def test_candidates(self, mock_read):
with tf.gfile.Open(os.path.join(self._test_dir,
'interaction_03.pbtxt')) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
_set_mock_read(mock_read, [interaction])
max_seq_length = 15
tf_example_utils._MAX_NUM_ROWS = 4
tf_example_utils._MAX_NUM_CANDIDATES = 10
pipeline = create_data.build_classifier_pipeline(
input_files=['input.tfrecord'],
output_files=[self._output_path],
config=_ClassifierConfig(
vocab_file=os.path.join(self._test_dir, 'vocab.txt'),
max_seq_length=max_seq_length,
max_column_id=4,
max_row_id=4,
strip_column_names=False,
add_aggregation_candidates=True,
),
)
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
output = _read_examples(self._output_path)
with tf.gfile.Open(os.path.join(self._test_dir,
'tf_example_03.pbtxt')) as input_file:
expected_example = text_format.ParseLines(input_file,
tf.train.Example())
actual_example = output[0]
logging.info('%s', actual_example)
# assertEqual struggles with NaNs inside protos
del actual_example.features.feature['numeric_values']
self.assertEqual(actual_example, expected_example)
def test_tfrecord_io(self, mock_read):
"""Reads from TFRecord and writes to TFRecord."""
with tf.gfile.Open(os.path.join(self._test_dir,
'interaction_03.pbtxt')) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
def dummy_read(file_pattern, coder, validate):
del file_pattern, coder, validate # Unused.
return beam.Create([interaction])
mock_read.side_effect = dummy_read
max_seq_length = 15
pipeline = create_data.build_classifier_pipeline(
input_files=['input.tfrecord'],
output_files=[self._output_path],
config=_ClassifierConfig(
vocab_file=os.path.join(self._test_dir, 'vocab.txt'),
max_seq_length=max_seq_length,
max_column_id=4,
max_row_id=4,
strip_column_names=False,
add_aggregation_candidates=False,
))
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
output = []
for value in tf.python_io.tf_record_iterator(self._output_path):
example = tf.train.Example()
example.ParseFromString(value)
output.append(example)
self.assertLen(output, 1)
sid = output[0].features.feature['segment_ids']
self.assertLen(sid.int64_list.value, max_seq_length)
def test_numeric_relations(self, mock_read):
input_file = 'interaction_00.pbtxt'
expected_counters = {
'Conversion success': 1,
'Example emitted': 1,
'Input question': 1,
'Relation Set Index: 2': 5,
'Relation Set Index: 4': 13,
'Found answers: <= 4': 1,
}
with tf.gfile.Open(os.path.join(self._test_dir,
input_file)) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
_set_mock_read(mock_read, [interaction])
max_seq_length = 512
pipeline = create_data.build_classifier_pipeline(
input_files=['input.tfrecord'],
output_files=[self._output_path],
config=_ClassifierConfig(
vocab_file=os.path.join(self._test_dir, 'vocab.txt'),
max_seq_length=max_seq_length,
max_column_id=512,
max_row_id=512,
strip_column_names=False,
add_aggregation_candidates=False,
))
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
self.assertEqual(
{
metric_result.key.metric.name: metric_result.committed
for metric_result in result.metrics().query()['counters']
}, expected_counters)
output = _read_examples(self._output_path)
self.assertLen(output, 1)
actual_example = output[0]
self.assertIn('numeric_relations',
actual_example.features.feature.keys())
relations = actual_example.features.feature[
'numeric_relations'].int64_list.value
with tf.gfile.Open(os.path.join(self._test_dir,
'vocab.txt')) as vocab_file:
vocab = [line.strip() for line in vocab_file]
inputs = actual_example.features.feature['input_ids'].int64_list.value
pairs = [(vocab[input_id], relation)
for (input_id, relation) in zip(inputs, relations)
if input_id > 0]
logging.info('pairs: %s', pairs)
self.assertSequenceEqual(pairs, [('[CLS]', 0), ('which', 0),
('cities', 0),
('had', 0), ('less', 0), ('than', 0),
('2', 0), (',', 0), ('000', 0),
('pass', 0), ('##en', 0), ('##ge', 0),
('##rs', 0), ('?', 0), ('[SEP]', 0),
('ran', 0), ('##k', 0), ('city', 0),
('pass', 0), ('##en', 0), ('##ge', 0),
('##rs', 0), ('ran', 0), ('##ki', 0),
('##ng', 0), ('air', 0), ('##li', 0),
('##ne', 0), ('1', 4), ('united', 0),
('states', 0), (',', 0), ('los', 0),
('angeles', 0), ('14', 2), (',', 2),
('7', 2), ('##4', 2), ('##9', 2),
('[EMPTY]', 0),
