Fix issues with MLJDecisionTreeInterface (#76)

* Add tests with MLJDecisionTreeInterface * Fix use of MLJ interface * Fix seed of DecisionTreeClassifier in reproducibility tests
TuringLang · Feb 27, 2023 · 5ad916f · 5ad916f · devmotion · Feb 27, 2023
1 parent 5955f82
commit 5ad916f
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Showing 4 changed files with 15 additions and 7 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "MCMCDiagnosticTools"
 uuid = "be115224-59cd-429b-ad48-344e309966f0"
 authors = ["David Widmann"]
-version = "0.3.0"
+version = "0.3.1"
 
 [deps]
 AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c"

diff --git a/src/rstar.jl b/src/rstar.jl
@@ -43,18 +43,21 @@ function rstar(
 
     xtable = _astable(x)
     ycategorical = MMI.categorical(ysplit)
-    xdata, ydata = MMI.reformat(classifier, xtable, ycategorical)
 
     # train classifier on training data
-    xtrain, ytrain = MMI.selectrows(classifier, train_ids, xdata, ydata)
-    fitresult, _ = MMI.fit(classifier, verbosity, xtrain, ytrain)
+    data = MMI.reformat(classifier, xtable, ycategorical)
+    train_data = MMI.selectrows(classifier, train_ids, data...)
+    fitresult, _ = MMI.fit(classifier, verbosity, train_data...)
 
     # compute predictions on test data
-    xtest, = MMI.selectrows(classifier, test_ids, xdata)
-    ytest = ycategorical[test_ids]
-    predictions = _predict(classifier, fitresult, xtest)
+    # we exploit that MLJ demands that
+    # reformat(model, args...)[1] = reformat(model, args[1])
+    # (https://alan-turing-institute.github.io/MLJ.jl/dev/adding_models_for_general_use/#Implementing-a-data-front-end)
+    test_data = MMI.selectrows(classifier, test_ids, data[1])
+    predictions = _predict(classifier, fitresult, test_data...)
 
     # compute statistic
+    ytest = ycategorical[test_ids]
     result = _rstar(MMI.scitype(predictions), predictions, ytest)
 
     return result

diff --git a/test/Project.toml b/test/Project.toml
@@ -6,6 +6,7 @@ FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
+MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
 MLJIteration = "614be32b-d00c-4edb-bd02-1eb411ab5e55"
 MLJLIBSVMInterface = "61c7150f-6c77-4bb1-949c-13197eac2a52"
 MLJModels = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
@@ -25,6 +26,7 @@ FFTW = "1.1"
 LogDensityProblems = "0.12, 1, 2"
 LogExpFunctions = "0.3"
 MLJBase = "0.19, 0.20, 0.21"
+MLJDecisionTreeInterface = "0.3"
 MLJIteration = "0.5"
 MLJLIBSVMInterface = "0.2"
 MLJModels = "0.16"

diff --git a/test/rstar.jl b/test/rstar.jl
@@ -3,6 +3,7 @@ using MCMCDiagnosticTools
 using Distributions
 using EvoTrees
 using MLJBase: MLJBase, Pipeline, predict_mode
+using MLJDecisionTreeInterface
 using MLJLIBSVMInterface
 using MLJModels
 using MLJXGBoostInterface
@@ -26,6 +27,7 @@ end
         classifiers = (
             EvoTreeClassifier(; nrounds=100, eta=0.3),
             Pipeline(EvoTreeClassifier(; nrounds=100, eta=0.3); operation=predict_mode),
+            DecisionTreeClassifier(),
             SVC(),
             XGBoostClassifiers...,
         )
@@ -131,6 +133,7 @@ end
             Pipeline(
                 EvoTreeClassifier(; rng=rng, nrounds=100, eta=0.3); operation=predict_mode
             ),
+            DecisionTreeClassifier(; rng=rng),
             SVC(),
             XGBoostClassifiers...,
         )