def main():
questions_path, answers_path = sys.argv[1:]
print("Reading Corpus:")
train_sentences = read_corpus('train_data', disp=True)
print('\nTraining on Corpus')
model = NGram.train_model(train_sentences, disp=True)
with open(answers_path, 'r') as answer_file:
answers = get_sentences(untokenized_text=answer_file.read(),
is_tokenized=True,
token_start_end=('<s>', '</s>'))
dev_sentences = answers[:520]
print('Calculating Probabilities for Dev Sentences:')
model.sentences_probabilities(dev_sentences, disp=True)
lambdas = optimize_lambdas(model)
with open(questions_path, 'r') as question_file:
questions = get_sentences(untokenized_text=question_file.read(),
is_tokenized=True,
token_start_end=('<s>', '</s>'))
print('Calculating Probabilities for Test Sentences:')
model.sentences_probabilities(sentences=questions, disp=True)
_, sentences_perplexity = model.perplexity(lambdas=lambdas)
print('Writing sentences and perplexities to file')
with open('output.txt', 'w') as out_file:
for i, perplexity in enumerate(sentences_perplexity):
out_file.write('{}\t{}\n'.format(' '.join(questions[i]).replace('<s0> <s1>', '<s>'), perplexity))
def main():
questions_path, answers_path = sys.argv[1:]
print("Reading Corpus:")
train_sentences = read_corpus('train_data', disp=True)
print('\nTraining on Corpus')
model = NGram.train_model(train_sentences, disp=True)
with open(answers_path, 'r') as answer_file:
answers = get_sentences(untokenized_text=answer_file.read(),
is_tokenized=True,
token_start_end=('<s>', '</s>'))
dev_sentences = answers[:520]
print('Calculating Probabilities for Dev Sentences:')
model.sentences_probabilities(dev_sentences, disp=True)
lambdas = optimize_lambdas(model)
with open(questions_path, 'r') as question_file:
questions = get_sentences(untokenized_text=question_file.read(),
is_tokenized=True,
token_start_end=('<s>', '</s>'))
print('Calculating Probabilities for Test Sentences:')
model.sentences_probabilities(sentences=questions, disp=True)
_, sentences_perplexity = model.perplexity(lambdas=lambdas)
print('Writing sentences and perplexities to file')
with open('output.txt', 'w') as out_file:
for i, perplexity in enumerate(sentences_perplexity):
out_file.write('{}\t{}\n'.format(
' '.join(questions[i]).replace('<s0> <s1>', '<s>'),
perplexity))
def read_corpus(path, disp=False):
sentences = []
if os.path.isdir(path):
files = [
join(path, file) for file in listdir(path)
if isfile(join(path, file))
]
for i, file in enumerate(files):
if disp and int((i / len(files)) * 100) % 2 == 0:
progress = int((i / len(files)) * 100)
progress_bar(progress)
with open(file, encoding='utf-8', errors='ignore') as data_file:
file_data = data_file.read().lower()
data_file.readlines()
sentences += get_sentences(file_data,
sentence_tokenizer=sent_tokenize)
if disp:
progress_bar(100)
print()
return sentences
def read_corpus(path, disp=False):
sentences = []
if os.path.isdir(path):
files = [join(path, file) for file in listdir(path) if isfile(join(path, file))]
for i, file in enumerate(files):
if disp and int((i / len(files)) * 100) % 2 == 0:
progress = int((i / len(files)) * 100)
progress_bar(progress)
with open(file, encoding='utf-8', errors='ignore') as data_file:
file_data = data_file.read().lower()
data_file.readlines()
sentences += get_sentences(file_data, sentence_tokenizer=sent_tokenize)
if disp:
progress_bar(100)
print()
return sentences
