pymc3 vs tensorflow probability

This document aims to explain the design and implementation of probabilistic programming in PyMC3, with comparisons to other PPL like TensorFlow Probability (TFP) and Pyro in mind. I think that a lot of TF probability is based on Edward. Bayesian Methods for Hackers, an introductory, hands-on tutorial,, December 10, 2018 then gives you a feel for the density in this windiness-cloudiness space. Intermediate #. ). These experiments have yielded promising results, but my ultimate goal has always been to combine these models with Hamiltonian Monte Carlo sampling to perform posterior inference. Real PyTorch code: With this backround, we can finally discuss the differences between PyMC3, Pyro Create an account to follow your favorite communities and start taking part in conversations. For our last release, we put out a "visual release notes" notebook. Ive kept quiet about Edward so far. New to TensorFlow Probability (TFP)? Not the answer you're looking for? The depreciation of its dependency Theano might be a disadvantage for PyMC3 in So PyMC is still under active development and it's backend is not "completely dead". You specify the generative model for the data. BUGS, perform so called approximate inference. Greta: If you want TFP, but hate the interface for it, use Greta. To this end, I have been working on developing various custom operations within TensorFlow to implement scalable Gaussian processes and various special functions for fitting exoplanet data (Foreman-Mackey et al., in prep, ha!). (Of course making sure good Share Improve this answer Follow The result is called a Pyro is built on PyTorch. Critically, you can then take that graph and compile it to different execution backends. ), extending Stan using custom C++ code and a forked version of pystan, who has written about a similar MCMC mashups, Theano docs for writing custom operations (ops). The distribution in question is then a joint probability Disconnect between goals and daily tasksIs it me, or the industry? I used 'Anglican' which is based on Clojure, and I think that is not good for me. Maybe pythonistas would find it more intuitive, but I didn't enjoy using it. analytical formulas for the above calculations. In this case, the shebang tells the shell to run flask/bin/python, and that file does not exist in your current location.. We're also actively working on improvements to the HMC API, in particular to support multiple variants of mass matrix adaptation, progress indicators, streaming moments estimation, etc. For MCMC sampling, it offers the NUTS algorithm. order, reverse mode automatic differentiation). I work at a government research lab and I have only briefly used Tensorflow probability. Are there examples, where one shines in comparison? You can use it from C++, R, command line, matlab, Julia, Python, Scala, Mathematica, Stata. TF as a whole is massive, but I find it questionably documented and confusingly organized. In plain What I really want is a sampling engine that does all the tuning like PyMC3/Stan, but without requiring the use of a specific modeling framework. When I went to look around the internet I couldn't really find any discussions or many examples about TFP. Secondly, what about building a prototype before having seen the data something like a modeling sanity check? I also think this page is still valuable two years later since it was the first google result. Pyro, and other probabilistic programming packages such as Stan, Edward, and This is where Making statements based on opinion; back them up with references or personal experience. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. if a model can't be fit in Stan, I assume it's inherently not fittable as stated. It is a good practice to write the model as a function so that you can change set ups like hyperparameters much easier. automatic differentiation (AD) comes in. In Julia, you can use Turing, writing probability models comes very naturally imo. modelling in Python. variational inference, supports composable inference algorithms. It started out with just approximation by sampling, hence the Especially to all GSoC students who contributed features and bug fixes to the libraries, and explored what could be done in a functional modeling approach. Magic! Otherwise you are effectively downweighting the likelihood by a factor equal to the size of your data set. We also would like to thank Rif A. Saurous and the Tensorflow Probability Team, who sponsored us two developer summits, with many fruitful discussions. Your home for data science. TL;DR: PyMC3 on Theano with the new JAX backend is the future, PyMC4 based on TensorFlow Probability will not be developed further. This left PyMC3, which relies on Theano as its computational backend, in a difficult position and prompted us to start work on PyMC4 which is based on TensorFlow instead. I have built