also changed for regression

.github/workflows/CI.yml

@@ -20,7 +20,6 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.9'
           - '1.10'
         os:
           - ubuntu-latest
@@ -29,10 +28,10 @@ jobs:
           - x64
         include:
           - os: windows-latest
-            version: '1.9'
+            version: '1'
             arch: x64
           - os: macOS-latest
-            version: '1.9'
+            version: '1'
             arch: x64
     steps:
       - uses: actions/checkout@v2

Project.toml

@@ -1,7 +1,7 @@
 name = "LaplaceRedux"
 uuid = "c52c1a26-f7c5-402b-80be-ba1e638ad478"
 authors = ["Patrick Altmeyer"]
-version = "1.1.0"
+version = "1.1.1"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
@@ -36,13 +36,13 @@ MLJModelInterface = "1.8.0"
 MLUtils = "0.4"
 Optimisers = "0.2, 0.3"
 ProgressMeter = "1.7.2"
-Random = "1.9, 1.10"
+Random = "1.10"
 Statistics = "1"
 Tables = "1.10.1"
-Test = "1.9, 1.10"
+Test = "1.10"
 Tullio = "0.3.5"
 Zygote = "0.6"
-julia = "1.9, 1.10"
+julia = "1.10"
 
 [extras]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"

dev/Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.10.5"
 manifest_format = "2.0"
-project_hash = "7c4391b00ad44ccd083aa89cf817ea4c83643a8f"
+project_hash = "6224e923196c94cc69f23bf59d6ea60b7ea2c919"
 
 [[deps.AbstractFFTs]]
 deps = ["LinearAlgebra"]
@@ -717,7 +717,7 @@ version = "1.3.1"
 deps = ["ChainRulesCore", "Compat", "ComputationalResources", "Distributions", "Flux", "LinearAlgebra", "MLJBase", "MLJFlux", "MLJModelInterface", "MLUtils", "Optimisers", "ProgressMeter", "Random", "Statistics", "Tables", "Tullio", "Zygote"]
 path = ".."
 uuid = "c52c1a26-f7c5-402b-80be-ba1e638ad478"
-version = "1.1.0"
+version = "1.1.1"
 
 [[deps.Latexify]]
 deps = ["Format", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Requires"]

dev/Project.toml

@@ -4,6 +4,7 @@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 LaplaceRedux = "c52c1a26-f7c5-402b-80be-ba1e638ad478"
 Luxor = "ae8d54c2-7ccd-5906-9d76-62fc9837b5bc"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
+Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 PlotThemes = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

dev/issues/predict_slow.jl

@@ -1,10 +1,11 @@
 using LaplaceRedux
 using MLJBase
+using Optimisers
 
 X = MLJBase.table(rand(Float32, 100, 3));
 y = coerce(rand("abc", 100), Multiclass);
-model = LaplaceClassification();
-fitresult, _, _ = MLJBase.fit(model, 0, X, y);
+model = LaplaceClassification(optimiser=Optimisers.Adam(0.1), epochs=100);
+fitresult, _, _ = MLJBase.fit(model, 2, X, y);
 la = fitresult[1];
 Xmat = matrix(X) |> permutedims;
 

src/mlj_flux.jl

@@ -247,21 +247,17 @@ function MLJFlux.train(
         push!(history, current_loss)
     end
 
-    if !isa(chain, AbstractLaplace)
-        la = LaplaceRedux.Laplace(
-            chain;
-            likelihood=:regression,
-            subset_of_weights=model.subset_of_weights,
-            subnetwork_indices=model.subnetwork_indices,
-            hessian_structure=model.hessian_structure,
-            backend=model.backend,
-            σ=model.σ,
-            μ₀=model.μ₀,
-            P₀=model.P₀,
-        )
-    else
-        la = chain
-    end
+    la = LaplaceRedux.Laplace(
+        chain;
+        likelihood=:regression,
+        subset_of_weights=model.subset_of_weights,
+        subnetwork_indices=model.subnetwork_indices,
+        hessian_structure=model.hessian_structure,
+        backend=model.backend,
+        σ=model.σ,
+        μ₀=model.μ₀,
+        P₀=model.P₀,
+    )
 
     # fit the Laplace model:
     LaplaceRedux.fit!(la, zip(X, y))
@@ -285,12 +281,9 @@ Predict the output for new input data using a Laplace regression model.
 
 """
 function MLJFlux.predict(model::LaplaceRegression, fitresult, Xnew)
-    Xnew = MLJBase.matrix(Xnew)
+    Xnew = MLJBase.matrix(Xnew) |> permutedims
     la = fitresult[1]
-    #convert in a vector of vectors because MLJ ask to do so
-    X_vec = collect(eachrow(Xnew))
-    # Predict using Laplace and collect the predictions
-    yhat = [map(x -> LaplaceRedux.predict(la, x; ret_distr=model.ret_distr), X_vec)...]
+    yhat = LaplaceRedux.predict(la, Xnew; ret_distr=model.ret_distr)
     return yhat
 end
 
@@ -416,21 +409,18 @@ function MLJFlux.train(
         push!(history, current_loss)
     end
 
-    if !isa(chain, AbstractLaplace)
-        la = LaplaceRedux.Laplace(
-            chain;
-            likelihood=:classification,
-            subset_of_weights=model.subset_of_weights,
-            subnetwork_indices=model.subnetwork_indices,
-            hessian_structure=model.hessian_structure,
-            backend=model.backend,
-            σ=model.σ,
-            μ₀=model.μ₀,
-            P₀=model.P₀,
-        )
-    else
-        la = chain
-    end
+    la = LaplaceRedux.Laplace(
+        chain;
+        likelihood=:classification,
+        subset_of_weights=model.subset_of_weights,
+        subnetwork_indices=model.subnetwork_indices,
+        hessian_structure=model.hessian_structure,
+        backend=model.backend,
+        σ=model.σ,
+        μ₀=model.μ₀,
+        P₀=model.P₀,
+    )
+
     # fit the Laplace model:
     LaplaceRedux.fit!(la, zip(X, y))
     optimize_prior!(la; verbose=verbose_laplace, n_steps=model.fit_prior_nsteps)