// Automated Hyperparameter Optimization Pipeline
//
// This module provides comprehensive hyperparameter optimization capabilities for neural
// architecture search, including Bayesian optimization, population-based methods,
// and multi-fidelity optimization strategies.

use scirs2_core::ndarray::{Array1, Array2, ArrayBase, Data, Dimension};
use scirs2_core::numeric::Float;
use std::fmt::Debug;
use std::collections::{HashMap, VecDeque, BTreeMap};
use std::time::{Duration, Instant};
use std::sync::{Arc, Mutex, RwLock};

#[allow(unused_imports)]
use crate::error::Result;
use super::{
    OptimizerArchitecture, SearchSpaceConfig, ParameterRange, EvaluationMetric,
    EvaluationResults, ResourceUsage, SearchResult
};

/// Automated Hyperparameter Optimization Pipeline
pub struct HyperparameterOptimizationPipeline<T: Float + Debug + Send + Sync + 'static> {
    /// Optimization configuration
    config: HPOConfig<T>,
    
    /// Active optimization strategies
    strategies: Vec<Box<dyn HPOStrategy<T>>>,
    
    /// Parameter space manager
    parameter_space: ParameterSpaceManager<T>,
    
    /// Evaluation scheduler
    evaluation_scheduler: EvaluationScheduler<T>,
    
    /// Result database
    result_database: HPOResultDatabase<T>,
    
    /// Multi-fidelity manager
    multi_fidelity_manager: Option<MultiFidelityManager<T>>,
    
    /// Early stopping controller
    early_stopping: EarlyStoppingController<T>,
    
    /// Ensemble optimizer
    ensemble_optimizer: EnsembleOptimizer<T>,
    
    /// Current optimization state
    optimization_state: OptimizationState<T>}

/// Hyperparameter optimization configuration
#[derive(Debug, Clone)]
pub struct HPOConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Optimization strategies to use
    pub strategies: Vec<HPOStrategyType>,
    
    /// Parameter search space
    pub parameter_space: ParameterSearchSpace,
    
    /// Evaluation budget
    pub evaluation_budget: EvaluationBudget,
    
    /// Multi-objective settings
    pub multi_objective: MultiObjectiveSettings<T>,
    
    /// Early stopping criteria
    pub early_stopping: EarlyStoppingCriteria<T>,
    
    /// Multi-fidelity settings
    pub multi_fidelity: Option<MultiFidelitySettings<T>>,
    
    /// Ensemble settings
    pub ensemble_settings: EnsembleSettings<T>,
    
    /// Parallelization settings
    pub parallelization: ParallelizationSettings,
    
    /// Resource constraints
    pub resource_constraints: ResourceConstraints<T>,
    
    /// Optimization objectives
    pub objectives: Vec<OptimizationObjective<T>>,
    
    /// Constraint functions
    pub constraints: Vec<ConstraintFunction<T>>}

/// HPO strategy types
#[derive(Debug, Clone, Copy)]
pub enum HPOStrategyType {
    /// Bayesian Optimization with Gaussian Processes
    BayesianOptimization,
    
    /// Population-Based Training
    PopulationBasedTraining,
    
    /// Hyperband algorithm
    Hyperband,
    
    /// BOHB (Bayesian Optimization and Hyperband)
    BOHB,
    
    /// Successive Halving
    SuccessiveHalving,
    
    /// Random Search
    RandomSearch,
    
    /// Grid Search
    GridSearch,
    
    /// Evolutionary Optimization
    EvolutionaryOptimization,
    
    /// Tree-Structured Parzen Estimator
    TPE,
    
    /// CMA-ES (Covariance Matrix Adaptation Evolution Strategy)
    CMAES,
    
    /// Differential Evolution
    DifferentialEvolution,
    
    /// Particle Swarm Optimization
    ParticleSwarmOptimization}

/// Parameter search space definition
#[derive(Debug, Clone)]
pub struct ParameterSearchSpace {
    /// Continuous parameters
    pub continuous_params: HashMap<String, ContinuousParameterSpace>,
    
    /// Discrete parameters
    pub discrete_params: HashMap<String, DiscreteParameterSpace>,
    
    /// Categorical parameters
    pub categorical_params: HashMap<String, CategoricalParameterSpace>,
    
    /// Conditional parameters
    pub conditional_params: HashMap<String, ConditionalParameterSpace>,
    
    /// Parameter dependencies
    pub dependencies: Vec<ParameterDependency>,
    
    /// Parameter constraints
    pub constraints: Vec<ParameterConstraint>}

/// Continuous parameter space
#[derive(Debug, Clone)]
pub struct ContinuousParameterSpace {
    /// Lower bound
    pub lower_bound: f64,
    
    /// Upper bound
    pub upper_bound: f64,
    
    /// Log scale
    pub log_scale: bool,
    
    /// Prior distribution
    pub prior: Option<PriorDistribution>,
    
    /// Default value
    pub default: Option<f64>}

/// Discrete parameter space
#[derive(Debug, Clone)]
pub struct DiscreteParameterSpace {
    /// Possible values
    pub values: Vec<i64>,
    
    /// Prior probabilities
    pub priors: Option<Vec<f64>>,
    
    /// Default value
    pub default: Option<i64>}

/// Categorical parameter space
#[derive(Debug, Clone)]
pub struct CategoricalParameterSpace {
    /// Possible categories
    pub categories: Vec<String>,
    
    /// Prior probabilities
    pub priors: Option<Vec<f64>>,
    
    /// Default category
    pub default: Option<String>}

/// Conditional parameter space
#[derive(Debug, Clone)]
pub struct ConditionalParameterSpace {
    /// Parent parameter
    pub parent_parameter: String,
    
    /// Condition for activation
    pub activation_condition: ParameterCondition,
    
    /// Child parameter space
    pub child_space: Box<ParameterSearchSpace>}

/// Parameter dependencies
#[derive(Debug, Clone)]
pub struct ParameterDependency {
    /// Dependent parameter
    pub dependent_param: String,
    
    /// Parent parameters
    pub parent_params: Vec<String>,
    
    /// Dependency type
    pub dependency_type: DependencyType,
    
    /// Dependency function
    pub dependency_function: DependencyFunction}

/// Parameter constraints
#[derive(Debug, Clone)]
pub struct ParameterConstraint {
    /// Parameters involved
    pub parameters: Vec<String>,
    
    /// Constraint type
    pub constraint_type: ConstraintType,
    
    /// Constraint expression
    pub expression: String,
    
    /// Violation penalty
    pub penalty: f64}

/// Prior distributions for parameters
#[derive(Debug, Clone)]
pub enum PriorDistribution {
    /// Uniform distribution
    Uniform,
    
    /// Normal distribution
    Normal { mean: f64, std: f64 },
    
    /// Log-normal distribution
    LogNormal { mu: f64, sigma: f64 },
    
    /// Beta distribution
    Beta { alpha: f64, beta: f64 },
    
    /// Gamma distribution
    Gamma { shape: f64, scale: f64 }}

/// Parameter conditions
#[derive(Debug, Clone)]
pub enum ParameterCondition {
    /// Equals specific value
    Equals(String),
    
    /// Not equals specific value
    NotEquals(String),
    
    /// In set of values
    In(Vec<String>),
    
    /// Not in set of values
    NotIn(Vec<String>),
    
    /// Greater than value
    GreaterThan(f64),
    
    /// Less than value
    LessThan(f64),
    
    /// Complex condition
    Complex(String)}

/// Dependency types
#[derive(Debug, Clone, Copy)]
pub enum DependencyType {
    /// Linear dependency
    Linear,
    
