diff options
9 files changed, 120 insertions, 112 deletions
diff --git a/gi/pipeline/evaluation-pipeline.pl b/gi/pipeline/evaluation-pipeline.pl index cc3d6fd5..c0cd9a69 100755 --- a/gi/pipeline/evaluation-pipeline.pl +++ b/gi/pipeline/evaluation-pipeline.pl @@ -146,8 +146,8 @@ $drefs = mydircat($corpdir, $drefs); my $test = mydircat($corpdir, $tests{$lp}); my $teval = mydircat($corpdir, $testevals{$lp}); -die "Can't find test: $test\n" unless -f $test; -assert_exec($teval); +#die "Can't find test: $test\n" unless -f $test; +#assert_exec($teval); `mkdir -p $outdir`; diff --git a/gi/posterior-regularisation/prjava/Makefile b/gi/posterior-regularisation/prjava/Makefile index abd9b964..a16adcde 100644 --- a/gi/posterior-regularisation/prjava/Makefile +++ b/gi/posterior-regularisation/prjava/Makefile @@ -1,5 +1,5 @@ all: - ant + ant dist clean: ant clean diff --git a/gi/posterior-regularisation/prjava/src/arr/F.java b/gi/posterior-regularisation/prjava/src/arr/F.java index 54dadeac..79de5d1a 100644 --- a/gi/posterior-regularisation/prjava/src/arr/F.java +++ b/gi/posterior-regularisation/prjava/src/arr/F.java @@ -56,6 +56,7 @@ public class F { }
public static double l1norm(double a[]){
+ // FIXME: this isn't the l1 norm for a < 0
double norm=0;
for(int i=0;i<a.length;i++){
norm += a[i];
@@ -63,6 +64,14 @@ public class F { return norm;
}
+ public static double l2norm(double a[]){
+ double norm=0;
+ for(int i=0;i<a.length;i++){
+ norm += a[i]*a[i];
+ }
+ return Math.sqrt(norm);
+ }
+
public static int argmax(double probs[])
{
double m = Double.NEGATIVE_INFINITY;
diff --git a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java index 0a4a5445..2fcb7990 100644 --- a/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java +++ b/gi/posterior-regularisation/prjava/src/optimization/gradientBasedMethods/AbstractGradientBaseMethod.java @@ -56,9 +56,10 @@ public abstract class AbstractGradientBaseMethod implements Optimizer{ stats.collectInitStats(this, o); direction = new double[o.getNumParameters()]; initializeStructures(o, stats, stop); - for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){ -// System.out.println("starting iterations: parameters:" ); -// o.printParameters(); + for (currentProjectionIteration = 1; currentProjectionIteration < maxNumberOfIterations; currentProjectionIteration++){ + //System.out.println("\tgradient descent iteration " + currentProjectionIteration); + //System.out.print("\tparameters:" ); + //o.printParameters(); previousValue = currValue; currValue = o.getValue(); gradient = o.getGradient(); @@ -76,7 +77,7 @@ public abstract class AbstractGradientBaseMethod implements Optimizer{ updateStructuresBeforeStep(o, stats, stop); lso.reset(direction); step = lineSearch.getStepSize(lso); -// System.out.println("Leave with step: " + step); + //System.out.println("\t\tLeave with step: " + step); if(step==-1){ System.out.println("Failed to find step"); stats.collectFinalStats(this, o); diff --git a/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java b/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java index eec11bcf..f22afcaf 100644 --- a/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java +++ b/gi/posterior-regularisation/prjava/src/optimization/projections/SimplexProjection.java @@ -40,7 +40,7 @@ public class SimplexProjection extends Projection{ for (int i = 0; i < ds.length; i++) { currentSum+=ds[i]; theta = (currentSum-scale)/(i+1); - if(ds[i]-theta <= 0){ + if(ds[i]-theta < -1e-10){ break; } previousTheta = theta; diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java index abd868c4..68148248 100644 --- a/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java +++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseCluster.java @@ -373,12 +373,13 @@ public class PhraseCluster { return primal;
}
+
+ double[] lambda;
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, scalePT, scaleCT);
