quantrs2-anneal 0.1.3

//! Neural Architecture Search for Meta-Learning Optimization
//!
//! This module contains all Neural Architecture Search (NAS) types and implementations
//! used by the meta-learning optimization system.

use super::config::{
    ActivationFunction, ArchitectureSpec, ConnectionPattern, EarlyStoppingCriteria, LayerSpec,
    LayerType, NeuralArchitectureSearchConfig, OptimizationSettings, OptimizerType,
    RegularizationConfig, ResourceConstraints, SearchSpace, SearchStrategy,
};
use super::features::ProblemFeatures;
use crate::applications::ApplicationResult;
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

/// Neural Architecture Search engine
#[derive(Debug)]
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,
}

/// 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,
}

/// Resource requirements for architectures
#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    /// Memory requirements (MB)
    pub memory: usize,
    /// Computational requirements (FLOPS)
    pub computation: f64,
    /// Training time estimate
    pub training_time: Duration,
    /// Model size (parameters)
    pub model_size: usize,
}

/// 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,
}

/// 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>),
}

impl NeuralArchitectureSearch {
    #[must_use]
    pub fn new(config: NeuralArchitectureSearchConfig) -> Self {
        Self {
            config: config.clone(),
            search_space: config.search_space,
            current_architectures: Vec::new(),
            search_history: VecDeque::new(),
            performance_predictor: PerformancePredictor {
                model: PredictorModel::RandomForest,
                training_data: Vec::new(),
                accuracy: 0.8,
                uncertainty_estimation: true,
            },
        }
    }

    pub fn search_architecture(
        &mut self,
        _features: &ProblemFeatures,
    ) -> ApplicationResult<ArchitectureSpec> {
        // Simplified architecture search
        let layers = vec![
            LayerSpec {
                layer_type: LayerType::Dense,
                input_dim: 100,
                output_dim: 256,
                activation: ActivationFunction::ReLU,
                dropout: 0.1,
                parameters: HashMap::new(),
            },
            LayerSpec {
                layer_type: LayerType::Dense,
                input_dim: 256,
                output_dim: 128,
                activation: ActivationFunction::ReLU,
                dropout: 0.1,
                parameters: HashMap::new(),
            },
            LayerSpec {
                layer_type: LayerType::Dense,
                input_dim: 128,
                output_dim: 1,
                activation: ActivationFunction::Sigmoid,
                dropout: 0.0,
                parameters: HashMap::new(),
            },
        ];

        Ok(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: 0.01,
                    dropout: 0.1,
                    batch_norm: true,
                    early_stopping: true,
                },
            },
        })
    }

    /// Generate a new architecture candidate
    pub fn generate_candidate(
        &mut self,
        method: GenerationMethod,
    ) -> ApplicationResult<ArchitectureCandidate> {
        let id = format!("arch_{}", Instant::now().elapsed().as_nanos());

        // Generate architecture based on method
        let architecture = match method {
            GenerationMethod::Random => self.generate_random_architecture()?,
            GenerationMethod::Mutation => self.mutate_existing_architecture()?,
            GenerationMethod::Crossover => self.crossover_architectures()?,
            _ => self.generate_random_architecture()?,
        };

        let candidate = ArchitectureCandidate {
            id,
            architecture,
            estimated_performance: 0.8,
            actual_performance: None,
            resource_requirements: ResourceRequirements {
                memory: 512,
                computation: 1e9,
                training_time: Duration::from_secs(300),
                model_size: 100_000,
            },
            generation_method: method,
        };

        Ok(candidate)
    }

    /// Evaluate an architecture candidate
    pub fn evaluate_candidate(
        &mut self,
        candidate: &mut ArchitectureCandidate,
    ) -> ApplicationResult<f64> {
        // Simulate architecture evaluation
        let performance = self
            .performance_predictor
            .predict(&candidate.architecture)?;
        candidate.actual_performance = Some(performance);

        // Update predictor with new data
        self.performance_predictor
            .training_data
            .push((candidate.architecture.clone(), performance));

        Ok(performance)
    }

    /// Update the search based on evaluation results
    pub fn update_search(&mut self, results: &[(String, f64)]) -> ApplicationResult<()> {
        // Update search strategy based on results
        let iteration = SearchIteration {
            iteration: self.search_history.len() + 1,
            architectures_evaluated: results.iter().map(|(id, _)| id.clone()).collect(),
            best_performance: results.iter().map(|(_, perf)| *perf).fold(0.0, f64::max),
            strategy_used: SearchStrategy::DifferentiableNAS,
            computational_cost: results.len() as f64 * 100.0,
            timestamp: Instant::now(),
        };

        self.search_history.push_back(iteration);

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

        Ok(())
    }

    /// Get the best architecture found so far
    #[must_use]
    pub fn get_best_architecture(&self) -> Option<&ArchitectureCandidate> {
        self.current_architectures.iter().max_by(|a, b| {
            let a_perf = a.actual_performance.unwrap_or(a.estimated_performance);
            let b_perf = b.actual_performance.unwrap_or(b.estimated_performance);
            a_perf
                .partial_cmp(&b_perf)
                .unwrap_or(std::cmp::Ordering::Equal)
        })
    }

