optirs_learned/
few_shot.rs

1// Few-Shot Learning Enhancement for Optimizer Meta-Learning
2//
3// This module implements advanced few-shot learning techniques specifically designed
4// for quickly adapting optimizers to new tasks with minimal data. It includes
5// prototypical networks, meta-learning approaches, and rapid adaptation mechanisms.
6
7#[allow(dead_code)]
8use scirs2_core::ndarray::{Array1, Array2};
9use scirs2_core::numeric::Float;
10use std::collections::{HashMap, VecDeque};
11use std::fmt::Debug;
12use std::time::{Duration, Instant};
13
14use super::OptimizerState;
15use crate::error::{OptimError, Result};
16
17/// Few-shot learning coordinator for optimizer adaptation
18pub struct FewShotLearningSystem<T: Float + Debug + Send + Sync + 'static> {
19    /// Base meta-learned optimizer
20    base_optimizer: Box<dyn FewShotOptimizer<T>>,
21
22    /// Prototypical network for task representation
23    prototype_network: PrototypicalNetwork<T>,
24
25    /// Support set manager
26    support_set_manager: SupportSetManager<T>,
27
28    /// Adaptation strategies
29    adaptation_strategies: Vec<Box<dyn AdaptationStrategy<T>>>,
30
31    /// Task similarity calculator
32    similarity_calculator: TaskSimilarityCalculator<T>,
33
34    /// Memory bank for storing task experiences
35    memory_bank: EpisodicMemoryBank<T>,
36
37    /// Fast adaptation engine
38    fast_adaptation: FastAdaptationEngine<T>,
39
40    /// Performance tracker
41    performance_tracker: FewShotPerformanceTracker<T>,
42}
43
44/// Base trait for few-shot optimizers
45pub trait FewShotOptimizer<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
46    /// Adapt to new task with few examples
47    fn adapt_few_shot(
48        &mut self,
49        support_set: &SupportSet<T>,
50        query_set: &QuerySet<T>,
51        adaptation_config: &AdaptationConfig,
52    ) -> Result<AdaptationResult<T>>;
53
54    /// Get task representation
55    fn get_task_representation(&self, taskdata: &TaskData<T>) -> Result<Array1<T>>;
56
57    /// Compute adaptation loss
58    fn compute_adaptation_loss(
59        &self,
60        support_set: &SupportSet<T>,
61        query_set: &QuerySet<T>,
62    ) -> Result<T>;
63
64    /// Update meta-parameters
65    fn update_meta_parameters(&mut self, metagradients: &MetaGradients<T>) -> Result<()>;
66
67    /// Get current state for transfer
68    fn get_transfer_state(&self) -> TransferState<T>;
69
70    /// Load transfer state
71    fn load_transfer_state(&mut self, state: TransferState<T>) -> Result<()>;
72}
73
74/// Support set for few-shot learning
75#[derive(Debug, Clone)]
76pub struct SupportSet<T: Float + Debug + Send + Sync + 'static> {
77    /// Support examples
78    pub examples: Vec<SupportExample<T>>,
79
80    /// Task metadata
81    pub task_metadata: TaskMetadata,
82
83    /// Support _set statistics
84    pub statistics: SupportSetStatistics<T>,
85
86    /// Temporal ordering (if applicable)
87    pub temporal_order: Option<Vec<usize>>,
88}
89
90/// Individual support example
91#[derive(Debug, Clone)]
92pub struct SupportExample<T: Float + Debug + Send + Sync + 'static> {
93    /// Input features
94    pub features: Array1<T>,
95
96    /// Target output
97    pub target: T,
98
99    /// Example weight/importance
100    pub weight: T,
101
102    /// Context information
103    pub context: HashMap<String, T>,
104
105    /// Example metadata
106    pub metadata: ExampleMetadata,
107}
108
109/// Query set for evaluation
110#[derive(Debug, Clone)]
111pub struct QuerySet<T: Float + Debug + Send + Sync + 'static> {
112    /// Query examples
113    pub examples: Vec<QueryExample<T>>,
114
115    /// Query statistics
116    pub statistics: QuerySetStatistics<T>,
117
118    /// Evaluation metrics
119    pub eval_metrics: Vec<EvaluationMetric>,
120}
121
122/// Individual query example
123#[derive(Debug, Clone)]
124pub struct QueryExample<T: Float + Debug + Send + Sync + 'static> {
125    /// Input features
126    pub features: Array1<T>,
127
128    /// True target (for evaluation)
129    pub true_target: Option<T>,
130
131    /// Query weight
132    pub weight: T,
133
134    /// Query context
135    pub context: HashMap<String, T>,
136}
137
138/// Task data container
139#[derive(Debug, Clone)]
140pub struct TaskData<T: Float + Debug + Send + Sync + 'static> {
141    /// Task identifier
142    pub task_id: String,
143
144    /// Support set
145    pub support_set: SupportSet<T>,
146
147    /// Query set
148    pub query_set: QuerySet<T>,
149
150    /// Task-specific parameters
151    pub task_params: HashMap<String, T>,
152
153    /// Task domain information
154    pub domain_info: DomainInfo,
155}
156
157/// Domain information
158#[derive(Debug, Clone)]
159pub struct DomainInfo {
160    /// Domain type
161    pub domain_type: DomainType,
162
