fit and computation of scores seems to work now

Project.toml

@@ -10,6 +10,7 @@ ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 InferOpt = "4846b161-c94e-4150-8dac-c7ae193c601f"
+LaplaceRedux = "c52c1a26-f7c5-402b-80be-ba1e638ad478"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJEnsembles = "50ed68f4-41fd-4504-931a-ed422449fee0"

src/conformal_models/inductive_bayes_regression.jl

@@ -1,4 +1,4 @@
- # Simple
+#using LaplaceRedux.LaplaceRegression
  "The `BayesRegressor` is the simplest approach to Inductive Conformalized Bayes."
  mutable struct BayesRegressor{Model <: Supervised}  <: ConformalInterval
      model::Model
@@ -9,19 +9,16 @@
  end
 
  function ConformalBayes(y, fμ, fvar)
-        # Ensure σ is positive
-    if fvar <= 0
-        throw(ArgumentError("variance must be positive"))
-    end
     std= sqrt.(fvar)
     # Compute the probability density
     coeff = 1 ./ (std .* sqrt(2 * π))
     exponent = -((y .- fμ).^2) ./ (2 .* std.^2)
     return -coeff .* exp.(exponent)
  end
 
- function BayesRegressor(model::Supervised; coverage::AbstractFloat=0.95, heuristic::Function=ConformalBayes(y, fμ, fvar), train_ratio::AbstractFloat=0.5)
-    #@assert typeof(model) == :Laplace "Model must be of type Laplace"
+ function BayesRegressor(model::Supervised; coverage::AbstractFloat=0.95, heuristic::Function=ConformalBayes, train_ratio::AbstractFloat=0.5)
+    #@assert typeof(model.model) == :Laplace "Model must be of type Laplace"
+    #@assert typeof(model)== LaplaceRegression "Model must be of type Laplace"
      return BayesRegressor(model, coverage, nothing, heuristic, train_ratio)
  end
 
@@ -37,15 +34,22 @@
  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::BayesRegressor, verbosity, X, y)
-
     # Data Splitting:
     Xtrain, ytrain, Xcal, ycal = split_data(conf_model, X, y)
 
      # Training: 
-     fitresult, cache, report = MMI.fit(conf_model.model, verbosity, MMI.reformat(conf_model.model, Xcal)...)
+    fitresult, cache, report = MMI.fit(
+        conf_model.model, verbosity, MMI.reformat(conf_model.model, Xtrain, ytrain)...)
+
 
      # Nonconformity Scores:
-     fμ, fvar = MMI.predict(conf_model.model, fitresult, Xcal)    
+    yhat  =  MMI.predict(conf_model.model, fitresult,  Xcal)
+
+    fμ = vcat([x[1] for x in yhat]...)
+    fvar = vcat([x[2] for x in yhat]...)
+
+
+
      conf_model.scores = @.(conf_model.heuristic(ycal, fμ, fvar))
 
      return (fitresult, cache, report)
@@ -67,5 +71,5 @@
     v = conf_model.scores
     q̂ = qplus(v, conf_model.coverage)
     #normal_distr = [Normal(fμ[i], fstd[i]) for i in 1:size(fμ, 2)]
-     return fμ
+     return fμ, fvar
  end