gam-sae 0.3.154

#![cfg(test)]
//! LOCAL zoo-micro loop (#1026) — the BSF-zoo m12 shape as an in-tree Rust
//! harness, no python wheel, no MSI round-trip.
//!
//! The committed fixture (`tests/data/zoo_micro/`, generated by the exact
//! `bench/bsf_manifold_zoo.py` `ZooData(12, 48, 3, seed=0, curved=0.5)`
//! generator the MSI m12 job used — same kinds list) drives:
//!
//!  1. the FULL production outer cascade (`OuterProblem::run`, the exact FFI
//!     entry), timed for observation and required to return an analytic
//!     convergence certificate;
//!  2. the three-way OOS discriminator the zoo's `ours_rust` arm reports:
//!     native train EV vs COLD re-encode(train) EV vs COLD encode(test) EV,
//!     where the cold arms run the genuine frozen-decoder OOS solve
//!     (production seeding via `seed_coords_by_decoder_projection`, then
//!     `run_fixed_decoder_arrow_schur`) — the same math as the
//!     `sae_manifold_predict_oos` FFI lane. `native >> cold(train)` localizes
//!     the historical OOS collapse to the cold ENCODE path (seeding/routing,
//!     not data novelty); `cold(train) >> cold(test)` is a genuine
//!     generalization gap.
//!
//! zz_measure discipline: eprintln elapsed measurements, but decide pass/fail
//! only from convergence and fitted-state invariants.

use super::tests_startup_validation_1782::{Topo, objective_and_seed};
use ndarray::Array2;
use std::time::Instant;

fn zoo_fixture(name: &str, n: usize, p: usize) -> Array2<f64> {
    let path = format!("{}/tests/data/zoo_micro/{name}", env!("CARGO_MANIFEST_DIR"));
    let bytes =
        std::fs::read(&path).unwrap_or_else(|e| panic!("zoo_micro fixture {path} unreadable: {e}"));
    assert_eq!(bytes.len(), n * p * 8, "fixture {name} size mismatch");
    let vals: Vec<f64> = bytes
        .chunks_exact(8)
        .map(|c| f64::from_le_bytes(c.try_into().unwrap()))
        .collect();
    Array2::from_shape_vec((n, p), vals).unwrap()
}

fn global_ev(target: &Array2<f64>, fitted: &Array2<f64>) -> f64 {
    let (n, p) = target.dim();
    let mut means = vec![0.0_f64; p];
    for col in 0..p {
        let mut acc = 0.0;
        for row in 0..n {
            acc += target[[row, col]];
        }
        means[col] = acc / n as f64;
    }
    let (mut ss_res, mut ss_tot) = (0.0_f64, 0.0_f64);
    for row in 0..n {
        for col in 0..p {
            let r = target[[row, col]] - fitted[[row, col]];
            ss_res += r * r;
            let c = target[[row, col]] - means[col];
            ss_tot += c * c;
        }
    }
    1.0 - ss_res / ss_tot.max(1e-12)
}

/// Cold frozen-decoder OOS solve of `x` against the FITTED dictionary: fresh
/// rows, cold logits, coords seeded by the production decoder-grid projection,
/// then the fixed-decoder Newton refinement — the `sae_manifold_predict_oos`
/// math without the FFI marshalling. Returns the reconstruction EV.
///
/// Delegates to the PRODUCTION-FAITHFUL helper in `tests_collapse_2132`
/// (decoder-projection coords + residual-seeded softmax logits + ρ*-threaded
/// fixed-decoder solve). The original inline version here seeded the logits
/// UNIFORMLY, which softmax-blends all K atoms into a near-mean reconstruction
/// and mis-attributes routing collapse to the encode path — the flaw the
/// #2132 status-diff caught.
fn cold_oos_ev(
    fitted_term: &super::SaeManifoldTerm,
    rho: &super::SaeManifoldRho,
    x: &Array2<f64>,
    label: &str,
) -> f64 {
    let t0 = Instant::now();
    let ev = super::tests_collapse_2132::oos_heldout_ev(fitted_term, rho, x.view());
    let secs = t0.elapsed().as_secs_f64();
    eprintln!(
        "[zoo-micro-local] cold OOS {label}: ev={ev:.4} solve={secs:.2}s n={}",
        x.nrows()
    );
    ev
}

