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ce85a7a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | """Figures for the BALLAST reproduction logbook (Plotly HTML + raw CSV)."""
from __future__ import annotations
import json
import os
import sys
import numpy as np
import plotly.graph_objects as go
OUT = "outputs"
COL = {
"unif": "#7f8c8d", "sobol": "#2980b9", "dist_sep": "#8e44ad",
"eig": "#e67e22", "ballast_opt": "#16a085", "ballast_true": "#c0392b",
}
NAME = {
"unif": "UNIF", "sobol": "SOBOL", "dist_sep": "DIST-SEP", "eig": "EIG",
"ballast_opt": "BALLAST-opt", "ballast_true": "BALLAST-true",
}
LAYOUT = dict(
template="plotly_white", font=dict(size=13), height=420,
margin=dict(l=60, r=20, t=50, b=50),
)
def _save(fig, name, csv_rows=None, header=None):
os.makedirs(OUT, exist_ok=True)
fig.write_html(f"{OUT}/{name}.html", include_plotlyjs="cdn")
if csv_rows is not None:
with open(f"{OUT}/{name}.csv", "w") as f:
f.write(",".join(header) + "\n")
for r in csv_rows:
f.write(",".join(str(x) for x in r) + "\n")
print("wrote", name)
def fig_spde_cost(path="outputs/raw/spde_cost/spde_cost.json"):
d = json.load(open(path))
rows = d["rows"]
n = [r["n_sampT"] for r in rows]
sp = [r["spde_s"] for r in rows]
nv = [r["naive_s"] for r in rows]
ok = [i for i, v in enumerate(nv) if v is not None]
fig = go.Figure()
fig.add_trace(go.Scatter(x=n, y=sp, name="SPDE (Sec. 4.1)", mode="lines+markers",
line=dict(color="#16a085", width=3)))
fig.add_trace(go.Scatter(x=[n[i] for i in ok], y=[nv[i] for i in ok],
name="naive dense", mode="lines+markers",
line=dict(color="#c0392b", width=3)))
# extrapolate the naive cubic to show where it goes
i0 = ok[-1]
ext = [x for x in n if x > n[i0]]
if ext:
fig.add_trace(go.Scatter(
x=[n[i0]] + ext,
y=[nv[i0]] + [nv[i0] * (x / n[i0]) ** 3 for x in ext],
name="naive (cubic extrapolation, out of memory)", mode="lines",
line=dict(color="#c0392b", width=2, dash="dot")))
fig.update_layout(
title="Posterior sampling cost: SPDE stays linear, naive dense goes cubic then out of memory",
xaxis=dict(title="N_sampT (sampled time slices)", type="log"),
yaxis=dict(title="seconds for J=20 samples (A100, fp64)", type="log"),
legend=dict(x=0.02, y=0.98), **LAYOUT)
_save(fig, "claim2_spde_cost",
[(r["n_sampT"], r["spde_s"], r["naive_s"], r["naive_cov_gb"]) for r in rows],
["n_sampT", "spde_seconds", "naive_seconds", "naive_cov_gb"])
def fig_ablation(path="outputs/claim5.json"):
d = json.load(open(path))
fig = go.Figure()
colors = {"3.0": "#2980b9", "5.0": "#16a085", "7.0": "#c0392b"}
rows = []
for t, r in d.items():
J = np.array(r["J"])
m = np.array(r["gap_mc_mean"])
s = np.array(r["gap_mc_se2"])
c = colors.get(str(t), "#333")
fig.add_trace(go.Scatter(x=J, y=m, name=f"BALLAST t={t}", mode="lines",
line=dict(color=c, width=2.5)))
fig.add_trace(go.Scatter(
x=np.concatenate([J, J[::-1]]), y=np.concatenate([m + s, (m - s)[::-1]]),
fill="toself", fillcolor=c.replace("#", "rgba(").replace("", "") if False else c,
opacity=0.15, line=dict(width=0), showlegend=False, hoverinfo="skip"))
fig.add_trace(go.Scatter(x=[J[0], J[-1]], y=[r["eig_gap_mc"]] * 2,
name=f"EIG t={t}", mode="lines",
line=dict(color=c, width=1.5, dash="dash")))
fig.add_trace(go.Scatter(x=[J[0], J[-1]], y=[r["unif_gap_mc"]] * 2,
name=f"UNIF t={t}", mode="lines",
