/*******************************************************************************
* 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;
/**
* The F-score (or F-measure) considers both the precision and the recall of the test
* to compute the score. The precision p is the number of correct positive results
* divided by the number of all positive results, and the recall r is the number of
* correct positive results divided by the number of positive results that should
* have been returned.
*
* The traditional or balanced F-score (F1 score) is the harmonic mean of
* precision and recall, where an F1 score reaches its best value at 1 and worst at 0.
*
* The general formula involves a positive real β so that F-score measures
* the effectiveness of retrieval with respect to a user who attaches β times
* as much importance to recall as precision.
*
* @author Haifeng Li
*/
public class FMeasure implements ClassificationMeasure {
/**
* A positive value such that F-score measures the effectiveness of
* retrieval with respect to a user who attaches β times
* as much importance to recall as precision. The default value 1.0
* corresponds to F1-score.
*/
private double beta2 = 1.0;
/** Constructor of F1 score. */
public FMeasure() {
}
/** Constructor of general F-score.
*
* @param beta a positive value such that F-score measures
* the effectiveness of retrieval with respect to a user who attaches β times
* as much importance to recall as precision.
*/
public FMeasure(double beta) {
if (beta <= 0.0)
throw new IllegalArgumentException("Negative beta");
this.beta2 = beta * beta;
}
@Override
public double measure(int[] truth, int[] prediction) {
double p = new Precision().measure(truth, prediction);
double r = new Recall().measure(truth, prediction);
return (1 + beta2) * (p * r) / (beta2 * p + r);
}
}