Subhod Lagade created SPARK-8627:
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Summary: ALS model predict error
Key: SPARK-8627
URL: https://issues.apache.org/jira/browse/SPARK-8627
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.4.0
Reporter: Subhod Lagade
/**
* Created by subhod lagade on 25/06/15.
*/
import org.apache.spark.SparkConf
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming._;
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.PrintStream;
import java.net.ServerSocket;
import java.net.Socket;
import java.util.Properties;
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.mllib.recommendation.Rating
object SparkStreamKafka {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("Simple Application");
val sc = new SparkContext(conf);
val data = sc.textFile("/home/appadmin/Disney/data.csv");
val ratings = data.map(_.split(',') match { case Array(user, product,
rate) => Rating(user.toInt, product.toInt, rate.toDouble) });
val rank = 3;
val numIterations = 2;
val model = ALS.train(ratings,rank,numIterations,0.01);
val usersProducts = ratings.map{ case Rating(user, product, rate) =>
(user, product)}
// Build the recommendation model using ALS
usersProducts.foreach(println)
val predictions = model.predict(usersProducts)
}
}
/*
ERROR Message
[ERROR]
/home/appadmin/disneypoc/src/main/scala/org/capgemini/SparkKafka.scala:53:
error: not enough arguments for method predict: (user: Int, product: Int)Double.
[INFO] Unspecified value parameter product.
[INFO] val predictions = model.predict(usersProducts)
*/
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