clusterMembership.put(topic, new Clusters());
JSONArray clusters = (JSONArray) ((JSONObject) topicObj.get(topic)).get("clusters");
Iterator<JSONArray> clusterIt = clusters.iterator();
while (clusterIt.hasNext()) { // for each cluster in the topic
JSONArray cluster = (JSONArray) clusterIt.next();
Cluster c = new Cluster();
Iterator<String> clusterMemberIt = cluster.iterator();
while (clusterMemberIt.hasNext()) { // for each docId in the cluster
String member = clusterMemberIt.next();
long memberId = Long.parseLong(member);
c.add(memberId);
}
clusterMembership.get(topic).add(c);
}
}
} catch (Exception e) {
err.println("Error reading training data.");
e.printStackTrace();
System.exit(-1);
}
// 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);
}
}