Refactor - rename GroupDifferentiators into SplitFinders

SplitRule would have made more sense but it was already taken.

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

@@ -180,8 +180,8 @@ public class Main {
 
     private static Settings defaultTemplate(){
 
-        final ObjectNode groupDifferentiatorSettings = new ObjectNode(JsonNodeFactory.instance);
+        final ObjectNode splitFinderSettings = new ObjectNode(JsonNodeFactory.instance);
-        groupDifferentiatorSettings.set("type", new TextNode("WeightedVarianceGroupDifferentiator"));
+        splitFinderSettings.set("type", new TextNode("WeightedVarianceSplitFinder"));
 
         final ObjectNode responseCombinerSettings = new ObjectNode(JsonNodeFactory.instance);
         responseCombinerSettings.set("type", new TextNode("MeanResponseCombiner"));
@@ -204,7 +204,7 @@ public class Main {
                 .validationDataLocation("validation_data.csv")
                 .responseCombinerSettings(responseCombinerSettings)
                 .treeCombinerSettings(treeCombinerSettings)
-                .groupDifferentiatorSettings(groupDifferentiatorSettings)
+                .splitFinderSettings(splitFinderSettings)
                 .yVarSettings(yVarSettings)
                 .maxNodeDepth(100000)
                 .mtry(2)

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

@@ -22,11 +22,11 @@ import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskRespons
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponseWithCensorTime;
 import ca.joeltherrien.randomforest.responses.competingrisk.combiner.CompetingRiskFunctionCombiner;
 import ca.joeltherrien.randomforest.responses.competingrisk.combiner.CompetingRiskResponseCombiner;
-import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.GrayLogRankDifferentiator;
+import ca.joeltherrien.randomforest.responses.competingrisk.splitfinder.GrayLogRankSplitFinder;
-import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.LogRankDifferentiator;
+import ca.joeltherrien.randomforest.responses.competingrisk.splitfinder.LogRankSplitFinder;
 import ca.joeltherrien.randomforest.responses.regression.MeanResponseCombiner;
-import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceGroupDifferentiator;
+import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceSplitFinder;
-import ca.joeltherrien.randomforest.tree.GroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.SplitFinder;
 import ca.joeltherrien.randomforest.tree.ResponseCombiner;
 import ca.joeltherrien.randomforest.utils.DataUtils;
 import ca.joeltherrien.randomforest.utils.Utils;
@@ -77,31 +77,31 @@ public class Settings {
         );
     }
 
-    private static Map<String, Function<ObjectNode, GroupDifferentiator>> GROUP_DIFFERENTIATOR_MAP = new HashMap<>();
+    private static Map<String, Function<ObjectNode, SplitFinder>> SPLIT_FINDER_MAP = new HashMap<>();
-    public static Function<ObjectNode, GroupDifferentiator> getGroupDifferentiatorConstructor(final String name){
+    public static Function<ObjectNode, SplitFinder> getSplitFinderConstructor(final String name){
-        return GROUP_DIFFERENTIATOR_MAP.get(name.toLowerCase());
+        return SPLIT_FINDER_MAP.get(name.toLowerCase());
     }
-    public static void registerGroupDifferentiatorConstructor(final String name, final Function<ObjectNode, GroupDifferentiator> groupDifferentiatorConstructor){
+    public static void registerSplitFinderConstructor(final String name, final Function<ObjectNode, SplitFinder> splitFinderConstructor){
-        GROUP_DIFFERENTIATOR_MAP.put(name.toLowerCase(), groupDifferentiatorConstructor);
+        SPLIT_FINDER_MAP.put(name.toLowerCase(), splitFinderConstructor);
     }
     static{
-        registerGroupDifferentiatorConstructor("WeightedVarianceGroupDifferentiator",
+        registerSplitFinderConstructor("WeightedVarianceSplitFinder",
-                (node) -> new WeightedVarianceGroupDifferentiator()
+                (node) -> new WeightedVarianceSplitFinder()
         );
-        registerGroupDifferentiatorConstructor("GrayLogRankDifferentiator",
+        registerSplitFinderConstructor("GrayLogRankSplitFinder",
                 (objectNode) -> {
                     final int[] eventsOfFocusArray = Utils.jsonToIntArray(objectNode.get("eventsOfFocus"));
                     final int[] eventArray = Utils.jsonToIntArray(objectNode.get("events"));
 
