THIS IS THE **OLD** PYMC PROJECT. PLEASE USE PYMC3 INSTEAD:

Related tags

Deep Learning pymc
Overview

Introduction

Version: 2.3.8
Authors: Chris Fonnesbeck
Anand Patil
David Huard
John Salvatier
Web site: https://github.com/pymc-devs/pymc
Documentation: http://bit.ly/pymc_docs
Copyright: This document has been placed in the public domain.
License: PyMC is released under the Academic Free License.
https://secure.travis-ci.org/pymc-devs/pymc.png http://img.shields.io/pypi/v/pymc.svg?style=flat http://img.shields.io/badge/license-AFL-blue.svg?style=flat

NOTE: The current version PyMC (version 3) has been moved to its own repository called pymc3. Unless you have a good reason for using this package, we recommend all new users adopt PyMC3.

Purpose

PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.

Features

PyMC provides functionalities to make Bayesian analysis as painless as possible. Here is a short list of some of its features:

  • Fits Bayesian statistical models with Markov chain Monte Carlo and other algorithms.
  • Includes a large suite of well-documented statistical distributions.
  • Uses NumPy for numerics wherever possible.
  • Includes a module for modeling Gaussian processes.
  • Sampling loops can be paused and tuned manually, or saved and restarted later.
  • Creates summaries including tables and plots.
  • Traces can be saved to the disk as plain text, Python pickles, SQLite or MySQL database, or hdf5 archives.
  • Several convergence diagnostics are available.
  • Extensible: easily incorporates custom step methods and unusual probability distributions.
  • MCMC loops can be embedded in larger programs, and results can be analyzed with the full power of Python.

What's new in version 2

This second version of PyMC benefits from a major rewrite effort. Substantial improvements in code extensibility, user interface as well as in raw performance have been achieved. Most notably, the PyMC 2 series provides:

  • New flexible object model and syntax (not backward-compatible).
  • Reduced redundant computations: only relevant log-probability terms are computed, and these are cached.
  • Optimized probability distributions.
  • New adaptive blocked Metropolis step method.
  • Much more!

Usage

First, define your model in a file, say mymodel.py (with comments, of course!):

# Import relevant modules
import pymc
import numpy as np

# Some data
n = 5 * np.ones(4, dtype=int)
x = np.array([-.86, -.3, -.05, .73])

# Priors on unknown parameters
alpha = pymc.Normal('alpha', mu=0, tau=.01)
beta = pymc.Normal('beta', mu=0, tau=.01)

# Arbitrary deterministic function of parameters
@pymc.deterministic
def theta(a=alpha, b=beta):
    """theta = logit^{-1}(a+b)"""
    return pymc.invlogit(a + b * x)

# Binomial likelihood for data
d = pymc.Binomial('d', n=n, p=theta, value=np.array([0., 1., 3., 5.]),
                  observed=True)

Save this file, then from a python shell (or another file in the same directory), call:

import pymc
import mymodel

S = pymc.MCMC(mymodel, db='pickle')
S.sample(iter=10000, burn=5000, thin=2)
pymc.Matplot.plot(S)

This example will generate 10000 posterior samples, thinned by a factor of 2, with the first half discarded as burn-in. The sample is stored in a Python serialization (pickle) database.

History

PyMC began development in 2003, as an effort to generalize the process of building Metropolis-Hastings samplers, with an aim to making Markov chain Monte Carlo (MCMC) more accessible to non-statisticians (particularly ecologists). The choice to develop PyMC as a python module, rather than a standalone application, allowed the use MCMC methods in a larger modeling framework. By 2005, PyMC was reliable enough for version 1.0 to be released to the public. A small group of regular users, most associated with the University of Georgia, provided much of the feedback necessary for the refinement of PyMC to a usable state.

In 2006, David Huard and Anand Patil joined Chris Fonnesbeck on the development team for PyMC 2.0. This iteration of the software strives for more flexibility, better performance and a better end-user experience than any previous version of PyMC.

PyMC 2.1 was released in early 2010. It contains numerous bugfixes and optimizations, as well as a few new features. This user guide is written for version 2.1.

Relationship to other packages

PyMC in one of many general-purpose MCMC packages. The most prominent among them is WinBUGS, which has made MCMC and with it Bayesian statistics accessible to a huge user community. Unlike PyMC, WinBUGS is a stand-alone, self-contained application. This can be an attractive feature for users without much programming experience, but others may find it constraining. A related package is JAGS, which provides a more UNIX-like implementation of the BUGS language. Other packages include Hierarchical Bayes Compiler and a number of R packages of varying scope.

It would be difficult to meaningfully benchmark PyMC against these other packages because of the unlimited variety in Bayesian probability models and flavors of the MCMC algorithm. However, it is possible to anticipate how it will perform in broad terms.

PyMC's number-crunching is done using a combination of industry-standard libraries (NumPy and the linear algebra libraries on which it depends) and hand-optimized Fortran routines. For models that are composed of variables valued as large arrays, PyMC will spend most of its time in these fast routines. In that case, it will be roughly as fast as packages written entirely in C and faster than WinBUGS. For finer-grained models containing mostly scalar variables, it will spend most of its time in coordinating Python code. In that case, despite our best efforts at optimization, PyMC will be significantly slower than packages written in C and on par with or slower than WinBUGS. However, as fine-grained models are often small and simple, the total time required for sampling is often quite reasonable despite this poorer performance.

We have chosen to spend time developing PyMC rather than using an existing package primarily because it allows us to build and efficiently fit any model we like within a full-fledged Python environment. We have emphasized extensibility throughout PyMC's design, so if it doesn't meet your needs out of the box chances are you can make it do so with a relatively small amount of code. See the testimonials page on the wiki for reasons why other users have chosen PyMC.

Getting started

This guide provides all the information needed to install PyMC, code a Bayesian statistical model, run the sampler, save and visualize the results. In addition, it contains a list of the statistical distributions currently available. More examples of usage as well as tutorials are available from the PyMC web site.

Comments
  • Rebuild conda packages

    Rebuild conda packages

    The conda packages for pymc 2.3 are not compatible with current anaconda versions. We thus need to rebuild and reupload. I can do linux 64bit, @fonnesbeck can you do osx 64bit?

    release 
    opened by twiecki 188
  • Installation Instructions for Pymc3 on Windows 10 using Anaconda 3

    Installation Instructions for Pymc3 on Windows 10 using Anaconda 3

    Hi @michaelosthege - here is the first version of the Installation Reference. Let me know what changes may be needed.

    For Pymc3 Windows users who may not have a programming background or have comfort with tool chains and such, the installation instructions on this page (https://github.com/pymc-devs/pymc3/wiki/Installation-Guide-(Windows)) may not be sufficient. The instructions posted below go beyond the basic installation process posted at the above link.

    In addition, there are a large community of users, who use both R and Python (Anaconda). The RTools mingw tool chain has to be first on the System Environment variables so that R packages that need compilation such as rstan, brms will run correctly. In this situation, the compilation of pymc3 models will break and additional post-install User Environment Variables has to be done so pymc3 works correctly.

    Versions and main components

    • PyMC3 Version: 3.11.2
    • Aesara/Theano Version: 1.1.2 ( using theano-pymc)
    • Python Version: 3.7 / 3.8 using Anaconda3 64-bit
    • Operating system: Windows 10 64-bit, with 1904 Update
    • How did you install PyMC3: pip
    • C & C++ compilers: Installed m2w64 tool chain from conda-forge
    • Microsoft VS C++ compiler(s) present? No
    • Any Competing C++ compiler(s) present? Yes - RTools mingw tool chain on the System PATH

    The essence of a solid PyMC3 installation on Windows is to install most of the dependencies through conda. The reason installation via PyPI is difficult is that Theano/Aesara require compilation against MKL, which is difficult to set up, while Conda comes with its own compilers and MKL installation.

    โš  Do not pip install without first installing dependencies with conda. โš 

    Method 1: Run conda env create -f environment.yml to create a fresh environment in one step - use Notepad++, if possible to create the said environment.yml file.

    environment.yml (copy from name: pm3env and ending with pymc3 and save it in C:\Users\Your_User_Name )

    channels:
      - conda-forge
      - defaults
    dependencies:
      - libpython
      - blas
      - mkl-service
      - m2w64-toolchain
      - numba
      - pip
      - python=3.8
      - python-graphviz
      - scipy
      - pip:
        - pymc3
    

    You can change name to something meaningful for you such as pym3 or env_pym3. You do not have to use pm3env. Keep it simple and meaningful so you can use the environment with ease when using the command line.

    Method 2: You can create pymc3 specific environment also directly from the Anaconda3 Command Prompt using the following command: conda create -n pm3env -c conda-forge "python=3.8" libpython mkl-service m2w64-toolchain numba python-graphviz scipy

    After you have created the environment, you can test it has been created successfully by typing: conda info --envs - the output of the command will show all the environments that exist in the current Anaconda3 install. Hopefully you will see your environment for pymc3

    Activate your environment by typing the command: conda activate pm3env (or whatever name you chose) Your command prompt will look like: (pm3env) C:\Users\Your_User_Name

    Check Packages Installed using the command conda list and all packages installed in pm3env will be shown. Check if either one of theano or theano-pymc has been installed. Make note of it (It should not be)

    Install Pymc3: Now you can install Pymc3 using the command: pip install pymc3 and then if all requirements are met, all packages from pip and their dependencies will be installed,. Of critical importance to note is whether theano-pymc has been installed along with pymc3 - pymc3 will not run without this - Here theano-pymc should be installed. In addition, in the pip output check that dependencies such as arviz (for working with pymc3 objects) and matplotlib (for general purpose data graphing) have also been installed.

    Sometimes these packages may not be installed in your new environment but will be installed likely in the location below on Windows 10: c:\users\your_user_name\appdata\roaming\python\python38\site-packages - note the number 38 next to python38 in the folder name. This means that versions relevant for python3.8 have been installed here. These packages will not appear in the output of conda list. Ensure that the python you specified (python=3.8) matches what you see in the folder name here (python38)

    [@michaelosthege The original instructions ask to install theano-pymc using conda-forge. However, I found that in the new dependency installation of pymc3, theano-pymc is being automatically being installed and it is working correctly. This is one where I need your input on whether we remove the note to update theano-pymc. I think this is outdated]

    Now there are additional ways to install ``pymc3and additional variants ofpymc3```. Refer to the next section, which may be more appropriate for advanced users.

    Developer Installation

    If you want to tinker with PyMC3 itself, first clone the repository and then make an "editable" installation: (You need to have already installed git for this to work, if not install git first)

    cd pymc3
    pip install --editable .
    

    Upgrading from Theano to Theano-PyMC - Just in case when you run conda list at the pm3env prompt and find that you have theano instead of theano-pymc (stranger things have and will happen!)

    1. Make a note of the channel where theano was installed from: it will show pypi or conda-forge.
    2. If you see pypi then use the command, pip uninstall theano - you should see a message stating theano has been uninstalled
    3. If you see conda-forge use the command, conda remove theano - you should answer with a y if prompted for the removal of theano
    4. Install theano-pymc using the command, conda install -c conda-forge theano-pymc.
    5. Once the installation is complete, run the command conda list and verify pymc3 & theano-pymc are installed.

    Optional Dependencies (before you install any packages, first check they have not been already installed using conda list

    1. The GLM submodule relies on Patsy. Patsy brings the convenience of "R-style formulas" to Python.
    2. pm.model_to_graphviz depends on Graphviz and pydot:
    3. Use the command conda install -c conda-forge python-graphviz and pip install pydot-ng
    4. In the package installations done so far, Jupyter Notebook or Jupyter Lab are not installed. If you are working with Anaconda3, you install these two Jupyter tools from Anaconda3 Navigator or from the pm3env command prompt using the command: conda install -c conda-forge notebook

    Do Not Close the Command Prompt window of the pm3env - we have to use it later on.

    Post Installation Checks - Do Not Skip This Step on Windows A. Assumes you have installed Jupyter Notebook B. Make note of whether you have R and in particular RTools installed on your laptop and location of its' install. C. Assumes you have either Admin or Power-User rights on your laptop so that you can make changes to the environment variables at the User level.

    1. Windows does not come pre-installed with C and C++ compilers (as Mac and Linux Distros do) so it is important to ensure your Anaconda3 environments are pointing to the correct internal compilers.

    2. Going back to the original creation / installation of pm3env environment, one of the packages installed is m2w64-toolchain. Here things can get complex in terms of having compiler tools specific to the version of Python and to that of the environment. So generally speaking, try to keep your Anaconda3 environments to a minimum, when you are starting out with tools such as pymc3. Also make sure you install a m2w64-toolchain in each environment you create. Conda will ensure that you have the most appropriate version of the compilers for the version of Python in that environment. There are exceptions, but it gets beyond the scope of this document.

    3. On Windows, search for "Edit System Environment Variables". You will be taken to the screen below image

    4. Click on the "Environment Variables" on the bottom right-hand corner and you will see a new window pop-up with the top Window for User variables and the bottom window for the System variables. Click on the entry labeled Path under User Variables for Your_User_Name and click edit. Here you should add the following Anaconda paths specific to your environment:

    Note the location of where Anaconda3 is installed by default (as shown below). If you have changed the location during the installation of Anaconda3, please make the changes accordingly.

    First: C:\ProgramData\Anaconda3\Library\mingw-w64\bin Second: C:\ProgramData\Anaconda3\Library\bin Third: C:\ProgramData\Anaconda3\Scripts

    Move each of the entries so that they appear in the exact order as shown above at the top of the User Variables Path using the Move Up and Move Down buttons

    Then click "OK" to accept the changes all the way.

    1. Go back to the pm3env Command Prompt window that is already open that displays (pm3env) C:\Users\Your_User_Name>

    2. Type jupyter-notebook at the prompt and (hopefully) a page should be opened in your Default Browser. image

    3. On the top right corner, click on the drop-down under New image

    4. Select Python 3 (ipykernel)

    5. You should see a new Jupyter Notebook open that looks as follows: image

    6. Make sure that at the top right corner, the button says Trusted. If this is the first time you are using a Jupyter Notebook, it might show up as Not Trusted. Click on it if it says "Not Trusted" and select the option to make it "Trusted".

    7. In the first Notebook cell, type import theano as tp and click on the Run button.

    8. You may see a warning as shown in the picture below (WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.) image

    9. In the next cell type the following commands and click Run import pymc3 as pm print(f"Running on PyMC3 v{pm.__version__}")

    10. You should see an output as follows: Running on PyMC3 v3.11.2

    11. You can now begin testing the full capabilities of Pymc3 by starting with the three examples from the Getting Started with Pymc3 - linked here - http://docs.pymc.io/notebooks/getting_started

    Good luck with using Pymc3!

    installation 
    opened by sreedat 134
  • Vectors of multivariate variables

    Vectors of multivariate variables

    It would be useful if we could model multiple independent multivariate variables in the same statement. For example, if I wanted four multivariate normal vectors with the same prior, I should be able to specify:

    f = pm.MvNormal('f', np.zeros(3), np.eye(3), shape=(4,3))
    

    but it currently returns a ValueError complaining of non-aligned matrices.

    enhancements 
    opened by fonnesbeck 88
  • Add MLDA stepper

    Add MLDA stepper

    The MLDA stepper

    The MLDA (Multi-Level Delayed Acceptance) [1] method employs a hierarchy of chains to sample from a posterior. The top-level chain samples from the fine model posterior (i.e. the most accurate one / the one we are interested in) and the lower level chains sample from coarse model posteriors (i.e. approximations of declining accuracy).

    MLDA uses samples generated in one level as proposals for the level above. A chain runs for a fixed number of iterations and then the last sample is used as the proposal for the higher-level chain. The bottom level is a Metropolis sampler, although an algorithm like pCN or adaptive MH is expected to be used in the future.

    The stepper is suitable for situations where a model is expensive to evaluate in high spatial or temporal resolution (e.g. realistic subsurface flow models where a PDE needs to be solved in each iteration). In those cases we can use cheaper, lower resolutions as coarse models. If the approximations are sufficiently good, this leads to good quality proposals, high acceptance rates and high ES/sec compared to other methods because only a few expensive fine model solves are needed to achieve the same ESS.

    Changes

    • MLDA is a new ArrayStepShared step method in metropolis.py. Its constructor instantiates step methods for all the levels in the hierarchy.
    • RecursiveDA is a new Proposal proposal method in metropolis.py. It recursively calls sample to run the hierarchy of chains in multiple levels, using the step methods instantiated by MLDA.init. Note that logging is switched off here to avoid unwanted console output. sample()each time generates a fixed number of samples and then we only keep the last and return control to the level above. We rerun sample() from that point each time control goes back to that level.
    • A new internal variable called is_mlda_base is added to Metropolis. This is detected within sample() to avoid running reset_tuning(). This is done because we want the Metropolis step in the bottom level to maintain the tuning information across subsequent sample() calls. Note that resetting was introduced by #3733. MLDA is not affected by the issue there, as it always uses cores=1 and chains=1 in each level of the hierarchy. A more elegant solution might be the detection of cores and chains and passing the info to _iter_sample to decide if reset_tuning runs.
    • Tests for MLDA and RecursiveDA and two new models for testing in tests/models.py
    • A notebook comparing the performance of MLDA with Metropolis for a groundwater flow model (a Bayesian inverse problem setting). The forward model requires a PDE solve which is done using the C-based FEniCS library (see notebook for details). Thus it is also a demonstration of using black box external code in the likelihood. The PR adds all the necessary code to solve the model. The example is also provided in the form of a python script.

    Performance

    Running the notebook on a MacBook Pro (see specs within), with 3 unknown parameters and model resolution (30, 30) and (120, 120), the resulting performance comparison is the following:

    Untitled

    Work in progress

    • I am trying to find ways to accelerate the method. It seems that there is a lot of overhead from creating/destroying traces and Theano parsing the likelihood when calling sample() multiple times. Stopping and restarting is necessary as I need to switch between chains and continue a chain from where it last left off. Are there faster ways to pause and resume a chain than saving the trace/tuning and calling a new sample() afterwards? Would iter_sample be more efficient in this case?
    • I am working on adding an adaptive bottom level sampler and also on applying an adaptive likelihood correction technique found in [2]. Is there any work going on adaptive samplers like adaptive MH or pCN within the pymc3 community?
    • I plan to add one more feature contained in [1] (the variance reduction technique to reduce variance of integral estimates).

    Usage

    Can be used as any other method but has one positional argument that needs to be provided (coarse_models). See notebook and tests for examples.

    References

    [1] Dodwell, Tim & Ketelsen, Chris & Scheichl, Robert & Teckentrup, Aretha. (2019). Multilevel Markov Chain Monte Carlo. SIAM Review. 61. 509-545.ย https://doi.org/10.1137/19M126966X [2] Cui, Tiangang & Fox, Colin & O'Sullivan, Michael. (2012). Adaptive Error Modelling in MCMC Sampling for Large Scale Inverse Problems.

    opened by gmingas 82
  • Running multiple chains causes RecursionError

    Running multiple chains causes RecursionError

    Setting the njobs parameter to run multiple chains results in an error:

    ---------------------------------------------------------------------------
    RecursionError                            Traceback (most recent call last)
    <ipython-input-59-548e16bedce3> in <module>()
          6 
          7 
    ----> 8     trace = sample(5000, njobs=2)
    
    /Users/fonnescj/Github/pymc3/pymc3/sampling.py in sample(draws, step, start, trace, chain, njobs, tune, progressbar, model, random_seed)
        153         sample_args = [draws, step, start, trace, chain,
        154                        tune, progressbar, model, random_seed]
    --> 155     return sample_func(*sample_args)
        156 
        157 
    
    /Users/fonnescj/Github/pymc3/pymc3/sampling.py in _mp_sample(njobs, args)
        274 def _mp_sample(njobs, args):
        275     p = mp.Pool(njobs)
    --> 276     traces = p.map(argsample, args)
        277     p.close()
        278     return merge_traces(traces)
    
    /Users/fonnescj/anaconda3/lib/python3.5/multiprocessing/pool.py in map(self, func, iterable, chunksize)
        258         in a list that is returned.
        259         '''
    --> 260         return self._map_async(func, iterable, mapstar, chunksize).get()
        261 
        262     def starmap(self, func, iterable, chunksize=None):
    
    /Users/fonnescj/anaconda3/lib/python3.5/multiprocessing/pool.py in get(self, timeout)
        606             return self._value
        607         else:
    --> 608             raise self._value
        609 
        610     def _set(self, i, obj):
    
    /Users/fonnescj/anaconda3/lib/python3.5/multiprocessing/pool.py in _handle_tasks(taskqueue, put, outqueue, pool, cache)
        383                         break
        384                     try:
    --> 385                         put(task)
        386                     except Exception as e:
        387                         job, ind = task[:2]
    
    /Users/fonnescj/anaconda3/lib/python3.5/multiprocessing/connection.py in send(self, obj)
        204         self._check_closed()
        205         self._check_writable()
    --> 206         self._send_bytes(ForkingPickler.dumps(obj))
        207 
        208     def recv_bytes(self, maxlength=None):
    
    /Users/fonnescj/anaconda3/lib/python3.5/multiprocessing/reduction.py in dumps(cls, obj, protocol)
         48     def dumps(cls, obj, protocol=None):
         49         buf = io.BytesIO()
    ---> 50         cls(buf, protocol).dump(obj)
         51         return buf.getbuffer()
         52 
    
    RecursionError: maximum recursion depth exceeded
    
    opened by fonnesbeck 78
  • Optimization

    Optimization

    Bayesian optimization over posterior latent space is an interesting sort of problem that becomes real with this PR. Notebook with toy example is provided

    @fonnesbeck you can find Histogram application for SVGD there

    opened by ferrine 73
  • Bring back distribution moments

    Bring back distribution moments

    With #4983 and #5087 finished, it's a good time to bring back moments for our distributions for more stable starting points.

    With this we can then do the switch in #5009

    We should also update the distribution developer guide to mention the implementation of moments: https://github.com/pymc-devs/pymc/blob/main/docs/source/developer_guide_implementing_distribution.md


    How to help?

    1. This PR should give a template on how to implement and test new moments for distributions: https://github.com/pymc-devs/pymc/pull/5087/files

    2. In most cases we should be able to copy the moments we were using in the V3 branch. For example here is what we were doing for the Beta https://github.com/pymc-devs/pymc/blob/efbaccee94159de0a15b04baf84b1250047f132a/pymc3/distributions/continuous.py#L1235 2.1 We used to have multiple moments for some distributions such as mean, median, mode. We only support one moment now, and probably the "higher-order" one is the most useful (that is mean > median > mode)... You might need to truncate the moment if you are dealing with a discrete distribution. 2.2 We left some of these moments commented out inside the distribution dist classmethod. Make sure they are removed when you implement them! https://github.com/pymc-devs/pymc/blob/8f3636daf7d9946f6eca4717f3bb0c6d77d9c6e9/pymc/distributions/continuous.py#L538-L539

    3. We have to be careful with size != None and broadcasting properly when when some parameters that are not used in the moment may nevertheless inform about the shape of the distribution. E.g. pm.Normal.dist(mu=0, sigma=np.arange(1, 6)) returns a moment of [mu, mu, mu, mu, mu]. Again #5087 should give some template to think about this https://github.com/pymc-devs/pymc/blob/8f3636daf7d9946f6eca4717f3bb0c6d77d9c6e9/pymc/distributions/continuous.py#L546-L550 3.1 In the case where you have to manually broadcast the parameters with each other it's important to add test conditions that would fail if you were not to do that. A straightforward way to do this is to make the used parameter a scalar, the unused one(s) a vector (one at a time) and size None

    4. Just to keep things uniformish, please add the get_moment immediately below the dist classmethod and before logp or logcdf methods.

    5. New tests have to be added in test_distributions_moments.py. Make sure to test different combinations of size and broadcasting to cover the cases mentioned in point 3.

    6. Don't hesitate to ask any questions. You can grab as many distributions to implement moments as you want. Just make sure to write in this issue so that we can keep track of it.

    7. Profit with your new open source KARMA!

    The following distributions don't have a moment method implemented:

    • [x] pymc.distributions.continuous.Beta #5145
    • [x] pymc.distributions.continuous.Kumaraswamy #5147
    • [x] pymc.distributions.continuous.Exponential #5147
    • [x] pymc.distributions.continuous.Laplace #5147
    • [x] pymc.distributions.continuous.StudentT #5147
    • [x] pymc.distributions.continuous.Cauchy #5147
    • [x] pymc.distributions.continuous.HalfCauchy https://github.com/pymc-devs/pymc/pull/5148
    • [x] pymc.distributions.continuous.Gamma https://github.com/pymc-devs/pymc/pull/5148
    • [x] pymc.distributions.continuous.Weibull https://github.com/pymc-devs/pymc/pull/5148
    • [x] pymc.distributions.continuous.LogNormal https://github.com/pymc-devs/pymc/pull/5148
    • [x] pymc.distributions.continuous.HalfStudentT https://github.com/pymc-devs/pymc/pull/5152
    • [x] pymc.distributions.continuous.ChiSquared https://github.com/pymc-devs/pymc/pull/5154
    • [x] pymc.distributions.continuous.Wald #5161
    • [x] pymc.distributions.continuous.Pareto #5161
    • [x] pymc.distributions.continuous.InverseGamma #5199
    • [x] pymc.distributions.continuous.ExGaussian #5165
    • [x] pymc.distributions.continuous.VonMises #5232
    • [x] pymc.distributions.discrete.Binomial https://github.com/pymc-devs/pymc/pull/5150
    • [x] pymc.distributions.discrete.BetaBinomial #5175
    • [x] pymc.distributions.discrete.Poisson https://github.com/pymc-devs/pymc/pull/5150
    • [x] pymc.distributions.discrete.NegativeBinomial #5163
    • [x] pymc.distributions.discrete.Constant #5156
    • [x] pymc.distributions.discrete.ZeroInflatedPoisson #5163
    • [x] pymc.distributions.discrete.ZeroInflatedNegativeBinomial #5206
    • [x] pymc.distributions.discrete.ZeroInflatedBinomial #5163
    • [x] pymc.distributions.discrete.DiscreteUniform #5167
    • [x] pymc.distributions.discrete.Geometric #5158
    • [x] pymc.distributions.discrete.HyperGeometric #5167
    • [x] pymc.distributions.discrete.Categorical #5176
    • [x] pymc.distributions.distribution.DensityDist #5159
    • [x] pymc.distributions.multivariate.MvNormal #5171
    • [x] pymc.distributions.multivariate.MatrixNormal #5173
    • [x] pymc.distributions.multivariate.KroneckerNormal #5235
    • [x] pymc.distributions.multivariate.MvStudentT #5173
    • [x] pymc.distributions.multivariate.Dirichlet #5174
    • [x] pymc.distributions.multivariate.Multinomial #5201
      • Note: This distribution had tests for the mode in test_distributions.py which should be moved/refactored to test_distributions_moments.py
      • https://github.com/pymc-devs/pymc/blob/bdd4d1992f11cab9202774b14b8044ddf0cb7674/pymc/tests/test_distributions.py#L2151
      • https://github.com/pymc-devs/pymc/blob/bdd4d1992f11cab9202774b14b8044ddf0cb7674/pymc/tests/test_distributions.py#L2189
    • [x] pymc.distributions.multivariate.DirichletMultinomial #5225
    • [x] pymc.distributions.continuous.AsymmetricLaplace #5188
    • [x] pymc.distributions.continuous.SkewNormal #5188
    • [x] pymc.distributions.continuous.Triangular #5180
    • [x] pymc.distributions.discrete.DiscreteWeibull #5407
    • [x] pymc.distributions.continuous.Gumbel #5180
    • [x] pymc.distributions.continuous.Logistic #5157
    • [x] pymc.distributions.continuous.LogitNormal #5180
    • [x] pymc.distributions.continuous.Interpolated #5222
    • [x] pymc.distributions.continuous.Rice #5190
    • [x] pymc.distributions.continuous.Moyal #5179
    • [x] pymc.distributions.simulator.Simulator #5208
    • [x] pymc.distributions.multivariate.CAR #5220
    • [x] pymc.distributions.continuous.PolyaGamma #5193
    • [x] pymc.bart.BART #5211
    beginner friendly help wanted v4 
    opened by ricardoV94 65
  • Variational inference with AD?

    Variational inference with AD?

    Can the theano infrastructure handle this?

    https://github.com/stan-dev/stan/pull/1421 http://andrewgelman.com/2015/02/18/vb-stan-black-box-black-box-variational-bayes/

    opened by datnamer 64
  • rewrite radon notebook using ArviZ and xarray

    rewrite radon notebook using ArviZ and xarray

    I am in the process of rewriting the notebook to exploit all ArviZ and xarray goodness. Comments are very welcome and I hope it is useful as a way to showcase xarray mostly and ArviZ capabilities inherited from xarray. Part of #3959

    Depending on what your PR does, here are a few things you might want to address in the description:

    • [x] important background, or details about the implementation
      • ~Doing this I have encountered a couple bugs in ArviZ so this notebook won't even run on ArviZ master, only on PR branch. I am not sure about the timeline of the doc sprint but it will end up either running on ArviZ master or on next release (there may be a release coming in the coming weeks :thinking:). Depends on https://github.com/arviz-devs/arviz/pull/1240 and https://github.com/arviz-devs/arviz/pull/1241~ All PRs have been merged and ArviZ 0.9.0 has been released!
      • I have checked doc generation locally to make sure xarray html repr is rendered properly
    • [x] right before it's ready to merge, mention the PR in the RELEASE-NOTES.md
    opened by OriolAbril 63
  • Leveraging SymPy for PyMC3

    Leveraging SymPy for PyMC3

    SymPy (http://sympy.org/en/index.html) is a Python library for symbolic mathematics.

    My initial motivation for looking at SymPy resulted from #172 and #173. Instead of recoding all probability distributions, samplers etc in Theano, maybe we could just use the ones provided by sympy.stats (http://docs.sympy.org/dev/modules/stats.html).

    For this to work we needed to convert the sympy computing graph to a theano one. It seems that there is some work that shows that this is possible (https://github.com/nouiz/theano_sympy)

    Looking at sympy (and sympy.stats) more closely it seems that there are potentially more areas where integrating this could help. Maybe this would give the best of both worlds: "Theano focuses more on tensor expressions than Sympy, and has more machinery for compilation. Sympy has more sophisticated algebra rules and can handle a wider variety of mathematical operations (such as series, limits, and integrals)."

    There is additional discussion here: https://github.com/nouiz/theano_sympy/issues/1.

    Copy pasting some chunks from @mrocklin response to move the discussion over here:

    Overlap

    There are some obvious points of overlap between the various projects

    • PyMC has distributions and SymPy has distributions (https://github.com/sympy/sympy/blob/master/sympy/stats/crv_types.py). SymPy doesn't currently have infinite discrete random variables like Poisson though. This could be fixed but is a current failing. SymPy's support for analytic solution of infinite sums is poor so this was a low priority. It seems like you're not really looking for that though.
    • PyMC has implemented some special functions that could be in Theano. I would encourage you to push these upstream. They'll probably get some useful attention from the Theano crowd.
    • Looking at the pymc2 readme it appears that you have created some sort of symbolic algebra class structure (you add two pymc.Normal objects). Presumably SymPy.core might be of use here.

    What is the relationship with statsmodels? They also have a home-grown internal algebraic system. My guess is that if everyone were to unite under one algebraic system there would be some pleasant efficiencies. I obviously have a bias about what that algebraic system should be :)

    Derivatives

    Both Theano and SymPy provide derivatives which, apparently, you need. SymPy provides analytic ones, Theano provides automatic ones. My suggestion would be to use SymPy if it works and fall back on Theano if it doesn't work. You don't need SymPy.stats for this (in case you didn't want to offload your distributions work.) SymPy.core would be just fine.

    Other benefits

    In general the benefits to using symbolic systems tend to be unexpected. SymPy can provide lots of general aesthetic fluff like awesome pretty printing, symbolic simplification, C/Fortran code snippet generation, etc....

    opened by twiecki 60
  • Update robust glm notebook

    Update robust glm notebook

    The first purpose of this PR is to update the GLM robust regression notebook already in the examples here: https://docs.pymc.io/notebooks/GLM-robust-with-outlier-detection.html

    Those updates are everything from v2.1 onwards:

    Version history:

    version | date | author | changes :--- | :--- | :--- | :--- 1.0 | 2015-12-21 | jonsedar | Create and publish 2.0 | 2018-07-24 | twiecki | Restate outlier model using pm.Normal.dist().logp() and pm.Potential() 2.1 | 2019-11-16 | jonsedar | Restate nu in StudentT model to be more efficient, drop explicit use of theano shared vars, generally improve plotting / explanations / layout 2.2 | 2020-05-21 | jonsedar | Minor tidyup for plots and warnings and rerun with pymc3.8

    The second purpose of this PR is to clarify the docstring within sampling.py, specifically the kwargs for step_kwargs when you have multiple steppers. I found the need for better clarity in the docstring during my rework of the notebook, so I hope it's valid to include a change in this single PR. I think this is also a fix for https://github.com/pymc-devs/pymc3/issues/3197

    opened by jonsedar 59
  • Spell-check code, docs and comments

    Spell-check code, docs and comments

    What is this PR about? I used typos to spell-check code, docs and comments.

    Checklist

    Major / Breaking Changes

    • ...

    New features

    • ...

    Bugfixes

    • ...

    Documentation

    • ...

    Maintenance

    • ...
    no releasenotes 
    opened by Armavica 2
  • Move pymc/distributions/logprob.py to pymc/logprob/

    Move pymc/distributions/logprob.py to pymc/logprob/

    What is this PR about? This PR shuffles around some of the logprob-related functions.

    • joint_logprob, a thin wrapper over factorized_joint_logprob, was only used in tests, so it was moved to tests.
    • _joint_logprob now lives as joint_logprob in pymc/logprob/joint_logprob.py
    • _get_scaling, _check_no_rvs, logp to pymc/logprob/joint_logprob.py
    • logcdf to logprob/abstract.py
    • ignore_logprob to logprob/utils.py

    Checklist

    Major / Breaking Changes

    • ...

    New features

    • ...

    Bugfixes

    • ...

    Documentation

    • ...

    Maintenance

    • ...
    opened by Armavica 1
  • BUG: Unable to import pymc (pymc.model module not found error)

    BUG: Unable to import pymc (pymc.model module not found error)

    Describe the issue:

    I'm trying to use a package dependent on pymc (HDDM) but run into the error below when I try to import pymc. I can't find a similar error online anywhere. Installing pymc using conda seems to run smoothly but when I try to import in python, it gives me an error (see below). What could be the culprit? A similar function elsewhere? Thanks in advance.

    Reproduceable code example:

    import pymc
    

    Error message:

    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "/opt/anaconda3/envs/pyHDDM/lib/python3.7/site-packages/pymc/__init__.py", line 32, in <module>
        from .distributions import *
      File "/opt/anaconda3/envs/pyHDDM/lib/python3.7/site-packages/pymc/distributions/__init__.py", line 22, in <module>
        from pymc.distributions.bound import Bound
      File "/opt/anaconda3/envs/pyHDDM/lib/python3.7/site-packages/pymc/distributions/bound.py", line 23, in <module>
        from pymc.distributions.continuous import BoundedContinuous, bounded_cont_transform
      File "/opt/anaconda3/envs/pyHDDM/lib/python3.7/site-packages/pymc/distributions/continuous.py", line 90, in <module>
        from pymc.distributions.distribution import DIST_PARAMETER_TYPES, Continuous
      File "/opt/anaconda3/envs/pyHDDM/lib/python3.7/site-packages/pymc/distributions/distribution.py", line 50, in <module>
        from pymc.printing import str_for_dist, str_for_symbolic_dist
      File "/opt/anaconda3/envs/pyHDDM/lib/python3.7/site-packages/pymc/printing.py", line 25, in <module>
        from pymc.model import Model
    ModuleNotFoundError: No module named 'pymc.model'
    

    PyMC version information:

    pymc 2.3.8 OS: Mojave 10.14.6 Installation: conda

    Context for the issue:

    I'm trying to use a package dependent on pymc (HDDM) but run into the error below when I try to import pymc. I can't find a similar error online anywhere. HDDM requires python 3.7 and pymc 2.3.8.

    bug 
    opened by LLouedKhen 0
  • add ref to template notebook in Jupyter style guide

    add ref to template notebook in Jupyter style guide

    What is this PR about? Closes #6420

    Checklist

    Documentation

    • Update Jupyter Style Guide: include template notebook for pymc examples

    cc: @drbenvincent

    opened by reshamas 0
  • Bump PyTensor to 2.8.12

    Bump PyTensor to 2.8.12

    What is this PR about? Updating to the latest PyTensor version, because I'm looking forward to a bugfix in the release after, and I want things to go smooth =)

    Checklist

    Major / Breaking Changes

    • Updated PyTensor 2.8.11โ†’2.8.12
    pytensor-related major 
    opened by michaelosthege 3
  • BUG: Class probabilities in `pm.Categorical` are split into observed and missing when `observed` has missing values

    BUG: Class probabilities in `pm.Categorical` are split into observed and missing when `observed` has missing values

    Describe the issue:

    Originally raised on discourse here.

    It appears that when pm.Categorical has observed data with missing values, the variable is re-instantiated inside model.make_obs_var with the wrong number of categories. This example shows that the new number of categories is indeed controlled by the shape of the data:

    import pymc as pm
    import numpy as np
    
    # No error
    data = np.ma.masked_equal([1, -1, -1], -1)
    with pm.Model():
        idx = pm.Categorical(f"hi_idx", p=[0.1, 0.2, 0.7], observed=data)
    pm.draw(idx, 100).max()
    >>>Out: 2.0
    
    data = np.ma.masked_equal([1, -1], -1)
    with pm.Model():
        idx = pm.Categorical(f"hi_idx", p=[0.1, 0.2, 0.7], observed=data)
    pm.draw(idx, 100).max()
    >>> Out: 1.0
    

    If the data are longer than the number of classes, the code will error out, as shown below:

    Reproduceable code example:

    import pymc as pm
    import numpy as np
    
    data = np.ma.masked_equal([1, 1, 0, 0, 2, -1, -1], -1)
    with pm.Model():
        idx = pm.Categorical(f"hi_idx", p=[0.1, 0.2, 0.7], observed=data)
    pm.draw(idx, 100).max()
    

    Error message:

    
    IndexError: index 5 is out of bounds for axis 0 with size 3
    Apply node that caused the error: AdvancedSubtensor1(TensorConstant{[0.1 0.2 0.7]}, TensorConstant{[5 6]})
    Toposort index: 1
    Inputs types: [TensorType(float64, (3,)), TensorType(uint8, (2,))]
    Inputs shapes: [(3,), (2,)]
    Inputs strides: [(8,), (1,)]
    Inputs values: [array([0.1, 0.2, 0.7]), array([5, 6], dtype=uint8)]
    Outputs clients: [[categorical_rv{0, (1,), int64, True}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x12AD72820>), TensorConstant{(1,) of 2}, TensorConstant{4}, AdvancedSubtensor1.0)]]
    
    Backtrace when the node is created (use PyTensor flag traceback__limit=N to make it longer):
      File "/Users/jessegrabowski/mambaforge/envs/econ/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3194, in run_cell_async
        has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
      File "/Users/jessegrabowski/mambaforge/envs/econ/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3373, in run_ast_nodes
        if await self.run_code(code, result, async_=asy):
      File "/Users/jessegrabowski/mambaforge/envs/econ/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3433, in run_code
        exec(code_obj, self.user_global_ns, self.user_ns)
      File "/var/folders/7b/rzxy96cj0w751_6td3g2yss00000gn/T/ipykernel_96907/3148429013.py", line 3, in <module>
        idx = pm.Categorical(f"hi_idx", p=[0.1, 0.2, 0.7], observed=data)
      File "/Users/jessegrabowski/mambaforge/envs/econ/lib/python3.9/site-packages/pymc/distributions/distribution.py", line 457, in __new__
        return super().__new__(cls, name, *args, **kwargs)
      File "/Users/jessegrabowski/mambaforge/envs/econ/lib/python3.9/site-packages/pymc/distributions/distribution.py", line 310, in __new__
        rv_out = model.register_rv(
      File "/Users/jessegrabowski/mambaforge/envs/econ/lib/python3.9/site-packages/pymc/model.py", line 1348, in register_rv
        rv_var = self.make_obs_var(rv_var, observed, dims, transform)
      File "/Users/jessegrabowski/mambaforge/envs/econ/lib/python3.9/site-packages/pymc/model.py", line 1425, in make_obs_var
        (missing_rv_var,) = local_subtensor_rv_lift.transform(fgraph, fgraph.outputs[0].owner)
    
    HINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.
    
    

    PyMC version information:

    PyMC version: 5.0.1 Pytensor version: 2.8.11

    Context for the issue:

    No response

    bug 
    opened by jessegrabowski 1
Releases(v5.0.1)
  • v5.0.1(Dec 21, 2022)

    What's Changed

    New Features ๐ŸŽ‰

    • Implement logp derivation for division, subtraction and negation by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6371
    • Extend logprob inference to power transforms by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6400

    Bugfixes ๐Ÿ›

    • Update PyTensor dependency and fix bugs in inferred mixture logprob by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6397

    Maintenance ๐Ÿ”ง

    • Update Release Notes template by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6392
    • Remove global RandomStream by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6396
    • Fix error in docstring of Truncated by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6395
    • added postprocessing_chunks option to sample_blackjax_nuts and sampleโ€ฆ by @wnorcbrown in https://github.com/pymc-devs/pymc/pull/6388
    • replaces numpy sqrt method with pytensor equivalent by @morganstrom in https://github.com/pymc-devs/pymc/pull/6405

    New Contributors

    • @wnorcbrown made their first contribution in https://github.com/pymc-devs/pymc/pull/6388

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v5.0.0...v5.0.1

    Source code(tar.gz)
    Source code(zip)
  • v5.0.0(Dec 12, 2022)

    What's Changed

    In this major release we are switching our graph computation backend from Aesara to PyTensor, which is a fork of Aesara under PyMC governance. Read the full announcement here: PyMC is Forking Aesara to PyTensor.

    The switch itself should be rather seamless and you can probably just update your imports:

    import aesara.tensor as at       # old (pymc >=4,< 5)
    import pytensor.tensor as pt     # new (pymc >=5)
    

    If you encounter problems updating please check the latest Discussions and don't hesitate to get in touch.

    Major Changes ๐Ÿ› 

    • โš  Switched the graph backend from Aesara to PyTensor
    • Merged AePPL into a new logprob submodule. Dispatch methods can be found in logprob.abstract
    • โš  The log_likelihood, needed for arviz.compare is no longer computed by default. It can be added with idata = pm.compute_log_likelihood(idata) or using pm.sample(idata_kwargs=dict(log_likelihood=True)) by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6374
    • Changed Minibatch API by @ferrine in https://github.com/pymc-devs/pymc/pull/6304
    • Fix ordering transformation for batched dimensions, and deprecate in favor of univariate_ordered and multivariate_ordered by @TimOliverMaier in #6255 and @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6375

    New Features & Bugfixes ๐ŸŽ‰

    • Support logp derivation in DensityDist when random function returns a PyTensor variable by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6361
    • Added alternative parametrization for AsymmetricLaplace by @aloctavodia in https://github.com/pymc-devs/pymc/pull/6337

    Docs & Maintenance ๐Ÿ”ง

    • Bugfixes to increase robustness against unnamed dims by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6339
    • Updated GOVERNANCE.md by @canyon289 in https://github.com/pymc-devs/pymc/pull/6358
    • Fixed overriding user provided mp_ctx strings to pm.sample() on M1 MacOS by @digicosmos86 in https://github.com/pymc-devs/pymc/pull/6363
    • Simplify measurable transform rewrites by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6370
    • Fix measurable stack and join with interdependent inputs by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6342
    • Allow transforms to work with multiple-valued nodes by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6341
    • Fix transformed Scan values by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6343
    • Add issue templates by @ferrine in https://github.com/pymc-devs/pymc/pull/6327
    • Fail docs build on errors in core notebooks by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6324
    • Curated ecosystem references by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6383
    • Switched run_mypy.py from pass-listing to fail-listing by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6381
    • Runing pydocstyle in pre-commit by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6382
    • Removed NoDistribution from docs by @stestoll in https://github.com/pymc-devs/pymc/pull/6316
    • Fix transforms example by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6333

    New Contributors

    • @digicosmos86 made their first contribution in https://github.com/pymc-devs/pymc/pull/6363

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.4.0...v5.0.0

    Source code(tar.gz)
    Source code(zip)
  • v4.4.0(Nov 19, 2022)

    What's Changed

    Major Changes ๐Ÿ› 

    • Removed support for selectively tracking variables via pm.sample(trace=[...]). by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6269
    • Do not rely on tag information for rv and logp conversions by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6281. This includes:
      • Deprecated accessing any of [value_variable|observations|transform|total_size] via var.tag in favor of model.rvs_to_[values|transforms|total_sizes]
      • Deprecated joint_logp in favor of model.logp
      • Deprecated aesaraf.rvs_to_value_vars in favor of model.replace_rvs_by_values
    • Using keyword seed for initial point no longer supported by @wd60622 in https://github.com/pymc-devs/pymc/pull/6291
    • Sampling of transformed variables from prior_predictive is no longer allowed by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6309
    • Require all step methods to return stats by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6313. This includes
      • Require all step methods to return stats from their step/astep method.
      • The BlockedStep.generates_stats attribute was removed.

    New Features & Bugfixes ๐ŸŽ‰

    • Fix shared variable latex by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6260
    • Fix bug when replacing random variables with nested value transforms by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6281
    • Do not infer graph_model node types based on variable Op class by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6259
    • Do not propagate dims to observed component of imputed variable by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6263
    • Fix Categorical and Multinomial bugs by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6265
    • Sample stats for blackjax nuts by @TimOliverMaier in https://github.com/pymc-devs/pymc/pull/6264
    • Add warning if observed in DensityDist is dict by @symeneses in https://github.com/pymc-devs/pymc/pull/6292
    • Fix versioneer config to match version tags by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6314

    Docs & Maintenance ๐Ÿ”ง

    • Split sampling.py into sampling.py and sampling_forward.py by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6257
    • Improve join_nonshared_inputs documentation by @wd60622 in https://github.com/pymc-devs/pymc/pull/6216
    • Move sampling code to a submodule by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6268
    • Fixed a typo in the overview notebook by @grtyvr in https://github.com/pymc-devs/pymc/pull/6274
    • Check that sampler vars correspond to value variables in the model by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6235
    • Updated pymc.DiscreteWeibull docstring by @hyosubkim in https://github.com/pymc-devs/pymc/pull/6283
    • Improve random seed processing for SMC sampling by @juanitorduz in https://github.com/pymc-devs/pymc/pull/6298
    • Remove wrong type-hints and stale docstrings from distributions by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6280
    • update theme by @OriolAbril in https://github.com/pymc-devs/pymc/pull/6296
    • Fix some typos and lints by @Armavica in https://github.com/pymc-devs/pymc/pull/6300
    • Update docstrings for sample_smc and smc.py by @rowangayleschaefer in https://github.com/pymc-devs/pymc/pull/6114
    • Fix Flaky Euler-Maruyama Tests by @wd60622 in https://github.com/pymc-devs/pymc/pull/6287

    New Contributors

    • @grtyvr made their first contribution in https://github.com/pymc-devs/pymc/pull/6274
    • @hyosubkim made their first contribution in https://github.com/pymc-devs/pymc/pull/6283

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.3.0...v4.4.0

    Source code(tar.gz)
    Source code(zip)
  • v4.3.0(Oct 31, 2022)

    What's Changed

    Major Changes ๐Ÿ› 

    • Remove samples and keep_size from sample_posterior_predictive by @pibieta in https://github.com/pymc-devs/pymc/pull/6029
    • Deprecate old or unused Model methods by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6237
    • Rename SMC files by @IMvision12 in https://github.com/pymc-devs/pymc/pull/6174
    • Require backends to record sample stats by @wd60622 in https://github.com/pymc-devs/pymc/pull/6205
    • Collect sampler warnings only through stats by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6192

    New Features & Bugfixes ๐ŸŽ‰

    • Refactor EulerMaruyama to work in v4 by @junpenglao in https://github.com/pymc-devs/pymc/pull/6227
    • Fix bug in get_vars_in_point_list when model does not have variables that exist in the trace by @lucianopaz in https://github.com/pymc-devs/pymc/pull/6203

    Docs & Maintenance ๐Ÿ”ง

    • Speed up posterior predictive sampling by @OriolAbril in https://github.com/pymc-devs/pymc/pull/6208
    • Add option to include transformed variables in InferenceData by @dfm in https://github.com/pymc-devs/pymc/pull/6232
    • Set start method to "fork" for MacOs ARM devices by @bchen93 in https://github.com/pymc-devs/pymc/pull/6218
    • Deprecate sample_posterior_predictive_w by @zaxtax in https://github.com/pymc-devs/pymc/pull/6254
    • Fix latex repr of symbolic distributions by @mattiadg in https://github.com/pymc-devs/pymc/pull/6231
    • Some doc fixes by @OriolAbril in https://github.com/pymc-devs/pymc/pull/6200
    • Modify logo_link to work with new sphinx schema by @hdnl in https://github.com/pymc-devs/pymc/pull/6209
    • Fix docstring of the ZeroInflatedPoisson distribution by @cscheffler in https://github.com/pymc-devs/pymc/pull/6213
    • Fix debug_print of wrong variable in notebook by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6225
    • Fix flaky TestMixture.test_component_choice_random by @bherwerth in https://github.com/pymc-devs/pymc/pull/6222
    • Seed flaky test TestSamplePPC.test_normal_scalar by @mattiadg in https://github.com/pymc-devs/pymc/pull/6220
    • Fix flaky TestTruncation.truncation_discrete_random by @mattiadg in https://github.com/pymc-devs/pymc/pull/6229
    • Seed pm.sample in BaseSampler(SeededTest) to make deriving test classes deterministic by @mattiadg in https://github.com/pymc-devs/pymc/pull/6251

    New Contributors

    • @hdnl made their first contribution in https://github.com/pymc-devs/pymc/pull/6209
    • @mattiadg made their first contribution in https://github.com/pymc-devs/pymc/pull/6220
    • @bchen93 made their first contribution in https://github.com/pymc-devs/pymc/pull/6218
    • @IMvision12 made their first contribution in https://github.com/pymc-devs/pymc/pull/6174

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.2.2...v4.3.0

    Source code(tar.gz)
    Source code(zip)
  • v4.2.2(Oct 10, 2022)

    What's Changed

    New Features & Bugfixes ๐ŸŽ‰

    • Add ZeroSumNormal distribution by @AlexAndorra in https://github.com/pymc-devs/pymc/pull/6121
    • Refactor Multivariate RandomWalk distributions by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6131

    Docs & Maintenance ๐Ÿ”ง

    • Finish restructuring the tests to follow the structure of the code by @Armavica in https://github.com/pymc-devs/pymc/pull/6125
    • Small typo corrections in Markdown for overview notebook by @willettk in https://github.com/pymc-devs/pymc/pull/6183
    • Run mypy outside of pre-commit in its own job by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6186
    • Update docstrings of set_data and Data by @bwengals in https://github.com/pymc-devs/pymc/pull/6087
    • Remove unused trace features by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6188

    New Contributors

    • @willettk made their first contribution in https://github.com/pymc-devs/pymc/pull/6183

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.2.1...v4.2.2

    Source code(tar.gz)
    Source code(zip)
  • v4.2.1(Sep 30, 2022)

    What's Changed

    New Features & Bugfixes ๐ŸŽ‰

    • Check shared variable values to determine volatility in posterior predictive sampling by @lucianopaz in https://github.com/pymc-devs/pymc/pull/6147
    • Log name of variables that are sampled in predictive sampling functions by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6142
    • Fix DiscreteUniformRV dropping degenerate dimension by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6151
    • Fix shape bug when creating a truncated normal via Truncated by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6165

    Docs & Maintenance ๐Ÿ”ง

    • Repair the plot of Interpolated and add an example for Deterministic by @Armavica in https://github.com/pymc-devs/pymc/pull/6126
    • Add constant_fold helper by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6160
    • Use sigma instead of noise in GP functions 6094 by @wd60622 in https://github.com/pymc-devs/pymc/pull/6145
    • Replace multinomial sampling with systematic sampling in sample_smc by @aloctavodia in https://github.com/pymc-devs/pymc/pull/6162
    • Assume default_output is the only measurable output in SymbolicRandomVariables by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6161

    New Contributors

    • @wd60622 made their first contribution in https://github.com/pymc-devs/pymc/pull/6145

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.2.0...v4.2.1

    Source code(tar.gz)
    Source code(zip)
  • v4.2.0(Sep 19, 2022)

    What's Changed

    Major Changes ๐Ÿ› 

    • Allow broadcasting via observed and dims by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6063
    • Remove support for specifying "dims on the fly" from the shapes of variables by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6112
    • Automatic versioning with versioneer by @cfonnesbeck in https://github.com/pymc-devs/pymc/pull/6078

    New Features & Bugfixes ๐ŸŽ‰

    • Implement Truncated distributions by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6113
    • Port GARCH11 to v4 by @junpenglao in https://github.com/pymc-devs/pymc/pull/6119
    • Implement Symbolic RVs and enable nested distribution factories (such as Mixtures of Mixtures) by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6072
    • Allow for batched alpha in StickBreakingWeights by @purna135 in https://github.com/pymc-devs/pymc/pull/6042
    • Remove NoDistribution and enable .dist API for Simulator and DensityDist by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6110
    • Add start_sigma to ADVI 2 by @markusschmaus in https://github.com/pymc-devs/pymc/pull/6132
    • Create .gitpod.yml by @ferrine in https://github.com/pymc-devs/pymc/pull/6070 and https://github.com/pymc-devs/pymc/pull/6109

    Docs & Maintenance ๐Ÿ”ง

    • Make rvs_to_values work with non-RandomVariables by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6101
    • Fix bug in Marginalapprox by @bwengals in https://github.com/pymc-devs/pymc/pull/6076
    • Fix bug in which TruncatedNormal returns -inf for all values if any value is out of bounds by @adrn in https://github.com/pymc-devs/pymc/pull/6128
    • Rename cov_func/cov to scale_func/scale for TP/MvStudentT by @fonnesbeck in https://github.com/pymc-devs/pymc/pull/6068
    • Ignore SpecifyShape when converting to JAX by @martiningram in https://github.com/pymc-devs/pymc/pull/6062
    • Remove reshape_t by @tjburch in https://github.com/pymc-devs/pymc/pull/6118
    • Fix Model docstring by @alekracicot in https://github.com/pymc-devs/pymc/pull/6048
    • Update opvi docs by @ferrine in https://github.com/pymc-devs/pymc/pull/6093
    • Fix formatting in documentation of AR distribution parameters by @daniel-saunders-phil in https://github.com/pymc-devs/pymc/pull/6080
    • Fix incorrect formula in NormalMixture docstring by @MatthewQuenneville in https://github.com/pymc-devs/pymc/pull/6073
    • Fix last remaining PyMC3 occurrences & broken link by @Armavica in https://github.com/pymc-devs/pymc/pull/6133
    • Update GOVERNANCE.md for PyMCon_2022 planning repo by @canyon289 in https://github.com/pymc-devs/pymc/pull/6088
    • Add new core contributors by @OriolAbril in https://github.com/pymc-devs/pymc/pull/6117
    • Pin pydata-sphinx-theme by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/6120
    • Mirror codebase structure in tests by @Armavica in https://github.com/pymc-devs/pymc/pull/6084
    • Clean up some warnings from the test suite by @Armavica in https://github.com/pymc-devs/pymc/pull/6067 and https://github.com/pymc-devs/pymc/pull/6074
    • Restructure the test suite to follow the code by @Armavica in https://github.com/pymc-devs/pymc/pull/6111

    New Contributors

    • @alekracicot made their first contribution in https://github.com/pymc-devs/pymc/pull/6048
    • @MatthewQuenneville made their first contribution in https://github.com/pymc-devs/pymc/pull/6073
    • @tjburch made their first contribution in https://github.com/pymc-devs/pymc/pull/6118
    • @markusschmaus made their first contribution in https://github.com/pymc-devs/pymc/pull/6096
    • @cfonnesbeck made their first contribution in https://github.com/pymc-devs/pymc/pull/6078
    • @adrn made their first contribution in https://github.com/pymc-devs/pymc/pull/6128

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.1.7...v4.2.0

    Source code(tar.gz)
    Source code(zip)
  • v4.1.7(Aug 26, 2022)

    What's Changed

    Docs & Maintenance ๐Ÿ”ง

    • Remove note that probs are automatically rescaled by @Armavica in https://github.com/pymc-devs/pymc/pull/6066
    • update the default value of jitter to JITTER_DEFAULT by @danhphan in https://github.com/pymc-devs/pymc/pull/6055

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.1.6...v4.1.7

    Source code(tar.gz)
    Source code(zip)
  • v4.1.6(Aug 25, 2022)

    What's Changed

    Docs & Maintenance ๐Ÿ”ง

    • adding markdown cell for Watermark by @reshamas in https://github.com/pymc-devs/pymc/pull/6051
    • DOC Adding "Git Bash command" to install virtual enviroment by @vitaliset in https://github.com/pymc-devs/pymc/pull/6056
    • Fix JAX sampling funcs overwriting existing var's dims and coords by @jhrcook in https://github.com/pymc-devs/pymc/pull/6041
    • Remove unused IS_FLOAT32 and IS_WINDOWS from test_ode by @maresb in https://github.com/pymc-devs/pymc/pull/6057
    • Add missing file test_printing.py to github runner by @Armavica in https://github.com/pymc-devs/pymc/pull/6058
    • Convert pip-installed dev dependencies to Conda by @maresb in https://github.com/pymc-devs/pymc/pull/6060
    • Upgrade to aesara=2.8.2 and aeppl=0.0.35 by @Armavica in https://github.com/pymc-devs/pymc/pull/6059

    New Contributors

    • @Armavica made their first contribution in https://github.com/pymc-devs/pymc/pull/6058

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.1.5...v4.1.6

    Source code(tar.gz)
    Source code(zip)
  • v4.1.5(Aug 17, 2022)

    What's Changed

    New Features & Bugfixes ๐ŸŽ‰

    • Constrain priors with symmetric mass distribution by @lucianopaz in https://github.com/pymc-devs/pymc/pull/5981
    • Fix AttributeError in HMC bad initial energy warning by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6037

    Docs & Maintenance ๐Ÿ”ง

    • Fix problems with specifying target_accept and nuts kwargs by @mschmidt87 in https://github.com/pymc-devs/pymc/pull/6018
    • Typehints and updated docstring for Blackjax NUTS sampling function by @jhrcook in https://github.com/pymc-devs/pymc/pull/6022
    • Revert numpy warnings workaround by @maresb in https://github.com/pymc-devs/pymc/pull/6025
    • Revert "Proposal: Readd 3.7" by @twiecki in https://github.com/pymc-devs/pymc/pull/6014
    • fixed some docstring spacing around colons by @daniel-saunders-phil in https://github.com/pymc-devs/pymc/pull/6027
    • issue6004 fixed example in docstring for set_data by @rowangayleschaefer in https://github.com/pymc-devs/pymc/pull/6028
    • Updating docstrings of distributions by @vitaliset in https://github.com/pymc-devs/pymc/pull/5998
    • Pass user-provided NUTS kwargs to Numpyro by @jhrcook in https://github.com/pymc-devs/pymc/pull/6021
    • โฌ†๏ธ UPGRADE: Autoupdate pre-commit config by @twiecki in https://github.com/pymc-devs/pymc/pull/6008
    • [DOCS] Fix aesara core notebook dprint error by @juanitorduz in https://github.com/pymc-devs/pymc/pull/6030
    • Removed assert_negative_support deprecated function call #5997 by @dihanster in https://github.com/pymc-devs/pymc/pull/6034
    • Update aeppl dependency to 0.0.34 by @cluhmann in https://github.com/pymc-devs/pymc/pull/6049
    • Updated pymc.simulator docstring (typos, defaults, type description) by @daniel-saunders-phil in https://github.com/pymc-devs/pymc/pull/6035
    • Added networkx export functionality by @jonititan in https://github.com/pymc-devs/pymc/pull/6046

    New Contributors

    • @mschmidt87 made their first contribution in https://github.com/pymc-devs/pymc/pull/6018
    • @daniel-saunders-phil made their first contribution in https://github.com/pymc-devs/pymc/pull/6027
    • @rowangayleschaefer made their first contribution in https://github.com/pymc-devs/pymc/pull/6028
    • @dihanster made their first contribution in https://github.com/pymc-devs/pymc/pull/6034
    • @jonititan made their first contribution in https://github.com/pymc-devs/pymc/pull/6046

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.1.4...v4.1.5

    Source code(tar.gz)
    Source code(zip)
  • v4.1.4(Jul 26, 2022)

    What's Changed

    Docs & Maintenance ๐Ÿ”ง

    • Updated docstrings of some distribution classes inside multivariate.py by @pibieta in https://github.com/pymc-devs/pymc/pull/5982
    • Fix error when passing coords and dims in sampling_jax by @bherwerth in https://github.com/pymc-devs/pymc/pull/5983
    • โฌ†๏ธ UPGRADE: Autoupdate pre-commit config by @twiecki in https://github.com/pymc-devs/pymc/pull/5984
    • Fix docker image build by @symeneses in https://github.com/pymc-devs/pymc/pull/5977
    • docs: Fix a few typos by @timgates42 in https://github.com/pymc-devs/pymc/pull/5988
    • contributing, jupyter style; author section more explicit by @reshamas in https://github.com/pymc-devs/pymc/pull/6000
    • Move MLDA to pymc-experimental by @michaelosthege in https://github.com/pymc-devs/pymc/pull/6007
    • Bump aesara to 2.7.8. by @twiecki in https://github.com/pymc-devs/pymc/pull/5995
    • Proposal: Readd 3.7 by @canyon289 in https://github.com/pymc-devs/pymc/pull/6010
    • Fix pm.Interpolated moment by @larryshamalama in https://github.com/pymc-devs/pymc/pull/5986
    • Bump aesara to 2.7.9 and aeppl to 0.0.33 by @twiecki in https://github.com/pymc-devs/pymc/pull/6012
    • Create arrow to observation nodes subject to arbitrary dtype casting in pm.model_to_graphviz by @larryshamalama in https://github.com/pymc-devs/pymc/pull/6011

    New Contributors

    • @pibieta made their first contribution in https://github.com/pymc-devs/pymc/pull/5982

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.1.3...v4.1.4

    Source code(tar.gz)
    Source code(zip)
  • v4.1.3(Jul 15, 2022)

    What's Changed

    Docs & Maintenance ๐Ÿ”ง

    • update docstrings in BetaBinomial class by @saurbhc in https://github.com/pymc-devs/pymc/pull/5960
    • Deprecate assert_negative_support by @vitaliset in https://github.com/pymc-devs/pymc/pull/5963
    • Updated docstrings to inform users that ODE solution may be slow. by @dmburt in https://github.com/pymc-devs/pymc/pull/5965
    • Add docker-image workflow by @symeneses in https://github.com/pymc-devs/pymc/pull/5966
    • โฌ†๏ธ UPGRADE: Autoupdate pre-commit config by @twiecki in https://github.com/pymc-devs/pymc/pull/5967
    • Provide a fix for sample_blackjax_nuts failing with chains=1 with prior parameters of different shapes by @bherwerth in https://github.com/pymc-devs/pymc/pull/5969
    • correct docstring in BetaBinomial Class by @SangamSwadiK in https://github.com/pymc-devs/pymc/pull/5957
    • Correct docs for Bernoulli, Poisson, Negative Binomial, Geometric and HyperGeometric by @SangamSwadiK in https://github.com/pymc-devs/pymc/pull/5958
    • update docstrings in ZeroInflatedPoisson, DiracDelta and OrderedLogistic classes by @saurbhc in https://github.com/pymc-devs/pymc/pull/5962
    • Bernoulli, OrderedProbit, ZeroInflatedBinomial, ZeroInflatedNegativeBinomial docstring update by @mariyayb in https://github.com/pymc-devs/pymc/pull/5961
    • Updated docstring for find_constrained_prior by @jlindbloom in https://github.com/pymc-devs/pymc/pull/5964
    • Point installation links to new installation guide in docs by @fonnesbeck in https://github.com/pymc-devs/pymc/pull/5873
    • Bump aesara dependency by @keesterbrugge in https://github.com/pymc-devs/pymc/pull/5970

    New Contributors

    • @saurbhc made their first contribution in https://github.com/pymc-devs/pymc/pull/5960
    • @vitaliset made their first contribution in https://github.com/pymc-devs/pymc/pull/5963
    • @dmburt made their first contribution in https://github.com/pymc-devs/pymc/pull/5965
    • @bherwerth made their first contribution in https://github.com/pymc-devs/pymc/pull/5969
    • @mariyayb made their first contribution in https://github.com/pymc-devs/pymc/pull/5961
    • @jlindbloom made their first contribution in https://github.com/pymc-devs/pymc/pull/5964
    • @keesterbrugge made their first contribution in https://github.com/pymc-devs/pymc/pull/5970

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.1.2...v4.1.3

    Source code(tar.gz)
    Source code(zip)
  • v4.1.2(Jul 8, 2022)

    What's Changed

    New Features & Bugfixes ๐ŸŽ‰

    • Fix model graph node name to remove RV from end only and not the start by @cscheffler in https://github.com/pymc-devs/pymc/pull/5953
    • Workaround to suppress (some) import warnings from NumPy by @michaelosthege in https://github.com/pymc-devs/pymc/pull/5956

    Docs & Maintenance ๐Ÿ”ง

    • include :: in name prefix check by @moshelooks in https://github.com/pymc-devs/pymc/pull/5951
    • correct docstrings in Binomial Class by @SangamSwadiK in https://github.com/pymc-devs/pymc/pull/5952
    • Bump Aesara to 2.7.5, aeppl to 0.0.32, update tests for aeppl by @maresb in https://github.com/pymc-devs/pymc/pull/5955

    New Contributors

    • @moshelooks made their first contribution in https://github.com/pymc-devs/pymc/pull/5951
    • @cscheffler made their first contribution in https://github.com/pymc-devs/pymc/pull/5953
    • @SangamSwadiK made their first contribution in https://github.com/pymc-devs/pymc/pull/5952

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.1.1...v4.1.2

    Source code(tar.gz)
    Source code(zip)
  • v4.1.1(Jul 4, 2022)

    What's Changed

    Docs & Maintenance ๐Ÿ”ง

    • Bump aesara to 2.7.4. by @twiecki in https://github.com/pymc-devs/pymc/pull/5947

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.1.0...v4.1.1

    Source code(tar.gz)
    Source code(zip)
  • v4.1.0(Jul 3, 2022)

    What's Changed

    Major Changes ๐Ÿ› 

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

    New Features & Bugfixes ๐ŸŽ‰

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

    Docs & Maintenance ๐Ÿ”ง

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

    New Contributors

    • @quantheory made their first contribution in https://github.com/pymc-devs/pymc/pull/5928
    • @isms made their first contribution in https://github.com/pymc-devs/pymc/pull/5936

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.0.1...v4.1.0

    Source code(tar.gz)
    Source code(zip)
  • v4.0.1(Jun 20, 2022)

    What's Changed

    Docs

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

    Bugfixes & Maintenance

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

    New Contributors

    • @kunalghosh made their first contribution in https://github.com/pymc-devs/pymc/pull/5853
    • @ryanrussell made their first contribution in https://github.com/pymc-devs/pymc/pull/5860
    • @hectormz made their first contribution in https://github.com/pymc-devs/pymc/pull/5896

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.0.0...v4.0.1

    Source code(tar.gz)
    Source code(zip)
  • v4.0.0(Jun 3, 2022)

    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 ๐Ÿพ

    Do not miss ๐Ÿšจ

    • โš ๏ธ 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.
    • โš ๏ธ 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).
    • โš ๏ธ Support for JAX and JAX samplers, also allows sampling on GPUs. This benchmark shows speed-ups of up to 11x.
    • โš ๏ธ The GLM submodule was removed, please use Bambi instead.
    • โš ๏ธ PyMC now requires SciPy version >= 1.4.1 (see #4857).

    v3 features not yet working in v4 โณ

    โš ๏ธ 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 ๐Ÿฅณ

    • 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:

      • โš ๏ธ 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 ๐Ÿ’”

    • 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) ๐Ÿ˜ฒ

    Very important โš ๏ธ

    • 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 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 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

    • โš ๏ธ 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).
    Source code(tar.gz)
    Source code(zip)
  • v4.0.0b6(Mar 30, 2022)

    What's Changed

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

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.0.0b5...v4.0.0b6

    Source code(tar.gz)
    Source code(zip)
  • v4.0.0b5(Mar 22, 2022)

    What's Changed

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

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.0.0b4...v4.0.0b5

    Source code(tar.gz)
    Source code(zip)
  • v4.0.0b4(Mar 17, 2022)

    This release adds the following major improvements:

    • Refactor Mixture distribution for V4 by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/5438
    • Adding NUTS sampler from blackjax to sampling_jax by @zaxtax in https://github.com/pymc-devs/pymc/pull/5477
    • Update aesara and aeppl dependencies to fix a memory leak in pymc models by @ricardoV94 in https://github.com/pymc-devs/pymc/pull/5582

    New Contributors

    • @mirko-m made their first contribution in https://github.com/pymc-devs/pymc/pull/5414
    • @chritter made their first contribution in https://github.com/pymc-devs/pymc/pull/5491
    • @5hv5hvnk made their first contribution in https://github.com/pymc-devs/pymc/pull/5601

    Full Changelog: https://github.com/pymc-devs/pymc/compare/v4.0.0b3...v4.0.0b4

    Source code(tar.gz)
    Source code(zip)
  • v3.11.5(Mar 15, 2022)

    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).
    Source code(tar.gz)
    Source code(zip)
  • v4.0.0b3(Mar 8, 2022)

  • v4.0.0b2(Jan 14, 2022)

    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).
    Source code(tar.gz)
    Source code(zip)
  • v4.0.0b1(Dec 16, 2021)

    PyMC 4.0.0 beta 1

    โš  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:

    • โš  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.)
    • โš  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
    • 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 get_moment keyword argument to pass in a function to compute the moment of the distribution (see 5026).
    • New features for BART:
      • Added partial dependence plots and individual conditional expectation plots 5091.
      • Modify how particle weights are computed. This improves the accuracy of the modeled function (see 5177).
      • Improve sampling, increase the default number of particles 5229.
    • pm.Data now passes additional kwargs to aesara.shared. #5098
    • ...

    Internal changes

    • โš  PyMC now requires Scipy version >= 1.4.1 (see 4857).
    • 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.
    • ...
    Source code(tar.gz)
    Source code(zip)
  • v3.11.4(Aug 24, 2021)

  • v3.11.3(Aug 20, 2021)

  • v3.11.2(Mar 14, 2021)

    PyMC3 3.11.2 (14 March 2021)

    DOI

    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 โ†’ pm.plot_trace
      • pm.compareplot โ†’ pm.plot_compare (here you might need to rename some columns in the input according to the arviz.plot_compare documentation)
      • pm.autocorrplot โ†’ pm.plot_autocorr
      • pm.forestplot โ†’ pm.plot_forest
      • pm.kdeplot โ†’ pm.plot_kde
      • pm.energyplot โ†’ pm.plot_energy
      • pm.densityplot โ†’ pm.plot_density
      • pm.pairplot โ†’ pm.plot_pair

    Maintenance

    • โš  Our memoization mechanism wasn't robust against hash collisions (#4506), sometimes resulting in incorrect values in, for example, posterior predictives. The pymc3.memoize module was removed and replaced with cachetools. The hashable function and WithMemoization class were moved to pymc3.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)

    Source code(tar.gz)
    Source code(zip)
  • v3.11.1(Feb 12, 2021)

    New Features

    • Automatic imputations now also work with ndarray data, not just pd.Series or pd.DataFrame (see#4439).
    • pymc3.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...
      • โš  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)

    Source code(tar.gz)
    Source code(zip)
  • v3.11.0(Jan 21, 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

    • โš  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)
    • โš  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.
    • โš  Python 3.6 support was dropped (by no longer testing) and Python 3.9 was added (see #4332).
    • โš  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)

    Source code(tar.gz)
    Source code(zip)
  • v3.10.0(Dec 7, 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 pymc3.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 pymc3.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)

    Source code(tar.gz)
    Source code(zip)
Decorator for PyMC3

sampled Decorator for reusable models in PyMC3 Provides syntactic sugar for reusable models with PyMC3. This lets you separate creating a generative m

Colin 50 Oct 8, 2021
Use evolutionary algorithms instead of gridsearch in scikit-learn

sklearn-deap Use evolutionary algorithms instead of gridsearch in scikit-learn. This allows you to reduce the time required to find the best parameter

rsteca 709 Jan 3, 2023
DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

Jason Antic 15.8k Jan 4, 2023
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

SeaLion is designed to teach today's aspiring ml-engineers the popular machine learning concepts of today in a way that gives both intuition and ways of application. We do this through concise algorithms that do the job in the least jargon possible and examples to guide you through every step of the way.

Anish 324 Dec 27, 2022
Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Face Identity Disentanglement via Latent Space Mapping - Implement in pytorch with StyleGAN 2 Description Pytorch implementation of the paper Face Ide

Daniel Roich 58 Dec 24, 2022
Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps

Proximal Backpropagation Proximal Backpropagation (ProxProp) is a neural network training algorithm that takes implicit instead of explicit gradient s

Thomas Frerix 40 Dec 17, 2022
MNIST, but with Bezier curves instead of pixels

bezier-mnist This is a work-in-progress vector version of the MNIST dataset. Samples Here are some samples from the training set. Note that, while the

Alex Nichol 15 Jan 16, 2022
Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photo Back to Life (CVPR 2020 oral)

Microsoft 11.3k Dec 30, 2022
Random-Afg - Afghanistan Random Old Idz Cloner Tools

AFGHANISTAN RANDOM OLD IDZ CLONER TOOLS Install $ apt update $ apt upgrade $ apt

MAHADI HASAN AFRIDI 5 Jan 26, 2022
this is a lite easy to use virtual keyboard project for anyone to use

virtual_Keyboard this is a lite easy to use virtual keyboard project for anyone to use motivation I made this for this year's recruitment for RobEn AA

Mohamed Emad 3 Oct 23, 2021
Project Aquarium is a SUSE-sponsored open source project aiming at becoming an easy to use, rock solid storage appliance based on Ceph.

Project Aquarium Project Aquarium is a SUSE-sponsored open source project aiming at becoming an easy to use, rock solid storage appliance based on Cep

Aquarist Labs 73 Jul 21, 2022
UpChecker is a simple opensource project to host it fast on your server and check is server up, view statistic, get messages if it is down. UpChecker - just run file and use project easy

UpChecker UpChecker is a simple opensource project to host it fast on your server and check is server up, view statistic, get messages if it is down.

Yan 4 Apr 7, 2022
A collection of easy-to-use, ready-to-use, interesting deep neural network models

Interesting and reproducible research works should be conserved. This repository wraps a collection of deep neural network models into a simple and un

Aria Ghora Prabono 16 Jun 16, 2022
Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

null 10 Dec 14, 2022
Transfer style api - An API to use with Tranfer Style App, where you can use two image and transfer the style

Transfer Style API It's an API to use with Tranfer Style App, where you can use

Brian Alejandro 1 Feb 13, 2022
This project demonstrates the use of neural networks and computer vision to create a classifier that interprets the Brazilian Sign Language.

LIBRAS-Image-Classifier This project demonstrates the use of neural networks and computer vision to create a classifier that interprets the Brazilian

Aryclenio Xavier Barros 26 Oct 14, 2022
ไธ€ไธชๅคš่ฏญ่จ€ๆ”ฏๆŒใ€ๆ˜“ไฝฟ็”จ็š„ OCR ้กน็›ฎใ€‚An easy-to-use OCR project with multilingual support.

AgentOCR ็ฎ€ไป‹ AgentOCR ๆ˜ฏไธ€ไธชๅŸบไบŽ PaddleOCR ๅ’Œ ONNXRuntime ้กน็›ฎๅผ€ๅ‘็š„ไธ€ไธชไฝฟ็”จ็ฎ€ๅ•ใ€่ฐƒ็”จๆ–นไพฟ็š„ OCR ้กน็›ฎ ๆœฌ้กน็›ฎ็›ฎๅ‰ๅŒ…ๅซ Python Package ใ€AgentOCRใ€‘ ๅ’Œ OCR ๆ ‡ๆณจ่ฝฏไปถ ใ€AgentOCRLabelingใ€‘ ไฝฟ็”จๆŒ‡ๅ— Pytho

AgentMaker 98 Nov 10, 2022
Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

Tom-R.T.Kvalvaag 2 Dec 17, 2021