Testing the ML method outside immuneML with a sample design matrix

When implementing a new ML method, it can be useful to test the method with a small sample design matrix before integrating it into the immuneML codebase. Example design matrices for any encoding can be exported to .csv format with the DesignMatrixExporter report and the ExploratoryAnalysis instruction. To quickly generate some random sample data, RandomRepertoireDataset or RandomReceptorDataset may be specified as import formats. Alternatively, you can import your own data. A full YAML specification for exporting a sample design matrix for a 3-mer encoding may look like this:

      format: RandomRepertoireDataset
        repertoire_count: 5 # a dataset with 5 repertoires
        sequence_count_probabilities: # each repertoire has 10 sequences
          10: 1
        sequence_length_probabilities: # each sequence has length 15
          15: 1
          my_label: # half of the repertoires has my_label = true, the rest has false
            false: 0.5
            true: 0.5
        k: 3
        name: my_design_matrix
    type: ExploratoryAnalysis
        dataset: my_simulated_data
        encoding: my_3mer_encoding
        - my_label
        report: my_design_matrix


Note that for design matrices beyond 2 dimensions (such as OneHotEncoder with flatten = False), the matrix is exported as a .npy file instead of a .csv file.

To generate the design matrix, save the YAML specification to specs.yaml and and run immuneML providing the path to the saved file and a path to the output directory:

immune-ml specs.yaml output_dir/

The resulting design matrix can be found in my_instruction/analysis_my_analysis/report/design_matrix.csv, and the true classes for each repertoire can be found in labels.csv. In immuneML, the design matrix is passed to the ML method as an EncodedData object, and the labels as a numpy ndarray. The EncodedData object has attribute examples which contains the design matrix, and feature_names and example_ids which contain the row and column names respectively.

Testing the ML method using immuneML for encoding the data

In immuneML, different encodings can be used to create encoded data. For the full list of possible encodings, see Encodings. Encoding works in the following way: given a dataset and encoding parameters, the specific encoder object creates an instance of EncodedData class and fills the attributes examples (design / feature matrix, where one row is one example), labels (a dictionary of numpy array where the keys are label names and values are arrays with class assignment for each example), feature_names (if available for the encoding, a list of feature names for each column in the examples). This object will be provided as input to the corresponding functions of the new ML method class.

To load the data encoded as described above into an EncodedData object, the function immuneML.dev_util.util.load_encoded_data can be used.