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[Repo Assist] test: increase sample size in linear classification auto-assignment test #1466

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[Repo Assist] test: increase sample size in linear classification auto-assignment test #1466

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4 changes: 3 additions & 1 deletion tests/gcm/test_auto.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,9 @@ def _generate_non_linear_regression_data():


def _generate_linear_classification_data():
X = np.random.normal(0, 1, (100, 5))
# 500 samples instead of 100 to give cross-validation a stable enough signal
# so that LogisticRegression reliably wins over SVC for linear data.
X = np.random.normal(0, 1, (500, 5))
Y = (np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) > 0).astype(str)
Comment on lines +46 to 47
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Copilot AI Apr 19, 2026

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This test data generator still relies on the global NumPy RNG state, so outcomes can remain non-deterministic (and potentially influenced by other tests consuming randomness). To fully eliminate flakiness, consider using a local, fixed-seed generator (e.g., rng = np.random.default_rng(seed) and calling rng.normal(...), rng.uniform(...)) inside _generate_linear_classification_data() so the dataset is reproducible across runs.

Suggested change
X = np.random.normal(0, 1, (500, 5))
Y = (np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) > 0).astype(str)
rng = np.random.default_rng(0)
X = rng.normal(0, 1, (500, 5))
Y = (np.sum(X * rng.uniform(-5, 5, X.shape[1]), axis=1) > 0).astype(str)

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return X, Y
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