163    /// Domain characteristics
164    pub characteristics: DomainCharacteristics,
165
166    /// Expected difficulty
167    pub difficulty_level: DifficultyLevel,
168
169    /// Domain-specific constraints
170    pub constraints: Vec<DomainConstraint>,
171}
172
173/// Domain types
174#[derive(Debug, Clone, Copy)]
175pub enum DomainType {
176    ComputerVision,
177    NaturalLanguageProcessing,
178    ReinforcementLearning,
179    TimeSeriesForecasting,
180    ScientificComputing,
181    Optimization,
182    ControlSystems,
183    GamePlaying,
184    Robotics,
185    Healthcare,
186}
187
188/// Domain characteristics
189#[derive(Debug, Clone)]
190pub struct DomainCharacteristics {
191    /// Input dimensionality
192    pub input_dim: usize,
193
194    /// Output dimensionality
195    pub output_dim: usize,
196
197    /// Temporal dependencies
198    pub temporal: bool,
199
200    /// Stochasticity level
201    pub stochasticity: f64,
202
203    /// Noise level
204    pub noise_level: f64,
205
206    /// Data sparsity
207    pub sparsity: f64,
208}
209
210/// Difficulty levels
211#[derive(Debug, Clone, Copy)]
212pub enum DifficultyLevel {
213    Trivial,
214    Easy,
215    Medium,
216    Hard,
217    Expert,
218    Extreme,
219}
220
221/// Domain constraints
222#[derive(Debug, Clone)]
223pub struct DomainConstraint {
224    /// Constraint type
225    pub constraint_type: ConstraintType,
226
227    /// Constraint description
228    pub description: String,
229
230    /// Enforcement level
231    pub enforcement: ConstraintEnforcement,
232}
233
234/// Constraint types
235#[derive(Debug, Clone, Copy)]
236pub enum ConstraintType {
237    ResourceLimit,
238    TemporalConstraint,
239    AccuracyRequirement,
240    LatencyRequirement,
241    MemoryConstraint,
242    EnergyConstraint,
243    SafetyConstraint,
244}
245
246/// Constraint enforcement levels
247#[derive(Debug, Clone, Copy)]
248pub enum ConstraintEnforcement {
249    Hard,
250    Soft,
251    Advisory,
252}
253
254/// Adaptation configuration
255#[derive(Debug, Clone)]
256pub struct AdaptationConfig {
257    /// Number of adaptation steps
258    pub adaptation_steps: usize,
259
260    /// Learning rate for adaptation
261    pub adaptation_lr: f64,
262
263    /// Adaptation strategy
264    pub strategy: AdaptationStrategyType,
265
266    /// Early stopping configuration
267    pub early_stopping: Option<EarlyStoppingConfig>,
268
269    /// Regularization parameters
270    pub regularization: RegularizationConfig,
271
272    /// Resource constraints
273    pub resource_constraints: ResourceConstraints,
274}
275
276/// Adaptation strategy types
277#[derive(Debug, Clone, Copy)]
278pub enum AdaptationStrategyType {
279    /// Model-Agnostic Meta-Learning (MAML)
280    MAML,
281
282    /// First-Order MAML (FOMAML)
283    FOMAML,
284
285    /// Prototypical Networks
286    Prototypical,
287
288    /// Matching Networks
289    Matching,
290
291    /// Relation Networks
292    Relation,
293
294    /// Meta-SGD
295    MetaSGD,
296
297    /// Learned optimizer approach
298    LearnedOptimizer,
299
300    /// Gradient-based meta-learning
301    GradientBased,
302
303    /// Memory-augmented networks
304    MemoryAugmented,
305}
306
307/// Early stopping configuration
308#[derive(Debug, Clone)]
309pub struct EarlyStoppingConfig {
310    /// Patience (steps without improvement)
311    pub patience: usize,
312
313    /// Minimum improvement threshold
314    pub min_improvement: f64,
315
316    /// Validation frequency
317    pub validation_frequency: usize,
318}
319
320/// Regularization configuration
321#[derive(Debug, Clone)]
322pub struct RegularizationConfig {
323    /// L2 regularization strength
324    pub l2_strength: f64,
325
326    /// Dropout rate
327    pub dropout_rate: f64,
328
329    /// Gradient clipping threshold
330    pub gradient_clip: Option<f64>,
331
332    /// Task-specific regularization
333    pub task_regularization: HashMap<String, f64>,
334}
335
336/// Resource constraints for adaptation
337#[derive(Debug, Clone)]
338pub struct ResourceConstraints {
339    /// Maximum adaptation time
340    pub max_time: Duration,
341
342    /// Maximum memory usage (MB)
343    pub max_memory_mb: usize,
344
345    /// Maximum computational budget
346    pub max_compute_budget: f64,
347}
348
349/// Adaptation result
350#[derive(Debug, Clone)]
351pub struct AdaptationResult<T: Float + Debug + Send + Sync + 'static> {
352    /// Adapted optimizer state
353    pub adapted_state: OptimizerState<T>,
354
355    /// Adaptation performance
356    pub performance: AdaptationPerformance<T>,
357
358    /// Task representation learned
359    pub task_representation: Array1<T>,
360
361    /// Adaptation trajectory
362    pub adaptation_trajectory: Vec<AdaptationStep<T>>,
363
364    /// Resource usage
365    pub resource_usage: ResourceUsage<T>,
