8000 ENH Add `eigh` solver to `FastICA` (#22527) · scikit-learn/scikit-learn@54c1503 · GitHub
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ENH Add eigh solver to FastICA (#22527)
Co-authored-by: Pierre Ablin <pierreablin@gmail.com> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
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‎doc/whats_new/v1.2.rst

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@@ -105,6 +105,20 @@ Changelog
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- |Efficiency| Improve runtime performance of :class:`ensemble.IsolationForest`
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by avoiding data copies. :pr:`23252` by :user:`Zhehao Liu <MaxwellLZH>`.
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:mod:`sklearn.decomposition`
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............................
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- |Enhancement| :class:`decomposition.FastICA` now allows the user to select
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how whitening is performed through the new `whiten_solver` parameter, which
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supports `svd` and `eigh`. `whiten_solver` defaults to `svd` although `eigh`
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may be faster and more memory efficient in cases where
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`num_features > num_samples`. An additional `sign_flip` parameter is added.
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When `sign_flip=True`, then the output of both solvers will be reconciled
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during `fit` so that their outputs match. This may change the output of the
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default solver, and hence may not be backwards compatible.
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:pr:`11860` by :user:`Pierre Ablin <pierreablin>`,
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:pr:`22527` by :user:`Meekail Zain <micky774>` and `Thomas Fan`_.
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:mod:`sklearn.impute`
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.....................
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‎sklearn/decomposition/_fastica.py

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@@ -13,7 +13,6 @@
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import numpy as np
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from scipy import linalg
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from ..base import BaseEstimator, TransformerMixin, _ClassNamePrefixFeaturesOutMixin
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from ..exceptions import ConvergenceWarning
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@@ -162,10 +161,12 @@ def fastica(
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max_iter=200,
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tol=1e-04,
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w_init=None,
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whiten_solver="svd",
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random_state=None,
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return_X_mean=False,
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compute_sources=True,
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return_n_iter=False,
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sign_flip=False,
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):
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"""Perform Fast Independent Component Analysis.
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@@ -228,6 +229,18 @@ def my_g(x):
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Initial un-mixing array. If `w_init=None`, then an array of values
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drawn from a normal distribution is used.
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whiten_solver : {"eigh", "svd"}, default="svd"
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The solver to use for whitening.
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- "svd" is more stable numerically if the problem is degenerate, and
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often faster when `n_samples <= n_features`.
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- "eigh" is generally more memory efficient when
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`n_samples >= n_features`, and can be faster when
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`n_samples >= 50 * n_features`.
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.. versionadded:: 1.2
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random_state : int, RandomState instance or None, default=None
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Used to initialize ``w_init`` when not specified, with a
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normal distribution. Pass an int, for reproducible results
@@ -244,6 +257,21 @@ def my_g(x):
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return_n_iter : bool, default=False
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Whether or not to return the number of iterations.
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sign_flip : bool, default=False
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Used to determine whether to enable sign flipping during whitening for
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consistency in output between solvers.
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- If `sign_flip=False` then the output of different choices for
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`whiten_solver` may not be equal. Both outputs will still be correct,
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but may differ numerically.
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- If `sign_flip=True` then the output of both solvers will be
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reconciled during fit so that their outputs match. This may produce
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a different output for each solver when compared to
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`sign_flip=False`.
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.. versionadded:: 1.2
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Returns
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-------
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K : ndarray of shape (n_components, n_features) or None
@@ -300,7 +328,9 @@ def my_g(x):
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max_iter=max_iter,
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tol=tol,
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w_init=w_init,
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whiten_solver=whiten_solver,
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random_state=random_state,
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sign_flip=sign_flip,
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)
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S = est._fit(X, compute_sources=compute_sources)
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@@ -378,12 +408,39 @@ def my_g(x):
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Initial un-mixing array. If `w_init=None`, then an array of values
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drawn from a normal distribution is used.
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whiten_solver : {"eigh", "svd"}, default="svd"
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The solver to use for whitening.
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- "svd" is more stable numerically if the problem is degenerate, and
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often faster when `n_samples <= n_features`.
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- "eigh" is generally more memory efficient when
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`n_samples >= n_features`, and can be faster when
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`n_samples >= 50 * n_features`.
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.. versionadded:: 1.2
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random_state : int, RandomState instance or None, default=None
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Used to initialize ``w_init`` when not specified, with a
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normal distribution. Pass an int, for reproducible results
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across multiple function calls.
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See :term:`Glossary <random_state>`.
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sign_flip : bool, default=False
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Used to determine whether to enable sign flipping during whitening for
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consistency in output between solvers.
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- If `sign_flip=False` then the output of different choices for
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`whiten_solver` may not be equal. Both outputs will still be correct,
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but may differ numerically.
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- If `sign_flip=True` then the output of both solvers will be
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reconciled during fit so that their outputs match. This may produce
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a different output for each solver when compared to
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`sign_flip=False`.
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.. versionadded:: 1.2
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Attributes
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----------
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components_ : ndarray of shape (n_components, n_features)
@@ -457,7 +514,9 @@ def __init__(
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max_iter=200,
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tol=1e-4,
459516
w_init=None,
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whiten_solver="svd",
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random_state=None,
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sign_flip=False,
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):
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super().__init__()
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self.n_components = n_components
@@ -468,7 +527,9 @@ def __init__(
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self.max_iter = max_iter
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self.tol = tol
470529
self.w_init = w_init
530+
self.whiten_solver = whiten_solver
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self.random_state = random_state
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self.sign_flip = sign_flip
472533

