hypersteeldb 0.2.4

//! Bitmap-program analytics over the infon index — the deterministic template library the agent
//! composes beyond bare `retrieve`. Ported from the mother's `hypergraph/programs.ts` +
//! `query-engine.ts` (`fd46262`): partitions (`breakdown`/`crosstab`), salience (`rank`), the
//! structural s-graph (`cooccurs`/`s_path`/`s_clusters`), and stepwise answerability (`narrow`).
//!
//! Everything reduces to roaring set-algebra: partitions are anchor ∩ token popcounts; the s-graph is
//! token→token overlap (shared situations) with a threshold `s`. Non-discriminative high-DF tokens and
//! contextual facets (geo/time/qty/…) are held out of the structural graph so the analytics reason over
//! signal, not stopword-like noise — the `registration()` fix (`71e1714`).

use crate::bitmap::{Postings, RoarPostings};
use crate::index::InfonIndex;
use crate::tokenql::evaluate;
use serde_json::{json, Value};
use std::collections::{HashMap, HashSet};

type Ix = InfonIndex<RoarPostings>;

/// Contextual / structural-marker facets: a situation's coordinates (geo/time), quantities, and
/// relation/polarity markers. They sit in huge numbers of situations, so including them pollutes
/// s-overlap — kept out of MEMBERSHIP but still first-class as scope/filters. (structure.ts
/// DEFAULT_CONTEXT_FACETS ∪ query-engine NON_CONCEPT.)
const CONTEXT_FACETS: &[&str] = &[
    "geo", "region", "location", "loc", "place", "country", "site", "city", "date", "time", "ts",
    "when", "year", "month", "qty", "unit", "value", "amount", "measure", "num", "metric", "price",
    "numeric", "dur", "rel", "pol", "doctype", "kano", "sentiment", "src",
];

/// Cap on the number of nodes admitted to the materialised s-graph (top-N by frequency), so
/// `s_path` / `s_clusters` neighbour expansion stays bounded on large corpora.
const MAX_STRUCT_NODES: usize = 2000;

fn facet_of(t: &str) -> &str {
    t.split('/').next().unwrap_or(t)
}
fn leaf_of(t: &str) -> &str {
    match t.find('/') {
        Some(i) => &t[i + 1..],
        None => t,
    }
}
fn is_context(t: &str) -> bool {
    CONTEXT_FACETS.contains(&facet_of(t))
}
/// A token is admissible to the structural graph / analytics when it is a concept facet (not
/// contextual), not a high-DF noise token, and actually indexed.
fn admissible(t: &str, noise: &HashSet<String>) -> bool {
    !is_context(t) && !noise.contains(t)
}

// ── partitions ──────────────────────────────────────────────────────────────────

/// Partition an anchor set by the values of a facet: for each `facet/value`, the anchor ∩ token count.
/// A breakdown of an explicitly requested facet shows ALL its values — the high-DF registration filter
/// (which belongs on the structural graph) must not hide the most frequent values here.
pub fn breakdown(ix: &Ix, anchor: &str, facet: &str, k: usize) -> Value {
    let base = evaluate(ix, anchor);
    let mut rows: Vec<(String, usize)> = Vec::new();
    for t in ix.facet_members(facet) {
        let n = base.and(&ix.post(t)).len();
        if n > 0 {
            rows.push((t.clone(), n));
        }
    }
    rows.sort_by(|a, b| b.1.cmp(&a.1).then(a.0.cmp(&b.0)));
    rows.truncate(k);
    json!({
        "program": "breakdown", "anchor": anchor, "facet": facet, "total": base.len(),
        "partition": rows.iter().map(|(v, n)| json!({ "value": leaf_of(v), "token": v, "count": n })).collect::<Vec<_>>(),
    })
}

