| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
| from typing import Dict, List |
|
|
| import numpy as np |
|
|
| METRICS = ["st_i", "st_a", "si_a", "msci"] |
|
|
|
|
| class CoherenceStats: |
| """ |
| Learns statistical thresholds from previous run logs. |
| """ |
|
|
| def __init__(self) -> None: |
| self.stats: Dict[str, Dict[str, float]] = {} |
|
|
| def fit(self, runs: List[Dict]) -> None: |
| """ |
| Learn mean, std, and percentiles from past runs. |
| """ |
| for metric in METRICS: |
| values = [ |
| r["scores"][metric] |
| for r in runs |
| if metric in r.get("scores", {}) |
| ] |
|
|
| if not values: |
| continue |
|
|
| arr = np.array(values) |
|
|
| self.stats[metric] = { |
| "mean": float(np.mean(arr)), |
| "std": float(np.std(arr)), |
| "p25": float(np.percentile(arr, 25)), |
| "p50": float(np.percentile(arr, 50)), |
| "p75": float(np.percentile(arr, 75)), |
| } |
|
|
| def save(self, path: Path) -> None: |
| path.write_text(json.dumps(self.stats, indent=2)) |
|
|
| @classmethod |
| def load(cls, path: Path) -> "CoherenceStats": |
| obj = cls() |
| obj.stats = json.loads(path.read_text()) |
| return obj |
|
|
