pom.xml

@@ -24,6 +24,19 @@
         </dependency>
 
 
+        <dependency>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter-api</artifactId>
+            <version>5.2.0</version>
+            <scope>test</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter-engine</artifactId>
+            <version>5.2.0</version>
+            <scope>test</scope>
+        </dependency>
 
     </dependencies>
 

src/main/java/ca/joeltherrien/randomforest/FactorCovariate.java

@@ -26,7 +26,7 @@ public final class FactorCovariate implements Covariate<String>{
         for(int i=0; i<levels.size()-1; i++){
             numberOfPossiblePairingsTemp *= 2;
         }
-        this.numberOfPossiblePairings = numberOfPossiblePairingsTemp;
+        this.numberOfPossiblePairings = numberOfPossiblePairingsTemp-1;
 
     }
 
@@ -37,7 +37,7 @@ public final class FactorCovariate implements Covariate<String>{
     }
 
     @Override
-    public Collection<FactorSplitRule> generateSplitRules(List<Value<String>> data, int number) {
+    public Set<FactorSplitRule> generateSplitRules(List<Value<String>> data, int number) {
         final Set<FactorSplitRule> splitRules = new HashSet<>();
 
         // This is to ensure we don't get stuck in an infinite loop for small factors

src/test/java/ca/joeltherrien/randomforest/covariates/FactorCovariateTest.java

@@ -0,0 +1,65 @@
+package ca.joeltherrien.randomforest.covariates;
+
+
+import ca.joeltherrien.randomforest.FactorCovariate;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.function.Executable;
+
+import java.util.Collection;
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+public class FactorCovariateTest {
+
+    @Test
+    void verifyEqualLevels() {
+        final FactorCovariate petCovariate = createTestCovariate();
+
+        final FactorCovariate.FactorValue dog1 = petCovariate.createValue("DOG");
+        final FactorCovariate.FactorValue dog2 = petCovariate.createValue("DO" + "G");
+
+        assertSame(dog1, dog2);
+
+        final FactorCovariate.FactorValue cat1 = petCovariate.createValue("CAT");
+        final FactorCovariate.FactorValue cat2 = petCovariate.createValue("CA" + "T");
+
+        assertSame(cat1, cat2);
+
+        final FactorCovariate.FactorValue mouse1 = petCovariate.createValue("MOUSE");
+        final FactorCovariate.FactorValue mouse2 = petCovariate.createValue("MOUS" + "E");
+
+        assertSame(mouse1, mouse2);
+
+
+    }
+
+    @Test
+    void verifyBadLevelException(){
+        final FactorCovariate petCovariate = createTestCovariate();
+        final Executable badCode = () -> petCovariate.createValue("vulcan");
+
+        assertThrows(IllegalArgumentException.class, badCode, "vulcan is not a level in FactorCovariate pet");
+    }
+
+    @Test
+    void testAllSubsets(){
+        final FactorCovariate petCovariate = createTestCovariate();
+
+        final Collection<FactorCovariate.FactorSplitRule> splitRules = petCovariate.generateSplitRules(null, 100);
+
+        assertEquals(splitRules.size(), 3);
+
+        // TODO verify the contents of the split rules
+
+    }
+
+
+    private FactorCovariate createTestCovariate(){
+        final List<String> levels = List.of("DOG", "CAT", "MOUSE");
+
+        return new FactorCovariate("pet", levels);
+    }
+
+
+}