-                    return new GrayLogRankDifferentiator(eventsOfFocusArray, eventArray);
+                    return new GrayLogRankSplitFinder(eventsOfFocusArray, eventArray);
                 }
         );
-        registerGroupDifferentiatorConstructor("LogRankDifferentiator",
+        registerSplitFinderConstructor("LogRankSplitFinder",
                 (objectNode) -> {
                     final int[] eventsOfFocusArray = Utils.jsonToIntArray(objectNode.get("eventsOfFocus"));
                     final int[] eventArray = Utils.jsonToIntArray(objectNode.get("events"));
 
-                    return new LogRankDifferentiator(eventsOfFocusArray, eventArray);
+                    return new LogRankSplitFinder(eventsOfFocusArray, eventArray);
                 }
         );
     }
@@ -153,7 +153,7 @@ public class Settings {
     private boolean checkNodePurity = false;
 
     private ObjectNode responseCombinerSettings = new ObjectNode(JsonNodeFactory.instance);
-    private ObjectNode groupDifferentiatorSettings = new ObjectNode(JsonNodeFactory.instance);
+    private ObjectNode splitFinderSettings = new ObjectNode(JsonNodeFactory.instance);
     private ObjectNode treeCombinerSettings = new ObjectNode(JsonNodeFactory.instance);
 
     private List<CovariateSettings> covariateSettings = new ArrayList<>();
@@ -194,10 +194,10 @@ public class Settings {
     }
 
     @JsonIgnore
-    public GroupDifferentiator getGroupDifferentiator(){
+    public SplitFinder getSplitFinder(){
-        final String type = groupDifferentiatorSettings.get("type").asText();
+        final String type = splitFinderSettings.get("type").asText();
 
-        return getGroupDifferentiatorConstructor(type).apply(groupDifferentiatorSettings);
+        return getSplitFinderConstructor(type).apply(splitFinderSettings);
     }
 
     @JsonIgnore

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/splitfinder/CompetingRiskSplitFinder.java

@@ -14,13 +14,13 @@
  *     along with this program.  If not, see <https://www.gnu.org/licenses/>.
  */
 
-package ca.joeltherrien.randomforest.responses.competingrisk.differentiator;
+package ca.joeltherrien.randomforest.responses.competingrisk.splitfinder;
 
 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.SplitFinder;
 import ca.joeltherrien.randomforest.tree.Split;
 import ca.joeltherrien.randomforest.tree.SplitAndScore;
 import lombok.AllArgsConstructor;
@@ -35,24 +35,24 @@ import java.util.stream.Collectors;
  * modifies the abstract method.
  *
  */
-public abstract class CompetingRiskGroupDifferentiator<Y extends CompetingRiskResponse> implements GroupDifferentiator<Y> {
+public abstract class CompetingRiskSplitFinder<Y extends CompetingRiskResponse> implements SplitFinder<Y> {
 
     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) {
+    public SplitAndScore<Y, ?> findBestSplit(Iterator<Split<Y, ?>> splitIterator) {
 
         if(splitIterator instanceof Covariate.SplitRuleUpdater){
-            return differentiateWithSplitUpdater((Covariate.SplitRuleUpdater) splitIterator);
+            return findBestSplitWithSplitUpdater((Covariate.SplitRuleUpdater) splitIterator);
         }
         else{
-            return differentiateWithBasicIterator(splitIterator);
+            return findBestSplitWithBasicIterator(splitIterator);
         }
     }
 
-    private SplitAndScore<Y, ?> differentiateWithBasicIterator(Iterator<Split<Y, ?>> splitIterator){
+    private SplitAndScore<Y, ?> findBestSplitWithBasicIterator(Iterator<Split<Y, ?>> splitIterator){
         Double bestScore = null;
         Split<Y, ?> bestSplit = null;
 
@@ -83,7 +83,7 @@ public abstract class CompetingRiskGroupDifferentiator<Y extends CompetingRiskRe
         return new SplitAndScore<>(bestSplit, bestScore);
     }
 
-    private SplitAndScore<Y, ?> differentiateWithSplitUpdater(Covariate.SplitRuleUpdater<Y, ?> splitRuleUpdater) {
+    private SplitAndScore<Y, ?> findBestSplitWithSplitUpdater(Covariate.SplitRuleUpdater<Y, ?> splitRuleUpdater) {
 
         final List<Y> leftInitialSplit = splitRuleUpdater.currentSplit().getLeftHand()
                 .stream().map(Row::getResponse).collect(Collectors.toList());

