Metadata-Version: 1.2
Name: igel
Version: 0.0.4
Summary: a machine learning tool that allows to train, test and use models without writing code
Home-page: https://github.com/nidhaloff/igel
Author: Nidhal Baccouri
Author-email: nidhalbacc@gmail.com
License: MIT license
Description: ====
        igel
        ====
        
        .. image:: assets/logo.png
            :width: 100%
            :scale: 50%
            :align: center
            :alt: igel-icon
        
        .. image:: https://img.shields.io/pypi/v/igel.svg
                :target: https://pypi.python.org/pypi/igel
        
        .. image:: https://img.shields.io/travis/nidhaloff/igel.svg
                :target: https://travis-ci.com/nidhaloff/igel
        
        .. image:: https://readthedocs.org/projects/igel/badge/?version=latest
                :target: https://igel.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        
        
        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 <https://packaging.python.org/guides/tool-recommendations/>`_
        
        .. code-block:: console
        
            $ pip install igel
        
        - Check the docs for other ways to install igel from source
        
        Quick Start
        ------------
        
        - First step is to provide a yaml file:
        
        .. code-block:: yaml
        
                # 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 <https://www.kaggle.com/luddarell/101-simple-linear-regressioncsv>`_ )
        `
        - Run this command in Terminal, where you provide the **path to your dataset** and the **path to the yaml file**
        
        .. code-block:: console
        
            $ 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.
        
Keywords: igel
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