diff --git a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java b/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java index ff135a3d..a9d3529c 100644 --- a/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java +++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseContextObjective.java @@ -32,7 +32,7 @@ public class PhraseContextObjective extends ProjectedObjective private PhraseCluster c;
- // un-regularized unnormalized posterior, p[edge][tag]
+ // un-regularized unnormalized posterior, p[edge][tag]
// P(tag|edge) \propto P(tag|phrase)P(context|tag)
private double p[][];
@@ -144,7 +144,7 @@ public class PhraseContextObjective extends ProjectedObjective gradient[ic]=-q[e][tag];
}
}
- //System.out.println("objective " + loglikelihood + " gradient: " + Arrays.toString(gradient));
+ //System.out.println("objective " + loglikelihood + " ||gradient||_2: " + arr.F.l2norm(gradient));
objectiveTime += System.currentTimeMillis() - begin;
}
@@ -154,106 +154,100 @@ public class PhraseContextObjective extends ProjectedObjective long begin = System.currentTimeMillis();
List<Future<?>> tasks = new ArrayList<Future<?>>();
- //System.out.println("projectPoint: " + Arrays.toString(point));
+ //System.out.println("\t\tprojectPoint: " + Arrays.toString(point));
Arrays.fill(newPoint, 0, newPoint.length, 0);
- if (scalePT > 0)
+ // first project using the phrase-tag constraints,
+ // for all p,t: sum_c lambda_ptc < scaleP
+ if (pool == null)
{
- // first project using the phrase-tag constraints,
- // for all p,t: sum_c lambda_ptc < scaleP
- if (pool == null)
+ for (int p = 0; p < c.c.getNumPhrases(); ++p)
{
- for (int p = 0; p < c.c.getNumPhrases(); ++p)
+ List<Edge> edges = c.c.getEdgesForPhrase(p);
+ double[] toProject = new double[edges.size()];
+ for(int tag=0;tag<c.K;tag++)
{
- List<Edge> edges = c.c.getEdgesForPhrase(p);
- double[] toProject = new double[edges.size()];
- for(int tag=0;tag<c.K;tag++)
- {
- for(int e=0; e<edges.size(); e++)
- toProject[e] = point[index(edges.get(e), tag, true)];
- long lbegin = System.currentTimeMillis();
- projectionPhrase.project(toProject);
- actualProjectionTime += System.currentTimeMillis() - lbegin;
- for(int e=0; e<edges.size(); e++)
- newPoint[index(edges.get(e), tag, true)] = toProject[e];
- }
+ for(int e=0; e<edges.size(); e++)
+ toProject[e] = point[index(edges.get(e), tag, true)];
+ long lbegin = System.currentTimeMillis();
+ projectionPhrase.project(toProject);
+ actualProjectionTime += System.currentTimeMillis() - lbegin;
+ for(int e=0; e<edges.size(); e++)
+ newPoint[index(edges.get(e), tag, true)] = toProject[e];
}
}
- else // do above in parallel using thread pool
- {
- for (int p = 0; p < c.c.getNumPhrases(); ++p)
+ }
+ else // do above in parallel using thread pool
+ {
+ for (int p = 0; p < c.c.getNumPhrases(); ++p)
+ {
+ final int phrase = p;
+ final double[] inPoint = point;
+ Runnable task = new Runnable()
{
- final int phrase = p;
- final double[] inPoint = point;
- Runnable task = new Runnable()
+ public void run()
{
- public void run()
+ List<Edge> edges = c.c.getEdgesForPhrase(phrase);
+ double toProject[] = new double[edges.size()];
+ for(int tag=0;tag<c.K;tag++)
{
- List<Edge> edges = c.c.getEdgesForPhrase(phrase);
- double toProject[] = new double[edges.size()];
- for(int tag=0;tag<c.K;tag++)
- {
- for(int e=0; e<edges.size(); e++)
- toProject[e] = inPoint[index(edges.get(e), tag, true)];
- projectionPhrase.project(toProject);
- for(int e=0; e<edges.size(); e++)
- newPoint[index(edges.get(e), tag, true)] = toProject[e];
- }
- }
- };
- tasks.add(pool.submit(task));
- }
+ for(int e=0; e<edges.size(); e++)
+ toProject[e] = inPoint[index(edges.get(e), tag, true)];
+ projectionPhrase.project(toProject);
+ for(int e=0; e<edges.size(); e++)
+ newPoint[index(edges.get(e), tag, true)] = toProject[e];
+ }
+ }
+ };
+ tasks.add(pool.submit(task));
}
}
//System.out.println("after PT " + Arrays.toString(newPoint));
- if (scaleCT > 1e-6)
+ // now project using the context-tag constraints,
+ // for all c,t: sum_p omega_pct < scaleC
+ if (pool == null)
