コード例 #1
0
ファイル: test_space.py プロジェクト: felker/deephyper
def test_normalize():
    # can you pass a Space instance to the Space constructor?
    space = Space([(0.0, 1.0), (-5, 5), ("a", "b", "c"),
                   (1.0, 5.0, "log-uniform"), ("e", "f")])
    space.set_transformer("normalize")
    X = [[0.0, -5, "a", 1.0, "e"]]
    Xt = np.zeros((1, 5))
    assert_array_equal(space.transform(X), Xt)
    assert_array_equal(space.inverse_transform(Xt), X)
    assert_array_equal(space.inverse_transform(space.transform(X)), X)
コード例 #2
0
ファイル: test_space.py プロジェクト: felker/deephyper
def test_normalize_types():
    # can you pass a Space instance to the Space constructor?
    space = Space([(0.0, 1.0), Integer(-5, 5, dtype=int), (True, False)])
    space.set_transformer("normalize")
    X = [[0.0, -5, False]]
    Xt = np.zeros((1, 3))
    assert_array_equal(space.transform(X), Xt)
    assert_array_equal(space.inverse_transform(Xt), X)
    assert_array_equal(space.inverse_transform(space.transform(X)), X)
    assert isinstance(space.inverse_transform(Xt)[0][0], float)
    assert isinstance(space.inverse_transform(Xt)[0][1], int)
    assert isinstance(space.inverse_transform(Xt)[0][2], (np.bool_, bool))
コード例 #3
0
ファイル: test_space.py プロジェクト: felker/deephyper
def test_normalize_bounds():
    bounds = [(-999, 189000), Categorical((True, False))]
    space = Space(normalize_dimensions(bounds))
    for a in np.linspace(1e-9, 0.4999, 1000):
        x = space.inverse_transform([[a, a]])
        check_limits(x[0][0], -999, 189000)
        y = space.transform(x)
        check_limits(y, 0.0, 1.0)
    for a in np.linspace(0.50001, 1e-9 + 1.0, 1000):
        x = space.inverse_transform([[a, a]])
        check_limits(x[0][0], -999, 189000)
        y = space.transform(x)
        check_limits(y, 0.0, 1.0)
コード例 #4
0
ファイル: test_space.py プロジェクト: felker/deephyper
def test_space_api():
    space = Space([(0.0, 1.0), (-5, 5), ("a", "b", "c"),
                   (1.0, 5.0, "log-uniform"), ("e", "f")])

    cat_space = Space([(1, "r"), (1.0, "r")])
    assert isinstance(cat_space.dimensions[0], Categorical)
    assert isinstance(cat_space.dimensions[1], Categorical)

    assert_equal(len(space.dimensions), 5)
    assert isinstance(space.dimensions[0], Real)
    assert isinstance(space.dimensions[1], Integer)
    assert isinstance(space.dimensions[2], Categorical)
    assert isinstance(space.dimensions[3], Real)
    assert isinstance(space.dimensions[4], Categorical)

    samples = space.rvs(n_samples=10, random_state=0)
    assert_equal(len(samples), 10)
    assert_equal(len(samples[0]), 5)

    assert isinstance(samples, list)
    for n in range(4):
        assert isinstance(samples[n], list)

    assert isinstance(samples[0][0], numbers.Real)
    assert isinstance(samples[0][1], numbers.Integral)
    assert isinstance(samples[0][2], str)
    assert isinstance(samples[0][3], numbers.Real)
    assert isinstance(samples[0][4], str)

    samples_transformed = space.transform(samples)
    assert_equal(samples_transformed.shape[0], len(samples))
    assert_equal(samples_transformed.shape[1], 1 + 1 + 3 + 1 + 1)

    # our space contains mixed types, this means we can't use
    # `array_allclose` or similar to check points are close after a round-trip
    # of transformations
    for orig, round_trip in zip(samples,
                                space.inverse_transform(samples_transformed)):
        assert space.distance(orig, round_trip) < 1.0e-8

    samples = space.inverse_transform(samples_transformed)
    assert isinstance(samples[0][0], numbers.Real)
    assert isinstance(samples[0][1], numbers.Integral)
    assert isinstance(samples[0][2], str)
    assert isinstance(samples[0][3], numbers.Real)
    assert isinstance(samples[0][4], str)

    for b1, b2 in zip(
            space.bounds,
        [
            (0.0, 1.0),
            (-5, 5),
            np.asarray(["a", "b", "c"]),
            (1.0, 5.0),
            np.asarray(["e", "f"]),
        ],
    ):
        assert_array_equal(b1, b2)

    for b1, b2 in zip(
            space.transformed_bounds,
        [
            (0.0, 1.0),
            (-5, 5),
            (0.0, 1.0),
            (0.0, 1.0),
            (0.0, 1.0),
            (np.log10(1.0), np.log10(5.0)),
            (0.0, 1.0),
        ],
    ):
        assert_array_equal(b1, b2)