code: /*
   * Copyright 2015 recommenders.net.
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   *      http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  package net.recommenders.rival.recommend.frameworks;
  
  import java.io.File;
  import java.io.FileInputStream;
  import java.io.IOException;
  import java.util.ArrayList;
  import java.util.HashSet;
  import java.util.List;
  import java.util.Properties;
  import java.util.Set;
  
  /**
   * Runner of multiple recommenders.
   *
   * @author <a href="http://github.com/alansaid">Alan</a>
   */
  public final class MultipleRecommendationRunner {
  
      /**
       * Property key.
       */
      public static final String INPUT = "input";
      /**
       * Property key.
       */
      public static final String MAHOUT_ITEMBASED_RECS = "mahout.rec.ib";
      /**
       * Property key.
       */
      public static final String MAHOUT_USERBASED_RECS = "mahout.rec.ub";
      /**
       * Property key.
       */
      public static final String MAHOUT_SIMILARITIES = "mahout.sim";
      /**
       * Property key.
       */
      public static final String MAHOUT_SVD_RECS = "mahout.rec.svd";
      /**
       * Property key.
       */
      public static final String MAHOUT_SVD_FACTORIZER = "mahout.svd.factorizer";
      /**
       * Property key.
       */
      public static final String LENSKIT_ITEMBASED_RECS = "lenskit.rec.ib";
      /**
       * Property key.
       */
      public static final String LENSKIT_USERBASED_RECS = "lenskit.rec.ub";
      /**
       * Property key.
       */
      public static final String LENSKIT_SIMILARITIES = "lenskit.sim";
      /**
       * Property key.
       */
      public static final String LENSKIT_SVD_RECS = "lenskit.rec.svd";
      /**
       * Property key.
       */
      public static final String RANKSYS_ITEMBASED_RECS = "ranksys.rec.ib";
      /**
       * Property key.
       */
      public static final String RANKSYS_USERBASED_RECS = "ranksys.rec.ub";
      /**
       * Property key.
       */
      public static final String RANKSYS_SIMILARITIES = "ranksys.sim";
      /**
       * Property key.
       */
      public static final String RANKSYS_SVD_RECS = "ranksys.rec.svd";
      /**
       * Property key.
       */
      public static final String RANKSYS_SVD_FACTORIZER = "ranksys.svd.factorizer";
      /**
       * Property key.
       */
      public static final String N = "neighborhood"; //also factors
      /**
       * Property key.
       */
      public static final String SVD_ITER = "svd.iterations";
      /**
       * Property key.
       */
      public static final String OUTPUT = "output";
  
      /**
       * Utility classes should not have a public or default constructor.
       */
      private MultipleRecommendationRunner() {
      }
  
      /**
       * Instantiates and runs the runners based on input (property) files.
       *
       * @param args not used.
       */
      public static void main(final String[] args) {
          final Set<String> paths = new HashSet<String>();
          final String propertyFile = System.getProperty("file");
          Properties properties = new Properties();
          try {
              properties.load(new FileInputStream(propertyFile));
          } catch (IOException e) {
              e.printStackTrace();
          }
  
          for (String pr : properties.stringPropertyNames()) {
              System.out.println(pr + " : " + properties.getProperty(pr));
          }
  
          listAllFiles(paths, properties.getProperty(INPUT));
  
          runLenskitRecommenders(paths, properties);
          runMahoutRecommenders(paths, properties);
          runRanksysRecommenders(paths, properties);
      }
  
      /**
       * Runs the Lenskit recommenders.
       *
       * @param paths the input and output paths.
       * @param properties the properties.
       */
      public static void runLenskitRecommenders(final Set<String> paths, final Properties properties) {
          for (AbstractRunner<Long, Long> rec : instantiateLenskitRecommenders(paths, properties)) {
              RecommendationRunner.run(rec);
          }
      }
  
