else:
X = tf.placeholder(tf.float32, shape=[None, len(texts_rep_train[0])], name="X")
print(X)
y = tf.placeholder(tf.int64, shape=[None, n_classes], name="y")
fnames_plc = tf.placeholder(tf.string, shape=[None], name="fnames_plc")
lr = tf.placeholder(tf.float32, shape=[], name="lr")
is_training = tf.placeholder_with_default(False, shape=[], name='is_training')
dropout_keep_prob = tf.placeholder_with_default(1.0, shape=(), name="dropout")
"""
GET THE MODEL
"""
if MODEL == "CNN":
logits = CNN.get_model(X, is_training=is_training, filters=filters, n_classes=n_classes, tf_idf=True, logger=logger)
elif MODEL == "RNN":
logits = RNN.get_model(X, dropout_keep_prob, hidden_size=HIDDEN_UNITS, n_classes=n_classes,
num_layers=NUM_LAYERS)
elif MODEL == "FF":
logits = FF.get_model(X, dropout_keep_prob, is_training=is_training, layers=layers, n_classes=n_classes)
logger.info(logits)
softmax = tf.nn.softmax(logits)
num_params = np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])
logger.info("{} params to train".format(num_params))
train_op, loss = train_ops.train_op(logits, y, learning_rate=lr, optimizer=OPTIMIZER)
""""""
"""Test de embeddings"""
train_dataset = tf.data.Dataset.from_tensor_slices((X, y, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
dev_dataset = tf.data.Dataset.from_tensor_slices((X, y, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
def __init__(
self,
x_train,
y_train,
x_test,
y_test,
fnames_train,
fnames_test,
embedding,
MAX_SEQUENCE_LENGTH,
EMBEDDING_DIM=300,
OPTIMIZER='adam',
logger=None,
opts=None,
by_acc=True,
n_classes=4,
):
"""
Vars
"""
MODEL = opts.model
#FOR RNN
HIDDEN_UNITS = 4
NUM_LAYERS = 1
BATCH_SIZE = 64 # Any size is accepted
labelencoder = LabelEncoder() # set
y_train_ = np.array(y_train).astype(str)
y_test_ = np.array(y_test).astype(str)
labelencoder.fit(y_train_)
y_train_ = labelencoder.transform(y_train_)
y_test_ = labelencoder.transform(y_test_)
n_values = len(np.unique(y_train_))
# To One hot
y_train = to_categorical(y_train_, n_values)
y_test = to_categorical(y_test_, n_values)
logger.info("texts_rep_train: {}".format(x_train.shape))
logger.info("y_train: {}".format(x_test.shape))
"""""" """""" """""" """""" """""" """""
# Tensorflow
""" """""" """""" """""" """""" """""" ""
batch_size = tf.placeholder(tf.int64, name="batch_size")
X = tf.placeholder(tf.int64, shape=[None, MAX_SEQUENCE_LENGTH])
fnames_plc = tf.placeholder(tf.string, shape=[None], name="fnames_plc")
y = tf.placeholder(tf.int64, shape=[None, n_classes])
is_training = tf.placeholder_with_default(False,
shape=[],
name='is_training')
dropout_keep_prob = tf.placeholder_with_default(1.0, shape=())
lr = tf.placeholder(tf.float32, shape=[])
glove_weights_initializer = tf.constant_initializer(embedding)
print(glove_weights_initializer)
embeddings = tf.Variable(
# tf.random_uniform([2000, EMBEDDING_DIM], -1.0, 1.0)
embedding)
print(embeddings)
"""
GET THE MODEL
"""
if MODEL == "CNN":
logits = CNN.get_model(X,
W=embeddings,
is_training=is_training,
filters=opts.filters,
n_classes=n_classes,
logger=logger)
elif MODEL == "RNN":
logits = RNN.get_model(X,
W=embeddings,
dropout_keep_prob=dropout_keep_prob,
hidden_size=HIDDEN_UNITS,
n_classes=n_classes,
num_layers=NUM_LAYERS)
print(logits)
softmax = tf.nn.softmax(logits)
num_params = np.sum([
np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()
])
print("{} params to train".format(num_params))
train_op, loss = train_ops.train_op(logits,
y=y,
learning_rate=lr,
optimizer=OPTIMIZER)
""""""
"""Test de embeddings"""
train_dataset = tf.data.Dataset.from_tensor_slices(
(X, y, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
test_dataset = tf.data.Dataset.from_tensor_slices(
(X, y, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
train_data = (x_train, y_train, fnames_train)
