13  Handling Noise

This chapter demonstrates how noisy functions can be handled by Spot and how noise can be simulated, i.e., added to the objective function.

13.1 Example: Spot and the Noisy Sphere Function

import numpy as np
from math import inf
from spotpython.fun.objectivefunctions import analytical
from spotpython.spot import spot
import matplotlib.pyplot as plt
from spotpython.utils.init import fun_control_init, get_spot_tensorboard_path
from spotpython.utils.init import fun_control_init, design_control_init, surrogate_control_init

PREFIX = "08"

13.1.1 The Objective Function: Noisy Sphere

The spotpython package provides several classes of objective functions, which return a one-dimensional output \(y=f(x)\) for a given input \(x\) (independent variable). Several objective functions allow one- or multidimensional input, some also combinations of real-valued and categorial input values.

An objective function is considered as “analytical” if it can be described by a closed mathematical formula, e.g., \[ f(x, y) = x^2 + y^2. \]

To simulate measurement errors, adding artificial noise to the function value \(y\) is a common practice, e.g.,:

\[ f(x, y) = x^2 + y^2 + \epsilon. \]

Usually, noise is assumed to be normally distributed with mean \(\mu=0\) and standard deviation \(\sigma\). spotpython uses numpy’s scale parameter, which specifies the standard deviation (spread or “width”) of the distribution is used. This must be a non-negative value, see https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html.

Example: The sphere function without noise

The default setting does not use any noise.

from spotpython.fun.objectivefunctions import analytical
fun = analytical().fun_sphere
x = np.linspace(-1,1,100).reshape(-1,1)
y = fun(x)
plt.figure()
plt.plot(x,y, "k")
plt.show()

Example: The sphere function with noise

Noise can be added to the sphere function as follows:

from spotpython.fun.objectivefunctions import analytical
fun = analytical(seed=123, sigma=0.02).fun_sphere
x = np.linspace(-1,1,100).reshape(-1,1)
y = fun(x)
plt.figure()
plt.plot(x,y, "k")
plt.show()

13.1.2 Reproducibility: Noise Generation and Seed Handling

spotpython provides two mechanisms for generating random noise:

1. The seed is initialized once, i.e., when the objective function is instantiated. This can be done using the following call: fun = analytical(sigma=0.02, seed=123).fun_sphere.
2. The seed is set every time the objective function is called. This can be done using the following call: y = fun(x, sigma=0.02, seed=123).

These two different ways lead to different results as explained in the following tables:

Example: Noise added to the sphere function

Since sigma is set to 0.02, noise is added to the function:

from spotpython.fun.objectivefunctions import analytical
fun = analytical(sigma=0.02, seed=123).fun_sphere
x = np.array([1]).reshape(-1,1)
for i in range(3):
    print(f"{i}: {fun(x)}")

0: [0.98021757]
1: [0.99264427]
2: [1.02575851]

The seed is set once. Every call to fun() results in a different value. The whole experiment can be repeated, the initial seed is used to generate the same sequence as shown below:

Example: Noise added to the sphere function

Since sigma is set to 0.02, noise is added to the function:

from spotpython.fun.objectivefunctions import analytical
fun = analytical(sigma=0.02, seed=123).fun_sphere
x = np.array([1]).reshape(-1,1)
for i in range(3):
    print(f"{i}: {fun(x)}")

0: [0.98021757]
1: [0.99264427]
2: [1.02575851]

If spotpython is used as a hyperparameter tuner, it is important that only one realization of the noise function is optimized. This behaviour can be accomplished by passing the same seed via the dictionary fun_control to every call of the objective function fun as shown below:

Example: The same noise added to the sphere function

Since sigma is set to 0.02, noise is added to the function:

from spotpython.fun.objectivefunctions import analytical
fun = analytical().fun_sphere
fun_control = fun_control_init(
    PREFIX=PREFIX,
    sigma=0.02)
y = fun(x, fun_control=fun_control)
x = np.array([1]).reshape(-1,1)
for i in range(3):
    print(f"{i}: {fun(x)}")

0: [0.98021757]
1: [0.98021757]
2: [0.98021757]

13.2 spotpython’s Noise Handling Approaches

The following setting will be used for the next steps:

fun = analytical().fun_sphere
fun_control = fun_control_init(
    PREFIX=PREFIX,
    sigma=0.02,
)

spotpython is adopted as follows to cope with noisy functions:

1. fun_repeats is set to a value larger than 1 (here: 2)
2. noise is set to true. Therefore, a nugget (Lambda) term is added to the correlation matrix
3. init size (of the design_control dictionary) is set to a value larger than 1 (here: 3)

spot_1_noisy = spot.Spot(fun=fun,
                   fun_control=fun_control_init(
                                    lower = np.array([-1]),
                                    upper = np.array([1]),
                                    fun_evals = 20,
                                    fun_repeats = 2,
                                    noise = True,
                                    show_models=True),
                   design_control=design_control_init(init_size=3, repeats=2),
                   surrogate_control=surrogate_control_init(noise=True))

spot_1_noisy.run()

spotpython tuning: 0.034752873669989026 [####------] 40.00% 

spotpython tuning: 0.03230817945928789 [#####-----] 50.00% 

spotpython tuning: 0.015578418800855254 [######----] 60.00% 

spotpython tuning: 0.0009550994714026289 [#######---] 70.00% 

spotpython tuning: 5.561590542963861e-05 [########--] 80.00% 

spotpython tuning: 7.181090066713707e-07 [#########-] 90.00% 

spotpython tuning: 4.5254143126895086e-07 [##########] 100.00% Done...

13.3 Print the Results

spot_1_noisy.print_results()

min y: 4.5254143126895086e-07
min mean y: 4.5254143126895086e-07
x0: 0.0006727119972684825

[['x0', 0.0006727119972684825]]

spot_1_noisy.plot_progress(log_y=False,
    filename="./figures/" + PREFIX + "_progress.png")

Progress plot. Black dots denote results from the initial design. Red dots illustrate the improvement found by the surrogate model based optimization.

13.4 Noise and Surrogates: The Nugget Effect

13.4.1 The Noisy Sphere

13.4.1.1 The Data

• We prepare some data first:

import numpy as np
import spotpython
from spotpython.fun.objectivefunctions import analytical
from spotpython.spot import spot
from spotpython.design.spacefilling import SpaceFilling
from spotpython.build.kriging import Kriging
import matplotlib.pyplot as plt

gen = SpaceFilling(1)
rng = np.random.RandomState(1)
lower = np.array([-10])
upper = np.array([10])
fun = analytical().fun_sphere
fun_control = fun_control_init(
    PREFIX=PREFIX,
    sigma=4)
X = gen.scipy_lhd(10, lower=lower, upper = upper)
y = fun(X, fun_control=fun_control)
X_train = X.reshape(-1,1)
y_train = y

• A surrogate without nugget is fitted to these data:

S = Kriging(name='kriging',
            n_theta=1,
            noise=False)
S.fit(X_train, y_train)

X_axis = np.linspace(start=-13, stop=13, num=1000).reshape(-1, 1)
mean_prediction, std_prediction, ei = S.predict(X_axis, return_val="all")

plt.scatter(X_train, y_train, label="Observations")
plt.plot(X_axis, mean_prediction, label="mue")
plt.legend()
plt.xlabel("$x$")
plt.ylabel("$f(x)$")
_ = plt.title("Sphere: Gaussian process regression on noisy dataset")

• In comparison to the surrogate without nugget, we fit a surrogate with nugget to the data:

S_nug = Kriging(name='kriging',
            n_theta=1,
            noise=True)
S_nug.fit(X_train, y_train)
X_axis = np.linspace(start=-13, stop=13, num=1000).reshape(-1, 1)
mean_prediction, std_prediction, ei = S_nug.predict(X_axis, return_val="all")
plt.scatter(X_train, y_train, label="Observations")
plt.plot(X_axis, mean_prediction, label="mue")
plt.legend()
plt.xlabel("$x$")
plt.ylabel("$f(x)$")
_ = plt.title("Sphere: Gaussian process regression with nugget on noisy dataset")

• The value of the nugget term can be extracted from the model as follows:

S.Lambda

S_nug.Lambda

0.0005592051705322895

• We see:
  • the first model S has no nugget,
  • whereas the second model has a nugget value (Lambda) larger than zero.

13.5 Exercises

13.5.1 Noisy fun_cubed

• Analyse the effect of noise on the fun_cubed function with the following settings:

fun = analytical().fun_cubed
fun_control = fun_control_init(
    sigma=10)
lower = np.array([-10])
upper = np.array([10])

13.5.2 fun_runge

• Analyse the effect of noise on the fun_runge function with the following settings:

lower = np.array([-10])
upper = np.array([10])
fun = analytical().fun_runge
fun_control = fun_control_init(
    sigma=0.25)

13.5.3 fun_forrester

• Analyse the effect of noise on the fun_forrester function with the following settings:

lower = np.array([0])
upper = np.array([1])
fun = analytical().fun_forrester
fun_control = fun_control_init(
    sigma=5)

13.5.4 fun_xsin

• Analyse the effect of noise on the fun_xsin function with the following settings:

lower = np.array([-1.])
upper = np.array([1.])
fun = analytical().fun_xsin
fun_control = fun_control_init(    
    sigma=0.5)