package mia.recommender.ch02;
import org.apache.mahout.cf.taste.common.TasteException;
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.eval.GenericRecommenderIRStatsEvaluator;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import org.apache.mahout.common.RandomUtils;
import java.io.File;
class IREvaluatorIntro {
private IREvaluatorIntro() {
}
public static void main(String[] args) throws Exception {
RandomUtils.useTestSeed();
File modelFile = null;
if (args.length > 0)
modelFile = new File(args[0]);
if(modelFile == null || !modelFile.exists())
modelFile = new File("intro.csv");
if(!modelFile.exists()) {
System.err.println("Please, specify name of file, or put file 'input.csv' into current directory!");
System.exit(1);
}
DataModel model = new FileDataModel(modelFile);
RecommenderIRStatsEvaluator evaluator =
new GenericRecommenderIRStatsEvaluator();
// Build the same recommender for testing that we did last time:
RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
@Override
public Recommender buildRecommender(DataModel model) throws TasteException {
UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
UserNeighborhood neighborhood =
new NearestNUserNeighborhood(2, similarity, model);
return new GenericUserBasedRecommender(model, neighborhood, similarity);
}
};
// Evaluate precision and recall "at 2":
IRStatistics stats = evaluator.evaluate(recommenderBuilder,
null, model, null, 2,
GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
1.0);
System.out.println(stats.getPrecision());
System.out.println(stats.getRecall());
}
}