print(__doc__)

import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
from skopt.learning import ExtraTreesRegressor
from skopt import Optimizer
from skopt.plots import plot_gaussian_process

"""
Explanation: Exploration vs exploitation
Sigurd Carlen, September 2019.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
We can control how much the acqusition function favors exploration and
exploitation by tweaking the two parameters kappa and xi. Higher values
means more exploration and less exploitation and vice versa with low values.
kappa is only used if acq_func is set to "LCB". xi is used when acq_func is
"EI" or "PI". By default the acqusition function is set to "gp_hedge" which
chooses the best of these three. Therefore I recommend not using gp_hedge
when tweaking exploration/exploitation, but instead choosing "LCB",
"EI" or "PI".
The way to pass kappa and xi to the optimizer is to use the named argument
"acq_func_kwargs". This is a dict of extra arguments for the aqcuisition
function.
If you want opt.ask() to give a new acquisition value immediately after
tweaking kappa or xi call opt.update_next(). This ensures that the next
value is updated with the new acquisition parameters.
This example uses :class:plots.plot_gaussian_process which is available
since version 0.8.
End of explanation
"""


noise_level = 0.1


# Our 1D toy problem, this is the function we are trying to
# minimize
def objective(x, noise_level=noise_level):
    return np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) +\
           np.random.randn() * noise_level


def objective_wo_noise(x):
    return objective(x, noise_level=0)

opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_optimizer="sampling")

"""
Explanation: Toy example
First we define our objective like in the ask-and-tell example notebook and
define a plotting function. We do however only use on initial random point.
All points after the first one is therefore chosen by the acquisition
function.
End of explanation
"""


plot_args = {"objective": objective_wo_noise,
             "noise_level": noise_level, "show_legend": True,
             "show_title": True, "show_next_point": False,
             "show_acq_func": True}

"""
Explanation: Plotting parameters
End of explanation
"""


for i in range(30):
    next_x = opt.ask()
    f_val = objective(next_x)
    opt.tell(next_x, f_val)
# The same output could be created with opt.run(objective, n_iter=30)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

"""
Explanation: We run a an optimization loop with standard settings
End of explanation
"""


acq_func_kwargs = {"xi": 10000, "kappa": 10000}

opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_optimizer="sampling",
                acq_func_kwargs=acq_func_kwargs)

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

"""
Explanation: We see that some minima is found and "exploited"
Now lets try to set kappa and xi using'to other values and
pass it to the optimizer:
End of explanation
"""


opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_func="LCB", acq_optimizer="sampling",
                acq_func_kwargs=acq_func_kwargs)

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

"""
Explanation: We see that the points are more random now.
This works both for kappa when using acq_func="LCB":
End of explanation
"""


opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_func="PI", acq_optimizer="sampling",
                acq_func_kwargs=acq_func_kwargs)

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

"""
Explanation: And for xi when using acq_func="EI": or acq_func="PI":
End of explanation
"""


acq_func_kwargs = {"xi": 0.000001, "kappa": 0.001}

opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_func="LCB", acq_optimizer="sampling",
                acq_func_kwargs=acq_func_kwargs)

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_func="EI", acq_optimizer="sampling",
                acq_func_kwargs=acq_func_kwargs)

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_func="PI", acq_optimizer="sampling",
                acq_func_kwargs=acq_func_kwargs)

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

"""
Explanation: We can also favor exploitaton:
End of explanation
"""


acq_func_kwargs = {"xi": -1000000000000}

opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_func="PI", acq_optimizer="sampling",
                acq_func_kwargs=acq_func_kwargs)

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_func="EI", acq_optimizer="sampling",
                acq_func_kwargs=acq_func_kwargs)

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

"""
Explanation: Note that negative values does not work with the "PI"-acquisition function
but works with "EI":
End of explanation
"""


acq_func_kwargs = {"kappa": 0}

opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                acq_func="LCB", acq_optimizer="sampling",
                acq_func_kwargs=acq_func_kwargs)

opt.acq_func_kwargs

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

acq_func_kwargs = {"kappa": 100000}

opt.acq_func_kwargs = acq_func_kwargs
opt.update_next()

opt.run(objective, n_iter=20)
_ = plot_gaussian_process(opt.get_result(), **plot_args)

"""
Explanation: Changing kappa and xi on the go
If we want to change kappa or ki at any point during our optimization
process we just replace opt.acq_func_kwargs. Remember to call
opt.update_next() after the change, in order for next point to be
recalculated.
End of explanation
"""


