Merge branch 'optimizations' of joel/RandomSurvivalForests into master

pmd-rules.xml

@@ -0,0 +1,93 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+<ruleset name="Default Maven PMD Plugin Ruleset"
+    xmlns="http://pmd.sourceforge.net/ruleset/2.0.0"
+    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+    xsi:schemaLocation="http://pmd.sourceforge.net/ruleset/2.0.0 http://pmd.sourceforge.net/ruleset_2_0_0.xsd">
+
+    <description>
+        The default ruleset used by the Maven PMD Plugin, when no other ruleset is specified.
+        It contains the rules of the old (pre PMD 6.0.0) rulesets java-basic, java-empty, java-imports,
+        java-unnecessary, java-unusedcode.
+
+        This ruleset might be used as a starting point for an own customized ruleset [0].
+
+        [0] https://pmd.github.io/latest/pmd_userdocs_understanding_rulesets.html
+    </description>
+
+    <rule ref="category/java/bestpractices.xml/AvoidUsingHardCodedIP" />
+    <rule ref="category/java/bestpractices.xml/CheckResultSet" />
+    <rule ref="category/java/bestpractices.xml/UnusedImports" />
+    <rule ref="category/java/bestpractices.xml/UnusedFormalParameter" />
+    <rule ref="category/java/bestpractices.xml/UnusedLocalVariable" />
+    <rule ref="category/java/bestpractices.xml/UnusedPrivateField" />
+    <rule ref="category/java/bestpractices.xml/UnusedPrivateMethod" />
+
+    <rule ref="category/java/codestyle.xml/DontImportJavaLang" />
+    <rule ref="category/java/codestyle.xml/DuplicateImports" />
+    <rule ref="category/java/codestyle.xml/ExtendsObject" />
+    <rule ref="category/java/codestyle.xml/ForLoopShouldBeWhileLoop" />
+    <rule ref="category/java/codestyle.xml/TooManyStaticImports" />
+    <rule ref="category/java/codestyle.xml/UnnecessaryFullyQualifiedName" />
+    <rule ref="category/java/codestyle.xml/UnnecessaryModifier" />
+    <rule ref="category/java/codestyle.xml/UnnecessaryReturn" />
+    <!--<rule ref="category/java/codestyle.xml/UselessParentheses" /> Sometimes parentheses are used for human eyes -->
+    <rule ref="category/java/codestyle.xml/UselessQualifiedThis" />
+
+    <rule ref="category/java/design.xml/CollapsibleIfStatements" />
+    <rule ref="category/java/design.xml/SimplifiedTernary" />
+    <rule ref="category/java/design.xml/UselessOverridingMethod" />
+
+    <rule ref="category/java/errorprone.xml/AvoidBranchingStatementAsLastInLoop" />
+    <rule ref="category/java/errorprone.xml/AvoidDecimalLiteralsInBigDecimalConstructor" />
+    <rule ref="category/java/errorprone.xml/AvoidMultipleUnaryOperators" />
+    <rule ref="category/java/errorprone.xml/AvoidUsingOctalValues" />
+    <rule ref="category/java/errorprone.xml/BrokenNullCheck" />
+    <rule ref="category/java/errorprone.xml/CheckSkipResult" />
+    <rule ref="category/java/errorprone.xml/ClassCastExceptionWithToArray" />
+    <rule ref="category/java/errorprone.xml/DontUseFloatTypeForLoopIndices" />
+    <rule ref="category/java/errorprone.xml/EmptyCatchBlock" />
+    <rule ref="category/java/errorprone.xml/EmptyFinallyBlock" />
+    <rule ref="category/java/errorprone.xml/EmptyIfStmt" />
+    <rule ref="category/java/errorprone.xml/EmptyInitializer" />
+    <rule ref="category/java/errorprone.xml/EmptyStatementBlock" />
+    <rule ref="category/java/errorprone.xml/EmptyStatementNotInLoop" />
+    <rule ref="category/java/errorprone.xml/EmptySwitchStatements" />
+    <rule ref="category/java/errorprone.xml/EmptySynchronizedBlock" />
+    <rule ref="category/java/errorprone.xml/EmptyTryBlock" />
+    <rule ref="category/java/errorprone.xml/EmptyWhileStmt" />
+    <rule ref="category/java/errorprone.xml/ImportFromSamePackage" />
+    <rule ref="category/java/errorprone.xml/JumbledIncrementer" />
+    <rule ref="category/java/errorprone.xml/MisplacedNullCheck" />
+    <rule ref="category/java/errorprone.xml/OverrideBothEqualsAndHashcode" />
+    <rule ref="category/java/errorprone.xml/ReturnFromFinallyBlock" />
+    <rule ref="category/java/errorprone.xml/UnconditionalIfStatement" />
+    <rule ref="category/java/errorprone.xml/UnnecessaryConversionTemporary" />
+    <rule ref="category/java/errorprone.xml/UnusedNullCheckInEquals" />
+    <rule ref="category/java/errorprone.xml/UselessOperationOnImmutable" />
+
+    <rule ref="category/java/multithreading.xml/AvoidThreadGroup" />
+    <rule ref="category/java/multithreading.xml/DontCallThreadRun" />
+    <rule ref="category/java/multithreading.xml/DoubleCheckedLocking" />
+
+    <rule ref="category/java/performance.xml/BigIntegerInstantiation" />
+    <rule ref="category/java/performance.xml/BooleanInstantiation" />
+
+</ruleset>

pom.xml

@@ -13,6 +13,9 @@
         <maven.compiler.target>1.8</maven.compiler.target>
         <maven.compiler.source>1.8</maven.compiler.source>
         <jackson.version>2.9.6</jackson.version>
+
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
     </properties>
 
 
@@ -89,9 +92,27 @@
                     </execution>
                 </executions>
             </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-pmd-plugin</artifactId>
+                <version>3.11.0</version>
+                <executions>
+                    <execution>
+                        <phase>package</phase> <!-- bind to the packaging phase -->
+                        <goals>
+                            <goal>check</goal>
+                        </goals>
+                    </execution>
+                </executions>
+                <configuration>
+                    <rulesets>
+                        <!-- Custom local file system rule set -->
+                        <ruleset>${project.basedir}/pmd-rules.xml</ruleset>
+                    </rulesets>
+                </configuration>
+            </plugin>
         </plugins>
     </build>
 
 
-
 </project>

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

@@ -4,20 +4,20 @@ import lombok.RequiredArgsConstructor;
 
 import java.util.ArrayList;
 import java.util.List;
-import java.util.concurrent.ThreadLocalRandom;
+import java.util.Random;
 
 @RequiredArgsConstructor
 public class Bootstrapper<T> {
 
     final private List<T> originalData;
 
-    public List<T> bootstrap(){
+    public List<T> bootstrap(Random random){
         final int n = originalData.size();
 
         final List<T> newList = new ArrayList<>(n);
 
         for(int i=0; i<n; i++){
-            final int index = ThreadLocalRandom.current().nextInt(n);
+            final int index = random.nextInt(n);
 
             newList.add(originalData.get(index));
         }

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

@@ -17,7 +17,7 @@ public class CovariateRow implements Serializable {
     @Getter
     private final int id;
 
-    public Covariate.Value<?> getCovariateValue(Covariate covariate){
+    public <V> Covariate.Value<V> getCovariateValue(Covariate<V> covariate){
         return valueArray[covariate.getIndex()];
     }
 

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

@@ -58,7 +58,7 @@ public class DataLoader {
             throw new IllegalArgumentException("Tree directory must be a directory!");
         }
 
-        final File[] treeFiles = folder.listFiles(((file, s) -> s.endsWith(".tree")));
+        final File[] treeFiles = folder.listFiles((file, s) -> s.endsWith(".tree"));
         final List<File> treeFileList = Arrays.asList(treeFiles);
 
         Collections.sort(treeFileList, Comparator.comparing(File::getName));

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

@@ -1,9 +1,9 @@
 package ca.joeltherrien.randomforest;
 
-import ca.joeltherrien.randomforest.covariates.BooleanCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.BooleanCovariateSettings;
 import ca.joeltherrien.randomforest.covariates.Covariate;
-import ca.joeltherrien.randomforest.covariates.FactorCovariateSettings;
-import ca.joeltherrien.randomforest.covariates.NumericCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.FactorCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.NumericCovariateSettings;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskErrorRateCalculator;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskFunctions;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;

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

@@ -17,6 +17,8 @@ public class Row<Y> extends CovariateRow {
     }
 
 
+
+
 	public Y getResponse() {
 		return this.response;
 	}

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

@@ -1,7 +1,7 @@
 package ca.joeltherrien.randomforest;
 
 import ca.joeltherrien.randomforest.covariates.Covariate;
-import ca.joeltherrien.randomforest.covariates.CovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.CovariateSettings;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponseWithCensorTime;
 import ca.joeltherrien.randomforest.responses.competingrisk.combiner.CompetingRiskFunctionCombiner;
@@ -10,7 +10,6 @@ import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.GrayL
 import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.GrayLogRankSingleGroupDifferentiator;
 import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.LogRankMultipleGroupDifferentiator;
 import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.LogRankSingleGroupDifferentiator;
-import ca.joeltherrien.randomforest.responses.regression.MeanGroupDifferentiator;
 import ca.joeltherrien.randomforest.responses.regression.MeanResponseCombiner;
 import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceGroupDifferentiator;
 import ca.joeltherrien.randomforest.tree.GroupDifferentiator;
@@ -68,9 +67,6 @@ public class Settings {
         GROUP_DIFFERENTIATOR_MAP.put(name.toLowerCase(), groupDifferentiatorConstructor);
     }
     static{
-        registerGroupDifferentiatorConstructor("MeanGroupDifferentiator",
-                (node) -> new MeanGroupDifferentiator()
-        );
         registerGroupDifferentiatorConstructor("WeightedVarianceGroupDifferentiator",
                 (node) -> new WeightedVarianceGroupDifferentiator()
         );

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

@@ -1,11 +1,12 @@
 package ca.joeltherrien.randomforest.covariates;
 
+import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.tree.Split;
+import ca.joeltherrien.randomforest.utils.SingletonIterator;
 import lombok.Getter;
 import lombok.RequiredArgsConstructor;
 
-import java.util.Collection;
-import java.util.Collections;
-import java.util.List;
+import java.util.*;
 
 @RequiredArgsConstructor
 public final class BooleanCovariate implements Covariate<Boolean> {
@@ -16,11 +17,13 @@ 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
-    public Collection<BooleanSplitRule> generateSplitRules(List<Value<Boolean>> data, int number) {
-        return Collections.singleton(splitRule);
+    public <Y> Iterator<Split<Y, Boolean>> generateSplitRuleUpdater(List<Row<Y>> data, int number, Random random) {
+        return new SingletonIterator<>(this.splitRule.applyRule(data));
     }
 
     @Override
@@ -31,6 +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);
         }
 
@@ -45,6 +49,11 @@ public final class BooleanCovariate implements Covariate<Boolean>{
         }
     }
 
+    @Override
+    public boolean hasNAs() {
+        return hasNAs;
+    }
+
     @Override
     public String toString(){
         return "BooleanCovariate(name=" + name + ")";
@@ -74,6 +83,7 @@ public final class BooleanCovariate implements Covariate<Boolean>{
         }
     }
 
+
     public class BooleanSplitRule implements SplitRule<Boolean>{
 
         @Override

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

@@ -5,10 +5,7 @@ import ca.joeltherrien.randomforest.Row;
 import ca.joeltherrien.randomforest.tree.Split;
 
 import java.io.Serializable;
-import java.util.ArrayList;
-import java.util.Collection;
-import java.util.LinkedList;
-import java.util.List;
+import java.util.*;
 import java.util.concurrent.ThreadLocalRandom;
 
 public interface Covariate<V> extends Serializable {
@@ -17,7 +14,7 @@ public interface Covariate<V> extends Serializable {
 
     int getIndex();
 
-    Collection<? extends SplitRule<V>> generateSplitRules(final List<Value<V>> data, final int number);
+    <Y> Iterator<Split<Y, V>> generateSplitRuleUpdater(final List<Row<Y>> data, final int number, final Random random);
 
     Value<V> createValue(V value);
 
@@ -29,6 +26,8 @@ public interface Covariate<V> extends Serializable {
      */
     Value<V> createValue(String value);
 
+    boolean hasNAs();
+
     interface Value<V> extends Serializable{
 
         Covariate<V> getParent();
@@ -39,6 +38,17 @@ public interface Covariate<V> extends Serializable {
 
     }
 
+    interface SplitRuleUpdater<Y, V> extends Iterator<Split<Y, V>>{
+        Split<Y, V> currentSplit();
+        boolean currentSplitValid();
+        SplitUpdate<Y, V> nextUpdate();
+    }
+
+    interface SplitUpdate<Y, V> {
+        SplitRule<V> getSplitRule();
+        Collection<Row<Y>> rowsMovedToLeftHand();
+    }
+
     interface SplitRule<V> extends Serializable{
 
         Covariate<V> getParent();
@@ -51,7 +61,7 @@ public interface Covariate<V> extends Serializable {
          * @param <Y>
          * @return
          */
-        default <Y> Split<Y> applyRule(List<Row<Y>> rows) {
+        default <Y> Split<Y, V> applyRule(List<Row<Y>> rows) {
             final List<Row<Y>> leftHand = new LinkedList<>();
             final List<Row<Y>> rightHand = new LinkedList<>();
 
@@ -59,7 +69,7 @@ public interface Covariate<V> extends Serializable {
 
 
             for(final Row<Y> row : rows) {
-                final Value<V> value = (Value<V>) row.getCovariateValue(getParent());
+                final Value<V> value = row.getCovariateValue(getParent());
 
                 if(value.isNA()){
                     missingValueRows.add(row);
@@ -77,11 +87,11 @@ public interface Covariate<V> extends Serializable {
             }
 
 
-            return new Split<>(leftHand, rightHand, missingValueRows);
+            return new Split<>(this, leftHand, rightHand, missingValueRows);
         }
 
         default boolean isLeftHand(CovariateRow row, final double probabilityNaLeftHand){
-            final Value<V> value = (Value<V>) row.getCovariateValue(getParent());
+            final Value<V> value = row.getCovariateValue(getParent());
 
             if(value.isNA()){
                 return ThreadLocalRandom.current().nextDouble() <= probabilityNaLeftHand;

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

@@ -1,10 +1,11 @@
 package ca.joeltherrien.randomforest.covariates;
 
