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PyMC3 3.11.0 (21 January 2021)
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@Spaak Spaak released this 21 Jan 08:36
v3.11.0
aedc8e9

This release breaks some APIs w.r.t. 3.10.0. It also brings some dreadfully awaited fixes, so be sure to go through the (breaking) changes below.

Breaking Changes

  • ⚠ Many plotting and diagnostic functions that were just aliasing ArviZ functions were removed (see 4397). This includes pm.summary, pm.traceplot, pm.ess and many more!
  • Changed shape behavior: No longer collapse length 1 vector shape into scalars. (see #4206 and #4214)
  • ⚠ We now depend on Theano-PyMC version 1.1.0 exactly (see #4405). Major refactorings were done in Theano-PyMC 1.1.0. If you implement custom Ops or interact with Theano in any way yourself, make sure to read the Theano-PyMC 1.1.0 release notes.
  • ⚠ Python 3.6 support was dropped (by no longer testing) and Python 3.9 was added (see #4332).
  • ⚠ Changed shape behavior: No longer collapse length 1 vector shape into scalars. (see #4206 and #4214)
    • Applies to random variables and also the .random(size=...) kwarg!
    • To create scalar variables you must now use shape=None or shape=().
    • shape=(1,) and shape=1 now become vectors. Previously they were collapsed into scalars
    • 0-length dimensions are now ruled illegal for random variables and raise a ValueError.
  • In sample_prior_predictive the vars kwarg was removed in favor of var_names (see #4327).
  • Removed theanof.set_theano_config because it illegally changed Theano's internal state (see #4329).

New Features

  • Option to set check_bounds=False when instantiating pymc3.Model(). This turns off bounds checks that ensure that input parameters of distributions are valid. For correctly specified models, this is unneccessary as all parameters get automatically transformed so that all values are valid. Turning this off should lead to faster sampling (see #4377).
  • OrderedProbit distribution added (see #4232).
  • plot_posterior_predictive_glm now works with arviz.InferenceData as well (see #4234)
  • Add logcdf method to all univariate discrete distributions (see #4387).
  • Add random method to MvGaussianRandomWalk (see #4388)
  • AsymmetricLaplace distribution added (see #4392).
  • DirichletMultinomial distribution added (see #4373).
  • Added a new predict method to BART to compute out of sample predictions (see #4310).

Maintenance

  • Fixed bug whereby partial traces returns after keyboard interrupt during parallel sampling had fewer draws than would've been available #4318
  • Make sample_shape same across all contexts in draw_values (see #4305).
  • The notebook gallery has been moved to https://github.com/pymc-devs/pymc-examples (see #4348).
  • math.logsumexp now matches scipy.special.logsumexp when arrays contain infinite values (see #4360).
  • Fixed mathematical formulation in MvStudentT random method. (see #4359)
  • Fix issue in logp method of HyperGeometric. It now returns -inf for invalid parameters (see 4367)
  • Fixed MatrixNormal random method to work with parameters as random variables. (see #4368)
  • Update the logcdf method of several continuous distributions to return -inf for invalid parameters and values, and raise an informative error when multiple values cannot be evaluated in a single call. (see 4393 and #4421)
  • Improve numerical stability in logp and logcdf methods of ExGaussian (see #4407)
  • Issue UserWarning when doing prior or posterior predictive sampling with models containing Potential factors (see #4419)
  • Dirichlet distribution's random method is now optimized and gives outputs in correct shape (see #4416)
  • Attempting to sample a named model with SMC will now raise a NotImplementedError. (see #4365)

Release manager for 3.11.0: Eelke Spaak (@Spaak)

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