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Optimize CompetingRiskResponseCombiner

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This commit is contained in:
Joel Therrien 2018-10-12 11:42:25 -07:00
parent aa733d5eba
commit 7fba964af9
3 changed files with 72 additions and 138 deletions
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18
src/main/java/ca/joeltherrien/randomforest/Settings.java
View file

@ -129,15 +129,8 @@ public class Settings {
node.get("events").elements().forEachRemaining(event -> eventList.add(event.asInt()));
final int[] events = eventList.stream().mapToInt(i -> i).toArray();
double[] times = null;
// note that times may be null
if(node.hasNonNull("times")){
final List<Double> timeList = new ArrayList<>();
node.get("times").elements().forEachRemaining(time -> timeList.add(time.asDouble()));
times = timeList.stream().mapToDouble(db -> db).toArray();
}
return new CompetingRiskResponseCombiner(events, times);
return new CompetingRiskResponseCombiner(events);
}
);
@ -167,15 +160,8 @@ public class Settings {
node.get("events").elements().forEachRemaining(event -> eventList.add(event.asInt()));
final int[] events = eventList.stream().mapToInt(i -> i).toArray();
double[] times = null;
// note that times may be null
if(node.hasNonNull("times")){
final List<Double> timeList = new ArrayList<>();
node.get("times").elements().forEachRemaining(time -> timeList.add(time.asDouble()));
times = timeList.stream().mapToDouble(db -> db).toArray();
}
final CompetingRiskResponseCombiner responseCombiner = new CompetingRiskResponseCombiner(events, times);
final CompetingRiskResponseCombiner responseCombiner = new CompetingRiskResponseCombiner(events);
return new CompetingRiskListCombiner(responseCombiner);
}

123
src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/combiner/CompetingRiskResponseCombiner.java
View file