{
- // now project using the context-tag constraints,
- // for all c,t: sum_p omega_pct < scaleC
- if (pool == null)
+ for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)
{
- for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)
+ List<Edge> edges = c.c.getEdgesForContext(ctx);
+ double toProject[] = new double[edges.size()];
+ for(int tag=0;tag<c.K;tag++)
{
- List<Edge> edges = c.c.getEdgesForContext(ctx);
- double toProject[] = new double[edges.size()];
- for(int tag=0;tag<c.K;tag++)
- {
- for(int e=0; e<edges.size(); e++)
- toProject[e] = point[index(edges.get(e), tag, false)];
- long lbegin = System.currentTimeMillis();
- projectionContext.project(toProject);
- actualProjectionTime += System.currentTimeMillis() - lbegin;
- for(int e=0; e<edges.size(); e++)
- newPoint[index(edges.get(e), tag, false)] = toProject[e];
- }
+ for(int e=0; e<edges.size(); e++)
+ toProject[e] = point[index(edges.get(e), tag, false)];
+ long lbegin = System.currentTimeMillis();
+ projectionContext.project(toProject);
+ actualProjectionTime += System.currentTimeMillis() - lbegin;
+ for(int e=0; e<edges.size(); e++)
+ newPoint[index(edges.get(e), tag, false)] = toProject[e];
}
}
- else
+ }
+ else
+ {
+ // do above in parallel using thread pool
+ for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)
{
- // do above in parallel using thread pool
- for (int ctx = 0; ctx < c.c.getNumContexts(); ++ctx)
+ final int context = ctx;
+ final double[] inPoint = point;
+ Runnable task = new Runnable()
{
- final int context = ctx;
- final double[] inPoint = point;
- Runnable task = new Runnable()
+ public void run()
{
- public void run()
+ List<Edge> edges = c.c.getEdgesForContext(context);
+ double toProject[] = new double[edges.size()];
+ for(int tag=0;tag<c.K;tag++)
{
- List<Edge> edges = c.c.getEdgesForContext(context);
- double toProject[] = new double[edges.size()];
- for(int tag=0;tag<c.K;tag++)
- {
- for(int e=0; e<edges.size(); e++)
- toProject[e] = inPoint[index(edges.get(e), tag, false)];
- projectionContext.project(toProject);
- for(int e=0; e<edges.size(); e++)
- newPoint[index(edges.get(e), tag, false)] = toProject[e];
- }
+ for(int e=0; e<edges.size(); e++)
+ toProject[e] = inPoint[index(edges.get(e), tag, false)];
+ projectionContext.project(toProject);
+ for(int e=0; e<edges.size(); e++)
+ newPoint[index(edges.get(e), tag, false)] = toProject[e];
}
- };
- tasks.add(pool.submit(task));
- }
+ }
+ };
+ tasks.add(pool.submit(task));
}
}
@@ -283,9 +277,8 @@ public class PhraseContextObjective extends ProjectedObjective double[] tmp = newPoint;
newPoint = point;
projectionTime += System.currentTimeMillis() - begin;
-
- //System.out.println("\treturning " + Arrays.toString(tmp));
+ //System.out.println("\t\treturning " + Arrays.toString(tmp));
return tmp;
}
@@ -405,6 +398,6 @@ public class PhraseContextObjective extends ProjectedObjective // L - KL(q||p) - scalePT * l1lmax_phrase - scaleCT * l1lmax_context
public double primal()
{
- return loglikelihood() - KL_divergence() - scalePT * phrase_l1lmax() - scalePT * context_l1lmax();
+ return loglikelihood() - KL_divergence() - scalePT * phrase_l1lmax() - scaleCT * 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 33167c20..0e2ab4b9 100644 --- a/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java +++ b/gi/posterior-regularisation/prjava/src/phrase/PhraseObjective.java @@ -1,5 +1,6 @@ package phrase;
+import java.util.Arrays;
import java.util.List;
import optimization.gradientBasedMethods.ProjectedGradientDescent;
@@ -155,7 +156,7 @@ public class PhraseObjective extends ProjectedObjective @Override
public String toString() {
- return "No need for pointless toString";
+ return Arrays.toString(parameters);
}
public double [][]posterior(){
