| from __future__ import annotations |
|
|
| from dataclasses import dataclass |
| from typing import Sequence |
|
|
|
|
| @dataclass |
| class WeightScheduleConfig: |
| sample_knots: tuple[int, int, int, int] = (20, 50, 100, 200) |
| weight_knots: tuple[float, float, float, float] = (0.1, 0.3, 0.5, 0.8) |
| max_weight: float = 0.9 |
| min_weight: float = 0.05 |
| instability_threshold: float = 2.0 |
| instability_decay: float = 0.25 |
|
|
|
|
| def _interp(x: float, xs: Sequence[float], ys: Sequence[float]) -> float: |
| if x <= xs[0]: |
| return float(ys[0]) |
| if x >= xs[-1]: |
| return float(ys[-1]) |
|
|
| for i in range(1, len(xs)): |
| if x <= xs[i]: |
| x0, x1 = xs[i - 1], xs[i] |
| y0, y1 = ys[i - 1], ys[i] |
| t = (x - x0) / max(1e-9, (x1 - x0)) |
| return float(y0 + t * (y1 - y0)) |
| return float(ys[-1]) |
|
|
|
|
| def compute_model_weight( |
| n_samples: int, |
| instability_ratio: float = 1.0, |
| config: WeightScheduleConfig | None = None, |
| ) -> float: |
| cfg = config or WeightScheduleConfig() |
|
|
| base = _interp(float(n_samples), cfg.sample_knots, cfg.weight_knots) |
| if n_samples > cfg.sample_knots[-1]: |
| extra = min(cfg.max_weight - base, 0.05 * (n_samples - cfg.sample_knots[-1]) / 100.0) |
| base += max(0.0, extra) |
|
|
| if instability_ratio > cfg.instability_threshold: |
| base *= max(0.1, 1.0 - cfg.instability_decay) |
|
|
| base = max(cfg.min_weight, min(cfg.max_weight, base)) |
| return float(base) |
|
|
