/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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 org.apache.mahout.cf.taste.impl.eval;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.DataModelBuilder;
import org.apache.mahout.cf.taste.eval.IRStatistics;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator;
import org.apache.mahout.cf.taste.impl.TasteTestCase;
import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.model.GenericBooleanPrefDataModel;
import org.apache.mahout.cf.taste.impl.recommender.GenericBooleanPrefItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.recommender.slopeone.SlopeOneRecommender;
import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.junit.Test;
public final class GenericRecommenderIRStatsEvaluatorImplTest extends TasteTestCase {
@Test
public void testEvaluate() throws Exception {
DataModel model = getDataModel();
RecommenderBuilder builder = new RecommenderBuilder() {
@Override
public Recommender buildRecommender(DataModel dataModel) throws TasteException {
return new SlopeOneRecommender(dataModel);
}
};
RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
IRStatistics stats = evaluator.evaluate(builder, null, model, null, 1, 0.2, 1.0);
assertNotNull(stats);
assertEquals(0.75, stats.getPrecision(), EPSILON);
assertEquals(0.75, stats.getRecall(), EPSILON);
assertEquals(0.75, stats.getF1Measure(), EPSILON);
assertEquals(0.75, stats.getFNMeasure(2.0), EPSILON);
assertEquals(0.75, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);
}
@Test
public void testBoolean() throws Exception {
DataModel model = getBooleanDataModel();
RecommenderBuilder builder = new RecommenderBuilder() {
@Override
public Recommender buildRecommender(DataModel dataModel) {
return new GenericBooleanPrefItemBasedRecommender(dataModel, new LogLikelihoodSimilarity(dataModel));
}
};
DataModelBuilder dataModelBuilder = new DataModelBuilder() {
@Override
public DataModel buildDataModel(FastByIDMap<PreferenceArray> trainingData) {
return new GenericBooleanPrefDataModel(GenericBooleanPrefDataModel.toDataMap(trainingData));
}
};
RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
IRStatistics stats = evaluator.evaluate(
builder, dataModelBuilder, model, null, 1, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
assertNotNull(stats);
assertEquals(0.666666666, stats.getPrecision(), EPSILON);
assertEquals(0.666666666, stats.getRecall(), EPSILON);
assertEquals(0.666666666, stats.getF1Measure(), EPSILON);
assertEquals(0.666666666, stats.getFNMeasure(2.0), EPSILON);
assertEquals(0.666666666, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);
}
@Test
public void testIRStats() {
IRStatistics stats = new IRStatisticsImpl(0.3, 0.1, 0.2, 0.05, 0.15);
assertEquals(0.3, stats.getPrecision(), EPSILON);
assertEquals(0.1, stats.getRecall(), EPSILON);
assertEquals(0.15, stats.getF1Measure(), EPSILON);
assertEquals(0.11538461538462, stats.getFNMeasure(2.0), EPSILON);
assertEquals(0.05, stats.getNormalizedDiscountedCumulativeGain(), EPSILON);
}
}