Implement naive version of concordance index.
Note that results DO NOT MATCH with randomForestSRC; so take these results with a grain of salt.
This commit is contained in:
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7a77851f94
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650579a430
4 changed files with 69 additions and 248 deletions
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@ -6,9 +6,7 @@ import ca.joeltherrien.randomforest.tree.Forest;
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import ca.joeltherrien.randomforest.tree.Tree;
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import lombok.RequiredArgsConstructor;
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import java.util.Collection;
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import java.util.Comparator;
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import java.util.List;
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import java.util.*;
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import java.util.stream.Collectors;
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/**
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@ -28,21 +26,31 @@ public class CompetingRiskErrorRateCalculator {
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this.combiner = new CompetingRiskFunctionCombiner(events, times);
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}
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public double[] calculateAll(final List<Row<CompetingRiskResponse>> rows, final Forest<CompetingRiskFunctions, CompetingRiskFunctions> forest){
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public double[] calculateConcordance(final List<Row<CompetingRiskResponse>> rows, final Forest<CompetingRiskFunctions, CompetingRiskFunctions> forest){
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final double tau = rows.stream().mapToDouble(row -> row.getResponse().getU()).max().orElse(0.0);
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return calculateConcordance(rows, forest, tau);
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}
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private double[] calculateConcordance(final List<Row<CompetingRiskResponse>> rows, final Forest<CompetingRiskFunctions, CompetingRiskFunctions> forest, final double tau){
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final Collection<Tree<CompetingRiskFunctions>> trees = forest.getTrees();
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// This predicts for rows based on their OOB trees.
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final List<CompetingRiskFunctions> riskFunctions = rows.stream()
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.map(row -> {
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return trees.stream().filter(tree -> !tree.idInBootstrapSample(row.getId())).map(tree -> tree.evaluate(row)).collect(Collectors.toList());
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})
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.map(list -> combiner.combine(list))
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.map(combiner::combine)
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.collect(Collectors.toList());
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//final List<CompetingRiskFunctions> riskFunctions = rows.stream().map(row -> forest.evaluate(row)).collect(Collectors.toList());
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final double[] errorRates = new double[events.length];
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final List<CompetingRiskResponse> responses = rows.stream().map(row -> row.getResponse()).collect(Collectors.toList());
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final List<CompetingRiskResponse> responses = rows.stream().map(Row::getResponse).collect(Collectors.toList());
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// Let \tau be the max time.
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@ -51,10 +59,10 @@ public class CompetingRiskErrorRateCalculator {
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final double[] mortalityList = riskFunctions.stream()
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.map(riskFunction -> riskFunction.getCumulativeIncidenceFunction(event))
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.mapToDouble(cif -> functionToMortality(cif))
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.mapToDouble(cif -> functionToMortality(cif, tau))
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.toArray();
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final double concordance = calculate(responses, mortalityList, event);
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final double concordance = calculateConcordance(responses, mortalityList, event);
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errorRates[e] = 1.0 - concordance;
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}
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@ -64,12 +72,12 @@ public class CompetingRiskErrorRateCalculator {
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}
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@VisibleForTesting
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public double calculate(final List<CompetingRiskResponse> responseList, final double[] mortalityArray, final int event){
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public double calculateConcordance(final List<CompetingRiskResponse> responseList, double[] mortalityArray, final int event){
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// Let \tau be the max time.
