Merge pull request #452 from sathvikbhagavan/sb/doc1

Update makedocs syntax and few cleanups

.github/workflows/CI.yml

@@ -15,7 +15,6 @@ jobs:
           - Core
         version:
           - '1'
-          - '1.6'
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v1

docs/make.jl

@@ -10,17 +10,11 @@ using Plots
 include("pages.jl")
 
 makedocs(sitename = "Surrogates.jl",
-         strict = [
-             :doctest,
-             :linkcheck,
-             :parse_error,
-             :example_block,
-             # Other available options are
-             # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
-         ],
-         format = Documenter.HTML(analytics = "UA-90474609-3",
-                                  assets = ["assets/favicon.ico"],
-                                  canonical = "https://docs.sciml.ai/Surrogates/stable/"),
-         pages = pages)
+    linkcheck = true,
+    warnonly = [:missing_docs],
+    format = Documenter.HTML(analytics = "UA-90474609-3",
+        assets = ["assets/favicon.ico"],
+        canonical = "https://docs.sciml.ai/Surrogates/stable/"),
+    pages = pages)
 
 deploydocs(repo = "github.com/SciML/Surrogates.jl.git")

docs/pages.jl

@@ -1,39 +1,39 @@
 pages = ["index.md"
-         "Tutorials" => [
-             "Basics" => "tutorials.md",
-             "Radials" => "radials.md",
-             "Kriging" => "kriging.md",
-             "Gaussian Process" => "abstractgps.md",
-             "Lobachevsky" => "lobachevsky.md",
-             "Linear" => "LinearSurrogate.md",
-             "InverseDistance" => "InverseDistance.md",
-             "RandomForest" => "randomforest.md",
-             "SecondOrderPolynomial" => "secondorderpoly.md",
-             "NeuralSurrogate" => "neural.md",
-             "Wendland" => "wendland.md",
-             "Polynomial Chaos" => "polychaos.md",
-             "Variable Fidelity" => "variablefidelity.md",
-             "Gradient Enhanced Kriging" => "gek.md",
-             "GEKPLS" => "gekpls.md",
-             "MOE" => "moe.md",
-             "Parallel Optimization" => "parallel.md"
-         ]
-         "User guide" => [
-             "Samples" => "samples.md",
-             "Surrogates" => "surrogate.md",
-             "Optimization" => "optimizations.md",
-         ]
-         "Benchmarks" => [
-             "Sphere function" => "sphere_function.md",
-             "Lp norm" => "lp.md",
-             "Rosenbrock" => "rosenbrock.md",
-             "Tensor product" => "tensor_prod.md",
-             "Cantilever beam" => "cantilever.md",
-             "Water Flow function" => "water_flow.md",
-             "Welded beam function" => "welded_beam.md",
-             "Branin function" => "BraninFunction.md",
-             "Ackley function" => "ackley.md",
-             "Gramacy & Lee Function" => "gramacylee.md",
-             "Salustowicz Benchmark" => "Salustowicz.md",
-             "Multi objective optimization" => "multi_objective_opt.md",
-         ]]
+    "Tutorials" => [
+    "Basics" => "tutorials.md",
+    "Radials" => "radials.md",
+    "Kriging" => "kriging.md",
+    "Gaussian Process" => "abstractgps.md",
+    "Lobachevsky" => "lobachevsky.md",
+    "Linear" => "LinearSurrogate.md",
+    "InverseDistance" => "InverseDistance.md",
+    "RandomForest" => "randomforest.md",
+    "SecondOrderPolynomial" => "secondorderpoly.md",
+    "NeuralSurrogate" => "neural.md",
+    "Wendland" => "wendland.md",
+    "Polynomial Chaos" => "polychaos.md",
+    "Variable Fidelity" => "variablefidelity.md",
+    "Gradient Enhanced Kriging" => "gek.md",
+    "GEKPLS" => "gekpls.md",
+    "MOE" => "moe.md",
+    "Parallel Optimization" => "parallel.md",
+]
+    "User guide" => [
+    "Samples" => "samples.md",
+    "Surrogates" => "surrogate.md",
+    "Optimization" => "optimizations.md",
+]
+    "Benchmarks" => [
+    "Sphere function" => "sphere_function.md",
+    "Lp norm" => "lp.md",
+    "Rosenbrock" => "rosenbrock.md",
+    "Tensor product" => "tensor_prod.md",
+    "Cantilever beam" => "cantilever.md",
+    "Water Flow function" => "water_flow.md",
+    "Welded beam function" => "welded_beam.md",
+    "Branin function" => "BraninFunction.md",
+    "Ackley function" => "ackley.md",
+    "Gramacy & Lee Function" => "gramacylee.md",
+    "Salustowicz Benchmark" => "Salustowicz.md",
+    "Multi objective optimization" => "multi_objective_opt.md",
+]]

