Update pymc requirement from ==5.16.* to >=5.16,<5.21

[dependabot]

Updates the requirements on pymc to permit the latest version.

Release notes

Sourced from pymc's releases.

v5.20.1

What's Changed

New Features 🎉

• Add ZarrTrace by @​lucianopaz in pymc-devs/pymc#7540

Bugfixes 🪲

• fix: deep copy nuts_sampler_kwarg to prevent .pop side effects by @​inclinedadarsh in pymc-devs/pymc#7652

Documentation 📖

• Adds shape/rate info to Gamma docs by @​Dpananos in pymc-devs/pymc#7625
• Probability distributions guide update by @​fonnesbeck in pymc-devs/pymc#7671

Maintenance 🔧

• Foward compile_kwargs to ADVI when init = "advi+..." by @​jessegrabowski in pymc-devs/pymc#7640
• Check for observed variables in the trace by @​zaxtax in pymc-devs/pymc#7641
• Use jaxified logp for initial point evaluation when sampling via Jax by @​nataziel in pymc-devs/pymc#7610
• Show one progress bar per chain when sampling by @​jessegrabowski in pymc-devs/pymc#7634
• Ignore inner unused RNG inputs in collect_default_updates by @​ricardoV94 in pymc-devs/pymc#7658

New Contributors

• @​nataziel made their first contribution in pymc-devs/pymc#7610
• @​inclinedadarsh made their first contribution in pymc-devs/pymc#7652

Full Changelog: pymc-devs/pymc@v5.20.0...v5.20.1

Changelog

Sourced from pymc's changelog.

Release Notes

⚠️ Moving forward we're no longer updating the RELEASE-NOTES.md document. ⚠️

⚠️ Instead, please check the release notes in the GitHub Releases. ⚠️

PyMC 4.0.0 (2022-06-03)

If you want a description of the highlights of this release, check out the release announcement on our new website. Feel free to read it, print it out, and give it to people on the street -- because everybody has to know PyMC 4.0 is officially out 🍾

Do not miss 🚨

• ⚠️ The project was renamed to "PyMC". Now the library is installed as "pip install pymc" and imported like import pymc as pm. See this migration guide for more details.
• ⚠️ Theano-PyMC has been replaced with Aesara, so all external references to theano and tt need to be replaced with aesara and at, respectively (see 4471).
• ⚠️ Support for JAX and JAX samplers, also allows sampling on GPUs. This benchmark shows speed-ups of up to 11x.
• ⚠️ Random seeding behavior changed (see #5787)!
  • Sampling results will differ from those of v3 when passing the same random_seed as before. They will be consistent across subsequent v4 releases unless mentioned otherwise.
  • Sampling functions no longer respect user-specified global seeding! Always pass random_seed to ensure reproducible behavior.
  • random_seed now accepts RandomState and Generators besides integers.
• ⚠️ The GLM submodule was removed, please use Bambi instead.
• ⚠️ PyMC now requires SciPy version >= 1.4.1 (see #4857).

v3 features not yet working in v4 ⏳

⚠️ We plan to get these working again, but at this point their inner workings have not been refactored.

• MvNormalRandomWalk, MvStudentTRandomWalk, GARCH11 and EulerMaruyama distributions (see #4642)
• Nested Mixture distributions (see #5533)
• pm.sample_posterior_predictive_w (see #4807)
• Partially observed Multivariate distributions (see #5260)

New features 🥳

• Distributions:

  • Univariate censored distributions are now available via pm.Censored. #5169

  • The CAR distribution has been added to allow for use of conditional autoregressions which often are used in spatial and network models.

  • Added a logcdf implementation for the Kumaraswamy distribution (see #4706).

  • The OrderedMultinomial distribution has been added for use on ordinal data which are aggregated by trial, like multinomial observations, whereas OrderedLogistic only accepts ordinal data in a disaggregated format, like categorical observations (see #4773).

  • The Polya-Gamma distribution has been added (see #4531). To make use of this distribution, the polyagamma>=1.3.1 library must be installed and available in the user's environment.

  • pm.DensityDist can now accept an optional logcdf keyword argument to pass in a function to compute the cumulative density function of the distribution (see 5026).

  • pm.DensityDist can now accept an optional moment keyword argument to pass in a function to compute the moment of the distribution (see 5026).

  • Added an alternative parametrization, logit_p to pm.Binomial and pm.Categorical distributions (see 5637).

• Model dimensions:

  • The dimensionality of model variables can now be parametrized through either of shape or dims (see #4696):
    • With shape the length of dimensions must be given numerically or as scalar Aesara Variables. Numeric entries in shape restrict the model variable to the exact length and re-sizing is no longer possible.
    • dims keeps model variables re-sizeable (for example through pm.Data) and leads to well defined coordinates in InferenceData objects.
    • An Ellipsis (...) in the last position of shape or dims can be used as short-hand notation for implied dimensions.

... (truncated)

Commits

• d7d2be2 bump pytensor version dependency
• 3b6e351 Probability distributions guide update (#7671)
• 268e13b remove the Futurewarning in the test
• e0e7511 Ignore inner unused RNG inputs in collect_default_updates
• 0db176c Show one progress bar per chain when sampling (#7634)
• 472da97 fix: deep copy nuts_sampler_kwarg to prevent .pop side effects (#7652)
• fa43eba Use jaxified logp for initial point evaluation when sampling via Jax (#7610)
• 7a995a0 [pre-commit.ci] pre-commit autoupdate (#7653)
• 892c37a Check for observed variables in the trace (#7641)
• 2012262 Create zizmor workflow
• Additional commits viewable in compare view

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[dependabot]

Looks like pymc is no longer updatable, so this is no longer needed.