nylon

An intelligent, flexible grammar of machine learning.

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:

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.

{
  "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.

 json_file = {
    "data": {
        "target": "ocean_proximity"
    },
    "preprocessor": {
        "fill": "ALL",
        "label-encode": "ocean_proximity"
    },
    "modeling": {
        "type": ["svms", "neighbors"]
    }
}

Now we can call,

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

sk_to_nylon

sk_to_nylon_second

GitHub

https://github.com/Palashio/nylon