation**: [the.forestobservatory.com/giants](https://the.forestobservatory.com/giants)

**Source code**: [forestobservatory/giants](https://github.com/forestobservatory/giants)

---

## Introduction

`giants` is a simple package that provides python support for tuning `sklearn` models via hyperparameter searches. There are a series of pre-defined configurations and hyperparameter grids defined for a series of models, which should be fairly easy to extend as needed.

It was originally developed for the [Big Trees project](https://github.com/forestobservatory/cfo-big-trees) but we found it useful enough to clean it up and publish it as a standalone package for easy re-use.

## Install

```bash
pip install giants
```

## Developed by

![Earth Chris](https://img.shields.io/twitter/follow/earth_chris)


