org.eclipse.agail.recommenderserver.Test.java Source code

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Here is the source code for org.eclipse.agail.recommenderserver.Test.java

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/*********************************************************************
 * Copyright (C) 2017 TUGraz.
 *
 * This program and the accompanying materials are made
 * available under the terms of the Eclipse Public License 2.0
 * which is available at https://www.eclipse.org/legal/epl-2.0/
 *
 * SPDX-License-Identifier: EPL-2.0
 **********************************************************************/
package org.eclipse.agail.recommenderserver;

import java.io.BufferedWriter;
import java.io.File;
import java.io.FileOutputStream;
import java.io.FileWriter;
import java.io.PrintWriter;
import java.net.URL;
import java.util.ArrayList;
import java.util.List;

import org.apache.commons.lang3.StringEscapeUtils;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
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.EuclideanDistanceSimilarity;
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.UserBasedRecommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import org.eclipse.agail.recommenderserver.collaborative.CollaborativeFiltering;
import org.eclipse.agail.recommenderserver.marketplaces.parsers.ParseCloud;
import org.eclipse.agail.recommenderserver.models.App;
import org.eclipse.agail.recommenderserver.models.GatewayProfile;
import org.eclipse.agail.recommenderserver.models.ListOfApps;
import org.eclipse.agail.recommenderserver.models.ListOfDevices;
import org.eclipse.agail.recommenderserver.models.ListOfWFs;

public class Test {

    public static CollaborativeFiltering cf = new CollaborativeFiltering();
    static Recommenders recommenders = new Recommenders();
    public static String testFile = "C:\\Users\\spolater\\Desktop\\AGILE\\AGILE-GITHUB\\Recommender\\Recommender\\files\\test.csv";
    public static String testFile2 = "C:\\Users\\spolater\\Desktop\\AGILE\\AGILE-GITHUB\\Recommender\\Recommender\\files\\test2.csv";

    public static void main(String[] args) {

        //testFiles();
        //testCollaborativeFiltering();
        //testgetAppRecommendation(); // size= 2
        //testgetDeviceRecommendation();  // size= 2
        //testgetWFRecommendation(); // size= 3

    }

    public static void printPath() {
        String path = String.format("%s/%s", System.getProperty("user.dir"),
                FileOperations.class.getClass().getPackage().getName().replace(".", "/"));
        System.out.println(path);
    }

    public static void relativePath() {
        FileOperations op = new FileOperations();
        String filename = "files\\Clouds";
        op.cleanFile(filename);
        op.appendNewLineToFile(filename, "hebele");
        //      try{
        //           String str = "World";
        //             BufferedWriter writer = new BufferedWriter(new FileWriter("files\\Clouds"));
        //             writer.append(' ');
        //             writer.append(str);
        //              
        //             writer.close();
        //       
        //       }
        //      catch(Exception ex)
        //      {}
    }

    public static void decodingHtml() {

        StringEscapeUtils util = new StringEscapeUtils();
        String test = "" < > = ' /";
        System.out.println("Before: " + test);
        test = util.unescapeHtml4(test);
        System.out.println("After: " + test);
    }

    public static void testFiles() {
        System.out.println(cf.userProfilesFile);
        System.out.println(cf.itemsFile);

        System.out.println(cf.getLastUserID());
        System.out.println(cf.getItem(0));

        int lastline = cf.appendToBottomOfFile("7,5,5.0", cf.userProfilesFile);
        int lastline2 = cf.appendToBottomOfFile("App,hebele", cf.itemsFile);

        System.out.println(cf.getLastUserID());
        System.out.println(cf.getItem(0));
    }

    public static void testgetAppRecommendation() {
        GatewayProfile profile = new GatewayProfile();
        List<App> appList = new ArrayList<App>();
        appList.add(new App("App", "hebele", 0, 0));
        profile.apps.setAppList(appList);

        ListOfApps recs = cf.getAppRecommendation(profile);
        System.out.println(recs.getAppList().size());

    }

    public static void testgetWFRecommendation() {
        GatewayProfile profile = new GatewayProfile();
        List<App> appList = new ArrayList<App>();
        appList.add(new App("App", "hebele", 0, 0));
        profile.apps.setAppList(appList);

        ListOfWFs recs = cf.getWorkflowRecommendation(profile);
        System.out.println(recs.getWfList().size());

    }

    public static void testgetDeviceRecommendation() {
        GatewayProfile profile = new GatewayProfile();
        List<App> appList = new ArrayList<App>();
        appList.add(new App("App", "hebele", 0, 0));
        profile.apps.setAppList(appList);

        ListOfDevices recs = cf.getDeviceRecommendation(profile);
        System.out.println(recs.getDeviceList().size());

    }

    public static void testCollaborativeFiltering() {

        System.out.println("testCollaborativeFiltering");

        try {
            // load the data from the file with format "userID,itemID,value"
            DataModel model = new FileDataModel(new File(testFile2));

            //  compute the correlation coefficient between their interactions
            UserSimilarity similarity = new EuclideanDistanceSimilarity(model);

            double similar = similarity.userSimilarity(1, 2);
            System.out.println(similar);

            // we'll use all that have a similarity greater than 0.1
            UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);

            // all the pieces to create our recommender
            UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);

            //  get three items recommended for the user with userID 2
            List<RecommendedItem> recommendations = recommender.recommend(2, 10);
            for (RecommendedItem recommendation : recommendations) {
                System.out.println(recommendation);
            }

        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }

    }

}