Documentation updates

.github/dependabot.yml

@@ -0,0 +1,15 @@
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "monthly"
+  - package-ecosystem: "julia"
+    directories: # Location of Julia projects
+      - "/"
+      - "/docs"
+    schedule:
+      interval: "weekly"
+    ignore:
+      - dependency-name: "NearestNeighborModels"

.github/workflows/ci.yml

@@ -17,19 +17,19 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6'
+          - 'lts'
           - '1'
         os:
           - ubuntu-latest
         arch:
           - x64
     steps:
       - uses: actions/checkout@v3
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@v3
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: actions/cache@v1
+      - uses: julia-actions/cache@v2
         env:
           cache-name: cache-artifacts
         with:

docs/Project.toml

@@ -5,7 +5,7 @@ NearestNeighborModels = "636a865e-7cf4-491e-846c-de09b730eb36"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 
 [compat]
-Documenter = "^0.24"
-MLJBase = "^0.19"
-StableRNGs = "^1.0"
-julia = "^1.0"
+Documenter = "1"
+MLJBase = "1"
+StableRNGs = "1.0"
+julia = "1.10"

docs/make.jl

@@ -2,21 +2,21 @@ using Documenter, MLJBase, NearestNeighborModels
 
 makedocs(;
     authors = """
-        Anthony D. Blaom <anthony.blaom@gmail.com>, 
-        Sebastian Vollmer <s.vollmer.4@warwick.ac.uk>, 
-        Thibaut Lienart <thibaut.lienart@gmail.com> and 
         Okon Samuel <okonsamuel50@gmail.com>
+        Thibaut Lienart <thibaut.lienart@gmail.com> and
+        Anthony D. Blaom <anthony.blaom@gmail.com>,
+        Sebastian Vollmer <s.vollmer.4@warwick.ac.uk>,
         """,
     format = Documenter.HTML(;
         prettyurls= get(ENV, "CI", "false") == "true"
     ),
     modules = [NearestNeighborModels],
-    pages=[
+    pages = [
         "Home" => "index.md",
         "API" => "api.md"
     ],
     doctest = false, # don't runt doctest as doctests are automatically run separately in ci.
-    repo = "https://github.com/JuliaAI/NearestNeighborModels.jl/blob/{commit}{path}#L{line}",
+    repo = Remotes.GitHub("JuliaAI", "NearestNeighborModels"),
     sitename = "NearestNeighborModels.jl",
 )
 

docs/src/index.md

@@ -1,16 +1,15 @@
 # NearestNeighborModels - Docs
 
-NearestNeighborModels is a julia package providing implemtation of various 
-k-nearest-neighbor classifiers and regressors models for use with 
-[MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) machine learning 
-framework. It also provides users with an array of weighting kernels to choose 
-from for prediction.
+NearestNeighborModels is a julia package providing implemtation of various
+k-nearest-neighbor classifiers and regressors models for use with
+[MLJ](https://juliaml.ai) machine learning framework. It also provides users with an array
+of weighting kernels to choose from for prediction.
 
-NearestNeighborModels builds on Kristoffer Carlsson's 
-[NearestNeighbors](https://github.com/KristofferC/NearestNeighbors.jl) package(for 
-performing efficient nearest neighbor searches) and earlier contributions from Thibaut 
-Lienart originally residing in 
-[MLJModels.jl](https://github.com/alan-turing-institute/MLJModels.jl/blob/98618d7be53f72054de284fa1796c5292d9071bb/src/NearestNeighbors.jl#L1).
+NearestNeighborModels builds on Kristoffer Carlsson's
+[NearestNeighbors](https://github.com/KristofferC/NearestNeighbors.jl) package(for
+performing efficient nearest neighbor searches) and earlier contributions from Thibaut
+Lienart originally residing in
+[MLJModels.jl](https://github.com/JuliaAI/MLJModels.jl/blob/98618d7be53f72054de284fa1796c5292d9071bb/src/NearestNeighbors.jl#L1).
 
 
 # Installation
@@ -20,28 +19,30 @@ as shown below.
 
 ```julia
 using Pkg
-Pkg.add("NearestNeighborModels") 
+Pkg.add("NearestNeighborModels")
 ```
 
