torsh-text 0.1.2

//! Semantic Analysis for Lexical Coherence
//!
//! This module provides sophisticated semantic relationship detection and analysis
//! capabilities for lexical coherence measurement. It handles word sense disambiguation,
//! semantic similarity calculation, and relationship classification.

use crate::metrics::coherence::lexical_coherence::config::{
    SemanticAnalysisConfig, SemanticRelationshipType,
};
use crate::metrics::coherence::lexical_coherence::results::{
    LexicalItem, SemanticAnalysisResult, SemanticCluster, SemanticRelationship,
};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use thiserror::Error;

/// Errors that can occur during semantic analysis
#[derive(Error, Debug)]
pub enum SemanticAnalysisError {
    #[error("Failed to load semantic lexicon: {0}")]
    LexiconLoadError(String),
    #[error("Word sense disambiguation failed: {0}")]
    DisambiguationError(String),
    #[error("Semantic similarity calculation error: {0}")]
    SimilarityError(String),
    #[error("Invalid semantic configuration: {0}")]
    ConfigurationError(String),
}

/// Core semantic analyzer for lexical coherence analysis
#[derive(Debug)]
pub struct SemanticAnalyzer {
    config: SemanticAnalysisConfig,

    // Semantic knowledge base
    wordnet_relations: HashMap<String, HashMap<String, Vec<String>>>,
    word_embeddings: HashMap<String, Vec<f64>>,
    semantic_networks: HashMap<String, SemanticNetwork>,

    // Analysis caches
    similarity_cache: HashMap<(String, String), f64>,
    relationship_cache: HashMap<(String, String), Vec<SemanticRelationship>>,
    word_sense_cache: HashMap<String, Vec<WordSense>>,

    // Processing components
    word_sense_disambiguator: WordSenseDisambiguator,
    similarity_calculator: SimilarityCalculator,
    relationship_classifier: RelationshipClassifier,
}

/// Semantic network structure for relationship modeling
#[derive(Debug, Clone)]
struct SemanticNetwork {
    nodes: HashMap<String, SemanticNode>,
    edges: HashMap<(String, String), SemanticEdge>,
    clusters: Vec<SemanticCluster>,
}

/// Individual semantic node in the network
#[derive(Debug, Clone)]
struct SemanticNode {
    word: String,
    senses: Vec<WordSense>,
    frequency: f64,
    centrality: f64,
    semantic_features: Vec<String>,
}

/// Semantic edge representing relationships between words
#[derive(Debug, Clone)]
struct SemanticEdge {
    source: String,
    target: String,
    relationship_type: SemanticRelationshipType,
    strength: f64,
    confidence: f64,
    distance: f64,
}

/// Word sense information for disambiguation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WordSense {
    pub sense_id: String,
    pub definition: String,
    pub examples: Vec<String>,
    pub frequency: f64,
    pub semantic_features: Vec<String>,
    pub hypernyms: Vec<String>,
    pub hyponyms: Vec<String>,
    pub synonyms: Vec<String>,
    pub antonyms: Vec<String>,
    pub meronyms: Vec<String>,
    pub holonyms: Vec<String>,
}

/// Word sense disambiguation component
#[derive(Debug)]
struct WordSenseDisambiguator {
    context_window: usize,
    disambiguation_threshold: f64,
    sense_embeddings: HashMap<String, Vec<f64>>,
    context_similarity_cache: HashMap<String, f64>,
}

/// Semantic similarity calculation component
#[derive(Debug)]
struct SimilarityCalculator {
    embedding_dim: usize,
    similarity_metrics: Vec<SimilarityMetric>,
    weight_combinations: HashMap<String, f64>,
}

/// Relationship classification component
#[derive(Debug)]
struct RelationshipClassifier {
    classification_rules: HashMap<SemanticRelationshipType, ClassificationRule>,
    feature_extractors: Vec<FeatureExtractor>,
    confidence_thresholds: HashMap<SemanticRelationshipType, f64>,
}

/// Similarity metric enumeration
#[derive(Debug, Clone)]
enum SimilarityMetric {
    Cosine,
    Jaccard,
    PathBased,
    InformationContent,
    Wu_Palmer,
    Leacock_Chodorow,
    Resnik,
    JiangConrath,
    Lin,
}

/// Classification rule for semantic relationships
#[derive(Debug, Clone)]
struct ClassificationRule {
    feature_patterns: Vec<String>,
    weight_vector: Vec<f64>,
    threshold: f64,
    confidence_adjustment: f64,
}

/// Feature extractor for relationship classification
#[derive(Debug, Clone)]
struct FeatureExtractor {
    name: String,
    extraction_function: fn(&str, &str, &HashMap<String, WordSense>) -> Vec<f64>,
}

impl SemanticAnalyzer {
    /// Create a new semantic analyzer with configuration
    pub fn new(config: SemanticAnalysisConfig) -> Result<Self, SemanticAnalysisError> {
        let mut analyzer = SemanticAnalyzer {
            config: config.clone(),
            wordnet_relations: HashMap::new(),
            word_embeddings: HashMap::new(),
            semantic_networks: HashMap::new(),
            similarity_cache: HashMap::new(),
            relationship_cache: HashMap::new(),
            word_sense_cache: HashMap::new(),
            word_sense_disambiguator: WordSenseDisambiguator::new(&config)?,
            similarity_calculator: SimilarityCalculator::new(&config)?,
            relationship_classifier: RelationshipClassifier::new(&config)?,
        };

        analyzer.initialize_semantic_resources()?;
        Ok(analyzer)
    }

    /// Perform comprehensive semantic analysis on text
    pub fn analyze_semantic_relationships(
        &mut self,
        lexical_items: &[LexicalItem],
        context: &[String],
    ) -> Result<SemanticAnalysisResult, SemanticAnalysisError> {
        // Step 1: Word sense disambiguation
        let disambiguated_items = self.disambiguate_word_senses(lexical_items, context)?;

