Repository: spark Updated Branches: refs/heads/master 9d1c02526 -> 244016a95
[SPARK-9149] [ML] [EXAMPLES] Add an example of spark.ml KMeans [SPARK-9149] Add an example of spark.ml KMeans - ASF JIRA https://issues.apache.org/jira/browse/SPARK-9149 jkbradley Should we support other data formats, such as TSV or CSV. I have implemented these examples which support only space separated file which is same as the example for `spark.mllib`'s `KMeans`. Author: Yu ISHIKAWA <yuu.ishik...@gmail.com> Closes #7697 from yu-iskw/SPARK-9149 and squashes the following commits: 7137bad [Yu ISHIKAWA] Fix the typo 56b9da2 [Yu ISHIKAWA] Fix the place of the wrong import statment 554e574 [Yu ISHIKAWA] Change the way to format input data in KMeansExample e7a948a [Yu ISHIKAWA] Import spark.ml.clustering.KMeans 1901e0c [Yu ISHIKAWA] Change how to initialize an array for a DataFrame schema d8043f5 [Yu ISHIKAWA] Return a value directly d81bf55 [Yu ISHIKAWA] Fix a typo and its access specifiers 3e0862d [Yu ISHIKAWA] Make KMeansExample more simple 51ce9c1 [Yu ISHIKAWA] Make JavaKMeansExample more simple a5a01e0 [Yu ISHIKAWA] Fix a Javadoc about the command to execute the example b09ec13 [Yu ISHIKAWA] [SPARK-9149][ML][Examples] Add an example of spark.ml KMeans Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/244016a9 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/244016a9 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/244016a9 Branch: refs/heads/master Commit: 244016a95c43ce6db422378e85a9d527bfe59bf1 Parents: 9d1c025 Author: Yu ISHIKAWA <yuu.ishik...@gmail.com> Authored: Sun Aug 2 09:00:32 2015 +0100 Committer: Sean Owen <so...@cloudera.com> Committed: Sun Aug 2 09:00:58 2015 +0100 ---------------------------------------------------------------------- .../spark/examples/ml/JavaKMeansExample.java | 97 ++++++++++++++++++++ examples/src/main/python/ml/kmeans_example.py | 71 ++++++++++++++ .../spark/examples/ml/KMeansExample.scala | 73 +++++++++++++++ 3 files changed, 241 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/244016a9/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java new file mode 100644 index 0000000..be2bf0c --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.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.ml; + +import java.util.regex.Pattern; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.ml.clustering.KMeansModel; +import org.apache.spark.ml.clustering.KMeans; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.VectorUDT; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.catalyst.expressions.GenericRow; +import org.apache.spark.sql.types.Metadata; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; + + +/** + * An example demonstrating a k-means clustering. + * Run with + * <pre> + * bin/run-example ml.JavaSimpleParamsExample <file> <k> + * </pre> + */ +public class JavaKMeansExample { + + private static class ParsePoint implements Function<String, Row> { + private static final Pattern separator = Pattern.compile(" "); + + @Override + public Row call(String line) { + String[] tok = separator.split(line); + double[] point = new double[tok.length]; + for (int i = 0; i < tok.length; ++i) { + point[i] = Double.parseDouble(tok[i]); + } + Vector[] points = {Vectors.dense(point)}; + return new GenericRow(points); + } + } + + public static void main(String[] args) { + if (args.length != 2) { + System.err.println("Usage: ml.JavaKMeansExample <file> <k>"); + System.exit(1); + } + String inputFile = args[0]; + int k = Integer.parseInt(args[1]); + + // Parses the arguments + SparkConf conf = new SparkConf().setAppName("JavaKMeansExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext sqlContext = new SQLContext(jsc); + + // Loads data + JavaRDD<Row> points = jsc.textFile(inputFile).map(new ParsePoint()); + StructField[] fields = {new StructField("features", new VectorUDT(), false, Metadata.empty())}; + StructType schema = new StructType(fields); + DataFrame dataset = sqlContext.createDataFrame(points, schema); + + // Trains a k-means model + KMeans kmeans = new