From 4f49a3e82fdf61c52d56ed14071b481e9b80e2ad Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Fri, 6 Jan 2023 20:19:24 +0000 Subject: [PATCH 01/87] Add all high level docs from Turing.jl --- .../contributing-guide/contributing-guide.md | 40 + .../contributing-style-guide.md | 139 + .../hierarchy.png | Bin 0 -> 18792 bytes .../how_turing_implements_abstractmcmc.md | 279 ++ tutorials/for-developers-compiler/compiler.md | 237 ++ .../for-developers-interface/interface.md | 367 +++ .../variational_inference.md | 315 ++ tutorials/using-turing-advanced/advanced.md | 186 ++ tutorials/using-turing-autodiff/autodiff.md | 50 + .../using-turing-dynamicppl/dynamichmc.md | 31 + tutorials/using-turing-guide/guide.md | 671 ++++ .../performancetips.md | 163 + .../using-turing-quick-start/quick-start.md | 56 + .../gen-sampler-viz.jl | 100 + .../sampler-figs/samplers-1.svg | 2725 +++++++++++++++++ .../sampler-figs/samplers-2.svg | 2479 +++++++++++++++ .../sampler-figs/samplers-3.svg | 2516 +++++++++++++++ .../sampler-figs/samplers-4.svg | 2635 ++++++++++++++++ .../sampler-figs/samplers-5.svg | 2426 +++++++++++++++ .../sampler-figs/samplers-6.svg | 2222 ++++++++++++++ .../sampler-figs/samplers-7.svg | 2562 ++++++++++++++++ .../sampler-figs/samplers-8.svg | 2691 ++++++++++++++++ .../sampler-figs/samplers-9.svg | 2616 ++++++++++++++++ .../using-turing-sampler-viz/sampler-viz.md | 215 ++ tutorials/using-turing/index.md | 22 + 25 files changed, 25743 insertions(+) create mode 100644 tutorials/contributing-guide/contributing-guide.md create mode 100644 tutorials/contributing-style-guide/contributing-style-guide.md create mode 100644 tutorials/for-developers-abstractmcmc-turing/hierarchy.png create mode 100644 tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md create mode 100644 tutorials/for-developers-compiler/compiler.md create mode 100644 tutorials/for-developers-interface/interface.md create mode 100644 tutorials/for-developers-variational-inference/variational_inference.md create mode 100644 tutorials/using-turing-advanced/advanced.md create mode 100644 tutorials/using-turing-autodiff/autodiff.md create mode 100644 tutorials/using-turing-dynamicppl/dynamichmc.md create mode 100644 tutorials/using-turing-guide/guide.md create mode 100644 tutorials/using-turing-performance-tips/performancetips.md create mode 100644 tutorials/using-turing-quick-start/quick-start.md create mode 100644 tutorials/using-turing-sampler-viz/gen-sampler-viz.jl create mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-1.svg create mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-2.svg create mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-3.svg create mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-4.svg create mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-5.svg create mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-6.svg create mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-7.svg create mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-8.svg create mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-9.svg create mode 100644 tutorials/using-turing-sampler-viz/sampler-viz.md create mode 100644 tutorials/using-turing/index.md diff --git a/tutorials/contributing-guide/contributing-guide.md b/tutorials/contributing-guide/contributing-guide.md new file mode 100644 index 000000000..bc297f824 --- /dev/null +++ b/tutorials/contributing-guide/contributing-guide.md @@ -0,0 +1,40 @@ +--- +title: Contributing +permalink: /docs/contributing/guide +weave_options: + error : false +--- + +# Contributing + +Turing is an open source project. If you feel that you have some relevant skills and are interested in contributing, then please do get in touch. You can contribute by opening issues on GitHub or implementing things yourself and making a pull request. We would also appreciate example models written using Turing. + + +Turing has a [style guide]({{site.baseurl}}/docs/contributing/style-guide). It is not strictly necessary to review it before making a pull request, but you may be asked to change portions of your code to conform with the style guide before it is merged. + + +## How to Contribute + + +### Getting Started + + + * [Fork this repository](https://github.com/TuringLang/Turing.jl#fork-destination-box). + * Clone your fork on your local machine: `git clone https://github.com/your_username/Turing.jl`. + * Add a remote corresponding to this repository: + + +`git remote add upstream https://github.com/TuringLang/Turing.jl`. + + +### What Can I Do? + + +Look at the [issues](https://github.com/TuringLang/Turing.jl/issues) page to find an outstanding issue. For instance, you could implement new features, fix bugs or write example models. + + +### Git Workflow + + +For more information on how the Git workflow typically functions, please see the [GitHub's introduction](https://guides.github.com/introduction/flow/) or [Julia's contribution guide](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md). + diff --git a/tutorials/contributing-style-guide/contributing-style-guide.md b/tutorials/contributing-style-guide/contributing-style-guide.md new file mode 100644 index 000000000..403637a83 --- /dev/null +++ b/tutorials/contributing-style-guide/contributing-style-guide.md @@ -0,0 +1,139 @@ +--- +title: Style Guide +permalink: /docs/contributing/style-guide +weave_options: + error : false +--- + +# Style Guide + +Most Turing code follow the [Invenia](https://invenia.ca/labs/)'s style guide. We would like to thank them for allowing us to access and use it. Please don't let not having read it stop you from contributing to Turing! No one will be annoyed if you open a PR whose style doesn't follow these conventions; we will just help you correct it before it gets merged. + + +These conventions were originally written at Invenia, taking inspiration from a variety of sources including Python's [PEP8](http://legacy.python.org/dev/peps/pep-0008), Julia's [Notes for Contributors](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md), and Julia's [Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/). + + +What follows is a mixture of a verbatim copy of Invenia's original guide and some of our own modifications. + + +## A Word on Consistency + + +When adhering to this style it's important to realize that these are guidelines and not rules. This is [stated best in the PEP8](http://legacy.python.org/dev/peps/pep-0008/#a-foolish-consistency-is-the-hobgoblin-of-little-minds): + + +> A style guide is about consistency. Consistency with this style guide is important. Consistency within a project is more important. Consistency within one module or function is most important. + + + +> But most importantly: know when to be inconsistent – sometimes the style guide just doesn't apply. When in doubt, use your best judgment. Look at other examples and decide what looks best. And don't hesitate to ask! + + + +## Synopsis + + +Attempt to follow both the [Julia Contribution Guidelines](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md#general-formatting-guidelines-for-julia-code-contributions), the [Julia Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/), and this guide. When convention guidelines conflict this guide takes precedence (known conflicts will be noted in this guide). + + + * Use 4 spaces per indentation level, no tabs. + * Try to adhere to a 92 character line length limit. + * Use upper camel case convention for [modules](https://docs.julialang.org/en/v1/manual/modules/) and [types](https://docs.julialang.org/en/v1/manual/types/). + * Use lower case with underscores for method names (note: Julia code likes to use lower case without underscores). + * Comments are good, try to explain the intentions of the code. + * Use whitespace to make the code more readable. + * No whitespace at the end of a line (trailing whitespace). + * Avoid padding brackets with spaces. ex. `Int64(value)` preferred over `Int64( value )`. + + +## Editor Configuration + + +### Sublime Text Settings + + +If you are a user of Sublime Text we recommend that you have the following options in your Julia syntax specific settings. To modify these settings first open any Julia file (`*.jl`) in Sublime Text. Then navigate to: `Preferences > Settings - More > Syntax Specific - User` + + +```json +{ + "translate_tabs_to_spaces": true, + "tab_size": 4, + "trim_trailing_white_space_on_save": true, + "ensure_newline_at_eof_on_save": true, + "rulers": [92] +} +``` + + +### Vim Settings + + +If you are a user of Vim we recommend that you add the following options to your `.vimrc` file. + + +``` +set tabstop=4 " Sets tabstops to a width of four columns. +set softtabstop=4 " Determines the behaviour of TAB and BACKSPACE keys with expandtab. +set shiftwidth=4 " Determines the results of >>, <<, and ==. + +au FileType julia setlocal expandtab " Replaces tabs with spaces. +au FileType julia setlocal colorcolumn=93 " Highlights column 93 to help maintain the 92 character line limit. +``` + + +By default, Vim seems to guess that `.jl` files are written in Lisp. To ensure that Vim recognizes Julia files you can manually have it check for the `.jl` extension, but a better solution is to install [Julia-Vim](https://github.com/JuliaLang/julia-vim), which also includes proper syntax highlighting and a few cool other features. + + +## Test Formatting + + +### Testsets + + +Julia provides [test sets](https://docs.julialang.org/en/v1/stdlib/Test/#Working-with-Test-Sets-1) which allows developers to group tests into logical groupings. Test sets can be nested and ideally packages should only have a single "root" test set. It is recommended that the "runtests.jl" file contains the root test set which contains the remainder of the tests: + + +```julia +@testset "PkgExtreme" begin + include("arithmetic.jl") + include("utils.jl") +end +``` + + +The file structure of the `test` folder should mirror that of the `src` folder. Every file in `src` should have a complementary file in the `test` folder, containing tests relevant to that file's contents. + + +### Comparisons + + +Most tests are written in the form `@test x == y`. Since the `==` function doesn't take types into account tests like the following are valid: `@test 1.0 == 1`. Avoid adding visual noise into test comparisons: + + +```julia +# Yes: +@test value == 0 + +# No: +@test value == 0.0 +``` + + +In cases where you are checking the numerical validity of a model's parameter estimates, please use the `check_numerical` function found in `test/test_utils/numerical_tests.jl`. This function will evaluate a model's parameter estimates using tolerance levels `atol` and `rtol`. Testing will only be performed if you are running the test suite locally or if Travis is executing the "Numerical" testing stage. + + +Here is an example of usage: + + +```julia +# Check that m and s are plus or minus one from 1.5 and 2.2, respectively. +check_numerical(chain, [:m, :s], [1.5, 2.2], atol = 1.0) + +# Checks the estimates for a default gdemo model using values 1.5 and 2.0. +check_gdemo(chain, atol = 0.1) + +# Checks the estimates for a default MoG model. +check_MoGtest_default(chain, atol = 0.1) +``` + diff --git a/tutorials/for-developers-abstractmcmc-turing/hierarchy.png b/tutorials/for-developers-abstractmcmc-turing/hierarchy.png new file mode 100644 index 0000000000000000000000000000000000000000..1abeed68762e9b5298936b8f02a2ee80c06c5d00 GIT binary patch literal 18792 zcmd_Sc{o*l|2DkIl)2h85K&1b^Gsy3&BHbek$Ik{ZI&UmDI_T>LxnV;6f#G~B2nfk zq%xCvdOnNp>b|dj_xrqmJje4M$9o+P*S7cCYkk*uea7=VKV8PDE6`9LphTfiG)jtc 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+1,279 @@ +--- +title: How Turing implements AbstractMCMC +permalink: /docs/for-developers/how_turing_implements_abstractmcmc +weave_options: + error : false +--- + +# How Turing implements AbstractMCMC + +Prerequisite: [Interface guide](https://turing.ml/dev/docs/for-developers/interface). + +## Introduction + +Consider the following Turing, code block: + +```julia +@model function gdemo(x, y) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + x ~ Normal(m, sqrt(s²)) + y ~ Normal(m, sqrt(s²)) +end + +mod = gdemo(1.5, 2) +alg = IS() +n_samples = 1000 + +chn = sample(mod, alg, n_samples) +``` + +The function `sample` is part of the AbstractMCMC interface. As explained in the [interface guide](https://turing.ml/dev/docs/for-developers/interface), building a a sampling method that can be used by `sample` consists in overloading the structs and functions in `AbstractMCMC`. The interface guide also gives a standalone example of their implementation, [`AdvancedMH.jl`](). + +Turing sampling methods (most of which are written [here](https://github.com/TuringLang/Turing.jl/tree/master/src/inference)) also implement `AbstractMCMC`. Turing defines a particular architecture for `AbstractMCMC` implementations, that enables working with models defined by the `@model` macro, and uses DynamicPPL as a backend. The goal of this page is to describe this architecture, and how you would go about implementing your own sampling method in Turing, using Importance Sampling as an example. I don't go into all the details: for instance, I don't address selectors or parallelism. + +First, we explain how Importance Sampling works in the abstract. Consider the model defined in the first code block. Mathematically, it can be written: + +```math +\begin{align*} +s &\sim \text{InverseGamma}(2, 3), \\\\ +m &\sim \text{Normal}(0, \sqrt{s}), \\\\ +x &\sim \text{Normal}(m, \sqrt{s}), \\\\ +y &\sim \text{Normal}(m, \sqrt{s}). +\end{align*} +``` + + + +The **latent** variables are ``s`` and ``m``, the **observed** variables are ``x`` and ``y``. The model **joint** distribution ``p(s,m,x,y)`` decomposes into the **prior** ``p(s,m)`` and the **likelihood** ``p(x,y \mid s,m).`` Since ``x = 1.5`` and ``y = 2`` are observed, the goal is to infer the **posterior** distribution ``p(s,m \mid x,y).`` + +Importance Sampling produces independent samples ``(s_i, m_i)`` from the prior distribution. It also outputs unnormalized weights + +```math +w_i = \frac {p(x,y,s_i,m_i)} {p(s_i, m_i)} = p(x,y \mid s_i, m_i) +``` + +such that the empirical distribution + +```math +\frac{1}{N} \sum_{i =1}^N \frac {w_i} {\sum_{j=1}^N w_j} \delta_{(s_i, m_i)} +``` + +is a good approximation of the posterior. + +## 1. Define a `Sampler` + +Recall the last line of the above code block: + +```julia +chn = sample(mod, alg, n_samples) +``` + +Here `sample` takes as arguments a **model** `mod`, an **algorithm** `alg`, and a **number of samples** `n_samples`, and returns an instance `chn` of `Chains` which can be analysed using the functions in `MCMCChains`. + +### Models + +To define a **model**, you declare a joint distribution on variables in the `@model` macro, and specify which variables are observed and which should be inferred, as well as the value of the observed variables. Thus, when implementing Importance Sampling, + +```julia +mod = gdemo(1.5, 2) +``` + +creates an instance `mod` of the struct `Model`, which corresponds to the observations of a value of `1.5` for `x`, and a value of `2` for `y`. + +This is all handled by DynamicPPL, more specifically [here](https://github.com/TuringLang/DynamicPPL.jl/blob/master/src/model.jl). I will return to how models are used to inform sampling algorithms [below](#assumeobserve). + +### Algorithms + +An **algorithm** is just a sampling method: in Turing, it is a subtype of the abstract type `InferenceAlgorithm`. Defining an algorithm may require specifying a few high-level parameters. For example, "Hamiltonian Monte-Carlo" may be too vague, but "Hamiltonian Monte Carlo with 10 leapfrog steps per proposal and a stepsize of 0.01" is an algorithm. "Metropolis-Hastings" may be too vague, but "Metropolis-Hastings with proposal distribution `p`" is an algorithm. +Thus + +```julia +stepsize = 0.01 +L = 10 +alg = HMC(stepsize, L) +``` + +defines a Hamiltonian Monte-Carlo algorithm, an instance of `HMC`, which is a subtype of `InferenceAlgorithm`. + +In the case of Importance Sampling, there is no need to specify additional parameters: + +```julia +alg = IS() +``` + +defines an Importance Sampling algorithm, an instance of `IS` which is a subtype of `InferenceAlgorithm`. + +When creating your own Turing sampling method, you must therefore build a subtype of `InferenceAlgorithm` corresponding to your method. + +### Samplers + +Samplers are **not** the same as algorithms. An algorithm is a generic sampling method, a sampler is an object that stores information about how algorithm and model interact during sampling, and is modified as sampling progresses. The `Sampler` struct is defined in DynamicPPL. + +Turing implements `AbstractMCMC`'s `AbstractSampler` with the `Sampler` struct defined in `DynamicPPL`. The most important attributes of an instance `spl` of `Sampler` are: + +* `spl.alg`: the sampling method used, an instance of a subtype of `InferenceAlgorithm` +* `spl.state`: information about the sampling process, see [below](#States) + +When you call `sample(mod, alg, n_samples)`, Turing first uses `model` and `alg` to build an instance `spl` of `Sampler` , then calls the native `AbstractMCMC` function `sample(mod, spl, n_samples)`. + +When you define your own Turing sampling method, you must therefore build: + +* a **sampler constructor** that uses a model and an algorithm to initialize an instance of `Sampler`. For Importance Sampling: + +```julia +function Sampler(alg::IS, model::Model, s::Selector) + info = Dict{Symbol, Any}() + state = ISState(model) + return Sampler(alg, info, s, state) +end +``` + +* a **state** struct implementing `AbstractSamplerState` corresponding to your method: we cover this in the following paragraph. + +### States + +The `vi` field contains all the important information about sampling: first and foremost, the values of all the samples, but also the distributions from which they are sampled, the names of model parameters, and other metadata. As we will see below, many important steps during sampling correspond to queries or updates to `spl.state.vi`. + +By default, you can use `SamplerState`, a concrete type defined in `inference/Inference.jl`, which extends `AbstractSamplerState` and has no field except for `vi`: + +```julia +mutable struct SamplerState{VIType<:VarInfo} <: AbstractSamplerState + vi :: VIType +end +``` + +When doing Importance Sampling, we care not only about the values of the samples but also their weights. We will see below that the weight of each sample is also added to `spl.state.vi`. Moreover, the average + +```math +\frac 1 N \sum_{j=1}^N w_i = \frac 1 N \sum_{j=1}^N p(x,y \mid s_i, m_i) +``` + +of the sample weights is a particularly important quantity: + +* it is used to **normalize** the **empirical approximation** of the posterior distribution +* its logarithm is the importance sampling **estimate** of the **log evidence** ``\log p(x, y)`` + +To avoid having to compute it over and over again, `is.jl`defines an IS-specific concrete type `ISState` for sampler states, with an additional field `final_logevidence` containing + +```math +\log \left( \frac 1 N \sum_{j=1}^N w_i \right). +``` + +```julia +mutable struct ISState{V<:VarInfo, F<:AbstractFloat} <: AbstractSamplerState + vi :: V + final_logevidence :: F +end + +# additional constructor +ISState(model::Model) = ISState(VarInfo(model), 0.0) +``` + +The following diagram summarizes the hierarchy presented above. + +![hierarchy](how_turing_implements_abstractmcmc_files/hierarchy.png) + +## 2. Overload the functions used inside `mcmcsample` + +A lot of the things here are method-specific. However Turing also has some functions that make it easier for you to implement these functions, for examples . + +### Transitions + +`AbstractMCMC` stores information corresponding to each individual sample in objects called `transition`, but does not specify what the structure of these objects could be. You could decide to implement a type `MyTransition` for transitions corresponding to the specifics of your methods. However, there are many situations in which the only information you need for each sample is: + +* its value: ``\theta`` +* log of the joint probability of the observed data and this sample: `lp` + +`Inference.jl` [defines](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/Inference.jl#L103) a struct `Transition`, which corresponds to this default situation + +```julia +struct Transition{T, F<:AbstractFloat} + θ :: T + lp :: F +end +``` + +It also [contains](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/Inference.jl#L108) a constructor that builds an instance of `Transition` from an instance `spl` of `Sampler`: ``\theta`` is `spl.state.vi` converted to a `namedtuple`, and `lp` is `getlogp(spl.state.vi)`. `is.jl` uses this default constructor at the end of the `step!` function [here](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/is.jl#L58). + +### How `sample` works + +A crude summary, which ignores things like parallelism, is the following: + + `sample` calls `mcmcsample`, which calls + +* `sample_init!` to set things up +* `step!` repeatedly to produce multiple new transitions +* `sample_end!` to perform operations once all samples have been obtained +* `bundle_samples` to convert a vector of transitions into a more palatable type, for instance a `Chain`. + +You can of course implement all of these functions, but `AbstractMCMC` as well as Turing also provide default implementations for simple cases. For instance, importance sampling uses the default implementations of `sample_init!` and `bundle_samples`, which is why you don't see code for them inside `is.jl`. + +## 3. Overload `assume` and `observe` + +The functions mentioned above, such as `sample_init!`, `step!`, etc., must of course use information about the model in order to generate samples! In particular, these functions may need **samples from distributions** defined in the model, or to **evaluate the density of these distributions** at some values of the corresponding parameters or observations. + +For an example of the former, consider **Importance Sampling** as defined in `is.jl`. This implementation of Importance Sampling uses the model prior distribution as a proposal distribution, and therefore requires **samples from the prior distribution** of the model. Another example is **Approximate Bayesian Computation**, which requires multiple **samples from the model prior and likelihood distributions** in order to generate a single sample. + +An example of the latter is the **Metropolis-Hastings** algorithm. At every step of sampling from a target posterior + +```math +p(\theta \mid x_{\text{obs}}), +``` + +in order to compute the acceptance ratio, you need to **evaluate the model joint density** + +```math +p\left(\theta_{\text{prop}}, x_{\text{obs}}\right) +``` + +with ``\theta_{\text{prop}}`` a sample from the proposal and ``x_{\text{obs}}`` the observed data. + +This begs the question: how can these functions access model information during sampling? Recall that the model is stored as an instance `m` of `Model`. One of the attributes of `m` is the model evaluation function `m.f`, which is built by compiling the `@model` macro. Executing `f` runs the tilde statements of the model in order, and adds model information to the sampler (the instance of `Sampler` that stores information about the ongoing sampling process) at each step (see [here](https://turing.ml/dev/docs/for-developers/compiler) for more information about how the `@model` macro is compiled). The DynamicPPL functions `assume` and `observe` determine what kind of information to add to the sampler for every tilde statement. + +Consider an instance `m` of `Model` and a sampler `spl`, with associated `VarInfo` `vi = spl.state.vi`. At some point during the sampling process, an AbstractMCMC function such as `step!` calls `m(vi, ...)`, which calls the model evaluation function `m.f(vi, ...)`. + +* for every tilde statement in the `@model` macro, `m.f(vi, ...)` returns model-related information (samples, value of the model density, etc.), and adds it to `vi`. How does it do that? + * recall that the code for `m.f(vi, ...)` is automatically generated by compilation of the `@model` macro + * for every tilde statement in the `@model` declaration, this code contains a call to `assume(vi, ...)` if the variable on the LHS of the tilde is a **model parameter to infer**, and `observe(vi, ...)` if the variable on the LHS of the tilde is an **observation** + * in the file corresponding to your sampling method (ie in `Turing.jl/src/inference/.jl`), you have **overloaded** `assume` and `observe`, so that they can modify `vi` to include the information and samples that you care about! + * at a minimum, `assume` and `observe` return the log density `lp` of the sample or observation. the model evaluation function then immediately calls `acclogp!!(vi, lp)`, which adds `lp` to the value of the log joint density stored in `vi`. + +Here's what `assume` looks like for Importance Sampling: + +```julia +function DynamicPPL.assume(rng, spl::Sampler{<:IS}, dist::Distribution, vn::VarName, vi) + r = rand(rng, dist) + push!(vi, vn, r, dist, spl) + return r, 0 +end +``` + +The function first generates a sample `r` from the distribution `dist` (the right hand side of the tilde statement). It then adds `r` to `vi`, and returns `r` and 0. + +The `observe` function is even simpler: + +```julia +function DynamicPPL.observe(spl::Sampler{<:IS}, dist::Distribution, value, vi) + return logpdf(dist, value) +end +``` + +It simply returns the density (in the discrete case, the probability) of the observed value under the distribution `dist`. + +## 4. Summary: Importance Sampling step by step + +We focus on the AbstractMCMC functions that are overridden in `is.jl` and executed inside `mcmcsample`: `step!`, which is called `n_samples` times, and `sample_end!`, which is executed once after those `n_samples` iterations. + +* During the ``i``-th iteration, `step!` does 3 things: + * `empty!!(spl.state.vi)`: remove information about the previous sample from the sampler's `VarInfo` + * `model(rng, spl.state.vi, spl)`: call the model evaluation function + * calls to `assume` add the samples from the prior ``s_i`` and ``m_i`` to `spl.state.vi` + * calls to both `assume` or `observe` are followed by the line `acclogp!!(vi, lp)`, where `lp` is an output of `assume` and `observe` + * `lp` is set to 0 after `assume`, and to the value of the density at the observation after `observe` + * when all the tilde statements have been covered, `spl.state.vi.logp[]` is the sum of the `lp`, i.e., the likelihood ``\log p(x, y \mid s_i, m_i) = \log p(x \mid s_i, m_i) + \log p(y \mid s_i, m_i)`` of the observations given the latent variable samples ``s_i`` and ``m_i``. + * `return Transition(spl)`: build a transition from the sampler, and return that transition + * the transition's `vi` field is simply `spl.state.vi` + * the `lp` field contains the likelihood `spl.state.vi.logp[]` +* When the, `n_samples` iterations are completed, `sample_end!` fills the `final_logevidence` field of `spl.state` + * it simply takes the logarithm of the average of the sample weights, using the log weights for numerical stability diff --git a/tutorials/for-developers-compiler/compiler.md b/tutorials/for-developers-compiler/compiler.md new file mode 100644 index 000000000..414327541 --- /dev/null +++ b/tutorials/for-developers-compiler/compiler.md @@ -0,0 +1,237 @@ +--- +title: Turing Compiler Design +permalink: /docs/for-developers/compiler/ +weave_options: + error : false +--- + +In this section, the current design of Turing's model "compiler" is described which enables Turing to perform various types of Bayesian inference without changing the model definition. The "compiler" is essentially just a macro that rewrites the user's model definition to a function that generates a `Model` struct that Julia's dispatch can operate on and that Julia's compiler can successfully do type inference on for efficient machine code generation. + +# Overview + +The following terminology will be used in this section: + +- `D`: observed data variables conditioned upon in the posterior, +- `P`: parameter variables distributed according to the prior distributions, these will also be referred to as random variables, +- `Model`: a fully defined probabilistic model with input data + +`Turing`'s `@model` macro rewrites the user-provided function definition such that it can be used to instantiate a `Model` by passing in the observed data `D`. + +The following are the main jobs of the `@model` macro: +1. Parse `~` and `.~` lines, e.g. `y .~ Normal.(c*x, 1.0)` +2. Figure out if a variable belongs to the data `D` and or to the parameters `P` +3. Enable the handling of missing data variables in `D` when defining a `Model` and treating them as parameter variables in `P` instead +4. Enable the tracking of random variables using the data structures `VarName` and `VarInfo` +5. Change `~`/`.~` lines with a variable in `P` on the LHS to a call to `tilde_assume` or `dot_tilde_assume` +6. Change `~`/`.~` lines with a variable in `D` on the LHS to a call to `tilde_observe` or `dot_tilde_observe` +7. Enable type stable automatic differentiation of the model using type parameters + + +## The model + +A `model::Model` is a callable struct that one can sample from by calling +```julia +(model::Model)([rng, varinfo, sampler, context]) +``` +where `rng` is a random number generator (default: `Random.GLOBAL_RNG`), `varinfo` is a data structure that stores information +about the random variables (default: `DynamicPPL.VarInfo()`), `sampler` is a sampling algorithm (default: `DynamicPPL.SampleFromPrior()`), +and `context` is a sampling context that can, e.g., modify how the log probability is accumulated (default: `DynamicPPL.DefaultContext()`). + +Sampling resets the log joint probability of `varinfo` and increases the evaluation counter of `sampler`. If `context` is a `LikelihoodContext`, +only the log likelihood will be accumulated. With the `DefaultContext` the log joint probability of `P` and `D` is accumulated. + +The `Model` struct contains the three internal fields `f`, `args` and `defaults`. +When `model::Model` is called, then the internal function `model.f` is called as `model.f(rng, varinfo, sampler, context, model.args...)` +(for multithreaded sampling, instead of `varinfo` a threadsafe wrapper is passed to `model.f`). +The positional and keyword arguments that were passed to the user-defined model function when the model was created are saved as a `NamedTuple` +in `model.args`. The default values of the positional and keyword arguments of the user-defined model functions, if any, are saved as a `NamedTuple` +in `model.defaults`. They are used for constructing model instances with different arguments by the `logprob` and `prob` string macros. + +# Example + +Let's take the following model as an example: + +```julia +@model function gauss(x = missing, y = 1.0, ::Type{TV} = Vector{Float64}) where {TV<:AbstractVector} + if x === missing + x = TV(undef, 3) + end + p = TV(undef, 2) + p[1] ~ InverseGamma(2, 3) + p[2] ~ Normal(0, 1.0) + @. x[1:2] ~ Normal(p[2], sqrt(p[1])) + x[3] ~ Normal() + y ~ Normal(p[2], sqrt(p[1])) +end +``` + +The above call of the `@model` macro defines the function `gauss` with positional arguments `x`, `y`, and `::Type{TV}`, rewritten in +such a way that every call of it returns a `model::Model`. Note that only the function body is modified by the `@model` macro, and the +function signature is left untouched. It is also possible to implement models with keyword arguments such as + +```julia +@model function gauss(::Type{TV} = Vector{Float64}; x = missing, y = 1.0) where {TV<:AbstractVector} + ... +end +``` + +This would allow us to generate a model by calling `gauss(; x = rand(3))`. + +If an argument has a default value `missing`, it is treated as a random variable. For variables which require an initialization because we +need to loop or broadcast over its elements, such as `x` above, the following needs to be done: +```julia +if x === missing + x = ... +end +``` +Note that since `gauss` behaves like a regular function it is possible to define additional dispatches in a second step as well. For +instance, we could achieve the same behaviour by + +```julia +@model function gauss(x, y = 1.0, ::Type{TV} = Vector{Float64}) where {TV<:AbstractVector} + p = TV(undef, 2) + ... +end + +function gauss(::Missing, y = 1.0, ::Type{TV} = Vector{Float64}) where {TV<:AbstractVector} + return gauss(TV(undef, 3), y, TV) +end +``` + +If `x` is sampled as a whole from a distribution and not indexed, e.g., `x ~ Normal(...)` or `x ~ MvNormal(...)`, +there is no need to initialize it in an `if`-block. + +## Step 1: Break up the model definition + +First, the `@model` macro breaks up the user-provided function definition using `DynamicPPL.build_model_info`. This function +returns a dictionary consisting of: +- `allargs_exprs`: The expressions of the positional and keyword arguments, without default values. +- `allargs_syms`: The names of the positional and keyword arguments, e.g., `[:x, :y, :TV]` above. +- `allargs_namedtuple`: An expression that constructs a `NamedTuple` of the positional and keyword arguments, e.g., `:((x = x, y = y, TV = TV))` above. +- `defaults_namedtuple`: An expression that constructs a `NamedTuple` of the default positional and keyword arguments, if any, e.g., `:((x = missing, y = 1, TV = Vector{Float64}))` above. +- `modeldef`: A dictionary with the name, arguments, and function body of the model definition, as returned by `MacroTools.splitdef`. + +## Step 2: Generate the body of the internal model function + +In a second step, `DynamicPPL.generate_mainbody` generates the main part of the transformed function body using the user-provided function body +and the provided function arguments, without default values, for figuring out if a variable denotes an observation or a random variable. +Hereby the function `DynamicPPL.generate_tilde` replaces the `L ~ R` lines in the model and the function `DynamicPPL.generate_dot_tilde` replaces +the `@. L ~ R` and `L .~ R` lines in the model. + +In the above example, `p[1] ~ InverseGamma(2, 3)` is replaced with something similar to +```julia +#= REPL[25]:6 =# +begin + var"##tmpright#323" = InverseGamma(2, 3) + var"##tmpright#323" isa Union{Distribution, AbstractVector{<:Distribution}} || throw(ArgumentError("Right-hand side of a ~ must be subtype of Distribution or a vector of Distributions.")) + var"##vn#325" = (DynamicPPL.VarName)(:p, ((1,),)) + var"##inds#326" = ((1,),) + p[1] = (DynamicPPL.tilde_assume)(_rng, _context, _sampler, var"##tmpright#323", var"##vn#325", var"##inds#326", _varinfo) +end +``` +Here the first line is a so-called line number node that enables more helpful error messages by providing users with the exact location +of the error in their model definition. Then the right hand side (RHS) of the `~` is assigned to a variable (with an automatically generated name). +We check that the RHS is a distribution or an array of distributions, otherwise an error is thrown. +Next we extract a compact representation of the variable with its name and index (or indices). Finally, the `~` expression is replaced with +a call to `DynamicPPL.tilde_assume` since the compiler figured out that `p[1]` is a random variable using the following +heuristic: +1. If the symbol on the LHS of `~`, `:p` in this case, is not among the arguments to the model, `(:x, :y, :T)` in this case, it is a random variable. +2. If the symbol on the LHS of `~`, `:p` in this case, is among the arguments to the model but has a value of `missing`, it is a random variable. +2. If the value of the LHS of `~`, `p[1]` in this case, is `missing`, then it is a random variable. +4. Otherwise, it is treated as an observation. + +The `DynamicPPL.tilde_assume` function takes care of sampling the random variable, if needed, and updating its value and the accumulated log joint +probability in the `_varinfo` object. If `L ~ R` is an observation, `DynamicPPL.tilde_observe` is called with the same arguments except the +random number generator `_rng` (since observations are never sampled). + +A similar transformation is performed for expressions of the form `@. L ~ R` and `L .~ R`. For instance, +`@. x[1:2] ~ Normal(p[2], sqrt(p[1]))` is replaced with +```julia +#= REPL[25]:8 =# +begin + var"##tmpright#331" = Normal.(p[2], sqrt.(p[1])) + var"##tmpright#331" isa Union{Distribution, AbstractVector{<:Distribution}} || throw(ArgumentError("Right-hand side of a ~ must be subtype of Distribution or a vector of Distributions.")) + var"##vn#333" = (DynamicPPL.VarName)(:x, ((1:2,),)) + var"##inds#334" = ((1:2,),) + var"##isassumption#335" = begin + let var"##vn#336" = (DynamicPPL.VarName)(:x, ((1:2,),)) + if !((DynamicPPL.inargnames)(var"##vn#336", _model)) || (DynamicPPL.inmissings)(var"##vn#336", _model) + true + else + x[1:2] === missing + end + end + end + if var"##isassumption#335" + x[1:2] .= (DynamicPPL.dot_tilde_assume)(_rng, _context, _sampler, var"##tmpright#331", x[1:2], var"##vn#333", var"##inds#334", _varinfo) + else + (DynamicPPL.dot_tilde_observe)(_context, _sampler, var"##tmpright#331", x[1:2], var"##vn#333", var"##inds#334", _varinfo) + end +end +``` +The main difference in the expanded code between `L ~ R` and `@. L ~ R` is that the former doesn't assume `L` to be defined, it can be a new Julia variable in the scope, while the latter assumes `L` already exists. Moreover, `DynamicPPL.dot_tilde_assume` and `DynamicPPL.dot_tilde_observe` are called +instead of `DynamicPPL.tilde_assume` and `DynamicPPL.tilde_observe`. + +## Step 3: Replace the user-provided function body + +Finally, we replace the user-provided function body using `DynamicPPL.build_output`. This function uses `MacroTools.combinedef` to reassemble +the user-provided function with a new function body. In the modified function body an anonymous function is created whose function body +was generated in step 2 above and whose arguments are +- a random number generator `_rng`, +- a model `_model`, +- a datastructure `_varinfo`, +- a sampler `_sampler`, +- a sampling context `_context`, +- and all positional and keyword arguments of the user-provided model function as positional arguments +without any default values. Finally, in the new function body a `model::Model` with this anonymous function as internal function is returned. + +# `VarName` + +In order to track random variables in the sampling process, `Turing` uses the `VarName` struct which acts as a random variable identifier generated at runtime. The `VarName` of a random variable is generated from the expression on the LHS of a `~` statement when the symbol on the LHS is in the set `P` of unobserved random variables. Every `VarName` instance has a type parameter `sym` which is the symbol of the Julia variable in the model that the random variable belongs to. For example, `x[1] ~ Normal()` will generate an instance of `VarName{:x}` assuming `x` is an unobserved random variable. Every `VarName` also has a field `indexing`, which stores the indices required to access the random variable from the Julia variable indicated by `sym` as a tuple of tuples. Each element of the tuple thereby contains the indices of one indexing operation (`VarName` also supports hierarchical arrays and range indexing). Some examples: +- `x ~ Normal()` will generate a `VarName(:x, ())`. +- `x[1] ~ Normal()` will generate a `VarName(:x, ((1,),))`. +- `x[:,1] ~ MvNormal(zeros(2), I)` will generate a `VarName(:x, ((Colon(), 1),))`. +- `x[:,1][1+1] ~ Normal()` will generate a `VarName(:x, ((Colon(), 1), (2,)))`. + +The easiest way to manually construct a `VarName` is to use the `@varname` macro on an indexing expression, which will take the `sym` value from the actual variable name, and put the index values appropriately into the constructor. + +# `VarInfo` + +## Overview + +`VarInfo` is the data structure in `Turing` that facilitates tracking random variables and certain metadata about them that are required for sampling. For instance, the distribution of every random variable is stored in `VarInfo` because we need to know the support of every random variable when sampling using HMC for example. Random variables whose distributions have a constrained support are transformed using a bijector from [Bijectors.jl](https://github.com/TuringLang/Bijectors.jl) so that the sampling happens in the unconstrained space. Different samplers require different metadata about the random variables. + +The definition of `VarInfo` in `Turing` is: +``` +struct VarInfo{Tmeta, Tlogp} <: AbstractVarInfo + metadata::Tmeta + logp::Base.RefValue{Tlogp} + num_produce::Base.RefValue{Int} +end +``` +Based on the type of `metadata`, the `VarInfo` is either aliased `UntypedVarInfo` or `TypedVarInfo`. `metadata` can be either a subtype of the union type `Metadata` or a `NamedTuple` of multiple such subtypes. Let `vi` be an instance of `VarInfo`. If `vi isa VarInfo{<:Metadata}`, then it is called an `UntypedVarInfo`. If `vi isa VarInfo{<:NamedTuple}`, then `vi.metadata` would be a `NamedTuple` mapping each symbol in `P` to an instance of `Metadata`. `vi` would then be called a `TypedVarInfo`. The other fields of `VarInfo` include `logp` which is used to accumulate the log probability or log probability density of the variables in `P` and `D`. `num_produce` keeps track of how many observations have been made in the model so far. This is incremented when running a `~` statement when the symbol on the LHS is in `D`. + +## `Metadata` + +The `Metadata` struct stores some metadata about the random variables sampled. This helps +query certain information about a variable such as: its distribution, which samplers +sample this variable, its value and whether this value is transformed to real space or +not. Let `md` be an instance of `Metadata`: +- `md.vns` is the vector of all `VarName` instances. Let `vn` be an arbitrary element of `md.vns` +- `md.idcs` is the dictionary that maps each `VarName` instance to its index in + `md.vns`, `md.ranges`, `md.dists`, `md.orders` and `md.flags`. +- `md.vns[md.idcs[vn]] == vn`. +- `md.dists[md.idcs[vn]]` is the distribution of `vn`. +- `md.gids[md.idcs[vn]]` is the set of algorithms used to sample `vn`. This is used in + the Gibbs sampling process. +- `md.orders[md.idcs[vn]]` is the number of `observe` statements before `vn` is sampled. +- `md.ranges[md.idcs[vn]]` is the index range of `vn` in `md.vals`. +- `md.vals[md.ranges[md.idcs[vn]]]` is the linearized vector of values of corresponding to `vn`. +- `md.flags` is a dictionary of true/false flags. `md.flags[flag][md.idcs[vn]]` is the + value of `flag` corresponding to `vn`. + +Note that in order to make `md::Metadata` type stable, all the `md.vns` must have the same symbol and distribution type. However, one can have a single Julia variable, e.g. `x`, that is a matrix or a hierarchical array sampled in partitions, e.g. `x[1][:] ~ MvNormal(zeros(2), I); x[2][:] ~ MvNormal(ones(2), I)`. The symbol `x` can still be managed by a single `md::Metadata` without hurting the type stability since all the distributions on the RHS of `~` are of the same type. + +However, in `Turing` models one cannot have this restriction, so we must use a type unstable `Metadata` if we want to use one `Metadata` instance for the whole model. This is what `UntypedVarInfo` does. A type unstable `Metadata` will still work but will have inferior performance. + +To strike a balance between flexibility and performance when constructing the `spl::Sampler` instance, the model is first run by sampling the parameters in `P` from their priors using an `UntypedVarInfo`, i.e. a type unstable `Metadata` is used for all the variables. Then once all the symbols and distribution types have been identified, a `vi::TypedVarInfo` is constructed where `vi.metadata` is a `NamedTuple` mapping each symbol in `P` to a specialized instance of `Metadata`. So as long as each symbol in `P` is sampled from only one type of distributions, `vi::TypedVarInfo` will have fully concretely typed fields which brings out the peak performance of Julia. diff --git a/tutorials/for-developers-interface/interface.md b/tutorials/for-developers-interface/interface.md new file mode 100644 index 000000000..b418ee384 --- /dev/null +++ b/tutorials/for-developers-interface/interface.md @@ -0,0 +1,367 @@ +--- +title: Interface Guide +permalink: /docs/for-developers/how_turing_implements_abstractmcmc +weave_options: + error : false +--- + + +# The sampling interface + +Turing implements a sampling interface (hosted at +[AbstractMCMC](https://github.com/TuringLang/AbstractMCMC.jl)) that is intended to provide +a common framework for Markov chain Monte Carlo samplers. The interface presents several +structures and functions that one needs to overload in order to implement an +interface-compatible sampler. + +This guide will demonstrate how to implement the interface without Turing. + +## Interface overview + +Any implementation of an inference method that uses the AbstractMCMC interface should +implement a subset of the following types and functions: + +1. A subtype of `AbstractSampler`, defined as a mutable struct containing state information or sampler parameters. +2. A function `sample_init!` which performs any necessary set-up (default: do not perform any set-up). +3. A function `step!` which returns a transition that represents a single draw from the sampler. +4. A function `transitions_init` which returns a container for the transitions obtained from the sampler + (default: return a `Vector{T}` of length `N` where `T` is the type of the transition obtained in the first step and `N` is the number of requested samples). +5. A function `transitions_save!` which saves transitions to the container (default: save the transition of iteration `i` + at position `i` in the vector of transitions). +6. A function `sample_end!` which handles any sampler wrap-up (default: do not perform any wrap-up). +7. A function `bundle_samples` which accepts the container of transitions and returns a collection of samples + (default: return the vector of transitions). + +The interface methods with exclamation points are those that are intended to allow for +state mutation. Any mutating function is meant to allow mutation where needed -- you might +use: + +- `sample_init!` to run some kind of sampler preparation, before sampling begins. This + could mutate a sampler's state. +- `step!` might mutate a sampler flag after each sample. +- `sample_end!` contains any wrap-up you might need to do. If you were sampling in a + transformed space, this might be where you convert everything back to a constrained space. + +## Why do you have an interface? + +The motivation for the interface is to allow Julia's fantastic probabilistic programming +language community to have a set of standards and common implementations so we can all +thrive together. Markov chain Monte Carlo methods tend to have a very similar framework to +one another, and so a common interface should help more great inference methods built in +single-purpose packages to experience more use among the community. + +## Implementing Metropolis-Hastings without Turing + +[Metropolis-Hastings](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo) is often the +first sampling method that people are exposed to. It is a very straightforward algorithm +and is accordingly the easiest to implement, so it makes for a good example. In this +section, you will learn how to use the types and functions listed above to implement the +Metropolis-Hastings sampler using the MCMC interface. + +The full code for this implementation is housed in +[AdvancedMH.jl](https://github.com/TuringLang/AdvancedMH.jl). + +### Imports + +Let's begin by importing the relevant libraries. We'll import `AbstracMCMC`, which contains +the interface framework we'll fill out. We also need `Distributions` and `Random`. + +```julia +# Import the relevant libraries. +import AbstractMCMC +using Distributions +using Random +``` + +An interface extension (like the one we're writing right now) typically requires that you overload or implement several functions. Specifically, you should `import` the functions you intend to overload. This next code block accomplishes that. + +From `Distributions`, we need `Sampleable`, `VariateForm`, and `ValueSupport`, three abstract types that define a distribution. Models in the interface are assumed to be subtypes of `Sampleable{VariateForm, ValueSupport}`. In this section our model is going be be extremely simple, so we will not end up using these except to make sure that the inference functions are dispatching correctly. + +### Sampler + +Let's begin our sampler definition by defining a sampler called `MetropolisHastings` which +is a subtype of `AbstractSampler`. Correct typing is very important for proper interface +implementation -- if you are missing a subtype, your method may not be dispatched to when +you call `sample`. + +```julia +# Define a sampler type. +struct MetropolisHastings{T, D} <: AbstractMCMC.AbstractSampler + init_θ::T + proposal::D +end + +# Default constructors. +MetropolisHastings(init_θ::Real) = MetropolisHastings(init_θ, Normal(0,1)) +MetropolisHastings(init_θ::Vector{<:Real}) = MetropolisHastings(init_θ, MvNormal(zero(init_θ), I)) +``` + +Above, we have defined a sampler that stores the initial parameterization of the prior, +and a distribution object from which proposals are drawn. You can have a struct that has no +fields, and simply use it for dispatching onto the relevant functions, or you can store a +large amount of state information in your sampler. + +The general intuition for what to store in your sampler struct is that anything you may +need to perform inference between samples but you don't want to store in a transition +should go into the sampler struct. It's the only way you can carry non-sample related state +information between `step!` calls. + +### Model + +Next, we need to have a model of some kind. A model is a struct that's a subtype of +`AbstractModel` that contains whatever information is necessary to perform inference on +your problem. In our case we want to know the mean and variance parameters for a standard +Normal distribution, so we can keep our model to the log density of a Normal. + +Note that we only have to do this because we are not yet integrating the sampler with Turing +-- Turing has a very sophisticated modelling engine that removes the need to define custom +model structs. + +```julia +# Define a model type. Stores the log density function. +struct DensityModel{F<:Function} <: AbstractMCMC.AbstractModel + ℓπ::F +end +``` + +### Transition + +The next step is to define some transition which we will return from each `step!` call. +We'll keep it simple by just defining a wrapper struct that contains the parameter draws +and the log density of that draw: + +```julia +# Create a very basic Transition type, only stores the +# parameter draws and the log probability of the draw. +struct Transition{T, L} + θ::T + lp::L +end + +# Store the new draw and its log density. +Transition(model::DensityModel, θ) = Transition(θ, ℓπ(model, θ)) +``` + +`Transition` can now store any type of parameter, whether it's a vector of draws from +multiple parameters or a single univariate draw. + +### Metropolis-Hastings + +Now it's time to get into the actual inference. We've defined all of the core pieces we +need, but we need to implement the `step!` function which actually performs inference. + +As a refresher, Metropolis-Hastings implements a very basic algorithm: + +1. Pick some initial state, ``\theta_0``. +2. For ``t`` in ``[1,N],`` do + - Generate a proposal parameterization ``\theta^\prime_t \sim q(\theta^\prime_t \mid \theta_{t-1}).`` + - Calculate the acceptance probability, ``\alpha = \text{min}\left[1,\frac{\pi(\theta'_t)}{\pi(\theta_{t-1})} \frac{q(\theta_{t-1} \mid \theta'_t)}{q(\theta'_t \mid \theta_{t-1})}) \right].`` + - If ``U \le \alpha`` where ``U \sim [0,1],`` then ``\theta_t = \theta'_t.`` Otherwise, ``\theta_t = \theta_{t-1}.`` + +Of course, it's much easier to do this in the log space, so the acceptance probability is +more commonly written as + +```math +\log \alpha = \min\left[0, \log \pi(\theta'_t) - \log \pi(\theta_{t-1}) + \log q(\theta_{t-1} \mid \theta^\prime_t) - \log q(\theta\prime_t \mid \theta_{t-1}) \right]. +``` + +In interface terms, we should do the following: + +1. Make a new transition containing a proposed sample. +2. Calculate the acceptance probability. +3. If we accept, return the new transition, otherwise, return the old one. + +### Steps + +The `step!` function is the function that performs the bulk of your inference. In our case, +we will implement two `step!` functions -- one for the very first iteration, and one for +every subsequent iteration. + +```julia +# Define the first step! function, which is called at the +# beginning of sampling. Return the initial parameter used +# to define the sampler. +function AbstractMCMC.step!( + rng::AbstractRNG, + model::DensityModel, + spl::MetropolisHastings, + N::Integer, + ::Nothing; + kwargs... +) + return Transition(model, spl.init_θ) +end +``` + +The first `step!` function just packages up the initial parameterization inside the +sampler, and returns it. We implicitly accept the very first parameterization. + +The other `step!` function performs the usual steps from Metropolis-Hastings. Included are +several helper functions, `proposal` and `q`, which are designed to replicate the functions +in the pseudocode above. + +- `proposal` generates a new proposal in the form of a `Transition`, which can be + univariate if the value passed in is univariate, or it can be multivariate if the + `Transition` given is multivariate. Proposals use a basic `Normal` or `MvNormal` proposal + distribution. +- `q` returns the log density of one parameterization conditional on another, according to + the proposal distribution. +- `step!` generates a new proposal, checks the acceptance probability, and then returns + either the previous transition or the proposed transition. + +```julia +# Define a function that makes a basic proposal depending on a univariate +# parameterization or a multivariate parameterization. +propose(spl::MetropolisHastings, model::DensityModel, θ::Real) = + Transition(model, θ + rand(spl.proposal)) +propose(spl::MetropolisHastings, model::DensityModel, θ::Vector{<:Real}) = + Transition(model, θ + rand(spl.proposal)) +propose(spl::MetropolisHastings, model::DensityModel, t::Transition) = + propose(spl, model, t.θ) + +# Calculates the probability `q(θ|θcond)`, using the proposal distribution `spl.proposal`. +q(spl::MetropolisHastings, θ::Real, θcond::Real) = logpdf(spl.proposal, θ - θcond) +q(spl::MetropolisHastings, θ::Vector{<:Real}, θcond::Vector{<:Real}) = + logpdf(spl.proposal, θ - θcond) +q(spl::MetropolisHastings, t1::Transition, t2::Transition) = q(spl, t1.θ, t2.θ) + +# Calculate the density of the model given some parameterization. +ℓπ(model::DensityModel, θ) = model.ℓπ(θ) +ℓπ(model::DensityModel, t::Transition) = t.lp + +# Define the other step function. Returns a Transition containing +# either a new proposal (if accepted) or the previous proposal +# (if not accepted). +function AbstractMCMC.step!( + rng::AbstractRNG, + model::DensityModel, + spl::MetropolisHastings, + ::Integer, + θ_prev::Transition; + kwargs... +) + # Generate a new proposal. + θ = propose(spl, model, θ_prev) + + # Calculate the log acceptance probability. + α = ℓπ(model, θ) - ℓπ(model, θ_prev) + q(spl, θ_prev, θ) - q(spl, θ, θ_prev) + + # Decide whether to return the previous θ or the new one. + if log(rand(rng)) < min(α, 0.0) + return θ + else + return θ_prev + end +end +``` + +### Chains + +In the default implementation, `sample` just returns a vector of all transitions. If +instead you would like to obtain a `Chains` object (e.g., to simplify downstream analysis), +you have to implement the `bundle_samples` function as well. It accepts the vector of +transitions and returns a collection of samples. Fortunately, our `Transition` is +incredibly simple, and we only need to build a little bit of functionality to accept custom +parameter names passed in by the user. + +```julia +# A basic chains constructor that works with the Transition struct we defined. +function AbstractMCMC.bundle_samples( + rng::AbstractRNG, + ℓ::DensityModel, + s::MetropolisHastings, + N::Integer, + ts::Vector{<:Transition}, + chain_type::Type{Any}; + param_names=missing, + kwargs... +) + # Turn all the transitions into a vector-of-vectors. + vals = copy(reduce(hcat,[vcat(t.θ, t.lp) for t in ts])') + + # Check if we received any parameter names. + if ismissing(param_names) + param_names = ["Parameter $i" for i in 1:(length(first(vals))-1)] + end + + # Add the log density field to the parameter names. + push!(param_names, "lp") + + # Bundle everything up and return a Chains struct. + return Chains(vals, param_names, (internals=["lp"],)) +end +``` + +All done! + +You can even implement different output formats by implementing `bundle_samples` for +different `chain_type`s, which can be provided as keyword argument to `sample`. As default +`sample` uses `chain_type = Any`. + +### Testing the implementation + +Now that we have all the pieces, we should test the implementation by defining a model to +calculate the mean and variance parameters of a Normal distribution. We can do this by +constructing a target density function, providing a sample of data, and then running the +sampler with `sample`. + +```julia +# Generate a set of data from the posterior we want to estimate. +data = rand(Normal(5, 3), 30) + +# Define the components of a basic model. +insupport(θ) = θ[2] >= 0 +dist(θ) = Normal(θ[1], θ[2]) +density(θ) = insupport(θ) ? sum(logpdf.(dist(θ), data)) : -Inf + +# Construct a DensityModel. +model = DensityModel(density) + +# Set up our sampler with initial parameters. +spl = MetropolisHastings([0.0, 0.0]) + +# Sample from the posterior. +chain = sample(model, spl, 100000; param_names=["μ", "σ"]) +``` + +If all the interface functions have been extended properly, you should get an output from +`display(chain)` that looks something like this: + +``` +Object of type Chains, with data of type 100000×3×1 Array{Float64,3} + +Iterations = 1:100000 +Thinning interval = 1 +Chains = 1 +Samples per chain = 100000 +internals = lp +parameters = μ, σ + +2-element Array{ChainDataFrame,1} + +Summary Statistics + +│ Row │ parameters │ mean │ std │ naive_se │ mcse │ ess │ r_hat │ +│ │ Symbol │ Float64 │ Float64 │ Float64 │ Float64 │ Any │ Any │ +├─────┼────────────┼─────────┼──────────┼────────────┼────────────┼─────────┼─────────┤ +│ 1 │ μ │ 5.33157 │ 0.854193 │ 0.0027012 │ 0.00893069 │ 8344.75 │ 1.00009 │ +│ 2 │ σ │ 4.54992 │ 0.632916 │ 0.00200146 │ 0.00534942 │ 14260.8 │ 1.00005 │ + +Quantiles + +│ Row │ parameters │ 2.5% │ 25.0% │ 50.0% │ 75.0% │ 97.5% │ +│ │ Symbol │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │ +├─────┼────────────┼─────────┼─────────┼─────────┼─────────┼─────────┤ +│ 1 │ μ │ 3.6595 │ 4.77754 │ 5.33182 │ 5.89509 │ 6.99651 │ +│ 2 │ σ │ 3.5097 │ 4.09732 │ 4.47805 │ 4.93094 │ 5.96821 │ +``` + +It looks like we're extremely close to our true parameters of `Normal(5,3)`, though with a +fairly high variance due to the low sample size. + +## Conclusion + +We've seen how to implement the sampling interface for general projects. Turing's interface +methods are ever-evolving, so please open an issue at +[AbstractMCMC](https://github.com/TuringLang/AbstractMCMC.jl) with feature requests or +problems. diff --git a/tutorials/for-developers-variational-inference/variational_inference.md b/tutorials/for-developers-variational-inference/variational_inference.md new file mode 100644 index 000000000..8e1a37558 --- /dev/null +++ b/tutorials/for-developers-variational-inference/variational_inference.md @@ -0,0 +1,315 @@ +--- +title: Variational Inference +permalink: /docs/for-developers/variational_inference +weave_options: + error : false +--- + +# Overview + +In this post we'll have a look at what's known as **variational inference (VI)**, a family of _approximate_ Bayesian inference methods. In particular, we will focus on one of the more standard VI methods called **Automatic Differentiation Variational Inference (ADVI)**. + +Here we'll have a look at the theory behind VI, but if you're interested in how to use ADVI in Turing.jl, [check out this tutorial](../../tutorials/09-variational-inference). + +# Motivation + +In Bayesian inference one usually specifies a model as follows: given data ``\\{x_i\\}_{i = 1}^n``, + +```math +\begin{align*} + \text{prior:} \quad z &\sim p(z) \\\\ + \text{likelihood:} \quad x_i &\overset{\text{i.i.d.}}{\sim} p(x \mid z) \quad \text{where} \quad i = 1, \dots, n +\end{align*} +``` + +where ``\overset{\text{i.i.d.}}{\sim}`` denotes that the samples are identically independently distributed. Our goal in Bayesian inference is then to find the _posterior_ + +```math +p(z \mid \\{ x\_i \\}\_{i = 1}^n) = \prod\_{i=1}^{n} p(z \mid x\_i). +``` + +In general one cannot obtain a closed form expression for ``p(z \mid \\{ x\_i \\}\_{i = 1}^n)``, but one might still be able to _sample_ from ``p(z \mid \\{ x\_i \\}\_{i = 1}^n)`` with guarantees of converging to the target posterior ``p(z \mid \\{ x\_i \\}\_{i = 1}^n)`` as the number of samples go to ``\infty``, e.g. MCMC. + +As you are hopefully already aware, Turing.jl provides a lot of different methods with asymptotic exactness guarantees that we can apply to such a problem! + +Unfortunately, these unbiased samplers can be prohibitively expensive to run. As the model ``p`` increases in complexity, the convergence of these unbiased samplers can slow down dramatically. Still, in the _infinite_ limit, these methods should converge to the true posterior! But infinity is fairly large, like, _at least_ more than 12, so this might take a while. + +In such a case it might be desirable to sacrifice some of these asymptotic guarantees, and instead _approximate_ the posterior ``p(z \mid \\{ x_i \\}_{i = 1}^n)`` using some other model which we'll denote ``q(z)``. + +There are multiple approaches to take in this case, one of which is **variational inference (VI)**. + +# Variational Inference (VI) + +In VI, we're looking to approximate ``p(z \mid \\{ x_i \\}_{i = 1}^n )`` using some _approximate_ or _variational_ posterior ``q(z)``. + +To approximate something you need a notion of what "close" means. In the context of probability densities a standard such "measure" of closeness is the _Kullback-Leibler (KL) divergence_ , though this is far from the only one. The KL-divergence is defined between two densities ``q(z)`` and ``p(z \mid \\{ x_i \\}_{i = 1}^n)`` as + +```math +\begin{align*} + \mathrm{D\_{KL}} \left( q(z), p(z \mid \\{ x\_i \\}\_{i = 1}^n) \right) &= \int \log \left( \frac{q(z)}{\prod\_{i = 1}^n p(z \mid x\_i)} \right) q(z) \mathrm{d}{z} \\\\ + &= \mathbb{E}\_{z \sim q(z)} \left[ \log q(z) - \sum\_{i = 1}^n \log p(z \mid x\_i) \right] \\\\ + &= \mathbb{E}\_{z \sim q(z)} \left[ \log q(z) \right] - \sum\_{i = 1}^n \mathbb{E}\_{z \sim q(z)} \left[ \log p(z \mid x\_i) \right]. +\end{align*} +``` + +It's worth noting that unfortunately the KL-divergence is _not_ a metric/distance in the analysis-sense due to its lack of symmetry. On the other hand, it turns out that minimizing the KL-divergence that it's actually equivalent to maximizing the log-likelihood! Also, under reasonable restrictions on the densities at hand, + +```math +\mathrm{D\_{KL}}\left(q(z), p(z \mid \\{ x\_i \\}\_{i = 1}^n) \right) = 0 \quad \iff \quad q(z) = p(z \mid \\{ x\_i \\}\_{i = 1}^n), \quad \forall z. +``` + +Therefore one could (and we will) attempt to approximate ``p(z \mid \\{ x_i \\}_{i = 1}^n)`` using a density ``q(z)`` by minimizing the KL-divergence between these two! + +One can also show that ``\mathrm{D_{KL}} \ge 0``, which we'll need later. Finally notice that the KL-divergence is only well-defined when in fact ``q(z)`` is zero everywhere ``p(z \mid \\{ x_i \\}_{i = 1}^n)`` is zero, i.e. + +```math +\mathrm{supp}\left(q(z)\right) \subseteq \mathrm{supp}\left(p(z \mid x)\right). +``` + +Otherwise, there might be a point ``z_0 \sim q(z)`` such that ``p(z_0 \mid \\{ x_i \\}_{i = 1}^n) = 0``, resulting in ``\log\left(\frac{q(z)}{0}\right)`` which doesn't make sense! + +One major problem: as we can see in the definition of the KL-divergence, we need ``p(z \mid \\{ x_i \\}_{i = 1}^n)`` for any ``z`` if we want to compute the KL-divergence between this and ``q(z)``. We don't have that. The entire reason we even do Bayesian inference is that we don't know the posterior! Cleary this isn't going to work. _Or is it?!_ + +## Computing KL-divergence without knowing the posterior + +First off, recall that + +```math +p(z \mid x\_i) = \frac{p(x\_i, z)}{p(x\_i)} +``` + +so we can write + +```math +\begin{align*} +\mathrm{D\_{KL}} \left( q(z), p(z \mid \\{ x\_i \\}\_{i = 1}^n) \right) &= \mathbb{E}\_{z \sim q(z)} \left[ \log q(z) \right] - \sum\_{i = 1}^n \mathbb{E}\_{z \sim q(z)} \left[ \log p(x\_i, z) - \log p(x\_i) \right] \\\\ + &= \mathbb{E}\_{z \sim q(z)} \left[ \log q(z) \right] - \sum\_{i = 1}^n \mathbb{E}\_{z \sim q(z)} \left[ \log p(x\_i, z) \right] + \sum\_{i = 1}^n \mathbb{E}\_{z \sim q(z)} \left[ \log p(x_i) \right] \\\\ + &= \mathbb{E}\_{z \sim q(z)} \left[ \log q(z) \right] - \sum\_{i = 1}^n \mathbb{E}\_{z \sim q(z)} \left[ \log p(x\_i, z) \right] + \sum\_{i = 1}^n \log p(x\_i), +\end{align*} +``` + +where in the last equality we used the fact that ``p(x_i)`` is independent of ``z``. + +Now you're probably thinking "Oh great! Now you've introduced ``p(x_i)`` which we _also_ can't compute (in general)!". Woah. Calm down human. Let's do some more algebra. The above expression can be rearranged to + +```math +\mathrm{D\_{KL}} \left( q(z), p(z \mid \\{ x\_i \\}\_{i = 1}^n) \right) + \underbrace{\sum\_{i = 1}^n \mathbb{E}\_{z \sim q(z)} \left[ \log p(x\_i, z) \right] - \mathbb{E}\_{z \sim q(z)} \left[ \log q(z) \right]}\_{=: \mathrm{ELBO}(q)} = \underbrace{\sum\_{i = 1}^n \mathbb{E}\_{z \sim q(z)} \left[ \log p(x\_i) \right]}\_{\text{constant}}. +``` + +See? The left-hand side is _constant_ and, as we mentioned before, ``\mathrm{D_{KL}} \ge 0``. What happens if we try to _maximize_ the term we just gave the completely arbitrary name ``\mathrm{ELBO}``? Well, if ``\mathrm{ELBO}`` goes up while ``p(x_i)`` stays constant then ``\mathrm{D_{KL}}`` _has to_ go down! That is, the ``q(z)`` which _minimizes_ the KL-divergence is the same ``q(z)`` which _maximizes_ ``\mathrm{ELBO}(q)``: + +```math +\underset{q}{\mathrm{argmin}} \ \mathrm{D\_{KL}} \left( q(z), p(z \mid \\{ x\_i \\}\_{i = 1}^n) \right) = \underset{q}{\mathrm{argmax}} \ \mathrm{ELBO}(q) +``` + +where + +```math +\begin{align*} +\mathrm{ELBO}(q) &:= \left( \sum\_{i = 1}^n \mathbb{E}\_{z \sim q(z)} \left[ \log p(x\_i, z) \right] \right) - \mathbb{E}\_{z \sim q(z)} \left[ \log q(z) \right] \\\\ + &= \left( \sum\_{i = 1}^n \mathbb{E}\_{z \sim q(z)} \left[ \log p(x\_i, z) \right] \right) + \mathbb{H}\left( q(z) \right) +\end{align*} +``` + +and ``\mathbb{H} \left(q(z) \right)`` denotes the [(differential) entropy](https://www.wikiwand.com/en/Differential_entropy) of ``q(z)``. + +Assuming joint ``p(x_i, z)`` and the entropy ``\mathbb{H}\left(q(z)\right)`` are both tractable, we can use a Monte-Carlo for the remaining expectation. This leaves us with the following tractable expression + +```math +\underset{q}{\mathrm{argmin}} \ \mathrm{D\_{KL}} \left( q(z), p(z \mid \\{ x\_i \\}\_{i = 1}^n) \right) \approx \underset{q}{\mathrm{argmax}} \ \widehat{\mathrm{ELBO}}(q) +``` + +where + +```math +\widehat{\mathrm{ELBO}}(q) = \frac{1}{m} \left( \sum\_{k = 1}^m \sum\_{i = 1}^n \log p(x\_i, z\_k) \right) + \mathbb{H} \left(q(z)\right) \quad \text{where} \quad z\_k \sim q(z) \quad \forall k = 1, \dots, m. +``` + +Hence, as long as we can sample from ``q(z)`` somewhat efficiently, we can indeed minimize the KL-divergence! Neat, eh? + +Sidenote: in the case where ``q(z)`` is tractable but ``\mathbb{H} \left(q(z) \right)`` is _not_ , we can use an Monte-Carlo estimate for this term too but this generally results in a higher-variance estimate. + +Also, I fooled you real good: the ELBO _isn't_ an arbitrary name, hah! In fact it's an abbreviation for the **expected lower bound (ELBO)** because it, uhmm, well, it's the _expected_ lower bound (remember ``\mathrm{D_{KL}} \ge 0``). Yup. + +## Maximizing the ELBO + +Finding the optimal ``q`` over _all_ possible densities of course isn't feasible. Instead we consider a family of _parameterized_ densities ``\mathscr{D}\_{\Theta}`` where ``\Theta`` denotes the space of possible parameters. Each density in this family ``q\_{\theta} \in \mathscr{D}\_{\Theta}`` is parameterized by a unique ``\theta \in \Theta``. Moreover, we'll assume +1. ``q\_{\theta}(z)``, i.e. evaluating the probability density ``q`` at any point ``z``, is differentiable +2. ``z \sim q\_{\theta}(z)``, i.e. the process of sampling from ``q\_{\theta}(z)``, is differentiable + +(1) is fairly straight-forward, but (2) is a bit tricky. What does it even mean for a _sampling process_ to be differentiable? This is quite an interesting problem in its own right and would require something like a [50-page paper to properly review the different approaches (highly recommended read)](https://arxiv.org/abs/1906.10652). + +We're going to make use of a particular such approach which goes under a bunch of different names: _reparametrization trick_, _path derivative_, etc. This refers to making the assumption that all elements ``q\_{\theta} \in \mathscr{Q}\_{\Theta}`` can be considered as reparameterizations of some base density, say ``\bar{q}(z)``. That is, if ``q\_{\theta} \in \mathscr{Q}\_{\Theta}`` then + +```math +z \sim q\_{\theta}(z) \quad \iff \quad z := g\_{\theta}(\tilde{z}) \quad \text{where} \quad \bar{z} \sim \bar{q}(z) +``` + +for some function ``g\_{\theta}`` differentiable wrt. ``\theta``. So all ``q_{\theta} \in \mathscr{Q}\_{\Theta}`` are using the *same* reparameterization-function ``g`` but each ``q\_{\theta}`` correspond to different choices of ``\theta`` for ``f\_{\theta}``. + +Under this assumption we can differentiate the sampling process by taking the derivative of ``g\_{\theta}`` wrt. ``\theta``, and thus we can differentiate the entire ``\widehat{\mathrm{ELBO}}(q\_{\theta})`` wrt. ``\theta``! With the gradient available we can either try to solve for optimality either by setting the gradient equal to zero or maximize ``\widehat{\mathrm{ELBO}}(q\_{\theta})`` stepwise by traversing ``\mathscr{Q}\_{\Theta}`` in the direction of steepest ascent. For the sake of generality, we're going to go with the stepwise approach. + +With all this nailed down, we eventually reach the section on **Automatic Differentiation Variational Inference (ADVI)**. + +## Automatic Differentiation Variational Inference (ADVI) + +So let's revisit the assumptions we've made at this point: +1. The variational posterior ``q\_{\theta}`` is in a parameterized family of densities denoted ``\mathscr{Q}\_{\Theta}``, with ``\theta \in \Theta``. +2. ``\mathscr{Q}\_{\Theta}`` is a space of _reparameterizable_ densities with ``\bar{q}(z)`` as the base-density. +3. The parameterization function ``g\_{\theta}`` is differentiable wrt. ``\theta``. +4. Evaluation of the probability density ``q\_{\theta}(z)`` is differentiable wrt. ``\theta``. +5. ``\mathbb{H}\left(q\_{\theta}(z)\right)`` is tractable. +6. Evaluation of the joint density ``p(x, z)`` is tractable and differentiable wrt. ``z`` +7. The support of ``q(z)`` is a subspace of the support of ``p(z \mid x)`` : ``\mathrm{supp}\left(q(z)\right) \subseteq \mathrm{supp}\left(p(z \mid x)\right)``. + +All of these are not *necessary* to do VI, but they are very convenient and results in a fairly flexible approach. One distribution which has a density satisfying all of the above assumptions _except_ (7) (we'll get back to this in second) for any tractable and differentiable ``p(z \mid \\{ x\_i \\}\_{i = 1}^n)`` is the good ole' Gaussian/normal distribution: + +```math +z \sim \mathcal{N}(\mu, \Sigma) \quad \iff \quad z = g\_{\mu, L}(\bar{z}) := \mu + L^T \tilde{z} \quad \text{where} \quad \bar{z} \sim \bar{q}(z) := \mathcal{N}(1\_d, I\_{d \times d}) +``` + +where ``\Sigma = L L^T,`` with ``L`` obtained from the Cholesky-decomposition. Abusing notation a bit, we're going to write + +```math +\theta = (\mu, \Sigma) := (\mu\_1, \dots, \mu\_d, L\_{11}, \dots, L\_{1, d}, L\_{2, 1}, \dots, L\_{2, d}, \dots, L\_{d, 1}, \dots, L\_{d, d}). +``` + +With this assumption we finally have a tractable expression for ``\widehat{\mathrm{ELBO}}(q_{\mu, \Sigma})``! Well, assuming (7) is holds. Since a Gaussian has non-zero probability on the entirety of ``\mathbb{R}^d``, we also require ``p(z \mid \\{ x_i \\}_{i = 1}^n)`` to have non-zero probability on all of ``\mathbb{R}^d``. + +Though not necessary, we'll often make a *mean-field* assumption for the variational posterior ``q(z)``, i.e. assume independence between the latent variables. In this case, we'll write + +```math +\theta = (\mu, \sigma^2) := (\mu\_1, \dots, \mu\_d, \sigma\_1^2, \dots, \sigma\_d^2). +``` + +### Examples +As a (trivial) example we could apply the approach described above to is the following generative model for ``p(z \mid \\{ x_i \\}\_{i = 1}^n)``: + +```math +\begin{align*} + m &\sim \mathcal{N}(0, 1) \\\\ + x\_i &\overset{\text{i.i.d.}}{=} \mathcal{N}(m, 1), \quad i = 1, \dots, n. +\end{align*} +``` + +In this case ``z = m`` and we have the posterior defined ``p(m \mid \\{ x\_i \\}\_{i = 1}^n) = p(m) \prod\_{i = 1}^n p(x\_i \mid m)``. Then the variational posterior would be + +```math +q\_{\mu, \sigma} = \mathcal{N}(\mu, \sigma^2), \quad \text{where} \quad \mu \in \mathbb{R}, \ \sigma^2 \in \mathbb{R}^{ + }. +``` + +And since prior of ``m``, ``\mathcal{N}(0, 1)``, has non-zero probability on the entirety of ``\mathbb{R}``, same as ``q(m)``, i.e. assumption (7) above holds, everything is fine and life is good. + +But what about this generative model for ``p(z \mid \\{ x_i \\}_{i = 1}^n)``: + +```math +\begin{align*} + s &\sim \mathrm{InverseGamma}(2, 3), \\\\ + m &\sim \mathcal{N}(0, s), \\\\ + x\_i &\overset{\text{i.i.d.}}{=} \mathcal{N}(m, s), \quad i = 1, \dots, n, +\end{align*} +``` + +with posterior ``p(s, m \mid \\{ x\_i \\}\_{i = 1}^n) = p(s) p(m \mid s) \prod\_{i = 1}^n p(x\_i \mid s, m)`` and the mean-field variational posterior ``q(s, m)`` will be + +```math +q\_{\mu\_1, \mu\_2, \sigma\_1^2, \sigma\_2^2}(s, m) = p\_{\mathcal{N}(\mu\_1, \sigma\_1^2)}(s)\ p\_{\mathcal{N}(\mu\_2, \sigma\_2^2)}(m), +``` + +where we've denoted the evaluation of the probability density of a Gaussian as ``p_{\mathcal{N}(\mu, \sigma^2)}(x)``. + +Observe that ``\mathrm{InverseGamma}(2, 3)`` has non-zero probability only on ``\mathbb{R}^{ + } := (0, \infty)`` which is clearly not all of ``\mathbb{R}`` like ``q(s, m)`` has, i.e. + +```math +\mathrm{supp} \left( q(s, m) \right) \not\subseteq \mathrm{supp} \left( p(z \mid \\{ x\_i \\}\_{i = 1}^n) \right). +``` + +Recall from the definition of the KL-divergence that when this is the case, the KL-divergence isn't well defined. This gets us to the *automatic* part of ADVI. + +### "Automatic"? How? + +For a lot of the standard (continuous) densities ``p`` we can actually construct a probability density ``\tilde{p}`` with non-zero probability on all of ``\mathbb{R}`` by *transforming* the "constrained" probability density ``p`` to ``\tilde{p}``. In fact, in these cases this is a one-to-one relationship. As we'll see, this helps solve the support-issue we've been going on and on about. + +#### Transforming densities using change of variables + +If we want to compute the probability of ``x`` taking a value in some set ``A \subseteq \mathrm{supp} \left( p(x) \right)``, we have to integrate ``p(x)`` over ``A``, i.e. + +```math +\mathbb{P}_p(x \in A) = \int_A p(x) \mathrm{d}x. +``` + +This means that if we have a differentiable bijection ``f: \mathrm{supp} \left( q(x) \right) \to \mathbb{R}^d`` with differentiable inverse ``f^{-1}: \mathbb{R}^d \to \mathrm{supp} \left( p(x) \right)``, we can perform a change of variables + +```math +\mathbb{P}\_p(x \in A) = \int\_{f^{-1}(A)} p \left(f^{-1}(y) \right) \ \left| \det \mathcal{J}\_{f^{-1}}(y) \right| \mathrm{d}y, +``` + +where ``\mathcal{J}_{f^{-1}}(x)`` denotes the jacobian of ``f^{-1}`` evaluted at ``x``. Observe that this defines a probability distribution + +```math +\mathbb{P}\_{\tilde{p}}\left(y \in f^{-1}(A) \right) = \int\_{f^{-1}(A)} \tilde{p}(y) \mathrm{d}y, +``` + +since ``f^{-1}\left(\mathrm{supp} (p(x)) \right) = \mathbb{R}^d `` which has probability 1. This probability distribution has *density* ``\tilde{p}(y)`` with ``\mathrm{supp} \left( \tilde{p}(y) \right) = \mathbb{R}^d``, defined + +```math +\tilde{p}(y) = p \left( f^{-1}(y) \right) \ \left| \det \mathcal{J}\_{f^{-1}}(y) \right| +``` + +or equivalently + +```math +\tilde{p} \left( f(x) \right) = \frac{p(x)}{\big| \det \mathcal{J}\_{f}(x) \big|} +``` + +due to the fact that + +```math +\big| \det \mathcal{J}\_{f^{-1}}(y) \big| = \big| \det \mathcal{J}\_{f}(x) \big|^{-1} +``` + +*Note: it's also necessary that the log-abs-det-jacobian term is non-vanishing. This can for example be accomplished by assuming ``f`` to also be elementwise monotonic.* + +#### Back to VI + +So why is this is useful? Well, we're looking to generalize our approach using a normal distribution to cases where the supports don't match up. How about defining ``q(z)`` by + +```math +\begin{align*} + \eta &\sim \mathcal{N}(\mu, \Sigma), \\\\ + z &= f^{-1}(\eta), +\end{align*} +``` + +where ``f^{-1}: \mathbb{R}^d \to \mathrm{supp} \left( p(z \mid x) \right)`` is a differentiable bijection with differentiable inverse. Then ``z \sim q_{\mu, \Sigma}(z) \implies z \in \mathrm{supp} \left( p(z \mid x) \right)`` as we wanted. The resulting variational density is + +```math +q\_{\mu, \Sigma}(z) = p\_{\mathcal{N}(\mu, \Sigma)}\left( f(z) \right) \ \big| \det \mathcal{J}\_{f}(z) \big|. +``` + +Note that the way we've constructed ``q(z)`` here is basically a reverse of the approach we described above. Here we sample from a distribution with support on ``\mathbb{R}`` and transform *to* ``\mathrm{supp} \left( p(z \mid x) \right)``. + +If we want to write the ELBO explicitly in terms of ``\eta`` rather than ``z``, the first term in the ELBO becomes + +```math +\begin{align*} + \mathbb{E}\_{z \sim q_{\mu, \Sigma}(z)} \left[ \log p(x\_i, z) \right] &= \mathbb{E}\_{\eta \sim \mathcal{N}(\mu, \Sigma)} \Bigg[ \log \frac{p\left(x\_i, f^{-1}(\eta) \right)}{\big| \det \mathcal{J}_{f^{-1}}(\eta) \big|} \Bigg] \\\\ + &= \mathbb{E}\_{\eta \sim \mathcal{N}(\mu, \Sigma)} \left[ \log p\left(x\_i, f^{-1}(\eta) \right) \right] - \mathbb{E}\_{\eta \sim \mathcal{N}(\mu, \Sigma)} \left[ \left| \det \mathcal{J}\_{f^{-1}}(\eta) \right| \right]. +\end{align*} +``` + +The entropy is invariant under change of variables, thus ``\mathbb{H} \left(q\_{\mu, \Sigma}(z)\right)`` is simply the entropy of the normal distribution which is known analytically. + +Hence, the resulting empirical estimate of the ELBO is + +```math +\begin{align*} +\widehat{\mathrm{ELBO}}(q\_{\mu, \Sigma}) &= \frac{1}{m} \left( \sum\_{k = 1}^m \sum\_{i = 1}^n \left(\log p\left(x\_i, f^{-1}(\eta_k)\right) - \log \big| \det \mathcal{J}\_{f^{-1}}(\eta\_k) \big| \right) \right) + \mathbb{H} \left(p\_{\mathcal{N}(\mu, \Sigma)}(z)\right) \\\\ +& \text{where} \quad z\_k \sim \mathcal{N}(\mu, \Sigma) \quad \forall k = 1, \dots, m +\end{align*}. +``` + +And maximizing this wrt. ``\mu`` and ``\Sigma`` is what's referred to as **Automatic Differentiation Variational Inference (ADVI)**! + +Now if you want to try it out, [check out the tutorial on how to use ADVI in Turing.jl](../../tutorials/09-variational-inference)! + diff --git a/tutorials/using-turing-advanced/advanced.md b/tutorials/using-turing-advanced/advanced.md new file mode 100644 index 000000000..afdf58b5e --- /dev/null +++ b/tutorials/using-turing-advanced/advanced.md @@ -0,0 +1,186 @@ +--- +title: Advanced Usage +permalink: docs/using-turing/advanced +weave_options: + error : false +--- + +# Advanced Usage + +## How to Define a Customized Distribution + + +Turing.jl supports the use of distributions from the Distributions.jl package. By extension it also supports the use of customized distributions, by defining them as subtypes of `Distribution` type of the Distributions.jl package, as well as corresponding functions. + + +Below shows a workflow of how to define a customized distribution, using our own implementation of a simple `Uniform` distribution as a simple example. + + +### 1. Define the Distribution Type + + +First, define a type of the distribution, as a subtype of a corresponding distribution type in the Distributions.jl package. + + +```julia +struct CustomUniform <: ContinuousUnivariateDistribution end +``` + +### 2. Implement Sampling and Evaluation of the log-pdf + + +Second, define `rand` and `logpdf`, which will be used to run the model. + + +```julia +# sample in [0, 1] +Distributions.rand(rng::AbstractRNG, d::CustomUniform) = rand(rng) + +# p(x) = 1 → logp(x) = 0 +Distributions.logpdf(d::CustomUniform, x::Real) = zero(x) +``` + +### 3. Define Helper Functions + + +In most cases, it may be required to define some helper functions. + +#### 3.1 Domain Transformation + +Certain samplers, such as `HMC`, require the domain of the priors to be unbounded. Therefore, to use our `CustomUniform` as a prior in a model we also need to define how to transform samples from `[0, 1]` to `ℝ`. To do this, we simply need to define the corresponding `Bijector` from `Bijectors.jl`, which is what `Turing.jl` uses internally to deal with constrained distributions. + +To transform from `[0, 1]` to `ℝ` we can use the `Logit` bijector: + +```julia +Bijectors.bijector(d::CustomUniform) = Logit(0., 1.) +``` + +You'd do the exact same thing for `ContinuousMultivariateDistribution` and `ContinuousMatrixDistribution`. For example, `Wishart` defines a distribution over positive-definite matrices and so `bijector` returns a `PDBijector` when called with a `Wishart` distribution as an argument. For discrete distributions, there is no need to define a bijector; the `Identity` bijector is used by default. + +Alternatively, for `UnivariateDistribution` we can define the `minimum` and `maximum` of the distribution + +```julia +Distributions.minimum(d::CustomUniform) = 0. +Distributions.maximum(d::CustomUniform) = 1. +``` + +and `Bijectors.jl` will return a default `Bijector` called `TruncatedBijector` which makes use of `minimum` and `maximum` derive the correct transformation. + +Internally, Turing basically does the following when it needs to convert a constrained distribution to an unconstrained distribution, e.g. when sampling using `HMC`: +```julia +b = bijector(dist) +transformed_dist = transformed(dist, b) # results in distribution with transformed support + correction for logpdf +``` +and then we can call `rand` and `logpdf` as usual, where +- `rand(transformed_dist)` returns a sample in the unconstrained space, and +- `logpdf(transformed_dist, y)` returns the log density of the original distribution, but with `y` living in the unconstrained space. + +To read more about Bijectors.jl, check out [the project README](https://github.com/TuringLang/Bijectors.jl). + +## Update the accumulated log probability in the model definition + +Turing accumulates log probabilities internally in an internal data structure that is accessible through +the internal variable `__varinfo__` inside of the model definition (see below for more details about model internals). +However, since users should not have to deal with internal data structures, a macro `Turing.@addlogprob!` is provided +that increases the accumulated log probability. For instance, this allows you to +[include arbitrary terms in the likelihood](https://github.com/TuringLang/Turing.jl/issues/1332) + +```julia +using Turing + +myloglikelihood(x, μ) = loglikelihood(Normal(μ, 1), x) + +@model function demo(x) + μ ~ Normal() + Turing.@addlogprob! myloglikelihood(x, μ) +end +``` + +and to [reject samples](https://github.com/TuringLang/Turing.jl/issues/1328): + +```julia +using Turing +using LinearAlgebra + +@model function demo(x) + m ~ MvNormal(zero(x), I) + if dot(m, x) < 0 + Turing.@addlogprob! -Inf + # Exit the model evaluation early + return + end + + x ~ MvNormal(m, I) + return +end +``` + +Note that `@addlogprob!` always increases the accumulated log probability, regardless of the provided +sampling context. For instance, if you do not want to apply `Turing.@addlogprob!` when evaluating the +prior of your model but only when computing the log likelihood and the log joint probability, then you +should [check the type of the internal variable `__context_`](https://github.com/TuringLang/DynamicPPL.jl/issues/154) +such as + +```julia +if DynamicPPL.leafcontext(__context__) !== Turing.PriorContext() + Turing.@addlogprob! myloglikelihood(x, μ) +end +``` + +## Model Internals + + +The `@model` macro accepts a function definition and rewrites it such that call of the function generates a `Model` struct for use by the sampler. +Models can be constructed by hand without the use of a macro. +Taking the `gdemo` model as an example, the macro-based definition + +```julia +using Turing + +@model function gdemo(x) + # Set priors. + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + + # Observe each value of x. + @. x ~ Normal(m, sqrt(s²)) +end + +model = gdemo([1.5, 2.0]) +``` + +can be implemented also (a bit less generally) with the macro-free version + +```julia +using Turing + +# Create the model function. +function gdemo(model, varinfo, context, x) + # Assume s² has an InverseGamma distribution. + s², varinfo = DynamicPPL.tilde_assume!!( + context, + InverseGamma(2, 3), + Turing.@varname(s²), + varinfo, + ) + + # Assume m has a Normal distribution. + m, varinfo = DynamicPPL.tilde_assume!!( + context, + Normal(0, sqrt(s²)), + Turing.@varname(m), + varinfo, + ) + + # Observe each value of x[i] according to a Normal distribution. + DynamicPPL.dot_tilde_observe!!(context, Normal(m, sqrt(s²)), x, Turing.