Merge pull request #26 from JuliaAI/mljtestinterface

For a 0.3.2 release: Use MLJTestInterface instead of MLJTestIntegration

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJXGBoostInterface"
 uuid = "54119dfa-1dab-4055-a167-80440f4f7a91"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.3.1"
+version = "0.3.2"
 
 [deps]
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
@@ -18,9 +18,9 @@ julia = "1.6"
 [extras]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
-MLJTestIntegration = "697918b4-fdc1-4f9e-8ff9-929724cee270"
+MLJTestInterface = "72560011-54dd-4dc2-94f3-c5de45b75ecd"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Distributions", "MLJBase", "MLJTestIntegration", "StableRNGs", "Test"]
+test = ["Distributions", "MLJBase", "MLJTestInterface", "StableRNGs", "Test"]

test/runtests.jl

@@ -2,7 +2,7 @@ using MLJBase
 using Test
 import XGBoost
 using MLJXGBoostInterface
-using MLJTestIntegration
+using MLJTestInterface
 using Distributions
 import StableRNGs
 const rng = StableRNGs.StableRNG(123)
@@ -37,44 +37,44 @@ end
     features = MLJBase.table(features)
     (fitresultR, cacheR, reportR) = MLJBase.fit(plain_regressor, 0, features, labels)
     rpred = predict(plain_regressor, fitresultR, features);
-    
+
     plain_regressor.objective = "gamma"
     labels = abs.(labels)
     fitresultR, cacheR, reportR = MLJBase.fit(plain_regressor, 0, features, labels)
     rpred = predict(plain_regressor, fitresultR, features);
-    
+
     # serialization:
     serializable_fitresult = MLJBase.save(plain_regressor, fitresultR)
-    
+
     restored_fitresult = MLJBase.restore(plain_regressor, serializable_fitresult)
     @test predict(plain_regressor, restored_fitresult, features) ≈ rpred
 end
 
 @testset "count" begin
     count_regressor = XGBoostCount(num_round=10)
-    
+
     X = randn(rng, 100, 3) .* randn(rng, 3)'
     Xtable = table(X)
-    
+
     α = 0.1
     β = [-0.3, 0.2, -0.1]
     λ = exp.(α .+ X * β)
     ycount_ = [rand(rng, Poisson(λᵢ)) for λᵢ ∈ λ]
     ycount = @view(ycount_[:]) # intention is to simulate issue #17
-    
+
     fitresultC, cacheC, reportC = MLJBase.fit(count_regressor, 0, Xtable, ycount);
     fitresultC_, cacheC_, reportC_ = MLJBase.fit(count_regressor, 0, Xtable, ycount_);
-    # the `cacheC` and `reportC` should be same for both models but the 
+    # the `cacheC` and `reportC` should be same for both models but the
     # `fitresultC`s might be different since they may have different pointers to same
-    # information. 
+    # information.
     @test cacheC == cacheC_
     @test reportC == reportC_
     cpred = predict(count_regressor, fitresultC, Xtable);
 end
 
 @testset "classifier" begin
     plain_classifier = XGBoostClassifier(num_round=100, seed=0)
-    
+
     # test binary case:
     N=2
     X = (x1=rand(rng, 1000), x2=rand(rng, 1000), x3=rand(rng, 1000))
@@ -88,31 +88,31 @@ end
     yhat = mode.(predict(plain_classifier, fitresult, selectrows(X, test)))
     misclassification_rate = sum(yhat .!= y[test])/length(test)
     @test misclassification_rate < 0.015
-    
+
     # Multiclass{10} case:
     N=10
     X = (x1=rand(rng, 1000), x2=rand(rng, 1000), x3=rand(rng, 1000))
     ycat = map(X.x1) do x
         string(mod(round(Int, 10*x), N))
     end |> categorical
     y = identity.(ycat) # make plain Vector with categ. elements
-    
+
     train, test = partition(eachindex(y), 0.6)
     fitresult, cache, report = MLJBase.fit(plain_classifier, 0,
                                                 selectrows(X, train), y[train];)
     fitresult_, cache_, report_ = MLJBase.fit(
         plain_classifier, 0, selectrows(X, train), @view(y[train]);
     ) # mimick issue #17
-    # the `cache` and `report` should be same for both models but the 
+    # the `cache` and `report` should be same for both models but the
     # `fitresult` might be different since they may have different pointers to same
-    # information. 
+    # information.
     @test cache == cache_
     @test report == report_
-    
+
     yhat = mode.(predict(plain_classifier, fitresult, selectrows(X, test)))
     misclassification_rate = sum(yhat .!= y[test])/length(test)
     @test misclassification_rate < 0.01
-    
+
     # check target pool preserved:
     X = (x1=rand(rng, 400), x2=rand(rng, 400), x3=rand(rng, 400))
     ycat = vcat(fill('x', 100), fill('y', 100), fill('z', 200)) |>categorical
@@ -124,12 +124,12 @@ end
                                                 selectrows(X, train), y[train];)
     yhat = predict_mode(plain_classifier, fitresult, selectrows(X, test))
     @test Set(MLJBase.classes(yhat[1])) == Set(MLJBase.classes(y[train][1]))
-    
+
     # serialization:
     serializable_fitresult = MLJBase.save(plain_classifier, fitresult)
-    
+
     restored_fitresult = MLJBase.restore(plain_classifier, serializable_fitresult)
-    
+
     @test predict_mode(plain_classifier, restored_fitresult, selectrows(X, test)) == yhat
 end
 
