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2188a91 | 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 | """Render the poster's PNG figures from the logged reproduction results.
Every number here comes from results/*.json produced by the runs; nothing is
hand-entered except the paper's own reported values, which are labelled as such.
"""
import json
import os
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
OUT = "poster/images"
os.makedirs(OUT, exist_ok=True)
ACCENT, GOLD, GREY, RED = "#17697B", "#B07C2B", "#8A8A8A", "#A33B3B"
FONT = dict(family="Helvetica, Arial, sans-serif", size=30, color="#1A1A1A")
SCALE = 3
def save(fig, name, w, h):
fig.update_layout(template="plotly_white", font=FONT,
margin=dict(l=90, r=30, t=80, b=70))
fig.write_image(f"{OUT}/{name}.png", width=w, height=h, scale=SCALE)
print(f"wrote {OUT}/{name}.png ({w*SCALE}x{h*SCALE} px)")
# ------------------------------------------------------------------ Fig 1: Claim 3
def fig_ablation():
s = json.load(open("results/adftd_summary.json"))
rows = {r["config"]: r for r in s["rows"]}
order = ["tsfp_scratch", "tsfp_rec", "tsfp_rec_div"]
order = [o for o in order if o in rows]
if len(order) < 3:
print("skip ablation fig (incomplete)")
return
xs = ["Scratch", "Pre-trained<br>(ℒ_rec)", "Pre-trained<br>(ℒ_rec + ℒ_div)"]
fig = go.Figure()
fig.add_bar(name="this reproduction (3 seeds)", x=xs,
y=[rows[o]["f1"] for o in order],
error_y=dict(type="data", array=[rows[o]["f1_sd"] for o in order],
thickness=3, width=14),
marker_color=ACCENT,
text=[f"{rows[o]['f1']:.2f}" for o in order],
textposition="outside", textfont=dict(size=32))
fig.add_bar(name="paper (Table 2)", x=xs,
y=[rows[o]["paper_f1"] for o in order], marker_color=GOLD,
text=[f"{rows[o]['paper_f1']:.2f}" for o in order],
textposition="outside", textfont=dict(size=32))
fig.update_layout(barmode="group", yaxis_title="ADFTD macro F1 (%)",
yaxis_range=[38, 58], height=620, width=1000,
legend=dict(orientation="h", y=1.13, x=0))
save(fig, "claim3_ablation", 1000, 620)
# ------------------------------------------------------------------ Fig 2: Claim 4
def fig_promotion():
s = json.load(open("results/adftd_summary.json"))
p = s["promotions"]
names, ours = [], []
for k in ("TS-Fingerprint", "SimMTM", "Ti-MAE"):
if p[k]["f1"]:
names.append(k)
ours.append(p[k]["f1"]["rel_pct"])
if not names:
print("skip promotion fig")
return
paper = {"TS-Fingerprint": 13.07, "SimMTM": 4.42, "Ti-MAE": -0.96}
fig = go.Figure()
fig.add_bar(name="this reproduction", x=names, y=ours, marker_color=ACCENT,
text=[f"{v:+.1f}%" for v in ours], textposition="outside",
textfont=dict(size=32))
fig.add_bar(name="paper (Table 4)", x=names, y=[paper[n] for n in names],
marker_color=GOLD, text=[f"{paper[n]:+.1f}%" for n in names],
textposition="outside", textfont=dict(size=32))
lo = min(ours + [paper[n] for n in names])
hi = max(ours + [paper[n] for n in names])
fig.add_hline(y=0, line=dict(color="#555", width=2))
fig.update_layout(barmode="group",
yaxis_title="relative F1 gain from pre-training (%)",
yaxis_range=[min(lo * 1.5, -3), hi * 1.35],
height=620, width=1000,
legend=dict(orientation="h", y=1.13, x=0))
save(fig, "claim4_promotion", 1000, 620)
# ------------------------------------------------------------------ Fig 3: Claim 6
def fig_sweep():
p = "results/sweep_summary.json"
if not os.path.exists(p):
print("skip sweep fig (not run yet)")
