View GitHub Repository Hyperactive

Schedule Inspection
Subscribe

Code Duplication    Length = 13-15 lines in 4 locations

examples/examples_v1.x.x/use_cases/sklearn_preprocessing_example.py 1 location

@@ 23-37 (lines=15) @@
20 
    return X
21 

                                

                                                            

                                    22
                                    
                                    

                                

                                                            

                                    23
                                    
                                    
def model(para, X, y):

                                

                                                            

                                    24
                                    
                                    
    model = GradientBoostingClassifier(

                                

                                                            

                                    25
                                    
                                    
        n_estimators=para["n_estimators"],

                                

                                                            

                                    26
                                    
                                    
        max_depth=para["max_depth"],

                                

                                                            

                                    27
                                    
                                    
        min_samples_split=para["min_samples_split"],

                                

                                                            

                                    28
                                    
                                    
        min_samples_leaf=para["min_samples_leaf"],

                                

                                                            

                                    29
                                    
                                    
    )

                                

                                                            

                                    30
                                    
                                    

                                

                                                            

                                    31
                                    
                                    
    X_pca = para["decomposition"](X)

                                

                                                            

                                    32
                                    
                                    
    X = np.hstack((X, X_pca))

                                

                                                            

                                    33
                                    
                                    

                                

                                                            

                                    34
                                    
                                    
    X = SelectKBest(f_classif, k=para["k"]).fit_transform(X, y)

                                

                                                            

                                    35
                                    
                                    
    scores = cross_val_score(model, X, y, cv=3)

                                

                                                            

                                    36
                                    
                                    

                                

                                                            

                                    37
                                    
                                    
    return scores.mean()

                                

                                                            

                                    38
                                    
                                    

                                

                                                            

                                    39
                                    
                                    

                                

                                                            

                                    40
                                    
                                    
search_config = {

                                

                                                                        

                

                                                            

                    
examples/use_cases/SklearnPreprocessing.py 1 location

                

                

                    

                                                
                                                    

                                
                                
                                    @@ 23-37 (lines=15) @@
                                
                            


                                                            

                                    20
                                    
                                    
    return X

                                

                                                            

                                    21
                                    
                                    

                                

                                                            

                                    22
                                    
                                    

                                

                                                            

                                    23
                                    
                                    
def model(para, X, y):

                                

                                                            

                                    24
                                    
                                    
    model = GradientBoostingClassifier(

                                

                                                            

                                    25
                                    
                                    
        n_estimators=para["n_estimators"],

                                

                                                            

                                    26
                                    
                                    
        max_depth=para["max_depth"],

                                

                                                            

                                    27
                                    
                                    
        min_samples_split=para["min_samples_split"],

                                

                                                            

                                    28
                                    
                                    
        min_samples_leaf=para["min_samples_leaf"],

                                

                                                            

                                    29
                                    
                                    
    )

                                

                                                            

                                    30
                                    
                                    

                                

                                                            

                                    31
                                    
                                    
    X_pca = para["decomposition"](X)

                                

                                                            

                                    32
                                    
                                    
    X = np.hstack((X, X_pca))

                                

                                                            

                                    33
                                    
                                    

                                

                                                            

                                    34
                                    
                                    
    X = SelectKBest(f_classif, k=para["k"]).fit_transform(X, y)

                                

                                                            

                                    35
                                    
                                    
    scores = cross_val_score(model, X, y, cv=3)

                                

                                                            

                                    36
                                    
                                    

                                

                                                            

                                    37
                                    
                                    
    return scores.mean()

                                

                                                            

                                    38
                                    
                                    

                                

                                                            

                                    39
                                    
                                    

                                

                                                            

                                    40
                                    
                                    
search_config = {

                                

                                                                        

                

                                                            

                    
examples/use_cases/SklearnPipeline.py 1 location

                

                

                    

                                                
                                                    

                                
                                
                                    @@ 21-33 (lines=13) @@
                                
                            


                                                            

                                    18
                                    
                                    
    return gbc

                                

                                                            

                                    19
                                    
                                    

                                

                                                            

                                    20
                                    
                                    

                                

                                                            

                                    21
                                    
                                    
def model(para, X, y):

                                

                                                            

                                    22
                                    
                                    
    gbc = GradientBoostingClassifier(

                                

                                                            

                                    23
                                    
                                    
        n_estimators=para["n_estimators"],

                                

                                                            

                                    24
                                    
                                    
        max_depth=para["max_depth"],

                                

                                                            

                                    25
                                    
                                    
        min_samples_split=para["min_samples_split"],

                                

                                                            

                                    26
                                    
                                    
        min_samples_leaf=para["min_samples_leaf"],

                                

                                                            

                                    27
                                    
                                    
    )

                                

                                                            

                                    28
                                    
                                    
    filter_ = SelectKBest(f_classif, k=para["k"])

                                

                                                            

                                    29
                                    
                                    
    model_ = para["pipeline"](filter_, gbc)

                                

                                                            

                                    30
                                    
                                    

                                

                                                            

                                    31
                                    
                                    
    scores = cross_val_score(model_, X, y, cv=3)

                                

                                                            

                                    32
                                    
                                    

                                

                                                            

                                    33
                                    
                                    
    return scores.mean()

                                

                                                            

                                    34
                                    
                                    

                                

                                                            

                                    35
                                    
                                    

                                

                                                            

                                    36
                                    
                                    
search_config = {

                                

                                                                        

                

                                                            

                    
examples/examples_v1.x.x/use_cases/sklearn_pipeline_example.py 1 location

                

                

                    

                                                
                                                    

                                
                                
                                    @@ 21-33 (lines=13) @@
                                
                            


                                                            

                                    18
                                    
                                    
    return gbc

                                

                                                            

                                    19
                                    
                                    

                                

                                                            

                                    20
                                    
                                    

                                

                                                            

                                    21
                                    
                                    
def model(para, X, y):

                                

                                                            

                                    22
                                    
                                    
    gbc = GradientBoostingClassifier(

                                

                                                            

                                    23
                                    
                                    
        n_estimators=para["n_estimators"],

                                

                                                            

                                    24
                                    
                                    
        max_depth=para["max_depth"],

                                

                                                            

                                    25
                                    
                                    
        min_samples_split=para["min_samples_split"],

                                

                                                            

                                    26
                                    
                                    
        min_samples_leaf=para["min_samples_leaf"],

                                

                                                            

                                    27
                                    
                                    
    )

                                

                                                            

                                    28
                                    
                                    
    filter_ = SelectKBest(f_classif, k=para["k"])

                                

                                                            

                                    29
                                    
                                    
    model_ = para["pipeline"](filter_, gbc)

                                

                                                            

                                    30
                                    
                                    

                                

                                                            

                                    31
                                    
                                    
    scores = cross_val_score(model_, X, y, cv=3)

                                

                                                            

                                    32
                                    
                                    

                                

                                                            

                                    33
                                    
                                    
    return scores.mean()

                                

                                                            

                                    34
                                    
                                    

                                

                                                            

                                    35
                                    
                                    

                                

                                                            

                                    36
                                    
                                    
search_config = {