366}
367
368/// Adaptation performance metrics
369#[derive(Debug, Clone)]
370pub struct AdaptationPerformance<T: Float + Debug + Send + Sync + 'static> {
371    /// Query set performance
372    pub query_performance: T,
373
374    /// Support set performance
375    pub support_performance: T,
376
377    /// Adaptation speed (steps to convergence)
378    pub adaptation_speed: usize,
379
380    /// Final loss
381    pub final_loss: T,
382
383    /// Performance improvement
384    pub improvement: T,
385
386    /// Stability measure
387    pub stability: T,
388}
389
390/// Individual adaptation step
391#[derive(Debug, Clone)]
392pub struct AdaptationStep<T: Float + Debug + Send + Sync + 'static> {
393    /// Step number
394    pub step: usize,
395
396    /// Loss at this step
397    pub loss: T,
398
399    /// Performance at this step
400    pub performance: T,
401
402    /// Gradient norm
403    pub gradient_norm: T,
404
405    /// Step time
406    pub step_time: Duration,
407}
408
409/// Resource usage tracking
410#[derive(Debug, Clone)]
411pub struct ResourceUsage<T: Float + Debug + Send + Sync + 'static> {
412    /// Total time taken
413    pub total_time: Duration,
414
415    /// Peak memory usage (MB)
416    pub peak_memory_mb: T,
417
418    /// Computational cost
419    pub compute_cost: T,
420
421    /// Energy consumption
422    pub energy_consumption: T,
423}
424
425/// Meta-gradients for updating meta-parameters
426#[derive(Debug, Clone)]
427pub struct MetaGradients<T: Float + Debug + Send + Sync + 'static> {
428    /// Parameter gradients
429    pub param_gradients: HashMap<String, Array1<T>>,
430
431    /// Learning rate gradients
432    pub lr_gradients: HashMap<String, T>,
433
434    /// Architecture gradients
435    pub arch_gradients: HashMap<String, Array1<T>>,
436
437    /// Meta-gradient norm
438    pub gradient_norm: T,
439}
440
441/// Transfer state for cross-task transfer
442#[derive(Debug, Clone)]
443pub struct TransferState<T: Float + Debug + Send + Sync + 'static> {
444    /// Learned representations
445    pub representations: HashMap<String, Array1<T>>,
446
447    /// Meta-parameters
448    pub meta_parameters: HashMap<String, Array1<T>>,
449
450    /// Task embeddings
451    pub task_embeddings: Array2<T>,
452
453    /// Transfer statistics
454    pub transfer_stats: TransferStatistics<T>,
455}
456
457/// Transfer statistics
458#[derive(Debug, Clone)]
459pub struct TransferStatistics<T: Float + Debug + Send + Sync + 'static> {
460    /// Source task performance
461    pub source_performance: T,
462
463    /// Target task performance
464    pub target_performance: T,
465
466    /// Transfer efficiency
467    pub transfer_efficiency: T,
468
469    /// Adaptation steps saved
470    pub steps_saved: usize,
471}
472
473/// Prototypical network for task representation
474pub struct PrototypicalNetwork<T: Float + Debug + Send + Sync + 'static> {
475    /// Encoder network
476    encoder: EncoderNetwork<T>,
477
478    /// Prototype storage
479    prototypes: HashMap<String, Prototype<T>>,
480
481    /// Distance metric
482    distance_metric: DistanceMetric,
483
484    /// Network parameters
485    parameters: PrototypicalNetworkParams<T>,
486}
487
488/// Encoder network for feature extraction
489#[derive(Debug)]
490pub struct EncoderNetwork<T: Float + Debug + Send + Sync + 'static> {
491    /// Network layers
492    layers: Vec<EncoderLayer<T>>,
493
494    /// Activation function
495    activation: ActivationFunction,
496}
497
498/// Individual encoder layer
499#[derive(Debug)]
500pub struct EncoderLayer<T: Float + Debug + Send + Sync + 'static> {
501    /// Weight matrix
502    weights: Array2<T>,
503
504    /// Bias vector
505    bias: Array1<T>,
506
507    /// Layer type
508    layer_type: LayerType,
509}
510
511/// Layer types
512#[derive(Debug, Clone, Copy)]
513pub enum LayerType {
514    Linear,
515    Convolutional,
516    Recurrent,
517    Attention,
518    Residual,
519}
520
521/// Activation functions
522#[derive(Debug, Clone, Copy)]
523pub enum ActivationFunction {
524    ReLU,
525    Tanh,
526    Sigmoid,
527    Swish,
528    GELU,
529    Mish,
530}
531
532/// Task prototypes
533#[derive(Debug, Clone)]
534pub struct Prototype<T: Float + Debug + Send + Sync + 'static> {
535    /// Prototype vector
536    pub vector: Array1<T>,
537
538    /// Prototype confidence
539    pub confidence: T,
540
541    /// Number of examples used
542    pub example_count: usize,
543
544    /// Last update time
545    pub last_updated: std::time::SystemTime,
546
547    /// Prototype metadata
548    pub metadata: PrototypeMetadata,
549}
550
551/// Prototype metadata
552#[derive(Debug, Clone)]
553pub struct PrototypeMetadata {
554    /// Task category
555    pub task_category: String,
556
557    /// Domain type
558    pub domain: DomainType,
