def decode():
'''Load dictionaries'''
# Load vocabularies.
print(os.getcwd())
doc_dict = data_util.load_dict(FLAGS.data_dir + "/doc_dict.txt")
sum_dict = data_util.load_dict(FLAGS.data_dir + "/sum_dict.txt")
if doc_dict is None or sum_dict is None:
logging.warning("Dict not found.")
print("Loading testing data")
data = data_util.load_test_data(FLAGS.test_file, doc_dict)
with tf.Session() as sess:
# Create model and load parameters.
logging.info("Creating %d layers of %d units." %
(FLAGS.num_layers, FLAGS.size))
model = create_model(sess, True)
result = []
for idx, token_ids in enumerate(data):
# Get a 1-element batch to feed the sentence to the model.
encoder_inputs, decoder_inputs, encoder_len, decoder_len =\
model.get_batch(
{0: [(token_ids, [data_util.ID_GO, data_util.ID_EOS])]}, 0)
if FLAGS.batch_size == 1 and FLAGS.geneos:
print('ran code')
loss, outputs = model.step(sess, encoder_inputs,
decoder_inputs, encoder_len,
decoder_len, True)
outputs = [np.argmax(item) for item in outputs[0]]
else:
outputs = model.step_beam(sess,
encoder_inputs,
encoder_len,
geneos=FLAGS.geneos)
# If there is an EOS symbol in outputs, cut them at that point.
if data_util.ID_EOS in outputs:
outputs = outputs[:outputs.index(data_util.ID_EOS)]
gen_sum = " ".join(data_util.sen_map2tok(outputs, sum_dict[1]))
print(gen_sum)
gen_sum = data_util.sen_postprocess(gen_sum)
print(gen_sum)
result.append(gen_sum)
logging.info("Finish {} samples. :: {}".format(idx, gen_sum[:75]))
with open(FLAGS.test_output, "w") as f:
for item in result:
print(item, file=f)
def decode():
# Load vocabularies.
doc_dict = data_util.load_dict(FLAGS.data_dir + "/doc_dict.txt")
sum_dict = data_util.load_dict(FLAGS.data_dir + "/sum_dict.txt")
en_dict = data_util.load_dict(FLAGS.data_dir + "/en_dict.txt")
if doc_dict is None or sum_dict is None:
logging.warning("Dict not found.")
data, en_data = data_util.load_test_data(
FLAGS.test_file, doc_dict, FLAGS.data_dir + "/test.entity.txt",
en_dict)
with tf.Session() as sess:
# Create model and load parameters.
logging.info("Creating %d layers of %d units." %
(FLAGS.num_layers, FLAGS.size))
model = create_model(sess, True, None, None, None)
result = []
for idx, token_ids in enumerate(data):
en_ids = en_data[idx]
if len(en_ids) == 0:
en_ids = [data_util.ID_PAD]
# token_ids, en_ids = d
#print(idx)
#print(token_ids)
# Get a 1-element batch to feed the sentence to the model.
shiva = model.get_batch(
{
0:
[(token_ids, [data_util.ID_GO, data_util.ID_EOS],
[data_util.ID_PAD, data_util.ID_PAD, data_util.ID_PAD] +
en_ids +
[data_util.ID_PAD, data_util.ID_PAD, data_util.ID_PAD])]
}, 0)
#print(shiva)
encoder_inputs, decoder_inputs, encoder_len, decoder_len, entity_inputs, entity_len = shiva
K = min(FLAGS.K, np.amax(entity_len) - 6)
#print("K", K)
if FLAGS.batch_size == 1 and FLAGS.geneos:
loss, outputs, att, t = model.step(sess, encoder_inputs,
decoder_inputs,
entity_inputs, encoder_len,
decoder_len, entity_len, K,
True)
#outputs = [np.argmax(item) for item in outputs[0]]
else:
outputs = model.step_beam(sess,
encoder_inputs,
encoder_len,
entity_inputs,
entity_len,
K,
geneos=FLAGS.geneos)
