[SPARK-11548][DOCS] Replaced example code in mllib-collaborative-filtering.md using include_example
Kindly review the changes. Author: Rishabh Bhardwaj <rbnext29@gmail.com> Closes #9519 from rishabhbhardwaj/SPARK-11337.
This commit is contained in:
Rishabh Bhardwaj
Xiangrui Meng
parent
51d41e4b1a
commit
b7720fa455
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@ -66,43 +66,7 @@ recommendation model by measuring the Mean Squared Error of rating prediction.
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Refer to the [`ALS` Scala docs](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS) for details on the API.
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{% highlight scala %}
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import org.apache.spark.mllib.recommendation.ALS
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import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
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import org.apache.spark.mllib.recommendation.Rating
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// Load and parse the data
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val data = sc.textFile("data/mllib/als/test.data")
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val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
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Rating(user.toInt, item.toInt, rate.toDouble)
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})
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// Build the recommendation model using ALS
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val rank = 10
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val numIterations = 10
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val model = ALS.train(ratings, rank, numIterations, 0.01)
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// Evaluate the model on rating data
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val usersProducts = ratings.map { case Rating(user, product, rate) =>
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(user, product)
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}
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val predictions =
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model.predict(usersProducts).map { case Rating(user, product, rate) =>
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((user, product), rate)
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}
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val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
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((user, product), rate)
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}.join(predictions)
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val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
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val err = (r1 - r2)
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err * err
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}.mean()
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println("Mean Squared Error = " + MSE)
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// Save and load model
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model.save(sc, "myModelPath")
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val sameModel = MatrixFactorizationModel.load(sc, "myModelPath")
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{% endhighlight %}
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{% include_example scala/org/apache/spark/examples/mllib/RecommendationExample.scala %}
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If the rating matrix is derived from another source of information (e.g., it is inferred from
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other signals), you can use the `trainImplicit` method to get better results.
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@ -123,81 +87,7 @@ that is equivalent to the provided example in Scala is given below:
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Refer to the [`ALS` Java docs](api/java/org/apache/spark/mllib/recommendation/ALS.html) for details on the API.
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{% highlight java %}
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import scala.Tuple2;
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import org.apache.spark.api.java.*;
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import org.apache.spark.api.java.function.Function;
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import org.apache.spark.mllib.recommendation.ALS;
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import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
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import org.apache.spark.mllib.recommendation.Rating;
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import org.apache.spark.SparkConf;
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public class CollaborativeFiltering {
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public static void main(String[] args) {
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SparkConf conf = new SparkConf().setAppName("Collaborative Filtering Example");
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JavaSparkContext sc = new JavaSparkContext(conf);
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// Load and parse the data
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String path = "data/mllib/als/test.data";
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JavaRDD<String> data = sc.textFile(path);
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JavaRDD<Rating> ratings = data.map(
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new Function<String, Rating>() {
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public Rating call(String s) {
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String[] sarray = s.split(",");
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return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]),
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Double.parseDouble(sarray[2]));
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}
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}
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);
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// Build the recommendation model using ALS
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int rank = 10;
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int numIterations = 10;
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MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01);
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// Evaluate the model on rating data
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JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(
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new Function<Rating, Tuple2<Object, Object>>() {
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public Tuple2<Object, Object> call(Rating r) {
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return new Tuple2<Object, Object>(r.user(), r.product());
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}
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}
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);
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JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(
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model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
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new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
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public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
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return new Tuple2<Tuple2<Integer, Integer>, Double>(
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new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
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}
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}
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));
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JavaRDD<Tuple2<Double, Double>> ratesAndPreds =
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JavaPairRDD.fromJavaRDD(ratings.map(
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new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
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public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
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return new Tuple2<Tuple2<Integer, Integer>, Double>(
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new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
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}
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}
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)).join(predictions).values();
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double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map(
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new Function<Tuple2<Double, Double>, Object>() {
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public Object call(Tuple2<Double, Double> pair) {
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Double err = pair._1() - pair._2();
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return err * err;
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}
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}
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).rdd()).mean();
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System.out.println("Mean Squared Error = " + MSE);
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// Save and load model
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model.save(sc.sc(), "myModelPath");
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MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(sc.sc(), "myModelPath");
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}
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}
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{% endhighlight %}
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{% include_example java/org/apache/spark/examples/mllib/JavaRecommendationExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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@ -207,29 +97,7 @@ recommendation by measuring the Mean Squared Error of rating prediction.
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Refer to the [`ALS` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.recommendation.ALS) for more details on the API.
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{% highlight python %}
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from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating
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# Load and parse the data
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data = sc.textFile("data/mllib/als/test.data")
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ratings = data.map(lambda l: l.split(',')).map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2])))
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# Build the recommendation model using Alternating Least Squares
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rank = 10
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numIterations = 10
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model = ALS.train(ratings, rank, numIterations)
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# Evaluate the model on training data
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testdata = ratings.map(lambda p: (p[0], p[1]))
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predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
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ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
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MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
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print("Mean Squared Error = " + str(MSE))
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# Save and load model
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model.save(sc, "myModelPath")
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sameModel = MatrixFactorizationModel.load(sc, "myModelPath")
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{% endhighlight %}
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{% include_example python/mllib/recommendation_example.py %}
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If the rating matrix is derived from other source of information (i.e., it is inferred from other
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signals), you can use the trainImplicit method to get better results.