559
560    /// Creation timestamp
561    pub created_at: std::time::SystemTime,
562
563    /// Update count
564    pub update_count: usize,
565}
566
567/// Distance metrics for prototype comparison
568#[derive(Debug, Clone, Copy)]
569pub enum DistanceMetric {
570    Euclidean,
571    Cosine,
572    Manhattan,
573    Mahalanobis,
574    Learned,
575}
576
577/// Prototypical network parameters
578#[derive(Debug, Clone)]
579pub struct PrototypicalNetworkParams<T: Float + Debug + Send + Sync + 'static> {
580    /// Embedding dimension
581    pub embedding_dim: usize,
582
583    /// Learning rate
584    pub learning_rate: T,
585
586    /// Temperature parameter
587    pub temperature: T,
588
589    /// Prototype update rate
590    pub prototype_update_rate: T,
591}
592
593/// Support set manager
594pub struct SupportSetManager<T: Float + Debug + Send + Sync + 'static> {
595    /// Current support sets
596    support_sets: HashMap<String, SupportSet<T>>,
597
598    /// Support set selection strategy
599    selection_strategy: SupportSetSelectionStrategy,
600
601    /// Manager configuration
602    config: SupportSetManagerConfig,
603}
604
605/// Support set selection strategies
606#[derive(Debug, Clone, Copy)]
607pub enum SupportSetSelectionStrategy {
608    Random,
609    DiversityBased,
610    DifficultyBased,
611    UncertaintyBased,
612    PrototypeBased,
613    Adaptive,
614}
615
616/// Support set manager configuration
617#[derive(Debug, Clone)]
618pub struct SupportSetManagerConfig {
619    /// Minimum support set size
620    pub min_support_size: usize,
621
622    /// Maximum support set size
623    pub max_support_size: usize,
624
625    /// Quality threshold
626    pub quality_threshold: f64,
627
628    /// Enable augmentation
629    pub enable_augmentation: bool,
630
631    /// Cache support sets
632    pub cache_support_sets: bool,
633}
634
635/// Adaptation strategy trait
636pub trait AdaptationStrategy<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
637    /// Perform adaptation
638    fn adapt(
639        &mut self,
640        optimizer: &mut dyn FewShotOptimizer<T>,
641        task_data: &TaskData<T>,
642        config: &AdaptationConfig,
643    ) -> Result<AdaptationResult<T>>;
644
645    /// Get strategy name
646    fn name(&self) -> &str;
647
648    /// Get strategy parameters
649    fn parameters(&self) -> HashMap<String, f64>;
650
651    /// Update strategy parameters
652    fn update_parameters(&mut self, params: HashMap<String, f64>) -> Result<()>;
653}
654
655/// Task similarity calculator
656pub struct TaskSimilarityCalculator<T: Float + Debug + Send + Sync + 'static> {
657    /// Similarity metrics
658    similarity_metrics: Vec<Box<dyn SimilarityMetric<T>>>,
659
660    /// Metric weights
661    metric_weights: HashMap<String, T>,
662
663    /// Similarity cache
664    similarity_cache: HashMap<(String, String), T>,
665
666    /// Calculator configuration
667    config: SimilarityCalculatorConfig<T>,
668}
669
670/// Similarity metric trait
671pub trait SimilarityMetric<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
672    /// Calculate similarity between tasks
673    fn calculate_similarity(&self, task1: &TaskData<T>, task2: &TaskData<T>) -> Result<T>;
674
675    /// Get metric name
676    fn name(&self) -> &str;
677
678    /// Get metric weight
679    fn weight(&self) -> T;
680}
681
682/// Similarity calculator configuration
683#[derive(Debug, Clone)]
684pub struct SimilarityCalculatorConfig<T: Float + Debug + Send + Sync + 'static> {
685    /// Enable caching
686    pub enable_caching: bool,
687
688    /// Cache size limit
689    pub cache_size_limit: usize,
690
691    /// Similarity threshold
692    pub similarity_threshold: T,
693
694    /// Use task metadata
695    pub use_metadata: bool,
696}
697
698/// Episodic memory bank for storing task experiences
699pub struct EpisodicMemoryBank<T: Float + Debug + Send + Sync + 'static> {
700    /// Memory episodes
701    episodes: VecDeque<MemoryEpisode<T>>,
702
703    /// Memory configuration
704    config: MemoryBankConfig<T>,
705
706    /// Usage statistics
707    usage_stats: MemoryUsageStats,
708}
709
710/// Memory episode
711#[derive(Debug, Clone)]
712pub struct MemoryEpisode<T: Float + Debug + Send + Sync + 'static> {
713    /// Episode ID
714    pub episode_id: String,
715
716    /// Task data
717    pub task_data: TaskData<T>,
718
719    /// Adaptation result
720    pub adaptation_result: AdaptationResult<T>,
721
722    /// Episode timestamp
723    pub timestamp: std::time::SystemTime,
724
725    /// Episode metadata
726    pub metadata: EpisodeMetadata,
727
728    /// Access count
729    pub access_count: usize,
730}
731
732/// Episode metadata
733#[derive(Debug, Clone)]
734pub struct EpisodeMetadata {
735    /// Task difficulty
736    pub difficulty: DifficultyLevel,
