riteshhf/tsfp-repro-code

TS-Fingerprint — independent reproduction code (ICML 2026 #7348)

Independent re-implementation of "Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization" (arXiv 2605.00130, OpenReview lrRBHFIgaK).

The paper released no code. A GitHub code search for TS-Fingerprint returns zero repositories and the paper carries no code link, so every component here was derived from the paper text (Sec. 3.2 architecture, Sec. 3.3 losses, Sec. 3.4 head, Sec. 4.1 hyperparameters). Data preprocessing follows the paper's own stated protocol reference, DL4mHealth/Medformer.

Full write-up, per-claim evidence and the agent session trace: the Trackio reproduction logbook.

Files

File	Purpose
model.py	TSFingerprint (Perceiver-style cross-attention bottleneck, masked F′-only decoder, TCR diversity loss, attention-pooling head) + TiMAE and SimMTM baselines
train.py	pre-train → fine-tune harness; subject-wise splits, Adam 1e-3/1e-4, ≤100 epochs, early stopping on val F1 (patience 10), macro Acc/P/R/F1/AUROC
schedule.py	small GPU-slot scheduler for running the config grids locally
prep_adftd.py	ADFTD (OpenNeuro ds004504) → 69,752 windows, reproducing Medformer's notebook
prep_ptbxl.py	PTB-XL (PhysioNet) → 213,880 windows, 5 diagnostic superclasses
audit_claim1.py	Claim 1: fixed k×d output, learnable Q, k ≪ T, strict X → F′ → X̂ chain
audit_theorem31.py	Claim 2: numerical audit of Theorem 3.1, both legs, with controls
audit_table1_rank.py	Claim 5: re-parses all 420 Table-1 values and recomputes the average rank
analyze_adftd.py, analyze_sweep.py	Claims 3/4 and Claim 6 summaries + figures
jobs_*.json	the exact config grids that were run

Data

Processed ADFTD arrays: riteshhf/tsfp-repro-adftd-processed. PTB-XL must be fetched from PhysioNet (see prep_ptbxl.py; the physionet-open S3 mirror works without credentials).

Reproducing

python prep_adftd.py --root data/ADFTD --out data/ADFTD/processed
python train.py --data-dir data/ADFTD/processed --model tsfp --mode pretrain_ft \
                --use-div 1 --k 8 --mask-ratio 0.6 --seeds 41 42 43 --out rec_div.json