def get_transducer_symbol_table(self):
transducer_symbol_table = fst.SymbolTable()
all_segments_string = "".join(self.get_segments_symbols())
all_segments_string += LEFT_APPLICATION_BRACKET + LEFT_CENTER_BRACKET + LEFT_IDENTITY_BRACKET
all_segments_string += RIGHT_APPLICATION_BRACKET + RIGHT_CENTER_BRACKET + RIGHT_IDENTITY_BRACKET
fst.linear_chain(all_segments_string, syms=transducer_symbol_table)
return transducer_symbol_table
def letter_constraint (letterstring,syms):
"Create an FSA that has the already typed letters followed by sigma*"
thisfst=fst.linear_chain(letterstring,syms);
sigmafst=sigma(syms).closure()
thisfst.concatenate(sigmafst)
thisfst.remove_epsilon()
return thisfst.determinize()
def load_nbest_iter(self, nbest_file):
"""
cat >text.fst <<EOF
0 1 a x .5
0 1 b y 1.5
1 2 c z 2.5
2 3.5
EOF
"""
a = fst.Acceptor()
sigma = fst.SymbolTable()
with open(nbest_file, 'r') as f_in:
for line in f_in:
line = line.strip()
b = fst.linear_chain(line.split(), sigma)
a = a.union(b)
a.remove_epsilon()
a = a.determinize()
a.minimize()
self.fsa = a
self.syms = sigma
def extract_from_poses(pos_seq, phrase_transducer, vocab=None, draw_composition=None, **kwargs):
"""returns span indices for phrases"""
# t0 = time.time()
if isinstance(phrase_transducer, (str,unicode)):
phrase_transducer = get_fst(phrase_transducer)
if vocab is None:
vocab = set(sym for sym,num in phrase_transducer.isyms.items())
oovize = lambda seq: [x if x in vocab else 'O' for x in seq]
a = set([(k,v) for k,v in kwargs.iteritems()])
b = set(pos_seq)
assert 'O' in vocab
pos_seq = oovize(pos_seq)
# print "setup elapsed %.3f ms" % ((time.time() - t0)*1e3)
input_transducer = fst.linear_chain(pos_seq, syms=phrase_transducer.isyms)
#print vocab
#print input_transducer
composed = input_transducer >> phrase_transducer
if len(composed)==0: return []
#draw_pdf(input_transducer, "t.pdf")
if draw_composition:
draw_pdf(composed, draw_composition)
# print "%s states" % len(composed)
# print "composition elapsed %.3f ms" % ((time.time() - t0)*1e3)
ret = extract_from_composition(composed, **kwargs)
ret = list(ret) # might not always be necessary?
# print "extraction elapsed %.3f ms" % ((time.time() - t0)*1e3)
return ret
def get_acceptor_for_word(self, word, syms):
try:
return self._word_acceptors_cache[word]
except KeyError:
acceptor = linear_chain(word, syms=syms)
self._word_acceptors_cache[word] = acceptor
return acceptor
def distance(a, b):
# Compose a o edit transducer o b
composed = fst.linear_chain(a, syms) >> edit >> fst.linear_chain(b, syms)
# Compute distance
distances = composed.shortest_distance(reverse=True)
dist = int(distances[0])
# Find best alignment
alignment = composed.shortest_path()
# Re-order states
alignment.top_sort()
