Github user BenFradet commented on a diff in the pull request:
https://github.com/apache/spark/pull/10411#discussion_r48418593
--- Diff:
examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java ---
@@ -0,0 +1,131 @@
+/*
+ * 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 org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.sql.SQLContext;
+
+// $example on$
+import java.io.Serializable;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.recommendation.ALS;
+import org.apache.spark.ml.recommendation.ALSModel;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.types.DataTypes;
+// $example off$
+
+public class JavaALSExample {
+
+ // $example on$
+ public static class Rating implements Serializable {
+ private int userId;
+ private int movieId;
+ private float rating;
+ private long timestamp;
+
+ public int getUserId() {
+ return userId;
+ }
+
+ public void setUserId(int userId) {
+ this.userId = userId;
+ }
+
+ public int getMovieId() {
+ return movieId;
+ }
+
+ public void setMovieId(int movieId) {
+ this.movieId = movieId;
+ }
+
+ public float getRating() {
+ return rating;
+ }
+
+ public void setRating(float rating) {
+ this.rating = rating;
+ }
+
+ public long getTimestamp() {
+ return timestamp;
+ }
+
+ public void setTimestamp(long timestamp) {
+ this.timestamp = timestamp;
+ }
+
+ public static Rating parseRating(String str) {
+ String[] fields = str.split("::");
+ assert(fields.length == 4);
+ Rating rating = new Rating();
+ rating.setUserId(Integer.parseInt(fields[0]));
+ rating.setMovieId(Integer.parseInt(fields[1]));
+ rating.setRating(Float.parseFloat(fields[2]));
+ rating.setTimestamp(Long.parseLong(fields[3]));
+ return rating;
+ }
+ }
+ // $example off$
+
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("JavaALSExample");
+ JavaSparkContext jsc = new JavaSparkContext(conf);
+ SQLContext sqlContext = new SQLContext(jsc);
+
+ // $example on$
+ JavaRDD<Rating> ratingsRDD =
jsc.textFile("data/mllib/als/sample_movielens_ratings.txt")
+ .map(new Function<String, Rating>() {
+ public Rating call(String str) {
+ return Rating.parseRating(str);
+ }
+ });
+ DataFrame ratings = sqlContext.createDataFrame(ratingsRDD,
Rating.class);
+ DataFrame[] splits = ratings.randomSplit(new double[]{0.8, 0.2});
+ DataFrame training = splits[0];
+ DataFrame test = splits[1];
+
+ // Build the recommendation model using ALS on the training data
+ ALS als = new ALS()
+ .setMaxIter(5)
+ .setRegParam(0.01)
+ .setUserCol("userId")
+ .setItemCol("movieId")
+ .setRatingCol("rating");
+ ALSModel model = als.fit(training);
+
+ // Evaluate the model by computing the RMSE on the test data
+ DataFrame rawPredictions = model.transform(test);
+ DataFrame predictions = rawPredictions
+ .withColumn("rating",
rawPredictions.col("rating").cast(DataTypes.DoubleType))
+ .withColumn("prediction",
rawPredictions.col("prediction").cast(DataTypes.DoubleType));
--- End diff --
There might be a better way to do this, input welcome.
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