/// Co-occurrence matrix of two facets over an anchor set (top-k values of each by anchor overlap).
/// Like `breakdown`, the requested facets' values are shown in full (no high-DF registration filter).
pub fn crosstab(ix: &Ix, anchor: &str, facet_a: &str, facet_b: &str, k: usize) -> Value {
    let base = evaluate(ix, anchor);
    let top = |facet: &str| -> Vec<String> {
        let mut v: Vec<(String, usize)> = ix
            .facet_members(facet)
            .into_iter()
            .map(|t| (t.clone(), base.and(&ix.post(t)).len()))
            .filter(|(_, n)| *n > 0)
            .collect();
        v.sort_by(|a, b| b.1.cmp(&a.1).then(a.0.cmp(&b.0)));
        v.truncate(k);
        v.into_iter().map(|(t, _)| t).collect()
    };
    let (a_toks, b_toks) = (top(facet_a), top(facet_b));
    let matrix: Vec<Value> = a_toks
        .iter()
        .map(|a| {
            let ba = base.and(&ix.post(a));
            let cells: Vec<Value> = b_toks
                .iter()
                .map(|b| json!({ "col": leaf_of(b), "count": ba.and(&ix.post(b)).len() }))
                .collect();
            json!({ "row": leaf_of(a), "cells": cells })
        })
        .collect();
    json!({
        "program": "crosstab", "anchor": anchor, "row_facet": facet_a, "col_facet": facet_b,
        "cols": b_toks.iter().map(|t| leaf_of(t)).collect::<Vec<_>>(), "matrix": matrix, "total": base.len(),
    })
}

// ── salience (MDUS) ───────────────────────────────────────────────────────────────

fn minmax(vals: &[f64]) -> Vec<f64> {
    let (lo, hi) = vals.iter().fold((f64::INFINITY, f64::NEG_INFINITY), |(lo, hi), &v| (lo.min(v), hi.max(v)));
    let rng = hi - lo;
    vals.iter().map(|&v| if rng > 0.0 { (v - lo) / rng } else { 0.0 }).collect()
}

/// Rank a facet's values by salience: a min-max-normalised blend of frequency (posting size) and
/// cross-concept breadth (how many distinct structural nodes it co-occurs with). Recency is omitted —
/// the Rust corpus has no reliable per-situation date column — so weights split freq/breadth 0.5/0.5.
pub fn rank(ix: &Ix, facet: &str, k: usize, noise: &HashSet<String>) -> Value {
    let st = Structure::build(ix, noise);
    // rank all values of the requested facet; salience (breadth) already down-weights ubiquitous ones,
    // so no need to hide high-DF values the way the structural graph does.
    let ents: Vec<String> = ix.facet_members(facet).into_iter().cloned().collect();
    if ents.is_empty() {
        return json!({ "program": "rank", "facet": facet, "ranked": [] });
    }
    let freq: Vec<f64> = ents.iter().map(|t| ix.post_len(t) as f64).collect();
    let breadth: Vec<f64> = ents.iter().map(|t| st.breadth_of(&ix.post(t)) as f64).collect();
    let (nf, nb) = (minmax(&freq), minmax(&breadth));
    let mut scored: Vec<(String, f64, f64, f64)> = ents
        .iter()
        .enumerate()
        .map(|(i, t)| {
            let mdus = 0.5 * nf[i] + 0.5 * nb[i];
            (t.clone(), mdus, nf[i], nb[i])
        })
        .collect();
    scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
    scored.truncate(k);
    json!({
        "program": "rank", "facet": facet,
        "ranked": scored.iter().map(|(t, m, f, b)| json!({
            "token": leaf_of(t), "mdus": (m * 1000.0).round() / 1000.0,
            "components": { "freq": (f * 1000.0).round() / 1000.0, "breadth": (b * 1000.0).round() / 1000.0 }
        })).collect::<Vec<_>>(),
    })
}

// ── structural s-graph ─────────────────────────────────────────────────────────────

