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

  1. Ax

  2. Hpbandster

  3. Sklearn

  4. Numpy

  5. Pandas

  6. scipy

  7. Tensorflow

  8. 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.