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@ -0,0 +1,97 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.examples.mllib;
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// $example on$
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import scala.Tuple2;
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import org.apache.spark.api.java.*;
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import org.apache.spark.api.java.function.Function;
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import org.apache.spark.mllib.recommendation.ALS;
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import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
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import org.apache.spark.mllib.recommendation.Rating;
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import org.apache.spark.SparkConf;
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// $example off$
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public class JavaRecommendationExample {
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public static void main(String args[]) {
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// $example on$
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SparkConf conf = new SparkConf().setAppName("Java Collaborative Filtering Example");
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JavaSparkContext jsc = new JavaSparkContext(conf);
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// Load and parse the data
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String path = "data/mllib/als/test.data";
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JavaRDD<String> data = jsc.textFile(path);
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JavaRDD<Rating> ratings = data.map(
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new Function<String, Rating>() {
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public Rating call(String s) {
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String[] sarray = s.split(",");
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return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]),
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Double.parseDouble(sarray[2]));
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}
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}
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);
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// Build the recommendation model using ALS
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int rank = 10;
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int numIterations = 10;
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MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01);
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// Evaluate the model on rating data
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JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(
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new Function<Rating, Tuple2<Object, Object>>() {
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public Tuple2<Object, Object> call(Rating r) {
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return new Tuple2<Object, Object>(r.user(), r.product());
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}
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}
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);
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JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(
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model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
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new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
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public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
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return new Tuple2<Tuple2<Integer, Integer>, Double>(
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new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
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}
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}
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));
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JavaRDD<Tuple2<Double, Double>> ratesAndPreds =
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JavaPairRDD.fromJavaRDD(ratings.map(
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new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
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public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
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return new Tuple2<Tuple2<Integer, Integer>, Double>(
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new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
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}
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}
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)).join(predictions).values();
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double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map(
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new Function<Tuple2<Double, Double>, Object>() {
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public Object call(Tuple2<Double, Double> pair) {
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Double err = pair._1() - pair._2();
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return err * err;
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}
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}
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).rdd()).mean();
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System.out.println("Mean Squared Error = " + MSE);
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// Save and load model
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model.save(jsc.sc(), "target/tmp/myCollaborativeFilter");
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MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(jsc.sc(),
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"target/tmp/myCollaborativeFilter");
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// $example off$
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}
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}
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54
examples/src/main/python/mllib/recommendation_example.py
Normal file
54
examples/src/main/python/mllib/recommendation_example.py
Normal file
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@ -0,0 +1,54 @@
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Collaborative Filtering Classification Example.
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"""
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from __future__ import print_function
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import sys
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from pyspark import SparkContext
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# $example on$
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from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating
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# $example off$
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if __name__ == "__main__":
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sc = SparkContext(appName="PythonCollaborativeFilteringExample")
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# $example on$
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# Load and parse the data
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data = sc.textFile("data/mllib/als/test.data")
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ratings = data.map(lambda l: l.split(','))\
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.map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2])))
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# Build the recommendation model using Alternating Least Squares
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rank = 10
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numIterations = 10
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model = ALS.train(ratings, rank, numIterations)
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# Evaluate the model on training data
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testdata = ratings.map(lambda p: (p[0], p[1]))
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predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
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ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
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MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
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print("Mean Squared Error = " + str(MSE))
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# Save and load model
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model.save(sc, "target/tmp/myCollaborativeFilter")
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sameModel = MatrixFactorizationModel.load(sc, "target/tmp/myCollaborativeFilter")
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# $example off$
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@ -0,0 +1,67 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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// scalastyle:off println
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package org.apache.spark.examples.mllib
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import org.apache.spark.{SparkContext, SparkConf}
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// $example on$
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import org.apache.spark.mllib.recommendation.ALS
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import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
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import org.apache.spark.mllib.recommendation.Rating
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// $example off$
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object RecommendationExample {
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def main(args: Array[String]): Unit = {
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val conf = new SparkConf().setAppName("CollaborativeFilteringExample")
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val sc = new SparkContext(conf)
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// $example on$
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// Load and parse the data
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val data = sc.textFile("data/mllib/als/test.data")
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val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
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Rating(user.toInt, item.toInt, rate.toDouble)
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})
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// Build the recommendation model using ALS
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val rank = 10
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val numIterations = 10
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val model = ALS.train(ratings, rank, numIterations, 0.01)
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// Evaluate the model on rating data
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val usersProducts = ratings.map { case Rating(user, product, rate) =>
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(user, product)
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}
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val predictions =
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model.predict(usersProducts).map { case Rating(user, product, rate) =>
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((user, product), rate)
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}
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val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
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((user, product), rate)
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}.join(predictions)
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val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
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val err = (r1 - r2)
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err * err
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}.mean()
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println("Mean Squared Error = " + MSE)
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// Save and load model
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model.save(sc, "target/tmp/myCollaborativeFilter")
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val sameModel = MatrixFactorizationModel.load(sc, "target/tmp/myCollaborativeFilter")
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// $example off$
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}
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}
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// scalastyle:on println
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