#[test]
fn zz_zoo_micro_local_full_fit_and_oos_discriminator() {
    gam_runtime::test_support::install_diagnostic_logger();
    let train = zoo_fixture("train_3000x48_f64le.bin", 3000, 48);
    let test = zoo_fixture("test_1500x48_f64le.bin", 1500, 48);

    // The m12 arm: K=12 with the configured assignment model
    // temperature, circle topology, single PCA seed — the configuration whose
    // MSI runs exposed incumbent churn.
    let (mut objective, seed) = objective_and_seed(
        train.view(),
        12,
        Topo::Circle,
        crate::assignment::AssignmentMode::softmax(1.0),
    );
    let n_params = seed.len();
    let t0 = Instant::now();
    let result = gam_solve::rho_optimizer::OuterProblem::new(n_params)
        .with_initial_rho(seed)
        .with_seed_config(gam_problem::SeedConfig {
            max_seeds: 1,
            seed_budget: 1,
            ..Default::default()
        })
        .run(&mut objective, "SAE manifold")
        .expect("zoo-micro full fit must not abort");
    assert!(result.converged(), "zoo fit must be analytically certified");
    let certificate = result
        .criterion_certificate
        .as_ref()
        .expect("converged zoo fit carries an analytic certificate");
    assert!(certificate.stationarity.projected_norm() <= certificate.stationarity.bound());
    let fit_secs = t0.elapsed().as_secs_f64();
    objective
        .certify_outer_result(&result)
        .expect("zoo outer result certifies the exact installed state");
    let fitted = objective.into_fitted().expect("outer fit was evaluated");
    let native_fitted = fitted.term.fitted();
    let native_ev = global_ev(&train, &native_fitted.to_owned());
    eprintln!(
        "[zoo-micro-local] FIT: {fit_secs:.1}s native_train_ev={native_ev:.4} (K=12, N=3000, p=48)"
    );

    let cold_train_ev = cold_oos_ev(&fitted.term, &fitted.rho, &train, "re-encode(train)");
    let cold_test_ev = cold_oos_ev(&fitted.term, &fitted.rho, &test, "encode(test)");
    eprintln!(
        "[zoo-micro-local] DISCRIMINATOR: native={native_ev:.4} cold(train)={cold_train_ev:.4} \
         cold(test)={cold_test_ev:.4} | encode-gap={:.4} generalization-gap={:.4}",
        native_ev - cold_train_ev,
        cold_train_ev - cold_test_ev
    );

    assert!(native_ev.is_finite() && cold_train_ev.is_finite() && cold_test_ev.is_finite());
    // Signal floor, not a quality bar: the fit must actually explain planted
    // structure (the m12 MSI incumbent reached 0.67 within minutes even under
    // the churn; PCA-12 on this mixture clears 0.5 easily).
    assert!(
        native_ev > 0.3,
        "zoo-micro native train EV {native_ev:.4} is below the signal floor — \
         the fit did not engage the planted mixture"
    );
}

/// #2022 canonical rank-charge zoo-micro measurement. Fits the m12 configuration
/// once under the noise-referenced, piecewise-constant
/// `½·d_eff·ln N_eff` charge and `rank_eff==0 ⇒ v→+∞` veto, reporting
/// births/deaths, co-collapse incidence, reconstruction quality, criterion, and
/// wall-clock. At fixed residual dispersion the integer rank changes only when a
/// decoder mode crosses an MP edge; under a physical unit rescale, decoder energy
/// and residual dispersion scale together and the count is invariant. This is an
/// ordinary named measurement test because ignored tests are forbidden; focused
/// runs select it by its `zz_rank_charge` name. Pass/fail uses finiteness plus
/// signal engagement.
#[derive(Debug)]
struct RankChargeArm {
    fit_secs: f64,
    final_value: f64,
    converged: bool,
    iterations: usize,
    grad_norm: Option<f64>,
    native_ev: f64,
    cold_train_ev: f64,
    cold_test_ev: f64,
    k_atoms: usize,
    collapse_events: usize,
    dict_cocollapse_reseeds: usize,
    struct_cocollapse_reseeds: usize,
    evidence_reanchors: usize,
}

