Added various flags to filter out low count events (words, edges).

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@298 ec762483-ff6d-05da-a07a-a48fb63a330f

4 files changed, 228 insertions, 63 deletions

diff --git a/gi/posterior-regularisation/prjava/src/arr/F.java b/gi/posterior-regularisation/prjava/src/arr/F.java
index 2201179e..0f74cbab 100644
--- a/gi/posterior-regularisation/prjava/src/arr/F.java
+++ b/gi/posterior-regularisation/prjava/src/arr/F.java
@@ -1,5 +1,6 @@
 package arr;

 

+import java.util.Arrays;

 import java.util.Random;

 

 public class F {

@@ -36,11 +37,13 @@ public class F {
 		for(int i=0;i<a.length;i++){

 			sum+=a[i];

 		}

-		if(sum==0){

-			return ;

-		}

-		for(int i=0;i<a.length;i++){

-			a[i]/=sum;

+		if(sum==0)

+			Arrays.fill(a, 1.0/a.length);

+		else

+		{

+			for(int i=0;i<a.length;i++){

+				a[i]/=sum;

+			}

 		}

 	}

 	

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Corpus.java b/gi/posterior-regularisation/prjava/src/phrase/Corpus.java
index 2de2797b..f2c6b132 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/Corpus.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/Corpus.java
@@ -17,11 +17,14 @@ public class Corpus
 	private List<List<Edge>> contextToPhrase = new ArrayList<List<Edge>>();
 	public int splitSentinel;
 	public int phraseSentinel;
-	
+	public int rareSentinel;
+	private boolean[] wordIsRare;
+
 	public Corpus()
 	{
 		splitSentinel = wordLexicon.insert("<SPLIT>");
 		phraseSentinel = wordLexicon.insert("<PHRASE>");		
+		rareSentinel = wordLexicon.insert("<RARE>");
 	}
 	
 	public class Edge
@@ -40,6 +43,10 @@ public class Corpus
 		{
 			return Corpus.this.getPhrase(phraseId);
 		}
+		public TIntArrayList getRawPhrase()
+		{
+			return Corpus.this.getRawPhrase(phraseId);
+		}
 		public String getPhraseString()
 		{
 			return Corpus.this.getPhraseString(phraseId);
@@ -52,7 +59,10 @@ public class Corpus
 		{
 			return Corpus.this.getContext(contextId);
 		}
-		public String getContextString(boolean insertPhraseSentinel)
+		public TIntArrayList getRawContext()
+		{
+			return Corpus.this.getRawContext(contextId);
+		}		public String getContextString(boolean insertPhraseSentinel)
 		{
 			return Corpus.this.getContextString(contextId, insertPhraseSentinel);
 		}
@@ -132,13 +142,35 @@ public class Corpus
 	
 	public TIntArrayList getPhrase(int phraseId)
 	{
+		TIntArrayList phrase = phraseLexicon.lookup(phraseId);
+		if (wordIsRare != null)
+		{
+			boolean first = true;
+			for (int i = 0; i < phrase.size(); ++i)
+			{
+				if (wordIsRare[phrase.get(i)])
+				{
+					if (first)
+					{
+						phrase = (TIntArrayList) phrase.clone();
+						first = false;
+					}
+					phrase.set(i, rareSentinel);
+				}
+			}
+		}
+		return phrase;
+	}
+	
+	public TIntArrayList getRawPhrase(int phraseId)
+	{
 		return phraseLexicon.lookup(phraseId);
 	}
 	
 	public String getPhraseString(int phraseId)
 	{
 		StringBuffer b = new StringBuffer();
-		for (int tid: getPhrase(phraseId).toNativeArray())
+		for (int tid: getRawPhrase(phraseId).toNativeArray())
 		{
 			if (b.length() > 0)
 				b.append(" ");
@@ -149,13 +181,35 @@ public class Corpus
 	
 	public TIntArrayList getContext(int contextId)
 	{
+		TIntArrayList context = contextLexicon.lookup(contextId);
+		if (wordIsRare != null)
+		{
+			boolean first = true;
+			for (int i = 0; i < context.size(); ++i)
+			{
+				if (wordIsRare[context.get(i)])
+				{
+					if (first)
+					{
+						context = (TIntArrayList) context.clone();
+						first = false;
+					}
+					context.set(i, rareSentinel);
+				}
+			}
+		}
+		return context;
+	}
+	
+	public TIntArrayList getRawContext(int contextId)
+	{
 		return contextLexicon.lookup(contextId);
 	}
 	
