fully implement MLJ (#22)

* add mlj docstring

* test with MLJTestInterface

* throw a helpful error if input data only has one class

* mljtestinterface is not a dep (oops)

* move allequal error to main function

* fix allequal error

* fix tests

* add MLJBase as docs dep

* fix mlj doctest

* attempt fix of multiclass printing

* use @example instead of jldoctest

* test for no failures in mlj interface test

Project.toml

@@ -36,7 +36,8 @@ 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"]

docs/Project.toml

@@ -1,3 +1,4 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 Maxnet = "81f79f80-22f2-4e41-ab86-00c11cf0f26f"

src/maxnet_function.jl

@@ -49,6 +49,11 @@ function maxnet(
     n_knots::Int = 50,
     kw...)
 
+    if allequal(presences) 
+        pa = first(presences) ? "presences" : "absences"
+        throw(ArgumentError("All data points are $pa. Maxnet will only work with at least some presences and some absences."))
+    end
+
     _maxnet(
         presences, 
         predictors, 

src/mlj_interface.jl

@@ -24,30 +24,6 @@ function MaxnetBinaryClassifier(;
     )
 end
 
-"""
-    MaxnetBinaryClassifier
-
-    A model type for fitting a maxnet model using `MLJ`.
-        
-    Use `MaxnetBinaryClassifier()` to create an instance with default parameters, or use keyword arguments to specify parameters.
-    
-    The keywords `link`, and `clamp` are passed to [`Maxnet.predict`](@ref), while all other keywords are passed to [`maxnet`](@ref).
-    See the documentation of these functions for the meaning of these parameters and their defaults.
-
-    # Example
-    ```jldoctest
-    using Maxnet, MLJBase
-    p_a, env = Maxnet.bradypus()
-
-    mach = machine(MaxnetBinaryClassifier(features = "lqp"), env, categorical(p_a))
-    fit!(mach)
-    yhat = MLJBase.predict(mach, env)
-    # output
-    ```
-
-"""
-MaxnetBinaryClassifier
-
 MMI.metadata_pkg(
     MaxnetBinaryClassifier;
     name = "Maxnet",
@@ -67,6 +43,26 @@ MMI.metadata_model(
     reports_feature_importances=false
 )
 
+"""
+$(MMI.doc_header(MaxnetBinaryClassifier))
+
+The keywords `link`, and `clamp` are passed to [`predict`](@ref), while all other keywords are passed to [`maxnet`](@ref).
+See the documentation of these functions for the meaning of these parameters and their defaults.
+
+# Example
+```@example
+using MLJBase
+p_a, env = Maxnet.bradypus()
+
+mach = machine(MaxnetBinaryClassifier(features = "lqp"), env, categorical(p_a), scitype_check_level = 0)
+fit!(mach, verbosity = 0)
+yhat = MLJBase.predict(mach, env)
+
+```
+
+"""
+MaxnetBinaryClassifier
+
 function MMI.fit(m::MaxnetBinaryClassifier, verbosity::Int, X, y)
     # convert categorical to boolean
     y_boolean = Bool.(MMI.int(y) .- 1)

test/runtests.jl

@@ -1,5 +1,7 @@
-using Maxnet, Test, Statistics, CategoricalArrays
+using Maxnet, Statistics, CategoricalArrays, MLJTestInterface
+using Test
 
+# read in Bradypus data
 p_a, env = Maxnet.bradypus()
 # Make the levels in ecoreg string to make sure that that works
 env = merge(env, (; ecoreg = recode(env.ecoreg, (l => string(l) for l in levels(env.ecoreg))...)))
@@ -82,9 +84,18 @@ end
     m = maxnet(p_a, env; features = "lq", addsamplestobackground = false)
     @test m_w.entropy > m.entropy
 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