"""Example of how to use this bayesian optimization package."""

import sys

sys.path.append("./")

from bayes_opt import BayesianOptimization

# Lets find the maximum of a simple quadratic function of two variables

# We create the bayes_opt object and pass the function to be maximized

# together with the parameters names and their bounds.

bo = BayesianOptimization(lambda x, y: -x ** 2 - (y - 1) ** 2 + 1,

{'x': (-4, 4), 'y': (-3, 3)})

# One of the things we can do with this object is pass points

# which we want the algorithm to probe. A dictionary with the

# parameters names and a list of values to include in the search

# must be given.

bo.explore({'x': [-1, 3], 'y': [-2, 2]})

# Additionally, if we have any prior knowledge of the behaviour of

# the target function (even if not totally accurate) we can also

# tell that to the optimizer.

# Here we pass a dictionary with 'target' and parameter names as keys and a

# list of corresponding values

bo.initialize(

{

'target': [-1, -1],

'x': [1, 1],

'y': [0, 2]

}

)

# Once we are satisfied with the initialization conditions

# we let the algorithm do its magic by calling the maximize()

# method.

bo.maximize(init_points=5, n_iter=15, kappa=2)

# The output values can be accessed with self.res

print((bo.res['max']))

# If we are not satisfied with the current results we can pickup from

# where we left, maybe pass some more exploration points to the algorithm

# change any parameters we may choose, and the let it run again.

bo.explore({'x': [0.6], 'y': [-0.23]})

# Making changes to the gaussian process can impact the algorithm

# dramatically.

gp_params = {'kernel': None,

'alpha': 1e-5}

# Run it again with different acquisition function

bo.maximize(n_iter=5, acq='ei', **gp_params)

# Finally, we take a look at the final results.

print((bo.res['max']))

print((bo.res['all']))