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TST use global_random_seed in sklearn/decomposition/tests/test_incremental_pca.py #31250

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TST use global_random_seed in sklearn/decomposition/tests/test_incremental_pca.py #31250

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dc4a9a2
Adding random seed
DeaMariaLeon Apr 25, 2025
ec01f9f
Run the CI for tests that take global_random_seed [all random seeds]
DeaMariaLeon Apr 25, 2025
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32 changes: 16 additions & 16 deletions sklearn/decomposition/tests/test_incremental_pca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,9 +87,9 @@ def test_incremental_pca_sparse(sparse_container):
ipca.partial_fit(X_sparse)


def test_incremental_pca_check_projection():
def test_incremental_pca_check_projection(global_random_seed):
# Test that the projection of data is correct.
rng = np.random.RandomState(1999)
rng = np.random.RandomState(global_random_seed)
n, p = 100, 3
X = rng.randn(n, p) * 0.1
X[:10] += np.array([3, 4, 5])
Expand All @@ -108,9 +108,9 @@ def test_incremental_pca_check_projection():
assert_almost_equal(np.abs(Yt[0][0]), 1.0, 1)


def test_incremental_pca_inverse():
def test_incremental_pca_inverse(global_random_seed):
# Test that the projection of data can be inverted.
rng = np.random.RandomState(1999)
rng = np.random.RandomState(global_random_seed)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= 0.00001 # make middle component relatively small
Expand Down Expand Up @@ -217,9 +217,9 @@ def test_incremental_pca_num_features_change():
ipca.partial_fit(X2)


def test_incremental_pca_batch_signs():
def test_incremental_pca_batch_signs(global_random_seed):
# Test that components_ sign is stable over batch sizes.
rng = np.random.RandomState(1999)
rng = np.random.RandomState(global_random_seed)
n_samples = 100
n_features = 3
X = rng.randn(n_samples, n_features)
Expand Down Expand Up @@ -254,9 +254,9 @@ def test_incremental_pca_partial_fit_small_batch():
assert_allclose(pca.components_, pipca.components_, atol=1e-3)


def test_incremental_pca_batch_values():
def test_incremental_pca_batch_values(global_random_seed):
# Test that components_ values are stable over batch sizes.
rng = np.random.RandomState(1999)
rng = np.random.RandomState(global_random_seed)
n_samples = 100
n_features = 3
X = rng.randn(n_samples, n_features)
Expand Down Expand Up @@ -286,9 +286,9 @@ def test_incremental_pca_batch_rank():
assert_allclose_dense_sparse(components_i, components_j)


def test_incremental_pca_partial_fit():
def test_incremental_pca_partial_fit(global_random_seed):
# Test that fit and partial_fit get equivalent results.
rng = np.random.RandomState(1999)
rng = np.random.RandomState(global_random_seed)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= 0.00001 # make middle component relatively small
Expand Down Expand Up @@ -316,9 +316,9 @@ def test_incremental_pca_against_pca_iris():
assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1)


def test_incremental_pca_against_pca_random_data():
def test_incremental_pca_against_pca_random_data(global_random_seed):
# Test that IncrementalPCA and PCA are approximate (to a sign flip).
rng = np.random.RandomState(1999)
rng = np.random.RandomState(global_random_seed)
n_samples = 100
n_features = 3
X = rng.randn(n_samples, n_features) + 5 * rng.rand(1, n_features)
Expand Down Expand Up @@ -348,10 +348,10 @@ def test_explained_variances():
assert_almost_equal(pca.noise_variance_, ipca.noise_variance_, decimal=prec)


def test_singular_values():
def test_singular_values(global_random_seed):
# Check that the IncrementalPCA output has the correct singular values

rng = np.random.RandomState(0)
rng = np.random.RandomState(global_random_seed)
n_samples = 1000
n_features = 100

Expand All @@ -360,7 +360,7 @@ def test_singular_values():
)

pca = PCA(n_components=10, svd_solver="full", random_state=rng).fit(X)
ipca = IncrementalPCA(n_components=10, batch_size=100).fit(X)
ipca = IncrementalPCA(n_components=10, batch_size=150).fit(X)
assert_array_almost_equal(pca.singular_values_, ipca.singular_values_, 2)

# Compare to the Frobenius norm
Expand All @@ -382,7 +382,7 @@ def test_singular_values():
)

# Set the singular values and see what we get back
rng = np.random.RandomState(0)
rng = np.random.RandomState(global_random_seed)
n_samples = 100
n_features = 110

Expand Down