immuneML.workflows.instructions.dataset_generation package

Submodules

immuneML.workflows.instructions.dataset_generation.DatasetExportInstruction module

class immuneML.workflows.instructions.dataset_generation.DatasetExportInstruction.DatasetExportInstruction(datasets: List[Dataset], number_of_processes: int = 1, preprocessing_sequence: List[Preprocessor] = None, result_path: Path = None, name: str = None)[source]

Bases: Instruction

DatasetExport instruction takes a list of datasets as input, optionally applies preprocessing steps, and outputs the data in specified formats.

Specification arguments:

  • datasets (list): a list of datasets to export in all given formats

  • preprocessing_sequence (list): which preprocessing sequence to use on the dataset(s), this item is optional and does not have to be specified. When specified, the same preprocessing sequence will be applied to all datasets.

  • number_of_processes (int): how many processes to use during repertoire export (not used for sequence datasets)

YAML specification:

instructions:
    my_dataset_export_instruction: # user-defined instruction name
        type: DatasetExport # which instruction to execute
        datasets: # list of datasets to export
            - my_generated_dataset
            - my_dataset_from_adaptive
        preprocessing_sequence: my_preprocessing_sequence
        number_of_processes: 4
static get_documentation()[source]
run(result_path: Path) → DatasetExportState[source]

immuneML.workflows.instructions.dataset_generation.DatasetExportState module

class immuneML.workflows.instructions.dataset_generation.DatasetExportState.DatasetExportState(datasets: List[immuneML.data_model.datasets.Dataset.Dataset], formats: List[str], preprocessing_sequence: List[immuneML.preprocessing.Preprocessor.Preprocessor], paths: dict, result_path: pathlib.Path, name: str)[source]

Bases: object

datasets: List[Dataset]
formats: List[str]
name: str
paths: dict
preprocessing_sequence: List[Preprocessor]
result_path: Path

Module contents