/* Copyright (C) 2010 Univ. of Massachusetts Amherst, Computer Science Dept.
This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
http://www.cs.umass.edu/~mccallum/mallet
This software is provided under the terms of the Common Public License,
version 1.0, as published by http://www.opensource.org. For further
information, see the file `LICENSE' included with this distribution. */
package cc.mallet.fst.semi_supervised.constraints;
import gnu.trove.TIntArrayList;
import gnu.trove.TIntObjectHashMap;
import java.util.ArrayList;
import java.util.BitSet;
import cc.mallet.fst.SumLattice;
import cc.mallet.fst.semi_supervised.StateLabelMap;
import cc.mallet.types.FeatureVector;
import cc.mallet.types.FeatureVectorSequence;
import cc.mallet.types.Instance;
import cc.mallet.types.InstanceList;
/**
* A set of constraints on distributions over single
* labels conditioned on the presence of input features.
*
* Subclasses are to be used with GE.
*
* Multiple constraints are grouped together here
* to make things more efficient.
*
* @author Gregory Druck
*/
public abstract class OneLabelGEConstraints implements GEConstraint {
// maps between input feature indices and constraints
protected TIntObjectHashMap<OneLabelGEConstraint> constraints;
protected StateLabelMap map;
// cache of set of constrained features that fire at last FeatureVector
// provided in preprocess call
protected TIntArrayList cache;
public OneLabelGEConstraints() {
this.constraints = new TIntObjectHashMap<OneLabelGEConstraint>();
this.cache = new TIntArrayList();
}
protected OneLabelGEConstraints(TIntObjectHashMap<OneLabelGEConstraint> constraints, StateLabelMap map) {
this.constraints = constraints;
this.map = map;
this.cache = new TIntArrayList();
}
public abstract void addConstraint(int fi, double[] target, double weight);
public boolean isOneStateConstraint() {
return true;
}
public void setStateLabelMap(StateLabelMap map) {
this.map = map;
}
public void preProcess(FeatureVector fv) {
cache.resetQuick();
int fi;
// cache constrained input features
for (int loc = 0; loc < fv.numLocations(); loc++) {
fi = fv.indexAtLocation(loc);
if (constraints.containsKey(fi)) {
cache.add(fi);
}
}
if (constraints.containsKey(fv.getAlphabet().size())) {
cache.add(fv.getAlphabet().size());
}
}
// find examples that contain constrained input features
public BitSet preProcess(InstanceList data) {
// count
int ii = 0;
int fi;
FeatureVector fv;
BitSet bitSet = new BitSet(data.size());
for (Instance instance : data) {
FeatureVectorSequence fvs = (FeatureVectorSequence)instance.getData();
for (int ip = 0; ip < fvs.size(); ip++) {
fv = fvs.get(ip);
for (int loc = 0; loc < fv.numLocations(); loc++) {
fi = fv.indexAtLocation(loc);
if (constraints.containsKey(fi)) {
constraints.get(fi).count += 1;
bitSet.set(ii);
}
}
if (constraints.containsKey(fv.getAlphabet().size())) {
bitSet.set(ii);
constraints.get(fv.getAlphabet().size()).count += 1;
}
}
ii++;
}
return bitSet;
}
public double getCompositeConstraintFeatureValue(FeatureVector fv, int ip, int si1, int si2) {
double value = 0;
int li2 = map.getLabelIndex(si2);
for (int i = 0; i < cache.size(); i++) {
value += constraints.get(cache.getQuick(i)).getValue(li2);
}
return value;
}
public abstract double getValue();
public void zeroExpectations() {
for (int fi : constraints.keys()) {
constraints.get(fi).expectation = new double[map.getNumLabels()];
}
}
public void computeExpectations(ArrayList<SumLattice> lattices) {
double[][] gammas;
TIntArrayList cache = new TIntArrayList();
for (int i = 0; i < lattices.size(); i++) {
if (lattices.get(i) == null) { continue; }
SumLattice lattice = lattices.get(i);
FeatureVectorSequence fvs = (FeatureVectorSequence)lattice.getInput();
gammas = lattice.getGammas();
for (int ip = 0; ip < fvs.size(); ++ip) {
cache.resetQuick();
FeatureVector fv = fvs.getFeatureVector(ip);
int fi;
for (int loc = 0; loc < fv.numLocations(); loc++) {
fi = fv.indexAtLocation(loc);
// binary constraint features
if (constraints.containsKey(fi)) {
cache.add(fi);
}
}
if (constraints.containsKey(fv.getAlphabet().size())) {
cache.add(fv.getAlphabet().size());
}
for (int s = 0; s < map.getNumStates(); ++s) {
int li = map.getLabelIndex(s);
if (li != StateLabelMap.START_LABEL) {
double gammaProb = Math.exp(gammas[ip+1][s]);
for (int j = 0; j < cache.size(); j++) {
constraints.get(cache.getQuick(j)).expectation[li] += gammaProb;
}
}
}
}
}
}
protected abstract class OneLabelGEConstraint {
protected double[] target;
protected double[] expectation;
protected double count;
protected double weight;
public OneLabelGEConstraint(double[] target, double weight) {
this.target = target;
this.weight = weight;
this.expectation = null;
this.count = 0;
}
public double getCount() {
return count;
}
public double[] getTarget() {
return target;
}
public double[] getExpectation() {
return expectation;
}
public double getWeight() {
return weight;
}
public abstract double getValue(int li);
}
}