@ -16,84 +16,108 @@ import java.util.*;
* See https://kogalur.github.io/randomForestSRC/theory.html for details.
*
*/
@RequiredArgsConstructor
public class CompetingRiskResponseCombiner implements ResponseCombiner<CompetingRiskResponse, CompetingRiskFunctions> {
private final int[] events;
private final double[] times; // We may restrict ourselves to specific times.
public CompetingRiskResponseCombiner(final int[] events){
this.events = events.clone();
// Check to make sure that events go from 1 to the right order
for(int i=0; i<events.length; i++){
if(events[i] != (i+1)){
throw new IllegalArgumentException("The events parameter must be in the form 1,2,3,...J with no gaps");
}
}
}
public int[] getEvents(){
return events.clone();
}
public double[] getTimes(){
return times.clone();
}
@Override
public CompetingRiskFunctions combine(List<CompetingRiskResponse> responses) {
final List<MathFunction> causeSpecificCumulativeHazardFunctionList = new ArrayList<>(events.length);
final List<MathFunction> cumulativeIncidenceFunctionList = new ArrayList<>(events.length);
final double[] timesToUse;
if(times != null){
timesToUse = this.times;
}
else{
timesToUse = responses.stream()
.filter(response -> !response.isCensored())
.mapToDouble(response -> response.getU())
.sorted().distinct()
.toArray();
}
Collections.sort(responses, (y1, y2) -> {
if(y1.getU() < y2.getU()){
return -1;
}
else if(y1.getU() > y2.getU()){
return 1;
}
else{
return 0;
}
});
final double[] individualsAtRiskArray = Arrays.stream(timesToUse).map(time -> riskSet(responses, time)).toArray();
final int n = responses.size();
int[] numberOfCurrentEvents = new int[events.length+1];
// First we need to develop the overall survival curve!
final List<Point> survivalPoints = new ArrayList<>(timesToUse.length);
double previousSurvivalValue = 1.0;
for(int i=0; i<timesToUse.length; i++){
final double time_k = timesToUse[i];
final double individualsAtRisk = individualsAtRiskArray[i]; // Y(t_k)
final List<Point> survivalPoints = new ArrayList<>(n); // better to be too large than too small
// Also track riskSet variables and numberOfEvents, and timesToUse
final List<Double> timesToUseList = new ArrayList<>(n);
final List<Integer> riskSetList = new ArrayList<>(n);
final List<int[]> numberOfEvents = new ArrayList<>(n);
for(int i=0; i<n; i++){
final CompetingRiskResponse currentResponse = responses.get(i);
final boolean lastOfTime = (i+1)==n || responses.get(i+1).getU() > currentResponse.getU();
numberOfCurrentEvents[currentResponse.getDelta()]++;
if(lastOfTime){
int totalNumberOfCurrentEvents = 0;
for(int e = 1; e < numberOfCurrentEvents.length; e++){ // exclude censored events
totalNumberOfCurrentEvents += numberOfCurrentEvents[e];
}
if(totalNumberOfCurrentEvents > 0){
// Add point
final double currentTime = currentResponse.getU();
final int riskSet = n - (i+1) + totalNumberOfCurrentEvents + numberOfCurrentEvents[0];
final double newValue = previousSurvivalValue * (1.0 - (double) totalNumberOfCurrentEvents / (double) riskSet);
survivalPoints.add(new Point(currentTime, newValue));
previousSurvivalValue = newValue;
timesToUseList.add(currentTime);
riskSetList.add(riskSet);
numberOfEvents.add(numberOfCurrentEvents);
}
// reset counters
numberOfCurrentEvents = new int[events.length+1];
if(individualsAtRisk == 0){
// if we continue we'll get NaN
break;
}
final double numberOfEventsAtTime = (double) responses.stream()
.filter(event -> !event.isCensored())
.filter(event -> event.getU() == time_k) // since delta != 0 we know censoring didn't occur prior to this
.count();
final double newValue = previousSurvivalValue * (1.0 - numberOfEventsAtTime / individualsAtRisk);
survivalPoints.add(new Point(time_k, newValue));
previousSurvivalValue = newValue;
}
final MathFunction survivalCurve = new MathFunction(survivalPoints, new Point(0.0, 1.0));
for(final int event : events){
final List<Point> hazardFunctionPoints = new ArrayList<>(timesToUse.length);
final List<Point> hazardFunctionPoints = new ArrayList<>(timesToUseList.size());
Point previousHazardFunctionPoint = new Point(0.0, 0.0);
final List<Point> cifPoints = new ArrayList<>(timesToUse.length);
final List<Point> cifPoints = new ArrayList<>(timesToUseList.size());
Point previousCIFPoint = new Point(0.0, 0.0);
for(int i=0; i<timesToUse.length; i++){
final double time_k = timesToUse[i];
final double individualsAtRisk = individualsAtRiskArray[i]; // Y(t_k)
for(int i=0; i<timesToUseList.size(); i++){
final double time_k = timesToUseList.get(i);
final double individualsAtRisk = riskSetList.get(i); // Y(t_k)
if(individualsAtRisk == 0){
// if we continue we'll get NaN
break;
}
final double numberEventsAtTime = numberOfEventsAtTime(event, responses, time_k); // d_j(t_k)
final double numberEventsAtTime = numberOfEvents.get(i)[event]; // d_j(t_k)
// Cause-specific cumulative hazard function
final double hazardDeltaY = numberEventsAtTime / individualsAtRisk;
@ -105,7 +129,7 @@ public class CompetingRiskResponseCombiner implements ResponseCombiner<Competing
// Cumulative incidence function
// TODO - confirm this behaviour
//final double previousSurvivalEvaluation = i > 0 ? survivalCurve.evaluate(timesToUse[i-1]).getY() : survivalCurve.evaluate(0.0).getY();
final double previousSurvivalEvaluation = i > 0 ? survivalCurve.evaluate(timesToUse[i-1]).getY() : 1.0;
final double previousSurvivalEvaluation = i > 0 ? survivalCurve.evaluate(timesToUseList.get(i-1)).getY() : 1.0;
final double cifDeltaY = previousSurvivalEvaluation * (numberEventsAtTime / individualsAtRisk);
final Point newCIFPoint = new Point(time_k, previousCIFPoint.getY() + cifDeltaY);
@ -130,18 +154,5 @@ public class CompetingRiskResponseCombiner implements ResponseCombiner<Competing
}
private double riskSet(List<CompetingRiskResponse> eventList, double time) {
return eventList.stream()
.filter(event -> event.getU() >= time)
.count();
}
private double numberOfEventsAtTime(int eventOfFocus, List<CompetingRiskResponse> eventList, double time){
return (double) eventList.stream()
.filter(event -> event.getDelta() == eventOfFocus)
.filter(event -> event.getU() == time) // since delta != 0 we know censoring didn't occur prior to this
.count();
}
}

69
src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRiskResponseCombiner.java
View file

