optirs-nas 0.2.0

// Automated Hyperparameter Optimization Pipeline
//
// This module implements advanced hyperparameter optimization techniques specifically
// for neural architecture search and optimizer design, including Bayesian optimization,
// population-based training, and multi-fidelity optimization.

#[allow(dead_code)]

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;
use std::fmt::Debug;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

use super::EvaluationResults;
#[allow(unused_imports)]
use crate::error::Result;

/// Main hyperparameter optimization coordinator
pub struct HyperparameterOptimizer<T: Float + Debug + Send + Sync + 'static> {
    /// Optimization strategy
    strategy: Box<dyn HyperOptStrategy<T>>,

    /// Search space definition
    search_space: HyperparameterSearchSpace<T>,

    /// Optimization history
    optimization_history: VecDeque<HyperOptResult<T>>,

    /// Current best configurations
    best_configurations: Vec<HyperparameterConfig<T>>,

    /// Multi-fidelity manager
    fidelity_manager: Option<MultiFidelityManager<T>>,

    /// Population-based training manager
    pbt_manager: Option<PopulationBasedTrainer<T>>,

    /// Early stopping manager
    early_stopping: EarlyStoppingManager<T>,

    /// Resource budget
    resource_budget: ResourceBudget<T>,

    /// Optimization statistics
    statistics: HyperOptStatistics<T>,

    /// Configuration cache
    config_cache: ConfigurationCache<T>,
}

/// Hyperparameter optimization strategies
pub trait HyperOptStrategy<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
    /// Initialize the strategy
    fn initialize(&mut self, searchspace: &HyperparameterSearchSpace<T>) -> Result<()>;

    /// Suggest next configuration to evaluate
    fn suggest_next(&mut self, history: &[HyperOptResult<T>]) -> Result<HyperparameterConfig<T>>;

    /// Update strategy with new results
    fn update(&mut self, result: &HyperOptResult<T>) -> Result<()>;

    /// Get strategy name
    fn name(&self) -> &str;

    /// Get strategy statistics
    fn statistics(&self) -> StrategyStatistics<T>;

    /// Check if optimization should continue
    fn should_continue(&self, history: &[HyperOptResult<T>]) -> bool;
}

/// Hyperparameter search space definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HyperparameterSearchSpace<T: Float + Debug + Send + Sync + 'static> {
    /// Continuous hyperparameters
    pub continuous_params: HashMap<String, ContinuousParameter<T>>,

    /// Integer hyperparameters
    pub integer_params: HashMap<String, IntegerParameter>,

    /// Categorical hyperparameters
    pub categorical_params: HashMap<String, CategoricalParameter>,

    /// Boolean hyperparameters
    pub boolean_params: Vec<String>,

    /// Conditional dependencies
    pub dependencies: Vec<ParameterDependency>,

    /// Constraints
    pub constraints: Vec<HyperparameterConstraint<T>>,

    /// Search _space metadata
    pub metadata: SearchSpaceMetadata,
}

/// Continuous parameter definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ContinuousParameter<T: Float + Debug + Send + Sync + 'static> {
    /// Minimum value
    pub min_value: T,

    /// Maximum value
    pub max_value: T,

    /// Distribution type
    pub distribution: ParameterDistribution,

    /// Default value
    pub default_value: Option<T>,

    /// Transformation (log, sqrt, etc.)
    pub transformation: Option<ParameterTransformation>,

    /// Prior distribution parameters
    pub prior_params: Option<PriorParameters<T>>,
}

/// Integer parameter definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IntegerParameter {
    /// Minimum value
    pub min_value: i32,

    /// Maximum value
    pub max_value: i32,

    /// Step size
    pub step: Option<i32>,

    /// Default value
    pub default_value: Option<i32>,

    /// Distribution type
    pub distribution: ParameterDistribution,
}

/// Categorical parameter definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CategoricalParameter {
    /// Possible values
    pub choices: Vec<String>,

    /// Default value
    pub default_value: Option<String>,

    /// Prior probabilities
    pub prior_probabilities: Option<Vec<f64>>,

    /// Ordinal ordering (if applicable)
    pub ordinal: bool,
}

/// Parameter distributions
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ParameterDistribution {
    Uniform,
    LogUniform,
    Normal,
    LogNormal,
    Beta,
    Gamma,
    Categorical,
}

/// Parameter transformations
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ParameterTransformation {
    Log,
    Sqrt,
    Square,
    Logit,
    Exp,
    Identity,
}

/// Prior distribution parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PriorParameters<T: Float + Debug + Send + Sync + 'static> {
    /// Distribution type
    pub distribution_type: PriorDistributionType,

    /// Distribution parameters
    pub parameters: Vec<T>,

    /// Confidence/strength
    pub confidence: T,
}

/// Prior distribution types
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum PriorDistributionType {
    Normal,
    Beta,
    Gamma,
    Uniform,
    Exponential,
}

