⚠️ CI failed on Linux_Docker.debian_32bit (last failure: Apr 19, 2025) ⚠️ #29802

scikit-learn-bot · 2024-09-08T03:35:56Z

CI is still failing on Linux_Docker.debian_32bit (Apr 19, 2025)

test_format_agnosticism[30-csr_matrix-RadiusNeighbors-float32]
test_format_agnosticism[30-csr_array-RadiusNeighbors-float32]

scikit-learn-bot · 2024-09-09T03:46:25Z

CI is no longer failing! ✅

Successful run on May 21, 2025

jeremiedbb · 2024-09-09T13:02:43Z

Probably a test too sensitive to the random seed.

lesteve · 2025-03-28T06:10:02Z

The March 28 failure build log is genuine. All the PRs with a recent push seem broken e.g. a polars related one #31095 or a pytest-related one #31074. A separated issue was created in #31098.

No idea why this would happen off the top of my head 😱 maybe a Debian Docker image upgrade somehow?

Failing tests:

FAILED tests/test_common.py::test_estimators[LinearRegression(positive=True)-check_sample_weight_equivalence_on_dense_data] - AssertionError: 
FAILED utils/tests/test_estimator_checks.py::test_check_estimator_clones - AssertionError:

The error message is something like:

Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.

Stacktraces for the two failing tests

=================================== FAILURES ===================================
_ test_estimators[LinearRegression(positive=True)-check_sample_weight_equivalence_on_dense_data] _

estimator = LinearRegression(positive=True)
check = functools.partial(<function check_sample_weight_equivalence_on_dense_data at 0xd91bde88>, 'LinearRegression')
request = <FixtureRequest for <Function test_estimators[LinearRegression(positive=True)-check_sample_weight_equivalence_on_dense_data]>>

    @parametrize_with_checks(
        list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks
    )
    def test_estimators(estimator, check, request):
        # Common tests for estimator instances
        with ignore_warnings(
            category=(FutureWarning, ConvergenceWarning, UserWarning, LinAlgWarning)
        ):
>           check(estimator)

check      = functools.partial(<function check_sample_weight_equivalence_on_dense_data at 0xd91bde88>, 'LinearRegression')
estimator  = LinearRegression(positive=True)
request    = <FixtureRequest for <Function test_estimators[LinearRegression(positive=True)-check_sample_weight_equivalence_on_dense_data]>>

