Refactored code to allow for a class of covariates to determine which

SplitRules are tested.

Most of the refactoring involved the creation of a Covariate class (one
instance per column); with SplitRule and Value being folded in as inner
classes.

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

@@ -0,0 +1,58 @@
+package ca.joeltherrien.randomforest;
+
+import java.io.Serializable;
+import java.util.Collection;
+import java.util.LinkedList;
+import java.util.List;
+
+public interface Covariate<V> extends Serializable {
+
+    String getName();
+
+    Collection<? extends SplitRule<V>> generateSplitRules(final List<Value<V>> data, final int number);
+
+    Value<V> createValue(V value);
+
+    interface Value<V> extends Serializable{
+
+        Covariate<V> getParent();
+
+        V getValue();
+
+    }
+
+    interface SplitRule<V> extends Serializable{
+
+        Covariate<V> getParent();
+
+        /**
+         * Applies the SplitRule to a list of rows and returns a Split object, which contains two lists for both sides.
+         * This method is primarily used during the training of a tree when splits are being tested.
+         *
+         * @param rows
+         * @param <Y>
+         * @return
+         */
+        default <Y> Split<Y> applyRule(List<Row<Y>> rows) {
+            final List<Row<Y>> leftHand = new LinkedList<>();
+            final List<Row<Y>> rightHand = new LinkedList<>();
+
+            for(final Row<Y> row : rows) {
+
+                if(isLeftHand(row)){
+                    leftHand.add(row);
+                }
+                else{
+                    rightHand.add(row);
+                }
+
+            }
+
+            return new Split<>(leftHand, rightHand);
+        }
+
+        boolean isLeftHand(CovariateRow row);
+    }
+
+
+}

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

@@ -8,12 +8,12 @@ import java.util.Map;
 @RequiredArgsConstructor
 public class CovariateRow {
 
-    private final Map<String, Value> valueMap;
+    private final Map<String, Covariate.Value> valueMap;
 
     @Getter
     private final int id;
 
-    public Value<?> getCovariate(String name){
+    public Covariate.Value<?> getCovariateValue(String name){
         return valueMap.get(name);
 
     }

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

@@ -0,0 +1,103 @@
+package ca.joeltherrien.randomforest;
+
+import ca.joeltherrien.randomforest.exceptions.MissingValueException;
+import lombok.Getter;
+import lombok.RequiredArgsConstructor;
+
+import java.util.*;
+import java.util.concurrent.ThreadLocalRandom;
+import java.util.stream.Collectors;
+
+@RequiredArgsConstructor
+public class NumericCovariate implements Covariate<Double>{
+
+    @Getter
+    private final String name;
+
+    @Override
+    public Collection<NumericSplitRule> generateSplitRules(List<Value<Double>> data, int number) {
+
+        // 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);
+        }
+        else{ // only need the top number entries
+            shuffledData = new ArrayList<>(number);
+            final Set<Integer> indexesToUse = new HashSet<>();
+
+            while(indexesToUse.size() < number){
+                final int index = ThreadLocalRandom.current().nextInt(data.size());
+
+                if(indexesToUse.add(index)){
+                    shuffledData.add(data.get(index));
+                }
+            }
+
+        }
+
+        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
+
+
+    }
+
+    @Override
+    public NumericValue createValue(Double value) {
+        return new NumericValue(value);
+    }
+
+    public class NumericValue implements Covariate.Value<Double>{
+
+        private final double value;
+
+        private NumericValue(final double value){
+            this.value = value;
+        }
+
+        @Override
+        public Covariate<Double> getParent() {
+            return NumericCovariate.this;
+        }
+
+        @Override
+        public Double getValue() {
+            return value;
+        }
+    }
+
+    public class NumericSplitRule implements Covariate.SplitRule<Double>{
+
+        private final double threshold;
+
+        private NumericSplitRule(final double threshold){
+            this.threshold = threshold;
+        }
+
+        @Override
+        public final String toString() {
+            return "NumericSplitRule on " + getParent().getName() + " at " + threshold;
+        }
+
+        @Override
+        public Covariate<Double> getParent() {
+            return NumericCovariate.this;
+        }
+
+        @Override
+        public boolean isLeftHand(CovariateRow row) {
+            final Covariate.Value<?> x = row.getCovariateValue(getParent().getName());
+            if(x == null) {
+                throw new MissingValueException(row, this);
+            }
+
+            final double xNum = (Double) x.getValue();
+
+            return xNum <= threshold;
+        }
+    }
+}

