consciousness/src/hippocampus/neuro/scoring.rs

// Consolidation scoring, replay queues, interference detection, and
// graph health metrics. Pure analysis — no store mutations.

use crate::store::{Store, now_epoch};
use crate::graph::{self, Graph};
use crate::spectral::{self, SpectralEmbedding, SpectralPosition};

use std::collections::HashMap;

const SECS_PER_DAY: f64 = 86400.0;

/// Consolidation priority: how urgently a node needs attention.
///
/// With spectral data:
///   priority = spectral_displacement × overdue × emotion
/// Without:
///   priority = (1 - cc) × overdue × emotion
///
/// Spectral displacement is the outlier_score clamped and normalized —
/// it measures how far a node sits from its community center in the
/// eigenspace. This is a global signal (considers all graph structure)
/// vs CC which is local (only immediate neighbors).
pub fn consolidation_priority(
    store: &Store,
    key: &str,
    graph: &Graph,
    spectral_outlier: Option<f64>,
) -> f64 {
    let node = match store.get_node(key).ok().flatten() {
        Some(n) => n,
        None => return 0.0,
    };

    // Integration factor: how poorly integrated is this node?
    let displacement = if let Some(outlier) = spectral_outlier {
        // outlier_score = dist_to_center / median_dist_in_community
        // 1.0 = typical position, >2 = unusual, >5 = extreme outlier
        // Use log scale for dynamic range: the difference between
        // outlier=5 and outlier=10 matters less than 1 vs 2.
        (outlier / 3.0).min(3.0)
    } else {
        let cc = graph.clustering_coefficient(key) as f64;
        1.0 - cc
    };

    // Spaced repetition: how overdue is this node for replay?
    let interval_secs = node.spaced_repetition_interval as f64 * SECS_PER_DAY;
    let time_since_replay = if node.last_replayed > 0 {
        (now_epoch() - node.last_replayed).max(0) as f64
    } else {
        interval_secs * 3.0
    };
    let overdue_ratio = (time_since_replay / interval_secs).min(5.0);

    // Emotional intensity: higher emotion = higher priority
    let emotion_factor = 1.0 + (node.emotion as f64 / 10.0);

    displacement * overdue_ratio * emotion_factor
}

/// Item in the replay queue
pub struct ReplayItem {
    pub key: String,
    pub priority: f64,
    pub interval_days: u32,
    pub emotion: f32,
    pub cc: f32,
    /// Spectral classification: "bridge", "outlier", "core", "peripheral"
    pub classification: &'static str,
    /// Raw spectral outlier score (distance / median)
    pub outlier_score: f64,
}

/// Generate the replay queue: nodes ordered by consolidation priority.
/// Automatically loads spectral embedding if available.
pub fn replay_queue(store: &Store, count: usize) -> Vec<ReplayItem> {
    let graph = store.build_graph();
    let emb = spectral::load_embedding().ok();
    replay_queue_with_graph(store, count, &graph, emb.as_ref())
}

/// Generate the replay queue using pre-built graph and optional spectral data.
pub fn replay_queue_with_graph(
    store: &Store,
    count: usize,
    graph: &Graph,
    emb: Option<&SpectralEmbedding>,
) -> Vec<ReplayItem> {
    // Build spectral position map if embedding is available
    let positions: HashMap<String, SpectralPosition> = if let Some(emb) = emb {
        let communities = graph.communities().clone();
        spectral::analyze_positions(emb, &communities)
            .into_iter()
            .map(|p| (p.key.clone(), p))
            .collect()
    } else {
        HashMap::new()
    };

    let all_keys = store.all_keys().unwrap_or_default();
    let mut items: Vec<ReplayItem> = all_keys.iter()
        .filter_map(|key| {
            let node = store.get_node(key).ok()??;
            let pos = positions.get(key);
            let outlier_score = pos.map(|p| p.outlier_score).unwrap_or(0.0);
            let classification = pos
                .map(spectral::classify_position)
                .unwrap_or("unknown");

            let priority = consolidation_priority(
                store, key, graph,
                pos.map(|p| p.outlier_score),
            );
            Some(ReplayItem {
                key: key.clone(),
                priority,
                interval_days: node.spaced_repetition_interval,
                emotion: node.emotion,
                cc: graph.clustering_coefficient(key),
                classification,
                outlier_score,
            })
        })
        .collect();

    items.sort_by(|a, b| b.priority.total_cmp(&a.priority));
    items.truncate(count);
    items
}