some model in both, but unfortunately, I am not getting the same answer. Building your models and training routines, writes and feels like any other Python code with some special rules and formulations that come with the probabilistic approach. We welcome all researchers, students, professionals, and enthusiasts looking to be a part of an online statistics community. Basically, suppose you have several groups, and want to initialize several variables per group, but you want to initialize different numbers of variables Then you need to use the quirky variables[index]notation. $\frac{\partial \ \text{model}}{\partial I chose TFP because I was already familiar with using Tensorflow for deep learning and have honestly enjoyed using it (TF2 and eager mode makes the code easier than what's shown in the book which uses TF 1.x standards). So you get PyTorchs dynamic programming and it was recently announced that Theano will not be maintained after an year. It's also a domain-specific tool built by a team who cares deeply about efficiency, interfaces, and correctness. So what tools do we want to use in a production environment? However, I must say that Edward is showing the most promise when it comes to the future of Bayesian learning (due to alot of work done in Bayesian Deep Learning). After graph transformation and simplification, the resulting Ops get compiled into their appropriate C analogues and then the resulting C-source files are compiled to a shared library, which is then called by Python. Thanks for contributing an answer to Stack Overflow! other two frameworks. maybe even cross-validate, while grid-searching hyper-parameters. I've been learning about Bayesian inference and probabilistic programming recently and as a jumping off point I started reading the book "Bayesian Methods For Hackers", mores specifically the Tensorflow-Probability (TFP) version . We just need to provide JAX implementations for each Theano Ops. Book: Bayesian Modeling and Computation in Python. inference by sampling and variational inference. use a backend library that does the heavy lifting of their computations. and content on it. This means that it must be possible to compute the first derivative of your model with respect to the input parameters. In fact, we can further check to see if something is off by calling the .log_prob_parts, which gives the log_prob of each nodes in the Graphical model: turns out the last node is not being reduce_sum along the i.i.d. PyMC3, [1] [2] [3] [4] It is a rewrite from scratch of the previous version of the PyMC software. Combine that with Thomas Wiecki's blog and you have a complete guide to data analysis with Python.. computations on N-dimensional arrays (scalars, vectors, matrices, or in general: How can this new ban on drag possibly be considered constitutional? PyMC (formerly known as PyMC3) is a Python package for Bayesian statistical modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Is there a proper earth ground point in this switch box? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. In this post we show how to fit a simple linear regression model using TensorFlow Probability by replicating the first example on the getting started guide for PyMC3.We are going to use Auto-Batched Joint Distributions as they simplify the model specification considerably. to use immediate execution / dynamic computational graphs in the style of Variational inference and Markov chain Monte Carlo. (allowing recursion). brms: An R Package for Bayesian Multilevel Models Using Stan [2] B. Carpenter, A. Gelman, et al. Multitude of inference approaches We currently have replica exchange (parallel tempering), HMC, NUTS, RWM, MH(your proposal), and in experimental.mcmc: SMC & particle filtering. As far as documentation goes, not quite extensive as Stan in my opinion but the examples are really good. In this Colab, we will show some examples of how to use JointDistributionSequential to achieve your day to day Bayesian workflow. This is where things become really interesting. It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. implemented NUTS in PyTorch without much effort telling. Acidity of alcohols and basicity of amines. Is there a single-word adjective for "having exceptionally strong moral principles"? There is also a language called Nimble which is great if you're coming from a BUGs background. inference, and we can easily explore many different models of the data. By now, it also supports variational inference, with automatic They all use a 'backend' library that does the heavy lifting of their computations. Have a use-case or research question with a potential hypothesis. See here for my course on Machine Learning and Deep Learning (Use code DEEPSCHOOL-MARCH to 85% off). results to a large population of users. It means working with the joint Furthermore, since I generally want to do my initial tests and make my plots in Python, I always ended up implementing two version of my model (one in Stan and one in Python) and it was frustrating to make sure that these always gave the same results. (For user convenience, aguments will be passed in reverse order of creation.) In R, there are librairies binding to Stan, which is probably the most complete language to date. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? computational graph. It was a very interesting and worthwhile experiment that let us learn a lot, but the main obstacle was TensorFlows eager mode, along with a variety of technical issues that we could not resolve ourselves. I think most people use pymc3 in Python, there's also Pyro and Numpyro though they are relatively younger. with respect to its parameters (i.e. In Julia, you can use Turing, writing probability models comes very naturally imo. When should you use Pyro, PyMC3, or something else still? To learn more, see our tips on writing great answers. layers and a `JointDistribution` abstraction. And we can now do inference! Multilevel Modeling Primer in TensorFlow Probability bookmark_border On this page Dependencies & Prerequisites Import 1 Introduction 2 Multilevel Modeling Overview A Primer on Bayesian Methods for Multilevel Modeling This example is ported from the PyMC3 example notebook A Primer on Bayesian Methods for Multilevel Modeling Run in Google Colab Anyhow it appears to be an exciting framework. resources on PyMC3 and the maturity of the framework are obvious advantages. StackExchange question however: Thus, variational inference is suited to large data sets and scenarios where Update as of 12/15/2020, PyMC4 has been discontinued. clunky API. is a rather big disadvantage at the moment. differences and limitations compared to For the most part anything I want to do in Stan I can do in BRMS with less effort. I have previousely used PyMC3 and am now looking to use tensorflow probability. the long term. Your file starts with a shebang telling the shell what program to load to run the script. We are looking forward to incorporating these ideas into future versions of PyMC3. It shouldnt be too hard to generalize this to multiple outputs if you need to, but I havent tried. What is the difference between 'SAME' and 'VALID' padding in tf.nn.max_pool of tensorflow? This is also openly available and in very early stages. enough experience with approximate inference to make claims; from this Trying to understand how to get this basic Fourier Series. Connect and share knowledge within a single location that is structured and easy to search. . x}$ and $\frac{\partial \ \text{model}}{\partial y}$ in the example). As far as I can tell, there are two popular libraries for HMC inference in Python: PyMC3 and Stan (via the pystan interface). (in which sampling parameters are not automatically updated, but should rather In parallel to this, in an effort to extend the life of PyMC3, we took over maintenance of Theano from the Mila team, hosted under Theano-PyMC. answer the research question or hypothesis you posed. What is the point of Thrower's Bandolier? We look forward to your pull requests. You can check out the low-hanging fruit on the Theano and PyMC3 repos. Here the PyMC3 devs The syntax isnt quite as nice as Stan, but still workable. I had sent a link introducing Edward is a newer one which is a bit more aligned with the workflow of deep Learning (since the researchers for it do a lot of bayesian deep Learning). value for this variable, how likely is the value of some other variable? In this post wed like to make a major announcement about where PyMC is headed, how we got here, and what our reasons for this direction are. PyTorch framework. Here's the gist: You can find more information from the docstring of JointDistributionSequential, but the gist is that you pass a list of distributions to initialize the Class, if some distributions in the list is depending on output from another upstream distribution/variable, you just wrap it with a lambda function. you have to give a unique name, and that represent probability distributions. Yeah I think thats one of the big selling points for TFP is the easy use of accelerators although I havent tried it myself yet. PyMC3 and Edward functions need to bottom out in Theano and TensorFlow functions to allow analytic derivatives and automatic differentiation respectively. +, -, *, /, tensor concatenation, etc. Instead, the PyMC team has taken over maintaining Theano and will continue to develop PyMC3 on a new tailored Theano build. The callable will have at most as many arguments as its index in the list. Imo Stan has the best Hamiltonian Monte Carlo implementation so if you're building models with continuous parametric variables the python version of stan is good. build and curate a dataset that relates to the use-case or research question. My personal opinion as a nerd on the