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/splitfinder/GrayLogRankSplitFinder.java

@@ -14,7 +14,7 @@
  *     along with this program.  If not, see <https://www.gnu.org/licenses/>.
  */
 
-package ca.joeltherrien.randomforest.responses.competingrisk.differentiator;
+package ca.joeltherrien.randomforest.responses.competingrisk.splitfinder;
 
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponseWithCensorTime;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSets;
@@ -27,12 +27,12 @@ import java.util.List;
  * See page 761 of Random survival forests for competing risks by Ishwaran et al.
  *
  */
-public class GrayLogRankDifferentiator extends CompetingRiskGroupDifferentiator<CompetingRiskResponseWithCensorTime> {
+public class GrayLogRankSplitFinder extends CompetingRiskSplitFinder<CompetingRiskResponseWithCensorTime> {
 
     private final int[] eventsOfFocus;
     private final int[] events;
 
-    public GrayLogRankDifferentiator(int[] eventsOfFocus, int[] events){
+    public GrayLogRankSplitFinder(int[] eventsOfFocus, int[] events){
         this.eventsOfFocus = eventsOfFocus;
         this.events = events;
 

src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/splitfinder/LogRankSplitFinder.java

@@ -14,7 +14,7 @@
  *     along with this program.  If not, see <https://www.gnu.org/licenses/>.
  */
 
-package ca.joeltherrien.randomforest.responses.competingrisk.differentiator;
+package ca.joeltherrien.randomforest.responses.competingrisk.splitfinder;
 
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskSets;
@@ -27,12 +27,12 @@ import java.util.List;
  * See page 761 of Random survival forests for competing risks by Ishwaran et al.
  *
  */
-public class LogRankDifferentiator extends CompetingRiskGroupDifferentiator<CompetingRiskResponse> {
+public class LogRankSplitFinder extends CompetingRiskSplitFinder<CompetingRiskResponse> {
 
     private final int[] eventsOfFocus;
     private final int[] events;
 
-    public LogRankDifferentiator(int[] eventsOfFocus, int[] events){
+    public LogRankSplitFinder(int[] eventsOfFocus, int[] events){
         this.eventsOfFocus = eventsOfFocus;
         this.events = events;
 

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

@@ -18,7 +18,7 @@ package ca.joeltherrien.randomforest.responses.regression;
 
 import ca.joeltherrien.randomforest.Row;
 import ca.joeltherrien.randomforest.covariates.Covariate;
-import ca.joeltherrien.randomforest.tree.GroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.SplitFinder;
 import ca.joeltherrien.randomforest.tree.Split;
 import ca.joeltherrien.randomforest.tree.SplitAndScore;
 
@@ -26,7 +26,7 @@ import java.util.Iterator;
 import java.util.List;
 import java.util.stream.Collectors;
 
-public class WeightedVarianceGroupDifferentiator implements GroupDifferentiator<Double> {
+public class WeightedVarianceSplitFinder implements SplitFinder<Double> {
 
     private Double getScore(Set leftHand, Set rightHand) {
 
@@ -44,17 +44,17 @@ public class WeightedVarianceGroupDifferentiator implements GroupDifferentiator<
     }
 
     @Override
-    public SplitAndScore<Double, ?> differentiate(Iterator<Split<Double, ?>> splitIterator) {
+    public SplitAndScore<Double, ?> findBestSplit(Iterator<Split<Double, ?>> splitIterator) {
 
         if(splitIterator instanceof Covariate.SplitRuleUpdater){
-            return differentiateWithSplitUpdater((Covariate.SplitRuleUpdater) splitIterator);
+            return findBestSplitWithSplitUpdater((Covariate.SplitRuleUpdater) splitIterator);
         }
         else{
-            return differentiateWithBasicIterator(splitIterator);
+            return findBestSplitWithBasicIterator(splitIterator);
         }
     }
 
-    private SplitAndScore<Double, ?> differentiateWithBasicIterator(Iterator<Split<Double, ?>> splitIterator){
+    private SplitAndScore<Double, ?> findBestSplitWithBasicIterator(Iterator<Split<Double, ?>> splitIterator){
         Double bestScore = null;
         Split<Double, ?> bestSplit = null;
 
