def get_conversation_turn_stats(folders, bucket_size=30, max_turns=3000):
start_time = time.time()
number_of_files_read = 0
# Array with occurrences of conversations with i turns. Array[i] get #conversations with i turns
turns = [0] * max_turns
exceeded_max_turns = 0
total_turns = 0.0
longest_conversation = 0
more_than_500 = 0
more_than_1000 = 0
more_than_2000 = 0
for folder in folders:
folder_path = "../../ubuntu-ranking-dataset-creator/src/dialogs/" + folder
for filename in os.listdir(folder_path):
number_of_files_read += 1
file_path = folder_path + "/" + filename
num_turns = get_num_turns_in_file(file_path, bucket_size,
max_turns)
if num_turns > 2000:
more_than_2000 += 1
elif (num_turns > 1000):
more_than_1000 += 1
elif num_turns > 500:
more_than_500 += 1
if num_turns < max_turns:
turns[num_turns] += 1
total_turns += num_turns
else:
exceeded_max_turns += 1
print("Done with folder: " + str(folder) + ", read " +
str(number_of_files_read) + " conversations")
print "Number of files read: " + str(number_of_files_read)
# Find stats
max_occ = 0
max_index = 0
for i in range(2, max_turns):
if turns[i] > max_occ:
max_occ = turns[i]
max_index = i
print("Mode is " + str(max_index) + " with " + str(max_occ) +
" occurrences")
print("Average turns per conversation is " +
str(total_turns / number_of_files_read))
#print("Average turns per conversation without two turns is " + str((total_turns-2*turns[2])/(number_of_files_read-turns[2])))
for occurrence in turns:
print(occurrence)
print("Exceeded max turns " + str(exceeded_max_turns))
print("More than 2000 " + str(more_than_2000))
print("More than 1000 " + str(more_than_1000))
print("More than 500 " + str(more_than_500))
print get_time(start_time)
def read_every_data_file_and_create_initial_files(folders, initial_x_file_path,
initial_y_file_path):
start_time = time.time()
number_of_files_read = 0
for folder in folders:
folder_path = "../../ubuntu-ranking-dataset-creator/src/dialogs/" + folder
for filename in os.listdir(folder_path):
number_of_files_read += 1
file_path = folder_path + "/" + filename
preprocess_training_file(file_path, initial_x_file_path,
initial_y_file_path)
print("Done with folder: " + str(folder) + ", read " +
str(number_of_files_read) + " files")
print "Number of files read: " + str(number_of_files_read)
print get_time(start_time)
def create_fast_text_model(folder, merged_spellcheck_path):
start_time_fasttext = time.time()
path = './' + str(folder) + '/model'
model = fasttext.skipgram(merged_spellcheck_path, path)
print("Time used to create Fasttext model: ",
get_time(start_time_fasttext))
return model
def get_most_similar_words_for_unk(unknown_words, vocab_words,
unknown_dict_pickle_path,
unk_to_vocab_txt_path, save_freq):
# The resulting dictionary consisting of 'unk_word' : 'most similar vocab word'
unknown_words_results = {}
# If a previously dictionary is saved, this one will be fed with words that has NOT computed a similar word
new_unk_words_dict = {}
# If pickle file exists, load into unknown_words_results
if os.path.exists(unknown_dict_pickle_path):
unknown_words_results = load_pickle_file(unknown_dict_pickle_path)
for key, value in unknown_words.iteritems():
if key not in unknown_words_results:
# If the word is not computed, add to new_unk_words_dict so it can be computed later
new_unk_words_dict[key] = value
# Set unknown_words to the words that is not computed
unknown_words = new_unk_words_dict
# Create lists for faster computation
known_words_list = [(key, value[0], value[1])
for key, value in vocab_words.iteritems()]
unknown_words_list = [(key, value)
for key, value in unknown_words.iteritems()]
counter = 1
start_time_unk = time.time()
# Loop all unknown_words
for unk_key, unk_values in unknown_words_list:
min_dist = 1
word = ""
if (counter % 5000) == 0:
print(" Calculated " + str(counter) + " unknown words")
# Loop all vocab words for calculating the distance
for key, value, dis in known_words_list:
cur_dist = distance(unk_values, value, dis)
# Save the word that is most similar
if cur_dist < min_dist:
min_dist = cur_dist
word = key
# Save most similar vocab_word to the unk_word
unknown_words_results[unk_key] = word
counter += 1
# Once in a while, save checkpoints
if counter % save_freq == 0:
save_to_pickle(unknown_dict_pickle_path, unknown_words_results)
print(" Saved temporarily unknown_words_dictionary")
print("Time to get similar words for all UNK:", get_time(start_time_unk))
save_to_pickle(unknown_dict_pickle_path, unknown_words_results)
save_dict_to_file(unk_to_vocab_txt_path, unknown_words_results)
return unknown_words_results
def read_every_data_file_and_create_initial_files(initial_x_file_path,
initial_y_file_path,
misspelled_vocabulary):
start_time = time.time()
number_of_files_read = 0
number_of_lines = 0
folder_path = "../../opensubtitles-parser/data"
for filename in os.listdir(folder_path):
if filename[-7:] == 'raw.txt':
filename_path = paths['source_folder_root'] + filename
number_of_files_read += 1
num_sentences = preprocess_training_file(filename_path,
initial_x_file_path,
initial_y_file_path,
misspelled_vocabulary)
number_of_lines += num_sentences
print("Done with filename: " + str(filename) + ", read " +
str(number_of_files_read) + " files, processed " +
str(number_of_lines) + " sentences")
else:
print(filename + " is not preprocessed")
print "Number of files read: " + str(number_of_files_read)
print get_time(start_time)
def train():
"""Train a en->fr translation model using WMT data."""
