in example_opt_root/scikit_optimizer.py [0:0]
def get_sk_dimensions(api_config, transform="normalize"):
"""Help routine to setup skopt search space in constructor.
Take api_config as argument so this can be static.
"""
# The ordering of iteration prob makes no difference, but just to be
# safe and consistnent with space.py, I will make sorted.
param_list = sorted(api_config.keys())
sk_dims = []
round_to_values = {}
for param_name in param_list:
param_config = api_config[param_name]
param_type = param_config["type"]
param_space = param_config.get("space", None)
param_range = param_config.get("range", None)
param_values = param_config.get("values", None)
# Some setup for case that whitelist of values is provided:
values_only_type = param_type in ("cat", "ordinal")
if (param_values is not None) and (not values_only_type):
assert param_range is None
param_values = np.unique(param_values)
param_range = (param_values[0], param_values[-1])
round_to_values[param_name] = interp1d(
param_values, param_values, kind="nearest", fill_value="extrapolate"
)
if param_type == "int":
# Integer space in sklearn does not support any warping => Need
# to leave the warping as linear in skopt.
sk_dims.append(Integer(param_range[0], param_range[-1], transform=transform, name=param_name))
elif param_type == "bool":
assert param_range is None
assert param_values is None
sk_dims.append(Integer(0, 1, transform=transform, name=param_name))
elif param_type in ("cat", "ordinal"):
assert param_range is None
# Leave x-form to one-hot as per skopt default
sk_dims.append(Categorical(param_values, name=param_name))
elif param_type == "real":
# Skopt doesn't support all our warpings, so need to pick
# closest substitute it does support.
prior = "log-uniform" if param_space in ("log", "logit") else "uniform"
sk_dims.append(Real(param_range[0], param_range[-1], prior=prior, transform=transform, name=param_name))
else:
assert False, "type %s not handled in API" % param_type
return sk_dims, round_to_values