/io/sklearn/tests/test_common.py:122: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/io/sklearn/utils/estimator_checks.py:1570: in check_sample_weight_equivalence_on_dense_data
    _check_sample_weight_equivalence(name, estimator_orig, sparse_container=None)
        estimator_orig = LinearRegression(positive=True)
        name       = 'LinearRegression'
/io/sklearn/utils/_testing.py:145: in wrapper
    return fn(*args, **kwargs)
        args       = ('LinearRegression', LinearRegression(positive=True))
        fn         = <function _check_sample_weight_equivalence at 0xd91bdde8>
        kwargs     = {'sparse_container': None}
        self       = _IgnoreWarnings(record=True)
/io/sklearn/utils/estimator_checks.py:1566: in _check_sample_weight_equivalence
    assert_allclose_dense_sparse(X_pred1, X_pred2, err_msg=err_msg)
        X          = array([[0.37454012, 0.95071431, 0.73199394, 0.59865848, 0.15601864,
        0.15599452, 0.05808361, 0.86617615, 0.6011..., 0.98663958, 0.3742708 , 0.37064215, 0.81279957,
        0.94724858, 0.98600106, 0.75337819, 0.37625959, 0.08350072]])
        X_pred1    = array([ 8.88178420e-16,  1.00000000e+00,  2.00000000e+00,  1.18549798e+00,
        4.06241761e+00,  1.00000000e+00,  2...5767e+00,  2.00000000e+00, -2.79936287e-02, -8.90642835e-01,
       -8.00809991e-01,  1.00000000e+00,  1.00000000e+00])
        X_pred2    = array([0.        , 1.        , 2.        , 0.94186541, 1.72670876,
       1.        , 2.        , 2.        , 1.8723887 , 2.        ,
       1.50877777, 0.76107365, 1.70933257, 1.        , 1.        ])
        X_repeated = array([[0.37454012, 0.95071431, 0.73199394, 0.59865848, 0.15601864,
        0.15599452, 0.05808361, 0.86617615, 0.6011..., 0.98663958, 0.3742708 , 0.37064215, 0.81279957,
        0.94724858, 0.98600106, 0.75337819, 0.37625959, 0.08350072]])
        X_weighted = array([[0.60754485, 0.17052412, 0.06505159, 0.94888554, 0.96563203,
        0.80839735, 0.30461377, 0.09767211, 0.6842..., 0.69673717, 0.62894285, 0.87747201, 0.73507104,
        0.80348093, 0.28203457, 0.17743954, 0.75061475, 0.80683474]])
        err_msg    = 'Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.'
        estimator_orig = LinearRegression(positive=True)
        estimator_repeated = LinearRegression(positive=True)
        estimator_weighted = LinearRegression(positive=True)
        method     = 'predict'
        n_samples  = 15
        name       = 'LinearRegression'
        rng        = RandomState(MT19937) at 0xCCEBACE8
        sparse_container = None
        sw         = array([3, 4, 0, 3, 1, 0, 4, 4, 0, 3, 0, 0, 3, 2, 0])
        y          = array([0, 1, 2, 2, 1, 1, 2, 2, 1, 2, 0, 0, 1, 1, 1])
        y_repeated = array([0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       1, 1, 1, 1, 1])
        y_weighted = array([1, 2, 1, 2, 1, 1, 2, 1, 0, 2, 0, 2, 0, 1, 1])
/io/sklearn/utils/_testing.py:283: in assert_allclose_dense_sparse
    assert_allclose(x, y, rtol=rtol, atol=atol, err_msg=err_msg)
        atol       = 1e-09
        err_msg    = 'Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.'
        rtol       = 1e-07
        x          = array([ 8.88178420e-16,  1.00000000e+00,  2.00000000e+00,  1.18549798e+00,
        4.06241761e+00,  1.00000000e+00,  2...5767e+00,  2.00000000e+00, -2.79936287e-02, -8.90642835e-01,
       -8.00809991e-01,  1.00000000e+00,  1.00000000e+00])
        y          = array([0.        , 1.        , 2.        , 0.94186541, 1.72670876,
       1.        , 2.        , 2.        , 1.8723887 , 2.        ,
       1.50877777, 0.76107365, 1.70933257, 1.        , 1.        ])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

actual = array([ 8.88178420e-16,  1.00000000e+00,  2.00000000e+00,  1.18549798e+00,
        4.06241761e+00,  1.00000000e+00,  2...5767e+00,  2.00000000e+00, -2.79936287e-02, -8.90642835e-01,
       -8.00809991e-01,  1.00000000e+00,  1.00000000e+00])
desired = array([0.        , 1.        , 2.        , 0.94186541, 1.72670876,
       1.        , 2.        , 2.        , 1.8723887 , 2.        ,
       1.50877777, 0.76107365, 1.70933257, 1.        , 1.        ])
rtol = 1e-07, atol = 1e-09, equal_nan = True
err_msg = 'Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.'
verbose = True

    def assert_allclose(
        actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True
    ):
        """dtype-aware variant of numpy.testing.assert_allclose
    
        This variant introspects the least precise floating point dtype
        in the input argument and automatically sets the relative tolerance
        parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64
        in scikit-learn).
    
        `atol` is always left to 0. by default. It should be adjusted manually
        to an assertion-specific value in case there are null values expected
        in `desired`.
    
        The aggregate tolerance is `atol + rtol * abs(desired)`.
    