/// Materialised structural token graph: the concept nodes (facet not contextual, not noise) capped to
/// the top `MAX_STRUCT_NODES` by frequency, with a forward `situation → node-idxs` map so neighbour
/// discovery is O(situation degree) rather than O(vocab). Two nodes are s-adjacent iff they share ≥ s
/// situations (overlap = popcount of the postings intersection).
struct Structure {
    names: Vec<String>,
    posts: Vec<RoarPostings>,
    index: HashMap<String, usize>,
    forward: HashMap<u32, Vec<usize>>,
}

impl Structure {
    fn build(ix: &Ix, noise: &HashSet<String>) -> Structure {
        let mut nodes: Vec<(&String, usize)> = ix
            .tokens()
            .filter(|t| admissible(t, noise))
            .map(|t| (t, ix.post_len(t)))
            .filter(|(_, n)| *n > 0)
            .collect();
        nodes.sort_by(|a, b| b.1.cmp(&a.1).then(a.0.cmp(b.0)));
        nodes.truncate(MAX_STRUCT_NODES);

        let names: Vec<String> = nodes.iter().map(|(t, _)| (*t).clone()).collect();
        let posts: Vec<RoarPostings> = names.iter().map(|t| ix.post(t)).collect();
        let index: HashMap<String, usize> = names.iter().enumerate().map(|(i, t)| (t.clone(), i)).collect();
        let mut forward: HashMap<u32, Vec<usize>> = HashMap::new();
        for (i, p) in posts.iter().enumerate() {
            for sid in p.to_sorted() {
                forward.entry(sid).or_default().push(i);
            }
        }
        Structure { names, posts, index, forward }
    }

    /// Distinct structural nodes that share ≥1 situation with the given posting set.
    fn candidates(&self, post: &RoarPostings) -> HashSet<usize> {
        let mut out = HashSet::new();
        for sid in post.to_sorted() {
            if let Some(idxs) = self.forward.get(&sid) {
                out.extend(idxs.iter().copied());
            }
        }
        out
    }

    /// Cross-concept breadth: number of distinct structural nodes co-occurring with `post`.
    fn breadth_of(&self, post: &RoarPostings) -> usize {
        self.candidates(post).len()
    }

    /// s-neighbours of node `i`: candidates sharing ≥ `s` situations.
    fn neighbors(&self, i: usize, s: usize) -> Vec<usize> {
        self.candidates(&self.posts[i])
            .into_iter()
            .filter(|&j| j != i && self.posts[i].and(&self.posts[j]).len() >= s)
            .collect()
    }
}


/// One level of an **s-filtration**: both readings of the incidence matrix at overlap threshold `s`.
///
/// The index is an incidence matrix — tags by situations — and it can be read in two directions:
///
/// * **primal** — situations are nodes, joined when they share at least `s` tags. *Which events are related?*
/// * **dual** — tags are nodes, joined when they co-occur in at least `s` situations. *Which concepts belong
///   together?*
///
/// Nothing is rebuilt to switch between them: it is the same matrix transposed. That is the structural reason a
/// hypergraph engine gets the dual for free where a pairwise graph needs a second index kept in step with the
/// first.
#[derive(Debug, Clone, serde::Serialize)]
pub struct Level {
    pub s: usize,
    pub primal: Graph,
    pub dual: Graph,
}

/// Edge and component counts for one reading at one threshold.
#[derive(Debug, Clone, serde::Serialize)]
pub struct Graph {
    pub nodes: usize,
    pub edges: usize,
    /// connected components — structure appearing as coincidental links are removed
    pub components: usize,
    /// a bounded sample of edges, for drawing
    pub sample: Vec<(usize, usize)>,
}

/// Connected components of an undirected edge list, by union-find.
fn components(n: usize, edges: &[(usize, usize)]) -> usize {
    let mut parent: Vec<usize> = (0..n).collect();
    fn find(p: &mut [usize], x: usize) -> usize {
        let mut r = x;
        while p[r] != r {
            r = p[r];
        }
        // path compression, iterative so a long chain cannot blow the stack
        let mut c = x;
        while p[c] != r {
            let next = p[c];
            p[c] = r;
            c = next;
        }
        r
    }
    for &(a, b) in edges {
        let (ra, rb) = (find(&mut parent, a), find(&mut parent, b));
        if ra != rb {
            parent[ra] = rb;
        }
    }
    let mut roots = HashSet::new();
    for i in 0..n {
        roots.insert(find(&mut parent, i));
    }
    roots.len()
}

