From 925087356b853e2099c1b60d8b757d7aa02121a9 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/PhraseCluster.java           | 540 ---------------------
 1 file changed, 540 deletions(-)
 delete mode 100644 gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java

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

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
deleted file mode 100644
index c032bb2b..00000000
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ /dev/null
@@ -1,540 +0,0 @@
-package phrase;
-
-import gnu.trove.TIntArrayList;
-import org.apache.commons.math.special.Gamma;
-
-import java.io.BufferedReader;
-import java.io.IOException;
-import java.io.PrintStream;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.List;
-import java.util.concurrent.Callable;
-import java.util.concurrent.ExecutionException;
-import java.util.concurrent.ExecutorService;
-import java.util.concurrent.Executors;
-import java.util.concurrent.Future;
-import java.util.concurrent.LinkedBlockingQueue;
-import java.util.concurrent.atomic.AtomicInteger;
-import java.util.concurrent.atomic.AtomicLong;
-import java.util.regex.Pattern;
-
-import phrase.Corpus.Edge;
-
-
-public class PhraseCluster {
-	
-	public int K;
-	private int n_phrases, n_words, n_contexts, n_positions;
-	public Corpus c;
-	public ExecutorService pool; 
-
-	double[] lambdaPTCT;
-	double[][] lambdaPT;
-	boolean cacheLambda = true;
-
-	// emit[tag][position][word] = p(word | tag, position in context)
-	double emit[][][];
-	// pi[phrase][tag] = p(tag | phrase)
-	double pi[][];
-	
-	public PhraseCluster(int numCluster, Corpus corpus)
-	{
-		K=numCluster;
-		c=corpus;
-		n_words=c.getNumWords();
-		n_phrases=c.getNumPhrases();
-		n_contexts=c.getNumContexts();
-		n_positions=c.getNumContextPositions();
-
-		emit=new double [K][n_positions][n_words];
-		pi=new double[n_phrases][K];
-		
-		for(double [][]i:emit)
-			for(double []j:i)
-				arr.F.randomise(j, true);
-
-		for(double []j:pi)
-			arr.F.randomise(j, true);
-	}
-	
-	void useThreadPool(ExecutorService pool)
-	{
-		this.pool = pool;
-	}
-
-	public double EM(int phraseSizeLimit)
-	{
-		double [][][]exp_emit=new double [K][n_positions][n_words];
-		double []exp_pi=new double[K];
-		
-		for(double [][]i:exp_emit)
-			for(double []j:i)
-				Arrays.fill(j, 1e-10);
-		
-		double loglikelihood=0;
-		
-		//E
-		for(int phrase=0; phrase < n_phrases; phrase++)
-		{
-			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit)
-				continue;
-
-			Arrays.fill(exp_pi, 1e-10);
-			
-			List<Edge> contexts = c.getEdgesForPhrase(phrase);
-
-			for (int ctx=0; ctx<contexts.size(); ctx++)
-			{
-				Edge edge = contexts.get(ctx);
-				
-				double p[]=posterior(edge);
-				double z = arr.F.l1norm(p);
-				assert z > 0;
-				loglikelihood += edge.getCount() * Math.log(z);
-				arr.F.l1normalize(p);
-				
-				double count = edge.getCount();
-				//increment expected count
-				TIntArrayList context = edge.getContext();
-				for(int tag=0;tag<K;tag++)
-				{
-					for(int pos=0;pos<n_positions;pos++){
-						exp_emit[tag][pos][context.get(pos)]+=p[tag]*count;
-					}
-					exp_pi[tag]+=p[tag]*count;
-				}
-			}
