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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | """Turn the ADFTD ablation result JSONs into the Claim 3 / Claim 4 tables and figures."""
import json
import os
import numpy as np
import plotly.graph_objects as go
RES = "results/adftd"
OUT = "results"
# paper values (Table 2 for the ablation, Table 4 for the promotion comparison)
PAPER = {
"tsfp_scratch": {"accuracy": 48.83, "f1": 45.80, "auroc": 65.29},
"tsfp_rec": {"accuracy": 53.51, "f1": 50.48, "auroc": 70.21},
"tsfp_rec_div": {"accuracy": 53.91, "f1": 51.79, "auroc": 72.11},
"timae_scratch": {"accuracy": 50.27, "f1": 45.88},
"timae_pretrain": {"accuracy": 50.48, "f1": 45.44},
"simmtm_scratch": {"accuracy": 51.98, "f1": 43.66},
"simmtm_pretrain": {"accuracy": 52.92, "f1": 45.59},
}
LABEL = {
"tsfp_scratch": "TS-Fingerprint · Scratch",
"tsfp_rec": "TS-Fingerprint · Pre-trained (L_rec)",
"tsfp_rec_div": "TS-Fingerprint · Pre-trained (L_rec + L_div)",
"timae_scratch": "Ti-MAE · Scratch",
"timae_pretrain": "Ti-MAE · Pre-trained",
"simmtm_scratch": "SimMTM · Scratch",
"simmtm_pretrain": "SimMTM · Pre-trained",
}
def load():
out = {}
for name in PAPER:
p = os.path.join(RES, f"{name}.json")
if os.path.exists(p) and os.path.getsize(p) > 0:
out[name] = json.load(open(p))
return out
def fmt(agg, m):
return f"{agg[m]['mean']:.2f} ± {agg[m]['std']:.2f}"
def main():
r = load()
print(f"available configs: {sorted(r)}\n")
rows = []
for name in PAPER:
if name not in r:
continue
a = r[name]["aggregate"]
rows.append({
"config": name, "label": LABEL[name],
"acc": a["accuracy"]["mean"], "acc_sd": a["accuracy"]["std"],
"f1": a["f1"]["mean"], "f1_sd": a["f1"]["std"],
"auroc": a["auroc"]["mean"], "auroc_sd": a["auroc"]["std"],
"paper_acc": PAPER[name].get("accuracy"),
"paper_f1": PAPER[name].get("f1"),
"paper_auroc": PAPER[name].get("auroc"),
"n_seeds": len(r[name]["runs"]),
"sec_per_seed": float(np.mean([x["seconds"] for x in r[name]["runs"]])),
"params": r[name]["runs"][0]["params"],
})
print(f"{'config':<18}{'Acc (ours)':>16}{'Acc (paper)':>13}"
f"{'F1 (ours)':>16}{'F1 (paper)':>12}{'AUROC (ours)':>17}{'AUROC (paper)':>15}")
for x in rows:
pa = f"{x['paper_auroc']:.2f}" if x["paper_auroc"] else "-"
print(f"{x['config']:<18}{x['acc']:>9.2f}±{x['acc_sd']:<5.2f}"
f"{x['paper_acc']:>13.2f}{x['f1']:>9.2f}±{x['f1_sd']:<5.2f}"
f"{x['paper_f1']:>12.2f}{x['auroc']:>10.2f}±{x['auroc_sd']:<5.2f}{pa:>15}")
# ---- relative promotion (Table 2 / Table 4 delta definition) ----
def promo(scratch, pre, key="f1"):
if scratch not in r or pre not in r:
return None
s = r[scratch]["aggregate"][key]["mean"]
p = r[pre]["aggregate"][key]["mean"]
return {"scratch": s, "pretrained": p, "abs": p - s, "rel_pct": (p - s) / s * 100}
promos = {
"TS-Fingerprint": {"f1": promo("tsfp_scratch", "tsfp_rec_div", "f1"),
"accuracy": promo("tsfp_scratch", "tsfp_rec_div", "accuracy")},
"Ti-MAE": {"f1": promo("timae_scratch", "timae_pretrain", "f1"),
"accuracy": promo("timae_scratch", "timae_pretrain", "accuracy")},
"SimMTM": {"f1": promo("simmtm_scratch", "simmtm_pretrain", "f1"),
"accuracy": promo("simmtm_scratch", "simmtm_pretrain", "accuracy")},
}
# the diversity-loss increment specifically (Claim 3)
div_effect = promo("tsfp_rec", "tsfp_rec_div", "f1")
print("\nrelative F1 promotion (scratch -> pre-trained):")
for k, v in promos.items():
if v["f1"]:
print(f" {k:<16} {v['f1']['scratch']:.2f} -> {v['f1']['pretrained']:.2f} "
f"(+{v['f1']['rel_pct']:.2f}%)")
if div_effect:
print(f"\nL_div increment on top of L_rec: F1 {div_effect['scratch']:.2f} -> "
