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ce9edc2 | 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 | """Shared utilities for FraudShield training and evaluation."""
from __future__ import annotations
import json
import os
import random
from pathlib import Path
from typing import Any, Iterable, Sequence
import numpy as np
def seed_everything(seed: int) -> None:
"""Seed Python, NumPy, and torch when available."""
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
try: # pragma: no cover - torch is optional at runtime
import torch
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
except Exception:
pass
def ensure_dir(path: str | Path) -> Path:
"""Create a directory if needed and return it as a ``Path``."""
resolved = Path(path)
resolved.mkdir(parents=True, exist_ok=True)
return resolved
def save_json(payload: Any, path: str | Path) -> None:
"""Write JSON with stable indentation."""
Path(path).write_text(json.dumps(payload, indent=2), encoding="utf-8")
def load_json(path: str | Path) -> Any:
"""Load JSON from disk."""
return json.loads(Path(path).read_text(encoding="utf-8"))
def extract_json_object(text: str) -> dict[str, Any]:
"""Extract the first JSON object from model output."""
start = text.find("{")
end = text.rfind("}")
if start == -1 or end == -1 or end < start:
raise ValueError("Model output did not contain a JSON object.")
return json.loads(text[start : end + 1])
def moving_average(values: Sequence[float], window: int = 10) -> list[float]:
"""Compute a simple moving average."""
if not values:
return []
window = max(1, int(window))
averaged: list[float] = []
for idx in range(len(values)):
start = max(0, idx - window + 1)
chunk = values[start : idx + 1]
averaged.append(sum(chunk) / len(chunk))
return averaged
def approximate_token_count(text: str) -> int:
"""Cheap token estimate that works without a tokenizer."""
stripped = text.strip()
if not stripped:
return 0
return max(1, int(len(stripped.split()) * 1.3))
def flatten_dict_items(mapping: dict[str, Any], prefix: str = "") -> Iterable[tuple[str, Any]]:
"""Flatten nested dictionaries for logging."""
for key, value in mapping.items():
full_key = f"{prefix}.{key}" if prefix else key
if isinstance(value, dict):
yield from flatten_dict_items(value, prefix=full_key)
else:
yield full_key, value
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