Source code for immuneML.workflows.instructions.subsampling.SubsamplingInstruction

import random
import shutil
from pathlib import Path
from typing import List

from immuneML.IO.dataset_export.DataExporter import DataExporter
from immuneML.data_model.dataset.Dataset import Dataset
from immuneML.util.PathBuilder import PathBuilder
from immuneML.util.ReflectionHandler import ReflectionHandler
from immuneML.workflows.instructions.Instruction import Instruction
from immuneML.workflows.instructions.subsampling.SubsamplingState import SubsamplingState
from scripts.specification_util import update_docs_per_mapping

[docs]class SubsamplingInstruction(Instruction): """ Subsampling is an instruction that subsamples a given dataset and creates multiple smaller dataset according to the parameters provided. Arguments: dataset (Dataset): original dataset which will be used as a basis for subsampling subsampled_dataset_sizes (list): a list of dataset sizes (number of examples) each subsampled dataset should have dataset_export_formats (list): in which formats to export the subsampled datasets. Valid formats are class names of any non-abstract class inheriting :py:obj:`~immuneML.IO.dataset_export.DataExporter.DataExporter`. YAML specification: .. indent with spaces .. code-block:: yaml my_subsampling_instruction: # user-defined name of the instruction type: Subsampling # which instruction to execute dataset: my_dataset # original dataset to be subsampled, with e.g., 300 examples subsampled_dataset_sizes: # how large the subsampled datasets should be, one dataset will be created for each list item - 200 # one subsampled dataset with 200 examples (200 repertoires if my_dataset was repertoire dataset) - 100 # the other subsampled dataset will have 100 examples dataset_export_formats: # in which formats to export the subsampled datasets - ImmuneML - AIRR """ def __init__(self, dataset: Dataset, subsampled_dataset_sizes: List[int], dataset_export_formats: list, result_path: Path = None, name: str = None): self.state = SubsamplingState(dataset, subsampled_dataset_sizes, dataset_export_formats, result_path, name)
[docs] def run(self, result_path: Path): self.state.result_path = / example_indices = list(range(self.state.dataset.get_example_count())) for index, dataset_size in enumerate(self.state.subsampled_dataset_sizes): new_dataset_name = f"{}_{dataset_size}_subsampled_{index+1}" new_dataset_path = / new_dataset_name) new_example_indices = random.sample(example_indices, k=dataset_size) new_dataset = self.state.dataset.make_subset(new_example_indices, new_dataset_path, Dataset.SUBSAMPLED) = new_dataset_name self.state.subsampled_datasets.append(new_dataset) self.export_dataset(new_dataset, new_dataset_path) return self.state
[docs] def export_dataset(self, new_dataset, new_dataset_path): self.state.subsampled_dataset_paths[] = {} for exporter in self.state.dataset_exporters: exporter_name = exporter.__name__[:-8].lower() export_path = new_dataset_path / f"exported/{exporter_name}/" exporter.export(new_dataset, export_path) zip_export_path = shutil.make_archive(new_dataset_path / f"exported_{exporter_name}_{}", "zip", export_path) self.state.subsampled_dataset_paths[][exporter_name] = zip_export_path
[docs] @staticmethod def get_documentation(): doc = str(SubsamplingInstruction.__doc__) valid_strategy_values = ReflectionHandler.all_nonabstract_subclass_basic_names(DataExporter, "Exporter", "dataset_export/") valid_strategy_values = str(valid_strategy_values)[1:-1].replace("'", "`") mapping = { "Valid formats are class names of any non-abstract class inheriting " ":py:obj:`~immuneML.IO.dataset_export.DataExporter.DataExporter`.": f"Valid values are: {valid_strategy_values}." } doc = update_docs_per_mapping(doc, mapping) return doc