v4.1.0

What's Changed

Major Changes 🛠

• Dropped support for Python 3.7 and added support for Python 3.10 by @ricardoV94 in #5917
• Default to pm.Data(mutable=False) by @michaelosthege in #5944
• Deprecating MLDA in anticipation of migrating it to pymc-experimental by @michaelosthege in #5944

New Features & Bugfixes 🎉

• Small improvements to early NUTS behaviour by @aseyboldt in #5824
• Correct the order of rvs sent to compile_dlogp in find_MAP by @quantheory in #5928
• Remove nan_is_num and nan_is_high limiters from find_MAP. by @quantheory in #5929
• Registering _as_tensor_variable converter for pandas objects by @juanitorduz in #5920
• Fix model and aesara_config kwargs for pm.Model by @ferrine in #5915

Docs & Maintenance 🔧

• Remove reference to old parameters in SMC docstring by @aloctavodia in #5914
• Get rid of python-version specific conda environments by @ricardoV94 in #5911
• Further fixes to VI docs by @ferrine in #5916
• Expand dimensionality notebook by @ricardoV94 in #5746
• Review docstrings checkmarcked as best practice by @OriolAbril in #5919
• Update conda environment name when running docker with jupyter notebook by @danhphan in #5933
• Update docs build and contributing instructions by @michaelosthege in #5938
• Add numpyro install to building docs instructions by @isms in #5936
• Add version string to conda install command. by @twiecki in #5946

New Contributors

• @quantheory made their first contribution in #5928
• @isms made their first contribution in #5936

Full Changelog: v4.0.1...v4.1.0

v4.0.1

What's Changed

Docs

• PyMC, Aesara and Aeppl intro notebook by @juanitorduz in #5721
• Moved wiki install guides to the docs by @fonnesbeck in #5869
• Fix Examples link in README by @ryanrussell in #5860
• Update dev guide by @michaelosthege in #5810
• Run black on core notebooks by @ricardoV94 in #5901
• Convert rng_seeder to random_seed in 'Prior and Posterior Predictive Checks' notebook by @hectormz in #5896
• Disable dark mode in docs by @ricardoV94 in #5904
• Fixed Student-t process docstring by @kunalghosh in #5853

Bugfixes & Maintenance

• Align advertised Metropolis.stats_dtypes with changes from 1e7d91f by @michaelosthege in #5882
• Added a check in Empirical approximation which does not yet support InferenceData inputs (see #5884) by @ferrine in #5874
• Compute some basic Slice sample stats by @ricardoV94 in #5889
• Fixed bug when sampling discrete variables with SMC by @ricardoV94 in #5887
• Removed t suffix from functions, Model methods and properties by @cuchoi in #5863
  • Model.logpt → Model.logp
  • Model.dlogpt → Model.dlogp
  • Model.d2logpt → Model.d2logp
  • Model.datalogpt → Model.datalogp
  • Model.varlogpt → Model.varlogp
  • Model.observedlogpt → Model.observedlogp
  • Model.potentiallogpt → Model.potentiallogp
  • Model.varlogp_nojact → Model.varlogp_nojac
  • logprob.joint_logpt → logprob.joint_logp
• Remove self-directing arrow in observed nodes by @larryshamalama in #5893
• Update clone_replace strict keyword name by @brandonwillard in #5849
• Renamed pm.Constant to pm.DiracDelta by @cluhmann in #5903
• Update Dockerfile to PyMC v4 by @danhphan in #5881
• Refactor sampling_jax postrocessing to avoid jit by @ferrine in #5908
• Fix compile_fn bug and reduce return type confusion by @michaelosthege in #5909
• Align conda envs and add Windows 3.9 env by @hectormz in #5895
• Include ConstantData in InferenceData returned by JAX samplers by @danhphan in #5807
• Updated Aesara dependency to 2.7.3 by @ricardoV94 in #5910

New Contributors

• @kunalghosh made their first contribution in #5853
• @ryanrussell made their first contribution in #5860
• @hectormz made their first contribution in #5896

Full Changelog: v4.0.0...v4.0.1

PyMC 4.0.0

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.
• ⚠️ 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 cummulative 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.
  • New features for pm.Data containers:
    • With pm.Data(..., mutable=False), or by using pm.ConstantData() one can now create TensorConstant data variables. These can be more performant and compatible in situations where a variable doesn't need to be changed via pm.set_data(). See #5295. If you do need to change the variable, use pm.Data(..., mutable=True), or pm.MutableData().
    • New named dimensions can be introduced to the model via pm.Data(..., dims=...). For mutable data variables (see above) the lengths of these dimensions are symbolic, so they can be re-sized via pm.set_data().
    • pm.Data now passes additional kwargs to aesara.shared/at.as_tensor. #5098.
  • The length of dims in the model is now tracked symbolically through Model.dim_lengths (see #4625).

