Merge branch '32-move-to-plot-recipes-instead-of-overloading'

.github/workflows/FormatCheck.yml

@@ -0,0 +1,28 @@
+name: Format Check
+
+on:
+  push:
+    branches:
+      - 'main'
+      - 'release-'
+    tags: ['*']
+  pull_request:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: 1
+      - uses: actions/checkout@v1
+      - name: Install JuliaFormatter
+        run: |
+          using Pkg
+          Pkg.add("JuliaFormatter")
+        shell: julia --color=yes {0}
+      - name: Format code
+        run: |
+          using JuliaFormatter
+          format("."; verbose=true)
+        shell: julia --color=yes {0}

src/ConformalPrediction/ConformalPrediction.jl

@@ -40,4 +40,4 @@ end
 
 include("regression.jl")
 include("bar.jl")
-include("classification.jl")
+include("classification.jl")

src/ConformalPrediction/bar.jl

@@ -7,11 +7,7 @@
 
 A `Plots.jl` recipe that can be used to visualize the set size distribution of a conformal predictor. In the regression case, prediction interval widths are stratified into discrete bins. It can be useful to plot the distribution of set sizes in order to visually asses how adaptive a conformal predictor is. For more adaptive predictors the distribution of set sizes is typically spread out more widely, which reflects that “the procedure is effectively distinguishing between easy and hard inputs”. This is desirable: when for a given sample it is difficult to make predictions, this should be reflected in the set size (or interval width in the regression case). Since ‘difficult’ lies on some spectrum that ranges from ‘very easy’ to ‘very difficult’ the set size should vary across the spectrum of ‘empty set’ to ‘all labels included’.
 """
-@recipe function plot(
-    conf_model::ConformalModel,
-    fitresult,
-    X
-)
+@recipe function plot(conf_model::ConformalModel, fitresult, X)
 
     # Plot attributes:
     xtickfontsize --> 6
@@ -28,5 +24,4 @@ A `Plots.jl` recipe that can be used to visualize the set size distribution of a
         label --> ""
         x, y
     end
-
-end
+end

src/ConformalPrediction/classification.jl

@@ -90,20 +90,21 @@ In the case of univariate inputs or higher dimensional inputs, a stacked area pl
         # Predictions:
         ŷ = MLJBase.predict(conf_model, fitresult, Xraw)
         nout = length(levels(y))
-        ŷ =
-            map(_y -> ismissing(_y) ? [0 for i = 1:nout] : pdf.(_y, levels(y)), ŷ) |> _y -> reduce(hcat, _y)
+        ŷ = (_y -> reduce(hcat, _y))(map(
+            _y -> ismissing(_y) ? [0 for i in 1:nout] : pdf.(_y, levels(y)), ŷ
+        ))
         ŷ = permutedims(ŷ)
         println(x)
         println(ŷ[sortperm(x), :])
 
         # Area chart
         args = (x, ŷ)
-        data = cumsum(args[end], dims=2)
+        data = cumsum(args[end]; dims=2)
         x = length(args) == 1 ? (axes(data, 1)) : args[1]
         seriestype := :line
         for i in axes(data, 2)
             @series begin
-                fillrange := i > 1 ? data[:, i-1] : 0
+                fillrange := i > 1 ? data[:, i - 1] : 0
                 x, data[:, i]
             end
         end
@@ -114,8 +115,21 @@ In the case of univariate inputs or higher dimensional inputs, a stacked area pl
 
         # Setup:
         x1, x2, x1range, x2range, Z, xlims, ylims, _default_title = setup_contour_cp(
-            conf_model, fitresult, X, y, xlims, ylims, zoom, ntest, target,
-            plot_set_size, plot_classification_loss, plot_set_loss, temp, κ, loss_matrix,
+            conf_model,
+            fitresult,
+            X,
+            y,
+            xlims,
+            ylims,
+            zoom,
+            ntest,
+            target,
+            plot_set_size,
+            plot_classification_loss,
+            plot_set_loss,
+            temp,
+            κ,
+            loss_matrix,
         )
 
