def _make_dataset(self, path, size) -> RepertoireDataset:
random_dataset = RandomDatasetGenerator.generate_repertoire_dataset(repertoire_count=size, sequence_count_probabilities={100: 1.},
sequence_length_probabilities={5: 1.}, labels={}, path=path)
signals = [Signal(identifier="disease", motifs=[Motif(identifier="m1", instantiation=GappedKmerInstantiation(), seed="AAA")],
implanting_strategy=HealthySequenceImplanting(implanting_computation=ImplantingComputation.ROUND,
implanting=GappedMotifImplanting())),
Signal(identifier="HLA", motifs=[Motif(identifier="m2", instantiation=GappedKmerInstantiation(), seed="CCC")],
implanting_strategy=HealthySequenceImplanting(implanting_computation=ImplantingComputation.ROUND,
implanting=GappedMotifImplanting())),
Signal(identifier="age", motifs=[Motif(identifier="m3", instantiation=GappedKmerInstantiation(), seed="GGG")],
implanting_strategy=HealthySequenceImplanting(implanting_computation=ImplantingComputation.ROUND,
implanting=GappedMotifImplanting()))]
simulation = Simulation([Implanting(dataset_implanting_rate=0.2, signals=signals, name='i1', repertoire_implanting_rate=0.25),
Implanting(dataset_implanting_rate=0.2, signals=[signals[0], signals[1]], name='i2', repertoire_implanting_rate=0.25),
Implanting(dataset_implanting_rate=0.1, signals=[signals[0]], name='i3', repertoire_implanting_rate=0.25),
Implanting(dataset_implanting_rate=0.2, signals=[signals[2]], name='i4', repertoire_implanting_rate=0.25),
Implanting(dataset_implanting_rate=0.1, signals=[signals[1]], name='i5', repertoire_implanting_rate=0.25)
])
dataset = SignalImplanter.run(SimulationState(signals=signals, dataset=random_dataset, formats=['Pickle'], result_path=path,
name='my_synthetic_dataset', simulation=simulation))
return dataset
def test_run_with_receptors(self):
path = PathBuilder.build(EnvironmentSettings.root_path /
"test/tmp/signalImplanter_receptor/")
dataset = RandomDatasetGenerator.generate_receptor_dataset(
10, {10: 1}, {12: 1}, {}, path / "dataset/")
motif1 = Motif(identifier="motif1",
instantiation=GappedKmerInstantiation(),
seed_chain1="AAA",
name_chain1=Chain.ALPHA,
seed_chain2="CCC",
name_chain2=Chain.BETA)
signal1 = Signal(identifier="signal1",
motifs=[motif1],
implanting_strategy=ReceptorImplanting(
GappedMotifImplanting()))
simulation = Simulation(
[Implanting(dataset_implanting_rate=0.5, signals=[signal1])])
sim_state = SimulationState(dataset=dataset,
result_path=path,
simulation=simulation,
signals=[signal1],
formats=["ImmuneML"])
new_dataset = SignalImplanter.run(sim_state)
self.assertEqual(10, new_dataset.get_example_count())
self.assertEqual(
5,
len([
receptor for receptor in new_dataset.get_data(40)
if receptor.metadata["signal1"] is True
]))
shutil.rmtree(path)
def _parse_simulation(key: str, simulation: dict, symbol_table: SymbolTable) -> SymbolTable:
location = "SimulationParser"
valid_implanting_keys = ["dataset_implanting_rate", "repertoire_implanting_rate", "signals", "is_noise"]
implantings = []
for impl_key, implanting in simulation.items():
ParameterValidator.assert_keys(implanting.keys(), valid_implanting_keys, location, impl_key, exclusive=False)
ParameterValidator.assert_keys(implanting["signals"], symbol_table.get_keys_by_type(SymbolType.SIGNAL), location, impl_key, False)
implanting_params = copy.deepcopy(implanting)
implanting_params["signals"] = [symbol_table.get(signal) for signal in implanting["signals"]]
implanting_params["name"] = impl_key
implantings.append(Implanting(**implanting_params))
assert sum([settings["dataset_implanting_rate"] for settings in simulation.values()]) <= 1, \
"The total dataset implanting rate can not exceed 1."
symbol_table.add(key, SymbolType.SIMULATION, Simulation(implantings))
return symbol_table
def test_run(self):
r = []
path = EnvironmentSettings.tmp_test_path / "signalImplanter/"
if not os.path.isdir(path):
os.makedirs(path)
sequences = [
ReceptorSequence("ACDEFG", identifier="1"),
ReceptorSequence("ACDEFG", identifier="2"),
ReceptorSequence("ACDEFG", identifier="3"),
ReceptorSequence("ACDEFG", identifier="4")
]
for i in range(10):
rep = Repertoire.build_from_sequence_objects(
sequence_objects=sequences, path=path, metadata={})
r.append(rep)
dataset = RepertoireDataset(repertoires=r)
m1 = Motif(identifier="m1",
instantiation=GappedKmerInstantiation(),
seed="CAS")
m2 = Motif(identifier="m2",
instantiation=GappedKmerInstantiation(),
seed="CCC")
s1 = Signal(identifier="s1",
motifs=[m1],
implanting_strategy=HealthySequenceImplanting(
GappedMotifImplanting(),
implanting_computation=ImplantingComputation.ROUND))
s2 = Signal(identifier="s2",
motifs=[m1, m2],
implanting_strategy=HealthySequenceImplanting(
GappedMotifImplanting(),
implanting_computation=ImplantingComputation.ROUND))
simulation = Simulation([
Implanting(dataset_implanting_rate=0.2,
repertoire_implanting_rate=0.5,
signals=[s1, s2],
name="i1"),
Implanting(dataset_implanting_rate=0.2,
repertoire_implanting_rate=0.5,
signals=[s2],
name="i2")
])
input_params = SimulationState(dataset=dataset,
result_path=path,
simulation=simulation,
signals=[s1, s2],
formats=["ImmuneML"])
new_dataset = SignalImplanter.run(input_params)
reps_with_s2 = sum([
rep.metadata[s2.id] is True
for rep in new_dataset.get_data(batch_size=10)
])
reps_with_s1 = sum([
rep.metadata[s1.id] is True
for rep in new_dataset.get_data(batch_size=10)
])
self.assertEqual(10, len(new_dataset.get_example_ids()))
self.assertTrue(
all([
s1.id in rep.metadata.keys()
for rep in new_dataset.get_data(batch_size=10)
]))
self.assertTrue(
all([
s2.id in rep.metadata.keys()
for rep in new_dataset.get_data(batch_size=10)
]))
self.assertTrue(reps_with_s2 == 4)
self.assertTrue(reps_with_s1 == 2)
self.assertEqual(10, len(new_dataset.get_example_ids()))
metadata_filenames = [
filename.name for filename in new_dataset.get_filenames()
]
self.assertTrue(
all([
repertoire.data_filename.name in metadata_filenames
for repertoire in new_dataset.repertoires
]))
shutil.rmtree(path)