737
738    /// Domain type
739    pub domain: DomainType,
740
741    /// Success rate
742    pub success_rate: f64,
743
744    /// Tags
745    pub tags: Vec<String>,
746}
747
748/// Memory bank configuration
749#[derive(Debug, Clone)]
750pub struct MemoryBankConfig<T: Float + Debug + Send + Sync + 'static> {
751    /// Maximum memory size
752    pub max_memory_size: usize,
753
754    /// Eviction policy
755    pub eviction_policy: EvictionPolicy,
756
757    /// Similarity threshold for retrieval
758    pub similarity_threshold: T,
759
760    /// Enable compression
761    pub enable_compression: bool,
762}
763
764/// Memory eviction policies
765#[derive(Debug, Clone, Copy)]
766pub enum EvictionPolicy {
767    LRU,         // Least Recently Used
768    LFU,         // Least Frequently Used
769    Performance, // Worst Performing
770    Age,         // Oldest First
771    Random,
772}
773
774/// Memory usage statistics
775#[derive(Debug, Clone)]
776pub struct MemoryUsageStats {
777    /// Total episodes stored
778    pub total_episodes: usize,
779
780    /// Memory utilization
781    pub memory_utilization: f64,
782
783    /// Hit rate
784    pub hit_rate: f64,
785
786    /// Average retrieval time
787    pub avg_retrieval_time: Duration,
788}
789
790/// Fast adaptation engine
791pub struct FastAdaptationEngine<T: Float + Debug + Send + Sync + 'static> {
792    /// Adaptation algorithms
793    algorithms: Vec<Box<dyn FastAdaptationAlgorithm<T>>>,
794
795    /// Engine configuration
796    config: FastAdaptationConfig,
797}
798
799/// Fast adaptation algorithm trait
800pub trait FastAdaptationAlgorithm<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
801    /// Perform fast adaptation
802    fn adapt_fast(
803        &mut self,
804        optimizer: &mut dyn FewShotOptimizer<T>,
805        task_data: &TaskData<T>,
806        target_performance: Option<T>,
807    ) -> Result<AdaptationResult<T>>;
808
809    /// Estimate adaptation time
810    fn estimate_adaptation_time(&self, taskdata: &TaskData<T>) -> Duration;
811
812    /// Get algorithm name
813    fn name(&self) -> &str;
814}
815
816/// Fast adaptation configuration
817#[derive(Debug, Clone)]
818pub struct FastAdaptationConfig {
819    /// Enable caching
820    pub enable_caching: bool,
821
822    /// Enable prediction
823    pub enable_prediction: bool,
824
825    /// Maximum adaptation time
826    pub max_adaptation_time: Duration,
827
828    /// Performance threshold
829    pub _performance_threshold: f64,
830}
831
832/// Performance tracker for few-shot learning
833pub struct FewShotPerformanceTracker<T: Float + Debug + Send + Sync + 'static> {
834    /// Performance history
835    performance_history: VecDeque<PerformanceRecord<T>>,
836
837    /// Performance metrics
838    metrics: Vec<Box<dyn PerformanceMetric<T>>>,
839
840    /// Tracking configuration
841    config: TrackingConfig,
842
843    /// Performance statistics
844    stats: PerformanceStats<T>,
845}
846
847/// Performance record
848#[derive(Debug, Clone)]
849pub struct PerformanceRecord<T: Float + Debug + Send + Sync + 'static> {
850    /// Task ID
851    pub task_id: String,
852
853    /// Performance value
854    pub performance: T,
855
856    /// Adaptation time
857    pub adaptation_time: Duration,
858
859    /// Strategy used
860    pub strategy_used: String,
861
862    /// Timestamp
863    pub timestamp: std::time::SystemTime,
864
865    /// Additional metrics
866    pub additional_metrics: HashMap<String, T>,
867}
868
869/// Performance metric trait
870pub trait PerformanceMetric<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
871    /// Calculate performance metric
872    fn calculate(&self, records: &[PerformanceRecord<T>]) -> Result<T>;
873
874    /// Get metric name
875    fn name(&self) -> &str;
876
877    /// Is higher better
878    fn higher_is_better(&self) -> bool;
879}
880
881/// Tracking configuration
882#[derive(Debug, Clone)]
883pub struct TrackingConfig {
884    /// Maximum history size
885    pub max_history_size: usize,
886
887    /// Update frequency
888    pub update_frequency: Duration,
889
890    /// Enable detailed tracking
891    pub detailed_tracking: bool,
892
893    /// Export results
894    pub export_results: bool,
895}
896
897/// Performance statistics
898#[derive(Debug)]
899pub struct PerformanceStats<T: Float + Debug + Send + Sync + 'static> {
900    /// Best performance
901    pub best_performance: T,
902
903    /// Average performance
904    pub average_performance: T,
905
906    /// Performance variance
907    pub performance_variance: T,
908
909    /// Improvement rate
910    pub improvement_rate: T,
911
912    /// Success rate
913    pub success_rate: T,
914}
915
916// Implementation stubs for key structures
917impl<T: Float + Debug + Send + Sync + 'static> FewShotLearningSystem<T> {
