def test_config_space_hp(self):
import ConfigSpace.hyperparameters as csh
from deephyper.problem import HpProblem
alpha = csh.UniformFloatHyperparameter(name="alpha", lower=0, upper=1)
beta = csh.UniformFloatHyperparameter(name="beta", lower=0, upper=1)
pb = HpProblem()
pb.add_hyperparameters([alpha, beta])
Example command line::
python -m deephyper.search.hps.ambs2 --evaluator threadPool --problem deephyper.benchmark.hps.toy.problem_basic_1.Problem --run deephyper.benchmark.hps.toy.problem_basic_1.run --max-evals 100 --kappa 0.001
"""
import ConfigSpace.hyperparameters as csh
import numpy as np
from deephyper.problem import HpProblem
# Problem definition
Problem = HpProblem()
x_hp = csh.UniformIntegerHyperparameter(name="x", lower=0, upper=10, log=False)
y_hp = csh.UniformIntegerHyperparameter(name="y", lower=0, upper=10, log=False)
Problem.add_hyperparameters([x_hp, y_hp])
Problem.add_starting_point(x=1, y=1)
# Definition of the function which runs the model
def run(param_dict):
x = param_dict["x"]
y = param_dict["y"]
res = x + y
return res # the objective
