GitHub user mouendless opened a pull request:
https://github.com/apache/spark/pull/13133
[SPARK-6706] [MLlib] Reduce duplicate computation in picking initial points
@mateiz @srowen
I state that the contribution is my original work and that I license the
work to the project under the project's open source license
There's some format problems with my last PR, with @HyukjinKwon 's help I
read the guidance, re-check my code and PR, then run the tests, finally
re-submit the PR request here.
The related JIRA issue though marked as resolved, this change may relate to
it I think.
## Proposed Change
After picking each new initial centers, it's unnecessary to compute the
distances between all the points and the old ones.
Instead this change keeps the distance between all the points and their
closest centers, and compare to the distance of them with the new center then
update them.
## Test result
One can find an easy test way in
(https://issues.apache.org/jira/browse/SPARK-6706)
I test the KMeans++ method for a small dataset with 16k points, and the
whole KMeans|| with a large one with 240k points.
The data has 4096 features and I tunes K from 100 to 500.
The test environment was on my 4 machine cluster, I also tested a 3M points
data on a larger cluster with 25 machines and got similar results, which I
would not draw the detail curve. The result of the first two exps are shown
below
### Local KMeans++ test:
Dataset:4m_ini_center
Data_size:16234
Dimension:4096
Lloyd's Iteration = 10
The y-axis is time in sec, the x-axis is tuning the K.
### On a larger dataset
An improve show in the graph but not commit in this file: In this
experiment I also have an improvement for calculation in normalization data
(the distance is convert to the cosine distance). As if the data is normalized
into (0,1), one improvement in the original vesion for
util.MLUtils.fastSauaredDistance would have no effect (the precisionBound 2.0 *
EPSILON * sumSquaredNorm / (normDiff * normDiff + EPSILON) will never less then
precision in this case). Therefore I design an early terminal method when
comparing two distance (used for findClosest). But I don't include this improve
in this file, you may only refer to the curves without "normalize" for
comparing the results.
Dataset:4k24
Data_size:243960
Dimension:4096
Normlize Enlarge Initialize Lloyd's_Iteration
NO 1 3 5
YES 10000 3 5
Notice: the normlized data is enlarged to ensure precision
The cost time: x-for value of K, y-for time in sec
SE for unnormalized data between two version, to ensure the correctness
Here is the SE between normalized data just for reference, it's also
correct.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/mouendless/spark patch-2
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/13133.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 #13133
----
commit c28db67f7bd3257ac87580072228fd9854a3c883
Author: DLucky <mouendl...@gmail.com>
Date: 2016-05-16T09:52:47Z
Reduce duplicate computation
Reduce duplicate computation when picking initial points
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