quantrs2-device 0.1.3

//! Algorithm Discovery Engine
//!
//! This module provides intelligent algorithm discovery capabilities including
//! semantic search, recommendation systems, and personalized algorithm suggestions.

use super::*;

/// Algorithm discovery engine
pub struct AlgorithmDiscoveryEngine {
    config: DiscoveryConfig,
    search_engine: Arc<RwLock<SemanticSearchEngine>>,
    recommendation_engine: Arc<RwLock<RecommendationEngine>>,
    ranking_system: Arc<RwLock<RankingSystem>>,
    personalization_engine: Arc<RwLock<PersonalizationEngine>>,
    cache: Arc<RwLock<DiscoveryCache>>,
}

/// Discovery criteria for searching algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DiscoveryCriteria {
    pub query: Option<String>,
    pub category: Option<AlgorithmCategory>,
    pub tags: Vec<String>,
    pub author: Option<String>,
    pub min_rating: Option<f64>,
    pub hardware_constraints: Option<HardwareConstraints>,
    pub performance_requirements: Option<PerformanceRequirements>,
    pub complexity_filter: Option<ComplexityFilter>,
    pub license_filter: Option<Vec<LicenseType>>,
    pub sort_by: SortBy,
    pub limit: usize,
    pub offset: usize,
    pub include_experimental: bool,
    pub user_context: Option<UserContext>,
}

/// Hardware constraints for discovery
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareConstraints {
    pub max_qubits: Option<usize>,
    pub min_qubits: Option<usize>,
    pub required_gates: Vec<String>,
    pub forbidden_gates: Vec<String>,
    pub topology_constraints: Vec<TopologyType>,
    pub platform_compatibility: Vec<String>,
    pub fidelity_threshold: Option<f64>,
}

/// Performance requirements for discovery
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceRequirements {
    pub max_execution_time: Option<Duration>,
    pub min_success_probability: Option<f64>,
    pub max_error_rate: Option<f64>,
    pub min_quantum_advantage: Option<f64>,
    pub resource_efficiency: Option<f64>,
}

/// Complexity filter for algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComplexityFilter {
    pub max_time_complexity: Option<String>,
    pub max_space_complexity: Option<String>,
    pub max_circuit_depth: Option<usize>,
    pub max_gate_count: Option<usize>,
}

/// Sorting options for discovery results
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum SortBy {
    Relevance,
    Popularity,
    Rating,
    RecentlyUpdated,
    Performance,
    Alphabetical,
    QuantumAdvantage,
    ResourceEfficiency,
    Custom(String),
}

/// User context for personalized discovery
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UserContext {
    pub user_id: String,
    pub experience_level: ExperienceLevel,
    pub research_areas: Vec<String>,
    pub preferred_platforms: Vec<String>,
    pub usage_history: Vec<String>,
    pub collaboration_preferences: Vec<String>,
}

/// Experience levels
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum ExperienceLevel {
    Beginner,
    Intermediate,
    Advanced,
    Expert,
    Researcher,
}

/// Algorithm information for discovery results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlgorithmInfo {
    pub algorithm_id: String,
    pub name: String,
    pub version: String,
    pub description: String,
    pub author: String,
    pub category: AlgorithmCategory,
    pub tags: Vec<String>,
    pub rating: f64,
    pub downloads: u64,
    pub last_updated: SystemTime,
    pub quantum_advantage: QuantumAdvantage,
    pub hardware_requirements: HardwareRequirements,
    pub complexity_info: ComplexityInfo,
    pub discovery_score: f64,
    pub personalization_score: Option<f64>,
}

/// Complexity information summary
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComplexityInfo {
    pub time_complexity: String,
    pub space_complexity: String,
    pub circuit_depth: usize,
    pub gate_count: usize,
    pub qubit_count: usize,
}

/// Semantic search engine
pub struct SemanticSearchEngine {
    word_embeddings: HashMap<String, Vec<f64>>,
    algorithm_embeddings: HashMap<String, Vec<f64>>,
    similarity_cache: HashMap<String, Vec<(String, f64)>>,
    search_index: InvertedIndex,
}

/// Inverted search index
#[derive(Debug, Clone)]
pub struct InvertedIndex {
    term_to_algorithms: HashMap<String, Vec<String>>,
    algorithm_to_terms: HashMap<String, Vec<String>>,
    term_frequencies: HashMap<String, HashMap<String, f64>>,
    document_frequencies: HashMap<String, usize>,
}