print("Checking for needed files")
check_for_needed_files_and_create()
train_path = paths['train_path']
shuffle_file(train_path, train_path)
print("Creating file queue")
filename_queue = input_pipeline(root=paths['preprocess_root_files'] ,start_name=paths['train_file'])
filename_queue_dev = input_pipeline(root=paths['preprocess_root_files'], start_name=paths['dev_file'])
perplexity_log_path = os.path.join(FLAGS.train_dir, paths['perplexity_log'])
if not os.path.exists(perplexity_log_path):
with open(perplexity_log_path, 'w') as fileObject:
fileObject.write("Learning_rate: %d \t Optimizer: %s \n" % (FLAGS.learning_rate, optimizer))
fileObject.write("Step \tPerplexity \tBucket perplexity \n")
# Avoid allocating all of the GPU memory
config = get_session_configs()
with tf.device(use_gpu):
with tf.Session(config=config) as sess:
# Create model.
print("Creating %d layers of %d units." % (FLAGS.num_layers, FLAGS.size))
model = create_model(sess, False)
# Stream data
print("Setting up coordinator")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# This is for the training loop.
train_set = [[] for _ in _buckets]
dev_set = [[] for _ in _buckets]
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
read_line = 0
reading_file_path = ""
# Create log writer object
print("Create log writer object")
summary_writer = tf.train.SummaryWriter(FLAGS.log_dir, graph=tf.get_default_graph())
reader_train_data = tf.TextLineReader() # skip_header_lines=int, number of lines to skip
key, txt_row_train_data = reader_train_data.read(filename_queue)
reader_dev_data = tf.TextLineReader()
_, txt_row_dev_data = reader_dev_data.read(filename_queue_dev)
lowest_perplexity = 20.0
train_time = time.time()
print("Starting training loop")
try:
while current_step < FLAGS.max_train_steps: # not coord.should_stop():
if current_step % FLAGS.print_frequency == 0:
print("Step number: " + str(current_step))
read_line, reading_file_path = check_and_shuffle_file(key, sess, read_line, paths['train_path'])
# Get a batch
train_set, bucket_id = get_batch(txt_row_train_data, train_set, FLAGS.batch_size)
start_time = time.time()
encoder_inputs, decoder_inputs, target_weights = model.get_batch(train_set, bucket_id)
# Clean out trained bucket
train_set[bucket_id] = []
# Make a step
_, step_loss, _ = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, False)
# Calculating variables
step_time += (time.time() - start_time) / FLAGS.steps_per_checkpoint
loss += step_loss / FLAGS.steps_per_checkpoint
current_step += 1
# Once in a while, we save checkpoint, print statistics, and run evals.
if current_step % FLAGS.steps_per_checkpoint == 0:
check_time = time.time()
print(get_time(train_time, "to train"))
# Print statistics for the previous epoch.
dev_set, bucket_id = get_batch(txt_row_dev_data, dev_set, FLAGS.batch_size, ac_function=min)
perplexity = exp(float(loss)) if loss < 300 else float("inf")
print("global step %d learning rate %.4f step-time %.2f perplexity "
"%.2f" % (model.global_step.eval(), model.learning_rate.eval(), step_time, perplexity))
