optirs_learned/transformer/training/

meta_learning.rs

// Meta-learning capabilities for transformer optimization
//
// This module implements meta-learning strategies that allow the transformer
// optimizer to quickly adapt to new tasks and optimization landscapes.

#[allow(dead_code)]
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;

use crate::error::{OptimError, Result};
use crate::transformer::TransformerNetwork;

/// Meta-learning strategies
#[derive(Debug, Clone, Copy)]
pub enum MetaLearningStrategy {
    /// Model-Agnostic Meta-Learning (MAML)
    MAML,
    /// Reptile algorithm
    Reptile,
    /// Gradient-based meta-learning
    GradientBased,
    /// Memory-augmented meta-learning
    MemoryAugmented,
    /// Task-agnostic meta-learning
    TaskAgnostic,
    /// Few-shot meta-learning
    FewShot,
    /// Continual meta-learning
    Continual,
}

/// Meta-learner for transformer optimizer
#[derive(Debug, Clone)]
pub struct TransformerMetaLearner<
    T: Float
        + Debug
        + Default
        + Clone
        + std::iter::Sum
        + scirs2_core::ndarray::ScalarOperand
        + Send
        + Sync
        + 'static,
> {
    /// Meta-learning strategy
    strategy: MetaLearningStrategy,

    /// Meta-transformer for higher-level learning
    meta_transformer: Option<TransformerNetwork<T>>,

    /// Task embeddings
    task_embeddings: HashMap<String, Array1<T>>,

    /// Meta-training history
    meta_history: VecDeque<MetaTrainingEvent<T>>,

    /// Domain adaptation module
    domain_adapter: DomainAdapter<T>,

    /// Few-shot learning capabilities
    few_shot_learner: FewShotLearner<T>,

    /// Continual learning state
    continual_learning: ContinualLearningState<T>,

    /// Meta-learning parameters
    meta_params: MetaLearningParams<T>,
}

/// Meta-training event
#[derive(Debug, Clone)]
pub struct MetaTrainingEvent<T: Float + Debug + Send + Sync + 'static> {
    /// Event type
    event_type: MetaEventType,

    /// Task information
    task_info: TaskInfo<T>,

    /// Performance metrics
    performance: MetaPerformanceMetrics<T>,

    /// Adaptation steps
    adaptation_steps: usize,

    /// Timestamp
    timestamp: usize,
}

/// Meta-event types
#[derive(Debug, Clone, Copy)]
pub enum MetaEventType {
    /// Task adaptation
    TaskAdaptation,
    /// Domain transfer
    DomainTransfer,
    /// Few-shot learning
    FewShotLearning,
    /// Continual learning
    ContinualLearning,
    /// Meta-validation
    MetaValidation,
}

/// Task information
#[derive(Debug, Clone)]
pub struct TaskInfo<T: Float + Debug + Send + Sync + 'static> {
    /// Task identifier
    task_id: String,

    /// Task characteristics
    characteristics: TaskCharacteristics<T>,

    /// Domain information
    domain: DomainInfo,

    /// Difficulty level
    difficulty: T,

    /// Expected performance
    expected_performance: Option<T>,
}

/// Task characteristics
#[derive(Debug, Clone)]
pub struct TaskCharacteristics<T: Float + Debug + Send + Sync + 'static> {
    /// Problem dimensionality
    dimensionality: usize,

    /// Landscape complexity
    landscape_complexity: T,

    /// Noise level
    noise_level: T,

    /// Conditioning number
    conditioning: T,

    /// Sparsity level
    sparsity: T,

    /// Temporal dependencies
    temporal_dependencies: T,

    /// Feature correlations
    feature_correlations: Array2<T>,
}

/// Domain information
#[derive(Debug, Clone)]
pub struct DomainInfo {
    /// Domain name
    name: String,

    /// Domain type
    domain_type: DomainType,

    /// Related domains
    related_domains: Vec<String>,

    /// Domain-specific features
    features: HashMap<String, f64>,
}

/// Domain types
#[derive(Debug, Clone, Copy)]
pub enum DomainType {
    /// Computer vision
    Vision,
    /// Natural language processing
    NLP,
    /// Reinforcement learning
    RL,
    /// Time series
    TimeSeries,
    /// Graph neural networks
    Graph,
    /// Scientific computing
    Scientific,
    /// General optimization
    General,
}

