134 breaking remove unicode characters

CHANGELOG.md

@@ -6,6 +6,16 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 
 *Note*: We try to adhere to these practices as of version [v0.2.1].
 
+
+## Version [2.0.0] - 2024-11-26
+
+
+
+### Added
+
+-  added support to MLJ [#126] [#134]
+
+
 ## Version [1.1.1] - 2024-09-12
 
 ### Changed

docs/src/tutorials/regression.qmd

@@ -128,7 +128,7 @@ then we can plot the calibration plot of our neural model
 
 ```{julia}
 #| output: true
-Calibration_Plot(la,y_test,vec(predicted_distributions);n_bins = 20)
+calibration_plot(la,y_test,vec(predicted_distributions);n_bins = 20)
 ``` 
 
 and compute the sharpness of the predictive distribution

src/direct_mlj.jl

@@ -19,9 +19,9 @@ MLJBase.@mlj_model mutable struct LaplaceClassifier <: MLJBase.Probabilistic
     hessian_structure::Union{HessianStructure,Symbol,String} =
         :full::(_ in (:full, :diagonal))
     backend::Symbol = :GGN::(_ in (:GGN, :EmpiricalFisher))
-    σ::Float64 = 1.0
-    μ₀::Float64 = 0.0
-    P₀::Union{AbstractMatrix,UniformScaling,Nothing} = nothing
+    observational_noise::Float64 = 1.0
+    prior_mean::Float64 = 0.0
+    prior_precision_matrix::Union{AbstractMatrix,UniformScaling,Nothing} = nothing
     fit_prior_nsteps::Int = 100::(_ > 0)
     link_approx::Symbol = :probit::(_ in (:probit, :plugin))
 end
@@ -37,14 +37,32 @@ MLJBase.@mlj_model mutable struct LaplaceRegressor <: MLJBase.Probabilistic
     hessian_structure::Union{HessianStructure,Symbol,String} =
         :full::(_ in (:full, :diagonal))
     backend::Symbol = :GGN::(_ in (:GGN, :EmpiricalFisher))
-    σ::Float64 = 1.0
-    μ₀::Float64 = 0.0
-    P₀::Union{AbstractMatrix,UniformScaling,Nothing} = nothing
+    observational_noise::Float64 = 1.0
+    prior_mean::Float64 = 0.0
+    prior_precision_matrix::Union{AbstractMatrix,UniformScaling,Nothing} = nothing
     fit_prior_nsteps::Int = 100::(_ > 0)
 end
 
 LaplaceModels = Union{LaplaceRegressor,LaplaceClassifier}
 
+# Aliases
+const LM = LaplaceModels
+function Base.getproperty(ce::LM, sym::Symbol)
+    sym = sym === :σ ? :observational_noise : sym
+    sym = sym === :μ₀ ? :prior_mean : sym
+    sym = sym === :P₀ ? :prior_precision_matrix : sym
+    return Base.getfield(ce, sym)
+end
+function Base.setproperty!(ce::LM, sym::Symbol, val)
+    sym = sym === :σ ? :observational_noise : sym
+    sym = sym === :μ₀ ? :prior_mean : sym
+    sym = sym === :P₀ ? :prior_precision_matrix : sym
+    return Base.setfield!(ce, sym, val)
+end
+
+
+
+
 # for fit:
 function MMI.reformat(::LaplaceRegressor, X, y)
     return (MLJBase.matrix(X) |> permutedims, (reshape(y, 1, :), nothing))
@@ -193,9 +211,9 @@ function MMI.fit(m::LaplaceModels, verbosity, X, y)
         subnetwork_indices=m.subnetwork_indices,
         hessian_structure=m.hessian_structure,
         backend=m.backend,
-        σ=m.σ,
-        μ₀=m.μ₀,
-        P₀=m.P₀,
+        σ=m.observational_noise,
+        μ₀=m.prior_mean,
+        P₀=m.prior_precision_matrix,
     )
 
