Merge branch 'master' into wct/improve-ad-docs

.github/workflows/publish.yml

@@ -11,6 +11,9 @@ concurrency:
   group: docs
   cancel-in-progress: true
 
+permissions:
+  contents: write
+
 jobs:
   build-and-deploy:
     runs-on: ubuntu-latest
@@ -105,18 +108,26 @@ jobs:
         run: |
           jq -s '.[0] + .[1]' _site/search_original.json fixed_main_search.json > _site/search.json
 
-      - name: Deploy versioned docs
-        uses: JamesIves/github-pages-deploy-action@v4
+      - name: Checkout gh-pages branch
+        uses: actions/checkout@v4
         with:
-          branch: gh-pages
-          folder: _site
-          target-folder: versions/${{ env.version }}
-          clean: false
+          ref: gh-pages
+          path: gh-pages
 
-      - name: Deploy latest docs to root
-        if: env.version == env.LATEST
-        uses: JamesIves/github-pages-deploy-action@v4
-        with:
-          branch: gh-pages
-          folder: _site
-          clean: false
+      - name: Update gh-pages branch
+        run: |
+          # Copy to versions/ subdirectory
+          mkdir -p gh-pages/versions/${{ env.version }}
+          cp -r _site/* gh-pages/versions/${{ env.version }}
+
+          # Find the latest version of the docs and copy that to the root
+          cd gh-pages/versions
+          LATEST_DOCS=$(ls -d * | sort -V | tail -n 1)
+          cp -r $LATEST_DOCS/* ../
+
+          # Commit and push
+          git config --global user.name "github-actions[bot]"
+          git config --global user.email "github-actions[bot]@users.noreply.github.com"
+          git add -A
+          git commit -m "Publish docs @ ${GITHUB_REPOSITORY}@${GITHUB_SHA}"
+          git push

README.md

@@ -8,8 +8,10 @@ This repository is part of [Turing.jl's](https://turinglang.org/) website (i.e.
 
 To get started with the docs website locally, you'll need to have [Quarto](https://quarto.org/docs/download/) installed.
 Make sure you have at least version 1.5 of Quarto installed, as this is required to correctly run [the native Julia engine](https://quarto.org/docs/computations/julia.html#using-the-julia-engine).
+Ideally, you should use Quarto 1.6.31 or later as this version fixes [a bug which causes random number generation between different cells to not be deterministic](https://github.com/TuringLang/docs/issues/533).
+Note that as of October 2024, Quarto 1.6 is a pre-release version, so you may need to install it from source rather than via a package manager like Homebrew.
 
-Once you have the prerequisite installed, you can follow these steps:
+Once you have Quarto installed, you can follow these steps:
 
 1. Clone this repository:
 

_quarto.yml

@@ -181,13 +181,13 @@ probabilistic-pca: tutorials/11-probabilistic-pca
 gplvm: tutorials/12-gplvm
 seasonal-time-series: tutorials/13-seasonal-time-series
 contexts: tutorials/16-contexts
-minituring: tutorial/14-minituring
+minituring: tutorials/14-minituring
 contributing-guide: tutorials/docs-01-contributing-guide
 using-turing-abstractmcmc: tutorials/docs-04-for-developers-abstractmc-turing
 using-turing-compiler: tutorials/docs-05-for-developers-compiler
 using-turing-interface: tutorials/docs-06-for-developers-interface
 using-turing-variational-inference: tutorials/docs-07-for-developers-variational-inference
-using-turing-advanced: tutorials/tutorials/docs-09-using-turing-advanced
+using-turing-advanced: tutorials/docs-09-using-turing-advanced
 using-turing-autodiff: tutorials/docs-10-using-turing-autodiff
 using-turing-dynamichmc: tutorials/docs-11-using-turing-dynamichmc
 using-turing: tutorials/docs-12-using-turing-guide
@@ -197,7 +197,7 @@ using-turing-external-samplers: tutorials/docs-16-using-turing-external-samplers
 using-turing-implementing-samplers: tutorials/docs-17-implementing-samplers
 using-turing-mode-estimation: tutorials/docs-17-mode-estimation
 usage-probability-interface: tutorials/usage-probability-interface
-usage-custom-distribution: tutorials/tutorials/usage-custom-distribution
-usage-generated-quantities: tutorials/tutorials/usage-generated-quantities
-usage-modifying-logprob: tutorials/tutorials/usage-modifying-logprob
+usage-custom-distribution: tutorials/usage-custom-distribution
+usage-generated-quantities: tutorials/usage-generated-quantities
+usage-modifying-logprob: tutorials/usage-modifying-logprob
 dev-model-manual: tutorials/dev-model-manual

