# Using the SMAC interface tuned for hyperparameter optimization for black-box optimization¶

import logging

import numpy as np
from ConfigSpace.hyperparameters import UniformFloatHyperparameter

# Import ConfigSpace and different types of parameters
from smac.configspace import ConfigurationSpace
# Import SMAC-utilities
from smac.scenario.scenario import Scenario

def rosenbrock_2d(x):
""" The 2 dimensional Rosenbrock function as a toy model
The Rosenbrock function is well know in the optimization community and
often serves as a toy problem. It can be defined for arbitrary
dimensions. The minimium is always at x_i = 1 with a function value of
zero. All input parameters are continuous. The search domain for
all x's is the interval [-5, 10].
"""
x1 = x["x0"]
x2 = x["x1"]

val = 100. * (x2 - x1 ** 2.) ** 2. + (1 - x1) ** 2.
return val

logging.basicConfig(level=logging.INFO)  # logging.DEBUG for debug output

# Build Configuration Space which defines all parameters and their ranges
cs = ConfigurationSpace()
x0 = UniformFloatHyperparameter("x0", -5, 10, default_value=-3)
x1 = UniformFloatHyperparameter("x1", -5, 10, default_value=-4)

# Scenario object
scenario = Scenario({"run_obj": "quality",  # we optimize quality (alternatively runtime)
"runcount-limit": 10,  # max. number of function evaluations; for this example set to a low number
"cs": cs,  # configuration space
"deterministic": "true"
})

# Example call of the function
# It returns: Status, Cost, Runtime, Additional Infos
def_value = rosenbrock_2d(cs.get_default_configuration())
print("Default Value: %.2f" % def_value)

# Optimize, using a SMAC-object
print("Optimizing! Depending on your machine, this might take a few minutes.")
smac = SMAC4HPO(scenario=scenario,
rng=np.random.RandomState(42),
tae_runner=rosenbrock_2d)

smac.optimize()


Total running time of the script: ( 0 minutes 0.000 seconds)

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