"""Render the baseline comparison figure (paper fig 7).""" import argparse import json import os import sys import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) mpl.rcParams.update({ "font.family": "sans-serif", "font.sans-serif": ["Inter", "Helvetica Neue", "Arial", "DejaVu Sans"], "font.size": 9, "axes.labelsize": 10, "xtick.labelsize": 8.5, "ytick.labelsize": 8.5, "legend.fontsize": 8, "legend.frameon": False, "figure.dpi": 200, "savefig.dpi": 400, "savefig.bbox": "tight", "pdf.fonttype": 42, "ps.fonttype": 42, "axes.linewidth": 0.7, "axes.spines.top": False, "axes.spines.right": False, }) # Order matters: weakest → strongest reads left-to-right. STRATEGIES = [ ("random", "Random\n(no diagnosis)", "#B0B7C3"), ("failed_only", "Failed only\n(no type balancing)", "#F0A357"), ("silver_bullet", "Silver bullet\n(ours)", "#2E7D32"), ] GREY_REF = "#999999" def _bootstrap(jsonl_path, n_boot=5000): 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): 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): 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): if p is None: return "" if p < 0.001: return "***" if p < 0.01: return "**" if p < 0.05: return "*" return "" def main(): parser = argparse.ArgumentParser() parser.add_argument("--baseline-root", required=True) parser.add_argument("--model", required=True) parser.add_argument("--quant", required=True) parser.add_argument("--benchmark", required=True) parser.add_argument("--metrics", required=True) parser.add_argument("--segmented", default="results/segmented") parser.add_argument("--output", required=True) args = parser.parse_args() base_out = _load_outcomes(os.path.join("results", "diagnosis", args.quant, args.model, f"{args.benchmark}_run0.jsonl")) names, means, los, his, colors, stars = [], [], [], [], [], [] for strat_key, strat_label, color in STRATEGIES: diag = os.path.join(args.baseline_root, strat_key, "diagnosis", f"{args.benchmark}_run0.jsonl") ci = _bootstrap(diag) if ci is None: continue p = None if base_out: rest_out = _load_outcomes(diag) if rest_out: p = _paired_p(base_out, rest_out) names.append(strat_label) means.append(ci[0] * 100); los.append(ci[1] * 100); his.append(ci[2] * 100) colors.append(color); stars.append(_stars(p)) if not names: print("No baseline results found.") return # Reference levels. base_path = os.path.join(args.metrics, f"{args.model}_{args.quant}_{args.benchmark}_run0_metrics.json") base_acc = json.load(open(base_path))["accuracy"] * 100 if os.path.exists(base_path) else None from eval_accuracy import accuracy as _lv_acc fp16_jsonl = os.path.join(args.segmented, "fp16", args.model, f"{args.benchmark}_run0.jsonl") fp16_v = _lv_acc(fp16_jsonl, args.benchmark) fp16_acc = fp16_v * 100 if fp16_v else None fig, ax = plt.subplots(figsize=(5.3, 3.2), constrained_layout=True) x = np.arange(len(names)) width = 0.5 # Shade the "quantization gap" region (baseline → FP16) in pale grey. if base_acc is not None and fp16_acc is not None: ax.axhspan(base_acc, fp16_acc, color="#EEEEEE", alpha=1.0, zorder=0) for i, (m, lo, hi, c, s) in enumerate(zip(means, los, his, colors, stars)): ax.bar(x[i], m, width, color=c, edgecolor="white", linewidth=0.9, zorder=2) # CI whisker ax.plot([x[i], x[i]], [lo, hi], color="#333333", linewidth=1.0, zorder=3, solid_capstyle="butt") # Value label ax.annotate(f"{m:.1f}", xy=(x[i], m), xytext=(0, 5), textcoords="offset points", ha="center", va="bottom", fontsize=9.5, color="#222", fontweight="bold") # Significance star (offset above the value). if s: ax.annotate(s, xy=(x[i], hi), xytext=(0, 4), textcoords="offset points", ha="center", va="bottom", fontsize=11, color=c, fontweight="bold") # Reference lines — labels anchored just OUTSIDE the right spine via # axes fraction, so they sit clearly in the right-margin whitespace # regardless of where bars end in data coordinates. if base_acc is not None: ax.axhline(base_acc, color=GREY_REF, linestyle=(0, (5, 3)), linewidth=1.0, zorder=1) ax.text(1.02, base_acc, f"Quantized\n{base_acc:.1f}%", transform=ax.get_yaxis_transform(), ha="left", va="center", fontsize=7.5, color=GREY_REF) if fp16_acc is not None: ax.axhline(fp16_acc, color="#333", linestyle=(0, (1, 2)), linewidth=1.0, zorder=1) ax.text(1.02, fp16_acc, f"FP16\n{fp16_acc:.1f}%", transform=ax.get_yaxis_transform(), ha="left", va="center", fontsize=7.5, color="#333") ax.set_xticks(x) ax.set_xticklabels(names) ax.set_ylabel("Accuracy (%)") ax.yaxis.grid(True, linewidth=0.4, color="#DDDDDD") ax.set_axisbelow(True) ys = means + los + his + [v for v in (base_acc, fp16_acc) if v is not None] ax.set_ylim(min(ys) - 3, max(ys) + 5) ax.set_xlim(-0.55, len(names) - 0.45) pretty_quant = {"awq_w4": "AWQ w4", "gptq_w4": "GPTQ w4", "bnb_nf4_w4": "BnB NF4"}.get(args.quant, args.quant) ax.text(1.0, 1.02, f"{args.model} · {pretty_quant} · {args.benchmark}", transform=ax.transAxes, ha="right", va="bottom", fontsize=8, color="#555") # Footnote-size key for the stars. fig.text(0.02, -0.03, r"Paired-bootstrap $p$ vs. quantized baseline: $*$: $p<.05$ $**$: $p<.01$ $***$: $p<.001$", ha="left", va="top", fontsize=7, color="#555") os.makedirs(os.path.dirname(args.output), exist_ok=True) fig.savefig(args.output) plt.close(fig) print(f" Paper fig 7 (baselines) saved: {args.output}") if __name__ == "__main__": main()