/// Meta-performance metrics
#[derive(Debug, Clone)]
pub struct MetaPerformanceMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Final performance
    final_performance: T,

    /// Convergence speed
    convergence_speed: T,

    /// Sample efficiency
    sample_efficiency: T,

    /// Generalization score
    generalization: T,

    /// Stability measure
    stability: T,

    /// Resource usage
    resource_usage: T,
}

/// Domain adapter for cross-domain transfer
#[derive(Debug, Clone)]
pub struct DomainAdapter<T: Float + Debug + Send + Sync + 'static> {
    /// Domain-specific adapters
    adapters: HashMap<String, DomainSpecificAdapter<T>>,

    /// Domain similarity estimator
    similarity_estimator: DomainSimilarityEstimator<T>,

    /// Adaptation strategies
    adaptation_strategies: Vec<AdaptationStrategy>,

    /// Transfer efficiency tracker
    transfer_tracker: TransferEfficiencyTracker<T>,
}

/// Domain-specific adapter
#[derive(Debug, Clone)]
pub struct DomainSpecificAdapter<T: Float + Debug + Send + Sync + 'static> {
    /// Adapter parameters
    parameters: HashMap<String, Array1<T>>,

    /// Domain features
    domain_features: Array1<T>,

    /// Adaptation history
    adaptation_history: Vec<AdaptationEvent<T>>,

    /// Performance on domain
    domain_performance: T,
}

/// Few-shot learner component
#[derive(Debug, Clone)]
pub struct FewShotLearner<T: Float + Debug + Send + Sync + 'static> {
    /// Support set memory
    support_memory: HashMap<String, Vec<Array1<T>>>,

    /// Prototype vectors
    prototypes: HashMap<String, Array1<T>>,

    /// Distance metric learner
    distance_learner: DistanceMetricLearner<T>,

    /// Few-shot adaptation parameters
    adaptation_params: FewShotParams<T>,
}

/// Continual learning state
#[derive(Debug, Clone)]
pub struct ContinualLearningState<T: Float + Debug + Send + Sync + 'static> {
    /// Elastic weight consolidation parameters
    ewc_params: HashMap<String, Array1<T>>,

    /// Fisher information matrix
    fisher_information: HashMap<String, Array2<T>>,

    /// Previous task importance scores
    task_importance: HashMap<String, T>,

    /// Memory replay buffer
    replay_buffer: Vec<ContinualLearningEvent<T>>,

    /// Catastrophic forgetting prevention strategy
    forgetting_prevention: ForgettingPreventionStrategy,
}

/// Meta-learning parameters
#[derive(Debug, Clone)]
pub struct MetaLearningParams<T: Float + Debug + Send + Sync + 'static> {
    /// Learning rate for meta-updates
    meta_learning_rate: T,

    /// Number of inner gradient steps
    inner_steps: usize,

    /// Meta-batch size
    meta_batch_size: usize,

    /// Task diversity weight
    diversity_weight: T,

    /// Transfer learning coefficient
    transfer_coefficient: T,

    /// Memory retention factor
    memory_retention: T,
}

// Additional supporting types
#[derive(Debug, Clone, Copy)]
pub enum AdaptationStrategy {
    FineTuning,
    ParameterSharing,
    ModularAdaptation,
    AttentionAdaptation,
}

#[derive(Debug, Clone)]
pub struct DomainSimilarityEstimator<T: Float + Debug + Send + Sync + 'static> {
    similarity_matrix: HashMap<(String, String), T>,
    feature_extractors: HashMap<String, Array2<T>>,
}

#[derive(Debug, Clone)]
pub struct TransferEfficiencyTracker<T: Float + Debug + Send + Sync + 'static> {
    transfer_history: Vec<TransferEvent<T>>,
    efficiency_metrics: HashMap<String, T>,
}

#[derive(Debug, Clone)]
pub struct AdaptationEvent<T: Float + Debug + Send + Sync + 'static> {
    timestamp: usize,
    adaptation_loss: T,
    performance_gain: T,
    adaptation_steps: usize,
}

