#![allow(clippy::unwrap_used, clippy::cast_possible_truncation)]
//! Multi-vector preference — **event-time anchoring** (end-to-end regression for
//! the W11 fix).
//!
//! A positive-engagement signal must anchor the user's per-cluster importance at
//! the signal's EVENT timestamp, not the ingestion wall-clock. Otherwise a
//! backfilled / out-of-order engagement masquerades as fresh in the top-M fan-out.
//! The engine plumbs the event timestamp through `try_update_preference_vector ->
//! MultiPreferenceVectors::update_at(.., timestamp.as_nanos())`; this test proves
//! that wiring through the real `signal_with_context` path (the unit tests cover
//! the kernel, but not that the live signal path reaches it with the event time).

use std::collections::HashMap;

use tidaldb::{
    TidalDb,
    schema::{DecaySpec, EntityId, EntityKind, SchemaBuilder, Timestamp, Window},
};

const DIM: usize = 8;
const DAY_NS: u64 = 24 * 3600 * 1_000_000_000;
const BASE_NS: u64 = 1_000_000_000;

fn one_hot(axis: usize) -> Vec<f32> {
    let mut v = vec![0.0_f32; DIM];
    v[axis] = 1.0;
    v
}

fn schema() -> tidaldb::schema::Schema {
    let mut b = SchemaBuilder::new();
    let _ = b
        .signal(
            "like",
            EntityKind::Item,
            DecaySpec::Exponential {
                half_life: std::time::Duration::from_secs(30 * 24 * 3600),
            },
        )
        .windows(&[Window::TwentyFourHours])
        .positive_engagement(true)
        .add();
    b.embedding_slot("content", EntityKind::Item, DIM);
    b.build().unwrap()
}

#[test]
fn preference_importance_anchors_at_event_time_not_wall_clock() {
    let db = TidalDb::builder()
        .ephemeral()
        .with_schema(schema())
        .open()
        .unwrap();

    // Interest A on axis 0, interest B on axis 3 (orthogonal ⇒ distinct clusters).
    let item_a = EntityId::new(1);
    let item_b = EntityId::new(2);
    for (id, axis) in [(item_a, 0usize), (item_b, 3usize)] {
        db.write_item_with_metadata(id, &HashMap::new()).unwrap();
        db.write_item_embedding(id, &one_hot(axis)).unwrap();
    }

    let user = 7u64;

    // Warm the user entirely on interest A at an OLD event time (timestamps offset
    // by 1ns each to avoid WAL dedup; the spread is negligible vs the half-life).
    // >= COLD_START_N "like"s crosses the user into the clustered tier on A.
    for i in 0..6u64 {
        db.signal_with_context(
            "like",
            item_a,
            1.0,
            Timestamp::from_nanos(BASE_NS + i),
            Some(user),
            Some(100),
        )
        .unwrap();
    }
    assert!(
        db.preference_vectors().is_warm(user),
        "user must be warm on interest A after >= COLD_START_N likes"
    );

    // A SINGLE engagement with interest B, but 120 days later in EVENT time
    // (4 importance half-lives — the importance half-life default is 30 days).
    let t_late = Timestamp::from_nanos(BASE_NS + 120 * DAY_NS);
    db.signal_with_context("like", item_b, 1.0, t_late, Some(user), Some(100))
        .unwrap();

    let prefs = db.preference_vectors();
    assert_eq!(
        prefs.cluster_count(user),
        2,
        "A and B are orthogonal ⇒ two distinct interest clusters"
    );

    // Rank the clusters by current importance at t_late. With correct EVENT-time
    // anchoring, interest A (anchored ~120 days ago, decayed ~16×) falls BELOW the
    // single fresh interest B. Under the wall-clock bug, all six A engagements would
    // anchor at ~now and A's 6× mass would dominate B — so "B ranks first" is the
    // negative control that distinguishes the two implementations.
    let fanout = prefs.query_vectors(user, t_late.as_nanos(), 2);
    assert_eq!(fanout.len(), 2);
    assert!(
        fanout[0][3] > 0.9 && fanout[0][0] < 0.1,
        "the FRESH interest B (axis 3) must outrank the STALE interest A (axis 0) \
         when importance is anchored at event time; got fanout[0]={:?}",
        fanout[0]
    );
}