        // Step 2: Calculate pairwise semantic similarities
        let similarity_matrix = self.calculate_similarity_matrix(&disambiguated_items)?;

        // Step 3: Identify semantic relationships
        let relationships =
            self.identify_semantic_relationships(&disambiguated_items, &similarity_matrix)?;

        // Step 4: Build semantic network
        let semantic_network = self.build_semantic_network(&disambiguated_items, &relationships)?;

        // Step 5: Perform network analysis
        let network_metrics = self.analyze_semantic_network(&semantic_network)?;

        // Step 6: Calculate semantic cohesion scores
        let cohesion_scores = self.calculate_semantic_cohesion(&relationships, &network_metrics)?;

        // Step 7: Generate semantic clusters
        let clusters = self.generate_semantic_clusters(&semantic_network, &similarity_matrix)?;

        Ok(SemanticAnalysisResult {
            disambiguated_items,
            similarity_matrix,
            relationships,
            semantic_network: network_metrics,
            cohesion_scores,
            clusters,
            analysis_metadata: self.generate_analysis_metadata(),
        })
    }

    /// Initialize semantic resources (WordNet, embeddings, etc.)
    fn initialize_semantic_resources(&mut self) -> Result<(), SemanticAnalysisError> {
        // Load WordNet relations
        if self.config.use_wordnet {
            self.load_wordnet_relations()?;
        }

        // Load word embeddings
        if self.config.use_embeddings {
            self.load_word_embeddings()?;
        }

        // Initialize semantic networks
        if self.config.build_semantic_networks {
            self.initialize_semantic_networks()?;
        }

        Ok(())
    }

    /// Load WordNet semantic relations
    fn load_wordnet_relations(&mut self) -> Result<(), SemanticAnalysisError> {
        // This would typically load from WordNet database
        // For now, we'll initialize with basic relations
        self.wordnet_relations = HashMap::new();

        // Basic synonym/antonym patterns
        let basic_relations = vec![
            (
                "good",
                vec![
                    ("great", "synonym"),
                    ("bad", "antonym"),
                    ("excellent", "synonym"),
                ],
            ),
            (
                "big",
                vec![
                    ("large", "synonym"),
                    ("small", "antonym"),
                    ("huge", "synonym"),
                ],
            ),
            (
                "happy",
                vec![
                    ("joyful", "synonym"),
                    ("sad", "antonym"),
                    ("cheerful", "synonym"),
                ],
            ),
        ];

        for (word, relations) in basic_relations {
            let mut word_relations = HashMap::new();
            for (related_word, relation_type) in relations {
                word_relations
                    .entry(relation_type.to_string())
                    .or_insert_with(Vec::new)
                    .push(related_word.to_string());
            }
            self.wordnet_relations
                .insert(word.to_string(), word_relations);
        }

        Ok(())
    }

    /// Load pre-trained word embeddings
    fn load_word_embeddings(&mut self) -> Result<(), SemanticAnalysisError> {
        // This would typically load from embedding files (Word2Vec, GloVe, etc.)
        // For now, we'll create dummy embeddings
        self.word_embeddings = HashMap::new();

        let common_words = vec![
            "the", "and", "or", "but", "good", "bad", "big", "small", "happy", "sad", "run",
            "walk", "fast", "slow", "time", "day",
        ];

        for word in common_words {
            // Generate dummy embedding vector
            let embedding: Vec<f64> = (0..self.config.embedding_dimension)
                .map(|_| rand::random::<f64>() * 2.0 - 1.0)
                .collect();
            self.word_embeddings.insert(word.to_string(), embedding);
        }

        Ok(())
    }

    /// Initialize semantic network structures
    fn initialize_semantic_networks(&mut self) -> Result<(), SemanticAnalysisError> {
        self.semantic_networks = HashMap::new();
        Ok(())
    }

    /// Disambiguate word senses based on context
    fn disambiguate_word_senses(
        &mut self,
        lexical_items: &[LexicalItem],
        context: &[String],
    ) -> Result<Vec<LexicalItem>, SemanticAnalysisError> {
        let mut disambiguated_items = Vec::new();

        for item in lexical_items {
            let disambiguated_item = if self.config.perform_disambiguation {
                self.word_sense_disambiguator.disambiguate(item, context)?
            } else {
                item.clone()
            };
            disambiguated_items.push(disambiguated_item);
        }

        Ok(disambiguated_items)
    }

    /// Calculate semantic similarity matrix between all lexical items
    fn calculate_similarity_matrix(
        &mut self,
        lexical_items: &[LexicalItem],
    ) -> Result<Vec<Vec<f64>>, SemanticAnalysisError> {
        let n = lexical_items.len();
        let mut matrix = vec![vec![0.0; n]; n];

        for i in 0..n {
            for j in i..n {
                if i == j {
                    matrix[i][j] = 1.0;
                } else {
                    let similarity = self
                        .similarity_calculator
                        .calculate_similarity(&lexical_items[i], &lexical_items[j])?;
                    matrix[i][j] = similarity;
                    matrix[j][i] = similarity;
                }
            }
        }

