org.apache.flink.examples.java.clustering.KMeans Maven / Gradle / Ivy
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package org.apache.flink.examples.java.clustering;
import java.io.Serializable;
import java.util.Collection;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.functions.FunctionAnnotation.ForwardedFields;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.examples.java.clustering.util.KMeansData;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.IterativeDataSet;
/**
* This example implements a basic K-Means clustering algorithm.
*
*
* K-Means is an iterative clustering algorithm and works as follows:
* K-Means is given a set of data points to be clustered and an initial set of K cluster centers.
* In each iteration, the algorithm computes the distance of each data point to each cluster center.
* Each point is assigned to the cluster center which is closest to it.
* Subsequently, each cluster center is moved to the center (mean) of all points that have been assigned to it.
* The moved cluster centers are fed into the next iteration.
* The algorithm terminates after a fixed number of iterations (as in this implementation)
* or if cluster centers do not (significantly) move in an iteration.
* This is the Wikipedia entry for the K-Means Clustering algorithm.
*
*
* This implementation works on two-dimensional data points.
* It computes an assignment of data points to cluster centers, i.e.,
* each data point is annotated with the id of the final cluster (center) it belongs to.
*
*
* Input files are plain text files and must be formatted as follows:
*
* - Data points are represented as two double values separated by a blank character.
* Data points are separated by newline characters.
* For example "1.2 2.3\n5.3 7.2\n" gives two data points (x=1.2, y=2.3) and (x=5.3, y=7.2).
* - Cluster centers are represented by an integer id and a point value.
* For example "1 6.2 3.2\n2 2.9 5.7\n" gives two centers (id=1, x=6.2, y=3.2) and (id=2, x=2.9, y=5.7).
*
*
*
* Usage: KMeans <points path> <centers path> <result path> <num iterations>
* If no parameters are provided, the program is run with default data from {@link KMeansData} and 10 iterations.
*
*
* This example shows how to use:
*
* - Bulk iterations
*
- Broadcast variables in bulk iterations
*
- Custom Java objects (PoJos)
*
*/
@SuppressWarnings("serial")
public class KMeans {
// *************************************************************************
// PROGRAM
// *************************************************************************
public static void main(String[] args) throws Exception {
if(!parseParameters(args)) {
return;
}
// set up execution environment
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
// get input data
DataSet points = getPointDataSet(env);
DataSet centroids = getCentroidDataSet(env);
// set number of bulk iterations for KMeans algorithm
IterativeDataSet loop = centroids.iterate(numIterations);
DataSet newCentroids = points
// compute closest centroid for each point
.map(new SelectNearestCenter()).withBroadcastSet(loop, "centroids")
// count and sum point coordinates for each centroid
.map(new CountAppender())
.groupBy(0).reduce(new CentroidAccumulator())
// compute new centroids from point counts and coordinate sums
.map(new CentroidAverager());
// feed new centroids back into next iteration
DataSet finalCentroids = loop.closeWith(newCentroids);
DataSet> clusteredPoints = points
// assign points to final clusters
.map(new SelectNearestCenter()).withBroadcastSet(finalCentroids, "centroids");
// emit result
if (fileOutput) {
clusteredPoints.writeAsCsv(outputPath, "\n", " ");
// since file sinks are lazy, we trigger the execution explicitly
env.execute("KMeans Example");
}
else {
clusteredPoints.print();
}
}
// *************************************************************************
// DATA TYPES
// *************************************************************************
/**
* A simple two-dimensional point.
*/
public static class Point implements Serializable {
public double x, y;
public Point() {}
public Point(double x, double y) {
this.x = x;
this.y = y;
}
public Point add(Point other) {
x += other.x;
y += other.y;
return this;
}
public Point div(long val) {
x /= val;
y /= val;
return this;
}
public double euclideanDistance(Point other) {
return Math.sqrt((x-other.x)*(x-other.x) + (y-other.y)*(y-other.y));
}
public void clear() {
x = y = 0.0;
}
@Override
public String toString() {
return x + " " + y;
}
}
/**
* A simple two-dimensional centroid, basically a point with an ID.
