Author: jeastman
Date: Mon Apr 18 04:20:36 2011
New Revision: 1094223
URL: http://svn.apache.org/viewvc?rev=1094223&view=rev
Log:
MAHOUT-479: Missed the new unit test
Added:
mahout/trunk/core/src/test/java/org/apache/mahout/clustering/TestClusterClassifier.java
(with props)
Added:
mahout/trunk/core/src/test/java/org/apache/mahout/clustering/TestClusterClassifier.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/test/java/org/apache/mahout/clustering/TestClusterClassifier.java?rev=1094223&view=auto
==============================================================================
---
mahout/trunk/core/src/test/java/org/apache/mahout/clustering/TestClusterClassifier.java
(added)
+++
mahout/trunk/core/src/test/java/org/apache/mahout/clustering/TestClusterClassifier.java
Mon Apr 18 04:20:36 2011
@@ -0,0 +1,254 @@
+/**
+ * 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;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+import org.apache.commons.lang.NotImplementedException;
+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.mahout.clustering.canopy.Canopy;
+import org.apache.mahout.clustering.dirichlet.models.GaussianCluster;
+import org.apache.mahout.clustering.fuzzykmeans.SoftCluster;
+import org.apache.mahout.clustering.kmeans.TestKmeansClustering;
+import org.apache.mahout.clustering.meanshift.MeanShiftCanopy;
+import org.apache.mahout.common.MahoutTestCase;
+import org.apache.mahout.common.distance.DistanceMeasure;
+import org.apache.mahout.common.distance.ManhattanDistanceMeasure;
+import org.apache.mahout.math.DenseVector;
+import org.apache.mahout.math.Vector;
+import org.junit.Test;
+
+public final class TestClusterClassifier extends MahoutTestCase {
+
+ private ClusterClassifier newDMClassifier() {
+ List<Cluster> models = new ArrayList<Cluster>();
+ DistanceMeasure measure = new ManhattanDistanceMeasure();
+ models.add(new DistanceMeasureCluster(new DenseVector(2).assign(1), 0,
+ measure));
+ models.add(new DistanceMeasureCluster(new DenseVector(2), 1, measure));
+ models.add(new DistanceMeasureCluster(new DenseVector(2).assign(-1), 2,
+ measure));
+ ClusterClassifier classifier = new ClusterClassifier(models);
+ return classifier;
+ }
+
+ private ClusterClassifier newClusterClassifier() {
+ List<Cluster> models = new ArrayList<Cluster>();
+ DistanceMeasure measure = new ManhattanDistanceMeasure();
+ models.add(new org.apache.mahout.clustering.kmeans.Cluster(new DenseVector(
+ 2).assign(1), 0, measure));
+ models.add(new org.apache.mahout.clustering.kmeans.Cluster(new DenseVector(
+ 2), 1, measure));
+ models.add(new org.apache.mahout.clustering.kmeans.Cluster(new DenseVector(
+ 2).assign(-1), 2, measure));
+ ClusterClassifier classifier = new ClusterClassifier(models);
+ return classifier;
+ }
+
+ private ClusterClassifier newSoftClusterClassifier() {
+ List<Cluster> models = new ArrayList<Cluster>();
+ DistanceMeasure measure = new ManhattanDistanceMeasure();
+ models.add(new SoftCluster(new DenseVector(2).assign(1), 0, measure));
+ models.add(new SoftCluster(new DenseVector(2), 1, measure));
+ models.add(new SoftCluster(new DenseVector(2).assign(-1), 2, measure));
+ ClusterClassifier classifier = new ClusterClassifier(models);
+ return classifier;
+ }
+
+ private ClusterClassifier newGaussianClassifier() {
+ List<Cluster> models = new ArrayList<Cluster>();
+ models.add(new GaussianCluster(new DenseVector(2).assign(1),
+ new DenseVector(2).assign(1), 0));
