/**
* Culls the test set using the relevant Events extracted from the training data.<br>
*
* @param relevantEvents the features from the EventSets which are going to be evaluated
* @param eventSetsToCull The test documents to be culled
* @return the culled test documents
* @throws Exception
*/
public List<EventSet> cullWithRespectToTraining(
List<EventSet> relevantEvents, List<EventSet> eventSetsToCull, CumulativeFeatureDriver cfd)
throws Exception {
List<EventSet> relevant = relevantEvents;
int numOfFeatureClasses = eventSetsToCull.size() - 1; // -1 to compensate for removing metadata
int i;
List<EventSet> culledUnknownEventSets = new LinkedList<EventSet>();
// remove the metadata prior to culling
EventSet metadata = eventSetsToCull.remove(eventSetsToCull.size() - 1);
// make sure all unknown sets would have only events that appear in the
// known sets
// UNLESS the event set contains a sole numeric value event - in that
// case take it anyway
for (i = 0; i < numOfFeatureClasses; i++) {
if (cfd.featureDriverAt(i).isCalcHist()) {
// initialize set of relevant events
EventSet es = relevant.get(i);
Set<String> relevantEventsString = new HashSet<String>(es.size());
for (Event e : es) relevantEventsString.add(e.getEvent());
// remove all non-relevant events from unknown event sets
EventSet unknown;
Event e;
unknown = eventSetsToCull.get(i);
Iterator<Event> iterator = unknown.iterator();
Event next = null;
// the test doc may not contain a given feature (ie it might not
// have any semi-colons)
if (iterator.hasNext()) next = (Event) iterator.next();
// while it has more of a feature
while (iterator.hasNext()) {
// copy the feature
e = next;
boolean remove = true;
// check to see if the feature is relevant
for (int l = 0; l < unknown.size(); l++) {
try {
if (e.equals(relevantEvents.get(i).eventAt(l))) {
remove = false; // if it is, break
break;
}
} catch (IndexOutOfBoundsException iobe) {
remove = true; // it is not relevant if we reach this point.
break;
}
}
// remove the feature if it isn't relevant
if (remove) {
iterator.remove();
}
// grab the next feature
next = iterator.next();
}
// add the culled event set
culledUnknownEventSets.add(unknown);
} else { // one unique numeric event
// add non-histogram if it is in the relevantEventSets list
boolean isRelevant = false;
for (EventSet res : relevantEvents) {
if (res.getEventSetID().equals(eventSetsToCull.get(i).getEventSetID())) {
isRelevant = true;
break;
}
}
if (isRelevant) culledUnknownEventSets.add(eventSetsToCull.get(i));
}
}
eventSetsToCull.add(metadata);
culledUnknownEventSets.add(metadata);
return culledUnknownEventSets;
}
/**
* Extracts a list of all features to be used for analysis.
*
* @param culledEventSets
* @param relevantEvents
* @param cumulativeFeatureDriver
* @return
* @throws Exception
*/
public List<String> getFeatureList(
List<List<EventSet>> culledEventSets,
List<EventSet> relevantEvents,
CumulativeFeatureDriver cumulativeFeatureDriver)
throws Exception {
// remove the metdata prior to generating attribute list
ArrayList<EventSet> docMetaData = new ArrayList<EventSet>();
for (List<EventSet> les : culledEventSets) {
docMetaData.add(les.remove(les.size() - 1));
}
// initialize useful things
int numOfFeatureClasses = relevantEvents.size();
List<EventSet> list;
List<String> features = new ArrayList<String>(numOfFeatureClasses);
// initialize list of sets of events, which will eventually become the
// attributes
List<EventSet> allEvents = new ArrayList<EventSet>(numOfFeatureClasses);
// Neither the doc title nor the author is in the List<List<EventSet>>, so this should work fine
for (int currEventSet = 0; currEventSet < numOfFeatureClasses; currEventSet++) {
// initialize relevant list of event sets and histograms
list = new ArrayList<EventSet>();
for (int i = 0; i < numOfFeatureClasses; i++) list.add(relevantEvents.get(i));
// initialize eventSet
EventSet events = new EventSet();
events.setEventSetID(relevantEvents.get(currEventSet).getEventSetID());
if (cumulativeFeatureDriver.featureDriverAt(currEventSet).isCalcHist()) { // histogram feature
// generate event histograms and unique event list
EventSet eventSet = list.get(currEventSet);
for (Event event : eventSet) {
events.addEvent(event);
}
allEvents.add(events);
} else { // one unique numeric event
// generate sole event (give placeholder value)
Event event = new Event("{-}");
events.addEvent(event);
allEvents.add(events);
}
}
// Adds all of the events to the fast vector
int featureIndex = 0;
for (EventSet es : allEvents) {
Iterator<Event> iterator = es.iterator();
if (cumulativeFeatureDriver.featureDriverAt(featureIndex).isCalcHist()) {
if (iterator.hasNext()) {
// grab first event; there should be at least one
Event nextEvent = (Event) iterator.next();
// get and add all middle events if they exist
while (iterator.hasNext()) {
features.add(nextEvent.getEvent());
nextEvent = (Event) iterator.next();
}
// add the last event
features.add(nextEvent.getEvent());
}
} else {
features.add(es.getEventSetID());
}
featureIndex++;
}
// add the metadata back in
int index = 0;
for (List<EventSet> les : culledEventSets) {
les.add(docMetaData.get(index));
index++;
}
return features;
}
