smile.validation.AUC Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2010 Haifeng Li
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
package smile.validation;
import smile.sort.QuickSort;
/**
* The area under the curve (AUC). When using normalized units, the area under
* the curve is equal to the probability that a classifier will rank a
* randomly chosen positive instance higher than a randomly chosen negative
* one (assuming 'positive' ranks higher than 'negative').
*
* In statistics, a receiver operating characteristic (ROC), or ROC curve,
* is a graphical plot that illustrates the performance of a binary classifier
* system as its discrimination threshold is varied. The curve is created by
* plotting the true positive rate (TPR) against the false positive rate (FPR)
* at various threshold settings.
*
* AUC is quite noisy as a classification measure and has some other
* significant problems in model comparison.
*
* @author Haifeng Li
*/
public class AUC {
public AUC() {
}
/**
* Caulculate AUC for binary classifier.
* @param truth The sample labels
* @param probability The posterior probability of positive class.
* @return AUC
*/
public double measure(int[] truth, double[] probability) {
if (truth.length != probability.length) {
throw new IllegalArgumentException(String.format("The vector sizes don't match: %d != %d.", truth.length, probability.length));
}
int totalPositive = 0;
int totalNegative = 0;
int[] label = truth.clone();
double[] predicition = probability.clone();
for (int i = 0; i < truth.length; i++) {
if (label[i] == 0) {
totalNegative++;
} else if (label[i] == 1) {
totalPositive++;
} else {
throw new IllegalArgumentException("AUC is only for binary classification. Invalid label: " + label[i]);
}
}
QuickSort.sort(predicition, label);
double fp = 0;
double tp = 0;
double fpPrev = 0;
double tpPrev = 0;
double area = 0;
double fPrev = Double.MIN_VALUE;
for (int i = 0; i < truth.length; i++) {
double curF = predicition[i];
if (curF != fPrev) {
area += Math.abs(fp - fpPrev) * ((tp + tpPrev) / 2.0);
fPrev = curF;
fpPrev = fp;
tpPrev = tp;
}
if (label[i] == +1) {
tp++;
} else {
fp++;
}
}
area += Math.abs(totalNegative - fpPrev) * ((totalPositive + tpPrev) / 2.0);
area /= ((double) totalPositive * totalNegative);
return area;
}
}