+import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.tree.Split;
 import lombok.EqualsAndHashCode;
 import lombok.Getter;
 
 import java.util.*;
-import java.util.concurrent.ThreadLocalRandom;
 
 public final class FactorCovariate implements Covariate<String>{
 
@@ -18,6 +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;
@@ -42,17 +45,14 @@ public final class FactorCovariate implements Covariate<String>{
 
 
     @Override
-    public Set<FactorSplitRule> generateSplitRules(List<Value<String>> data, int number) {
-        final Set<FactorSplitRule> splitRules = new HashSet<>();
+    public <Y> Iterator<Split<Y, String>> generateSplitRuleUpdater(List<Row<Y>> data, int number, Random random) {
+        final Set<Split<Y, String>> splits = new HashSet<>();
 
         // This is to ensure we don't get stuck in an infinite loop for small factors
         number = Math.min(number, numberOfPossiblePairings);
-        final Random random = ThreadLocalRandom.current();
         final List<FactorValue> levels = new ArrayList<>(factorLevels.values());
 
-
-
-        while(splitRules.size() < number){
+        while(splits.size() < number){
             Collections.shuffle(levels, random);
             final Set<FactorValue> leftSideValues = new HashSet<>();
             leftSideValues.add(levels.get(0));
@@ -63,16 +63,18 @@ public final class FactorCovariate implements Covariate<String>{
                 }
             }
 
-            splitRules.add(new FactorSplitRule(leftSideValues));
+            splits.add(new FactorSplitRule(leftSideValues).applyRule(data));
         }
 
-        return splitRules;
+        return splits.iterator();
 
     }
 
+
     @Override
     public FactorValue createValue(String value) {
         if(value == null || value.equalsIgnoreCase("na")){
+            this.hasNAs = true;
             return this.naValue;
         }
 
@@ -85,6 +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

@@ -1,13 +1,18 @@
-package ca.joeltherrien.randomforest.covariates;
+package ca.joeltherrien.randomforest.covariates.numeric;
 
+import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.covariates.Covariate;
+import ca.joeltherrien.randomforest.utils.IndexedIterator;
+import ca.joeltherrien.randomforest.utils.UniqueSubsetValueIterator;
+import ca.joeltherrien.randomforest.utils.UniqueValueIterator;
 import lombok.EqualsAndHashCode;
 import lombok.Getter;
 import lombok.RequiredArgsConstructor;
 import lombok.ToString;
 
 import java.util.*;
-import java.util.concurrent.ThreadLocalRandom;
 import java.util.stream.Collectors;
+import java.util.stream.Stream;
 
 @RequiredArgsConstructor
 @ToString
@@ -19,42 +24,51 @@ public final class NumericCovariate implements Covariate<Double>{
     @Getter
     private final int index;
 
+    private boolean hasNAs = false;
+
     @Override
-    public Collection<NumericSplitRule> generateSplitRules(List<Value<Double>> data, int number) {
+    public <Y> NumericSplitRuleUpdater<Y> generateSplitRuleUpdater(List<Row<Y>> data, int number, Random random) {
+        Stream<Row<Y>> stream = data.stream();
 
-        final Random random = ThreadLocalRandom.current();
-
-        // only work with non-NA values
-        data = data.stream().filter(value -> !value.isNA()).collect(Collectors.toList());
-        //data = data.stream().filter(value -> !value.isNA()).distinct().collect(Collectors.toList()); // TODO which to use?
-
-        // for this implementation we need to shuffle the data
-        final List<Value<Double>> shuffledData;
-        if(number >= data.size()){
-            shuffledData = new ArrayList<>(data);
-            Collections.shuffle(shuffledData, random);
+        if(hasNAs()){
+            stream = stream.filter(row -> !row.getCovariateValue(this).isNA());
         }
-        else{ // only need the top number entries
-            shuffledData = new ArrayList<>(number);
-            final Set<Integer> indexesToUse = new HashSet<>();
-            //final List<Integer> indexesToUse = new ArrayList<>(); // TODO which to use?
 
-            while(indexesToUse.size() < number){
-                final int index = random.nextInt(data.size());
+        data = stream
+                .sorted((r1, r2) -> {
+                    Double d1 = r1.getCovariateValue(this).getValue();
+                    Double d2 = r2.getCovariateValue(this).getValue();
 
-                if(indexesToUse.add(index)){
-                    shuffledData.add(data.get(index));
+                    return d1.compareTo(d2);
+                })
+                .collect(Collectors.toList());
+
+        Iterator<Double> sortedDataIterator = data.stream()
+                .map(row -> row.getCovariateValue(this).getValue())
+                .iterator();
+
+
+        final IndexedIterator<Double> dataIterator;
+        if(number == 0){
+            dataIterator = new UniqueValueIterator<>(sortedDataIterator);
         }
+        else{
+            final TreeSet<Integer> indexSet = new TreeSet<>();
+
+            final int maxIndex = data.size();
+
+            for(int i=0; i<number; i++){
+                indexSet.add(random.nextInt(maxIndex));
             }
 
+            dataIterator = new UniqueSubsetValueIterator<>(
+                    new UniqueValueIterator<>(sortedDataIterator),
+                    indexSet.toArray(new Integer[indexSet.size()])
+            );
+
         }
 
-        return shuffledData.stream()
-                .mapToDouble(v -> v.getValue())
-                .mapToObj(threshold -> new NumericSplitRule(threshold))
-                .collect(Collectors.toSet());
-        // by returning a set we'll make everything far more efficient as a lot of rules can repeat due to bootstrapping
-
+        return new NumericSplitRuleUpdater<>(this, data, dataIterator);
 
     }
 
@@ -66,12 +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>{
 
@@ -102,7 +123,7 @@ public final class NumericCovariate implements Covariate<Double>{
 
         private final double threshold;
 
-        private NumericSplitRule(final double threshold){
+        NumericSplitRule(final double threshold){
             this.threshold = threshold;
         }
 

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

@@ -0,0 +1,84 @@
+package ca.joeltherrien.randomforest.covariates.numeric;
+
+import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.covariates.Covariate;
+import ca.joeltherrien.randomforest.tree.Split;
+import ca.joeltherrien.randomforest.utils.IndexedIterator;
+
+import java.util.Collections;
+import java.util.List;
+
+public class NumericSplitRuleUpdater<Y> implements Covariate.SplitRuleUpdater<Y, Double>  {
+
+    private final NumericCovariate covariate;
+    private final List<Row<Y>> orderedData;
+    private final IndexedIterator<Double> dataIterator;
+
+    private Split<Y, Double> currentSplit;
+
+    public NumericSplitRuleUpdater(final NumericCovariate covariate, final List<Row<Y>> orderedData, final IndexedIterator<Double> iterator){
+        this.covariate = covariate;
+        this.orderedData = orderedData;
+        this.dataIterator = iterator;
+
+        final List<Row<Y>> leftHandList = Collections.emptyList();
+        final List<Row<Y>> rightHandList = orderedData;
+
+        this.currentSplit = new Split<>(
+                covariate.new NumericSplitRule(Double.MIN_VALUE),
+                leftHandList,
+                rightHandList,
+                Collections.emptyList());
+
+    }
+
+    @Override
+    public Split<Y, Double> currentSplit() {
+        return this.currentSplit;
+    }
+
+    @Override
+    public boolean currentSplitValid() {
+        return currentSplit.getLeftHand().size() > 0 && currentSplit.getRightHand().size() > 0;
+    }
+
+    @Override
+    public NumericSplitUpdate<Y> nextUpdate() {
+        if(hasNext()){
+            final int currentPosition = dataIterator.getIndex();
+            final Double splitValue = dataIterator.next();
+            final int newPosition = dataIterator.getIndex();
+
+            final List<Row<Y>> rowsMoved = orderedData.subList(currentPosition, newPosition);
+
+            final NumericCovariate.NumericSplitRule splitRule = covariate.new NumericSplitRule(splitValue);
+
+            // Update current split
+            this.currentSplit = new Split<>(
+                    splitRule,
+                    Collections.unmodifiableList(orderedData.subList(0, newPosition)),
+                    Collections.unmodifiableList(orderedData.subList(newPosition, orderedData.size())),
+                    Collections.emptyList());
+
+
+            return new NumericSplitUpdate<>(splitRule, rowsMoved);
+        }
+
+        return null;
+    }
+
+    @Override
+    public boolean hasNext() {
+        return dataIterator.hasNext();
+    }
+
+    @Override
+    public Split<Y, Double> next() {
+        if(hasNext()){
+            nextUpdate();
+        }
+
+        return this.currentSplit();
+    }
+
+}

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

@@ -0,0 +1,24 @@
+package ca.joeltherrien.randomforest.covariates.numeric;
+
+import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.covariates.Covariate;
+import lombok.AllArgsConstructor;
+
+import java.util.Collection;
+
+@AllArgsConstructor
+public class NumericSplitUpdate<Y> implements Covariate.SplitUpdate<Y, Double> {
+
+    private final NumericCovariate.NumericSplitRule numericSplitRule;
+    private final Collection<Row<Y>> rowsMoved;
+
+    @Override
+    public NumericCovariate.NumericSplitRule getSplitRule() {
+        return numericSplitRule;
+    }
+
+    @Override
+    public Collection<Row<Y>> rowsMovedToLeftHand() {
+        return rowsMoved;
+    }
+}

src/main/java/ca/joeltherrien/randomforest/covariates/settings/BooleanCovariateSettings.java

@@ -1,5 +1,6 @@
-package ca.joeltherrien.randomforest.covariates;
+package ca.joeltherrien.randomforest.covariates.settings;
 
+import ca.joeltherrien.randomforest.covariates.BooleanCovariate;
 import lombok.Data;
 import lombok.NoArgsConstructor;
 

src/main/java/ca/joeltherrien/randomforest/covariates/settings/CovariateSettings.java

@@ -1,5 +1,6 @@
-package ca.joeltherrien.randomforest.covariates;
+package ca.joeltherrien.randomforest.covariates.settings;
 
+import ca.joeltherrien.randomforest.covariates.Covariate;
 import com.fasterxml.jackson.annotation.JsonSubTypes;
 import com.fasterxml.jackson.annotation.JsonTypeInfo;
 import lombok.Getter;

src/main/java/ca/joeltherrien/randomforest/covariates/settings/FactorCovariateSettings.java

@@ -1,5 +1,6 @@
-package ca.joeltherrien.randomforest.covariates;
+package ca.joeltherrien.randomforest.covariates.settings;
 
+import ca.joeltherrien.randomforest.covariates.FactorCovariate;
 import lombok.Data;
 import lombok.NoArgsConstructor;
 

src/main/java/ca/joeltherrien/randomforest/covariates/settings/NumericCovariateSettings.java

@@ -1,5 +1,6 @@
-package ca.joeltherrien.randomforest.covariates;
+package ca.joeltherrien.randomforest.covariates.settings;
 
+import ca.joeltherrien.randomforest.covariates.numeric.NumericCovariate;
 import lombok.Data;
 import lombok.NoArgsConstructor;
 

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskGraySetsImpl.java

@@ -1,37 +1,77 @@
 package ca.joeltherrien.randomforest.responses.competingrisk;
 
-import ca.joeltherrien.randomforest.utils.MathFunction;
-import lombok.Builder;
-import lombok.Getter;
+import java.util.Arrays;
 
-import java.util.List;
-import java.util.Map;
+public class CompetingRiskGraySetsImpl implements CompetingRiskSets<CompetingRiskResponseWithCensorTime> {
 
-/**
- * Represents a response from CompetingRiskUtils#calculateGraySetsEfficiently
- *
- */
-@Builder
-@Getter
-public class CompetingRiskGraySetsImpl implements CompetingRiskSets{
+    final double[] times; // length m array
+    int[][] riskSetLeft; // J x m array
+    final int[][] riskSetTotal; // J x m array
+    int[][] numberOfEventsLeft; // J+1 x m array
+    final int[][] numberOfEventsTotal; // J+1 x m array
 
-    private final List<Double> eventTimes;
-    private final MathFunction[] riskSet;
-    private final Map<Double, int[]> numberOfEvents;
-
-    @Override
-    public MathFunction getRiskSet(int event){
-        return(riskSet[event-1]);
+    public CompetingRiskGraySetsImpl(double[] times, int[][] riskSetLeft, int[][] riskSetTotal, int[][] numberOfEventsLeft, int[][] numberOfEventsTotal) {
+        this.times = times;
+        this.riskSetLeft = riskSetLeft;
+        this.riskSetTotal = riskSetTotal;
+        this.numberOfEventsLeft = numberOfEventsLeft;
+        this.numberOfEventsTotal = numberOfEventsTotal;
     }
 
     @Override
-    public int getNumberOfEvents(Double time, int event){
-        if(numberOfEvents.containsKey(time)){
-            return numberOfEvents.get(time)[event];
+    public double[] getDistinctTimes() {
+        return times;
     }
 
-        return 0;
+    @Override
+    public int getRiskSetLeft(int timeIndex, int event) {
+        return riskSetLeft[event-1][timeIndex];
+    }
+
+    @Override
+    public int getRiskSetTotal(int timeIndex, int event) {
+        return riskSetTotal[event-1][timeIndex];
     }
 
 
+    @Override
+    public int getNumberOfEventsLeft(int timeIndex, int event) {
+        return numberOfEventsLeft[event][timeIndex];
+    }
+
+    @Override
+    public int getNumberOfEventsTotal(int timeIndex, int event) {
+        return numberOfEventsTotal[event][timeIndex];
+    }
+
+    @Override
+    public void update(CompetingRiskResponseWithCensorTime rowMovedToLeft) {
+        final double time = rowMovedToLeft.getU();
+        final int k = Arrays.binarySearch(times, time);
+        final int delta_m_1 = rowMovedToLeft.getDelta() - 1;
+        final double censorTime = rowMovedToLeft.getC();
+
+        for(int j=0; j<riskSetLeft.length; j++){
+            final int[] riskSetLeftJ = riskSetLeft[j];
+
+            // first iteration; perform normal increment as if Y is normal
+            // corresponds to the first part, U_i >= t, in I(...)
+            for(int i=0; i<=k; i++){
+                riskSetLeftJ[i]++;
+            }
+
+            // second iteration; only if delta-1 != j
+            // corresponds to the second part, U_i < t & delta_i != j & C_i > t
+            if(delta_m_1 != j && !rowMovedToLeft.isCensored()){
+                int i = k+1;
+                while(i < times.length && times[i] < censorTime){
+                    riskSetLeftJ[i]++;
+                    i++;
+                }
+            }
+
+        }
+
+        numberOfEventsLeft[rowMovedToLeft.getDelta()][k]++;
+    }
 }

