import copy
from immuneML.dsl.symbol_table.SymbolTable import SymbolTable
from immuneML.dsl.symbol_table.SymbolType import SymbolType
from immuneML.simulation.Implanting import Implanting
from immuneML.simulation.Simulation import Simulation
from immuneML.util.Logger import log
from immuneML.util.ParameterValidator import ParameterValidator
[docs]
class SimulationParser:
"""
YAML specification:
.. highlight:: yaml
.. code-block:: yaml
definitions:
dataset:
my_dataset:
...
motifs:
m1:
seed: AAC # "/" character denotes the gap in the seed if present (e.g. AA/C)
instantiation:
GappedKmer:
# probability that when hamming distance is allowed a letter in the seed will be replaced by
# other alphabet letters - alphabet_weights
alphabet_weights:
A: 0.2
C: 0.2
D: 0.4
E: 0.2
# Relative probabilities of choosing each position in the seed for hamming distance modification.
# The probabilities will be scaled to sum to one - position_weights
position_weights:
0: 1
1: 0
2: 0
hamming_distance_probabilities:
0: 0.5 # Hamming distance of 0 (no change) with probability 0.5
1: 0.5 # Hamming distance of 1 (one letter change) with probability 0.5
min_gap: 0
max_gap: 1
signals:
s1:
motifs: # list of all motifs for signal which will be uniformly sampled to get a motif instance for implanting
- m1
sequence_position_weights: # likelihood of implanting at IMGT position of receptor sequence
107: 0.5
implanting: HealthySequence # choose only sequences with no other signals for to implant one of the motifs
simulations:
sim1: # one Simulation object consists of a dict of Implanting objects
i1:
dataset_implanting_rate: 0.5 # percentage of repertoire where the signals will be implanted
repertoire_implanting_rate: 0.01 # percentage of sequences within repertoire where the signals will be implanted
signals:
- s1
instructions:
my_simulation_instruction:
type: Simulation
dataset: my_dataset
simulation: sim1
batch_size: 5 # number of repertoires that can be loaded at the same time
# (only affects the speed)
export_formats: [AIRR, ImmuneML]
"""
[docs]
@staticmethod
def parse_simulations(simulations: dict, symbol_table: SymbolTable):
for key, simulation in simulations.items():
symbol_table = SimulationParser._parse_simulation(key, simulation, symbol_table)
return symbol_table, simulations
@staticmethod
@log
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