/// Recommendation engine
pub struct RecommendationEngine {
    user_profiles: HashMap<String, UserProfile>,
    algorithm_similarities: HashMap<String, Vec<(String, f64)>>,
    collaborative_filters: Vec<Box<dyn CollaborativeFilter + Send + Sync>>,
    content_filters: Vec<Box<dyn ContentFilter + Send + Sync>>,
    hybrid_weights: HybridWeights,
}

/// User profile for recommendations
#[derive(Debug, Clone)]
pub struct UserProfile {
    pub user_id: String,
    pub interests: Vec<String>,
    pub expertise_areas: Vec<String>,
    pub interaction_history: Vec<UserInteraction>,
    pub preference_vector: Vec<f64>,
    pub temporal_patterns: TemporalPatterns,
}

/// User interaction data
#[derive(Debug, Clone)]
pub struct UserInteraction {
    pub algorithm_id: String,
    pub interaction_type: InteractionType,
    pub timestamp: SystemTime,
    pub duration: Option<Duration>,
    pub rating: Option<f64>,
    pub feedback: Option<String>,
}

/// Interaction types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum InteractionType {
    View,
    Download,
    Deploy,
    Rate,
    Review,
    Share,
    Bookmark,
    Fork,
    Contribute,
}

/// Temporal patterns in user behavior
#[derive(Debug, Clone)]
pub struct TemporalPatterns {
    pub activity_by_hour: Vec<f64>,
    pub activity_by_day: Vec<f64>,
    pub seasonal_patterns: Vec<f64>,
    pub burst_periods: Vec<(SystemTime, Duration)>,
}

/// Collaborative filtering trait
pub trait CollaborativeFilter {
    fn recommend(&self, user_id: &str, candidates: &[String]) -> DeviceResult<Vec<(String, f64)>>;
    fn update_model(&mut self, interactions: &[UserInteraction]) -> DeviceResult<()>;
}

/// Content-based filtering trait
pub trait ContentFilter {
    fn recommend(
        &self,
        user_profile: &UserProfile,
        candidates: &[String],
    ) -> DeviceResult<Vec<(String, f64)>>;
    fn extract_features(&self, algorithm_id: &str) -> DeviceResult<Vec<f64>>;
}

/// Hybrid recommendation weights
#[derive(Debug, Clone)]
pub struct HybridWeights {
    pub collaborative_weight: f64,
    pub content_weight: f64,
    pub popularity_weight: f64,
    pub temporal_weight: f64,
    pub semantic_weight: f64,
}

/// Ranking system for algorithm discovery
pub struct RankingSystem {
    ranking_algorithms: Vec<Box<dyn RankingAlgorithm + Send + Sync>>,
    feature_extractors: Vec<Box<dyn FeatureExtractor + Send + Sync>>,
    learning_to_rank_model: Option<LearningToRankModel>,
    ranking_cache: HashMap<String, Vec<RankedResult>>,
}

/// Ranking algorithm trait
pub trait RankingAlgorithm {
    fn rank(
        &self,
        candidates: &[AlgorithmInfo],
        criteria: &DiscoveryCriteria,
    ) -> DeviceResult<Vec<RankedResult>>;
    fn get_algorithm_name(&self) -> String;
}

/// Feature extractor trait
pub trait FeatureExtractor {
    fn extract_features(
        &self,
        algorithm: &AlgorithmInfo,
        context: &DiscoveryCriteria,
    ) -> DeviceResult<Vec<f64>>;
    fn get_feature_names(&self) -> Vec<String>;
}

/// Ranked result with score
#[derive(Debug, Clone)]
pub struct RankedResult {
    pub algorithm_id: String,
    pub rank: usize,
    pub score: f64,
    pub feature_scores: HashMap<String, f64>,
    pub explanation: RankingExplanation,
}

/// Ranking explanation
#[derive(Debug, Clone)]
pub struct RankingExplanation {
    pub primary_factors: Vec<String>,
    pub relevance_score: f64,
    pub popularity_score: f64,
    pub quality_score: f64,
    pub personalization_boost: f64,
    pub detailed_explanation: String,
}

/// Learning-to-rank model
#[derive(Debug, Clone)]
pub struct LearningToRankModel {
    pub model_type: String,
    pub feature_weights: Vec<f64>,
    pub training_data: Vec<TrainingExample>,
    pub model_performance: ModelPerformance,
}

/// Training example for learning-to-rank
#[derive(Debug, Clone)]
pub struct TrainingExample {
    pub query_id: String,
    pub algorithm_id: String,
    pub features: Vec<f64>,
    pub relevance_score: f64,
    pub user_action: Option<InteractionType>,
}

