package edu.stanford.nlp.classify;
import edu.stanford.nlp.ling.Datum;
import edu.stanford.nlp.ling.RVFDatum;
import edu.stanford.nlp.math.ArrayMath;
import edu.stanford.nlp.optimization.AbstractCachingDiffFunction;
import edu.stanford.nlp.stats.ClassicCounter;
import edu.stanford.nlp.stats.Counter;
import edu.stanford.nlp.util.Triple;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;
/**
* Implementation of Generalized Expectation Objective function for an I.I.D. log-linear model. See Mann and McCallum, ACL 2008.
* IMPORTANT : the current implementation is only correct as long as the labeled features passed to GE are binary.
* However, other features are allowed to be real valued.
* The original paper also discusses GE only for binary features.
* @author Ramesh Nallapati (nmramesh@cs.stanford.edu)
*/
public class GeneralizedExpectationObjectiveFunction<L,F> extends AbstractCachingDiffFunction {
private GeneralDataset<L,F> labeledDataset;
private List<? extends Datum<L,F>> unlabeledDataList;
private List<F> geFeatures;
private LinearClassifier<L,F> classifier;
private double[][] geFeature2EmpiricalDist; //empirical label distributions of each feature.
private List<List<Integer>> geFeature2DatumList; //an inverted list of active unlabeled documents for each feature.
protected int numFeatures = 0;
protected int numClasses = 0;
@Override
public int domainDimension() {
return numFeatures * numClasses;
}
int classOf(int index) {
return index % numClasses;
}
int featureOf(int index) {
return index / numClasses;
}
protected int indexOf(int f, int c) {
return f * numClasses + c;
}
public double[][] to2D(double[] x) {
double[][] x2 = new double[numFeatures][numClasses];
for (int i = 0; i < numFeatures; i++) {
for (int j = 0; j < numClasses; j++) {
x2[i][j] = x[indexOf(i, j)];
}
}
return x2;
}
@Override
protected void calculate(double[] x) {
classifier.setWeights(to2D(x));
if (derivative == null) {
derivative = new double[x.length];
} else {
Arrays.fill(derivative, 0.0);
}
Counter<Triple<Integer,Integer,Integer>> feature2classPairDerivatives = new ClassicCounter<Triple<Integer,Integer,Integer>>();
value = 0.0;
for(int n = 0; n < geFeatures.size(); n++){
//F feature = geFeatures.get(n);
double[] modelDist = new double[numClasses];
Arrays.fill(modelDist,0);
//go over the unlabeled active data to compute expectations
List<Integer> activeData = geFeature2DatumList.get(n);
for(int i = 0; i < activeData.size(); i++){
Datum<L,F> datum = unlabeledDataList.get(activeData.get(i));
double[] probs = getModelProbs(datum);
for(int c = 0; c < numClasses; c++)
modelDist[c]+= probs[c];
updateDerivative(datum,probs,feature2classPairDerivatives); //computes p(y_d)*(1-p(y_d))*f_d for all active features.
}
//now compute the value (KL-divergence) and the final value of the derivative.
if(activeData.size()>0){
for(int c = 0; c < numClasses; c++)
modelDist[c]/= activeData.size();
smoothDistribution(modelDist);
for(int c = 0; c < numClasses; c++)
value += -geFeature2EmpiricalDist[n][c]*Math.log(modelDist[c]);
for(int f = 0; f < labeledDataset.featureIndex().size(); f++){
for(int c = 0; c < numClasses; c++){
int wtIndex = indexOf(f,c);
for(int cPrime = 0; cPrime < numClasses; cPrime++){
derivative[wtIndex] += feature2classPairDerivatives.getCount(new Triple<Integer,Integer,Integer>(f,c,cPrime))*geFeature2EmpiricalDist[n][cPrime]/modelDist[cPrime];
}
derivative[wtIndex] /= activeData.size();
}
} // loop over each feature for derivative computation
} //end of if condition
} //loop over each GE feature
}
private void updateDerivative(Datum<L,F> datum, double[] probs,Counter<Triple<Integer,Integer,Integer>> feature2classPairDerivatives){
for(F feature : datum.asFeatures()){
int fID = labeledDataset.featureIndex.indexOf(feature);
if(fID >= 0){
for(int c = 0; c < numClasses; c++){
for(int cPrime = 0; cPrime < numClasses; cPrime++){
if(cPrime == c)
feature2classPairDerivatives.incrementCount(new Triple<Integer,Integer,Integer>(fID,c,cPrime), - probs[c]*(1-probs[c])*valueOfFeature(feature,datum));
else
feature2classPairDerivatives.incrementCount(new Triple<Integer,Integer,Integer>(fID,c,cPrime), probs[c]*probs[cPrime]*valueOfFeature(feature,datum));
}
}
}
}
}
/*
* This method assumes the feature already exists in the datum.
