scripts/make_tables.py

"""Generate auto-populated LaTeX tables for the paper.

Emits two supplementary tables from whatever data currently exists on disk:

    table_ablation.tex   — Silver-bullet dataset-size ablation numerics
                           (companion to fig_paper_6_ablation.pdf).
    table_baselines.tex  — Sampling-strategy baseline comparison (silver_bullet
                           vs failed_only vs random; companion to fig_paper_7).
    table_interventions.tex — Prompt-prefix vs QLoRA comparison (companion to
                           fig_paper_10), if prefix runs have completed.

Each file is safe to \\input{} from paper/main.tex; if the underlying
experiment hasn't run yet, the script writes a minimal placeholder table
with a \\textit{(not yet computed)} note so LaTeX still compiles.
"""

import argparse
import glob
import json
import os
import re
import sys
from typing import List, Optional, Tuple

import numpy as np

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))


# ---------------------------------------------------------------------------
# Shared helpers
# ---------------------------------------------------------------------------

def _bootstrap_acc(jsonl_path: str, n_boot: int = 5000) -> Optional[Tuple[float, float, float]]:
    if not os.path.exists(jsonl_path):
        return None
    v = []
    with open(jsonl_path) as f:
        for line in f:
            t = json.loads(line)
            v.append(1.0 if t.get("is_correct_final") else 0.0)
    if not v:
        return None
    v = np.array(v)
    rng = np.random.default_rng(0)
    s = np.empty(n_boot)
    for i in range(n_boot):
        idx = rng.integers(0, len(v), size=len(v))
        s[i] = v[idx].mean()
    return float(v.mean()), float(np.percentile(s, 2.5)), float(np.percentile(s, 97.5))


def _paired_p(base_out, rest_out, n_boot=5000) -> Optional[float]:
    common = sorted(set(base_out) & set(rest_out))
    if not common:
        return None
    b = np.array([base_out[k] for k in common])
    r = np.array([rest_out[k] for k in common])
    rng = np.random.default_rng(0)
    deltas = np.empty(n_boot)
    for i in range(n_boot):
        idx = rng.integers(0, len(common), size=len(common))
        deltas[i] = r[idx].mean() - b[idx].mean()
    return float(2 * min((deltas <= 0).mean(), (deltas >= 0).mean()))


def _load_outcomes(jsonl_path: str):
    if not os.path.exists(jsonl_path):
        return None
    out = {}
    with open(jsonl_path) as f:
        for line in f:
            t = json.loads(line)
            out[t.get("problem_id")] = 1.0 if t.get("is_correct_final") else 0.0
    return out


def _stars(p: Optional[float]) -> str:
    if p is None:
        return ""
    if p < 0.001: return "$^{***}$"
    if p < 0.01:  return "$^{**}$"
    if p < 0.05:  return "$^{*}$"
    return ""


def _placeholder(label: str, caption: str, note: str) -> str:
    return (
        "\\begin{table}[t]\n\\centering\\small\n"
        f"\\caption{{{caption}}}\n\\label{{{label}}}\n"
        "\\begin{tabular}{l}\n\\toprule\n"
        f"\\textit{{{note}}} \\\\\n"
        "\\bottomrule\n\\end{tabular}\n\\end{table}\n"
    )


# ---------------------------------------------------------------------------
# Table: dataset-size ablation
# ---------------------------------------------------------------------------

def table_ablation(ablation_root: str, benchmark: str, baseline_acc: Optional[float],
                   output: str) -> None:
    rows = []
    if os.path.isdir(ablation_root):
        for entry in sorted(os.listdir(ablation_root)):
            m = re.match(r"n(\d+)$", entry)
            if not m:
                continue
            N = int(m.group(1))
            diag = os.path.join(ablation_root, entry, "diagnosis", f"{benchmark}_run0.jsonl")
            ci = _bootstrap_acc(diag)
            if ci is None:
                continue
            rows.append((N, ci))

    if not rows:
        with open(output, "w") as f:
            f.write(_placeholder("tab:ablation",
                                 "Silver-bullet dataset-size ablation on primary cell.",
                                 "(not yet computed — run \\texttt{bash run\\_ablation.sh})"))
        print(f"  placeholder written: {output}")
        return