/// Model performance metrics
#[derive(Debug, Clone)]
pub struct ModelPerformance {
    pub ndcg_at_10: f64,
    pub map_score: f64,
    pub precision_at_k: Vec<f64>,
    pub recall_at_k: Vec<f64>,
    pub click_through_rate: f64,
}

/// Personalization engine
pub struct PersonalizationEngine {
    personalization_models: HashMap<String, PersonalizationModel>,
    context_analyzers: Vec<Box<dyn ContextAnalyzer + Send + Sync>>,
    adaptation_strategies: Vec<Box<dyn AdaptationStrategy + Send + Sync>>,
    privacy_preserving: bool,
}

/// Personalization model
#[derive(Debug, Clone)]
pub struct PersonalizationModel {
    pub user_id: String,
    pub model_parameters: Vec<f64>,
    pub context_features: Vec<f64>,
    pub adaptation_history: Vec<AdaptationEvent>,
    pub model_confidence: f64,
}

/// Context analyzer trait
pub trait ContextAnalyzer {
    fn analyze_context(&self, user_context: &UserContext) -> DeviceResult<Vec<f64>>;
    fn get_context_dimensions(&self) -> Vec<String>;
}

/// Adaptation strategy trait
pub trait AdaptationStrategy {
    fn adapt_recommendations(
        &self,
        base_recommendations: Vec<(String, f64)>,
        user_context: &UserContext,
    ) -> DeviceResult<Vec<(String, f64)>>;
    fn update_strategy(&mut self, feedback: &[UserInteraction]) -> DeviceResult<()>;
}

/// Adaptation event
#[derive(Debug, Clone)]
pub struct AdaptationEvent {
    pub timestamp: SystemTime,
    pub adaptation_type: AdaptationType,
    pub parameters_changed: Vec<String>,
    pub performance_impact: f64,
}

/// Adaptation types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum AdaptationType {
    UserFeedback,
    PerformanceOptimization,
    ConceptDrift,
    ContextChange,
    ColdStart,
}

/// Discovery cache
#[derive(Debug, Clone)]
pub struct DiscoveryCache {
    query_cache: HashMap<String, CachedResult>,
    recommendation_cache: HashMap<String, CachedRecommendations>,
    similarity_cache: HashMap<String, HashMap<String, f64>>,
    cache_stats: CacheStatistics,
}

/// Cached search result
#[derive(Debug, Clone)]
pub struct CachedResult {
    pub results: Vec<AlgorithmInfo>,
    pub cached_at: SystemTime,
    pub expires_at: SystemTime,
    pub hit_count: usize,
}

/// Cached recommendations
#[derive(Debug, Clone)]
pub struct CachedRecommendations {
    pub user_id: String,
    pub recommendations: Vec<(String, f64)>,
    pub cached_at: SystemTime,
    pub context_hash: String,
}

/// Cache statistics
#[derive(Debug, Clone, Default)]
pub struct CacheStatistics {
    pub total_requests: u64,
    pub cache_hits: u64,
    pub cache_misses: u64,
    pub evictions: u64,
    pub memory_usage: usize,
}

impl AlgorithmDiscoveryEngine {
    /// Create a new discovery engine
    pub fn new(config: &DiscoveryConfig) -> DeviceResult<Self> {
        let search_engine = Arc::new(RwLock::new(SemanticSearchEngine::new()?));
        let recommendation_engine = Arc::new(RwLock::new(RecommendationEngine::new()?));
        let ranking_system = Arc::new(RwLock::new(RankingSystem::new()?));
        let personalization_engine = Arc::new(RwLock::new(PersonalizationEngine::new()?));
        let cache = Arc::new(RwLock::new(DiscoveryCache::new()));

        Ok(Self {
            config: config.clone(),
            search_engine,
            recommendation_engine,
            ranking_system,
            personalization_engine,
            cache,
        })
    }

    /// Initialize the discovery engine
    pub async fn initialize(&self) -> DeviceResult<()> {
        // Initialize all components
        Ok(())
    }

    /// Search for algorithms based on criteria
    pub async fn search_algorithms(
        &self,
        criteria: DiscoveryCriteria,
    ) -> DeviceResult<Vec<AlgorithmInfo>> {
        // Check cache first
        let cache_key = self.generate_cache_key(&criteria);
        if let Some(cached_result) = self.check_cache(&cache_key).await? {
            return Ok(cached_result);
        }

        // Perform search
        let mut results = self.perform_search(&criteria).await?;

        // Apply ranking
        results = self.apply_ranking(results, &criteria).await?;

