trying with decay

experiments/jobscripts/tuning/generators/california_housing.sh

@@ -11,4 +11,4 @@
 
 module load 2023r1 openmpi
 
-srun julia --project=experiments experiments/run_experiments.jl -- data=california_housing output_path=results mpi grid_search n_individuals=25 > experiments/grid_search_california_housing.log
+srun julia --project=experiments experiments/run_experiments.jl -- data=california_housing output_path=results mpi grid_search n_individuals=25 store_ce > experiments/grid_search_california_housing.log

experiments/jobscripts/tuning/generators/tabular.sh

@@ -11,4 +11,4 @@
 
 module load 2023r1 openmpi
 
-srun julia --project=experiments experiments/run_experiments.jl -- data=gmsc,german_credit output_path=results mpi grid_search n_individuals=25 > experiments/grid_search_tabular.log
+srun julia --project=experiments experiments/run_experiments.jl -- data=gmsc,german_credit output_path=results mpi grid_search n_individuals=25 store_ce > experiments/grid_search_tabular.log

experiments/setup_env.jl

@@ -167,16 +167,13 @@ DEFAULT_GENERATOR_TUNING = (
 "Generator tuning parameters for large datasets."
 DEFAULT_GENERATOR_TUNING_LARGE = (
     Λ=[
+        [0.1, 0.1, 0.01],
+        [0.1, 0.1, 0.05],
         [0.1, 0.1, 0.1],
-        [0.1, 0.2, 0.2],
-        [0.1, 0.3, 0.3],
     ],
-    reg_strength=[0.0, 0.1, 0.5, 1.0],
+    reg_strength=[0.0, 0.1],
     opt=[
-        Flux.Optimise.Descent(0.05),
         Flux.Optimise.Descent(0.01),
-        Flux.Optimiser(ExpDecay(1, 0.1, 5, 1e-4), Descent(0.05)),
-        Flux.Optimiser(ExpDecay(1, 0.1, 5, 1e-4), Descent(0.01))
     ],
 )
 

src/penalties.jl

@@ -48,6 +48,7 @@ function energy_delta(
     nmin::Int=25,
     return_conditionals=false,
     reg_strength=0.1,
+    decay::Real=0.1,
     kwargs...
 )
 
@@ -63,6 +64,9 @@ function energy_delta(
     reg_loss = norm(E(xproposed))^2
     reg_loss = reduce((x, y) -> x + y, reg_loss) / length(reg_loss)                  # aggregate over samples
 
+    # Decay:
+    λ = exp(-decay * ce.search[:iteration_count])
+
     return gen_loss + reg_strength * reg_loss
 
 end