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

@@ -1,33 +0,0 @@
-package ca.joeltherrien.randomforest;
-
-import java.util.LinkedList;
-import java.util.List;
-
-import ca.joeltherrien.randomforest.exceptions.MissingValueException;
-import lombok.AllArgsConstructor;
-
-@AllArgsConstructor
-public class NumericSplitRule extends SplitRule{
-
-	public final String covariateName;	
-	public final double threshold;
-
-	@Override
-	public final String toString() {
-		return "NumericSplitRule on " + covariateName + " at " + threshold;
-	}
-
-    @Override
-    public boolean isLeftHand(CovariateRow row) {
-        final Value<?> x = row.getCovariate(covariateName);
-        if(x == null) {
-            throw new MissingValueException(row, this);
-        }
-
-        final double xNum = (Double) x.getValue();
-
-        return xNum <= threshold;
-    }
-
-
-}

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

@@ -1,24 +0,0 @@
-package ca.joeltherrien.randomforest;
-
-import lombok.RequiredArgsConstructor;
-
-@RequiredArgsConstructor
-public class NumericValue implements Value<Double> {
-
-    private final double value;
-
-    @Override
-    public Double getValue() {
-        return value;
-    }
-
-    @Override
-    public SplitRule generateSplitRule(final String covariateName) {
-        return new NumericSplitRule(covariateName, value);
-    }
-
-    @Override
-    public String toString(){
-        return "" + value;
-    }
-}

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

@@ -7,7 +7,7 @@ public class Row<Y> extends CovariateRow {
 
 	private final Y response;
 
-	public Row(Map<String, Value> valueMap, int id, Y response){
+	public Row(Map<String, Covariate.Value> valueMap, int id, Y response){
 	    super(valueMap, id);
 	    this.response = response;
     }

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

@@ -1,37 +0,0 @@
-package ca.joeltherrien.randomforest;
-
-import java.io.Serializable;
-import java.util.LinkedList;
-import java.util.List;
-
-public abstract class SplitRule implements Serializable {
-
-    /**
-     * Applies the SplitRule to a list of rows and returns a Split object, which contains two lists for both sides.
-     * This method is primarily used during the training of a tree when splits are being tested.
-     *
-     * @param rows
-     * @param <Y>
-     * @return
-     */
-    public <Y> Split<Y> applyRule(List<Row<Y>> rows) {
-        final List<Row<Y>> leftHand = new LinkedList<>();
-        final List<Row<Y>> rightHand = new LinkedList<>();
-
-        for(final Row<Y> row : rows) {
-
-            if(isLeftHand(row)){
-                leftHand.add(row);
-            }
-            else{
-                rightHand.add(row);
-            }
-
-        }
-
-        return new Split<>(leftHand, rightHand);
-    }
-
-    public abstract boolean isLeftHand(CovariateRow row);
-	
-}

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

@@ -1,11 +0,0 @@
-package ca.joeltherrien.randomforest;
-
-
-public interface Value<V> {
-
-    V getValue();
-
-    SplitRule generateSplitRule(String covariateName);
-
-
-}

src/main/java/ca/joeltherrien/randomforest/exceptions/MissingValueException.java

@@ -1,8 +1,7 @@
 package ca.joeltherrien.randomforest.exceptions;
 
+import ca.joeltherrien.randomforest.Covariate;
 import ca.joeltherrien.randomforest.CovariateRow;
-import ca.joeltherrien.randomforest.Row;
-import ca.joeltherrien.randomforest.SplitRule;
 
 public class MissingValueException extends RuntimeException{
 
@@ -11,7 +10,7 @@ public class MissingValueException extends RuntimeException{
 	 */
 	private static final long serialVersionUID = 6808060079431207726L;
 	
-	public MissingValueException(CovariateRow row, SplitRule rule) {
+	public MissingValueException(CovariateRow row, Covariate.SplitRule rule) {
 		super("Missing value at CovariateRow " + row + rule);
 	}
 

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

@@ -1,6 +1,7 @@
 package ca.joeltherrien.randomforest.tree;
 
 import ca.joeltherrien.randomforest.Bootstrapper;
+import ca.joeltherrien.randomforest.Covariate;
 import ca.joeltherrien.randomforest.ResponseCombiner;
 import ca.joeltherrien.randomforest.Row;
 import lombok.Builder;
@@ -21,7 +22,7 @@ import java.util.stream.Stream;
 public class ForestTrainer<Y> {
 
     private final TreeTrainer<Y> treeTrainer;
-    private final List<String> covariatesToTry;
+    private final List<Covariate> covariatesToTry;
     private final ResponseCombiner<Y, ?> treeResponseCombiner;
     private final List<Row<Y>> data;
 
