diff --git a/executable/src/main/java/ca/joeltherrien/randomforest/Main.java b/executable/src/main/java/ca/joeltherrien/randomforest/Main.java
index 7d5dda2..aeeddc6 100644
--- a/executable/src/main/java/ca/joeltherrien/randomforest/Main.java
+++ b/executable/src/main/java/ca/joeltherrien/randomforest/Main.java
@@ -227,9 +227,9 @@ public class Main {
return Settings.builder()
.covariateSettings(Utils.easyList(
- new NumericCovariateSettings("x1"),
- new BooleanCovariateSettings("x2"),
- new FactorCovariateSettings("x3", Utils.easyList("cat", "mouse", "dog"))
+ new NumericCovariateSettings("x1", true),
+ new BooleanCovariateSettings("x2", false),
+ new FactorCovariateSettings("x3", Utils.easyList("cat", "mouse", "dog"), true)
)
)
.trainingDataLocation("training_data.csv")
diff --git a/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/BooleanCovariateSettings.java b/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/BooleanCovariateSettings.java
index 3ed9028..0c7ba46 100644
--- a/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/BooleanCovariateSettings.java
+++ b/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/BooleanCovariateSettings.java
@@ -24,12 +24,12 @@ import lombok.NoArgsConstructor;
@Data
public final class BooleanCovariateSettings extends CovariateSettings<Boolean> {
- public BooleanCovariateSettings(String name){
- super(name);
+ public BooleanCovariateSettings(String name, boolean naSplitPenalty){
+ super(name, naSplitPenalty);
}
@Override
public BooleanCovariate build(int index) {
- return new BooleanCovariate(name, index);
+ return new BooleanCovariate(name, index, naSplitPenalty);
}
}
diff --git a/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/CovariateSettings.java b/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/CovariateSettings.java
index 1b967ee..e658dc0 100644
--- a/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/CovariateSettings.java
+++ b/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/CovariateSettings.java
@@ -40,9 +40,11 @@ import lombok.NoArgsConstructor;
public abstract class CovariateSettings<V> {
String name;
+ boolean naSplitPenalty;
- CovariateSettings(String name){
+ CovariateSettings(String name, boolean naSplitPenalty){
this.name = name;
+ this.naSplitPenalty = naSplitPenalty;
}
public abstract Covariate<V> build(int index);
diff --git a/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/FactorCovariateSettings.java b/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/FactorCovariateSettings.java
index 03ccd3b..499bba1 100644
--- a/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/FactorCovariateSettings.java
+++ b/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/FactorCovariateSettings.java
@@ -29,13 +29,13 @@ public final class FactorCovariateSettings extends CovariateSettings<String> {
private List<String> levels;
- public FactorCovariateSettings(String name, List<String> levels){
- super(name);
+ public FactorCovariateSettings(String name, List<String> levels, boolean naSplitPenalty){
+ super(name, naSplitPenalty);
this.levels = new ArrayList<>(levels); // Jackson struggles with List.of(...)
}
@Override
public FactorCovariate build(int index) {
- return new FactorCovariate(name, index, levels);
+ return new FactorCovariate(name, index, levels, naSplitPenalty);
}
}
diff --git a/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/NumericCovariateSettings.java b/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/NumericCovariateSettings.java
index 8d15031..6820dde 100644
--- a/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/NumericCovariateSettings.java
+++ b/executable/src/main/java/ca/joeltherrien/randomforest/covariates/settings/NumericCovariateSettings.java
@@ -24,12 +24,12 @@ import lombok.NoArgsConstructor;
@Data
public final class NumericCovariateSettings extends CovariateSettings<Double> {
- public NumericCovariateSettings(String name){
- super(name);
+ public NumericCovariateSettings(String name, boolean naSplitPenalty){
+ super(name, naSplitPenalty);
}
@Override
public NumericCovariate build(int index) {
- return new NumericCovariate(name, index);
+ return new NumericCovariate(name, index, naSplitPenalty);
}
}
diff --git a/executable/src/test/java/ca/joeltherrien/randomforest/TestPersistence.java b/executable/src/test/java/ca/joeltherrien/randomforest/TestPersistence.java
index b0449af..acc53c3 100644
--- a/executable/src/test/java/ca/joeltherrien/randomforest/TestPersistence.java
+++ b/executable/src/test/java/ca/joeltherrien/randomforest/TestPersistence.java
@@ -49,9 +49,9 @@ public class TestPersistence {
final Settings settingsOriginal = Settings.builder()
.covariateSettings(Utils.easyList(
- new NumericCovariateSettings("x1"),
- new BooleanCovariateSettings("x2"),
- new FactorCovariateSettings("x3", Utils.easyList("cat", "mouse", "dog"))
+ new NumericCovariateSettings("x1", true),
+ new BooleanCovariateSettings("x2", false),
+ new FactorCovariateSettings("x3", Utils.easyList("cat", "mouse", "dog"), true)
)
)
.trainingDataLocation("training_data.csv")
diff --git a/executable/src/test/java/ca/joeltherrien/randomforest/csv/TestLoadingCSV.java b/executable/src/test/java/ca/joeltherrien/randomforest/csv/TestLoadingCSV.java
index bf164b0..3c5991f 100644
--- a/executable/src/test/java/ca/joeltherrien/randomforest/csv/TestLoadingCSV.java
