optirs-nas 0.3.1

//! Performance predictor for NAS evaluation
//!
//! Provides models for predicting optimizer performance without full evaluation.

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;
use std::collections::HashMap;
use std::fmt::Debug;
use std::time::{Instant, SystemTime};

use super::types::*;
use crate::error::Result;
use crate::nas_engine::results::EvaluationResults;
use crate::{EvaluationConfig, OptimizerArchitecture};

/// Performance predictor
#[derive(Debug)]
pub struct PerformancePredictor<T: Float + Debug + Send + Sync + 'static> {
    /// Predictor model
    predictor_model: PredictorModel<T>,

    /// Feature extractor
    feature_extractor: FeatureExtractor<T>,

    /// Training data
    training_data: PredictorTrainingData<T>,

    /// Prediction cache
    prediction_cache: PredictionCache<T>,

    /// Uncertainty estimator
    uncertainty_estimator: UncertaintyEstimator<T>,
}

/// Predictor model
#[derive(Debug)]
pub struct PredictorModel<T: Float + Debug + Send + Sync + 'static> {
    /// Model type
    model_type: PredictorModelType,

    /// Model parameters
    parameters: ModelParameters<T>,

    /// Model architecture
    architecture: ModelArchitecture,

    /// Training state
    training_state: ModelTrainingState<T>,
}

/// Model parameters
#[derive(Debug)]
pub struct ModelParameters<T: Float + Debug + Send + Sync + 'static> {
    /// Weights
    weights: Vec<Array2<T>>,

    /// Biases
    biases: Vec<Array1<T>>,

    /// Hyperparameters
    hyperparameters: HashMap<String, T>,

    /// Regularization parameters
    regularization: RegularizationParameters<T>,
}

/// Model architecture specification
#[derive(Debug, Clone)]
pub struct ModelArchitecture {
    /// Layer sizes
    layer_sizes: Vec<usize>,

    /// Activation functions
    activations: Vec<ActivationFunction>,

    /// Dropout rates
    dropout_rates: Vec<f64>,

    /// Skip connections
    skip_connections: Vec<(usize, usize)>,
}

/// Regularization parameters
#[derive(Debug)]
pub struct RegularizationParameters<T: Float + Debug + Send + Sync + 'static> {
    /// L1 regularization strength
    l1_strength: T,

    /// L2 regularization strength
    l2_strength: T,

    /// Dropout probability
    dropout_prob: T,

    /// Batch normalization flag
    batch_norm: bool,
}

/// Model training state
#[derive(Debug)]
pub struct ModelTrainingState<T: Float + Debug + Send + Sync + 'static> {
    /// Current epoch
    current_epoch: usize,

    /// Training loss history
    loss_history: Vec<T>,

    /// Validation loss history
    validation_loss_history: Vec<T>,

    /// Learning rate schedule
    learning_rate_schedule: LearningRateSchedule<T>,

    /// Early stopping state
    early_stopping_state: EarlyStoppingState<T>,
}

/// Learning rate schedule
#[derive(Debug)]
pub struct LearningRateSchedule<T: Float + Debug + Send + Sync + 'static> {
    /// Schedule type
    schedule_type: ScheduleType,

    /// Initial learning rate
    initial_lr: T,

    /// Current learning rate
    current_lr: T,

    /// Schedule parameters
    parameters: HashMap<String, T>,
}

/// Early stopping state
#[derive(Debug)]
pub struct EarlyStoppingState<T: Float + Debug + Send + Sync + 'static> {
    /// Best validation loss
    best_val_loss: T,

    /// Patience counter
    patience_counter: usize,

    /// Maximum patience
    max_patience: usize,

    /// Should stop flag
    should_stop: bool,
}

/// Feature extractor for performance prediction
#[derive(Debug)]
pub struct FeatureExtractor<T: Float + Debug + Send + Sync + 'static> {
    /// Feature extraction methods
    extraction_methods: Vec<FeatureExtractionMethod>,

    /// Feature engineering pipeline
    engineering_pipeline: FeatureEngineeringPipeline<T>,

    /// Feature selection
    feature_selection: FeatureSelection<T>,

    /// Feature cache
    feature_cache: FeatureCache<T>,
}

/// Feature engineering pipeline
#[derive(Debug)]
pub struct FeatureEngineeringPipeline<T: Float + Debug + Send + Sync + 'static> {
    /// Normalization method
    normalization: NormalizationMethod,