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def _fit(self, X, compute_sources=False):
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"""Fit the model.
@@ -557,9 +618,33 @@ def g(x, fun_args):
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XT -= X_mean[:, np.newaxis]
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# Whitening and preprocessing by PCA
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u, d, _ = linalg.svd(XT, full_matrices=False, check_finite=False)
621+
if self.whiten_solver == "eigh":
622+
# Faster when num_samples >> n_features
623+
d, u = linalg.eigh(XT.dot(X))
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sort_indices = np.argsort(d)[::-1]
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eps = np.finfo(d.dtype).eps
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degenerate_idx = d < eps
627+
if np.any(degenerate_idx):
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warnings.warn(
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"There are some small singular values, using "
630+
"whiten_solver = 'svd' might lead to more "
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"accurate results."
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)
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d[degenerate_idx] = eps # For numerical issues
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np.sqrt(d, out=d)
635+
d, u = d[sort_indices], u[:, sort_indices]
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elif self.whiten_solver == "svd":
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u, d = linalg.svd(XT, full_matrices=False, check_finite=False)[:2]
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else:
639+
raise ValueError(
640+
"`whiten_solver` must be 'eigh' or 'svd' but got"
641+
f" {self.whiten_solver} instead"
642+
)
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644+
# Give consistent eigenvectors for both svd solvers
645+
if self.sign_flip:
646+
u *= np.sign(u[0])
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562-
del _
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K = (u / d).T[:n_components] # see (6.33) p.140
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del u, d
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X1 = np.dot(K, XT)

‎sklearn/decomposition/tests/test_fastica.py

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@@ -7,6 +7,7 @@
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import numpy as np
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from scipy import stats
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from sklearn.datasets import make_low_rank_matrix
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_allclose
@@ -422,7 +423,10 @@ def test_fastica_whiten_backwards_compatibility():
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423424
# No warning must be raised in this case.
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av_ica = FastICA(
425-
n_components=n_components, whiten="arbitrary-variance", random_state=0
426+
n_components=n_components,
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whiten="arbitrary-variance",
428+
random_state=0,
429+
whiten_solver="svd",
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)
427431
with warnings.catch_warnings():
428432
warnings.simplefilter("error", FutureWarning)
@@ -457,3 +461,60 @@ def test_fastica_output_shape(whiten, return_X_mean, return_n_iter):
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assert len(out) == expected_len
458462
if not whiten:
459463
assert out[0] is None
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@pytest.mark.parametrize("add_noise", [True, False])
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def test_fastica_simple_different_solvers(add_noise, global_random_seed):
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"""Test FastICA is consistent between whiten_solvers when `sign_flip=True`."""
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rng = np.random.RandomState(global_random_seed)
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n_samples = 1000
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# Generate two sources:
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s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
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s2 = stats.t.rvs(1, size=n_samples, random_state=rng)
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s = np.c_[s1, s2].T
475+
center_and_norm(s)
476+
s1, s2 = s
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478+
# Mixing angle
479+
phi = rng.rand() * 2 * np.pi
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mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi), -np.cos(phi)]])
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m = np.dot(mixing, s)
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if add_noise:
484+
m += 0.1 * rng.randn(2, 1000)
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center_and_norm(m)
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outs = {}
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for solver in ("svd", "eigh"):
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ica = FastICA(
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random_state=0, whiten="unit-variance", whiten_solver=solver, sign_flip=True
492+
)
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sources = ica.fit_transform 2851 (m.T)
494+
outs[solver] = sources
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assert ica.components_.shape == (2, 2)
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assert sources.shape == (1000, 2)
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assert_allclose(outs["eigh"], outs["svd"])
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def test_fastica_eigh_low_rank_warning(global_random_seed):
502+
"""Test FastICA eigh solver raises warning for low-rank data."""
503+
rng = np.random.RandomState(global_random_seed)
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X = make_low_rank_matrix(
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n_samples=10, n_features=10, random_state=rng, effective_rank=2
506+
)
507+
ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver="eigh")
508+
msg = "There are some small singular values"
509+
with pytest.warns(UserWarning, match=msg):
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ica.fit(X)
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@pytest.mark.parametrize("whiten_solver", ["this_should_fail", "test", 1, None])
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def test_fastica_whiten_solver_validation(whiten_solver):
515+
rng = np.random.RandomState(0)
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X = rng.random_sample((10, 2))
517+
ica = FastICA(random_state=rng, whiten_solver=whiten_solver, whiten="unit-variance")
518+
msg = f"`whiten_solver` must be 'eigh' or 'svd' but got {whiten_solver} instead"
519+
with pytest.raises(ValueError, match=msg):
520+
ica.fit_transform(X)

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