@@ -140,7 +141,7 @@ public class ForestTrainer<Y> {
     }
 
     private Node<Y> trainTree(final Bootstrapper<Row<Y>> bootstrapper){
-        final List<String> treeCovariates = new ArrayList<>(covariatesToTry);
+        final List<Covariate> treeCovariates = new ArrayList<>(covariatesToTry);
         Collections.shuffle(treeCovariates);
 
         for(int treeIndex = covariatesToTry.size()-1; treeIndex >= mtry; treeIndex--){

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

@@ -1,8 +1,7 @@
 package ca.joeltherrien.randomforest.tree;
 
+import ca.joeltherrien.randomforest.Covariate;
 import ca.joeltherrien.randomforest.CovariateRow;
-import ca.joeltherrien.randomforest.Row;
-import ca.joeltherrien.randomforest.SplitRule;
 import lombok.Builder;
 
 @Builder
@@ -10,7 +9,7 @@ public class SplitNode<Y> implements Node<Y> {
 
     private final Node<Y> leftHand;
     private final Node<Y> rightHand;
-    private final SplitRule splitRule;
+    private final Covariate.SplitRule splitRule;
 
     @Override
     public Y evaluate(CovariateRow row) {

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

@@ -1,8 +1,6 @@
 package ca.joeltherrien.randomforest.tree;
 
 import ca.joeltherrien.randomforest.*;
-import ca.joeltherrien.randomforest.regression.MeanResponseCombiner;
-import ca.joeltherrien.randomforest.regression.WeightedVarianceGroupDifferentiator;
 import lombok.Builder;
 
 import java.util.*;
@@ -22,17 +20,15 @@ public class TreeTrainer<Y> {
     private final int nodeSize;
     private final int maxNodeDepth;
 
-    private final Random random = new Random();
 
-
+    public Node<Y> growTree(List<Row<Y>> data, List<Covariate> covariatesToTry){
-    public Node<Y> growTree(List<Row<Y>> data, List<String> covariatesToTry){
         return growNode(data, covariatesToTry, 0);
     }
 
-    private Node<Y> growNode(List<Row<Y>> data, List<String> covariatesToTry, int depth){
+    private Node<Y> growNode(List<Row<Y>> data, List<Covariate> covariatesToTry, int depth){
         // TODO; what is minimum per tree?
         if(data.size() >= 2*nodeSize && depth < maxNodeDepth && !nodeIsPure(data)){
-            final SplitRule bestSplitRule = findBestSplitRule(data, covariatesToTry);
+            final Covariate.SplitRule bestSplitRule = findBestSplitRule(data, covariatesToTry);
 
             if(bestSplitRule == null){
                 return new TerminalNode<>(
@@ -63,37 +59,24 @@ public class TreeTrainer<Y> {
 
     }
 
-    private SplitRule findBestSplitRule(List<Row<Y>> data, List<String> covariatesToTry){
+    private Covariate.SplitRule findBestSplitRule(List<Row<Y>> data, List<Covariate> covariatesToTry){
-        SplitRule bestSplitRule = null;
+        Covariate.SplitRule bestSplitRule = null;
         double bestSplitScore = 0.0;
         boolean first = true;
 
-        for(final String covariate : covariatesToTry){
+        for(final Covariate covariate : covariatesToTry){
 
-            final List<Row<Y>> shuffledData;
+            final int numberToTry = numberOfSplits==0 ? data.size() : numberOfSplits;
-            if(numberOfSplits == 0 || numberOfSplits > data.size()){
-                shuffledData = new ArrayList<>(data);
-                Collections.shuffle(shuffledData);
-            }
-            else{ // only need the top numberOfSplits entries
-                shuffledData = new ArrayList<>(numberOfSplits);
-                final Set<Integer> indexesToUse = new HashSet<>();
 
-                while(indexesToUse.size() < numberOfSplits){
+            final Collection<Covariate.SplitRule> splitRulesToTry = covariate
-                    final int index = random.nextInt(data.size());
+                    .generateSplitRules(
+                            data
+                                    .stream()
+                                    .map(row -> row.getCovariateValue(covariate.getName()))
+                                    .collect(Collectors.toList())
+                            , numberToTry);
 
