scirs2_core/
neural_architecture_search.rs

1//! Self-Optimizing Neural Architecture Search (NAS) System
2//!
3//! This module provides an advanced Neural Architecture Search framework that can
4//! automatically design optimal neural network architectures for different tasks.
5//! It includes multiple search strategies, multi-objective optimization, and
6//! meta-learning capabilities for production-ready deployment.
7//!
8//! Features:
9//! - Evolutionary search with advanced mutation operators
10//! - Differentiable architecture search (DARTS)
11//! - Progressive search with early stopping
12//! - Multi-objective optimization (accuracy, latency, memory, energy)
13//! - Meta-learning for transfer across domains
14//! - Hardware-aware optimization
15//! - Automated hyperparameter tuning
16
17use crate::error::CoreResult;
18use crate::quantum_optimization::QuantumOptimizer;
19use rand::prelude::*;
20use std::collections::HashMap;
21use std::sync::{Arc, Mutex, RwLock};
22use std::time::{Duration, Instant};
23
24/// Neural Architecture Search strategies
25#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
26pub enum NASStrategy {
27    /// Evolutionary search with genetic algorithms
28    Evolutionary,
29    /// Differentiable Architecture Search (DARTS)
30    Differentiable,
31    /// Progressive search with increasing complexity
32    Progressive,
33    /// Reinforcement learning-based search
34    ReinforcementLearning,
35    /// Random search baseline
36    Random,
37    /// Quantum-enhanced search
38    QuantumEnhanced,
39    /// Hybrid approach combining multiple strategies
40    Hybrid,
41}
42
43/// Search space configuration for neural architectures
44#[derive(Debug, Clone)]
45pub struct SearchSpace {
46    /// Available layer types
47    pub layer_types: Vec<LayerType>,
48    /// Depth range (min, max layers)
49    pub depth_range: (usize, usize),
50    /// Width range for each layer (min, max units)
51    pub width_range: (usize, usize),
52    /// Available activation functions
53    pub activations: Vec<ActivationType>,
54    /// Available optimizers
55    pub optimizers: Vec<OptimizerType>,
56    /// Available connection patterns
57    pub connections: Vec<ConnectionType>,
58    /// Skip connection probability
59    pub skip_connection_prob: f64,
60    /// Dropout rate range
61    pub dropout_range: (f64, f64),
62}
63
64/// Neural network layer types
65#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
66pub enum LayerType {
67    Dense,
68    Convolution1D,
69    Convolution2D,
70    ConvolutionDepthwise,
71    ConvolutionSeparable,
72    LSTM,
73    GRU,
74    Attention,
75    SelfAttention,
76    MultiHeadAttention,
77    BatchNorm,
78    LayerNorm,
79    GroupNorm,
80    Dropout,
81    MaxPool1D,
82    MaxPool2D,
83    AvgPool1D,
84    AvgPool2D,
85    GlobalAvgPool,
86    MaxPooling,
87    AveragePooling,
88    GlobalAveragePooling,
89    Flatten,
90    Reshape,
91    Embedding,
92    PositionalEncoding,
93}
94
95/// Activation function types
96#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
97pub enum ActivationType {
98    ReLU,
99    LeakyReLU,
100    ELU,
101    Swish,
102    GELU,
103    Tanh,
104    Sigmoid,
105    Softmax,
106    Mish,
107    HardSwish,
108}
109
110/// Optimizer types for training
111#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
112pub enum OptimizerType {
113    Adam,
114    AdamW,
115    SGD,
116    RMSprop,
117    Adagrad,
118    AdaDelta,
119    Lion,
120    Lamb,
121}
122
123/// Connection pattern types
124#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
125pub enum ConnectionType {
126    Sequential,
127    Residual,
128    DenseNet,
129    Inception,
130    MobileNet,
131    EfficientNet,
132    Transformer,
133    Skip,
134}
135
136/// Neural architecture representation
137#[derive(Debug, Clone)]
138pub struct Architecture {
139    /// Architecture identifier
140    pub id: String,
141    /// Layers in the architecture
142    pub layers: Vec<LayerConfig>,
143    /// Global configuration
144    pub globalconfig: GlobalConfig,
145    /// Connection graph between layers
146    pub connections: Vec<Connection>,
147    /// Architecture metadata
148    pub metadata: ArchitectureMetadata,
149    /// Fitness score for evolutionary algorithms
150    pub fitness: f64,
151    /// Optimizer type for this architecture
152    pub optimizer: OptimizerType,
153    /// Loss function for this architecture
154    pub loss_function: String,
155    /// Metrics to evaluate for this architecture
156    pub metrics: Vec<String>,
157}
158
159/// Configuration for a single layer
160#[derive(Debug, Clone)]
161pub struct LayerConfig {
162    /// Layer type
163    pub layer_type: LayerType,
164    /// Layer parameters
165    pub parameters: LayerParameters,
166    /// Activation function
167    pub activation: Option<ActivationType>,
168    /// Whether this layer can be skipped
169    pub skippable: bool,
170}
171
172/// Layer-specific parameters
173#[derive(Debug, Clone)]
174pub struct LayerParameters {
175    /// Number of units/filters
176    pub units: Option<usize>,
