Subject: bug#37433: python-scikit-learn has two failing tests

From: Ricardo Wurmus <[email-masked]>
Date: Tue, 17 Sep 2019 00:46:01 +0200

The python-scikit-learn package fails to build due to two failing tests:

--8<---------------cut here---------------start------------->8---
=================================== FAILURES ===================================
___________________________ test_scale_and_stability ___________________________

    def test_scale_and_stability():
        # We test scale=True parameter
        # This allows to check numerical stability over platforms as well
    
        d = load_linnerud()
        X1 = d.data
        Y1 = d.target
        # causes X[:, -1].std() to be zero
        X1[:, -1] = 1.0
    
        # From bug #2821
        # Test with X2, T2 s.t. clf.x_score[:, 1] == 0, clf.y_score[:, 1] == 0
        # This test robustness of algorithm when dealing with value close to 0
        X2 = np.array([[0., 0., 1.],
                       [1., 0., 0.],
                       [2., 2., 2.],
                       [3., 5., 4.]])
        Y2 = np.array([[0.1, -0.2],
                       [0.9, 1.1],
                       [6.2, 5.9],
                       [11.9, 12.3]])
    
        for (X, Y) in [(X1, Y1), (X2, Y2)]:
            X_std = X.std(axis=0, ddof=1)
            X_std[X_std == 0] = 1
            Y_std = Y.std(axis=0, ddof=1)
            Y_std[Y_std == 0] = 1
    
            X_s = (X - X.mean(axis=0)) / X_std
            Y_s = (Y - Y.mean(axis=0)) / Y_std
    
            for clf in [CCA(), pls_.PLSCanonical(), pls_.PLSRegression(),
                        pls_.PLSSVD()]:
                clf.set_params(scale=True)
                X_score, Y_score = clf.fit_transform(X, Y)
                clf.set_params(scale=False)
                X_s_score, Y_s_score = clf.fit_transform(X_s, Y_s)
                assert_array_almost_equal(X_s_score, X_score)
                assert_array_almost_equal(Y_s_score, Y_score)
                # Scaling should be idempotent
                clf.set_params(scale=True)
                X_score, Y_score = clf.fit_transform(X_s, Y_s)
>               assert_array_almost_equal(X_s_score, X_score)
E               AssertionError: 
E               Arrays are not almost equal to 6 decimals
E               
E               (mismatch 50.0%)
E                x: array([-1.337317, -0.041705, -1.108472,  0.098154,  0.407632, -0.103084,
E                       2.038158,  0.046634])
E                y: array([-1.337317, -0.041776, -1.108472,  0.0982  ,  0.407632, -0.103027,
E                       2.038158,  0.046602])

sklearn/cross_decomposition/tests/test_pls.py:365: AssertionError
____________________________ test_unsorted_indices _____________________________

    def test_unsorted_indices():
        # test that the result with sorted and unsorted indices in csr is the same
        # we use a subset of digits as iris, blobs or make_classification didn't
        # show the problem
        digits = load_digits()
        X, y = digits.data[:50], digits.target[:50]
        X_test = sparse.csr_matrix(digits.data[50:100])
    
        X_sparse = sparse.csr_matrix(X)
        coef_dense = svm.SVC(kernel='linear', probability=True,
                             random_state=0).fit(X, y).coef_
        sparse_svc = svm.SVC(kernel='linear', probability=True,
                             random_state=0).fit(X_sparse, y)
        coef_sorted = sparse_svc.coef_
        # make sure dense and sparse SVM give the same result
        assert_array_almost_equal(coef_dense, coef_sorted.toarray())
    
        X_sparse_unsorted = X_sparse[np.arange(X.shape[0])]
        X_test_unsorted = X_test[np.arange(X_test.shape[0])]
    
        # make sure we scramble the indices
>       assert_false(X_sparse_unsorted.has_sorted_indices)

sklearn/svm/tests/test_sparse.py:118: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <sklearn.utils._unittest_backport.TestCase testMethod=__init__>, expr = 1
msg = '1 is not false'

    def assertFalse(self, expr, msg=None):
        """Check that the expression is false."""
        if expr:
            msg = self._formatMessage(msg, "%s is not false" % safe_repr(expr))
>           raise self.failureException(msg)
E           AssertionError: 1 is not false

/gnu/store/b7fqhszxl02g6pfm3vw6b3cjz472qrly-python-3.7.0/lib/python3.7/unittest/case.py:686: AssertionError
[…]
2 failed, 10415 passed, 30 skipped, 1 deselected, 1 xfailed, 1026 warnings in 895.50 seconds 
--8<---------------cut here---------------end--------------->8---

-- 
Ricardo