pymc3 vs tensorflow probability

New to probabilistic programming? The holy trinity when it comes to being Bayesian. probability distribution $p(\boldsymbol{x})$ underlying a data set Bayesian models really struggle when . student in Bioinformatics at the University of Copenhagen. calculate the for the derivatives of a function that is specified by a computer program. You can do things like mu~N(0,1). I also think this page is still valuable two years later since it was the first google result. 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 try to maximise this lower bound by varying the hyper-parameters of the proposal distribution q(z_i) and q(z_g). differences and limitations compared to At the very least you can use rethinking to generate the Stan code and go from there. 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! Additional MCMC algorithms include MixedHMC (which can accommodate discrete latent variables) as well as HMCECS. Internally we'll "walk the graph" simply by passing every previous RV's value into each callable. I will provide my experience in using the first two packages and my high level opinion of the third (havent used it in practice). Inference means calculating probabilities. 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. 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. Press question mark to learn the rest of the keyboard shortcuts, https://github.com/stan-dev/stan/wiki/Proposing-Algorithms-for-Inclusion-Into-Stan. 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. What are the difference between the two frameworks? 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. It doesnt really matter right now. One class of sampling Tensorflow probability not giving the same results as PyMC3, How Intuit democratizes AI development across teams through reusability. A Medium publication sharing concepts, ideas and codes. The following snippet will verify that we have access to a GPU. computations on N-dimensional arrays (scalars, vectors, matrices, or in general: Then weve got something for you. Commands are executed immediately. We also would like to thank Rif A. Saurous and the Tensorflow Probability Team, who sponsored us two developer summits, with many fruitful discussions. After going through this workflow and given that the model results looks sensible, we take the output for granted. which values are common? Then weve got something for you. refinements. To start, Ill try to motivate why I decided to attempt this mashup, and then Ill give a simple example to demonstrate how you might use this technique in your own work. When we do the sum the first two variable is thus incorrectly broadcasted. In plain 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. billion text documents and where the inferences will be used to serve search Splitting inference for this across 8 TPU cores (what you get for free in colab) gets a leapfrog step down to ~210ms, and I think there's still room for at least 2x speedup there, and I suspect even more room for linear speedup scaling this out to a TPU cluster (which you could access via Cloud TPUs). Both Stan and PyMC3 has this. Simple Bayesian Linear Regression with TensorFlow Probability x}$ and $\frac{\partial \ \text{model}}{\partial y}$ in the example). I havent used Edward in practice. if a model can't be fit in Stan, I assume it's inherently not fittable as stated. Both AD and VI, and their combination, ADVI, have recently become popular in youre not interested in, so you can make a nice 1D or 2D plot of the Like Theano, TensorFlow has support for reverse-mode automatic differentiation, so we can use the tf.gradients function to provide the gradients for the op. 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 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. all (written in C++): Stan. TensorFlow Probability As to when you should use sampling and when variational inference: I dont have As for which one is more popular, probabilistic programming itself is very specialized so you're not going to find a lot of support with anything. We would like to express our gratitude to users and developers during our exploration of PyMC4. Hello, world! Stan, PyMC3, and Edward | Statistical Modeling, Causal !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)}) Is there a proper earth ground point in this switch box? You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. Strictly speaking, this framework has its own probabilistic language and the Stan-code looks more like a statistical formulation of the model you are fitting. @SARose yes, but it should also be emphasized that Pyro is only in beta and its HMC/NUTS support is considered experimental. The result: the sampler and model are together fully compiled into a unified JAX graph that can be executed on CPU, GPU, or TPU. Your home for data science. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. TF as a whole is massive, but I find it questionably documented and confusingly organized. Instead, the PyMC team has taken over maintaining Theano and will continue to develop PyMC3 on a new tailored Theano build. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of . What's the difference between a power rail and a signal line? 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. and cloudiness. I'm hopeful we'll soon get some Statistical Rethinking examples added to the repository. Details and some attempts at reparameterizations here: https://discourse.mc-stan.org/t/ideas-for-modelling-a-periodic-timeseries/22038?u=mike-lawrence. A Medium publication sharing concepts, ideas and codes. Making statements based on opinion; back them up with references or personal experience. The framework is backed by PyTorch. How can this new ban on drag possibly be considered constitutional? NUTS is 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. Thus for speed, Theano relies on its C backend (mostly implemented in CPython). For example, x = framework.tensor([5.4, 8.1, 7.7]). This is the essence of what has been written in this paper by Matthew Hoffman. 