immuneML.encodings.deeprc package

Submodules

immuneML.encodings.deeprc.DeepRCEncoder module

class immuneML.encodings.deeprc.DeepRCEncoder.DeepRCEncoder(context: dict = None, name: str = None)

Bases: DatasetEncoder

DeepRCEncoder should be used in combination with the DeepRC ML method (DeepRC). This encoder writes the data in a RepertoireDataset to .tsv files. For each repertoire, one .tsv file is created containing the amino acid sequences and the counts. Additionally, one metadata .tsv file is created, which describes the subset of repertoires that is encoded by a given instance of the DeepRCEncoder.

Note: sequences where count is None, the count value will be set to 1

Dataset type:

• RepertoireDatasets

YAML specification:

definitions:
    encodings:
        my_deeprc_encoder: DeepRC

COUNTS_COLUMN = 'templates'

EXTENSION = 'tsv'

ID_COLUMN = 'ID'

METADATA_EXTENSION = 'csv'

METADATA_SEP = ','

SEP = '\t'

SEQUENCE_COLUMN = 'amino_acid'

static build_object(dataset, **params)

Creates an instance of the relevant subclass of the DatasetEncoder class using the given parameters. This method will be called during parsing time (early in the immuneML run), such that parameters and dataset type can be tested here.

The build_object method should do the following:

1. Check parameters: immuneML should crash if wrong user parameters are specified. The ParameterValidator utility class may be used for parameter testing.

2. Check the dataset type: immuneML should crash if the wrong dataset type is specified for this encoder. For example, DeepRCEncoder should only work for RepertoireDatasets and crash if the dataset is of another type.

3. Create an instance of the correct Encoder class, using the given parameters. Return this object. Some encoders have different subclasses depending on the dataset type. Make sure to return an instance of the correct subclass. For instance: KmerFrequencyEncoder has different subclasses for each dataset type. When the dataset is a Repertoire dataset, KmerFreqRepertoireEncoder should be returned.

Parameters:

**params – keyword arguments that will be provided by users in the specification (if immuneML is used as a command line tool) or in the dictionary when calling the method from the code, and which should be used to create the Encoder object

Returns:

the object of the appropriate Encoder class

encode(dataset, params: EncoderParams) → RepertoireDataset

This is the main encoding method of the Encoder. It takes in a given dataset, computes an EncodedData object, and returns a copy of the dataset with the attached EncodedData object.

Parameters:

• dataset – A dataset object (Sequence, Receptor or RepertoireDataset)

• params – An EncoderParams object containing few utility parameters which may be used during encoding (e.g., number of parallel processes to use).

Returns:

A copy of the original dataset, with an EncodedData object added to the dataset.encoded_data field.

export_metadata_file(dataset, labels, output_folder)

export_repertoire_tsv_files(output_folder: Path, params: EncoderParams)

set_context(context: dict)

This method can be used to attach the full dataset (as part of a dictionary), as opposed to the dataset which is passed to the .encode() method. When training ML models, that data split is usually a training/validation subset of the total dataset.

In most cases, an encoder should only use the ‘dataset’ argument passed to the .encode() method to compute the encoded data. Using information from the full dataset, which includes the test data, may result in data leakage. For example, some encoders normalise the computed feature values (e.g., KmerFrequencyEncoder). Such normalised feature values should be based only on the current data split, and test data should remain unseen.

To avoid confusion about which version of the dataset to use, the full dataset is by default not attached, and attaching the full dataset should be done explicitly when required. For instance, if the encoded data is some kind of distance matrix (e.g., DistanceEncoder), the distance between examples in the training and test dataset should be included. Note that this does not entail data leakage: the test examples are not used to improve the computation of distances. The distances to test examples are determined by an algorithm which does not ‘learn’ from test data.

To explicitly enable using the full dataset in the encoder, the contents of this method should be as follows:

self.context = context return self

Parameters:

context – a dictionary containing the full dataset

Module contents