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metaprob

(★ 170)

An embedded language for probabilistic programming and meta-programming.

파일 탐색기

최종 버전 다운로드 (.zip)

- convert
- convert-all
- gnuplot-hist
- gnuplot-hist.gp
- parse-all
- interaction.md
- language.md
- metaprob-in-clojure.txt
- - autotrace.cljc
  - code_handlers.cljc
  - distributions.cljc
  - expander.cljc
  - generative_functions.cljc
  - inference.cljc
  - prelude.cljc
  - trace.cljc
- - compositional_test.cljc
  - distributions_test.cljc
  - inference_test.cljc
  - prelude_test.cljc
  - syntax_test.cljc
  - test_runner.cljs
  - trace_test.cljc
- - plot-trace.js
  - plotly-latest.min.js
- README.md
.gitignore
.travis.yml
deps.edn
docker-compose.yaml
Dockerfile
INSTALL.md
LICENSE
Makefile
project.clj
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/probcomp/metaprob

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd metaprob

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Docker

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.

docker compose up -d --build

정의된 모든 컨테이너(서버, DB 등)를 한 번에 빌드하고 백그라운드에서 실행합니다.

✅ 터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.

3. Make

보통

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Make Linux/macOS는 보통 기본 설치되어 있습니다. Windows는 별도 설치(예: MSYS2, WSL)가 필요합니다.

make

생성된 빌드 설정을 바탕으로 실제 컴파일을 진행해 실행 파일을 만듭니다.

✅ 에러 없이 끝나면 성공입니다. 생성된 실행 파일을 직접 실행해보세요.

// repository documentation

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# Metaprob

[![Build Status](https://travis-ci.org/probcomp/metaprob.svg?branch=master)](https://travis-ci.org/probcomp/metaprob)
![Stability: Experimental](https://img.shields.io/badge/stability-experimental-orange.svg)

A language for probabilistic programming and metaprogramming, embedded in [Clojure](https://clojure.org/).

**Note: Metaprob is currently an unstable research prototype, with little documentation and low test coverage. Also, future versions may not be backwards compatible with this version. We do not recommend using it for any purpose other than basic research, and are not yet able to support users outside of the MIT Probabilistic Computing Project.**

## Key features

* Models can be represented via generative code, i.e. ordinary code that makes stochastic choices
* Models can also be represented via approximations, e.g. importance samplers with nontrivial weights
* Custom inference algorithms can be written in user-space code, via reflective language constructs for:
  * tracing program executions
  * using partial traces to specify interventions and constraints
* Generic inference algorithms are provided via user-space code in a standard library; adding new algorithms does not require modifying the language implementation
* All Inference algorithms are ordinary generative code and can be traced and treated as models
* New probability distributions and inference algorithms are first-class citizens that can be created dynamically during program execution

## Motivations

* Lightweight embeddings of probabilistic programming and inference metaprogramming
  * Interactive, browser-based data analysis tools (via [ClojureScript](https://clojurescript.org/))
  * Smart data pipelines suitable for enterprise deployment (via Clojure on the JVM)
* “Small core” language potentially suitable for formal specification and verification
* Teaching
  * Undergraduates and graduate students interested in implementing their own minimal PPL
  * Software engineers and data engineers interested in probabilistic modeling and inference
* Research in artificial intelligence and cognitive science
  * Combining symbolic and probabilistic reasoning, e.g. via integration with Clojure’s [core.logic](https://github.com/clojure/core.logic)
  * “Theory of mind” models, where an agent’s reasoning is modeled as an inference metaprogram acting on a generative model
  * Reinforcement learning and other “nested” applications of modeling and approximate inference
  * Causal reasoning, via a notion of interventions that extends Pearl's “do” operator
* Research in probabilistic meta-programming, e.g. synthesis, reflection, runtime code generation

## Modeling and tracing

Generative models are represented as ordinary functions that make stochastic choices.

```clojure
;; Flip a fair coin n times
(def fair-coin-model
 (gen [n]
   (map (fn [i] (at i flip 0.5)) (range n))))
;; Flip a possibly weighted coin n times
(def biased-coin-model
 (gen [n]
   (let [p (at "p" uniform 0 1)]
     (map (fn [i] (at i flip p)) (range n)))))
```

Execution traces of models, which record the random choices they make, are first-class values that inference algorithms can manipulate.

We obtain scored traces using `infer-and-score`, which invokes a “tracing interpreter” that is itself a Metaprob program.

```clojure
(infer-and-score :procedure fair-coin-model, :inputs [3])
```

## Documentation

* [Contributor installation instructions](INSTALL.md)
  * [Using Metaprob](doc/interaction.md)
  * [Language reference](doc/language.md)