#[derive(Debug, Clone)]
pub struct DistanceMetricLearner<T: Float + Debug + Send + Sync + 'static> {
    metric_parameters: Array2<T>,
    learned_similarities: HashMap<String, T>,
}

#[derive(Debug, Clone)]
pub struct FewShotParams<T: Float + Debug + Send + Sync + 'static> {
    support_size: usize,
    query_size: usize,
    adaptation_lr: T,
    temperature: T,
}

#[derive(Debug, Clone)]
pub struct ContinualLearningEvent<T: Float + Debug + Send + Sync + 'static> {
    task_id: String,
    gradients: Array1<T>,
    performance: T,
    timestamp: usize,
}

#[derive(Debug, Clone, Copy)]
pub enum ForgettingPreventionStrategy {
    EWC,
    PackNet,
    ProgressiveNetworks,
    GEM,
}

#[derive(Debug, Clone)]
pub struct TransferEvent<T: Float + Debug + Send + Sync + 'static> {
    source_domain: String,
    target_domain: String,
    transfer_performance: T,
    adaptation_time: usize,
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > TransformerMetaLearner<T>
{
    /// Create new meta-learner
    pub fn new(strategy: MetaLearningStrategy) -> Result<Self> {
        Ok(Self {
            strategy,
            meta_transformer: None,
            task_embeddings: HashMap::new(),
            meta_history: VecDeque::new(),
            domain_adapter: DomainAdapter::new()?,
            few_shot_learner: FewShotLearner::new()?,
            continual_learning: ContinualLearningState::new()?,
            meta_params: MetaLearningParams::default(),
        })
    }

    /// Adapt to a new task
    pub fn adapt_to_task(
        &mut self,
        task_info: &TaskInfo<T>,
        support_data: &[Array1<T>],
        query_data: &[Array1<T>],
    ) -> Result<T> {
        match self.strategy {
            MetaLearningStrategy::MAML => self.maml_adaptation(task_info, support_data, query_data),
            MetaLearningStrategy::Reptile => {
                self.reptile_adaptation(task_info, support_data, query_data)
            }
            MetaLearningStrategy::FewShot => {
                self.few_shot_adaptation(task_info, support_data, query_data)
            }
            MetaLearningStrategy::Continual => {
                self.continual_adaptation(task_info, support_data, query_data)
            }
            _ => self.generic_adaptation(task_info, support_data, query_data),
        }
    }

    /// MAML adaptation
    fn maml_adaptation(
        &mut self,
        task_info: &TaskInfo<T>,
        support_data: &[Array1<T>],
        query_data: &[Array1<T>],
    ) -> Result<T> {
        // Simplified MAML implementation
        let mut adaptation_loss = T::zero();

        // Perform inner loop updates
        for _ in 0..self.meta_params.inner_steps {
            // Compute gradients on support set
            let support_loss = self.compute_support_loss(support_data)?;

            // Update parameters (simplified)
            adaptation_loss = adaptation_loss + support_loss;
        }

        // Evaluate on query set
        let query_loss = self.compute_query_loss(query_data)?;

        // Record adaptation event
        let event = MetaTrainingEvent {
            event_type: MetaEventType::TaskAdaptation,
            task_info: task_info.clone(),
            performance: MetaPerformanceMetrics {
                final_performance: query_loss,
                convergence_speed: scirs2_core::numeric::NumCast::from(
                    1.0 / self.meta_params.inner_steps as f64,
                )
                .unwrap_or_else(|| T::zero()),
                sample_efficiency: T::from(support_data.len() as f64).unwrap(),
                generalization: T::one() / (T::one() + query_loss),
                stability: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
                resource_usage: scirs2_core::numeric::NumCast::from(
                    self.meta_params.inner_steps as f64,
                )
                .unwrap_or_else(|| T::zero()),
            },
            adaptation_steps: self.meta_params.inner_steps,
            timestamp: self.meta_history.len(),
        };

        self.meta_history.push_back(event);

        Ok(query_loss)
    }

    /// Reptile adaptation
    fn reptile_adaptation(
        &mut self,
        task_info: &TaskInfo<T>,
        support_data: &[Array1<T>],
        _query_data: &[Array1<T>],
    ) -> Result<T> {
        // Simplified Reptile implementation
        let initial_loss = self.compute_support_loss(support_data)?;

        // Perform multiple gradient steps
        let mut final_loss = initial_loss;
        for _ in 0..self.meta_params.inner_steps {
            final_loss =
                final_loss * scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero());
            // Simplified decay
        }

        Ok(final_loss)
    }

    /// Few-shot adaptation
    fn few_shot_adaptation(
        &mut self,
        task_info: &TaskInfo<T>,
        support_data: &[Array1<T>],
        query_data: &[Array1<T>],
    ) -> Result<T> {
        self.few_shot_learner
            .adapt(task_info, support_data, query_data)
    }

    /// Continual learning adaptation
    fn continual_adaptation(
        &mut self,
        task_info: &TaskInfo<T>,
        support_data: &[Array1<T>],
        query_data: &[Array1<T>],
    ) -> Result<T> {
        // Update continual learning state
        self.continual_learning
            .update_for_task(task_info, support_data)?;

        // Compute adaptation loss with forgetting prevention
        let base_loss = self.compute_support_loss(support_data)?;
        let forgetting_penalty = self.continual_learning.compute_forgetting_penalty()?;

        Ok(base_loss + forgetting_penalty)
    }

    /// Generic adaptation fallback
    fn generic_adaptation(
        &mut self,
        _task_info: &TaskInfo<T>,
        support_data: &[Array1<T>],
        query_data: &[Array1<T>],
    ) -> Result<T> {
        let support_loss = self.compute_support_loss(support_data)?;
        let query_loss = self.compute_query_loss(query_data)?;
        Ok((support_loss + query_loss)
            / scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero()))
    }

    /// Compute loss on support set
    fn compute_support_loss(&self, support_data: &[Array1<T>]) -> Result<T> {
        if support_data.is_empty() {
            return Ok(T::zero());
        }

        let mut total_loss = T::zero();
        for data in support_data {
            // Simplified loss computation
            let loss = data.iter().map(|&x| x * x).fold(T::zero(), |a, b| a + b);
            total_loss = total_loss + loss;
        }

        Ok(total_loss / T::from(support_data.len() as f64).unwrap())
    }

    /// Compute loss on query set
    fn compute_query_loss(&self, query_data: &[Array1<T>]) -> Result<T> {
        if query_data.is_empty() {
            return Ok(T::zero());
        }

        let mut total_loss = T::zero();
        for data in query_data {
            // Simplified loss computation
            let loss = data.iter().map(|&x| x * x).fold(T::zero(), |a, b| a + b);
            total_loss = total_loss + loss;
        }

        Ok(total_loss / T::from(query_data.len() as f64).unwrap())
    }

    /// Get meta-learning statistics
    pub fn get_meta_statistics(&self) -> HashMap<String, T> {
        let mut stats = HashMap::new();

        stats.insert(
            "meta_events_count".to_string(),
            T::from(self.meta_history.len() as f64).unwrap(),
        );
        stats.insert(
            "task_embeddings_count".to_string(),
            T::from(self.task_embeddings.len() as f64).unwrap(),
        );

        // Compute average performance
        if !self.meta_history.is_empty() {
            let avg_performance = self
                .meta_history
                .iter()
                .map(|event| event.performance.final_performance)
                .fold(T::zero(), |a, b| a + b)
                / T::from(self.meta_history.len() as f64).unwrap();
            stats.insert("average_performance".to_string(), avg_performance);
        }

        stats
    }

    /// Update meta-parameters
    pub fn update_meta_parameters(&mut self, params: MetaLearningParams<T>) {
        self.meta_params = params;
    }

    /// Get domain adapter
    pub fn domain_adapter(&self) -> &DomainAdapter<T> {
        &self.domain_adapter
    }

    /// Reset meta-learner state
    pub fn reset(&mut self) {
        self.task_embeddings.clear();
        self.meta_history.clear();
        self.domain_adapter.reset();
        self.few_shot_learner.reset();
        self.continual_learning.reset();
    }
}

