18 interface mlj

.JuliaFormatter.toml

@@ -0,0 +1 @@
+style = "blue"

.github/workflows/CI.yml

@@ -1,6 +1,9 @@
 name: CI
 on:
   push:
+    branches:
+      - main
+    tags: '*'
   pull_request:
     branches: [main]
   workflow_dispatch:

.github/workflows/CompatHelper.yml

@@ -0,0 +1,16 @@
+name: CompatHelper
+on:
+  schedule:
+    - cron: 0 0 * * *
+  workflow_dispatch:
+jobs:
+  CompatHelper:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Pkg.add("CompatHelper")
+        run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
+      - name: CompatHelper.main()
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
+        run: julia -e 'using CompatHelper; CompatHelper.main()'

.github/workflows/TagBot.yml

@@ -0,0 +1,15 @@
+name: TagBot
+on:
+  issue_comment:
+    types:
+      - created
+  workflow_dispatch:
+jobs:
+  TagBot:
+    if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: JuliaRegistries/TagBot@v1
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          ssh: ${{ secrets.DOCUMENTER_KEY }}

README.md

@@ -1,9 +1,10 @@
+# LaplaceRedux
 
 ![](docs/src/assets/wide_logo.png)
 
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/stable) [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/dev) [![Build Status](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/actions/workflows/CI.yml?query=branch%3Amain) [![Coverage](https://codecov.io/gh/JuliaTrustworthyAI/LaplaceRedux.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/JuliaTrustworthyAI/LaplaceRedux.jl) [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle) [![ColPrac: Contributor’s Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet.png)](https://github.com/SciML/ColPrac) [![Twitter Badge](https://img.shields.io/twitter/url/https/twitter.com/paltmey.svg?style=social&label=Follow%20%40paltmey)](https://twitter.com/paltmey)
 
-`LaplaceRedux.jl` is a library written in pure Julia that can be used for effortless Bayesian Deep Learning trough Laplace Approximation (LA). In the development of this package I have drawn inspiration from this Python [library](https://aleximmer.github.io/Laplace/index.html#setup) and its companion [paper](https://arxiv.org/abs/2106.14806) (Daxberger et al. 2021).
+`LaplaceRedux.jl` is a library written in pure Julia that can be used for effortless Bayesian Deep Learning through Laplace Approximation (LA). In the development of this package I have drawn inspiration from this Python [library](https://aleximmer.github.io/Laplace/index.html#setup) and its companion [paper](https://arxiv.org/abs/2106.14806) (Daxberger et al. 2021).
 
 ## 🚩 Installation
 
@@ -36,7 +37,7 @@ optimize_prior!(la)
 plot(la, X, y; zoom=-5, size=(500,500))
 ```
 
-![](README_files/figure-commonmark/cell-4-output-1.png)
+![](README_files/figure-commonmark/cell-4-output-1.svg)
 
 ### Binary Classification
 
@@ -58,7 +59,7 @@ p_laplace = plot(la, X, ys; title="LA - tuned (λ=$(round(unique(diag(la.P₀))[
 plot(p_plugin, p_untuned, p_laplace, layout=(1,3), size=(1700,400))
 ```
 
-![](README_files/figure-commonmark/cell-6-output-1.svg)
+![](README_files/figure-commonmark/cell-7-output-1.svg)
 
 ## 📢 JuliaCon 2022
 

README.qmd

@@ -12,7 +12,7 @@ crossref:
   fig-prefix: Figure
   tbl-prefix: Table
 bibliography: https://raw.githubusercontent.com/pat-alt/bib/main/bib.bib
-jupyter: julia-1.8
+jupyter: julia-1.9
 ---
 
 ![](docs/src/assets/wide_logo.png)