      /**
       *
       * Instantiates recommenders based on the provided properties.
       *
       * @param paths the input and output paths.
       * @param properties the properties.
       * @return an array of recommenders, prepared to be run.
       */
      @SuppressWarnings("unchecked")
      public static AbstractRunner<Long, Long>[] instantiateLenskitRecommenders(final Set<String> paths, final Properties properties) {
          List<AbstractRunner<Long, Long>> recList = new ArrayList<AbstractRunner<Long, Long>>();
          try {
              String[] ibRecs = properties.getProperty(LENSKIT_ITEMBASED_RECS).split(",");
              String[] ubRecs = properties.getProperty(LENSKIT_USERBASED_RECS).split(",");
              String[] svdRecs = properties.getProperty(LENSKIT_SVD_RECS).split(",");
              String[] similarities = properties.getProperty(LENSKIT_SIMILARITIES).split(",");
              String[] neighborhoods = properties.getProperty(N).split(",");
  
              for (String path : paths) {
                  Properties prop = new Properties();
                  prop.setProperty(RecommendationRunner.TRAINING_SET, path + "_train.dat");
                  prop.setProperty(RecommendationRunner.TEST_SET, path + "_test.dat");
                  prop.setProperty(RecommendationRunner.OUTPUT, properties.getProperty(OUTPUT, ""));
                  prop.setProperty(RecommendationRunner.FRAMEWORK, "lenskit");
                  for (String ubRec : ubRecs) {
                      if (ubRec.trim().isEmpty()) {
                          continue;
                      }
                      prop.setProperty(RecommendationRunner.RECOMMENDER, ubRec);
                      for (String sim : similarities) {
                          prop.setProperty(RecommendationRunner.SIMILARITY, sim);
                          for (String n : neighborhoods) {
                              prop.setProperty(RecommendationRunner.NEIGHBORHOOD, n);
                              AbstractRunner<Long, Long> ar = RecommendationRunner.instantiateRecommender(prop);
                              recList.add(ar);
                          }
                          prop.remove(RecommendationRunner.SIMILARITY);
  //                    prop.remove(RecommendationRunner.neighborhood);
                      }
                  }
                  for (String ibRec : ibRecs) {
                      if (ibRec.trim().isEmpty()) {
                          continue;
                      }
                      prop.setProperty(RecommendationRunner.RECOMMENDER, ibRec);
                      for (String sim : similarities) {
                          prop.setProperty(RecommendationRunner.SIMILARITY, sim);
                          AbstractRunner<Long, Long> ar = RecommendationRunner.instantiateRecommender(prop);
                          recList.add(ar);
                          prop.remove(RecommendationRunner.SIMILARITY);
                      }
                  }
                  for (String svdRec : svdRecs) {
                      if (svdRec.trim().isEmpty()) {
                          continue;
                      }
                      prop.setProperty(RecommendationRunner.RECOMMENDER, svdRec);
                      for (String f : neighborhoods) {
                          prop.setProperty(RecommendationRunner.FACTORS, f);
                          AbstractRunner<Long, Long> ar = RecommendationRunner.instantiateRecommender(prop);
                          recList.add(ar);
                      }
                      prop.remove(RecommendationRunner.FACTORIZER);
                  }
              }
          } catch (NullPointerException e) {
              System.out.println("Properties not set (Lenskit recommenders)");
          }
          AbstractRunner<Long, Long>[] recs = recList.toArray(new AbstractRunner[0]);
          return recs;
      }
  
      /**
       * Runs Mahout-based recommenders.
       *
       * @param paths the input and output paths.
       * @param properties the properties.
       */
      public static void runMahoutRecommenders(final Set<String> paths, final Properties properties) {
          for (AbstractRunner<Long, Long> rec : instantiateMahoutRecommenders(paths, properties)) {
              RecommendationRunner.run(rec);
          }
      }
  
