/**
* 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.mahout.clustering.canopy;
import java.util.Collection;
import java.util.List;
import java.util.Set;
import com.google.common.collect.Iterables;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.clustering.ClusteringTestUtils;
import org.apache.mahout.clustering.iterator.ClusterWritable;
import org.apache.mahout.common.DummyRecordWriter;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.common.Pair;
import org.apache.mahout.common.commandline.DefaultOptionCreator;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.common.distance.ManhattanDistanceMeasure;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileValueIterable;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.junit.Before;
import org.junit.Test;
import com.google.common.collect.Lists;
import com.google.common.io.Closeables;
@Deprecated
public final class TestCanopyCreation extends MahoutTestCase {
private static final double[][] RAW = { { 1, 1 }, { 2, 1 }, { 1, 2 },
{ 2, 2 }, { 3, 3 }, { 4, 4 }, { 5, 4 }, { 4, 5 }, { 5, 5 } };
private List<Canopy> referenceManhattan;
private final DistanceMeasure manhattanDistanceMeasure = new ManhattanDistanceMeasure();
private List<Vector> manhattanCentroids;
private List<Canopy> referenceEuclidean;
private final DistanceMeasure euclideanDistanceMeasure = new EuclideanDistanceMeasure();
private List<Vector> euclideanCentroids;
private FileSystem fs;
private static List<VectorWritable> getPointsWritable() {
List<VectorWritable> points = Lists.newArrayList();
for (double[] fr : RAW) {
Vector vec = new RandomAccessSparseVector(fr.length);
vec.assign(fr);
points.add(new VectorWritable(vec));
}
return points;
}
private static List<Vector> getPoints() {
List<Vector> points = Lists.newArrayList();
for (double[] fr : RAW) {
Vector vec = new RandomAccessSparseVector(fr.length);
vec.assign(fr);
points.add(vec);
}
return points;
}
/**
* Print the canopies to the transcript
*
* @param canopies
* a List<Canopy>
*/
private static void printCanopies(Iterable<Canopy> canopies) {
for (Canopy canopy : canopies) {
System.out.println(canopy.asFormatString(null));
}
}
@Override
@Before
public void setUp() throws Exception {
super.setUp();
fs = FileSystem.get(getConfiguration());
referenceManhattan = CanopyClusterer.createCanopies(getPoints(),
manhattanDistanceMeasure, 3.1, 2.1);
manhattanCentroids = CanopyClusterer.getCenters(referenceManhattan);
referenceEuclidean = CanopyClusterer.createCanopies(getPoints(),
euclideanDistanceMeasure, 3.1, 2.1);
euclideanCentroids = CanopyClusterer.getCenters(referenceEuclidean);
}
/**
* Story: User can cluster points using a ManhattanDistanceMeasure and a
* reference implementation
*/
@Test
public void testReferenceManhattan() throws Exception {
// see setUp for cluster creation
printCanopies(referenceManhattan);
assertEquals("number of canopies", 3, referenceManhattan.size());
for (int canopyIx = 0; canopyIx < referenceManhattan.size(); canopyIx++) {
Canopy testCanopy = referenceManhattan.get(canopyIx);
int[] expectedNumPoints = { 4, 4, 3 };
double[][] expectedCentroids = { { 1.5, 1.5 }, { 4.0, 4.0 },
{ 4.666666666666667, 4.6666666666666667 } };
assertEquals("canopy points " + canopyIx, testCanopy.getNumObservations(),
expectedNumPoints[canopyIx]);
double[] refCentroid = expectedCentroids[canopyIx];
Vector testCentroid = testCanopy.computeCentroid();
for (int pointIx = 0; pointIx < refCentroid.length; pointIx++) {
assertEquals("canopy centroid " + canopyIx + '[' + pointIx + ']',
refCentroid[pointIx], testCentroid.get(pointIx), EPSILON);
}
}
}
/**
* Story: User can cluster points using a EuclideanDistanceMeasure and a
* reference implementation
*/
@Test
public void testReferenceEuclidean() throws Exception {
// see setUp for cluster creation
printCanopies(referenceEuclidean);
assertEquals("number of canopies", 3, referenceEuclidean.size());
int[] expectedNumPoints = { 5, 5, 3 };
double[][] expectedCentroids = { { 1.8, 1.8 }, { 4.2, 4.2 },
{ 4.666666666666667, 4.666666666666667 } };
for (int canopyIx = 0; canopyIx < referenceEuclidean.size(); canopyIx++) {
Canopy testCanopy = referenceEuclidean.get(canopyIx);
assertEquals("canopy points " + canopyIx, testCanopy.getNumObservations(),
expectedNumPoints[canopyIx]);
double[] refCentroid = expectedCentroids[canopyIx];
Vector testCentroid = testCanopy.computeCentroid();
for (int pointIx = 0; pointIx < refCentroid.length; pointIx++) {
assertEquals("canopy centroid " + canopyIx + '[' + pointIx + ']',
refCentroid[pointIx], testCentroid.get(pointIx), EPSILON);
}
}
}
/**
* Story: User can produce initial canopy centers using a
* ManhattanDistanceMeasure and a CanopyMapper which clusters input points to
* produce an output set of canopy centroid points.
