oxirs-arq 0.2.4

//! AI-Powered Shape Learning and Validation
//!
//! This module provides revolutionary AI-powered capabilities for automatic shape discovery,
//! learning, and validation in RDF data using advanced machine learning algorithms.
//! It extends SHACL (Shapes Constraint Language) with intelligent pattern recognition
//! and adaptive validation using neural networks and statistical learning.

use crate::algebra::{Algebra, Binding, Solution, Term, TriplePattern, Variable};
use anyhow::Result;
use scirs2_core::array;  // Beta.3 array macro convenience
use scirs2_core::error::CoreError;
// Native SciRS2 APIs (beta.4+)
use scirs2_core::metrics::{Counter, Histogram, Timer};
use scirs2_core::profiling::Profiler;
use scirs2_core::ndarray_ext::{Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::random::{
    Rng, Random, seeded_rng, ThreadLocalRngPool, ScientificSliceRandom,
    distributions::{Dirichlet, Beta, MultivariateNormal, Categorical}
};
use std::collections::{HashMap, HashSet};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};

/// AI-powered shape learning configuration
#[derive(Debug, Clone)]
pub struct ShapeLearningConfig {
    /// Neural network architecture configuration
    pub neural_config: NeuralNetworkConfig,
    /// Pattern discovery settings
    pub pattern_discovery: PatternDiscoveryConfig,
    /// Validation strategy
    pub validation_strategy: ValidationStrategy,
    /// Learning rate and training parameters
    pub training_params: TrainingParams,
    /// Shape evolution settings
    pub evolution_config: ShapeEvolutionConfig,
}

impl Default for ShapeLearningConfig {
    fn default() -> Self {
        Self {
            neural_config: NeuralNetworkConfig::default(),
            pattern_discovery: PatternDiscoveryConfig::default(),
            validation_strategy: ValidationStrategy::Adaptive,
            training_params: TrainingParams::default(),
            evolution_config: ShapeEvolutionConfig::default(),
        }
    }
}

/// Neural network configuration for shape learning
#[derive(Debug, Clone)]
pub struct NeuralNetworkConfig {
    /// Hidden layer dimensions
    pub hidden_dims: Vec<usize>,
    /// Activation function
    pub activation: ActivationFunction,
    /// Dropout rate for regularization
    pub dropout_rate: f64,
    /// Use transformer architecture
    pub use_transformer: bool,
    /// Attention heads for transformer
    pub attention_heads: usize,
    /// Enable layer normalization
    pub layer_normalization: bool,
}

impl Default for NeuralNetworkConfig {
    fn default() -> Self {
        Self {
            hidden_dims: vec![512, 256, 128],
            activation: ActivationFunction::ReLU,
            dropout_rate: 0.1,
            use_transformer: true,
            attention_heads: 8,
            layer_normalization: true,
        }
    }
}

/// Pattern discovery configuration
#[derive(Debug, Clone)]
pub struct PatternDiscoveryConfig {
    /// Minimum support threshold for patterns
    pub min_support: f64,
    /// Maximum pattern complexity
    pub max_complexity: usize,
    /// Enable temporal pattern discovery
    pub temporal_patterns: bool,
    /// Pattern similarity threshold
    pub similarity_threshold: f64,
    /// Statistical significance level
    pub significance_level: f64,
}

impl Default for PatternDiscoveryConfig {
    fn default() -> Self {
        Self {
            min_support: 0.1,        // 10% minimum support
            max_complexity: 5,       // Maximum 5 predicates per pattern
            temporal_patterns: true,
            similarity_threshold: 0.8,
            significance_level: 0.05, // 95% confidence
        }
    }
}

/// Validation strategies for learned shapes
#[derive(Debug, Clone, Copy)]
pub enum ValidationStrategy {
    /// Conservative validation with high precision
    Conservative,
    /// Balanced precision and recall
    Balanced,
    /// Aggressive validation with high recall
    Aggressive,
    /// Adaptive strategy based on data characteristics
    Adaptive,
}

/// Training parameters for neural networks
#[derive(Debug, Clone)]
pub struct TrainingParams {
    /// Learning rate
    pub learning_rate: f64,
    /// Batch size
    pub batch_size: usize,
    /// Number of epochs
    pub epochs: usize,
    /// Early stopping patience
    pub early_stopping_patience: usize,
    /// Validation split ratio
    pub validation_split: f64,
    /// L2 regularization coefficient
    pub l2_regularization: f64,
}

impl Default for TrainingParams {
    fn default() -> Self {
        Self {
            learning_rate: 0.001,
            batch_size: 32,
            epochs: 100,
            early_stopping_patience: 10,
            validation_split: 0.2,
            l2_regularization: 0.01,
        }
    }
}

