A mock logger with some mock defaults

Get a sequence on ensembles that are on the pareto front Raises ------ SklearnNotFittedError If ``fit`` has not b

Select the predictions of the selected model. Parameters ---------- base_models_predictions : np.ndarray shape =

The predict function calls the predict function of the underlying scikit-learn model and returns an array with the predictions. Param

The predict function calls the predict function of the underlying scikit-learn model and returns an array with the predictions. Param

Predict probabilities. Parameters ---------- X : array-like, shape = (n_samples, n_features) Returns -------

Path to the cached losses we store between runs

Parameters ---------- parser : Parser The parser to add options to

Modifys the colelction of tests that are captured

Used to register marks

Run before each test

Clean up any child processes

Called after the ``Session`` object has been created and before performing collection and entering the run test loop. Parameters --------

Refit all models found with fit to new data. Necessary when using cross-validation. During training, auto-sklearn fits each model k t

Delete this caches items

Creates a backend mock

Creates a backend mock

Returns a dictionary containing dictionaries of ensemble models. Each model in the ensemble can be accessed by giving its ``model_id`` as key