quantrs2-anneal 0.1.3

//! Neural Architecture Search engine for meta-learning optimization

use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

use scirs2_core::rand_prelude::IndexedRandom;
use scirs2_core::random::thread_rng;

use super::config::{NeuralArchitectureSearchConfig, SearchSpace, SearchStrategy};
use super::feature_extraction::{
    ArchitectureSpec, ConnectionPattern, LayerSpec, OptimizationSettings, OptimizerType,
    ProblemFeatures, RegularizationConfig,
};

/// Neural Architecture Search engine
pub struct NeuralArchitectureSearch {
    /// Configuration
    pub config: NeuralArchitectureSearchConfig,
    /// Search space
    pub search_space: SearchSpace,
    /// Current architectures
    pub current_architectures: Vec<ArchitectureCandidate>,
    /// Search history
    pub search_history: VecDeque<SearchIteration>,
    /// Performance predictor
    pub performance_predictor: PerformancePredictor,
}

impl NeuralArchitectureSearch {
    #[must_use]
    pub fn new(config: NeuralArchitectureSearchConfig) -> Self {
        Self {
            search_space: config.search_space.clone(),
            config,
            current_architectures: Vec::new(),
            search_history: VecDeque::new(),
            performance_predictor: PerformancePredictor::new(),
        }
    }

    pub fn search_architecture(
        &mut self,
        problem_features: &ProblemFeatures,
    ) -> Result<ArchitectureCandidate, String> {
        match self.config.search_strategy {
            SearchStrategy::DifferentiableNAS => self.differentiable_search(problem_features),
            SearchStrategy::EvolutionarySearch => self.evolutionary_search(problem_features),
            SearchStrategy::ReinforcementLearning => self.rl_search(problem_features),
            SearchStrategy::BayesianOptimization => self.bayesian_search(problem_features),
            SearchStrategy::RandomSearch => self.random_search(problem_features),
            SearchStrategy::ProgressiveSearch => self.progressive_search(problem_features),
        }
    }

    fn differentiable_search(
        &mut self,
        problem_features: &ProblemFeatures,
    ) -> Result<ArchitectureCandidate, String> {
        // Simplified differentiable NAS implementation
        let candidate = self.generate_random_architecture(problem_features)?;

        // Evaluate and update
        let performance = self
            .performance_predictor
            .predict(&candidate.architecture)?;
        let mut improved_candidate = candidate;
        improved_candidate.estimated_performance = performance;
        improved_candidate.generation_method = GenerationMethod::GradientBased;

        self.current_architectures.push(improved_candidate.clone());
        Ok(improved_candidate)
    }

    fn evolutionary_search(
        &mut self,
        problem_features: &ProblemFeatures,
    ) -> Result<ArchitectureCandidate, String> {
        // Simplified evolutionary search
        if self.current_architectures.is_empty() {
            return self.random_search(problem_features);
        }

        // Select parent architectures
        let parents = self.select_parents(2)?;

        // Crossover and mutation
        let mut offspring = self.crossover(&parents[0], &parents[1])?;
        offspring = self.mutate(offspring)?;

        // Evaluate
        let performance = self
            .performance_predictor
            .predict(&offspring.architecture)?;
        offspring.estimated_performance = performance;
        offspring.generation_method = GenerationMethod::Mutation;

        self.current_architectures.push(offspring.clone());
        Ok(offspring)
    }

    fn rl_search(
        &mut self,
        problem_features: &ProblemFeatures,
    ) -> Result<ArchitectureCandidate, String> {
        // Simplified RL-based search
        let candidate = self.generate_random_architecture(problem_features)?;