    rows.sort()
    with open(output, "w") as f:
        f.write("\\begin{table}[t]\n\\centering\\small\n")
        f.write("\\caption{Dataset-size ablation. Accuracy (\\%) of the restored "
                "model on MATH-500 under the primary configuration "
                "(\\texttt{qwen25-7b} / GPTQ w4) as the silver-bullet dataset size "
                "$N$ varies. 95\\% bootstrap CIs in brackets. $\\Delta$ is "
                "restored $-$ quantized baseline.}\n\\label{tab:ablation}\n")
        f.write("\\begin{tabular}{@{}rccc@{}}\n\\toprule\n")
        f.write("$N$ & Acc (\\%) & 95\\% CI & $\\Delta$ vs.\\ baseline (pp) \\\\\n\\midrule\n")
        for N, (acc, lo, hi) in rows:
            delta = (acc - baseline_acc) * 100 if baseline_acc is not None else None
            delta_str = "--" if delta is None else f"{delta:+.1f}"
            f.write(f"{N} & {acc*100:.1f} & [{lo*100:.1f}, {hi*100:.1f}] & {delta_str} \\\\\n")
        f.write("\\bottomrule\n\\end{tabular}\n\\end{table}\n")
    print(f"  wrote: {output}")


# ---------------------------------------------------------------------------
# Table: sampling-strategy baseline comparison
# ---------------------------------------------------------------------------

def table_baselines(baseline_root: str, model: str, quant: str, benchmark: str,
                    baseline_acc: Optional[float], output: str) -> None:
    strategies = ["silver_bullet", "failed_only", "random"]
    rows = []
    if os.path.isdir(baseline_root):
        base_out = _load_outcomes(os.path.join(
            "results", "diagnosis", quant, model, f"{benchmark}_run0.jsonl"))
        for strat in strategies:
            diag = os.path.join(baseline_root, strat, "diagnosis", f"{benchmark}_run0.jsonl")
            ci = _bootstrap_acc(diag)
            if ci is None:
                continue
            rest_out = _load_outcomes(diag)
            p = _paired_p(base_out, rest_out) if base_out and rest_out else None
            rows.append((strat, ci, p))

    if not rows:
        with open(output, "w") as f:
            f.write(_placeholder("tab:baselines",
                                 "Sampling-strategy baseline comparison.",
                                 "(not yet computed — run \\texttt{bash run\\_baselines.sh})"))
        print(f"  placeholder written: {output}")
        return

    pretty_strat = {
        "silver_bullet": "\\textbf{Silver bullet} (ours)",
        "failed_only":   "Failed only (no type balancing)",
        "random":        "Random (no diagnosis)",
    }
    with open(output, "w") as f:
        f.write("\\begin{table}[t]\n\\centering\\small\n")
        f.write("\\caption{Sampling-strategy baselines. All three adapters are "
                "trained with identical QLoRA hyperparameters on the same "
                "underlying problem set; the only difference is which problems "
                "are drawn. Accuracy in \\%; 95\\% bootstrap CI in brackets; "
                "$p$-value from paired bootstrap against the quantized baseline.}\n"
                "\\label{tab:baselines}\n")
        f.write("\\begin{tabular}{@{}lccc@{}}\n\\toprule\n")
        f.write("Sampling strategy & Acc (\\%) & 95\\% CI & $p$ \\\\\n\\midrule\n")
        for strat, (acc, lo, hi), p in rows:
            p_str = "--" if p is None else f"{p:.3f}{_stars(p)}"
            f.write(f"{pretty_strat.get(strat, strat)} & {acc*100:.1f} "
                    f"& [{lo*100:.1f}, {hi*100:.1f}] & {p_str} \\\\\n")
        if baseline_acc is not None:
            f.write("\\midrule\n")
            f.write(f"\\textit{{Quantized baseline (no restoration)}} "
                    f"& {baseline_acc*100:.1f} & -- & -- \\\\\n")
        f.write("\\bottomrule\n\\end{tabular}\n\\end{table}\n")
    print(f"  wrote: {output}")


