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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreemnets. 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.util;
import java.util.Arrays;
import java.util.List;
import opennlp.model.MaxentModel;
/**
* Performs k-best search over sequence. This is based on the description in
* Ratnaparkhi (1998), PhD diss, Univ. of Pennsylvania.
*
* @see Sequence
* @see SequenceValidator
* @see BeamSearchContextGenerator
*/
public class BeamSearch {
private static final Object[] EMPTY_ADDITIONAL_CONTEXT = new Object[0];
protected int size;
protected BeamSearchContextGenerator cg;
protected MaxentModel model;
private SequenceValidator validator;
private double[] probs;
private Cache contextsCache;
private static final int zeroLog = -100000;
/**
* Creates new search object.
*
* @param size The size of the beam (k).
* @param cg the context generator for the model.
* @param model the model for assigning probabilities to the sequence outcomes.
*/
public BeamSearch(int size, BeamSearchContextGenerator cg, MaxentModel model) {
this(size, cg, model, null, 0);
}
public BeamSearch(int size, BeamSearchContextGenerator cg, MaxentModel model,
int cacheSize) {
this (size, cg, model, null, cacheSize);
}
public BeamSearch(int size, BeamSearchContextGenerator cg, MaxentModel model,
SequenceValidator validator, int cacheSize) {
this.size = size;
this.cg = cg;
this.model = model;
this.validator = validator;
if (cacheSize > 0) {
contextsCache = new Cache(cacheSize);
}
this.probs = new double[model.getNumOutcomes()];
}
/**
* Note:
* This method will be private in the future because clients can now
* pass a validator to validate the sequence.
*
* @see SequenceValidator
*/
private boolean validSequence(int i, T[] inputSequence, String[] outcomesSequence, String outcome) {
if (validator != null) {
return validator.validSequence(i, inputSequence, outcomesSequence, outcome);
}
else {
return true;
}
}
public Sequence[] bestSequences(int numSequences, T[] sequence, Object[] additionalContext) {
return bestSequences(numSequences, sequence, additionalContext, zeroLog);
}
/**
* Returns the best sequence of outcomes based on model for this object.
*
* @param numSequences The maximum number of sequences to be returned.
* @param sequence The input sequence.
* @param additionalContext An Object[] of additional context. This is passed to the context generator blindly with the assumption that the context are appropiate.
* @param minSequenceScore A lower bound on the score of a returned sequence.
* @return An array of the top ranked sequences of outcomes.
*/
public Sequence[] bestSequences(int numSequences, T[] sequence, Object[] additionalContext, double minSequenceScore) {
Heap prev = new ListHeap(size);
Heap next = new ListHeap(size);
Heap tmp;
prev.add(new Sequence());
if (additionalContext == null) {
additionalContext = EMPTY_ADDITIONAL_CONTEXT;
}
for (int i = 0; i < sequence.length; i++) {
int sz = Math.min(size, prev.size());
for (int sc = 0; prev.size() > 0 && sc < sz; sc++) {
Sequence top = (Sequence) prev.extract();
List tmpOutcomes = top.getOutcomes();
String[] outcomes = (String[]) tmpOutcomes.toArray(new String[tmpOutcomes.size()]);
String[] contexts = cg.getContext(i, sequence, outcomes, additionalContext);
double[] scores;
if (contextsCache != null) {
scores = (double[]) contextsCache.get(contexts);
if (scores == null) {
scores = model.eval(contexts, probs);
contextsCache.put(contexts,scores);
}
}
else {
scores = model.eval(contexts, probs);
}
double[] temp_scores = new double[scores.length];
for (int c = 0; c < scores.length; c++) {
temp_scores[c] = scores[c];
}
Arrays.sort(temp_scores);
double min = temp_scores[Math.max(0,scores.length-size)];
for (int p = 0; p < scores.length; p++) {
if (scores[p] < min)
continue; //only advance first "size" outcomes
String out = model.getOutcome(p);
if (validSequence(i, sequence, outcomes, out)) {
Sequence ns = new Sequence(top, out, scores[p]);
if (ns.getScore() > minSequenceScore) {
next.add(ns);
}
}
}
if (next.size() == 0) {//if no advanced sequences, advance all valid
for (int p = 0; p < scores.length; p++) {
String out = model.getOutcome(p);
if (validSequence(i, sequence, outcomes, out)) {
Sequence ns = new Sequence(top, out, scores[p]);
if (ns.getScore() > minSequenceScore) {
next.add(ns);
}
}
}
}
}
// make prev = next; and re-init next (we reuse existing prev set once we clear it)
prev.clear();
tmp = prev;
prev = next;
next = tmp;
}
int numSeq = Math.min(numSequences, prev.size());
Sequence[] topSequences = new Sequence[numSeq];
for (int seqIndex = 0; seqIndex < numSeq; seqIndex++) {
topSequences[seqIndex] = (Sequence) prev.extract();
}
return topSequences;
}
/**
* Returns the best sequence of outcomes based on model for this object.
*
* @param sequence The input sequence.
* @param additionalContext An Object[] of additional context. This is passed to the context generator blindly with the assumption that the context are appropiate.
*
* @return The top ranked sequence of outcomes or null if no sequence could be found
*/
public Sequence bestSequence(T[] sequence, Object[] additionalContext) {
Sequence sequences[] = bestSequences(1, sequence, additionalContext,zeroLog);
if (sequences.length > 0)
return sequences[0];
else
return null;
}
}
