ai-content-maker/.venv/Lib/site-packages/sklearn/decomposition/tests/test_fastica.py

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"""
Test the fastica algorithm.
"""
import itertools
import os
import warnings
import numpy as np
import pytest
from scipy import stats
from sklearn.decomposition import PCA, FastICA, fastica
from sklearn.decomposition._fastica import _gs_decorrelation
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils._testing import assert_allclose
def center_and_norm(x, axis=-1):
"""Centers and norms x **in place**
Parameters
-----------
x: ndarray
Array with an axis of observations (statistical units) measured on
random variables.
axis: int, optional
Axis along which the mean and variance are calculated.
"""
x = np.rollaxis(x, axis)
x -= x.mean(axis=0)
x /= x.std(axis=0)
def test_gs():
# Test gram schmidt orthonormalization
# generate a random orthogonal matrix
rng = np.random.RandomState(0)
W, _, _ = np.linalg.svd(rng.randn(10, 10))
w = rng.randn(10)
_gs_decorrelation(w, W, 10)
assert (w**2).sum() < 1.0e-10
w = rng.randn(10)
u = _gs_decorrelation(w, W, 5)
tmp = np.dot(u, W.T)
assert (tmp[:5] ** 2).sum() < 1.0e-10
def test_fastica_attributes_dtypes(global_dtype):
rng = np.random.RandomState(0)
X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
fica = FastICA(
n_components=5, max_iter=1000, whiten="unit-variance", random_state=0
).fit(X)
assert fica.components_.dtype == global_dtype
assert fica.mixing_.dtype == global_dtype
assert fica.mean_.dtype == global_dtype
assert fica.whitening_.dtype == global_dtype
def test_fastica_return_dtypes(global_dtype):
rng = np.random.RandomState(0)
X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
k_, mixing_, s_ = fastica(
X, max_iter=1000, whiten="unit-variance", random_state=rng
)
assert k_.dtype == global_dtype
assert mixing_.dtype == global_dtype
assert s_.dtype == global_dtype
@pytest.mark.parametrize("add_noise", [True, False])
def test_fastica_simple(add_noise, global_random_seed, global_dtype):
if (
global_random_seed == 20
and global_dtype == np.float32
and not add_noise
and os.getenv("DISTRIB") == "ubuntu"
):
pytest.xfail(
"FastICA instability with Ubuntu Atlas build with float32 "
"global_dtype. For more details, see "
"https://github.com/scikit-learn/scikit-learn/issues/24131#issuecomment-1208091119" # noqa
)
# Test the FastICA algorithm on very simple data.
rng = np.random.RandomState(global_random_seed)
n_samples = 1000
# Generate two sources:
s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
s2 = stats.t.rvs(1, size=n_samples, random_state=global_random_seed)
s = np.c_[s1, s2].T
center_and_norm(s)
s = s.astype(global_dtype)
s1, s2 = s
# Mixing angle
phi = 0.6
mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi), -np.cos(phi)]])
mixing = mixing.astype(global_dtype)
m = np.dot(mixing, s)
if add_noise:
m += 0.1 * rng.randn(2, 1000)
center_and_norm(m)
# function as fun arg
def g_test(x):
return x**3, (3 * x**2).mean(axis=-1)
algos = ["parallel", "deflation"]
nls = ["logcosh", "exp", "cube", g_test]
whitening = ["arbitrary-variance", "unit-variance", False]
for algo, nl, whiten in itertools.product(algos, nls, whitening):
if whiten:
k_, mixing_, s_ = fastica(
m.T, fun=nl, whiten=whiten, algorithm=algo, random_state=rng
)
with pytest.raises(ValueError):
fastica(m.T, fun=np.tanh, whiten=whiten, algorithm=algo)
else:
pca = PCA(n_components=2, whiten=True, random_state=rng)
X = pca.fit_transform(m.T)
k_, mixing_, s_ = fastica(
X, fun=nl, algorithm=algo, whiten=False, random_state=rng
)
with pytest.raises(ValueError):
fastica(X, fun=np.tanh, algorithm=algo)
s_ = s_.T
# Check that the mixing model described in the docstring holds:
if whiten:
# XXX: exact reconstruction to standard relative tolerance is not
# possible. This is probably expected when add_noise is True but we
# also need a non-trivial atol in float32 when add_noise is False.
#
# Note that the 2 sources are non-Gaussian in this test.
atol = 1e-5 if global_dtype == np.float32 else 0
assert_allclose(np.dot(np.dot(mixing_, k_), m), s_, atol=atol)
center_and_norm(s_)
s1_, s2_ = s_
# Check to see if the sources have been estimated
# in the wrong order
if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
s2_, s1_ = s_
s1_ *= np.sign(np.dot(s1_, s1))
s2_ *= np.sign(np.dot(s2_, s2))
# Check that we have estimated the original sources
if not add_noise:
assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-2)
assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-2)
else:
assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-1)
assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-1)
# Test FastICA class
_, _, sources_fun = fastica(
m.T, fun=nl, algorithm=algo, random_state=global_random_seed
)
ica = FastICA(fun=nl, algorithm=algo, random_state=global_random_seed)
sources = ica.fit_transform(m.T)
assert ica.components_.shape == (2, 2)
assert sources.shape == (1000, 2)
assert_allclose(sources_fun, sources)
# Set atol to account for the different magnitudes of the elements in sources
# (from 1e-4 to 1e1).
atol = np.max(np.abs(sources)) * (1e-5 if global_dtype == np.float32 else 1e-7)
assert_allclose(sources, ica.transform(m.T), atol=atol)
assert ica.mixing_.shape == (2, 2)
ica = FastICA(fun=np.tanh, algorithm=algo)
with pytest.raises(ValueError):
ica.fit(m.T)
def test_fastica_nowhiten():
m = [[0, 1], [1, 0]]
# test for issue #697
ica = FastICA(n_components=1, whiten=False, random_state=0)
warn_msg = "Ignoring n_components with whiten=False."
with pytest.warns(UserWarning, match=warn_msg):
ica.fit(m)
assert hasattr(ica, "mixing_")
def test_fastica_convergence_fail():
# Test the FastICA algorithm on very simple data
# (see test_non_square_fastica).
# Ensure a ConvergenceWarning raised if the tolerance is sufficiently low.
rng = np.random.RandomState(0)
n_samples = 1000
# Generate two sources:
t = np.linspace(0, 100, n_samples)
s1 = np.sin(t)
s2 = np.ceil(np.sin(np.pi * t))
s = np.c_[s1, s2].T
center_and_norm(s)
# Mixing matrix
mixing = rng.randn(6, 2)
m = np.dot(mixing, s)
# Do fastICA with tolerance 0. to ensure failing convergence
warn_msg = (
"FastICA did not converge. Consider increasing tolerance "
"or the maximum number of iterations."
)
with pytest.warns(ConvergenceWarning, match=warn_msg):
ica = FastICA(
algorithm="parallel", n_components=2, random_state=rng, max_iter=2, tol=0.0
)
ica.fit(m.T)
@pytest.mark.parametrize("add_noise", [True, False])
def test_non_square_fastica(add_noise):
# Test the FastICA algorithm on very simple data.
rng = np.random.RandomState(0)
n_samples = 1000
# Generate two sources:
t = np.linspace(0, 100, n_samples)
s1 = np.sin(t)
s2 = np.ceil(np.sin(np.pi * t))
s = np.c_[s1, s2].T
center_and_norm(s)
s1, s2 = s
# Mixing matrix
mixing = rng.randn(6, 2)
m = np.dot(mixing, s)
if add_noise:
m += 0.1 * rng.randn(6, n_samples)
center_and_norm(m)
k_, mixing_, s_ = fastica(
m.T, n_components=2, whiten="unit-variance", random_state=rng
)
s_ = s_.T
# Check that the mixing model described in the docstring holds:
assert_allclose(s_, np.dot(np.dot(mixing_, k_), m))
center_and_norm(s_)
s1_, s2_ = s_
# Check to see if the sources have been estimated
# in the wrong order
if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
s2_, s1_ = s_
s1_ *= np.sign(np.dot(s1_, s1))
s2_ *= np.sign(np.dot(s2_, s2))
# Check that we have estimated the original sources
if not add_noise:
assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-3)
assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-3)
def test_fit_transform(global_random_seed, global_dtype):
"""Test unit variance of transformed data using FastICA algorithm.
Check that `fit_transform` gives the same result as applying
`fit` and then `transform`.
Bug #13056
"""
# multivariate uniform data in [0, 1]
rng = np.random.RandomState(global_random_seed)
X = rng.random_sample((100, 10)).astype(global_dtype)
max_iter = 300
for whiten, n_components in [["unit-variance", 5], [False, None]]:
n_components_ = n_components if n_components is not None else X.shape[1]
ica = FastICA(
n_components=n_components, max_iter=max_iter, whiten=whiten, random_state=0
)
with warnings.catch_warnings():
# make sure that numerical errors do not cause sqrt of negative
# values
warnings.simplefilter("error", RuntimeWarning)
# XXX: for some seeds, the model does not converge.
# However this is not what we test here.
warnings.simplefilter("ignore", ConvergenceWarning)
Xt = ica.fit_transform(X)
assert ica.components_.shape == (n_components_, 10)
assert Xt.shape == (X.shape[0], n_components_)
ica2 = FastICA(
n_components=n_components, max_iter=max_iter, whiten=whiten, random_state=0
)
with warnings.catch_warnings():
# make sure that numerical errors do not cause sqrt of negative
# values
warnings.simplefilter("error", RuntimeWarning)
warnings.simplefilter("ignore", ConvergenceWarning)
ica2.fit(X)
assert ica2.components_.shape == (n_components_, 10)
Xt2 = ica2.transform(X)
# XXX: we have to set atol for this test to pass for all seeds when
# fitting with float32 data. Is this revealing a bug?
if global_dtype:
atol = np.abs(Xt2).mean() / 1e6
else:
atol = 0.0 # the default rtol is enough for float64 data
assert_allclose(Xt, Xt2, atol=atol)
@pytest.mark.filterwarnings("ignore:Ignoring n_components with whiten=False.")
