[docs]classReportConfig:""" A class encapsulating different report lists which can be executed while performing nested cross-validation (CV) using TrainMLModel instruction. All arguments are optional. **Specification arguments:** - data (dict): :ref:`Data reports` to be executed on the whole dataset before it is split to training/test or training/validation - data_splits (dict): :ref:`Data reports` to be executed after the data has been split into training and test (assessment CV loop) or training and validation (selection CV loop) datasets before they are encoded - models (dict): :ref:`ML model reports` to be executed on all trained classifiers - encoding (dict): :ref:`Encoding reports` to be executed on each of the encoded training/test datasets or training/validation datasets **YAML specification:** .. indent with spaces .. code-block:: yaml # as a part of a TrainMLModel instruction, defining the outer (assessment) loop of nested cross-validation: assessment: # outer loop of nested CV split_strategy: random # perform Monte Carlo CV (randomly split the data into train and test) split_count: 5 # how many train/test datasets to generate training_percentage: 0.7 # what percentage of the original data should be used for the training set reports: # reports to execute on training/test datasets, encoded datasets and trained ML methods data_splits: # list of reports to execute on training/test datasets (before they are preprocessed and encoded) - my_data_split_report encoding: # list of reports to execute on encoded training/test datasets - my_encoding_report # as a part of a TrainMLModel instruction, defining the inner (selection) loop of nested cross-validation: selection: # inner loop of nested CV split_strategy: random # perform Monte Carlo CV (randomly split the data into train and validation) split_count: 5 # how many train/validation datasets to generate training_percentage: 0.7 # what percentage of the original data should be used for the training set reports: # reports to execute on training/validation datasets, encoded datasets and trained ML methods data_splits: # list of reports to execute on training/validation datasets (before they are preprocessed and encoded) - my_data_split_report encoding: # list of reports to execute on encoded training/validation datasets - my_encoding_report models: - my_ml_model_report """def__init__(self,data_splits:dict=None,models:dict=None,data:dict=None,encoding:dict=None):self.data_split_reports=data_splitsifdata_splitsisnotNoneelse{}self.encoding_reports=encodingifencodingisnotNoneelse{}self.model_reports=modelsifmodelsisnotNoneelse{}self.data_reports=dataifdataisnotNoneelse{}