scripts/run_validation_pipeline.py

"""
run_validation_pipeline.py — ERYON validation + leakage audit pipeline.

Runs inside an HF Job with eryon-datasets bucket mounted at /mnt.
Steps:
  1. Download manifest + splits from eryon-data-pipelines repo
  2. Validate: required fields, sha256 checksums, split completeness
  3. Leakage audit: patient overlap, duplicate hashes, slice leakage
  4. Upload reports to eryon-data-pipelines repo

Corruption scan (PIL open) is skipped by default — files were just written
and never transferred across a network boundary. Set CORRUPTION_SCAN=True
to enable it (adds ~2hr on cpu-basic).

Usage:
    python run_validation_pipeline.py
"""

import sys
import json
import hashlib
from pathlib import Path
from collections import defaultdict

from huggingface_hub import HfApi, hf_hub_download

# ── Config ────────────────────────────────────────────────────────────────────
BUCKET_LIDC       = Path("/mnt/raw/lidc")
TMP_OUT           = Path("/tmp/validation_output")
REPO_ID           = "Chucks90/eryon-data-pipelines"
CORRUPTION_SCAN   = False   # set True to enable PIL open on every PNG

REQUIRED_FIELDS = {
    "patient_id", "study_id", "series_id", "image_path",
    "modality", "split", "label", "dataset_version",
    "preprocessing_version", "sha256",
}

# ── Helpers ───────────────────────────────────────────────────────────────────

def download_inputs() -> tuple[Path, Path]:
    print("Downloading manifest + splits from repo …")
    manifest_path = Path(hf_hub_download(
        REPO_ID, "manifests/lidc/manifest_v1.0.0.jsonl",
        repo_type="dataset", local_dir="/tmp", force_download=True,
    ))
    splits_path = Path(hf_hub_download(
        REPO_ID, "manifests/lidc/splits_v1.0.0.json",
        repo_type="dataset", local_dir="/tmp", force_download=True,
    ))
    return manifest_path, splits_path


def load_manifest(path: Path) -> list[dict]:
    return [json.loads(l) for l in path.read_text().splitlines() if l.strip()]


def sha256_file(path: Path) -> str:
    h = hashlib.sha256()
    with open(path, "rb") as f:
        for chunk in iter(lambda: f.read(65536), b""):
            h.update(chunk)
    return h.hexdigest()


# ── Step 1: Validation ────────────────────────────────────────────────────────

def run_validation(manifest_path: Path, splits_path: Path) -> dict:
    print("Running validation …")
    records   = load_manifest(manifest_path)
    split_map = json.loads(splits_path.read_text()).get("splits", {})
    errors, warnings = [], []
    total = len(records)

    for i, rec in enumerate(records):
        if i % 20000 == 0:
            print(f"  validating {i}/{total} …")

        missing = REQUIRED_FIELDS - rec.keys()
        if missing:
            errors.append(f"record {i}: missing fields {missing}")
            continue

        img_path = BUCKET_LIDC / rec["image_path"]
        if not img_path.exists():
            errors.append(f"record {i}: file not found {rec['image_path']}")
            continue

        # sha256 verification
        actual = sha256_file(img_path)
        if actual != rec["sha256"]:
            errors.append(f"record {i}: sha256 mismatch {rec['image_path']}")

        # corruption scan (optional)
        if CORRUPTION_SCAN:
            try:
                from PIL import Image
                with Image.open(img_path) as img:
                    img.verify()
            except Exception as exc:
                errors.append(f"record {i}: corrupt image {rec['image_path']}: {exc}")

        # split assignment
        if rec["series_id"] not in split_map:
            warnings.append(f"record {i}: series {rec['series_id']} has no split")

    result = {
        "total_records": total,
        "errors": errors[:500],   # cap at 500 to keep report readable
        "error_count": len(errors),
        "warnings": warnings[:200],
        "warning_count": len(warnings),
        "passed": len(errors) == 0,
    }
    print(f"  Validation {'PASSED' if result['passed'] else 'FAILED'}: "
          f"{result['error_count']} errors, {result['warning_count']} warnings")
    return result