     if typeof(m) == LaplaceClassifier
@@ -282,9 +300,9 @@ function MMI.update(m::LaplaceModels, verbosity, old_fitresult, old_cache, X, y)
                 subnetwork_indices=m.subnetwork_indices,
                 hessian_structure=m.hessian_structure,
                 backend=m.backend,
-                σ=m.σ,
-                μ₀=m.μ₀,
-                P₀=m.P₀,
+                σ=m.observational_noise,
+                μ₀=m.prior_mean,
+                P₀=m.prior_precision_matrix,
             )
             if typeof(m) == LaplaceClassifier
                 la.likelihood = :classification
@@ -315,9 +333,9 @@ function MMI.update(m::LaplaceModels, verbosity, old_fitresult, old_cache, X, y)
         :subnetwork_indices,
         :hessian_structure,
         :backend,
-        :σ,
-        :μ₀,
-        :P₀,
+        :observational_noise,
+        :prior_mean,
+        :prior_precision_matrix,
     )
         println(" updating only the laplace optimization part")
         old_la = old_fitresult[1]
@@ -329,9 +347,9 @@ function MMI.update(m::LaplaceModels, verbosity, old_fitresult, old_cache, X, y)
             subnetwork_indices=m.subnetwork_indices,
             hessian_structure=m.hessian_structure,
             backend=m.backend,
-            σ=m.σ,
-            μ₀=m.μ₀,
-            P₀=m.P₀,
+            σ=m.observational_noise,
+            μ₀=m.prior_mean,
+            P₀=m.prior_precision_matrix,
         )
         if typeof(m) == LaplaceClassifier
             la.likelihood = :classification
@@ -452,10 +470,10 @@ end
 
  # Returns
  A named tuple containing:
- - `μ`: The mean of the posterior distribution.
+ - `mean`: The mean of the posterior distribution.
  - `H`: The Hessian of the posterior distribution.
  - `P`: The precision matrix of the posterior distribution.
- - `Σ`: The covariance matrix of the posterior distribution.
+ - `cov_matrix`: The covariance matrix of the posterior distribution.
  - `n_data`: The number of data points.
  - `n_params`: The number of parameters.
  - `n_out`: The number of outputs.
@@ -466,10 +484,10 @@ function MMI.fitted_params(model::LaplaceModels, fitresult)
     la, decode = fitresult
     posterior = la.posterior
     return (
-        μ=posterior.μ,
+        mean=posterior.μ,
         H=posterior.H,
         P=posterior.P,
-        Σ=posterior.Σ,
+        cov_matrix=posterior.Σ,
         n_data=posterior.n_data,
         n_params=posterior.n_params,
         n_out=posterior.n_out,
@@ -517,7 +535,7 @@ function MMI.predict(m::LaplaceModels, fitresult, Xnew)
         means, variances = yhat
 
         # Create Normal distributions from the means and variances
-        return vec([Normal(μ, sqrt(σ)) for (μ, σ) in zip(means, variances)])
+        return vec([Normal(mean, sqrt(variance)) for (mean, variance) in zip(means, variances)])
 
     else
         predictions =
@@ -551,7 +569,7 @@ MMI.metadata_pkg(
 MLJBase.metadata_model(
     LaplaceClassifier;
     input_scitype=Union{
-        AbstractMatrix{<:Union{MLJBase.Finite,MLJBase.Continuous}}, # matrix with mixed types
+        AbstractMatrix{<:Union{MLJBase.Finite,MLJBase.Infinite}}, # matrix with mixed types
         MLJBase.Table(MLJBase.Finite, MLJBase.Continuous), # table with mixed types
     },
     target_scitype=AbstractArray{<:MLJBase.Finite}, # ordered factor or multiclass
@@ -562,8 +580,8 @@ MLJBase.metadata_model(
 MLJBase.metadata_model(
     LaplaceRegressor;
     input_scitype=Union{
-        AbstractMatrix{<:Union{MLJBase.Finite,MLJBase.Continuous}}, # matrix with mixed types
-        MLJBase.Table(MLJBase.Finite, MLJBase.Continuous), # table with mixed types
+        AbstractMatrix{<:Union{MLJBase.Finite, MLJBase.Infinite}}, # matrix with mixed types
+        MLJBase.Table(MLJBase.Finite, MLJBase.Infinite), # table with mixed types
     },
     target_scitype=AbstractArray{MLJBase.Continuous},
     supports_training_losses=true,
@@ -626,11 +644,11 @@ Train the machine using `fit!(mach, rows=...)`.
 
