def from_config()

in aepsych/generators/monotonic_rejection_generator.py [0:0]

• 28 lines of code
• 1 McCabe index (conditional complexity)

    def from_config(cls, config: Config):
        classname = cls.__name__
        acqf = config.getobj("common", "acqf", fallback=None)
        extra_acqf_args = cls._get_acqf_options(acqf, config)

        options = {}
        options["num_restarts"] = config.getint(classname, "restarts", fallback=10)
        options["raw_samples"] = config.getint(classname, "samps", fallback=1000)
        options["verbosity_freq"] = config.getint(
            classname, "verbosity_freq", fallback=-1
        )
        options["lr"] = config.getfloat(classname, "lr", fallback=0.01)  # type: ignore
        options["momentum"] = config.getfloat(classname, "momentum", fallback=0.9)  # type: ignore
        options["nesterov"] = config.getboolean(classname, "nesterov", fallback=True)
        options["epochs"] = config.getint(classname, "epochs", fallback=50)
        options["milestones"] = config.getlist(
            classname, "milestones", fallback=[25, 40]  # type: ignore
        )
        options["gamma"] = config.getfloat(classname, "gamma", fallback=0.1)  # type: ignore
        options["loss_constraint_fun"] = config.getobj(
            classname, "loss_constraint_fun", fallback=default_loss_constraint_fun
        )

        explore_features = config.getlist(classname, "explore_idxs", fallback=None)  # type: ignore

        return cls(
            acqf=acqf,
            acqf_kwargs=extra_acqf_args,
            model_gen_options=options,
            explore_features=explore_features,
        )