From 5517e0b82f9503c59c10fc0167fa9d7fbdca1e64 Mon Sep 17 00:00:00 2001
From: "trevor.cohn" <trevor.cohn@ec762483-ff6d-05da-a07a-a48fb63a330f>
Date: Wed, 7 Jul 2010 14:11:42 +0000
Subject: git-svn-id: https://ws10smt.googlecode.com/svn/trunk@173
 ec762483-ff6d-05da-a07a-a48fb63a330f

---
 .../PhraseContextModel.java                        |   2 +-
 gi/posterior-regularisation/prjava.jar             |   1 +
 .../prjava/prjava-20100707.jar                     | Bin 0 -> 933814 bytes
 .../prjava/src/io/FileUtil.java                    |  12 ++-
 .../prjava/src/phrase/PhraseCluster.java           | 110 +++++++++++++++------
 .../prjava/src/phrase/PhraseCorpus.java            |  19 ++--
 .../prjava/src/phrase/PhraseObjective.java         |  19 ++--
 gi/posterior-regularisation/train_pr_global.py     |   8 +-
 gi/posterior-regularisation/train_pr_parallel.py   |  25 +++--
 9 files changed, 132 insertions(+), 64 deletions(-)
 create mode 120000 gi/posterior-regularisation/prjava.jar
 create mode 100644 gi/posterior-regularisation/prjava/prjava-20100707.jar

(limited to 'gi/posterior-regularisation')

diff --git a/gi/posterior-regularisation/PhraseContextModel.java b/gi/posterior-regularisation/PhraseContextModel.java
index db152e73..85bcfb89 100644
--- a/gi/posterior-regularisation/PhraseContextModel.java
+++ b/gi/posterior-regularisation/PhraseContextModel.java
@@ -149,7 +149,7 @@ class PhraseContextModel
 					System.out.println("Failed to optimize");
 				//System.out.println("Ended optimization in " + optimizer.getCurrentIteration());				
 
-				lambda = objective.getParameters();
+				//lambda = objective.getParameters();
 				llh = objective.primal();
 				
 				for (int i = 0; i < training.getNumPhrases(); ++i)
diff --git a/gi/posterior-regularisation/prjava.jar b/gi/posterior-regularisation/prjava.jar
new file mode 120000
index 00000000..7cd1a3ff
--- /dev/null
+++ b/gi/posterior-regularisation/prjava.jar
@@ -0,0 +1 @@
+prjava/prjava-20100707.jar
\ No newline at end of file
diff --git a/gi/posterior-regularisation/prjava/prjava-20100707.jar b/gi/posterior-regularisation/prjava/prjava-20100707.jar
new file mode 100644
index 00000000..195374d9
Binary files /dev/null and b/gi/posterior-regularisation/prjava/prjava-20100707.jar differ
diff --git a/gi/posterior-regularisation/prjava/src/io/FileUtil.java b/gi/posterior-regularisation/prjava/src/io/FileUtil.java
index 7d9f2bc5..67ce571e 100644
--- a/gi/posterior-regularisation/prjava/src/io/FileUtil.java
+++ b/gi/posterior-regularisation/prjava/src/io/FileUtil.java
@@ -1,5 +1,7 @@
 package io;
 import java.util.*;
+import java.util.zip.GZIPInputStream;
+import java.util.zip.GZIPOutputStream;
 import java.io.*;
 public class FileUtil {
 	public static Scanner openInFile(String filename){
@@ -18,7 +20,10 @@ public class FileUtil {
 		BufferedReader r=null;
 		try
 		{
-			r=(new BufferedReader(new FileReader(new File(filename))));
+			if (filename.endsWith(".gz"))
+				r=(new BufferedReader(new InputStreamReader(new GZIPInputStream(new FileInputStream(new File(filename))))));
+			else
+				r=(new BufferedReader(new FileReader(new File(filename))));
 		}catch(IOException ioe){
 			System.out.println(ioe.getMessage());
 		}
@@ -29,7 +34,10 @@ public class FileUtil {
 		PrintStream localps=null;
 		try
 		{
-			localps=new PrintStream (new FileOutputStream(filename));
+			if (filename.endsWith(".gz"))
+				localps=new PrintStream (new GZIPOutputStream(new FileOutputStream(filename)));
+			else
+				localps=new PrintStream (new FileOutputStream(filename));
 