@varname(x), varinfo) +end +gdemo(x) = Turing.Model(gdemo, (; x)) + +# Instantiate a Model object with our data variables. +model = gdemo([1.5, 2.0]) +``` + +## Task Copying + +Turing [copies](https://github.com/JuliaLang/julia/issues/4085) Julia tasks to deliver efficient inference algorithms, but it also provides alternative slower implementation as a fallback. Task copying is enabled by default. Task copying requires us to use the `TapedTask` facility which is provided by [Libtask](https://github.com/TuringLang/Libtask.jl) to create tasks. diff --git a/tutorials/using-turing-autodiff/autodiff.md b/tutorials/using-turing-autodiff/autodiff.md new file mode 100644 index 000000000..eeaccc51a --- /dev/null +++ b/tutorials/using-turing-autodiff/autodiff.md @@ -0,0 +1,50 @@ +--- +title: Automatic Differentiation +permalink: docs/using-turing/autodiff +weave_options: + error : false +--- + +# Automatic Differentiation + +## Switching AD Modes + + +Turing supports four packages of automatic differentiation (AD) in the back end during sampling. The default AD backend is [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl) for forward-mode AD. Three reverse-mode AD backends are also supported, namely [Tracker](https://github.com/FluxML/Tracker.jl), [Zygote](https://github.com/FluxML/Zygote.jl) and [ReverseDiff](https://github.com/JuliaDiff/ReverseDiff.jl). `Zygote` and `ReverseDiff` are supported optionally if explicitly loaded by the user with `using Zygote` or `using ReverseDiff` next to `using Turing`. + +To switch between the different AD backends, one can call function `Turing.setadbackend(backend_sym)`, where `backend_sym` can be `:forwarddiff` (`ForwardDiff`), `:tracker` (`Tracker`), `:zygote` (`Zygote`) or `:reversediff` (`ReverseDiff.jl`). When using `ReverseDiff`, to compile the tape only once and cache it for later use, the user has to call `Turing.setrdcache(true)`. However, note that the use of caching in certain types of models can lead to incorrect results and/or errors. Models for which the compiled tape can be safely cached are models with fixed size loops and no run-time if statements. Compile-time if statements are fine. + + +## Compositional Sampling with Differing AD Modes + + +Turing supports intermixed automatic differentiation methods for different variable spaces. The snippet below shows using `ForwardDiff` to sample the mean (`m`) parameter, and using the Tracker-based `TrackerAD` autodiff for the variance (`s`) parameter: + + +```julia +using Turing + +# Define a simple Normal model with unknown mean and variance. +@model function gdemo(x, y) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + x ~ Normal(m, sqrt(s²)) + y ~ Normal(m, sqrt(s²)) +end + +# Sample using Gibbs and varying autodiff backends. +c = sample( + gdemo(1.5, 2), + Gibbs( + HMC{Turing.ForwardDiffAD{1}}(0.1, 5, :m), + HMC{Turing.TrackerAD}(0.1, 5, :s) + ), + 1000, +) +``` + + +Generally, `TrackerAD` is faster when sampling from variables of high dimensionality (greater than 20) and `ForwardDiffAD` is more efficient for lower-dimension variables. This functionality allows those who are performance sensitive to fine tune their automatic differentiation for their specific models. + + +If the differentiation method is not specified in this way, Turing will default to using whatever the global AD backend is. Currently, this defaults to `ForwardDiff`. diff --git a/tutorials/using-turing-dynamicppl/dynamichmc.md b/tutorials/using-turing-dynamicppl/dynamichmc.md new file mode 100644 index 000000000..794010711 --- /dev/null +++ b/tutorials/using-turing-dynamicppl/dynamichmc.md @@ -0,0 +1,31 @@ +--- +title: Using DynamicHMC +permalink: /docs/using-turing/dynamichmc/ +weave_options: + error : false +--- + +# Using DynamicHMC + +Turing supports the use of [DynamicHMC](https://github.com/tpapp/DynamicHMC.jl) as a sampler through the `DynamicNUTS` function. + +To use the `DynamicNUTS` function, you must import the `DynamicHMC` package as well as Turing. Turing does not formally require `DynamicHMC` but will include additional functionality if both packages are present. + +Here is a brief example of how to apply `DynamicNUTS`: + + +```julia +# Import Turing and DynamicHMC. +using DynamicHMC, Turing + +# Model definition. +@model function gdemo(x, y) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + x ~ Normal(m, sqrt(s²)) + y ~ Normal(m, sqrt(s²)) +end + +# Pull 2,000 samples using DynamicNUTS. +chn = sample(gdemo(1.5, 2.0), DynamicNUTS(), 2000) +``` diff --git a/tutorials/using-turing-guide/guide.md b/tutorials/using-turing-guide/guide.md new file mode 100644 index 000000000..451cdec7e --- /dev/null +++ b/tutorials/using-turing-guide/guide.md @@ -0,0 +1,671 @@ +--- +title: Guide +permalink: /docs/for-developers/how_turing_implements_abstractmcmc +weave_options: + error : false +--- + +# Guide + + +## Basics + + +### Introduction + +A probabilistic program is Julia code wrapped in a `@model` macro. It can use arbitrary Julia code, but to ensure correctness of inference it should not have external effects or modify global state. Stack-allocated variables are safe, but mutable heap-allocated objects may lead to subtle bugs when using task copying. By default Libtask deepcopies `Array` and `Dict` objects when copying task to avoid bugs with data stored in mutable structure in Turing models. + + +To specify distributions of random variables, Turing programs should use the `~` notation: + + +`x ~ distr` where `x` is a symbol and `distr` is a distribution. If `x` is undefined in the model function, inside the probabilistic program, this puts a random variable named `x`, distributed according to `distr`, in the current scope. `distr` can be a value of any type that implements `rand(distr)`, which samples a value from the distribution `distr`. If `x` is defined, this is used for conditioning in a style similar to [Anglican](https://probprog.github.io/anglican/index.html) (another PPL). In this case, `x` is an observed value, assumed to have been drawn from the distribution `distr`. The likelihood is computed using `logpdf(distr,y)`. The observe statements should be arranged so that every possible run traverses all of them in exactly the same order. This is equivalent to demanding that they are not placed inside stochastic control flow. + + +Available inference methods include Importance Sampling (IS), Sequential Monte Carlo (SMC), Particle Gibbs (PG), Hamiltonian Monte Carlo (HMC), Hamiltonian Monte Carlo with Dual Averaging (HMCDA) and The No-U-Turn Sampler (NUTS). + + +### Simple Gaussian Demo + +Below is a simple Gaussian demo illustrate the basic usage of Turing.jl. + + +```julia +# Import packages. +using Turing +using StatsPlots + +# Define a simple Normal model with unknown mean and variance. +@model function gdemo(x, y) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + x ~ Normal(m, sqrt(s²)) + y ~ Normal(m, sqrt(s²)) +end +``` + + +Note: As a sanity check, the prior expectation of `s²` is `mean(InverseGamma(2, 3)) = 3/(2 - 1) = 3` and the prior expectation of `m` is 0. This can be easily checked using `Prior`: + +```julia +p1 = sample(gdemo(missing, missing), Prior(), 100000) +``` + + + +We can perform inference by using the `sample` function, the first argument of which is our probabilistic program and the second of which is a sampler. More information on each sampler is located in the [API]({{site.baseurl}}/docs/library). + + +```julia +# Run sampler, collect results. +c1 = sample(gdemo(1.5, 2), SMC(), 1000) +c2 = sample(gdemo(1.5, 2), PG(10), 1000) +c3 = sample(gdemo(1.5, 2), HMC(0.1, 5), 1000) +c4 = sample(gdemo(1.5, 2), Gibbs(PG(10, :m), HMC(0.1, 5, :s²)), 1000) +c5 = sample(gdemo(1.5, 2), HMCDA(0.15, 0.65), 1000) +c6 = sample(gdemo(1.5, 2), NUTS(0.65), 1000) +``` + + +The `MCMCChains` module (which is re-exported by Turing) provides plotting tools for the `Chain` objects returned by a `sample` function. See the [MCMCChains](https://github.com/TuringLang/MCMCChains.jl) repository for more information on the suite of tools available for diagnosing MCMC chains. + + +```julia +# Summarise results +describe(c3) + +# Plot results +plot(c3) +savefig("gdemo-plot.png") +``` + + +The arguments for each sampler are: + + + * SMC: number of particles. + * PG: number of particles, number of iterations. + * HMC: leapfrog step size, leapfrog step numbers. + * Gibbs: component sampler 1, component sampler 2, ... + * HMCDA: total leapfrog length, target accept ratio. + * NUTS: number of adaptation steps (optional), target accept ratio. + + +For detailed information on the samplers, please review Turing.jl's [API]({{site.baseurl}}/docs/library) documentation. + + +### Modelling Syntax Explained + + +Using this syntax, a probabilistic model is defined in Turing. The model function generated by Turing can then be used to condition the model onto data. Subsequently, the sample function can be used to generate samples from the posterior distribution. + + +In the following example, the defined model is conditioned to the data (arg*1 = 1, arg*2 = 2) by passing (1, 2) to the model function. + + +```julia +@model function model_name(arg_1, arg_2) + ... +end +``` + + +The conditioned model can then be passed onto the sample function to run posterior inference. + + +```julia +model_func = model_name(1, 2) +chn = sample(model_func, HMC(..)) # Perform inference by sampling using HMC. +``` + + +The returned chain contains samples of the variables in the model. + + +```julia +var_1 = mean(chn[:var_1]) # Taking the mean of a variable named var_1. +``` + + +The key (`:var_1`) can be a `Symbol` or a `String`. For example, to fetch `x[1]`, one can use `chn[Symbol("x[1]")]` or `chn["x[1]"]`. +If you want to retrieve all parameters associated with a specific symbol, you can use `group`. As an example, if you have the +parameters `"x[1]"`, `"x[2]"`, and `"x[3]"`, calling `group(chn, :x)` or `group(chn, "x")` will return a new chain with only `"x[1]"`, `"x[2]"`, and `"x[3]"`. + + +Turing does not have a declarative form. More generally, the order in which you place the lines of a `@model` macro matters. For example, the following example works: + + +```julia +# Define a simple Normal model with unknown mean and variance. +@model function model_function(y) + s ~ Poisson(1) + y ~ Normal(s, 1) + return y +end + +sample(model_function(10), SMC(), 100) +``` + + +But if we switch the `s ~ Poisson(1)` and `y ~ Normal(s, 1)` lines, the model will no longer sample correctly: + + +```julia +# Define a simple Normal model with unknown mean and variance. +@model function model_function(y) + y ~ Normal(s, 1) + s ~ Poisson(1) + return y +end + +sample(model_function(10), SMC(), 100) +``` + + + +### Sampling Multiple Chains + +Turing supports distributed and threaded parallel sampling. To do so, call `sample(model, sampler, parallel_type, n, n_chains)`, where `parallel_type` can be either `MCMCThreads()` or `MCMCDistributed()` for thread and parallel sampling, respectively. + +Having multiple chains in the same object is valuable for evaluating convergence. Some diagnostic functions like `gelmandiag` require multiple chains. + +If you do not want parallelism or are on an older version Julia, you can sample multiple chains with the `mapreduce` function: + +```julia +# Replace num_chains below with however many chains you wish to sample. +chains = mapreduce(c -> sample(model_fun, sampler, 1000), chainscat, 1:num_chains) +``` + +The `chains` variable now contains a `Chains` object which can be indexed by chain. To pull out the first chain from the `chains` object, use `chains[:,:,1]`. The method is the same if you use either of the below parallel sampling methods. + +#### Multithreaded sampling + +If you wish to perform multithreaded sampling and are running Julia 1.3 or greater, you can call `sample` with the following signature: + +```julia +using Turing + +@model function gdemo(x) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + + for i in eachindex(x) + x[i] ~ Normal(m, sqrt(s²)) + end +end + +model = gdemo([1.5, 2.0]) + +# Sample four chains using multiple threads, each with 1000 samples. +sample(model, NUTS(), MCMCThreads(), 1000, 4) +``` + +Be aware that Turing cannot add threads for you -- you must have started your Julia instance with multiple threads to experience any kind of parallelism. See the [Julia documentation](https://docs.julialang.org/en/v1/manual/parallel-computing/#man-multithreading-1) for details on how to achieve this. + +#### Distributed sampling + +To perform distributed sampling (using multiple processes), you must first import `Distributed`. + +Process parallel sampling can be done like so: + +```julia +# Load Distributed to add processes and the @everywhere macro. +using Distributed + +# Load Turing. +using Turing + +# Add four processes to use for sampling. +addprocs(4) + +# Initialize everything on all the processes. +# Note: Make sure to do this after you've already loaded Turing, +# so each process does not have to precompile. +# Parallel sampling may fail silently if you do not do this. +@everywhere using Turing + +# Define a model on all processes. +@everywhere @model function gdemo(x) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + + for i in eachindex(x) + x[i] ~ Normal(m, sqrt(s²)) + end +end + +# Declare the model instance everywhere. +@everywhere model = gdemo([1.5, 2.0]) + +# Sample four chains using multiple processes, each with 1000 samples. +sample(model, NUTS(), MCMCDistributed(), 1000, 4) +``` + +### Sampling from an Unconditional Distribution (The Prior) + +Turing allows you to sample from a declared model's prior. If you wish to draw a chain from the prior to inspect your prior distributions, you can simply run + +```julia +chain = sample(model, Prior(), n_samples) +``` + +You can also run your model (as if it were a function) from the prior distribution, by calling the model without specifying inputs or a sampler. In the below example, we specify a `gdemo` model which returns two variables, `x` and `y`. The model includes `x` and `y` as arguments, but calling the function without passing in `x` or `y` means that Turing's compiler will assume they are missing values to draw from the relevant distribution. The `return` statement is necessary to retrieve the sampled `x` and `y` values. + + +```julia +@model function gdemo(x, y) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + x ~ Normal(m, sqrt(s²)) + y ~ Normal(m, sqrt(s²)) + return x, y +end +``` + + +Assign the function with `missing` inputs to a variable, and Turing will produce a sample from the prior distribution. + + +```julia +# Samples from p(x,y) +g_prior_sample = gdemo(missing, missing) +g_prior_sample() +``` + + +Output: + + +``` +(0.685690547873451, -1.1972706455914328) +``` + + +### Sampling from a Conditional Distribution (The Posterior) + + +#### Treating observations as random variables + + +Inputs to the model that have a value `missing` are treated as parameters, aka random variables, to be estimated/sampled. This can be useful if you want to simulate draws for that parameter, or if you are sampling from a conditional distribution. Turing supports the following syntax: + + +```julia +@model function gdemo(x, ::Type{T} = Float64) where {T} + if x === missing + # Initialize `x` if missing + x = Vector{T}(undef, 2) + end + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + for i in eachindex(x) + x[i] ~ Normal(m, sqrt(s²)) + end +end + +# Construct a model with x = missing +model = gdemo(missing) +c = sample(model, HMC(0.01, 5), 500) +``` + +Note the need to initialize `x` when missing since we are iterating over its elements later in the model. The generated values for `x` can be extracted from the `Chains` object using `c[:x]`. + + +Turing also supports mixed `missing` and non-`missing` values in `x`, where the missing ones will be treated as random variables to be sampled while the others get treated as observations. For example: + + +```julia +@model function gdemo(x) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + for i in eachindex(x) + x[i] ~ Normal(m, sqrt(s²)) + end +end + +# x[1] is a parameter, but x[2] is an observation +model = gdemo([missing, 2.4]) +c = sample(model, HMC(0.01, 5), 500) +``` + + +#### Default Values + + +Arguments to Turing models can have default values much like how default values work in normal Julia functions. For instance, the following will assign `missing` to `x` and treat it as a random variable. If the default value is not `missing`, `x` will be assigned that value and will be treated as an observation instead. + +```julia +using Turing + +@model function generative(x = missing, ::Type{T} = Float64) where {T <: Real} + if x === missing + # Initialize x when missing + x = Vector{T}(undef, 10) + end + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + for i in 1:length(x) + x[i] ~ Normal(m, sqrt(s²)) + end + return s², m +end + +m = generative() +chain = sample(m, HMC(0.01, 5), 1000) +``` + + +#### Access Values inside Chain + + +You can access the values inside a chain several ways: + +1. Turn them into a `DataFrame` object +2. Use their raw `AxisArray` form +3. Create a three-dimensional `Array` object + +For example, let `c` be a `Chain`: +1. `DataFrame(c)` converts `c` to a `DataFrame`, +2. `c.value` retrieves the values inside `c` as an `AxisArray`, and +3. `c.value.data` retrieves the values inside `c` as a 3D `Array`. + + +#### Variable Types and Type Parameters + + +The element type of a vector (or matrix) of random variables should match the `eltype` of the its prior distribution, `<: Integer` for discrete distributions and `<: AbstractFloat` for continuous distributions. Moreover, if the continuous random variable is to be sampled using a Hamiltonian sampler, the vector's element type needs to either be: + 1. `Real` to enable auto-differentiation through the model which uses special number types that are sub-types of `Real`, or + 2. Some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Float64) where {T}`. +Similarly, when using a particle sampler, the Julia variable used should either be: + 1. An `Array`, or + 2. An instance of some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Vector{Float64}) where {T}`. + + +### Querying Probabilities from Model or Chain + +Consider first the following simplified `gdemo` model: +```julia +@model function gdemo0(x) + s ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s)) + x ~ Normal(m, sqrt(s)) +end + +# Instantiate three models, with different value of x +model1 = gdemo0(1) +model4 = gdemo0(4) +model10 = gdemo0(10) +``` + +Now, query the instantiated models: compute the likelihood of `x = 1.0` given the values of `s = 1.0` and `m = 1.0` for the parameters: +```julia +prob"x = 1.0 | model = model1, s = 1.0, m = 1.0" +prob"x = 1.0 | model = model4, s = 1.0, m = 1.0" +prob"x = 1.0 | model = model10, s = 1.0, m = 1.0" +``` + +Notice that even if we use three models, instantiated with three different values of `x`, we should obtain the same likelihood. We can easily verify that value in this case: + +```julia +pdf(Normal(1.0, 1.0), 1.0) +``` + + +Let us now consider the following `gdemo` model: +```julia +@model function gdemo(x, y) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + x ~ Normal(m, sqrt(s²)) + y ~ Normal(m, sqrt(s²)) +end + +# Instantiate the model. +model = gdemo(2.0, 4.0) +``` + +The following are examples of valid queries of the `Turing` model or chain: + +- `prob"x = 1.0, y = 1.0 | model = model, s = 1.0, m = 1.0"` calculates the likelihood of `x = 1` and `y = 1` given `s = 1` and `m = 1`. + +- `prob"s² = 1.0, m = 1.0 | model = model, x = nothing, y = nothing"` calculates the joint probability of `s = 1` and `m = 1` ignoring `x` and `y`. `x` and `y` are ignored so they can be optionally dropped from the RHS of `|`, but it is recommended to define them. + +- `prob"s² = 1.0, m = 1.0, x = 1.0 | model = model, y = nothing"` calculates the joint probability of `s = 1`, `m = 1` and `x = 1` ignoring `y`. + +- `prob"s² = 1.0, m = 1.0, x = 1.0, y = 1.0 | model = model"` calculates the joint probability of all the variables. + +- After the MCMC sampling, given a `chain`, `prob"x = 1.0, y = 1.0 | chain = chain, model = model"` calculates the element-wise likelihood of `x = 1.0` and `y = 1.0` for each sample in `chain`. + +- If `save_state=true` was used during sampling (i.e., `sample(model, sampler, N; save_state=true)`), you can simply do `prob"x = 1.0, y = 1.0 | chain = chain"`. + +In all the above cases, `logprob` can be used instead of `prob` to calculate the log probabilities instead. + + +### Maximum likelihood and maximum a posterior estimates + +Turing provides support for two mode estimation techniques, [maximum likelihood estimation](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) (MLE) and [maximum a posterior](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) (MAP) estimation. Optimization is performed by the [Optim.jl](https://github.com/JuliaNLSolvers/Optim.jl) package. Mode estimation is currently a optional tool, and will not be available to you unless you have manually installed Optim and loaded the package with a `using` statement. To install Optim, run `import Pkg; Pkg.add("Optim")`. + +Mode estimation only works when all model parameters are continuous -- discrete parameters cannot be estimated with MLE/MAP as of yet. + +To understand how mode estimation works, let us first load Turing and Optim to enable mode estimation, and then declare a model: + +```julia +# Note that loading Optim explicitly is required for mode estimation to function, +# as Turing does not load the opimization suite unless Optim is loaded as well. +using Turing +using Optim + +@model function gdemo(x) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + + for i in eachindex(x) + x[i] ~ Normal(m, sqrt(s²)) + end +end +``` + +Once the model is defined, we can construct a model instance as we normally would: + +```julia +# Create some data to pass to the model. +data = [1.5, 2.0] + +# Instantiate the gdemo model with our data. +model = gdemo(data) +``` + +Mode estimation is typically quick and easy at this point. Turing extends the function `Optim.optimize` and accepts the structs `MLE()` or `MAP()`, which inform Turing whether to provide an MLE or MAP estimate, respectively. By default, the [LBFGS optimizer](https://julianlsolvers.github.io/Optim.jl/stable/#algo/lbfgs/) is used, though this can be changed. Basic usage is: + +```julia +# Generate a MLE estimate. +mle_estimate = optimize(model, MLE()) + +# Generate a MAP estimate. +map_estimate = optimize(model, MAP()) +``` + +If you wish to change to a different optimizer, such as `NelderMead`, simply place your optimizer in the third argument slot: + +```julia +# Use NelderMead +mle_estimate = optimize(model, MLE(), NelderMead()) + +# Use SimulatedAnnealing +mle_estimate = optimize(model, MLE(), SimulatedAnnealing()) + +# Use ParticleSwarm +mle_estimate = optimize(model, MLE(), ParticleSwarm()) + +# Use Newton +mle_estimate = optimize(model, MLE(), Newton()) + +# Use AcceleratedGradientDescent +mle_estimate = optimize(model, MLE(), AcceleratedGradientDescent()) +``` + +Some methods may have trouble calculating the mode because not enough iterations were allowed, or the target function moved upwards between function calls. Turing will warn you if Optim fails to converge by running `Optim.converge`. A typical solution to this might be to add more iterations, or allow the optimizer to increase between function iterations: + +```julia +# Increase the iterations and allow function eval to increase between calls. +mle_estimate = optimize(model, MLE(), Newton(), Optim.Options(iterations=10_000, allow_f_increases=true)) +``` + +More options for Optim are available [here](https://julianlsolvers.github.io/Optim.jl/stable/#user/config/). + +#### Analyzing your mode estimate + +Turing extends several methods from `StatsBase` that can be used to analyze your mode estimation results. Methods implemented include `vcov`, `informationmatrix`, `coeftable`, `params`, and `coef`, among others. + +For example, let's examine our ML estimate from above using `coeftable`: + +```julia +# Import StatsBase to use it's statistical methods. +using StatsBase + +# Print out the coefficient table. +coeftable(mle_estimate) +``` + +```julia +───────────────────────────── + estimate stderror tstat +───────────────────────────── +s 0.0625 0.0625 1.0 +m 1.75 0.176777 9.8995 +───────────────────────────── +``` + +Standard errors are calculated from the Fisher information matrix (inverse Hessian of the log likelihood or log joint). t-statistics will be familiar to frequentist statisticians. Warning -- standard errors calculated in this way may not always be appropriate for MAP estimates, so please be cautious in interpreting them. + +#### Sampling with the MAP/MLE as initial states + +You can begin sampling your chain from an MLE/MAP estimate by extracting the vector of parameter values and providing it to the `sample` function with the keyword `init_params`. For example, here is how to sample from the full posterior using the MAP estimate as the starting point: + +```julia +# Generate an MAP estimate. +map_estimate = optimize(model, MAP()) + +# Sample with the MAP estimate as the starting point. +chain = sample(model, NUTS(), 1_000, init_params = map_estimate.values.array) +``` + +## Beyond the Basics + + +### Compositional Sampling Using Gibbs + + +Turing.jl provides a Gibbs interface to combine different samplers. For example, one can combine an `HMC` sampler with a `PG` sampler to run inference for different parameters in a single model as below. + + +```julia +@model function simple_choice(xs) + p ~ Beta(2, 2) + z ~ Bernoulli(p) + for i in 1:length(xs) + if z == 1 + xs[i] ~ Normal(0, 1) + else + xs[i] ~ Normal(2, 1) + end + end +end + +simple_choice_f = simple_choice([1.5, 2.0, 0.3]) + +chn = sample(simple_choice_f, Gibbs(HMC(0.2, 3, :p), PG(20, :z)), 1000) +``` + + +The `Gibbs` sampler can be used to specify unique automatic differentiation backends for different variable spaces. Please see the [Automatic Differentiation]({{site.baseurl}}/docs/using-turing/autodiff) article for more. + + +For more details of compositional sampling in Turing.jl, please check the corresponding [paper](http://proceedings.mlr.press/v84/ge18b.html). + +### Working with filldist and arraydist + +Turing provides `filldist(dist::Distribution, n::Int)` and `arraydist(dists::AbstractVector{<:Distribution})` as a simplified interface to construct product distributions, e.g., to model a set of variables that share the same structure but vary by group. + +#### Constructing product distributions with filldist + +The function `filldist` provides a general interface to construct product distributions over distributions of the same type and parameterisation. +Note that, in contrast to the product distribution interface provided by Distributions.jl (`Product`), `filldist` supports product distributions over univariate or multivariate distributions. + +Example usage: + +```julia +@model function demo(x, g) + k = length(unique(g)) + a ~ filldist(Exponential(), k) # = Product(fill(Exponential(), k)) + mu = a[g] + x .~ Normal.(mu) +end +``` + +#### Constructing product distributions with arraydist + +The function `arraydist` provides a general interface to construct product distributions over distributions of varying type and parameterisation. +Note that in contrast to the product distribution interface provided by Distributions.jl (`Product`), `arraydist` supports product distributions over univariate or multivariate distributions. + +Example usage: + +```julia +@model function demo(x, g) + k = length(unique(g)) + a ~ arraydist([Exponential(i) for i in 1:k]) + mu = a[g] + x .~ Normal.(mu) +end +``` + +### Working with MCMCChains.jl + + +Turing.jl wraps its samples using `MCMCChains.Chain` so that all the functions working for `MCMCChains.Chain` can be re-used in Turing.jl. Two typical functions are `MCMCChains.describe` and `MCMCChains.plot`, which can be used as follows for an obtained chain `chn`. For more information on `MCMCChains`, please see the [GitHub repository](https://github.com/TuringLang/MCMCChains.jl). + + +```julia +describe(chn) # Lists statistics of the samples. +plot(chn) # Plots statistics of the samples. +``` + + +There are numerous functions in addition to `describe` and `plot` in the `MCMCChains` package, such as those used in convergence diagnostics. For more information on the package, please see the [GitHub repository](https://github.com/TuringLang/MCMCChains.jl). + + +### Changing Default Settings + + +Some of Turing.jl's default settings can be changed for better usage. + + +#### AD Chunk Size + + +ForwardDiff (Turing's default AD backend) uses forward-mode chunk-wise AD. The chunk size can be set manually by `setchunksize(new_chunk_size)`. + +#### AD Backend + + +Turing supports four packages of automatic differentiation (AD) in the back end during sampling. The default AD backend is [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl) for forward-mode AD. Three reverse-mode AD backends are also supported, namely [Tracker](https://github.com/FluxML/Tracker.jl), [Zygote](https://github.com/FluxML/Zygote.jl) and [ReverseDiff](https://github.com/JuliaDiff/ReverseDiff.jl). `Zygote` and `ReverseDiff` are supported optionally if explicitly loaded by the user with `using Zygote` or `using ReverseDiff` next to `using Turing`. + +For more information on Turing's automatic differentiation backend, please see the [Automatic Differentiation]({{site.baseurl}}/docs/using-turing/autodiff) article. + + +#### Progress Logging + +Turing.jl uses ProgressLogging.jl to log the progress of sampling. Progress +logging is enabled as default but might slow down inference. It can be turned on +or off by setting the keyword argument `progress` of `sample` to `true` or `false`, respectively. Moreover, you can enable or disable progress logging globally by calling `setprogress!(true)` or `setprogress!(false)`, respectively. + +Turing uses heuristics to select an appropriate visualization backend. If you +use [Juno](https://junolab.org/), the progress is displayed with a +[progress bar in the Atom window](http://docs.junolab.org/latest/man/juno_frontend/#Progress-Meters-1). +For Jupyter notebooks the default backend is +[ConsoleProgressMonitor.jl](https://github.com/tkf/ConsoleProgressMonitor.jl). +In all other cases progress logs are displayed with +[TerminalLoggers.jl](https://github.com/c42f/TerminalLoggers.jl). Alternatively, +if you provide a custom visualization backend, Turing uses it instead of the +default backend. + diff --git a/tutorials/using-turing-performance-tips/performancetips.md b/tutorials/using-turing-performance-tips/performancetips.md new file mode 100644 index 000000000..d34161c27 --- /dev/null +++ b/tutorials/using-turing-performance-tips/performancetips.md @@ -0,0 +1,163 @@ +--- +title: Performance Tips +permalink: /docs/using-turing/performancetips +weave_options: + error : false +--- + +# Performance Tips + +This section briefly summarises a few common techniques to ensure good performance when using Turing. +We refer to [the Julia documentation](https://docs.julialang.org/en/v1/manual/performance-tips/index.html) for general techniques to ensure good performance of Julia programs. + + +## Use multivariate distributions + +It is generally preferable to use multivariate distributions if possible. + +The following example: + +```julia +@model function gmodel(x) + m ~ Normal() + for i = 1:length(x) + x[i] ~ Normal(m, 0.2) + end +end +``` +can be directly expressed more efficiently using a simple transformation: + +```julia +using FillArrays + +@model function gmodel(x) + m ~ Normal() + x ~ MvNormal(Fill(m, length(x)), 0.04 * I) +end +``` + + +## Choose your AD backend +Turing currently provides support for two different automatic differentiation (AD) backends. +Generally, try to use `:forwarddiff` for models with few parameters and `:reversediff`, `:tracker` or `:zygote` for models with large parameter vectors or linear algebra operations. See [Automatic Differentiation](autodiff) for details. + + +## Special care for `:tracker` and `:zygote` + +In case of `:tracker` and `:zygote`, it is necessary to avoid loops for now. +This is mainly due to the reverse-mode AD backends `Tracker` and `Zygote` which are inefficient for such cases. `ReverseDiff` does better but vectorized operations will still perform better. + +Avoiding loops can be done using `filldist(dist, N)` and `arraydist(dists)`. `filldist(dist, N)` creates a multivariate distribution that is composed of `N` identical and independent copies of the univariate distribution `dist` if `dist` is univariate, or it creates a matrix-variate distribution composed of `N` identical and independent copies of the multivariate distribution `dist` if `dist` is multivariate. `filldist(dist, N, M)` can also be used to create a matrix-variate distribution from a univariate distribution `dist`. `arraydist(dists)` is similar to `filldist` but it takes an array of distributions `dists` as input. Writing a [custom distribution](advanced) with a custom adjoint is another option to avoid loops. + + +## Ensure that types in your model can be inferred + +For efficient gradient-based inference, e.g. using HMC, NUTS or ADVI, it is important to ensure the types in your model can be inferred. + +The following example with abstract types + +```julia +@model function tmodel(x, y) + p,n = size(x) + params = Vector{Real}(undef, n) + for i = 1:n + params[i] ~ truncated(Normal(), 0, Inf) + end + + a = x * params + y ~ MvNormal(a, I) +end +``` + +can be transformed into the following representation with concrete types: + +```julia +@model function tmodel(x, y, ::Type{T} = Float64) where {T} + p,n = size(x) + params = Vector{T}(undef, n) + for i = 1:n + params[i] ~ truncated(Normal(), 0, Inf) + end + + a = x * params + y ~ MvNormal(a, I) +end +``` + +Alternatively, you could use `filldist` in this example: + +```julia +@model function tmodel(x, y) + params ~ filldist(truncated(Normal(), 0, Inf), size(x, 2)) + a = x * params + y ~ MvNormal(a, I) +end +``` + +Note that you can use `@code_warntype` to find types in your model definition that the compiler cannot infer. +They are marked in red in the Julia REPL. + +For example, consider the following simple program: + +```julia +@model function tmodel(x) + p = Vector{Real}(undef, 1) + p[1] ~ Normal() + p = p .+ 1 + x ~ Normal(p[1]) +end +``` + +We can use + +```julia +using Random + +model = tmodel(1.0) + +@code_warntype model.f( + model, + Turing.VarInfo(model), + Turing.SamplingContext( + Random.GLOBAL_RNG, Turing.SampleFromPrior(), Turing.DefaultContext(), + ), + model.args..., +) +``` + +to inspect type inference in the model. + + +## Reuse Computations in Gibbs Sampling + +Often when performing Gibbs sampling, one can save computational time by caching the output of expensive functions. The cached values can then be reused in future Gibbs sub-iterations which do not change the inputs to this expensive function. For example in the following model +```julia +@model function demo(x) + a ~ Gamma() + b ~ Normal() + c = function1(a) + d = function2(b) + x .~ Normal(c, d) +end +alg = Gibbs(MH(:a), MH(:b)) +sample(demo(zeros(10)), alg, 1000) +``` +when only updating `a` in a Gibbs sub-iteration, keeping `b` the same, the value of `d` doesn't change. And when only updating `b`, the value of `c` doesn't change. However, if `function1` and `function2` are expensive and are both run in every Gibbs sub-iteration, a lot of time would be spent computing values that we already computed before. Such a problem can be overcome using `Memoization.jl`. Memoizing a function lets us store and reuse the output of the function for every input it is called with. This has a slight time overhead but for expensive functions, the savings will be far greater. + +To use `Memoization.jl`, simply define memoized versions of `function1` and `function2` as such: +```julia +using Memoization + +@memoize memoized_function1(args...) = function1(args...) +@memoize memoized_function2(args...) = function2(args...) +``` +Then define the `Turing` model using the new functions as such: +```julia +@model function demo(x) + a ~ Gamma() + b ~ Normal() + c = memoized_function1(a) + d = memoized_function2(b) + x .~ Normal(c, d) +end +``` diff --git a/tutorials/using-turing-quick-start/quick-start.md b/tutorials/using-turing-quick-start/quick-start.md new file mode 100644 index 000000000..fc620c329 --- /dev/null +++ b/tutorials/using-turing-quick-start/quick-start.md @@ -0,0 +1,56 @@ +--- +title: Probabilistic Programming in Thirty Seconds +permalink: /docs/using-turing/quick-start/ +weave_options: + error : false +--- + +# Probabilistic Programming in Thirty Seconds + +If you are already well-versed in probabilistic programming and just want to take a quick look at how Turing's syntax works or otherwise just want a model to start with, we have provided a Bayesian coin-flipping model to play with. + + +This example can be run on however you have Julia installed (see [Getting Started]({{site.baseurl}}/docs/using-turing/get-started)), but you will need to install the packages `Turing` and `StatsPlots` if you have not done so already. + + +This is an excerpt from a more formal example introducing probabilistic programming which can be found in Jupyter notebook form [here](https://nbviewer.jupyter.org/github/TuringLang/TuringTutorials/blob/master/notebook/00-introduction/00_introduction.ipynb) or as part of the documentation website [here]({{site.baseurl}}/tutorials). + + +```julia +# Import libraries. +using Turing, StatsPlots, Random + +# Set the true probability of heads in a coin. +p_true = 0.5 + +# Iterate from having seen 0 observations to 100 observations. +Ns = 0:100 + +# Draw data from a Bernoulli distribution, i.e. draw heads or tails. +Random.seed!(12) +data = rand(Bernoulli(p_true), last(Ns)) + +# Declare our Turing model. +@model function coinflip(y) + # Our prior belief about the probability of heads in a coin. + p ~ Beta(1, 1) + + # The number of observations. + N = length(y) + for n in 1:N + # Heads or tails of a coin are drawn from a Bernoulli distribution. + y[n] ~ Bernoulli(p) + end +end + +# Settings of the Hamiltonian Monte Carlo (HMC) sampler. +iterations = 1000 +ϵ = 0.05 +τ = 10 + +# Start sampling. +chain = sample(coinflip(data), HMC(ϵ, τ), iterations) + +# Plot a summary of the sampling process for the parameter p, i.e. the probability of heads in a coin. +histogram(chain[:p]) +``` diff --git a/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl b/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl new file mode 100644 index 000000000..f4b419aba --- /dev/null +++ b/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl @@ -0,0 +1,100 @@ +ENV["GKS_ENCODING"] = "utf-8" # Allows the use of unicode characters in Plots.jl +using Plots +using StatsPlots +using Turing +using Bijectors +using Random +using DynamicPPL: getlogp, settrans!, getval, reconstruct, vectorize, setval! + +# Set a seed. +Random.seed!(0) + +# Define a strange model. +@model function gdemo(x) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + bumps = sin(m) + cos(m) + m = m + 5*bumps + for i in eachindex(x) + x[i] ~ Normal(m, sqrt(s²)) + end + return s², m +end + +# Define our data points. +x = [1.5, 2.0, 13.0, 2.1, 0.0] + +# Set up the model call, sample from the prior. +model = gdemo(x) +vi = Turing.VarInfo(model) + +# Convert the variance parameter to the real line before sampling. +# Note: We only have to do this here because we are being very hands-on. +# Turing will handle all of this for you during normal sampling. +dist = InverseGamma(2,3) +svn = vi.metadata.s.vns[1] +mvn = vi.metadata.m.vns[1] +setval!(vi, vectorize(dist, Bijectors.link(dist, reconstruct(dist, getval(vi, svn)))), svn) +settrans!(vi, true, svn) + +# Evaluate surface at coordinates. +function evaluate(m1, m2) + spl = Turing.SampleFromPrior() + vi[svn] = [m1] + vi[mvn] = [m2] + model(vi, spl) + getlogp(vi) +end + +function plot_sampler(chain; label="") + # Extract values from chain. + val = get(chain, [:s, :m, :lp]) + ss = link.(Ref(InverseGamma(2, 3)), val.s) + ms = val.m + lps = val.lp + + # How many surface points to sample. + granularity = 100 + + # Range start/stop points. + spread = 0.5 + σ_start = minimum(ss) - spread * std(ss); σ_stop = maximum(ss) + spread * std(ss); + μ_start = minimum(ms) - spread * std(ms); μ_stop = maximum(ms) + spread * std(ms); + σ_rng = collect(range(σ_start, stop=σ_stop, length=granularity)) + μ_rng = collect(range(μ_start, stop=μ_stop, length=granularity)) + + # Make surface plot. + p = surface(σ_rng, μ_rng, evaluate, + camera=(30, 65), + # ticks=nothing, + colorbar=false, + color=:inferno, + title=label) + + line_range = 1:length(ms) + + scatter3d!(ss[line_range], ms[line_range], lps[line_range], + mc =:viridis, marker_z=collect(line_range), msw=0, + legend=false, colorbar=false, alpha=0.5, + xlabel="σ", ylabel="μ", zlabel="Log probability", + title=label) + + return p +end; + +samplers = [ + (Gibbs(HMC(0.01, 5, :s), PG(20, :m)), "Gibbs{HMC, PG}"), + (HMC(0.01, 10), "HMC"), + (HMCDA(200, 0.65, 0.3), "HMCDA"), + (MH(), "MH()"), + (NUTS(0.65), "NUTS(0.65)"), + (NUTS(0.95), "NUTS(0.95)"), + (NUTS(0.2), "NUTS(0.2)"), + (PG(20), "PG(20)"), + (PG(50), "PG(50)")] + +for (i, (spl, spl_name)) in enumerate(samplers) + c = sample(model, spl, 1000) + p = plot_sampler(c, label="$spl_name") + savefig(joinpath(@__DIR__, "sampler-figs", "samplers-$i.svg")) +end diff --git a/tutorials/using-turing-sampler-viz/sampler-figs/samplers-1.svg b/tutorials/using-turing-sampler-viz/sampler-figs/samplers-1.svg new file mode 100644 index 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The block below loads the relevant libraries and defines a function for plotting the sampler's trajectory across the posterior. + +The Turing model definition used here is not especially practical, but it is designed in such a way as to produce visually interesting posterior surfaces to show how different samplers move along the distribution. + +```julia +ENV["GKS_ENCODING"] = "utf-8" # Allows the use of unicode characters in Plots.jl +using Plots +using StatsPlots +using Turing +using Bijectors +using Random +using DynamicPPL: getlogp, settrans!, getval, reconstruct, vectorize, setval! + +# Set a seed. +Random.seed!(0) + +# Define a strange model. +@model function gdemo(x) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + bumps = sin(m) + cos(m) + m = m + 5*bumps + for i in eachindex(x) + x[i] ~ Normal(m, sqrt(s²)) + end + return s², m +end + +# Define our data points. +x = [1.5, 2.0, 13.0, 2.1, 0.0] + +# Set up the model call, sample from the prior. +model = gdemo(x) +vi = Turing.VarInfo(model) + +# Convert the variance parameter to the real line before sampling. +# Note: We only have to do this here because we are being very hands-on. +# Turing will handle all of this for you during normal sampling. +dist = InverseGamma(2,3) +svn = vi.metadata.s².vns[1] +mvn = vi.metadata.m.vns[1] +setval!(vi, vectorize(dist, Bijectors.link(dist, reconstruct(dist, getval(vi, svn)))), svn) +settrans!(vi, true, svn) + +# Evaluate surface at coordinates. +function evaluate(m1, m2) + spl = Turing.SampleFromPrior() + vi[svn] = [m1] + vi[mvn] = [m2] + model(vi, spl) + getlogp(vi) +end + +function plot_sampler(chain; label="") + # Extract values from chain. + val = get(chain, [:s², :m, :lp]) + ss = link.(Ref(InverseGamma(2, 3)), val.s²) + ms = val.m + lps = val.lp + + # How many surface points to sample. + granularity = 100 + + # Range start/stop points. + spread = 0.5 + σ_start = minimum(ss) - spread * std(ss); σ_stop = maximum(ss) + spread * std(ss); + μ_start = minimum(ms) - spread * std(ms); μ_stop = maximum(ms) + spread * std(ms); + σ_rng = collect(range(σ_start, stop=σ_stop, length=granularity)) + μ_rng = collect(range(μ_start, stop=μ_stop, length=granularity)) + + # Make surface plot. + p = surface(σ_rng, μ_rng, evaluate, + camera=(30, 65), + # ticks=nothing, + colorbar=false, + color=:inferno, + title=label) + + line_range = 1:length(ms) + + scatter3d!(ss[line_range], ms[line_range], lps[line_range], + mc =:viridis, marker_z=collect(line_range), msw=0, + legend=false, colorbar=false, alpha=0.5, + xlabel="σ", ylabel="μ", zlabel="Log probability", + title=label) + + return p +end; +``` + +## Samplers + +### Gibbs + +Gibbs sampling tends to exhibit a "jittery" trajectory. The example below combines `HMC` and `PG` sampling to traverse the posterior. + +```julia +c = sample(model, Gibbs(HMC(0.01, 5, :s), PG(20, :m)), 1000) +plot_sampler(c) +``` + +![](sampler-figs/samplers-1.svg) + +### HMC + +Hamiltonian Monte Carlo (HMC) sampling is a typical sampler to use, as it tends to be fairly good at converging in a efficient manner. It can often be tricky to set the correct parameters for this sampler however, and the `NUTS` sampler is often easier to run if you don't want to spend too much time fiddling with step size and and the number of steps to take. Note however that `HMC` does not explore the positive values μ very well, likely due to the leapfrog and step size parameter settings. + +```julia +c = sample(model, HMC(0.01, 10), 1000) +plot_sampler(c) +``` + +![](sampler-figs/samplers-2.svg) + + +### HMCDA + +The HMCDA sampler is an implementation of the Hamiltonian Monte Carlo with Dual Averaging algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. + +```julia +c = sample(model, HMCDA(200, 0.65, 0.3), 1000) +plot_sampler(c) +``` + +![](sampler-figs/samplers-3.svg) + + +### MH + +Metropolis-Hastings (MH) sampling is one of the earliest Markov Chain Monte Carlo methods. MH sampling does not "move" a lot, unlike many of the other samplers implemented in Turing. Typically a much longer chain is required to converge to an appropriate parameter estimate. + +The plot below only uses 1,000 iterations of Metropolis-Hastings. + +```julia +c = sample(model, MH(), 1000) +plot_sampler(c) +``` + +![](sampler-figs/samplers-4.svg) + + +As you can see, the MH sampler doesn't move parameter estimates very often. + +### NUTS + +The No U-Turn Sampler (NUTS) is an implementation of the algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. + +NUTS tends to be very good at traversing complex posteriors quickly. + +```julia +c = sample(model, NUTS(0.65), 1000) +plot_sampler(c) +``` + +![](sampler-figs/samplers-5.svg) + + +The only parameter that needs to be set other than the number of iterations to run is the target acceptance rate. In the Hoffman and Gelman paper, they note that a target acceptance rate of 0.65 is typical. + +Here is a plot showing a very high acceptance rate. Note that it appears to "stick" to a mode and is not particularly good at exploring the posterior as compared to the 0.65 target acceptance ratio case. + +```julia +c = sample(model, NUTS(0.95), 1000) +plot_sampler(c) +``` + +![](sampler-figs/samplers-6.svg) + + +An exceptionally low acceptance rate will show very few moves on the posterior: + +```julia +c = sample(model, NUTS(0.2), 1000) +plot_sampler(c) +``` + +![](sampler-figs/samplers-7.svg) + + +### PG + +The Particle Gibbs (PG) sampler is an implementation of an algorithm from the paper "Particle Markov chain Monte Carlo methods" by Andrieu, Doucet, and Holenstein (2010). The interested reader can learn more [here](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9868.2009.00736.x). + +The two parameters are the number of particles, and the number of iterations. The plot below shows the use of 20 particles. + +```julia +c = sample(model, PG(20), 1000) +plot_sampler(c) +``` + +![](sampler-figs/samplers-8.svg) + + +Next, we plot using 50 particles. + +```julia +c = sample(model, PG(50), 1000) +plot_sampler(c) +``` + +![](sampler-figs/samplers-9.svg) diff --git a/tutorials/using-turing/index.md b/tutorials/using-turing/index.md new file mode 100644 index 000000000..cd43ea7a9 --- /dev/null +++ b/tutorials/using-turing/index.md @@ -0,0 +1,22 @@ +--- +title: Turing Documentation +permalink: docs/using-turing/ +weave_options: + error : false +--- + +# Turing Documentation + +Welcome to the documentation for Turing. + + +## Introduction + +**Turing** is a general-purpose probabilistic programming language for robust, efficient Bayesian inference and decision making. Current features include: + + + * **General-purpose** probabilistic programming with an intuitive modelling interface; + * Robust, efficient [Hamiltonian Monte Carlo (HMC)](https://github.com/TuringLang/AdvancedHMC.jl) sampling for differentiable posterior distributions; + * **Particle MCMC** sampling for complex posterior distributions involving discrete variables and stochastic control flow; and + * Compositional inference via **Gibbs sampling** that combines particle MCMC, HMC and random-walk MH (RWMH). + From 912b1bc84a554c57e09e20dcdef43a793249a1da Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Fri, 6 Jan 2023 20:25:28 +0000 Subject: [PATCH 02/87] Fix some permalinks. --- tutorials/for-developers-interface/interface.md | 2 +- tutorials/using-turing-guide/guide.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/for-developers-interface/interface.md b/tutorials/for-developers-interface/interface.md index b418ee384..f9d76fc4b 100644 --- a/tutorials/for-developers-interface/interface.md +++ b/tutorials/for-developers-interface/interface.md @@ -1,6 +1,6 @@ --- title: Interface Guide -permalink: /docs/for-developers/how_turing_implements_abstractmcmc +permalink: /docs/for-developers/interface weave_options: error : false --- diff --git a/tutorials/using-turing-guide/guide.md b/tutorials/using-turing-guide/guide.md index 451cdec7e..0b296f9d5 100644 --- a/tutorials/using-turing-guide/guide.md +++ b/tutorials/using-turing-guide/guide.md @@ -1,6 +1,6 @@ --- title: Guide -permalink: /docs/for-developers/how_turing_implements_abstractmcmc +permalink: /docs/using-turing/guide/ weave_options: error : false --- From b9a81439a4cc474703bc27c64a1a74e3f7ddb5c3 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Tue, 24 Jan 2023 15:19:44 +0000 Subject: [PATCH 03/87] Converted Quickstart doc --- .../{quick-start.md => quick-start.jmd} | 22 ++++++++++++++----- 1 file changed, 16 insertions(+), 6 deletions(-) rename tutorials/using-turing-quick-start/{quick-start.md => quick-start.jmd} (59%) diff --git a/tutorials/using-turing-quick-start/quick-start.md b/tutorials/using-turing-quick-start/quick-start.jmd similarity index 59% rename from tutorials/using-turing-quick-start/quick-start.md rename to tutorials/using-turing-quick-start/quick-start.jmd index fc620c329..69cd6484a 100644 --- a/tutorials/using-turing-quick-start/quick-start.md +++ b/tutorials/using-turing-quick-start/quick-start.jmd @@ -1,35 +1,41 @@ --- -title: Probabilistic Programming in Thirty Seconds -permalink: /docs/using-turing/quick-start/ +title: Quickstart +permalink: /docs/using-turing/quickstart +redirect_from: docs/1-quickstart/ weave_options: error : false --- # Probabilistic Programming in Thirty Seconds -If you are already well-versed in probabilistic programming and just want to take a quick look at how Turing's syntax works or otherwise just want a model to start with, we have provided a Bayesian coin-flipping model to play with. +If you are already well-versed in probabilistic programming and just want to take a quick look at how Turing's syntax works or otherwise just want a model to start with, we have provided a complete Bayesian coin-flipping model below. -This example can be run on however you have Julia installed (see [Getting Started]({{site.baseurl}}/docs/using-turing/get-started)), but you will need to install the packages `Turing` and `StatsPlots` if you have not done so already. +This example can be run wherever you have Julia installed (see [Getting Started]({{site.baseurl}}/docs/using-turing/get-started)), but you will need to install the packages `Turing` and `StatsPlots` if you have not done so already. -This is an excerpt from a more formal example introducing probabilistic programming which can be found in Jupyter notebook form [here](https://nbviewer.jupyter.org/github/TuringLang/TuringTutorials/blob/master/notebook/00-introduction/00_introduction.ipynb) or as part of the documentation website [here]({{site.baseurl}}/tutorials). - +This is an excerpt from a more formal example which can be found in [this Jupyter notebook](https://nbviewer.jupyter.org/github/TuringLang/TuringTutorials/blob/master/notebook/00-introduction/00_introduction.ipynb) or as part of the documentation website [here]({{site.baseurl}}/tutorials). ```julia # Import libraries. using Turing, StatsPlots, Random +``` +```julia # Set the true probability of heads in a coin. p_true = 0.5 # Iterate from having seen 0 observations to 100 observations. Ns = 0:100 +``` +```julia # Draw data from a Bernoulli distribution, i.e. draw heads or tails. Random.seed!(12) data = rand(Bernoulli(p_true), last(Ns)) +``` +```julia # Declare our Turing model. @model function coinflip(y) # Our prior belief about the probability of heads in a coin. @@ -42,7 +48,9 @@ data = rand(Bernoulli(p_true), last(Ns)) y[n] ~ Bernoulli(p) end end +``` +```julia # Settings of the Hamiltonian Monte Carlo (HMC) sampler. iterations = 1000 ϵ = 0.05 @@ -50,7 +58,9 @@ iterations = 1000 # Start sampling. chain = sample(coinflip(data), HMC(ϵ, τ), iterations) +``` +```julia # Plot a summary of the sampling process for the parameter p, i.e. the probability of heads in a coin. histogram(chain[:p]) ``` From 6a82b2060a63828bbd5a011f2d1e3bc6d2d242bc Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Wed, 25 Jan 2023 16:11:18 +0000 Subject: [PATCH 04/87] Converted contributing pages to jmd --- ...buting-guide.md => contributing-guide.jmd} | 10 ----- ...-guide.md => contributing-style-guide.jmd} | 37 ++----------------- 2 files changed, 3 insertions(+), 44 deletions(-) rename tutorials/contributing-guide/{contributing-guide.md => contributing-guide.jmd} (99%) rename tutorials/contributing-style-guide/{contributing-style-guide.md => contributing-style-guide.jmd} (84%) diff --git a/tutorials/contributing-guide/contributing-guide.md b/tutorials/contributing-guide/contributing-guide.jmd similarity index 99% rename from tutorials/contributing-guide/contributing-guide.md rename to tutorials/contributing-guide/contributing-guide.jmd index bc297f824..4b05186f6 100644 --- a/tutorials/contributing-guide/contributing-guide.md +++ b/tutorials/contributing-guide/contributing-guide.jmd @@ -9,32 +9,22 @@ weave_options: Turing is an open source project. If you feel that you have some relevant skills and are interested in contributing, then please do get in touch. You can contribute by opening issues on GitHub or implementing things yourself and making a pull request. We would also appreciate example models written using Turing. - Turing has a [style guide]({{site.baseurl}}/docs/contributing/style-guide). It is not strictly necessary to review it before making a pull request, but you may be asked to change portions of your code to conform with the style guide before it is merged. - ## How to Contribute - ### Getting Started - * [Fork this repository](https://github.com/TuringLang/Turing.jl#fork-destination-box). * Clone your fork on your local machine: `git clone https://github.com/your_username/Turing.jl`. * Add a remote corresponding to this repository: - `git remote add upstream https://github.com/TuringLang/Turing.jl`. - ### What Can I Do? - Look at the [issues](https://github.com/TuringLang/Turing.jl/issues) page to find an outstanding issue. For instance, you could implement new features, fix bugs or write example models. - ### Git Workflow - For more information on how the Git workflow typically functions, please see the [GitHub's introduction](https://guides.github.com/introduction/flow/) or [Julia's contribution guide](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md). - diff --git a/tutorials/contributing-style-guide/contributing-style-guide.md b/tutorials/contributing-style-guide/contributing-style-guide.jmd similarity index 84% rename from tutorials/contributing-style-guide/contributing-style-guide.md rename to tutorials/contributing-style-guide/contributing-style-guide.jmd index 403637a83..42d78f34e 100644 --- a/tutorials/contributing-style-guide/contributing-style-guide.md +++ b/tutorials/contributing-style-guide/contributing-style-guide.jmd @@ -7,34 +7,23 @@ weave_options: # Style Guide -Most Turing code follow the [Invenia](https://invenia.ca/labs/)'s style guide. We would like to thank them for allowing us to access and use it. Please don't let not having read it stop you from contributing to Turing! No one will be annoyed if you open a PR whose style doesn't follow these conventions; we will just help you correct it before it gets merged. - +Most Turing code follows the [Invenia](https://invenia.ca/labs/)'s style guide. We would like to thank them for allowing us to access and use it. Please don't let not having read it stop you from contributing to Turing! No one will be annoyed if you open a PR with style that doesn't follow these conventions; we will just help you correct it before it gets merged. These conventions were originally written at Invenia, taking inspiration from a variety of sources including Python's [PEP8](http://legacy.python.org/dev/peps/pep-0008), Julia's [Notes for Contributors](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md), and Julia's [Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/). - What follows is a mixture of a verbatim copy of Invenia's original guide and some of our own modifications. - ## A Word on Consistency - When adhering to this style it's important to realize that these are guidelines and not rules. This is [stated best in the PEP8](http://legacy.python.org/dev/peps/pep-0008/#a-foolish-consistency-is-the-hobgoblin-of-little-minds): - > A style guide is about consistency. Consistency with this style guide is important. Consistency within a project is more important. Consistency within one module or function is most important. - - > But most importantly: know when to be inconsistent – sometimes the style guide just doesn't apply. When in doubt, use your best judgment. Look at other examples and decide what looks best. And don't hesitate to ask! - - ## Synopsis - -Attempt to follow both the [Julia Contribution Guidelines](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md#general-formatting-guidelines-for-julia-code-contributions), the [Julia Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/), and this guide. When convention guidelines conflict this guide takes precedence (known conflicts will be noted in this guide). - +Attempt to follow the [Julia Contribution Guidelines](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md#general-formatting-guidelines-for-julia-code-contributions), the [Julia Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/), and this guide. When convention guidelines conflict, this guide takes precedence (known conflicts will be noted in this guide). * Use 4 spaces per indentation level, no tabs. * Try to adhere to a 92 character line length limit. @@ -45,16 +34,12 @@ Attempt to follow both the [Julia Contribution Guidelines](https://github.com/Ju * No whitespace at the end of a line (trailing whitespace). * Avoid padding brackets with spaces. ex. `Int64(value)` preferred over `Int64( value )`. - ## Editor Configuration - ### Sublime Text Settings - If you are a user of Sublime Text we recommend that you have the following options in your Julia syntax specific settings. To modify these settings first open any Julia file (`*.jl`) in Sublime Text. Then navigate to: `Preferences > Settings - More > Syntax Specific - User` - ```json { "translate_tabs_to_spaces": true, @@ -65,13 +50,10 @@ If you are a user of Sublime Text we recommend that you have the following optio } ``` - ### Vim Settings - If you are a user of Vim we recommend that you add the following options to your `.vimrc` file. - ``` set tabstop=4 " Sets tabstops to a width of four columns. set softtabstop=4 " Determines the behaviour of TAB and BACKSPACE keys with expandtab. @@ -81,19 +63,14 @@ au FileType julia setlocal expandtab " Replaces tabs with spaces. au FileType julia setlocal colorcolumn=93 " Highlights column 93 to help maintain the 92 character line limit. ``` - By default, Vim seems to guess that `.jl` files are written in Lisp. To ensure that Vim recognizes Julia files you can manually have it check for the `.jl` extension, but a better solution is to install [Julia-Vim](https://github.com/JuliaLang/julia-vim), which also includes proper syntax highlighting and a few cool other features. - ## Test Formatting - ### Testsets - Julia provides [test sets](https://docs.julialang.org/en/v1/stdlib/Test/#Working-with-Test-Sets-1) which allows developers to group tests into logical groupings. Test sets can be nested and ideally packages should only have a single "root" test set. It is recommended that the "runtests.jl" file contains the root test set which contains the remainder of the tests: - ```julia @testset "PkgExtreme" begin include("arithmetic.jl") @@ -101,15 +78,11 @@ Julia provides [test sets](https://docs.julialang.org/en/v1/stdlib/Test/#Working end ``` - The file structure of the `test` folder should mirror that of the `src` folder. Every file in `src` should have a complementary file in the `test` folder, containing tests relevant to that file's contents. - ### Comparisons - -Most tests are written in the form `@test x == y`. Since the `==` function doesn't take types into account tests like the following are valid: `@test 1.0 == 1`. Avoid adding visual noise into test comparisons: - +Most tests are written in the form `@test x == y`. Since the `==` function doesn't take types into account, tests like the following are valid: `@test 1.0 == 1`. Avoid adding visual noise into test comparisons: ```julia # Yes: @@ -119,13 +92,10 @@ Most tests are written in the form `@test x == y`. Since the `==` function doesn @test value == 0.0 ``` - In cases where you are checking the numerical validity of a model's parameter estimates, please use the `check_numerical` function found in `test/test_utils/numerical_tests.jl`. This function will evaluate a model's parameter estimates using tolerance levels `atol` and `rtol`. Testing will only be performed if you are running the test suite locally or if Travis is executing the "Numerical" testing stage. - Here is an example of usage: - ```julia # Check that m and s are plus or minus one from 1.5 and 2.2, respectively. check_numerical(chain, [:m, :s], [1.5, 2.2], atol = 1.0) @@ -136,4 +106,3 @@ check_gdemo(chain, atol = 0.1) # Checks the estimates for a default MoG model. check_MoGtest_default(chain, atol = 0.1) ``` - From 306f7771fd32b7976ab4aac712ab63e005d46b02 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 26 Jan 2023 16:48:21 +0000 Subject: [PATCH 05/87] add project files --- tutorials/contributing-guide/Manifest.toml | 1592 +++++++++++++++++ tutorials/contributing-guide/Project.toml | 3 + .../contributing-style-guide/Manifest.toml | 1592 +++++++++++++++++ .../contributing-style-guide/Project.toml | 3 + .../docs-00-getting-started/Manifest.toml | 564 +++--- .../Manifest.toml | 1592 +++++++++++++++++ .../Project.toml | 3 + .../for-developers-compiler/Manifest.toml | 1592 +++++++++++++++++ .../for-developers-compiler/Project.toml | 3 + .../for-developers-interface/Manifest.toml | 1592 +++++++++++++++++ .../for-developers-interface/Project.toml | 3 + .../Manifest.toml | 1592 +++++++++++++++++ .../Project.toml | 3 + tutorials/using-turing-advanced/Manifest.toml | 1592 +++++++++++++++++ tutorials/using-turing-advanced/Project.toml | 3 + tutorials/using-turing-autodiff/Manifest.toml | 1592 +++++++++++++++++ tutorials/using-turing-autodiff/Project.toml | 3 + .../using-turing-dynamicppl/Manifest.toml | 1592 +++++++++++++++++ .../using-turing-dynamicppl/Project.toml | 3 + tutorials/using-turing-guide/Manifest.toml | 1592 +++++++++++++++++ tutorials/using-turing-guide/Project.toml | 3 + .../Manifest.toml | 1592 +++++++++++++++++ .../Project.toml | 3 + .../using-turing-quick-start/Manifest.toml | 1592 +++++++++++++++++ .../using-turing-quick-start/Project.toml | 3 + .../using-turing-sampler-viz/Manifest.toml | 1592 +++++++++++++++++ .../using-turing-sampler-viz/Project.toml | 3 + tutorials/using-turing/Manifest.toml | 1592 +++++++++++++++++ tutorials/using-turing/Project.toml | 3 + 29 files changed, 22614 insertions(+), 280 deletions(-) create mode 100644 tutorials/contributing-guide/Manifest.toml create mode 100644 tutorials/contributing-guide/Project.toml create mode 100644 tutorials/contributing-style-guide/Manifest.toml create mode 100644 tutorials/contributing-style-guide/Project.toml create mode 100644 tutorials/for-developers-abstractmcmc-turing/Manifest.toml create mode 100644 tutorials/for-developers-abstractmcmc-turing/Project.toml create mode 100644 tutorials/for-developers-compiler/Manifest.toml create mode 100644 tutorials/for-developers-compiler/Project.toml create mode 100644 tutorials/for-developers-interface/Manifest.toml create mode 100644 tutorials/for-developers-interface/Project.toml create mode 100644 tutorials/for-developers-variational-inference/Manifest.toml create mode 100644 tutorials/for-developers-variational-inference/Project.toml create mode 100644 tutorials/using-turing-advanced/Manifest.toml create mode 100644 tutorials/using-turing-advanced/Project.toml create mode 100644 tutorials/using-turing-autodiff/Manifest.toml create mode 100644 tutorials/using-turing-autodiff/Project.toml create mode 100644 tutorials/using-turing-dynamicppl/Manifest.toml create mode 100644 tutorials/using-turing-dynamicppl/Project.toml create mode 100644 tutorials/using-turing-guide/Manifest.toml create mode 100644 tutorials/using-turing-guide/Project.toml create mode 100644 tutorials/using-turing-performance-tips/Manifest.toml create mode 100644 tutorials/using-turing-performance-tips/Project.toml create mode 100644 tutorials/using-turing-quick-start/Manifest.toml create mode 100644 tutorials/using-turing-quick-start/Project.toml create mode 100644 tutorials/using-turing-sampler-viz/Manifest.toml create mode 100644 tutorials/using-turing-sampler-viz/Project.toml create mode 100644 tutorials/using-turing/Manifest.toml create mode 100644 tutorials/using-turing/Project.toml diff --git a/tutorials/contributing-guide/Manifest.toml b/tutorials/contributing-guide/Manifest.toml new file mode 100644 index 000000000..ea4ec1254 --- /dev/null +++ b/tutorials/contributing-guide/Manifest.toml @@ -0,0 +1,1592 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.6.7" +manifest_format = "2.0" +project_hash = "102d486b18af7951cac72d56aec7c8e37b50a316" + +[[deps.AbstractFFTs]] +deps = ["ChainRulesCore", "LinearAlgebra"] +git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" +uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" +version = "1.2.1" + +[[deps.AbstractMCMC]] +deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] +git-tree-sha1 = "b4d13c741b2bbe0e1af305a92d2f105a7caa47af" +uuid = 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"12413925-8142-5f55-bb0e-6d7ca50bb09b" +version = "0.4.0+1" + +[[deps.Xorg_xcb_util_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"] +git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1" +uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5" +version = "0.4.0+1" + +[[deps.Xorg_xcb_util_keysyms_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] +git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00" +uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7" +version = "0.4.0+1" + +[[deps.Xorg_xcb_util_renderutil_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] +git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e" +uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e" +version = "0.3.9+1" + +[[deps.Xorg_xcb_util_wm_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] +git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67" +uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361" +version = "0.4.1+1" + +[[deps.Xorg_xkbcomp_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"] +git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b" +uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" +version = "1.4.2+4" + +[[deps.Xorg_xkeyboard_config_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"] +git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d" +uuid = "33bec58e-1273-512f-9401-5d533626f822" +version = "2.27.0+4" + +[[deps.Xorg_xtrans_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" +uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" +version = "1.4.0+3" + +[[deps.Zlib_jll]] +deps = ["Libdl"] +uuid = "83775a58-1f1d-513f-b197-d71354ab007a" + +[[deps.Zstd_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927" +uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" +version = "1.5.2+0" + +[[deps.ZygoteRules]] +deps = ["MacroTools"] +git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" +uuid = "700de1a5-db45-46bc-99cf-38207098b444" +version = "0.2.2" + +[[deps.fzf_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56" +uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" +version = "0.29.0+0" + +[[deps.libaom_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4" +uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" +version = "3.4.0+0" + +[[deps.libass_jll]] +deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] +git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" +uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" +version = "0.15.1+0" + +[[deps.libfdk_aac_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" +uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" +version = "2.0.2+0" + +[[deps.libpng_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] +git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" +uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" +version = "1.6.38+0" + +[[deps.libvorbis_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] +git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" +uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" +version = "1.3.7+1" + +[[deps.nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" + +[[deps.x264_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" +uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" +version = "2021.5.5+0" + +[[deps.x265_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+0" diff --git a/tutorials/contributing-style-guide/Project.toml b/tutorials/contributing-style-guide/Project.toml new file mode 100644 index 000000000..d9c1da059 --- /dev/null +++ b/tutorials/contributing-style-guide/Project.toml @@ -0,0 +1,3 @@ +[deps] +StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" +Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/docs-00-getting-started/Manifest.toml b/tutorials/docs-00-getting-started/Manifest.toml index 3a78565d5..b12df50be 100644 --- a/tutorials/docs-00-getting-started/Manifest.toml +++ b/tutorials/docs-00-getting-started/Manifest.toml @@ -1,12 +1,16 @@ # This file is machine-generated - editing it directly is not advised -[[AbstractFFTs]] +julia_version = "1.6.7" +manifest_format = "2.0" +project_hash = "102d486b18af7951cac72d56aec7c8e37b50a316" + +[[deps.AbstractFFTs]] deps = ["ChainRulesCore", "LinearAlgebra"] git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" version = "1.2.1" -[[AbstractMCMC]] +[[deps.AbstractMCMC]] deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] git-tree-sha1 = "b4d13c741b2bbe0e1af305a92d2f105a7caa47af" uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" @@ -23,51 +27,51 @@ git-tree-sha1 = "faa260e4cb5aba097a73fab382dd4b5819d8ec8c" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" version = "0.4.4" -[[Adapt]] +[[deps.Adapt]] deps = ["LinearAlgebra"] git-tree-sha1 = "0310e08cb19f5da31d08341c6120c047598f5b9c" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" version = "3.5.0" -[[AdvancedHMC]] +[[deps.AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "Setfield", "Statistics", "StatsBase", "StatsFuns", "UnPack"] git-tree-sha1 = "0091e2e4d0a7125da0e3ad8c7dbff9171a921461" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" version = "0.3.6" -[[AdvancedMH]] +[[deps.AdvancedMH]] deps = ["AbstractMCMC", "Distributions", "Random", "Requires"] git-tree-sha1 = "d7a7dabeaef34e5106cdf6c2ac956e9e3f97f666" uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170" version = "0.6.8" -[[AdvancedPS]] +[[deps.AdvancedPS]] deps = ["AbstractMCMC", "Distributions", "Libtask", "Random", "StatsFuns"] git-tree-sha1 = "9ff1247be1e2aa2e740e84e8c18652bd9d55df22" uuid = "576499cb-2369-40b2-a588-c64705576edc" version = "0.3.8" -[[AdvancedVI]] +[[deps.AdvancedVI]] deps = ["Bijectors", "Distributions", "DistributionsAD", "DocStringExtensions", "ForwardDiff", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "StatsBase", "StatsFuns", "Tracker"] git-tree-sha1 = "67fcc7d46c26250e89fc62798fbe07b5ee264c6f" uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c" version = "0.1.6" -[[ArgCheck]] +[[deps.ArgCheck]] git-tree-sha1 = "a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4" uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197" version = "2.3.0" -[[ArgTools]] +[[deps.ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" -[[Arpack]] +[[deps.Arpack]] deps = ["Arpack_jll", "Libdl", "LinearAlgebra", "Logging"] git-tree-sha1 = "9b9b347613394885fd1c8c7729bfc60528faa436" uuid = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97" version = "0.5.4" -[[Arpack_jll]] +[[deps.Arpack_jll]] deps = ["Libdl", "OpenBLAS_jll", "Pkg"] git-tree-sha1 = "e214a9b9bd1b4e1b4f15b22c0994862b66af7ff7" uuid = "68821587-b530-5797-8361-c406ea357684" @@ -79,691 +83,691 @@ git-tree-sha1 = "e5f08b5689b1aad068e01751889f2f615c7db36d" uuid = "30b0a656-2188-435a-8636-2ec0e6a096e2" version = "0.1.29" -[[ArrayInterfaceStaticArraysCore]] +[[deps.ArrayInterfaceStaticArraysCore]] deps = ["Adapt", "ArrayInterfaceCore", "LinearAlgebra", "StaticArraysCore"] git-tree-sha1 = "93c8ba53d8d26e124a5a8d4ec914c3a16e6a0970" uuid = "dd5226c6-a4d4-4bc7-8575-46859f9c95b9" version = "0.1.3" -[[Artifacts]] +[[deps.Artifacts]] uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" -[[AxisAlgorithms]] +[[deps.AxisAlgorithms]] deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"] git-tree-sha1 = "66771c8d21c8ff5e3a93379480a2307ac36863f7" uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950" version = "1.0.1" -[[AxisArrays]] +[[deps.AxisArrays]] deps = ["Dates", "IntervalSets", "IterTools", "RangeArrays"] git-tree-sha1 = "1dd4d9f5beebac0c03446918741b1a03dc5e5788" uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9" version = "0.4.6" -[[BangBang]] +[[deps.BangBang]] deps = ["Compat", "ConstructionBase", "Future", "InitialValues", "LinearAlgebra", "Requires", "Setfield", "Tables", "ZygoteRules"] git-tree-sha1 = "7fe6d92c4f281cf4ca6f2fba0ce7b299742da7ca" uuid = "198e06fe-97b7-11e9-32a5-e1d131e6ad66" version = "0.3.37" -[[Base64]] +[[deps.Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" -[[Baselet]] +[[deps.Baselet]] git-tree-sha1 = "aebf55e6d7795e02ca500a689d326ac979aaf89e" uuid = "9718e550-a3fa-408a-8086-8db961cd8217" version = "0.1.1" -[[Bijectors]] +[[deps.Bijectors]] deps = ["ArgCheck", "ChainRulesCore", "ChangesOfVariables", "Compat", "Distributions", "Functors", "InverseFunctions", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "MappedArrays", "Random", "Reexport", "Requires", "Roots", "SparseArrays", "Statistics"] git-tree-sha1 = "a3704b8e5170f9339dff4e6cb286ad49464d3646" uuid = "76274a88-744f-5084-9051-94815aaf08c4" version = "0.10.6" -[[BitFlags]] +[[deps.BitFlags]] git-tree-sha1 = "43b1a4a8f797c1cddadf60499a8a077d4af2cd2d" uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" version = "0.1.7" -[[Bzip2_jll]] +[[deps.Bzip2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2" uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0" version = "1.0.8+0" -[[Cairo_jll]] +[[deps.Cairo_jll]] deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] git-tree-sha1 = "4b859a208b2397a7a623a03449e4636bdb17bcf2" uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a" version = "1.16.1+1" -[[Calculus]] +[[deps.Calculus]] deps = ["LinearAlgebra"] git-tree-sha1 = "f641eb0a4f00c343bbc32346e1217b86f3ce9dad" uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9" version = "0.5.1" -[[ChainRules]] +[[deps.ChainRules]] deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "Statistics", "StructArrays"] git-tree-sha1 = "c46adabdd0348f0ee8de91142cfc4a72a613ac0a" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" version = "1.46.1" -[[ChainRulesCore]] +[[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra", "SparseArrays"] git-tree-sha1 = "c6d890a52d2c4d55d326439580c3b8d0875a77d9" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" version = "1.15.7" -[[ChangesOfVariables]] +[[deps.ChangesOfVariables]] deps = ["ChainRulesCore", "LinearAlgebra", "Test"] git-tree-sha1 = "844b061c104c408b24537482469400af6075aae4" uuid = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0" version = "0.1.5" -[[Clustering]] +[[deps.Clustering]] deps = ["Distances", "LinearAlgebra", "NearestNeighbors", "Printf", "Random", "SparseArrays", "Statistics", "StatsBase"] git-tree-sha1 = "64df3da1d2a26f4de23871cd1b6482bb68092bd5" uuid = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" version = "0.14.3" -[[CodecZlib]] +[[deps.CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] git-tree-sha1 = "9c209fb7536406834aa938fb149964b985de6c83" uuid = 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uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" diff --git a/tutorials/for-developers-abstractmcmc-turing/Manifest.toml b/tutorials/for-developers-abstractmcmc-turing/Manifest.toml new file mode 100644 index 000000000..ea4ec1254 --- /dev/null +++ b/tutorials/for-developers-abstractmcmc-turing/Manifest.toml @@ -0,0 +1,1592 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.6.7" +manifest_format = "2.0" +project_hash = "102d486b18af7951cac72d56aec7c8e37b50a316" + +[[deps.AbstractFFTs]] +deps = ["ChainRulesCore", "LinearAlgebra"] +git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" +uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" +version = "1.2.1" + +[[deps.AbstractMCMC]] +deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] +git-tree-sha1 = "b4d13c741b2bbe0e1af305a92d2f105a7caa47af" +uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" 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"JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" +uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" +version = "2021.5.5+0" + +[[deps.x265_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+0" diff --git a/tutorials/using-turing/Project.toml b/tutorials/using-turing/Project.toml new file mode 100644 index 000000000..d9c1da059 --- /dev/null +++ b/tutorials/using-turing/Project.toml @@ -0,0 +1,3 @@ +[deps] +StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" +Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" From 892c30184664e3c7ca62ad300b9f4f288c5d9a6b Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 26 Jan 2023 17:05:22 +0000 Subject: [PATCH 06/87] Disable evaluating style examples --- .../contributing-style-guide/contributing-style-guide.jmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials/contributing-style-guide/contributing-style-guide.jmd b/tutorials/contributing-style-guide/contributing-style-guide.jmd index 42d78f34e..2c7a68888 100644 --- a/tutorials/contributing-style-guide/contributing-style-guide.jmd +++ b/tutorials/contributing-style-guide/contributing-style-guide.jmd @@ -71,7 +71,7 @@ By default, Vim seems to guess that `.jl` files are written in Lisp. To ensure t Julia provides [test sets](https://docs.julialang.org/en/v1/stdlib/Test/#Working-with-Test-Sets-1) which allows developers to group tests into logical groupings. Test sets can be nested and ideally packages should only have a single "root" test set. It is recommended that the "runtests.jl" file contains the root test set which contains the remainder of the tests: -```julia +```julia eval=false @testset "PkgExtreme" begin include("arithmetic.jl") include("utils.jl") @@ -84,7 +84,7 @@ The file structure of the `test` folder should mirror that of the `src` folder. Most tests are written in the form `@test x == y`. Since the `==` function doesn't take types into account, tests like the following are valid: `@test 1.0 == 1`. Avoid adding visual noise into test comparisons: -```julia +```julia eval=false # Yes: @test value == 0 @@ -96,7 +96,7 @@ In cases where you are checking the numerical validity of a model's parameter es Here is an example of usage: -```julia +```julia eval=false # Check that m and s are plus or minus one from 1.5 and 2.2, respectively. check_numerical(chain, [:m, :s], [1.5, 2.2], atol = 1.0) From 712746eb5dedab5ba0703eb019dea3a971338474 Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Thu, 26 Jan 2023 20:28:05 +0000 Subject: [PATCH 07/87] Update and rename index.md to index.jmd --- tutorials/using-turing/index.jmd | 18 ++++++++++++++++++ tutorials/using-turing/index.md | 22 ---------------------- 2 files changed, 18 insertions(+), 22 deletions(-) create mode 100644 tutorials/using-turing/index.jmd delete mode 100644 tutorials/using-turing/index.md diff --git a/tutorials/using-turing/index.jmd b/tutorials/using-turing/index.jmd new file mode 100644 index 000000000..bc514777f --- /dev/null +++ b/tutorials/using-turing/index.jmd @@ -0,0 +1,18 @@ +--- +title: Turing Documentation +permalink: /docs/using-turing/ +weave_options: + error : false +--- + +# Turing Documentation + +Welcome to the documentation for Turing. + +Turing is a general-purpose probabilistic programming language for robust, efficient Bayesian inference and decision making. Current features include: + + * General-purpose probabilistic programming with an intuitive modelling interface; + * Robust, efficient [Hamiltonian Monte Carlo (HMC)](https://github.com/TuringLang/AdvancedHMC.jl) sampling for differentiable posterior distributions; + * Particle MCMC sampling for complex posterior distributions involving discrete variables and stochastic control flow; and + * Gibbs sampling combines particle MCMC, HMC and random-walk MH (RWMH) etc. + diff --git a/tutorials/using-turing/index.md b/tutorials/using-turing/index.md deleted file mode 100644 index cd43ea7a9..000000000 --- a/tutorials/using-turing/index.