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskSets.java

@@ -1,13 +1,13 @@
 package ca.joeltherrien.randomforest.responses.competingrisk;
 
-import ca.joeltherrien.randomforest.utils.MathFunction;
+public interface CompetingRiskSets<T extends CompetingRiskResponse> {
 
-import java.util.List;
+    double[] getDistinctTimes();
+    int getRiskSetLeft(int timeIndex, int event);
+    int getRiskSetTotal(int timeIndex, int event);
+    int getNumberOfEventsLeft(int timeIndex, int event);
+    int getNumberOfEventsTotal(int timeIndex, int event);
 
-public interface CompetingRiskSets {
-
-    MathFunction getRiskSet(int event);
-    int getNumberOfEvents(Double time, int event);
-    List<Double> getEventTimes();
+    void update(T rowMovedToLeft);
 
 }

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskSetsImpl.java

@@ -1,36 +1,59 @@
 package ca.joeltherrien.randomforest.responses.competingrisk;
 
-import ca.joeltherrien.randomforest.utils.MathFunction;
-import lombok.Builder;
-import lombok.Getter;
+import java.util.Arrays;
 
-import java.util.List;
-import java.util.Map;
+public class CompetingRiskSetsImpl implements CompetingRiskSets<CompetingRiskResponse> {
 
-/**
- * Represents a response from CompetingRiskUtils#calculateSetsEfficiently
- *
- */
-@Builder
-@Getter
-public class CompetingRiskSetsImpl implements CompetingRiskSets{
+    final double[] times; // length m array
+    int[] riskSetLeft; // length m array
+    final int[] riskSetTotal; // length m array
+    int[][] numberOfEventsLeft; // J+1 x m array
+    final int[][] numberOfEventsTotal; // J+1 x m array
 
-    private final List<Double> eventTimes;
-    private final MathFunction riskSet;
-    private final Map<Double, int[]> numberOfEvents;
 
-    @Override
-    public MathFunction getRiskSet(int event){
-        return riskSet;
+    public CompetingRiskSetsImpl(double[] times, int[] riskSetLeft, int[] riskSetTotal, int[][] numberOfEventsLeft, int[][] numberOfEventsTotal) {
+        this.times = times;
+        this.riskSetLeft = riskSetLeft;
+        this.riskSetTotal = riskSetTotal;
+        this.numberOfEventsLeft = numberOfEventsLeft;
+        this.numberOfEventsTotal = numberOfEventsTotal;
     }
 
     @Override
-    public int getNumberOfEvents(Double time, int event){
-        if(numberOfEvents.containsKey(time)){
-            return numberOfEvents.get(time)[event];
+    public double[] getDistinctTimes() {
+        return times;
     }
 
-        return 0;
+    @Override
+    public int getRiskSetLeft(int timeIndex, int event) {
+        return riskSetLeft[timeIndex];
     }
 
+    @Override
+    public int getRiskSetTotal(int timeIndex, int event) {
+        return riskSetTotal[timeIndex];
+    }
+
+
+    @Override
+    public int getNumberOfEventsLeft(int timeIndex, int event) {
+        return numberOfEventsLeft[event][timeIndex];
+    }
+
+    @Override
+    public int getNumberOfEventsTotal(int timeIndex, int event) {
+        return numberOfEventsTotal[event][timeIndex];
+    }
+
+    @Override
+    public void update(CompetingRiskResponse rowMovedToLeft) {
+        final double time = rowMovedToLeft.getU();
+        final int k = Arrays.binarySearch(times, time);
+
+        for(int i=0; i<=k; i++){
+            riskSetLeft[i]++;
+        }
+
+        numberOfEventsLeft[rowMovedToLeft.getDelta()][k]++;
+    }
 }

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskUtils.java

@@ -1,11 +1,9 @@
 package ca.joeltherrien.randomforest.responses.competingrisk;
 
-import ca.joeltherrien.randomforest.utils.LeftContinuousStepFunction;
 import ca.joeltherrien.randomforest.utils.StepFunction;
-import ca.joeltherrien.randomforest.utils.VeryDiscontinuousStepFunction;
 
 import java.util.*;
-import java.util.stream.DoubleStream;
+import java.util.stream.Stream;
 
 public class CompetingRiskUtils {
 
@@ -102,18 +100,30 @@ public class CompetingRiskUtils {
 
     }
 
-    public static CompetingRiskSetsImpl calculateSetsEfficiently(final List<CompetingRiskResponse> responses, int[] eventsOfFocus){
-        final int n = responses.size();
-        int[] numberOfCurrentEvents = new int[eventsOfFocus.length+1];
 
-        final Map<Double, int[]> numberOfEvents = new HashMap<>();
+    public static CompetingRiskSetsImpl calculateSetsEfficiently(final List<CompetingRiskResponse> initialLeftHand,
+                                                                 final List<CompetingRiskResponse> initialRightHand,
+                                                                 int[] eventsOfFocus,
+                                                                 boolean calculateRiskSets){
 
-        final List<Double> eventTimes = new ArrayList<>(n);
-        final List<Double> eventAndCensorTimes = new ArrayList<>(n);
-        final List<Integer> riskSetNumberList = new ArrayList<>(n);
+        final double[] distinctEventTimes = Stream.concat(
+                initialLeftHand.stream(),
+                initialRightHand.stream())
+                //.filter(y -> !y.isCensored())
+                .map(CompetingRiskResponse::getU)
+                .mapToDouble(Double::doubleValue)
+                .sorted()
+                .distinct()
+                .toArray();
+
+
+        final int m = distinctEventTimes.length;
+        final int[][] numberOfCurrentEventsTotal = new int[eventsOfFocus.length+1][m];
+
+        // Left Hand First
 
         // need to first sort responses
-        Collections.sort(responses, (y1, y2) -> {
+        Collections.sort(initialLeftHand, (y1, y2) -> {
             if(y1.getU() < y2.getU()){
                 return -1;
             }
@@ -125,127 +135,191 @@ public class CompetingRiskUtils {
             }
         });
 
+        final int nLeft = initialLeftHand.size();
+        final int nRight = initialRightHand.size();
+
+        final int[][] numberOfCurrentEventsLeft = new int[eventsOfFocus.length+1][m];
+        final int[] riskSetArrayLeft = new int[m];
+        final int[] riskSetArrayTotal = new int[m];
 
 
-        for(int i=0; i<n; i++){
-            final CompetingRiskResponse currentResponse = responses.get(i);
-            final boolean lastOfTime = (i+1)==n || responses.get(i+1).getU() > currentResponse.getU();
 
-            numberOfCurrentEvents[currentResponse.getDelta()]++;
+        for(int k=0; k<m; k++){
+            riskSetArrayLeft[k] = nLeft;
+            riskSetArrayTotal[k] = nLeft + nRight;
+        }
+
+        // Left Hand
+        for(int i=0; i<nLeft; i++){
+            final CompetingRiskResponse currentResponse = initialLeftHand.get(i);
+            final boolean lastOfTime = (i+1)==nLeft || initialLeftHand.get(i+1).getU() > currentResponse.getU();
+
+            final int k = Arrays.binarySearch(distinctEventTimes, currentResponse.getU());
+
+            numberOfCurrentEventsLeft[currentResponse.getDelta()][k]++;
+            numberOfCurrentEventsTotal[currentResponse.getDelta()][k]++;
 
             if(lastOfTime){
                 int totalNumberOfCurrentEvents = 0;
-                for(int e = 1; e < numberOfCurrentEvents.length; e++){ // exclude censored events
-                    totalNumberOfCurrentEvents += numberOfCurrentEvents[e];
+                for(int e = 1; e < eventsOfFocus.length+1; e++){ // exclude censored events
+                    totalNumberOfCurrentEvents += numberOfCurrentEventsLeft[e][k];
                 }
 
-                final double currentTime = currentResponse.getU();
+                // Calculate risk set values
+                // Note that we only decrease values in the *future*
+                if(calculateRiskSets){
+                    final int decreaseBy = totalNumberOfCurrentEvents + numberOfCurrentEventsLeft[0][k];
+                    for(int j=k+1; j<m; j++){
+                        riskSetArrayLeft[j] = riskSetArrayLeft[j] - decreaseBy;
+                        riskSetArrayTotal[j] = riskSetArrayTotal[j] - decreaseBy;
 
-                if(totalNumberOfCurrentEvents > 0){ // add numberOfCurrentEvents
-                    // Add point
-                    eventTimes.add(currentTime);
-                    numberOfEvents.put(currentTime, numberOfCurrentEvents);
+                    }
                 }
 
-                // Always do risk set
-                // remember that the LeftContinuousFunction takes into account that at this currentTime the risk value is the previous value
-                final int riskSet = n - (i+1);
-                riskSetNumberList.add(riskSet);
-                eventAndCensorTimes.add(currentTime);
-
-                // reset counters
-                numberOfCurrentEvents = new int[eventsOfFocus.length+1];
 
             }
 
         }
 
-        final double[] riskSetArray = new double[eventAndCensorTimes.size()];
-        final double[] timesArray = new double[eventAndCensorTimes.size()];
-        for(int i=0; i<riskSetArray.length; i++){
-            timesArray[i] = eventAndCensorTimes.get(i);
-            riskSetArray[i] = riskSetNumberList.get(i);
-        }
-
-        final LeftContinuousStepFunction riskSetFunction = new LeftContinuousStepFunction(timesArray, riskSetArray, n);
-
-        return CompetingRiskSetsImpl.builder()
-                .numberOfEvents(numberOfEvents)
-                .riskSet(riskSetFunction)
-                .eventTimes(eventTimes)
-                .build();
-
-    }
-
-    public static CompetingRiskGraySetsImpl calculateGraySetsEfficiently(final List<CompetingRiskResponseWithCensorTime> responses, int[] eventsOfFocus){
-        final List sillyList = responses; // annoying Java generic work-around
-        final CompetingRiskSetsImpl originalSets = calculateSetsEfficiently(sillyList, eventsOfFocus);
-
-        final double[] allTimes = DoubleStream.concat(
-                responses.stream()
-                        .mapToDouble(CompetingRiskResponseWithCensorTime::getC),
-                responses.stream()
-                        .mapToDouble(CompetingRiskResponseWithCensorTime::getU)
-        ).sorted().distinct().toArray();
-
-
-
-        final VeryDiscontinuousStepFunction[] riskSets = new VeryDiscontinuousStepFunction[eventsOfFocus.length];
-
-        for(final int event : eventsOfFocus){
-            final double[] yAt = new double[allTimes.length];
-            final double[] yRight = new double[allTimes.length];
-
-            for(final CompetingRiskResponseWithCensorTime response : responses){
-                if(response.getDelta() == event){
-                    // traditional case only; increment on time t when I(t <= Ui)
-                    final double time = response.getU();
-                    final int index = Arrays.binarySearch(allTimes, time);
-
-                    if(index < 0){ // TODO remove once code is stable
-                        throw new IllegalStateException("Index shouldn't be negative!");
-                    }
-
-                    // All yAts up to and including index are incremented;
-                    // All yRights up to index are incremented
-                    yAt[index]++;
-                    for(int i=0; i<index; i++){
-                        yAt[i]++;
-                        yRight[i]++;
+
+        // Right Hand Next. Note that we only need to keep track of the Left Hand and the Total
+
+        // need to first sort responses
+        Collections.sort(initialRightHand, (y1, y2) -> {
+            if(y1.getU() < y2.getU()){
+                return -1;
             }
+            else if(y1.getU() > y2.getU()){
+                return 1;
             }
             else{
-                    // need to increment on time t on following conditions; I(t <= Ui | t < Ci)
-                    // Fact: Ci >= Ui.
+                return 0;
+            }
+        });
 
-                    // increment yAt up to Ci. If Ui==Ci, increment yAt at Ci.
-                    final double time = response.getC();
-                    final int index = Arrays.binarySearch(allTimes, time);
+        // Right Hand
+        int[] currentEventsRight = new int[eventsOfFocus.length+1];
+        for(int i=0; i<nRight; i++){
+            final CompetingRiskResponse currentResponse = initialRightHand.get(i);
+            final boolean lastOfTime = (i+1)==nRight || initialRightHand.get(i+1).getU() > currentResponse.getU();
 
-                    if(index < 0){ // TODO remove once code is stable
-                        throw new IllegalStateException("Index shouldn't be negative!");
-                    }
-
-                    for(int i=0; i<index; i++){
-                        yAt[i]++;
-                        yRight[i]++;
-                    }
-                    if(response.getU() == response.getC()){
-                        yAt[index]++;
+            final int k = Arrays.binarySearch(distinctEventTimes, currentResponse.getU());
+
+            currentEventsRight[currentResponse.getDelta()]++;
+            numberOfCurrentEventsTotal[currentResponse.getDelta()][k]++;
+
+            if(lastOfTime){
+                int totalNumberOfCurrentEvents = 0;
+                for(int e = 1; e < eventsOfFocus.length+1; e++){ // exclude censored events
+                    totalNumberOfCurrentEvents += currentEventsRight[e];
                 }
 
+                // Calculate risk set values
+                // Note that we only decrease values in the *future*
+                if(calculateRiskSets){
+                    final int decreaseBy = totalNumberOfCurrentEvents + currentEventsRight[0];
+                    for(int j=k+1; j<m; j++){
+                        riskSetArrayTotal[j] = riskSetArrayTotal[j] - decreaseBy;
                     }
                 }
 
-            riskSets[event-1] = new VeryDiscontinuousStepFunction(allTimes, yAt, yRight, responses.size());
+                // Reset
+                currentEventsRight = new int[eventsOfFocus.length+1];
 