    /// Multiplicative dependency
    Multiplicative,
    
    /// Conditional dependency
    Conditional,
    
    /// Functional dependency
    Functional}

/// Dependency functions
#[derive(Debug, Clone)]
pub enum DependencyFunction {
    /// Linear function
    Linear { coefficients: Vec<f64>, intercept: f64 },
    
    /// Polynomial function
    Polynomial { coefficients: Vec<f64> },
    
    /// Exponential function
    Exponential { base: f64, scale: f64 },
    
    /// Custom function
    Custom(String)}

/// Constraint types
#[derive(Debug, Clone, Copy)]
pub enum ConstraintType {
    /// Equality constraint
    Equality,
    
    /// Inequality constraint
    Inequality,
    
    /// Range constraint
    Range,
    
    /// Custom constraint
    Custom}

/// Multi-objective settings
#[derive(Debug, Clone)]
pub struct MultiObjectiveSettings<T: Float + Debug + Send + Sync + 'static> {
    /// Objective functions
    pub objectives: Vec<ObjectiveFunction<T>>,
    
    /// Scalarization method
    pub scalarization: ScalarizationMethod,
    
    /// Pareto front approximation
    pub pareto_approximation: ParetoApproximationMethod,
    
    /// Reference point (for reference point methods)
    pub reference_point: Option<Vec<T>>,
    
    /// Weights for weighted sum
    pub weights: Option<Vec<T>>}

/// Objective functions
#[derive(Debug, Clone)]
pub struct ObjectiveFunction<T: Float + Debug + Send + Sync + 'static> {
    /// Objective name
    pub name: String,
    
    /// Optimization direction
    pub direction: OptimizationDirection,
    
    /// Weight in multi-objective optimization
    pub weight: T,
    
    /// Normalization method
    pub normalization: NormalizationMethod,
    
    /// Target value (for goal programming)
    pub target: Option<T>}

/// Optimization directions
#[derive(Debug, Clone, Copy)]
pub enum OptimizationDirection {
    Minimize,
    Maximize}

/// Scalarization methods for multi-objective optimization
#[derive(Debug, Clone, Copy)]
pub enum ScalarizationMethod {
    /// Weighted sum
    WeightedSum,
    
    /// Weighted Chebyshev
    WeightedChebyshev,
    
    /// Augmented Chebyshev
    AugmentedChebyshev,
    
    /// Achievement scalarization
    Achievement,
    
    /// Epsilon constraint
    EpsilonConstraint}

/// Pareto front approximation methods
#[derive(Debug, Clone, Copy)]
pub enum ParetoApproximationMethod {
    /// Non-dominated sorting
    NonDominatedSorting,
    
    /// Hypervolume indicator
    Hypervolume,
    
    /// Spacing metric
    Spacing,
    
    /// Generational distance
    GenerationalDistance}

/// Normalization methods
#[derive(Debug, Clone, Copy)]
pub enum NormalizationMethod {
    /// No normalization
    None,
    
    /// Min-max normalization
    MinMax,
    
    /// Z-score normalization
    ZScore,
    
    /// Robust normalization
    Robust}

/// Early stopping criteria
#[derive(Debug, Clone)]
pub struct EarlyStoppingCriteria<T: Float + Debug + Send + Sync + 'static> {
    /// Enable early stopping
    pub enabled: bool,
    
    /// Patience (iterations without improvement)
    pub patience: usize,
    
    /// Minimum improvement threshold
    pub min_improvement: T,
    
    /// Relative improvement threshold
    pub relative_improvement: T,
    
    /// Target performance
    pub target_performance: Option<T>,
    
    /// Maximum evaluations
    pub max_evaluations: Option<usize>,
    
    /// Maximum time budget
    pub max_time: Option<Duration>}

/// Multi-fidelity optimization settings
#[derive(Debug, Clone)]
pub struct MultiFidelitySettings<T: Float + Debug + Send + Sync + 'static> {
    /// Fidelity levels
    pub fidelity_levels: Vec<FidelityLevel<T>>,
    
    /// Promotion criteria
    pub promotion_criteria: PromotionCriteria<T>,
    
    /// Resource allocation strategy
    pub resource_allocation: ResourceAllocationStrategy,
    
    /// Correlation model between fidelities
    pub correlation_model: FidelityCorrelationModel<T>}

/// Fidelity levels
#[derive(Debug, Clone)]
pub struct FidelityLevel<T: Float + Debug + Send + Sync + 'static> {
    /// Fidelity identifier
    pub id: String,
    
    /// Resource cost
    pub cost: T,
    
    /// Expected accuracy
    pub accuracy: T,
    
    /// Configuration parameters
    pub config: HashMap<String, T>}

/// Promotion criteria for multi-fidelity
#[derive(Debug, Clone)]
pub struct PromotionCriteria<T: Float + Debug + Send + Sync + 'static> {
    /// Performance threshold
    pub performance_threshold: T,
    
    /// Confidence threshold
    pub confidence_threshold: T,
    
    /// Minimum evaluations at current fidelity
    pub min_evaluations: usize,
    
    /// Promotion strategy
    pub strategy: PromotionStrategy}

/// Resource allocation strategies
#[derive(Debug, Clone, Copy)]
pub enum ResourceAllocationStrategy {
    /// Equal allocation
    Equal,
    
    /// Performance-based allocation
    PerformanceBased,
    
    /// Bandit-based allocation
    Bandit,
    
    /// Adaptive allocation
    Adaptive}

/// Promotion strategies
#[derive(Debug, Clone, Copy)]
pub enum PromotionStrategy {
    /// Top-k promotion
    TopK,
    
    /// Threshold-based promotion
    Threshold,
    
    /// Probabilistic promotion
    Probabilistic,
    
    /// Adaptive promotion
    Adaptive}

/// Fidelity correlation models
#[derive(Debug, Clone)]
pub struct FidelityCorrelationModel<T: Float + Debug + Send + Sync + 'static> {
    /// Model type
    pub model_type: CorrelationModelType,
    
    /// Model parameters
    pub parameters: Vec<T>,
    
    /// Correlation matrix
    pub correlation_matrix: Option<Array2<T>>}

/// Correlation model types
#[derive(Debug, Clone, Copy)]
pub enum CorrelationModelType {
    /// Linear correlation
    Linear,
    
    /// Exponential correlation
    Exponential,
    
    /// Power law correlation
    PowerLaw,
    
    /// Learned correlation
    Learned}

/// Ensemble optimization settings
#[derive(Debug, Clone)]
pub struct EnsembleSettings<T: Float + Debug + Send + Sync + 'static> {
    /// Enable ensemble optimization
    pub enabled: bool,
    
    /// Ensemble strategies
    pub strategies: Vec<HPOStrategyType>,
    
    /// Strategy weights
    pub strategy_weights: Vec<T>,
    
    /// Combination method
    pub combination_method: EnsembleCombinationMethod,
    
    /// Adaptation strategy
    pub adaptation_strategy: EnsembleAdaptationStrategy,
    
    /// Performance tracking window
    pub tracking_window: usize}

/// Ensemble combination methods
#[derive(Debug, Clone, Copy)]
pub enum EnsembleCombinationMethod {
    /// Weighted average
    WeightedAverage,
    
    /// Voting
    Voting,
    
    /// Best performer
    BestPerformer,
    
    /// Dynamic selection
    DynamicSelection,
    
    /// Stacking
    Stacking}

/// Ensemble adaptation strategies
#[derive(Debug, Clone, Copy)]
pub enum EnsembleAdaptationStrategy {
    /// Fixed weights
    Fixed,
    
    /// Performance-based adaptation
    PerformanceBased,
    
    /// Bandit-based adaptation
    Bandit,
    
    /// Contextual adaptation
    Contextual}

/// Parallelization settings
#[derive(Debug, Clone)]
pub struct ParallelizationSettings {
    /// Number of parallel workers
    pub num_workers: usize,
    
    /// Synchronization strategy
    pub synchronization: SynchronizationStrategy,
    
    /// Load balancing strategy
    pub load_balancing: LoadBalancingStrategy,
    
    /// Communication overhead limit
    pub communication_overhead_limit: f64}

/// Synchronization strategies for parallel HPO
#[derive(Debug, Clone, Copy)]
pub enum SynchronizationStrategy {
    /// Synchronous evaluation
    Synchronous,
    
    /// Asynchronous evaluation
    Asynchronous,
    
    /// Batch synchronous
    BatchSynchronous,
    
    /// Hybrid synchronization
    Hybrid}

/// Load balancing strategies
#[derive(Debug, Clone, Copy)]
pub enum LoadBalancingStrategy {
    /// Round robin
    RoundRobin,
    
    /// Work stealing
    WorkStealing,
    
    /// Performance-based
    PerformanceBased,
    
    /// Dynamic balancing
    Dynamic}

/// Resource constraints for HPO
#[derive(Debug, Clone)]
pub struct ResourceConstraints<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum memory usage
    pub max_memory_gb: T,
    
    /// Maximum computation time
    pub max_time_hours: T,
    
    /// Maximum financial cost
    pub max_cost: T,
    
    /// Available compute resources
    pub compute_resources: ComputeResources}

/// Available compute resources
#[derive(Debug, Clone)]
pub struct ComputeResources {
    /// CPU cores
    pub cpu_cores: usize,
    
    /// GPU devices
    pub gpu_devices: usize,
    
    /// Memory per device
    pub memory_per_device_gb: f64,
    
    /// Network bandwidth
    pub network_bandwidth_gbps: f64}

/// Optimization objectives
#[derive(Debug, Clone)]
pub struct OptimizationObjective<T: Float + Debug + Send + Sync + 'static> {
    /// Objective name
    pub name: String,
    
    /// Objective function
    pub function: ObjectiveFunctionType,
    
    /// Optimization direction
    pub direction: OptimizationDirection,
    
    /// Weight in multi-objective setting
    pub weight: T,
    
    /// Constraint bounds
    pub bounds: Option<(T, T)>}

/// Objective function types
#[derive(Debug, Clone)]
pub enum ObjectiveFunctionType {
    /// Performance metric
    Performance(String),
    
    /// Resource consumption
    ResourceConsumption(String),
    
    /// Training time
    TrainingTime,
    
    /// Model complexity
    ModelComplexity,
    
    /// Custom objective
    Custom(String)}

/// Constraint functions
#[derive(Debug, Clone)]
pub struct ConstraintFunction<T: Float + Debug + Send + Sync + 'static> {
    /// Constraint name
    pub name: String,
    
    /// Constraint type
    pub constraint_type: ConstraintType,
    
    /// Constraint function
    pub function: ConstraintFunctionType,
    
    /// Tolerance
    pub tolerance: T,
    
    /// Penalty weight
    pub penalty_weight: T}

/// Constraint function types
#[derive(Debug, Clone)]
pub enum ConstraintFunctionType {
    /// Linear constraint
    Linear { coefficients: Vec<f64>, bound: f64 },
    
    /// Quadratic constraint
    Quadratic { matrix: Vec<Vec<f64>>, bound: f64 },
    
    /// Resource constraint
    Resource(String),
    
    /// Custom constraint
    Custom(String)}

/// Base trait for HPO strategies
pub trait HPOStrategy<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
    /// Initialize the strategy
    fn initialize(&mut self, config: &HPOConfig<T>) -> Result<()>;
    
    /// Suggest next parameter configuration
    fn suggest(&mut self, history: &[HPOResult<T>]) -> Result<ParameterConfiguration<T>>;
    
    /// Update strategy with new results
    fn update(&mut self, result: &HPOResult<T>) -> Result<()>;
    
    /// Check if strategy should stop
    fn should_stop(&self) -> bool;
    
    /// Get strategy statistics
    fn get_statistics(&self) -> HPOStrategyStatistics<T>;
    
    /// Get strategy name
    fn name(&self) -> &str;
}

/// Parameter configuration
#[derive(Debug, Clone)]
pub struct ParameterConfiguration<T: Float + Debug + Send + Sync + 'static> {
    /// Parameter values
    pub parameters: HashMap<String, ParameterValue>,
    
    /// Configuration quality score
    pub quality_score: Option<T>,
    
    /// Expected performance
    pub expected_performance: Option<T>,
    
    /// Configuration source
    pub source: ConfigurationSource,
    
    /// Generation metadata
    pub metadata: ConfigurationMetadata}

/// Parameter values
#[derive(Debug, Clone)]
pub enum ParameterValue {
    /// Continuous value
    Continuous(f64),
    
    /// Discrete value
    Discrete(i64),
    
    /// Categorical value
    Categorical(String),
    
    /// Boolean value
    Boolean(bool)}

/// Configuration sources
#[derive(Debug, Clone, Copy)]
pub enum ConfigurationSource {
    /// Random sampling
    Random,
    
    /// Bayesian optimization
    Bayesian,
    
    /// Evolutionary algorithm
    Evolutionary,
    
    /// Gradient-based
    GradientBased,
    
    /// Transfer learning
    Transfer,
    
    /// User-defined
    UserDefined}

/// Configuration metadata
#[derive(Debug, Clone)]
pub struct ConfigurationMetadata {
    /// Generation timestamp
    pub timestamp: Instant,
    
    /// Generation strategy
    pub strategy: String,
    
    /// Acquisition function value
    pub acquisition_value: Option<f64>,
    
    /// Uncertainty estimate
    pub uncertainty: Option<f64>}

/// HPO result
#[derive(Debug, Clone)]
pub struct HPOResult<T: Float + Debug + Send + Sync + 'static> {
    /// Parameter configuration
    pub configuration: ParameterConfiguration<T>,
    
    /// Objective values
    pub objectives: HashMap<String, T>,
    
    /// Constraint violations
    pub constraints: HashMap<String, T>,
    
    /// Evaluation metadata
    pub metadata: EvaluationMetadata,
    
    /// Resource usage
    pub resource_usage: ResourceUsage<T>,
    
    /// Fidelity level
    pub fidelity: Option<String>}

/// Evaluation metadata
#[derive(Debug, Clone)]
pub struct EvaluationMetadata {
    /// Evaluation start time
    pub start_time: Instant,
    
    /// Evaluation duration
    pub duration: Duration,
    
    /// Evaluation success flag
    pub success: bool,
    
    /// Error message if failed
    pub error_message: Option<String>,
    
    /// Additional metrics
    pub additional_metrics: HashMap<String, f64>}

/// HPO strategy statistics
#[derive(Debug, Clone)]
pub struct HPOStrategyStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Number of suggestions made
    pub suggestions_made: usize,
    
