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TST Use global_random_seed in sklearn/decomposition/tests/test_fastica.py #31203

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Merged
merged 3 commits into from
Apr 15, 2025
Merged

TST Use global_random_seed in sklearn/decomposition/tests/test_fastica.py #31203

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9b96ee6
Adding global random seed
DeaMariaLeon Apr 14, 2025
da26c34
Merge remote-tracking branch 'upstream/main' into tests
DeaMariaLeon Apr 14, 2025
6fb71f8
Run the CI for tests that take global_random_seed [all random seeds]
DeaMariaLeon Apr 14, 2025
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16 changes: 8 additions & 8 deletions sklearn/decomposition/tests/test_fastica.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,10 +32,10 @@ def center_and_norm(x, axis=-1):
x /= x.std(axis=0)


def test_gs():
def test_gs(global_random_seed):
# Test gram schmidt orthonormalization
# generate a random orthogonal matrix
rng = np.random.RandomState(0)
rng = np.random.RandomState(global_random_seed)
W, _, _ = np.linalg.svd(rng.randn(10, 10))
w = rng.randn(10)
_gs_decorrelation(w, W, 10)
Expand Down Expand Up @@ -188,11 +188,11 @@ def test_fastica_nowhiten():
assert hasattr(ica, "mixing_")


def test_fastica_convergence_fail():
def test_fastica_convergence_fail(global_random_seed):
# 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)
rng = np.random.RandomState(global_random_seed)

n_samples = 1000
# Generate two sources:
Expand All @@ -219,9 +219,9 @@ def test_fastica_convergence_fail():


@pytest.mark.parametrize("add_noise", [True, False])
def test_non_square_fastica(add_noise):
def test_non_square_fastica(global_random_seed, add_noise):
# Test the FastICA algorithm on very simple data.
rng = np.random.RandomState(0)
rng = np.random.RandomState(global_random_seed)

n_samples = 1000
# Generate two sources:
Expand Down Expand Up @@ -372,12 +372,12 @@ def test_fastica_errors():
fastica(X, w_init=w_init)


def test_fastica_whiten_unit_variance():
def test_fastica_whiten_unit_variance(global_random_seed):
"""Test unit variance of transformed data using FastICA algorithm.

Bug #13056
"""
rng = np.random.RandomState(0)
rng = np.random.RandomState(global_random_seed)
X = rng.random_sample((100, 10))
n_components = X.shape[1]
ica = FastICA(n_components=n_components, whiten="unit-variance", random_state=0)
Expand Down