+++ b/executable/src/test/java/ca/joeltherrien/randomforest/csv/TestLoadingCSV.java
@@ -55,9 +55,9 @@ public class TestLoadingCSV {
final Settings settings = Settings.builder()
.trainingDataLocation(filename)
.covariateSettings(
- Utils.easyList(new NumericCovariateSettings("x1"),
- new FactorCovariateSettings("x2", Utils.easyList("dog", "cat", "mouse")),
- new BooleanCovariateSettings("x3"))
+ Utils.easyList(new NumericCovariateSettings("x1", true),
+ new FactorCovariateSettings("x2", Utils.easyList("dog", "cat", "mouse"), false),
+ new BooleanCovariateSettings("x3", true))
)
.yVarSettings(yVarSettings)
.build();
@@ -71,14 +71,14 @@ public class TestLoadingCSV {
}
@Test
- public void verifyLoadingNormal(final List<Covariate> covariates) throws IOException {
+ public void testLoadingNormal(final List<Covariate> covariates) throws IOException {
final List<Row<Double>> data = loadData("src/test/resources/testCSV.csv");
assertData(data, covariates);
}
@Test
- public void verifyLoadingGz(final List<Covariate> covariates) throws IOException {
+ public void testLoadingGz(final List<Covariate> covariates) throws IOException {
final List<Row<Double>> data = loadData("src/test/resources/testCSV.csv.gz");
assertData(data, covariates);
diff --git a/library/src/main/java/ca/joeltherrien/randomforest/covariates/Covariate.java b/library/src/main/java/ca/joeltherrien/randomforest/covariates/Covariate.java
index f0a8cc1..172a944 100644
--- a/library/src/main/java/ca/joeltherrien/randomforest/covariates/Covariate.java
+++ b/library/src/main/java/ca/joeltherrien/randomforest/covariates/Covariate.java
@@ -49,6 +49,8 @@ public interface Covariate<V> extends Serializable, Comparable<Covariate> {
return getIndex() - other.getIndex();
}
+ boolean haveNASplitPenalty();
+
interface Value<V> extends Serializable{
Covariate<V> getParent();
diff --git a/library/src/main/java/ca/joeltherrien/randomforest/covariates/bool/BooleanCovariate.java b/library/src/main/java/ca/joeltherrien/randomforest/covariates/bool/BooleanCovariate.java
index 824c5eb..e87e0d0 100644
--- a/library/src/main/java/ca/joeltherrien/randomforest/covariates/bool/BooleanCovariate.java
+++ b/library/src/main/java/ca/joeltherrien/randomforest/covariates/bool/BooleanCovariate.java
@@ -25,6 +25,7 @@ import lombok.Getter;
import java.util.Iterator;
import java.util.List;
import java.util.Random;
+import java.util.stream.Collectors;
public final class BooleanCovariate implements Covariate<Boolean> {
@@ -40,14 +41,26 @@ public final class BooleanCovariate implements Covariate<Boolean> {
private final BooleanSplitRule splitRule; // there's only one possible rule for BooleanCovariates.
- public BooleanCovariate(String name, int index){
+ private final boolean haveNASplitPenalty;
+ @Override
+ public boolean haveNASplitPenalty(){
+ // penalty would add worthless computational time if there are no NAs
+ return hasNAs && haveNASplitPenalty;
+ }
+
+ public BooleanCovariate(String name, int index, boolean haveNASplitPenalty){
this.name = name;
this.index = index;
- splitRule = new BooleanSplitRule(this);
+ this.splitRule = new BooleanSplitRule(this);
+ this.haveNASplitPenalty = haveNASplitPenalty;
}
@Override
public <Y> Iterator<Split<Y, Boolean>> generateSplitRuleUpdater(List<Row<Y>> data, int number, Random random) {
+ if(hasNAs){
+ data = data.stream().filter(row -> !row.getValueByIndex(index).isNA()).collect(Collectors.toList());
+ }
+
return new SingletonIterator<>(this.splitRule.applyRule(data));
}
diff --git a/library/src/main/java/ca/joeltherrien/randomforest/covariates/factor/FactorCovariate.java b/library/src/main/java/ca/joeltherrien/randomforest/covariates/factor/FactorCovariate.java
index 63db922..919f345 100644
--- a/library/src/main/java/ca/joeltherrien/randomforest/covariates/factor/FactorCovariate.java
+++ b/library/src/main/java/ca/joeltherrien/randomforest/covariates/factor/FactorCovariate.java
@@ -23,6 +23,7 @@ import lombok.EqualsAndHashCode;
import lombok.Getter;
import java.util.*;
+import java.util.stream.Collectors;
public final class FactorCovariate implements Covariate<String> {
@@ -40,8 +41,15 @@ public final class FactorCovariate implements Covariate<String> {
private boolean hasNAs;
+ private final boolean haveNASplitPenalty;
+ @Override
+ public boolean haveNASplitPenalty(){
+ // penalty would add worthless computational time if there are no NAs
+ return hasNAs && haveNASplitPenalty;
+ }
- public FactorCovariate(final String name, final int index, List<String> levels){
+
+ public FactorCovariate(final String name, final int index, List<String> levels, final boolean haveNASplitPenalty){
this.name = name;
this.index = index;
this.factorLevels = new HashMap<>();
@@ -63,12 +71,22 @@ public final class FactorCovariate implements Covariate<String> {
this.numberOfPossiblePairings = numberOfPossiblePairingsTemp-1;
this.naValue = new FactorValue(null);
+
+ this.haveNASplitPenalty = haveNASplitPenalty;
}
@Override
public <Y> Iterator<Split<Y, String>> generateSplitRuleUpdater(List<Row<Y>> data, int number, Random random) {
+ if(hasNAs()){
+ data = data.stream().filter(row -> !row.getCovariateValue(this).isNA()).collect(Collectors.toList());
+ }
+
+ if(number == 0){ // nsplit = 0 => try every possibility, although we limit it to the number of observations.
