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Open Source Chemistry Library
package com.actelion.research.calc.regression;
import com.actelion.research.calc.Matrix;
import com.actelion.research.calc.MatrixFunctions;
import com.actelion.research.calc.classification.PrecisionAndRecall;
import com.actelion.research.util.Formatter;
import com.actelion.research.util.datamodel.DoubleArray;
import java.util.*;
/**
* ModelError
*
* This class is a data model for the error. It is not the error of the model.
*
* Copyright: Actelion Ltd., Inc. All Rights Reserved
* This software is the proprietary information of Actelion Pharmaceuticals, Ltd.
* Use is subject to license terms.
* @author Modest von Korff
* Aug 14, 2015 MvK Start implementation
*/
public class ModelError {
// Average from the sum of |errors|
public double error;
public double errorMedian;
public double errorRelative;
public double errorRelativeMedian;
public double errorRelativeWeighted;
public double errSumSquared;
public double errMax;
public double errMin;
public double corrSquared;
public double corrSquaredSpearman;
// Classification
public boolean classification;
public PrecisionAndRecall precisionAndRecall;
public boolean failed;
public int nNotFiniteRelError;
/**
*
*/
public ModelError() {
failed = false;
}
public void setFailed() {
this.failed = true;
}
public boolean isFailed() {
return failed;
}
/* (non-Javadoc)
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
if(failed) {
sb.append("ModelError [error=failed]");
} else {
sb.append("ModelError [error=");
sb.append(Formatter.format3(error));
sb.append(", errRelativeMedian=");
sb.append(Formatter.format3(errorRelativeMedian));
if(nNotFiniteRelError!=0){
sb.append(", NotFiniteRelError=");
sb.append(nNotFiniteRelError);
}
sb.append(", errMax=");
sb.append(Formatter.format3(errMax));
sb.append(", errMin=");
sb.append(Formatter.format3(errMin));
sb.append(", corrSquared=");
sb.append(Formatter.format3(corrSquared));
if(precisionAndRecall!=null){
sb.append(", Cohen's kappa=");
sb.append(Formatter.format3(precisionAndRecall.calculateCohensKappa()));
}
sb.append("]");
}
return sb.toString();
}
/**
* Calculates the absolute and the relative error.
* @param Y
* @param YHat
* @return
*/
public static ModelError calculateError(Matrix Y, Matrix YHat){
ModelError modelError = new ModelError();
modelError.errMax = 0;
modelError.errMin = Integer.MAX_VALUE;
DoubleArray daError = new DoubleArray(Y.rows()*YHat.cols());
double sumSquared = 0;
for (int i = 0; i < YHat.cols(); i++) {
for (int j = 0; j < YHat.rows(); j++) {
double e = Math.abs(Y.get(j, i) - YHat.get(j, i));
sumSquared += e*e;
modelError.errMax = Math.max(modelError.errMax, e);
modelError.errMin = Math.min(modelError.errMin, e);
modelError.error += e;
daError.add(e);
}
}
modelError.error = modelError.error / (YHat.rows()*YHat.cols());
modelError.errSumSquared = sumSquared;
modelError.errorMedian = daError.median();
DoubleArray daErrorRelative = new DoubleArray(YHat.rows()*YHat.cols());
modelError.nNotFiniteRelError=0;
for (int i = 0; i < YHat.cols(); i++) {
for (int j = 0; j < YHat.rows(); j++) {
double y = Y.get(j, i);
double yHat = YHat.get(j, i);
double er = getRelativeError(y, yHat);
if(Double.isFinite(er)){
daErrorRelative.add(er);
}else {
modelError.nNotFiniteRelError++;
}
}
}
if(daErrorRelative.size()>0) {
modelError.errorRelative = daErrorRelative.avr();
modelError.errorRelativeMedian = daErrorRelative.median();
}
//
// Weighted error
//
DoubleArray daErrorRelativeWeighted = new DoubleArray(YHat.rows()*YHat.cols());
