returning one-hot encoded directly

src/direct_mlj.jl

@@ -44,30 +44,22 @@ MLJBase.@mlj_model mutable struct LaplaceRegressor <: MLJBase.Probabilistic
     fit_prior_nsteps::Int = 100::(_ > 0)
 end
 
-Laplace_Models = Union{LaplaceRegressor,LaplaceClassifier}
+LaplaceModels = Union{LaplaceRegressor,LaplaceClassifier}
 
 # for fit:
-MMI.reformat(::LaplaceRegressor, X, y) = (MLJBase.matrix(X) |> permutedims, (reshape(y, 1, :),nothing))
-
+MMI.reformat(::LaplaceRegressor, X, y) = (MLJBase.matrix(X) |> permutedims, reshape(y, 1, :))
 
 function MMI.reformat(::LaplaceClassifier, X, y) 
 
     X = MLJBase.matrix(X) |> permutedims
+    y = categorical(y)
+    unique_labels = y.pool.levels
+    y = Flux.onehotbatch(y, unique_labels) # One-hot encoding
 
-
-    y= reshape(y, 1, :)
-    # Convert labels to integer format starting from 0 for one-hot encoding
-    y_plain = MLJBase.int(y[1, :]) .- 1
-    # One-hot encoding of labels
-    unique_labels = unique(y_plain) # Ensure unique labels for one-hot encoding
-    y_onehot = Flux.onehotbatch(y_plain, unique_labels) # One-hot encoding
-    return X,(y_onehot, y[1])
+    return X, y
 end
 
-#MMI.selectrows(::LaplaceClassifier, I, Xmatrix, y) = (view(Xmatrix, :, I), (view(y[1],I),y[2]))
-#MMI.selectrows(::LaplaceRegressor, I, Xmatrix, y) = (view(Xmatrix, :,I), (view(y[1],I),y[2]) )
-#for predict:
-MMI.reformat(::Laplace_Models, X) = (MLJBase.matrix(X) |> permutedims,)
+MMI.reformat(::LaplaceModels, X) = (MLJBase.matrix(X) |> permutedims,)
 
 @doc """
     MMI.fit(m::Union{LaplaceRegressor,LaplaceClassifier}, verbosity, X, y)
@@ -85,7 +77,7 @@ Fit a Laplace model using the provided features and target values.
 - `cache`: a tuple containing a deepcopy of the model, the current state of the optimiser and the training loss history.
 - `report`: A Namedtuple containing the loss history of the fitting process.
 """
-function MMI.fit(m::Laplace_Models, verbosity, X, y)
+function MMI.fit(m::LaplaceModels, verbosity, X, y)
     decode = y[2]
 
     y= y[1] 
@@ -172,7 +164,7 @@ Update the Laplace model using the provided new data points.
 - `cache`: a tuple containing a deepcopy of the model, the updated current state of the optimiser and training loss history.
 - `report`: A Namedtuple containing the complete loss history of the fitting process.
 """
-function MMI.update(m::Laplace_Models, verbosity, old_fitresult, old_cache, X, y)
+function MMI.update(m::LaplaceModels, verbosity, old_fitresult, old_cache, X, y)
     decode = y[2]
     y_up=y[1]
 
@@ -302,7 +294,7 @@ function MMI.update(m::Laplace_Models, verbosity, old_fitresult, old_cache, X, y
 end
 
 @doc """
-    function MMI.is_same_except(m1::Laplace_Models, m2::Laplace_Models, exceptions::Symbol...) 
+    function MMI.is_same_except(m1::LaplaceModels, m2::LaplaceModels, exceptions::Symbol...) 
 
 If both `m1` and `m2` are of `MLJType`, return `true` if the
 following conditions all hold, and `false` otherwise:
@@ -329,7 +321,7 @@ meaning; see [`deep_properties`](@ref)) for details.
 If `m1` or `m2` are not `MLJType` objects, then return `==(m1, m2)`.
 
 """
-function MMI.is_same_except(m1::Laplace_Models, m2::Laplace_Models, exceptions::Symbol...)
+function MMI.is_same_except(m1::LaplaceModels, m2::LaplaceModels, exceptions::Symbol...)
     typeof(m1) === typeof(m2) || return false
     names = propertynames(m1)
     propertynames(m2) === names || return false
@@ -420,7 +412,7 @@ end
  - `loss`: The loss value of the posterior distribution.
 
