test with MLJTestInterface

Project.toml

@@ -11,6 +11,7 @@ GLMNet = "8d5ece8b-de18-5317-b113-243142960cc6"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
 Lasso = "b4fcebef-c861-5a0f-a7e2-ba9dc32b180a"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
+MLJTestInterface = "72560011-54dd-4dc2-94f3-c5de45b75ecd"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
@@ -26,6 +27,7 @@ Interpolations = "0.15"
 Lasso = "0.7"
 MLJBase = "1"
 MLJModelInterface = "1"
+MLJTestInterface = "0.2.6"
 Statistics = "1"
 StatsAPI = "1"
 StatsBase = "0.34"
@@ -36,7 +38,8 @@ julia = "1.9"
 [extras]
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
+MLJTestInterface = "72560011-54dd-4dc2-94f3-c5de45b75ecd"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["DelimitedFiles", "MLJBase", "Test"]
+test = ["DelimitedFiles", "MLJBase", "MLJTestInterface", "Test"]

test/runtests.jl

@@ -1,4 +1,4 @@
-using Maxnet, Test, Statistics, CategoricalArrays
+using Maxnet, Test, Statistics, CategoricalArrays, MLJTestInterface
 
 p_a, env = Maxnet.bradypus()
 # Make the levels in ecoreg string to make sure that that works
@@ -85,6 +85,16 @@ end
 m = maxnet(p_a, env; features = "lq", addsamplestobackground = false)
 
 @testset "MLJ" begin
+    data = MLJTestInterface.make_binary()
+    failures, summary = MLJTestInterface.test(
+        [MaxnetBinaryClassifier],
+        data...;
+        mod=@__MODULE__,
+        verbosity=0, # bump to debug
+        throw=false, # set to true to debug
+    )
+    @test isempty(failures)
+
     using MLJBase
     mn = Maxnet.MaxnetBinaryClassifier