def suggest()

in bayesmark/builtin_opt/pysot_optimizer.py [0:0]

• 23 lines of code
• 4 McCabe index (conditional complexity)

    def suggest(self, n_suggestions=1):
        """Get a suggestion from the optimizer.

        Parameters
        ----------
        n_suggestions : int
            Desired number of parallel suggestions in the output

        Returns
        -------
        next_guess : list of dict
            List of `n_suggestions` suggestions to evaluate the objective
            function. Each suggestion is a dictionary where each key
            corresponds to a parameter being optimized.
        """

        if self.batch_size is None:  # First call to suggest
            self.batch_size = n_suggestions
            self.start(self.max_evals)

        # Set the tolerances pretending like we are running batch
        d, p = float(self.opt.dim), float(n_suggestions)
        self.strategy.failtol = p * int(max(np.ceil(d / p), np.ceil(4 / p)))

        # Now we can make suggestions
        x_w = []
        self.proposals = []
        for _ in range(n_suggestions):
            proposal = self.strategy.propose_action()
            record = EvalRecord(proposal.args, status="pending")
            proposal.record = record
            proposal.accept()  # This triggers all the callbacks

            # It is possible that pySOT proposes a previously evaluated point
            # when all variables are integers, so we just abort in this case
            # since we have likely converged anyway. See PySOT issue #30.
            x = list(proposal.record.params)  # From tuple to list
            x_unwarped, = self.space_x.unwarp(x)
            if x_unwarped in self.history:
                warnings.warn("pySOT proposed the same point twice")
                self.start(self.max_evals)
                return self.suggest(n_suggestions=n_suggestions)

            # NOTE: Append unwarped to avoid rounding issues
            self.history.append(copy(x_unwarped))
            self.proposals.append(proposal)
            x_w.append(copy(x_unwarped))

        return x_w