Consistent failure on OSX in draft 0.20.2

While trying to build wheels for 0.20.2, all Mac builds have failed (e.g. https://travis-ci.org/MacPython/scikit-learn-wheels/jobs/469752588) with:

___________________ test_pca_dtype_preservation[randomized] ____________________
svd_solver = 'randomized'
    @pytest.mark.parametrize('svd_solver', solver_list)
    def test_pca_dtype_preservation(svd_solver):
>       check_pca_float_dtype_preservation(svd_solver)
svd_solver = 'randomized'
../venv/lib/python2.7/site-packages/sklearn/decomposition/tests/test_pca.py:707: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
svd_solver = 'randomized'
    def check_pca_float_dtype_preservation(svd_solver):
        # Ensure that PCA does not upscale the dtype when input is float32
        X_64 = np.random.RandomState(0).rand(1000, 4).astype(np.float64)
        X_32 = X_64.astype(np.float32)
    
        pca_64 = PCA(n_components=3, svd_solver=svd_solver,
                     random_state=0).fit(X_64)
        pca_32 = PCA(n_components=3, svd_solver=svd_solver,
                     random_state=0).fit(X_32)
    
        assert pca_64.components_.dtype == np.float64
        assert pca_32.components_.dtype == np.float32
        assert pca_64.transform(X_64).dtype == np.float64
        assert pca_32.transform(X_32).dtype == np.float32
    
        assert_array_almost_equal(pca_64.components_, pca_32.components_,
>                                 decimal=5)
E       AssertionError: 
E       Arrays are not almost equal to 5 decimals
E       
E       (mismatch 16.6666666667%)
E        x: array([[ 0.62022,  0.15983, -0.38317, -0.66555],
E              [ 0.26318,  0.24085,  0.90801, -0.21966],
E              [-0.12498, -0.88109,  0.16727, -0.42437]])
E        y: array([[ 0.62022,  0.15983, -0.38317, -0.66555],
E              [ 0.26318,  0.24084,  0.90801, -0.21967],
E              [-0.12498, -0.88109,  0.16726, -0.42436]], dtype=float32)
X_32       = array([[ 0.54881352,  0.71518934,  0.60276335,  0.54488319],
       [ 0.423654...],
       [ 0.43487364,  0.83000296,  0.93280619,  0.30833843]], dtype=float32)
X_64       = array([[ 0.5488135 ,  0.71518937,  0.60276338,  0.54488318],
       [ 0.423654...91,  0.34963937],
       [ 0.43487363,  0.83000295,  0.93280618,  0.30833843]])
pca_32     = PCA(copy=True, iterated_power='auto', n_components=3, random_state=0,
  svd_solver='randomized', tol=0.0, whiten=False)
pca_64     = PCA(copy=True, iterated_power='auto', n_components=3, random_state=0,
  svd_solver='randomized', tol=0.0, whiten=False)
svd_solver = 'randomized'

I can’t see any change in 0.20.2 that could have caused this new failure.

[qinhanmin2014]

I guess this is likely to be a scipy issue, since we always use the latest version of scipy and scipy just released 1.2.0, but I don’t have a mac.

[jeremiedbb]

leave it open for further investigation?

I don’t think there’s need for further investigation. We can’t expect the same level of accuracy when comparing against float32. The default rtol we use to compare float64 is 1e-7. It’s reasonable to use a rtol between 1e-3 and 1e-4 for float32 since precision is halved.