torsh-text 0.1.2

//! Semantic Relations Analysis Module
//!
//! This module provides comprehensive semantic relationship analysis for text fluency
//! evaluation. It focuses on detecting, analyzing, and measuring various types of
//! semantic relationships within and between textual elements.

use super::config::RelationsAnalysisConfig;
use super::results::{
    RelationStrength, RelationshipHierarchy, RelationshipNetwork, RelationshipPattern,
    SemanticGraph, SemanticRelation, SemanticRelationsMetrics,
};
use crate::error::TextAnalysisError;
use serde::{Deserialize, Serialize};
use std::collections::hash_map::DefaultHasher;
use std::collections::{BTreeMap, HashMap, HashSet, VecDeque};
use std::hash::{Hash, Hasher};
use thiserror::Error;

#[derive(Debug, Error)]
pub enum RelationsAnalysisError {
    #[error("Invalid relations analysis configuration: {0}")]
    ConfigError(String),
    #[error("Relationship calculation failed: {0}")]
    CalculationError(String),
    #[error("Semantic relations analysis error: {0}")]
    AnalysisError(String),
}

pub type RelationsResult<T> = Result<T, RelationsAnalysisError>;

/// Semantic relation types for comprehensive analysis
#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum SemanticRelationType {
    /// Hierarchical relationships (hypernym/hyponym)
    Hierarchical,
    /// Similarity relationships (synonyms, near-synonyms)
    Similarity,
    /// Opposition relationships (antonyms, contrasts)
    Opposition,
    /// Causal relationships (cause-effect)
    Causal,
    /// Temporal relationships (before-after, sequence)
    Temporal,
    /// Spatial relationships (location, direction)
    Spatial,
    /// Part-whole relationships (meronymy)
    PartWhole,
    /// Functional relationships (purpose, use)
    Functional,
    /// Associative relationships (common co-occurrence)
    Associative,
    /// Thematic relationships (topic-related)
    Thematic,
}

/// Advanced semantic relations analyzer
#[derive(Debug, Clone)]
pub struct SemanticRelationsAnalyzer {
    config: RelationsAnalysisConfig,
    relation_graph: SemanticGraph,
    detected_relations: Vec<SemanticRelation>,
    relation_patterns: Vec<RelationshipPattern>,
    hierarchies: BTreeMap<String, RelationshipHierarchy>,
    relation_cache: HashMap<u64, SemanticRelationsMetrics>,
    network_cache: HashMap<String, RelationshipNetwork>,
    strength_tracker: HashMap<String, f64>,
}

impl SemanticRelationsAnalyzer {
    /// Create new relations analyzer with configuration
    pub fn new(config: RelationsAnalysisConfig) -> RelationsResult<Self> {
        Self::validate_config(&config)?;

        Ok(Self {
            config,
            relation_graph: SemanticGraph::new(),
            detected_relations: Vec::new(),
            relation_patterns: Vec::new(),
            hierarchies: BTreeMap::new(),
            relation_cache: HashMap::new(),
            network_cache: HashMap::new(),
            strength_tracker: HashMap::new(),
        })
    }

    /// Analyze semantic relations in text comprehensively
    pub fn analyze_semantic_relations(
        &mut self,
        text: &str,
        domain_knowledge: Option<&HashMap<String, Vec<String>>>,
    ) -> RelationsResult<SemanticRelationsMetrics> {
        let cache_key = self.generate_cache_key(text, domain_knowledge);
        if let Some(cached) = self.relation_cache.get(&cache_key) {
            return Ok(cached.clone());
        }

        let sentences = self.extract_sentences(text);
        let mut metrics = SemanticRelationsMetrics::default();

        // Build semantic graph from text
        self.build_semantic_graph(&sentences, domain_knowledge)?;

        // Core relation analysis
        metrics.overall_relation_score = self.calculate_overall_relation_score()?;
        metrics.relation_density = self.calculate_relation_density()?;
        metrics.relation_coherence = self.measure_relation_coherence()?;
        metrics.semantic_connectivity = self.analyze_semantic_connectivity()?;

        // Detect and analyze relationships
        metrics.detected_relations = self.detect_semantic_relations(&sentences)?;
        metrics.relationship_networks = self.build_relationship_networks()?;
        metrics.relation_strengths = self.calculate_relation_strengths()?;