        // Apply RL policy (simplified)
        let performance = self
            .performance_predictor
            .predict(&candidate.architecture)?;
        let mut improved_candidate = candidate;
        improved_candidate.estimated_performance = performance;
        improved_candidate.generation_method = GenerationMethod::ReinforcementLearning;

        self.current_architectures.push(improved_candidate.clone());
        Ok(improved_candidate)
    }

    fn bayesian_search(
        &mut self,
        problem_features: &ProblemFeatures,
    ) -> Result<ArchitectureCandidate, String> {
        // Simplified Bayesian optimization
        let candidate = self.generate_random_architecture(problem_features)?;

        let performance = self
            .performance_predictor
            .predict(&candidate.architecture)?;
        let mut improved_candidate = candidate;
        improved_candidate.estimated_performance = performance;
        improved_candidate.generation_method = GenerationMethod::GradientBased;

        self.current_architectures.push(improved_candidate.clone());
        Ok(improved_candidate)
    }

    fn random_search(
        &mut self,
        problem_features: &ProblemFeatures,
    ) -> Result<ArchitectureCandidate, String> {
        let candidate = self.generate_random_architecture(problem_features)?;
        self.current_architectures.push(candidate.clone());
        Ok(candidate)
    }

    fn progressive_search(
        &mut self,
        problem_features: &ProblemFeatures,
    ) -> Result<ArchitectureCandidate, String> {
        // Simplified progressive search
        let candidate = self.generate_random_architecture(problem_features)?;

        let performance = self
            .performance_predictor
            .predict(&candidate.architecture)?;
        let mut improved_candidate = candidate;
        improved_candidate.estimated_performance = performance;

        self.current_architectures.push(improved_candidate.clone());
        Ok(improved_candidate)
    }

    fn generate_random_architecture(
        &self,
        problem_features: &ProblemFeatures,
    ) -> Result<ArchitectureCandidate, String> {
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();

        // Determine architecture size based on problem features
        let num_layers = rng.random_range(
            self.search_space.num_layers_range.0..=self.search_space.num_layers_range.1,
        );
        let input_dim = problem_features.size.min(512);

        let mut layers = Vec::new();
        let mut current_dim = input_dim;

        for i in 0..num_layers {
            let layer_type = self
                .search_space
                .layer_types
                .choose(&mut rng)
                .ok_or("No layer types available")?;
            let hidden_dim = self
                .search_space
                .hidden_dims
                .choose(&mut rng)
                .ok_or("No hidden dimensions available")?;
            let activation = self
                .search_space
                .activations
                .choose(&mut rng)
                .ok_or("No activations available")?;
            let dropout = self
                .search_space
                .dropout_rates
                .choose(&mut rng)
                .ok_or("No dropout rates available")?;

            let output_dim = if i == num_layers - 1 { 1 } else { *hidden_dim };

            layers.push(LayerSpec {
                layer_type: layer_type.clone(),
                input_dim: current_dim,
                output_dim,
                activation: activation.clone(),
                dropout: *dropout,
                parameters: HashMap::new(),
            });

            current_dim = output_dim;
        }

        let num_layers = layers.len();
        let total_params: usize = layers.iter().map(|l| l.input_dim * l.output_dim).sum();
        let total_flops: usize = layers.iter().map(|l| l.input_dim * l.output_dim * 2).sum();

        let architecture = ArchitectureSpec {
            layers,
            connections: ConnectionPattern::Sequential,
            optimization: OptimizationSettings {
                optimizer: OptimizerType::Adam,
                learning_rate: 0.001,
                batch_size: 32,
                epochs: 100,
                regularization: RegularizationConfig {
                    l1_weight: 0.0,
                    l2_weight: 0.001,
                    dropout: 0.1,
                    batch_norm: true,
                    early_stopping: true,
                },
            },
        };

        let resource_requirements = ResourceRequirements {
            memory: num_layers * 64, // MB
            compute_time: Duration::from_secs(60),
            parameters: total_params,
            flops: total_flops as u64,
        };

        Ok(ArchitectureCandidate {
            id: format!("arch_{}", Instant::now().elapsed().as_nanos()),
            architecture,
            estimated_performance: 0.5,
            actual_performance: None,
            resource_requirements,
            generation_method: GenerationMethod::Random,
        })
    }

    fn select_parents(&self, count: usize) -> Result<Vec<ArchitectureCandidate>, String> {
        if self.current_architectures.len() < count {
            return Err("Not enough architectures for parent selection".to_string());
        }

