def run_test(inferrer):
if args.candidatesfile != None:
target2candidates = read_candidates(args.candidatesfile)
else:
target2candidates = None
tfi = open(args.testfile, 'r')
tfo = open(args.resultsfile, 'w')
tfo_ranked = open(args.resultsfile+'.ranked', 'w')
tfo_generated_oot = open(args.resultsfile+'.generated.oot', 'w')
tfo_generated_best = open(args.resultsfile+'.generated.best', 'w')
lines = 0
while True:
context_line = tfi.readline()
if not context_line:
break;
lst_instance = ContextInstance(context_line, args.no_pos)
lines += 1
if (args.debug == True):
tfo.write("\nTest context:\n")
tfo.write("***************\n")
tfo.write(lst_instance.decorate_context())
result_vec = inferrer.find_inferred(lst_instance, tfo)
generated_results = inferrer.generate_inferred(result_vec, lst_instance.target, lst_instance.target_lemma, lst_instance.pos)
tfo.write("\nGenerated lemmatized results\n")
tfo.write("***************\n")
tfo.write("GENERATED\t" + ' '.join([lst_instance.full_target_key, lst_instance.target_id]) + " ::: " + vec_to_str_generated(generated_results.iteritems(), args.topgenerated)+"\n")
tfo_generated_oot.write(' '.join([lst_instance.full_target_key, lst_instance.target_id]) + " ::: " + vec_to_str_generated(generated_results.iteritems(), args.topgenerated)+"\n")
tfo_generated_best.write(' '.join([lst_instance.full_target_key, lst_instance.target_id]) + " :: " + vec_to_str_generated(generated_results.iteritems(), 1)+"\n")
filtered_results = inferrer.filter_inferred(result_vec, target2candidates[lst_instance.target_key], lst_instance.pos)
tfo.write("\nFiltered results\n")
tfo.write("***************\n")
tfo.write("RANKED\t" + ' '.join([lst_instance.full_target_key, lst_instance.target_id]) + "\t" + vec_to_str(filtered_results.iteritems(), len(filtered_results))+"\n")
tfo_ranked.write("RANKED\t" + ' '.join([lst_instance.full_target_key, lst_instance.target_id]) + "\t" + vec_to_str(filtered_results.iteritems(), len(filtered_results))+"\n")
# print "end %f" % time.time()
if lines % 10 == 0:
print "Read %d lines" % lines
print "Read %d lines in total" % lines
print "Time per word: %f msec" % inferrer.msec_per_word()
tfi.close()
tfo.close()
tfo_ranked.close()
tfo_generated_oot.close()
tfo_generated_best.close()
def run_test(inferrer):
if args.candidatesfile is not None:
target2candidates = read_candidates(args.candidatesfile)
else:
target2candidates = None
tfi = open(args.testfile, 'r')
tfo = open(args.resultsfile, 'w')
tfo_ranked = open(args.resultsfile + '.ranked', 'w')
tfo_generated_oot = open(args.resultsfile + '.generated.oot', 'w')
tfo_generated_best = open(args.resultsfile + '.generated.best', 'w')
lines = 0
while True:
context_line = tfi.readline()
if not context_line:
break
lst_instance = ContextInstance(context_line, args.no_pos)
lines += 1
if args.debug:
tfo.write("\nTest context:\n")
tfo.write("***************\n")
tfo.write(lst_instance.decorate_context())
result_vec = inferrer.find_inferred(lst_instance, tfo)
generated_results = inferrer.generate_inferred(
result_vec, lst_instance.target, lst_instance.target_lemma,
lst_instance.pos)
tfo.write("\nGenerated lemmatized results\n")
tfo.write("***************\n")
tfo.write(
"GENERATED\t" +
' '.join([lst_instance.full_target_key, lst_instance.target_id]) +
" ::: " + vec_to_str_generated(iter(generated_results.items()),
