a2a7810d41524ee038baf5508ef374e1bc970fd3,lightning/impl/tests/test_sag.py,,test_l1_regularized_saga,#,252

Before Change

                              penalty="l1", random_state=0)
    saga = SAGAClassifier(eta=1e-3, alpha=0.0, beta=beta, max_iter=10,
                          penalty="l1", random_state=0)
    pysaga.fit(X_bin, y_bin)
    saga.fit(X_bin, y_bin)
    np.testing.assert_array_almost_equal(pysaga.coef_, saga.coef_)

After Change

def test_l1_regularized_saga():
    X_sparse = sparse.rand(100, 50, density=.5, random_state=0).tocsr()

    y_sparse = np.random.randint(0, high=2, size=100)

    for (X, y) in ((X_bin, y_bin), (X_sparse, y_sparse)):
        for beta in np.logspace(-3, 3, 10):
            pysaga = PySAGAClassifier(eta=1e-3, alpha=0.0, beta=beta,

Instances