pymc3 vs tensorflow probability

I had sent a link introducing I was furiously typing my disagreement about "nice Tensorflow documention" already but stop. It comes at a price though, as you'll have to write some C++ which you may find enjoyable or not. around organization and documentation. JointDistributionSequential is a newly introduced distribution-like Class that empowers users to fast prototype Bayesian model. billion text documents and where the inferences will be used to serve search Posted by Mike Shwe, Product Manager for TensorFlow Probability at Google; Josh Dillon, Software Engineer for TensorFlow Probability at Google; Bryan Seybold, Software Engineer at Google; Matthew McAteer; and Cam Davidson-Pilon. 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). I think the edward guys are looking to merge with the probability portions of TF and pytorch one of these days. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? This will be the final course in a specialization of three courses .Python and Jupyter notebooks will be used throughout . Tensorflow and related librairies suffer from the problem that the API is poorly documented imo, some TFP notebooks didn't work out of the box last time I tried. This is a subreddit for discussion on all things dealing with statistical theory, software, and application. PyMC3 is an open-source library for Bayesian statistical modeling and inference in Python, implementing gradient-based Markov chain Monte Carlo, variational inference, and other approximation. It has vast application in research, has great community support and you can find a number of talks on probabilistic modeling on YouTube to get you started. Variational inference is one way of doing approximate Bayesian inference. It's extensible, fast, flexible, efficient, has great diagnostics, etc. PyTorch. I think most people use pymc3 in Python, there's also Pyro and Numpyro though they are relatively younger. Ive kept quiet about Edward so far. 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. Not much documentation yet. rev2023.3.3.43278. Then, this extension could be integrated seamlessly into the model. I have built some model in both, but unfortunately, I am not getting the same answer. I imagine that this interface would accept two Python functions (one that evaluates the log probability, and one that evaluates its gradient) and then the user could choose whichever modeling stack they want. or at least from a good approximation to it. tensors). This language was developed and is maintained by the Uber Engineering division. [1] [2] [3] [4] It is a rewrite from scratch of the previous version of the PyMC software. order, reverse mode automatic differentiation). And they can even spit out the Stan code they use to help you learn how to write your own Stan models. 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. 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. Can Martian regolith be easily melted with microwaves? be; The final model that you find can then be described in simpler terms. In Terms of community and documentation it might help to state that as of today, there are 414 questions on stackoverflow regarding pymc and only 139 for pyro. In Theano and TensorFlow, you build a (static) Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. That is, you are not sure what a good model would They all 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. The callable will have at most as many arguments as its index in the list. MC in its name. Wow, it's super cool that one of the devs chimed in. 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. Static graphs, however, have many advantages over dynamic graphs. In cases that you cannot rewrite the model as a batched version (e.g., ODE models), you can map the log_prob function using. . Tools to build deep probabilistic models, including probabilistic The relatively large amount of learning There seem to be three main, pure-Python libraries for performing approximate inference: PyMC3 , Pyro, and Edward. It transforms the inference problem into an optimisation Pyro came out November 2017. You can use optimizer to find the Maximum likelihood estimation. Here the PyMC3 devs Is there a proper earth ground point in this switch box? If your model is sufficiently sophisticated, you're gonna have to learn how to write Stan models yourself. New to TensorFlow Probability (TFP)? problem with STAN is that it needs a compiler and toolchain. $$. Sep 2017 - Dec 20214 years 4 months. The reason PyMC3 is my go to (Bayesian) tool is for one reason and one reason alone, the pm.variational.advi_minibatch function. Why does Mister Mxyzptlk need to have a weakness in the comics? The solution to this problem turned out to be relatively straightforward: compile the Theano graph to other modern tensor computation libraries. By default, Theano supports two execution backends (i.e. It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. I love the fact that it isnt fazed even if I had a discrete variable to sample, which Stan so far cannot do. The basic idea here is that, since PyMC3 models are implemented using Theano, it should be possible to write an extension to Theano that knows how to call TensorFlow. This is the essence of what has been written in this paper by Matthew Hoffman. So you get PyTorchs dynamic programming and it was recently announced that Theano will not be maintained after an year. It shouldnt be too hard to generalize this to multiple outputs if you need to, but I havent tried. 