GitHub user viirya opened a pull request:
https://github.com/apache/spark/pull/7121
[SPARK-8708][MLlib] Paritition ALS ratings based on both users and products
JIRA: https://issues.apache.org/jira/browse/SPARK-8708
Previously the partitions of ratings are only based on the given products.
So if the `usersProducts` given for prediction contains only few products or
even one product, the generated ratings will be pushed into few or single
partition and can't use high parallelism.
The following codes are the example reported in the JIRA. Because it asks
the predictions for users on product 2. There is only one partition in the
result.
>>> r1 = (1, 1, 1.0)
>>> r2 = (1, 2, 2.0)
>>> r3 = (2, 1, 2.0)
>>> r4 = (2, 2, 2.0)
>>> r5 = (3, 1, 1.0)
>>> ratings = sc.parallelize([r1, r2, r3, r4, r5], 5)
>>> users = ratings.map(itemgetter(0)).distinct()
>>> model = ALS.trainImplicit(ratings, 1, seed=10)
>>> predictions_for_2 = model.predictAll(users.map(lambda u: (u, 2)))
>>> predictions_for_2.glom().map(len).collect()
[0, 0, 3, 0, 0]
This PR uses user and product instead of only product to partition the
ratings.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/viirya/spark-1 mfm_fix_partition
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/7121.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 #7121
----
commit b534dc899c908be3622cf9b61da902babd8a8f90
Author: Liang-Chi Hsieh <[email protected]>
Date: 2015-06-30T10:00:51Z
Paritition ratings based on both users and products.
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