Updated launcher to include agreement model.

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@226 ec762483-ff6d-05da-a07a-a48fb63a330f

5 files changed, 104 insertions, 75 deletions

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Agree.java b/gi/posterior-regularisation/prjava/src/phrase/Agree.java
index d5b949b0..d61e6eef 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/Agree.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/Agree.java
@@ -12,8 +12,8 @@ import java.util.List;
 import phrase.Corpus.Edge;

 

 public class Agree {

-	private PhraseCluster model1;

-	private C2F model2;

+	PhraseCluster model1;

+	C2F model2;

 	Corpus c;

 	private int K,n_phrases, n_words, n_contexts, n_positions1,n_positions2;

 	

@@ -32,7 +32,7 @@ public class Agree {
 	 */

 	public Agree(int numCluster, Corpus corpus){

 		

-		model1=new PhraseCluster(numCluster, corpus, 0, 0, 0);

+		model1=new PhraseCluster(numCluster, corpus);

 		model2=new C2F(numCluster,corpus);

 		c=corpus;

 		n_words=c.getNumWords();

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
index 7bc63c33..abd868c4 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
@@ -18,21 +18,16 @@ import util.MathUtil;
 public class PhraseCluster {

 	

 	public int K;

-	public double scalePT, scaleCT;

 	private int n_phrases, n_words, n_contexts, n_positions;

 	public Corpus c;

 	public ExecutorService pool; 

 	

 	// emit[tag][position][word] = p(word | tag, position in context)

-	private double emit[][][];

+	double emit[][][];

 	// pi[phrase][tag] = p(tag | phrase)

-	private double pi[][];

+	double pi[][];

 	

-	double alphaEmit;

-	double alphaPi;

-	

-	public PhraseCluster(int numCluster, Corpus corpus, double scalep, double scalec, int threads,

-						 double alphaEmit, double alphaPi)

+	public PhraseCluster(int numCluster, Corpus corpus)

 	{

 		K=numCluster;

 		c=corpus;

@@ -40,33 +35,34 @@ public class PhraseCluster {
 		n_phrases=c.getNumPhrases();

 		n_contexts=c.getNumContexts();

 		n_positions=c.getNumContextPositions();

-		this.scalePT = scalep;

-		this.scaleCT = scalec;

-		if (threads > 0)

-			pool = Executors.newFixedThreadPool(threads);

-		

+

 		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, alphaEmit <= 0);

-				if (alphaEmit > 0) 

-					digammaNormalize(j, alphaEmit);

-			}

-		}

-		

+				arr.F.randomise(j, true);

 		for(double []j:pi)

-		{

-			arr.F.randomise(j, alphaPi <= 0);

-			if (alphaPi > 0) 

-				digammaNormalize(j, alphaPi);

-		}

+			arr.F.randomise(j, true);

+	}

+	

+	public void initialiseVB(double alphaEmit, double alphaPi)

+	{

+		assert alphaEmit > 0;

+		assert alphaPi > 0;

+		

+		for(double [][]i:emit)

+			for(double []j:i)

+				digammaNormalize(j, alphaEmit);

 		

-		this.alphaEmit = alphaEmit;

-		this.alphaPi = alphaPi;

+		for(double []j:pi)

+			digammaNormalize(j, alphaPi);

+	}

+	

+	void useThreadPool(int threads)

+	{

+		assert threads > 0;

+		pool = Executors.newFixedThreadPool(threads);

 	}

 

 	public double EM()

@@ -116,7 +112,7 @@ public class PhraseCluster {
 		return loglikelihood;

 	}

 	

-	public double VBEM()

+	public double VBEM(double alphaEmit, double alphaPi)

 	{

 		// FIXME: broken - needs to be done entirely in log-space

 		

@@ -216,9 +212,22 @@ public class PhraseCluster {
 		return kl;

 	}

 	

-	public double PREM_phrase_constraints(){

-		assert (scaleCT <= 0);

-		

+	public double PREM(double scalePT, double scaleCT)

+	{

+		if (scaleCT == 0)

+		{

+			if (pool != null)

+				return PREM_phrase_constraints_parallel(scalePT);

+			else

+				return PREM_phrase_constraints(scalePT);

+		}

+		else

+			return this.PREM_phrase_context_constraints(scalePT, scaleCT);

+	}

+

+	

+	public double PREM_phrase_constraints(double scalePT)

+	{

 		double [][][]exp_emit=new double[K][n_positions][n_words];

 		double [][]exp_pi=new double[n_phrases][K];

 		

@@ -226,7 +235,7 @@ public class PhraseCluster {
 		int failures=0, iterations=0;

 		//E

 		for(int phrase=0; phrase<n_phrases; phrase++){

-			PhraseObjective po=new PhraseObjective(this,phrase);