 		}catch(IOException ioe){
 			System.out.println(ioe.getMessage());
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
index cd28c12e..731d03ac 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java
@@ -1,11 +1,16 @@
 package phrase;
 
 import io.FileUtil;
+
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.OutputStream;
 import java.io.PrintStream;
 import java.util.Arrays;
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
 import java.util.concurrent.LinkedBlockingQueue;
+import java.util.zip.GZIPOutputStream;
 
 public class PhraseCluster {
 	
@@ -26,28 +31,46 @@ public class PhraseCluster {
 	public static void main(String[] args) {
 		String input_fname = args[0];
 		int tags = Integer.parseInt(args[1]);
-		String outputDir = args[2];
+		String output_fname = args[2];
 		int iterations = Integer.parseInt(args[3]);
 		double scale = Double.parseDouble(args[4]);
 		int threads = Integer.parseInt(args[5]);
+		boolean runEM = Boolean.parseBoolean(args[6]);
 		
 		PhraseCorpus corpus = new PhraseCorpus(input_fname);
 		PhraseCluster cluster = new PhraseCluster(tags, corpus, scale, threads);
 		
-		PhraseObjective.ps = FileUtil.openOutFile(outputDir + "/phrase_stat.out");
+		//PhraseObjective.ps = FileUtil.openOutFile(outputDir + "/phrase_stat.out");
 		
+		double last = 0;
 		for(int i=0;i<iterations;i++){
-			double o = cluster.PREM();
-		    //double o = cluster.EM();
-			PhraseObjective.ps.println("ITER: "+i+" objective: " + o);
+			
+			double o;
+			if (runEM || i < 3) 
+				o = cluster.EM();
+			else
+				o = cluster.PREM();
+			//PhraseObjective.ps.
+			System.out.println("ITER: "+i+" objective: " + o);
+			last = o;
+		}
+		
+		if (runEM)
+		{
+			double l1lmax = cluster.posterior_l1lmax();
+			System.out.println("Final l1lmax term " + l1lmax + ", total PR objective " + (last - scale*l1lmax));
+			// nb. KL is 0 by definition
 		}
 		
-		PrintStream ps=io.FileUtil.openOutFile(outputDir + "/posterior.out");
+		PrintStream ps=io.FileUtil.openOutFile(output_fname);
 		cluster.displayPosterior(ps);
-		ps.println();
-		cluster.displayModelParam(ps);
 		ps.close();
-		PhraseObjective.ps.close();
+		
+		//PhraseObjective.ps.close();
+
+		//ps = io.FileUtil.openOutFile(outputDir + "/parameters.out");
+		//cluster.displayModelParam(ps);
+		//ps.close();
 		
 		cluster.finish();
 	}
@@ -61,7 +84,7 @@ public class PhraseCluster {
 		if (threads > 0)
 			pool = Executors.newFixedThreadPool(threads);
 		
-		emit=new double [K][PhraseCorpus.NUM_CONTEXT][n_words];
+		emit=new double [K][c.numContexts][n_words];
 		pi=new double[n_phrase][K];
 		
 		for(double [][]i:emit){
@@ -82,7 +105,7 @@ public class PhraseCluster {
 	}
 		
 	public double EM(){
-		double [][][]exp_emit=new double [K][PhraseCorpus.NUM_CONTEXT][n_words];
+		double [][][]exp_emit=new double [K][c.numContexts][n_words];
 		double [][]exp_pi=new double[n_phrase][K];
 		
 		double loglikelihood=0;
@@ -93,7 +116,9 @@ public class PhraseCluster {
 			for(int ctx=0;ctx<data.length;ctx++){
 				int context[]=data[ctx];
 				double p[]=posterior(phrase,context);
-				loglikelihood+=Math.log(arr.F.l1norm(p));
+				double z = arr.F.l1norm(p);
+				assert z > 0;
+				loglikelihood+=Math.log(z);
 				arr.F.l1normalize(p);
 				
 				int contextCnt=context[context.length-1];
@@ -132,7 +157,7 @@ public class PhraseCluster {
 		if (pool != null)
 			return PREMParallel();
 		
-		double [][][]exp_emit=new double [K][PhraseCorpus.NUM_CONTEXT][n_words];
+		double [][][]exp_emit=new double [K][c.numContexts][n_words];
 		double [][]exp_pi=new double[n_phrase][K];
 		
 		double loglikelihood=0;
@@ -142,7 +167,7 @@ public class PhraseCluster {
 			PhraseObjective po=new PhraseObjective(this,phrase);
 			po.optimizeWithProjectedGradientDescent();
 			double [][] q=po.posterior();
-			loglikelihood+=po.getValue();
+			loglikelihood+=po.llh;
 			primal+=po.primal();
 			for(int edge=0;edge<q.length;edge++){
 				int []context=c.data[phrase][edge];
@@ -184,7 +209,7 @@ public class PhraseCluster {
 		final LinkedBlockingQueue<PhraseObjective> expectations 
 			= new LinkedBlockingQueue<PhraseObjective>();
 		