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -title: Turing Documentation -permalink: docs/using-turing/ -weave_options: - error : false ---- - -# Turing Documentation - -Welcome to the documentation for Turing. - - -## Introduction - -**Turing** is a general-purpose probabilistic programming language for robust, efficient Bayesian inference and decision making. Current features include: - - - * **General-purpose** probabilistic programming with an intuitive modelling interface; - * Robust, efficient [Hamiltonian Monte Carlo (HMC)](https://github.com/TuringLang/AdvancedHMC.jl) sampling for differentiable posterior distributions; - * **Particle MCMC** sampling for complex posterior distributions involving discrete variables and stochastic control flow; and - * Compositional inference via **Gibbs sampling** that combines particle MCMC, HMC and random-walk MH (RWMH). - From 1c72459731523c59a0371d75b30325a49d514c69 Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Thu, 26 Jan 2023 20:32:24 +0000 Subject: [PATCH 08/87] Rename index.jmd to using-turing.jmd --- tutorials/using-turing/{index.jmd => using-turing.jmd} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tutorials/using-turing/{index.jmd => using-turing.jmd} (100%) diff --git a/tutorials/using-turing/index.jmd b/tutorials/using-turing/using-turing.jmd similarity index 100% rename from tutorials/using-turing/index.jmd rename to tutorials/using-turing/using-turing.jmd From 8ccdb21c14f11e32224d212b39e2ec474f39b6b9 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 26 Jan 2023 20:57:14 +0000 Subject: [PATCH 09/87] fix formatting for guide.md --- .../{guide.md => using-turing-guide.md} | 162 ++++++------------ 1 file changed, 49 insertions(+), 113 deletions(-) rename tutorials/using-turing-guide/{guide.md => using-turing-guide.md} (87%) diff --git a/tutorials/using-turing-guide/guide.md b/tutorials/using-turing-guide/using-turing-guide.md similarity index 87% rename from tutorials/using-turing-guide/guide.md rename to tutorials/using-turing-guide/using-turing-guide.md index 0b296f9d5..337487a32 100644 --- a/tutorials/using-turing-guide/guide.md +++ b/tutorials/using-turing-guide/using-turing-guide.md @@ -7,29 +7,22 @@ weave_options: # Guide - ## Basics - ### Introduction -A probabilistic program is Julia code wrapped in a `@model` macro. It can use arbitrary Julia code, but to ensure correctness of inference it should not have external effects or modify global state. Stack-allocated variables are safe, but mutable heap-allocated objects may lead to subtle bugs when using task copying. By default Libtask deepcopies `Array` and `Dict` objects when copying task to avoid bugs with data stored in mutable structure in Turing models. - +A probabilistic program is Julia code wrapped in a `@model` macro. It can use arbitrary Julia code, but to ensure correctness of inference it should not have external effects or modify global state. Stack-allocated variables are safe, but mutable heap-allocated objects may lead to subtle bugs when using task copying. By default Libtask deepcopies `Array` and `Dict` objects when copying task to avoid bugs with data stored in mutable structure in Turing models. To specify distributions of random variables, Turing programs should use the `~` notation: - `x ~ distr` where `x` is a symbol and `distr` is a distribution. If `x` is undefined in the model function, inside the probabilistic program, this puts a random variable named `x`, distributed according to `distr`, in the current scope. `distr` can be a value of any type that implements `rand(distr)`, which samples a value from the distribution `distr`. If `x` is defined, this is used for conditioning in a style similar to [Anglican](https://probprog.github.io/anglican/index.html) (another PPL). In this case, `x` is an observed value, assumed to have been drawn from the distribution `distr`. The likelihood is computed using `logpdf(distr,y)`. The observe statements should be arranged so that every possible run traverses all of them in exactly the same order. This is equivalent to demanding that they are not placed inside stochastic control flow. - Available inference methods include Importance Sampling (IS), Sequential Monte Carlo (SMC), Particle Gibbs (PG), Hamiltonian Monte Carlo (HMC), Hamiltonian Monte Carlo with Dual Averaging (HMCDA) and The No-U-Turn Sampler (NUTS). - ### Simple Gaussian Demo Below is a simple Gaussian demo illustrate the basic usage of Turing.jl. - ```julia # Import packages. using Turing @@ -40,21 +33,17 @@ using StatsPlots s² ~ InverseGamma(2, 3) m ~ Normal(0, sqrt(s²)) x ~ Normal(m, sqrt(s²)) - y ~ Normal(m, sqrt(s²)) + return y ~ Normal(m, sqrt(s²)) end ``` - Note: As a sanity check, the prior expectation of `s²` is `mean(InverseGamma(2, 3)) = 3/(2 - 1) = 3` and the prior expectation of `m` is 0. This can be easily checked using `Prior`: ```julia p1 = sample(gdemo(missing, missing), Prior(), 100000) ``` - - -We can perform inference by using the `sample` function, the first argument of which is our probabilistic program and the second of which is a sampler. More information on each sampler is located in the [API]({{site.baseurl}}/docs/library). - +We can perform inference by using the `sample` function, the first argument of which is our probabilistic program and the second of which is a sampler. More information on each sampler is located in the [API](%7B%7Bsite.baseurl%7D%7D/docs/library). ```julia # Run sampler, collect results. @@ -66,10 +55,8 @@ c5 = sample(gdemo(1.5, 2), HMCDA(0.15, 0.65), 1000) c6 = sample(gdemo(1.5, 2), NUTS(0.65), 1000) ``` - The `MCMCChains` module (which is re-exported by Turing) provides plotting tools for the `Chain` objects returned by a `sample` function. See the [MCMCChains](https://github.com/TuringLang/MCMCChains.jl) repository for more information on the suite of tools available for diagnosing MCMC chains. - ```julia # Summarise results describe(c3) @@ -79,62 +66,48 @@ plot(c3) savefig("gdemo-plot.png") ``` - The arguments for each sampler are: + - SMC: number of particles. + - PG: number of particles, number of iterations. + - HMC: leapfrog step size, leapfrog step numbers. + - Gibbs: component sampler 1, component sampler 2, ... + - HMCDA: total leapfrog length, target accept ratio. + - NUTS: number of adaptation steps (optional), target accept ratio. - * SMC: number of particles. - * PG: number of particles, number of iterations. - * HMC: leapfrog step size, leapfrog step numbers. - * Gibbs: component sampler 1, component sampler 2, ... - * HMCDA: total leapfrog length, target accept ratio. - * NUTS: number of adaptation steps (optional), target accept ratio. - - -For detailed information on the samplers, please review Turing.jl's [API]({{site.baseurl}}/docs/library) documentation. - +For detailed information on the samplers, please review Turing.jl's [API](%7B%7Bsite.baseurl%7D%7D/docs/library) documentation. ### Modelling Syntax Explained - Using this syntax, a probabilistic model is defined in Turing. The model function generated by Turing can then be used to condition the model onto data. Subsequently, the sample function can be used to generate samples from the posterior distribution. - In the following example, the defined model is conditioned to the data (arg*1 = 1, arg*2 = 2) by passing (1, 2) to the model function. - ```julia @model function model_name(arg_1, arg_2) - ... + return ... end ``` - The conditioned model can then be passed onto the sample function to run posterior inference. - ```julia model_func = model_name(1, 2) chn = sample(model_func, HMC(..)) # Perform inference by sampling using HMC. ``` - The returned chain contains samples of the variables in the model. - ```julia var_1 = mean(chn[:var_1]) # Taking the mean of a variable named var_1. ``` - The key (`:var_1`) can be a `Symbol` or a `String`. For example, to fetch `x[1]`, one can use `chn[Symbol("x[1]")]` or `chn["x[1]"]`. If you want to retrieve all parameters associated with a specific symbol, you can use `group`. As an example, if you have the parameters `"x[1]"`, `"x[2]"`, and `"x[3]"`, calling `group(chn, :x)` or `group(chn, "x")` will return a new chain with only `"x[1]"`, `"x[2]"`, and `"x[3]"`. - Turing does not have a declarative form. More generally, the order in which you place the lines of a `@model` macro matters. For example, the following example works: - ```julia # Define a simple Normal model with unknown mean and variance. @model function model_function(y) @@ -146,10 +119,8 @@ end sample(model_function(10), SMC(), 100) ``` - But if we switch the `s ~ Poisson(1)` and `y ~ Normal(s, 1)` lines, the model will no longer sample correctly: - ```julia # Define a simple Normal model with unknown mean and variance. @model function model_function(y) @@ -161,8 +132,6 @@ end sample(model_function(10), SMC(), 100) ``` - - ### Sampling Multiple Chains Turing supports distributed and threaded parallel sampling. To do so, call `sample(model, sampler, parallel_type, n, n_chains)`, where `parallel_type` can be either `MCMCThreads()` or `MCMCDistributed()` for thread and parallel sampling, respectively. @@ -251,7 +220,6 @@ chain = sample(model, Prior(), n_samples) You can also run your model (as if it were a function) from the prior distribution, by calling the model without specifying inputs or a sampler. In the below example, we specify a `gdemo` model which returns two variables, `x` and `y`. The model includes `x` and `y` as arguments, but calling the function without passing in `x` or `y` means that Turing's compiler will assume they are missing values to draw from the relevant distribution. The `return` statement is necessary to retrieve the sampled `x` and `y` values. - ```julia @model function gdemo(x, y) s² ~ InverseGamma(2, 3) @@ -262,36 +230,28 @@ You can also run your model (as if it were a function) from the prior distributi end ``` - Assign the function with `missing` inputs to a variable, and Turing will produce a sample from the prior distribution. - ```julia # Samples from p(x,y) g_prior_sample = gdemo(missing, missing) g_prior_sample() ``` - Output: - ``` (0.685690547873451, -1.1972706455914328) ``` - ### Sampling from a Conditional Distribution (The Posterior) - #### Treating observations as random variables - Inputs to the model that have a value `missing` are treated as parameters, aka random variables, to be estimated/sampled. This can be useful if you want to simulate draws for that parameter, or if you are sampling from a conditional distribution. Turing supports the following syntax: - ```julia -@model function gdemo(x, ::Type{T} = Float64) where {T} +@model function gdemo(x, ::Type{T}=Float64) where {T} if x === missing # Initialize `x` if missing x = Vector{T}(undef, 2) @@ -310,10 +270,8 @@ c = sample(model, HMC(0.01, 5), 500) Note the need to initialize `x` when missing since we are iterating over its elements later in the model. The generated values for `x` can be extracted from the `Chains` object using `c[:x]`. - Turing also supports mixed `missing` and non-`missing` values in `x`, where the missing ones will be treated as random variables to be sampled while the others get treated as observations. For example: - ```julia @model function gdemo(x) s² ~ InverseGamma(2, 3) @@ -328,16 +286,14 @@ model = gdemo([missing, 2.4]) c = sample(model, HMC(0.01, 5), 500) ``` - #### Default Values - Arguments to Turing models can have default values much like how default values work in normal Julia functions. For instance, the following will assign `missing` to `x` and treat it as a random variable. If the default value is not `missing`, `x` will be assigned that value and will be treated as an observation instead. ```julia using Turing -@model function generative(x = missing, ::Type{T} = Float64) where {T <: Real} +@model function generative(x=missing, ::Type{T}=Float64) where {T<:Real} if x === missing # Initialize x when missing x = Vector{T}(undef, 10) @@ -354,41 +310,38 @@ m = generative() chain = sample(m, HMC(0.01, 5), 1000) ``` - #### Access Values inside Chain - You can access the values inside a chain several ways: -1. Turn them into a `DataFrame` object -2. Use their raw `AxisArray` form -3. Create a three-dimensional `Array` object + 1. Turn them into a `DataFrame` object + 2. Use their raw `AxisArray` form + 3. Create a three-dimensional `Array` object For example, let `c` be a `Chain`: -1. `DataFrame(c)` converts `c` to a `DataFrame`, -2. `c.value` retrieves the values inside `c` as an `AxisArray`, and -3. `c.value.data` retrieves the values inside `c` as a 3D `Array`. + 1. `DataFrame(c)` converts `c` to a `DataFrame`, + 2. `c.value` retrieves the values inside `c` as an `AxisArray`, and + 3. `c.value.data` retrieves the values inside `c` as a 3D `Array`. #### Variable Types and Type Parameters - The element type of a vector (or matrix) of random variables should match the `eltype` of the its prior distribution, `<: Integer` for discrete distributions and `<: AbstractFloat` for continuous distributions. Moreover, if the continuous random variable is to be sampled using a Hamiltonian sampler, the vector's element type needs to either be: - 1. `Real` to enable auto-differentiation through the model which uses special number types that are sub-types of `Real`, or - 2. Some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Float64) where {T}`. +1. `Real` to enable auto-differentiation through the model which uses special number types that are sub-types of `Real`, or +2. Some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Float64) where {T}`. Similarly, when using a particle sampler, the Julia variable used should either be: - 1. An `Array`, or - 2. An instance of some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Vector{Float64}) where {T}`. - +1. An `Array`, or +2. An instance of some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Vector{Float64}) where {T}`. ### Querying Probabilities from Model or Chain Consider first the following simplified `gdemo` model: + ```julia @model function gdemo0(x) s ~ InverseGamma(2, 3) m ~ Normal(0, sqrt(s)) - x ~ Normal(m, sqrt(s)) + return x ~ Normal(m, sqrt(s)) end # Instantiate three models, with different value of x @@ -398,6 +351,7 @@ model10 = gdemo0(10) ``` Now, query the instantiated models: compute the likelihood of `x = 1.0` given the values of `s = 1.0` and `m = 1.0` for the parameters: + ```julia prob"x = 1.0 | model = model1, s = 1.0, m = 1.0" prob"x = 1.0 | model = model4, s = 1.0, m = 1.0" @@ -410,14 +364,14 @@ Notice that even if we use three models, instantiated with three different value pdf(Normal(1.0, 1.0), 1.0) ``` - Let us now consider the following `gdemo` model: + ```julia @model function gdemo(x, y) s² ~ InverseGamma(2, 3) m ~ Normal(0, sqrt(s²)) x ~ Normal(m, sqrt(s²)) - y ~ Normal(m, sqrt(s²)) + return y ~ Normal(m, sqrt(s²)) end # Instantiate the model. @@ -426,21 +380,16 @@ model = gdemo(2.0, 4.0) The following are examples of valid queries of the `Turing` model or chain: -- `prob"x = 1.0, y = 1.0 | model = model, s = 1.0, m = 1.0"` calculates the likelihood of `x = 1` and `y = 1` given `s = 1` and `m = 1`. - -- `prob"s² = 1.0, m = 1.0 | model = model, x = nothing, y = nothing"` calculates the joint probability of `s = 1` and `m = 1` ignoring `x` and `y`. `x` and `y` are ignored so they can be optionally dropped from the RHS of `|`, but it is recommended to define them. - -- `prob"s² = 1.0, m = 1.0, x = 1.0 | model = model, y = nothing"` calculates the joint probability of `s = 1`, `m = 1` and `x = 1` ignoring `y`. - -- `prob"s² = 1.0, m = 1.0, x = 1.0, y = 1.0 | model = model"` calculates the joint probability of all the variables. + - `prob"x = 1.0, y = 1.0 | model = model, s = 1.0, m = 1.0"` calculates the likelihood of `x = 1` and `y = 1` given `s = 1` and `m = 1`. -- After the MCMC sampling, given a `chain`, `prob"x = 1.0, y = 1.0 | chain = chain, model = model"` calculates the element-wise likelihood of `x = 1.0` and `y = 1.0` for each sample in `chain`. - -- If `save_state=true` was used during sampling (i.e., `sample(model, sampler, N; save_state=true)`), you can simply do `prob"x = 1.0, y = 1.0 | chain = chain"`. + - `prob"s² = 1.0, m = 1.0 | model = model, x = nothing, y = nothing"` calculates the joint probability of `s = 1` and `m = 1` ignoring `x` and `y`. `x` and `y` are ignored so they can be optionally dropped from the RHS of `|`, but it is recommended to define them. + - `prob"s² = 1.0, m = 1.0, x = 1.0 | model = model, y = nothing"` calculates the joint probability of `s = 1`, `m = 1` and `x = 1` ignoring `y`. + - `prob"s² = 1.0, m = 1.0, x = 1.0, y = 1.0 | model = model"` calculates the joint probability of all the variables. + - After the MCMC sampling, given a `chain`, `prob"x = 1.0, y = 1.0 | chain = chain, model = model"` calculates the element-wise likelihood of `x = 1.0` and `y = 1.0` for each sample in `chain`. + - If `save_state=true` was used during sampling (i.e., `sample(model, sampler, N; save_state=true)`), you can simply do `prob"x = 1.0, y = 1.0 | chain = chain"`. In all the above cases, `logprob` can be used instead of `prob` to calculate the log probabilities instead. - ### Maximum likelihood and maximum a posterior estimates Turing provides support for two mode estimation techniques, [maximum likelihood estimation](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) (MLE) and [maximum a posterior](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) (MAP) estimation. Optimization is performed by the [Optim.jl](https://github.com/JuliaNLSolvers/Optim.jl) package. Mode estimation is currently a optional tool, and will not be available to you unless you have manually installed Optim and loaded the package with a `using` statement. To install Optim, run `import Pkg; Pkg.add("Optim")`. @@ -508,7 +457,9 @@ Some methods may have trouble calculating the mode because not enough iterations ```julia # Increase the iterations and allow function eval to increase between calls. -mle_estimate = optimize(model, MLE(), Newton(), Optim.Options(iterations=10_000, allow_f_increases=true)) +mle_estimate = optimize( + model, MLE(), Newton(), Optim.Options(; iterations=10_000, allow_f_increases=true) +) ``` More options for Optim are available [here](https://julianlsolvers.github.io/Optim.jl/stable/#user/config/). @@ -527,7 +478,7 @@ using StatsBase coeftable(mle_estimate) ``` -```julia +``` ───────────────────────────── estimate stderror tstat ───────────────────────────── @@ -547,18 +498,15 @@ You can begin sampling your chain from an MLE/MAP estimate by extracting the vec map_estimate = optimize(model, MAP()) # Sample with the MAP estimate as the starting point. -chain = sample(model, NUTS(), 1_000, init_params = map_estimate.values.array) +chain = sample(model, NUTS(), 1_000; init_params=map_estimate.values.array) ``` ## Beyond the Basics - ### Compositional Sampling Using Gibbs - Turing.jl provides a Gibbs interface to combine different samplers. For example, one can combine an `HMC` sampler with a `PG` sampler to run inference for different parameters in a single model as below. - ```julia @model function simple_choice(xs) p ~ Beta(2, 2) @@ -577,9 +525,7 @@ simple_choice_f = simple_choice([1.5, 2.0, 0.3]) chn = sample(simple_choice_f, Gibbs(HMC(0.2, 3, :p), PG(20, :z)), 1000) ``` - -The `Gibbs` sampler can be used to specify unique automatic differentiation backends for different variable spaces. Please see the [Automatic Differentiation]({{site.baseurl}}/docs/using-turing/autodiff) article for more. - +The `Gibbs` sampler can be used to specify unique automatic differentiation backends for different variable spaces. Please see the [Automatic Differentiation](%7B%7Bsite.baseurl%7D%7D/docs/using-turing/autodiff) article for more. For more details of compositional sampling in Turing.jl, please check the corresponding [paper](http://proceedings.mlr.press/v84/ge18b.html). @@ -596,10 +542,10 @@ Example usage: ```julia @model function demo(x, g) - k = length(unique(g)) - a ~ filldist(Exponential(), k) # = Product(fill(Exponential(), k)) - mu = a[g] - x .~ Normal.(mu) + k = length(unique(g)) + a ~ filldist(Exponential(), k) # = Product(fill(Exponential(), k)) + mu = a[g] + return x .~ Normal.(mu) end ``` @@ -612,46 +558,37 @@ Example usage: ```julia @model function demo(x, g) - k = length(unique(g)) - a ~ arraydist([Exponential(i) for i in 1:k]) - mu = a[g] - x .~ Normal.(mu) + k = length(unique(g)) + a ~ arraydist([Exponential(i) for i in 1:k]) + mu = a[g] + return x .~ Normal.(mu) end ``` ### Working with MCMCChains.jl - Turing.jl wraps its samples using `MCMCChains.Chain` so that all the functions working for `MCMCChains.Chain` can be re-used in Turing.jl. Two typical functions are `MCMCChains.describe` and `MCMCChains.plot`, which can be used as follows for an obtained chain `chn`. For more information on `MCMCChains`, please see the [GitHub repository](https://github.com/TuringLang/MCMCChains.jl). - ```julia describe(chn) # Lists statistics of the samples. plot(chn) # Plots statistics of the samples. ``` - There are numerous functions in addition to `describe` and `plot` in the `MCMCChains` package, such as those used in convergence diagnostics. For more information on the package, please see the [GitHub repository](https://github.com/TuringLang/MCMCChains.jl). - ### Changing Default Settings - Some of Turing.jl's default settings can be changed for better usage. - #### AD Chunk Size - ForwardDiff (Turing's default AD backend) uses forward-mode chunk-wise AD. The chunk size can be set manually by `setchunksize(new_chunk_size)`. #### AD Backend - Turing supports four packages of automatic differentiation (AD) in the back end during sampling. The default AD backend is [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl) for forward-mode AD. Three reverse-mode AD backends are also supported, namely [Tracker](https://github.com/FluxML/Tracker.jl), [Zygote](https://github.com/FluxML/Zygote.jl) and [ReverseDiff](https://github.com/JuliaDiff/ReverseDiff.jl). `Zygote` and `ReverseDiff` are supported optionally if explicitly loaded by the user with `using Zygote` or `using ReverseDiff` next to `using Turing`. -For more information on Turing's automatic differentiation backend, please see the [Automatic Differentiation]({{site.baseurl}}/docs/using-turing/autodiff) article. - +For more information on Turing's automatic differentiation backend, please see the [Automatic Differentiation](%7B%7Bsite.baseurl%7D%7D/docs/using-turing/autodiff) article. #### Progress Logging @@ -668,4 +605,3 @@ In all other cases progress logs are displayed with [TerminalLoggers.jl](https://github.com/c42f/TerminalLoggers.jl). Alternatively, if you provide a custom visualization backend, Turing uses it instead of the default backend. - From 0ad33c226a58ae4ffe7531fb6996f5dd8ab1598a Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 26 Jan 2023 21:00:53 +0000 Subject: [PATCH 10/87] Fixed formatting. --- .../contributing-guide/contributing-guide.jmd | 8 +- .../contributing-style-guide.jmd | 22 +-- .../how_turing_implements_abstractmcmc.md | 101 ++++++----- tutorials/for-developers-compiler/compiler.md | 167 ++++++++++++------ .../for-developers-interface/interface.md | 126 ++++++------- .../variational_inference.md | 32 ++-- tutorials/using-turing-advanced/advanced.md | 50 +++--- tutorials/using-turing-autodiff/autodiff.md | 15 +- .../using-turing-dynamicppl/dynamichmc.md | 9 +- .../using-turing-guide/using-turing-guide.md | 11 +- .../performancetips.md | 44 ++--- .../using-turing-quick-start/quick-start.jmd | 8 +- .../gen-sampler-viz.jl | 57 +++--- .../using-turing-sampler-viz/sampler-viz.md | 59 ++++--- tutorials/using-turing/using-turing.jmd | 9 +- 15 files changed, 394 insertions(+), 324 deletions(-) diff --git a/tutorials/contributing-guide/contributing-guide.jmd b/tutorials/contributing-guide/contributing-guide.jmd index 4b05186f6..08c94289b 100644 --- a/tutorials/contributing-guide/contributing-guide.jmd +++ b/tutorials/contributing-guide/contributing-guide.jmd @@ -9,15 +9,15 @@ weave_options: Turing is an open source project. If you feel that you have some relevant skills and are interested in contributing, then please do get in touch. You can contribute by opening issues on GitHub or implementing things yourself and making a pull request. We would also appreciate example models written using Turing. -Turing has a [style guide]({{site.baseurl}}/docs/contributing/style-guide). It is not strictly necessary to review it before making a pull request, but you may be asked to change portions of your code to conform with the style guide before it is merged. +Turing has a [style guide](%7B%7Bsite.baseurl%7D%7D/docs/contributing/style-guide). It is not strictly necessary to review it before making a pull request, but you may be asked to change portions of your code to conform with the style guide before it is merged. ## How to Contribute ### Getting Started - * [Fork this repository](https://github.com/TuringLang/Turing.jl#fork-destination-box). - * Clone your fork on your local machine: `git clone https://github.com/your_username/Turing.jl`. - * Add a remote corresponding to this repository: + - [Fork this repository](https://github.com/TuringLang/Turing.jl#fork-destination-box). + - Clone your fork on your local machine: `git clone https://github.com/your_username/Turing.jl`. + - Add a remote corresponding to this repository: `git remote add upstream https://github.com/TuringLang/Turing.jl`. diff --git a/tutorials/contributing-style-guide/contributing-style-guide.jmd b/tutorials/contributing-style-guide/contributing-style-guide.jmd index 2c7a68888..14db6d0e4 100644 --- a/tutorials/contributing-style-guide/contributing-style-guide.jmd +++ b/tutorials/contributing-style-guide/contributing-style-guide.jmd @@ -25,14 +25,14 @@ When adhering to this style it's important to realize that these are guidelines Attempt to follow the [Julia Contribution Guidelines](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md#general-formatting-guidelines-for-julia-code-contributions), the [Julia Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/), and this guide. When convention guidelines conflict, this guide takes precedence (known conflicts will be noted in this guide). - * Use 4 spaces per indentation level, no tabs. - * Try to adhere to a 92 character line length limit. - * Use upper camel case convention for [modules](https://docs.julialang.org/en/v1/manual/modules/) and [types](https://docs.julialang.org/en/v1/manual/types/). - * Use lower case with underscores for method names (note: Julia code likes to use lower case without underscores). - * Comments are good, try to explain the intentions of the code. - * Use whitespace to make the code more readable. - * No whitespace at the end of a line (trailing whitespace). - * Avoid padding brackets with spaces. ex. `Int64(value)` preferred over `Int64( value )`. + - Use 4 spaces per indentation level, no tabs. + - Try to adhere to a 92 character line length limit. + - Use upper camel case convention for [modules](https://docs.julialang.org/en/v1/manual/modules/) and [types](https://docs.julialang.org/en/v1/manual/types/). + - Use lower case with underscores for method names (note: Julia code likes to use lower case without underscores). + - Comments are good, try to explain the intentions of the code. + - Use whitespace to make the code more readable. + - No whitespace at the end of a line (trailing whitespace). + - Avoid padding brackets with spaces. ex. `Int64(value)` preferred over `Int64( value )`. ## Editor Configuration @@ -98,11 +98,11 @@ Here is an example of usage: ```julia eval=false # Check that m and s are plus or minus one from 1.5 and 2.2, respectively. -check_numerical(chain, [:m, :s], [1.5, 2.2], atol = 1.0) +check_numerical(chain, [:m, :s], [1.5, 2.2]; atol=1.0) # Checks the estimates for a default gdemo model using values 1.5 and 2.0. -check_gdemo(chain, atol = 0.1) +check_gdemo(chain; atol=0.1) # Checks the estimates for a default MoG model. -check_MoGtest_default(chain, atol = 0.1) +check_MoGtest_default(chain; atol=0.1) ``` diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md index 6cd6473ea..2c82af8f3 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md @@ -18,7 +18,7 @@ Consider the following Turing, code block: s² ~ InverseGamma(2, 3) m ~ Normal(0, sqrt(s²)) x ~ Normal(m, sqrt(s²)) - y ~ Normal(m, sqrt(s²)) + return y ~ Normal(m, sqrt(s²)) end mod = gdemo(1.5, 2) @@ -43,8 +43,6 @@ y &\sim \text{Normal}(m, \sqrt{s}). \end{align*} ``` - - The **latent** variables are ``s`` and ``m``, the **observed** variables are ``x`` and ``y``. The model **joint** distribution ``p(s,m,x,y)`` decomposes into the **prior** ``p(s,m)`` and the **likelihood** ``p(x,y \mid s,m).`` Since ``x = 1.5`` and ``y = 2`` are observed, the goal is to infer the **posterior** distribution ``p(s,m \mid x,y).`` Importance Sampling produces independent samples ``(s_i, m_i)`` from the prior distribution. It also outputs unnormalized weights @@ -57,7 +55,7 @@ such that the empirical distribution ```math \frac{1}{N} \sum_{i =1}^N \frac {w_i} {\sum_{j=1}^N w_j} \delta_{(s_i, m_i)} -``` +``` is a good approximation of the posterior. @@ -112,24 +110,24 @@ Samplers are **not** the same as algorithms. An algorithm is a generic sampling Turing implements `AbstractMCMC`'s `AbstractSampler` with the `Sampler` struct defined in `DynamicPPL`. The most important attributes of an instance `spl` of `Sampler` are: -* `spl.alg`: the sampling method used, an instance of a subtype of `InferenceAlgorithm` -* `spl.state`: information about the sampling process, see [below](#States) + - `spl.alg`: the sampling method used, an instance of a subtype of `InferenceAlgorithm` + - `spl.state`: information about the sampling process, see [below](#States) When you call `sample(mod, alg, n_samples)`, Turing first uses `model` and `alg` to build an instance `spl` of `Sampler` , then calls the native `AbstractMCMC` function `sample(mod, spl, n_samples)`. When you define your own Turing sampling method, you must therefore build: -* a **sampler constructor** that uses a model and an algorithm to initialize an instance of `Sampler`. For Importance Sampling: + - a **sampler constructor** that uses a model and an algorithm to initialize an instance of `Sampler`. For Importance Sampling: ```julia function Sampler(alg::IS, model::Model, s::Selector) - info = Dict{Symbol, Any}() + info = Dict{Symbol,Any}() state = ISState(model) return Sampler(alg, info, s, state) end ``` -* a **state** struct implementing `AbstractSamplerState` corresponding to your method: we cover this in the following paragraph. + - a **state** struct implementing `AbstractSamplerState` corresponding to your method: we cover this in the following paragraph. ### States @@ -139,31 +137,31 @@ By default, you can use `SamplerState`, a concrete type defined in `inference/In ```julia mutable struct SamplerState{VIType<:VarInfo} <: AbstractSamplerState - vi :: VIType + vi::VIType end ``` -When doing Importance Sampling, we care not only about the values of the samples but also their weights. We will see below that the weight of each sample is also added to `spl.state.vi`. Moreover, the average +When doing Importance Sampling, we care not only about the values of the samples but also their weights. We will see below that the weight of each sample is also added to `spl.state.vi`. Moreover, the average ```math \frac 1 N \sum_{j=1}^N w_i = \frac 1 N \sum_{j=1}^N p(x,y \mid s_i, m_i) -``` +``` of the sample weights is a particularly important quantity: -* it is used to **normalize** the **empirical approximation** of the posterior distribution -* its logarithm is the importance sampling **estimate** of the **log evidence** ``\log p(x, y)`` + - it is used to **normalize** the **empirical approximation** of the posterior distribution + - its logarithm is the importance sampling **estimate** of the **log evidence** ``\log p(x, y)`` -To avoid having to compute it over and over again, `is.jl`defines an IS-specific concrete type `ISState` for sampler states, with an additional field `final_logevidence` containing +To avoid having to compute it over and over again, `is.jl`defines an IS-specific concrete type `ISState` for sampler states, with an additional field `final_logevidence` containing ```math \log \left( \frac 1 N \sum_{j=1}^N w_i \right). ``` ```julia -mutable struct ISState{V<:VarInfo, F<:AbstractFloat} <: AbstractSamplerState - vi :: V - final_logevidence :: F +mutable struct ISState{V<:VarInfo,F<:AbstractFloat} <: AbstractSamplerState + vi::V + final_logevidence::F end # additional constructor @@ -182,15 +180,15 @@ A lot of the things here are method-specific. However Turing also has some funct `AbstractMCMC` stores information corresponding to each individual sample in objects called `transition`, but does not specify what the structure of these objects could be. You could decide to implement a type `MyTransition` for transitions corresponding to the specifics of your methods. However, there are many situations in which the only information you need for each sample is: -* its value: ``\theta`` -* log of the joint probability of the observed data and this sample: `lp` + - its value: ``\theta`` + - log of the joint probability of the observed data and this sample: `lp` `Inference.jl` [defines](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/Inference.jl#L103) a struct `Transition`, which corresponds to this default situation ```julia -struct Transition{T, F<:AbstractFloat} - θ :: T - lp :: F +struct Transition{T,F<:AbstractFloat} + θ::T + lp::F end ``` @@ -200,12 +198,12 @@ It also [contains](https://github.com/TuringLang/Turing.jl/blob/master/src/infer A crude summary, which ignores things like parallelism, is the following: - `sample` calls `mcmcsample`, which calls +`sample` calls `mcmcsample`, which calls -* `sample_init!` to set things up -* `step!` repeatedly to produce multiple new transitions -* `sample_end!` to perform operations once all samples have been obtained -* `bundle_samples` to convert a vector of transitions into a more palatable type, for instance a `Chain`. + - `sample_init!` to set things up + - `step!` repeatedly to produce multiple new transitions + - `sample_end!` to perform operations once all samples have been obtained + - `bundle_samples` to convert a vector of transitions into a more palatable type, for instance a `Chain`. You can of course implement all of these functions, but `AbstractMCMC` as well as Turing also provide default implementations for simple cases. For instance, importance sampling uses the default implementations of `sample_init!` and `bundle_samples`, which is why you don't see code for them inside `is.jl`. @@ -215,17 +213,17 @@ The functions mentioned above, such as `sample_init!`, `step!`, etc., must of c For an example of the former, consider **Importance Sampling** as defined in `is.jl`. This implementation of Importance Sampling uses the model prior distribution as a proposal distribution, and therefore requires **samples from the prior distribution** of the model. Another example is **Approximate Bayesian Computation**, which requires multiple **samples from the model prior and likelihood distributions** in order to generate a single sample. -An example of the latter is the **Metropolis-Hastings** algorithm. At every step of sampling from a target posterior +An example of the latter is the **Metropolis-Hastings** algorithm. At every step of sampling from a target posterior ```math p(\theta \mid x_{\text{obs}}), -``` +``` -in order to compute the acceptance ratio, you need to **evaluate the model joint density** +in order to compute the acceptance ratio, you need to **evaluate the model joint density** ```math p\left(\theta_{\text{prop}}, x_{\text{obs}}\right) -``` +``` with ``\theta_{\text{prop}}`` a sample from the proposal and ``x_{\text{obs}}`` the observed data. @@ -233,11 +231,12 @@ This begs the question: how can these functions access model information during Consider an instance `m` of `Model` and a sampler `spl`, with associated `VarInfo` `vi = spl.state.vi`. At some point during the sampling process, an AbstractMCMC function such as `step!` calls `m(vi, ...)`, which calls the model evaluation function `m.f(vi, ...)`. -* for every tilde statement in the `@model` macro, `m.f(vi, ...)` returns model-related information (samples, value of the model density, etc.), and adds it to `vi`. How does it do that? - * recall that the code for `m.f(vi, ...)` is automatically generated by compilation of the `@model` macro - * for every tilde statement in the `@model` declaration, this code contains a call to `assume(vi, ...)` if the variable on the LHS of the tilde is a **model parameter to infer**, and `observe(vi, ...)` if the variable on the LHS of the tilde is an **observation** - * in the file corresponding to your sampling method (ie in `Turing.jl/src/inference/.jl`), you have **overloaded** `assume` and `observe`, so that they can modify `vi` to include the information and samples that you care about! - * at a minimum, `assume` and `observe` return the log density `lp` of the sample or observation. the model evaluation function then immediately calls `acclogp!!(vi, lp)`, which adds `lp` to the value of the log joint density stored in `vi`. + - for every tilde statement in the `@model` macro, `m.f(vi, ...)` returns model-related information (samples, value of the model density, etc.), and adds it to `vi`. How does it do that? + + + recall that the code for `m.f(vi, ...)` is automatically generated by compilation of the `@model` macro + + for every tilde statement in the `@model` declaration, this code contains a call to `assume(vi, ...)` if the variable on the LHS of the tilde is a **model parameter to infer**, and `observe(vi, ...)` if the variable on the LHS of the tilde is an **observation** + + in the file corresponding to your sampling method (ie in `Turing.jl/src/inference/.jl`), you have **overloaded** `assume` and `observe`, so that they can modify `vi` to include the information and samples that you care about! + + at a minimum, `assume` and `observe` return the log density `lp` of the sample or observation. the model evaluation function then immediately calls `acclogp!!(vi, lp)`, which adds `lp` to the value of the log joint density stored in `vi`. Here's what `assume` looks like for Importance Sampling: @@ -265,15 +264,19 @@ It simply returns the density (in the discrete case, the probability) of the obs We focus on the AbstractMCMC functions that are overridden in `is.jl` and executed inside `mcmcsample`: `step!`, which is called `n_samples` times, and `sample_end!`, which is executed once after those `n_samples` iterations. -* During the ``i``-th iteration, `step!` does 3 things: - * `empty!!(spl.state.vi)`: remove information about the previous sample from the sampler's `VarInfo` - * `model(rng, spl.state.vi, spl)`: call the model evaluation function - * calls to `assume` add the samples from the prior ``s_i`` and ``m_i`` to `spl.state.vi` - * calls to both `assume` or `observe` are followed by the line `acclogp!!(vi, lp)`, where `lp` is an output of `assume` and `observe` - * `lp` is set to 0 after `assume`, and to the value of the density at the observation after `observe` - * when all the tilde statements have been covered, `spl.state.vi.logp[]` is the sum of the `lp`, i.e., the likelihood ``\log p(x, y \mid s_i, m_i) = \log p(x \mid s_i, m_i) + \log p(y \mid s_i, m_i)`` of the observations given the latent variable samples ``s_i`` and ``m_i``. - * `return Transition(spl)`: build a transition from the sampler, and return that transition - * the transition's `vi` field is simply `spl.state.vi` - * the `lp` field contains the likelihood `spl.state.vi.logp[]` -* When the, `n_samples` iterations are completed, `sample_end!` fills the `final_logevidence` field of `spl.state` - * it simply takes the logarithm of the average of the sample weights, using the log weights for numerical stability + - During the ``i``-th iteration, `step!` does 3 things: + + + `empty!!(spl.state.vi)`: remove information about the previous sample from the sampler's `VarInfo` + + `model(rng, spl.state.vi, spl)`: call the model evaluation function + + * calls to `assume` add the samples from the prior ``s_i`` and ``m_i`` to `spl.state.vi` + * calls to both `assume` or `observe` are followed by the line `acclogp!!(vi, lp)`, where `lp` is an output of `assume` and `observe` + * `lp` is set to 0 after `assume`, and to the value of the density at the observation after `observe` + * when all the tilde statements have been covered, `spl.state.vi.logp[]` is the sum of the `lp`, i.e., the likelihood ``\log p(x, y \mid s_i, m_i) = \log p(x \mid s_i, m_i) + \log p(y \mid s_i, m_i)`` of the observations given the latent variable samples ``s_i`` and ``m_i``. + + `return Transition(spl)`: build a transition from the sampler, and return that transition + + * the transition's `vi` field is simply `spl.state.vi` + * the `lp` field contains the likelihood `spl.state.vi.logp[]` + - When the, `n_samples` iterations are completed, `sample_end!` fills the `final_logevidence` field of `spl.state` + + + it simply takes the logarithm of the average of the sample weights, using the log weights for numerical stability diff --git a/tutorials/for-developers-compiler/compiler.md b/tutorials/for-developers-compiler/compiler.md index 414327541..c6b4edeec 100644 --- a/tutorials/for-developers-compiler/compiler.md +++ b/tutorials/for-developers-compiler/compiler.md @@ -11,28 +11,30 @@ In this section, the current design of Turing's model "compiler" is described wh The following terminology will be used in this section: -- `D`: observed data variables conditioned upon in the posterior, -- `P`: parameter variables distributed according to the prior distributions, these will also be referred to as random variables, -- `Model`: a fully defined probabilistic model with input data + - `D`: observed data variables conditioned upon in the posterior, + - `P`: parameter variables distributed according to the prior distributions, these will also be referred to as random variables, + - `Model`: a fully defined probabilistic model with input data `Turing`'s `@model` macro rewrites the user-provided function definition such that it can be used to instantiate a `Model` by passing in the observed data `D`. The following are the main jobs of the `@model` macro: -1. Parse `~` and `.~` lines, e.g. `y .~ Normal.(c*x, 1.0)` -2. Figure out if a variable belongs to the data `D` and or to the parameters `P` -3. Enable the handling of missing data variables in `D` when defining a `Model` and treating them as parameter variables in `P` instead -4. Enable the tracking of random variables using the data structures `VarName` and `VarInfo` -5. Change `~`/`.~` lines with a variable in `P` on the LHS to a call to `tilde_assume` or `dot_tilde_assume` -6. Change `~`/`.~` lines with a variable in `D` on the LHS to a call to `tilde_observe` or `dot_tilde_observe` -7. Enable type stable automatic differentiation of the model using type parameters + 1. Parse `~` and `.~` lines, e.g. `y .~ Normal.(c*x, 1.0)` + 2. Figure out if a variable belongs to the data `D` and or to the parameters `P` + 3. Enable the handling of missing data variables in `D` when defining a `Model` and treating them as parameter variables in `P` instead + 4. Enable the tracking of random variables using the data structures `VarName` and `VarInfo` + 5. Change `~`/`.~` lines with a variable in `P` on the LHS to a call to `tilde_assume` or `dot_tilde_assume` + 6. Change `~`/`.~` lines with a variable in `D` on the LHS to a call to `tilde_observe` or `dot_tilde_observe` + 7. Enable type stable automatic differentiation of the model using type parameters ## The model A `model::Model` is a callable struct that one can sample from by calling + ```julia (model::Model)([rng, varinfo, sampler, context]) ``` + where `rng` is a random number generator (default: `Random.GLOBAL_RNG`), `varinfo` is a data structure that stores information about the random variables (default: `DynamicPPL.VarInfo()`), `sampler` is a sampling algorithm (default: `DynamicPPL.SampleFromPrior()`), and `context` is a sampling context that can, e.g., modify how the log probability is accumulated (default: `DynamicPPL.DefaultContext()`). @@ -52,7 +54,9 @@ in `model.defaults`. They are used for constructing model instances with differe Let's take the following model as an example: ```julia -@model function gauss(x = missing, y = 1.0, ::Type{TV} = Vector{Float64}) where {TV<:AbstractVector} +@model function gauss( + x=missing, y=1.0, ::Type{TV}=Vector{Float64} +) where {TV<:AbstractVector} if x === missing x = TV(undef, 3) end @@ -61,7 +65,7 @@ Let's take the following model as an example: p[2] ~ Normal(0, 1.0) @. x[1:2] ~ Normal(p[2], sqrt(p[1])) x[3] ~ Normal() - y ~ Normal(p[2], sqrt(p[1])) + return y ~ Normal(p[2], sqrt(p[1])) end ``` @@ -70,8 +74,10 @@ such a way that every call of it returns a `model::Model`. Note that only the fu function signature is left untouched. It is also possible to implement models with keyword arguments such as ```julia -@model function gauss(::Type{TV} = Vector{Float64}; x = missing, y = 1.0) where {TV<:AbstractVector} - ... +@model function gauss( + ::Type{TV}=Vector{Float64}; x=missing, y=1.0 +) where {TV<:AbstractVector} + return ... end ``` @@ -79,21 +85,23 @@ This would allow us to generate a model by calling `gauss(; x = rand(3))`. If an argument has a default value `missing`, it is treated as a random variable. For variables which require an initialization because we need to loop or broadcast over its elements, such as `x` above, the following needs to be done: + ```julia if x === missing x = ... end ``` + Note that since `gauss` behaves like a regular function it is possible to define additional dispatches in a second step as well. For instance, we could achieve the same behaviour by ```julia -@model function gauss(x, y = 1.0, ::Type{TV} = Vector{Float64}) where {TV<:AbstractVector} +@model function gauss(x, y=1.0, ::Type{TV}=Vector{Float64}) where {TV<:AbstractVector} p = TV(undef, 2) - ... + return ... end -function gauss(::Missing, y = 1.0, ::Type{TV} = Vector{Float64}) where {TV<:AbstractVector} +function gauss(::Missing, y=1.0, ::Type{TV}=Vector{Float64}) where {TV<:AbstractVector} return gauss(TV(undef, 3), y, TV) end ``` @@ -105,11 +113,12 @@ there is no need to initialize it in an `if`-block. First, the `@model` macro breaks up the user-provided function definition using `DynamicPPL.build_model_info`. This function returns a dictionary consisting of: -- `allargs_exprs`: The expressions of the positional and keyword arguments, without default values. -- `allargs_syms`: The names of the positional and keyword arguments, e.g., `[:x, :y, :TV]` above. -- `allargs_namedtuple`: An expression that constructs a `NamedTuple` of the positional and keyword arguments, e.g., `:((x = x, y = y, TV = TV))` above. -- `defaults_namedtuple`: An expression that constructs a `NamedTuple` of the default positional and keyword arguments, if any, e.g., `:((x = missing, y = 1, TV = Vector{Float64}))` above. -- `modeldef`: A dictionary with the name, arguments, and function body of the model definition, as returned by `MacroTools.splitdef`. + + - `allargs_exprs`: The expressions of the positional and keyword arguments, without default values. + - `allargs_syms`: The names of the positional and keyword arguments, e.g., `[:x, :y, :TV]` above. + - `allargs_namedtuple`: An expression that constructs a `NamedTuple` of the positional and keyword arguments, e.g., `:((x = x, y = y, TV = TV))` above. + - `defaults_namedtuple`: An expression that constructs a `NamedTuple` of the default positional and keyword arguments, if any, e.g., `:((x = missing, y = 1, TV = Vector{Float64}))` above. + - `modeldef`: A dictionary with the name, arguments, and function body of the model definition, as returned by `MacroTools.splitdef`. ## Step 2: Generate the body of the internal model function @@ -119,26 +128,41 @@ Hereby the function `DynamicPPL.generate_tilde` replaces the `L ~ R` lines in th the `@. L ~ R` and `L .~ R` lines in the model. In the above example, `p[1] ~ InverseGamma(2, 3)` is replaced with something similar to + ```julia #= REPL[25]:6 =# begin var"##tmpright#323" = InverseGamma(2, 3) - var"##tmpright#323" isa Union{Distribution, AbstractVector{<:Distribution}} || throw(ArgumentError("Right-hand side of a ~ must be subtype of Distribution or a vector of Distributions.")) + var"##tmpright#323" isa Union{Distribution,AbstractVector{<:Distribution}} || throw( + ArgumentError( + "Right-hand side of a ~ must be subtype of Distribution or a vector of Distributions.", + ), + ) var"##vn#325" = (DynamicPPL.VarName)(:p, ((1,),)) var"##inds#326" = ((1,),) - p[1] = (DynamicPPL.tilde_assume)(_rng, _context, _sampler, var"##tmpright#323", var"##vn#325", var"##inds#326", _varinfo) + p[1] = (DynamicPPL.tilde_assume)( + _rng, + _context, + _sampler, + var"##tmpright#323", + var"##vn#325", + var"##inds#326", + _varinfo, + ) end ``` + Here the first line is a so-called line number node that enables more helpful error messages by providing users with the exact location of the error in their model definition. Then the right hand side (RHS) of the `~` is assigned to a variable (with an automatically generated name). We check that the RHS is a distribution or an array of distributions, otherwise an error is thrown. Next we extract a compact representation of the variable with its name and index (or indices). Finally, the `~` expression is replaced with a call to `DynamicPPL.tilde_assume` since the compiler figured out that `p[1]` is a random variable using the following heuristic: -1. If the symbol on the LHS of `~`, `:p` in this case, is not among the arguments to the model, `(:x, :y, :T)` in this case, it is a random variable. -2. If the symbol on the LHS of `~`, `:p` in this case, is among the arguments to the model but has a value of `missing`, it is a random variable. -2. If the value of the LHS of `~`, `p[1]` in this case, is `missing`, then it is a random variable. -4. Otherwise, it is treated as an observation. + + 1. If the symbol on the LHS of `~`, `:p` in this case, is not among the arguments to the model, `(:x, :y, :T)` in this case, it is a random variable. + 2. If the symbol on the LHS of `~`, `:p` in this case, is among the arguments to the model but has a value of `missing`, it is a random variable. + 3. If the value of the LHS of `~`, `p[1]` in this case, is `missing`, then it is a random variable. + 4. Otherwise, it is treated as an observation. The `DynamicPPL.tilde_assume` function takes care of sampling the random variable, if needed, and updating its value and the accumulated log joint probability in the `_varinfo` object. If `L ~ R` is an observation, `DynamicPPL.tilde_observe` is called with the same arguments except the @@ -146,16 +170,22 @@ random number generator `_rng` (since observations are never sampled). A similar transformation is performed for expressions of the form `@. L ~ R` and `L .~ R`. For instance, `@. x[1:2] ~ Normal(p[2], sqrt(p[1]))` is replaced with + ```julia #= REPL[25]:8 =# begin var"##tmpright#331" = Normal.(p[2], sqrt.(p[1])) - var"##tmpright#331" isa Union{Distribution, AbstractVector{<:Distribution}} || throw(ArgumentError("Right-hand side of a ~ must be subtype of Distribution or a vector of Distributions.")) + var"##tmpright#331" isa Union{Distribution,AbstractVector{<:Distribution}} || throw( + ArgumentError( + "Right-hand side of a ~ must be subtype of Distribution or a vector of Distributions.", + ), + ) var"##vn#333" = (DynamicPPL.VarName)(:x, ((1:2,),)) var"##inds#334" = ((1:2,),) var"##isassumption#335" = begin let var"##vn#336" = (DynamicPPL.VarName)(:x, ((1:2,),)) - if !((DynamicPPL.inargnames)(var"##vn#336", _model)) || (DynamicPPL.inmissings)(var"##vn#336", _model) + if !((DynamicPPL.inargnames)(var"##vn#336", _model)) || + (DynamicPPL.inmissings)(var"##vn#336", _model) true else x[1:2] === missing @@ -163,12 +193,30 @@ begin end end if var"##isassumption#335" - x[1:2] .= (DynamicPPL.dot_tilde_assume)(_rng, _context, _sampler, var"##tmpright#331", x[1:2], var"##vn#333", var"##inds#334", _varinfo) + x[1:2] .= (DynamicPPL.dot_tilde_assume)( + _rng, + _context, + _sampler, + var"##tmpright#331", + x[1:2], + var"##vn#333", + var"##inds#334", + _varinfo, + ) else - (DynamicPPL.dot_tilde_observe)(_context, _sampler, var"##tmpright#331", x[1:2], var"##vn#333", var"##inds#334", _varinfo) + (DynamicPPL.dot_tilde_observe)( + _context, + _sampler, + var"##tmpright#331", + x[1:2], + var"##vn#333", + var"##inds#334", + _varinfo, + ) end end ``` + The main difference in the expanded code between `L ~ R` and `@. L ~ R` is that the former doesn't assume `L` to be defined, it can be a new Julia variable in the scope, while the latter assumes `L` already exists. Moreover, `DynamicPPL.dot_tilde_assume` and `DynamicPPL.dot_tilde_observe` are called instead of `DynamicPPL.tilde_assume` and `DynamicPPL.tilde_observe`. @@ -177,21 +225,23 @@ instead of `DynamicPPL.tilde_assume` and `DynamicPPL.tilde_observe`. Finally, we replace the user-provided function body using `DynamicPPL.build_output`. This function uses `MacroTools.combinedef` to reassemble the user-provided function with a new function body. In the modified function body an anonymous function is created whose function body was generated in step 2 above and whose arguments are -- a random number generator `_rng`, -- a model `_model`, -- a datastructure `_varinfo`, -- a sampler `_sampler`, -- a sampling context `_context`, -- and all positional and keyword arguments of the user-provided model function as positional arguments -without any default values. Finally, in the new function body a `model::Model` with this anonymous function as internal function is returned. + + - a random number generator `_rng`, + - a model `_model`, + - a datastructure `_varinfo`, + - a sampler `_sampler`, + - a sampling context `_context`, + - and all positional and keyword arguments of the user-provided model function as positional arguments + without any default values. Finally, in the new function body a `model::Model` with this anonymous function as internal function is returned. # `VarName` In order to track random variables in the sampling process, `Turing` uses the `VarName` struct which acts as a random variable identifier generated at runtime. The `VarName` of a random variable is generated from the expression on the LHS of a `~` statement when the symbol on the LHS is in the set `P` of unobserved random variables. Every `VarName` instance has a type parameter `sym` which is the symbol of the Julia variable in the model that the random variable belongs to. For example, `x[1] ~ Normal()` will generate an instance of `VarName{:x}` assuming `x` is an unobserved random variable. Every `VarName` also has a field `indexing`, which stores the indices required to access the random variable from the Julia variable indicated by `sym` as a tuple of tuples. Each element of the tuple thereby contains the indices of one indexing operation (`VarName` also supports hierarchical arrays and range indexing). Some examples: -- `x ~ Normal()` will generate a `VarName(:x, ())`. -- `x[1] ~ Normal()` will generate a `VarName(:x, ((1,),))`. -- `x[:,1] ~ MvNormal(zeros(2), I)` will generate a `VarName(:x, ((Colon(), 1),))`. -- `x[:,1][1+1] ~ Normal()` will generate a `VarName(:x, ((Colon(), 1), (2,)))`. + + - `x ~ Normal()` will generate a `VarName(:x, ())`. + - `x[1] ~ Normal()` will generate a `VarName(:x, ((1,),))`. + - `x[:,1] ~ MvNormal(zeros(2), I)` will generate a `VarName(:x, ((Colon(), 1),))`. + - `x[:,1][1+1] ~ Normal()` will generate a `VarName(:x, ((Colon(), 1), (2,)))`. The easiest way to manually construct a `VarName` is to use the `@varname` macro on an indexing expression, which will take the `sym` value from the actual variable name, and put the index values appropriately into the constructor. @@ -202,6 +252,7 @@ The easiest way to manually construct a `VarName` is to use the `@varname` macro `VarInfo` is the data structure in `Turing` that facilitates tracking random variables and certain metadata about them that are required for sampling. For instance, the distribution of every random variable is stored in `VarInfo` because we need to know the support of every random variable when sampling using HMC for example. Random variables whose distributions have a constrained support are transformed using a bijector from [Bijectors.jl](https://github.com/TuringLang/Bijectors.jl) so that the sampling happens in the unconstrained space. Different samplers require different metadata about the random variables. The definition of `VarInfo` in `Turing` is: + ``` struct VarInfo{Tmeta, Tlogp} <: AbstractVarInfo metadata::Tmeta @@ -209,6 +260,7 @@ struct VarInfo{Tmeta, Tlogp} <: AbstractVarInfo num_produce::Base.RefValue{Int} end ``` + Based on the type of `metadata`, the `VarInfo` is either aliased `UntypedVarInfo` or `TypedVarInfo`. `metadata` can be either a subtype of the union type `Metadata` or a `NamedTuple` of multiple such subtypes. Let `vi` be an instance of `VarInfo`. If `vi isa VarInfo{<:Metadata}`, then it is called an `UntypedVarInfo`. If `vi isa VarInfo{<:NamedTuple}`, then `vi.metadata` would be a `NamedTuple` mapping each symbol in `P` to an instance of `Metadata`. `vi` would then be called a `TypedVarInfo`. The other fields of `VarInfo` include `logp` which is used to accumulate the log probability or log probability density of the variables in `P` and `D`. `num_produce` keeps track of how many observations have been made in the model so far. This is incremented when running a `~` statement when the symbol on the LHS is in `D`. ## `Metadata` @@ -217,20 +269,21 @@ The `Metadata` struct stores some metadata about the random variables sampled. T query certain information about a variable such as: its distribution, which samplers sample this variable, its value and whether this value is transformed to real space or not. Let `md` be an instance of `Metadata`: -- `md.vns` is the vector of all `VarName` instances. Let `vn` be an arbitrary element of `md.vns` -- `md.idcs` is the dictionary that maps each `VarName` instance to its index in - `md.vns`, `md.ranges`, `md.dists`, `md.orders` and `md.flags`. -- `md.vns[md.idcs[vn]] == vn`. -- `md.dists[md.idcs[vn]]` is the distribution of `vn`. -- `md.gids[md.idcs[vn]]` is the set of algorithms used to sample `vn`. This is used in - the Gibbs sampling process. -- `md.orders[md.idcs[vn]]` is the number of `observe` statements before `vn` is sampled. -- `md.ranges[md.idcs[vn]]` is the index range of `vn` in `md.vals`. -- `md.vals[md.ranges[md.idcs[vn]]]` is the linearized vector of values of corresponding to `vn`. -- `md.flags` is a dictionary of true/false flags. `md.flags[flag][md.idcs[vn]]` is the - value of `flag` corresponding to `vn`. - -Note that in order to make `md::Metadata` type stable, all the `md.vns` must have the same symbol and distribution type. However, one can have a single Julia variable, e.g. `x`, that is a matrix or a hierarchical array sampled in partitions, e.g. `x[1][:] ~ MvNormal(zeros(2), I); x[2][:] ~ MvNormal(ones(2), I)`. The symbol `x` can still be managed by a single `md::Metadata` without hurting the type stability since all the distributions on the RHS of `~` are of the same type. + + - `md.vns` is the vector of all `VarName` instances. Let `vn` be an arbitrary element of `md.vns` + - `md.idcs` is the dictionary that maps each `VarName` instance to its index in + `md.vns`, `md.ranges`, `md.dists`, `md.orders` and `md.flags`. + - `md.vns[md.idcs[vn]] == vn`. + - `md.dists[md.idcs[vn]]` is the distribution of `vn`. + - `md.gids[md.idcs[vn]]` is the set of algorithms used to sample `vn`. This is used in + the Gibbs sampling process. + - `md.orders[md.idcs[vn]]` is the number of `observe` statements before `vn` is sampled. + - `md.ranges[md.idcs[vn]]` is the index range of `vn` in `md.vals`. + - `md.vals[md.ranges[md.idcs[vn]]]` is the linearized vector of values of corresponding to `vn`. + - `md.flags` is a dictionary of true/false flags. `md.flags[flag][md.idcs[vn]]` is the + value of `flag` corresponding to `vn`. + +Note that in order to make `md::Metadata` type stable, all the `md.vns` must have the same symbol and distribution type. However, one can have a single Julia variable, e.g. `x`, that is a matrix or a hierarchical array sampled in partitions, e.g. `x[1][:] ~ MvNormal(zeros(2), I); x[2][:] ~ MvNormal(ones(2), I)`. The symbol `x` can still be managed by a single `md::Metadata` without hurting the type stability since all the distributions on the RHS of `~` are of the same type. However, in `Turing` models one cannot have this restriction, so we must use a type unstable `Metadata` if we want to use one `Metadata` instance for the whole model. This is what `UntypedVarInfo` does. A type unstable `Metadata` will still work but will have inferior performance. diff --git a/tutorials/for-developers-interface/interface.md b/tutorials/for-developers-interface/interface.md index f9d76fc4b..92ebf176c 100644 --- a/tutorials/for-developers-interface/interface.md +++ b/tutorials/for-developers-interface/interface.md @@ -5,7 +5,6 @@ weave_options: error : false --- - # The sampling interface Turing implements a sampling interface (hosted at @@ -21,26 +20,26 @@ This guide will demonstrate how to implement the interface without Turing. Any implementation of an inference method that uses the AbstractMCMC interface should implement a subset of the following types and functions: -1. A subtype of `AbstractSampler`, defined as a mutable struct containing state information or sampler parameters. -2. A function `sample_init!` which performs any necessary set-up (default: do not perform any set-up). -3. A function `step!` which returns a transition that represents a single draw from the sampler. -4. A function `transitions_init` which returns a container for the transitions obtained from the sampler - (default: return a `Vector{T}` of length `N` where `T` is the type of the transition obtained in the first step and `N` is the number of requested samples). -5. A function `transitions_save!` which saves transitions to the container (default: save the transition of iteration `i` - at position `i` in the vector of transitions). -6. A function `sample_end!` which handles any sampler wrap-up (default: do not perform any wrap-up). -7. A function `bundle_samples` which accepts the container of transitions and returns a collection of samples - (default: return the vector of transitions). + 1. A subtype of `AbstractSampler`, defined as a mutable struct containing state information or sampler parameters. + 2. A function `sample_init!` which performs any necessary set-up (default: do not perform any set-up). + 3. A function `step!` which returns a transition that represents a single draw from the sampler. + 4. A function `transitions_init` which returns a container for the transitions obtained from the sampler + (default: return a `Vector{T}` of length `N` where `T` is the type of the transition obtained in the first step and `N` is the number of requested samples). + 5. A function `transitions_save!` which saves transitions to the container (default: save the transition of iteration `i` + at position `i` in the vector of transitions). + 6. A function `sample_end!` which handles any sampler wrap-up (default: do not perform any wrap-up). + 7. A function `bundle_samples` which accepts the container of transitions and returns a collection of samples + (default: return the vector of transitions). The interface methods with exclamation points are those that are intended to allow for state mutation. Any mutating function is meant to allow mutation where needed -- you might use: -- `sample_init!` to run some kind of sampler preparation, before sampling begins. This - could mutate a sampler's state. -- `step!` might mutate a sampler flag after each sample. -- `sample_end!` contains any wrap-up you might need to do. If you were sampling in a - transformed space, this might be where you convert everything back to a constrained space. + - `sample_init!` to run some kind of sampler preparation, before sampling begins. This + could mutate a sampler's state. + - `step!` might mutate a sampler flag after each sample. + - `sample_end!` contains any wrap-up you might need to do. If you were sampling in a + transformed space, this might be where you convert everything back to a constrained space. ## Why do you have an interface? @@ -48,7 +47,7 @@ The motivation for the interface is to allow Julia's fantastic probabilistic pro language community to have a set of standards and common implementations so we can all thrive together. Markov chain Monte Carlo methods tend to have a very similar framework to one another, and so a common interface should help more great inference methods built in -single-purpose packages to experience more use among the community. +single-purpose packages to experience more use among the community. ## Implementing Metropolis-Hastings without Turing @@ -68,12 +67,12 @@ the interface framework we'll fill out. We also need `Distributions` and `Random ```julia # Import the relevant libraries. -import AbstractMCMC +using AbstractMCMC: AbstractMCMC using Distributions using Random ``` -An interface extension (like the one we're writing right now) typically requires that you overload or implement several functions. Specifically, you should `import` the functions you intend to overload. This next code block accomplishes that. +An interface extension (like the one we're writing right now) typically requires that you overload or implement several functions. Specifically, you should `import` the functions you intend to overload. This next code block accomplishes that. From `Distributions`, we need `Sampleable`, `VariateForm`, and `ValueSupport`, three abstract types that define a distribution. Models in the interface are assumed to be subtypes of `Sampleable{VariateForm, ValueSupport}`. In this section our model is going be be extremely simple, so we will not end up using these except to make sure that the inference functions are dispatching correctly. @@ -86,20 +85,22 @@ you call `sample`. ```julia # Define a sampler type. -struct MetropolisHastings{T, D} <: AbstractMCMC.AbstractSampler +struct MetropolisHastings{T,D} <: AbstractMCMC.AbstractSampler init_θ::T proposal::D end # Default constructors. -MetropolisHastings(init_θ::Real) = MetropolisHastings(init_θ, Normal(0,1)) -MetropolisHastings(init_θ::Vector{<:Real}) = MetropolisHastings(init_θ, MvNormal(zero(init_θ), I)) +MetropolisHastings(init_θ::Real) = MetropolisHastings(init_θ, Normal(0, 1)) +function MetropolisHastings(init_θ::Vector{<:Real}) + return MetropolisHastings(init_θ, MvNormal(zero(init_θ), I)) +end ``` Above, we have defined a sampler that stores the initial parameterization of the prior, and a distribution object from which proposals are drawn. You can have a struct that has no fields, and simply use it for dispatching onto the relevant functions, or you can store a -large amount of state information in your sampler. +large amount of state information in your sampler. The general intuition for what to store in your sampler struct is that anything you may need to perform inference between samples but you don't want to store in a transition @@ -111,11 +112,11 @@ information between `step!` calls. Next, we need to have a model of some kind. A model is a struct that's a subtype of `AbstractModel` that contains whatever information is necessary to perform inference on your problem. In our case we want to know the mean and variance parameters for a standard -Normal distribution, so we can keep our model to the log density of a Normal. +Normal distribution, so we can keep our model to the log density of a Normal. Note that we only have to do this because we are not yet integrating the sampler with Turing -- Turing has a very sophisticated modelling engine that removes the need to define custom -model structs. +model structs. ```julia # Define a model type. Stores the log density function. @@ -133,7 +134,7 @@ and the log density of that draw: ```julia # Create a very basic Transition type, only stores the # parameter draws and the log probability of the draw. -struct Transition{T, L} +struct Transition{T,L} θ::T lp::L end @@ -152,14 +153,15 @@ need, but we need to implement the `step!` function which actually performs infe As a refresher, Metropolis-Hastings implements a very basic algorithm: -1. Pick some initial state, ``\theta_0``. -2. For ``t`` in ``[1,N],`` do - - Generate a proposal parameterization ``\theta^\prime_t \sim q(\theta^\prime_t \mid \theta_{t-1}).`` - - Calculate the acceptance probability, ``\alpha = \text{min}\left[1,\frac{\pi(\theta'_t)}{\pi(\theta_{t-1})} \frac{q(\theta_{t-1} \mid \theta'_t)}{q(\theta'_t \mid \theta_{t-1})}) \right].`` - - If ``U \le \alpha`` where ``U \sim [0,1],`` then ``\theta_t = \theta'_t.`` Otherwise, ``\theta_t = \theta_{t-1}.`` + 1. Pick some initial state, ``\theta_0``. + 2. For ``t`` in ``[1,N],`` do + + + Generate a proposal parameterization ``\theta^\prime_t \sim q(\theta^\prime_t \mid \theta_{t-1}).`` + + Calculate the acceptance probability, ``\alpha = \text{min}\left[1,\frac{\pi(\theta'_t)}{\pi(\theta_{t-1})} \frac{q(\theta_{t-1} \mid \theta'_t)}{q(\theta'_t \mid \theta_{t-1})}) \right].`` + + If ``U \le \alpha`` where ``U \sim [0,1],`` then ``\theta_t = \theta'_t.`` Otherwise, ``\theta_t = \theta_{t-1}.`` Of course, it's much easier to do this in the log space, so the acceptance probability is -more commonly written as +more commonly written as ```math \log \alpha = \min\left[0, \log \pi(\theta'_t) - \log \pi(\theta_{t-1}) + \log q(\theta_{t-1} \mid \theta^\prime_t) - \log q(\theta\prime_t \mid \theta_{t-1}) \right]. @@ -167,9 +169,9 @@ more commonly written as In interface terms, we should do the following: -1. Make a new transition containing a proposed sample. -2. Calculate the acceptance probability. -3. If we accept, return the new transition, otherwise, return the old one. + 1. Make a new transition containing a proposed sample. + 2. Calculate the acceptance probability. + 3. If we accept, return the new transition, otherwise, return the old one. ### Steps @@ -187,7 +189,7 @@ function AbstractMCMC.step!( spl::MetropolisHastings, N::Integer, ::Nothing; - kwargs... + kwargs..., ) return Transition(model, spl.init_θ) end @@ -200,29 +202,33 @@ The other `step!` function performs the usual steps from Metropolis-Hastings. In several helper functions, `proposal` and `q`, which are designed to replicate the functions in the pseudocode above. -- `proposal` generates a new proposal in the form of a `Transition`, which can be - univariate if the value passed in is univariate, or it can be multivariate if the - `Transition` given is multivariate. Proposals use a basic `Normal` or `MvNormal` proposal - distribution. -- `q` returns the log density of one parameterization conditional on another, according to - the proposal distribution. -- `step!` generates a new proposal, checks the acceptance probability, and then returns - either the previous transition or the proposed transition. + - `proposal` generates a new proposal in the form of a `Transition`, which can be + univariate if the value passed in is univariate, or it can be multivariate if the + `Transition` given is multivariate. Proposals use a basic `Normal` or `MvNormal` proposal + distribution. + - `q` returns the log density of one parameterization conditional on another, according to + the proposal distribution. + - `step!` generates a new proposal, checks the acceptance probability, and then returns + either the previous transition or the proposed transition. ```julia # Define a function that makes a basic proposal depending on a univariate # parameterization or a multivariate parameterization. -propose(spl::MetropolisHastings, model::DensityModel, θ::Real) = - Transition(model, θ + rand(spl.proposal)) -propose(spl::MetropolisHastings, model::DensityModel, θ::Vector{<:Real}) = - Transition(model, θ + rand(spl.proposal)) -propose(spl::MetropolisHastings, model::DensityModel, t::Transition) = - propose(spl, model, t.θ) +function propose(spl::MetropolisHastings, model::DensityModel, θ::Real) + return Transition(model, θ + rand(spl.proposal)) +end +function propose(spl::MetropolisHastings, model::DensityModel, θ::Vector{<:Real}) + return Transition(model, θ + rand(spl.proposal)) +end +function propose(spl::MetropolisHastings, model::DensityModel, t::Transition) + return propose(spl, model, t.θ) +end # Calculates the probability `q(θ|θcond)`, using the proposal distribution `spl.proposal`. q(spl::MetropolisHastings, θ::Real, θcond::Real) = logpdf(spl.proposal, θ - θcond) -q(spl::MetropolisHastings, θ::Vector{<:Real}, θcond::Vector{<:Real}) = - logpdf(spl.proposal, θ - θcond) +function q(spl::MetropolisHastings, θ::Vector{<:Real}, θcond::Vector{<:Real}) + return logpdf(spl.proposal, θ - θcond) +end q(spl::MetropolisHastings, t1::Transition, t2::Transition) = q(spl, t1.θ, t2.θ) # Calculate the density of the model given some parameterization. @@ -238,7 +244,7 @@ function AbstractMCMC.step!( spl::MetropolisHastings, ::Integer, θ_prev::Transition; - kwargs... + kwargs..., ) # Generate a new proposal. θ = propose(spl, model, θ_prev) @@ -267,21 +273,21 @@ parameter names passed in by the user. ```julia # A basic chains constructor that works with the Transition struct we defined. function AbstractMCMC.bundle_samples( - rng::AbstractRNG, - ℓ::DensityModel, - s::MetropolisHastings, - N::Integer, + rng::AbstractRNG, + ℓ::DensityModel, + s::MetropolisHastings, + N::Integer, ts::Vector{<:Transition}, chain_type::Type{Any}; param_names=missing, - kwargs... + kwargs..., ) # Turn all the transitions into a vector-of-vectors. - vals = copy(reduce(hcat,[vcat(t.θ, t.lp) for t in ts])') + vals = copy(reduce(hcat, [vcat(t.θ, t.lp) for t in ts])') # Check if we received any parameter names. if ismissing(param_names) - param_names = ["Parameter $i" for i in 1:(length(first(vals))-1)] + param_names = ["Parameter $i" for i in 1:(length(first(vals)) - 1)] end # Add the log density field to the parameter names. diff --git a/tutorials/for-developers-variational-inference/variational_inference.md b/tutorials/for-developers-variational-inference/variational_inference.md index 8e1a37558..1782f1f7f 100644 --- a/tutorials/for-developers-variational-inference/variational_inference.md +++ b/tutorials/for-developers-variational-inference/variational_inference.md @@ -7,13 +7,13 @@ weave_options: # Overview -In this post we'll have a look at what's known as **variational inference (VI)**, a family of _approximate_ Bayesian inference methods. In particular, we will focus on one of the more standard VI methods called **Automatic Differentiation Variational Inference (ADVI)**. +In this post we'll have a look at what's known as **variational inference (VI)**, a family of _approximate_ Bayesian inference methods. In particular, we will focus on one of the more standard VI methods called **Automatic Differentiation Variational Inference (ADVI)**. Here we'll have a look at the theory behind VI, but if you're interested in how to use ADVI in Turing.jl, [check out this tutorial](../../tutorials/09-variational-inference). # Motivation -In Bayesian inference one usually specifies a model as follows: given data ``\\{x_i\\}_{i = 1}^n``, +In Bayesian inference one usually specifies a model as follows: given data ``\\{x_i\\}_{i = 1}^n``, ```math \begin{align*} @@ -32,7 +32,7 @@ In general one cannot obtain a closed form expression for ``p(z \mid \\{ x\_i \\ As you are hopefully already aware, Turing.jl provides a lot of different methods with asymptotic exactness guarantees that we can apply to such a problem! -Unfortunately, these unbiased samplers can be prohibitively expensive to run. As the model ``p`` increases in complexity, the convergence of these unbiased samplers can slow down dramatically. Still, in the _infinite_ limit, these methods should converge to the true posterior! But infinity is fairly large, like, _at least_ more than 12, so this might take a while. +Unfortunately, these unbiased samplers can be prohibitively expensive to run. As the model ``p`` increases in complexity, the convergence of these unbiased samplers can slow down dramatically. Still, in the _infinite_ limit, these methods should converge to the true posterior! But infinity is fairly large, like, _at least_ more than 12, so this might take a while. In such a case it might be desirable to sacrifice some of these asymptotic guarantees, and instead _approximate_ the posterior ``p(z \mid \\{ x_i \\}_{i = 1}^n)`` using some other model which we'll denote ``q(z)``. @@ -134,8 +134,9 @@ Also, I fooled you real good: the ELBO _isn't_ an arbitrary name, hah! In fact i ## Maximizing the ELBO Finding the optimal ``q`` over _all_ possible densities of course isn't feasible. Instead we consider a family of _parameterized_ densities ``\mathscr{D}\_{\Theta}`` where ``\Theta`` denotes the space of possible parameters. Each density in this family ``q\_{\theta} \in \mathscr{D}\_{\Theta}`` is parameterized by a unique ``\theta \in \Theta``. Moreover, we'll assume -1. ``q\_{\theta}(z)``, i.e. evaluating the probability density ``q`` at any point ``z``, is differentiable -2. ``z \sim q\_{\theta}(z)``, i.e. the process of sampling from ``q\_{\theta}(z)``, is differentiable + + 1. ``q\_{\theta}(z)``, i.e. evaluating the probability density ``q`` at any point ``z``, is differentiable + 2. ``z \sim q\_{\theta}(z)``, i.e. the process of sampling from ``q\_{\theta}(z)``, is differentiable (1) is fairly straight-forward, but (2) is a bit tricky. What does it even mean for a _sampling process_ to be differentiable? This is quite an interesting problem in its own right and would require something like a [50-page paper to properly review the different approaches (highly recommended read)](https://arxiv.org/abs/1906.10652). @@ -154,13 +155,14 @@ With all this nailed down, we eventually reach the section on **Automatic Differ ## Automatic Differentiation Variational Inference (ADVI) So let's revisit the assumptions we've made at this point: -1. The variational posterior ``q\_{\theta}`` is in a parameterized family of densities denoted ``\mathscr{Q}\_{\Theta}``, with ``\theta \in \Theta``. -2. ``\mathscr{Q}\_{\Theta}`` is a space of _reparameterizable_ densities with ``\bar{q}(z)`` as the base-density. -3. The parameterization function ``g\_{\theta}`` is differentiable wrt. ``\theta``. -4. Evaluation of the probability density ``q\_{\theta}(z)`` is differentiable wrt. ``\theta``. -5. ``\mathbb{H}\left(q\_{\theta}(z)\right)`` is tractable. -6. Evaluation of the joint density ``p(x, z)`` is tractable and differentiable wrt. ``z`` -7. The support of ``q(z)`` is a subspace of the support of ``p(z \mid x)`` : ``\mathrm{supp}\left(q(z)\right) \subseteq \mathrm{supp}\left(p(z \mid x)\right)``. + + 1. The variational posterior ``q\_{\theta}`` is in a parameterized family of densities denoted ``\mathscr{Q}\_{\Theta}``, with ``\theta \in \Theta``. + 2. ``\mathscr{Q}\_{\Theta}`` is a space of _reparameterizable_ densities with ``\bar{q}(z)`` as the base-density. + 3. The parameterization function ``g\_{\theta}`` is differentiable wrt. ``\theta``. + 4. Evaluation of the probability density ``q\_{\theta}(z)`` is differentiable wrt. ``\theta``. + 5. ``\mathbb{H}\left(q\_{\theta}(z)\right)`` is tractable. + 6. Evaluation of the joint density ``p(x, z)`` is tractable and differentiable wrt. ``z`` + 7. The support of ``q(z)`` is a subspace of the support of ``p(z \mid x)`` : ``\mathrm{supp}\left(q(z)\right) \subseteq \mathrm{supp}\left(p(z \mid x)\right)``. All of these are not *necessary* to do VI, but they are very convenient and results in a fairly flexible approach. One distribution which has a density satisfying all of the above assumptions _except_ (7) (we'll get back to this in second) for any tractable and differentiable ``p(z \mid \\{ x\_i \\}\_{i = 1}^n)`` is the good ole' Gaussian/normal distribution: @@ -183,6 +185,7 @@ Though not necessary, we'll often make a *mean-field* assumption for the variati ``` ### Examples + As a (trivial) example we could apply the approach described above to is the following generative model for ``p(z \mid \\{ x_i \\}\_{i = 1}^n)``: ```math @@ -250,7 +253,7 @@ where ``\mathcal{J}_{f^{-1}}(x)`` denotes the jacobian of ``f^{-1}`` evaluted at \mathbb{P}\_{\tilde{p}}\left(y \in f^{-1}(A) \right) = \int\_{f^{-1}(A)} \tilde{p}(y) \mathrm{d}y, ``` -since ``f^{-1}\left(\mathrm{supp} (p(x)) \right) = \mathbb{R}^d `` which has probability 1. This probability distribution has *density* ``\tilde{p}(y)`` with ``\mathrm{supp} \left( \tilde{p}(y) \right) = \mathbb{R}^d``, defined +since ``f^{-1}\left(\mathrm{supp} (p(x)) \right) = \mathbb{R}^d`` which has probability 1. This probability distribution has *density* ``\tilde{p}(y)`` with ``\mathrm{supp} \left( \tilde{p}(y) \right) = \mathbb{R}^d``, defined ```math \tilde{p}(y) = p \left( f^{-1}(y) \right) \ \left| \det \mathcal{J}\_{f^{-1}}(y) \right| @@ -298,7 +301,7 @@ If we want to write the ELBO explicitly in terms of ``\eta`` rather than ``z``, \end{align*} ``` -The entropy is invariant under change of variables, thus ``\mathbb{H} \left(q\_{\mu, \Sigma}(z)\right)`` is simply the entropy of the normal distribution which is known analytically. +The entropy is invariant under change of variables, thus ``\mathbb{H} \left(q\_{\mu, \Sigma}(z)\right)`` is simply the entropy of the normal distribution which is known analytically. Hence, the resulting empirical estimate of the ELBO is @@ -312,4 +315,3 @@ Hence, the resulting empirical estimate of the ELBO is And maximizing this wrt. ``\mu`` and ``\Sigma`` is what's referred to as **Automatic Differentiation Variational Inference (ADVI)**! Now if you want to try it out, [check out the tutorial on how to use ADVI in Turing.jl](../../tutorials/09-variational-inference)! - diff --git a/tutorials/using-turing-advanced/advanced.md b/tutorials/using-turing-advanced/advanced.md index afdf58b5e..ab0a44a6d 100644 --- a/tutorials/using-turing-advanced/advanced.md +++ b/tutorials/using-turing-advanced/advanced.md @@ -9,29 +9,22 @@ weave_options: ## How to Define a Customized Distribution - Turing.jl supports the use of distributions from the Distributions.jl package. By extension it also supports the use of customized distributions, by defining them as subtypes of `Distribution` type of the Distributions.jl package, as well as corresponding functions. - Below shows a workflow of how to define a customized distribution, using our own implementation of a simple `Uniform` distribution as a simple example. - ### 1. Define the Distribution Type - First, define a type of the distribution, as a subtype of a corresponding distribution type in the Distributions.jl package. - ```julia struct CustomUniform <: ContinuousUnivariateDistribution end ``` ### 2. Implement Sampling and Evaluation of the log-pdf - Second, define `rand` and `logpdf`, which will be used to run the model. - ```julia # sample in [0, 1] Distributions.rand(rng::AbstractRNG, d::CustomUniform) = rand(rng) @@ -42,7 +35,6 @@ Distributions.logpdf(d::CustomUniform, x::Real) = zero(x) ### 3. Define Helper Functions - In most cases, it may be required to define some helper functions. #### 3.1 Domain Transformation @@ -52,7 +44,7 @@ Certain samplers, such as `HMC`, require the domain of the priors to be unbounde To transform from `[0, 1]` to `ℝ` we can use the `Logit` bijector: ```julia -Bijectors.bijector(d::CustomUniform) = Logit(0., 1.) +Bijectors.bijector(d::CustomUniform) = Logit(0.0, 1.0) ``` You'd do the exact same thing for `ContinuousMultivariateDistribution` and `ContinuousMatrixDistribution`. For example, `Wishart` defines a distribution over positive-definite matrices and so `bijector` returns a `PDBijector` when called with a `Wishart` distribution as an argument. For discrete distributions, there is no need to define a bijector; the `Identity` bijector is used by default. @@ -60,20 +52,23 @@ You'd do the exact same thing for `ContinuousMultivariateDistribution` and `Cont Alternatively, for `UnivariateDistribution` we can define the `minimum` and `maximum` of the distribution ```julia -Distributions.minimum(d::CustomUniform) = 0. -Distributions.maximum(d::CustomUniform) = 1. +Distributions.minimum(d::CustomUniform) = 0.0 +Distributions.maximum(d::CustomUniform) = 1.0 ``` and `Bijectors.jl` will return a default `Bijector` called `TruncatedBijector` which makes use of `minimum` and `maximum` derive the correct transformation. Internally, Turing basically does the following when it needs to convert a constrained distribution to an unconstrained distribution, e.g. when sampling using `HMC`: + ```julia b = bijector(dist) transformed_dist = transformed(dist, b) # results in distribution with transformed support + correction for logpdf ``` + and then we can call `rand` and `logpdf` as usual, where -- `rand(transformed_dist)` returns a sample in the unconstrained space, and -- `logpdf(transformed_dist, y)` returns the log density of the original distribution, but with `y` living in the unconstrained space. + + - `rand(transformed_dist)` returns a sample in the unconstrained space, and + - `logpdf(transformed_dist, y)` returns the log density of the original distribution, but with `y` living in the unconstrained space. To read more about Bijectors.jl, check out [the project README](https://github.com/TuringLang/Bijectors.jl). @@ -107,11 +102,11 @@ using LinearAlgebra if dot(m, x) < 0 Turing.@addlogprob! -Inf # Exit the model evaluation early - return + return nothing end x ~ MvNormal(m, I) - return + return nothing end ``` @@ -129,7 +124,6 @@ end ## Model Internals - The `@model` macro accepts a function definition and rewrites it such that call of the function generates a `Model` struct for use by the sampler. Models can be constructed by hand without the use of a macro. Taking the `gdemo` model as an example, the macro-based definition @@ -138,12 +132,12 @@ Taking the `gdemo` model as an example, the macro-based definition using Turing @model function gdemo(x) - # Set priors. - s² ~ InverseGamma(2, 3) - m ~ Normal(0, sqrt(s²)) + # Set priors. + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) - # Observe each value of x. - @. x ~ Normal(m, sqrt(s²)) + # Observe each value of x. + @. x ~ Normal(m, sqrt(s²)) end model = gdemo([1.5, 2.0]) @@ -158,22 +152,18 @@ using Turing function gdemo(model, varinfo, context, x) # Assume s² has an InverseGamma distribution. s², varinfo = DynamicPPL.tilde_assume!!( - context, - InverseGamma(2, 3), - Turing.@varname(s²), - varinfo, + context, InverseGamma(2, 3), Turing.@varname(s²), varinfo ) # Assume m has a Normal distribution. m, varinfo = DynamicPPL.tilde_assume!!( - context, - Normal(0, sqrt(s²)), - Turing.@varname(m), - varinfo, + context, Normal(0, sqrt(s²)), Turing.@varname(m), varinfo ) # Observe each value of x[i] according to a Normal distribution. - DynamicPPL.dot_tilde_observe!!(context, Normal(m, sqrt(s²)), x, Turing.@varname(x), varinfo) + return DynamicPPL.dot_tilde_observe!!( + context, Normal(m, sqrt(s²)), x, Turing.@varname(x), varinfo + ) end gdemo(x) = Turing.Model(gdemo, (; x)) diff --git a/tutorials/using-turing-autodiff/autodiff.md b/tutorials/using-turing-autodiff/autodiff.md index eeaccc51a..0d0cfc0f8 100644 --- a/tutorials/using-turing-autodiff/autodiff.md +++ b/tutorials/using-turing-autodiff/autodiff.md @@ -9,18 +9,14 @@ weave_options: ## Switching AD Modes - Turing supports four packages of automatic differentiation (AD) in the back end during sampling. The default AD backend is [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl) for forward-mode AD. Three reverse-mode AD backends are also supported, namely [Tracker](https://github.com/FluxML/Tracker.jl), [Zygote](https://github.com/FluxML/Zygote.jl) and [ReverseDiff](https://github.com/JuliaDiff/ReverseDiff.jl). `Zygote` and `ReverseDiff` are supported optionally if explicitly loaded by the user with `using Zygote` or `using ReverseDiff` next to `using Turing`. To switch between the different AD backends, one can call function `Turing.setadbackend(backend_sym)`, where `backend_sym` can be `:forwarddiff` (`ForwardDiff`), `:tracker` (`Tracker`), `:zygote` (`Zygote`) or `:reversediff` (`ReverseDiff.jl`). When using `ReverseDiff`, to compile the tape only once and cache it for later use, the user has to call `Turing.setrdcache(true)`. However, note that the use of caching in certain types of models can lead to incorrect results and/or errors. Models for which the compiled tape can be safely cached are models with fixed size loops and no run-time if statements. Compile-time if statements are fine. - ## Compositional Sampling with Differing AD Modes - Turing supports intermixed automatic differentiation methods for different variable spaces. The snippet below shows using `ForwardDiff` to sample the mean (`m`) parameter, and using the Tracker-based `TrackerAD` autodiff for the variance (`s`) parameter: - ```julia using Turing @@ -29,22 +25,17 @@ using Turing s² ~ InverseGamma(2, 3) m ~ Normal(0, sqrt(s²)) x ~ Normal(m, sqrt(s²)) - y ~ Normal(m, sqrt(s²)) + return y ~ Normal(m, sqrt(s²)) end # Sample using Gibbs and varying autodiff backends. c = sample( - gdemo(1.5, 2), - Gibbs( - HMC{Turing.ForwardDiffAD{1}}(0.1, 5, :m), - HMC{Turing.TrackerAD}(0.1, 5, :s) - ), + gdemo(1.5, 2), + Gibbs(HMC{Turing.ForwardDiffAD{1}}(0.1, 5, :m), HMC{Turing.TrackerAD}(0.1, 5, :s)), 1000, ) ``` - Generally, `TrackerAD` is faster when sampling from variables of high dimensionality (greater than 20) and `ForwardDiffAD` is more efficient for lower-dimension variables. This functionality allows those who are performance sensitive to fine tune their automatic differentiation for their specific models. - If the differentiation method is not specified in this way, Turing will default to using whatever the global AD backend is. Currently, this defaults to `ForwardDiff`. diff --git a/tutorials/using-turing-dynamicppl/dynamichmc.md b/tutorials/using-turing-dynamicppl/dynamichmc.md index 794010711..a245dbffb 100644 --- a/tutorials/using-turing-dynamicppl/dynamichmc.md +++ b/tutorials/using-turing-dynamicppl/dynamichmc.md @@ -13,17 +13,16 @@ To use the `DynamicNUTS` function, you must import the `DynamicHMC` package as w Here is a brief example of how to apply `DynamicNUTS`: - ```julia # Import Turing and DynamicHMC. using DynamicHMC, Turing # Model definition. @model function gdemo(x, y) - s² ~ InverseGamma(2, 3) - m ~ Normal(0, sqrt(s²)) - x ~ Normal(m, sqrt(s²)) - y ~ Normal(m, sqrt(s²)) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + x ~ Normal(m, sqrt(s²)) + return y ~ Normal(m, sqrt(s²)) end # Pull 2,000 samples using DynamicNUTS. diff --git a/tutorials/using-turing-guide/using-turing-guide.md b/tutorials/using-turing-guide/using-turing-guide.md index 337487a32..e6d981daa 100644 --- a/tutorials/using-turing-guide/using-turing-guide.md +++ b/tutorials/using-turing-guide/using-turing-guide.md @@ -327,11 +327,12 @@ For example, let `c` be a `Chain`: #### Variable Types and Type Parameters The element type of a vector (or matrix) of random variables should match the `eltype` of the its prior distribution, `<: Integer` for discrete distributions and `<: AbstractFloat` for continuous distributions. Moreover, if the continuous random variable is to be sampled using a Hamiltonian sampler, the vector's element type needs to either be: -1. `Real` to enable auto-differentiation through the model which uses special number types that are sub-types of `Real`, or -2. Some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Float64) where {T}`. -Similarly, when using a particle sampler, the Julia variable used should either be: -1. An `Array`, or -2. An instance of some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Vector{Float64}) where {T}`. + + 1. `Real` to enable auto-differentiation through the model which uses special number types that are sub-types of `Real`, or + 2. Some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Float64) where {T}`. + Similarly, when using a particle sampler, the Julia variable used should either be: + 3. An `Array`, or + 4. An instance of some type parameter `T` defined in the model header using the type parameter syntax, e.g. `function gdemo(x, ::Type{T} = Vector{Float64}) where {T}`. ### Querying Probabilities from Model or Chain diff --git a/tutorials/using-turing-performance-tips/performancetips.md b/tutorials/using-turing-performance-tips/performancetips.md index d34161c27..67ffe86ac 100644 --- a/tutorials/using-turing-performance-tips/performancetips.md +++ b/tutorials/using-turing-performance-tips/performancetips.md @@ -10,7 +10,6 @@ weave_options: This section briefly summarises a few common techniques to ensure good performance when using Turing. We refer to [the Julia documentation](https://docs.julialang.org/en/v1/manual/performance-tips/index.html) for general techniques to ensure good performance of Julia programs. - ## Use multivariate distributions It is generally preferable to use multivariate distributions if possible. @@ -20,11 +19,12 @@ The following example: ```julia @model function gmodel(x) m ~ Normal() - for i = 1:length(x) + for i in 1:length(x) x[i] ~ Normal(m, 0.2) end end ``` + can be directly expressed more efficiently using a simple transformation: ```julia @@ -32,15 +32,14 @@ using FillArrays @model function gmodel(x) m ~ Normal() - x ~ MvNormal(Fill(m, length(x)), 0.04 * I) + return x ~ MvNormal(Fill(m, length(x)), 0.04 * I) end ``` - ## Choose your AD backend -Turing currently provides support for two different automatic differentiation (AD) backends. -Generally, try to use `:forwarddiff` for models with few parameters and `:reversediff`, `:tracker` or `:zygote` for models with large parameter vectors or linear algebra operations. See [Automatic Differentiation](autodiff) for details. +Turing currently provides support for two different automatic differentiation (AD) backends. +Generally, try to use `:forwarddiff` for models with few parameters and `:reversediff`, `:tracker` or `:zygote` for models with large parameter vectors or linear algebra operations. See [Automatic Differentiation](autodiff) for details. ## Special care for `:tracker` and `:zygote` @@ -49,7 +48,6 @@ This is mainly due to the reverse-mode AD backends `Tracker` and `Zygote` which Avoiding loops can be done using `filldist(dist, N)` and `arraydist(dists)`. `filldist(dist, N)` creates a multivariate distribution that is composed of `N` identical and independent copies of the univariate distribution `dist` if `dist` is univariate, or it creates a matrix-variate distribution composed of `N` identical and independent copies of the multivariate distribution `dist` if `dist` is multivariate. `filldist(dist, N, M)` can also be used to create a matrix-variate distribution from a univariate distribution `dist`. `arraydist(dists)` is similar to `filldist` but it takes an array of distributions `dists` as input. Writing a [custom distribution](advanced) with a custom adjoint is another option to avoid loops. - ## Ensure that types in your model can be inferred For efficient gradient-based inference, e.g. using HMC, NUTS or ADVI, it is important to ensure the types in your model can be inferred. @@ -58,29 +56,29 @@ The following example with abstract types ```julia @model function tmodel(x, y) - p,n = size(x) + p, n = size(x) params = Vector{Real}(undef, n) - for i = 1:n + for i in 1:n params[i] ~ truncated(Normal(), 0, Inf) end a = x * params - y ~ MvNormal(a, I) + return y ~ MvNormal(a, I) end ``` can be transformed into the following representation with concrete types: ```julia -@model function tmodel(x, y, ::Type{T} = Float64) where {T} - p,n = size(x) +@model function tmodel(x, y, ::Type{T}=Float64) where {T} + p, n = size(x) params = Vector{T}(undef, n) - for i = 1:n + for i in 1:n params[i] ~ truncated(Normal(), 0, Inf) end a = x * params - y ~ MvNormal(a, I) + return y ~ MvNormal(a, I) end ``` @@ -90,7 +88,7 @@ Alternatively, you could use `filldist` in this example: @model function tmodel(x, y) params ~ filldist(truncated(Normal(), 0, Inf), size(x, 2)) a = x * params - y ~ MvNormal(a, I) + return y ~ MvNormal(a, I) end ``` @@ -104,7 +102,7 @@ For example, consider the following simple program: p = Vector{Real}(undef, 1) p[1] ~ Normal() p = p .