Prettyfied output regarding optimization failure.

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@210 ec762483-ff6d-05da-a07a-a48fb63a330f

2 files changed, 28 insertions, 9 deletions

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
index 7d7c46dd..b9b1b98c 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
@@ -9,6 +9,7 @@ import java.util.List;
 import java.util.concurrent.ExecutorService;

 import java.util.concurrent.Executors;

 import java.util.concurrent.LinkedBlockingQueue;

+import java.util.concurrent.atomic.AtomicInteger;

 

 import phrase.Corpus.Edge;

 

@@ -110,10 +111,13 @@ public class PhraseCluster {
 		double [][]exp_pi=new double[n_phrases][K];

 		

 		double loglikelihood=0, kl=0, l1lmax=0, primal=0;

+		int failures=0, iterations=0;

 		//E

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

 			PhraseObjective po=new PhraseObjective(this,phrase);

-			po.optimizeWithProjectedGradientDescent();

+			boolean ok = po.optimizeWithProjectedGradientDescent();

+			if (!ok) ++failures;

+			iterations += po.iterations;

 			double [][] q=po.posterior();

 			loglikelihood += po.loglikelihood();

 			kl += po.KL_divergence();

@@ -136,6 +140,9 @@ public class PhraseCluster {
 			}

 		}

 		

+		if (failures > 0)

+			System.out.println("WARNING: failed to converge in " + failures + "/" + n_phrases + " cases");

+		System.out.println("\tmean iters: 	  " + iterations/(double)n_phrases);

 		System.out.println("\tllh:            " + loglikelihood);

 		System.out.println("\tKL:             " + kl);

 		System.out.println("\tphrase l1lmax:  " + l1lmax);

@@ -170,6 +177,8 @@ public class PhraseCluster {
 		double [][]exp_pi=new double[n_phrases][K];

 		

 		double loglikelihood=0, kl=0, l1lmax=0, primal=0;

+		final AtomicInteger failures = new AtomicInteger(0);

+		int iterations=0;

 

 		//E

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

@@ -179,7 +188,8 @@ public class PhraseCluster {
 					try {

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

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

-						po.optimizeWithProjectedGradientDescent();

+						boolean ok = po.optimizeWithProjectedGradientDescent();

+						if (!ok) failures.incrementAndGet();

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

 						expectations.put(po);

 						//System.out.println("" + Thread.currentThread().getId() + " added to queue " + p);

@@ -206,6 +216,8 @@ public class PhraseCluster {
 				kl += po.KL_divergence();

 				l1lmax += po.l1lmax();

 				primal += po.primal();

+				iterations += po.iterations;

+

 				

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

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

@@ -227,6 +239,9 @@ public class PhraseCluster {
 			}

 		}

 		

+		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);

 		System.out.println("\tllh:            " + loglikelihood);

 		System.out.println("\tKL:             " + kl);

 		System.out.println("\tphrase l1lmax:  " + l1lmax);

diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
index 3314f74a..f24b903d 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
@@ -22,7 +22,7 @@ public class PhraseObjective extends ProjectedObjective
 {

 	static final double GRAD_DIFF = 0.00002;

 	static double INIT_STEP_SIZE = 300;

-	static double VAL_DIFF = 1e-4; // FIXME needs to be tuned - and this might be too weak

+	static double VAL_DIFF = 1e-6; // FIXME needs to be tuned - and this might be too weak

 	static int ITERATIONS = 100;

 	//private double c1=0.0001; // wolf stuff

 	//private double c2=0.9;

@@ -164,7 +164,9 @@ public class PhraseObjective extends ProjectedObjective
 		return q;

 	}

 	

-	public void optimizeWithProjectedGradientDescent(){

+	public int iterations = 0;

+	

+	public boolean optimizeWithProjectedGradientDescent(){

 		LineSearchMethod ls =

 			new ArmijoLineSearchMinimizationAlongProjectionArc

 				(new InterpolationPickFirstStep(INIT_STEP_SIZE));

@@ -181,13 +183,14 @@ public class PhraseObjective extends ProjectedObjective
 		compositeStop.add(stopValue);

 		optimizer.setMaxIterations(ITERATIONS);

 		updateFunction();

-		boolean succed = optimizer.optimize(this,stats,compositeStop);

+		boolean success = optimizer.optimize(this,stats,compositeStop);

+		iterations += optimizer.getCurrentIteration();

 //		System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));

-		if(succed){

+		//if(succed){

 			//System.out.println("Ended optimization in " + optimizer.getCurrentIteration());

-		}else{

-			System.out.println("Failed to optimize");

-		}

+		//}else{

+//			System.out.println("Failed to optimize");

+		//}

 		lambda[phrase]=parameters;

 		//	ps.println(Arrays.toString(parameters));

 		

@@ -195,6 +198,7 @@ public class PhraseObjective extends ProjectedObjective
 		//	ps.println(Arrays.toString(q[edge]));

 		//	}

 		

+		return success;

 	}

 	

 	public double KL_divergence()