Feature Ranking

This Python project implements a compute-intensive data mining task on workstation devices capable of parallel computation. A particular data science algorithm for feature ranking is implemented in the OpenCL framework and compared to an unaccelerated version written in Python (NumPy).

How to run this project

Requirements

This project was built and tested on Windows 10 64-bit, with up-to-date CPU and GPU drivers. You'll need at least one OpenCL device discoverable on your workstation, a "recent" version of Anaconda (eg, 2021.05), a Git client like Git Bash available at the Anaconda command line, and a local directory on your workstation where your code repositories are housed.

Setup from scratch

• Open the Anaconda CMD prompt into the Conda (base) environment.

• Change to your preferred local repository base directory:

  cd C:\Users\your-user-name\...\your-local-repos>

• Clone the FeatureRanking project GitHub repository:

  git clone https://github.com/cbmira01/FeatureRanking

• Change to the FeatureRanking project root directory:

  cd .\FeatureRanking

• Create the Conda environment for the project:

  conda env create --file environment.yml

• After packages have loaded, activate the new Conda environment:

  conda activate feature_ranking

Test the installation

• To run Python programs, change to the .\src folder:

  cd .\src

• The quick-demo program will dump a list of available OpenCL devices and run a very small OpenCL workload on each one:

  python quick_demo.py

• A few unit tests can be run:

  python run_tests.py

After a successful installation

• Run the main program:

  python feature_ranking.py

• More information on setup is available in the project Wiki.

More information

More information about feature ranking, OpenCL, the experimental approach of this project, and results are described in the project Wiki.

Credits

Credits, attributions, and works consulted are summarized in the project Wiki.

Notes for Code Louisville project grading

This project fullfilled requirements for the Summer 2021 Code Louisville Python session.

• Implements a “master loop” console application where the user can repeatedly enter commands/perform actions, as seen in

  ./src/feature_ranking.py

• Creates and uses a dictionary or list, as seen in

  ./src/prep_data.py

• Reads data from external JSON and CSV files, as seen in

  ./src/prep_data.py

• Function calls that return values are used throughout, for example

  ./src/fr_opencl.py ./src/feature_ranking.py

• Implements a "stretch" goal: setup and testing of the OpenCL framework.