README.md

Important

This repository has been archived and is no longer maintained. An updated version of the Feature Selection demo can be found here.

---

Feature Selection

This notebook develops a QPU programming model for an optimization problem that selects a subset and demonstrates it using Ocean software's dwave-hybrid on an example of feature selection for machine learning.

The notebook has the following sections:

1. What is Feature Selection? defines and explains the feature-selection problem.
2. Feature Selection by Mutual Information describes a particular method of feature selection that is demonstrated in this notebook.
3. Solving Feature Selection on a Quantum Computer shows how such optimization problems can be formulated for solution on a D-Wave quantum computer.
4. Example Application: Predicting Survival of Titanic Passengers demonstrates the use of Kerberos, an out-of-the-box classical-quantum hybrid sampler, to select optimal features for a public-domain dataset.

What is Feature Selection?

Statistical and machine-learning models use sets of input variables ("features") to predict output variables of interest. Feature selection can be part of the model design process: selecting from a large set of potential features a highly informative subset simplifies the model and reduces dimensionality.

For systems with large numbers of potential input information—for example, weather forecasting or image recognition—model complexity and required compute resources can be daunting. Feature selection can help make such models tractable.

However, optimal feature selection can itself be a hard problem. This example introduces a powerful method of optimizing feature selection based on a complex probability calculation. This calculation is submitted for solution to a quantum computer.

Installation

You can run this example without installation in cloud-based IDEs that support the Development Containers specification (aka "devcontainers").

For development environments that do not support devcontainers, install requirements:

pip install -r requirements.txt

If you are cloning the repo to your local system, working in a virtual environment is recommended.

Usage

Your development environment should be configured to access Leap’s Solvers. You can see information about supported IDEs and authorizing access to your Leap account here.

The notebook can be opened by clicking on the 01-feature-selection.ipynb file in VS Code-based IDEs.

To run a locally installed notebook:

jupyter notebook

License

See LICENSE file.