        Parameters
        ----------
        actual : array_like
            Array obtained.
        desired : array_like
            Array desired.
        rtol : float, optional, default=None
            Relative tolerance.
            If None, it is set based on the provided arrays' dtypes.
        atol : float, optional, default=0.
            Absolute tolerance.
        equal_nan : bool, optional, default=True
            If True, NaNs will compare equal.
        err_msg : str, optional, default=''
            The error message to be printed in case of failure.
        verbose : bool, optional, default=True
            If True, the conflicting values are appended to the error message.
    
        Raises
        ------
        AssertionError
            If actual and desired are not equal up to specified precision.
    
        See Also
        --------
        numpy.testing.assert_allclose
    
        Examples
        --------
        >>> import numpy as np
        >>> from sklearn.utils._testing import assert_allclose
        >>> x = [1e-5, 1e-3, 1e-1]
        >>> y = np.arccos(np.cos(x))
        >>> assert_allclose(x, y, rtol=1e-5, atol=0)
        >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32)
        >>> assert_allclose(a, 1e-5)
        """
        dtypes = []
    
        actual, desired = np.asanyarray(actual), np.asanyarray(desired)
        dtypes = [actual.dtype, desired.dtype]
    
        if rtol is None:
            rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes]
            rtol = max(rtols)
    
>       np_assert_allclose(
            actual,
            desired,
            rtol=rtol,
            atol=atol,
            equal_nan=equal_nan,
            err_msg=err_msg,
            verbose=verbose,
        )
E       AssertionError: 
E       Not equal to tolerance rtol=1e-07, atol=1e-09
E       Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.
E       Mismatched elements: 6 / 15 (40%)
E       Max absolute difference among violations: 2.51014256
E       Max relative difference among violations: 2.17024526
E        ACTUAL: array([ 8.881784e-16,  1.000000e+00,  2.000000e+00,  1.185498e+00,
E               4.062418e+00,  1.000000e+00,  2.000000e+00,  2.000000e+00,
E               4.105658e+00,  2.000000e+00, -2.799363e-02, -8.906428e-01,
E              -8.008100e-01,  1.000000e+00,  1.000000e+00])
E        DESIRED: array([0.      , 1.      , 2.      , 0.941865, 1.726709, 1.      ,
E              2.      , 2.      , 1.872389, 2.      , 1.508778, 0.761074,
E              1.709333, 1.      , 1.      ])

actual     = array([ 8.88178420e-16,  1.00000000e+00,  2.00000000e+00,  1.18549798e+00,
        4.06241761e+00,  1.00000000e+00,  2...5767e+00,  2.00000000e+00, -2.79936287e-02, -8.90642835e-01,
       -8.00809991e-01,  1.00000000e+00,  1.00000000e+00])
atol       = 1e-09
desired    = array([0.        , 1.        , 2.        , 0.94186541, 1.72670876,
       1.        , 2.        , 2.        , 1.8723887 , 2.        ,
       1.50877777, 0.76107365, 1.70933257, 1.        , 1.        ])
dtypes     = [dtype('float64'), dtype('float64')]
equal_nan  = True
err_msg    = 'Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.'
rtol       = 1e-07
verbose    = True

/io/sklearn/utils/_testing.py:237: AssertionError
_________________________ test_check_estimator_clones __________________________

    def test_check_estimator_clones():
        # check that check_estimator doesn't modify the estimator it receives
    
        iris = load_iris()
    
        for Estimator in [
            GaussianMixture,
            LinearRegression,
            SGDClassifier,
            PCA,
            MiniBatchKMeans,
        ]:
            # without fitting
            with ignore_warnings(category=ConvergenceWarning):
                est = Estimator()
                set_random_state(est)
                old_hash = joblib.hash(est)
>               check_estimator(
                    est, expected_failed_checks=_get_expected_failed_checks(est)
                )

