| |
| """Cold-start top-1 fixture reproduction using only NumPy and packaged files.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import csv |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
|
|
| def read_tsv(path: Path) -> list[dict[str, str]]: |
| with path.open("r", encoding="utf-8", newline="") as handle: |
| return list(csv.DictReader(handle, delimiter="\t")) |
|
|
|
|
| def deterministic_top1(scores: np.ndarray, labels: list[str]) -> int: |
| maximum = float(scores.max()) |
| tied = np.flatnonzero(scores == maximum).tolist() |
| return min(tied, key=lambda index: labels[index]) |
|
|
|
|
| def main() -> int: |
| parser = argparse.ArgumentParser() |
| parser.add_argument("kernel", type=Path) |
| parser.add_argument("--out", type=Path) |
| args = parser.parse_args() |
| root = args.kernel.resolve() |
| queries = np.load(root / "e0-query-features-fp16.npy", mmap_mode="r", allow_pickle=False) |
| prototypes = np.load(root / "final-species-centroids-f64.npy", mmap_mode="r", allow_pickle=False) |
| species_rows = read_tsv(root / "species-prototype-map.tsv") |
| fixtures = read_tsv(root / "minimal-reproduction-fixtures.tsv") |
| labels = [row["canonical_taxon_key"] for row in species_rows] |
| if queries.shape != (6719, 1024) or prototypes.shape != (19144, 1024): |
| raise ValueError("kernel array shape mismatch") |
| results = [] |
| for fixture in fixtures: |
| query = np.asarray(queries[int(fixture["query_tensor_row"])], dtype=np.float64) |
| scores = query @ prototypes.T |
| top1_index = deterministic_top1(scores, labels) |
| prediction = labels[top1_index] |
| observed_hit = int(prediction == fixture["evaluation_label_key"]) |
| expected_hit = int(fixture["expected_top1_hit"]) |
| results.append( |
| { |
| "fixture_order": int(fixture["fixture_order"]), |
| "query_id": fixture["query_id"], |
| "evaluation_label_key": fixture["evaluation_label_key"], |
| "predicted_top1_label_key": prediction, |
| "expected_top1_hit": expected_hit, |
| "observed_top1_hit": observed_hit, |
| "status": "PASS" if observed_hit == expected_hit else "FAIL", |
| } |
| ) |
| passed = sum(row["status"] == "PASS" for row in results) |
| summary = { |
| "status": "PASS" if passed == len(results) else "FAIL", |
| "implementation": "NumPy only; no network, pixels, PyTorch, or absolute source paths", |
| "fixtures": len(results), |
| "passed": passed, |
| "failed": len(results) - passed, |
| "results": results, |
| } |
| text = json.dumps(summary, indent=2, sort_keys=True) + "\n" |
| if args.out: |
| args.out.write_text(text, encoding="utf-8") |
| print(text, end="") |
| return 0 if summary["status"] == "PASS" else 1 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main()) |
|
|
