igel 0.0.4

a machine learning tool that allows to train, test and use models without writing code

igel

A machine learning tool that allows to train/fit, test and use models without writing code

• Free software: MIT license

• Documentation: https://igel.readthedocs.io.

Note

The project is still under heavy development. Feel free to open an issue if you encountered any

Intro

igel is built on top of scikit-learn. It provides a simple way to use machine learning without writing a single line of code

All you need is a yaml file, where you need to describe what you are trying to do. That’s it!

Installation

• The easiest way is to install igel using pip

$ pip install igel

• Check the docs for other ways to install igel from source

Quick Start

• First step is to provide a yaml file:

# model definition
model:
    type: regression
    algorithm: forest

# target you want to predict
target:
    - GPA

In the example above, we declare that we have a regression problem and we want to use the random forest model to solve it. Furthermore, the target we want to predict is GPA (since I’m using this simple dataset ) ` - Run this command in Terminal, where you provide the path to your dataset and the path to the yaml file

$ igel fit --data_path 'path_to_your_csv_dataset' --model_definition_file 'path_to_your_yaml_file'

That’s it. Your “trained” model can be now found in the model_results folder (automatically created for you in your current working directory). Furthermore, a description can be found in the description.json file inside the model_results folder.

Examples

Check the examples folder, where you can use the csv data to run a simple example from terminal

TODO

• add option as arguments to the models

• add multiple file support

Contributors

None yet. Why not be the first? Contributions are always welcome. Please check the contribution guidelines first.

History

0.0.3 (2020-08-30)

• First functional package

0.0.1 (2020-08-27)

• First release on PyPI.