Installation
============

To use HPO, you must first install Python3. 

In case you prefer convenient access to the source code and examples, you have the option to install HPO through cloning the HPO/Hyperparameter-Optimization::

    git clone https://github.com/ComputationalDesignLab/Hyperparameter-Optimization.git

Then, open the terminal and ``cd`` into the downloaded repository and pip install the package::

    pip install -e .

This will install the package in editable mode. If you change anything in the source code of the package, then you don't need to re-install the package.

.. note::

	Or you can just download the zip file and extract

Open the terminal and ``cd`` into the cloned repository and run::

    git pull


Requirements
------------------------------------

IFEWs model depends external packages to do hyperparameter optimization. 
The following packages are required to download

#. Ax
#. Hpbandster
#. Sklearn
#. Numpy
#. Pandas
#. scipy
#. Tensorflow
#. Matplotlib

.. note::

   Numpy and Pandas will be automatically installed along with scikit-learn because they are dependencies of Sklearn.

.. note::

   Matplotlib is for plotting the result.
   
Installation instructions:
You can simply download the requirements by using ``pip`` from your python environment (we recommend Anaconda).

Examples are::

	pip install ax-platform

	pip install scikit-learn
	
To ensure the installation of optional dependencies, use the ``[all]`` suffix with the install command. However, it is not necessary for a basic installation.