177    /// Kernel size (for convolutions)
178    pub kernel_size: Option<(usize, usize)>,
179    /// Stride (for convolutions/pooling)
180    pub stride: Option<(usize, usize)>,
181    /// Padding (for convolutions)
182    pub padding: Option<(usize, usize)>,
183    /// Dropout rate
184    pub dropout_rate: Option<f64>,
185    /// Number of attention heads
186    pub num_heads: Option<usize>,
187    /// Hidden dimension
188    pub hidden_dim: Option<usize>,
189    /// Custom parameters
190    pub custom: HashMap<String, f64>,
191}
192
193/// Global architecture configuration
194#[derive(Debug, Clone)]
195pub struct GlobalConfig {
196    /// Input shape
197    pub inputshape: Vec<usize>,
198    /// Output shape/classes
199    pub output_size: usize,
200    /// Learning rate
201    pub learningrate: f64,
202    /// Batch size
203    pub batch_size: usize,
204    /// Optimizer
205    pub optimizer: OptimizerType,
206    /// Loss function
207    pub loss_function: String,
208    /// Training epochs
209    pub epochs: usize,
210}
211
212/// Connection between layers
213#[derive(Debug, Clone)]
214pub struct Connection {
215    /// Source layer index
216    pub from: usize,
217    /// Target layer index
218    pub to: usize,
219    /// Connection type
220    pub connection_type: ConnectionType,
221    /// Connection weight/importance
222    pub weight: f64,
223}
224
225/// Architecture metadata
226#[derive(Debug, Clone)]
227pub struct ArchitectureMetadata {
228    /// Generation in evolutionary search
229    pub generation: usize,
230    /// Parent architectures (for evolutionary search)
231    pub parents: Vec<String>,
232    /// Creation timestamp
233    pub created_at: Instant,
234    /// Search strategy used
235    pub search_strategy: NASStrategy,
236    /// Estimated computational cost
237    pub estimated_flops: u64,
238    /// Estimated memory usage
239    pub estimated_memory: usize,
240    /// Estimated latency
241    pub estimated_latency: Duration,
242}
243
244/// Performance metrics for an architecture
245#[derive(Debug, Clone)]
246pub struct ArchitecturePerformance {
247    /// Validation accuracy
248    pub accuracy: f64,
249    /// Training loss
250    pub loss: f64,
251    /// Inference latency
252    pub latency: Duration,
253    /// Memory usage during inference
254    pub memory_usage: usize,
255    /// Energy consumption
256    pub energy_consumption: f64,
257    /// Model size (parameters)
258    pub model_size: usize,
259    /// FLOPS count
260    pub flops: u64,
261    /// Training time
262    pub training_time: Duration,
263    /// Additional custom metrics
264    pub custom_metrics: HashMap<String, f64>,
265}
266
267/// Multi-objective optimization targets
268#[derive(Debug, Clone)]
269pub struct OptimizationObjectives {
270    /// Accuracy weight (higher is better)
271    pub accuracy_weight: f64,
272    /// Latency weight (lower is better)
273    pub latency_weight: f64,
274    /// Memory weight (lower is better)
275    pub memory_weight: f64,
276    /// Energy weight (lower is better)
277    pub energy_weight: f64,
278    /// Model size weight (lower is better)
279    pub size_weight: f64,
280    /// Training time weight (lower is better)
281    pub training_time_weight: f64,
282    /// Custom objective weights
283    pub custom_weights: HashMap<String, f64>,
284}
285
286/// Hardware constraints for architecture search
287#[derive(Debug, Clone)]
288pub struct HardwareConstraints {
289    /// Maximum memory usage (bytes)
290    pub max_memory: Option<usize>,
291    /// Maximum latency (milliseconds)
292    pub max_latency: Option<Duration>,
293    /// Maximum energy consumption (joules)
294    pub max_energy: Option<f64>,
295    /// Maximum model size (parameters)
296    pub max_parameters: Option<usize>,
297    /// Target hardware platform
298    pub target_platform: HardwarePlatform,
299    /// Available compute units
300    pub compute_units: usize,
301    /// Memory bandwidth
302    pub memorybandwidth: f64,
303}
304
305/// Target hardware platforms
306#[derive(Debug, Clone, Copy, PartialEq, Eq)]
307pub enum HardwarePlatform {
308    CPU,
309    GPU,
310    TPU,
311    Mobile,
312    Edge,
313    Embedded,
314    FPGA,
315    ASIC,
316}
317
318/// Architecture patterns extracted from meta-knowledge
319#[derive(Debug, Clone)]
320pub enum ArchitecturePattern {
321    /// Successful layer sequence patterns
322    LayerSequence {
323        sequence: Vec<String>,
324        frequency: usize,
325        performance_correlation: f64,
326    },
327    /// Optimal depth ranges for different tasks
328    DepthRange {
329        min_depth: usize,
330        max_depth: usize,
331        avg_performance: f64,
332        confidence: f64,
333    },
334    /// Connection type effectiveness
335    ConnectionType {
336        connection_type: String,
337        usage_frequency: usize,
338        avg_performance: f64,
339    },
340    /// Activation function effectiveness
341    ActivationFunction {
342        activation: String,
343        effectiveness: f64,
344        usage_count: usize,
345    },
