8000 check_supervised_y_2d fails with estimator GaussianProcess on travis with macOS machines · Issue #11439 · scikit-learn/scikit-learn · GitHub
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check_supervised_y_2d fails with estimator GaussianProcess on travis with macOS machines #11439

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ogrisel opened this issue Jul 5, 2018 · 4 comments
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check_supervised_y_2d fails with estimator GaussianProcess on travis with macOS machines #11439

ogrisel opened this issue Jul 5, 2018 · 4 comments
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@ogrisel
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ogrisel commented Jul 5, 2018

Found when trying to build the wheels for 0.19.2:

________________________ test_non_meta_estimators[969] _________________________
self = <sklearn.utils.testing._named_check object at 0x1091aaa58>
args = ('GaussianProcess', GaussianProcess(beta0=None, corr='squared_exponential', normalize=True,
        nugget=2.220446049...state=None, regr='constant',
        storage_mode='full', theta0=0.1, thetaL=None, thetaU=None,
        verbose=False))
kwargs = {}
    def __call__(self, *args, **kwargs):
>       return self.check(*args, **kwargs)
args       = ('GaussianProcess', GaussianProcess(beta0=None, corr='squared_exponential', normalize=True,
        nugget=2.220446049...state=None, regr='constant',
        storage_mode='full', theta0=0.1, thetaL=None, thetaU=None,
        verbose=False))
kwargs     = {}
self       = <sklearn.utils.testing._named_check object at 0x1091aaa58>
../venv/lib/python3.4/site-packages/sklearn/utils/testing.py:775: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../venv/lib/python3.4/site-packages/sklearn/utils/testing.py:291: in wrapper
    return fn(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 'GaussianProcess'
estimator_orig = GaussianProcess(beta0=None, corr='squared_exponential', normalize=True,
        nugget=2.2204460492503131e-15, optimiz..._state=None, regr='constant',
        storage_mode='full', theta0=0.1, thetaL=None, thetaU=None,
        verbose=False)
    @ignore_warnings(category=(DeprecationWarning, FutureWarning))
    def check_supervised_y_2d(name, estimator_orig):
        if "MultiTask" in name:
            # These only work on 2d, so this test makes no sense
            return
        rnd = np.random.RandomState(0)
        X = rnd.uniform(size=(10, 3))
        y = np.arange(10) % 3
        estimator = clone(estimator_orig)
        set_random_state(estimator)
        # fit
        estimator.fit(X, y)
        y_pred = estimator.predict(X)
    
        set_random_state(estimator)
        # Check that when a 2D y is given, a DataConversionWarning is
        # raised
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter("always", DataConversionWarning)
            warnings.simplefilter("ignore", RuntimeWarning)
            estimator.fit(X, y[:, np.newaxis])
        y_pred_2d = estimator.predict(X)
        msg = "expected 1 DataConversionWarning, got: %s" % (
            ", ".join([str(w_x) for w_x in w]))
        if name not in MULTI_OUTPUT:
            # check that we warned if we don't support multi-output
            assert_greater(len(w), 0, msg)
            assert_true("DataConversionWarning('A column-vector y"
                        " was passed when a 1d array was expected" in msg)
>       assert_allclose(y_pred.ravel(), y_pred_2d.ravel())
E       AssertionError: 
E       Not equal to tolerance rtol=1e-07, atol=0
E       
E       (mismatch 40.0%)
E        x: array([ -1.086908e-13,   1.000000e+00,   2.000000e+00,   1.155742e-13,
E                1.000000e+00,   2.000000e+00,   5.062617e-14,   1.000000e+00,
E                2.000000e+00,   9.325873e-15])
E        y: array([ -1.089129e-13,   1.000000e+00,   2.000000e+00,   1.272316e-13,
E                1.000000e+00,   2.000000e+00,   5.051515e-14,   1.000000e+00,
E                2.000000e+00,   9.214851e-15])
X          = array([[ 0.5488135 ,  0.71518937,  0.60276338],
       [ 0.54488318,  0.4236548 ,  0.64589411],
       [ 0.43758721,  ...6147936,  0.78052918],
       [ 0.11827443,  0.63992102,  0.14335329],
       [ 0.94466892,  0.52184832,  0.41466194]])
estimator  = GaussianProcess(beta0=None,
        corr=<function squared_exponential at 0x1063c0730>, normalize=True,
        nugget...constant at 0x1063c02f0>, storage_mode='full',
        theta0=array([[ 0.1]]), thetaL=None, thetaU=None, verbose=False)
estimator_orig = GaussianProcess(beta0=None, corr='squared_exponential', normalize=True,
        nugget=2.2204460492503131e-15, optimiz..._state=None, regr='constant',
        storage_mode='full', theta0=0.1, thetaL=None, thetaU=None,
        verbose=False)
msg        = 'expected 1 DataConversionWarning, got: '
name       = 'GaussianProcess'
rnd        = <mtrand.RandomState object at 0x10ab87e58>
w          = []
y          = array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0])
y_pred     = array([ -1.08690834e-13,   1.00000000e+00,   2.00000000e+00,
         1.15574217e-13,   1.00000000e+00,   2.00000000e+00,
         5.06261699e-14,   1.00000000e+00,   2.00000000e+00,
         9.32587341e-15])
y_pred_2d  = array([[ -1.08912879e-13],
       [  1.00000000e+00],
       [  2.00000000e+00],
       [  1.27231559e-13],
       [  ...0000e+00],
       [  5.05151476e-14],
       [  1.00000000e+00],
       [  2.00000000e+00],
       [  9.21485110e-15]])
../venv/lib/python3.4/site-packages/sklearn/utils/estimator_checks.py:1226: AssertionError

Details:

https://travis-ci.org/MacPython/scikit-learn-wheels/builds/400338565

we have not changed anything between 0.19.1 and 0.19.2 for this model so it's probably a change on the travis environment that caused this issue to appear now.

It would be great if someone with a mac could try to reproduce those failures both on master and 0.19.X branches.
@ogrisel ogrisel added the Bug label Jul 5, 2018
@ogrisel
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ogrisel commented Jul 5, 2018

Actually, closing as a duplicate of #10562.

@ogrisel ogrisel closed this as completed Jul 5, 2018
@ogrisel
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ogrisel commented Jul 5, 2018

Actually reopening this issue to track the resolution of this particular failing test on macOS as the discussion in #10562 is wider.

@ogrisel ogrisel reopened this Jul 5, 2018
@cmarmo
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cmarmo commented Apr 23, 2021

@ogrisel as we are now at 0.24 (almost 1.0) and I can't remind of this problem recently, shall we just close this one?

@cmarmo cmarmo added the Needs Decision - Close Requires decision for closing label Jan 17, 2022
@thomasjpfan
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I am closing this issue because this test has not failed on OSX for a while on any of our CI jobs, Azure or wheel building.

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