KMeans() + .setK(k); + KMeansModel model = kmeans.fit(dataset); + + // Shows the result + Vector[] centers = model.clusterCenters(); + System.out.println("Cluster Centers: "); + for (Vector center: centers) { + System.out.println(center); + } + + jsc.stop(); + } +} http://git-wip-us.apache.org/repos/asf/spark/blob/244016a9/examples/src/main/python/ml/kmeans_example.py ---------------------------------------------------------------------- diff --git a/examples/src/main/python/ml/kmeans_example.py b/examples/src/main/python/ml/kmeans_example.py new file mode 100644 index 0000000..150dadd --- /dev/null +++ b/examples/src/main/python/ml/kmeans_example.py @@ -0,0 +1,71 @@ +# +# 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. +# + +from __future__ import print_function + +import sys +import re + +import numpy as np +from pyspark import SparkContext +from pyspark.ml.clustering import KMeans, KMeansModel +from pyspark.mllib.linalg import VectorUDT, _convert_to_vector +from pyspark.sql import SQLContext +from pyspark.sql.types import Row, StructField, StructType + +""" +A simple example demonstrating a k-means clustering. +Run with: + bin/spark-submit examples/src/main/python/ml/kmeans_example.py <input> <k> + +This example requires NumPy (http://www.numpy.org/). +""" + + +def parseVector(line): + array = np.array([float(x) for x in line.split(' ')]) + return _convert_to_vector(array) + + +if __name__ == "__main__": + + FEATURES_COL = "features" + + if len(sys.argv) != 3: + print("Usage: kmeans_example.py <file> <k>", file=sys.stderr) + exit(-1) + path = sys.argv[1] + k = sys.argv[2] + + sc = SparkContext(appName="PythonKMeansExample") + sqlContext = SQLContext(sc) + + lines = sc.textFile(path) + data = lines.map(parseVector) + row_rdd = data.map(lambda x: Row(x)) + schema = StructType([StructField(FEATURES_COL, VectorUDT(), False)]) + df = sqlContext.createDataFrame(row_rdd, schema) + + kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol(FEATURES_COL) + model = kmeans.fit(df) + centers = model.clusterCenters() + + print("Cluster Centers: ") + for center in centers: + print(center) + + sc.stop() http://git-wip-us.apache.org/repos/asf/spark/blob/244016a9/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala ---------------------------------------------------------------------- diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala new file mode 100644 index 0000000..5ce3846 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala @@ -0,0 +1,73 @@ +/* + * 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.{SparkContext, SparkConf} +import org.apache.spark.mllib.linalg.{VectorUDT, Vectors} +import org.apache.spark.ml.clustering.KMeans +import org.apache.spark.sql.{Row, SQLContext} +import org.apache.spark.sql.types.{StructField, StructType} + + +/** + * An example demonstrating a k-means clustering. + * Run with + * {{{ + * bin/run-example ml.KMeansExample <file> <k> + * }}} + */ +object KMeansExample { + + final val FEATURES_COL = "features" + + def main(args: Array[String]): Unit = { + if (args.length != 2) { + // scalastyle:off println + System.err.println("Usage: ml.KMeansExample <file> <k>") + // scalastyle:on println + System.exit(1) + } + val input = args(0) + val k = args(1).toInt + + // Creates a Spark context and a SQL context + val conf = new SparkConf().setAppName(s"${this.getClass.getSimpleName}") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + + // Loads data + val rowRDD = sc.textFile(input).filter(_.nonEmpty) + .map(_.split(" ").map(_.toDouble)).map(Vectors.dense).map(Row(_)) + val schema = StructType(Array(StructField(FEATURES_COL, new VectorUDT, false))) + val dataset = sqlContext.createDataFrame(rowRDD, schema) + + // Trains a k-means model + val kmeans = new KMeans() + .setK(k) + .setFeaturesCol(FEATURES_COL) + val model = kmeans.fit(dataset) + + // Shows the result + // scalastyle:off println + println("Final Centers: ") + model.clusterCenters.foreach(println) + // scalastyle:on println + + sc.stop() + } +} --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