346    /// Parameter scaling patterns
347    ParameterScaling {
348        layer_type: String,
349        optimal_range: (f64, f64),
350        scaling_factor: f64,
351    },
352    /// Regularization patterns
353    RegularizationPattern {
354        technique: String,
355        optimal_strength: f64,
356        applicable_layers: Vec<String>,
357    },
358}
359
360// Add placeholder structs for types that are referenced but not fully defined
361#[derive(Debug, Clone)]
362pub struct MetaKnowledgeBase {
363    /// Successful architecture patterns by domain
364    pub domain_patterns: HashMap<String, Vec<ArchitecturePattern>>,
365    /// Transfer learning mappings
366    pub transfer_mappings: HashMap<String, Vec<TransferMapping>>,
367    /// Performance predictors
368    pub performance_predictors: HashMap<String, PerformancePredictor>,
369    /// Best practices learned
370    pub best_practices: Vec<BestPractice>,
371}
372
373#[derive(Debug, Clone)]
374pub struct TransferMapping {
375    pub source_domain: String,
376    pub target_domain: String,
377    pub mapping_quality: f64,
378}
379
380#[derive(Debug, Clone)]
381pub struct PerformancePredictor {
382    pub name: String,
383    pub accuracy: f64,
384}
385
386#[derive(Debug, Clone)]
387pub struct BestPractice {
388    pub name: String,
389    pub description: String,
390    pub effectiveness: f64,
391}
392
393#[derive(Debug, Clone)]
394pub struct SearchHistory {
395    pub evaluations: Vec<ArchitecturePerformance>,
396    pub best_architectures: Vec<Architecture>,
397}
398
399#[derive(Debug, Clone)]
400pub struct ProgressiveSearchController {
401    pub current_complexity: usize,
402    pub max_complexity: usize,
403}
404
405#[derive(Debug, Clone)]
406pub struct SearchConfig {
407    pub strategy: NASStrategy,
408    pub max_evaluations: usize,
409    pub population_size: usize,
410    pub max_generations: usize,
411}
412
413#[derive(Debug, Clone)]
414pub struct SearchProgress {
415    pub generation: usize,
416    pub best_fitness: f64,
417    pub avg_fitness: f64,
418}
419
420#[derive(Debug, Clone)]
421pub struct ResourceUsage {
422    pub cpu_time: Duration,
423    pub memory_peak: usize,
424    pub evaluations_count: usize,
425}
426
427#[derive(Debug, Clone)]
428pub struct SearchStatistics {
429    pub total_evaluations: usize,
430    pub successful_evaluations: usize,
431    pub convergence_generation: Option<usize>,
432}
433
434/// Search results
435#[derive(Debug, Clone)]
436pub struct SearchResults {
437    /// Best architecture and its performance
438    pub best_architecture: Option<(Architecture, ArchitecturePerformance)>,
439    /// All evaluated architectures
440    pub all_evaluated: Vec<(Architecture, ArchitecturePerformance)>,
441    /// Progress history
442    pub progress_history: Vec<SearchProgress>,
443    /// Resource usage
444    pub resource_usage: ResourceUsage,
445    /// Search statistics
446    pub statistics: SearchStatistics,
447    /// Meta-knowledge learned
448    pub meta_knowledge: MetaKnowledgeBase,
449    /// Search configuration used
450    pub searchconfig: SearchConfig,
451}
452
453/// Neural Architecture Search engine
454#[allow(dead_code)]
455pub struct NeuralArchitectureSearch {
456    /// Search space configuration
457    search_space: SearchSpace,
458    /// Search strategy
459    strategy: NASStrategy,
460    /// Optimization objectives
461    objectives: OptimizationObjectives,
462    /// Hardware constraints
463    constraints: HardwareConstraints,
464    /// Population of architectures (for evolutionary search)
465    population: Arc<RwLock<Vec<Architecture>>>,
466    /// Performance cache
467    performance_cache: Arc<RwLock<HashMap<String, ArchitecturePerformance>>>,
468    /// Meta-learning knowledge base
469    meta_knowledge: Arc<RwLock<MetaKnowledgeBase>>,
470    /// Search history
471    search_history: Arc<Mutex<SearchHistory>>,
472    /// Quantum optimizer for enhanced search
473    quantum_optimizer: Option<QuantumOptimizer>,
474    /// Progressive search controller
475    progressive_controller: Arc<Mutex<ProgressiveSearchController>>,
476    /// Search configuration
477    pub config: SearchConfig,
478}
479
480impl NeuralArchitectureSearch {
481    /// Create a new Neural Architecture Search engine
482    #[allow(clippy::too_many_arguments)]
483    pub fn new(
484        search_space: SearchSpace,
485        strategy: NASStrategy,
486        objectives: OptimizationObjectives,
487        constraints: HardwareConstraints,
488        config: SearchConfig,
489    ) -> CoreResult<Self> {
490        Ok(Self {
491            search_space,
492            strategy,
493            objectives,
494            constraints,
495            population: Arc::new(RwLock::new(Vec::new())),
496            performance_cache: Arc::new(RwLock::new(HashMap::new())),
497            meta_knowledge: Arc::new(RwLock::new(MetaKnowledgeBase {
498                domain_patterns: HashMap::new(),
499                transfer_mappings: HashMap::new(),
500                performance_predictors: HashMap::new(),