_freeze/docs/src/index/execute-results/md.json

@@ -1,7 +1,7 @@
 {
   "hash": "78c5dc332bd7b51e7b48569a1e1728f5",
   "result": {
-    "markdown": "```@meta\nCurrentModule = LaplaceRedux\n```\n\n# LaplaceRedux\n\nDocumentation for [LaplaceRedux.jl](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl).\n\n\n\n`LaplaceRedux.jl` is a library written in pure Julia that can be used for effortless Bayesian Deep Learning trough Laplace Approximation (LA). In the development of this package I have drawn inspiration from this Python [library](https://aleximmer.github.io/Laplace/index.html#setup) and its companion [paper](https://arxiv.org/abs/2106.14806) [@daxberger2021laplace].\n\n## 🚩 Installation\n\nThe stable version of this package can be installed as follows:\n\n```{.julia}\nusing Pkg\nPkg.add(\"LaplaceRedux.jl\")\n```\n\nThe development version can be installed like so:\n\n```{.julia}\nusing Pkg\nPkg.add(\"https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl\")\n```\n\n## 🖥️ Basic Usage\n\n`LaplaceRedux.jl` can be used for any neural network trained in [`Flux.jl`](https://fluxml.ai/Flux.jl/dev/). Below we show basic usage examples involving two simple models for a regression and a classification task, respectively.\n\n### Regression\n\n\n\nA complete worked example for a regression model can be found in the [docs](https://www.paltmeyer.com/LaplaceRedux.jl/dev/tutorials/regression/). Here we jump straight to Laplace Approximation and take the pre-trained model `nn` as given. Then LA can be implemented as follows, where we specify the model `likelihood`. The plot show the fitted values overlayed with a 95% confidence interval. As expected, predictive uncertainty quickly increases in areas that are not populated by any training data.\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nla = Laplace(nn; likelihood=:regression)\nfit!(la, data)\noptimize_prior!(la)\nplot(la, X, y; zoom=-5, size=(500,500))\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n![](index_files/figure-commonmark/cell-4-output-1.png){}\n:::\n:::\n\n\n### Binary Classification\n\n\n\nOnce again we jump straight to LA and refer to the [docs](https://www.paltmeyer.com/LaplaceRedux.jl/dev/tutorials/mlp/) for a complete worked example involving binary classification. In this case we need to specify `likelihood=:classification`. The plot below shows the resulting posterior predictive distributions as contours in the two-dimensional feature space: note how the **Plugin** Approximation on the left compares to the Laplace Approximation on the right.\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\ntheme(:lime)\n\nla = Laplace(nn; likelihood=:classification)\nfit!(la, data)\nla_untuned = deepcopy(la)   # saving for plotting\noptimize_prior!(la; n_steps=500)\n\n# Plot the posterior predictive distribution:\nzoom=0\np_plugin = plot(la, X, ys; title=\"Plugin\", link_approx=:plugin, clim=(0,1))\np_untuned = plot(la_untuned, X, ys; title=\"LA - raw (λ=$(unique(diag(la_untuned.P₀))[1]))\", clim=(0,1), zoom=zoom)\np_laplace = plot(la, X, ys; title=\"LA - tuned (λ=$(round(unique(diag(la.P₀))[1],digits=2)))\", clim=(0,1), zoom=zoom)\nplot(p_plugin, p_untuned, p_laplace, layout=(1,3), size=(1700,400))\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n![](index_files/figure-commonmark/cell-6-output-1.svg){}\n:::\n:::\n\n\n## 📢 JuliaCon 2022\n\nThis project was presented at JuliaCon 2022 in July 2022. See [here](https://pretalx.com/juliacon-2022/talk/Z7MXFS/) for details.\n\n## 🛠️ Contribute\n\nContributions are very much welcome! Please follow the [SciML ColPrac guide](https://github.com/SciML/ColPrac). You may want to start by having a look at any open [issues](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/issues). \n\n## 🎓 References\n\n",