• Sampling:

  • ⚠️ 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.
  • A small change to the mass matrix tuning methods jitter+adapt_diag (the default) and adapt_diag improves performance early on during tuning for some models. #5004
  • New experimental mass matrix tuning method jitter+adapt_diag_grad. #5004
  • Support for samplers written in JAX:
    • Adding support for numpyro's NUTS sampler via pymc.sampling_jax.sample_numpyro_nuts()
    • Adding support for blackjax's NUTS sampler via pymc.sampling_jax.sample_blackjax_nuts() (see #5477)
    • pymc.sampling_jax samplers support log_likelihood, observed_data, and sample_stats in returned InferenceData object (see #5189)
    • Adding support for pm.Deterministic in pymc.sampling_jax (see #5182)

• Miscellaneous:

  • The new pm.find_constrained_prior function can be used to find optimized prior parameters of a distribution under some
    constraints (e.g lower and upper bound). See #5231.
  • Nested models now inherit the parent model's coordinates. #5344
  • softmax and log_softmax functions added to math module (see #5279).
  • Added the low level compile_forward_sampling_function method to compile the aesara function responsible for generating forward samples (see #5759).

Expected breaking changes 💔

• pm.sample(return_inferencedata=True) is now the default (see #4744).
• ArviZ plots and stats wrappers were removed. The functions are now just available by their original names (see #4549 and 3.11.2 release notes).
• pm.sample_posterior_predictive(vars=...) kwarg was removed in favor of var_names (see #4343).
• ElemwiseCategorical step method was removed (see #4701)
• LKJCholeskyCov's compute_corr keyword argument is now set to True by default (see#5382)
• Alternative sd keyword argument has been removed from all distributions. sigma should be used instead (see #5583).

Read on if you're a developer. Or curious. Or both.

Unexpected breaking changes (action needed) 😲

Very important ⚠️

• pm.Bound interface no longer accepts a callable class as argument, instead it requires an instantiated distribution (created via the .dist() API) to be passed as an argument. In addition, Bound no longer returns a class instance but works as a normal PyMC distribution. Finally, it is no longer possible to do predictive random sampling from Bounded variables. Please, consult the new documentation for details on how to use Bounded variables (see 4815).
• BART has received various updates (5091, 5177, 5229, 4914) but was removed from the main package in #5566. It is now available from pymc-experimental.
• Removed AR1. AR of order 1 should be used instead. (see 5734).
• The pm.EllipticalSlice sampler was removed (see #5756).
• BaseStochasticGradient was removed (see #5630)
• pm.Distribution(...).logp(x) is now pm.logp(pm.Distribution(...), x).
• pm.Distribution(...).logcdf(x) is now pm.logcdf(pm.Distribution(...), x).
• pm.Distribution(...).random(size=x) is now pm.draw(pm.Distribution(...), draws=x).
• pm.draw_values(...) and `pm.genera...

4.0.0 beta 6

What's Changed

• Implemented default transform for Mixtures by @ricardoV94 in #5636
• Scope separator for netcdf by @ferrine in #5663
• Fix default update bug by @ricardoV94 in #5667
• Pandas dependency was removed by @thomasjpfan in #5633
• Recognize cast data in InferenceData by @zaxtax in #5646
• Updated docstrings of multiple distributions by @purna135 in #5595, #5596 and #5600
• Refine Interval docstrings and fix typo by @ricardoV94 in #5640
• Add test for interactions between missing, default and explicit updates in compile_pymc by @ricardoV94 in #5645
• Test reshape from observed by @ricardoV94 in #5670
• Upgraded all CI cache actions to v3 by @michaelosthege in #5647

Full Changelog: v4.0.0b5...v4.0.0b6

4.0.0 beta 5

What's Changed

• Generalize multinomial moment to arbitrary dimensions by @markvrma in #5476
• Remove sd optional kwarg from distributions by @purna135 in #5583
• Improve scoped models by @ferrine in #5607
• Add helper wrapper aound Interval transform by @ricardoV94 in #5347
• Rename logp_transform to _get_default_transform by @ricardoV94 in #5612
• Do not set RNG updates inplace in compile_pymc by @ricardoV94 in #5615
• Refine trigger filter for both PRs and pushes by @michaelosthege in #5619
• Update contributing guide with etiquette section by @michaelosthege in #5611
• Combine test workflows into one by @michaelosthege in #5623
• Raise ValueError if random variables are present in the logp graph by @ricardoV94 in #5614
• Run float32 jobs separately by @michaelosthege in #5630
• Bring back sampler argument target_accept by @aloctavodia in #5622
• Parametrize Binomial and Categorical distributions via logit_p by @purna135 in #5637
• Remove SGMCMC and fix flaky mypy results by @michaelosthege in #5631

