adds Aqua checks; fixes some issues highlighted by Aqua

.github/workflows/ci.yml

@@ -51,25 +51,3 @@ jobs:
       - uses: codecov/codecov-action@v1
         with:
           file: lcov.info
-  docs:
-    name: Documentation
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@v1
-        with:
-          version: '1'
-      - run: |
-          julia --project=docs -e '
-            using Pkg
-            Pkg.develop(PackageSpec(path=pwd()))
-            Pkg.instantiate()'
-      - run: |
-          julia --project=docs -e '
-            using Documenter: doctest
-            using KNearestCenters
-            doctest(KNearestCenters)'
-      - run: julia --project=docs docs/make.jl
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}

.github/workflows/documentation.yml

@@ -0,0 +1,24 @@
+name: Documentation
+
+on:
+  push:
+    branches:
+      - main
+    tags: '*'
+  pull_request:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: '1.6'
+      - name: Install dependencies
+        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
+      - name: Build and deploy
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # If authenticating with GitHub Actions token
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # If authenticating with SSH deploy key
+        run: julia --project=docs/ docs/make.jl

Project.toml

@@ -5,12 +5,9 @@ version = "0.7.2"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
-Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 KCenters = "5d8de97f-65f8-4dd6-a15b-0f89c36a43ce"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LossFunctions = "30fc2ffe-d236-52d8-8643-a9d8f7c094a7"
-MLLabelUtils = "66a33bbf-0c2b-5fc8-a008-9da813334f0a"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SearchModels = "0e966ebe-b704-4a65-8279-db954bfe5da0"
@@ -20,16 +17,21 @@ StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
+Aqua = "0.5"
 CategoricalArrays = "0.8, 0.9, 0.10"
 KCenters = "0.7"
+LossFunctions = "0.8"
+MLUtils = "0.2.9"
 SearchModels = "0.3"
 SimilaritySearch = "0.8, 0.9"
+StatsAPI = "1.4"
 StatsBase = "0.32, 0.33"
 julia = "1.6"
 
 [extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "CSV"]
+test = ["Aqua", "Test", "CSV"]

src/KNearestCenters.jl

@@ -9,7 +9,7 @@ import SearchModels: combine, mutate
 import StatsAPI: predict, fit
 using MLUtils
 
-export Knc, KncConfig, KncConfigSpace, KncProto, KncProtoConfig, KncProtoConfigSpace, KncPerClassConfigSpace, KncGlobalConfigSpace
+export Knc, KncConfig, KncConfigSpace, KncProto, KncProtoConfig, KncProtoConfigSpace
 export transform, predict, fit, categorical
 
 include("scores.jl") 

src/criterions.jl

@@ -1,6 +1,6 @@
 # This file is a part of KNearestCenters.jl
 
-export size_criterion, sqrt_criterion, change_criterion, fun_criterion, log_criterion, epsilon_criterion, salesman_criterion
+export size_criterion, sqrt_criterion, change_criterion, fun_criterion, epsilon_criterion, salesman_criterion
 
 
 """

src/knn.jl

@@ -124,14 +124,11 @@ function fit(::Type{KnnModel}, index::AbstractSearchIndex, labels::CategoricalAr
     KnnModel(k, 1, KnnSingleLabelPrediction(imap), weight, index, meta_)
 end
 
-function fit(::Type{KnnModel}, examples, labels::CategoricalArray; k=3, weight=KnnUniformWeightKernel(), dist=L2Distance())
-    meta_, imap = onehotenc(labels)
-    if examples isa AbstractArray
-        db = MatrixDatabase(examples)
-    else
-        db = examples
-    end
+fit(::Type{KnnModel}, examples::AbstractMatrix, labels::CategoricalArray; k=3, weight=KnnUniformWeightKernel(), dist=L2Distance()) =
+    fit(KnnModel, MatrixDatabase(examples), labels; k, weight, dist)
 
+function fit(::Type{KnnModel}, db::AbstractDatabase, labels::CategoricalArray; k=3, weight=KnnUniformWeightKernel(), dist=L2Distance())
+    meta_, imap = onehotenc(labels)
     index = ParallelExhaustiveSearch(; db, dist)
     KnnModel(k, 1, KnnSingleLabelPrediction(imap), weight, index, meta_)
 end

src/knnopt.jl

@@ -31,7 +31,7 @@ function optimize!(
         ]
     )
     optimize!(model, klist, wlist) do
-        predict.(model, Xtest)
+        ypred = predict.(model, Xtest)
         value(loss, ytest, ypred)
     end
 end

test/runtests.jl

@@ -1,7 +1,10 @@
 # This file is a part of KCenters.jl
 
-using Test
-using KNearestCenters
+using Test, KNearestCenters
+
+using Aqua
+Aqua.test_all(KNearestCenters, ambiguities=false)
+Aqua.test_ambiguities([KNearestCenters, Core])
 
 @testset "Scores" begin
     @test accuracy_score([1,1,1,1,1], [1,1,1,1,1]) == 1.0
@@ -16,6 +19,6 @@ using KNearestCenters
     @test f1_score([0,1,1,1,0,1], [0,1,1,1,1,1], weight=:macro) ≈ (2 * 0.5 / 1.5 + 2 * 0.8 / 1.8) / 2
 end
 
-#include("knc.jl")
+include("knc.jl")
 include("kncproto.jl")
-#include("knn.jl")
+include("knn.jl")