legacy/final/predict_eval.py

"""Score the released BioDCASE-2026 Task 5 evaluation clips with the deployed unanimous-3
agreement-gate ensemble and write the official submission .txt.

Pipeline (mirrors final/infer.py, but on the eval set instead of cached test):
  1. Perch (1536-d) and BirdMAE (1024-d) embeddings are read from parquets produced by the
     sibling repo's scripts/extract_fm_embeddings.py (frozen FMs, cannot run in this repo).
  2. Harmonic (102-d) and background-whitened (257-d) features are computed here, directly
     from each eval wav, with the SAME helpers the deployed members were trained on.
  3. GatedEnsemble(bundle.pt).predict_proba(perch, harmonic, birdmae, bgwhiten) -> (N,9).
  4. argmax -> class index 0..8 -> OFFICIAL 1-based predicted_species_id via framework.metadata.
  5. write file_id,predicted_species_id rows (file_id without .wav).

Run with the sibling TF venv python (has torch + librosa + soundfile + pyarrow):
  .../Cross-Domain-Mosquito-Species-Classification-Tensorflow/.venv/bin/python final/predict_eval.py
Add --self-test to first confirm the bundle reproduces dev BA_unseen ~= 0.3616 on cached test.
"""
import os, sys, glob, argparse, json
import numpy as np

HERE = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.dirname(HERE)
sys.path.insert(0, HERE)   # final/ : ensemble_model, harmonic_features, bgwhiten_features, model
sys.path.insert(0, ROOT)   # repo root : framework/

from framework.metadata import SPECIES_NAMES, SPECIES_ID_TO_NAME, DOMAIN_NAMES  # noqa: E402
import harmonic_features as HF      # noqa: E402
import bgwhiten_features as BG      # noqa: E402

# class index 0..8 -> official 1-based species id, DERIVED from metadata (not a literal +1)
_NAME_TO_SPECIES_ID = {name: int(sid) for sid, name in SPECIES_ID_TO_NAME.items()}
IDX_TO_SPECIES_ID = [_NAME_TO_SPECIES_ID[SPECIES_NAMES[i]] for i in range(len(SPECIES_NAMES))]

EVAL_PARQUET_DIR = "/home/alaska/Projects/Cross-Domain-Mosquito-Species-Classification-Tensorflow/reports/fm_embeddings/eval"
DEV_BIRDMAE_PARQUET = "/home/alaska/Projects/Cross-Domain-Mosquito-Species-Classification-Tensorflow/reports/fm_embeddings/birdmae.parquet"


def read_ids(path):
    with open(path) as fh:
        return [ln.strip() for ln in fh if ln.strip()]


def load_parquet_emb(path, ids, dim):
    """Return (len(ids), dim) array aligned to `ids` by file_id; missing -> zeros (counted)."""
    import pyarrow.parquet as pq
    t = pq.read_table(path)
    fid = np.array(t.column("file_id").to_pylist())
    emb = t.column("embedding").combine_chunks().values.to_numpy().reshape(-1, dim).astype(np.float32)
    idx = {f: i for i, f in enumerate(fid)}
    out = np.zeros((len(ids), dim), np.float32)
    missing = 0
    for j, f in enumerate(ids):
        i = idx.get(f)
        if i is None:
            missing += 1
        else:
            out[j] = emb[i]
    return out, missing


def _feat_one(args):
    """Worker: load one wav once, return (harmonic102, bgwhiten257, ok)."""
    audio_root, fid = args
    try:
        y = HF.load_wav(os.path.join(audio_root, f"{fid}.wav"))
        return HF.harmonic_feature(y), BG.bgwhiten_feature(y), True
    except Exception:
        return np.zeros(HF.HARM_DIM, np.float32), np.zeros(BG.BGW_DIM, np.float32), False


def compute_handcrafted(ids, audio_root, workers):
    """Harmonic (N,102) + bgwhiten (N,257) for ids, in order. Returns (harm, bgw, n_failed)."""
    harm = np.zeros((len(ids), HF.HARM_DIM), np.float32)
    bgw = np.zeros((len(ids), BG.BGW_DIM), np.float32)
    failed = 0
    work = [(audio_root, f) for f in ids]
    if workers and workers > 1:
        from concurrent.futures import ProcessPoolExecutor
        with ProcessPoolExecutor(max_workers=workers) as ex:
            for j, (h, b, ok) in enumerate(ex.map(_feat_one, work, chunksize=32)):
                harm[j], bgw[j] = h, b
                if not ok:
                    failed += 1
                if (j + 1) % 2000 == 0:
                    print(f"  handcrafted features {j+1}/{len(ids)}", flush=True)
    else:
        for j, w in enumerate(work):
            h, b, ok = _feat_one(w)
            harm[j], bgw[j] = h, b
            if not ok:
                failed += 1
            if (j + 1) % 2000 == 0:
                print(f"  handcrafted features {j+1}/{len(ids)}", flush=True)
    return harm, bgw, failed


