tests.test_performance.test_local_opt.test_local_perf()

Complexity

Conditions

2

Size

Total Lines	29
Code Lines	22

Duplication

Lines	29
Ratio	100 %

Importance

Changes

0

import pytest
@pytest.mark.parametrize(*opt_local_l)

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def test_local_perf(Optimizer):
    def objective_function(para):
        score = -para["x1"] * para["x1"]
        return score

    search_space = {"x1": np.arange(-100, 101, 1)}
    initialize = {"vertices": 2}

    n_opts = 33
    n_iter = 100

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = Optimizer(search_space, initialize=initialize)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            verbosity=False,
        )

        scores.append(opt.best_score)
    score_mean = np.array(scores).mean()

    print("\n score_mean", score_mean)

    assert score_mean > -5