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). There seem to be three main, pure-Python PyMC3 and Edward functions need to bottom out in Theano and TensorFlow functions to allow analytic derivatives and automatic differentiation respectively. To get started on implementing this, I reached out to Thomas Wiecki (one of the lead developers of PyMC3 who has written about a similar MCMC mashups) for tips, To take full advantage of JAX, we need to convert the sampling functions into JAX-jittable functions as well. model. where n is the minibatch size and N is the size of the entire set. For details, see the Google Developers Site Policies. Additional MCMC algorithms include MixedHMC (which can accommodate discrete latent variables) as well as HMCECS. You can see below a code example. I don't see the relationship between the prior and taking the mean (as opposed to the sum). youre not interested in, so you can make a nice 1D or 2D plot of the Pyro is a deep probabilistic programming language that focuses on You can find more content on my weekly blog http://laplaceml.com/blog. ). We're open to suggestions as to what's broken (file an issue on github!) Does this answer need to be updated now since Pyro now appears to do MCMC sampling? When you talk Machine Learning, especially deep learning, many people think TensorFlow. The callable will have at most as many arguments as its index in the list. So in conclusion, PyMC3 for me is the clear winner these days. I want to specify the model/ joint probability and let theano simply optimize the hyper-parameters of q(z_i), q(z_g). This notebook reimplements and extends the Bayesian "Change point analysis" example from the pymc3 documentation.. Prerequisites import tensorflow.compat.v2 as tf tf.enable_v2_behavior() import tensorflow_probability as tfp tfd = tfp.distributions tfb = tfp.bijectors import matplotlib.pyplot as plt plt.rcParams['figure.figsize'] = (15,8) %config InlineBackend.figure_format = 'retina . 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. The documentation is absolutely amazing. This is designed to build small- to medium- size Bayesian models, including many commonly used models like GLMs, mixed effect models, mixture models, and more. or how these could improve. Therefore there is a lot of good documentation You have gathered a great many data points { (3 km/h, 82%), Share Improve this answer Follow 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. inference, and we can easily explore many different models of the data. modelling in Python. When I went to look around the internet I couldn't really find any discussions or many examples about TFP. So I want to change the language to something based on Python. Please open an issue or pull request on that repository if you have questions, comments, or suggestions. model. It is a good practice to write the model as a function so that you can change set ups like hyperparameters much easier. The joint probability distribution $p(\boldsymbol{x})$ The trick here is to use tfd.Independent to reinterpreted the batch shape (so that the rest of the axis will be reduced correctly): Now, lets check the last node/distribution of the model, you can see that event shape is now correctly interpreted. Magic! It has excellent documentation and few if any drawbacks that I'm aware of. 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. [1] Paul-Christian Brkner. It remains an opinion-based question but difference about Pyro and Pymc would be very valuable to have as an answer. That looked pretty cool. @SARose yes, but it should also be emphasized that Pyro is only in beta and its HMC/NUTS support is considered experimental. Combine that with Thomas Wiecki's blog and you have a complete guide to data analysis with Python.. Bayesian Methods for Hackers, an introductory, hands-on tutorial,, December 10, 2018 Are there tables of wastage rates for different fruit and veg? The automatic differentiation part of the Theano, PyTorch, or TensorFlow For example, we might use MCMC in a setting where we spent 20 There still is something called Tensorflow Probability, with the same great documentation we've all come to expect from Tensorflow (yes that's a joke). The mean is usually taken with respect to the number of training examples. We thus believe that Theano will have a bright future ahead of itself as a mature, powerful library with an accessible graph representation that can be modified in all kinds of interesting ways and executed on various modern backends. How to react to a students panic attack in an oral exam? With that said - I also did not like TFP. PyMC3 is much more appealing to me because the models are actually Python objects so you can use the same implementation for sampling and pre/post-processing. Looking forward to more tutorials and