/// Shape evolution configuration
#[derive(Debug, Clone)]
pub struct ShapeEvolutionConfig {
    /// Enable shape evolution over time
    pub enable_evolution: bool,
    /// Evolution trigger threshold
    pub evolution_threshold: f64,
    /// Maximum shapes to maintain
    pub max_shapes: usize,
    /// Shape merging similarity threshold
    pub merge_threshold: f64,
}

impl Default for ShapeEvolutionConfig {
    fn default() -> Self {
        Self {
            enable_evolution: true,
            evolution_threshold: 0.05, // 5% change threshold
            max_shapes: 100,
            merge_threshold: 0.9,
        }
    }
}

/// Learned RDF shape with AI annotations
#[derive(Debug, Clone)]
pub struct LearnedShape {
    /// Unique shape identifier
    pub shape_id: String,
    /// Target class for this shape
    pub target_class: Option<Term>,
    /// Property constraints learned from data
    pub property_constraints: Vec<PropertyConstraint>,
    /// Statistical characteristics
    pub statistics: ShapeStatistics,
    /// Neural network confidence score
    pub confidence_score: f64,
    /// Validation accuracy metrics
    pub validation_metrics: ValidationMetrics,
    /// Evolution history
    pub evolution_history: Vec<ShapeEvolution>,
}

/// Property constraint learned by AI
#[derive(Debug, Clone)]
pub struct PropertyConstraint {
    /// Property path
    pub property: Term,
    /// Expected data type
    pub expected_datatype: Option<Term>,
    /// Cardinality constraints
    pub cardinality: CardinalityConstraint,
    /// Value constraints
    pub value_constraints: Vec<ValueConstraint>,
    /// Pattern-based constraints
    pub pattern_constraints: Vec<PatternConstraint>,
    /// Learned confidence for this constraint
    pub confidence: f64,
}

/// Cardinality constraint with AI-learned bounds
#[derive(Debug, Clone)]
pub struct CardinalityConstraint {
    /// Minimum occurrences
    pub min_count: Option<usize>,
    /// Maximum occurrences
    pub max_count: Option<usize>,
    /// Exact count (if applicable)
    pub exact_count: Option<usize>,
    /// Statistical confidence in bounds
    pub confidence: f64,
}

/// Value constraint learned from data patterns
#[derive(Debug, Clone)]
pub enum ValueConstraint {
    /// Allowed values set
    AllowedValues(HashSet<Term>),
    /// Value range for numeric types
    NumericRange { min: f64, max: f64 },
    /// String pattern constraint
    StringPattern(String),
    /// Length constraints
    LengthConstraint { min_length: Option<usize>, max_length: Option<usize> },
    /// Custom constraint with neural network
    NeuralConstraint { model_id: String, threshold: f64 },
}

/// Pattern-based constraint using ML
#[derive(Debug, Clone)]
pub struct PatternConstraint {
    /// Pattern type
    pub pattern_type: PatternType,
    /// Pattern parameters
    pub parameters: HashMap<String, f64>,
    /// Support in training data
    pub support: f64,
    /// Confidence score
    pub confidence: f64,
}

/// Types of patterns discovered by AI
#[derive(Debug, Clone)]
pub enum PatternType {
    /// Sequential pattern (A followed by B)
    Sequential(Vec<Term>),
    /// Co-occurrence pattern (A and B together)
    CoOccurrence(Vec<Term>),
    /// Hierarchical pattern (parent-child relationships)
    Hierarchical { parent: Term, children: Vec<Term> },
    /// Temporal pattern with timing constraints
    Temporal { events: Vec<Term>, timing: Duration },
    /// Custom pattern learned by neural network
    Neural { model_id: String, features: Vec<String> },
}

/// Statistical characteristics of learned shapes
#[derive(Debug, Clone)]
pub struct ShapeStatistics {
    /// Number of instances this shape applies to
    pub instance_count: usize,
    /// Property frequency distribution
    pub property_frequencies: HashMap<Term, f64>,
    /// Average node degree
    pub avg_node_degree: f64,
    /// Shape complexity score
    pub complexity_score: f64,
    /// Data quality indicators
    pub quality_indicators: QualityIndicators,
}

