application.Application.java Source code

Introduction

Here is the source code for application.Application.java

Source

// Copyright (C) 2015 Fabio Petroni
// Contact: http://www.fabiopetroni.com
//
// This file is part of LCBM (a fast and lightweight collaborative filtering algorithm for binary ratings).
//
// LCBM is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// LCBM is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with LCBM.  If not, see <http://www.gnu.org/licenses/>.
//
// Based on the publication:
// - Fabio Petroni, Leonardo Querzoni, Roberto Beraldi, Mario Paolucci: 
//   "LCBM: Statistics-Based Parallel Collaborative Filtering".
//   BIS, 2014.

package application;

import java.util.HashMap;

import job.ItemPDFJob;
import job.OutputReader;
import job.UserProfileJob;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.ToolRunner;

import structures.ItemProfile;
import structures.UserProfile;

// Classe Main
public class Application {

    /**
     *    il metodo main prende in ingresso gli argomenti passati da riga di comando, inizialmente svolge la fase di training
     *    esegue il job mapreduce per gli item, una volta ottenuto l'output del job esegue il secondo job mapreduce per gli user,
     *    infine svolge fase di working in cui stima la valutazione dell'utente e confronta con l'effettivo voto espresso dall'utente
     * @param args [] la prima cella di memoria dell'array contiene l'input path, 
     *  la seconda l'output path per l'esecuzione ItemPDFJob la terza l'output path per l'esecuzione di UserProfileJob, 
     *  la quarta e' opzionale e contiene il valore K con cui eseguire il processo
     * @throws Exception
     */
    public static void main(String[] args) throws Exception {

        long START_TIME = System.currentTimeMillis();

        System.out.println("\n--------------------------------------------------");
        System.out.println(
                " LCBM mapreduce: a fast and lightweight collaborative filtering algorithm for binary ratings.");
        System.out.println(" author: Fabio Petroni (http://www.fabiopetroni.com)");
        System.out.println("--------------------------------------------------\n");
        Globals GLOBALS = new Globals(args);
        System.out.println(" Parameters:");
        GLOBALS.print();
        System.out.println("\n");

        HashMap<Integer, ItemProfile> ITEM_STATE;
        HashMap<Integer, UserProfile> USER_STATE;
        Configuration conf = new Configuration();

        //begin ITEM PROFILING PROCEDURE
        System.out.println(" JOB1 - Computing item profiles... ");
        long local_START = System.currentTimeMillis();
        ItemPDFJob ItemPDF_DRIVER = new ItemPDFJob(GLOBALS, conf);
        ToolRunner.run(conf, ItemPDF_DRIVER, args);
        long local_END = System.currentTimeMillis();
        System.out.println("\ttime item profiling procedure: " + (local_END - local_START));
        System.out.println("\n");
        //end ITEM PROFILING PROCEDURE

        System.out.println(" JOB2 - Computing user profiles... ");
        local_START = System.currentTimeMillis();
        UserProfileJob UserProfiler_DRIVER = new UserProfileJob(GLOBALS, conf);
        ToolRunner.run(conf, UserProfiler_DRIVER, args);
        local_END = System.currentTimeMillis();
        System.out.println("\ttime user profiling procedure: " + (local_END - local_START));
        System.out.println("\n");
        //end USER PROFILING PROCEDURE

        ITEM_STATE = OutputReader.readOutputItemPDFJob(GLOBALS.getOUTPUT1(), conf);
        USER_STATE = OutputReader.readOutputUserProfileJob(GLOBALS.getOUTPUT2(), conf);

        //begin EVALUATION
        System.out.println("\n Evaluating algorithm performace... ");
        local_START = System.currentTimeMillis();
        RecommenderEvaluator RE = new RecommenderEvaluator(ITEM_STATE, USER_STATE, GLOBALS, conf);
        RE.evaluate();
        local_END = System.currentTimeMillis();
        double MCC = RE.getMCC();
        System.out.println("\ttime evaluator: " + (local_END - local_START));
        System.out.println("\tMCC: " + MCC);
        System.out.println("\n");
        //end EVALUATION   

        long END_TIME = System.currentTimeMillis();
        long TOTAL_TIME = END_TIME - START_TIME;
        System.out.println("  Total Time: " + TOTAL_TIME + "\n");
    }
}