/// Parameter dependencies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParameterDependency {
    /// Child parameter name
    pub child_param: String,

    /// Parent parameter name
    pub parent_param: String,

    /// Dependency condition
    pub condition: DependencyCondition,

    /// Child parameter constraints when condition is met
    pub child_constraints: DependencyConstraints,
}

/// Dependency conditions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DependencyCondition {
    /// Parent equals specific value
    Equals(String),

    /// Parent in set of values
    In(Vec<String>),

    /// Parent greater than threshold
    GreaterThan(f64),

    /// Parent less than threshold
    LessThan(f64),

    /// Parent in range
    InRange(f64, f64),

    /// Custom condition
    Custom(String),
}

/// Dependency constraints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DependencyConstraints {
    /// Enable/disable parameter
    Active(bool),

    /// Modify continuous parameter range
    ContinuousRange(f64, f64),

    /// Modify integer parameter range
    IntegerRange(i32, i32),

    /// Modify categorical choices
    CategoricalChoices(Vec<String>),

    /// Custom constraints
    Custom(HashMap<String, String>),
}

/// Hyperparameter constraints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HyperparameterConstraint<T: Float + Debug + Send + Sync + 'static> {
    /// Constraint type
    pub constraint_type: ConstraintType,

    /// Involved parameters
    pub parameters: Vec<String>,

    /// Constraint expression
    pub expression: ConstraintExpression<T>,

    /// Penalty for violation
    pub violation_penalty: T,

    /// Hard or soft constraint
    pub constraint_level: ConstraintLevel,
}

/// Constraint types
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ConstraintType {
    Linear,
    Nonlinear,
    Logical,
    Resource,
    Performance,
}

/// Constraint expressions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConstraintExpression<T: Float + Debug + Send + Sync + 'static> {
    /// Linear: sum(a_i * x_i) <= b
    Linear {
        coefficients: Vec<T>,
        bound: T,
        inequality: InequalityType,
    },

    /// Nonlinear constraint
    Nonlinear {
        expression: String,
        bound: T,
        inequality: InequalityType,
    },

    /// Logical constraint
    Logical { expression: LogicalExpression },

    /// Custom constraint
    Custom {
        evaluator: String,
        parameters: HashMap<String, T>,
    },
}

/// Inequality types
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum InequalityType {
    LessEqual,
    GreaterEqual,
    Equal,
    NotEqual,
}

/// Logical expressions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LogicalExpression {
    And(Vec<String>),
    Or(Vec<String>),
    Not(String),
    Implies(String, String),
    IfThenElse(String, String, String),
}

/// Constraint levels
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ConstraintLevel {
    Hard,    // Must be satisfied
    Soft,    // Preferred but not required
    Penalty, // Penalize violations
}

/// Search space metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SearchSpaceMetadata {
    /// Total number of parameters
    pub total_parameters: usize,

    /// Estimated search space size
    pub estimated_space_size: f64,

    /// Complexity score
    pub complexity_score: f64,

    /// Creation timestamp
    pub created_at: String,

    /// Version
    pub version: String,
}

/// Hyperparameter configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HyperparameterConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Configuration ID
    pub id: String,

    /// Parameter values
    pub parameters: HashMap<String, ParameterValue<T>>,

    /// Configuration metadata
    pub metadata: ConfigMetadata,

    /// Generation information
    pub generation_info: GenerationInfo,

    /// Validation status
    pub validation_status: ValidationStatus,
}

/// Parameter values
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ParameterValue<T: Float + Debug + Send + Sync + 'static> {
    Continuous(T),
    Integer(i32),
    Categorical(String),
    Boolean(bool),
}

/// Configuration metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConfigMetadata {
    /// Creation timestamp
    pub created_at: std::time::SystemTime,

    /// Source strategy
    pub source_strategy: String,

    /// Parent configurations (if derived)
    pub parent_configs: Vec<String>,

    /// Hash for deduplication
    pub config_hash: u64,

    /// Priority score
    pub priority_score: f64,
}

/// Generation information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GenerationInfo {
    /// Strategy used to generate this config
    pub strategy: String,

    /// Generation method
    pub method: GenerationMethod,

    /// Exploitation vs exploration score
    pub exploration_score: f64,

    /// Confidence in this configuration
    pub confidence: f64,
}

/// Generation methods
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum GenerationMethod {
    Random,
    Bayesian,
    Evolutionary,
    GradientBased,
    PopulationBased,
    MultiFidelity,
    Bandit,
    HyperBand,
}

/// Validation status
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ValidationStatus {
    Valid,
    Invalid,
    Pending,
    ConstraintViolation,
    ResourceExceeded,
}