/// Sweep the overlap threshold from 1 to `max_s`, reporting both topologies at each level.
///
/// This is the **s-filtration**. At `s = 1` a single shared item connects almost everything, which is the
/// regime where an unguarded walk drifts to somewhere unrelated to where it began. Raising `s` demands more
/// agreement per step: edges fall away while component counts climb, so genuine structure separates from
/// coincidence.
///
/// `sample_cap` bounds the edges returned per level; the counts are always exact.
pub fn s_filtration(ix: &Ix, max_s: usize, noise: &HashSet<String>, sample_cap: usize) -> Vec<Level> {
    // tags worth considering, and the situations each covers
    let tags: Vec<(&String, &RoarPostings)> = ix
        .postings()
        .filter(|(t, _)| !noise.contains(t.as_str()))
        .collect();
    let n_sit = ix.situations() as usize;

    // primal rows: which tags each situation carries, as indices into `tags`
    let mut tags_of: Vec<Vec<usize>> = vec![Vec::new(); n_sit];
    for (ti, (_, post)) in tags.iter().enumerate() {
        for sid in post.to_sorted() {
            if let Some(slot) = tags_of.get_mut(sid as usize) {
                slot.push(ti);
            }
        }
    }

    let overlap = |a: &[usize], b: &[usize]| -> usize {
        // both lists are ascending, so this is a merge rather than a nested scan
        let (mut i, mut j, mut n) = (0, 0, 0);
        while i < a.len() && j < b.len() {
            match a[i].cmp(&b[j]) {
                std::cmp::Ordering::Equal => {
                    n += 1;
                    i += 1;
                    j += 1;
                }
                std::cmp::Ordering::Less => i += 1,
                std::cmp::Ordering::Greater => j += 1,
            }
        }
        n
    };

    (1..=max_s.clamp(1, 16))
        .map(|s| {
            let mut p_edges: Vec<(usize, usize)> = Vec::new();
            for i in 0..n_sit {
                for j in (i + 1)..n_sit {
                    if overlap(&tags_of[i], &tags_of[j]) >= s {
                        p_edges.push((i, j));
                    }
                }
            }
            let mut d_edges: Vec<(usize, usize)> = Vec::new();
            for a in 0..tags.len() {
                for b in (a + 1)..tags.len() {
                    if tags[a].1.and(tags[b].1).len() >= s {
                        d_edges.push((a, b));
                    }
                }
            }
            Level {
                s,
                primal: Graph {
                    nodes: n_sit,
                    edges: p_edges.len(),
                    components: components(n_sit, &p_edges),
                    sample: p_edges.iter().take(sample_cap).copied().collect(),
                },
                dual: Graph {
                    nodes: tags.len(),
                    edges: d_edges.len(),
                    components: components(tags.len(), &d_edges),
                    sample: d_edges.iter().take(sample_cap).copied().collect(),
                },
            }
        })
        .collect()
}

/// The names behind the dual node indices of [`s_filtration`], in the same order.
pub fn dual_node_names(ix: &Ix, noise: &HashSet<String>) -> Vec<String> {
    ix.postings().filter(|(t, _)| !noise.contains(t.as_str())).map(|(t, _)| t.clone()).collect()
}

/// Tokens that co-occur most with `token` (shared-situation overlap), over the structural node space.
pub fn cooccurs(ix: &Ix, token: &str, k: usize, noise: &HashSet<String>) -> Value {
    let st = Structure::build(ix, noise);
    let focus = ix.post(token);
    let mut scored: Vec<(usize, usize)> = st
        .candidates(&focus)
        .into_iter()
        .filter(|&j| st.names[j] != token)
        .map(|j| (j, focus.and(&st.posts[j]).len()))
        .filter(|(_, n)| *n > 0)
        .collect();
    scored.sort_by(|a, b| b.1.cmp(&a.1).then(st.names[a.0].cmp(&st.names[b.0])));
    scored.truncate(k);
    json!({
        "program": "structure", "op": "cooccurs", "focus": token,
        "cooccurs": scored.iter().map(|(j, n)| json!({ "token": st.names[*j], "shared": n })).collect::<Vec<_>>(),
    })
}

