| """Figures for the reproduction logbook. |
| |
| Palette: validated categorical slots (validate_palette.js, light mode, ALL CHECKS PASS). |
| Every figure ships its raw data as CSV alongside the HTML, which serves as the table |
| view the contrast WARN obligates. |
| """ |
| import json, os, sys |
| import numpy as np |
| import plotly.graph_objects as go |
|
|
| OUT = "figs" |
| P = ["#2a78d6", "#e87ba4", "#eda100", "#1baf7a", "#4a3aa7"] |
| INK, INK2, GRID = "#0b0b0b", "#52514e", "rgba(120,118,110,0.22)" |
|
|
|
|
| def base(fig, title, xt, yt, logx=False, logy=False): |
| fig.update_layout( |
| title=dict(text=title, font=dict(size=15, color=INK)), |
| paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)", |
| font=dict(family="Inter, system-ui, sans-serif", size=12, color=INK2), |
| margin=dict(l=64, r=132, t=54, b=52), hovermode="x unified", |
| legend=dict(bgcolor="rgba(0,0,0,0)", borderwidth=0, font=dict(color=INK2)), |
| width=780, height=420) |
| for ax, t, lg in ((fig.update_xaxes, xt, logx), (fig.update_yaxes, yt, logy)): |
| ax(title_text=t, type="log" if lg else "linear", gridcolor=GRID, |
| zeroline=False, linecolor=GRID, ticks="outside", tickcolor=GRID, |
| title_font=dict(color=INK2)) |
| return fig |
|
|
|
|
| def endlabel(fig, x, y, text, color): |
| fig.add_annotation(x=np.log10(x) if fig.layout.xaxis.type == "log" else x, |
| y=np.log10(y) if fig.layout.yaxis.type == "log" else y, |
| text=text, showarrow=False, xanchor="left", xshift=8, |
| font=dict(color=color, size=11)) |
|
|
|
|
| def save(fig, name, rows, header): |
| os.makedirs(OUT, exist_ok=True) |
| fig.write_html(f"{OUT}/{name}.html", include_plotlyjs="cdn", full_html=True) |
| with open(f"{OUT}/{name}.csv", "w") as f: |
| f.write(",".join(header) + "\n") |
| for r in rows: |
| f.write(",".join(str(v) for v in r) + "\n") |
| print(f"wrote {OUT}/{name}.html + .csv") |
|
|
|
|
| |
| def fig_rate(path="outputs/claim1_rate.json"): |
| """Exploitation-stage regret vs T: the sqrt(T) half of Theorem 2, confirmed.""" |
| f = json.load(open(path))["fits"] |
| fig = go.Figure(); rows = [] |
| keys = [k for k in f if k.startswith("T:")] |
| for i, k in enumerate(keys): |
| T, R, s = f[k]["x"], f[k]["exploit"], f[k]["slope_exploit"] |
| lab = k[2:].replace("_", ", ").replace("K", "K=").replace("d", "d=") |
| fig.add_trace(go.Scatter(x=T, y=R, mode="lines+markers", name=f"{lab} (slope {s:.3f})", |
| line=dict(color=P[i], width=2), marker=dict(size=8))) |
| endlabel(fig, T[-1], R[-1], f"{s:.3f}", P[i]) |
| rows += [[lab, t, r] for t, r in zip(T, R)] |
| T = np.array(f[keys[0]]["x"], float) |
| ref = f[keys[0]]["exploit"][0] * np.sqrt(T / T[0]) |
| fig.add_trace(go.Scatter(x=T, y=ref, mode="lines", name="√T reference (slope 0.5)", |
| line=dict(color=INK2, width=2, dash="dot"))) |
| base(fig, "Claim 1 — exploitation-stage regret vs horizon T (5 seeds, T up to 2¹⁷)", |
| "T (rounds, log)", "regret accrued after the cold start (log)", True, True) |
| save(fig, "claim1_rate_T", rows, ["config", "T", "exploitation_regret"]) |
|
|
|
|
| def fig_rate_Kd(path="outputs/claim1_rate.json"): |
| """Exploitation-stage regret vs K and vs d: sqrt(d) holds, sqrt(K) does not.""" |
| f = json.load(open(path))["fits"] |
| fig = go.Figure(); rows = [] |
| for i, (k, lab) in enumerate([("K", "vs K (d=5)"), ("d", "vs d (K=5)")]): |
| x, y, s = f[k]["x"], f[k]["exploit"], f[k]["slope_exploit"] |
| fig.add_trace(go.Scatter(x=x, y=y, mode="lines+markers", |
| name=f"{lab} — slope {s:.3f}", |
| line=dict(color=P[i], width=2), marker=dict(size=8))) |
| endlabel(fig, x[-1], y[-1], f"{s:.3f}", P[i]) |
| rows += [[lab, a, b] for a, b in zip(x, y)] |
| xs = np.array([2, 20], float) |
| y0 = f["K"]["exploit"][0] |
| fig.add_trace(go.Scatter(x=xs, y=y0 * np.sqrt(xs / 2), mode="lines", |
| name="√· reference — Theorem 2's claimed slope 0.5", |
| line=dict(color=INK2, width=2, dash="dot"))) |
| base(fig, "Claim 1 — exploitation regret vs arms K and dimension d (T = 2¹⁷)", |
| "K or d (log)", "exploitation-stage regret (log)", True, True) |
| save(fig, "claim1_rate_Kd", rows, ["sweep", "value", "exploitation_regret"]) |
|
|
|
|
| def fig_warfarin_ablation(path="outputs/warfarin_ablation.json"): |
| """Which reading of the paper's under-specified §5.1 setup reproduces Table 2? |
| Distance is the L1 distance between the 3x3 dosage-correction matrix and Table 2.""" |
| d = json.load(open(path)) |
| fig = go.Figure(); rows = [] |
| labs, vals, cols = [], [], [] |
| for c in d["ceilings"]: |
| labs.append(f"offline ceiling<br>{c['reward']} reward") |
| vals.append(c["conf_L1_vs_paper"]); cols.append(P[4]) |
| rows.append(["ceiling", c["reward"], "-", "-", c["conf_L1_vs_paper"], |
| c["error"], c["score"]]) |
| for e in d["spec_grid"]: |
| labs.append(f"{e['reward']}<br>E_F={e['EF']}, φ₀={e['phi0']}") |
| vals.append(e["conf_L1_vs_paper"]) |
| cols.append(P[0] if e["reward"] == "binary" else P[1]) |
| rows.append(["online RCB", e["reward"], e["EF"], e["phi0"], |
| e["conf_L1_vs_paper"], e["error"], e["score"]]) |
| order = np.argsort(vals) |
| fig.add_trace(go.Bar(x=[labs[i] for i in order], y=[vals[i] for i in order], |
| marker_color=[cols[i] for i in order], |
| hovertemplate="%{x}<br>L1 distance to Table 2: %{y:.3f}<extra></extra>")) |
| base(fig, "Warfarin §5.1 — distance to the paper's Table 2 under each reading " |
| "of the under-specified setup", |
| "specification", "L1 distance of the 3×3 correction matrix to Table 2") |
| fig.update_layout(height=470, margin=dict(l=64, r=40, t=54, b=120)) |
| save(fig, "warfarin_ablation", rows, |
| ["policy", "reward", "EF", "phi0", "L1_to_table2", "error", "weighted_risk_score"]) |
|
|
|
|
| def fig_warfarin_N(path="outputs/warfarin_ablation.json"): |
| """Cold-start length vs clinical quality: the Claim-2 tradeoff on real data.""" |
| d = json.load(open(path)) |
| s = [e for e in d["N_sweep"] if e["Tcold"] < 5528] |
| fig = go.Figure() |
| fig.add_trace(go.Scatter(x=[e["Tcold"] for e in s], y=[e["score"] for e in s], |
| mode="lines+markers", name="RCB weighted risk score", |
| line=dict(color=P[0], width=2), marker=dict(size=9), |
| text=[f"N={e['N']}" for e in s], |
| hovertemplate="T_cold=%{x:.0f} (%{text})<br>score=%{y:.3f}<extra></extra>")) |
| for yv, lab, col in [(0.291, "paper's reported RCB score 0.291", "#c0392b"), |
| (d["ceilings"][0]["score"], "offline full-data oracle ceiling 0.352", P[4]), |
| (d["meta"]["physician_score"], "physician baseline 0.224", INK2)]: |
| fig.add_hline(y=yv, line=dict(color=col, width=2, dash="dot"), |
| annotation_text=lab, annotation_font_color=col) |
| base(fig, "Warfarin — the price of incentives, measured: longer cold start ⇒ worse clinical score", |
| "cold-start length T_cold (patients)", "weighted risk score") |
| save(fig, "warfarin_N", [[e["N"], e["Tcold"], e["error"], e["score"]] for e in d["N_sweep"]], |
| ["N", "Tcold", "error", "weighted_risk_score"]) |
|
|
|
|
| def fig_gain(path="outputs/syn_s1.json"): |
| d = json.load(open(path))["setting1"] |
| fig = go.Figure(); rows = [] |
| sel = [e for e in d if e["d"] == 5][:4] |
| for i, e in enumerate(sel): |
| fig.add_trace(go.Bar(x=[f"K={e['K']}"], y=[e["gain_frac_ok"]], |
| marker_color=P[i], name=f"K={e['K']}", showlegend=False, |
| text=[f"{e['gain_frac_ok']:.3f}"], textposition="outside")) |
| rows.append([e["K"], e["d"], e["gain_frac_ok"], e["gain_min"]]) |
| fig.add_hline(y=1.0, line=dict(color=INK2, width=2, dash="dot"), |
| annotation_text="required by Definition 1", annotation_font_color=INK2) |
| base(fig, "Claim 1 — fraction of rounds satisfying the ε-DBIC constraint (d=5, T=10⁵)", |
| "arms", "fraction of rounds with expected gain ≥ −ε") |
| fig.update_yaxes(range=[0, 1.15]) |
| save(fig, "claim1_dbic", rows, ["K", "d", "frac_gain_ge_-eps", "min_gain"]) |
|
|
|
|
| |
| def fig_feasibility(path="outputs/feas.json"): |
| d = json.load(open(path))["thm1_feasibility"] |
| fig = go.Figure() |
| tags = [e["tag"] for e in d]; ratio = [e["ratio"] for e in d] |
| fig.add_trace(go.Bar(y=tags, x=ratio, orientation="h", marker_color=P[0], |
| text=[f"{r:,.0f}×" for r in ratio], textposition="outside", |
| showlegend=False)) |
| fig.add_vline(x=1.0, line=dict(color="#c0392b", width=2, dash="dot"), |
| annotation_text="cold start = the paper's own horizon T", |
| annotation_font_color="#c0392b") |
| base(fig, "Claim 2 — Theorem 1's prescribed cold start K·L·N(ε), as a multiple of T", |
| "K·L·N(ε) / T (log scale)", "", True) |
| fig.update_layout(height=460, margin=dict(l=190, r=110, t=54, b=52)) |
| save(fig, "claim2_feasibility", [[e["tag"].replace(",", ";"), e["T"], e["N"], e["L"], e["cold"], e["ratio"]] |
| for e in d], |
| ["config", "T", "N_eps", "L", "K_L_N", "ratio_to_T"]) |
|
|
|
|
| def fig_tradeoff(path="outputs/syn_s3.json"): |
| d = json.load(open(path))["setting3"] |
| fig = go.Figure(); rows = [] |
| for i, lam in enumerate([3, 5, 10]): |
| sub = sorted([e for e in d if e["lam"] == lam], key=lambda e: e["eps"]) |
| fig.add_trace(go.Scatter(x=[e["eps"] for e in sub], y=[e["regret"] for e in sub], |
| mode="lines+markers", name=f"Σ₀ = (1/{lam})·I", |
| line=dict(color=P[i], width=2), marker=dict(size=9))) |
| endlabel(fig, sub[-1]["eps"], sub[-1]["regret"], f"1/{lam}", P[i]) |
| rows += [[lam, e["eps"], e["N"], e["Tcold"], e["regret"], e["frac_ok"]] for e in sub] |
| base(fig, "Claim 2 — incentive budget ε vs cumulative regret (Setting 3, T=5×10⁴, K=d=5)", |
| "incentive budget ε", "cumulative regret R(T) (log)", False, True) |
| save(fig, "claim2_tradeoff", rows, ["inv_lambda", "eps", "N", "Tcold", "regret", "frac_dbic_ok"]) |
|
|
|
|
| def fig_Neps(): |
| from rcb.core import N_eps |
| fig = go.Figure(); rows = [] |
| specs = [("K (cubic)", [2, 3, 5, 10, 20, 40], lambda v: N_eps(v, 5, .05, .05, .01, 1.), 3.0), |
| ("d at σ=0.05 (paper's own noise)", [2, 5, 10, 20, 50, 100, 200], |
| lambda v: N_eps(5, v, .05, .05, .01, 1.), None), |
| ("τ+ε (inverse quadratic)", [.02, .03, .04, .06, .11], |