docs/src/index.md

@@ -113,56 +113,59 @@ surrogate_optimize(f,SRBF(),lb,ub,my_lobachevsky,RandomSample())
 value = my_lobachevsky(5.0)
 ```
 ## Reproducibility
+
 ```@raw html
 <details><summary>The documentation of this SciML package was built using these direct dependencies,</summary>
 ```
+
 ```@example
 using Pkg # hide
 Pkg.status() # hide
 ```
+
 ```@raw html
 </details>
 ```
+
 ```@raw html
 <details><summary>and using this machine and Julia version.</summary>
 ```
+
 ```@example
 using InteractiveUtils # hide
 versioninfo() # hide
 ```
+
 ```@raw html
 </details>
 ```
+
 ```@raw html
 <details><summary>A more complete overview of all dependencies and their versions is also provided.</summary>
 ```
+
 ```@example
 using Pkg # hide
-Pkg.status(;mode = PKGMODE_MANIFEST) # hide
+Pkg.status(; mode = PKGMODE_MANIFEST) # hide
 ```
+
 ```@raw html
 </details>
 ```
-```@raw html
-You can also download the 
-<a href="
-```
-```@eval
-using TOML
-version = TOML.parse(read("../../Project.toml",String))["version"]
-name = TOML.parse(read("../../Project.toml",String))["name"]
-link = "https://github.com/SciML/"*name*".jl/tree/gh-pages/v"*version*"/assets/Manifest.toml"
-```
-```@raw html
-">manifest</a> file and the
-<a href="
-```
+
 ```@eval
 using TOML
-version = TOML.parse(read("../../Project.toml",String))["version"]
-name = TOML.parse(read("../../Project.toml",String))["name"]
-link = "https://github.com/SciML/"*name*".jl/tree/gh-pages/v"*version*"/assets/Project.toml"
-```
-```@raw html
-">project</a> file.
+using Markdown
+version = TOML.parse(read("../../Project.toml", String))["version"]
+name = TOML.parse(read("../../Project.toml", String))["name"]
+link_manifest = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+                "/assets/Manifest.toml"
+link_project = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+               "/assets/Project.toml"
+Markdown.parse("""You can also download the
+[manifest]($link_manifest)
+file and the
+[project]($link_project)
+file.
+""")
 ```

lib/SurrogatesAbstractGPs/test/runtests.jl

@@ -27,7 +27,7 @@ using Surrogates: sample, SobolSample
         x_points = sample(5, lb, ub, SobolSample())
         y_points = f.(x_points)
         agp1D = AbstractGPSurrogate([x_points[1]], [y_points[1]],
-                                    gp = GP(SqExponentialKernel()), Σy = 0.05)
+            gp = GP(SqExponentialKernel()), Σy = 0.05)
         x_new = 2.5
         y_actual = f.(x_new)
         for i in 2:length(x_points)
@@ -88,7 +88,7 @@ using Surrogates: sample, SobolSample
         b = 6
         my_k_EI1 = AbstractGPSurrogate(x, y)
         surrogate_optimize(objective_function, EI(), a, b, my_k_EI1, RandomSample(),
-                           maxiters = 200, num_new_samples = 155)
+            maxiters = 200, num_new_samples = 155)
     end
 
     @testset "Optimization ND" begin

lib/SurrogatesFlux/src/SurrogatesFlux.jl

@@ -26,8 +26,8 @@ NeuralSurrogate(x,y,lb,ub,model,loss,opt,n_echos)
 
 """
 function NeuralSurrogate(x, y, lb, ub; model = Chain(Dense(length(x[1]), 1), first),
-                         loss = (x, y) -> Flux.mse(model(x), y), opt = Descent(0.01),
-                         n_echos::Int = 1)
+        loss = (x, y) -> Flux.mse(model(x), y), opt = Descent(0.01),
