We had people from high energy physics argue strongly in the other direction.
I think for most people negative sample weights make no sense.
A possible way around this could be to add a global flag that optionally allows sample weights and deprecate the use of negative sample weights if the flag is not set.

Original issue in #3774. Maybe @ndawe is still around and could say if he still thinks the feature is useful and if having to explicitly enable it with a global flag would be acceptable.

also ping @alexpearce

Apologies I missed that other thread; I did a quick search but it didn't show up.

Please feel free to close this issue and continue the discussion on the other one should you wish to.

Thanks for the ping @amueller.

I still think the feature is very useful. Samples with negative weights play a critical role in the area of high energy physics that I work in. But one does need to understand that one can easily arrive at nonsensical results when using them, so having a warning/error is probably a good sanity check for many use-cases.

I think a global flag is a nice idea, as then the user has to admit "I think I know what I'm doing" and has to interpret any results accordingly.

Would like to check the status of this open item, having a global flag enabling negative sample weight is helpful in my use case. Alternatively, I uses a direct implementation to allow -ve weights:

xw = x * w
xwx = np.dot(xw.T, x)
xwx_inv = np.linalg.inv(xwx)
coef = np.dot(xwx_inv,np.dot(xw.T,y))
xf = np.dot(x, coef)
res = y - xf
res_wss = np.dot(w.T, (res ** 2))
tot_wss = np.dot(w.T, (y ** 2))
wr2 = 1 - res_wss / tot_wss