Merge branch '85-overshooting' of https://github.com/pat-alt/ECCCo.jl into 85-overshooting

experiments/daic/testing/lin_sep.sh

+#!/bin/bash
+
+#SBATCH --job-name="Grid-search Linearly Separable (ECCCo)"
+#SBATCH --time=01:00:00
+#SBATCH --ntasks=100
+#SBATCH --cpus-per-task=1
+#SBATCH --partition=general
+#SBATCH --mem-per-cpu=4GB
+#SBATCH --mail-type=END     # Set mail type to 'END' to receive a mail when the job finishes. 
+
+module use /opt/insy/modulefiles          # Use DAIC INSY software collection
+module load openmpi
+
+srun julia --project=experiments experiments/run_experiments.jl -- data=linearly_separable output_path=results mpi grid_search n_individuals=10 > experiments/grid_search_linearly_separable.log

experiments/daic/testing/lin_sep_4each.sh

+#!/bin/bash
+
+#SBATCH --job-name="Grid-search Linearly Separable (ECCCo)"
+#SBATCH --time=01:00:00
+#SBATCH --ntasks=100
+#SBATCH --cpus-per-task=1
+#SBATCH --partition=general
+#SBATCH --mem-per-cpu=4GB
+#SBATCH --mail-type=END     # Set mail type to 'END' to receive a mail when the job finishes. 
+
+module use /opt/insy/modulefiles          # Use DAIC INSY software collection
+module load openmpi
+
+srun julia --project=experiments experiments/run_experiments.jl -- data=linearly_separable output_path=results mpi grid_search n_individuals=10 n_each=4 > experiments/grid_search_linearly_separable.log

experiments/daic/testing/lin_sep_threaded.sh

+#!/bin/bash
+
+#SBATCH --job-name="Grid-search Linearly Separable (ECCCo)"
+#SBATCH --time=01:00:00
+#SBATCH --ntasks=10
+#SBATCH --cpus-per-task=10
+#SBATCH --partition=general
+#SBATCH --mem-per-cpu=4GB
+#SBATCH --mail-type=END     # Set mail type to 'END' to receive a mail when the job finishes. 
+
+module use /opt/insy/modulefiles          # Use DAIC INSY software collection
+module load openmpi
+
+srun julia --project=experiments experiments/run_experiments.jl -- data=linearly_separable output_path=results mpi grid_search n_individuals=10 threaded > experiments/grid_search_linearly_separable.log

experiments/daic/tuning/generators/linearly_separable.sh

+#!/bin/bash
+
+#SBATCH --job-name="Grid-search Linearly Separable (ECCCo)"
+#SBATCH --time=02:00:00
+#SBATCH --ntasks=1000
+#SBATCH --cpus-per-task=1
+#SBATCH --partition=general
+#SBATCH --mem-per-cpu=4GB
+#SBATCH --mail-type=END     # Set mail type to 'END' to receive a mail when the job finishes. 
+
+module use /opt/insy/modulefiles          # Use DAIC INSY software collection
+module load openmpi
+
+srun julia --project=experiments experiments/run_experiments.jl -- data=linearly_separable output_path=results mpi grid_search > experiments/grid_search_linearly_separable.log

experiments/experiment.jl

@@ -139,7 +139,7 @@ function run_experiment(
             joinpath(exper.output_path, "$(exper.save_name)_bmk.jls"),
             outcome.bmk,
         )
-        meta(outcome; save_output = true)
+        all_meta(outcome; save_output = true)
     end
 
     # Final barrier:

experiments/post_processing/meta_data.jl

 """
-    meta(exper::Experiment)
+    all_meta(exper::Experiment)
 
 Extract and save meta data about the experiment.
 """
-function meta(
+function all_meta(
     outcome::ExperimentOutcome;
     save_output::Bool = false,
     params_path::Union{Nothing,String} = nothing,

experiments/save_best.jl

@@ -23,7 +23,7 @@ function save_best(outcomes_file_path::String)
         joinpath(output_path, "$(exper.save_name)_models.jls"),
         outcome.model_dict,
     )
-    meta(outcome; save_output = true, params_path = params_path)
+    all_meta(outcome; save_output = true, params_path = params_path)
 end
 
 function bmk2csv(dataname::String)

experiments/setup_env.jl

@@ -182,12 +182,13 @@ const GRID_SEARCH = "grid_search" ∈ ARGS
 
 "Generator tuning parameters."
 DEFAULT_GENERATOR_TUNING = (
-    Λ = [[0.1, 0.1, 0.05], [0.1, 0.1, 0.1], [0.1, 0.1, 0.5], [0.1, 0.1, 1.0]],
-    reg_strength = [0.0, 0.1, 0.25, 0.5, 1.0],
+    Λ=[[0.1, 0.1, 0.1], [0.1, 0.1, 0.5],],
+    reg_strength = [0.0, 0.1, 1.0],
     opt = [
         Descent(0.01),
         Descent(0.05),
-        Descent(0.1),
+        Optimiser(ClipValue(0.01), Descent(0.01)),
+        Optimiser(ClipValue(0.05), Descent(0.05)),
     ],
     decay = [(0.0, 1), (0.01, 1), (0.1, 1)],
 )