Add a test for the composite log rank splitting rule.

Add some debug toString capabilities on Nodes and Trees

src/main/java/ca/joeltherrien/randomforest/covariates/NumericCovariate.java

@@ -31,8 +31,7 @@ public final class NumericCovariate implements Covariate<Double>{
         // for this implementation we need to shuffle the data
         final List<Value<Double>> shuffledData;
         if(number >= data.size()){
-            shuffledData = new ArrayList<>(data);
+            shuffledData = data;
-            Collections.shuffle(shuffledData, random);
         }
         else{ // only need the top number entries
             shuffledData = new ArrayList<>(number);

src/main/java/ca/joeltherrien/randomforest/tree/SplitNode.java

@@ -3,8 +3,10 @@ package ca.joeltherrien.randomforest.tree;
 import ca.joeltherrien.randomforest.CovariateRow;
 import ca.joeltherrien.randomforest.covariates.Covariate;
 import lombok.Builder;
+import lombok.ToString;
 
 @Builder
+@ToString
 public class SplitNode<Y> implements Node<Y> {
 
     private final Node<Y> leftHand;

src/main/java/ca/joeltherrien/randomforest/tree/TerminalNode.java

@@ -2,8 +2,10 @@ package ca.joeltherrien.randomforest.tree;
 
 import ca.joeltherrien.randomforest.CovariateRow;
 import lombok.RequiredArgsConstructor;
+import lombok.ToString;
 
 @RequiredArgsConstructor
+@ToString
 public class TerminalNode<Y> implements Node<Y> {
 
     private final Y responseValue;
@@ -14,6 +16,4 @@ public class TerminalNode<Y> implements Node<Y> {
     }
 
 
-
-
 }

src/main/java/ca/joeltherrien/randomforest/tree/Tree.java

@@ -29,4 +29,8 @@ public class Tree<Y> implements Node<Y> {
         return Arrays.binarySearch(this.bootstrapRowIds, id) >= 0;
     }
 
+    @Override
+    public String toString(){
+        return rootNode.toString();
+    }
 }

src/main/java/ca/joeltherrien/randomforest/tree/TreeTrainer.java

@@ -3,9 +3,7 @@ package ca.joeltherrien.randomforest.tree;
 import ca.joeltherrien.randomforest.Row;
 import ca.joeltherrien.randomforest.Settings;
 import ca.joeltherrien.randomforest.covariates.Covariate;
-import lombok.AccessLevel;
+import lombok.*;
-import lombok.AllArgsConstructor;
-import lombok.Builder;
 
 import java.util.*;
 import java.util.concurrent.ThreadLocalRandom;
@@ -108,6 +106,7 @@ public class TreeTrainer<Y, O> {
     }
 
     private SplitRuleAndSplit findBestSplitRule(List<Row<Y>> data, List<Covariate> covariatesToTry){
+        final Random random = ThreadLocalRandom.current();
         SplitRuleAndSplit bestSplitRuleAndSplit = new SplitRuleAndSplit();
         double bestSplitScore = 0.0;
         boolean first = true;
@@ -134,7 +133,6 @@ public class TreeTrainer<Y, O> {
 
                 final double probabilityLeftHand = (double) possibleSplit.leftHand.size() / (double) (possibleSplit.leftHand.size() + possibleSplit.rightHand.size());
 
-                final Random random = ThreadLocalRandom.current();
                 for(final Row<Y> missingValueRow : possibleSplit.naHand){
                     final boolean randomDecision = random.nextDouble() <= probabilityLeftHand;
                     if(randomDecision){

src/test/java/ca/joeltherrien/randomforest/competingrisk/TestLogRankMultipleGroupDifferentiator.java

