cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 540 deletions

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;

-		}

-	}

-}