Examples with org.haiku.haikudepotserver.dataobjects.auto._User org.haiku.haikudepotserver.dataobjects.auto._User used on opensource projects

Example 41 with org.haiku.haikudepotserver.dataobjects.auto._User

use of org.haiku.haikudepotserver.dataobjects.auto._User in project IR_Base by Linda-sunshine.

the class UserAnalyzer method loadSVDFile.

// Added by Lin: Load the svd file to get the low dim representation of users.
public void loadSVDFile(String filename) {
    try {
        // Construct the <userID, user> map first.
        int count = 0;
        HashMap<String, double[]> IDLowDimMap = new HashMap<String, double[]>();
        int skip = 3;
        File file = new File(filename);
        BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream(file), "UTF-8"));
        String line, userID;
        String[] strs;
        double[] lowDims;
        // Skip the first three lines.
        while (skip-- > 0) reader.readLine();
        while ((line = reader.readLine()) != null) {
            strs = line.split("\\s+");
            userID = strs[0];
            lowDims = new double[strs.length - 1];
            for (int i = 1; i < strs.length; i++) lowDims[i - 1] = Double.valueOf(strs[i]);
            IDLowDimMap.put(userID, lowDims);
            count++;
        }
        // Currently, there are missing low dimension representation of users.
        for (_User u : m_users) {
            if (IDLowDimMap.containsKey(u.getUserID()))
                u.setLowDimProfile(IDLowDimMap.get(u.getUserID()));
            else {
                System.out.println("[Warning]" + u.getUserID() + " : low dim profile missing.");
                u.setLowDimProfile(new double[11]);
            }
        }
        reader.close();
        System.out.format("Ther are %d users and %d users' low dimension profile are loaded.\n", m_users.size(), count);
    } catch (IOException e) {
        e.printStackTrace();
    }
}

Example 42 with org.haiku.haikudepotserver.dataobjects.auto._User

use of org.haiku.haikudepotserver.dataobjects.auto._User in project IR_Base by Linda-sunshine.

the class MultiThreadedLinkPredAnalyzer method checkFriendSize.

public void checkFriendSize() {
    int train = 0, test = 0;
    for (_User u : m_users) {
        if (u.getFriendSize() != 0)
            train++;
        if (u.getTestFriendSize() != 0)
            test++;
    }
    System.out.format("[Check]%d users have train friends, %d users have test friends.\n", train, test);
}

Example 43 with org.haiku.haikudepotserver.dataobjects.auto._User

use of org.haiku.haikudepotserver.dataobjects.auto._User in project IR_Base by Linda-sunshine.

the class MultiThreadedLinkPredAnalyzer method loadTestFriendship.

// load the test user friends, for link prediction only
public void loadTestFriendship(String filename) {
    try {
        m_testMap.clear();
        File file = new File(filename);
        BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream(file), "UTF-8"));
        String line;
        String[] users, friends;
        while ((line = reader.readLine()) != null) {
            users = line.trim().split("\t");
            friends = Arrays.copyOfRange(users, 1, users.length);
            if (friends.length == 0) {
                continue;
            }
            m_testMap.put(users[0], friends);
        }
        reader.close();
        // map friends to users.
        for (_User u : m_users) {
            if (m_testMap.containsKey(u.getUserID()))
                u.setTestFriends(m_testMap.get(u.getUserID()));
        }
        checkFriendSize();
    } catch (IOException e) {
        e.printStackTrace();
    }
}

Example 44 with org.haiku.haikudepotserver.dataobjects.auto._User

use of org.haiku.haikudepotserver.dataobjects.auto._User in project IR_Base by Linda-sunshine.

the class MultiThreadedLinkPredAnalyzer method saveUserUserPairs.

// save the user-user pairs to graphlab for model training.
public void saveUserUserPairs(String dir) {
    int trainUser = 0, testUser = 0, trainPair = 0, testPair = 0;
    try {
        PrintWriter trainWriter = new PrintWriter(new File(dir + "train.csv"));
        PrintWriter testWriter = new PrintWriter(new File(dir + "test.csv"));
        trainWriter.write("user_id,item_id,rating\n");
        testWriter.write("user_id,item_id,rating\n");
        for (_User u : m_users) {
            if (u.getFriendSize() != 0) {
                trainUser++;
                for (String frd : u.getFriends()) {
                    trainPair++;
                    trainWriter.write(String.format("%s,%s,%d\n", u.getUserID(), frd, 1));
                    trainWriter.write(String.format("%s,%s,%d\n", frd, u.getUserID(), 1));
                }
            }
            // for test users, we also need to write out non-friends
            if (u.getTestFriendSize() != 0) {
                testUser++;
                for (_User nei : m_users) {
                    String neiID = nei.getUserID();
                    if (u.hasFriend(neiID) || u.getUserID().equals(neiID))
                        continue;
                    else if (u.hasTestFriend(neiID)) {
                        testPair++;
                        testWriter.write(String.format("%s,%s,%d\n", u.getUserID(), neiID, 1));
                        testWriter.write(String.format("%s,%s,%d\n", neiID, u.getUserID(), 1));
                    } else if (m_trainMap.containsKey(neiID)) {
                        testPair++;
                        testWriter.write(String.format("%s,%s,%d\n", u.getUserID(), neiID, 0));
                        testWriter.write(String.format("%s,%s,%d\n", neiID, u.getUserID(), 0));
                    }
                }
            }
        }
        trainWriter.close();
        testWriter.close();
        System.out.format("[Info]Finish writing (%d,%d) training users/pairs, (%d,%d) testing users/pairs.\n", trainUser, trainPair, testUser, testPair);
    } catch (IOException e) {
        e.printStackTrace();
    }
}

Example 45 with org.haiku.haikudepotserver.dataobjects.auto._User

use of org.haiku.haikudepotserver.dataobjects.auto._User in project IR_Base by Linda-sunshine.

the class MultiThreadedLinkPredAnalyzer method rmMultipleReviews4OneItem.

public void rmMultipleReviews4OneItem() {
    Set<String> items = new HashSet<String>();
    ArrayList<Integer> indexes = new ArrayList<Integer>();
    int uCount = 0, rCount = 0;
    boolean flag = false;
    for (_User u : m_users) {
        ArrayList<_Review> reviews = u.getReviews();
        items.clear();
        indexes.clear();
        for (int i = 0; i < reviews.size(); i++) {
            _Review r = reviews.get(i);
            if (items.contains(r.getItemID())) {
                indexes.add(i);
                rCount++;
                flag = true;
            } else {
                items.add(r.getItemID());
            }
        }
        // record the user number
        if (flag) {
            uCount++;
            flag = false;
        }
        // remove the reviews.
        Collections.sort(indexes, Collections.reverseOrder());
        for (int idx : indexes) {
            reviews.remove(idx);
        }
        u.constructTrainTestReviews();
    }
    System.out.format("%d users have %d duplicate reviews for items.\n", uCount, rCount);
}