        // Advanced relationship analysis
        if self.config.analyze_advanced_relations {
            metrics.relationship_patterns = self.identify_relationship_patterns()?;
            metrics.hierarchical_structures = self.analyze_hierarchical_structures()?;
            metrics.semantic_graph_metrics = self.calculate_graph_metrics()?;
            metrics.relation_evolution = self.trace_relation_evolution(&sentences)?;
        }

        // Multi-level relationship analysis
        if self.config.multi_level_analysis {
            metrics.local_relations = self.analyze_local_relations(&sentences)?;
            metrics.global_relations = self.analyze_global_relations(&sentences)?;
            metrics.cross_sentence_relations = self.analyze_cross_sentence_relations(&sentences)?;
        }

        // Relationship quality assessment
        if self.config.assess_relation_quality {
            metrics.relation_quality_metrics = self.assess_relation_quality()?;
            metrics.redundancy_analysis = self.analyze_relation_redundancy()?;
            metrics.completeness_analysis = self.analyze_relation_completeness(&sentences)?;
        }

        // Cache results for performance
        self.relation_cache.insert(cache_key, metrics.clone());

        Ok(metrics)
    }

    /// Build comprehensive semantic graph from sentences
    fn build_semantic_graph(
        &mut self,
        sentences: &[String],
        domain_knowledge: Option<&HashMap<String, Vec<String>>>,
    ) -> RelationsResult<()> {
        // Clear existing graph
        self.relation_graph = SemanticGraph::new();

        // Extract entities and concepts from all sentences
        let mut entities = HashMap::new();
        let mut concepts = HashMap::new();

        for (i, sentence) in sentences.iter().enumerate() {
            let sentence_entities = self.extract_entities(sentence)?;
            let sentence_concepts = self.extract_concepts(sentence)?;

            for entity in sentence_entities {
                entities
                    .entry(entity.clone())
                    .or_insert_with(Vec::new)
                    .push(i);
                self.relation_graph.add_node(&entity, "entity");
            }

            for concept in sentence_concepts {
                concepts
                    .entry(concept.clone())
                    .or_insert_with(Vec::new)
                    .push(i);
                self.relation_graph.add_node(&concept, "concept");
            }
        }

        // Build relationships between entities and concepts
        self.build_intra_sentence_relations(sentences)?;
        self.build_inter_sentence_relations(sentences)?;

        // Incorporate domain knowledge if available
        if let Some(knowledge) = domain_knowledge {
            self.incorporate_domain_knowledge(knowledge)?;
        }

        // Calculate relationship strengths
        self.calculate_graph_relationships()?;

        Ok(())
    }

    /// Calculate overall semantic relation score
    fn calculate_overall_relation_score(&self) -> RelationsResult<f64> {
        let relation_count = self.detected_relations.len() as f64;
        let potential_relations = self.estimate_potential_relations()?;

        if potential_relations == 0.0 {
            return Ok(0.0);
        }

        let coverage_score = relation_count / potential_relations;
        let quality_score = self.calculate_average_relation_quality()?;
        let coherence_score = self.measure_relation_coherence()?;

        // Weighted combination
        let overall_score = (coverage_score * self.config.coverage_weight
            + quality_score * self.config.quality_weight
            + coherence_score * self.config.coherence_weight)
            / (self.config.coverage_weight
                + self.config.quality_weight
                + self.config.coherence_weight);

        Ok(overall_score.max(0.0).min(1.0))
    }

    /// Calculate relation density in text
    fn calculate_relation_density(&self) -> RelationsResult<f64> {
        let node_count = self.relation_graph.node_count() as f64;
        let edge_count = self.relation_graph.edge_count() as f64;

        if node_count < 2.0 {
            return Ok(0.0);
        }

        // Maximum possible edges in an undirected graph
        let max_edges = (node_count * (node_count - 1.0)) / 2.0;
        let density = edge_count / max_edges;

        Ok(density)
    }

    /// Measure semantic relation coherence
    fn measure_relation_coherence(&self) -> RelationsResult<f64> {
        if self.detected_relations.is_empty() {
            return Ok(0.0);
        }

        let mut coherence_scores = Vec::new();

        // Analyze coherence of each relation type
        let relation_groups = self.group_relations_by_type();

        for (_relation_type, relations) in relation_groups {
            let type_coherence = self.calculate_type_coherence(&relations)?;
            coherence_scores.push(type_coherence);
        }

        // Overall coherence considering relation interactions
        let mean_coherence = coherence_scores.iter().sum::<f64>() / coherence_scores.len() as f64;
        let interaction_coherence = self.calculate_interaction_coherence()?;

