SimonBlanke / Gradient-Free-Optimizers

    assert diff_time1 < 0.5

def test_memory_warm_start():

    data = load_breast_cancer()

    X, y = data.data, data.target

    def objective_function(para):

        dtc = DecisionTreeClassifier(

            max_depth=para["max_depth"],

            min_samples_split=para["min_samples_split"],

        )

        scores = cross_val_score(dtc, X, y, cv=5)

        return scores.mean()

    search_space = {

        "max_depth": np.arange(1, 10),

        "min_samples_split": np.arange(2, 20),

    }

    c_time1 = time.time()

    opt0 = RandomSearchOptimizer(search_space)

    opt0.search(objective_function, n_iter=300)

    diff_time1 = time.time() - c_time1

    c_time2 = time.time()

    opt1 = RandomSearchOptimizer(search_space)

    opt1.search(objective_function, n_iter=300, memory_warm_start=opt0.search_data)

    diff_time2 = time.time() - c_time2

    print("\n diff_time1 ", diff_time1)

    print("\n diff_time2 ", diff_time2)

    assert diff_time2 < diff_time1 * 0.5

def test_memory_warm_start_manual():

}

def test_memory_timeSave_0():

    data = load_breast_cancer()

    X, y = data.data, data.target

    def objective_function(para):

        dtc = DecisionTreeClassifier(min_samples_split=para["min_samples_split"])

        scores = cross_val_score(dtc, X, y, cv=5)

        return scores.mean()

    search_space = {

        "min_samples_split": np.arange(2, 20),

    }

    c_time1 = time.time()

    opt = RandomSearchOptimizer(search_space)

    opt.search(objective_function, n_iter=100)

    diff_time1 = time.time() - c_time1

    c_time2 = time.time()

    opt = RandomSearchOptimizer(search_space)

    opt.search(objective_function, n_iter=100, memory=False)

    diff_time2 = time.time() - c_time2

    print("\n diff_time1 ", diff_time1)

    print("\n diff_time2 ", diff_time2)

    assert diff_time1 < diff_time2 * 0.5

def test_memory_timeSave_1():