    /// Number of successful evaluations
    pub successful_evaluations: usize,
    
    /// Best performance found
    pub best_performance: Option<T>,
    
    /// Average performance
    pub average_performance: Option<T>,
    
    /// Convergence rate
    pub convergence_rate: Option<T>,
    
    /// Strategy-specific metrics
    pub custom_metrics: HashMap<String, T>}

/// Parameter space manager
#[derive(Debug)]
pub struct ParameterSpaceManager<T: Float + Debug + Send + Sync + 'static> {
    /// Parameter space definition
    space: ParameterSearchSpace,
    
    /// Cached transformations
    transformations: HashMap<String, ParameterTransformation>,
    
    /// Validation rules
    validation_rules: Vec<ValidationRule>,
    
    /// Sampling strategies
    sampling_strategies: HashMap<String, SamplingStrategy>}

/// Parameter transformations
#[derive(Debug, Clone)]
pub enum ParameterTransformation {
    /// Log transformation
    Log,
    
    /// Inverse transformation
    Inverse,
    
    /// Box-Cox transformation
    BoxCox(f64),
    
    /// Custom transformation
    Custom(String)}

/// Validation rules
#[derive(Debug, Clone)]
pub struct ValidationRule {
    /// Rule name
    pub name: String,
    
    /// Parameters to validate
    pub parameters: Vec<String>,
    
    /// Validation function
    pub validator: ValidationFunction,
    
    /// Error message
    pub error_message: String}

/// Validation functions
#[derive(Debug, Clone)]
pub enum ValidationFunction {
    /// Range validation
    Range { min: f64, max: f64 },
    
    /// Sum constraint
    Sum { target: f64, tolerance: f64 },
    
    /// Dependency validation
    Dependency(String),
    
    /// Custom validation
    Custom(String)}

/// Sampling strategies
#[derive(Debug, Clone, Copy)]
pub enum SamplingStrategy {
    /// Uniform sampling
    Uniform,
    
    /// Latin hypercube sampling
    LatinHypercube,
    
    /// Sobol sampling
    Sobol,
    
    /// Halton sampling
    Halton,
    
    /// Grid sampling
    Grid}

/// Evaluation scheduler
#[derive(Debug)]
pub struct EvaluationScheduler<T: Float + Debug + Send + Sync + 'static> {
    /// Pending evaluations
    pending_queue: VecDeque<EvaluationTask<T>>,
    
    /// Running evaluations
    running_evaluations: HashMap<String, RunningEvaluation<T>>,
    
    /// Completed evaluations
    completed_evaluations: VecDeque<HPOResult<T>>,
    
    /// Scheduler configuration
    config: SchedulerConfig,
    
    /// Resource monitor
    resource_monitor: ResourceMonitor<T>}

/// Evaluation task
#[derive(Debug, Clone)]
pub struct EvaluationTask<T: Float + Debug + Send + Sync + 'static> {
    /// Task identifier
    pub id: String,
    
    /// Parameter configuration
    pub configuration: ParameterConfiguration<T>,
    
    /// Priority
    pub priority: TaskPriority,
    
    /// Fidelity level
    pub fidelity: Option<String>,
    
    /// Estimated resource requirements
    pub resource_requirements: ResourceRequirements<T>}

/// Running evaluation
#[derive(Debug)]
pub struct RunningEvaluation<T: Float + Debug + Send + Sync + 'static> {
    /// Evaluation task
    pub task: EvaluationTask<T>,
    
    /// Start time
    pub start_time: Instant,
    
    /// Worker assignment
    pub worker_id: Option<String>,
    
    /// Intermediate results
    pub intermediate_results: Vec<IntermediateResult<T>>}

/// Task priorities
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum TaskPriority {
    Low = 1,
    Medium = 2,
    High = 3,
    Critical = 4}

/// Resource requirements
#[derive(Debug, Clone)]
pub struct ResourceRequirements<T: Float + Debug + Send + Sync + 'static> {
    /// CPU cores required
    pub cpu_cores: usize,
    
    /// Memory required (GB)
    pub memory_gb: T,
    
    /// GPU devices required
    pub gpu_devices: usize,
    
    /// Estimated duration
    pub estimated_duration: Duration}

/// Intermediate results during evaluation
#[derive(Debug, Clone)]
pub struct IntermediateResult<T: Float + Debug + Send + Sync + 'static> {
    /// Timestamp
    pub timestamp: Instant,
    
    /// Partial objective values
    pub partial_objectives: HashMap<String, T>,
    
    /// Progress indicator
    pub progress: T,
    
    /// Additional metrics
    pub metrics: HashMap<String, T>}

/// Scheduler configuration
#[derive(Debug, Clone)]
pub struct SchedulerConfig {
    /// Maximum concurrent evaluations
    pub max_concurrent: usize,
    
    /// Scheduling strategy
    pub strategy: SchedulingStrategy,
    
    /// Resource allocation policy
    pub resource_allocation: ResourceAllocationPolicy,
    
    /// Preemption policy
    pub preemption_policy: PreemptionPolicy}

/// Scheduling strategies
#[derive(Debug, Clone, Copy)]
pub enum SchedulingStrategy {
    /// First-come, first-served
    FCFS,
    
    /// Priority-based scheduling
    Priority,
    
    /// Shortest job first
    SJF,
    
    /// Fair share scheduling
    FairShare,
    
    /// Performance-aware scheduling
    PerformanceAware}

/// Resource allocation policies
#[derive(Debug, Clone, Copy)]
pub enum ResourceAllocationPolicy {
    /// Exclusive allocation
    Exclusive,
    
    /// Shared allocation
    Shared,
    
    /// Adaptive allocation
    Adaptive,
    
    /// Best-fit allocation
    BestFit}

/// Preemption policies
#[derive(Debug, Clone, Copy)]
pub enum PreemptionPolicy {
    /// No preemption
    None,
    
    /// Priority-based preemption
    Priority,
    
    /// Resource-based preemption
    Resource,
    
    /// Performance-based preemption
    Performance}

/// HPO result database
#[derive(Debug)]
pub struct HPOResultDatabase<T: Float + Debug + Send + Sync + 'static> {
    /// All results
    results: Vec<HPOResult<T>>,
    
    /// Results by configuration hash
    results_by_config: HashMap<u64, Vec<usize>>,
    
    /// Results by objective value
    results_by_objective: BTreeMap<String, BTreeMap<OrderedFloat<f64>, usize>>,
    
    /// Pareto front
    pareto_front: Vec<usize>,
    
    /// Database statistics
    statistics: DatabaseStatistics<T>}

/// Ordered float wrapper for BTreeMap keys
#[derive(Debug, Clone, Copy, PartialEq, PartialOrd)]
struct OrderedFloat<T: Float + Debug + Send + Sync + 'static>(T);

impl<T: Float + Debug + Send + Sync + 'static> Eq for OrderedFloat<T> {}

impl<T: Float + Debug + Send + Sync + 'static> Ord for OrderedFloat<T> {
    fn cmp(&self, other: &Self) -> std::cmp::Ordering {
        self.partial_cmp(other).unwrap_or(std::cmp::Ordering::Equal)
    }
}

/// Database statistics
#[derive(Debug, Clone)]
pub struct DatabaseStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Total evaluations
    pub total_evaluations: usize,
    