f"{div_effect['pretrained']:.2f} ({div_effect['abs']:+.2f} pts, "
f"{div_effect['rel_pct']:+.2f}%)")
# per-seed paired test for the diversity loss
paired = None
if "tsfp_rec" in r and "tsfp_rec_div" in r:
a = {x["seed"]: x["test"]["f1"] for x in r["tsfp_rec"]["runs"]}
b = {x["seed"]: x["test"]["f1"] for x in r["tsfp_rec_div"]["runs"]}
seeds = sorted(set(a) & set(b))
d = [b[s] - a[s] for s in seeds]
paired = {"seeds": seeds, "per_seed_delta_f1": d,
"mean_delta": float(np.mean(d)),
"n_positive": int(sum(x > 0 for x in d))}
print(f"\npaired per-seed F1 delta (rec+div minus rec): "
f"{[round(x, 2) for x in d]} mean {np.mean(d):+.2f}, "
f"{paired['n_positive']}/{len(d)} seeds positive")
json.dump({"rows": rows, "promotions": promos, "div_effect": div_effect,
"paired_div_test": paired},
open(os.path.join(OUT, "adftd_summary.json"), "w"), indent=2)
# ---------------- figures ----------------
# Fig 1: Claim 3 ablation, ours vs paper
order = ["tsfp_scratch", "tsfp_rec", "tsfp_rec_div"]
have = [x for x in order if x in r]
if have:
xs = ["Scratch", "Pre-trained\n(L_rec)", "Pre-trained\n(L_rec + L_div)"][:len(have)]
fig = go.Figure()
for m, col in (("f1", "#2F6F8F"), ("accuracy", "#B07C2B"), ("auroc", "#7E7E7E")):
ours = [r[c]["aggregate"][m]["mean"] for c in have]
sd = [r[c]["aggregate"][m]["std"] for c in have]
pap = [PAPER[c].get(m) for c in have]
fig.add_bar(name=f"ours · {m}", x=xs, y=ours,
error_y=dict(type="data", array=sd), marker_color=col)
fig.add_scatter(name=f"paper · {m}", x=xs, y=pap, mode="markers",
marker=dict(symbol="line-ew", size=26, line=dict(
width=3, color=col)))
fig.update_layout(
title="Claim 3 — ADFTD ablation: this reproduction (bars, 3 seeds) "
"vs the paper's Table 2 (ticks)",
yaxis_title="score (%)", barmode="group", template="plotly_white",
height=460, legend=dict(orientation="h", y=-0.18))
fig.write_html(os.path.join(OUT, "claim3_ablation.html"),
include_plotlyjs="cdn")
# Fig 2: Claim 4 promotion comparison
if all(promos[k]["f1"] for k in promos):
fig2 = go.Figure()
names = list(promos)
fig2.add_bar(name="this reproduction", x=names,
y=[promos[k]["f1"]["rel_pct"] for k in names],
marker_color="#2F6F8F",
text=[f"{promos[k]['f1']['rel_pct']:+.2f}%" for k in names],
textposition="outside")
paper_promo = {"TS-Fingerprint": 13.07, "Ti-MAE": -0.96, "SimMTM": 4.42}
fig2.add_bar(name="paper (Table 4)", x=names,
y=[paper_promo[k] for k in names], marker_color="#B07C2B",
text=[f"{paper_promo[k]:+.2f}%" for k in names],
textposition="outside")
fig2.update_layout(
title="Claim 4 — relative F1 promotion from pre-training on ADFTD",
yaxis_title="relative F1 gain (%)", barmode="group",
template="plotly_white", height=440,
legend=dict(orientation="h", y=-0.15))
fig2.write_html(os.path.join(OUT, "claim4_promotion.html"),
include_plotlyjs="cdn")
# raw CSV for the figure cells
with open(os.path.join(OUT, "adftd_results.csv"), "w") as f:
f.write("config,label,n_seeds,acc,acc_sd,f1,f1_sd,auroc,auroc_sd,"
"paper_acc,paper_f1,paper_auroc,sec_per_seed,params\n")
for x in rows:
f.write(f"{x['config']},{x['label']},{x['n_seeds']},{x['acc']:.3f},"
f"{x['acc_sd']:.3f},{x['f1']:.3f},{x['f1_sd']:.3f},"
f"{x['auroc']:.3f},{x['auroc_sd']:.3f},{x['paper_acc']},"
f"{x['paper_f1']},{x['paper_auroc']},{x['sec_per_seed']:.0f},"
f"{x['params']}\n")
print("\nwrote results/adftd_summary.json, adftd_results.csv, "
"claim3_ablation.html, claim4_promotion.html")
if __name__ == "__main__":
main()
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