+ number = data.size();
+ }
+
final Set<Split<Y, String>> splits = new HashSet<>();
// This is to ensure we don't get stuck in an infinite loop for small factors
diff --git a/library/src/main/java/ca/joeltherrien/randomforest/covariates/numeric/NumericCovariate.java b/library/src/main/java/ca/joeltherrien/randomforest/covariates/numeric/NumericCovariate.java
index 9b65187..b08512b 100644
--- a/library/src/main/java/ca/joeltherrien/randomforest/covariates/numeric/NumericCovariate.java
+++ b/library/src/main/java/ca/joeltherrien/randomforest/covariates/numeric/NumericCovariate.java
@@ -47,6 +47,13 @@ public final class NumericCovariate implements Covariate<Double> {
private boolean hasNAs = false;
+ private final boolean haveNASplitPenalty;
+ @Override
+ public boolean haveNASplitPenalty(){
+ // penalty would add worthless computational time if there are no NAs
+ return hasNAs && haveNASplitPenalty;
+ }
+
@Override
public <Y> NumericSplitRuleUpdater<Y> generateSplitRuleUpdater(List<Row<Y>> data, int number, Random random) {
Stream<Row<Y>> stream = data.stream();
diff --git a/library/src/main/java/ca/joeltherrien/randomforest/tree/SplitAndScore.java b/library/src/main/java/ca/joeltherrien/randomforest/tree/SplitAndScore.java
index 7b8d9d4..f232a2c 100644
--- a/library/src/main/java/ca/joeltherrien/randomforest/tree/SplitAndScore.java
+++ b/library/src/main/java/ca/joeltherrien/randomforest/tree/SplitAndScore.java
@@ -17,15 +17,13 @@
package ca.joeltherrien.randomforest.tree;
import lombok.AllArgsConstructor;
-import lombok.Getter;
+import lombok.Data;
@AllArgsConstructor
+@Data
public class SplitAndScore<Y, V> {
- @Getter
- private final Split<Y, V> split;
-
- @Getter
- private final Double score;
+ private Split<Y, V> split;
+ private Double score;
}
diff --git a/library/src/main/java/ca/joeltherrien/randomforest/tree/TreeTrainer.java b/library/src/main/java/ca/joeltherrien/randomforest/tree/TreeTrainer.java
index 653856a..b41213d 100644
--- a/library/src/main/java/ca/joeltherrien/randomforest/tree/TreeTrainer.java
+++ b/library/src/main/java/ca/joeltherrien/randomforest/tree/TreeTrainer.java
@@ -17,7 +17,9 @@
package ca.joeltherrien.randomforest.tree;
import ca.joeltherrien.randomforest.Row;
+import ca.joeltherrien.randomforest.VisibleForTesting;
import ca.joeltherrien.randomforest.covariates.Covariate;
+import ca.joeltherrien.randomforest.utils.SingletonIterator;
import lombok.AccessLevel;
import lombok.AllArgsConstructor;
import lombok.Builder;
@@ -72,31 +74,12 @@ public class TreeTrainer<Y, O> {
}
-
// 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(covariates.get(bestSplit.getSplitRule().getParentCovariateIndex()).hasNAs()){
- bestSplit = bestSplit.modifiableClone(); // the lists in bestSplit are otherwise usually unmodifiable lists
-
- for(Row<Y> row : data) {
- final int covariateIndex = bestSplit.getSplitRule().getParentCovariateIndex();
-
- if(row.getValueByIndex(covariateIndex).isNA()) {
- final boolean randomDecision = random.nextDouble() <= probabilityLeftHand;
-
- if(randomDecision){
- bestSplit.getLeftHand().add(row);
- }
- else{
- bestSplit.getRightHand().add(row);
- }
-
- }
- }
- }
+ bestSplit = randomlyAssignNAs(data, bestSplit, random);
final Node<O> leftNode;
final Node<O> rightNode;
@@ -144,7 +127,8 @@ public class TreeTrainer<Y, O> {
return splitCovariates;
}
- private Split<Y, ?> findBestSplitRule(List<Row<Y>> data, List<Covariate> covariatesToTry, Random random){
+ @VisibleForTesting
+ public Split<Y, ?> findBestSplitRule(List<Row<Y>> data, List<Covariate> covariatesToTry, Random random){
SplitAndScore<Y, ?> bestSplitAndScore = null;
final SplitFinder noGenericSplitFinder = splitFinder; // cause Java generics are sometimes too frustrating
@@ -157,10 +141,32 @@ public class TreeTrainer<Y, O> {
continue;
}
- final SplitAndScore<Y, ?> candidateSplitAndScore = noGenericSplitFinder.findBestSplit(iterator);
+ SplitAndScore<Y, ?> candidateSplitAndScore = noGenericSplitFinder.findBestSplit(iterator);
- if(candidateSplitAndScore != null && (bestSplitAndScore == null ||
- candidateSplitAndScore.getScore() > bestSplitAndScore.getScore())) {
+
+ if(candidateSplitAndScore == null){
+ continue;
+ }
+
+ // This score was based on splitting only non-NA values. However, there might be a similar covariate we are also considering
+ // that is just as good at splitting but has less NAs; we should thus penalize the split score for variables with NAs
+ // We do this by randomly assigning the NAs and then recalculating the split score on the best split we already have.
+ //
+ // We only have to penalize the score though if we know it's possible that this might be the best split. If it's not,
+ // then we can skip the computations.
+ final boolean mayBeGoodSplit = bestSplitAndScore == null ||
+ candidateSplitAndScore.getScore() > bestSplitAndScore.getScore();
+ if(mayBeGoodSplit && covariate.haveNASplitPenalty()){
+ Split<Y, ?> candiateSplitWithNAs = randomlyAssignNAs(data, candidateSplitAndScore.getSplit(), random);
+ final Iterator<Split<Y, ?>> newSplitWithRandomNAs = new SingletonIterator<>(candiateSplitWithNAs);
+ final double newScore = splitFinder.findBestSplit(newSplitWithRandomNAs).getScore();
+
+ // There's a chance that NAs might add noise to *improve* the score; but we want to ensure we penalize it.