/// Hyperparameter optimization result
#[derive(Debug, Clone)]
pub struct HyperOptResult<T: Float + Debug + Send + Sync + 'static> {
    /// Configuration that was evaluated
    pub config: HyperparameterConfig<T>,

    /// Evaluation results
    pub evaluation: EvaluationResults<T>,

    /// Objective values (for multi-objective)
    pub objectives: HashMap<String, T>,

    /// Constraint violations
    pub constraint_violations: Vec<ConstraintViolation<T>>,

    /// Resource usage
    pub resource_usage: ResourceUsage<T>,

    /// Evaluation metadata
    pub eval_metadata: EvaluationMetadata,

    /// Fidelity level (for multi-fidelity optimization)
    pub fidelity_level: Option<FidelityLevel>,
}

/// Constraint violations
#[derive(Debug, Clone)]
pub struct ConstraintViolation<T: Float + Debug + Send + Sync + 'static> {
    /// Constraint that was violated
    pub constraint_id: String,

    /// Violation amount
    pub violation_amount: T,

    /// Constraint type
    pub constraint_type: ConstraintType,

    /// Severity
    pub severity: ViolationSeverity,
}

/// Violation severity
#[derive(Debug, Clone, Copy)]
pub enum ViolationSeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Resource usage tracking
#[derive(Debug, Clone)]
pub struct ResourceUsage<T: Float + Debug + Send + Sync + 'static> {
    /// Evaluation time
    pub evaluation_time: Duration,

    /// Memory usage (MB)
    pub memory_mb: T,

    /// CPU hours
    pub cpu_hours: T,

    /// GPU hours
    pub gpu_hours: T,

    /// Energy consumption (kWh)
    pub energy_kwh: T,

    /// Financial cost
    pub cost: T,
}

/// Evaluation metadata
#[derive(Debug, Clone)]
pub struct EvaluationMetadata {
    /// Evaluation timestamp
    pub timestamp: std::time::SystemTime,

    /// Evaluator version
    pub evaluator_version: String,

    /// Evaluation environment
    pub environment: EvaluationEnvironment,

    /// Random seed used
    pub random_seed: Option<u64>,

    /// Cross-validation fold (if applicable)
    pub cv_fold: Option<usize>,
}

/// Evaluation environment
#[derive(Debug, Clone)]
pub struct EvaluationEnvironment {
    /// Hardware specification
    pub hardware: HardwareSpec,

    /// Software environment
    pub software: SoftwareSpec,

    /// Dataset information
    pub dataset: DatasetSpec,
}

/// Hardware specification
#[derive(Debug, Clone)]
pub struct HardwareSpec {
    pub cpu_model: String,
    pub cpu_cores: usize,
    pub memory_gb: usize,
    pub gpu_model: Option<String>,
    pub gpu_memory_gb: Option<usize>,
}

/// Software specification
#[derive(Debug, Clone)]
pub struct SoftwareSpec {
    pub os: String,
    pub rust_version: String,
    pub dependencies: HashMap<String, String>,
}

/// Dataset specification
#[derive(Debug, Clone)]
pub struct DatasetSpec {
    pub name: String,
    pub size: usize,
    pub features: usize,
    pub task_type: String,
}

/// Multi-fidelity optimization manager
pub struct MultiFidelityManager<T: Float + Debug + Send + Sync + 'static> {
    /// Available fidelity levels
    fidelity_levels: Vec<FidelityLevel>,

    /// Fidelity allocation strategy
    allocation_strategy: FidelityAllocationStrategy,

    /// Performance models for each fidelity
    fidelity_models: HashMap<String, Box<dyn PerformanceModel<T>>>,

    /// Budget allocation
    budget_allocation: BudgetAllocation<T>,

    /// Promotion criteria
    promotion_criteria: PromotionCriteria<T>,
}

/// Fidelity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FidelityLevel {
    /// Fidelity name/identifier
    pub name: String,

    /// Fidelity value (0.0 to 1.0)
    pub level: f64,

    /// Resource multiplier
    pub resource_multiplier: f64,

    /// Correlation with full fidelity
    pub correlation: Option<f64>,

    /// Fidelity parameters
    pub parameters: FidelityParameters,
}

/// Fidelity parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FidelityParameters {
    /// Training epochs/iterations
    pub epochs: Option<usize>,

    /// Dataset fraction
    pub dataset_fraction: Option<f64>,

    /// Model size reduction
    pub model_size_fraction: Option<f64>,

    /// Evaluation budget
    pub evaluation_budget: Option<usize>,

    /// Custom parameters
    pub custom: HashMap<String, f64>,
}

/// Fidelity allocation strategies
#[derive(Debug, Clone, Copy)]
pub enum FidelityAllocationStrategy {
    /// Successive halving
    SuccessiveHalving,