        // Apply personalization if user context is provided
        if let Some(user_context) = &criteria.user_context {
            results = self.apply_personalization(results, user_context).await?;
        }

        // Cache results
        self.cache_results(&cache_key, &results).await?;

        Ok(results)
    }

    /// Get recommendations for a user
    pub async fn get_recommendations(
        &self,
        user_id: &str,
        count: usize,
    ) -> DeviceResult<Vec<AlgorithmInfo>> {
        let recommendation_engine = self
            .recommendation_engine
            .read()
            .unwrap_or_else(|e| e.into_inner());

        // Get base recommendations
        let recommendations = recommendation_engine.get_recommendations(user_id, count)?;

        // Convert to AlgorithmInfo (simplified)
        let mut results = Vec::new();
        for (algorithm_id, score) in recommendations {
            let info = AlgorithmInfo {
                algorithm_id: algorithm_id.clone(),
                name: format!("Algorithm {algorithm_id}"),
                version: "1.0.0".to_string(),
                description: "Recommended algorithm".to_string(),
                author: "Unknown".to_string(),
                category: AlgorithmCategory::Optimization,
                tags: vec![],
                rating: 4.5,
                downloads: 1000,
                last_updated: SystemTime::now(),
                quantum_advantage: QuantumAdvantage {
                    advantage_type: AdvantageType::Quadratic,
                    speedup_factor: Some(2.0),
                    problem_size_threshold: Some(100),
                    verification_method: "Theoretical".to_string(),
                    theoretical_basis: "Grover's algorithm".to_string(),
                    experimental_validation: false,
                },
                hardware_requirements: HardwareRequirements {
                    min_qubits: 1,
                    recommended_qubits: 10,
                    max_circuit_depth: 100,
                    required_gates: vec!["H".to_string(), "CNOT".to_string()],
                    connectivity_requirements: ConnectivityRequirements {
                        topology_type: TopologyType::AllToAll,
                        connectivity_degree: None,
                        all_to_all_required: false,
                        specific_connections: vec![],
                    },
                    fidelity_requirements: FidelityRequirements {
                        min_gate_fidelity: 0.99,
                        min_readout_fidelity: 0.95,
                        min_state_preparation_fidelity: 0.98,
                        coherence_time_requirement: Duration::from_micros(100),
                        error_budget: 0.01,
                    },
                    supported_platforms: vec!["IBM".to_string()],
                    special_hardware: vec![],
                },
                complexity_info: ComplexityInfo {
                    time_complexity: "O(sqrt(N))".to_string(),
                    space_complexity: "O(log N)".to_string(),
                    circuit_depth: 50,
                    gate_count: 100,
                    qubit_count: 10,
                },
                discovery_score: score,
                personalization_score: Some(score),
            };
            results.push(info);
        }

        Ok(results)
    }

    // Helper methods
    async fn perform_search(
        &self,
        criteria: &DiscoveryCriteria,
    ) -> DeviceResult<Vec<AlgorithmInfo>> {
        // Implement actual search logic
        Ok(vec![])
    }

    async fn apply_ranking(
        &self,
        mut results: Vec<AlgorithmInfo>,
        criteria: &DiscoveryCriteria,
    ) -> DeviceResult<Vec<AlgorithmInfo>> {
        let ranking_system = self
            .ranking_system
            .read()
            .unwrap_or_else(|e| e.into_inner());
        let ranked_results = ranking_system.rank_algorithms(&results, criteria)?;

        // Sort by rank
        results.sort_by(|a, b| {
            let rank_a = ranked_results
                .iter()
                .find(|r| r.algorithm_id == a.algorithm_id)
                .map_or(usize::MAX, |r| r.rank);
            let rank_b = ranked_results
                .iter()
                .find(|r| r.algorithm_id == b.algorithm_id)
                .map_or(usize::MAX, |r| r.rank);
            rank_a.cmp(&rank_b)
        });

        Ok(results)
    }

    async fn apply_personalization(
        &self,
        mut results: Vec<AlgorithmInfo>,
        user_context: &UserContext,
    ) -> DeviceResult<Vec<AlgorithmInfo>> {
        let personalization_engine = self
            .personalization_engine
            .read()
            .unwrap_or_else(|e| e.into_inner());
        let personalized_scores =
            personalization_engine.personalize_results(&results, user_context)?;

        // Update personalization scores
        for (i, score) in personalized_scores.iter().enumerate() {
            if i < results.len() {
                results[i].personalization_score = Some(*score);
            }
        }

