Comments (2)
One thought at the moment is you can subclass the scipy_optimizer
via something like this for now:
class scipy_optimizer_hess(pyhf.optimize.scipy_optimizer):
def _get_minimizer(self, *args, hess=None, **kwargs):
return lambda *a, **k: scipy.optimize.minimize(*a, **k, hess=hess)
and then you can use it like so:
pyhf.set_backend(pyhf.tensorlib, scipy_optimizer_hess())
just to get a quick way of having it working for right now. This is probably something that needs a bit more thought as minuit
doesn't support the hess
, so we'd need a way to handle it seamlessly.
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Thanks, yes, that can work. It's trickier than I first thought, though, because of how it must interact with fixed_params.
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