+			PhraseObjective po=new PhraseObjective(this, phrase, scalePT);

 			boolean ok = po.optimizeWithProjectedGradientDescent();

 			if (!ok) ++failures;

 			iterations += po.getNumberUpdateCalls();

@@ -234,7 +243,7 @@ public class PhraseCluster {
 			loglikelihood += po.loglikelihood();

 			kl += po.KL_divergence();

 			l1lmax += po.l1lmax();

-			primal += po.primal();

+			primal += po.primal(scalePT);

 			List<Edge> edges = c.getEdgesForPhrase(phrase);

 

 			for(int edge=0;edge<q.length;edge++){

@@ -272,10 +281,9 @@ public class PhraseCluster {
 		return primal;

 	}

 

-	public double PREM_phrase_constraints_parallel()

+	public double PREM_phrase_constraints_parallel(final double scalePT)

 	{

 		assert(pool != null);

-		assert(scaleCT <= 0);

 		

 		final LinkedBlockingQueue<PhraseObjective> expectations 

 			= new LinkedBlockingQueue<PhraseObjective>();

@@ -294,7 +302,7 @@ public class PhraseCluster {
 				public void run() {

 					try {

 						//System.out.println("" + Thread.currentThread().getId() + " optimising lambda for " + p);

-						PhraseObjective po = new PhraseObjective(PhraseCluster.this, p);

+						PhraseObjective po = new PhraseObjective(PhraseCluster.this, p, scalePT);

 						boolean ok = po.optimizeWithProjectedGradientDescent();

 						if (!ok) failures.incrementAndGet();

 						//System.out.println("" + Thread.currentThread().getId() + " done optimising lambda for " + p);

@@ -322,7 +330,7 @@ public class PhraseCluster {
 				loglikelihood += po.loglikelihood();

 				kl += po.KL_divergence();

 				l1lmax += po.l1lmax();

-				primal += po.primal();

+				primal += po.primal(scalePT);

 				iterations += po.getNumberUpdateCalls();

 

 				

@@ -366,15 +374,14 @@ public class PhraseCluster {
 		return primal;

 	}

 

-	public double PREM_phrase_context_constraints(){

-		assert (scaleCT > 0);

-		

+	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];

 		double[] lambda = null;

 

 		//E step

-		PhraseContextObjective pco = new PhraseContextObjective(this, lambda, pool);

+		PhraseContextObjective pco = new PhraseContextObjective(this, lambda, pool, scalePT, scaleCT);

 		lambda = pco.optimizeWithProjectedGradientDescent();

 

 		//now extract expectations

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
index 15bd29c2..ff135a3d 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java
@@ -59,12 +59,18 @@ public class PhraseContextObjective extends ProjectedObjective
 	private long actualProjectionTime;

 	private ExecutorService pool;

 	

-	public PhraseContextObjective(PhraseCluster cluster, double[] startingParameters, 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)

@@ -73,8 +79,8 @@ public class PhraseContextObjective extends ProjectedObjective
 		newPoint = new double[n_param];

 		gradient = new double[n_param];

 		initP();

-		projectionPhrase = new SimplexProjection(c.scalePT);

-		projectionContext = new SimplexProjection(c.scaleCT);

+		projectionPhrase = new SimplexProjection(scalePT);

+		projectionContext = new SimplexProjection(scaleCT);

 		q=new double [data.size()][c.K];

 		

 		edgeIndex = new HashMap<Edge, Integer>();

@@ -151,7 +157,7 @@ public class PhraseContextObjective extends ProjectedObjective
 		//System.out.println("projectPoint: " + Arrays.toString(point));

 		Arrays.fill(newPoint, 0, newPoint.length, 0);

 		

-		if (c.scalePT > 0)

+		if (scalePT > 0)

 		{

 			// first project using the phrase-tag constraints,

 			// for all p,t: sum_c lambda_ptc < scaleP 

@@ -201,7 +207,7 @@ public class PhraseContextObjective extends ProjectedObjective
 		}

 		//System.out.println("after PT " + Arrays.toString(newPoint));

 	

-		if (c.scaleCT > 1e-6)

+		if (scaleCT > 1e-6)

 		{

 			// now project using the context-tag constraints,

 			// for all c,t: sum_p omega_pct < scaleC

@@ -399,6 +405,6 @@ public class PhraseContextObjective extends ProjectedObjective
 	// L - KL(q||p) - scalePT * l1lmax_phrase - scaleCT * l1lmax_context

 	public double primal()

 	{

-		return loglikelihood() - KL_divergence() - c.scalePT * phrase_l1lmax() - c.scalePT * context_l1lmax();

+		return loglikelihood() - KL_divergence() - scalePT * phrase_l1lmax() - scalePT * context_l1lmax();

