def fmin_smac(func: callable,
x0: list,
bounds: list,
maxfun: int = -1,
maxtime: int = -1,
rng: np.random.RandomState = None):
""" Minimize a function func using the SMAC algorithm.
This function is a convenience wrapper for the SMAC class.
Parameters
----------
func : callable f(x)
Function to minimize.
x0 : list
Initial guess/default configuration.
bounds : list
``(min, max)`` pairs for each element in ``x``, defining the bound on
that parameters.
maxtime : int, optional
Maximum runtime in seconds.
maxfun : int, optional
Maximum number of function evaluations.
rng : np.random.RandomState, optional
Random number generator used by SMAC.
Returns
-------
x : list
Estimated position of the minimum.
f : float
Value of `func` at the minimum.
s : :class:`smac.facade.smac_facade.SMAC`
SMAC objects which enables the user to get
e.g., the trajectory and runhistory.
"""
# create configuration space
cs = ConfigurationSpace()
for idx, (lower_bound, upper_bound) in enumerate(bounds):
parameter = UniformFloatHyperparameter(name="x%d" % (idx + 1),
lower=lower_bound,
upper=upper_bound,
default_value=x0[idx])
cs.add_hyperparameter(parameter)
# Create target algorithm runner
ta = ExecuteTAFuncArray(ta=func)
# create scenario
scenario_dict = {
"run_obj": "quality",
"cs": cs,
"deterministic": "true",
"initial_incumbent": "DEFAULT"
}
if maxfun > 0:
scenario_dict["runcount_limit"] = maxfun
if maxtime > 0:
scenario_dict["wallclock_limit"] = maxtime
scenario = Scenario(scenario_dict)
smac = SMAC(scenario=scenario, tae_runner=ta, rng=rng)
smac.logger = logging.getLogger(smac.__module__ + "." +
smac.__class__.__name__)
incumbent = smac.optimize()
config_id = smac.solver.runhistory.config_ids[incumbent]
run_key = RunKey(config_id, None, 0)
incumbent_performance = smac.solver.runhistory.data[run_key]
incumbent = np.array(
[incumbent['x%d' % (idx + 1)] for idx in range(len(bounds))],
dtype=np.float)
return incumbent, incumbent_performance.cost, \
smac
def test_check_random_states(self, patch):
patch.return_value = None
smac = SMAC()
smac.logger = unittest.mock.MagicMock()
# Check some properties
# Check whether different seeds give different random states
_, rng_1 = get_rng(1)
_, rng_2 = get_rng(2)
self.assertNotEqual(sum(rng_1.get_state()[1] - rng_2.get_state()[1]),
0)
# Check whether no seeds gives different random states
_, rng_1 = get_rng(logger=smac.logger)
self.assertEqual(smac.logger.debug.call_count, 1)
_, rng_2 = get_rng(logger=smac.logger)
self.assertEqual(smac.logger.debug.call_count, 2)
self.assertNotEqual(sum(rng_1.get_state()[1] - rng_2.get_state()[1]),
0)
# Check whether the same int seeds give the same random states
_, rng_1 = get_rng(1)
_, rng_2 = get_rng(1)
self.assertEqual(sum(rng_1.get_state()[1] - rng_2.get_state()[1]), 0)
# Check all execution paths
self.assertRaisesRegex(
TypeError,
"Argument rng accepts only arguments of type None, int or np.random.RandomState, "
"you provided <class 'str'>.",
get_rng,
rng='ABC',
)
self.assertRaisesRegex(
TypeError,
"Argument run_id accepts only arguments of type None, int or np.random.RandomState, "
"you provided <class 'str'>.",
get_rng,
run_id='ABC')
run_id, rng_1 = get_rng(rng=None, run_id=None, logger=smac.logger)
self.assertIsInstance(run_id, int)
self.assertIsInstance(rng_1, np.random.RandomState)
self.assertEqual(smac.logger.debug.call_count, 3)
run_id, rng_1 = get_rng(rng=None, run_id=1, logger=smac.logger)
self.assertEqual(run_id, 1)
self.assertIsInstance(rng_1, np.random.RandomState)
run_id, rng_1 = get_rng(rng=1, run_id=None, logger=smac.logger)
self.assertEqual(run_id, 1)
self.assertIsInstance(rng_1, np.random.RandomState)
run_id, rng_1 = get_rng(rng=1, run_id=1337, logger=smac.logger)
self.assertEqual(run_id, 1337)
self.assertIsInstance(rng_1, np.random.RandomState)
rs = np.random.RandomState(1)
run_id, rng_1 = get_rng(rng=rs, run_id=None, logger=smac.logger)
self.assertIsInstance(run_id, int)
self.assertIs(rng_1, rs)
run_id, rng_1 = get_rng(rng=rs, run_id=2505, logger=smac.logger)
self.assertEqual(run_id, 2505)
self.assertIs(rng_1, rs)