test_data = (x_test, y_test, fnames_test)
# create a iterator of the correct shape and type
iter = tf.data.Iterator.from_structure(train_dataset.output_types,
train_dataset.output_shapes)
iter_test = tf.data.Iterator.from_structure(test_dataset.output_types,
test_dataset.output_shapes)
# create the initialisation operations
train_init_op = iter.make_initializer(train_dataset)
test_init_op = iter_test.make_initializer(test_dataset)
epoch_start = 0
## Train
sess = tf.Session()
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_g)
sess.run(init_l)
best_loss = 99999
best_acc = 0
for epoch in range(epoch_start, opts.epochs + 1):
sess.run(train_init_op,
feed_dict={
X: train_data[0],
y: train_data[1],
fnames_plc: train_data[2],
batch_size: BATCH_SIZE,
})
current_batch_index = 0
next_element = iter.get_next()
loss_count = 0
while True:
try:
data = sess.run([next_element])
except tf.errors.OutOfRangeError:
break
current_batch_index += 1
data = data[0]
batch_x, batch_tgt, batch_fnames = data
_, loss_result = sess.run(
[train_op, loss],
feed_dict={
X: batch_x,
y: batch_tgt,
lr: train_ops.lr_scheduler(epoch),
batch_size: BATCH_SIZE,
is_training: True,
dropout_keep_prob: 0.3,
})
# print("Loss: {}".format(loss_result))
loss_count += loss_result
loss_count = loss_count / current_batch_index
acc = 0
if not by_acc:
if loss_count < best_loss:
best_loss = loss_count
logger.info("New best_loss : {}".format(best_loss))
sesions.save_best(sess, opts.work_dir)
else:
"""
---------------- dev
"""
sess.run(test_init_op,
feed_dict={
X: test_data[0],
y: test_data[1],
fnames_plc: test_data[2],
batch_size: BATCH_SIZE,
})
current_batch_index = 0
next_element = iter_test.get_next()
while True:
try:
data = sess.run([next_element])
except tf.errors.OutOfRangeError:
break
current_batch_index += 1
data = data[0]
batch_x, batch_tgt, batch_fnames = data
results = sess.run(
[softmax],
feed_dict={
X: batch_x,
y: batch_tgt,
fnames_plc: batch_fnames,
batch_size: BATCH_SIZE,
dropout_keep_prob: 1.0,
lr: train_ops.lr_scheduler(1)
})
acc_aux = metrics.accuracy(X=results[0], y=batch_tgt)
acc += acc_aux
acc = acc / current_batch_index
logger.info("----------")
logger.info("Loss on epoch {} : {} - LR: {}".format(
epoch, loss_count, train_ops.lr_scheduler(epoch)))
logger.info("Acc Val Test {}".format(acc))
if acc > best_acc:
best_acc = acc
logger.info("New acc : {}".format(best_acc))
sesions.save_best(sess, opts.work_dir)
"""
----------------- TEST -----------------
"""
logger.info("\n-- TEST --\n")
logger.info("Restoring the best Checkpoint.")
# restore_file = sesions.restore_from_best(sess, save_path)
restore_file = sesions.restore_from_best(sess, opts.work_dir)
if restore_file:
logger.info("Best model restored")
else:
logger.info("Cant restore the best model.")
exit()
sess.run(test_init_op,
feed_dict={
X: test_data[0],
y: test_data[1],
fnames_plc: test_data[2],
batch_size: BATCH_SIZE,
})
current_batch_index = 0
next_element = iter_test.get_next()
classifieds = []
while True:
try:
data = sess.run([next_element])
except tf.errors.OutOfRangeError:
break
current_batch_index += 1
data = data[0]
batch_x, batch_tgt, batch_fnames = data
results = sess.run(
[softmax],
feed_dict={
X: batch_x,
y: batch_tgt,
fnames_plc: batch_fnames,
batch_size: BATCH_SIZE,
dropout_keep_prob: 1.0,
lr: train_ops.lr_scheduler(1)
})
results = results[0]
acc_aux = metrics.accuracy(X=results, y=batch_tgt)
acc += acc_aux
for i in range(len(results)):
hyp = [np.argmax(results[i], axis=-1)]
hyp = labelencoder.inverse_transform(hyp)[
0] # real label #set
doc_name = batch_fnames[i].decode("utf-8").split("/")[-1]
tgt = [np.argmax(batch_tgt[i], axis=-1)]
tgt = labelencoder.inverse_transform(tgt)[
0] # real label #set
# to vote
classifieds.append((hyp, doc_name, tgt))