             }
 
-        return CompetingRiskGraySetsImpl.builder()
-                .numberOfEvents(originalSets.getNumberOfEvents())
-                .eventTimes(originalSets.getEventTimes())
-                .riskSet(riskSets)
-                .build();
+        }
+
+        return new CompetingRiskSetsImpl(distinctEventTimes, riskSetArrayLeft, riskSetArrayTotal, numberOfCurrentEventsLeft, numberOfCurrentEventsTotal);
+
+    }
+
+
+    public static CompetingRiskGraySetsImpl calculateGraySetsEfficiently(final List<CompetingRiskResponseWithCensorTime> initialLeftHand,
+                                                                         final List<CompetingRiskResponseWithCensorTime> initialRightHand,
+                                                                         int[] eventsOfFocus){
+
+        final List leftHandGenericsSuck = initialLeftHand;
+        final List rightHandGenericsSuck = initialRightHand;
+
+        final CompetingRiskSetsImpl normalSets = calculateSetsEfficiently(
+                leftHandGenericsSuck,
+                rightHandGenericsSuck,
+                eventsOfFocus, false);
+
+        final double[] times = normalSets.times;
+        final int[][] numberOfEventsLeft = normalSets.numberOfEventsLeft;
+        final int[][] numberOfEventsTotal = normalSets.numberOfEventsTotal;
+
+        // FYI; initialLeftHand and initialRightHand have both now been sorted
+        // Time to calculate the Gray modified risk sets
+        final int[][] riskSetsLeft = new int[eventsOfFocus.length][times.length];
+        final int[][] riskSetsTotal = new int[eventsOfFocus.length][times.length];
+
+        // Left hand first
+        for(final CompetingRiskResponseWithCensorTime response : initialLeftHand){
+            final double time = response.getU();
+            final int k = Arrays.binarySearch(times, time);
+            final int delta_m_1 = response.getDelta() - 1;
+            final double censorTime = response.getC();
+
+            for(int j=0; j<eventsOfFocus.length; j++){
+                final int[] riskSetLeftJ = riskSetsLeft[j];
+                final int[] riskSetTotalJ = riskSetsTotal[j];
+
+                // first iteration; perform normal increment as if Y is normal
+                // corresponds to the first part, U_i >= t, in I(...)
+                for(int i=0; i<=k; i++){
+                    riskSetLeftJ[i]++;
+                    riskSetTotalJ[i]++;
+                }
+
+                // second iteration; only if delta-1 != j
+                // corresponds to the second part, U_i < t & delta_i != j & C_i > t
+                if(delta_m_1 != j && !response.isCensored()){
+                    int i = k+1;
+                    while(i < times.length && times[i] < censorTime){
+                        riskSetLeftJ[i]++;
+                        riskSetTotalJ[i]++;
+                        i++;
+                    }
+                }
+
+            }
+
+        }
+
+        // Repeat for right hand
+        for(final CompetingRiskResponseWithCensorTime response : initialRightHand){
+            final double time = response.getU();
+            final int k = Arrays.binarySearch(times, time);
+            final int delta_m_1 = response.getDelta() - 1;
+            final double censorTime = response.getC();
+
+            for(int j=0; j<eventsOfFocus.length; j++){
+                final int[] riskSetTotalJ = riskSetsTotal[j];
+
+                // first iteration; perform normal increment as if Y is normal
+                // corresponds to the first part, U_i >= t, in I(...)
+                for(int i=0; i<=k; i++){
+                    riskSetTotalJ[i]++;
+                }
+
+                // second iteration; only if delta-1 != j
+                // corresponds to the second part, U_i < t & delta_i != j & C_i > t
+                if(delta_m_1 != j && !response.isCensored()){
+                    int i = k+1;
+                    while(i < times.length && times[i] < censorTime){
+                        riskSetTotalJ[i]++;
+                        i++;
+                    }
+                }
+
+            }
+
+        }
+
+        return new CompetingRiskGraySetsImpl(times, riskSetsLeft, riskSetsTotal, numberOfEventsLeft, numberOfEventsTotal);
 
     }
 

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/differentiator/CompetingRiskGroupDifferentiator.java

@@ -1,13 +1,18 @@
 package ca.joeltherrien.randomforest.responses.competingrisk.differentiator;
 
+import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.covariates.Covariate;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSets;
 import ca.joeltherrien.randomforest.tree.GroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.Split;
+import ca.joeltherrien.randomforest.tree.SplitAndScore;
 import lombok.AllArgsConstructor;
 import lombok.Data;
 
+import java.util.Iterator;
 import java.util.List;
-import java.util.stream.Stream;
+import java.util.stream.Collectors;
 
 /**
  * See page 761 of Random survival forests for competing risks by Ishwaran et al. The class is abstract as Gray's test
@@ -16,45 +21,112 @@ import java.util.stream.Stream;
  */
 public abstract class CompetingRiskGroupDifferentiator<Y extends CompetingRiskResponse> implements GroupDifferentiator<Y> {
 
-    @Override
-    public abstract Double differentiate(List<Y> leftHand, List<Y> rightHand);
+    abstract protected CompetingRiskSets<Y> createCompetingRiskSets(List<Y> leftHand, List<Y> rightHand);
 
+    abstract protected Double getScore(final CompetingRiskSets<Y> competingRiskSets);
+
+    @Override
+    public SplitAndScore<Y, ?> differentiate(Iterator<Split<Y, ?>> splitIterator) {
+
+        if(splitIterator instanceof Covariate.SplitRuleUpdater){
+            return differentiateWithSplitUpdater((Covariate.SplitRuleUpdater) splitIterator);
+        }
+        else{
+            return differentiateWithBasicIterator(splitIterator);
+        }
+    }
+
+    private SplitAndScore<Y, ?> differentiateWithBasicIterator(Iterator<Split<Y, ?>> splitIterator){
+        Double bestScore = null;
+        Split<Y, ?> bestSplit = null;
+
+        while(splitIterator.hasNext()){
+            final Split<Y, ?> candidateSplit = splitIterator.next();
+
+            final List<Y> leftHand = candidateSplit.getLeftHand().stream().map(Row::getResponse).collect(Collectors.toList());
+            final List<Y> rightHand = candidateSplit.getRightHand().stream().map(Row::getResponse).collect(Collectors.toList());
+
+            if(leftHand.isEmpty() || rightHand.isEmpty()){
+                continue;
+            }
+
+            final CompetingRiskSets<Y> competingRiskSets = createCompetingRiskSets(leftHand, rightHand);
+
+            final Double score = getScore(competingRiskSets);
+
+            if(Double.isFinite(score) && (bestScore == null || score > bestScore)){
+                bestScore = score;
+                bestSplit = candidateSplit;
+            }
+        }
+
+        if(bestSplit == null){
+            return null;
+        }
+
+        return new SplitAndScore<>(bestSplit, bestScore);
+    }
+
+    private SplitAndScore<Y, ?> differentiateWithSplitUpdater(Covariate.SplitRuleUpdater<Y, ?> splitRuleUpdater) {
+
+        final List<Y> leftInitialSplit = splitRuleUpdater.currentSplit().getLeftHand()
+                .stream().map(Row::getResponse).collect(Collectors.toList());
+        final List<Y> rightInitialSplit = splitRuleUpdater.currentSplit().getRightHand()
+                .stream().map(Row::getResponse).collect(Collectors.toList());
+
+        final CompetingRiskSets<Y> competingRiskSets = createCompetingRiskSets(leftInitialSplit, rightInitialSplit);
+
+        Double bestScore = null;
+        Split<Y, ?> bestSplit = null;
+
+        while(splitRuleUpdater.hasNext()){
+            for(Row<Y> rowMoved : splitRuleUpdater.nextUpdate().rowsMovedToLeftHand()){
+                competingRiskSets.update(rowMoved.getResponse());
+            }
+
+            final Double score = getScore(competingRiskSets);
+
+            if(Double.isFinite(score) && (bestScore == null || score > bestScore)){
+                bestScore = score;
+                bestSplit = splitRuleUpdater.currentSplit();
+            }
+        }
+
+        if(bestSplit == null){
+            return null;
+        }
+
+        return new SplitAndScore<>(bestSplit, bestScore);
+
+    }
 
     /**
      * Calculates the log rank value (or the Gray's test value) for a *specific* event cause.
      *
      * @param eventOfFocus
-     * @param competingRiskSetsLeft A summary of the different sets used in the calculation for the left side
-     * @param competingRiskSetsRight A summary of the different sets used in the calculation for the right side
+     * @param competingRiskSets A summary of the different sets used in the calculation
      * @return
      */
-    LogRankValue specificLogRankValue(final int eventOfFocus, final CompetingRiskSets competingRiskSetsLeft, final CompetingRiskSets competingRiskSetsRight){
-
-        final double[] distinctEventTimes = Stream.concat(
-                competingRiskSetsLeft.getEventTimes().stream(),
-                competingRiskSetsRight.getEventTimes().stream())
-                .mapToDouble(Double::doubleValue)
-                .sorted()
-                .distinct()
-                .toArray();
+    LogRankValue specificLogRankValue(final int eventOfFocus, final CompetingRiskSets<Y> competingRiskSets){
 
         double summation = 0.0;
         double variance = 0.0;
 
-        for(final double time_k : distinctEventTimes){
+        final double[] distinctTimes = competingRiskSets.getDistinctTimes();
+
+        for(int k = 0; k<distinctTimes.length; k++){
+            final double time_k = distinctTimes[k];
             final double weight = weight(time_k); // W_j(t_k)
-            final double numberEventsAtTimeDaughterLeft = competingRiskSetsLeft.getNumberOfEvents(time_k, eventOfFocus); // // d_{j,l}(t_k)
-            final double numberEventsAtTimeDaughterRight = competingRiskSetsRight.getNumberOfEvents(time_k, eventOfFocus); // d_{j,r}(t_k)
-            final double numberOfEventsAtTime = numberEventsAtTimeDaughterLeft + numberEventsAtTimeDaughterRight; // d_j(t_k)
+            final double numberEventsAtTimeDaughterLeft = competingRiskSets.getNumberOfEventsLeft(k, eventOfFocus); // // d_{j,l}(t_k)
+            final double numberEventsAtTimeDaughterTotal = competingRiskSets.getNumberOfEventsTotal(k, eventOfFocus); // d_j(t_k)
 
-            final double individualsAtRiskDaughterLeft = competingRiskSetsLeft.getRiskSet(eventOfFocus).evaluate(time_k); // Y_l(t_k)
-            final double individualsAtRiskDaughterRight = competingRiskSetsRight.getRiskSet(eventOfFocus).evaluate(time_k); // Y_r(t_k)
-            final double individualsAtRisk = individualsAtRiskDaughterLeft + individualsAtRiskDaughterRight; // Y(t_k)
+            final double individualsAtRiskDaughterLeft = competingRiskSets.getRiskSetLeft(k, eventOfFocus); // Y_l(t_k)
+            final double individualsAtRiskDaughterTotal = competingRiskSets.getRiskSetTotal(k, eventOfFocus); // Y(t_k)
 
-            final double deltaSummation = weight*(numberEventsAtTimeDaughterLeft - numberOfEventsAtTime*individualsAtRiskDaughterLeft/individualsAtRisk);
-            final double deltaVariance = weight*weight*numberOfEventsAtTime*individualsAtRiskDaughterLeft/individualsAtRisk
-                    * (1.0 - individualsAtRiskDaughterLeft / individualsAtRisk)
-                    * ((individualsAtRisk - numberOfEventsAtTime) / (individualsAtRisk - 1.0));
+            final double deltaSummation = weight*(numberEventsAtTimeDaughterLeft - numberEventsAtTimeDaughterTotal*individualsAtRiskDaughterLeft/individualsAtRiskDaughterTotal);
+            final double deltaVariance = weight*weight*numberEventsAtTimeDaughterTotal*individualsAtRiskDaughterLeft/individualsAtRiskDaughterTotal
+                    * (1.0 - individualsAtRiskDaughterLeft / individualsAtRiskDaughterTotal)
+                    * ((individualsAtRiskDaughterTotal - numberEventsAtTimeDaughterTotal) / (individualsAtRiskDaughterTotal - 1.0));
 
             // Note - notation differs slightly with what is found in STAT 855 notes, but they are equivalent.
             // Note - if individualsAtRisk == 1 then variance will be NaN.
@@ -62,10 +134,6 @@ public abstract class CompetingRiskGroupDifferentiator<Y extends CompetingRiskRe
                 summation += deltaSummation;
                 variance += deltaVariance;
             }
-            else{
-                // Do nothing; else statement left for breakpoints.
-            }
-
         }
 
         return new LogRankValue(summation, variance);

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/differentiator/GrayLogRankMultipleGroupDifferentiator.java

@@ -1,7 +1,7 @@
 package ca.joeltherrien.randomforest.responses.competingrisk.differentiator;
 
-import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskGraySetsImpl;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponseWithCensorTime;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSets;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskUtils;
 import lombok.RequiredArgsConstructor;
 
@@ -17,19 +17,17 @@ public class GrayLogRankMultipleGroupDifferentiator extends CompetingRiskGroupDi
     private final int[] events;
 
     @Override
-    public Double differentiate(List<CompetingRiskResponseWithCensorTime> leftHand, List<CompetingRiskResponseWithCensorTime> rightHand) {
-        if(leftHand.size() == 0 || rightHand.size() == 0){
-            return null;
+    protected CompetingRiskSets<CompetingRiskResponseWithCensorTime> createCompetingRiskSets(List<CompetingRiskResponseWithCensorTime> leftHand, List<CompetingRiskResponseWithCensorTime> rightHand){
+        return CompetingRiskUtils.calculateGraySetsEfficiently(leftHand, rightHand, events);
     }
 
-        final CompetingRiskGraySetsImpl competingRiskSetsLeft = CompetingRiskUtils.calculateGraySetsEfficiently(leftHand, events);
-        final CompetingRiskGraySetsImpl competingRiskSetsRight = CompetingRiskUtils.calculateGraySetsEfficiently(rightHand, events);
-
+    @Override
+    protected Double getScore(final CompetingRiskSets<CompetingRiskResponseWithCensorTime> competingRiskSets){
         double numerator = 0.0;
         double denominatorSquared = 0.0;
 
         for(final int eventOfFocus : events){
-            final LogRankValue valueOfInterest = specificLogRankValue(eventOfFocus, competingRiskSetsLeft, competingRiskSetsRight);
+            final LogRankValue valueOfInterest = specificLogRankValue(eventOfFocus, competingRiskSets);
 
             numerator += valueOfInterest.getNumerator()*valueOfInterest.getVarianceSqrt();
             denominatorSquared += valueOfInterest.getVariance();
@@ -37,7 +35,6 @@ public class GrayLogRankMultipleGroupDifferentiator extends CompetingRiskGroupDi
         }
 
         return Math.abs(numerator / Math.sqrt(denominatorSquared));
-
     }
 
 

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/differentiator/GrayLogRankSingleGroupDifferentiator.java

@@ -1,7 +1,7 @@
 package ca.joeltherrien.randomforest.responses.competingrisk.differentiator;
 
-import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskGraySetsImpl;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponseWithCensorTime;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSets;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskUtils;
 import lombok.RequiredArgsConstructor;
 
@@ -18,18 +18,14 @@ public class GrayLogRankSingleGroupDifferentiator extends CompetingRiskGroupDiff
     private final int[] events;
 
