def test_pca(n_components, whiten):
"""Test PCA equivalence."""
from sklearn.decomposition import PCA
n_samples, n_dim = 1000, 10
X = np.random.RandomState(0).randn(n_samples, n_dim)
X[:, -1] = np.mean(X[:, :-1], axis=-1) # true X dim is ndim - 1
X_orig = X.copy()
pca_skl = PCA(n_components, whiten=whiten, svd_solver='full')
pca_mne = _PCA(n_components, whiten=whiten)
X_skl = pca_skl.fit_transform(X)
assert_array_equal(X, X_orig)
X_mne = pca_mne.fit_transform(X)
assert_array_equal(X, X_orig)
assert_allclose(X_skl, X_mne)
for key in ('mean_', 'components_', 'explained_variance_',
'explained_variance_ratio_'):
val_skl, val_mne = getattr(pca_skl, key), getattr(pca_mne, key)
assert_allclose(val_skl, val_mne)
if isinstance(n_components, float):
assert 1 < pca_mne.n_components_ < n_dim
elif isinstance(n_components, int):
assert pca_mne.n_components_ == n_components
elif n_components == 'mle':
assert pca_mne.n_components_ == n_dim - 1
else:
assert n_components is None
assert pca_mne.n_components_ == n_dim
def test_pca(n_components, whiten):
"""Test PCA equivalence."""
import sklearn
from sklearn.decomposition import PCA
n_samples, n_dim = 1000, 10
X = np.random.RandomState(0).randn(n_samples, n_dim)
X[:, -1] = np.mean(X[:, :-1], axis=-1) # true X dim is ndim - 1
X_orig = X.copy()
pca_skl = PCA(n_components, whiten=whiten, svd_solver='full')
pca_mne = _PCA(n_components, whiten=whiten)
X_skl = pca_skl.fit_transform(X)
assert_array_equal(X, X_orig)
X_mne = pca_mne.fit_transform(X)
assert_array_equal(X, X_orig)
old_sklearn = LooseVersion(sklearn.__version__) < LooseVersion('0.19')
if whiten and old_sklearn:
X_skl *= np.sqrt((n_samples - 1.) / n_samples)
assert_allclose(X_skl, X_mne)
for key in ('mean_', 'components_',
'explained_variance_', 'explained_variance_ratio_'):
val_skl, val_mne = getattr(pca_skl, key), getattr(pca_mne, key)
if key == 'explained_variance_' and old_sklearn:
val_skl *= n_samples / (n_samples - 1.) # old bug
assert_allclose(val_skl, val_mne)
if isinstance(n_components, float):
assert 1 < pca_mne.n_components_ < n_dim
elif isinstance(n_components, int):
assert pca_mne.n_components_ == n_components
elif n_components == 'mle':
assert pca_mne.n_components_ == n_dim - 1
else:
assert n_components is None
assert pca_mne.n_components_ == n_dim