    /// Successful evaluations
    pub successful_evaluations: usize,
    
    /// Best objective values
    pub best_objectives: HashMap<String, T>,
    
    /// Average objective values
    pub average_objectives: HashMap<String, T>,
    
    /// Objective value distributions
    pub objective_distributions: HashMap<String, Vec<T>>}

/// Multi-fidelity manager
#[derive(Debug)]
pub struct MultiFidelityManager<T: Float + Debug + Send + Sync + 'static> {
    /// Fidelity settings
    settings: MultiFidelitySettings<T>,
    
    /// Current fidelity assignments
    fidelity_assignments: HashMap<String, String>,
    
    /// Promotion queue
    promotion_queue: VecDeque<PromotionCandidate<T>>,
    
    /// Resource usage tracker
    resource_tracker: FidelityResourceTracker<T>}

/// Promotion candidate
#[derive(Debug, Clone)]
pub struct PromotionCandidate<T: Float + Debug + Send + Sync + 'static> {
    /// Configuration ID
    pub config_id: String,
    
    /// Current fidelity
    pub current_fidelity: String,
    
    /// Target fidelity
    pub target_fidelity: String,
    
    /// Promotion score
    pub promotion_score: T,
    
    /// Performance history
    pub performance_history: Vec<T>}

/// Resource tracker for fidelities
#[derive(Debug)]
pub struct FidelityResourceTracker<T: Float + Debug + Send + Sync + 'static> {
    /// Resource usage per fidelity
    usage_per_fidelity: HashMap<String, ResourceUsage<T>>,
    
    /// Total resource budget
    total_budget: ResourceUsage<T>,
    
    /// Used resources
    used_resources: ResourceUsage<T>}

/// Early stopping controller
#[derive(Debug)]
pub struct EarlyStoppingController<T: Float + Debug + Send + Sync + 'static> {
    /// Stopping criteria
    criteria: EarlyStoppingCriteria<T>,
    
    /// Performance history
    performance_history: VecDeque<T>,
    
    /// Best performance seen
    best_performance: Option<T>,
    
    /// Iterations without improvement
    iterations_without_improvement: usize,
    
    /// Controller state
    state: EarlyStoppingState}

/// Early stopping states
#[derive(Debug, Clone, Copy)]
pub enum EarlyStoppingState {
    /// Active monitoring
    Active,
    
    /// Stop triggered
    Triggered,
    
    /// Disabled
    Disabled}

/// Ensemble optimizer
#[derive(Debug)]
pub struct EnsembleOptimizer<T: Float + Debug + Send + Sync + 'static> {
    /// Ensemble settings
    settings: EnsembleSettings<T>,
    
    /// Individual strategies
    strategies: Vec<Box<dyn HPOStrategy<T>>>,
    
    /// Strategy performance tracker
    performance_tracker: StrategyPerformanceTracker<T>,
    
    /// Weight adaptation controller
    weight_controller: WeightAdaptationController<T>,
    
    /// Combination engine
    combination_engine: CombinationEngine<T>}

/// Strategy performance tracker
#[derive(Debug)]
pub struct StrategyPerformanceTracker<T: Float + Debug + Send + Sync + 'static> {
    /// Performance history per strategy
    performance_history: HashMap<String, VecDeque<T>>,
    
    /// Success rates
    success_rates: HashMap<String, T>,
    
    /// Average performance
    average_performance: HashMap<String, T>,
    
    /// Performance trends
    performance_trends: HashMap<String, PerformanceTrend<T>>}

/// Performance trends
#[derive(Debug, Clone)]
pub struct PerformanceTrend<T: Float + Debug + Send + Sync + 'static> {
    /// Trend direction
    pub direction: TrendDirection,
    
    /// Trend magnitude
    pub magnitude: T,
    
    /// Trend confidence
    pub confidence: T}

/// Trend directions
#[derive(Debug, Clone, Copy)]
pub enum TrendDirection {
    Improving,
    Declining,
    Stable}

/// Weight adaptation controller
#[derive(Debug)]
pub struct WeightAdaptationController<T: Float + Debug + Send + Sync + 'static> {
    /// Current weights
    current_weights: Vec<T>,
    
    /// Adaptation strategy
    adaptation_strategy: EnsembleAdaptationStrategy,
    
    /// Adaptation rate
    adaptation_rate: T,
    
    /// Weight constraints
    weight_constraints: WeightConstraints<T>}

/// Weight constraints
#[derive(Debug, Clone)]
pub struct WeightConstraints<T: Float + Debug + Send + Sync + 'static> {
    /// Minimum weight
    pub min_weight: T,
    
    /// Maximum weight
    pub max_weight: T,
    
    /// Sum constraint
    pub sum_constraint: Option<T>,
    
    /// Smoothness constraint
    pub smoothness_constraint: Option<T>}

/// Combination engine
#[derive(Debug)]
pub struct CombinationEngine<T: Float + Debug + Send + Sync + 'static> {
    /// Combination method
    method: EnsembleCombinationMethod,
    
    /// Combination history
    combination_history: VecDeque<CombinationResult<T>>,
    
    /// Meta-learner (for stacking)
    meta_learner: Option<MetaLearner<T>>}

/// Combination result
#[derive(Debug, Clone)]
pub struct CombinationResult<T: Float + Debug + Send + Sync + 'static> {
    /// Combined suggestion
    pub suggestion: ParameterConfiguration<T>,
    
    /// Individual suggestions
    pub individual_suggestions: Vec<ParameterConfiguration<T>>,
    
    /// Combination weights used
    pub weights: Vec<T>,
    
    /// Combination quality score
    pub quality_score: T}

/// Meta-learner for ensemble stacking
#[derive(Debug)]
pub struct MetaLearner<T: Float + Debug + Send + Sync + 'static> {
    /// Meta-features
    meta_features: Array2<T>,
    
    /// Meta-targets
    meta_targets: Array1<T>,
    
    /// Meta-model parameters
    parameters: Array1<T>,
    
    /// Training history
    training_history: Vec<MetaTrainingStep<T>>}

/// Meta-training step
#[derive(Debug, Clone)]
pub struct MetaTrainingStep<T: Float + Debug + Send + Sync + 'static> {
    /// Step number
    pub step: usize,
    
    /// Loss value
    pub loss: T,
    
    /// Gradient norm
    pub gradient_norm: T,
    
    /// Learning rate
    pub learning_rate: T}

/// Optimization state
#[derive(Debug)]
pub struct OptimizationState<T: Float + Debug + Send + Sync + 'static> {
    /// Current iteration
    pub iteration: usize,
    
    /// Total evaluations performed
    pub total_evaluations: usize,
    
    /// Current best configuration
    pub best_configuration: Option<ParameterConfiguration<T>>,
    
    /// Current best performance
    pub best_performance: Option<T>,
    
    /// Optimization start time
    pub start_time: Instant,
    
    /// Current phase
    pub phase: OptimizationPhase,
    
    /// Resource usage so far
    pub resource_usage: ResourceUsage<T>}

/// Optimization phases
#[derive(Debug, Clone, Copy)]
pub enum OptimizationPhase {
    /// Initialization phase
    Initialization,
    
    /// Exploration phase
    Exploration,
    
    /// Exploitation phase
    Exploitation,
    
    /// Refinement phase
    Refinement,
    
    /// Termination phase
    Termination}

/// Resource monitor
#[derive(Debug)]
pub struct ResourceMonitor<T: Float + Debug + Send + Sync + 'static> {
    /// Current resource usage
    current_usage: ResourceUsage<T>,
    