        Ok(matrix)
    }

    /// Identify semantic relationships between lexical items
    fn identify_semantic_relationships(
        &mut self,
        lexical_items: &[LexicalItem],
        similarity_matrix: &[Vec<f64>],
    ) -> Result<Vec<SemanticRelationship>, SemanticAnalysisError> {
        let mut relationships = Vec::new();

        for i in 0..lexical_items.len() {
            for j in (i + 1)..lexical_items.len() {
                if similarity_matrix[i][j] >= self.config.relationship_threshold {
                    let relationship = self.relationship_classifier.classify_relationship(
                        &lexical_items[i],
                        &lexical_items[j],
                        similarity_matrix[i][j],
                    )?;

                    if relationship.confidence >= self.config.confidence_threshold {
                        relationships.push(relationship);
                    }
                }
            }
        }

        Ok(relationships)
    }

    /// Build semantic network from relationships
    fn build_semantic_network(
        &mut self,
        lexical_items: &[LexicalItem],
        relationships: &[SemanticRelationship],
    ) -> Result<SemanticNetwork, SemanticAnalysisError> {
        let mut network = SemanticNetwork {
            nodes: HashMap::new(),
            edges: HashMap::new(),
            clusters: Vec::new(),
        };

        // Add nodes
        for item in lexical_items {
            let node = SemanticNode {
                word: item.word.clone(),
                senses: item.word_senses.clone(),
                frequency: item.frequency,
                centrality: 0.0, // Calculate later
                semantic_features: item.semantic_features.clone(),
            };
            network.nodes.insert(item.word.clone(), node);
        }

        // Add edges
        for relationship in relationships {
            let edge = SemanticEdge {
                source: relationship.source_word.clone(),
                target: relationship.target_word.clone(),
                relationship_type: relationship.relationship_type.clone(),
                strength: relationship.strength,
                confidence: relationship.confidence,
                distance: relationship.distance,
            };
            network.edges.insert(
                (
                    relationship.source_word.clone(),
                    relationship.target_word.clone(),
                ),
                edge,
            );
        }

        Ok(network)
    }

    /// Analyze semantic network properties
    fn analyze_semantic_network(
        &self,
        network: &SemanticNetwork,
    ) -> Result<HashMap<String, f64>, SemanticAnalysisError> {
        let mut metrics = HashMap::new();

        // Calculate basic network metrics
        metrics.insert("node_count".to_string(), network.nodes.len() as f64);
        metrics.insert("edge_count".to_string(), network.edges.len() as f64);

        if !network.nodes.is_empty() {
            let density = network.edges.len() as f64
                / (network.nodes.len() * (network.nodes.len() - 1) / 2) as f64;
            metrics.insert("density".to_string(), density);
        }

        // Calculate centrality measures
        let centrality_scores = self.calculate_centrality_measures(network)?;
        for (measure, score) in centrality_scores {
            metrics.insert(format!("centrality_{}", measure), score);
        }

        // Calculate clustering coefficient
        let clustering_coefficient = self.calculate_clustering_coefficient(network)?;
        metrics.insert("clustering_coefficient".to_string(), clustering_coefficient);

        Ok(metrics)
    }

    /// Calculate semantic cohesion scores
    fn calculate_semantic_cohesion(
        &self,
        relationships: &[SemanticRelationship],
        network_metrics: &HashMap<String, f64>,
    ) -> Result<HashMap<String, f64>, SemanticAnalysisError> {
        let mut cohesion_scores = HashMap::new();

        // Overall semantic cohesion
        let total_strength: f64 = relationships.iter().map(|r| r.strength).sum();
        let avg_strength = if !relationships.is_empty() {
            total_strength / relationships.len() as f64
        } else {
            0.0
        };
        cohesion_scores.insert("overall_cohesion".to_string(), avg_strength);

        // Relationship type specific cohesion
        let mut type_groups: HashMap<String, Vec<f64>> = HashMap::new();
        for relationship in relationships {
            type_groups
                .entry(format!("{:?}", relationship.relationship_type))
                .or_default()
                .push(relationship.strength);
        }

        for (rel_type, strengths) in type_groups {
            let avg_type_strength = strengths.iter().sum::<f64>() / strengths.len() as f64;
            cohesion_scores.insert(format!("{}_cohesion", rel_type), avg_type_strength);
        }

        // Network-based cohesion
        if let Some(density) = network_metrics.get("density") {
            cohesion_scores.insert("network_cohesion".to_string(), *density);
        }

        Ok(cohesion_scores)
    }

    /// Generate semantic clusters
    fn generate_semantic_clusters(
        &self,
        network: &SemanticNetwork,
        similarity_matrix: &[Vec<f64>],
    ) -> Result<Vec<SemanticCluster>, SemanticAnalysisError> {
        let mut clusters = Vec::new();

        if network.nodes.is_empty() {
            return Ok(clusters);
        }

        // Simple clustering based on similarity threshold
        let words: Vec<String> = network.nodes.keys().cloned().collect();
        let mut assigned = HashSet::new();
        let mut cluster_id = 0;

        for i in 0..words.len() {
            if assigned.contains(&i) {
                continue;
            }

            let mut cluster_words = vec![words[i].clone()];
            assigned.insert(i);

            for j in (i + 1)..words.len() {
                if assigned.contains(&j) {
                    continue;
                }

                if similarity_matrix[i][j] >= self.config.clustering_threshold {
                    cluster_words.push(words[j].clone());
                    assigned.insert(j);
                }
            }

            if cluster_words.len() > 1 {
                let avg_coherence =
                    self.calculate_cluster_coherence(&cluster_words, similarity_matrix, &words)?;

                clusters.push(SemanticCluster {
                    cluster_id,
                    words: cluster_words,
                    coherence_score: avg_coherence,
                    size: cluster_words.len(),
                    centroid_word: None,  // Could be calculated
                    semantic_theme: None, // Could be inferred
                });

                cluster_id += 1;
            }
        }

        Ok(clusters)
    }

    /// Calculate centrality measures for network analysis
    fn calculate_centrality_measures(
        &self,
        network: &SemanticNetwork,
    ) -> Result<HashMap<String, f64>, SemanticAnalysisError> {
        let mut measures = HashMap::new();

        // Degree centrality
        let max_degree = network
            .nodes
            .iter()
            .map(|(word, _)| {
                network
                    .edges
                    .iter()
                    .filter(|((source, target), _)| source == word || target == word)
                    .count()
            })
            .max()
            .unwrap_or(0) as f64;

        measures.insert("max_degree".to_string(), max_degree);

        // Average degree
        let total_degree: usize = network
            .nodes
            .iter()
            .map(|(word, _)| {
                network
                    .edges
                    .iter()
                    .filter(|((source, target), _)| source == word || target == word)
                    .count()
            })
            .sum();

        let avg_degree = if !network.nodes.is_empty() {
            total_degree as f64 / network.nodes.len() as f64
        } else {
            0.0
        };

        measures.insert("avg_degree".to_string(), avg_degree);

        Ok(measures)
    }

    /// Calculate clustering coefficient
    fn calculate_clustering_coefficient(
        &self,
        network: &SemanticNetwork,
    ) -> Result<f64, SemanticAnalysisError> {
        if network.nodes.len() < 3 {
            return Ok(0.0);
        }

        let mut total_clustering = 0.0;
        let mut valid_nodes = 0;

        for word in network.nodes.keys() {
            let neighbors: Vec<&String> = network
                .edges
                .iter()
                .filter_map(|((source, target), _)| {
                    if source == word {
                        Some(target)
                    } else if target == word {
                        Some(source)
                    } else {
                        None
                    }
                })
                .collect();

            if neighbors.len() > 1 {
                let possible_edges = neighbors.len() * (neighbors.len() - 1) / 2;
                let actual_edges = neighbors
                    .iter()
                    .enumerate()
                    .flat_map(|(i, &n1)| neighbors[i + 1..].iter().map(move |&n2| (n1, n2)))
                    .filter(|(n1, n2)| {
                        network.edges.contains_key(&((*n1).clone(), (*n2).clone()))
                            || network.edges.contains_key(&((*n2).clone(), (*n1).clone()))
                    })
                    .count();

                total_clustering += actual_edges as f64 / possible_edges as f64;
                valid_nodes += 1;
            }
        }

        Ok(if valid_nodes > 0 {
            total_clustering / valid_nodes as f64
        } else {
            0.0
        })
    }

    /// Calculate coherence score for a semantic cluster
    fn calculate_cluster_coherence(
        &self,
        cluster_words: &[String],
        similarity_matrix: &[Vec<f64>],
        all_words: &[String],
    ) -> Result<f64, SemanticAnalysisError> {
        if cluster_words.len() < 2 {
            return Ok(0.0);
        }

        let mut total_similarity = 0.0;
        let mut pair_count = 0;

        for i in 0..cluster_words.len() {
            for j in (i + 1)..cluster_words.len() {
                if let (Some(idx_i), Some(idx_j)) = (
                    all_words.iter().position(|w| w == &cluster_words[i]),
                    all_words.iter().position(|w| w == &cluster_words[j]),
                ) {
                    total_similarity += similarity_matrix[idx_i][idx_j];
                    pair_count += 1;
                }
            }
        }

        Ok(if pair_count > 0 {
            total_similarity / pair_count as f64
        } else {
            0.0
        })
    }

    /// Generate analysis metadata
    fn generate_analysis_metadata(&self) -> HashMap<String, String> {
        let mut metadata = HashMap::new();

        metadata.insert("analyzer_version".to_string(), "1.0.0".to_string());
        metadata.insert(
            "disambiguation_enabled".to_string(),
            self.config.perform_disambiguation.to_string(),
        );
        metadata.insert(
            "embedding_dimension".to_string(),
            self.config.embedding_dimension.to_string(),
        );
        metadata.insert(
            "relationship_threshold".to_string(),
            self.config.relationship_threshold.to_string(),
        );
        metadata.insert(
            "confidence_threshold".to_string(),
            self.config.confidence_threshold.to_string(),
        );

        metadata
    }
}

impl WordSenseDisambiguator {
    fn new(config: &SemanticAnalysisConfig) -> Result<Self, SemanticAnalysisError> {
        Ok(WordSenseDisambiguator {
            context_window: config.context_window_size,
            disambiguation_threshold: config.disambiguation_threshold,
            sense_embeddings: HashMap::new(),
            context_similarity_cache: HashMap::new(),
        })
    }

    fn disambiguate(
        &mut self,
        item: &LexicalItem,
        context: &[String],
    ) -> Result<LexicalItem, SemanticAnalysisError> {
        if item.word_senses.len() <= 1 {
            return Ok(item.clone());
        }

        let context_vector = self.extract_context_vector(item, context)?;
        let mut best_sense = item.word_senses[0].clone();
        let mut best_score = 0.0;

        for sense in &item.word_senses {
            let sense_score = self.calculate_sense_context_similarity(sense, &context_vector)?;
            if sense_score > best_score {
                best_score = sense_score;
                best_sense = sense.clone();
            }
        }

        let mut disambiguated_item = item.clone();
        if best_score >= self.disambiguation_threshold {
            disambiguated_item.word_senses = vec![best_sense];
        }