tutorials/01-gaussian-mixture-model/index.qmd

@@ -142,8 +142,7 @@ let
         # μ[1] and μ[2] can switch places, so we sort the values first.
         chain = Array(chains[:, ["μ[1]", "μ[2]"], i])
         μ_mean = vec(mean(chain; dims=1))
-        # TODO: https://github.com/TuringLang/docs/issues/533
-        # @assert isapprox(sort(μ_mean), μ; rtol=0.1) "Difference between estimated mean of μ ($(sort(μ_mean))) and data-generating μ ($μ) unexpectedly large!"
+        @assert isapprox(sort(μ_mean), μ; rtol=0.1) "Difference between estimated mean of μ ($(sort(μ_mean))) and data-generating μ ($μ) unexpectedly large!"
     end
 end
 ```
@@ -208,8 +207,7 @@ let
         # μ[1] and μ[2] can no longer switch places. Check that they've found the mean
         chain = Array(chains[:, ["μ[1]", "μ[2]"], i])
         μ_mean = vec(mean(chain; dims=1))
-        # TODO: https://github.com/TuringLang/docs/issues/533
-        # @assert isapprox(sort(μ_mean), μ; rtol=0.4) "Difference between estimated mean of μ ($(sort(μ_mean))) and data-generating μ ($μ) unexpectedly large!"
+        @assert isapprox(sort(μ_mean), μ; rtol=0.4) "Difference between estimated mean of μ ($(sort(μ_mean))) and data-generating μ ($μ) unexpectedly large!"
     end
 end
 ```
@@ -349,8 +347,7 @@ let
         # μ[1] and μ[2] can no longer switch places. Check that they've found the mean
         chain = Array(chains[:, ["μ[1]", "μ[2]"], i])
         μ_mean = vec(mean(chain; dims=1))
-        # TODO: https://github.com/TuringLang/docs/issues/533
-        # @assert isapprox(sort(μ_mean), μ; rtol=0.4) "Difference between estimated mean of μ ($(sort(μ_mean))) and data-generating μ ($μ) unexpectedly large!"
+        @assert isapprox(sort(μ_mean), μ; rtol=0.4) "Difference between estimated mean of μ ($(sort(μ_mean))) and data-generating μ ($μ) unexpectedly large!"
     end
 end
 ```

tutorials/09-variational-inference/index.qmd

@@ -155,9 +155,8 @@ var(x), mean(x)
 #| echo: false
 let
     v, m = (mean(rand(q, 2000); dims=2)...,)
-    # TODO: Fix these as they randomly fail https://github.com/TuringLang/docs/issues/533
-    # @assert isapprox(v, 1.022; atol=0.1) "Mean of s (VI posterior, 1000 samples): $v"
-    # @assert isapprox(m, -0.027; atol=0.03) "Mean of m (VI posterior, 1000 samples): $m"
+    @assert isapprox(v, 1.022; atol=0.1) "Mean of s (VI posterior, 1000 samples): $v"
+    @assert isapprox(m, -0.027; atol=0.03) "Mean of m (VI posterior, 1000 samples): $m"
 end
 ```
 

tutorials/11-probabilistic-pca/index.qmd

@@ -246,13 +246,10 @@ heatmap(
 We can quantitatively check the absolute magnitudes of the column average of the gap between `mat_exp` and `mat_rec`:
 
 ```{julia}
-#| echo: false
-# let
-#     diff_matrix = mat_exp .- mat_rec
-#     @assert abs(mean(diff_matrix[:, 4])) <= 0.5 #0.327
-#     @assert abs(mean(diff_matrix[:, 5])) <= 0.5 #0.390
-#     @assert abs(mean(diff_matrix[:, 6])) <= 0.5 #0.326
-# end
+diff_matrix = mat_exp .- mat_rec
+for col in 4:6
+    @assert abs(mean(diff_matrix[:, col])) <= 0.5
+end
 ```
 