# If there is an EOS symbol in outputs, cut them at that point.
#print(outputs)
f2 = open(FLAGS.test_output + '.disambig', 'a')
f2.write(' '.join(
str(y) + ":" + str(x.mean())
for x, y in zip(t[0], entity_inputs[0][3:])) + '\n')
f2.close()
f2 = open(FLAGS.test_output + '.attention', 'a')
f2.write(' '.join(
str(y) + ":" + str(x)
for x, y in zip(att[0], entity_inputs[0][3:])) + '\n')
f2.close()
outputs = list(outputs[0])
if data_util.ID_EOS in output:
outputs = outputs[:outputs.index(data_util.ID_EOS)]
#outputs = list(outputs)
gen_sum = " ".join(data_util.sen_map2tok(
outputs, sum_dict[1])) #sum_dict[1])) #lvt_str
gen_sum = data_util.sen_postprocess(gen_sum)
result.append(gen_sum)
logging.info("Finish {} samples. :: {}".format(idx, gen_sum[:75]))
with open(FLAGS.test_output, "w") as f:
for item in result:
print(item, file=f)
def decode():
# Load vocabularies.
doc_dict = data_util.load_dict(FLAGS.data_dir + "/doc_dict.txt")
sum_dict = data_util.load_dict(FLAGS.data_dir + "/sum_dict.txt")
if doc_dict is None or sum_dict is None:
logging.warning("Dict not found.")
docs, data = data_util.load_test_data(FLAGS.test_file, doc_dict)
with tf.Session() as sess:
# Create model and load parameters.
logging.info("Creating %d layers of %d units." %
(FLAGS.num_layers, FLAGS.size))
model = create_model(sess, True)
class_model = create_class_model(sess, True)
result = []
for idx, token_ids in enumerate(data):
# Get a 1-element batch to feed the sentence to the model.
encoder_inputs, decoder_inputs, encoder_len, decoder_len, class_output, class_len =\
data_util.get_batch(
{0: [(token_ids, [data_util.ID_GO, data_util.ID_EOS],[0,0])]}, _buckets, 0, FLAGS.batch_size, False, 0)
if FLAGS.batch_size == 1 and FLAGS.geneos:
loss, outputs = model.step(sess, encoder_inputs,
decoder_inputs, encoder_len,
decoder_len, True)
outputs = [np.argmax(item) for item in outputs[0]]
else:
outputs = model.step_beam(sess,
encoder_inputs,
encoder_len,
geneos=FLAGS.geneos)
# If there is an EOS symbol in outputs, cut them at that point.
if data_util.ID_EOS in outputs:
outputs = outputs[:outputs.index(data_util.ID_EOS)]
gen_sum = " ".join(data_util.sen_map2tok(outputs, sum_dict[1]))
gen_sum = data_util.sen_postprocess(gen_sum)
result.append(gen_sum)
logging.info("Finish {} samples. :: {}".format(idx, gen_sum[:75]))
#Get Encoder outputs
batchidx = 0
final_inputs = []
final_outputs = []
final_len = []
while batchidx + FLAGS.batch_size <= len(data):
encoder_inputs, decoder_inputs, encoder_len, decoder_len, class_output, class_len =\
data_util.get_batch(
{0: [(token_ids, [data_util.ID_GO, data_util.ID_EOS],[0,0])]}, _buckets, 0, FLAGS.batch_size, False, 0)
_, _, enc_outputs = model.step(sess, encoder_inputs,
decoder_inputs, encoder_len,
decoder_len, True)
enc_outputs = data_util.add_pad_for_hidden(enc_outputs,
_buckets[0][0])
final_inputs.append(enc_outputs)
final_outputs.append(class_output)
final_len.append(class_len)
batchidx += FLAGS.batch_size
final_inputs = np.asarray(final_inputs)
final_inputs = np.concatenate(final_inputs, 0)
final_outputs = np.asarray(final_outputs)
final_outputs = np.concatenate(final_outputs, 0)
final_len = np.asarray(final_len)
final_len = np.concatenate(final_len, 0)
print(final_inputs.shape, final_outputs.shape, final_len.shape)
#Hidden classifier
step_loss, output = class_model.step(sess, final_inputs[:],
final_outputs[:], final_len[:],
True)
clipped = np.array(output > 0.5, dtype=np.int)
#label = data_util.hidden_label_gen(FLAGS.test_file, "data/test.1981.msg.txt")
#make confusion matrix to get precision
#tn, fp, fn, tp = confusion_matrix(label.flatten(), clipped.flatten()).ravel()
#print("Test precision : ", tp/(tp+fp))
with open(FLAGS.test_output, "w") as f:
for idx, item in enumerate(result):
print(item, file=f)
for j in range(len(docs[idx])):
if clipped[idx][j] == 1:
print("Recommended identifier: " + docs[idx][j] + " ",
file=f)
print("\n", file=f)
