Datasets:

fromhope
/

acubench

---
license: cc-by-4.0
language:
  - en
pretty_name: "AcuBench: Indication-based Acupoint-Set Recommendation"
task_categories:
  - text-classification
  - other
tags:
  - acupuncture
  - traditional-chinese-medicine
  - multi-label-classification
  - conformal-prediction
  - knowledge-graph
  - health
  - build-script-dataset
size_categories:
  - n<1K
configs:
  - config_name: sample
    data_files: sample_labels.jsonl
---

# AcuBench

**Indication-based acupoint-set recommendation.** Given an indication /
symptom string (e.g. `"Headache"`), predict the set of WHO-standard acupoints
indicated for it, grounded in AcuKG's `Indication` table, over a fixed
361-point label space. AcuBench is a small (446-sample) benchmark with a
dedicated conformal-prediction calibration split.

> **Not a clinical prescription benchmark.** A row's acupoint set is
> *"acupoints indicated for this symptom in AcuKG"*, i.e. a **candidate pool**,
> **not** a set a practitioner would prescribe together in one session. Large
> sets (e.g. 63 points for "Headache") are a symptom of pooling, not a
> 63-point prescription. See the datasheet caveat below.

## Why the labels are not shipped here (BUILD-SCRIPT pattern)

AcuBench's labels are derived from [AcuKG](https://github.com/) `Indication.csv`,
and **AcuKG has no license file** (confirmed via GitHub API: `"license": null`).
Under default copyright we do **not** have redistribution rights to AcuKG's raw
files or to a substantially complete derived copy of them.

So this repo does **not** contain the assembled labels (`acubench.jsonl`) or the
raw AcuKG clone. Instead it ships a **deterministic build script** that
regenerates the exact same labels + splits **locally on your machine** from a
copy of AcuKG that **you** clone yourself. What ships here:

| File | What it is | AcuKG-derived? |
|---|---|---|
| `build_acubench.py` | Deterministic build script (seed=42) | No (code) |
| `who_acupoints.csv` | From-scratch WHO 361-acupoint skeleton (14 meridians) | **No** |
| `meridian_adjacency.csv` | 347 on-meridian adjacency edges | **No** |
| `eval.py` | Self-contained metric harness | No (code) |
| `sample_labels.jsonl` | 20-row illustrative sample, attributed | Small attributed excerpt |
| `LICENSE`, `NOTICE` | License + AcuKG-dependency notice | No |

## Reproduce the full benchmark

```bash
# 1. Clone AcuKG yourself (under its terms, not ours):
git clone https://github.com/<acukg-owner>/AcuKG.git /path/to/acukg

# 2. Regenerate the exact 446-row benchmark + splits locally:
python3 build_acubench.py --acukg /path/to/acukg --out ./build
```

This writes `build/acubench.jsonl` (446 rows) and
`build/splits/{train,val,calib,test}_ids.txt`. The script is byte-for-byte
equivalent to the reference research pipeline (seed=42); it prints per-file
SHA-256 checksums and asserts the 446 / 266-70-65-45 counts so you can confirm
you regenerated the canonical dataset.

Each `acubench.jsonl` row:

```json
{"id": 0, "indication": "Abdomen Skin Itching", "acupoints": ["CV15"],
 "meridians_present": ["CV"], "n_points": 1, "split": "train"}
```

## Splits

60/15/15/10 train/val/calib/test, **stratified by target-set-size bin**
(`{1, 2, 3-5, 6-8, 9-12, 13-20, 21-40, 41+}`) with deterministic
largest-remainder allocation (seed=42). Realized sizes (446 total):

| split | n | purpose |
|---|---|---|
| train | 266 | model fitting |
| val | 70 | model selection / threshold tuning |
| calib | 65 | **conformal-prediction calibration** (disjoint from val/test) |
| test | 45 | held-out final reporting |

Target-set-size distribution is heavily right-skewed: min 1, **median 2**,
mean ~5.3, max 63; 88% of indications have ≤12 points. 360 of the 361 points
appear as a label (`ST17` never does).

## Metric suite (`eval.py`)

Self-contained (numpy + scikit-learn only). Scores a predictions JSONL against
a gold JSONL over the 361-point space:

- **Set metrics**: `jaccard_mean`, `f1_micro`, `f1_macro` (example-based)
- **Ranking metrics** (need per-point scores): `precision_at_k`, `recall_at_k`,
  `ndcg_at_k` for k∈{5,10,20}, `prauc_mean`
- **`invalid_combination_rate`**: a **structural meridian-scatter PROXY** for
  prescription plausibility (analogous in spirit to a DDI-rate), **not** a
  clinical-safety number.

Prediction format (JSONL, one object per line):

```json
{"id": 0, "acupoints": ["CV15"], "scores": {"CV15": 0.9, "LU1": 0.1}}
```

- `acupoints` drives the set metrics + validity proxy.
- `scores` (optional but recommended) drives the ranking metrics; without it,
  ranking falls back to alphabetical order of the predicted set.

```bash
# score the test split of your locally-built gold:
python3 eval.py --pred preds.jsonl --gold build/acubench.jsonl \
    --split test --who who_acupoints.csv
# score against the shipped 20-row sample (keyed by symptom string):
python3 eval.py --pred preds.jsonl --gold sample_labels.jsonl \
    --gold-key symptoms --who who_acupoints.csv
```

## Reference baselines (from the AcuBench paper, TEST n=45)

| model | Jaccard | F1-micro | F1-macro | P@5 | R@10 | NDCG@10 | PRAUC |
|---|---|---|---|---|---|---|---|
| popularity | 0.0137 | 0.0328 | 0.0260 | 0.0089 | 0.0219 | 0.0169 | 0.0291 |
| association (TF-IDF kNN) | 0.0440 | 0.0595 | 0.0672 | 0.0711 | 0.1602 | 0.1299 | 0.1114 |
| mlp (OvR-LogReg) | 0.0430 | 0.1304 | 0.0624 | 0.0756 | 0.1345 | 0.1148 | 0.1032 |
| RAkEL (Label-Powerset) | 0.0358 | 0.0659 | 0.0584 | 0.0622 | 0.1331 | 0.1069 | 0.0978 |
| MatrixFactorization | 0.0277 | 0.0637 | 0.0492 | 0.0800 | 0.1789 | 0.1274 | 0.0988 |

Absolute numbers are modest by design (the task has ~50% single-point targets
over 361 classes). Learned baselines beat popularity by ~2.5-5x on most metrics.

## Limitations (honest)

- **Single label source**: all labels derive from one structured source (AcuKG
  `Indication.csv`); no independent source cross-validates it.
- **Small scale**: 446 samples / 361 classes — underpowered for strong
  coverage guarantees. Conformal results in the paper are a small-scale pilot
  (strict full-containment conformal is data-sparsity-bound at this scale;
  it degenerates to ~100% abstention at a 30-point cap).
- **Indication strings are not deduplicated / normalized** (near-synonyms are
  distinct rows).
- **Random split only** (stratified by set size); no temporal/population split,
  so it tests interpolation within AcuKG's vocabulary, not novel indications.
- **`invalid_combination_rate` is a structural proxy**, not clinical validity.

## Citation

```bibtex
@misc{acubench2026,
  title  = {AcuBench: A Benchmark for Indication-based Acupoint-Set Recommendation},
  author = {You, Taewan},
  year   = {2026},
  note   = {Labels derived from AcuKG via a local build script; see NOTICE.}
}
```

Please also cite **AcuKG** (the upstream source of the derived labels) per its
authors' request.