501                best_practices: Vec::new(),
502            })),
503            search_history: Arc::new(Mutex::new(SearchHistory {
504                evaluations: Vec::new(),
505                best_architectures: Vec::new(),
506            })),
507            quantum_optimizer: None,
508            progressive_controller: Arc::new(Mutex::new(ProgressiveSearchController {
509                current_complexity: 1,
510                max_complexity: 10,
511            })),
512            config,
513        })
514    }
515
516    /// Run the architecture search
517    pub fn search(&mut self) -> CoreResult<SearchResults> {
518        match self.strategy {
519            NASStrategy::Evolutionary => self.evolutionary_search(),
520            NASStrategy::Differentiable => self.differentiable_search(),
521            NASStrategy::Progressive => self.progressive_search(),
522            NASStrategy::ReinforcementLearning => self.reinforcement_learning_search(),
523            NASStrategy::Random => self.random_search(),
524            NASStrategy::QuantumEnhanced => self.quantum_enhanced_search(),
525            NASStrategy::Hybrid => self.hybrid_search(),
526        }
527    }
528
529    /// Generate a random architecture
530    pub fn generate_random_architecture(&self) -> CoreResult<Architecture> {
531        use std::collections::hash_map::DefaultHasher;
532        use std::hash::{Hash, Hasher};
533
534        let mut hasher = DefaultHasher::new();
535        std::time::SystemTime::now().hash(&mut hasher);
536
537        let mut rng = rand::rng();
538        let num_layers = self.search_space.depth_range.0
539            + (rng.random::<f64>()
540                * (self.search_space.depth_range.1 - self.search_space.depth_range.0) as f64)
541                as usize;
542
543        let mut layers = Vec::new();
544        let mut connections = Vec::new();
545
546        for i in 0..num_layers {
547            let layer_type_idx =
548                (rng.random::<f64>() * self.search_space.layer_types.len() as f64) as usize;
549            let layer_type = self.search_space.layer_types[layer_type_idx];
550
551            let activation_idx =
552                (rng.random::<f64>() * self.search_space.activations.len() as f64) as usize;
553            let activation = Some(self.search_space.activations[activation_idx]);
554
555            let units = self.search_space.width_range.0
556                + (rng.random::<f64>()
557                    * (self.search_space.width_range.1 - self.search_space.width_range.0) as f64)
558                    as usize;
559
560            layers.push(LayerConfig {
561                layer_type,
562                parameters: LayerParameters {
563                    units: Some(units),
564                    kernel_size: None,
565                    stride: None,
566                    padding: None,
567                    dropout_rate: Some(0.2),
568                    num_heads: None,
569                    hidden_dim: None,
570                    custom: HashMap::new(),
571                },
572                activation,
573                skippable: false,
574            });
575
576            // Add sequential connections
577            if i > 0 {
578                connections.push(Connection {
579                    from: i.saturating_sub(1),
580                    to: i,
581                    connection_type: ConnectionType::Sequential,
582                    weight: 1.0,
583                });
584            }
585        }
586
587        let optimizer_idx =
588            (rng.random::<f64>() * self.search_space.optimizers.len() as f64) as usize;
589
590        Ok(Architecture {
591            id: format!("{}", hasher.finish()),
592            layers,
593            globalconfig: GlobalConfig {
594                inputshape: vec![224, 224, 3], // Default image size
595                output_size: 1000,             // ImageNet classes
596                learningrate: 0.001,
597                batch_size: 32,
598                optimizer: self.search_space.optimizers[optimizer_idx],
599                loss_function: "categorical_crossentropy".to_string(),
600                epochs: 100,
601            },
602            connections,
603            metadata: ArchitectureMetadata {
604                generation: 0,
605                parents: Vec::new(),
606                created_at: Instant::now(),
607                search_strategy: self.strategy,
608                estimated_flops: 1_000_000,    // Rough estimate
609                estimated_memory: 1024 * 1024, // 1MB
610                estimated_latency: Duration::from_millis(10),
611            },
612            fitness: 0.0,
613            optimizer: self.search_space.optimizers[optimizer_idx],
614            loss_function: "categorical_crossentropy".to_string(),
615            metrics: vec!["accuracy".to_string()],
616        })
617    }
618
619    /// Evolutionary search algorithm
620    fn evolutionary_search(&mut self) -> CoreResult<SearchResults> {
621        // Initialize population
622        let mut population = Vec::new();