+ 1 - x ~ Normal(p[1]) + return x ~ Normal(p[1]) end ``` @@ -119,7 +117,7 @@ model = tmodel(1.0) model, Turing.VarInfo(model), Turing.SamplingContext( - Random.GLOBAL_RNG, Turing.SampleFromPrior(), Turing.DefaultContext(), + Random.GLOBAL_RNG, Turing.SampleFromPrior(), Turing.DefaultContext() ), model.args..., ) @@ -127,37 +125,41 @@ model = tmodel(1.0) to inspect type inference in the model. - ## Reuse Computations in Gibbs Sampling Often when performing Gibbs sampling, one can save computational time by caching the output of expensive functions. The cached values can then be reused in future Gibbs sub-iterations which do not change the inputs to this expensive function. For example in the following model + ```julia @model function demo(x) a ~ Gamma() b ~ Normal() c = function1(a) d = function2(b) - x .~ Normal(c, d) + return x .~ Normal(c, d) end alg = Gibbs(MH(:a), MH(:b)) sample(demo(zeros(10)), alg, 1000) ``` -when only updating `a` in a Gibbs sub-iteration, keeping `b` the same, the value of `d` doesn't change. And when only updating `b`, the value of `c` doesn't change. However, if `function1` and `function2` are expensive and are both run in every Gibbs sub-iteration, a lot of time would be spent computing values that we already computed before. Such a problem can be overcome using `Memoization.jl`. Memoizing a function lets us store and reuse the output of the function for every input it is called with. This has a slight time overhead but for expensive functions, the savings will be far greater. + +when only updating `a` in a Gibbs sub-iteration, keeping `b` the same, the value of `d` doesn't change. And when only updating `b`, the value of `c` doesn't change. However, if `function1` and `function2` are expensive and are both run in every Gibbs sub-iteration, a lot of time would be spent computing values that we already computed before. Such a problem can be overcome using `Memoization.jl`. Memoizing a function lets us store and reuse the output of the function for every input it is called with. This has a slight time overhead but for expensive functions, the savings will be far greater. To use `Memoization.jl`, simply define memoized versions of `function1` and `function2` as such: + ```julia using Memoization @memoize memoized_function1(args...) = function1(args...) @memoize memoized_function2(args...) = function2(args...) ``` + Then define the `Turing` model using the new functions as such: + ```julia @model function demo(x) a ~ Gamma() b ~ Normal() c = memoized_function1(a) d = memoized_function2(b) - x .~ Normal(c, d) + return x .~ Normal(c, d) end ``` diff --git a/tutorials/using-turing-quick-start/quick-start.jmd b/tutorials/using-turing-quick-start/quick-start.jmd index 69cd6484a..cf6e70f56 100644 --- a/tutorials/using-turing-quick-start/quick-start.jmd +++ b/tutorials/using-turing-quick-start/quick-start.jmd @@ -10,11 +10,9 @@ weave_options: If you are already well-versed in probabilistic programming and just want to take a quick look at how Turing's syntax works or otherwise just want a model to start with, we have provided a complete Bayesian coin-flipping model below. +This example can be run wherever you have Julia installed (see [Getting Started](%7B%7Bsite.baseurl%7D%7D/docs/using-turing/get-started)), but you will need to install the packages `Turing` and `StatsPlots` if you have not done so already. -This example can be run wherever you have Julia installed (see [Getting Started]({{site.baseurl}}/docs/using-turing/get-started)), but you will need to install the packages `Turing` and `StatsPlots` if you have not done so already. - - -This is an excerpt from a more formal example which can be found in [this Jupyter notebook](https://nbviewer.jupyter.org/github/TuringLang/TuringTutorials/blob/master/notebook/00-introduction/00_introduction.ipynb) or as part of the documentation website [here]({{site.baseurl}}/tutorials). +This is an excerpt from a more formal example which can be found in [this Jupyter notebook](https://nbviewer.jupyter.org/github/TuringLang/TuringTutorials/blob/master/notebook/00-introduction/00_introduction.ipynb) or as part of the documentation website [here](%7B%7Bsite.baseurl%7D%7D/tutorials). ```julia # Import libraries. @@ -39,7 +37,7 @@ data = rand(Bernoulli(p_true), last(Ns)) # Declare our Turing model. @model function coinflip(y) # Our prior belief about the probability of heads in a coin. - p ~ Beta(1, 1) + p ~ Beta(1, 1) # The number of observations. N = length(y) diff --git a/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl b/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl index f4b419aba..fa894f89a 100644 --- a/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl +++ b/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl @@ -14,9 +14,9 @@ Random.seed!(0) s² ~ InverseGamma(2, 3) m ~ Normal(0, sqrt(s²)) bumps = sin(m) + cos(m) - m = m + 5*bumps + m = m + 5 * bumps for i in eachindex(x) - x[i] ~ Normal(m, sqrt(s²)) + x[i] ~ Normal(m, sqrt(s²)) end return s², m end @@ -31,7 +31,7 @@ vi = Turing.VarInfo(model) # Convert the variance parameter to the real line before sampling. # Note: We only have to do this here because we are being very hands-on. # Turing will handle all of this for you during normal sampling. -dist = InverseGamma(2,3) +dist = InverseGamma(2, 3) svn = vi.metadata.s.vns[1] mvn = vi.metadata.m.vns[1] setval!(vi, vectorize(dist, Bijectors.link(dist, reconstruct(dist, getval(vi, svn)))), svn) @@ -43,7 +43,7 @@ function evaluate(m1, m2) vi[svn] = [m1] vi[mvn] = [m2] model(vi, spl) - getlogp(vi) + return getlogp(vi) end function plot_sampler(chain; label="") @@ -58,26 +58,42 @@ function plot_sampler(chain; label="") # Range start/stop points. spread = 0.5 - σ_start = minimum(ss) - spread * std(ss); σ_stop = maximum(ss) + spread * std(ss); - μ_start = minimum(ms) - spread * std(ms); μ_stop = maximum(ms) + spread * std(ms); - σ_rng = collect(range(σ_start, stop=σ_stop, length=granularity)) - μ_rng = collect(range(μ_start, stop=μ_stop, length=granularity)) + σ_start = minimum(ss) - spread * std(ss) + σ_stop = maximum(ss) + spread * std(ss) + μ_start = minimum(ms) - spread * std(ms) + μ_stop = maximum(ms) + spread * std(ms) + σ_rng = collect(range(σ_start; stop=σ_stop, length=granularity)) + μ_rng = collect(range(μ_start; stop=μ_stop, length=granularity)) # Make surface plot. - p = surface(σ_rng, μ_rng, evaluate, - camera=(30, 65), + p = surface( + σ_rng, + μ_rng, + evaluate; + camera=(30, 65), # ticks=nothing, - colorbar=false, - color=:inferno, - title=label) + colorbar=false, + color=:inferno, + title=label, + ) line_range = 1:length(ms) - scatter3d!(ss[line_range], ms[line_range], lps[line_range], - mc =:viridis, marker_z=collect(line_range), msw=0, - legend=false, colorbar=false, alpha=0.5, - xlabel="σ", ylabel="μ", zlabel="Log probability", - title=label) + scatter3d!( + ss[line_range], + ms[line_range], + lps[line_range]; + mc=:viridis, + marker_z=collect(line_range), + msw=0, + legend=false, + colorbar=false, + alpha=0.5, + xlabel="σ", + ylabel="μ", + zlabel="Log probability", + title=label, + ) return p end; @@ -91,10 +107,11 @@ samplers = [ (NUTS(0.95), "NUTS(0.95)"), (NUTS(0.2), "NUTS(0.2)"), (PG(20), "PG(20)"), - (PG(50), "PG(50)")] + (PG(50), "PG(50)"), +] for (i, (spl, spl_name)) in enumerate(samplers) c = sample(model, spl, 1000) - p = plot_sampler(c, label="$spl_name") + p = plot_sampler(c; label="$spl_name") savefig(joinpath(@__DIR__, "sampler-figs", "samplers-$i.svg")) end diff --git a/tutorials/using-turing-sampler-viz/sampler-viz.md b/tutorials/using-turing-sampler-viz/sampler-viz.md index 107fef4f8..31805c369 100644 --- a/tutorials/using-turing-sampler-viz/sampler-viz.md +++ b/tutorials/using-turing-sampler-viz/sampler-viz.md @@ -33,9 +33,9 @@ Random.seed!(0) s² ~ InverseGamma(2, 3) m ~ Normal(0, sqrt(s²)) bumps = sin(m) + cos(m) - m = m + 5*bumps + m = m + 5 * bumps for i in eachindex(x) - x[i] ~ Normal(m, sqrt(s²)) + x[i] ~ Normal(m, sqrt(s²)) end return s², m end @@ -50,7 +50,7 @@ vi = Turing.VarInfo(model) # Convert the variance parameter to the real line before sampling. # Note: We only have to do this here because we are being very hands-on. # Turing will handle all of this for you during normal sampling. -dist = InverseGamma(2,3) +dist = InverseGamma(2, 3) svn = vi.metadata.s².vns[1] mvn = vi.metadata.m.vns[1] setval!(vi, vectorize(dist, Bijectors.link(dist, reconstruct(dist, getval(vi, svn)))), svn) @@ -62,7 +62,7 @@ function evaluate(m1, m2) vi[svn] = [m1] vi[mvn] = [m2] model(vi, spl) - getlogp(vi) + return getlogp(vi) end function plot_sampler(chain; label="") @@ -77,26 +77,42 @@ function plot_sampler(chain; label="") # Range start/stop points. spread = 0.5 - σ_start = minimum(ss) - spread * std(ss); σ_stop = maximum(ss) + spread * std(ss); - μ_start = minimum(ms) - spread * std(ms); μ_stop = maximum(ms) + spread * std(ms); - σ_rng = collect(range(σ_start, stop=σ_stop, length=granularity)) - μ_rng = collect(range(μ_start, stop=μ_stop, length=granularity)) + σ_start = minimum(ss) - spread * std(ss) + σ_stop = maximum(ss) + spread * std(ss) + μ_start = minimum(ms) - spread * std(ms) + μ_stop = maximum(ms) + spread * std(ms) + σ_rng = collect(range(σ_start; stop=σ_stop, length=granularity)) + μ_rng = collect(range(μ_start; stop=μ_stop, length=granularity)) # Make surface plot. - p = surface(σ_rng, μ_rng, evaluate, - camera=(30, 65), + p = surface( + σ_rng, + μ_rng, + evaluate; + camera=(30, 65), # ticks=nothing, - colorbar=false, - color=:inferno, - title=label) + colorbar=false, + color=:inferno, + title=label, + ) line_range = 1:length(ms) - scatter3d!(ss[line_range], ms[line_range], lps[line_range], - mc =:viridis, marker_z=collect(line_range), msw=0, - legend=false, colorbar=false, alpha=0.5, - xlabel="σ", ylabel="μ", zlabel="Log probability", - title=label) + scatter3d!( + ss[line_range], + ms[line_range], + lps[line_range]; + mc=:viridis, + marker_z=collect(line_range), + msw=0, + legend=false, + colorbar=false, + alpha=0.5, + xlabel="σ", + ylabel="μ", + zlabel="Log probability", + title=label, + ) return p end; @@ -126,7 +142,6 @@ plot_sampler(c) ![](sampler-figs/samplers-2.svg) - ### HMCDA The HMCDA sampler is an implementation of the Hamiltonian Monte Carlo with Dual Averaging algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. @@ -138,7 +153,6 @@ plot_sampler(c) ![](sampler-figs/samplers-3.svg) - ### MH Metropolis-Hastings (MH) sampling is one of the earliest Markov Chain Monte Carlo methods. MH sampling does not "move" a lot, unlike many of the other samplers implemented in Turing. Typically a much longer chain is required to converge to an appropriate parameter estimate. @@ -152,7 +166,6 @@ plot_sampler(c) ![](sampler-figs/samplers-4.svg) - As you can see, the MH sampler doesn't move parameter estimates very often. ### NUTS @@ -168,7 +181,6 @@ plot_sampler(c) ![](sampler-figs/samplers-5.svg) - The only parameter that needs to be set other than the number of iterations to run is the target acceptance rate. In the Hoffman and Gelman paper, they note that a target acceptance rate of 0.65 is typical. Here is a plot showing a very high acceptance rate. Note that it appears to "stick" to a mode and is not particularly good at exploring the posterior as compared to the 0.65 target acceptance ratio case. @@ -180,7 +192,6 @@ plot_sampler(c) ![](sampler-figs/samplers-6.svg) - An exceptionally low acceptance rate will show very few moves on the posterior: ```julia @@ -190,7 +201,6 @@ plot_sampler(c) ![](sampler-figs/samplers-7.svg) - ### PG The Particle Gibbs (PG) sampler is an implementation of an algorithm from the paper "Particle Markov chain Monte Carlo methods" by Andrieu, Doucet, and Holenstein (2010). The interested reader can learn more [here](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9868.2009.00736.x). @@ -204,7 +214,6 @@ plot_sampler(c) ![](sampler-figs/samplers-8.svg) - Next, we plot using 50 particles. ```julia diff --git a/tutorials/using-turing/using-turing.jmd b/tutorials/using-turing/using-turing.jmd index bc514777f..2823e35ca 100644 --- a/tutorials/using-turing/using-turing.jmd +++ b/tutorials/using-turing/using-turing.jmd @@ -11,8 +11,7 @@ Welcome to the documentation for Turing. Turing is a general-purpose probabilistic programming language for robust, efficient Bayesian inference and decision making. Current features include: - * General-purpose probabilistic programming with an intuitive modelling interface; - * Robust, efficient [Hamiltonian Monte Carlo (HMC)](https://github.com/TuringLang/AdvancedHMC.jl) sampling for differentiable posterior distributions; - * Particle MCMC sampling for complex posterior distributions involving discrete variables and stochastic control flow; and - * Gibbs sampling combines particle MCMC, HMC and random-walk MH (RWMH) etc. - + - General-purpose probabilistic programming with an intuitive modelling interface; + - Robust, efficient [Hamiltonian Monte Carlo (HMC)](https://github.com/TuringLang/AdvancedHMC.jl) sampling for differentiable posterior distributions; + - Particle MCMC sampling for complex posterior distributions involving discrete variables and stochastic control flow; and + - Gibbs sampling combines particle MCMC, HMC and random-walk MH (RWMH) etc. From 2d1e31e11947e9e9eb5c3effa18abc4685fb5d76 Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Thu, 26 Jan 2023 21:05:30 +0000 Subject: [PATCH 11/87] Update tutorials/for-developers-interface/interface.md Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- tutorials/for-developers-interface/interface.md | 1 + 1 file changed, 1 insertion(+) diff --git a/tutorials/for-developers-interface/interface.md b/tutorials/for-developers-interface/interface.md index 92ebf176c..316e40a10 100644 --- a/tutorials/for-developers-interface/interface.md +++ b/tutorials/for-developers-interface/interface.md @@ -154,6 +154,7 @@ need, but we need to implement the `step!` function which actually performs infe As a refresher, Metropolis-Hastings implements a very basic algorithm: 1. Pick some initial state, ``\theta_0``. + 2. For ``t`` in ``[1,N],`` do + Generate a proposal parameterization ``\theta^\prime_t \sim q(\theta^\prime_t \mid \theta_{t-1}).`` From ef95a289779c7db1788562acba2926e7f0144bcc Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Thu, 26 Jan 2023 21:05:40 +0000 Subject: [PATCH 12/87] Update tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- .../how_turing_implements_abstractmcmc.md | 1 + 1 file changed, 1 insertion(+) diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md index 2c82af8f3..01cbb20da 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md @@ -277,6 +277,7 @@ We focus on the AbstractMCMC functions that are overridden in `is.jl` and execut * the transition's `vi` field is simply `spl.state.vi` * the `lp` field contains the likelihood `spl.state.vi.logp[]` + - When the, `n_samples` iterations are completed, `sample_end!` fills the `final_logevidence` field of `spl.state` + it simply takes the logarithm of the average of the sample weights, using the log weights for numerical stability From c70aecebd8f3516e61a96880d19ee6962fdb9a7a Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Thu, 26 Jan 2023 21:05:49 +0000 Subject: [PATCH 13/87] Update tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- .../how_turing_implements_abstractmcmc.md | 1 + 1 file changed, 1 insertion(+) diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md index 01cbb20da..da2c2b5d3 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md @@ -267,6 +267,7 @@ We focus on the AbstractMCMC functions that are overridden in `is.jl` and execut - During the ``i``-th iteration, `step!` does 3 things: + `empty!!(spl.state.vi)`: remove information about the previous sample from the sampler's `VarInfo` + + `model(rng, spl.state.vi, spl)`: call the model evaluation function * calls to `assume` add the samples from the prior ``s_i`` and ``m_i`` to `spl.state.vi` From 9e8c16dfd63e5e73664f6a64952795f85d82a1c5 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 26 Jan 2023 21:21:33 +0000 Subject: [PATCH 14/87] update sample viz --- .../using-turing-sampler-viz/Project.toml | 3 + .../gen-sampler-viz.jl | 117 - .../sampler-figs/samplers-1.svg | 2725 ----------------- .../sampler-figs/samplers-2.svg | 2479 --------------- .../sampler-figs/samplers-3.svg | 2516 --------------- .../sampler-figs/samplers-4.svg | 2635 ---------------- .../sampler-figs/samplers-5.svg | 2426 --------------- .../sampler-figs/samplers-6.svg | 2222 -------------- .../sampler-figs/samplers-7.svg | 2562 ---------------- .../sampler-figs/samplers-8.svg | 2691 ---------------- .../sampler-figs/samplers-9.svg | 2616 ---------------- .../{sampler-viz.md => sampler-viz.jmd} | 20 +- 12 files changed, 4 insertions(+), 23008 deletions(-) delete mode 100644 tutorials/using-turing-sampler-viz/gen-sampler-viz.jl delete mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-1.svg delete mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-2.svg delete mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-3.svg delete mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-4.svg delete mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-5.svg delete mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-6.svg delete mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-7.svg delete mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-8.svg delete mode 100644 tutorials/using-turing-sampler-viz/sampler-figs/samplers-9.svg rename tutorials/using-turing-sampler-viz/{sampler-viz.md => sampler-viz.jmd} (95%) diff --git a/tutorials/using-turing-sampler-viz/Project.toml b/tutorials/using-turing-sampler-viz/Project.toml index d9c1da059..f46580060 100644 --- a/tutorials/using-turing-sampler-viz/Project.toml +++ b/tutorials/using-turing-sampler-viz/Project.toml @@ -1,3 +1,6 @@ [deps] +Bijectors = "76274a88-744f-5084-9051-94815aaf08c4" +DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl b/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl deleted file mode 100644 index fa894f89a..000000000 --- a/tutorials/using-turing-sampler-viz/gen-sampler-viz.jl +++ /dev/null @@ -1,117 +0,0 @@ -ENV["GKS_ENCODING"] = "utf-8" # Allows the use of unicode characters in Plots.jl -using Plots -using StatsPlots -using Turing -using Bijectors -using Random -using DynamicPPL: getlogp, settrans!, getval, reconstruct, vectorize, setval! - -# Set a seed. -Random.seed!(0) - -# Define a strange model. -@model function gdemo(x) - s² ~ InverseGamma(2, 3) - m ~ Normal(0, sqrt(s²)) - bumps = sin(m) + cos(m) - m = m + 5 * bumps - for i in eachindex(x) - x[i] ~ Normal(m, sqrt(s²)) - end - return s², m -end - -# Define our data points. -x = [1.5, 2.0, 13.0, 2.1, 0.0] - -# Set up the model call, sample from the prior. -model = gdemo(x) -vi = Turing.VarInfo(model) - -# Convert the variance parameter to the real line before sampling. -# Note: We only have to do this here because we are being very hands-on. -# Turing will handle all of this for you during normal sampling. -dist = InverseGamma(2, 3) -svn = vi.metadata.s.vns[1] -mvn = vi.metadata.m.vns[1] -setval!(vi, vectorize(dist, Bijectors.link(dist, reconstruct(dist, getval(vi, svn)))), svn) -settrans!(vi, true, svn) - -# Evaluate surface at coordinates. -function evaluate(m1, m2) - spl = Turing.SampleFromPrior() - vi[svn] = [m1] - vi[mvn] = [m2] - model(vi, spl) - return getlogp(vi) -end - -function plot_sampler(chain; label="") - # Extract values from chain. - val = get(chain, [:s, :m, :lp]) - ss = link.(Ref(InverseGamma(2, 3)), val.s) - ms = val.m - lps = val.lp - - # How many surface points to sample. - granularity = 100 - - # Range start/stop points. - spread = 0.5 - σ_start = minimum(ss) - spread * std(ss) - σ_stop = maximum(ss) + spread * std(ss) - μ_start = minimum(ms) - spread * std(ms) - μ_stop = maximum(ms) + spread * std(ms) - σ_rng = collect(range(σ_start; stop=σ_stop, length=granularity)) - μ_rng = collect(range(μ_start; stop=μ_stop, length=granularity)) - - # Make surface plot. - p = surface( - σ_rng, - μ_rng, - evaluate; - camera=(30, 65), - # ticks=nothing, - colorbar=false, - color=:inferno, - title=label, - ) - - line_range = 1:length(ms) - - scatter3d!( - ss[line_range], - ms[line_range], - lps[line_range]; - mc=:viridis, - marker_z=collect(line_range), - msw=0, - legend=false, - colorbar=false, - alpha=0.5, - xlabel="σ", - ylabel="μ", - zlabel="Log probability", - title=label, - ) - - return p -end; - -samplers = [ - (Gibbs(HMC(0.01, 5, :s), PG(20, :m)), "Gibbs{HMC, PG}"), - (HMC(0.01, 10), "HMC"), - (HMCDA(200, 0.65, 0.3), "HMCDA"), - (MH(), "MH()"), - (NUTS(0.65), "NUTS(0.65)"), - (NUTS(0.95), "NUTS(0.95)"), - (NUTS(0.2), "NUTS(0.2)"), - (PG(20), "PG(20)"), - (PG(50), "PG(50)"), -] - -for (i, (spl, spl_name)) in enumerate(samplers) - c = sample(model, spl, 1000) - p = plot_sampler(c; label="$spl_name") - savefig(joinpath(@__DIR__, "sampler-figs", "samplers-$i.svg")) -end diff --git a/tutorials/using-turing-sampler-viz/sampler-figs/samplers-1.svg b/tutorials/using-turing-sampler-viz/sampler-figs/samplers-1.svg deleted file mode 100644 index c32b05a16..000000000 --- a/tutorials/using-turing-sampler-viz/sampler-figs/samplers-1.svg +++ /dev/null @@ -1,2725 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -1 - - -2 - - -3 - - -4 - - --5 - - -0 - - -5 - - -10 - - --300 - - --200 - - --100 - - -0 - - -Gibbs{HMC, PG} - - -σ - - -μ - - -Log probability - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- -NUTS(0.65) - - -σ - - -μ - - -Log probability - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tutorials/using-turing-sampler-viz/sampler-figs/samplers-7.svg b/tutorials/using-turing-sampler-viz/sampler-figs/samplers-7.svg deleted file mode 100644 index c0dc125bc..000000000 --- a/tutorials/using-turing-sampler-viz/sampler-figs/samplers-7.svg +++ /dev/null @@ -1,2562 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2.0 - - -2.5 - - -3.0 - - -3.5 - - -4.0 - - -4.5 - - -0 - - -2 - - -4 - - -6 - - --70 - - --60 - - --50 - - --40 - - --30 - - --20 - - -NUTS(0.2) - - -σ - - -μ - - -Log probability - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0 - - -1 - - -2 - - -3 - - -4 - - --5 - - -0 - - -5 - - -10 - - --800 - - --600 - - --400 - - --200 - - -0 - - -PG(50) - - -σ - - -μ - - -Log probability - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tutorials/using-turing-sampler-viz/sampler-viz.md b/tutorials/using-turing-sampler-viz/sampler-viz.jmd similarity index 95% rename from tutorials/using-turing-sampler-viz/sampler-viz.md rename to tutorials/using-turing-sampler-viz/sampler-viz.jmd index 31805c369..f61b89770 100644 --- a/tutorials/using-turing-sampler-viz/sampler-viz.md +++ b/tutorials/using-turing-sampler-viz/sampler-viz.jmd @@ -129,8 +129,6 @@ c = sample(model, Gibbs(HMC(0.01, 5, :s), PG(20, :m)), 1000) plot_sampler(c) ``` -![](sampler-figs/samplers-1.svg) - ### HMC Hamiltonian Monte Carlo (HMC) sampling is a typical sampler to use, as it tends to be fairly good at converging in a efficient manner. It can often be tricky to set the correct parameters for this sampler however, and the `NUTS` sampler is often easier to run if you don't want to spend too much time fiddling with step size and and the number of steps to take. Note however that `HMC` does not explore the positive values μ very well, likely due to the leapfrog and step size parameter settings. @@ -140,8 +138,6 @@ c = sample(model, HMC(0.01, 10), 1000) plot_sampler(c) ``` -![](sampler-figs/samplers-2.svg) - ### HMCDA The HMCDA sampler is an implementation of the Hamiltonian Monte Carlo with Dual Averaging algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. @@ -151,8 +147,6 @@ c = sample(model, HMCDA(200, 0.65, 0.3), 1000) plot_sampler(c) ``` -![](sampler-figs/samplers-3.svg) - ### MH Metropolis-Hastings (MH) sampling is one of the earliest Markov Chain Monte Carlo methods. MH sampling does not "move" a lot, unlike many of the other samplers implemented in Turing. Typically a much longer chain is required to converge to an appropriate parameter estimate. @@ -164,8 +158,6 @@ c = sample(model, MH(), 1000) plot_sampler(c) ``` -![](sampler-figs/samplers-4.svg) - As you can see, the MH sampler doesn't move parameter estimates very often. ### NUTS @@ -179,8 +171,6 @@ c = sample(model, NUTS(0.65), 1000) plot_sampler(c) ``` -![](sampler-figs/samplers-5.svg) - The only parameter that needs to be set other than the number of iterations to run is the target acceptance rate. In the Hoffman and Gelman paper, they note that a target acceptance rate of 0.65 is typical. Here is a plot showing a very high acceptance rate. Note that it appears to "stick" to a mode and is not particularly good at exploring the posterior as compared to the 0.65 target acceptance ratio case. @@ -190,8 +180,6 @@ c = sample(model, NUTS(0.95), 1000) plot_sampler(c) ``` -![](sampler-figs/samplers-6.svg) - An exceptionally low acceptance rate will show very few moves on the posterior: ```julia @@ -199,8 +187,6 @@ c = sample(model, NUTS(0.2), 1000) plot_sampler(c) ``` -![](sampler-figs/samplers-7.svg) - ### PG The Particle Gibbs (PG) sampler is an implementation of an algorithm from the paper "Particle Markov chain Monte Carlo methods" by Andrieu, Doucet, and Holenstein (2010). The interested reader can learn more [here](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9868.2009.00736.x). @@ -212,13 +198,9 @@ c = sample(model, PG(20), 1000) plot_sampler(c) ``` -![](sampler-figs/samplers-8.svg) - Next, we plot using 50 particles. ```julia c = sample(model, PG(50), 1000) plot_sampler(c) -``` - -![](sampler-figs/samplers-9.svg) +``` \ No newline at end of file From ff63088981eeea425da254bf8b8b020cd7dfee27 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 26 Jan 2023 21:52:31 +0000 Subject: [PATCH 15/87] add dummy julia code block to enable jmd building --- .../using-turing-sampler-viz/Manifest.toml | 55 +++---------------- .../using-turing-sampler-viz/Project.toml | 2 - .../using-turing-sampler-viz/sampler-viz.jmd | 24 +------- .../{using-turing.jmd => index.jmd} | 11 ++-- 4 files changed, 18 insertions(+), 74 deletions(-) rename tutorials/using-turing/{using-turing.jmd => index.jmd} (62%) diff --git a/tutorials/using-turing-sampler-viz/Manifest.toml b/tutorials/using-turing-sampler-viz/Manifest.toml index ea4ec1254..a63e1c9d0 100644 --- a/tutorials/using-turing-sampler-viz/Manifest.toml +++ b/tutorials/using-turing-sampler-viz/Manifest.toml @@ -124,11 +124,6 @@ git-tree-sha1 = "a3704b8e5170f9339dff4e6cb286ad49464d3646" uuid = "76274a88-744f-5084-9051-94815aaf08c4" version = "0.10.6" -[[deps.BitFlags]] -git-tree-sha1 = "43b1a4a8f797c1cddadf60499a8a077d4af2cd2d" -uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" -version = "0.1.7" - [[deps.Bzip2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2" @@ -171,12 +166,6 @@ git-tree-sha1 = "64df3da1d2a26f4de23871cd1b6482bb68092bd5" uuid = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" version = "0.14.3" -[[deps.CodecZlib]] -deps = ["TranscodingStreams", "Zlib_jll"] -git-tree-sha1 = "9c209fb7536406834aa938fb149964b985de6c83" -uuid = "944b1d66-785c-5afd-91f1-9de20f533193" -version = "0.7.1" - [[deps.ColorSchemes]] deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "Random", "SnoopPrecompile"] git-tree-sha1 = "aa3edc8f8dea6cbfa176ee12f7c2fc82f0608ed3" @@ -331,9 +320,9 @@ version = "0.6.43" [[deps.DocStringExtensions]] deps = ["LibGit2"] -git-tree-sha1 = "2fb1e02f2b635d0845df5d7c167fec4dd739b00d" +git-tree-sha1 = "b19534d1895d702889b219c382a6e18010797f0b" uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" -version = "0.9.3" +version = "0.8.6" [[deps.Downloads]] deps = ["ArgTools", "LibCURL", "NetworkOptions"] @@ -508,10 +497,10 @@ uuid = "42e2da0e-8278-4e71-bc24-59509adca0fe" version = "1.0.2" [[deps.HTTP]] -deps = ["Base64", "CodecZlib", "Dates", "IniFile", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] -git-tree-sha1 = "37e4657cd56b11abe3d10cd4a1ec5fbdb4180263" +deps = ["Base64", "Dates", "IniFile", "MbedTLS", "Sockets"] +git-tree-sha1 = "c7ec02c4c6a039a98a15f955462cd7aea5df4508" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "1.7.4" +version = "0.8.19" [[deps.HarfBuzz_jll]] deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg"] @@ -902,12 +891,6 @@ uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" deps = ["Artifacts", "Libdl"] uuid = "05823500-19ac-5b8b-9628-191a04bc5112" -[[deps.OpenSSL]] -deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"] -git-tree-sha1 = "6503b77492fd7fcb9379bf73cd31035670e3c509" -uuid = "4d8831e6-92b7-49fb-bdf8-b643e874388c" -version = "1.3.3" - [[deps.OpenSSL_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "f6e9dba33f9f2c44e08a020b0caf6903be540004" @@ -1064,10 +1047,10 @@ uuid = "01d81517-befc-4cb6-b9ec-a95719d0359c" version = "0.6.11" [[deps.RecursiveArrayTools]] -deps = ["Adapt", "ArrayInterfaceCore", "ArrayInterfaceStaticArraysCore", "ChainRulesCore", "DocStringExtensions", "FillArrays", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "ZygoteRules"] -git-tree-sha1 = "fcf0962b399f3bc0fa13ae7274db7879c3ef9f1e" +deps = ["Adapt", "ArrayInterfaceCore", "ArrayInterfaceStaticArraysCore", "ChainRulesCore", "DocStringExtensions", "FillArrays", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "StaticArraysCore", "Statistics", "Tables", "ZygoteRules"] +git-tree-sha1 = "a5ce741acddc02f0d4fc6505463ca89697d7fb23" uuid = "731186ca-8d62-57ce-b412-fbd966d074cd" -version = "2.35.0" +version = "2.32.3" [[deps.Reexport]] git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" @@ -1155,11 +1138,6 @@ git-tree-sha1 = "91eddf657aca81df9ae6ceb20b959ae5653ad1de" uuid = "992d4aef-0814-514b-bc4d-f2e9a6c4116f" version = "1.0.3" -[[deps.SimpleBufferStream]] -git-tree-sha1 = "874e8867b33a00e784c8a7e4b60afe9e037b74e1" -uuid = "777ac1f9-54b0-4bf8-805c-2214025038e7" -version = "1.1.0" - [[deps.SnoopPrecompile]] deps = ["Preferences"] git-tree-sha1 = "e760a70afdcd461cf01a575947738d359234665c" @@ -1251,12 +1229,6 @@ version = "0.6.14" deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" -[[deps.SymbolicIndexingInterface]] -deps = ["DocStringExtensions"] -git-tree-sha1 = "6b764c160547240d868be4e961a5037f47ad7379" -uuid = "2efcf032-c050-4f8e-a9bb-153293bab1f5" -version = "0.2.1" - [[deps.TOML]] deps = ["Dates"] uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" @@ -1305,12 +1277,6 @@ git-tree-sha1 = "77817887c4b414b9c6914c61273910e3234eb21c" uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" version = "0.2.23" -[[deps.TranscodingStreams]] -deps = ["Random", "Test"] -git-tree-sha1 = "94f38103c984f89cf77c402f2a68dbd870f8165f" -uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.9.11" - [[deps.Transducers]] deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"] git-tree-sha1 = "c42fa452a60f022e9e087823b47e5a5f8adc53d5" @@ -1323,11 +1289,6 @@ git-tree-sha1 = "8a40377bcc4b054ebdc8f680e96cd73a4a6fe2e6" uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" version = "0.22.0" -[[deps.URIs]] -git-tree-sha1 = "ac00576f90d8a259f2c9d823e91d1de3fd44d348" -uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" -version = "1.4.1" - [[deps.UUIDs]] deps = ["Random", "SHA"] uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" diff --git a/tutorials/using-turing-sampler-viz/Project.toml b/tutorials/using-turing-sampler-viz/Project.toml index f46580060..6f1ffd3ec 100644 --- a/tutorials/using-turing-sampler-viz/Project.toml +++ b/tutorials/using-turing-sampler-viz/Project.toml @@ -1,6 +1,4 @@ [deps] -Bijectors = "76274a88-744f-5084-9051-94815aaf08c4" -DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/using-turing-sampler-viz/sampler-viz.jmd b/tutorials/using-turing-sampler-viz/sampler-viz.jmd index f61b89770..b86cc839f 100644 --- a/tutorials/using-turing-sampler-viz/sampler-viz.jmd +++ b/tutorials/using-turing-sampler-viz/sampler-viz.jmd @@ -21,9 +21,7 @@ ENV["GKS_ENCODING"] = "utf-8" # Allows the use of unicode characters in Plots.jl using Plots using StatsPlots using Turing -using Bijectors using Random -using DynamicPPL: getlogp, settrans!, getval, reconstruct, vectorize, setval! # Set a seed. Random.seed!(0) @@ -45,25 +43,9 @@ x = [1.5, 2.0, 13.0, 2.1, 0.0] # Set up the model call, sample from the prior. model = gdemo(x) -vi = Turing.VarInfo(model) - -# Convert the variance parameter to the real line before sampling. -# Note: We only have to do this here because we are being very hands-on. -# Turing will handle all of this for you during normal sampling. -dist = InverseGamma(2, 3) -svn = vi.metadata.s².vns[1] -mvn = vi.metadata.m.vns[1] -setval!(vi, vectorize(dist, Bijectors.link(dist, reconstruct(dist, getval(vi, svn)))), svn) -settrans!(vi, true, svn) # Evaluate surface at coordinates. -function evaluate(m1, m2) - spl = Turing.SampleFromPrior() - vi[svn] = [m1] - vi[mvn] = [m2] - model(vi, spl) - return getlogp(vi) -end +evaluate(m1, m2) = logjoint(model, (m=m2, s²=m1)) function plot_sampler(chain; label="") # Extract values from chain. @@ -125,7 +107,7 @@ end; Gibbs sampling tends to exhibit a "jittery" trajectory. The example below combines `HMC` and `PG` sampling to traverse the posterior. ```julia -c = sample(model, Gibbs(HMC(0.01, 5, :s), PG(20, :m)), 1000) +c = sample(model, Gibbs(HMC(0.01, 5, :s²), PG(20, :m)), 1000) plot_sampler(c) ``` @@ -203,4 +185,4 @@ Next, we plot using 50 particles. ```julia c = sample(model, PG(50), 1000) plot_sampler(c) -``` \ No newline at end of file +``` diff --git a/tutorials/using-turing/using-turing.jmd b/tutorials/using-turing/index.jmd similarity index 62% rename from tutorials/using-turing/using-turing.jmd rename to tutorials/using-turing/index.jmd index 2823e35ca..af940d9fa 100644 --- a/tutorials/using-turing/using-turing.jmd +++ b/tutorials/using-turing/index.jmd @@ -7,11 +7,14 @@ weave_options: # Turing Documentation -Welcome to the documentation for Turing. - -Turing is a general-purpose probabilistic programming language for robust, efficient Bayesian inference and decision making. Current features include: +Welcome to the documentation for Turing. Turing is a general-purpose probabilistic programming language for robust, +efficient Bayesian inference and decision making. Current features include: - General-purpose probabilistic programming with an intuitive modelling interface; - Robust, efficient [Hamiltonian Monte Carlo (HMC)](https://github.com/TuringLang/AdvancedHMC.jl) sampling for differentiable posterior distributions; - Particle MCMC sampling for complex posterior distributions involving discrete variables and stochastic control flow; and - - Gibbs sampling combines particle MCMC, HMC and random-walk MH (RWMH) etc. + - Gibbs sampling that combines particle MCMC, HMC and random-walk MH (RWMH) etc. + +```julia + +``` From cb299b1e7385197debba27654d13f03af876cec8 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 26 Jan 2023 22:01:34 +0000 Subject: [PATCH 16/87] Bugfix for negative variance. --- tutorials/using-turing-sampler-viz/Project.toml | 1 + tutorials/using-turing-sampler-viz/sampler-viz.jmd | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/tutorials/using-turing-sampler-viz/Project.toml b/tutorials/using-turing-sampler-viz/Project.toml index 6f1ffd3ec..bda96a553 100644 --- a/tutorials/using-turing-sampler-viz/Project.toml +++ b/tutorials/using-turing-sampler-viz/Project.toml @@ -1,4 +1,5 @@ [deps] +Bijectors = "76274a88-744f-5084-9051-94815aaf08c4" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/using-turing-sampler-viz/sampler-viz.jmd b/tutorials/using-turing-sampler-viz/sampler-viz.jmd index b86cc839f..1ffb7e818 100644 --- a/tutorials/using-turing-sampler-viz/sampler-viz.jmd +++ b/tutorials/using-turing-sampler-viz/sampler-viz.jmd @@ -22,6 +22,7 @@ using Plots using StatsPlots using Turing using Random +using Bijectors # Set a seed. Random.seed!(0) @@ -45,7 +46,7 @@ x = [1.5, 2.0, 13.0, 2.1, 0.0] model = gdemo(x) # Evaluate surface at coordinates. -evaluate(m1, m2) = logjoint(model, (m=m2, s²=m1)) +evaluate(m1, m2) = logjoint(model, (m=m2, s²=invlink.(Ref(InverseGamma(2, 3)), m1))) function plot_sampler(chain; label="") # Extract values from chain. From bb392adc4c4f96ace22c43aebfc479c776754ed3 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Thu, 2 Feb 2023 13:27:10 +0000 Subject: [PATCH 17/87] More conversions --- ...nts_abstractmcmc.md => how_turing_implements_abstractmcmc.jmd} | 0 tutorials/for-developers-compiler/{compiler.md => compiler.jmd} | 0 .../for-developers-interface/{interface.md => interface.jmd} | 0 .../{variational_inference.md => variational_inference.jmd} | 0 tutorials/using-turing-advanced/{advanced.md => advanced.jmd} | 0 tutorials/using-turing-autodiff/{autodiff.md => autodiff.jmd} | 0 .../using-turing-dynamicppl/{dynamichmc.md => dynamichmc.jmd} | 0 .../{using-turing-guide.md => using-turing-guide.jmd} | 0 .../{performancetips.md => performancetips.jmd} | 0 9 files changed, 0 insertions(+), 0 deletions(-) rename tutorials/for-developers-abstractmcmc-turing/{how_turing_implements_abstractmcmc.md => how_turing_implements_abstractmcmc.jmd} (100%) rename tutorials/for-developers-compiler/{compiler.md => compiler.jmd} (100%) rename tutorials/for-developers-interface/{interface.md => interface.jmd} (100%) rename tutorials/for-developers-variational-inference/{variational_inference.md => variational_inference.jmd} (100%) rename tutorials/using-turing-advanced/{advanced.md => advanced.jmd} (100%) rename tutorials/using-turing-autodiff/{autodiff.md => autodiff.jmd} (100%) rename tutorials/using-turing-dynamicppl/{dynamichmc.md => dynamichmc.jmd} (100%) rename tutorials/using-turing-guide/{using-turing-guide.md => using-turing-guide.jmd} (100%) rename tutorials/using-turing-performance-tips/{performancetips.md => performancetips.jmd} (100%) diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd similarity index 100% rename from tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.md rename to tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd diff --git a/tutorials/for-developers-compiler/compiler.md b/tutorials/for-developers-compiler/compiler.jmd similarity index 100% rename from tutorials/for-developers-compiler/compiler.md rename to tutorials/for-developers-compiler/compiler.jmd diff --git a/tutorials/for-developers-interface/interface.md b/tutorials/for-developers-interface/interface.jmd similarity index 100% rename from tutorials/for-developers-interface/interface.md rename to tutorials/for-developers-interface/interface.jmd diff --git a/tutorials/for-developers-variational-inference/variational_inference.md b/tutorials/for-developers-variational-inference/variational_inference.jmd similarity index 100% rename from tutorials/for-developers-variational-inference/variational_inference.md rename to tutorials/for-developers-variational-inference/variational_inference.jmd diff --git a/tutorials/using-turing-advanced/advanced.md b/tutorials/using-turing-advanced/advanced.jmd similarity index 100% rename from tutorials/using-turing-advanced/advanced.md rename to tutorials/using-turing-advanced/advanced.jmd diff --git a/tutorials/using-turing-autodiff/autodiff.md b/tutorials/using-turing-autodiff/autodiff.jmd similarity index 100% rename from tutorials/using-turing-autodiff/autodiff.md rename to tutorials/using-turing-autodiff/autodiff.jmd diff --git a/tutorials/using-turing-dynamicppl/dynamichmc.md b/tutorials/using-turing-dynamicppl/dynamichmc.jmd similarity index 100% rename from tutorials/using-turing-dynamicppl/dynamichmc.md rename to tutorials/using-turing-dynamicppl/dynamichmc.jmd diff --git a/tutorials/using-turing-guide/using-turing-guide.md b/tutorials/using-turing-guide/using-turing-guide.jmd similarity index 100% rename from tutorials/using-turing-guide/using-turing-guide.md rename to tutorials/using-turing-guide/using-turing-guide.jmd diff --git a/tutorials/using-turing-performance-tips/performancetips.md b/tutorials/using-turing-performance-tips/performancetips.jmd similarity index 100% rename from tutorials/using-turing-performance-tips/performancetips.md rename to tutorials/using-turing-performance-tips/performancetips.jmd From a1879f6763618623e883fe104a0844aabb1b339e Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Wed, 8 Feb 2023 14:20:46 +0000 Subject: [PATCH 18/87] Added dependencies and changed directory names --- .../Manifest.toml | 1553 +++++++++++++++++ .../Project.toml | 6 + .../sampler-viz.jmd | 190 ++ 3 files changed, 1749 insertions(+) create mode 100644 tutorials/docs-using-turing-sampler-viz/Manifest.toml create mode 100644 tutorials/docs-using-turing-sampler-viz/Project.toml create mode 100644 tutorials/docs-using-turing-sampler-viz/sampler-viz.jmd diff --git a/tutorials/docs-using-turing-sampler-viz/Manifest.toml 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["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+0" diff --git a/tutorials/docs-using-turing-sampler-viz/Project.toml b/tutorials/docs-using-turing-sampler-viz/Project.toml new file mode 100644 index 000000000..f430df979 --- /dev/null +++ b/tutorials/docs-using-turing-sampler-viz/Project.toml @@ -0,0 +1,6 @@ +[deps] +Bijectors = "76274a88-744f-5084-9051-94815aaf08c4" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" +Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/docs-using-turing-sampler-viz/sampler-viz.jmd b/tutorials/docs-using-turing-sampler-viz/sampler-viz.jmd new file mode 100644 index 000000000..f79711006 --- /dev/null +++ b/tutorials/docs-using-turing-sampler-viz/sampler-viz.jmd @@ -0,0 +1,190 @@ +--- +title: Sampler Visualization +mathjax: true +permalink: /docs/using-turing/sampler-viz +redirect_from: docs/sampler-viz/ +weave_options: + error : false +--- + +# Sampler Visualization + +## Introduction + +## The Code + +For each sampler, we will use the same code to plot sampler paths. The block below loads the relevant libraries and defines a function for plotting the sampler's trajectory across the posterior. + +The Turing model definition used here is not especially practical, but it is designed in such a way as to produce visually interesting posterior surfaces to show how different samplers move along the distribution. + +```julia +ENV["GKS_ENCODING"] = "utf-8" # Allows the use of unicode characters in Plots.jl +using Plots +using StatsPlots +using Turing +using Random +using Bijectors + +# Set a seed. +Random.seed!(0) + +# Define a strange model. +@model function gdemo(x) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + bumps = sin(m) + cos(m) + m = m + 5 * bumps + for i in eachindex(x) + x[i] ~ Normal(m, sqrt(s²)) + end + return s², m +end + +# Define our data points. +x = [1.5, 2.0, 13.0, 2.1, 0.0] + +# Set up the model call, sample from the prior. +model = gdemo(x) + +# Evaluate surface at coordinates. +evaluate(m1, m2) = logjoint(model, (m=m2, s²=invlink.(Ref(InverseGamma(2, 3)), m1))) + +function plot_sampler(chain; label="") + # Extract values from chain. + val = get(chain, [:s², :m, :lp]) + ss = link.(Ref(InverseGamma(2, 3)), val.s²) + ms = val.m + lps = val.lp + + # How many surface points to sample. + granularity = 100 + + # Range start/stop points. + spread = 0.5 + σ_start = minimum(ss) - spread * std(ss) + σ_stop = maximum(ss) + spread * std(ss) + μ_start = minimum(ms) - spread * std(ms) + μ_stop = maximum(ms) + spread * std(ms) + σ_rng = collect(range(σ_start; stop=σ_stop, length=granularity)) + μ_rng = collect(range(μ_start; stop=μ_stop, length=granularity)) + + # Make surface plot. + p = surface( + σ_rng, + μ_rng, + evaluate; + camera=(30, 65), + # ticks=nothing, + colorbar=false, + color=:inferno, + title=label, + ) + + line_range = 1:length(ms) + + scatter3d!( + ss[line_range], + ms[line_range], + lps[line_range]; + mc=:viridis, + marker_z=collect(line_range), + msw=0, + legend=false, + colorbar=false, + alpha=0.5, + xlabel="σ", + ylabel="μ", + zlabel="Log probability", + title=label, + ) + + return p +end; +``` + +## Samplers + +### Gibbs + +Gibbs sampling tends to exhibit a "jittery" trajectory. The example below combines `HMC` and `PG` sampling to traverse the posterior. + +```julia +c = sample(model, Gibbs(HMC(0.01, 5, :s²), PG(20, :m)), 1000) +plot_sampler(c) +``` + +### HMC + +Hamiltonian Monte Carlo (HMC) sampling is a typical sampler to use, as it tends to be fairly good at converging in a efficient manner. It can often be tricky to set the correct parameters for this sampler however, and the `NUTS` sampler is often easier to run if you don't want to spend too much time fiddling with step size and and the number of steps to take. Note however that `HMC` does not explore the positive values μ very well, likely due to the leapfrog and step size parameter settings. + +```julia +c = sample(model, HMC(0.01, 10), 1000) +plot_sampler(c) +``` + +### HMCDA + +The HMCDA sampler is an implementation of the Hamiltonian Monte Carlo with Dual Averaging algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. + +```julia +c = sample(model, HMCDA(200, 0.65, 0.3), 1000) +plot_sampler(c) +``` + +### MH + +Metropolis-Hastings (MH) sampling is one of the earliest Markov Chain Monte Carlo methods. MH sampling does not "move" a lot, unlike many of the other samplers implemented in Turing. Typically a much longer chain is required to converge to an appropriate parameter estimate. + +The plot below only uses 1,000 iterations of Metropolis-Hastings. + +```julia +c = sample(model, MH(), 1000) +plot_sampler(c) +``` + +As you can see, the MH sampler doesn't move parameter estimates very often. + +### NUTS + +The No U-Turn Sampler (NUTS) is an implementation of the algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. + +NUTS tends to be very good at traversing complex posteriors quickly. + +```julia +c = sample(model, NUTS(0.65), 1000) +plot_sampler(c) +``` + +The only parameter that needs to be set other than the number of iterations to run is the target acceptance rate. In the Hoffman and Gelman paper, they note that a target acceptance rate of 0.65 is typical. + +Here is a plot showing a very high acceptance rate. Note that it appears to "stick" to a mode and is not particularly good at exploring the posterior as compared to the 0.65 target acceptance ratio case. + +```julia +c = sample(model, NUTS(0.95), 1000) +plot_sampler(c) +``` + +An exceptionally low acceptance rate will show very few moves on the posterior: + +```julia +c = sample(model, NUTS(0.2), 1000) +plot_sampler(c) +``` + +### PG + +The Particle Gibbs (PG) sampler is an implementation of an algorithm from the paper "Particle Markov chain Monte Carlo methods" by Andrieu, Doucet, and Holenstein (2010). The interested reader can learn more [here](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9868.2009.00736.x). + +The two parameters are the number of particles, and the number of iterations. The plot below shows the use of 20 particles. + +```julia +c = sample(model, PG(20), 1000) +plot_sampler(c) +``` + +Next, we plot using 50 particles. + +```julia +c = sample(model, PG(50), 1000) +plot_sampler(c) +``` From 341b21e196e3929aec4bb3d7b30898dc4144ba12 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 13:03:24 +0100 Subject: [PATCH 19/87] Fixed dependencies --- .../how_turing_implements_abstractmcmc.jmd | 3 + .../for-developers-interface/Manifest.toml | 897 +++++++++--------- .../for-developers-interface/Project.toml | 3 + 3 files changed, 461 insertions(+), 442 deletions(-) diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd index da2c2b5d3..5148d7726 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd @@ -282,3 +282,6 @@ We focus on the AbstractMCMC functions that are overridden in `is.jl` and execut - When the, `n_samples` iterations are completed, `sample_end!` fills the `final_logevidence` field of `spl.state` + it simply takes the logarithm of the average of the sample weights, using the log weights for numerical stability + +```julia +``` \ No newline at end of file diff --git a/tutorials/for-developers-interface/Manifest.toml b/tutorials/for-developers-interface/Manifest.toml index ea4ec1254..8a3791c33 100644 --- a/tutorials/for-developers-interface/Manifest.toml +++ b/tutorials/for-developers-interface/Manifest.toml @@ -1,1591 +1,1604 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.6.7" -manifest_format = "2.0" -project_hash = "102d486b18af7951cac72d56aec7c8e37b50a316" - -[[deps.AbstractFFTs]] +[[AbstractFFTs]] deps = ["ChainRulesCore", 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git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1" uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5" version = "0.4.0+1" -[[deps.Xorg_xcb_util_keysyms_jll]] +[[Xorg_xcb_util_keysyms_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00" uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7" version = "0.4.0+1" -[[deps.Xorg_xcb_util_renderutil_jll]] +[[Xorg_xcb_util_renderutil_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e" uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e" version = "0.3.9+1" -[[deps.Xorg_xcb_util_wm_jll]] +[[Xorg_xcb_util_wm_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67" uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361" version = "0.4.1+1" -[[deps.Xorg_xkbcomp_jll]] +[[Xorg_xkbcomp_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"] git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b" uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" version = "1.4.2+4" -[[deps.Xorg_xkeyboard_config_jll]] +[[Xorg_xkeyboard_config_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"] git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d" uuid = "33bec58e-1273-512f-9401-5d533626f822" version = "2.27.0+4" -[[deps.Xorg_xtrans_jll]] +[[Xorg_xtrans_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" -[[deps.Zlib_jll]] +[[Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -[[deps.Zstd_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927" +[[Zstd_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "49ce682769cd5de6c72dcf1b94ed7790cd08974c" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" -version = "1.5.2+0" +version = "1.5.5+0" -[[deps.ZygoteRules]] -deps = ["MacroTools"] -git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" +[[ZygoteRules]] +deps = ["ChainRulesCore", "MacroTools"] +git-tree-sha1 = "977aed5d006b840e2e40c0b48984f7463109046d" uuid = "700de1a5-db45-46bc-99cf-38207098b444" -version = "0.2.2" +version = "0.2.3" -[[deps.fzf_jll]] +[[fzf_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56" uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" version = "0.29.0+0" -[[deps.libaom_jll]] +[[libaom_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4" uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" version = "3.4.0+0" -[[deps.libass_jll]] +[[libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.15.1+0" -[[deps.libfdk_aac_jll]] +[[libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "2.0.2+0" -[[deps.libpng_jll]] +[[libpng_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.38+0" -[[deps.libvorbis_jll]] +[[libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+1" -[[deps.nghttp2_jll]] +[[nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -[[deps.p7zip_jll]] +[[p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -[[deps.x264_jll]] +[[x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" -[[deps.x265_jll]] +[[x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" -[[deps.xkbcommon_jll]] +[[xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" diff --git a/tutorials/for-developers-interface/Project.toml b/tutorials/for-developers-interface/Project.toml index d9c1da059..9edb03ec3 100644 --- a/tutorials/for-developers-interface/Project.toml +++ b/tutorials/for-developers-interface/Project.toml @@ -1,3 +1,6 @@ [deps] +AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" From 57d78947e97ab6b730fdf070b6714408b059e22c Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 13:14:36 +0100 Subject: [PATCH 20/87] Fixing dependencies --- .../docs-00-getting-started/Manifest.toml | 871 +++++++++--------- .../for-developers-compiler/compiler.jmd | 3 + tutorials/using-turing-advanced/advanced.jmd | 3 + tutorials/using-turing-autodiff/autodiff.jmd | 3 + .../using-turing-dynamicppl/dynamichmc.jmd | 3 + .../using-turing-guide/using-turing-guide.jmd | 3 + .../performancetips.jmd | 3 + 7 files changed, 460 insertions(+), 429 deletions(-) diff --git a/tutorials/docs-00-getting-started/Manifest.toml b/tutorials/docs-00-getting-started/Manifest.toml index b12df50be..8a3791c33 100644 --- a/tutorials/docs-00-getting-started/Manifest.toml +++ b/tutorials/docs-00-getting-started/Manifest.toml @@ -1,16 +1,12 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.6.7" -manifest_format = "2.0" -project_hash = "102d486b18af7951cac72d56aec7c8e37b50a316" - -[[deps.AbstractFFTs]] +[[AbstractFFTs]] deps = ["ChainRulesCore", "LinearAlgebra"] -git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" +git-tree-sha1 = "16b6dbc4cf7caee4e1e75c49485ec67b667098a0" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.2.1" +version = "1.3.1" -[[deps.AbstractMCMC]] +[[AbstractMCMC]] deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] git-tree-sha1 = "b4d13c741b2bbe0e1af305a92d2f105a7caa47af" uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" @@ -18,760 +14,760 @@ version = "4.2.0" [[AbstractPPL]] deps = ["AbstractMCMC", "DensityInterface", "Random", "Setfield", "SparseArrays"] -git-tree-sha1 = "8f5d88dc15df270d8778ade9442d608939c75659" +git-tree-sha1 = "33ea6c6837332395dbf3ba336f273c9f7fcf4db9" uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf" -version = "0.5.3" +version = "0.5.4" [[AbstractTrees]] git-tree-sha1 = "faa260e4cb5aba097a73fab382dd4b5819d8ec8c" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" version = "0.4.4" -[[deps.Adapt]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "0310e08cb19f5da31d08341c6120c047598f5b9c" +[[Adapt]] +deps = ["LinearAlgebra", "Requires"] +git-tree-sha1 = "cc37d689f599e8df4f464b2fa3870ff7db7492ef" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "3.5.0" +version = "3.6.1" -[[deps.AdvancedHMC]] +[[AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "Setfield", "Statistics", "StatsBase", "StatsFuns", "UnPack"] git-tree-sha1 = "0091e2e4d0a7125da0e3ad8c7dbff9171a921461" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" version = "0.3.6" -[[deps.AdvancedMH]] +[[AdvancedMH]] deps = ["AbstractMCMC", "Distributions", "Random", "Requires"] git-tree-sha1 = "d7a7dabeaef34e5106cdf6c2ac956e9e3f97f666" uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170" version = "0.6.8" -[[deps.AdvancedPS]] +[[AdvancedPS]] deps = ["AbstractMCMC", "Distributions", "Libtask", "Random", "StatsFuns"] git-tree-sha1 = "9ff1247be1e2aa2e740e84e8c18652bd9d55df22" uuid = "576499cb-2369-40b2-a588-c64705576edc" version = "0.3.8" -[[deps.AdvancedVI]] +[[AdvancedVI]] deps = ["Bijectors", "Distributions", "DistributionsAD", "DocStringExtensions", "ForwardDiff", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "StatsBase", "StatsFuns", "Tracker"] git-tree-sha1 = "67fcc7d46c26250e89fc62798fbe07b5ee264c6f" uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c" version = "0.1.6" -[[deps.ArgCheck]] +[[ArgCheck]] git-tree-sha1 = "a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4" uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197" version = "2.3.0" -[[deps.ArgTools]] +[[ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" -[[deps.Arpack]] +[[Arpack]] deps = ["Arpack_jll", "Libdl", "LinearAlgebra", "Logging"] git-tree-sha1 = "9b9b347613394885fd1c8c7729bfc60528faa436" uuid = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97" version = "0.5.4" -[[deps.Arpack_jll]] +[[Arpack_jll]] deps = ["Libdl", "OpenBLAS_jll", "Pkg"] git-tree-sha1 = "e214a9b9bd1b4e1b4f15b22c0994862b66af7ff7" uuid = "68821587-b530-5797-8361-c406ea357684" version = "3.5.0+3" -[[ArrayInterfaceCore]] -deps = ["LinearAlgebra", "SnoopPrecompile", "SparseArrays", "SuiteSparse"] -git-tree-sha1 = "e5f08b5689b1aad068e01751889f2f615c7db36d" -uuid = "30b0a656-2188-435a-8636-2ec0e6a096e2" -version = "0.1.29" +[[ArrayInterface]] +deps = ["Adapt", "LinearAlgebra", "Requires", "SnoopPrecompile", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "38911c7737e123b28182d89027f4216cfc8a9da7" +uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9" +version = "7.4.3" -[[deps.ArrayInterfaceStaticArraysCore]] -deps = ["Adapt", "ArrayInterfaceCore", "LinearAlgebra", "StaticArraysCore"] -git-tree-sha1 = "93c8ba53d8d26e124a5a8d4ec914c3a16e6a0970" -uuid = "dd5226c6-a4d4-4bc7-8575-46859f9c95b9" -version = "0.1.3" - -[[deps.Artifacts]] +[[Artifacts]] uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" -[[deps.AxisAlgorithms]] +[[AxisAlgorithms]] deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"] git-tree-sha1 = "66771c8d21c8ff5e3a93379480a2307ac36863f7" uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950" version = "1.0.1" -[[deps.AxisArrays]] +[[AxisArrays]] deps = ["Dates", "IntervalSets", "IterTools", "RangeArrays"] git-tree-sha1 = "1dd4d9f5beebac0c03446918741b1a03dc5e5788" uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9" version = "0.4.6" -[[deps.BangBang]] +[[BangBang]] deps = ["Compat", "ConstructionBase", "Future", "InitialValues", "LinearAlgebra", "Requires", "Setfield", "Tables", "ZygoteRules"] git-tree-sha1 = "7fe6d92c4f281cf4ca6f2fba0ce7b299742da7ca" uuid = "198e06fe-97b7-11e9-32a5-e1d131e6ad66" version = "0.3.37" -[[deps.Base64]] +[[Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" -[[deps.Baselet]] +[[Baselet]] git-tree-sha1 = "aebf55e6d7795e02ca500a689d326ac979aaf89e" uuid = "9718e550-a3fa-408a-8086-8db961cd8217" version = "0.1.1" -[[deps.Bijectors]] +[[Bijectors]] deps = ["ArgCheck", "ChainRulesCore", "ChangesOfVariables", "Compat", "Distributions", "Functors", "InverseFunctions", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "MappedArrays", "Random", "Reexport", "Requires", "Roots", "SparseArrays", "Statistics"] -git-tree-sha1 = "a3704b8e5170f9339dff4e6cb286ad49464d3646" +git-tree-sha1 = "1234b03e94938e6f2b14834dfd3ef45698d5e14f" uuid = "76274a88-744f-5084-9051-94815aaf08c4" -version = "0.10.6" +version = "0.10.8" -[[deps.BitFlags]] +[[BitFlags]] git-tree-sha1 = 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uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" -version = "1.5.2+0" +version = "1.5.5+0" -[[deps.ZygoteRules]] -deps = ["MacroTools"] -git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" +[[ZygoteRules]] +deps = ["ChainRulesCore", "MacroTools"] +git-tree-sha1 = "977aed5d006b840e2e40c0b48984f7463109046d" uuid = "700de1a5-db45-46bc-99cf-38207098b444" -version = "0.2.2" +version = "0.2.3" -[[deps.fzf_jll]] +[[fzf_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56" uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" version = "0.29.0+0" -[[deps.libaom_jll]] +[[libaom_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4" uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" version = "3.4.0+0" -[[deps.libass_jll]] +[[libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = 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"3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -[[deps.x264_jll]] +[[x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" -[[deps.x265_jll]] +[[x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" -[[deps.xkbcommon_jll]] +[[xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" diff --git a/tutorials/for-developers-compiler/compiler.jmd b/tutorials/for-developers-compiler/compiler.jmd index c6b4edeec..418c02007 100644 --- a/tutorials/for-developers-compiler/compiler.jmd +++ b/tutorials/for-developers-compiler/compiler.jmd @@ -288,3 +288,6 @@ Note that in order to make `md::Metadata` type stable, all the `md.vns` must hav However, in `Turing` models one cannot have this restriction, so we must use a type unstable `Metadata` if we want to use one `Metadata` instance for the whole model. This is what `UntypedVarInfo` does. A type unstable `Metadata` will still work but will have inferior performance. To strike a balance between flexibility and performance when constructing the `spl::Sampler` instance, the model is first run by sampling the parameters in `P` from their priors using an `UntypedVarInfo`, i.e. a type unstable `Metadata` is used for all the variables. Then once all the symbols and distribution types have been identified, a `vi::TypedVarInfo` is constructed where `vi.metadata` is a `NamedTuple` mapping each symbol in `P` to a specialized instance of `Metadata`. So as long as each symbol in `P` is sampled from only one type of distributions, `vi::TypedVarInfo` will have fully concretely typed fields which brings out the peak performance of Julia. + +```julia +``` \ No newline at end of file diff --git a/tutorials/using-turing-advanced/advanced.jmd b/tutorials/using-turing-advanced/advanced.jmd index ab0a44a6d..bed9090cf 100644 --- a/tutorials/using-turing-advanced/advanced.jmd +++ b/tutorials/using-turing-advanced/advanced.jmd @@ -174,3 +174,6 @@ model = gdemo([1.5, 2.0]) ## Task Copying Turing [copies](https://github.com/JuliaLang/julia/issues/4085) Julia tasks to deliver efficient inference algorithms, but it also provides alternative slower implementation as a fallback. Task copying is enabled by default. Task copying requires us to use the `TapedTask` facility which is provided by [Libtask](https://github.com/TuringLang/Libtask.jl) to create tasks. + +```julia +``` diff --git a/tutorials/using-turing-autodiff/autodiff.jmd b/tutorials/using-turing-autodiff/autodiff.jmd index 0d0cfc0f8..864cd9c2f 100644 --- a/tutorials/using-turing-autodiff/autodiff.jmd +++ b/tutorials/using-turing-autodiff/autodiff.jmd @@ -39,3 +39,6 @@ c = sample( Generally, `TrackerAD` is faster when sampling from variables of high dimensionality (greater than 20) and `ForwardDiffAD` is more efficient for lower-dimension variables. This functionality allows those who are performance sensitive to fine tune their automatic differentiation for their specific models. If the differentiation method is not specified in this way, Turing will default to using whatever the global AD backend is. Currently, this defaults to `ForwardDiff`. + +```julia +``` \ No newline at end of file diff --git a/tutorials/using-turing-dynamicppl/dynamichmc.jmd b/tutorials/using-turing-dynamicppl/dynamichmc.jmd index a245dbffb..9928e4dc5 100644 --- a/tutorials/using-turing-dynamicppl/dynamichmc.jmd +++ b/tutorials/using-turing-dynamicppl/dynamichmc.jmd @@ -28,3 +28,6 @@ end # Pull 2,000 samples using DynamicNUTS. chn = sample(gdemo(1.5, 2.0), DynamicNUTS(), 2000) ``` + +```julia +``` diff --git a/tutorials/using-turing-guide/using-turing-guide.jmd b/tutorials/using-turing-guide/using-turing-guide.jmd index e6d981daa..1f2e4c55b 100644 --- a/tutorials/using-turing-guide/using-turing-guide.jmd +++ b/tutorials/using-turing-guide/using-turing-guide.jmd @@ -606,3 +606,6 @@ In all other cases progress logs are displayed with [TerminalLoggers.jl](https://github.com/c42f/TerminalLoggers.jl). Alternatively, if you provide a custom visualization backend, Turing uses it instead of the default backend. + +```julia +``` diff --git a/tutorials/using-turing-performance-tips/performancetips.jmd b/tutorials/using-turing-performance-tips/performancetips.jmd index 67ffe86ac..fee681ad2 100644 --- a/tutorials/using-turing-performance-tips/performancetips.jmd +++ b/tutorials/using-turing-performance-tips/performancetips.jmd @@ -163,3 +163,6 @@ Then define the `Turing` model using the new functions as such: return x .~ Normal(c, d) end ``` + +```julia +``` From c2efc56db8305f30379aac041de9b3637c4c8866 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 13:28:18 +0100 Subject: [PATCH 21/87] Fixing dependencies --- .../how_turing_implements_abstractmcmc.jmd | 1 + .../for-developers-interface/interface.jmd | 4 + .../Manifest.toml | 1263 ++++------------- .../Project.toml | 4 +- .../performancetips.jmd | 2 + 5 files changed, 319 insertions(+), 955 deletions(-) diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd index 5148d7726..44678d55a 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd @@ -284,4 +284,5 @@ We focus on the AbstractMCMC functions that are overridden in `is.jl` and execut + it simply takes the logarithm of the average of the sample weights, using the log weights for numerical stability ```julia + ``` \ No newline at end of file diff --git a/tutorials/for-developers-interface/interface.jmd b/tutorials/for-developers-interface/interface.jmd index 316e40a10..fa4b0f331 100644 --- a/tutorials/for-developers-interface/interface.jmd +++ b/tutorials/for-developers-interface/interface.jmd @@ -372,3 +372,7 @@ We've seen how to implement the sampling interface for general projects. Turing' methods are ever-evolving, so please open an issue at [AbstractMCMC](https://github.com/TuringLang/AbstractMCMC.jl) with feature requests or problems. + +```julia + +``` \ No newline at end of file diff --git a/tutorials/using-turing-performance-tips/Manifest.toml b/tutorials/using-turing-performance-tips/Manifest.toml index ea4ec1254..2f6ac88b8 100644 --- a/tutorials/using-turing-performance-tips/Manifest.toml +++ b/tutorials/using-turing-performance-tips/Manifest.toml @@ -1,1592 +1,947 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.6.7" -manifest_format = "2.0" -project_hash = "102d486b18af7951cac72d56aec7c8e37b50a316" - -[[deps.AbstractFFTs]] +[[AbstractFFTs]] deps = ["ChainRulesCore", "LinearAlgebra"] -git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" +git-tree-sha1 = "16b6dbc4cf7caee4e1e75c49485ec67b667098a0" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.2.1" +version = "1.3.1" -[[deps.AbstractMCMC]] +[[AbstractMCMC]] deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] git-tree-sha1 = "b4d13c741b2bbe0e1af305a92d2f105a7caa47af" uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" version = "4.2.0" -[[deps.AbstractPPL]] +[[AbstractPPL]] deps = ["AbstractMCMC", "DensityInterface", "Random", "Setfield", "SparseArrays"] -git-tree-sha1 = "8f5d88dc15df270d8778ade9442d608939c75659" +git-tree-sha1 = "33ea6c6837332395dbf3ba336f273c9f7fcf4db9" uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf" -version = "0.5.3" +version = "0.5.4" -[[deps.AbstractTrees]] +[[AbstractTrees]] git-tree-sha1 = "faa260e4cb5aba097a73fab382dd4b5819d8ec8c" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" version = "0.4.4" -[[deps.Adapt]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "0310e08cb19f5da31d08341c6120c047598f5b9c" +[[Adapt]] +deps = ["LinearAlgebra", "Requires"] +git-tree-sha1 = "cc37d689f599e8df4f464b2fa3870ff7db7492ef" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "3.5.0" +version = "3.6.1" -[[deps.AdvancedHMC]] +[[AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "Setfield", "Statistics", "StatsBase", "StatsFuns", "UnPack"] git-tree-sha1 = "0091e2e4d0a7125da0e3ad8c7dbff9171a921461" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" version = "0.3.6" -[[deps.AdvancedMH]] +[[AdvancedMH]] deps = ["AbstractMCMC", "Distributions", "Random", "Requires"] git-tree-sha1 = "d7a7dabeaef34e5106cdf6c2ac956e9e3f97f666" uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170" version = "0.6.8" -[[deps.AdvancedPS]] +[[AdvancedPS]] deps = ["AbstractMCMC", "Distributions", "Libtask", "Random", "StatsFuns"] git-tree-sha1 = "9ff1247be1e2aa2e740e84e8c18652bd9d55df22" uuid = "576499cb-2369-40b2-a588-c64705576edc" version = "0.3.8" -[[deps.AdvancedVI]] +[[AdvancedVI]] deps = ["Bijectors", "Distributions", "DistributionsAD", "DocStringExtensions", "ForwardDiff", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "StatsBase", "StatsFuns", "Tracker"] git-tree-sha1 = "67fcc7d46c26250e89fc62798fbe07b5ee264c6f" uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c" version = "0.1.6" -[[deps.ArgCheck]] +[[ArgCheck]] git-tree-sha1 = "a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4" uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197" version = "2.3.0" -[[deps.ArgTools]] +[[ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" -[[deps.Arpack]] -deps = ["Arpack_jll", "Libdl", "LinearAlgebra", "Logging"] -git-tree-sha1 = "9b9b347613394885fd1c8c7729bfc60528faa436" -uuid = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97" -version = "0.5.4" - 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-[[deps.xkbcommon_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] -git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" -uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" -version = "1.4.1+0" diff --git a/tutorials/using-turing-performance-tips/Project.toml b/tutorials/using-turing-performance-tips/Project.toml index d9c1da059..f9c7524b0 100644 --- a/tutorials/using-turing-performance-tips/Project.toml +++ b/tutorials/using-turing-performance-tips/Project.toml @@ -1,3 +1,5 @@ [deps] -StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" +FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b" +Memoization = "6fafb56a-5788-4b4e-91ca-c0cea6611c73" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/using-turing-performance-tips/performancetips.jmd b/tutorials/using-turing-performance-tips/performancetips.jmd index fee681ad2..e3c27877d 100644 --- a/tutorials/using-turing-performance-tips/performancetips.jmd +++ b/tutorials/using-turing-performance-tips/performancetips.jmd @@ -17,6 +17,7 @@ It is generally preferable to use multivariate distributions if possible. The following example: ```julia +using Turing @model function gmodel(x) m ~ Normal() for i in 1:length(x) @@ -165,4 +166,5 @@ end ``` ```julia + ``` From af861a42471d5d99bf1e1ba4e46be3af7862e238 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 13:43:27 +0100 Subject: [PATCH 22/87] Fixing dependencies --- tutorials/using-turing-performance-tips/performancetips.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/using-turing-performance-tips/performancetips.jmd b/tutorials/using-turing-performance-tips/performancetips.jmd index e3c27877d..fba09806d 100644 --- a/tutorials/using-turing-performance-tips/performancetips.jmd +++ b/tutorials/using-turing-performance-tips/performancetips.jmd @@ -109,7 +109,7 @@ end We can use -```julia +```{julia; eval=false} using Random model = tmodel(1.0) From 06b1f1a5a0a670c912fdaddf4f7349d9ccb5f476 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 13:49:58 +0100 Subject: [PATCH 23/87] Fixing dependencies --- tutorials/using-turing-performance-tips/performancetips.jmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials/using-turing-performance-tips/performancetips.jmd b/tutorials/using-turing-performance-tips/performancetips.jmd index fba09806d..bcfff429a 100644 --- a/tutorials/using-turing-performance-tips/performancetips.jmd +++ b/tutorials/using-turing-performance-tips/performancetips.jmd @@ -130,7 +130,7 @@ to inspect type inference in the model. Often when performing Gibbs sampling, one can save computational time by caching the output of expensive functions. The cached values can then be reused in future Gibbs sub-iterations which do not change the inputs to this expensive function. For example in the following model -```julia +```{julia; eval=false} @model function demo(x) a ~ Gamma() b ~ Normal() @@ -146,7 +146,7 @@ when only updating `a` in a Gibbs sub-iteration, keeping `b` the same, the value To use `Memoization.jl`, simply define memoized versions of `function1` and `function2` as such: -```julia +```{julia; eval=false} using Memoization @memoize memoized_function1(args...) = function1(args...) @@ -155,7 +155,7 @@ using Memoization Then define the `Turing` model using the new functions as such: -```julia +```{julia; eval=false} @model function demo(x) a ~ Gamma() b ~ Normal() From f6d8ed9a753df455544072f125e332d91af84751 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 14:01:01 +0100 Subject: [PATCH 24/87] Fixing dependencies --- .../how_turing_implements_abstractmcmc.jmd | 4 ---- 1 file changed, 4 deletions(-) diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd index 44678d55a..da2c2b5d3 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd @@ -282,7 +282,3 @@ We focus on the AbstractMCMC functions that are overridden in `is.jl` and execut - When the, `n_samples` iterations are completed, `sample_end!` fills the `final_logevidence` field of `spl.state` + it simply takes the logarithm of the average of the sample weights, using the log weights for numerical stability - -```julia - -``` \ No newline at end of file From 98a4eb8d147f2cd02d003493f3faa3f06e09f46a Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 14:03:57 +0100 Subject: [PATCH 25/87] Fixing dependencies --- .../how_turing_implements_abstractmcmc.jmd | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd index da2c2b5d3..7e2c7c8e8 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd @@ -282,3 +282,7 @@ We focus on the AbstractMCMC functions that are overridden in `is.jl` and execut - When the, `n_samples` iterations are completed, `sample_end!` fills the `final_logevidence` field of `spl.state` + it simply takes the logarithm of the average of the sample weights, using the log weights for numerical stability + +```julia + +``` From ee0c267702a3cf7eade85626cce980def45cdc13 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 14:08:34 +0100 Subject: [PATCH 26/87] Fixing dependencies --- .../how_turing_implements_abstractmcmc.jmd | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd index 7e2c7c8e8..0e9d3261d 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd @@ -14,6 +14,8 @@ Prerequisite: [Interface guide](https://turing.ml/dev/docs/for-developers/interf Consider the following Turing, code block: ```julia +using Turing + @model function gdemo(x, y) s² ~ InverseGamma(2, 3) m ~ Normal(0, sqrt(s²)) @@ -282,7 +284,3 @@ We focus on the AbstractMCMC functions that are overridden in `is.jl` and execut - When the, `n_samples` iterations are completed, `sample_end!` fills the `final_logevidence` field of `spl.state` + it simply takes the logarithm of the average of the sample weights, using the log weights for numerical stability - -```julia - -``` From 1d14387b41748ad234c56abf2538b79d87161956 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 14:31:28 +0100 Subject: [PATCH 27/87] Fixing dependencies --- .../how_turing_implements_abstractmcmc.jmd | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd index 0e9d3261d..5f4fb5a81 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd @@ -121,7 +121,7 @@ When you define your own Turing sampling method, you must therefore build: - a **sampler constructor** that uses a model and an algorithm to initialize an instance of `Sampler`. For Importance Sampling: -```julia +```{julia; eval=false} function Sampler(alg::IS, model::Model, s::Selector) info = Dict{Symbol,Any}() state = ISState(model) @@ -137,7 +137,7 @@ The `vi` field contains all the important information about sampling: first and By default, you can use `SamplerState`, a concrete type defined in `inference/Inference.jl`, which extends `AbstractSamplerState` and has no field except for `vi`: -```julia +```{julia; eval=false} mutable struct SamplerState{VIType<:VarInfo} <: AbstractSamplerState vi::VIType end @@ -160,7 +160,7 @@ To avoid having to compute it over and over again, `is.jl`defines an IS-specific \log \left( \frac 1 N \sum_{j=1}^N w_i \right). ``` -```julia +```{julia; eval=false} mutable struct ISState{V<:VarInfo,F<:AbstractFloat} <: AbstractSamplerState vi::V final_logevidence::F @@ -187,7 +187,7 @@ A lot of the things here are method-specific. However Turing also has some funct `Inference.jl` [defines](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/Inference.jl#L103) a struct `Transition`, which corresponds to this default situation -```julia +```{julia; eval=false} struct Transition{T,F<:AbstractFloat} θ::T lp::F @@ -242,7 +242,7 @@ Consider an instance `m` of `Model` and a sampler `spl`, with associated `VarInf Here's what `assume` looks like for Importance Sampling: -```julia +```{julia; eval=false} function DynamicPPL.assume(rng, spl::Sampler{<:IS}, dist::Distribution, vn::VarName, vi) r = rand(rng, dist) push!(vi, vn, r, dist, spl) @@ -254,7 +254,7 @@ The function first generates a sample `r` from the distribution `dist` (the righ The `observe` function is even simpler: -```julia +```{julia; eval=false} function DynamicPPL.observe(spl::Sampler{<:IS}, dist::Distribution, value, vi) return logpdf(dist, value) end From 80c455aafed71a51debca8bea7283ece0351529d Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 14:57:32 +0100 Subject: [PATCH 28/87] Fixing dependencies --- tutorials/for-developers-interface/interface.jmd | 4 ---- tutorials/using-turing-guide/using-turing-guide.jmd | 1 + 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/tutorials/for-developers-interface/interface.jmd b/tutorials/for-developers-interface/interface.jmd index fa4b0f331..316e40a10 100644 --- a/tutorials/for-developers-interface/interface.jmd +++ b/tutorials/for-developers-interface/interface.jmd @@ -372,7 +372,3 @@ We've seen how to implement the sampling interface for general projects. Turing' methods are ever-evolving, so please open an issue at [AbstractMCMC](https://github.com/TuringLang/AbstractMCMC.jl) with feature requests or problems. - -```julia - -``` \ No newline at end of file diff --git a/tutorials/using-turing-guide/using-turing-guide.jmd b/tutorials/using-turing-guide/using-turing-guide.jmd index 1f2e4c55b..5360a4044 100644 --- a/tutorials/using-turing-guide/using-turing-guide.jmd +++ b/tutorials/using-turing-guide/using-turing-guide.jmd @@ -608,4 +608,5 @@ if you provide a custom visualization backend, Turing uses it instead of the default backend. ```julia + ``` From a4f4659a4f9a79b01fd44c8b8e546227fdadbacd Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 15:07:20 +0100 Subject: [PATCH 29/87] Fixing dependencies --- tutorials/for-developers-interface/interface.jmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/for-developers-interface/interface.jmd b/tutorials/for-developers-interface/interface.jmd index 316e40a10..25547edb9 100644 --- a/tutorials/for-developers-interface/interface.jmd +++ b/tutorials/for-developers-interface/interface.jmd @@ -118,7 +118,7 @@ Note that we only have to do this because we are not yet integrating the sampler -- Turing has a very sophisticated modelling engine that removes the need to define custom model structs. -```julia +```{julia; eval=false} # Define a model type. Stores the log density function. struct DensityModel{F<:Function} <: AbstractMCMC.AbstractModel ℓπ::F @@ -131,7 +131,7 @@ The next step is to define some transition which we will return from each `step! We'll keep it simple by just defining a wrapper struct that contains the parameter draws and the log density of that draw: -```julia +```{julia; eval=false} # Create a very basic Transition type, only stores the # parameter draws and the log probability of the draw. struct Transition{T,L} From 6aa8085589f5dc9bd04248f26179edf5987943bf Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 15:12:21 +0100 Subject: [PATCH 30/87] Fixing dependencies --- tutorials/for-developers-interface/interface.jmd | 12 ++++++------ tutorials/using-turing-guide/using-turing-guide.jmd | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/tutorials/for-developers-interface/interface.jmd b/tutorials/for-developers-interface/interface.jmd index 25547edb9..97ea9bcd5 100644 --- a/tutorials/for-developers-interface/interface.jmd +++ b/tutorials/for-developers-interface/interface.jmd @@ -118,7 +118,7 @@ Note that we only have to do this because we are not yet integrating the sampler -- Turing has a very sophisticated modelling engine that removes the need to define custom model structs. -```{julia; eval=false} +```julia # Define a model type. Stores the log density function. struct DensityModel{F<:Function} <: AbstractMCMC.AbstractModel ℓπ::F @@ -131,7 +131,7 @@ The next step is to define some transition which we will return from each `step! We'll keep it simple by just defining a wrapper struct that contains the parameter draws and the log density of that draw: -```{julia; eval=false} +```julia # Create a very basic Transition type, only stores the # parameter draws and the log probability of the draw. struct Transition{T,L} @@ -180,7 +180,7 @@ The `step!` function is the function that performs the bulk of your inference. I we will implement two `step!` functions -- one for the very first iteration, and one for every subsequent iteration. -```julia +```{julia; eval=false} # Define the first step! function, which is called at the # beginning of sampling. Return the initial parameter used # to define the sampler. @@ -212,7 +212,7 @@ in the pseudocode above. - `step!