GPyOpt - how to run a physical experiment?

Question

I'm trying to do some physical experiments to find a formulation that optimizes some parameters. By physical experiments I mean I have a chemistry bench, I'm mixing stuff together, then measuring the properties of that formulation. Historically I've used traditional DOEs, but I need to speed up my time to getting to the ideal formulation. I'm aware of simplex optimization, but I'm interested in trying out Bayesian optimization. I found GPyOpt which claims (even in the SO Tag description) to support physical experiments. However, it's not clear how to enable this kind of behavior.

One thing I've tried is to collect user input via input, and I suppose I could pickle off the optimizer and function, but this feels kludgy. In the example code below, I use the function from the GPyOpt example but I have to type in the actual value.

from GPyOpt.methods import BayesianOptimization
import numpy as np


# --- Define your problem
def f(x):
    return (6*x-2)**2*np.sin(12*x-4)


def g(x):
    print(f(x))
    return float(input("Result?"))


domain = [{'name': 'var_1', 'type': 'continuous', 'domain': (0, 1)}]

myBopt = BayesianOptimization(f=g,
                              domain=domain,
                              X=np.array([[0.745], [0.766], [0], [1], [0.5]]),
                              Y=np.array([[f(0.745)], [f(0.766)], [f(0)], [f(1)], [f(0.5)]]),
                              acquisition_type='LCB')
myBopt.run_optimization(max_iter=15, eps=0.001)

So, my questions is, what is the intended way of using GPyOpt for physical experimentation?

Answer 1

A few things.

First, set f=None. Note that this has the side-effect of causing the BO object to ignore the maximize=True, if you happen to be using this.

Second, rather than use run_optimization, you want suggest_next_locations. The former runs the entire optimization, whereas the latter just runs a single iteration. This method returns a vector with parameter combinations ("locations") to go test in the lab.

Third, you'll need to make some decisions regarding batch size. The number of combinations/locations that you get are controlled by the batch_size parameter that you use to initialize the BayesianOptimization object. Choice of acquisition function is important here, because some are closely tied to a batch_size of 1. If you need larger batches, then you'll need to read the docs for combinations suitable to your situation (e.g. acquisition_type=EI and evaluator_type=local_penalization.

Fourth, you'll need to explicitly manage the data between iterations. There are at least two ways to approach this. One is to pickle the BO object and add more data to it. An alternative that I think is more elegant is to instead create a completely fresh BO object each time. When instantiating it, you concatenate the new data to the old data, and just run a single iteration on the whole set (again, using suggest_next_locations). This might be kind of insane if you were using BO to optimize a function in silico, but considering how slow the chemistry steps are likely to be, this might be cleanest (and easier to make mid-course corrections.)

Hope this helps!