Covariates track if they have any NA values, and skip NA handling code if possible

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

@@ -17,6 +17,8 @@ public final class BooleanCovariate implements Covariate<Boolean> {
     @Getter
     private final int index;
 
+    private boolean hasNAs = false;
+
     private final BooleanSplitRule splitRule = new BooleanSplitRule(); // there's only one possible rule for BooleanCovariates.
 
     @Override
@@ -32,6 +34,7 @@ public final class BooleanCovariate implements Covariate<Boolean> {
     @Override
     public Value<Boolean> createValue(String value) {
         if(value == null || value.equalsIgnoreCase("na")){
+            hasNAs = true;
             return createValue( (Boolean) null);
         }
 
@@ -46,6 +49,11 @@ public final class BooleanCovariate implements Covariate<Boolean> {
         }
     }
 
+    @Override
+    public boolean hasNAs() {
+        return hasNAs;
+    }
+
     @Override
     public String toString(){
         return "BooleanCovariate(name=" + name + ")";

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

@@ -26,6 +26,8 @@ public interface Covariate<V> extends Serializable {
      */
     Value<V> createValue(String value);
 
+    boolean hasNAs();
+
     interface Value<V> extends Serializable{
 
         Covariate<V> getParent();

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

@@ -19,6 +19,8 @@ public final class FactorCovariate implements Covariate<String>{
     private final FactorValue naValue;
     private final int numberOfPossiblePairings;
 
+    private boolean hasNAs;
+
 
     public FactorCovariate(final String name, final int index, List<String> levels){
         this.name = name;
@@ -72,6 +74,7 @@ public final class FactorCovariate implements Covariate<String>{
     @Override
     public FactorValue createValue(String value) {
         if(value == null || value.equalsIgnoreCase("na")){
+            this.hasNAs = true;
             return this.naValue;
         }
 
@@ -84,6 +87,12 @@ public final class FactorCovariate implements Covariate<String>{
         return factorValue;
     }
 
+
+    @Override
+    public boolean hasNAs() {
+        return hasNAs;
+    }
+
     @Override
     public String toString(){
         return "FactorCovariate(name=" + name + ")";

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

@@ -12,6 +12,7 @@ import lombok.ToString;
 
 import java.util.*;
 import java.util.stream.Collectors;
+import java.util.stream.Stream;
 
 @RequiredArgsConstructor
 @ToString
@@ -23,10 +24,17 @@ public final class NumericCovariate implements Covariate<Double> {
     @Getter
     private final int index;
 
+    private boolean hasNAs = false;
+
     @Override
     public <Y> NumericSplitRuleUpdater<Y> generateSplitRuleUpdater(List<Row<Y>> data, int number, Random random) {
-        data = data.stream()
+        Stream<Row<Y>> stream = data.stream();
-                .filter(row -> !row.getCovariateValue(this).isNA())
+
+        if(hasNAs()){
+            stream = stream.filter(row -> !row.getCovariateValue(this).isNA());
+        }
+
+        data = stream
                 .sorted((r1, r2) -> {
                     Double d1 = r1.getCovariateValue(this).getValue();
                     Double d2 = r2.getCovariateValue(this).getValue();
@@ -37,7 +45,6 @@ public final class NumericCovariate implements Covariate<Double> {
 
         Iterator<Double> sortedDataIterator = data.stream()
                 .map(row -> row.getCovariateValue(this).getValue())
-                .filter(v -> v != null)
                 .iterator();
 
 
@@ -56,7 +63,7 @@ public final class NumericCovariate implements Covariate<Double> {
 
             dataIterator = new UniqueSubsetValueIterator<>(
                     new UniqueValueIterator<>(sortedDataIterator),
-                    indexSet.toArray(new Integer[indexSet.size()]) // TODO verify this is ordered
+                    indexSet.toArray(new Integer[indexSet.size()])
             );
 
         }
@@ -73,12 +80,19 @@ public final class NumericCovariate implements Covariate<Double> {
     @Override
     public NumericValue createValue(String value) {
         if(value == null || value.equalsIgnoreCase("na")){
+            this.hasNAs = true;
             return createValue((Double) null);
         }
 
         return createValue(Double.parseDouble(value));
     }
 
+
+    @Override
+    public boolean hasNAs() {
+        return hasNAs;
+    }
+
     @EqualsAndHashCode
     public class NumericValue implements Covariate.Value<Double>{
 

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

@@ -76,7 +76,8 @@ public class TreeTrainer<Y, O> {
             final double probabilityLeftHand = (double) bestSplit.leftHand.size() /
                     (double) (bestSplit.leftHand.size() + bestSplit.rightHand.size());
 
-            // Assign missing values to the split
+            // Assign missing values to the split if necessary
+            if(bestSplit.getSplitRule().getParent().hasNAs()){
                 for(Row<Y> row : data) {
                     if(row.getCovariateValue(bestSplit.getSplitRule().getParent()).isNA()) {
                         final boolean randomDecision = random.nextDouble() <= probabilityLeftHand;
@@ -90,6 +91,8 @@ public class TreeTrainer<Y, O> {
 
                     }
                 }
+            }
+
 
 
             final Node<O> leftNode = growNode(bestSplit.leftHand, depth+1, random);