f00a6eb3828b0d1861cef489a172c0dbb465e323,optuna/integration/botorch.py,BoTorchSampler,sample_relative,#BoTorchSampler#Any#Any#Any#,450

Before Change

        values = numpy.empty((n_trials, n_objectives), dtype=numpy.float64)
        params = numpy.empty((n_trials, trans.bounds.shape[0]), dtype=numpy.float64)
        if self._constraints_func is not None:
            n_constraints = len(next(iter(self._trial_constraints.values())))
            con = numpy.empty((n_trials, n_constraints), dtype=numpy.float64)
        else:
            con = None

After Change

                // `constraints` can be `None` if `constraints_func` raised an error. The best we
                // can do is to fill with NaN.
                if constraints is not None:
                    n_constraints = len(constraints)
                    if con is None:
                        con = numpy.full((n_trials, n_constraints), numpy.nan, dtype=numpy.float64)

                    con[trial_idx] = constraints

Instances