def get_sentence_posteriors(sentence, iterations=1, extra_meaning=None):
meaning_probs = {}
# parse sentence with charniak and apply surgeries
print 'parsing ...'
modparse = get_modparse(sentence)
t = ParentedTree.parse(modparse)
print '\n%s\n' % t.pprint()
num_ancestors = count_lmk_phrases(t) - 1
for _ in xrange(iterations):
(lmk, _, _), (rel, _, _) = get_meaning(num_ancestors=num_ancestors)
meaning = m2s(lmk,rel)
if meaning not in meaning_probs:
ps = get_tree_probs(t, lmk, rel)[0]
# print "Tree probs: ", zip(ps,rls)
meaning_probs[meaning] = np.prod(ps)
print '.'
if extra_meaning:
meaning = m2s(*extra_meaning)
if meaning not in meaning_probs:
ps = get_tree_probs(t, lmk, rel)[0]
# print "Tree prob: ", zip(ps,rls)
meaning_probs[meaning] = np.prod(ps)
print '.'
summ = sum(meaning_probs.values())
for key in meaning_probs:
meaning_probs[key] /= summ
return meaning_probs.items()
def get_modparse(sentence):
"""returns the modified parse tree for a sentence"""
sp_db = SentenceParse.get_sentence_parse(sentence)
try:
res = sp_db.all()[0]
parsetree = res.original_parse
modparsetree = res.modified_parse
except:
print "parse.py: 103: " + sentence
parses = parse_sentences([sentence])
if len(parses) == 0:
raise ParseError(printcolors.WARNING + ('ParseError: a sentence was empty'))
modparses = modify_parses(parses)
for i,chunk in enumerate(modparses[:]):
for j,modparse in enumerate(chunk):
if 'LANDMARK-PHRASE' in modparse:
modparses[i] = modparse
parses[i] = parses[i][j]
break
if isinstance(modparses[i],list):
modparses[i] = modparses[i][0]
parses[i] = parses[i][0]
parsetree = parses[0]
modparsetree = modparses[0]
try:
SentenceParse.add_sentence_parse(sentence, parsetree, modparsetree)
except Exception as e:
print e
if count_lmk_phrases(ParentedTree.parse(modparsetree)) < 1:
raise ParseError(printcolors.WARNING + ('ParseError: Parse contained no Landmark phrase.\nSentence: %s\nParse: %s\nModparse: %s' % (sentence,parsetree,modparsetree)))
return parsetree, modparsetree
def get_all_sentence_posteriors(sentence, meanings, golden=False, printing=True):
print 'parsing ...'
_, modparse = get_modparse(sentence)
t = ParentedTree.parse(modparse)
print '\n%s\n' % t.pprint()
num_ancestors = count_lmk_phrases(t) - 1
# logger(len(meanings))
lmks, rels = zip(*meanings)
lmks = set(lmks)
rels = set(rels)
# logger('num things ' + str(len(lmks))+' '+str(len(rels)))
syms = ['\\', '|', '/', '-']
sys.stdout.write('processing...\\')
sys.stdout.flush()
posteriors = {}
for i,lmk in enumerate(lmks):
if lmk.get_ancestor_count() != num_ancestors:
p = 0
else:
ps = get_tree_probs(t[1], lmk, golden=golden, printing=printing)[0]
p = np.prod(ps)
posteriors[lmk] = p
sys.stdout.write("\b%s" % syms[i % len(syms)])
sys.stdout.flush()
for i,rel in enumerate(rels):
ps = get_tree_probs(t[0], rel=rel, golden=golden, printing=printing)[0]
posteriors[rel] = np.prod(ps)
sys.stdout.write("\b%s" % syms[i % len(syms)])
sys.stdout.flush()
for j in range(50):
sys.stdout.write("\b.%s" % syms[(i+j) % len(syms)])
sys.stdout.flush()
print
# for meaning in meanings:
# lmk,rel = meaning
# if lmk.get_ancestor_count() != num_ancestors:
# p = 0
# else:
# ps = get_tree_probs(t, lmk, rel, printing=False)[0]
# p = np.prod(ps)
# posteriors.append(p)
# print p, lmk, lmk.ori_relations, rel, (rel.distance, rel.measurement.best_degree_class, rel.measurement.best_distance_class ) if hasattr(rel,'measurement') else 'No measurement'
return posteriors
def get_all_sentence_posteriors(sentence, meanings):
print 'parsing ...'
modparse = get_modparse(sentence)
t = ParentedTree.parse(modparse)
print '\n%s\n' % t.pprint()
num_ancestors = count_lmk_phrases(t) - 1
posteriors = []
for meaning in meanings:
lmk,rel = meaning
if lmk.get_ancestor_count() != num_ancestors:
p = 0
else:
ps = get_tree_probs(t, lmk, rel)[0]
p = np.prod(ps)
posteriors.append(p)
return posteriors
def get_sentence_posteriors(sentence, iterations=1):
probs = []
meanings = []
# parse sentence with charniak and apply surgeries
print 'parsing ...'
modparse = get_modparse(sentence)
t = ParentedTree.parse(modparse)
print '\n%s\n' % t.pprint()
num_ancestors = count_lmk_phrases(t) - 1
for _ in xrange(iterations):
meaning = get_meaning(num_ancestors=num_ancestors)
lmk, rel = meaning
probs.append(get_tree_prob(t, *meaning))
meanings.append(m2s(lmk,rel))
print '.'
probs = np.array(probs) / sum(probs)
return uniquify_distribution(meanings, probs)
for i, row in enumerate(reader, start=1):
print "sentence", i
# unpack row
xloc, yloc, sentence, parse, modparse = row
# convert variables to the right types
xloc = float(xloc)
yloc = float(yloc)
loc = (xloc, yloc)
parse = ParentedTree.parse(parse)
modparse = ParentedTree.parse(modparse)
# how many ancestors should the sampled landmark have?
num_ancestors = count_lmk_phrases(modparse) - 1
# sample `args.iterations` times for each sentence
for _ in xrange(args.iterations):
lmk, rel = get_meaning(loc, num_ancestors)
if args.verbose:
print "utterance:", repr(sentence)
print "location: %s" % repr(loc)
print "landmark: %s (%s)" % (lmk, lmk_id(lmk))
print "relation: %s" % rel_type(rel)
print "parse:"
print parse.pprint()
print "modparse:"
print modparse.pprint()
print "-" * 70
for i,row in enumerate(reader, start=1):
print 'sentence', i
# unpack row
xloc, yloc, sentence, parse, modparse = row
unique_sentences[sentence] = (parse, modparse)
# convert variables to the right types
xloc = float(xloc)
yloc = float(yloc)
loc = (xloc, yloc)
parse = ParentedTree.parse(parse)
modparse = ParentedTree.parse(modparse)
# how many ancestors should the sampled landmark have?
num_ancestors = count_lmk_phrases(modparse) - 1
if num_ancestors == -1:
print 'Failed to parse %d [%s] [%s] [%s]' % (i, sentence, parse, modparse)
continue
# sample `args.iterations` times for each sentence
for _ in xrange(args.iterations):
lmk, rel = get_meaning(loc, num_ancestors)
lmk, _, _ = lmk
rel, _, _ = rel
assert(not isinstance(lmk, tuple))
assert(not isinstance(rel, tuple))
if args.verbose:
