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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.
    • 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:

    • 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.generate_samples(...) were removed.
  • 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#).
  • Setting of initial values:
    • Setting initial values through pm.Distribution(testval=...) is now pm.Distribution(initval=...).
    • 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.
  • transforms module is no longer accessible at the root level. It is accessible at pymc.distributions.transforms (see#5347).
  • logp, dlogp, and d2logp and nojac variations were removed. Use Model.compile_logp, compile_dlgop and compile_d2logp with jacobian keyword instead.
  • pm.DensityDist no longer accepts the logp as its first position 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).

Important:

  • Signature and default parameters changed for several distributions:

    • pm.StudentT now requires either sigma or lam as kwarg (see #5628)
    • pm.StudentT now requires nu to be specified (no longer defaults to 1) (see #5628)
    • pm.AsymmetricLaplace positional arguments re-ordered (see #5628)
    • pm.AsymmetricLaplace now requires mu to be specified (no longer defaults to 0) (see #5628)
    • ZeroInflatedPoisson theta parameter was renamed to mu (see #5584).
    • pm.GaussianRandomWalk initial distribution defaults to zero-centered normal with sigma=100 instead of flat (see#5779)
    • pm.AR initial distribution defaults to unit normal instead of flat (see#5779)

  • logpt, logpt_sum, logp_elemwiset and nojac variations were removed. Use Model.logpt(jacobian=True/False, sum=True/False) instead.

  • dlogp_nojact and d2logp_nojact were removed. Use Model.dlogpt and d2logpt with jacobian=False instead.

  • model.makefn is now called Model.compile_fn, and model.fn was removed.

  • Methods starting with fast_*, such as Model.fast_logp, were removed. Same applies to PointFunc classes

  • 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).

  • Transform.forward and Transform.backward signatures changed.

  • Changes to the Gaussian Process (GP) submodule (see 5055):

    • The gp.prior(..., shape=...) kwarg was renamed to size.
    • Multiple methods including gp.prior now require explicit kwargs.
    • 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 version 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 arguement 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.

  • Removed MixtureSameFamily. Mixture is now capable of handling batched multivariate components (see #5438).

Documentation

  • Switched to the pydata-sphinx-theme
  • Updated our documentation tooling to use MyST, MyST-NB, sphinx-design, notfound.extension, sphinx-copybutton and sphinx-remove-toctrees.
  • Separated the builds of the example notebooks and of the versioned docs.
  • Restructured the documentation to facilitate learning paths
  • Updated API docs to document objects at the path users should use to import them

Maintenance

  • ⚠️ Fixed old-time bug in Slice sampler that resulted in biased samples (see #5816).
  • Removed float128 dtype support (see #4514).
  • Logp method of Uniform and DiscreteUniform no longer depends on pymc.distributions.dist_math.bound for proper evaluation (see #4541).
  • We now include cloudpickle as a required dependency, and no longer depend on dill (see #4858).
  • The incomplete_beta function in pymc.distributions.dist_math was replaced by aesara.tensor.betainc (see 4857).
  • math.log1mexp and math.log1mexp_numpy will expect negative inputs in the future. A FutureWarning is now raised unless negative_input=True is set (see #4860).
  • Changed name of Lognormal distribution to LogNormal to harmonize CamelCase usage for distribution names.
  • Attempt to iterate over MultiTrace will raise NotImplementedError.
  • Removed silent normalisation of p parameters in Categorical and Multinomial distributions (see #5370).

PyMC 3.11.2 (14 March 2021)

New Features

  • pm.math.cartesian can now handle inputs that are themselves >1D (see #4482).
  • Statistics and plotting functions that were removed in 3.11.0 were brought back, albeit with deprecation warnings if an old naming scheme is used (see #4536). In order to future proof your code, rename these function calls:
    • pm.traceplot → pm.plot_trace
    • pm.compareplot → pm.plot_compare (here you might need to rename some columns in the input according to the arviz.plot_compare documentation)
    • pm.autocorrplot → pm.plot_autocorr
    • pm.forestplot → pm.plot_forest
    • pm.kdeplot → pm.plot_kde
    • pm.energyplot → pm.plot_energy
    • pm.densityplot → pm.plot_density
    • pm.pairplot → pm.plot_pair

Maintenance

  • ⚠ Our memoization mechanism wasn't robust against hash collisions (#4506), sometimes resulting in incorrect values in, for example, posterior predictives. The pymc.memoize module was removed and replaced with cachetools. The hashable function and WithMemoization class were moved to pymc.util (see #4525).
  • pm.make_shared_replacements now retains broadcasting information which fixes issues with Metropolis samplers (see #4492).

Release manager for 3.11.2: Michael Osthege (@michaelosthege)

PyMC 3.11.1 (12 February 2021)

New Features

  • Automatic imputations now also work with ndarray data, not just pd.Series or pd.DataFrame (see#4439).
  • pymc.sampling_jax.sample_numpyro_nuts now returns samples from transformed random variables, rather than from the unconstrained representation (see #4427).

Maintenance

  • We upgraded to Theano-PyMC v1.1.2 which includes bugfixes for...
    • ⚠ a problem with tt.switch that affected the behavior of several distributions, including at least the following special cases (see #4448)
      1. Bernoulli when all the observed values were the same (e.g., [0, 0, 0, 0, 0]).
      2. TruncatedNormal when sigma was constant and mu was being automatically broadcasted to match the shape of observations.
    • Warning floods and compiledir locking (see #4444)
  • math.log1mexp_numpy no longer raises RuntimeWarning when given very small inputs. These were commonly observed during NUTS sampling (see #4428).
  • ScalarSharedVariable can now be used as an input to other RVs directly (see #4445).
  • pm.sample and pm.find_MAP no longer change the start argument (see #4458).
  • Fixed Dirichlet.logp method to work with unit batch or event shapes (see #4454).
  • Bugfix in logp and logcdf methods of Triangular distribution (see #4470).

Release manager for 3.11.1: Michael Osthege (@michaelosthege)

PyMC3 3.11.0 (21 January 2021)

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!
  • ⚠ 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 pymc.Model(). This turns off bounds checks that ensure that input parameters of distributions are valid. For correctly specified models, this is unnecessary 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)

PyMC3 3.10.0 (7 December 2020)

This is a major release with many exciting new features. The biggest change is that we now rely on our own fork of Theano-PyMC. This is in line with our big announcement about our commitment to PyMC3 and Theano.

When upgrading, make sure that Theano-PyMC and not Theano are installed (the imports remain unchanged, however). If not, you can uninstall Theano:

conda remove theano

And to install:

conda install -c conda-forge theano-pymc

Or, if you are using pip (not recommended):

pip uninstall theano

And to install:

pip install theano-pymc

This new version of Theano-PyMC comes with an experimental JAX backend which, when combined with the new and experimental JAX samplers in PyMC3, can greatly speed up sampling in your model. As this is still very new, please do not use it in production yet but do test it out and let us know if anything breaks and what results you are seeing, especially speed-wise.

New features

  • New experimental JAX samplers in pymc.sample_jax (see notebook and #4247). Requires JAX and either TFP or numpyro.
  • Add MLDA, a new stepper for multilevel sampling. MLDA can be used when a hierarchy of approximate posteriors of varying accuracy is available, offering improved sampling efficiency especially in high-dimensional problems and/or where gradients are not available (see #3926)
  • Add Bayesian Additive Regression Trees (BARTs) #4183)
  • Added pymc.gp.cov.Circular kernel for Gaussian Processes on circular domains, e.g. the unit circle (see #4082).
  • Added a new MixtureSameFamily distribution to handle mixtures of arbitrary dimensions in vectorized form for improved speed (see #4185).
  • sample_posterior_predictive_w can now feed on xarray.Dataset - e.g. from InferenceData.posterior. (see #4042)
  • Change SMC metropolis kernel to independent metropolis kernel #4115)
  • Add alternative parametrization to NegativeBinomial distribution in terms of n and p (see #4126)
  • Added semantically meaningful str representations to PyMC3 objects for console, notebook, and GraphViz use (see #4076, #4065, #4159, #4217, #4243, and #4260).
  • Add Discrete HyperGeometric Distribution (see #4249)

Maintenance

  • Switch the dependency of Theano to our own fork, Theano-PyMC.
  • Removed non-NDArray (Text, SQLite, HDF5) backends and associated tests.
  • Use dill to serialize user defined logp functions in DensityDist. The previous serialization code fails if it is used in notebooks on Windows and Mac. dill is now a required dependency. (see #3844).
  • Fixed numerical instability in ExGaussian's logp by preventing logpow from returning -inf (see #4050).
  • Numerically improved stickbreaking transformation - e.g. for the Dirichlet distribution. #4129
  • Enabled the Multinomial distribution to handle batch sizes that have more than 2 dimensions. #4169
  • Test model logp before starting any MCMC chains (see #4211)
  • Fix bug in model.check_test_point that caused the test_point argument to be ignored. (see PR #4211)
  • Refactored MvNormal.random method with better handling of sample, batch and event shapes. #4207
  • The InverseGamma distribution now implements a logcdf. #3944
  • Make starting jitter methods for nuts sampling more robust by resampling values that lead to non-finite probabilities. A new optional argument jitter-max-retries can be passed to pm.sample() and pm.init_nuts() to control the maximum number of retries per chain. 4298

Documentation

  • Added a new notebook demonstrating how to incorporate sampling from a conjugate Dirichlet-multinomial posterior density in conjunction with other step methods (see #4199).
  • Mentioned the way to do any random walk with theano.tensor.cumsum() in GaussianRandomWalk docstrings (see #4048).

Release manager for 3.10.0: Eelke Spaak (@Spaak)

PyMC3 3.9.3 (11 August 2020)

New features

  • Introduce optional arguments to pm.sample: mp_ctx to control how the processes for parallel sampling are started, and pickle_backend to specify which library is used to pickle models in parallel sampling when the multiprocessing context is not of type fork (see #3991).
  • Add sampler stats process_time_diff, perf_counter_diff and perf_counter_start, that record wall and CPU times for each NUTS and HMC sample (see #3986).
  • Extend keep_size argument handling for sample_posterior_predictive and fast_sample_posterior_predictive, to work on ArviZ InferenceData and xarray Dataset input values (see PR #4006 and issue #4004).
  • SMC-ABC: add the Wasserstein and energy distance functions. Refactor API, the distance, sum_stats and epsilon arguments are now passed pm.Simulator instead of pm.sample_smc. Add random method to pm.Simulator. Add option to save the simulated data. Improved LaTeX representation #3996.
  • SMC-ABC: Allow use of potentials by adding them to the prior term. #4016.

Maintenance

  • Fix an error on Windows and Mac where error message from unpickling models did not show up in the notebook, or where sampling froze when a worker process crashed (see #3991).
  • Require Theano >= 1.0.5 (see #4032).

Documentation

  • Notebook on multilevel modeling has been rewritten to showcase ArviZ and xarray usage for inference result analysis (see #3963).

NB: The docs/* folder is still removed from the tarball due to an upload size limit on PyPi.

Release manager for 3.9.3: Kyle Beauchamp (@kyleabeauchamp)

PyMC3 3.9.2 (24 June 2020)

Maintenance

  • Warning added in GP module when input_dim is lower than the number of columns in X to compute the covariance function (see #3974).
  • Pass the tune argument from sample when using advi+adapt_diag_grad (see issue #3965, fixed by #3979).
  • Add simple test case for new coords and dims feature in pm.Model (see #3977).
  • Require ArviZ >= 0.9.0 (see #3977).
  • Fixed issue #3962 by making a change in the _random() method of GaussianRandomWalk class (see PR #3985). Further testing revealed a new issue which is being tracked by #4010.

NB: The docs/* folder is still removed from the tarball due to an upload size limit on PyPi.

Release manager for 3.9.2: Alex Andorra (@AlexAndorra)

PyMC3 3.9.1 (16 June 2020)

The v3.9.0 upload to PyPI didn't include a tarball, which is fixed in this release. Though we had to temporarily remove the docs/* folder from the tarball due to a size limit.

Release manager for 3.9.1: Michael Osthege (@michaelosthege)

PyMC3 3.9.0 (16 June 2020)

New features

  • Use fastprogress instead of tqdm #3693.
  • DEMetropolis can now tune both lambda and scaling parameters, but by default neither of them are tuned. See #3743 for more info.
  • DEMetropolisZ, an improved variant of DEMetropolis brings better parallelization and higher efficiency with fewer chains with a slower initial convergence. This implementation is experimental. See #3784 for more info.
  • Notebooks that give insight into DEMetropolis, DEMetropolisZ and the DifferentialEquation interface are now located in the Tutorials/Deep Dive section.
  • Add fast_sample_posterior_predictive, a vectorized alternative to sample_posterior_predictive. This alternative is substantially faster for large models.
  • GP covariance functions can now be exponentiated by a scalar. See PR #3852
  • sample_posterior_predictive can now feed on xarray.Dataset - e.g. from InferenceData.posterior. (see #3846)
  • SamplerReport (MultiTrace.report) now has properties n_tune, n_draws, t_sampling for increased convenience (see #3827)
  • pm.sample(..., return_inferencedata=True) can now directly return the trace as arviz.InferenceData (see #3911)
  • pm.sample now has support for adapting dense mass matrix using QuadPotentialFullAdapt (see #3596, #3705, #3858, and #3893). Use init="adapt_full" or init="jitter+adapt_full" to use.
  • Moyal distribution added (see #3870).
  • pm.LKJCholeskyCov now automatically computes and returns the unpacked Cholesky decomposition, the correlations and the standard deviations of the covariance matrix (see #3881).
  • pm.Data container can now be used for index variables, i.e with integer data and not only floats (issue #3813, fixed by #3925).
  • pm.Data container can now be used as input for other random variables (issue #3842, fixed by #3925).
  • Allow users to specify coordinates and dimension names instead of numerical shapes when specifying a model. This makes interoperability with ArviZ easier. (see #3551)
  • Plots and Stats API sections now link to ArviZ documentation #3927
  • Add SamplerReport with properties n_draws, t_sampling and n_tune to SMC. n_tune is always 0 #3931.
  • SMC-ABC: add option to define summary statistics, allow to sample from more complex models, remove redundant distances #3940

Maintenance

  • Tuning results no longer leak into sequentially sampled Metropolis chains (see #3733 and #3796).
  • We'll deprecate the Text and SQLite backends and the save_trace/load_trace functions, since this is now done with ArviZ. (see #3902)
  • ArviZ v0.8.3 is now the minimum required version
  • In named models, pm.Data objects now get model-relative names (see #3843).
  • pm.sample now takes 1000 draws and 1000 tuning samples by default, instead of 500 previously (see #3855).
  • Moved argument division out of NegativeBinomial random method. Fixes #3864 in the style of #3509.
  • The Dirichlet distribution now raises a ValueError when it's initialized with <= 0 values (see #3853).
  • Dtype bugfix in MvNormal and MvStudentT (see 3836).
  • End of sampling report now uses arviz.InferenceData internally and avoids storing pointwise log likelihood (see #3883).
  • The multiprocessing start method on MacOS is now set to "forkserver", to avoid crashes (see issue #3849, solved by #3919).
  • The AR1 logp now uses the precision of the whole AR1 process instead of just the innovation precision (see issue #3892, fixed by #3899).
  • Forced the Beta distribution's random method to generate samples that are in the open interval $(0, 1)$, i.e. no value can be equal to zero or equal to one (issue #3898 fixed by #3924).
  • Fixed an issue that happened on Windows, that was introduced by the clipped beta distribution rvs function (#3924). Windows does not support the float128 dtype, but we had assumed that it had to be available. The solution was to only support float128 on Linux and Darwin systems (see issue #3929 fixed by #3930).

Deprecations

  • Remove sample_ppc and sample_ppc_w that were deprecated in 3.6.
  • Deprecated sd has been replaced by sigma (already in version 3.7) in continuous, mixed and timeseries distributions and now raises DeprecationWarning when sd is used. (see #3837 and #3688).
  • We'll deprecate the Text and SQLite backends and the save_trace/load_trace functions, since this is now done with ArviZ. (see #3902)
  • Dropped some deprecated kwargs and functions (see #3906)
  • Dropped the outdated 'nuts' initialization method for pm.sample (see #3863).

Release manager for 3.9.0: Michael Osthege (@michaelosthege)

PyMC3 3.8 (November 29 2019)

New features

  • Implemented robust u turn check in NUTS (similar to stan-dev/stan#2800). See PR [#3605]
  • Add capabilities to do inference on parameters in a differential equation with DifferentialEquation. See #3590 and #3634.
  • Distinguish between Data and Deterministic variables when graphing models with graphviz. PR #3491.
  • Sequential Monte Carlo - Approximate Bayesian Computation step method is now available. The implementation is in an experimental stage and will be further improved.
  • Added Matern12 covariance function for Gaussian processes. This is the Matern kernel with nu=1/2.
  • Progressbar reports number of divergences in real time, when available #3547.
  • Sampling from variational approximation now allows for alternative trace backends [#3550].
  • Infix @ operator now works with random variables and deterministics #3619.
  • ArviZ is now a requirement, and handles plotting, diagnostics, and statistical checks.
  • Can use GaussianRandomWalk in sample_prior_predictive and sample_prior_predictive #3682
  • Now 11 years of S&P returns in data set#3682

Maintenance

  • Moved math operations out of Rice, TruncatedNormal, Triangular and ZeroInflatedNegativeBinomial random methods. Math operations on values returned by draw_values might not broadcast well, and all the size aware broadcasting is left to generate_samples. Fixes #3481 and #3508
  • Parallelization of population steppers (DEMetropolis) is now set via the cores argument. (#3559)
  • Fixed a bug in Categorical.logp. In the case of multidimensional p's, the indexing was done wrong leading to incorrectly shaped tensors that consumed O(n**2) memory instead of O(n). This fixes issue #3535
  • Fixed a defect in OrderedLogistic.__init__ that unnecessarily increased the dimensionality of the underlying p. Related to issue issue #3535 but was not the true cause of it.
  • SMC: stabilize covariance matrix 3573
  • SMC: is no longer a step method of pm.sample now it should be called using pm.sample_smc 3579
  • SMC: improve computation of the proposal scaling factor 3594 and 3625
  • SMC: reduce number of logp evaluations 3600
  • SMC: remove scaling and tune_scaling arguments as is a better idea to always allow SMC to automatically compute the scaling factor 3625
  • Now uses multiprocessong rather than psutil to count CPUs, which results in reliable core counts on Chromebooks.
  • sample_posterior_predictive now preallocates the memory required for its output to improve memory usage. Addresses problems raised in this discourse thread.
  • Fixed a bug in Categorical.logp. In the case of multidimensional p's, the indexing was done wrong leading to incorrectly shaped tensors that consumed O(n**2) memory instead of O(n). This fixes issue #3535
  • Fixed a defect in OrderedLogistic.__init__ that unnecessarily increased the dimensionality of the underlying p. Related to issue issue #3535 but was not the true cause of it.
  • Wrapped DensityDist.rand with generate_samples to make it aware of the distribution's shape. Added control flow attributes to still be able to behave as in earlier versions, and to control how to interpret the size parameter in the random callable signature. Fixes 3553
  • Added theano.gof.graph.Constant to type checks done in _draw_value (fixes issue 3595)
  • HalfNormal did not used to work properly in draw_values, sample_prior_predictive, or sample_posterior_predictive (fixes issue 3686)
  • Random variable transforms were inadvertently left out of the API documentation. Added them. (See PR 3690).
  • Refactored pymc.model.get_named_nodes_and_relations to use the ancestors and descendents, in a way that is consistent with theano's naming convention.
  • Changed the way in which pymc.model.get_named_nodes_and_relations computes nodes without ancestors to make it robust to changes in var_name orderings (issue #3643)

PyMC3 3.7 (May 29 2019)

New features

  • Add data container class (Data) that wraps the theano SharedVariable class and let the model be aware of its inputs and outputs.
  • Add function set_data to update variables defined as Data.
  • Mixture now supports mixtures of multidimensional probability distributions, not just lists of 1D distributions.
  • GLM.from_formula and LinearComponent.from_formula can extract variables from the calling scope. Customizable via the new eval_env argument. Fixing #3382.
  • Added the distributions.shape_utils module with functions used to help broadcast samples drawn from distributions using the size keyword argument.
  • Used numpy.vectorize in distributions.distribution._compile_theano_function. This enables sample_prior_predictive and sample_posterior_predictive to ask for tuples of samples instead of just integers. This fixes issue #3422.

Maintenance

  • All occurrences of sd as a parameter name have been renamed to sigma. sd will continue to function for backwards compatibility.
  • HamiltonianMC was ignoring certain arguments like target_accept, and not using the custom step size jitter function with expectation 1.
  • Made BrokenPipeError for parallel sampling more verbose on Windows.
  • Added the broadcast_distribution_samples function that helps broadcasting arrays of drawn samples, taking into account the requested size and the inferred distribution shape. This sometimes is needed by distributions that call several rvs separately within their random method, such as the ZeroInflatedPoisson (fixes issue #3310).
  • The Wald, Kumaraswamy, LogNormal, Pareto, Cauchy, HalfCauchy, Weibull and ExGaussian distributions random method used a hidden _random function that was written with scalars in mind. This could potentially lead to artificial correlations between random draws. Added shape guards and broadcasting of the distribution samples to prevent this (Similar to issue #3310).
  • Added a fix to allow the imputation of single missing values of observed data, which previously would fail (fixes issue #3122).
  • The draw_values function was too permissive with what could be grabbed from inside point, which lead to an error when sampling posterior predictives of variables that depended on shared variables that had changed their shape after pm.sample() had been called (fix issue #3346).
  • draw_values now adds the theano graph descendants of TensorConstant or SharedVariables to the named relationship nodes stack, only if these descendants are ObservedRV or MultiObservedRV instances (fixes issue #3354).
  • Fixed bug in broadcast_distrution_samples, which did not handle correctly cases in which some samples did not have the size tuple prepended.
  • Changed MvNormal.random's usage of tensordot for Cholesky encoded covariances. This lead to wrong axis broadcasting and seemed to be the cause for issue #3343.
  • Fixed defect in Mixture.random when multidimensional mixtures were involved. The mixture component was not preserved across all the elements of the dimensions of the mixture. This meant that the correlations across elements within a given draw of the mixture were partly broken.
  • Restructured Mixture.random to allow better use of vectorized calls to comp_dists.random.
  • Added tests for mixtures of multidimensional distributions to the test suite.
  • Fixed incorrect usage of broadcast_distribution_samples in DiscreteWeibull.
  • Mixture's default dtype is now determined by theano.config.floatX.
  • dist_math.random_choice now handles nd-arrays of category probabilities, and also handles sizes that are not None. Also removed unused k kwarg from dist_math.random_choice.
  • Changed Categorical.mode to preserve all the dimensions of p except the last one, which encodes each category's probability.
  • Changed initialization of Categorical.p. p is now normalized to sum to 1 inside logp and random, but not during initialization. This could hide negative values supplied to p as mentioned in #2082.
  • Categorical now accepts elements of p equal to 0. logp will return -inf if there are values that index to the zero probability categories.
  • Add sigma, tau, and sd to signature of NormalMixture.
  • Set default lower and upper values of -inf and inf for pm.distributions.continuous.TruncatedNormal. This avoids errors caused by their previous values of None (fixes issue #3248).
  • Converted all calls to pm.distributions.bound._ContinuousBounded and pm.distributions.bound._DiscreteBounded to use only and all positional arguments (fixes issue #3399).
  • Restructured distributions.distribution.generate_samples to use the shape_utils module. This solves issues #3421 and #3147 by using the size aware broadcating functions in shape_utils.
  • Fixed the Multinomial.random and Multinomial.random_ methods to make them compatible with the new generate_samples function. In the process, a bug of the Multinomial.random_ shape handling was discovered and fixed.
  • Fixed a defect found in Bound.random where the point dictionary was passed to generate_samples as an arg instead of in not_broadcast_kwargs.
  • Fixed a defect found in Bound.random_ where total_size could end up as a float64 instead of being an integer if given size=tuple().
  • Fixed an issue in model_graph that caused construction of the graph of the model for rendering to hang: replaced a search over the powerset of the nodes with a breadth-first search over the nodes. Fix for #3458.
  • Removed variable annotations from model_graph but left type hints (Fix for #3465). This means that we support python>=3.5.4.
  • Default target_acceptfor HamiltonianMC is now 0.65, as suggested in Beskos et. al. 2010 and Neal 2001.
  • Fixed bug in draw_values that lead to intermittent errors in python3.5. This happened with some deterministic nodes that were drawn but not added to givens.

Deprecations

  • nuts_kwargs and step_kwargs have been deprecated in favor of using the standard kwargs to pass optional step method arguments.
  • SGFS and CSG have been removed (Fix for #3353). They have been moved to pymc-experimental.
  • References to live_plot and corresponding notebooks have been removed.
  • Function approx_hessian was removed, due to numdifftools becoming incompatible with current scipy. The function was already optional, only available to a user who installed numdifftools separately, and not hit on any common codepaths. #3485.
  • Deprecated vars parameter of sample_posterior_predictive in favor of varnames.
  • References to live_plot and corresponding notebooks have been removed.
  • Deprecated vars parameters of sample_posterior_predictive and sample_prior_predictive in favor of var_names. At least for the latter, this is more accurate, since the vars parameter actually took names.

Contributors sorted by number of commits

45  Luciano Paz
38  Thomas Wiecki
23  Colin Carroll
19  Junpeng Lao
15  Chris Fonnesbeck
13  Juan Martín Loyola
13  Ravin Kumar
 8  Robert P. Goldman
 5  Tim Blazina
 4  chang111
 4  adamboche
 3  Eric Ma
 3  Osvaldo Martin
 3  Sanmitra Ghosh
 3  Saurav Shekhar
 3  chartl
 3  fredcallaway
 3  Demetri
 2  Daisuke Kondo
 2  David Brochart
 2  George Ho
 2  Vaibhav Sinha
 1  rpgoldman
 1  Adel Tomilova
 1  Adriaan van der Graaf
 1  Bas Nijholt
 1  Benjamin Wild
 1  Brigitta Sipocz
 1  Daniel Emaasit
 1  Hari
 1  Jeroen
 1  Joseph Willard
 1  Juan Martin Loyola
 1  Katrin Leinweber
 1  Lisa Martin
 1  M. Domenzain
 1  Matt Pitkin
 1  Peadar Coyle
 1  Rupal Sharma
 1  Tom Gilliss
 1  changjiangeng
 1  michaelosthege
 1  monsta
 1  579397

PyMC3 3.6 (Dec 21 2018)

This will be the last release to support Python 2.

New features

  • Track the model log-likelihood as a sampler stat for NUTS and HMC samplers (accessible as trace.get_sampler_stats('model_logp')) (#3134)
  • Add Incomplete Beta function incomplete_beta(a, b, value)
  • Add log CDF functions to continuous distributions: Beta, Cauchy, ExGaussian, Exponential, Flat, Gumbel, HalfCauchy, HalfFlat, HalfNormal, Laplace, Logistic, LogNormal, Normal, Pareto, StudentT, Triangular, Uniform, Wald, Weibull.
  • Behavior of sample_posterior_predictive is now to produce posterior predictive samples, in order, from all values of the trace. Previously, by default it would produce 1 chain worth of samples, using a random selection from the trace (#3212)
  • Show diagnostics for initial energy errors in HMC and NUTS.
  • PR #3273 has added the distributions.distribution._DrawValuesContext context manager. This is used to store the values already drawn in nested random and draw_values calls, enabling draw_values to draw samples from the joint probability distribution of RVs and not the marginals. Custom distributions that must call draw_values several times in their random method, or that invoke many calls to other distribution's random methods (e.g. mixtures) must do all of these calls under the same _DrawValuesContext context manager instance. If they do not, the conditional relations between the distribution's parameters could be broken, and random could return values drawn from an incorrect distribution.
  • Rice distribution is now defined with either the noncentrality parameter or the shape parameter (#3287).

Maintenance

  • Big rewrite of documentation (#3275)
  • Fixed Triangular distribution c attribute handling in random and updated sample codes for consistency (#3225)
  • Refactor SMC and properly compute marginal likelihood (#3124)
  • Removed use of deprecated ymin keyword in matplotlib's Axes.set_ylim (#3279)
  • Fix for #3210. Now distribution.draw_values(params), will draw the params values from their joint probability distribution and not from combinations of their marginals (Refer to PR #3273).
  • Removed dependence on pandas-datareader for retrieving Yahoo Finance data in examples (#3262)
  • Rewrote Multinomial._random method to better handle shape broadcasting (#3271)
  • Fixed Rice distribution, which inconsistently mixed two parametrizations (#3286).
  • Rice distribution now accepts multiple parameters and observations and is usable with NUTS (#3289).
  • sample_posterior_predictive no longer calls draw_values to initialize the shape of the ppc trace. This called could lead to ValueError's when sampling the ppc from a model with Flat or HalfFlat prior distributions (Fix issue #3294).
  • Added explicit conversion to floatX and int32 for the continuous and discrete probability distribution parameters (addresses issue #3223).

Deprecations

  • Renamed sample_ppc() and sample_ppc_w() to sample_posterior_predictive() and sample_posterior_predictive_w(), respectively.

PyMC3 3.5 (July 21 2018)

New features

  • Add documentation section on survival analysis and censored data models
  • Add check_test_point method to pm.Model
  • Add Ordered Transformation and OrderedLogistic distribution
  • Add Chain transformation
  • Improve error message Mass matrix contains zeros on the diagonal. Some derivatives might always be zero during tuning of pm.sample
  • Improve error message NaN occurred in optimization. during ADVI
  • Save and load traces without pickle using pm.save_trace and pm.load_trace
  • Add Kumaraswamy distribution
  • Add TruncatedNormal distribution
  • Rewrite parallel sampling of multiple chains on py3. This resolves long standing issues when transferring large traces to the main process, avoids pickling issues on UNIX, and allows us to show a progress bar for all chains. If parallel sampling is interrupted, we now return partial results.
  • Add sample_prior_predictive which allows for efficient sampling from the unconditioned model.
  • SMC: remove experimental warning, allow sampling using sample, reduce autocorrelation from final trace.
  • Add model_to_graphviz (which uses the optional dependency graphviz) to plot a directed graph of a PyMC3 model using plate notation.
  • Add beta-ELBO variational inference as in beta-VAE model (Christopher P. Burgess et al. NIPS, 2017)
  • Add __dir__ to SingleGroupApproximation to improve autocompletion in interactive environments

Fixes

  • Fixed grammar in divergence warning, previously There were 1 divergences ... could be raised.
  • Fixed KeyError raised when only subset of variables are specified to be recorded in the trace.
  • Removed unused repeat=None arguments from all random() methods in distributions.
  • Deprecated the sigma argument in MarginalSparse.marginal_likelihood in favor of noise
  • Fixed unexpected behavior in random. Now the random functionality is more robust and will work better for sample_prior when that is implemented.
  • Fixed scale_cost_to_minibatch behaviour, previously this was not working and always False

PyMC3 3.4.1 (April 18 2018)

New features

  • Add logit_p keyword to pm.Bernoulli, so that users can specify the logit of the success probability. This is faster and more stable than using p=tt.nnet.sigmoid(logit_p).
  • Add random keyword to pm.DensityDist thus enabling users to pass custom random method which in turn makes sampling from a DensityDist possible.
  • Effective sample size computation is updated. The estimation uses Geyer's initial positive sequence, which no longer truncates the autocorrelation series inaccurately. pm.diagnostics.effective_n now can reports N_eff>N.
  • Added KroneckerNormal distribution and a corresponding MarginalKron Gaussian Process implementation for efficient inference, along with lower-level functions such as cartesian and kronecker products.
  • Added Coregion covariance function.
  • Add new 'pairplot' function, for plotting scatter or hexbin matrices of sampled parameters. Optionally it can plot divergences.
  • Plots of discrete distributions in the docstrings
  • Add logitnormal distribution
  • Densityplot: add support for discrete variables
  • Fix the Binomial likelihood in .glm.families.Binomial, with the flexibility of specifying the n.
  • Add offset kwarg to .glm.
  • Changed the compare function to accept a dictionary of model-trace pairs instead of two separate lists of models and traces.
  • add test and support for creating multivariate mixture and mixture of mixtures
  • distribution.draw_values, now is also able to draw values from conditionally dependent RVs, such as autotransformed RVs (Refer to PR #2902).

Fixes

  • VonMises does not overflow for large values of kappa. i0 and i1 have been removed and we now use log_i0 to compute the logp.
  • The bandwidth for KDE plots is computed using a modified version of Scott's rule. The new version uses entropy instead of standard deviation. This works better for multimodal distributions. Functions using KDE plots has a new argument bw controlling the bandwidth.
  • fix PyMC3 variable is not replaced if provided in more_replacements (#2890)
  • Fix for issue #2900. For many situations, named node-inputs do not have a random method, while some intermediate node may have it. This meant that if the named node-input at the leaf of the graph did not have a fixed value, theano would try to compile it and fail to find inputs, raising a theano.gof.fg.MissingInputError. This was fixed by going through the theano variable's owner inputs graph, trying to get intermediate named-nodes values if the leafs had failed.
  • In distribution.draw_values, some named nodes could be theano.tensor.TensorConstants or theano.tensor.sharedvar.SharedVariables. Nevertheless, in distribution._draw_value, these would be passed to distribution._compile_theano_function as if they were theano.tensor.TensorVariables. This could lead to the following exceptions TypeError: ('Constants not allowed in param list', ...) or TypeError: Cannot use a shared variable (...). The fix was to not add theano.tensor.TensorConstant or theano.tensor.sharedvar.SharedVariable named nodes into the givens dict that could be used in distribution._compile_theano_function.
  • Exponential support changed to include zero values.

Deprecations

  • DIC and BPIC calculations have been removed
  • df_summary have been removed, use summary instead
  • njobs and nchains kwarg are deprecated in favor of cores and chains for sample
  • lag kwarg in pm.stats.autocorr and pm.stats.autocov is deprecated.

PyMC3 3.3 (January 9, 2018)

New features

  • Improve NUTS initialization advi+adapt_diag_grad and add jitter+adapt_diag_grad (#2643)
  • Added MatrixNormal class for representing vectors of multivariate normal variables
  • Implemented HalfStudentT distribution
  • New benchmark suite added (see http://pandas.pydata.org/speed/pymc/)
  • Generalized random seed types
  • Update loo, new improved algorithm (#2730)
  • New CSG (Constant Stochastic Gradient) approximate posterior sampling algorithm (#2544)
  • Michael Osthege added support for population-samplers and implemented differential evolution metropolis (DEMetropolis). For models with correlated dimensions that can not use gradient-based samplers, the DEMetropolis sampler can give higher effective sampling rates. (also see PR#2735)
  • Forestplot supports multiple traces (#2736)
  • Add new plot, densityplot (#2741)
  • DIC and BPIC calculations have been deprecated
  • Refactor HMC and implemented new warning system (#2677, #2808)

Fixes

  • Fixed compareplot to use loo output.
  • Improved posteriorplot to scale fonts
  • sample_ppc_w now broadcasts
  • df_summary function renamed to summary
  • Add test for model.logp_array and model.bijection (#2724)
  • Fixed sample_ppc and sample_ppc_w to iterate all chains(#2633, #2748)
  • Add Bayesian R2 score (for GLMs) stats.r2_score (#2696) and test (#2729).
  • SMC works with transformed variables (#2755)
  • Speedup OPVI (#2759)
  • Multiple minor fixes and improvements in the docs (#2775, #2786, #2787, #2789, #2790, #2794, #2799, #2809)

Deprecations

  • Old (minibatch-)advi is removed (#2781)

PyMC3 3.2 (October 10, 2017)

New features

This version includes two major contributions from our Google Summer of Code 2017 students:

  • Maxim Kochurov extended and refactored the variational inference module. This primarily adds two important classes, representing operator variational inference (OPVI) objects and Approximation objects. These make it easier to extend existing variational classes, and to derive inference from variational optimizations, respectively. The variational module now also includes normalizing flows (NFVI).
  • Bill Engels added an extensive new Gaussian processes (gp) module. Standard GPs can be specified using either Latent or Marginal classes, depending on the nature of the underlying function. A Student-T process TP has been added. In order to accommodate larger datasets, approximate marginal Gaussian processes (MarginalSparse) have been added.

Documentation has been improved as the result of the project's monthly "docathons".

An experimental stochastic gradient Fisher scoring (SGFS) sampling step method has been added.

The API for find_MAP was enhanced.

SMC now estimates the marginal likelihood.

Added Logistic and HalfFlat distributions to set of continuous distributions.

Bayesian fraction of missing information (bfmi) function added to stats.

Enhancements to compareplot added.

QuadPotential adaptation has been implemented.

Script added to build and deploy documentation.

MAP estimates now available for transformed and non-transformed variables.

The Constant variable class has been deprecated, and will be removed in 3.3.

DIC and BPIC calculations have been sped up.

Arrays are now accepted as arguments for the Bound class.

random method was added to the Wishart and LKJCorr distributions.

Progress bars have been added to LOO and WAIC calculations.

All example notebooks updated to reflect changes in API since 3.1.

Parts of the test suite have been refactored.

Fixes

Fixed sampler stats error in NUTS for non-RAM backends

Matplotlib is no longer a hard dependency, making it easier to use in settings where installing Matplotlib is problematic. PyMC3 will only complain if plotting is attempted.

Several bugs in the Gaussian process covariance were fixed.

All chains are now used to calculate WAIC and LOO.

AR(1) log-likelihood function has been fixed.

Slice sampler fixed to sample from 1D conditionals.

Several docstring fixes.

Contributors

The following people contributed to this release (ordered by number of commits):

Maxim Kochurov [email protected] Bill Engels [email protected] Chris Fonnesbeck [email protected] Junpeng Lao [email protected] Adrian Seyboldt [email protected] AustinRochford [email protected] Osvaldo Martin [email protected] Colin Carroll [email protected] Hannes Vasyura-Bathke [email protected] Thomas Wiecki [email protected] michaelosthege [email protected] Marco De Nadai [email protected] Kyle Beauchamp [email protected] Massimo [email protected] ctm22396 [email protected] Max Horn [email protected] Hennadii Madan [email protected] Hassan Naseri [email protected] Peadar Coyle [email protected] Saurav R. Tuladhar [email protected] Shashank Shekhar [email protected] Eric Ma [email protected] Ed Herbst [email protected] tsdlovell [email protected] zaxtax [email protected] Dan Nichol [email protected] Benjamin Yetton [email protected] jackhansom [email protected] Jack Tsai [email protected] Andrés Asensio Ramos [email protected]

PyMC3 3.1 (June 23, 2017)

New features

Fixes

  • Bound now works for discrete distributions as well.

  • Random sampling now returns the correct shape even for higher dimensional RVs.

  • Use theano Psi and GammaLn functions to enable GPU support for them.

