Closed
Description
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.