File size: 5,739 Bytes
91ac3cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | from __future__ import annotations
import argparse
import json
import random
from collections import Counter
from dataclasses import asdict
from pathlib import Path
from typing import Any
import numpy as np
from tqdm import tqdm
from kawk50m.cli import configure_utf8_stdio
from kawk50m.io import discover_paths, iter_documents
from kawk50m.text import (
FilterConfig,
measure_text,
quality_score,
rejection_reason,
)
METRIC_FIELDS = (
"characters",
"hangul_visible_ratio",
"hangul_letter_ratio",
"digit_ratio",
"symbol_ratio",
"unique_line_ratio",
"unique_word_ratio",
"short_line_ratio",
"list_line_ratio",
"table_line_ratio",
"boilerplate_hits_per_1k",
"repeated_character_run",
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Audit Korean corpus quality and write deterministic samples."
)
parser.add_argument("--input", nargs="+", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--limit", type=int, default=100_000)
parser.add_argument("--sample-size", type=int, default=200)
parser.add_argument("--lowest-quality-size", type=int, default=200)
parser.add_argument("--seed", type=int, default=42)
return parser.parse_args()
def quantiles(values: list[float]) -> dict[str, float]:
if not values:
return {}
result = np.quantile(
np.asarray(values, dtype=np.float64),
[0.0, 0.01, 0.05, 0.25, 0.50, 0.75, 0.95, 0.99, 1.0],
)
names = ("min", "p01", "p05", "p25", "p50", "p75", "p95", "p99", "max")
return {name: float(value) for name, value in zip(names, result)}
def compact_row(
document: dict[str, Any],
*,
score: float,
reason: str | None,
metrics: Any,
) -> dict[str, Any]:
return {
"id": document.get("id"),
"source": document.get("source"),
"url": document.get("url"),
"quality_score": round(score, 6),
"rejection_reason": reason,
"metrics": {
field: getattr(metrics, field)
for field in METRIC_FIELDS
},
"text": document["text"],
}
def write_jsonl(path: Path, rows: list[dict[str, Any]]) -> None:
with path.open("w", encoding="utf-8") as handle:
for row in rows:
handle.write(json.dumps(row, ensure_ascii=False) + "\n")
def main() -> None:
configure_utf8_stdio()
args = parse_args()
if args.limit <= 0:
raise ValueError("--limit must be positive")
if args.sample_size < 0 or args.lowest_quality_size < 0:
raise ValueError("sample sizes must be non-negative")
paths = discover_paths(args.input)
if not paths:
raise FileNotFoundError("No supported audit inputs were found")
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
expected_outputs = (
output_dir / "audit_summary.json",
output_dir / "random_sample.jsonl",
output_dir / "lowest_quality.jsonl",
)
for path in expected_outputs:
if path.exists():
raise FileExistsError(
f"{path} already exists. Choose a new audit output directory."
)
rng = random.Random(args.seed)
filter_config = FilterConfig()
counters: Counter[str] = Counter()
sources: Counter[str] = Counter()
values: dict[str, list[float]] = {
field: [] for field in (*METRIC_FIELDS, "quality_score")
}
sample: list[dict[str, Any]] = []
lowest: list[dict[str, Any]] = []
documents = iter_documents(paths)
for index, document in enumerate(
tqdm(documents, total=args.limit, unit="docs"),
start=1,
):
if index > args.limit:
break
metrics = measure_text(document["text"])
reason = rejection_reason(
document["text"], filter_config, metrics=metrics
)
score = quality_score(metrics, filter_config)
row = compact_row(
document,
score=score,
reason=reason,
metrics=metrics,
)
counters["documents"] += 1
counters["accepted" if reason is None else f"rejected_{reason}"] += 1
sources[str(document.get("source") or "unknown")] += 1
for field in METRIC_FIELDS:
values[field].append(float(getattr(metrics, field)))
values["quality_score"].append(score)
if len(sample) < args.sample_size:
sample.append(row)
elif args.sample_size:
replacement = rng.randrange(index)
if replacement < args.sample_size:
sample[replacement] = row
if args.lowest_quality_size:
lowest.append(row)
lowest.sort(key=lambda item: item["quality_score"])
if len(lowest) > args.lowest_quality_size:
lowest.pop()
summary = {
"inputs": [str(path) for path in paths],
"limit": args.limit,
"seed": args.seed,
"filter": asdict(filter_config),
"counts": dict(sorted(counters.items())),
"sources": dict(sources.most_common()),
"quantiles": {
field: quantiles(field_values)
for field, field_values in values.items()
},
}
with (output_dir / "audit_summary.json").open(
"w", encoding="utf-8"
) as handle:
json.dump(summary, handle, ensure_ascii=False, indent=2)
handle.write("\n")
write_jsonl(output_dir / "random_sample.jsonl", sample)
write_jsonl(output_dir / "lowest_quality.jsonl", lowest)
print(json.dumps(summary, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
|