@ -13,7 +13,7 @@ import java.util.List;
public class TestCompetingRiskResponseCombiner {
private CompetingRiskFunctions generateFunctions(double[] times){
private CompetingRiskFunctions generateFunctions(){
final List<CompetingRiskResponse> data = new ArrayList<>();
data.add(new CompetingRiskResponse(1, 1.0));
@ -24,14 +24,14 @@ public class TestCompetingRiskResponseCombiner {
data.add(new CompetingRiskResponse(0, 1.5));
data.add(new CompetingRiskResponse(0, 2.5));
final CompetingRiskResponseCombiner combiner = new CompetingRiskResponseCombiner(new int[]{1,2}, times);
final CompetingRiskResponseCombiner combiner = new CompetingRiskResponseCombiner(new int[]{1,2});
return combiner.combine(data);
}
@Test
public void testCompetingRiskResponseCombiner(){
final CompetingRiskFunctions functions = generateFunctions(null);
final CompetingRiskFunctions functions = generateFunctions();
final MathFunction survivalCurve = functions.getSurvivalCurve();
@ -86,68 +86,5 @@ public class TestCompetingRiskResponseCombiner {
}
@Test
public void testCompetingRiskResponseCombinerWithSetTimes(){
// By including time 3.0 (which extends past the data),
// we verify that we don't get NaNs past 3.0, which was a previous bug.
final CompetingRiskFunctions functions = generateFunctions(new double[]{1.0, 1.5, 2.0, 2.5, 3.0});
final MathFunction survivalCurve = functions.getSurvivalCurve();
// time = 1.0 1.5 2.0 2.5
// surv = 0.7142857 0.5714286 0.1904762 0.1904762
final double margin = 0.0000001;
closeEnough(0.7142857, survivalCurve.evaluate(1.0).getY(), margin);
closeEnough(0.5714286, survivalCurve.evaluate(1.5).getY(), margin);
closeEnough(0.1904762, survivalCurve.evaluate(2.0).getY(), margin);
closeEnough(0.1904762, survivalCurve.evaluate(2.5).getY(), margin);
closeEnough(0.1904762, survivalCurve.evaluate(3.0).getY(), margin);
// Time = 1.0 1.5 2.0 2.5
/* Cumulative hazard function. Each row for one event.
[,1] [,2] [,3] [,4]
[1,] 0.2857143 0.2857143 0.6190476 0.6190476
[2,] 0.0000000 0.2000000 0.5333333 0.5333333
*/
final MathFunction cumHaz1 = functions.getCauseSpecificHazardFunction(1);
closeEnough(0.2857143, cumHaz1.evaluate(1.0).getY(), margin);
closeEnough(0.2857143, cumHaz1.evaluate(1.5).getY(), margin);
closeEnough(0.6190476, cumHaz1.evaluate(2.0).getY(), margin);
closeEnough(0.6190476, cumHaz1.evaluate(2.5).getY(), margin);
closeEnough(0.6190476, cumHaz1.evaluate(3.0).getY(), margin);
final MathFunction cumHaz2 = functions.getCauseSpecificHazardFunction(2);
closeEnough(0.0, cumHaz2.evaluate(1.0).getY(), margin);
closeEnough(0.2, cumHaz2.evaluate(1.5).getY(), margin);
closeEnough(0.5333333, cumHaz2.evaluate(2.0).getY(), margin);
closeEnough(0.5333333, cumHaz2.evaluate(2.5).getY(), margin);
closeEnough(0.5333333, cumHaz2.evaluate(3.0).getY(), margin);
/* Time = 1.0 1.5 2.0 2.5
Cumulative Incidence Curve. Each row for one event.
[,1] [,2] [,3] [,4]
[1,] 0.2857143 0.2857143 0.4761905 0.4761905
[2,] 0.0000000 0.1428571 0.3333333 0.3333333
*/
final MathFunction cic1 = functions.getCumulativeIncidenceFunction(1);
closeEnough(0.2857143, cic1.evaluate(1.0).getY(), margin);
closeEnough(0.2857143, cic1.evaluate(1.5).getY(), margin);
closeEnough(0.4761905, cic1.evaluate(2.0).getY(), margin);
closeEnough(0.4761905, cic1.evaluate(2.5).getY(), margin);
closeEnough(0.4761905, cic1.evaluate(3.0).getY(), margin);
final MathFunction cic2 = functions.getCumulativeIncidenceFunction(2);
closeEnough(0.0, cic2.evaluate(1.0).getY(), margin);
closeEnough(0.1428571, cic2.evaluate(1.5).getY(), margin);
closeEnough(0.3333333, cic2.evaluate(2.0).getY(), margin);
closeEnough(0.3333333, cic2.evaluate(2.5).getY(), margin);
closeEnough(0.3333333, cic2.evaluate(3.0).getY(), margin);
}
}