     @Override
-    public Double differentiate(List<CompetingRiskResponseWithCensorTime> leftHand, List<CompetingRiskResponseWithCensorTime> rightHand) {
-        if(leftHand.size() == 0 || rightHand.size() == 0){
-            return null;
+    protected CompetingRiskSets<CompetingRiskResponseWithCensorTime> createCompetingRiskSets(List<CompetingRiskResponseWithCensorTime> leftHand, List<CompetingRiskResponseWithCensorTime> rightHand){
+        return CompetingRiskUtils.calculateGraySetsEfficiently(leftHand, rightHand, events);
     }
 
-        final CompetingRiskGraySetsImpl competingRiskSetsLeft = CompetingRiskUtils.calculateGraySetsEfficiently(leftHand, events);
-        final CompetingRiskGraySetsImpl competingRiskSetsRight = CompetingRiskUtils.calculateGraySetsEfficiently(rightHand, events);
-
-        final LogRankValue valueOfInterest = specificLogRankValue(eventOfFocus, competingRiskSetsLeft, competingRiskSetsRight);
-
+    @Override
+    protected Double getScore(final CompetingRiskSets<CompetingRiskResponseWithCensorTime> competingRiskSets){
+        final LogRankValue valueOfInterest = specificLogRankValue(eventOfFocus, competingRiskSets);
         return Math.abs(valueOfInterest.getNumerator() / valueOfInterest.getVarianceSqrt());
-
     }
 
 }

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/differentiator/LogRankMultipleGroupDifferentiator.java

@@ -1,7 +1,7 @@
 package ca.joeltherrien.randomforest.responses.competingrisk.differentiator;
 
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
-import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSetsImpl;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSets;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskUtils;
 import lombok.RequiredArgsConstructor;
 
@@ -17,19 +17,17 @@ public class LogRankMultipleGroupDifferentiator extends CompetingRiskGroupDiffer
     private final int[] events;
 
     @Override
-    public Double differentiate(List<CompetingRiskResponse> leftHand, List<CompetingRiskResponse> rightHand) {
-        if(leftHand.size() == 0 || rightHand.size() == 0){
-            return null;
+    protected CompetingRiskSets<CompetingRiskResponse> createCompetingRiskSets(List<CompetingRiskResponse> leftHand, List<CompetingRiskResponse> rightHand){
+        return CompetingRiskUtils.calculateSetsEfficiently(leftHand, rightHand, events, true);
     }
 
-        final CompetingRiskSetsImpl competingRiskSetsLeft = CompetingRiskUtils.calculateSetsEfficiently(leftHand, events);
-        final CompetingRiskSetsImpl competingRiskSetsRight = CompetingRiskUtils.calculateSetsEfficiently(rightHand, events);
-
+    @Override
+    protected Double getScore(final CompetingRiskSets<CompetingRiskResponse> competingRiskSets){
         double numerator = 0.0;
         double denominatorSquared = 0.0;
 
         for(final int eventOfFocus : events){
-            final LogRankValue valueOfInterest = specificLogRankValue(eventOfFocus, competingRiskSetsLeft, competingRiskSetsRight);
+            final LogRankValue valueOfInterest = specificLogRankValue(eventOfFocus, competingRiskSets);
 
             numerator += valueOfInterest.getNumerator()*valueOfInterest.getVarianceSqrt();
             denominatorSquared += valueOfInterest.getVariance();
@@ -37,7 +35,7 @@ public class LogRankMultipleGroupDifferentiator extends CompetingRiskGroupDiffer
         }
 
         return Math.abs(numerator / Math.sqrt(denominatorSquared));
-
     }
 
+
 }

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/differentiator/LogRankSingleGroupDifferentiator.java

@@ -1,7 +1,7 @@
 package ca.joeltherrien.randomforest.responses.competingrisk.differentiator;
 
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
-import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSetsImpl;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSets;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskUtils;
 import lombok.RequiredArgsConstructor;
 
@@ -18,18 +18,14 @@ public class LogRankSingleGroupDifferentiator extends CompetingRiskGroupDifferen
     private final int[] events;
 
     @Override
-    public Double differentiate(List<CompetingRiskResponse> leftHand, List<CompetingRiskResponse> rightHand) {
-        if(leftHand.size() == 0 || rightHand.size() == 0){
-            return null;
+    protected CompetingRiskSets<CompetingRiskResponse> createCompetingRiskSets(List<CompetingRiskResponse> leftHand, List<CompetingRiskResponse> rightHand){
+        return CompetingRiskUtils.calculateSetsEfficiently(leftHand, rightHand, events, true);
     }
 
-        final CompetingRiskSetsImpl competingRiskSetsLeft = CompetingRiskUtils.calculateSetsEfficiently(leftHand, events);
-        final CompetingRiskSetsImpl competingRiskSetsRight = CompetingRiskUtils.calculateSetsEfficiently(rightHand, events);
-
-        final LogRankValue valueOfInterest = specificLogRankValue(eventOfFocus, competingRiskSetsLeft, competingRiskSetsRight);
-
+    @Override
+    protected Double getScore(final CompetingRiskSets<CompetingRiskResponse> competingRiskSets){
+        final LogRankValue valueOfInterest = specificLogRankValue(eventOfFocus, competingRiskSets);
         return Math.abs(valueOfInterest.getNumerator() / valueOfInterest.getVarianceSqrt());
-
     }
 
 }

src/main/java/ca/joeltherrien/randomforest/responses/regression/MeanGroupDifferentiator.java

@@ -1,26 +0,0 @@
-package ca.joeltherrien.randomforest.responses.regression;
-
-import ca.joeltherrien.randomforest.tree.GroupDifferentiator;
-
-import java.util.List;
-
-public class MeanGroupDifferentiator implements GroupDifferentiator<Double> {
-
-    @Override
-    public Double differentiate(List<Double> leftHand, List<Double> rightHand) {
-
-        double leftHandSize = leftHand.size();
-        double rightHandSize = rightHand.size();
-
-        if(leftHandSize == 0 || rightHandSize == 0){
-            return null;
-        }
-
-        double leftHandMean = leftHand.stream().mapToDouble(db -> db/leftHandSize).sum();
-        double rightHandMean = rightHand.stream().mapToDouble(db -> db/rightHandSize).sum();
-
-        return Math.abs(leftHandMean - rightHandMean);
-
-    }
-
-}

src/main/java/ca/joeltherrien/randomforest/responses/regression/WeightedVarianceGroupDifferentiator.java

@@ -1,13 +1,13 @@
 package ca.joeltherrien.randomforest.responses.regression;
 
-import ca.joeltherrien.randomforest.tree.GroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.SimpleGroupDifferentiator;
 
 import java.util.List;
 
-public class WeightedVarianceGroupDifferentiator implements GroupDifferentiator<Double> {
+public class WeightedVarianceGroupDifferentiator extends SimpleGroupDifferentiator<Double> {
 
     @Override
-    public Double differentiate(List<Double> leftHand, List<Double> rightHand) {
+    public Double getScore(List<Double> leftHand, List<Double> rightHand) {
 
         final double leftHandSize = leftHand.size();
         final double rightHandSize = rightHand.size();

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

@@ -14,8 +14,10 @@ import java.io.IOException;
 import java.io.ObjectOutputStream;
 import java.util.ArrayList;
 import java.util.List;
+import java.util.Random;
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
+import java.util.concurrent.ThreadLocalRandom;
 import java.util.concurrent.atomic.AtomicInteger;
 import java.util.stream.Collectors;
 import java.util.stream.Stream;
@@ -45,17 +47,17 @@ public class ForestTrainer<Y, TO, FO> {
         this.covariates = covariates;
         this.treeResponseCombiner = settings.getTreeCombiner();
         this.treeTrainer = new TreeTrainer<>(settings, covariates);
-
     }
 
     public Forest<TO, FO> trainSerial(){
 
         final List<Tree<TO>> trees = new ArrayList<>(ntree);
         final Bootstrapper<Row<Y>> bootstrapper = new Bootstrapper<>(data);
+        final Random random = new Random();
 
         for(int j=0; j<ntree; j++){
 
-            trees.add(trainTree(bootstrapper));
+            trees.add(trainTree(bootstrapper, random));
 
             if(displayProgress){
                 if(j==0) {
@@ -130,7 +132,7 @@ public class ForestTrainer<Y, TO, FO> {
         }
 
 
-        final File[] treeFiles = folder.listFiles(((file, s) -> s.endsWith(".tree")));
+        final File[] treeFiles = folder.listFiles((file, s) -> s.endsWith(".tree"));
 
         final ExecutorService executorService = Executors.newFixedThreadPool(threads);
         final AtomicInteger treeCount = new AtomicInteger(treeFiles.length); // tracks how many trees are finished
@@ -162,9 +164,9 @@ public class ForestTrainer<Y, TO, FO> {
 
     }
 
-    private Tree<TO> trainTree(final Bootstrapper<Row<Y>> bootstrapper){
-        final List<Row<Y>> bootstrappedData = bootstrapper.bootstrap();
-        return treeTrainer.growTree(bootstrappedData);
+    private Tree<TO> trainTree(final Bootstrapper<Row<Y>> bootstrapper, Random random){
+        final List<Row<Y>> bootstrappedData = bootstrapper.bootstrap(random);
+        return treeTrainer.growTree(bootstrappedData, random);
     }
 
     public void saveTree(final Tree<TO> tree, String name) throws IOException {
@@ -193,7 +195,8 @@ public class ForestTrainer<Y, TO, FO> {
         @Override
         public void run() {
 
-            final Tree<TO> tree = trainTree(bootstrapper);
+            // ThreadLocalRandom should make sure we don't duplicate seeds
+            final Tree<TO> tree = trainTree(bootstrapper, ThreadLocalRandom.current());
 
             // should be okay as the list structure isn't changing
             treeList.set(treeIndex, tree);
@@ -216,7 +219,8 @@ public class ForestTrainer<Y, TO, FO> {
         @Override
         public void run() {
 
-            final Tree<TO> tree = trainTree(bootstrapper);
+            // ThreadLocalRandom should make sure we don't duplicate seeds
+            final Tree<TO> tree = trainTree(bootstrapper, ThreadLocalRandom.current());
 
             try {
                 saveTree(tree, filename);

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

@@ -1,15 +1,17 @@
 package ca.joeltherrien.randomforest.tree;
 
-import java.util.List;
+import java.util.Iterator;
 
 /**
  * When choosing an optimal node to split on, we choose the split that maximizes the difference between the two groups.
- * The GroupDifferentiator has one method that outputs a score to show how different groups are. The larger the score,
- * the greater the difference.
+ * The GroupDifferentiator has one method that cycles through an iterator of Splits (FYI; check if the iterator is an
+ * instance of Covariate.SplitRuleUpdater; in which case you get access to the rows that change between splits)
  *
+ *  If you want to implement a very trivial GroupDifferentiator that just takes two Lists as arguments, try extending
+ *  SimpleGroupDifferentiator.
  */
 public interface GroupDifferentiator<Y> {
 
-    Double differentiate(List<Y> leftHand, List<Y> rightHand);
+    SplitAndScore<Y, ?> differentiate(Iterator<Split<Y, ?>> splitIterator);
 
 }

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

@@ -0,0 +1,50 @@
+package ca.joeltherrien.randomforest.tree;
+
+import ca.joeltherrien.randomforest.Row;
+
+import java.util.Iterator;
+import java.util.List;
+import java.util.stream.Collectors;
+
+public abstract class SimpleGroupDifferentiator<Y> implements GroupDifferentiator<Y> {
+
+    @Override
+    public SplitAndScore<Y, ?> differentiate(Iterator<Split<Y, ?>> splitIterator) {
+        Double bestScore = null;
+        Split<Y, ?> bestSplit = null;
+
+        while(splitIterator.hasNext()){
+            final Split<Y, ?> candidateSplit = splitIterator.next();
+
+            final List<Y> leftHand = candidateSplit.getLeftHand().stream().map(Row::getResponse).collect(Collectors.toList());
+            final List<Y> rightHand = candidateSplit.getRightHand().stream().map(Row::getResponse).collect(Collectors.toList());
+
+            if(leftHand.isEmpty() || rightHand.isEmpty()){
+                continue;
+            }
+
+            final Double score = getScore(leftHand, rightHand);
+
+            if(score != null && (bestScore == null || score > bestScore)){
+                bestScore = score;
+                bestSplit = candidateSplit;
+            }
+        }
+
+        if(bestSplit == null){
+            return null;
+        }
+
+        return new SplitAndScore<>(bestSplit, bestScore);
+    }
+
+    /**
+     * Return a score; higher is better.
+     *
+     * @param leftHand
+     * @param rightHand
+     * @return
+     */
+    public abstract Double getScore(List<Y> leftHand, List<Y> rightHand);
+
+}

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

@@ -1,19 +1,21 @@
 package ca.joeltherrien.randomforest.tree;
 
 import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.covariates.Covariate;
 import lombok.Data;
 
 import java.util.List;
 
 /**
- * Very simple class that contains three lists; it's essentially a thruple.
+ * Very simple class that contains three lists and a SplitRule.
  * 
  * @author joel
  *
  */
 @Data
-public class Split<Y> {
+public final class Split<Y, V> {
 
+	public final Covariate.SplitRule<V> splitRule;
 	public final List<Row<Y>> leftHand;
 	public final List<Row<Y>> rightHand;
 	public final List<Row<Y>> naHand;

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

@@ -0,0 +1,15 @@
+package ca.joeltherrien.randomforest.tree;
+
+import lombok.AllArgsConstructor;
+import lombok.Getter;
+
+@AllArgsConstructor
+public class SplitAndScore<Y, V> {
+
+    @Getter
+    private final Split<Y, V> split;
+
+    @Getter
+    private final Double score;
+
+}

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

@@ -8,7 +8,6 @@ import lombok.AllArgsConstructor;
 import lombok.Builder;
 
 import java.util.*;
-import java.util.concurrent.ThreadLocalRandom;
 import java.util.stream.Collectors;
 
 @Builder
@@ -47,20 +46,21 @@ public class TreeTrainer<Y, O> {
         this.covariates = covariates;
     }
 
-    public Tree<O> growTree(List<Row<Y>> data){
+    public Tree<O> growTree(List<Row<Y>> data, Random random){
 
-        final Node<O> rootNode = growNode(data, 0);
+        final Node<O> rootNode = growNode(data, 0, random);
         return new Tree<>(rootNode, data.stream().mapToInt(Row::getId).toArray());
 
     }
 
-    private Node<O> growNode(List<Row<Y>> data, int depth){
+    private Node<O> growNode(List<Row<Y>> data, int depth, Random random){
         // 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);
-            final SplitRuleAndSplit bestSplitRuleAndSplit = findBestSplitRule(data, covariatesToTry);
+            final List<Covariate> covariatesToTry = selectCovariates(this.mtry, random);
+            final Split<Y,?> bestSplit = findBestSplitRule(data, covariatesToTry, random);
 