Estimator  = <class 'sklearn.linear_model._base.LinearRegression'>
est        = LinearRegression()
iris       = {'data': array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2],
       [4.7, 3.2, 1.3, 0.2],
       [4.6, 3.1, 1.5,... width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
old_hash   = 'fdcbee8ed611695d1e19a9bdabd615ac'

/io/sklearn/utils/tests/test_estimator_checks.py:919: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/io/sklearn/utils/_param_validation.py:218: in wrapper
    return func(*args, **kwargs)
        args       = (LinearRegression(),)
        func       = <function check_estimator at 0xd91bd668>
        func_sig   = <Signature (estimator=None, generate_only=False, *, legacy: 'bool' = True, expected_failed_checks: 'dict[str, str] | N...al['warn'] | None" = 'warn', on_fail: "Literal['raise', 'warn'] | None" = 'raise', callback: 'Callable | None' = None)>
        global_skip_validation = False
        kwargs     = {'expected_failed_checks': {}}
        parameter_constraints = {'callback': [<built-in function callable>, None], 'expected_failed_checks': [<class 'dict'>, None], 'generate_only': ['boolean'], 'legacy': ['boolean'], ...}
        params     = {'callback': None, 'estimator': LinearRegression(), 'expected_failed_checks': {}, 'generate_only': False, ...}
        prefer_skip_nested_validation = False
        to_ignore  = ['self', 'cls']
/io/sklearn/utils/estimator_checks.py:856: in check_estimator
    check(estimator)
        callback   = None
        check      = functools.partial(<function check_sample_weight_equivalence_on_dense_data at 0xd91bde88>, 'LinearRegression')
        check_result = {'check_name': 'check_sample_weight_equivalence_on_dense_data', 'estimator': LinearRegression(), 'exception': None, 'expected_to_fail': False, ...}
        estimator  = LinearRegression(positive=True)
        expected_failed_checks = {}
        generate_only = False
        legacy     = True
        name       = 'LinearRegression'
        on_fail    = 'raise'
        on_skip    = 'warn'
        reason     = 'Check is not expected to fail'
        test_can_fail = False
        test_results = [{'check_name': 'check_estimator_cloneable', 'estimator': LinearRegression(), 'exception': None, 'expected_to_fail': F...k_no_attributes_set_in_init', 'estimator': LinearRegression(), 'exception': None, 'expected_to_fail': False, ...}, ...]
/io/sklearn/utils/estimator_checks.py:1570: in check_sample_weight_equivalence_on_dense_data
    _check_sample_weight_equivalence(name, estimator_orig, sparse_container=None)
        estimator_orig = LinearRegression(positive=True)
        name       = 'LinearRegression'
/io/sklearn/utils/_testing.py:145: in wrapper
    return fn(*args, **kwargs)
        args       = ('LinearRegression', LinearRegression(positive=True))
        fn         = <function _check_sample_weight_equivalence at 0xd91bdde8>
        kwargs     = {'sparse_container': None}
        self       = _IgnoreWarnings(record=True)