@@ -86,7 +86,7 @@ public class WeightedVarianceGroupDifferentiator implements GroupDifferentiator<
         return new SplitAndScore<>(bestSplit, bestScore);
     }
 
-    private SplitAndScore<Double, ?> differentiateWithSplitUpdater(Covariate.SplitRuleUpdater<Double, ?> splitRuleUpdater) {
+    private SplitAndScore<Double, ?> findBestSplitWithSplitUpdater(Covariate.SplitRuleUpdater<Double, ?> splitRuleUpdater) {
 
         final List<Double> leftInitialSplit = splitRuleUpdater.currentSplit().getLeftHand()
                 .stream().map(Row::getResponse).collect(Collectors.toList());

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

@@ -22,10 +22,10 @@ import java.util.Iterator;
 import java.util.List;
 import java.util.stream.Collectors;
 
-public abstract class SimpleGroupDifferentiator<Y> implements GroupDifferentiator<Y> {
+public abstract class SimpleSplitFinder<Y> implements SplitFinder<Y> {
 
     @Override
-    public SplitAndScore<Y, ?> differentiate(Iterator<Split<Y, ?>> splitIterator) {
+    public SplitAndScore<Y, ?> findBestSplit(Iterator<Split<Y, ?>> splitIterator) {
         Double bestScore = null;
         Split<Y, ?> bestSplit = null;
 

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

@@ -21,14 +21,14 @@ 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 cycles through an iterator of Splits (FYI; check if the iterator is an
+ * The SplitFinder 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
+ *  If you want to implement a very trivial SplitFinder that just takes two Lists as arguments, try extending
- *  SimpleGroupDifferentiator.
+ *  SimpleSplitFinder.
  */
-public interface GroupDifferentiator<Y> extends Serializable {
+public interface SplitFinder<Y> extends Serializable {
 
-    SplitAndScore<Y, ?> differentiate(Iterator<Split<Y, ?>> splitIterator);
+    SplitAndScore<Y, ?> findBestSplit(Iterator<Split<Y, ?>> splitIterator);
 
 }

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

@@ -31,7 +31,7 @@ import java.util.stream.Collectors;
 public class TreeTrainer<Y, O> {
 
     private final ResponseCombiner<Y, O> responseCombiner;
-    private final GroupDifferentiator<Y> groupDifferentiator;
+    private final SplitFinder<Y> splitFinder;
 
     /**
      * The number of splits to perform on each covariate. A value of 0 means all possible splits are tried.
@@ -58,7 +58,7 @@ public class TreeTrainer<Y, O> {
         this.checkNodePurity = settings.isCheckNodePurity();
 
         this.responseCombiner = settings.getResponseCombiner();
-        this.groupDifferentiator = settings.getGroupDifferentiator();
+        this.splitFinder = settings.getSplitFinder();
         this.covariates = covariates;
     }
 
@@ -160,7 +160,7 @@ public class TreeTrainer<Y, O> {
     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
+        final SplitFinder noGenericSplitFinder = splitFinder; // cause Java generics are sometimes too frustrating
 
         for(final Covariate covariate : covariatesToTry) {
             final Iterator<Split> iterator = covariate.generateSplitRuleUpdater(data, this.numberOfSplits, random);
@@ -170,7 +170,7 @@ public class TreeTrainer<Y, O> {
                 continue;
             }
 
-            final SplitAndScore<Y, ?> candidateSplitAndScore = noGenericDifferentiator.differentiate(iterator);
+            final SplitAndScore<Y, ?> candidateSplitAndScore = noGenericSplitFinder.findBestSplit(iterator);
 
             if(candidateSplitAndScore != null && (bestSplitAndScore == null ||
                     candidateSplitAndScore.getScore() > bestSplitAndScore.getScore())) {

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

@@ -44,12 +44,12 @@ public class TestSavingLoading {
      * @return
      */
     public Settings getSettings(){
-        final ObjectNode groupDifferentiatorSettings = new ObjectNode(JsonNodeFactory.instance);
+        final ObjectNode splitRuleSettings = new ObjectNode(JsonNodeFactory.instance);
-        groupDifferentiatorSettings.set("type", new TextNode("LogRankDifferentiator"));
+        splitRuleSettings.set("type", new TextNode("LogRankSplitFinder"));
-        groupDifferentiatorSettings.set("eventsOfFocus",
+        splitRuleSettings.set("eventsOfFocus",
                 new ArrayNode(JsonNodeFactory.instance, Utils.easyList(new IntNode(1)))
         );
-        groupDifferentiatorSettings.set("events",
+        splitRuleSettings.set("events",
                 new ArrayNode(JsonNodeFactory.instance, Utils.easyList(new IntNode(1), new IntNode(2)))
         );
 
