Caching factor of MatrixFactorizationModel in PySpark

Question

after loading a saved MatrixFactorizationModel I get the warnings: MatrixFactorizationModelWrapper: Product factor does not have a partitioner. Prediction on individual records could be slow. MatrixFactorizationModelWrapper: Product factor is not cached. Prediction could be slow.

and indeed the computation is slow and will not scale well

how do I set a partitioner and cache the Product factor?

adding code that demonstrates the problem:

from pyspark import SparkContext
import sys

sc = SparkContext("spark://hadoop-m:7077", "recommend")    
from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating
model = MatrixFactorizationModel.load(sc, "model")
model.productFeatures.cache()

i get:

Traceback (most recent call last): File "/home/me/recommend.py", line 7, in model.productFeatures.cache() AttributeError: 'function' object has no attribute 'cache'

Answer 1

Concerning the caching, like I wrote in the comment box, you can cache your rdd doing the following :

rdd.cache() # for Scala, Java and Python

EDIT: The userFeatures and the productFeatures are both of type RDD[(Int, Array[Double]). (Ref. Official Documentation)

To cache the productFeature, you can do the following

model.productFeatures().cache() 

Of course I consider that loaded model is called model.

Example :

r1 = (1, 1, 1.0)
r2 = (1, 2, 2.0)
r3 = (2, 1, 2.0)

ratings = sc.parallelize([r1, r2, r3])

from pyspark.mllib.recommendation import ALS

model = ALS.trainImplicit(ratings, 1, seed=10)
model.predict(2, 2)

feats = model.productFeatures()

type(feats)

>> MapPartitionsRDD[137] at mapPartitions at PythonMLLibAPI.scala:1074
feats.cache()

As for the warning concerning the partitioner, even if you partition your model, let's say by feature with .partitionBy() to balance it it would still be too expensive performance.

There is a JIRA ticket (SPARK-8708) concerning this issue that should be resolved in the next release of Spark (1.5).

Nevertheless, if you want to learning more about partitioning algorithms, I invite you to read the the discussion in this ticket SPARK-3717 that argues about partitioning by features within the DecisionTree and RandomForest algorithms.