Refactor tests

Author: juliohm

test/runtests.jl

@@ -13,18 +13,25 @@ import MLJ, MLJDecisionTreeInterface
 const SLM = StatsLearnModels
 
 @testset "StatsLearnModels.jl" begin
-  iris = DataFrame(MLJ.load_iris())
-  input = iris[:, Not(:target)]
-  output = iris[:, [:target]]
-  train, test = MLJ.partition(1:nrow(input), 0.7, rng=123)
-
-  @testset "show" begin
+  @testset "Basic" begin
+    # show method
+    x1 = rand(1:0.1:10, 100)
+    x2 = rand(1:0.1:10, 100)
+    y = 2x1 + x2
+    input = DataFrame(; x1, x2)
+    output = DataFrame(; y)
+    train, test = 1:70, 71:100
     model = DecisionTreeClassifier()
     fmodel = SLM.fit(model, input[train, :], output[train, :])
     @test sprint(show, fmodel) == "FittedStatsLearnModel{DecisionTreeClassifier}"
-  end
 
-  @testset "StatsLearnModel" begin
+    # show method
+    lmodel = SLM.StatsLearnModel(DecisionTreeClassifier(), [:a, :b], :c)
+    @test sprint(show, lmodel) == """
+    StatsLearnModel{DecisionTreeClassifier}
+    ├─ features: [:a, :b]
+    └─ targets: :c"""
+
     # accessor functions
     model = DecisionTreeClassifier()
     feats = selector([:a, :b])
@@ -33,46 +40,26 @@ const SLM = StatsLearnModels
     @test lmodel.model === model
     @test lmodel.feats === feats
     @test lmodel.targs === targs
-    # show
-    lmodel = SLM.StatsLearnModel(DecisionTreeClassifier(), [:a, :b], :c)
-    @test sprint(show, lmodel) == """
-    StatsLearnModel{DecisionTreeClassifier}
-    ├─ features: [:a, :b]
-    └─ targets: :c"""
   end
 
-  @testset "models" begin
-    @testset "MLJ" begin
-      Random.seed!(123)
-      Tree = MLJ.@load(DecisionTreeClassifier, pkg = DecisionTree, verbosity = 0)
-      fmodel = SLM.fit(Tree(), input[train, :], output[train, :])
-      pred = SLM.predict(fmodel, input[test, :])
-      accuracy = count(pred.target .== output.target[test]) / length(test)
-      @test accuracy > 0.9
-    end
-
-    @testset "DecisionTree" begin
-      Random.seed!(123)
-      model = DecisionTreeClassifier()
-      fmodel = SLM.fit(model, input[train, :], output[train, :])
-      pred = SLM.predict(fmodel, input[test, :])
-      accuracy = count(pred.target .== output.target[test]) / length(test)
-      @test accuracy > 0.9
-    end
-
+  @testset "Models" begin
     @testset "NearestNeighbors" begin
       Random.seed!(123)
+      iris = DataFrame(MLJ.load_iris())
+      input = iris[:, Not(:target)]
+      output = iris[:, [:target]]
+      train, test = MLJ.partition(1:nrow(input), 0.7, rng=123)
       model = KNNClassifier(5)
       fmodel = SLM.fit(model, input[train, :], output[train, :])
       pred = SLM.predict(fmodel, input[test, :])
       accuracy = count(pred.target .== output.target[test]) / length(test)
       @test accuracy > 0.9
 
       Random.seed!(123)
-      a = rand(1:0.1:10, 100)
-      b = rand(1:0.1:10, 100)
-      y = 2a + b
-      input = DataFrame(; a, b)
+      x1 = rand(1:0.1:10, 100)
+      x2 = rand(1:0.1:10, 100)
+      y = 2x1 + x2
+      input = DataFrame(; x1, x2)
       output = DataFrame(; y)
       model = KNNRegressor(5)
       fmodel = SLM.fit(model, input, output)
@@ -101,10 +88,27 @@ const SLM = StatsLearnModels
       pred = SLM.predict(fmodel, input)
       @test all(isapprox.(pred.y, output.y, atol=0.5))
     end
+
+    @testset "DecisionTree" begin
+      Random.seed!(123)
+      iris = DataFrame(MLJ.load_iris())
+      input = iris[:, Not(:target)]
+      output = iris[:, [:target]]
+      train, test = MLJ.partition(1:nrow(input), 0.7, rng=123)
+      model = DecisionTreeClassifier()
+      fmodel = SLM.fit(model, input[train, :], output[train, :])
+      pred = SLM.predict(fmodel, input[test, :])
+      accuracy = count(pred.target .== output.target[test]) / length(test)
+      @test accuracy > 0.9
+    end
   end
 
   @testset "Learn" begin
     Random.seed!(123)
+    iris = DataFrame(MLJ.load_iris())
+    input = iris[:, Not(:target)]
+    output = iris[:, [:target]]
+    train, test = MLJ.partition(1:nrow(input), 0.7, rng=123)
     outvar = :target
     feats = setdiff(propertynames(iris), [outvar])
     targs = outvar
@@ -119,4 +123,17 @@ const SLM = StatsLearnModels
     # training data is not a table
     @test_throws ArgumentError Learn(nothing, model, feats => targs)
   end
+
+  @testset "MLJ" begin
+    Random.seed!(123)
+    iris = DataFrame(MLJ.load_iris())
+    input = iris[:, Not(:target)]
+    output = iris[:, [:target]]
+    train, test = MLJ.partition(1:nrow(input), 0.7, rng=123)
+    Tree = MLJ.@load(DecisionTreeClassifier, pkg = DecisionTree, verbosity = 0)
+    fmodel = SLM.fit(Tree(), input[train, :], output[train, :])
+    pred = SLM.predict(fmodel, input[test, :])
+    accuracy = count(pred.target .== output.target[test]) / length(test)
+    @test accuracy > 0.9
+  end
 end