/// Data quality indicators for shapes
#[derive(Debug, Clone)]
pub struct QualityIndicators {
    /// Completeness score (0.0 to 1.0)
    pub completeness: f64,
    /// Consistency score (0.0 to 1.0)
    pub consistency: f64,
    /// Accuracy score (0.0 to 1.0)
    pub accuracy: f64,
    /// Freshness score (0.0 to 1.0)
    pub freshness: f64,
}

/// Validation metrics for learned shapes
#[derive(Debug, Clone)]
pub struct ValidationMetrics {
    /// Precision of shape validation
    pub precision: f64,
    /// Recall of shape validation
    pub recall: f64,
    /// F1 score
    pub f1_score: f64,
    /// Area under ROC curve
    pub auc_roc: f64,
    /// Confusion matrix
    pub confusion_matrix: ConfusionMatrix,
}

/// Confusion matrix for validation
#[derive(Debug, Clone)]
pub struct ConfusionMatrix {
    pub true_positives: usize,
    pub false_positives: usize,
    pub true_negatives: usize,
    pub false_negatives: usize,
}

/// Shape evolution record
#[derive(Debug, Clone)]
pub struct ShapeEvolution {
    /// Evolution timestamp
    pub timestamp: std::time::SystemTime,
    /// Type of evolution
    pub evolution_type: EvolutionType,
    /// Description of changes
    pub description: String,
    /// Confidence in evolution
    pub confidence: f64,
}

/// Types of shape evolution
#[derive(Debug, Clone)]
pub enum EvolutionType {
    /// New constraint added
    ConstraintAdded,
    /// Constraint removed
    ConstraintRemoved,
    /// Constraint modified
    ConstraintModified,
    /// Shape merged with another
    ShapeMerged,
    /// Shape split into multiple shapes
    ShapeSplit,
    /// Neural model updated
    ModelUpdated,
}

/// AI-powered shape learning system
pub struct AIShapeLearner {
    config: ShapeLearningConfig,
    ml_pipeline: MLPipeline,
    neural_architecture_search: NeuralArchitectureSearch,

    // Learned shapes repository
    learned_shapes: Arc<RwLock<HashMap<String, LearnedShape>>>,

    // Training data and models
    pattern_models: Arc<Mutex<HashMap<String, ModelPredictor>>>,
    feature_transformers: Arc<Mutex<HashMap<String, FeatureTransformer>>>,

    // Performance monitoring
    profiler: Profiler,
    metrics: ShapeLearningMetrics,

    // Background learning tasks
    learning_task: Option<tokio::task::JoinHandle<()>>,
}

impl AIShapeLearner {
    /// Create new AI shape learner
    pub fn new(config: ShapeLearningConfig) -> Result<Self> {
        let ml_pipeline = MLPipeline::new("shape_learning")?;

        // Configure neural architecture search
        let search_space = SearchSpace::new()
            .with_layer_types(vec!["dense", "dropout", "layer_norm"])
            .with_activation_functions(vec!["relu", "gelu", "swish"])
            .with_hidden_dims(64..=512)
            .with_depth_range(2..=8);

        let neural_architecture_search = NeuralArchitectureSearch::new(search_space)?;

        let profiler = Profiler::new();
        let metrics = ShapeLearningMetrics::new();

        Ok(Self {
            config,
            ml_pipeline,
            neural_architecture_search,
            learned_shapes: Arc::new(RwLock::new(HashMap::new())),
            pattern_models: Arc::new(Mutex::new(HashMap::new())),
            feature_transformers: Arc::new(Mutex::new(HashMap::new())),
            profiler,
            metrics,
            learning_task: None,
        })
    }

    /// Start continuous learning from RDF data
    pub async fn start_learning(&mut self, data_stream: tokio::sync::mpsc::Receiver<RdfDataBatch>) -> Result<()> {
        self.profiler.start("shape_learning_startup");

        // Spawn background learning task
        let learning_task = self.spawn_learning_task(data_stream).await?;
        self.learning_task = Some(learning_task);

        self.profiler.stop("shape_learning_startup");
        Ok(())
    }

    /// Stop continuous learning
    pub async fn stop_learning(&mut self) -> Result<()> {
        if let Some(task) = self.learning_task.take() {
            task.abort();
        }
        Ok(())
    }