@@ -85,7 +85,7 @@ public class TestSavingLoading {
                 .validationDataLocation("src/test/resources/wihs.csv")
                 .responseCombinerSettings(responseCombinerSettings)
                 .treeCombinerSettings(treeCombinerSettings)
-                .groupDifferentiatorSettings(groupDifferentiatorSettings)
+                .splitFinderSettings(splitRuleSettings)
                 .yVarSettings(yVarSettings)
                 .maxNodeDepth(100000)
                 // TODO fill in these settings

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

@@ -53,12 +53,12 @@ public class TestCompetingRisk {
      * @return
      */
     public Settings getSettings(){
-        final ObjectNode groupDifferentiatorSettings = new ObjectNode(JsonNodeFactory.instance);
+        final ObjectNode splitFinderSettings = new ObjectNode(JsonNodeFactory.instance);
-        groupDifferentiatorSettings.set("type", new TextNode("LogRankDifferentiator"));
+        splitFinderSettings.set("type", new TextNode("LogRankSplitFinder"));
-        groupDifferentiatorSettings.set("eventsOfFocus",
+        splitFinderSettings.set("eventsOfFocus",
                 new ArrayNode(JsonNodeFactory.instance, Utils.easyList(new IntNode(1)))
         );
-        groupDifferentiatorSettings.set("events",
+        splitFinderSettings.set("events",
                 new ArrayNode(JsonNodeFactory.instance, Utils.easyList(new IntNode(1), new IntNode(2)))
         );
 
@@ -93,7 +93,7 @@ public class TestCompetingRisk {
                 .trainingDataLocation("src/test/resources/wihs.csv")
                 .responseCombinerSettings(responseCombinerSettings)
                 .treeCombinerSettings(treeCombinerSettings)
-                .groupDifferentiatorSettings(groupDifferentiatorSettings)
+                .splitFinderSettings(splitFinderSettings)
                 .yVarSettings(yVarSettings)
                 .maxNodeDepth(100000)
                 // TODO fill in these settings
@@ -222,7 +222,7 @@ public class TestCompetingRisk {
     }
 
     @Test
-    public void testLogRankSingleGroupDifferentiatorTwoBooleans() throws IOException {
+    public void testLogRankSplitFinderTwoBooleans() throws IOException {
         final Settings settings = getSettings();
         settings.setCovariateSettings(Utils.easyList(
                 new BooleanCovariateSettings("idu"),
@@ -337,7 +337,7 @@ public class TestCompetingRisk {
     }
 
     @Test
-    public void testLogRankSingleGroupDifferentiatorAllCovariates() throws IOException {
+    public void testLogRankSplitFinderAllCovariates() throws IOException {
 
         final Settings settings = getSettings();
         settings.setNtree(300); // results are too variable at 100

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

@@ -21,7 +21,7 @@ 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.LogRankDifferentiator;
+import ca.joeltherrien.randomforest.responses.competingrisk.splitfinder.LogRankSplitFinder;
 import ca.joeltherrien.randomforest.tree.Split;
 import ca.joeltherrien.randomforest.utils.DataUtils;
 import ca.joeltherrien.randomforest.utils.SingletonIterator;
@@ -39,7 +39,7 @@ import java.util.List;
 
 import static org.junit.jupiter.api.Assertions.assertTrue;
 
-public class TestLogRankDifferentiator {
+public class TestLogRankSplitFinder {
 
     private Iterator<Split<CompetingRiskResponse, ?>> turnIntoSplitIterator(List<Row<CompetingRiskResponse>> leftList,
                                                                                  List<Row<CompetingRiskResponse>> rightList){
@@ -70,14 +70,14 @@ public class TestLogRankDifferentiator {
 
