def test_random_sampling(self, seed):
interaction = _to_interaction(
[[1.0, 3.0, 6.0], [2.0, 0.0, None], [1.0, None, 0.0]], 3.0)
rng = random.Random(seed)
interpretation_utils._MAX_NUM_CANDIDATES = 1
actual = interpretation_utils.find_candidates(rng, interaction.table,
interaction.questions[0])
self.assertLen(actual, 3)
def test_selection_answer(self, seed):
interaction = _to_interaction(
[[1.0, 3.0, 6.0], [2.0, 0.0, None], [1.0, None, 0.0]], 100.0)
coords = interaction.questions[0].answer.answer_coordinates.add()
coords.row_index = 1
coords.column_index = 2
rng = random.Random(seed)
actual = interpretation_utils.find_candidates(rng, interaction.table,
interaction.questions[0])
expected = [_Candidate(_AggFun.NONE, 2, (1, ))]
self.assertEqual(expected, actual)
def test_random_exploration(self, seed):
interaction = _to_interaction(
[[1.0, 3.0, 6.0], [2.0, 0.0, None], [1.0, None, 0.0]], 3.0)
rng = random.Random(seed)
interpretation_utils._MAX_INDICES_TO_EXPLORE = 1
actual = interpretation_utils.find_candidates(rng, interaction.table,
interaction.questions[0])
expected = [
_Candidate(_AggFun.COUNT, 0, (0, 1, 2)),
_Candidate(_AggFun.COUNT, 1, (0, 1, 2)),
_Candidate(_AggFun.COUNT, 2, (0, 1, 2)),
_Candidate(_AggFun.SUM, 1, (0, )),
_Candidate(_AggFun.AVERAGE, 1, (0, )),
]
self.assertEqual(expected, actual)
def test_float_conversion(self, seed):
interaction = _to_interaction(
[[1.0, 3.0, 6.0], [2.0, 0.0, None], [1.0, None, 0.0]], 3.0)
rng = random.Random(seed)
actual = interpretation_utils.find_candidates(rng, interaction.table,
interaction.questions[0])
expected = [
_Candidate(_AggFun.COUNT, 0, (0, 1, 2)),
_Candidate(_AggFun.COUNT, 1, (0, 1, 2)),
_Candidate(_AggFun.COUNT, 2, (0, 1, 2)),
_Candidate(_AggFun.SUM, 0, (0, 1)),
_Candidate(_AggFun.SUM, 0, (1, 2)),
_Candidate(_AggFun.SUM, 1, (0, )),
_Candidate(_AggFun.SUM, 1, (0, 1)),
_Candidate(_AggFun.AVERAGE, 1, (0, )),
_Candidate(_AggFun.AVERAGE, 2, (0, 2)),
]
self.assertEqual(expected, actual)
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 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))
