https://github.com/stewartpark/sklearn2gem

⚡ sklearn2gem ports your scikit-learn model into a fast ruby C binding!
https://github.com/stewartpark/sklearn2gem

ruby rubygem scikit-learn sklearn

Last synced: about 2 months ago
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⚡ sklearn2gem ports your scikit-learn model into a fast ruby C binding!

• Host: GitHub
• URL: https://github.com/stewartpark/sklearn2gem
• Owner: stewartpark
• Created: 2018-06-07T09:23:35.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-06-08T16:35:36.000Z (over 6 years ago)
• Last Synced: 2024-12-15T17:28:38.573Z (2 months ago)
• Topics: ruby, rubygem, scikit-learn, sklearn
• Language: Python
• Size: 18.6 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# sklearn2gem

[![Build Status](https://travis-ci.org/stewartpark/sklearn2gem.svg?branch=master)](https://travis-ci.org/stewartpark/sklearn2gem)

[![Requirements Status](https://requires.io/github/stewartpark/sklearn2gem/requirements.svg?branch=master)](https://requires.io/github/stewartpark/sklearn2gem/requirements/?branch=master)

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⚡ sklearn2gem ports your scikit-learn model into a fast ruby C binding!

# Requirements

Python 3.6+

# Getting started

Install sklearn2gem using `pip`:

```

pip install sklearn2gem

```

or via `easy_install`:

```

easy_install sklearn2gem

```

After that, dump your scikit-learn model with `sklearn.externals.joblib`, and run `sklearn2gem model_name@version your_model.pkl foo/bar/model_name`. You should be able to see a newly created folder named `model_name` under `foo/bar/`.

See [`examples/iris.py`](https://github.com/stewartpark/sklearn2gem/blob/master/examples/iris.py) to try it out.

To produce a pre-compiled binary gem, use [gem-compiler](https://github.com/luislavena/gem-compiler).

# What machine learning algorithms are supported?

Since sklearn2gem uses `nok/sklearn-porter` to convert a model into a C file, you can refer to [this page](https://github.com/nok/sklearn-porter#machine-learning-algorithms).