# Decrease learning rate if no improvement was seen over last 3 times.
if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
# Save checkpoint and zero timer and loss.
print("Save checkpoint")
checkpoint_path = os.path.join(FLAGS.train_dir, "Ola.ckpt")
model.saver.save(sess, checkpoint_path, global_step=model.global_step)
step_time, loss = 0.0, 0.0
# Adding perplexity to tensorboard
perplexity_summary = tf.Summary()
overall_value = perplexity_summary.value.add()
overall_value.tag = "perplexity_overall"
overall_value.simple_value = perplexity
# Run evals on development set and print their perplexity.
print("Run evaluation on development set")
bucket_perplexity = ""
for bucket_id in xrange(len(_buckets)):
if len(dev_set[bucket_id]) == 0:
print(" eval: empty bucket %d" % bucket_id)
continue
encoder_inputs, decoder_inputs, target_weights = model.get_batch(dev_set, bucket_id)
# Clean out used bucket
del dev_set[bucket_id][:FLAGS.batch_size]
_, eval_loss, _ = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True)
eval_ppx = exp(float(eval_loss)) if eval_loss < 300 else float("inf")
print(" eval: bucket %d perplexity %.2f" % (bucket_id, eval_ppx))
bucket_perplexity += "\t" + str(eval_ppx)
# Adding bucket perplexity to tensorboard
bucket_value = perplexity_summary.value.add()
bucket_value.tag = "perplexity_bucket %d" % bucket_id
bucket_value.simple_value = eval_ppx
summary_writer.add_summary(perplexity_summary, model.global_step.eval())
with open(os.path.join(FLAGS.train_dir, paths['perplexity_log']), 'a') as fileObject:
fileObject.write(str(model.global_step) + " \t" + str(perplexity) + bucket_perplexity + "\n")
# Save model if checkpoint was the best one
if perplexity < lowest_perplexity: # and current_step > 400000:
lowest_perplexity = perplexity
checkpoint_path = os.path.join(FLAGS.train_dir, "Ola_best_.ckpt")
model.saver.save(sess, checkpoint_path, global_step=model.global_step)
sys.stdout.flush()
print(get_time(check_time), "to do checkpoint")
train_time = time.time()
except tf.errors.OutOfRangeError:
print('Done training, epoch reached')
finally:
coord.request_stop()
coord.join(threads)
def train():
"""Train a en->fr translation model using WMT data."""
print("Checking for needed files")
check_for_needed_files_and_create()
print("Creating file queues")
filename_queue = input_pipeline(root=paths['stateful_datafiles'], start_name="merged_train", shuffle=False)
filename_queue_dev = input_pipeline(root=paths['stateful_datafiles'], start_name="merged_dev", shuffle=False)
perplexity_log_path = os.path.join(FLAGS.train_dir, paths['perplexity_log'])
if not os.path.exists(perplexity_log_path):
with open(perplexity_log_path, 'w') as fileObject:
fileObject.write(
"Learning_rate: %d \t Optimizer: %s \t Lstm %s \n" % (FLAGS.learning_rate, optimizer, FLAGS.use_lstm))
fileObject.write("Step \tPerplexity \tBucket perplexity \n")
# Avoid allocating all of the GPU memory
config = get_session_configs()
with tf.device(use_gpu):
with tf.Session(config=config) as sess:
# Create model.
print("Creating %d layers of %d units." % (FLAGS.num_layers, FLAGS.size))
model = create_model(sess, False)
# Stream data
print("Setting up coordinator")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# This is for the training loop.
step_time, loss = 0.0, 0.0
current_step = 0
train_set = [[] for _ in range(batch_size)]
dev_set = [[] for _ in range(batch_size)]
previous_losses = []
read_line = 0
read_line_dev = 0
reading_file_path = paths['merged_train_stateful_path_file1']
reading_dev_file_path = paths['merged_dev_stateful_path']
# Create log writer object
print("Create log writer object")
summary_writer = tf.train.SummaryWriter(FLAGS.log_dir, graph=tf.get_default_graph())
key, txt_row_train_data = tf.TextLineReader().read(filename_queue)
key_dev, txt_row_dev_data = tf.TextLineReader().read(filename_queue_dev)
lowest_perplexity = 20.0
train_time = time.time()
# Need an initial state for the encoder rnn
if FLAGS.use_lstm:
initial_state = np.zeros((num_layers, 2, batch_size, size))
else:
initial_state = np.zeros((num_layers, batch_size, size))
state = initial_state
dev_state = initial_state
print("Starts training loop")
try:
while FLAGS.max_train_steps >= current_step: # not coord.should_stop():
if current_step % FLAGS.print_frequency == 0:
print("Step number" + str(current_step))
# Get a batch
# Find empty holders in training set
empty_conversations = [index for index, conversation in enumerate(train_set) if conversation == []]
if empty_conversations != []:
init_key, init_line = sess.run([key, txt_row_train_data])
read_line, reading_file_path = check_and_shuffle_file(init_key, sess, read_line, reading_file_path, stateful=True)
train_set, batch_train_set, state = get_stateful_batch(txt_row_train_data, train_set, empty_conversations, init_line, state, size, FLAGS.use_lstm)
start_time = time.time()
encoder_inputs, decoder_inputs, target_weights = model.get_batch(batch_train_set)
# Make a step
_, step_loss, _, state = model.step(sess, encoder_inputs, decoder_inputs, target_weights, state, False)
