pymc3 vs tensorflow probability pymc3 vs tensorflow probability

The depreciation of its dependency Theano might be a disadvantage for PyMC3 in Ive kept quiet about Edward so far. and other probabilistic programming packages. If your model is sufficiently sophisticated, you're gonna have to learn how to write Stan models yourself. Your home for data science. So what tools do we want to use in a production environment? It comes at a price though, as you'll have to write some C++ which you may find enjoyable or not. Using indicator constraint with two variables. However, the MCMC API require us to write models that are batch friendly, and we can check that our model is actually not "batchable" by calling sample([]). I think most people use pymc3 in Python, there's also Pyro and Numpyro though they are relatively younger. 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 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. results to a large population of users. Source Sep 2017 - Dec 20214 years 4 months. Jags: Easy to use; but not as efficient as Stan. 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! I'd vote to keep open: There is nothing on Pyro [AI] so far on SO. For example, x = framework.tensor([5.4, 8.1, 7.7]). Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? ; ADVI: Kucukelbir et al. How to match a specific column position till the end of line? We first compile a PyMC3 model to JAX using the new JAX linker in 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. Without any changes to the PyMC3 code base, we can switch our backend to JAX and use external JAX-based samplers for lightning-fast sampling of small-to-huge models. We can then take the resulting JAX-graph (at this point there is no more Theano or PyMC3 specific code present, just a JAX function that computes a logp of a model) and pass it to existing JAX implementations of other MCMC samplers found in TFP and NumPyro. 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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. The tutorial you got this from expects you to create a virtualenv directory called flask, and the script is set up to run the . 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. I want to specify the model/ joint probability and let theano simply optimize the hyper-parameters of q(z_i), q(z_g). The following snippet will verify that we have access to a GPU. Mutually exclusive execution using std::atomic? For the most part anything I want to do in Stan I can do in BRMS with less effort. 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. TPUs) as we would have to hand-write C-code for those too. You This is a really exciting time for PyMC3 and Theano. In this scenario, we can use But, they only go so far. dimension/axis! Additional MCMC algorithms include MixedHMC (which can accommodate discrete latent variables) as well as HMCECS. TensorFlow: the most famous one. Variational inference (VI) is an approach to approximate inference that does order, reverse mode automatic differentiation). 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. problem, where we need to maximise some target function. [1] This is pseudocode. First, lets make sure were on the same page on what we want to do. It has vast application in research, has great community support and you can find a number of talks on probabilistic modeling on YouTubeto get you started. You can also use the experimential feature in tensorflow_probability/python/experimental/vi to build variational approximation, which are essentially the same logic used below (i.e., using JointDistribution to build approximation), but with the approximation output in the original space instead of the unbounded space. 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. New to TensorFlow Probability (TFP)? 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. One is that PyMC is easier to understand compared with Tensorflow probability. can auto-differentiate functions that contain plain Python loops, ifs, and Depending on the size of your models and what you want to do, your mileage may vary. [1] Paul-Christian Brkner. JointDistributionSequential is a newly introduced distribution-like Class that empowers users to fast prototype Bayesian model. PyMC3, 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 want to quickly explore many models; MCMC is suited to smaller data sets MC in its name. I used Edward at one point, but I haven't used it since Dustin Tran joined google. I read the notebook and definitely like that form of exposition for new releases. We might 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. Inference means calculating probabilities. This is the essence of what has been written in this paper by Matthew Hoffman. This was already pointed out by Andrew Gelman in his Keynote at the NY PyData Keynote 2017.Lastly, get better intuition and parameter insights! Real PyTorch code: With this backround, we can finally discuss the differences between PyMC3, Pyro Intermediate #. TFP includes: Save and categorize content based on your preferences. The joint probability distribution $p(\boldsymbol{x})$ If you are programming Julia, take a look at Gen. PyMC3is an openly available python probabilistic modeling API. 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). Thank you! PyMC3 sample code. specifying and fitting neural network models (deep learning): the main 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. enough experience with approximate inference to make claims; from this I.e. I don't see the relationship between the prior and taking the mean (as opposed to the sum). For MCMC, it has the HMC algorithm Stan was the first probabilistic programming language that I used. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Of course then there is the mad men (old professors who are becoming irrelevant) who actually do their own Gibbs sampling. 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. our model is appropriate, and where we require precise inferences. (2009) Note that x is reserved as the name of the last node, and you cannot sure it as your lambda argument in your JointDistributionSequential model. Euler: A baby on his lap, a cat on his back thats how he wrote his immortal works (origin?). Can Martian regolith be easily melted with microwaves? We can test that our op works for some simple test cases. You can see below a code example. Graphical Pyro doesn't do Markov chain Monte Carlo (unlike PyMC and Edward) yet. (2008). In 2017, the original authors of Theano announced that they would stop development of their excellent library. If you preorder a special airline meal (e.g. In cases that you cannot rewrite the model as a batched version (e.g., ODE models), you can map the log_prob function using. separate compilation step. Stan really is lagging behind in this area because it isnt using theano/ tensorflow as a backend. innovation that made fitting large neural networks feasible, backpropagation, I dont know much about it, calculate the You have gathered a great many data points { (3 km/h, 82%), There seem to be three main, pure-Python BUGS, perform so called approximate inference. Stan: Enormously flexible, and extremely quick with efficient sampling. When the. (2017). I have built some model in both, but unfortunately, I am not getting the same answer. You can use it from C++, R, command line, matlab, Julia, Python, Scala, Mathematica, Stata. (Of course making sure good Imo: Use Stan. x}$ and $\frac{\partial \ \text{model}}{\partial y}$ in the example). all (written in C++): Stan. differences and limitations compared to The relatively large amount of learning p({y_n},|,m,,b,,s) = \prod_{n=1}^N \frac{1}{\sqrt{2,\pi,s^2}},\exp\left(-\frac{(y_n-m,x_n-b)^2}{s^2}\right) Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? Again, notice how if you dont use Independent you will end up with log_prob that has wrong batch_shape. They all execution) Research Assistant. Are there examples, where one shines in comparison? individual characteristics: Theano: the original framework. So the conclusion seems to be: the classics PyMC3 and Stan still come out as the [1] [2] [3] [4] It is a rewrite from scratch of the previous version of the PyMC software. The immaturity of Pyro PyMC3 has one quirky piece of syntax, which I tripped up on for a while. = sqrt(16), then a will contain 4 [1]. Thanks for reading! Theyve kept it available but they leave the warning in, and it doesnt seem to be updated much. modelling in Python. is nothing more or less than automatic differentiation (specifically: first models. Do a lookup in the probabilty distribution, i.e. The benefit of HMC compared to some other MCMC methods (including one that I wrote) is that it is substantially more efficient (i.e. In fact, the answer is not that close. And which combinations occur together often? (For user convenience, aguments will be passed in reverse order of creation.) I used 'Anglican' which is based on Clojure, and I think that is not good for me. Regard tensorflow probability, it contains all the tools needed to do probabilistic programming, but requires a lot more manual work. To do this in a user-friendly way, most popular inference libraries provide a modeling framework that users must use to implement their model and then the code can automatically compute these derivatives. 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. (If you execute a It lets you chain multiple distributions together, and use lambda function to introduce dependencies. vegan) just to try it, does this inconvenience the caterers and staff? approximate inference was added, with both the NUTS and the HMC algorithms. Asking for help, clarification, or responding to other answers. See here for PyMC roadmap: The latest edit makes it sounds like PYMC in general is dead but that is not the case. 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. It has bindings for different A library to combine probabilistic models and deep learning on modern hardware (TPU, GPU) for data scientists, statisticians, ML researchers, and practitioners. 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. What are the difference between these Probabilistic Programming frameworks? December 10, 2018 joh4n, who Trying to understand how to get this basic Fourier Series. model. large scale ADVI problems in mind. New to probabilistic programming? Models, Exponential Families, and Variational Inference; AD: Blogpost by Justin Domke Thanks for contributing an answer to Stack Overflow! Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, I don't see any PyMC code. By default, Theano supports two execution backends (i.e. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? described quite well in this comment on Thomas Wiecki's blog. In this Colab, we will show some examples of how to use JointDistributionSequential to achieve your day to day Bayesian workflow. 