/// Shortest ≥s-overlap path (fewest hops) between two tokens over the structural graph; null if
/// disconnected at `s`.
pub fn s_path(ix: &Ix, a: &str, b: &str, s: usize, noise: &HashSet<String>) -> Value {
    let st = Structure::build(ix, noise);
    let path = match (st.index.get(a), st.index.get(b)) {
        (Some(&src), Some(&dst)) => bfs_path(&st, src, dst, s.max(1)),
        _ => None,
    };
    json!({
        "program": "structure", "op": "s_path", "a": a, "b": b, "s": s,
        "path": path.map(|p| p.iter().map(|&i| st.names[i].clone()).collect::<Vec<_>>()),
    })
}

fn bfs_path(st: &Structure, src: usize, dst: usize, s: usize) -> Option<Vec<usize>> {
    if src == dst {
        return Some(vec![src]);
    }
    let mut prev: HashMap<usize, Option<usize>> = HashMap::new();
    prev.insert(src, None);
    let mut queue = std::collections::VecDeque::from([src]);
    while let Some(u) = queue.pop_front() {
        for v in st.neighbors(u, s) {
            if let std::collections::hash_map::Entry::Vacant(e) = prev.entry(v) {
                e.insert(Some(u));
                if v == dst {
                    let mut path = vec![dst];
                    let mut n = dst;
                    while let Some(Some(p)) = prev.get(&n) {
                        path.push(*p);
                        n = *p;
                    }
                    path.reverse();
                    return Some(path);
                }
                queue.push_back(v);
            }
        }
    }
    None
}

/// Connected components of the structural graph at overlap threshold `s` — the token clusters.
pub fn s_clusters(ix: &Ix, s: usize, k: usize, noise: &HashSet<String>) -> Value {
    let st = Structure::build(ix, noise);
    let s = s.max(1);
    let mut seen = vec![false; st.names.len()];
    let mut clusters: Vec<Vec<usize>> = Vec::new();
    for start in 0..st.names.len() {
        if seen[start] {
            continue;
        }
        let mut comp = Vec::new();
        let mut stack = vec![start];
        seen[start] = true;
        while let Some(u) = stack.pop() {
            comp.push(u);
            for v in st.neighbors(u, s) {
                if !seen[v] {
                    seen[v] = true;
                    stack.push(v);
                }
            }
        }
        if comp.len() > 1 {
            clusters.push(comp);
        }
    }
    clusters.sort_by(|a, b| b.len().cmp(&a.len()));
    clusters.truncate(k);
    json!({
        "program": "structure", "op": "s_clusters", "s": s,
        "clusters": clusters.iter().map(|c| json!({
            "size": c.len(),
            "tokens": c.iter().take(12).map(|&i| st.names[i].clone()).collect::<Vec<_>>(),
        })).collect::<Vec<_>>(),
    })
}

// ── stepwise answerability ──────────────────────────────────────────────────────────

/// Add scope/filter tokens one at a time and report how the matched set shrinks — where it hits zero
/// tells you which constraint the corpus cannot satisfy.
pub fn narrow(ix: &Ix, scope: &[String], filters: &[String]) -> Value {
    let mut parts: Vec<String> = Vec::new();
    let mut steps: Vec<Value> = Vec::new();
    let mut empty_at: Option<String> = None;
    let seq: Vec<(&str, &String)> = scope
        .iter()
        .map(|t| ("concept", t))
        .chain(filters.iter().map(|t| ("filter", t)))
        .collect();
    for (kind, piece) in seq {
        parts.push(piece.clone());
        let cur = if parts.len() == 1 { parts[0].clone() } else { format!("(and {})", parts.join(" ")) };
        let n = evaluate(ix, &cur).len();
        steps.push(json!({ "add": piece, "kind": kind, "expr": cur, "remaining": n }));
        if n == 0 && empty_at.is_none() {
            empty_at = Some(piece.clone());
        }
    }
    let answerable = steps.last().and_then(|s| s.get("remaining")).and_then(|n| n.as_u64()).unwrap_or(0) > 0;
    json!({
        "program": "narrow", "steps": steps,
        "verdict": if answerable { "ANSWERABLE" } else { "NOT ANSWERABLE" }, "empty_at": empty_at,
    })
}