@pytest.mark.parametrize(
"whiten, n_components, expected_mixing_shape",
[
("arbitrary-variance", 5, (10, 5)),
("arbitrary-variance", 10, (10, 10)),
("unit-variance", 5, (10, 5)),
("unit-variance", 10, (10, 10)),
(False, 5, (10, 10)),
(False, 10, (10, 10)),
],
)
def test_inverse_transform(
whiten, n_components, expected_mixing_shape, global_random_seed, global_dtype
):
# Test FastICA.inverse_transform
n_samples = 100
rng = np.random.RandomState(global_random_seed)
X = rng.random_sample((n_samples, 10)).astype(global_dtype)
ica = FastICA(n_components=n_components, random_state=rng, whiten=whiten)
with warnings.catch_warnings():
# For some dataset (depending on the value of global_dtype) the model
# can fail to converge but this should not impact the definition of
# a valid inverse transform.
warnings.simplefilter("ignore", ConvergenceWarning)
Xt = ica.fit_transform(X)
assert ica.mixing_.shape == expected_mixing_shape
X2 = ica.inverse_transform(Xt)
assert X.shape == X2.shape
# reversibility test in non-reduction case
if n_components == X.shape[1]:
# XXX: we have to set atol for this test to pass for all seeds when
# fitting with float32 data. Is this revealing a bug?
if global_dtype:
# XXX: dividing by a smaller number makes
# tests fail for some seeds.
atol = np.abs(X2).mean() / 1e5
else:
atol = 0.0 # the default rtol is enough for float64 data
assert_allclose(X, X2, atol=atol)
def test_fastica_errors():
n_features = 3
n_samples = 10
rng = np.random.RandomState(0)
X = rng.random_sample((n_samples, n_features))
w_init = rng.randn(n_features + 1, n_features + 1)
with pytest.raises(ValueError, match=r"alpha must be in \[1,2\]"):
fastica(X, fun_args={"alpha": 0})
with pytest.raises(
ValueError, match="w_init has invalid shape.+" r"should be \(3L?, 3L?\)"
):
fastica(X, w_init=w_init)
def test_fastica_whiten_unit_variance():
"""Test unit variance of transformed data using FastICA algorithm.
Bug #13056
"""
rng = np.random.RandomState(0)
X = rng.random_sample((100, 10))
n_components = X.shape[1]
ica = FastICA(n_components=n_components, whiten="unit-variance", random_state=0)
Xt = ica.fit_transform(X)
assert np.var(Xt) == pytest.approx(1.0)
@pytest.mark.parametrize("whiten", ["arbitrary-variance", "unit-variance", False])
@pytest.mark.parametrize("return_X_mean", [True, False])
@pytest.mark.parametrize("return_n_iter", [True, False])
def test_fastica_output_shape(whiten, return_X_mean, return_n_iter):
n_features = 3
n_samples = 10
rng = np.random.RandomState(0)
X = rng.random_sample((n_samples, n_features))
expected_len = 3 + return_X_mean + return_n_iter
out = fastica(
X, whiten=whiten, return_n_iter=return_n_iter, return_X_mean=return_X_mean
)
assert len(out) == expected_len
if not whiten:
assert out[0] is None
@pytest.mark.parametrize("add_noise", [True, False])
def test_fastica_simple_different_solvers(add_noise, global_random_seed):
"""Test FastICA is consistent between whiten_solvers."""
rng = np.random.RandomState(global_random_seed)
n_samples = 1000
# Generate two sources:
s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
s2 = stats.t.rvs(1, size=n_samples, random_state=rng)
s = np.c_[s1, s2].T
center_and_norm(s)
s1, s2 = s
# Mixing angle
phi = rng.rand() * 2 * np.pi
mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi), -np.cos(phi)]])
m = np.dot(mixing, s)
if add_noise:
m += 0.1 * rng.randn(2, 1000)
center_and_norm(m)
outs = {}
for solver in ("svd", "eigh"):
ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver=solver)
sources = ica.fit_transform(m.T)
outs[solver] = sources
assert ica.components_.shape == (2, 2)
assert sources.shape == (1000, 2)
# compared numbers are not all on the same magnitude. Using a small atol to
# make the test less brittle
assert_allclose(outs["eigh"], outs["svd"], atol=1e-12)
def test_fastica_eigh_low_rank_warning(global_random_seed):
"""Test FastICA eigh solver raises warning for low-rank data."""
rng = np.random.RandomState(global_random_seed)
A = rng.randn(10, 2)
X = A @ A.T
ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver="eigh")
msg = "There are some small singular values"
with pytest.warns(UserWarning, match=msg):
ica.fit(X)