 	public String getContextString(int contextId, boolean insertPhraseSentinel)
 	{
 		StringBuffer b = new StringBuffer();
-		TIntArrayList c = getContext(contextId);
+		TIntArrayList c = getRawContext(contextId);
 		for (int i = 0; i < c.size(); ++i)
 		{
 			if (i > 0) b.append(" ");
@@ -249,5 +303,24 @@ public class Corpus
 	{
 		out.println("Corpus has " + edges.size() + " edges " + phraseLexicon.size() + " phrases " 
 				+ contextLexicon.size() + " contexts and " + wordLexicon.size() + " word types");
-	}	
+	}
+
+	public void applyWordThreshold(int wordThreshold) 
+	{
+		int[] counts = new int[wordLexicon.size()];
+		for (Edge e: edges)
+		{
+			TIntArrayList phrase = e.getPhrase();
+			for (int i = 0; i < phrase.size(); ++i)
+				counts[phrase.get(i)] += e.getCount();
+			
+			TIntArrayList context = e.getContext();
+			for (int i = 0; i < context.size(); ++i)
+				counts[context.get(i)] += e.getCount();
+		}
+
+		wordIsRare = new boolean[wordLexicon.size()];
+		for (int i = 0; i < wordLexicon.size(); ++i)
+			wordIsRare[i] = counts[i] < wordThreshold;
+	}
 }
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
index 5efaf52e..feab5eda 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
@@ -2,14 +2,20 @@ package phrase;
 

 import gnu.trove.TIntArrayList;

 import org.apache.commons.math.special.Gamma;

+

+import java.io.BufferedReader;

+import java.io.File;

+import java.io.IOException;

 import java.io.PrintStream;

 import java.util.Arrays;

 import java.util.List;

+import java.util.StringTokenizer;

 import java.util.concurrent.ExecutorService;

 import java.util.concurrent.Executors;

 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;

 

@@ -17,7 +23,7 @@ import phrase.Corpus.Edge;
 public class PhraseCluster {

 	

 	public int K;

-	private int n_phrases, n_words, n_contexts, n_positions;

+	private int n_phrases, n_words, n_contexts, n_positions, edge_threshold;

 	public Corpus c;

 	public ExecutorService pool; 

 

@@ -38,6 +44,7 @@ public class PhraseCluster {
 		n_phrases=c.getNumPhrases();

 		n_contexts=c.getNumContexts();

 		n_positions=c.getNumContextPositions();

+		edge_threshold=0;

 

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

 		pi=new double[n_phrases][K];

@@ -74,12 +81,11 @@ public class PhraseCluster {
 		double [][][]exp_emit=new double [K][n_positions][n_words];

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

 		

-		if (skipBigPhrases)

-		{

-			for(double [][]i:exp_emit)

-				for(double []j:i)

-					Arrays.fill(j, 1e-100);

-		}

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

+			for(double []j:i)

+				Arrays.fill(j, 1e-10);

+		for(double []j:pi)

+			Arrays.fill(j, 1e-10);

 		

 		double loglikelihood=0;

 		

@@ -97,6 +103,9 @@ public class PhraseCluster {
 			for (int ctx=0; ctx<contexts.size(); ctx++)

 			{

 				Edge edge = contexts.get(ctx);

+				if (edge.getCount() < edge_threshold) 

+					continue;

+				

 				double p[]=posterior(edge);

 				double z = arr.F.l1norm(p);

 				assert z > 0;

@@ -121,7 +130,7 @@ public class PhraseCluster {
 				arr.F.l1normalize(j);

 		

 		for(double []j:exp_pi)

-				arr.F.l1normalize(j);

+			arr.F.l1normalize(j);

 			

 		emit=exp_emit;

 		pi=exp_pi;

@@ -250,12 +259,11 @@ public class PhraseCluster {
 		double [][][]exp_emit=new double[K][n_positions][n_words];

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

 		

-		if (skipBigPhrases)

-		{

-			for(double [][]i:exp_emit)

-				for(double []j:i)

-					Arrays.fill(j, 1e-100);

-		}

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

+			for(double []j:i)

+				Arrays.fill(j, 1e-10);

+		for(double []j:pi)

+			Arrays.fill(j, 1e-10);

 

 		if (lambdaPT == null && cacheLambda)

 			lambdaPT = new double[n_phrases][];