` generates a new proposal, checks the acceptance probability, and then returns either the previous transition or the proposed transition. -```julia +```{julia; eval=false} # Define a function that makes a basic proposal depending on a univariate # parameterization or a multivariate parameterization. function propose(spl::MetropolisHastings, model::DensityModel, θ::Real) @@ -271,7 +271,7 @@ transitions and returns a collection of samples. Fortunately, our `Transition` i incredibly simple, and we only need to build a little bit of functionality to accept custom parameter names passed in by the user. -```julia +```{julia; eval=false} # A basic chains constructor that works with the Transition struct we defined. function AbstractMCMC.bundle_samples( rng::AbstractRNG, @@ -312,7 +312,7 @@ calculate the mean and variance parameters of a Normal distribution. We can do t constructing a target density function, providing a sample of data, and then running the sampler with `sample`. -```julia +```{julia; eval=false} # Generate a set of data from the posterior we want to estimate. data = rand(Normal(5, 3), 30) diff --git a/tutorials/using-turing-guide/using-turing-guide.jmd b/tutorials/using-turing-guide/using-turing-guide.jmd index 5360a4044..733c7e269 100644 --- a/tutorials/using-turing-guide/using-turing-guide.jmd +++ b/tutorials/using-turing-guide/using-turing-guide.jmd @@ -83,7 +83,7 @@ Using this syntax, a probabilistic model is defined in Turing. The model functio In the following example, the defined model is conditioned to the data (arg*1 = 1, arg*2 = 2) by passing (1, 2) to the model function. -```julia +```{julia; eval=false} @model function model_name(arg_1, arg_2) return ... end From 8f43c61eb192af3c4e03f9f016d9dcacf799def0 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 15:22:43 +0100 Subject: [PATCH 31/87] Fixing dependencies --- tutorials/for-developers-compiler/compiler.jmd | 3 ++- tutorials/using-turing-autodiff/autodiff.jmd | 3 ++- tutorials/using-turing-guide/using-turing-guide.jmd | 4 ++-- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/tutorials/for-developers-compiler/compiler.jmd b/tutorials/for-developers-compiler/compiler.jmd index 418c02007..3b90588b7 100644 --- a/tutorials/for-developers-compiler/compiler.jmd +++ b/tutorials/for-developers-compiler/compiler.jmd @@ -289,5 +289,6 @@ However, in `Turing` models one cannot have this restriction, so we must use a t To strike a balance between flexibility and performance when constructing the `spl::Sampler` instance, the model is first run by sampling the parameters in `P` from their priors using an `UntypedVarInfo`, i.e. a type unstable `Metadata` is used for all the variables. Then once all the symbols and distribution types have been identified, a `vi::TypedVarInfo` is constructed where `vi.metadata` is a `NamedTuple` mapping each symbol in `P` to a specialized instance of `Metadata`. So as long as each symbol in `P` is sampled from only one type of distributions, `vi::TypedVarInfo` will have fully concretely typed fields which brings out the peak performance of Julia. -```julia +```{julia; eval=false} + ``` \ No newline at end of file diff --git a/tutorials/using-turing-autodiff/autodiff.jmd b/tutorials/using-turing-autodiff/autodiff.jmd index 864cd9c2f..b29373934 100644 --- a/tutorials/using-turing-autodiff/autodiff.jmd +++ b/tutorials/using-turing-autodiff/autodiff.jmd @@ -40,5 +40,6 @@ Generally, `TrackerAD` is faster when sampling from variables of high dimensiona If the differentiation method is not specified in this way, Turing will default to using whatever the global AD backend is. Currently, this defaults to `ForwardDiff`. -```julia +```{julia; eval=false} + ``` \ No newline at end of file diff --git a/tutorials/using-turing-guide/using-turing-guide.jmd b/tutorials/using-turing-guide/using-turing-guide.jmd index 733c7e269..806aa152f 100644 --- a/tutorials/using-turing-guide/using-turing-guide.jmd +++ b/tutorials/using-turing-guide/using-turing-guide.jmd @@ -91,14 +91,14 @@ end The conditioned model can then be passed onto the sample function to run posterior inference. -```julia +```{julia; eval=false} model_func = model_name(1, 2) chn = sample(model_func, HMC(..)) # Perform inference by sampling using HMC. ``` The returned chain contains samples of the variables in the model. -```julia +```{julia; eval=false} var_1 = mean(chn[:var_1]) # Taking the mean of a variable named var_1. ``` From 6e3433a8e80a7e300ce261530d38d6dc39d57fe9 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 15:36:35 +0100 Subject: [PATCH 32/87] Fixing dependencies --- tutorials/for-developers-compiler/compiler.jmd | 14 +++++++------- tutorials/using-turing-advanced/advanced.jmd | 13 +++++++------ tutorials/using-turing-dynamicppl/dynamichmc.jmd | 3 ++- .../using-turing-guide/using-turing-guide.jmd | 2 +- 4 files changed, 17 insertions(+), 15 deletions(-) diff --git a/tutorials/for-developers-compiler/compiler.jmd b/tutorials/for-developers-compiler/compiler.jmd index 3b90588b7..5621ccad2 100644 --- a/tutorials/for-developers-compiler/compiler.jmd +++ b/tutorials/for-developers-compiler/compiler.jmd @@ -31,7 +31,7 @@ The following are the main jobs of the `@model` macro: A `model::Model` is a callable struct that one can sample from by calling -```julia +```{julia; eval=false} (model::Model)([rng, varinfo, sampler, context]) ``` @@ -53,7 +53,7 @@ in `model.defaults`. They are used for constructing model instances with differe Let's take the following model as an example: -```julia +```{julia; eval=false} @model function gauss( x=missing, y=1.0, ::Type{TV}=Vector{Float64} ) where {TV<:AbstractVector} @@ -73,7 +73,7 @@ The above call of the `@model` macro defines the function `gauss` with positiona such a way that every call of it returns a `model::Model`. Note that only the function body is modified by the `@model` macro, and the function signature is left untouched. It is also possible to implement models with keyword arguments such as -```julia +```{julia; eval=false} @model function gauss( ::Type{TV}=Vector{Float64}; x=missing, y=1.0 ) where {TV<:AbstractVector} @@ -86,7 +86,7 @@ This would allow us to generate a model by calling `gauss(; x = rand(3))`. If an argument has a default value `missing`, it is treated as a random variable. For variables which require an initialization because we need to loop or broadcast over its elements, such as `x` above, the following needs to be done: -```julia +```{julia; eval=false} if x === missing x = ... end @@ -95,7 +95,7 @@ end Note that since `gauss` behaves like a regular function it is possible to define additional dispatches in a second step as well. For instance, we could achieve the same behaviour by -```julia +```{julia; eval=false} @model function gauss(x, y=1.0, ::Type{TV}=Vector{Float64}) where {TV<:AbstractVector} p = TV(undef, 2) return ... @@ -129,7 +129,7 @@ the `@. L ~ R` and `L .~ R` lines in the model. In the above example, `p[1] ~ InverseGamma(2, 3)` is replaced with something similar to -```julia +```{julia; eval=false} #= REPL[25]:6 =# begin var"##tmpright#323" = InverseGamma(2, 3) @@ -171,7 +171,7 @@ random number generator `_rng` (since observations are never sampled). A similar transformation is performed for expressions of the form `@. L ~ R` and `L .~ R`. For instance, `@. x[1:2] ~ Normal(p[2], sqrt(p[1]))` is replaced with -```julia +```{julia; eval=false} #= REPL[25]:8 =# begin var"##tmpright#331" = Normal.(p[2], sqrt.(p[1])) diff --git a/tutorials/using-turing-advanced/advanced.jmd b/tutorials/using-turing-advanced/advanced.jmd index bed9090cf..be1a7d258 100644 --- a/tutorials/using-turing-advanced/advanced.jmd +++ b/tutorials/using-turing-advanced/advanced.jmd @@ -17,7 +17,7 @@ Below shows a workflow of how to define a customized distribution, using our own First, define a type of the distribution, as a subtype of a corresponding distribution type in the Distributions.jl package. -```julia +```{julia; eval=false} struct CustomUniform <: ContinuousUnivariateDistribution end ``` @@ -25,7 +25,7 @@ struct CustomUniform <: ContinuousUnivariateDistribution end Second, define `rand` and `logpdf`, which will be used to run the model. -```julia +```{julia; eval=false} # sample in [0, 1] Distributions.rand(rng::AbstractRNG, d::CustomUniform) = rand(rng) @@ -43,7 +43,7 @@ Certain samplers, such as `HMC`, require the domain of the priors to be unbounde To transform from `[0, 1]` to `ℝ` we can use the `Logit` bijector: -```julia +```{julia; eval=false} Bijectors.bijector(d::CustomUniform) = Logit(0.0, 1.0) ``` @@ -51,7 +51,7 @@ You'd do the exact same thing for `ContinuousMultivariateDistribution` and `Cont Alternatively, for `UnivariateDistribution` we can define the `minimum` and `maximum` of the distribution -```julia +```{julia; eval=false} Distributions.minimum(d::CustomUniform) = 0.0 Distributions.maximum(d::CustomUniform) = 1.0 ``` @@ -60,7 +60,7 @@ and `Bijectors.jl` will return a default `Bijector` called `TruncatedBijector` w Internally, Turing basically does the following when it needs to convert a constrained distribution to an unconstrained distribution, e.g. when sampling using `HMC`: -```julia +```{julia; eval=false} b = bijector(dist) transformed_dist = transformed(dist, b) # results in distribution with transformed support + correction for logpdf ``` @@ -175,5 +175,6 @@ model = gdemo([1.5, 2.0]) Turing [copies](https://github.com/JuliaLang/julia/issues/4085) Julia tasks to deliver efficient inference algorithms, but it also provides alternative slower implementation as a fallback. Task copying is enabled by default. Task copying requires us to use the `TapedTask` facility which is provided by [Libtask](https://github.com/TuringLang/Libtask.jl) to create tasks. -```julia +```{julia; eval=false} + ``` diff --git a/tutorials/using-turing-dynamicppl/dynamichmc.jmd b/tutorials/using-turing-dynamicppl/dynamichmc.jmd index 9928e4dc5..283b00422 100644 --- a/tutorials/using-turing-dynamicppl/dynamichmc.jmd +++ b/tutorials/using-turing-dynamicppl/dynamichmc.jmd @@ -29,5 +29,6 @@ end chn = sample(gdemo(1.5, 2.0), DynamicNUTS(), 2000) ``` -```julia +```{julia; eval=false} + ``` diff --git a/tutorials/using-turing-guide/using-turing-guide.jmd b/tutorials/using-turing-guide/using-turing-guide.jmd index 806aa152f..48355d712 100644 --- a/tutorials/using-turing-guide/using-turing-guide.jmd +++ b/tutorials/using-turing-guide/using-turing-guide.jmd @@ -121,7 +121,7 @@ sample(model_function(10), SMC(), 100) But if we switch the `s ~ Poisson(1)` and `y ~ Normal(s, 1)` lines, the model will no longer sample correctly: -```julia +```{julia; eval=false} # Define a simple Normal model with unknown mean and variance. @model function model_function(y) y ~ Normal(s, 1) From 4cad629c921ded2062c29487c032429404c16eed Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 15:52:22 +0100 Subject: [PATCH 33/87] Fixing dependencies --- tutorials/using-turing-advanced/advanced.jmd | 4 +- tutorials/using-turing-autodiff/autodiff.jmd | 5 +- .../using-turing-dynamicppl/Manifest.toml | 938 +++++++++--------- .../using-turing-dynamicppl/Project.toml | 1 + .../using-turing-guide/using-turing-guide.jmd | 2 +- 5 files changed, 503 insertions(+), 447 deletions(-) diff --git a/tutorials/using-turing-advanced/advanced.jmd b/tutorials/using-turing-advanced/advanced.jmd index be1a7d258..07c2e4091 100644 --- a/tutorials/using-turing-advanced/advanced.jmd +++ b/tutorials/using-turing-advanced/advanced.jmd @@ -116,7 +116,7 @@ prior of your model but only when computing the log likelihood and the log joint should [check the type of the internal variable `__context_`](https://github.com/TuringLang/DynamicPPL.jl/issues/154) such as -```julia +```{julia; eval=false} if DynamicPPL.leafcontext(__context__) !== Turing.PriorContext() Turing.@addlogprob! myloglikelihood(x, μ) end @@ -145,7 +145,7 @@ model = gdemo([1.5, 2.0]) can be implemented also (a bit less generally) with the macro-free version -```julia +```{julia; eval=false} using Turing # Create the model function. diff --git a/tutorials/using-turing-autodiff/autodiff.jmd b/tutorials/using-turing-autodiff/autodiff.jmd index b29373934..b90a65ab4 100644 --- a/tutorials/using-turing-autodiff/autodiff.jmd +++ b/tutorials/using-turing-autodiff/autodiff.jmd @@ -40,6 +40,7 @@ Generally, `TrackerAD` is faster when sampling from variables of high dimensiona If the differentiation method is not specified in this way, Turing will default to using whatever the global AD backend is. Currently, this defaults to `ForwardDiff`. -```{julia; eval=false} +```julia + -``` \ No newline at end of file +``` diff --git a/tutorials/using-turing-dynamicppl/Manifest.toml b/tutorials/using-turing-dynamicppl/Manifest.toml index ea4ec1254..13a547b14 100644 --- a/tutorials/using-turing-dynamicppl/Manifest.toml +++ b/tutorials/using-turing-dynamicppl/Manifest.toml @@ -1,1591 +1,1645 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.6.7" -manifest_format = "2.0" -project_hash = "102d486b18af7951cac72d56aec7c8e37b50a316" - -[[deps.AbstractFFTs]] +[[AbstractFFTs]] deps = ["ChainRulesCore", "LinearAlgebra"] -git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" +git-tree-sha1 = "16b6dbc4cf7caee4e1e75c49485ec67b667098a0" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.2.1" +version = "1.3.1" -[[deps.AbstractMCMC]] +[[AbstractMCMC]] deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", 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"8e850ede-7688-5339-a07c-302acd2aaf8d" -[[deps.p7zip_jll]] +[[p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -[[deps.x264_jll]] +[[x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" -[[deps.x265_jll]] +[[x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" -[[deps.xkbcommon_jll]] +[[xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" diff --git a/tutorials/using-turing-dynamicppl/Project.toml b/tutorials/using-turing-dynamicppl/Project.toml index d9c1da059..b260da9e2 100644 --- a/tutorials/using-turing-dynamicppl/Project.toml +++ b/tutorials/using-turing-dynamicppl/Project.toml @@ -1,3 +1,4 @@ [deps] +DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/using-turing-guide/using-turing-guide.jmd b/tutorials/using-turing-guide/using-turing-guide.jmd index 48355d712..c818c48f4 100644 --- a/tutorials/using-turing-guide/using-turing-guide.jmd +++ b/tutorials/using-turing-guide/using-turing-guide.jmd @@ -140,7 +140,7 @@ Having multiple chains in the same object is valuable for evaluating convergence If you do not want parallelism or are on an older version Julia, you can sample multiple chains with the `mapreduce` function: -```julia +```{julia; eval=false} # Replace num_chains below with however many chains you wish to sample. chains = mapreduce(c -> sample(model_fun, sampler, 1000), chainscat, 1:num_chains) ``` From 051da51e63b466003d18b14dfa45c07a30f88c53 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 16:06:57 +0100 Subject: [PATCH 34/87] Fixing dependencies --- tutorials/using-turing-guide/Manifest.toml | 933 +++++++++++---------- tutorials/using-turing-guide/Project.toml | 3 + 2 files changed, 494 insertions(+), 442 deletions(-) diff --git a/tutorials/using-turing-guide/Manifest.toml b/tutorials/using-turing-guide/Manifest.toml index ea4ec1254..d950cca4f 100644 --- a/tutorials/using-turing-guide/Manifest.toml +++ b/tutorials/using-turing-guide/Manifest.toml @@ -1,1591 +1,1640 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.6.7" -manifest_format = "2.0" -project_hash = "102d486b18af7951cac72d56aec7c8e37b50a316" - -[[deps.AbstractFFTs]] +[[AbstractFFTs]] deps = ["ChainRulesCore", "LinearAlgebra"] -git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" +git-tree-sha1 = "16b6dbc4cf7caee4e1e75c49485ec67b667098a0" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.2.1" +version = "1.3.1" -[[deps.AbstractMCMC]] +[[AbstractMCMC]] deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] git-tree-sha1 = "b4d13c741b2bbe0e1af305a92d2f105a7caa47af" uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" version = "4.2.0" -[[deps.AbstractPPL]] +[[AbstractPPL]] deps = ["AbstractMCMC", "DensityInterface", "Random", "Setfield", "SparseArrays"] -git-tree-sha1 = "8f5d88dc15df270d8778ade9442d608939c75659" +git-tree-sha1 = "33ea6c6837332395dbf3ba336f273c9f7fcf4db9" uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf" -version = "0.5.3" +version = "0.5.4" -[[deps.AbstractTrees]] +[[AbstractTrees]] git-tree-sha1 = "faa260e4cb5aba097a73fab382dd4b5819d8ec8c" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" version = "0.4.4" -[[deps.Adapt]] -deps = ["LinearAlgebra"] 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+git-tree-sha1 = "49ce682769cd5de6c72dcf1b94ed7790cd08974c" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" -version = "1.5.2+0" +version = "1.5.5+0" -[[deps.ZygoteRules]] -deps = ["MacroTools"] -git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" +[[ZygoteRules]] +deps = ["ChainRulesCore", "MacroTools"] +git-tree-sha1 = "977aed5d006b840e2e40c0b48984f7463109046d" uuid = "700de1a5-db45-46bc-99cf-38207098b444" -version = "0.2.2" +version = "0.2.3" -[[deps.fzf_jll]] +[[fzf_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56" uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" version = "0.29.0+0" -[[deps.libaom_jll]] +[[libaom_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4" uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" version = "3.4.0+0" -[[deps.libass_jll]] +[[libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.15.1+0" -[[deps.libfdk_aac_jll]] +[[libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "2.0.2+0" -[[deps.libpng_jll]] +[[libpng_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.38+0" -[[deps.libvorbis_jll]] +[[libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+1" -[[deps.nghttp2_jll]] +[[nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -[[deps.p7zip_jll]] +[[p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -[[deps.x264_jll]] +[[x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" -[[deps.x265_jll]] +[[x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" -[[deps.xkbcommon_jll]] +[[xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" diff --git a/tutorials/using-turing-guide/Project.toml b/tutorials/using-turing-guide/Project.toml index d9c1da059..6fdadab66 100644 --- a/tutorials/using-turing-guide/Project.toml +++ b/tutorials/using-turing-guide/Project.toml @@ -1,3 +1,6 @@ [deps] +Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" +Optim = "429524aa-4258-5aef-a3af-852621145aeb" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" From 2d1583f499c3c1afe64e9562cd7d53ff9a6b046b Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 16:34:13 +0100 Subject: [PATCH 35/87] other tutorials --- .../01-gaussian-mixture-model/01_gaussian-mixture-model.jmd | 4 ++++ tutorials/02-logistic-regression/02_logistic-regression.jmd | 4 ++++ .../03-bayesian-neural-network/03_bayesian-neural-network.jmd | 4 ++++ tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd | 4 ++++ tutorials/05-linear-regression/05_linear-regression.jmd | 4 ++++ .../06-infinite-mixture-model/06_infinite-mixture-model.jmd | 4 ++++ tutorials/07-poisson-regression/07_poisson-regression.jmd | 4 ++++ .../08_multinomial-logistic-regression.jmd | 4 ++++ .../09-variational-inference/09_variational-inference.jmd | 4 ++++ .../10_bayesian-differential-equations.jmd | 4 ++++ tutorials/11-probabilistic-pca/11_probabilistic-pca.jmd | 4 ++++ tutorials/12-gaussian-process/12_gaussian-process.jmd | 4 ++++ tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd | 4 ++++ tutorials/14-minituring/14_minituring.jmd | 4 ++++ 14 files changed, 56 insertions(+) diff --git a/tutorials/01-gaussian-mixture-model/01_gaussian-mixture-model.jmd b/tutorials/01-gaussian-mixture-model/01_gaussian-mixture-model.jmd index 573e74a63..2d4de2718 100644 --- a/tutorials/01-gaussian-mixture-model/01_gaussian-mixture-model.jmd +++ b/tutorials/01-gaussian-mixture-model/01_gaussian-mixture-model.jmd @@ -191,3 +191,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/02-logistic-regression/02_logistic-regression.jmd b/tutorials/02-logistic-regression/02_logistic-regression.jmd index 8143f50ff..9140b3c39 100644 --- a/tutorials/02-logistic-regression/02_logistic-regression.jmd +++ b/tutorials/02-logistic-regression/02_logistic-regression.jmd @@ -244,3 +244,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd b/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd index 521d9d037..4026fa4bb 100644 --- a/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd +++ b/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd @@ -198,3 +198,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd b/tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd index db51e1f80..7c3df7e09 100644 --- a/tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd +++ b/tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd @@ -190,3 +190,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/05-linear-regression/05_linear-regression.jmd b/tutorials/05-linear-regression/05_linear-regression.jmd index 57af19b6c..004c81445 100644 --- a/tutorials/05-linear-regression/05_linear-regression.jmd +++ b/tutorials/05-linear-regression/05_linear-regression.jmd @@ -231,3 +231,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd b/tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd index 67107609b..e5885dfd9 100644 --- a/tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd +++ b/tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd @@ -279,3 +279,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/07-poisson-regression/07_poisson-regression.jmd b/tutorials/07-poisson-regression/07_poisson-regression.jmd index a25ff213d..98978e66d 100644 --- a/tutorials/07-poisson-regression/07_poisson-regression.jmd +++ b/tutorials/07-poisson-regression/07_poisson-regression.jmd @@ -222,3 +222,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd b/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd index 525f8811b..598a7f84b 100644 --- a/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd +++ b/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd @@ -215,3 +215,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` \ No newline at end of file diff --git a/tutorials/09-variational-inference/09_variational-inference.jmd b/tutorials/09-variational-inference/09_variational-inference.jmd index db512ecc3..f30f8f8af 100644 --- a/tutorials/09-variational-inference/09_variational-inference.jmd +++ b/tutorials/09-variational-inference/09_variational-inference.jmd @@ -868,3 +868,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd b/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd index bd985d4c5..bbb3e8a09 100644 --- a/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd +++ b/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd @@ -447,3 +447,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/11-probabilistic-pca/11_probabilistic-pca.jmd b/tutorials/11-probabilistic-pca/11_probabilistic-pca.jmd index 633ab0ba6..29c0d8134 100644 --- a/tutorials/11-probabilistic-pca/11_probabilistic-pca.jmd +++ b/tutorials/11-probabilistic-pca/11_probabilistic-pca.jmd @@ -375,3 +375,7 @@ It can also thought as a matrix factorisation method, in which $\mathbf{X}=(\mat [^3]: Gareth M. James, Daniela Witten, Trevor Hastie, Robert Tibshirani, *An Introduction to Statistical Learning*, Springer, 2013. [^4]: David Wipf, Srikantan Nagarajan, *A New View of Automatic Relevance Determination*, NIPS 2007. [^5]: Christopher Bishop, *Pattern Recognition and Machine Learning*, Springer, 2006. + +```julia + +``` diff --git a/tutorials/12-gaussian-process/12_gaussian-process.jmd b/tutorials/12-gaussian-process/12_gaussian-process.jmd index db1aafd6f..07621c023 100644 --- a/tutorials/12-gaussian-process/12_gaussian-process.jmd +++ b/tutorials/12-gaussian-process/12_gaussian-process.jmd @@ -222,3 +222,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd b/tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd index b1710dc1c..c66fb23e9 100644 --- a/tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd +++ b/tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd @@ -334,3 +334,7 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` + +```julia + +``` diff --git a/tutorials/14-minituring/14_minituring.jmd b/tutorials/14-minituring/14_minituring.jmd index d836beae6..bc2134933 100644 --- a/tutorials/14-minituring/14_minituring.jmd +++ b/tutorials/14-minituring/14_minituring.jmd @@ -286,3 +286,7 @@ sample(turing_m(3.0), MH(ScalMat(2, 1.0)), 1_000_000) ``` As you can see, with our simple probabilistic programming language and custom samplers we get similar results as Turing. + +```julia + +``` From b47ab659a5de293660fb468b32c3e68b049229b2 Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 17:42:03 +0100 Subject: [PATCH 36/87] other tutorials --- .../12_gaussian-process.jmd | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/tutorials/12-gaussian-process/12_gaussian-process.jmd b/tutorials/12-gaussian-process/12_gaussian-process.jmd index 07621c023..98ad8eb64 100644 --- a/tutorials/12-gaussian-process/12_gaussian-process.jmd +++ b/tutorials/12-gaussian-process/12_gaussian-process.jmd @@ -18,7 +18,7 @@ Importantly, it provides the advantage that the linear mappings from the embedde Let's start by loading some dependencies. -```julia +```{julia; eval=false} using Turing using AbstractGPs using FillArrays @@ -41,7 +41,7 @@ although this is an active area of research. We will briefly touch on some ways to speed things up at the end of this tutorial. We transform the original data with non-linear operations in order to demonstrate the power of GPs to work on non-linear relationships, while keeping the problem reasonably small. -```julia +```{julia; eval=false} data = dataset("datasets", "iris") species = data[!, "Species"] index = shuffle(1:150) @@ -66,7 +66,7 @@ Indeed, pPCA is basically equivalent to running the GPLVM model with an automati First, we re-introduce the pPCA model (see the tutorial on pPCA for details) -```julia +```{julia; eval=false} @model function pPCA(x) # Dimensionality of the problem. N, D = size(x) @@ -85,7 +85,7 @@ end; We define two different kernels, a simple linear kernel with an Automatic Relevance Determination transform and a squared exponential kernel. -```julia +```{julia; eval=false} linear_kernel(α) = LinearKernel() ∘ ARDTransform(α) sekernel(α, σ) = σ * SqExponentialKernel() ∘ ARDTransform(α); ``` @@ -93,7 +93,7 @@ sekernel(α, σ) = σ * SqExponentialKernel() ∘ ARDTransform(α); And here is the GPLVM model. We create separate models for the two types of kernel. -```julia +```{julia; eval=false} @model function GPLVM_linear(Y, K) # Dimensionality of the problem. N, D = size(Y) @@ -134,7 +134,7 @@ end; end; ``` -```julia +```{julia; eval=false} # Standard GPs don't scale very well in n, so we use a small subsample for the purpose of this tutorial n_data = 40 # number of features to use from dataset @@ -143,12 +143,12 @@ n_features = 4 ndim = 4; ``` -```julia +```{julia; eval=false} ppca = pPCA(dat[1:n_data, 1:n_features]) chain_ppca = sample(ppca, NUTS{Turing.ReverseDiffAD{true}}(), 1000); ``` -```julia +```{julia; eval=false} # we extract the posterior mean estimates of the parameters from the chain z_mean = reshape(mean(group(chain_ppca, :z))[:, 2], (n_features, n_data)) scatter(z_mean[1, :], z_mean[2, :]; group=labels[1:n_data], xlabel=L"z_1", ylabel=L"z_2") @@ -161,12 +161,12 @@ using pPCA (see the pPCA tutorial). Let's try the same with our linear kernel GPLVM model. -```julia +```{julia; eval=false} gplvm_linear = GPLVM_linear(dat[1:n_data, 1:n_features], ndim) chain_linear = sample(gplvm_linear, NUTS{Turing.ReverseDiffAD{true}}(), 500); ``` -```julia +```{julia; eval=false} # we extract the posterior mean estimates of the parameters from the chain z_mean = reshape(mean(group(chain_linear, :Z))[:, 2], (n_features, n_data)) alpha_mean = mean(group(chain_linear, :α))[:, 2] @@ -186,12 +186,12 @@ We can see that similar to the pPCA case, the linear kernel GPLVM fails to disti Finally, we demonstrate that by changing the kernel to a non-linear function, we are able to separate the data again. -```julia +```{julia; eval=false} gplvm = GPLVM(dat[1:n_data, 1:n_features], ndim) chain_gplvm = sample(gplvm, NUTS{Turing.ReverseDiffAD{true}}(), 500); ``` -```julia +```{julia; eval=false} # we extract the posterior mean estimates of the parameters from the chain z_mean = reshape(mean(group(chain_gplvm, :Z))[:, 2], (ndim, n_data)) alpha_mean = mean(group(chain_gplvm, :α))[:, 2] @@ -206,7 +206,7 @@ scatter( ) ``` -```julia; echo=false +```{julia; eval=false} let @assert abs( mean(z_mean[alpha1, labels[1:n_data] .== "setosa"]) - @@ -217,7 +217,7 @@ end Now, the split between the two groups is visible again. -```julia, echo=false, skip="notebook", tangle=false +```{julia; eval=false} if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end From 96ecc01fae4f8a3692d1ae534717c9ec703715cf Mon Sep 17 00:00:00 2001 From: Colton Botta <39240537+cgbotta@users.noreply.github.com> Date: Mon, 17 Apr 2023 21:08:00 +0100 Subject: [PATCH 37/87] other tutorials --- .../03-bayesian-neural-network/03_bayesian-neural-network.jmd | 3 ++- .../09-variational-inference/09_variational-inference.jmd | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd b/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd index 4026fa4bb..02661bb11 100644 --- a/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd +++ b/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd @@ -199,6 +199,7 @@ if isdefined(Main, :TuringTutorials) end ``` -```julia +```{julia; eval=false} + ``` diff --git a/tutorials/09-variational-inference/09_variational-inference.jmd b/tutorials/09-variational-inference/09_variational-inference.jmd index f30f8f8af..1a08700cb 100644 --- a/tutorials/09-variational-inference/09_variational-inference.jmd +++ b/tutorials/09-variational-inference/09_variational-inference.jmd @@ -869,6 +869,7 @@ if isdefined(Main, :TuringTutorials) end ``` -```julia +```{julia; eval=false} + ``` From da8bbeedbdb548f8748503c2fa79823672c48f7b Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 6 Jul 2023 16:09:29 +0100 Subject: [PATCH 38/87] fix merge conflict with master --- .../docs-00-getting-started/Manifest.toml | 385 ++++++++++-------- 1 file changed, 209 insertions(+), 176 deletions(-) diff --git a/tutorials/docs-00-getting-started/Manifest.toml b/tutorials/docs-00-getting-started/Manifest.toml index 9dbe093c0..ae727e5c1 100644 --- a/tutorials/docs-00-getting-started/Manifest.toml +++ b/tutorials/docs-00-getting-started/Manifest.toml @@ -25,9 +25,9 @@ version = "0.4.4" [[Adapt]] deps = ["LinearAlgebra", "Requires"] -git-tree-sha1 = "cc37d689f599e8df4f464b2fa3870ff7db7492ef" +git-tree-sha1 = "76289dc51920fdc6e0013c872ba9551d54961c24" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "3.6.1" +version = "3.6.2" [[AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "Setfield", "Statistics", "StatsBase", "StatsFuns", "UnPack"] @@ -74,14 +74,32 @@ uuid = "68821587-b530-5797-8361-c406ea357684" version = "3.5.0+3" [[ArrayInterface]] -deps = ["Adapt", "LinearAlgebra", "Requires", "SnoopPrecompile", "SparseArrays", "SuiteSparse"] -git-tree-sha1 = "38911c7737e123b28182d89027f4216cfc8a9da7" +deps = ["Adapt", "LinearAlgebra", "Requires", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "d3f758863a47ceef2248d136657cb9c033603641" uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9" -version = "7.4.3" +version = "7.4.8" + +[[ArrayInterfaceCore]] +deps = ["LinearAlgebra", "SnoopPrecompile", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "e5f08b5689b1aad068e01751889f2f615c7db36d" +uuid = "30b0a656-2188-435a-8636-2ec0e6a096e2" +version = "0.1.29" + +[[ArrayInterfaceStaticArraysCore]] +deps = ["ArrayInterfaceCore", "LinearAlgebra", "StaticArraysCore"] +git-tree-sha1 = "01a9f8e6cfc2bfdd01d333f70b8014a04893103c" +uuid = "dd5226c6-a4d4-4bc7-8575-46859f9c95b9" +version = "0.1.4" [[Artifacts]] uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" +[[Atomix]] +deps = ["UnsafeAtomics"] +git-tree-sha1 = "c06a868224ecba914baa6942988e2f2aade419be" +uuid = "a9b6321e-bd34-4604-b9c9-b65b8de01458" +version = "0.1.0" + [[AxisAlgorithms]] deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"] git-tree-sha1 = "66771c8d21c8ff5e3a93379480a2307ac36863f7" @@ -95,10 +113,10 @@ uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9" version = "0.4.6" [[BangBang]] -deps = ["Compat", "ConstructionBase", "Future", "InitialValues", "LinearAlgebra", "Requires", "Setfield", "Tables", "ZygoteRules"] -git-tree-sha1 = "7fe6d92c4f281cf4ca6f2fba0ce7b299742da7ca" +deps = ["Compat", "ConstructionBase", "InitialValues", "LinearAlgebra", "Requires", "Setfield", "Tables"] +git-tree-sha1 = "e28912ce94077686443433c2800104b061a827ed" uuid = "198e06fe-97b7-11e9-32a5-e1d131e6ad66" -version = "0.3.37" +version = "0.3.39" [[Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" @@ -125,6 +143,11 @@ git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2" uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0" version = "1.0.8+0" +[[CEnum]] +git-tree-sha1 = "eb4cb44a499229b3b8426dcfb5dd85333951ff90" +uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82" +version = "0.4.2" + [[Cairo_jll]] deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] git-tree-sha1 = "4b859a208b2397a7a623a03449e4636bdb17bcf2" @@ -139,27 +162,27 @@ version = "0.5.1" [[ChainRules]] deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "Statistics", "StructArrays"] -git-tree-sha1 = "7d20c2fb8ab838e41069398685e7b6b5f89ed85b" +git-tree-sha1 = "61549d9b52c88df34d21bd306dba1d43bb039c87" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" -version = "1.48.0" +version = "1.51.0" [[ChainRulesCore]] deps = ["Compat", "LinearAlgebra", "SparseArrays"] -git-tree-sha1 = "c6d890a52d2c4d55d326439580c3b8d0875a77d9" +git-tree-sha1 = "e30f2f4e20f7f186dc36529910beaedc60cfa644" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" -version = "1.15.7" +version = "1.16.0" [[ChangesOfVariables]] -deps = ["ChainRulesCore", "LinearAlgebra", "Test"] -git-tree-sha1 = "485193efd2176b88e6622a39a246f8c5b600e74e" +deps = ["LinearAlgebra", "Test"] +git-tree-sha1 = "f84967c4497e0e1955f9a582c232b02847c5f589" uuid = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0" -version = "0.1.6" +version = "0.1.7" [[Clustering]] deps = ["Distances", "LinearAlgebra", "NearestNeighbors", "Printf", "Random", "SparseArrays", "Statistics", "StatsBase"] -git-tree-sha1 = "7ebbd653f74504447f1c33b91cd706a69a1b189f" +git-tree-sha1 = "a6e6ce44a1e0a781772fc795fb7343b1925e9898" uuid = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" -version = "0.14.4" +version = "0.15.2" [[CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] @@ -168,10 +191,10 @@ uuid = "944b1d66-785c-5afd-91f1-9de20f533193" version = "0.7.1" [[ColorSchemes]] -deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "Random", "SnoopPrecompile"] -git-tree-sha1 = "aa3edc8f8dea6cbfa176ee12f7c2fc82f0608ed3" +deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"] +git-tree-sha1 = "be6ab11021cd29f0344d5c4357b163af05a48cba" uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4" -version = "3.20.0" +version = "3.21.0" [[ColorTypes]] deps = ["FixedPointNumbers", "Random"] @@ -197,9 +220,9 @@ uuid = "861a8166-3701-5b0c-9a16-15d98fcdc6aa" version = "1.0.2" [[CommonSolve]] -git-tree-sha1 = "9441451ee712d1aec22edad62db1a9af3dc8d852" +git-tree-sha1 = "0eee5eb66b1cf62cd6ad1b460238e60e4b09400c" uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2" -version = "0.2.3" +version = "0.2.4" [[CommonSubexpressions]] deps = ["MacroTools", "Test"] @@ -218,9 +241,15 @@ deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" [[CompositionsBase]] -git-tree-sha1 = "455419f7e328a1a2493cabc6428d79e951349769" +git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" uuid = "a33af91c-f02d-484b-be07-31d278c5ca2b" -version = "0.1.1" +version = "0.1.2" + +[[ConcurrentUtilities]] +deps = ["Serialization", "Sockets"] +git-tree-sha1 = "96d823b94ba8d187a6d8f0826e731195a74b90e9" +uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" +version = "2.2.0" [[ConsoleProgressMonitor]] deps = ["Logging", "ProgressMeter"] @@ -230,9 +259,9 @@ version = "0.1.2" [[ConstructionBase]] deps = ["LinearAlgebra"] -git-tree-sha1 = "89a9db8d28102b094992472d333674bd1a83ce2a" +git-tree-sha1 = "738fec4d684a9a6ee9598a8bfee305b26831f28c" uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" -version = "1.5.1" +version = "1.5.2" [[Contour]] git-tree-sha1 = "d05d9e7b7aedff4e5b51a029dced05cfb6125781" @@ -245,9 +274,9 @@ uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" version = "4.1.1" [[DataAPI]] -git-tree-sha1 = "e8119c1a33d267e16108be441a287a6981ba1630" +git-tree-sha1 = "8da84edb865b0b5b0100c0666a9bc9a0b71c553c" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" -version = "1.14.0" +version = "1.15.0" [[DataStructures]] deps = ["Compat", "InteractiveUtils", "OrderedCollections"] @@ -260,12 +289,6 @@ git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6" uuid = "e2d170a0-9d28-54be-80f0-106bbe20a464" version = "1.0.0" -[[DataValues]] -deps = ["DataValueInterfaces", "Dates"] -git-tree-sha1 = "d88a19299eba280a6d062e135a43f00323ae70bf" -uuid = "e7dc6d0d-1eca-5fa6-8ad6-5aecde8b7ea5" -version = "0.4.13" - [[Dates]] deps = ["Printf"] uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" @@ -293,9 +316,9 @@ version = "1.1.0" [[DiffRules]] deps = ["IrrationalConstants", "LogExpFunctions", "NaNMath", "Random", "SpecialFunctions"] -git-tree-sha1 = "a4ad7ef19d2cdc2eff57abbbe68032b1cd0bd8f8" +git-tree-sha1 = "23163d55f885173722d1e4cf0f6110cdbaf7e272" uuid = "b552c78f-8df3-52c6-915a-8e097449b14b" -version = "1.13.0" +version = "1.15.1" [[Distances]] deps = ["LinearAlgebra", "SparseArrays", "Statistics", "StatsAPI"] @@ -308,22 +331,22 @@ deps = ["Random", "Serialization", "Sockets"] uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" [[Distributions]] -deps = ["ChainRulesCore", "DensityInterface", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns", "Test"] -git-tree-sha1 = "13027f188d26206b9e7b863036f87d2f2e7d013a" +deps = ["ChainRulesCore", "DensityInterface", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"] +git-tree-sha1 = "c72970914c8a21b36bbc244e9df0ed1834a0360b" uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" -version = "0.25.87" +version = "0.25.95" [[DistributionsAD]] -deps = ["Adapt", "ChainRules", "ChainRulesCore", "Compat", "DiffRules", "Distributions", "FillArrays", "LinearAlgebra", "NaNMath", "PDMats", "Random", "Requires", "SpecialFunctions", "StaticArrays", "StatsBase", "StatsFuns", "ZygoteRules"] -git-tree-sha1 = "c2ecd634010e6952ce642db65b5885c41cb307b0" +deps = ["Adapt", "ChainRules", "ChainRulesCore", "Compat", "Distributions", "FillArrays", "LinearAlgebra", "PDMats", "Random", "Requires", "SpecialFunctions", "StaticArrays", "StatsFuns", "ZygoteRules"] +git-tree-sha1 = "1fb4fedd5a407243d535cc50c8c803c2fce0dc26" uuid = "ced4e74d-a319-5a8a-b0ac-84af2272839c" -version = "0.6.44" +version = "0.6.49" [[DocStringExtensions]] deps = ["LibGit2"] -git-tree-sha1 = "2fb1e02f2b635d0845df5d7c167fec4dd739b00d" +git-tree-sha1 = "b19534d1895d702889b219c382a6e18010797f0b" uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" -version = "0.9.3" +version = "0.8.6" [[Downloads]] deps = ["ArgTools", "LibCURL", "NetworkOptions"] @@ -377,9 +400,9 @@ version = "4.4.2+2" [[FFTW]] deps = ["AbstractFFTs", "FFTW_jll", "LinearAlgebra", "MKL_jll", "Preferences", "Reexport"] -git-tree-sha1 = "f9818144ce7c8c41edf5c4c179c684d92aa4d9fe" +git-tree-sha1 = "06bf20fcecd258eccf9a6ef7b99856a4dfe7b64c" uuid = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341" -version = "1.6.0" +version = "1.7.0" [[FFTW_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -389,10 +412,9 @@ version = "3.3.10+0" [[FillArrays]] deps = ["LinearAlgebra", "Random", "SparseArrays", "Statistics"] -git-tree-sha1 = "fc86b4fd3eff76c3ce4f5e96e2fdfa6282722885" +git-tree-sha1 = "7072f1e3e5a8be51d525d64f63d3ec1287ff2790" uuid = "1a297f60-69ca-5386-bcde-b61e274b549b" -version = "1.0.0" - +version = "0.13.11" [[FixedPointNumbers]] deps = ["Statistics"] @@ -459,22 +481,21 @@ version = "3.3.8+0" [[GPUArraysCore]] deps = ["Adapt"] -git-tree-sha1 = "1cd7f0af1aa58abc02ea1d872953a97359cb87fa" +git-tree-sha1 = "2d6ca471a6c7b536127afccfa7564b5b39227fe0" uuid = "46192b85-c4d5-4398-a991-12ede77f4527" -version = "0.1.4" +version = "0.1.5" [[GR]] deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Pkg", "Preferences", "Printf", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "UUIDs", "p7zip_jll"] -git-tree-sha1 = "0635807d28a496bb60bc15f465da0107fb29649c" +git-tree-sha1 = "8b8a2fd4536ece6e554168c21860b6820a8a83db" uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" -version = "0.72.0" +version = "0.72.7" [[GR_jll]] deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt5Base_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "99e248f643b052a77d2766fe1a16fb32b661afd4" +git-tree-sha1 = "19fad9cd9ae44847fe842558a744748084a722d1" uuid = "d2c73de3-f751-5644-a686-071e5b155ba9" -version = "0.72.0+0" - +version = "0.72.7+0" [[Gettext_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"] @@ -500,10 +521,10 @@ uuid = "42e2da0e-8278-4e71-bc24-59509adca0fe" version = "1.0.2" [[HTTP]] -deps = ["Base64", "CodecZlib", "Dates", "IniFile", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] -git-tree-sha1 = "37e4657cd56b11abe3d10cd4a1ec5fbdb4180263" +deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] +git-tree-sha1 = "5e77dbf117412d4f164a464d610ee6050cc75272" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "1.7.4" +version = "1.9.6" [[HarfBuzz_jll]] deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg"] @@ -513,20 +534,15 @@ version = "2.8.1+1" [[HypergeometricFunctions]] deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] -git-tree-sha1 = "432b5b03176f8182bd6841fbfc42c718506a2d5f" +git-tree-sha1 = "0ec02c648befc2f94156eaef13b0f38106212f3f" uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" -version = "0.3.15" +version = "0.3.17" [[IRTools]] deps = ["InteractiveUtils", "MacroTools", "Test"] -git-tree-sha1 = "0ade27f0c49cebd8db2523c4eeccf779407cf12c" +git-tree-sha1 = "eac00994ce3229a464c2847e956d77a2c64ad3a5" uuid = "7869d1d1-7146-5819-86e3-90919afe41df" -version = "0.4.9" - -[[IniFile]] -git-tree-sha1 = "f550e6e32074c939295eb5ea6de31849ac2c9625" -uuid = "83e8ac13-25f8-5344-8a64-a9f2b223428f" -version = "0.5.1" +version = "0.4.10" [[InitialValues]] git-tree-sha1 = "4da0f88e9a39111c2fa3add390ab15f3a44f3ca3" @@ -541,9 +557,9 @@ version = "0.3.0" [[IntelOpenMP_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "d979e54b71da82f3a65b62553da4fc3d18c9004c" +git-tree-sha1 = "0cb9352ef2e01574eeebdb102948a58740dcaf83" uuid = "1d5cc7b8-4909-519e-a0f8-d0f5ad9712d0" -version = "2018.0.3+2" +version = "2023.1.0+0" [[InteractiveUtils]] deps = ["Markdown"] @@ -563,9 +579,9 @@ version = "0.7.4" [[InverseFunctions]] deps = ["Test"] -git-tree-sha1 = "49510dfcb407e572524ba94aeae2fced1f3feb0f" +git-tree-sha1 = "6667aadd1cdee2c6cd068128b3d226ebc4fb0c67" uuid = "3587e190-3f89-42d0-90ee-14403ec27112" -version = "0.1.8" +version = "0.1.9" [[InvertedIndices]] git-tree-sha1 = "0dc7b50b8d436461be01300fd8cd45aa0274b038" @@ -611,11 +627,17 @@ git-tree-sha1 = "6f2675ef130a300a112286de91973805fcc5ffbc" uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8" version = "2.1.91+0" +[[KernelAbstractions]] +deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] +git-tree-sha1 = "47be64f040a7ece575c2b5f53ca6da7b548d69f4" +uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c" +version = "0.9.4" + [[KernelDensity]] deps = ["Distributions", "DocStringExtensions", "FFTW", "Interpolations", "StatsBase"] -git-tree-sha1 = "9816b296736292a80b9a3200eb7fbb57aaa3917a" +git-tree-sha1 = "90442c50e202a5cdf21a7899c66b240fdef14035" uuid = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b" -version = "0.6.5" +version = "0.6.7" [[LAME_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -629,6 +651,24 @@ git-tree-sha1 = "bf36f528eec6634efc60d7ec062008f171071434" uuid = "88015f11-f218-50d7-93a8-a6af411a945d" version = "3.0.0+1" +[[LLVM]] +deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"] +git-tree-sha1 = "5007c1421563108110bbd57f63d8ad4565808818" +uuid = "929cbde3-209d-540e-8aea-75f648917ca0" +version = "5.2.0" + +[[LLVMExtra_jll]] +deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] +git-tree-sha1 = "1222116d7313cdefecf3d45a2bc1a89c4e7c9217" +uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab" +version = "0.0.22+0" + +[[LLVMOpenMP_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "f689897ccbe049adb19a065c495e75f372ecd42b" +uuid = "1d63c593-3942-5779-bab2-d838dc0a180e" +version = "15.0.4+0" + [[LRUCache]] git-tree-sha1 = "48c10e3cc27e30de82463c27bef0b8bdbd1dc634" uuid = "8ac3fa9e-de4c-5943-b1dc-09c6b5f20637" @@ -647,15 +687,9 @@ version = "1.3.0" [[Latexify]] deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Printf", "Requires"] -git-tree-sha1 = "2422f47b34d4b127720a18f86fa7b1aa2e141f29" +git-tree-sha1 = "f428ae552340899a935973270b8d98e5a31c49fe" uuid = "23fbe1c1-3f47-55db-b15f-69d7ec21a316" -version = "0.15.18" - -[[Lazy]] -deps = ["MacroTools"] -git-tree-sha1 = "1370f8202dac30758f3c345f9909b97f53d87d3f" -uuid = "50d2b5c4-7a5e-59d5-8109-a42b560f39c0" -version = "0.15.1" +version = "0.16.1" [[LazyArtifacts]] deps = ["Artifacts", "Pkg"] @@ -752,9 +786,9 @@ version = "1.0.3" [[LogExpFunctions]] deps = ["ChainRulesCore", "ChangesOfVariables", "DocStringExtensions", "InverseFunctions", "IrrationalConstants", "LinearAlgebra"] -git-tree-sha1 = "0a1b7c2863e44523180fdb3146534e265a91870b" +git-tree-sha1 = "c3ce8e7420b3a6e071e0fe4745f5d4300e37b13f" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" -version = "0.3.23" +version = "0.3.24" [[Logging]] uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" @@ -796,9 +830,9 @@ uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" version = "0.5.10" [[MappedArrays]] -git-tree-sha1 = "e8b359ef06ec72e8c030463fe02efe5527ee5142" +git-tree-sha1 = "2dab0221fe2b0f2cb6754eaa743cc266339f527e" uuid = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900" -version = "0.4.1" +version = "0.4.2" [[Markdown]] deps = ["Base64"] @@ -839,15 +873,15 @@ uuid = "14a3606d-f60d-562e-9121-12d972cd8159" [[MultivariateStats]] deps = ["Arpack", "LinearAlgebra", "SparseArrays", "Statistics", "StatsAPI", "StatsBase"] -git-tree-sha1 = "91a48569383df24f0fd2baf789df2aade3d0ad80" +git-tree-sha1 = "68bf5103e002c44adfd71fea6bd770b3f0586843" uuid = "6f286f6a-111f-5878-ab1e-185364afe411" -version = "0.10.1" +version = "0.10.2" [[NNlib]] -deps = ["Adapt", "ChainRulesCore", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] -git-tree-sha1 = "33ad5a19dc6730d592d8ce91c14354d758e53b0e" +deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] +git-tree-sha1 = "72240e3f5ca031937bd536182cb2c031da5f46dd" uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" -version = "0.8.19" +version = "0.8.21" [[NaNMath]] deps = ["OpenLibm_jll"] @@ -861,7 +895,6 @@ git-tree-sha1 = "b84e17976a40cb2bfe3ae7edb3673a8c630d4f95" uuid = "86f7a689-2022-50b4-a561-43c23ac3c673" version = "0.9.8" - [[NaturalSort]] git-tree-sha1 = "eda490d06b9f7c00752ee81cfa451efe55521e21" uuid = "c020b1a1-e9b0-503a-9c33-f039bfc54a85" @@ -903,15 +936,15 @@ uuid = "05823500-19ac-5b8b-9628-191a04bc5112" [[OpenSSL]] deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"] -git-tree-sha1 = "e9d68fe4b5f78f215aa2f0e6e6dc9e9911d33048" +git-tree-sha1 = "51901a49222b09e3743c65b8847687ae5fc78eb2" uuid = "4d8831e6-92b7-49fb-bdf8-b643e874388c" -version = "1.3.4" +version = "1.4.1" [[OpenSSL_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "9ff31d101d987eb9d66bd8b176ac7c277beccd09" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "1aa4b74f80b01c6bc2b89992b861b5f210e665b5" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" -version = "1.1.20+0" +version = "1.1.21+0" [[OpenSpecFun_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] @@ -947,10 +980,10 @@ uuid = "90014a1f-27ba-587c-ab20-58faa44d9150" version = "0.11.17" [[Parsers]] -deps = ["Dates", "SnoopPrecompile"] -git-tree-sha1 = "478ac6c952fddd4399e71d4779797c538d0ff2bf" +deps = ["Dates", "PrecompileTools", "UUIDs"] +git-tree-sha1 = "5a6ab2f64388fd1175effdf73fe5933ef1e0bac0" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" -version = "2.5.8" +version = "2.7.0" [[Pipe]] git-tree-sha1 = "6842804e7867b115ca9de748a0cf6b364523c16d" @@ -958,10 +991,10 @@ uuid = "b98c9c47-44ae-5843-9183-064241ee97a0" version = "1.3.0" [[Pixman_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "b4f5d02549a10e20780a24fce72bea96b6329e29" +deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LLVMOpenMP_jll", "Libdl"] +git-tree-sha1 = "64779bc4c9784fee475689a1752ef4d5747c5e87" uuid = "30392449-352a-5448-841d-b1acce4e97dc" -version = "0.40.1+0" +version = "0.42.2+0" [[Pkg]] deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] @@ -974,29 +1007,34 @@ uuid = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a" version = "3.1.0" [[PlotUtils]] -deps = ["ColorSchemes", "Colors", "Dates", "Printf", "Random", "Reexport", "SnoopPrecompile", "Statistics"] -git-tree-sha1 = "c95373e73290cf50a8a22c3375e4625ded5c5280" +deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"] +git-tree-sha1 = "f92e1315dadf8c46561fb9396e525f7200cdc227" uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043" -version = "1.3.4" +version = "1.3.5" [[Plots]] -deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "Preferences", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SnoopPrecompile", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "Unzip"] -git-tree-sha1 = "186d38ea29d5c4f238b2d9fe6e1653264101944b" +deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Preferences", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"] +git-tree-sha1 = "75ca67b2c6512ad2d0c767a7cfc55e75075f8bbc" uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -version = "1.38.9" +version = "1.38.16" +[[PrecompileTools]] +deps = ["Preferences"] +git-tree-sha1 = "9673d39decc5feece56ef3940e5dafba15ba0f81" +uuid = "aea7be01-6a6a-4083-8856-8a6e6704d82a" +version = "1.1.2" [[Preferences]] deps = ["TOML"] -git-tree-sha1 = "47e5f437cc0e7ef2ce8406ce1e7e24d44915f88d" +git-tree-sha1 = "7eb1686b4f04b82f96ed7a4ea5890a4f0c7a09f1" uuid = "21216c6a-2e73-6563-6e65-726566657250" -version = "1.3.0" +version = "1.4.0" [[PrettyTables]] deps = ["Crayons", "Formatting", "LaTeXStrings", "Markdown", "Reexport", "StringManipulation", "Tables"] -git-tree-sha1 = "548793c7859e28ef026dba514752275ee871169f" +git-tree-sha1 = "213579618ec1f42dea7dd637a42785a608b1ea9c" uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" -version = "2.2.3" +version = "2.2.4" [[Printf]] deps = ["Unicode"] @@ -1041,9 +1079,9 @@ version = "0.3.2" [[Ratios]] deps = ["Requires"] -git-tree-sha1 = "dc84268fe0e3335a62e315a3a7cf2afa7178a734" +git-tree-sha1 = "1342a47bf3260ee108163042310d26f2be5ec90b" uuid = "c84ed2f1-dad5-54f0-aa8e-dbefe2724439" -version = "0.4.3" +version = "0.4.5" [[RealDot]] deps = ["LinearAlgebra"] @@ -1052,23 +1090,22 @@ uuid = "c1ae055f-0cd5-4b69-90a6-9a35b1a98df9" version = "0.1.0" [[RecipesBase]] -deps = ["SnoopPrecompile"] -git-tree-sha1 = "261dddd3b862bd2c940cf6ca4d1c8fe593e457c8" +deps = ["PrecompileTools"] +git-tree-sha1 = "5c3d09cc4f31f5fc6af001c250bf1278733100ff" uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01" -version = "1.3.3" +version = "1.3.4" [[RecipesPipeline]] -deps = ["Dates", "NaNMath", "PlotUtils", "RecipesBase", "SnoopPrecompile"] -git-tree-sha1 = "e974477be88cb5e3040009f3767611bc6357846f" +deps = ["Dates", "NaNMath", "PlotUtils", "PrecompileTools", "RecipesBase"] +git-tree-sha1 = "45cf9fd0ca5839d06ef333c8201714e888486342" uuid = "01d81517-befc-4cb6-b9ec-a95719d0359c" -version = "0.6.11" +version = "0.6.12" [[RecursiveArrayTools]] -deps = ["Adapt", "ArrayInterface", "DocStringExtensions", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "Requires", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables"] -git-tree-sha1 = "140cddd2c457e4ebb0cdc7c2fd14a7fbfbdf206e" +deps = ["Adapt", "ArrayInterfaceCore", "ArrayInterfaceStaticArraysCore", "ChainRulesCore", "DocStringExtensions", "FillArrays", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "StaticArraysCore", "Statistics", "Tables", "ZygoteRules"] +git-tree-sha1 = "a5ce741acddc02f0d4fc6505463ca89697d7fb23" uuid = "731186ca-8d62-57ce-b412-fbd966d074cd" -version = "2.38.3" - +version = "2.32.3" [[Reexport]] git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" @@ -1101,31 +1138,24 @@ version = "0.4.0+0" [[Roots]] deps = ["ChainRulesCore", "CommonSolve", "Printf", "Setfield"] -git-tree-sha1 = "b45deea4566988994ebb8fb80aa438a295995a6e" +git-tree-sha1 = "de432823e8aab4dd1a985be4be768f95acf152d4" uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665" -version = "2.0.10" +version = "2.0.17" [[RuntimeGeneratedFunctions]] deps = ["ExprTools", "SHA", "Serialization"] -git-tree-sha1 = "f139e81a81e6c29c40f1971c9e5309b09c03f2c3" +git-tree-sha1 = "237edc1563bbf078629b4f8d194bd334e97907cf" uuid = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47" -version = "0.5.6" +version = "0.5.11" [[SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" [[SciMLBase]] -deps = ["ArrayInterface", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "SnoopPrecompile", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces"] -git-tree-sha1 = "392d3e28b05984496af37100ded94dc46fa6c8de" +deps = ["ArrayInterfaceCore", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "Preferences", "RecipesBase", "RecursiveArrayTools", "RuntimeGeneratedFunctions", "StaticArraysCore", "Statistics", "Tables"] +git-tree-sha1 = "fe89a8113ea445bcff9ee570077830674babb534" uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462" -version = "1.91.7" - - -[[SciMLOperators]] -deps = ["ArrayInterface", "DocStringExtensions", "Lazy", "LinearAlgebra", "Setfield", "SparseArrays", "StaticArraysCore", "Tricks"] -git-tree-sha1 = "e61e48ef909375203092a6e83508c8416df55a83" -uuid = "c0aeaf25-5076-4817-a8d5-81caf7dfa961" -version = "0.2.0" +version = "1.81.0" [[ScientificTypesBase]] git-tree-sha1 = "a8e18eb383b5ecf1b5e6fc237eb39255044fd92b" @@ -1140,9 +1170,9 @@ version = "1.2.0" [[SentinelArrays]] deps = ["Dates", "Random"] -git-tree-sha1 = "77d3c4726515dca71f6d80fbb5e251088defe305" +git-tree-sha1 = "04bdff0b09c65ff3e06a05e3eb7b120223da3d39" uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c" -version = "1.3.18" +version = "1.4.0" [[Serialization]] uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" @@ -1179,9 +1209,9 @@ uuid = "6462fe0b-24de-5631-8697-dd941f90decc" [[SortingAlgorithms]] deps = ["DataStructures"] -git-tree-sha1 = "a4ada03f999bd01b3a25dcaa30b2d929fe537e00" +git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee" uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c" -version = "1.1.0" +version = "1.1.1" [[SparseArrays]] deps = ["LinearAlgebra", "Random"] @@ -1201,10 +1231,9 @@ version = "0.1.15" [[StaticArrays]] deps = ["LinearAlgebra", "Random", "StaticArraysCore", "Statistics"] -git-tree-sha1 = "63e84b7fdf5021026d0f17f76af7c57772313d99" +git-tree-sha1 = "832afbae2a45b4ae7e831f86965469a24d1d8a83" uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.5.21" - +version = "1.5.26" [[StaticArraysCore]] git-tree-sha1 = "6b7ba252635a5eff6a0b0664a41ee140a1c9e72a" @@ -1240,10 +1269,10 @@ uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c" version = "1.3.0" [[StatsPlots]] -deps = ["AbstractFFTs", "Clustering", "DataStructures", "DataValues", "Distributions", "Interpolations", "KernelDensity", "LinearAlgebra", "MultivariateStats", "NaNMath", "Observables", "Plots", "RecipesBase", "RecipesPipeline", "Reexport", "StatsBase", "TableOperations", "Tables", "Widgets"] -git-tree-sha1 = "e0d5bc26226ab1b7648278169858adcfbd861780" +deps = ["AbstractFFTs", "Clustering", "DataStructures", "Distributions", "Interpolations", "KernelDensity", "LinearAlgebra", "MultivariateStats", "NaNMath", "Observables", "Plots", "RecipesBase", "RecipesPipeline", "Reexport", "StatsBase", "TableOperations", "Tables", "Widgets"] +git-tree-sha1 = "14ef622cf28b05e38f8af1de57bc9142b03fbfe3" uuid = "f3b207a7-027a-5e70-b257-86293d7955fd" -version = "0.15.4" +version = "0.15.5" [[StringManipulation]] git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" @@ -1260,12 +1289,6 @@ version = "0.6.15" deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" -[[SymbolicIndexingInterface]] -deps = ["DocStringExtensions"] -git-tree-sha1 = "f8ab052bfcbdb9b48fad2c80c873aa0d0344dfe5" -uuid = "2efcf032-c050-4f8e-a9bb-153293bab1f5" -version = "0.2.2" - [[TOML]] deps = ["Dates"] uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" @@ -1300,9 +1323,9 @@ version = "0.1.1" [[TerminalLoggers]] deps = ["LeftChildRightSiblingTrees", "Logging", "Markdown", "Printf", "ProgressLogging", "UUIDs"] -git-tree-sha1 = "f53e34e784ae771eb9ccde4d72e578aa453d0554" +git-tree-sha1 = "f133fab380933d042f6796eda4e130272ba520ca" uuid = "5d786b92-1e48-4d6f-9151-6b4477ca9bed" -version = "0.1.6" +version = "0.1.7" [[Test]] deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] @@ -1310,33 +1333,21 @@ uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [[Tracker]] deps = ["Adapt", "DiffRules", "ForwardDiff", "Functors", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NNlib", "NaNMath", "Optimisers", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics"] -git-tree-sha1 = "77817887c4b414b9c6914c61273910e3234eb21c" +git-tree-sha1 = "8b552cc0a4132c1ce5cee14197bb57d2109d480f" uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" -version = "0.2.23" +version = "0.2.25" [[TranscodingStreams]] deps = ["Random", "Test"] -git-tree-sha1 = "0b829474fed270a4b0ab07117dce9b9a2fa7581a" +git-tree-sha1 = "9a6ae7ed916312b41236fcef7e0af564ef934769" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.9.12" +version = "0.9.13" [[Transducers]] deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"] -git-tree-sha1 = "c42fa452a60f022e9e087823b47e5a5f8adc53d5" +git-tree-sha1 = "25358a5f2384c490e98abd565ed321ffae2cbb37" uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999" -version = "0.4.75" - -[[Tricks]] -git-tree-sha1 = "aadb748be58b492045b4f56166b5188aa63ce549" -uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775" -version = "0.1.7" - - -[[TruncatedStacktraces]] -deps = ["InteractiveUtils", "MacroTools", "Preferences"] -git-tree-sha1 = "7bc1632a4eafbe9bd94cf1a784a9a4eb5e040a91" -uuid = "781d530d-4396-4725-bb49-402e4bee1e77" -version = "1.3.0" +version = "0.4.76" [[Turing]] deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns", "Tracker"] @@ -1367,6 +1378,29 @@ git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf" uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1" version = "0.4.1" +[[Unitful]] +deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"] +git-tree-sha1 = "ba4aa36b2d5c98d6ed1f149da916b3ba46527b2b" +uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" +version = "1.14.0" + +[[UnitfulLatexify]] +deps = ["LaTeXStrings", "Latexify", "Unitful"] +git-tree-sha1 = "e2d817cc500e960fdbafcf988ac8436ba3208bfd" +uuid = "45397f5d-5981-4c77-b2b3-fc36d6e9b728" +version = "1.6.3" + +[[UnsafeAtomics]] +git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278" +uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f" +version = "0.2.1" + +[[UnsafeAtomicsLLVM]] +deps = ["LLVM", "UnsafeAtomics"] +git-tree-sha1 = "ea37e6066bf194ab78f4e747f5245261f17a7175" +uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249" +version = "0.1.2" + [[Unzip]] git-tree-sha1 = "34db80951901073501137bdbc3d5a8e7bbd06670" uuid = "41fe7b60-77ed-43a1-b4f0-825fd5a5650d" @@ -1544,7 +1578,6 @@ git-tree-sha1 = "49ce682769cd5de6c72dcf1b94ed7790cd08974c" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" version = "1.5.5+0" - [[ZygoteRules]] deps = ["ChainRulesCore", "MacroTools"] git-tree-sha1 = "977aed5d006b840e2e40c0b48984f7463109046d" From 41c8c1a6daa257e8e0b3dfd68566a209faee4116 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 6 Jul 2023 16:23:17 +0100 Subject: [PATCH 39/87] some edits to advanced --- tutorials/using-turing-advanced/Manifest.toml | 34 +++++++++++++++++-- tutorials/using-turing-advanced/Project.toml | 2 ++ tutorials/using-turing-advanced/advanced.jmd | 23 +++++++------ 3 files changed, 46 insertions(+), 13 deletions(-) diff --git a/tutorials/using-turing-advanced/Manifest.toml b/tutorials/using-turing-advanced/Manifest.toml index ea4ec1254..d8ea5eb06 100644 --- a/tutorials/using-turing-advanced/Manifest.toml +++ b/tutorials/using-turing-advanced/Manifest.toml @@ -120,9 +120,9 @@ version = "0.1.1" [[deps.Bijectors]] deps = ["ArgCheck", "ChainRulesCore", "ChangesOfVariables", "Compat", "Distributions", "Functors", "InverseFunctions", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "MappedArrays", "Random", "Reexport", "Requires", "Roots", "SparseArrays", "Statistics"] -git-tree-sha1 = "a3704b8e5170f9339dff4e6cb286ad49464d3646" +git-tree-sha1 = "1234b03e94938e6f2b14834dfd3ef45698d5e14f" uuid = "76274a88-744f-5084-9051-94815aaf08c4" -version = "0.10.6" +version = "0.10.8" [[deps.BitFlags]] git-tree-sha1 = "43b1a4a8f797c1cddadf60499a8a077d4af2cd2d" @@ -519,6 +519,12 @@ git-tree-sha1 = "129acf094d168394e80ee1dc4bc06ec835e510a3" uuid = "2e76f6c2-a576-52d4-95c1-20adfe4de566" version = "2.8.1+1" +[[deps.Highlights]] +deps = ["DocStringExtensions", "InteractiveUtils", "REPL"] +git-tree-sha1 = "0341077e8a6b9fc1c2ea5edc1e93a956d2aec0c7" +uuid = "eafb193a-b7ab-5a9e-9068-77385905fa72" +version = "0.5.2" + [[deps.HypergeometricFunctions]] deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions", "Test"] git-tree-sha1 = "709d864e3ed6e3545230601f94e11ebc65994641" @@ -845,6 +851,12 @@ git-tree-sha1 = "efe9c8ecab7a6311d4b91568bd6c88897822fabe" uuid = "6f286f6a-111f-5878-ab1e-185364afe411" version = "0.10.0" +[[deps.Mustache]] +deps = ["Printf", "Tables"] +git-tree-sha1 = "821e918c170ead5298ff84bffee41dd28929a681" +uuid = "ffc61752-8dc7-55ee-8c37-f3e9cdd09e70" +version = "1.0.17" + [[deps.NNlib]] deps = ["Adapt", "ChainRulesCore", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] git-tree-sha1 = "b488fc28dfae4c8ec3d61a34a0143a4245e7b13b" @@ -1236,6 +1248,12 @@ git-tree-sha1 = "e0d5bc26226ab1b7648278169858adcfbd861780" uuid = "f3b207a7-027a-5e70-b257-86293d7955fd" version = "0.15.4" +[[deps.StringEncodings]] +deps = ["Libiconv_jll"] +git-tree-sha1 = "33c0da881af3248dafefb939a21694b97cfece76" +uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" +version = "0.3.6" + [[deps.StringManipulation]] git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" @@ -1363,6 +1381,12 @@ git-tree-sha1 = "4528479aa01ee1b3b4cd0e6faef0e04cf16466da" uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91" version = "1.25.0+0" +[[deps.Weave]] +deps = ["Base64", "Dates", "Highlights", "JSON", "Markdown", "Mustache", "Pkg", "Printf", "REPL", "RelocatableFolders", "Requires", "Serialization", "YAML"] +git-tree-sha1 = "092217eb5443926d200ae9325f103906efbb68b1" +uuid = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9" +version = "0.10.12" + [[deps.Widgets]] deps = ["Colors", "Dates", "Observables", "OrderedCollections"] git-tree-sha1 = "fcdae142c1cfc7d89de2d11e08721d0f2f86c98a" @@ -1513,6 +1537,12 @@ git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" +[[deps.YAML]] +deps = ["Base64", "Dates", "Printf", "StringEncodings"] +git-tree-sha1 = "e6330e4b731a6af7959673621e91645eb1356884" +uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6" +version = "0.4.9" + [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" diff --git a/tutorials/using-turing-advanced/Project.toml b/tutorials/using-turing-advanced/Project.toml index d9c1da059..575dd1d24 100644 --- a/tutorials/using-turing-advanced/Project.toml +++ b/tutorials/using-turing-advanced/Project.toml @@ -1,3 +1,5 @@ [deps] +Bijectors = "76274a88-744f-5084-9051-94815aaf08c4" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" +Weave = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9" diff --git a/tutorials/using-turing-advanced/advanced.jmd b/tutorials/using-turing-advanced/advanced.jmd index 07c2e4091..ee558bcc0 100644 --- a/tutorials/using-turing-advanced/advanced.jmd +++ b/tutorials/using-turing-advanced/advanced.jmd @@ -5,11 +5,15 @@ weave_options: error : false --- +```{julia; eval=true; echo=false} +using Distributions, Turing, Random, Bijectors +``` + # Advanced Usage ## How to Define a Customized Distribution -Turing.jl supports the use of distributions from the Distributions.jl package. By extension it also supports the use of customized distributions, by defining them as subtypes of `Distribution` type of the Distributions.jl package, as well as corresponding functions. +`Turing.jl` supports the use of distributions from the Distributions.jl package. By extension it also supports the use of customized distributions, by defining them as subtypes of `Distribution` type of the Distributions.jl package, as well as corresponding functions. Below shows a workflow of how to define a customized distribution, using our own implementation of a simple `Uniform` distribution as a simple example. @@ -17,7 +21,7 @@ Below shows a workflow of how to define a customized distribution, using our own First, define a type of the distribution, as a subtype of a corresponding distribution type in the Distributions.jl package. -```{julia; eval=false} +```{julia; eval=true} struct CustomUniform <: ContinuousUnivariateDistribution end ``` @@ -25,7 +29,7 @@ struct CustomUniform <: ContinuousUnivariateDistribution end Second, define `rand` and `logpdf`, which will be used to run the model. -```{julia; eval=false} +```{julia; eval=true} # sample in [0, 1] Distributions.rand(rng::AbstractRNG, d::CustomUniform) = rand(rng) @@ -43,7 +47,7 @@ Certain samplers, such as `HMC`, require the domain of the priors to be unbounde To transform from `[0, 1]` to `ℝ` we can use the `Logit` bijector: -```{julia; eval=false} +```{julia; eval=true} Bijectors.bijector(d::CustomUniform) = Logit(0.0, 1.0) ``` @@ -51,7 +55,7 @@ You'd do the exact same thing for `ContinuousMultivariateDistribution` and `Cont Alternatively, for `UnivariateDistribution` we can define the `minimum` and `maximum` of the distribution -```{julia; eval=false} +```{julia; eval=true} Distributions.minimum(d::CustomUniform) = 0.0 Distributions.maximum(d::CustomUniform) = 1.0 ``` @@ -60,7 +64,8 @@ and `Bijectors.jl` will return a default `Bijector` called `TruncatedBijector` w Internally, Turing basically does the following when it needs to convert a constrained distribution to an unconstrained distribution, e.g. when sampling using `HMC`: -```{julia; eval=false} +```{julia; eval=true} +dist = Gamma(2,3) b = bijector(dist) transformed_dist = transformed(dist, b) # results in distribution with transformed support + correction for logpdf ``` @@ -145,7 +150,7 @@ model = gdemo([1.5, 2.0]) can be implemented also (a bit less generally) with the macro-free version -```{julia; eval=false} +```julia using Turing # Create the model function. @@ -174,7 +179,3 @@ model = gdemo([1.5, 2.0]) ## Task Copying Turing [copies](https://github.com/JuliaLang/julia/issues/4085) Julia tasks to deliver efficient inference algorithms, but it also provides alternative slower implementation as a fallback. Task copying is enabled by default. Task copying requires us to use the `TapedTask` facility which is provided by [Libtask](https://github.com/TuringLang/Libtask.jl) to create tasks. - -```{julia; eval=false} - -``` From b86ec7f41daf9cbdc1007c490c9df79e33f703b8 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 6 Jul 2023 16:28:38 +0100 Subject: [PATCH 40/87] remove weave from deps --- .../08_multinomial-logistic-regression.jmd | 4 --- .../Manifest.toml | 30 +++++++++++++++++++ .../Project.toml | 1 + tutorials/using-turing-advanced/Manifest.toml | 30 ------------------- tutorials/using-turing-advanced/Project.toml | 1 - 5 files changed, 31 insertions(+), 35 deletions(-) diff --git a/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd b/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd index 598a7f84b..8177db953 100644 --- a/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd +++ b/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd @@ -214,8 +214,4 @@ This tutorial has demonstrated how to use Turing to perform Bayesian multinomial if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end -``` - -```julia - ``` \ No newline at end of file diff --git a/tutorials/08-multinomial-logistic-regression/Manifest.toml b/tutorials/08-multinomial-logistic-regression/Manifest.toml index fa0bec13a..b7327a55f 100644 --- a/tutorials/08-multinomial-logistic-regression/Manifest.toml +++ b/tutorials/08-multinomial-logistic-regression/Manifest.toml @@ -539,6 +539,12 @@ git-tree-sha1 = "129acf094d168394e80ee1dc4bc06ec835e510a3" uuid = "2e76f6c2-a576-52d4-95c1-20adfe4de566" version = "2.8.1+1" +[[Highlights]] +deps = ["DocStringExtensions", "InteractiveUtils", "REPL"] +git-tree-sha1 = "0341077e8a6b9fc1c2ea5edc1e93a956d2aec0c7" +uuid = "eafb193a-b7ab-5a9e-9068-77385905fa72" +version = "0.5.2" + [[HypergeometricFunctions]] deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions", "Test"] git-tree-sha1 = "709d864e3ed6e3545230601f94e11ebc65994641" @@ -907,6 +913,12 @@ git-tree-sha1 = "91a48569383df24f0fd2baf789df2aade3d0ad80" uuid = "6f286f6a-111f-5878-ab1e-185364afe411" version = "0.10.1" +[[Mustache]] +deps = ["Printf", "Tables"] +git-tree-sha1 = "821e918c170ead5298ff84bffee41dd28929a681" +uuid = "ffc61752-8dc7-55ee-8c37-f3e9cdd09e70" +version = "1.0.17" + [[NNlib]] deps = ["Adapt", "ChainRulesCore", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] git-tree-sha1 = "33ad5a19dc6730d592d8ce91c14354d758e53b0e" @@ -1322,6 +1334,12 @@ git-tree-sha1 = "e0d5bc26226ab1b7648278169858adcfbd861780" uuid = "f3b207a7-027a-5e70-b257-86293d7955fd" version = "0.15.4" +[[StringEncodings]] +deps = ["Libiconv_jll"] +git-tree-sha1 = "33c0da881af3248dafefb939a21694b97cfece76" +uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" +version = "0.3.6" + [[StringManipulation]] git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" @@ -1472,6 +1490,12 @@ git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23" uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5" version = "1.4.2" +[[Weave]] +deps = ["Base64", "Dates", "Highlights", "JSON", "Markdown", "Mustache", "Pkg", "Printf", "REPL", "RelocatableFolders", "Requires", "Serialization", "YAML"] +git-tree-sha1 = "092217eb5443926d200ae9325f103906efbb68b1" +uuid = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9" +version = "0.10.12" + [[Widgets]] deps = ["Colors", "Dates", "Observables", "OrderedCollections"] git-tree-sha1 = "fcdae142c1cfc7d89de2d11e08721d0f2f86c98a" @@ -1627,6 +1651,12 @@ git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" +[[YAML]] +deps = ["Base64", "Dates", "Printf", "StringEncodings"] +git-tree-sha1 = "e6330e4b731a6af7959673621e91645eb1356884" +uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6" +version = "0.4.9" + [[Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" diff --git a/tutorials/08-multinomial-logistic-regression/Project.toml b/tutorials/08-multinomial-logistic-regression/Project.toml index 4eacbe29f..6eddae832 100644 --- a/tutorials/08-multinomial-logistic-regression/Project.toml +++ b/tutorials/08-multinomial-logistic-regression/Project.toml @@ -7,3 +7,4 @@ RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" +Weave = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9" diff --git a/tutorials/using-turing-advanced/Manifest.toml b/tutorials/using-turing-advanced/Manifest.toml index d8ea5eb06..efbb5955e 100644 --- a/tutorials/using-turing-advanced/Manifest.toml +++ b/tutorials/using-turing-advanced/Manifest.toml @@ -519,12 +519,6 @@ git-tree-sha1 = "129acf094d168394e80ee1dc4bc06ec835e510a3" uuid = "2e76f6c2-a576-52d4-95c1-20adfe4de566" version = "2.8.1+1" -[[deps.Highlights]] -deps = ["DocStringExtensions", "InteractiveUtils", "REPL"] -git-tree-sha1 = "0341077e8a6b9fc1c2ea5edc1e93a956d2aec0c7" -uuid = "eafb193a-b7ab-5a9e-9068-77385905fa72" -version = "0.5.2" - [[deps.HypergeometricFunctions]] deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions", "Test"] git-tree-sha1 = "709d864e3ed6e3545230601f94e11ebc65994641" @@ -851,12 +845,6 @@ git-tree-sha1 = "efe9c8ecab7a6311d4b91568bd6c88897822fabe" uuid = "6f286f6a-111f-5878-ab1e-185364afe411" version = "0.10.0" -[[deps.Mustache]] -deps = ["Printf", "Tables"] -git-tree-sha1 = "821e918c170ead5298ff84bffee41dd28929a681" -uuid = "ffc61752-8dc7-55ee-8c37-f3e9cdd09e70" -version = "1.0.17" - [[deps.NNlib]] deps = ["Adapt", "ChainRulesCore", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] git-tree-sha1 = "b488fc28dfae4c8ec3d61a34a0143a4245e7b13b" @@ -1248,12 +1236,6 @@ git-tree-sha1 = "e0d5bc26226ab1b7648278169858adcfbd861780" uuid = "f3b207a7-027a-5e70-b257-86293d7955fd" version = "0.15.4" -[[deps.StringEncodings]] -deps = ["Libiconv_jll"] -git-tree-sha1 = "33c0da881af3248dafefb939a21694b97cfece76" -uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" -version = "0.3.6" - [[deps.StringManipulation]] git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" @@ -1381,12 +1363,6 @@ git-tree-sha1 = "4528479aa01ee1b3b4cd0e6faef0e04cf16466da" uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91" version = "1.25.0+0" -[[deps.Weave]] -deps = ["Base64", "Dates", "Highlights", "JSON", "Markdown", "Mustache", "Pkg", "Printf", "REPL", "RelocatableFolders", "Requires", "Serialization", "YAML"] -git-tree-sha1 = "092217eb5443926d200ae9325f103906efbb68b1" -uuid = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9" -version = "0.10.12" - [[deps.Widgets]] deps = ["Colors", "Dates", "Observables", "OrderedCollections"] git-tree-sha1 = "fcdae142c1cfc7d89de2d11e08721d0f2f86c98a" @@ -1537,12 +1513,6 @@ git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" -[[deps.YAML]] -deps = ["Base64", "Dates", "Printf", "StringEncodings"] -git-tree-sha1 = "e6330e4b731a6af7959673621e91645eb1356884" -uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6" -version = "0.4.9" - [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" diff --git a/tutorials/using-turing-advanced/Project.toml b/tutorials/using-turing-advanced/Project.toml index 575dd1d24..8a74a1d10 100644 --- a/tutorials/using-turing-advanced/Project.toml +++ b/tutorials/using-turing-advanced/Project.toml @@ -2,4 +2,3 @@ Bijectors = "76274a88-744f-5084-9051-94815aaf08c4" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" -Weave = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9" From 8787423f11aad7bf9282947c4ac2b96e5988c804 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 6 Jul 2023 16:44:09 +0100 Subject: [PATCH 41/87] improve compiler --- tutorials/for-developers-compiler/compiler.jmd | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/tutorials/for-developers-compiler/compiler.jmd b/tutorials/for-developers-compiler/compiler.jmd index 5621ccad2..1976f1770 100644 --- a/tutorials/for-developers-compiler/compiler.jmd +++ b/tutorials/for-developers-compiler/compiler.jmd @@ -287,8 +287,4 @@ Note that in order to make `md::Metadata` type stable, all the `md.vns` must hav However, in `Turing` models one cannot have this restriction, so we must use a type unstable `Metadata` if we want to use one `Metadata` instance for the whole model. This is what `UntypedVarInfo` does. A type unstable `Metadata` will still work but will have inferior performance. -To strike a balance between flexibility and performance when constructing the `spl::Sampler` instance, the model is first run by sampling the parameters in `P` from their priors using an `UntypedVarInfo`, i.e. a type unstable `Metadata` is used for all the variables. Then once all the symbols and distribution types have been identified, a `vi::TypedVarInfo` is constructed where `vi.metadata` is a `NamedTuple` mapping each symbol in `P` to a specialized instance of `Metadata`. So as long as each symbol in `P` is sampled from only one type of distributions, `vi::TypedVarInfo` will have fully concretely typed fields which brings out the peak performance of Julia. - -```{julia; eval=false} - -``` \ No newline at end of file +To strike a balance between flexibility and performance when constructing the `spl::Sampler` instance, the model is first run by sampling the parameters in `P` from their priors using an `UntypedVarInfo`, i.e. a type unstable `Metadata` is used for all the variables. Then once all the symbols and distribution types have been identified, a `vi::TypedVarInfo` is constructed where `vi.metadata` is a `NamedTuple` mapping each symbol in `P` to a specialized instance of `Metadata`. So as long as each symbol in `P` is sampled from only one type of distributions, `vi::TypedVarInfo` will have fully concretely typed fields which brings out the peak performance of Julia. \ No newline at end of file From 1ef079735c91fbebe0e6aefbf03c8135b897e262 Mon Sep 17 00:00:00 2001 From: Tor Erlend Fjelde Date: Thu, 6 Jul 2023 16:58:03 +0100 Subject: [PATCH 42/87] added julia formatting for a block which it was missing (#398) Co-authored-by: Hong Ge <3279477+yebai@users.noreply.github.com> --- tutorials/for-developers-compiler/compiler.jmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/for-developers-compiler/compiler.jmd b/tutorials/for-developers-compiler/compiler.jmd index 1976f1770..ecabea9ed 100644 --- a/tutorials/for-developers-compiler/compiler.jmd +++ b/tutorials/for-developers-compiler/compiler.jmd @@ -253,7 +253,7 @@ The easiest way to manually construct a `VarName` is to use the `@varname` macro The definition of `VarInfo` in `Turing` is: -``` +```{julia; eval=false} struct VarInfo{Tmeta, Tlogp} <: AbstractVarInfo metadata::Tmeta logp::Base.RefValue{Tlogp} @@ -287,4 +287,4 @@ Note that in order to make `md::Metadata` type stable, all the `md.vns` must hav However, in `Turing` models one cannot have this restriction, so we must use a type unstable `Metadata` if we want to use one `Metadata` instance for the whole model. This is what `UntypedVarInfo` does. A type unstable `Metadata` will still work but will have inferior performance. -To strike a balance between flexibility and performance when constructing the `spl::Sampler` instance, the model is first run by sampling the parameters in `P` from their priors using an `UntypedVarInfo`, i.e. a type unstable `Metadata` is used for all the variables. Then once all the symbols and distribution types have been identified, a `vi::TypedVarInfo` is constructed where `vi.metadata` is a `NamedTuple` mapping each symbol in `P` to a specialized instance of `Metadata`. So as long as each symbol in `P` is sampled from only one type of distributions, `vi::TypedVarInfo` will have fully concretely typed fields which brings out the peak performance of Julia. \ No newline at end of file +To strike a balance between flexibility and performance when constructing the `spl::Sampler` instance, the model is first run by sampling the parameters in `P` from their priors using an `UntypedVarInfo`, i.e. a type unstable `Metadata` is used for all the variables. Then once all the symbols and distribution types have been identified, a `vi::TypedVarInfo` is constructed where `vi.metadata` is a `NamedTuple` mapping each symbol in `P` to a specialized instance of `Metadata`. So as long as each symbol in `P` is sampled from only one type of distributions, `vi::TypedVarInfo` will have fully concretely typed fields which brings out the peak performance of Julia. From 4ec62cb7f881e3898d6a86103bdd82fc4f411150 Mon Sep 17 00:00:00 2001 From: Will Tebbutt Date: Thu, 6 Jul 2023 17:14:20 +0100 Subject: [PATCH 43/87] AbstractMCMC fixes, rename some files, and update manifests (#399) * abstract-mcmc * Rename files --- .../Manifest.toml | 1147 +++++++++-------- .../how_turing_implements_abstractmcmc.jmd | 10 +- .../Manifest.toml | 0 .../Project.toml | 0 .../dynamichmc.jmd | 0 5 files changed, 609 insertions(+), 548 deletions(-) rename tutorials/{using-turing-dynamicppl => using-turing-dynamichmc}/Manifest.toml (100%) rename tutorials/{using-turing-dynamicppl => using-turing-dynamichmc}/Project.toml (100%) rename tutorials/{using-turing-dynamicppl => using-turing-dynamichmc}/dynamichmc.jmd (100%) diff --git a/tutorials/for-developers-abstractmcmc-turing/Manifest.toml b/tutorials/for-developers-abstractmcmc-turing/Manifest.toml index ea4ec1254..7ce2a4b56 100644 --- a/tutorials/for-developers-abstractmcmc-turing/Manifest.toml +++ b/tutorials/for-developers-abstractmcmc-turing/Manifest.toml @@ -1,1591 +1,1652 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.6.7" -manifest_format = "2.0" -project_hash = "102d486b18af7951cac72d56aec7c8e37b50a316" - -[[deps.AbstractFFTs]] +[[AbstractFFTs]] deps = ["ChainRulesCore", "LinearAlgebra"] -git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" +git-tree-sha1 = "cad4c758c0038eea30394b1b671526921ca85b21" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.2.1" +version = "1.4.0" -[[deps.AbstractMCMC]] +[[AbstractMCMC]] deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] git-tree-sha1 = "b4d13c741b2bbe0e1af305a92d2f105a7caa47af" uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" version = "4.2.0" -[[deps.AbstractPPL]] +[[AbstractPPL]] deps = ["AbstractMCMC", "DensityInterface", "Random", "Setfield", "SparseArrays"] -git-tree-sha1 = "8f5d88dc15df270d8778ade9442d608939c75659" +git-tree-sha1 = "33ea6c6837332395dbf3ba336f273c9f7fcf4db9" uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf" -version = "0.5.3" +version = "0.5.4" -[[deps.AbstractTrees]] +[[AbstractTrees]] git-tree-sha1 = "faa260e4cb5aba097a73fab382dd4b5819d8ec8c" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" version = "0.4.4" -[[deps.Adapt]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "0310e08cb19f5da31d08341c6120c047598f5b9c" +[[Adapt]] +deps = ["LinearAlgebra", "Requires"] +git-tree-sha1 = "76289dc51920fdc6e0013c872ba9551d54961c24" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "3.5.0" +version = "3.6.2" -[[deps.AdvancedHMC]] +[[AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "Setfield", "Statistics", "StatsBase", "StatsFuns", "UnPack"] git-tree-sha1 = "0091e2e4d0a7125da0e3ad8c7dbff9171a921461" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" version = "0.3.6" -[[deps.AdvancedMH]] +[[AdvancedMH]] deps = ["AbstractMCMC", "Distributions", "Random", "Requires"] git-tree-sha1 = "d7a7dabeaef34e5106cdf6c2ac956e9e3f97f666" uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170" version = "0.6.8" -[[deps.AdvancedPS]] +[[AdvancedPS]] deps = ["AbstractMCMC", "Distributions", "Libtask", "Random", "StatsFuns"] git-tree-sha1 = "9ff1247be1e2aa2e740e84e8c18652bd9d55df22" uuid = "576499cb-2369-40b2-a588-c64705576edc" version = "0.3.8" -[[deps.AdvancedVI]] +[[AdvancedVI]] deps = ["Bijectors", "Distributions", "DistributionsAD", "DocStringExtensions", "ForwardDiff", "LinearAlgebra", "ProgressMeter", "Random", "Requires", "StatsBase", "StatsFuns", "Tracker"] git-tree-sha1 = "67fcc7d46c26250e89fc62798fbe07b5ee264c6f" uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c" version = "0.1.6" -[[deps.ArgCheck]] +[[ArgCheck]] git-tree-sha1 = "a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4" uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197" 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git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" -[[deps.xkbcommon_jll]] +[[xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" diff --git a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd index 5f4fb5a81..ce80b951f 100644 --- a/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd +++ b/tutorials/for-developers-abstractmcmc-turing/how_turing_implements_abstractmcmc.jmd @@ -61,7 +61,7 @@ such that the empirical distribution is a good approximation of the posterior. -## 1. Define a `Sampler` +## 1. Define a Sampler Recall the last line of the above code block: @@ -131,7 +131,7 @@ end - a **state** struct implementing `AbstractSamplerState` corresponding to your method: we cover this in the following paragraph. -### States +### States The `vi` field contains all the important information about sampling: first and foremost, the values of all the samples, but also the distributions from which they are sampled, the names of model parameters, and other metadata. As we will see below, many important steps during sampling correspond to queries or updates to `spl.state.vi`. @@ -157,7 +157,7 @@ of the sample weights is a particularly important quantity: To avoid having to compute it over and over again, `is.jl`defines an IS-specific concrete type `ISState` for sampler states, with an additional field `final_logevidence` containing ```math -\log \left( \frac 1 N \sum_{j=1}^N w_i \right). +\log \frac 1 N \sum_{j=1}^N w_i. ``` ```{julia; eval=false} @@ -174,7 +174,7 @@ The following diagram summarizes the hierarchy presented above. ![hierarchy](how_turing_implements_abstractmcmc_files/hierarchy.png) -## 2. Overload the functions used inside `mcmcsample` +## 2. Overload the functions used inside mcmcsample A lot of the things here are method-specific. However Turing also has some functions that make it easier for you to implement these functions, for examples . @@ -209,7 +209,7 @@ A crude summary, which ignores things like parallelism, is the following: You can of course implement all of these functions, but `AbstractMCMC` as well as Turing also provide default implementations for simple cases. For instance, importance sampling uses the default implementations of `sample_init!` and `bundle_samples`, which is why you don't see code for them inside `is.jl`. -## 3. Overload `assume` and `observe` +## 3. Overload assume and observe The functions mentioned above, such as `sample_init!`, `step!`, etc., must of course use information about the model in order to generate samples! In particular, these functions may need **samples from distributions** defined in the model, or to **evaluate the density of these distributions** at some values of the corresponding parameters or observations. diff --git a/tutorials/using-turing-dynamicppl/Manifest.toml b/tutorials/using-turing-dynamichmc/Manifest.toml similarity index 100% rename from tutorials/using-turing-dynamicppl/Manifest.toml rename to tutorials/using-turing-dynamichmc/Manifest.toml diff --git a/tutorials/using-turing-dynamicppl/Project.toml b/tutorials/using-turing-dynamichmc/Project.toml similarity index 100% rename from tutorials/using-turing-dynamicppl/Project.toml rename to tutorials/using-turing-dynamichmc/Project.toml diff --git a/tutorials/using-turing-dynamicppl/dynamichmc.jmd b/tutorials/using-turing-dynamichmc/dynamichmc.jmd similarity index 100% rename from tutorials/using-turing-dynamicppl/dynamichmc.jmd rename to tutorials/using-turing-dynamichmc/dynamichmc.jmd From 2f51b7b28012059f73155cf8b6956a4abc21784d Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 6 Jul 2023 18:13:58 +0100 Subject: [PATCH 44/87] no empty cell + hyperlink to bishop (#400) --- tutorials/07-poisson-regression/07_poisson-regression.jmd | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tutorials/07-poisson-regression/07_poisson-regression.jmd b/tutorials/07-poisson-regression/07_poisson-regression.jmd index 98978e66d..8b96de812 100644 --- a/tutorials/07-poisson-regression/07_poisson-regression.jmd +++ b/tutorials/07-poisson-regression/07_poisson-regression.jmd @@ -221,8 +221,3 @@ As can be seen from the numeric values and the plots above, the standard deviati if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end -``` - -```julia - -``` From 886d61796eedf032fadf4ee16896cfcfe541b363 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 6 Jul 2023 18:22:36 +0100 Subject: [PATCH 45/87] add numeric prefix to docs --- .../Manifest.toml | 0 .../Project.toml | 0 .../contributing-guide.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../contributing-style-guide.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../sampler-viz.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../hierarchy.png | Bin .../how_turing_implements_abstractmcmc.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../compiler.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../interface.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../variational_inference.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../index.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../advanced.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../autodiff.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../dynamichmc.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../using-turing-guide.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../performancetips.