# Replace <epsilon> -> "-"
alignment.relabel({fst.EPSILON: '-'}, {fst.EPSILON: '-'})
# Read alignment on the arcs of the transducer
arcs = (next(state.arcs) for state in alignment)
labels = ((arc.ilabel, arc.olabel) for arc in arcs)
align = [(alignment.isyms.find(x), alignment.osyms.find(y)) for x, y in labels]
return dist, align
def test_closure():
t = fst.linear_chain('ab')
result = t.closure_plus()
eq_(len(result), len(t))
result.remove_epsilon()
expected = t + t.closure()
expected.remove_epsilon()
eq_(result, expected)
def test_closure():
t = fst.linear_chain('ab')
result = t.closure_plus()
eq_(len(result), len(t))
result.remove_epsilon()
expected = t + t.closure()
expected.remove_epsilon()
eq_(result, expected)
def distance(a, b):
# Compose a o edit transducer o b
composed = fst.linear_chain(a, syms) >> edit >> fst.linear_chain(
b, syms)
# Compute distance
distances = composed.shortest_distance(reverse=True)
dist = int(distances[0])
# Find best alignment
alignment = composed.shortest_path()
# Re-order states
alignment.top_sort()
# Replace <epsilon> -> "-"
alignment.relabel({fst.EPSILON: '-'}, {fst.EPSILON: '-'})
# Read alignment on the arcs of the transducer
arcs = (next(state.arcs) for state in alignment)
labels = ((arc.ilabel, arc.olabel) for arc in arcs)
align = [(alignment.isyms.find(x), alignment.osyms.find(y))
for x, y in labels]
return dist, align
def create_symbol_table(words):
symbol_set = set()
for word in words:
for symbol in list(word):
if symbol not in symbol_set:
symbol_set.add(symbol)
symbol_fst = fst.linear_chain(''.join(symbol_set))
return symbol_fst.isyms
def create_min_fst(words, symbol_table):
fst_union = fst.StdVectorFst(isyms=symbol_table, osyms=symbol_table)
for word in words:
fst_union |= fst.linear_chain(word, symbol_table)
print '- naive FST has {0} states and {1} arcs'.format(*automata_size(fst_union))
fst_union.remove_epsilon()
dfst_union = fst_union.determinize()
dfst_union.minimize()
print '- minimized FST has {0} states and {1} arcs'.format(*automata_size(dfst_union))
return dfst_union
def test_simple():
t = fst.Transducer()
for i, (ic, oc) in enumerate(zip('hello', 'olleh')):
t.add_arc(i, i+1, ic, oc)
t[i+1].final = True
eq_(len(t), 6)
ok_(t[5].final)
a = fst.Acceptor()
for i, c in enumerate('hello'):
a.add_arc(i, i+1, c)
a[i+1].final = True
eq_(len(a), 6)
ok_(a[5].final)
l = fst.linear_chain('hello')
eq_(a, l)
def extract_from_poses(pos_seq, phrase_transducer, vocab=None, draw_composition=None, tagset='auto', **kwargs):
"""
tagset could be: 'ark', 'ptb', 'coarse', 'auto'
ark: Gimpel et al 2011's twitter tagset
ptb: Penn Treebank
coarse: Petrov et al's Universal POS tagset
auto: try to detect. this may waste time.
(todo: use NLTK's tagset naming system. nathan contributed to it)
RETURNS: span indices for phrases
"""
# t0 = time.time()
if isinstance(phrase_transducer, (str,unicode)):
tname = phrase_transducer
phrase_transducer = get_fst(tname)
vocab = FSTS[tname + "_vocab"]
if vocab is None:
vocab = set(sym for sym,num in phrase_transducer.isyms.items())
# print "0setup elapsed %.3f ms" % ((time.time() - t0)*1e3)
# t0 = time.time()
pos_seq = preprocess_tags(pos_seq, vocab, tagset)
# print "1tagpreproc elapsed %.3f ms" % ((time.time() - t0)*1e3)
# t0 = time.time()
input_transducer = fst.linear_chain(pos_seq, syms=phrase_transducer.isyms)
# print "2linchain elapsed %.3f ms" % ((time.time() - t0)*1e3)
# t0 = time.time()
composed = input_transducer >> phrase_transducer
if len(composed)==0: return []
if draw_composition:
draw_pdf(composed, draw_composition)
# print "3composition elapsed %.3f ms" % ((time.time() - t0)*1e3)
# t0 = time.time()
ret = extract_from_composition2(composed, **kwargs)
# print "4extraction elapsed %.3f ms" % ((time.time() - t0)*1e3)
return ret
def load_nbest(self, nbest_file):
"""
cat >text.fst <<EOF
0 1 a x .5
0 1 b y 1.5
1 2 c z 2.5
2 3.5
EOF
"""
a = fst.Acceptor(syms=self.syms)
with open(nbest_file, 'r') as f_in:
for line in f_in:
line = line.strip()
b = fst.linear_chain(line.split(), self.syms)
a = a.union(b)
a.remove_epsilon()
d = a.determinize()
d.minimize()
self.fsa = d
self.fsa.top_sort()
def PrintOutputsForInput(transducer, input_str):
inp = fst.linear_chain(input_str, syms=transducer.isyms, semiring=semiring)
combined = (inp >> transducer)
PrintFullPaths(combined)
pron = oov_dict[unicode(w,'utf-8')]
except KeyError:
'''ipa_pron = False
#print 'word is OOV, attempting phonetisaurus lookup'
w = unicode(w, 'utf-8')
pron = subprocess.check_output(['phonetisaurus-g2p', '--model='+AR_FST_FILE,'--input='+w.encode('utf-8')])
pron = pron.rstrip()
endnum = pron.index('\t')
pron = pron[endnum+1:]
pron = ''.join(pron.split())
pron = pron.decode('utf-8')
#manually handle characters not in my phonetisaurus fst
pron = pron.replace(u'\u0644', u'\006c')
pron = pron.replace(u'\u0622', 'CA')
print 'reverting to phonetisaurus' '''
wfst = fst.linear_chain(unicode(w,'utf-8'), syms=alt_ar_fst.isyms)
ppath = wfst.compose(alt_ar_fst).shortest_path(1)
pron = ''
for path in ppath:
pron += ''.join(ppath.osyms.find(arc.olabel) for arc in path)
#try:
pron = pron.encode('utf-8')
pron = pron.replace('\x06', '')
#if ipa_pron:
pron = unicode(pron, 'utf-8')
longcs = [] #long consonants to be replaced
extra_long = [] #long consonants that would be replaced by two colons if this didn't exist
for i in range(len(pron)-1):
if pron[i] == pron[i+1] and pron[i] not in [u'a', u'u', u'i',u"\u02D0"]:
longcs.append(pron[i])
if i < len(pron)-2 and pron[i+2] == u"\u02D0":
pron = oov_dict[unicode(w, 'utf-8')]
except KeyError:
'''ipa_pron = False
#print 'word is OOV, attempting phonetisaurus lookup'
w = unicode(w, 'utf-8')
pron = subprocess.check_output(['phonetisaurus-g2p', '--model='+AR_FST_FILE,'--input='+w.encode('utf-8')])
pron = pron.rstrip()
endnum = pron.index('\t')
pron = pron[endnum+1:]
pron = ''.join(pron.split())
pron = pron.decode('utf-8')
#manually handle characters not in my phonetisaurus fst
pron = pron.replace(u'\u0644', u'\006c')
pron = pron.replace(u'\u0622', 'CA')
print 'reverting to phonetisaurus' '''
wfst = fst.linear_chain(unicode(w, 'utf-8'),
syms=alt_ar_fst.isyms)
ppath = wfst.compose(alt_ar_fst).shortest_path(1)
pron = ''
for path in ppath:
pron += ''.join(
ppath.osyms.find(arc.olabel) for arc in path)
#try:
pron = pron.encode('utf-8')
pron = pron.replace('\x06', '')
#if ipa_pron:
pron = unicode(pron, 'utf-8')
longcs = [] #long consonants to be replaced
extra_long = [
] #long consonants that would be replaced by two colons if this didn't exist
for i in range(len(pron) - 1):
if pron[i] == pron[i + 1] and pron[i] not in [