Source code for immuneML.data_model.dataset.ReceptorDataset

import copy
import logging
import math
from pathlib import Path
from typing import List

import numpy as np
import pandas as pd

from immuneML.data_model.dataset.ElementDataset import ElementDataset
from immuneML.data_model.receptor.Receptor import Receptor

[docs] class ReceptorDataset(ElementDataset): """A dataset class for receptor datasets (paired chain). All the functionality is implemented in ElementDataset class, except creating a new dataset and obtaining metadata. """
[docs] @classmethod def build(cls, **kwargs): return ReceptorDataset(**kwargs)
[docs] @classmethod def build_from_objects(cls, receptors: List[Receptor], file_size: int, path: Path, name: str = None): file_count = math.ceil(len(receptors) / file_size) file_names = [path / f"batch{''.join(['0' for i in range(1, len(str(file_count)) - len(str(index)) + 1)])}{index}.npy" for index in range(1, file_count + 1)] for index in range(file_count): receptor_matrix = np.core.records.fromrecords( [receptor.get_record() for receptor in receptors[index * file_size:(index + 1) * file_size]], names=type(receptors[0]).get_record_names())[index]), receptor_matrix, allow_pickle=False) return ReceptorDataset(filenames=file_names, file_size=file_size, name=name, element_class_name=type(receptors[0]).__name__ if len(receptors) > 0 else None)
[docs] def get_metadata(self, field_names: list, return_df: bool = False): """Returns a dict or an equivalent pandas DataFrame with metadata information from Receptor objects for provided field names""" result = {field: [] for field in field_names} for receptor in self.get_data(): for field in field_names: result[field].append(receptor.metadata[field] if receptor.metadata and field in receptor.metadata else None) for field in field_names: if all(item is None for item in result[field]): logging.warning(f"{ReceptorDataset.__name__}: none of the receptors in the dataset {} have metadata field '{field}'. " f"Returning 'None' instead...") result[field] = None return pd.DataFrame(result) if return_df else result
[docs] def clone(self, keep_identifier: bool = False): dataset = ReceptorDataset(self.labels, copy.deepcopy(self.encoded_data), copy.deepcopy(self.filenames), file_size=self.file_size,, element_class_name=self.element_generator.element_class_name) if keep_identifier: dataset.identifier = self.identifier dataset.element_ids = self.element_ids return dataset