 - `backend::Symbol = :GGN::(_ in (:GGN, :EmpiricalFisher))`:                                        the backend to use, either `:GGN` or `:EmpiricalFisher`.
 
-- `σ::Float64 = 1.0`:                                                                               the standard deviation of the prior distribution.
+- `observational_noise (alias σ)::Float64 = 1.0`:                                                   the standard deviation of the prior distribution.
 
-- `μ₀::Float64 = 0.0`:                                                                              the mean of the prior distribution.
+- `prior_mean (alias μ₀)::Float64 = 0.0`:                                                           the mean of the prior distribution.
 
-- `P₀::Union{AbstractMatrix,UniformScaling,Nothing} = nothing`:                                     the covariance matrix of the prior distribution.
+- `prior_precision_matrix (alias P₀)::Union{AbstractMatrix,UniformScaling,Nothing} = nothing`:      the covariance matrix of the prior distribution.
 
 - `fit_prior_nsteps::Int = 100::(_ > 0) `:                                                          the number of steps used to fit the priors.
 
@@ -650,13 +668,13 @@ Train the machine using `fit!(mach, rows=...)`.
 
 The fields of `fitted_params(mach)` are:
 
- - `μ`: The mean of the posterior distribution.
+ - `mean`: The mean of the posterior distribution.
 
  - `H`: The Hessian of the posterior distribution.
 
  - `P`: The precision matrix of the posterior distribution.
 
- - `Σ`: The covariance matrix of the posterior distribution.
+ - `cov_matrix`: The covariance matrix of the posterior distribution.
 
  - `n_data`: The number of data points.
 
@@ -766,11 +784,11 @@ Train the machine using `fit!(mach, rows=...)`.
 
 - `backend::Symbol = :GGN::(_ in (:GGN, :EmpiricalFisher))`:                                        the backend to use, either `:GGN` or `:EmpiricalFisher`.
 
-- `σ::Float64 = 1.0`:                                                                               the standard deviation of the prior distribution.
+- `observational_noise (alias σ)::Float64 = 1.0`:                                                   the standard deviation of the prior distribution.
 
-- `μ₀::Float64 = 0.0`:                                                                              the mean of the prior distribution.
+- `prior_mean (alias μ₀)::Float64 = 0.0`:                                                           the mean of the prior distribution.
 
-- `P₀::Union{AbstractMatrix,UniformScaling,Nothing} = nothing`:                                     the covariance matrix of the prior distribution.
+- `prior_precision_matrix (alias P₀)::Union{AbstractMatrix,UniformScaling,Nothing} = nothing`:      the covariance matrix of the prior distribution.
 
 - `fit_prior_nsteps::Int = 100::(_ > 0) `:                                                          the number of steps used to fit the priors.
 
@@ -789,13 +807,13 @@ Train the machine using `fit!(mach, rows=...)`.
 
 The fields of `fitted_params(mach)` are:
 
- - `μ`: The mean of the posterior distribution.
+ - `mean`: The mean of the posterior distribution.
 
  - `H`: The Hessian of the posterior distribution.
 
  - `P`: The precision matrix of the posterior distribution.
 
- - `Σ`: The covariance matrix of the posterior distribution.
+ - `cov_matrix`: The covariance matrix of the posterior distribution.
 
  - `n_data`: The number of data points.