Original RLDAS as a baseline

.pre-commit-config.yaml

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+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.15.0
+  hooks:
+    - id: ruff-format

.python-version

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+3.11

CDB_study.slurm

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+#!/bin/bash
+#SBATCH --job-name=rl_das_experiment
+#SBATCH --output=logs/experiment_%A_%a.out
+#SBATCH --error=logs/experiment_%A_%a.err
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=1
+#SBATCH --mem=32G
+#SBATCH --time=48:00:00
+#SBATCH --partition=plgrid-gpu-a100
+#SBATCH --array=0-9           # 10 tasks total
+
+CDB_VAL=${1:-1.5}
+
+if [ "$#" -gt 0 ]; then
+    shift
+fi
+
+if [ "$#" -eq 0 ]; then
+    PORTFOLIO=('JDE21' 'MADDE' 'NL_SHADE_RSP')
+else
+    PORTFOLIO=("$@")
+fi
+PORTFOLIO_STR=$(IFS="_"; echo "${PORTFOLIO[*]}")
+
+
+# CONFIGURATION
+ENV_PATH="$SCRATCH/DynamicAlgorithmSelection/.venv/bin/activate"
+source "$ENV_PATH"
+mkdir -p logs
+
+# Array of Dimensions
+DIMS=(2 3 5 10)
+
+# 1. Dimension-specific CV-LOIO (Indices 0-3)
+if [[ $SLURM_ARRAY_TASK_ID -ge 0 && $SLURM_ARRAY_TASK_ID -le 3 ]]; then
+    MODE="CV-LOIO"
+    DIM=${DIMS[$SLURM_ARRAY_TASK_ID]}
+    echo "Running Mode: $MODE | Dimension: $DIM"
+
+    python3 dynamicalgorithmselection/main.py ${PORTFOLIO_STR}_PG_${MODE}_${CDB_VAL}_${DIM} \
+      -p "${PORTFOLIO[@]}" -r ELA --mode $MODE --dimensionality $DIM \
+      --cdb $CDB_VAL --n_epochs 3 --agent policy-gradient
+
+# 2. Dimension-specific CV-LOPO (Indices 4-7)
+elif [[ $SLURM_ARRAY_TASK_ID -ge 4 && $SLURM_ARRAY_TASK_ID -le 7 ]]; then
+    MODE="CV-LOPO"
+    DIM=${DIMS[$((SLURM_ARRAY_TASK_ID - 4))]}
+    echo "Running Mode: $MODE | Dimension: $DIM"
+
+    python3 dynamicalgorithmselection/main.py ${PORTFOLIO_STR}_PG_${MODE}_${CDB_VAL}_${DIM} \
+      -p "${PORTFOLIO[@]}" -r ELA --mode $MODE --dimensionality $DIM \
+      --cdb $CDB_VAL --n_epochs 3 --agent policy-gradient
+
+# 3. Multidimensional CV-LOIO (Index 8)
+elif [[ $SLURM_ARRAY_TASK_ID -eq 8 ]]; then
+    MODE="CV-LOIO"
+    echo "Running Mode: $MODE | Multidimensional PG"
+    python3 dynamicalgorithmselection/main.py ${PORTFOLIO_STR}_PG_MULTIDIMENSIONAL_${MODE}_${CDB_VAL} \
+      -p "${PORTFOLIO[@]}" -r ELA --mode $MODE --cdb $CDB_VAL --agent policy-gradient
+
+# 4. Multidimensional CV-LOPO (Index 9)
+elif [[ $SLURM_ARRAY_TASK_ID -eq 9 ]]; then
+    MODE="CV-LOPO"
+    echo "Running Mode: $MODE | Multidimensional PG"
+    python3 dynamicalgorithmselection/main.py ${PORTFOLIO_STR}_PG_MULTIDIMENSIONAL_${MODE}_${CDB_VAL} \
+      -p "${PORTFOLIO[@]}" -r ELA --mode $MODE --cdb $CDB_VAL --agent policy-gradient
+fi