I have data set which contains user and purchase data. Here is an example, where first element is userId, second is productId, and third indicate boolean.
(2147481832,23355149,1) (2147481832,973010692,1) (2147481832,2134870842,1) (2147481832,541023347,1) (2147481832,1682206630,1) (2147481832,1138211459,1) (2147481832,852202566,1) (2147481832,201375938,1) (2147481832,486538879,1) (2147481832,919187908,1) ... I want to make sure I only take 80% of each users data and build an RDD while take the rest of the 20% and build a another RDD. Lets call train and test. I would like to stay away from using groupBy to start with since it can create memory problem since data set is large. Whats the best way to do this?
I could do following but this will not give 80% of each user.
val percentData = data.map(x => ((math.random * 100).toInt, x._1. x._2, x._3) val train = percentData.filter(x => x._1 < 80).values.repartition(10).cache() 2 Answers
Answers 1
One possibility is in Holden's answer, and this is another one :
You can use the sampleByKeyExact transformation, from the PairRDDFunctions class.
sampleByKeyExact(boolean withReplacement, scala.collection.Map fractions, long seed) Return a subset of this RDD sampled by key (via stratified sampling) containing exactly math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
And this is how I would do :
Considering the following list :
val list = List((2147481832,23355149,1),(2147481832,973010692,1),(2147481832,2134870842,1),(2147481832,541023347,1),(2147481832,1682206630,1),(2147481832,1138211459,1),(2147481832,852202566,1),(2147481832,201375938,1),(2147481832,486538879,1),(2147481832,919187908,1),(214748183,919187908,1),(214748183,91187908,1)) I would create an RDD Pair, mapping all the users as keys :
val data = sc.parallelize(list.toSeq).map(x => (x._1,(x._2,x._3))) Then I'll set up fractions for each key as following, since you've noticed that sampleByKeyExact takes a Map of fraction for each key :
val fractions = data.map(_._1).distinct.map(x => (x,0.8)).collectAsMap What I have done here is, actually, mapping on the keys to find distinct and then associate each key to a fraction equals to 0.8 then I collect the whole as a Map.
To sample now, all I have to do is :
import org.apache.spark.rdd.PairRDDFunctions val sampleData = data.sampleByKeyExact(false, fractions, 2L) or
val sampleData = data.sampleByKeyExact(withReplacement = false, fractions = fractions,seed = 2L) You can check the count on your keys or data or data sample :
scala > data.count // [...] // res10: Long = 12 scala > sampleData.count // [...] // res11: Long = 10 Answers 2
Something like this is may be well suited to something like "Blink DB", but lets look at the question. There are two ways to interpret what you've asked one is:
1) You want 80% of your users, and you want all of the data for them. 2) You want 80% of each users data
For #1 you could do a map to get the user ids, call distinct, and then sample 80% of them (you may want to look at kFold in MLUtils or BernoulliCellSampler). You can then filter your input data to just the set of IDs you want.
For #2 you could look at BernoulliCellSampler and simply apply it directly.
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