/// Agent allocation from the control loop.
/// Agent types and counts are data-driven — add agents by adding
/// entries to the counts map.
#[derive(Default)]
pub struct ConsolidationPlan {
    /// agent_name → run count
    pub counts: std::collections::HashMap<String, usize>,
    pub run_health: bool,
    pub rationale: Vec<String>,
}

impl ConsolidationPlan {
    pub fn count(&self, agent: &str) -> usize {
        self.counts.get(agent).copied().unwrap_or(0)
    }

    pub fn set(&mut self, agent: &str, count: usize) {
        self.counts.insert(agent.to_string(), count);
    }

    pub fn add(&mut self, agent: &str, count: usize) {
        *self.counts.entry(agent.to_string()).or_default() += count;
    }

    pub fn total(&self) -> usize {
        self.counts.values().sum::<usize>() + if self.run_health { 1 } else { 0 }
    }

    /// Expand the plan into a flat list of (agent_name, batch_size) runs.
    /// Interleaves agent types so different types alternate.
    pub fn to_agent_runs(&self, batch_size: usize) -> Vec<(String, usize)> {
        let mut runs = Vec::new();
        if self.run_health {
            runs.push(("health".to_string(), 0));
        }

        // Sort by count descending so high-volume agents interleave well
        let mut types: Vec<(&String, &usize)> = self.counts.iter()
            .filter(|(_, c)| **c > 0)
            .collect();
        types.sort_by(|a, b| b.1.cmp(a.1));

        let mut queues: Vec<Vec<(String, usize)>> = types.iter().map(|(name, count)| {
            let mut q = Vec::new();
            let mut remaining = **count;
            while remaining > 0 {
                let batch = remaining.min(batch_size);
                q.push((name.to_string(), batch));
                remaining -= batch;
            }
            q
        }).collect();

        // Round-robin interleave
        loop {
            let mut added = false;
            for q in &mut queues {
                if let Some(run) = q.first() {
                    runs.push(run.clone());
                    q.remove(0);
                    added = true;
                }
            }
            if !added { break; }
        }
        runs
    }
}

/// Analyze metrics and decide how much each agent needs to run.
///
/// This is the control loop: metrics → error signal → agent allocation.
/// Target values are based on healthy small-world networks.
pub fn consolidation_plan(store: &Store) -> ConsolidationPlan {
    consolidation_plan_inner(store, true)
}

/// Cheap version: skip O(n²) interference detection (for daemon status).
pub fn consolidation_plan_quick(store: &Store) -> ConsolidationPlan {
    consolidation_plan_inner(store, false)
}

fn consolidation_plan_inner(store: &Store, _detect_interf: bool) -> ConsolidationPlan {
    let graph = store.build_graph();
    let alpha = graph.degree_power_law_exponent();
    let gini = graph.degree_gini();
    let _avg_cc = graph.avg_clustering_coefficient();

    let all_keys = store.all_keys().unwrap_or_default();
    let episodic_count = all_keys.iter()
        .filter_map(|k| store.get_node(k).ok()?)
        .filter(|n| matches!(n.node_type, crate::store::NodeType::EpisodicSession))
        .count();
    let _episodic_ratio = if all_keys.is_empty() { 0.0 }
        else { episodic_count as f32 / all_keys.len() as f32 };

    let mut plan = ConsolidationPlan {
        counts: std::collections::HashMap::new(),
        run_health: true,
        rationale: Vec::new(),
    };