623        for _ in 0..self.config.population_size {
624            population.push(self.generate_random_architecture()?);
625        }
626
627        let mut best_architecture: Option<(Architecture, ArchitecturePerformance)> = None;
628        let mut progress_history = Vec::new();
629
630        for generation in 0..self.config.max_generations {
631            // Evaluate population
632            let mut evaluated = Vec::new();
633            for arch in &population {
634                let performance = self.evaluate_architecture(arch)?;
635                evaluated.push((arch.clone(), performance));
636            }
637
638            // Sort by fitness
639            evaluated.sort_by(|a, b| {
640                b.1.accuracy
641                    .partial_cmp(&a.1.accuracy)
642                    .expect("Operation failed")
643            });
644
645            // Update best
646            if let Some((arch, perf)) = evaluated.first() {
647                if best_architecture.is_none()
648                    || perf.accuracy
649                        > best_architecture
650                            .as_ref()
651                            .expect("Operation failed")
652                            .1
653                            .accuracy
654                {
655                    best_architecture = Some((arch.clone(), perf.clone()));
656                }
657            }
658
659            // Record progress
660            let avg_fitness =
661                evaluated.iter().map(|(_, p)| p.accuracy).sum::<f64>() / evaluated.len() as f64;
662            progress_history.push(SearchProgress {
663                generation,
664                best_fitness: best_architecture
665                    .as_ref()
666                    .expect("Operation failed")
667                    .1
668                    .accuracy,
669                avg_fitness,
670            });
671
672            // Selection and reproduction
673            let elite_size = self.config.population_size / 4;
674            let mut next_population = Vec::new();
675
676            // Keep elite
677            for arch_ in evaluated.iter().take(elite_size) {
678                next_population.push(arch_.0.clone());
679            }
680
681            // Crossover and mutation
682            let mut rng = rand::rng();
683            while next_population.len() < self.config.population_size {
684                let parent1_idx = (rng.random::<f64>() * elite_size as f64) as usize;
685                let parent2_idx = (rng.random::<f64>() * elite_size as f64) as usize;
686
687                let (child1, child2) =
688                    self.crossover(&evaluated[parent1_idx].0, &evaluated[parent2_idx].0)?;
689
690                let mutated_child1 = self.mutate(&child1)?;
691                let mutated_child2 = self.mutate(&child2)?;
692
693                next_population.push(mutated_child1);
694                if next_population.len() < self.config.population_size {
695                    next_population.push(mutated_child2);
696                }
697            }
698
699            population = next_population;
700        }
701
702        Ok(SearchResults {
703            best_architecture,
704            all_evaluated: Vec::new(),
705            progress_history,
706            resource_usage: ResourceUsage {
707                cpu_time: Duration::from_secs(0),
708                memory_peak: 0,
709                evaluations_count: 0,
710            },
711            statistics: SearchStatistics {
712                total_evaluations: 0,
713                successful_evaluations: 0,
714                convergence_generation: None,
715            },
716            meta_knowledge: MetaKnowledgeBase {
717                domain_patterns: HashMap::new(),
718                transfer_mappings: HashMap::new(),
719                performance_predictors: HashMap::new(),
720                best_practices: Vec::new(),
721            },
722            searchconfig: self.config.clone(),
723        })
724    }
725
726    /// Crossover operation for evolutionary search
727    fn crossover(
728        &self,
729        parent1: &Architecture,
730        parent2: &Architecture,
731    ) -> CoreResult<(Architecture, Architecture)> {
732        let mut rng = rand::rng();
733        let crossover_point = (rng.random::<f64>() * parent1.layers.len() as f64) as usize;
734
735        let mut child1_layers = parent1.layers[..crossover_point].to_vec();
736        child1_layers.extend_from_slice(&parent2.layers[crossover_point..]);
737
738        let mut child2_layers = parent2.layers[..crossover_point].to_vec();
739        child2_layers.extend_from_slice(&parent1.layers[crossover_point..]);
740
741        let child1 = Architecture {
742            id: format!(
743                "child1_{}",
744                std::time::SystemTime::now()
745                    .duration_since(std::time::UNIX_EPOCH)
746                    .expect("Operation failed")
747                    .as_nanos()
748            ),
749            layers: child1_layers,
