def test_non_padded_dataset(self):
# Set up test data.
test_data_directory = os.path.join(
FLAGS.test_srcdir,
'./testdata'
)
label_vocab_array = [
'EMBL:AE017224', 'RefSeq:WP_002966386.1', 'ProteinModelPortal:P0CB34',
'SMR:P0CB34', 'EnsemblBacteria:AAX75635', 'GeneID:29595679',
'KEGG:bmb:BruAb2_0191', 'HOGENOM:HOG000133897', 'KO:K04078',
'OMA:PGRIDDN', 'Proteomes:UP000000540', 'GO:GO:0005737',
'GO:GO:0005524', 'GO:GO:0006457', 'CDD:cd00320', 'Gene3D:2.30.33.40',
'HAMAP:MF_00580', 'InterPro:IPR020818', 'InterPro:IPR037124',
'InterPro:IPR018369', 'InterPro:IPR011032', 'PANTHER:PTHR10772',
'Pfam:PF00166', 'PRINTS:PR00297', 'SMART:SM00883', 'SUPFAM:SSF50129',
'PROSITE:PS00681'
]
with tf.Graph().as_default():
sess = tf.Session()
dataset = protein_dataset.non_batched_dataset(
# Dev fold instead of train fold because the train fold is repeated.
train_dev_or_test=protein_dataset.DEV_FOLD,
label_vocab=label_vocab_array,
data_root_dir=test_data_directory)
example_itr = dataset.make_initializable_iterator()
sess.run(tf.tables_initializer())
sess.run(tf.global_variables_initializer())
sess.run(example_itr.initializer)
# Compute actual output
actual_examples = _dataset_iterator_to_list(example_itr, sess)
expected_length = 4
# Compute expected values
expected_sequence = 'MADIKFRPLHDRVVVRRVESEAKTAGGIIIPDTAKEKPQEGEVVAAGAGARDEAGKLVPLDVKAGDRVLFGKWSGTEVKIGGEDLLIMKESDILGIVG'
expected_sequence_indexes = [
utils.AMINO_ACID_VOCABULARY.index(x) for x in expected_sequence
]
expected_sequence_one_hot = _numpy_one_hot(
expected_sequence_indexes, depth=len(utils.AMINO_ACID_VOCABULARY))
# Because the label vocab is exactly the labels in the first example, we
# just get range(len(label_vocab_array))
expected_label_indexes = range(len(label_vocab_array))
expected_id = b'P0CB34'
# Assert values correct
self.assertLen(actual_examples, expected_length)
np.testing.assert_equal(actual_examples[0][protein_dataset.SEQUENCE_KEY],
expected_sequence_one_hot)
np.testing.assert_equal(
actual_examples[0][protein_dataset.SEQUENCE_LENGTH_KEY],
len(expected_sequence))
np.testing.assert_equal(actual_examples[0][protein_dataset.LABEL_KEY],
expected_label_indexes)
np.testing.assert_equal(actual_examples[0][protein_dataset.SEQUENCE_ID_KEY],
expected_id)
def test_padded_dataset(self):
# Set up test data.
test_data_directory = os.path.join(
FLAGS.test_srcdir,
'./testdata'
)
label_vocab_array = ['EMBL:AE017224']
batch_size = 3
with tf.Graph().as_default():
sess = tf.Session()
non_padded_dataset = protein_dataset.non_batched_dataset(
# Dev fold instead of train fold because the train fold is repeated.
train_dev_or_test=protein_dataset.DEV_FOLD,
label_vocab=label_vocab_array,
data_root_dir=test_data_directory)
batched_dataset = protein_dataset.batched_dataset(
non_padded_dataset, batch_size=batch_size, bucket_boundaries=[11000])
batch_itr = batched_dataset.make_initializable_iterator()
sess.run(tf.tables_initializer())
sess.run(tf.global_variables_initializer())
sess.run(batch_itr.initializer)
# Compute actual output
actual_examples = _dataset_iterator_to_list(batch_itr, sess)
# Examine correctness of first element.
actual_sequence_batch_shape = actual_examples[0][
protein_dataset.SEQUENCE_KEY].shape
expected_longest_sequence_len_in_first_batch = 98
expected_first_batch_sequence_shape = (
batch_size, expected_longest_sequence_len_in_first_batch,
len(utils.AMINO_ACID_VOCABULARY))
self.assertEqual(actual_sequence_batch_shape,
expected_first_batch_sequence_shape)
actual_label_batch_shape = actual_examples[0][
protein_dataset.LABEL_KEY].shape
# Because the label vocab contains the labels in the first example, we
# get len(label_vocab_array) as the number of labels.
expected_batch_label_shape = (batch_size, len(label_vocab_array))
self.assertEqual(actual_label_batch_shape, expected_batch_label_shape)