Beispiel #1
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def test_sbo_params():
    params = {"alpha": 0.9, "p_mutation": 0.05, "z": 0.02}

    new_sbo = sbo.SBO(params=params)

    assert new_sbo.alpha == 0.9

    assert new_sbo.p_mutation == 0.05

    assert new_sbo.z == 0.02
Beispiel #2
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def test_sbo_hyperparams():
    hyperparams = {'alpha': 0.9, 'p_mutation': 0.05, 'z': 0.02}

    new_sbo = sbo.SBO(hyperparams=hyperparams)

    assert new_sbo.alpha == 0.9

    assert new_sbo.p_mutation == 0.05

    assert new_sbo.z == 0.02
Beispiel #3
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def test_sbo_update():
    def square(x):
        return np.sum(x**2)

    search_space = search.SearchSpace(
        n_agents=2, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]
    )

    new_sbo = sbo.SBO()
    new_sbo.compile(search_space)
    new_sbo.p_mutation = 1

    new_sbo.update(search_space, square)
Beispiel #4
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def test_sbo_compile():
    search_space = search.SearchSpace(
        n_agents=2, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]
    )

    new_sbo = sbo.SBO()
    new_sbo.compile(search_space)

    try:
        new_sbo.sigma = 1
    except:
        new_sbo.sigma = []

    assert new_sbo.sigma == []
Beispiel #5
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def test_sbo_update():
    def square(x):
        return np.sum(x**2)

    new_function = function.Function(pointer=square)

    new_sbo = sbo.SBO()

    search_space = search.SearchSpace(n_agents=2, n_iterations=10,
                                      n_variables=2, lower_bound=[1, 1],
                                      upper_bound=[10, 10])

    new_sbo._update(search_space.agents,
                    search_space.best_agent, new_function, np.array([0.5, 0.5]))

    assert search_space.agents[0].position[0] != 0
Beispiel #6
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def test_sbo_run():
    def square(x):
        return np.sum(x**2)

    def hook(optimizer, space, function):
        return

    new_function = function.Function(pointer=square)

    new_sbo = sbo.SBO()

    search_space = search.SearchSpace(n_agents=10, n_iterations=30,
                                      n_variables=2, lower_bound=[0, 0],
                                      upper_bound=[10, 10])

    history = new_sbo.run(search_space, new_function, pre_evaluation=hook)

    assert len(history.agents) > 0
    assert len(history.best_agent) > 0

    best_fitness = history.best_agent[-1][1]
    assert best_fitness <= constants.TEST_EPSILON, 'The algorithm sbo failed to converge.'
Beispiel #7
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def test_sbo_hyperparams_setter():
    new_sbo = sbo.SBO()

    try:
        new_sbo.alpha = 'a'
    except:
        new_sbo.alpha = 0.75

    try:
        new_sbo.alpha = -1
    except:
        new_sbo.alpha = 0.75

    assert new_sbo.alpha == 0.75

    try:
        new_sbo.p_mutation = 'b'
    except:
        new_sbo.p_mutation = 0.05

    try:
        new_sbo.p_mutation = 1.5
    except:
        new_sbo.p_mutation = 0.05

    assert new_sbo.p_mutation == 0.05

    try:
        new_sbo.z = 'c'
    except:
        new_sbo.z = 0.02

    try:
        new_sbo.z = 1.5
    except:
        new_sbo.z = 0.02

    assert new_sbo.z == 0.02
Beispiel #8
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def test_sbo_params_setter():
    new_sbo = sbo.SBO()

    try:
        new_sbo.alpha = "a"
    except:
        new_sbo.alpha = 0.75

    try:
        new_sbo.alpha = -1
    except:
        new_sbo.alpha = 0.75

    assert new_sbo.alpha == 0.75

    try:
        new_sbo.p_mutation = "b"
    except:
        new_sbo.p_mutation = 0.05

    try:
        new_sbo.p_mutation = 1.5
    except:
        new_sbo.p_mutation = 0.05

    assert new_sbo.p_mutation == 0.05

    try:
        new_sbo.z = "c"
    except:
        new_sbo.z = 0.02

    try:
        new_sbo.z = 1.5
    except:
        new_sbo.z = 0.02

    assert new_sbo.z == 0.02
Beispiel #9
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def test_sbo_build():
    new_sbo = sbo.SBO()

    assert new_sbo.built == True