def _encode_new_dataset(self, dataset, params: EncoderParams):
encoded_dataset = RepertoireDataset(repertoires=dataset.repertoires, params=dataset.params,
metadata_file=dataset.metadata_file)
encoded_repertoires, labels = self._encode_repertoires(dataset, params)
feature_annotations = self._get_feature_info()
encoded_dataset.add_encoded_data(EncodedData(
examples=encoded_repertoires,
labels=labels,
feature_names=list(feature_annotations["sequence_id"]),
feature_annotations=feature_annotations,
example_ids=[repertoire.identifier for repertoire in dataset.get_data()],
encoding=MatchedSequencesEncoder.__name__
))
return encoded_dataset
def _encode_new_dataset(self, dataset, params: EncoderParams):
self._load_regex_df()
encoded_dataset = RepertoireDataset(
repertoires=dataset.repertoires,
params=dataset.params,
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