def query_to_encoder_features(sentence, vocabs, FLAGS):
"""
Convert a natural language query into feature vectors used by the encoder.
"""
if FLAGS.channel == 'char':
tokens = data_utils.nl_to_characters(sentence)
init_vocab = data_utils.CHAR_INIT_VOCAB
elif FLAGS.channel == 'partial.token':
tokens = data_utils.nl_to_partial_tokens(sentence, tokenizer.basic_tokenizer)
init_vocab = data_utils.TOKEN_INIT_VOCAB
else:
if FLAGS.normalized:
tokens = data_utils.nl_to_tokens(sentence, tokenizer.ner_tokenizer)
else:
tokens = data_utils.nl_to_tokens(sentence, tokenizer.basic_tokenizer)
init_vocab = data_utils.TOKEN_INIT_VOCAB
sc_ids = data_utils.tokens_to_ids(tokens, vocabs.sc_vocab)
encoder_features = [[sc_ids]]
if FLAGS.use_copy and FLAGS.copy_fun == 'copynet':
csc_ids = []
for i, t in enumerate(tokens):
if not t in init_vocab and t in vocabs.tg_vocab:
csc_ids.append(vocabs.tg_vocab[t])
else:
csc_ids.append(len(vocabs.tg_vocab) + i)
encoder_features.append([csc_ids])
return encoder_features
def query_to_copy_tokens(sentence, FLAGS):
if FLAGS.channel == 'char':
tokens = data_utils.nl_to_characters(sentence)
elif FLAGS.channel == 'partial.token':
tokens = data_utils.nl_to_partial_tokens(
sentence, tokenizer.basic_tokenizer, to_lower_case=False,
lemmatization=False)
else:
tokens = data_utils.nl_to_tokens(
sentence, tokenizer.basic_tokenizer, to_lower_case=False,
lemmatization=False)
return tokens
def main():
with open(os.path.join(os.getcwd(), 'data/nl2bash-data.json')) as fr:
d_nl2bash = json.load(fr)
with open(os.path.join(os.getcwd(), 'data/ainix_data.json')) as fr:
d_ainix = json.load(fr)
tuple_nl2bash = [(d_nl2bash[key]['invocation'], d_nl2bash[key]['cmd'])
for key in d_nl2bash]
tuple_ainix = [(d_ainix[key]['invocation'], d_ainix[key]['cmd'])
for key in d_ainix]
tuple_all = list(set(tuple_nl2bash + tuple_ainix))
d = {}
for idx, t in enumerate(tuple_all):
temp = {}
temp['invocation'] = t[0]
temp['cmd'] = [t[1]]
d[str(idx + 1)] = temp
def normalizer(text):
for keyword in KEYWORD_LIST:
if keyword in text:
return 'ARG'
return text
input_template_predictor = []
input_argument_predictor = []
for t in tuple_all:
nl = t[0][0].lower() + t[0][1:]
norm_nl = ' '.join(
tokenizer.basic_tokenizer(nl,
to_lower_case=False,
lemmatization=False,
remove_stop_words=True,
correct_spell=False)[0])
norm_nl_arg_replace = ' '.join([
normalizer(item) for item in data_utils.nl_to_tokens(
nl, tokenizer=tokenizer.ner_tokenizer)
])
cm = t[1]
norm_cm = [
normalizer(item.split('<FLAG_SUFFIX>')[0])
for item in data_utils.cm_to_tokens(
cm, data_tools.bash_tokenizer, arg_type_only=True)
]
norm_cm_ref = [
normalizer(item.split('<FLAG_SUFFIX>')[0])
for item in data_utils.cm_to_tokens(
cm, data_tools.bash_tokenizer, arg_type_only=False)
]
args = []
for c, r in zip(norm_cm, norm_cm_ref):
if c != r:
args.append(r)
norm_cm = ' '.join(norm_cm)
if len(args) > 0:
source = norm_nl + ' SEP ' + norm_cm
target = ' SEP '.join(args)
input_argument_predictor.append((source, target))
input_template_predictor.append((norm_nl_arg_replace, norm_cm))
random.seed(18015651)
random.shuffle(input_template_predictor)
random.shuffle(input_argument_predictor)
os.makedirs(os.path.join(os.getcwd(), 'corpus/template_predictor'),
exist_ok=True)
with open(os.path.join(os.getcwd(), 'corpus/template_predictor/train.nl'), 'w') as fwn, \
open(os.path.join(os.getcwd(), 'corpus/template_predictor/train.cm'), 'w') as fwc:
for example in input_template_predictor:
fwn.write(example[0] + '\n')
fwc.write(example[1] + '\n')
with open(os.path.join(os.getcwd(), 'corpus/template_predictor/valid.nl'), 'w') as fwn, \
open(os.path.join(os.getcwd(), 'corpus/template_predictor/valid.cm'), 'w') as fwc:
for example in input_template_predictor[10247:]:
fwn.write(example[0] + '\n')
fwc.write(example[1] + '\n')
with open(os.path.join(os.getcwd(), 'corpus/template_predictor/test.nl'), 'w') as fwn, \
open(os.path.join(os.getcwd(), 'corpus/template_predictor/test.cm'), 'w') as fwc:
for example in input_template_predictor[10247:]:
fwn.write(example[0] + '\n')
fwc.write(example[1] + '\n')
os.makedirs(os.path.join(os.getcwd(), 'corpus/argument_predictor'),
exist_ok=True)
with open(os.path.join(os.getcwd(), 'corpus/argument_predictor/train.ctx'), 'w') as fwc, \
open(os.path.join(os.getcwd(), 'corpus/argument_predictor/train.arg'), 'w') as fwa:
for example in input_argument_predictor:
fwc.write(example[0] + '\n')
fwa.write(example[1] + '\n')
with open(os.path.join(os.getcwd(), 'corpus/argument_predictor/valid.ctx'), 'w') as fwc, \
open(os.path.join(os.getcwd(), 'corpus/argument_predictor/valid.arg'), 'w') as fwa:
for example in input_argument_predictor[9830:]:
fwc.write(example[0] + '\n')
fwa.write(example[1] + '\n')
with open(os.path.join(os.getcwd(), 'corpus/argument_predictor/test.ctx'), 'w') as fwc, \
open(os.path.join(os.getcwd(), 'corpus/argument_predictor/test.arg'), 'w') as fwa:
for example in input_argument_predictor[9830:]:
fwc.write(example[0] + '\n')
fwa.write(example[1] + '\n')