    /// Resource limits
    limits: ResourceConstraints<T>,
    
    /// Usage history
    usage_history: VecDeque<(Instant, ResourceUsage<T>)>,
    
    /// Monitoring interval
    monitoring_interval: Duration}

impl<T: Float + Debug + Send + Sync + 'static> Default for HPOConfig<T> {
    fn default() -> Self {
        Self {
            strategies: vec![HPOStrategyType::BayesianOptimization, HPOStrategyType::RandomSearch],
            parameter_space: ParameterSearchSpace::default(),
            evaluation_budget: EvaluationBudget::default(),
            multi_objective: MultiObjectiveSettings::default(),
            early_stopping: EarlyStoppingCriteria::default(),
            multi_fidelity: None,
            ensemble_settings: EnsembleSettings::default(),
            parallelization: ParallelizationSettings::default(),
            resource_constraints: ResourceConstraints::default(),
            objectives: vec![
                OptimizationObjective {
                    name: "performance".to_string(),
                    function: ObjectiveFunctionType::Performance("accuracy".to_string()),
                    direction: OptimizationDirection::Maximize,
                    weight: T::one(),
                    bounds: None}
            ],
            constraints: vec![]}
    }
}

impl Default for ParameterSearchSpace {
    fn default() -> Self {
        Self {
            continuous_params: HashMap::new(),
            discrete_params: HashMap::new(),
            categorical_params: HashMap::new(),
            conditional_params: HashMap::new(),
            dependencies: Vec::new(),
            constraints: Vec::new()}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for MultiObjectiveSettings<T> {
    fn default() -> Self {
        Self {
            objectives: vec![],
            scalarization: ScalarizationMethod::WeightedSum,
            pareto_approximation: ParetoApproximationMethod::NonDominatedSorting,
            reference_point: None,
            weights: None}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for EarlyStoppingCriteria<T> {
    fn default() -> Self {
        Self {
            enabled: true,
            patience: 50,
            min_improvement: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
            relative_improvement: scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()),
            target_performance: None,
            max_evaluations: Some(1000),
            max_time: Some(Duration::from_hours(24))}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for EnsembleSettings<T> {
    fn default() -> Self {
        Self {
            enabled: false,
            strategies: vec![],
            strategy_weights: vec![],
            combination_method: EnsembleCombinationMethod::WeightedAverage,
            adaptation_strategy: EnsembleAdaptationStrategy::PerformanceBased,
            tracking_window: 100}
    }
}

impl Default for ParallelizationSettings {
    fn default() -> Self {
        Self {
            num_workers: 4,
            synchronization: SynchronizationStrategy::Asynchronous,
            load_balancing: LoadBalancingStrategy::Dynamic,
            communication_overhead_limit: 0.1}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for ResourceConstraints<T> {
    fn default() -> Self {
        Self {
            max_memory_gb: scirs2_core::numeric::NumCast::from(32.0).unwrap_or_else(|| T::zero()),
            max_time_hours: scirs2_core::numeric::NumCast::from(24.0).unwrap_or_else(|| T::zero()),
            max_cost: scirs2_core::numeric::NumCast::from(1000.0).unwrap_or_else(|| T::zero()),
            compute_resources: ComputeResources::default()}
    }
}

impl Default for ComputeResources {
    fn default() -> Self {
        Self {
            cpu_cores: 8,
            gpu_devices: 1,
            memory_per_device_gb: 16.0,
            network_bandwidth_gbps: 10.0}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for EvaluationBudget {
    fn default() -> Self {
        Self {
            max_epochs: 1000,
            max_time_seconds: 86400, // 24 hours
            max_flops: 1_000_000_000_000, // 1 TFLOP
            early_stopping_patience: 50,
            min_evaluation_time: Duration::from_secs(60)}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> HyperparameterOptimizationPipeline<T> {
    /// Create new hyperparameter optimization pipeline
    pub fn new(config: HPOConfig<T>) -> Result<Self> {
        let strategies = Self::create_strategies(&_config)?;
        let parameter_space = ParameterSpaceManager::new(_config.parameter_space.clone())?;
        let evaluation_scheduler = EvaluationScheduler::new(
            SchedulerConfig::default(),
            config.resource_constraints.clone(),
        )?;
        let result_database = HPOResultDatabase::new();
        let multi_fidelity_manager = if let Some(mf_config) = config.multi_fidelity.clone() {
            Some(MultiFidelityManager::new(mf_config)?)
        } else {
            None
        };
        let early_stopping = EarlyStoppingController::new(_config.early_stopping.clone());
        let ensemble_optimizer = EnsembleOptimizer::new(_config.ensemble_settings.clone())?;
        
        Ok(Self {
            config,
            strategies,
            parameter_space,
            evaluation_scheduler,
            result_database,
            multi_fidelity_manager,
            early_stopping,
            ensemble_optimizer,
            optimization_state: OptimizationState::new()})
    }
    
    /// Run the hyperparameter optimization
    pub fn optimize(&mut self) -> Result<HPOResults<T>> {
        let start_time = Instant::now();
        
        // Initialize optimization
        self.initialize_optimization()?;
        
        // Main optimization loop
        while !self.should_terminate() {
            // Generate candidate configurations
            let candidates = self.generate_candidates()?;
            
            // Schedule evaluations
            self.schedule_evaluations(candidates)?;
            
            // Process completed evaluations
            self.process_completed_evaluations()?;
            
            // Update optimization state
            self.update_optimization_state()?;
            
            // Check early stopping
            if self.early_stopping.should_stop() {
                break;
            }
        }
        
        // Finalize optimization
        self.finalize_optimization(start_time.elapsed())
    }
    
    /// Initialize optimization process
    fn initialize_optimization(&mut self) -> Result<()> {
        // Initialize all strategies
        for strategy in &mut self.strategies {
            strategy.initialize(&self.config)?;
        }
        
        // Initialize ensemble optimizer
        if self.config.ensemble_settings.enabled {
            self.ensemble_optimizer.initialize(&self.config)?;
        }
        
        // Start resource monitoring
        self.evaluation_scheduler.start_monitoring()?;
        
        Ok(())
    }
    
    /// Generate candidate configurations
    fn generate_candidates(&mut self) -> Result<Vec<ParameterConfiguration<T>>> {
        let mut candidates = Vec::new();
        let history = self.result_database.get_all_results();
        
        if self.config.ensemble_settings.enabled {
            // Use ensemble optimizer
            let ensemble_candidates = self.ensemble_optimizer.suggest_batch(
                &self.config.parallelization.num_workers,
                &history,
            )?;
            candidates.extend(ensemble_candidates);
        } else {
            // Use individual strategies
            for strategy in &mut self.strategies {
                let candidate = strategy.suggest(&history)?;
                candidates.push(candidate);
            }
        }
        
        // Validate candidates
        candidates = self.parameter_space.validate_configurations(candidates)?;
        
        // Apply multi-fidelity logic
        if let Some(ref mut mf_manager) = self.multi_fidelity_manager {
            candidates = mf_manager.assign_fidelities(candidates)?;
        }
        