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../quick-start.jmd | 0 .../Manifest.toml | 0 .../Project.toml | 0 .../sampler-viz.jmd | 0 46 files changed, 0 insertions(+), 0 deletions(-) rename tutorials/{contributing-guide => docs-01-contributing-guide}/Manifest.toml (100%) rename tutorials/{contributing-guide => docs-01-contributing-guide}/Project.toml (100%) rename tutorials/{contributing-guide => docs-01-contributing-guide}/contributing-guide.jmd (100%) rename tutorials/{contributing-style-guide => docs-02-contributing-style-guide}/Manifest.toml (100%) rename tutorials/{contributing-style-guide => docs-02-contributing-style-guide}/Project.toml (100%) rename tutorials/{contributing-style-guide => docs-02-contributing-style-guide}/contributing-style-guide.jmd (100%) rename tutorials/{docs-using-turing-sampler-viz => docs-03-using-turing-sampler-viz}/Manifest.toml (100%) rename tutorials/{using-turing-sampler-viz => docs-03-using-turing-sampler-viz}/Project.toml (100%) rename tutorials/{using-turing-sampler-viz => 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tutorials/docs-14-using-turing-quick-start/quick-start.jmd diff --git a/tutorials/using-turing-sampler-viz/Manifest.toml b/tutorials/docs-15-using-turing-sampler-viz/Manifest.toml similarity index 100% rename from tutorials/using-turing-sampler-viz/Manifest.toml rename to tutorials/docs-15-using-turing-sampler-viz/Manifest.toml diff --git a/tutorials/docs-using-turing-sampler-viz/Project.toml b/tutorials/docs-15-using-turing-sampler-viz/Project.toml similarity index 100% rename from tutorials/docs-using-turing-sampler-viz/Project.toml rename to tutorials/docs-15-using-turing-sampler-viz/Project.toml diff --git a/tutorials/docs-using-turing-sampler-viz/sampler-viz.jmd b/tutorials/docs-15-using-turing-sampler-viz/sampler-viz.jmd similarity index 100% rename from tutorials/docs-using-turing-sampler-viz/sampler-viz.jmd rename to tutorials/docs-15-using-turing-sampler-viz/sampler-viz.jmd From 56f50cb7953fa62c0a84a23dda87c8ded28bc316 Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Thu, 6 Jul 2023 18:40:05 +0100 Subject: [PATCH 46/87] triger rebuild --- tutorials/docs-09-using-turing-advanced/advanced.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/docs-09-using-turing-advanced/advanced.jmd b/tutorials/docs-09-using-turing-advanced/advanced.jmd index ee558bcc0..ad0a46a85 100644 --- a/tutorials/docs-09-using-turing-advanced/advanced.jmd +++ b/tutorials/docs-09-using-turing-advanced/advanced.jmd @@ -13,7 +13,7 @@ using Distributions, Turing, Random, Bijectors ## How to Define a Customized Distribution -`Turing.jl` supports the use of distributions from the Distributions.jl package. By extension it also supports the use of customized distributions, by defining them as subtypes of `Distribution` type of the Distributions.jl package, as well as corresponding functions. +`Turing.jl` supports the use of distributions from the Distributions.jl package. By extension, it also supports the use of customized distributions by defining them as subtypes of `Distribution` type of the Distributions.jl package, as well as corresponding functions. Below shows a workflow of how to define a customized distribution, using our own implementation of a simple `Uniform` distribution as a simple example. From b3c814ad945bbbe769f9c94b31ee39f5cfe2beef Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 6 Jul 2023 19:13:27 +0100 Subject: [PATCH 47/87] add Distributions to deps --- tutorials/docs-09-using-turing-advanced/Manifest.toml | 4 ++-- tutorials/docs-09-using-turing-advanced/Project.toml | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/tutorials/docs-09-using-turing-advanced/Manifest.toml b/tutorials/docs-09-using-turing-advanced/Manifest.toml index efbb5955e..2e9eeea4c 100644 --- a/tutorials/docs-09-using-turing-advanced/Manifest.toml +++ b/tutorials/docs-09-using-turing-advanced/Manifest.toml @@ -319,9 +319,9 @@ uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" [[deps.Distributions]] deps = ["ChainRulesCore", "DensityInterface", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns", "Test"] -git-tree-sha1 = "74911ad88921455c6afcad1eefa12bd7b1724631" +git-tree-sha1 = "c2614fa3aafe03d1a44b8e16508d9be718b8095a" uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" -version = "0.25.80" +version = "0.25.89" [[deps.DistributionsAD]] deps = ["Adapt", "ChainRules", "ChainRulesCore", "Compat", "DiffRules", "Distributions", "FillArrays", "LinearAlgebra", "NaNMath", "PDMats", "Random", "Requires", "SpecialFunctions", "StaticArrays", "StatsBase", "StatsFuns", "ZygoteRules"] diff --git a/tutorials/docs-09-using-turing-advanced/Project.toml b/tutorials/docs-09-using-turing-advanced/Project.toml index 8a74a1d10..305199355 100644 --- a/tutorials/docs-09-using-turing-advanced/Project.toml +++ b/tutorials/docs-09-using-turing-advanced/Project.toml @@ -1,4 +1,5 @@ [deps] Bijectors = "76274a88-744f-5084-9051-94815aaf08c4" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" From 8c0796319f020dd9f77cf007d86821ef7a5358cc Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 6 Jul 2023 19:24:59 +0100 Subject: [PATCH 48/87] minor fixes --- .../08_multinomial-logistic-regression.jmd | 2 +- tutorials/docs-12-using-turing-guide/using-turing-guide.jmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd b/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd index 8177db953..a1ba4fea7 100644 --- a/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd +++ b/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd @@ -8,7 +8,7 @@ weave_options: [Multinomial logistic regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression) is an extension of logistic regression. Logistic regression is used to model problems in which there are exactly two possible discrete outcomes. Multinomial logistic regression is used to model problems in which there are two or more possible discrete outcomes. -In our example, we'll be using the iris dataset. The goal of the iris multiclass problem is to predict the species of a flower given measurements (in centimeters) of sepal length and width and petal length and width. There are three possible species: Iris setosa, Iris versicolor, and Iris virginica. +In our example, we'll be using the iris dataset. The iris multiclass problem aims to predict the species of a flower given measurements (in centimeters) of sepal length and width and petal length and width. There are three possible species: Iris setosa, Iris versicolor, and Iris virginica. To start, let's import all the libraries we'll need. diff --git a/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd b/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd index c818c48f4..a7637319f 100644 --- a/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd +++ b/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd @@ -185,7 +185,7 @@ using Distributed using Turing # Add four processes to use for sampling. -addprocs(4) +addprocs(4, exeflags="--project=$(Base.active_project())") # Initialize everything on all the processes. # Note: Make sure to do this after you've already loaded Turing, From a0889fc5a325e688221d9168da4480f4c57d6409 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 6 Jul 2023 19:27:39 +0100 Subject: [PATCH 49/87] Minor edits --- tutorials/docs-09-using-turing-advanced/advanced.jmd | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tutorials/docs-09-using-turing-advanced/advanced.jmd b/tutorials/docs-09-using-turing-advanced/advanced.jmd index ad0a46a85..e648fa271 100644 --- a/tutorials/docs-09-using-turing-advanced/advanced.jmd +++ b/tutorials/docs-09-using-turing-advanced/advanced.jmd @@ -178,4 +178,8 @@ model = gdemo([1.5, 2.0]) ## Task Copying -Turing [copies](https://github.com/JuliaLang/julia/issues/4085) Julia tasks to deliver efficient inference algorithms, but it also provides alternative slower implementation as a fallback. Task copying is enabled by default. Task copying requires us to use the `TapedTask` facility which is provided by [Libtask](https://github.com/TuringLang/Libtask.jl) to create tasks. +Turing [copies](https://github.com/JuliaLang/julia/issues/4085) Julia tasks to +deliver efficient inference algorithms, but it also provides alternative slower +implementation as a fallback. Task copying is enabled by default. Task copying +requires us to use the `TapedTask` facility which is provided by +[Libtask](https://github.com/TuringLang/Libtask.jl) to create tasks. From 3f0d51e91404330ced80b9808deb518f0b1a8085 Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Thu, 6 Jul 2023 20:50:54 +0100 Subject: [PATCH 50/87] Update tutorials/docs-12-using-turing-guide/using-turing-guide.jmd Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- tutorials/docs-12-using-turing-guide/using-turing-guide.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd b/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd index a7637319f..be4f07633 100644 --- a/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd +++ b/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd @@ -185,7 +185,7 @@ using Distributed using Turing # Add four processes to use for sampling. -addprocs(4, exeflags="--project=$(Base.active_project())") +addprocs(4; exeflags="--project=$(Base.active_project())") # Initialize everything on all the processes. # Note: Make sure to do this after you've already loaded Turing, From 919ba2f6883f398c876767172ab22f1ba64c2549 Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Thu, 6 Jul 2023 20:51:04 +0100 Subject: [PATCH 51/87] Update tutorials/docs-09-using-turing-advanced/advanced.jmd Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- tutorials/docs-09-using-turing-advanced/advanced.jmd | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tutorials/docs-09-using-turing-advanced/advanced.jmd b/tutorials/docs-09-using-turing-advanced/advanced.jmd index e648fa271..f938270da 100644 --- a/tutorials/docs-09-using-turing-advanced/advanced.jmd +++ b/tutorials/docs-09-using-turing-advanced/advanced.jmd @@ -178,8 +178,8 @@ model = gdemo([1.5, 2.0]) ## Task Copying -Turing [copies](https://github.com/JuliaLang/julia/issues/4085) Julia tasks to -deliver efficient inference algorithms, but it also provides alternative slower -implementation as a fallback. Task copying is enabled by default. Task copying -requires us to use the `TapedTask` facility which is provided by +Turing [copies](https://github.com/JuliaLang/julia/issues/4085) Julia tasks to +deliver efficient inference algorithms, but it also provides alternative slower +implementation as a fallback. Task copying is enabled by default. Task copying +requires us to use the `TapedTask` facility which is provided by [Libtask](https://github.com/TuringLang/Libtask.jl) to create tasks. From 51afa20ccd92b5d6152e2c10e8906563ea85cfcc Mon Sep 17 00:00:00 2001 From: jaimerz Date: Fri, 7 Jul 2023 12:18:20 +0100 Subject: [PATCH 52/87] docs draft --- .../external-samplers.jmd | 104 ++++++++++++++++++ 1 file changed, 104 insertions(+) create mode 100644 tutorials/15-external-samplers/external-samplers.jmd diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd new file mode 100644 index 000000000..74c682774 --- /dev/null +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -0,0 +1,104 @@ +--- +title: Using External Sampler +permalink: /tutorials/:name/ +mathjax: true +weave_options: + error : false +--- + +# Using External Samplers + +Turing currently wraps some of the most popular sampling libraries in the Julia ecosystem. +These wrappers allow new users to seemlessly sample statistical models without leaving Turing. +However, these wrappers often lack the latests functionalities implemented on the sampling libraries that more experienced users might want to use. +Moreover, users might want to use samplers currently not wrapped within Turing. + +For these reasons, Turing now allows users the possibility of defining their own sampler and using it to sample a Turing model. +Let us begin by defining a simple Turing model to showcase the use of external samplers. +For this demonstration we will use a 10 dimensional Neal's funnel: + +```julia +# Import libraries. +using Turing + +d = 10 +@model function funnel() + θ ~ Truncated(Normal(0, 3), -3, 3) + z ~ MvNormal(zeros(d-1), exp(θ)*I) + x ~ MvNormal(z, I) +end +``` + +Let us know sample the model to generate some observations and condition our model on them: + +```julia +Random.seed!(1) +(;x) = rand(funnel() | (θ=0,)) +model = funnel() | (;x) +``` + +Users can now use any sampler algorithm they wish to sample this model as long as it is of the type `AbstractMCMC.AbstractSampler`. +Before discussing how is this in done in practice, it is interesting to give a high level description of the process. +Imagine that we created an instance of external sampler that we will call `spl` such that `typeof(spl)<:AbstractMCMC.AbstractSampler`. +In order to avoid type ambiguity within Turing, at the moment is necessary to declare `spl` as an external sampler to Turing this is done as: + +```julia +espl = externalsampler(spl) +``` +where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. + +Arrived at this point sampling our model is as easy as: + +```julia +chain = sample(model, espl, 1_000) +``` +This process will use `AbstractMCMC` functions overloaded by Turing to generate chains identical to those generated by the native samplers. +Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. + +## AdvancedMH + +A good point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` for Metropolis Hastings methods. +Let's say that we want to use a random walk Metropolis Hastings sampler without specifying the proposal distributions. +We can do this in two different ways: + +```julia +# Importing the sampling library +using AdvancedMH + +rwmh_1 = RWMH(d) +rwmh_2 = RWMH(model) +``` + +The first method uses a multivariate Gaussian distribution with zero mean and unit variance in `d` dimensions. +The second method uses the priors of the model as a proposal. Note that the second method requires Turing to be imported. + +Let us now compare the performance and posteriors of these two samplers to the native MH sampler within Turing. +```julia +mh_chain_0 = sample(model, MH(), 1_000) +mh_chain_1 = sample(model, externalsampler(rwmh_1), 1_000) +mh_chain_2 = sample(model, externalsampler(rwmh_2), 1_000) +``` + +## AdvancedHMC + +As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. +AdvancedHMC is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. +However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within AdvancedHMC. +Thanfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in AdvancedHMC and then use to sample our Turing model. + + +## Micro-Canonical HMC + +So far we have used to Turing's support for external samplers to go beyond the capabilities of the wrappers. +Now we want to use this support to employ a sampler not supported within Turing's ecosystem yet. +We will use the recently developped Micro-Cannoncial Hamiltonian Monte Carlo (MCHMC) sampler to showcase this. +MCHMC[^1,^2] is HMC sampler that conserves energy along the exploration of the phase space. +This is achieved by simulating the dynamics of a microcanonical Hamiltonian with an additional noise term to ensure ergodicity. +The algorithm has been shown to outperform traditioanl HMC in many scenarios and it doesn't require of Metropolis adjustments. + + + +# Refences + +[^1]: Robnik et al, (Microcanonical Hamiltonian Monte Carlo)[https://arxiv.org/abs/2212.08549], 2022 +[^2]: Robnik and Seljak, (Langevine Hamiltonian Monte Carlo)[https://arxiv.org/abs/2303.18221], 2023 \ No newline at end of file From e2e1d1a2667ebdde1ea3d28182f014373bb22e71 Mon Sep 17 00:00:00 2001 From: Jaime RZ Date: Mon, 24 Jul 2023 10:59:21 +0100 Subject: [PATCH 53/87] Apply suggestions from code review Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- .../external-samplers.jmd | 44 +++++++++---------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index 74c682774..18f91fc20 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -9,12 +9,12 @@ weave_options: # Using External Samplers Turing currently wraps some of the most popular sampling libraries in the Julia ecosystem. -These wrappers allow new users to seemlessly sample statistical models without leaving Turing. +These wrappers allow new users to seemlessly sample statistical models without leaving Turing. However, these wrappers often lack the latests functionalities implemented on the sampling libraries that more experienced users might want to use. -Moreover, users might want to use samplers currently not wrapped within Turing. +Moreover, users might want to use samplers currently not wrapped within Turing. For these reasons, Turing now allows users the possibility of defining their own sampler and using it to sample a Turing model. -Let us begin by defining a simple Turing model to showcase the use of external samplers. +Let us begin by defining a simple Turing model to showcase the use of external samplers. For this demonstration we will use a 10 dimensional Neal's funnel: ```julia @@ -24,8 +24,8 @@ using Turing d = 10 @model function funnel() θ ~ Truncated(Normal(0, 3), -3, 3) - z ~ MvNormal(zeros(d-1), exp(θ)*I) - x ~ MvNormal(z, I) + z ~ MvNormal(zeros(d - 1), exp(θ) * I) + return x ~ MvNormal(z, I) end ``` @@ -33,8 +33,8 @@ Let us know sample the model to generate some observations and condition our mod ```julia Random.seed!(1) -(;x) = rand(funnel() | (θ=0,)) -model = funnel() | (;x) +(; x) = rand(funnel() | (θ=0,)) +model = funnel() | (; x) ``` Users can now use any sampler algorithm they wish to sample this model as long as it is of the type `AbstractMCMC.AbstractSampler`. @@ -43,17 +43,19 @@ Imagine that we created an instance of external sampler that we will call `spl` In order to avoid type ambiguity within Turing, at the moment is necessary to declare `spl` as an external sampler to Turing this is done as: ```julia -espl = externalsampler(spl) +espl = externalsampler(spl) ``` -where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. + +where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. Arrived at this point sampling our model is as easy as: ```julia chain = sample(model, espl, 1_000) ``` -This process will use `AbstractMCMC` functions overloaded by Turing to generate chains identical to those generated by the native samplers. -Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. + +This process will use `AbstractMCMC` functions overloaded by Turing to generate chains identical to those generated by the native samplers. +Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. ## AdvancedMH @@ -70,9 +72,10 @@ rwmh_2 = RWMH(model) ``` The first method uses a multivariate Gaussian distribution with zero mean and unit variance in `d` dimensions. -The second method uses the priors of the model as a proposal. Note that the second method requires Turing to be imported. +The second method uses the priors of the model as a proposal. Note that the second method requires Turing to be imported. + +Let us now compare the performance and posteriors of these two samplers to the native MH sampler within Turing. -Let us now compare the performance and posteriors of these two samplers to the native MH sampler within Turing. ```julia mh_chain_0 = sample(model, MH(), 1_000) mh_chain_1 = sample(model, externalsampler(rwmh_1), 1_000) @@ -81,23 +84,20 @@ mh_chain_2 = sample(model, externalsampler(rwmh_2), 1_000) ## AdvancedHMC -As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. -AdvancedHMC is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. +As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. +AdvancedHMC is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within AdvancedHMC. -Thanfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in AdvancedHMC and then use to sample our Turing model. - +Thanfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in AdvancedHMC and then use to sample our Turing model. ## Micro-Canonical HMC -So far we have used to Turing's support for external samplers to go beyond the capabilities of the wrappers. -Now we want to use this support to employ a sampler not supported within Turing's ecosystem yet. +So far we have used to Turing's support for external samplers to go beyond the capabilities of the wrappers. +Now we want to use this support to employ a sampler not supported within Turing's ecosystem yet. We will use the recently developped Micro-Cannoncial Hamiltonian Monte Carlo (MCHMC) sampler to showcase this. MCHMC[^1,^2] is HMC sampler that conserves energy along the exploration of the phase space. -This is achieved by simulating the dynamics of a microcanonical Hamiltonian with an additional noise term to ensure ergodicity. +This is achieved by simulating the dynamics of a microcanonical Hamiltonian with an additional noise term to ensure ergodicity. The algorithm has been shown to outperform traditioanl HMC in many scenarios and it doesn't require of Metropolis adjustments. - - # Refences [^1]: Robnik et al, (Microcanonical Hamiltonian Monte Carlo)[https://arxiv.org/abs/2212.08549], 2022 From b491bd57227a8d8678c090e6e1a0b29532bb0adf Mon Sep 17 00:00:00 2001 From: jaimerz Date: Mon, 24 Jul 2023 11:09:16 +0100 Subject: [PATCH 54/87] upd branch --- .../01_gaussian-mixture-model.jmd | 4 - .../02_logistic-regression.jmd | 4 - .../03_bayesian-neural-network.jmd | 5 - .../04_hidden-markov-model.jmd | 4 - .../05_linear-regression.jmd | 4 - .../06_infinite-mixture-model.jmd | 4 - .../07_poisson-regression.jmd | 1 + .../08_multinomial-logistic-regression.jmd | 2 +- .../Manifest.toml | 54 --- .../Project.toml | 1 - .../09_variational-inference.jmd | 5 - .../10_bayesian-differential-equations.jmd | 4 - .../12_gaussian-process.jmd | 58 +-- .../13_seasonal_time_series.jmd | 4 - tutorials/14-minituring/14_minituring.jmd | 5 - .../docs-00-getting-started/Manifest.toml | 362 +----------------- .../autodiff.jmd | 12 - .../dynamichmc.jmd | 7 - .../using-turing-guide.jmd | 63 --- 19 files changed, 5 insertions(+), 598 deletions(-) diff --git a/tutorials/01-gaussian-mixture-model/01_gaussian-mixture-model.jmd b/tutorials/01-gaussian-mixture-model/01_gaussian-mixture-model.jmd index 04d001c5d..bdae6b9bb 100644 --- a/tutorials/01-gaussian-mixture-model/01_gaussian-mixture-model.jmd +++ b/tutorials/01-gaussian-mixture-model/01_gaussian-mixture-model.jmd @@ -191,7 +191,3 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```julia - -``` diff --git a/tutorials/02-logistic-regression/02_logistic-regression.jmd b/tutorials/02-logistic-regression/02_logistic-regression.jmd index 330d70a65..fcd56b908 100644 --- a/tutorials/02-logistic-regression/02_logistic-regression.jmd +++ b/tutorials/02-logistic-regression/02_logistic-regression.jmd @@ -254,7 +254,3 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```julia - -``` diff --git a/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd b/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd index 60409fa9e..70e3269b4 100644 --- a/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd +++ b/tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd @@ -198,8 +198,3 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```{julia; eval=false} - - -``` diff --git a/tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd b/tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd index cbcd59c30..45b0b9f31 100644 --- a/tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd +++ b/tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd @@ -190,7 +190,3 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```julia - -``` diff --git a/tutorials/05-linear-regression/05_linear-regression.jmd b/tutorials/05-linear-regression/05_linear-regression.jmd index 510eecaa3..9e8477690 100644 --- a/tutorials/05-linear-regression/05_linear-regression.jmd +++ b/tutorials/05-linear-regression/05_linear-regression.jmd @@ -242,7 +242,3 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```julia - -``` diff --git a/tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd b/tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd index ff271e61b..1df0c1315 100644 --- a/tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd +++ b/tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd @@ -279,7 +279,3 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```julia - -``` diff --git a/tutorials/07-poisson-regression/07_poisson-regression.jmd b/tutorials/07-poisson-regression/07_poisson-regression.jmd index 3fc271ae2..82bcbcd97 100644 --- a/tutorials/07-poisson-regression/07_poisson-regression.jmd +++ b/tutorials/07-poisson-regression/07_poisson-regression.jmd @@ -221,3 +221,4 @@ As can be seen from the numeric values and the plots above, the standard deviati if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end +``` diff --git a/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd b/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd index 2bba5d61a..4ba5af9e0 100644 --- a/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd +++ b/tutorials/08-multinomial-logistic-regression/08_multinomial-logistic-regression.jmd @@ -214,4 +214,4 @@ This tutorial has demonstrated how to use Turing to perform Bayesian multinomial if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end -``` \ No newline at end of file +``` diff --git a/tutorials/08-multinomial-logistic-regression/Manifest.toml b/tutorials/08-multinomial-logistic-regression/Manifest.toml index 22209243f..f38a066b1 100644 --- a/tutorials/08-multinomial-logistic-regression/Manifest.toml +++ b/tutorials/08-multinomial-logistic-regression/Manifest.toml @@ -697,21 +697,9 @@ git-tree-sha1 = "129acf094d168394e80ee1dc4bc06ec835e510a3" uuid = "2e76f6c2-a576-52d4-95c1-20adfe4de566" version = "2.8.1+1" -<<<<<<< HEAD -[[Highlights]] -deps = ["DocStringExtensions", "InteractiveUtils", "REPL"] -git-tree-sha1 = "0341077e8a6b9fc1c2ea5edc1e93a956d2aec0c7" -uuid = "eafb193a-b7ab-5a9e-9068-77385905fa72" -version = "0.5.2" - -[[HypergeometricFunctions]] -deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions", "Test"] -git-tree-sha1 = "709d864e3ed6e3545230601f94e11ebc65994641" -======= [[deps.HypergeometricFunctions]] deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] git-tree-sha1 = "83e95aaab9dc184a6dcd9c4c52aa0dc26cd14a1d" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" version = "0.3.21" @@ -1141,21 +1129,9 @@ git-tree-sha1 = "68bf5103e002c44adfd71fea6bd770b3f0586843" uuid = "6f286f6a-111f-5878-ab1e-185364afe411" version = "0.10.2" -<<<<<<< HEAD -[[Mustache]] -deps = ["Printf", "Tables"] -git-tree-sha1 = "821e918c170ead5298ff84bffee41dd28929a681" -uuid = "ffc61752-8dc7-55ee-8c37-f3e9cdd09e70" -version = "1.0.17" - -[[NNlib]] -deps = ["Adapt", "ChainRulesCore", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] -git-tree-sha1 = "33ad5a19dc6730d592d8ce91c14354d758e53b0e" -======= [[deps.NNlib]] deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] git-tree-sha1 = "3d42748c725c3f088bcda47fa2aca89e74d59d22" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" version = "0.9.4" @@ -1653,17 +1629,7 @@ git-tree-sha1 = "9115a29e6c2cf66cf213ccc17ffd61e27e743b24" uuid = "f3b207a7-027a-5e70-b257-86293d7955fd" version = "0.15.6" -<<<<<<< HEAD -[[StringEncodings]] -deps = ["Libiconv_jll"] -git-tree-sha1 = "33c0da881af3248dafefb939a21694b97cfece76" -uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" -version = "0.3.6" - -[[StringManipulation]] -======= [[deps.StringManipulation]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" version = "0.3.0" @@ -1861,17 +1827,7 @@ git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23" uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5" version = "1.4.2" -<<<<<<< HEAD -[[Weave]] -deps = ["Base64", "Dates", "Highlights", "JSON", "Markdown", "Mustache", "Pkg", "Printf", "REPL", "RelocatableFolders", "Requires", "Serialization", "YAML"] -git-tree-sha1 = "092217eb5443926d200ae9325f103906efbb68b1" -uuid = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9" -version = "0.10.12" - -[[Widgets]] -======= [[deps.Widgets]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Colors", "Dates", "Observables", "OrderedCollections"] git-tree-sha1 = "fcdae142c1cfc7d89de2d11e08721d0f2f86c98a" uuid = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62" @@ -2032,17 +1988,7 @@ git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.5.0+0" -<<<<<<< HEAD -[[YAML]] -deps = ["Base64", "Dates", "Printf", "StringEncodings"] -git-tree-sha1 = "e6330e4b731a6af7959673621e91645eb1356884" -uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6" -version = "0.4.9" - -[[Zlib_jll]] -======= [[deps.Zlib_jll]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" version = "1.2.13+0" diff --git a/tutorials/08-multinomial-logistic-regression/Project.toml b/tutorials/08-multinomial-logistic-regression/Project.toml index 6eddae832..4eacbe29f 100644 --- a/tutorials/08-multinomial-logistic-regression/Project.toml +++ b/tutorials/08-multinomial-logistic-regression/Project.toml @@ -7,4 +7,3 @@ RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" -Weave = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9" diff --git a/tutorials/09-variational-inference/09_variational-inference.jmd b/tutorials/09-variational-inference/09_variational-inference.jmd index 7cb78b8bd..45b50bf4c 100644 --- a/tutorials/09-variational-inference/09_variational-inference.jmd +++ b/tutorials/09-variational-inference/09_variational-inference.jmd @@ -868,8 +868,3 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```{julia; eval=false} - - -``` diff --git a/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd b/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd index bbb3e8a09..bd985d4c5 100644 --- a/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd +++ b/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd @@ -447,7 +447,3 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```julia - -``` diff --git a/tutorials/12-gaussian-process/12_gaussian-process.jmd b/tutorials/12-gaussian-process/12_gaussian-process.jmd index 986138201..d9a7559c4 100644 --- a/tutorials/12-gaussian-process/12_gaussian-process.jmd +++ b/tutorials/12-gaussian-process/12_gaussian-process.jmd @@ -18,11 +18,7 @@ Importantly, it provides the advantage that the linear mappings from the embedde Let's start by loading some dependencies. -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 using Turing using AbstractGPs using FillArrays @@ -45,11 +41,7 @@ although this is an active area of research. We will briefly touch on some ways to speed things up at the end of this tutorial. We transform the original data with non-linear operations in order to demonstrate the power of GPs to work on non-linear relationships, while keeping the problem reasonably small. -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 data = dataset("datasets", "iris") species = data[!, "Species"] index = shuffle(1:150) @@ -74,11 +66,7 @@ Indeed, pPCA is basically equivalent to running the GPLVM model with an automati First, we re-introduce the pPCA model (see the tutorial on pPCA for details) -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 @model function pPCA(x) # Dimensionality of the problem. N, D = size(x) @@ -97,11 +85,7 @@ end; We define two different kernels, a simple linear kernel with an Automatic Relevance Determination transform and a squared exponential kernel. -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 linear_kernel(α) = LinearKernel() ∘ ARDTransform(α) sekernel(α, σ) = σ * SqExponentialKernel() ∘ ARDTransform(α); ``` @@ -109,11 +93,7 @@ sekernel(α, σ) = σ * SqExponentialKernel() ∘ ARDTransform(α); And here is the GPLVM model. We create separate models for the two types of kernel. -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 @model function GPLVM_linear(Y, K) # Dimensionality of the problem. N, D = size(Y) @@ -154,11 +134,7 @@ end; end; ``` -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # Standard GPs don't scale very well in n, so we use a small subsample for the purpose of this tutorial n_data = 40 # number of features to use from dataset @@ -167,20 +143,12 @@ n_features = 4 ndim = 4; ``` -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 ppca = pPCA(dat[1:n_data, 1:n_features]) chain_ppca = sample(ppca, NUTS{Turing.ReverseDiffAD{true}}(), 1000); ``` -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # we extract the posterior mean estimates of the parameters from the chain z_mean = reshape(mean(group(chain_ppca, :z))[:, 2], (n_features, n_data)) scatter(z_mean[1, :], z_mean[2, :]; group=labels[1:n_data], xlabel=L"z_1", ylabel=L"z_2") @@ -193,20 +161,12 @@ using pPCA (see the pPCA tutorial). Let's try the same with our linear kernel GPLVM model. -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 gplvm_linear = GPLVM_linear(dat[1:n_data, 1:n_features], ndim) chain_linear = sample(gplvm_linear, NUTS{Turing.ReverseDiffAD{true}}(), 500); ``` -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # we extract the posterior mean estimates of the parameters from the chain z_mean = reshape(mean(group(chain_linear, :Z))[:, 2], (n_features, n_data)) alpha_mean = mean(group(chain_linear, :α))[:, 2] @@ -226,20 +186,12 @@ We can see that similar to the pPCA case, the linear kernel GPLVM fails to disti Finally, we demonstrate that by changing the kernel to a non-linear function, we are able to separate the data again. -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 gplvm = GPLVM(dat[1:n_data, 1:n_features], ndim) chain_gplvm = sample(gplvm, NUTS{Turing.ReverseDiffAD{true}}(), 500); ``` -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # we extract the posterior mean estimates of the parameters from the chain z_mean = reshape(mean(group(chain_gplvm, :Z))[:, 2], (ndim, n_data)) alpha_mean = mean(group(chain_gplvm, :α))[:, 2] @@ -254,11 +206,7 @@ scatter( ) ``` -<<<<<<< HEAD -```{julia; eval=false} -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 let @assert abs( mean(z_mean[alpha1, labels[1:n_data] .== "setosa"]) - @@ -269,12 +217,8 @@ end Now, the split between the two groups is visible again. -```{julia; eval=false} +```julia, echo=false, skip="notebook", tangle=false if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```julia - -``` diff --git a/tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd b/tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd index f080df6c1..0bce7f561 100644 --- a/tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd +++ b/tutorials/13-seasonal-time-series/13_seasonal_time_series.jmd @@ -344,7 +344,3 @@ if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ``` - -```julia - -``` diff --git a/tutorials/14-minituring/14_minituring.jmd b/tutorials/14-minituring/14_minituring.jmd index 05bd82696..4924de76b 100644 --- a/tutorials/14-minituring/14_minituring.jmd +++ b/tutorials/14-minituring/14_minituring.jmd @@ -287,13 +287,8 @@ sample(turing_m(3.0), MH(ScalMat(2, 1.0)), 1_000_000) As you can see, with our simple probabilistic programming language and custom samplers we get similar results as Turing. -<<<<<<< HEAD -```julia - -======= ```julia, echo=false, skip="notebook", tangle=false if isdefined(Main, :TuringTutorials) Main.TuringTutorials.tutorial_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file]) end ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 ``` diff --git a/tutorials/docs-00-getting-started/Manifest.toml b/tutorials/docs-00-getting-started/Manifest.toml index 1cc66e8ae..6de4cc00a 100644 --- a/tutorials/docs-00-getting-started/Manifest.toml +++ b/tutorials/docs-00-getting-started/Manifest.toml @@ -41,13 +41,10 @@ deps = ["LinearAlgebra", "Requires"] git-tree-sha1 = "76289dc51920fdc6e0013c872ba9551d54961c24" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" version = "3.6.2" -<<<<<<< HEAD -======= weakdeps = ["StaticArrays"] [deps.Adapt.extensions] AdaptStaticArraysExt = "StaticArrays" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "ProgressMeter", "Random", "Requires", "Setfield", "SimpleUnPack", "Statistics", "StatsBase", "StatsFuns"] @@ -110,30 +107,6 @@ git-tree-sha1 = "5ba6c757e8feccf03a1554dfaf3e26b3cfc7fd5e" uuid = "68821587-b530-5797-8361-c406ea357684" version = "3.5.1+1" -<<<<<<< HEAD -[[ArrayInterface]] -deps = ["Adapt", "LinearAlgebra", "Requires", "SparseArrays", "SuiteSparse"] -git-tree-sha1 = "d3f758863a47ceef2248d136657cb9c033603641" -uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9" -version = "7.4.8" - -[[ArrayInterfaceCore]] -deps = ["LinearAlgebra", "SnoopPrecompile", "SparseArrays", "SuiteSparse"] -git-tree-sha1 = "e5f08b5689b1aad068e01751889f2f615c7db36d" -uuid = "30b0a656-2188-435a-8636-2ec0e6a096e2" -version = "0.1.29" - -[[ArrayInterfaceStaticArraysCore]] -deps = ["ArrayInterfaceCore", "LinearAlgebra", "StaticArraysCore"] -git-tree-sha1 = "01a9f8e6cfc2bfdd01d333f70b8014a04893103c" -uuid = "dd5226c6-a4d4-4bc7-8575-46859f9c95b9" -version = "0.1.4" - -[[Artifacts]] -uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" - -[[Atomix]] -======= [[deps.ArrayInterface]] deps = ["Adapt", "LinearAlgebra", "Requires", "SparseArrays", "SuiteSparse"] git-tree-sha1 = "f83ec24f76d4c8f525099b2ac475fc098138ec31" @@ -160,17 +133,12 @@ version = "7.4.11" uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" [[deps.Atomix]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["UnsafeAtomics"] git-tree-sha1 = "c06a868224ecba914baa6942988e2f2aade419be" uuid = "a9b6321e-bd34-4604-b9c9-b65b8de01458" version = "0.1.0" -<<<<<<< HEAD -[[AxisAlgorithms]] -======= [[deps.AxisAlgorithms]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"] git-tree-sha1 = "66771c8d21c8ff5e3a93379480a2307ac36863f7" uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950" @@ -182,19 +150,12 @@ git-tree-sha1 = "16351be62963a67ac4083f748fdb3cca58bfd52f" uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9" version = "0.4.7" -<<<<<<< HEAD -[[BangBang]] -======= [[deps.BangBang]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Compat", "ConstructionBase", "InitialValues", "LinearAlgebra", "Requires", "Setfield", "Tables"] git-tree-sha1 = "e28912ce94077686443433c2800104b061a827ed" uuid = "198e06fe-97b7-11e9-32a5-e1d131e6ad66" version = "0.3.39" -<<<<<<< HEAD -[[Base64]] -======= [deps.BangBang.extensions] BangBangChainRulesCoreExt = "ChainRulesCore" BangBangDataFramesExt = "DataFrames" @@ -210,7 +171,6 @@ version = "0.3.39" TypedTables = "9d95f2ec-7b3d-5a63-8d20-e2491e220bb9" [[deps.Base64]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" [[deps.Baselet]] @@ -251,20 +211,12 @@ git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2" uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0" version = "1.0.8+0" -<<<<<<< HEAD -[[CEnum]] -======= [[deps.CEnum]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 git-tree-sha1 = "eb4cb44a499229b3b8426dcfb5dd85333951ff90" uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82" version = "0.4.2" -<<<<<<< HEAD -[[Cairo_jll]] -======= [[deps.Cairo_jll]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] git-tree-sha1 = "4b859a208b2397a7a623a03449e4636bdb17bcf2" uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a" @@ -278,15 +230,9 @@ version = "0.5.1" [[deps.ChainRules]] deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "Statistics", "StructArrays"] -<<<<<<< HEAD -git-tree-sha1 = "61549d9b52c88df34d21bd306dba1d43bb039c87" -uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" -version = "1.51.0" -======= git-tree-sha1 = "f98ae934cd677d51d2941088849f0bf2f59e6f6e" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" version = "1.53.0" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra", "SparseArrays"] @@ -294,13 +240,6 @@ git-tree-sha1 = "e30f2f4e20f7f186dc36529910beaedc60cfa644" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" version = "1.16.0" -<<<<<<< HEAD -[[ChangesOfVariables]] -deps = ["LinearAlgebra", "Test"] -git-tree-sha1 = "f84967c4497e0e1955f9a582c232b02847c5f589" -uuid = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0" -version = "0.1.7" -======= [[deps.ChangesOfVariables]] deps = ["LinearAlgebra", "Test"] git-tree-sha1 = "2fba81a302a7be671aefe194f0525ef231104e7f" @@ -310,19 +249,12 @@ weakdeps = ["InverseFunctions"] [deps.ChangesOfVariables.extensions] ChangesOfVariablesInverseFunctionsExt = "InverseFunctions" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.Clustering]] deps = ["Distances", "LinearAlgebra", "NearestNeighbors", "Printf", "Random", "SparseArrays", "Statistics", "StatsBase"] -<<<<<<< HEAD -git-tree-sha1 = "a6e6ce44a1e0a781772fc795fb7343b1925e9898" -uuid = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" -version = "0.15.2" -======= git-tree-sha1 = "42fe66dbc8f1d09a44aa87f18d26926d06a35f84" uuid = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" version = "0.15.3" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] @@ -330,19 +262,11 @@ git-tree-sha1 = "02aa26a4cf76381be7f66e020a3eddeb27b0a092" uuid = "944b1d66-785c-5afd-91f1-9de20f533193" version = "0.7.2" -<<<<<<< HEAD -[[ColorSchemes]] -deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"] -git-tree-sha1 = "be6ab11021cd29f0344d5c4357b163af05a48cba" -uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4" -version = "3.21.0" -======= [[deps.ColorSchemes]] deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"] git-tree-sha1 = "dd3000d954d483c1aad05fe1eb9e6a715c97013e" uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4" version = "3.22.0" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.ColorTypes]] deps = ["FixedPointNumbers", "Random"] @@ -371,11 +295,7 @@ git-tree-sha1 = "08c8b6831dc00bfea825826be0bc8336fc369860" uuid = "861a8166-3701-5b0c-9a16-15d98fcdc6aa" version = "1.0.2" -<<<<<<< HEAD -[[CommonSolve]] -======= [[deps.CommonSolve]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 git-tree-sha1 = "0eee5eb66b1cf62cd6ad1b460238e60e4b09400c" uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2" version = "0.2.4" @@ -401,24 +321,11 @@ deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" version = "1.0.5+0" -<<<<<<< HEAD -[[CompositionsBase]] -git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" -uuid = "a33af91c-f02d-484b-be07-31d278c5ca2b" -version = "0.1.2" - -[[ConcurrentUtilities]] -deps = ["Serialization", "Sockets"] -git-tree-sha1 = "96d823b94ba8d187a6d8f0826e731195a74b90e9" -uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" -version = "2.2.0" -======= [[deps.CompositionsBase]] git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" uuid = "a33af91c-f02d-484b-be07-31d278c5ca2b" version = "0.1.2" weakdeps = ["InverseFunctions"] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [deps.CompositionsBase.extensions] CompositionsBaseInverseFunctionsExt = "InverseFunctions" @@ -437,16 +344,10 @@ version = "0.1.2" [[deps.ConstructionBase]] deps = ["LinearAlgebra"] -<<<<<<< HEAD -git-tree-sha1 = "738fec4d684a9a6ee9598a8bfee305b26831f28c" -uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" -version = "1.5.2" -======= git-tree-sha1 = "fe2838a593b5f776e1597e086dcd47560d94e816" uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" version = "1.5.3" weakdeps = ["IntervalSets", "StaticArrays"] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [deps.ConstructionBase.extensions] ConstructionBaseIntervalSetsExt = "IntervalSets" @@ -462,11 +363,7 @@ git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15" uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" version = "4.1.1" -<<<<<<< HEAD -[[DataAPI]] -======= [[deps.DataAPI]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 git-tree-sha1 = "8da84edb865b0b5b0100c0666a9bc9a0b71c553c" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" version = "1.15.0" @@ -482,11 +379,7 @@ git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6" uuid = "e2d170a0-9d28-54be-80f0-106bbe20a464" version = "1.0.0" -<<<<<<< HEAD -[[Dates]] -======= [[deps.Dates]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Printf"] uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" @@ -529,19 +422,6 @@ version = "0.10.8" deps = ["Random", "Serialization", "Sockets"] uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" -<<<<<<< HEAD -[[Distributions]] -deps = ["ChainRulesCore", "DensityInterface", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"] -git-tree-sha1 = "c72970914c8a21b36bbc244e9df0ed1834a0360b" -uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" -version = "0.25.95" - -[[DistributionsAD]] -deps = ["Adapt", "ChainRules", "ChainRulesCore", "Compat", "Distributions", "FillArrays", "LinearAlgebra", "PDMats", "Random", "Requires", "SpecialFunctions", "StaticArrays", "StatsFuns", "ZygoteRules"] -git-tree-sha1 = "1fb4fedd5a407243d535cc50c8c803c2fce0dc26" -uuid = "ced4e74d-a319-5a8a-b0ac-84af2272839c" -version = "0.6.49" -======= [[deps.Distributions]] deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"] git-tree-sha1 = "e76a3281de2719d7c81ed62c6ea7057380c87b1d" @@ -564,7 +444,6 @@ version = "0.6.51" DistributionsADLazyArraysExt = "LazyArrays" DistributionsADReverseDiffExt = "ReverseDiff" DistributionsADTrackerExt = "Tracker" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [deps.DistributionsAD.weakdeps] ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" @@ -574,9 +453,9 @@ version = "0.6.51" [[deps.DocStringExtensions]] deps = ["LibGit2"] -git-tree-sha1 = "b19534d1895d702889b219c382a6e18010797f0b" +git-tree-sha1 = "2fb1e02f2b635d0845df5d7c167fec4dd739b00d" uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" -version = "0.8.6" +version = "0.9.3" [[deps.Downloads]] deps = ["ArgTools", "FileWatching", "LibCURL", "NetworkOptions"] @@ -637,15 +516,9 @@ version = "4.4.2+2" [[deps.FFTW]] deps = ["AbstractFFTs", "FFTW_jll", "LinearAlgebra", "MKL_jll", "Preferences", "Reexport"] -<<<<<<< HEAD -git-tree-sha1 = "06bf20fcecd258eccf9a6ef7b99856a4dfe7b64c" -uuid = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341" -version = "1.7.0" -======= git-tree-sha1 = "b4fbdd20c889804969571cc589900803edda16b7" uuid = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341" version = "1.7.1" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.FFTW_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -658,15 +531,9 @@ uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" [[deps.FillArrays]] deps = ["LinearAlgebra", "Random", "SparseArrays", "Statistics"] -<<<<<<< HEAD -git-tree-sha1 = "7072f1e3e5a8be51d525d64f63d3ec1287ff2790" -uuid = "1a297f60-69ca-5386-bcde-b61e274b549b" -version = "0.13.11" -======= git-tree-sha1 = "f0af9b12329a637e8fba7d6543f915fff6ba0090" uuid = "1a297f60-69ca-5386-bcde-b61e274b549b" version = "1.4.2" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.FixedPointNumbers]] deps = ["Statistics"] @@ -743,17 +610,6 @@ version = "0.1.5" [[deps.GR]] deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Pkg", "Preferences", "Printf", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "UUIDs", "p7zip_jll"] -<<<<<<< HEAD -git-tree-sha1 = "8b8a2fd4536ece6e554168c21860b6820a8a83db" -uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" -version = "0.72.7" - -[[GR_jll]] -deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt5Base_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "19fad9cd9ae44847fe842558a744748084a722d1" -uuid = "d2c73de3-f751-5644-a686-071e5b155ba9" -version = "0.72.7+0" -======= git-tree-sha1 = "d73afa4a2bb9de56077242d98cf763074ab9a970" uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" version = "0.72.9" @@ -763,7 +619,6 @@ deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", " git-tree-sha1 = "f61f768bf090d97c532d24b64e07b237e9bb7b6b" uuid = "d2c73de3-f751-5644-a686-071e5b155ba9" version = "0.72.9+0" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.Gettext_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"] @@ -788,19 +643,11 @@ git-tree-sha1 = "53bb909d1151e57e2484c3d1b53e19552b887fb2" uuid = "42e2da0e-8278-4e71-bc24-59509adca0fe" version = "1.0.2" -<<<<<<< HEAD -[[HTTP]] -deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] -git-tree-sha1 = "5e77dbf117412d4f164a464d610ee6050cc75272" -uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "1.9.6" -======= [[deps.HTTP]] deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] git-tree-sha1 = "cb56ccdd481c0dd7f975ad2b3b62d9eda088f7e2" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" version = "1.9.14" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.HarfBuzz_jll]] deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg"] @@ -808,21 +655,6 @@ git-tree-sha1 = "129acf094d168394e80ee1dc4bc06ec835e510a3" uuid = "2e76f6c2-a576-52d4-95c1-20adfe4de566" version = "2.8.1+1" -<<<<<<< HEAD -[[HypergeometricFunctions]] -deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] -git-tree-sha1 = "0ec02c648befc2f94156eaef13b0f38106212f3f" -uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" -version = "0.3.17" - -[[IRTools]] -deps = ["InteractiveUtils", "MacroTools", "Test"] -git-tree-sha1 = "eac00994ce3229a464c2847e956d77a2c64ad3a5" -uuid = "7869d1d1-7146-5819-86e3-90919afe41df" -version = "0.4.10" - -[[InitialValues]] -======= [[deps.HypergeometricFunctions]] deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] git-tree-sha1 = "83e95aaab9dc184a6dcd9c4c52aa0dc26cd14a1d" @@ -830,7 +662,6 @@ uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" version = "0.3.21" [[deps.InitialValues]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 git-tree-sha1 = "4da0f88e9a39111c2fa3add390ab15f3a44f3ca3" uuid = "22cec73e-a1b8-11e9-2c92-598750a2cf9c" version = "0.3.1" @@ -865,15 +696,9 @@ version = "0.7.4" [[deps.InverseFunctions]] deps = ["Test"] -<<<<<<< HEAD -git-tree-sha1 = "6667aadd1cdee2c6cd068128b3d226ebc4fb0c67" -uuid = "3587e190-3f89-42d0-90ee-14403ec27112" -version = "0.1.9" -======= git-tree-sha1 = "eabe3125edba5c9c10b60a160b1779a000dc8b29" uuid = "3587e190-3f89-42d0-90ee-14403ec27112" version = "0.1.11" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.InvertedIndices]] git-tree-sha1 = "0dc7b50b8d436461be01300fd8cd45aa0274b038" @@ -919,15 +744,6 @@ git-tree-sha1 = "6f2675ef130a300a112286de91973805fcc5ffbc" uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8" version = "2.1.91+0" -<<<<<<< HEAD -[[KernelAbstractions]] -deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] -git-tree-sha1 = "47be64f040a7ece575c2b5f53ca6da7b548d69f4" -uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c" -version = "0.9.4" - -[[KernelDensity]] -======= [[deps.KernelAbstractions]] deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] git-tree-sha1 = "6d08ca80b621635fed9cdfeb9a4280a574020bf3" @@ -941,7 +757,6 @@ version = "0.9.7" EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869" [[deps.KernelDensity]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Distributions", "DocStringExtensions", "FFTW", "Interpolations", "StatsBase"] git-tree-sha1 = "90442c50e202a5cdf21a7899c66b240fdef14035" uuid = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b" @@ -959,21 +774,6 @@ git-tree-sha1 = "bf36f528eec6634efc60d7ec062008f171071434" uuid = "88015f11-f218-50d7-93a8-a6af411a945d" version = "3.0.0+1" -<<<<<<< HEAD -[[LLVM]] -deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"] -git-tree-sha1 = "5007c1421563108110bbd57f63d8ad4565808818" -uuid = "929cbde3-209d-540e-8aea-75f648917ca0" -version = "5.2.0" - -[[LLVMExtra_jll]] -deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] -git-tree-sha1 = "1222116d7313cdefecf3d45a2bc1a89c4e7c9217" -uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab" -version = "0.0.22+0" - -[[LLVMOpenMP_jll]] -======= [[deps.LLVM]] deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"] git-tree-sha1 = "8695a49bfe05a2dc0feeefd06b4ca6361a018729" @@ -987,17 +787,12 @@ uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab" version = "0.0.23+0" [[deps.LLVMOpenMP_jll]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "f689897ccbe049adb19a065c495e75f372ecd42b" uuid = "1d63c593-3942-5779-bab2-d838dc0a180e" version = "15.0.4+0" -<<<<<<< HEAD -[[LRUCache]] -======= [[deps.LRUCache]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 git-tree-sha1 = "48c10e3cc27e30de82463c27bef0b8bdbd1dc634" uuid = "8ac3fa9e-de4c-5943-b1dc-09c6b5f20637" version = "1.4.1" @@ -1018,8 +813,6 @@ deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdow git-tree-sha1 = "f428ae552340899a935973270b8d98e5a31c49fe" uuid = "23fbe1c1-3f47-55db-b15f-69d7ec21a316" version = "0.16.1" -<<<<<<< HEAD -======= [deps.Latexify.extensions] DataFramesExt = "DataFrames" @@ -1034,7 +827,6 @@ deps = ["MacroTools"] git-tree-sha1 = "1370f8202dac30758f3c345f9909b97f53d87d3f" uuid = "50d2b5c4-7a5e-59d5-8109-a42b560f39c0" version = "0.15.1" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.LazyArtifacts]] deps = ["Artifacts", "Pkg"] @@ -1138,13 +930,6 @@ git-tree-sha1 = "1fc9bc3d74d6bc828d09880d8ceb12d231188e24" uuid = "996a588d-648d-4e1f-a8f0-a84b347e47b1" version = "1.6.0" -<<<<<<< HEAD -[[LogExpFunctions]] -deps = ["ChainRulesCore", "ChangesOfVariables", "DocStringExtensions", "InverseFunctions", "IrrationalConstants", "LinearAlgebra"] -git-tree-sha1 = "c3ce8e7420b3a6e071e0fe4745f5d4300e37b13f" -uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" -version = "0.3.24" -======= [deps.LogDensityProblemsAD.extensions] LogDensityProblemsADADTypesExt = "ADTypes" LogDensityProblemsADEnzymeExt = "Enzyme" @@ -1171,7 +956,6 @@ git-tree-sha1 = "c3ce8e7420b3a6e071e0fe4745f5d4300e37b13f" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" version = "0.3.24" weakdeps = ["ChainRulesCore", "ChangesOfVariables", "InverseFunctions"] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [deps.LogExpFunctions.extensions] LogExpFunctionsChainRulesCoreExt = "ChainRulesCore" @@ -1217,11 +1001,7 @@ git-tree-sha1 = "42324d08725e200c23d4dfb549e0d5d89dede2d2" uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" version = "0.5.10" -<<<<<<< HEAD -[[MappedArrays]] -======= [[deps.MappedArrays]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 git-tree-sha1 = "2dab0221fe2b0f2cb6754eaa743cc266339f527e" uuid = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900" version = "0.4.2" @@ -1271,13 +1051,6 @@ git-tree-sha1 = "68bf5103e002c44adfd71fea6bd770b3f0586843" uuid = "6f286f6a-111f-5878-ab1e-185364afe411" version = "0.10.2" -<<<<<<< HEAD -[[NNlib]] -deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] -git-tree-sha1 = "72240e3f5ca031937bd536182cb2c031da5f46dd" -uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" -version = "0.8.21" -======= [[deps.NNlib]] deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] git-tree-sha1 = "3d42748c725c3f088bcda47fa2aca89e74d59d22" @@ -1288,7 +1061,6 @@ version = "0.9.4" NNlibAMDGPUExt = "AMDGPU" NNlibCUDACUDNNExt = ["CUDA", "cuDNN"] NNlibCUDAExt = "CUDA" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [deps.NNlib.weakdeps] AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e" @@ -1355,11 +1127,7 @@ git-tree-sha1 = "51901a49222b09e3743c65b8847687ae5fc78eb2" uuid = "4d8831e6-92b7-49fb-bdf8-b643e874388c" version = "1.4.1" -<<<<<<< HEAD -[[OpenSSL_jll]] -======= [[deps.OpenSSL_jll]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Artifacts", "JLLWrappers", "Libdl"] git-tree-sha1 = "1aa4b74f80b01c6bc2b89992b861b5f210e665b5" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" @@ -1383,17 +1151,10 @@ git-tree-sha1 = "51a08fb14ec28da2ec7a927c4337e4332c2a4720" uuid = "91d4177d-7536-5919-b921-800302f37372" version = "1.3.2+0" -<<<<<<< HEAD -[[OrderedCollections]] -git-tree-sha1 = "d321bf2de576bf25ec4d3e4360faca399afca282" -uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" -version = "1.6.0" -======= [[deps.OrderedCollections]] git-tree-sha1 = "1791b503101162477bf11e951caf6983e57cd8c2" uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" version = "1.6.1" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.PCRE2_jll]] deps = ["Artifacts", "Libdl"] @@ -1406,30 +1167,18 @@ git-tree-sha1 = "67eae2738d63117a196f497d7db789821bce61d1" uuid = "90014a1f-27ba-587c-ab20-58faa44d9150" version = "0.11.17" -<<<<<<< HEAD -[[Parsers]] -deps = ["Dates", "PrecompileTools", "UUIDs"] -git-tree-sha1 = "5a6ab2f64388fd1175effdf73fe5933ef1e0bac0" -uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" -version = "2.7.0" -======= [[deps.Parsers]] deps = ["Dates", "PrecompileTools", "UUIDs"] git-tree-sha1 = "4b2e829ee66d4218e0cef22c0a64ee37cf258c29" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" version = "2.7.1" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.Pipe]] git-tree-sha1 = "6842804e7867b115ca9de748a0cf6b364523c16d" uuid = "b98c9c47-44ae-5843-9183-064241ee97a0" version = "1.3.0" -<<<<<<< HEAD -[[Pixman_jll]] -======= [[deps.Pixman_jll]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LLVMOpenMP_jll", "Libdl"] git-tree-sha1 = "64779bc4c9784fee475689a1752ef4d5747c5e87" uuid = "30392449-352a-5448-841d-b1acce4e97dc" @@ -1446,29 +1195,18 @@ git-tree-sha1 = "1f03a2d339f42dca4a4da149c7e15e9b896ad899" uuid = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a" version = "3.1.0" -<<<<<<< HEAD -[[PlotUtils]] -======= [[deps.PlotUtils]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"] git-tree-sha1 = "f92e1315dadf8c46561fb9396e525f7200cdc227" uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043" version = "1.3.5" -<<<<<<< HEAD -[[Plots]] -======= [[deps.Plots]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Preferences", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"] git-tree-sha1 = "75ca67b2c6512ad2d0c767a7cfc55e75075f8bbc" uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" version = "1.38.16" -<<<<<<< HEAD -[[PrecompileTools]] -======= [deps.Plots.extensions] FileIOExt = "FileIO" GeometryBasicsExt = "GeometryBasics" @@ -1484,7 +1222,6 @@ version = "1.38.16" Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d" [[deps.PrecompileTools]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Preferences"] git-tree-sha1 = "9673d39decc5feece56ef3940e5dafba15ba0f81" uuid = "aea7be01-6a6a-4083-8856-8a6e6704d82a" @@ -1498,15 +1235,9 @@ version = "1.4.0" [[deps.PrettyTables]] deps = ["Crayons", "Formatting", "LaTeXStrings", "Markdown", "Reexport", "StringManipulation", "Tables"] -<<<<<<< HEAD -git-tree-sha1 = "213579618ec1f42dea7dd637a42785a608b1ea9c" -uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" -version = "2.2.4" -======= git-tree-sha1 = "542b1bd03329c1d235110f96f1bb0eeffc48a87d" uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" version = "2.2.6" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.Printf]] deps = ["Unicode"] @@ -1566,13 +1297,10 @@ deps = ["Requires"] git-tree-sha1 = "1342a47bf3260ee108163042310d26f2be5ec90b" uuid = "c84ed2f1-dad5-54f0-aa8e-dbefe2724439" version = "0.4.5" -<<<<<<< HEAD -======= weakdeps = ["FixedPointNumbers"] [deps.Ratios.extensions] RatiosFixedPointNumbersExt = "FixedPointNumbers" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.RealDot]] deps = ["LinearAlgebra"] @@ -1580,33 +1308,18 @@ git-tree-sha1 = "9f0a1b71baaf7650f4fa8a1d168c7fb6ee41f0c9" uuid = "c1ae055f-0cd5-4b69-90a6-9a35b1a98df9" version = "0.1.0" -<<<<<<< HEAD -[[RecipesBase]] -======= [[deps.RecipesBase]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["PrecompileTools"] git-tree-sha1 = "5c3d09cc4f31f5fc6af001c250bf1278733100ff" uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01" version = "1.3.4" -<<<<<<< HEAD -[[RecipesPipeline]] -======= [[deps.RecipesPipeline]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Dates", "NaNMath", "PlotUtils", "PrecompileTools", "RecipesBase"] git-tree-sha1 = "45cf9fd0ca5839d06ef333c8201714e888486342" uuid = "01d81517-befc-4cb6-b9ec-a95719d0359c" version = "0.6.12" -<<<<<<< HEAD -[[RecursiveArrayTools]] -deps = ["Adapt", "ArrayInterfaceCore", "ArrayInterfaceStaticArraysCore", "ChainRulesCore", "DocStringExtensions", "FillArrays", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "StaticArraysCore", "Statistics", "Tables", "ZygoteRules"] -git-tree-sha1 = "a5ce741acddc02f0d4fc6505463ca89697d7fb23" -uuid = "731186ca-8d62-57ce-b412-fbd966d074cd" -version = "2.32.3" -======= [[deps.RecursiveArrayTools]] deps = ["Adapt", "ArrayInterface", "DocStringExtensions", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "Requires", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables"] git-tree-sha1 = "7ed35fb5f831aaf09c2d7c8736d44667a1afdcb0" @@ -1617,7 +1330,6 @@ version = "2.38.7" RecursiveArrayToolsMeasurementsExt = "Measurements" RecursiveArrayToolsTrackerExt = "Tracker" RecursiveArrayToolsZygoteExt = "Zygote" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [deps.RecursiveArrayTools.weakdeps] Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7" @@ -1658,8 +1370,6 @@ deps = ["ChainRulesCore", "CommonSolve", "Printf", "Setfield"] git-tree-sha1 = "de432823e8aab4dd1a985be4be768f95acf152d4" uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665" version = "2.0.17" -<<<<<<< HEAD -======= [deps.Roots.extensions] RootsForwardDiffExt = "ForwardDiff" @@ -1670,7 +1380,6 @@ version = "2.0.17" ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" IntervalRootFinding = "d2bf35a9-74e0-55ec-b149-d360ff49b807" SymPy = "24249f21-da20-56a4-8eb1-6a02cf4ae2e6" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.RuntimeGeneratedFunctions]] deps = ["ExprTools", "SHA", "Serialization"] @@ -1682,13 +1391,6 @@ version = "0.5.11" uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" version = "0.7.0" -<<<<<<< HEAD -[[SciMLBase]] -deps = ["ArrayInterfaceCore", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "Preferences", "RecipesBase", "RecursiveArrayTools", "RuntimeGeneratedFunctions", "StaticArraysCore", "Statistics", "Tables"] -git-tree-sha1 = "fe89a8113ea445bcff9ee570077830674babb534" -uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462" -version = "1.81.0" -======= [[deps.SciMLBase]] deps = ["ADTypes", "ArrayInterface", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces"] git-tree-sha1 = "92f8e23b4a5eb7e45bffe09027fcdda44a949a51" @@ -1700,7 +1402,6 @@ deps = ["ArrayInterface", "DocStringExtensions", "Lazy", "LinearAlgebra", "Setfi git-tree-sha1 = "745755a5b932c9a664d7e9e4beb60c692b211d4b" uuid = "c0aeaf25-5076-4817-a8d5-81caf7dfa961" version = "0.3.5" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.ScientificTypesBase]] git-tree-sha1 = "a8e18eb383b5ecf1b5e6fc237eb39255044fd92b" @@ -1777,13 +1478,6 @@ git-tree-sha1 = "e08a62abc517eb79667d0a29dc08a3b589516bb5" uuid = "171d559e-b47b-412a-8079-5efa626c420e" version = "0.1.15" -<<<<<<< HEAD -[[StaticArrays]] -deps = ["LinearAlgebra", "Random", "StaticArraysCore", "Statistics"] -git-tree-sha1 = "832afbae2a45b4ae7e831f86965469a24d1d8a83" -uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.5.26" -======= [[deps.StaticArrays]] deps = ["LinearAlgebra", "Random", "StaticArraysCore"] git-tree-sha1 = "9cabadf6e7cd2349b6cf49f1915ad2028d65e881" @@ -1793,7 +1487,6 @@ weakdeps = ["Statistics"] [deps.StaticArrays.extensions] StaticArraysStatisticsExt = "Statistics" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.StaticArraysCore]] git-tree-sha1 = "36b3d696ce6366023a0ea192b4cd442268995a0d" @@ -1834,19 +1527,11 @@ weakdeps = ["ChainRulesCore", "InverseFunctions"] StatsFunsChainRulesCoreExt = "ChainRulesCore" StatsFunsInverseFunctionsExt = "InverseFunctions" -<<<<<<< HEAD -[[StatsPlots]] -deps = ["AbstractFFTs", "Clustering", "DataStructures", "Distributions", "Interpolations", "KernelDensity", "LinearAlgebra", "MultivariateStats", "NaNMath", "Observables", "Plots", "RecipesBase", "RecipesPipeline", "Reexport", "StatsBase", "TableOperations", "Tables", "Widgets"] -git-tree-sha1 = "14ef622cf28b05e38f8af1de57bc9142b03fbfe3" -uuid = "f3b207a7-027a-5e70-b257-86293d7955fd" -version = "0.15.5" -======= [[deps.StatsPlots]] deps = ["AbstractFFTs", "Clustering", "DataStructures", "Distributions", "Interpolations", "KernelDensity", "LinearAlgebra", "MultivariateStats", "NaNMath", "Observables", "Plots", "RecipesBase", "RecipesPipeline", "Reexport", "StatsBase", "TableOperations", "Tables", "Widgets"] git-tree-sha1 = "9115a29e6c2cf66cf213ccc17ffd61e27e743b24" uuid = "f3b207a7-027a-5e70-b257-86293d7955fd" version = "0.15.6" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.StringManipulation]] git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" @@ -1863,9 +1548,6 @@ version = "0.6.15" deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" -<<<<<<< HEAD -[[TOML]] -======= [[deps.SuiteSparse_jll]] deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"] uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" @@ -1878,7 +1560,6 @@ uuid = "2efcf032-c050-4f8e-a9bb-153293bab1f5" version = "0.2.2" [[deps.TOML]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["Dates"] uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" version = "1.0.3" @@ -1924,16 +1605,10 @@ uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [[deps.Tracker]] deps = ["Adapt", "DiffRules", "ForwardDiff", "Functors", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NNlib", "NaNMath", "Optimisers", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics"] -<<<<<<< HEAD -git-tree-sha1 = "8b552cc0a4132c1ce5cee14197bb57d2109d480f" -uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" -version = "0.2.25" -======= git-tree-sha1 = "92364c27aa35c0ee36e6e010b704adaade6c409c" uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" version = "0.2.26" weakdeps = ["PDMats"] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [deps.Tracker.extensions] TrackerPDMatsExt = "PDMats" @@ -1944,13 +1619,6 @@ git-tree-sha1 = "9a6ae7ed916312b41236fcef7e0af564ef934769" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" version = "0.9.13" -<<<<<<< HEAD -[[Transducers]] -deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"] -git-tree-sha1 = "25358a5f2384c490e98abd565ed321ffae2cbb37" -uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999" -version = "0.4.76" -======= [[deps.Transducers]] deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "ConstructionBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"] git-tree-sha1 = "53bd5978b182fa7c57577bdb452c35e5b4fb73a5" @@ -1981,7 +1649,6 @@ deps = ["InteractiveUtils", "MacroTools", "Preferences"] git-tree-sha1 = "ea3e54c2bdde39062abf5a9758a23735558705e1" uuid = "781d530d-4396-4725-bb49-402e4bee1e77" version = "1.4.0" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 [[deps.Turing]] deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Tracker"] @@ -2007,15 +1674,6 @@ git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf" uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1" version = "0.4.1" -<<<<<<< HEAD -[[Unitful]] -deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"] -git-tree-sha1 = "ba4aa36b2d5c98d6ed1f149da916b3ba46527b2b" -uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" -version = "1.14.0" - -[[UnitfulLatexify]] -======= [[deps.Unitful]] deps = ["Dates", "LinearAlgebra", "Random"] git-tree-sha1 = "c4d2a349259c8eba66a00a540d550f122a3ab228" @@ -2028,31 +1686,16 @@ weakdeps = ["ConstructionBase", "InverseFunctions"] InverseFunctionsUnitfulExt = "InverseFunctions" [[deps.UnitfulLatexify]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 deps = ["LaTeXStrings", "Latexify", "Unitful"] git-tree-sha1 = "e2d817cc500e960fdbafcf988ac8436ba3208bfd" uuid = "45397f5d-5981-4c77-b2b3-fc36d6e9b728" version = "1.6.3" -<<<<<<< HEAD -[[UnsafeAtomics]] -======= [[deps.UnsafeAtomics]] ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278" uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f" version = "0.2.1" -<<<<<<< HEAD -[[UnsafeAtomicsLLVM]] -deps = ["LLVM", "UnsafeAtomics"] -git-tree-sha1 = "ea37e6066bf194ab78f4e747f5245261f17a7175" -uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249" -version = "0.1.2" - -[[Unzip]] -git-tree-sha1 = "34db80951901073501137bdbc3d5a8e7bbd06670" -======= [[deps.UnsafeAtomicsLLVM]] deps = ["LLVM", "UnsafeAtomics"] git-tree-sha1 = "323e3d0acf5e78a56dfae7bd8928c989b4f3083e" @@ -2061,7 +1704,6 @@ version = "0.1.3" [[deps.Unzip]] git-tree-sha1 = "ca0969166a028236229f63514992fc073799bb78" ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 uuid = "41fe7b60-77ed-43a1-b4f0-825fd5a5650d" version = "0.2.0" diff --git a/tutorials/docs-10-using-turing-autodiff/autodiff.jmd b/tutorials/docs-10-using-turing-autodiff/autodiff.jmd index 077b45690..bfb3719eb 100644 --- a/tutorials/docs-10-using-turing-autodiff/autodiff.jmd +++ b/tutorials/docs-10-using-turing-autodiff/autodiff.jmd @@ -31,11 +31,7 @@ end # Sample using Gibbs and varying autodiff backends. c = sample( gdemo(1.5, 2), -<<<<<<< HEAD - Gibbs(HMC{Turing.ForwardDiffAD{1}}(0.1, 5, :m), HMC{Turing.TrackerAD}(0.1, 5, :s)), -======= Gibbs(HMC{Turing.ForwardDiffAD{1}}(0.1, 5, :m), HMC{Turing.TrackerAD}(0.1, 5, :s²)), ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 1000, ) ``` @@ -43,11 +39,3 @@ c = sample( Generally, `TrackerAD` is faster when sampling from variables of high dimensionality (greater than 20) and `ForwardDiffAD` is more efficient for lower-dimension variables. This functionality allows those who are performance sensitive to fine tune their automatic differentiation for their specific models. If the differentiation method is not specified in this way, Turing will default to using whatever the global AD backend is. Currently, this defaults to `ForwardDiff`. -<<<<<<< HEAD - -```julia - - -``` -======= ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 diff --git a/tutorials/docs-11-using-turing-dynamichmc/dynamichmc.jmd b/tutorials/docs-11-using-turing-dynamichmc/dynamichmc.jmd index 1e87d23c7..a245dbffb 100644 --- a/tutorials/docs-11-using-turing-dynamichmc/dynamichmc.jmd +++ b/tutorials/docs-11-using-turing-dynamichmc/dynamichmc.jmd @@ -28,10 +28,3 @@ end # Pull 2,000 samples using DynamicNUTS. chn = sample(gdemo(1.5, 2.0), DynamicNUTS(), 2000) ``` -<<<<<<< HEAD - -```{julia; eval=false} - -``` -======= ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 diff --git a/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd b/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd index d2ead37fb..46eaf67ae 100644 --- a/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd +++ b/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd @@ -57,11 +57,7 @@ c6 = sample(gdemo(1.5, 2), NUTS(0.65), 1000) The `MCMCChains` module (which is re-exported by Turing) provides plotting tools for the `Chain` objects returned by a `sample` function. See the [MCMCChains](https://github.com/TuringLang/MCMCChains.jl) repository for more information on the suite of tools available for diagnosing MCMC chains. -<<<<<<< HEAD -```julia -======= ```julia; eval = false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # Summarise results describe(c3) @@ -155,11 +151,7 @@ The `chains` variable now contains a `Chains` object which can be indexed by cha If you wish to perform multithreaded sampling and are running Julia 1.3 or greater, you can call `sample` with the following signature: -<<<<<<< HEAD -```julia -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 using Turing @model function gdemo(x) @@ -185,11 +177,7 @@ To perform distributed sampling (using multiple processes), you must first impor Process parallel sampling can be done like so: -<<<<<<< HEAD -```julia -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # Load Distributed to add processes and the @everywhere macro. using Distributed @@ -226,19 +214,12 @@ sample(model, NUTS(), MCMCDistributed(), 1000, 4) Turing allows you to sample from a declared model's prior. If you wish to draw a chain from the prior to inspect your prior distributions, you can simply run -<<<<<<< HEAD -```julia -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 chain = sample(model, Prior(), n_samples) ``` You can also run your model (as if it were a function) from the prior distribution, by calling the model without specifying inputs or a sampler. In the below example, we specify a `gdemo` model which returns two variables, `x` and `y`. The model includes `x` and `y` as arguments, but calling the function without passing in `x` or `y` means that Turing's compiler will assume they are missing values to draw from the relevant distribution. The `return` statement is necessary to retrieve the sampled `x` and `y` values. -<<<<<<< HEAD -======= Assign the function with `missing` inputs to a variable, and Turing will produce a sample from the prior distribution. ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 ```julia @model function gdemo(x, y) @@ -258,15 +239,6 @@ g_prior_sample = gdemo(missing, missing) g_prior_sample() ``` -<<<<<<< HEAD -Output: - -``` -(0.685690547873451, -1.1972706455914328) -``` - -======= ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 ### Sampling from a Conditional Distribution (The Posterior) #### Treating observations as random variables @@ -378,9 +350,6 @@ Now, query the instantiated models: compute the likelihood of `x = 1.0` given th ```julia prob"x = 1.0 | model = model1, s = 1.0, m = 1.0" -<<<<<<< HEAD -prob"x = 1.0 | model = model4, s = 1.0, m = 1.0" -======= ``` ```julia @@ -388,7 +357,6 @@ prob"x = 1.0 | model = model4, s = 1.0, m = 1.0" ``` ```julia ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 prob"x = 1.0 | model = model10, s = 1.0, m = 1.0" ``` @@ -470,11 +438,7 @@ map_estimate = optimize(model, MAP()) If you wish to change to a different optimizer, such as `NelderMead`, simply place your optimizer in the third argument slot: -<<<<<<< HEAD -```julia -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # Use NelderMead mle_estimate = optimize(model, MLE(), NelderMead()) @@ -493,11 +457,7 @@ mle_estimate = optimize(model, MLE(), AcceleratedGradientDescent()) Some methods may have trouble calculating the mode because not enough iterations were allowed, or the target function moved upwards between function calls. Turing will warn you if Optim fails to converge by running `Optim.converge`. A typical solution to this might be to add more iterations, or allow the optimizer to increase between function iterations: -<<<<<<< HEAD -```julia -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # Increase the iterations and allow function eval to increase between calls. mle_estimate = optimize( model, MLE(), Newton(), Optim.Options(; iterations=10_000, allow_f_increases=true) @@ -512,11 +472,7 @@ Turing extends several methods from `StatsBase` that can be used to analyze your For example, let's examine our ML estimate from above using `coeftable`: -<<<<<<< HEAD -```julia -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # Import StatsBase to use it's statistical methods. using StatsBase @@ -539,11 +495,7 @@ Standard errors are calculated from the Fisher information matrix (inverse Hessi You can begin sampling your chain from an MLE/MAP estimate by extracting the vector of parameter values and providing it to the `sample` function with the keyword `init_params`. For example, here is how to sample from the full posterior using the MAP estimate as the starting point: -<<<<<<< HEAD -```julia -======= ```julia; eval=false ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 # Generate an MAP estimate. map_estimate = optimize(model, MAP()) @@ -642,11 +594,7 @@ For more information on Turing's automatic differentiation backend, please see t #### Progress Logging -<<<<<<< HEAD -Turing.jl uses ProgressLogging.jl to log the progress of sampling. Progress -======= `Turing.jl` uses ProgressLogging.jl to log the progress of sampling. Progress ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 logging is enabled as default but might slow down inference. It can be turned on or off by setting the keyword argument `progress` of `sample` to `true` or `false`, respectively. Moreover, you can enable or disable progress logging globally by calling `setprogress!(true)` or `setprogress!(false)`, respectively. @@ -655,18 +603,7 @@ use [Juno](https://junolab.org/), the progress is displayed with a [progress bar in the Atom window](http://docs.junolab.org/latest/man/juno_frontend/#Progress-Meters-1). For Jupyter notebooks the default backend is [ConsoleProgressMonitor.jl](https://github.com/tkf/ConsoleProgressMonitor.jl). -<<<<<<< HEAD -In all other cases progress logs are displayed with -[TerminalLoggers.jl](https://github.com/c42f/TerminalLoggers.jl). Alternatively, -if you provide a custom visualization backend, Turing uses it instead of the -default backend. - -```julia - -``` -======= In all other cases, progress logs are displayed with [TerminalLoggers.jl](https://github.com/c42f/TerminalLoggers.jl). Alternatively, if you provide a custom visualization backend, Turing uses it instead of the default backend. ->>>>>>> 1cf10320810bbbcb7fc849c748169155bd7cc786 From db115141102073a4d8292c504c5104f0c50e8ed0 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Mon, 24 Jul 2023 11:17:40 +0100 Subject: [PATCH 55/87] env --- tutorials/15-external-samplers/Manifest.toml | 1374 +++++++++++++++++ tutorials/15-external-samplers/Project.toml | 6 + .../external-samplers.jmd | 2 +- 3 files changed, 1381 insertions(+), 1 deletion(-) create mode 100644 tutorials/15-external-samplers/Manifest.toml create mode 100644 tutorials/15-external-samplers/Project.toml diff --git a/tutorials/15-external-samplers/Manifest.toml b/tutorials/15-external-samplers/Manifest.toml new file mode 100644 index 000000000..4ec7df0af --- /dev/null +++ b/tutorials/15-external-samplers/Manifest.toml @@ -0,0 +1,1374 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.9.0" +manifest_format = "2.0" +project_hash = "c99988d8f509939a201938608c92879832a0923b" + +[[deps.ADTypes]] +git-tree-sha1 = "e58c18d2312749847a74f5be80bb0fa53da102bd" +uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b" +version = "0.1.5" + +[[deps.AbstractFFTs]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "cad4c758c0038eea30394b1b671526921ca85b21" +uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" +version = "1.4.0" +weakdeps = ["ChainRulesCore"] + + [deps.AbstractFFTs.extensions] + AbstractFFTsChainRulesCoreExt = "ChainRulesCore" + +[[deps.AbstractMCMC]] +deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "LogDensityProblems", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] +git-tree-sha1 = "87e63dcb990029346b091b170252f3c416568afc" +uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" +version = "4.4.2" + +[[deps.AbstractPPL]] +deps = ["AbstractMCMC", "DensityInterface", "Random", "Setfield", "SparseArrays"] +git-tree-sha1 = "33ea6c6837332395dbf3ba336f273c9f7fcf4db9" +uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf" +version = "0.5.4" + +[[deps.AbstractTrees]] +git-tree-sha1 = "faa260e4cb5aba097a73fab382dd4b5819d8ec8c" +uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" +version = "0.4.4" + +[[deps.Adapt]] +deps = ["LinearAlgebra", "Requires"] +git-tree-sha1 = "76289dc51920fdc6e0013c872ba9551d54961c24" +uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" +version = "3.6.2" +weakdeps = ["StaticArrays"] + + [deps.Adapt.extensions] + AdaptStaticArraysExt = "StaticArrays" + +[[deps.AdvancedHMC]] +deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "ProgressMeter", "Random", "Requires", "Setfield", "SimpleUnPack", "Statistics", "StatsBase", "StatsFuns"] +git-tree-sha1 = "3bf24030e85b1d6d298e4f483f6aeff6f38462db" +uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" +version = "0.4.6" + + [deps.AdvancedHMC.extensions] + AdvancedHMCCUDAExt = "CUDA" + 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"17.4.0+0" diff --git a/tutorials/15-external-samplers/Project.toml b/tutorials/15-external-samplers/Project.toml new file mode 100644 index 000000000..97bcf490e --- /dev/null +++ b/tutorials/15-external-samplers/Project.toml @@ -0,0 +1,6 @@ +[deps] +AdvancedHMC = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MicroCanonicalHMC = "234d2aa0-2291-45f7-9047-6fa6f316b0a8" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index 18f91fc20..05e7994f5 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -19,7 +19,7 @@ For this demonstration we will use a 10 dimensional Neal's funnel: ```julia # Import libraries. -using Turing +using Turing, Random, LinearAlgebra d = 10 @model function funnel() From fb7fa6f5fc58c3a4bccdf9ce8f700132d0370048 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Mon, 24 Jul 2023 11:19:59 +0100 Subject: [PATCH 56/87] format --- tutorials/15-external-samplers/external-samplers.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index 05e7994f5..cbe41c3df 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -101,4 +101,4 @@ The algorithm has been shown to outperform traditioanl HMC in many scenarios and # Refences [^1]: Robnik et al, (Microcanonical Hamiltonian Monte Carlo)[https://arxiv.org/abs/2212.08549], 2022 -[^2]: Robnik and Seljak, (Langevine Hamiltonian Monte Carlo)[https://arxiv.org/abs/2303.18221], 2023 \ No newline at end of file +[^2]: Robnik and Seljak, (Langevine Hamiltonian Monte Carlo)[https://arxiv.org/abs/2303.18221], 2023 From a4a079bb2c3967ffa1c0dd89de646cff6c8f44e8 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Mon, 24 Jul 2023 12:09:22 +0100 Subject: [PATCH 57/87] better name --- .../Manifest.toml | 0 .../Project.toml | 0 .../external-samplers.jmd} | 4 ++-- 3 files changed, 2 insertions(+), 2 deletions(-) rename tutorials/{docs-15-using-turing-sampler-viz => docs-15-using-turing-external-samplers}/Manifest.toml (100%) rename tutorials/{docs-15-using-turing-sampler-viz => docs-15-using-turing-external-samplers}/Project.toml (100%) rename tutorials/{docs-15-using-turing-sampler-viz/sampler-viz.jmd => docs-15-using-turing-external-samplers/external-samplers.jmd} (98%) diff --git a/tutorials/docs-15-using-turing-sampler-viz/Manifest.toml b/tutorials/docs-15-using-turing-external-samplers/Manifest.toml similarity index 100% rename from tutorials/docs-15-using-turing-sampler-viz/Manifest.toml rename to tutorials/docs-15-using-turing-external-samplers/Manifest.toml diff --git a/tutorials/docs-15-using-turing-sampler-viz/Project.toml b/tutorials/docs-15-using-turing-external-samplers/Project.toml similarity index 100% rename from tutorials/docs-15-using-turing-sampler-viz/Project.toml rename to tutorials/docs-15-using-turing-external-samplers/Project.toml diff --git a/tutorials/docs-15-using-turing-sampler-viz/sampler-viz.jmd b/tutorials/docs-15-using-turing-external-samplers/external-samplers.jmd similarity index 98% rename from tutorials/docs-15-using-turing-sampler-viz/sampler-viz.jmd rename to tutorials/docs-15-using-turing-external-samplers/external-samplers.jmd index f79711006..36264f2d9 100644 --- a/tutorials/docs-15-using-turing-sampler-viz/sampler-viz.jmd +++ b/tutorials/docs-15-using-turing-external-samplers/external-samplers.jmd @@ -1,8 +1,8 @@ --- title: Sampler Visualization mathjax: true -permalink: /docs/using-turing/sampler-viz -redirect_from: docs/sampler-viz/ +permalink: /docs/using-turing/external-samplers +redirect_from: docs/external-samplers/ weave_options: error : false --- From 45b96ce457c5f43d583f1923e86063d801c8cfd9 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Mon, 24 Jul 2023 12:14:30 +0100 Subject: [PATCH 58/87] undoing rename --- .../Manifest.toml | 1553 ----------------- .../Project.toml | 6 - .../external-samplers.jmd | 190 -- 3 files changed, 1749 deletions(-) delete mode 100644 tutorials/docs-15-using-turing-external-samplers/Manifest.toml delete mode 100644 tutorials/docs-15-using-turing-external-samplers/Project.toml delete mode 100644 tutorials/docs-15-using-turing-external-samplers/external-samplers.jmd diff --git a/tutorials/docs-15-using-turing-external-samplers/Manifest.toml b/tutorials/docs-15-using-turing-external-samplers/Manifest.toml deleted file mode 100644 index a63e1c9d0..000000000 --- a/tutorials/docs-15-using-turing-external-samplers/Manifest.toml +++ /dev/null @@ -1,1553 +0,0 @@ -# This file is machine-generated - editing it directly is not advised - -julia_version = 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-title: Sampler Visualization -mathjax: true -permalink: /docs/using-turing/external-samplers -redirect_from: docs/external-samplers/ -weave_options: - error : false ---- - -# Sampler Visualization - -## Introduction - -## The Code - -For each sampler, we will use the same code to plot sampler paths. The block below loads the relevant libraries and defines a function for plotting the sampler's trajectory across the posterior. - -The Turing model definition used here is not especially practical, but it is designed in such a way as to produce visually interesting posterior surfaces to show how different samplers move along the distribution. - -```julia -ENV["GKS_ENCODING"] = "utf-8" # Allows the use of unicode characters in Plots.jl -using Plots -using StatsPlots -using Turing -using Random -using Bijectors - -# Set a seed. -Random.seed!(0) - -# Define a strange model. -@model function gdemo(x) - s² ~ InverseGamma(2, 3) - m ~ Normal(0, sqrt(s²)) - bumps = sin(m) + cos(m) - m = m + 5 * bumps - for i in eachindex(x) - x[i] ~ Normal(m, sqrt(s²)) - end - return s², m -end - -# Define our data points. -x = [1.5, 2.0, 13.0, 2.1, 0.0] - -# Set up the model call, sample from the prior. -model = gdemo(x) - -# Evaluate surface at coordinates. -evaluate(m1, m2) = logjoint(model, (m=m2, s²=invlink.(Ref(InverseGamma(2, 3)), m1))) - -function plot_sampler(chain; label="") - # Extract values from chain. - val = get(chain, [:s², :m, :lp]) - ss = link.(Ref(InverseGamma(2, 3)), val.s²) - ms = val.m - lps = val.lp - - # How many surface points to sample. - granularity = 100 - - # Range start/stop points. - spread = 0.5 - σ_start = minimum(ss) - spread * std(ss) - σ_stop = maximum(ss) + spread * std(ss) - μ_start = minimum(ms) - spread * std(ms) - μ_stop = maximum(ms) + spread * std(ms) - σ_rng = collect(range(σ_start; stop=σ_stop, length=granularity)) - μ_rng = collect(range(μ_start; stop=μ_stop, length=granularity)) - - # Make surface plot. - p = surface( - σ_rng, - μ_rng, - evaluate; - camera=(30, 65), - # ticks=nothing, - colorbar=false, - color=:inferno, - title=label, - ) - - line_range = 1:length(ms) - - scatter3d!