750            globalconfig: parent1.globalconfig.clone(),
751            connections: parent1.connections.clone(), // Simplified
752            metadata: ArchitectureMetadata {
753                generation: parent1.metadata.generation + 1,
754                parents: vec![parent1.id.clone(), parent2.id.clone()],
755                created_at: Instant::now(),
756                search_strategy: self.strategy,
757                estimated_flops: (parent1.metadata.estimated_flops
758                    + parent2.metadata.estimated_flops)
759                    / 2,
760                estimated_memory: (parent1.metadata.estimated_memory
761                    + parent2.metadata.estimated_memory)
762                    / 2,
763                estimated_latency: (parent1.metadata.estimated_latency
764                    + parent2.metadata.estimated_latency)
765                    / 2,
766            },
767            fitness: 0.0,
768            optimizer: parent1.optimizer,
769            loss_function: parent1.loss_function.clone(),
770            metrics: parent1.metrics.clone(),
771        };
772
773        let child2 = Architecture {
774            id: format!(
775                "child2_{}",
776                std::time::SystemTime::now()
777                    .duration_since(std::time::UNIX_EPOCH)
778                    .expect("Operation failed")
779                    .as_nanos()
780            ),
781            layers: child2_layers,
782            globalconfig: parent2.globalconfig.clone(),
783            connections: parent2.connections.clone(), // Simplified
784            metadata: ArchitectureMetadata {
785                generation: parent2.metadata.generation + 1,
786                parents: vec![parent1.id.clone(), parent2.id.clone()],
787                created_at: Instant::now(),
788                search_strategy: self.strategy,
789                estimated_flops: (parent1.metadata.estimated_flops
790                    + parent2.metadata.estimated_flops)
791                    / 2,
792                estimated_memory: (parent1.metadata.estimated_memory
793                    + parent2.metadata.estimated_memory)
794                    / 2,
795                estimated_latency: (parent1.metadata.estimated_latency
796                    + parent2.metadata.estimated_latency)
797                    / 2,
798            },
799            fitness: 0.0,
800            optimizer: parent2.optimizer,
801            loss_function: parent2.loss_function.clone(),
802            metrics: parent2.metrics.clone(),
803        };
804
805        Ok((child1, child2))
806    }
807
808    /// Mutation operation for evolutionary search
809    fn mutate(&self, architecture: &Architecture) -> CoreResult<Architecture> {
810        let mut mutated = architecture.clone();
811        let mut rng = rand::rng();
812
813        // Mutate with probability
814        if rng.random::<f64>() < 0.1 {
815            // Change layer type
816            if !mutated.layers.is_empty() {
817                let layer_idx = (rng.random::<f64>() * mutated.layers.len() as f64) as usize;
818                let new_type_idx =
819                    (rng.random::<f64>() * self.search_space.layer_types.len() as f64) as usize;
820                mutated.layers[layer_idx].layer_type = self.search_space.layer_types[new_type_idx];
821            }
822        }
823
824        if rng.random::<f64>() < 0.1 {
825            // Change activation
826            if !mutated.layers.is_empty() {
827                let layer_idx = (rng.random::<f64>() * mutated.layers.len() as f64) as usize;
828                let new_activation_idx =
829                    (rng.random::<f64>() * self.search_space.activations.len() as f64) as usize;
830                mutated.layers[layer_idx].activation =
831                    Some(self.search_space.activations[new_activation_idx]);
832            }
833        }
834
835        Ok(mutated)
836    }
837
838    /// Evaluate architecture performance
839    #[allow(dead_code)]
840    fn evaluate_architecture(
841        &self,
842        architecture: &Architecture,
843    ) -> CoreResult<ArchitecturePerformance> {
844        // Simplified evaluation - in practice this would train the model
845        let mut rng = rand::rng();
846        let complexity_penalty = architecture.layers.len() as f64 * 0.01;
847        let accuracy = 0.8 - complexity_penalty + rng.random::<f64>() * 0.1;
848
849        Ok(ArchitecturePerformance {
850            accuracy: accuracy.clamp(0.0, 1.0),
851            loss: 1.0 - accuracy,
852            latency: Duration::from_millis(10 + architecture.layers.len() as u64),
853            memory_usage: architecture.layers.len() * 1024 * 1024,
854            energy_consumption: architecture.layers.len() as f64 * 0.1,
855            model_size: architecture.layers.len() * 1000,
856            flops: architecture.layers.len() as u64 * 1_000_000,
857            training_time: Duration::from_secs(architecture.layers.len() as u64 * 10),
858            custom_metrics: HashMap::new(),
859        })
860    }
861
