def run_lstm_conditional(config,
split=True,
outputpath='output/lstm_conditional',
final=False):
## Get data
config, data_dict = get_data(
config,
filename_embeddings='/../../glove.6B.50d.txt',
pickle_path='/data/glove50d_h_ids_b_ids_pickle.p',
concat=False)
## pass data into local namespace:
y = data_dict['y']
h = data_dict['h_np']
b = data_dict['b_np']
h_len = data_dict['h_seqlen']
b_len = data_dict['b_seqlen']
# config, data_dict1 = get_data(config,
# filename_embeddings = '/../../glove.6B.50d.txt',
# pickle_path = '/data/glove50d_h_ids_b_ids_comp_pickle.p',
# concat = False)
#
# ## pass data into local namespace:
# y1 = data_dict1['y']
# h1 = data_dict1['h_np']
# b1 = data_dict1['b_np']
# h1_len = data_dict1['h_seqlen']
# b1_len = data_dict1['b_seqlen']
#
#
#
# if config.b_max_len is not None:
# b_max_len = config.b_max_len
# if np.shape(b1)[1] > b_max_len:
# b1 = b1[:, 0:b_max_len]
# b1_len = np.minimum(b1_len, b_max_len)
# Do shortening of dataset ## affects number of samples and max_len.
if config.num_samples is not None:
## Random seed
np.random.seed(1)
ind = range(np.shape(h)[0])
random.shuffle(ind)
indices = ind[0:config.num_samples]
h = h[indices, :]
b = b[indices, :]
h_len = h_len[indices]
b_len = b_len[indices]
y = y[indices]
# Truncate headlines and bodies
if config.h_max_len is not None:
h_max_len = config.h_max_len
if np.shape(h)[1] > h_max_len:
h = h[:, 0:h_max_len]
h_len = np.minimum(h_len, h_max_len)
if config.b_max_len is not None:
b_max_len = config.b_max_len
if np.shape(b)[1] > b_max_len:
b = b[:, 0:b_max_len]
b_len = np.minimum(b_len, b_max_len)
if split:
# Split data
train_indices, dev_indices, test_indices = split_indices(
np.shape(h)[0])
# Divide data
train_h = h[train_indices, :]
train_b = b[train_indices, :]
train_h_len = h_len[train_indices]
train_b_len = b_len[train_indices]
train_y = y[train_indices]
# test
dev_h = h[dev_indices, :]
dev_b = b[dev_indices, :]
dev_h_len = h_len[dev_indices]
dev_b_len = b_len[dev_indices]
dev_y = y[dev_indices]
if final:
# Combine train and dev
train_dev_indices = train_indices + dev_indices
train_h = h[train_dev_indices, :]
train_b = b[train_dev_indices, :]
train_h_len = h_len[train_dev_indices]
train_b_len = b_len[train_dev_indices]
train_y = y[train_dev_indices]
# Set dev to test
dev_h = h[test_indices, :]
dev_b = b[test_indices, :]
dev_h_len = h_len[test_indices]
dev_b_len = b_len[test_indices]
dev_y = y[test_indices]
print np.shape(train_h)
# dev_h = h1
# dev_b = b1
# dev_h_len = h1_len
# dev_b_len = b1_len
# dev_y = y1
## Passing parameter_dict to config settings
## Changes to config based on data shape
assert (np.shape(train_h)[0] == np.shape(train_b)[0] ==
np.shape(train_y)[0] == np.shape(train_h_len)[0] ==
np.shape(train_b_len)[0])
# assert(np.shape(dev_h)[0] == np.shape(dev_b)[0] == np.shape(dev_y)[0] == np.shape(dev_h_len)[0] == np.shape(dev_b_len)[0])
config.num_samples = np.shape(train_h)[0]
config.h_max_len = np.shape(train_h)[1]
config.b_max_len = np.shape(train_b)[1]
data_path = '/home/neha/sem2/nlu/project/stance_detection-master/code/models/lstm_conditional/'
# model_save_name= 'lstm-conditional-50blen-2l-60e'
model_save_name = 'lstm-conditional-test2'
## Start Tensorflow!
print('Starting TensorFlow operations')
print 'With hidden layers: ', config.n_layers ## hidden layer?