-		double [][][]exp_emit=new double [K][PhraseCorpus.NUM_CONTEXT][n_words];
+		double [][][]exp_emit=new double [K][c.numContexts][n_words];
 		double [][]exp_pi=new double[n_phrase][K];
 		
 		double loglikelihood=0;
@@ -220,7 +245,7 @@ public class PhraseCluster {
 				int phrase = po.phrase;
 				//System.out.println("" + Thread.currentThread().getId() + " taken phrase " + phrase);
 				double [][] q=po.posterior();
-				loglikelihood+=po.getValue();
+				loglikelihood+=po.llh;
 				primal+=po.primal();
 				for(int edge=0;edge<q.length;edge++){
 					int []context=c.data[phrase][edge];
@@ -295,18 +320,19 @@ public class PhraseCluster {
 				// emit phrase
 				ps.print(c.phraseList[i]);
 				ps.print("\t");
-				ps.print(c.getContextString(e));
-				ps.print("||| C=" + e[e.length-1] + " |||");
-
+				ps.print(c.getContextString(e, true));
 				int t=arr.F.argmax(probs);
+				ps.println(" ||| C=" + t);
+
+				//ps.print("||| C=" + e[e.length-1] + " |||");
 				
-				ps.print(t+"||| [");
-				for(t=0;t<K;t++){
-					ps.print(probs[t]+", ");
-				}
+				//ps.print(t+"||| [");
+				//for(t=0;t<K;t++){
+				//	ps.print(probs[t]+", ");
+				//}
 				// for (int t = 0; t < numTags; ++t)
 				// System.out.print(" " + probs[t]);
-				ps.println("]");
+				//ps.println("]");
 			}
 		}
 	}
@@ -329,14 +355,14 @@ public class PhraseCluster {
 		ps.println("P(word|tag,position)");
 		for (int i = 0; i < K; ++i)
 		{
-			ps.println(i);
-			for(int position=0;position<PhraseCorpus.NUM_CONTEXT;position++){
-				ps.println(position);
+			for(int position=0;position<c.numContexts;position++){
+				ps.println("tag " + i + " position " + position);
 				for(int word=0;word<emit[i][position].length;word++){
-					if((word+1)%100==0){
-						ps.println();
-					}
-					ps.print(c.wordList[word]+"="+emit[i][position][word]+"\t");
+					//if((word+1)%100==0){
+					//	ps.println();
+					//}
+					if (emit[i][position][word] > 1e-10)
+						ps.print(c.wordList[word]+"="+emit[i][position][word]+"\t");
 				}
 				ps.println();
 			}
@@ -344,4 +370,26 @@ public class PhraseCluster {
 		}
 		
 	}
+	
+	double posterior_l1lmax()
+	{
+		double sum=0;
+		for(int phrase=0;phrase<c.data.length;phrase++)
+		{
+			int [][] data = c.data[phrase];
+			double [] maxes = new double[K];
+			for(int ctx=0;ctx<data.length;ctx++)
+			{
+				int context[]=data[ctx];
+				double p[]=posterior(phrase,context);
+				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;
+	}
 }
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java
index 99545371..b8f1f24a 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseCorpus.java
@@ -9,11 +9,9 @@ import java.util.HashMap;
 import java.util.Scanner;
 
 public class PhraseCorpus {
-
 	
 	public static String LEX_FILENAME="../pdata/lex.out";
 	public static String DATA_FILENAME="../pdata/btec.con";
-	public static int NUM_CONTEXT=4;
 	
 	public HashMap<String,Integer>wordLex;
 	public HashMap<String,Integer>phraseLex;
@@ -23,6 +21,7 @@ public class PhraseCorpus {
 	
 	//data[phrase][num context][position]
 	public int data[][][];
+	public int numContexts;
 	
 	public static void main(String[] args) {
 		// TODO Auto-generated method stub
@@ -40,6 +39,7 @@ public class PhraseCorpus {
 		