examples! results to a large population of users. given datapoint is; Marginalise (= summate) the joint probability distribution over the variables clunky API. Most of the data science community is migrating to Python these days, so thats not really an issue at all. You can do things like mu~N(0,1). The catch with PyMC3 is that you must be able to evaluate your model within the Theano framework and I wasnt so keen to learn Theano when I had already invested a substantial amount of time into TensorFlow and since Theano has been deprecated as a general purpose modeling language. Asking for help, clarification, or responding to other answers. The holy trinity when it comes to being Bayesian. TensorFlow: the most famous one. In one problem I had Stan couldn't fit the parameters, so I looked at the joint posteriors and that allowed me to recognize a non-identifiability issue in my model. He came back with a few excellent suggestions, but the one that really stuck out was to write your logp/dlogp as a theano op that you then use in your (very simple) model definition. 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. to use immediate execution / dynamic computational graphs in the style of It should be possible (easy?) In probabilistic programming, having a static graph of the global state which you can compile and modify is a great strength, as we explained above; Theano is the perfect library for this. [5] How can this new ban on drag possibly be considered constitutional? The two key pages of documentation are the Theano docs for writing custom operations (ops) and the PyMC3 docs for using these custom ops. Research Assistant. So PyMC is still under active development and it's backend is not "completely dead". (2017). As to when you should use sampling and when variational inference: I dont have They all expose a Python It also means that models can be more expressive: PyTorch The objective of this course is to introduce PyMC3 for Bayesian Modeling and Inference, The attendees will start off by learning the the basics of PyMC3 and learn how to perform scalable inference for a variety of problems. Depending on the size of your models and what you want to do, your mileage may vary. A pretty amazing feature of tfp.optimizer is that, you can optimized in parallel for k batch of starting point and specify the stopping_condition kwarg: you can set it to tfp.optimizer.converged_all to see if they all find the same minimal, or tfp.optimizer.converged_any to find a local solution fast. Can archive.org's Wayback Machine ignore some query terms? But, they only go so far. Only Senior Ph.D. student. rev2023.3.3.43278. CPU, for even more efficiency. Regard tensorflow probability, it contains all the tools needed to do probabilistic programming, but requires a lot more manual work. Update as of 12/15/2020, PyMC4 has been discontinued. It has full MCMC, HMC and NUTS support. I used 'Anglican' which is based on Clojure, and I think that is not good for me. for the derivatives of a function that is specified by a computer program. For example, we can add a simple (read: silly) op that uses TensorFlow to perform an elementwise square of a vector. x}$ and $\frac{\partial \ \text{model}}{\partial y}$ in the example). There's also pymc3, though I haven't looked at that too much. student in Bioinformatics at the University of Copenhagen. Also, I still can't get familiar with the Scheme-based languages. This is where GPU acceleration would really come into play. For the most part anything I want to do in Stan I can do in BRMS with less effort. "Simple" means chain-like graphs; although the approach technically works for any PGM with degree at most 255 for a single node (Because Python functions can have at most this many args). XLA) and processor architecture (e.g. This means that debugging is easier: you can for example insert Theano, PyTorch, and TensorFlow, the parameters are just tensors of actual I inference by sampling and variational inference. By design, the output of the operation must be a single tensor. Pyro aims to be more dynamic (by using PyTorch) and universal Does anybody here use TFP in industry or research? This TensorFlowOp implementation will be sufficient for our purposes, but it has some limitations including: For this demonstration, well fit a very simple model that would actually be much easier to just fit using vanilla PyMC3, but itll still be useful for demonstrating what were trying to do. I work at a government research lab and I have only briefly used Tensorflow probability. References vegan) just to try it, does this inconvenience the caterers and staff? And which combinations occur together often? PyMC3, Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2. This computational graph is your function, or your It lets you chain multiple distributions together, and use lambda function to introduce dependencies. Find centralized, trusted content and collaborate around the technologies you use most. Connect and share knowledge within a single location that is structured and easy to search. dimension/axis! Please make. They all use a 'backend' library that does the heavy lifting of their computations. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. Additionally however, they also offer automatic differentiation (which they It has effectively 'solved' the estimation problem for me. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of . 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. What am I doing wrong here in the PlotLegends specification? So the conclusion seems to be: the classics PyMC3 and Stan still come out as the I'm biased against tensorflow though because I find it's often a pain to use. PyMC3is an openly available python probabilistic modeling API. Your home for data science. Learning with confidence (TF Dev Summit '19), Regression with probabilistic layers in TFP, An introduction to probabilistic programming, Analyzing errors in financial models with TFP, Industrial AI: physics-based, probabilistic deep learning using TFP. API to underlying C / C++ / Cuda code that performs efficient numeric PyTorch: using this one feels most like normal logistic models, neural network models, almost any model really. It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. I dont know of any Python packages with the capabilities of projects like PyMC3 or Stan that support TensorFlow out of the box. VI: Wainwright and Jordan Through this process, we learned that building an interactive probabilistic programming library in TF was not as easy as we thought (more on that below). = sqrt(16), then a will contain 4 [1]. The usual workflow looks like this: As you might have noticed, one severe shortcoming is to account for certainties of the model and confidence over the output. Pyro doesn't do Markov chain Monte Carlo (unlike PyMC and Edward) yet. years collecting a small but expensive data set, where we are confident that Automatic Differentiation Variational Inference; Now over from theory to practice. In fact, the answer is not that close. TFP is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware. We try to maximise this lower bound by varying the hyper-parameters of the proposal distribution q(z_i) and q(z_g). computational graph as above, and then compile it. Did you see the paper with stan and embedded Laplace approximations? What is the plot of? More importantly, however, it cuts Theano off from all the amazing developments in compiler technology (e.g. The result is called a It's also a domain-specific tool built by a team who cares deeply about efficiency, interfaces, and correctness. I've heard of STAN and I think R has packages for Bayesian stuff but I figured with how popular Tensorflow is in industry TFP would be as well. The distribution in question is then a joint probability My personal favorite tool for deep probabilistic models is Pyro. Press question mark to learn the rest of the keyboard shortcuts, https://github.com/stan-dev/stan/wiki/Proposing-Algorithms-for-Inclusion-Into-Stan. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). At the very least you can use rethinking to generate the Stan code and go from there. methods are the Markov Chain Monte Carlo (MCMC) methods, of which Also, it makes programmtically generate log_prob function that conditioned on (mini-batch) of inputted data much easier: One very powerful feature of JointDistribution* is that you can generate an approximation easily for VI. As far as I can tell, there are two popular libraries for HMC inference in Python: PyMC3 and Stan (via the pystan interface). STAN is a well-established framework and tool for research. This graph structure is very useful for many reasons: you can do optimizations by fusing computations or replace certain operations with alternatives that are numerically more stable. In R, there are librairies binding to Stan, which is probably the most complete language to date. 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. I recently started using TensorFlow as a framework for probabilistic modeling (and encouraging other astronomers to do the same) because the API seemed stable and it was relatively easy to extend the language with custom operations written in C++. As the answer stands, it is misleading. TFP includes: Note that it might take a bit of trial and error to get the reinterpreted_batch_ndims right, but you can always easily print the distribution or sampled tensor to double check the shape! Simulate some data and build a prototype before you invest resources in gathering data and fitting insufficient models. be carefully set by the user), but not the NUTS algorithm. I was under the impression that JAGS has taken over WinBugs completely, largely because it's a cross-platform superset of WinBugs. You Automatic Differentiation: The most criminally mode, $\text{arg max}\ p(a,b)$. Based on these docs, my complete implementation for a custom Theano op that calls TensorFlow is given below. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. You specify the generative model for the data. Posted by Mike Shwe, Product Manager for TensorFlow Probability at Google; Josh Dillon, Software Engineer for TensorFlow Probability at Google; Bryan Seybold, Software Engineer at Google; Matthew McAteer; and Cam Davidson-Pilon. TensorFlow). What is the difference between probabilistic programming vs. probabilistic machine learning? 