('al', 0), ('##as', 0), ('##ka', 0),
('air', 0), ('##li', 0), ('##ne', 0),
('##s', 0), ('2', 4), ('united', 0),
('states', 0), (',', 0), ('h', 0),
('##ous', 0), ('##ton', 0), ('5', 2),
(',', 2), ('4', 2), ('##6', 2),
('##5', 2), ('[EMPTY]', 0),
('united', 0), ('e', 0), ('##x', 0),
('##p', 0), ('##re', 0), ('##s', 0),
('##s', 0), ('3', 4), ('canada', 0),
(',', 0), ('c', 0), ('##al', 0),
('##ga', 0), ('##ry', 0), ('3', 2),
(',', 2), ('7', 2), ('##6', 2),
('##1', 2), ('[EMPTY]', 0),
('air', 0), ('t', 0), ('##ra', 0),
('##ns', 0), ('##a', 0), ('##t', 0),
(',', 0), ('west', 0), ('##j', 0),
('##et', 0), ('4', 4), ('canada', 0),
(',', 0), ('s', 0), ('##as', 0),
('##ka', 0), ('##to', 0), ('##on', 0),
('2', 2), (',', 2), ('28', 2),
('##2', 2), ('4', 0), ('[EMPTY]', 0),
('5', 4), ('canada', 0), (',', 0),
('van', 0), ('##co', 0), ('##u', 0),
('##ve', 0), ('##r', 0), ('2', 2),
(',', 2), ('10', 2), ('##3', 2),
('[EMPTY]', 0), ('air', 0), ('t', 0),
('##ra', 0), ('##ns', 0), ('##a', 0),
('##t', 0), ('6', 4), ('united', 0),
('states', 0), (',', 0), ('p', 0),
('##h', 0), ('##o', 0), ('##en', 0),
('##i', 0), ('##x', 0), ('1', 4),
(',', 4), ('8', 4), ('##2', 4),
('##9', 4), ('1', 0), ('us', 0),
('air', 0), ('##w', 0), ('##a', 0),
('##y', 0), ('##s', 0), ('7', 4),
('canada', 0), (',', 0), ('to', 0),
('##ro', 0), ('##nt', 0), ('##o', 0),
('1', 4), (',', 4), ('20', 4),
('##2', 4), ('1', 0), ('air', 0),
('t', 0), ('##ra', 0), ('##ns', 0),
('##a', 0), ('##t', 0), (',', 0),
('can', 0), ('##j', 0), ('##et', 0),
('8', 4), ('canada', 0), (',', 0),
('ed', 0), ('##m', 0), ('##on', 0),
('##ton', 0), ('11', 4), ('##0', 4),
('[EMPTY]', 0), ('[EMPTY]', 0),
('9', 4), ('united', 0),
('states', 0), (',', 0), ('o', 0),
('##a', 0), ('##k', 0), ('##land', 0),
('10', 4), ('##7', 4), ('[EMPTY]', 0),
('[EMPTY]', 0)])
def test_gracefully_handle_big_examples(self, max_seq_length,
max_column_id, max_row_id,
expected_counters, mock_read):
with tf.gfile.Open(os.path.join(self._test_dir,
'interaction_02.pbtxt')) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
_set_mock_read(mock_read, [interaction])
pipeline = create_data.build_classifier_pipeline(
input_files=['input.tfrecord'],
output_files=[self._output_path],
config=_ClassifierConfig(
vocab_file=self._vocab_path,
max_seq_length=60
if max_seq_length is None else max_seq_length,
max_column_id=5 if max_column_id is None else max_column_id,
max_row_id=10 if max_row_id is None else max_row_id,
strip_column_names=False,
add_aggregation_candidates=False,
))
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
self.assertEqual(
{
metric_result.key.metric.name: metric_result.committed
for metric_result in result.metrics().query()['counters']
}, expected_counters)
if max_seq_length is None and max_column_id is None and max_row_id is None:
output = _read_examples(self._output_path)
with tf.gfile.Open(
os.path.join(self._test_dir,
'tf_example_02.pbtxt')) as input_file:
expected_example = text_format.ParseLines(
input_file, tf.train.Example())
with tf.gfile.Open(
os.path.join(self._test_dir,
'tf_example_02_conv.pbtxt')) as input_file:
expected_conversational_example = text_format.ParseLines(
input_file, tf.train.Example())
self.assertLen(output, 2)
actual_example = output[0]
del actual_example.features.feature['column_ranks']
del actual_example.features.feature['inv_column_ranks']
del actual_example.features.feature['numeric_relations']
del actual_example.features.feature['numeric_values']
del actual_example.features.feature['numeric_values_scale']
del actual_example.features.feature['question_id_ints']
# assertEqual struggles with NaNs inside protos
del actual_example.features.feature['answer']
self.assertEqual(actual_example, expected_example)
actual_example = output[1]
del actual_example.features.feature['column_ranks']
del actual_example.features.feature['inv_column_ranks']
del actual_example.features.feature['numeric_relations']
del actual_example.features.feature['numeric_values']
del actual_example.features.feature['numeric_values_scale']
del actual_example.features.feature['question_id_ints']
# assertEqual struggles with NaNs inside protos
del actual_example.features.feature['answer']
self.assertEqual(actual_example, expected_conversational_example)