+ // Thus we only change the score if its worse.
+ candidateSplitAndScore.setScore(Math.min(newScore, candidateSplitAndScore.getScore()));
+ }
+
+ if(bestSplitAndScore == null || candidateSplitAndScore.getScore() > bestSplitAndScore.getScore()) {
bestSplitAndScore = candidateSplitAndScore;
}
@@ -174,6 +180,38 @@ public class TreeTrainer<Y, O> {
}
+ private <V> Split<Y, V> randomlyAssignNAs(List<Row<Y>> data, Split<Y, V> existingSplit, Random random){
+
+ // Now that we have the best split; we need to handle any NAs that were dropped off
+ final double probabilityLeftHand = (double) existingSplit.leftHand.size() /
+ (double) (existingSplit.leftHand.size() + existingSplit.rightHand.size());
+
+
+ final int covariateIndex = existingSplit.getSplitRule().getParentCovariateIndex();
+
+ // Assign missing values to the split if necessary
+ if(covariates.get(existingSplit.getSplitRule().getParentCovariateIndex()).hasNAs()){
+ existingSplit = existingSplit.modifiableClone(); // the lists in bestSplit are otherwise usually unmodifiable lists
+
+ for(Row<Y> row : data) {
+ if(row.getValueByIndex(covariateIndex).isNA()) {
+ final boolean randomDecision = random.nextDouble() <= probabilityLeftHand;
+
+ if(randomDecision){
+ existingSplit.getLeftHand().add(row);
+ }
+ else{
+ existingSplit.getRightHand().add(row);
+ }
+
+ }
+ }
+ }
+
+ return existingSplit;
+
+ }
+
private boolean nodeIsPure(List<Row<Y>> data){
if(!checkNodePurity){
return false;
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/TestDeterministicForests.java b/library/src/test/java/ca/joeltherrien/randomforest/TestDeterministicForests.java
index 506e939..feb9190 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/TestDeterministicForests.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/TestDeterministicForests.java
@@ -45,20 +45,20 @@ public class TestDeterministicForests {
int index = 0;
for(int j=0; j<5; j++){
- final NumericCovariate numericCovariate = new NumericCovariate("numeric"+j, index);
+ final NumericCovariate numericCovariate = new NumericCovariate("numeric"+j, index, false);
covariateList.add(numericCovariate);
index++;
}
for(int j=0; j<5; j++){
- final BooleanCovariate booleanCovariate = new BooleanCovariate("boolean"+j, index);
+ final BooleanCovariate booleanCovariate = new BooleanCovariate("boolean"+j, index, false);
covariateList.add(booleanCovariate);
index++;
}
final List<String> levels = Utils.easyList("cat", "dog", "mouse");
for(int j=0; j<5; j++){
- final FactorCovariate factorCovariate = new FactorCovariate("factor"+j, index, levels);
+ final FactorCovariate factorCovariate = new FactorCovariate("factor"+j, index, levels, false);
covariateList.add(factorCovariate);
index++;
}
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/TestProvidingInitialForest.java b/library/src/test/java/ca/joeltherrien/randomforest/TestProvidingInitialForest.java
index 15835b1..3ff2876 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/TestProvidingInitialForest.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/TestProvidingInitialForest.java
@@ -44,7 +44,7 @@ public class TestProvidingInitialForest {
private List<Row<Double>> data;
public TestProvidingInitialForest(){
- covariateList = Collections.singletonList(new NumericCovariate("x", 0));
+ covariateList = Collections.singletonList(new NumericCovariate("x", 0, false));
data = Utils.easyList(
Row.createSimple(Utils.easyMap("x", "1.0"), covariateList, 1, 1.0),
@@ -198,7 +198,7 @@ public class TestProvidingInitialForest {
it's not clear if the forest being provided is the same one that trees were saved from.
*/
@Test
- public void verifyExceptions(){
+ public void testExceptions(){
final String filePath = "src/test/resources/trees/";
final File directory = new File(filePath);
if(directory.exists()){
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/TestSavingLoading.java b/library/src/test/java/ca/joeltherrien/randomforest/TestSavingLoading.java
index cff464f..41c70d4 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/TestSavingLoading.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/TestSavingLoading.java
@@ -47,10 +47,10 @@ public class TestSavingLoading {
public List<Covariate> getCovariates(){
return Utils.easyList(
- new NumericCovariate("ageatfda", 0),
- new BooleanCovariate("idu", 1),
- new BooleanCovariate("black", 2),
- new NumericCovariate("cd4nadir", 3)
+ new NumericCovariate("ageatfda", 0, false),
+ new BooleanCovariate("idu", 1, false),
+ new BooleanCovariate("black", 2, false),
+ new NumericCovariate("cd4nadir", 3, false)
);
}
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/TestUtils.java b/library/src/test/java/ca/joeltherrien/randomforest/TestUtils.java
index 1b26a03..e290597 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/TestUtils.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/TestUtils.java
@@ -156,7 +156,7 @@ public class TestUtils {
}
@Test
- public void reduceListToSize(){
+ public void testReduceListToSize(){
final List<Integer> testList = Utils.easyList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
final Random random = new Random();
for(int i=0; i<100; i++) { // want to test many times to ensure it doesn't work just due to randomness
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/IBSCalculatorTest.java b/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/IBSCalculatorTest.java
index dd3a2af..5598856 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/IBSCalculatorTest.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/IBSCalculatorTest.java
@@ -52,7 +52,7 @@ public class IBSCalculatorTest {
*/
@Test
- public void resultsWithoutCensoringDistribution(){
+ public void testResultsWithoutCensoringDistribution(){
final IBSCalculator calculator = new IBSCalculator();
final double errorDifferentEvent = calculator.calculateError(
@@ -74,7 +74,7 @@ public class IBSCalculatorTest {
}
@Test
- public void resultsWithCensoringDistribution(){
+ public void testResultsWithCensoringDistribution(){
final RightContinuousStepFunction censorSurvivalFunction = RightContinuousStepFunction.constructFromPoints(
Utils.easyList(
new Point(0.0, 0.75),
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRisk.java b/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRisk.java
index afbd199..e72d7fd 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRisk.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRisk.java
@@ -53,10 +53,10 @@ public class TestCompetingRisk {
public List<Covariate> getCovariates(){
return Utils.easyList(
- new NumericCovariate("ageatfda", 0),
- new BooleanCovariate("idu", 1),
- new BooleanCovariate("black", 2),
- new NumericCovariate("cd4nadir", 3)
+ new NumericCovariate("ageatfda", 0, false),
+ new BooleanCovariate("idu", 1, false),
+ new BooleanCovariate("black", 2, false),
+ new NumericCovariate("cd4nadir", 3, false)
);
}
@@ -109,8 +109,8 @@ public class TestCompetingRisk {
// by only using BooleanCovariates (only one split rule) we can guarantee identical results with randomForestSRC on one tree.