*/
@Test
public void testCanopyMapperManhattan() throws Exception {
CanopyMapper mapper = new CanopyMapper();
Configuration conf = getConfiguration();
conf.set(CanopyConfigKeys.DISTANCE_MEASURE_KEY, manhattanDistanceMeasure
.getClass().getName());
conf.set(CanopyConfigKeys.T1_KEY, String.valueOf(3.1));
conf.set(CanopyConfigKeys.T2_KEY, String.valueOf(2.1));
conf.set(CanopyConfigKeys.CF_KEY, "0");
DummyRecordWriter<Text, VectorWritable> writer = new DummyRecordWriter<Text, VectorWritable>();
Mapper<WritableComparable<?>, VectorWritable, Text, VectorWritable>.Context context = DummyRecordWriter
.build(mapper, conf, writer);
mapper.setup(context);
List<VectorWritable> points = getPointsWritable();
// map the data
for (VectorWritable point : points) {
mapper.map(new Text(), point, context);
}
mapper.cleanup(context);
assertEquals("Number of map results", 1, writer.getData().size());
// now verify the output
List<VectorWritable> data = writer.getValue(new Text("centroid"));
assertEquals("Number of centroids", 3, data.size());
for (int i = 0; i < data.size(); i++) {
assertEquals("Centroid error",
manhattanCentroids.get(i).asFormatString(), data.get(i).get()
.asFormatString());
}
}
/**
* Story: User can produce initial canopy centers using a
* EuclideanDistanceMeasure and a CanopyMapper/Combiner which clusters input
* points to produce an output set of canopy centroid points.
*/
@Test
public void testCanopyMapperEuclidean() throws Exception {
CanopyMapper mapper = new CanopyMapper();
Configuration conf = getConfiguration();
conf.set(CanopyConfigKeys.DISTANCE_MEASURE_KEY, euclideanDistanceMeasure
.getClass().getName());
conf.set(CanopyConfigKeys.T1_KEY, String.valueOf(3.1));
conf.set(CanopyConfigKeys.T2_KEY, String.valueOf(2.1));
conf.set(CanopyConfigKeys.CF_KEY, "0");
DummyRecordWriter<Text, VectorWritable> writer = new DummyRecordWriter<Text, VectorWritable>();
Mapper<WritableComparable<?>, VectorWritable, Text, VectorWritable>.Context context = DummyRecordWriter
.build(mapper, conf, writer);
mapper.setup(context);
List<VectorWritable> points = getPointsWritable();
// map the data
for (VectorWritable point : points) {
mapper.map(new Text(), point, context);
}
mapper.cleanup(context);
assertEquals("Number of map results", 1, writer.getData().size());
// now verify the output
List<VectorWritable> data = writer.getValue(new Text("centroid"));
assertEquals("Number of centroids", 3, data.size());
for (int i = 0; i < data.size(); i++) {
assertEquals("Centroid error",
euclideanCentroids.get(i).asFormatString(), data.get(i).get()
.asFormatString());
}
}
/**
* Story: User can produce final canopy centers using a
* ManhattanDistanceMeasure and a CanopyReducer which clusters input centroid
* points to produce an output set of final canopy centroid points.
