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diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index 8e724fbf06..44720147be 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -49,27 +49,7 @@ optimal *k* is usually one where there is an "elbow" in the WSSSE graph. Refer to the [`KMeans` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.KMeans) and [`KMeansModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.KMeansModel) for details on the API. -{% highlight scala %} -import org.apache.spark.mllib.clustering.{KMeans, KMeansModel} -import org.apache.spark.mllib.linalg.Vectors - -// Load and parse the data -val data = sc.textFile("data/mllib/kmeans_data.txt") -val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble))).cache() - -// Cluster the data into two classes using KMeans -val numClusters = 2 -val numIterations = 20 -val clusters = KMeans.train(parsedData, numClusters, numIterations) - -// Evaluate clustering by computing Within Set Sum of Squared Errors -val WSSSE = clusters.computeCost(parsedData) -println("Within Set Sum of Squared Errors = " + WSSSE) - -// Save and load model -clusters.save(sc, "myModelPath") -val sameModel = KMeansModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/KMeansExample.scala %} </div> <div data-lang="java" markdown="1"> @@ -81,51 +61,7 @@ that is equivalent to the provided example in Scala is given below: Refer to the [`KMeans` Java docs](api/java/org/apache/spark/mllib/clustering/KMeans.html) and [`KMeansModel` Java docs](api/java/org/apache/spark/mllib/clustering/KMeansModel.html) for details on the API. -{% highlight java %} -import org.apache.spark.api.java.*; -import org.apache.spark.api.java.function.Function; -import org.apache.spark.mllib.clustering.KMeans; -import org.apache.spark.mllib.clustering.KMeansModel; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.SparkConf; - -public class KMeansExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("K-means Example"); - JavaSparkContext sc = new JavaSparkContext(conf); - - // Load and parse data - String path = "data/mllib/kmeans_data.txt"; - JavaRDD<String> data = sc.textFile(path); - JavaRDD<Vector> parsedData = data.map( - new Function<String, Vector>() { - public Vector call(String s) { - String[] sarray = s.split(" "); - double[] values = new double[sarray.length]; - for (int i = 0; i < sarray.length; i++) - values[i] = Double.parseDouble(sarray[i]); - return Vectors.dense(values); - } - } - ); - parsedData.cache(); - - // Cluster the data into two classes using KMeans - int numClusters = 2; - int numIterations = 20; - KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations); - - // Evaluate clustering by computing Within Set Sum of Squared Errors - double WSSSE = clusters.computeCost(parsedData.rdd()); - System.out.println("Within Set Sum of Squared Errors = " + WSSSE); - - // Save and load model - clusters.save(sc.sc(), "myModelPath"); - KMeansModel sameModel = KMeansModel.load(sc.sc(), "myModelPath"); - } -} -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaKMeansExample.java %} </div> <div data-lang="python" markdown="1"> @@ -138,27 +74,7 @@ fact the optimal *k* is usually one where there is an "elbow" in the WSSSE graph Refer to the [`KMeans` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.KMeans) and [`KMeansModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.KMeansModel) for more details on the API. -{% highlight python %} -from pyspark.mllib.clustering import KMeans, KMeansModel -from numpy import array -from math import sqrt - -# Load and parse the data -data = sc.textFile("data/mllib/kmeans_data.txt") -parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')])) - -# Build the model (cluster the data) -clusters = KMeans.train(parsedData, 2, maxIterations=10, - runs=10, initializationMode="random") - -# Evaluate clustering by computing Within Set Sum of Squared Errors -WSSSE = clusters.computeCost(parsedData) -print("Within Set Sum of Squared Error = " + str(WSSSE)) - -# Save and load model -clusters.save(sc, "myModelPath") -sameModel = KMeansModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example python/mllib/k_means_example.py %} </div> </div> @@ -188,29 +104,7 @@ to the algorithm. We then output the parameters of the mixture model. Refer to the [`GaussianMixture` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture) and [`GaussianMixtureModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixtureModel) for details on the API. -{% highlight scala %} -import org.apache.spark.mllib.clustering.GaussianMixture -import org.apache.spark.mllib.clustering.GaussianMixtureModel -import org.apache.spark.mllib.linalg.Vectors - -// Load and parse the data -val data = sc.textFile("data/mllib/gmm_data.txt") -val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble))).cache() - -// Cluster the data into two classes using GaussianMixture -val gmm = new GaussianMixture().setK(2).run(parsedData) - -// Save and load model -gmm.save(sc, "myGMMModel") -val sameModel = GaussianMixtureModel.load(sc, "myGMMModel") - -// output parameters of max-likelihood model -for (i <- 0 until gmm.k) { - println("weight=%f\nmu=%s\nsigma=\n%s\n" format - (gmm.weights(i), gmm.gaussians(i).mu, gmm.gaussians(i).sigma)) -} - -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/GaussianMixtureExample.scala %} </div> <div data-lang="java" markdown="1"> @@ -222,50 +116,7 @@ that is equivalent to the provided example in Scala is given below: Refer to the [`GaussianMixture` Java docs](api/java/org/apache/spark/mllib/clustering/GaussianMixture.html) and [`GaussianMixtureModel` Java docs](api/java/org/apache/spark/mllib/clustering/GaussianMixtureModel.html) for details on the API. -{% highlight java %} -import org.apache.spark.api.java.