src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTreeFactor.java

@@ -0,0 +1,190 @@
+package ca.joeltherrien.randomforest.workshop;
+
+
+import ca.joeltherrien.randomforest.*;
+import ca.joeltherrien.randomforest.regression.MeanResponseCombiner;
+import ca.joeltherrien.randomforest.regression.WeightedVarianceGroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.Node;
+import ca.joeltherrien.randomforest.tree.TreeTrainer;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+import java.util.stream.Collectors;
+import java.util.stream.DoubleStream;
+import java.util.stream.Stream;
+
+public class TrainSingleTreeFactor {
+
+	public static void main(String[] args) {
+		System.out.println("Hello world!");
+
+        final Random random = new Random(123);
+
+        final int n = 10000;
+        final List<Row<Double>> trainingSet = new ArrayList<>(n);
+
+        final Covariate<Double> x1Covariate = new NumericCovariate("x1");
+        final Covariate<Double> x2Covariate = new NumericCovariate("x2");
+        final FactorCovariate x3Covariate = new FactorCovariate("x3", List.of("cat", "dog", "mouse"));
+
+        final List<Covariate.Value<Double>> x1List = DoubleStream
+                .generate(() -> random.nextDouble()*10.0)
+                .limit(n)
+                .mapToObj(x1 -> x1Covariate.createValue(x1))
+                .collect(Collectors.toList());
+
+        final List<Covariate.Value<Double>> x2List = DoubleStream
+                .generate(() -> random.nextDouble()*10.0)
+                .limit(n)
+                .mapToObj(x2 -> x1Covariate.createValue(x2))
+                .collect(Collectors.toList());
+
+        final List<Covariate.Value<String>> x3List = DoubleStream
+                .generate(() -> random.nextDouble())
+                .limit(n)
+                .mapToObj(db -> {
+                    if(db < 0.333){
+                        return "cat";
+                    }
+                    else if(db < 0.5){
+                        return "mouse";
+                    }
+                    else{
+                        return "dog";
+                    }
+                })
+                .map(str -> x3Covariate.createValue(str))
+                .collect(Collectors.toList());
+
+
+        for(int i=0; i<n; i++){
+            final Covariate.Value<Double> x1 = x1List.get(i);
+            final Covariate.Value<Double> x2 = x2List.get(i);
+            final Covariate.Value<String> x3 = x3List.get(i);
+
+            trainingSet.add(generateRow(x1, x2, x3, i));
+        }
+
+        final TreeTrainer<Double> treeTrainer = TreeTrainer.<Double>builder()
+                .groupDifferentiator(new WeightedVarianceGroupDifferentiator())
+                .responseCombiner(new MeanResponseCombiner())
+                .maxNodeDepth(30)
+                .nodeSize(5)
+                .numberOfSplits(5)
+                .build();
+
+        final List<Covariate> covariateNames = List.of(x1Covariate, x2Covariate);
+
+        final long startTime = System.currentTimeMillis();
+        final Node<Double> baseNode = treeTrainer.growTree(trainingSet, covariateNames);
+        final long endTime = System.currentTimeMillis();
+
+        System.out.println(((double)(endTime - startTime))/1000.0);
+
+
+
+        final Covariate.Value<String> cat = x3Covariate.createValue("cat");
+        final Covariate.Value<String> dog = x3Covariate.createValue("dog");
+        final Covariate.Value<String> mouse = x3Covariate.createValue("mouse");
+
+
+        final List<CovariateRow> testSet = new ArrayList<>();
+        testSet.add(generateCovariateRow(x1Covariate.createValue(9.0), x2Covariate.createValue(2.0), cat, 1)); // expect 1
+        testSet.add(generateCovariateRow(x1Covariate.createValue(5.0), x2Covariate.createValue(2.0), dog, 5));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(2.0), x2Covariate.createValue(2.0), cat, 3));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(9.0), x2Covariate.createValue(5.0), dog, 0));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(6.0), x2Covariate.createValue(5.0), cat, 8));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(3.0), x2Covariate.createValue(5.0), dog, 10));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(1.0), x2Covariate.createValue(5.0), cat, 3));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(7.0), x2Covariate.createValue(9.0), dog, 2));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(1.0), x2Covariate.createValue(9.0), cat, 4));
+
+
+        testSet.add(generateCovariateRow(x1Covariate.createValue(3.0), x2Covariate.createValue(9.0), mouse, 0));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(7.0), x2Covariate.createValue(9.0), mouse, 5));
+
+        for(final CovariateRow testCase : testSet){
+            System.out.println(testCase);
+            System.out.println(baseNode.evaluate(testCase));
+            System.out.println();
+
+
+        }
+
+
+
+
+
+    }
+
+	public static Row<Double> generateRow(Covariate.Value<Double> x1, Covariate.Value<Double> x2, Covariate.Value<String> x3, int id){
+	    double y = generateResponse(x1.getValue(), x2.getValue(), x3.getValue());
+
+	    final Map<String, Covariate.Value> map = Map.of("x1", x1, "x2", x2);
+
+        return new Row<>(map, id, y);
+
+    }
+
+
+    public static CovariateRow generateCovariateRow(Covariate.Value x1, Covariate.Value x2, Covariate.Value x3, int id){
+        final Map<String, Covariate.Value> map = Map.of("x1", x1, "x2", x2, "x3", x3);
+
+        return new CovariateRow(map, id);
+
+    }
+
+
+	public static double generateResponse(double x1, double x2, String x3){
+
+	    if(x3.equalsIgnoreCase("mouse")){
+	        if(x1 <= 5){
+	            return 0;
+            }
+            else{
+	            return 5;
+            }
+        }
+
+        // cat & dog below
+
+	    if(x2 <= 3){
+	        if(x1 <= 3){
+	            return 3;
+            }
+            else if(x1 <= 7){
+	            return 5;
+            }
+            else{
+	            return 1;
+            }
+        }
+        else if(x1 >= 5){
+	        if(x2 > 6){
+	            return 2;
+            }
+            else if(x1 >= 8){
+	            return 0;
+            }
+            else{
+	            return 8;
+            }
+        }
+        else if(x1 <= 2){
+	        if(x2 >= 7){
+	            return 4;
+            }
+            else{
+	            return 3;
+            }
+        }
+        else{
+	        return 10;
+        }
+
+
+    }
+
+}