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find_best_hyperpara()   A

↳ Parent: hyperactive.extensions.meta_learn.predictor

Complexity

Conditions 1

Size

Total Lines 10
Code Lines 7

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
cc 1
eloc 7
nop 2
dl 0
loc 10
rs 10
c 0
b 0
f 0
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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 os

            


            
                                                                                                            
                            
            
                                    
            
            

                
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import numpy as np

            


            
                                                                                                            
                            
            
                                    
            
            

                
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from sklearn.externals import joblib

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def find_best_hyperpara(features, scores):

            


            
                                                                        
                            
            
                                    
            
            

                
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    N_best_features = 1

            


            
                                                                        
                            
            
                                    
            
            

                
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    scores = np.array(scores)

            


            
                                                                        
                            
            
                                    
            
            

                
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    index_best_scores = list(scores.argsort()[-N_best_features:][::-1])

            


            
                                                                        
                            
            
                                    
            
            

                
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    best_score = scores[index_best_scores][0]

            


            
                                                                        
                            
            
                                    
            
            

                
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    best_features = features.iloc[index_best_scores]

            


            
                                                                        
                            
            
                                    
            
            

                
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    return best_features, best_score

            


            
                                                                                                            
                            
            
                                    
            
            

                
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class Predictor:

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    def __init__(self):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        pass

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    def search(self, X_test):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        best_para, best_score = self._predict(X_test)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        return best_para, best_score

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    def load_model(self, path):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        if os.path.isfile(path):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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            self.meta_reg = joblib.load(path)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        else:

            


            
                                                                                                            
                            
            
                                    
            
            

                
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            print("File at path", path, "not found")

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    def _predict(self, X_test):

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        score_pred = self.meta_reg.predict(X_test)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        best_features, best_score = find_best_hyperpara(X_test, score_pred)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        return best_features, best_score

            


            
                                                        
            
                                    
            
            

                
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