From e26434979adc33bd949566ba7bf02dff64e80a3e Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cab.ark.cs.cmu.edu>
Date: Tue, 2 Oct 2012 00:19:43 -0400
Subject: cdec cleanup, remove bayesian stuff, parsing stuff

---
 .../prjava/src/phrase/PhraseContextObjective.java  | 436 ---------------------
 1 file changed, 436 deletions(-)
 delete mode 100644 gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java

(limited to 'gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java')

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
deleted file mode 100644
index 646ff392..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
+++ /dev/null
@@ -1,436 +0,0 @@
-package phrase;
-
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.concurrent.ExecutionException;
-import java.util.concurrent.ExecutorService;
-import java.util.concurrent.Future;
-
-import optimization.gradientBasedMethods.ProjectedGradientDescent;
-import optimization.gradientBasedMethods.ProjectedObjective;
-import optimization.gradientBasedMethods.stats.OptimizerStats;
-import optimization.linesearch.ArmijoLineSearchMinimizationAlongProjectionArc;
-import optimization.linesearch.InterpolationPickFirstStep;
-import optimization.linesearch.LineSearchMethod;
-import optimization.projections.SimplexProjection;
-import optimization.stopCriteria.CompositeStopingCriteria;
-import optimization.stopCriteria.ProjectedGradientL2Norm;
-import optimization.stopCriteria.StopingCriteria;
-import optimization.stopCriteria.ValueDifference;
-import optimization.util.MathUtils;
-import phrase.Corpus.Edge;
-
-public class PhraseContextObjective extends ProjectedObjective
-{
-	private static final double GRAD_DIFF = 0.00002;
-	private static double INIT_STEP_SIZE = 300;
-	private static double VAL_DIFF = 1e-8;
-	private static int ITERATIONS = 20;
-	boolean debug = false;
-	
-	private PhraseCluster c;
-	
-	// un-regularized unnormalized posterior, p[edge][tag]
-	// P(tag|edge) \propto P(tag|phrase)P(context|tag)
-	private double p[][];
-
-	// regularized unnormalized posterior 
-	// q[edge][tag] propto p[edge][tag]*exp(-lambda)
-	private double q[][];
-	private List<Corpus.Edge> data;
-	
-	// log likelihood under q
-	private double loglikelihood;
-	private SimplexProjection projectionPhrase;
-	private SimplexProjection projectionContext;
-	
-	double[] newPoint;
-	private int n_param;
-	
-	// likelihood under p
-	public double llh;
-	
-	private static Map<Corpus.Edge, Integer> edgeIndex;
-	
-	private long projectionTime;
-	private long objectiveTime;
-	private long actualProjectionTime;
-	private ExecutorService pool;
-	
-	double scalePT;
-	double scaleCT;
-	
-	public PhraseContextObjective(PhraseCluster cluster, double[] startingParameters, ExecutorService pool,
-			double scalePT, double scaleCT)
-	{
-		c=cluster;
-		data=c.c.getEdges();
-		n_param=data.size()*c.K*2;
-		this.pool=pool;
-		this.scalePT = scalePT;
-		this.scaleCT = scaleCT;
-		
-		parameters = startingParameters;
-		if (parameters == null)
-			parameters = new double[n_param];
-		
-		System.out.println("Num parameters " + n_param);
-		newPoint = new double[n_param];
-		gradient = new double[n_param];
-		initP();
-		projectionPhrase = new SimplexProjection(scalePT);
-		projectionContext = new SimplexProjection(scaleCT);
-		q=new double [data.size()][c.K];
-		