@@ -0,0 +1,84 @@
+package ca.joeltherrien.randomforest.competingrisk;
+
+import ca.joeltherrien.randomforest.DataLoader;
+import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.Settings;
+import ca.joeltherrien.randomforest.covariates.Covariate;
+import ca.joeltherrien.randomforest.covariates.NumericCovariateSettings;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
+import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.LogRankMultipleGroupDifferentiator;
+import ca.joeltherrien.randomforest.utils.Utils;
+import com.fasterxml.jackson.databind.node.JsonNodeFactory;
+import com.fasterxml.jackson.databind.node.ObjectNode;
+import com.fasterxml.jackson.databind.node.TextNode;
+import lombok.AllArgsConstructor;
+import org.junit.jupiter.api.Test;
+
+import java.io.IOException;
+import java.util.List;
+import java.util.stream.Collectors;
+
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class TestLogRankMultipleGroupDifferentiator {
+
+    public static Data<CompetingRiskResponse> loadData(String filename) throws IOException {
+        final ObjectNode yVarSettings = new ObjectNode(JsonNodeFactory.instance);
+        yVarSettings.set("type", new TextNode("CompetingRiskResponse"));
+        yVarSettings.set("delta", new TextNode("delta"));
+        yVarSettings.set("u", new TextNode("u"));
+
+        final Settings settings = Settings.builder()
+                .trainingDataLocation(filename)
+                .covariateSettings(
+                        Utils.easyList(new NumericCovariateSettings("x2"))
+                )
+                .yVarSettings(yVarSettings)
+                .build();
+
+        final List<Covariate> covariates = settings.getCovariates();
+
+        final DataLoader.ResponseLoader loader = settings.getResponseLoader();
+        final List<Row<CompetingRiskResponse>> rows = DataLoader.loadData(covariates, loader, settings.getTrainingDataLocation());
+
+        return new Data<>(rows, covariates);
+    }
+
+    @Test
+    public void testSplitRule() throws IOException {
+        final LogRankMultipleGroupDifferentiator groupDifferentiator = new LogRankMultipleGroupDifferentiator(new int[]{1,2});
+
+        final List<Row<CompetingRiskResponse>> data = loadData("src/test/resources/test_split_data.csv").getRows();
+
+        final List<Row<CompetingRiskResponse>> group1Bad = data.subList(0, 196);
+        final List<Row<CompetingRiskResponse>> group2Bad = data.subList(196, data.size());
+
+        final double scoreBad = groupDifferentiator.differentiate(
+                group1Bad.stream().map(Row::getResponse).collect(Collectors.toList()),
+                group2Bad.stream().map(Row::getResponse).collect(Collectors.toList()));
+
+        final List<Row<CompetingRiskResponse>> group1Good = data.subList(0, 199);
+        final List<Row<CompetingRiskResponse>> group2Good= data.subList(199, data.size());
+
+        final double scoreGood = groupDifferentiator.differentiate(
+                group1Good.stream().map(Row::getResponse).collect(Collectors.toList()),
+                group2Good.stream().map(Row::getResponse).collect(Collectors.toList()));
+
+        // expected results calculated manually using survival::survdiff in R; see issue #10 in Gitea
+        closeEnough(71.41135, scoreBad, 0.00001);
+        closeEnough(71.5354, scoreGood, 0.00001);
+
+    }
+
+    private void closeEnough(double expected, double actual, double margin){
+        assertTrue(Math.abs(expected - actual) < margin, "Expected " + expected + " but saw " + actual);
+    }
+
+    @lombok.Data
+    @AllArgsConstructor
+    public static class Data<Y> {
+        private List<Row<Y>> rows;
+        private List<Covariate> covariateList;
+    }
+
+}

src/test/java/ca/joeltherrien/randomforest/competingrisk/TestLogRankSingleGroupDifferentiator.java

@@ -1,12 +1,17 @@
 package ca.joeltherrien.randomforest.competingrisk;
 
+import ca.joeltherrien.randomforest.Row;
 import ca.joeltherrien.randomforest.responses.competingrisk.*;
+import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.LogRankMultipleGroupDifferentiator;
 import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.LogRankSingleGroupDifferentiator;
 import org.junit.jupiter.api.Test;
 
+import java.io.IOException;
 import java.util.ArrayList;
 import java.util.List;
+import java.util.stream.Collectors;
 
+import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertTrue;
 
 public class TestLogRankSingleGroupDifferentiator {
@@ -55,6 +60,32 @@ public class TestLogRankSingleGroupDifferentiator {
 
     }
 
+    @Test
+    public void testCorrectSplit() throws IOException {
+        final LogRankSingleGroupDifferentiator groupDifferentiator =
+                new LogRankSingleGroupDifferentiator(1, new int[]{1,2});
+
+        final List<Row<CompetingRiskResponse>> data = TestLogRankMultipleGroupDifferentiator.
+                loadData("src/test/resources/test_single_split.csv").getRows();
+
+        final List<Row<CompetingRiskResponse>> group1Good = data.subList(0, 221);
+        final List<Row<CompetingRiskResponse>> group2Good = data.subList(221, data.size());
+
+        final double scoreGood = groupDifferentiator.differentiate(
+                group1Good.stream().map(Row::getResponse).collect(Collectors.toList()),
+                group2Good.stream().map(Row::getResponse).collect(Collectors.toList()));
+
+        final List<Row<CompetingRiskResponse>> group1Bad = data.subList(0, 222);
+        final List<Row<CompetingRiskResponse>> group2Bad = data.subList(222, data.size());
+
+        final double scoreBad = groupDifferentiator.differentiate(
+                group1Bad.stream().map(Row::getResponse).collect(Collectors.toList()),
+                group2Bad.stream().map(Row::getResponse).collect(Collectors.toList()));
+
+        // Apparently not all groups are unique when splitting
+        assertEquals(scoreGood, scoreBad);
+    }
+
     private void closeEnough(double expected, double actual, double margin){
         assertTrue(Math.abs(expected - actual) < margin, "Expected " + expected + " but saw " + actual);
     }

src/test/resources/test_single_split.csv

@@ -0,0 +1,437 @@
+"T","J","x1","x2","x3","set","C","u","delta"
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