 We observe that, using posterior mean, the recovered data matrix `mat_rec` has values align with the original data matrix - particularly the same pattern in the first and last 3 gene features are captured, which implies the inference and p-PCA decomposition are successful.
@@ -281,12 +278,12 @@ Another way to put it: 2 dimensions is enough to capture the main structure of t
 A direct question arises from above practice is: how many principal components do we want to keep, in order to sufficiently represent the latent structure in the data?
 This is a very central question for all latent factor models, i.e. how many dimensions are needed to represent that data in the latent space.
 In the case of PCA, there exist a lot of heuristics to make that choice.
-For example, We can tune the number of principal components using empirical methods such as cross-validation based some criteria such as MSE between the posterior predicted (e.g. mean predictions) data matrix and the original data matrix or the percentage of variation explained [3].
+For example, We can tune the number of principal components using empirical methods such as cross-validation based some criteria such as MSE between the posterior predicted (e.g. mean predictions) data matrix and the original data matrix or the percentage of variation explained [^3].
 
 For p-PCA, this can be done in an elegant and principled way, using a technique called *Automatic Relevance Determination* (ARD).
-ARD can help pick the correct number of principal directions by regularizing the solution space using a parameterized, data-dependent prior distribution that effectively prunes away redundant or superfluous features [4].
+ARD can help pick the correct number of principal directions by regularizing the solution space using a parameterized, data-dependent prior distribution that effectively prunes away redundant or superfluous features [^4].
 Essentially, we are using a specific prior over the factor loadings $\mathbf{W}$ that allows us to prune away dimensions in the latent space. The prior is determined by a precision hyperparameter $\alpha$. Here, smaller values of $\alpha$ correspond to more important components.
-You can find more details about this in e.g. [5].
+You can find more details about this in, for example, Bishop (2006) [^5].
 
 ```{julia}
 @model function pPCA_ARD(X)
@@ -383,4 +380,4 @@ It can also thought as a matrix factorisation method, in which $\mathbf{X}=(\mat
 [^2]: Probabilistic PCA by TensorFlow, "https://www.tensorflow.org/probability/examples/Probabilistic_PCA".
 [^3]: Gareth M. James, Daniela Witten, Trevor Hastie, Robert Tibshirani, *An Introduction to Statistical Learning*, Springer, 2013.
 [^4]: David Wipf, Srikantan Nagarajan, *A New View of Automatic Relevance Determination*, NIPS 2007.
-[^5]: Christopher Bishop, *Pattern Recognition and Machine Learning*, Springer, 2006.
+[^5]: Christopher Bishop, *Pattern Recognition and Machine Learning*, Springer, 2006.

tutorials/16-contexts/index.qmd

@@ -14,7 +14,7 @@ In the [Mini Turing]({{< meta minituring >}}) tutorial we developed a miniature
 
 # Mini Turing expanded, now with more contexts
 
-If you haven't read [Mini Turing]({{< meta minituring >}}t) yet, you should do that first. We start by repeating verbatim much of the code from there. Define the type for holding values for variables:
+If you haven't read [Mini Turing]({{< meta minituring >}}) yet, you should do that first. We start by repeating verbatim much of the code from there. Define the type for holding values for variables:
 
 ```{julia}
 import MacroTools, Random, AbstractMCMC

tutorials/docs-01-contributing-guide/index.qmd

@@ -27,16 +27,16 @@ Other sections of the website (anything that isn't a package, or a tutorial) –
 
 ### Tests
 
-Turing, like most software libraries, has a test suite. You can run the whole suite the usual Julia way with
+Turing, like most software libraries, has a test suite. You can run the whole suite by running `julia --project=.` from the root of the Turing repository, and then running
 
 ```julia
-Pkg.test("Turing")
+import Pkg; Pkg.test("Turing")
 ```
 
 The test suite subdivides into files in the `test` folder, and you can run only some of them using commands like
 
 ```julia
-Pkg.test("Turing"; test_args=["optim", "hmc", "--skip", "ext"])
+import Pkg; Pkg.test("Turing"; test_args=["optim", "hmc", "--skip", "ext"])
 ```
 
 This one would run all files with "optim" or "hmc" in their path, such as `test/optimisation/Optimisation.jl`, but not files with "ext" in their path. Alternatively, you can set these arguments as command line arguments when you run Julia