Simple Multi-Objective

__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
__license__ = "3-clause BSD"

import numpy as np
from ConfigSpace.hyperparameters import UniformFloatHyperparameter
from matplotlib import pyplot as plt

from smac.configspace import ConfigurationSpace
from smac.facade.smac_bb_facade import SMAC4BB
from smac.scenario.scenario import Scenario


def schaffer(x):
    f1 = np.square(x)
    f2 = np.square(np.sqrt(f1) - 2)

    return f1, f2


def plot(all_x):
    plt.figure()
    for x in all_x:
        f1, f2 = schaffer(x)
        plt.scatter(f1, f2, c="blue", alpha=0.1, zorder=3000)

    plt.vlines([1], 0, 4, linestyles="dashed", colors=["red"])
    plt.hlines([1], 0, 4, linestyles="dashed", colors=["red"])

    plt.show()


def plot_from_smac(smac):
    rh = smac.get_runhistory()
    all_x = []
    for (config_id, _, _, _) in rh.data.keys():
        config = rh.ids_config[config_id]
        all_x.append(config["x"])

    plot(all_x)


def tae(cfg):
    f1, f2 = schaffer(cfg["x"])
    return {"metric1": f1, "metric2": f2}


if __name__ == "__main__":
    MIN_V = -2
    MAX_V = 2

    # Simple configspace
    cs = ConfigurationSpace()
    cs.add_hyperparameter(UniformFloatHyperparameter("x", lower=MIN_V, upper=MAX_V))

    # Scenario object
    scenario = Scenario(
        {
            "run_obj": "quality",  # we optimize quality (alternatively runtime)
            "runcount-limit": 50,  # max. number of function evaluations
            "cs": cs,  # configuration space
            "multi_objectives": "metric1, metric2",
            "limit_resources": False,
        }
    )

    smac = SMAC4BB(
        scenario=scenario,
        rng=np.random.RandomState(5),
        tae_runner=tae,
    )
    incumbent = smac.optimize()

    # Plot the evaluated points
    plot_from_smac(smac)

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Synthetic Function with BOinG as optimizer