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.

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: classification
    algorithm: random forest

# target you want to predict
# Here, as an example, I'm using the famous indians-diabetes dataset, where I want to predict whether someone have diabetes or not.
# Depending on your data, you need to provide the target(s) you want to predict here
target:
    - sick

In the example above, I'm using random forest to classify whether someone have diabetes or not depending on some features in the dataset I used this indian-diabetes 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 --help

# or just

$ 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.

E2E Example

A complete end to end solution is provided in this section to prove the capabilities of igel. As explained previously, you need to create a yaml configuration file. Here is an end to end example for predicting whether someone have diabetes or not using the decision tree algorithm. The dataset can be found in the examples folder.

• Fit/Train a model:

model:
    type: classification
    algorithm: decision tree

target:
    - sick

$ igel fit -dp path_to_the_dataset -yml path_to_the_yaml_file

That's it, igel will now fit the model for you and save it in a model_results folder in your current directory.

• Evaluate the model:

Evaluate the pre-fitted model. Igel will load the pre-fitted model from the model_results directory and evaluate it for you. You just need to run the evaluate command and provide the path to your evaluation data.

$ igel evaluate -dp path_to_the_evaluation_dataset

That's it! Igel will evaluate the model and store statistics/results in an evaluation.json file inside the model_results folder

• Predict:

Use the pre-fitted model to predict on new data. This is done automatically by igel, you just need to provide the path to your data that you want to use prediction on.

$ igel predict -dp path_to_the_new_dataset

That's it! Igel will use the pre-fitted model to make predictions and save it in a predictions.csv file inside the model_results folder

Advanced Usage

You can also carry out some preprocessing methods or other operations by providing the it in the yaml file. Here is an example, where the data is split to 80% for training and 20% for validation/testing. Also, the data are shuffled while splitting.

Furthermore, the data are preprocessed by replacing missing values with the mean ( you can also use median, mode etc..). check this link for more information

# dataset operations
dataset:
    split:
        test_size: 0.2
        shuffle: True
        stratify: None
    preprocess:
        missing_values: mean
# model definition
model:
    type: classification
    algorithm: random forest

# target you want to predict
target:
    - sick

Then, you can fit the model by running the igel command as shown in the other examples

$ igel fit -dp path_to_the_dataset -yml path_to_the_yaml_file

For evaluation

$ igel evaluate -dp path_to_the_evaluation_dataset

For production

$ igel predict -dp path_to_the_new_dataset

Examples

Check the examples folder, where you will find the indian-diabetes data and a yaml file example. You can clone the repo or just download the dataset then try running the example afterwards.

Contributions

Contributions are always welcome. Make sure you read the guidelines first