def test_repertoire_flattened(self):
path = EnvironmentSettings.root_path / "test/tmp/onehot_recep_flat/"
PathBuilder.build(path)
dataset, lc = self._construct_test_repertoiredataset(path, positional=False)
encoder = OneHotEncoder.build_object(dataset, **{"use_positional_info": False, "distance_to_seq_middle": None,
"flatten": True})
encoded_data = encoder.encode(dataset, EncoderParams(
result_path=path,
label_config=lc,
pool_size=1,
learn_model=True,
model={},
filename="dataset.pkl"
))
self.assertTrue(isinstance(encoded_data, RepertoireDataset))
onehot_a = [1.0] + [0.0] * 19
onehot_t = [0.0] * 16 + [1.0] + [0] * 3
onehot_empty = [0] * 20
self.assertListEqual(list(encoded_data.encoded_data.examples[0]), onehot_a+onehot_a+onehot_a+onehot_a+onehot_a+onehot_t+onehot_a+onehot_empty+onehot_a+onehot_t+onehot_a+onehot_empty)
self.assertListEqual(list(encoded_data.encoded_data.examples[1]), onehot_a+onehot_t+onehot_a+onehot_empty+onehot_t+onehot_a+onehot_a+onehot_empty+onehot_empty+onehot_empty+onehot_empty+onehot_empty)
self.assertListEqual(list(encoded_data.encoded_data.feature_names), [f"{seq}_{pos}_{char}" for seq in range(3) for pos in range(4) for char in EnvironmentSettings.get_sequence_alphabet()])
shutil.rmtree(path)
def test(self):
path = EnvironmentSettings.tmp_test_path / "onehot_sequence/"
PathBuilder.build(path)
dataset, lc = self._construct_test_dataset(path)
encoder = OneHotEncoder.build_object(
dataset, **{
"use_positional_info": False,
'sequence_type': 'amino_acid',
"distance_to_seq_middle": None,
"flatten": False
})
encoded_data = encoder.encode(
dataset,
EncoderParams(result_path=path / "encoded/",
label_config=lc,
learn_model=True,
model={},
filename="dataset.pkl"))
self.assertTrue(isinstance(encoded_data, SequenceDataset))
onehot_a = [1] + [0] * 19
onehot_t = [0] * 16 + [1] + [0] * 3
onehot_empty = [0] * 20
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[0]],
[onehot_a for i in range(4)])
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[1]],
[onehot_a, onehot_t, onehot_a, onehot_empty])
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[2]],
[onehot_a, onehot_t, onehot_t, onehot_empty])
self.assertListEqual(
encoded_data.encoded_data.example_ids,
[receptor.identifier for receptor in dataset.get_data()])
self.assertDictEqual(
encoded_data.encoded_data.labels, {
"l1": [
receptor_seq.get_attribute("l1")
for receptor_seq in dataset.get_data()
],
"l2": [
receptor_seq.get_attribute("l2")
for receptor_seq in dataset.get_data()
]
})
shutil.rmtree(path)
def test_receptor_flattened(self):
path = EnvironmentSettings.root_path / "test/tmp/onehot_recep_flat/"
PathBuilder.build(path)
dataset = self.construct_test_flatten_dataset(path)
encoder = OneHotEncoder.build_object(
dataset, **{
"use_positional_info": False,
"distance_to_seq_middle": None,
'sequence_type': 'amino_acid',
"flatten": True
})
encoded_data = encoder.encode(
dataset,
EncoderParams(result_path=path,
label_config=LabelConfiguration([
Label(name="l1",
values=[1, 0],
positive_class="1")
]),
pool_size=1,
learn_model=True,
model={},
filename="dataset.pkl"))
self.assertTrue(isinstance(encoded_data, ReceptorDataset))
onehot_a = [1.0] + [0.0] * 19
onehot_t = [0.0] * 16 + [1.0] + [0] * 3
self.assertListEqual(
list(encoded_data.encoded_data.examples[0]),
onehot_a + onehot_a + onehot_a + onehot_t + onehot_t + onehot_t +
onehot_a + onehot_t + onehot_a + onehot_t + onehot_a + onehot_t)
self.assertListEqual(list(encoded_data.encoded_data.examples[1]),
onehot_a * 12)
self.assertListEqual(list(encoded_data.encoded_data.examples[2]),
onehot_a * 12)
self.assertListEqual(list(encoded_data.encoded_data.feature_names), [
f"{chain}_{pos}_{char}" for chain in ("alpha", "beta")