        Ok(results)
    }

    fn generate_cache_key(&self, criteria: &DiscoveryCriteria) -> String {
        // Generate a unique cache key based on criteria
        format!("search:{criteria:?}")
    }

    async fn check_cache(&self, cache_key: &str) -> DeviceResult<Option<Vec<AlgorithmInfo>>> {
        let cache = self.cache.read().unwrap_or_else(|e| e.into_inner());
        if let Some(cached_result) = cache.query_cache.get(cache_key) {
            if cached_result.expires_at > SystemTime::now() {
                return Ok(Some(cached_result.results.clone()));
            }
        }
        Ok(None)
    }

    async fn cache_results(&self, cache_key: &str, results: &[AlgorithmInfo]) -> DeviceResult<()> {
        let mut cache = self.cache.write().unwrap_or_else(|e| e.into_inner());
        let cached_result = CachedResult {
            results: results.to_vec(),
            cached_at: SystemTime::now(),
            expires_at: SystemTime::now() + self.config.cache_ttl,
            hit_count: 0,
        };
        cache
            .query_cache
            .insert(cache_key.to_string(), cached_result);
        Ok(())
    }
}

// Implementation stubs for sub-components
impl SemanticSearchEngine {
    fn new() -> DeviceResult<Self> {
        Ok(Self {
            word_embeddings: HashMap::new(),
            algorithm_embeddings: HashMap::new(),
            similarity_cache: HashMap::new(),
            search_index: InvertedIndex::new(),
        })
    }
}

impl InvertedIndex {
    fn new() -> Self {
        Self {
            term_to_algorithms: HashMap::new(),
            algorithm_to_terms: HashMap::new(),
            term_frequencies: HashMap::new(),
            document_frequencies: HashMap::new(),
        }
    }
}

impl RecommendationEngine {
    fn new() -> DeviceResult<Self> {
        Ok(Self {
            user_profiles: HashMap::new(),
            algorithm_similarities: HashMap::new(),
            collaborative_filters: vec![],
            content_filters: vec![],
            hybrid_weights: HybridWeights {
                collaborative_weight: 0.4,
                content_weight: 0.3,
                popularity_weight: 0.15,
                temporal_weight: 0.1,
                semantic_weight: 0.05,
            },
        })
    }

    fn get_recommendations(
        &self,
        _user_id: &str,
        count: usize,
    ) -> DeviceResult<Vec<(String, f64)>> {
        // Simplified recommendation logic
        let mut recommendations = Vec::new();
        for i in 0..count {
            recommendations.push((format!("algorithm_{i}"), (i as f64).mul_add(-0.1, 0.8)));
        }
        Ok(recommendations)
    }
}

impl RankingSystem {
    fn new() -> DeviceResult<Self> {
        Ok(Self {
            ranking_algorithms: vec![],
            feature_extractors: vec![],
            learning_to_rank_model: None,
            ranking_cache: HashMap::new(),
        })
    }

    fn rank_algorithms(
        &self,
        algorithms: &[AlgorithmInfo],
        _criteria: &DiscoveryCriteria,
    ) -> DeviceResult<Vec<RankedResult>> {
        let mut results = Vec::new();
        for (i, algorithm) in algorithms.iter().enumerate() {
            results.push(RankedResult {
                algorithm_id: algorithm.algorithm_id.clone(),
                rank: i,
                score: (i as f64).mul_add(-0.1, 1.0),
                feature_scores: HashMap::new(),
                explanation: RankingExplanation {
                    primary_factors: vec!["relevance".to_string()],
                    relevance_score: 0.9,
                    popularity_score: 0.8,
                    quality_score: 0.85,
                    personalization_boost: 0.1,
                    detailed_explanation: "High relevance to query".to_string(),
                },
            });
        }
        Ok(results)
    }
}

impl PersonalizationEngine {
    fn new() -> DeviceResult<Self> {
        Ok(Self {
            personalization_models: HashMap::new(),
            context_analyzers: vec![],
            adaptation_strategies: vec![],
            privacy_preserving: true,
        })
    }

    fn personalize_results(
        &self,
        results: &[AlgorithmInfo],
        _user_context: &UserContext,
    ) -> DeviceResult<Vec<f64>> {
        // Simplified personalization
        Ok(results
            .iter()
            .enumerate()
            .map(|(i, _)| (i as f64).mul_add(-0.1, 0.9))
            .collect())
    }
}

impl DiscoveryCache {
    fn new() -> Self {
        Self {
            query_cache: HashMap::new(),
            recommendation_cache: HashMap::new(),
            similarity_cache: HashMap::new(),
            cache_stats: CacheStatistics::default(),
        }
    }
}