blob: 09ae68c6b89232b6e16745c0b22fa0622d3d7e74 [file] [log] [blame]
/*
* Hivemall: Hive scalable Machine Learning Library
*
* Copyright (C) 2015 Makoto YUI
* Copyright (C) 2013-2015 National Institute of Advanced Industrial Science and Technology (AIST)
*
* 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 hivemall.evaluation;
import java.util.List;
import javax.annotation.Nonnull;
/**
* Utility class of various measures.
*
* See http://recsyswiki.com/wiki/Discounted_Cumulative_Gain
*/
public final class BinaryResponsesMeasures {
private BinaryResponsesMeasures() {}
public static double nDCG(@Nonnull final List<?> rankedList, @Nonnull final List<?> groundTruth) {
double dcg = 0.d;
double idcg = IDCG(groundTruth.size());
for (int i = 0, n = rankedList.size(); i < n; i++) {
Object item_id = rankedList.get(i);
if (!groundTruth.contains(item_id)) {
continue;
}
int rank = i + 1;
dcg += Math.log(2) / Math.log(rank + 1);
}
return dcg / idcg;
}
/**
* Computes the ideal DCG
*
* @param n the number of positive items
* @return ideal DCG
*/
public static double IDCG(final int n) {
double idcg = 0.d;
for (int i = 0; i < n; i++) {
idcg += Math.log(2) / Math.log(i + 2);
}
return idcg;
}
}