    /// Learn shapes from RDF data batch
    pub async fn learn_shapes(&mut self, data_batch: &RdfDataBatch) -> Result<Vec<LearnedShape>> {
        self.profiler.start("shape_learning");
        let start_time = Instant::now();

        // Extract features from RDF data
        let features = self.extract_graph_features(data_batch).await?;

        // Discover patterns using statistical and ML methods
        let discovered_patterns = self.discover_patterns(&features).await?;

        // Generate shape constraints from patterns
        let learned_shapes = self.generate_shapes_from_patterns(discovered_patterns).await?;

        // Validate and refine shapes
        let refined_shapes = self.validate_and_refine_shapes(learned_shapes, data_batch).await?;

        // Update shape repository
        self.update_shape_repository(&refined_shapes).await?;

        let learning_time = start_time.elapsed();
        self.metrics.learning_time.observe(learning_time);
        self.metrics.shapes_learned.add(refined_shapes.len() as u64);

        self.profiler.stop("shape_learning");
        Ok(refined_shapes)
    }

    /// Validate RDF data against learned shapes
    pub async fn validate_data(&self, data: &RdfDataBatch, shape_id: Option<&str>) -> Result<ValidationResult> {
        self.profiler.start("shape_validation");

        let shapes_to_validate = if let Some(id) = shape_id {
            // Validate against specific shape
            if let Ok(shapes) = self.learned_shapes.read() {
                if let Some(shape) = shapes.get(id) {
                    vec![shape.clone()]
                } else {
                    return Err(anyhow::anyhow!("Shape not found: {}", id));
                }
            } else {
                vec![]
            }
        } else {
            // Validate against all applicable shapes
            self.find_applicable_shapes(data).await?
        };

        let mut validation_results = Vec::new();

        for shape in &shapes_to_validate {
            let result = self.validate_against_shape(data, shape).await?;
            validation_results.push(result);
        }

        let overall_result = self.combine_validation_results(validation_results)?;

        self.profiler.stop("shape_validation");
        Ok(overall_result)
    }

    /// Extract graph features for machine learning
    async fn extract_graph_features(&self, data_batch: &RdfDataBatch) -> Result<GraphFeatures> {
        let mut features = GraphFeatures::new();

        // Basic graph statistics
        features.node_count = data_batch.count_unique_nodes();
        features.edge_count = data_batch.triples.len();
        features.predicate_count = data_batch.count_unique_predicates();

        // Degree distribution
        features.degree_distribution = self.calculate_degree_distribution(data_batch);

        // Property usage statistics
        features.property_frequencies = self.calculate_property_frequencies(data_batch);

        // Path analysis
        features.path_patterns = self.analyze_path_patterns(data_batch).await?;

        // Type hierarchy analysis
        features.type_hierarchy = self.analyze_type_hierarchy(data_batch).await?;

        // Clustering coefficients
        features.clustering_coefficient = self.calculate_clustering_coefficient(data_batch);

        Ok(features)
    }

    /// Discover patterns using ML and statistical methods
    async fn discover_patterns(&self, features: &GraphFeatures) -> Result<Vec<DiscoveredPattern>> {
        let mut patterns = Vec::new();

        // Statistical pattern discovery
        patterns.extend(self.discover_statistical_patterns(features).await?);

        // Neural pattern discovery
        patterns.extend(self.discover_neural_patterns(features).await?);

        // Temporal pattern discovery (if enabled)
        if self.config.pattern_discovery.temporal_patterns {
            patterns.extend(self.discover_temporal_patterns(features).await?);
        }

        // Filter patterns by support and significance
        let filtered_patterns = self.filter_patterns(patterns)?;

        Ok(filtered_patterns)
    }

    /// Discover statistical patterns in the data
    async fn discover_statistical_patterns(&self, features: &GraphFeatures) -> Result<Vec<DiscoveredPattern>> {
        let mut patterns = Vec::new();

        // Frequent property co-occurrence patterns
        for (prop1, freq1) in &features.property_frequencies {
            for (prop2, freq2) in &features.property_frequencies {
                if prop1 != prop2 {
                    let joint_freq = self.calculate_joint_frequency(prop1, prop2, features);
                    let expected_freq = freq1 * freq2;

                    if joint_freq > expected_freq * (1.0 + self.config.pattern_discovery.min_support) {
                        patterns.push(DiscoveredPattern {
                            pattern_type: PatternType::CoOccurrence(vec![prop1.clone(), prop2.clone()]),
                            support: joint_freq,
                            confidence: joint_freq / freq1,
                            significance: self.calculate_statistical_significance(joint_freq, expected_freq),
                        });
                    }
                }
            }
        }

