Skip to content

TST Use global_random_seed in test_huber.py #30912

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 6 commits into
base: main
Choose a base branch
from
Open

TST Use global_random_seed in test_huber.py #30912

Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
0bf6c06
Replaced all hardcoded randon seeds with global_random_seed
ArturoSbr Feb 27, 2025
cd151cf
Black compliant file
ArturoSbr Feb 27, 2025
b404b02
empty commit to trigger all CI jobs [all random seeds]
ArturoSbr Feb 27, 2025
8790327
Merge branch 'scikit-learn:main' into feature/global_random_seed_hube…
ArturoSbr Mar 3, 2025
f8e8f11
Empty commit to trigger CI jobs
ArturoSbr Mar 3, 2025
0de17e6
Merge branch 'main' into feature/global_random_seed_huber_tests
ArturoSbr Apr 11, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
57 changes: 31 additions & 26 deletions sklearn/linear_model/tests/test_huber.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,11 +16,14 @@
from sklearn.utils.fixes import CSR_CONTAINERS


def make_regression_with_outliers(n_samples=50, n_features=20):
rng = np.random.RandomState(0)
def make_regression_with_outliers(global_random_seed, n_samples=50, n_features=20):
rng = np.random.RandomState(global_random_seed)
# Generate data with outliers by replacing 10% of the samples with noise.
X, y = make_regression(
n_samples=n_samples, n_features=n_features, random_state=0, noise=0.05
n_samples=n_samples,
n_features=n_features,
random_state=global_random_seed,
noise=0.05,
)

# Replace 10% of the sample with noise.
Expand All @@ -30,9 +33,9 @@ def make_regression_with_outliers(n_samples=50, n_features=20):
return X, y


def test_huber_equals_lr_for_high_epsilon():
def test_huber_equals_lr_for_high_epsilon(global_random_seed):
# Test that Ridge matches LinearRegression for large epsilon
X, y = make_regression_with_outliers()
X, y = make_regression_with_outliers(global_random_seed)
lr = LinearRegression()
lr.fit(X, y)
huber = HuberRegressor(epsilon=1e3, alpha=0.0)
Expand All @@ -41,17 +44,17 @@ def test_huber_equals_lr_for_high_epsilon():
assert_almost_equal(huber.intercept_, lr.intercept_, 2)


def test_huber_max_iter():
X, y = make_regression_with_outliers()
def test_huber_max_iter(global_random_seed):
X, y = make_regression_with_outliers(global_random_seed)
huber = HuberRegressor(max_iter=1)
huber.fit(X, y)
assert huber.n_iter_ == huber.max_iter


def test_huber_gradient():
def test_huber_gradient(global_random_seed):
# Test that the gradient calculated by _huber_loss_and_gradient is correct
rng = np.random.RandomState(1)
X, y = make_regression_with_outliers()
rng = np.random.RandomState(global_random_seed)
X, y = make_regression_with_outliers(global_random_seed)
sample_weight = rng.randint(1, 3, (y.shape[0]))

def loss_func(x, *args):
Expand All @@ -73,10 +76,10 @@ def grad_func(x, *args):


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_huber_sample_weights(csr_container):
def test_huber_sample_weights(csr_container, global_random_seed):
# Test sample_weights implementation in HuberRegressor"""

X, y = make_regression_with_outliers()
X, y = make_regression_with_outliers(global_random_seed)
huber = HuberRegressor()
huber.fit(X, y)
huber_coef = huber.coef_
Expand All @@ -92,7 +95,7 @@ def test_huber_sample_weights(csr_container):
assert_array_almost_equal(huber.coef_ / scale, huber_coef / scale)
assert_array_almost_equal(huber.intercept_ / scale, huber_intercept / scale)

X, y = make_regression_with_outliers(n_samples=5, n_features=20)
X, y = make_regression_with_outliers(global_random_seed, n_samples=5, n_features=20)
X_new = np.vstack((X, np.vstack((X[1], X[1], X[3]))))
y_new = np.concatenate((y, [y[1]], [y[1]], [y[3]]))
huber.fit(X_new, y_new)
Expand All @@ -114,8 +117,8 @@ def test_huber_sample_weights(csr_container):


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_huber_sparse(csr_container):
X, y = make_regression_with_outliers()
def test_huber_sparse(csr_container, global_random_seed):
X, y = make_regression_with_outliers(global_random_seed)
huber = HuberRegressor(alpha=0.1)
huber.fit(X, y)

Expand All @@ -126,9 +129,9 @@ def test_huber_sparse(csr_container):
assert_array_equal(huber.outliers_, huber_sparse.outliers_)


def test_huber_scaling_invariant():
def test_huber_scaling_invariant(global_random_seed):
# Test that outliers filtering is scaling independent.
X, y = make_regression_with_outliers()
X, y = make_regression_with_outliers(global_random_seed)
huber = HuberRegressor(fit_intercept=False, alpha=0.0)
huber.fit(X, y)
n_outliers_mask_1 = huber.outliers_
Expand All @@ -143,10 +146,10 @@ def test_huber_scaling_invariant():
assert_array_equal(n_outliers_mask_3, n_outliers_mask_1)


def test_huber_and_sgd_same_results():
def test_huber_and_sgd_same_results(global_random_seed):
# Test they should converge to same coefficients for same parameters

X, y = make_regression_with_outliers(n_samples=10, n_features=2)
X, y = make_regression_with_outliers(global_random_seed, n_samples=10, n_features=2)

# Fit once to find out the scale parameter. Scale down X and y by scale
# so that the scale parameter is optimized to 1.0
Expand All @@ -161,7 +164,7 @@ def test_huber_and_sgd_same_results():
alpha=0.0,
loss="huber",
shuffle=True,
random_state=0,
random_state=global_random_seed,
max_iter=10000,
fit_intercept=False,
epsilon=1.35,
Expand All @@ -171,8 +174,8 @@ def test_huber_and_sgd_same_results():
assert_array_almost_equal(huber.coef_, sgdreg.coef_, 1)


def test_huber_warm_start():
X, y = make_regression_with_outliers()
def test_huber_warm_start(global_random_seed):
X, y = make_regression_with_outliers(global_random_seed)
huber_warm = HuberRegressor(alpha=1.0, max_iter=10000, warm_start=True, tol=1e-1)

huber_warm.fit(X, y)
Expand All @@ -186,9 +189,9 @@ def test_huber_warm_start():
assert huber_warm.n_iter_ == 0


def test_huber_better_r2_score():
def test_huber_better_r2_score(global_random_seed):
# Test that huber returns a better r2 score than non-outliers"""
X, y = make_regression_with_outliers()
X, y = make_regression_with_outliers(global_random_seed)
huber = HuberRegressor(alpha=0.01)
huber.fit(X, y)
linear_loss = np.dot(X, huber.coef_) + huber.intercept_ - y
Expand All @@ -209,8 +212,10 @@ def test_huber_better_r2_score():
assert ridge_outlier_score > huber_outlier_score


def test_huber_bool():
def test_huber_bool(global_random_seed):
# Test that it does not crash with bool data
X, y = make_regression(n_samples=200, n_features=2, noise=4.0, random_state=0)
X, y = make_regression(
n_samples=200, n_features=2, noise=4.0, random_state=global_random_seed
)
X_bool = X > 0
HuberRegressor().fit(X_bool, y)
Loading