Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3720#discussion_r22735895
--- Diff:
examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala ---
@@ -0,0 +1,173 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.examples.ml
+
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.examples.mllib.AbstractParams
+import org.apache.spark.ml.recommendation.ALS
+import org.apache.spark.sql.{Row, SQLContext}
+
+/**
+ * An example app for ALS on MovieLens data
(http://grouplens.org/datasets/movielens/).
+ * Run with
+ * {{{
+ * bin/run-example ml.MovieLensALS
+ * }}}
+ */
+object MovieLensALS {
+
+ case class Rating(userId: Int, movieId: Int, rating: Float, timestamp:
Long)
+
+ object Rating {
+ def parseRating(str: String): Rating = {
+ val fields = str.split("::")
+ assert(fields.size == 4)
+ Rating(fields(0).toInt, fields(1).toInt, fields(2).toFloat,
fields(3).toLong)
+ }
+ }
+
+ case class Movie(movieId: Int, title: String, genres: Seq[String])
+
+ object Movie {
+ def parseMovie(str: String): Movie = {
+ val fields = str.split("::")
+ assert(fields.size == 3)
+ Movie(fields(0).toInt, fields(1), fields(2).split("|"))
+ }
+ }
+
+ case class Params(
+ ratings: String = null,
+ movies: String = null,
+ maxIter: Int = 10,
+ regParam: Double = 0.1,
+ rank: Int = 10,
+ numBlocks: Int = 10) extends AbstractParams[Params]
+
+ def main(args: Array[String]) {
+ val defaultParams = Params()
+
+ val parser = new OptionParser[Params]("MovieLensALS") {
+ head("MovieLensALS: an example app for ALS on MovieLens data.")
+ opt[String]("ratings")
+ .required()
+ .text("path to a MovieLens dataset of ratings")
+ .action((x, c) => c.copy(ratings = x))
+ opt[String]("movies")
+ .required()
+ .text("path to a MovieLens dataset of movies")
+ .action((x, c) => c.copy(movies = x))
+ opt[Int]("rank")
+ .text(s"rank, default: ${defaultParams.rank}}")
+ .action((x, c) => c.copy(rank = x))
+ opt[Int]("maxIter")
+ .text(s"max number of iterations, default:
${defaultParams.maxIter}")
+ .action((x, c) => c.copy(maxIter = x))
+ opt[Double]("regParam")
+ .text(s"regularization parameter, default:
${defaultParams.regParam}")
+ .action((x, c) => c.copy(regParam = x))
+ opt[Int]("numBlocks")
+ .text(s"number of blocks, default: ${defaultParams.numBlocks}")
+ .action((x, c) => c.copy(numBlocks = x))
+ note(
+ """
+ |Example command line to run this app:
+ |
+ | bin/spark-submit --class
org.apache.spark.examples.ml.MovieLensALS \
+ | examples/target/scala-*/spark-examples-*.jar \
+ | --rank 10 --maxIter 15 --regParam 0.1 \
+ | --movies path/to/movielens/movies.dat \
+ | --ratings path/to/movielens/ratings.dat
+ """.stripMargin)
+ }
+
+ parser.parse(args, defaultParams).map { params =>
+ run(params)
+ } getOrElse {
+ System.exit(1)
+ }
+ }
+
+ def run(params: Params) {
+ val conf = new SparkConf().setAppName(s"MovieLensALS with $params")
+ val sc = new SparkContext(conf)
+ val sqlContext = new SQLContext(sc)
+ import sqlContext._
+
+ val ratings =
sc.textFile(params.ratings).map(Rating.parseRating).cache()
+
+ val numRatings = ratings.count()
+ val numUsers = ratings.map(_.userId).distinct().count()
+ val numMovies = ratings.map(_.movieId).distinct().count()
+
+ println(s"Got $numRatings ratings from $numUsers users on $numMovies
movies.")
+
+ val splits = ratings.randomSplit(Array(0.8, 0.2), 0L)
+ val training = splits(0).cache()
+ val test = splits(1).cache()
+
+ val numTraining = training.count()
+ val numTest = test.count()
+ println(s"Training: $numTraining, test: $numTest.")
+
+ ratings.unpersist(blocking = false)
+
+ val als = new ALS()
+ .setUserCol("userId")
+ .setItemCol("movieId")
+ .setRank(params.rank)
+ .setMaxIter(params.maxIter)
+ .setRegParam(params.regParam)
+ .setNumBlocks(params.numBlocks)
+
+ val model = als.fit(training)
+
+ val predictions = model.transform(test).cache()
+
+ // Evaluate the model.
+ val mse = predictions.select('rating, 'prediction)
+ .flatMap { case Row(rating: Float, prediction: Float) =>
+ val err = rating.toDouble - prediction
+ val err2 = err * err
+ if (err2.isNaN) {
+ Iterator.empty
--- End diff --
Done.
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