Improve stability example in docs (#90)

.github/workflows/CI.yml

@@ -21,29 +21,30 @@ jobs:
       actions: write
       # Required by julia-actions/cache.
       contents: read
-    runs-on: ${{ matrix.os }}
+    runs-on: ${{ matrix.config.os }}
     timeout-minutes: 30
     strategy:
       fail-fast: false
       matrix:
-        version:
-          - '1.6'
-          - '1'
-        os:
-          - ubuntu-latest
+        config:
+          - {os: ubuntu-latest, version: '1.6'}
+          # R crashes on ubuntu-latest with newer versions of Julia.
+          - {os: macos-latest, version: '1'}
 
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v2
         with:
-          version: ${{ matrix.version }}
+          version: ${{ matrix.config.version }}
       - uses: julia-actions/cache@v2.0.0
+        with:
+          cache-name: 'test-${{ matrix.config.os }}-${{ matrix.config.version }}'
       - uses: r-lib/actions/setup-r@v2
         with:
           use-public-rspm: true
           r-version: '4'
       - run: echo "LD_LIBRARY_PATH=$(R RHOME)/lib:$LD_LIBRARY_PATH" >> $GITHUB_ENV
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.config.os == 'ubuntu-latest'
       - run: Rscript -e 'install.packages("sirus")'
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1

docs/Project.toml

@@ -19,7 +19,7 @@ StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 
 [compat]
 CSV = "0.10"
-CairoMakie = "0.11"
+CairoMakie = "0.12"
 CategoricalArrays = "0.10"
 DataDeps = "0.7"
 DataFrames = "1"

docs/src/basic-example.jl

@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.32
+# v0.19.42
 
 using Markdown
 using InteractiveUtils

docs/src/binary-classification.jl

@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.32
+# v0.19.42
 
 using Markdown
 using InteractiveUtils
@@ -171,6 +171,7 @@ Say, we take the following subset of length `0.7 * length(nodes)`:
 # ╔═╡ ee12350a-627b-4a11-99cb-38c496977d18
 md"""
 Now, the algorithm would choose a different location and, hence, introduce instability.
+
 To solve this, Bénard et al. decided to limit the splitpoints that the algorithm can use to split to data to a pre-defined set of points.
 For each feature, they find `q` empirical quantiles where `q` is typically 10.
 Let's overlay these quantiles on top of the `age` feature:
@@ -181,6 +182,20 @@ md"""
 Next, let's see where the cutpoints are when we take the same random subset as above:
 """
 
+# ╔═╡ ede038b3-d92e-4208-b8ab-984f3ca1810e
+# hideall
+function _plot_cutpoints(data::DataFrame; q=10)
+	fig = Figure(; size=(800, 100))
+	ax = Axis(fig[1, 1])
+	cps = Float64.(unique(cutpoints(data.nodes, q)))
+	scatter!(ax, data.nodes, fill(1, nrow(data)); color=data.color, marker=data.marker)
+	vlines!(ax, cps; color=:black, linestyle=:dash)
+	xlims!(ax, -1, 48)
+	ylims!(ax, 0.82, 1.2)
+	hideydecorations!(ax)
+	return fig
+end;
+
 # ╔═╡ 01b08d44-4b9b-42e2-bb20-f34cb9b407f3
 md"""
 As can be seen, many cutpoints are at the same location as before.
@@ -331,23 +346,6 @@ md"""
 ## Appendix
 """
 
-# ╔═╡ ede038b3-d92e-4208-b8ab-984f3ca1810e
-function _plot_cutpoints(data::AbstractVector)
-	fig = Figure(; size=(800, 100))
-	ax = Axis(fig[1, 1])
-	cps = Float64.(unique(cutpoints(data, 10)))
-	scatter!(ax, data, fill(1, length(data)))
-	vlines!(ax, cps; color=:black, linestyle=:dash)
-	textlocs = [(c, 1.1) for c in cps]
-	for cutpoint in cps
-		annotation = string(round(cutpoint; digits=2))::String
-		text!(ax, cutpoint + 0.2, 1.08; text=annotation, fontsize=13)
-	end
-	ylims!(ax, 0.9, 1.2)
-	hideydecorations!(ax)
-	return fig
-end;
-
 # ╔═╡ 93a7dd3b-7810-4021-bf6e-ae9c04acea46
 _rng(seed::Int=1) = StableRNG(seed);
 
@@ -421,47 +419,66 @@ The `then` or `else` outcome is chosen for all the rules and, finally, the outco
 """
 
 # ╔═╡ 172d3263-2e39-483c-9d82-8c22059e63c3
-nodes = sort(data.age);
+# hideall
+processed = let
+	df = sort(data, :nodes)
+	df[!, :marker] = [survival == 1 ? :circle : :cross for survival in df.survival]
+	df[!, :color] = [survival == 1 ? :black : :gray for survival in df.survival]
+	df
+end;
 
 # ╔═╡ cf1816e5-4e8d-4e60-812f-bd6ae7011d6c
 # hideall
-ln = length(nodes);
+l = length(processed.nodes);
+
+# ╔═╡ 8d1b30bd-0ad2-416e-a36a-f263ef781289
+# hideall
+index = l - 21;
+
+# ╔═╡ bfcb5e17-8937-4448-b090-2782818c6b6c
+# hideall
+subset_indexes = collect(S._rand_subset(_rng(3), 1:l, round(Int, 0.6 * l)));
 
