Source code for immuneML.encodings.onehot.OneHotSequenceEncoder

from immuneML.data_model.dataset.SequenceDataset import SequenceDataset
from immuneML.data_model.encoded_data.EncodedData import EncodedData
from immuneML.encodings.EncoderParams import EncoderParams
from immuneML.encodings.onehot.OneHotEncoder import OneHotEncoder

[docs]class OneHotSequenceEncoder(OneHotEncoder): """ One-hot encoded repertoire data is represented in a matrix with dimensions: [sequences, sequence_lengths, one_hot_characters] when use_positional_info is true, the last 3 indices in one_hot_characters represents the positional information: - start position (high when close to start) - middle position (high in the middle of the sequence) - end position (high when close to end) """ def _encode_new_dataset(self, dataset: SequenceDataset, params: EncoderParams): encoded_data = self._encode_data(dataset, params) encoded_dataset = SequenceDataset(filenames=dataset.get_filenames(), encoded_data=encoded_data, labels=dataset.labels, file_size=dataset.file_size) return encoded_dataset def _encode_data(self, dataset: SequenceDataset, params: EncoderParams): sequence_objs = [obj for obj in dataset.get_data(params.pool_size)] sequences = [obj.get_sequence(self.sequence_type) for obj in sequence_objs] if any(seq is None for seq in sequences): raise ValueError( f"{OneHotEncoder.__name__}: sequence dataset {} (id: {dataset.identifier}) contains empty sequences for the specified " f"sequence type {}. Please check that the dataset is imported correctly.") example_ids = dataset.get_example_ids() max_seq_len = max([len(seq) for seq in sequences]) labels = self._get_labels(sequence_objs, params) if params.encode_labels else None examples = self._encode_sequence_list(sequences, pad_n_sequences=len(sequence_objs), pad_sequence_len=max_seq_len) feature_names = self._get_feature_names(max_seq_len) if self.flatten: examples = examples.reshape((len(sequence_objs), max_seq_len*len(self.onehot_dimensions))) feature_names = [item for sublist in feature_names for item in sublist] encoded_data = EncodedData(examples=examples, labels=labels, example_ids=example_ids, feature_names=feature_names, encoding=OneHotEncoder.__name__) return encoded_data def _get_feature_names(self, max_seq_len): return [[f"{pos}_{dim}" for dim in self.onehot_dimensions] for pos in range(max_seq_len)] def _get_labels(self, sequence_objs, params: EncoderParams): label_names = params.label_config.get_labels_by_name() labels = {name: [None] * len(sequence_objs) for name in label_names} for idx, sequence in enumerate(sequence_objs): for name in label_names: labels[name][idx] = sequence.get_attribute(name) return labels