diff options
Diffstat (limited to 'gi/posterior-regularisation/train_pr_global.py')
| -rw-r--r-- | gi/posterior-regularisation/train_pr_global.py | 117 |
1 files changed, 66 insertions, 51 deletions
diff --git a/gi/posterior-regularisation/train_pr_global.py b/gi/posterior-regularisation/train_pr_global.py index 467069ef..da32fa18 100644 --- a/gi/posterior-regularisation/train_pr_global.py +++ b/gi/posterior-regularisation/train_pr_global.py @@ -40,6 +40,8 @@ print 'Read in', num_edges, 'edges and', len(types), 'word types' num_tags = 5 num_types = len(types) +num_phrases = len(edges_phrase_to_context) +num_contexts = len(edges_context_to_phrase) delta = int(sys.argv[1]) gamma = int(sys.argv[2]) @@ -51,7 +53,8 @@ tagDist = normalise(random(num_tags)+1) # Pr(context at pos i = w | tag) indexed by i, tag, word contextWordDist = [[normalise(random(num_types)+1) for t in range(num_tags)] for i in range(4)] # PR langrange multipliers -lamba = zeros(num_edges * num_tags) +lamba = zeros(2 * num_edges * num_tags) +omega_offset = num_edges * num_tags lamba_index = {} next = 0 for phrase, ccs in edges_phrase_to_context.items(): @@ -88,7 +91,8 @@ for iteration in range(20): local_z = 0 for t in range(num_tags): - local_z += conditionals[t] * exp(-ls[lamba_index[phrase,context] + t]) + li = lamba_index[phrase,context] + t + local_z += conditionals[t] * exp(-ls[li] - ls[omega_offset+li]) logz += log(local_z) * count #print 'ls', ls @@ -96,12 +100,8 @@ for iteration in range(20): #print 'dual', logz return logz - def primal(ls): - # FIXME: returns negative values for KL (impossible) - logz = dual(ls) - kl = -logz - - expectations = zeros(lamba.shape) + def loglikelihood(): + llh = 0 for phrase, ccs in edges_phrase_to_context.items(): for context, count in ccs.items(): conditionals = zeros(num_tags) @@ -111,17 +111,15 @@ for iteration in range(20): prob *= contextWordDist[i][t][types[context[i]]] conditionals[t] = prob cz = sum(conditionals) - conditionals /= cz + llh += log(cz) * count + return llh - scores = zeros(num_tags) - for t in range(num_tags): - scores[t] = conditionals[t] * exp(-ls[lamba_index[phrase,context] + t]) - local_z = sum(scores) - - for t in range(num_tags): - li = lamba_index[phrase,context] + t - expectations[li] = scores[t] / local_z * count - kl -= expectations[li] * ls[li] + def primal(ls): + # FIXME: returns negative values for KL (impossible) + logz = dual(ls) + expectations = -dual_deriv(ls) + kl = -logz - dot(ls, expectations) + llh = loglikelihood() pt_l1linf = 0 for phrase, ccs in edges_phrase_to_context.items(): @@ -143,11 +141,11 @@ for iteration in range(20): if s > best: best = s ct_l1linf += best - return kl, pt_l1linf, ct_l1linf, 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] - deriv = zeros(num_edges * num_tags) + deriv = zeros(2 * num_edges * num_tags) for phrase, ccs in edges_phrase_to_context.items(): for context, count in ccs.items(): conditionals = zeros(num_tags) @@ -161,58 +159,74 @@ for iteration in range(20): scores = zeros(num_tags) for t in range(num_tags): - scores[t] = conditionals[t] * exp(-ls[lamba_index[phrase,context] + t]) + li = lamba_index[phrase,context] + t + scores[t] = conditionals[t] * exp(-ls[li] - ls[omega_offset + li]) local_z = sum(scores) for t in range(num_tags): - deriv[lamba_index[phrase,context] + t] -= count * scores[t] / local_z + 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 #print 'ddual', deriv return deriv def constraints(ls): - cons = [] - if delta > 0: - for phrase, ccs in edges_phrase_to_context.items(): - for t in range(num_tags): + cons = zeros(num_phrases * num_tags + num_edges * num_tags) + + index = 0 + for phrase, ccs in edges_phrase_to_context.items(): + for t in range(num_tags): + if delta > 0: total = delta for cprime in ccs.keys(): total -= ls[lamba_index[phrase, cprime] + t] - cons.append(total) + cons[index] = total + index += 1 - if gamma > 0: - for context, pcs in edges_context_to_phrase.items(): - for t in range(num_tags): + for context, pcs in edges_context_to_phrase.items(): + for t in range(num_tags): + if gamma > 0: total = gamma for pprime in pcs.keys(): - total -= ls[lamba_index[pprime, context] + t] - cons.append(total) + total -= ls[omega_offset + lamba_index[pprime, context] + t] + cons[index] = total + index += 1 + #print 'cons', cons return cons def constraints_deriv(ls): - cons = [] - if delta > 0: - for phrase, ccs in edges_phrase_to_context.items(): - for t in range(num_tags): - d = zeros(num_edges * num_tags) + cons = zeros((num_phrases * num_tags + num_edges * num_tags, 2 * num_edges * num_tags)) + + index = 0 + for phrase, ccs in edges_phrase_to_context.items(): + for t in range(num_tags): + if delta > 0: + d = cons[index,:]#zeros(num_edges * num_tags) for cprime in ccs.keys(): d[lamba_index[phrase, cprime] + t] = -1 - cons.append(d) + #cons[index] = d + index += 1 - if gamma > 0: - for context, pcs in edges_context_to_phrase.items(): - for t in range(num_tags): - d = zeros(num_edges * num_tags) + for context, pcs in edges_context_to_phrase.items(): + for t in range(num_tags): + if gamma > 0: + d = cons[index,:]#d = zeros(num_edges * num_tags) for pprime in pcs.keys(): - d[lamba_index[pprime, context] + t] = -1 - cons.append(d) + d[omega_offset + lamba_index[pprime, context] + t] = -1 + #cons[index] = d + index += 1 #print 'dcons', cons return cons print 'Pre lambda optimisation dual', dual(lamba), 'primal', primal(lamba) + #print 'lambda', lamba, lamba.shape + #print 'bounds', [(0, max(delta, gamma))] * (2 * num_edges * num_tags) + lamba = scipy.optimize.fmin_slsqp(dual, lamba, - bounds=[(0, delta)] * (num_edges * num_tags), + bounds=[(0, max(delta, gamma))] * (2 * num_edges * num_tags), f_ieqcons=constraints, fprime=dual_deriv, fprime_ieqcons=constraints_deriv, @@ -236,7 +250,7 @@ for iteration in range(20): scores = zeros(num_tags) li = lamba_index[phrase, context] for t in range(num_tags): - scores[t] = conditionals[t] * exp(-lamba[li + t]) + scores[t] = conditionals[t] * exp(-lamba[li + t] - lamba[omega_offset + li + t]) z += count * sum(scores) tagCounts += count * scores @@ -264,9 +278,10 @@ for phrase, ccs in edges_phrase_to_context.items(): 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]) + #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(scores)), scores / sum(scores) + print '%s\t%s ||| C=%d ||| %d |||' % (phrase, context, count, argmax(conditionals)), conditionals |