/io/sklearn/utils/estimator_checks.py:1566: in _check_sample_weight_equivalence
    assert_allclose_dense_sparse(X_pred1, X_pred2, err_msg=err_msg)
        X          = array([[0.37454012, 0.95071431, 0.73199394, 0.59865848, 0.15601864,
        0.15599452, 0.05808361, 0.86617615, 0.6011..., 0.98663958, 0.3742708 , 0.37064215, 0.81279957,
        0.94724858, 0.98600106, 0.75337819, 0.37625959, 0.08350072]])
        X_pred1    = array([ 8.88178420e-16,  1.00000000e+00,  2.00000000e+00,  1.18549798e+00,
        4.06241761e+00,  1.00000000e+00,  2...5767e+00,  2.00000000e+00, -2.79936287e-02, -8.90642835e-01,
       -8.00809991e-01,  1.00000000e+00,  1.00000000e+00])
        X_pred2    = array([0.        , 1.        , 2.        , 0.94186541, 1.72670876,
       1.        , 2.        , 2.        , 1.8723887 , 2.        ,
       1.50877777, 0.76107365, 1.70933257, 1.        , 1.        ])
        X_repeated = array([[0.37454012, 0.95071431, 0.73199394, 0.59865848, 0.15601864,
        0.15599452, 0.05808361, 0.86617615, 0.6011..., 0.98663958, 0.3742708 , 0.37064215, 0.81279957,
        0.94724858, 0.98600106, 0.75337819, 0.37625959, 0.08350072]])
        X_weighted = array([[0.60754485, 0.17052412, 0.06505159, 0.94888554, 0.96563203,
        0.80839735, 0.30461377, 0.09767211, 0.6842..., 0.69673717, 0.62894285, 0.87747201, 0.73507104,
        0.80348093, 0.28203457, 0.17743954, 0.75061475, 0.80683474]])
        err_msg    = 'Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.'
        estimator_orig = LinearRegression(positive=True)
        estimator_repeated = LinearRegression(positive=True)
        estimator_weighted = LinearRegression(positive=True)
        method     = 'predict'
        n_samples  = 15
        name       = 'LinearRegression'
        rng        = RandomState(MT19937) at 0xC9302628
        sparse_container = None
        sw         = array([3, 4, 0, 3, 1, 0, 4, 4, 0, 3, 0, 0, 3, 2, 0])
        y          = array([0, 1, 2, 2, 1, 1, 2, 2, 1, 2, 0, 0, 1, 1, 1])
        y_repeated = array([0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       1, 1, 1, 1, 1])
        y_weighted = array([1, 2, 1, 2, 1, 1, 2, 1, 0, 2, 0, 2, 0, 1, 1])
/io/sklearn/utils/_testing.py:283: in assert_allclose_dense_sparse
    assert_allclose(x, y, rtol=rtol, atol=atol, err_msg=err_msg)
        atol       = 1e-09
        err_msg    = 'Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.'
        rtol       = 1e-07
        x          = array([ 8.88178420e-16,  1.00000000e+00,  2.00000000e+00,  1.18549798e+00,
        4.06241761e+00,  1.00000000e+00,  2...5767e+00,  2.00000000e+00, -2.79936287e-02, -8.90642835e-01,
       -8.00809991e-01,  1.00000000e+00,  1.00000000e+00])
        y          = array([0.        , 1.        , 2.        , 0.94186541, 1.72670876,
       1.        , 2.        , 2.        , 1.8723887 , 2.        ,
       1.50877777, 0.76107365, 1.70933257, 1.        , 1.        ])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