        Ok(candidates)
    }
    
    /// Schedule evaluations for candidates
    fn schedule_evaluations(
        &mut self,
        candidates: Vec<ParameterConfiguration<T>>,
    ) -> Result<()> {
        for candidate in candidates {
            let task = EvaluationTask {
                id: format!("eval_{}", uuid::Uuid::new_v4()),
                configuration: candidate,
                priority: TaskPriority::Medium,
                fidelity: None, // Will be set by multi-fidelity manager
                resource_requirements: self.estimate_resource_requirements(&candidate)?};
            
            self.evaluation_scheduler.schedule_task(task)?;
        }
        Ok(())
    }
    
    /// Process completed evaluations
    fn process_completed_evaluations(&mut self) -> Result<()> {
        let completed = self.evaluation_scheduler.get_completed_evaluations();
        
        for result in completed {
            // Add to database
            self.result_database.add_result(result.clone())?;
            
            // Update strategies
            for strategy in &mut self.strategies {
                strategy.update(&result)?;
            }
            
            // Update ensemble optimizer
            if self.config.ensemble_settings.enabled {
                self.ensemble_optimizer.update(&result)?;
            }
            
            // Update multi-fidelity manager
            if let Some(ref mut mf_manager) = self.multi_fidelity_manager {
                mf_manager.update_with_result(&result)?;
            }
            
            // Update early stopping controller
            if let Some(performance) = result.objectives.get("performance") {
                self.early_stopping.update(*performance);
            }
        }
        
        Ok(())
    }
    
    /// Update optimization state
    fn update_optimization_state(&mut self) -> Result<()> {
        self.optimization_state.iteration += 1;
        self.optimization_state.total_evaluations = self.result_database.total_evaluations();
        
        // Update best configuration and performance
        if let Some(best_result) = self.result_database.get_best_result("performance") {
            self.optimization_state.best_configuration = Some(best_result.configuration.clone());
            self.optimization_state.best_performance = best_result.objectives.get("performance").copied();
        }
        
        // Update optimization phase
        self.optimization_state.phase = self.determine_optimization_phase();
        
        Ok(())
    }
    
    /// Check if optimization should terminate
    fn should_terminate(&self) -> bool {
        // Check evaluation budget
        if self.optimization_state.total_evaluations >= self.config.evaluation_budget.max_epochs {
            return true;
        }
        
        // Check time budget
        if self.optimization_state.start_time.elapsed() >= Duration::from_secs(
            self.config.evaluation_budget.max_time_seconds
        ) {
            return true;
        }
        
        // Check early stopping
        if self.early_stopping.should_stop() {
            return true;
        }
        
        // Check resource constraints
        if self.evaluation_scheduler.resource_limit_exceeded() {
            return true;
        }
        
        false
    }
    
    /// Finalize optimization and return results
    fn finalize_optimization(&self, totaltime: Duration) -> Result<HPOResults<T>> {
        let best_result = self.result_database.get_best_result("performance");
        let pareto_front = self.result_database.get_pareto_front();
        let statistics = self.collect_optimization_statistics(total_time);
        
        Ok(HPOResults {
            best_configuration: best_result.map(|r| r.configuration.clone()),
            best_performance: best_result.and_then(|r| r.objectives.get("performance").copied()),
            pareto_front,
            all_results: self.result_database.get_all_results(),
            statistics,
            optimization_trace: self.get_optimization_trace()})
    }
    
    // Helper methods would be implemented here...
    fn create_strategies(config: &HPOConfig<T>) -> Result<Vec<Box<dyn HPOStrategy<T>>>> {
        // Implementation would create strategy instances based on _config
        Ok(vec![])
    }
    
    fn estimate_resource_requirements(&self,
        config: &ParameterConfiguration<T>) -> Result<ResourceRequirements<T>> {
        // Placeholder implementation
        Ok(ResourceRequirements {
            cpu_cores: 1,
            memory_gb: scirs2_core::numeric::NumCast::from(4.0).unwrap_or_else(|| T::zero()),
            gpu_devices: 0,
            estimated_duration: Duration::from_secs(300)})
    }
    
    fn determine_optimization_phase(&self) -> OptimizationPhase {
        // Simple phase determination logic
        let progress = self.optimization_state.total_evaluations as f64 / self.config.evaluation_budget.max_epochs as f64;
        
        if progress < 0.1 {
            OptimizationPhase::Initialization
        } else if progress < 0.5 {
            OptimizationPhase::Exploration
        } else if progress < 0.8 {
            OptimizationPhase::Exploitation
        } else if progress < 0.95 {
            OptimizationPhase::Refinement
        } else {
            OptimizationPhase::Termination
        }
    }
    
    fn collect_optimization_statistics(&self, totaltime: Duration) -> OptimizationStatistics<T> {
        OptimizationStatistics {
            total_evaluations: self.optimization_state.total_evaluations,
            successful_evaluations: self.result_database.successful_evaluations(),
            total_time,
            best_performance: self.optimization_state.best_performance,
            convergence_iteration: None, // Would be computed
            strategy_statistics: HashMap::new(), // Would be collected from strategies
        }
    }
    
    fn get_optimization_trace(&self) -> Vec<OptimizationTracePoint<T>> {
        // Would return the optimization trace
        vec![]
    }
}

/// HPO optimization results
#[derive(Debug, Clone)]
pub struct HPOResults<T: Float + Debug + Send + Sync + 'static> {
    /// Best configuration found
    pub best_configuration: Option<ParameterConfiguration<T>>,
    
    /// Best performance achieved
    pub best_performance: Option<T>,
    
    /// Pareto front (for multi-objective)
    pub pareto_front: Vec<HPOResult<T>>,
    
    /// All evaluation results
    pub all_results: Vec<HPOResult<T>>,
    
    /// Optimization statistics
    pub statistics: OptimizationStatistics<T>,
    
    /// Optimization trace
    pub optimization_trace: Vec<OptimizationTracePoint<T>>}

/// Optimization statistics
#[derive(Debug, Clone)]
pub struct OptimizationStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Total evaluations performed
    pub total_evaluations: usize,
    
    /// Successful evaluations
    pub successful_evaluations: usize,
    
    /// Total optimization time
    pub total_time: Duration,
    
    /// Best performance found
    pub best_performance: Option<T>,
    
    /// Iteration where convergence was detected
    pub convergence_iteration: Option<usize>,
    
    /// Per-strategy statistics
    pub strategy_statistics: HashMap<String, HPOStrategyStatistics<T>>}

/// Optimization trace point
#[derive(Debug, Clone)]
pub struct OptimizationTracePoint<T: Float + Debug + Send + Sync + 'static> {
    /// Iteration number
    pub iteration: usize,
    
    /// Best performance so far
    pub best_performance: T,
    
    /// Current configuration being evaluated
    pub current_configuration: ParameterConfiguration<T>,
    