fn rank_charge_zoo_arm(train: &Array2<f64>, test: &Array2<f64>) -> RankChargeArm {
    let (mut objective, seed) = objective_and_seed(
        train.view(),
        12,
        Topo::Circle,
        crate::assignment::AssignmentMode::softmax(1.0),
    );
    let n_params = seed.len();
    let t0 = Instant::now();
    let result = gam_solve::rho_optimizer::OuterProblem::new(n_params)
        .with_initial_rho(seed)
        .with_seed_config(gam_problem::SeedConfig {
            max_seeds: 1,
            seed_budget: 1,
            ..Default::default()
        })
        .run(&mut objective, "SAE manifold")
        .expect("zoo-micro rank-charge fit must not abort");
    let fit_secs = t0.elapsed().as_secs_f64();
    objective
        .certify_outer_result(&result)
        .expect("zoo rank-charge outer result certifies the installed state");
    let grad_norm = result
        .criterion_certificate
        .as_ref()
        .map(|certificate| certificate.stationarity.projected_norm());
    let fitted = objective.into_fitted().expect("outer fit was evaluated");
    let native_ev = global_ev(train, &fitted.term.fitted().to_owned());
    let cold_train_ev = cold_oos_ev(&fitted.term, &fitted.rho, train, "re-encode(train)");
    let cold_test_ev = cold_oos_ev(&fitted.term, &fitted.rho, test, "encode(test)");
    RankChargeArm {
        fit_secs,
        final_value: result.final_value,
        converged: result.converged(),
        iterations: result.iterations,
        grad_norm,
        native_ev,
        cold_train_ev,
        cold_test_ev,
        k_atoms: fitted.term.k_atoms(),
        collapse_events: fitted.term.collapse_events().len(),
        dict_cocollapse_reseeds: fitted.term.dictionary_cocollapse_reseeds,
        struct_cocollapse_reseeds: fitted.term.structural_cocollapse_reseeds,
        evidence_reanchors: fitted.term.criterion_gauge_deflation_reanchors,
    }
}

#[test]
// zz_measure: one full outer fit; focused runs select it by its zz_ name.
fn zz_rank_charge_zoo_micro_2022() {
    gam_runtime::test_support::install_diagnostic_logger();
    let train = zoo_fixture("train_3000x48_f64le.bin", 3000, 48);
    let test = zoo_fixture("test_1500x48_f64le.bin", 1500, 48);

    let arm = rank_charge_zoo_arm(&train, &test);
    eprintln!(
        "[#2022 rank-charge zoo] fit={:.1}s conv={} iters={} \
             grad={:?} | native_ev={:.4} cold_train={:.4} cold_test={:.4} | \
             K={} deaths={} dict_reseed={} struct_reseed={} reanchor={} | crit={:.6e}",
        arm.fit_secs,
        arm.converged,
        arm.iterations,
        arm.grad_norm,
        arm.native_ev,
        arm.cold_train_ev,
        arm.cold_test_ev,
        arm.k_atoms,
        arm.collapse_events,
        arm.dict_cocollapse_reseeds,
        arm.struct_cocollapse_reseeds,
        arm.evidence_reanchors,
        arm.final_value,
    );

    assert!(
        arm.native_ev.is_finite() && arm.cold_train_ev.is_finite() && arm.cold_test_ev.is_finite(),
        "rank-charge fit produced a non-finite EV"
    );
    assert!(
        arm.native_ev > 0.3,
        "rank-charge native EV {:.4} below signal floor — fit did not engage",
        arm.native_ev
    );
}