training_classifiers/src_bash/nn_uncertainty.bash

#!/bin/bash
#SBATCH --job-name=refit-seed-array
#SBATCH --partition=dgx-b200
#SBATCH --gpus=1
#SBATCH --cpus-per-task=10
#SBATCH --mem=100G
#SBATCH --time=12:00:00
#SBATCH --output=%x_%A_%a.out
#SBATCH --array=0-4          # 5 seeds → indices 0..4

HOME_LOC=~/
SCRIPT_LOC=$HOME_LOC/PeptiVerse/training_classifiers
DATA_LOC=$HOME_LOC/PeptiVerse/training_data_cleaned
# ── Configure per submission ──────────────────────────────────────────
OBJECTIVE='permeability_pampa'         # nf / solubility / hemolysis / permeability_penetrance/ permeability_pampa / permeability_caco2 
WT='chemberta'                   # wt / smiles / chemberta / peptideclm
MODEL_TYPE='mlp'              # mlp / cnn / transformer
DATA_FILE="hemo_${WT}_with_embeddings_unpooled" # nf / sol/ hemo / perm / pampa/ caco2
# Points to the directory where Optuna already saved best_model.pt
BASE_OUT_DIR="${SCRIPT_LOC}/${OBJECTIVE}/${MODEL_TYPE}_${WT}"
DATASET_PATH="${DATA_LOC}/permeability_${WT}/${DATA_FILE}"
# ────────────────────────────────────────────────────────────────────────────

SEEDS=(1986 42 0 123 12345)
SEED=${SEEDS[$SLURM_ARRAY_TASK_ID]}

LOG_LOC=$SCRIPT_LOC/src_bash/logs
mkdir -p $LOG_LOC
DATE=$(date +%m_%d)

cd $SCRIPT_LOC

echo "Running seed=$SEED  model=$MODEL_TYPE  objective=$OBJECTIVE  wt=$WT"

START_TIME=$(date +%s%N)

python -u refit_nn_seed.py \
  --dataset_path "${DATASET_PATH}" \
  --base_out_dir  "${BASE_OUT_DIR}" \
  --model         "${MODEL_TYPE}" \
  --seed          "${SEED}" \
  > "${LOG_LOC}/${DATE}_refit_${MODEL_TYPE}_${OBJECTIVE}_${WT}_seed${SEED}.log" 2>&1

END_TIME=$(date +%s%N)
ELAPSED_S=$(( (END_TIME - START_TIME) / 1000000000 ))

echo "Seed $SEED done at $(date) — wall clock: ${ELAPSED_S}s"
echo "{\"model\": \"${MODEL_TYPE}\", \"objective\": \"${OBJECTIVE}\", \"wt\": \"${WT}\", \"seed\": ${SEED}, \"wall_s\": ${ELAPSED_S}}" \
  >> "${LOG_LOC}/${DATE}_wall_clock_refit.jsonl"