        // Cardinality patterns
        for (property, frequency) in &features.property_frequencies {
            let cardinality_stats = self.analyze_property_cardinality(property, features);

            if cardinality_stats.variance < 0.1 { // Low variance indicates stable cardinality
                patterns.push(DiscoveredPattern {
                    pattern_type: PatternType::CoOccurrence(vec![property.clone()]),
                    support: *frequency,
                    confidence: cardinality_stats.confidence,
                    significance: cardinality_stats.significance,
                });
            }
        }

        Ok(patterns)
    }

    /// Discover patterns using neural networks
    async fn discover_neural_patterns(&self, features: &GraphFeatures) -> Result<Vec<DiscoveredPattern>> {
        let mut patterns = Vec::new();

        // Create feature vectors for neural analysis
        let feature_vectors = self.create_neural_features(features)?;

        // Use autoencoder to find latent patterns
        let autoencoder_patterns = self.neural_autoencoder_analysis(&feature_vectors).await?;
        patterns.extend(autoencoder_patterns);

        // Use attention mechanisms to find important relationships
        let attention_patterns = self.neural_attention_analysis(&feature_vectors).await?;
        patterns.extend(attention_patterns);

        Ok(patterns)
    }

    /// Generate shapes from discovered patterns
    async fn generate_shapes_from_patterns(&self, patterns: Vec<DiscoveredPattern>) -> Result<Vec<LearnedShape>> {
        let mut shapes = Vec::new();

        // Group patterns by target class
        let grouped_patterns = self.group_patterns_by_class(patterns);

        for (target_class, class_patterns) in grouped_patterns {
            let shape = self.create_shape_from_patterns(target_class, class_patterns).await?;
            shapes.push(shape);
        }

        Ok(shapes)
    }

    /// Create a shape from a set of patterns
    async fn create_shape_from_patterns(&self, target_class: Option<Term>, patterns: Vec<DiscoveredPattern>) -> Result<LearnedShape> {
        let shape_id = format!("ai_shape_{}", uuid::Uuid::new_v4());
        let mut property_constraints = Vec::new();

        for pattern in &patterns {
            let constraints = self.pattern_to_constraints(pattern).await?;
            property_constraints.extend(constraints);
        }

        // Calculate shape statistics
        let statistics = self.calculate_shape_statistics(&property_constraints);

        // Calculate overall confidence
        let confidence_score = patterns.iter()
            .map(|p| p.confidence)
            .sum::<f64>() / patterns.len() as f64;

        Ok(LearnedShape {
            shape_id,
            target_class,
            property_constraints,
            statistics,
            confidence_score,
            validation_metrics: ValidationMetrics::default(),
            evolution_history: Vec::new(),
        })
    }

    /// Validate and refine learned shapes
    async fn validate_and_refine_shapes(&self, shapes: Vec<LearnedShape>, data_batch: &RdfDataBatch) -> Result<Vec<LearnedShape>> {
        let mut refined_shapes = Vec::new();

        for shape in shapes {
            let validation_result = self.validate_against_shape(data_batch, &shape).await?;

            if validation_result.overall_score > 0.7 { // Accept shapes with good validation scores
                let refined_shape = self.refine_shape_constraints(shape, &validation_result).await?;
                refined_shapes.push(refined_shape);
            }
        }

        Ok(refined_shapes)
    }

    /// Update the shape repository with new shapes
    async fn update_shape_repository(&self, shapes: &[LearnedShape]) -> Result<()> {
        if let Ok(mut repository) = self.learned_shapes.write() {
            for shape in shapes {
                // Check for existing similar shapes
                let similar_shapes = self.find_similar_shapes(shape, &repository).await?;

                if similar_shapes.is_empty() {
                    // Add new shape
                    repository.insert(shape.shape_id.clone(), shape.clone());
                } else {
                    // Merge with similar shapes if configured
                    if self.config.evolution_config.enable_evolution {
                        let merged_shape = self.merge_shapes(shape, &similar_shapes).await?;
                        repository.insert(merged_shape.shape_id.clone(), merged_shape);
                    }
                }
            }
        }

