EvaluatorIntro.java

package mia.recommender.ch02;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.eval.RecommenderEvaluator;
import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;
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 EvaluatorIntro {

  private EvaluatorIntro() {
  }

  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);

    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
    // 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);
      }
    };
    // Use 70% of the data to train; test using the other 30%.
    double score = evaluator.evaluate(recommenderBuilder, null, model, 0.7, 1.0);
    System.out.println(score);
  }
}