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dadf189 | 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 | from __future__ import annotations
from itertools import combinations
import numpy as np
try:
from scipy.stats import ks_2samp
except Exception: # pragma: no cover
ks_2samp = None
def sensor_ks_test(scores_by_sensor: dict[str, np.ndarray]) -> list[dict[str, float | str]]:
"""Pairwise two-sample KS test across sensors."""
rows: list[dict[str, float | str]] = []
for s1, s2 in combinations(sorted(scores_by_sensor.keys()), 2):
a = np.asarray(scores_by_sensor[s1], dtype=np.float64)
b = np.asarray(scores_by_sensor[s2], dtype=np.float64)
if len(a) == 0 or len(b) == 0:
continue
if ks_2samp is not None:
stat, pval = ks_2samp(a, b)
else:
# Fallback approximation using ECDF max difference.
xa = np.sort(a)
xb = np.sort(b)
grid = np.unique(np.concatenate([xa, xb]))
cdfa = np.searchsorted(xa, grid, side="right") / len(xa)
cdfb = np.searchsorted(xb, grid, side="right") / len(xb)
stat = float(np.max(np.abs(cdfa - cdfb)))
pval = float("nan")
rows.append(
{
"sensor_a": s1,
"sensor_b": s2,
"ks_stat": float(stat),
"p_value": float(pval),
}
)
return rows
def cross_sensor_correlation(paired_scores: list[tuple[float, float]]) -> float:
"""Pearson correlation over matched cross-sensor quality pairs."""
if len(paired_scores) < 2:
return 0.0
arr = np.asarray(paired_scores, dtype=np.float64)
return float(np.corrcoef(arr[:, 0], arr[:, 1])[0, 1])
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