T5A — Molecules / graphs (QM9-style)¶
Paper evidence: main.pdf · Block findings
Lemma: D5 · Stack: pytorch
Nuisance key: compositional
Production change: Conformer / position blocks move; property label fixed.
Notebook (Run All, built-in demo): t05a-qm9-molecule.ipynb
pip install matching-pmh torch
# Open the notebook and Run All
What this task achieved (headline)¶
E1 matched position PMH: clean MAE 24.921 (−0.155 vs B0); σ=0.2 Å noise MAE 47.415 vs B0.
| B0 MAE | E1 MAE | @ σ=0.2 |
|---|---|---|
| 25.076 | 24.921 | 47.415 |
Paper preset: t5_compositional_d5 · from pmh.benchmark.presets import get_preset
Subtasks (paper)¶
QM9 train + noise + embedding eval¶
E1 clean MAE 24.921.
Preset: t5_compositional_d5
MolGCN training¶
Preset: t5_compositional_d5
Position-noise eval sweep¶
Preset: t5_compositional_d5
Run with matching-pmh¶
from pmh import PMHTrainer, evaluate_robust_fit
# nuisance="compositional"
Do not use PMH when¶
Property definition changes at deploy.
Replace demo data with yours¶
Swap demo loaders for your train_loader, source_batches, target_batches, and deploy holdout. Hook the backbone before your task head.