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35d483e | 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 | #!/usr/bin/env python3
"""Create a privacy-safe failure queue from development predictions."""
from __future__ import annotations
import argparse
import hashlib
import json
from collections import defaultdict
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--metrics", required=True)
parser.add_argument("--predictions", help="default: <metrics stem>.predictions.jsonl")
parser.add_argument("--output", required=True)
parser.add_argument("--examples-per-type", type=int, default=20)
parser.add_argument("--min-slice-count", type=int, default=5)
return parser.parse_args()
def _resolve(value: str) -> Path:
path = Path(value)
return path if path.is_absolute() else ROOT / path
def _case_id(record_id: object) -> str:
return "case_" + hashlib.sha256(str(record_id).encode()).hexdigest()[:12]
def _load_predictions(path: Path) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
seen: set[str] = set()
with path.open(encoding="utf-8") as handle:
for line_number, line in enumerate(handle, start=1):
if not line.strip():
continue
try:
row = json.loads(line)
except json.JSONDecodeError as exc:
raise ValueError(f"{path}:{line_number}: invalid JSON") from exc
if not isinstance(row, dict):
raise ValueError(f"{path}:{line_number}: expected an object")
record_id = str(row.get("record_id", ""))
if not record_id or record_id in seen:
raise ValueError(f"{path}:{line_number}: missing or duplicate record_id")
seen.add(record_id)
rows.append(row)
if not rows:
raise ValueError("prediction file is empty")
return rows
def _review_case(row: dict[str, Any], prediction: int) -> dict[str, Any]:
return {
"case_id": _case_id(row["record_id"]),
"target": "END" if int(row["label"]) else "HOLD",
"predicted": "END" if prediction else "HOLD",
"p_end": float(row["probability"]),
"language": row.get("language"),
"dataset": row.get("dataset"),
"synthetic": row.get("synthetic"),
"filler_type": row.get("filler_type"),
"duration_bin": row.get("duration_bin"),
"review_note": "Listen under authorized local access; do not export audio or transcript.",
}
def main() -> int:
args = parse_args()
if args.examples_per_type < 1 or args.min_slice_count < 1:
raise SystemExit("example and slice counts must be positive")
metrics_path = _resolve(args.metrics)
try:
metrics = json.loads(metrics_path.read_text(encoding="utf-8"))
except (OSError, json.JSONDecodeError) as exc:
raise SystemExit(f"invalid metrics JSON: {metrics_path}") from exc
threshold = metrics.get("threshold")
if not isinstance(threshold, int | float) or not 0.0 <= threshold <= 1.0:
raise SystemExit("metrics JSON has no valid threshold")
predictions_path = (
_resolve(args.predictions)
if args.predictions
else metrics_path.with_name(metrics_path.stem + ".predictions.jsonl")
)
rows = _load_predictions(predictions_path)
false_interruptions: list[dict[str, Any]] = []
missed_ends: list[dict[str, Any]] = []
slices: dict[str, dict[str, dict[str, int]]] = {
dimension: defaultdict(lambda: {"count": 0, "false_interruptions": 0, "missed_ends": 0})
for dimension in ("language", "dataset", "synthetic", "filler_type", "duration_bin")
}
for row in rows:
label = int(row["label"])
probability = float(row["probability"])
if label not in (0, 1) or not 0.0 <= probability <= 1.0:
raise SystemExit("predictions contain invalid labels or probabilities")
prediction = int(probability >= threshold)
is_false_interruption = prediction == 1 and label == 0
is_missed_end = prediction == 0 and label == 1
if is_false_interruption:
false_interruptions.append(_review_case(row, prediction))
elif is_missed_end:
missed_ends.append(_review_case(row, prediction))
for dimension, values in slices.items():
value = str(row.get(dimension, "<missing>"))
values[value]["count"] += 1
values[value]["false_interruptions"] += int(is_false_interruption)
values[value]["missed_ends"] += int(is_missed_end)
false_interruptions.sort(key=lambda row: float(row["p_end"]), reverse=True)
missed_ends.sort(key=lambda row: float(row["p_end"]))
filtered_slices = {
dimension: {
value: counts
for value, counts in sorted(values.items())
if counts["count"] >= args.min_slice_count
}
for dimension, values in slices.items()
}
report = {
"scope": metrics.get("data_scope"),
"split": metrics.get("split"),
"development_only": metrics.get("development_only"),
"threshold": threshold,
"privacy": (
"Case IDs are one-way hashes. No audio, transcript, raw record ID, or source path "
"is included. Hypotheses require authorized local listening."
),
"counts": {
"examples": len(rows),
"false_interruptions": len(false_interruptions),
"missed_ends": len(missed_ends),
},
"highest_confidence_false_interruptions": false_interruptions[: args.examples_per_type],
"highest_confidence_missed_ends": missed_ends[: args.examples_per_type],
"failure_counts_by_slice": filtered_slices,
}
output = _resolve(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(
json.dumps(report, indent=2, sort_keys=True, allow_nan=False) + "\n",
encoding="utf-8",
)
print(json.dumps(report["counts"], indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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