//! `PageRank` centrality algorithm.
//!
//! Computes the importance of nodes based on link structure.
//! Higher scores indicate more "important" nodes.
//!
//! The core power-iteration algorithm is provided by [`graphops`];
//! this module wraps it with a `KnowledgeGraph`-specific API that maps
//! petgraph node indices back to entity IDs.

use crate::KnowledgeGraph;
use std::collections::HashMap;

/// `PageRank` configuration.
#[derive(Debug, Clone, Copy)]
pub struct PageRankConfig {
    /// Damping factor (probability of following a link vs teleporting).
    /// Typically 0.85.
    pub damping_factor: f64,
    /// Maximum iterations before stopping.
    pub max_iterations: usize,
    /// Convergence tolerance (L1 norm of score changes).
    pub tolerance: f64,
}

impl Default for PageRankConfig {
    fn default() -> Self {
        Self {
            damping_factor: 0.85,
            max_iterations: 100,
            tolerance: 1e-6,
        }
    }
}

/// Compute `PageRank` for all entities.
///
/// Returns a map of `EntityId` -> Score, where scores sum to 1.0.
///
/// Delegates to [`graphops::pagerank::pagerank`] for the core iteration
/// and maps petgraph node indices back to entity IDs.
#[must_use]
pub fn pagerank(kg: &KnowledgeGraph, config: PageRankConfig) -> HashMap<String, f64> {
    let graph = kg.as_petgraph();
    let n = graph.node_count();
    if n == 0 {
        return HashMap::new();
    }

    let gp_config = graphops::pagerank::PageRankConfig {
        damping: config.damping_factor,
        max_iterations: config.max_iterations,
        tolerance: config.tolerance,
    };

    let scores = graphops::pagerank::pagerank(graph, gp_config);

    // Map indices back to entity IDs
    let mut result = HashMap::with_capacity(n);
    for (idx, score) in scores.into_iter().enumerate() {
        let entity = &graph[petgraph::graph::NodeIndex::new(idx)];
        result.insert(entity.id.as_str().to_owned(), score);
    }
    result
}

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

    #[test]
    fn test_pagerank_cycle() {
        let mut kg = KnowledgeGraph::new();
        // A -> B -> C -> A (cycle)
        kg.add_triple(Triple::new("A", "rel", "B"));
        kg.add_triple(Triple::new("B", "rel", "C"));
        kg.add_triple(Triple::new("C", "rel", "A"));

        let scores = pagerank(&kg, PageRankConfig::default());

        // Symmetric cycle: all scores should be equal
        let a = scores.get("A").unwrap();
        let b = scores.get("B").unwrap();
        let c = scores.get("C").unwrap();

        assert!((a - b).abs() < 1e-4, "A={a} B={b}");
        assert!((b - c).abs() < 1e-4, "B={b} C={c}");
        assert!((a - 1.0 / 3.0).abs() < 0.01);
    }

    #[test]
    fn test_pagerank_star() {
        let mut kg = KnowledgeGraph::new();
        // Hub -> A, Hub -> B, Hub -> C (star topology)
        kg.add_triple(Triple::new("Hub", "rel", "A"));
        kg.add_triple(Triple::new("Hub", "rel", "B"));
        kg.add_triple(Triple::new("Hub", "rel", "C"));

        let scores = pagerank(&kg, PageRankConfig::default());

        // Hub has outlinks but no inlinks from the graph
        // A, B, C are dangling (no outlinks)
        let hub = scores.get("Hub").unwrap();
        let a = scores.get("A").unwrap();

        // Dangling nodes receive mass from Hub + teleport
        // Hub only gets teleport (no inlinks)
        assert!(a > hub, "Leaf A ({a}) should rank higher than Hub ({hub})",);
    }

    #[test]
    fn test_pagerank_sums_to_one() {
        let mut kg = KnowledgeGraph::new();
        kg.add_triple(Triple::new("A", "rel", "B"));
        kg.add_triple(Triple::new("B", "rel", "C"));
        kg.add_triple(Triple::new("C", "rel", "A"));
        kg.add_triple(Triple::new("A", "rel", "D"));

        let scores = pagerank(&kg, PageRankConfig::default());
        let total: f64 = scores.values().sum();

        assert!(
            (total - 1.0).abs() < 1e-6,
            "Scores should sum to 1.0, got {total}",
        );
    }
}