-		if (edgeIndex == null) {
-			edgeIndex = new HashMap<Edge, Integer>();
-			for (int e=0; e<data.size(); e++)
-			{
-				edgeIndex.put(data.get(e), e);
-				//if (debug) System.out.println("Edge " + data.get(e) + " index " + e);
-			}
-		}
-		
-		setParameters(parameters);
-	}
-
-	private void initP(){
-		p=new double[data.size()][];
-		for(int edge=0;edge<data.size();edge++)
-		{
-			p[edge]=c.posterior(data.get(edge));
-			llh += data.get(edge).getCount() * Math.log(arr.F.l1norm(p[edge]));
-			arr.F.l1normalize(p[edge]);
-		}
-	}
-	
-	@Override
-	public void setParameters(double[] params) {
-		//System.out.println("setParameters " + Arrays.toString(parameters));
-		// TODO: test if params have changed and skip update otherwise
-		super.setParameters(params);
-		updateFunction();
-	}
-	
-	private void updateFunction()
-	{
-		updateCalls++;
-		loglikelihood=0;
-
-		System.out.print(".");
-		System.out.flush();
-
-		long begin = System.currentTimeMillis();
-		for (int e=0; e<data.size(); e++) 
-		{
-			Edge edge = data.get(e);
-			for(int tag=0; tag<c.K; tag++)
-			{
-				int ip = index(e, tag, true);
-				int ic = index(e, tag, false);
-				q[e][tag] = p[e][tag]*
-					Math.exp((-parameters[ip]-parameters[ic]) / edge.getCount());
-				//if (debug)
-					//System.out.println("\tposterior " + edge + " with tag " + tag + " p " + p[e][tag] + " params " + parameters[ip] + " and " + parameters[ic] + " q " + q[e][tag]);
-			}
-		}
-	
-		for(int edge=0;edge<data.size();edge++) {
-			loglikelihood+=data.get(edge).getCount() * Math.log(arr.F.l1norm(q[edge]));
-			arr.F.l1normalize(q[edge]);
-		}
-		
-		for (int e=0; e<data.size(); e++) 
-		{
-			for(int tag=0; tag<c.K; tag++)
-			{
-				int ip = index(e, tag, true);
-				int ic = index(e, tag, false);
-				gradient[ip]=-q[e][tag];
-				gradient[ic]=-q[e][tag];
-			}
-		}
-		//if (debug) {
-			//System.out.println("objective " + loglikelihood + " ||gradient||_2: " + arr.F.l2norm(gradient));		
-			//System.out.println("gradient " + Arrays.toString(gradient));
-		//}
-		objectiveTime += System.currentTimeMillis() - begin;
-	}
-	
-	@Override
-	public double[] projectPoint(double[] point) 
-	{
-		long begin = System.currentTimeMillis();
-		List<Future<?>> tasks = new ArrayList<Future<?>>();
-		
-		System.out.print(",");
-		System.out.flush();
-
-		Arrays.fill(newPoint, 0, newPoint.length, 0);
-		
-		// first project using the phrase-tag constraints,
-		// for all p,t: sum_c lambda_ptc < scaleP 
-		if (pool == null)
-		{
-			for (int p = 0; p < c.c.getNumPhrases(); ++p)
-			{
-				List<Edge> edges = c.c.getEdgesForPhrase(p);
-				double[] toProject = new double[edges.size()];
-				for(int tag=0;tag<c.K;tag++)
-				{
-					// FIXME: slow hash lookup for e (twice)
-					for(int e=0; e<edges.size(); e++) 						
-						toProject[e] = point[index(edges.get(e), tag, true)];
-					long lbegin = System.currentTimeMillis();
-					projectionPhrase.project(toProject);
-					actualProjectionTime += System.currentTimeMillis() - lbegin;
-					for(int e=0; e<edges.size(); e++)
-						newPoint[index(edges.get(e), tag, true)] = toProject[e];
-				}
-			}
-		}
-		else // do above in parallel using thread pool
-		{	
-			for (int p = 0; p < c.c.getNumPhrases(); ++p)
-			{
-				final int phrase = p;
-				final double[] inPoint = point;
-				Runnable task = new Runnable()