-            if(bestSplitRuleAndSplit.splitRule == null){
+
+            if(bestSplit == null){
 
                 return new TerminalNode<>(
                         responseCombiner.combine(
@@ -71,14 +71,34 @@ public class TreeTrainer<Y, O> {
 
             }
 
-            final Split<Y> split = bestSplitRuleAndSplit.split;
-            // Note that NAs have already been handled
+
+            // Now that we have the best split; we need to handle any NAs that were dropped off
+            final double probabilityLeftHand = (double) bestSplit.leftHand.size() /
+                    (double) (bestSplit.leftHand.size() + bestSplit.rightHand.size());
+
+            // 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;
+
+                        if(randomDecision){
+                            bestSplit.getLeftHand().add(row);
+                        }
+                        else{
+                            bestSplit.getRightHand().add(row);
+                        }
+
+                    }
+                }
+            }
 
 
-            final Node<O> leftNode = growNode(split.leftHand, depth+1);
-            final Node<O> rightNode = growNode(split.rightHand, depth+1);
 
-            return new SplitNode<>(leftNode, rightNode, bestSplitRuleAndSplit.splitRule, bestSplitRuleAndSplit.probabilityLeftHand);
+            final Node<O> leftNode = growNode(bestSplit.leftHand, depth+1, random);
+            final Node<O> rightNode = growNode(bestSplit.rightHand, depth+1, random);
+
+            return new SplitNode<>(leftNode, rightNode, bestSplit.getSplitRule(), probabilityLeftHand);
 
         }
         else{
@@ -92,13 +112,13 @@ public class TreeTrainer<Y, O> {
 
     }
 
-    private List<Covariate> selectCovariates(int mtry){
+    private List<Covariate> selectCovariates(int mtry, Random random){
         if(mtry >= covariates.size()){
             return covariates;
         }
 
         final List<Covariate> splitCovariates = new ArrayList<>(covariates);
-        Collections.shuffle(splitCovariates, ThreadLocalRandom.current());
+        Collections.shuffle(splitCovariates, random);
 
         if (splitCovariates.size() > mtry) {
             splitCovariates.subList(mtry, splitCovariates.size()).clear();
@@ -107,63 +127,28 @@ public class TreeTrainer<Y, O> {
         return splitCovariates;
     }
 
-    private SplitRuleAndSplit findBestSplitRule(List<Row<Y>> data, List<Covariate> covariatesToTry){
-        SplitRuleAndSplit bestSplitRuleAndSplit = new SplitRuleAndSplit();
-        double bestSplitScore = 0.0;
-        boolean first = true;
+    private Split<Y, ?> findBestSplitRule(List<Row<Y>> data, List<Covariate> covariatesToTry, Random random){
+
+        SplitAndScore<Y, ?> bestSplitAndScore = null;
+        final GroupDifferentiator noGenericDifferentiator = groupDifferentiator; // cause Java generics suck
 
         for(final Covariate covariate : covariatesToTry) {
+            final Iterator<Split> iterator = covariate.generateSplitRuleUpdater(data, this.numberOfSplits, random);
 
-            final int numberToTry = numberOfSplits==0 ? data.size() : numberOfSplits;
+            final SplitAndScore<Y, ?> candidateSplitAndScore = noGenericDifferentiator.differentiate(iterator);
 
-            final Collection<Covariate.SplitRule> splitRulesToTry = covariate
-                    .generateSplitRules(
-                            data
-                                    .stream()
-                                    .map(row -> row.getCovariateValue(covariate))
-                                    .collect(Collectors.toList())
-                            , numberToTry);
-
-            for(final Covariate.SplitRule possibleRule : splitRulesToTry){
-                final Split<Y> possibleSplit = possibleRule.applyRule(data);
-
-                // We have to handle any NAs
-                if(possibleSplit.leftHand.size() == 0 && possibleSplit.rightHand.size() == 0 && possibleSplit.naHand.size() > 0){
-                    throw new IllegalArgumentException("Can't apply " + this + " when there are rows with missing data and no non-missing value rows");
-                }
-
-                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){
-                        possibleSplit.leftHand.add(missingValueRow);
-                    }
-                    else{
-                        possibleSplit.rightHand.add(missingValueRow);
-                    }
-                }
-
-                final Double score = groupDifferentiator.differentiate(
-                        possibleSplit.leftHand.stream().map(row -> row.getResponse()).collect(Collectors.toList()),
-                        possibleSplit.rightHand.stream().map(row -> row.getResponse()).collect(Collectors.toList())
-                );
-
-
-                if(score != null && !Double.isNaN(score) && (score > bestSplitScore || first)){
-                    bestSplitRuleAndSplit.splitRule = possibleRule;
-                    bestSplitRuleAndSplit.split = possibleSplit;
-                    bestSplitRuleAndSplit.probabilityLeftHand = probabilityLeftHand;
-
-                    bestSplitScore = score;
-                    first = false;
-                }
+            if(candidateSplitAndScore != null && (bestSplitAndScore == null ||
+                    candidateSplitAndScore.getScore() > bestSplitAndScore.getScore())) {
+                bestSplitAndScore = candidateSplitAndScore;
             }
 
         }
 
-        return bestSplitRuleAndSplit;
+        if(bestSplitAndScore == null){
+            return null;
+        }
+
+        return bestSplitAndScore.getSplit();
 
     }
 
@@ -186,10 +171,4 @@ public class TreeTrainer<Y, O> {
         return true;
     }
 
-    private class SplitRuleAndSplit{
-        private Covariate.SplitRule splitRule = null;
-        private Split<Y> split = null;
-        private double probabilityLeftHand;
-    }
-
 }

src/main/java/ca/joeltherrien/randomforest/utils/IndexedIterator.java

@@ -0,0 +1,9 @@
+package ca.joeltherrien.randomforest.utils;
+
+import java.util.Iterator;
+
+public interface IndexedIterator<E> extends Iterator<E> {
+
+    int getIndex();
+
+}

src/main/java/ca/joeltherrien/randomforest/utils/SingletonIterator.java

@@ -0,0 +1,29 @@
+package ca.joeltherrien.randomforest.utils;
+
+import lombok.RequiredArgsConstructor;
+
+import java.util.Iterator;
+
+@RequiredArgsConstructor
+public class SingletonIterator<E> implements Iterator<E> {
+
+    private final E value;
+
+    private boolean beenCalled = false;
+
+
+    @Override
+    public boolean hasNext() {
+        return !beenCalled;
+    }
+
+    @Override
+    public E next() {
+        if(!beenCalled){
+            beenCalled = true;
+            return value;
+        }
+
+        return null;
+    }
+}

src/main/java/ca/joeltherrien/randomforest/utils/UniqueSubsetValueIterator.java

@@ -0,0 +1,61 @@
+package ca.joeltherrien.randomforest.utils;
+
+
+/**
+ * Iterator that wraps around a UniqueValueIterator. It continues to iterate until it gets to one of the prespecified indexes,
+ * and then proceeds just past that to the end of the existing values it's at.
+ *
+ * The wrapped iterator must be from a sorted collection of some sort such that equal values are clumped together.
+ *  I.e. "b b c c c d d a a" is okay but "a b b c c a" is not as 'a' appears twice at different locations
+ *
+ * @param <E>
+ */
+public class UniqueSubsetValueIterator<E> implements IndexedIterator<E> {
+
+    private final UniqueValueIterator<E> iterator;
+    private final Integer[] indexValues;
+
+    private int currentIndexSpot = 0;
+
+    public UniqueSubsetValueIterator(final UniqueValueIterator<E> iterator, final Integer[] indexValues){
+        this.iterator = iterator;
+        this.indexValues = indexValues;
+    }
+
+    @Override
+    public boolean hasNext() {
+        return iterator.hasNext() && iterator.getIndex() <= indexValues[indexValues.length-1];
+    }
+
+    @Override
+    public E next() {
+        if(hasNext()){
+            final int indexToStopBy = indexValues[currentIndexSpot];
+
+            while(iterator.getIndex() <= indexToStopBy){
+                iterator.next();
+            }
+
+            for(int i = currentIndexSpot + 1; i < indexValues.length; i++){
+                if(iterator.getIndex() <= indexValues[i]){
+                    currentIndexSpot = i;
+                    break;
+                }
+            }
+
+
+            return iterator.getCurrentValue();
+
+        }
+
+        return null;
+
+    }
+
+    @Override
+    public int getIndex(){
+        return iterator.getIndex();
+    }
+
+
+}

src/main/java/ca/joeltherrien/randomforest/utils/UniqueValueIterator.java

@@ -0,0 +1,71 @@
+package ca.joeltherrien.randomforest.utils;
+
+
+import lombok.Getter;
+
+import java.util.Iterator;
+
+/**
+ * Iterator that wraps around another iterator. It continues to iterate until it gets to the *end* of a sequence of identical values.
+ * It also tracks the current index in the original iterator.
+ *
+ * The wrapped iterator must be from a sorted collection of some sort such that equal values are clumped together.
+ *  I.e. "b b c c c d d a a" is okay but "a b b c c a" is not as 'a' appears twice at different locations
+ *
+ * @param <E>
+ */
+public class UniqueValueIterator<E> implements IndexedIterator<E> {
+
+    private final Iterator<E> wrappedIterator;
+
+    @Getter private E currentValue = null;
+    @Getter private E nextValue;
+
+    public UniqueValueIterator(final Iterator<E> wrappedIterator){
+        this.wrappedIterator = wrappedIterator;
+        this.nextValue = wrappedIterator.next();
+    }
+
+    // Count must return the index of the last value of the sequence returned by next()
+    @Getter
+    private int index = 0;
+
+    @Override
+    public boolean hasNext() {
+        return nextValue != null;
+    }
+
+    @Override
+    public E next() {
+
+        int count = 1;
+        while(wrappedIterator.hasNext()){
+            final E currentIteratorValue = wrappedIterator.next();
+
+            if(currentIteratorValue.equals(nextValue)){
+                count++;
+            }
+            else{
+                index +=count;
+                currentValue = nextValue;
+                nextValue = currentIteratorValue;
+
+                return currentValue;
+            }
+
+        }
+
+        if(nextValue != null){
+            index += count;
+            currentValue = nextValue;
+            nextValue = null;
+
+            return currentValue;
+        }
+        else{
+            return null;
+        }
+
+
+    }
+}

src/test/java/ca/joeltherrien/randomforest/TestSavingLoading.java

@@ -1,8 +1,8 @@
 package ca.joeltherrien.randomforest;
 
-import ca.joeltherrien.randomforest.covariates.BooleanCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.BooleanCovariateSettings;
 import ca.joeltherrien.randomforest.covariates.Covariate;
-import ca.joeltherrien.randomforest.covariates.NumericCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.NumericCovariateSettings;
 import ca.joeltherrien.randomforest.responses.competingrisk.combiner.CompetingRiskFunctionCombiner;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskFunctions;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;