# Calculating variables
step_time += (time.time() - start_time) / FLAGS.steps_per_checkpoint
loss += step_loss / FLAGS.steps_per_checkpoint
current_step += 1
# Once in a while, we save checkpoint, print statistics, and run evals.
if current_step % FLAGS.steps_per_checkpoint == 0:
check_time = time.time()
print(get_time(train_time), "to train")
# Print statistics for the previous epoch.
empty_dev_conversations = [index for index, conversation in enumerate(dev_set) if
conversation == []]
if empty_dev_conversations != []:
init_key_dev, init_line_dev = sess.run([key_dev, txt_row_dev_data])
read_line_dev, reading_dev_file_path = check_and_shuffle_file(init_key_dev, sess, read_line_dev, reading_dev_file_path, stateful=True, dev=True)
dev_set, batch_dev_set, dev_state = get_stateful_batch(txt_row_dev_data, dev_set, empty_dev_conversations, init_line_dev, dev_state, size, FLAGS.use_lstm)
perplexity = exp(float(loss)) if loss < 300 else float("inf")
print("global step %d learning rate %.4f step-time %.2f perplexity "
"%.2f" % (model.global_step.eval(), model.learning_rate.eval(), step_time, perplexity))
# Decrease learning rate if no improvement was seen over last 3 times.
if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
# Save checkpoint and zero timer and loss.
print("Save checkpoint")
checkpoint_path = os.path.join(FLAGS.train_dir, "Vinyals.ckpt")
model.saver.save(sess, checkpoint_path, global_step=model.global_step)
step_time, loss = 0.0, 0.0
# Adding perplexity to tensorboard
perplexity_summary = tf.Summary()
overall_value = perplexity_summary.value.add()
overall_value.tag = "perplexity_overall"
overall_value.simple_value = perplexity
# Run evals on development set and print their perplexity.
print("Run evaluation on development set")
step_perplexity = ""
# Run eval on three steps
# 1
encoder_inputs, decoder_inputs, target_weights = model.get_batch(batch_dev_set)
_, eval_loss, _, dev_state = model.step(sess, encoder_inputs, decoder_inputs, target_weights, dev_state, True)
eval_ppx = exp(float(eval_loss)) if eval_loss < 300 else float("inf")
print(" eval: step %d perplexity %.2f" % (1.0, eval_ppx))
step_perplexity += "\t" + str(eval_ppx)
# Adding step perplexity to tensorboard
step_value = perplexity_summary.value.add()
step_value.tag = "perplexity_step %d" % 1.0
step_value.simple_value = eval_ppx
# 2
empty_dev_conversations = [index for index, conversation in enumerate(dev_set) if
conversation == []]
if empty_dev_conversations != []:
init_key_dev, init_line_dev = sess.run([key_dev, txt_row_dev_data])
read_line_dev, reading_dev_file_path = check_and_shuffle_file(init_key_dev, sess, read_line_dev, reading_dev_file_path, stateful=True, dev=True)
dev_set, batch_dev_set, dev_state = get_stateful_batch(txt_row_dev_data, dev_set,empty_dev_conversations, init_line_dev, dev_state, size, FLAGS.use_lstm)
encoder_inputs, decoder_inputs, target_weights = model.get_batch(batch_dev_set)
_, eval_loss, _, dev_state = model.step(sess, encoder_inputs, decoder_inputs, target_weights, dev_state,
True)
eval_ppx = exp(float(eval_loss)) if eval_loss < 300 else float("inf")
print(" eval: step %d perplexity %.2f" % (2.0, eval_ppx))
step_perplexity += "\t" + str(eval_ppx)
# Adding step perplexity to tensorboard
step_value = perplexity_summary.value.add()
step_value.tag = "perplexity_step %d" % 2.0
step_value.simple_value = eval_ppx
# 3
empty_dev_conversations = [index for index, conversation in enumerate(dev_set) if
conversation == []]
if empty_dev_conversations != []:
init_key_dev, init_line_dev = sess.run([key_dev, txt_row_dev_data])
