https://github.com/palashio/nylon

An intelligent, flexible grammar of machine learning.
https://github.com/palashio/nylon

auto-ml data-science grammar machine-learning

Last synced: 6 days ago
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An intelligent, flexible grammar of machine learning.

• Host: GitHub
• URL: https://github.com/palashio/nylon
• Owner: Palashio
• License: mit
• Created: 2021-06-04T17:33:49.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-07-29T20:34:04.000Z (over 3 years ago)
• Last Synced: 2024-10-31T19:51:40.753Z (13 days ago)
• Topics: auto-ml, data-science, grammar, machine-learning
• Language: Python
• Homepage: http://docs.paraglide.ai
• Size: 1.89 MB
• Stars: 83
• Watchers: 7
• Forks: 8
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        



 



 

 

` `  

 **An english representation of machine learning. Modify what you want, let us handle the rest.**


 

[![Build Status](https://www.travis-ci.com/Palashio/nylon.svg?branch=main)](https://www.travis-ci.com/Palashio/nylon)

[![Downloads](https://pepy.tech/badge/nylon-ai)](https://pepy.tech/project/nylon-ai)

 

 

## Overview


Nylon is a python library that lets you customize automated machine learning workflows through a concise, JSON syntax. It provides a built in grammar, in which you can access different operations in ML with the english language.

 

## Installation

Install latest release version:

```

pip install -U nylon-ai

```

Install directory from github:

```

git clone https://github.com/Palashio/nylon.git

cd nylon-ai

pip install .

```

## Usage: the basics

A new `Polymer` object should be created everytime you're working with a new dataset. When initializing an object, a dataset in the form of a ```.csv``` or ```.xs``` file should be passed to it by path:

```python

nylon_object = Polymer('housing.csv')

```

Now, it's time to create a specifications file using the nylon grammar. Here's a basic one, that lets Nylon handle most of the work. Nylon currently has four major parts in it's grammar: the data reader, preprocessor, modeler, and analysis modules. In the example below, you can see that we're specifying the target column under data (which is always required), and manually specifying the type of preprocessing we'd like. **Everything we haven't specified will be handled for us.**

```json

{

  "data": {

    "target": "ocean_proximity"

  },

  "preprocessor": {

    "fill": "ALL",

    "label-encode": "ocean_proximity"

  }

}

```

Now, we can override more components to take advantage of the built in ensembling of SVM's, and nearest neighbors modeling in nylon.

```python

 json_file = {

    "data": {

        "target": "ocean_proximity"

    },

    "preprocessor": {

        "fill": "ALL",

        "label-encode": "ocean_proximity"

    },

    "modeling": {

        "type": ["svms", "neighbors"]

    }

}

```

Now we can call,

```python

nylon_object.run(json_file)

```

This will return a fully trained nylon object. You can access all information about this particular iteration in the ```.results``` field of the object.

## Demos



 

![alt text](/data_storage/github_images/sk_to_nylon.png)

![alt text](/data_storage/github_images/sk_to_nylon_second.png)

 



## Asking for help

Welcome to the Nylon community!

If you have any questions, feel free to:

1. [Read the Docs](https://docs.paraglide.ai/)

2. [Search through the issues](https://github.com/Palashio/nylon/issues)

3. [Join our Discord](https://discord.gg/udZSbhws9D)

## Contact

Shoot me an email at [[email protected]](mailto:[email protected]) if you'd like to get in touch!

Follow me on [twitter](https://twitter.com/_pshah) for updates and my insights about modern AI!