def _evaluate(self, X, out, *args, **kwargs):
# Convert X to a DataSet
X = DataSet(np.atleast_2d(X), columns=self.X_columns)
# Add in any fixed columns (i.e., values for cateogricals)
if self.fixed_variables is not None:
for key, value in self.fixed_variables.items():
X[key] = value
F = np.zeros([X.shape[0], self.n_obj])
for i in range(self.n_obj):
F[:, i] = self.models[i].predict(X)
# Negate objectives that are need to be maximized
for i, v in enumerate(self.domain.output_variables):
if v.maximize:
F[:, i] *= -1
out["F"] = F
# Add constraints if necessary
if self.domain.constraints:
constraint_res = [
X.eval(c.lhs, resolvers=[X]) for c in self.domain.constraints
]
out["G"] = [c.tolist()[0] for c in constraint_res]
def problem_wrapper(X):
X = DataSet(np.atleast_2d(X), columns=input_columns)
X[("strategy", "METADATA")] = "NSGAII"
result = self.experiment.run_experiments(X)
if self.domain.constraints:
constraint_res = [
X.eval(c.lhs, resolvers=[X]) for c in self.domain.constraints
]
constraint_res = [c.tolist()[0] for c in constraint_res]
return result[output_columns].to_numpy()[0,:], constraint_res
else:
return result[output_columns].to_numpy()[0,:]
def _evaluate(self, X, out, *args, **kwargs):
X = DataSet(np.atleast_2d(X), columns=self.input_columns)
X[("strategy", "METADATA")] = "NSGAII"
F = self.experiment.run_experiments(X)
F = F[self.output_columns].data_to_numpy()
# Negate objectives that need to be maximized
for i, v in enumerate(self.domain.output_variables):
if v.maximize:
F[:,i] *= -1
out["F"] = F
# Add constraints if necessary
if self.domain.constraints:
constraint_res = [
X.eval(c.lhs, resolvers=[X]) for c in self.domain.constraints
]
out["G"] = [c.tolist()[0] for c in constraint_res]