PyMC3 3.0 (January 9, 2017)

We are proud and excited to release the first stable version of PyMC3, the product of more than 5 years of ongoing development and contributions from over 80 individuals. PyMC3 is a Python module for Bayesian modeling which focuses on modern Bayesian computational methods, primarily gradient-based (Hamiltonian) MCMC sampling and variational inference. Models are specified in Python, which allows for great flexibility. The main technological difference in PyMC3 relative to previous versions is the reliance on Theano for the computational backend, rather than on Fortran extensions.

New features

Since the beta release last year, the following improvements have been implemented:

  • Added variational submodule, which features the automatic differentiation variational inference (ADVI) fitting method. Also supports mini-batch ADVI for large data sets. Much of this work was due to the efforts of Taku Yoshioka, and important guidance was provided by the Stan team (specifically Alp Kucukelbir and Daniel Lee).

  • Added model checking utility functions, including leave-one-out (LOO) cross-validation, BPIC, WAIC, and DIC.

  • Implemented posterior predictive sampling (sample_ppc).

  • Implemented auto-assignment of step methods by sample function.

  • Enhanced IPython Notebook examples, featuring more complete narratives accompanying code.

  • Extensive debugging of NUTS sampler.

  • Updated documentation to reflect changes in code since beta.

  • Refactored test suite for better efficiency.

  • Added von Mises, zero-inflated negative binomial, and Lewandowski, Kurowicka and Joe (LKJ) distributions.

  • Adopted joblib for managing parallel computation of chains.

  • Added contributor guidelines, contributor code of conduct and governance document.

Deprecations

  • Argument order of tau and sd was switched for distributions of the normal family:
  • Normal()
  • LogNormal()
  • HalfNormal()

Old: Normal(name, mu, tau) New: Normal(name, mu, sd) (supplying keyword arguments is unaffected).

  • MvNormal calling signature changed: Old: MvNormal(name, mu, tau) New: MvNormal(name, mu, cov) (supplying keyword arguments is unaffected).

We on the PyMC3 core team would like to thank everyone for contributing and now feel that this is ready for the big time. We look forward to hearing about all the cool stuff you use PyMC3 for, and look forward to continued development on the package.

Contributors

The following authors contributed to this release:

Chris Fonnesbeck [email protected] John Salvatier [email protected] Thomas Wiecki [email protected] Colin Carroll [email protected] Maxim Kochurov [email protected] Taku Yoshioka [email protected] Peadar Coyle (springcoil) [email protected] Austin Rochford [email protected] Osvaldo Martin [email protected] Shashank Shekhar [email protected]

In addition, the following community members contributed to this release:

A Kuz [email protected] A. Flaxman [email protected] Abraham Flaxman [email protected] Alexey Goldin [email protected] Anand Patil [email protected] Andrea Zonca [email protected] Andreas Klostermann [email protected] Andres Asensio Ramos Andrew Clegg [email protected] Anjum48 Benjamin Edwards [email protected] Boris Avdeev [email protected] Brian Naughton [email protected] Byron Smith Chad Heyne [email protected] Corey Farwell [email protected] David Huard [email protected] David Stück [email protected] DeliciousHair [email protected] Dustin Tran Eigenblutwurst [email protected] Gideon Wulfsohn [email protected] Gil Raphaelli [email protected] Gogs [email protected] Ilan Man Imri Sofer [email protected] Jake Biesinger [email protected] James Webber [email protected] John McDonnell [email protected] Jon Sedar [email protected] Jordi Diaz Jordi Warmenhoven [email protected] Karlson Pfannschmidt [email protected] Kyle Bishop [email protected] Kyle Meyer [email protected] Lin Xiao Mack Sweeney [email protected] Matthew Emmett [email protected] Michael Gallaspy [email protected] Nick [email protected] Osvaldo Martin [email protected] Patricio Benavente [email protected] Raymond Roberts Rodrigo Benenson [email protected] Sergei Lebedev [email protected] Skipper Seabold [email protected] Thomas Kluyver [email protected] Tobias Knuth [email protected] Volodymyr Kazantsev Wes McKinney [email protected] Zach Ploskey [email protected] akuz [email protected] brandon willard [email protected] dstuck [email protected] ingmarschuster [email protected] jan-matthis [email protected] jason JasonTam22@gmailcom kiudee [email protected] maahnman [email protected] macgyver [email protected] mwibrow [email protected] olafSmits [email protected] paul sorenson [email protected] redst4r [email protected] santon [email protected] sgenoud [email protected] stonebig Tal Yarkoni [email protected] x2apps [email protected] zenourn [email protected]

PyMC3 3.0b (June 16th, 2015)

Probabilistic programming allows for flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source probabilistic programmer framework with an intuitive, readable and concise, yet powerful, syntax that is close to the natural notation statisticians use to describe models. It features next-generation fitting techniques, such as the No U-Turn Sampler, that allow fitting complex models with thousands of parameters without specialized knowledge of fitting algorithms.

PyMC3 has recently seen rapid development. With the addition of two new major features: automatic transforms and missing value imputation, PyMC3 has become ready for wider use. PyMC3 is now refined enough that adding features is easy, so we don't expect adding features in the future will require drastic changes. It has also become user friendly enough for a broader audience. Automatic transformations mean NUTS and find_MAP work with less effort, and friendly error messages mean its easy to diagnose problems with your model.

Thus, Thomas, Chris and I are pleased to announce that PyMC3 is now in Beta.

Highlights

  • Transforms now automatically applied to constrained distributions
  • Transforms now specified with a transform= argument on Distributions. model.TransformedVar is gone.
  • Transparent missing value imputation support added with MaskedArrays or pandas.DataFrame NaNs.
  • Bad default values now ignored
  • Profile theano functions using model.profile(model.logpt)

Contributors since 3.0a