Hi,
The MatrixFactorizationModel consists of two RDD's. When you use the second
method, Spark tries to serialize both RDD's for the .map() function,
which is not possible, because RDD's are not serializable. Therefore you
receive the NULLPointerException. You must use the first method.
Best,
Burak
----- Original Message -----
From: "Franco Barrientos" <franco.barrien...@exalitica.com>
To: user@spark.apache.org
Sent: Wednesday, December 24, 2014 7:44:24 AM
Subject: null Error in ALS model predict
Hi all!,
I have a RDD[(int,int,double,double)] where the first two int values are id
and product, respectively. I trained an implicit ALS algorithm and want to
make predictions from this RDD. I make two things but I think both ways are
same.
1- Convert this RDD to RDD[(int,int)] and use
model.predict(RDD(int,int)), this works to me!
2- Make a map and apply model.predict(int,int), for example:
val ratings = RDD[(int,int,double,double)].map{ case (id, rubro, rating,
resp)=>
model.predict(id,rubro)
}
Where ratings is a RDD[Double].
Now, the second way when I apply a ratings.first() I get the follow error:
Why this happend? I need to use this second way.
Thanks in advance,
Franco Barrientos
Data Scientist
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Santiago, Chile.
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