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test_predict()   A

↳ Parent: Project

Complexity

Conditions 2

Size

Total Lines 10

Duplication

Lines 10
Ratio 100 %

Code Coverage

Tests 9
CRAP Score 2

Importance

Changes 2
Bugs 0 Features 0
Metric Value
cc 2
c 2
b 0
f 0
dl 10
loc 10
ccs 9
cts 9
cp 1
crap 2
rs 9.4285
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import numpy as np
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import numpy.random as rnd
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from libtlda.suba import SubspaceAlignedClassifier
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def test_init01():

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    """Test for object type."""

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    clf = SubspaceAlignedClassifier()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    assert type(clf) == SubspaceAlignedClassifier

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def test_init02():

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    """Test for is_trained model."""

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    clf = SubspaceAlignedClassifier()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    assert not clf.is_trained

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def test_fit():

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    """Test for fitting the model."""

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    X = rnd.randn(10, 2)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    y = np.hstack((-np.ones((5,)), np.ones((5,))))

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    Z = rnd.randn(10, 2) + 1

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    clf = SubspaceAlignedClassifier()

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    clf.fit(X, y, Z)

            


            
                                                                                                            
                            
            
                                    
            
            

                
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    assert clf.is_trained

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def test_predict():

            


                            

                    
                        
                    
                    
                    
                    
                                                                                                    
                        

                            
                            
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    """Test for making predictions."""

            


            
                                                        
            
                                    
            
            

                
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    X = rnd.randn(10, 2)

            


            
                                                        
            
                                    
            
            

                
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    y = np.hstack((-np.ones((5,)), np.ones((5,))))

            


            
                                                        
            
                                    
            
            

                
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    Z = rnd.randn(10, 2) + 1

            


            
                                                        
            
                                    
            
            

                
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    clf = SubspaceAlignedClassifier()

            


            
                                                        
            
                                    
            
            

                
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    clf.fit(X, y, Z)

            


            
                                                        
            
                                    
            
            

                
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    u_pred = clf.predict(Z)

            


            
                                                        
            
                                    
            
            

                
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    labels = np.unique(y)

            


            
                                                        
            
                                    
            
            

                
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    assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0

            


            
                                                        
            
                                    
            
            

                
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