read_line_dev, reading_dev_file_path = check_and_shuffle_file(init_key_dev, sess,
read_line_dev,
reading_dev_file_path,
stateful=True, dev=True)
dev_set, batch_dev_set, dev_state = get_stateful_batch(txt_row_dev_data, dev_set,
empty_dev_conversations, init_line_dev,
dev_state, size, FLAGS.use_lstm)
encoder_inputs, decoder_inputs, target_weights = model.get_batch(batch_dev_set)
_, eval_loss, _, dev_state = model.step(sess, encoder_inputs, decoder_inputs, target_weights, dev_state,
True)
eval_ppx = exp(float(eval_loss)) if eval_loss < 300 else float("inf")
print(" eval: step %d perplexity %.2f" % (3.0, eval_ppx))
step_perplexity += "\t" + str(eval_ppx)
# Adding step perplexity to tensorboard
step_value = perplexity_summary.value.add()
step_value.tag = "perplexity_step %d" % 3.0
step_value.simple_value = eval_ppx
summary_writer.add_summary(perplexity_summary, model.global_step.eval())
with open(os.path.join(FLAGS.train_dir, paths['perplexity_log']), 'a') as fileObject:
fileObject.write(str(model.global_step) + " \t" + str(perplexity) + step_perplexity + "\n")
# Save model if checkpoint was the best one
if perplexity < lowest_perplexity:
lowest_perplexity = perplexity
checkpoint_path = os.path.join(FLAGS.train_dir, "Vinyals_stateful_best_.ckpt")
model.saver.save(sess, checkpoint_path, global_step=model.global_step)
sys.stdout.flush()
get_time(check_time, "to do checkpoint")
train_time = time.time()
except tf.errors.OutOfRangeError:
print('Done training, epoch reached')
finally:
coord.request_stop()
coord.join(threads)
def get_conversation_stats_for_context(folders, fit_1, fit_2, fit_3, fit_4,
fit_5, fit_6):
start_time = time.time()
number_of_files_checked = 0
fits_1_conv = 0
fits_1_turns = 0
fits_2_conv = 0
fits_2_turns = 0
fits_3_conv = 0
fits_3_turns = 0
fits_4_conv = 0
fits_4_turns = 0
fits_5_conv = 0
fits_5_turns = 0
fits_6_conv = 0
fits_6_turns = 0
counter = 0
nice_files = []
for folder in folders:
folder_path = "../../ubuntu-ranking-dataset-creator/src/dialogs/" + folder
for filename in os.listdir(folder_path):
number_of_files_checked += 1
file_path = folder_path + "/" + filename
num_turns, fit_1_bool, fit_2_bool, fit_3_bool, fit_4_bool, fit_5_bool, fit_6_bool, = non_turns_exceed_max_turns_in_conv(
file_path, fit_1, fit_2, fit_3, fit_4, fit_5, fit_6)
if fit_1_bool:
fits_1_conv += 1
fits_1_turns += num_turns
if counter < 40:
counter += 1
nice_files.append(file_path)
if fit_2_bool:
fits_2_conv += 1
fits_2_turns += num_turns
if fit_3_bool:
fits_3_conv += 1
fits_3_turns += num_turns
if fit_4_bool:
fits_4_conv += 1
fits_4_turns += num_turns
if fit_5_bool:
fits_5_conv += 1
fits_5_turns += num_turns
if fit_6_bool:
fits_6_conv += 1
fits_6_turns += num_turns
print("Done with folder: " + str(folder) + ", read " +
str(number_of_files_checked) + " files")
print "Number of files read: " + str(number_of_files_checked)
print(
str(fits_1_conv) + " conversations fits with max len " + str(fit_1) +
". Has " + str(fits_1_turns) + " turns")
print(
str(fits_2_conv) + " conversations fits with max len " + str(fit_2) +
". Has " + str(fits_2_turns) + " turns")
print(
str(fits_3_conv) + " conversations fits with max len " + str(fit_3) +
". Has " + str(fits_3_turns) + " turns")
print(
str(fits_4_conv) + " conversations fits with max len " + str(fit_4) +
". Has " + str(fits_4_turns) + " turns")
print(
str(fits_5_conv) + " conversations fits with max len " + str(fit_5) +
". Has " + str(fits_5_turns) + " turns")
print(
str(fits_6_conv) + " conversations fits with max len " + str(fit_6) +
". Has " + str(fits_6_turns) + " turns")
for files in nice_files:
print(files)
print get_time(start_time)
