diff options
4 files changed, 221 insertions, 135 deletions
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index 1ad52123c7..7cd1b894e7 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -66,43 +66,7 @@ recommendation model by measuring the Mean Squared Error of rating prediction. Refer to the [`ALS` Scala docs](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS) for details on the API. -{% highlight scala %} -import org.apache.spark.mllib.recommendation.ALS -import org.apache.spark.mllib.recommendation.MatrixFactorizationModel -import org.apache.spark.mllib.recommendation.Rating - -// 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, "myModelPath") -val sameModel = MatrixFactorizationModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/RecommendationExample.scala %} If the rating matrix is derived from another source of information (e.g., it is inferred from other signals), you can use the `trainImplicit` method to get better results. @@ -123,81 +87,7 @@ that is equivalent to the provided example in Scala is given below: Refer to the [`ALS` Java docs](api/java/org/apache/spark/mllib/recommendation/ALS.html) for details on the API. -{% highlight java %} -import scala.Tuple2; - -import org.apache.spark.api.java.*; -import org.apache.spark.api.java.function.Function; -import org.apache.spark.mllib.recommendation.ALS; -import org.apache.spark.mllib.recommendation.MatrixFactorizationModel; -import org.apache.spark.mllib.recommendation.Rating; -import org.apache.spark.SparkConf; - -public class CollaborativeFiltering { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("Collaborative Filtering Example"); - JavaSparkContext sc = new JavaSparkContext(conf); - - // Load and parse the data - String path = "data/mllib/als/test.data"; - JavaRDD<String> data = sc.textFile(path); - JavaRDD<Rating> ratings = data.map( - new Function<String, Rating>() { - public Rating call(String s) { - String[] sarray = s.split(","); - return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]), - Double.parseDouble(sarray[2])); - } - } - ); - - // Build the recommendation model using ALS - int rank = 10; - int numIterations = 10; - MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01); - - // Evaluate the model on rating data - JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map( - new Function<Rating, Tuple2<Object, Object>>() { - public Tuple2<Object, Object> call(Rating r) { - return new Tuple2<Object, Object>(r.user(), r.product()); - } - } - ); - JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD( - model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map( - new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() { - public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){ - return new Tuple2<Tuple2<Integer, Integer>, Double>( - new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating()); - } - } - )); - JavaRDD<Tuple2<Double, Double>> ratesAndPreds = - JavaPairRDD.fromJavaRDD(ratings.map( - new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() { - public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){ - return new Tuple2<Tuple2<Integer, Integer>, Double>( - new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating()); - } - } - )).join(predictions).values(); - double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map( - new Function<Tuple2<Double, Double>, Object>() { - public Object call(Tuple2<Double, Double> pair) { - Double err = pair._1() - pair._2(); - return err * err; - } - } - ).rdd()).mean(); - System.out.println("Mean Squared Error = " + MSE); - - // Save and load model - model.save(sc.sc(), "myModelPath"); - MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(sc.sc(), "myModelPath"); - } -} -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaRecommendationExample.java %} </div> <div data-lang="python" markdown="1"> @@ -207,29 +97,7 @@ recommendation by measuring the Mean Squared Error of rating prediction. Refer to the [`ALS` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.recommendation.ALS) for more details on the API. -{% highlight python %} -from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating - -# Load and parse the data -data = sc.textFile("data/mllib/als/test.data") -ratings = data.map(lambda l: l.split(',')).map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2]))) - -# Build the recommendation model using Alternating Least Squares -rank = 10 -numIterations = 10 -model = ALS.train(ratings, rank, numIterations) - -# Evaluate the model on training data -testdata = ratings.map(lambda p: (p[0], p[1])) -predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2])) -ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions) -MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean() -print("Mean Squared Error = " + str(MSE)) - -# Save and load model -model.save(sc, "myModelPath") -sameModel = MatrixFactorizationModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example python/mllib/recommendation_example.py %} If the rating matrix is derived from other source of information (i.e., it is inferred from other signals), you can use the trainImplicit method to get better results. diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java new file mode 100644 index 0000000000..1065fde953 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java @@ -0,0 +1,97 @@ +/* + * 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.mllib; + +// $example on$ +import scala.Tuple2; + +import org.apache.spark.api.java.*; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.mllib.recommendation.ALS; +import org.apache.spark.mllib.recommendation.MatrixFactorizationModel; +import org.apache.spark.mllib.recommendation.Rating; +import org.apache.spark.SparkConf; +// $example off$ + +public class JavaRecommendationExample { + public static void main(String args[]) { + // $example on$ + SparkConf conf = new SparkConf().setAppName("Java Collaborative Filtering Example"); + JavaSparkContext jsc = new JavaSparkContext(conf); + + // Load and parse the data + String path = "data/mllib/als/test.data"; + JavaRDD<String> data = jsc.textFile(path); + JavaRDD<Rating> ratings = data.map( + new Function<String, Rating>() { + public Rating call(String s) { + String[] sarray = s.split(","); + return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]), + Double.parseDouble(sarray[2])); + } + } + ); + + // Build the recommendation model using ALS + int rank = 10; + int numIterations = 10; + MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01); + + // Evaluate the model on rating data + JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map( + new Function<Rating, Tuple2<Object, Object>>() { + public Tuple2<Object, Object> call(Rating r) { + return new Tuple2<Object, Object>(r.user(), r.product()); + } + } + ); + JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD( + model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map( + new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() { + public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){ + return new Tuple2<Tuple2<Integer, Integer>, Double>( + new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating()); + } + } + )); + JavaRDD<Tuple2<Double, Double>> ratesAndPreds = + JavaPairRDD.fromJavaRDD(ratings.map( + new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() { + public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){ + return new Tuple2<Tuple2<Integer, Integer>, Double>( + new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating()); + } + } + )).join(predictions).values(); + double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map( + new Function<Tuple2<Double, Double>, Object>() { + public Object call(Tuple2<Double, Double> pair) { + Double err = pair._1() - pair._2(); + return err * err; + } + } + ).rdd()).mean(); + System.out.println("Mean Squared Error = " + MSE); + + // Save and load model + model.save(jsc.sc(), "target/tmp/myCollaborativeFilter"); + MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(jsc.sc(), + "target/tmp/myCollaborativeFilter"); + // $example off$ + } +} diff --git a/examples/src/main/python/mllib/recommendation_example.py b/examples/src/main/python/mllib/recommendation_example.py new file mode 100644 index 0000000000..615db0749b --- /dev/null +++ b/examples/src/main/python/mllib/recommendation_example.py @@ -0,0 +1,54 @@ +# +# 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. +# + +""" +Collaborative Filtering Classification Example. +""" +from __future__ import print_function + +import sys + +from pyspark import SparkContext + +# $example on$ +from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="PythonCollaborativeFilteringExample") + # $example on$ + # Load and parse the data + data = sc.textFile("data/mllib/als/test.data") + ratings = data.map(lambda l: l.split(','))\ + .map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2]))) + + # Build the recommendation model using Alternating Least Squares + rank = 10 + numIterations = 10 + model = ALS.train(ratings, rank, numIterations) + + # Evaluate the model on training data + testdata = ratings.map(lambda p: (p[0], p[1])) + predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2])) + ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions) + MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean() + print("Mean Squared Error = " + str(MSE)) + + # Save and load model + model.save(sc, "target/tmp/myCollaborativeFilter") + sameModel = MatrixFactorizationModel.load(sc, "target/tmp/myCollaborativeFilter") + # $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RecommendationExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RecommendationExample.scala new file mode 100644 index 0000000000..64e4602465 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RecommendationExample.scala @@ -0,0 +1,67 @@ +/* + * 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.{SparkContext, SparkConf} +// $example on$ +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 |