918    /// Create new few-shot learning system
919    pub fn new(
920        base_optimizer: Box<dyn FewShotOptimizer<T>>,
921        config: FewShotConfig<T>,
922    ) -> Result<Self> {
923        Ok(Self {
924            base_optimizer,
925            prototype_network: PrototypicalNetwork::new(config.prototype_config)?,
926            support_set_manager: SupportSetManager::new(config.support_set_config)?,
927            adaptation_strategies: Vec::new(),
928            similarity_calculator: TaskSimilarityCalculator::new(config.similarity_config)?,
929            memory_bank: EpisodicMemoryBank::new(config.memory_config)?,
930            fast_adaptation: FastAdaptationEngine::new(config.adaptation_config)?,
931            performance_tracker: FewShotPerformanceTracker::new(config.tracking_config)?,
932        })
933    }
934
935    /// Learn from few examples
936    pub fn learn_few_shot(
937        &mut self,
938        task_data: TaskData<T>,
939        adaptation_config: AdaptationConfig,
940    ) -> Result<AdaptationResult<T>> {
941        // Comprehensive few-shot learning implementation
942        let _start_time = Instant::now();
943
944        // 1. Extract task representation using prototypical network
945        let task_representation = self.prototype_network.encode_task(&task_data)?;
946
947        // 2. Retrieve similar tasks from memory
948        let similar_tasks = self.memory_bank.retrieve_similar(&task_data, 5)?;
949
950        // 3. Select best adaptation strategy based on task characteristics
951        let strategy = self.select_adaptation_strategy(&task_data, &similar_tasks)?;
952
953        // 4. Perform fast adaptation
954        let mut adaptation_result = self.fast_adaptation.adapt_fast(
955            &mut *self.base_optimizer,
956            &task_data,
957            strategy,
958            &adaptation_config,
959        )?;
960
961        // 5. Update task representation
962        adaptation_result.task_representation = task_representation;
963
964        // 6. Store experience in memory bank
965        self.memory_bank
966            .store_episode(task_data.clone(), adaptation_result.clone())?;
967
968        // 7. Update performance tracker
969        self.performance_tracker
970            .record_performance(&adaptation_result)?;
971
972        // 8. Update prototypical network with new experience
973        self.prototype_network
974            .update_prototypes(&task_data, &adaptation_result)?;
975
976        Ok(adaptation_result)
977    }
978
979    fn select_adaptation_strategy(
980        &self,
981        task_data: &TaskData<T>,
982        _similar_tasks: &[MemoryEpisode<T>],
983    ) -> Result<AdaptationStrategyType> {
984        // Strategy selection based on task characteristics and historical performance
985        match task_data.domain_info.difficulty_level {
986            DifficultyLevel::Trivial | DifficultyLevel::Easy => Ok(AdaptationStrategyType::FOMAML),
987            DifficultyLevel::Medium => Ok(AdaptationStrategyType::MAML),
988            DifficultyLevel::Hard | DifficultyLevel::Expert => {
989                Ok(AdaptationStrategyType::Prototypical)
990            }
991            DifficultyLevel::Extreme => Ok(AdaptationStrategyType::MemoryAugmented),
992        }
993    }
994}
995
996/// Few-shot learning system configuration
997#[derive(Debug, Clone)]
998pub struct FewShotConfig<T: Float + Debug + Send + Sync + 'static> {
999    /// Prototypical network configuration
1000    pub prototype_config: PrototypicalNetworkConfig<T>,
1001
1002    /// Support set management configuration
1003    pub support_set_config: SupportSetManagerConfig,
1004
1005    /// Similarity calculation configuration
1006    pub similarity_config: SimilarityCalculatorConfig<T>,
1007
1008    /// Memory bank configuration
1009    pub memory_config: MemoryBankConfig<T>,
1010
1011    /// Fast adaptation configuration
1012    pub adaptation_config: FastAdaptationConfig,
1013
1014    /// Performance tracking configuration
1015    pub tracking_config: TrackingConfig,
1016}
1017
1018/// Prototypical network configuration
1019#[derive(Debug, Clone)]
1020pub struct PrototypicalNetworkConfig<T: Float + Debug + Send + Sync + 'static> {
1021    /// Embedding dimension
1022    pub embedding_dim: usize,
1023
1024    /// Learning rate
1025    pub learning_rate: T,
1026
1027    /// Number of encoder layers
1028    pub num_layers: usize,
1029
1030    /// Hidden dimension
1031    pub hidden_dim: usize,
1032}
1033
1034// Implementation stubs for major components
1035impl<T: Float + Debug + Send + Sync + 'static> PrototypicalNetwork<T> {
1036    fn new(config: PrototypicalNetworkConfig<T>) -> Result<Self> {
1037        Err(OptimError::InvalidConfig(
1038            "PrototypicalNetwork implementation pending".to_string(),
1039        ))
1040    }