args.topgenerated) + "\n")
tfo_generated_oot.write(
' '.join([lst_instance.full_target_key, lst_instance.target_id]) +
" ::: " + vec_to_str_generated(iter(generated_results.items()),
args.topgenerated) + "\n")
tfo_generated_best.write(
' '.join([lst_instance.full_target_key, lst_instance.target_id]) +
" :: " + vec_to_str_generated(iter(generated_results.items()), 1) +
"\n")
filtered_results = inferrer.filter_inferred(
result_vec, target2candidates[lst_instance.target_key],
lst_instance.pos)
tfo.write("\nFiltered results\n")
tfo.write("***************\n")
tfo.write(
"RANKED\t" +
' '.join([lst_instance.full_target_key, lst_instance.target_id]) +
"\t" +
vec_to_str(iter(filtered_results.items()), len(filtered_results)) +
"\n")
tfo_ranked.write(
"RANKED\t" +
' '.join([lst_instance.full_target_key, lst_instance.target_id]) +
"\t" +
vec_to_str(iter(filtered_results.items()), len(filtered_results)) +
"\n")
# print "end %f" % time.time()
if lines % 10 == 0:
print("Read %d lines" % lines)
print("Read %d lines in total" % lines)
print("Time per word: %f msec" % inferrer.msec_per_word())
tfi.close()
tfo.close()
tfo_ranked.close()
tfo_generated_oot.close()
tfo_generated_best.close()
def candidate_ranking_out(data_f, words2elmo_token, target_ws, sents,
position_lst, target_w2candidates, w2index, w2elmo,
vocab_all):
output_f_rank = open(
data_f.split('/')[-1] + '.' + model + 'vocab80000.ranked', 'w')
output_f_oot = open(
data_f.split('/')[-1] + '.' + model + 'vocab80000.oot', 'w')
output_f_best = open(
data_f.split('/')[-1] + '.' + model + 'vocab80000.best', 'w')
print('normalizing vectors')
w2elmo = normalize(w2elmo)
words2elmo_token = normalize(words2elmo_token)
sim_matrix = w2elmo.dot(words2elmo_token.T).T
# w2elmo = w2elmo / np.sqrt((w2elmo * w2elmo).sum())
# words2elmo_token=words2elmo_token / np.sqrt((words2elmo_token * words2elmo_token).sum())
print('normalizing completed')
for i in range(len(words2elmo_token)):
if i % 100 == 0 and i >= 100:
print(i)
w2elmo_token = words2elmo_token[i]
target_w_out = target_ws[i]
target_w = target_w_out.split()[0]
pos = target_w.split('.')[-1]
target_w_lemma = target_w.split('.')[0]
#similarity matrix
# similarity=(w2elmo.dot(w2elmo_token)+1.0)/2
similarity = (sim_matrix[i] + 1.0) / 2
result_vec = sorted(zip(vocab_all, similarity),
reverse=True,
key=lambda x: x[1])
try:
candidates = target_w2candidates['.'.join(target_w.split('.')[:2])]
except KeyError as e:
print(
'target w does not occur in gold candidates list: {0}'.format(
e))
continue
#ranked result
filtered_results = filter_inferred(result_vec, candidates, pos)
# candis_cos=sorted(filtered_results.items(),key=lambda x:x[1],reverse=True)
# candis_cos='\t'.join([res[0]+' '+str(res[1]) for res in candis_cos])
# out_line='RANKED\t{0}\t{1}\n'.format(target_w,candis_cos)
# output_f_rank.write(out_line)
output_f_rank.write(
"RANKED\t" + target_w_out + "\t" +
vec_to_str(filtered_results.items(), len(filtered_results)) + "\n")
#generate result
generated_results = generate_inferred(result_vec,
sents[i][position_lst[i]],
target_w_lemma, pos)
output_f_oot.write(
target_w_out + " ::: " +
vec_to_str_generated(generated_results.items(), 10) + "\n")
output_f_best.write(
target_w_out + " :: " +
vec_to_str_generated(generated_results.items(), 1) + "\n")
output_f_rank.close()
output_f_best.close()
output_f_oot.close()