-			arr.F.l1normalize(exp_pi);
-			System.arraycopy(exp_pi, 0, pi[phrase], 0, K);
-		}
-
-		//M
-		for(double [][]i:exp_emit)
-			for(double []j:i)
-				arr.F.l1normalize(j);
-			
-		emit=exp_emit;
-
-		return loglikelihood;
-	}
-	
-	public double PREM(double scalePT, double scaleCT, int phraseSizeLimit)
-	{
-		if (scaleCT == 0)
-		{
-			if (pool != null)
-				return PREM_phrase_constraints_parallel(scalePT, phraseSizeLimit);
-			else
-				return PREM_phrase_constraints(scalePT, phraseSizeLimit);
-		}
-		else // FIXME: ignores phraseSizeLimit
-			return this.PREM_phrase_context_constraints(scalePT, scaleCT);
-	}
-
-	
-	public double PREM_phrase_constraints(double scalePT, int phraseSizeLimit)
-	{
-		double [][][]exp_emit=new double[K][n_positions][n_words];
-		double []exp_pi=new double[K];
-		
-		for(double [][]i:exp_emit)
-			for(double []j:i)
-				Arrays.fill(j, 1e-10);
-		
-		if (lambdaPT == null && cacheLambda)
-			lambdaPT = new double[n_phrases][];
-		
-		double loglikelihood=0, kl=0, l1lmax=0, primal=0;
-		int failures=0, iterations=0;
-		long start = System.currentTimeMillis();
-		//E
-		for(int phrase=0; phrase<n_phrases; phrase++)
-		{
-			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit)
-			{
-				//System.arraycopy(pi[phrase], 0, exp_pi[phrase], 0, K);
-				continue;
-			}
-			
-			Arrays.fill(exp_pi, 1e-10);
-			
-			// FIXME: add rare edge check to phrase objective & posterior processing
-			PhraseObjective po = new PhraseObjective(this, phrase, scalePT, (cacheLambda) ? lambdaPT[phrase] : null);
-			boolean ok = po.optimizeWithProjectedGradientDescent();
-			if (!ok) ++failures;
-			if (cacheLambda) lambdaPT[phrase] = po.getParameters();
-			iterations += po.getNumberUpdateCalls();
-			double [][] q=po.posterior();
-			loglikelihood += po.loglikelihood();
-			kl += po.KL_divergence();
-			l1lmax += po.l1lmax();
-			primal += po.primal(scalePT);
-			List<Edge> edges = c.getEdgesForPhrase(phrase);
-
-			for(int edge=0;edge<q.length;edge++){
-				Edge e = edges.get(edge);
-				TIntArrayList context = e.getContext();
-				double contextCnt = e.getCount();
-				//increment expected count
-				for(int tag=0;tag<K;tag++){
-					for(int pos=0;pos<n_positions;pos++){
-						exp_emit[tag][pos][context.get(pos)]+=q[edge][tag]*contextCnt;
-					}
-					
-					exp_pi[tag]+=q[edge][tag]*contextCnt;
-					
-				}
-			}
-			arr.F.l1normalize(exp_pi);
-			System.arraycopy(exp_pi, 0, pi[phrase], 0, K);
-		}
-		
-		long end = System.currentTimeMillis();
-		if (failures > 0)
-			System.out.println("WARNING: failed to converge in " + failures + "/" + n_phrases + " cases");
-		System.out.println("\tmean iters:     " + iterations/(double)n_phrases + " elapsed time " + (end - start) / 1000.0);
-		System.out.println("\tllh:            " + loglikelihood);
-		System.out.println("\tKL:             " + kl);
-		System.out.println("\tphrase l1lmax:  " + l1lmax);
-		
-		//M
-		for(double [][]i:exp_emit)
-			for(double []j:i)
-				arr.F.l1normalize(j);
-		emit=exp_emit;
-		
-		return primal;
-	}
-
-	public double PREM_phrase_constraints_parallel(final double scalePT, int phraseSizeLimit)
-	{
-		assert(pool != null);
-		
