oxirs-vec 0.2.4

//! Main Neural Architecture Search engine implementation

use crate::neural_architecture_search::{
    architecture::*,
    config::*,
    types::*,
    evaluator::*,
    history::*,
    dataset::*,
    monitoring::*,
};
use anyhow::{Result, Context};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use uuid::Uuid;
use scirs2_core::random::{Random, Rng};

/// Neural Architecture Search Engine for embedding optimization
pub struct NeuralArchitectureSearch {
    /// Search configuration
    config: NASConfig,
    /// Architecture search space
    search_space: ArchitectureSearchSpace,
    /// Performance evaluator
    evaluator: Arc<PerformanceEvaluator>,
    /// Search strategy
    strategy: SearchStrategy,
    /// Architecture history and performance tracking
    history: Arc<RwLock<SearchHistory>>,
    /// Current generation of architectures
    population: Vec<Architecture>,
    /// Random number generator for reproducibility
    rng: Random,
}

impl NeuralArchitectureSearch {
    /// Create a new NAS instance
    pub fn new(
        config: NASConfig,
        search_space: ArchitectureSearchSpace,
        evaluator: Arc<PerformanceEvaluator>,
        strategy: SearchStrategy,
    ) -> Result<Self> {
        let mut rng = Random::seed(config.random_seed);
        let history = Arc::new(RwLock::new(SearchHistory::default()));

        // Initialize population
        let population = Self::initialize_population(
            &search_space,
            config.population_size,
            &mut rng,
        )?;

        Ok(Self {
            config,
            search_space,
            evaluator,
            strategy,
            history,
            population,
            rng,
        })
    }

    /// Run the neural architecture search
    pub async fn search(&mut self) -> Result<Architecture> {
        let mut best_architecture = None;
        let mut best_performance = f64::NEG_INFINITY;
        let mut generations_without_improvement = 0;

        for generation in 0..self.config.max_generations {
            // Evaluate current population
            self.evaluate_population(generation).await?;

            // Update best architecture
            if let Some(arch) = self.get_best_architecture()? {
                if let Some(perf) = &arch.performance {
                    if perf.composite_score > best_performance {
                        best_performance = perf.composite_score;
                        best_architecture = Some(arch.clone());
                        generations_without_improvement = 0;

                        // Check if target performance is reached
                        if best_performance >= self.config.target_performance {
                            break;
                        }
                    } else {
                        generations_without_improvement += 1;
                    }
                }
            }

            // Early stopping check
            if generations_without_improvement >= self.config.early_stopping_patience {
                break;
            }

            // Generate next generation
            self.evolve_population(generation)?;

            // Update search statistics
            self.update_search_statistics(generation, best_performance)?;
        }

        best_architecture.ok_or_else(|| anyhow::anyhow!("No valid architecture found"))
    }

    /// Initialize the population with random architectures
    fn initialize_population(
        search_space: &ArchitectureSearchSpace,
        population_size: usize,
        rng: &mut Random,
    ) -> Result<Vec<Architecture>> {
        let mut population = Vec::with_capacity(population_size);

        for _ in 0..population_size {
            let architecture = Self::generate_random_architecture(search_space, rng)?;
            population.push(architecture);
        }

        Ok(population)
    }

    /// Generate a random architecture
    fn generate_random_architecture(
        search_space: &ArchitectureSearchSpace,
        rng: &mut Random,
    ) -> Result<Architecture> {
        let depth = rng.gen_range(search_space.depth_range.0..=search_space.depth_range.1);
        let mut layers = Vec::with_capacity(depth);

        // Generate random layers
        for _i in 0..depth {
            let layer_type = if search_space.layer_types.is_empty() {
                return Err(anyhow::anyhow!("No layer types available"));
            } else {
                let idx = rng.random_range(0..search_space.layer_types.len());
                search_space.layer_types[idx].clone()
            };

            let activation = if search_space.activations.is_empty() {
                return Err(anyhow::anyhow!("No activation functions available"));
            } else {
                let idx = rng.random_range(0..search_space.activations.len());
                search_space.activations[idx].clone()
            };

            let normalization = if search_space.normalizations.is_empty() {
                return Err(anyhow::anyhow!("No normalization types available"));
            } else {
                let idx = rng.random_range(0..search_space.normalizations.len());
                search_space.normalizations[idx].clone()
            };

            let skip_pattern = if search_space.skip_patterns.is_empty() {
                return Err(anyhow::anyhow!("No skip patterns available"));
            } else {
                let idx = rng.random_range(0..search_space.skip_patterns.len());
                search_space.skip_patterns[idx].clone()
            };

