GitHub user srowen opened a pull request:
https://github.com/apache/spark/pull/16328
[SPARK-18808][ML][MLLIB] ml.KMeansModel.transform is very inefficient
## What changes were proposed in this pull request?
mllib.KMeansModel.clusterCentersWithNorm is a method than ends up being
called every time `predict` is called on a single vector, which is bad news for
now the ml.KMeansModel Transformer works, which necessarily transforms one
vector at a time.
This causes the model to just store the vectors with norms upfront. The
extra norm should be small compared to the vectors. This would avoid this form
of overhead on this and other code paths.
## How was this patch tested?
Existing tests.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/srowen/spark SPARK-18808
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/16328.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #16328
----
commit cddf553511e5a9684020b2e1a8ad04d3858db9ec
Author: Sean Owen <[email protected]>
Date: 2016-12-18T09:55:03Z
Store vector norms upfront in KMeansModel to avoid computing every time in
ml.KmeansModel.predict, etc
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