 	}

 }
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
index cc12546d..33167c20 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
@@ -63,7 +63,7 @@ public class PhraseObjective extends ProjectedObjective
 	 */

 	public double llh;

 	

-	public PhraseObjective(PhraseCluster cluster, int phraseIdx){

+	public PhraseObjective(PhraseCluster cluster, int phraseIdx, double scale){

 		phrase=phraseIdx;

 		c=cluster;

 		data=c.c.getEdgesForPhrase(phrase);

@@ -81,7 +81,7 @@ public class PhraseObjective extends ProjectedObjective
 		newPoint  = new double[n_param];

 		gradient = new double[n_param];

 		initP();

-		projection=new SimplexProjection(c.scalePT);

+		projection=new SimplexProjection(scale);

 		q=new double [data.size()][c.K];

 

 		setParameters(parameters);

@@ -220,8 +220,8 @@ public class PhraseObjective extends ProjectedObjective
 		return sum;

 	}

 

-	public double primal()

+	public double primal(double scale)

 	{

-		return loglikelihood() - KL_divergence() - c.scalePT * l1lmax();	

+		return loglikelihood() - KL_divergence() - scale * l1lmax();	

 	}

 }

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java b/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
index 439fb337..240c4d64 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
@@ -30,6 +30,7 @@ public class Trainer
         parser.accepts("variational-bayes");
         parser.accepts("alpha-emit").withRequiredArg().ofType(Double.class).defaultsTo(0.1);
         parser.accepts("alpha-pi").withRequiredArg().ofType(Double.class).defaultsTo(0.01);
+        parser.accepts("agree");
         OptionSet options = parser.parse(args);
 
         if (options.has("help") || !options.has("in"))
@@ -37,7 +38,7 @@ public class Trainer
         	try {
 				parser.printHelpOn(System.err);
 			} catch (IOException e) {
-				System.err.println("This should never happen. Really.");
+				System.err.println("This should never happen.");
 				e.printStackTrace();
 			}
         	System.exit(1);     
@@ -75,34 +76,46 @@ public class Trainer
 			System.exit(1);
 		}
 		
- 		System.out.println("Running with " + tags + " tags " +
- 				"for " + em_iterations + " EM and " + pr_iterations + " PR iterations " +
- 				"with scale " + scale_phrase + " phrase and " + scale_context + " context " +
- 				"and " + threads + " threads");
- 		System.out.println();
+		if (!options.has("agree"))
+			System.out.println("Running with " + tags + " tags " +
+					"for " + em_iterations + " EM and " + pr_iterations + " PR iterations " +
+					"with scale " + scale_phrase + " phrase and " + scale_context + " context " +
+					"and " + threads + " threads");
+		else
+			System.out.println("Running agreement model with " + tags + " tags " +
+	 				"for " + em_iterations);
+
+	 	System.out.println();
 		
-		PhraseCluster cluster = new PhraseCluster(tags, corpus, scale_phrase, scale_context, threads, alphaEmit, alphaPi);
+ 		PhraseCluster cluster = null;
+ 		Agree agree = null;
+ 		if (options.has("agree"))
+ 			agree = new Agree(tags, corpus);
+ 		else
+ 		{
+ 			cluster = new PhraseCluster(tags, corpus);
+ 			if (threads > 0) cluster.useThreadPool(threads);
+ 			if (vb)	cluster.initialiseVB(alphaEmit, alphaPi);
+ 		}
 				
 		double last = 0;
 		for (int i=0; i<em_iterations+pr_iterations; i++)
 		{
 			double o;
-			if (i < em_iterations) 
-			{
-				if (!vb)
-					o = cluster.EM();
-				else
-					o = cluster.VBEM();
-			}
-			else if (scale_context == 0)
+			if (agree != null)
+				o = agree.EM();
+			else
 			{
-				if (threads >= 1)
-					o = cluster.PREM_phrase_constraints_parallel();
+				if (i < em_iterations)
+				{
+					if (!vb)
+						o = cluster.EM();
+					else
+						o = cluster.VBEM(alphaEmit, alphaPi);	
+				}
 				else
-					o = cluster.PREM_phrase_constraints();
+					o = cluster.PREM(scale_phrase, scale_context);
 			}
-			else 
-				o = cluster.PREM_phrase_context_constraints();
 			
 			System.out.println("ITER: "+i+" objective: " + o);
 			
@@ -120,6 +133,9 @@ public class Trainer
 			last = o;
 		}
 		
+		if (cluster == null)
+			cluster = agree.model1;
+
 		double pl1lmax = cluster.phrase_l1lmax();
 		double cl1lmax = cluster.context_l1lmax();
 		System.out.println("\nFinal posterior phrase l1lmax " + pl1lmax + " context l1lmax " + cl1lmax);