/*******************************************************************************
* 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;
/**
* Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a
* statistical measures of the performance of a binary classification test.
* Sensitivity is the proportion of actual positives which are correctly
* identified as such.
* <p>
* TPR = TP / P = TP / (TP + FN)
* <p>
* Sensitivity and specificity are closely related to the concepts of type
* I and type II errors. For any test, there is usually a trade-off between
* the measures. This trade-off can be represented graphically using an ROC curve.
* <p>
* In this implementation, the class label 1 is regarded as positive and all others
* are regarded as negative.
*
* @author Haifeng Li
*/
public class Sensitivity implements ClassificationMeasure {
@Override
public double measure(int[] truth, int[] prediction) {
if (truth.length != prediction.length) {
throw new IllegalArgumentException(String.format("The vector sizes don't match: %d != %d.", truth.length, prediction.length));
}
int tp = 0;
int p = 0;
for (int i = 0; i < truth.length; i++) {
if (truth[i] == 1) {
p++;
if (prediction[i] == 1) {
tp++;
}
}
}
return (double) tp / p;
}
@Override
public String toString() {
return "Sensitivity";
}
}