*/
@Test
public void testCanopyReducerManhattan() throws Exception {
CanopyReducer reducer = new CanopyReducer();
Configuration conf = getConfiguration();
conf.set(CanopyConfigKeys.DISTANCE_MEASURE_KEY,
"org.apache.mahout.common.distance.ManhattanDistanceMeasure");
conf.set(CanopyConfigKeys.T1_KEY, String.valueOf(3.1));
conf.set(CanopyConfigKeys.T2_KEY, String.valueOf(2.1));
conf.set(CanopyConfigKeys.CF_KEY, "0");
DummyRecordWriter<Text, ClusterWritable> writer = new DummyRecordWriter<Text, ClusterWritable>();
Reducer<Text, VectorWritable, Text, ClusterWritable>.Context context = DummyRecordWriter
.build(reducer, conf, writer, Text.class, VectorWritable.class);
reducer.setup(context);
List<VectorWritable> points = getPointsWritable();
reducer.reduce(new Text("centroid"), points, context);
Iterable<Text> keys = writer.getKeysInInsertionOrder();
assertEquals("Number of centroids", 3, Iterables.size(keys));
int i = 0;
for (Text key : keys) {
List<ClusterWritable> data = writer.getValue(key);
ClusterWritable clusterWritable = data.get(0);
Canopy canopy = (Canopy) clusterWritable.getValue();
assertEquals(manhattanCentroids.get(i).asFormatString() + " is not equal to "
+ canopy.computeCentroid().asFormatString(),
manhattanCentroids.get(i), canopy.computeCentroid());
i++;
}
}
/**
* Story: User can produce final canopy centers using a
* EuclideanDistanceMeasure and a CanopyReducer which clusters input centroid
* points to produce an output set of final canopy centroid points.
*/
@Test
public void testCanopyReducerEuclidean() throws Exception {
CanopyReducer reducer = new CanopyReducer();
Configuration conf = getConfiguration();
conf.set(CanopyConfigKeys.DISTANCE_MEASURE_KEY, "org.apache.mahout.common.distance.EuclideanDistanceMeasure");
conf.set(CanopyConfigKeys.T1_KEY, String.valueOf(3.1));
conf.set(CanopyConfigKeys.T2_KEY, String.valueOf(2.1));
conf.set(CanopyConfigKeys.CF_KEY, "0");
DummyRecordWriter<Text, ClusterWritable> writer = new DummyRecordWriter<Text, ClusterWritable>();
Reducer<Text, VectorWritable, Text, ClusterWritable>.Context context =
DummyRecordWriter.build(reducer, conf, writer, Text.class, VectorWritable.class);
reducer.setup(context);
List<VectorWritable> points = getPointsWritable();
reducer.reduce(new Text("centroid"), points, context);
Iterable<Text> keys = writer.getKeysInInsertionOrder();
assertEquals("Number of centroids", 3, Iterables.size(keys));
int i = 0;
for (Text key : keys) {
List<ClusterWritable> data = writer.getValue(key);
ClusterWritable clusterWritable = data.get(0);
Canopy canopy = (Canopy) clusterWritable.getValue();
assertEquals(euclideanCentroids.get(i).asFormatString() + " is not equal to "
+ canopy.computeCentroid().asFormatString(),
euclideanCentroids.get(i), canopy.computeCentroid());
i++;
}
}
/**
* Story: User can produce final canopy centers using a Hadoop map/reduce job
* and a ManhattanDistanceMeasure.