*; -import org.apache.spark.api.java.function.Function; -import org.apache.spark.mllib.clustering.GaussianMixture; -import org.apache.spark.mllib.clustering.GaussianMixtureModel; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.SparkConf; - -public class GaussianMixtureExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("GaussianMixture Example"); - JavaSparkContext sc = new JavaSparkContext(conf); - - // Load and parse data - String path = "data/mllib/gmm_data.txt"; - JavaRDD<String> data = sc.textFile(path); - JavaRDD<Vector> parsedData = data.map( - new Function<String, Vector>() { - public Vector call(String s) { - String[] sarray = s.trim().split(" "); - double[] values = new double[sarray.length]; - for (int i = 0; i < sarray.length; i++) - values[i] = Double.parseDouble(sarray[i]); - return Vectors.dense(values); - } - } - ); - parsedData.cache(); - - // Cluster the data into two classes using GaussianMixture - GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd()); - - // Save and load GaussianMixtureModel - gmm.save(sc.sc(), "myGMMModel"); - GaussianMixtureModel sameModel = GaussianMixtureModel.load(sc.sc(), "myGMMModel"); - // Output the parameters of the mixture model - for(int j=0; j<gmm.k(); j++) { - System.out.printf("weight=%f\nmu=%s\nsigma=\n%s\n", - gmm.weights()[j], gmm.gaussians()[j].mu(), gmm.gaussians()[j].sigma()); - } - } -} -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaGaussianMixtureExample.java %} </div> <div data-lang="python" markdown="1"> @@ -276,23 +127,7 @@ to the algorithm. We then output the parameters of the mixture model. Refer to the [`GaussianMixture` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.GaussianMixture) and [`GaussianMixtureModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.GaussianMixtureModel) for more details on the API. -{% highlight python %} -from pyspark.mllib.clustering import GaussianMixture -from numpy import array - -# Load and parse the data -data = sc.textFile("data/mllib/gmm_data.txt") -parsedData = data.map(lambda line: array([float(x) for x in line.strip().split(' ')])) - -# Build the model (cluster the data) -gmm = GaussianMixture.train(parsedData, 2) - -# output parameters of model -for i in range(2): - print ("weight = ", gmm.weights[i], "mu = ", gmm.gaussians[i].mu, - "sigma = ", gmm.gaussians[i].sigma.toArray()) - -{% endhighlight %} +{% include_example python/mllib/gaussian_mixture_example.py %} </div> </div> @@ -334,31 +169,7 @@ which contains the computed clustering assignments. Refer to the [`PowerIterationClustering` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.PowerIterationClustering) and [`PowerIterationClusteringModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.PowerIterationClusteringModel) for details on the API. -{% highlight scala %} -import org.apache.spark.mllib.clustering.{PowerIterationClustering, PowerIterationClusteringModel} -import org.apache.spark.mllib.linalg.Vectors - -// Load and parse the data -val data = sc.textFile("data/mllib/pic_data.txt") -val similarities = data.map { line => - val parts = line.split(' ') - (parts(0).toLong, parts(1).toLong, parts(2).toDouble) -} - -// Cluster the data into two classes using PowerIterationClustering -val pic = new PowerIterationClustering() - .setK(2) - .setMaxIterations(10) -val model = pic.run(similarities) - -model.assignments.foreach { a => - println(s"${a.id} -> ${a.cluster}") -} - -// Save and load model -model.save(sc, "myModelPath") -val sameModel = PowerIterationClusteringModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala %} A full example that produces the experiment described in the PIC paper can be found under [`examples/`](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala). @@ -377,40 +188,7 @@ which contains the computed clustering assignments. Refer to the [`PowerIterationClustering` Java docs](api/java/org/apache/spark/mllib/clustering/PowerIterationClustering.html) and [`PowerIterationClusteringModel` Java docs](api/java/org/apache/spark/mllib/clustering/PowerIterationClusteringModel.html) for details on the API. -{% highlight java %} -import scala.Tuple2; -import scala.Tuple3; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.function.Function; -import org.apache.spark.mllib.clustering.PowerIterationClustering; -import org.apache.spark.mllib.clustering.PowerIterationClusteringModel; - -// Load and parse the data -JavaRDD<String> data = sc.textFile("data/mllib/pic_data.txt"); -JavaRDD<Tuple3<Long, Long, Double>> similarities = data.map( - new