    /// HyperBand
    HyperBand,

    /// Adaptive allocation
    Adaptive,

    /// Multi-armed bandit
    Bandit,

    /// Bayesian optimization
    Bayesian,
}

/// Performance models for fidelity estimation
pub trait PerformanceModel<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
    /// Predict performance at higher fidelity
    fn predict(
        &self,
        config: &HyperparameterConfig<T>,
        target_fidelity: &FidelityLevel,
    ) -> Result<T>;

    /// Update model with new observations
    fn update(
        &mut self,
        observations: &[(HyperparameterConfig<T>, FidelityLevel, T)],
    ) -> Result<()>;

    /// Get prediction uncertainty
    fn uncertainty(&self, config: &HyperparameterConfig<T>, fidelity: &FidelityLevel) -> Result<T>;
}

/// Budget allocation for multi-fidelity
#[derive(Debug, Clone)]
pub struct BudgetAllocation<T: Float + Debug + Send + Sync + 'static> {
    /// Total budget
    pub total_budget: T,

    /// Budget per _fidelity level
    pub _fidelity_budgets: HashMap<String, T>,

    /// Used budget tracking
    pub used_budget: HashMap<String, T>,

    /// Budget allocation strategy
    pub strategy: BudgetStrategy,
}

/// Budget allocation strategies
#[derive(Debug, Clone, Copy)]
pub enum BudgetStrategy {
    Uniform,
    Geometric,
    Adaptive,
    PerformanceBased,
}

/// Promotion criteria for multi-fidelity
#[derive(Debug, Clone)]
pub struct PromotionCriteria<T: Float + Debug + Send + Sync + 'static> {
    /// Performance threshold for promotion
    pub performance_threshold: T,

    /// Top-k promotion
    pub top_k: Option<usize>,

    /// Percentile-based promotion
    pub percentile: Option<f64>,

    /// Minimum evaluations before promotion
    pub min_evaluations: usize,

    /// Uncertainty threshold
    pub uncertainty_threshold: Option<T>,
}

/// Population-based training manager
pub struct PopulationBasedTrainer<T: Float + Debug + Send + Sync + 'static> {
    /// Current population
    population: Vec<PBTIndividual<T>>,

    /// Population size
    population_size: usize,

    /// Exploration/exploitation parameters
    exploration_params: ExplorationParameters<T>,

    /// Selection strategy
    selection_strategy: SelectionStrategy,

    /// Mutation strategy
    mutation_strategy: MutationStrategy<T>,

    /// Population statistics
    population_stats: PopulationStatistics<T>,
}

/// PBT individual
#[derive(Debug, Clone)]
pub struct PBTIndividual<T: Float + Debug + Send + Sync + 'static> {
    /// Individual ID
    pub id: String,

    /// Current configuration
    pub config: HyperparameterConfig<T>,

    /// Performance history
    pub performance_history: Vec<T>,

    /// Age (number of training steps)
    pub age: usize,

    /// Resource allocation
    pub resource_allocation: T,

    /// Ancestry information
    pub ancestry: Vec<String>,
}

/// Exploration parameters for PBT
#[derive(Debug, Clone)]
pub struct ExplorationParameters<T: Float + Debug + Send + Sync + 'static> {
    /// Exploitation probability
    pub exploit_prob: T,

    /// Exploration probability
    pub explore_prob: T,

    /// Perturbation strength
    pub perturbation_strength: T,

    /// Resample probability
    pub resample_prob: T,
}

/// Selection strategies for PBT
#[derive(Debug, Clone, Copy)]
pub enum SelectionStrategy {
    Tournament,
    Rank,
    Roulette,
    Truncation,
    Elite,
}

/// Mutation strategies for PBT
#[derive(Debug, Clone)]
pub struct MutationStrategy<T: Float + Debug + Send + Sync + 'static> {
    /// Mutation type
    pub mutation_type: MutationType,

    /// Mutation rate
    pub mutation_rate: T,

    /// Mutation strength
    pub mutation_strength: T,

    /// Adaptive mutation
    pub adaptive: bool,
}

/// Mutation types
#[derive(Debug, Clone, Copy)]
pub enum MutationType {
    Gaussian,
    Uniform,
    Cauchy,
    Adaptive,
    Crossover,
}

/// Population statistics
#[derive(Debug, Clone)]
pub struct PopulationStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Best performance
    pub best_performance: T,

    /// Average performance
    pub average_performance: T,

    /// Performance diversity
    pub performance_diversity: T,

    /// Configuration diversity
    pub config_diversity: T,

    /// Convergence measure
    pub convergence: T,
}

/// Early stopping manager
pub struct EarlyStoppingManager<T: Float + Debug + Send + Sync + 'static> {
    /// Stopping criteria
    criteria: Vec<EarlyStoppingCriterion<T>>,

