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6835659 | 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 | 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
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