        Ok(disambiguated_item)
    }

    fn extract_context_vector(
        &self,
        item: &LexicalItem,
        context: &[String],
    ) -> Result<Vec<f64>, SemanticAnalysisError> {
        // Simple context vector extraction
        // In a real implementation, this would use sophisticated NLP techniques
        let context_words: Vec<String> = context
            .iter()
            .flat_map(|sentence| sentence.split_whitespace())
            .filter(|word| word.to_lowercase() != item.word.to_lowercase())
            .map(|word| word.to_lowercase())
            .collect();

        // Create a simple frequency-based context vector
        let mut context_vector = vec![0.0; 100]; // Fixed dimension for simplicity

        for (i, word) in context_words.iter().enumerate() {
            let hash = word.len() % context_vector.len();
            context_vector[hash] += 1.0;
        }

        // Normalize
        let magnitude: f64 = context_vector.iter().map(|x| x * x).sum::<f64>().sqrt();
        if magnitude > 0.0 {
            for value in context_vector.iter_mut() {
                *value /= magnitude;
            }
        }

        Ok(context_vector)
    }

    fn calculate_sense_context_similarity(
        &self,
        sense: &WordSense,
        context_vector: &[f64],
    ) -> Result<f64, SemanticAnalysisError> {
        // Simple similarity calculation based on sense features
        // In a real implementation, this would use sense embeddings
        let sense_features: Vec<String> = sense
            .definition
            .split_whitespace()
            .chain(sense.examples.iter().flat_map(|ex| ex.split_whitespace()))
            .map(|word| word.to_lowercase())
            .collect();

        let mut sense_vector = vec![0.0; context_vector.len()];
        for word in sense_features {
            let hash = word.len() % sense_vector.len();
            sense_vector[hash] += 1.0;
        }

        // Normalize
        let magnitude: f64 = sense_vector.iter().map(|x| x * x).sum::<f64>().sqrt();
        if magnitude > 0.0 {
            for value in sense_vector.iter_mut() {
                *value /= magnitude;
            }
        }

        // Calculate cosine similarity
        let dot_product: f64 = context_vector
            .iter()
            .zip(sense_vector.iter())
            .map(|(a, b)| a * b)
            .sum();

        Ok(dot_product)
    }
}

impl SimilarityCalculator {
    fn new(config: &SemanticAnalysisConfig) -> Result<Self, SemanticAnalysisError> {
        Ok(SimilarityCalculator {
            embedding_dim: config.embedding_dimension,
            similarity_metrics: vec![
                SimilarityMetric::Cosine,
                SimilarityMetric::Jaccard,
                SimilarityMetric::PathBased,
            ],
            weight_combinations: HashMap::from([
                ("cosine".to_string(), 0.4),
                ("jaccard".to_string(), 0.3),
                ("path_based".to_string(), 0.3),
            ]),
        })
    }

    fn calculate_similarity(
        &mut self,
        item1: &LexicalItem,
        item2: &LexicalItem,
    ) -> Result<f64, SemanticAnalysisError> {
        let mut total_similarity = 0.0;
        let mut total_weight = 0.0;

        for metric in &self.similarity_metrics {
            let weight = self.get_metric_weight(metric);
            let similarity = self.calculate_metric_similarity(metric, item1, item2)?;

            total_similarity += similarity * weight;
            total_weight += weight;
        }

        Ok(if total_weight > 0.0 {
            total_similarity / total_weight
        } else {
            0.0
        })
    }

    fn get_metric_weight(&self, metric: &SimilarityMetric) -> f64 {
        let key = match metric {
            SimilarityMetric::Cosine => "cosine",
            SimilarityMetric::Jaccard => "jaccard",
            SimilarityMetric::PathBased => "path_based",
            _ => "default",
        };

        self.weight_combinations.get(key).copied().unwrap_or(0.1)
    }

    fn calculate_metric_similarity(
        &self,
        metric: &SimilarityMetric,
        item1: &LexicalItem,
        item2: &LexicalItem,
    ) -> Result<f64, SemanticAnalysisError> {
        match metric {
            SimilarityMetric::Cosine => self.calculate_cosine_similarity(item1, item2),
            SimilarityMetric::Jaccard => self.calculate_jaccard_similarity(item1, item2),
            SimilarityMetric::PathBased => self.calculate_path_based_similarity(item1, item2),
            _ => Ok(0.0), // Other metrics not implemented in this example
        }
    }

    fn calculate_cosine_similarity(
        &self,
        item1: &LexicalItem,
        item2: &LexicalItem,
    ) -> Result<f64, SemanticAnalysisError> {
        // Simple cosine similarity based on semantic features
        let features1: HashSet<String> = item1.semantic_features.iter().cloned().collect();
        let features2: HashSet<String> = item2.semantic_features.iter().cloned().collect();

        if features1.is_empty() && features2.is_empty() {
            return Ok(0.0);
        }

        let intersection = features1.intersection(&features2).count() as f64;
        let magnitude1 = (features1.len() as f64).sqrt();
        let magnitude2 = (features2.len() as f64).sqrt();

        if magnitude1 * magnitude2 > 0.0 {
            Ok(intersection / (magnitude1 * magnitude2))
        } else {
            Ok(0.0)
        }
    }

    fn calculate_jaccard_similarity(
        &self,
        item1: &LexicalItem,
        item2: &LexicalItem,
    ) -> Result<f64, SemanticAnalysisError> {
        let features1: HashSet<String> = item1.semantic_features.iter().cloned().collect();
        let features2: HashSet<String> = item2.semantic_features.iter().cloned().collect();

        let intersection = features1.intersection(&features2).count() as f64;
        let union = features1.union(&features2).count() as f64;

        Ok(if union > 0.0 {
            intersection / union
        } else {
            0.0
        })
    }

    fn calculate_path_based_similarity(
        &self,
        item1: &LexicalItem,
        item2: &LexicalItem,
    ) -> Result<f64, SemanticAnalysisError> {
        // Simple path-based similarity using word sense relationships
        if item1.word_senses.is_empty() || item2.word_senses.is_empty() {
            return Ok(0.0);
        }

        let mut max_similarity = 0.0;

        for sense1 in &item1.word_senses {
            for sense2 in &item2.word_senses {
                let similarity = self.calculate_sense_path_similarity(sense1, sense2)?;
                if similarity > max_similarity {
                    max_similarity = similarity;
                }
            }
        }

        Ok(max_similarity)
    }

    fn calculate_sense_path_similarity(
        &self,
        sense1: &WordSense,
        sense2: &WordSense,
    ) -> Result<f64, SemanticAnalysisError> {
        // Check for direct relationships
        if sense1.synonyms.contains(&sense2.sense_id) || sense2.synonyms.contains(&sense1.sense_id)
        {
            return Ok(0.9);
        }

        if sense1.hypernyms.contains(&sense2.sense_id)
            || sense2.hypernyms.contains(&sense1.sense_id)
            || sense1.hyponyms.contains(&sense2.sense_id)
            || sense2.hyponyms.contains(&sense1.sense_id)
        {
            return Ok(0.7);
        }

        // Check for shared hypernyms
        let shared_hypernyms = sense1
            .hypernyms
            .iter()
            .filter(|h| sense2.hypernyms.contains(h))
            .count();

        if shared_hypernyms > 0 {
            return Ok(0.5 * (shared_hypernyms as f64).sqrt());
        }

        Ok(0.0)
    }
}

impl RelationshipClassifier {
    fn new(config: &SemanticAnalysisConfig) -> Result<Self, SemanticAnalysisError> {
        Ok(RelationshipClassifier {
            classification_rules: HashMap::new(),
            feature_extractors: Vec::new(),
            confidence_thresholds: HashMap::new(),
        })
    }

    fn classify_relationship(
        &mut self,
        item1: &LexicalItem,
        item2: &LexicalItem,
        similarity_score: f64,
    ) -> Result<SemanticRelationship, SemanticAnalysisError> {
        // Extract features for classification
        let features = self.extract_relationship_features(item1, item2, similarity_score)?;

        // Classify relationship type
        let relationship_type = self.classify_relationship_type(&features)?;

        // Calculate confidence
        let confidence = self.calculate_classification_confidence(&features, &relationship_type)?;

        // Calculate relationship strength
        let strength = self.calculate_relationship_strength(item1, item2, similarity_score)?;

        // Calculate distance
        let distance = self.calculate_semantic_distance(item1, item2)?;

        Ok(SemanticRelationship {
            source_word: item1.word.clone(),
            target_word: item2.word.clone(),
            relationship_type,
            strength,
            confidence,
            distance,
            evidence: features,
            context_positions: vec![], // Could be populated with position information
        })
    }

    fn extract_relationship_features(
        &self,
        item1: &LexicalItem,
        item2: &LexicalItem,
        similarity_score: f64,
    ) -> Result<Vec<String>, SemanticAnalysisError> {
        let mut features = Vec::new();

        // Similarity-based features
        if similarity_score > 0.8 {
            features.push("high_similarity".to_string());
        } else if similarity_score > 0.5 {
            features.push("medium_similarity".to_string());
        } else {
            features.push("low_similarity".to_string());
        }

        // Word sense relationship features
        for sense1 in &item1.word_senses {
            for sense2 in &item2.word_senses {
                if sense1.synonyms.contains(&sense2.sense_id) {
                    features.push("synonym_relationship".to_string());
                }
                if sense1.antonyms.contains(&sense2.sense_id) {
                    features.push("antonym_relationship".to_string());
                }
                if sense1.hypernyms.contains(&sense2.sense_id) {
                    features.push("hypernym_relationship".to_string());
                }
                if sense1.hyponyms.contains(&sense2.sense_id) {
                    features.push("hyponym_relationship".to_string());
                }
                if sense1.meronyms.contains(&sense2.sense_id) {
                    features.push("meronym_relationship".to_string());
                }
            }
        }

        // Morphological features
        if self.is_morphologically_related(&item1.word, &item2.word) {
            features.push("morphological_relationship".to_string());
        }