         # Contour:
@@ -136,13 +150,19 @@ In the case of univariate inputs or higher dimensional inputs, a stacked area pl
                 x1[group_idx], x2[group_idx]
             end
         end
-
     end
-
 end
 
 function setup_contour_cp(
-    conf_model, fitresult, X, y, xlims, ylims, zoom, ntest, target,
+    conf_model,
+    fitresult,
+    X,
+    y,
+    xlims,
+    ylims,
+    zoom,
+    ntest,
+    target,
     plot_set_size,
     plot_classification_loss,
     plot_set_loss,
@@ -201,23 +221,14 @@ function setup_contour_cp(
         elseif plot_classification_loss
             _target = categorical([target]; levels=levels(y))
             z = ConformalPrediction.ConformalTraining.classification_loss(
-                conf_model,
-                fitresult,
-                [x1 x2],
-                _target;
-                temp=temp,
-                loss_matrix=loss_matrix,
+                conf_model, fitresult, [x1 x2], _target; temp=temp, loss_matrix=loss_matrix
             )
         elseif plot_set_loss
             z = ConformalPrediction.ConformalTraining.smooth_size_loss(
-                conf_model,
-                fitresult,
-                [x1 x2];
-                κ=κ,
-                temp=temp,
+                conf_model, fitresult, [x1 x2]; κ=κ, temp=temp
             )
         else
-            z = ismissing(p̂) ? [missing for i = 1:length(levels(y))] : pdf.(p̂, levels(y))
+            z = ismissing(p̂) ? [missing for i in 1:length(levels(y))] : pdf.(p̂, levels(y))
             z = replace(z, 0 => missing)
         end
         push!(Z, z)
@@ -226,4 +237,4 @@ function setup_contour_cp(
     Z = Z[findall(levels(y) .== target)[1][1], :]
 
     return x1, x2, x1range, x2range, Z, xlims, ylims, _default_title
-end
+end

src/ConformalPrediction/regression.jl

@@ -56,11 +56,9 @@ A `Plots.jl` recipe that can be used to visualize the conformal predictions of a
         label := train_lab
         vec(x), vec(y)
     end
-
 end
 
 function setup_ci(X, y, input_var, xlims, ylims, zoom)
-
     Xraw = deepcopy(X)
     _names = get_names(Xraw)
     X = permutedims(MLJBase.matrix(X))
@@ -88,4 +86,4 @@ function setup_ci(X, y, input_var, xlims, ylims, zoom)
     xlims, ylims = generate_lims(x, y, xlims, ylims, zoom)
 
     return x, y, xlims, ylims, Xraw
-end
+end

src/CounterfactualExplations/counterfactuals.jl

@@ -21,10 +21,9 @@ Calling `Plots.plot` on a `CounterfactualExplanation` object will plot the train
     dim_red=:pca,
     plot_loss=false,
     loss_fun=nothing,
-    plot_up_to = nothing,
-    n_points = nothing,
+    plot_up_to=nothing,
+    n_points=nothing,
 )
-
     if !isnothing(n_points)
         if n_points < size(ce.data.X, 2)
             @info "Undersampling to $(n_points) points."
@@ -35,7 +34,7 @@ Calling `Plots.plot` on a `CounterfactualExplanation` object will plot the train
         ce = deepcopy(ce)
         ce.data = DataPreprocessing.subsample(ce.data, n_points)
     else
-        xlims, ylims  = nothing, nothing
+        xlims, ylims = nothing, nothing
     end
 
     # Asserts
@@ -45,23 +44,14 @@ Calling `Plots.plot` on a `CounterfactualExplanation` object will plot the train
     xlims = get(plotattributes, :xlims, xlims)
     ylims = get(plotattributes, :ylims, ylims)
     ms = get(plotattributes, :markersize, 3)
-    mspath = ms*2
-    msfinal = mspath*2
+    mspath = ms * 2
+    msfinal = mspath * 2
 
     # Plot attributes
     linewidth --> 0.1
 
     contour_series, X, y, xlims, ylims = setup_model_plot(
-        ce.M,
-        ce.data,
-        target,
-        length_out,
-        zoom,
-        dim_red,
-        plot_loss,
-        loss_fun,
-        xlims,
-        ylims,
+        ce.M, ce.data, target, length_out, zoom, dim_red, plot_loss, loss_fun, xlims, ylims
     )
 
     xlims --> xlims
@@ -95,20 +85,21 @@ Calling `Plots.plot` on a `CounterfactualExplanation` object will plot the train
     path_x, path_y = setup_ce_plot(ce)
 