-		final LinkedBlockingQueue<PhraseObjective> expectations 
-			= new LinkedBlockingQueue<PhraseObjective>();
-		
-		double [][][]exp_emit=new double [K][n_positions][n_words];
-		double [][]exp_pi=new double[n_phrases][K];
-		
-		for(double [][]i:exp_emit)
-			for(double []j:i)
-				Arrays.fill(j, 1e-10);
-		for(double []j:exp_pi)
-			Arrays.fill(j, 1e-10);
-		
-		double loglikelihood=0, kl=0, l1lmax=0, primal=0;
-		final AtomicInteger failures = new AtomicInteger(0);
-		final AtomicLong elapsed = new AtomicLong(0l);
-		int iterations=0;
-		long start = System.currentTimeMillis();
-		List<Future<PhraseObjective>> results = new ArrayList<Future<PhraseObjective>>();
-		
-		if (lambdaPT == null && cacheLambda)
-			lambdaPT = new double[n_phrases][];
-
-		//E
-		for(int phrase=0;phrase<n_phrases;phrase++) {
-			if (phraseSizeLimit >= 1 && c.getPhrase(phrase).size() > phraseSizeLimit) {
-				System.arraycopy(pi[phrase], 0, exp_pi[phrase], 0, K);
-				continue;
-			}
-
-			final int p=phrase;
-			results.add(pool.submit(new Callable<PhraseObjective>() {
-				public PhraseObjective call() {
-					//System.out.println("" + Thread.currentThread().getId() + " optimising lambda for " + p);
-					long start = System.currentTimeMillis();
-					PhraseObjective po = new PhraseObjective(PhraseCluster.this, p, scalePT, (cacheLambda) ? lambdaPT[p] : null);
-					boolean ok = po.optimizeWithProjectedGradientDescent();
-					if (!ok) failures.incrementAndGet();
-					long end = System.currentTimeMillis();
-					elapsed.addAndGet(end - start);
-					//System.out.println("" + Thread.currentThread().getId() + " done optimising lambda for " + p);
-					return po;
-				}
-			}));
-		}
-		
-		// aggregate the expectations as they become available
-		for (Future<PhraseObjective> fpo : results)
-		{
-			try {
-				//System.out.println("" + Thread.currentThread().getId() + " reading queue #" + count);
-
-				// wait (blocking) until something is ready
-				PhraseObjective po = fpo.get();
-				// process
-				int phrase = po.phrase;
-				if (cacheLambda) lambdaPT[phrase] = po.getParameters();
-				//System.out.println("" + Thread.currentThread().getId() + " taken phrase " + phrase);
-				double [][] q=po.posterior();
-				loglikelihood += po.loglikelihood();
-				kl += po.KL_divergence();
-				l1lmax += po.l1lmax();
-				primal += po.primal(scalePT);
-				iterations += po.getNumberUpdateCalls();
-
-				List<Edge> edges = c.getEdgesForPhrase(phrase);
-				for(int edge=0;edge<q.length;edge++){
-					Edge e = edges.get(edge);
-					TIntArrayList context = e.getContext();
-					double contextCnt = e.getCount();
-					//increment expected count
-					for(int tag=0;tag<K;tag++){
-						for(int pos=0;pos<n_positions;pos++){
-							exp_emit[tag][pos][context.get(pos)]+=q[edge][tag]*contextCnt;
-						}
-						exp_pi[phrase][tag]+=q[edge][tag]*contextCnt;
-					}
-				}
-			} catch (InterruptedException e) {
-				System.err.println("M-step thread interrupted. Probably fatal!");
-				throw new RuntimeException(e);
-			} catch (ExecutionException e) {
-				System.err.println("M-step thread execution died. Probably fatal!");
-				throw new RuntimeException(e);