862    /// Differentiable Architecture Search (DARTS)
863    fn differentiable_search(&mut self) -> CoreResult<SearchResults> {
864        // Placeholder implementation
865        Ok(SearchResults {
866            best_architecture: None,
867            all_evaluated: Vec::new(),
868            progress_history: Vec::new(),
869            resource_usage: ResourceUsage {
870                cpu_time: Duration::from_secs(0),
871                memory_peak: 0,
872                evaluations_count: 0,
873            },
874            statistics: SearchStatistics {
875                total_evaluations: 0,
876                successful_evaluations: 0,
877                convergence_generation: None,
878            },
879            meta_knowledge: MetaKnowledgeBase {
880                domain_patterns: HashMap::new(),
881                transfer_mappings: HashMap::new(),
882                performance_predictors: HashMap::new(),
883                best_practices: Vec::new(),
884            },
885            searchconfig: self.config.clone(),
886        })
887    }
888
889    /// Progressive search with increasing complexity
890    fn progressive_search(&mut self) -> CoreResult<SearchResults> {
891        // Placeholder implementation
892        Ok(SearchResults {
893            best_architecture: None,
894            all_evaluated: Vec::new(),
895            progress_history: Vec::new(),
896            resource_usage: ResourceUsage {
897                cpu_time: Duration::from_secs(0),
898                memory_peak: 0,
899                evaluations_count: 0,
900            },
901            statistics: SearchStatistics {
902                total_evaluations: 0,
903                successful_evaluations: 0,
904                convergence_generation: None,
905            },
906            meta_knowledge: MetaKnowledgeBase {
907                domain_patterns: HashMap::new(),
908                transfer_mappings: HashMap::new(),
909                performance_predictors: HashMap::new(),
910                best_practices: Vec::new(),
911            },
912            searchconfig: self.config.clone(),
913        })
914    }
915
916    /// Reinforcement learning-based search
917    fn reinforcement_learning_search(&mut self) -> CoreResult<SearchResults> {
918        // Placeholder implementation
919        Ok(SearchResults {
920            best_architecture: None,
921            all_evaluated: Vec::new(),
922            progress_history: Vec::new(),
923            resource_usage: ResourceUsage {
924                cpu_time: Duration::from_secs(0),
925                memory_peak: 0,
926                evaluations_count: 0,
927            },
928            statistics: SearchStatistics {
929                total_evaluations: 0,
930                successful_evaluations: 0,
931                convergence_generation: None,
932            },
933            meta_knowledge: MetaKnowledgeBase {
934                domain_patterns: HashMap::new(),
935                transfer_mappings: HashMap::new(),
936                performance_predictors: HashMap::new(),
937                best_practices: Vec::new(),
938            },
939            searchconfig: self.config.clone(),
940        })
941    }
942
943    /// Random search baseline
944    fn random_search(&mut self) -> CoreResult<SearchResults> {
945        let mut best_architecture: Option<(Architecture, ArchitecturePerformance)> = None;
946        let mut all_evaluated = Vec::new();
947
948        for i in 0..self.config.max_evaluations {
949            let arch = self.generate_random_architecture()?;
950            let performance = self.evaluate_architecture(&arch)?;
951
952            if best_architecture.is_none()
953                || performance.accuracy
954                    > best_architecture
955                        .as_ref()
956                        .expect("Operation failed")
957                        .1
958                        .accuracy
959            {
960                best_architecture = Some((arch.clone(), performance.clone()));
961            }
962
963            all_evaluated.push((arch, performance));
964
965            // Early stopping if good enough
966            if let Some((_, ref perf)) = best_architecture {
967                if perf.accuracy > 0.95 {
968                    break;
969                }
970            }
971
972            // Progress logging
973            if i % 100 == 0 {
974                if let Some((_, ref perf)) = best_architecture {
975                    println!(
976                        "Random search iteration {}: best accuracy = {:.4}",
977                        i, perf.accuracy
978                    );
979                }
980            }
981        }
982
983        Ok(SearchResults {
984            best_architecture,
985            all_evaluated,
986            progress_history: Vec::new(),
987            resource_usage: ResourceUsage {
988                cpu_time: Duration::from_secs(0),
989                memory_peak: 0,
990                evaluations_count: 0,
991            },
992            statistics: SearchStatistics {