*/
@Test
public void testCanopyGenManhattanMR() throws Exception {
List<VectorWritable> points = getPointsWritable();
Configuration config = getConfiguration();
ClusteringTestUtils.writePointsToFile(points,
getTestTempFilePath("testdata/file1"), fs, config);
ClusteringTestUtils.writePointsToFile(points,
getTestTempFilePath("testdata/file2"), fs, config);
// now run the Canopy Driver
Path output = getTestTempDirPath("output");
CanopyDriver.run(config, getTestTempDirPath("testdata"), output,
manhattanDistanceMeasure, 3.1, 2.1, false, 0.0, false);
// verify output from sequence file
Path path = new Path(output, "clusters-0-final/part-r-00000");
FileSystem fs = FileSystem.get(path.toUri(), config);
SequenceFile.Reader reader = new SequenceFile.Reader(fs, path, config);
try {
Writable key = new Text();
ClusterWritable clusterWritable = new ClusterWritable();
assertTrue("more to come", reader.next(key, clusterWritable));
assertEquals("1st key", "C-0", key.toString());
List<Pair<Double,Double>> refCenters = Lists.newArrayList();
refCenters.add(new Pair<Double,Double>(1.5,1.5));
refCenters.add(new Pair<Double,Double>(4.333333333333334,4.333333333333334));
Pair<Double,Double> c = new Pair<Double,Double>(clusterWritable.getValue() .getCenter().get(0),
clusterWritable.getValue().getCenter().get(1));
assertTrue("center "+c+" not found", findAndRemove(c, refCenters, EPSILON));
assertTrue("more to come", reader.next(key, clusterWritable));
assertEquals("2nd key", "C-1", key.toString());
c = new Pair<Double,Double>(clusterWritable.getValue().getCenter().get(0),
clusterWritable.getValue().getCenter().get(1));
assertTrue("center " + c + " not found", findAndRemove(c, refCenters, EPSILON));
assertFalse("more to come", reader.next(key, clusterWritable));
} finally {
Closeables.close(reader, true);
}
}
static boolean findAndRemove(Pair<Double, Double> target, Collection<Pair<Double, Double>> list, double epsilon) {
for (Pair<Double,Double> curr : list) {
if ( (Math.abs(target.getFirst() - curr.getFirst()) < epsilon)
&& (Math.abs(target.getSecond() - curr.getSecond()) < epsilon) ) {
list.remove(curr);
return true;
}
}
return false;
}
/**
* Story: User can produce final canopy centers using a Hadoop map/reduce job
* and a EuclideanDistanceMeasure.
*/
@Test
public void testCanopyGenEuclideanMR() throws Exception {
List<VectorWritable> points = getPointsWritable();
Configuration config = getConfiguration();
ClusteringTestUtils.writePointsToFile(points,
getTestTempFilePath("testdata/file1"), fs, config);
ClusteringTestUtils.writePointsToFile(points,
getTestTempFilePath("testdata/file2"), fs, config);
// now run the Canopy Driver
Path output = getTestTempDirPath("output");
CanopyDriver.run(config, getTestTempDirPath("testdata"), output,
euclideanDistanceMeasure, 3.1, 2.1, false, 0.0, false);
// verify output from sequence file
Path path = new Path(output, "clusters-0-final/part-r-00000");
FileSystem fs = FileSystem.get(path.toUri(), config);
SequenceFile.Reader reader = new SequenceFile.Reader(fs, path, config);
try {
Writable key = new Text();
ClusterWritable clusterWritable = new ClusterWritable();
assertTrue("more to come", reader.next(key, clusterWritable));
assertEquals("1st key", "C-0", key.toString());
List<Pair<Double,Double>> refCenters = Lists.newArrayList();
refCenters.add(new Pair<Double,Double>(1.8,1.8));
refCenters.add(new Pair<Double,Double>(4.433333333333334, 4.433333333333334));
Pair<Double,Double> c = new Pair<Double,Double>(clusterWritable.getValue().getCenter().get(0),
clusterWritable.getValue().getCenter().get(1));
assertTrue("center "+c+" not found", findAndRemove(c, refCenters, EPSILON));
assertTrue("more to come", reader.next(key, clusterWritable));
assertEquals("2nd key", "C-1", key.toString());
c = new Pair<Double,Double>(clusterWritable.getValue().getCenter().get(0),
clusterWritable.getValue().getCenter().get(1));
assertTrue("center "+c+" not found", findAndRemove(c, refCenters, EPSILON));
assertFalse("more to come", reader.next(key, clusterWritable));
} finally {
Closeables.close(reader, true);
}
}
/** Story: User can cluster points using sequential execution */
@Test
public void testClusteringManhattanSeq() throws Exception {
