def _convert_photon_to_smac_param(hyperparam: object, name: str):
"""
Helper function: Convert PHOTON hyperparameter to SMAC hyperparameter.
Parameters
----------
* `hyperparam` [object]:
One of photonai.optimization.hyperparameters.
* `name` [str]
Name of hyperparameter.
"""
if not hyperparam:
return None
if isinstance(hyperparam, PhotonCategorical):
return CategoricalHyperparameter(name, hyperparam.values)
elif isinstance(hyperparam, list):
return CategoricalHyperparameter(name, hyperparam)
elif isinstance(hyperparam, FloatRange):
if hyperparam.range_type == 'linspace':
return UniformFloatHyperparameter(name, hyperparam.start, hyperparam.stop)
elif hyperparam.range_type == 'logspace':
raise NotImplementedError("Logspace in your float hyperparameter is not implemented in SMAC.")
else:
return UniformFloatHyperparameter(name, hyperparam.start, hyperparam.stop)
elif isinstance(hyperparam, IntegerRange):
return UniformIntegerHyperparameter(name, hyperparam.start, hyperparam.stop)
def _get_acm_cs(self):
"""
returns a configuration space
designed for querying ~smac.optimizer.smbo._component_builder
Returns
-------
ConfigurationSpace
"""
cs = ConfigurationSpace()
cs.seed(self.rng.randint(0,2**20))
model = CategoricalHyperparameter("model", choices=("RF", "GP"))
num_trees = Constant("num_trees", value=10)
bootstrap = CategoricalHyperparameter("do_bootstrapping", choices=(True, False), default_value=True)
ratio_features = CategoricalHyperparameter("ratio_features", choices=(3 / 6, 4 / 6, 5 / 6, 1), default_value=1)
min_split = UniformIntegerHyperparameter("min_samples_to_split", lower=1, upper=10, default_value=2)
min_leaves = UniformIntegerHyperparameter("min_samples_in_leaf", lower=1, upper=10, default_value=1)
cs.add_hyperparameters([model, num_trees, bootstrap, ratio_features, min_split, min_leaves])
inc_num_trees = InCondition(num_trees, model, ["RF"])
inc_bootstrap = InCondition(bootstrap, model, ["RF"])
inc_ratio_features = InCondition(ratio_features, model, ["RF"])
inc_min_split = InCondition(min_split, model, ["RF"])
inc_min_leavs = InCondition(min_leaves, model, ["RF"])
cs.add_conditions([inc_num_trees, inc_bootstrap, inc_ratio_features, inc_min_split, inc_min_leavs])
acq = CategoricalHyperparameter("acq_func", choices=("EI", "LCB", "PI", "LogEI"))
par_ei = UniformFloatHyperparameter("par_ei", lower=-10, upper=10)
par_pi = UniformFloatHyperparameter("par_pi", lower=-10, upper=10)
par_logei = UniformFloatHyperparameter("par_logei", lower=0.001, upper=100, log=True)
par_lcb = UniformFloatHyperparameter("par_lcb", lower=0.0001, upper=0.9999)
cs.add_hyperparameters([acq, par_ei, par_pi, par_logei, par_lcb])
inc_par_ei = InCondition(par_ei, acq, ["EI"])
inc_par_pi = InCondition(par_pi, acq, ["PI"])
inc_par_logei = InCondition(par_logei, acq, ["LogEI"])
inc_par_lcb = InCondition(par_lcb, acq, ["LCB"])
cs.add_conditions([inc_par_ei, inc_par_pi, inc_par_logei, inc_par_lcb])
return cs
def setUp(self):
logging.basicConfig()
self.logger = logging.getLogger(self.__module__ + "." + self.__class__.__name__)
self.logger.setLevel(logging.DEBUG)
self.value = 0
self.cs = ConfigurationSpace()
self.cs.add_hyperparameters([
UniformFloatHyperparameter('param_a', -0.2, 1.77, 1.1),
UniformIntegerHyperparameter('param_b', -3, 10, 1),
Constant('param_c', 'value'),
CategoricalHyperparameter('ambigous_categorical', choices=['True', True, 5]), # True is ambigous here
])
self.test_config = Configuration(self.cs, {'param_a': 0.5,
'param_b': 1,
'param_c': 'value',
'ambigous_categorical': 5})
def _convert_PHOTON_to_smac_space(self, hyperparam: object, name: str):
if not hyperparam:
return None
self.hyperparameter_list.append(name)
if isinstance(hyperparam, PhotonCategorical):
return CategoricalHyperparameter(choices=hyperparam.values, name=name)
elif isinstance(hyperparam, list):
return CategoricalHyperparameter(choices=hyperparam, name=name)
elif isinstance(hyperparam, FloatRange):
return UniformFloatHyperparameter(
lower=hyperparam.start, upper=hyperparam.stop, name=name
)
elif isinstance(hyperparam, IntegerRange):
return UniformIntegerHyperparameter(
lower=hyperparam.start, upper=hyperparam.stop, name=name
)
elif isinstance(hyperparam, BooleanSwitch):
return CategoricalHyperparameter(choices=["true", "false"], name=name)
def _convert_photon_to_smac_space(hyperparam: object, name: str):
"""
Helper function: Convert PHOTON hyperparams to smac params.
"""
if not hyperparam:
return None
if isinstance(hyperparam, PhotonCategorical):
return CategoricalHyperparameter(name, hyperparam.values)
elif isinstance(hyperparam, list):
return CategoricalHyperparameter(name, hyperparam)
elif isinstance(hyperparam, FloatRange):
if hyperparam.range_type == 'linspace':
return UniformFloatHyperparameter(name, hyperparam.start,
hyperparam.stop)
elif hyperparam.range_type == 'logspace':
raise NotImplementedError(
"Logspace in your float hyperparameter is not implemented in SMAC."
)
else:
return UniformFloatHyperparameter(name, hyperparam.start,
hyperparam.stop)
elif isinstance(hyperparam, IntegerRange):
return UniformIntegerHyperparameter(name, hyperparam.start,
hyperparam.stop)