 		ArrayList<int[][]>dataList=new ArrayList<int[][]>();
 		String line=null;
+		numContexts = 0;
 		
 		while((line=readLine(r))!=null){
 			
@@ -54,7 +54,12 @@ public class PhraseCorpus {
 			for(int i=0;i<toks.length;i+=2){
 				String ctx=toks[i];
 				String words[]=ctx.split(" ");
-				int []context=new int [NUM_CONTEXT+1];
+				if (numContexts == 0)
+					numContexts = words.length - 1;
+				else
+					assert numContexts == words.length - 1;
+				
+				int []context=new int [numContexts+1];
 				int idx=0;
 				for(String word:words){
 					if(word.equals("<PHRASE>")){
@@ -68,9 +73,7 @@ public class PhraseCorpus {
 				String count=toks[i+1];
 				context[idx]=Integer.parseInt(count.trim().substring(2));
 				
-				
 				ctxList.add(context);
-				
 			}
 			
 			dataList.add(ctxList.toArray(new int [0][]));
@@ -157,13 +160,17 @@ public class PhraseCorpus {
 		return dict;
 	}
 	
-	public String getContextString(int context[])
+	public String getContextString(int context[], boolean addPhraseMarker)
 	{
 		StringBuffer b = new StringBuffer();
 		for (int i=0;i<context.length-1;i++)
 		{
 			if (b.length() > 0)
 				b.append(" ");
+
+			if (i == context.length/2)
+				b.append("<PHRASE> ");
+			
 			b.append(wordList[context[i]]);
 		}
 		return b.toString();
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
index 71c91b96..b7c62261 100644
--- a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
+++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java
@@ -20,17 +20,17 @@ import optimization.util.MathUtils;
 public class PhraseObjective extends ProjectedObjective{
 
 	private static final double GRAD_DIFF = 0.002;
-	public static double INIT_STEP_SIZE=1;
-	public static double VAL_DIFF=0.001;
-	private double c1=0.0001;
-	private double c2=0.9;
+	public static double INIT_STEP_SIZE = 10;
+	public static double VAL_DIFF = 0.001; // FIXME needs to be tuned
+	//private double c1=0.0001; // wolf stuff
+	//private double c2=0.9;
 	
 	private PhraseCluster c;
 	
 	/**@brief
 	 *  for debugging purposes
 	 */
-	public static PrintStream ps;
+	//public static PrintStream ps;
 	
 	/**@brief current phrase being optimzed*/
 	public int phrase;
@@ -61,7 +61,7 @@ public class PhraseObjective extends ProjectedObjective{
 	/**@brief likelihood under p
 	 * 
 	 */
-	private double llh;
+	public double llh;
 	
 	public PhraseObjective(PhraseCluster cluster, int phraseIdx){
 		phrase=phraseIdx;
@@ -181,7 +181,7 @@ public class PhraseObjective extends ProjectedObjective{
 		boolean succed = optimizer.optimize(this,stats,compositeStop);
 //		System.out.println("Ended optimzation Projected Gradient Descent\n" + stats.prettyPrint(1));
 		if(succed){
-			System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
+			//System.out.println("Ended optimization in " + optimizer.getCurrentIteration());
 		}else{
 			System.out.println("Failed to optimize");
 		}
@@ -208,6 +208,10 @@ public class PhraseObjective extends ProjectedObjective{
 		double kl=-loglikelihood
 			+MathUtils.dotProduct(parameters, gradient);
 //		ps.print(", "+kl);
+		//System.out.println("llh " + llh);
+		//System.out.println("kl " + kl);
+		
+
 		l=l-kl;
 		double sum=0;
 		for(int tag=0;tag<c.K;tag++){
@@ -219,6 +223,7 @@ public class PhraseObjective extends ProjectedObjective{
 			}
 			sum+=max;
 		}
+		//System.out.println("l1lmax " + sum);
 //		ps.println(", "+sum);
 		l=l-c.scale*sum;
 		return l;
diff --git a/gi/posterior-regularisation/train_pr_global.py b/gi/posterior-regularisation/train_pr_global.py
index 8b80c6bc..f2806b6e 100644
--- a/gi/posterior-regularisation/train_pr_global.py
+++ b/gi/posterior-regularisation/train_pr_global.py
@@ -290,10 +290,4 @@ for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
         cz = sum(conditionals)
         conditionals /= cz
 
-        #scores = zeros(num_tags)
-        #li = lamba_index[phrase, context]
-        #for t in range(num_tags):
-        #    scores[t] = conditionals[t] * exp(-lamba[li + t])
-
-        #print '%s\t%s ||| C=%d ||| %d |||' % (phrase, context, count, argmax(scores)), scores / sum(scores)
-        print '%s\t%s ||| C=%d ||| %d |||' % (phrase, context, count, argmax(conditionals)), conditionals
+        print '%s\t%s ||| C=%d |||' % (phrase, context, argmax(conditionals)), conditionals
diff --git a/gi/posterior-regularisation/train_pr_parallel.py b/gi/posterior-regularisation/train_pr_parallel.py
index 4de7f504..3b9cefed 100644
--- a/gi/posterior-regularisation/train_pr_parallel.py
+++ b/gi/posterior-regularisation/train_pr_parallel.py
@@ -41,7 +41,7 @@ for line in sys.stdin:
 # Step 2: initialise the model parameters
 #
 
