def guess(self, questions: List[str],
n_guesses: Optional[int]) -> List[List[Tuple[str, float]]]:
log.info('Generating {} guesses for each of {} questions'.format(
n_guesses, len(questions)))
log.info('Converting text to embedding indices...')
x_test = [
convert_text_to_embeddings_indices(tokenize_question(q),
self.embedding_lookup)
for q in questions
]
log.info('Computing question lengths...')
x_test_lengths = compute_lengths(x_test)
log.info('Converting questions to tensorflow format...')
x_test = tf_format(x_test, self.max_len, self.embeddings.shape[0])
x_test = np.array(x_test)
self.model = AuxDanModel(self.dan_params, self.max_len, self.n_classes,
self.n_ans_type_classes,
self.n_gender_classes,
self.n_category_classes)
log.info('Starting Tensorflow model guessing...')
guess_labels, guess_scores = self.model.guess(x_test, x_test_lengths,
n_guesses)
log.info(
'Guess generation and fetching top guesses done, converting to output format'
)
all_guesses = []
for i_row, score_row in zip(guess_labels, guess_scores):
guesses = []
for label, score in zip(i_row, score_row):
guesses.append((self.i_to_class[label], score))
all_guesses.append(guesses)
return all_guesses
def guess(self, questions: List[QuestionText], max_n_guesses: Optional[int]) -> List[List[Tuple[Answer, float]]]:
x_test = [convert_text_to_embeddings_indices(
tokenize_question(q), self.embedding_lookup)
for q in questions
]
for r in x_test:
if len(r) == 0:
log.warn('Found an empty question, adding an UNK token to it so that NaNs do not occur')
r.append(self.embedding_lookup['UNK'])
x_test = np.array(x_test)
y_test = np.zeros(len(x_test))
_, t_x_batches, t_offset_batches, t_y_batches = batchify(
self.batch_size, x_test, y_test, truncate=False, shuffle=False
)
self.model.eval()
self.model.cuda()
guesses = []
for b in range(len(t_x_batches)):
t_x = Variable(t_x_batches[b], volatile=True)
t_offset = Variable(t_offset_batches[b], volatile=True)
out = self.model(t_x, t_offset)
probs = F.softmax(out)
scores, preds = torch.max(probs, 1)
scores = scores.data.cpu().numpy()
preds = preds.data.cpu().numpy()
for p, s in zip(preds, scores):
guesses.append([(self.i_to_class[p], s)])
return guesses
def guess(self, questions, max_n_guesses):
x_test = [convert_text_to_embeddings_indices(
tokenize_question(q), self.embedding_lookup)
for q in questions
]
for r in x_test:
if len(r) == 0:
log.warn('Found an empty question, adding an UNK token to it so that NaNs do not occur')
r.append(self.embedding_lookup['UNK'])
x_test = np.array(x_test)
y_test = np.zeros(len(x_test))
_, t_x_batches, lengths, masks, t_y_batches = batchify(
self.batch_size, x_test, y_test,
truncate=False, shuffle=False
)
self.model.eval()
self.model.cuda()
guesses = []
for b in range(len(t_x_batches)):
t_x = Variable(t_x_batches[b], volatile=True)
length_batch = lengths[b]
mask_batch = masks[b]
probs = self.model(t_x, length_batch, mask_batch)
scores, preds = torch.max(probs, 1)
scores = scores.data.cpu().numpy()
preds = preds.data.cpu().numpy()
for p, s in zip(preds, scores):
guesses.append([(self.i_to_class[p], s)])
return guesses
def guess(self, questions: List[QuestionText], max_n_guesses: Optional[int]) -> List[List[Tuple[Answer, float]]]:
log.info('Generating {} guesses for each of {} questions'.format(max_n_guesses, len(questions)))
x_test = [nn.convert_text_to_embeddings_indices(
tokenize_question(q), self.embedding_lookup)
for q in questions]
x_test = np.array(nn.tf_format(x_test, self.max_len, 0))
class_probabilities = self.model.predict_proba(x_test, batch_size=self.batch_size)
