Fix bug where NAs cause crash

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

@@ -20,6 +20,7 @@ import ca.joeltherrien.randomforest.Row;
 import ca.joeltherrien.randomforest.covariates.Covariate;
 import lombok.Data;
 
+import java.util.ArrayList;
 import java.util.List;
 
 /**
@@ -36,4 +37,11 @@ public final class Split<Y, V> {
 	public final List<Row<Y>> rightHand;
 	public final List<Row<Y>> naHand;
 
+	public Split<Y, V> modifiableClone(){
+		return new Split<>(splitRule,
+				new ArrayList<>(leftHand),
+				new ArrayList<>(rightHand),
+				new ArrayList<>(naHand));
+	}
+
 }

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

@@ -72,7 +72,7 @@ public class TreeTrainer<Y, O> {
         // See https://kogalur.github.io/randomForestSRC/theory.html#section3.1 (near bottom)
         if(data.size() >= 2*nodeSize && depth < maxNodeDepth && !nodeIsPure(data)){
             final List<Covariate> covariatesToTry = selectCovariates(this.mtry, random);
-            final Split<Y,?> bestSplit = findBestSplitRule(data, covariatesToTry, random);
+            Split<Y,?> bestSplit = findBestSplitRule(data, covariatesToTry, random);
 
 
             if(bestSplit == null){
@@ -92,8 +92,12 @@ public class TreeTrainer<Y, O> {
 
             // Assign missing values to the split if necessary
             if(bestSplit.getSplitRule().getParent().hasNAs()){
+                bestSplit = bestSplit.modifiableClone(); // the lists in bestSplit are otherwise usually unmodifiable lists
+
                 for(Row<Y> row : data) {
-                    if(row.getCovariateValue(bestSplit.getSplitRule().getParent()).isNA()) {
+                    final Covariate<?> covariate = bestSplit.getSplitRule().getParent();
+
+                    if(row.getCovariateValue(covariate).isNA()) {
                         final boolean randomDecision = random.nextDouble() <= probabilityLeftHand;
 
                         if(randomDecision){

src/test/java/ca/joeltherrien/randomforest/nas/TestNAs.java

@@ -0,0 +1,61 @@
+package ca.joeltherrien.randomforest.nas;
+
+import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.covariates.Covariate;
+import ca.joeltherrien.randomforest.covariates.numeric.NumericCovariate;
+import ca.joeltherrien.randomforest.responses.regression.MeanResponseCombiner;
+import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceGroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.TreeTrainer;
+import ca.joeltherrien.randomforest.utils.Utils;
+import org.junit.jupiter.api.Test;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.Random;
+
+public class TestNAs {
+
+    private List<Row<Double>> generateData(List<Covariate> covariates){
+        final List<Row<Double>> dataList = new ArrayList<>();
+
+
+        // We must include an NA for one of the values
+        dataList.add(Row.createSimple(Utils.easyMap("x", "NA"), covariates, 1, 5.0));
+        dataList.add(Row.createSimple(Utils.easyMap("x", "1"), covariates, 1, 6.0));
+        dataList.add(Row.createSimple(Utils.easyMap("x", "2"), covariates, 1, 5.5));
+        dataList.add(Row.createSimple(Utils.easyMap("x", "7"), covariates, 1, 0.0));
+        dataList.add(Row.createSimple(Utils.easyMap("x", "8"), covariates, 1, 1.0));
+        dataList.add(Row.createSimple(Utils.easyMap("x", "8.4"), covariates, 1, 1.0));
+
+
+        return dataList;
+    }
+
+    @Test
+    public void testException(){
+        // There was a bug with NAs where when we tried to randomly assign NAs during a split to the best split produced by NumericSplitRuleUpdater,
+        // but NumericSplitRuleUpdater had unmodifiable lists when creating the split.
+        // This bug verifies that this no longer causes a crash
+
+        final List<Covariate> covariates = Collections.singletonList(new NumericCovariate("x", 0));
+        final List<Row<Double>> dataset = generateData(covariates);
+
+        final TreeTrainer<Double, Double> treeTrainer = TreeTrainer.<Double, Double>builder()
+                .checkNodePurity(false)
+                .covariates(covariates)
+                .numberOfSplits(0)
+                .nodeSize(1)
+                .maxNodeDepth(1000)
+                .groupDifferentiator(new WeightedVarianceGroupDifferentiator())
+                .responseCombiner(new MeanResponseCombiner())
+                .build();
+
+        treeTrainer.growTree(dataset, new Random(123));
+
+        // As long as no exception occurs, we passed
+
+
+    }
+
+}