cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 400 deletions

diff --git a/gi/posterior-regularisation/train_pr_agree.py b/gi/posterior-regularisation/train_pr_agree.py
deleted file mode 100644
index 9d41362d..00000000
--- a/gi/posterior-regularisation/train_pr_agree.py
+++ /dev/null
@@ -1,400 +0,0 @@
-import sys
-import scipy.optimize
-from scipy.stats import geom
-from numpy import *
-from numpy.random import random, seed
-
-style = sys.argv[1]
-if len(sys.argv) >= 3:
-     seed(int(sys.argv[2]))
-
-#
-# Step 1: load the concordance counts
-# 
-
-edges = []
-word_types = {}
-phrase_types = {}
-context_types = {}
-
-for line in sys.stdin:
-    phrase, rest = line.strip().split('\t')
-    ptoks = tuple(map(lambda t: word_types.setdefault(t, len(word_types)), phrase.split()))
-    pid = phrase_types.setdefault(ptoks, len(phrase_types))
-
-    parts = rest.split('|||')
-    for i in range(0, len(parts), 2):
-        context, count = parts[i:i+2]
-
-        ctx = filter(lambda x: x != '<PHRASE>', context.split())
-        ctoks = tuple(map(lambda t: word_types.setdefault(t, len(word_types)), ctx))
-        cid = context_types.setdefault(ctoks, len(context_types))
-
-        cnt = int(count.strip()[2:])
-        edges.append((pid, cid, cnt))
-
-word_type_list = [None] * len(word_types)
-for typ, index in word_types.items():
-    word_type_list[index] = typ
-
-phrase_type_list = [None] * len(phrase_types)
-for typ, index in phrase_types.items():
-    phrase_type_list[index] = typ
-
-context_type_list = [None] * len(context_types)
-for typ, index in context_types.items():
-    context_type_list[index] = typ
-
-num_tags = 5
-num_types = len(word_types)
-num_phrases = len(phrase_types)
-num_contexts = len(context_types)
-num_edges = len(edges)
-
-print 'Read in', num_edges, 'edges', num_phrases, 'phrases', num_contexts, 'contexts and', num_types, 'word types'
-
-#
-# Step 2:  expectation maximisation 
-#
-
-def normalise(a):
-    return a / float(sum(a))
-
-class PhraseToContextModel:
-    def __init__(self):
-        # Pr(tag | phrase)
-        self.tagDist = [normalise(random(num_tags)+1) for p in range(num_phrases)]
-        # Pr(context at pos i = w | tag) indexed by i, tag, word
-        self.contextWordDist = [[normalise(random(num_types)+1) for t in range(num_tags)] for i in range(4)]
-
-    def prob(self, pid, cid):
-        # return distribution p(tag, context | phrase) as vector of length |tags|
-        context = context_type_list[cid]
-        dist = zeros(num_tags)
-        for t in range(num_tags):
-            prob = self.tagDist[pid][t]
-            for k, tokid in enumerate(context):
-                prob *= self.contextWordDist[k][t][tokid]
-            dist[t] = prob
-        return dist
-
-    def expectation_maximisation_step(self, lamba=None):
-        tagCounts = zeros((num_phrases, num_tags))
-        contextWordCounts = zeros((4, num_tags, num_types))
-
-        # E-step
-        llh = 0
-        for pid, cid, cnt in edges:
-            q = self.prob(pid, cid)
-            z = sum(q)
-            q /= z
-            llh += log(z)
-            context = context_type_list[cid]
-            if lamba != None:
-                q *= exp(lamba)
-                q /= sum(q)
-            for t in range(num_tags):
-                tagCounts[pid][t] += cnt * q[t]
-            for i in range(4):
-                for t in range(num_tags):
-                    contextWordCounts[i][t][context[i]] += cnt * q[t]
-
-        # M-step
-        for p in range(num_phrases):
-            self.tagDist[p] = normalise(tagCounts[p])
