| """Wide two-panel versions of the Claim 3 and Claim 4 figures. |
| |
| Why: the single-panel square versions render at ~42-45% of a poster column's |
| width (posterly polish: FIG/SQUARE, FIG/WIDE want >=55-75%). Rather than shrink |
| the cards, we widen the figures by adding a genuinely informative second panel. |
| """ |
| import json, matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| METHODS = ["EduMirror", "LLMob", "BabyAGI", "D2A", "ReAct"] |
|
|
| |
| d3 = json.load(open("/tmp/res/claim3/claim3.json")) |
| recs = [r for r in d3["records"] if r.get("delta_rses") is not None] |
| fig, (a1, a2) = plt.subplots(1, 2, figsize=(11.4, 4.5)) |
| arms = ["neglectful", "authoritative_punitive", "supportive_individual", "supportive_cooperative"] |
| cols = {"neglectful": "#C44E52", "authoritative_punitive": "#DD8452", |
| "supportive_individual": "#55A868", "supportive_cooperative": "#4C72B0"} |
| for arm in arms: |
| pts = [(r["delta_value"], r["delta_rses"]) for r in recs if r["arm"] == arm] |
| if pts: |
| a1.scatter([p[0] for p in pts], [p[1] for p in pts], s=90, alpha=.85, |
| color=cols[arm], label=arm.replace("_", " "), edgecolor="w", zorder=3) |
| xs = [r["delta_value"] for r in recs]; ys = [r["delta_rses"] for r in recs] |
| coef = np.polyfit(xs, ys, 1); xr = np.linspace(min(xs), max(xs), 50) |
| a1.plot(xr, np.polyval(coef, xr), "--", color="#333", lw=1.3, zorder=2) |
| a1.axhline(0, color="#ccc", lw=.8); a1.axvline(0, color="#ccc", lw=.8) |
| a1.set_xlabel("Δ internal value ('self worth', 'sense of respect')") |
| a1.set_ylabel("Δ RSES total (external, 10–40)") |
| a1.set_title("Internal state vs external instrument\nr = 0.943, ρ = 0.951, n = 12") |
| a1.legend(fontsize=7.5); a1.grid(alpha=.3) |
|
|
| pre = [np.mean([r["rses_pre"] for r in recs if r["arm"] == a]) for a in arms] |
| post = [np.mean([r["rses_post"] for r in recs if r["arm"] == a]) for a in arms] |
| x = np.arange(4); w = .38 |
| a2.bar(x - w/2, pre, w, label="RSES pre", color="#BBB") |
| a2.bar(x + w/2, post, w, label="RSES post", color=[cols[a] for a in arms]) |
| a2.set_xticks(x); a2.set_xticklabels(["neglect\n(control)", "authoritative\npunitive", |
| "supportive\nindividual", "supportive\ncooperative"], fontsize=8) |
| a2.set_ylabel("RSES total (10–40)"); a2.set_ylim(10, 40) |
| a2.set_title("Victim self-esteem, pre → post\nNeglect: 31.0 → 19.7; every intervention arrests it") |
| a2.legend(fontsize=8); a2.grid(axis="y", alpha=.3) |
| for i, (b, p) in enumerate(zip(pre, post)): |
| a2.text(i + w/2, p + .6, f"{p:.1f}", ha="center", fontsize=8, |
| fontweight="bold" if arms[i] == "neglectful" else "normal") |
| fig.tight_layout(); fig.savefig("outputs/figures/claim3_rses_validity.png", dpi=150); plt.close(fig) |
| print("claim3 wide written") |
|
|
| |
| d4 = json.load(open("/tmp/res/claim4_72b/claim4.json")) |
| mat = np.array([[d4["matrix"][r].get(c, np.nan) for c in METHODS] for r in METHODS], float) |
| fig, (b1, b2) = plt.subplots(1, 2, figsize=(11.4, 4.6), |
| gridspec_kw={"width_ratios": [1.25, 1]}) |
| im = b1.imshow(mat, cmap="RdYlBu_r", vmin=0, vmax=1) |
| b1.set_xticks(range(5)); b1.set_xticklabels(METHODS, rotation=30, ha="right", fontsize=8) |
| b1.set_yticks(range(5)); b1.set_yticklabels(METHODS, fontsize=8) |
| b1.set_xlabel("Column model"); b1.set_ylabel("Row model") |
| for i in range(5): |
| for j in range(5): |
| v = mat[i, j] |
| b1.text(j, i, "--" if v != v else f"{v:.2f}", ha="center", va="center", |
| fontsize=8, color="white" if (v == v and (v < .28 or v > .72)) else "black") |
| fig.colorbar(im, ax=b1, fraction=.046, label="win rate, column vs row") |
| b1.set_title("Pairwise win rates (72B validated judge)\n6 scenarios, 120 comparisons, all decided") |
|
|
| aw = d4["average_win_rate"] |
| order = sorted(METHODS, key=lambda m: -aw[m]) |
| vals = [aw[m] for m in order] |
| colors = ["#4C72B0" if m == "EduMirror" else "#BBB" for m in order] |
| b2.barh(range(5), vals, color=colors) |
| b2.set_yticks(range(5)); b2.set_yticklabels(order, fontsize=9); b2.invert_yaxis() |
| b2.axvline(.5, color="#333", ls=":", lw=1) |
| b2.set_xlabel("Average win rate vs all opponents"); b2.set_xlim(0, .8) |
| b2.set_title("EduMirror is NOT top: LLMob leads\n(paper claims EduMirror strongest)") |
| for i, v in enumerate(vals): |
| b2.text(v + .012, i, f"{v:.3f}", va="center", fontsize=9, |
| fontweight="bold" if order[i] == "EduMirror" else "normal") |
| b2.grid(axis="x", alpha=.3) |
| fig.tight_layout(); fig.savefig("outputs/figures/claim4_heatmap.png", dpi=150); plt.close(fig) |
| print("claim4 wide written | avg win:", {m: aw[m] for m in order}) |
|
|
