# scripts/data -- Data pipeline for the biopesticide-AI project

This package builds the training CSV consumed by the downstream CNN/GAN
model code. It produces a table where each row is a 21-nt siRNA sequence
with:

- the binarized efficacy label (`pest_label`; 1 if measured knockdown >=
  0.7, else 0) -- this is the SCIENTIFICALLY CORRECT label, replacing
  the original merged code's wrong heuristic of labeling any 100-nt tile
  from a pest gene as `pest_label=1`,
- 8 Reynolds 2004 siRNA efficacy features + a `reynolds_score`,
- per-species off-target risk against the 6-species safety panel
  (apis_mellifera, bos_taurus, bos_indicus, gallus_gallus, danio_rerio,
  homo_sapiens).

## Files

| File                      | Purpose                                                                              |
| ------------------------- | ------------------------------------------------------------------------------------ |
| `__init__.py`             | Empty; marks `scripts/data` as a Python package.                                     |
| `_legacy_imports.py`      | Shim that re-exports `fasta_iter`, `KmerOffTargetIndex`, `tile_sequence`, etc. from the original `upload/merged_biopesticide_ai.py`. Copied verbatim (not imported) so the data pipeline does not depend on the soon-to-be-refactored `src/` layout. |
| `reynolds_features.py`    | `ReynoldsFeaturizer` class implementing the 8 Reynolds 2004 siRNA efficacy criteria. Pure Python + pandas, no torch. |
| `download_sources.py`     | Attempts to download siRecords, DRSC/TRiP, and genomeRNAi into `data/external/`. Verifies (but never re-downloads) the NCBI `rna.fna` files expected under `data/<species>/ncbi_dataset/data/<assembly>/`. All download failures are logged and skipped. |
| `generate_synthetic.py`   | Generates a small synthetic mini-dataset (50 pest transcripts, 1000 siRNAs split 500 high-efficacy / 500 low-efficacy, 50 safety transcripts across 5 species) so the pipeline can run end-to-end without 20GB of NCBI data. |
| `build_training_csv.py`   | The main orchestrator. Loads siRNAs, builds the off-target index, computes Reynolds features + off-target risk, binarizes the label, writes the final CSV. |

## How to run

All commands are run from the project root (the directory containing the
`bioai/` and `scripts/` packages) and use `python -m` so that intra-package
imports resolve correctly.

### Synthetic path (recommended for development; no external data needed)

```bash
# Step 1: generate the synthetic mini-dataset.
python -m scripts.data.generate_synthetic

# Step 2: build the final training CSV.
python -m scripts.data.build_training_csv --source synthetic
```

Output:
- `data/synthetic/pest_transcripts.fasta`
- `data/synthetic/safety_transcripts.fasta`
- `data/synthetic/sirna_training.csv`
- `data/processed/training_data.csv`  <-- consumed by the model code

### Real path (requires real siRNA CSV + NCBI rna.fna files on disk)

```bash
# Step 1: attempt to fetch public RNAi databases (best-effort; will log
#         and skip on failure). This also verifies which NCBI files are
#         on disk.
python -m scripts.data.download_sources

# Step 2: build the final training CSV from real siRNA data + real NCBI
#         safety panel. Place your real siRNA CSV (with columns
#         `sirna_seq,target_gene,knockdown_pct`) at
#         `data/external/sirna_real.csv`, or pass --input <path>.
python -m scripts.data.build_training_csv --source real
```

Output:
- `data/external/sirecords.csv` (only if the download succeeded)
- `data/external/drsc_trip.csv` (only if the download succeeded)
- `data/external/genomernai_phenotypes.txt` (only if the download succeeded)
- `data/processed/training_data_real.csv`  <-- consumed by the model code

## Output CSV schema

`data/processed/training_data.csv` (synthetic) and
`data/processed/training_data_real.csv` (real) have identical schemas:

```
sirna_seq, target_gene, knockdown_pct, pest_label,
gc_content, no_repeats, at_pos19, a_pos3, t_pos10, ag_pos13,
t_pos16, thermo_asymmetry, reynolds_score,
offtarget_apis_mellifera, offtarget_bos_taurus,
offtarget_bos_indicus, offtarget_gallus_gallus,
offtarget_danio_rerio, offtarget_homo_sapiens
```

- `pest_label` = 1 if `knockdown_pct >= 0.7` else 0.
- `reynolds_score` = integer 0-8, the count of Reynolds 2004 criteria met.
- `offtarget_<species>` = fraction of the siRNA's 21-mers (and their
  reverse complements) that appear in that species' transcriptome. 0.0
  means no exact 21-mer overlap.

## Dependencies

`pandas`, `numpy`, `pyyaml`, `biopython`, `tqdm`, `requests`. All CPU-only.
No torch is required to run any of these scripts (the model code in
`src/models/` will import the produced CSV).

## Reproducibility

All synthetic generation seeds BOTH `numpy.random.default_rng(13)` and
Python's `random.Random(13)` with the same seed (13). Re-running
`generate_synthetic.py` produces byte-identical output.

## Known limitations

- The synthetic siRNAs are random 21-mers, and the synthetic safety
  transcripts are also random. Exact 21-mer overlap between the two is
  effectively zero, so all `offtarget_<species>` columns are 0.0 in the
  synthetic path. This is expected; the real-data path will produce
  meaningful off-target signal.
- `KmerOffTargetIndex` uses exact 21-mer matching, which is strict.
  Real RNAi off-target effects often involve seed-region matches with
  mismatches; that's a future enhancement (out of scope for Task 3-B).
- The public RNAi databases (siRecords, DRSC/TRiP, genomeRNAi) often
  have unstable download endpoints. `download_sources.py` tries several
  candidate URLs per source and logs+skips on failure; it will never
  block the pipeline.
- `pest_label` is binarized from `knockdown_pct` at threshold 0.7. This
  is a simple heuristic; the spec asks for this exact threshold.