final List<Covariate> covariates = Utils.easyList(
- new BooleanCovariate("idu", 0),
- new BooleanCovariate("black", 1)
+ new BooleanCovariate("idu", 0, false),
+ new BooleanCovariate("black", 1, false)
);
final List<Row<CompetingRiskResponse>> dataset = getData(covariates, "src/test/resources/wihs.bootstrapped.csv");
@@ -210,8 +210,8 @@ public class TestCompetingRisk {
public void testLogRankSplitFinderTwoBooleans() throws IOException {
// by only using BooleanCovariates (only one split rule) we can guarantee identical results with randomForestSRC on one tree.
final List<Covariate> covariates = Utils.easyList(
- new BooleanCovariate("idu", 0),
- new BooleanCovariate("black", 1)
+ new BooleanCovariate("idu", 0, false),
+ new BooleanCovariate("black", 1, false)
);
@@ -259,7 +259,7 @@ public class TestCompetingRisk {
}
@Test
- public void verifyDataset() throws IOException {
+ public void testDataset() throws IOException {
final List<Covariate> covariates = getCovariates();
final List<Row<CompetingRiskResponse>> dataset = getData(covariates, DEFAULT_FILEPATH);
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestLogRankSplitFinder.java b/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestLogRankSplitFinder.java
index 99a30e4..3513f5d 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestLogRankSplitFinder.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestLogRankSplitFinder.java
@@ -46,7 +46,7 @@ public class TestLogRankSplitFinder {
public static Data<CompetingRiskResponse> loadData(String filename) throws IOException {
final List<Covariate> covariates = Utils.easyList(
- new NumericCovariate("x2", 0)
+ new NumericCovariate("x2", 0, false)
);
final List<Row<CompetingRiskResponse>> rows = TestUtils.loadData(covariates, new ResponseLoader.CompetingRisksResponseLoader("delta", "u"), filename);
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/covariates/FactorCovariateTest.java b/library/src/test/java/ca/joeltherrien/randomforest/covariates/FactorCovariateTest.java
index 84c134d..c214c9f 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/covariates/FactorCovariateTest.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/covariates/FactorCovariateTest.java
@@ -17,12 +17,15 @@
package ca.joeltherrien.randomforest.covariates;
+import ca.joeltherrien.randomforest.Row;
import ca.joeltherrien.randomforest.covariates.factor.FactorCovariate;
+import ca.joeltherrien.randomforest.tree.Split;
import ca.joeltherrien.randomforest.utils.Utils;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.function.Executable;
import java.util.ArrayList;
+import java.util.Collections;
import java.util.List;
import java.util.Random;
@@ -31,7 +34,7 @@ import static org.junit.jupiter.api.Assertions.*;
public class FactorCovariateTest {
@Test
- void verifyEqualLevels() {
+ public void testEqualLevels() {
final FactorCovariate petCovariate = createTestCovariate();
final FactorCovariate.FactorValue dog1 = petCovariate.createValue("DOG");
@@ -53,7 +56,7 @@ public class FactorCovariateTest {
}
@Test
- void verifyBadLevelException(){
+ public void testBadLevelException(){
final FactorCovariate petCovariate = createTestCovariate();
final Executable badCode = () -> petCovariate.createValue("vulcan");
@@ -61,25 +64,169 @@ public class FactorCovariateTest {
}
@Test
- void testAllSubsets(){
+ public void testAllSubsets(){
+ final int n = 2*3; // ensure that n is a multiple of 3 for the test
final FactorCovariate petCovariate = createTestCovariate();
+ final List<Row<Double>> data = generateSampleData(petCovariate, n);
- final List<SplitRule<String>> splitRules = new ArrayList<>();
+ final List<Split<Double, String>> splits = new ArrayList<>();
- petCovariate.generateSplitRuleUpdater(null, 100, new Random())
- .forEachRemaining(split -> splitRules.add(split.getSplitRule()));
+ petCovariate.generateSplitRuleUpdater(data, 100, new Random())
+ .forEachRemaining(split -> splits.add(split));
- assertEquals(splitRules.size(), 3);
+ assertEquals(splits.size(), 3);
- // TODO verify the contents of the split rules
+ // These are the 3 possibilities
+ boolean dog_catmouse = false;
+ boolean cat_dogmouse = false;
+ boolean mouse_dogcat = false;
+ for(Split<Double, String> split : splits){
+ List<Row<Double>> smallerHand;
+ List<Row<Double>> largerHand;
+
+ if(split.getLeftHand().size() < split.getRightHand().size()){
+ smallerHand = split.getLeftHand();
+ largerHand = split.getRightHand();
+ } else{
+ smallerHand = split.getRightHand();
+ largerHand = split.getLeftHand();
+ }
+
+ // There should be exactly one distinct value in the smaller list
+ assertEquals(n/3, smallerHand.size());
+ assertEquals(1,
+ smallerHand.stream()
+ .map(row -> row.getCovariateValue(petCovariate).getValue())
+ .distinct()
+ .count()
+ );
+
+ // There should be exactly two distinct values in the smaller list
+ assertEquals(2*n/3, largerHand.size());
+ assertEquals(2,
+ largerHand.stream()
+ .map(row -> row.getCovariateValue(petCovariate).getValue())
+ .distinct()
+ .count()
+ );
+
+ switch(smallerHand.get(0).getCovariateValue(petCovariate).getValue()){
+ case "DOG":
+ dog_catmouse = true;
+ case "CAT":
+ cat_dogmouse = true;
+ case "MOUSE":
+ mouse_dogcat = true;
+ }
+
+ }
+
+ assertTrue(dog_catmouse);
+ assertTrue(cat_dogmouse);
+ assertTrue(mouse_dogcat);
+
+ }
+
+ /*
+ * There was a bug where if number==0 in generateSplitRuleUpdater, then the result was empty.