( - ss[line_range], - ms[line_range], - lps[line_range]; - mc=:viridis, - marker_z=collect(line_range), - msw=0, - legend=false, - colorbar=false, - alpha=0.5, - xlabel="σ", - ylabel="μ", - zlabel="Log probability", - title=label, - ) - - return p -end; -``` - -## Samplers - -### Gibbs - -Gibbs sampling tends to exhibit a "jittery" trajectory. The example below combines `HMC` and `PG` sampling to traverse the posterior. - -```julia -c = sample(model, Gibbs(HMC(0.01, 5, :s²), PG(20, :m)), 1000) -plot_sampler(c) -``` - -### HMC - -Hamiltonian Monte Carlo (HMC) sampling is a typical sampler to use, as it tends to be fairly good at converging in a efficient manner. It can often be tricky to set the correct parameters for this sampler however, and the `NUTS` sampler is often easier to run if you don't want to spend too much time fiddling with step size and and the number of steps to take. Note however that `HMC` does not explore the positive values μ very well, likely due to the leapfrog and step size parameter settings. - -```julia -c = sample(model, HMC(0.01, 10), 1000) -plot_sampler(c) -``` - -### HMCDA - -The HMCDA sampler is an implementation of the Hamiltonian Monte Carlo with Dual Averaging algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. - -```julia -c = sample(model, HMCDA(200, 0.65, 0.3), 1000) -plot_sampler(c) -``` - -### MH - -Metropolis-Hastings (MH) sampling is one of the earliest Markov Chain Monte Carlo methods. MH sampling does not "move" a lot, unlike many of the other samplers implemented in Turing. Typically a much longer chain is required to converge to an appropriate parameter estimate. - -The plot below only uses 1,000 iterations of Metropolis-Hastings. - -```julia -c = sample(model, MH(), 1000) -plot_sampler(c) -``` - -As you can see, the MH sampler doesn't move parameter estimates very often. - -### NUTS - -The No U-Turn Sampler (NUTS) is an implementation of the algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. - -NUTS tends to be very good at traversing complex posteriors quickly. - -```julia -c = sample(model, NUTS(0.65), 1000) -plot_sampler(c) -``` - -The only parameter that needs to be set other than the number of iterations to run is the target acceptance rate. In the Hoffman and Gelman paper, they note that a target acceptance rate of 0.65 is typical. - -Here is a plot showing a very high acceptance rate. Note that it appears to "stick" to a mode and is not particularly good at exploring the posterior as compared to the 0.65 target acceptance ratio case. - -```julia -c = sample(model, NUTS(0.95), 1000) -plot_sampler(c) -``` - -An exceptionally low acceptance rate will show very few moves on the posterior: - -```julia -c = sample(model, NUTS(0.2), 1000) -plot_sampler(c) -``` - -### PG - -The Particle Gibbs (PG) sampler is an implementation of an algorithm from the paper "Particle Markov chain Monte Carlo methods" by Andrieu, Doucet, and Holenstein (2010). The interested reader can learn more [here](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9868.2009.00736.x). - -The two parameters are the number of particles, and the number of iterations. The plot below shows the use of 20 particles. - -```julia -c = sample(model, PG(20), 1000) -plot_sampler(c) -``` - -Next, we plot using 50 particles. - -```julia -c = sample(model, PG(50), 1000) -plot_sampler(c) -``` From ad9fea4d02842c5e14574273011428560881dec3 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Mon, 24 Jul 2023 12:15:23 +0100 Subject: [PATCH 59/87] undoing fck up --- .../Manifest.toml | 1553 +++++++++++++++++ .../Project.toml | 6 + .../sampler-viz.jmd | 190 ++ 3 files changed, 1749 insertions(+) create mode 100644 tutorials/docs-15-using-turing-sampler-viz/Manifest.toml create mode 100644 tutorials/docs-15-using-turing-sampler-viz/Project.toml create mode 100644 tutorials/docs-15-using-turing-sampler-viz/sampler-viz.jmd diff --git a/tutorials/docs-15-using-turing-sampler-viz/Manifest.toml b/tutorials/docs-15-using-turing-sampler-viz/Manifest.toml new file mode 100644 index 000000000..a63e1c9d0 --- /dev/null +++ b/tutorials/docs-15-using-turing-sampler-viz/Manifest.toml @@ 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"ee567a171cce03570d77ad3a43e90218e38937a9" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+0" diff --git a/tutorials/docs-15-using-turing-sampler-viz/Project.toml b/tutorials/docs-15-using-turing-sampler-viz/Project.toml new file mode 100644 index 000000000..f430df979 --- /dev/null +++ b/tutorials/docs-15-using-turing-sampler-viz/Project.toml @@ -0,0 +1,6 @@ +[deps] +Bijectors = "76274a88-744f-5084-9051-94815aaf08c4" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" +Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/docs-15-using-turing-sampler-viz/sampler-viz.jmd b/tutorials/docs-15-using-turing-sampler-viz/sampler-viz.jmd new file mode 100644 index 000000000..f79711006 --- /dev/null +++ b/tutorials/docs-15-using-turing-sampler-viz/sampler-viz.jmd @@ -0,0 +1,190 @@ +--- +title: Sampler Visualization +mathjax: true +permalink: /docs/using-turing/sampler-viz +redirect_from: docs/sampler-viz/ +weave_options: + error : false +--- + +# Sampler Visualization + +## Introduction + +## The Code + +For each sampler, we will use the same code to plot sampler paths. The block below loads the relevant libraries and defines a function for plotting the sampler's trajectory across the posterior. + +The Turing model definition used here is not especially practical, but it is designed in such a way as to produce visually interesting posterior surfaces to show how different samplers move along the distribution. + +```julia +ENV["GKS_ENCODING"] = "utf-8" # Allows the use of unicode characters in Plots.jl +using Plots +using StatsPlots +using Turing +using Random +using Bijectors + +# Set a seed. +Random.seed!(0) + +# Define a strange model. +@model function gdemo(x) + s² ~ InverseGamma(2, 3) + m ~ Normal(0, sqrt(s²)) + bumps = sin(m) + cos(m) + m = m + 5 * bumps + for i in eachindex(x) + x[i] ~ Normal(m, sqrt(s²)) + end + return s², m +end + +# Define our data points. +x = [1.5, 2.0, 13.0, 2.1, 0.0] + +# Set up the model call, sample from the prior. +model = gdemo(x) + +# Evaluate surface at coordinates. +evaluate(m1, m2) = logjoint(model, (m=m2, s²=invlink.(Ref(InverseGamma(2, 3)), m1))) + +function plot_sampler(chain; label="") + # Extract values from chain. + val = get(chain, [:s², :m, :lp]) + ss = link.(Ref(InverseGamma(2, 3)), val.s²) + ms = val.m + lps = val.lp + + # How many surface points to sample. + granularity = 100 + + # Range start/stop points. + spread = 0.5 + σ_start = minimum(ss) - spread * std(ss) + σ_stop = maximum(ss) + spread * std(ss) + μ_start = minimum(ms) - spread * std(ms) + μ_stop = maximum(ms) + spread * std(ms) + σ_rng = collect(range(σ_start; stop=σ_stop, length=granularity)) + μ_rng = collect(range(μ_start; stop=μ_stop, length=granularity)) + + # Make surface plot. + p = surface( + σ_rng, + μ_rng, + evaluate; + camera=(30, 65), + # ticks=nothing, + colorbar=false, + color=:inferno, + title=label, + ) + + line_range = 1:length(ms) + + scatter3d!( + ss[line_range], + ms[line_range], + lps[line_range]; + mc=:viridis, + marker_z=collect(line_range), + msw=0, + legend=false, + colorbar=false, + alpha=0.5, + xlabel="σ", + ylabel="μ", + zlabel="Log probability", + title=label, + ) + + return p +end; +``` + +## Samplers + +### Gibbs + +Gibbs sampling tends to exhibit a "jittery" trajectory. The example below combines `HMC` and `PG` sampling to traverse the posterior. + +```julia +c = sample(model, Gibbs(HMC(0.01, 5, :s²), PG(20, :m)), 1000) +plot_sampler(c) +``` + +### HMC + +Hamiltonian Monte Carlo (HMC) sampling is a typical sampler to use, as it tends to be fairly good at converging in a efficient manner. It can often be tricky to set the correct parameters for this sampler however, and the `NUTS` sampler is often easier to run if you don't want to spend too much time fiddling with step size and and the number of steps to take. Note however that `HMC` does not explore the positive values μ very well, likely due to the leapfrog and step size parameter settings. + +```julia +c = sample(model, HMC(0.01, 10), 1000) +plot_sampler(c) +``` + +### HMCDA + +The HMCDA sampler is an implementation of the Hamiltonian Monte Carlo with Dual Averaging algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. + +```julia +c = sample(model, HMCDA(200, 0.65, 0.3), 1000) +plot_sampler(c) +``` + +### MH + +Metropolis-Hastings (MH) sampling is one of the earliest Markov Chain Monte Carlo methods. MH sampling does not "move" a lot, unlike many of the other samplers implemented in Turing. Typically a much longer chain is required to converge to an appropriate parameter estimate. + +The plot below only uses 1,000 iterations of Metropolis-Hastings. + +```julia +c = sample(model, MH(), 1000) +plot_sampler(c) +``` + +As you can see, the MH sampler doesn't move parameter estimates very often. + +### NUTS + +The No U-Turn Sampler (NUTS) is an implementation of the algorithm found in the paper "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo" by Hoffman and Gelman (2011). The paper can be found on [arXiv](https://arxiv.org/abs/1111.4246) for the interested reader. + +NUTS tends to be very good at traversing complex posteriors quickly. + +```julia +c = sample(model, NUTS(0.65), 1000) +plot_sampler(c) +``` + +The only parameter that needs to be set other than the number of iterations to run is the target acceptance rate. In the Hoffman and Gelman paper, they note that a target acceptance rate of 0.65 is typical. + +Here is a plot showing a very high acceptance rate. Note that it appears to "stick" to a mode and is not particularly good at exploring the posterior as compared to the 0.65 target acceptance ratio case. + +```julia +c = sample(model, NUTS(0.95), 1000) +plot_sampler(c) +``` + +An exceptionally low acceptance rate will show very few moves on the posterior: + +```julia +c = sample(model, NUTS(0.2), 1000) +plot_sampler(c) +``` + +### PG + +The Particle Gibbs (PG) sampler is an implementation of an algorithm from the paper "Particle Markov chain Monte Carlo methods" by Andrieu, Doucet, and Holenstein (2010). The interested reader can learn more [here](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9868.2009.00736.x). + +The two parameters are the number of particles, and the number of iterations. The plot below shows the use of 20 particles. + +```julia +c = sample(model, PG(20), 1000) +plot_sampler(c) +``` + +Next, we plot using 50 particles. + +```julia +c = sample(model, PG(50), 1000) +plot_sampler(c) +``` From dc5387ae3d400a06a1a129e4fa0a46c5eecb9d5d Mon Sep 17 00:00:00 2001 From: jaimerz Date: Mon, 24 Jul 2023 17:17:03 +0100 Subject: [PATCH 60/87] working on Pathfinder example --- tutorials/15-external-samplers/Manifest.toml | 161 +++++++++++++++++- tutorials/15-external-samplers/Project.toml | 1 + .../external-samplers.jmd | 2 +- 3 files changed, 155 insertions(+), 9 deletions(-) diff --git a/tutorials/15-external-samplers/Manifest.toml b/tutorials/15-external-samplers/Manifest.toml index 4ec7df0af..c5ed20c2a 100644 --- a/tutorials/15-external-samplers/Manifest.toml +++ b/tutorials/15-external-samplers/Manifest.toml @@ -2,7 +2,7 @@ julia_version = "1.9.0" manifest_format = "2.0" -project_hash = "c99988d8f509939a201938608c92879832a0923b" +project_hash = 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OptimizationTrackerExt = "Tracker" + OptimizationZygoteExt = "Zygote" + + [deps.Optimization.weakdeps] + Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9" + FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41" + ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" + ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78" + ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" + Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" + Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" + +[[deps.OptimizationOptimJL]] +deps = ["Optim", "Optimization", "Reexport", "SparseArrays"] +git-tree-sha1 = "76a57a8484370bb26ae82a0a330abfff96d561aa" +uuid = "36348300-93cb-4f02-beb5-3c3902f8871e" +version = "0.1.9" + [[deps.OrderedCollections]] git-tree-sha1 = "2e73fe17cac3c62ad1aebe70d44c963c3cfdc3e3" uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" @@ -887,12 +980,41 @@ git-tree-sha1 = "67eae2738d63117a196f497d7db789821bce61d1" uuid = "90014a1f-27ba-587c-ab20-58faa44d9150" version = "0.11.17" +[[deps.PSIS]] +deps 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+version = "0.7.6" + + [deps.Pathfinder.extensions] + PathfinderDynamicHMCExt = "DynamicHMC" + PathfinderTuringExt = ["Accessors", "DynamicPPL", "MCMCChains", "Turing"] + + [deps.Pathfinder.weakdeps] + Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697" + DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb" + DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8" + MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d" + Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" + [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" @@ -904,6 +1026,12 @@ git-tree-sha1 = "a6062fe4063cdafe78f4a0a81cfffb89721b30e7" uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720" version = "1.4.2" +[[deps.PositiveFactorizations]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "17275485f373e6673f7e7f97051f703ed5b15b20" +uuid = "85a6dd25-e78a-55b7-8502-1745935b8125" +version = "0.2.4" + [[deps.PrecompileTools]] deps = ["Preferences"] git-tree-sha1 = "9673d39decc5feece56ef3940e5dafba15ba0f81" @@ -1014,6 +1142,12 @@ git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" uuid = "189a3867-3050-52da-a836-e630ba90ab69" version = "1.2.2" +[[deps.Referenceables]] +deps = ["Adapt"] +git-tree-sha1 = "e681d3bfa49cd46c3c161505caddf20f0e62aaa9" +uuid = "42d2dcc6-99eb-4e98-b66c-637b7d73030e" +version = "0.1.2" + [[deps.Requires]] deps = ["UUIDs"] git-tree-sha1 = "838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7" @@ -1168,9 +1302,9 @@ version = "1.6.0" [[deps.StatsBase]] deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"] -git-tree-sha1 = "75ebe04c5bed70b91614d684259b661c9e6274a4" +git-tree-sha1 = "d1bf48bfcc554a3761a133fe3a9bb01488e06916" uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" -version = "0.34.0" +version = "0.33.21" [[deps.StatsFuns]] deps = ["HypergeometricFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"] @@ -1247,6 +1381,12 @@ version = "0.1.7" deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" +[[deps.ThreadedScans]] +deps = ["ArgCheck"] +git-tree-sha1 = "ca1ba3000289eacba571aaa4efcefb642e7a1de6" +uuid = "24d252fe-5d94-4a69-83ea-56a14333d47a" +version = "0.1.0" + [[deps.Tracker]] deps = ["Adapt", "DiffRules", "ForwardDiff", "Functors", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NNlib", "NaNMath", "Optimisers", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics"] git-tree-sha1 = "92364c27aa35c0ee36e6e010b704adaade6c409c" @@ -1304,6 +1444,11 @@ version = "0.26.6" deps = ["Random", "SHA"] uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" +[[deps.UnPack]] +git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b" +uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed" +version = "1.0.2" + [[deps.Unicode]] uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" diff --git a/tutorials/15-external-samplers/Project.toml b/tutorials/15-external-samplers/Project.toml index 97bcf490e..64acf13c6 100644 --- a/tutorials/15-external-samplers/Project.toml +++ b/tutorials/15-external-samplers/Project.toml @@ -2,5 +2,6 @@ AdvancedHMC = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MicroCanonicalHMC = "234d2aa0-2291-45f7-9047-6fa6f316b0a8" +Pathfinder = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index cbe41c3df..38f2f7478 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -87,7 +87,7 @@ mh_chain_2 = sample(model, externalsampler(rwmh_2), 1_000) As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. AdvancedHMC is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within AdvancedHMC. -Thanfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in AdvancedHMC and then use to sample our Turing model. +Thankfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in AdvancedHMC and then use it to sample our Turing model. ## Micro-Canonical HMC From 46773bd3ac6a20ba3ff414e1bdc5e22d066b3eef Mon Sep 17 00:00:00 2001 From: Jaime RZ Date: Tue, 25 Jul 2023 09:36:27 +0100 Subject: [PATCH 61/87] Apply suggestions from code review Co-authored-by: Tor Erlend Fjelde --- .../15-external-samplers/external-samplers.jmd | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index 38f2f7478..a88771d17 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -8,14 +8,13 @@ weave_options: # Using External Samplers -Turing currently wraps some of the most popular sampling libraries in the Julia ecosystem. +Turing provides several wrapped samplers from external sampling libraries, e.g. HMC smaplers from AdvancedHMC.jl. These wrappers allow new users to seemlessly sample statistical models without leaving Turing. -However, these wrappers often lack the latests functionalities implemented on the sampling libraries that more experienced users might want to use. +However, these wrappers might not always be complete, missing some functionality from the wrapped sampling library. Moreover, users might want to use samplers currently not wrapped within Turing. -For these reasons, Turing now allows users the possibility of defining their own sampler and using it to sample a Turing model. -Let us begin by defining a simple Turing model to showcase the use of external samplers. -For this demonstration we will use a 10 dimensional Neal's funnel: +For these reasons, Turing also makes it easy to run external samplers on Turing models without any modifications or wrapping being necessary! +Throughout we will use a 10 dimensional Neal's funnel as a running example:: ```julia # Import libraries. @@ -29,7 +28,7 @@ d = 10 end ``` -Let us know sample the model to generate some observations and condition our model on them: +Now we sample the model to generate some observations, which we can then subsequently condition on. ```julia Random.seed!(1) @@ -37,7 +36,7 @@ Random.seed!(1) model = funnel() | (; x) ``` -Users can now use any sampler algorithm they wish to sample this model as long as it is of the type `AbstractMCMC.AbstractSampler`. +Users can use any sampler algorithm they wish to sample this model as long as it is of the type `AbstractMCMC.AbstractSampler`. Before discussing how is this in done in practice, it is interesting to give a high level description of the process. Imagine that we created an instance of external sampler that we will call `spl` such that `typeof(spl)<:AbstractMCMC.AbstractSampler`. In order to avoid type ambiguity within Turing, at the moment is necessary to declare `spl` as an external sampler to Turing this is done as: @@ -48,7 +47,7 @@ espl = externalsampler(spl) where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. -Arrived at this point sampling our model is as easy as: +At this point, sampling from our model is as easy as doing ```julia chain = sample(model, espl, 1_000) From 150091be1b14389a2d1484d9464d076ea8a71c4d Mon Sep 17 00:00:00 2001 From: jaimerz Date: Tue, 25 Jul 2023 11:24:48 +0100 Subject: [PATCH 62/87] citations --- .../15-external-samplers/external-samplers.jmd | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index a88771d17..c60fe0a0b 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -58,7 +58,7 @@ Note that if the external sampler produces transitions that Turing cannot parse ## AdvancedMH -A good point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` for Metropolis Hastings methods. +A good point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH`[^*] for Metropolis Hastings methods. Let's say that we want to use a random walk Metropolis Hastings sampler without specifying the proposal distributions. We can do this in two different ways: @@ -84,8 +84,8 @@ mh_chain_2 = sample(model, externalsampler(rwmh_2), 1_000) ## AdvancedHMC As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. -AdvancedHMC is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. -However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within AdvancedHMC. +`AdvancedHMC`[^2] is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. +However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within `AdvancedHMC`. Thankfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in AdvancedHMC and then use it to sample our Turing model. ## Micro-Canonical HMC @@ -93,11 +93,14 @@ Thankfully, we can use Turing's support for external samplers to define a HMC sa So far we have used to Turing's support for external samplers to go beyond the capabilities of the wrappers. Now we want to use this support to employ a sampler not supported within Turing's ecosystem yet. We will use the recently developped Micro-Cannoncial Hamiltonian Monte Carlo (MCHMC) sampler to showcase this. -MCHMC[^1,^2] is HMC sampler that conserves energy along the exploration of the phase space. +MCHMC[^3,^4] is HMC sampler that conserves energy along the exploration of the phase space. This is achieved by simulating the dynamics of a microcanonical Hamiltonian with an additional noise term to ensure ergodicity. The algorithm has been shown to outperform traditioanl HMC in many scenarios and it doesn't require of Metropolis adjustments. # Refences -[^1]: Robnik et al, (Microcanonical Hamiltonian Monte Carlo)[https://arxiv.org/abs/2212.08549], 2022 -[^2]: Robnik and Seljak, (Langevine Hamiltonian Monte Carlo)[https://arxiv.org/abs/2303.18221], 2023 +[^*]: AdvancedMH, [https://github.com/TuringLang/AdvancedMH.jl] +[^1]: Xu et al, (AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms)[http://proceedings.mlr.press/v118/xu20a/xu20a.pdf], 2019 +[^2]: Zhang et al, (Pathfinder: Parallel quasi-Newton variational inference)[https://arxiv.org/abs/2108.03782], 2021 +[^3]: Robnik et al, (Microcanonical Hamiltonian Monte Carlo)[https://arxiv.org/abs/2212.08549], 2022 +[^4]: Robnik and Seljak, (Langevine Hamiltonian Monte Carlo)[https://arxiv.org/abs/2303.18221], 2023 From 2fbb15ec581e1401aeb5687943c23831fd9de445 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Tue, 25 Jul 2023 11:25:35 +0100 Subject: [PATCH 63/87] no propaganda --- tutorials/15-external-samplers/external-samplers.jmd | 1 - 1 file changed, 1 deletion(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index c60fe0a0b..be94d5651 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -95,7 +95,6 @@ Now we want to use this support to employ a sampler not supported within Turing' We will use the recently developped Micro-Cannoncial Hamiltonian Monte Carlo (MCHMC) sampler to showcase this. MCHMC[^3,^4] is HMC sampler that conserves energy along the exploration of the phase space. This is achieved by simulating the dynamics of a microcanonical Hamiltonian with an additional noise term to ensure ergodicity. -The algorithm has been shown to outperform traditioanl HMC in many scenarios and it doesn't require of Metropolis adjustments. # Refences From 3d0ccf4f1087b08d2c73567e7e8d649c36ab4c1f Mon Sep 17 00:00:00 2001 From: jaimerz Date: Tue, 25 Jul 2023 12:21:27 +0100 Subject: [PATCH 64/87] flesing out the examples --- .../external-samplers.jmd | 53 ++++++++++++++++--- 1 file changed, 45 insertions(+), 8 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index be94d5651..4d4750bfa 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -58,7 +58,7 @@ Note that if the external sampler produces transitions that Turing cannot parse ## AdvancedMH -A good point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH`[^*] for Metropolis Hastings methods. +A good point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` ((`AdvancedMH`'s GitHub)[[https://github.com/TuringLang/AdvancedMH.jl]) for Metropolis Hastings methods. Let's say that we want to use a random walk Metropolis Hastings sampler without specifying the proposal distributions. We can do this in two different ways: @@ -76,29 +76,66 @@ The second method uses the priors of the model as a proposal. Note that the seco Let us now compare the performance and posteriors of these two samplers to the native MH sampler within Turing. ```julia -mh_chain_0 = sample(model, MH(), 1_000) -mh_chain_1 = sample(model, externalsampler(rwmh_1), 1_000) -mh_chain_2 = sample(model, externalsampler(rwmh_2), 1_000) +mh_chain_0 = sample(model, MH(), 10_000) +mh_chain_1 = sample(model, externalsampler(rwmh_1), 10_000) +mh_chain_2 = sample(model, externalsampler(rwmh_2), 10_000) ``` ## AdvancedHMC As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. -`AdvancedHMC`[^2] is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. +`AdvancedHMC`[^1] ((`AdvancedHMC`'s GitHub)[https://github.com/TuringLang/AdvancedHMC.jl]) is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within `AdvancedHMC`. -Thankfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in AdvancedHMC and then use it to sample our Turing model. +Thankfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in `AdvancedHMC` and then use it to sample our Turing model. + +We will use the library `Pathfinder`[^2] ((`Pathfinder`'s GitHub)[https://github.com/mlcolab/Pathfinder.jl]) to construct our estimate of mass matrix. +`Pathfinder` is a variational inference algorithm which first finds the maximum a posteriori (MAP) estimate of a target posterior distribution and then uses the trace of the optimization to construct a sequence of multivariate normal approximations to the target distribution. +In this process, `Pathfinder` computes an estimate of the mass matrix that the user can access. + +The code below shows can this be done in practice. + +```julia +using AdvancedHMC, Pathfidner +# Running pathfidner +draws = 1_000 +result_multi = multipathfinder(model, draws; nruns=8) + +# Estimating the metric +inv_metric = result_multi.pathfinder_results[1].fit_distribution.Σ +metric = Pathfinder.RankUpdateEuclideanMetric(inv_metric) + +# Creating an AdvancedHMC NUTS sampler with the custom metric. +n_adapts = 1000 # Number of adaptation steps +tap = 0.9 # Large target acceptance probability to deal with the funel structure of the posterior +nuts = AdvancedHMC.NUTS(n_adapts, tap; metric=metric) + +# Sample +chain = sample(model, externalsampler(nuts), 10_000) +``` ## Micro-Canonical HMC So far we have used to Turing's support for external samplers to go beyond the capabilities of the wrappers. Now we want to use this support to employ a sampler not supported within Turing's ecosystem yet. We will use the recently developped Micro-Cannoncial Hamiltonian Monte Carlo (MCHMC) sampler to showcase this. -MCHMC[^3,^4] is HMC sampler that conserves energy along the exploration of the phase space. +MCHMC[^3,^4] ((MCHMC's GitHub)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl]) is HMC sampler that conserves energy along the exploration of the phase space. This is achieved by simulating the dynamics of a microcanonical Hamiltonian with an additional noise term to ensure ergodicity. +Using this as well as other inference methods outside the Turing ecosystem is as simple as executing the code shown below: + +```julia +using MicroCanonicalHMC +# Create MCHMC sampler +n_adapts = 1_000 # adaptation steps +tev = 0.01 # target energy variance +mchmc = MCHMC(n_adapts, tev; adaptive=true) + +# Sample +chain = sample(model, externalsampler(mchmc), 10_000) +``` + # Refences -[^*]: AdvancedMH, [https://github.com/TuringLang/AdvancedMH.jl] [^1]: Xu et al, (AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms)[http://proceedings.mlr.press/v118/xu20a/xu20a.pdf], 2019 [^2]: Zhang et al, (Pathfinder: Parallel quasi-Newton variational inference)[https://arxiv.org/abs/2108.03782], 2021 [^3]: Robnik et al, (Microcanonical Hamiltonian Monte Carlo)[https://arxiv.org/abs/2212.08549], 2022 From 7eeaa20f557220c101f37c3007479a5edad417d1 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Tue, 25 Jul 2023 12:22:16 +0100 Subject: [PATCH 65/87] format --- tutorials/15-external-samplers/external-samplers.jmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index 4d4750bfa..a0c14d05d 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -88,9 +88,9 @@ As previously mentioned the Turing wrappers can often limit the capabilities of However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within `AdvancedHMC`. Thankfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in `AdvancedHMC` and then use it to sample our Turing model. -We will use the library `Pathfinder`[^2] ((`Pathfinder`'s GitHub)[https://github.com/mlcolab/Pathfinder.jl]) to construct our estimate of mass matrix. -`Pathfinder` is a variational inference algorithm which first finds the maximum a posteriori (MAP) estimate of a target posterior distribution and then uses the trace of the optimization to construct a sequence of multivariate normal approximations to the target distribution. -In this process, `Pathfinder` computes an estimate of the mass matrix that the user can access. +We will use the library `Pathfinder`[^2] ((`Pathfinder`'s GitHub)[https://github.com/mlcolab/Pathfinder.jl]) to construct our estimate of mass matrix. +`Pathfinder` is a variational inference algorithm which first finds the maximum a posteriori (MAP) estimate of a target posterior distribution and then uses the trace of the optimization to construct a sequence of multivariate normal approximations to the target distribution. +In this process, `Pathfinder` computes an estimate of the mass matrix that the user can access. The code below shows can this be done in practice. From 34a6ede35106f7026a26e70d38d8311d112f490c Mon Sep 17 00:00:00 2001 From: jaimerz Date: Tue, 25 Jul 2023 14:18:25 +0100 Subject: [PATCH 66/87] more concise --- .../external-samplers.jmd | 53 ++++++------------- 1 file changed, 16 insertions(+), 37 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index a0c14d05d..e2624a254 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -8,12 +8,12 @@ weave_options: # Using External Samplers -Turing provides several wrapped samplers from external sampling libraries, e.g. HMC smaplers from AdvancedHMC.jl. -These wrappers allow new users to seemlessly sample statistical models without leaving Turing. +`Turing` provides several wrapped samplers from external sampling libraries, e.g. HMC smaplers from `AdvancedHMC`. +These wrappers allow new users to seemlessly sample statistical models without leaving `Turing` However, these wrappers might not always be complete, missing some functionality from the wrapped sampling library. -Moreover, users might want to use samplers currently not wrapped within Turing. +Moreover, users might want to use samplers currently not wrapped within `Turing`. -For these reasons, Turing also makes it easy to run external samplers on Turing models without any modifications or wrapping being necessary! +For these reasons, `Turing` also makes it easy to run external samplers on Turing models without any modifications or wrapping being necessary! Throughout we will use a 10 dimensional Neal's funnel as a running example:: ```julia @@ -31,7 +31,6 @@ end Now we sample the model to generate some observations, which we can then subsequently condition on. ```julia -Random.seed!(1) (; x) = rand(funnel() | (θ=0,)) model = funnel() | (; x) ``` @@ -39,49 +38,29 @@ model = funnel() | (; x) Users can use any sampler algorithm they wish to sample this model as long as it is of the type `AbstractMCMC.AbstractSampler`. Before discussing how is this in done in practice, it is interesting to give a high level description of the process. Imagine that we created an instance of external sampler that we will call `spl` such that `typeof(spl)<:AbstractMCMC.AbstractSampler`. -In order to avoid type ambiguity within Turing, at the moment is necessary to declare `spl` as an external sampler to Turing this is done as: +In order to avoid type ambiguity within Turing, at the moment is necessary to declare `spl` as an external sampler to Turing `espl = externalsampler(spl)` where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. -```julia -espl = externalsampler(spl) -``` - -where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. - -At this point, sampling from our model is as easy as doing - -```julia -chain = sample(model, espl, 1_000) -``` - -This process will use `AbstractMCMC` functions overloaded by Turing to generate chains identical to those generated by the native samplers. -Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. - -## AdvancedMH - -A good point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` ((`AdvancedMH`'s GitHub)[[https://github.com/TuringLang/AdvancedMH.jl]) for Metropolis Hastings methods. +A good point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` ((`AdvancedMH`'s GitHub)[[https://github.com/TuringLang/AdvancedMH.jl]) for Metropolis Hastings (MH) methods. Let's say that we want to use a random walk Metropolis Hastings sampler without specifying the proposal distributions. -We can do this in two different ways: +The code below constructs a MH sampler using a multivariate Gaussian distribution with zero mean and unit variance in `d` dimensions as a random walk proposal. ```julia # Importing the sampling library using AdvancedMH - -rwmh_1 = RWMH(d) -rwmh_2 = RWMH(model) +rwmh = RWMH(d) ``` -The first method uses a multivariate Gaussian distribution with zero mean and unit variance in `d` dimensions. -The second method uses the priors of the model as a proposal. Note that the second method requires Turing to be imported. - -Let us now compare the performance and posteriors of these two samplers to the native MH sampler within Turing. +Sampling is then as easy as: ```julia -mh_chain_0 = sample(model, MH(), 10_000) -mh_chain_1 = sample(model, externalsampler(rwmh_1), 10_000) -mh_chain_2 = sample(model, externalsampler(rwmh_2), 10_000) +chain = sample(model, externalsampler(rwmh), 10_000) ``` -## AdvancedHMC +This process will use `AbstractMCMC` functions overloaded by Turing to generate chains identical to those generated by the native samplers. +Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. + + +## Going beyond the `Turing` API As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. `AdvancedHMC`[^1] ((`AdvancedHMC`'s GitHub)[https://github.com/TuringLang/AdvancedHMC.jl]) is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. @@ -113,7 +92,7 @@ nuts = AdvancedHMC.NUTS(n_adapts, tap; metric=metric) chain = sample(model, externalsampler(nuts), 10_000) ``` -## Micro-Canonical HMC +## Using new inference methods So far we have used to Turing's support for external samplers to go beyond the capabilities of the wrappers. Now we want to use this support to employ a sampler not supported within Turing's ecosystem yet. From a1680a34f516d372838cf1ece9a9a49298d12011 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Tue, 25 Jul 2023 14:21:00 +0100 Subject: [PATCH 67/87] format --- tutorials/15-external-samplers/external-samplers.jmd | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index e2624a254..9f7f7bbe0 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -59,8 +59,7 @@ chain = sample(model, externalsampler(rwmh), 10_000) This process will use `AbstractMCMC` functions overloaded by Turing to generate chains identical to those generated by the native samplers. Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. - -## Going beyond the `Turing` API +## Going beyond the `Turing` API As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. `AdvancedHMC`[^1] ((`AdvancedHMC`'s GitHub)[https://github.com/TuringLang/AdvancedHMC.jl]) is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. From ee426dbaff8f03b4dae2da4e85d421cdbec46782 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Tue, 25 Jul 2023 14:24:44 +0100 Subject: [PATCH 68/87] advancedMH --- tutorials/15-external-samplers/Manifest.toml | 2 +- tutorials/15-external-samplers/Project.toml | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tutorials/15-external-samplers/Manifest.toml b/tutorials/15-external-samplers/Manifest.toml index c5ed20c2a..6a2d15089 100644 --- a/tutorials/15-external-samplers/Manifest.toml +++ b/tutorials/15-external-samplers/Manifest.toml @@ -2,7 +2,7 @@ julia_version = "1.9.0" manifest_format = "2.0" -project_hash = "cfa0c7f363549f06f9109ba61c6eeee1d5a526b7" +project_hash = "6559f27b923d65d4f9f21538db968bd03760e383" [[deps.ADTypes]] git-tree-sha1 = "e58c18d2312749847a74f5be80bb0fa53da102bd" diff --git a/tutorials/15-external-samplers/Project.toml b/tutorials/15-external-samplers/Project.toml index 64acf13c6..292767c3c 100644 --- a/tutorials/15-external-samplers/Project.toml +++ b/tutorials/15-external-samplers/Project.toml @@ -1,5 +1,6 @@ [deps] AdvancedHMC = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" +AdvancedMH = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MicroCanonicalHMC = "234d2aa0-2291-45f7-9047-6fa6f316b0a8" Pathfinder = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454" From 207d68b9d489a263cb8c80aa97524feaaf7317b3 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Wed, 26 Jul 2023 16:25:49 +0100 Subject: [PATCH 69/87] for devs --- .../external-samplers.jmd | 46 ++++++++++++++++++- 1 file changed, 45 insertions(+), 1 deletion(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index 9f7f7bbe0..c91053ccc 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -35,7 +35,7 @@ Now we sample the model to generate some observations, which we can then subsequ model = funnel() | (; x) ``` -Users can use any sampler algorithm they wish to sample this model as long as it is of the type `AbstractMCMC.AbstractSampler`. +Users can use any sampler algorithm they wish to sample this model as long as it follows the `AbstractMCMC` API. Before discussing how is this in done in practice, it is interesting to give a high level description of the process. Imagine that we created an instance of external sampler that we will call `spl` such that `typeof(spl)<:AbstractMCMC.AbstractSampler`. In order to avoid type ambiguity within Turing, at the moment is necessary to declare `spl` as an external sampler to Turing `espl = externalsampler(spl)` where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. @@ -112,6 +112,50 @@ mchmc = MCHMC(n_adapts, tev; adaptive=true) chain = sample(model, externalsampler(mchmc), 10_000) ``` +## The `AbstractMCMC` API + +As previously stated, in order to use external sampling libraries within `Turing` they must follow the `AbstractMCMC` API. +In this section we will briefly dwell in what this entails. +First and foremost, the inference method must be of the type `typeof(spl)<:AbstractMCMC.AbstractSampler`. +Second, the stepping function of the MCMC algorithm must be made into instances of `AbstractMCMC.step` and follow the structure below: + +``` +# First step +function AbstractMCMC.step{T<:AbstractMCMC.AbstractSampler}( + rng::Random.AbstractRNG, + model::AbstractMCMC.LogDensityModel, + spl::T; + kwargs..., +) + [...] + return transition, sample +end + +# N+1 step +function AbstractMCMC.step{T<:AbstractMCMC.AbstractSampler}( + rng::Random.AbstractRNG, + model::AbstractMCMC.LogDensityModel, + sampler::T, + state; + kwargs..., +) + [...] + return transition, sample +end +``` +There are several characteristic to note in these functions: +- There must be two `step` functions: + - A function that performs the first step which and initializes the sampler. + - A function that performs the following steps and which takes and extra input, `state`, which carries the initialization information. +- The functions must follow the displayed signatures. +- The output of the functions must be a transition, the current state of the sampler, and a sample, what is saved to the MCMC chain. + +The last requirement is that the transition must be structure with a field named `θ` which constains the values of the parameters of the model for said transition. +This allows `Turing` to seemlessly extract the parameter values at each step of the chain when bundling the chains. + +For a practical example of how to adapt a sampling library to the `AbstractMCMC` interface the readers can consult the following (code)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl] within `MicroCanonicalHMC`. + + # Refences [^1]: Xu et al, (AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms)[http://proceedings.mlr.press/v118/xu20a/xu20a.pdf], 2019 From 1af81004735b117b297074ea884e4d707654f55e Mon Sep 17 00:00:00 2001 From: Jaime RZ Date: Thu, 27 Jul 2023 17:19:16 +0100 Subject: [PATCH 70/87] Apply suggestions from code review Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- .../15-external-samplers/external-samplers.jmd | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index c91053ccc..515106ead 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -115,7 +115,7 @@ chain = sample(model, externalsampler(mchmc), 10_000) ## The `AbstractMCMC` API As previously stated, in order to use external sampling libraries within `Turing` they must follow the `AbstractMCMC` API. -In this section we will briefly dwell in what this entails. +In this section we will briefly dwell in what this entails. First and foremost, the inference method must be of the type `typeof(spl)<:AbstractMCMC.AbstractSampler`. Second, the stepping function of the MCMC algorithm must be made into instances of `AbstractMCMC.step` and follow the structure below: @@ -143,14 +143,17 @@ function AbstractMCMC.step{T<:AbstractMCMC.AbstractSampler}( return transition, sample end ``` + There are several characteristic to note in these functions: -- There must be two `step` functions: - - A function that performs the first step which and initializes the sampler. - - A function that performs the following steps and which takes and extra input, `state`, which carries the initialization information. -- The functions must follow the displayed signatures. -- The output of the functions must be a transition, the current state of the sampler, and a sample, what is saved to the MCMC chain. -The last requirement is that the transition must be structure with a field named `θ` which constains the values of the parameters of the model for said transition. + - There must be two `step` functions: + + + A function that performs the first step which and initializes the sampler. + + A function that performs the following steps and which takes and extra input, `state`, which carries the initialization information. + - The functions must follow the displayed signatures. + - The output of the functions must be a transition, the current state of the sampler, and a sample, what is saved to the MCMC chain. + +The last requirement is that the transition must be structure with a field named `θ` which constains the values of the parameters of the model for said transition. This allows `Turing` to seemlessly extract the parameter values at each step of the chain when bundling the chains. For a practical example of how to adapt a sampling library to the `AbstractMCMC` interface the readers can consult the following (code)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl] within `MicroCanonicalHMC`. From 1f1cb9d46c0098df064dd80841699e54715dbaaa Mon Sep 17 00:00:00 2001 From: Jaime RZ Date: Thu, 27 Jul 2023 18:59:16 +0100 Subject: [PATCH 71/87] Update tutorials/15-external-samplers/external-samplers.jmd Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- tutorials/15-external-samplers/external-samplers.jmd | 1 + 1 file changed, 1 insertion(+) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index 515106ead..5972d79a1 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -150,6 +150,7 @@ There are several characteristic to note in these functions: + A function that performs the first step which and initializes the sampler. + A function that performs the following steps and which takes and extra input, `state`, which carries the initialization information. + - The functions must follow the displayed signatures. - The output of the functions must be a transition, the current state of the sampler, and a sample, what is saved to the MCMC chain. From 2761834f3d6b4c3184d4c10aba126838269418ff Mon Sep 17 00:00:00 2001 From: jaimerz Date: Thu, 27 Jul 2023 19:15:14 +0100 Subject: [PATCH 72/87] bug --- tutorials/15-external-samplers/external-samplers.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index c91053ccc..2a8a26cad 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -47,7 +47,7 @@ The code below constructs a MH sampler using a multivariate Gaussian distributio ```julia # Importing the sampling library using AdvancedMH -rwmh = RWMH(d) +rwmh = AdvancedMH.RWMH(d) ``` Sampling is then as easy as: From 1b39ea7452e6646dce719108f6a8d09fcf0c53e5 Mon Sep 17 00:00:00 2001 From: Jaime RZ Date: Fri, 28 Jul 2023 14:37:09 +0100 Subject: [PATCH 73/87] Apply suggestions from code review Co-authored-by: Tor Erlend Fjelde --- tutorials/15-external-samplers/external-samplers.jmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index 41be629c3..2f5ba8fac 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -84,7 +84,7 @@ metric = Pathfinder.RankUpdateEuclideanMetric(inv_metric) # Creating an AdvancedHMC NUTS sampler with the custom metric. n_adapts = 1000 # Number of adaptation steps -tap = 0.9 # Large target acceptance probability to deal with the funel structure of the posterior +tap = 0.9 # Large target acceptance probability to deal with the funnel structure of the posterior nuts = AdvancedHMC.NUTS(n_adapts, tap; metric=metric) # Sample @@ -112,11 +112,11 @@ mchmc = MCHMC(n_adapts, tev; adaptive=true) chain = sample(model, externalsampler(mchmc), 10_000) ``` -## The `AbstractMCMC` API +The only requirement to work with `externalsampler` is that the provided `sampler` must implement the AbstractMCMC.jl-interface [INSERT LINK] for a `model` of type `AbstractMCMC.LogDensityModel` [INSERT LINK]. As previously stated, in order to use external sampling libraries within `Turing` they must follow the `AbstractMCMC` API. In this section we will briefly dwell in what this entails. -First and foremost, the inference method must be of the type `typeof(spl)<:AbstractMCMC.AbstractSampler`. +First and foremost, the sampler should be a subtype of `AbstractMCMC.AbstractSampler`. Second, the stepping function of the MCMC algorithm must be made into instances of `AbstractMCMC.step` and follow the structure below: ``` From 5ed43db359f70766b457eb1413e5e47cd3eaf234 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Fri, 28 Jul 2023 15:31:34 +0100 Subject: [PATCH 74/87] upd --- .../external-samplers.jmd | 21 ++++++++++--------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index 2f5ba8fac..ac7a154c8 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -38,7 +38,7 @@ model = funnel() | (; x) Users can use any sampler algorithm they wish to sample this model as long as it follows the `AbstractMCMC` API. Before discussing how is this in done in practice, it is interesting to give a high level description of the process. Imagine that we created an instance of external sampler that we will call `spl` such that `typeof(spl)<:AbstractMCMC.AbstractSampler`. -In order to avoid type ambiguity within Turing, at the moment is necessary to declare `spl` as an external sampler to Turing `espl = externalsampler(spl)` where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. +In order to avoid type ambiguity within Turing, at the moment it is necessary to declare `spl` as an external sampler to Turing `espl = externalsampler(spl)`, where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. A good point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` ((`AdvancedMH`'s GitHub)[[https://github.com/TuringLang/AdvancedMH.jl]) for Metropolis Hastings (MH) methods. Let's say that we want to use a random walk Metropolis Hastings sampler without specifying the proposal distributions. @@ -56,9 +56,6 @@ Sampling is then as easy as: chain = sample(model, externalsampler(rwmh), 10_000) ``` -This process will use `AbstractMCMC` functions overloaded by Turing to generate chains identical to those generated by the native samplers. -Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. - ## Going beyond the `Turing` API As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. @@ -85,10 +82,10 @@ metric = Pathfinder.RankUpdateEuclideanMetric(inv_metric) # Creating an AdvancedHMC NUTS sampler with the custom metric. n_adapts = 1000 # Number of adaptation steps tap = 0.9 # Large target acceptance probability to deal with the funnel structure of the posterior -nuts = AdvancedHMC.NUTS(n_adapts, tap; metric=metric) +nuts = AdvancedHMC.NUTS(tap; metric=metric) # Sample -chain = sample(model, externalsampler(nuts), 10_000) +chain = sample(model, externalsampler(nuts), 10_000; n_adapts=1_000) ``` ## Using new inference methods @@ -96,7 +93,7 @@ chain = sample(model, externalsampler(nuts), 10_000) So far we have used to Turing's support for external samplers to go beyond the capabilities of the wrappers. Now we want to use this support to employ a sampler not supported within Turing's ecosystem yet. We will use the recently developped Micro-Cannoncial Hamiltonian Monte Carlo (MCHMC) sampler to showcase this. -MCHMC[^3,^4] ((MCHMC's GitHub)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl]) is HMC sampler that conserves energy along the exploration of the phase space. +MCHMC[^3,^4] ((MCHMC's GitHub)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl]) is HMC sampler that uses one single Hamiltonian energy level to explore the whole parameter space. This is achieved by simulating the dynamics of a microcanonical Hamiltonian with an additional noise term to ensure ergodicity. Using this as well as other inference methods outside the Turing ecosystem is as simple as executing the code shown below: @@ -117,7 +114,7 @@ The only requirement to work with `externalsampler` is that the provided `sample As previously stated, in order to use external sampling libraries within `Turing` they must follow the `AbstractMCMC` API. In this section we will briefly dwell in what this entails. First and foremost, the sampler should be a subtype of `AbstractMCMC.AbstractSampler`. -Second, the stepping function of the MCMC algorithm must be made into instances of `AbstractMCMC.step` and follow the structure below: +Second, the stepping function of the MCMC algorithm must be made defined using `AbstractMCMC.step` and follow the structure below: ``` # First step @@ -154,10 +151,14 @@ There are several characteristic to note in these functions: - The functions must follow the displayed signatures. - The output of the functions must be a transition, the current state of the sampler, and a sample, what is saved to the MCMC chain. -The last requirement is that the transition must be structure with a field named `θ` which constains the values of the parameters of the model for said transition. +The last requirement is that the transition must be structure with a field `θ` which constains the values of the parameters of the model for said transition. This allows `Turing` to seemlessly extract the parameter values at each step of the chain when bundling the chains. +Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. -For a practical example of how to adapt a sampling library to the `AbstractMCMC` interface the readers can consult the following (code)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl] within `MicroCanonicalHMC`. +For a practical examples of how to adapt a sampling library to the `AbstractMCMC` interface the readers can consult the following libraries: +- (AdvancedMH)[https://github.com/TuringLang/AdvancedMH.jl/blob/458a602ac32a8514a117d4c671396a9ba8acbdab/src/mh-core.jl#L73-L115] +- (AdvancedHMC)[https://github.com/TuringLang/AdvancedHMC.jl/blob/762e55f894d142495a41a6eba0eed9201da0a600/src/abstractmcmc.jl#L102-L170] +- (MicroCanonicalHMC)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl] within `MicroCanonicalHMC`. # Refences From 4d1340342138479f2dba97ee2704a6aef008f2b8 Mon Sep 17 00:00:00 2001 From: Jaime RZ Date: Fri, 28 Jul 2023 16:44:32 +0100 Subject: [PATCH 75/87] Update tutorials/15-external-samplers/external-samplers.jmd Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- tutorials/15-external-samplers/external-samplers.jmd | 3 --- 1 file changed, 3 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index ac7a154c8..b0fa21ace 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -156,9 +156,6 @@ This allows `Turing` to seemlessly extract the parameter values at each step of Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. For a practical examples of how to adapt a sampling library to the `AbstractMCMC` interface the readers can consult the following libraries: -- (AdvancedMH)[https://github.com/TuringLang/AdvancedMH.jl/blob/458a602ac32a8514a117d4c671396a9ba8acbdab/src/mh-core.jl#L73-L115] -- (AdvancedHMC)[https://github.com/TuringLang/AdvancedHMC.jl/blob/762e55f894d142495a41a6eba0eed9201da0a600/src/abstractmcmc.jl#L102-L170] -- (MicroCanonicalHMC)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl] within `MicroCanonicalHMC`. # Refences From 7796e1123b2cead0f5d1b1ee8fb19dda7b7ccd84 Mon Sep 17 00:00:00 2001 From: Jaime RZ Date: Fri, 28 Jul 2023 16:44:43 +0100 Subject: [PATCH 76/87] Update tutorials/15-external-samplers/external-samplers.jmd Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- tutorials/15-external-samplers/external-samplers.jmd | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index b0fa21ace..a0e007ced 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -157,6 +157,9 @@ Note that if the external sampler produces transitions that Turing cannot parse For a practical examples of how to adapt a sampling library to the `AbstractMCMC` interface the readers can consult the following libraries: + - (AdvancedMH)[https://github.com/TuringLang/AdvancedMH.jl/blob/458a602ac32a8514a117d4c671396a9ba8acbdab/src/mh-core.jl#L73-L115] + - (AdvancedHMC)[https://github.com/TuringLang/AdvancedHMC.jl/blob/762e55f894d142495a41a6eba0eed9201da0a600/src/abstractmcmc.jl#L102-L170] + - (MicroCanonicalHMC)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl] within `MicroCanonicalHMC`. # Refences From 24f1cc3c3ee13343b58efb26d9edc4277ca12544 Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Sun, 30 Jul 2023 22:29:18 +0100 Subject: [PATCH 77/87] Update external-samplers.jmd --- .../external-samplers.jmd | 52 +++++++++---------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/15-external-samplers/external-samplers.jmd index a0e007ced..595ddc8e5 100644 --- a/tutorials/15-external-samplers/external-samplers.jmd +++ b/tutorials/15-external-samplers/external-samplers.jmd @@ -8,13 +8,13 @@ weave_options: # Using External Samplers -`Turing` provides several wrapped samplers from external sampling libraries, e.g. HMC smaplers from `AdvancedHMC`. -These wrappers allow new users to seemlessly sample statistical models without leaving `Turing` -However, these wrappers might not always be complete, missing some functionality from the wrapped sampling library. +`Turing` provides several wrapped samplers from external sampling libraries, e.g., HMC samplers from `AdvancedHMC`. +These wrappers allow new users to seamlessly sample statistical models without leaving `Turing` +However, these wrappers might only sometimes be complete, missing some functionality from the wrapped sampling library. Moreover, users might want to use samplers currently not wrapped within `Turing`. -For these reasons, `Turing` also makes it easy to run external samplers on Turing models without any modifications or wrapping being necessary! -Throughout we will use a 10 dimensional Neal's funnel as a running example:: +For these reasons, `Turing` also makes it easy to run external samplers on Turing models without any necessary modifications or wrapping! +Throughout we will use a 10-dimensional Neal's funnel as a running example:: ```julia # Import libraries. @@ -28,21 +28,21 @@ d = 10 end ``` -Now we sample the model to generate some observations, which we can then subsequently condition on. +Now we sample the model to generate some observations, which we can then condition on. ```julia (; x) = rand(funnel() | (θ=0,)) model = funnel() | (; x) ``` -Users can use any sampler algorithm they wish to sample this model as long as it follows the `AbstractMCMC` API. -Before discussing how is this in done in practice, it is interesting to give a high level description of the process. -Imagine that we created an instance of external sampler that we will call `spl` such that `typeof(spl)<:AbstractMCMC.AbstractSampler`. +Users can use any sampler algorithm to sample this model if it follows the `AbstractMCMC` API. +Before discussing how this is done in practice, giving a high-level description of the process is interesting. +Imagine that we created an instance of an external sampler that we will call `spl` such that `typeof(spl)<:AbstractMCMC.AbstractSampler`. In order to avoid type ambiguity within Turing, at the moment it is necessary to declare `spl` as an external sampler to Turing `espl = externalsampler(spl)`, where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. -A good point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` ((`AdvancedMH`'s GitHub)[[https://github.com/TuringLang/AdvancedMH.jl]) for Metropolis Hastings (MH) methods. -Let's say that we want to use a random walk Metropolis Hastings sampler without specifying the proposal distributions. -The code below constructs a MH sampler using a multivariate Gaussian distribution with zero mean and unit variance in `d` dimensions as a random walk proposal. +An excellent point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` ((`AdvancedMH`'s GitHub)[[https://github.com/TuringLang/AdvancedMH.jl]) for Metropolis Hastings (MH) methods. +Let's say we want to use a random walk Metropolis-Hastings sampler without specifying the proposal distributions. +The code below constructs an MH sampler using a multivariate Gaussian distribution with zero mean and unit variance in `d` dimensions as a random walk proposal. ```julia # Importing the sampling library @@ -61,13 +61,13 @@ chain = sample(model, externalsampler(rwmh), 10_000) As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. `AdvancedHMC`[^1] ((`AdvancedHMC`'s GitHub)[https://github.com/TuringLang/AdvancedHMC.jl]) is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within `AdvancedHMC`. -Thankfully, we can use Turing's support for external samplers to define a HMC sampler with a custom mass matrix in `AdvancedHMC` and then use it to sample our Turing model. +Thankfully, we can use Turing's support for external samplers to define an HMC sampler with a custom mass matrix in `AdvancedHMC` and then use it to sample our Turing model. We will use the library `Pathfinder`[^2] ((`Pathfinder`'s GitHub)[https://github.com/mlcolab/Pathfinder.jl]) to construct our estimate of mass matrix. -`Pathfinder` is a variational inference algorithm which first finds the maximum a posteriori (MAP) estimate of a target posterior distribution and then uses the trace of the optimization to construct a sequence of multivariate normal approximations to the target distribution. -In this process, `Pathfinder` computes an estimate of the mass matrix that the user can access. +`Pathfinder` is a variational inference algorithm that first finds the maximum a posteriori (MAP) estimate of a target posterior distribution and then uses the trace of the optimization to construct a sequence of multivariate normal approximations to the target distribution. +In this process, `Pathfinder` computes an estimate of the mass matrix the user can access. -The code below shows can this be done in practice. +The code below shows this can be done in practice. ```julia using AdvancedHMC, Pathfidner @@ -90,9 +90,9 @@ chain = sample(model, externalsampler(nuts), 10_000; n_adapts=1_000) ## Using new inference methods -So far we have used to Turing's support for external samplers to go beyond the capabilities of the wrappers. -Now we want to use this support to employ a sampler not supported within Turing's ecosystem yet. -We will use the recently developped Micro-Cannoncial Hamiltonian Monte Carlo (MCHMC) sampler to showcase this. +So far we have used Turing's support for external samplers to go beyond the capabilities of the wrappers. +We want to use this support to employ a sampler not supported within Turing's ecosystem yet. +We will use the recently developed Micro-Cannoncial Hamiltonian Monte Carlo (MCHMC) sampler to showcase this. MCHMC[^3,^4] ((MCHMC's GitHub)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl]) is HMC sampler that uses one single Hamiltonian energy level to explore the whole parameter space. This is achieved by simulating the dynamics of a microcanonical Hamiltonian with an additional noise term to ensure ergodicity. @@ -112,7 +112,7 @@ chain = sample(model, externalsampler(mchmc), 10_000) The only requirement to work with `externalsampler` is that the provided `sampler` must implement the AbstractMCMC.jl-interface [INSERT LINK] for a `model` of type `AbstractMCMC.LogDensityModel` [INSERT LINK]. As previously stated, in order to use external sampling libraries within `Turing` they must follow the `AbstractMCMC` API. -In this section we will briefly dwell in what this entails. +In this section, we will briefly dwell on what this entails. First and foremost, the sampler should be a subtype of `AbstractMCMC.AbstractSampler`. Second, the stepping function of the MCMC algorithm must be made defined using `AbstractMCMC.step` and follow the structure below: @@ -141,21 +141,21 @@ function AbstractMCMC.step{T<:AbstractMCMC.AbstractSampler}( end ``` -There are several characteristic to note in these functions: +There are several characteristics to note in these functions: - There must be two `step` functions: - + A function that performs the first step which and initializes the sampler. - + A function that performs the following steps and which takes and extra input, `state`, which carries the initialization information. + + A function that performs the first step and initializes the sampler. + + A function that performs the following steps and takes an extra input, `state`, which carries the initialization information. - The functions must follow the displayed signatures. - The output of the functions must be a transition, the current state of the sampler, and a sample, what is saved to the MCMC chain. -The last requirement is that the transition must be structure with a field `θ` which constains the values of the parameters of the model for said transition. -This allows `Turing` to seemlessly extract the parameter values at each step of the chain when bundling the chains. +The last requirement is that the transition must be structured with a field `θ` which contains the values of the parameters of the model for said transition. +This allows `Turing` to seamlessly extract the parameter values at each step of the chain when bundling the chains. Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. -For a practical examples of how to adapt a sampling library to the `AbstractMCMC` interface the readers can consult the following libraries: +For practical examples of how to adapt a sampling library to the `AbstractMCMC` interface, the readers can consult the following libraries: - (AdvancedMH)[https://github.com/TuringLang/AdvancedMH.jl/blob/458a602ac32a8514a117d4c671396a9ba8acbdab/src/mh-core.jl#L73-L115] - (AdvancedHMC)[https://github.com/TuringLang/AdvancedHMC.jl/blob/762e55f894d142495a41a6eba0eed9201da0a600/src/abstractmcmc.jl#L102-L170] From 6ec06d331be626ad2a2532774e86ee9119c782c7 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Sun, 30 Jul 2023 22:45:03 +0100 Subject: [PATCH 78/87] update manifest.toml --- .../Manifest.toml | 118 +++++++++--------- .../Project.toml | 0 .../external-samplers.jmd | 0 3 files changed, 60 insertions(+), 58 deletions(-) rename tutorials/{15-external-samplers => docs-16-external-samplers}/Manifest.toml (95%) rename tutorials/{15-external-samplers => docs-16-external-samplers}/Project.toml (100%) rename tutorials/{15-external-samplers => docs-16-external-samplers}/external-samplers.jmd (100%) diff --git a/tutorials/15-external-samplers/Manifest.toml b/tutorials/docs-16-external-samplers/Manifest.toml similarity index 95% rename from tutorials/15-external-samplers/Manifest.toml rename to tutorials/docs-16-external-samplers/Manifest.toml index 6a2d15089..635e9f215 100644 --- a/tutorials/15-external-samplers/Manifest.toml +++ b/tutorials/docs-16-external-samplers/Manifest.toml @@ -1,23 +1,24 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.9.0" +julia_version = "1.9.2" manifest_format = "2.0" project_hash = "6559f27b923d65d4f9f21538db968bd03760e383" [[deps.ADTypes]] -git-tree-sha1 = "e58c18d2312749847a74f5be80bb0fa53da102bd" +git-tree-sha1 = "f5c25e8a5b29b5e941b7408bc8cc79fea4d9ef9a" uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b" -version = "0.1.5" +version = "0.1.6" [[deps.AbstractFFTs]] deps = ["LinearAlgebra"] -git-tree-sha1 = "cad4c758c0038eea30394b1b671526921ca85b21" +git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.4.0" -weakdeps = ["ChainRulesCore"] +version = "1.5.0" +weakdeps = ["ChainRulesCore", "Test"] [deps.AbstractFFTs.extensions] AbstractFFTsChainRulesCoreExt = "ChainRulesCore" + AbstractFFTsTestExt = "Test" [[deps.AbstractMCMC]] deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "LogDensityProblems", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] @@ -66,9 +67,9 @@ weakdeps = ["StaticArrays"] [[deps.AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "ProgressMeter", "Random", "Requires", "Setfield", "SimpleUnPack", "Statistics", "StatsBase", "StatsFuns"] -git-tree-sha1 = "3bf24030e85b1d6d298e4f483f6aeff6f38462db" +git-tree-sha1 = "d4986b2575ad5710b322a1bbc4939e0208f9f498" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" -version = "0.4.6" +version = "0.5.3" [deps.AdvancedHMC.extensions] AdvancedHMCCUDAExt = "CUDA" @@ -275,9 +276,9 @@ version = "0.3.0" [[deps.Compat]] deps = ["UUIDs"] -git-tree-sha1 = "4e88377ae7ebeaf29a047aa1ee40826e0b708a5d" +git-tree-sha1 = "5ce999a19f4ca23ea484e92a1774a61b8ca4cf8e" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "4.7.0" +version = "4.8.0" weakdeps = ["Dates", "LinearAlgebra"] [deps.Compat.extensions] @@ -286,7 +287,7 @@ weakdeps = ["Dates", "LinearAlgebra"] [[deps.CompilerSupportLibraries_jll]] deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" -version = "1.0.2+0" +version = "1.0.5+0" [[deps.CompositionsBase]] git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" @@ -442,9 +443,9 @@ uuid = "4e289a0a-7415-4d19-859d-a7e5c4648b56" version = "1.0.4" [[deps.ExprTools]] -git-tree-sha1 = "c1d06d129da9f55715c6c212866f5b1bddc5fa00" +git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec" uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04" -version = "0.1.9" +version = "0.1.10" [[deps.ExternalDocstrings]] git-tree-sha1 = "1224740fc4d07c989949e1c1b508ebd49a65a5f6" @@ -557,9 +558,9 @@ version = "0.1.5" [[deps.HypergeometricFunctions]] deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] -git-tree-sha1 = "83e95aaab9dc184a6dcd9c4c52aa0dc26cd14a1d" +git-tree-sha1 = "f218fe3736ddf977e0e772bc9a586b2383da2685" uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" -version = "0.3.21" +version = "0.3.23" [[deps.InitialValues]] git-tree-sha1 = "4da0f88e9a39111c2fa3add390ab15f3a44f3ca3" @@ -595,10 +596,14 @@ uuid = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59" version = "0.14.7" [[deps.IntervalSets]] -deps = ["Dates", "Random", "Statistics"] -git-tree-sha1 = "16c0cc91853084cb5f58a78bd209513900206ce6" +deps = ["Dates", "Random"] +git-tree-sha1 = "8e59ea773deee525c99a8018409f64f19fb719e6" uuid = "8197267c-284f-5f27-9208-e0e47529a953" -version = "0.7.4" +version = "0.7.7" +weakdeps = ["Statistics"] + + [deps.IntervalSets.extensions] + IntervalSetsStatisticsExt = "Statistics" [[deps.InverseFunctions]] deps = ["Test"] @@ -612,9 +617,9 @@ uuid = "41ab1584-1d38-5bbf-9106-f11c6c58b48f" version = "1.3.0" [[deps.IrrationalConstants]] -git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2" +git-tree-sha1 = "7fd44fd4ff43fc60815f8e764c0f352b83c49151" uuid = "92d709cd-6900-40b7-9082-c6be49f344b6" -version = "0.2.2" +version = "0.1.1" [[deps.IterTools]] git-tree-sha1 = "4ced6667f9974fc5c5943fa5e2ef1ca43ea9e450" @@ -740,9 +745,9 @@ version = "2.1.1" [[deps.LogDensityProblemsAD]] deps = ["DocStringExtensions", "LogDensityProblems", "Requires", "SimpleUnPack"] -git-tree-sha1 = "1fc9bc3d74d6bc828d09880d8ceb12d231188e24" +git-tree-sha1 = "7841ebecc60703fa978cbe9623e1a37dcaf96b75" uuid = "996a588d-648d-4e1f-a8f0-a84b347e47b1" -version = "1.6.0" +version = "1.6.1" [deps.LogDensityProblemsAD.extensions] LogDensityProblemsADADTypesExt = "ADTypes" @@ -786,16 +791,16 @@ uuid = "e6f89c97-d47a-5376-807f-9c37f3926c36" version = "1.0.0" [[deps.MCMCChains]] -deps = ["AbstractMCMC", "AxisArrays", "Dates", "Distributions", "Formatting", "IteratorInterfaceExtensions", "KernelDensity", "LinearAlgebra", "MCMCDiagnosticTools", "MLJModelInterface", "NaturalSort", "OrderedCollections", "PrettyTables", "Random", "RecipesBase", "Serialization", "Statistics", "StatsBase", "StatsFuns", "TableTraits", "Tables"] -git-tree-sha1 = "c659f7508035a7bdd5102aef2de028ab035f289a" +deps = ["AbstractMCMC", "AxisArrays", "Dates", "Distributions", "Formatting", "IteratorInterfaceExtensions", "KernelDensity", "LinearAlgebra", "MCMCDiagnosticTools", "MLJModelInterface", "NaturalSort", "OrderedCollections", "PrettyTables", "Random", "RecipesBase", "Statistics", "StatsBase", "StatsFuns", "TableTraits", "Tables"] +git-tree-sha1 = "8778ea7283a0bf0d7e507a0235adfff38071493b" uuid = "c7f686f2-ff18-58e9-bc7b-31028e88f75d" -version = "5.7.1" +version = "6.0.3" [[deps.MCMCDiagnosticTools]] deps = ["AbstractFFTs", "DataAPI", "DataStructures", "Distributions", "LinearAlgebra", "MLJModelInterface", "Random", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns", "Tables"] -git-tree-sha1 = "8b862779314f9299cbf7bdbf2413bcbd9c8e77b2" +git-tree-sha1 = "695e91605361d1932c3e89a812be78480a4a4595" uuid = "be115224-59cd-429b-ad48-344e309966f0" -version = "0.2.6" +version = "0.3.4" [[deps.MKL_jll]] deps = ["Artifacts", "IntelOpenMP_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "Pkg"] @@ -831,9 +836,9 @@ version = "2.28.2+0" [[deps.MicroCanonicalHMC]] deps = ["AbstractMCMC", "BlockDiagonals", "CSV", "DataFrames", "Distributed", "Distributions", "DistributionsAD", "DynamicPPL", "ForwardDiff", "Interpolations", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "MCMCDiagnosticTools", "NPZ", "ProgressMeter", "Random", "Statistics", "Turing", "YAML"] -git-tree-sha1 = "3ece57a4711b4378e128203672bb6dcb3ea94fa5" +git-tree-sha1 = "4ba945aa51b383cb7d90849b3e6c4287bb67cd27" repo-rev = "master" -repo-url = "https://github.com/JaimeRZP/MicroCanonicalHMC.jl.git" +repo-url = "https://github.com/jaimerzp/MicroCanonicalHMC.jl" uuid = "234d2aa0-2291-45f7-9047-6fa6f316b0a8" version = "0.1.0" @@ -935,9 +940,9 @@ version = "1.7.6" [[deps.Optimisers]] deps = ["ChainRulesCore", "Functors", "LinearAlgebra", "Random", "Statistics"] -git-tree-sha1 = "6a01f65dd8583dee82eecc2a19b0ff21521aa749" +git-tree-sha1 = "16776280310aa5553c370b9c7b17f34aadaf3c8e" uuid = "3bd65402-5787-11e9-1adc-39752487f4e2" -version = "0.2.18" +version = "0.2.19" [[deps.Optimization]] deps = ["ADTypes", "ArrayInterface", "ConsoleProgressMonitor", "DocStringExtensions", "LinearAlgebra", "Logging", "LoggingExtras", "Pkg", "Printf", "ProgressLogging", "Reexport", "Requires", "SciMLBase", "SparseArrays", "TerminalLoggers"] @@ -982,9 +987,9 @@ version = "0.11.17" [[deps.PSIS]] deps = ["LinearAlgebra", "LogExpFunctions", "Printf", "RecipesBase", "Statistics"] -git-tree-sha1 = "18f1f130619068d6461e61e11e78b887c5f860f5" +git-tree-sha1 = "72a16ef4d76e0cb071d704ff50dc086b6715d2f9" uuid = "ce719bf2-d5d0-4fb9-925d-10a81b42ad04" -version = "0.9.1" +version = "0.7.2" [[deps.Parameters]] deps = ["OrderedCollections", "UnPack"] @@ -994,31 +999,20 @@ version = "0.12.3" [[deps.Parsers]] deps = ["Dates", "PrecompileTools", "UUIDs"] -git-tree-sha1 = "4b2e829ee66d4218e0cef22c0a64ee37cf258c29" +git-tree-sha1 = "716e24b21538abc91f6205fd1d8363f39b442851" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" -version = "2.7.1" +version = "2.7.2" [[deps.Pathfinder]] -deps = ["Distributions", "Folds", "ForwardDiff", "IrrationalConstants", "LinearAlgebra", "LogDensityProblems", "Optim", "Optimization", "OptimizationOptimJL", "PDMats", "PSIS", "ProgressLogging", "Random", "Requires", "SciMLBase", "Statistics", "StatsBase", "Transducers", "UnPack"] -git-tree-sha1 = "35a7f63ca409ba5951f64b5eaffdcbb0a21936eb" +deps = ["Accessors", "Distributions", "Folds", "ForwardDiff", "IrrationalConstants", "LinearAlgebra", "LogDensityProblems", "Optim", "Optimization", "OptimizationOptimJL", "PDMats", "PSIS", "ProgressLogging", "Random", "Requires", "SciMLBase", "Statistics", "StatsBase", "Transducers", "UnPack"] +git-tree-sha1 = "9276ab48cb3a900e0bca214912c756d52625dadb" uuid = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454" -version = "0.7.6" - - [deps.Pathfinder.extensions] - PathfinderDynamicHMCExt = "DynamicHMC" - PathfinderTuringExt = ["Accessors", "DynamicPPL", "MCMCChains", "Turing"] - - [deps.Pathfinder.weakdeps] - Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697" - DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb" - DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8" - MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d" - Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" +version = "0.7.0" [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" -version = "1.9.0" +version = "1.9.2" [[deps.PooledArrays]] deps = ["DataAPI", "Future"] @@ -1045,10 +1039,10 @@ uuid = "21216c6a-2e73-6563-6e65-726566657250" version = "1.4.0" [[deps.PrettyTables]] -deps = ["Crayons", "Formatting", "LaTeXStrings", "Markdown", "Reexport", "StringManipulation", "Tables"] -git-tree-sha1 = "542b1bd03329c1d235110f96f1bb0eeffc48a87d" +deps = ["Crayons", "LaTeXStrings", "Markdown", "Printf", "Reexport", "StringManipulation", "Tables"] +git-tree-sha1 = "ee094908d720185ddbdc58dbe0c1cbe35453ec7a" uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" -version = "2.2.6" +version = "2.2.7" [[deps.Printf]] deps = ["Unicode"] @@ -1200,9 +1194,9 @@ version = "0.7.0" [[deps.SciMLBase]] deps = ["ADTypes", "ArrayInterface", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces"] -git-tree-sha1 = "92f8e23b4a5eb7e45bffe09027fcdda44a949a51" +git-tree-sha1 = "04370090604cd399db5bebddb636d80ab9d338e9" uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462" -version = "1.93.4" +version = "1.94.0" [[deps.SciMLOperators]] deps = ["ArrayInterface", "DocStringExtensions", "Lazy", "LinearAlgebra", "Setfield", "SparseArrays", "StaticArraysCore", "Tricks"] @@ -1436,9 +1430,17 @@ version = "1.4.0" [[deps.Turing]] deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Tracker"] -git-tree-sha1 = "9c10ad4bf838360d3187a76664ce9558e75e5ed5" +git-tree-sha1 = "89fa5db8e86044c15bfe6d86a1231b46aebe2e70" uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" -version = "0.26.6" +version = "0.28.0" + + [deps.Turing.extensions] + TuringDynamicHMCExt = "DynamicHMC" + TuringOptimExt = "Optim" + + [deps.Turing.weakdeps] + DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb" + Optim = "429524aa-4258-5aef-a3af-852621145aeb" [[deps.UUIDs]] deps = ["Random", "SHA"] @@ -1506,7 +1508,7 @@ version = "0.2.3" [[deps.libblastrampoline_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.7.0+0" +version = "5.8.0+0" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] diff --git a/tutorials/15-external-samplers/Project.toml b/tutorials/docs-16-external-samplers/Project.toml similarity index 100% rename from tutorials/15-external-samplers/Project.toml rename to tutorials/docs-16-external-samplers/Project.toml diff --git a/tutorials/15-external-samplers/external-samplers.jmd b/tutorials/docs-16-external-samplers/external-samplers.jmd similarity index 100% rename from tutorials/15-external-samplers/external-samplers.jmd rename to tutorials/docs-16-external-samplers/external-samplers.jmd From f8e9b03fcf6099aff681ed1015efb1c77abc4317 Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Thu, 17 Aug 2023 10:06:40 +0100 Subject: [PATCH 79/87] Update external-samplers.jmd --- .../docs-16-external-samplers/external-samplers.jmd | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tutorials/docs-16-external-samplers/external-samplers.jmd b/tutorials/docs-16-external-samplers/external-samplers.jmd index 595ddc8e5..9d7d331f1 100644 --- a/tutorials/docs-16-external-samplers/external-samplers.jmd +++ b/tutorials/docs-16-external-samplers/external-samplers.jmd @@ -13,8 +13,8 @@ These wrappers allow new users to seamlessly sample statistical models without l However, these wrappers might only sometimes be complete, missing some functionality from the wrapped sampling library. Moreover, users might want to use samplers currently not wrapped within `Turing`. -For these reasons, `Turing` also makes it easy to run external samplers on Turing models without any necessary modifications or wrapping! -Throughout we will use a 10-dimensional Neal's funnel as a running example:: +For these reasons, `Turing` also makes running external samplers on Turing models easy without any necessary modifications or wrapping! +Throughout, we will use a 10-dimensional Neal's funnel as a running example:: ```julia # Import libraries. @@ -38,9 +38,9 @@ model = funnel() | (; x) Users can use any sampler algorithm to sample this model if it follows the `AbstractMCMC` API. Before discussing how this is done in practice, giving a high-level description of the process is interesting. Imagine that we created an instance of an external sampler that we will call `spl` such that `typeof(spl)<:AbstractMCMC.AbstractSampler`. -In order to avoid type ambiguity within Turing, at the moment it is necessary to declare `spl` as an external sampler to Turing `espl = externalsampler(spl)`, where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adquately. +In order to avoid type ambiguity within Turing, at the moment it is necessary to declare `spl` as an external sampler to Turing `espl = externalsampler(spl)`, where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adequately. -An excellent point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` ((`AdvancedMH`'s GitHub)[[https://github.com/TuringLang/AdvancedMH.jl]) for Metropolis Hastings (MH) methods. +An excellent point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` ((`AdvancedMH`'s GitHub)[[https://github.com/TuringLang/AdvancedMH.jl]) for Metropolis-Hastings (MH) methods. Let's say we want to use a random walk Metropolis-Hastings sampler without specifying the proposal distributions. The code below constructs an MH sampler using a multivariate Gaussian distribution with zero mean and unit variance in `d` dimensions as a random walk proposal. @@ -58,7 +58,7 @@ chain = sample(model, externalsampler(rwmh), 10_000) ## Going beyond the `Turing` API -As previously mentioned the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. +As previously mentioned, the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. `AdvancedHMC`[^1] ((`AdvancedHMC`'s GitHub)[https://github.com/TuringLang/AdvancedHMC.jl]) is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix. However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within `AdvancedHMC`. Thankfully, we can use Turing's support for external samplers to define an HMC sampler with a custom mass matrix in `AdvancedHMC` and then use it to sample our Turing model. From 7cc1703e465c0319ea137c6a454ddcbdc1ac8ed5 Mon Sep 17 00:00:00 2001 From: Hong Ge Date: Thu, 17 Aug 2023 10:10:15 +0100 Subject: [PATCH 80/87] update manifest file --- .../docs-16-external-samplers/Manifest.toml | 121 +++++++++--------- 1 file changed, 63 insertions(+), 58 deletions(-) diff --git a/tutorials/docs-16-external-samplers/Manifest.toml b/tutorials/docs-16-external-samplers/Manifest.toml index 635e9f215..60e870a65 100644 --- a/tutorials/docs-16-external-samplers/Manifest.toml +++ b/tutorials/docs-16-external-samplers/Manifest.toml @@ -1,6 +1,6 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.9.2" +julia_version = "1.10.0-beta1" manifest_format = "2.0" project_hash = "6559f27b923d65d4f9f21538db968bd03760e383" @@ -28,9 +28,9 @@ version = "4.4.2" [[deps.AbstractPPL]] deps = ["AbstractMCMC", "DensityInterface", "Random", "Setfield", "SparseArrays"] -git-tree-sha1 = "33ea6c6837332395dbf3ba336f273c9f7fcf4db9" +git-tree-sha1 = "caa9b62583577b0d6b222f11f54aa29fabbdb5ca" uuid = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf" -version = "0.5.4" +version = "0.6.2" [[deps.AbstractTrees]] git-tree-sha1 = "faa260e4cb5aba097a73fab382dd4b5819d8ec8c" @@ -67,9 +67,9 @@ weakdeps = ["StaticArrays"] [[deps.AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "ProgressMeter", "Random", "Requires", "Setfield", "SimpleUnPack", "Statistics", "StatsBase", "StatsFuns"] -git-tree-sha1 = "d4986b2575ad5710b322a1bbc4939e0208f9f498" +git-tree-sha1 = "d782efeacf92a1dcc65a77ea1507577d4dc843fe" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" -version = "0.5.3" +version = "0.5.4" [deps.AdvancedHMC.extensions] AdvancedHMCCUDAExt = "CUDA" @@ -186,10 +186,10 @@ uuid = "9718e550-a3fa-408a-8086-8db961cd8217" version = "0.1.1" [[deps.Bijectors]] -deps = ["ArgCheck", "ChainRulesCore", "ChangesOfVariables", "Compat", "Distributions", "Functors", "InverseFunctions", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "MappedArrays", "Random", "Reexport", "Requires", "Roots", "SparseArrays", "Statistics"] -git-tree-sha1 = "ff192d037dee3c05fe842a207f8c6b840b04cca2" +deps = ["ArgCheck", "ChainRules", "ChainRulesCore", "ChangesOfVariables", "Compat", "Distributions", "Functors", "InverseFunctions", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "MappedArrays", "Random", "Reexport", "Requires", "Roots", "SparseArrays", "Statistics"] +git-tree-sha1 = "af192c7c235264bdc6f67321fd1c57be0dd7ffb5" uuid = "76274a88-744f-5084-9051-94815aaf08c4" -version = "0.12.8" +version = "0.13.6" [deps.Bijectors.extensions] BijectorsDistributionsADExt = "DistributionsAD" @@ -276,9 +276,9 @@ version = "0.3.0" [[deps.Compat]] deps = ["UUIDs"] -git-tree-sha1 = "5ce999a19f4ca23ea484e92a1774a61b8ca4cf8e" +git-tree-sha1 = "e460f044ca8b99be31d35fe54fc33a5c33dd8ed7" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "4.8.0" +version = "4.9.0" weakdeps = ["Dates", "LinearAlgebra"] [deps.Compat.extensions] @@ -333,9 +333,9 @@ version = "1.6.1" [[deps.DataStructures]] deps = ["Compat", "InteractiveUtils", "OrderedCollections"] -git-tree-sha1 = "cf25ccb972fec4e4817764d01c82386ae94f77b4" +git-tree-sha1 = "3dbd312d370723b6bb43ba9d02fc36abade4518d" uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" -version = "0.18.14" +version = "0.18.15" [[deps.DataValueInterfaces]] git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6" @@ -380,10 +380,10 @@ deps = ["Random", "Serialization", "Sockets"] uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" [[deps.Distributions]] -deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"] -git-tree-sha1 = "e76a3281de2719d7c81ed62c6ea7057380c87b1d" +deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"] +git-tree-sha1 = "938fe2981db009f531b6332e31c58e9584a2f9bd" uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" -version = "0.25.98" +version = "0.25.100" weakdeps = ["ChainRulesCore", "DensityInterface"] [deps.Distributions.extensions] @@ -427,9 +427,13 @@ version = "0.6.8" [[deps.DynamicPPL]] deps = ["AbstractMCMC", "AbstractPPL", "BangBang", "Bijectors", "ChainRulesCore", "ConstructionBase", "Distributions", "DocStringExtensions", "LinearAlgebra", "LogDensityProblems", "MacroTools", "OrderedCollections", "Random", "Setfield", "Test", "ZygoteRules"] -git-tree-sha1 = "e3518c62a998defe74c5cc64b2bf2a33b7a788b8" +git-tree-sha1 = "c85676be0049fd673c71d655b7a308c09eb83ed6" uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8" -version = "0.23.0" +version = "0.23.13" +weakdeps = ["MCMCChains"] + + [deps.DynamicPPL.extensions] + DynamicPPLMCMCChainsExt = ["MCMCChains"] [[deps.EllipticalSliceSampling]] deps = ["AbstractMCMC", "ArrayInterface", "Distributions", "Random", "Statistics"] @@ -503,9 +507,9 @@ version = "2.21.1" [[deps.FiniteDifferences]] deps = ["ChainRulesCore", "LinearAlgebra", "Printf", "Random", "Richardson", "SparseArrays", "StaticArrays"] -git-tree-sha1 = "549636fd813ddf1816d8407efb23f486822f4b63" +git-tree-sha1 = "98a4571ba51bf172c798e7cde9b16c11c1e22e6d" uuid = "26cc04aa-876d-5657-8c51-4c34ba976000" -version = "0.12.29" +version = "0.12.30" [[deps.Folds]] deps = ["Accessors", "BangBang", "Baselet", "DefineSingletons", "Distributed", "ExternalDocstrings", "InitialValues", "MicroCollections", "Referenceables", "Requires", "Test", "ThreadedScans", "Transducers"] @@ -521,9 +525,9 @@ version = "0.4.2" [[deps.ForwardDiff]] deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "LinearAlgebra", "LogExpFunctions", "NaNMath", "Preferences", "Printf", "Random", "SpecialFunctions"] -git-tree-sha1 = "00e252f4d706b3d55a8863432e742bf5717b498d" +git-tree-sha1 = "cf0fe81336da9fb90944683b8c41984b08793dad" uuid = "f6369f11-7733-5829-9624-2563aa707210" -version = "0.10.35" +version = "0.10.36" weakdeps = ["StaticArrays"] [deps.ForwardDiff.extensions] @@ -581,9 +585,9 @@ version = "0.3.0" [[deps.IntelOpenMP_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "0cb9352ef2e01574eeebdb102948a58740dcaf83" +git-tree-sha1 = "ad37c091f7d7daf900963171600d7c1c5c3ede32" uuid = "1d5cc7b8-4909-519e-a0f8-d0f5ad9712d0" -version = "2023.1.0+0" +version = "2023.2.0+0" [[deps.InteractiveUtils]] deps = ["Markdown"] @@ -607,9 +611,9 @@ weakdeps = ["Statistics"] [[deps.InverseFunctions]] deps = ["Test"] -git-tree-sha1 = "eabe3125edba5c9c10b60a160b1779a000dc8b29" +git-tree-sha1 = "68772f49f54b479fa88ace904f6127f0a3bb2e46" uuid = "3587e190-3f89-42d0-90ee-14403ec27112" -version = "0.1.11" +version = "0.1.12" [[deps.InvertedIndices]] git-tree-sha1 = "0dc7b50b8d436461be01300fd8cd45aa0274b038" @@ -632,10 +636,10 @@ uuid = "82899510-4779-5014-852e-03e436cf321d" version = "1.0.0" [[deps.JLLWrappers]] -deps = ["Preferences"] -git-tree-sha1 = "abc9885a7ca2052a736a600f7fa66209f96506e1" +deps = ["Artifacts", "Preferences"] +git-tree-sha1 = "a7e91ef94114d5bc8952bcaa8d6ff952cf709808" uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210" -version = "1.4.1" +version = "1.4.2" [[deps.KernelAbstractions]] deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] @@ -696,12 +700,12 @@ version = "0.2.0" [[deps.LibCURL]] deps = ["LibCURL_jll", "MozillaCACerts_jll"] uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" -version = "0.6.3" +version = "0.6.4" [[deps.LibCURL_jll]] deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" -version = "7.84.0+0" +version = "8.0.1+0" [[deps.LibGit2]] deps = ["Base64", "NetworkOptions", "Printf", "SHA"] @@ -798,21 +802,21 @@ version = "6.0.3" [[deps.MCMCDiagnosticTools]] deps = ["AbstractFFTs", "DataAPI", "DataStructures", "Distributions", "LinearAlgebra", "MLJModelInterface", "Random", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns", "Tables"] -git-tree-sha1 = "695e91605361d1932c3e89a812be78480a4a4595" +git-tree-sha1 = "3e6db72c2ab9cadfa3278ff388473a01fc0cfb9d" uuid = "be115224-59cd-429b-ad48-344e309966f0" -version = "0.3.4" +version = "0.3.5" [[deps.MKL_jll]] deps = ["Artifacts", "IntelOpenMP_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "Pkg"] -git-tree-sha1 = "154d7aaa82d24db6d8f7e4ffcfe596f40bff214b" +git-tree-sha1 = "eb006abbd7041c28e0d16260e50a24f8f9104913" uuid = "856f044c-d86e-5d09-b602-aeab76dc8ba7" -version = "2023.1.0+0" +version = "2023.2.0+0" [[deps.MLJModelInterface]] deps = ["Random", "ScientificTypesBase", "StatisticalTraits"] -git-tree-sha1 = "c8b7e632d6754a5e36c0d94a4b466a5ba3a30128" +git-tree-sha1 = "e89d1ea12c5a50057bfb0c124d905669e5ed4ec9" uuid = "e80e1ace-859a-464e-9ed9-23947d8ae3ea" -version = "1.8.0" +version = "1.9.1" [[deps.MacroTools]] deps = ["Markdown", "Random"] @@ -836,9 +840,9 @@ version = "2.28.2+0" [[deps.MicroCanonicalHMC]] deps = ["AbstractMCMC", "BlockDiagonals", "CSV", "DataFrames", "Distributed", "Distributions", "DistributionsAD", "DynamicPPL", "ForwardDiff", "Interpolations", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "MCMCDiagnosticTools", "NPZ", "ProgressMeter", "Random", "Statistics", "Turing", "YAML"] -git-tree-sha1 = "4ba945aa51b383cb7d90849b3e6c4287bb67cd27" +git-tree-sha1 = "3291385729348de7bbc0f6ecafc349808e61d30c" repo-rev = "master" -repo-url = "https://github.com/jaimerzp/MicroCanonicalHMC.jl" +repo-url = "https://github.com/JaimeRZP/MicroCanonicalHMC.jl" uuid = "234d2aa0-2291-45f7-9047-6fa6f316b0a8" version = "0.1.0" @@ -859,7 +863,7 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" -version = "2022.10.11" +version = "2023.1.10" [[deps.NLSolversBase]] deps = ["DiffResults", "Distributed", "FiniteDiff", "ForwardDiff"] @@ -897,9 +901,9 @@ version = "1.0.2" [[deps.NamedArrays]] deps = ["Combinatorics", "DataStructures", "DelimitedFiles", "InvertedIndices", "LinearAlgebra", "Random", "Requires", "SparseArrays", "Statistics"] -git-tree-sha1 = "b84e17976a40cb2bfe3ae7edb3673a8c630d4f95" +git-tree-sha1 = "6d42eca6c3a27dc79172d6d947ead136d88751bb" uuid = "86f7a689-2022-50b4-a561-43c23ac3c673" -version = "0.9.8" +version = "0.10.0" [[deps.NaturalSort]] git-tree-sha1 = "eda490d06b9f7c00752ee81cfa451efe55521e21" @@ -919,7 +923,7 @@ version = "1.12.10" [[deps.OpenBLAS_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" -version = "0.3.21+4" +version = "0.3.23+0" [[deps.OpenLibm_jll]] deps = ["Artifacts", "Libdl"] @@ -934,9 +938,9 @@ version = "0.5.5+0" [[deps.Optim]] deps = ["Compat", "FillArrays", "ForwardDiff", "LineSearches", "LinearAlgebra", "NLSolversBase", "NaNMath", "Parameters", "PositiveFactorizations", "Printf", "SparseArrays", "StatsBase"] -git-tree-sha1 = "e3a6546c1577bfd701771b477b794a52949e7594" +git-tree-sha1 = "963b004d15216f8129f6c0f7d187efa136570be0" uuid = "429524aa-4258-5aef-a3af-852621145aeb" -version = "1.7.6" +version = "1.7.7" [[deps.Optimisers]] deps = ["ChainRulesCore", "Functors", "LinearAlgebra", "Random", "Statistics"] @@ -1012,7 +1016,7 @@ version = "0.7.0" [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" -version = "1.9.2" +version = "1.10.0" [[deps.PooledArrays]] deps = ["DataAPI", "Future"] @@ -1071,7 +1075,7 @@ deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" [[deps.Random]] -deps = ["SHA", "Serialization"] +deps = ["SHA"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" [[deps.Random123]] @@ -1168,9 +1172,9 @@ version = "0.4.0+0" [[deps.Roots]] deps = ["ChainRulesCore", "CommonSolve", "Printf", "Setfield"] -git-tree-sha1 = "de432823e8aab4dd1a985be4be768f95acf152d4" +git-tree-sha1 = "ff42754a57bb0d6dcfe302fd0d4272853190421f" uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665" -version = "2.0.17" +version = "2.0.19" [deps.Roots.extensions] RootsForwardDiffExt = "ForwardDiff" @@ -1184,9 +1188,9 @@ version = "2.0.17" [[deps.RuntimeGeneratedFunctions]] deps = ["ExprTools", "SHA", "Serialization"] -git-tree-sha1 = "237edc1563bbf078629b4f8d194bd334e97907cf" +git-tree-sha1 = "6aacc5eefe8415f47b3e34214c1d79d2674a0ba2" uuid = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47" -version = "0.5.11" +version = "0.5.12" [[deps.SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" @@ -1200,9 +1204,9 @@ version = "1.94.0" [[deps.SciMLOperators]] deps = ["ArrayInterface", "DocStringExtensions", "Lazy", "LinearAlgebra", "Setfield", "SparseArrays", "StaticArraysCore", "Tricks"] -git-tree-sha1 = "745755a5b932c9a664d7e9e4beb60c692b211d4b" +git-tree-sha1 = "65c2e6ced6f62ea796af251eb292a0e131a3613b" uuid = "c0aeaf25-5076-4817-a8d5-81caf7dfa961" -version = "0.3.5" +version = "0.3.6" [[deps.ScientificTypesBase]] git-tree-sha1 = "a8e18eb383b5ecf1b5e6fc237eb39255044fd92b" @@ -1245,12 +1249,13 @@ version = "1.1.1" [[deps.SparseArrays]] deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" +version = "1.10.0" [[deps.SpecialFunctions]] deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] -git-tree-sha1 = "7beb031cf8145577fbccacd94b8a8f4ce78428d3" +git-tree-sha1 = "e2cfc4012a19088254b3950b85c3c1d8882d864d" uuid = "276daf66-3868-5448-9aa4-cd146d93841b" -version = "2.3.0" +version = "2.3.1" weakdeps = ["ChainRulesCore"] [deps.SpecialFunctions.extensions] @@ -1335,7 +1340,7 @@ uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" [[deps.SuiteSparse_jll]] deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"] uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" -version = "5.10.1+6" +version = "7.2.0+0" [[deps.SymbolicIndexingInterface]] deps = ["DocStringExtensions"] @@ -1430,9 +1435,9 @@ version = "1.4.0" [[deps.Turing]] deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Tracker"] -git-tree-sha1 = "89fa5db8e86044c15bfe6d86a1231b46aebe2e70" +git-tree-sha1 = "172b7a658d7ea91dad3203e711d83d9c91d01fed" uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" -version = "0.28.0" +version = "0.28.2" [deps.Turing.extensions] TuringDynamicHMCExt = "DynamicHMC" @@ -1513,7 +1518,7 @@ version = "5.8.0+0" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.48.0+0" +version = "1.52.0+0" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] From f79aa964168ea25604486057d6aea6c730ae5ee7 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Tue, 19 Sep 2023 20:45:42 +0100 Subject: [PATCH 81/87] bug --- tutorials/docs-16-external-samplers/external-samplers.jmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/docs-16-external-samplers/external-samplers.jmd b/tutorials/docs-16-external-samplers/external-samplers.jmd index 9d7d331f1..342cfff22 100644 --- a/tutorials/docs-16-external-samplers/external-samplers.jmd +++ b/tutorials/docs-16-external-samplers/external-samplers.jmd @@ -77,7 +77,7 @@ result_multi = multipathfinder(model, draws; nruns=8) # Estimating the metric inv_metric = result_multi.pathfinder_results[1].fit_distribution.