    /// Feature scaling
    scaling: FeatureScaling<T>,

    /// Feature interactions
    interactions: FeatureInteractions,

    /// Polynomial features
    polynomial_features: PolynomialFeatures,
}

/// Feature scaling
#[derive(Debug)]
pub struct FeatureScaling<T: Float + Debug + Send + Sync + 'static> {
    /// Scaling method
    method: ScalingMethod,

    /// Scale parameters
    scale_params: HashMap<String, T>,

    /// Feature ranges
    feature_ranges: HashMap<String, (T, T)>,
}

/// Feature interactions
#[derive(Debug, Clone)]
pub struct FeatureInteractions {
    /// Interaction order
    interaction_order: usize,

    /// Include bias term
    include_bias: bool,

    /// Selected interactions
    selected_interactions: Vec<Vec<usize>>,
}

/// Polynomial features
#[derive(Debug, Clone)]
pub struct PolynomialFeatures {
    /// Polynomial degree
    degree: usize,

    /// Include bias term
    include_bias: bool,

    /// Interaction only flag
    interaction_only: bool,
}

/// Feature selection
#[derive(Debug)]
pub struct FeatureSelection<T: Float + Debug + Send + Sync + 'static> {
    /// Selection method
    selection_method: FeatureSelectionMethod,

    /// Selection parameters
    parameters: HashMap<String, T>,

    /// Selected features
    selected_features: Vec<usize>,

    /// Feature importance scores
    importance_scores: Vec<T>,
}

/// Feature cache
#[derive(Debug)]
pub struct FeatureCache<T: Float + Debug + Send + Sync + 'static> {
    /// Cached features
    cached_features: HashMap<String, Array1<T>>,

    /// Cache hit rate
    hit_rate: f64,

    /// Cache size limit
    size_limit: usize,

    /// Eviction policy
    eviction_policy: CacheEvictionPolicy,
}

/// Predictor training data
#[derive(Debug)]
pub struct PredictorTrainingData<T: Float + Debug + Send + Sync + 'static> {
    /// Architecture features
    architecture_features: Vec<Array1<T>>,

    /// Performance targets
    performance_targets: Vec<T>,

    /// Training metadata
    metadata: Vec<TrainingMetadata>,

    /// Data splits
    data_splits: DataSplits,
}

/// Training metadata
#[derive(Debug, Clone)]
pub struct TrainingMetadata {
    /// Architecture ID
    architecture_id: String,

    /// Benchmark name
    benchmark_name: String,

    /// Evaluation timestamp
    timestamp: SystemTime,

    /// Resource usage
    resource_usage: ResourceUsageRecord,
}

/// Resource usage record
#[derive(Debug, Clone)]
pub struct ResourceUsageRecord {
    /// Memory usage (MB)
    memory_mb: f64,

    /// CPU time (seconds)
    cpu_time_seconds: f64,

    /// GPU time (seconds)
    gpu_time_seconds: f64,

    /// Energy consumption (kWh)
    energy_kwh: f64,
}

/// Data splits for training
#[derive(Debug, Clone)]
pub struct DataSplits {
    /// Training indices
    train_indices: Vec<usize>,

    /// Validation indices
    validation_indices: Vec<usize>,

    /// Test indices
    test_indices: Vec<usize>,

    /// Split ratios
    split_ratios: (f64, f64, f64),
}

/// Prediction cache
#[derive(Debug)]
pub struct PredictionCache<T: Float + Debug + Send + Sync + 'static> {
    /// Cached predictions
    predictions: HashMap<String, PredictionResult<T>>,

    /// Cache statistics
    statistics: CacheStatistics,

    /// Cache configuration
    config: CacheConfig,
}

/// Prediction result
#[derive(Debug, Clone)]
pub struct PredictionResult<T: Float + Debug + Send + Sync + 'static> {
    /// Predicted performance
    predicted_performance: T,