Full Changelog: v4.0.0b4...v4.0.0b5

v4.0.0 beta 4

This release adds the following major improvements:

• Refactor Mixture distribution for V4 by @ricardoV94 in #5438
• Adding NUTS sampler from blackjax to sampling_jax by @zaxtax in #5477
• Update aesara and aeppl dependencies to fix a memory leak in pymc models by @ricardoV94 in #5582

New Contributors

• @mirko-m made their first contribution in #5414
• @chritter made their first contribution in #5491
• @5hv5hvnk made their first contribution in #5601

Full Changelog: v4.0.0b3...v4.0.0b4

PyMC 3.11.5

PyMC 3.11.5 (15 March 2022)

This is a backport & bugfix release that eases the transition to pymc >=4.0.0.

Backports

• The pm.logp(rv, x) syntax is now available and recommended to make your model code v4-ready. Note that this backport is just an alias and much less capable than what's available with pymc >=4 (see #5083).
• The pm.Distribution(testval=...) kwarg was deprecated and will be replaced by pm.Distribution(initval=...)in pymc >=4 (see #5226).
• The pm.sample(start=...) kwarg was deprecated and will be replaced by pm.sample(initvals=...)in pymc >=4 (see #5226).
• pm.LogNormal is now available as an alias for pm.Lognormal (see #5389).

Bugfixes

• The upper limit for the SciPy version is <1.8.0 and will most probably remain for all future 3.x.x releases. For compatibility with newer SciPy versions please update to pymc>=4.0.0. Also see #5448.
• A hotfix is applied on import to remain compatible with NumPy 1.22 (see #5316).

PyMC 4.0.0 beta 3

Here is the full list of changes compared to 4.0.0b2.

For a current list of changes w.r.t. the upcoming v3.11.5 see RELEASE-NOTES.md.

Notable changes & features

• ADVI has been ported to PyMC 4
• LKJ has been ported to PyMC 4 (#5382)
• Dependencies have been updated

v4.0.0b2

PyMC 4.0.0 beta 2

This beta release includes the removal of warnings, polishing of APIs, more distributions and internal refactorings.

Here is the full list of changes compared to 4.0.0b1.

For a current list of changes w.r.t. the upcoming v3.11.5 see RELEASE-NOTES.md.

Notable changes & features

• Introduction of pm.Data(..., mutable=False/True) and corresponding pm.ConstantData/pm.MutableData wrappers (see #5295).
• The warning about theano or pymc3 being installed in parallel was removed.
• dims can again be specified alongside shape or size (see #5325).
• pm.draw was added to draw prior samples from a variable (see #5340).
• Renames of model properties & methods like Model.logpt.
• A function to find a prior based on lower/upper bounds (see #5231).

v4.0.0b1

PyMC 4.0.0 beta 1

⚠ This is the first beta of the next major release for PyMC 4.0.0 (formerly PyMC3). 4.0.0 is a rewrite of large parts of the PyMC code base which make it faster, adds many new features, and introduces some breaking changes. For the most part, the API remains stable and we expect that most models will work without any changes.

Not-yet working features

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

• Timeseries distributions (see #4642)
• Mixture distributions (see #4781)
• Cholesky distributions (see WIP PR #4784)
• Variational inference submodule (see WIP PR #4582)
• Elliptical slice sampling (see #5137)
• BaseStochasticGradient (see #5138)
• pm.sample_posterior_predictive_w (see #4807)
• Partially observed Multivariate distributions (see #5260)

Also, check out the milestones for a potentially more complete list.

Unexpected breaking changes (action needed)

• New API is not available in v3.11.5.
• Old API does not work in v4.0.0.

All of the above applies to:

• ⚠ The library is now named, installed, and imported as "pymc". For example: pip install pymc. (Use pip install pymc --pre while we are in the pre-release phase.)
• ⚠ Theano-PyMC has been replaced with Aesara, so all external references to theano, tt, and pymc3.theanof need to be replaced with aesara, at, and pymc.aesaraf (see 4471).
• pm.Distribution(...).logp(x) is now pm.logp(pm.Distribution(...), x)
• pm.Distribution(...).logcdf(x) is now pm.logcdf(pm.Distribution(...), x)
• pm.Distribution(...).random() is now pm.Distribution(...).eval()
• pm.draw_values(...) and pm.generate_samples(...) were removed. The tensors can now be evaluated with .eval().
• pm.fast_sample_posterior_predictive was removed.
• pm.sample_prior_predictive, pm.sample_posterior_predictive and pm.sample_posterior_predictive_w now return an InferenceData object by default, instead of a dictionary (see #5073).
• pm.sample_prior_predictive no longer returns transformed variable values by default. Pass them by name in var_names if you want to obtain these draws (see 4769).
• pm.sample(trace=...) no longer accepts MultiTrace or len(.) > 0 traces (see 5019#).
• The GLM submodule was removed, please use Bambi instead.
• pm.Bound interface no longer accepts a callable class as an argument, instead, it requires an instantiated distribution (created via the .dist() API) to be passed as an argument. In addition, Bound no longer returns a class instance but works as a normal PyMC distribution. Finally, it is no longer possible to do predictive random sampling from Bounded variables. Please, consult the new documentation for details on how to use Bounded variables (see 4815).
• pm.logpt(transformed=...) kwarg was removed (816b5f).
• Model(model=...) kwarg was removed
• Model(theano_config=...) kwarg was removed
• Model.size property was removed (use Model.ndim instead).
• dims and coords handling:
  • Model.RV_dims and Model.coords are now read-only properties. To modify the coords dictionary use Model.add_coord.
  • dims or coordinate values that are None will be auto-completed (see #4625).
  • Coordinate values passed to Model.add_coord are always converted to tuples (see #5061).
• Model.update_start_values(...) was removed. Initial values can be set in the Model.initial_values dictionary directly.
• Test values can no longer be set through pm.Distribution(testval=...) and must be assigned manually.
• Transform.forward and Transform.backward signatures changed.
• pm.DensityDist no longer accepts the logp as its first positional argument. It is now an optional keyword argument. If you pass a callable as the first positional argument, a TypeError will be raised (see 5026).
• pm.DensityDist now accepts distribution parameters as positional arguments. Passing them as a dictionary in the observed keyword argument is no longer supported and will raise an error (see 5026).
• The signature of the logp and random functions that can be passed into a pm.DensityDist has been changed (see 5026).
• Changes to the Gaussian process (gp) submodule:
  • The gp.prior(..., shape=...) kwarg was renamed to size.
  • Multiple methods including gp.prior now require explicit kwargs.
• Changes to the BART implementation:
  • A BART variable can be combined with other random variables. The inv_link argument has been removed (see 4914).
  • Moved BART to its own module (see 5058).
• Changes to the Gaussian Process (GP) submodule (see 5055):
  • For all implementations, gp.Latent, gp.Marginal etc., cov_func and mean_func are required kwargs.
  • In Windows test conda environment the mkl version is fixed to verison 2020.4, and mkl-service is fixed to 2.3.0. This was required for gp.MarginalKron to function properly.
  • gp.MvStudentT uses rotated samples from StudentT directly now, instead of sampling from pm.Chi2 and then from pm.Normal.
  • The "jitter" parameter, or the diagonal noise term added to Gram matrices such that the Cholesky is numerically stable, is now exposed to the user instead of hard-coded. See the function gp.util.stabilize.
  • The is_observed argument for gp.Marginal* implementations has been deprecated.
  • In the gp.utils file, the kmeans_inducing_points function now passes through kmeans_kwargs to scipy's k-means function.
  • The function replace_with_values function has been added to gp.utils.
  • MarginalSparse has been renamed MarginalApprox.

Expected breaks

• New API was already available in v3.
• Old API had deprecation warnings since at least 3.11.0 (2021-01).
• Old API stops working in v4 (preferably with informative errors).

All of the above apply to:

• pm.sample(return_inferencedata=True) is now the default (see #4744).
• ArviZ plots and stats wrappers were removed. The functions are now just available by their original names (see #4549 and 3.11.2 release notes).
• pm.sample_posterior_predictive(vars=...) kwarg was removed in favor of var_names (see #4343).
• ElemwiseCategorical step method was removed (see #4701)

Ongoing deprecations

• Old API still works in v4 and has a deprecation warning.
• Preferably the new API should be available in v3 already

New features

• The length of dims in the model is now tracked symbolically through Model.dim_lengths (see #4625).
• The CAR distribution has been added to allow for use of conditional autoregressions which often are used in spatial and network models.
• The dimensionality of model variables can now be parametrized through either of shape, dims or size (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.
  • The size kwarg behaves as it does in Aesara/NumPy. For univariate RVs it is the same as shape, but for multivariate RVs it depends on how the RV implements broadcasting to dimensionality greater than RVOp.ndim_supp.
  • An Ellipsis (...) in the last position of shape or dims can be used as shorthand notation for implied dimensions.
• 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.
• A small change to the mass matrix tuning methods jitter+adapt_diag (the default) and adapt_diag improves performance early on during tuning for some models. #5004
• New experimental mass matrix tuning method jitter+adapt_diag_grad. [#5004](https://github.com/pymc-devs/pymc/pu...