Add tests for density estimators

test/density_estimation.jl

@@ -0,0 +1,79 @@
+using Test
+using MLJTuning
+using MLJBase
+using StatisticalMeasures
+using StableRNGs
+import MLJModelInterface
+import StatisticalMeasures: CategoricalDistributions, Distributions
+
+
+# We define a density estimator to fit a `UnivariateFinite` distribution to some
+# Categorical data, with a Laplace smoothing option, α.
+
+mutable struct UnivariateFiniteFitter <: MLJModelInterface.Probabilistic
+    alpha::Float64
+end
+UnivariateFiniteFitter(;alpha=1.0) = UnivariateFiniteFitter(alpha)
+
+function MLJModelInterface.fit(model::UnivariateFiniteFitter,
+                               verbosity, X, y)
+
+    α = model.alpha
+    N = length(y)
+    _classes = classes(y)
+    d = length(_classes)
+
+    frequency_given_class = Distributions.countmap(y)
+    prob_given_class =
+        Dict(c => (get(frequency_given_class, c, 0) + α)/(N + α*d) for c in _classes)
+
+    fitresult = CategoricalDistributions.UnivariateFinite(prob_given_class)
+
+    report = (params=Distributions.params(fitresult),)
+    cache = nothing
+
+    verbosity > 0 && @info "Fitted a $fitresult"
+
+    return fitresult, cache, report
+end
+
+MLJModelInterface.predict(model::UnivariateFiniteFitter,
+                          fitresult,
+                          X) = fitresult
+
+
+MLJModelInterface.input_scitype(::Type{<:UnivariateFiniteFitter}) =
+    Nothing
+MLJModelInterface.target_scitype(::Type{<:UnivariateFiniteFitter}) =
+    AbstractVector{<:Finite}
+
+# This test will fail if MLJ test dependency MLJBase is < 1.11
+@testset "tuning for density estimators" begin
+    y = coerce(collect("abbabbc"), Multiclass)
+    X = nothing
+
+    train, test = partition(eachindex(y), 3/7)
+    # For above train-test split, hand calculation determines, when optimizing against
+    # log loss, that:
+    best_alpha = 2.0
+    best_loss = (4log(9) - log(3) - 2log(4) - log(2))/4
+
+    model = UnivariateFiniteFitter(alpha=0)
+    r = range(model, :alpha, values=[0.1, 1, 1.5, 2, 2.5, 10])
+    tmodel = TunedModel(
+        model,
+        tuning=Grid(shuffle=false),
+        range=r,
+        resampling=[(train, test),],
+        measure=log_loss,
+        compact_history=false,
+    )
+
+    mach = machine(tmodel, X, y)
+    fit!(mach, verbosity=0)
+    best = report(mach).best_history_entry
+    @test best.model.alpha == best_alpha
+    @test best.evaluation.measurement[1] ≈ best_loss
+end
+
+true

test/runtests.jl

@@ -60,7 +60,6 @@ end
     @test include("serialization.jl")
 end
 
-# @testset "julia bug" begin
-#     @test include("julia_bug.jl")
-# end
-
+@testset "density estimatation" begin
+    @test include("density_estimation.jl")
+end