GRN/run_gene_noise.sh

#!/bin/bash
#PJM -L rscgrp=b-batch
#PJM -L gpu=1
#PJM -L elapse=48:00:00
#PJM -N scdfm_gene_noise
#PJM -j
#PJM -o /home/hp250092/ku50001222/qian/aivc/lfj/GRN/baseline/logs/gene_noise_%j.out

module load cuda/12.2.2
module load cudnn/8.9.7
module load gcc-toolset/12

source /home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/activate

cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM

export PYTHONPATH=./
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256

# === scDFM with gene-specific noise scaling ===
# Identical to baseline run.sh except --gene_noise_scale is enabled.
# Per-gene noise is scaled by delta_std / median(delta_std), clamped to [0.1, 3.0].
python src/script/run.py \
    --batch_size=48 \
    --devices='0' \
    --model_type=origin \
    --lr=5e-5 \
    --steps=200000 \
    --data_name=norman \
    --d_model=128 \
    --eta_min=1e-6 \
    --fusion_method=differential_perceiver \
    --infer_top_gene=1000 \
    --n_top_genes=5000 \
    --result_path=/home/hp250092/ku50001222/qian/aivc/lfj/GRN/baseline \
    --perturbation_function=crisper \
    --noise_type=Gaussian \
    --mode=predict_y \
    --gamma=0.5 \
    --split_method=additive \
    --use_mmd_loss \
    --fold=1 \
    --topk=30 \
    --use_negative_edge \
    --gene_noise_scale