igel 0.0.6

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. I appreciate any feedback in order to improve the package ;)

Motivation & Goal

The goal of the project is to provide machine learning for everyone, both technical and non technical users.

I needed a tool sometimes, which I can use to fast create a machine learning prototype. Whether to build some proof of concept or create a fast draft model to prove a point. I find myself often stuck at writing boilerplate code and/or thinking too much of how to start this.

Therefore, I decided to create igel. Hopefully, it will make it easier for technical and non technical users to build machine learning models.

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:
    # in the type field, you can write the type of problem you want to solve. Whether regression or classification
    # Then, provide the algorithm you want to use on the data. Here I'm using the random forest algorithm
    type: regression
    algorithm: random forest

# target you want to predict
# Here, as an example, I'm using a dataset, where I want to predict the GPA values.
# Depending on your data, you need to provide the target(s) you want to predict here
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.csv' --yaml_file 'path_to_your_yaml_file.yaml'

# or shorter
$ igel fit -dp 'path_to_your_csv_dataset.csv' -yml 'path_to_your_yaml_file.yaml'

you can run this command to get instruction on how to use the model:

$ igel -h

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

• provide an api to evaluate models

Contributors

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

History

0.0.6 (2020-09-01)

• Added validation on arguments and provided an example

0.0.5 (2020-08-31)

• Added logging and changed file keyword to yaml_file

0.0.3 (2020-08-30)

• First functional package

0.0.1 (2020-08-27)

• First release on PyPI.