for pos in range(6)
for char in EnvironmentSettings.get_sequence_alphabet()
])
shutil.rmtree(path)
def test_not_positional(self):
path = EnvironmentSettings.root_path / "test/tmp/onehot_vanilla/"
PathBuilder.build(path)
dataset, lc = self._construct_test_repertoiredataset(path, positional=False)
encoder = OneHotEncoder.build_object(dataset, **{"use_positional_info": False,
"distance_to_seq_middle": 6,
"flatten": False})
encoded_data = encoder.encode(dataset, EncoderParams(
result_path=path,
label_config=lc,
learn_model=True,
model={},
filename="dataset.pkl"
))
self.assertTrue(isinstance(encoded_data, RepertoireDataset))
onehot_a = [1] + [0] * 19
onehot_t = [0] * 16 + [1] + [0] * 3
onehot_empty = [0] * 20
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[0][0]], [onehot_a for i in range(4)])
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[0][1]],
[onehot_a, onehot_t, onehot_a, onehot_empty])
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[0][2]],
[onehot_a, onehot_t, onehot_a, onehot_empty])
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[1][0]],
[onehot_a, onehot_t, onehot_a, onehot_empty])
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[1][1]],
[onehot_t, onehot_a, onehot_a, onehot_empty])
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[1][2]], [onehot_empty for i in range(4)])
self.assertListEqual(list(encoded_data.encoded_data.example_ids), [repertoire.identifier for repertoire in dataset.get_data()])
self.assertDictEqual(encoded_data.encoded_data.labels,
{"l1": [repertoire.metadata["l1"] for repertoire in dataset.get_data()],
"l2": [repertoire.metadata["l2"] for repertoire in dataset.get_data()]})
shutil.rmtree(path)
def test(self):
path = EnvironmentSettings.tmp_test_path / "onehot_sequence_1/"
PathBuilder.build(path)
dataset, lc = self._construct_test_dataset(path)
encoder = OneHotEncoder.build_object(
dataset, **{
"use_positional_info": False,
'sequence_type': 'amino_acid',
"distance_to_seq_middle": 6,
"flatten": False
})
encoded_data = encoder.encode(
dataset,
EncoderParams(result_path=path / "encoded",
label_config=lc,
learn_model=True,
model={},
filename="dataset.pkl"))
self.assertTrue(isinstance(encoded_data, ReceptorDataset))
onehot_a = [1] + [0] * 19
onehot_t = [0] * 16 + [1] + [0] * 3
onehot_empty = [0] * 20
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[0, 0]],
[onehot_a for i in range(4)])
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[0, 1]],
[onehot_a, onehot_t, onehot_a, onehot_empty])
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[1, 0]],
[onehot_a, onehot_t, onehot_a, onehot_empty])
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[1, 1]],
[onehot_a, onehot_t, onehot_t, onehot_empty])
shutil.rmtree(path)
def test_imgt_weights(self):
path = EnvironmentSettings.root_path / "test/tmp/onehot_imgt/"
PathBuilder.build(path)
encoder = OneHotEncoder.build_object(RepertoireDataset(), **{"use_positional_info": True,
"distance_to_seq_middle": 4,
"flatten": False})
# testing positional information for 'middle' (idx = 0) section
self.assertListEqual(list(encoder._get_imgt_position_weights(9)[1]), [0, 1 / 4, 2 / 4, 3 / 4, 1, 3 / 4, 2 / 4, 1 / 4, 0])
self.assertListEqual(list(encoder._get_imgt_position_weights(8)[1]), [0, 1 / 4, 2 / 4, 3 / 4, 3 / 4, 2 / 4, 1 / 4, 0])
self.assertListEqual(list(encoder._get_imgt_position_weights(6)[1]), [0, 1 / 4, 2 / 4, 2 / 4, 1 / 4, 0])
self.assertListEqual(list(encoder._get_imgt_position_weights(5)[1]), [0, 1 / 4, 2 / 4, 1 / 4, 0])
# testing positional information for 'start' (idx = 1) and 'end' (idx = 2) section
self.assertListEqual(list(encoder._get_imgt_position_weights(6, 8)[0]), [1, 3 / 4, 2 / 4, 1 / 4, 0, 0, 0, 0])