# print(classifieds)
# exit()
acc = acc / current_batch_index
logger.info("----------")
logger.info("Acc Val Test {}".format(acc))
#
# per_doc = classify_per_doc(classifieds, logger=logger)
# logger.info("----------")
# logger.info("Acc Val Test Per document votation {}".format(metrics.accuracy_per_doc(per_doc)))
# logger.info("----------")
# [print(x) for x in classifieds_to_write]
self.results = classifieds
def __init__(self,
layers=[512, 256, 128, 64, 32],
filters=[64, 128, 256, 512],
MODEL="FF",
HIDDEN_UNITS=32,
NUM_LAYERS=2,
do_val=False,
OPTIMIZER='adam',
DEV_SPLIT=0.2,
NUM_EPOCH=50,
min_ngram=1,
up=5,
max_features=None,
dataset="PAN2019",
logger=None,
opts=None,
DEBUG=False,
lang="es"):
"""
Vars
"""
BATCH_SIZE = opts.batch_size
logger = logger or logging.getLogger(__name__)
# MODEL = "RNN"
# MODEL = "CNN"
if not DEBUG:
## PAN
path = opts.tr_data + '/' + lang
path_test = opts.i + '/' + lang
sent = 0
if lang == 'en': sent = 0
if do_val:
txt_train = opts.file_i + "/{}/truth-train.txt".format(lang)
txt_dev = opts.file_i + "/{}/truth-dev.txt".format(lang)
dt_train = process.PAN2019(path=path,
txt=txt_train,
join_all=MODEL == "FF",
sentiment_id=sent)
dt_dev = process.PAN2019(path=path,
txt=txt_dev,
join_all=MODEL == "FF",
sentiment_id=sent)
fnames_dev = dt_dev.fnames
y_dev = dt_dev.y
x_dev = dt_dev.X
# sent_dev = dt_dev.sentiment
del dt_dev
else:
txt_train = opts.file_i + "/{}/truth.txt".format(lang)
dt_train = process.PAN2019(path=path,
txt=txt_train,
join_all=MODEL == "FF",
sentiment_id=sent)
dt_test = process.PAN2019_Test(path=path_test,
join_all=MODEL == "FF",
sentiment_id=sent)
n_classes = 2 # bot or not bot
# sent_train = dt_train.sentiment
# sent_test = dt_test.sentiment
x_train = dt_train.X
y_train = dt_train.y
y2_train = dt_train.y2
print(len(x_train))
print(len(y_train))
print(len(y2_train))
x_test = dt_test.X
# y_test = dt_test.y
fnames_train = dt_train.fnames
fnames_test = dt_test.fnames
labelencoder = LabelEncoder() #set
y_train_ = np.array(y_train).astype(str)
# y_test_ = np.array(y_test).astype(str)
labelencoder.fit(y_train_)
y_train_ = labelencoder.transform(y_train_)
# y_test_ = labelencoder.transform(y_test_)
n_values = len(np.unique(y_train_))
# To One hot
y_train = to_categorical(y_train_, n_values)
# y_test = to_categorical(y_test_, n_values)
if max_features:
rep = TfidfVectorizer(ngram_range=(min_ngram, up),
max_features=max_features)
else:
rep = TfidfVectorizer(ngram_range=(min_ngram, up))
del dt_train
del dt_test
logger.info("fit_transform tfidf")
texts_rep_train = rep.fit_transform(x_train)
logger.info("To array")
texts_rep_train = texts_rep_train.toarray()
logger.info("transform tfidf")
text_test_rep = rep.transform(x_test)
logger.info("To array")
text_test_rep = text_test_rep.toarray()
if do_val:
text_dev_rep = rep.transform(x_dev)
text_dev_rep = text_dev_rep.toarray()
y_dev_ = np.array(y_dev).astype(str)
y_dev_ = labelencoder.transform(y_dev_)
y_dev = to_categorical(y_dev_, n_values)
if MODEL == "CNN":
num = opts.num_tweets
texts_rep_train = texts_rep_train.reshape(
int(texts_rep_train.shape[0] / num), num,
texts_rep_train.shape[1])
text_test_rep = text_test_rep.reshape(
int(text_test_rep.shape[0] / num), num,
text_test_rep.shape[1])
else:
logger.info(" --------------- DEBUG ON ------------------")
n_classes = 2
n_vcab = 10000
train_data = 128
dev_data = 50
texts_rep_train = np.random.randn(train_data, 100, n_vcab)
text_test_rep = np.random.randn(dev_data, 100, n_vcab)
y_train = np.eye(n_classes)[np.random.choice(
n_classes, train_data)]
y_test = np.eye(n_classes)[np.random.choice(n_classes, dev_data)]
alphabet = list(
'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'
)
np_alphabet = np.array(alphabet, dtype="|S1")
fnames_train = np.random.choice(np_alphabet, [train_data])