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int permissible = 0;
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int numerator = 0;
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double numerator = 0;
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for(int i = 0; i<mortalityArray.length; i++){
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final CompetingRiskResponse responseI = responseList.get(i);
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@ -86,7 +94,12 @@ public class CompetingRiskErrorRateCalculator {
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permissible++;
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final double mortalityJ = mortalityArray[j];
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numerator += mortalityI > mortalityJ ? 1 : 0;
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if(mortalityI > mortalityJ){
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numerator += 1.0;
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}
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else if(mortalityI == mortalityJ){
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numerator += 0.5; // Edge case that can happen in trees with only a few BooleanCovariates, when you're looking at training error
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}
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}
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@ -94,194 +107,29 @@ public class CompetingRiskErrorRateCalculator {
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}
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return (double) numerator / (double) permissible;
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return numerator / (double) permissible;
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}
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/*
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public double[] calculateAll(final List<Row<CompetingRiskResponse>> rows, final Forest<CompetingRiskFunctions, CompetingRiskFunctions> forest){
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rows.sort(Comparator.comparing(row -> row.getResponse().getU())); // optimization for later loop
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final Collection<Tree<CompetingRiskFunctions>> trees = forest.getTrees();
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final List<CompetingRiskFunctions> riskFunctions = rows.stream()
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.map(row -> {
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return trees.stream().filter(tree -> !tree.idInBootstrapSample(row.getId())).map(tree -> tree.evaluate(row)).collect(Collectors.toList());
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})
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.map(list -> combiner.combine(list))
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.collect(Collectors.toList());
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final double[] errorRates = new double[events.length];
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for(int e=0; e<events.length; e++){
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final int event = events[e];
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final double[] mortalityList = riskFunctions.stream()
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.map(riskFunction -> riskFunction.getCumulativeIncidenceFunction(event))
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.mapToDouble(cif -> functionToMortality(cif))
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.toArray();
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int permissible = 0;
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double c = 0.0;
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outer_mortality:
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for(int i = 0; i< mortalityList.length; i++){
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final Row<CompetingRiskResponse> leftRow = rows.get(i);
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final double mortalityLeft = mortalityList[i];
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final CompetingRiskResponse leftResponse = leftRow.getResponse();
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for(int j=i+1; j<mortalityList.length; j++){
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final Row<CompetingRiskResponse> rightRow = rows.get(j);
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final double mortalityRight = mortalityList[j];
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final CompetingRiskResponse rightResponse = rightRow.getResponse();
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if(leftResponse.getDelta() != event && rightResponse.getU() > leftResponse.getU()){
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// because we've sorted the responses earlier we will never get a permissable result for greater j.
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continue outer_mortality;
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}
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// check and see if pair is permissable
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if(isPermissablePair(leftResponse, rightResponse, event)){
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permissible++;
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final double comparisonScore = compare(leftResponse, rightResponse, event);
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if(comparisonScore < 0) { // left > right
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// right has shorter time
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if(mortalityRight > mortalityLeft){
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c += 1.0;
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}
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else if(mortalityRight == mortalityLeft){
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c += 0.5;
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}
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}
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else if(comparisonScore > 0){ // left < right
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// left has shorter term
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if(mortalityRight < mortalityLeft){
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c += 1.0;
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}
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else if(mortalityRight == mortalityLeft){
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c += 0.5;
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}
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}
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else{ // comparisonScore == 0
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c += (mortalityLeft == mortalityRight) ? 1.0 : 0.5;
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}
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}
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else{
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continue;
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}
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}
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}
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final double concordance = c / (double) permissible;
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errorRates[e] = 1.0 - concordance;
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}
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return errorRates;
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}
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*/
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/*
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private boolean isPermissablePair(final CompetingRiskResponse left, final CompetingRiskResponse right){
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if(left.isCensored() && right.isCensored()){
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return false;
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}
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if(left.getU() < right.getU() && left.isCensored()){
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return false;
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}
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if(left.getU() > right.getU() && right.isCensored()){
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return false;
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}
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return true;
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}
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*/
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/*
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private boolean isPermissablePair(final CompetingRiskResponse left, final CompetingRiskResponse right, int event){
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if(left.getDelta() != event && right.getDelta() != event){
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return false;
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}
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if(left.getU() < right.getU() && left.getDelta() != event){
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return false;
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}
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if(left.getU() > right.getU() && right.getDelta() != event){
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return false;
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}
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return true;
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}
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*/
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private double functionToMortality(final MathFunction cif){
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private double functionToMortality(final MathFunction cif, final double tau){
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double summation = 0.0;
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double previousTime = 0.0;
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Point previousPoint = null;
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for(final Point point : cif.getPoints()){
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summation += point.getY() * (point.getTime() - previousTime);
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previousTime = point.getTime();
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if(previousPoint != null){
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summation += previousPoint.getY() * (point.getTime() - previousPoint.getTime());
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}
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previousPoint = point;
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}
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// this is to ensure that we integrate over the same range for every function and get comparable results.