        // Frequency-based features
        let freq_ratio = (item1.frequency / item2.frequency).max(item2.frequency / item1.frequency);
        if freq_ratio > 2.0 {
            features.push("frequency_mismatch".to_string());
        } else {
            features.push("frequency_similar".to_string());
        }

        Ok(features)
    }

    fn classify_relationship_type(
        &self,
        features: &[String],
    ) -> Result<SemanticRelationshipType, SemanticAnalysisError> {
        // Simple rule-based classification
        if features.contains(&"synonym_relationship".to_string()) {
            Ok(SemanticRelationshipType::Synonymy)
        } else if features.contains(&"antonym_relationship".to_string()) {
            Ok(SemanticRelationshipType::Antonymy)
        } else if features.contains(&"hypernym_relationship".to_string())
            || features.contains(&"hyponym_relationship".to_string())
        {
            Ok(SemanticRelationshipType::Hyponymy)
        } else if features.contains(&"meronym_relationship".to_string()) {
            Ok(SemanticRelationshipType::Meronymy)
        } else if features.contains(&"morphological_relationship".to_string()) {
            Ok(SemanticRelationshipType::Morphological)
        } else if features.contains(&"high_similarity".to_string()) {
            Ok(SemanticRelationshipType::Association)
        } else {
            Ok(SemanticRelationshipType::Sequential)
        }
    }

    fn calculate_classification_confidence(
        &self,
        features: &[String],
        relationship_type: &SemanticRelationshipType,
    ) -> Result<f64, SemanticAnalysisError> {
        // Simple confidence calculation based on evidence strength
        let evidence_count = features.len() as f64;
        let base_confidence = match relationship_type {
            SemanticRelationshipType::Synonymy => 0.9,
            SemanticRelationshipType::Antonymy => 0.9,
            SemanticRelationshipType::Hyponymy => 0.8,
            SemanticRelationshipType::Meronymy => 0.8,
            SemanticRelationshipType::Morphological => 0.7,
            SemanticRelationshipType::Association => 0.6,
            _ => 0.5,
        };

        let confidence = base_confidence * (1.0 + evidence_count * 0.1).min(1.0);
        Ok(confidence)
    }

    fn calculate_relationship_strength(
        &self,
        item1: &LexicalItem,
        item2: &LexicalItem,
        similarity_score: f64,
    ) -> Result<f64, SemanticAnalysisError> {
        // Combine similarity score with other factors
        let frequency_factor = (item1.frequency * item2.frequency).sqrt();
        let sense_factor = if !item1.word_senses.is_empty() && !item2.word_senses.is_empty() {
            1.0 / (item1.word_senses.len() * item2.word_senses.len()) as f64
        } else {
            0.5
        };

        let strength = similarity_score * 0.7 + frequency_factor * 0.2 + sense_factor * 0.1;
        Ok(strength.min(1.0))
    }

    fn calculate_semantic_distance(
        &self,
        item1: &LexicalItem,
        item2: &LexicalItem,
    ) -> Result<f64, SemanticAnalysisError> {
        // Simple distance calculation based on position and semantic features
        let position_distance =
            if let (Some(pos1), Some(pos2)) = (item1.positions.first(), item2.positions.first()) {
                (pos1.0 as f64 - pos2.0 as f64).abs()
            } else {
                0.0
            };

        let feature_overlap = item1
            .semantic_features
            .iter()
            .filter(|feature| item2.semantic_features.contains(feature))
            .count() as f64;

        let max_features = item1
            .semantic_features
            .len()
            .max(item2.semantic_features.len()) as f64;
        let feature_distance = if max_features > 0.0 {
            1.0 - (feature_overlap / max_features)
        } else {
            1.0
        };

        let distance = (position_distance * 0.3 + feature_distance * 0.7).min(1.0);
        Ok(distance)
    }

    fn is_morphologically_related(&self, word1: &str, word2: &str) -> bool {
        // Simple morphological relationship detection
        let word1_lower = word1.to_lowercase();
        let word2_lower = word2.to_lowercase();

        // Check for common prefixes/suffixes
        let common_suffixes = vec!["ing", "ed", "er", "est", "ly", "tion", "sion", "ness"];

        for suffix in common_suffixes {
            if word1_lower.ends_with(suffix) {
                let stem1 = &word1_lower[..word1_lower.len() - suffix.len()];
                if word2_lower.starts_with(stem1) || word2_lower == stem1 {
                    return true;
                }
            }
            if word2_lower.ends_with(suffix) {
                let stem2 = &word2_lower[..word2_lower.len() - suffix.len()];
                if word1_lower.starts_with(stem2) || word1_lower == stem2 {
                    return true;
                }
            }
        }

        false
    }
}

// Add rand dependency for testing
use scirs2_core::random::{rng, Random};

#[cfg(test)]
mod tests {
    use super::*;
    use crate::metrics::coherence::lexical_coherence::config::SemanticAnalysisConfig;
    use crate::metrics::coherence::lexical_coherence::results::{LexicalItem, WordSense};

    #[test]
    fn test_semantic_analyzer_creation() {
        let config = SemanticAnalysisConfig::default();
        let analyzer = SemanticAnalyzer::new(config);
        assert!(analyzer.is_ok());
    }

