A series of classes to match mentors and mentees
This is a package to help match mentees and mentors. It's specifically designed for a volunteer programme I support, but you could probably extend or alter it to suit whatever you're doing.
It uses this implementation of Munkres to find the most effective pairings. The Munkres algorithm works on a grid of scores.
Full details of how the matches are calculated can be read in the code itself. Customisable configurations are on the roadmap but are not planned for any upcoming releases.
You can install this project with
python -m pip install mentor-match
To use this library, first install it (see above). You may need to munge your data for the system to be happy with it. Use the example CSV file as guides for your mentor and mentee data, then put them together in the same folder.
The software will run three matching exercises. Participants who don't match in the first round are more heavily weighted in the next round. The aim is to improve the experience for everyone.
The weightings are as follows:
|property||First run||Second run||Third run|
|mentee's target profession is the same as mentor's profession||4||4||0|
|grade is one or two grades different||3||3||3|
|bonus for anyone's who's not got a match yet||0||50||100|
Here is a snippet that outlines a minimal use in a Python project:
from matching import process data_folder = "Documents/mentoring-data" mentors, mentees = process.conduct_matching_from_file(data_folder) output_folder = data_folder / "output" process.create_mailing_list(mentors, output_folder) process.create_mailing_list(mentees, output_folder)
This creates a mailing list according to a set template, ready for processing by your favourite/enterprise mandated email solution
Alternatively, you can run this software from the command line as follows
python -m matching /path/to/participant/data
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