List<VectorWritable> points = getPointsWritable();
Configuration config = getConfiguration();
ClusteringTestUtils.writePointsToFile(points,
getTestTempFilePath("testdata/file1"), fs, config);
// now run the Canopy Driver in sequential mode
Path output = getTestTempDirPath("output");
CanopyDriver.run(config, getTestTempDirPath("testdata"), output,
manhattanDistanceMeasure, 3.1, 2.1, true, 0.0, true);
// verify output from sequence file
Path path = new Path(output, "clusters-0-final/part-r-00000");
int ix = 0;
for (ClusterWritable clusterWritable : new SequenceFileValueIterable<ClusterWritable>(path, true,
config)) {
assertEquals("Center [" + ix + ']', manhattanCentroids.get(ix), clusterWritable.getValue()
.getCenter());
ix++;
}
path = new Path(output, "clusteredPoints/part-m-0");
long count = HadoopUtil.countRecords(path, config);
assertEquals("number of points", points.size(), count);
}
/** Story: User can cluster points using sequential execution */
@Test
public void testClusteringEuclideanSeq() throws Exception {
List<VectorWritable> points = getPointsWritable();
Configuration config = getConfiguration();
ClusteringTestUtils.writePointsToFile(points,
getTestTempFilePath("testdata/file1"), fs, config);
// now run the Canopy Driver in sequential mode
Path output = getTestTempDirPath("output");
String[] args = { optKey(DefaultOptionCreator.INPUT_OPTION),
getTestTempDirPath("testdata").toString(),
optKey(DefaultOptionCreator.OUTPUT_OPTION), output.toString(),
optKey(DefaultOptionCreator.DISTANCE_MEASURE_OPTION),
EuclideanDistanceMeasure.class.getName(),
optKey(DefaultOptionCreator.T1_OPTION), "3.1",
optKey(DefaultOptionCreator.T2_OPTION), "2.1",
optKey(DefaultOptionCreator.CLUSTERING_OPTION),
optKey(DefaultOptionCreator.OVERWRITE_OPTION),
optKey(DefaultOptionCreator.METHOD_OPTION),
DefaultOptionCreator.SEQUENTIAL_METHOD };
ToolRunner.run(config, new CanopyDriver(), args);
// verify output from sequence file
Path path = new Path(output, "clusters-0-final/part-r-00000");
int ix = 0;
for (ClusterWritable clusterWritable : new SequenceFileValueIterable<ClusterWritable>(path, true,
config)) {
assertEquals("Center [" + ix + ']', euclideanCentroids.get(ix), clusterWritable.getValue()
.getCenter());
ix++;
}
path = new Path(output, "clusteredPoints/part-m-0");
long count = HadoopUtil.countRecords(path, config);
assertEquals("number of points", points.size(), count);
}
/** Story: User can remove outliers while clustering points using sequential execution */
@Test
public void testClusteringEuclideanWithOutlierRemovalSeq() throws Exception {
List<VectorWritable> points = getPointsWritable();
Configuration config = getConfiguration();
ClusteringTestUtils.writePointsToFile(points,
getTestTempFilePath("testdata/file1"), fs, config);
// now run the Canopy Driver in sequential mode
Path output = getTestTempDirPath("output");
String[] args = { optKey(DefaultOptionCreator.INPUT_OPTION),
getTestTempDirPath("testdata").toString(),
optKey(DefaultOptionCreator.OUTPUT_OPTION), output.toString(),
optKey(DefaultOptionCreator.DISTANCE_MEASURE_OPTION),
EuclideanDistanceMeasure.class.getName(),
optKey(DefaultOptionCreator.T1_OPTION), "3.1",
optKey(DefaultOptionCreator.T2_OPTION), "2.1",
optKey(DefaultOptionCreator.OUTLIER_THRESHOLD), "0.5",
optKey(DefaultOptionCreator.CLUSTERING_OPTION),
optKey(DefaultOptionCreator.OVERWRITE_OPTION),
optKey(DefaultOptionCreator.METHOD_OPTION),
DefaultOptionCreator.SEQUENTIAL_METHOD };
ToolRunner.run(config, new CanopyDriver(), args);
// verify output from sequence file
Path path = new Path(output, "clusters-0-final/part-r-00000");
int ix = 0;
for (ClusterWritable clusterWritable : new SequenceFileValueIterable<ClusterWritable>(path, true,
config)) {
assertEquals("Center [" + ix + ']', euclideanCentroids.get(ix), clusterWritable.getValue()
.getCenter());
ix++;
}
path = new Path(output, "clusteredPoints/part-m-0");
long count = HadoopUtil.countRecords(path, config);
int expectedPointsHavingPDFGreaterThanThreshold = 6;
assertEquals("number of points", expectedPointsHavingPDFGreaterThanThreshold, count);
}
/**
* Story: User can produce final point clustering using a Hadoop map/reduce
* job and a ManhattanDistanceMeasure.
