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LightGBM.model()   A

↳ Parent: LightGBM

Complexity

Conditions 1

Size

Total Lines 9
Code Lines 7

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
eloc 7
dl 0
loc 9
rs 10
c 0
b 0
f 0
cc 1
nop 3
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from sklearn.model_selection import cross_val_score
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from lightgbm import LGBMRegressor
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from sklearn.datasets import load_breast_cancer
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from hyperactive import Hyperactive
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data = load_breast_cancer()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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X, y = data.data, data.target

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def model(para, X, y):

            


            
                                                                        
                            
            
                                    
            
            

                
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    lgbm = LGBMRegressor(

            


            
                                                                        
                            
            
                                    
            
            

                
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        num_leaves=para["num_leaves"],

            


            
                                                                        
                            
            
                                    
            
            

                
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        bagging_freq=para["bagging_freq"],

            


            
                                                                        
                            
            
                                    
            
            

                
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        learning_rate=para["learning_rate"],

            


            
                                                                        
                            
            
                                    
            
            

                
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    )

            


            
                                                                        
                            
            
                                    
            
            

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

            


            
                                                                        
                            
            
                                    
            
            

                
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    return scores.mean()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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search_config = {

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    model: {

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        "num_leaves": range(2, 20),

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        "bagging_freq": range(2, 12),

            


            
                                                                                                            
                            
            
                                    
            
            

                
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        "learning_rate": [1e-3, 1e-2, 1e-1, 0.5, 1.0],

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    }

            


            
                                                                                                            
                            
            
                                    
            
            

                
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}

            


            
                                                                                                            
                            
            
                                    
            
            

                
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opt = Hyperactive(X, y)

            


            
                                                                                                            
                                                                
            
                                    
            
            

                
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opt.search(search_config, n_iter=30)

            


            
                                                        
            
                                    
            
            

                
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