-                    if(indexesToUse.add(index)){
+            for(final Covariate.SplitRule possibleRule : splitRulesToTry){
-                        shuffledData.add(data.get(index));
-                    }
-                }
-
-            }
-
-
-            int tries = 0;
-
-            while(tries < shuffledData.size()){
-                final SplitRule possibleRule = shuffledData.get(tries).getCovariate(covariate).generateSplitRule(covariate);
                 final Split<Y> possibleSplit = possibleRule.applyRule(data);
 
                 final Double score = groupDifferentiator.differentiate(
@@ -106,8 +89,6 @@ public class TreeTrainer<Y> {
                     bestSplitScore = score;
                     first = false;
                 }
-
-                tries++;
             }
 
         }

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

@@ -19,21 +19,28 @@ public class TrainForest {
         final int n = 10000;
         final int p = 5;
 
+
         final Random random = new Random();
 
         final List<Row<Double>> data = new ArrayList<>(n);
 
         double minY = 1000.0;
 
+        final List<Covariate> covariateList = new ArrayList<>(p);
+        for(int j =0; j < p; j++){
+            final NumericCovariate covariate = new NumericCovariate("x"+j);
+            covariateList.add(covariate);
+        }
+
         for(int i=0; i<n; i++){
             double y = 0.0;
-            final Map<String, Value> map = new HashMap<>();
+            final Map<String, Covariate.Value> map = new HashMap<>();
 
-            for(int j=0; j<p; j++){
+            for(final Covariate covariate : covariateList) {
                 final double x = random.nextDouble();
                 y += x;
 
-                map.put("x"+j, new NumericValue(x));
+                map.put(covariate.getName(), covariate.createValue(x));
             }
 
             data.add(i, new Row<>(map, i, y));
@@ -44,10 +51,8 @@ public class TrainForest {
 
         }
 
-        final List<String> covariateNames = IntStream.range(0, p).mapToObj(j -> "x"+j).collect(Collectors.toList());
 
-
+        final TreeTrainer<Double> treeTrainer = TreeTrainer.<Double>builder()
-        TreeTrainer<Double> treeTrainer = TreeTrainer.<Double>builder()
                 .numberOfSplits(5)
                 .nodeSize(5)
                 .maxNodeDepth(100000000)
@@ -58,7 +63,7 @@ public class TrainForest {
         final ForestTrainer<Double> forestTrainer = ForestTrainer.<Double>builder()
                 .treeTrainer(treeTrainer)
                 .data(data)
-                .covariatesToTry(covariateNames)
+                .covariatesToTry(covariateList)
                 .mtry(4)
                 .ntree(100)
                 .treeResponseCombiner(new MeanResponseCombiner())
@@ -69,7 +74,7 @@ public class TrainForest {
         final long startTime = System.currentTimeMillis();
 
         //final Forest<Double> forest = forestTrainer.trainSerial();
-        //final Forest<Double> forest = forestTrainer.trainParallel(8);
+        //final Forest<Double> forest = forestTrainer.trainParallelInMemory(3);
         forestTrainer.trainParallelOnDisk(3);
 
         final long endTime  = System.currentTimeMillis();
@@ -88,9 +93,9 @@ public class TrainForest {
 
         System.out.println(forest.evaluate(testRow1));
         System.out.println(forest.evaluate(testRow2));
-
-        System.out.println("MinY = " + minY);
         */
+        System.out.println("MinY = " + minY);
+
     }
 
 }

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

@@ -1,11 +1,10 @@
 package ca.joeltherrien.randomforest.workshop;
 
 
+import ca.joeltherrien.randomforest.Covariate;
 import ca.joeltherrien.randomforest.CovariateRow;
-import ca.joeltherrien.randomforest.NumericValue;
+import ca.joeltherrien.randomforest.NumericCovariate;
 import ca.joeltherrien.randomforest.Row;
-import ca.joeltherrien.randomforest.Value;
-import ca.joeltherrien.randomforest.regression.MeanGroupDifferentiator;
 import ca.joeltherrien.randomforest.regression.MeanResponseCombiner;
 import ca.joeltherrien.randomforest.regression.WeightedVarianceGroupDifferentiator;
 import ca.joeltherrien.randomforest.tree.Node;
@@ -25,30 +24,30 @@ public class TrainSingleTree {
         final int n = 1000;
         final List<Row<Double>> trainingSet = new ArrayList<>(n);
 
-        final List<Value<Double>> x1List = DoubleStream
+        final Covariate<Double> x1Covariate = new NumericCovariate("x1");
+        final Covariate<Double> x2Covariate = new NumericCovariate("x2");
+
+        final List<Covariate.Value<Double>> x1List = DoubleStream
                 .generate(() -> random.nextDouble()*10.0)
                 .limit(n)
-                .mapToObj(x1 -> new NumericValue(x1))
+                .mapToObj(x1 -> x1Covariate.createValue(x1))
                 .collect(Collectors.toList());
 