*/
@Test
public void testClusteringManhattanMR() throws Exception {
List<VectorWritable> points = getPointsWritable();
Configuration conf = getConfiguration();
ClusteringTestUtils.writePointsToFile(points, true,
getTestTempFilePath("testdata/file1"), fs, conf);
ClusteringTestUtils.writePointsToFile(points, true,
getTestTempFilePath("testdata/file2"), fs, conf);
// now run the Job
Path output = getTestTempDirPath("output");
CanopyDriver.run(conf, getTestTempDirPath("testdata"), output,
manhattanDistanceMeasure, 3.1, 2.1, true, 0.0, false);
Path path = new Path(output, "clusteredPoints/part-m-00000");
long count = HadoopUtil.countRecords(path, conf);
assertEquals("number of points", points.size(), count);
}
/**
* Story: User can produce final point clustering using a Hadoop map/reduce
* job and a EuclideanDistanceMeasure.
*/
@Test
public void testClusteringEuclideanMR() throws Exception {
List<VectorWritable> points = getPointsWritable();
Configuration conf = getConfiguration();
ClusteringTestUtils.writePointsToFile(points, true,
getTestTempFilePath("testdata/file1"), fs, conf);
ClusteringTestUtils.writePointsToFile(points, true,
getTestTempFilePath("testdata/file2"), fs, conf);
// now run the Job using the run() command. Others can use runJob().
Path output = getTestTempDirPath("output");
String[] args = { optKey(DefaultOptionCreator.INPUT_OPTION),
getTestTempDirPath("testdata").toString(),
optKey(DefaultOptionCreator.OUTPUT_OPTION), output.toString(),
optKey(DefaultOptionCreator.DISTANCE_MEASURE_OPTION),
EuclideanDistanceMeasure.class.getName(),
optKey(DefaultOptionCreator.T1_OPTION), "3.1",
optKey(DefaultOptionCreator.T2_OPTION), "2.1",
optKey(DefaultOptionCreator.CLUSTERING_OPTION),
optKey(DefaultOptionCreator.OVERWRITE_OPTION) };
ToolRunner.run(getConfiguration(), new CanopyDriver(), args);
Path path = new Path(output, "clusteredPoints/part-m-00000");
long count = HadoopUtil.countRecords(path, conf);
assertEquals("number of points", points.size(), count);
}
/**
* Story: User can produce final point clustering using a Hadoop map/reduce
* job and a EuclideanDistanceMeasure and outlier removal threshold.
*/
@Test
public void testClusteringEuclideanWithOutlierRemovalMR() throws Exception {
List<VectorWritable> points = getPointsWritable();
Configuration conf = getConfiguration();
ClusteringTestUtils.writePointsToFile(points, true,
getTestTempFilePath("testdata/file1"), fs, conf);
ClusteringTestUtils.writePointsToFile(points, true,
getTestTempFilePath("testdata/file2"), fs, conf);
// now run the Job using the run() command. Others can use runJob().