fnames_test = np.random.choice(np_alphabet, [dev_data])
logger.info("Random data created")
# print(len(sent_train))
# print(texts_rep_train.shape)
# texts_rep_train = np.concatenate((texts_rep_train, np.expand_dims(sentiment.append([blob.sentiment.polarity, blob.sentiment.subjectivity]),axis=1)), axis=-1)
# text_test_rep = np.concatenate((text_test_rep, np.expand_dims(sent_test,axis=1)), axis=-1)
# text_dev_rep = np.concatenate((text_dev_rep, np.expand_dims(sent_dev,axis=1)), axis=-1)
# texts_rep_train = np.concatenate((texts_rep_train, sent_train), axis=-1)
logger.info("texts_rep_train: {}".format(texts_rep_train.shape))
logger.info("y_train: {}".format(y_train.shape))
# X_train, X_val, X_test, y_train, y_val, y_test, MAX_SEQUENCE_LENGTH = prepare_data(
# dir_word_embeddings, fname_vocab, train_path, test_path, EMBEDDING_DIM,
# VALIDATION_SPLIT=DEV_SPLIT, MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LENGTH
# )
"""""" """""" """""" """""" """""" """""
# Tensorflow
""" """""" """""" """""" """""" """""" ""
batch_size = tf.placeholder(tf.int64, name="batch_size")
if MODEL == "CNN":
X = tf.placeholder(tf.float32,
shape=[
None, texts_rep_train.shape[1],
texts_rep_train.shape[2]
],
name="X")
else:
X = tf.placeholder(tf.float32,
shape=[None, len(texts_rep_train[0])],
name="X")
print(X)
y = tf.placeholder(tf.int64, shape=[None, n_classes], name="y")
fnames_plc = tf.placeholder(tf.string, shape=[None], name="fnames_plc")
lr = tf.placeholder(tf.float32, shape=[], name="lr")
is_training = tf.placeholder_with_default(False,
shape=[],
name='is_training')
dropout_keep_prob = tf.placeholder_with_default(1.0,
shape=(),
name="dropout")
"""
GET THE MODEL
"""
if MODEL == "CNN":
logits = CNN.get_model(X,
is_training=is_training,
filters=filters,
n_classes=n_classes,
tf_idf=True,
logger=logger)
elif MODEL == "RNN":
logits = RNN.get_model(X,
dropout_keep_prob,
hidden_size=HIDDEN_UNITS,
n_classes=n_classes,
num_layers=NUM_LAYERS)
elif MODEL == "FF":
logits = FF.get_model(X,
dropout_keep_prob,
is_training=is_training,
layers=layers,
n_classes=n_classes)
logger.info(logits)
softmax = tf.nn.softmax(logits)
num_params = np.sum([
np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()
])
logger.info("{} params to train".format(num_params))
train_op, loss = train_ops.train_op(logits,
y,
learning_rate=lr,
optimizer=OPTIMIZER)
""""""
"""Test de embeddings"""
train_dataset = tf.data.Dataset.from_tensor_slices(
(X, y, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
dev_dataset = tf.data.Dataset.from_tensor_slices(
(X, y, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
test_dataset = tf.data.Dataset.from_tensor_slices(
(X, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
train_data = (texts_rep_train, y_train, fnames_train)
if do_val:
dev_data = (text_dev_rep, y_dev, fnames_dev)
test_data = (text_test_rep, fnames_test)
print(text_test_rep.shape)
print(len(fnames_test))
# create a iterator of the correct shape and type
iter = tf.data.Iterator.from_structure(train_dataset.output_types,
train_dataset.output_shapes)
iter_test = tf.data.Iterator.from_structure(test_dataset.output_types,
test_dataset.output_shapes)
# create the initialisation operations
train_init_op = iter.make_initializer(train_dataset)
dev_init_op = iter.make_initializer(dev_dataset)
test_init_op = iter_test.make_initializer(test_dataset)
epoch_start = 0
## Train
sess = tf.Session()
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_g)
sess.run(init_l)
best_acc = 0
for epoch in range(epoch_start, NUM_EPOCH + 1):
sess.run(train_init_op,
feed_dict={
X: train_data[0],
y: train_data[1],
fnames_plc: train_data[2],
batch_size: BATCH_SIZE,
})
current_batch_index = 0
next_element = iter.get_next()
loss_count = 0
while True:
try:
data = sess.run([next_element])