-        for i in range(4):
-            for t in range(num_tags):
-                self.contextWordDist[i][t] = normalise(contextWordCounts[i,t])
-
-        return llh
-
-class ContextToPhraseModel:
-    def __init__(self):
-        # Pr(tag | context) = Multinomial
-        self.tagDist = [normalise(random(num_tags)+1) for p in range(num_contexts)]
-        # Pr(phrase = w | tag) = Multinomial
-        self.phraseSingleDist = [normalise(random(num_types)+1) for t in range(num_tags)]
-        # Pr(phrase_1 = w | tag) = Multinomial
-        self.phraseLeftDist = [normalise(random(num_types)+1) for t in range(num_tags)]
-        # Pr(phrase_-1 = w | tag) = Multinomial
-        self.phraseRightDist = [normalise(random(num_types)+1) for t in range(num_tags)]
-        # Pr(|phrase| = l | tag) = Geometric
-        self.phraseLengthDist = [0.5] * num_tags
-        # n.b. internal words for phrases of length >= 3 are drawn from uniform distribution
-
-    def prob(self, pid, cid):
-        # return distribution p(tag, phrase | context) as vector of length |tags|
-        phrase = phrase_type_list[pid]
-        dist = zeros(num_tags)
-        for t in range(num_tags):
-            prob = self.tagDist[cid][t]
-            f = self.phraseLengthDist[t]
-            prob *= geom.pmf(len(phrase), f)
-            if len(phrase) == 1:
-                prob *= self.phraseSingleDist[t][phrase[0]]
-            else:
-                prob *= self.phraseLeftDist[t][phrase[0]]
-                prob *= self.phraseRightDist[t][phrase[-1]]
-            dist[t] = prob
-        return dist
-
-    def expectation_maximisation_step(self, lamba=None):
-        tagCounts = zeros((num_contexts, num_tags))
-        phraseSingleCounts = zeros((num_tags, num_types))
-        phraseLeftCounts = zeros((num_tags, num_types))
-        phraseRightCounts = zeros((num_tags, num_types))
-        phraseLength = zeros(num_types)
-
-        # E-step
-        llh = 0
-        for pid, cid, cnt in edges:
-            q = self.prob(pid, cid)
-            z = sum(q)
-            q /= z
-            llh += log(z)
-            if lamba != None:
-                q *= exp(lamba)
-                q /= sum(q)
-            #print 'p', phrase_type_list[pid], 'c', context_type_list[cid], 'q', q
-            phrase = phrase_type_list[pid]
-            for t in range(num_tags):
-                tagCounts[cid][t] += cnt * q[t]
-                phraseLength[t] += cnt * len(phrase) * q[t]
-                if len(phrase) == 1:
-                    phraseSingleCounts[t][phrase[0]] += cnt * q[t]
-                else:
-                    phraseLeftCounts[t][phrase[0]] += cnt * q[t]
-                    phraseRightCounts[t][phrase[-1]] += cnt * q[t]
-
-        # M-step
-        for t in range(num_tags):
-            self.phraseLengthDist[t] = min(max(sum(tagCounts[:,t]) / phraseLength[t], 1e-6), 1-1e-6)
-            self.phraseSingleDist[t] = normalise(phraseSingleCounts[t])
-            self.phraseLeftDist[t] = normalise(phraseLeftCounts[t])
-            self.phraseRightDist[t] = normalise(phraseRightCounts[t])
-        for c in range(num_contexts):
-            self.tagDist[c] = normalise(tagCounts[c])
-
-        #print 't', self.tagDist
-        #print 'l', self.phraseLengthDist
-        #print 's', self.phraseSingleDist
-        #print 'L', self.phraseLeftDist
-        #print 'R', self.phraseRightDist
-
-        return llh
-
-class ProductModel:
-    """
-    WARNING: I haven't verified the maths behind this model. It's quite likely to be incorrect.