     @Test
     public void testSplitRule() throws IOException {
-        final LogRankDifferentiator groupDifferentiator = new LogRankDifferentiator(new int[]{1,2}, new int[]{1,2});
+        final LogRankSplitFinder splitFinder = new LogRankSplitFinder(new int[]{1,2}, 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 double scoreBad = splitFinder.findBestSplit(turnIntoSplitIterator(group1Bad, group2Bad)).getScore();
 
         // expected results calculated manually using survival::survdiff in R; see issue #10 in Gitea
         closeEnough(9.413002, scoreBad, 0.00001);

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

@@ -18,8 +18,8 @@ package ca.joeltherrien.randomforest.competingrisk;
 
 import ca.joeltherrien.randomforest.Row;
 import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskResponse;
-import ca.joeltherrien.randomforest.responses.competingrisk.differentiator.LogRankDifferentiator;
+import ca.joeltherrien.randomforest.responses.competingrisk.splitfinder.LogRankSplitFinder;
-import ca.joeltherrien.randomforest.tree.GroupDifferentiator;
+import ca.joeltherrien.randomforest.tree.SplitFinder;
 import ca.joeltherrien.randomforest.tree.Split;
 import ca.joeltherrien.randomforest.utils.SingletonIterator;
 import org.junit.jupiter.api.Test;
@@ -33,13 +33,13 @@ import java.util.List;
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertTrue;
 
-public class TestLogRankSingleGroupDifferentiator {
+public class TestLogRankSplitFinderSingleEvent {
 
-    private double getScore(final GroupDifferentiator<CompetingRiskResponse> groupDifferentiator, List<Row<CompetingRiskResponse>> left, List<Row<CompetingRiskResponse>> right){
+    private double getScore(final SplitFinder<CompetingRiskResponse> splitFinder, List<Row<CompetingRiskResponse>> left, List<Row<CompetingRiskResponse>> right){
         final Iterator<Split<CompetingRiskResponse, ?>> iterator = new SingletonIterator<>(
                 new Split<>(null, left, right, Collections.emptyList()));
 
-        return groupDifferentiator.differentiate(iterator).getScore();
+        return splitFinder.findBestSplit(iterator).getScore();
 
     }
 
@@ -81,9 +81,9 @@ public class TestLogRankSingleGroupDifferentiator {
         final List<Row<CompetingRiskResponse>> data1 = generateData1();
         final List<Row<CompetingRiskResponse>> data2 = generateData2();
 
-        final LogRankDifferentiator differentiator = new LogRankDifferentiator(new int[]{1}, new int[]{1});
+        final LogRankSplitFinder splitFinder = new LogRankSplitFinder(new int[]{1}, new int[]{1});
 
-        final double score = getScore(differentiator, data1, data2);
+        final double score = getScore(splitFinder, data1, data2);
         final double margin = 0.000001;
 
         // Tested using 855 method
@@ -94,21 +94,21 @@ public class TestLogRankSingleGroupDifferentiator {
 
     @Test
     public void testCorrectSplit() throws IOException {
-        final LogRankDifferentiator groupDifferentiator =
+        final LogRankSplitFinder splitFinder =
-                new LogRankDifferentiator(new int[]{1}, new int[]{1,2});
+                new LogRankSplitFinder(new int[]{1}, new int[]{1,2});
 
-        final List<Row<CompetingRiskResponse>> data = TestLogRankDifferentiator.
+        final List<Row<CompetingRiskResponse>> data = TestLogRankSplitFinder.
                 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 double scoreGood = getScore(splitFinder, 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);
+        final double scoreBad = getScore(splitFinder, group1Bad, group2Bad);
 
         // Apparently not all groups are unique when splitting
         assertEquals(scoreGood, scoreBad);

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

@@ -4,7 +4,7 @@ import ca.joeltherrien.randomforest.Row;
 import ca.joeltherrien.randomforest.covariates.Covariate;
 import ca.joeltherrien.randomforest.covariates.numeric.NumericCovariate;
 import ca.joeltherrien.randomforest.responses.regression.MeanResponseCombiner;
-import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceGroupDifferentiator;
+import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceSplitFinder;
 import ca.joeltherrien.randomforest.tree.TreeTrainer;
 import ca.joeltherrien.randomforest.utils.Utils;
 import org.junit.jupiter.api.Test;
@@ -47,7 +47,7 @@ public class TestNAs {
                 .numberOfSplits(0)
                 .nodeSize(1)
                 .maxNodeDepth(1000)
-                .groupDifferentiator(new WeightedVarianceGroupDifferentiator())
+                .splitFinder(new WeightedVarianceSplitFinder())
                 .responseCombiner(new MeanResponseCombiner())
                 .build();
 