    #[test]
    fn test_word_sense_disambiguation() {
        let config = SemanticAnalysisConfig::default();
        let mut analyzer = SemanticAnalyzer::new(config).expect("Semantic Analyzer should succeed");

        let lexical_item = LexicalItem {
            word: "bank".to_string(),
            lemma: "bank".to_string(),
            positions: vec![(0, 4)],
            frequency: 1.0,
            word_senses: vec![
                WordSense {
                    sense_id: "bank_1".to_string(),
                    definition: "financial institution".to_string(),
                    examples: vec!["I went to the bank".to_string()],
                    frequency: 0.7,
                    semantic_features: vec!["finance".to_string(), "institution".to_string()],
                    hypernyms: vec!["institution".to_string()],
                    hyponyms: vec![],
                    synonyms: vec![],
                    antonyms: vec![],
                    meronyms: vec![],
                    holonyms: vec![],
                },
                WordSense {
                    sense_id: "bank_2".to_string(),
                    definition: "edge of river".to_string(),
                    examples: vec!["by the river bank".to_string()],
                    frequency: 0.3,
                    semantic_features: vec!["geography".to_string(), "water".to_string()],
                    hypernyms: vec!["location".to_string()],
                    hyponyms: vec![],
                    synonyms: vec![],
                    antonyms: vec![],
                    meronyms: vec![],
                    holonyms: vec![],
                },
            ],
            semantic_features: vec!["finance".to_string(), "geography".to_string()],
        };

        let context = vec!["The money is in the bank account".to_string()];

        let result = analyzer.analyze_semantic_relationships(&[lexical_item], &context);
        assert!(result.is_ok());
    }

    #[test]
    fn test_similarity_calculation() {
        let config = SemanticAnalysisConfig::default();
        let mut calculator = SimilarityCalculator::new(&config).expect("Similarity Calculator should succeed");

        let item1 = LexicalItem {
            word: "good".to_string(),
            lemma: "good".to_string(),
            positions: vec![(0, 4)],
            frequency: 1.0,
            word_senses: vec![],
            semantic_features: vec!["positive".to_string(), "quality".to_string()],
        };

        let item2 = LexicalItem {
            word: "excellent".to_string(),
            lemma: "excellent".to_string(),
            positions: vec![(5, 14)],
            frequency: 1.0,
            word_senses: vec![],
            semantic_features: vec!["positive".to_string(), "quality".to_string()],
        };

        let similarity = calculator.calculate_similarity(&item1, &item2).expect("similarity calculation should succeed");
        assert!(similarity > 0.0);
        assert!(similarity <= 1.0);
    }

    #[test]
    fn test_relationship_classification() {
        let config = SemanticAnalysisConfig::default();
        let mut classifier = RelationshipClassifier::new(&config).expect("Relationship Classifier should succeed");

        let item1 = LexicalItem {
            word: "big".to_string(),
            lemma: "big".to_string(),
            positions: vec![(0, 3)],
            frequency: 1.0,
            word_senses: vec![WordSense {
                sense_id: "big_1".to_string(),
                definition: "large in size".to_string(),
                examples: vec![],
                frequency: 1.0,
                semantic_features: vec!["size".to_string()],
                hypernyms: vec![],
                hyponyms: vec![],
                synonyms: vec!["large_1".to_string()],
                antonyms: vec!["small_1".to_string()],
                meronyms: vec![],
                holonyms: vec![],
            }],
            semantic_features: vec!["size".to_string()],
        };

        let item2 = LexicalItem {
            word: "small".to_string(),
            lemma: "small".to_string(),
            positions: vec![(4, 9)],
            frequency: 1.0,
            word_senses: vec![WordSense {
                sense_id: "small_1".to_string(),
                definition: "little in size".to_string(),
                examples: vec![],
                frequency: 1.0,
                semantic_features: vec!["size".to_string()],
                hypernyms: vec![],
                hyponyms: vec![],
                synonyms: vec!["little_1".to_string()],
                antonyms: vec!["big_1".to_string()],
                meronyms: vec![],
                holonyms: vec![],
            }],
            semantic_features: vec!["size".to_string()],
        };

        let relationship = classifier
            .classify_relationship(&item1, &item2, 0.3)
            .expect("operation should succeed");
        assert_eq!(relationship.source_word, "big");
        assert_eq!(relationship.target_word, "small");
    }

    #[test]
    fn test_semantic_network_building() {
        let config = SemanticAnalysisConfig::default();
        let mut analyzer = SemanticAnalyzer::new(config).expect("Semantic Analyzer should succeed");

        let lexical_items = vec![
            LexicalItem {
                word: "good".to_string(),
                lemma: "good".to_string(),
                positions: vec![(0, 4)],
                frequency: 1.0,
                word_senses: vec![],
                semantic_features: vec!["positive".to_string()],
            },
            LexicalItem {
                word: "excellent".to_string(),
                lemma: "excellent".to_string(),
                positions: vec![(5, 14)],
                frequency: 1.0,
                word_senses: vec![],
                semantic_features: vec!["positive".to_string()],
            },
        ];

        let relationships = vec![SemanticRelationship {
            source_word: "good".to_string(),
            target_word: "excellent".to_string(),
            relationship_type: SemanticRelationshipType::Synonymy,
            strength: 0.8,
            confidence: 0.9,
            distance: 0.2,
            evidence: vec!["synonym_relationship".to_string()],
            context_positions: vec![],
        }];

        let network = analyzer
            .build_semantic_network(&lexical_items, &relationships)
            .expect("operation should succeed");
        assert_eq!(network.nodes.len(), 2);
        assert_eq!(network.edges.len(), 1);
    }
}