-        final List<Value<Double>> x2List = DoubleStream
+        final List<Covariate.Value<Double>> x2List = DoubleStream
                 .generate(() -> random.nextDouble()*10.0)
                 .limit(n)
-                .mapToObj(x1 -> new NumericValue(x1))
+                .mapToObj(x2 -> x1Covariate.createValue(x2))
                 .collect(Collectors.toList());
 
 
 
         for(int i=0; i<n; i++){
-            double x1 = x1List.get(i).getValue();
+            final Covariate.Value<Double> x1 = x1List.get(i);
-            double x2 = x2List.get(i).getValue();
+            final Covariate.Value<Double> x2 = x2List.get(i);
 
             trainingSet.add(generateRow(x1, x2, i));
         }
 
-
-        final long startTime = System.currentTimeMillis();
-
         final TreeTrainer<Double> treeTrainer = TreeTrainer.<Double>builder()
                 .groupDifferentiator(new WeightedVarianceGroupDifferentiator())
                 .responseCombiner(new MeanResponseCombiner())
@@ -57,25 +56,29 @@ public class TrainSingleTree {
                 .numberOfSplits(0)
                 .build();
 
+        final List<Covariate> covariateNames = List.of(x1Covariate, x2Covariate);
+
+        final long startTime = System.currentTimeMillis();
+        final Node<Double> baseNode = treeTrainer.growTree(trainingSet, covariateNames);
         final long endTime = System.currentTimeMillis();
 
         System.out.println(((double)(endTime - startTime))/1000.0);
 
-        final List<String> covariateNames = List.of("x1", "x2");
 
-        final Node<Double> baseNode = treeTrainer.growTree(trainingSet, covariateNames);
+
+
 
 
         final List<CovariateRow> testSet = new ArrayList<>();
-        testSet.add(generateCovariateRow(9, 2, 1)); // expect 1
+        testSet.add(generateCovariateRow(x1Covariate.createValue(9.0), x2Covariate.createValue(2.0), 1)); // expect 1
-        testSet.add(generateCovariateRow(5, 2, 5));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(5.0), x2Covariate.createValue(2.0), 5));
-        testSet.add(generateCovariateRow(2, 2, 3));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(2.0), x2Covariate.createValue(2.0), 3));
-        testSet.add(generateCovariateRow(9, 5, 0));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(9.0), x2Covariate.createValue(5.0), 0));
-        testSet.add(generateCovariateRow(6, 5, 8));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(6.0), x2Covariate.createValue(5.0), 8));
-        testSet.add(generateCovariateRow(3, 5, 10));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(3.0), x2Covariate.createValue(5.0), 10));
-        testSet.add(generateCovariateRow(1, 5, 3));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(1.0), x2Covariate.createValue(5.0), 3));
-        testSet.add(generateCovariateRow(7, 9, 2));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(7.0), x2Covariate.createValue(9.0), 2));
-        testSet.add(generateCovariateRow(1, 9, 4));
+        testSet.add(generateCovariateRow(x1Covariate.createValue(1.0), x2Covariate.createValue(9.0), 4));
 
         for(final CovariateRow testCase : testSet){
             System.out.println(testCase);
@@ -91,18 +94,18 @@ public class TrainSingleTree {
 
     }
 
-	public static Row<Double> generateRow(double x1, double x2, int id){
+	public static Row<Double> generateRow(Covariate.Value<Double> x1, Covariate.Value<Double> x2, int id){
-	    double y = generateResponse(x1, x2);
+	    double y = generateResponse(x1.getValue(), x2.getValue());
 
-	    final Map<String, Value> map = Map.of("x1", new NumericValue(x1), "x2", new NumericValue(x2));
+	    final Map<String, Covariate.Value> map = Map.of("x1", x1, "x2", x2);
 
         return new Row<>(map, id, y);
 
     }
 
 
-    public static CovariateRow generateCovariateRow(double x1, double x2, int id){
+    public static CovariateRow generateCovariateRow(Covariate.Value x1, Covariate.Value x2, int id){
-        final Map<String, Value> map = Map.of("x1", new NumericValue(x1), "x2", new NumericValue(x2));
+        final Map<String, Covariate.Value> map = Map.of("x1", x1, "x2", x2);
 
         return new CovariateRow(map, id);