For a 0.4.3 release

.github/workflows/ci.yml

@@ -17,9 +17,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6'
+          - '1.10'
           - '1'
-          - 'nightly'
         os:
           - ubuntu-latest
         arch:
@@ -30,7 +29,7 @@ jobs:
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: actions/cache@v1
+      - uses: julia-actions/cache@v2
         env:
           cache-name: cache-artifacts
         with:

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJDecisionTreeInterface"
 uuid = "c6f25543-311c-4c74-83dc-3ea6d1015661"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.4.2"
+version = "0.4.3"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -11,11 +11,11 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
-CategoricalArrays = "0.10"
+CategoricalArrays = "1"
 DecisionTree = "0.12"
 MLJModelInterface = "1.5"
 Tables = "1.6"
-julia = "1.6"
+julia = "1.10"
 
 [extras]
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"

src/MLJDecisionTreeInterface.jl

@@ -23,10 +23,6 @@ end
 Base.show(stream::IO, c::TreePrinter) =
     print(stream, "TreePrinter object (call with display depth)")
 
-function classes(y)
-    p = CategoricalArrays.pool(y)
-    [p[i] for i in 1:length(p)]
-end
 
 # # DECISION TREE CLASSIFIER
 
@@ -79,7 +75,7 @@ function MMI.fit(
 end
 
 # returns a dictionary of categorical elements keyed on ref integer:
-get_encoding(classes_seen) = Dict(MMI.int(c) => c for c in classes(classes_seen))
+get_encoding(classes_seen) = Dict(MMI.int(c) => c for c in levels(classes_seen))
 
 # given such a dictionary, return printable class labels, ordered by corresponding ref
 # integer:
@@ -459,7 +455,7 @@ _columnnames(X, ::Val{false}) = Tables.columnnames(first(Tables.rows(X)))
 
 # for fit:
 MMI.reformat(::Classifier, X, y) =
-    (Tables.matrix(X), MMI.int(y), _columnnames(X), classes(y))
+    (Tables.matrix(X), MMI.int(y), _columnnames(X), levels(y))
 MMI.reformat(::Regressor, X, y) =
     (Tables.matrix(X), float(y), _columnnames(X))
 MMI.selectrows(::TreeModel, I, Xmatrix, y, meta...) =

test/runtests.jl

@@ -1,6 +1,7 @@
 using Test
 import CategoricalArrays
 import CategoricalArrays.categorical
+import CategoricalArrays.levels
 using MLJBase
 using StableRNGs
 using Random
@@ -48,7 +49,7 @@ stable_rng() = StableRNGs.StableRNG(123)
 Xraw, yraw = @load_iris
 X = Tables.matrix(Xraw);
 y = int(yraw);
-_classes = MLJDecisionTreeInterface.classes(yraw)
+_classes = levels(yraw)
 features = MLJDecisionTreeInterface._columnnames(Xraw)
 
 baretree = DecisionTreeClassifier(rng=stable_rng())
@@ -74,7 +75,7 @@ yhat = MLJBase.predict(baretree, fitresult, X);
 
 # check preservation of levels:
 yyhat = predict_mode(baretree, fitresult, X[1:3, :])
-@test MLJBase.classes(yyhat[1]) == MLJBase.classes(yraw)
+@test MLJBase.levels(yyhat[1]) == MLJBase.levels(yraw)
 
 # check report and fitresult fields:
 @test Set([:classes_seen, :print_tree, :features]) == Set(keys(report))