opennlp.model.OnePassRealValueDataIndexer.java Source code

Introduction

Here is the source code for opennlp.model.OnePassRealValueDataIndexer.java

Source

/*
 * 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.model;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;

/**
 * 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 {
    private static final Log LOG = LogFactory.getLog(OnePassRealValueDataIndexer.class);

    float[][] values;

    public OnePassRealValueDataIndexer(EventStream eventStream, int cutoff, boolean sort) throws IOException {
        super(eventStream, cutoff, sort);
    }

    /**
     * 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.
     */
    public OnePassRealValueDataIndexer(EventStream eventStream, int cutoff) throws IOException {
        super(eventStream, cutoff);
    }

    public float[][] getValues() {
        return values;
    }

    protected int sortAndMerge(List eventsToCompare, boolean sort) {
        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 = (ComparableEvent) eventsToCompare.get(i);
            if (null == evt) {
                continue; // this was a dupe, skip over it.
            }
            values[j++] = evt.values;
        }
        return numUniqueEvents;
    }

    protected List index(LinkedList<Event> events, Map<String, Integer> predicateIndex) {
        Map<String, Integer> omap = new HashMap<String, Integer>();

        int numEvents = events.size();
        int outcomeCount = 0;
        List eventsToCompare = new ArrayList(numEvents);
        List<Integer> indexedContext = new ArrayList<Integer>();

        for (int eventIndex = 0; eventIndex < numEvents; eventIndex++) {
            Event ev = (Event) events.removeFirst();
            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 (int i = 0; i < econtext.length; i++) {
                String pred = econtext[i];
                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 {
                LOG.debug("Dropped event " + ev.getOutcome() + ":" + Arrays.asList(ev.getContext()));
            }
            //    recycle the TIntArrayList
            indexedContext.clear();
        }
        outcomeLabels = toIndexedStringArray(omap);
        predLabels = toIndexedStringArray(predicateIndex);
        return eventsToCompare;
    }

}