# ---------------------------------------------------------------------------
# Table: intervention comparison (prompt-prefix vs QLoRA)
# ---------------------------------------------------------------------------

def table_interventions(prefix_root: str, model: str, quant: str, benchmark: str,
                        baseline_acc: Optional[float], metrics_dir: str,
                        output: str) -> None:
    rest_path = os.path.join(metrics_dir,
                              f"{model}_{quant}_restored_{benchmark}_run0_metrics.json")
    rest_acc = (json.load(open(rest_path))["accuracy"]
                if os.path.exists(rest_path) else None)

    rows = []
    if os.path.isdir(prefix_root):
        for entry in sorted(os.listdir(prefix_root)):
            m = re.match(r"k(\d+)$", entry)
            if not m:
                continue
            k = int(m.group(1))
            diag = os.path.join(prefix_root, entry, "diagnosis", f"{benchmark}_run0.jsonl")
            ci = _bootstrap_acc(diag)
            if ci is None:
                continue
            rows.append((k, ci))
    rows.sort()

    if not rows and rest_acc is None:
        with open(output, "w") as f:
            f.write(_placeholder("tab:interventions",
                                 "Training-free vs. training-based interventions.",
                                 "(not yet computed — run \\texttt{bash run\\_prompt\\_prefix.sh})"))
        print(f"  placeholder written: {output}")
        return

    with open(output, "w") as f:
        f.write("\\begin{table}[t]\n\\centering\\small\n")
        f.write("\\caption{Diagnosis-enabled interventions on the primary cell "
                "(\\texttt{qwen25-7b} / GPTQ w4 / MATH-500). "
                "\\emph{Prompt-prefix $k$} prepends the first $k$ FP16 reference "
                "steps to the quantized model's prompt (no training). "
                "\\emph{QLoRA restored} is the adapter from \\S\\ref{sec:results-restoration}. "
                "Accuracy in \\%, 95\\% bootstrap CIs in brackets.}\n"
                "\\label{tab:interventions}\n")
        f.write("\\begin{tabular}{@{}lcc@{}}\n\\toprule\n")
        f.write("Intervention & Acc (\\%) & 95\\% CI \\\\\n\\midrule\n")
        if baseline_acc is not None:
            f.write(f"Quantized baseline & {baseline_acc*100:.1f} & -- \\\\\n")
        for k, (acc, lo, hi) in rows:
            label = f"Prompt-prefix $k={k}$"
            f.write(f"{label} & {acc*100:.1f} & [{lo*100:.1f}, {hi*100:.1f}] \\\\\n")
        if rest_acc is not None:
            f.write(f"\\textbf{{QLoRA restored (ours)}} & {rest_acc*100:.1f} & -- \\\\\n")
        f.write("\\bottomrule\n\\end{tabular}\n\\end{table}\n")
    print(f"  wrote: {output}")


# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", default="qwen25-7b")
    parser.add_argument("--quant", default="gptq_w4")
    parser.add_argument("--benchmark", default="math500")
    parser.add_argument("--metrics-dir", default="results/metrics")
    parser.add_argument("--ablation-root", default=None,
                        help="Default: results/ablation/<model>_<quant>")
    parser.add_argument("--baselines-root", default=None,
                        help="Default: results/baselines/<model>_<quant>")
    parser.add_argument("--prefix-root", default=None,
                        help="Default: results/prefix_injection/<model>_<quant>")
    parser.add_argument("--output-dir", default="figures/paper",
                        help="Where to drop the .tex files")
    args = parser.parse_args()

    os.makedirs(args.output_dir, exist_ok=True)

    # Quantized baseline accuracy (read from metrics file).
    base_path = os.path.join(args.metrics_dir,
                             f"{args.model}_{args.quant}_{args.benchmark}_run0_metrics.json")
    base_acc = (json.load(open(base_path))["accuracy"]
                if os.path.exists(base_path) else None)

    ablation_root  = args.ablation_root  or f"results/ablation/{args.model}_{args.quant}"
    baselines_root = args.baselines_root or f"results/baselines/{args.model}_{args.quant}"
    prefix_root    = args.prefix_root    or f"results/prefix_injection/{args.model}_{args.quant}"

    table_ablation(ablation_root, args.benchmark, base_acc,
                   os.path.join(args.output_dir, "table_ablation.tex"))
    table_baselines(baselines_root, args.model, args.quant, args.benchmark, base_acc,
                    os.path.join(args.output_dir, "table_baselines.tex"))
    table_interventions(prefix_root, args.model, args.quant, args.benchmark, base_acc,
                        args.metrics_dir,
                        os.path.join(args.output_dir, "table_interventions.tex"))


if __name__ == "__main__":
    main()