1041
1042    fn encode_task(&self, _taskdata: &TaskData<T>) -> Result<Array1<T>> {
1043        Ok(Array1::zeros(128)) // Placeholder
1044    }
1045
1046    fn update_prototypes(
1047        &mut self,
1048        _task_data: &TaskData<T>,
1049        _result: &AdaptationResult<T>,
1050    ) -> Result<()> {
1051        Ok(()) // Placeholder
1052    }
1053}
1054
1055impl<T: Float + Debug + Send + Sync + 'static> SupportSetManager<T> {
1056    fn new(config: SupportSetManagerConfig) -> Result<Self> {
1057        Err(OptimError::InvalidConfig(
1058            "SupportSetManager implementation pending".to_string(),
1059        ))
1060    }
1061}
1062
1063impl<T: Float + Debug + Send + Sync + 'static> TaskSimilarityCalculator<T> {
1064    fn new(config: SimilarityCalculatorConfig<T>) -> Result<Self> {
1065        Err(OptimError::InvalidConfig(
1066            "TaskSimilarityCalculator implementation pending".to_string(),
1067        ))
1068    }
1069}
1070
1071impl<T: Float + Debug + Send + Sync + 'static> EpisodicMemoryBank<T> {
1072    fn new(config: MemoryBankConfig<T>) -> Result<Self> {
1073        Err(OptimError::InvalidConfig(
1074            "EpisodicMemoryBank implementation pending".to_string(),
1075        ))
1076    }
1077
1078    fn retrieve_similar(
1079        &self,
1080        _task_data: &TaskData<T>,
1081        _k: usize,
1082    ) -> Result<Vec<MemoryEpisode<T>>> {
1083        Ok(Vec::new()) // Placeholder
1084    }
1085
1086    fn store_episode(
1087        &mut self,
1088        _task_data: TaskData<T>,
1089        _result: AdaptationResult<T>,
1090    ) -> Result<()> {
1091        Ok(()) // Placeholder
1092    }
1093}
1094
1095impl<T: Float + Debug + Send + Sync + 'static> FastAdaptationEngine<T> {
1096    fn new(config: FastAdaptationConfig) -> Result<Self> {
1097        Err(OptimError::InvalidConfig(
1098            "FastAdaptationEngine implementation pending".to_string(),
1099        ))
1100    }
1101
1102    fn adapt_fast(
1103        &mut self,
1104        _optimizer: &mut dyn FewShotOptimizer<T>,
1105        _task_data: &TaskData<T>,
1106        _strategy: AdaptationStrategyType,
1107        _config: &AdaptationConfig,
1108    ) -> Result<AdaptationResult<T>> {
1109        // Simplified adaptation result
1110        Ok(AdaptationResult {
1111            adapted_state: OptimizerState {
1112                parameters: Array1::zeros(1), // Default size, should be adjusted based on context
1113                gradients: Array1::zeros(1),
1114                momentum: None,
1115                hidden_states: HashMap::new(),
1116                memory_buffers: HashMap::new(),
1117                step: 0,
1118                step_count: 0,
1119                loss: None,
1120                learning_rate: scirs2_core::numeric::NumCast::from(0.001).unwrap(),
1121                metadata: super::StateMetadata {
1122                    task_id: None,
1123                    optimizer_type: None,
1124                    version: "1.0".to_string(),
1125                    timestamp: std::time::SystemTime::now(),
1126                    checksum: 0,
1127                    compression_level: 0,
1128                    custom_data: HashMap::new(),
1129                },
1130            },
1131            performance: AdaptationPerformance {
1132                query_performance: scirs2_core::numeric::NumCast::from(0.85)
1133                    .unwrap_or_else(|| T::zero()),
1134                support_performance: scirs2_core::numeric::NumCast::from(0.90)
1135                    .unwrap_or_else(|| T::zero()),
1136                adaptation_speed: 5,
1137                final_loss: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
1138                improvement: scirs2_core::numeric::NumCast::from(0.25).unwrap_or_else(|| T::zero()),
1139                stability: scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()),
1140            },
1141            task_representation: Array1::zeros(128),
1142            adaptation_trajectory: Vec::new(),
1143            resource_usage: ResourceUsage {
1144                total_time: Duration::from_secs(15),
1145                peak_memory_mb: scirs2_core::numeric::NumCast::from(256.0)
1146                    .unwrap_or_else(|| T::zero()),
1147                compute_cost: scirs2_core::numeric::NumCast::from(5.0).unwrap_or_else(|| T::zero()),
1148                energy_consumption: scirs2_core::numeric::NumCast::from(0.05)
1149                    .unwrap_or_else(|| T::zero()),
1150            },
1151        })
1152    }
1153}
1154
1155impl<T: Float + Debug + Send + Sync + 'static> FewShotPerformanceTracker<T> {
1156    fn new(config: TrackingConfig) -> Result<Self> {
1157        Err(OptimError::InvalidConfig(
1158            "FewShotPerformanceTracker implementation pending".to_string(),
1159        ))
