def select(ast, utility_set):
for ut in data_tools.get_utilities(ast):
if not ut in utility_set:
print('Utility currently not handled: {} - {}'.format(
ut, data_tools.ast2command(ast, loose_constraints=True)))
return False
return True
def stable_slot_filling(template_tokens,
sc_fillers,
tg_slots,
pointer_targets,
encoder_outputs,
decoder_outputs,
slot_filling_classifier,
verbose=False):
"""
Fills the argument slots using learnt local alignment scores and a greedy
global alignment algorithm (stable marriage).
:param template_tokens: list of tokens in the command template
:param sc_fillers: the slot fillers extracted from the source sequence,
indexed by token id
:param tg_slots: the argument slots in the command template, indexed by
token id
:param pointer_targets: [encoder_length, decoder_length], local alignment
scores between source and target tokens
:param encoder_outputs: [encoder_length, dim] sequence of encoder hidden states
:param decoder_outputs: [decoder_length, dim] sequence of decoder hidden states
:param slot_filling_classifier: the classifier that produces the local
alignment scores
:param verbose: print all local alignment scores if set to true
"""
# Step a): prepare (binary) type alignment matrix based on type info
M = np.zeros([len(encoder_outputs), len(decoder_outputs)], dtype=np.int32)
for f in sc_fillers:
assert (f <= len(encoder_outputs))
surface, filler_type = sc_fillers[f]
matched = False
for s in tg_slots:
assert (s <= len(decoder_outputs))
slot_value, slot_type = tg_slots[s]
if slot_filler_type_match(slot_type, filler_type):
M[f, s] = 1
matched = True
if not matched:
# If no target slot can hold a source filler, skip the alignment
# step and return None
return None, None, None
# Step b): compute local alignment scores if they are not provided already
if pointer_targets is None:
assert (encoder_outputs is not None)
assert (decoder_outputs is not None)
assert (slot_filling_classifier is not None)
pointer_targets = np.zeros(
[len(encoder_outputs), len(decoder_outputs)])
for f in xrange(M.shape[0]):
if np.sum(M[f]) > 1:
X = []
# use reversed index for the encoder embeddings matrix
ff = len(encoder_outputs) - f - 1
cm_slots_keys = list(tg_slots.keys())
for s in cm_slots_keys:
X.append(
np.concatenate([
encoder_outputs[ff:ff + 1],
decoder_outputs[s:s + 1]
],
axis=1))
X = np.concatenate(X, axis=0)
X = X / norm(X, axis=1)[:, None]
raw_scores = slot_filling_classifier.predict(X)
for ii in xrange(len(raw_scores)):
s = cm_slots_keys[ii]
pointer_targets[f, s] = raw_scores[ii]
if verbose:
print('• alignment ({}, {}): {}\t{}\t{}'.format(
f, s, sc_fillers[f], tg_slots[s], raw_scores[ii]))
M = M + M * pointer_targets
# convert M into a dictinary representation of a sparse matrix
M_dict = collections.defaultdict(dict)
for i in xrange(M.shape[0]):
if np.sum(M[i]) > 0:
for j in xrange(M.shape[1]):
if M[i, j] > 0:
M_dict[i][j] = M[i, j]
mappings, remained_fillers = stable_marriage_alignment(M_dict)
if not remained_fillers:
for f, s in mappings:
template_tokens[s] = get_fill_in_value(tg_slots[s], sc_fillers[f])
cmd = ' '.join(template_tokens)
tree = data_tools.bash_parser(cmd)
if not tree is None:
data_tools.fill_default_value(tree)
temp = data_tools.ast2command(tree,
loose_constraints=True,
ignore_flag_order=False)
else:
tree, temp = None, None
return tree, temp, mappings
def decode_set(sess, model, dataset, top_k, FLAGS, verbose=False):
"""
Compute top-k predictions on the dev/test dataset and write the predictions
to disk.
:param sess: A TensorFlow session.
:param model: Prediction model object.
:param top_k: Number of top predictions to compute.
:param FLAGS: Training/testing hyperparameter settings.
