gradient_free_optimizers.optimizers.smb_opt.acquisition_function.expected_improvement.ExpectedImprovement.calculate() - Code Metrics - Inspection of "refactor expected-improvement into separate module" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( e7a955...cd6747 )

by Simon

created 2022-06-16 10:30 UTC

ExpectedImprovement.calculate() A

↳ Parent: gradient_free_optimizers.optimizers.smb_opt.acquisition_function.expected_improvement

Complexity

Conditions

Size

Total Lines	19
Code Lines	12

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	12
dl	0
loc	19
rs	9.8
c	0
b	0
f	0
cc	1
nop	3

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License


import numpy as np
from scipy.stats import norm


def normalize(array):
    num = array - array.min()
    den = array.max() - array.min()

    if den == 0:
        return np.random.random_sample(array.shape)
    else:
        return ((num / den) + 0) / 1


class ExpectedImprovement:
    def __init__(self, surrogate_model, position_l, xi):
        self.surrogate_model = surrogate_model
        self.position_l = position_l
        self.xi = xi

    def calculate(self, X_sample, Y_sample):
        mu, sigma = self.surrogate_model.predict(self.position_l, return_std=True)
        # TODO mu_sample = self.surrogate_model.predict(X_sample)
        mu = mu.reshape(-1, 1)
        sigma = sigma.reshape(-1, 1)

        # with normalization this is always 1
        Y_sample = normalize(np.array(Y_sample)).reshape(-1, 1)

        imp = mu - np.max(Y_sample) - self.xi
        Z = np.divide(imp, sigma, out=np.zeros_like(sigma), where=sigma != 0)

        exploit = imp * norm.cdf(Z)
        explore = sigma * norm.pdf(Z)

        aqu_func = exploit + explore
        aqu_func[sigma == 0.0] = 0.0

        return aqu_func[:, 0]


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5
6			import numpy as np
7			from scipy.stats import norm
8
9
10			def normalize(array):
11			num = array - array.min()
12			den = array.max() - array.min()
13
14			if den == 0:
15			return np.random.random_sample(array.shape)
16			else:
17			return ((num / den) + 0) / 1
18
19
20			class ExpectedImprovement:
21			def __init__(self, surrogate_model, position_l, xi):
22			self.surrogate_model = surrogate_model
23			self.position_l = position_l
24			self.xi = xi
25
26			def calculate(self, X_sample, Y_sample):
27			mu, sigma = self.surrogate_model.predict(self.position_l, return_std=True)
28			# TODO mu_sample = self.surrogate_model.predict(X_sample)
29			mu = mu.reshape(-1, 1)
30			sigma = sigma.reshape(-1, 1)
31
32			# with normalization this is always 1
33			Y_sample = normalize(np.array(Y_sample)).reshape(-1, 1)
34
35			imp = mu - np.max(Y_sample) - self.xi
36			Z = np.divide(imp, sigma, out=np.zeros_like(sigma), where=sigma != 0)
37
38			exploit = imp * norm.cdf(Z)
39			explore = sigma * norm.pdf(Z)
40
41			aqu_func = exploit + explore
42			aqu_func[sigma == 0.0] = 0.0
43
44			return aqu_func[:, 0]
45

SimonBlanke / Gradient-Free-Optimizers

Push — master ( e7a955...cd6747 )

ExpectedImprovement.calculate() A

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