-    """
-
-    def __init__(self):
-        self.pcm = PhraseToContextModel()
-        self.cpm = ContextToPhraseModel()
-
-    def prob(self, pid, cid):
-        p1 = self.pcm.prob(pid, cid)
-        p2 = self.cpm.prob(pid, cid)
-        return (p1 / sum(p1)) * (p2 / sum(p2))
-
-    def expectation_maximisation_step(self):
-        tagCountsGivenPhrase = zeros((num_phrases, num_tags))
-        contextWordCounts = zeros((4, num_tags, num_types))
-
-        tagCountsGivenContext = zeros((num_contexts, num_tags))
-        phraseSingleCounts = zeros((num_tags, num_types))
-        phraseLeftCounts = zeros((num_tags, num_types))
-        phraseRightCounts = zeros((num_tags, num_types))
-        phraseLength = zeros(num_types)
-
-        kl = llh1 = llh2 = 0
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            llh1 += log(sum(p1)) * cnt
-            p2 = self.cpm.prob(pid, cid)
-            llh2 += log(sum(p2)) * cnt
-
-            q = (p1 / sum(p1)) * (p2 / sum(p2))
-            kl += log(sum(q)) * cnt
-            qi = sqrt(q)
-            qi /= sum(qi)
-
-            phrase = phrase_type_list[pid]
-            context = context_type_list[cid]
-            for t in range(num_tags):
-                tagCountsGivenPhrase[pid][t] += cnt * qi[t]
-                tagCountsGivenContext[cid][t] += cnt * qi[t]
-                phraseLength[t] += cnt * len(phrase) * qi[t]
-                if len(phrase) == 1:
-                    phraseSingleCounts[t][phrase[0]] += cnt * qi[t]
-                else:
-                    phraseLeftCounts[t][phrase[0]] += cnt * qi[t]
-                    phraseRightCounts[t][phrase[-1]] += cnt * qi[t]
-                for i in range(4):
-                    contextWordCounts[i][t][context[i]] += cnt * qi[t]
-
-        kl *= -2
-
-        for t in range(num_tags):
-            for i in range(4):
-                self.pcm.contextWordDist[i][t] = normalise(contextWordCounts[i,t])
-            self.cpm.phraseLengthDist[t] = min(max(sum(tagCountsGivenContext[:,t]) / phraseLength[t], 1e-6), 1-1e-6)
-            self.cpm.phraseSingleDist[t] = normalise(phraseSingleCounts[t])
-            self.cpm.phraseLeftDist[t] = normalise(phraseLeftCounts[t])
-            self.cpm.phraseRightDist[t] = normalise(phraseRightCounts[t])
-        for p in range(num_phrases):
-            self.pcm.tagDist[p] = normalise(tagCountsGivenPhrase[p])
-        for c in range(num_contexts):
-            self.cpm.tagDist[c] = normalise(tagCountsGivenContext[c])
-
-        # return the overall objective
-        return llh1 + llh2 + kl
-
-class RegularisedProductModel:
-    # as above, but with a slack regularisation term which kills the
-    # closed-form solution for the E-step
-
-    def __init__(self, epsilon):
-        self.pcm = PhraseToContextModel()
-        self.cpm = ContextToPhraseModel()
-        self.epsilon = epsilon
-        self.lamba = zeros(num_tags)
-
-    def prob(self, pid, cid):
-        p1 = self.pcm.prob(pid, cid)
-        p2 = self.cpm.prob(pid, cid)
-        return (p1 / sum(p1)) * (p2 / sum(p2))
-
-    def dual(self, lamba):
-        return self.logz(lamba) + self.epsilon * dot(lamba, lamba) ** 0.5
-
-    def dual_gradient(self, lamba):
-        return self.expected_features(lamba) + self.epsilon * 2 * lamba
-
-    def expectation_maximisation_step(self):
-        # PR-step: optimise lambda to minimise log(z_lambda) + eps ||lambda||_2
-        self.lamba = scipy.optimize.fmin_slsqp(self.dual, self.lamba,
-                                bounds=[(0, 1e100)] * num_tags,
-                                fprime=self.dual_gradient, iprint=1)
-
-        # E,M-steps: collect expected counts under q_lambda and normalise
-        llh1 = self.pcm.expectation_maximisation_step(self.lamba)
-        llh2 = self.cpm.expectation_maximisation_step(-self.lamba)
-
-        # return the overall objective: llh - KL(q||p1.p2)
-        # llh = llh1 + llh2
-        # kl = sum q log q / p1 p2 = sum q { lambda . phi } - log Z
