def observe()

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

• 5 lines of code
• 7 McCabe index (conditional complexity)

    def observe(self, X, y):
        """Send an observation of a suggestion back to the optimizer.

        Parameters
        ----------
        X : list of dict-like
            Places where the objective function has already been evaluated.
            Each suggestion is a dictionary where each key corresponds to a
            parameter being optimized.
        y : array-like, shape (n,)
            Corresponding values where objective has been evaluated
        """
        # Supposedly skopt can handle blocks, but not sure about interface for
        # that. Just do loop to be safe for now.
        for xx, yy in zip(X, y):
            # skopt needs lists instead of dicts
            xx = [xx[dim_name] for dim_name in self.dimensions_list]
            # Just ignore, any inf observations we got, unclear if right thing
            if np.isfinite(yy):
                self.skopt.tell(xx, yy)