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// Don't need to assert whether previousPoint is null or not; if it is null then the MathFunction was incorrectly made as there will always be at least one point for a response
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summation += previousPoint.getY() * (tau - previousPoint.getTime());
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return summation;
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}
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/**
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* Compare two CompetingRiskResponses to see which is larger than the other (if it can be determined).
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*
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* @param left
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* @param right
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* @param event Event of interest. All other events are treated as censoring.
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* @return -1 if left is strictly greater than right, -0.5 if left is greater than right, 0 if both are equal, 0.5 if right is greater than left, and 1 if right is strictly greater than left.
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*//*
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@VisibleForTesting
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public double compare(final CompetingRiskResponse left, final CompetingRiskResponse right, int event){
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if(left.getU() > right.getU() && right.getDelta()==event){
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// left is greater
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return -1;
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}
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else if(right.getU() > left.getU() && left.getDelta()==event){
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// right is greater
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return 1;
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}
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else if(left.getU() == right.getU() && left.getDelta()==event && right.getDelta()==event){
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// they are equal
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return 0;
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}
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else if(left.getU() == right.getU() && left.getDelta()!=event && right.getDelta()==event){
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// left is greater (note; could be unknown depending on definitions)
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//return -0.5;
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return 0;
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}
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else if(left.getU() == right.getU() && left.getDelta()==event && right.getDelta()!=event){
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// right is greater (note; could be unknown depending on definitions)
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//return 0.5;
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return 0;
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}
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else{
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throw new IllegalArgumentException("Invalid comparison of " + left + " and " + right + "; did you call isPermissablePair first?");
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}
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}*/
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}
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@ -72,6 +72,21 @@ output.many.trees.all$cif[,103,1]
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output.many.trees.all$cif[,103,2]
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err.rate.1 = c()
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err.rate.2 = c()
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for(j in 1:100){
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many.trees.all <- rfsrc(Surv(time, status) ~ ageatfda + cd4nadir + idu + black, wihs, nsplit = 5, ntree = 100, splitrule="logrank", cause=1, mtry=2, membership=TRUE);
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err.rate.1 = c(err.rate.1, many.trees.all$err.rate[100,1])
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err.rate.2 = c(err.rate.2, many.trees.all$err.rate[100,2])
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}
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quant.1 = quantile(err.rate.1, probs=c(0.025, 0.5, 0.975)) # 0.4727131 0.4792391 0.4862286
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quant.2 = quantile(err.rate.2, probs=c(0.025, 0.5, 0.975)) # 0.4898299 0.4978300 0.5064539
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(quant.1[3] + quant.1[1]) / 2
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(quant.1[3] - quant.1[1]) / 2
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(quant.2[3] + quant.2[1]) / 2
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(quant.2[3] - quant.2[1]) / 2
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@ -228,11 +228,20 @@ public class TestCompetingRisk {
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closeEnough(0.195, functions.getCumulativeIncidenceFunction(2).evaluate(10.8).getY(), 0.01);
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final CompetingRiskErrorRateCalculator errorRateCalculator = new CompetingRiskErrorRateCalculator(new int[]{1,2}, null);
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final double[] errorRates = errorRateCalculator.calculateAll(dataset, forest);
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final double[] errorRates = errorRateCalculator.calculateConcordance(dataset, forest);
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// Error rates happen to be about the same
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/* randomForestSRC results; ignored for now
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closeEnough(0.4795, errorRates[0], 0.007);
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closeEnough(0.478, errorRates[1], 0.008);
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*/
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System.out.println(errorRates[0]);
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System.out.println(errorRates[1]);
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closeEnough(0.452, errorRates[0], 0.01);
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closeEnough(0.446, errorRates[1], 0.01);
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}
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@ -299,15 +308,20 @@ public class TestCompetingRisk {
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// We seem to consistently underestimate the results.