    /// Confidence interval
    confidence_interval: (T, T),

    /// Prediction uncertainty
    uncertainty: T,

    /// Feature importance
    feature_importance: Vec<T>,

    /// Prediction timestamp
    timestamp: Instant,
}

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

    /// Cache hits
    cache_hits: usize,

    /// Cache misses
    cache_misses: usize,

    /// Hit rate
    hit_rate: f64,

    /// Average prediction time
    avg_prediction_time_ms: f64,
}

/// Cache configuration
#[derive(Debug, Clone)]
pub struct CacheConfig {
    /// Maximum cache size
    max_size: usize,

    /// TTL for entries (seconds)
    ttl_seconds: u64,

    /// Eviction policy
    eviction_policy: CacheEvictionPolicy,

    /// Enable persistence
    enable_persistence: bool,
}

/// Uncertainty estimator
#[derive(Debug)]
pub struct UncertaintyEstimator<T: Float + Debug + Send + Sync + 'static> {
    /// Estimation method
    estimation_method: UncertaintyEstimationMethod,

    /// Model ensemble (if using ensemble methods)
    model_ensemble: Vec<PredictorModel<T>>,

    /// Uncertainty parameters
    parameters: UncertaintyParameters<T>,

    /// Calibration data
    calibration_data: CalibrationData<T>,
}

/// Uncertainty parameters
#[derive(Debug)]
pub struct UncertaintyParameters<T: Float + Debug + Send + Sync + 'static> {
    /// Number of samples for MC methods
    num_samples: usize,

    /// Confidence level
    confidence_level: T,

    /// Calibration alpha
    calibration_alpha: T,

    /// Method-specific parameters
    method_params: HashMap<String, T>,
}

/// Calibration data
#[derive(Debug)]
pub struct CalibrationData<T: Float + Debug + Send + Sync + 'static> {
    /// Calibration predictions
    predictions: Vec<T>,

    /// Calibration targets
    targets: Vec<T>,

    /// Calibration scores
    scores: Vec<T>,

    /// Calibration curve
    calibration_curve: CalibrationCurve<T>,
}

/// Calibration curve
#[derive(Debug)]
pub struct CalibrationCurve<T: Float + Debug + Send + Sync + 'static> {
    /// Bin edges
    bin_edges: Vec<T>,

    /// Bin accuracies
    bin_accuracies: Vec<T>,

    /// Bin confidences
    bin_confidences: Vec<T>,

    /// Bin counts
    bin_counts: Vec<usize>,
}

// Implementations

impl<T: Float + Debug + Default + Send + Sync> PerformancePredictor<T> {
    pub fn new(_config: &EvaluationConfig) -> Result<Self> {
        Ok(Self {
            predictor_model: PredictorModel::new()?,
            feature_extractor: FeatureExtractor::new()?,
            training_data: PredictorTrainingData::new(),
            prediction_cache: PredictionCache::new(),
            uncertainty_estimator: UncertaintyEstimator::new()?,
        })
    }

    pub fn predict_performance(
        &self,
        _architecture: &OptimizerArchitecture,
    ) -> Result<EvaluationResults<T>> {
        // Simple placeholder implementation
        Ok(EvaluationResults {
            metric_scores: std::collections::HashMap::new(),
            overall_score: scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()),
            confidence_intervals: std::collections::HashMap::new(),
            evaluation_time: std::time::Duration::from_millis(100),
            success: true,
            error_message: None,
            cv_results: None,
            benchmark_results: std::collections::HashMap::new(),
            training_trajectory: Vec::new(),
        })
    }

    pub fn update_with_results(&mut self, _results: &[EvaluationResults<T>]) -> Result<()> {
        // Simple placeholder implementation
        Ok(())
    }
}