 # ╔═╡ de90efc9-2171-4406-93a1-9a213ab32259
 # hideall
 let
 	fig = Figure(; size=(800, 100))
 	ax = Axis(fig[1, 1])
-	scatter!(ax, nodes, fill(1, ln))
+	color = (; colormap=:tab10, colorrange=(1, 10))
+	scatter!(ax, processed.nodes, fill(1, l); color=processed.color, marker=processed.marker)
 	hideydecorations!(ax)
+	xlims!(ax, -1, 48)
+	ylims!(ax, 0.82, 1.2)
 	fig
 end
 
-# ╔═╡ 8d1b30bd-0ad2-416e-a36a-f263ef781289
-# hideall
-index = length(nodes) - 3;
-
 # ╔═╡ 2c1adef4-822e-4dc0-946b-dc574e50b305
 # hideall
 let
+	V = processed.nodes
 	fig = Figure(; size=(800, 100))
 	ax = Axis(fig[1, 1])
-	scatter!(ax, nodes, fill(1, ln))
-	vlines!(ax, [nodes[index]]; color=:red)
-	annotation = string(round(nodes[index]; digits=2))
-	text!(ax, nodes[index] + 0.003, 1.08; text=annotation, fontsize=11)
+	scatter!(ax, V, fill(1, l); color=processed.color, marker=processed.marker)
+	vlines!(ax, [V[index]]; color=:red)
+	annotation = string(round(V[index]; digits=2))
+	text!(ax, V[index] + 0.003, 1.08; text=annotation, fontsize=11)
 	hideydecorations!(ax)
-	ylims!(ax, 0.9, 1.2)
+	xlims!(ax, -1, 48)
+	ylims!(ax, 0.82, 1.2)
 	fig
 end
 
-# ╔═╡ bfcb5e17-8937-4448-b090-2782818c6b6c
+# ╔═╡ 6fb30208-cf39-42cd-bdda-a7941173822e
 # hideall
-subset = collect(S._rand_subset(_rng(3), nodes, round(Int, 0.7 * ln)));
+subset = let
+	df = processed[subset_indexes, :]
+	sort!(df, :nodes)
+	df
+end
 
 # ╔═╡ dff9eb71-a853-4186-8245-a64206379b6f
 # hideall
-ls = length(subset);
+ls = nrow(subset);
 
 # ╔═╡ 8fdc24d9-1f6b-4094-9722-6b5b6c713f12
 # hideall
@@ -470,20 +487,23 @@ _plot_cutpoints(subset)
 # ╔═╡ 25ad7a18-f989-40f7-8ef1-4ca506446478
 # hideall
 let
+	V = subset.nodes
 	fig = Figure(; size=(800, 100))
 	ax = Axis(fig[1, 1])
-	scatter!(ax, subset, fill(1, ls))
-	vlines!(ax, [nodes[index]]; color=:red, linestyle=:dash)
-	annotation = string(round(nodes[index]; digits=2))
-	text!(ax, nodes[index] + 0.003, 1.08; text=annotation, fontsize=11)
+	scatter!(ax, V, fill(1, ls); color=subset.color, marker=subset.marker)
+	loc = processed.nodes[index]
+	vlines!(ax, [loc]; color=:red, linestyle=:dash)
+	annotation = string(round(loc; digits=2))
+	text!(ax, loc + 0.003, 1.08; text=annotation, fontsize=11)
 	hideydecorations!(ax)
-	ylims!(ax, 0.9, 1.2)
+	xlims!(ax, -1, 48)
+	ylims!(ax, 0.82, 1.2)
 	fig
 end
 
 # ╔═╡ 4935d8f5-32e1-429c-a8c1-84c242eff4bf
 # hideall
-_plot_cutpoints(nodes)
+_plot_cutpoints(processed; q=10)
 
 # ╔═╡ a64dae3c-3b97-4076-98f4-3c9a0e5c0621
 function _odds_plot(models::Vector{<:StableRules}, feat_names::Vector{String})
@@ -701,12 +721,14 @@ end
 # ╠═2c1adef4-822e-4dc0-946b-dc574e50b305
 # ╠═896e00dc-2ce9-4a9f-acc1-519aec21dd83
 # ╠═bfcb5e17-8937-4448-b090-2782818c6b6c
+# ╠═6fb30208-cf39-42cd-bdda-a7941173822e
 # ╠═dff9eb71-a853-4186-8245-a64206379b6f
 # ╠═25ad7a18-f989-40f7-8ef1-4ca506446478
 # ╠═ee12350a-627b-4a11-99cb-38c496977d18
 # ╠═4935d8f5-32e1-429c-a8c1-84c242eff4bf
 # ╠═0cc970cd-b7ed-4782-a520-ff0a76fe0453
 # ╠═8fdc24d9-1f6b-4094-9722-6b5b6c713f12
+# ╠═ede038b3-d92e-4208-b8ab-984f3ca1810e
 # ╠═01b08d44-4b9b-42e2-bb20-f34cb9b407f3
 # ╠═7e1d46b4-5f93-478d-9105-a5b0db1eaf08
 # ╠═ab103b4e-24eb-4575-8c04-ae3fd9ec1673
@@ -733,7 +755,6 @@ end
 # ╠═e7f396dc-38a7-40f7-9e5b-6fbea9d61789
 # ╠═7c688412-d1b4-492d-bda2-0b9181057d4d
 # ╠═e1890517-7a44-4814-999d-6af27e2a136a
-# ╠═ede038b3-d92e-4208-b8ab-984f3ca1810e
 # ╠═93a7dd3b-7810-4021-bf6e-ae9c04acea46
 # ╠═be324728-1b60-4584-b8ea-c4fe9e3466af
 # ╠═7ad3cf67-2acd-44c6-aa91-7d5ae809dfbc