-			}
-		}
-		
-		long end = System.currentTimeMillis();
-		
-		if (failures.get() > 0)
-			System.out.println("WARNING: failed to converge in " + failures.get() + "/" + n_phrases + " cases");
-		System.out.println("\tmean iters:     " + iterations/(double)n_phrases + " walltime " + (end-start)/1000.0 + " threads " + elapsed.get() / 1000.0);
-		System.out.println("\tllh:            " + loglikelihood);
-		System.out.println("\tKL:             " + kl);
-		System.out.println("\tphrase l1lmax:  " + l1lmax);
-		
-		//M
-		for(double [][]i:exp_emit)
-			for(double []j:i)
-				arr.F.l1normalize(j);
-		emit=exp_emit;
-		
-		for(double []j:exp_pi)
-			arr.F.l1normalize(j);
-		pi=exp_pi;
-		
-		return primal;
-	}
-	
-	public double PREM_phrase_context_constraints(double scalePT, double scaleCT)
-	{	
-		double[][][] exp_emit = new double [K][n_positions][n_words];
-		double[][] exp_pi = new double[n_phrases][K];
-
-		//E step
-		PhraseContextObjective pco = new PhraseContextObjective(this, lambdaPTCT, pool, scalePT, scaleCT);
-		boolean ok = pco.optimizeWithProjectedGradientDescent();
-		if (cacheLambda) lambdaPTCT = pco.getParameters();
-
-		//now extract expectations
-		List<Corpus.Edge> edges = c.getEdges();
-		for(int e = 0; e < edges.size(); ++e)
-		{
-			double [] q = pco.posterior(e);
-			Corpus.Edge edge = edges.get(e);
-
-			TIntArrayList context = edge.getContext();
-			double contextCnt = edge.getCount();
-			//increment expected count
-			for(int tag=0;tag<K;tag++)
-			{
-				for(int pos=0;pos<n_positions;pos++)
-					exp_emit[tag][pos][context.get(pos)]+=q[tag]*contextCnt;
-				exp_pi[edge.getPhraseId()][tag]+=q[tag]*contextCnt;
-			}
-		}
-		
-		System.out.println("\tllh:            " + pco.loglikelihood());
-		System.out.println("\tKL:             " + pco.KL_divergence());
-		System.out.println("\tphrase l1lmax:  " + pco.phrase_l1lmax());
-		System.out.println("\tcontext l1lmax: " + pco.context_l1lmax());
-		
-		//M step
-		for(double [][]i:exp_emit)
-			for(double []j:i)
-				arr.F.l1normalize(j);
-		emit=exp_emit;
-		
-		for(double []j:exp_pi)
-			arr.F.l1normalize(j);
-		pi=exp_pi;
-		
-		return pco.primal();
-	}	
-		
-	/**
-	 * @param phrase index of phrase
-	 * @param ctx array of context
-	 * @return unnormalized posterior
-	 */
-	public double[] posterior(Corpus.Edge edge) 
-	{
-		double[] prob;
-		
-		if(edge.getTag()>=0){
-			prob=new double[K];
-			prob[edge.getTag()]=1;
-			return prob;
-		}
-		
-		if (edge.getPhraseId() < n_phrases)
-			prob = Arrays.copyOf(pi[edge.getPhraseId()], K);
-		else
-		{
-			prob = new double[K];
-			Arrays.fill(prob, 1.0);
-		}
-		
-		TIntArrayList ctx = edge.getContext();
-		for(int tag=0;tag<K;tag++)
-		{
-			for(int c=0;c<n_positions;c++)
-			{
-				int word = ctx.get(c);
-				if (!this.c.isSentinel(word) && word < n_words)
-					prob[tag]*=emit[tag][c][word];
-			}
-		}
-		
-		return prob;
-	}
-	
-	public void displayPosterior(PrintStream ps, List<Edge> testing)
-	{	
-		for (Edge edge : testing)
-		{
-			double probs[] = posterior(edge);
-			arr.F.l1normalize(probs);
-
-			// emit phrase
-			ps.print(edge.getPhraseString());
-			ps.print("\t");