diff --git a/gi/posterior-regularisation/train_pr_global.py b/gi/posterior-regularisation/train_pr_global.py index f2806b6e..8521bccb 100644 --- a/gi/posterior-regularisation/train_pr_global.py +++ b/gi/posterior-regularisation/train_pr_global.py @@ -45,7 +45,7 @@ print 'edges_phrase_to_context', edges_phrase_to_context # Step 2: initialise the model parameters # -num_tags = 5 +num_tags = 10 num_types = len(types) num_phrases = len(edges_phrase_to_context) num_contexts = len(edges_context_to_phrase) @@ -56,11 +56,11 @@ def normalise(a): return a / float(sum(a)) # Pr(tag | phrase) -#tagDist = [normalise(random(num_tags)+1) for p in range(num_phrases)] -tagDist = [normalise(array(range(1,num_tags+1))) for p in range(num_phrases)] +tagDist = [normalise(random(num_tags)+1) for p in range(num_phrases)] +#tagDist = [normalise(array(range(1,num_tags+1))) for p in range(num_phrases)] # Pr(context at pos i = w | tag) indexed by i, tag, word -contextWordDist = [[normalise(array(range(1,num_types+1))) for t in range(num_tags)] for i in range(4)] -#contextWordDist = [[normalise(random(num_types)+1) for t in range(num_tags)] for i in range(4)] +#contextWordDist = [[normalise(array(range(1,num_types+1))) for t in range(num_tags)] for i in range(4)] +contextWordDist = [[normalise(random(num_types)+1) for t in range(num_tags)] for i in range(4)] # PR langrange multipliers lamba = zeros(2 * num_edges * num_tags) omega_offset = num_edges * num_tags @@ -99,6 +99,8 @@ for iteration in range(20): cz = sum(conditionals) conditionals /= cz + #print 'dual', phrase, context, count, 'p =', conditionals + local_z = 0 for t in range(num_tags): li = lamba_index[phrase,context] + t @@ -106,8 +108,8 @@ for iteration in range(20): logz += log(local_z) * count #print 'ls', ls - print 'lambda', list(ls) - print 'dual', logz + #print 'lambda', list(ls) + #print 'dual', logz return logz def loglikelihood(): @@ -146,12 +148,12 @@ for iteration in range(20): for t in range(num_tags): best = -1e500 for phrase, count in pcs: - li = lamba_index[phrase,context] + t + li = omega_offset + lamba_index[phrase,context] + t s = expectations[li] if s > best: best = s ct_l1linf += best - return llh, kl, pt_l1linf, ct_l1linf, llh + kl + delta * pt_l1linf + gamma * ct_l1linf + return llh, kl, pt_l1linf, ct_l1linf, llh - kl - delta * pt_l1linf - gamma * ct_l1linf def dual_deriv(ls): # d/dl log(z) = E_q[phi] @@ -173,13 +175,13 @@ for iteration in range(20): scores[t] = conditionals[t] * exp(-ls[li] - ls[omega_offset + li]) local_z = sum(scores) + #print 'ddual', phrase, context, count, 'q =', scores / local_z + for t in range(num_tags): - if delta > 0: - deriv[lamba_index[phrase,context] + t] -= count * scores[t] / local_z - if gamma > 0: - deriv[omega_offset + lamba_index[phrase,context] + t] -= count * scores[t] / local_z + deriv[lamba_index[phrase,context] + t] -= count * scores[t] / local_z + deriv[omega_offset + lamba_index[phrase,context] + t] -= count * scores[t] / local_z - print 'ddual', list(deriv) + #print 'ddual', list(deriv) return deriv def constraints(ls): @@ -244,7 +246,7 @@ for iteration in range(20): print 'Post lambda optimisation dual', dual(lamba), 'primal', primal(lamba) # E-step - llh = z = 0 + llh = log_z = 0 for p, (phrase, ccs) in enumerate(edges_phrase_to_context): for context, count in ccs: conditionals = zeros(num_tags) @@ -257,20 +259,21 @@ for iteration in range(20): conditionals /= cz llh += log(cz) * count - scores = zeros(num_tags) + q = zeros(num_tags) li = lamba_index[phrase, context] for t in range(num_tags): - scores[t] = conditionals[t] * exp(-lamba[li + t] - lamba[omega_offset + li + t]) - z += count * sum(scores) + q[t] = conditionals[t] * exp(-lamba[li + t] - lamba[omega_offset + li + t]) + qz = sum(q) + log_z += count * log(qz) for t in range(num_tags): - tagCounts[p][t] += count * scores[t] + tagCounts[p][t] += count * q[t] / qz for i in range(4): for t in range(num_tags): - contextWordCounts[i][t][types[context[i]]] += count * scores[t] + contextWordCounts[i][t][types[context[i]]] += count * q[t] / qz - print 'iteration', iteration, 'llh', llh, 'logz', log(z) + print 'iteration', iteration, 'llh', llh, 'logz', log_z # M-step for p in range(num_phrases): |