package zad2;

import org.apache.mahout.cf.taste.common.NoSuchUserException;
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.RecommenderEvaluator;
import org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator;
import org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator;
import org.apache.mahout.cf.taste.impl.eval.RMSRecommenderEvaluator;
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.neighborhood.ThresholdUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;
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.RecommendedItem;
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.BufferedWriter;
import java.io.File;
import java.io.FileWriter;
import java.util.List;

public class UserRecommender {

    public static final double TRAINING_PERCENTAGE = 0.35;
    public static final double EVALUATION_PERCENTAGE = 0.65;
    public static final String JESTER_RATINGS = "/home/marin/Dropbox/fax/4. godina/rovkp/dz3/jester_ratings.dat";

    public static void main(String[] args) throws Exception {

        RandomUtils.useTestSeed();

        //inicijaliziraj model učitavanjem podataka o korisnicima
        DataModel model = new FileDataModel(new File(JESTER_RATINGS), "\t+");

        RecommenderBuilder paersonCorrelationBuilder = new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
                UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
                return new GenericUserBasedRecommender(model, neighborhood, similarity);
            }
        };


        //nađi preporuke
        List<RecommendedItem> recommendedItems = paersonCorrelationBuilder.buildRecommender(model).recommend(22, 10);
        for (RecommendedItem recommendedItem : recommendedItems) {
            System.out.println(recommendedItem);
        }

        //evaluiraj procjenu za paerson correlation buildera
        RecommenderEvaluator recEvaluator = new RMSRecommenderEvaluator();
        double score = recEvaluator.evaluate(paersonCorrelationBuilder, null, model, TRAINING_PERCENTAGE, EVALUATION_PERCENTAGE);
        System.out.println("Paerson correlation score = " + score);


        //ispiši 10 preporuka prvih 100 korisnika na disk

        BufferedWriter writer = new BufferedWriter(new FileWriter("result.txt"));
        BufferedWriter usersWriter = new BufferedWriter(new FileWriter("users.txt"));
        for (int i = 0,userId=1; i < 100; userId++) {
            try {
                String line = userId + "\t { ";
                List<RecommendedItem> recommendations = paersonCorrelationBuilder.buildRecommender(model).recommend(userId, 10);
                for (RecommendedItem recommendedItem : recommendations) {
                    line += recommendedItem.getItemID() + " : " + recommendedItem.getValue() + ", ";
                }
                line += "}";
                writer.write(line);
                writer.newLine();

                usersWriter.write(String.valueOf(userId));
                usersWriter.newLine();
                i++;
            } catch (NoSuchUserException e) {
                System.err.println("No such user with id " + userId);
            }

        }
        writer.close();
        usersWriter.close();


        //log likelihood builder
        RecommenderBuilder logLikelihoodBuilder = new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                UserSimilarity similarity = new LogLikelihoodSimilarity(model);
                UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
                return new GenericUserBasedRecommender(model, neighborhood, similarity);
            }
        };

        //evaluiraj procjenu za log likelihood buildera
        RecommenderEvaluator recEvaluator2 = new RMSRecommenderEvaluator();
        double score2 = recEvaluator.evaluate(logLikelihoodBuilder, null, model, TRAINING_PERCENTAGE, EVALUATION_PERCENTAGE);
        System.out.println("Log Likelihood score = " + score2);
    }
}