fixed predict so that it return a vector of distributions-> fixed eva…

…luate!
JuliaTrustworthyAI · Oct 18, 2024 · 80784bb · 80784bb
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commit 80784bb
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Showing 2 changed files with 17 additions and 9 deletions.
diff --git a/docs/Manifest.toml b/docs/Manifest.toml
@@ -2,7 +2,7 @@
 
 julia_version = "1.10.5"
 manifest_format = "2.0"
-project_hash = "0bd11d5fa58aad2714bf7893e520fc7c086ef3ca"
+project_hash = "616a9e89f5c520a58672ad91b5525001e0dadab3"
 
 [[deps.ANSIColoredPrinters]]
 git-tree-sha1 = "574baf8110975760d391c710b6341da1afa48d8c"
@@ -955,10 +955,10 @@ uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
 version = "1.3.1"
 
 [[deps.LaplaceRedux]]
-deps = ["ChainRulesCore", "Compat", "ComputationalResources", "Distributions", "Flux", "LinearAlgebra", "MLJBase", "MLJFlux", "MLJModelInterface", "MLUtils", "Optimisers", "ProgressMeter", "Random", "Statistics", "Tables", "Tullio", "Zygote"]
-git-tree-sha1 = "a84b72a27c93c72a6af5d22216eb81a419b1b97a"
+deps = ["CategoricalDistributions", "ChainRulesCore", "Compat", "ComputationalResources", "Distributions", "Flux", "LinearAlgebra", "MLJBase", "MLJFlux", "MLJModelInterface", "MLUtils", "Optimisers", "ProgressMeter", "Random", "Statistics", "Tables", "Tullio", "Zygote"]
+path = "C:\\Users\\Pasqu\\Documents\\julia_projects\\LaplaceRedux.jl"
 uuid = "c52c1a26-f7c5-402b-80be-ba1e638ad478"
-version = "1.0.2"
+version = "1.1.1"
 
 [[deps.Latexify]]
 deps = ["Format", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Requires"]

diff --git a/test/direct_mlj_interface.jl b/test/direct_mlj_interface.jl
@@ -18,11 +18,12 @@ cv = CV(; nfolds=3)
     model = LaplaceRegressor(model=flux_model,epochs=50)
 
     X, y = make_regression(100, 4; noise=0.5, sparse=0.2, outliers=0.1)
+    #train, test = partition(eachindex(y), 0.7); # 70:30 split
     mach = machine(model, X, y) #|> MLJBase.fit! #|> (fitresult,cache,report)
-    MLJBase.fit!(mach,verbosity=1)
-    Xnew, _ = make_regression(3, 4; rng=123)
-    yhat = MLJBase.predict(mach, Xnew) # probabilistic predictions
-    MLJBase.predict_mode(mach, Xnew)   # point predictions
+    MLJBase.fit!(mach, verbosity=1)
+    #Xnew, ynew = make_regression(3, 4; rng=123)
+    yhat = MLJBase.predict(mach, X) # probabilistic predictions
+    MLJBase.predict_mode(mach, X)   # point predictions
     MLJBase.fitted_params(mach)   #fitted params function 
     MLJBase.training_losses(mach) #training loss history
     model.epochs= 100 #changing number of epochs
@@ -31,7 +32,14 @@ cv = CV(; nfolds=3)
     MLJBase.fit!(mach) #testing update function
     model.fit_prior_nsteps = 200 #changing LaplaceRedux fit steps
     MLJBase.fit!(mach) #testing update function (the laplace part)
-    # evaluate!(mach, resampling=cv, measure=l2, verbosity=0)
+    yhat = MLJBase.predict(mach, X) # probabilistic predictions
+    println( typeof(yhat) )
+    println( size(yhat) )
+    println( typeof(y) )
+    println( size(y) )
+
+    evaluate!(mach, resampling=cv, measure=log_loss, verbosity=0)
+
 end