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Improve performance of CompetingRiskFunctionCombiner

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Estimate of time improvement is at least 10x faster
This commit is contained in:
Joel Therrien 2019-03-27 21:06:15 -07:00
parent c8269ae285
commit bf168bc2a5
1 changed files with 44 additions and 20 deletions
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src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/combiner/CompetingRiskFunctionCombiner.java
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@ -19,6 +19,7 @@ package ca.joeltherrien.randomforest.responses.competingrisk.combiner;
import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskFunctions; import ca.joeltherrien.randomforest.responses.competingrisk.CompetingRiskFunctions;
import ca.joeltherrien.randomforest.tree.ResponseCombiner; import ca.joeltherrien.randomforest.tree.ResponseCombiner;
import ca.joeltherrien.randomforest.utils.RightContinuousStepFunction; import ca.joeltherrien.randomforest.utils.RightContinuousStepFunction;
import ca.joeltherrien.randomforest.utils.Utils;
import lombok.RequiredArgsConstructor; import lombok.RequiredArgsConstructor;
import java.util.ArrayList; import java.util.ArrayList;
@ -57,40 +58,63 @@ public class CompetingRiskFunctionCombiner implements ResponseCombiner<Competing
final double n = responses.size(); final double n = responses.size();
final double[] survivalY = new double[timesToUse.length]; final double[] survivalY = new double[timesToUse.length];
final double[][] csCHFY = new double[events.length][timesToUse.length];
final double[][] cifY = new double[events.length][timesToUse.length];
/*
We're going to try to efficiently put our predictions together -
Assumptions - for each event on a response, the hazard and CIF functions share the same x points
Plan - go through the time on each response and make use of that so that when we search for a time index
to evaluate the function at, we don't need to re-search the earlier times.
*/
for(final CompetingRiskFunctions currentFunctions : responses){
final double[] survivalXPoints = currentFunctions.getSurvivalCurve().getX();
final double[][] eventSpecificXPoints = new double[events.length][];
for(final int event : events){
eventSpecificXPoints[event-1] = currentFunctions.getCumulativeIncidenceFunction(event)
.getX();
}
int previousSurvivalIndex = 0;
final int[] previousEventSpecificIndex = new int[events.length]; // relying on 0 being default value
for(int i=0; i<timesToUse.length; i++){ for(int i=0; i<timesToUse.length; i++){
final double time = timesToUse[i]; final double time = timesToUse[i];
survivalY[i] = responses.stream()
.mapToDouble(functions -> functions.getSurvivalCurve().evaluate(time) / n) // Survival curve
.sum(); final int survivalTimeIndex = Utils.binarySearchLessThan(previousSurvivalIndex, survivalXPoints.length, survivalXPoints, time);
survivalY[i] = survivalY[i] + currentFunctions.getSurvivalCurve().evaluateByIndex(survivalTimeIndex) / n;
previousSurvivalIndex = Math.max(survivalTimeIndex, 0); // if our current time is less than the smallest time in xPoints then binarySearchLessThan returned a -1.
// -1's not an issue for evaluateByIndex, but it is an issue for the next time binarySearchLessThan is called.
// CHFs and CIFs
for(final int event : events){
final double[] xPoints = eventSpecificXPoints[event-1];
final int eventTimeIndex = Utils.binarySearchLessThan(previousEventSpecificIndex[event-1], xPoints.length,
xPoints, time);
csCHFY[event-1][i] = csCHFY[event-1][i] + currentFunctions.getCauseSpecificHazardFunction(event)
.evaluateByIndex(eventTimeIndex) / n;
cifY[event-1][i] = cifY[event-1][i] + currentFunctions.getCumulativeIncidenceFunction(event)
.evaluateByIndex(eventTimeIndex) / n;
previousEventSpecificIndex[event-1] = Math.max(eventTimeIndex, 0);
}
}
} }
final RightContinuousStepFunction survivalFunction = new RightContinuousStepFunction(timesToUse, survivalY, 1.0); final RightContinuousStepFunction survivalFunction = new RightContinuousStepFunction(timesToUse, survivalY, 1.0);
final List<RightContinuousStepFunction> causeSpecificCumulativeHazardFunctionList = new ArrayList<>(events.length); final List<RightContinuousStepFunction> causeSpecificCumulativeHazardFunctionList = new ArrayList<>(events.length);
final List<RightContinuousStepFunction> cumulativeIncidenceFunctionList = new ArrayList<>(events.length); final List<RightContinuousStepFunction> cumulativeIncidenceFunctionList = new ArrayList<>(events.length);
for(final int event : events){ for(final int event : events){
causeSpecificCumulativeHazardFunctionList.add(event-1, new RightContinuousStepFunction(timesToUse, csCHFY[event-1], 0));
final double[] cumulativeHazardFunctionY = new double[timesToUse.length]; cumulativeIncidenceFunctionList.add(event-1, new RightContinuousStepFunction(timesToUse, cifY[event-1], 0));
final double[] cumulativeIncidenceFunctionY = new double[timesToUse.length];
for(int i=0; i<timesToUse.length; i++){
final double time = timesToUse[i];
cumulativeHazardFunctionY[i] = responses.stream()
.mapToDouble(functions -> functions.getCauseSpecificHazardFunction(event).evaluate(time) / n)
.sum();
cumulativeIncidenceFunctionY[i] = responses.stream()
.mapToDouble(functions -> functions.getCumulativeIncidenceFunction(event).evaluate(time) / n)
.sum();
}
causeSpecificCumulativeHazardFunctionList.add(event-1, new RightContinuousStepFunction(timesToUse, cumulativeHazardFunctionY, 0));
cumulativeIncidenceFunctionList.add(event-1, new RightContinuousStepFunction(timesToUse, cumulativeIncidenceFunctionY, 0));
} }
return CompetingRiskFunctions.builder() return CompetingRiskFunctions.builder()