| """Commands do not run evaluations unless `evaluate` is explicitly invoked.""" |
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(prog="clc-mini-bench", description="CLC NKp46 benchmark preparation and frozen-feature evaluation") |
| sub = parser.add_subparsers(dest="command", required=True) |
| build = sub.add_parser("build", help="Prepare annotations, deduplicated datasets, and family folds") |
| build.add_argument("--raw", type=Path, default=Path("data/raw/measurements.csv")) |
| build.add_argument("--output", type=Path, default=Path("data/processed")) |
| build.add_argument("--protocol", type=Path) |
| check = sub.add_parser("validate", help="Validate both prepared datasets and file checksums") |
| check.add_argument("--data-dir", type=Path, default=Path("data/processed")) |
| embeddings = sub.add_parser("embed", help="Extract frozen embeddings with ESM-2 or a custom adapter") |
| embeddings.add_argument("--dataset", type=Path, required=True) |
| embeddings.add_argument("--output", type=Path, required=True) |
| embeddings.add_argument("--adapter", help="Python module:function receiving label-free sequence records") |
| embeddings.add_argument("--model", default="facebook/esm2_t33_650M_UR50D") |
| embeddings.add_argument("--revision", help="Model repository revision or commit") |
| embeddings.add_argument("--batch-size", type=int, default=16) |
| embeddings.add_argument("--device", default="cpu") |
| evaluate = sub.add_parser("evaluate", help="Run grouped outer CV with nested Ridge model selection") |
| evaluate.add_argument("--dataset", type=Path, required=True) |
| feature_source = evaluate.add_mutually_exclusive_group(required=True) |
| feature_source.add_argument("--features", type=Path) |
| feature_source.add_argument("--composition-baseline", action="store_true") |
| evaluate.add_argument("--output", type=Path, required=True) |
| evaluate.add_argument("--protocol", type=Path) |
| args = parser.parse_args() |
| if args.command == "build": |
| from .data import build as run |
| result = run(args.raw, args.output, args.protocol) |
| print(json.dumps(result["datasets"], indent=2)) |
| elif args.command == "validate": |
| import pandas as pd |
| from .data import sha256_file, validate_dataset |
| manifest = json.loads((args.data_dir / "manifest.json").read_text()) |
| for name, expected in manifest["files"].items(): |
| if sha256_file(args.data_dir / name) != expected: |
| raise ValueError(f"File checksum mismatch: {name}") |
| for platform in ("omniflic", "omniclic"): |
| frame = pd.read_csv(args.data_dir / f"{platform}_nkp46.csv") |
| validate_dataset(frame) |
| print(f"{platform}: {len(frame)} samples, {frame.family_id.nunique()} families, {frame.fold_id.nunique()} folds; valid") |
| elif args.command == "embed": |
| from .embeddings import extract_embeddings |
| result = extract_embeddings(args.dataset, args.output, adapter=args.adapter, |
| model_name=args.model, batch_size=args.batch_size, device=args.device, revision=args.revision) |
| print(json.dumps(result, indent=2)) |
| elif args.command == "evaluate": |
| from .evaluation import evaluate as run |
| result = run(args.dataset, args.output, features=args.features, |
| baseline=args.composition_baseline, protocol_path=args.protocol) |
| print(json.dumps(result["pooled_out_of_fold"], indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
