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//! Loss function implementations for diagram fitting.
//!
//! This module provides simple loss functions that measure the difference
//! between fitted and target region areas.
use crate::geometry::diagram::RegionMask;
use std::collections::HashMap;
/// Loss function type
#[derive(Debug, Clone, Copy, PartialEq, Default)]
pub enum LossType {
/// Sum of squared errors: Σ(fitted - target)²
#[default]
SumSquared,
/// Sums of absolute errors: Σ|fitted - target|
SumAbsoute,
/// SumRegionError sum(|fitted / sum(fitted) - target / sum(target)|)
SumAbsoluteRegionError,
/// SumSquaredRegionError sum((fitted / sum(fitted) - target / sum(target))²)
SumSquaredRegionError,
/// Maximum absolute error: max(|fitted - target|)
MaxAbsolute,
/// Maximum squared error: max((fitted - target)²)
MaxSquared,
/// Root mean squared error: sqrt(mean((fitted - target)²))
RootMeanSquared,
/// Stress (venneuler-style)
Stress,
/// DiagError max(|fit / sum(fit) - target / sum(target)|), EulerAPE style
DiagError,
}
impl LossType {
/// Sum of squared errors
pub fn sse() -> Self {
Self::SumSquared
}
/// Root mean squared error
pub fn rmse() -> Self {
Self::RootMeanSquared
}
/// Stress loss (venneuler-style)
pub fn stress() -> Self {
Self::Stress
}
/// Maximum absolute error
pub fn max_absolute() -> Self {
Self::MaxAbsolute
}
/// Maximum squared error
pub fn max_squared() -> Self {
Self::MaxSquared
}
/// Sum of absolute errors
pub fn sum_absolute() -> Self {
Self::SumAbsoute
}
/// Sum of absolute region errors
pub fn sum_absolute_region_error() -> Self {
Self::SumAbsoluteRegionError
}
/// Sum of squared region errors
pub fn sum_squared_region_error() -> Self {
Self::SumSquaredRegionError
}
/// Diagonal error (EulerAPE style)
pub fn diag_error() -> Self {
Self::DiagError
}
/// Compute loss between fitted and target region areas
pub fn compute(
&self,
fitted: &HashMap<RegionMask, f64>,
target: &HashMap<RegionMask, f64>,
) -> f64 {
// Collect all unique region masks from both fitted and target
let all_masks: std::collections::HashSet<RegionMask> =
fitted.keys().chain(target.keys()).copied().collect();
if all_masks.is_empty() {
return 0.0;
}
match self {
LossType::SumSquared => all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
(f - t).powi(2)
})
.sum(),
LossType::RootMeanSquared => {
let sum_squared: f64 = all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
(f - t).powi(2)
})
.sum();
(sum_squared / all_masks.len() as f64).sqrt()
}
LossType::Stress => {
// venneuler-style stress (matches eulerr):
// stress = Σ(f - β·t)² / Σf² where β = Σ(f·t) / Σt²
let sum_ft: f64 = all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
f * t
})
.sum();
let sum_t2: f64 = target.values().map(|&v| v * v).sum();
let sum_f2: f64 = fitted.values().map(|&v| v * v).sum();
if sum_t2 < 1e-20 || sum_f2 < 1e-20 {
return 0.0;
}
let beta = sum_ft / sum_t2;
let numerator: f64 = all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
(f - beta * t).powi(2)
})
.sum();
numerator / sum_f2
}
LossType::MaxAbsolute => all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
(f - t).abs()
})
.fold(0.0, f64::max),
LossType::MaxSquared => all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
(f - t).powi(2)
})
.fold(0.0, f64::max),
LossType::DiagError => {
// eulerr's diagError: max|f_i/Σf - t_i/Σt| (linear sum normalization)
let ssf = fitted.values().sum::<f64>();
let sst = target.values().sum::<f64>();
if ssf.abs() < 1e-10 || sst.abs() < 1e-10 {
return f64::MAX;
}
all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
(f / ssf - t / sst).abs()
})
.fold(0.0, f64::max)
}
LossType::SumAbsoute => all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
(f - t).abs()
})
.sum(),
LossType::SumAbsoluteRegionError => {
let ssf = fitted.values().sum::<f64>();
let sst = target.values().sum::<f64>();
if ssf.abs() < 1e-10 || sst.abs() < 1e-10 {
return f64::MAX;
}
all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
