Image Classification
LiteRT
LiteRT
agriculture
maize
plant-disease
edge-ai
research-preview
human-in-the-loop
Instructions to use sinuosity/okuafo-maizeguard-edge-v1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use sinuosity/okuafo-maizeguard-edge-v1.4 with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Create eval/audit_split_manifest.py
Browse files- eval/audit_split_manifest.py +115 -0
eval/audit_split_manifest.py
ADDED
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#!/usr/bin/env python3
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"""Audit a MaizeGuard CSV manifest for group leakage and source concentration.
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The manifest is expected to contain ``split``, ``label``, ``source_id``,
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``record_id`` and ``group_id`` columns. The command prints a deterministic JSON
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report and exits non-zero when a biological/collection group or record appears
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in more than one split.
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"""
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from __future__ import annotations
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import argparse
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import csv
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import json
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from collections import Counter, defaultdict
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from pathlib import Path
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REQUIRED_COLUMNS = {"split", "label", "source_id", "record_id", "group_id"}
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("manifest", type=Path, help="CSV split manifest to audit")
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parser.add_argument(
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"--max-source-share",
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type=float,
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default=0.80,
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help="Flag classes where one source supplies more than this share (default: 0.80)",
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)
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return parser.parse_args()
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def audit(path: Path, max_source_share: float) -> dict[str, object]:
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groups: dict[tuple[str, str], set[str]] = defaultdict(set)
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records: dict[str, set[str]] = defaultdict(set)
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split_counts: Counter[str] = Counter()
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label_counts: Counter[str] = Counter()
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label_sources: dict[str, Counter[str]] = defaultdict(Counter)
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with path.open(newline="", encoding="utf-8") as handle:
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reader = csv.DictReader(handle)
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missing = REQUIRED_COLUMNS - set(reader.fieldnames or ())
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| 43 |
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if missing:
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raise ValueError(f"manifest is missing required columns: {sorted(missing)}")
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for row in reader:
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split = row["split"].strip()
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label = row["label"].strip()
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source = row["source_id"].strip()
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record = row["record_id"].strip()
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group = row["group_id"].strip()
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if not all((split, label, source, record, group)):
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raise ValueError("manifest contains an empty required field")
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groups[(source, group)].add(split)
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records[record].add(split)
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split_counts[split] += 1
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label_counts[label] += 1
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label_sources[label][source] += 1
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cross_split_groups = sorted(
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(source, group, sorted(splits))
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| 61 |
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for (source, group), splits in groups.items()
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| 62 |
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if len(splits) > 1
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)
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cross_split_records = sorted(
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| 65 |
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(record, sorted(splits)) for record, splits in records.items() if len(splits) > 1
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)
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concentration: dict[str, object] = {}
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concentrated_labels: list[str] = []
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for label in sorted(label_sources):
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total = label_counts[label]
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source, count = label_sources[label].most_common(1)[0]
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share = count / total
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concentration[label] = {
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"record_count": total,
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"largest_source": source,
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| 76 |
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"largest_source_count": count,
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"largest_source_share": round(share, 6),
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}
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| 79 |
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if share > max_source_share:
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concentrated_labels.append(label)
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| 82 |
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return {
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| 83 |
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"schema_version": "1",
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| 84 |
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"manifest": path.name,
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| 85 |
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"record_count": sum(split_counts.values()),
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| 86 |
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"group_count": len(groups),
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| 87 |
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"split_counts": dict(sorted(split_counts.items())),
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| 88 |
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"cross_split_group_count": len(cross_split_groups),
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| 89 |
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"cross_split_group_examples": [
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| 90 |
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{"source_id": source, "group_id": group, "splits": splits}
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| 91 |
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for source, group, splits in cross_split_groups[:20]
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| 92 |
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],
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| 93 |
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"cross_split_record_count": len(cross_split_records),
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| 94 |
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"cross_split_record_examples": [
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| 95 |
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{"record_id": record, "splits": splits}
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| 96 |
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for record, splits in cross_split_records[:20]
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| 97 |
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],
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| 98 |
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"class_source_concentration": concentration,
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| 99 |
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"classes_above_source_share_threshold": concentrated_labels,
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| 100 |
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"max_source_share_threshold": max_source_share,
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| 101 |
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"leakage_gate_passed": not cross_split_groups and not cross_split_records,
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| 102 |
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}
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| 103 |
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| 104 |
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| 105 |
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def main() -> int:
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| 106 |
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args = parse_args()
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| 107 |
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if not 0 < args.max_source_share <= 1:
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| 108 |
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raise SystemExit("--max-source-share must be greater than 0 and at most 1")
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| 109 |
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report = audit(args.manifest, args.max_source_share)
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| 110 |
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print(json.dumps(report, indent=2, sort_keys=True))
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| 111 |
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return 0 if report["leakage_gate_passed"] else 2
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| 112 |
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| 113 |
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| 114 |
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if __name__ == "__main__":
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| 115 |
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raise SystemExit(main())
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