/**
* Copyright 2014, Emory University
*
* 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 edu.emory.clir.clearnlp.component.evaluation;
import edu.emory.clir.clearnlp.util.MathUtils;
/**
* @since 3.0.0
* @author Jinho D. Choi ({@code jinho.choi@emory.edu})
*/
abstract public class AbstractF1Eval<LabelType> extends AbstractEval<LabelType>
{
protected int p_total;
protected int r_total;
protected int n_correct;
public AbstractF1Eval()
{
clear();
}
@Override
public void clear()
{
p_total = 0;
r_total = 0;
n_correct = 0;
}
@Override
public double getScore()
{
return getScores()[0];
}
@Override
public String toString()
{
double[] d = getScores();
return String.format("F1: %5.2f, P: %5.2f, R: %5.2f", d[0], d[1], d[2]);
}
private double[] getScores()
{
double precision = 100d * n_correct / p_total;
double recall = 100d * n_correct / r_total;
return new double[]{MathUtils.getF1(precision, recall), precision, recall};
}
}