line=dict(color=c, width=1.5, dash="dot")))
for j, v in zip(J, m):
rows.append((t, j, v))
fig.add_hline(y=1.0, line=dict(color="black", width=1))
fig.add_annotation(x=np.log10(120), y=np.log10(1.0), text="1% cut-off",
showarrow=False, yshift=10)
fig.add_vline(x=20, line=dict(color="#888", width=1, dash="dash"))
fig.update_layout(
title="Percentage utility gap vs Monte Carlo sample number J (2 s.e. bands)",
xaxis=dict(title="J (posterior field samples)", type="log"),
yaxis=dict(title="Monte Carlo % utility gap", type="log"),
**LAYOUT)
_save(fig, "claim5_ablation", rows, ["decision_time", "J", "gap_mc_pct"])
def fig_policy(path, tag, title):
d = json.load(open(path))
pols = d["policies"]
n_dep = d["n_deploy"]
x = np.arange(1, n_dep + 1)
# --- average rank
fig = go.Figure()
for i, p in enumerate(pols):
m = np.array(d["rank_mean"][i])
s = np.array(d["rank_se2"][i])
fig.add_trace(go.Scatter(x=x, y=m, name=NAME[p], mode="lines+markers",
line=dict(color=COL[p], width=2.5),
error_y=dict(type="data", array=s, visible=True, thickness=1)))
fig.update_layout(
title=f"{title}: average policy rank (1 = best), {d['n_runs']} runs",
xaxis=dict(title="drifters deployed"),
yaxis=dict(title="average rank"), **LAYOUT)
_save(fig, f"{tag}_rank",
[(p, i + 1, d["rank_mean"][j][i]) for j, p in enumerate(pols) for i in range(n_dep)],
["policy", "n_drifters", "mean_rank"])
# --- iso-performance
fig = go.Figure()
for p in pols:
if p == "unif":
continue
m = np.array(d["iso"][p]["mean"])
s = np.array(d["iso"][p]["se2"])
fig.add_trace(go.Scatter(x=x, y=m, name=NAME[p], mode="lines+markers",
line=dict(color=COL[p], width=2.5),
error_y=dict(type="data", array=s, visible=True, thickness=1)))
fig.add_hline(y=0, line=dict(color="#7f8c8d", width=1, dash="dash"))
fig.update_layout(
title=f"{title}: drifters saved vs UNIF (iso-performance), {d['n_runs']} runs",
xaxis=dict(title="drifters deployed"),
yaxis=dict(title="drifters saved (positive = better)"), **LAYOUT)
_save(fig, f"{tag}_iso",
[(p, i + 1, d["iso"][p]["mean"][i], d["iso"][p]["se2"][i])
for p in pols for i in range(n_dep)],
["policy", "n_drifters", "drifters_saved", "se2"])
# --- error curves
fig = go.Figure()
for p in pols:
m = np.array(d["err_mean"][p])
s = np.array(d["err_se2"][p])
fig.add_trace(go.Scatter(x=x, y=m, name=NAME[p], mode="lines",
line=dict(color=COL[p], width=2.5),
error_y=dict(type="data", array=s, visible=True, thickness=1)))
fig.update_layout(
title=f"{title}: field error vs drifters deployed, {d['n_runs']} runs",
xaxis=dict(title="drifters deployed"),
yaxis=dict(title="mean L2 error of posterior mean field"), **LAYOUT)
_save(fig, f"{tag}_error",
[(p, i + 1, d["err_mean"][p][i]) for p in pols for i in range(n_dep)],
["policy", "n_drifters", "mean_l2_error"])
if __name__ == "__main__":
which = sys.argv[1] if len(sys.argv) > 1 else "all"
if which in ("all", "spde"):
fig_spde_cost()
if which in ("all", "ablation") and os.path.exists("outputs/claim5.json"):
fig_ablation()
if which in ("all", "synth") and os.path.exists("outputs/claim3.json"):
fig_policy("outputs/claim3.json", "claim3", "Temporal Helmholtz ground truth")
if which in ("all", "suntans") and os.path.exists("outputs/claim4.json"):
fig_policy("outputs/claim4.json", "claim4", "SUNTANS ground truth")
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