Function<String, Tuple3<Long, Long, Double>>() { - public Tuple3<Long, Long, Double> call(String line) { - String[] parts = line.split(" "); - return new Tuple3<>(new Long(parts[0]), new Long(parts[1]), new Double(parts[2])); - } - } -); - -// Cluster the data into two classes using PowerIterationClustering -PowerIterationClustering pic = new PowerIterationClustering() - .setK(2) - .setMaxIterations(10); -PowerIterationClusteringModel model = pic.run(similarities); - -for (PowerIterationClustering.Assignment a: model.assignments().toJavaRDD().collect()) { - System.out.println(a.id() + " -> " + a.cluster()); -} - -// Save and load model -model.save(sc.sc(), "myModelPath"); -PowerIterationClusteringModel sameModel = PowerIterationClusteringModel.load(sc.sc(), "myModelPath"); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaPowerIterationClusteringExample.java %} </div> <div data-lang="python" markdown="1"> @@ -425,23 +203,7 @@ which contains the computed clustering assignments. Refer to the [`PowerIterationClustering` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.PowerIterationClustering) and [`PowerIterationClusteringModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.PowerIterationClusteringModel) for more details on the API. -{% highlight python %} -from __future__ import print_function -from pyspark.mllib.clustering import PowerIterationClustering, PowerIterationClusteringModel - -# Load and parse the data -data = sc.textFile("data/mllib/pic_data.txt") -similarities = data.map(lambda line: tuple([float(x) for x in line.split(' ')])) - -# Cluster the data into two classes using PowerIterationClustering -model = PowerIterationClustering.train(similarities, 2, 10) - -model.assignments().foreach(lambda x: print(str(x.id) + " -> " + str(x.cluster))) - -# Save and load model -model.save(sc, "myModelPath") -sameModel = PowerIterationClusteringModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example python/mllib/power_iteration_clustering_example.py %} </div> </div> @@ -587,129 +349,19 @@ to the algorithm. We then output the topics, represented as probability distribu <div data-lang="scala" markdown="1"> Refer to the [`LDA` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) and [`DistributedLDAModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.DistributedLDAModel) for details on the API. -{% highlight scala %} -import org.apache.spark.mllib.clustering.{LDA, DistributedLDAModel} -import org.apache.spark.mllib.linalg.Vectors - -// Load and parse the data -val data = sc.textFile("data/mllib/sample_lda_data.txt") -val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble))) -// Index documents with unique IDs -val corpus = parsedData.zipWithIndex.map(_.swap).cache() - -// Cluster the documents into three topics using LDA -val ldaModel = new LDA().setK(3).run(corpus) - -// Output topics. Each is a distribution over words (matching word count vectors) -println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize + " words):") -val topics = ldaModel.topicsMatrix -for (topic <- Range(0, 3)) { - print("Topic " + topic + ":") - for (word <- Range(0, ldaModel.vocabSize)) { print(" " + topics(word, topic)); } - println() -} - -// Save and load model. -ldaModel.save(sc, "myLDAModel") -val sameModel = DistributedLDAModel.load(sc, "myLDAModel") - -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/LatentDirichletAllocationExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [`LDA` Java docs](api/java/org/apache/spark/mllib/clustering/LDA.html) and [`DistributedLDAModel` Java docs](api/java/org/apache/spark/mllib/clustering/DistributedLDAModel.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.clustering.DistributedLDAModel; -import org.apache.spark.mllib.clustering.LDA; -import org.apache.spark.mllib.linalg.Matrix; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.SparkConf; - -public class JavaLDAExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("LDA Example"); - JavaSparkContext sc = new JavaSparkContext(conf); - - // Load and parse the data - String path = "data/mllib/sample_lda_data.txt"; - JavaRDD<String> data = sc.textFile(path); - JavaRDD<Vector> parsedData = data.map( - new Function<String, Vector>() { - public Vector call(String s) { - String[] sarray = s.trim().split(" "); - double[] values = new double[sarray.length]; - for (int i = 0; i < sarray.length; i++) - values[i] = Double.parseDouble(sarray[i]); - return Vectors.dense(values); - } - } - ); - // Index documents with unique IDs - JavaPairRDD<Long, Vector> corpus = JavaPairRDD.fromJavaRDD(parsedData.zipWithIndex().map( - new Function<Tuple2<Vector, Long>, Tuple2<Long, Vector>>() { - public Tuple2<Long, Vector> call(Tuple2<Vector, Long> doc_id) { - return doc_id.swap(); - } - } - )); - corpus.cache(); - - // Cluster the documents into three topics using LDA - DistributedLDAModel ldaModel = new LDA().setK(3).run(corpus); - - // Output topics. Each is a distribution over words (matching word count vectors) - System.out.println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize() - + " words):"); - Matrix topics = ldaModel.topicsMatrix(); - for (int