+        n_echos::Int = 1)
     X = vec.(collect.(x))
     data = zip(X, y)
     ps = Flux.params(model)
@@ -59,7 +59,7 @@ function add_point!(my_n::NeuralSurrogate, x_new, y_new)
     end
     X = vec.(collect.(my_n.x))
     data = zip(X, my_n.y)
-    for epoch in 1:my_n.n_echos
+    for epoch in 1:(my_n.n_echos)
         Flux.train!(my_n.loss, my_n.ps, data, my_n.opt)
     end
     nothing

lib/SurrogatesFlux/test/runtests.jl

@@ -19,7 +19,7 @@ using SafeTestsets
     my_opt = Descent(0.01)
     n_echos = 1
     my_neural = NeuralSurrogate(x, y, a, b, model = my_model, loss = my_loss, opt = my_opt,
-                                n_echos = 1)
+        n_echos = 1)
     my_neural_kwargs = NeuralSurrogate(x, y, a, b)
     add_point!(my_neural, 8.5, 20.0)
     add_point!(my_neural, [3.2, 3.5], [7.4, 8.0])
@@ -37,7 +37,7 @@ using SafeTestsets
     my_opt = Descent(0.01)
     n_echos = 1
     my_neural = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                opt = my_opt, n_echos = 1)
+        opt = my_opt, n_echos = 1)
     my_neural_kwargs = NeuralSurrogate(x, y, lb, ub)
     my_neural((3.5, 1.49))
     my_neural([3.4, 1.4])
@@ -54,7 +54,7 @@ using SafeTestsets
     my_model = Chain(Dense(1, 2))
     my_loss(x, y) = Flux.mse(my_model(x), y)
     surrogate = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                opt = my_opt, n_echos = 1)
+        opt = my_opt, n_echos = 1)
     surr_kwargs = NeuralSurrogate(x, y, lb, ub)
 
     f = x -> [x[1], x[2]^2]
@@ -66,7 +66,7 @@ using SafeTestsets
     my_model = Chain(Dense(2, 2))
     my_loss(x, y) = Flux.mse(my_model(x), y)
     surrogate = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                opt = my_opt, n_echos = 1)
+        opt = my_opt, n_echos = 1)
     surrogate_kwargs = NeuralSurrogate(x, y, lb, ub)
     surrogate((1.0, 2.0))
     x_new = (2.0, 2.0)
@@ -85,7 +85,7 @@ using SafeTestsets
     n_echos = 1
     my_neural_ND_neural = NeuralSurrogate(x, y, lb, ub)
     surrogate_optimize(objective_function_ND, SRBF(), lb, ub, my_neural_ND_neural,
-                       SobolSample(), maxiters = 15)
+        SobolSample(), maxiters = 15)
 
     # AD Compatibility
     lb = 0.0
@@ -101,7 +101,7 @@ using SafeTestsets
         my_opt = Descent(0.01)
         n_echos = 1
         my_neural = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                    opt = my_opt, n_echos = 1)
+            opt = my_opt, n_echos = 1)
         g = x -> my_neural'(x)
         g(3.4)
     end
@@ -120,7 +120,7 @@ using SafeTestsets
         my_opt = Descent(0.01)
         n_echos = 1
         my_neural = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                    opt = my_opt, n_echos = 1)
+            opt = my_opt, n_echos = 1)
         g = x -> Zygote.gradient(my_neural, x)
         g((2.0, 5.0))
     end
@@ -141,7 +141,7 @@ using SafeTestsets
         my_opt = Descent(0.01)
         n_echos = 1
         my_neural = NeuralSurrogate(x, y, lb, ub, model = my_model, loss = my_loss,
-                                    opt = my_opt, n_echos = 1)
+            opt = my_opt, n_echos = 1)
         Zygote.gradient(x -> sum(my_neural(x)), (2.0, 5.0))
 
         my_rad = RadialBasis(x, y, lb, ub, rad = linearRadial())