# Bayesian Optimization with GPopt Part 2 (save and resume)

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Two weeks ago, I presented GPopt: a Python package for Bayesian optimization. In particular, I’ve presented a way to **stop the optimizer and resume** it later by adding more iterations.

This week, I present a way to **save and resume**, that makes the optimizer’s data persistent. Behind this saving feature, are hidden Python shelves which are – sort of – hash tables on disk.

We start by installing packages necessary for the demo.

!pip install GPopt !pip install matplotlib==3.1.3

Import packages.

import GPopt as gp import numpy as np import matplotlib.pyplot as plt

Objective function to be minimized.

# branin def branin(x): x1 = x[0] x2 = x[1] term1 = (x2 - (5.1*x1**2)/(4*np.pi**2) + (5*x1)/np.pi - 6)**2 term2 = 10*(1-1/(8*np.pi))*np.cos(x1) return (term1 + term2 + 10)

Start the optimizer, and **save on disk after 25 iterations**.

print("Saving after 25 iterations") gp_opt3 = gp.GPOpt(objective_func=branin, lower_bound = np.array([-5, 0]), upper_bound = np.array([10, 15]), n_init=10, n_iter=25, save = "./save") # will create save.db in the current directory gp_opt3.optimize(verbose=1) print("current number of iterations:") print(gp_opt3.n_iter) gp_opt3.close_shelve() print("\n") print("current minimum:") print(gp_opt3.x_min) print(gp_opt3.y_min) plt.plot(gp_opt3.max_ei)

current number of iterations: 25 current minimum: [3.17337036 2.07962036] 0.4318831996378023

On this figure, we observe that there’s still room for advancement in the convergence of expected improvement (EI). We can **add more iterations to the procedure by loading the saved object**.

print("---------- \n") print("loading previously saved object") gp_optload = gp.GPOpt(objective_func=branin, lower_bound = np.array([-5, 0]), upper_bound = np.array([10, 15])) gp_optload.load(path="./save") # loading the saved object print("current number of iterations:") print(gp_optload.n_iter) gp_optload.optimize(verbose=2, n_more_iter=190, abs_tol=1e-4) # early stopping based on expected improvement print("current number of iterations:") print(gp_optload.n_iter) print("\n") print("current minimum:") print(gp_optload.x_min) print(gp_optload.y_min) plt.plot(gp_optload.max_ei)

current number of iterations: 51 current minimum: [9.44061279 2.48199463] 0.3991320518189241

Now that the **EI has effectively converged to 0**, we can stop the optimization procedure.

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