YannCabanes
commented
2 years ago Here is the continuous integration test error message:
=================================== FAILURES =================================== _ test_allestimators[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
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).