*/
private double valueOfFeature(F feature, Datum<L,F> datum){
if(datum instanceof RVFDatum)
return ((RVFDatum<L,F>)datum).asFeaturesCounter().getCount(feature);
else return 1.0;
}
private void computeEmpiricalStatistics(List<F> geFeatures){
//allocate memory to the containers and initialize them
geFeature2EmpiricalDist = new double[geFeatures.size()][labeledDataset.labelIndex.size()];
geFeature2DatumList = new ArrayList<List<Integer>>(geFeatures.size());
Map<F,Integer> geFeatureMap = new HashMap<F,Integer>();
Set<Integer> activeUnlabeledExamples = new HashSet<Integer>();
for(int n = 0; n < geFeatures.size(); n++){
F geFeature = geFeatures.get(n);
geFeature2DatumList.add(new ArrayList<Integer>());
Arrays.fill(geFeature2EmpiricalDist[n], 0);
geFeatureMap.put(geFeature,n);
}
//compute the empirical label distribution for each GE feature
for(int i = 0; i < labeledDataset.size(); i++){
Datum<L,F> datum = labeledDataset.getDatum(i);
int labelID = labeledDataset.labelIndex.indexOf(datum.label());
for(F feature : datum.asFeatures()){
if(geFeatureMap.containsKey(feature)){
int geFnum = geFeatureMap.get(feature);
geFeature2EmpiricalDist[geFnum][labelID]++;
}
}
}
//now normalize and smooth the label distribution for each feature.
for(int n = 0; n < geFeatures.size(); n++){
ArrayMath.normalize(geFeature2EmpiricalDist[n]);
smoothDistribution(geFeature2EmpiricalDist[n]);
}
//now build the inverted index from each GE feature to unlabeled datums that contain it.
for(int i = 0; i < unlabeledDataList.size(); i++){
Datum<L,F> datum = unlabeledDataList.get(i);
for(F feature : datum.asFeatures()){
if(geFeatureMap.containsKey(feature)){
int geFnum = geFeatureMap.get(feature);
geFeature2DatumList.get(geFnum).add(i);
activeUnlabeledExamples.add(i);
}
}
}
System.out.println("Number of active unlabeled examples:"+activeUnlabeledExamples.size());
}
private void smoothDistribution(double [] dist){
//perform Laplace smoothing
double epsilon = 1e-6;
for(int i = 0; i < dist.length; i++)
dist[i] += epsilon;
ArrayMath.normalize(dist);
}
private double[] getModelProbs(Datum<L,F> datum){
double[] condDist = new double[labeledDataset.numClasses()];
Counter<L> probCounter = classifier.probabilityOf(datum);
for(L label : probCounter.keySet()){
int labelID = labeledDataset.labelIndex.indexOf(label);
condDist[labelID] = probCounter.getCount(label);
}
return condDist;
}
public GeneralizedExpectationObjectiveFunction(GeneralDataset<L, F> labeledDataset, List<? extends Datum<L,F>> unlabeledDataList,List<F> geFeatures) {
System.out.println("Number of labeled examples:"+labeledDataset.size+"\nNumber of unlabeled examples:"+unlabeledDataList.size());
System.out.println("Number of GE features:"+geFeatures.size());
this.numFeatures = labeledDataset.numFeatures();
this.numClasses = labeledDataset.numClasses();
this.labeledDataset = labeledDataset;
this.unlabeledDataList = unlabeledDataList;
this.geFeatures = geFeatures;
this.classifier = new LinearClassifier<L,F>(null,labeledDataset.featureIndex,labeledDataset.labelIndex);
computeEmpiricalStatistics(geFeatures);
//empirical distributions don't change with iterations, so compute them only once.
//model distributions will have to be recomputed every iteration though.
}
}
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