Antony Mayi created SPARK-8708:
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Summary: MatrixFactorizationModel.predictAll() populates single
partition only
Key: SPARK-8708
URL: https://issues.apache.org/jira/browse/SPARK-8708
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.3.0
Reporter: Antony Mayi
When using mllib.recommendation.ALS the RDD returned by .predictAll() has all
values pushed into single partition despite using quite high parallelism.
This degrades performance of further processing (I can obviously run
.partitionBy()) to balance it but that's still too costly (ie if running
.predictAll() in loop for thousands of products) and should be possible to do
it rather somehow on the model (automatically)).
Bellow is an example on tiny sample (same on large dataset):
{code:title=pyspark}
>>> 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)
>>> ratings.getNumPartitions()
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]
{code}
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