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