Source code for immuneML.reports.ml_reports.MLReport

from pathlib import Path

from immuneML.data_model.dataset.Dataset import Dataset
from immuneML.hyperparameter_optimization.HPSetting import HPSetting
from immuneML.ml_methods.MLMethod import MLMethod
from immuneML.reports.Report import Report

[docs]class MLReport(Report): """ ML model reports show some type of features or statistics about one trained ML model. In the :ref:`TrainMLModel` instruction, ML model reports can be specified inside the 'selection' or 'assessment' specification under the key 'reports:models'. When using the reports with TrainMLModel instruction, the arguments defined below are set at runtime by the instruction. Concrete classes inheriting MLReport may include additional parameters that will be set by the user in the form of input arguments. Arguments: train_dataset (Dataset): a dataset object (repertoire, receptor or sequence dataset) with encoded_data attribute set to an EncodedData object that was used for training the ML method test_dataset (Dataset): same as train_dataset, except it is not used for training and then maybe be used for testing the method method (MLMethod): a trained instance of a concrete subclass of MLMethod object result_path (Path): location where the report results will be stored hp_setting (HPSetting): a HPSetting object describing the ML method, encoding and preprocessing used label (str): name of the label for which the model was trained name (str): user-defined name of the report used in the HTML overview automatically generated by the platform """ def __init__(self, train_dataset: Dataset = None, test_dataset: Dataset = None, method: MLMethod = None, result_path: Path = None, name: str = None, hp_setting: HPSetting = None, label=None): super().__init__(name) self.train_dataset = train_dataset self.test_dataset = test_dataset self.method = method self.result_path = result_path = name self.hp_setting = hp_setting self.label = label
[docs] @staticmethod def get_title(): return "ML model reports"