Path output = getTestTempDirPath("output");
String[] args = { optKey(DefaultOptionCreator.INPUT_OPTION),
getTestTempDirPath("testdata").toString(),
optKey(DefaultOptionCreator.OUTPUT_OPTION), output.toString(),
optKey(DefaultOptionCreator.DISTANCE_MEASURE_OPTION),
EuclideanDistanceMeasure.class.getName(),
optKey(DefaultOptionCreator.T1_OPTION), "3.1",
optKey(DefaultOptionCreator.T2_OPTION), "2.1",
optKey(DefaultOptionCreator.OUTLIER_THRESHOLD), "0.7",
optKey(DefaultOptionCreator.CLUSTERING_OPTION),
optKey(DefaultOptionCreator.OVERWRITE_OPTION) };
ToolRunner.run(getConfiguration(), new CanopyDriver(), args);
Path path = new Path(output, "clusteredPoints/part-m-00000");
long count = HadoopUtil.countRecords(path, conf);
int expectedPointsAfterOutlierRemoval = 8;
assertEquals("number of points", expectedPointsAfterOutlierRemoval, count);
}
/**
* Story: User can set T3 and T4 values to be used by the reducer for its T1
* and T2 thresholds
*/
@Test
public void testCanopyReducerT3T4Configuration() throws Exception {
CanopyReducer reducer = new CanopyReducer();
Configuration conf = getConfiguration();
conf.set(CanopyConfigKeys.DISTANCE_MEASURE_KEY,
"org.apache.mahout.common.distance.ManhattanDistanceMeasure");
conf.set(CanopyConfigKeys.T1_KEY, String.valueOf(3.1));
conf.set(CanopyConfigKeys.T2_KEY, String.valueOf(2.1));
conf.set(CanopyConfigKeys.T3_KEY, String.valueOf(1.1));
conf.set(CanopyConfigKeys.T4_KEY, String.valueOf(0.1));
conf.set(CanopyConfigKeys.CF_KEY, "0");
DummyRecordWriter<Text, ClusterWritable> writer = new DummyRecordWriter<Text, ClusterWritable>();
Reducer<Text, VectorWritable, Text, ClusterWritable>.Context context = DummyRecordWriter
.build(reducer, conf, writer, Text.class, VectorWritable.class);
reducer.setup(context);
assertEquals(1.1, reducer.getCanopyClusterer().getT1(), EPSILON);
assertEquals(0.1, reducer.getCanopyClusterer().getT2(), EPSILON);
}
/**
* Story: User can specify a clustering limit that prevents output of small
* clusters
*/
@Test
public void testCanopyMapperClusterFilter() throws Exception {
CanopyMapper mapper = new CanopyMapper();
Configuration conf = getConfiguration();
conf.set(CanopyConfigKeys.DISTANCE_MEASURE_KEY, manhattanDistanceMeasure
.getClass().getName());
conf.set(CanopyConfigKeys.T1_KEY, String.valueOf(3.1));
conf.set(CanopyConfigKeys.T2_KEY, String.valueOf(2.1));
conf.set(CanopyConfigKeys.CF_KEY, "3");
DummyRecordWriter<Text, VectorWritable> writer = new DummyRecordWriter<Text, VectorWritable>();
Mapper<WritableComparable<?>, VectorWritable, Text, VectorWritable>.Context context = DummyRecordWriter
.build(mapper, conf, writer);
mapper.setup(context);
List<VectorWritable> points = getPointsWritable();
// map the data
for (VectorWritable point : points) {
mapper.map(new Text(), point, context);
}
mapper.cleanup(context);
assertEquals("Number of map results", 1, writer.getData().size());
// now verify the output
List<VectorWritable> data = writer.getValue(new Text("centroid"));
assertEquals("Number of centroids", 2, data.size());
}
/**
* Story: User can specify a cluster filter that limits the minimum size of
* canopies produced by the reducer
*/
@Test
public void testCanopyReducerClusterFilter() throws Exception {
CanopyReducer reducer = new CanopyReducer();
Configuration conf = getConfiguration();
conf.set(CanopyConfigKeys.DISTANCE_MEASURE_KEY,
"org.apache.mahout.common.distance.ManhattanDistanceMeasure");
conf.set(CanopyConfigKeys.T1_KEY, String.valueOf(3.1));
conf.set(CanopyConfigKeys.T2_KEY, String.valueOf(2.1));
conf.set(CanopyConfigKeys.CF_KEY, "3");
DummyRecordWriter<Text, ClusterWritable> writer = new DummyRecordWriter<Text, ClusterWritable>();
Reducer<Text, VectorWritable, Text, ClusterWritable>.Context context = DummyRecordWriter
.build(reducer, conf, writer, Text.class, VectorWritable.class);
reducer.setup(context);
List<VectorWritable> points = getPointsWritable();
reducer.reduce(new Text("centroid"), points, context);
Set<Text> keys = writer.getKeys();
assertEquals("Number of centroids", 2, keys.size());
}
}