Merge pull request #4 from alan-turing-institute/compat

For a 0.1.2 relese

.github/workflows/CompatHelper.yml

@@ -1,19 +1,16 @@
 name: CompatHelper
-
 on:
   schedule:
     - cron: '00 00 * * *'
-
+  workflow_dispatch:
 jobs:
   CompatHelper:
     runs-on: ubuntu-latest
     steps:
-      - uses: julia-actions/setup-julia@latest
-        with:
-          version: 1.3
       - name: Pkg.add("CompatHelper")
         run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
-      - name: CompatHelper.main
+      - name: CompatHelper.main()
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: julia -e 'using CompatHelper; CompatHelper.main(; master_branch = "master")'
+          COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_PRIV }}  # optional
+        run: julia -e 'using CompatHelper; CompatHelper.main()'

Project.toml

@@ -1,15 +1,15 @@
 name = "MLJXGBoostInterface"
 uuid = "54119dfa-1dab-4055-a167-80440f4f7a91"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.1.1"
+version = "0.1.2"
 
 [deps]
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 XGBoost = "009559a3-9522-5dbb-924b-0b6ed2b22bb9"
 
 [compat]
-MLJModelInterface = "^0.3.5"
+MLJModelInterface = "^0.3.5,^0.4"
 Tables = "^1.0.5"
 XGBoost = "1.1.1"
 julia = "1.3"

test/runtests.jl

@@ -20,7 +20,6 @@ labels = features * weights;
 features = MLJBase.table(features)
 fitresultR, cacheR, reportR = MLJBase.fit(plain_regressor, 0, features, labels);
 rpred = predict(plain_regressor, fitresultR, features);
-info_dict(XGBoostRegressor)
 
 plain_regressor.objective = "gamma"
 labels = abs.(labels)
@@ -53,7 +52,6 @@ ycount = [rand(rng, Poisson(λᵢ)) for λᵢ ∈ λ]
 
 fitresultC, cacheC, reportC = MLJBase.fit(count_regressor, 0, Xtable, ycount);
 cpred = predict(count_regressor, fitresultC, Xtable);
-info_dict(XGBoostCount)
 
 importances = reportC.feature_importances
 
@@ -105,8 +103,6 @@ fitresult, cache, report = MLJBase.fit(plain_classifier, 0,
 yhat = predict_mode(plain_classifier, fitresult, selectrows(X, test))
 @test Set(MLJBase.classes(yhat[1])) == Set(MLJBase.classes(y[train][1]))
 
-info_dict(XGBoostClassifier)
-
 # serialization:
 serializable_fitresult =
     MLJBase.save("mymodel", plain_classifier, fitresult)