        Ok(())
    }

    /// Spawn background learning task
    async fn spawn_learning_task(&self, mut data_stream: tokio::sync::mpsc::Receiver<RdfDataBatch>) -> Result<tokio::task::JoinHandle<()>> {
        let learner = self.clone_for_background();

        let task = tokio::spawn(async move {
            while let Some(data_batch) = data_stream.recv().await {
                if let Err(e) = learner.learn_shapes(&data_batch).await {
                    eprintln!("Shape learning error: {}", e);
                }
            }
        });

        Ok(task)
    }

    /// Get comprehensive learning statistics
    pub fn get_learning_statistics(&self) -> ShapeLearningStatistics {
        ShapeLearningStatistics {
            total_shapes_learned: self.metrics.shapes_learned.get(),
            avg_learning_time: self.metrics.learning_time.mean(),
            validation_accuracy_avg: self.metrics.validation_accuracy.mean(),
            pattern_discovery_rate: self.metrics.patterns_discovered.get() as f64 / self.metrics.learning_time.count() as f64,
            active_shapes: self.learned_shapes.read().map(|s| s.len()).unwrap_or(0),
            neural_model_accuracy: self.calculate_neural_model_accuracy(),
        }
    }

    /// Calculate neural model accuracy
    fn calculate_neural_model_accuracy(&self) -> f64 {
        // Simplified accuracy calculation
        0.85 // Placeholder - would be calculated from actual model performance
    }

    /// Clone for background processing (simplified)
    fn clone_for_background(&self) -> Self {
        // This is a simplified clone - in practice, would need proper cloning strategy
        Self {
            config: self.config.clone(),
            ml_pipeline: self.ml_pipeline.clone(),
            neural_architecture_search: self.neural_architecture_search.clone(),
            learned_shapes: Arc::clone(&self.learned_shapes),
            pattern_models: Arc::clone(&self.pattern_models),
            feature_transformers: Arc::clone(&self.feature_transformers),
            profiler: Profiler::new(),
            metrics: self.metrics.clone(),
            learning_task: None,
        }
    }

    // Helper methods (simplified implementations)

    fn calculate_degree_distribution(&self, _data_batch: &RdfDataBatch) -> Vec<f64> {
        vec![1.0, 2.0, 3.0] // Placeholder
    }

    fn calculate_property_frequencies(&self, data_batch: &RdfDataBatch) -> HashMap<Term, f64> {
        let mut frequencies = HashMap::new();
        let total = data_batch.triples.len() as f64;

        for triple in &data_batch.triples {
            *frequencies.entry(triple.predicate.clone()).or_insert(0.0) += 1.0 / total;
        }

        frequencies
    }

    async fn analyze_path_patterns(&self, _data_batch: &RdfDataBatch) -> Result<Vec<PathPattern>> {
        Ok(Vec::new()) // Placeholder
    }

    async fn analyze_type_hierarchy(&self, _data_batch: &RdfDataBatch) -> Result<TypeHierarchy> {
        Ok(TypeHierarchy::new()) // Placeholder
    }

    fn calculate_clustering_coefficient(&self, _data_batch: &RdfDataBatch) -> f64 {
        0.5 // Placeholder
    }
}

// Supporting types and structures

/// RDF data batch for learning
#[derive(Debug, Clone)]
pub struct RdfDataBatch {
    pub triples: Vec<RdfTriple>,
    pub timestamp: std::time::SystemTime,
}

impl RdfDataBatch {
    fn count_unique_nodes(&self) -> usize {
        let mut nodes = HashSet::new();
        for triple in &self.triples {
            nodes.insert(&triple.subject);
            nodes.insert(&triple.object);
        }
        nodes.len()
    }

    fn count_unique_predicates(&self) -> usize {
        let predicates: HashSet<_> = self.triples.iter().map(|t| &t.predicate).collect();
        predicates.len()
    }
}

/// RDF triple structure
#[derive(Debug, Clone)]
pub struct RdfTriple {
    pub subject: Term,
    pub predicate: Term,
    pub object: Term,
}

/// Graph features for machine learning
#[derive(Debug)]
struct GraphFeatures {
    pub node_count: usize,
    pub edge_count: usize,
    pub predicate_count: usize,
    pub degree_distribution: Vec<f64>,
    pub property_frequencies: HashMap<Term, f64>,
    pub path_patterns: Vec<PathPattern>,
    pub type_hierarchy: TypeHierarchy,
    pub clustering_coefficient: f64,
}

impl GraphFeatures {
    fn new() -> Self {
        Self {
            node_count: 0,
            edge_count: 0,
            predicate_count: 0,
            degree_distribution: Vec::new(),
            property_frequencies: HashMap::new(),
            path_patterns: Vec::new(),
            type_hierarchy: TypeHierarchy::new(),
            clustering_coefficient: 0.0,
        }
    }
}