self.assertListEqual(list(encoder._get_imgt_position_weights(6, 8)[2]), [0, 0, 1 / 4, 2 / 4, 3 / 4, 1, 0, 0])
self.assertListEqual(list(encoder._get_imgt_position_weights(3)[0]), [1, 3 / 4, 2 / 4])
self.assertListEqual(list(encoder._get_imgt_position_weights(3)[2]), [2 / 4, 3 / 4, 1])
self.assertListEqual(list(encoder._get_imgt_position_weights(3, 6)[2]), [2 / 4, 3 / 4, 1, 0, 0, 0])
shutil.rmtree(path)
def test_positional(self):
path = EnvironmentSettings.root_path / "test/tmp/onehot_positional/"
PathBuilder.build(path)
dataset, lc = self._construct_test_repertoiredataset(path, positional=True)
encoder = OneHotEncoder.build_object(dataset, **{"use_positional_info": True,
"distance_to_seq_middle": 6,
"flatten": False})
encoded_data = encoder.encode(dataset, EncoderParams(
result_path=path,
label_config=lc,
pool_size=1,
learn_model=True,
model={},
filename="dataset.pkl"
))
self.assertTrue(isinstance(encoded_data, RepertoireDataset))
onehot_a = [1.0] + [0.0] * 19
onehot_t = [0.0] * 16 + [1.0] + [0] * 3
onehot_empty = [0.0] * 20
# testing onehot dimensions, all but the last 3 value (last 3 = positional info)
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[0, 0, :, :-3]], [onehot_a for i in range(17)])
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[0, 1, :, :-3]], [onehot_a for i in range(17)])
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[1, 0, :, :-3]],
[onehot_t for i in range(13)] + [onehot_empty for i in range(4)])
# testing positional dimensions
precision = 5
# Dimension: middle
self.assertListEqual([round(val, precision) for val in encoded_data.encoded_data.examples[0, 0, :, 21]],
[round(val, precision) for val in
[0, 1 / 6, 2 / 6, 3 / 6, 4 / 6, 5 / 6, 1, 1, 1, 1, 1, 5 / 6, 4 / 6, 3 / 6, 2 / 6, 1 / 6, 0]])
self.assertListEqual([round(val, precision) for val in encoded_data.encoded_data.examples[0, 1, :, 21]],
[round(val, precision) for val in
[0, 1 / 6, 2 / 6, 3 / 6, 4 / 6, 5 / 6, 1, 1, 1, 1, 1, 5 / 6, 4 / 6, 3 / 6, 2 / 6, 1 / 6, 0]])
self.assertListEqual([round(val, precision) for val in encoded_data.encoded_data.examples[1, 0, :, 21]],
[round(val, precision) for val in
[0, 1 / 6, 2 / 6, 3 / 6, 4 / 6, 5 / 6, 1, 5 / 6, 4 / 6, 3 / 6, 2 / 6, 1 / 6, 0, 0, 0, 0, 0]])
# Dimension: start
self.assertListEqual([round(val, precision) for val in encoded_data.encoded_data.examples[0, 0, :, 20]],
[round(val, precision) for val in [1, 5 / 6, 4 / 6, 3 / 6, 2 / 6, 1 / 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
self.assertListEqual([round(val, precision) for val in encoded_data.encoded_data.examples[0, 1, :, 20]],
[round(val, precision) for val in [1, 5 / 6, 4 / 6, 3 / 6, 2 / 6, 1 / 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
self.assertListEqual([round(val, precision) for val in encoded_data.encoded_data.examples[1, 0, :, 20]],
[round(val, precision) for val in [1, 5 / 6, 4 / 6, 3 / 6, 2 / 6, 1 / 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
# Dimension: end
self.assertListEqual([round(val, precision) for val in encoded_data.encoded_data.examples[0, 0, :, 22]],
[round(val, precision) for val in [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 / 6, 2 / 6, 3 / 6, 4 / 6, 5 / 6, 1]])
self.assertListEqual([round(val, precision) for val in encoded_data.encoded_data.examples[0, 1, :, 22]],
[round(val, precision) for val in [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 / 6, 2 / 6, 3 / 6, 4 / 6, 5 / 6, 1]])
self.assertListEqual([round(val, precision) for val in encoded_data.encoded_data.examples[1, 0, :, 22]],
[round(val, precision) for val in [0, 0, 0, 0, 0, 0, 0, 1 / 6, 2 / 6, 3 / 6, 4 / 6, 5 / 6, 1, 0, 0, 0, 0]])
shutil.rmtree(path)