with tf.Graph().as_default():
tf.set_random_seed(1)
model = LSTMCondModel(config)
init = tf.global_variables_initializer()
# saver = tf.train.Saver()
with tf.Session() as session:
session.run(init)
# saver.restore(session, tf.train.latest_checkpoint(checkpoint_dir = '/home/neha/sem2/nlu/project/stance_detection-master/code/models/lstm_conditional/'))
# saver = tf.train.Saver()
losses_ep, dev_performances_ep, dev_predicted_classes_ep, dev_predictions_ep = model.fit(
session, train_h, train_b, train_h_len, train_b_len, train_y,
dev_h, dev_b, dev_h_len, dev_b_len, dev_y) #M
# saver.save(session, data_path + model_save_name + '-final')
# Write results to csv
convertOutputs(outputpath, config, losses_ep, dev_performances_ep)
print('Losses ', losses_ep)
print('Dev Performance ', dev_performances_ep) #M
return losses_ep, dev_predicted_classes_ep, dev_performances_ep #M
def run_lstm_conditional(config,
split=True,
outputpath='../../xp',
final=False):
## Get data
config, data_dict = get_data(
config,
filename_embeddings='/../../glove/glove.twitter.27B.50d.txt',
pickle_path='/../../glove/twitter50d_h_ids_b_ids_pickle.p',
concat=False)
config1, lex_data_dict = get_lexicon_data_bilstm_sentiment(
config,
filename_embeddings='/../../glove/glove.twitter.27B.50d.txt',
pickle_path='lexiconp.p',
concat=False)
# print(data_dict_l)
## pass data into local namespace:
y = data_dict['y']
h = data_dict['h_np']
b = data_dict['b_np']
h_len = data_dict['h_seqlen']
b_len = data_dict['b_seqlen']
# Do shortening of dataset ## affects number of samples and max_len.
if config.num_samples is not None:
## Random seed
np.random.seed(1)
ind = range(np.shape(h)[0])
random.shuffle(ind)
indices = ind[0:config.num_samples]
h = h[indices, :]
b = b[indices, :]
h_len = h_len[indices]
b_len = b_len[indices]
y = y[indices]
# Truncate headlines and bodies
if config.h_max_len is not None:
h_max_len = config.h_max_len
if np.shape(h)[1] > h_max_len:
h = h[:, 0:h_max_len]
h_len = np.minimum(h_len, h_max_len)
if config.b_max_len is not None:
b_max_len = config.b_max_len
if np.shape(b)[1] > b_max_len:
b = b[:, 0:b_max_len]
b_len = np.minimum(b_len, b_max_len)
####################### LEX DATA #######################
#lex_y = lex_data_dict['y']
#lex_h = lex_data_dict['h_np']
#lex_h_len = lex_data_dict['h_seqlen']
lex_b = lex_data_dict['h_b_np']
lex_b_len = lex_data_dict['seqlen']
# lex_y = data_dict_l['y']
# lex_h_b_np = data_dict_l['h_b_np']
# lex_seqlen = data_dict_l['seqlen']
# lex_data = pack_labels(lex_h_b_np, lex_y, lex_seqlen)
# if config1.num_samples is not None:
# lex_num_samples = config1.num_samples
# lex_data = lex_data[0:num_samples - 1]
# lex_train_data, lex_dev_data, lex_test_data, lex_train_indices, lex_dev_indices, lex_test_indices = split_data(lex_data, prop_train = 0.6, prop_dev = 0.2, seed = 56)
# config1.num_samples = len(lex_train_indices)
# config1.max_length = 75
# print("lex_train_data", lex_train_data[0])
if split:
# Split data
train_indices, dev_indices, test_indices = split_indices(
np.shape(h)[0])
# Divide data
train_h = h[train_indices, :]
train_b = b[train_indices, :]
train_h_len = h_len[train_indices]
train_b_len = b_len[train_indices]
train_y = y[train_indices]
####################### LEX DATA #######################
#lex_train_h = lex_h[train_indices,:]
#lex_train_h_len = lex_h_len[train_indices]
lex_train_b = lex_b[train_indices, :]
lex_train_b_len = lex_b_len[train_indices]
# test
dev_h = h[dev_indices, :]
dev_b = b[dev_indices, :]
dev_h_len = h_len[dev_indices]
dev_b_len = b_len[dev_indices]
dev_y = y[dev_indices]
################## LEX ########################
#lex_dev_h = lex_h[dev_indices,:]
#lex_dev_h_len = lex_h_len[dev_indices]
lex_dev_b = lex_b[dev_indices, :]
lex_dev_b_len = lex_b_len[dev_indices]
if final:
# Combine train and dev
train_dev_indices = train_indices + dev_indices
train_h = h[train_dev_indices, :]
train_b = b[train_dev_indices, :]
train_h_len = h_len[train_dev_indices]
train_b_len = b_len[train_dev_indices]
train_y = y[train_dev_indices]
################## LEX train ########################
#lex_train_h = lex_h[train_dev_indices,:]
#lex_train_h_len = lex_h_len[train_dev_indices]
lex_train_b = lex_b[train_dev_indices, :]
lex_train_b_len = lex_b_len[train_dev_indices]
# Set dev to test
dev_h = h[test_indices, :]
dev_b = b[test_indices, :]
dev_h_len = h_len[test_indices]
dev_b_len = b_len[test_indices]
dev_y = y[test_indices]
################### LEX dev ######################
#lex_dev_h = lex_h[test_indices,:]
#lex_dev_h_len = lex_h_len[test_indices]
lex_dev_b = lex_b[test_indices, :]
lex_dev_b_len = lex_b_len[test_indices]
## Passing parameter_dict to config settings
## Changes to config based on data shape
assert (np.shape(train_h)[0] == np.shape(train_b)[0] ==
np.shape(train_y)[0] == np.shape(train_h_len)[0] ==
np.shape(train_b_len)[0] == np.shape(lex_train_b_len)[0])
config.num_samples = np.shape(train_h)[0]
config.h_max_len = np.shape(train_h)[1]
config.b_max_len = np.shape(train_b)[1]
## Start Tensorflow!
print('Starting TensorFlow operations')
print 'With hidden layers: ', config.n_layers ## hidden layer?
