File size: 1,391 Bytes
fc329a3 | 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 | """Coverage metrics."""
import numpy as np
def marginal_coverage(covered: np.ndarray) -> float:
"""Overall empirical coverage."""
return covered.mean()
def stratified_coverage(
covered: np.ndarray,
strata: np.ndarray,
) -> dict[int, float]:
"""Stratified coverage in prediction space.
Args:
covered: boolean array (n,)
strata: integer group labels (n,)
Returns:
dict mapping stratum label -> empirical coverage
"""
result = {}
for s in np.unique(strata):
mask = strata == s
if mask.sum() > 0:
result[int(s)] = covered[mask].mean()
return result
def max_disparity(
covered: np.ndarray,
strata: np.ndarray,
alpha: float,
) -> float:
"""Max |stratified_coverage - (1-alpha)| across strata."""
sc = stratified_coverage(covered, strata)
target = 1.0 - alpha
return max(abs(v - target) for v in sc.values())
def worst_stratum_coverage(
covered: np.ndarray,
strata: np.ndarray,
) -> float:
"""Minimum empirical coverage across strata."""
sc = stratified_coverage(covered, strata)
return min(sc.values())
def coverage_variance(
covered: np.ndarray,
strata: np.ndarray,
) -> float:
"""Variance of empirical coverage across strata."""
sc = stratified_coverage(covered, strata)
return float(np.var(list(sc.values())))
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