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