Source code for immuneML.data_model.dataset.RepertoireDataset

# quality: gold
import copy
import uuid
from pathlib import Path

import pandas as pd

from immuneML.data_model.dataset.Dataset import Dataset
from immuneML.data_model.encoded_data.EncodedData import EncodedData
from immuneML.data_model.repertoire.Repertoire import Repertoire
from immuneML.environment.Constants import Constants
from immuneML.util.ParameterValidator import ParameterValidator

[docs]class RepertoireDataset(Dataset):
[docs] @classmethod def build(cls, **kwargs): ParameterValidator.assert_keys_present(list(kwargs.keys()), ['metadata_file', 'name', 'repertoire_ids', 'metadata_fields'], RepertoireDataset.__name__, "repertoire dataset") repertoires = [] metadata_df = pd.read_csv(kwargs['metadata_file'], comment=Constants.COMMENT_SIGN) for index, row in metadata_df.iterrows(): filename = Path(kwargs['metadata_file']).parent / row['filename'] if not filename.is_file() and 'repertoires' in str(filename): filename = filename.parent.parent / Path(row['filename']).name repertoire = Repertoire(data_filename=filename, metadata_filename=filename.parent / f'{filename.stem}_metadata.yaml', identifier=row['identifier']) repertoires.append(repertoire) if "repertoire_ids" in kwargs.keys() and "repertoires" not in kwargs.keys() and kwargs['repertoire_ids'] is not None: assert all(rep.identifier == kwargs['repertoire_ids'][i] for i, rep in enumerate(repertoires)), \ f"{RepertoireDataset.__name__}: repertoire ids from the iml_dataset file and metadata file don't match for the dataset " \ f"{kwargs['name']} with identifier {kwargs['identifier']}." return RepertoireDataset(**{**kwargs, **{"repertoires": repertoires}})
def __init__(self, labels: dict = None, encoded_data: EncodedData = None, repertoires: list = None, identifier: str = None, metadata_file: Path = None, name: str = None, metadata_fields: list = None, repertoire_ids: list = None): super().__init__(encoded_data, name, identifier if identifier is not None else uuid.uuid4().hex, labels) self.metadata_file = Path(metadata_file) if metadata_file is not None else None self.metadata_fields = metadata_fields self.repertoire_ids = repertoire_ids self.repertoires = repertoires
[docs] def clone(self, keep_identifier: bool = False): dataset = RepertoireDataset(self.labels, copy.deepcopy(self.encoded_data), copy.deepcopy(self.repertoires), metadata_file=self.metadata_file, if keep_identifier: dataset.identifier = self.identifier return dataset
[docs] def add_encoded_data(self, encoded_data: EncodedData): self.encoded_data = encoded_data
[docs] def get_data(self, batch_size: int = 1): return self.repertoires
[docs] def get_batch(self, batch_size: int = 1): return self.repertoires
[docs] def get_repertoire(self, index: int = -1, repertoire_identifier: str = "") -> Repertoire: assert index != -1 or repertoire_identifier != "", \ "RepertoireDataset: cannot import_dataset repertoire since the index nor identifier are set." return self.repertoires[index] if index != -1 else [rep for rep in self.repertoires if rep.identifier == repertoire_identifier][0]
[docs] def get_example_count(self): return len(self.repertoires)
[docs] def get_metadata_fields(self, refresh=False): """Returns the list of metadata fields, includes also the fields that will typically not be used as labels, like filename or identifier""" if self.metadata_fields is None or refresh: df = pd.read_csv(self.metadata_file, sep=",", nrows=0, comment=Constants.COMMENT_SIGN) self.metadata_fields = df.columns.values.tolist() return self.metadata_fields
[docs] def get_label_names(self, refresh=False): """Returns the list of metadata fields which can be used as labels; if refresh=True, it reloads the fields from disk""" all_metadata_fields = set(self.get_metadata_fields(refresh)) for non_label in ["subject_id", "filename", "repertoire_identifier", "identifier"]: if non_label in all_metadata_fields: all_metadata_fields.remove(non_label) return all_metadata_fields
[docs] def get_metadata(self, field_names: list, return_df: bool = False): """ A function to get the metadata of the repertoires. It can be useful in encodings or reports when the repertoire information needed is not present only in the label chosen for the ML model (e.g., disease), but also other information (e.g., age, HLA). Args: field_names (list): list of the metadata fields to return; the fields must be present in the metadata files. To find fields available, use :py:obj:`~immuneML.data_model.dataset.RepertoireDataset.RepertoireDataset.get_label_names` function. return_df (bool): determines if the results should be returned as a dataframe where each column corresponds to a field or as a dictionary Returns: a dictionary where keys are fields names and values are lists of field values for each repertoire; alternatively returns the same information in dataframe format """ assert isinstance(self.metadata_file, Path) and self.metadata_file.is_file(), \ f"RepertoireDataset: for dataset {} (id: {self.identifier}) metadata file is not set properly. The metadata file points to " \ f"{self.metadata_file}." df = pd.read_csv(self.metadata_file, sep=",", usecols=field_names, comment=Constants.COMMENT_SIGN) if return_df: return df else: return df.to_dict("list")
[docs] def get_filenames(self): """Returns the paths to files in which repertoire information is stored""" return [Path(filename) for filename in self.get_metadata(["filename"])["filename"]]
def _build_new_metadata(self, indices, path: Path) -> Path: if self.metadata_file: df = pd.read_csv(self.metadata_file, comment=Constants.COMMENT_SIGN) df = df.iloc[indices, :] df.to_csv(path, index=False) return path else: return None
[docs] def make_subset(self, example_indices, path: Path, dataset_type: str): """ Creates a new dataset object with only those examples (repertoires) available which were given by index in example_indices argument. Args: example_indices (list): a list of indices of examples (repertoires) to use in the new dataset path (Path): a path where to store the newly created dataset dataset_type (str): a type of the dataset used as a part of the name of the resulting dataset; the values are defined as constants in :py:obj:`~immuneML.data_model.dataset.Dataset.Dataset` Returns: a new RepertoireDataset object which includes only the repertoires specified under example_indices """ metadata_file = self._build_new_metadata(example_indices, path / f"{dataset_type}_metadata.csv") new_dataset = RepertoireDataset(repertoires=[self.repertoires[i] for i in example_indices], labels=copy.deepcopy(self.labels), metadata_file=metadata_file, identifier=str(uuid.uuid1())) return new_dataset
[docs] def get_repertoire_ids(self) -> list: """Returns a list of repertoire identifiers, same as get_example_ids()""" if self.repertoire_ids is None: self.repertoire_ids = [str(repertoire.identifier) for repertoire in self.repertoires] return self.repertoire_ids
[docs] def get_example_ids(self): """Returns a list of example identifiers""" return self.get_repertoire_ids()