Merge pull request #85 from JuliaTrustworthyAI/84-update-docs-and-doc…

…strings

84 update docs and docstrings

Project.toml

@@ -1,7 +1,7 @@
 name = "LaplaceRedux"
 uuid = "c52c1a26-f7c5-402b-80be-ba1e638ad478"
 authors = ["Patrick Altmeyer"]
-version = "0.1.8"
+version = "0.2.0"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
@@ -12,7 +12,6 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
-Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -30,7 +29,6 @@ LinearAlgebra = "1.6, 1.7, 1.8, 1.9, 1.10"
 MLJFlux = "0.2.10, 0.3, 0.4"
 MLJModelInterface = "1.8.0"
 MLUtils = "0.4.3"
-Parameters = "0.12"
 ProgressMeter = "1.7.2"
 Random = "1.6, 1.7, 1.8, 1.9, 1.10"
 Statistics = "1"

README.md

@@ -58,8 +58,8 @@ optimize_prior!(la; n_steps=100)
 # Plot the posterior predictive distribution:
 zoom=0
 p_plugin = plot(la, X, ys; title="Plugin", link_approx=:plugin, clim=(0,1))
-p_untuned = plot(la_untuned, X, ys; title="LA - raw (λ=$(unique(diag(la_untuned.P₀))[1]))", clim=(0,1), zoom=zoom)
-p_laplace = plot(la, X, ys; title="LA - tuned (λ=$(round(unique(diag(la.P₀))[1],digits=2)))", clim=(0,1), zoom=zoom)
+p_untuned = plot(la_untuned, X, ys; title="LA - raw (λ=$(unique(diag(la_untuned.prior.P₀))[1]))", clim=(0,1), zoom=zoom)
+p_laplace = plot(la, X, ys; title="LA - tuned (λ=$(round(unique(diag(la.prior.P₀))[1],digits=2)))", clim=(0,1), zoom=zoom)
 plot(p_plugin, p_untuned, p_laplace, layout=(1,3), size=(1700,400))
 ```
 

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

@@ -2,7 +2,7 @@
   "hash": "0b3babc9ba412d09f74672b1ac5c443d",
   "result": {
     "engine": "jupyter",
-    "markdown": "```@meta\nCurrentModule = LaplaceRedux\n```\n\n![](assets/wide_logo.png)\n\nDocumentation for [LaplaceRedux.jl](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl).\n\n\n# LaplaceRedux\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## 🏃 Getting Started\n\nIf you are new to Deep Learning in Julia or simply prefer learning through videos, check out this awesome YouTube [tutorial](https://www.youtube.com/channel/UCQwQVlIkbalDzmMnr-0tRhw) by [doggo.jl](https://www.youtube.com/@doggodotjl/about) 🐶. Additionally, you can also find a [video](https://www.youtube.com/watch?v=oWko8FRj_64) of my presentation at JuliaCon 2022 on YouTube. \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 shows 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=28}\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}\nla = Laplace(nn; likelihood=:classification)\nfit!(la, data)\nla_untuned = deepcopy(la)   # saving for plotting\noptimize_prior!(la; n_steps=100)\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=31}\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",
+    "markdown": "```@meta\nCurrentModule = LaplaceRedux\n```\n\n![](assets/wide_logo.png)\n\nDocumentation for [LaplaceRedux.jl](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl).\n\n\n# LaplaceRedux\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## 🏃 Getting Started\n\nIf you are new to Deep Learning in Julia or simply prefer learning through videos, check out this awesome YouTube [tutorial](https://www.youtube.com/channel/UCQwQVlIkbalDzmMnr-0tRhw) by [doggo.jl](https://www.youtube.com/@doggodotjl/about) 🐶. Additionally, you can also find a [video](https://www.youtube.com/watch?v=oWko8FRj_64) of my presentation at JuliaCon 2022 on YouTube. \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 shows 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}\nla = Laplace(nn; likelihood=:classification)\nfit!(la, data)\nla_untuned = deepcopy(la)   # saving for plotting\noptimize_prior!(la; n_steps=100)\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=$(unique(diag(la_untuned.prior.P₀))[1]))\", clim=(0,1), zoom=zoom)\np_laplace = plot(la, X, ys; title=\"LA - tuned (λ=$(round(unique(diag(la.prior.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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