uff

docs/src/intro.qmd

@@ -97,7 +97,7 @@ Predictions can then be computed using the generic `predict` method. The code be
 
 ```{julia}
 #| output: true
-n = 10
+n = 5
 Xtest = selectrows(X, first(test,n))
 ytest = y[first(test,n)]
 predict(mach, Xtest)

src/ConformalModels/inductive_bayes.jl

@@ -0,0 +1,74 @@
+# Simple
+"The `SimpleInductiveBayes` is the simplest approach to Inductive Conformalized Bayes."
+mutable struct SimpleInductiveBayes{Model <: Supervised} <: ConformalModel
+    model::Model
+    coverage::AbstractFloat
+    scores::Union{Nothing,AbstractArray}
+    heuristic::Function
+    train_ratio::AbstractFloat
+end
+
+function SimpleInductiveBayes(model::Supervised; coverage::AbstractFloat=0.95, heuristic::Function=f(y, ŷ)=-ŷ, train_ratio::AbstractFloat=0.5)
+    return SimpleInductiveBayes(model, coverage, nothing, heuristic, train_ratio)
+end
+
+@doc raw"""
+    MMI.fit(conf_model::SimpleInductiveBayes, verbosity, X, y)
+
+For the [`SimpleInductiveBayes`](@ref) nonconformity scores are computed as follows:
+
+``
+S_i^{\text{CAL}} = s(X_i, Y_i) = h(\hat\mu(X_i), Y_i), \ i \in \mathcal{D}_{\text{calibration}}
+``
+
+A typical choice for the heuristic function is ``h(\hat\mu(X_i), Y_i)=1-\hat\mu(X_i)_{Y_i}`` where ``\hat\mu(X_i)_{Y_i}`` denotes the softmax output of the true class and ``\hat\mu`` denotes the model fitted on training data ``\mathcal{D}_{\text{train}}``. The simple approach only takes the softmax probability of the true label into account.
+"""
+function MMI.fit(conf_model::SimpleInductiveBayes, verbosity, X, y)
+
+    # Data Splitting:
+    train, calibration = partition(eachindex(y), conf_model.train_ratio)
+    Xtrain = selectrows(X, train)
+    ytrain = y[train]
+    Xtrain, ytrain = MMI.reformat(conf_model.model, Xtrain, ytrain)
+    Xcal = selectrows(X, calibration)
+    ycal = y[calibration]
+    Xcal, ycal = MMI.reformat(conf_model.model, Xcal, ycal)
+
+    # Training: 
+    fitresult, cache, report = MMI.fit(conf_model.model, verbosity, Xtrain, ytrain)
+
+    # Nonconformity Scores:
+    ŷ = pdf.(MMI.predict(conf_model.model, fitresult, Xcal), ycal)      # predict returns a vector of distributions
+    conf_model.scores = @.(conf_model.heuristic(ycal, ŷ))
+
+    return (fitresult, cache, report)
+end
+
+@doc raw"""
+    MMI.predict(conf_model::SimpleInductiveBayes, fitresult, Xnew)
+
+For the [`SimpleInductiveBayes`](@ref) prediction sets are computed as follows,
+
+``
+\hat{C}_{n,\alpha}(X_{n+1}) = \left\{y: s(X_{n+1},y) \le \hat{q}_{n, \alpha}^{+} \{S_i^{\text{CAL}}\} \right\}, \ i \in \mathcal{D}_{\text{calibration}}
+``
+
+where ``\mathcal{D}_{\text{calibration}}`` denotes the designated calibration data.
+"""
+function MMI.predict(conf_model::SimpleInductiveBayes, fitresult, Xnew)
+    p̂ = MMI.predict(conf_model.model, fitresult, MMI.reformat(conf_model.model, Xnew)...)
+    v = conf_model.scores
+    q̂ = Statistics.quantile(v, conf_model.coverage)
+    p̂ = map(p̂) do pp
+        L = p̂.decoder.classes
+        probas = pdf.(pp, L)
+        is_in_set = 1.0 .- probas .<= q̂
+        if !all(is_in_set .== false)
+            pp = UnivariateFinite(L[is_in_set], probas[is_in_set])
+        else
+            pp = missing
+        end
+        return pp
+    end
+    return p̂
+end