opennlp.tools.ml.model.AbstractDataIndexer 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.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;
import opennlp.tools.ml.AbstractTrainer;
import opennlp.tools.util.InsufficientTrainingDataException;
import opennlp.tools.util.TrainingParameters;
/**
* Abstract class for collecting event and context counts used in training.
*
*/
public abstract class AbstractDataIndexer implements DataIndexer {
public static final String CUTOFF_PARAM = AbstractTrainer.CUTOFF_PARAM;
public static final int CUTOFF_DEFAULT = AbstractTrainer.CUTOFF_DEFAULT;
public static final String SORT_PARAM = "sort";
public static final boolean SORT_DEFAULT = true;
protected TrainingParameters trainingParameters;
protected Map reportMap;
protected boolean printMessages;
public void init(TrainingParameters indexingParameters,Map reportMap) {
this.reportMap = reportMap;
if (this.reportMap == null) reportMap = new HashMap<>();
trainingParameters = indexingParameters;
printMessages = trainingParameters.getBooleanParameter(AbstractTrainer.VERBOSE_PARAM,
AbstractTrainer.VERBOSE_DEFAULT);
}
private int numEvents;
/** The integer contexts associated with each unique event. */
protected int[][] contexts;
/** The integer outcome associated with each unique event. */
protected int[] outcomeList;
/** The number of times an event occured in the training data. */
protected int[] numTimesEventsSeen;
/** The predicate/context names. */
protected String[] predLabels;
/** The names of the outcomes. */
protected String[] outcomeLabels;
/** The number of times each predicate occured. */
protected int[] predCounts;
public int[][] getContexts() {
return contexts;
}
public int[] getNumTimesEventsSeen() {
return numTimesEventsSeen;
}
public int[] getOutcomeList() {
return outcomeList;
}
public String[] getPredLabels() {
return predLabels;
}
public String[] getOutcomeLabels() {
return outcomeLabels;
}
public int[] getPredCounts() {
return predCounts;
}
/**
* Sorts and uniques the array of comparable events and return the number of unique events.
* This method will alter the eventsToCompare array -- it does an in place
* sort, followed by an in place edit to remove duplicates.
*
* @param eventsToCompare a ComparableEvent[] value
* @return The number of unique events in the specified list.
* @throws InsufficientTrainingDataException if not enough events are provided
* @since maxent 1.2.6
*/
protected int sortAndMerge(List eventsToCompare, boolean sort)
throws InsufficientTrainingDataException {
int numUniqueEvents = 1;
numEvents = eventsToCompare.size();
if (sort && eventsToCompare.size() > 0) {
Collections.sort(eventsToCompare);
ComparableEvent ce = eventsToCompare.get(0);
for (int i = 1; i < numEvents; i++) {
ComparableEvent ce2 = eventsToCompare.get(i);
if (ce.compareTo(ce2) == 0) {
ce.seen++; // increment the seen count
eventsToCompare.set(i, null); // kill the duplicate
}
else {
ce = ce2; // a new champion emerges...
numUniqueEvents++; // increment the # of unique events
}
}
}
else {
numUniqueEvents = eventsToCompare.size();
}
if (numUniqueEvents == 0) {
throw new InsufficientTrainingDataException("Insufficient training data to create model.");
}
if (sort) display("done. Reduced " + numEvents + " events to " + numUniqueEvents + ".\n");
contexts = new int[numUniqueEvents][];
outcomeList = new int[numUniqueEvents];
numTimesEventsSeen = new int[numUniqueEvents];
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.
}
numTimesEventsSeen[j] = evt.seen;
outcomeList[j] = evt.outcome;
contexts[j] = evt.predIndexes;
++j;
}
return numUniqueEvents;
}
public int getNumEvents() {
return numEvents;
}
/**
* Updates the set of predicated and counter with the specified event contexts and cutoff.
* @param ec The contexts/features which occur in a event.
* @param predicateSet The set of predicates which will be used for model building.
* @param counter The predicate counters.
* @param cutoff The cutoff which determines whether a predicate is included.
*/
protected static void update(String[] ec, Set predicateSet,
Map counter, int cutoff) {
for (String s : ec) {
Integer i = counter.get(s);
if (i == null) {
counter.put(s, 1);
}
else {
counter.put(s, i + 1);
}
if (!predicateSet.contains(s) && counter.get(s) >= cutoff) {
predicateSet.add(s);
}
}
}
/**
* Utility method for creating a String[] array from a map whose
* keys are labels (Strings) to be stored in the array and whose
* values are the indices (Integers) at which the corresponding
* labels should be inserted.
*
* @param labelToIndexMap a TObjectIntHashMap value
* @return a String[] value
* @since maxent 1.2.6
*/
protected static String[] toIndexedStringArray(Map labelToIndexMap) {
final String[] array = new String[labelToIndexMap.size()];
for (String label : labelToIndexMap.keySet()) {
array[labelToIndexMap.get(label)] = label;
}
return array;
}
public float[][] getValues() {
return null;
}
protected void display(String s) {
if (printMessages) {
System.out.print(s);
}
}
}