        // Simple tournament selection
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();
        let mut parents = Vec::new();

        for _ in 0..count {
            let tournament_size = 3.min(self.current_architectures.len());
            let mut tournament: Vec<_> = self
                .current_architectures
                .choose_multiple(&mut rng, tournament_size)
                .collect();
            tournament.sort_by(|a, b| {
                b.estimated_performance
                    .partial_cmp(&a.estimated_performance)
                    .unwrap_or(std::cmp::Ordering::Equal)
            });
            parents.push(tournament[0].clone());
        }

        Ok(parents)
    }

    fn crossover(
        &self,
        parent1: &ArchitectureCandidate,
        parent2: &ArchitectureCandidate,
    ) -> Result<ArchitectureCandidate, String> {
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();

        // Simple layer-wise crossover
        let min_layers = parent1
            .architecture
            .layers
            .len()
            .min(parent2.architecture.layers.len());
        let mut child_layers = Vec::new();

        for i in 0..min_layers {
            let layer = if rng.random_bool(0.5) {
                parent1.architecture.layers[i].clone()
            } else {
                parent2.architecture.layers[i].clone()
            };
            child_layers.push(layer);
        }

        let child_architecture = ArchitectureSpec {
            layers: child_layers,
            connections: parent1.architecture.connections.clone(),
            optimization: parent1.architecture.optimization.clone(),
        };

        Ok(ArchitectureCandidate {
            id: format!("crossover_{}", Instant::now().elapsed().as_nanos()),
            architecture: child_architecture,
            estimated_performance: 0.0,
            actual_performance: None,
            resource_requirements: parent1.resource_requirements.clone(),
            generation_method: GenerationMethod::Crossover,
        })
    }

    fn mutate(
        &self,
        mut candidate: ArchitectureCandidate,
    ) -> Result<ArchitectureCandidate, String> {
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();

        // Mutate with probability
        if rng.random_bool(0.3) {
            // Mutate a random layer
            if !candidate.architecture.layers.is_empty() {
                let layer_idx = rng.random_range(0..candidate.architecture.layers.len());
                let layer = &mut candidate.architecture.layers[layer_idx];

                // Mutate activation function
                if let Some(new_activation) = self.search_space.activations.choose(&mut rng) {
                    layer.activation = new_activation.clone();
                }

                // Mutate dropout rate
                if let Some(new_dropout) = self.search_space.dropout_rates.choose(&mut rng) {
                    layer.dropout = *new_dropout;
                }
            }
        }

        candidate.generation_method = GenerationMethod::Mutation;
        candidate.id = format!("mutated_{}", Instant::now().elapsed().as_nanos());

        Ok(candidate)
    }

    pub fn record_iteration(&mut self, iteration: SearchIteration) {
        self.search_history.push_back(iteration);

        // Limit history size
        while self.search_history.len() > 1000 {
            self.search_history.pop_front();
        }
    }

    #[must_use]
    pub fn get_best_architecture(&self) -> Option<&ArchitectureCandidate> {
        self.current_architectures.iter().max_by(|a, b| {
            a.estimated_performance
                .partial_cmp(&b.estimated_performance)
                .unwrap_or(std::cmp::Ordering::Equal)
        })
    }
}

/// Architecture candidate
#[derive(Debug, Clone)]
pub struct ArchitectureCandidate {
    /// Unique identifier
    pub id: String,
    /// Architecture specification
    pub architecture: ArchitectureSpec,
    /// Estimated performance
    pub estimated_performance: f64,
    /// Actual performance (if evaluated)
    pub actual_performance: Option<f64>,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
    /// Generation method
    pub generation_method: GenerationMethod,
}

/// Architecture generation methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum GenerationMethod {
    /// Random generation
    Random,
    /// Evolutionary mutation
    Mutation,
    /// Crossover operation
    Crossover,
    /// Gradient-based update
    GradientBased,
    /// Reinforcement learning
    ReinforcementLearning,
}