-        return llh1 + llh2 + self.logz(self.lamba) \
-            - dot(self.lamba, self.expected_features(self.lamba))
-
-    def logz(self, lamba):
-        lz = 0
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            z1 = dot(p1 / sum(p1), exp(lamba))
-            lz += log(z1) * cnt
-
-            p2 = self.cpm.prob(pid, cid)
-            z2 = dot(p2 / sum(p2), exp(-lamba))
-            lz += log(z2) * cnt
-        return lz
-
-    def expected_features(self, lamba):
-        fs = zeros(num_tags)
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            q1 = (p1 / sum(p1)) * exp(lamba)
-            fs += cnt * q1 / sum(q1)
-
-            p2 = self.cpm.prob(pid, cid)
-            q2 = (p2 / sum(p2)) * exp(-lamba)
-            fs -= cnt * q2 / sum(q2)
-        return fs
-
-
-class InterpolatedModel:
-    def __init__(self, epsilon):
-        self.pcm = PhraseToContextModel()
-        self.cpm = ContextToPhraseModel()
-        self.epsilon = epsilon
-        self.lamba = zeros(num_tags)
-
-    def prob(self, pid, cid):
-        p1 = self.pcm.prob(pid, cid)
-        p2 = self.cpm.prob(pid, cid)
-        return (p1 + p2) / 2
-
-    def dual(self, lamba):
-        return self.logz(lamba) + self.epsilon * dot(lamba, lamba) ** 0.5
-
-    def dual_gradient(self, lamba):
-        return self.expected_features(lamba) + self.epsilon * 2 * lamba
-
-    def expectation_maximisation_step(self):
-        # PR-step: optimise lambda to minimise log(z_lambda) + eps ||lambda||_2
-        self.lamba = scipy.optimize.fmin_slsqp(self.dual, self.lamba,
-                                bounds=[(0, 1e100)] * num_tags,
-                                fprime=self.dual_gradient, iprint=2)
-
-        # E,M-steps: collect expected counts under q_lambda and normalise
-        llh1 = self.pcm.expectation_maximisation_step(self.lamba)
-        llh2 = self.cpm.expectation_maximisation_step(self.lamba)
-
-        # return the overall objective: llh1 + llh2 - KL(q||p1.p2)
-        # kl = sum_y q log q / 0.5 * (p1 + p2) = sum_y q(y) { -lambda . phi(y) } - log Z
-        #    = -log Z + lambda . (E_q1[-phi] + E_q2[-phi]) / 2
-        kl = -self.logz(self.lamba) + dot(self.lamba, self.expected_features(self.lamba))
-        return llh1 + llh2 - kl, llh1, llh2, kl
-        # FIXME: KL comes out negative...
-
-    def logz(self, lamba):
-        lz = 0
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            q1 = p1 / sum(p1) * exp(-lamba)
-            q1z = sum(q1)
-
-            p2 = self.cpm.prob(pid, cid)
-            q2 = p2 / sum(p2) * exp(-lamba)
-            q2z = sum(q2)
-
-            lz += log(0.5 * (q1z + q2z)) * cnt
-        return lz
-
-    # z = 1/2 * (sum_y p1(y|x) exp (-lambda . phi(y)) + sum_y p2(y|x) exp (-lambda . phi(y)))
-    #   = 1/2 (z1 + z2)
-    # d (log z) / dlambda = 1/2 (E_q1 [ -phi ] + E_q2 [ -phi ] )
-    def expected_features(self, lamba):
-        fs = zeros(num_tags)
-        for pid, cid, cnt in edges:
-            p1 = self.pcm.prob(pid, cid)
-            q1 = (p1 / sum(p1)) * exp(-lamba)
-            fs -= 0.5 * cnt * q1 / sum(q1)
-
-            p2 = self.cpm.prob(pid, cid)
-            q2 = (p2 / sum(p2)) * exp(-lamba)
-            fs -= 0.5 * cnt * q2 / sum(q2)
-        return fs
-
-if style == 'p2c':
-    m = PhraseToContextModel()
-elif style == 'c2p':
-    m = ContextToPhraseModel()
-elif style == 'prod':
-    m = ProductModel()
-elif style == 'prodslack':
-    m = RegularisedProductModel(0.5)
-elif style == 'sum':
-    m = InterpolatedModel(0.5)
-
-for iteration in range(30):
-    obj = m.expectation_maximisation_step()
-    print 'iteration', iteration, 'objective', obj
-
-for pid, cid, cnt in edges:
-    p = m.prob(pid, cid)
-    phrase = phrase_type_list[pid]
-    phrase_str = ' '.join(map(word_type_list.__getitem__, phrase))
-    context = context_type_list[cid]
-    context_str = ' '.join(map(word_type_list.__getitem__, context))
-    print '%s\t%s ||| C=%d' % (phrase_str, context_str, argmax(p))