(f / ssf - t / sst).abs()
})
.sum()
}
LossType::SumSquaredRegionError => {
let ssf = fitted.values().sum::<f64>();
let sst = target.values().sum::<f64>();
if ssf.abs() < 1e-10 || sst.abs() < 1e-10 {
return f64::MAX;
}
all_masks
.iter()
.map(|&mask| {
let f = fitted.get(&mask).copied().unwrap_or(0.0);
let t = target.get(&mask).copied().unwrap_or(0.0);
(f / ssf - t / sst).powi(2)
})
.sum()
}
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_sse() {
let loss = LossType::sse();
let mut fitted = HashMap::new();
fitted.insert(0b001, 10.0);
fitted.insert(0b010, 20.0);
fitted.insert(0b100, 30.0);
let mut target = HashMap::new();
target.insert(0b001, 12.0);
target.insert(0b010, 18.0);
target.insert(0b100, 28.0);
// (10-12)² + (20-18)² + (30-28)² = 4 + 4 + 4 = 12
assert_eq!(loss.compute(&fitted, &target), 12.0);
}
#[test]
fn test_rmse() {
let loss = LossType::rmse();
let mut fitted = HashMap::new();
fitted.insert(0b001, 10.0);
fitted.insert(0b010, 20.0);
fitted.insert(0b100, 30.0);
let mut target = HashMap::new();
target.insert(0b001, 12.0);
target.insert(0b010, 18.0);
target.insert(0b100, 28.0);
// sqrt((4 + 4 + 4) / 3) = sqrt(4) = 2.0
assert_eq!(loss.compute(&fitted, &target), 2.0);
}
#[test]
fn test_stress() {
let loss = LossType::stress();
let mut fitted = HashMap::new();
fitted.insert(0b001, 10.0);
fitted.insert(0b010, 20.0);
let mut target = HashMap::new();
target.insert(0b001, 12.0);
target.insert(0b010, 18.0);
// venneuler/eulerr stress: Σ(f - β·t)² / Σf² where β = Σ(f·t) / Σt²
// Σft = 10·12 + 20·18 = 480
// Σt² = 144 + 324 = 468 → β = 480/468 = 40/39
// (10 - 40/39·12)² + (20 - 40/39·18)² = (90/39)² + (60/39)² = 11700/1521
// Σf² = 100 + 400 = 500 → stress = 11700/1521/500 ≈ 0.015385
let result = loss.compute(&fitted, &target);
assert!(
(result - 0.015385).abs() < 1e-5,
"expected 0.015385, got {}",
result
);
}
#[test]
fn test_max_absolute() {
let loss = LossType::max_absolute();
let mut fitted = HashMap::new();
fitted.insert(0b001, 10.0);
fitted.insert(0b010, 20.0);
fitted.insert(0b100, 30.0);
let mut target = HashMap::new();
target.insert(0b001, 8.0);
target.insert(0b010, 25.0);
target.insert(0b100, 28.0);
// max(|10-8|, |20-25|, |30-28|) = max(2, 5, 2) = 5
assert_eq!(loss.compute(&fitted, &target), 5.0);
}
#[test]
fn test_empty_target() {
let loss = LossType::sse();
let fitted = HashMap::new();
let target = HashMap::new();
assert_eq!(loss.compute(&fitted, &target), 0.0);
}
#[test]
fn test_missing_fitted_area() {
let loss = LossType::sse();
let fitted = HashMap::new(); // Empty - no fitted areas
let mut target = HashMap::new();
target.insert(0b001, 5.0);
target.insert(0b010, 3.0);
// (0-5)² + (0-3)² = 25 + 9 = 34
assert_eq!(loss.compute(&fitted, &target), 34.0);
}
#[test]
fn test_extra_fitted_area() {
let loss = LossType::sse();
let mut fitted = HashMap::new();
fitted.insert(0b001, 5.0);
fitted.insert(0b010, 3.0);
fitted.insert(0b100, 7.0); // Extra region not in target
let mut target = HashMap::new();
target.insert(0b001, 5.0);
target.insert(0b010, 3.0);
// 0b100 missing from target
// (5-5)² + (3-3)² + (7-0)² = 0 + 0 + 49 = 49
assert_eq!(loss.compute(&fitted, &target), 49.0);
}
#[test]
fn test_stress_with_zero_target() {
let loss = LossType::stress();
let mut fitted = HashMap::new();
fitted.insert(0b001, 5.0);
fitted.insert(0b010, 0.0);
fitted.insert(0b100, 3.0);
let mut target = HashMap::new();
target.insert(0b001, 0.0);
target.insert(0b010, 0.0);
target.insert(0b100, 3.0);
// Σft = 0 + 0 + 9 = 9; Σt² = 9 → β = 1
// numerator = (5-0)² + (0-0)² + (3-3)² = 25
// Σf² = 25 + 0 + 9 = 34 → stress = 25/34 ≈ 0.735294
let result = loss.compute(&fitted, &target);
assert!(
(result - 25.0 / 34.0).abs() < 1e-10,
"expected 0.735294, got {}",
result
);
}
#[test]
fn test_equality() {
assert_eq!(LossType::sse(), LossType::SumSquared);
assert_eq!(LossType::stress(), LossType::Stress);
assert_ne!(LossType::sse(), LossType::rmse());
}
#[test]
fn test_clone() {
let loss = LossType::sse();
let cloned = loss;
assert_eq!(loss, cloned);
}
}