libatlas 3.10.3 related failures on debian

[Lars Grüter]

From #7391 (comment):

Details

I did, the mentioned ones are gone, but now there is another one; this time in amd64 (and obviousy unrelated):

_____________________ test_polynomial_weighted_estimation ______________________

    def test_polynomial_weighted_estimation():
        # Over-determined solution with same points, and unity weights
        tform = estimate_transform('polynomial', SRC, DST, order=10)
        tform_w = estimate_transform(
            'polynomial', SRC, DST, order=10, weights=np.ones(SRC.shape[0])
        )
        assert_almost_equal(tform.params, tform_w.params)
    
        # Repeating a point, but setting its weight small, should give nearly
        # the same result.
        point_weights = np.ones(SRC.shape[0] + 1)
        point_weights[0] = 1.0e-15
        tform1 = estimate_transform('polynomial', SRC, DST, order=10)
        tform2 = estimate_transform(
            'polynomial',
            SRC[np.arange(-1, SRC.shape[0]), :],
            DST[np.arange(-1, SRC.shape[0]), :],
            order=10,
            weights=point_weights,
        )
>       assert_almost_equal(tform1.params, tform2.params, decimal=4)

skimage/transform/tests/test_geometric.py:666: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.11/contextlib.py:81: in inner
    return func(*args, **kwds)
/usr/lib/python3.11/contextlib.py:81: in inner
    return func(*args, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = (<function assert_array_almost_equal.<locals>.compare at 0x7ffad9691580>, array([[ 1.16431672e-10, -1.37662973e-11,  1...       -7.77541170e-06,  1.12016980e-06,  3.45277758e-05,
        -1.55254763e-05, -1.16166818e-05, -6.45696044e-06]]))
kwds = {'err_msg': '', 'header': 'Arrays are not almost equal to 4 decimals', 'precision': 4, 'verbose': True}

    @wraps(func)
    def inner(*args, **kwds):
        with self._recreate_cm():
>           return func(*args, **kwds)
E           AssertionError: 
E           Arrays are not almost equal to 4 decimals
E           
E           Mismatched elements: 3 / 132 (2.27%)
E           Max absolute difference: 0.00044363
E           Max relative difference: 26.96123959
E            x: array([[ 1.1643e-10, -1.3766e-11,  1.5589e-07,  1.0080e-07,  9.3082e-06,
E                    2.4074e-05,  2.8658e-05, -5.3848e-05,  1.0834e-10,  5.3736e-09,
E                   -1.7943e-07,  2.8586e-08, -9.4791e-06,  7.8816e-05, -2.8878e-05,...
E            y: array([[-9.0369e-07, -1.1004e-05, -2.1400e-07,  6.5641e-06,  9.2999e-06,
E                    1.9720e-05, -3.2870e-05, -5.0437e-05, -1.3570e-05, -9.9079e-05,
E                    2.0259e-04, -1.4415e-05, -1.1206e-05,  1.7683e-05, -2.8268e-05,...

/usr/lib/python3.11/contextlib.py:81: AssertionError

In ppc64el and loongarch64, I get:

_______________________ test_ellipse_parameter_stability _______________________
_____________________________ test_reproducibility _____________________________

    def test_reproducibility():
        """ensure cut_normalized returns the same output for the same input,
        when specifying random seed
        """
        img = data.coffee()
        labels1 = segmentation.slic(img, compactness=30, n_segments=400, start_label=0)
        g = graph.rag_mean_color(img, labels1, mode='similarity')
        results = [None] * 4
        for i in range(len(results)):
            results[i] = graph.cut_normalized(
                labels1, g, in_place=False, thresh=1e-3, rng=1234
            )
        graph.cut_normalized(labels1, g, in_place=False, thresh=1e-3, rng=1234)
    
        for i in range(len(results) - 1):
>           assert_array_equal(results[i], results[i + 1])

skimage/graph/tests/test_rag.py:224: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = (<built-in function eq>, array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0...35, 135, ...,  11,  11,  11],
       [135, 135, 135, ...,  11,  11,  11],
       [135, 135, 135, ...,  11,  11,  11]]))
kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True}

    @wraps(func)
    def inner(*args, **kwds):
        with self._recreate_cm():
>           return func(*args, **kwds)
E           AssertionError: 
E           Arrays are not equal
E           
E           Mismatched elements: 231553 / 240000 (96.5%)
E           Max absolute difference: 394
E           Max relative difference: 1.
E            x: array([[0, 0, 0, ..., 0, 0, 0],
E                  [0, 0, 0, ..., 0, 0, 0],
E                  [0, 0, 0, ..., 0, 0, 0],...
E            y: array([[  0,   0,   0, ...,  11,  11,  11],
E                  [  0,   0,   0, ...,  11,  11,  11],
E                  [  0,   0,   0, ...,  11,  11,  11],...

/usr/lib/python3.12/contextlib.py:81: AssertionError

All logs here.

There are some arch specific failures on Debian that seem related to libatlas. This affects test_polynomial_weighted_estimation for amd64 and test_ellipse_parameter_stability for ppc64el and loongarch64.

See also #7391 (comment) for possible dependencies that might have caused the test assertion failures:

The main difference seems to be that the failing build used libatlas 3.10.3 instead of blas 3.12.0 / libopenblas 0.3.26. All other differences seem minor:

• Python distutils, lib2to3, tk from 3.12.2 to 3.12.3
• pyproject-metadata only in the Debian release number,
• liblua 5.46, gringo 5.6.2, clasp 3.3.5, aspcud 1.9.6 are only in the failed (experimental) build
• Python defcon 0.10.3 is only in the failed (experimental) build
• Python ufolib2 0.16.0 is only in the successfull (unstable) build

I must say that I don't know why in the experimental build libatlas was preferred over blas; it is a numpy dependency and libblas is there the preferred one. However, they should give the same results, shouldn't they?

cc @olebole