 """
-function MMI.fitted_params(model::Laplace_Models, fitresult)
+function MMI.fitted_params(model::LaplaceModels, fitresult)
     la, decode = fitresult
     posterior = la.posterior
     return (
@@ -447,7 +439,7 @@ Retrieve the training loss history from the given `report`.
 # Returns
 - A collection representing the loss history from the training report.
 """
-function MMI.training_losses(model::Laplace_Models, report)
+function MMI.training_losses(model::LaplaceModels, report)
     return report.loss_history
 end
 
@@ -467,7 +459,7 @@ function MMI.predict(m::LaplaceRegressor, fitresult, Xnew)
     for LaplaceClassifier:
     - `MLJBase.UnivariateFinite`: The predicted class probabilities for the new data.
 """
-function MMI.predict(m::Laplace_Models, fitresult, Xnew)
+function MMI.predict(m::LaplaceModels, fitresult, Xnew)
     la, decode = fitresult
     if typeof(m) == LaplaceRegressor
         yhat = LaplaceRedux.predict(la, Xnew; ret_distr=false)

test/direct_mlj_interface.jl

@@ -7,6 +7,7 @@ using MLJ
 using MLJ:predict,fit!
 using LaplaceRedux
 
+cv = CV(; nfolds=3)
 
 @testset "Regression" begin
     flux_model = Chain(
@@ -36,41 +37,40 @@ end
 
 
 @testset "Classification" begin
-# Define the model
-flux_model = Chain(
-    Dense(4, 10, relu),
-    Dense(10, 3)
-)
+    # Define the model
+    flux_model = Chain(
+        Dense(4, 10, relu),
+        Dense(10, 3)
+    )
 
-model = LaplaceClassifier(model=flux_model,epochs=50)
+    model = LaplaceClassifier(model=flux_model,epochs=50)
 
-X, y = @load_iris
-mach = machine(model, X, y)
-MLJBase.fit!(mach,verbosity=1)
-Xnew = (sepal_length = [6.4, 7.2, 7.4],
-        sepal_width = [2.8, 3.0, 2.8],
-        petal_length = [5.6, 5.8, 6.1],
-        petal_width = [2.1, 1.6, 1.9],)
-yhat = MLJBase.predict(mach, Xnew) # probabilistic predictions
-predict_mode(mach, Xnew)   # point predictions
-pdf.(yhat, "virginica")    # probabilities for the "verginica" class
-MLJBase.fitted_params(mach)  # fitted params 
-MLJBase.training_losses(mach) #training loss history
-model.epochs= 100 #changing number of epochs
-MLJBase.fit!(mach) #testing update function
-model.epochs= 50 #changing number of epochs to a lower number
-MLJBase.fit!(mach) #testing update function
-model.fit_prior_nsteps = 200 #changing LaplaceRedux fit steps
-MLJBase.fit!(mach) #testing update function (the laplace part)
+    X, y = @load_iris
+    mach = machine(model, X, y)
+    MLJBase.fit!(mach,verbosity=1)
+    Xnew = (sepal_length = [6.4, 7.2, 7.4],
+            sepal_width = [2.8, 3.0, 2.8],
+            petal_length = [5.6, 5.8, 6.1],
+            petal_width = [2.1, 1.6, 1.9],)
+    yhat = MLJBase.predict(mach, Xnew) # probabilistic predictions
+    predict_mode(mach, Xnew)   # point predictions
+    pdf.(yhat, "virginica")    # probabilities for the "verginica" class
+    MLJBase.fitted_params(mach)  # fitted params 
+    MLJBase.training_losses(mach) #training loss history
+    model.epochs= 100 #changing number of epochs
+    MLJBase.fit!(mach) #testing update function
+    model.epochs= 50 #changing number of epochs to a lower number
+    MLJBase.fit!(mach) #testing update function
+    model.fit_prior_nsteps = 200 #changing LaplaceRedux fit steps
+    MLJBase.fit!(mach) #testing update function (the laplace part)
 
-# Define a different model
-flux_model_two = Chain(
-    Dense(4, 6, relu),
-    Dense(6, 3)
-)
+    # Define a different model
+    flux_model_two = Chain(
+        Dense(4, 6, relu),
+        Dense(6, 3)
+    )
 
-model.model = flux_model_two
+    model.model = flux_model_two
 
-MLJBase.fit!(mach)
-
+    MLJBase.fit!(mach)
 end