     # Outer loop over number of counterfactuals:
-    for (num_counterfactual, X) in enumerate(eachslice(path_x, dims=3))
+    for (num_counterfactual, X) in enumerate(eachslice(path_x; dims=3))
         # Inner loop over counterfactual search steps:
-        steps = zip(eachcol(X), path_y)       
-        for (i,(x,y)) in enumerate(steps)
+        steps = zip(eachcol(X), path_y)
+        for (i, (x, y)) in enumerate(steps)
             i <= max_iter || break
             _final_iter = i == length(steps) || i == max_iter
             _annotate = i == length(steps) && ce.num_counterfactuals > 1
             @series begin
                 seriestype := :scatter
                 markercolor := CategoricalArrays.levelcode.(y[num_counterfactual])
                 markersize := _final_iter ? msfinal : mspath
-                series_annotation := _annotate ? text("C$(num_counterfactual)", mspath) : nothing
+                series_annotation :=
+                    _annotate ? text("C$(num_counterfactual)", mspath) : nothing
                 label := :none
-                x[1,:], x[2,:]
+                x[1, :], x[2, :]
             end
         end
     end
@@ -131,12 +122,11 @@ animate_path(ce)
 """
 function animate_path(
     ce::CounterfactualExplanation,
-    path = tempdir();
-    plot_up_to::Union{Nothing,Int} = nothing,
-    legend = :topright,
+    path=tempdir();
+    plot_up_to::Union{Nothing,Int}=nothing,
+    legend=:topright,
     kwrgs...,
 )
-
     max_iter = total_steps(ce)
     max_iter = if isnothing(plot_up_to)
         total_steps(ce)
@@ -145,7 +135,7 @@ function animate_path(
     end
     max_iter += 1
 
-    anim = @animate for t = 1:max_iter
+    anim = @animate for t in 1:max_iter
         plot(ce; plot_up_to=t, legend=legend, kwrgs...)
     end
     return anim

src/CounterfactualExplations/data.jl

@@ -1,6 +1,6 @@
 using MLUtils
 
-function embed(data::CounterfactualData, X::AbstractArray = nothing; dim_red::Symbol = :pca)
+function embed(data::CounterfactualData, X::AbstractArray=nothing; dim_red::Symbol=:pca)
 
     # Training compressor:
     if typeof(data.input_encoder) <: MultivariateStats.AbstractDimensionalityReduction
@@ -12,19 +12,19 @@ function embed(data::CounterfactualData, X::AbstractArray = nothing; dim_red::Sy
         else
             @info "Training model to compress data."
             if dim_red == :pca
-                tfn = MultivariateStats.fit(PCA, X_train; maxoutdim = 2)
+                tfn = MultivariateStats.fit(PCA, X_train; maxoutdim=2)
             elseif dim_red == :tsne
-                tfn = MultivariateStats.fit(TSNE, X_train; maxoutdim = 2)
+                tfn = MultivariateStats.fit(TSNE, X_train; maxoutdim=2)
             end
             data.input_encoder = nothing
             X = isnothing(X) ? X_train : X
         end
     end
 
     # Transforming:
-    X = typeof(X) <: Vector{<:Matrix} ? MLUtils.stack(X, dims = 2) : X
+    X = typeof(X) <: Vector{<:Matrix} ? MLUtils.stack(X; dims=2) : X
     if !isnothing(tfn) && !isnothing(X)
-        X = mapslices(x -> MultivariateStats.predict(tfn, x), X, dims = 1)
+        X = mapslices(x -> MultivariateStats.predict(tfn, x), X; dims=1)
     else
         X = isnothing(X) ? X_train : X
     end
@@ -42,13 +42,13 @@ function embed_path(ce::CounterfactualExplanation)
     return embed(data_, path(ce))
 end
 
-function prepare_for_plotting(data::CounterfactualData; dim_red::Symbol = :pca)
+function prepare_for_plotting(data::CounterfactualData; dim_red::Symbol=:pca)
     X, _ = DataPreprocessing.unpack_data(data)
     y = data.output_encoder.labels
     @assert size(X, 1) != 1 "Don't know how to plot 1-dimensional data."
     multi_dim = size(X, 1) > 2
     if multi_dim
-        X = embed(data, X; dim_red = dim_red)
+        X = embed(data, X; dim_red=dim_red)
     end
     return X', y, multi_dim
 end
@@ -58,10 +58,10 @@ end
 
 Calling `Plots.plot` on a `data::CounterfactualData` object will generate a scatter plot of the data.
 """
-@recipe function plot(data::CounterfactualData; dim_red = :pca)
+@recipe function plot(data::CounterfactualData; dim_red=:pca)
 
     # Set up:
-    X, y, _ = prepare_for_plotting(data; dim_red = dim_red)
+    X, y, _ = prepare_for_plotting(data; dim_red=dim_red)
 
     # Scatter plot:
     for (i, x) in enumerate(unique(sort(y)))