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assertTrue(causeOneCIFPoints.get(causeOneCIFPoints.size()-1).getY() > 0.75, "Results should match randomForestSRC; had " + causeOneCIFPoints.get(causeOneCIFPoints.size()-1).getY()); // note; most observations from randomForestSRC hover around 0.78 but I've seen it as low as 0.72
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final CompetingRiskErrorRateCalculator errorRate = new CompetingRiskErrorRateCalculator((CompetingRiskFunctionCombiner) settings.getTreeCombiner(), new int[]{1,2});
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final double[] errorRates = errorRate.calculateAll(dataset, forest);
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final CompetingRiskErrorRateCalculator errorRate = new CompetingRiskErrorRateCalculator(new int[]{1,2}, null);
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final double[] errorRates = errorRate.calculateConcordance(dataset, forest);
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System.out.println(errorRates[0]);
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System.out.println(errorRates[1]);
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closeEnough(0.41, errorRates[0], 0.02);
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closeEnough(0.38, errorRates[1], 0.02);
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/* randomForestSRC results; ignored for now
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closeEnough(0.412, errorRates[0], 0.007);
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closeEnough(0.384, errorRates[1], 0.007);
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*/
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// Consistency results
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closeEnough(0.395, errorRates[0], 0.01);
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closeEnough(0.345, errorRates[1], 0.01);
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}
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/**
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@ -10,62 +10,6 @@ import static org.junit.jupiter.api.Assertions.*;
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public class TestCompetingRiskErrorRateCalculator {
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/*
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@Test
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public void testComparingResponses(){
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// Large, uncensored
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CompetingRiskResponse responseA = new CompetingRiskResponse(1, 10.0);
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// Large, censored
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CompetingRiskResponse responseB = new CompetingRiskResponse(0, 10.0);
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// Large, other event
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CompetingRiskResponse responseC = new CompetingRiskResponse(2, 10.0);
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// Medium, uncensored
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CompetingRiskResponse responseD = new CompetingRiskResponse(1, 5.0);
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// Medium, censored
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CompetingRiskResponse responseE = new CompetingRiskResponse(0, 5.0);
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// Medium, other event
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CompetingRiskResponse responseF = new CompetingRiskResponse(2, 5.0);
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final int event = 1;
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final CompetingRiskErrorRateCalculator errorRateCalculator = new CompetingRiskErrorRateCalculator(null, null);
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseB, responseB, event));
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseC, responseC, event));
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assertEquals(0.5, errorRateCalculator.compare(responseA, responseB, event));
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assertEquals(-0.5, errorRateCalculator.compare(responseB, responseA, event));
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assertEquals(0.0, errorRateCalculator.compare(responseA, responseA, event));
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseB, responseE, event));
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseE, responseB, event));
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseB, responseF, event));
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseF, responseB, event));
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assertEquals(-1.0, errorRateCalculator.compare(responseB, responseD, event));
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assertEquals(1.0, errorRateCalculator.compare(responseD, responseB, event));
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assertEquals(-1.0, errorRateCalculator.compare(responseC, responseD, event));
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assertEquals(1.0, errorRateCalculator.compare(responseD, responseC, event));
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assertEquals(-1.0, errorRateCalculator.compare(responseA, responseD, event));
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assertEquals(1.0, errorRateCalculator.compare(responseD, responseA, event));
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseA, responseE, event));
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseE, responseA, event));
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseA, responseF, event));
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assertThrows(IllegalArgumentException.class, () -> errorRateCalculator.compare(responseF, responseA, event));
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}
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*/
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@Test
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public void testConcordance(){
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@ -81,7 +25,7 @@ public class TestCompetingRiskErrorRateCalculator {
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final CompetingRiskErrorRateCalculator errorRateCalculator = new CompetingRiskErrorRateCalculator(new int[]{1,2}, null);
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final double concordance = errorRateCalculator.calculate(responseList, mortalityArray, event);
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final double concordance = errorRateCalculator.calculateConcordance(responseList, mortalityArray, event);
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// Expected value found through calculations by hand
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assertEquals(3.0/5.0, concordance);
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