    /// Grace period
    grace_period: usize,

    /// Performance history
    performance_history: VecDeque<T>,

    /// Patience counter
    patience_counter: usize,

    /// Best performance seen
    best_performance: Option<T>,
}

/// Early stopping criteria
#[derive(Debug, Clone)]
pub struct EarlyStoppingCriterion<T: Float + Debug + Send + Sync + 'static> {
    /// Criterion type
    pub criterion_type: StoppingCriterionType,

    /// Threshold value
    pub threshold: T,

    /// Patience (evaluations to wait)
    pub patience: usize,

    /// Minimum improvement
    pub min_improvement: T,

    /// Relative improvement
    pub relative_improvement: bool,
}

/// Stopping criterion types
#[derive(Debug, Clone, Copy)]
pub enum StoppingCriterionType {
    NoImprovement,
    PerformanceThreshold,
    ResourceBudget,
    TimeLimit,
    ConvergenceDetection,
    UserDefined,
}

/// Resource budget management
#[derive(Debug, Clone)]
pub struct ResourceBudget<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum evaluations
    pub max_evaluations: usize,

    /// Maximum time budget
    pub max_time: Duration,

    /// Maximum financial cost
    pub max_cost: T,

    /// Maximum memory usage
    pub max_memory_gb: T,

    /// Maximum energy consumption
    pub max_energy_kwh: T,

    /// Current usage tracking
    pub current_usage: ResourceUsage<T>,
}

/// Optimization statistics
#[derive(Debug, Clone)]
pub struct HyperOptStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Total evaluations performed
    pub total_evaluations: usize,

    /// Total time spent
    pub total_time: Duration,

    /// Best performance found
    pub best_performance: T,

    /// Average performance
    pub average_performance: T,

    /// Performance improvement rate
    pub improvement_rate: T,

    /// Strategy effectiveness
    pub strategy_effectiveness: HashMap<String, T>,

    /// Convergence metrics
    pub convergence_metrics: ConvergenceMetrics<T>,
}

/// Convergence metrics
#[derive(Debug, Clone)]
pub struct ConvergenceMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Convergence rate
    pub convergence_rate: T,

    /// Plateau detection
    pub plateau_length: usize,

    /// Diversity measure
    pub diversity: T,

    /// Exploration efficiency
    pub exploration_efficiency: T,
}

/// Configuration cache for avoiding re-evaluation
pub struct ConfigurationCache<T: Float + Debug + Send + Sync + 'static> {
    /// Cache storage
    cache: HashMap<u64, HyperOptResult<T>>,

    /// Cache statistics
    cache_stats: CacheStatistics,

    /// Cache policy
    cache_policy: CachePolicy,

    /// Maximum cache size
    max_size: usize,
}

/// Cache statistics
#[derive(Debug, Clone)]
pub struct CacheStatistics {
    /// Cache hits
    pub hits: usize,

    /// Cache misses
    pub misses: usize,

    /// Cache size
    pub current_size: usize,

    /// Evictions
    pub evictions: usize,
}

/// Cache policies
#[derive(Debug, Clone, Copy)]
pub enum CachePolicy {
    LRU,  // Least Recently Used
    LFU,  // Least Frequently Used
    TTL,  // Time To Live
    Size, // Size-based eviction
}

/// Strategy statistics
#[derive(Debug, Clone)]
pub struct StrategyStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Number of suggestions made
    pub suggestions_made: usize,

    /// Average performance of suggestions
    pub avg_performance: T,

    /// Best performance found
    pub best_performance: T,

    /// Exploration vs exploitation ratio
    pub exploration_ratio: T,

    /// Strategy-specific metrics
    pub custom_metrics: HashMap<String, T>,
}

/// Bayesian Optimization Strategy
pub struct BayesianOptimizationStrategy<T: Float + Debug + Send + Sync + 'static> {
    /// Gaussian Process model
    gp_model: GaussianProcess<T>,

    /// Acquisition function
    acquisition_fn: AcquisitionFunction<T>,

    /// Observed configurations and performances
    observations: Vec<(HyperparameterConfig<T>, T)>,

    /// Strategy statistics
    stats: StrategyStatistics<T>,

    /// Hyperparameters
    hyperparams: BayesianOptParams<T>,
}

/// Gaussian Process model
#[derive(Debug)]
pub struct GaussianProcess<T: Float + Debug + Send + Sync + 'static> {
    /// Kernel function
    kernel: KernelFunction<T>,

    /// Training data
    training_data: Vec<(Array1<T>, T)>,

    /// Kernel hyperparameters
    kernel_params: KernelParameters<T>,

    /// Noise parameter
    noise_variance: T,

    /// Covariance matrix inverse
    k_inv: Option<Array2<T>>,
}

/// Kernel functions
#[derive(Debug, Clone)]
pub enum KernelFunction<T: Float + Debug + Send + Sync + 'static> {
    RBF { lengthscale: T, variance: T },
    Matern32 { lengthscale: T, variance: T },
    Matern52 { lengthscale: T, variance: T },
    Linear { variance: T },
    Polynomial { degree: i32, variance: T },
    Composite(Box<KernelFunction<T>>, Box<KernelFunction<T>>),
}