@@ -139,29 +139,29 @@ end
     count_regressor = XGBoostCount(num_round=10)
 
     plain_classifier = XGBoostClassifier(num_round=100, seed=0)
- 
+
     X = randn(rng, 100, 3) .* randn(rng, 3)'
     Xtable = table(X)
-    
+
     α = 0.1
     β = [-0.3, 0.2, -0.1]
     λ = exp.(α .+ X * β)
     ycount_ = [rand(rng, Poisson(λᵢ)) for λᵢ ∈ λ]
     ycount = @view(ycount_[:]) # intention is to simulate issue #17
-   
+
     mach = machine(count_regressor, Xtable, ycount)
     fit!(mach, verbosity=0)
     yhat = predict(mach, Xtable)
-    
+
     # serialize:
     io = IOBuffer()
     MLJBase.save(io, mach)
-    
+
     # deserialize:
     seekstart(io)
     mach2 = machine(io)
     close(io)
-    
+
     # compare:
     @test predict(mach2, Xtable) ≈ yhat
 
@@ -171,42 +171,42 @@ end
         string(mod(round(Int, 10*x), N))
     end |> categorical
     yclass = identity.(ycat) # make plain Vector with categ. elements
-    
+
     # classifier
     mach = machine(plain_classifier, X, yclass)
     fit!(mach, verbosity=0)
     yhat = predict_mode(mach, X);
 
     imps = feature_importances(mach)
     @test Set(string.([imp[1] for imp ∈ imps])) == Set(["x1", "x2", "x3"])
-    
+
     # serialize:
     io = IOBuffer()
     MLJBase.save(io, mach)
-    
+
     # deserialize:
     seekstart(io)
     mach2 = machine(io)
-    
+
     # compare:
     @test predict_mode(mach2, X) == yhat
 end
 
 @testset "generic interface tests" begin
     @testset "XGBoostRegressor" begin
-        failures, summary = MLJTestIntegration.test(
+        failures, summary = MLJTestInterface.test(
             [XGBoostRegressor,],
-            MLJTestIntegration.make_regression()...;
+            MLJTestInterface.make_regression()...;
             mod=@__MODULE__,
             verbosity=0, # bump to debug
             throw=false, # set to true to debug
         )
         @test isempty(failures)
     end
     @testset "XGBoostCount" begin
-        failures, summary = MLJTestIntegration.test(
+        failures, summary = MLJTestInterface.test(
             [XGBoostCount],
-            MLJTestIntegration.make_count()...;
+            MLJTestInterface.make_count()...;
             mod=@__MODULE__,
             verbosity=0, # bump to debug
             throw=false, # set to true to debug
@@ -215,10 +215,10 @@ end
     end
     @testset "XGBoostClassifier" begin
         for data in [
-            MLJTestIntegration.make_binary(),
-            MLJTestIntegration.make_multiclass(),
+            MLJTestInterface.make_binary(),
+            MLJTestInterface.make_multiclass(),
         ]
-            failures, summary = MLJTestIntegration.test(
+            failures, summary = MLJTestInterface.test(
                 [XGBoostClassifier],
                 data...;
                 mod=@__MODULE__,
@@ -229,4 +229,3 @@ end
         end
     end
 end
-