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sklearn_example.model()   A
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↳ Parent: sklearn_example

Complexity

Conditions 1

Size

Total Lines 9
Code Lines 7

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
cc 1
eloc 7
nop 1
dl 0
loc 9
rs 10
c 0
b 0
f 0
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from sklearn.model_selection import cross_val_score
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from sklearn.ensemble import GradientBoostingRegressor
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from sklearn.datasets import load_diabetes
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from hyperactive import Hyperactive
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data = load_diabetes()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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X, y = data.data, data.target

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def model(opt):

            


            
                                                                        
                            
            
                                    
            
            

                
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    gbr = GradientBoostingRegressor(

            


            
                                                                        
                            
            
                                    
            
            

                
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        n_estimators=opt["n_estimators"],

            


            
                                                                        
                            
            
                                    
            
            

                
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        max_depth=opt["max_depth"],

            


            
                                                                        
                            
            
                                    
            
            

                
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        min_samples_split=opt["min_samples_split"],

            


            
                                                                        
                            
            
                                    
            
            

                
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    )

            


            
                                                                        
                            
            
                                    
            
            

                
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    scores = cross_val_score(gbr, X, y, cv=3)

            


            
                                                                        
                            
            
                                    
            
            

                
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    return scores.mean()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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search_space = {

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    "n_estimators": list(range(10, 150, 5)),

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    "max_depth": list(range(2, 12)),

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    "min_samples_split": list(range(2, 22)),

            


            
                                                                                                            
                            
            
                                    
            
            

                
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}

            


            
                                                                                                            
                            
            
                                    
            
            

                
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hyper = Hyperactive()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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hyper.add_search(model, search_space, n_iter=20)

            


            
                                                                                                            
                                                                
            
                                    
            
            

                
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hyper.run()

            


            
                                                        
            
                                    
            
            

                
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