https://github.com/theogf/Turkie.jl

Turing + Makie = Turkie
https://github.com/theogf/Turkie.jl

Last synced: about 2 months ago
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Turing + Makie = Turkie

• Host: GitHub
• URL: https://github.com/theogf/Turkie.jl
• Owner: theogf
• License: mit
• Created: 2020-09-19T13:44:06.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2024-06-07T00:35:44.000Z (3 months ago)
• Last Synced: 2024-07-07T02:03:53.307Z (2 months ago)
• Language: Julia
• Size: 10.7 MB
• Stars: 68
• Watchers: 2
• Forks: 6
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-sciml - theogf/Turkie.jl: Turing + Makie = Turkie

README

        
# Turing + Makie -> Turkie

[![Docs Latest](https://img.shields.io/badge/docs-dev-blue.svg)](https://theogf.dev/Turkie.jl/dev)

[![Docs Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://theogf.dev/Turkie.jl/stable)

[![Coverage Status](https://coveralls.io/repos/github/theogf/Turkie.jl/badge.svg?branch=master)](https://coveralls.io/github/theogf/Turkie.jl?branch=master)

[![CI](https://github.com/theogf/Turkie.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/theogf/Turkie.jl/actions/workflows/ci.yml)



  





  



WIP for an inference visualization package.


## Roadmap

### To plot during sampling

- [x] Trace of the chains

- [x] Statistics (mean and var)

- [x] Marginals (KDE/Histograms)

- [x] Autocorrelation plots

- [ ] Show multiple chains

### Additional features

- [x] Selecting which variables are plotted

- [x] Selecting what plots to show

- [x] Giving a recording option

- [ ] Additional fine tuning features like

  - [ ] Thinning

  - [x] Creating a buffer to limit the viewing

### Extra Features

- [ ] Using a color mapping given some statistics

- [ ] Allow to apply transformation before plotting

## Usage

Small example:

```julia

using Turing

using Turkie

using GLMakie # You could also use CairoMakie or another backend

@model function demo(x) # Some random Turing model

    m0 ~ Normal(0, 2)

    s ~ InverseGamma(2, 3)

    m ~ Normal(m0, √s)

    for i in eachindex(x)

        x[i] ~ Normal(m, √s)

    end

end

xs = randn(100) .+ 1 # Create some random data

m = demo(xs) # Create the model

cb = TurkieCallback(m) # Create a callback function to be given to the sample

chain = sample(m, NUTS(0.65), 300; callback = cb) # Sample and plot at the same time

```

If you want to show only some variables you can give a `Dict` to `TurkieCallback` :

```julia

cb = TurkieCallback(

            (m0 = [:trace, :mean], s = [:autocov, :var])

          )

```

You can also directly pass `OnlineStats` object:

```julia

using OnlineStats

cb = TurkieCallback(

            (v = [Mean(), AutoCov(20)],)

          )

```

If you want to record the video do

```julia

using Makie

record(cb.figure, joinpath(@__DIR__, "video.webm")) do io

    addIO!(cb, io)

    sample(m,  NUTS(0.65), 300; callback = cb)

end

```