def generate_model(author, steps):
"""Given an author name, processes the data/<author>.txt input for steps number
of iterations into the model input to be used by the lambda_handler
function.
"""
predictor = Predictor(128)
# Filenames.
author_models_dir = get_dir_for_author(author)
if not os.path.exists(author_models_dir):
os.mkdir(author_models_dir)
model_file = author_models_dir + author + ".model"
vocab_file = author_models_dir + author + ".vocab"
commons_file = author_models_dir + author + ".commons"
raw_text_file = "../data/" + author + ".txt"
# Read in the 'frequently used words' as common vocab.
frequent = read_common_vocab("../data/20k_most_common.txt")
# Clean the content.
with open(raw_text_file, 'r') as raw:
raw_words = raw.read().split(' ')
data, _ = clean_input_data(raw_words, frequent)
# Write out the words that occur in the clean data to the commons file.
record_common_vocab(data, commons_file)
# Train the model. This step takes the longest.
predictor.train(data, steps)
# Save the model that we have trained to disk.
predictor.save(model_file, vocab_file)
return predictor
def generate_model(author, steps):
"""Given an author name, processes the data/<author>.txt input for steps number
of iterations into the model input to be used by the lambda_handler
function.
"""
predictor = Predictor(128)
# Filenames.
author_models_dir = get_dir_for_author(author)
if not os.path.exists(author_models_dir):
os.mkdir(author_models_dir)
model_file = author_models_dir + author + ".model"
vocab_file = author_models_dir + author + ".vocab"
commons_file = author_models_dir + author + ".commons"
raw_text_file = "../data/" + author + ".txt"
# Read in the 'frequently used words' as common vocab.
frequent = read_common_vocab("../data/20k_most_common.txt")
# Clean the content.
with open(raw_text_file, 'r') as raw:
raw_words = raw.read().split(' ')
data, _ = clean_input_data(raw_words, frequent)
# Write out the words that occur in the clean data to the commons file.
record_common_vocab(data, commons_file)
# Train the model. This step takes the longest.
predictor.train(data, steps)
# Save the model that we have trained to disk.
predictor.save(model_file, vocab_file)
return predictor
def lambda_handler(event, context):
"""Use a model for an existing author to generate length words, interleaved
with user text input."""
author = event["author"]
user_text = event["userText"]
length = event["length"]
# Load in the predictor
model_file = get_dir_for_author(author) + author + ".model"
vocab_file = get_dir_for_author(author) + author + ".vocab"
predictor = Predictor(128, model=model_file, vocab=vocab_file)
# Clean the user data and separate out unknown words.
common_vocab = read_common_cocab(get_dir_for_author(author) + author + ".commons")
data, unique_user_words = clean_input_data(user_text, common_vocab)
generated_sample = predictor.sample(length)
return clean_generated_data(' '.join(generated_sample), unique_user_words)