except tf.errors.OutOfRangeError:
break
current_batch_index += 1
data = data[0]
batch_x, batch_tgt, batch_fnames = data
_, loss_result = sess.run(
[train_op, loss],
feed_dict={
X: batch_x,
y: batch_tgt,
lr: train_ops.lr_scheduler(epoch),
batch_size: BATCH_SIZE,
is_training: True,
dropout_keep_prob: 0.3,
})
# print("Loss: {}".format(loss_result))
loss_count += loss_result
loss_count = loss_count / current_batch_index
logger.info("Loss on epoch {} : {} - LR: {}".format(
epoch, loss_count, train_ops.lr_scheduler(epoch)))
acc = 0
if do_val:
print("Eval")
## Eval
sess.run(
dev_init_op,
feed_dict={
# sess.run(dev_init_op, feed_dict={
X: dev_data[0],
y: dev_data[1],
fnames_plc: dev_data[2],
batch_size: BATCH_SIZE,
})
current_batch_index = 0
next_element = iter.get_next()
while True:
try:
data = sess.run([next_element])
except tf.errors.OutOfRangeError:
break
current_batch_index += 1
data = data[0]
batch_x, batch_tgt, batch_fnames = data
results = sess.run(
[softmax],
feed_dict={
X: batch_x,
y: batch_tgt,
lr: train_ops.lr_scheduler(epoch),
batch_size: BATCH_SIZE,
is_training: False,
dropout_keep_prob: 1.0
})
results = results[0]
acc_aux = metrics.accuracy(X=results, y=batch_tgt)
acc += acc_aux
acc = acc / current_batch_index
print("Acc Val epoch {} : {}".format(epoch, acc))
print("----------")
if acc > best_acc:
best_acc = acc
logger.info("New acc : {}".format(best_acc))
save_best(sess, opts.work_dir)
if opts.testing and opts.do_val:
logger.info("Model: {}".format(MODEL))
logger.info("layers: {}".format(layers))
logger.info("max_features: {}".format(max_features))
logger.info("Min and max features: {} - {}".format(min_ngram, up))
logger.info("Best acc : {}".format(best_acc))
exit()
"""
----------------- TEST -----------------
"""
logger.info("\n-- TEST --\n")
logger.info("Restoring the best Checkpoint.")
# restore_file = sesions.restore_from_best(sess, save_path)
restore_file = restore_from_best(sess, opts.work_dir)
if restore_file:
logger.info("Best model restored")
else:
logger.info("Cant restore the best model.")
exit()
Y_FALSA = np.random.randint(1, size=(BATCH_SIZE, n_classes))
print(Y_FALSA.shape)
logger.info("\n-- TEST --\n")
sess.run(test_init_op,
feed_dict={
X: test_data[0],
y: Y_FALSA,
fnames_plc: test_data[1],
batch_size: BATCH_SIZE,
})
current_batch_index = 0
next_element = iter_test.get_next()
loss_count = 0
classifieds = []
classifieds_to_write = []
while True:
try:
data = sess.run([next_element])
except tf.errors.OutOfRangeError:
break
current_batch_index += 1
data = data[0]
batch_x, batch_fnames = data
results = sess.run(
[softmax],
feed_dict={
X: batch_x,
y: Y_FALSA,
batch_size: BATCH_SIZE,
dropout_keep_prob: 1.0,
lr: train_ops.lr_scheduler(1)
})
for i in range(len(results[0])):
# to write
hyp = [np.argmax(results[0][i], axis=-1)]
hyp = labelencoder.inverse_transform(hyp)[
0] #real label #set
doc_name = batch_fnames[i].decode("utf-8").split("/")[-1]
classifieds_to_write.append((doc_name, lang, hyp))
logger.info("----------")
logger.info("Writting results in output dir {}".format("{}/{}".format(
opts.o, lang)))
if sent != 2 and lang == 'en':
dt_train = process.PAN2019(path=path,
txt=txt_train,
join_all=MODEL == "FF",
sentiment_id=2)
x_train = dt_train.X
y2_train = dt_train.y2
del dt_train
if max_features:
rep = TfidfVectorizer(ngram_range=(min_ngram, up),
max_features=max_features)
else:
rep = TfidfVectorizer(ngram_range=(min_ngram, up))
texts_rep_train = rep.fit_transform(x_train).toarray()
dt_test = process.PAN2019_Test(path=path_test,
join_all=MODEL == "FF",
sentiment_id=2)
del dt_test
x_test = dt_test.X
text_test_rep = rep.transform(x_test).toarray()
process.write_from_array(classifieds_to_write,
"{}/{}".format(opts.o, lang), x_train,
texts_rep_train, y2_train, x_test,
text_test_rep, fnames_test)