+    "markdown": "---\ntitle: LaplaceRedux\n---\n\n\n```@meta\nCurrentModule = LaplaceRedux\n```\n\n\nDocumentation for [LaplaceRedux.jl](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl).\n\n\n# LaplaceRedux\n\n\n\n\n`LaplaceRedux.jl` is a library written in pure Julia that can be used for effortless Bayesian Deep Learning through Laplace Approximation (LA). In the development of this package I have drawn inspiration from this Python [library](https://aleximmer.github.io/Laplace/index.html#setup) and its companion [paper](https://arxiv.org/abs/2106.14806) [@daxberger2021laplace].\n\n## 🚩 Installation\n\nThe stable version of this package can be installed as follows:\n\n```{.julia}\nusing Pkg\nPkg.add(\"LaplaceRedux.jl\")\n```\n\nThe development version can be installed like so:\n\n```{.julia}\nusing Pkg\nPkg.add(\"https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl\")\n```\n\n## 🖥️ Basic Usage\n\n`LaplaceRedux.jl` can be used for any neural network trained in [`Flux.jl`](https://fluxml.ai/Flux.jl/dev/). Below we show basic usage examples involving two simple models for a regression and a classification task, respectively.\n\n### Regression\n\n\n\nA complete worked example for a regression model can be found in the [docs](https://www.paltmeyer.com/LaplaceRedux.jl/dev/tutorials/regression/). Here we jump straight to Laplace Approximation and take the pre-trained model `nn` as given. Then LA can be implemented as follows, where we specify the model `likelihood`. The plot show the fitted values overlaid with a 95% confidence interval. As expected, predictive uncertainty quickly increases in areas that are not populated by any training data.\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nla = Laplace(nn; likelihood=:regression)\nfit!(la, data)\noptimize_prior!(la)\nplot(la, X, y; zoom=-5, size=(500,500))\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n![](index_files/figure-commonmark/cell-4-output-1.svg){}\n:::\n:::\n\n\n\n\n### Binary Classification\n\n\n\nOnce again we jump straight to LA and refer to the [docs](https://www.paltmeyer.com/LaplaceRedux.jl/dev/tutorials/mlp/) for a complete worked example involving binary classification. In this case we need to specify `likelihood=:classification`. The plot below shows the resulting posterior predictive distributions as contours in the two-dimensional feature space: note how the **Plugin** Approximation on the left compares to the Laplace Approximation on the right.\n\n::: {.cell execution_count=6}\n``` {.julia .cell-code}\ntheme(:lime)\n\nla = Laplace(nn; likelihood=:classification)\nfit!(la, data)\nla_untuned = deepcopy(la)   # saving for plotting\noptimize_prior!(la; n_steps=500)\n\n# Plot the posterior predictive distribution:\nzoom=0\np_plugin = plot(la, X, ys; title=\"Plugin\", link_approx=:plugin, clim=(0,1))\np_untuned = plot(la_untuned, X, ys; title=\"LA - raw (λ=$(unique(diag(la_untuned.P₀))[1]))\", clim=(0,1), zoom=zoom)\np_laplace = plot(la, X, ys; title=\"LA - tuned (λ=$(round(unique(diag(la.P₀))[1],digits=2)))\", clim=(0,1), zoom=zoom)\nplot(p_plugin, p_untuned, p_laplace, layout=(1,3), size=(1700,400))\n```\n\n::: {.cell-output .cell-output-display execution_count=7}\n![](index_files/figure-commonmark/cell-7-output-1.svg){}\n:::\n:::\n\n\n## 📢 JuliaCon 2022\n\nThis project was presented at JuliaCon 2022 in July 2022. See [here](https://pretalx.com/juliacon-2022/talk/Z7MXFS/) for details.\n\n## 🛠️ Contribute\n\nContributions are very much welcome! Please follow the [SciML ColPrac guide](https://github.com/SciML/ColPrac). You may want to start by having a look at any open [issues](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/issues). \n\n## 🎓 References\n\n",
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