File size: 4,273 Bytes
92cfea3 | 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 | # Reconstructing the BioCandidateRanker training corpus
This guide rebuilds the exact development corpus, frozen split, and baseline
evaluation from the public upstream source plus the artifacts in this repository.
It never requires the measurement rows to pass through this dataset repository.
## 1. Obtain the original corpus
Download `Kcat_combination_0918_wildtype_mutant.json` from the original
[UniKP repository](https://github.com/Luo-SynBioLab/UniKP). The same wildtype+mutant
kcat compilation was distributed with DLKcat
([Zenodo record 6438262](https://zenodo.org/records/6438262)).
## 2. Verify the frozen identity
The project's frozen manifests pin the source bytes. Compute:
```bash
sha256sum Kcat_combination_0918_wildtype_mutant.json
```
Expected:
```
13643b0b36374f8d3f64d8b014882cf1b3b58946eeaae2b9dcd59e8b2c2d6719 (12,132,719 bytes, 17,010 rows)
```
If the hash differs, stop: the split assignments in `data/homology_split.parquet`
are keyed by row index and are only valid for a byte-identical copy. Cross-check
against `configs/unikp_source_manifest.json` and `data/unikp_audit.json` in this
repository. A verified copy was confirmed on 2026-10-24.
## 3. Audit the corpus with the project CLI
Unpack `release/software-implementation-v10-redacted.zip`, then:
```powershell
python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -e ".[dev]"
$env:PYTHONPATH="src"
python -m biocandidate.cli audit-data `
--data "<path>\Kcat_combination_0918_wildtype_mutant.json" `
--output artifacts\unikp_audit.json
```
Expected adapter statistics (recorded in `configs/unikp_source_manifest.json`):
| field | value |
|---|---|
| total rows | 17,010 |
| accepted | 16,838 |
| duplicates | 395 |
| conflicting duplicates | 215 |
| rejected | 172 |
## 4. Apply the frozen homology-cold split
The split in `data/homology_split.parquet` / `data/homology_split.json` maps each
0-based `source_row` to an MMseqs2 cluster and a partition (MMseqs2 `linclust`,
min identity 0.3, coverage 0.8, coverage mode 0, seed 42; 2,204 clusters;
13,470 / 1,684 / 1,684 rows). Join it to the source file by row index:
```python
import json
import pandas as pd
source = json.load(open("Kcat_combination_0918_wildtype_mutant.json", encoding="utf-8"))
split = pd.read_parquet("data/homology_split.parquet").set_index("source_row")
train = [source[i] for i in split.index[split["split"] == "train"]]
```
Or let the CLI bind the identity itself (recommended — it refuses a source that
does not match the audit manifest):
```powershell
python -m biocandidate.cli train `
--data "<path>\Kcat_combination_0918_wildtype_mutant.json" `
--manifest artifacts\unikp_audit.json `
--split-manifest artifacts\homology-final\homology_split.json `
--output-dir artifacts\training
```
(`artifacts/homology-final/homology_split.json` is included byte-identical inside
the release archive; the copy under `data/` here is the same file with its one
embedded local path redacted.)
## 5. Reproduce the frozen baseline evaluation
The baseline checkpoint `artifacts/full-homology-baseline/best.pt` ships inside
the release archive. Evaluate the test partition:
```powershell
python -m biocandidate.cli evaluate `
--checkpoint artifacts\full-homology-baseline\best.pt `
--data "<path>\Kcat_combination_0918_wildtype_mutant.json" `
--manifest artifacts\unikp_audit.json `
--split-manifest artifacts\homology-final\homology_split.json `
--partition test `
--output artifacts\full-homology-baseline\test_metrics.json
```
Reference result: `data/full-homology-baseline/test_metrics.json`.
> torch >= 2.6 note: `predict`/`evaluate` checkpoint loading with
> `weights_only=True` rejects pre-2.6 checkpoints. Install `torch<2.6` for CLI
> reproduction, or use the workbench server path documented in the README.
## 6. License obligations
- The corpus rows stay out of public archives: upstream terms are unresolved
(`configs/data_license_inventory.json`, corpus `unikp-kcat-development-corpus`).
- If you redistribute anything derived from the corpus, resolve the upstream terms
first and cite the UniKP and DLKcat papers.
- EnzEngDB v1 (CC-BY-4.0) and the Lunzer IMDH landscape (CC0) have their own
attribution requirements documented in the dataset card.
|