Function test_distributions

hyperopt/tests/unit/test_vectorize.py:202–273

()

def test_distributions():
    # test that the distributions come out right

    # XXX: test more distributions
    space = {
        "loss": (
            hp_loguniform("lu", -2, 2)
            + hp_qloguniform("qlu", np.log(1 + 0.01), np.log(20), 2)
            + hp_quniform("qu", -4.999, 5, 1)
            + hp_uniform("u", 0, 10)
        ),
        "status": "ok",
    }
    trials = base.Trials()
    N = 1000
    fmin(
        lambda x: x,
        space=space,
        algo=rand.suggest,
        trials=trials,
        max_evals=N,
        rstate=np.random.default_rng(124),
        catch_eval_exceptions=False,
    )
    assert len(trials) == N
    idxs, vals = base.miscs_to_idxs_vals(trials.miscs)
    print(list(idxs.keys()))

    COUNTMAX = 130
    COUNTMIN = 70

    # -- loguniform
    log_lu = np.log(vals["lu"])
    assert len(log_lu) == N
    assert -2 < np.min(log_lu)
    assert np.max(log_lu) < 2
    h = np.histogram(log_lu)[0]
    print(h)
    assert np.all(COUNTMIN < h)
    assert np.all(h < COUNTMAX)

    # -- quantized log uniform
    qlu = vals["qlu"]
    assert np.all(np.fmod(qlu, 2) == 0)
    assert np.min(qlu) == 2
    assert np.max(qlu) == 20
    bc_qlu = np.bincount(qlu)
    assert bc_qlu[2] > bc_qlu[4] > bc_qlu[6] > bc_qlu[8]

    # -- quantized uniform
    qu = vals["qu"]
    assert np.min(qu) == -5
    assert np.max(qu) == 5
    assert np.all(np.fmod(qu, 1) == 0)
    bc_qu = np.bincount(np.asarray(qu).astype("int") + 5)
    assert np.all(40 < bc_qu), bc_qu  # XXX: how to get the distribution flat
    # with new rounding rule?
    assert np.all(bc_qu < 125), bc_qu