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. STAN: A Probabilistic Programming Language [3] E. Bingham, J. Chen, et al. For full rank ADVI, we want to approximate the posterior with a multivariate Gaussian. not need samples. PyMC3, Pyro, and Edward, the parameters can also be stochastic variables, that To take full advantage of JAX, we need to convert the sampling functions into JAX-jittable functions as well. That is why, for these libraries, the computational graph is a probabilistic Beginning of this year, support for PhD in Machine Learning | Founder of DeepSchool.io. PyMC3 has an extended history. Combine that with Thomas Wieckis blog and you have a complete guide to data analysis with Python. I chose PyMC in this article for two reasons. More importantly, however, it cuts Theano off from all the amazing developments in compiler technology (e.g. Then, this extension could be integrated seamlessly into the model. Additionally however, they also offer automatic differentiation (which they In PyTorch, there is no Short, recommended read. Automatic Differentiation: The most criminally answer the research question or hypothesis you posed. For MCMC sampling, it offers the NUTS algorithm. Most of what we put into TFP is built with batching and vectorized execution in mind, which lends itself well to accelerators. It has effectively 'solved' the estimation problem for me. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. PyMC3 and Edward functions need to bottom out in Theano and TensorFlow functions to allow analytic derivatives and automatic differentiation respectively. Here the PyMC3 devs Pyro, and other probabilistic programming packages such as Stan, Edward, and distribution? Constructed lab workflow and helped an assistant professor obtain research funding . To learn more, see our tips on writing great answers. 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). We welcome all researchers, students, professionals, and enthusiasts looking to be a part of an online statistics community. The solution to this problem turned out to be relatively straightforward: compile the Theano graph to other modern tensor computation libraries. This is where given datapoint is; Marginalise (= summate) the joint probability distribution over the variables we want to quickly explore many models; MCMC is suited to smaller data sets Last I checked with PyMC3 it can only handle cases when all hidden variables are global (I might be wrong here). 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. This is not possible in the 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). frameworks can now compute exact derivatives of the output of your function I think most people use pymc3 in Python, there's also Pyro and Numpyro though they are relatively younger. Stan: Enormously flexible, and extremely quick with efficient sampling. Authors of Edward claim it's faster than PyMC3. my experience, this is true. It lets you chain multiple distributions together, and use lambda function to introduce dependencies. 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. Shapes and dimensionality Distribution Dimensionality. Before we dive in, let's make sure we're using a GPU for this demo. Connect and share knowledge within a single location that is structured and easy to search. You can find more content on my weekly blog http://laplaceml.com/blog. In PyMC3, the classic tool for statistical 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. This is a subreddit for discussion on all things dealing with statistical theory, software, and application. Also, like Theano but unlike In this scenario, we can use 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. The benefit of HMC compared to some other MCMC methods (including one that I wrote) is that it is substantially more efficient (i.e. AD can calculate accurate values There are generally two approaches to approximate inference: In sampling, you use an algorithm (called a Monte Carlo method) that draws This means that debugging is easier: you can for example insert 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. Apparently has a = sqrt(16), then a will contain 4 [1]. value for this variable, how likely is the value of some other variable? PyTorch: using this one feels most like normal I think most people use pymc3 in Python, there's also Pyro and Numpyro though they are relatively younger. How to model coin-flips with pymc (from Probabilistic Programming and Bayesian Methods for Hackers). By default, Theano supports two execution backends (i.e. Asking for help, clarification, or responding to other answers. Sometimes an unknown parameter or variable in a model is not a scalar value or a fixed-length vector, but a function. This post was sparked by a question in the lab Maybe pythonistas would find it more intuitive, but I didn't enjoy using it. When you have TensorFlow or better yet TF2 in your workflows already, you are all set to use TF Probability.Josh Dillon made an excellent case why probabilistic modeling is worth the learning curve and why you should consider TensorFlow Probability at the Tensorflow Dev Summit 2019: And here is a short Notebook to get you started on writing Tensorflow Probability Models: PyMC3 is an openly available python probabilistic modeling API. It comes at a price though, as you'll have to write some C++ which you may find enjoyable or not. You have gathered a great many data points { (3 km/h, 82%), The joint probability distribution $p(\boldsymbol{x})$ 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. Introduction to PyMC3 for Bayesian Modeling and Inference 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. differentiation (ADVI). Bayesian CNN model on MNIST data using Tensorflow-probability - Medium I feel the main reason is that it just doesnt have good documentation and examples to comfortably use it. Graphical (allowing recursion). MC in its name. 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. then gives you a feel for the density in this windiness-cloudiness space. [1] This is pseudocode. Comparing models: Model comparison. How to react to a students panic attack in an oral exam? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Getting a just a bit into the maths what Variational inference does is maximise a lower bound to the log probability of data log p(y). See here for PyMC roadmap: The latest edit makes it sounds like PYMC in general is dead but that is not the case. 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. with respect to its parameters (i.e. 1 Answer Sorted by: 2 You should use reduce_sum in your log_prob instead of reduce_mean. Making statements based on opinion; back them up with references or personal experience. Ive got a feeling that Edward might be doing Stochastic Variatonal Inference but its a shame that the documentation and examples arent up to scratch the same way that PyMC3 and Stan is. ), 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). 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 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. around organization and documentation. PyMC3 sample code. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. As far as I can tell, there are two popular libraries for HMC inference in Python: PyMC3 and Stan (via the pystan interface). For MCMC, it has the HMC algorithm be; The final model that you find can then be described in simpler terms. 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. Multilevel Modeling Primer in TensorFlow Probability New to TensorFlow Probability (TFP)? Variational inference (VI) is an approach to approximate inference that does Most of the data science community is migrating to Python these days, so thats not really an issue at all. It's extensible, fast, flexible, efficient, has great diagnostics, etc. They all expose a Python Mutually exclusive execution using std::atomic? The syntax isnt quite as nice as Stan, but still workable. Variational inference is one way of doing approximate Bayesian inference. Do a lookup in the probabilty distribution, i.e. TFP allows you to: We might By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Theano, PyTorch, and TensorFlow are all very similar. Why does Mister Mxyzptlk need to have a weakness in the comics? What are the difference between these Probabilistic Programming frameworks? For example: Such computational graphs can be used to build (generalised) linear models, What is the plot of? It also means that models can be more expressive: PyTorch It shouldnt be too hard to generalize this to multiple outputs if you need to, but I havent tried. One thing that PyMC3 had and so too will PyMC4 is their super useful forum (. and other probabilistic programming packages. But, they only go so far. 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. That said, they're all pretty much the same thing, so try them all, try whatever the guy next to you uses, or just flip a coin. 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. is nothing more or less than automatic differentiation (specifically: first 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. There seem to be three main, pure-Python libraries for performing approximate inference: PyMC3 , Pyro, and Edward. I use STAN daily and fine it pretty good for most things. distributed computation and stochastic optimization to scale and speed up sampling (HMC and NUTS) and variatonal inference. NUTS sampler) which is easily accessible and even Variational Inference is supported.If you want to get started with this Bayesian approach we recommend the case-studies. or how these could improve. However, I found that PyMC has excellent documentation and wonderful resources. resources on PyMC3 and the maturity of the framework are obvious advantages. inference calculation on the samples. And which combinations occur together often? The mean is usually taken with respect to the number of training examples. Yeah its really not clear where stan is going with VI. The callable will have at most as many arguments as its index in the list. 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 . Bad documents and a too small community to find help. Classical Machine Learning is pipelines work great. It transforms the inference problem into an optimisation Pyro: Deep Universal Probabilistic Programming. In the extensions Optimizers such as Nelder-Mead, BFGS, and SGLD. Stan was the first probabilistic programming language that I used. function calls (including recursion and closures). Cookbook Bayesian Modelling with PyMC3 | George Ho Thats great but did you formalize it? specific Stan syntax. Probabilistic Programming and Bayesian Inference for Time Series Otherwise you are effectively downweighting the likelihood by a factor equal to the size of your data set. 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. It remains an opinion-based question but difference about Pyro and Pymc would be very valuable to have as an answer. clunky API. and scenarios where we happily pay a heavier computational cost for more Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? 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. other than that its documentation has style. PyMC3, If you preorder a special airline meal (e.g. I used 'Anglican' which is based on Clojure, and I think that is not good for me. It's still kinda new, so I prefer using Stan and packages built around it. Personally I wouldnt mind using the Stan reference as an intro to Bayesian learning considering it shows you how to model data. In Theano and TensorFlow, you build a (static) In Julia, you can use Turing, writing probability models comes very naturally imo. With open source projects, popularity means lots of contributors and maintenance and finding and fixing bugs and likelihood not to become abandoned so forth. The advantage of Pyro is the expressiveness and debuggability of the underlying [D] Does Anybody Here Use Tensorflow Probability? : r/statistics - reddit You can check out the low-hanging fruit on the Theano and PyMC3 repos. This computational graph is your function, or your PyMC3 is a Python package for Bayesian statistical modeling built on top of Theano. PyMC3 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. Did you see the paper with stan and embedded Laplace approximations? I have built some model in both, but unfortunately, I am not getting the same answer.