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
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
//! Uncertainty quantification with conformal prediction
/// Uncertainty estimator for routing decisions
pub struct UncertaintyEstimator {
/// Calibration quantile for conformal prediction
calibration_quantile: f32,
}
impl UncertaintyEstimator {
/// Create a new uncertainty estimator
pub fn new() -> Self {
Self {
calibration_quantile: 0.9, // 90% confidence
}
}
/// Create with custom calibration quantile
pub fn with_quantile(quantile: f32) -> Self {
Self {
calibration_quantile: quantile,
}
}
/// Estimate uncertainty for a prediction
///
/// Uses a simple heuristic based on:
/// 1. Distance from decision boundary (0.5)
/// 2. Feature variance
/// 3. Model confidence
pub fn estimate(&self, _features: &[f32], prediction: f32) -> f32 {
// Distance from decision boundary (0.5)
let boundary_distance = (prediction - 0.5).abs();
// Higher uncertainty when close to boundary
let boundary_uncertainty = 1.0 - (boundary_distance * 2.0);
// Clip to [0, 1]
boundary_uncertainty.max(0.0).min(1.0)
}
/// Calibrate the estimator with a set of predictions and outcomes
pub fn calibrate(&mut self, _predictions: &[f32], _outcomes: &[bool]) {
// TODO: Implement conformal prediction calibration
// This would compute the quantile of non-conformity scores
}
/// Get the calibration quantile
pub fn calibration_quantile(&self) -> f32 {
self.calibration_quantile
}
}
impl Default for UncertaintyEstimator {
fn default() -> Self {
Self::new()
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_uncertainty_estimation() {
let estimator = UncertaintyEstimator::new();
// High confidence prediction should have low uncertainty
let features = vec![0.5; 10];
let high_conf = estimator.estimate(&features, 0.95);
assert!(high_conf < 0.5);
// Low confidence prediction should have high uncertainty
let low_conf = estimator.estimate(&features, 0.52);
assert!(low_conf > 0.5);
}
#[test]
fn test_boundary_uncertainty() {
let estimator = UncertaintyEstimator::new();
let features = vec![0.5; 10];
// Prediction exactly at boundary (0.5) should have maximum uncertainty
let boundary = estimator.estimate(&features, 0.5);
assert!((boundary - 1.0).abs() < 0.01);
}
}