      /**
       *
       * Instantiates recommenders based on the provided properties.
       *
       * @param paths the input and output paths.
       * @param properties the properties.
       * @return an array of recommenders, prepared to be run.
       */
      @SuppressWarnings("unchecked")
      public static AbstractRunner<Long, Long>[] instantiateMahoutRecommenders(final Set<String> paths, final Properties properties) {
          List<AbstractRunner<Long, Long>> recList = new ArrayList<AbstractRunner<Long, Long>>();
          try {
              String[] ibRecs = properties.getProperty(MAHOUT_ITEMBASED_RECS).split(",");
              String[] ubRecs = properties.getProperty(MAHOUT_USERBASED_RECS).split(",");
              String[] svdRecs = properties.getProperty(MAHOUT_SVD_RECS).split(",");
              String[] similarities = properties.getProperty(MAHOUT_SIMILARITIES).split(",");
              String[] neighborhoods = properties.getProperty(N).split(",");
  
              String[] factorizers = properties.getProperty(MAHOUT_SVD_FACTORIZER).split(",");
  
              for (String path : paths) {
                  Properties prop = new Properties();
                  prop.setProperty(RecommendationRunner.TRAINING_SET, path + "_train.dat");
                  prop.setProperty(RecommendationRunner.TEST_SET, path + "_test.dat");
                  prop.setProperty(RecommendationRunner.OUTPUT, properties.getProperty(OUTPUT, ""));
                  prop.setProperty(RecommendationRunner.FRAMEWORK, "mahout");
                  // first IB because it (should) does not have neighborhood
                  for (String ibRec : ibRecs) {
                      if (ibRec.trim().isEmpty()) {
                          continue;
                      }
                      prop.setProperty(RecommendationRunner.RECOMMENDER, ibRec);
                      for (String sim : similarities) {
                          prop.setProperty(RecommendationRunner.SIMILARITY, sim);
                          AbstractRunner<Long, Long> ar = RecommendationRunner.instantiateRecommender(prop);
                          recList.add(ar);
                          prop.remove(RecommendationRunner.SIMILARITY);
                      }
                  }
                  for (String ubRec : ubRecs) {
                      if (ubRec.trim().isEmpty()) {
                          continue;
                      }
                      prop.setProperty(RecommendationRunner.RECOMMENDER, ubRec);
                      for (String sim : similarities) {
                          prop.setProperty(RecommendationRunner.SIMILARITY, sim);
                          for (String n : neighborhoods) {
                              prop.setProperty(RecommendationRunner.NEIGHBORHOOD, n);
                              AbstractRunner<Long, Long> ar = RecommendationRunner.instantiateRecommender(prop);
                              recList.add(ar);
                          }
                          prop.remove(RecommendationRunner.SIMILARITY);
  //                    prop.remove(RecommendationRunner.neighborhood);
                      }
                  }
                  for (String svdRec : svdRecs) {
                      if (svdRec.trim().isEmpty()) {
                          continue;
                      }
                      prop.setProperty(RecommendationRunner.RECOMMENDER, svdRec);
                      for (String fact : factorizers) {
                          prop.setProperty(RecommendationRunner.FACTORIZER, fact);
                          for (String f : neighborhoods) {
                              prop.setProperty(RecommendationRunner.FACTORS, f);
                              AbstractRunner<Long, Long> ar = RecommendationRunner.instantiateRecommender(prop);
                              recList.add(ar);
                          }
                          prop.remove(RecommendationRunner.FACTORIZER);
  
                      }
                  }
              }
          } catch (NullPointerException e) {
              System.out.println("Properties not set (Mahout recommenders)");
          }
          AbstractRunner<Long, Long>[] recs = recList.toArray(new AbstractRunner[0]);
          return recs;
      }
  
      /**
       * Runs Ranksys-based recommenders.
       *
       * @param paths the input and output paths.
       * @param properties the properties.
       */
      public static void runRanksysRecommenders(final Set<String> paths, final Properties properties) {
          for (AbstractRunner<Long, Long> rec : instantiateRanksysRecommenders(paths, properties)) {
              RecommendationRunner.run(rec);
          }
      }
  
