Function test_base_example

nevergrad/optimization/test_doc.py:32–99

()

# pylint: disable=function-redefined
def test_base_example() -> None:
    # DOC_BASE_0
    import nevergrad as ng

    def square(x, y=12):
        return sum((x - 0.5) ** 2) + abs(y)

    # optimization on x as an array of shape (2,)
    optimizer = ng.optimizers.NGOpt(parametrization=2, budget=100)
    recommendation = optimizer.minimize(square)  # best value
    print(recommendation.value)
    # >>> [0.49971112 0.5002944 ]
    # DOC_BASE_1
    instrum = ng.p.Instrumentation(
        ng.p.Array(shape=(2,)).set_bounds(lower=-12, upper=12),
        y=ng.p.Scalar()
    )
    optimizer = ng.optimizers.NGOpt(parametrization=instrum, budget=100)
    recommendation = optimizer.minimize(square)
    print(recommendation.value)
    # >>> ((array([0.52213095, 0.45030925]),), {'y': -0.0003603100877068604})
    # DOC_BASE_2
    from concurrent import futures

    optimizer = ng.optimizers.NGOpt(parametrization=instrum, budget=10, num_workers=2)
    # We use ThreadPoolExecutor for CircleCI but please
    # use the line just below, with ProcessPoolExecutor instead (unless your
    # code is I/O bound rather than CPU bound):
    with futures.ThreadPoolExecutor(max_workers=optimizer.num_workers) as executor:
    #with futures.ProcessPoolExecutor(max_workers=optimizer.num_workers) as executor:
        recommendation = optimizer.minimize(square, executor=executor, batch_mode=False)
    # DOC_BASE_3
    import nevergrad as ng

    def square(x, y=12):
        return sum((x - 0.5) ** 2) + abs(y)

    instrum = ng.p.Instrumentation(ng.p.Array(shape=(2,)), y=ng.p.Scalar())  # We are working on R^2 x R.
    optimizer = ng.optimizers.NGOpt(parametrization=instrum, budget=100, num_workers=1)

    for _ in range(optimizer.budget):
        x = optimizer.ask()
        loss = square(*x.args, **x.kwargs)
        optimizer.tell(x, loss)

    recommendation = optimizer.provide_recommendation()
    print(recommendation.value)
    # DOC_BASE_4
    import nevergrad as ng

    def onemax(x):
        return len(x) - x.count(1)

    # Discrete, ordered
    param = ng.p.TransitionChoice(range(7), repetitions=10)
    optimizer = ng.optimizers.DiscreteOnePlusOne(parametrization=param, budget=100, num_workers=1)

    recommendation = optimizer.provide_recommendation()