Science Score: 41.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
✓Committers with academic emails
1 of 19 committers (5.3%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.8%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Probabilistic programming via source rewriting
Basic Info
- Host: GitHub
- Owner: cscherrer
- License: mit
- Language: Julia
- Default Branch: master
- Homepage: https://cscherrer.github.io/Soss.jl/stable/
- Size: 5.81 MB
Statistics
- Stars: 420
- Watchers: 19
- Forks: 30
- Open Issues: 112
- Releases: 48
Topics
Metadata Files
README.md
Soss
Soss is a library for probabilistic programming.
Let's look at an example. First we'll load things:
julia
using MeasureTheory
using Soss
MeasureTheory.jl is designed specifically with PPLs like Soss in mind, though you can also use Distributions.jl.
Now for a model. Here's a linear regression:
julia
m = @model x begin
α ~ Lebesgue(ℝ)
β ~ Normal()
σ ~ Exponential()
y ~ For(x) do xj
Normal(α + β * xj, σ)
end
return y
end
Next we'll generate some fake data to work with. For x-values, let's use
julia
x = randn(20)
Now loosely speaking, Lebesgue(ℝ) is uniform over the real numbers, so we can't really sample from it. Instead, let's transform the model and make α an argument:
julia
julia> predα = predictive(m, :α)
@model (x, α) begin
σ ~ Exponential()
β ~ Normal()
y ~ For(x) do xj
Normal(α + β * xj, σ)
end
return y
end
Now we can do
julia
julia> y = rand(predα(x=x,α=10.0))
20-element Vector{Float64}:
10.554133456468438
9.378065258831002
12.873667041657287
8.940799408080496
10.737189595204965
9.500536439014208
11.327606120726893
10.899892855024445
10.18488773139243
10.386969795947177
10.382195272387214
8.358407507910297
10.727173015711768
10.452311211064654
11.076232496702387
11.362009520020141
9.539433052406448
10.61851691333643
11.586170856832645
9.197496058151618
Now for inference! Let's use DynamicHMC, which we have wrapped in SampleChainsDynamicHMC.
```julia julia> using SampleChainsDynamicHMC [ Info: Precompiling SampleChainsDynamicHMC [6d9fd711-e8b2-4778-9c70-c1dfb499d4c4]
julia> post = sample(m(x=x) | (y=y,), dynamichmc()) 4000-element MultiChain with 4 chains and schema (σ = Float64, β = Float64, α = Float64) (σ = 1.0±0.15, β = 0.503±0.26, α = 10.2±0.25) ```
How is Soss different from Turing?
First, a fine point: When people say "the Turing PPL" they usually mean what's technically called "DynamicPPL".
- In Soss, models are first class, and can be composed or nested. For example, you can define a model and later nest it inside another model, and inference will handle both together. DynamicPPL can also handle nested models (see this PR) though I'm not aware of a way to combine independently-defined DynamicPPL models for a single inference pass.
- Soss has been updated to use MeasureTheory.jl, though everything from Distributions.jl is still available.
- Soss allows model transformations. This can be used, for example, to easily express predictive distributions or Markov blanket as a new model.
- Most of the focus of Soss is at the syntactic level; inference works in terms of "primitives" that transform the model's abstract syntax tree (AST) to new code. This adds the same benefits as using Julia's macros and generated functions, as opposed to higher-order functions alone.
- Soss can evaluate log-densities symbolically, which can then be used to produce optimized evaluations for much faster inference. This capability is in relatively early stages, and will be made more robust in our ongoing development.
- The Soss team is much smaller than that of DynamicPPL. But I hope that will change (contributors welcome!)
Soss and DynamicPPL are both maturing and becoming more complete, so the above will change over time. It's also worth noting that we (the Turing team and I) hope to move toward a natural way of using these systems together to arrive at the best of both.
How can I get involved?
I'm glad you asked! Lots of things:
- Contribute documentation or tests
- Ask questions on Discourse or Zulip
- File issues for bugs (or other problems) or feature requests
- Use Soss in your applications, teaching, or blogging
- Get involved in other libraries in the Soss ecosystem:
For more details, please see the documentation.
Stargazers over time
Owner
- Name: Chad Scherrer
- Login: cscherrer
- Kind: user
- Location: Seattle, WA
- Company: Redpoll
- Website: cscherrer.github.io
- Repositories: 128
- Profile: https://github.com/cscherrer
Probabilistic programming in Rust and Julia
Citation (CITATION.bib)
@misc{Soss.jl,
author = {Chad Scherrer},
title = {{Soss.jl}},
url = {https://github.com/cscherrer/Soss.jl},
year = {2019},
month = {9}
}
GitHub Events
Total
- Watch event: 4
Last Year
- Watch event: 4
Committers
Last synced: 8 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Chad Scherrer | c****r@g****m | 907 |
| Joey Miller | a****0@g****m | 29 |
| github-actions[bot] | 4****] | 26 |
| Joseph | j****5@a****m | 12 |
| Seth Axen | s****n@g****m | 9 |
| Dilum Aluthge | d****m@a****m | 8 |
| Taine Zhao | t****e@o****m | 6 |
| mohamed82008 | m****8@g****m | 3 |
| David Widmann | d****n | 2 |
| catethos | c****s@y****m | 2 |
| Fredrik Bagge Carlson | b****n@g****m | 1 |
| Julia TagBot | 5****t | 1 |
| Moritz Schauer | m****r@w****e | 1 |
| Thibaut Lienart | l****b@m****m | 1 |
| Vassily Trubetskoy | 3****a | 1 |
| jfb-h | 6****h | 1 |
| vargonis | v****s@g****m | 1 |
| $(ci_cfg.username) | $****) | 1 |
| willtebbutt | w****1@m****k | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 21
- Total pull requests: 85
- Average time to close issues: 7 days
- Average time to close pull requests: about 1 month
- Total issue authors: 15
- Total pull request authors: 4
- Average comments per issue: 5.71
- Average comments per pull request: 0.73
- Merged pull requests: 9
- Bot issues: 0
- Bot pull requests: 70
Past Year
- Issues: 0
- Pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 3
Top Authors
Issue Authors
- sethaxen (3)
- marius311 (2)
- paschermayr (2)
- jariji (2)
- FalkoSp (2)
- jtrakk (1)
- cscherrer (1)
- aelmokadem (1)
- gzagatti (1)
- AxLamelas (1)
- SebastianCallh (1)
- ParadaCarleton (1)
- JuliaTagBot (1)
- Tuebel (1)
- cwoode (1)
Pull Request Authors
- github-actions[bot] (73)
- cscherrer (12)
- thautwarm (2)
- sethaxen (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- julia 3 total
- Total dependent packages: 3
- Total dependent repositories: 0
- Total versions: 48
juliahub.com: Soss
Probabilistic programming via source rewriting
- Homepage: https://cscherrer.github.io/Soss.jl/stable/
- Documentation: https://docs.juliahub.com/General/Soss/stable/
- License: MIT
-
Latest release: 0.21.2
published over 3 years ago
Rankings
Dependencies
- JuliaRegistries/TagBot v1 composite
- actions/checkout v1.0.0 composite
- actions/github-script 0.3.0 composite
- julia-actions/setup-julia latest composite
- actions/cache v1 composite
- actions/checkout v2 composite
- codecov/codecov-action v1 composite
- julia-actions/julia-buildpkg latest composite
- julia-actions/julia-processcoverage v1 composite
- julia-actions/julia-runtest latest composite
- julia-actions/setup-julia v1 composite