Example usage for org.apache.mahout.cf.taste.recommender UserBasedRecommender estimatePreference

Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.recommender UserBasedRecommender estimatePreference.

Prototype

float estimatePreference(long userID, long itemID) throws TasteException;

Source Link

Usage

From source file:StudyResults.ResultEvaluator.java

/**
 * */*w  ww.  j  a  v  a 2s . co m*/
 *
 * @param filename must be a cvs file: userID, ItemID, rating
 * @return
 */
public static HashMap<String, Double> GetRMSEPerUser(String filename)
        throws FileNotFoundException, IOException, TasteException {
    HashMap<String, Integer> idToNumericId = new HashMap<>();
    HashMap<Integer, HashMap<Integer, Double>> ratingsPerUser = new HashMap<>();
    HashMap<String, ArrayList<Double>> allErrors = new HashMap<>();
    HashMap<String, Double> result = new HashMap<>();
    BufferedReader br = new BufferedReader(new FileReader(filename));
    String line;
    int userCount = -1;
    while ((line = br.readLine()) != null) {
        // use comma as separator
        String[] columns = line.split(",");
        if (!idToNumericId.containsKey(columns[0])) {
            userCount++;
            idToNumericId.put(columns[0], userCount);
        }
        if (!ratingsPerUser.containsKey(userCount)) {
            ratingsPerUser.put(userCount, new HashMap<>());
        }
        ratingsPerUser.get(userCount).put(Integer.parseInt(columns[1]), Double.parseDouble(columns[2]));
    }

    for (String userID : idToNumericId.keySet()) {
        int intUserID = idToNumericId.get(userID);
        if (ratingsPerUser.get(intUserID).keySet().size() > 1) {
            for (int scenarioID : ratingsPerUser.get(intUserID).keySet()) {
                PrintWriter writer = new PrintWriter("temp.csv", "UTF-8");
                for (String userID2 : idToNumericId.keySet()) {
                    int intUserID2 = idToNumericId.get(userID2);
                    for (int scenarioID2 : ratingsPerUser.get(intUserID2).keySet()) {
                        if (intUserID2 != intUserID || (intUserID2 == intUserID && scenarioID != scenarioID2)) {
                            writer.println(intUserID2 + "," + scenarioID2 + ","
                                    + ratingsPerUser.get(intUserID2).get(scenarioID2));
                        }
                    }
                }
                writer.close();
                DataModel model = new FileDataModel(new File("temp.csv"));
                UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
                UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
                UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood,
                        similarity);
                double predicted = recommender.estimatePreference(intUserID, scenarioID);
                if (!Double.isNaN(predicted)) {
                    double error = ratingsPerUser.get(intUserID).get(scenarioID) - predicted;
                    error *= error;
                    if (!allErrors.containsKey(userID)) {
                        allErrors.put(userID, new ArrayList<>());
                    }
                    allErrors.get(userID).add(error);
                }
            }
        }
    }

    for (String userID : allErrors.keySet()) {
        double mean = 0;
        for (Double error : allErrors.get(userID)) {
            mean += error;
        }
        mean /= allErrors.get(userID).size();
        mean = Math.sqrt(mean);
        result.put(userID, mean);
    }
    return result;
}

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