opennlp.tools.ml.model.OnePassDataIndexer Maven / Gradle / Ivy
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* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
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*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
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* KIND, either express or implied. See the License for the
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* under the License.
*/
package opennlp.tools.ml.model;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Set;
import opennlp.tools.util.ObjectStream;
/**
* An indexer for maxent model data which handles cutoffs for uncommon
* contextual predicates and provides a unique integer index for each of the
* predicates.
*/
public class OnePassDataIndexer extends AbstractDataIndexer {
/**
* One argument constructor for DataIndexer which calls the two argument
* constructor assuming no cutoff.
*
* @param eventStream
* An Event[] which contains the a list of all the Events seen in the
* training data.
*/
@Deprecated
public OnePassDataIndexer(ObjectStream eventStream) throws IOException {
this(eventStream, 0);
}
@Deprecated
public OnePassDataIndexer(ObjectStream eventStream, int cutoff)
throws IOException {
this(eventStream, cutoff, true);
}
/**
* Two argument constructor for DataIndexer.
*
* @param eventStream
* An Event[] which contains the a list of all the Events seen in the
* training data.
* @param cutoff
* The minimum number of times a predicate must have been observed in
* order to be included in the model.
*/
@Deprecated
public OnePassDataIndexer(ObjectStream eventStream, int cutoff, boolean sort)
throws IOException {
Map predicateIndex = new HashMap<>();
List events;
List eventsToCompare;
System.out.println("Indexing events using cutoff of " + cutoff + "\n");
System.out.print("\tComputing event counts... ");
events = computeEventCounts(eventStream, predicateIndex, cutoff);
System.out.println("done. " + events.size() + " events");
System.out.print("\tIndexing... ");
eventsToCompare = index(events, predicateIndex);
// done with event list
events = null;
// done with predicates
predicateIndex = null;
System.out.println("done.");
System.out.print("Sorting and merging events... ");
sortAndMerge(eventsToCompare, sort);
System.out.println("Done indexing.");
}
public OnePassDataIndexer(){}
@Override
public void index(ObjectStream eventStream) throws IOException {
int cutoff = trainingParameters.getIntParameter(CUTOFF_PARAM, CUTOFF_DEFAULT);
boolean sort = trainingParameters.getBooleanParameter(SORT_PARAM, SORT_DEFAULT);
Map predicateIndex = new HashMap<>();
List events;
List eventsToCompare;
System.out.println("Indexing events using cutoff of " + cutoff + "\n");
System.out.print("\tComputing event counts... ");
events = computeEventCounts(eventStream, predicateIndex, cutoff);
System.out.println("done. " + events.size() + " events");
System.out.print("\tIndexing... ");
eventsToCompare = index(events, predicateIndex);
// done with event list
events = null;
// done with predicates
predicateIndex = null;
System.out.println("done.");
System.out.print("Sorting and merging events... ");
sortAndMerge(eventsToCompare, sort);
System.out.println("Done indexing.");
}
/**
* Reads events from eventStream into a linked list. The predicates
* associated with each event are counted and any which occur at least
* cutoff times are added to the predicatesInOut map along
* with a unique integer index.
*
* @param eventStream
* an EventStream value
* @param predicatesInOut
* a TObjectIntHashMap value
* @param cutoff
* an int value
* @return a TLinkedList value
*/
private List computeEventCounts(ObjectStream eventStream,
Map predicatesInOut, int cutoff) throws IOException {
Set predicateSet = new HashSet<>();
Map counter = new HashMap<>();
List events = new LinkedList<>();
Event ev;
while ((ev = eventStream.read()) != null) {
events.add(ev);
update(ev.getContext(), predicateSet, counter, cutoff);
}
predCounts = new int[predicateSet.size()];
int index = 0;
for (Iterator pi = predicateSet.iterator(); pi.hasNext(); index++) {
String predicate = pi.next();
predCounts[index] = counter.get(predicate);
predicatesInOut.put(predicate, index);
}
return events;
}
protected List index(List events,
Map predicateIndex) {
Map omap = new HashMap<>();
int numEvents = events.size();
int outcomeCount = 0;
List eventsToCompare = new ArrayList<>(numEvents);
List indexedContext = new ArrayList<>();
for (Event ev:events) {
String[] econtext = ev.getContext();
ComparableEvent ce;
int ocID;
String oc = ev.getOutcome();
if (omap.containsKey(oc)) {
ocID = omap.get(oc);
} else {
ocID = outcomeCount++;
omap.put(oc, ocID);
}
for (String pred : econtext) {
if (predicateIndex.containsKey(pred)) {
indexedContext.add(predicateIndex.get(pred));
}
}
// drop events with no active features
if (indexedContext.size() > 0) {
int[] cons = new int[indexedContext.size()];
for (int ci = 0; ci < cons.length; ci++) {
cons[ci] = indexedContext.get(ci);
}
ce = new ComparableEvent(ocID, cons);
eventsToCompare.add(ce);
} else {
System.err.println("Dropped event " + ev.getOutcome() + ":"
+ Arrays.asList(ev.getContext()));
}
// recycle the TIntArrayList
indexedContext.clear();
}
outcomeLabels = toIndexedStringArray(omap);
predLabels = toIndexedStringArray(predicateIndex);
return eventsToCompare;
}
}