Package edu.gslis.ttg.searchers

Examples of edu.gslis.ttg.searchers.SimpleSearcher


   
    // instantiate search client
    TrecSearchThriftClient client = new TrecSearchThriftClient(params.getParamValue(HOST_OPTION),
        trainingPort, group, token);

    SimpleSearcher searcher = new SimpleSearcher(client, numResults);
   
    err.println("=== Train Queries ===");
   
    List<Double> thresholds = new ArrayList<Double>();
    double averageThreshold = 0;
    Iterator<GQuery> queryIterator = trainingQueries.iterator();
    while(queryIterator.hasNext()) {
      GQuery query = queryIterator.next();
     
      Map<Long, TResult> seenResults = searcher.search(query);
     
      SimpleJaccardClusterer clusterer = new SimpleJaccardClusterer(new ArrayList<TResult>(seenResults.values()));
     
      // sweep through jaccard steps, calculating F1
      double maxF1 = 0;
      double maxF1Threshold = 1;
      for (double j = 1.0; j >= 0.0; j -= stepSize) { // for each jaccard threshold step
        Clusters clusters = clusterer.cluster(j);
       
        // all clusters are created now, get a finalized set of results
        Set<Long> allResults = new HashSet<Long>(seenResults.keySet());
        allResults.removeAll(clusters.getAllClusteredResults()); // allResults includes unclustered plus one representative from each cluster
        for (Cluster c : clusters) {
          allResults.add(c.getFirstMember());
        }
       
        // calculate f1 on the finalized set
        Clusters seenClusters = new Clusters();
        Clusters trueClusters = clusterMembership.get(query.getTitle());
        Iterator<Long> resultIt = allResults.iterator();
        while (resultIt.hasNext()) {
          long result = resultIt.next();
          Cluster trueCluster = trueClusters.findCluster(result);
          if (trueCluster != null) { // if it is relevant, it will have a true cluster; if this is null, it's non-relevant
            seenClusters.add(trueCluster);
          }
        }
       
        int numRetrievedClusters = seenClusters.size();
        int numResultsReturned = allResults.size();
        int numTrueClusters = trueClusters.size();

        double precision = 0;
        double recall = 0;
        double f1 = 0;
        if (evalType.equals("unweighted")) {
          precision = numRetrievedClusters / (double) numResultsReturned;
          recall = numRetrievedClusters / (double) numTrueClusters;
          f1 = 2 * precision * recall / (precision + recall);
        } else {       
          // for weighted measurements, we need the weight of each cluster
          int retrievedWeight = 0;
          for (Cluster cluster : seenClusters) {
            int w = cluster.getWeight(query, qrels);
            retrievedWeight += w;
          }
          int resultsWeight = 0;
          for (long result : allResults) {
            int w = 0;
            if (seenClusters.findCluster(result) == null)
            resultsWeight += w;
          }
          int trueWeight = 0;
          for (Cluster cluster : trueClusters) {
            int w = cluster.getWeight(query, qrels);
            trueWeight += w;
          }
         
          precision = retrievedWeight / (double) resultsWeight; // <--- ??????
          recall = retrievedWeight / (double) trueWeight;
          f1 = 2 * precision * recall / (precision + recall);
        }
        if (f1 > maxF1) {
          maxF1 = f1;
          maxF1Threshold = j;
        }
      }
      thresholds.add(maxF1Threshold);
      err.println("F1: "+df.format(maxF1)+"; Jaccard: "+df.format(maxF1Threshold));
     
    }
   
    // get the average threshold
    for (double threshold : thresholds) {
      averageThreshold += threshold;
    }
    averageThreshold /= thresholds.size();
    err.println("Average Jaccard: "+averageThreshold);
   
    err.println("=== Test Queries ===");
   
    // now cluster the test queries and output
    queryIterator = queries.iterator();
    while(queryIterator.hasNext()) {
      GQuery query = queryIterator.next();
      err.println(query.getTitle());
     
      client = new TrecSearchThriftClient(params.getParamValue(HOST_OPTION), testingPort, group, token);
      searcher = new SimpleSearcher(client, numResults);
      Map<Long, TResult> seenResults = searcher.search(query);
     
      SimpleJaccardClusterer clusterer = new SimpleJaccardClusterer(new ArrayList<TResult>(seenResults.values()));
      Clusters clusters = clusterer.cluster(averageThreshold);
     
      // all clusters are created now, get a finalized set of results
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