Merge pull request #126 from JuliaTrustworthyAI/direct_mlj_interface

Direct mlj interface

.github/workflows/CI.yml

@@ -22,6 +22,7 @@ jobs:
         version:
           - '1.9'
           - '1.10'
+          - '1'
         os:
           - ubuntu-latest
           - windows-latest

Project.toml

@@ -4,11 +4,14 @@ authors = ["Patrick Altmeyer"]
 version = "1.1.1"
 
 [deps]
+CategoricalDistributions = "af321ab8-2d2e-40a6-b165-3d674595d28e"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
+MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -19,20 +22,23 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 Aqua = "0.8"
+CategoricalDistributions = "0.1.15"
 ChainRulesCore = "1.23.0"
 Compat = "4.7.0"
 Distributions = "0.25.109"
 Flux = "0.12, 0.13, 0.14"
 LinearAlgebra = "1.7, 1.10"
+MLJBase = "1"
+MLJModelInterface = "1.8.0"
 MLUtils = "0.4"
 Optimisers = "0.2, 0.3"
 Random = "1.9, 1.10"
 Statistics = "1"
 Tables = "1.10.1"
-Test = "1.9, 1.10"
+Test = "1"
 Tullio = "0.3.5"
 Zygote = "0.6"
-julia = "1.9, 1.10"
+julia = "1.10"
 
 [extras]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"

dev/issues/predict_slow.jl

@@ -4,16 +4,16 @@ using Optimisers
 
 X = MLJBase.table(rand(Float32, 100, 3));
 y = coerce(rand("abc", 100), Multiclass);
-model = LaplaceClassification(optimiser=Optimisers.Adam(0.1), epochs=100);
+model = LaplaceClassification(; optimiser=Optimisers.Adam(0.1), epochs=100);
 fitresult, _, _ = MLJBase.fit(model, 2, X, y);
 la = fitresult[1];
 Xmat = matrix(X) |> permutedims;
 
 # Single test sample:
-Xtest = Xmat[:,1:10];
+Xtest = Xmat[:, 1:10];
 Xtest_tab = MLJBase.table(Xtest');
 MLJBase.predict(model, fitresult, Xtest_tab);       # warm up
 LaplaceRedux.predict(la, Xmat);                     # warm up
 @time MLJBase.predict(model, fitresult, Xtest_tab);
 @time LaplaceRedux.predict(la, Xtest);
-@time glm_predictive_distribution(la, Xtest);
+@time glm_predictive_distribution(la, Xtest);