:param verbose: If set, also print decoding results to screen.
"""
nl2bash = FLAGS.dataset.startswith('bash') and not FLAGS.explain
tokenizer_selector = 'cm' if FLAGS.explain else 'nl'
grouped_dataset = data_utils.group_parallel_data(
dataset, okenizer_selector=tokenizer_selector)
vocabs = data_utils.load_vocabulary(FLAGS)
rev_sc_vocab = vocabs.rev_sc_vocab
ts = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H%M%S')
pred_file_path = os.path.join(model.model_dir, 'predictions.{}.{}'.format(
model.decode_sig, ts))
pred_file = open(pred_file_path, 'w')
eval_file_path = os.path.join(model.model_dir, 'predictions.{}.{}.csv'.format(
model.decode_sig, ts))
eval_file = open(eval_file_path, 'w')
eval_file.write('example_id, description, ground_truth, prediction, ' +
'correct template, correct command\n')
for example_id in xrange(len(grouped_dataset)):
key, data_group = grouped_dataset[example_id]
sc_txt = data_group[0].sc_txt.strip()
sc_tokens = [rev_sc_vocab[i] for i in data_group[0].sc_ids]
if FLAGS.channel == 'char':
sc_temp = ''.join(sc_tokens)
sc_temp = sc_temp.replace(constants._SPACE, ' ')
else:
sc_temp = ' '.join(sc_tokens)
tg_txts = [dp.tg_txt for dp in data_group]
tg_asts = [data_tools.bash_parser(tg_txt) for tg_txt in tg_txts]
if verbose:
print('\nExample {}:'.format(example_id))
print('Original Source: {}'.format(sc_txt.encode('utf-8')))
print('Source: {}'.format(sc_temp.encode('utf-8')))
for j in xrange(len(data_group)):
print('GT Target {}: {}'.format(j+1, data_group[j].tg_txt.encode('utf-8')))
if FLAGS.fill_argument_slots:
slot_filling_classifier = get_slot_filling_classifer(FLAGS)
batch_outputs, sequence_logits = translate_fun(data_group, sess, model,
vocabs, FLAGS, slot_filling_classifier=slot_filling_classifier)
else:
batch_outputs, sequence_logits = translate_fun(data_group, sess, model,
vocabs, FLAGS)
if FLAGS.tg_char:
batch_outputs, batch_char_outputs = batch_outputs
eval_row = '{},"{}",'.format(example_id, sc_txt.replace('"', '""'))
if batch_outputs:
if FLAGS.token_decoding_algorithm == 'greedy':
tree, pred_cmd = batch_outputs[0]
if nl2bash:
pred_cmd = data_tools.ast2command(
tree, loose_constraints=True)
score = sequence_logits[0]
if verbose:
print('Prediction: {} ({})'.format(pred_cmd, score))
pred_file.write('{}\n'.format(pred_cmd))
elif FLAGS.token_decoding_algorithm == 'beam_search':
top_k_predictions = batch_outputs[0]
if FLAGS.tg_char:
top_k_char_predictions = batch_char_outputs[0]
top_k_scores = sequence_logits[0]
num_preds = min(FLAGS.beam_size, top_k, len(top_k_predictions))
for j in xrange(num_preds):
if j > 0:
eval_row = ',,'
if j < len(tg_txts):
eval_row += '"{}",'.format(tg_txts[j].strip().replace('"', '""'))
else:
eval_row += ','
top_k_pred_tree, top_k_pred_cmd = top_k_predictions[j]
if nl2bash:
pred_cmd = data_tools.ast2command(
top_k_pred_tree, loose_constraints=True)
else:
pred_cmd = top_k_pred_cmd
pred_file.write('{}|||'.format(pred_cmd.encode('utf-8')))
eval_row += '"{}",'.format(pred_cmd.replace('"', '""'))
temp_match = tree_dist.one_match(
tg_asts, top_k_pred_tree, ignore_arg_value=True)
str_match = tree_dist.one_match(
tg_asts, top_k_pred_tree, ignore_arg_value=False)
if temp_match:
eval_row += 'y,'
if str_match:
eval_row += 'y'
eval_file.write('{}\n'.format(eval_row.encode('utf-8')))
if verbose:
print('Prediction {}: {} ({})'.format(
j+1, pred_cmd.encode('utf-8'), top_k_scores[j]))
if FLAGS.tg_char:
print('Character-based prediction {}: {}'.format(
j+1, top_k_char_predictions[j].encode('utf-8')))
pred_file.write('\n')
else:
print(APOLOGY_MSG)
pred_file.write('\n')
eval_file.write('{}\n'.format(eval_row))
eval_file.write('\n')
eval_file.write('\n')
pred_file.close()
eval_file.close()
shutil.copyfile(pred_file_path, os.path.join(FLAGS.model_dir,
'predictions.{}.latest'.format(model.decode_sig)))
shutil.copyfile(eval_file_path, os.path.join(FLAGS.model_dir,
'predictions.{}.latest.csv'.format(model.decode_sig)))
def decode_set(sess, model, dataset, top_k, FLAGS, verbose=True):
"""
Compute top-k predictions on the dev/test dataset and write the predictions
to disk.
