For a 0.8.9 release

.github/workflows/ci.yml

@@ -19,19 +19,19 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6'
+          - 'lts'
           - '1'
         os:
           - ubuntu-latest
         arch:
           - x64
     steps:
       - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: actions/cache@v1
+      - uses: julia-actions/cache@v2
         env:
           cache-name: cache-artifacts
         with:

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJTuning"
 uuid = "03970b2e-30c4-11ea-3135-d1576263f10f"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.8.8"
+version = "0.8.9"
 
 [deps]
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"

src/plotrecipes.jl

@@ -1,10 +1,14 @@
+const MAX_AXIS_LABEL_WIDTH = 20
+
 @recipe function f(mach::MLJBase.Machine{<:EitherTunedModel})
     rep = report(mach)
     measurement = repr(rep.best_history_entry.measure[1])
     r = rep.plotting
     z = r.measurements
     X = r.parameter_values
-    guides = r.parameter_names
+    guides = map(r.parameter_names) do name
+        trim(name, MAX_AXIS_LABEL_WIDTH)
+    end 
     scales = r.parameter_scales
     n = size(X, 2)
     indices = LinearIndices((n, n))'

src/tuned_models.jl

@@ -3,7 +3,7 @@
 const ERR_SPECIFY_MODEL = ArgumentError(
 "You need to specify `model=...`, unless `tuning=Explicit()`. ")
 const ERR_SPECIFY_RANGE = ArgumentError(
-    "You need to specify `range=...`, unless `tuning=Explicit()` and "*
+    "You need to specify `range=...`, unless `tuning=Explicit()` "*
     "and `models=...` is specified instead. ")
 const ERR_SPECIFY_RANGE_OR_MODELS = ArgumentError(
     "No `model` specified. Either specify an explicit iterator "*

src/utilities.jl

@@ -39,3 +39,22 @@ signature(measure) =
     else
         0
     end
+
+# function to trim a string like "transformed_target_model_deterministic.model.K" to `N`
+# characters. For example, if `N=20`, return `…model.K`. Used in plotrecipes.jl.
+function trim(str, N)
+    n = length(str)
+    n <= N && return str
+    fits = false
+    parts = split(str, ".") |> reverse
+    # removes parts until what remains fits, with room for ellipsis (1 character), or if
+    # there is only one part left:
+    while !fits && length(parts) > 1
+        removed = pop!(parts)
+        n -= length(removed) + 1 # the `1` is for the dot, `.`
+        if n < N
+            fits = true
+        end
+    end
+    "…"*join(reverse(parts), ".")
+end

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

test/utilities.jl

@@ -22,5 +22,16 @@ end
     @test MLJTuning.signature.(measures) == [-1, 0, 1]
 end
 
+@testset "trim" begin
+    str = "some.long.name" # 14 characters
+    @test MLJTuning.trim(str, 14) == str
+    @test MLJTuning.trim(str, 13) == "…long.name" # 10 characters
+    @test MLJTuning.trim(str, 12) == "…long.name"
+    @test MLJTuning.trim(str, 11) == "…long.name"
+    @test MLJTuning.trim(str, 10) == "…long.name"
+    @test MLJTuning.trim(str, 9) == "…name"
+    @test MLJTuning.trim(str, 1) == "…name" # cannot go any smaller
+end
+
 true