+ */
+ @Test
+ public void testNumber0Subsets(){
+ final int n = 2*3; // ensure that n is a multiple of 3 for the test
+ final FactorCovariate petCovariate = createTestCovariate();
+ final List<Row<Double>> data = generateSampleData(petCovariate, n);
+
+ final List<Split<Double, String>> splits = new ArrayList<>();
+
+ petCovariate.generateSplitRuleUpdater(data, 0, new Random())
+ .forEachRemaining(split -> splits.add(split));
+
+ assertEquals(splits.size(), 3);
+
+ // These are the 3 possibilities
+ boolean dog_catmouse = false;
+ boolean cat_dogmouse = false;
+ boolean mouse_dogcat = false;
+
+ for(Split<Double, String> split : splits){
+ List<Row<Double>> smallerHand;
+ List<Row<Double>> largerHand;
+
+ if(split.getLeftHand().size() < split.getRightHand().size()){
+ smallerHand = split.getLeftHand();
+ largerHand = split.getRightHand();
+ } else{
+ smallerHand = split.getRightHand();
+ largerHand = split.getLeftHand();
+ }
+
+ // There should be exactly one distinct value in the smaller list
+ assertEquals(n/3, smallerHand.size());
+ assertEquals(1,
+ smallerHand.stream()
+ .map(row -> row.getCovariateValue(petCovariate).getValue())
+ .distinct()
+ .count()
+ );
+
+ // There should be exactly two distinct values in the smaller list
+ assertEquals(2*n/3, largerHand.size());
+ assertEquals(2,
+ largerHand.stream()
+ .map(row -> row.getCovariateValue(petCovariate).getValue())
+ .distinct()
+ .count()
+ );
+
+ switch(smallerHand.get(0).getCovariateValue(petCovariate).getValue()){
+ case "DOG":
+ dog_catmouse = true;
+ case "CAT":
+ cat_dogmouse = true;
+ case "MOUSE":
+ mouse_dogcat = true;
+ }
+
+ }
+
+ assertTrue(dog_catmouse);
+ assertTrue(cat_dogmouse);
+ assertTrue(mouse_dogcat);
+
+ }
+
+ @Test
+ public void testSpitRuleUpdaterWithNAs(){
+ // When some NAs were present calling generateSplitRuleUpdater caused an exception.
+
+ final FactorCovariate covariate = createTestCovariate();
+ final List<Row<Double>> sampleData = generateSampleData(covariate, 10);
+ sampleData.add(Row.createSimple(Utils.easyMap("pet", "NA"), Collections.singletonList(covariate), 11, 5.0));
+
+ covariate.generateSplitRuleUpdater(sampleData, 0, new Random());
+
+ // Test passes if no exception has occurred.
}
private FactorCovariate createTestCovariate(){
final List<String> levels = Utils.easyList("DOG", "CAT", "MOUSE");
- return new FactorCovariate("pet", 0, levels);
+ return new FactorCovariate("pet", 0, levels, false);
+ }
+
+ private List<Row<Double>> generateSampleData(Covariate covariate, int n){
+ final List<Covariate> covariateList = Collections.singletonList(covariate);
+ final List<Row<Double>> dataList = new ArrayList<>(n);
+
+ final String[] levels = new String[]{"DOG", "CAT", "MOUSE"};
+
+ for(int i=0; i<n; i++){
+ dataList.add(Row.createSimple(Utils.easyMap("pet", levels[i % 3]), covariateList, 1, 1.0));
+ }
+
+ return dataList;
}
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/covariates/NumericCovariateTest.java b/library/src/test/java/ca/joeltherrien/randomforest/covariates/NumericCovariateTest.java
index 614aeeb..1dfb85c 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/covariates/NumericCovariateTest.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/covariates/NumericCovariateTest.java
@@ -70,7 +70,7 @@ public class NumericCovariateTest {
@Test
public void testNumericCovariateDeterministic(){
- final NumericCovariate covariate = new NumericCovariate("x", 0);
+ final NumericCovariate covariate = new NumericCovariate("x", 0, false);
final List<Row<Double>> dataset = createTestDataset(covariate);
@@ -158,7 +158,7 @@ public class NumericCovariateTest {
@Test
public void testNumericSplitRuleUpdaterWithIndexes(){
- final NumericCovariate covariate = new NumericCovariate("x", 0);
+ final NumericCovariate covariate = new NumericCovariate("x", 0, false);
final List<Row<Double>> dataset = createTestDataset(covariate);
@@ -223,7 +223,7 @@ public class NumericCovariateTest {
*/
@Test
public void testNumericSplitRuleUpdaterWithIndexesAllMissingData(){
- final NumericCovariate covariate = new NumericCovariate("x", 0);
+ final NumericCovariate covariate = new NumericCovariate("x", 0, false);
final List<Row<Double>> dataset = createTestDatasetMissingValues(covariate);
final NumericSplitRuleUpdater<Double> updater = covariate.generateSplitRuleUpdater(dataset, 5, new Random());
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/nas/TestNAs.java b/library/src/test/java/ca/joeltherrien/randomforest/nas/TestNAs.java