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

@@ -1,214 +0,0 @@
-package ca.joeltherrien.randomforest.competingrisk;
-
-import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskGraySetsImpl;
-import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponseWithCensorTime;
-import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSetsImpl;
-import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskUtils;
-import org.junit.jupiter.api.Test;
-
-import java.util.ArrayList;
-import java.util.List;
-
-import static org.junit.jupiter.api.Assertions.assertEquals;
-
-public class TestCalculatingCompetingRiskSets {
-
-    public List<CompetingRiskResponseWithCensorTime> generateData(){
-        final List<CompetingRiskResponseWithCensorTime> data = new ArrayList<>();
-
-        data.add(new CompetingRiskResponseWithCensorTime(1, 1, 3));
-        data.add(new CompetingRiskResponseWithCensorTime(1, 1, 3));
-        data.add(new CompetingRiskResponseWithCensorTime(0, 1, 1));
-        data.add(new CompetingRiskResponseWithCensorTime(1, 2, 2.5));
-        data.add(new CompetingRiskResponseWithCensorTime(2, 3, 4));
-        data.add(new CompetingRiskResponseWithCensorTime(0, 3, 3));
-        data.add(new CompetingRiskResponseWithCensorTime(1, 4, 4));
-        data.add(new CompetingRiskResponseWithCensorTime(0, 5, 5));
-        data.add(new CompetingRiskResponseWithCensorTime(2, 6, 7));
-
-        return data;
-    }
-
-    @Test
-    public void testCalculatingSets(){
-        final List data = generateData();
-
-        final CompetingRiskSetsImpl sets = CompetingRiskUtils.calculateSetsEfficiently(data, new int[]{1,2});
-
-        final List<Double> times = sets.getEventTimes();
-        assertEquals(5, times.size());
-
-        // Times
-        assertEquals(1.0, times.get(0).doubleValue());
-        assertEquals(2.0, times.get(1).doubleValue());
-        assertEquals(3.0, times.get(2).doubleValue());
-        assertEquals(4.0, times.get(3).doubleValue());
-        assertEquals(6.0, times.get(4).doubleValue());
-
-        // Number of Events
-        assertEquals(2, sets.getNumberOfEvents(1.0, 1));
-        assertEquals(0, sets.getNumberOfEvents(1.0, 2));
-
-        assertEquals(1, sets.getNumberOfEvents(2.0, 1));
-        assertEquals(0, sets.getNumberOfEvents(2.0, 2));
-
-        assertEquals(0, sets.getNumberOfEvents(3.0, 1));
-        assertEquals(1, sets.getNumberOfEvents(3.0, 2));
-
-        assertEquals(1, sets.getNumberOfEvents(4.0, 1));
-        assertEquals(0, sets.getNumberOfEvents(4.0, 2));
-
-        assertEquals(0, sets.getNumberOfEvents(6.0, 1));
-        assertEquals(1, sets.getNumberOfEvents(6.0, 2));
-
-        // Make sure it doesn't break for other times
-        assertEquals(0, sets.getNumberOfEvents(5.5, 1));
-        assertEquals(0, sets.getNumberOfEvents(5.5, 2));
-
-
-        // Risk set
-        assertEquals(9, sets.getRiskSet(1).evaluate(0.5));
-        assertEquals(9, sets.getRiskSet(2).evaluate(0.5));
-
-        assertEquals(9, sets.getRiskSet(1).evaluate(1.0));
-        assertEquals(9, sets.getRiskSet(2).evaluate(1.0));
-
-        assertEquals(6, sets.getRiskSet(1).evaluate(1.5));
-        assertEquals(6, sets.getRiskSet(2).evaluate(1.5));
-
-        assertEquals(6, sets.getRiskSet(1).evaluate(2.0));
-        assertEquals(6, sets.getRiskSet(2).evaluate(2.0));
-
-        assertEquals(5, sets.getRiskSet(1).evaluate(2.3));
-        assertEquals(5, sets.getRiskSet(2).evaluate(2.3));
-
-        assertEquals(5, sets.getRiskSet(1).evaluate(2.5));
-        assertEquals(5, sets.getRiskSet(2).evaluate(2.5));
-
-        assertEquals(5, sets.getRiskSet(1).evaluate(2.7));
-        assertEquals(5, sets.getRiskSet(2).evaluate(2.7));
-
-        assertEquals(5, sets.getRiskSet(1).evaluate(3.0));
-        assertEquals(5, sets.getRiskSet(2).evaluate(3.0));
-
-        assertEquals(3, sets.getRiskSet(1).evaluate(3.5));
-        assertEquals(3, sets.getRiskSet(2).evaluate(3.5));
-
-        assertEquals(3, sets.getRiskSet(1).evaluate(4.0));
-        assertEquals(3, sets.getRiskSet(2).evaluate(4.0));
-
-        assertEquals(2, sets.getRiskSet(1).evaluate(4.5));
-        assertEquals(2, sets.getRiskSet(2).evaluate(4.5));
-
-        assertEquals(2, sets.getRiskSet(1).evaluate(5.0));
-        assertEquals(2, sets.getRiskSet(2).evaluate(5.0));
-
-        assertEquals(1, sets.getRiskSet(1).evaluate(5.5));
-        assertEquals(1, sets.getRiskSet(2).evaluate(5.5));
-
-        assertEquals(1, sets.getRiskSet(1).evaluate(6.0));
-        assertEquals(1, sets.getRiskSet(2).evaluate(6.0));
-
-        assertEquals(0, sets.getRiskSet(1).evaluate(6.5));
-        assertEquals(0, sets.getRiskSet(2).evaluate(6.5));
-
-        assertEquals(0, sets.getRiskSet(1).evaluate(7.0));
-        assertEquals(0, sets.getRiskSet(2).evaluate(7.0));
-
-        assertEquals(0, sets.getRiskSet(1).evaluate(7.5));
-        assertEquals(0, sets.getRiskSet(2).evaluate(7.5));
-
-    }
-
-    @Test
-    public void testCalculatingGraySets(){
-        final List<CompetingRiskResponseWithCensorTime> data = generateData();
-
-        final CompetingRiskGraySetsImpl sets = CompetingRiskUtils.calculateGraySetsEfficiently(data, new int[]{1,2});
-
-        final List<Double> times = sets.getEventTimes();
-        assertEquals(5, times.size());
-
-        // Times
-        assertEquals(1.0, times.get(0).doubleValue());
-        assertEquals(2.0, times.get(1).doubleValue());
-        assertEquals(3.0, times.get(2).doubleValue());
-        assertEquals(4.0, times.get(3).doubleValue());
-        assertEquals(6.0, times.get(4).doubleValue());
-
-        // Number of Events
-        assertEquals(2, sets.getNumberOfEvents(1.0, 1));
-        assertEquals(0, sets.getNumberOfEvents(1.0, 2));
-
-        assertEquals(1, sets.getNumberOfEvents(2.0, 1));
-        assertEquals(0, sets.getNumberOfEvents(2.0, 2));
-
-        assertEquals(0, sets.getNumberOfEvents(3.0, 1));
-        assertEquals(1, sets.getNumberOfEvents(3.0, 2));
-
-        assertEquals(1, sets.getNumberOfEvents(4.0, 1));
-        assertEquals(0, sets.getNumberOfEvents(4.0, 2));
-
-        assertEquals(0, sets.getNumberOfEvents(6.0, 1));
-        assertEquals(1, sets.getNumberOfEvents(6.0, 2));
-
-        // Make sure it doesn't break for other times
-        assertEquals(0, sets.getNumberOfEvents(5.5, 1));
-        assertEquals(0, sets.getNumberOfEvents(5.5, 2));
-
-
-        // Risk set
-        assertEquals(9, sets.getRiskSet(1).evaluate(0.5));
-        assertEquals(9, sets.getRiskSet(2).evaluate(0.5));
-
-        assertEquals(9, sets.getRiskSet(1).evaluate(1.0));
-        assertEquals(9, sets.getRiskSet(2).evaluate(1.0));
-
-        assertEquals(6, sets.getRiskSet(1).evaluate(1.5));
-        assertEquals(8, sets.getRiskSet(2).evaluate(1.5));
-
-        assertEquals(6, sets.getRiskSet(1).evaluate(2.0));
-        assertEquals(8, sets.getRiskSet(2).evaluate(2.0));
-
-        assertEquals(5, sets.getRiskSet(1).evaluate(2.3));
-        assertEquals(8, sets.getRiskSet(2).evaluate(2.3));
-
-        assertEquals(5, sets.getRiskSet(1).evaluate(2.5));
-        assertEquals(7, sets.getRiskSet(2).evaluate(2.5));
-
-        assertEquals(5, sets.getRiskSet(1).evaluate(2.7));
-        assertEquals(7, sets.getRiskSet(2).evaluate(2.7));
-
-        assertEquals(5, sets.getRiskSet(1).evaluate(3.0));
-        assertEquals(5, sets.getRiskSet(2).evaluate(3.0));
-
-        assertEquals(4, sets.getRiskSet(1).evaluate(3.5));
-        assertEquals(3, sets.getRiskSet(2).evaluate(3.5));
-
-        assertEquals(3, sets.getRiskSet(1).evaluate(4.0));
-        assertEquals(3, sets.getRiskSet(2).evaluate(4.0));
-
-        assertEquals(2, sets.getRiskSet(1).evaluate(4.5));
-        assertEquals(2, sets.getRiskSet(2).evaluate(4.5));
-
-        assertEquals(2, sets.getRiskSet(1).evaluate(5.0));
-        assertEquals(2, sets.getRiskSet(2).evaluate(5.0));
-
-        assertEquals(1, sets.getRiskSet(1).evaluate(5.5));
-        assertEquals(1, sets.getRiskSet(2).evaluate(5.5));
-
-        assertEquals(1, sets.getRiskSet(1).evaluate(6.0));
-        assertEquals(1, sets.getRiskSet(2).evaluate(6.0));
-
-        assertEquals(1, sets.getRiskSet(1).evaluate(6.5));
-        assertEquals(0, sets.getRiskSet(2).evaluate(6.5));
-
-        assertEquals(0, sets.getRiskSet(1).evaluate(7.0));
-        assertEquals(0, sets.getRiskSet(2).evaluate(7.0));
-
-        assertEquals(0, sets.getRiskSet(1).evaluate(7.5));
-        assertEquals(0, sets.getRiskSet(2).evaluate(7.5));
-
-    }
-
-}

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

@@ -1,8 +1,15 @@
 package ca.joeltherrien.randomforest.competingrisk;
 
-import ca.joeltherrien.randomforest.*;
-import ca.joeltherrien.randomforest.covariates.*;
-import ca.joeltherrien.randomforest.responses.competingrisk.*;
+import ca.joeltherrien.randomforest.CovariateRow;
+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.settings.BooleanCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.NumericCovariateSettings;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskErrorRateCalculator;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskFunctions;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
 import ca.joeltherrien.randomforest.tree.Forest;
 import ca.joeltherrien.randomforest.tree.ForestTrainer;
 import ca.joeltherrien.randomforest.tree.Node;
@@ -12,12 +19,14 @@ import ca.joeltherrien.randomforest.utils.Utils;
 import com.fasterxml.jackson.databind.node.*;
 import org.junit.jupiter.api.Test;
 
-import static ca.joeltherrien.randomforest.TestUtils.assertCumulativeFunction;
-import static ca.joeltherrien.randomforest.TestUtils.closeEnough;
-import static org.junit.jupiter.api.Assertions.*;
-
 import java.io.IOException;
 import java.util.List;
+import java.util.Random;
+
+import static ca.joeltherrien.randomforest.TestUtils.assertCumulativeFunction;
+import static ca.joeltherrien.randomforest.TestUtils.closeEnough;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertTrue;
 
 public class TestCompetingRisk {
 
@@ -104,7 +113,7 @@ public class TestCompetingRisk {
         final List<Row<CompetingRiskResponse>> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(), settings.getTrainingDataLocation());
 
         final TreeTrainer<CompetingRiskResponse, CompetingRiskFunctions> treeTrainer = new TreeTrainer<>(settings, covariates);
-        final Node<CompetingRiskFunctions> node = treeTrainer.growTree(dataset);
+        final Node<CompetingRiskFunctions> node = treeTrainer.growTree(dataset, new Random());
 
         final CovariateRow newRow = getPredictionRow(covariates);
 
@@ -157,7 +166,7 @@ public class TestCompetingRisk {
         final List<Row<CompetingRiskResponse>> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(), settings.getTrainingDataLocation());
 
         final TreeTrainer<CompetingRiskResponse, CompetingRiskFunctions> treeTrainer = new TreeTrainer<>(settings, covariates);
-        final Node<CompetingRiskFunctions> node = treeTrainer.growTree(dataset);
+        final Node<CompetingRiskFunctions> node = treeTrainer.growTree(dataset, new Random());
 
         final CovariateRow newRow = getPredictionRow(covariates);
 
@@ -281,6 +290,34 @@ public class TestCompetingRisk {
         assertEquals(359, countEventTwo);
     }
 
+    /**
+     * Used to time how long the algorithm takes
+     *
+     * @param args Not used.
+     * @throws IOException
+     */
+    public static void main(String[] args) throws IOException {
+        // timing
+        final TestCompetingRisk tcr = new TestCompetingRisk();
+
+        final Settings settings = tcr.getSettings();
+        settings.setNtree(300); // results are too variable at 100
+
+        final List<Covariate> covariates = settings.getCovariates();
+        final List<Row<CompetingRiskResponse>> dataset = DataLoader.loadData(covariates, settings.getResponseLoader(),
+                settings.getTrainingDataLocation());
+        final ForestTrainer<CompetingRiskResponse, CompetingRiskFunctions, CompetingRiskFunctions> forestTrainer = new ForestTrainer<>(settings, dataset, covariates);
+
+        final long startTime = System.currentTimeMillis();
+        for(int i=0; i<50; i++){
+            forestTrainer.trainSerial();
+        }
+        final long endTime = System.currentTimeMillis();
+
+        final double diffTime = endTime - startTime;
+        System.out.println(diffTime / 1000.0 / 50.0);
+    }
+
     @Test
     public void testLogRankSingleGroupDifferentiatorAllCovariates() throws IOException {
 

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

@@ -0,0 +1,89 @@
+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.settings.NumericCovariateSettings;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
+import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.LogRankMultipleGroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.Split;
+import ca.joeltherrien.randomforest.utils.SingletonIterator;
+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.Collections;
+import java.util.Iterator;
+import java.util.List;
+import java.util.stream.Collectors;
+
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class TestLogRankMultipleGroupDifferentiator {
+
+    private Iterator<Split<CompetingRiskResponse, ?>> turnIntoSplitIterator(List<Row<CompetingRiskResponse>> leftList,
+                                                                                 List<Row<CompetingRiskResponse>> rightList){
+        return new SingletonIterator<Split<CompetingRiskResponse, ?>>(new Split(null, leftList, rightList, Collections.emptyList()));
+    }
+
+    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(turnIntoSplitIterator(group1Bad, group2Bad)).getScore();
+
+        final List<Row<CompetingRiskResponse>> group1Good = data.subList(0, 199);
+        final List<Row<CompetingRiskResponse>> group2Good= data.subList(199, data.size());
+
+        final double scoreGood = groupDifferentiator.differentiate(turnIntoSplitIterator(group1Good, group2Good)).getScore();
+
+        // 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

@@ -1,52 +1,74 @@
 package ca.joeltherrien.randomforest.competingrisk;
 
-import ca.joeltherrien.randomforest.responses.competingrisk.*;
+import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
 import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.LogRankSingleGroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.GroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.Split;
+import ca.joeltherrien.randomforest.utils.SingletonIterator;
 import org.junit.jupiter.api.Test;
 
+import java.io.IOException;
 import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Iterator;
 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 {
 
-    private List<CompetingRiskResponse> generateData1(){
-        final List<CompetingRiskResponse> data = new ArrayList<>();
+    private double getScore(final GroupDifferentiator<CompetingRiskResponse> groupDifferentiator, List<Row<CompetingRiskResponse>> left, List<Row<CompetingRiskResponse>> right){
+        final Iterator<Split<CompetingRiskResponse, ?>> iterator = new SingletonIterator<>(
+                new Split<>(null, left, right, Collections.emptyList()));
 
-        data.add(new CompetingRiskResponse(1, 1.0));
-        data.add(new CompetingRiskResponse(1, 1.0));
-        data.add(new CompetingRiskResponse(1, 2.0));
-        data.add(new CompetingRiskResponse(1, 1.5));
-        data.add(new CompetingRiskResponse(0, 2.0));
-        data.add(new CompetingRiskResponse(0, 1.5));
-        data.add(new CompetingRiskResponse(0, 2.5));
+        return groupDifferentiator.differentiate(iterator).getScore();
+
+    }
+
+    int count = 1;
+    private <Y> Row<Y> createRow(Y response){
+        return new Row<>(null, count++, response);
+    }
+
+    private List<Row<CompetingRiskResponse>> generateData1(){
+        final List<Row<CompetingRiskResponse>> data = new ArrayList<>();
+
+        data.add(createRow(new CompetingRiskResponse(1, 1.0)));
+        data.add(createRow(new CompetingRiskResponse(1, 1.0)));
+        data.add(createRow(new CompetingRiskResponse(1, 2.0)));
+        data.add(createRow(new CompetingRiskResponse(1, 1.5)));
+        data.add(createRow(new CompetingRiskResponse(0, 2.0)));
+        data.add(createRow(new CompetingRiskResponse(0, 1.5)));
+        data.add(createRow(new CompetingRiskResponse(0, 2.5)));
 
         return data;
     }
 
-    private List<CompetingRiskResponse> generateData2(){
-        final List<CompetingRiskResponse> data = new ArrayList<>();
+    private List<Row<CompetingRiskResponse>> generateData2(){
+        final List<Row<CompetingRiskResponse>> data = new ArrayList<>();
 