return
d = json.load(open(p))
ks, rs = [6, 8, 10], [0.5, 0.6, 0.7, 0.8]
ours = np.full((3, 4), np.nan)
for row in d["rows"]:
ours[ks.index(row["k"]), rs.index(row["r"])] = row["f1"]
paper = np.array([[54.81, 56.68, 55.41, 54.32],
[62.60, 63.51, 62.10, 59.19],
[51.76, 50.70, 56.33, 55.70]])
fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.14,
subplot_titles=("this reproduction (PTB-XL, 1 seed)",
"paper (Table 3)"))
for j, (m, lbl) in enumerate(((ours, "ours"), (paper, "paper"))):
fig.add_heatmap(z=m, x=[f"r={r}" for r in rs], y=[f"k={k}" for k in ks],
colorscale=[[0, "#F2F7F8"], [1, ACCENT]], showscale=False,
text=[[("" if np.isnan(v) else f"{v:.2f}") for v in row]
for row in m],
texttemplate="%{text}", textfont=dict(size=30),
row=1, col=j + 1)
fig.update_layout(height=560, width=1300,
title_text="macro F1 (%) across bottleneck size k and mask ratio r")
save(fig, "claim6_sweep", 1300, 560)
# ------------------------------------------------------------------ Fig 4: Claim 2
def fig_theorem():
rng = np.random.default_rng(0)
LOG2PI = np.log(2 * np.pi)
n, dd, s2, b = 20000, 6, 0.7, 0.6
x = rng.normal(size=(n, dd))
mses, gll, lll = [], [], []
for s in np.linspace(0.05, 2.0, 25):
xh = x + s * rng.normal(size=(n, dd))
mses.append(float(((x - xh) ** 2).mean()))
sq = ((x - xh) ** 2).sum(1)
gll.append(float((-0.5 * dd * (LOG2PI + np.log(s2)) - sq / (2 * s2)).mean()))
lll.append(float((-dd * np.log(2 * b) - np.abs(x - xh).sum(1) / b).mean()))
mses, gll, lll = np.array(mses), np.array(gll), np.array(lll)
def resid(xv, yv):
A = np.vstack([xv, np.ones_like(xv)]).T
c, *_ = np.linalg.lstsq(A, yv, rcond=None)
return np.abs(A @ c - yv)
fig = go.Figure()
fig.add_scatter(x=mses, y=np.maximum(resid(mses, gll), 1e-16), mode="lines+markers",
name="Gaussian decoder (theorem's assumption)",
line=dict(color=ACCENT, width=4), marker=dict(size=11))
fig.add_scatter(x=mses, y=resid(mses, lll), mode="lines+markers",
name="Laplace decoder — CONTROL",
line=dict(color=RED, width=4, dash="dash"), marker=dict(size=11))
fig.update_yaxes(type="log", title_text="|residual| from affine fit [nats]",
exponentformat="power")
fig.update_xaxes(title_text="reconstruction MSE (ℒ_rec)")
fig.update_layout(height=560, width=1000,
legend=dict(orientation="h", y=1.16, x=0))
save(fig, "claim2_control", 1000, 560)
# ------------------------------------------------------------------ Fig 5: Claim 5
def fig_rank():
d = json.load(open("results/table1_rank_audit.json"))
ranks = d["avg_rank_all_methods_5metrics"]
items = sorted(ranks.items(), key=lambda kv: kv[1])
names = [k if k != "Ours" else "TS-Fingerprint" for k, _ in items]
vals = [v for _, v in items]
cols = [GOLD if n == "TS-Fingerprint" else
(ACCENT if n == "Medformer" else GREY) for n in names]
fig = go.Figure()
fig.add_bar(x=vals, y=names, orientation="h", marker_color=cols,
text=[f"{v:.2f}" for v in vals], textposition="outside",
textfont=dict(size=28))
fig.update_layout(xaxis_title="average rank over 7 datasets × 5 metrics (lower better)",
height=760, width=1000, yaxis=dict(autorange="reversed"),
xaxis_range=[0, 12])
save(fig, "claim5_rank", 1000, 760)
if __name__ == "__main__":
for f in (fig_ablation, fig_promotion, fig_sweep, fig_theorem, fig_rank):
try:
f()
except Exception as e: # noqa: BLE001
print(f"skip {f.__name__}: {type(e).__name__}: {e}")
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