    // Target: α ≥ 2.5 (healthy scale-free)
    if alpha < 2.0 {
        plan.add("linker", 100);
        plan.rationale.push(format!(
            "α={:.2} (target ≥2.5): extreme hub dominance → 100 linker", alpha));
    } else if alpha < 2.5 {
        plan.add("linker", 50);
        plan.rationale.push(format!(
            "α={:.2} (target ≥2.5): moderate hub dominance → 50 linker", alpha));
    } else {
        plan.add("linker", 20);
        plan.rationale.push(format!(
            "α={:.2}: healthy — 20 linker for maintenance", alpha));
    }

    // Target: Gini ≤ 0.4
    if gini > 0.5 {
        plan.add("linker", 50);
        plan.rationale.push(format!(
            "Gini={:.3} (target ≤0.4): high inequality → +50 linker", gini));
    }

    // Organize: proportional to linker — synthesizes what linker connects
    let linker = plan.count("linker");
    plan.set("organize", linker / 2);
    plan.rationale.push(format!(
        "Organize: {} (half of linker count)", plan.count("organize")));

    // Distill: core concept maintenance
    let organize = plan.count("organize");
    let mut distill = organize;
    if gini > 0.4 { distill += 20; }
    if alpha < 2.0 { distill += 20; }
    plan.set("distill", distill);
    plan.rationale.push(format!(
        "Distill: {} (synthesize hub content)", plan.count("distill")));

    // Split: handle oversized nodes
    plan.set("split", 5);

    plan
}

/// Format the consolidation plan for display
pub fn format_plan(plan: &ConsolidationPlan) -> String {
    let mut out = String::from("Consolidation Plan\n==================\n\n");

    out.push_str("Analysis:\n");
    for r in &plan.rationale {
        out.push_str(&format!("  • {}\n", r));
    }

    out.push_str("\nAgent allocation:\n");
    if plan.run_health {
        out.push_str("  1. health — system audit\n");
    }
    let mut step = 2;
    let mut sorted: Vec<_> = plan.counts.iter()
        .filter(|(_, c)| **c > 0)
        .collect();
    sorted.sort_by(|a, b| b.1.cmp(a.1));
    for (agent, count) in &sorted {
        out.push_str(&format!("  {}. {} ×{}\n", step, agent, count));
        step += 1;
    }

    out.push_str(&format!("\nTotal agent runs: {}\n", plan.total()));
    out
}

/// Brief daily check: compare current metrics to last snapshot
pub fn daily_check(store: &Store) -> String {
    let graph_obj = store.build_graph();
    let snap = graph::current_metrics(&graph_obj);

    let history = graph::load_metrics_history();
    let prev = history.last();

    let mut out = String::from("Memory daily check\n");

    // Current state
    out.push_str(&format!("  σ={:.1}  α={:.2}  gini={:.3}  cc={:.4}\n",
        snap.sigma, snap.alpha, snap.gini, snap.avg_cc));

    // Trend
    if let Some(p) = prev {
        let d_sigma = snap.sigma - p.sigma;
        let d_alpha = snap.alpha - p.alpha;
        let d_gini = snap.gini - p.gini;

        out.push_str(&format!("  Δσ={:+.1}  Δα={:+.2}  Δgini={:+.3}\n",
            d_sigma, d_alpha, d_gini));

        // Assessment
        let mut issues = Vec::new();
        if snap.alpha < 2.0 { issues.push("hub dominance critical"); }
        if snap.gini > 0.5 { issues.push("high inequality"); }
        if snap.avg_cc < 0.1 { issues.push("poor integration"); }
        if d_sigma < -5.0 { issues.push("σ declining"); }
        if d_alpha < -0.1 { issues.push("α declining"); }
        if d_gini > 0.02 { issues.push("inequality increasing"); }

        if issues.is_empty() {
            out.push_str("  Status: healthy\n");
        } else {
            out.push_str(&format!("  Status: needs attention — {}\n", issues.join(", ")));
            out.push_str("  Run: poc-memory consolidate-session\n");
        }
    } else {
        out.push_str("  (first snapshot, no trend data yet)\n");
    }

    // Persist the snapshot
    graph::save_metrics_snapshot(&snap);

    out
}