for (int i = 0; i < YHat.cols(); i++) {
for (int j = 0; j < YHat.rows(); j++) {
double y = Y.get(j, i);
double yHat = YHat.get(j, i);
double w = Math.log10(10+y);
if(Math.abs(y) > Matrix.TINY04) {
double er = Math.abs((yHat - y) / y) * (1.0/w);
if(Double.isFinite(er)) {
daErrorRelativeWeighted.add(er);
}
} else {
double er = Math.abs((yHat - y) / Matrix.TINY04) * (1.0/w);
if(Double.isFinite(er)) {
daErrorRelativeWeighted.add(er);
}
}
}
}
modelError.errorRelativeWeighted = daErrorRelativeWeighted.avr();
double corr = 0;
double corrSpearman = 0;
try {
corr = MatrixFunctions.getCorrPearson(YHat, Y);
corrSpearman = MatrixFunctions.getCorrSpearman(YHat, Y);
} catch (Exception e) {
e.printStackTrace();
System.err.println("YHat");
System.err.println(YHat.toString());
System.err.println("Y");
System.err.println(Y.toString());
}
if(!Double.isFinite(corr)){
corr=0;
}
if(!Double.isFinite(corrSpearman)){
corrSpearman=0;
}
modelError.corrSquared = corr*corr;
modelError.corrSquaredSpearman = corrSpearman*corrSpearman;
return modelError;
}
public static double getRelativeError(double y, double yHat){
double er = 0;
if(Math.abs(y) > Matrix.TINY04) {
er = Math.abs((yHat - y) / y);
} else {
er = Math.abs((yHat - y) / Matrix.TINY04);
}
return er;
}
public static ModelError calculateError(Matrix Y, Matrix YHat, double threshold, boolean above){
ModelError me = calculateError(Y, YHat);
me.precisionAndRecall = new PrecisionAndRecall();
for (int i = 0; i < YHat.cols(); i++) {
for (int j = 0; j < YHat.rows(); j++) {
double y = Y.get(j, i);
double yHat = YHat.get(j, i);
if(above) {
if(y>=threshold && yHat>=threshold) {
me.precisionAndRecall.truePositive++;
} else if(y < threshold && yHat < threshold) {
me.precisionAndRecall.trueNegative++;
} else if (yHat>=threshold){
me.precisionAndRecall.falsePositive++;
} else if (yHat threshold && yHat > threshold) {
me.precisionAndRecall.trueNegative++;
} else if (yHat<=threshold){
me.precisionAndRecall.falsePositive++;
} else if (yHat>threshold){
me.precisionAndRecall.falseNegative++;
}
}
}
}
me.classification = true;
return me;
}
public static List getError(List liME){
List li = new ArrayList();
for (ModelError modelError : liME) {
li.add(modelError.error);
}
return li;
}
public static ModelError getErrorAverage(List liME){
ModelError modelErrorAvr = new ModelError();
for (ModelError modelError : liME) {
modelErrorAvr.errMax += modelError.errMax;
modelErrorAvr.errMin += modelError.errMin;
modelErrorAvr.error += modelError.error;
modelErrorAvr.corrSquared += modelError.corrSquared;
}
int n = liME.size();
modelErrorAvr.errMax /= n;
modelErrorAvr.errMin /= n;
modelErrorAvr.error /= n;
modelErrorAvr.corrSquared /= n;
return modelErrorAvr;
}
public static Comparator getComparatorError(){
return new Comparator() {
@Override
public int compare(ModelError o1, ModelError o2) {
int cmp = 0;
if(o1.error > o2.error){
cmp=1;
}else if(o1.error < o2.error){
cmp=-1;
}
return cmp;
}
};
}
public static void main(String[] args) {
int n = 11;
double fracNoise = 0.1;
Random random = new Random();
double [] a = new double[n];
double [] b = new double[n];
for (int i = 0; i < n; i++) {
a[i] = random.nextDouble();
b[i] = random.nextDouble();
}
ModelError meRaw = ModelError.calculateError(new Matrix(false, a), new Matrix(false, b));
System.out.println(meRaw.toString());
Arrays.sort(a);
Arrays.sort(b);
ModelError meSort = ModelError.calculateError(new Matrix(false, a), new Matrix(false, b));
System.out.println(meSort.toString());
for (int i = 0; i < n; i++) {
if(random.nextDouble() © 2015 - 2025 Weber Informatics LLC | Privacy Policy