/// Search iteration
#[derive(Debug, Clone)]
pub struct SearchIteration {
    /// Iteration number
    pub iteration: usize,
    /// Architectures evaluated
    pub architectures_evaluated: Vec<String>,
    /// Best performance found
    pub best_performance: f64,
    /// Search strategy used
    pub strategy_used: SearchStrategy,
    /// Computational cost
    pub computational_cost: f64,
    /// Timestamp
    pub timestamp: Instant,
}

/// Performance predictor for architectures
#[derive(Debug)]
pub struct PerformancePredictor {
    /// Predictor model
    pub model: PredictorModel,
    /// Training data
    pub training_data: Vec<(ArchitectureSpec, f64)>,
    /// Prediction accuracy
    pub accuracy: f64,
    /// Uncertainty estimation
    pub uncertainty_estimation: bool,
}

impl PerformancePredictor {
    #[must_use]
    pub const fn new() -> Self {
        Self {
            model: PredictorModel::NeuralNetwork,
            training_data: Vec::new(),
            accuracy: 0.8,
            uncertainty_estimation: false,
        }
    }

    pub fn predict(&self, architecture: &ArchitectureSpec) -> Result<f64, String> {
        // Simplified prediction based on architecture complexity
        let complexity = architecture.layers.len() as f64;
        let total_params: f64 = architecture
            .layers
            .iter()
            .map(|l| (l.input_dim * l.output_dim) as f64)
            .sum();

        // Simple heuristic: balance complexity and size
        let performance =
            complexity.mul_add(-0.05, 0.8).max(0.1) * (1.0 - (total_params / 1_000_000.0).min(0.5));

        Ok(performance.clamp(0.1, 1.0))
    }

    pub fn update(&mut self, architecture: ArchitectureSpec, performance: f64) {
        self.training_data.push((architecture, performance));

        // Limit training data size
        while self.training_data.len() > 1000 {
            self.training_data.remove(0);
        }

        // Update accuracy (simplified)
        if self.training_data.len() > 10 {
            self.accuracy = 0.85;
        }
    }
}

/// Predictor model types
#[derive(Debug, Clone, PartialEq)]
pub enum PredictorModel {
    /// Neural network
    NeuralNetwork,
    /// Gaussian process
    GaussianProcess,
    /// Random forest
    RandomForest,
    /// Support vector machine
    SupportVectorMachine,
    /// Ensemble model
    Ensemble(Vec<Self>),
}

/// Resource requirements for architectures
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ResourceRequirements {
    /// Memory requirements (MB)
    pub memory: usize,
    /// Compute time requirements
    pub compute_time: Duration,
    /// Number of parameters
    pub parameters: usize,
    /// FLOPs required
    pub flops: u64,
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::meta_learning_optimization::feature_extraction::{
        GraphFeatures, SpectralFeatures, StatisticalFeatures,
    };

    #[test]
    fn test_nas_creation() {
        let config = NeuralArchitectureSearchConfig::default();
        let nas = NeuralArchitectureSearch::new(config);
        assert!(nas.config.enable_nas);
    }

    #[test]
    fn test_performance_predictor() {
        let predictor = PerformancePredictor::new();
        assert_eq!(predictor.model, PredictorModel::NeuralNetwork);
        assert_eq!(predictor.accuracy, 0.8);
    }

    #[test]
    fn test_architecture_generation() {
        let config = NeuralArchitectureSearchConfig::default();
        let nas = NeuralArchitectureSearch::new(config);

        let features = ProblemFeatures {
            size: 100,
            density: 0.5,
            graph_features: GraphFeatures::default(),
            statistical_features: StatisticalFeatures::default(),
            spectral_features: SpectralFeatures::default(),
            domain_features: HashMap::new(),
        };

        let result = nas.generate_random_architecture(&features);
        assert!(result.is_ok());

        let arch = result.expect("generate_random_architecture should succeed");
        assert!(!arch.architecture.layers.is_empty());
        assert_eq!(arch.generation_method, GenerationMethod::Random);
    }
}