-num_tags = 5
+num_tags = 25
 num_types = len(types)
 num_phrases = len(edges_phrase_to_context)
 num_contexts = len(edges_context_to_phrase)
@@ -86,7 +86,7 @@ class GlobalDualObjective:
                     self.posterior[index,t] = prob
                 z = sum(self.posterior[index,:])
                 self.posterior[index,:] /= z
-                self.llh += log(z)
+                self.llh += log(z) * count
                 index += 1
 
     def objective(self, ls):
@@ -192,7 +192,7 @@ class LocalDualObjective:
                 self.posterior[i,t] = prob
             z = sum(self.posterior[i,:])
             self.posterior[i,:] /= z
-            self.llh += log(z)
+            self.llh += log(z) * count
 
     def objective(self, ls):
         edges = edges_phrase_to_context[self.phraseId][1]
@@ -243,9 +243,10 @@ class LocalDualObjective:
                 gradient[t,i,t] -= count
         return gradient.reshape((num_tags, len(edges)*num_tags))
 
-    def optimize(self):
+    def optimize(self, ls=None):
         edges = edges_phrase_to_context[self.phraseId][1]
-        ls = zeros(len(edges) * num_tags)
+        if ls == None:
+            ls = zeros(len(edges) * num_tags)
         #print '\tpre lambda optimisation dual', self.objective(ls) #, 'primal', primal(lamba)
         ls = scipy.optimize.fmin_slsqp(self.objective, ls,
                                 bounds=[(0, self.scale)] * len(edges) * num_tags,
@@ -253,6 +254,7 @@ class LocalDualObjective:
                                 fprime=self.gradient,
                                 fprime_ieqcons=self.constraints_gradient,
                                 iprint=0) # =2 for verbose
+        #print '\tlambda', list(ls)
         #print '\tpost lambda optimisation dual', self.objective(ls) #, 'primal', primal(lamba)
 
         # returns llh, kl and l1lmax contribution
@@ -263,8 +265,9 @@ class LocalDualObjective:
                 lmax = max(lmax, self.q[i,t])
             l1lmax += lmax
 
-        return self.llh, -self.objective(ls) + dot(ls, self.gradient(ls)), l1lmax
+        return self.llh, -self.objective(ls) + dot(ls, self.gradient(ls)), l1lmax, ls
 
+ls = [None] * num_phrases
 for iteration in range(20):
     tagCounts = [zeros(num_tags) for p in range(num_phrases)]
     contextWordCounts = [[zeros(num_types) for t in range(num_tags)] for i in range(4)]
@@ -275,11 +278,13 @@ for iteration in range(20):
         for p in range(num_phrases):
             o = LocalDualObjective(p, delta)
             #print '\toptimising lambda for phrase', p, '=', edges_phrase_to_context[p][0]
-            obj = o.optimize()
-            print '\tphrase', p, 'deltas', obj
+            #print '\toptimising lambda for phrase', p, 'ls', ls[p]
+            obj = o.optimize(ls[p])
+            #print '\tphrase', p, 'deltas', obj
             llh += obj[0]
             kl += obj[1]
             l1lmax += obj[2]
+            ls[p] = obj[3]
 
             edges = edges_phrase_to_context[p][1]
             for j, (context, count) in enumerate(edges):
@@ -305,7 +310,7 @@ for iteration in range(20):
                         contextWordCounts[i][t][types[context[i]]] += count * o.q[index,t]
                 index += 1
 
-    print 'iteration', iteration, 'objective', (llh + kl + delta * l1lmax), 'llh', llh, 'kl', kl, 'l1lmax', l1lmax
+    print 'iteration', iteration, 'objective', (llh - kl - delta * l1lmax), 'llh', llh, 'kl', kl, 'l1lmax', l1lmax
 
     # M-step
     for p in range(num_phrases):
@@ -325,4 +330,4 @@ for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
         cz = sum(conditionals)
         conditionals /= cz
 
-        print '%s\t%s ||| C=%d ||| %d |||' % (phrase, context, count, argmax(conditionals)), conditionals
+        print '%s\t%s ||| C=%d |||' % (phrase, context, argmax(conditionals)), conditionals
-- 
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