| # Initial 2,000-protein dataset |
|
|
| This is the canonical local root for the first complete router dataset: 1,000 |
| single-dominant structured-state proteins (label `0`) and 1,000 dynamic or |
| heterogeneous-state proteins (label `1`). The fixed split is 1,400 train, 300 |
| validation, and 300 test proteins. |
|
|
| Place Colab's completed ESMFold result files (`<sequence_sha256>.npz`) in |
| `esmfold_results/`. Then import them with: |
|
|
| ```bash |
| uv run python scripts/esmfold_dataset.py import |
| ``` |
|
|
| The importer validates every NPZ against `esmfold_input_manifest.csv` and |
| writes the reusable package-native bundles into `embeddings/esmfold_v1/`. It |
| also writes `embeddings/esmfold_v1/manifest.parquet`, the index consumed by the |
| training commands. Do not train directly from `esmfold_results/`; it is only a |
| transfer/staging location and can be deleted after a successful import. |
|
|
| Key files: |
|
|
| - `catalog.parquet` and `catalog.csv`: canonical labels, sequences, metadata, |
| and fixed split assignments. |
| - `catalog_esmfold_eligible.parquet` and `.csv`: the 1,988 rows with verified |
| ESMFold bundles; use these for embedding-only or combined model training. |
| - `esmfold_input_manifest.csv`: exact sequence hashes and model input contract. |
| - `embeddings/esmfold_v1/manifest.parquet`: verified embedding-bundle index. |
| - `embeddings/esmfold_v1/excluded.csv`: the 12 sequences outside ESMFold's |
| 1,022-residue limit. |
|
|
| For CPU/MPS training, pass `--dataset data/initial_2000_dataset/catalog.parquet` |
| and `--bundle-manifest data/initial_2000_dataset/embeddings/esmfold_v1/manifest.parquet`. |
|
|
