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ExpectedImprovement.calculate()   A
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↳ Parent: gradient_free_optimizers.optimizers.smb_opt.acquisition_function.expected_improvement

Complexity

Conditions 1

Size

Total Lines 19
Code Lines 12

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
eloc 12
dl 0
loc 19
rs 9.8
c 0
b 0
f 0
cc 1
nop 3
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# Author: Simon Blanke
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# Email: [email protected]
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# License: MIT License
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import numpy as np

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from scipy.stats import norm

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def normalize(array):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    num = array - array.min()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    den = array.max() - array.min()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    if den == 0:

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        return np.random.random_sample(array.shape)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    else:

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        return ((num / den) + 0) / 1

            


            
                                                                                                            
                            
            
                                    
            
            

                
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class ExpectedImprovement:

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    def __init__(self, surrogate_model, position_l, xi):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        self.surrogate_model = surrogate_model

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        self.position_l = position_l

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        self.xi = xi

            


            
                                                                                                            
                                                                
            
                                    
            
            

                
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    def calculate(self, X_sample, Y_sample):

            


            
                                                        
            
                                    
            
            

                
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        mu, sigma = self.surrogate_model.predict(self.position_l, return_std=True)

            


            
                                                        
            
                                    
            
            

                
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        # TODO mu_sample = self.surrogate_model.predict(X_sample)

            


            
                                                        
            
                                    
            
            

                
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        mu = mu.reshape(-1, 1)

            


            
                                                        
            
                                    
            
            

                
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        sigma = sigma.reshape(-1, 1)

            


            
                                                        
            
                                    
            
            

                
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        # with normalization this is always 1

            


            
                                                        
            
                                    
            
            

                
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        Y_sample = normalize(np.array(Y_sample)).reshape(-1, 1)

            


            
                                                        
            
                                    
            
            

                
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        imp = mu - np.max(Y_sample) - self.xi

            


            
                                                        
            
                                    
            
            

                
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        Z = np.divide(imp, sigma, out=np.zeros_like(sigma), where=sigma != 0)

            


            
                                                        
            
                                    
            
            

                
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        exploit = imp * norm.cdf(Z)

            


            
                                                        
            
                                    
            
            

                
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        explore = sigma * norm.pdf(Z)

            


            
                                                        
            
                                    
            
            

                
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        aqu_func = exploit + explore

            


            
                                                        
            
                                    
            
            

                
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        aqu_func[sigma == 0.0] = 0.0

            


            
                                                        
            
                                    
            
            

                
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        return aqu_func[:, 0]

            


            
                                                        
            
                                    
            
            

                
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