/// Kernel parameters
#[derive(Debug, Clone)]
pub struct KernelParameters<T: Float + Debug + Send + Sync + 'static> {
    /// Lengthscale parameters
    pub lengthscales: Array1<T>,

    /// Signal variance
    pub signal_variance: T,

    /// Noise variance
    pub noise_variance: T,

    /// Additional parameters
    pub additional_params: HashMap<String, T>,
}

/// Acquisition functions
#[derive(Debug)]
pub enum AcquisitionFunction<T: Float + Debug + Send + Sync + 'static> {
    ExpectedImprovement { xi: T },
    ProbabilityOfImprovement { xi: T },
    UpperConfidenceBound { kappa: T },
    EntropySearch,
    KnowledgeGradient,
    ThompsonSampling,
}

/// Bayesian optimization parameters
#[derive(Debug, Clone)]
pub struct BayesianOptParams<T: Float + Debug + Send + Sync + 'static> {
    /// Number of initial random samples
    pub n_initial_samples: usize,

    /// Acquisition optimization settings
    pub acq_opt_config: AcquisitionOptConfig<T>,

    /// GP hyperparameter optimization
    pub gp_opt_config: GPOptConfig<T>,

    /// Convergence criteria
    pub convergence_config: ConvergenceConfig<T>,
}

/// Acquisition optimization configuration
#[derive(Debug, Clone)]
pub struct AcquisitionOptConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Number of restarts for optimization
    pub n_restarts: usize,

    /// Maximum iterations
    pub max_iterations: usize,

    /// Convergence tolerance
    pub tolerance: T,

    /// Optimization method
    pub method: AcquisitionOptMethod,
}

/// Acquisition optimization methods
#[derive(Debug, Clone, Copy)]
pub enum AcquisitionOptMethod {
    LBFGS,
    Adam,
    CmaEs,
    DifferentialEvolution,
    RandomSearch,
}

/// GP optimization configuration
#[derive(Debug, Clone)]
pub struct GPOptConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Whether to optimize kernel hyperparameters
    pub optimize_hyperparams: bool,

    /// Optimization frequency
    pub opt_frequency: usize,

    /// Maximum likelihood optimization method
    pub ml_method: MLOptMethod,

    /// Prior distributions
    pub priors: HashMap<String, PriorParameters<T>>,
}

/// Maximum likelihood optimization methods
#[derive(Debug, Clone, Copy)]
pub enum MLOptMethod {
    LBFGS,
    Adam,
    MCMC,
    VariationalInference,
}

/// Convergence configuration
#[derive(Debug, Clone)]
pub struct ConvergenceConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Minimum number of iterations
    pub min_iterations: usize,

    /// Maximum number of iterations
    pub max_iterations: usize,

    /// Improvement threshold
    pub improvement_threshold: T,

    /// Patience for early stopping
    pub patience: usize,
}

impl<T: Float + Debug + Send + Sync + 'static> HyperparameterOptimizer<T> {
    /// Create new hyperparameter optimizer
    pub fn new(
        strategy: Box<dyn HyperOptStrategy<T>>,
        search_space: HyperparameterSearchSpace<T>,
        resource_budget: ResourceBudget<T>,
    ) -> Self {
        Self {
            strategy,
            search_space,
            optimization_history: VecDeque::new(),
            best_configurations: Vec::new(),
            fidelity_manager: None,
            pbt_manager: None,
            early_stopping: EarlyStoppingManager::new(),
            resource_budget,
            statistics: HyperOptStatistics::default(),
            config_cache: ConfigurationCache::new(1000),
        }
    }

    /// Run hyperparameter optimization
    pub fn optimize(&mut self) -> Result<OptimizationResults<T>> {
        let start_time = Instant::now();

        // Initialize strategy
        self.strategy.initialize(&self.search_space)?;

        // Main optimization loop
        while self.should_continue_optimization() {
            // Suggest next configuration
            let config = self.strategy.suggest_next(
                &self
                    .optimization_history
                    .iter()
                    .cloned()
                    .collect::<Vec<_>>(),
            )?;

            // Check cache first
            if let Some(cached_result) = self.config_cache.get(&config) {
                self.optimization_history.push_back(cached_result.clone());
                continue;
            }

            // Evaluate configuration
            let result = self.evaluate_configuration(config)?;

            // Update strategy
            self.strategy.update(&result)?;

            // Store result
            self.optimization_history.push_back(result.clone());
            self.config_cache.insert(result.clone());