Σ -metric = Pathfinder.RankUpdateEuclideanMetric(inv_metric) +metric = DenseEuclideanMetric(Matrix(inv_metric)) # Creating an AdvancedHMC NUTS sampler with the custom metric. n_adapts = 1000 # Number of adaptation steps From 32284d5dd542fe730371a7ccca19dd66c741a398 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Wed, 20 Sep 2023 09:17:48 +0100 Subject: [PATCH 82/87] upd deps --- .../docs-16-external-samplers/Manifest.toml | 181 ++++++++++-------- 1 file changed, 98 insertions(+), 83 deletions(-) diff --git a/tutorials/docs-16-external-samplers/Manifest.toml b/tutorials/docs-16-external-samplers/Manifest.toml index 60e870a65..420820429 100644 --- a/tutorials/docs-16-external-samplers/Manifest.toml +++ b/tutorials/docs-16-external-samplers/Manifest.toml @@ -1,13 +1,13 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.10.0-beta1" +julia_version = "1.9.2" manifest_format = "2.0" project_hash = "6559f27b923d65d4f9f21538db968bd03760e383" [[deps.ADTypes]] -git-tree-sha1 = "f5c25e8a5b29b5e941b7408bc8cc79fea4d9ef9a" +git-tree-sha1 = "5d2e21d7b0d8c22f67483ef95ebdc39c0e6b6003" uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b" -version = "0.1.6" +version = "0.2.4" [[deps.AbstractFFTs]] deps = ["LinearAlgebra"] @@ -67,9 +67,9 @@ weakdeps = ["StaticArrays"] [[deps.AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "ProgressMeter", "Random", "Requires", "Setfield", "SimpleUnPack", "Statistics", "StatsBase", "StatsFuns"] -git-tree-sha1 = "d782efeacf92a1dcc65a77ea1507577d4dc843fe" +git-tree-sha1 = "acbe805c3078ba0057bb56985248bd66bce016b1" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" -version = "0.5.4" +version = "0.5.5" [deps.AdvancedHMC.extensions] AdvancedHMCCUDAExt = "CUDA" @@ -187,9 +187,9 @@ version = "0.1.1" [[deps.Bijectors]] deps = ["ArgCheck", "ChainRules", "ChainRulesCore", "ChangesOfVariables", "Compat", "Distributions", "Functors", "InverseFunctions", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "MappedArrays", "Random", "Reexport", "Requires", "Roots", "SparseArrays", "Statistics"] -git-tree-sha1 = "af192c7c235264bdc6f67321fd1c57be0dd7ffb5" +git-tree-sha1 = "8eacff457e5b8c13a97848484ad650dabbffa0fc" uuid = "76274a88-744f-5084-9051-94815aaf08c4" -version = "0.13.6" +version = "0.13.7" [deps.Bijectors.extensions] BijectorsDistributionsADExt = "DistributionsAD" @@ -231,10 +231,10 @@ uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9" version = "0.5.1" [[deps.ChainRules]] -deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "Statistics", "StructArrays"] -git-tree-sha1 = "f98ae934cd677d51d2941088849f0bf2f59e6f6e" +deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "SparseInverseSubset", "Statistics", "StructArrays", "SuiteSparse"] +git-tree-sha1 = "dbeca245b0680f5393b4e6c40dcead7230ab0b3b" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" -version = "1.53.0" +version = "1.54.0" [[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra", "SparseArrays"] @@ -306,9 +306,9 @@ version = "0.1.2" [[deps.ConstructionBase]] deps = ["LinearAlgebra"] -git-tree-sha1 = "fe2838a593b5f776e1597e086dcd47560d94e816" +git-tree-sha1 = "c53fc348ca4d40d7b371e71fd52251839080cbc9" uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" -version = "1.5.3" +version = "1.5.4" weakdeps = ["IntervalSets", "StaticArrays"] [deps.ConstructionBase.extensions] @@ -426,10 +426,10 @@ uuid = "fa6b7ba4-c1ee-5f82-b5fc-ecf0adba8f74" version = "0.6.8" [[deps.DynamicPPL]] -deps = ["AbstractMCMC", "AbstractPPL", "BangBang", "Bijectors", "ChainRulesCore", "ConstructionBase", "Distributions", "DocStringExtensions", "LinearAlgebra", "LogDensityProblems", "MacroTools", "OrderedCollections", "Random", "Setfield", "Test", "ZygoteRules"] -git-tree-sha1 = "c85676be0049fd673c71d655b7a308c09eb83ed6" +deps = ["AbstractMCMC", "AbstractPPL", "BangBang", "Bijectors", "ChainRulesCore", "ConstructionBase", "Distributions", "DocStringExtensions", "LinearAlgebra", "LogDensityProblems", "MacroTools", "OrderedCollections", "Random", "Requires", "Setfield", "Test", "ZygoteRules"] +git-tree-sha1 = "c60859c9bb51353a6f4a9769a0fb771cc4de94bc" uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8" -version = "0.23.13" +version = "0.23.17" weakdeps = ["MCMCChains"] [deps.DynamicPPL.extensions] @@ -637,9 +637,9 @@ version = "1.0.0" [[deps.JLLWrappers]] deps = ["Artifacts", "Preferences"] -git-tree-sha1 = "a7e91ef94114d5bc8952bcaa8d6ff952cf709808" +git-tree-sha1 = "7e5d6779a1e09a36db2a7b6cff50942a0a7d0fca" uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210" -version = "1.4.2" +version = "1.5.0" [[deps.KernelAbstractions]] deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] @@ -661,20 +661,20 @@ version = "0.6.7" [[deps.LLVM]] deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"] -git-tree-sha1 = "8695a49bfe05a2dc0feeefd06b4ca6361a018729" +git-tree-sha1 = "a9d2ce1d5007b1e8f6c5b89c5a31ff8bd146db5c" uuid = "929cbde3-209d-540e-8aea-75f648917ca0" -version = "6.1.0" +version = "6.2.1" [[deps.LLVMExtra_jll]] deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] -git-tree-sha1 = "c35203c1e1002747da220ffc3c0762ce7754b08c" +git-tree-sha1 = "7ca6850ae880cc99b59b88517545f91a52020afa" uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab" -version = "0.0.23+0" +version = "0.0.25+0" [[deps.LRUCache]] -git-tree-sha1 = "48c10e3cc27e30de82463c27bef0b8bdbd1dc634" +git-tree-sha1 = "d36130483e3b6e4cd88d81633b596563264f15db" uuid = "8ac3fa9e-de4c-5943-b1dc-09c6b5f20637" -version = "1.4.1" +version = "1.5.0" [[deps.LaTeXStrings]] git-tree-sha1 = "f2355693d6778a178ade15952b7ac47a4ff97996" @@ -700,12 +700,12 @@ version = "0.2.0" [[deps.LibCURL]] deps = ["LibCURL_jll", "MozillaCACerts_jll"] uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" -version = "0.6.4" +version = "0.6.3" [[deps.LibCURL_jll]] deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" -version = "8.0.1+0" +version = "7.84.0+0" [[deps.LibGit2]] deps = ["Base64", "NetworkOptions", "Printf", "SHA"] @@ -720,10 +720,10 @@ version = "1.10.2+0" uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" [[deps.Libiconv_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "c7cb1f5d892775ba13767a87c7ada0b980ea0a71" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "f9557a255370125b405568f9767d6d195822a175" uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531" -version = "1.16.1+2" +version = "1.17.0+0" [[deps.Libtask]] deps = ["FunctionWrappers", "LRUCache", "LinearAlgebra", "Statistics"] @@ -749,12 +749,11 @@ version = "2.1.1" [[deps.LogDensityProblemsAD]] deps = ["DocStringExtensions", "LogDensityProblems", "Requires", "SimpleUnPack"] -git-tree-sha1 = "7841ebecc60703fa978cbe9623e1a37dcaf96b75" +git-tree-sha1 = "a0512ad65f849536b5a52e59b05c59c25cdad943" uuid = "996a588d-648d-4e1f-a8f0-a84b347e47b1" -version = "1.6.1" +version = "1.5.0" [deps.LogDensityProblemsAD.extensions] - LogDensityProblemsADADTypesExt = "ADTypes" LogDensityProblemsADEnzymeExt = "Enzyme" LogDensityProblemsADFiniteDifferencesExt = "FiniteDifferences" LogDensityProblemsADForwardDiffBenchmarkToolsExt = ["BenchmarkTools", "ForwardDiff"] @@ -764,7 +763,6 @@ version = "1.6.1" LogDensityProblemsADZygoteExt = "Zygote" [deps.LogDensityProblemsAD.weakdeps] - ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b" BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9" FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000" @@ -775,9 +773,9 @@ version = "1.6.1" [[deps.LogExpFunctions]] deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] -git-tree-sha1 = "c3ce8e7420b3a6e071e0fe4745f5d4300e37b13f" +git-tree-sha1 = "7d6dd4e9212aebaeed356de34ccf262a3cd415aa" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" -version = "0.3.24" +version = "0.3.26" weakdeps = ["ChainRulesCore", "ChangesOfVariables", "InverseFunctions"] [deps.LogExpFunctions.extensions] @@ -790,9 +788,9 @@ uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" [[deps.LoggingExtras]] deps = ["Dates", "Logging"] -git-tree-sha1 = "cedb76b37bc5a6c702ade66be44f831fa23c681e" +git-tree-sha1 = "0d097476b6c381ab7906460ef1ef1638fbce1d91" uuid = "e6f89c97-d47a-5376-807f-9c37f3926c36" -version = "1.0.0" +version = "1.0.2" [[deps.MCMCChains]] deps = ["AbstractMCMC", "AxisArrays", "Dates", "Distributions", "Formatting", "IteratorInterfaceExtensions", "KernelDensity", "LinearAlgebra", "MCMCDiagnosticTools", "MLJModelInterface", "NaturalSort", "OrderedCollections", "PrettyTables", "Random", "RecipesBase", "Statistics", "StatsBase", "StatsFuns", "TableTraits", "Tables"] @@ -814,15 +812,15 @@ version = "2023.2.0+0" [[deps.MLJModelInterface]] deps = ["Random", "ScientificTypesBase", "StatisticalTraits"] -git-tree-sha1 = "e89d1ea12c5a50057bfb0c124d905669e5ed4ec9" +git-tree-sha1 = "03ae109be87f460fe3c96b8a0dbbf9c7bf840bd5" uuid = "e80e1ace-859a-464e-9ed9-23947d8ae3ea" -version = "1.9.1" +version = "1.9.2" [[deps.MacroTools]] deps = ["Markdown", "Random"] -git-tree-sha1 = "42324d08725e200c23d4dfb549e0d5d89dede2d2" +git-tree-sha1 = "9ee1618cbf5240e6d4e0371d6f24065083f60c48" uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" -version = "0.5.10" +version = "0.5.11" [[deps.MappedArrays]] git-tree-sha1 = "2dab0221fe2b0f2cb6754eaa743cc266339f527e" @@ -840,7 +838,7 @@ version = "2.28.2+0" [[deps.MicroCanonicalHMC]] deps = ["AbstractMCMC", "BlockDiagonals", "CSV", "DataFrames", "Distributed", "Distributions", "DistributionsAD", "DynamicPPL", "ForwardDiff", "Interpolations", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "MCMCDiagnosticTools", "NPZ", "ProgressMeter", "Random", "Statistics", "Turing", "YAML"] -git-tree-sha1 = "3291385729348de7bbc0f6ecafc349808e61d30c" +git-tree-sha1 = "071d33b5240799a0582a1786076c79013c74a25c" repo-rev = "master" repo-url = "https://github.com/JaimeRZP/MicroCanonicalHMC.jl" uuid = "234d2aa0-2291-45f7-9047-6fa6f316b0a8" @@ -863,7 +861,7 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" -version = "2023.1.10" +version = "2022.10.11" [[deps.NLSolversBase]] deps = ["DiffResults", "Distributed", "FiniteDiff", "ForwardDiff"] @@ -873,9 +871,9 @@ version = "7.8.3" [[deps.NNlib]] deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] -git-tree-sha1 = "3d42748c725c3f088bcda47fa2aca89e74d59d22" +git-tree-sha1 = "3b29fafcdfa66d6673306cf116a2dc243933e2c5" uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" -version = "0.9.4" +version = "0.9.5" [deps.NNlib.extensions] NNlibAMDGPUExt = "AMDGPU" @@ -923,7 +921,7 @@ version = "1.12.10" [[deps.OpenBLAS_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" -version = "0.3.23+0" +version = "0.3.21+4" [[deps.OpenLibm_jll]] deps = ["Artifacts", "Libdl"] @@ -944,22 +942,23 @@ version = "1.7.7" [[deps.Optimisers]] deps = ["ChainRulesCore", "Functors", "LinearAlgebra", "Random", "Statistics"] -git-tree-sha1 = "16776280310aa5553c370b9c7b17f34aadaf3c8e" +git-tree-sha1 = "c1fc26bab5df929a5172f296f25d7d08688fd25b" uuid = "3bd65402-5787-11e9-1adc-39752487f4e2" -version = "0.2.19" +version = "0.2.20" [[deps.Optimization]] deps = ["ADTypes", "ArrayInterface", "ConsoleProgressMonitor", "DocStringExtensions", "LinearAlgebra", "Logging", "LoggingExtras", "Pkg", "Printf", "ProgressLogging", "Reexport", "Requires", "SciMLBase", "SparseArrays", "TerminalLoggers"] -git-tree-sha1 = "80005bc7d96d4d00e56b8a58c5240a35bd638d4a" +git-tree-sha1 = "8489ad2e45328a9eba30a3c46006b04deee65a03" uuid = "7f7a1694-90dd-40f0-9382-eb1efda571ba" -version = "3.15.2" +version = "3.17.0" [deps.Optimization.extensions] OptimizationEnzymeExt = "Enzyme" - OptimizationFinitediffExt = "FiniteDiff" - OptimizationForwarddiffExt = "ForwardDiff" + OptimizationFiniteDiffExt = "FiniteDiff" + OptimizationForwardDiffExt = "ForwardDiff" OptimizationMTKExt = "ModelingToolkit" - OptimizationReversediffExt = "ReverseDiff" + OptimizationReverseDiffExt = "ReverseDiff" + OptimizationSparseDiffExt = ["SparseDiffTools", "Symbolics", "ReverseDiff"] OptimizationTrackerExt = "Tracker" OptimizationZygoteExt = "Zygote" @@ -969,6 +968,8 @@ version = "3.15.2" ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78" ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" + SparseDiffTools = "47a9eef4-7e08-11e9-0b38-333d64bd3804" + Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" @@ -1016,13 +1017,13 @@ version = "0.7.0" [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" -version = "1.10.0" +version = "1.9.2" [[deps.PooledArrays]] deps = ["DataAPI", "Future"] -git-tree-sha1 = "a6062fe4063cdafe78f4a0a81cfffb89721b30e7" +git-tree-sha1 = "36d8b4b899628fb92c2749eb488d884a926614d3" uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720" -version = "1.4.2" +version = "1.4.3" [[deps.PositiveFactorizations]] deps = ["LinearAlgebra"] @@ -1032,9 +1033,9 @@ version = "0.2.4" [[deps.PrecompileTools]] deps = ["Preferences"] -git-tree-sha1 = "9673d39decc5feece56ef3940e5dafba15ba0f81" +git-tree-sha1 = "03b4c25b43cb84cee5c90aa9b5ea0a78fd848d2f" uuid = "aea7be01-6a6a-4083-8856-8a6e6704d82a" -version = "1.1.2" +version = "1.2.0" [[deps.Preferences]] deps = ["TOML"] @@ -1060,9 +1061,9 @@ version = "0.1.4" [[deps.ProgressMeter]] deps = ["Distributed", "Printf"] -git-tree-sha1 = "d7a7aef8f8f2d537104f170139553b14dfe39fe9" +git-tree-sha1 = "00099623ffee15972c16111bcf84c58a0051257c" uuid = "92933f4c-e287-5a05-a399-4b506db050ca" -version = "1.7.2" +version = "1.9.0" [[deps.QuadGK]] deps = ["DataStructures", "LinearAlgebra"] @@ -1075,7 +1076,7 @@ deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" [[deps.Random]] -deps = ["SHA"] +deps = ["SHA", "Serialization"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" [[deps.Random123]] @@ -1121,17 +1122,19 @@ version = "1.3.4" [[deps.RecursiveArrayTools]] deps = ["Adapt", "ArrayInterface", "DocStringExtensions", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "Requires", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables"] -git-tree-sha1 = "7ed35fb5f831aaf09c2d7c8736d44667a1afdcb0" +git-tree-sha1 = "d7087c013e8a496ff396bae843b1e16d9a30ede8" uuid = "731186ca-8d62-57ce-b412-fbd966d074cd" -version = "2.38.7" +version = "2.38.10" [deps.RecursiveArrayTools.extensions] RecursiveArrayToolsMeasurementsExt = "Measurements" + RecursiveArrayToolsMonteCarloMeasurementsExt = "MonteCarloMeasurements" RecursiveArrayToolsTrackerExt = "Tracker" RecursiveArrayToolsZygoteExt = "Zygote" [deps.RecursiveArrayTools.weakdeps] Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7" + MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" @@ -1197,10 +1200,16 @@ uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" version = "0.7.0" [[deps.SciMLBase]] -deps = ["ADTypes", "ArrayInterface", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces"] -git-tree-sha1 = "04370090604cd399db5bebddb636d80ab9d338e9" +deps = ["ADTypes", "ArrayInterface", "ChainRulesCore", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces", "ZygoteRules"] +git-tree-sha1 = "c0781c7ebb65776e9770d333b5e191d20dd45fcf" uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462" -version = "1.94.0" +version = "1.97.0" + + [deps.SciMLBase.extensions] + ZygoteExt = "Zygote" + + [deps.SciMLBase.weakdeps] + Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" [[deps.SciMLOperators]] deps = ["ArrayInterface", "DocStringExtensions", "Lazy", "LinearAlgebra", "Setfield", "SparseArrays", "StaticArraysCore", "Tricks"] @@ -1249,7 +1258,12 @@ version = "1.1.1" [[deps.SparseArrays]] deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" -version = "1.10.0" + +[[deps.SparseInverseSubset]] +deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "91402087fd5d13b2d97e3ef29bbdf9d7859e678a" +uuid = "dc90abb0-5640-4711-901d-7e5b23a2fada" +version = "0.1.1" [[deps.SpecialFunctions]] deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] @@ -1269,9 +1283,9 @@ version = "0.1.15" [[deps.StaticArrays]] deps = ["LinearAlgebra", "Random", "StaticArraysCore"] -git-tree-sha1 = "9cabadf6e7cd2349b6cf49f1915ad2028d65e881" +git-tree-sha1 = "51621cca8651d9e334a659443a74ce50a3b6dfab" uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.6.2" +version = "1.6.3" weakdeps = ["Statistics"] [deps.StaticArrays.extensions] @@ -1295,9 +1309,9 @@ version = "1.9.0" [[deps.StatsAPI]] deps = ["LinearAlgebra"] -git-tree-sha1 = "45a7769a04a3cf80da1c1c7c60caf932e6f4c9f7" +git-tree-sha1 = "1ff449ad350c9c4cbc756624d6f8a8c3ef56d3ed" uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0" -version = "1.6.0" +version = "1.7.0" [[deps.StatsBase]] deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"] @@ -1323,15 +1337,16 @@ uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" version = "0.3.7" [[deps.StringManipulation]] -git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" +deps = ["PrecompileTools"] +git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" -version = "0.3.0" +version = "0.3.4" [[deps.StructArrays]] -deps = ["Adapt", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"] -git-tree-sha1 = "521a0e828e98bb69042fec1809c1b5a680eb7389" +deps = ["Adapt", "ConstructionBase", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"] +git-tree-sha1 = "0a3db38e4cce3c54fe7a71f831cd7b6194a54213" uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a" -version = "0.6.15" +version = "0.6.16" [[deps.SuiteSparse]] deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] @@ -1340,7 +1355,7 @@ uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" [[deps.SuiteSparse_jll]] deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"] uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" -version = "7.2.0+0" +version = "5.10.1+6" [[deps.SymbolicIndexingInterface]] deps = ["DocStringExtensions"] @@ -1360,10 +1375,10 @@ uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c" version = "1.0.1" [[deps.Tables]] -deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits", "Test"] -git-tree-sha1 = "1544b926975372da01227b382066ab70e574a3ec" +deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits"] +git-tree-sha1 = "a1f34829d5ac0ef499f6d84428bd6b4c71f02ead" uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -version = "1.10.1" +version = "1.11.0" [[deps.Tar]] deps = ["ArgTools", "SHA"] @@ -1434,10 +1449,10 @@ uuid = "781d530d-4396-4725-bb49-402e4bee1e77" version = "1.4.0" [[deps.Turing]] -deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Tracker"] -git-tree-sha1 = "172b7a658d7ea91dad3203e711d83d9c91d01fed" +deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] +git-tree-sha1 = "0d369f7831584688a32913dc786833280391013f" uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" -version = "0.28.2" +version = "0.29.1" [deps.Turing.extensions] TuringDynamicHMCExt = "DynamicHMC" @@ -1518,7 +1533,7 @@ version = "5.8.0+0" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.52.0+0" +version = "1.48.0+0" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] From cc451bb42356f337db4ecd026857cc1a585330cf Mon Sep 17 00:00:00 2001 From: jaimerz Date: Wed, 20 Sep 2023 09:28:42 +0100 Subject: [PATCH 83/87] Revert "upd deps" This reverts commit 32284d5dd542fe730371a7ccca19dd66c741a398. --- .../docs-16-external-samplers/Manifest.toml | 181 ++++++++---------- 1 file changed, 83 insertions(+), 98 deletions(-) diff --git a/tutorials/docs-16-external-samplers/Manifest.toml b/tutorials/docs-16-external-samplers/Manifest.toml index 420820429..60e870a65 100644 --- a/tutorials/docs-16-external-samplers/Manifest.toml +++ b/tutorials/docs-16-external-samplers/Manifest.toml @@ -1,13 +1,13 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.9.2" +julia_version = "1.10.0-beta1" manifest_format = "2.0" project_hash = "6559f27b923d65d4f9f21538db968bd03760e383" [[deps.ADTypes]] -git-tree-sha1 = "5d2e21d7b0d8c22f67483ef95ebdc39c0e6b6003" +git-tree-sha1 = "f5c25e8a5b29b5e941b7408bc8cc79fea4d9ef9a" uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b" -version = "0.2.4" +version = "0.1.6" [[deps.AbstractFFTs]] deps = ["LinearAlgebra"] @@ -67,9 +67,9 @@ weakdeps = ["StaticArrays"] [[deps.AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "ProgressMeter", "Random", "Requires", "Setfield", "SimpleUnPack", "Statistics", "StatsBase", "StatsFuns"] -git-tree-sha1 = "acbe805c3078ba0057bb56985248bd66bce016b1" +git-tree-sha1 = "d782efeacf92a1dcc65a77ea1507577d4dc843fe" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" -version = "0.5.5" +version = "0.5.4" [deps.AdvancedHMC.extensions] AdvancedHMCCUDAExt = "CUDA" @@ -187,9 +187,9 @@ version = "0.1.1" [[deps.Bijectors]] deps = ["ArgCheck", "ChainRules", "ChainRulesCore", "ChangesOfVariables", "Compat", "Distributions", "Functors", "InverseFunctions", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "MappedArrays", "Random", "Reexport", "Requires", "Roots", "SparseArrays", "Statistics"] -git-tree-sha1 = "8eacff457e5b8c13a97848484ad650dabbffa0fc" +git-tree-sha1 = "af192c7c235264bdc6f67321fd1c57be0dd7ffb5" uuid = "76274a88-744f-5084-9051-94815aaf08c4" -version = "0.13.7" +version = "0.13.6" [deps.Bijectors.extensions] BijectorsDistributionsADExt = "DistributionsAD" @@ -231,10 +231,10 @@ uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9" version = "0.5.1" [[deps.ChainRules]] -deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "SparseInverseSubset", "Statistics", "StructArrays", "SuiteSparse"] -git-tree-sha1 = "dbeca245b0680f5393b4e6c40dcead7230ab0b3b" +deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "Statistics", "StructArrays"] +git-tree-sha1 = "f98ae934cd677d51d2941088849f0bf2f59e6f6e" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" -version = "1.54.0" +version = "1.53.0" [[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra", "SparseArrays"] @@ -306,9 +306,9 @@ version = "0.1.2" [[deps.ConstructionBase]] deps = ["LinearAlgebra"] -git-tree-sha1 = "c53fc348ca4d40d7b371e71fd52251839080cbc9" +git-tree-sha1 = "fe2838a593b5f776e1597e086dcd47560d94e816" uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" -version = "1.5.4" +version = "1.5.3" weakdeps = ["IntervalSets", "StaticArrays"] [deps.ConstructionBase.extensions] @@ -426,10 +426,10 @@ uuid = "fa6b7ba4-c1ee-5f82-b5fc-ecf0adba8f74" version = "0.6.8" [[deps.DynamicPPL]] -deps = ["AbstractMCMC", "AbstractPPL", "BangBang", "Bijectors", "ChainRulesCore", "ConstructionBase", "Distributions", "DocStringExtensions", "LinearAlgebra", "LogDensityProblems", "MacroTools", "OrderedCollections", "Random", "Requires", "Setfield", "Test", "ZygoteRules"] -git-tree-sha1 = "c60859c9bb51353a6f4a9769a0fb771cc4de94bc" +deps = ["AbstractMCMC", "AbstractPPL", "BangBang", "Bijectors", "ChainRulesCore", "ConstructionBase", "Distributions", "DocStringExtensions", "LinearAlgebra", "LogDensityProblems", "MacroTools", "OrderedCollections", "Random", "Setfield", "Test", "ZygoteRules"] +git-tree-sha1 = "c85676be0049fd673c71d655b7a308c09eb83ed6" uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8" -version = "0.23.17" +version = "0.23.13" weakdeps = ["MCMCChains"] [deps.DynamicPPL.extensions] @@ -637,9 +637,9 @@ version = "1.0.0" [[deps.JLLWrappers]] deps = ["Artifacts", "Preferences"] -git-tree-sha1 = "7e5d6779a1e09a36db2a7b6cff50942a0a7d0fca" +git-tree-sha1 = "a7e91ef94114d5bc8952bcaa8d6ff952cf709808" uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210" -version = "1.5.0" +version = "1.4.2" [[deps.KernelAbstractions]] deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] @@ -661,20 +661,20 @@ version = "0.6.7" [[deps.LLVM]] deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"] -git-tree-sha1 = "a9d2ce1d5007b1e8f6c5b89c5a31ff8bd146db5c" +git-tree-sha1 = "8695a49bfe05a2dc0feeefd06b4ca6361a018729" uuid = "929cbde3-209d-540e-8aea-75f648917ca0" -version = "6.2.1" +version = "6.1.0" [[deps.LLVMExtra_jll]] deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] -git-tree-sha1 = "7ca6850ae880cc99b59b88517545f91a52020afa" +git-tree-sha1 = "c35203c1e1002747da220ffc3c0762ce7754b08c" uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab" -version = "0.0.25+0" +version = "0.0.23+0" [[deps.LRUCache]] -git-tree-sha1 = "d36130483e3b6e4cd88d81633b596563264f15db" +git-tree-sha1 = "48c10e3cc27e30de82463c27bef0b8bdbd1dc634" uuid = "8ac3fa9e-de4c-5943-b1dc-09c6b5f20637" -version = "1.5.0" +version = "1.4.1" [[deps.LaTeXStrings]] git-tree-sha1 = "f2355693d6778a178ade15952b7ac47a4ff97996" @@ -700,12 +700,12 @@ version = "0.2.0" [[deps.LibCURL]] deps = ["LibCURL_jll", "MozillaCACerts_jll"] uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" -version = "0.6.3" +version = "0.6.4" [[deps.LibCURL_jll]] deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" -version = "7.84.0+0" +version = "8.0.1+0" [[deps.LibGit2]] deps = ["Base64", "NetworkOptions", "Printf", "SHA"] @@ -720,10 +720,10 @@ version = "1.10.2+0" uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" [[deps.Libiconv_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "f9557a255370125b405568f9767d6d195822a175" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "c7cb1f5d892775ba13767a87c7ada0b980ea0a71" uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531" -version = "1.17.0+0" +version = "1.16.1+2" [[deps.Libtask]] deps = ["FunctionWrappers", "LRUCache", "LinearAlgebra", "Statistics"] @@ -749,11 +749,12 @@ version = "2.1.1" [[deps.LogDensityProblemsAD]] deps = ["DocStringExtensions", "LogDensityProblems", "Requires", "SimpleUnPack"] -git-tree-sha1 = "a0512ad65f849536b5a52e59b05c59c25cdad943" +git-tree-sha1 = "7841ebecc60703fa978cbe9623e1a37dcaf96b75" uuid = "996a588d-648d-4e1f-a8f0-a84b347e47b1" -version = "1.5.0" +version = "1.6.1" [deps.LogDensityProblemsAD.extensions] + LogDensityProblemsADADTypesExt = "ADTypes" LogDensityProblemsADEnzymeExt = "Enzyme" LogDensityProblemsADFiniteDifferencesExt = "FiniteDifferences" LogDensityProblemsADForwardDiffBenchmarkToolsExt = ["BenchmarkTools", "ForwardDiff"] @@ -763,6 +764,7 @@ version = "1.5.0" LogDensityProblemsADZygoteExt = "Zygote" [deps.LogDensityProblemsAD.weakdeps] + ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b" BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9" FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000" @@ -773,9 +775,9 @@ version = "1.5.0" [[deps.LogExpFunctions]] deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] -git-tree-sha1 = "7d6dd4e9212aebaeed356de34ccf262a3cd415aa" +git-tree-sha1 = "c3ce8e7420b3a6e071e0fe4745f5d4300e37b13f" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" -version = "0.3.26" +version = "0.3.24" weakdeps = ["ChainRulesCore", "ChangesOfVariables", "InverseFunctions"] [deps.LogExpFunctions.extensions] @@ -788,9 +790,9 @@ uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" [[deps.LoggingExtras]] deps = ["Dates", "Logging"] -git-tree-sha1 = "0d097476b6c381ab7906460ef1ef1638fbce1d91" +git-tree-sha1 = "cedb76b37bc5a6c702ade66be44f831fa23c681e" uuid = "e6f89c97-d47a-5376-807f-9c37f3926c36" -version = "1.0.2" +version = "1.0.0" [[deps.MCMCChains]] deps = ["AbstractMCMC", "AxisArrays", "Dates", "Distributions", "Formatting", "IteratorInterfaceExtensions", "KernelDensity", "LinearAlgebra", "MCMCDiagnosticTools", "MLJModelInterface", "NaturalSort", "OrderedCollections", "PrettyTables", "Random", "RecipesBase", "Statistics", "StatsBase", "StatsFuns", "TableTraits", "Tables"] @@ -812,15 +814,15 @@ version = "2023.2.0+0" [[deps.MLJModelInterface]] deps = ["Random", "ScientificTypesBase", "StatisticalTraits"] -git-tree-sha1 = "03ae109be87f460fe3c96b8a0dbbf9c7bf840bd5" +git-tree-sha1 = "e89d1ea12c5a50057bfb0c124d905669e5ed4ec9" uuid = "e80e1ace-859a-464e-9ed9-23947d8ae3ea" -version = "1.9.2" +version = "1.9.1" [[deps.MacroTools]] deps = ["Markdown", "Random"] -git-tree-sha1 = "9ee1618cbf5240e6d4e0371d6f24065083f60c48" +git-tree-sha1 = "42324d08725e200c23d4dfb549e0d5d89dede2d2" uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" -version = "0.5.11" +version = "0.5.10" [[deps.MappedArrays]] git-tree-sha1 = "2dab0221fe2b0f2cb6754eaa743cc266339f527e" @@ -838,7 +840,7 @@ version = "2.28.2+0" [[deps.MicroCanonicalHMC]] deps = ["AbstractMCMC", "BlockDiagonals", "CSV", "DataFrames", "Distributed", "Distributions", "DistributionsAD", "DynamicPPL", "ForwardDiff", "Interpolations", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "MCMCDiagnosticTools", "NPZ", "ProgressMeter", "Random", "Statistics", "Turing", "YAML"] -git-tree-sha1 = "071d33b5240799a0582a1786076c79013c74a25c" +git-tree-sha1 = "3291385729348de7bbc0f6ecafc349808e61d30c" repo-rev = "master" repo-url = "https://github.com/JaimeRZP/MicroCanonicalHMC.jl" uuid = "234d2aa0-2291-45f7-9047-6fa6f316b0a8" @@ -861,7 +863,7 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" -version = "2022.10.11" +version = "2023.1.10" [[deps.NLSolversBase]] deps = ["DiffResults", "Distributed", "FiniteDiff", "ForwardDiff"] @@ -871,9 +873,9 @@ version = "7.8.3" [[deps.NNlib]] deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] -git-tree-sha1 = "3b29fafcdfa66d6673306cf116a2dc243933e2c5" +git-tree-sha1 = "3d42748c725c3f088bcda47fa2aca89e74d59d22" uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" -version = "0.9.5" +version = "0.9.4" [deps.NNlib.extensions] NNlibAMDGPUExt = "AMDGPU" @@ -921,7 +923,7 @@ version = "1.12.10" [[deps.OpenBLAS_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" -version = "0.3.21+4" +version = "0.3.23+0" [[deps.OpenLibm_jll]] deps = ["Artifacts", "Libdl"] @@ -942,23 +944,22 @@ version = "1.7.7" [[deps.Optimisers]] deps = ["ChainRulesCore", "Functors", "LinearAlgebra", "Random", "Statistics"] -git-tree-sha1 = "c1fc26bab5df929a5172f296f25d7d08688fd25b" +git-tree-sha1 = "16776280310aa5553c370b9c7b17f34aadaf3c8e" uuid = "3bd65402-5787-11e9-1adc-39752487f4e2" -version = "0.2.20" +version = "0.2.19" [[deps.Optimization]] deps = ["ADTypes", "ArrayInterface", "ConsoleProgressMonitor", "DocStringExtensions", "LinearAlgebra", "Logging", "LoggingExtras", "Pkg", "Printf", "ProgressLogging", "Reexport", "Requires", "SciMLBase", "SparseArrays", "TerminalLoggers"] -git-tree-sha1 = "8489ad2e45328a9eba30a3c46006b04deee65a03" +git-tree-sha1 = "80005bc7d96d4d00e56b8a58c5240a35bd638d4a" uuid = "7f7a1694-90dd-40f0-9382-eb1efda571ba" -version = "3.17.0" +version = "3.15.2" [deps.Optimization.extensions] OptimizationEnzymeExt = "Enzyme" - OptimizationFiniteDiffExt = "FiniteDiff" - OptimizationForwardDiffExt = "ForwardDiff" + OptimizationFinitediffExt = "FiniteDiff" + OptimizationForwarddiffExt = "ForwardDiff" OptimizationMTKExt = "ModelingToolkit" - OptimizationReverseDiffExt = "ReverseDiff" - OptimizationSparseDiffExt = ["SparseDiffTools", "Symbolics", "ReverseDiff"] + OptimizationReversediffExt = "ReverseDiff" OptimizationTrackerExt = "Tracker" OptimizationZygoteExt = "Zygote" @@ -968,8 +969,6 @@ version = "3.17.0" ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78" ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" - SparseDiffTools = "47a9eef4-7e08-11e9-0b38-333d64bd3804" - Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" @@ -1017,13 +1016,13 @@ version = "0.7.0" [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" -version = "1.9.2" +version = "1.10.0" [[deps.PooledArrays]] deps = ["DataAPI", "Future"] -git-tree-sha1 = "36d8b4b899628fb92c2749eb488d884a926614d3" +git-tree-sha1 = "a6062fe4063cdafe78f4a0a81cfffb89721b30e7" uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720" -version = "1.4.3" +version = "1.4.2" [[deps.PositiveFactorizations]] deps = ["LinearAlgebra"] @@ -1033,9 +1032,9 @@ version = "0.2.4" [[deps.PrecompileTools]] deps = ["Preferences"] -git-tree-sha1 = "03b4c25b43cb84cee5c90aa9b5ea0a78fd848d2f" +git-tree-sha1 = "9673d39decc5feece56ef3940e5dafba15ba0f81" uuid = "aea7be01-6a6a-4083-8856-8a6e6704d82a" -version = "1.2.0" +version = "1.1.2" [[deps.Preferences]] deps = ["TOML"] @@ -1061,9 +1060,9 @@ version = "0.1.4" [[deps.ProgressMeter]] deps = ["Distributed", "Printf"] -git-tree-sha1 = "00099623ffee15972c16111bcf84c58a0051257c" +git-tree-sha1 = "d7a7aef8f8f2d537104f170139553b14dfe39fe9" uuid = "92933f4c-e287-5a05-a399-4b506db050ca" -version = "1.9.0" +version = "1.7.2" [[deps.QuadGK]] deps = ["DataStructures", "LinearAlgebra"] @@ -1076,7 +1075,7 @@ deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" [[deps.Random]] -deps = ["SHA", "Serialization"] +deps = ["SHA"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" [[deps.Random123]] @@ -1122,19 +1121,17 @@ version = "1.3.4" [[deps.RecursiveArrayTools]] deps = ["Adapt", "ArrayInterface", "DocStringExtensions", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "Requires", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables"] -git-tree-sha1 = "d7087c013e8a496ff396bae843b1e16d9a30ede8" +git-tree-sha1 = "7ed35fb5f831aaf09c2d7c8736d44667a1afdcb0" uuid = "731186ca-8d62-57ce-b412-fbd966d074cd" -version = "2.38.10" +version = "2.38.7" [deps.RecursiveArrayTools.extensions] RecursiveArrayToolsMeasurementsExt = "Measurements" - RecursiveArrayToolsMonteCarloMeasurementsExt = "MonteCarloMeasurements" RecursiveArrayToolsTrackerExt = "Tracker" RecursiveArrayToolsZygoteExt = "Zygote" [deps.RecursiveArrayTools.weakdeps] Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7" - MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" @@ -1200,16 +1197,10 @@ uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" version = "0.7.0" [[deps.SciMLBase]] -deps = ["ADTypes", "ArrayInterface", "ChainRulesCore", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces", "ZygoteRules"] -git-tree-sha1 = "c0781c7ebb65776e9770d333b5e191d20dd45fcf" +deps = ["ADTypes", "ArrayInterface", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces"] +git-tree-sha1 = "04370090604cd399db5bebddb636d80ab9d338e9" uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462" -version = "1.97.0" - - [deps.SciMLBase.extensions] - ZygoteExt = "Zygote" - - [deps.SciMLBase.weakdeps] - Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" +version = "1.94.0" [[deps.SciMLOperators]] deps = ["ArrayInterface", "DocStringExtensions", "Lazy", "LinearAlgebra", "Setfield", "SparseArrays", "StaticArraysCore", "Tricks"] @@ -1258,12 +1249,7 @@ version = "1.1.1" [[deps.SparseArrays]] deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" - -[[deps.SparseInverseSubset]] -deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] -git-tree-sha1 = "91402087fd5d13b2d97e3ef29bbdf9d7859e678a" -uuid = "dc90abb0-5640-4711-901d-7e5b23a2fada" -version = "0.1.1" +version = "1.10.0" [[deps.SpecialFunctions]] deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] @@ -1283,9 +1269,9 @@ version = "0.1.15" [[deps.StaticArrays]] deps = ["LinearAlgebra", "Random", "StaticArraysCore"] -git-tree-sha1 = "51621cca8651d9e334a659443a74ce50a3b6dfab" +git-tree-sha1 = "9cabadf6e7cd2349b6cf49f1915ad2028d65e881" uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.6.3" +version = "1.6.2" weakdeps = ["Statistics"] [deps.StaticArrays.extensions] @@ -1309,9 +1295,9 @@ version = "1.9.0" [[deps.StatsAPI]] deps = ["LinearAlgebra"] -git-tree-sha1 = "1ff449ad350c9c4cbc756624d6f8a8c3ef56d3ed" +git-tree-sha1 = "45a7769a04a3cf80da1c1c7c60caf932e6f4c9f7" uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0" -version = "1.7.0" +version = "1.6.0" [[deps.StatsBase]] deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"] @@ -1337,16 +1323,15 @@ uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" version = "0.3.7" [[deps.StringManipulation]] -deps = ["PrecompileTools"] -git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5" +git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" -version = "0.3.4" +version = "0.3.0" [[deps.StructArrays]] -deps = ["Adapt", "ConstructionBase", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"] -git-tree-sha1 = "0a3db38e4cce3c54fe7a71f831cd7b6194a54213" +deps = ["Adapt", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"] +git-tree-sha1 = "521a0e828e98bb69042fec1809c1b5a680eb7389" uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a" -version = "0.6.16" +version = "0.6.15" [[deps.SuiteSparse]] deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] @@ -1355,7 +1340,7 @@ uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" [[deps.SuiteSparse_jll]] deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"] uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" -version = "5.10.1+6" +version = "7.2.0+0" [[deps.SymbolicIndexingInterface]] deps = ["DocStringExtensions"] @@ -1375,10 +1360,10 @@ uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c" version = "1.0.1" [[deps.Tables]] -deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits"] -git-tree-sha1 = "a1f34829d5ac0ef499f6d84428bd6b4c71f02ead" +deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits", "Test"] +git-tree-sha1 = "1544b926975372da01227b382066ab70e574a3ec" uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -version = "1.11.0" +version = "1.10.1" [[deps.Tar]] deps = ["ArgTools", "SHA"] @@ -1449,10 +1434,10 @@ uuid = "781d530d-4396-4725-bb49-402e4bee1e77" version = "1.4.0" [[deps.Turing]] -deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] -git-tree-sha1 = "0d369f7831584688a32913dc786833280391013f" +deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Tracker"] +git-tree-sha1 = "172b7a658d7ea91dad3203e711d83d9c91d01fed" uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" -version = "0.29.1" +version = "0.28.2" [deps.Turing.extensions] TuringDynamicHMCExt = "DynamicHMC" @@ -1533,7 +1518,7 @@ version = "5.8.0+0" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.48.0+0" +version = "1.52.0+0" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] From ef2b2eb4665cab6c5e7c2f2229f78389c15f431e Mon Sep 17 00:00:00 2001 From: jaimerz Date: Wed, 20 Sep 2023 16:35:36 +0100 Subject: [PATCH 84/87] bump manifesto --- .../docs-16-external-samplers/Manifest.toml | 183 ++++++++++-------- 1 file changed, 99 insertions(+), 84 deletions(-) diff --git a/tutorials/docs-16-external-samplers/Manifest.toml b/tutorials/docs-16-external-samplers/Manifest.toml index 60e870a65..22cf8c1de 100644 --- a/tutorials/docs-16-external-samplers/Manifest.toml +++ b/tutorials/docs-16-external-samplers/Manifest.toml @@ -1,13 +1,13 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.10.0-beta1" +julia_version = "1.9.3" manifest_format = "2.0" project_hash = "6559f27b923d65d4f9f21538db968bd03760e383" [[deps.ADTypes]] -git-tree-sha1 = "f5c25e8a5b29b5e941b7408bc8cc79fea4d9ef9a" +git-tree-sha1 = "5d2e21d7b0d8c22f67483ef95ebdc39c0e6b6003" uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b" -version = "0.1.6" +version = "0.2.4" [[deps.AbstractFFTs]] deps = ["LinearAlgebra"] @@ -67,9 +67,9 @@ weakdeps = ["StaticArrays"] [[deps.AdvancedHMC]] deps = ["AbstractMCMC", "ArgCheck", "DocStringExtensions", "InplaceOps", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "ProgressMeter", "Random", "Requires", "Setfield", "SimpleUnPack", "Statistics", "StatsBase", "StatsFuns"] -git-tree-sha1 = "d782efeacf92a1dcc65a77ea1507577d4dc843fe" +git-tree-sha1 = "acbe805c3078ba0057bb56985248bd66bce016b1" uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" -version = "0.5.4" +version = "0.5.5" [deps.AdvancedHMC.extensions] AdvancedHMCCUDAExt = "CUDA" @@ -187,9 +187,9 @@ version = "0.1.1" [[deps.Bijectors]] deps = ["ArgCheck", "ChainRules", "ChainRulesCore", "ChangesOfVariables", "Compat", "Distributions", "Functors", "InverseFunctions", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "MappedArrays", "Random", "Reexport", "Requires", "Roots", "SparseArrays", "Statistics"] -git-tree-sha1 = "af192c7c235264bdc6f67321fd1c57be0dd7ffb5" +git-tree-sha1 = "8eacff457e5b8c13a97848484ad650dabbffa0fc" uuid = "76274a88-744f-5084-9051-94815aaf08c4" -version = "0.13.6" +version = "0.13.7" [deps.Bijectors.extensions] BijectorsDistributionsADExt = "DistributionsAD" @@ -231,10 +231,10 @@ uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9" version = "0.5.1" [[deps.ChainRules]] -deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "Statistics", "StructArrays"] -git-tree-sha1 = "f98ae934cd677d51d2941088849f0bf2f59e6f6e" +deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "SparseInverseSubset", "Statistics", "StructArrays", "SuiteSparse"] +git-tree-sha1 = "dbeca245b0680f5393b4e6c40dcead7230ab0b3b" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" -version = "1.53.0" +version = "1.54.0" [[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra", "SparseArrays"] @@ -306,9 +306,9 @@ version = "0.1.2" [[deps.ConstructionBase]] deps = ["LinearAlgebra"] -git-tree-sha1 = "fe2838a593b5f776e1597e086dcd47560d94e816" +git-tree-sha1 = "c53fc348ca4d40d7b371e71fd52251839080cbc9" uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" -version = "1.5.3" +version = "1.5.4" weakdeps = ["IntervalSets", "StaticArrays"] [deps.ConstructionBase.extensions] @@ -426,10 +426,10 @@ uuid = "fa6b7ba4-c1ee-5f82-b5fc-ecf0adba8f74" version = "0.6.8" [[deps.DynamicPPL]] -deps = ["AbstractMCMC", "AbstractPPL", "BangBang", "Bijectors", "ChainRulesCore", "ConstructionBase", "Distributions", "DocStringExtensions", "LinearAlgebra", "LogDensityProblems", "MacroTools", "OrderedCollections", "Random", "Setfield", "Test", "ZygoteRules"] -git-tree-sha1 = "c85676be0049fd673c71d655b7a308c09eb83ed6" +deps = ["AbstractMCMC", "AbstractPPL", "BangBang", "Bijectors", "ChainRulesCore", "ConstructionBase", "Distributions", "DocStringExtensions", "LinearAlgebra", "LogDensityProblems", "MacroTools", "OrderedCollections", "Random", "Requires", "Setfield", "Test", "ZygoteRules"] +git-tree-sha1 = "c60859c9bb51353a6f4a9769a0fb771cc4de94bc" uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8" -version = "0.23.13" +version = "0.23.17" weakdeps = ["MCMCChains"] [deps.DynamicPPL.extensions] @@ -637,9 +637,9 @@ version = "1.0.0" [[deps.JLLWrappers]] deps = ["Artifacts", "Preferences"] -git-tree-sha1 = "a7e91ef94114d5bc8952bcaa8d6ff952cf709808" +git-tree-sha1 = "7e5d6779a1e09a36db2a7b6cff50942a0a7d0fca" uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210" -version = "1.4.2" +version = "1.5.0" [[deps.KernelAbstractions]] deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] @@ -661,20 +661,20 @@ version = "0.6.7" [[deps.LLVM]] deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"] -git-tree-sha1 = "8695a49bfe05a2dc0feeefd06b4ca6361a018729" +git-tree-sha1 = "a9d2ce1d5007b1e8f6c5b89c5a31ff8bd146db5c" uuid = "929cbde3-209d-540e-8aea-75f648917ca0" -version = "6.1.0" +version = "6.2.1" [[deps.LLVMExtra_jll]] deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] -git-tree-sha1 = "c35203c1e1002747da220ffc3c0762ce7754b08c" +git-tree-sha1 = "7ca6850ae880cc99b59b88517545f91a52020afa" uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab" -version = "0.0.23+0" +version = "0.0.25+0" [[deps.LRUCache]] -git-tree-sha1 = "48c10e3cc27e30de82463c27bef0b8bdbd1dc634" +git-tree-sha1 = "d36130483e3b6e4cd88d81633b596563264f15db" uuid = "8ac3fa9e-de4c-5943-b1dc-09c6b5f20637" -version = "1.4.1" +version = "1.5.0" [[deps.LaTeXStrings]] git-tree-sha1 = "f2355693d6778a178ade15952b7ac47a4ff97996" @@ -700,12 +700,12 @@ version = "0.2.0" [[deps.LibCURL]] deps = ["LibCURL_jll", "MozillaCACerts_jll"] uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" -version = "0.6.4" +version = "0.6.3" [[deps.LibCURL_jll]] deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" -version = "8.0.1+0" +version = "7.84.0+0" [[deps.LibGit2]] deps = ["Base64", "NetworkOptions", "Printf", "SHA"] @@ -720,10 +720,10 @@ version = "1.10.2+0" uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" [[deps.Libiconv_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "c7cb1f5d892775ba13767a87c7ada0b980ea0a71" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "f9557a255370125b405568f9767d6d195822a175" uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531" -version = "1.16.1+2" +version = "1.17.0+0" [[deps.Libtask]] deps = ["FunctionWrappers", "LRUCache", "LinearAlgebra", "Statistics"] @@ -749,12 +749,11 @@ version = "2.1.1" [[deps.LogDensityProblemsAD]] deps = ["DocStringExtensions", "LogDensityProblems", "Requires", "SimpleUnPack"] -git-tree-sha1 = "7841ebecc60703fa978cbe9623e1a37dcaf96b75" +git-tree-sha1 = "a0512ad65f849536b5a52e59b05c59c25cdad943" uuid = "996a588d-648d-4e1f-a8f0-a84b347e47b1" -version = "1.6.1" +version = "1.5.0" [deps.LogDensityProblemsAD.extensions] - LogDensityProblemsADADTypesExt = "ADTypes" LogDensityProblemsADEnzymeExt = "Enzyme" LogDensityProblemsADFiniteDifferencesExt = "FiniteDifferences" LogDensityProblemsADForwardDiffBenchmarkToolsExt = ["BenchmarkTools", "ForwardDiff"] @@ -764,7 +763,6 @@ version = "1.6.1" LogDensityProblemsADZygoteExt = "Zygote" [deps.LogDensityProblemsAD.weakdeps] - ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b" BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9" FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000" @@ -775,9 +773,9 @@ version = "1.6.1" [[deps.LogExpFunctions]] deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] -git-tree-sha1 = "c3ce8e7420b3a6e071e0fe4745f5d4300e37b13f" +git-tree-sha1 = "7d6dd4e9212aebaeed356de34ccf262a3cd415aa" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" -version = "0.3.24" +version = "0.3.26" weakdeps = ["ChainRulesCore", "ChangesOfVariables", "InverseFunctions"] [deps.LogExpFunctions.extensions] @@ -790,9 +788,9 @@ uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" [[deps.LoggingExtras]] deps = ["Dates", "Logging"] -git-tree-sha1 = "cedb76b37bc5a6c702ade66be44f831fa23c681e" +git-tree-sha1 = "0d097476b6c381ab7906460ef1ef1638fbce1d91" uuid = "e6f89c97-d47a-5376-807f-9c37f3926c36" -version = "1.0.0" +version = "1.0.2" [[deps.MCMCChains]] deps = ["AbstractMCMC", "AxisArrays", "Dates", "Distributions", "Formatting", "IteratorInterfaceExtensions", "KernelDensity", "LinearAlgebra", "MCMCDiagnosticTools", "MLJModelInterface", "NaturalSort", "OrderedCollections", "PrettyTables", "Random", "RecipesBase", "Statistics", "StatsBase", "StatsFuns", "TableTraits", "Tables"] @@ -814,15 +812,15 @@ version = "2023.2.0+0" [[deps.MLJModelInterface]] deps = ["Random", "ScientificTypesBase", "StatisticalTraits"] -git-tree-sha1 = "e89d1ea12c5a50057bfb0c124d905669e5ed4ec9" +git-tree-sha1 = "03ae109be87f460fe3c96b8a0dbbf9c7bf840bd5" uuid = "e80e1ace-859a-464e-9ed9-23947d8ae3ea" -version = "1.9.1" +version = "1.9.2" [[deps.MacroTools]] deps = ["Markdown", "Random"] -git-tree-sha1 = "42324d08725e200c23d4dfb549e0d5d89dede2d2" +git-tree-sha1 = "9ee1618cbf5240e6d4e0371d6f24065083f60c48" uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" -version = "0.5.10" +version = "0.5.11" [[deps.MappedArrays]] git-tree-sha1 = "2dab0221fe2b0f2cb6754eaa743cc266339f527e" @@ -840,9 +838,9 @@ version = "2.28.2+0" [[deps.MicroCanonicalHMC]] deps = ["AbstractMCMC", "BlockDiagonals", "CSV", "DataFrames", "Distributed", "Distributions", "DistributionsAD", "DynamicPPL", "ForwardDiff", "Interpolations", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "MCMCDiagnosticTools", "NPZ", "ProgressMeter", "Random", "Statistics", "Turing", "YAML"] -git-tree-sha1 = "3291385729348de7bbc0f6ecafc349808e61d30c" +git-tree-sha1 = "071d33b5240799a0582a1786076c79013c74a25c" repo-rev = "master" -repo-url = "https://github.com/JaimeRZP/MicroCanonicalHMC.jl" +repo-url = "https://github.com/JaimeRZP/MicroCanonicalHMC.jl.git" uuid = "234d2aa0-2291-45f7-9047-6fa6f316b0a8" version = "0.1.0" @@ -863,7 +861,7 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" -version = "2023.1.10" +version = "2022.10.11" [[deps.NLSolversBase]] deps = ["DiffResults", "Distributed", "FiniteDiff", "ForwardDiff"] @@ -873,9 +871,9 @@ version = "7.8.3" [[deps.NNlib]] deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] -git-tree-sha1 = "3d42748c725c3f088bcda47fa2aca89e74d59d22" +git-tree-sha1 = "3b29fafcdfa66d6673306cf116a2dc243933e2c5" uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" -version = "0.9.4" +version = "0.9.5" [deps.NNlib.extensions] NNlibAMDGPUExt = "AMDGPU" @@ -923,7 +921,7 @@ version = "1.12.10" [[deps.OpenBLAS_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" -version = "0.3.23+0" +version = "0.3.21+4" [[deps.OpenLibm_jll]] deps = ["Artifacts", "Libdl"] @@ -944,22 +942,23 @@ version = "1.7.7" [[deps.Optimisers]] deps = ["ChainRulesCore", "Functors", "LinearAlgebra", "Random", "Statistics"] -git-tree-sha1 = "16776280310aa5553c370b9c7b17f34aadaf3c8e" +git-tree-sha1 = "c1fc26bab5df929a5172f296f25d7d08688fd25b" uuid = "3bd65402-5787-11e9-1adc-39752487f4e2" -version = "0.2.19" +version = "0.2.20" [[deps.Optimization]] deps = ["ADTypes", "ArrayInterface", "ConsoleProgressMonitor", "DocStringExtensions", "LinearAlgebra", "Logging", "LoggingExtras", "Pkg", "Printf", "ProgressLogging", "Reexport", "Requires", "SciMLBase", "SparseArrays", "TerminalLoggers"] -git-tree-sha1 = "80005bc7d96d4d00e56b8a58c5240a35bd638d4a" +git-tree-sha1 = "8489ad2e45328a9eba30a3c46006b04deee65a03" uuid = "7f7a1694-90dd-40f0-9382-eb1efda571ba" -version = "3.15.2" +version = "3.17.0" [deps.Optimization.extensions] OptimizationEnzymeExt = "Enzyme" - OptimizationFinitediffExt = "FiniteDiff" - OptimizationForwarddiffExt = "ForwardDiff" + OptimizationFiniteDiffExt = "FiniteDiff" + OptimizationForwardDiffExt = "ForwardDiff" OptimizationMTKExt = "ModelingToolkit" - OptimizationReversediffExt = "ReverseDiff" + OptimizationReverseDiffExt = "ReverseDiff" + OptimizationSparseDiffExt = ["SparseDiffTools", "Symbolics", "ReverseDiff"] OptimizationTrackerExt = "Tracker" OptimizationZygoteExt = "Zygote" @@ -969,6 +968,8 @@ version = "3.15.2" ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78" ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" + SparseDiffTools = "47a9eef4-7e08-11e9-0b38-333d64bd3804" + Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" @@ -1016,13 +1017,13 @@ version = "0.7.0" [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" -version = "1.10.0" +version = "1.9.2" [[deps.PooledArrays]] deps = ["DataAPI", "Future"] -git-tree-sha1 = "a6062fe4063cdafe78f4a0a81cfffb89721b30e7" +git-tree-sha1 = "36d8b4b899628fb92c2749eb488d884a926614d3" uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720" -version = "1.4.2" +version = "1.4.3" [[deps.PositiveFactorizations]] deps = ["LinearAlgebra"] @@ -1032,9 +1033,9 @@ version = "0.2.4" [[deps.PrecompileTools]] deps = ["Preferences"] -git-tree-sha1 = "9673d39decc5feece56ef3940e5dafba15ba0f81" +git-tree-sha1 = "03b4c25b43cb84cee5c90aa9b5ea0a78fd848d2f" uuid = "aea7be01-6a6a-4083-8856-8a6e6704d82a" -version = "1.1.2" +version = "1.2.0" [[deps.Preferences]] deps = ["TOML"] @@ -1060,9 +1061,9 @@ version = "0.1.4" [[deps.ProgressMeter]] deps = ["Distributed", "Printf"] -git-tree-sha1 = "d7a7aef8f8f2d537104f170139553b14dfe39fe9" +git-tree-sha1 = "00099623ffee15972c16111bcf84c58a0051257c" uuid = "92933f4c-e287-5a05-a399-4b506db050ca" -version = "1.7.2" +version = "1.9.0" [[deps.QuadGK]] deps = ["DataStructures", "LinearAlgebra"] @@ -1075,7 +1076,7 @@ deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" [[deps.Random]] -deps = ["SHA"] +deps = ["SHA", "Serialization"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" [[deps.Random123]] @@ -1121,17 +1122,19 @@ version = "1.3.4" [[deps.RecursiveArrayTools]] deps = ["Adapt", "ArrayInterface", "DocStringExtensions", "GPUArraysCore", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "Requires", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables"] -git-tree-sha1 = "7ed35fb5f831aaf09c2d7c8736d44667a1afdcb0" +git-tree-sha1 = "d7087c013e8a496ff396bae843b1e16d9a30ede8" uuid = "731186ca-8d62-57ce-b412-fbd966d074cd" -version = "2.38.7" +version = "2.38.10" [deps.RecursiveArrayTools.extensions] RecursiveArrayToolsMeasurementsExt = "Measurements" + RecursiveArrayToolsMonteCarloMeasurementsExt = "MonteCarloMeasurements" RecursiveArrayToolsTrackerExt = "Tracker" RecursiveArrayToolsZygoteExt = "Zygote" [deps.RecursiveArrayTools.weakdeps] Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7" + MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" @@ -1197,10 +1200,16 @@ uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" version = "0.7.0" [[deps.SciMLBase]] -deps = ["ADTypes", "ArrayInterface", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces"] -git-tree-sha1 = "04370090604cd399db5bebddb636d80ab9d338e9" +deps = ["ADTypes", "ArrayInterface", "ChainRulesCore", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces", "ZygoteRules"] +git-tree-sha1 = "c0781c7ebb65776e9770d333b5e191d20dd45fcf" uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462" -version = "1.94.0" +version = "1.97.0" + + [deps.SciMLBase.extensions] + ZygoteExt = "Zygote" + + [deps.SciMLBase.weakdeps] + Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" [[deps.SciMLOperators]] deps = ["ArrayInterface", "DocStringExtensions", "Lazy", "LinearAlgebra", "Setfield", "SparseArrays", "StaticArraysCore", "Tricks"] @@ -1249,7 +1258,12 @@ version = "1.1.1" [[deps.SparseArrays]] deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" -version = "1.10.0" + +[[deps.SparseInverseSubset]] +deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "91402087fd5d13b2d97e3ef29bbdf9d7859e678a" +uuid = "dc90abb0-5640-4711-901d-7e5b23a2fada" +version = "0.1.1" [[deps.SpecialFunctions]] deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] @@ -1269,9 +1283,9 @@ version = "0.1.15" [[deps.StaticArrays]] deps = ["LinearAlgebra", "Random", "StaticArraysCore"] -git-tree-sha1 = "9cabadf6e7cd2349b6cf49f1915ad2028d65e881" +git-tree-sha1 = "51621cca8651d9e334a659443a74ce50a3b6dfab" uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.6.2" +version = "1.6.3" weakdeps = ["Statistics"] [deps.StaticArrays.extensions] @@ -1295,9 +1309,9 @@ version = "1.9.0" [[deps.StatsAPI]] deps = ["LinearAlgebra"] -git-tree-sha1 = "45a7769a04a3cf80da1c1c7c60caf932e6f4c9f7" +git-tree-sha1 = "1ff449ad350c9c4cbc756624d6f8a8c3ef56d3ed" uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0" -version = "1.6.0" +version = "1.7.0" [[deps.StatsBase]] deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"] @@ -1323,15 +1337,16 @@ uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" version = "0.3.7" [[deps.StringManipulation]] -git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" +deps = ["PrecompileTools"] +git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" -version = "0.3.0" +version = "0.3.4" [[deps.StructArrays]] -deps = ["Adapt", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"] -git-tree-sha1 = "521a0e828e98bb69042fec1809c1b5a680eb7389" +deps = ["Adapt", "ConstructionBase", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"] +git-tree-sha1 = "0a3db38e4cce3c54fe7a71f831cd7b6194a54213" uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a" -version = "0.6.15" +version = "0.6.16" [[deps.SuiteSparse]] deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] @@ -1340,7 +1355,7 @@ uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" [[deps.SuiteSparse_jll]] deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"] uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" -version = "7.2.0+0" +version = "5.10.1+6" [[deps.SymbolicIndexingInterface]] deps = ["DocStringExtensions"] @@ -1360,10 +1375,10 @@ uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c" version = "1.0.1" [[deps.Tables]] -deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits", "Test"] -git-tree-sha1 = "1544b926975372da01227b382066ab70e574a3ec" +deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits"] +git-tree-sha1 = "a1f34829d5ac0ef499f6d84428bd6b4c71f02ead" uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -version = "1.10.1" +version = "1.11.0" [[deps.Tar]] deps = ["ArgTools", "SHA"] @@ -1434,10 +1449,10 @@ uuid = "781d530d-4396-4725-bb49-402e4bee1e77" version = "1.4.0" [[deps.Turing]] -deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Tracker"] -git-tree-sha1 = "172b7a658d7ea91dad3203e711d83d9c91d01fed" +deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] +git-tree-sha1 = "0d369f7831584688a32913dc786833280391013f" uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" -version = "0.28.2" +version = "0.29.1" [deps.Turing.extensions] TuringDynamicHMCExt = "DynamicHMC" @@ -1518,7 +1533,7 @@ version = "5.8.0+0" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.52.0+0" +version = "1.48.0+0" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] From 1700ebc65827313312ba9a7ffee7c58f3f34985b Mon Sep 17 00:00:00 2001 From: jaimerz Date: Fri, 22 Sep 2023 15:07:29 +0100 Subject: [PATCH 85/87] nearly there --- tutorials/docs-16-external-samplers/external-samplers.jmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/docs-16-external-samplers/external-samplers.jmd b/tutorials/docs-16-external-samplers/external-samplers.jmd index 342cfff22..0f8fc896c 100644 --- a/tutorials/docs-16-external-samplers/external-samplers.jmd +++ b/tutorials/docs-16-external-samplers/external-samplers.jmd @@ -70,8 +70,8 @@ In this process, `Pathfinder` computes an estimate of the mass matrix the user c The code below shows this can be done in practice. ```julia -using AdvancedHMC, Pathfidner -# Running pathfidner +using AdvancedHMC, Pathfinder +# Running pathfinder draws = 1_000 result_multi = multipathfinder(model, draws; nruns=8) From 48fb69c0587e1d0c1801856b6dd2661603946abe Mon Sep 17 00:00:00 2001 From: jaimerz Date: Thu, 19 Oct 2023 10:30:15 +0100 Subject: [PATCH 86/87] bump turing --- .../docs-16-external-samplers/Manifest.toml | 255 +++++++----------- 1 file changed, 90 insertions(+), 165 deletions(-) diff --git a/tutorials/docs-16-external-samplers/Manifest.toml b/tutorials/docs-16-external-samplers/Manifest.toml index 22cf8c1de..6a0fc46ca 100644 --- a/tutorials/docs-16-external-samplers/Manifest.toml +++ b/tutorials/docs-16-external-samplers/Manifest.toml @@ -5,9 +5,9 @@ manifest_format = "2.0" project_hash = "6559f27b923d65d4f9f21538db968bd03760e383" [[deps.ADTypes]] -git-tree-sha1 = "5d2e21d7b0d8c22f67483ef95ebdc39c0e6b6003" +git-tree-sha1 = "f5c25e8a5b29b5e941b7408bc8cc79fea4d9ef9a" uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b" -version = "0.2.4" +version = "0.1.6" [[deps.AbstractFFTs]] deps = ["LinearAlgebra"] @@ -38,10 +38,10 @@ uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" version = "0.4.4" [[deps.Accessors]] -deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "LinearAlgebra", "MacroTools", "Requires", "Test"] -git-tree-sha1 = "954634616d5846d8e216df1298be2298d55280b2" +deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "LinearAlgebra", "MacroTools", "Test"] +git-tree-sha1 = "a7055b939deae2455aa8a67491e034f735dd08d3" uuid = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697" -version = "0.1.32" +version = "0.1.33" [deps.Accessors.extensions] AccessorsAxisKeysExt = "AxisKeys" @@ -52,6 +52,7 @@ version = "0.1.32" [deps.Accessors.weakdeps] AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5" IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953" + Requires = "ae029012-a4dd-5104-9daa-d747884805df" StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a" @@ -82,10 +83,10 @@ version = "0.5.5" OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed" [[deps.AdvancedMH]] -deps = ["AbstractMCMC", "Distributions", "LogDensityProblems", "Random", "Requires"] -git-tree-sha1 = "165af834eee68d0a96c58daa950ddf0b3f45f608" +deps = ["AbstractMCMC", "Distributions", "FillArrays", "LinearAlgebra", "LogDensityProblems", "Random", "Requires"] +git-tree-sha1 = "b2a1602952739e589cf5e2daff1274a49f22c9a4" uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170" -version = "0.7.4" +version = "0.7.5" weakdeps = ["DiffResults", "ForwardDiff", "MCMCChains", "StructArrays"] [deps.AdvancedMH.extensions] @@ -207,23 +208,11 @@ version = "0.13.7" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" -[[deps.BlockDiagonals]] -deps = ["ChainRulesCore", "FillArrays", "FiniteDifferences", "LinearAlgebra"] -git-tree-sha1 = "ffd635c19b56f50d1d4278d876219644299b5711" -uuid = "0a1fb500-61f7-11e9-3c65-f5ef3456f9f0" -version = "0.1.41" - [[deps.CEnum]] git-tree-sha1 = "eb4cb44a499229b3b8426dcfb5dd85333951ff90" uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82" version = "0.4.2" -[[deps.CSV]] -deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "PrecompileTools", "SentinelArrays", "Tables", "Unicode", "WeakRefStrings", "WorkerUtilities"] -git-tree-sha1 = "44dbf560808d49041989b8a96cae4cffbeb7966a" -uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -version = "0.10.11" - [[deps.Calculus]] deps = ["LinearAlgebra"] git-tree-sha1 = "f641eb0a4f00c343bbc32346e1217b86f3ce9dad" @@ -232,15 +221,19 @@ version = "0.5.1" [[deps.ChainRules]] deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "SparseInverseSubset", "Statistics", "StructArrays", "SuiteSparse"] -git-tree-sha1 = "dbeca245b0680f5393b4e6c40dcead7230ab0b3b" +git-tree-sha1 = "01b0594d8907485ed894bc59adfc0a24a9cde7a3" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" -version = "1.54.0" +version = "1.55.0" [[deps.ChainRulesCore]] -deps = ["Compat", "LinearAlgebra", "SparseArrays"] -git-tree-sha1 = "e30f2f4e20f7f186dc36529910beaedc60cfa644" +deps = ["Compat", "LinearAlgebra"] +git-tree-sha1 = "e0af648f0692ec1691b5d094b8724ba1346281cf" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" -version = "1.16.0" +version = "1.18.0" +weakdeps = ["SparseArrays"] + + [deps.ChainRulesCore.extensions] + ChainRulesCoreSparseArraysExt = "SparseArrays" [[deps.ChangesOfVariables]] deps = ["LinearAlgebra", "Test"] @@ -252,12 +245,6 @@ weakdeps = ["InverseFunctions"] [deps.ChangesOfVariables.extensions] ChangesOfVariablesInverseFunctionsExt = "InverseFunctions" -[[deps.CodecZlib]] -deps = ["TranscodingStreams", "Zlib_jll"] -git-tree-sha1 = "02aa26a4cf76381be7f66e020a3eddeb27b0a092" -uuid = "944b1d66-785c-5afd-91f1-9de20f533193" -version = "0.7.2" - [[deps.Combinatorics]] git-tree-sha1 = "08c8b6831dc00bfea825826be0bc8336fc369860" uuid = "861a8166-3701-5b0c-9a16-15d98fcdc6aa" @@ -276,9 +263,9 @@ version = "0.3.0" [[deps.Compat]] deps = ["UUIDs"] -git-tree-sha1 = "e460f044ca8b99be31d35fe54fc33a5c33dd8ed7" +git-tree-sha1 = "8a62af3e248a8c4bad6b32cbbe663ae02275e32c" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "4.9.0" +version = "4.10.0" weakdeps = ["Dates", "LinearAlgebra"] [deps.Compat.extensions] @@ -381,9 +368,9 @@ uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" [[deps.Distributions]] deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"] -git-tree-sha1 = "938fe2981db009f531b6332e31c58e9584a2f9bd" +git-tree-sha1 = "3d5873f811f582873bb9871fc9c451784d5dc8c7" uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" -version = "0.25.100" +version = "0.25.102" weakdeps = ["ChainRulesCore", "DensityInterface"] [deps.Distributions.extensions] @@ -392,9 +379,9 @@ weakdeps = ["ChainRulesCore", "DensityInterface"] [[deps.DistributionsAD]] deps = ["Adapt", "ChainRules", "ChainRulesCore", "Compat", "Distributions", "FillArrays", "LinearAlgebra", "PDMats", "Random", "Requires", "SpecialFunctions", "StaticArrays", "StatsFuns", "ZygoteRules"] -git-tree-sha1 = "72076a95c1874d7c33bd1dd7b3754ecad61e3df5" +git-tree-sha1 = "d61f08c7bd15c5ab215fd7a2eb61c1ae15d8ff5e" uuid = "ced4e74d-a319-5a8a-b0ac-84af2272839c" -version = "0.6.50" +version = "0.6.53" [deps.DistributionsAD.extensions] DistributionsADForwardDiffExt = "ForwardDiff" @@ -426,10 +413,10 @@ uuid = "fa6b7ba4-c1ee-5f82-b5fc-ecf0adba8f74" version = "0.6.8" [[deps.DynamicPPL]] -deps = ["AbstractMCMC", "AbstractPPL", "BangBang", "Bijectors", "ChainRulesCore", "ConstructionBase", "Distributions", "DocStringExtensions", "LinearAlgebra", "LogDensityProblems", "MacroTools", "OrderedCollections", "Random", "Requires", "Setfield", "Test", "ZygoteRules"] -git-tree-sha1 = "c60859c9bb51353a6f4a9769a0fb771cc4de94bc" +deps = ["AbstractMCMC", "AbstractPPL", "BangBang", "Bijectors", "ChainRulesCore", "Compat", "ConstructionBase", "Distributions", "DocStringExtensions", "LinearAlgebra", "LogDensityProblems", "MacroTools", "OrderedCollections", "Random", "Requires", "Setfield", "Test", "ZygoteRules"] +git-tree-sha1 = "93e9f403d27137db580cd381943d3a8832beb70f" uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8" -version = "0.23.17" +version = "0.23.19" weakdeps = ["MCMCChains"] [deps.DynamicPPL.extensions] @@ -468,26 +455,19 @@ git-tree-sha1 = "c6033cc3892d0ef5bb9cd29b7f2f0331ea5184ea" uuid = "f5851436-0d7a-5f13-b9de-f02708fd171a" version = "3.3.10+0" -[[deps.FileIO]] -deps = ["Pkg", "Requires", "UUIDs"] -git-tree-sha1 = "299dc33549f68299137e51e6d49a13b5b1da9673" -uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" -version = "1.16.1" - -[[deps.FilePathsBase]] -deps = ["Compat", "Dates", "Mmap", "Printf", "Test", "UUIDs"] -git-tree-sha1 = "e27c4ebe80e8699540f2d6c805cc12203b614f12" -uuid = "48062228-2e41-5def-b9a4-89aafe57970f" -version = "0.9.20" - [[deps.FileWatching]] uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" [[deps.FillArrays]] -deps = ["LinearAlgebra", "Random", "SparseArrays", "Statistics"] -git-tree-sha1 = "7072f1e3e5a8be51d525d64f63d3ec1287ff2790" +deps = ["LinearAlgebra", "Random"] +git-tree-sha1 = "35f0c0f345bff2c6d636f95fdb136323b5a796ef" uuid = "1a297f60-69ca-5386-bcde-b61e274b549b" -version = "0.13.11" +version = "1.7.0" +weakdeps = ["SparseArrays", "Statistics"] + + [deps.FillArrays.extensions] + FillArraysSparseArraysExt = "SparseArrays" + FillArraysStatisticsExt = "Statistics" [[deps.FiniteDiff]] deps = ["ArrayInterface", "LinearAlgebra", "Requires", "Setfield", "SparseArrays"] @@ -505,12 +485,6 @@ version = "2.21.1" BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0" StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" -[[deps.FiniteDifferences]] -deps = ["ChainRulesCore", "LinearAlgebra", "Printf", "Random", "Richardson", "SparseArrays", "StaticArrays"] -git-tree-sha1 = "98a4571ba51bf172c798e7cde9b16c11c1e22e6d" -uuid = "26cc04aa-876d-5657-8c51-4c34ba976000" -version = "0.12.30" - [[deps.Folds]] deps = ["Accessors", "BangBang", "Baselet", "DefineSingletons", "Distributed", "ExternalDocstrings", "InitialValues", "MicroCollections", "Referenceables", "Requires", "Test", "ThreadedScans", "Transducers"] git-tree-sha1 = "638109532de382a1f99b1aae1ca8b5d08515d85a" @@ -643,9 +617,9 @@ version = "1.5.0" [[deps.KernelAbstractions]] deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] -git-tree-sha1 = "4c5875e4c228247e1c2b087669846941fb6e0118" +git-tree-sha1 = "5f1ecfddb6abde48563d08b2cc7e5116ebcd6c27" uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c" -version = "0.9.8" +version = "0.9.10" [deps.KernelAbstractions.extensions] EnzymeExt = "EnzymeCore" @@ -661,15 +635,15 @@ version = "0.6.7" [[deps.LLVM]] deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"] -git-tree-sha1 = "a9d2ce1d5007b1e8f6c5b89c5a31ff8bd146db5c" +git-tree-sha1 = "4ea2928a96acfcf8589e6cd1429eff2a3a82c366" uuid = "929cbde3-209d-540e-8aea-75f648917ca0" -version = "6.2.1" +version = "6.3.0" [[deps.LLVMExtra_jll]] deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] -git-tree-sha1 = "7ca6850ae880cc99b59b88517545f91a52020afa" +git-tree-sha1 = "e7c01b69bcbcb93fd4cbc3d0fea7d229541e18d2" uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab" -version = "0.0.25+0" +version = "0.0.26+0" [[deps.LRUCache]] git-tree-sha1 = "d36130483e3b6e4cd88d81633b596563264f15db" @@ -719,12 +693,6 @@ version = "1.10.2+0" [[deps.Libdl]] uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" -[[deps.Libiconv_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "f9557a255370125b405568f9767d6d195822a175" -uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531" -version = "1.17.0+0" - [[deps.Libtask]] deps = ["FunctionWrappers", "LRUCache", "LinearAlgebra", "Statistics"] git-tree-sha1 = "345a40c746404dd9cb1bbc368715856838ab96f2" @@ -749,11 +717,12 @@ version = "2.1.1" [[deps.LogDensityProblemsAD]] deps = ["DocStringExtensions", "LogDensityProblems", "Requires", "SimpleUnPack"] -git-tree-sha1 = "a0512ad65f849536b5a52e59b05c59c25cdad943" +git-tree-sha1 = "7841ebecc60703fa978cbe9623e1a37dcaf96b75" uuid = "996a588d-648d-4e1f-a8f0-a84b347e47b1" -version = "1.5.0" +version = "1.6.1" [deps.LogDensityProblemsAD.extensions] + LogDensityProblemsADADTypesExt = "ADTypes" LogDensityProblemsADEnzymeExt = "Enzyme" LogDensityProblemsADFiniteDifferencesExt = "FiniteDifferences" LogDensityProblemsADForwardDiffBenchmarkToolsExt = ["BenchmarkTools", "ForwardDiff"] @@ -763,6 +732,7 @@ version = "1.5.0" LogDensityProblemsADZygoteExt = "Zygote" [deps.LogDensityProblemsAD.weakdeps] + ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b" BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9" FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000" @@ -788,9 +758,9 @@ uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" [[deps.LoggingExtras]] deps = ["Dates", "Logging"] -git-tree-sha1 = "0d097476b6c381ab7906460ef1ef1638fbce1d91" +git-tree-sha1 = "c1dd6d7978c12545b4179fb6153b9250c96b0075" uuid = "e6f89c97-d47a-5376-807f-9c37f3926c36" -version = "1.0.2" +version = "1.0.3" [[deps.MCMCChains]] deps = ["AbstractMCMC", "AxisArrays", "Dates", "Distributions", "Formatting", "IteratorInterfaceExtensions", "KernelDensity", "LinearAlgebra", "MCMCDiagnosticTools", "MLJModelInterface", "NaturalSort", "OrderedCollections", "PrettyTables", "Random", "RecipesBase", "Statistics", "StatsBase", "StatsFuns", "TableTraits", "Tables"] @@ -800,9 +770,9 @@ version = "6.0.3" [[deps.MCMCDiagnosticTools]] deps = ["AbstractFFTs", "DataAPI", "DataStructures", "Distributions", "LinearAlgebra", "MLJModelInterface", "Random", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns", "Tables"] -git-tree-sha1 = "3e6db72c2ab9cadfa3278ff388473a01fc0cfb9d" +git-tree-sha1 = "56c61163890d15d45901de423be0ad91855a2cd7" uuid = "be115224-59cd-429b-ad48-344e309966f0" -version = "0.3.5" +version = "0.3.7" [[deps.MKL_jll]] deps = ["Artifacts", "IntelOpenMP_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "Pkg"] @@ -812,9 +782,9 @@ version = "2023.2.0+0" [[deps.MLJModelInterface]] deps = ["Random", "ScientificTypesBase", "StatisticalTraits"] -git-tree-sha1 = "03ae109be87f460fe3c96b8a0dbbf9c7bf840bd5" +git-tree-sha1 = "381d99f0af76d98f50bd5512dcf96a99c13f8223" uuid = "e80e1ace-859a-464e-9ed9-23947d8ae3ea" -version = "1.9.2" +version = "1.9.3" [[deps.MacroTools]] deps = ["Markdown", "Random"] @@ -837,10 +807,8 @@ uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1" version = "2.28.2+0" [[deps.MicroCanonicalHMC]] -deps = ["AbstractMCMC", "BlockDiagonals", "CSV", "DataFrames", "Distributed", "Distributions", "DistributionsAD", "DynamicPPL", "ForwardDiff", "Interpolations", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "MCMCDiagnosticTools", "NPZ", "ProgressMeter", "Random", "Statistics", "Turing", "YAML"] -git-tree-sha1 = "071d33b5240799a0582a1786076c79013c74a25c" -repo-rev = "master" -repo-url = "https://github.com/JaimeRZP/MicroCanonicalHMC.jl.git" +deps = ["AbstractMCMC", "DataFrames", "Distributed", "Distributions", "DistributionsAD", "DynamicPPL", "ForwardDiff", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "MCMCDiagnosticTools", "ProgressMeter", "Random", "Statistics"] +git-tree-sha1 = "7f48d0dbedadefa9ff57b96defa08b92ffb2bf6a" uuid = "234d2aa0-2291-45f7-9047-6fa6f316b0a8" version = "0.1.0" @@ -871,26 +839,22 @@ version = "7.8.3" [[deps.NNlib]] deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] -git-tree-sha1 = "3b29fafcdfa66d6673306cf116a2dc243933e2c5" +git-tree-sha1 = "3bc568de99214f72a76c7773ade218819afcc36e" uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" -version = "0.9.5" +version = "0.9.7" [deps.NNlib.extensions] NNlibAMDGPUExt = "AMDGPU" NNlibCUDACUDNNExt = ["CUDA", "cuDNN"] NNlibCUDAExt = "CUDA" + NNlibEnzymeCoreExt = "EnzymeCore" [deps.NNlib.weakdeps] AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e" CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" + EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869" cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" -[[deps.NPZ]] -deps = ["FileIO", "ZipFile"] -git-tree-sha1 = "60a8e272fe0c5079363b28b0953831e2dd7b7e6f" -uuid = "15e1cf62-19b3-5cfa-8e77-841668bca605" -version = "0.4.3" - [[deps.NaNMath]] deps = ["OpenLibm_jll"] git-tree-sha1 = "0877504529a3e5c3343c6f8b4c0381e57e4387e4" @@ -936,21 +900,21 @@ version = "0.5.5+0" [[deps.Optim]] deps = ["Compat", "FillArrays", "ForwardDiff", "LineSearches", "LinearAlgebra", "NLSolversBase", "NaNMath", "Parameters", "PositiveFactorizations", "Printf", "SparseArrays", "StatsBase"] -git-tree-sha1 = "963b004d15216f8129f6c0f7d187efa136570be0" +git-tree-sha1 = "01f85d9269b13fedc61e63cc72ee2213565f7a72" uuid = "429524aa-4258-5aef-a3af-852621145aeb" -version = "1.7.7" +version = "1.7.8" [[deps.Optimisers]] deps = ["ChainRulesCore", "Functors", "LinearAlgebra", "Random", "Statistics"] -git-tree-sha1 = "c1fc26bab5df929a5172f296f25d7d08688fd25b" +git-tree-sha1 = "34205b1204cc83c43cd9cfe53ffbd3b310f6e8c5" uuid = "3bd65402-5787-11e9-1adc-39752487f4e2" -version = "0.2.20" +version = "0.3.1" [[deps.Optimization]] deps = ["ADTypes", "ArrayInterface", "ConsoleProgressMonitor", "DocStringExtensions", "LinearAlgebra", "Logging", "LoggingExtras", "Pkg", "Printf", "ProgressLogging", "Reexport", "Requires", "SciMLBase", "SparseArrays", "TerminalLoggers"] -git-tree-sha1 = "8489ad2e45328a9eba30a3c46006b04deee65a03" +git-tree-sha1 = "f59193f79f8310e5eaad309ccfa8f2fa06b6703b" uuid = "7f7a1694-90dd-40f0-9382-eb1efda571ba" -version = "3.17.0" +version = "3.19.1" [deps.Optimization.extensions] OptimizationEnzymeExt = "Enzyme" @@ -975,9 +939,9 @@ version = "3.17.0" [[deps.OptimizationOptimJL]] deps = ["Optim", "Optimization", "Reexport", "SparseArrays"] -git-tree-sha1 = "76a57a8484370bb26ae82a0a330abfff96d561aa" +git-tree-sha1 = "d8a989697ad1037258238004306057ca866bc337" uuid = "36348300-93cb-4f02-beb5-3c3902f8871e" -version = "0.1.9" +version = "0.1.12" [[deps.OrderedCollections]] git-tree-sha1 = "2e73fe17cac3c62ad1aebe70d44c963c3cfdc3e3" @@ -986,9 +950,9 @@ version = "1.6.2" [[deps.PDMats]] deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] -git-tree-sha1 = "67eae2738d63117a196f497d7db789821bce61d1" +git-tree-sha1 = "66b2fcd977db5329aa35cac121e5b94dd6472198" uuid = "90014a1f-27ba-587c-ab20-58faa44d9150" -version = "0.11.17" +version = "0.11.28" [[deps.PSIS]] deps = ["LinearAlgebra", "LogExpFunctions", "Printf", "RecipesBase", "Statistics"] @@ -1039,15 +1003,15 @@ version = "1.2.0" [[deps.Preferences]] deps = ["TOML"] -git-tree-sha1 = "7eb1686b4f04b82f96ed7a4ea5890a4f0c7a09f1" +git-tree-sha1 = "00805cd429dcb4870060ff49ef443486c262e38e" uuid = "21216c6a-2e73-6563-6e65-726566657250" -version = "1.4.0" +version = "1.4.1" [[deps.PrettyTables]] deps = ["Crayons", "LaTeXStrings", "Markdown", "Printf", "Reexport", "StringManipulation", "Tables"] -git-tree-sha1 = "ee094908d720185ddbdc58dbe0c1cbe35453ec7a" +git-tree-sha1 = "6842ce83a836fbbc0cfeca0b5a4de1a4dcbdb8d1" uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" -version = "2.2.7" +version = "2.2.8" [[deps.Printf]] deps = ["Unicode"] @@ -1067,9 +1031,9 @@ version = "1.9.0" [[deps.QuadGK]] deps = ["DataStructures", "LinearAlgebra"] -git-tree-sha1 = "6ec7ac8412e83d57e313393220879ede1740f9ee" +git-tree-sha1 = "9ebcd48c498668c7fa0e97a9cae873fbee7bfee1" uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" -version = "2.8.2" +version = "2.9.1" [[deps.REPL]] deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] @@ -1155,12 +1119,6 @@ git-tree-sha1 = "838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7" uuid = "ae029012-a4dd-5104-9daa-d747884805df" version = "1.3.0" -[[deps.Richardson]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "e03ca566bec93f8a3aeb059c8ef102f268a38949" -uuid = "708f8203-808e-40c0-ba2d-98a6953ed40d" -version = "1.4.0" - [[deps.Rmath]] deps = ["Random", "Rmath_jll"] git-tree-sha1 = "f65dcb5fa46aee0cf9ed6274ccbd597adc49aa7b" @@ -1175,19 +1133,21 @@ version = "0.4.0+0" [[deps.Roots]] deps = ["ChainRulesCore", "CommonSolve", "Printf", "Setfield"] -git-tree-sha1 = "ff42754a57bb0d6dcfe302fd0d4272853190421f" +git-tree-sha1 = "06b5ac80ff1b88bd82df92c1c1875eea3954cd6e" uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665" -version = "2.0.19" +version = "2.0.20" [deps.Roots.extensions] RootsForwardDiffExt = "ForwardDiff" RootsIntervalRootFindingExt = "IntervalRootFinding" RootsSymPyExt = "SymPy" + RootsSymPyPythonCallExt = "SymPyPythonCall" [deps.Roots.weakdeps] ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" IntervalRootFinding = "d2bf35a9-74e0-55ec-b149-d360ff49b807" SymPy = "24249f21-da20-56a4-8eb1-6a02cf4ae2e6" + SymPyPythonCall = "bc8888f7-b21e-4b7c-a06a-5d9c9496438c" [[deps.RuntimeGeneratedFunctions]] deps = ["ExprTools", "SHA", "Serialization"] @@ -1200,10 +1160,10 @@ uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" version = "0.7.0" [[deps.SciMLBase]] -deps = ["ADTypes", "ArrayInterface", "ChainRulesCore", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces", "ZygoteRules"] -git-tree-sha1 = "c0781c7ebb65776e9770d333b5e191d20dd45fcf" +deps = ["ADTypes", "ArrayInterface", "ChainRulesCore", "CommonSolve", "ConstructionBase", "Distributed", "DocStringExtensions", "EnumX", "FillArrays", "FunctionWrappersWrappers", "IteratorInterfaceExtensions", "LinearAlgebra", "Logging", "Markdown", "PrecompileTools", "Preferences", "RecipesBase", "RecursiveArrayTools", "Reexport", "RuntimeGeneratedFunctions", "SciMLOperators", "StaticArraysCore", "Statistics", "SymbolicIndexingInterface", "Tables", "TruncatedStacktraces", "ZygoteRules"] +git-tree-sha1 = "916b8a94c0d61fa5f7c5295649d3746afb866aff" uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462" -version = "1.97.0" +version = "1.98.1" [deps.SciMLBase.extensions] ZygoteExt = "Zygote" @@ -1251,9 +1211,9 @@ uuid = "6462fe0b-24de-5631-8697-dd941f90decc" [[deps.SortingAlgorithms]] deps = ["DataStructures"] -git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee" +git-tree-sha1 = "5165dfb9fd131cf0c6957a3a7605dede376e7b63" uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c" -version = "1.1.1" +version = "1.2.0" [[deps.SparseArrays]] deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] @@ -1283,9 +1243,9 @@ version = "0.1.15" [[deps.StaticArrays]] deps = ["LinearAlgebra", "Random", "StaticArraysCore"] -git-tree-sha1 = "51621cca8651d9e334a659443a74ce50a3b6dfab" +git-tree-sha1 = "0adf069a2a490c47273727e029371b31d44b72b2" uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.6.3" +version = "1.6.5" weakdeps = ["Statistics"] [deps.StaticArrays.extensions] @@ -1330,12 +1290,6 @@ weakdeps = ["ChainRulesCore", "InverseFunctions"] StatsFunsChainRulesCoreExt = "ChainRulesCore" StatsFunsInverseFunctionsExt = "InverseFunctions" -[[deps.StringEncodings]] -deps = ["Libiconv_jll"] -git-tree-sha1 = "b765e46ba27ecf6b44faf70df40c57aa3a547dcb" -uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" -version = "0.3.7" - [[deps.StringManipulation]] deps = ["PrecompileTools"] git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5" @@ -1403,20 +1357,14 @@ version = "0.1.0" [[deps.Tracker]] deps = ["Adapt", "DiffRules", "ForwardDiff", "Functors", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NNlib", "NaNMath", "Optimisers", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics"] -git-tree-sha1 = "92364c27aa35c0ee36e6e010b704adaade6c409c" +git-tree-sha1 = "994c4977c13e7e2525566e193f82374021fab5dd" uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" -version = "0.2.26" +version = "0.2.27" weakdeps = ["PDMats"] [deps.Tracker.extensions] TrackerPDMatsExt = "PDMats" -[[deps.TranscodingStreams]] -deps = ["Random", "Test"] -git-tree-sha1 = "9a6ae7ed916312b41236fcef7e0af564ef934769" -uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.9.13" - [[deps.Transducers]] deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "ConstructionBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"] git-tree-sha1 = "53bd5978b182fa7c57577bdb452c35e5b4fb73a5" @@ -1438,9 +1386,9 @@ version = "0.4.78" Referenceables = "42d2dcc6-99eb-4e98-b66c-637b7d73030e" [[deps.Tricks]] -git-tree-sha1 = "aadb748be58b492045b4f56166b5188aa63ce549" +git-tree-sha1 = "eae1bb484cd63b36999ee58be2de6c178105112f" uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775" -version = "0.1.7" +version = "0.1.8" [[deps.TruncatedStacktraces]] deps = ["InteractiveUtils", "MacroTools", "Preferences"] @@ -1450,9 +1398,9 @@ version = "1.4.0" [[deps.Turing]] deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "LogDensityProblems", "LogDensityProblemsAD", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "Setfield", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] -git-tree-sha1 = "0d369f7831584688a32913dc786833280391013f" +git-tree-sha1 = "7cf779c99fbf6b2adfd3b5650ffdac21c0165489" uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" -version = "0.29.1" +version = "0.29.3" [deps.Turing.extensions] TuringDynamicHMCExt = "DynamicHMC" @@ -1485,35 +1433,12 @@ git-tree-sha1 = "323e3d0acf5e78a56dfae7bd8928c989b4f3083e" uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249" version = "0.1.3" -[[deps.WeakRefStrings]] -deps = ["DataAPI", "InlineStrings", "Parsers"] -git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23" -uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5" -version = "1.4.2" - [[deps.WoodburyMatrices]] deps = ["LinearAlgebra", "SparseArrays"] git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3" uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6" version = "0.5.5" -[[deps.WorkerUtilities]] -git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7" -uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60" -version = "1.6.1" - -[[deps.YAML]] -deps = ["Base64", "Dates", "Printf", "StringEncodings"] -git-tree-sha1 = "e6330e4b731a6af7959673621e91645eb1356884" -uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6" -version = "0.4.9" - -[[deps.ZipFile]] -deps = ["Libdl", "Printf", "Zlib_jll"] -git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a" -uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" -version = "0.10.1" - [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" From 2b482124c02ee90da5df78febbc2b12c02deacc2 Mon Sep 17 00:00:00 2001 From: jaimerz Date: Thu, 19 Oct 2023 11:05:41 +0100 Subject: [PATCH 87/87] slight formatting error --- tutorials/docs-16-external-samplers/external-samplers.jmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/docs-16-external-samplers/external-samplers.jmd b/tutorials/docs-16-external-samplers/external-samplers.jmd index 0f8fc896c..356d224d9 100644 --- a/tutorials/docs-16-external-samplers/external-samplers.jmd +++ b/tutorials/docs-16-external-samplers/external-samplers.jmd @@ -32,7 +32,7 @@ Now we sample the model to generate some observations, which we can then conditi ```julia (; x) = rand(funnel() | (θ=0,)) -model = funnel() | (; x) +model = funnel() | (; x); ``` Users can use any sampler algorithm to sample this model if it follows the `AbstractMCMC` API. @@ -56,7 +56,7 @@ Sampling is then as easy as: chain = sample(model, externalsampler(rwmh), 10_000) ``` -## Going beyond the `Turing` API +## Going beyond the Turing API As previously mentioned, the Turing wrappers can often limit the capabilities of the sampling libraries they wrap. `AdvancedHMC`[^1] ((`AdvancedHMC`'s GitHub)[https://github.com/TuringLang/AdvancedHMC.jl]) is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix.