Function test_infnan

nevergrad/optimization/test_optimizerlib.py:205–250

(name: str)

@pytest.mark.parametrize("name", short_registry)  # type: ignore
@testing.suppress_nevergrad_warnings()  # hides bad loss
def test_infnan(name: str) -> None:
    if any(x in name for x in ["SMAC", "BO", "AX"]) and os.environ.get("CIRCLECI", False):
        raise SkipTest("too slow for CircleCI!")
    if "Force" in name:
        raise SkipTest("Forced methods not tested for infnan")

    def doint(s):  # Converting a string into an int.
        return 7 + sum([ord(c) * i for i, c in enumerate(s)])

    if doint(name) % 5 > 0:
        raise SkipTest("too many tests for CircleCI!")

    optim_cls = registry[name]
    optim = optim_cls(parametrization=2, budget=70)
    if not (
        any(
            x in name
            for x in [
                "EDA",
                "EMNA",
                "Stupid",
                "NEWUOA",
                "Large",
                "Fmin2",
                "NLOPT",
                "TBPSA",
                "SMAC",
                "BO",
                "Noisy",
                "Chain",
                "chain",  # TODO: remove when possible
            ]
        )
    ):
        recom = optim.minimize(buggy_function)
        result = buggy_function(recom.value)
        if result < 2.0:
            return
        assert (  # The "bad" algorithms, most of them originating in CMA's recommendation rule.
            any(x == name for x in ["WidePSO", "SPSA", "NGOptBase", "Shiwa", "NGO"])
            or isinstance(optim, (optlib.Portfolio, optlib._CMA, optlib.recaster.SequentialRecastOptimizer))
            or "NGOpt" in name
            or "HS" in name
            or "Adapti" in name
            or "MetaModelDiagonalCMA" in name
        )


@skip_win_perf  # type: ignore