-        data.add(new CompetingRiskResponse(1, 2.0));
-        data.add(new CompetingRiskResponse(1, 2.0));
-        data.add(new CompetingRiskResponse(1, 4.0));
-        data.add(new CompetingRiskResponse(1, 3.0));
-        data.add(new CompetingRiskResponse(0, 4.0));
-        data.add(new CompetingRiskResponse(0, 3.0));
-        data.add(new CompetingRiskResponse(0, 5.0));
+        data.add(createRow(new CompetingRiskResponse(1, 2.0)));
+        data.add(createRow(new CompetingRiskResponse(1, 2.0)));
+        data.add(createRow(new CompetingRiskResponse(1, 4.0)));
+        data.add(createRow(new CompetingRiskResponse(1, 3.0)));
+        data.add(createRow(new CompetingRiskResponse(0, 4.0)));
+        data.add(createRow(new CompetingRiskResponse(0, 3.0)));
+        data.add(createRow(new CompetingRiskResponse(0, 5.0)));
 
         return data;
     }
 
     @Test
     public void testCompetingRiskResponseCombiner(){
-        final List<CompetingRiskResponse> data1 = generateData1();
-        final List<CompetingRiskResponse> data2 = generateData2();
+        final List<Row<CompetingRiskResponse>> data1 = generateData1();
+        final List<Row<CompetingRiskResponse>> data2 = generateData2();
 
         final LogRankSingleGroupDifferentiator differentiator = new LogRankSingleGroupDifferentiator(1, new int[]{1});
 
-        final double score = differentiator.differentiate(data1, data2);
+        final double score = getScore(differentiator, data1, data2);
         final double margin = 0.000001;
 
         // Tested using 855 method
@@ -55,6 +77,28 @@ 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 = getScore(groupDifferentiator, group1Good, group2Good);
+
+        final List<Row<CompetingRiskResponse>> group1Bad = data.subList(0, 222);
+        final List<Row<CompetingRiskResponse>> group2Bad = data.subList(222, data.size());
+
+        final double scoreBad = getScore(groupDifferentiator, group1Bad, group2Bad);
+
+        // 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/java/ca/joeltherrien/randomforest/covariates/FactorCovariateTest.java

@@ -5,8 +5,9 @@ import ca.joeltherrien.randomforest.utils.Utils;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.function.Executable;
 
-import java.util.Collection;
+import java.util.ArrayList;
 import java.util.List;
+import java.util.Random;
 
 import static org.junit.jupiter.api.Assertions.*;
 
@@ -46,7 +47,10 @@ public class FactorCovariateTest {
     void testAllSubsets(){
         final FactorCovariate petCovariate = createTestCovariate();
 
-        final Collection<FactorCovariate.FactorSplitRule> splitRules = petCovariate.generateSplitRules(null, 100);
+        final List<Covariate.SplitRule<String>> splitRules = new ArrayList<>();
+
+        petCovariate.generateSplitRuleUpdater(null, 100, new Random())
+                .forEachRemaining(split -> splitRules.add(split.getSplitRule()));
 
         assertEquals(splitRules.size(), 3);
 

src/test/java/ca/joeltherrien/randomforest/csv/TestLoadingCSV.java

@@ -3,10 +3,10 @@ package ca.joeltherrien.randomforest.csv;
 import ca.joeltherrien.randomforest.DataLoader;
 import ca.joeltherrien.randomforest.Row;
 import ca.joeltherrien.randomforest.Settings;
-import ca.joeltherrien.randomforest.covariates.BooleanCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.BooleanCovariateSettings;
 import ca.joeltherrien.randomforest.covariates.Covariate;
-import ca.joeltherrien.randomforest.covariates.FactorCovariateSettings;
-import ca.joeltherrien.randomforest.covariates.NumericCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.FactorCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.NumericCovariateSettings;
 import ca.joeltherrien.randomforest.utils.Utils;
 import com.fasterxml.jackson.databind.node.JsonNodeFactory;
 import com.fasterxml.jackson.databind.node.ObjectNode;
@@ -15,7 +15,6 @@ 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.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertTrue;

src/test/java/ca/joeltherrien/randomforest/settings/TestPersistence.java

@@ -3,7 +3,9 @@ package ca.joeltherrien.randomforest.settings;
 import ca.joeltherrien.randomforest.Settings;
 import static org.junit.jupiter.api.Assertions.assertEquals;
 
-import ca.joeltherrien.randomforest.covariates.*;
+import ca.joeltherrien.randomforest.covariates.settings.BooleanCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.FactorCovariateSettings;
+import ca.joeltherrien.randomforest.covariates.settings.NumericCovariateSettings;
 import ca.joeltherrien.randomforest.utils.Utils;
 import com.fasterxml.jackson.databind.node.JsonNodeFactory;
 import com.fasterxml.jackson.databind.node.ObjectNode;

src/test/java/ca/joeltherrien/randomforest/utils/TestSingletonIterator.java

@@ -0,0 +1,25 @@
+package ca.joeltherrien.randomforest.utils;
+
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+public class TestSingletonIterator {
+
+    @Test
+    public void verifyBehaviour(){
+        final Integer element = 5;
+
+        final SingletonIterator<Integer> iterator = new SingletonIterator<>(element);
+
+        assertTrue(iterator.hasNext());
+        assertTrue(iterator.hasNext());
+
+        assertEquals(Integer.valueOf(5), iterator.next());
+
+        assertFalse(iterator.hasNext());
+        assertNull(iterator.next());
+
+    }
+
+}

src/test/java/ca/joeltherrien/randomforest/utils/TestUniqueSubsetValueIterator.java

@@ -0,0 +1,68 @@
+package ca.joeltherrien.randomforest.utils;
+
+import org.junit.jupiter.api.Test;
+
+import java.util.Arrays;
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+public class TestUniqueSubsetValueIterator {
+
+    @Test
+    public void testIterator1(){
+        final List<Integer> testData = Arrays.asList(
+                1,1,2,3,5,5,5,6,7,7
+        );
+
+        final Integer[] indexes = new Integer[]{2,3,4,5};
+
+        final UniqueValueIterator<Integer> uniqueValueIterator = new UniqueValueIterator<>(testData.iterator());
+        final UniqueSubsetValueIterator<Integer> iterator = new UniqueSubsetValueIterator<>(uniqueValueIterator, indexes);
+
+        // we expect to get 2, 3, and 5 back. 5 should happen only once
+
+        assertEquals(iterator.getIndex(), 0);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 2);
+        assertEquals(iterator.getIndex(), 3);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 3);
+        assertEquals(iterator.getIndex(), 4);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 5);
+        assertEquals(iterator.getIndex(), 7);
+        assertFalse(iterator.hasNext());
+
+
+    }
+
+    @Test
+    public void testIterator2(){
+        final List<Integer> testData = Arrays.asList(
+                1,1,2,3,5,5,5,6,7,7
+        );
+
+        final Integer[] indexes = new Integer[]{1,8};
+
+        final UniqueValueIterator<Integer> uniqueValueIterator = new UniqueValueIterator<>(testData.iterator());
+        final UniqueSubsetValueIterator<Integer> iterator = new UniqueSubsetValueIterator<>(uniqueValueIterator, indexes);
+
+        assertEquals(iterator.getIndex(), 0);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 1);
+        assertEquals(iterator.getIndex(), 2);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 7);
+        assertEquals(iterator.getIndex(), 10);
+        assertFalse(iterator.hasNext());
+
+
+    }
+
+}

src/test/java/ca/joeltherrien/randomforest/utils/TestUniqueValueIterator.java

@@ -0,0 +1,112 @@
+package ca.joeltherrien.randomforest.utils;
+
+import org.junit.jupiter.api.Test;
+
+import java.util.Arrays;
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+public class TestUniqueValueIterator {
+
+    @Test
+    public void testIterator1(){
+        final List<Integer> testData = Arrays.asList(
+                1,1,2,3,5,5,5,6,7,7
+        );
+
+        final UniqueValueIterator<Integer> iterator = new UniqueValueIterator<>(testData.iterator());
+
+        assertEquals(iterator.getIndex(), 0);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 1);
+        assertEquals(iterator.getIndex(), 2);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 2);
+        assertEquals(iterator.getIndex(), 3);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 3);
+        assertEquals(iterator.getIndex(), 4);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 5);
+        assertEquals(iterator.getIndex(), 7);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 6);
+        assertEquals(iterator.getIndex(), 8);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 7);
+        assertEquals(iterator.getIndex(), 10);
+        assertTrue(!iterator.hasNext());
+
+    }
+
+    @Test
+    public void testIterator2(){
+        final List<Integer> testData = Arrays.asList(
+                1,2,3,5,5,5,6,7 // same numbers; but 1 and 7 only appear once each
+        );
+
+        final UniqueValueIterator<Integer> iterator = new UniqueValueIterator<>(testData.iterator());
+
+        assertEquals(iterator.getIndex(), 0);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 1);
+        assertEquals(iterator.getIndex(), 1);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 2);
+        assertEquals(iterator.getIndex(), 2);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 3);
+        assertEquals(iterator.getIndex(), 3);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 5);
+        assertEquals(iterator.getIndex(), 6);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 6);
+        assertEquals(iterator.getIndex(), 7);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 7);
+        assertEquals(iterator.getIndex(), 8);
+        assertFalse(iterator.hasNext());
+
+    }
+
+    @Test
+    public void testIterator3(){
+        final List<Integer> testData = Arrays.asList(
+                1,1,1,1,1,1,1,2,2,2,2,2,3
+        );
+
+        final UniqueValueIterator<Integer> iterator = new UniqueValueIterator<>(testData.iterator());
+
+        assertEquals(iterator.getIndex(), 0);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 1);
+        assertEquals(iterator.getIndex(), 7);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 2);
+        assertEquals(iterator.getIndex(), 12);
+        assertTrue(iterator.hasNext());
+
+        assertEquals(iterator.next().intValue(), 3);
+        assertEquals(iterator.getIndex(), 13);
+        assertFalse(iterator.hasNext());
+
+
+    }
+
+}

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

@@ -2,7 +2,7 @@ package ca.joeltherrien.randomforest.workshop;
 
 import ca.joeltherrien.randomforest.*;
 import ca.joeltherrien.randomforest.covariates.Covariate;
-import ca.joeltherrien.randomforest.covariates.NumericCovariate;
+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.ForestTrainer;

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

@@ -3,7 +3,7 @@ package ca.joeltherrien.randomforest.workshop;
 
 import ca.joeltherrien.randomforest.covariates.Covariate;
 import ca.joeltherrien.randomforest.CovariateRow;
-import ca.joeltherrien.randomforest.covariates.NumericCovariate;
+import ca.joeltherrien.randomforest.covariates.numeric.NumericCovariate;
 import ca.joeltherrien.randomforest.Row;
 import ca.joeltherrien.randomforest.responses.regression.MeanResponseCombiner;
 import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceGroupDifferentiator;
@@ -54,13 +54,14 @@ public class TrainSingleTree {
                 .covariates(covariateNames)
                 .responseCombiner(new MeanResponseCombiner())
                 .maxNodeDepth(30)
+                .mtry(2)
                 .nodeSize(5)
                 .numberOfSplits(0)
                 .build();
 
 
         final long startTime = System.currentTimeMillis();
-        final Node<Double> baseNode = treeTrainer.growTree(trainingSet);
+        final Node<Double> baseNode = treeTrainer.growTree(trainingSet, new Random());
         final long endTime = System.currentTimeMillis();
 
         System.out.println(((double)(endTime - startTime))/1000.0);

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

@@ -4,7 +4,7 @@ package ca.joeltherrien.randomforest.workshop;
 import ca.joeltherrien.randomforest.*;
 import ca.joeltherrien.randomforest.covariates.Covariate;
 import ca.joeltherrien.randomforest.covariates.FactorCovariate;
-import ca.joeltherrien.randomforest.covariates.NumericCovariate;
+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.Node;
@@ -13,7 +13,6 @@ import ca.joeltherrien.randomforest.utils.Utils;
 
 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;
@@ -21,8 +20,6 @@ import java.util.stream.DoubleStream;
 public class TrainSingleTreeFactor {
 
 	public static void main(String[] args) {
-		System.out.println("Hello world!");
-
         final Random random = new Random(123);
 
         final int n = 10000;
@@ -84,7 +81,7 @@ public class TrainSingleTreeFactor {
 
 
         final long startTime = System.currentTimeMillis();
-        final Node<Double> baseNode = treeTrainer.growTree(trainingSet);
+        final Node<Double> baseNode = treeTrainer.growTree(trainingSet, random);
         final long endTime = System.currentTimeMillis();
 
         System.out.println(((double)(endTime - startTime))/1000.0);

src/test/resources/test_single_split.csv

@@ -0,0 +1,437 @@
+"T","J","x1","x2","x3","set","C","u","delta"
+2.36878896318231,2,-0.940736926269951,-2.74741651651697,-0.664166655908226,"d1",5.29132863866294,2.36878896318231,2
+0.0400548702796549,2,-0.22357985281977,-2.6784315261127,-1.45393397969899,"d1",24.0450198381188,0.0400548702796549,2
+6.07273173432729,1,-0.541321943849921,-2.61982060211246,-0.313865486168637,"d1",73.2294178857516,6.07273173432729,1
+0.0586427948437631,2,-4.08384650953597,-2.57905490896322,-1.94148492991394,"d1",29.575672428309,0.0586427948437631,2
+1.12241411577435,2,-0.343115442654736,-2.5261239240197,-0.568482592792157,"d1",10.7716199103743,1.12241411577435,2
+7.17758750180007,1,-0.0549158329059284,-2.49595311098652,0.777187867970255,"d1",0.770628582686186,0.770628582686186,0
+5.87972604899089,1,-0.417519608027854,-2.32569598105725,-1.23410968014014,"d1",21.7603224188032,5.87972604899089,1
+6.09850069496217,1,-0.624229581484001,-2.20950230906333,0.847654716653119,"d1",47.9509777923055,6.09850069496217,1
+5.24709419887559,1,-0.494413435039913,-2.1881842156793,-0.530500950041165,"d1",9.29604623673615,5.24709419887559,1
+5.49139738108154,1,-0.399023904593903,-2.17411513837698,-0.886241025366943,"d1",4.66279121115804,4.66279121115804,0
+7.49679934937549,1,-1.503172816431,-2.11416482594655,-0.784022459577515,"d1",38.599446339715,7.49679934937549,1
+0.120167720597237,2,-0.0111153155803973,-2.07922849793458,-0.0864173413157577,"d1",8.30861929266284,0.120167720597237,2
+0.0120866466313601,2,-1.13246179303068,-2.07388986542802,-0.108705951800557,"d1",74.5770950200868,0.0120866466313601,2
+1.93336439869347,2,-0.953063484590791,-2.06875275630198,0.391474935780971,"d1",8.68413874879479,1.93336439869347,2
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