def convert(self, interaction,
index):
"""Converts question at 'index' to example."""
table = interaction.table
num_rows = self._get_num_rows(table, self._drop_rows_to_fit)
num_columns = self._get_num_columns(table)
question = interaction.questions[index]
if not interaction.questions[index].answer.is_valid:
raise ValueError('Invalid answer')
text_tokens = self._tokenize_extended_question(question, table)
tokenized_table = self._tokenize_table(table)
serialized_example, features = self._to_trimmed_features(
question=question,
table=table,
question_tokens=text_tokens,
tokenized_table=tokenized_table,
num_columns=num_columns,
num_rows=num_rows,
drop_rows_to_fit=self._drop_rows_to_fit)
column_ids = serialized_example.column_ids
row_ids = serialized_example.row_ids
def get_answer_ids(question):
if self._update_answer_coordinates:
return _find_answer_ids_from_answer_texts(
column_ids,
row_ids,
tokenized_table,
answer_texts=[
self._tokenizer.tokenize(at)
for at in question.answer.answer_texts
],
)
return _get_answer_ids(column_ids, row_ids, question)
answer_ids = get_answer_ids(question)
self._pad_to_seq_length(answer_ids)
features['label_ids'] = create_int_feature(answer_ids)
if index == 0:
prev_answer_ids = [0] * len(column_ids)
else:
prev_answer_ids = get_answer_ids(interaction.questions[index - 1],)
self._pad_to_seq_length(prev_answer_ids)
features['prev_label_ids'] = create_int_feature(prev_answer_ids)
features['question_id'] = create_string_feature(
[question.id.encode('utf8')])
features['question_id_ints'] = create_int_feature(
text_utils.str_to_ints(
question.id, length=text_utils.DEFAULT_INTS_LENGTH))
features['aggregation_function_id'] = create_int_feature(
[question.answer.aggregation_function])
features['classification_class_index'] = create_int_feature(
[question.answer.class_index])
answer = question.answer.float_value if question.answer.HasField(
'float_value') else _NAN
features['answer'] = create_float_feature([answer])
self._add_question_numeric_values(question, features)
if self._add_aggregation_candidates:
rng = random.Random(fingerprint(question.id))
candidates = interpretation_utils.find_candidates(
rng, table, question)
num_initial_candidates = len(candidates)
candidates = [c for c in candidates if len(c.rows) < _MAX_NUM_ROWS]
candidates = candidates[:_MAX_NUM_CANDIDATES]
funs = [0] * _MAX_NUM_CANDIDATES
sizes = [0] * _MAX_NUM_CANDIDATES
indexes = []
num_final_candidates = 0
for index, candidate in enumerate(candidates):
token_indexes = []
for row in candidate.rows:
token_indexes += _get_cell_token_indexes(column_ids, row_ids,
candidate.column, row)
if len(indexes) + len(serialized_example.tokens) > _MAX_INDEX_LENGTH:
break
num_final_candidates += 1
sizes[index] = len(token_indexes)
funs[index] = candidate.agg_function
indexes += token_indexes
# <int>[1]
features['cand_num'] = create_int_feature([num_final_candidates])
# <int>[_MAX_NUM_CANDIDATES]
features['can_aggregation_function_ids'] = create_int_feature(funs)
# <int>[_MAX_NUM_CANDIDATES]
features['can_sizes'] = create_int_feature(sizes)
# <int>[_MAX_INDEX_LENGTH]
# Actual length is sum(sizes).
features['can_indexes'] = create_int_feature(indexes)
return tf.train.Example(features=tf.train.Features(feature=features))
def test_str_to_ints(self, text, length):
ints = text_utils.str_to_ints(text, length)
self.assertLen(ints, length)
logging.info("ints: %s %s ", ints, text)
self.assertEqual(text_utils.ints_to_str(ints), text)
def convert(
self,
interaction,
index,
negative_example,
):
"""Converts question at 'index' to example."""
table = interaction.table
num_rows = len(table.rows)
if num_rows >= self._max_row_id:
num_rows = self._max_row_id - 1
num_columns = len(table.columns)
if num_columns >= self._max_column_id:
num_columns = self._max_column_id - 1
title = table.document_title
if not self._use_document_title:
title = ''
title_tokens = self._tokenizer.tokenize(title)
tokenized_table = self._tokenize_table(table)
while True:
try:
_, features = self._to_trimmed_features(
question=None,
table=table,
question_tokens=title_tokens,
tokenized_table=tokenized_table,
num_columns=num_columns,
num_rows=num_rows)
break
except ValueError:
pass
# Since this is retrieval we might get away with removing some cells of
# the table.
# TODO(thomasmueller) Consider taking the token length into account.
if num_columns >= num_rows:
num_columns -= 1
else:
num_rows -= 1
if num_columns == 0 or num_rows == 0:
raise ValueError('Cannot fit table into sequence.')