with tf.Graph().as_default():
tf.set_random_seed(1)
model = LSTMCondModel(config)
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
losses_ep, dev_performances_ep, dev_predicted_classes_ep, dev_predictions_ep = model.fit(
session, train_h, train_b, train_h_len, train_b_len, train_y,
dev_h, dev_b, dev_h_len, dev_b_len, dev_y, lex_train_b,
lex_train_b_len, lex_dev_b, lex_dev_b_len) #M
# Write results to csv
convertOutputs(outputpath, config, losses_ep, dev_performances_ep)
print('Losses ', losses_ep)
print('Dev Performance ', dev_performances_ep) #M
return losses_ep, dev_predicted_classes_ep, dev_performances_ep #M
def run_lstm_conditional(config,
split=True,
outputpath='../../xp',
final=False):
## Get data
config, data_dict = get_data(
config,
filename_embeddings='/../../glove/glove.twitter.27B.50d.txt',
pickle_path='/../../glove/twitter50d_h_ids_b_ids_pickle.p',
concat=False)
## pass data into local namespace:
y = data_dict['y']
h = data_dict['h_np']
b = data_dict['b_np']
print("===========Number of head=========", len(h))
print("===========Number of body=========", len(b))
h_len = data_dict['h_seqlen']
b_len = data_dict['b_seqlen']
# Do shortening of dataset ## affects number of samples and max_len.
if config.num_samples is not None:
## Random seed
np.random.seed(1)
ind = range(np.shape(h)[0])
random.shuffle(ind)
indices = ind[0:config.num_samples]
h = h[indices, :]
b = b[indices, :]
h_len = h_len[indices]
b_len = b_len[indices]
y = y[indices]
# Truncate headlines and bodies
if config.h_max_len is not None:
h_max_len = config.h_max_len
if np.shape(h)[1] > h_max_len:
h = h[:, 0:h_max_len]
h_len = np.minimum(h_len, h_max_len)
if config.b_max_len is not None:
b_max_len = config.b_max_len
if np.shape(b)[1] > b_max_len:
b = b[:, 0:b_max_len]
b_len = np.minimum(b_len, b_max_len)
if split:
# Split data
train_indices, dev_indices, test_indices = split_indices(
np.shape(h)[0])
# Divide data
train_h = h[train_indices, :]
train_b = b[train_indices, :]
train_h_len = h_len[train_indices]
train_b_len = b_len[train_indices]
train_y = y[train_indices]
# test
dev_h = h[dev_indices, :]
dev_b = b[dev_indices, :]
dev_h_len = h_len[dev_indices]
dev_b_len = b_len[dev_indices]
dev_y = y[dev_indices]
if final:
# Combine train and dev
train_dev_indices = train_indices + dev_indices
train_h = h[train_dev_indices, :]
train_b = b[train_dev_indices, :]
train_h_len = h_len[train_dev_indices]
train_b_len = b_len[train_dev_indices]
train_y = y[train_dev_indices]
# Set dev to test
dev_h = h[test_indices, :]
dev_b = b[test_indices, :]
dev_h_len = h_len[test_indices]
dev_b_len = b_len[test_indices]
dev_y = y[test_indices]
## Passing parameter_dict to config settings
## Changes to config based on data shape
assert (np.shape(train_h)[0] == np.shape(train_b)[0] ==
np.shape(train_y)[0] == np.shape(train_h_len)[0] ==
np.shape(train_b_len)[0])
config.num_samples = np.shape(train_h)[0]
config.h_max_len = np.shape(train_h)[1]
config.b_max_len = np.shape(train_b)[1]
## Start Tensorflow!
print('Starting TensorFlow operations')
print 'With hidden layers: ', config.n_layers ## hidden layer?
with tf.Graph().as_default():
tf.set_random_seed(1)
model = LSTMCondModel(config)
# saver = tf.train.Saver()
saver = tf.train.Saver(tf.global_variables())
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
losses_ep, dev_performances_ep, dev_predicted_classes_ep, dev_predictions_ep = model.fit(
session, train_h, train_b, train_h_len, train_b_len, train_y,
dev_h, dev_b, dev_h_len, dev_b_len, dev_y) #M
save_path = saver.save(session, "model/model.ckpt")
print("Model saved in file: %s" % save_path)
# Write results to csv
convertOutputs(outputpath, config, losses_ep, dev_performances_ep)
print('Losses ', losses_ep)
print('Dev Performance ', dev_performances_ep)
return losses_ep, dev_predicted_classes_ep, dev_performances_ep