+ models.add(new GaussianCluster(new DenseVector(2), new DenseVector(2)
+ .assign(1), 1));
+ models.add(new GaussianCluster(new DenseVector(2).assign(-1),
+ new DenseVector(2).assign(1), 2));
+ ClusterClassifier classifier = new ClusterClassifier(models);
+ return classifier;
+ }
+
+ private ClusterClassifier writeAndRead(ClusterClassifier classifier)
+ throws IOException {
+ Configuration config = new Configuration();
+ Path path = new Path(getTestTempDirPath(), "output");
+ FileSystem fs = FileSystem.get(path.toUri(), config);
+ SequenceFile.Writer writer = new SequenceFile.Writer(fs, config, path,
+ Text.class, ClusterClassifier.class);
+ Writable key = new Text("test");
+ writer.append(key, classifier);
+ writer.close();
+
+ SequenceFile.Reader reader = new SequenceFile.Reader(fs, path, config);
+ key = new Text();
+ ClusterClassifier classifierOut = new ClusterClassifier();
+ reader.next(key, classifierOut);
+ reader.close();
+ return classifierOut;
+ }
+
+ @Test
+ public void testDMClusterClassification() {
+ ClusterClassifier classifier = newDMClassifier();
+ Vector pdf = classifier.classify(new DenseVector(2));
+ assertEquals("[0,0]", "[0.107, 0.787, 0.107]",
+ AbstractCluster.formatVector(pdf, null));
+ pdf = classifier.classify(new DenseVector(2).assign(2));
+ assertEquals("[2,2]", "[0.867, 0.117, 0.016]",
+ AbstractCluster.formatVector(pdf, null));
+ }
+
+ @Test
+ public void testCanopyClassification() {
+ List<Cluster> models = new ArrayList<Cluster>();
+ DistanceMeasure measure = new ManhattanDistanceMeasure();
+ models.add(new Canopy(new DenseVector(2).assign(1), 0, measure));
+ models.add(new Canopy(new DenseVector(2), 1, measure));
+ models.add(new Canopy(new DenseVector(2).assign(-1), 2, measure));
+ ClusterClassifier classifier = new ClusterClassifier(models);
+ Vector pdf = classifier.classify(new DenseVector(2));
+ assertEquals("[0,0]", "[0.107, 0.787, 0.107]",
+ AbstractCluster.formatVector(pdf, null));
+ pdf = classifier.classify(new DenseVector(2).assign(2));
+ assertEquals("[2,2]", "[0.867, 0.117, 0.016]",
+ AbstractCluster.formatVector(pdf, null));
+ }
+
+ @Test
+ public void testClusterClassification() {
+ ClusterClassifier classifier = newClusterClassifier();
+ Vector pdf = classifier.classify(new DenseVector(2));
+ assertEquals("[0,0]", "[0.107, 0.787, 0.107]",
+ AbstractCluster.formatVector(pdf, null));
+ pdf = classifier.classify(new DenseVector(2).assign(2));
+ assertEquals("[2,2]", "[0.867, 0.117, 0.016]",
+ AbstractCluster.formatVector(pdf, null));
+ }
+
+ @Test
+ public void testMSCanopyClassification() {
+ List<Cluster> models = new ArrayList<Cluster>();
+ DistanceMeasure measure = new ManhattanDistanceMeasure();
+ models.add(new MeanShiftCanopy(new DenseVector(2).assign(1), 0, measure));
+ models.add(new MeanShiftCanopy(new DenseVector(2), 1, measure));
+ models.add(new MeanShiftCanopy(new DenseVector(2).assign(-1), 2, measure));
+ ClusterClassifier classifier = new ClusterClassifier(models);
+ try {
+ classifier.classify(new DenseVector(2));
+ fail("Expected NotImplementedException");
+ } catch (NotImplementedException e) {}
+ }
+
+ @Test
+ public void testSoftClusterClassification() {
+ ClusterClassifier classifier = newSoftClusterClassifier();
+ Vector pdf = classifier.classify(new DenseVector(2));
+ assertEquals("[0,0]", "[0.000, 1.000, 0.000]",
+ AbstractCluster.formatVector(pdf, null));
+ pdf = classifier.classify(new DenseVector(2).assign(2));
