com.arosbio.ml.cp.CPRegressionPrediction Maven / Gradle / Ivy
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Conformal AI package, including all data IO, transformations, machine learning models and predictor classes. Without inclusion of chemistry-dependent code.
/*
* Copyright (C) Aros Bio AB.
*
* CPSign is an Open Source Software that is dual licensed to allow you to choose a license that best suits your requirements:
*
* 1) GPLv3 (GNU General Public License Version 3) with Additional Terms, including an attribution clause as well as a limitation to use the software for commercial purposes.
*
* 2) CPSign Proprietary License that allows you to use CPSign for commercial activities, such as in a revenue-generating operation or environment, or integrate CPSign in your proprietary software without worrying about disclosing the source code of your proprietary software, which is required if you choose to use the software under GPLv3 license. See arosbio.com/cpsign/commercial-license for details.
*/
package com.arosbio.ml.cp;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import com.arosbio.ml.IntervalUtils;
import com.google.common.collect.Range;
import com.google.common.math.DoubleMath;
/**
* The CPRegressionPrediction holds the result of a Conformal Regressor prediction.
*
* @author staffan
*/
public class CPRegressionPrediction {
private static final double EQUIV_TOL=1e-5;
private double y_hat; //The midpoint
private double intervalScaling;
private double minObs;
private double maxObs;
/** Confidence dependent prediction intervals */
private Map intervals = new HashMap<>();
/** Width dependent prediction intervals */
private Map predictionWidthBasedIntervals = new HashMap<>();
public class PredictedInterval implements Comparable{
private double predictedHalfIntervalWidth;
private double confidence;
public PredictedInterval(double confidence, double intervalHalfWidth) {
this.confidence = confidence;
if (intervalHalfWidth < 0)
throw new IllegalArgumentException("Interval widths must be positive, got: " + intervalHalfWidth);
this.predictedHalfIntervalWidth=intervalHalfWidth; // Width must be positive
}
public double getIntervalHalfWidth() {
return predictedHalfIntervalWidth;
}
public double getIntervalWidth() {
return predictedHalfIntervalWidth*2;
}
public Range getInterval(){
return IntervalUtils.getInterval(y_hat, predictedHalfIntervalWidth);
}
public Range getCappedInterval(){
return IntervalUtils.getCappedInterval(y_hat, predictedHalfIntervalWidth, minObs, maxObs);
}
public double getConfidence() {
return confidence;
}
public Map asMap(){
Map map = new HashMap<>();
map.put("confidence", confidence);
map.put("interval", getInterval());
map.put("cappedInterval", getCappedInterval());
map.put("intervalWidth", predictedHalfIntervalWidth*2);
return map;
}
public String toString() {
return asMap().toString();
}
@Override
public int compareTo(PredictedInterval o) {
return Double.compare(this.confidence, o.confidence);
}
public boolean equals(Object o){
if (! (o instanceof PredictedInterval))
return false;
return equals((PredictedInterval)o);
}
public boolean equals(PredictedInterval o){
return (DoubleMath.fuzzyEquals(o.confidence, confidence, EQUIV_TOL)) &&
(DoubleMath.fuzzyEquals(o.predictedHalfIntervalWidth, predictedHalfIntervalWidth, EQUIV_TOL));
}
}
/* ========================================
* CONSTRUCTORS
* ========================================
*/
public CPRegressionPrediction(double y_hat, double intervalScaling, double minObservation, double maxObservation) {
this.y_hat = y_hat;
this.intervalScaling = intervalScaling;
this.minObs = minObservation;
this.maxObs = maxObservation;
}
public CPRegressionPrediction(double y_hat, double intervalScaling, double minObservation, double maxObservation, Map intervals) {
this(y_hat, intervalScaling, minObservation, maxObservation);
this.intervals = intervals;
}
/**
* y_hat is the midpoint of the prediction
* @return The midpoint of the prediction
*/
public double getY_hat() {
return y_hat;
}
public double getMinObs() {
return minObs;
}
public double getMaxObs() {
return maxObs;
}
/**
* Interval scaling is the value that the NCS should be multiplied with to yield the +/- interval for
* a given confidence
* @return the scaling
*/
public double getIntervalScaling() {
return intervalScaling;
}
public void setPredictedIntervals(Map intervals) {
this.intervals = intervals;
}
public void setWidthBasedIntervals(Map intervals) {
this.predictionWidthBasedIntervals = intervals;
}
public Map getIntervals(){
return intervals;
}
public Map getWidthToConfidenceBasedIntervals(){
return predictionWidthBasedIntervals;
}
public List getConfidences(){
List confs = new ArrayList<>(intervals.keySet());
Collections.sort(confs);
return confs;
}
/**
* Get the widths used for predicted widths -> confidence
* @return A list of the widths used
*/
public List getPredictedWidths(){
if (predictionWidthBasedIntervals==null || predictionWidthBasedIntervals.isEmpty())
return new ArrayList<>();
List dists = new ArrayList<>(predictionWidthBasedIntervals.keySet());
Collections.sort(dists);
return dists;
}
public PredictedInterval getInterval(double confidence) {
if (intervals != null)
return intervals.get(confidence);
return null;
}
public Map asMap(){
Map map = new HashMap<>();
map.put("y_hat", y_hat);
map.put("intervalScaling", intervalScaling);
map.put("minObs", minObs);
map.put("maxObs", maxObs);
if (intervals != null && ! intervals.isEmpty()) {
Map> inters = new HashMap<>();
for (double conf: intervals.keySet()) {
inters.put(conf, intervals.get(conf).asMap());
}
map.put("intervals", inters);
}
if (predictionWidthBasedIntervals != null && ! predictionWidthBasedIntervals.isEmpty()) {
Map> inters = new HashMap<>();
for (double dist: predictionWidthBasedIntervals.keySet()) {
inters.put(dist, predictionWidthBasedIntervals.get(dist).asMap());
}
map.put("widthBasedIntervals", inters);
}
return map;
}
public String toString(){
return "CPRegressionPrediction: "+ asMap().toString();
}
public boolean equals(Object o){
if (! (o instanceof CPRegressionPrediction))
return false;
CPRegressionPrediction other = (CPRegressionPrediction)o;
// Check the scalar values first
if (! DoubleMath.fuzzyEquals(y_hat, other.y_hat, EQUIV_TOL) &&
DoubleMath.fuzzyEquals(intervalScaling, other.intervalScaling, EQUIV_TOL) &&
DoubleMath.fuzzyEquals(minObs, other.minObs, EQUIV_TOL) &&
DoubleMath.fuzzyEquals(maxObs, other.maxObs, EQUIV_TOL))
return false;
// Check the standard CIs after
if (intervals.size() != other.intervals.size())
return false;
if (!intervals.isEmpty()){
// Should have the same keys
if (!intervals.keySet().equals(other.intervals.keySet()))
return false;
// Check each CI
for (double c : intervals.keySet()){
if (!intervals.get(c).equals(other.intervals.get(c)))
return false;
}
}
// Check the CI width -> conf predictions
if (predictionWidthBasedIntervals.size() != other.predictionWidthBasedIntervals.size())
return false;
if (!predictionWidthBasedIntervals.isEmpty()){
// Should have the same keys - going width->conf this is the only requirement
if (!predictionWidthBasedIntervals.keySet().equals(other.predictionWidthBasedIntervals.keySet()))
return false;
}
// Everything equals
return true;
}
}