cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 296 deletions

diff --git a/gi/posterior-regularisation/train_pr_global.py b/gi/posterior-regularisation/train_pr_global.py
deleted file mode 100644
index 8521bccb..00000000
--- a/gi/posterior-regularisation/train_pr_global.py
+++ /dev/null
@@ -1,296 +0,0 @@
-import sys
-import scipy.optimize
-from numpy import *
-from numpy.random import random
-
-#
-# Step 1: load the concordance counts
-# 
-
-edges_phrase_to_context = []
-edges_context_to_phrase = []
-types = {}
-context_types = {}
-num_edges = 0
-
-for line in sys.stdin:
-    phrase, rest = line.strip().split('\t')
-    parts = rest.split('|||')
-    edges_phrase_to_context.append((phrase, []))
-    for i in range(0, len(parts), 2):
-        context, count = parts[i:i+2]
-
-        ctx = tuple(filter(lambda x: x != '<PHRASE>', context.split()))
-        cnt = int(count.strip()[2:])
-        edges_phrase_to_context[-1][1].append((ctx, cnt))
-
-        cid = context_types.get(ctx, len(context_types))
-        if cid == len(context_types):
-            context_types[ctx] = cid
-            edges_context_to_phrase.append((ctx, []))
-        edges_context_to_phrase[cid][1].append((phrase, cnt))
-
-        for token in ctx:
-            types.setdefault(token, len(types))
-        for token in phrase.split():
-            types.setdefault(token, len(types))
-
-        num_edges += 1
-
-print 'Read in', num_edges, 'edges and', len(types), 'word types'
-
-print 'edges_phrase_to_context', edges_phrase_to_context
-
-#
-# Step 2: initialise the model parameters
-#
-
-num_tags = 10
-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])
-
-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)]
-# 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)]
-# PR langrange multipliers
-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:
-    for context, count in ccs:
-        lamba_index[phrase,context] = next
-        next += num_tags
-#print lamba_index
-
-#
-# Step 3: expectation maximisation
-#
-
-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)]
-
-    #print 'tagDist', tagDist
-    #print 'contextWordCounts[0][0]', contextWordCounts[0][0]
-
-    # Tune lambda
-    # dual: min log Z(lamba) s.t. lamba >= 0;
-    # sum_c lamba_pct <= delta; sum_p lamba_pct <= gamma
-    def dual(ls):
-        logz = 0
-        for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-            for context, count in ccs:
-                conditionals = zeros(num_tags)
-                for t in range(num_tags):
-                    prob = tagDist[p][t]
-                    for i in range(4):
-                        prob *= contextWordDist[i][t][types[context[i]]]
-                    conditionals[t] = prob
-                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
-                    local_z += conditionals[t] * exp(-ls[li] - ls[omega_offset+li])
-                logz += log(local_z) * count
-
-        #print 'ls', ls
-        #print 'lambda', list(ls)
-        #print 'dual', logz
-        return logz
-
-    def loglikelihood():
-        llh = 0
-        for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-            for context, count in ccs:
-                conditionals = zeros(num_tags)
-                for t in range(num_tags):
-                    prob = tagDist[p][t]
-                    for i in range(4):
-                        prob *= contextWordDist[i][t][types[context[i]]]
-                    conditionals[t] = prob
-                cz = sum(conditionals)
-                llh += log(cz) * count
-        return llh
-
-    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:
-            for t in range(num_tags):
-                best = -1e500
-                for context, count in ccs:
-                    li = lamba_index[phrase,context] + t
-                    s = expectations[li]
-                    if s > best: best = s
-                pt_l1linf += best
-
-        ct_l1linf = 0
-        for context, pcs in edges_context_to_phrase:
-            for t in range(num_tags):
-                best = -1e500
-                for phrase, count in pcs:
-                    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
-
-    def dual_deriv(ls):
-        # d/dl log(z) = E_q[phi]
-        deriv = zeros(2 * num_edges * num_tags)
-        for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-            for context, count in ccs:
-                conditionals = zeros(num_tags)
-                for t in range(num_tags):
-                    prob = tagDist[p][t]
-                    for i in range(4):
-                        prob *= contextWordDist[i][t][types[context[i]]]
-                    conditionals[t] = prob
-                cz = sum(conditionals)
-                conditionals /= cz
-
-                scores = zeros(num_tags)
-                for t in range(num_tags):
-                    li = lamba_index[phrase,context] + t
-                    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):
-                    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)
-        return deriv
-
-    def constraints(ls):
-        cons = zeros(num_phrases * num_tags + num_edges * num_tags)
-
-        index = 0
-        for phrase, ccs in edges_phrase_to_context:
-            for t in range(num_tags):
-                if delta > 0:
-                    total = delta
-                    for cprime, count in ccs:
-                        total -= ls[lamba_index[phrase, cprime] + t]
-                    cons[index] = total
-                index += 1
-
-        for context, pcs in edges_context_to_phrase:
-            for t in range(num_tags):
-                if gamma > 0:
-                    total = gamma
-                    for pprime, count in pcs:
-                        total -= ls[omega_offset + lamba_index[pprime, context] + t]
-                    cons[index] = total
-                index += 1
-
-        #print 'cons', cons
-        return cons
-
-    def constraints_deriv(ls):
-        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:
-            for t in range(num_tags):
-                if delta > 0:
-                    d = cons[index,:]#zeros(num_edges * num_tags)
-                    for cprime, count in ccs:
-                        d[lamba_index[phrase, cprime] + t] = -1
-                    #cons[index] = d
-                index += 1
-
-        for context, pcs in edges_context_to_phrase:
-            for t in range(num_tags):
-                if gamma > 0:
-                    d = cons[index,:]#d = zeros(num_edges * num_tags)
-                    for pprime, count in pcs:
-                        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, max(delta, gamma))] * (2 * num_edges * num_tags),
-                            f_ieqcons=constraints,
-                            fprime=dual_deriv,
-                            fprime_ieqcons=constraints_deriv,
-                            iprint=0)
-    print 'Post lambda optimisation dual', dual(lamba), 'primal', primal(lamba)
-
-    # E-step
-    llh = log_z = 0
-    for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-        for context, count in ccs:
-            conditionals = zeros(num_tags)
-            for t in range(num_tags):
-                prob = tagDist[p][t]
-                for i in range(4):
-                    prob *= contextWordDist[i][t][types[context[i]]]
-                conditionals[t] = prob
-            cz = sum(conditionals)
-            conditionals /= cz
-            llh += log(cz) * count
-
-            q = zeros(num_tags)
-            li = lamba_index[phrase, context]
-            for t in range(num_tags):
-                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 * q[t] / qz
-
-            for i in range(4):
-                for t in range(num_tags):
-                    contextWordCounts[i][t][types[context[i]]] += count * q[t] / qz
-
-    print 'iteration', iteration, 'llh', llh, 'logz', log_z
-
-    # M-step
-    for p in range(num_phrases):
-        tagDist[p] = normalise(tagCounts[p])
-    for i in range(4):
-        for t in range(num_tags):
-            contextWordDist[i][t] = normalise(contextWordCounts[i][t])
-
-for p, (phrase, ccs) in enumerate(edges_phrase_to_context):
-    for context, count in ccs:
-        conditionals = zeros(num_tags)
-        for t in range(num_tags):
-            prob = tagDist[p][t]
-            for i in range(4):
-                prob *= contextWordDist[i][t][types[context[i]]]
-            conditionals[t] = prob
-        cz = sum(conditionals)
-        conditionals /= cz
-
-        print '%s\t%s ||| C=%d |||' % (phrase, context, argmax(conditionals)), conditionals