+ assertEquals("[2,2]", "[0.735, 0.184, 0.082]",
+ AbstractCluster.formatVector(pdf, null));
+ }
+
+ @Test
+ public void testGaussianClusterClassification() {
+ ClusterClassifier classifier = newGaussianClassifier();
+ Vector pdf = classifier.classify(new DenseVector(2));
+ assertEquals("[0,0]", "[0.212, 0.576, 0.212]",
+ AbstractCluster.formatVector(pdf, null));
+ pdf = classifier.classify(new DenseVector(2).assign(2));
+ assertEquals("[2,2]", "[0.952, 0.047, 0.000]",
+ AbstractCluster.formatVector(pdf, null));
+ }
+
+ @Test
+ public void testDMClassifierSerialization() throws Exception {
+ ClusterClassifier classifier = newDMClassifier();
+ ClusterClassifier classifierOut = writeAndRead(classifier);
+ assertEquals(classifier.getModels().size(), classifierOut.getModels()
+ .size());
+ assertEquals(classifier.getModels().get(0).getClass().getName(),
+ classifierOut.getModels().get(0).getClass().getName());
+ }
+
+ @Test
+ public void testClusterClassifierSerialization() throws Exception {
+ ClusterClassifier classifier = newClusterClassifier();
+ ClusterClassifier classifierOut = writeAndRead(classifier);
+ assertEquals(classifier.getModels().size(), classifierOut.getModels()
+ .size());
+ assertEquals(classifier.getModels().get(0).getClass().getName(),
+ classifierOut.getModels().get(0).getClass().getName());
+ }
+
+ @Test
+ public void testSoftClusterClassifierSerialization() throws Exception {
+ ClusterClassifier classifier = newSoftClusterClassifier();
+ ClusterClassifier classifierOut = writeAndRead(classifier);
+ assertEquals(classifier.getModels().size(), classifierOut.getModels()
+ .size());
+ assertEquals(classifier.getModels().get(0).getClass().getName(),
+ classifierOut.getModels().get(0).getClass().getName());
+ }
+
+ @Test
+ public void testGaussianClassifierSerialization() throws Exception {
+ ClusterClassifier classifier = newGaussianClassifier();
+ ClusterClassifier classifierOut = writeAndRead(classifier);
+ assertEquals(classifier.getModels().size(), classifierOut.getModels()
+ .size());
+ assertEquals(classifier.getModels().get(0).getClass().getName(),
+ classifierOut.getModels().get(0).getClass().getName());
+ }
+
+ @Test
+ public void testClusterIteratorKMeans() {
+ List<Vector> data = TestKmeansClustering
+ .getPoints(TestKmeansClustering.REFERENCE);
+ ClusteringPolicy policy = new KMeansClusteringPolicy();
+ ClusterClassifier prior = newClusterClassifier();
+ ClusterIterator iterator = new ClusterIterator(policy);
+ ClusterClassifier posterior = iterator.iterate(data, prior, 5);
+ assertEquals(3, posterior.getModels().size());
+ for (Cluster cluster : posterior.getModels()) {
+ System.out
+ .println(cluster.asFormatString(null));
+ }
+ }
+
+ @Test
+ public void testClusterIteratorDirichlet() {
+ List<Vector> data = TestKmeansClustering
+ .getPoints(TestKmeansClustering.REFERENCE);
+ ClusteringPolicy policy = new DirichletClusteringPolicy(3, 1);
+ ClusterClassifier prior = newClusterClassifier();
+ ClusterIterator iterator = new ClusterIterator(policy);
+ ClusterClassifier posterior = iterator.iterate(data, prior, 5);
+ assertEquals(3, posterior.getModels().size());
+ for (Cluster cluster : posterior.getModels()) {
+ System.out
+ .println(cluster.asFormatString(null));
+ }
+ }
+}
Propchange:
mahout/trunk/core/src/test/java/org/apache/mahout/clustering/TestClusterClassifier.java
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svn:mime-type = text/plain