index d60b13d..4e63b1c 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/nas/TestNAs.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/nas/TestNAs.java
@@ -18,31 +18,34 @@ package ca.joeltherrien.randomforest.nas;
import ca.joeltherrien.randomforest.Row;
import ca.joeltherrien.randomforest.covariates.Covariate;
+import ca.joeltherrien.randomforest.covariates.bool.BooleanCovariate;
+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.WeightedVarianceSplitFinder;
+import ca.joeltherrien.randomforest.tree.Split;
import ca.joeltherrien.randomforest.tree.TreeTrainer;
import ca.joeltherrien.randomforest.utils.Utils;
import org.junit.jupiter.api.Test;
import java.util.ArrayList;
-import java.util.Collections;
import java.util.List;
import java.util.Random;
+import static org.junit.jupiter.api.Assertions.*;
+
public class TestNAs {
- private List<Row<Double>> generateData(List<Covariate> covariates){
+ private List<Row<Double>> generateData1(List<Covariate> covariates){
final List<Row<Double>> dataList = new ArrayList<>();
-
// We must include an NA for one of the values
- dataList.add(Row.createSimple(Utils.easyMap("x", "NA"), covariates, 1, 5.0));
- dataList.add(Row.createSimple(Utils.easyMap("x", "1"), covariates, 1, 6.0));
- dataList.add(Row.createSimple(Utils.easyMap("x", "2"), covariates, 1, 5.5));
- dataList.add(Row.createSimple(Utils.easyMap("x", "7"), covariates, 1, 0.0));
- dataList.add(Row.createSimple(Utils.easyMap("x", "8"), covariates, 1, 1.0));
- dataList.add(Row.createSimple(Utils.easyMap("x", "8.4"), covariates, 1, 1.0));
+ dataList.add(Row.createSimple(Utils.easyMap("x", "NA", "y", "true", "z", "green"), covariates, 1, 5.0));
+ dataList.add(Row.createSimple(Utils.easyMap("x", "1", "y", "NA", "z", "blue"), covariates, 2, 6.0));
+ dataList.add(Row.createSimple(Utils.easyMap("x", "2", "y", "true", "z", "NA"), covariates, 3, 5.5));
+ dataList.add(Row.createSimple(Utils.easyMap("x", "7", "y", "false", "z", "green"), covariates, 4, 0.0));
+ dataList.add(Row.createSimple(Utils.easyMap("x", "8", "y", "true", "z", "blue"), covariates, 5, 1.0));
+ dataList.add(Row.createSimple(Utils.easyMap("x", "8.4", "y", "false", "z", "yellow"), covariates, 6, 1.0));
return dataList;
@@ -54,14 +57,19 @@ public class TestNAs {
// but NumericSplitRuleUpdater had unmodifiable lists when creating the split.
// This bug verifies that this no longer causes a crash
- final List<Covariate> covariates = Collections.singletonList(new NumericCovariate("x", 0));
- final List<Row<Double>> dataset = generateData(covariates);
+ final List<Covariate> covariates = Utils.easyList(
+ new NumericCovariate("x", 0, false),
+ new BooleanCovariate("y", 1, true),
+ new FactorCovariate("z", 2, Utils.easyList("green", "blue", "yellow"), true)
+ );
+ final List<Row<Double>> dataset = generateData1(covariates);
final TreeTrainer<Double, Double> treeTrainer = TreeTrainer.<Double, Double>builder()
.checkNodePurity(false)
.covariates(covariates)
.numberOfSplits(0)
.nodeSize(1)
+ .mtry(3)
.maxNodeDepth(1000)
.splitFinder(new WeightedVarianceSplitFinder())
.responseCombiner(new MeanResponseCombiner())
@@ -70,6 +78,87 @@ public class TestNAs {
treeTrainer.growTree(dataset, new Random(123));
// As long as no exception occurs, we passed
+ }
+
+ private List<Row<Double>> generateData2(List<Covariate> covariates){
+ final List<Row<Double>> dataList = new ArrayList<>();
+ // Idea - when ignoring NAs, BadVar gives a perfect split.
+ // GoodVar is slightly worse than BadVar when NAs are excluded.
+ // However, BadVar has a ton of NAs that will degrade its performance.
+ dataList.add(Row.createSimple(
+ Utils.easyMap("BadVar", "-1.0", "GoodVar", "true") // GoodVars one error
+ , covariates, 1, 5.0)
+ );
+ dataList.add(Row.createSimple(
+ Utils.easyMap("BadVar", "NA", "GoodVar", "false")
+ , covariates, 2, 5.0)
+ );
+ dataList.add(Row.createSimple(
+ Utils.easyMap("BadVar", "NA", "GoodVar", "false")
+ , covariates, 3, 5.0)
+ );
+ dataList.add(Row.createSimple(
+ Utils.easyMap("BadVar", "0.5", "GoodVar", "true")
+ , covariates, 4, 10.0)
+ );
+ dataList.add(Row.createSimple(
+ Utils.easyMap("BadVar", "NA", "GoodVar", "true")
+ , covariates, 5, 10.0)
+ );
+ dataList.add(Row.createSimple(
+ Utils.easyMap("BadVar", "NA", "GoodVar", "true")
+ , covariates, 6, 10.0)
+ );
+
+ return dataList;
+ }
+
+ @Test
+ // Test that the NA penalty works when selecting a best split.
