Update NumCosmo walkers

[combet]

From @vitenti - API has slightly changed with the latest conda released of NC (v0.15.4). In NumCosmo examples, e.g. example2...:

Ncm.FitESMCMCWalkerAPS.new (nwalkers, mset3.fparams_len ())
should be replaced by
Ncm.FitESMCMCWalkerAPES.new (nwalkers, mset3.fparams_len ())

Also, with this new version of the sampler, the number of walkers needs to be increase
nwalkers = 100 --> nwalkers=800

[hsinfan1996]

The version number of NC seems to be 1 after NC gets imported. Not sure if it is possible to distinguish between different versions of NC.
Cell 22:

try:
    nwalkers = 100 # Number of walkers
    walker = Ncm.FitESMCMCWalkerAPS.new (nwalkers, mset3.fparams_len ())
except AttributeError:
    nwalkers=800
    walker = Ncm.FitESMCMCWalkerAPES.new (nwalkers, mset3.fparams_len ())

Cell 26:

try:
    nwalkers = 100
    stretch = Ncm.FitESMCMCWalkerAPS.new (nwalkers, mset3.fparams_len ())
except AttributeError:
    nwalkers=800
    stretch = Ncm.FitESMCMCWalkerAPES.new (nwalkers, mset3.fparams_len ())

[hsinfan1996]

@vitenti In Example2, I tested cell 26 with different versions of NC and the results are:
v0.15.3: 100 walkers, ~23 min (10000 steps)
v0.15.4: 800 walkers, ~37 min (16000 steps)

[vitenti]

@hsinfan1996 In the lastest version (on github) you can try the old behavior of APES using:

Ncm.FitESMCMCWalkerAPES.new_full (nwalkers, mset.fparams_len (), Ncm.FitESMCMCWalkerAPESMethod.KDE, Ncm.FitESMCMCWalkerAPESKType.CAUCHY, 1.0, True)

I'll be releasing a new version of NumCosmo that includes this possibility in the next days. If you like, you could test using this constructor and 100 walkers to see if the previous results is recovered.

The default method used by apes now is Ncm.FitESMCMCWalkerAPESMethod.VKDE which is more costly but much more powerful to probe complicated posteriors.

[hsinfan1996]

@hsinfan1996 In the lastest version (on github) you can try the old behavior of APES using:

Ncm.FitESMCMCWalkerAPES.new_full (nwalkers, mset.fparams_len (), Ncm.FitESMCMCWalkerAPESMethod.KDE, Ncm.FitESMCMCWalkerAPESKType.CAUCHY, 1.0, True)

I'll be releasing a new version of NumCosmo that includes this possibility in the next days. If you like, you could test using this constructor and 100 walkers to see if the previous results is recovered.

The default method used by apes now is Ncm.FitESMCMCWalkerAPESMethod.VKDE which is more costly but much more powerful to probe complicated posteriors.

Thanks. It is good that I do not have to switch between different versions now. I am able to install NC from github, and I will get back to you once I finish the tests.

[hsinfan1996]

@vitenti @combet
NC 0.15.4
old method: ~33 min, 14000 moves
new method: same as previous
I am not sure how should I proceed given what I have right now, because I don't know how NC determines how many steps are needed for each MCMC run. It looks like time per step is the same regardless of the algorithms, so the total number of steps is more important.