opennlp.tools.ml.model.OnePassRealValueDataIndexer Maven / Gradle / Ivy
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* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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 opennlp.tools.ml.model;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import opennlp.tools.util.InsufficientTrainingDataException;
/**
* An indexer for maxent model data which handles cutoffs for uncommon
* contextual predicates and provides a unique integer index for each of the
* predicates and maintains event values.
*/
public class OnePassRealValueDataIndexer extends OnePassDataIndexer {
float[][] values;
public OnePassRealValueDataIndexer() {
}
public float[][] getValues() {
return values;
}
protected int sortAndMerge(List eventsToCompare,boolean sort)
throws InsufficientTrainingDataException {
int numUniqueEvents = super.sortAndMerge(eventsToCompare,sort);
values = new float[numUniqueEvents][];
int numEvents = eventsToCompare.size();
for (int i = 0, j = 0; i < numEvents; i++) {
ComparableEvent evt = eventsToCompare.get(i);
if (null == evt) {
continue; // this was a dupe, skip over it.
}
values[j++] = evt.values;
}
return numUniqueEvents;
}
@Override
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, ev.getValues());
eventsToCompare.add(ce);
} else {
System.err.println("Dropped event " + ev.getOutcome() + ":" + Arrays.asList(ev.getContext()));
}
indexedContext.clear();
}
outcomeLabels = toIndexedStringArray(omap);
predLabels = toIndexedStringArray(predicateIndex);
return eventsToCompare;
}
}