Update pymc requirement from ==5.20.* to >=5.20,<5.22

[dependabot]

Updates the requirements on pymc to permit the latest version.

Release notes

Sourced from pymc's releases.

v5.21.0

What's Changed

Major Changes 🛠

• Bump PyTensor and support numpy>2.0 and Python 3.13 by @​ricardoV94 in pymc-devs/pymc#7688
  • Note: Checking if variable:, equivalent to bool(variable) now raises for PyMC variables. Use if variable is not None or whatever is appropriate in your context.
• Remove deprecated generator data by @​wd60622 in pymc-devs/pymc#7664

Bugfixes 🪲

• Fix bug with chained CustomSymbolicDists by @​ricardoV94 in pymc-devs/pymc#7690
• Fix bug when reusing jax logp for initial point generation by @​ricardoV94 in pymc-devs/pymc#7695

Maintenance 🔧

• Relax observe to allow observing already observed variables by @​zaxtax in pymc-devs/pymc#7679
• Reuse jaxified logp when sampling via jax by @​nataziel in pymc-devs/pymc#7681

Full Changelog: pymc-devs/pymc@v5.20.1...v5.21.0

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

• 62335ac Fix bug when reusing jax logp for initial point generation
• 355b475 Bump PyTensor and support Numpy>2.0 and Python=3.13
• 2832e98 Mypy doesn't believe you can call max on numpy arrays
• 7163032 More precise type-hints
• f9e0f8c Chaining classmethod and property no longer supported in Python 3.13
• f374411 Fix bool usages with PyTensor variables
• 0035ab7 Replace arange by range for iteration
• 9343436 Remove PolyaGamma pin and add to conda env
• b7b969a Remove m2w64-toolchain windows conda dependency
• 4170386 Remove h5py conda dependency
• Additional commits viewable in compare view

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