uh

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.8.5"
 manifest_format = "2.0"
-project_hash = "7dee1a21a38b8ebe5b8194926ba2e4b4df21760e"
+project_hash = "a3f5e7cf561325560c244a222fe34c51dfd04226"
 
 [[deps.AbstractFFTs]]
 deps = ["ChainRulesCore", "LinearAlgebra"]

Project.toml

@@ -8,6 +8,7 @@ ConformalPrediction = "98bfc277-1877-43dc-819b-a3e38c30242f"
 CounterfactualExplanations = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"

notebooks/conformal.qmd

+
+```{julia}
+using CCE
+using ConformalPrediction
+using CounterfactualExplanations
+using CounterfactualExplanations.Data
+using MLJFlux
+```
+
+```{julia}
+counterfactual_data = load_linearly_separable()
+```
+
+```{julia}
+clf = NeuralNetworkClassifier()
+```
+
+
+```{julia}
+X = table(permutedims(counterfactual_data.X))
+y =  counterfactual_data.output_encoder.labels
+conf_model = conformal_model(clf; method=:simple_inductive)
+mach = machine(conf_model, X, y)
+fit!(mach)
+```

src/model.jl

@@ -6,14 +6,14 @@ using SliceMap
 using Statistics
 
 """
-    Models.ConformalModel <: AbstractDifferentiableJuliaModel
+    ConformalModel <: Models.AbstractDifferentiableJuliaModel
 
 Constructor for models trained in `Flux.jl`. 
 """
-struct Models.ConformalModel <: AbstractDifferentiableJuliaModel
+struct ConformalModel <: Models.AbstractDifferentiableJuliaModel
     model::ConformalPrediction.ConformalProbabilisticSet
     likelihood::Symbol
-    function FluxModel(model, likelihood)
+    function ConformalModel(model, likelihood)
         if likelihood ∈ [:classification_binary, :classification_multi]
             new(model, likelihood)
         else
@@ -27,16 +27,16 @@ struct Models.ConformalModel <: AbstractDifferentiableJuliaModel
 end
 
 # Outer constructor method:
-function Models.ConformalModel(model; likelihood::Symbol=:classification_binary)
-    Models.ConformalModel(model, likelihood)
+function ConformalModel(model; likelihood::Symbol=:classification_binary)
+    ConformalModel(model, likelihood)
 end
 
 # Methods
-function logits(M::Models.ConformalModel, X::AbstractArray)
+function logits(M::ConformalModel, X::AbstractArray)
     return SliceMap.slicemap(x -> M.model(x), X, dims=(1, 2))
 end
 
-function probs(M::Models.ConformalModel, X::AbstractArray)
+function probs(M::ConformalModel, X::AbstractArray)
     if M.likelihood == :classification_binary
         output = σ.(logits(M, X))
     elseif M.likelihood == :classification_multi