diff options
Diffstat (limited to 'gi/posterior-regularisation/train_pr_parallel.py')
| -rw-r--r-- | gi/posterior-regularisation/train_pr_parallel.py | 25 |
1 files changed, 15 insertions, 10 deletions
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 |