Apply Bayesian Optimization to SVC parameters.

def optimize_svc(data, targets):
    """Apply Bayesian Optimization to SVC parameters."""
    def svc_crossval(expC, expGamma):
        """Wrapper of SVC cross validation.

        Notice how we transform between regular and log scale. While this
        is not technically necessary, it greatly improves the performance
        of the optimizer.
        """
        C = 10 ** expC
        gamma = 10 ** expGamma
        return svc_cv(C=C, gamma=gamma, data=data, targets=targets)

    optimizer = BayesianOptimization(
        f=svc_crossval,
        pbounds={"expC": (-3, 2), "expGamma": (-4, -1)},
        random_state=1234,
        verbose=2
    )
    optimizer.maximize(n_iter=10)

    print("Final result:", optimizer.max)