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execute_keyphrase_extraction.py
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execute_keyphrase_extraction.py
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import time
import numpy as np
import tensorflow as tf
from models import GAT
from utils import process
from keyphrase_data.SemEval2010 import parse_input
import pickle
dataset = 'semeval'
checkpt_file = 'pre_trained/cora/mod_cora.ckpt'
train_file = 'data/' + dataset + '.train'
test_file = 'data/' + dataset + '.test'
# training params
batch_size = 1
nb_epochs = 20
patience = 10
lr = 0.0001 # learning rate
l2_coef = 0.0005 # weight decay
hid_units = [32] # numbers of hidden units per each attention head in each layer
n_heads = [16, 1] # additional entry for the output layer
residual = True
nonlinearity = tf.nn.elu
model = GAT
print('Dataset: ' + dataset)
print('----- Opt. hyperparams -----')
print('lr: ' + str(lr))
print('l2_coef: ' + str(l2_coef))
print('----- Archi. hyperparams -----')
print('nb. layers: ' + str(len(hid_units)))
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = None, None, None, None, None, None, None, None
try:
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = pickle.load(open(train_file + str(0), 'rb')), \
pickle.load(open(train_file + str(1), 'rb')), pickle.load(open(train_file + str(2), 'rb')), \
pickle.load(open(train_file + str(3), 'rb')), pickle.load(open(train_file + str(4), 'rb')), \
pickle.load(open(train_file + str(5), 'rb')), pickle.load(open(train_file + str(6), 'rb')), \
pickle.load(open(train_file + str(7), 'rb'))
except Exception as e:
print (e)
X = parse_input.preprocessor()
lgraphs, lgraph_features = X.extract('keyphrase_data/SemEval2010/train/',
'keyphrase_data/SemEval2010/train/train.combined.final')
for i, items in enumerate(list(lgraph_features)):
pickle.dump(items, open(train_file + str(i), 'wb'))
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = lgraph_features
print (adj.shape, features.shape, y_train.shape, y_val.shape, y_test.shape, train_mask.shape, val_mask.shape, test_mask.shape )
nb_nodes = features[0].shape[0]
ft_size = features[0].shape[1]
nb_classes = y_train[0].shape[1]
biases = adj #.transpose(0,2,1)
with tf.Graph().as_default():
with tf.name_scope('input'):
ftr_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, ft_size))
bias_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, nb_nodes))
lbl_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes, nb_classes))
msk_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes))
attn_drop = tf.placeholder(dtype=tf.float32, shape=())
ffd_drop = tf.placeholder(dtype=tf.float32, shape=())
is_train = tf.placeholder(dtype=tf.bool, shape=())
model=model()
logits = model.inference(ftr_in, nb_classes, nb_nodes, is_train,
attn_drop, ffd_drop,
bias_mat=bias_in,
hid_units=hid_units, n_heads=n_heads,
residual=residual, activation=nonlinearity)
log_resh = tf.reshape(logits, [-1, nb_classes])
lab_resh = tf.reshape(lbl_in, [-1, nb_classes])
msk_resh = tf.reshape(msk_in, [-1])
#log_resh = tf.Print(log_resh, [log_resh])
#lab_resh = tf.Print(lab_resh, [lab_resh])
loss = model.masked_sigmoid_cross_entropy(log_resh, lab_resh, msk_resh)
accuracy = model.masked_accuracy(log_resh, lab_resh, msk_resh)
train_op = model.training(loss, lr, l2_coef)
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
vlss_mn = np.inf
vacc_mx = 0.0
curr_step = 0
with tf.Session() as sess:
sess.run(init_op)
writer = tf.summary.FileWriter('./temp/graph', sess.graph)
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
for epoch in range(nb_epochs):
step = 0
tr_step = 0
tr_size = features.shape[0]
while step * batch_size < tr_size:
if train_mask[step * batch_size:(step + 1) * batch_size][0][0] == 1.0:
_, loss_value_tr, acc_tr = sess.run([train_op, loss, accuracy],
feed_dict={
ftr_in: features[step*batch_size:(step+1)*batch_size],
bias_in: biases[step*batch_size:(step+1)*batch_size],
lbl_in: y_train[step*batch_size:(step+1)*batch_size],
msk_in: train_mask[step*batch_size:(step+1)*batch_size],
is_train: True,
attn_drop: 0.6, ffd_drop: 0.6})
train_loss_avg += loss_value_tr
train_acc_avg += acc_tr
tr_step += 1
step += 1
step = 0
vl_step = 0
vl_size = features.shape[0]
count_in = 0.0
while step * batch_size < vl_size:
if val_mask[step * batch_size:(step+1) * batch_size][0][0] == 1.0:
out_vl, loss_value_vl, acc_vl = sess.run([log_resh, loss, accuracy],
feed_dict={
ftr_in: features[step*batch_size:(step+1)*batch_size],
bias_in: biases[step*batch_size:(step+1)*batch_size],
lbl_in: y_val[step*batch_size:(step+1)*batch_size],
msk_in: val_mask[step*batch_size:(step+1)*batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
val_loss_avg += loss_value_vl
val_acc_avg += acc_vl
vl_step += 1
unique, counts = np.unique(np.argmax(out_vl, axis=1), return_counts=True)
#print (dict(zip(unique, counts)))
out_vl_report = out_vl[:, 1].argsort()[-25:][::-1]
y_val_report = np.where(y_val[step * batch_size][:, 1] == 1)
#print (set(out_vl_report).intersection(set(list(y_val_report[0]))))
count_in += len(set(out_vl_report).intersection(set(list(y_val_report[0]))))*1.0 / len(set(list(y_val_report[0])))
step += 1
print ('Keywords in top 25: %.5f' % (count_in/vl_step))
print('Training: loss = %.5f, acc = %.5f | Val: loss = %.5f, acc = %.5f' %
(train_loss_avg/tr_step, train_acc_avg/tr_step,
val_loss_avg/vl_step, val_acc_avg/vl_step))
if val_acc_avg/vl_step >= vacc_mx or val_loss_avg/vl_step <= vlss_mn:
if val_acc_avg/vl_step >= vacc_mx and val_loss_avg/vl_step <= vlss_mn:
vacc_early_model = val_acc_avg/vl_step
vlss_early_model = val_loss_avg/vl_step
saver.save(sess, checkpt_file)
vacc_mx = np.max((val_acc_avg/vl_step, vacc_mx))
vlss_mn = np.min((val_loss_avg/vl_step, vlss_mn))
curr_step = 0
else:
curr_step += 1
if curr_step == patience:
print('Early stop! Min loss: ', vlss_mn, ', Max accuracy: ', vacc_mx)
print('Early stop model validation loss: ', vlss_early_model, ', accuracy: ', vacc_early_model)
break
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
saver.restore(sess, checkpt_file)
ts_size = features.shape[0]
step = 0
ts_step = 0
ts_loss = 0.0
ts_acc = 0.0
while step * batch_size < ts_size:
if test_mask[step * batch_size:(step + 1) * batch_size][0][0] == 1.0:
out_ts, loss_value_ts, acc_ts = sess.run([log_resh, loss, accuracy],
feed_dict={
ftr_in: features[step*batch_size:(step+1)*batch_size],
bias_in: biases[step*batch_size:(step+1)*batch_size],
lbl_in: y_test[step*batch_size:(step+1)*batch_size],
msk_in: test_mask[step*batch_size:(step+1)*batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
step += 1
print('Test loss:', ts_loss/ts_step, '; Test accuracy:', ts_acc/ts_step)
sess.close()