            // Generate random hyperparameters
            let mut hyperparameters = HashMap::new();
            hyperparameters.insert("learning_rate".to_string(), rng.random_range(1e-5, 1e-1));
            hyperparameters.insert("dropout_rate".to_string(), rng.gen_range(0.0..0.5));
            hyperparameters.insert("weight_decay".to_string(), rng.random_range(1e-6, 1e-2));

            let layer_config = LayerConfig {
                layer_type,
                activation,
                normalization,
                skip_pattern,
                hyperparameters,
            };

            layers.push(layer_config);
        }

        // Generate global configuration
        let embedding_dim = if search_space.embedding_dims.is_empty() {
            return Err(anyhow::anyhow!("No embedding dimensions available"));
        } else {
            let idx = rng.random_range(0..search_space.embedding_dims.len());
            &search_space.embedding_dims[idx]
        };

        let global_config = GlobalArchConfig {
            input_dim: rng.random_range(128..2048),
            output_dim: *embedding_dim,
            learning_rate: rng.random_range(1e-5, 1e-2),
            optimizer: OptimizerType::Adam {
                beta1: 0.9,
                beta2: 0.999,
                eps: 1e-8,
            },
            regularization: RegularizationConfig {
                l1_weight: rng.gen_range(0.0..1e-3),
                l2_weight: rng.gen_range(0.0..1e-2),
                dropout_rate: rng.gen_range(0.0..0.5),
                label_smoothing: rng.gen_range(0.0..0.1),
                early_stopping_patience: rng.random_range(5..20),
            },
            training_config: TrainingConfig {
                batch_size: {
                    let batch_sizes = [16, 32, 64, 128, 256];
                    let idx = rng.random_range(0..batch_sizes.len());
                    batch_sizes[idx]
                },
                epochs: rng.random_range(10..100),
                validation_split: rng.gen_range(0.1..0.3),
                lr_schedule: LRScheduleType::CosineAnnealingLR { t_max: 50 },
                loss_function: LossFunction::CosineSimilarity,
            },
        };

        Ok(Architecture::new(layers, global_config))
    }

    /// Evaluate the current population
    async fn evaluate_population(&mut self, generation: usize) -> Result<()> {
        // Evaluate each architecture
        for arch in &mut self.population {
            arch.generation = generation;

            // Placeholder evaluation - in real implementation, this would train and evaluate the model
            let performance = PerformanceMetrics {
                embedding_quality: self.rng.gen_range(0.0..1.0),
                training_loss: self.rng.gen_range(0.01..1.0),
                validation_loss: self.rng.gen_range(0.01..1.0),
                inference_latency_ms: self.rng.gen_range(1.0..100.0),
                model_size_params: arch.estimate_complexity(),
                memory_usage_mb: self.rng.gen_range(50.0..500.0),
                flops: self.rng.random_range(1_000_000, 100_000_000),
                training_time_minutes: self.rng.gen_range(5.0..120.0),
                energy_consumption: self.rng.gen_range(10.0..200.0),
                task_metrics: HashMap::new(),
            };

            arch.performance = Some(performance);
        }

        // Update history
        {
            let mut history = self.history
            .write()
            .expect("history write lock should not be poisoned");
            for arch in &self.population {
                history.add_architecture(arch.clone());
            }
        }

        Ok(())
    }

    /// Get the best architecture from the current population
    fn get_best_architecture(&self) -> Result<Option<Architecture>> {
        let best = self.population.iter()
            .filter_map(|arch| arch.performance.as_ref().map(|perf| (arch, perf)))
            .max_by(|(_, perf1), (_, perf2)| {
                perf1.composite_score.partial_cmp(&perf2.composite_score)
                    .unwrap_or(std::cmp::Ordering::Equal)
            });

        Ok(best.map(|(arch, _)| arch.clone()))
    }

    /// Evolve the population to the next generation
    fn evolve_population(&mut self, generation: usize) -> Result<()> {
        match &self.strategy {
            SearchStrategy::EvolutionaryAlgorithm => {
                // Placeholder evolutionary algorithm
                Ok(())
            }
            SearchStrategy::ReinforcementLearning => {
                // Placeholder RL-based evolution
                Ok(())
            }
            SearchStrategy::BayesianOptimization => {
                // Placeholder Bayesian optimization
                Ok(())
            }
            SearchStrategy::RandomSearch => {
                // Replace some architectures with random ones
                let num_to_replace = self.population.len() / 4;
                for i in 0..num_to_replace {
                    let new_arch = Self::generate_random_architecture(&self.search_space, &mut self.rng)?;
                    self.population[i] = new_arch;
                }
                Ok(())
            }
            SearchStrategy::GridSearch => {
                // Placeholder grid search
                Ok(())
            }
            SearchStrategy::GradientBased => {
                // Placeholder gradient-based search
                Ok(())
            }
            SearchStrategy::Hybrid { .. } => {
                // Placeholder hybrid approach
                Ok(())
            }
        }
    }

    /// Update search statistics
    fn update_search_statistics(&mut self, generation: usize, best_performance: f64) -> Result<()> {
        let mut history = self.history
            .write()
            .expect("history write lock should not be poisoned");
        history.update_best(
            self.get_best_architecture()?
                .expect("best architecture should exist after evaluation")
                .id,
            best_performance,
            generation,
        );
        Ok(())
    }

    /// Get current search history
    pub fn get_search_history(&self) -> Arc<RwLock<SearchHistory>> {
        self.history.clone()
    }

    /// Get current population
    pub fn get_population(&self) -> &[Architecture] {
        &self.population
    }

    /// Get search configuration
    pub fn get_config(&self) -> &NASConfig {
        &self.config
    }

    /// Get search space
    pub fn get_search_space(&self) -> &ArchitectureSearchSpace {
        &self.search_space
    }
}