def test_domain(self):
ps = ParamSpace()
ps.add_value("value", True)
domain = ps.domain("value")
self.assertIn("domain", domain)
self.assertIn("dtype", domain)
self.assertEqual(DTypes.CATEGORICAL.value, domain["dtype"])
ps.add_list("bool", [True, False, True])
domain = ps.domain("bool")
self.assertIn("domain", domain)
self.assertIn("dtype", domain)
self.assertEqual(DTypes.CATEGORICAL.value, domain["dtype"])
self.assertListEqual([True, False], domain["domain"])
ps.add_range("bounds", 0, 10, dtype=float)
domain = ps.domain("bounds")
self.assertIn("domain", domain)
self.assertIn("dtype", domain)
self.assertIn("prior", domain)
self.assertEqual("float", domain["dtype"])
self.assertEqual("uniform", domain["prior"])
ps.add_random("random", 0, 10, prior="log-uniform", dtype=float)
domain = ps.domain("bounds")
self.assertIn("domain", domain)
self.assertIn("dtype", domain)
self.assertIn("prior", domain)
self.assertEqual("float", domain["dtype"])
self.assertEqual("uniform", domain["prior"])
def params_to_skopt(param_space: ParamSpace):
""" Converts a parameter space to a list of Dimention objects that can be used with
a skopt Optimizer.
A skopt Optimizer only receives 3 types of Dimensions: Categorical, Real, or Integer
we convert parameters from our parameter space into one of those 3 types. Note that we only
convert parameters that have either bounds or with a categorical domain with more than 1 value.
If we have constant values in our parameter space, these don't need to be optimized anyway.
Another function is provided to convert skopt output values back into a dictionary with
a full configuration according to the parameter space (@see values_to_params).
Args:
param_space: a ParameterSpace where we can get the domain of each parameter
Returns:
a list of Dimension that can be passed to a skopt Optimizer
"""
dimensions = []
for param_name in param_space.param_names():
domain_param = param_space.domain(param_name)
domain = domain_param["domain"]
dtype = DTypes.from_type(domain_param["dtype"])
if len(domain) > 1:
if dtype == DTypes.INT:
low = min(domain)
high = max(domain)
dimensions.append(Integer(low, high, name=param_name))
elif dtype == DTypes.FLOAT:
low = min(domain)
high = max(domain)
prior = domain_param.get("prior", None)
dimensions.append(Real(low, high, prior=prior,
name=param_name))
elif dtype == DTypes.CATEGORICAL:
prior = domain_param.get("prior", None)
dimensions.append(
Categorical(domain,
prior,
transform="onehot",
name=param_name))
return dimensions