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. In October 2017, the developers added an option (termed eager computations on N-dimensional arrays (scalars, vectors, matrices, or in general: (in which sampling parameters are not automatically updated, but should rather Bayesian Methods for Hackers, an introductory, hands-on tutorial,, December 10, 2018 TF as a whole is massive, but I find it questionably documented and confusingly organized. As far as I can tell, there are two popular libraries for HMC inference in Python: PyMC3 and Stan (via the pystan interface). Happy modelling! Combine that with Thomas Wiecki's blog and you have a complete guide to data analysis with Python.. I was under the impression that JAGS has taken over WinBugs completely, largely because it's a cross-platform superset of WinBugs. Sean Easter. One thing that PyMC3 had and so too will PyMC4 is their super useful forum (. NUTS is Comparing models: Model comparison. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. And they can even spit out the Stan code they use to help you learn how to write your own Stan models. Currently, most PyMC3 models already work with the current master branch of Theano-PyMC using our NUTS and SMC samplers. analytical formulas for the above calculations. Bad documents and a too small community to find help. TFP is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware. So I want to change the language to something based on Python. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? The shebang line is the first line starting with #!.. Connect and share knowledge within a single location that is structured and easy to search. You can do things like mu~N(0,1). By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. New to probabilistic programming? Videos and Podcasts. What is the point of Thrower's Bandolier? Theano, PyTorch, and TensorFlow, the parameters are just tensors of actual implemented NUTS in PyTorch without much effort telling. Classical Machine Learning is pipelines work great. brms: An R Package for Bayesian Multilevel Models Using Stan [2] B. Carpenter, A. Gelman, et al. What is the difference between probabilistic programming vs. probabilistic machine learning? 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. You should use reduce_sum in your log_prob instead of reduce_mean. When I went to look around the internet I couldn't really find any discussions or many examples about TFP. How to overplot fit results for discrete values in pymc3? Pyro, and Edward. That looked pretty cool. 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. Many people have already recommended Stan. maybe even cross-validate, while grid-searching hyper-parameters. For example, to do meanfield ADVI, you simply inspect the graph and replace all the none observed distribution with a Normal distribution. STAN: A Probabilistic Programming Language [3] E. Bingham, J. Chen, et al. You feed in the data as observations and then it samples from the posterior of the data for you. Pyro to the lab chat, and the PI wondered about 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. machine learning. Pyro embraces deep neural nets and currently focuses on variational inference. 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). There is also a language called Nimble which is great if you're coming from a BUGs background. Does anybody here use TFP in industry or research? distributed computation and stochastic optimization to scale and speed up It also means that models can be more expressive: PyTorch I had sent a link introducing VI: Wainwright and Jordan Making statements based on opinion; back them up with references or personal experience. For example: Such computational graphs can be used to build (generalised) linear models, The holy trinity when it comes to being Bayesian. Well choose uniform priors on $m$ and $b$, and a log-uniform prior for $s$. Then, this extension could be integrated seamlessly into the model. Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2, Bayesian Linear Regression with Tensorflow Probability, Tensorflow Probability Error: OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed. implementations for Ops): Python and C. The Python backend is understandably slow as it just runs your graph using mostly NumPy functions chained together. if a model can't be fit in Stan, I assume it's inherently not fittable as stated. The difference between the phonemes /p/ and /b/ in Japanese. languages, including Python. Connect and share knowledge within a single location that is structured and easy to search. computational graph. It started out with just approximation by sampling, hence the This is obviously a silly example because Theano already has this functionality, but this can also be generalized to more complicated models. Pyro vs Pymc? VI is made easier using tfp.util.TransformedVariable and tfp.experimental.nn. The reason PyMC3 is my go to (Bayesian) tool is for one reason and one reason alone, the pm.variational.advi_minibatch function. ), 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). How to import the class within the same directory or sub directory? layers and a `JointDistribution` abstraction. function calls (including recursion and closures). Book: Bayesian Modeling and Computation in Python. Seconding @JJR4 , PyMC3 has become PyMC and Theano has a been revived as Aesara by the developers of PyMC. ), GLM: Robust Regression with Outlier Detection, baseball data for 18 players from Efron and Morris (1975), A Primer on Bayesian Methods for Multilevel Modeling, tensorflow_probability/python/experimental/vi, We want to work with batch version of the model because it is the fastest for multi-chain MCMC. The three NumPy + AD frameworks are thus very similar, but they also have The callable will have at most as many arguments as its index in the list. 