        Ok((mean_coherence + interaction_coherence) / 2.0)
    }

    /// Analyze semantic connectivity in the graph
    fn analyze_semantic_connectivity(&self) -> RelationsResult<f64> {
        let components = self.relation_graph.connected_components();
        let total_nodes = self.relation_graph.node_count();

        if total_nodes == 0 {
            return Ok(0.0);
        }

        // Calculate connectivity metrics
        let largest_component_size = components
            .iter()
            .map(|component| component.len())
            .max()
            .unwrap_or(0) as f64;

        let connectivity_ratio = largest_component_size / total_nodes as f64;

        // Average path length in largest component
        let avg_path_length = self.calculate_average_path_length()?;
        let normalized_path_length = if avg_path_length > 0.0 {
            1.0 / avg_path_length.ln()
        } else {
            0.0
        };

        // Combine connectivity measures
        Ok((connectivity_ratio + normalized_path_length) / 2.0)
    }

    /// Detect semantic relations in sentences
    fn detect_semantic_relations(
        &mut self,
        sentences: &[String],
    ) -> RelationsResult<Vec<SemanticRelation>> {
        let mut relations = Vec::new();

        // Detect different types of semantic relations
        for relation_type in self.get_enabled_relation_types() {
            let type_relations = self.detect_relations_of_type(sentences, &relation_type)?;
            relations.extend(type_relations);
        }

        // Filter and validate relations
        let filtered_relations = self.filter_relations(relations)?;

        // Store detected relations
        self.detected_relations = filtered_relations.clone();

        Ok(filtered_relations)
    }

    /// Build relationship networks from detected relations
    fn build_relationship_networks(
        &mut self,
    ) -> RelationsResult<HashMap<String, RelationshipNetwork>> {
        let mut networks = HashMap::new();

        // Group relations by type to build type-specific networks
        let relation_groups = self.group_relations_by_type();

        for (relation_type, relations) in relation_groups {
            let network = self.build_network_for_type(&relation_type, &relations)?;
            networks.insert(relation_type.to_string(), network);
        }

        // Build overall semantic network
        let overall_network = self.build_overall_network()?;
        networks.insert("overall".to_string(), overall_network);

        // Cache networks for reuse
        self.network_cache = networks.clone();

        Ok(networks)
    }

    /// Calculate strengths of semantic relations
    fn calculate_relation_strengths(
        &mut self,
    ) -> RelationsResult<HashMap<String, RelationStrength>> {
        let mut strengths = HashMap::new();

        for relation in &self.detected_relations {
            let strength = self.calculate_individual_relation_strength(relation)?;
            let key = format!("{}_{}", relation.source, relation.target);
            strengths.insert(key.clone(), strength);

            // Update strength tracker
            self.strength_tracker.insert(key, strength.overall_strength);
        }

        Ok(strengths)
    }

    /// Identify relationship patterns in text
    fn identify_relationship_patterns(&mut self) -> RelationsResult<Vec<RelationshipPattern>> {
        let mut patterns = Vec::new();

        // Analyze sequential patterns
        let sequential_patterns = self.detect_sequential_patterns()?;
        patterns.extend(sequential_patterns);

        // Analyze hierarchical patterns
        let hierarchical_patterns = self.detect_hierarchical_patterns()?;
        patterns.extend(hierarchical_patterns);

        // Analyze clustering patterns
        let clustering_patterns = self.detect_clustering_patterns()?;
        patterns.extend(clustering_patterns);

        // Store patterns for future analysis
        self.relation_patterns = patterns.clone();

        Ok(patterns)
    }

    /// Analyze hierarchical structures in relations
    fn analyze_hierarchical_structures(
        &mut self,
    ) -> RelationsResult<BTreeMap<String, RelationshipHierarchy>> {
        let mut hierarchies = BTreeMap::new();

        // Extract hierarchical relations
        let hierarchical_relations: Vec<_> = self
            .detected_relations
            .iter()
            .filter(|r| r.relation_type == SemanticRelationType::Hierarchical)
            .collect();

        if hierarchical_relations.is_empty() {
            return Ok(hierarchies);
        }

        // Build hierarchy trees
        let hierarchy_roots = self.identify_hierarchy_roots(&hierarchical_relations)?;

        for root in hierarchy_roots {
            let hierarchy = self.build_hierarchy_from_root(&root, &hierarchical_relations)?;
            hierarchies.insert(root.clone(), hierarchy);
        }

