org.apache.spark.examples.mllib.RecommendationExample.scala Maven / Gradle / Ivy

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/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.mllib.recommendation.Rating
// $example off$

object RecommendationExample {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("CollaborativeFilteringExample")
    val sc = new SparkContext(conf)
    // $example on$
    // Load and parse the data
    val data = sc.textFile("data/mllib/als/test.data")
    val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
      Rating(user.toInt, item.toInt, rate.toDouble)
    })

    // Build the recommendation model using ALS
    val rank = 10
    val numIterations = 10
    val model = ALS.train(ratings, rank, numIterations, 0.01)

    // Evaluate the model on rating data
    val usersProducts = ratings.map { case Rating(user, product, rate) =>
      (user, product)
    }
    val predictions =
      model.predict(usersProducts).map { case Rating(user, product, rate) =>
        ((user, product), rate)
      }
    val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
      ((user, product), rate)
    }.join(predictions)
    val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
      val err = (r1 - r2)
      err * err
    }.mean()
    println("Mean Squared Error = " + MSE)

    // Save and load model
    model.save(sc, "target/tmp/myCollaborativeFilter")
    val sameModel = MatrixFactorizationModel.load(sc, "target/tmp/myCollaborativeFilter")
    // $example off$
  }
}
// scalastyle:on println