      /**
       *
       * Instantiates recommenders based on the provided properties.
       *
       * @param paths the input and output paths.
       * @param properties the properties.
       * @return an array of recommenders, prepared to be run.
       */
      @SuppressWarnings("unchecked")
      public static AbstractRunner<Long, Long>[] instantiateRanksysRecommenders(final Set<String> paths, final Properties properties) {
          List<AbstractRunner<Long, Long>> recList = new ArrayList<AbstractRunner<Long, Long>>();
          try {
              String[] ibRecs = properties.getProperty(RANKSYS_ITEMBASED_RECS).split(",");
              String[] ubRecs = properties.getProperty(RANKSYS_USERBASED_RECS).split(",");
              String[] svdRecs = properties.getProperty(RANKSYS_SVD_RECS).split(",");
              String[] similarities = properties.getProperty(RANKSYS_SIMILARITIES).split(",");
              String[] neighborhoods = properties.getProperty(N).split(",");
  
              String[] factorizers = properties.getProperty(RANKSYS_SVD_FACTORIZER).split(",");
  
              for (String path : paths) {
                  Properties prop = new Properties();
                  prop.setProperty(RecommendationRunner.TRAINING_SET, path + "_train.dat");
                  prop.setProperty(RecommendationRunner.TEST_SET, path + "_test.dat");
                  prop.setProperty(RecommendationRunner.OUTPUT, properties.getProperty(OUTPUT, ""));
                  prop.setProperty(RecommendationRunner.FRAMEWORK, "ranksys");
                  // first IB because it (should) does not have neighborhood
                  for (String ibRec : ibRecs) {
                      if (ibRec.trim().isEmpty()) {
                          continue;
                      }
                      prop.setProperty(RecommendationRunner.RECOMMENDER, ibRec);
                      for (String sim : similarities) {
                          prop.setProperty(RecommendationRunner.SIMILARITY, sim);
                          AbstractRunner<Long, Long> ar = RecommendationRunner.instantiateRecommender(prop);
                          recList.add(ar);
                          prop.remove(RecommendationRunner.SIMILARITY);
                      }
                  }
                  for (String ubRec : ubRecs) {
                      if (ubRec.trim().isEmpty()) {
                          continue;
                      }
                      prop.setProperty(RecommendationRunner.RECOMMENDER, ubRec);
                      for (String sim : similarities) {
                          prop.setProperty(RecommendationRunner.SIMILARITY, sim);
                          for (String n : neighborhoods) {
                              prop.setProperty(RecommendationRunner.NEIGHBORHOOD, n);
                              AbstractRunner<Long, Long> ar = RecommendationRunner.instantiateRecommender(prop);
                              recList.add(ar);
                          }
                          prop.remove(RecommendationRunner.SIMILARITY);
  //                    prop.remove(RecommendationRunner.neighborhood);
                      }
                  }
                  for (String svdRec : svdRecs) {
                      if (svdRec.trim().isEmpty()) {
                          continue;
                      }
                      prop.setProperty(RecommendationRunner.RECOMMENDER, svdRec);
                      for (String fact : factorizers) {
                          prop.setProperty(RecommendationRunner.FACTORIZER, fact);
                          for (String f : neighborhoods) {
                              prop.setProperty(RecommendationRunner.FACTORS, f);
                              AbstractRunner<Long, Long> ar = RecommendationRunner.instantiateRecommender(prop);
                              recList.add(ar);
                          }
                          prop.remove(RecommendationRunner.FACTORIZER);
  
                      }
                  }
              }
          } catch (NullPointerException e) {
              System.out.println("Properties not set (Ranksys recommenders)");
          }
          AbstractRunner<Long, Long>[] recs = recList.toArray(new AbstractRunner[0]);
          return recs;
      }
  
      /**
       * List all files at a certain path.
       *
       * @param setOfPaths the set of files at a certain path
       * @param inputPath the path to check
       */
      public static void listAllFiles(final Set<String> setOfPaths, final String inputPath) {
          if (inputPath == null) {
              return;
          }
          File[] files = new File(inputPath).listFiles();
          if (files == null) {
              return;
          }
          for (File file : files) {
              if (file.isDirectory()) {
                  listAllFiles(setOfPaths, file.getAbsolutePath());
              } else if (file.getName().contains("_train.dat")) {
                  setOfPaths.add(file.getAbsolutePath().replaceAll("_train.dat", ""));
              }
          }
      }
  }
  