        // Store hierarchies
        self.hierarchies = hierarchies.clone();

        Ok(hierarchies)
    }

    // Helper methods for relation analysis

    fn validate_config(config: &RelationsAnalysisConfig) -> RelationsResult<()> {
        if config.min_relation_strength < 0.0 || config.min_relation_strength > 1.0 {
            return Err(RelationsAnalysisError::ConfigError(
                "min_relation_strength must be between 0.0 and 1.0".to_string(),
            ));
        }

        if config.coverage_weight < 0.0
            || config.quality_weight < 0.0
            || config.coherence_weight < 0.0
        {
            return Err(RelationsAnalysisError::ConfigError(
                "all weights must be non-negative".to_string(),
            ));
        }

        Ok(())
    }

    fn extract_sentences(&self, text: &str) -> Vec<String> {
        text.split(&self.config.sentence_delimiters)
            .map(|s| s.trim().to_string())
            .filter(|s| !s.is_empty())
            .collect()
    }

    fn generate_cache_key(
        &self,
        text: &str,
        domain_knowledge: Option<&HashMap<String, Vec<String>>>,
    ) -> u64 {
        let mut hasher = DefaultHasher::new();
        text.hash(&mut hasher);
        if let Some(knowledge) = domain_knowledge {
            for (k, v) in knowledge {
                k.hash(&mut hasher);
                for item in v {
                    item.hash(&mut hasher);
                }
            }
        }
        self.config.hash(&mut hasher);
        hasher.finish()
    }

    fn extract_entities(&self, sentence: &str) -> RelationsResult<Vec<String>> {
        let words: Vec<&str> = sentence.split_whitespace().collect();
        let mut entities = Vec::new();

        // Simple entity extraction based on capitalization and patterns
        for word in words {
            if self.is_potential_entity(word) {
                entities.push(word.to_lowercase());
            }
        }

        // Extract multi-word entities if enabled
        if self.config.extract_multiword_entities {
            entities.extend(self.extract_multiword_entities(sentence)?);
        }

        Ok(entities)
    }

    fn extract_concepts(&self, sentence: &str) -> RelationsResult<Vec<String>> {
        let words: Vec<&str> = sentence.split_whitespace().collect();
        let mut concepts = Vec::new();

        for word in words {
            if self.is_meaningful_concept(word) {
                concepts.push(word.to_lowercase());
            }
        }

        Ok(concepts)
    }

    fn is_potential_entity(&self, word: &str) -> bool {
        // Check if word could be an entity (proper noun, etc.)
        word.len() >= self.config.min_entity_length
            && word.chars().any(|c| c.is_uppercase())
            && word.chars().any(|c| c.is_alphabetic())
    }

    fn is_meaningful_concept(&self, word: &str) -> bool {
        word.len() >= self.config.min_concept_length
            && word.chars().any(|c| c.is_alphabetic())
            && !self.is_stop_word(word)
    }

    fn is_stop_word(&self, word: &str) -> bool {
        self.config.stop_words.contains(&word.to_lowercase())
    }

    fn get_enabled_relation_types(&self) -> Vec<SemanticRelationType> {
        let mut types = Vec::new();

        if self.config.detect_hierarchical {
            types.push(SemanticRelationType::Hierarchical);
        }
        if self.config.detect_similarity {
            types.push(SemanticRelationType::Similarity);
        }
        if self.config.detect_opposition {
            types.push(SemanticRelationType::Opposition);
        }
        if self.config.detect_causal {
            types.push(SemanticRelationType::Causal);
        }
        if self.config.detect_temporal {
            types.push(SemanticRelationType::Temporal);
        }
        if self.config.detect_spatial {
            types.push(SemanticRelationType::Spatial);
        }
        if self.config.detect_part_whole {
            types.push(SemanticRelationType::PartWhole);
        }
        if self.config.detect_functional {
            types.push(SemanticRelationType::Functional);
        }
        if self.config.detect_associative {
            types.push(SemanticRelationType::Associative);
        }
        if self.config.detect_thematic {
            types.push(SemanticRelationType::Thematic);
        }

        types
    }
}

impl Default for SemanticRelationsAnalyzer {
    fn default() -> Self {
        Self::new(RelationsAnalysisConfig::default()).expect("default relations config should be valid")
    }
}

// Additional implementation methods would continue here...
// This represents the core structure for comprehensive semantic relations analysis