Merge pull request #28 from alan-turing-institute/dev

For a 0.1.8 release
JuliaAI · Jan 28, 2021 · 095e7a8 · 095e7a8
2 parents a5ab41d + 17dde1d
commit 095e7a8
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diff --git a/.github/workflows/CompatHelper.yml b/.github/workflows/CompatHelper.yml
@@ -0,0 +1,16 @@
+name: CompatHelper
+on:
+  schedule:
+    - cron: '00 00 * * *'
+  workflow_dispatch:
+jobs:
+  CompatHelper:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Pkg.add("CompatHelper")
+        run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
+      - name: CompatHelper.main()
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_PRIV }}  # optional
+        run: julia -e 'using CompatHelper; CompatHelper.main()'
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -0,0 +1,47 @@
+name: CI
+env:
+  PYTHON: Conda
+on:
+  pull_request:
+    branches:
+      - master
+  push:
+    branches:
+      - master
+    tags: '*'
+jobs:
+  test:
+    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        version:
+          - '1.0'
+          - '1'
+        os:
+          - ubuntu-latest
+        arch:
+          - x64
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@v1
+        with:
+          version: ${{ matrix.version }}
+          arch: ${{ matrix.arch }}
+      - uses: actions/cache@v1
+        env:
+          cache-name: cache-artifacts
+        with:
+          path: ~/.julia/artifacts
+          key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
+          restore-keys: |
+            ${{ runner.os }}-test-${{ env.cache-name }}-
+            ${{ runner.os }}-test-
+            ${{ runner.os }}-
+      - uses: julia-actions/julia-buildpkg@v1
+      - uses: julia-actions/julia-runtest@v1
+      - uses: julia-actions/julia-processcoverage@v1
+      - uses: codecov/codecov-action@v1
+        with:
+          file: lcov.info
diff --git a/.travis.yml b/.travis.yml
diff --git a/Project.toml b/Project.toml
@@ -1,15 +1,15 @@
 name = "MLJScikitLearnInterface"
 uuid = "5ae90465-5518-4432-b9d2-8a1def2f0cab"
 authors = ["Thibaut Lienart, Anthony Blaom"]
-version = "0.1.7"
+version = "0.1.8"
 
 [deps]
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
 ScikitLearn = "3646fa90-6ef7-5e7e-9f22-8aca16db6324"
 
 [compat]
-MLJModelInterface = "0.3.6"
+MLJModelInterface = "0.3.6,0.4"
 PyCall = "1"
 ScikitLearn = "0.5,0.6"
 julia = "1"

diff --git a/README.md b/README.md
@@ -9,9 +9,4 @@ Repository implementing MLJ interface for
 **Note**: for errors with MKL on Mac, please refer to [ScikitLearn.jl](https://github.com/cstjean/ScikitLearn.jl) as it's a known issue.
 
 
-## Todo
-
-**June 2020**
-
-* Maybe more integration tests
 
diff --git a/test/models/clustering.jl b/test/models/clustering.jl
@@ -29,7 +29,7 @@ models = (
 
        if m in (AffinityPropagation, MeanShift)
           p = MB.predict(m, f, X)
-          @test p isa MB.CategoricalArray 
+          @test p isa MB.CategoricalArray
        end
 
        @test keys(fp) == getproperty(fparams, Symbol(mod))
@@ -46,12 +46,11 @@ end
 @testset "Scitypes" begin
     for mod in models
         m = mod()
-        infos = MB.info_dict(m)
-        @test infos[:input_scitype] == MB.Table(MB.Continuous)
+        @test input_scitype(mod) == MB.Table(MB.Continuous)
         m == AffinityPropagation &&
-            @test infos[:target_scitype] == AbstractVector{MB.Multiclass}
-        m == MeanShift && 
-            @test infos[:output_scitype] == MB.Table(MB.Continuous)
+            @test target_scitype(mod) == AbstractVector{MB.Multiclass}
+        m == MeanShift &&
+            @test output_scitype(mod) == MB.Table(MB.Continuous)
     end
 end
 
@@ -69,10 +68,10 @@ models = (
        fp = MB.fitted_params(m, f)
        @test size(fp.cluster_centers) == (4, 3)
        p = MB.predict(m, f, X)
-       @test p isa MB.CategoricalArray 
+       @test p isa MB.CategoricalArray
        Xt = MB.transform(m, f, X)
        @test Xt isa MB.Tables.MatrixTable
-            
+
        @test keys(fp) == getproperty(fparams, Symbol(mod))
     end
 end
@@ -87,10 +86,9 @@ end
 @testset "Scitypes" begin
     for mod in models
         m = mod()
-        infos = MB.info_dict(m)
-        @test infos[:input_scitype] == MB.Table(MB.Continuous)
-        @test infos[:target_scitype] == AbstractVector{MB.Multiclass}
-        @test infos[:output_scitype] == MB.Table(MB.Continuous)
+        @test input_scitype(mod) == MB.Table(MB.Continuous)
+        @test target_scitype(mod) == AbstractVector{MB.Multiclass}
+        @test output_scitype(mod) == MB.Table(MB.Continuous)
     end
 end
 
@@ -107,19 +105,19 @@ models = (
        f, = MB.fit(m, 1, X)
        fp = MB.fitted_params(m, f)
        @test fp.labels isa MB.CategoricalArray
-          
+
        Xt = MB.transform(m, f, X)
        if mod == FeatureAgglomeration
-           @test fp.distances === nothing 
-           Xit = MB.inverse_transform(m, f, Xt)            
+           @test fp.distances === nothing
+           Xit = MB.inverse_transform(m, f, Xt)
           # NOTE: they're not equal (not sure why)
            @test Xit isa MB.Tables.MatrixTable
-       else 
+       else
         p = MB.predict(m, f, X)
-        @test p isa MB.CategoricalArray  
+        @test p isa MB.CategoricalArray
         @test Xt isa MB.Tables.MatrixTable
-       end     
-            
+       end
+
        @test keys(fp) == getproperty(fparams, Symbol(mod))
     end
 end
@@ -133,11 +131,10 @@ end
 
 @testset "Scitypes" begin
     for mod in models
-        infos = MB.info_dict(mod)
-        @test infos[:input_scitype] == MB.Table(MB.Continuous)
+        @test input_scitype(mod) == MB.Table(MB.Continuous)
         mod == Birch &&
-            @test infos[:target_scitype] == AbstractVector{MB.Multiclass}
-        @test infos[:output_scitype] == MB.Table(MB.Continuous)
+            @test target_scitype(mod) == AbstractVector{MB.Multiclass}
+        @test output_scitype(mod) == MB.Table(MB.Continuous)
     end
 end
 
@@ -152,7 +149,7 @@ model = (
        m=mod()
        f, = MB.fit(m, 1, X)
        fp = MB.fitted_params(m, f)
-       @test fp.labels isa MB.CategoricalArray            
+       @test fp.labels isa MB.CategoricalArray
        @test keys(fp) == getproperty(fparams, Symbol(mod))
     end
 end
@@ -167,7 +164,6 @@ end
 @testset "Scitypes" begin
     for mod in models
         m = mod()
-        infos = MB.info_dict(m)
-        @test infos[:input_scitype] == MB.Table(MB.Continuous)
+        @test input_scitype(mod) == MB.Table(MB.Continuous)
     end
 end
diff --git a/test/runtests.jl b/test/runtests.jl
@@ -3,6 +3,7 @@ using MLJScikitLearnInterface
 using Test
 import MLJBase
 import ScikitLearn
+import MLJBase: target_scitype, input_scitype, output_scitype
 
 const MB = MLJBase