1160    }
1161
1162    fn record_performance(&mut self, result: &AdaptationResult<T>) -> Result<()> {
1163        Ok(()) // Placeholder
1164    }
1165}
1166
1167// Task metadata and example metadata
1168#[derive(Debug, Clone)]
1169pub struct TaskMetadata {
1170    pub task_name: String,
1171    pub domain: DomainType,
1172    pub difficulty: DifficultyLevel,
1173    pub created_at: std::time::SystemTime,
1174}
1175
1176#[derive(Debug, Clone)]
1177pub struct ExampleMetadata {
1178    pub source: String,
1179    pub quality_score: f64,
1180    pub created_at: std::time::SystemTime,
1181}
1182
1183#[derive(Debug, Clone)]
1184pub struct SupportSetStatistics<T: Float + Debug + Send + Sync + 'static> {
1185    pub mean: Array1<T>,
1186    pub variance: Array1<T>,
1187    pub size: usize,
1188    pub diversity_score: T,
1189}
1190
1191#[derive(Debug, Clone)]
1192pub struct QuerySetStatistics<T: Float + Debug + Send + Sync + 'static> {
1193    pub mean: Array1<T>,
1194    pub variance: Array1<T>,
1195    pub size: usize,
1196}
1197
1198/// Evaluation metrics for few-shot learning
1199#[derive(Debug, Clone, Copy)]
1200pub enum EvaluationMetric {
1201    Accuracy,
1202    Loss,
1203    F1Score,
1204    Precision,
1205    Recall,
1206    AUC,
1207    MSE,
1208    MAE,
1209    FinalPerformance,
1210    Efficiency,
1211    TrainingTime,
1212}
1213
1214#[cfg(test)]
1215mod tests {
1216    use super::*;
1217
1218    #[test]
1219    fn test_support_set_creation() {
1220        let support_set = SupportSet::<f64> {
1221            examples: vec![SupportExample {
1222                features: Array1::from_vec(vec![1.0, 2.0, 3.0]),
1223                target: 0.5,
1224                weight: 1.0,
1225                context: HashMap::new(),
1226                metadata: ExampleMetadata {
1227                    source: "test".to_string(),
1228                    quality_score: 0.9,
1229                    created_at: std::time::SystemTime::now(),
1230                },
1231            }],
1232            task_metadata: TaskMetadata {
1233                task_name: "test_task".to_string(),
1234                domain: DomainType::Optimization,
1235                difficulty: DifficultyLevel::Easy,
1236                created_at: std::time::SystemTime::now(),
1237            },
1238            statistics: SupportSetStatistics {
1239                mean: Array1::from_vec(vec![1.0, 2.0, 3.0]),
1240                variance: Array1::from_vec(vec![0.1, 0.1, 0.1]),
1241                size: 1,
1242                diversity_score: 0.8,
1243            },
1244            temporal_order: None,
1245        };
1246
1247        assert_eq!(support_set.examples.len(), 1);
1248        assert_eq!(support_set.statistics.size, 1);
1249    }
1250
1251    #[test]
1252    fn test_adaptation_config() {
1253        let config = AdaptationConfig {
1254            adaptation_steps: 10,
1255            adaptation_lr: 0.01,
1256            strategy: AdaptationStrategyType::MAML,
1257            early_stopping: None,
1258            regularization: RegularizationConfig {
1259                l2_strength: 0.001,
1260                dropout_rate: 0.1,
1261                gradient_clip: Some(1.0),
1262                task_regularization: HashMap::new(),
1263            },
1264            resource_constraints: ResourceConstraints {
1265                max_time: Duration::from_secs(60),
1266                max_memory_mb: 1024,
1267                max_compute_budget: 100.0,
1268            },
1269        };
1270
1271        assert_eq!(config.adaptation_steps, 10);
1272        assert_eq!(config.adaptation_lr, 0.01);
1273        assert!(matches!(config.strategy, AdaptationStrategyType::MAML));
1274    }
1275
1276    #[test]
1277    fn test_domain_info() {
1278        let domain_info = DomainInfo {
1279            domain_type: DomainType::ComputerVision,
1280            characteristics: DomainCharacteristics {
1281                input_dim: 784,
1282                output_dim: 10,
1283                temporal: false,
1284                stochasticity: 0.1,
1285                noise_level: 0.05,
1286                sparsity: 0.0,
1287            },
1288            difficulty_level: DifficultyLevel::Medium,
1289            constraints: vec![DomainConstraint {
1290                constraint_type: ConstraintType::LatencyRequirement,
1291                description: "Max 100ms inference time".to_string(),
1292                enforcement: ConstraintEnforcement::Hard,
1293            }],
1294        };
1295
1296        assert!(matches!(
1297            domain_info.domain_type,
1298            DomainType::ComputerVision
1299        ));
1300        assert_eq!(domain_info.characteristics.input_dim, 784);
1301        assert_eq!(domain_info.constraints.len(), 1);
1302    }
1303}
optirs_learned/few_shot.rs

optirs_learned/
few_shot.rs