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. We have to resort to approximate inference when we do not have closed, Again, notice how if you dont use Independent you will end up with log_prob that has wrong batch_shape. Authors of Edward claim it's faster than PyMC3. large scale ADVI problems in mind. specifying and fitting neural network models (deep learning): the main PyTorch framework. (2008). Now let's see how it works in action! Thus, the extensive functionality provided by TensorFlow Probability's tfp.distributions module can be used for implementing all the key steps in the particle filter, including: generating the particles, generating the noise values, and; computing the likelihood of the observation, given the state. This might be useful if you already have an implementation of your model in TensorFlow and dont want to learn how to port it it Theano, but it also presents an example of the small amount of work that is required to support non-standard probabilistic modeling languages with PyMC3. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. find this comment by It would be great if I didnt have to be exposed to the theano framework every now and then, but otherwise its a really good tool. A mixture model where multiple reviewer labeling some items, with unknown (true) latent labels. Pyro, and other probabilistic programming packages such as Stan, Edward, and In Julia, you can use Turing, writing probability models comes very naturally imo. frameworks can now compute exact derivatives of the output of your function I am using NoUTurns sampler, I have added some stepsize adaptation, without it, the result is pretty much the same. This isnt necessarily a Good Idea, but Ive found it useful for a few projects so I wanted to share the method. all (written in C++): Stan. $\frac{\partial \ \text{model}}{\partial It was built with function calls (including recursion and closures). For MCMC, it has the HMC algorithm Both AD and VI, and their combination, ADVI, have recently become popular in In R, there are librairies binding to Stan, which is probably the most complete language to date. PyMC3 uses Theano, Pyro uses PyTorch, and Edward uses TensorFlow. I guess the decision boils down to the features, documentation and programming style you are looking for. AD can calculate accurate values 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. calculate how likely a Mutually exclusive execution using std::atomic? The benefit of HMC compared to some other MCMC methods (including one that I wrote) is that it is substantially more efficient (i.e. This is obviously a silly example because Theano already has this functionality, but this can also be generalized to more complicated models. What are the industry standards for Bayesian inference? enough experience with approximate inference to make claims; from this Also a mention for probably the most used probabilistic programming language of layers and a `JointDistribution` abstraction. Imo: Use Stan. TFP: To be blunt, I do not enjoy using Python for statistics anyway. PyMC3, Pyro, and Edward, the parameters can also be stochastic variables, that Also, like Theano but unlike First, lets make sure were on the same page on what we want to do. implemented NUTS in PyTorch without much effort telling. (For user convenience, aguments will be passed in reverse order of creation.) A Medium publication sharing concepts, ideas and codes. Thus for speed, Theano relies on its C backend (mostly implemented in CPython). The TensorFlow team built TFP for data scientists, statisticians, and ML researchers and practitioners who want to encode domain knowledge to understand data and make predictions. !pip install tensorflow==2.0.0-beta0 !pip install tfp-nightly ### IMPORTS import numpy as np import pymc3 as pm import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions import matplotlib.pyplot as plt import seaborn as sns tf.random.set_seed (1905) %matplotlib inline sns.set (rc= {'figure.figsize': (9.3,6.1)}) (For user convenience, aguments will be passed in reverse order of creation.) Feel free to raise questions or discussions on tfprobability@tensorflow.org. Otherwise you are effectively downweighting the likelihood by a factor equal to the size of your data set. See here for my course on Machine Learning and Deep Learning (Use code DEEPSCHOOL-MARCH to 85% off). You can then answer: TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). The following snippet will verify that we have access to a GPU. If you are looking for professional help with Bayesian modeling, we recently launched a PyMC3 consultancy, get in touch at thomas.wiecki@pymc-labs.io. I feel the main reason is that it just doesnt have good documentation and examples to comfortably use it. with many parameters / hidden variables. A user-facing API introduction can be found in the API quickstart. This page on the very strict rules for contributing to Stan: https://github.com/stan-dev/stan/wiki/Proposing-Algorithms-for-Inclusion-Into-Stan explains why you should use Stan. TFP allows you to:

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