-				{
-					public void run()
-					{
-						List<Edge> edges = c.c.getEdgesForPhrase(phrase);
-						double toProject[] = new double[edges.size()];
-						for(int tag=0;tag<c.K;tag++)
-						{
-							// FIXME: slow hash lookup for e
-							for(int e=0; e<edges.size(); e++)
-								toProject[e] = inPoint[index(edges.get(e), tag, true)];
-							projectionPhrase.project(toProject);
-							for(int e=0; e<edges.size(); e++)
-								newPoint[index(edges.get(e), tag, true)] = toProject[e];
-						}
-					}		
-				};
-				tasks.add(pool.submit(task));
-			}
-		}
-		//System.out.println("after PT " + Arrays.toString(newPoint));
-	
-		// now project using the context-tag constraints,
-		// for all c,t: sum_p omega_pct < scaleC
-		if (pool == null)
-		{
-			for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)
-			{
-				List<Edge> edges = c.c.getEdgesForContext(ctx);
-				double toProject[] = new double[edges.size()];
-				for(int tag=0;tag<c.K;tag++)
-				{
-					// FIXME: slow hash lookup for e
-					for(int e=0; e<edges.size(); e++)
-						toProject[e] = point[index(edges.get(e), tag, false)];
-					long lbegin = System.currentTimeMillis();
-					projectionContext.project(toProject);
-					actualProjectionTime += System.currentTimeMillis() - lbegin;
-					for(int e=0; e<edges.size(); e++)
-						newPoint[index(edges.get(e), tag, false)] = toProject[e];
-				}
-			}
-		}
-		else
-		{
-			// do above in parallel using thread pool
-			for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)
-			{
-				final int context = ctx;
-				final double[] inPoint = point;
-				Runnable task = new Runnable()
-				{
-					public void run()
-					{
-						List<Edge> edges = c.c.getEdgesForContext(context);
-						double toProject[] = new double[edges.size()];
-						for(int tag=0;tag<c.K;tag++)
-						{
-							// FIXME: slow hash lookup for e
-							for(int e=0; e<edges.size(); e++)
-								toProject[e] = inPoint[index(edges.get(e), tag, false)];
-							projectionContext.project(toProject);
-							for(int e=0; e<edges.size(); e++)
-								newPoint[index(edges.get(e), tag, false)] = toProject[e];
-						}
-					}
-				};
-				tasks.add(pool.submit(task));
-			}
-		}
-		
-		if (pool != null)
-		{
-			// wait for all the jobs to complete
-			Exception failure = null;
-			for (Future<?> task: tasks)
-			{
-				try {
-					task.get();
-				} catch (InterruptedException e) {
-					System.err.println("ERROR: Projection thread interrupted");
-					e.printStackTrace();
-					failure = e;
-				} catch (ExecutionException e) {
-					System.err.println("ERROR: Projection thread died");
-					e.printStackTrace();
-					failure = e;
-				}
-			}
-			// rethrow the exception
-			if (failure != null)
-			{
-				pool.shutdownNow();
-				throw new RuntimeException(failure);
-			}
-		}
-		
-		double[] tmp = newPoint;
-		newPoint = point;
-		projectionTime += System.currentTimeMillis() - begin;
-		
-		//if (debug)
-			//System.out.println("\t\treturning " + Arrays.toString(tmp));
-		return tmp;
-	}
-	
-	private int index(Edge edge, int tag, boolean phrase)
-	{
-		// NB if indexing changes must also change code in updateFunction and constructor
-		if (phrase)
-			return tag * edgeIndex.size() + edgeIndex.get(edge);
-		else
-			return (c.K + tag) * edgeIndex.size() + edgeIndex.get(edge);
-	}
