def _update_archive_and_bests()

in nevergrad/optimization/base.py [0:0]

• 27 lines of code
• 18 McCabe index (conditional complexity)

    def _update_archive_and_bests(self, candidate: p.Parameter, loss: tp.FloatLoss) -> None:
        x = candidate.get_standardized_data(reference=self.parametrization)
        if not isinstance(
            loss, (Real, float)
        ):  # using "float" along "Real" because mypy does not understand "Real" for now Issue #3186
            raise TypeError(
                f'"tell" method only supports float values but the passed loss was: {loss} (type: {type(loss)}.'
            )
        if np.isnan(loss) or loss == np.inf:
            self._warn(f"Updating fitness with {loss} value", errors.BadLossWarning)
        mvalue: tp.Optional[utils.MultiValue] = None
        if x not in self.archive:
            self.archive[x] = utils.MultiValue(candidate, loss, reference=self.parametrization)
        else:
            mvalue = self.archive[x]
            mvalue.add_evaluation(loss)
            # both parameters should be non-None
            if mvalue.parameter.loss > candidate.loss:  # type: ignore
                mvalue.parameter = candidate  # keep best candidate
        # update current best records
        # this may have to be improved if we want to keep more kinds of best losss

        for name in self.current_bests:
            if mvalue is self.current_bests[name]:  # reboot
                best = min(self.archive.values(), key=lambda mv, n=name: mv.get_estimation(n))  # type: ignore
                # rebuild best point may change, and which value did not track the updated value anyway
                self.current_bests[name] = best
            else:
                if self.archive[x].get_estimation(name) <= self.current_bests[name].get_estimation(name):
                    self.current_bests[name] = self.archive[x]
                # deactivated checks
                # if not (np.isnan(loss) or loss == np.inf):
                #     if not self.current_bests[name].x in self.archive:
                #         bval = self.current_bests[name].get_estimation(name)
                #         avals = (min(v.get_estimation(name) for v in self.archive.values()),
                #                  max(v.get_estimation(name) for v in self.archive.values()))
                #         raise RuntimeError(f"Best value should exist in the archive at num_tell={self.num_tell})\n"
                #                            f"Best value is {bval} and archive is within range {avals} for {name}")
        if self.pruning is not None:
            self.archive = self.pruning(self.archive)