def __init__()

in bayesmark/builtin_opt/scikit_optimizer.py [0:0]

• 14 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, api_config, base_estimator="GP", acq_func="gp_hedge", n_initial_points=5, **kwargs):
        """Build wrapper class to use an optimizer in benchmark.

        Parameters
        ----------
        api_config : dict-like of dict-like
            Configuration of the optimization variables. See API description.
        base_estimator : {'GP', 'RF', 'ET', 'GBRT'}
            How to estimate the objective function.
        acq_func : {'LCB', 'EI', 'PI', 'gp_hedge', 'EIps', 'PIps'}
            Acquisition objective to decide next suggestion.
        n_initial_points : int
            Number of points to sample randomly before actual Bayes opt.
        """
        AbstractOptimizer.__init__(self, api_config)

        dimensions, self.round_to_values = ScikitOptimizer.get_sk_dimensions(api_config)

        # Older versions of skopt don't copy over the dimensions names during
        # normalization and hence the names are missing in
        # self.skopt.space.dimensions. Therefore, we save our own copy of
        # dimensions list to be safe. If we can commit to using the newer
        # versions of skopt we can delete self.dimensions.
        self.dimensions_list = tuple(dd.name for dd in dimensions)

        # Undecided where we want to pass the kwargs, so for now just make sure
        # they are blank
        assert len(kwargs) == 0

        self.skopt = SkOpt(
            dimensions,
            n_initial_points=n_initial_points,
            base_estimator=base_estimator,
            acq_func=acq_func,
            acq_optimizer="auto",
            acq_func_kwargs={},
            acq_optimizer_kwargs={},
        )