-
-	private int index(int e, int tag, boolean phrase)
-	{
-		// NB if indexing changes must also change code in updateFunction and constructor
-		if (phrase)
-			return tag * edgeIndex.size() + e;
-		else
-			return (c.K + tag) * edgeIndex.size() + e;
-	}
-	
-	@Override
-	public double[] getGradient() {
-		gradientCalls++;
-		return gradient;
-	}
-
-	@Override
-	public double getValue() {
-		functionCalls++;
-		return loglikelihood;
-	}
-
-	@Override
-	public String toString() {
-		return "No need for pointless toString";
-	}
-
-	public double []posterior(int edgeIndex){
-		return q[edgeIndex];
-	}
-	
-	public boolean optimizeWithProjectedGradientDescent()
-	{
-		projectionTime = 0;
-		actualProjectionTime = 0;
-		objectiveTime = 0;
-		long start = System.currentTimeMillis();
-
-		LineSearchMethod ls =
-			new ArmijoLineSearchMinimizationAlongProjectionArc
-				(new InterpolationPickFirstStep(INIT_STEP_SIZE));
-		//LineSearchMethod  ls = new WolfRuleLineSearch(
-		//		(new InterpolationPickFirstStep(INIT_STEP_SIZE)), c1, c2);
-		OptimizerStats stats = new OptimizerStats();
-		
-		
-		ProjectedGradientDescent optimizer = new ProjectedGradientDescent(ls);
-		StopingCriteria stopGrad = new ProjectedGradientL2Norm(GRAD_DIFF);
-		StopingCriteria stopValue = new ValueDifference(VAL_DIFF*(-llh));
-		CompositeStopingCriteria compositeStop = new CompositeStopingCriteria();
-		compositeStop.add(stopGrad);
-		compositeStop.add(stopValue);
-		optimizer.setMaxIterations(ITERATIONS);
-		updateFunction();
-		boolean success = optimizer.optimize(this,stats,compositeStop);
-
-		System.out.println();
-		System.out.println(stats.prettyPrint(1));
-		
-		if (success)
-			System.out.print("\toptimization took " + optimizer.getCurrentIteration() + " iterations");
-	 	else
-			System.out.print("\toptimization failed to converge");
-		long total = System.currentTimeMillis() - start;
-		System.out.println(" and " + total + " ms: projection " + projectionTime + 
-				" actual " + actualProjectionTime + " objective " + objectiveTime);
-
-		return success;
-	}
-	
-	double loglikelihood()
-	{
-		return llh;
-	}
-	
-	double KL_divergence()
-	{
-		return -loglikelihood + MathUtils.dotProduct(parameters, gradient);
-	}
-	
-	double phrase_l1lmax()
-	{
-		// \sum_{tag,phrase} max_{context} P(tag|context,phrase)
-		double sum=0;
-		for (int p = 0; p < c.c.getNumPhrases(); ++p)
-		{
-			List<Edge> edges = c.c.getEdgesForPhrase(p);
-			for(int tag=0;tag<c.K;tag++)
-			{
-				double max=0;
-				for (Edge edge: edges)
-					max = Math.max(max, q[edgeIndex.get(edge)][tag]);
-				sum+=max;
-			}	
-		}
-		return sum;
-	}
-	
-	double context_l1lmax()
-	{
-		// \sum_{tag,context} max_{phrase} P(tag|context,phrase)
-		double sum=0;
-		for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)
-		{
-			List<Edge> edges = c.c.getEdgesForContext(ctx);
-			for(int tag=0; tag<c.K; tag++)
-			{
-				double max=0;
-				for (Edge edge: edges)
-					max = Math.max(max, q[edgeIndex.get(edge)][tag]);
-				sum+=max;
-			}	
-		}
-		return sum;
-	}
-	
-	// L - KL(q||p) - scalePT * l1lmax_phrase - scaleCT * l1lmax_context
-	public double primal()
-	{
-		return loglikelihood() - KL_divergence() - scalePT * phrase_l1lmax() - scaleCT * context_l1lmax();
-	}
-}
\ No newline at end of file
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