Increase the coverage of clustering algorithm unit testing

chen0040 · Jun 2, 2017 · deb0311 · deb0311
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diff --git a/README.md b/README.md
@@ -21,6 +21,49 @@ Add the following dependency to your POM file:
 </dependency>
 ```
 
+### Spatial Segmentation using Hierarchical Clustering
+
+The following sample code shows how to use hierarchical clustering to separate two clusters:
+
+```java
+DataQuery.DataFrameQueryBuilder schema = DataQuery.blank()
+      .newInput("c1")
+      .newInput("c2")
+      .newOutput("designed")
+      .end();
+
+Sampler.DataSampleBuilder negativeSampler = new Sampler()
+      .forColumn("c1").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
+      .forColumn("c2").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
+      .forColumn("designed").generate((name, index) -> 0.0)
+      .end();
+
+Sampler.DataSampleBuilder positiveSampler = new Sampler()
+      .forColumn("c1").generate((name, index) -> rand(-4, -2))
+      .forColumn("c2").generate((name, index) -> rand(-2, -4))
+      .forColumn("designed").generate((name, index) -> 1.0)
+      .end();
+
+DataFrame data = schema.build();
+
+data = negativeSampler.sample(data, 50);
+data = positiveSampler.sample(data, 50);
+
+System.out.println(data.head(10));
+
+HierarchicalClustering algorithm = new HierarchicalClustering();
+algorithm.setLinkage(linkageCriterion);
+algorithm.setClusterCount(2);
+
+DataFrame learnedData = algorithm.fitAndTransform(data);
+
+for(int i = 0; i < learnedData.rowCount(); ++i){
+ DataRow tuple = learnedData.row(i);
+ String clusterId = tuple.getCategoricalTargetCell("cluster");
+ System.out.println("learned: " + clusterId +"\tknown: "+tuple.target());
+}
+```
+
 ### Spatial Segmentation using DBSCAN
 
 The following sample code shows how to use DBSCAN to perform clustering:

diff --git a/src/main/java/com/github/chen0040/clustering/hierarchical/HierarchicalClustering.java b/src/main/java/com/github/chen0040/clustering/hierarchical/HierarchicalClustering.java
@@ -20,10 +20,10 @@
 @Setter
 public class HierarchicalClustering {
 
-    @Setter(AccessLevel.NONE)
     private int clusterCount = 1;
 
     private LinkageCriterion linkage = LinkageCriterion.AverageLinkage;
+
     private BiFunction<DataRow, DataRow, Double> distanceMeasure;
 
 

diff --git a/...test/java/com/github/chen0040/clustering/hierarchical/HierarchicalClusteringUnitTest.java b/...test/java/com/github/chen0040/clustering/hierarchical/HierarchicalClusteringUnitTest.java
@@ -0,0 +1,101 @@
+package com.github.chen0040.clustering.hierarchical;
+
+
+import com.github.chen0040.clustering.density.DBSCAN;
+import com.github.chen0040.data.frame.DataFrame;
+import com.github.chen0040.data.frame.DataQuery;
+import com.github.chen0040.data.frame.DataRow;
+import com.github.chen0040.data.frame.Sampler;
+import org.testng.annotations.Test;
+
+import java.util.Random;
+
+import static org.testng.Assert.*;
+
+
+/**
+ * Created by xschen on 2/6/2017.
+ */
+public class HierarchicalClusteringUnitTest {
+   private static Random random = new Random();
+
+   public static double rand(){
+      return random.nextDouble();
+   }
+
+   public static double rand(double lower, double upper){
+      return rand() * (upper - lower) + lower;
+   }
+
+   public static double randn(){
+      double u1 = rand();
+      double u2 = rand();
+      double r = Math.sqrt(-2.0 * Math.log(u1));
+      double theta = 2.0 * Math.PI * u2;
+      return r * Math.sin(theta);
+   }
+
+   @Test
+   public void test_average_linkage() {
+      testSimple(HierarchicalClustering.LinkageCriterion.AverageLinkage);
+   }
+
+   @Test
+   public void test_centroid_linkage() {
+      testSimple(HierarchicalClustering.LinkageCriterion.CentroidLinkage);
+   }
+
+   @Test
+   public void test_complete_linkage() {
+      testSimple(HierarchicalClustering.LinkageCriterion.CompleteLinkage);
+   }
+
+   @Test
+   public void test_single_linkage() {
+      testSimple(HierarchicalClustering.LinkageCriterion.SingleLinkage);
+   }
+
+   // unit testing based on example from http://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html#
+   public void testSimple(HierarchicalClustering.LinkageCriterion linkageCriterion){
+
+
+      DataQuery.DataFrameQueryBuilder schema = DataQuery.blank()
+              .newInput("c1")
+              .newInput("c2")
+              .newOutput("designed")
+              .end();
+
+      Sampler.DataSampleBuilder negativeSampler = new Sampler()
+              .forColumn("c1").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
+              .forColumn("c2").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
+              .forColumn("designed").generate((name, index) -> 0.0)
+              .end();
+
+      Sampler.DataSampleBuilder positiveSampler = new Sampler()
+              .forColumn("c1").generate((name, index) -> rand(-4, -2))
+              .forColumn("c2").generate((name, index) -> rand(-2, -4))
+              .forColumn("designed").generate((name, index) -> 1.0)
+              .end();
+
+      DataFrame data = schema.build();
+
+      data = negativeSampler.sample(data, 50);
+      data = positiveSampler.sample(data, 50);
+
+      System.out.println(data.head(10));
+
+      HierarchicalClustering algorithm = new HierarchicalClustering();
+      algorithm.setLinkage(linkageCriterion);
+      algorithm.setClusterCount(2);
+
+      DataFrame learnedData = algorithm.fitAndTransform(data);
+
+      for(int i = 0; i < learnedData.rowCount(); ++i){
+         DataRow tuple = learnedData.row(i);
+         String clusterId = tuple.getCategoricalTargetCell("cluster");
+         System.out.println("learned: " + clusterId +"\tknown: "+tuple.target());
+      }
+
+
+   }
+}