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 = PathBuilder.build(result_path / self.state.name)
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"{self.state.dataset.name}_{dataset_size}_subsampled_{index+1}"
new_dataset_path = PathBuilder.build(self.state.result_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 = 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[new_dataset.name] = {}
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}_{new_dataset.name}", "zip", export_path)
self.state.subsampled_dataset_paths[new_dataset.name][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