| lambda v: N_eps(5, 5, .05, v - .01, .01, 1.), -2.0)] |
| for i, (lab, xs, f, th) in enumerate(specs): |
| ys = [f(v) for v in xs] |
| s = float(np.polyfit(np.log(xs), np.log(ys), 1)[0]) |
| fig.add_trace(go.Scatter(x=xs, y=ys, mode="lines+markers", |
| name=f"{lab} — fitted {s:+.3f}" |
| + (f" (paper {th:+.0f})" if th else " (paper +1)"), |
| line=dict(color=P[i], width=2), marker=dict(size=8))) |
| endlabel(fig, xs[-1], ys[-1], f"{s:+.2f}", P[i]) |
| rows += [[lab, v, y] for v, y in zip(xs, ys)] |
| base(fig, "Claim 2 — measured exponents of Theorem 1's N(ε)", |
| "parameter value (log)", "N(ε) (log)", True, True) |
| save(fig, "claim2_Neps", rows, ["sweep", "value", "N_eps"]) |
|
|
|
|
| |
| def fig_warfarin(path="outputs/warfarin.json"): |
| d = json.load(open(path)) |
| fig = go.Figure(); rows = [] |
| for i, eps in enumerate([0.025, 0.035, 0.045]): |
| e = [x for x in d["runs"] if x["eps"] == eps and x["prior_var"] == 0.4][0] |
| y = e["error_curve"]; x = list(range(0, 20 * len(y), 20)) |
| fig.add_trace(go.Scatter(x=x[5:], y=y[5:], mode="lines", name=f"RCB, ε={eps}", |
| line=dict(color=P[i], width=2))) |
| endlabel(fig, x[-1], y[-1], f"{y[-1]:.2f}", P[i]) |
| rows += [[eps, a, b] for a, b in zip(x, y)] |
| for yv, lab, col in [(0.35, "paper's reported RCB error ≈ 0.35", "#c0392b"), |
| (d["meta"]["physician_error"], "physician (always Medium)", INK2), |
| (0.3238, "offline full-data oracle ceiling 0.324", "#4a3aa7")]: |
| fig.add_hline(y=yv, line=dict(color=col, width=2, dash="dot"), |
| annotation_text=lab, annotation_font_color=col, |
| annotation_position="top right") |
| base(fig, "Warfarin (§5.1) — fraction of incorrect dosing decisions, Σ₀=0.4·I, 10 permutations", |
| "patients seen", "cumulative error rate") |
| fig.update_yaxes(range=[0.28, 0.62]) |
| save(fig, "warfarin_error", rows, ["eps", "t", "error_rate"]) |
|
|
|
|
| def fig_gamma(path="outputs/warfarin_gamma_sweep.json"): |
| d = json.load(open(path)) |
| fig = go.Figure() |
| g = [e["gamma_mult"] for e in d["sweep"]] |
| fig.add_trace(go.Scatter(x=g, y=[e["error"] for e in d["sweep"]], mode="lines+markers", |
| name="RCB error rate", line=dict(color=P[0], width=2), |
| marker=dict(size=9))) |
| fig.add_trace(go.Scatter(x=g, y=[e["explore"] for e in d["sweep"]], mode="lines+markers", |
| name="forced exploration fraction", |
| line=dict(color=P[3], width=2), marker=dict(size=9))) |
| for yv, lab, col in [(0.35, "paper's reported 0.35", "#c0392b"), |
| (d["ceiling_error"], "offline oracle ceiling", "#4a3aa7"), |
| (d["physician_error"], "physician baseline", INK2)]: |
| fig.add_hline(y=yv, line=dict(color=col, width=2, dash="dot"), |
| annotation_text=lab, annotation_font_color=col) |
| base(fig, "Warfarin — RCB error rate vs exploration aggressiveness (γ multiplier)", |
| "multiplier on the spread parameter γ_m (log)", "rate", True) |
| fig.update_yaxes(range=[0, 0.62]) |
| save(fig, "warfarin_gamma", [[e["gamma_mult"], e["error"], e["explore"], e["b_acc"], |
| e["score"]] for e in d["sweep"]], |
| ["gamma_mult", "error", "explore_frac", "oracle_b_acc", "weighted_risk_score"]) |
|
|
|
|
| if __name__ == "__main__": |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) |
| for name in sys.argv[1:] or ["warfarin", "gamma", "feasibility", "Neps"]: |
| globals()[f"fig_{name}"]() |
|
|
