from immuneML.IO.dataset_import.DataImport import DataImport
from immuneML.data_model.dataset.ReceptorDataset import ReceptorDataset
from immuneML.simulation.dataset_generation.RandomDatasetGenerator import RandomDatasetGenerator
from immuneML.util.ParameterValidator import ParameterValidator
[docs]
class RandomReceptorDatasetImport(DataImport):
"""
Returns a ReceptorDataset consisting of randomly generated sequences, which can be used for benchmarking purposes.
The sequences consist of uniformly chosen amino acids or nucleotides.
Arguments:
receptor_count (int): The number of receptors the ReceptorDataset should contain.
chain_1_length_probabilities (dict): A mapping where the keys correspond to different sequence lengths for chain 1, and the values are the probabilities for choosing each sequence length. For example, to create a random ReceptorDataset where 40% of the sequences for chain 1 would be of length 10, and 60% of the sequences would have length 12, this mapping would need to be specified:
.. indent with spaces
.. code-block:: yaml
10: 0.4
12: 0.6
chain_2_length_probabilities (dict): Same as chain_1_length_probabilities, but for chain 2.
labels (dict): A mapping that specifies randomly chosen labels to be assigned to the receptors. One or multiple labels can be specified here. The keys of this mapping are the labels, and the values consist of another mapping between label classes and their probabilities. For example, to create a random ReceptorDataset with the label cmv_epitope where 70% of the receptors has class binding and the remaining 30% has class not_binding, the following mapping should be specified:
.. indent with spaces
.. code-block:: yaml
cmv_epitope:
binding: 0.7
not_binding: 0.3
YAML specification:
.. indent with spaces
.. code-block:: yaml
my_random_dataset:
format: RandomReceptorDataset
params:
receptor_count: 100 # number of random receptors to generate
chain_1_length_probabilities:
14: 0.8 # 80% of all generated sequences for all receptors (for chain 1) will have length 14
15: 0.2 # 20% of all generated sequences across all receptors (for chain 1) will have length 15
chain_2_length_probabilities:
14: 0.8 # 80% of all generated sequences for all receptors (for chain 2) will have length 14
15: 0.2 # 20% of all generated sequences across all receptors (for chain 2) will have length 15
labels:
epitope1: # label name
True: 0.5 # 50% of the receptors will have class True
False: 0.5 # 50% of the receptors will have class False
epitope2: # next label with classes that will be assigned to receptors independently of the previous label or other parameters
1: 0.3 # 30% of the generated receptors will have class 1
0: 0.7 # 70% of the generated receptors will have class 0
"""
[docs]
@staticmethod
def import_dataset(params, name: str) -> ReceptorDataset:
"""
Returns randomly generated receptor dataset according to the parameters;
YAML specification:
result_path: path/where/to/store/results/
receptor_count: 100 # number of random receptors to generate
chain_1_length_probabilities:
14: 0.8 # 80% of all generated sequences for all receptors (for chain 1) will have length 14
15: 0.2 # 20% of all generated sequences across all receptors (for chain 1) will have length 15
chain_2_length_probabilities:
14: 0.8 # 80% of all generated sequences for all receptors (for chain 2) will have length 14
15: 0.2 # 20% of all generated sequences across all receptors (for chain 2) will have length 15
labels:
epitope1: # label name
True: 0.5 # 50% of the receptors will have class True
False: 0.5 # 50% of the receptors will have class False
epitope2: # next label with classes that will be assigned to receptors independently of the previous label or other parameters
1: 0.3 # 30% of the generated receptors will have class 1
0: 0.7 # 70% of the generated receptors will have class 0
"""
valid_keys = ["receptor_count", "chain_1_length_probabilities", "chain_2_length_probabilities", "labels", "result_path"]
ParameterValidator.assert_all_in_valid_list(list(params.keys()), valid_keys, "RandomReceptorDatasetImport", "params")
return RandomDatasetGenerator.generate_receptor_dataset(receptor_count=params["receptor_count"],
chain_1_length_probabilities=params["chain_1_length_probabilities"],
chain_2_length_probabilities=params["chain_2_length_probabilities"],
labels=params["labels"],
path=params["result_path"])