Continuous integration failing test on Linux for test check_pipeline_consistency of class LearningShapelets

[YannCabanes]

This bug was first noticed in continuous integration tests of the PR #411 (which is now merged), but this bug seems unrelated to the PR.
The continuous integration tests are failing with Linux but they pass with Windows and MacOS.
I use Linux and Python 3.8, and the tests pass on my local computer.
The failing test is related to the test the class tslearn.shapelets.shapelets.LearningShapelets by functions:
test_all_estimators (tslearn/tests/test_estimators.py) --> check_estimator (tslearn/tests/test_estimators.py) --> check_pipeline_consistency (tslearn/tests/sklearn_patches.py).

[YannCabanes]

Here is the continuous integration test error message:

=================================== FAILURES ===================================
___________ test_all_estimators[LearningShapelets-LearningShapelets] ___________

name = 'LearningShapelets'
Estimator = <class 'tslearn.shapelets.shapelets.LearningShapelets'>

@pytest.mark.parametrize('name, Estimator', get_estimators('all')) def test_all_estimators(name, Estimator): """Test all the estimators in tslearn.""" allow_nan = (hasattr(checks, 'ALLOW_NAN') and Estimator().get_tags()["allow_nan"]) if allow_nan: checks.ALLOW_NAN.append(name) if name in ["GlobalAlignmentKernelKMeans", "ShapeletModel", "SerializableShapeletModel"]: # Deprecated models return check_estimator(Estimator)

tslearn/tests/test_estimators.py:215:
tslearn/tests/test_estimators.py:197: in check_estimator
check(estimator)
/opt/hostedtoolcache/Python/3.9.14/x64/lib/python3.9/site-packages/sklearn/utils/_testing.py:311: in wrapper
return fn(*args, **kwargs)
tslearn/tests/sklearn_patches.py:558: in check_pipeline_consistency
assert_allclose_dense_sparse(result, result_pipe)

x = array([[3.7043095e-03],
[6.7453969e-01],
[6.3824987e-01],
[1.2295246e-03],
[2.0980835e-05]...4e-03],
[8.6247969e-01],
[1.4195442e-03],
[5.0067902e-06],
[9.4977307e-01]], dtype=float32)
y = array([[0.40121353],
[0.06187719],
[0.05123574],
[0.21641088],
[0.2602595 ],
[0.076... [0.25475943],
[0.12683961],
[0.27159142],
[0.29283226],
[0.16161257]], dtype=float32)
rtol = 1e-07, atol = 1e-09, err_msg = ''

`def assert_allclose_dense_sparse(x, y, rtol=1e-07, atol=1e-9, err_msg=""):
"""Assert allclose for sparse and dense data.

Both x and y need to be either sparse or dense, they
can't be mixed.

Parameters
----------
x : {array-like, sparse matrix}
    First array to compare.

y : {array-like, sparse matrix}
    Second array to compare.

rtol : float, default=1e-07
    relative tolerance; see numpy.allclose.

atol : float, default=1e-9
    absolute tolerance; see numpy.allclose. Note that the default here is
    more tolerant than the default for numpy.testing.assert_allclose, where
    atol=0.

err_msg : str, default=''
    Error message to raise.
"""
if sp.sparse.issparse(x) and sp.sparse.issparse(y):
    x = x.tocsr()
    y = y.tocsr()
    x.sum_duplicates()
    y.sum_duplicates()
    assert_array_equal(x.indices, y.indices, err_msg=err_msg)
    assert_array_equal(x.indptr, y.indptr, err_msg=err_msg)
    assert_allclose(x.data, y.data, rtol=rtol, atol=atol, err_msg=err_msg)
elif not sp.sparse.issparse(x) and not sp.sparse.issparse(y):
    # both dense
    assert_allclose(x, y, rtol=rtol, atol=atol, err_msg=err_msg)`

E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=1e-09
E
E Mismatched elements: 30 / 30 (100%)
E Max absolute difference: 0.7881605
E Max relative difference: 23.541649
E x: array([[3.704309e-03],
E [6.745397e-01],
E [6.382499e-01],...
E y: array([[0.401214],
E [0.061877],
E [0.051236],...

/opt/hostedtoolcache/Python/3.9.14/x64/lib/python3.9/site-packages/sklearn/utils/_testing.py:418: AssertionError