    /// Resource usage at this point
    pub resource_usage: ResourceUsage<T>,
    
    /// Timestamp
    pub timestamp: Instant}

// Implementation stubs for complex components - these would be fully implemented in practice
impl<T: Float + Debug + Send + Sync + 'static> ParameterSpaceManager<T> {
    fn new(space: ParameterSearchSpace) -> Result<Self> {
        Ok(Self {
            _space: space,
            transformations: HashMap::new(),
            validation_rules: Vec::new(),
            sampling_strategies: HashMap::new()})
    }
    
    fn validate_configurations(&self, configs: Vec<ParameterConfiguration<T>>) -> Result<Vec<ParameterConfiguration<T>>> {
        // Would implement validation logic
        Ok(configs)
    }
}

impl<T: Float + Debug + Send + Sync + 'static> EvaluationScheduler<T> {
    fn new(config: SchedulerConfig, constraints: ResourceConstraints<T>) -> Result<Self> {
        Ok(Self {
            pending_queue: VecDeque::new(),
            running_evaluations: HashMap::new(),
            completed_evaluations: VecDeque::new(),
            _config: config,
            resource_monitor: ResourceMonitor::new(_constraints)})
    }
    
    fn start_monitoring(&mut self) -> Result<()> {
        Ok(())
    }
    
    fn schedule_task(&mut self,
        task: EvaluationTask<T>) -> Result<()> {
        Ok(())
    }
    
    fn get_completed_evaluations(&mut self) -> Vec<HPOResult<T>> {
        vec![]
    }
    
    fn resource_limit_exceeded(&self) -> bool {
        false
    }
}

impl<T: Float + Debug + Send + Sync + 'static> HPOResultDatabase<T> {
    fn new() -> Self {
        Self {
            results: Vec::new(),
            results_by_config: HashMap::new(),
            results_by_objective: BTreeMap::new(),
            pareto_front: Vec::new(),
            statistics: DatabaseStatistics::default()}
    }
    
    fn add_result(&mut self,
        result: HPOResult<T>) -> Result<()> {
        Ok(())
    }
    
    fn get_all_results(&self) -> Vec<HPOResult<T>> {
        self.results.clone()
    }
    
    fn get_best_result(&self,
        objective: &str) -> Option<&HPOResult<T>> {
        None
    }
    
    fn get_pareto_front(&self) -> Vec<HPOResult<T>> {
        vec![]
    }
    
    fn total_evaluations(&self) -> usize {
        self.results.len()
    }
    
    fn successful_evaluations(&self) -> usize {
        self.results.len() // Simplified
    }
}

impl<T: Float + Debug + Send + Sync + 'static> MultiFidelityManager<T> {
    fn new(settings: MultiFidelitySettings<T>) -> Result<Self> {
        Ok(Self {
            _settings: settings,
            fidelity_assignments: HashMap::new(),
            promotion_queue: VecDeque::new(),
            resource_tracker: FidelityResourceTracker::new()})
    }
    
    fn assign_fidelities(&mut self, configs: Vec<ParameterConfiguration<T>>) -> Result<Vec<ParameterConfiguration<T>>> {
        Ok(configs)
    }
    
    fn update_with_result(&mut self,
        result: &HPOResult<T>) -> Result<()> {
        Ok(())
    }
}

impl<T: Float + Debug + Send + Sync + 'static> EarlyStoppingController<T> {
    fn new(criteria: EarlyStoppingCriteria<T>) -> Self {
        Self {
            _criteria: criteria,
            performance_history: VecDeque::new(),
            best_performance: None,
            iterations_without_improvement: 0,
            state: EarlyStoppingState::Active}
    }
    
    fn update(&mut self,
        performance: T) {
        // Would implement early stopping logic
    }
    
    fn should_stop(&self) -> bool {
        matches!(self.state, EarlyStoppingState::Triggered)
    }
}

impl<T: Float + Debug + Send + Sync + 'static> EnsembleOptimizer<T> {
    fn new(settings: EnsembleSettings<T>) -> Result<Self> {
        Ok(Self {
            _settings: settings,
            strategies: Vec::new(),
            performance_tracker: StrategyPerformanceTracker::new(),
            weight_controller: WeightAdaptationController::new(),
            combination_engine: CombinationEngine::new()})
    }
    
    fn initialize(&mut self,
        config: &HPOConfig<T>) -> Result<()> {
        Ok(())
    }
    
    fn suggest_batch(&mut self, _batch_size: &usize, history: &[HPOResult<T>]) -> Result<Vec<ParameterConfiguration<T>>> {
        Ok(vec![])
    }
    
    fn update(&mut self,
        result: &HPOResult<T>) -> Result<()> {
        Ok(())
    }
}

impl<T: Float + Debug + Send + Sync + 'static> OptimizationState<T> {
    fn new() -> Self {
        Self {
            iteration: 0,
            total_evaluations: 0,
            best_configuration: None,
            best_performance: None,
            start_time: Instant::now(),
            phase: OptimizationPhase::Initialization,
            resource_usage: ResourceUsage {
                memory_gb: T::zero(),
                cpu_time_seconds: T::zero(),
                gpu_time_seconds: T::zero(),
                energy_kwh: T::zero(),
                cost_usd: T::zero(),
                network_gb: T::zero()}}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> ResourceMonitor<T> {
    fn new(constraints: ResourceConstraints<T>) -> Self {
        Self {
            current_usage: ResourceUsage {
                memory_gb: T::zero(),
                cpu_time_seconds: T::zero(),
                gpu_time_seconds: T::zero(),
                energy_kwh: T::zero(),
                cost_usd: T::zero(),
                network_gb: T::zero()},
            limits: constraints,
            usage_history: VecDeque::new(),
            monitoring_interval: Duration::from_secs(60)}
    }
}

// Additional implementation stubs
impl<T: Float + Debug + Send + Sync + 'static> FidelityResourceTracker<T> {
    fn new() -> Self {
        Self {
            usage_per_fidelity: HashMap::new(),
            total_budget: ResourceUsage {
                memory_gb: T::zero(),
                cpu_time_seconds: T::zero(),
                gpu_time_seconds: T::zero(),
                energy_kwh: T::zero(),
                cost_usd: T::zero(),
                network_gb: T::zero()},
            used_resources: ResourceUsage {
                memory_gb: T::zero(),
                cpu_time_seconds: T::zero(),
                gpu_time_seconds: T::zero(),
                energy_kwh: T::zero(),
                cost_usd: T::zero(),
                network_gb: T::zero()}}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> StrategyPerformanceTracker<T> {
    fn new() -> Self {
        Self {
            performance_history: HashMap::new(),
            success_rates: HashMap::new(),
            average_performance: HashMap::new(),
            performance_trends: HashMap::new()}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> WeightAdaptationController<T> {
    fn new() -> Self {
        Self {
            current_weights: Vec::new(),
            adaptation_strategy: EnsembleAdaptationStrategy::PerformanceBased,
            adaptation_rate: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
            weight_constraints: WeightConstraints::default()}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> CombinationEngine<T> {
    fn new() -> Self {
        Self {
            method: EnsembleCombinationMethod::WeightedAverage,
            combination_history: VecDeque::new(),
            meta_learner: None}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for DatabaseStatistics<T> {
    fn default() -> Self {
        Self {
            total_evaluations: 0,
            successful_evaluations: 0,
            best_objectives: HashMap::new(),
            average_objectives: HashMap::new(),
            objective_distributions: HashMap::new()}
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for WeightConstraints<T> {
    fn default() -> Self {
        Self {
            min_weight: scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()),
            max_weight: T::one(),
            sum_constraint: Some(T::one()),
            smoothness_constraint: None}
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_hpo_config_creation() {
        let config = HPOConfig::<f64>::default();
        assert!(!config.strategies.is_empty());
        assert!(config.early_stopping.enabled);
    }

    #[test]
    fn test_parameter_space_creation() {
        let space = ParameterSearchSpace::default();
        assert!(space.continuous_params.is_empty());
        assert!(space.discrete_params.is_empty());
    }

    #[test]
    fn test_hpo_pipeline_creation() {
        let config = HPOConfig::<f64>::default();
        let pipeline = HyperparameterOptimizationPipeline::new(config);
        assert!(pipeline.is_ok());
    }
}
