MI values when cholesky failed

[EtienneCmb]

@nvoges @brovelli

The current version of the code failed on Nicoles' simulated data, because of the cholesky decomposition when the data are redundant.

@brovelli suggested np.clip(mi, 0, mi). This avoid negative MI values (I think it should also works on negative infinite). @nvoges do you get infinite values or NaN values? Because if NaN, the clip is not going to make a difference. In addition, I've seen negative MI because of the bias correction so I'm not sure it's a good idea to put a hard threshold at 0, especially because we also compute permutations and I'm afraid of having many permutations to zero because of that.

What do u think?

[brovelli]

You are right, np.clip(mi, 0, mi) cannot work for biased corrected GCMI values.

BUT, you can make the np.clip(mi, 0, mi) before computing the bias corrected values

[EtienneCmb]

Ok, it should works indeed. But @nvoges should clarify if she gets infinite or nan values before fixing it

[nvoges]

hi & sorry for the late reply, but I received this mail on my old Juelich account... I'll try to find and change these settings in git. My initial problem was that I received an error (LinAlg error) and no values at all. If I "force" the code to work (e.g. by including a selected set of nodes) then I can get unreasonable high values (MI~21) but neither nan nor infinite. Maybe you are refering to other values, but which ones?

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[EtienneCmb]

Ok, in that case, the np.clip is not going to fix your issue. One possibility :

try:
    chcx = np.linalg.cholesky(cx)
except:
    return 0

If the cholesky decomposition failed, the returned MI is set to 0. But I'm hesitating of putting it in the gcmi_nd.py functions because I'm afraid of having new silent errors with MI and permuted MI set to zero because it silently fail...

I'm really not sure this should be included in Frites, I think it might be dangerous for us

[EtienneCmb]

Maybe with an additional error message...

@brovelli what do you think?