actual = array([ 8.88178420e-16,  1.00000000e+00,  2.00000000e+00,  1.18549798e+00,
        4.06241761e+00,  1.00000000e+00,  2...5767e+00,  2.00000000e+00, -2.79936287e-02, -8.90642835e-01,
       -8.00809991e-01,  1.00000000e+00,  1.00000000e+00])
desired = array([0.        , 1.        , 2.        , 0.94186541, 1.72670876,
       1.        , 2.        , 2.        , 1.8723887 , 2.        ,
       1.50877777, 0.76107365, 1.70933257, 1.        , 1.        ])
rtol = 1e-07, atol = 1e-09, equal_nan = True
err_msg = 'Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.'
verbose = True

    def assert_allclose(
        actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True
    ):
        """dtype-aware variant of numpy.testing.assert_allclose
    
        This variant introspects the least precise floating point dtype
        in 
8A6E
the input argument and automatically sets the relative tolerance
        parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64
        in scikit-learn).
    
        `atol` is always left to 0. by default. It should be adjusted manually
        to an assertion-specific value in case there are null values expected
        in `desired`.
    
        The aggregate tolerance is `atol + rtol * abs(desired)`.
    
        Parameters
        ----------
        actual : array_like
            Array obtained.
        desired : array_like
            Array desired.
        rtol : float, optional, default=None
            Relative tolerance.
            If None, it is set based on the provided arrays' dtypes.
        atol : float, optional, default=0.
            Absolute tolerance.
        equal_nan : bool, optional, default=True
            If True, NaNs will compare equal.
        err_msg : str, optional, default=''
            The error message to be printed in case of failure.
        verbose : bool, optional, default=True
            If True, the conflicting values are appended to the error message.
    
        Raises
        ------
        AssertionError
            If actual and desired are not equal up to specified precision.
    
        See Also
        --------
        numpy.testing.assert_allclose
    
        Examples
        --------
        >>> import numpy as np
        >>> from sklearn.utils._testing import assert_allclose
        >>> x = [1e-5, 1e-3, 1e-1]
        >>> y = np.arccos(np.cos(x))
        >>> assert_allclose(x, y, rtol=1e-5, atol=0)
        >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32)
        >>> assert_allclose(a, 1e-5)
        """
        dtypes = []
    
        actual, desired = np.asanyarray(actual), np.asanyarray(desired)
        dtypes = [actual.dtype, desired.dtype]
    
        if rtol is None:
            rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes]
            rtol = max(rtols)
    
>       np_assert_allclose(
            actual,
            desired,
            rtol=rtol,
            atol=atol,
            equal_nan=equal_nan,
            err_msg=err_msg,
            verbose=verbose,
        )
E       AssertionError: 
E       Not equal to tolerance rtol=1e-07, atol=1e-09
E       Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.
E       Mismatched elements: 6 / 15 (40%)
E       Max absolute difference among violations: 2.51014256
E       Max relative difference among violations: 2.17024526
E        ACTUAL: array([ 8.881784e-16,  1.000000e+00,  2.000000e+00,  1.185498e+00,
E               4.062418e+00,  1.000000e+00,  2.000000e+00,  2.000000e+00,
E               4.105658e+00,  2.000000e+00, -2.799363e-02, -8.906428e-01,
E              -8.008100e-01,  1.000000e+00,  1.000000e+00])
E        DESIRED: array([0.      , 1.      , 2.      , 0.941865, 1.726709, 1.      ,
E              2.      , 2.      , 1.872389, 2.      , 1.508778, 0.761074,
E              1.709333, 1.      , 1.      ])

actual     = array([ 8.88178420e-16,  1.00000000e+00,  2.00000000e+00,  1.18549798e+00,
        4.06241761e+00,  1.00000000e+00,  2...5767e+00,  2.00000000e+00, -2.79936287e-02, -8.90642835e-01,
       -8.00809991e-01,  1.00000000e+00,  1.00000000e+00])
atol       = 1e-09
desired    = array([0.        , 1.        , 2.        , 0.94186541, 1.72670876,
       1.        , 2.        , 2.        , 1.8723887 , 2.        ,
       1.50877777, 0.76107365, 1.70933257, 1.        , 1.        ])
dtypes     = [dtype('float64'), dtype('float64')]
equal_nan  = True
err_msg    = 'Comparing the output of LinearRegression.predict revealed that fitting with `sample_weight` is not equivalent to fitting with removed or repeated data points.'
rtol       = 1e-07
verbose    = True

/io/sklearn/utils/_testing.py:237: AssertionError

github-actions bot added the Needs Triage Issue requires triage label Sep 8, 2024

jeremiedbb removed the Needs Triage Issue requires triage label Sep 9, 2024

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lesteve mentioned this issue Mar 28, 2025

MNT Add function to generate pytest IDs for yield_namespace_device_dtype_combinations #31074

Merged

lesteve closed this as completed Mar 30, 2025

lesteve reopened this Mar 30, 2025

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⚠️ CI failed on Linux_Docker.debian_32bit (last failure: Apr 19, 2025) ⚠️ #29802

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⚠️ CI failed on Linux_Docker.debian_32bit (last failure: Apr 19, 2025) ⚠️ #29802

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CI is no longer failing! ✅