info & background on `merge_closest()` method

[jGaboardi]

I am curious about the history & commenting out of the merge_closest() method. A quick search didn't lead to any previous issues or PRs (though I likely overlooked something). Is there anything you can point me to for that, @carsonfarmer?

[carsonfarmer]

Yeh, to be honest, I'm not totally sure? I suspect it was commented out, because I didn't have a very robust test for it. I recall essentially wanting to create random clusters, and then testing that they were recoverable via the merge_closest() flow. But I didn't end up implementing that.

[jGaboardi]

So do you think we want to:

• [ ] 1. ... keep it in as it is (commented out),
• [ ] 2. ... remove it entirely, or
• [ ] 3. ... try to create MWE confirmed test case

cc @gegen07

[carsonfarmer]

Well, obviously 3 is the ideal outcome. But given I no longer have a core use case for this, I would likely find it difficult to devote the time. If @gegen07 has a use case/data set/example, I'm sure we could use that!

Alternatively, we can just enable it, do the minimal test, and see if folks find it useful? For instance, the merge_closest method is really just a wrapper around the example/demo, which does indeed work.

[jGaboardi]

I found the commit where merge_closest() and interact() were removed -- https://github.com/carsonfarmer/fastpair/commit/92364962f6b695661f35a117bf11f96584128a8d

• merge_closest
• interact

[carsonfarmer]

Ah yes, interesting. So based on the comment, it looks like there was some weirdness with merging and not getting consistent results in tests. Having said that, it looks like there's no good reason to not support this, and just allow someone to supply their own merge function as input to merge_closest().

[jGaboardi]

Putting this chunk from the README in here to keep around for a notebook for when these become part of fastpair again -- xref #45 .

To illustrate the mergeing methods, here is a simple example of hierarchical clustering, treating points as the 'centroids' of various clusters:

for i in range(len(fp)-1):
   # First method... do it manually:
   dist, (a, b) = fp.closest_pair()
   c = interact(a, b)  # Compute mean centroid
   fp -= b
   fp -= a
   fp += c
   # Alternatively... do it all in one step:
   # fp.merge_closest()
len(fp)  # 1

[jGaboardi]

I'm just now realizing that interact() is actually defined within test_fastpair.py and then used for a merge_closest() test there, as well. Not sure how I missed this before.

[jGaboardi]

@carsonfarmer Should we go ahead and get those moved back into base.py and flesh out some tests for them?