            // Update best configurations
            self.update_best_configurations(&result);

            // Update statistics
            self.update_statistics();
        }

        // Finalize results
        let total_time = start_time.elapsed();
        self.statistics.total_time = total_time;

        Ok(OptimizationResults {
            best_configs: self.best_configurations.clone(),
            history: self.optimization_history.iter().cloned().collect(),
            statistics: self.statistics.clone(),
            resource_usage: self.resource_budget.current_usage.clone(),
        })
    }

    fn should_continue_optimization(&self) -> bool {
        // Check resource budget
        if self.optimization_history.len() >= self.resource_budget.max_evaluations {
            return false;
        }

        if self.statistics.total_time >= self.resource_budget.max_time {
            return false;
        }

        // Check early stopping
        self.early_stopping.should_continue(
            &self
                .optimization_history
                .iter()
                .cloned()
                .collect::<Vec<_>>(),
        )
    }

    fn evaluate_configuration(&self, config: HyperparameterConfig<T>) -> Result<HyperOptResult<T>> {
        // Simplified evaluation - in practice would run actual optimization
        let performance = scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()); // Placeholder

        let evaluation = EvaluationResults {
            metric_scores: HashMap::new(),
            overall_score: performance,
            confidence_intervals: HashMap::new(),
            evaluation_time: Duration::from_secs(60),
            success: true,
            error_message: None,
        };

        Ok(HyperOptResult {
            config,
            evaluation,
            objectives: HashMap::new(),
            constraint_violations: Vec::new(),
            resource_usage: ResourceUsage {
                evaluation_time: Duration::from_secs(60),
                memory_mb: scirs2_core::numeric::NumCast::from(1024.0).unwrap_or_else(|| T::zero()),
                cpu_hours: scirs2_core::numeric::NumCast::from(1.0).unwrap_or_else(|| T::zero()),
                gpu_hours: T::zero(),
                energy_kwh: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
                cost: scirs2_core::numeric::NumCast::from(1.0).unwrap_or_else(|| T::zero()),
            },
            eval_metadata: EvaluationMetadata {
                timestamp: std::time::SystemTime::now(),
                evaluator_version: "1.0".to_string(),
                environment: EvaluationEnvironment {
                    hardware: HardwareSpec {
                        cpu_model: "Unknown".to_string(),
                        cpu_cores: 8,
                        memory_gb: 16,
                        gpu_model: None,
                        gpu_memory_gb: None,
                    },
                    software: SoftwareSpec {
                        os: "Linux".to_string(),
                        rust_version: "1.70".to_string(),
                        dependencies: HashMap::new(),
                    },
                    dataset: DatasetSpec {
                        name: "benchmark".to_string(),
                        size: 10000,
                        features: 784,
                        task_type: "classification".to_string(),
                    },
                },
                random_seed: Some(42),
                cv_fold: None,
            },
            fidelity_level: None,
        })
    }

    fn update_best_configurations(&mut self, result: &HyperOptResult<T>) {
        self.best_configurations.push(result.config.clone());

        // Keep only top 10
        self.best_configurations.sort_by(|_a, _b| {
            // Sort by performance (would need to look up performance)
            std::cmp::Ordering::Equal
        });

        if self.best_configurations.len() > 10 {
            self.best_configurations.truncate(10);
        }
    }

    fn update_statistics(&mut self) {
        self.statistics.total_evaluations = self.optimization_history.len();
        // Update other statistics...
    }
}

impl<T: Float + Debug + Send + Sync + 'static> EarlyStoppingManager<T> {
    fn new() -> Self {
        Self {
            criteria: Vec::new(),
            grace_period: 10,
            performance_history: VecDeque::with_capacity(1000),
            patience_counter: 0,
            best_performance: None,
        }
    }

    fn should_continue(&self, history: &[HyperOptResult<T>]) -> bool {
        // Simplified - would implement actual early stopping logic
        true
    }
}

impl<T: Float + Debug + Send + Sync + 'static> ConfigurationCache<T> {
    fn new(maxsize: usize) -> Self {
        Self {
            cache: HashMap::new(),
            cache_stats: CacheStatistics {
                hits: 0,
                misses: 0,
                current_size: 0,
                evictions: 0,
            },
            cache_policy: CachePolicy::LRU,
            max_size: maxsize,
        }
    }

    fn get(&mut self, config: &HyperparameterConfig<T>) -> Option<&HyperOptResult<T>> {
        let hash = config.metadata.config_hash;
        if let Some(result) = self.cache.get(&hash) {
            self.cache_stats.hits += 1;
            Some(result)
        } else {
            self.cache_stats.misses += 1;
            None
        }
    }

    fn insert(&mut self, result: HyperOptResult<T>) {
        let hash = result.config.metadata.config_hash;

        if self.cache.len() >= self.max_size {
            // Simple eviction - remove first entry
            if let Some((key, _)) = self.cache.iter().next() {
                let key = *key;
                self.cache.remove(&key);
                self.cache_stats.evictions += 1;
            }
        }

        self.cache.insert(hash, result);
        self.cache_stats.current_size = self.cache.len();
    }
}