+ public void testNAPenalty(){
+ final List<Covariate> covariates1 = Utils.easyList(
+ new NumericCovariate("BadVar", 0, true),
+ new BooleanCovariate("GoodVar", 1, false)
+ );
+
+ final List<Row<Double>> dataList1 = generateData2(covariates1);
+
+ final TreeTrainer<Double, Double> treeTrainer1 = TreeTrainer.<Double, Double>builder()
+ .checkNodePurity(false)
+ .covariates(covariates1)
+ .numberOfSplits(0)
+ .nodeSize(1)
+ .mtry(2)
+ .maxNodeDepth(1000)
+ .splitFinder(new WeightedVarianceSplitFinder())
+ .responseCombiner(new MeanResponseCombiner())
+ .build();
+
+ final Split<Double, ?> bestSplit1 = treeTrainer1.findBestSplitRule(dataList1, covariates1, new Random(123));
+ assertEquals(1, bestSplit1.getSplitRule().getParentCovariateIndex()); // 1 corresponds to GoodVar
+
+ // Run again without the penalty; verify that we get different results
+
+ final List<Covariate> covariates2 = Utils.easyList(
+ new NumericCovariate("BadVar", 0, false),
+ new BooleanCovariate("GoodVar", 1, false)
+ );
+
+ final List<Row<Double>> dataList2 = generateData2(covariates2);
+
+ final TreeTrainer<Double, Double> treeTrainer2 = TreeTrainer.<Double, Double>builder()
+ .checkNodePurity(false)
+ .covariates(covariates2)
+ .numberOfSplits(0)
+ .nodeSize(1)
+ .mtry(2)
+ .maxNodeDepth(1000)
+ .splitFinder(new WeightedVarianceSplitFinder())
+ .responseCombiner(new MeanResponseCombiner())
+ .build();
+
+ final Split<Double, ?> bestSplit2 = treeTrainer2.findBestSplitRule(dataList2, covariates2, new Random(123));
+ assertEquals(0, bestSplit2.getSplitRule().getParentCovariateIndex()); // 1 corresponds to GoodVar
}
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/tree/vimp/VariableImportanceCalculatorTest.java b/library/src/test/java/ca/joeltherrien/randomforest/tree/vimp/VariableImportanceCalculatorTest.java
index b656ce0..e8a83a9 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/tree/vimp/VariableImportanceCalculatorTest.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/tree/vimp/VariableImportanceCalculatorTest.java
@@ -39,10 +39,10 @@ public class VariableImportanceCalculatorTest {
*/
public VariableImportanceCalculatorTest(){
- final BooleanCovariate booleanCovariate = new BooleanCovariate("x", 0);
- final NumericCovariate numericCovariate = new NumericCovariate("y", 1);
+ final BooleanCovariate booleanCovariate = new BooleanCovariate("x", 0, false);
+ final NumericCovariate numericCovariate = new NumericCovariate("y", 1, false);
final FactorCovariate factorCovariate = new FactorCovariate("z", 2,
- Utils.easyList("red", "blue", "green"));
+ Utils.easyList("red", "blue", "green"), false);
this.covariates = Utils.easyList(booleanCovariate, numericCovariate, factorCovariate);
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainForest.java b/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainForest.java
index c9a031a..679fef7 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainForest.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainForest.java
@@ -43,7 +43,7 @@ public class TrainForest {
final List<Covariate> covariateList = new ArrayList<>(p);
for(int j =0; j < p; j++){
- final NumericCovariate covariate = new NumericCovariate("x"+j, j);
+ final NumericCovariate covariate = new NumericCovariate("x"+j, j, false);
covariateList.add(covariate);
}
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTree.java b/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTree.java
index 8e1e361..6c9e0ed 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTree.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTree.java
@@ -39,8 +39,8 @@ public class TrainSingleTree {
final int n = 1000;
final List<Row<Double>> trainingSet = new ArrayList<>(n);
- final Covariate<Double> x1Covariate = new NumericCovariate("x1", 0);
- final Covariate<Double> x2Covariate = new NumericCovariate("x2", 1);
+ final Covariate<Double> x1Covariate = new NumericCovariate("x1", 0, false);
+ final Covariate<Double> x2Covariate = new NumericCovariate("x2", 1, false);
final List<Covariate.Value<Double>> x1List = DoubleStream
.generate(() -> random.nextDouble()*10.0)
diff --git a/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTreeFactor.java b/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTreeFactor.java
index 1d2b404..bc0bd6e 100644
--- a/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTreeFactor.java
+++ b/library/src/test/java/ca/joeltherrien/randomforest/workshop/TrainSingleTreeFactor.java
@@ -41,9 +41,9 @@ public class TrainSingleTreeFactor {
final int n = 10000;
final List<Row<Double>> trainingSet = new ArrayList<>(n);
- final Covariate<Double> x1Covariate = new NumericCovariate("x1", 0);
- final Covariate<Double> x2Covariate = new NumericCovariate("x2", 1);
- final FactorCovariate x3Covariate = new FactorCovariate("x3", 2, Utils.easyList("cat", "dog", "mouse"));
+ final Covariate<Double> x1Covariate = new NumericCovariate("x1", 0, false);
+ final Covariate<Double> x2Covariate = new NumericCovariate("x2", 1, false);
+ final FactorCovariate x3Covariate = new FactorCovariate("x3", 2, Utils.easyList("cat", "dog", "mouse"), false);
final List<Covariate.Value<Double>> x1List = DoubleStream
.generate(() -> random.nextDouble()*10.0)