Source code for immuneML.simulation.Implanting

from typing import List

from immuneML.simulation.implants.Signal import Signal

[docs]class Implanting: """ When performing a Simulation, one or more implantings can be specified. An implanting represents a set of signals which are implanted in a RepertoireDataset with given rates. Multiple implantings may be specified in one simulation. In this case, each implanting will only affect its own partition of the dataset, so each repertoire can only receive implanted signals from one implanting. This way, implantings can be used to ensure signals do not overlap (one implanting per signal), or to ensure signals always occur together (multiple signals per implanting). Arguments: signals (list): The list of :ref:`Signal` objects to be implanted in a subset of the repertoires in a RepertoireDataset. When multiple signals are specified, this means that all of these signals are implanted in the same repertoires in a RepertoireDataset, although they may not be implanted in the same sequences within those repertoires (this depends on the :py:obj:`~immuneML.simulation.signal_implanting_strategy.SignalImplantingStrategy.SignalImplantingStrategy`). dataset_implanting_rate (float): The proportion of repertoires in the RepertoireDataset in which the signals should be implanted. When specifying multiple implantings, the sum of all dataset_implanting_rates should not exceed 1. repertoire_implanting_rate (float): The proportion of sequences in a Repertoire where a motif associated with one of the signals should be implanted. is_noise (bool): indicates whether the implanting should be regarded as noise; if it is True, the signals will be implanted as specified, but the repertoire/receptor in question will have negative class. YAML specification: .. indent with spaces .. code-block:: yaml simulations: # definitions of simulations should be under key simulations in the definitions part of the specification # one simulation with multiple implanting objects, a part of definition section my_simulation: my_implanting_1: signals: - my_signal dataset_implanting_rate: 0.5 repertoire_implanting_rate: 0.25 my_implanting_2: signals: - my_signal dataset_implanting_rate: 0.2 repertoire_implanting_rate: 0.75 # a simulation where the signals is present in the negative class as well (e.g. wrong labels, or by chance) noisy_simulation: positive_class_implanting: signals: - my_signal dataset_implanting_rate: 0.5 repertoire_implanting_rate: 0.1 # 10% of the repertoire includes the signal in the positive class negative_class_implanting: signals: - my_signal is_noise: True # means that signal will be implanted, but the label will have negative class dataset_implanting_rate: 0.5 repertoire_implanting_rate: 0.01 # 1% of negative class repertoires has the signal # in case of defining implanting for paired chain immune receptor data the simulation with implanting objects would be: my_receptor_simulation: my_receptor_implanting_1: # repertoire_implanting_rate is omitted in this case, as it is not applicable signals: - my_receptor_signal dataset_implanting_rate: 0.4 # 40% of the receptors will have signal my_receptor_signal implanted and 60% will not """ def __init__(self, dataset_implanting_rate: float, signals: List[Signal], name: str = "", repertoire_implanting_rate: float = None, is_noise: bool = False): self.dataset_implanting_rate = dataset_implanting_rate self.repertoire_implanting_rate = repertoire_implanting_rate self.signals = signals self.is_noise = is_noise = name def __str__(self): return + ":\n dataset_implanting_rate: {}, \n" \ "repertoire_implanting_rate: {}, \n" \ "signals: {}".format(self.dataset_implanting_rate, self.repertoire_implanting_rate, str([str(s) for s in self.signals])[1:-1])