impl<T: Float + Debug + Default + Send + Sync> PredictorModel<T> {
    fn new() -> Result<Self> {
        Ok(Self {
            model_type: PredictorModelType::NeuralNetwork,
            parameters: ModelParameters {
                weights: Vec::new(),
                biases: Vec::new(),
                hyperparameters: HashMap::new(),
                regularization: RegularizationParameters {
                    l1_strength: T::zero(),
                    l2_strength: scirs2_core::numeric::NumCast::from(0.01)
                        .unwrap_or_else(|| T::zero()),
                    dropout_prob: scirs2_core::numeric::NumCast::from(0.1)
                        .unwrap_or_else(|| T::zero()),
                    batch_norm: true,
                },
            },
            architecture: ModelArchitecture {
                layer_sizes: vec![64, 128, 64, 1],
                activations: vec![ActivationFunction::ReLU; 3],
                dropout_rates: vec![0.1, 0.2, 0.1],
                skip_connections: Vec::new(),
            },
            training_state: ModelTrainingState {
                current_epoch: 0,
                loss_history: Vec::new(),
                validation_loss_history: Vec::new(),
                learning_rate_schedule: LearningRateSchedule {
                    schedule_type: ScheduleType::Exponential,
                    initial_lr: scirs2_core::numeric::NumCast::from(0.001)
                        .unwrap_or_else(|| T::zero()),
                    current_lr: scirs2_core::numeric::NumCast::from(0.001)
                        .unwrap_or_else(|| T::zero()),
                    parameters: HashMap::new(),
                },
                early_stopping_state: EarlyStoppingState {
                    best_val_loss: T::infinity(),
                    patience_counter: 0,
                    max_patience: 10,
                    should_stop: false,
                },
            },
        })
    }
}

impl<T: Float + Debug + Default + Send + Sync> FeatureExtractor<T> {
    fn new() -> Result<Self> {
        Ok(Self {
            extraction_methods: vec![
                FeatureExtractionMethod::ArchitectureEmbedding,
                FeatureExtractionMethod::HyperparameterEncoding,
            ],
            engineering_pipeline: FeatureEngineeringPipeline {
                normalization: NormalizationMethod::ZScore,
                scaling: FeatureScaling {
                    method: ScalingMethod::Standard,
                    scale_params: HashMap::new(),
                    feature_ranges: HashMap::new(),
                },
                interactions: FeatureInteractions {
                    interaction_order: 2,
                    include_bias: true,
                    selected_interactions: Vec::new(),
                },
                polynomial_features: PolynomialFeatures {
                    degree: 2,
                    include_bias: true,
                    interaction_only: false,
                },
            },
            feature_selection: FeatureSelection {
                selection_method: FeatureSelectionMethod::VarianceThreshold,
                parameters: HashMap::new(),
                selected_features: Vec::new(),
                importance_scores: Vec::new(),
            },
            feature_cache: FeatureCache {
                cached_features: HashMap::new(),
                hit_rate: 0.0,
                size_limit: 1000,
                eviction_policy: CacheEvictionPolicy::LRU,
            },
        })
    }
}

impl<T: Float + Debug + Default + Send + Sync> PredictorTrainingData<T> {
    fn new() -> Self {
        Self {
            architecture_features: Vec::new(),
            performance_targets: Vec::new(),
            metadata: Vec::new(),
            data_splits: DataSplits {
                train_indices: Vec::new(),
                validation_indices: Vec::new(),
                test_indices: Vec::new(),
                split_ratios: (0.7, 0.15, 0.15),
            },
        }
    }
}

impl<T: Float + Debug + Default + Send + Sync> PredictionCache<T> {
    fn new() -> Self {
        Self {
            predictions: HashMap::new(),
            statistics: CacheStatistics {
                total_requests: 0,
                cache_hits: 0,
                cache_misses: 0,
                hit_rate: 0.0,
                avg_prediction_time_ms: 0.0,
            },
            config: CacheConfig {
                max_size: 1000,
                ttl_seconds: 3600,
                eviction_policy: CacheEvictionPolicy::LRU,
                enable_persistence: false,
            },
        }
    }
}

impl<T: Float + Debug + Default + Send + Sync> UncertaintyEstimator<T> {
    fn new() -> Result<Self> {
        Ok(Self {
            estimation_method: UncertaintyEstimationMethod::MonteCarloDropout,
            model_ensemble: Vec::new(),
            parameters: UncertaintyParameters {
                num_samples: 100,
                confidence_level: scirs2_core::numeric::NumCast::from(0.95)
                    .unwrap_or_else(|| T::zero()),
                calibration_alpha: scirs2_core::numeric::NumCast::from(0.05)
                    .unwrap_or_else(|| T::zero()),
                method_params: HashMap::new(),
            },
            calibration_data: CalibrationData {
                predictions: Vec::new(),
                targets: Vec::new(),
                scores: Vec::new(),
                calibration_curve: CalibrationCurve {
                    bin_edges: Vec::new(),
                    bin_accuracies: Vec::new(),
                    bin_confidences: Vec::new(),
                    bin_counts: Vec::new(),
                },
            },
        })
    }
}