def from_dict(cls, d):
tsemo = super().from_dict(d)
ae = d["strategy_params"]["all_experiments"]
if ae is not None:
tsemo.all_experiments = DataSet.from_dict(ae)
return tsemo
def from_dict(cls, d):
snobfit = super().from_dict(d)
params = d["strategy_params"]["prev_param"]
if params is not None:
params[0] = (np.array(params[0][0]), params[0][1], np.array(params[0][2]))
params[1] = [DataSet.from_dict(p) for p in params[1]]
snobfit.prev_param = params
return snobfit
def from_dict(cls, d):
nm = super().from_dict(d)
prev_param = d["strategy_params"]["prev_param"]
if prev_param is not None:
nm.prev_param = [
unjsonify_dict(prev_param[0]),
DataSet.from_dict(prev_param[1]),
]
return nm
def from_dict(variable_dict):
ds = variable_dict["ds"]
ds = DataSet.from_dict(ds) if ds is not None else None
return CategoricalVariable(
name=variable_dict["name"],
description=variable_dict["description"],
levels=variable_dict["levels"],
descriptors=ds,
is_objective=variable_dict["is_objective"],
)
def from_dict(cls, d):
domain = Domain.from_dict(d["domain"])
experiment_params = d.get("experiment_params", {})
exp = cls(domain=domain, **experiment_params)
exp._data = DataSet.from_dict(d["data"])
for e in d["extras"]:
if type(e) == dict:
exp.extras.append(unjsonify_dict(e))
elif type(e) == list:
exp.extras.append(np.array(e))
else:
exp.extras.append(e)
return exp
def from_dict(cls, d):
dataset = d["experiment_params"]["dataset"]
d["experiment_params"]["dataset"] = DataSet.from_dict(dataset)
exp = super().from_dict(d)
exp.emulator.output_models = d["experiment_params"]["output_models"]
return exp
