| """Grafici della corsa di conferma: PNG + un HTML autonomo (SVG in linea, nessuna dipendenza esterna). |
| Uso: ~/.hermes/cache/scratch/mpl-venv/bin/python analysis/charts.py (matplotlib solo per disegnare)""" |
| import io |
| import json |
| from pathlib import Path |
|
|
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
|
|
| import stats as S |
|
|
| OUT = Path(__file__).resolve().parents[1] / "charts" |
| OUT.mkdir(exist_ok=True) |
| A = Path(__file__).resolve().parent |
| summ = json.load(open(A / "summary.json")) |
| xsum = json.load(open(A / "extra.json")) |
| POLS = ["A", "D", "B", "T"] |
| NAMES = {"A": "full window", "D": "mnemonic-proxy", "B": "summary (LLM compaction)", "T": "truncation"} |
| COL = {"A": "#7f7f7f", "D": "#1f77b4", "B": "#ff7f0e", "T": "#2ca02c"} |
| SVGS = {} |
| LAB = {"unite": "64k + 128k pooled"} |
|
|
|
|
| def save(fig, name): |
| fig.savefig(OUT / (name + ".png"), dpi=130, bbox_inches="tight") |
| buf = io.StringIO() |
| fig.savefig(buf, format="svg", bbox_inches="tight") |
| s = buf.getvalue() |
| SVGS[name] = s[s.index("<svg"):] |
| plt.close(fig) |
|
|
|
|
| |
| def bars(data, title, name, groups, ylabel="fully correct (official score = 1), %"): |
| fig, ax = plt.subplots(figsize=(8, 4.2)) |
| w = 0.2 |
| for k, p in enumerate(POLS): |
| xs, ys, lo, hi = [], [], [], [] |
| for g, key in enumerate(groups): |
| x, n = data[key][p] |
| l, u = S.wilson(x, n) |
| xs.append(g + (k - 1.5) * w) |
| ys.append(100 * x / n) |
| lo.append(100 * (x / n - l)) |
| hi.append(100 * (u - x / n)) |
| ax.bar(xs, ys, w, color=COL[p], label=NAMES[p], yerr=[lo, hi], capsize=3, error_kw={"lw": 1}) |
| for xx, yy in zip(xs, ys): |
| ax.text(xx, 2, f"{yy:.0f}", ha="center", va="bottom", fontsize=7, color="white") |
| ax.set_xticks(range(len(groups))) |
| ax.set_xticklabels([f"{LAB.get(g, g)} (n={data[g]['A'][1]})" for g in groups]) |
| ax.set_ylim(0, 100) |
| ax.set_ylabel(ylabel) |
| ax.set_title(title, fontsize=10) |
| ax.legend(fontsize=8, ncol=4, loc="upper center", bbox_to_anchor=(0.5, -0.1)) |
| ax.grid(axis="y", alpha=0.3) |
| save(fig, name) |
|
|
|
|
| bars(summ["correct"]["off"], "Confirmation run. AgentLongBench kf-c tool_response, Qwen3.8 Flash Next IQ3_XXS,\n" |
| "window 32K (full window: 80K / 160K). Error bars: 95% Wilson", "correct_by_policy", ["64k", "128k", "unite"]) |
|
|
| |
| mvc = xsum["main_vs_confirm"]["unite"] |
| data = {"main run (ambiguous question)": {p: [mvc[p]["main"], mvc[p]["n"]] for p in POLS}, |
| "confirmation run (question + note)": {p: [mvc[p]["confirm"], mvc[p]["n"]] for p in POLS}} |
| bars(data, "Same 100 questions, same engine and settings. Only change: one sentence added to the question\n" |
| "(64k + 128k pooled, error bars: 95% Wilson)", "main_vs_confirm", |
| ["main run (ambiguous question)", "confirmation run (question + note)"]) |
|
|
| |
| fig, ax = plt.subplots(figsize=(8, 4.2)) |
| w = 0.2 |
| for k, p in enumerate(POLS): |
| for g, L in enumerate(["64k", "128k"]): |
| c = summ["cost"][f"{L}/{p}"] |
| x = g + (k - 1.5) * w |
| ax.bar(x, c["prep_med"], w, color=COL[p], alpha=0.45, hatch="//", edgecolor="white") |
| ax.bar(x, c["q_med"], w, bottom=c["prep_med"], color=COL[p], label=NAMES[p] if g == 0 else None) |
| ax.text(x, c["prep_med"] + c["q_med"] + 3, f"{c['wall_med']:.0f}s", ha="center", fontsize=7) |
| from matplotlib.patches import Patch |
| ax.set_xticks([0, 1]) |