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

@@ -35,8 +35,8 @@ public class TestPersistence {
 
     @Test
     public void testSaving() throws IOException {
-        final ObjectNode groupDifferentiatorSettings = new ObjectNode(JsonNodeFactory.instance);
+        final ObjectNode splitFinderSettings = new ObjectNode(JsonNodeFactory.instance);
-        groupDifferentiatorSettings.set("type", new TextNode("WeightedVarianceGroupDifferentiator"));
+        splitFinderSettings.set("type", new TextNode("WeightedVarianceSplitFinder"));
 
         final ObjectNode responseCombinerSettings = new ObjectNode(JsonNodeFactory.instance);
         responseCombinerSettings.set("type", new TextNode("MeanResponseCombiner"));
@@ -59,7 +59,7 @@ public class TestPersistence {
                 .validationDataLocation("validation_data.csv")
                 .responseCombinerSettings(responseCombinerSettings)
                 .treeCombinerSettings(treeCombinerSettings)
-                .groupDifferentiatorSettings(groupDifferentiatorSettings)
+                .splitFinderSettings(splitFinderSettings)
                 .yVarSettings(yVarSettings)
                 .maxNodeDepth(100000)
                 .mtry(2)

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

@@ -20,7 +20,7 @@ import ca.joeltherrien.randomforest.*;
 import ca.joeltherrien.randomforest.covariates.Covariate;
 import ca.joeltherrien.randomforest.covariates.numeric.NumericCovariate;
 import ca.joeltherrien.randomforest.responses.regression.MeanResponseCombiner;
-import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceGroupDifferentiator;
+import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceSplitFinder;
 import ca.joeltherrien.randomforest.tree.ForestTrainer;
 import ca.joeltherrien.randomforest.tree.TreeTrainer;
 
@@ -72,7 +72,7 @@ public class TrainForest {
                 .nodeSize(5)
                 .mtry(4)
                 .maxNodeDepth(100000000)
-                .groupDifferentiator(new WeightedVarianceGroupDifferentiator())
+                .splitFinder(new WeightedVarianceSplitFinder())
                 .responseCombiner(new MeanResponseCombiner())
                 .build();
 

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

@@ -22,7 +22,7 @@ import ca.joeltherrien.randomforest.CovariateRow;
 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;
+import ca.joeltherrien.randomforest.responses.regression.WeightedVarianceSplitFinder;
 import ca.joeltherrien.randomforest.tree.Node;
 import ca.joeltherrien.randomforest.tree.TreeTrainer;
 import ca.joeltherrien.randomforest.utils.Utils;
@@ -66,7 +66,7 @@ public class TrainSingleTree {
         final List<Covariate> covariateNames = Utils.easyList(x1Covariate, x2Covariate);
 
         final TreeTrainer<Double, Double> treeTrainer = TreeTrainer.<Double, Double>builder()
-                .groupDifferentiator(new WeightedVarianceGroupDifferentiator())
+                .splitFinder(new WeightedVarianceSplitFinder())
                 .covariates(covariateNames)
                 .responseCombiner(new MeanResponseCombiner())
                 .maxNodeDepth(30)

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

@@ -22,7 +22,7 @@ import ca.joeltherrien.randomforest.covariates.Covariate;
 import ca.joeltherrien.randomforest.covariates.factor.FactorCovariate;
 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.responses.regression.WeightedVarianceSplitFinder;
 import ca.joeltherrien.randomforest.tree.Node;
 import ca.joeltherrien.randomforest.tree.TreeTrainer;
 import ca.joeltherrien.randomforest.utils.Utils;
@@ -86,7 +86,7 @@ public class TrainSingleTreeFactor {
         final List<Covariate> covariateNames = Utils.easyList(x1Covariate, x2Covariate);
 
         final TreeTrainer<Double, Double> treeTrainer = TreeTrainer.<Double, Double>builder()
-                .groupDifferentiator(new WeightedVarianceGroupDifferentiator())
+                .splitFinder(new WeightedVarianceSplitFinder())
                 .responseCombiner(new MeanResponseCombiner())
                 .covariates(covariateNames)
                 .maxNodeDepth(30)