def self_test():
    """Confirm GatedEnsemble(bundle) reproduces the deployed dev BA_unseen (~0.3616) on cached test."""
    from ensemble_model import GatedEnsemble
    P = os.path.join(ROOT, "data/perch")
    d = np.load(f"{P}/test.npz", allow_pickle=True)
    perch = d["emb"].astype(np.float32)
    yte = d["species"].astype(int); dte = d["domain"].astype(int)
    fte = np.array([str(f) for f in d["file_id"]])

    def by_fid(npz, key):
        h = np.load(npz, allow_pickle=True)
        idx = {str(f): i for i, f in enumerate(h["file_id"])}
        return h[key].astype(np.float32)[np.array([idx[f] for f in fte])]

    harm = by_fid(f"{P}/harmonic_test.npz", "harm")
    bgw = by_fid(f"{P}/bgwhiten_test.npz", "bgw")
    bird, miss = load_parquet_emb(DEV_BIRDMAE_PARQUET, list(fte), 1024)
    sm = json.load(open(f"{ROOT}/data/metadata/split_summary.json"))
    ud = {SPECIES_NAMES.index(k): DOMAIN_NAMES.index(v) for k, v in sm["unseen_domain_by_species"].items()}
    unseen = np.array([dte[i] == ud.get(int(yte[i]), -1) for i in range(len(yte))])

    ens = GatedEnsemble(os.path.join(HERE, "bundle.pt"))
    probs = ens.predict_proba(perch, harm, bird, bgw)
    pred = probs.argmax(1)
    yy = yte[unseen]; pp = pred[unseen]
    rec = [(pp[yy == c] == c).mean() for c in range(9) if (yy == c).any()]
    ba = float(np.mean(rec))
    print(f"[self-test] birdmae missing={miss}  gated BA_unseen={ba:.4f}  (expect ~0.3616)")
    assert abs(ba - 0.3616) < 0.01, f"bundle gate mismatch: {ba:.4f} != 0.3616"
    print("[self-test] OK\n")


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--audio-root", default=os.path.join(ROOT, "eval"))
    ap.add_argument("--ids", default=os.path.join(ROOT, "data/metadata_eval/Test_ids.txt"))
    ap.add_argument("--parquet-dir", default=EVAL_PARQUET_DIR)
    ap.add_argument("--bundle", default=os.path.join(HERE, "bundle.pt"))
    ap.add_argument("--out", default=os.path.join(ROOT, "final_submission/architecture_1/predictions.txt"))
    ap.add_argument("--workers", type=int, default=max(1, (os.cpu_count() or 2) - 1))
    ap.add_argument("--self-test", action="store_true")
    args = ap.parse_args()

    if args.self_test:
        self_test()

    ids = read_ids(args.ids)
    print(f"eval clips: {len(ids)}")

    print("loading Perch + BirdMAE eval embeddings ...")
    perch, miss_p = load_parquet_emb(os.path.join(args.parquet_dir, "perch.parquet"), ids, 1536)
    bird, miss_b = load_parquet_emb(os.path.join(args.parquet_dir, "birdmae.parquet"), ids, 1024)
    print(f"  perch missing={miss_p}  birdmae missing={miss_b}")

    print(f"computing harmonic + bgwhiten features ({args.workers} workers) ...")
    harm, bgw, failed = compute_handcrafted(ids, args.audio_root, args.workers)
    print(f"  handcrafted done; unreadable clips (zero-filled)={failed}")

    print("running gated ensemble ...")
    from ensemble_model import GatedEnsemble
    ens = GatedEnsemble(args.bundle)
    probs = ens.predict_proba(perch, harm, bird, bgw)
    idx = probs.argmax(1)
    species_id = np.array([IDX_TO_SPECIES_ID[i] for i in idx], dtype=int)

    os.makedirs(os.path.dirname(args.out), exist_ok=True)
    with open(args.out, "w") as fh:
        fh.write("file_id,predicted_species_id\n")
        for fid, sid in zip(ids, species_id):
            fh.write(f"{fid},{sid}\n")

    # report
    counts = {int(s): int((species_id == s).sum()) for s in range(1, 10)}
    print(f"\nwrote {len(ids)} rows -> {args.out}")
    print("predicted_species_id histogram (1-based):")
    for s in range(1, 10):
        print(f"  {s:>2} {SPECIES_ID_TO_NAME[str(s)]:<26} {counts[s]:>6}  ({100*counts[s]/len(ids):.1f}%)")


if __name__ == "__main__":
    main()