# evo3 eval subsets — `standard` and `hard`

The exact rows the evo3 `standard` and `hard` eval suites score, one parquet
per dataset. Exported so these subsets can be scored elsewhere without running
the evo3 eval or reimplementing its selection logic.

- Exported: 2026-07-24T19:52:15.198474+00:00
- evo3 commit: `48089c9b12ce559086bfa4a0d0f77f332cf1e378`
- pandas: 3.0.3

## Layout

```
standard/<dataset>.parquet
hard/<dataset>.parquet
manifest.json      # full per-dataset provenance (source path, seed, sha256, columns)
```

## Contents

| suite | dataset | rows | pos | neg | selection | source rows |
| --- | --- | ---: | ---: | ---: | --- | ---: |
| standard | clinvar | 5000 | 1675 | 3325 | `stratified_representative` n=5000 seed=1234 | 200036 |
| standard | splicevar | 5000 | 2979 | 2021 | `stratified_representative` n=5000 seed=1234 | 11978 |
| standard | gnomad_balanced | 5000 | 2500 | 2500 | `stratified_representative` n=5000 seed=1234 | 11992284 |
| standard | cosmic | 2000 | 183 | 1817 | `stratified_representative` n=2000 seed=1234 | 18903 |
| standard | traitgym_mendelian | 3380 | 338 | 3042 | full dataset (no subsampling) | 3380 |
| hard | traitgym_complex | 6840 | 1140 | 5700 | `matched_group_representative` n=None seed=1234 | 11400 |
| hard | denovodb | 4999 | 2569 | 2430 | `stratified_representative` n=5000 seed=1234 | 68669 |
| hard | causal_mpra | 5000 | 1024 | 3976 | `stratified_representative` n=5000 seed=1234 | 12256 |

## Columns

Each parquet carries its source dataset's own columns plus what the evo3 eval
loader derives:

- **`_label`** — 1 = positive, 0 = negative. Use this rather than the raw label
  column; the per-dataset positive/negative values are in `manifest.json`.
- **`chrom`** — normalized *without* a `chr` prefix (`1`, not `chr1`).
- **`strand`** — present where the dataset declares strand annotations (ClinVar);
  minus-strand windows are meant to be reverse-complemented before scoring.
- **`_subset_stratum`** — audit key naming the stratum a row was drawn from.

## Notes

- Selection is seeded and order-invariant: rows are sorted into a canonical
  `(chrom, pos, ref, alt)` order before sampling, so the same seed picks the same
  variants regardless of the source parquet's on-disk row order. Every subset here
  was built twice and compared on the variant key before being written.
- `traitgym_mendelian` carries no subset and is exported in full (~3.4k rows).
- `traitgym_complex` keeps every positive with its matched controls, so its size is
  `n_pos * (1 + negs_per_pos)` rather than a round `n`.
- `cosmic` keeps all 183 positives and fills the rest of n=2000 with negatives.
- `denovodb` holds 4999 rows, not 5000: the subset draws 5000 but one row has a
  null `group_tag`, which the eval loader drops when deriving `_label`. The eval
  scores 4999 too.

Regenerate with:

```bash
PYTHONPATH=src python scripts/eval/export_subset_parquets.py \
    --suites standard hard --out /mnt/weka/shared_datasets/nvidia_subset_parquets
```