topic = 0; topic < 3; topic++) { - System.out.print("Topic " + topic + ":"); - for (int word = 0; word < ldaModel.vocabSize(); word++) { - System.out.print(" " + topics.apply(word, topic)); - } - System.out.println(); - } - - ldaModel.save(sc.sc(), "myLDAModel"); - DistributedLDAModel sameModel = DistributedLDAModel.load(sc.sc(), "myLDAModel"); - } -} -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaLatentDirichletAllocationExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [`LDA` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.LDA) and [`LDAModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.LDAModel) for more details on the API. -{% highlight python %} -from pyspark.mllib.clustering import LDA, LDAModel -from pyspark.mllib.linalg import Vectors - -# Load and parse the data -data = sc.textFile("data/mllib/sample_lda_data.txt") -parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')])) -# Index documents with unique IDs -corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache() - -# Cluster the documents into three topics using LDA -ldaModel = LDA.train(corpus, k=3) - -# Output topics. Each is a distribution over words (matching word count vectors) -print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize()) + " words):") -topics = ldaModel.topicsMatrix() -for topic in range(3): - print("Topic " + str(topic) + ":") - for word in range(0, ldaModel.vocabSize()): - print(" " + str(topics[word][topic])) - -# Save and load model -model.save(sc, "myModelPath") -sameModel = LDAModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example python/mllib/latent_dirichlet_allocation_example.py %} </div> </div> @@ -785,96 +437,16 @@ This example shows how to estimate clusters on streaming data. <div data-lang="scala" markdown="1"> Refer to the [`StreamingKMeans` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.StreamingKMeans) for details on the API. +And Refer to [Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) for details on StreamingContext. -First we import the necessary classes. - -{% highlight scala %} - -import org.apache.spark.mllib.linalg.Vectors -import org.apache.spark.mllib.regression.LabeledPoint -import org.apache.spark.mllib.clustering.StreamingKMeans - -{% endhighlight %} - -Then we make an input stream of vectors for training, as well as a stream of labeled data -points for testing. We assume a StreamingContext `ssc` has been created, see -[Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) for more info. - -{% highlight scala %} - -val trainingData = ssc.textFileStream("/training/data/dir").map(Vectors.parse) -val testData = ssc.textFileStream("/testing/data/dir").map(LabeledPoint.parse) - -{% endhighlight %} - -We create a model with random clusters and specify the number of clusters to find - -{% highlight scala %} - -val numDimensions = 3 -val numClusters = 2 -val model = new StreamingKMeans() - .setK(numClusters) - .setDecayFactor(1.0) - .setRandomCenters(numDimensions, 0.0) - -{% endhighlight %} - -Now register the streams for training and testing and start the job, printing -the predicted cluster assignments on new data points as they arrive. - -{% highlight scala %} - -model.trainOn(trainingData) -model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print() - -ssc.start() -ssc.awaitTermination() - -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/StreamingKMeansExample.scala %} </div> <div data-lang="python" markdown="1"> Refer to the [`StreamingKMeans` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.StreamingKMeans) for more details on the API. +And Refer to [Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) for details on StreamingContext. -First we import the necessary classes. - -{% highlight python %} -from pyspark.mllib.linalg import Vectors -from pyspark.mllib.regression import LabeledPoint -from pyspark.mllib.clustering import StreamingKMeans -{% endhighlight %} - -Then we make an input stream of vectors for training, as well as a stream of labeled data -points for testing. We assume a StreamingContext `ssc` has been created, see -[Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) for more info. - -{% highlight python %} -def parse(lp): - label = float(lp[lp.find('(') + 1: lp.find(',')]) - vec = Vectors.dense(lp[lp.find('[') + 1: lp.find(']')].split(',')) - return LabeledPoint(label, vec) - -trainingData = ssc.textFileStream("/training/data/dir").map(Vectors.parse) -testData = ssc.textFileStream("/testing/data/dir").map(parse) -{% endhighlight %} - -We create a model with random clusters and specify the number of clusters to find - -{% highlight python %} -model = StreamingKMeans(k=2, decayFactor=1.0).setRandomCenters(3, 1.0, 0) -{% endhighlight %} - -Now register the streams for training and testing and start the job, printing -the predicted cluster assignments on new data points as they arrive. - -{% highlight python %} -model.trainOn(trainingData) -print(model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features)))) - -ssc.start() -ssc.awaitTermination() -{% endhighlight %} +{% include_example python/mllib/streaming_k_means_example.py %} </div> </div> |