*/
public static class Centroid extends Point {
public int id;
public Centroid() {}
public Centroid(int id, double x, double y) {
super(x,y);
this.id = id;
}
public Centroid(int id, Point p) {
super(p.x, p.y);
this.id = id;
}
@Override
public String toString() {
return id + " " + super.toString();
}
}
// *************************************************************************
// USER FUNCTIONS
// *************************************************************************
/** Converts a {@code Tuple2} into a Point. */
@ForwardedFields("0->x; 1->y")
public static final class TuplePointConverter implements MapFunction, Point> {
@Override
public Point map(Tuple2 t) throws Exception {
return new Point(t.f0, t.f1);
}
}
/** Converts a {@code Tuple3} into a Centroid. */
@ForwardedFields("0->id; 1->x; 2->y")
public static final class TupleCentroidConverter implements MapFunction, Centroid> {
@Override
public Centroid map(Tuple3 t) throws Exception {
return new Centroid(t.f0, t.f1, t.f2);
}
}
/** Determines the closest cluster center for a data point. */
@ForwardedFields("*->1")
public static final class SelectNearestCenter extends RichMapFunction> {
private Collection centroids;
/** Reads the centroid values from a broadcast variable into a collection. */
@Override
public void open(Configuration parameters) throws Exception {
this.centroids = getRuntimeContext().getBroadcastVariable("centroids");
}
@Override
public Tuple2 map(Point p) throws Exception {
double minDistance = Double.MAX_VALUE;
int closestCentroidId = -1;
// check all cluster centers
for (Centroid centroid : centroids) {
// compute distance
double distance = p.euclideanDistance(centroid);
// update nearest cluster if necessary
if (distance < minDistance) {
minDistance = distance;
closestCentroidId = centroid.id;
}
}
// emit a new record with the center id and the data point.
return new Tuple2(closestCentroidId, p);
}
}
/** Appends a count variable to the tuple. */
@ForwardedFields("f0;f1")
public static final class CountAppender implements MapFunction, Tuple3> {
@Override
public Tuple3 map(Tuple2 t) {
return new Tuple3(t.f0, t.f1, 1L);
}
}
/** Sums and counts point coordinates. */
@ForwardedFields("0")
public static final class CentroidAccumulator implements ReduceFunction> {
@Override
public Tuple3 reduce(Tuple3 val1, Tuple3 val2) {
return new Tuple3(val1.f0, val1.f1.add(val2.f1), val1.f2 + val2.f2);
}
}
/** Computes new centroid from coordinate sum and count of points. */
@ForwardedFields("0->id")
public static final class CentroidAverager implements MapFunction, Centroid> {
@Override
public Centroid map(Tuple3 value) {
return new Centroid(value.f0, value.f1.div(value.f2));
}
}
// *************************************************************************
// UTIL METHODS
// *************************************************************************
private static boolean fileOutput = false;
private static String pointsPath = null;
private static String centersPath = null;
private static String outputPath = null;
private static int numIterations = 10;
private static boolean parseParameters(String[] programArguments) {
if(programArguments.length > 0) {
// parse input arguments
fileOutput = true;
if(programArguments.length == 4) {
pointsPath = programArguments[0];
centersPath = programArguments[1];
outputPath = programArguments[2];
numIterations = Integer.parseInt(programArguments[3]);
} else {
System.err.println("Usage: KMeans ");
return false;
}
} else {
System.out.println("Executing K-Means example with default parameters and built-in default data.");
System.out.println(" Provide parameters to read input data from files.");
System.out.println(" See the documentation for the correct format of input files.");
System.out.println(" We provide a data generator to create synthetic input files for this program.");
System.out.println(" Usage: KMeans ");
}
return true;
}
private static DataSet getPointDataSet(ExecutionEnvironment env) {
if(fileOutput) {
// read points from CSV file
return env.readCsvFile(pointsPath)
.fieldDelimiter(" ")
.includeFields(true, true)
.types(Double.class, Double.class)
.map(new TuplePointConverter());
} else {
return KMeansData.getDefaultPointDataSet(env);
}
}
private static DataSet getCentroidDataSet(ExecutionEnvironment env) {
if(fileOutput) {
return env.readCsvFile(centersPath)
.fieldDelimiter(" ")
.includeFields(true, true, true)
.types(Integer.class, Double.class, Double.class)
.map(new TupleCentroidConverter());
} else {
return KMeansData.getDefaultCentroidDataSet(env);
}
}
}
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