NEW: Implement compact clustering methods for mini-batch selection

[carterbox]

Purpose

Related to #145. Implements an algorithm for compact batch selection.

Approach

Uses a modified k-means clustering algorithm which limits the cluster sizes to be approximately equal. Starts with kmeans++ to initialize the clusters, then cycles through the points trying to swap them such that the total distance from point to cluster centroid is minimized. This swapping heuristic technically does not minimize the kmeans objective, but it does a good job of creating clusters without enclaves.

Pre-Merge Checklists

Submitter

• [x] Write a helpfully descriptive pull request title.
• [x] Organize changes into logically grouped commits with descriptive commit messages.
• [x] Document all new functions.
• [x] Write tests for new functions or explain why they are not needed.
• [x] Build the documentation successfully
• [x] Use yapf to format python code.

Reviewer

• [x] Actually read all of the code.
• [x] Run the new code yourself.
• [x] Write a summary of the changes as you understand them.
• [x] Thank the submitter.

[pep8speaks]

Hello @carterbox! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers:

Comment last updated at 2021-12-01 17:13:15 UTC

[stevehenke]

This feature introduces a clustering approach to mini-batch selection. The code is clean and tests are passing. Thank you for your contribution!