Creating a list of potential features that may be nice to have, but aren't necessarily

>0.2.0 feature list about torchsde HOT 3 OPEN

google-research commented on June 27, 2024

>0.2.0 feature list

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Comments (3)

jparkhill commented on June 27, 2024

My number one desired feature is antithetic sampling as an option for sdeint. It's pretty cumbersome to achieve this without editing the package by changing the BrownianInterval's W.

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patrick-kidger commented on June 27, 2024

I've not filled in every detail, but I'd have thought something like this should work:

class AntitheticBrownian(BaseBrownian):
   def __init__(self, bm):
       self.bm = bm

    def __call__(self, ta, tb):
        return -self.bm(ta, tb)

bm = BrownianInterval(...)
ys = torchsde(bm=bm, ...)

abm = AntitheticBrownian(bm)
ays = torchsde(bm=abm, ...)

ys = torch.cat([ys, ays])

Does this approach fail / am I missing something / are you trying to achieve something beyond this?

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jparkhill commented on June 27, 2024

Does work, but it's still much uglier than it needs to be vs sdeint(...,antithetic=True). For innumerable financial applications of this library, this type of option is... priceless ;p Here's a filled in version in case anyone else is following along:

ts = tch.linspace(0,1.,365)
ntraj = 1000
bm = torchsde.BrownianInterval(ts[0], ts[-1], size=(ntraj,sde.brownian_size), levy_area_approximation='space-time')
ys_ = torchsde.sdeint(sde,sde.y0(ntraj=ntraj), ts, bm=bm)
class AntitheticBrownian(torchsde.BrownianInterval):
    def __init__(self, t0, t1, size, bm):
        super(AntitheticBrownian, self).__init__(t0,t1,size, levy_area_approximation='space-time')
        self.bm = bm
    def __call__(self, ta, tb=None, return_U=False, return_A=False):
        Z = self.bm(ta, tb, return_U, return_A)
        if return_U:
            if return_A:
                return -Z[0], Z[1], Z[2]
            else:
                return -Z[0], Z[1]
        else:
            if return_A:
                return -Z[0], Z[1]
            else:
                return -Z
abm = AntitheticBrownian(ts[0], ts[-1], (ntraj,sde.brownian_size), bm)
ays = torchsde.sdeint(sde,sde.y0(ntraj=ntraj), ts, bm=abm)
ys = torch.cat([ys_, ays],1)```

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