-			ps.print(edge.getContextString(true));
-			int t=arr.F.argmax(probs);
-			ps.println(" ||| C=" + t + " T=" + edge.getCount() + " P=" + probs[t]);
-			//ps.println("# probs " + Arrays.toString(probs));
-		}
-	}
-	
-	public void displayModelParam(PrintStream ps)
-	{
-		final double EPS = 1e-6;
-		ps.println("phrases " + n_phrases + " tags " + K + " positions " + n_positions);
-		
-		for (int i = 0; i < n_phrases; ++i)
-			for(int j=0;j<pi[i].length;j++)
-				if (pi[i][j] > EPS)
-					ps.println(i + " " + j + " " + pi[i][j]);
-
-		ps.println();
-		for (int i = 0; i < K; ++i)
-		{
-			for(int position=0;position<n_positions;position++)
-			{
-				for(int word=0;word<emit[i][position].length;word++)
-				{
-					if (emit[i][position][word] > EPS)
-						ps.println(i + " " + position + " " + word + " " + emit[i][position][word]);
-				}
-			}
-		}
-	}
-	
-	double phrase_l1lmax()
-	{
-		double sum=0;
-		for(int phrase=0; phrase<n_phrases; phrase++)
-		{
-			double [] maxes = new double[K];
-			for (Edge edge : c.getEdgesForPhrase(phrase))
-			{
-				double p[] = posterior(edge);
-				arr.F.l1normalize(p);
-				for(int tag=0;tag<K;tag++)
-					maxes[tag] = Math.max(maxes[tag], p[tag]);
-			}
-			for(int tag=0;tag<K;tag++)
-				sum += maxes[tag];
-		}
-		return sum;
-	}
-
-	double context_l1lmax()
-	{
-		double sum=0;
-		for(int context=0; context<n_contexts; context++)
-		{
-			double [] maxes = new double[K];
-			for (Edge edge : c.getEdgesForContext(context))
-			{
-				double p[] = posterior(edge);
-				arr.F.l1normalize(p);
-				for(int tag=0;tag<K;tag++)
-					maxes[tag] = Math.max(maxes[tag], p[tag]);
-			}
-			for(int tag=0;tag<K;tag++)
-				sum += maxes[tag];
-		}
-		return sum;
-	}
-
-	public void loadParameters(BufferedReader input) throws IOException
-	{	
-		final double EPS = 1e-50;
-		
-		// overwrite pi, emit with ~zeros
-		for(double [][]i:emit)
-			for(double []j:i)
-				Arrays.fill(j, EPS);
-
-		for(double []j:pi)
-			Arrays.fill(j, EPS);
-
-		String line = input.readLine();
-		assert line != null;
-
-		Pattern space = Pattern.compile(" +");
-		String[] parts = space.split(line);
-		assert parts.length == 6;
-
-		assert parts[0].equals("phrases");
-		int phrases = Integer.parseInt(parts[1]);
-		int tags = Integer.parseInt(parts[3]);
-		int positions = Integer.parseInt(parts[5]);
-		
-		assert phrases == n_phrases;
-		assert tags == K;
-		assert positions == n_positions;
-
-		// read in pi
-		while ((line = input.readLine()) != null)
-		{
-			line = line.trim();
-			if (line.isEmpty()) break;
-			
-			String[] tokens = space.split(line);
-			assert tokens.length == 3;
-			int p = Integer.parseInt(tokens[0]);
-			int t = Integer.parseInt(tokens[1]);
-			double v = Double.parseDouble(tokens[2]);
-
-			pi[p][t] = v;
-		}
-		
-		// read in emissions
-		while ((line = input.readLine()) != null)
-		{
-			String[] tokens = space.split(line);
-			assert tokens.length == 4;
-			int t = Integer.parseInt(tokens[0]);
-			int p = Integer.parseInt(tokens[1]);
-			int w = Integer.parseInt(tokens[2]);
-			double v = Double.parseDouble(tokens[3]);
-
-			emit[t][p][w] = v;
-		}
-	}
-}
-- 
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