question = interaction.questions[index]
features['question_id'] = base.create_string_feature(
[question.id.encode('utf8')])
features['question_id_ints'] = base.create_int_feature(
text_utils.str_to_ints(question.id,
length=text_utils.DEFAULT_INTS_LENGTH))
q_tokens = self._tokenizer.tokenize(question.text)
q_tokens = self._serialize_text(q_tokens)[0]
q_tokens.append(base.Token(_SEP, _SEP))
q_input_ids = self._to_token_ids(q_tokens)
self._pad_to_seq_length(q_input_ids)
q_input_mask = [1] * len(q_tokens)
self._pad_to_seq_length(q_input_mask)
features['question_input_ids'] = base.create_int_feature(q_input_ids)
features['question_input_mask'] = base.create_int_feature(q_input_mask)
if question:
features['question_hash'] = base.create_int_feature(
[base.fingerprint(question.text) % _MAX_INT])
if negative_example is not None:
n_table = negative_example.table
n_title_tokens = self._tokenizer.tokenize(n_table.document_title)
n_tokenized_table = self._tokenize_table(n_table)
n_num_rows = self._get_num_rows(n_table, drop_rows_to_fit=True)
n_num_columns = self._get_num_columns(n_table)
_, n_example_features = self._to_trimmed_features(
question=None,
table=n_table,
question_tokens=n_title_tokens,
tokenized_table=n_tokenized_table,
num_columns=n_num_columns,
num_rows=n_num_rows,
drop_rows_to_fit=True)
_join_features(features, n_example_features)
return tf.train.Example(features=tf.train.Features(feature=features))
def test_str_to_ints(self, text, length): ints = text_utils.str_to_ints(text, length) self.assertLen(ints, length) self.assertEqual(text_utils.ints_to_str(ints), text)
def convert(self, interaction,
index):
"""Converts question at 'index' to example."""
table = interaction.table
num_rows = self._get_num_rows(table, self._drop_rows_to_fit)
num_columns = self._get_num_columns(table)
question = interaction.questions[index]
if not interaction.questions[index].answer.is_valid:
beam_metrics.Metrics.counter(
_NS, 'Conversion skipped (answer not valid)').inc()
raise ValueError('Invalid answer')
text_tokens = self._tokenize_extended_question(question, table)
tokenized_table = self._tokenize_table(table)
table_selection_ext = table_selection_pb2.TableSelection.table_selection_ext
if table_selection_ext in question.Extensions:
table_selection = question.Extensions[table_selection_ext]
if not tokenized_table.selected_tokens:
raise ValueError('No tokens selected')
if table_selection.selected_tokens:
selected_tokens = {(t.row_index, t.column_index, t.token_index)
for t in table_selection.selected_tokens}
tokenized_table.selected_tokens = [
t for t in tokenized_table.selected_tokens
if (t.row_index, t.column_index, t.token_index) in selected_tokens
]
serialized_example, features = self._to_trimmed_features(
question=question,
table=table,
question_tokens=text_tokens,
tokenized_table=tokenized_table,
num_columns=num_columns,
num_rows=num_rows,
drop_rows_to_fit=self._drop_rows_to_fit)
column_ids = serialized_example.column_ids
row_ids = serialized_example.row_ids
def get_answer_ids(question):
if self._update_answer_coordinates:
return _find_answer_ids_from_answer_texts(
column_ids,
row_ids,
tokenized_table,
answer_texts=[
self._tokenizer.tokenize(at)
for at in question.answer.answer_texts
],
)
return _get_answer_ids(column_ids, row_ids, question)
answer_ids = get_answer_ids(question)
self._pad_to_seq_length(answer_ids)
features['label_ids'] = create_int_feature(answer_ids)
if index > 0:
prev_answer_ids = get_answer_ids(interaction.questions[index - 1],)
else:
prev_answer_ids = [0] * len(column_ids)
self._pad_to_seq_length(prev_answer_ids)
features['prev_label_ids'] = create_int_feature(prev_answer_ids)
features['question_id'] = create_string_feature(
[question.id.encode('utf8')])
if self._trim_question_ids:
question_id = question.id[-text_utils.DEFAULT_INTS_LENGTH:]
else:
question_id = question.id
features['question_id_ints'] = create_int_feature(
text_utils.str_to_ints(
question_id, length=text_utils.DEFAULT_INTS_LENGTH))
features['aggregation_function_id'] = create_int_feature(
[question.answer.aggregation_function])
features['classification_class_index'] = create_int_feature(
[question.answer.class_index])
answer = question.answer.float_value if question.answer.HasField(
'float_value') else _NAN
features['answer'] = create_float_feature([answer])
if self._add_aggregation_candidates:
rng = random.Random(fingerprint(question.id))
candidates = interpretation_utils.find_candidates(rng, table, question)
num_initial_candidates = len(candidates)
candidates = [c for c in candidates if len(c.rows) < _MAX_NUM_ROWS]
candidates = candidates[:_MAX_NUM_CANDIDATES]
funs = [0] * _MAX_NUM_CANDIDATES
sizes = [0] * _MAX_NUM_CANDIDATES
indexes = []
num_final_candidates = 0
for index, candidate in enumerate(candidates):
token_indexes = []
for row in candidate.rows:
token_indexes += _get_cell_token_indexes(column_ids, row_ids,
candidate.column, row)
if len(indexes) + len(serialized_example.tokens) > _MAX_INDEX_LENGTH:
break
num_final_candidates += 1
sizes[index] = len(token_indexes)
funs[index] = candidate.agg_function
indexes += token_indexes
# <int>[1]
features['cand_num'] = create_int_feature([num_final_candidates])
# <int>[_MAX_NUM_CANDIDATES]
features['can_aggregation_function_ids'] = create_int_feature(funs)
# <int>[_MAX_NUM_CANDIDATES]
features['can_sizes'] = create_int_feature(sizes)
# <int>[_MAX_INDEX_LENGTH]
# Actual length is sum(sizes).
features['can_indexes'] = create_int_feature(indexes)
if num_initial_candidates > 0:
beam_metrics.Metrics.counter(
_NS,
_get_buckets(num_initial_candidates,
[10, 20, 50, 100, 200, 500, 1000, 1200, 1500],
'Candidates Size:')).inc()
beam_metrics.Metrics.counter(_NS, 'Candidates: Input').inc()
if num_final_candidates != num_initial_candidates:
beam_metrics.Metrics.counter(_NS,
'Candidates: Dropped candidates').inc()
return tf.train.Example(features=tf.train.Features(feature=features))