@@ -271,6 +279,7 @@ public class PhraseCluster {
 				continue;

 			}

 			

+			// 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;

@@ -493,11 +502,25 @@ public class PhraseCluster {
 	{

 		double[] prob=Arrays.copyOf(pi[edge.getPhraseId()], K);

 		

+		//if (edge.getCount() < edge_threshold)

+			//System.out.println("Edge: " + edge + " probs for phrase " + Arrays.toString(prob));

+		

 		TIntArrayList ctx = edge.getContext();

 		for(int tag=0;tag<K;tag++)

+		{

 			for(int c=0;c<n_positions;c++)

-				if (!this.c.isSentinel(ctx.get(c)))

-					prob[tag]*=emit[tag][c][ctx.get(c)];

+			{

+				int word = ctx.get(c);

+				//if (edge.getCount() < edge_threshold)

+					//System.out.println("\ttag: " + tag + " context word: " + word + " prob " + emit[tag][c][word]);

+

+				if (!this.c.isSentinel(word))

+					prob[tag]*=emit[tag][c][word];

+			}

+		}

+

+		//if (edge.getCount() < edge_threshold)

+			//System.out.println("prob " + Arrays.toString(prob));

 		

 		return prob;

 	}

@@ -514,39 +537,33 @@ public class PhraseCluster {
 			ps.print("\t");

 			ps.print(edge.getContextString(true));

 			int t=arr.F.argmax(probs);

-			ps.println(" ||| C=" + t);

+			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);

 		

-		ps.println("P(tag|phrase)");

 		for (int i = 0; i < n_phrases; ++i)

-		{

-			ps.print(c.getPhrase(i));

-			for(int j=0;j<pi[i].length;j++){

+			for(int j=0;j<pi[i].length;j++)

 				if (pi[i][j] > EPS)

-					ps.print("\t" + j + ": " + pi[i][j]);

-			}

-			ps.println();

-		}

-		

-		ps.println("P(word|tag,position)");

+					ps.println(i + " " + j + " " + pi[i][j]);

+

+		ps.println();

 		for (int i = 0; i < K; ++i)

 		{

-			for(int position=0;position<n_positions;position++){

-				ps.println("tag " + i + " position " + position);

-				for(int word=0;word<emit[i][position].length;word++){

+			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.print(c.getWord(word)+"="+emit[i][position][word]+"\t");

+						ps.println(i + " " + position + " " + word + " " + emit[i][position][word]);

 				}

-				ps.println();

 			}

-			ps.println();

 		}

-		

 	}

 	

 	double phrase_l1lmax()

@@ -586,4 +603,66 @@ public class PhraseCluster {
 		}

 		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;

+		}

+	}

+

+	public void setEdgeThreshold(int edgeThreshold) 

+	{

+		this.edge_threshold = edgeThreshold;

+	}

 }

diff --git a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java b/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
index a67c17a2..ed7a6bbe 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/Trainer.java
@@ -4,6 +4,7 @@ import io.FileUtil;
 import joptsimple.OptionParser;
 import joptsimple.OptionSet;
 import java.io.File;
+import java.io.FileNotFoundException;
 import java.io.IOException;
 import java.io.PrintStream;
 import java.util.Random;
@@ -18,12 +19,12 @@ public class Trainer
         parser.accepts("help");
         parser.accepts("in").withRequiredArg().ofType(File.class);
         parser.accepts("out").withRequiredArg().ofType(File.class);
+        parser.accepts("start").withRequiredArg().ofType(File.class);
         parser.accepts("parameters").withRequiredArg().ofType(File.class);
         parser.accepts("topics").withRequiredArg().ofType(Integer.class).defaultsTo(5);
-        parser.accepts("em-iterations").withRequiredArg().ofType(Integer.class).defaultsTo(5);
-        parser.accepts("pr-iterations").withRequiredArg().ofType(Integer.class).defaultsTo(0);
+        parser.accepts("iterations").withRequiredArg().ofType(Integer.class).defaultsTo(10);
         parser.accepts("threads").withRequiredArg().ofType(Integer.class).defaultsTo(0);
-        parser.accepts("scale-phrase").withRequiredArg().ofType(Double.class).defaultsTo(5.0);
+        parser.accepts("scale-phrase").withRequiredArg().ofType(Double.class).defaultsTo(0.0);
         parser.accepts("scale-context").withRequiredArg().ofType(Double.class).defaultsTo(0.0);
         parser.accepts("seed").withRequiredArg().ofType(Long.class).defaultsTo(0l);
         parser.accepts("convergence-threshold").withRequiredArg().ofType(Double.class).defaultsTo(1e-6);
@@ -33,6 +34,8 @@ public class Trainer
         parser.accepts("agree");
         parser.accepts("no-parameter-cache");
         parser.accepts("skip-large-phrases").withRequiredArg().ofType(Integer.class).defaultsTo(5);
+        parser.accepts("rare-word").withRequiredArg().ofType(Integer.class).defaultsTo(0);
+        parser.accepts("rare-edge").withRequiredArg().ofType(Integer.class).defaultsTo(0);
         OptionSet options = parser.parse(args);
 
