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package predict
import org.rogach.scallop._
import org.apache.spark.rdd.RDD
import ujson._
import org.apache.spark.sql.SparkSession
import org.apache.log4j.Logger
import org.apache.log4j.Level
import scala.math
import shared.predictions._
class kNNConf(arguments: Seq[String]) extends ScallopConf(arguments) {
val train = opt[String](required = true)
val test = opt[String](required = true)
val separator = opt[String](default=Some("\t"))
val num_measurements = opt[Int](default=Some(0))
val json = opt[String]()
verify()
}
object kNN extends App {
// Remove these lines if encountering/debugging Spark
Logger.getLogger("org").setLevel(Level.OFF)
Logger.getLogger("akka").setLevel(Level.OFF)
val spark = SparkSession.builder()
.master("local[1]")
.getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
println("")
println("******************************************************")
var conf = new PersonalizedConf(args)
println("Loading training data from: " + conf.train())
val train = load(spark, conf.train(), conf.separator()).collect()
println("Loading test data from: " + conf.test())
val test = load(spark, conf.test(), conf.separator()).collect()
val measurements = (1 to conf.num_measurements()).map(x => timingInMs(() => {
Thread.sleep(1000) // Do everything here from train and test
42 // Output answer as last value
}))
val timings = measurements.map(t => t._2) // Retrieve the timing measurements
// Save answers as JSON
def printToFile(content: String,
location: String = "./answers.json") =
Some(new java.io.PrintWriter(location)).foreach{
f => try{
f.write(content)
} finally{ f.close }
}
conf.json.toOption match {
case None => ;
case Some(jsonFile) => {
val answers = ujson.Obj(
"Meta" -> ujson.Obj(
"1.Train" -> conf.train(),
"2.Test" -> conf.test(),
"3.Measurements" -> conf.num_measurements()
),
"N.1" -> ujson.Obj(
"1.k10u1v1" -> ujson.Num(0.0), // Similarity between user 1 and user 1 (k=10)
"2.k10u1v864" -> ujson.Num(0.0), // Similarity between user 1 and user 864 (k=10)
"3.k10u1v886" -> ujson.Num(0.0), // Similarity between user 1 and user 886 (k=10)
"4.PredUser1Item1" -> ujson.Num(0.0) // Prediction of item 1 for user 1 (k=10)
),
"N.2" -> ujson.Obj(
"1.kNN-Mae" -> List(10,30,50,100,200,300,400,800,943).map(k =>
List(
k,
0.0 // Compute MAE
)
).toList
),
"N.3" -> ujson.Obj(
"1.kNN" -> ujson.Obj(
"average (ms)" -> ujson.Num(mean(timings)),
"stddev (ms)" -> ujson.Num(std(timings))
)
)
)
val json = write(answers, 4)
println(json)
println("Saving answers in: " + jsonFile)
printToFile(json, jsonFile)
}
}
println("")
spark.close()
}