internet is that Tensorflow is a beast of a library that was built predicated on the very Googley assumption that it would be both possible and cost-effective to employ multiple full teams to support this code in production, which isn't realistic for most organizations let alone individual researchers. There seem to be three main, pure-Python libraries for performing approximate inference: PyMC3 , Pyro, and Edward. For deep-learning models you need to rely on a platitude of tools like SHAP and plotting libraries to explain what your model has learned.For probabilistic approaches, you can get insights on parameters quickly. Connect and share knowledge within a single location that is structured and easy to search. inference calculation on the samples. I have previously blogged about extending Stan using custom C++ code and a forked version of pystan, but I havent actually been able to use this method for my research because debugging any code more complicated than the one in that example ended up being far too tedious. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Pyro, and Edward. Models must be defined as generator functions, using a yield keyword for each random variable. ; ADVI: Kucukelbir et al. However it did worse than Stan on the models I tried. This implemetation requires two theano.tensor.Op subclasses, one for the operation itself (TensorFlowOp) and one for the gradient operation (_TensorFlowGradOp). Python development, according to their marketing and to their design goals. (2009) In PyTorch, there is no For full rank ADVI, we want to approximate the posterior with a multivariate Gaussian. PyTorch: using this one feels most like normal Moreover, there is a great resource to get deeper into this type of distribution: Auto-Batched Joint Distributions: A . What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Why is there a voltage on my HDMI and coaxial cables? Does this answer need to be updated now since Pyro now appears to do MCMC sampling? That looked pretty cool. rev2023.3.3.43278. Hamiltonian/Hybrid Monte Carlo (HMC) and No-U-Turn Sampling (NUTS) are See here for PyMC roadmap: The latest edit makes it sounds like PYMC in general is dead but that is not the case. The following snippet will verify that we have access to a GPU. Asking for help, clarification, or responding to other answers. Imo: Use Stan. The tutorial you got this from expects you to create a virtualenv directory called flask, and the script is set up to run the . Pyro aims to be more dynamic (by using PyTorch) and universal Introductory Overview of PyMC shows PyMC 4.0 code in action. and other probabilistic programming packages. Has 90% of ice around Antarctica disappeared in less than a decade? And which combinations occur together often? TPUs) as we would have to hand-write C-code for those too. The other reason is that Tensorflow probability is in the process of migrating from Tensorflow 1.x to Tensorflow 2.x, and the documentation of Tensorflow probability for Tensorflow 2.x is lacking. Next, define the log-likelihood function in TensorFlow: And then we can fit for the maximum likelihood parameters using an optimizer from TensorFlow: Here is the maximum likelihood solution compared to the data and the true relation: Finally, lets use PyMC3 to generate posterior samples for this model: After sampling, we can make the usual diagnostic plots. When the. implementations for Ops): Python and C. The Python backend is understandably slow as it just runs your graph using mostly NumPy functions chained together. I think most people use pymc3 in Python, there's also Pyro and Numpyro though they are relatively younger. Since JAX shares almost an identical API with NumPy/SciPy this turned out to be surprisingly simple, and we had a working prototype within a few days. New to TensorFlow Probability (TFP)? use variational inference when fitting a probabilistic model of text to one For example, we can add a simple (read: silly) op that uses TensorFlow to perform an elementwise square of a vector. The second term can be approximated with. Theano, PyTorch, and TensorFlow are all very similar. I love the fact that it isnt fazed even if I had a discrete variable to sample, which Stan so far cannot do. What are the industry standards for Bayesian inference? Edward is also relatively new (February 2016). You can use optimizer to find the Maximum likelihood estimation. I dont know much about it, You should use reduce_sum in your log_prob instead of reduce_mean. What is the plot of? Another alternative is Edward built on top of Tensorflow which is more mature and feature rich than pyro atm. A library to combine probabilistic models and deep learning on modern hardware (TPU, GPU) for data scientists, statisticians, ML researchers, and practitioners. Research Assistant. TensorFlow). What am I doing wrong here in the PlotLegends specification? [1] This is pseudocode. VI: Wainwright and Jordan Combine that with Thomas Wieckis blog and you have a complete guide to data analysis with Python. Now, let's set up a linear model, a simple intercept + slope regression problem: You can then check the graph of the model to see the dependence. In our limited experiments on small models, the C-backend is still a bit faster than the JAX one, but we anticipate further improvements in performance. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2. pymc3 how to code multi-state discrete Bayes net CPT? around organization and documentation. There are a lot of use-cases and already existing model-implementations and examples. PyMC3 has one quirky piece of syntax, which I tripped up on for a while. distribution? One class of models I was surprised to discover that HMC-style samplers cant handle is that of periodic timeseries, which have inherently multimodal likelihoods when seeking inference on the frequency of the periodic signal. given datapoint is; Marginalise (= summate) the joint probability distribution over the variables Find centralized, trusted content and collaborate around the technologies you use most. Save and categorize content based on your preferences. We try to maximise this lower bound by varying the hyper-parameters of the proposal distribution q(z_i) and q(z_g). Sean Easter. Variational inference (VI) is an approach to approximate inference that does Many people have already recommended Stan. Automatic Differentiation: The most criminally You can see below a code example. The benefit of HMC compared to some other MCMC methods (including one that I wrote) is that it is substantially more efficient (i.e. It enables all the necessary features for a Bayesian workflow: prior predictive sampling, It could be plug-in to another larger Bayesian Graphical model or neural network. Exactly! individual characteristics: Theano: the original framework. So what is missing?First, we have not accounted for missing or shifted data that comes up in our workflow.Some of you might interject and say that they have some augmentation routine for their data (e.g. specific Stan syntax. Maybe Pyro or PyMC could be the case, but I totally have no idea about both of those. This isnt necessarily a Good Idea, but Ive found it useful for a few projects so I wanted to share the method. encouraging other astronomers to do the same, various special functions for fitting exoplanet data (Foreman-Mackey et al., in prep, ha! I feel the main reason is that it just doesnt have good documentation and examples to comfortably use it. youre not interested in, so you can make a nice 1D or 2D plot of the I like python as a language, but as a statistical tool, I find it utterly obnoxious. tensors). Seconding @JJR4 , PyMC3 has become PyMC and Theano has a been revived as Aesara by the developers of PyMC. precise samples. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Shapes and dimensionality Distribution Dimensionality. PyMC4 will be built on Tensorflow, replacing Theano. The best library is generally the one you actually use to make working code, not the one that someone on StackOverflow says is the best. This language was developed and is maintained by the Uber Engineering division. PyMC3 In R, there are librairies binding to Stan, which is probably the most complete language to date. I guess the decision boils down to the features, documentation and programming style you are looking for. For example, to do meanfield ADVI, you simply inspect the graph and replace all the none observed distribution with a Normal distribution. The coolest part is that you, as a user, wont have to change anything on your existing PyMC3 model code in order to run your models on a modern backend, modern hardware, and JAX-ified samplers, and get amazing speed-ups for free. methods are the Markov Chain Monte Carlo (MCMC) methods, of which mode, $\text{arg max}\ p(a,b)$. By default, Theano supports two execution backends (i.e. There's some useful feedback in here, esp. The examples are quite extensive. Therefore there is a lot of good documentation With the ability to compile Theano graphs to JAX and the availability of JAX-based MCMC samplers, we are at the cusp of a major transformation of PyMC3. can thus use VI even when you dont have explicit formulas for your derivatives. It is true that I can feed in PyMC3 or Stan models directly to Edward but by the sound of it I need to write Edward specific code to use Tensorflow acceleration. This is not possible in the But in order to achieve that we should find out what is lacking. I know that Edward/TensorFlow probability has an HMC sampler, but it does not have a NUTS implementation, tuning heuristics, or any of the other niceties that the MCMC-first libraries provide. Tensorflow probability not giving the same results as PyMC3, How Intuit democratizes AI development across teams through reusability.