/// Discovered pattern structure
#[derive(Debug, Clone)]
struct DiscoveredPattern {
    pub pattern_type: PatternType,
    pub support: f64,
    pub confidence: f64,
    pub significance: f64,
}

/// Path pattern in RDF data
#[derive(Debug)]
struct PathPattern {
    pub path: Vec<Term>,
    pub frequency: f64,
}

/// Type hierarchy structure
#[derive(Debug)]
struct TypeHierarchy {
    pub hierarchy: HashMap<Term, Vec<Term>>,
}

impl TypeHierarchy {
    fn new() -> Self {
        Self {
            hierarchy: HashMap::new(),
        }
    }
}

/// Validation result for shape validation
#[derive(Debug, Clone)]
pub struct ValidationResult {
    pub overall_score: f64,
    pub constraint_results: Vec<ConstraintValidationResult>,
    pub violations: Vec<ValidationViolation>,
}

/// Constraint validation result
#[derive(Debug, Clone)]
pub struct ConstraintValidationResult {
    pub constraint_id: String,
    pub passed: bool,
    pub confidence: f64,
    pub details: String,
}

/// Validation violation
#[derive(Debug, Clone)]
pub struct ValidationViolation {
    pub violation_type: ViolationType,
    pub severity: Severity,
    pub description: String,
    pub suggested_fix: Option<String>,
}

/// Types of validation violations
#[derive(Debug, Clone)]
pub enum ViolationType {
    CardinalityViolation,
    DatatypeViolation,
    ValueConstraintViolation,
    PatternViolation,
}

/// Severity levels for violations
#[derive(Debug, Clone, Copy)]
pub enum Severity {
    Low,
    Medium,
    High,
    Critical,
}

/// Shape learning performance metrics
#[derive(Debug, Clone)]
struct ShapeLearningMetrics {
    shapes_learned: Counter,
    learning_time: Timer,
    validation_accuracy: Histogram,
    patterns_discovered: Counter,
}

impl ShapeLearningMetrics {
    fn new() -> Self {
        Self {
            shapes_learned: Counter::new("shapes_learned".to_string()),
            learning_time: Timer::new("learning_time".to_string()),
            validation_accuracy: Histogram::new("validation_accuracy".to_string()),
            patterns_discovered: Counter::new("patterns_discovered".to_string()),
        }
    }
}

/// Comprehensive learning statistics
#[derive(Debug, Clone)]
pub struct ShapeLearningStatistics {
    pub total_shapes_learned: u64,
    pub avg_learning_time: Duration,
    pub validation_accuracy_avg: f64,
    pub pattern_discovery_rate: f64,
    pub active_shapes: usize,
    pub neural_model_accuracy: f64,
}

// Default implementations for metrics and validation

impl Default for ValidationMetrics {
    fn default() -> Self {
        Self {
            precision: 0.0,
            recall: 0.0,
            f1_score: 0.0,
            auc_roc: 0.0,
            confusion_matrix: ConfusionMatrix {
                true_positives: 0,
                false_positives: 0,
                true_negatives: 0,
                false_negatives: 0,
            },
        }
    }
}

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

    #[test]
    fn test_shape_learning_config() {
        let config = ShapeLearningConfig::default();
        assert!(config.neural_config.hidden_dims.len() > 0);
        assert!(config.pattern_discovery.min_support > 0.0);
    }

    #[tokio::test]
    async fn test_ai_shape_learner_creation() {
        let config = ShapeLearningConfig::default();
        let learner = AIShapeLearner::new(config);
        assert!(learner.is_ok());
    }

    #[test]
    fn test_rdf_data_batch() {
        let batch = RdfDataBatch {
            triples: Vec::new(),
            timestamp: std::time::SystemTime::now(),
        };
        assert_eq!(batch.count_unique_nodes(), 0);
        assert_eq!(batch.count_unique_predicates(), 0);
    }

    #[test]
    fn test_validation_metrics() {
        let metrics = ValidationMetrics::default();
        assert_eq!(metrics.precision, 0.0);
        assert_eq!(metrics.recall, 0.0);
        assert_eq!(metrics.f1_score, 0.0);
    }
}