 # Usage
 
-To use any model implemented in this package, the model must first be wrapped in an MLJ 
-machine alongside the required data. Users also get additional features from MLJ including 
+To use any model implemented in this package, the model must first be wrapped in an MLJ
+machine alongside the required data. Users also get additional features from MLJ including
 performance evaluation, hyper-parameter tuning, stacking etc.
 The following example shows how to train a `KNNClassifier` on the crabs dataset.
 
 ```julia
-using NearestNeighborModels, MLJBase
-X, y = @load_crabs; # loads the crabs dataset from MLJBase
+import NearestNeighborModels: KNNClassifier, Inverse
+import MLJBase: @load_crabs, fit!, machine, nrows, partition, predict, predict_mode
+X, y = @load_crabs; # a table and a vector
 train_inds, test_inds = partition(1:nrows(X), 0.7, shuffle=false);
 knnc = KNNClassifier(weights = Inverse()) # KNNClassifier instantiation
-knnc_mach = machine(knnc, X, y) # wrap model and required data in an MLJ machine
-fit!(knnc_mach, rows=train_inds) # train machine on a subset of the wrapped data `X`
+knnc_mach = machine(knnc, X, y)           # wrap model and required data in an MLJ machine
+fit!(knnc_mach, rows=train_inds)          # train machine on a subset of the wrapped data `X`
 ```
 `UnivariateFinite` predictions can be obtained from the trained machine as shown below
 ```@meta
-DocTestSetup = quote
-    using NearestNeighborModels, MLJBase
+    DocTestSetup = quote
+    import NearestNeighborModels: KNNClassifier, Inverse
+    import MLJBase: @load_crabs, fit!, machine, nrows, partition, predict, predict_mode
     X, y = @load_crabs;
     train_inds, test_inds = partition(1:nrows(X), 0.7, shuffle=false);
     knnc = KNNClassifier(weights = Inverse())
@@ -51,7 +52,7 @@ end
 ```
 ```jldoctest ex1
 julia> predict(knnc_mach, rows=test_inds)
-60-element UnivariateFiniteVector{Multiclass{2}, String, UInt32, Float64}:
+60-element CategoricalDistributions.UnivariateFiniteVector{ScientificTypesBase.Multiclass{2}, String, UInt32, Float64}:
  UnivariateFinite{Multiclass{2}}(B=>0.315, O=>0.685)
  UnivariateFinite{Multiclass{2}}(B=>1.0, O=>0.0)
  UnivariateFinite{Multiclass{2}}(B=>1.0, O=>0.0)
@@ -114,24 +115,27 @@ function custom_kernel(dists::AbstractMatrix)
     return weights
 end
 
-# Then we wrap it in a `UserDefinedKernel`
-# `sort = true` because our `custom_kernel` function relies on `dists` being sorted in 
+# Then we wrap it in a `UserDefinedKernel` with
+# `sort = true` because our `custom_kernel` function relies on `dists` being sorted in
 # ascending order.
+import NearestNeighborModels.UserDefinedKernel
 weighting_kernel = UserDefinedKernel(func=custom_kernel, sort=true)
 ```
-We will now train a `MultitargetKNNRegressor` that makes use of our simple custom-defined 
+We will now train a `MultitargetKNNRegressor` that makes use of our simple custom-defined
 `weighting_kernel` for prediction.
 ```julia
-using NearestNeighborModels, MLJBase
+import NearestNeighborModels: MultitargetKNNRegressor, Inverse
+import MLJBase: @load_crabs, fit!, machine, nrows, partition, predict, predict_mode
+import MLJBase
 using StableRNGs #for reproducibility of this example
 
 n = 50
 p = 5
 l = 2
 rng = StableRNG(100)
 # `table` converts an `AbstractMatrix` into a `Tables.jl` compactible table
-X = table(randn(rng, (n, p))) # feature table
-Y = table(randn(rng, (n, l))) # target table
+X = MLJBase.table(randn(rng, (n, p))) # feature table
+Y = MLJBase.table(randn(rng, (n, l))) # target table
 
 train_inds, test_inds = partition(1:nrows(X), 0.8, shuffle=false);
 multi_knnr = MultitargetKNNRegressor(weights=weighting_kernel)
@@ -141,7 +145,11 @@ fit!(multi_knnr_mach, rows=train_inds) # train machine on a subset of the wrappe
 And of course predicting with the test-dataset gives:
 ```@meta
 DocTestSetup = quote
-    using NearestNeighborModels, MLJBase, StableRNGs
+    import NearestNeighborModels: MultitargetKNNRegressor, Inverse
+	import NearestNeighborModels.UserDefinedKernel
+    import MLJBase: @load_crabs, fit!, machine, nrows, partition, predict, predict_mode
+	import MLJBase
+	using StableRNGs
     function custom_kernel(dists::AbstractMatrix)
         weights = similar(Array{Float16}, size(dists))
         weights[:, 1] .= 2.0
@@ -153,8 +161,8 @@ DocTestSetup = quote
     p = 5
     l = 2
     rng = StableRNG(100)
-    X = table(randn(rng, (n, p))) # feature table
-    Y = table(randn(rng, (n, l))) # target table
+    X = MLJBase.table(randn(rng, (n, p))) # feature table
+    Y = MLJBase.table(randn(rng, (n, l))) # target table
     train_inds, test_inds = partition(1:nrows(X), 0.8, shuffle=false);
     multi_knnr = MultitargetKNNRegressor(weights=weighting_kernel)
     multi_knnr_mach = machine(multi_knnr, X, Y) #wrap model and required data in an MLJ machine
@@ -183,5 +191,5 @@ julia> MLJBase.matrix(table_predictions)
 ```@meta
 DocTestSetup = nothing
 ```
-see [MLJ docs](https://alan-turing-institute.github.io/MLJ.jl/dev/) for help on additional 
+see [MLJ docs](https://JuliaAI.github.io/MLJ.jl/stable/) for help on additional
 features such as hyper-parameter tuning, performance evaluation, stacking etc.