def gradient(theta):
write_learned_features(theta)
print 'getting counts...'
exp_counts = [fst.LogWeight.ZERO] * (len(f_names) + 1)
obs_counts = [fst.LogWeight.ZERO] * (len(f_names) + 1)
for idx, (exp_file, obs_chain_file) in enumerate(zip(exp_machines, obs_chain)):
sys.stdout.write('%d \r' % idx)
sys.stdout.flush()
exp = fst.read(path + exp_file)
obs_c = fst.read(path + obs_chain_file)
exp_wt = apply_weights(exp, theta)
(e_counts, o_counts) = get_counts_for_machine(exp_wt, obs_c)
exp_counts = accumilate_counts(e_counts, exp_counts)
obs_counts = accumilate_counts(o_counts, obs_counts)
grad = np.zeros(len(theta))
for i, o in f_names:
k = f_names[i, o]
ok = obs_counts[k]
ek = exp_counts[k]
#exp(c)-exp(e)
s1 = expm(-float(ok))
s2 = expm(-float(ek))
grad[k] = s1 - s2
#print grad[k], '=', s2, '-', s1, i, o
#pdb.set_trace()
print '\ngrad computed'
return grad
def value(theta):
likelihood = 0.0
print 'likelihoods'
for idx, (e_file, o_chain_file) in enumerate(zip(exp_machines, obs_chain)):
sys.stdout.write('%d \r' % idx)
sys.stdout.flush()
#print e_file
e = fst.read(path + e_file)
o_chain = fst.read(path + o_chain_file)
likelihood += get_likelihood(e, o_chain, theta)
reg = np.linalg.norm(theta, ord=1)
print 'll', likelihood, 'reg', reg
return likelihood
def get_likelihood(inp_file, E_file, o_chain_file, theta):
inp = fst.read(path + inp_file)
E = fst.read(path + E_file)
o_chain = fst.read(path + o_chain_file)
E_wt = apply_weights(E, theta)
exp_wt = inp.compose(E_wt) #apply_weights(exp, theta)
#e_wt.write('e_wt.fst', e_wt.isyms, e_wt.osyms)
exp_wt = renormalize(exp_wt)
#e_wt.write('e_norm.fst', e_wt.isyms, e_wt.osyms)
o = exp_wt.compose(o_chain)
#o.write('obs.after.fst', o.isyms, o.osyms)
ll = o.shortest_distance(True)[0]
return float(ll)
def main(args):
L = fst.read(args.fst_file)
for state in L:
for arc in state:
arc.weight = L.SEMIRING(0.0)
L.write(args.fst_file, keep_isyms=True, keep_osyms=True)
def main(args):
L = fst.read(args.fst_file)
for state in L:
for arc in state:
arc.weight = L.SEMIRING(0.0)
L.write(args.fst_file, keep_isyms=True, keep_osyms=True)
def main(args):
L = fst.read(args.fst_file)
for state in L:
for arc in state:
arc.weight = fst.TropicalWeight(0.0)
L.write(args.fst_file, keep_isyms=True, keep_osyms=True)
def main(args):
L = fst.read(args.fst_file)
for state in L:
for arc in state:
arc.weight = fst.TropicalWeight(0.0)
L.write(args.fst_file, keep_isyms=True, keep_osyms=True)
def main(args):
L = fst.read(args.fst_file)
for state in L:
for arc in state:
if arc.weight != fst.TropicalWeight(0.0):
sys.stderr.write(
"Nonzero weight in the fst: node {} arc {}".format(state, arc))
exit(1)
def main(args):
L = fst.read(args.fst_file)
for state in L:
for arc in state:
if arc.weight != fst.TropicalWeight(0.0):
sys.stderr.write(
"Nonzero weight in the fst: node {} arc {}".format(state, arc))
exit(1)
def get_fst(name):
global FSTS
if name not in FSTS:
here = os.path.dirname(__file__)
fstdir = os.path.join(here, "grammar", "compiled_fsts")
filename = os.path.join(fstdir, "%s.bin" % name)
assert os.path.exists(filename), "FST file does not exist: " + filename
FSTS[name] = fst.read(filename)
return FSTS[name]
def get_fst(name):
global FSTS
if name not in FSTS:
here = os.path.dirname(__file__)
fstdir = os.path.join(here, "grammar", "compiled_fsts")
filename = os.path.join(fstdir, "%s.bin" % name)
assert os.path.exists(filename), "FST file does not exist: " + filename
FSTS[name] = fst.read(filename)
FSTS[name + "_vocab"] = set(sym for sym,num in FSTS[name].isyms.items())
return FSTS[name]
def value(theta):
likelihood = 0.0
print 'likelihoods'
for idx, obs_trellis_file in enumerate(obs_machines):
sys.stdout.write('%d \r' % idx)
sys.stdout.flush()
obs_trellis = fst.read(path + obs_trellis_file)
likelihood += get_likelihood(obs_trellis, theta)
#reg = np.linalg.norm(theta, ord=1)
print 'll', likelihood #, 'reg', reg
return likelihood
def posibilities(path, words):
''' Return list of posibilities for every word in fstVector '''
fstVector = fst.read(path);
fstVector.remove_epsilon()
posibilities = [];
_posibilities(fstVector,fstVector.start, words, posibilities, 0)
for i in posibilities:
# remove empty sets
if len(i) == 0:
posibilities.remove(i)
return posibilities
def generate_suggestions(prefix):
"""
To extract suggestions the first step was to traverse the fst
in fstfile following the charecters of the given prefix. From
there the state of the final letter of prefix is saved and the next
part constructs an fst of the branch the grows from the saved state.
It is done in bds approach. Later, extract all paths from acceptor in
a dfs manner is done with path weight calculation. Then all paths
are sorted by weights and the first three are jsoned.
INPUT:
a string
OUTPUT:
a json file with up to three values for Suggestion entry
"""
fstfile = "/Users/dudy/CSLU/summerIntern/src/prfx_tree.fst"
sym = fst.read_symbols("/Users/dudy/CSLU/summerIntern/src/syms")
lm = fst.read(fstfile)
prefix = prefix.lower()
# look for subtree given prefix
stateid = 0
for ch in prefix:
state = lm[stateid]
for arc in state.arcs:
if sym.find(arc.ilabel)==ch:
print ch
stateid = arc.nextstate
break
# construct desired subtree (bds)
reduced = bfs(stateid, lm, sym)
# read strings (dfs)
top3 = dfs(reduced, sym)
# take first three (if exists)
suggest = []
for (suffix, _) in top3:
suggest.append(suffix)
# dict it
result = {}
result["Suggestions:"] = suggest
# json it
json_file = "auto.json"
with open(json_file, "w") as fp:
json.dump(result, fp)
def generate_suggestions(prefix):
"""
To extract suggestions the first step was to traverse the fst
in fstfile following the charecters of the given prefix. From
there the state of the final letter of prefix is saved and the next
part constructs an fst of the branch the grows from the saved state.
It is done in bds approach. Later, extract all paths from acceptor in
a dfs manner is done with path weight calculation. Then all paths
are sorted by weights and the first three are jsoned.
INPUT:
a string
OUTPUT:
a json file with up to three values for Suggestion entry
"""
fstfile = "/Users/dudy/CSLU/summerIntern/src/prfx_tree.fst"
sym = fst.read_symbols("/Users/dudy/CSLU/summerIntern/src/syms")
lm = fst.read(fstfile)
prefix = prefix.lower()
# look for subtree given prefix
stateid = 0
for ch in prefix:
state = lm[stateid]
for arc in state.arcs:
if sym.find(arc.ilabel) == ch:
print ch
stateid = arc.nextstate
break
# construct desired subtree (bds)
reduced = bfs(stateid, lm, sym)
# read strings (dfs)
top3 = dfs(reduced, sym)
# take first three (if exists)
suggest = []
for (suffix, _) in top3:
suggest.append(suffix)
# dict it
result = {}
result["Suggestions:"] = suggest
# json it
json_file = "auto.json"
with open(json_file, "w") as fp:
json.dump(result, fp)
def main(args):
L = fst.read(args.fst_file)
for state in L:
ilab = []
for arc in state:
ilab.append(arc.ilabel)
ilabs = set(ilab)
if 0 in ilabs and len(ilab) != 1:
sys.stderr.write(
"Node {} has a non-epsilon arc that is not unique: {}".format(
state, ilab))
exit(1)
if len(ilabs) != len(ilab):
sys.stderr.write(
"Node {} has duplicated ilabels on edges: {}".format(
state, ilab))
exit(1)
def main(args):
L = fst.read(args.fst_file)
for state in L:
ilab = []
for arc in state:
ilab.append(arc.ilabel)
ilabs = set(ilab)
if 0 in ilabs and len(ilab) != 1:
sys.stderr.write(
"Node {} has a non-epsilon arc that is not unique: {}"
.format(state, ilab))
exit(1)
if len(ilabs) != len(ilab):
sys.stderr.write(
"Node {} has duplicated ilabels on edges: {}"
.format(state, ilab))
exit(1)
def load_lat(fn):
lat = fst.read(fn)
lat = fst.StdVectorFst(lat)
return lat
learned_weights = dict(
(int(l.split('\t')[0]), float(l.split('\t')[-1])) for l in codecs.open(learned_weight_file, 'r', 'utf-8').readlines())
filenames = codecs.open(path + 'filenames', 'r', 'utf-8').readlines()[1:]
nat_sort_filenames = natural_sort(filenames)
inp_machines, obs_chain, exp_machines = zip(*[tuple(l.split()) for l in nat_sort_filenames])
obs_trelis = [o.replace('y', 'obs') for o in obs_chain]
source = [l.split() for l in codecs.open(path + 'en', 'r', 'utf-8').readlines()]
target = [l.split() for l in codecs.open(path + 'fr', 'r', 'utf-8').readlines()]
all_alignments = []
for idx, (ot, s, t) in enumerate(zip(obs_trelis, source, target)[:53]):
print idx, s, t
obs_t = fst.read(path + ot)
sym_features = obs_t.isyms
sym_targets = obs_t.osyms
print path + ot
obs_t.write('obs_t.fst')
obs_wt = apply_weights(obs_t, learned_weights)
obs_wt.write('obs_wt.fst', obs_t.isyms, obs_t.osyms)
os.system('fstmap --map_type="to_standard" obs_wt.fst > obs_wt.std.fst')
obs_wt_std = fst.read('obs_wt.std.fst')
best_path = obs_wt_std.shortest_path()
best_path.write('best_path.fst', obs_t.isyms, obs_t.osyms)
all_alignments += do_align(idx + 1, best_path, s, t)
writer = codecs.open('never.gonna.work.20.alignments.out', 'w')
writer.write('\n'.join(all_alignments))
writer.flush()
writer.close()
def __init__(self, path):
self.path = path
self.fst = fst.read(self.path)
self.isyms = dict(self.fst.isyms.items())
c.start = c.add_state()
space_id = syms["<space>"]
c.add_arc(0, 0, space_id, syms["<eps>"])
c.add_arc(0, 0, space_id, syms["+C+"])
c.add_arc(0, 0, space_id, syms["+D+"])
for word_id in word_ids:
c.add_arc(0, 0, word_id, word_id)
c[0].final = True
return c
if __name__ == '__main__':
if len(sys.argv) != 3:
print("Usage: %s G.fst words.txt" % sys.argv[0], file=sys.stderr)
g = fst.read(sys.argv[1])
syms = {}
syms_list = []
for l in open(sys.argv[2]):
ss = l.split()
syms[ss[0]] = int(ss[1])
syms_list.append(ss[0])
unk_id = syms["<unk>"]
# Following is needed to avoid line buffering
while 1:
l = sys.stdin.readline()
if not l: break
unks = []
words = l.split()
c.start = c.add_state()
space_id = syms["<space>"]
c.add_arc(0, 0, space_id, syms["<eps>"])
c.add_arc(0, 0, space_id, syms["+C+"])
c.add_arc(0, 0, space_id, syms["+D+"])
for word_id in word_ids:
c.add_arc(0, 0, word_id, word_id)
c[0].final = True
return c
if __name__ == '__main__':
if len(sys.argv) != 3:
print >> sys.stderr, "Usage: %s G.fst words.txt" % sys.argv[0]
g = fst.read(sys.argv[1])
syms = {}
syms_list = []
for l in open(sys.argv[2]):
ss = l.split()
syms[ss[0]] = int(ss[1])
syms_list.append(ss[0])
unk_id = syms["<unk>"]
# Following is needed to avoid line buffering
while 1:
l = sys.stdin.readline()
if not l: break
unks = []
words = l.split()
import graphviz
import pydot
# import pywrapfst
import fst
import nltk
import re
import os
LexM = fst.read("lex_model/lex-uw.fst")
LM_expr = "^(.*)\.pru$"
folder_name = "lang_model"
file_list = [os.path.join(folder_name, fname) for fname in os.listdir(folder_name)]
pruned_models = [re.match(LM_expr, filename).group(1) for filename in file_list if re.match(LM_expr, filename)]
i_table = LexM.isyms
o_table = LexM.osyms
mod_name = "lang_model/3-gram-3"
LG = fst.read(mod_name + ".pi").copy()
test_word = fst.Acceptor(syms=i_table)
test_word.add_arc(0, 1, 'HH')
test_word.add_arc(1, 2, 'EY')
test_word[2].final = True
test_comp = test_word >> LG
__author__ = 'arenduchintala'
import sys, fst
def parseargs(args):
try:
in_fst_path = args[args.index('-in') + 1]
out_fst_path = args[args.index('-out') + 1]
n = int(args[args.index('-n') + 1])
return [in_fst_path, out_fst_path, n]
except (ValueError, IndexError):
sys.stderr.write('Usage: -in [name of fst(final)] -out [name of shortest path fst] -n [number of paths]')
exit()
if __name__ == '__main__':
[in_fst, out_fst, n] = parseargs(sys.argv)
sym_f = fst.read_symbols('data/symf.bin')
sym_e = fst.read_symbols('data/syme.bin')
f = fst.read(in_fst)
sp = f.shortest_path(n)
sp.remove_epsilon()
sp.write(out_fst, sym_f, sym_e)
def load(self):
self.fst = fst.read(self.path)
self.isyms = dict(self.fst.isyms.items())
def load_lat(fn):
lat = fst.read(fn)
lat = fst.StdVectorFst(lat)
return lat
def load(self):
self.fst = fst.read(self.path)
self.isyms = dict(self.fst.isyms.items())