// Implementation for supporting types
impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > DomainAdapter<T>
{
    fn new() -> Result<Self> {
        Ok(Self {
            adapters: HashMap::new(),
            similarity_estimator: DomainSimilarityEstimator::new()?,
            adaptation_strategies: vec![AdaptationStrategy::FineTuning],
            transfer_tracker: TransferEfficiencyTracker::new()?,
        })
    }

    fn reset(&mut self) {
        self.adapters.clear();
    }
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > FewShotLearner<T>
{
    fn new() -> Result<Self> {
        Ok(Self {
            support_memory: HashMap::new(),
            prototypes: HashMap::new(),
            distance_learner: DistanceMetricLearner::new()?,
            adaptation_params: FewShotParams::default(),
        })
    }

    fn adapt(
        &mut self,
        _task_info: &TaskInfo<T>,
        support_data: &[Array1<T>],
        query_data: &[Array1<T>],
    ) -> Result<T> {
        // Simplified few-shot adaptation
        let support_loss = support_data
            .iter()
            .map(|x| x.iter().map(|&v| v * v).fold(T::zero(), |a, b| a + b))
            .fold(T::zero(), |a, b| a + b);
        let query_loss = query_data
            .iter()
            .map(|x| x.iter().map(|&v| v * v).fold(T::zero(), |a, b| a + b))
            .fold(T::zero(), |a, b| a + b);

        Ok((support_loss + query_loss)
            / T::from((support_data.len() + query_data.len()) as f64).unwrap())
    }

    fn reset(&mut self) {
        self.support_memory.clear();
        self.prototypes.clear();
    }
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > ContinualLearningState<T>
{
    fn new() -> Result<Self> {
        Ok(Self {
            ewc_params: HashMap::new(),
            fisher_information: HashMap::new(),
            task_importance: HashMap::new(),
            replay_buffer: Vec::new(),
            forgetting_prevention: ForgettingPreventionStrategy::EWC,
        })
    }

    fn update_for_task(
        &mut self,
        task_info: &TaskInfo<T>,
        _support_data: &[Array1<T>],
    ) -> Result<()> {
        self.task_importance
            .insert(task_info.task_id.clone(), task_info.difficulty);
        Ok(())
    }

    fn compute_forgetting_penalty(&self) -> Result<T> {
        // Simplified forgetting penalty
        Ok(scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()))
    }

    fn reset(&mut self) {
        self.ewc_params.clear();
        self.fisher_information.clear();
        self.task_importance.clear();
        self.replay_buffer.clear();
    }
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > DomainSimilarityEstimator<T>
{
    fn new() -> Result<Self> {
        Ok(Self {
            similarity_matrix: HashMap::new(),
            feature_extractors: HashMap::new(),
        })
    }
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > TransferEfficiencyTracker<T>
{
    fn new() -> Result<Self> {
        Ok(Self {
            transfer_history: Vec::new(),
            efficiency_metrics: HashMap::new(),
        })
    }
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > DistanceMetricLearner<T>
{
    fn new() -> Result<Self> {
        Ok(Self {
            metric_parameters: Array2::eye(10), // Default 10x10 identity matrix
            learned_similarities: HashMap::new(),
        })
    }
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > Default for MetaLearningParams<T>
{
    fn default() -> Self {
        Self {
            meta_learning_rate: scirs2_core::numeric::NumCast::from(0.001)
                .unwrap_or_else(|| T::zero()),
            inner_steps: 5,
            meta_batch_size: 32,
            diversity_weight: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
            transfer_coefficient: scirs2_core::numeric::NumCast::from(0.5)
                .unwrap_or_else(|| T::zero()),
            memory_retention: scirs2_core::numeric::NumCast::from(0.95)
                .unwrap_or_else(|| T::zero()),
        }
    }
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > Default for FewShotParams<T>
{
    fn default() -> Self {
        Self {
            support_size: 5,
            query_size: 15,
            adaptation_lr: scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()),
            temperature: scirs2_core::numeric::NumCast::from(1.0).unwrap_or_else(|| T::zero()),
        }
    }
}