    // Helper methods for architecture generation
    fn generate_random_architecture(&self) -> ApplicationResult<ArchitectureSpec> {
        let num_layers = 2 + (3 % 4); // Random between 2-5 layers
        let mut layers = Vec::new();

        let mut input_dim = 100;
        for i in 0..num_layers {
            let output_dim = if i == num_layers - 1 {
                1 // Final layer
            } else {
                [64, 128, 256, 512][i % 4]
            };

            layers.push(LayerSpec {
                layer_type: LayerType::Dense,
                input_dim,
                output_dim,
                activation: if i == num_layers - 1 {
                    ActivationFunction::Sigmoid
                } else {
                    ActivationFunction::ReLU
                },
                dropout: if i == num_layers - 1 { 0.0 } else { 0.1 },
                parameters: HashMap::new(),
            });

            input_dim = output_dim;
        }

        Ok(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: 0.01,
                    dropout: 0.1,
                    batch_norm: true,
                    early_stopping: true,
                },
            },
        })
    }

    fn mutate_existing_architecture(&self) -> ApplicationResult<ArchitectureSpec> {
        // If we have existing architectures, mutate one of them
        if let Some(base_arch) = self.current_architectures.first() {
            let mut mutated = base_arch.architecture.clone();

            // Simple mutation: change the size of a random layer
            if !mutated.layers.is_empty() {
                let layer_idx = 0; // Simplified: always mutate first layer
                if layer_idx < mutated.layers.len() - 1 {
                    // Don't mutate output layer
                    let new_size = [64, 128, 256, 512][layer_idx % 4];
                    mutated.layers[layer_idx].output_dim = new_size;

                    // Update next layer's input dimension
                    if layer_idx + 1 < mutated.layers.len() {
                        mutated.layers[layer_idx + 1].input_dim = new_size;
                    }
                }
            }

            Ok(mutated)
        } else {
            // No existing architectures, generate random
            self.generate_random_architecture()
        }
    }

    fn crossover_architectures(&self) -> ApplicationResult<ArchitectureSpec> {
        // If we have at least 2 architectures, perform crossover
        if self.current_architectures.len() >= 2 {
            let parent1 = &self.current_architectures[0].architecture;
            let parent2 = &self.current_architectures[1].architecture;

            // Simple crossover: take layers from both parents
            let mut child_layers = Vec::new();
            let min_layers = parent1.layers.len().min(parent2.layers.len());

            for i in 0..min_layers {
                let layer = if i % 2 == 0 {
                    parent1.layers[i].clone()
                } else {
                    parent2.layers[i].clone()
                };
                child_layers.push(layer);
            }

            // Ensure proper dimensions
            self.fix_layer_dimensions(&mut child_layers);

            Ok(ArchitectureSpec {
                layers: child_layers,
                connections: parent1.connections.clone(),
                optimization: parent1.optimization.clone(),
            })
        } else {
            // Not enough parents, generate random
            self.generate_random_architecture()
        }
    }

    fn fix_layer_dimensions(&self, layers: &mut Vec<LayerSpec>) {
        // Fix dimension mismatches between layers
        for i in 1..layers.len() {
            layers[i].input_dim = layers[i - 1].output_dim;
        }

        // Ensure final layer outputs 1 value
        if let Some(last_layer) = layers.last_mut() {
            last_layer.output_dim = 1;
            last_layer.activation = ActivationFunction::Sigmoid;
        }
    }
}

impl PerformancePredictor {
    /// Predict performance for an architecture
    pub fn predict(&self, architecture: &ArchitectureSpec) -> ApplicationResult<f64> {
        // Simplified performance prediction based on architecture complexity
        let complexity = self.calculate_complexity(architecture);
        let base_performance = 0.8;
        let complexity_penalty = (complexity - 1.0) * 0.1;

        let predicted_performance = (base_performance - complexity_penalty).max(0.1).min(1.0);
        Ok(predicted_performance)
    }

    /// Calculate architecture complexity metric
    fn calculate_complexity(&self, architecture: &ArchitectureSpec) -> f64 {
        let num_layers = architecture.layers.len() as f64;
        let total_params = architecture
            .layers
            .iter()
            .map(|layer| layer.input_dim * layer.output_dim)
            .sum::<usize>() as f64;

        // Normalize complexity
        (num_layers / 10.0) + (total_params / 1_000_000.0)
    }

    /// Update predictor with new training data
    pub fn update(&mut self, architecture: ArchitectureSpec, performance: f64) {
        self.training_data.push((architecture, performance));

        // Limit training data size
        if self.training_data.len() > 10_000 {
            self.training_data.remove(0);
        }

        // Update accuracy estimate (simplified)
        self.accuracy = (self.training_data.len() as f64 / 10_000.0).mul_add(0.15, 0.8);
    }
}

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

    #[test]
    fn test_nas_creation() {
        let config = NeuralArchitectureSearchConfig::default();
        let nas = NeuralArchitectureSearch::new(config);
        assert!(nas.current_architectures.is_empty());
        assert_eq!(nas.performance_predictor.accuracy, 0.8);
    }

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

        let candidate = nas.generate_candidate(GenerationMethod::Random);
        assert!(candidate.is_ok());

        let arch = candidate.expect("architecture generation should succeed");
        assert!(!arch.architecture.layers.is_empty());
        assert!(!arch.id.is_empty());
    }

    #[test]
    fn test_performance_prediction() {
        let predictor = PerformancePredictor {
            model: PredictorModel::RandomForest,
            training_data: Vec::new(),
            accuracy: 0.8,
            uncertainty_estimation: true,
        };

        let architecture = ArchitectureSpec {
            layers: vec![LayerSpec {
                layer_type: LayerType::Dense,
                input_dim: 100,
                output_dim: 1,
                activation: ActivationFunction::Sigmoid,
                dropout: 0.0,
                parameters: HashMap::new(),
            }],
            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: 0.01,
                    dropout: 0.1,
                    batch_norm: true,
                    early_stopping: true,
                },
            },
        };

        let performance = predictor.predict(&architecture);
        assert!(performance.is_ok());

        let perf_value = performance.expect("performance prediction should succeed");
        assert!(perf_value >= 0.0 && perf_value <= 1.0);
    }
}