| ax.set_xticklabels(["64k (~74K-token history)", "128k (~150K-token history)"]) |
| ax.set_ylim(0, 330) |
| h, l = ax.get_legend_handles_labels() |
| h += [Patch(facecolor="#bbbbbb", hatch="//", edgecolor="white"), Patch(facecolor="#555555")] |
| l += ["preparation", "at question time"] |
| ax.set_ylabel("seconds per question (median)") |
| ax.set_title("Median time per question. Hatched = preparation (proxy: history replay through the proxy;\n" |
| "summary: compactions). Solid = work at question time. Label = median end-to-end wall time.", fontsize=9) |
| ax.legend(h, l, fontsize=8, ncol=3, loc="upper center", bbox_to_anchor=(0.5, -0.1)) |
| ax.grid(axis="y", alpha=0.3) |
| save(fig, "time_per_question") |
|
|
| |
| fig, axs = plt.subplots(1, 3, figsize=(10, 3.4), constrained_layout=True) |
| for ax, key in zip(axs, ["64k", "128k", "unite"]): |
| c = summ["compare"]["main"][f"D-B/{key}"] |
| m = [[c["a"], c["b"]], [c["c"], c["d"]]] |
| for i in range(2): |
| for j in range(2): |
| ax.text(j, i, m[i][j], ha="center", va="center", fontsize=14, |
| color="white" if m[i][j] >= 20 else "black") |
| ax.imshow(m, cmap="Blues", vmin=0, vmax=45) |
| ax.set_xticks([0, 1]) |
| ax.set_xticklabels(["summary right", "summary wrong"]) |
| ax.set_yticks([0, 1]) |
| ax.set_yticklabels(["proxy right", "proxy wrong"]) |
| ax.set_title(f"{LAB.get(key, key)}: n = {c['n']}, McNemar exact p = {c['p_mcnemar']:.2g}", fontsize=9) |
| fig.suptitle("Primary comparison (confirmation run): mnemonic-proxy vs summary, paired", fontsize=10) |
| save(fig, "discordance_D_B") |
|
|
| |
| html = ["<!doctype html><html><head><meta charset='utf-8'><title>AgentLongBench confirmation run — mnemonic-proxy</title>", |
| "<style>body{font-family:sans-serif;max-width:980px;margin:2em auto;color:#222}svg{max-width:100%;height:auto}" |
| "table{border-collapse:collapse;font-size:13px}td,th{border:1px solid #ccc;padding:3px 7px;text-align:right}" |
| "</style></head><body>", |
| "<h1>AgentLongBench confirmation run (window 32K, 64k + 128k)</h1>", |
| "<p>Qwen3.8 Flash Next IQ3_XXS on Strata 0.1.40, RTX 4070 Ti 12 GB, KV int8, seed 1. kf-c tool_response, " |
| "Count Frequency + Find Duplicates, 25 + 25 questions per length, max_tokens 4,096. Full window = 81,920 / 163,840; " |
| "mnemonic-proxy 0.3.5 (b8d4920), summary (LLM compaction, at most 2,048 tokens) and truncation at 32,768. " |
| "Same questions as the main run plus one note that says which tool output \u201cround N\u201d refers to; " |
| "official answers and scorer unchanged.</p>"] |
| html.append("<table><tr><th>set</th>" + "".join(f"<th>{NAMES[p]}</th>" for p in POLS) + "</tr>") |
| for key in ["64k", "128k", "unite"]: |
| html.append(f"<tr><td>{LAB.get(key, key)}</td>" + "".join( |
| f"<td>{summ['correct']['off'][key][p][0]}/{summ['correct']['off'][key][p][1]}</td>" for p in POLS) + "</tr>") |
| html.append("</table>") |
| for name, cap in (("correct_by_policy", "Fully correct answers per method and length."), |
| ("main_vs_confirm", "Main run vs confirmation run."), |
| ("discordance_D_B", "Primary comparison: mnemonic-proxy vs summary, paired."), |
| ("time_per_question", "Median time per question.")): |
| html.append(f"<h2>{cap}</h2>") |
| html.append(SVGS[name]) |
| html.append("</body></html>") |
| (OUT / "confirm-charts.html").write_text("\n".join(html)) |
| print("ok", sorted(p.name for p in OUT.iterdir())) |
|
|