:param sess: A TensorFlow session.
:param model: Prediction model object.
:param top_k: Number of top predictions to compute.
:param FLAGS: Training/testing hyperparameter settings.
:param verbose: If set, also print decoding results to screen.
"""
nl2bash = FLAGS.dataset.startswith('bash') and not FLAGS.explain
tokenizer_selector = 'cm' if FLAGS.explain else 'nl'
grouped_dataset = data_utils.group_data(
dataset,
use_bucket=model.buckets,
use_temp=FLAGS.normalized,
tokenizer_selector=tokenizer_selector)
vocabs = data_utils.load_vocab(FLAGS)
rev_sc_vocab = vocabs.rev_sc_vocab
if FLAGS.fill_argument_slots:
# create slot filling classifier
mapping_param_dir = os.path.join(FLAGS.model_dir,
'train.mappings.X.Y.npz')
train_X, train_Y = data_utils.load_slot_filling_data(mapping_param_dir)
slot_filling_classifier = classifiers.KNearestNeighborModel(
FLAGS.num_nn_slot_filling, train_X, train_Y)
print('Slot filling classifier parameters loaded.')
else:
slot_filling_classifier = None
ts = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d-%H%M%S')
pred_file_path = os.path.join(
model.model_dir, 'predictions.{}.{}'.format(model.decode_sig, ts))
pred_file = open(pred_file_path, 'w')
for example_id in xrange(len(grouped_dataset)):
key, data_group = grouped_dataset[example_id]
sc_txt = data_group[0].sc_txt
sc_temp = ' '.join([rev_sc_vocab[i] for i in data_group[0].sc_ids])
if verbose:
print('Example {}:'.format(example_id))
print('Original Source: {}'.format(sc_txt))
print('Source: {}'.format(sc_temp))
for j in xrange(len(data_group)):
print('GT Target {}: {}'.format(j + 1, data_group[j].tg_txt))
batch_outputs, output_logits = translate_fun(
data_group,
sess,
model,
vocabs,
FLAGS,
slot_filling_classifier=slot_filling_classifier)
if FLAGS.tg_char:
batch_outputs, batch_char_outputs = batch_outputs
if batch_outputs:
if FLAGS.token_decoding_algorithm == 'greedy':
tree, pred_cmd = batch_outputs[0]
if nl2bash:
pred_cmd = data_tools.ast2command(tree,
loose_constraints=True)
score = output_logits[0]
if verbose:
print('Prediction: {} ({})'.format(pred_cmd, score))
pred_file.write('{}\n'.format(pred_cmd))
elif FLAGS.token_decoding_algorithm == 'beam_search':
top_k_predictions = batch_outputs[0]
if FLAGS.tg_char:
top_k_char_predictions = batch_char_outputs[0]
top_k_scores = output_logits[0]
num_preds = min(FLAGS.beam_size, top_k, len(top_k_predictions))
for j in xrange(num_preds):
top_k_pred_tree, top_k_pred_cmd = top_k_predictions[j]
if nl2bash:
pred_cmd = data_tools.ast2command(
top_k_pred_tree, loose_constraints=True)
else:
pred_cmd = top_k_pred_cmd
pred_file.write('{}|||'.format(pred_cmd))
if verbose:
print('Prediction {}: {} ({})'.format(
j + 1, pred_cmd, top_k_scores[j]))
if FLAGS.tg_char:
print('Character-based prediction {}: {}'.format(
