#!/usr/bin/env bash
# Frozen C2 causal diagnosis after validation-only task-0 development.
#
# Development selection (task seed 0, model seeds 0--2, d4 only):
#   * K1 estimator/LR screen selected layerwise eta=0.03.
#   * At eta=0.03, K4 reached 95.183% and K16 reached 97.017% mean
#     validation accuracy.  The predeclared rule selected the smaller K only
#     when it was within one point of the best, so K16 is frozen here.
#
# This independent panel uses the same untouched task/data seeds 3--5 and
# student seeds 0--4 as the earlier context-vectorizer panel.  It evaluates
# validation once after training; the independent synthetic test sets remain
# untouched.  The direct targets are deliberately query-expensive: this is a
# causal diagnostic and unamortized baseline, not a proposed efficient method.
# Usage: c2_nodepert_validation.sh <gpu> "<model seeds>"
set -eu

cd "$(dirname "$0")/.."
GPU="${1:?GPU index required}"
MODEL_SEEDS="${2:-0 1 2 3 4}"
PYTHON="${PYTHON:-/home/yurenh2/miniconda3/envs/ep_pascal/bin/python3}"

for task_seed in 3 4 5; do
  for model_seed in $MODEL_SEEDS; do
    for depth in 1 4; do
      tag="c2_nodepert_val_v1_tent_l2_w8_nodepert_d${depth}_t${task_seed}_s${model_seed}"
      out="results/${tag}.json"
      if [ -s "$out" ]; then
        echo "[$tag] exists; skipping"
        continue
      fi
      lesion=0
      if [ "$depth" -eq 4 ]; then
        lesion=0.3333333333333333
      fi
      CUDA_VISIBLE_DEVICES="$GPU" "$PYTHON" experiments/run.py \
        --mode nodepert --dataset tentmap --device cuda \
        --depth "$depth" --width 8 --act relu --residual 1 \
        --residual_lesion_fraction "$lesion" \
        --epochs 80 --batch_size 256 --eta 0.03 --momentum 0.9 \
        --pert_sigma 0.01 --pert_every 1 --pert_ndirs 16 \
        --pert_mode layerwise \
        --traffic_mode none --nuis_rho 0 \
        --use_residual 0 --learn_A 0 --learn_P 0 --p_neutral 1 \
        --task_train_examples 10000 --task_test_examples 5000 \
        --task_levels 2 --task_n_in 1 --task_seed "$task_seed" \
        --val_examples 2000 --split_seed 2027 --eval_split validation --eval_every 0 \
        --diagnostics alignment --diagnostics_schedule final --probe_bs 512 \
        --seed "$model_seed" --log_every 100000 --tag "$tag"
    done
  done
done