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Orange.classification.KNNLearner   A
last analyzed

↳ Parent: Project

Complexity

Total Complexity 1

Size/Duplication

Total Lines 9
Duplicated Lines 0 %
Metric Value
dl 0
loc 9
rs 10
wmc 1

1 Method

Rating   Name   Duplication   Size   Complexity  
A __init__() 0 5 1
1 
import sklearn.neighbors as skl_neighbors
0 ignored issues
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Configuration introduced by
The import sklearn.neighbors could not be resolved.

This can be caused by one of the following:

1. Missing Dependencies

This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands.

# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version.

2. Missing __init__.py files

This error could also result from missing __init__.py files in your module folders. Make sure that you place one file in each sub-folder.

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2 
from Orange.classification import SklLearner
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__all__ = ["KNNLearner"]

            


            
                                                                                                            
                            
            
                                    
            
            

                
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class KNNLearner(SklLearner):

            


            
                                                        
            
                                    
            
            

                
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    __wraps__ = skl_neighbors.KNeighborsClassifier

            


            
                                                        
            
                                    
            
            

                
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    name = 'knn'

            


            
                                                        
            
                                    
            
            

                
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    def __init__(self, n_neighbors=5, metric="euclidean", weights="uniform",

            


                            

                    
                        
                    
                    
                    
                    
                                                                                                                                                                                                            
                        

                            
                            
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The argument n_neighbors seems to be unused.
                                                                                                

                    
                                                    

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                 algorithm='auto',

            


                            

                    
                        
                    
                    
                    
                    
                                                                                                    
                        

                            
                            
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                 preprocessors=None):

            


            
                                                        
            
                                    
            
            

                
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        super().__init__(preprocessors=preprocessors)

            


            
                                                        
            
                                    
            
            

                
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        self.params = vars()

            


            
                                                        
            
                                    
            
            

                
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