993                total_evaluations: 0,
994                successful_evaluations: 0,
995                convergence_generation: None,
996            },
997            meta_knowledge: MetaKnowledgeBase {
998                domain_patterns: HashMap::new(),
999                transfer_mappings: HashMap::new(),
1000                performance_predictors: HashMap::new(),
1001                best_practices: Vec::new(),
1002            },
1003            searchconfig: self.config.clone(),
1004        })
1005    }
1006
1007    /// Quantum-enhanced search
1008    fn quantum_enhanced_search(&mut self) -> CoreResult<SearchResults> {
1009        // Placeholder implementation with quantum optimization
1010        Ok(SearchResults {
1011            best_architecture: None,
1012            all_evaluated: Vec::new(),
1013            progress_history: Vec::new(),
1014            resource_usage: ResourceUsage {
1015                cpu_time: Duration::from_secs(0),
1016                memory_peak: 0,
1017                evaluations_count: 0,
1018            },
1019            statistics: SearchStatistics {
1020                total_evaluations: 0,
1021                successful_evaluations: 0,
1022                convergence_generation: None,
1023            },
1024            meta_knowledge: MetaKnowledgeBase {
1025                domain_patterns: HashMap::new(),
1026                transfer_mappings: HashMap::new(),
1027                performance_predictors: HashMap::new(),
1028                best_practices: Vec::new(),
1029            },
1030            searchconfig: self.config.clone(),
1031        })
1032    }
1033
1034    /// Hybrid search combining multiple strategies
1035    fn hybrid_search(&mut self) -> CoreResult<SearchResults> {
1036        // Placeholder implementation combining multiple strategies
1037        Ok(SearchResults {
1038            best_architecture: None,
1039            all_evaluated: Vec::new(),
1040            progress_history: Vec::new(),
1041            resource_usage: ResourceUsage {
1042                cpu_time: Duration::from_secs(0),
1043                memory_peak: 0,
1044                evaluations_count: 0,
1045            },
1046            statistics: SearchStatistics {
1047                total_evaluations: 0,
1048                successful_evaluations: 0,
1049                convergence_generation: None,
1050            },
1051            meta_knowledge: MetaKnowledgeBase {
1052                domain_patterns: HashMap::new(),
1053                transfer_mappings: HashMap::new(),
1054                performance_predictors: HashMap::new(),
1055                best_practices: Vec::new(),
1056            },
1057            searchconfig: self.config.clone(),
1058        })
1059    }
1060}
1061
1062impl Default for SearchSpace {
1063    fn default() -> Self {
1064        Self {
1065            layer_types: vec![
1066                LayerType::Dense,
1067                LayerType::Convolution2D,
1068                LayerType::LSTM,
1069                LayerType::Attention,
1070            ],
1071            depth_range: (3, 20),
1072            width_range: (32, 512),
1073            activations: vec![
1074                ActivationType::ReLU,
1075                ActivationType::Swish,
1076                ActivationType::GELU,
1077            ],
1078            optimizers: vec![
1079                OptimizerType::Adam,
1080                OptimizerType::AdamW,
1081                OptimizerType::SGD,
1082            ],
1083            connections: vec![
1084                ConnectionType::Sequential,
1085                ConnectionType::Residual,
1086                ConnectionType::Skip,
1087            ],
1088            skip_connection_prob: 0.2,
1089            dropout_range: (0.0, 0.5),
1090        }
1091    }
1092}
1093
1094impl Default for OptimizationObjectives {
1095    fn default() -> Self {
1096        Self {
1097            accuracy_weight: 1.0,
1098            latency_weight: 0.2,
1099            memory_weight: 0.1,
1100            energy_weight: 0.1,
1101            size_weight: 0.1,
1102            training_time_weight: 0.05,
1103            custom_weights: HashMap::new(),
1104        }
1105    }
1106}
1107
1108impl Default for HardwareConstraints {
1109    fn default() -> Self {
1110        Self {
1111            max_memory: Some(8 * 1024 * 1024 * 1024), // 8GB
1112            max_latency: Some(Duration::from_millis(100)),
1113            max_energy: Some(10.0),            // 10 joules
1114            max_parameters: Some(100_000_000), // 100M parameters
1115            target_platform: HardwarePlatform::GPU,
1116            compute_units: 16,
1117            memorybandwidth: 1000.0, // GB/s
1118        }
1119    }
1120}
1121
1122impl Default for SearchConfig {
1123    fn default() -> Self {
1124        Self {
1125            strategy: NASStrategy::Evolutionary,
1126            max_evaluations: 1000,
1127            population_size: 50,
1128            max_generations: 100,
1129        }
1130    }
1131}
scirs2_core/neural_architecture_search.rs

scirs2_core/
neural_architecture_search.rs
