Shift to the pymc current version

[miemiekurisu]

Update to pymc current version and add some Arviz content

[miemiekurisu]

I'm reading this book again these days and find some of the contents a bit outdated even in pymc3 version. So I plan to update it to pymc current version. I believe that I've tested these new codes in the file well, and it works well under the pymc current version.

[CamDavidsonPilon]

Great, thanks @miemiekurisu. This is pymc4, correct?

[miemiekurisu]

Great, thanks @miemiekurisu. This is pymc4, correct?

Definitely, based on the pymc4. And I found some gaps between pymc4 and 3, different APIs, different usages, etc. But I'll try my best ヾ(≧▽≦*)o

[twiecki]

Thanks for taking this on @miemiekurisu!

[miemiekurisu]

Update Chapter 5, some codes work, but I'm afraid it needs some review or check =￣ω￣=

[twiecki]

@CamDavidsonPilon Can you enable reviewnb for this repo?

[miemiekurisu]

♪(´▽｀) Finished Ch1-6 and plan to check codes again. And add some Arviz content to make it look more "pymc4". ★,°:.☆(￣▽￣)/$:.°★ 。

[twiecki]

Once we have reviewnb added looking forward to reviewing!

[CamDavidsonPilon]

I've added reviewnb to the repo. It's my first time using it, so I'm not 100% where it's accessed by others. Can ya'll access this page?

[miemiekurisu]

I've added reviewnb to the repo. It's my first time using it, so I'm not 100% where it's accessed by others. Can ya'll access this page?

Affirmative~ ヾ(•ω•`)o

[Slepetys]

Hi @miemiekurisu Miemiekurisu,

I restarted reading Davidson-Pilon book last weekend, in parallel with Oswaldo Martin´s book, and both were written with previous versions of PyMC and I was struggling to replicate the results with the current code until I found your fork.

Thank you very much for the effort! And also for adding some Arviz steps, which since PyMC3 makes the data analysis and model development check much easier.

Running it locally or even in Colab, I realized that your running time in the sampling step, even using exactly the same sampler and settings was much faster than mine, do you have any advice on how to speed up it?

Thanks

[miemiekurisu]

@Slepetys Hi，glad you found it helpful q(≧▽≦q) I'm not so sure about the speed difference, maybe it is the hardware difference? I just use i3 12th gen to do this work. To be honest, I think it's so so slow in some cases.

I provide my hardware brief below:

OS: Ubuntu 22.04.1 LTS x86_64 Kernel: 5.15.0-46-generic Shell: bash 5.1.16 CPU: 12th Gen Intel i3-12100 (8) @ 5.500GHz GPU: Intel Device 4692 Memory: 6280MiB / 15744MiB

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twiecki commented on 2022-09-30T13:18:45Z ----------------------------------------------------------------

This should be replaced by parameter.initval.

miemiekurisu commented on 2022-11-08T09:05:14Z ----------------------------------------------------------------

Yeah, you're right, and in fact I've noticed this initval and test_value problem when I upgrade it to pymc4.

According to this URL: https://github.com/pymc-devs/pymc/issues/562#issuecomment-932146862

test_val is a misusing, initval parameter should be used.

I try to keep the original content as much as possible at first, so I used the

aesara.config.compute_test_value = 'warn' 

to keep compatibility.

Maybe I should add some brif comments to explain this problem to previous version readers?

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twiecki commented on 2022-09-30T13:18:45Z ----------------------------------------------------------------

Here as well. There is no more test_value.

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twiecki commented on 2022-09-30T13:18:46Z ----------------------------------------------------------------

import aesara.tensor as at

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twiecki commented on 2022-09-30T13:18:47Z ----------------------------------------------------------------

replace theano -> aesara everywhere.

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twiecki commented on 2022-09-30T13:18:47Z ----------------------------------------------------------------

again usage of test_value.

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twiecki commented on 2022-09-30T13:18:48Z ----------------------------------------------------------------

esara.config.compute_test_value = 'off'  you can probably remove this.

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twiecki commented on 2022-09-30T13:18:49Z ----------------------------------------------------------------

instead of accessing test_value or initval, if you want to sample from a random-variable, you can do pm.draw(first_coin_flips).

And you shouldn't have to set initval here at all then.

miemiekurisu commented on 2022-12-03T11:03:25Z ----------------------------------------------------------------

Got it.

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twiecki commented on 2022-09-30T13:18:50Z ----------------------------------------------------------------

initval should not be required.

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twiecki commented on 2022-09-30T13:18:51Z ----------------------------------------------------------------

import aesara.tensor as at

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twiecki commented on 2022-09-30T13:18:52Z ----------------------------------------------------------------

here I would just pm.draw().

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twiecki commented on 2022-09-30T13:18:52Z ----------------------------------------------------------------

PyMC4 -> PyMC 4

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twiecki commented on 2022-09-30T13:18:53Z ----------------------------------------------------------------

Unfortunately this chapter is quite out-of-date, but since we're just porting here it's oK.

miemiekurisu commented on 2022-12-03T12:10:02Z ----------------------------------------------------------------

LOL

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twiecki commented on 2022-09-30T13:18:55Z ----------------------------------------------------------------

I thought the syntax to pm.Potential has changed and you need to pass pm.Potential(logp=logp).Which version of PyMC 4 is this?

miemiekurisu commented on 2022-12-29T03:49:45Z ----------------------------------------------------------------

PYMC4 version is 4.1.5, let me check the API again

miemiekurisu commented on 2022-12-29T04:24:35Z ----------------------------------------------------------------

Accroding to the last version here: pymc/model.py at main · pymc-devs/pymc (github.com)

def Potential(name, var, model=None):
	    """Add an arbitrary factor potential to the model likelihood
    Parameters
    ----------
    name: str
    var: PyTensor variables

    Returns
    -------
    var: var, with name attribute
    """

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twiecki commented on 2022-09-30T13:18:56Z ----------------------------------------------------------------

Try with just pm.sample().

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twiecki commented on 2022-09-30T13:18:57Z ----------------------------------------------------------------

PyMC3 -> PyMC

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twiecki commented on 2022-09-30T13:18:57Z ----------------------------------------------------------------

T -> at

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twiecki commented on 2022-09-30T13:18:58Z ----------------------------------------------------------------

Maybe add a note that Wishart is discouraged these days and that people should use LKJ.

[twiecki]

@miemiekurisu Sorry this took a while but your port looks really good. There are some minor things, mainly about the usage of test_values but those should be easy to fix.