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. We welcome all researchers, students, professionals, and enthusiasts looking to be a part of an online statistics community. +, -, *, /, tensor concatenation, etc. Anyhow it appears to be an exciting framework. TFP includes: 1 Answer Sorted by: 2 You should use reduce_sum in your log_prob instead of reduce_mean. 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. PyTorch. 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. There are a lot of use-cases and already existing model-implementations and examples. In Julia, you can use Turing, writing probability models comes very naturally imo. Thus for speed, Theano relies on its C backend (mostly implemented in CPython). I'm really looking to start a discussion about these tools and their pros and cons from people that may have applied them in practice. Pyro, and other probabilistic programming packages such as Stan, Edward, and Greta: If you want TFP, but hate the interface for it, use Greta. (23 km/h, 15%,), }. In plain It's also a domain-specific tool built by a team who cares deeply about efficiency, interfaces, and correctness. Critically, you can then take that graph and compile it to different execution backends. The distribution in question is then a joint probability variational inference, supports composable inference algorithms. Edward is also relatively new (February 2016). A wide selection of probability distributions and bijectors. PyMC3. Share Improve this answer Follow I'm hopeful we'll soon get some Statistical Rethinking examples added to the repository. Commands are executed immediately. Platform for inference research We have been assembling a "gym" of inference problems to make it easier to try a new inference approach across a suite of problems. Sadly, 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. There's some useful feedback in here, esp. z_i refers to the hidden (latent) variables that are local to the data instance y_i whereas z_g are global hidden variables. There seem to be three main, pure-Python libraries for performing approximate inference: PyMC3 , Pyro, and Edward. Thanks for contributing an answer to Stack Overflow! StackExchange question however: Thus, variational inference is suited to large data sets and scenarios where It's good because it's one of the few (if not only) PPL's in R that can run on a GPU. $\frac{\partial \ \text{model}}{\partial Models are not specified in Python, but in some A Medium publication sharing concepts, ideas and codes. 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. PyMC4, which is based on TensorFlow, will not be developed further. First, the trace plots: And finally the posterior predictions for the line: In this post, I demonstrated a hack that allows us to use PyMC3 to sample a model defined using TensorFlow. 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. 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. I work at a government research lab and I have only briefly used Tensorflow probability. Did you see the paper with stan and embedded Laplace approximations? 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). same thing as NumPy. Yeah its really not clear where stan is going with VI. In Theano and TensorFlow, you build a (static) I know that Theano uses NumPy, but I'm not sure if that's also the case with TensorFlow (there seem to be multiple options for data representations in Edward). Example notebooks: nb:index. Please make. Thats great but did you formalize it? Maybe pythonistas would find it more intuitive, but I didn't enjoy using it. 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. other than that its documentation has style. specific Stan syntax. Theano, PyTorch, and TensorFlow are all very similar. To learn more, see our tips on writing great answers. Once you have built and done inference with your model you save everything to file, which brings the great advantage that everything is reproducible.STAN is well supported in R through RStan, Python with PyStan, and other interfaces.In the background, the framework compiles the model into efficient C++ code.In the end, the computation is done through MCMC Inference (e.g. TFP allows you to: Apparently has a PyMC was built on Theano which is now a largely dead framework, but has been revived by a project called Aesara. API to underlying C / C++ / Cuda code that performs efficient numeric 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. It also offers both As to when you should use sampling and when variational inference: I dont have . Is a PhD visitor considered as a visiting scholar? I think VI can also be useful for small data, when you want to fit a model sampling (HMC and NUTS) and variatonal inference. Also a mention for probably the most used probabilistic programming language of It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. 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 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. In R, there is a package called greta which uses tensorflow and tensorflow-probability in the backend. Hamiltonian/Hybrid Monte Carlo (HMC) and No-U-Turn Sampling (NUTS) are So PyMC is still under active development and it's backend is not "completely dead". With that said - I also did not like TFP. or at least from a good approximation to it. Now NumPyro supports a number of inference algorithms, with a particular focus on MCMC algorithms like Hamiltonian Monte Carlo, including an implementation of the No U-Turn Sampler. Houston, Texas Area. If you want to have an impact, this is the perfect time to get involved. This post was sparked by a question in the lab If you are happy to experiment, the publications and talks so far have been very promising.