test hinge, mlj metadata, and different feature classes

Author: tiemvanderdeure

test/runtests.jl

@@ -14,19 +14,39 @@ env1 = map(e -> [e[1]], env) # just the first row
     @test Maxnet.features_from_string("lqpt") == [LinearFeature(), CategoricalFeature(), QuadraticFeature(), ProductFeature(), ThresholdFeature()]
 
     @test Maxnet.default_features(100) == [LinearFeature(), CategoricalFeature(), QuadraticFeature(), HingeFeature(), ProductFeature()]
+
+    @test Maxnet.hinge(1:5, 3) ==    [ 
+     #  1:5   3:5  1:3  1:5 
+        0.0   0.0  0.0  0.0
+        0.25  0.0  0.5  0.25
+        0.5   0.0  1.0  0.5
+        0.75  0.5  1.0  0.75
+        1.0   1.0  1.0  1.0
+    ]
+    @test size(Maxnet.hinge(1:200)) == (200, 98)
+
+    presence, predictors = Maxnet.addsamples([true, false, false], (a = [1,2,3], b = [1,2,3]))
+    @test presence == [true, false, false, false]
+    @test predictors == (a = [1,2,3,1], b = [1,2,3,1])
 end
 
 @testset "Maxnet" begin
     # some class combinations and keywords
-    m = Maxnet.maxnet(p_a, env; features = "lq");
-    Maxnet.maxnet(p_a, env; features = "lqp", regularization_multiplier = 2.);
-    Maxnet.maxnet(p_a, env; features = "lqh", regularization_multiplier = 5., nknots = 10);
-    Maxnet.maxnet(p_a, env; features = "lqph", weight_factor = 10.);
+    m = maxnet(p_a, env; features = "lq");
+    m2 = maxnet(p_a, env)
+    m3 = maxnet(p_a, env[(:cld6190_ann, :h_dem)])
+    m4 = maxnet(p_a, env[(:ecoreg,)], addsamplestobackground =false)
+    m5 = maxnet(p_a, env[(:cld6190_ann, :h_dem)]; features = "ht", n_knots = 3)
 
-    # test the result
+    # test the results
     @test m.entropy ≈ 6.114650341746531
     @test complexity(m) == 21
-
+    @test m2.features == [LinearFeature(), CategoricalFeature(), QuadraticFeature(), HingeFeature(), ProductFeature()]
+    @test m3.features == [LinearFeature(), QuadraticFeature(), HingeFeature(), ProductFeature()]
+    @test m4.features == [CategoricalFeature()]
+    @test m5.features == [HingeFeature(), ThresholdFeature()]
+    @test length(m5.columns) == 14 # (n-1)*2 hinge columns and n threshold columns for each variable
+    
     # predictions
     prediction = predict(m, env)
     @test Statistics.mean(prediction[p_a]) > Statistics.mean(prediction[.~p_a])
@@ -57,6 +77,18 @@ end
     using MLJBase
     mn = Maxnet.MaxnetBinaryClassifier
 
+    # Test model metadata
+    @test name(mn) == "MaxnetBinaryClassifier"
+    @test human_name(mn) == "Maxnet"
+    @test package_name(mn) == "Maxnet"
+    @test !supports_weights(mn)
+    @test !is_pure_julia(mn)
+    @test is_supervised(mn)
+    @test package_license(mn) == "MIT"
+    @test prediction_type(mn) == :probabilistic
+    @test input_scitype(mn) == Table{<:Union{AbstractVector{<:Continuous}, AbstractVector{<:Finite}}}
+    @test hyperparameters(mn) == (:features, :regularization_multiplier, :regularization_function, :weight_factor, :link, :clamp, :kw)
+
     # convert to continuous
     cont_keys = collect(key => Continuous for key in keys(env) if key !== :ecoreg)
     env_typed = MLJBase.coerce(env, cont_keys...)
@@ -71,7 +103,6 @@ end
     # make the equivalent model without mlj
     model = Maxnet.maxnet((p_a), env_typed; features = "lqph");
 
-
     # predict via MLJBase
     mljprediction = MLJBase.predict(mach2, env_typed)
     mlj_true_probability = pdf.(mljprediction, true)