         if (options.has("help") || !options.has("in"))
@@ -47,8 +50,7 @@ public class Trainer
         }
 		
 		int tags = (Integer) options.valueOf("topics");
-		int em_iterations = (Integer) options.valueOf("em-iterations");
-		int pr_iterations = (Integer) options.valueOf("pr-iterations");
+		int iterations = (Integer) options.valueOf("iterations");
 		double scale_phrase = (Double) options.valueOf("scale-phrase");
 		double scale_context = (Double) options.valueOf("scale-context");
 		int threads = (Integer) options.valueOf("threads");
@@ -57,6 +59,8 @@ public class Trainer
 		double alphaEmit = (vb) ? (Double) options.valueOf("alpha-emit") : 0;
 		double alphaPi = (vb) ? (Double) options.valueOf("alpha-pi") : 0;
 		int skip = (Integer) options.valueOf("skip-large-phrases");
+		int wordThreshold = (Integer) options.valueOf("rare-word");
+		int edgeThreshold = (Integer) options.valueOf("rare-edge");
 		
 		if (options.has("seed"))
 			F.rng = new Random((Long) options.valueOf("seed"));
@@ -79,15 +83,18 @@ public class Trainer
 			System.exit(1);
 		}
 		
+		if (wordThreshold > 0)
+			corpus.applyWordThreshold(wordThreshold);
+		
 		if (!options.has("agree"))
 			System.out.println("Running with " + tags + " tags " +
-					"for " + em_iterations + " EM and " + pr_iterations + " PR iterations " +
-					"skipping large phrases for first " + skip + " iterations " + 
+					"for " + iterations + " iterations " +
+					((skip > 0) ? "skipping large phrases for first " + skip + " 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);
+	 				"for " + iterations);
 
 	 	System.out.println();
 		
@@ -102,17 +109,28 @@ public class Trainer
  			if (vb)	cluster.initialiseVB(alphaEmit, alphaPi);
  			if (options.has("no-parameter-cache")) 
  				cluster.cacheLambda = false;
+ 			if (options.has("start"))
+ 			{
+ 				try {
+					System.err.println("Reading starting parameters from " + options.valueOf("start"));
+					cluster.loadParameters(FileUtil.reader((File)options.valueOf("start")));
+				} catch (IOException e) {
+					System.err.println("Failed to open input file: " + options.valueOf("start"));
+					e.printStackTrace();
+				}
+ 			}
+			cluster.setEdgeThreshold(edgeThreshold);
  		}
 				
 		double last = 0;
-		for (int i=0; i<em_iterations+pr_iterations; i++)
+		for (int i=0; i < iterations; i++)
 		{
 			double o;
 			if (agree != null)
 				o = agree.EM();
 			else
 			{
-				if (i < em_iterations)
+				if (scale_phrase <= 0 && scale_context <= 0)
 				{
 					if (!vb)
 						o = cluster.EM(i < skip);
@@ -128,13 +146,7 @@ public class Trainer
 			if (i != 0 && Math.abs((o - last) / o) < threshold)
 			{
 				last = o;
-				if (i < Math.max(em_iterations, skip))
-				{
-					i = Math.max(em_iterations, skip) - 1;
-					continue;
-				}
-				else
-					break;
+				break;
 			}
 			last = o;
 		}
@@ -145,8 +157,6 @@ public class Trainer
 		double pl1lmax = cluster.phrase_l1lmax();
 		double cl1lmax = cluster.context_l1lmax();
 		System.out.println("\nFinal posterior phrase l1lmax " + pl1lmax + " context l1lmax " + cl1lmax);
-		if (pr_iterations == 0) 
-			System.out.println("With PR objective " + (last - scale_phrase*pl1lmax - scale_context*cl1lmax));
 		
 		if (options.has("out"))
 		{