oxirs_embed/
evolutionary_nas_eval.rs

1//! Evolutionary NAS — Evaluation
2//!
3//! Architecture fitness evaluation, proxy metrics, and early stopping.
4
5use crate::evolutionary_nas_types::{
6    ArchitectureCandidate, EvaluationDataset, HardwareTarget, PerformanceMetrics, ProfilingResult,
7};
8use anyhow::Result;
9use scirs2_core::random::Random;
10use std::collections::HashMap;
11use std::sync::Arc;
12use tokio::sync::RwLock;
13
14// ── Hardware Profiler ─────────────────────────────────────────────────────────
15
16/// Hardware profiler for performance measurement
17pub struct HardwareProfiler {
18    pub target_hardware: HardwareTarget,
19    pub profiling_history: Vec<ProfilingResult>,
20}
21
22// ── Fitness Evaluator ─────────────────────────────────────────────────────────
23
24/// Fitness evaluator for architecture candidates
25pub struct FitnessEvaluator {
26    pub datasets: HashMap<String, EvaluationDataset>,
27    pub hardware_profiler: HardwareProfiler,
28    pub evaluation_cache: Arc<RwLock<HashMap<String, PerformanceMetrics>>>,
29}
30
31impl FitnessEvaluator {
32    /// Evaluate the performance of a single candidate.
33    ///
34    /// In a real implementation this would train and evaluate the full architecture; here we use
35    /// synthetic metrics that are deterministic enough for testing.
36    pub async fn evaluate_candidate_performance(
37        &self,
38        _candidate: &ArchitectureCandidate,
39    ) -> Result<PerformanceMetrics> {
40        let mut random = Random::default();
41        Ok(PerformanceMetrics {
42            training_accuracy: random.random_range(0.7f32..0.95f32),
43            validation_accuracy: random.random_range(0.65f32..0.9f32),
44            test_accuracy: None,
45            training_time: random.random_range(100.0f64..1000.0f64),
46            inference_time_ms: random.random_range(0.1f32..10.0f32),
47            memory_usage_mb: random.random_range(100.0f32..2000.0f32),
48            energy_consumption: Some(random.random_range(10.0f32..100.0f32)),
49            model_size: random.random_range(1_000_000usize..50_000_000usize),
50            flops: random.random_range(1_000_000u64..100_000_000u64),
51        })
52    }
53}
54
55// ── Proxy metrics ─────────────────────────────────────────────────────────────
56
57/// Compute a lightweight proxy accuracy score for an architecture candidate without full training.
58///
59/// The proxy is computed from structural features of the genome: more nodes and active connections
60/// generally correlate with higher representational capacity, bounded to a plausible range.
61pub fn compute_proxy_accuracy(candidate: &ArchitectureCandidate) -> f32 {
62    let num_nodes = candidate.genome.nodes.len() as f32;
63    let num_active = candidate
64        .genome
65        .connections
66        .iter()
67        .filter(|c| c.active)
68        .count() as f32;
69
70    // Simple heuristic: log-scale capacity score, clipped to [0.5, 0.95]
71    let capacity = (num_nodes.ln_1p() + num_active.ln_1p()) / 10.0;
72    capacity.clamp(0.5, 0.95)
73}
74
75/// Proxy memory estimate in MB based on genome structure.
76pub fn estimate_memory_mb(candidate: &ArchitectureCandidate) -> f32 {
77    // Each node contributes ~0.5 MB on average at the 128-dim baseline
78    candidate.genome.nodes.len() as f32 * 0.5 + candidate.genome.connections.len() as f32 * 0.01
79}
80
81/// Proxy FLOPs estimate for a forward pass.
82pub fn estimate_flops(candidate: &ArchitectureCandidate) -> u64 {
83    // Very rough: each active connection involves ~1k multiply-add operations
84    let active_conns = candidate
85        .genome
86        .connections
87        .iter()
88        .filter(|c| c.active)
89        .count() as u64;
90    active_conns * 1_000
91}
92
93// ── Early stopping ────────────────────────────────────────────────────────────
94
95/// Decide whether evolution should stop early based on the recent fitness trend.
96///
97/// Returns `true` if the best fitness has not improved by more than `min_delta` over the last
98/// `patience` generations.
99pub fn should_stop_early(generation_best_fitness: &[f32], patience: usize, min_delta: f32) -> bool {
100    if generation_best_fitness.len() < patience + 1 {
101        return false;
102    }
103    let window = &generation_best_fitness[generation_best_fitness.len() - patience - 1..];
104    let oldest = window[0];
105    let newest = *window.last().expect("window must have elements");
106    (newest - oldest).abs() < min_delta
107}
oxirs_embed/evolutionary_nas_eval.rs

oxirs_embed/
evolutionary_nas_eval.rs
