Random Forest Visualisation Tools

About

These tools aim to provide a reproducible and consistent data visualisation platform where experimental and computational researchers can perform Random Forest (RF) to explore their data and perform regression tasks without requiring a line of code. Random Forest is a supervised machine learning model that learns to map data (features or descriptors) by constructing a multitude of decision trees to outputs (target variables) in the training phase of the model. It uses bagging and feature randomness when building each individual tree to try to create an uncorrelated forest of trees that predict target variables more accurately than a single decision tree.

App Manual

• The app manual, explaining data file upload requirements, features of the tool and how to read the plot outputs can be found here

Deploy app on server (production and development)

Requirements

• Download Python 3 if not already installed
• Install Git -- Installation instructions using command line can be found here Note: If you are using Git after upgrading to macOS Catalina and get the following error...

xcrun: error: invalid active developer path (/Library/Developer/CommandLineTools), missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun

...run the following in your terminal:

xcode-select --install

• Install virtualenv to create virtual environments

pip install virtualenv

Running the app

To run this app first clone repository and then open a terminal to the app folder.

git clone https://github.com/aaml-analytics/rf-explorer/
cd rf-explorer

Create and activate a new virtual environment (recommended) by running the following:

On Windows:

virtualenv venv
\venv\scripts\activate

Or if using macOS or linux

python3 -m venv myvenv
source myvenv/bin/activate

Install the requirements:

pip install -r requirements.txt

Running the Random Forest App:

• When running a high number of iterations (n>=100), the development server is preferred. When running (n<=100) a production server is preferred.

Run the app on development server:

python app.py

You can then run the app on your browser at http://127.0.0.1:8050

• To quit the app press (CTRL +C). You will have to run the app again with this command everytime you leave your terminal/ quit the app.

Run the app on production server by Gunicorn:

The suggested number of workers is (2*CPU)+1. For a dual-core (2 CPU) machine, 5 is the suggested workers value. The number of workers should equal the number of threads. The user can either use the below or change the worker and threads number accordingly.

gunicorn app:server --workers=5 --threads=5 --bind 0.0.0.0:8040

You can then run the app on your browser at http://0.0.0.0:8040

Screenshots

Contributing

When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change. You can also contact the AAML research group to discuss further contributions and collaborations. Please read CONTRIBUTING.md for details on our code of conduct.

Contact Us

Email: Mythili Sutharson, Nakul Rampal, Rocio Bueno Perez, David Fairen Jimenez
Website: http://aam.ceb.cam.ac.uk/
Address:
Department of Chemical Engineering and Biotechnology
Cambridge University
Philippa Fawcett Dr
Cambridge
CB3 0AS

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• AAML Research Group for developing this dashboard for the MOF community. Click here to read more about our work
• Dash - the python framework used to build this web application