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test_subspace_alignment()   A

↳ Parent: Project

Complexity

Conditions 4

Size

Total Lines 9

Duplication

Lines 0
Ratio 0 %

Code Coverage

Tests 8
CRAP Score 4

Importance

Changes 0
Metric Value
cc 4
c 0
b 0
f 0
dl 0
loc 9
ccs 8
cts 8
cp 1
crap 4
rs 9.2
1 1 
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_init():

            


            
                                                                                                            
                            
            
                                    
            
            

                
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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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    assert not clf.is_trained

            


            
                                                                                                            
                            
            
                                    
            
            

                
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def test_subspace_alignment():

            


            
                                                                        
                            
            
                                    
            
            

                
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    """Test the alignment between datasets."""

            


            
                                                                        
                            
            
                                    
            
            

                
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    X = rnd.randn(100, 10)

            


            
                                                                        
                            
            
                                    
            
            

                
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    Z = np.dot(rnd.randn(100, 10), np.diag(np.arange(1, 11)))

            


            
                                                                        
                            
            
                                    
            
            

                
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    clf = SubspaceAlignedClassifier()

            


            
                                                                        
                            
            
                                    
            
            

                
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    V, CX, CZ = clf.subspace_alignment(X, Z, num_components=3)

            


            
                                                                        
                            
            
                                    
            
            

                
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    assert not np.any(np.isnan(V))

            


            
                                                                        
                            
            
                                    
            
            

                
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    assert CX.shape[1] == 3

            


            
                                                                        
                            
            
                                    
            
            

                
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    assert CZ.shape[1] == 3

            


            
                                                                                                            
                            
            
                                    
            
            

                
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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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