Source code for immuneML.encodings.reference_encoding.MatchedRegexRepertoireEncoder

import re
import warnings

import numpy as np
import pandas as pd

from immuneML.data_model.dataset.RepertoireDataset import RepertoireDataset
from immuneML.data_model.encoded_data.EncodedData import EncodedData
from immuneML.data_model.receptor.receptor_sequence.Chain import Chain
from immuneML.data_model.repertoire.Repertoire import Repertoire
from immuneML.encodings.EncoderParams import EncoderParams
from immuneML.util.Logger import print_log
from immuneML.util.ReadsType import ReadsType
from immuneML.encodings.reference_encoding.MatchedRegexEncoder import MatchedRegexEncoder


[docs] class MatchedRegexRepertoireEncoder(MatchedRegexEncoder): def _encode_new_dataset(self, dataset, params: EncoderParams): self._load_regex_df() encoded_dataset = RepertoireDataset(repertoires=dataset.repertoires, labels=dataset.labels, metadata_file=dataset.metadata_file) feature_annotations = self._get_feature_info() encoded_repertoires, labels = self._encode_repertoires(dataset, params) encoded_dataset.add_encoded_data(EncodedData( examples=encoded_repertoires, example_ids=list(dataset.get_metadata(["subject_id"]).values())[0], feature_names=list(feature_annotations["chain_id"]), feature_annotations=feature_annotations, labels=labels, encoding=MatchedRegexEncoder.__name__ )) return encoded_dataset def _get_feature_info(self): """ returns a pandas dataframe containing: - feature id (id_CHAIN) - regex - v_gene (if match_v_genes == True) only for the motifs for which a regex was specified """ features = {"receptor_id": [], "chain_id": [], "chain": [], "regex": []} if self.match_v_genes: features["v_gene"] = [] for index, row in self.regex_df.iterrows(): for chain_type in self.chains: regex = row[f"{chain_type}_regex"] if regex is not None: features["receptor_id"].append(f"{row['id']}") features["chain_id"].append(f"{row['id']}_{chain_type}") features["chain"].append(Chain.get_chain(chain_type).name.lower()) features["regex"].append(regex) if self.match_v_genes: v_gene = row[f"{chain_type}V"] if f"{chain_type}V" in row else None features["v_gene"].append(v_gene) return pd.DataFrame(features) def _encode_repertoires(self, dataset: RepertoireDataset, params: EncoderParams): # Rows = repertoires, Columns = regex matches (one chain per column) encoded_repertoires = np.zeros((dataset.get_example_count(), self.feature_count), dtype=int) labels = {label: [] for label in params.label_config.get_labels_by_name()} if params.encode_labels else None n_repertoires = dataset.get_example_count() for i, repertoire in enumerate(dataset.get_data()): print_log(f"Encoding repertoire {i+1}/{n_repertoires}", include_datetime=True) encoded_repertoires[i] = self._match_repertoire_to_regexes(repertoire) if labels is not None: for label_name in params.label_config.get_labels_by_name(): labels[label_name].append(repertoire.metadata[label_name]) return encoded_repertoires, labels def _match_repertoire_to_regexes(self, repertoire: Repertoire): matches = np.zeros(self.feature_count, dtype=int) rep_seqs = repertoire.sequences match_idx = 0 for index, row in self.regex_df.iterrows(): for chain_type in self.chains: regex = row[f"{chain_type}_regex"] if regex is not None: v_gene = row[f"{chain_type}V"] if f"{chain_type}V" in row else None for rep_seq in rep_seqs: if rep_seq.metadata.chain is not None: if rep_seq.metadata.chain.value == chain_type: if self._matches(rep_seq, regex, v_gene): n_matches = 1 if self.reads == ReadsType.UNIQUE else rep_seq.metadata.count if n_matches is None: warnings.warn(f"MatchedRegexRepertoireEncoder: count not defined for sequence with id {rep_seq.identifier} in repertoire {repertoire.identifier}, ignoring sequence...") n_matches = 0 matches[match_idx] += n_matches else: warnings.warn(f"{MatchedRegexRepertoireEncoder.__class__.__name__}: chain was not set for sequence {rep_seq.identifier}, skipping the sequence for matching...") match_idx += 1 return matches def _matches(self, receptor_sequence, regex, v_gene=None): if v_gene is not None and receptor_sequence.metadata.v_gene != v_gene: matches = False else: matches = bool(re.search(regex, receptor_sequence.amino_acid_sequence)) return matches