guesses = []
for row in class_probabilities:
sorted_labels = np.argsort(-row)[:max_n_guesses]
sorted_guesses = [self.i_to_class[i] for i in sorted_labels]
sorted_scores = np.copy(row[sorted_labels])
guesses.append(list(zip(sorted_guesses, sorted_scores)))
return guesses
def guess(self, questions: List[QuestionText], max_n_guesses: Optional[int]) -> List[List[Tuple[Page, float]]]:
y_data = np.zeros((len(questions)))
x_data = [tokenize_question(q) for q in questions]
batches = batchify(x_data, y_data, shuffle=False, batch_size=32)
guesses = []
for x_batch, y_batch, length_batch in batches:
out = self.model(x_batch.cuda(), length_batch.cuda())
probs = F.softmax(out).data.cpu().numpy()
preds = np.argsort(-probs, axis=1)
n_examples = probs.shape[0]
for i in range(n_examples):
example_guesses = []
for p in preds[i][:max_n_guesses]:
example_guesses.append((self.i_to_class[p], probs[i][p]))
guesses.append(example_guesses)
return guesses
def guess(self, questions: List[QuestionText], max_n_guesses: Optional[int]) -> List[List[Tuple[Page, float]]]:
y_data = np.zeros((len(questions)))
x_data = [tokenize_question(q) for q in questions]
batches = batchify(x_data, y_data, shuffle=False, batch_size=32)
guesses = []
for x_batch, y_batch, length_batch in batches:
out = self.model(x_batch.cuda(), length_batch.cuda())
probs = F.softmax(out).data.cpu().numpy()
preds = np.argsort(-probs, axis=1)
n_examples = probs.shape[0]
for i in range(n_examples):
example_guesses = []
for p in preds[i][:max_n_guesses]:
example_guesses.append((self.i_to_class[p], probs[i][p]))
guesses.append(example_guesses)
return guesses
def run(self):
tagme.GCUBE_TOKEN = TAGME_GCUBE_TOKEN
with open('output/tagme/batches.pickle', 'rb') as f:
batch_dict = pickle.load(f)
batch_questions = batch_dict[self.question_batch]
dict_annotations = {}
for q in batch_questions:
annotated_sentences = {}
for s, text in q.text.items():
# This insures that preprocessing is matched for neural models
text = ' '.join(tokenize_question(text))
annotation = annotation_to_dict(tagme.annotate(text))
annotated_sentences[s] = annotation
dict_annotations[q.qnum] = annotated_sentences
with open(
'output/tagme/tagged_batch_{}.pickle'.format(
self.question_batch), 'wb') as f:
pickle.dump(dict_annotations, f)
def guess(self, questions: List[QuestionText],
max_n_guesses: Optional[int]):
x_test = [
convert_text_to_embeddings_indices(tokenize_question(q),
self.embedding_lookup)
for q in questions
]
for r in x_test:
if len(r) == 0:
log.warn(
'Found an empty question, adding an UNK token to it so that NaNs do not occur'
)
r.append(self.embedding_lookup['UNK'])
x_test = np.array(x_test)
y_test = np.zeros(len(x_test))
_, t_x_batches, lengths, t_y_batches, sort_batches = batchify(
self.batch_size, x_test, y_test, truncate=False, shuffle=False)
self.model.eval()
self.model.cuda()
guesses = []
hidden = self.model.init_hidden(self.batch_size)
for b in range(len(t_x_batches)):
t_x = Variable(t_x_batches[b], volatile=True)
length_batch = lengths[b]
sort = sort_batches[b]
if len(length_batch) != self.batch_size:
# This could happen for the last batch which is shorter than batch_size
hidden = self.model.init_hidden(len(length_batch))
else:
hidden = repackage_hidden(hidden, reset=True)
out, hidden = self.model(t_x, length_batch, hidden)
probs = F.softmax(out)
scores, preds = torch.max(probs, 1)
scores = scores.data.cpu().numpy()[np.argsort(sort)]
preds = preds.data.cpu().numpy()[np.argsort(sort)]
for p, s in zip(preds, scores):
guesses.append([(self.i_to_class[p], s)])
return guesses