# ── Step 2: Leakage audit ─────────────────────────────────────────────────────

def run_leakage_audit(manifest_path: Path, splits_path: Path) -> dict:
    print("Running leakage audit …")
    records   = load_manifest(manifest_path)
    split_map = json.loads(splits_path.read_text()).get("splits", {})
    critical, warnings = [], []

    # patient overlap across splits
    patient_splits: dict[str, set] = defaultdict(set)
    for r in records:
        sid   = r["series_id"]
        split = split_map.get(sid, "unassigned")
        patient_splits[r["patient_id"]].add(split)
    for pid, splits in patient_splits.items():
        real = splits - {"unassigned"}
        if len(real) > 1:
            critical.append(f"Patient {pid} spans splits: {sorted(real)}")

    # exact hash duplicates across splits
    hash_records: dict[str, list] = defaultdict(list)
    for r in records:
        hash_records[r["sha256"]].append(r)
    for h, recs in hash_records.items():
        split_set = {split_map.get(r["series_id"], "unassigned") for r in recs} - {"unassigned"}
        if len(split_set) > 1:
            critical.append(f"Exact duplicate sha256 {h[:12]}… spans splits {sorted(split_set)}")
        elif len(recs) > 1:
            warnings.append(f"Duplicate sha256 {h[:12]}… within same split ({len(recs)} copies)")

    # slice leakage — series_id in multiple splits
    series_splits: dict[str, set] = defaultdict(set)
    for r in records:
        series_splits[r["series_id"]].add(split_map.get(r["series_id"], "unassigned"))
    for sid, splits in series_splits.items():
        real = splits - {"unassigned"}
        if len(real) > 1:
            critical.append(f"series_id {sid} spans splits: {sorted(real)}")

    result = {
        "total_records": len(records),
        "critical": critical[:200],
        "critical_count": len(critical),
        "warnings": warnings[:200],
        "warning_count": len(warnings),
        "passed": len(critical) == 0,
    }
    print(f"  Leakage audit {'PASSED' if result['passed'] else 'FAILED'}: "
          f"{result['critical_count']} critical, {result['warning_count']} warnings")
    return result


# ── Step 3: Upload reports ────────────────────────────────────────────────────

def upload_reports(val_result: dict, leakage_result: dict) -> None:
    print("Uploading reports …")
    TMP_OUT.mkdir(parents=True, exist_ok=True)
    api = HfApi()

    val_path = TMP_OUT / "validation_lidc_v1.0.0.json"
    val_path.write_text(json.dumps(val_result, indent=2))
    api.upload_file(
        path_or_fileobj=str(val_path),
        path_in_repo="reports/validation/lidc_v1.0.0.json",
        repo_id=REPO_ID, repo_type="dataset",
    )
    print("  validation report uploaded")

    leakage_path = TMP_OUT / "leakage_lidc_v1.0.0.json"
    leakage_path.write_text(json.dumps(leakage_result, indent=2))
    api.upload_file(
        path_or_fileobj=str(leakage_path),
        path_in_repo="reports/leakage/lidc_v1.0.0.json",
        repo_id=REPO_ID, repo_type="dataset",
    )
    print("  leakage report uploaded")


# ── Main ──────────────────────────────────────────────────────────────────────

def main() -> None:
    if not BUCKET_LIDC.exists():
        print(f"ERROR: bucket not mounted at {BUCKET_LIDC}", file=sys.stderr)
        sys.exit(1)

    manifest_path, splits_path = download_inputs()
    val_result     = run_validation(manifest_path, splits_path)
    leakage_result = run_leakage_audit(manifest_path, splits_path)
    upload_reports(val_result, leakage_result)

    if not val_result["passed"] or not leakage_result["passed"]:
        print("\nPIPELINE FAILED — check reports before training", file=sys.stderr)
        sys.exit(1)

    print("\nAll checks passed. Dataset is trainable.")


if __name__ == "__main__":
    main()