j + 1, top_k_char_predictions[j]))
pred_file.write('\n')
else:
print(APOLOGY_MSG)
pred_file.write('\n')
pred_file.close()
shutil.copyfile(
pred_file_path,
os.path.join(FLAGS.model_dir,
'predictions.{}.latest'.format(model.decode_sig)))
def decode_set(model, dataset, rev_sc_vocab, rev_tg_vocab, verbose=True):
grouped_dataset = data_utils.group_data_by_nl(dataset, use_bucket=False,
use_temp=False)
with DBConnection() as db:
db.remove_model(model_name)
num_eval = 0
for sc_temp in grouped_dataset:
batch_sc_strs, batch_tg_strs, batch_scs, batch_cmds = \
grouped_dataset[sc_temp]
_, entities = tokenizer.ner_tokenizer(sc_temp)
nl_fillers = entities[-1]
if nl_fillers is not None:
cm_slots = {}
sc_str = batch_sc_strs[0]
nl = batch_scs[0]
if verbose:
print("Example {}".format(num_eval+1))
print("Original English: " + sc_str.strip())
print("English: " + sc_temp)
for j in xrange(len(batch_tg_strs)):
print("GT Command {}: {}".format(j+1, batch_tg_strs[j].strip()))
# retrieve top-ranked command template
top_k_results = model.test(nl, 100)
count = 0
for i in xrange(len(top_k_results)):
nn, output_tokens, score = top_k_results[i]
nn_str = ' '.join([rev_sc_vocab[j] for j in nn])
tokens = []
for j in xrange(1, len(output_tokens)-1):
pred_token = rev_tg_vocab[output_tokens[j]]
if "@@" in pred_token:
pred_token = pred_token.split("@@")[-1]
if nl_fillers is not None and \
pred_token in constants._ENTITIES:
if j > 0 and slot_filling.is_min_flag(
rev_tg_vocab[output_tokens[j-1]]):
pred_token_type = 'Timespan'
else:
pred_token_type = pred_token
cm_slots[j] = (pred_token, pred_token_type)
tokens.append(pred_token)
pred_cmd = ' '.join(tokens)
# check if the predicted command templates have enough slots to
# hold the fillers (to rule out templates that are trivially
# unqualified)
if FLAGS.dataset.startswith("bash"):
pred_cmd = re.sub('( ;\s+)|( ;$)', ' \\; ', pred_cmd)
tree = data_tools.bash_parser(pred_cmd)
else:
tree = data_tools.paren_parser(pred_cmd)
if nl_fillers is None or len(cm_slots) >= len(nl_fillers):
# Step 2: check if the predicted command template is grammatical
# filter out non-grammatical output
if tree is not None:
matched = slot_filling.heuristic_slot_filling(tree, nl_fillers)
if tree is not None:
slot_filling.fill_default_value(tree)
pred_cmd = data_tools.ast2command(tree)
if verbose:
print("NN: {}".format(nn_str))
print("Prediction {}: {} ({})".format(i, pred_cmd, score))
db.add_prediction(model_name, sc_str, pred_cmd, float(score),
update_mode=False)
count += 1
if count == 10:
break
print("")
num_eval += 1
def test_rewrite(cmd):
with DBConnection() as db:
ast = data_tools.bash_parser(cmd)
rewrites = list(db.get_rewrites(ast))
for i in xrange(len(rewrites)):
print("rewrite %d: %s" % (i, data_tools.ast2command(rewrites[i])))
