def make_gec_instances(raw_file, pos_file, correct_file=None): global nf, vf, df, pf, prof instances = FgExampleMemoryStore() raw_lines = [[BOS] + rl.strip().split() + [EOS] for rl in codecs.open(raw_file, 'r', 'utf8').readlines()] pos_lines = [[BOS] + pl.strip().split() + [EOS] for pl in codecs.open(pos_file, 'r', 'utf8').readlines()] if correct_file is not None: correct_lines = [[BOS] + cl.strip().split() + [EOS] for cl in codecs.open(correct_file, 'r', 'utf8').readlines()] pass else: correct_lines = [None] * len(raw_lines) pass for line_idx in range(len(raw_lines)): factor_graph = FactorGraph() vc = VarConfig() prev_hc = None for w_idx, (w,p,c) in enumerate(zip(raw_lines[line_idx], pos_lines[line_idx], correct_lines[line_idx])): candidates = generate_correction_candidates(p,w) print w, p, c print 'can', candidates, c hc = Var(Var.VarType.PREDICTED, len(candidates), "TAG_" + str(w_idx) , candidates) vc.put(hc, c) if prev_hc: t_varset = VarSet(hc) t_varset.add(prev_hc) t_factor = CRFFactor(t_varset, [prev_hc, hc], "BIGRAM") factor_graph.addFactor(t_factor) else: pass prev_hc = hc pass instances.add(LabeledFgExample(factor_graph, vc)) return instances
def make_instances(txt_file, tag_list, obs_list):
instances = FgExampleMemoryStore()
text_train = [t.strip() for t in open(txt_file).read().split('###/###') if t.strip() != '']
for x in range(len(text_train)):
factor_graph = FactorGraph()
vc = VarConfig()
prev_hc = None
for i, line in enumerate(text_train[x].split('\n')):
hidden_state = line.split('/')[1].strip()
observed_state = line.split('/')[0].strip()
# print 'h', hidden_state, 'o', observed_state
# make variables with their configurations
#r = random.randint(0, len(tag_list))
#s = set(tag_list[:r])
#s.add(hidden_state)
#s = list(s)
s = tag_list
hc = Var(Var.VarType.PREDICTED, len(s), "TAG_" + str(i), s)
vc.put(hc, hidden_state)
# o = Var(Var.VarType.PREDICTED , len(obs_list), "OBS_" + str(i), obs_list)
# vc.put(o, observed_state)
# make transition factor
if prev_hc:
t_varset = VarSet(hc)
t_varset.add(prev_hc)
t_factor = CRFFactor(t_varset, [prev_hc, hc], 'TAG-TAG')
factor_graph.addFactor(t_factor)
else:
pass
prev_hc = hc
# make emission factor
e_varset = VarSet(hc)
# e_varset.add(o)
# e_factor = CRFFactor(e_varset, [hc, o], 'TAG-OBS')
e_factor = ObservedFactor(e_varset, [hc], 'TAG-OBS', observed_state)
factor_graph.addFactor(e_factor)
# make clamp factor
# c_factor = Clamper(o, obs_list.index(observed_state))
# factor_graph.addFactor(c_factor)
instances.add(LabeledFgExample(factor_graph, vc))
return instances
def make_gec_instances(raw_file, pos_file, aspect_file):
global cl
instances = FgExampleMemoryStore()
#raw_lines = [[BOS] + rl.strip().split() + [EOS] for rl in codecs.open(raw_file, 'r', 'utf8').readlines()]
#pos_lines = [[BOS] + pl.strip().split() + [EOS] for pl in codecs.open(pos_file, 'r', 'utf8').readlines()]
with codecs.open(aspect_file, 'r', 'utf8') as aspect_f, codecs.open(
raw_file, 'r',
'utf8') as raw_f, codecs.open(pos_file, 'r', 'utf8') as pos_f:
for raw_line, pos_line, aspect_line in zip(raw_f, pos_f, aspect_f):
rl = [BOS] + raw_line.strip().split() + [EOS]
pl = [BOS] + pos_line.strip().split() + [EOS]
al = [BOS] + aspect_line.strip().split() + [EOS]
sys.stderr.write('.')
factor_graph = FactorGraph()
vc = VarConfig()
prev_hc = None
for w_idx, (w, p, a) in enumerate(zip(rl, pl, al)):
#w does not contain aspet
candidates = get_candidates(cl, w, p)
if len(candidates) == 1:
sys.stderr.write('no cans:' + w + ' ' + p + '\n')
else:
pass
hc = Var(Var.VarType.PREDICTED, len(candidates),
"TAG_" + str(w_idx), candidates)
vc.put(hc, w + '|||' + a)
assert w + '|||' + a in candidates
if prev_hc:
t_varset = VarSet(hc)
t_varset.add(prev_hc)
t_factor = CRFFactor(t_varset, [prev_hc, hc], "BIGRAM")
factor_graph.addFactor(t_factor)
else:
pass
prev_hc = hc
pass
instances.add(LabeledFgExample(factor_graph, vc))
return instances