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bf801fa c7b5dd7 bf801fa 1413cec bf801fa | 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 | #!/usr/bin/env python3
"""Export a canonical paper_data.yaml for the autopaper pipeline.
Single source of truth for the PINO paper's facts/numbers. Sources (all read
verbatim, no recomputation beyond trivial deltas):
- artifacts/representation_ablation/ablation_results.json (frozen ablation)
- artifacts/v10_line_drawn_metrics.json (substantivity GBM)
- artifacts/frozen_eval_{morgan,openpom_256}.json (A/B readout)
- dataset curation stats (trainable corpus, repair chain)
Macro naming contract (autopaper build_paper.py): keys are CamelCase letters
only; the builder prefixes each with "D" (e.g. pomThreshRho -> \\DPomThreshRho)
and also emits a lowercase legacy alias. Digits in a key are spelled to words,
so keep digits OUT of key names (use pomDim -> \\DPomDim, value carries 256).
"""
from __future__ import annotations
import json
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
OUT = ROOT / "artifacts" / "paper_data.yaml"
def load(rel: str):
p = ROOT / rel
return json.loads(p.read_text()) if p.exists() else None
def r(x, n=3):
return round(float(x), n) if x is not None else None
def main() -> int:
abl = load("artifacts/representation_ablation/ablation_results.json") or {}
v10 = load("artifacts/v10_line_drawn_metrics.json") or {}
# Prefer the authoritative GPU (20-epoch) readout; fall back to the CPU
# (6-epoch) run only if the GPU evals are absent. Prevents regenerating
# paper_data.yaml with the superseded small-n CPU numbers.
frozen_m = load("artifacts/frozen_eval_morgan_gpu.json") or load("artifacts/frozen_eval_morgan.json") or {}
frozen_p = load("artifacts/frozen_eval_openpom_256_gpu.json") or load("artifacts/frozen_eval_openpom_256.json") or {}
tasks = abl.get("tasks", {})
thr = tasks.get("odor_threshold", {})
sub = tasks.get("substantivity", {})
desc = tasks.get("descriptor", {})
subst = tasks.get("substitution", {})
def rep(task, name):
return (task.get("representations", {}) or {}).get(name, {})
def delta(task, name):
return (task.get("paired_deltas", {}) or {}).get(name, {})
# substantivity GBM locked holdout
sub_lock = (v10.get("part1", {}).get("substantivity", {}).get("locked_holdout", {}))
sub_cv = (v10.get("part1", {}).get("substantivity", {}).get("cv", {}))
# A/B frozen readout (verbatim)
def tr_acc(d):
return (d.get("substitution_triplets", {}) or {}).get("accuracy")
def tr_n(d):
return (d.get("substitution_triplets", {}) or {}).get("n_triplets")
def pr_cos(d):
return (d.get("prospective_formulas", {}) or {}).get("mean_family_profile_cosine")
def pr_n(d):
return (d.get("prospective_formulas", {}) or {}).get("n_formulas")
dm = {
# ---- representation dims ----
"morganDim": 138,
"pomDim": 256,
"abMorganInputDim": 151,
"abPomInputDim": 269,
# ---- frozen ablation: odor threshold ----
"threshN": thr.get("n"),
"threshMorganRho": r(rep(thr, "morgan").get("spearman_mean")),
"threshPomRho": r(rep(thr, "real_pom").get("spearman_mean")),
"threshPomPhysRho": r(rep(thr, "pom_plus_physics").get("spearman_mean")),
"threshPomVsMorganDelta": r(delta(thr, "real_pom_vs_morgan").get("spearman_mean_delta")),
# ---- frozen ablation: substantivity ----
"substN": sub.get("n"),
"substMorganRho": r(rep(sub, "morgan").get("spearman_mean")),
"substPomRho": r(rep(sub, "real_pom").get("spearman_mean")),
"substPomPhysRho": r(rep(sub, "pom_plus_physics").get("spearman_mean")),
"substPomVsMorganDelta": r(delta(sub, "real_pom_vs_morgan").get("spearman_mean_delta")),
# ---- frozen ablation: descriptor (macro-F1) ----
"descN": desc.get("n"),
"descNClasses": desc.get("n_classes"),
"descMorganMacroF": r(rep(desc, "morgan").get("macro_f1_mean")),
"descPomMacroF": r(rep(desc, "real_pom").get("macro_f1_mean")),
"descPomPhysMacroF": r(rep(desc, "pom_plus_physics").get("macro_f1_mean")),
"descPomVsMorganDelta": r(delta(desc, "real_pom_vs_morgan").get("macro_f1_mean_delta")),
# ---- frozen ablation: substitution retrieval ----
"substPairsN": subst.get("n_pairs"),
"substMorganTopFive": r(rep(subst, "morgan").get("top5_accuracy")),
"substPomTopFive": r(rep(subst, "real_pom").get("top5_accuracy")),
"substPomPhysTopFive": r(rep(subst, "pom_plus_physics").get("top5_accuracy")),
# ---- substantivity GBM locked holdout (v10_line_drawn) ----
"gbmNModelReady": v10.get("part1", {}).get("substantivity", {}).get("n_model_ready"),
"gbmLockedRho": r(sub_lock.get("spearman_rho")),
"gbmLockedRtwo": r(sub_lock.get("r2")),
"gbmLockedMae": r(sub_lock.get("mae")),
"gbmCvRho": r(sub_cv.get("mean_spearman_rho")),
# ---- two-arm A/B frozen readout (verbatim, CPU run) ----
"abMorganInputDimReadout": frozen_m.get("input_embedding_dim"),
"abPomInputDimReadout": frozen_p.get("input_embedding_dim"),
"abMorganTripletAcc": r(tr_acc(frozen_m)),
"abPomTripletAcc": r(tr_acc(frozen_p)),
"abTripletN": tr_n(frozen_m) or tr_n(frozen_p) or 20,
"abMorganProspectiveCos": r(pr_cos(frozen_m)),
"abPomProspectiveCos": r(pr_cos(frozen_p)),
"abProspectiveN": pr_n(frozen_m) or pr_n(frozen_p) or 40,
"abMorganValTotal": r(frozen_m.get("final_val_total")),
"abPomValTotal": r(frozen_p.get("final_val_total")),
# ---- dataset curation stats ----
"dsTotalRows": 5708,
"dsTrainableRows": 5678,
"dsTrainablePct": 99.47,
"dsBannedCasBefore": 309,
"dsBannedCasAfter": 0,
"dsLiteratureRepairPairs": 106,
"dsTrajSteps": 49,
"dsTrajDim": 138,
"dsTripletN": 20,
"dsProspectiveN": 40,
# ---- honest negatives ----
"negRetrievalTopFive": 0,
"negCharacterMacroF": 0.134,
}
# drop Nones so the builder never emits an empty macro
dm = {k: v for k, v in dm.items() if v is not None}
lines = [
"# Auto-generated by scripts/export_paper_data.py. Do not edit by hand.",
"# Canonical facts/numbers for the PINO paper (autopaper pipeline).",
"data_macros:",
]
for k, v in dm.items():
lines.append(f" {k}: {v}")
OUT.write_text("\n".join(lines) + "\n")
print(f"wrote {OUT} with {len(dm)} data_macros")
for k, v in dm.items():
print(f" \\D{k[0].upper()+k[1:]} = {v}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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