/// Final optimization results
#[derive(Debug, Clone)]
pub struct OptimizationResults<T: Float + Debug + Send + Sync + 'static> {
    /// Best configurations found
    pub best_configs: Vec<HyperparameterConfig<T>>,

    /// Optimization history
    pub history: Vec<HyperOptResult<T>>,

    /// Final statistics
    pub statistics: HyperOptStatistics<T>,

    /// Resource usage summary
    pub resource_usage: ResourceUsage<T>,
}

impl<T: Float + Debug + Send + Sync + 'static> Default for OptimizationResults<T> {
    fn default() -> Self {
        Self {
            best_configs: Vec::new(),
            history: Vec::new(),
            statistics: HyperOptStatistics::default(),
            resource_usage: ResourceUsage {
                evaluation_time: Duration::new(0, 0),
                memory_mb: T::zero(),
                cpu_hours: T::zero(),
                gpu_hours: T::zero(),
                energy_kwh: T::zero(),
                cost: T::zero(),
            },
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for HyperOptStatistics<T> {
    fn default() -> Self {
        Self {
            total_evaluations: 0,
            total_time: Duration::new(0, 0),
            best_performance: T::zero(),
            average_performance: T::zero(),
            improvement_rate: T::zero(),
            strategy_effectiveness: HashMap::new(),
            convergence_metrics: ConvergenceMetrics {
                convergence_rate: T::zero(),
                plateau_length: 0,
                diversity: T::one(),
                exploration_efficiency: T::zero(),
            },
        }
    }
}

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

    #[test]
    fn test_hyperparameter_search_space_creation() {
        let mut search_space = HyperparameterSearchSpace::<f64> {
            continuous_params: HashMap::new(),
            integer_params: HashMap::new(),
            categorical_params: HashMap::new(),
            boolean_params: Vec::new(),
            dependencies: Vec::new(),
            constraints: Vec::new(),
            metadata: SearchSpaceMetadata {
                total_parameters: 0,
                estimated_space_size: 1e6,
                complexity_score: 5.0,
                created_at: "2024-01-01T00:00:00Z".to_string(),
                version: "1.0".to_string(),
            },
        };

        // Add learning rate parameter
        search_space.continuous_params.insert(
            "learning_rate".to_string(),
            ContinuousParameter {
                min_value: 1e-6,
                max_value: 1e-1,
                distribution: ParameterDistribution::LogUniform,
                default_value: Some(1e-3),
                transformation: Some(ParameterTransformation::Log),
                prior_params: None,
            },
        );

        assert_eq!(search_space.continuous_params.len(), 1);
        assert!(search_space.continuous_params.contains_key("learning_rate"));
    }

    #[test]
    fn test_hyperparameter_config_creation() {
        let mut parameters = HashMap::new();
        parameters.insert(
            "learning_rate".to_string(),
            ParameterValue::Continuous(0.001),
        );
        parameters.insert("batch_size".to_string(), ParameterValue::Integer(32));
        parameters.insert(
            "optimizer".to_string(),
            ParameterValue::Categorical("Adam".to_string()),
        );
        parameters.insert("use_dropout".to_string(), ParameterValue::Boolean(true));

        let config = HyperparameterConfig {
            id: "config_001".to_string(),
            parameters,
            metadata: ConfigMetadata {
                created_at: std::time::SystemTime::now(),
                source_strategy: "random".to_string(),
                parent_configs: Vec::new(),
                config_hash: 12345,
                priority_score: 0.8,
            },
            generation_info: GenerationInfo {
                strategy: "random".to_string(),
                method: GenerationMethod::Random,
                exploration_score: 0.9,
                confidence: 0.5,
            },
            validation_status: ValidationStatus::Valid,
        };

        assert_eq!(config.parameters.len(), 4);
        assert!(matches!(config.validation_status, ValidationStatus::Valid));
    }

    #[test]
    fn test_resource_budget_tracking() {
        let budget = ResourceBudget {
            max_evaluations: 1000,
            max_time: Duration::from_secs(3600),
            max_cost: 100.0,
            max_memory_gb: 16.0,
            max_energy_kwh: 10.0,
            current_usage: ResourceUsage {
                evaluation_time: Duration::from_secs(0),
                memory_mb: 0.0,
                cpu_hours: 0.0,
                gpu_hours: 0.0,
                energy_kwh: 0.0,
                cost: 0.0,
            },
        };

        assert_eq!(budget.max_evaluations, 1000);
        assert_eq!(budget.max_time.as_secs(), 3600);
    }
}