gam-sae 0.3.149

//! #2231 §2a — the crosscoder per-block weight `log λ_ℓ` as an outer-REML
//! coordinate: value/gradient consistency and closed-form-fixed-point coherence.
//!
//! These are PURE-MATH gates on the block-criterion trio
//! ([`profiled_reml_criterion`], [`profiled_reml_block_log_lambda_gradient`],
//! [`profiled_reml_block_efs_log_lambda_steps`]) — no fit is run, so they isolate
//! the analytic derivative from the inner solve exactly as the FD-ban gate
//! (#2087 objective↔gradient desync class) demands: the analytic block gradient
//! must be the finite-difference derivative of the block criterion it is paired
//! with, and the analytic gradient's zero must coincide with the closed-form
//! variance-ratio EFS step's zero. The rho flat-layout round-trip pins that the
//! appended block sub-vector survives `to_flat`/`from_flat` and that an empty
//! block vector is byte-identical to the plain-SAE layout.

use super::{
    ArdSharing, SaeManifoldRho, profiled_reml_block_efs_log_lambda_steps,
    profiled_reml_block_log_lambda_gradient, profiled_reml_criterion,
};
use ndarray::{Array1, arr1};

/// Central-difference FD of [`profiled_reml_criterion`] with respect to each
/// block `log λ_ℓ` must match [`profiled_reml_block_log_lambda_gradient`] to high
/// precision — the consistent-pair (#2087) requirement for the new coordinate.
#[test]
fn block_log_lambda_gradient_matches_central_difference() {
    let n_obs = 50usize;
    let p_x = 8usize;
    let rss_x = 40.0_f64;
    let block_rss = [30.0_f64, 12.0, 7.5];
    let dims = [5usize, 3, 2];
    let log_lambda = [0.2_f64, -0.5, 0.9];

    let analytic =
        profiled_reml_block_log_lambda_gradient(n_obs, p_x, rss_x, &block_rss, &dims, &log_lambda);
    assert_eq!(analytic.len(), block_rss.len());

    let h = 1e-6_f64;
    for l in 0..block_rss.len() {
        let mut plus = log_lambda;
        let mut minus = log_lambda;
        plus[l] += h;
        minus[l] -= h;
        let c_plus = profiled_reml_criterion(n_obs, p_x, rss_x, &block_rss, &dims, &plus);
        let c_minus = profiled_reml_criterion(n_obs, p_x, rss_x, &block_rss, &dims, &minus);
        let fd = (c_plus - c_minus) / (2.0 * h);
        let tol = 1e-5 * (1.0 + analytic[l].abs());
        assert!(
            (analytic[l] - fd).abs() < tol,
            "block {l}: analytic {} vs FD {} (diff {})",
            analytic[l],
            fd,
            (analytic[l] - fd).abs()
        );
    }
}

/// At the joint variance-ratio fixed point `λ_ℓ = (R_x/p_x)/(R_ℓ/p_ℓ)` the analytic
/// block gradient vanishes for EVERY block simultaneously (the per-coordinate
/// stationary condition `λ_ℓ R_ℓ = (p_ℓ/p̃)·pooled` is met there), and the
/// closed-form EFS step is exactly zero — so the quasi-Newton gradient lane and
/// the Fellner–Schall fixed-point lane agree on the same root.
#[test]
fn gradient_and_efs_step_vanish_at_variance_ratio_fixed_point() {
    let n_obs = 64usize;
    let p_x = 6usize;
    let rss_x = 24.0_f64;
    let block_rss = [50.0_f64, 9.0];
    let dims = [4usize, 3];

    // Each block seeded at its variance-ratio root; because the pooled residual
    // couples the coordinates, the per-coordinate gradient only vanishes when ALL
    // blocks sit at their root together (which they do here).
    let var_x = rss_x / p_x as f64;
    let log_lambda: Vec<f64> = block_rss
        .iter()
        .zip(dims.iter())
        .map(|(&rss, &dim)| (var_x / (rss / dim as f64)).ln())
        .collect();

    let grad =
        profiled_reml_block_log_lambda_gradient(n_obs, p_x, rss_x, &block_rss, &dims, &log_lambda);
    for (l, g) in grad.iter().enumerate() {
        assert!(
            g.abs() < 1e-9,
            "block {l} gradient {g} not ~0 at fixed point"
        );
    }

    let steps =
        profiled_reml_block_efs_log_lambda_steps(p_x, rss_x, &block_rss, &dims, &log_lambda);
    for (l, s) in steps.iter().enumerate() {
        assert!(
            s.abs() < 1e-12,
            "block {l} EFS step {s} not ~0 at fixed point"
        );
    }
}

/// One closed-form EFS step from an arbitrary start lands exactly on the
/// variance-ratio root (the step is `log λ_ℓ* − log λ_ℓ`, so `log λ_ℓ + step` is
/// `log λ_ℓ*` independent of the other blocks — the decoupled per-block update the
/// M1 driver takes).
#[test]
fn one_efs_step_reaches_the_variance_ratio_root() {
    let p_x = 10usize;
    let rss_x = 55.0_f64;
    let block_rss = [8.0_f64, 40.0];
    let dims = [3usize, 5];
    let log_lambda = [1.3_f64, -2.1]; // far from the root

    let steps =
        profiled_reml_block_efs_log_lambda_steps(p_x, rss_x, &block_rss, &dims, &log_lambda);
    let var_x = rss_x / p_x as f64;
    for l in 0..block_rss.len() {
        let root = (var_x / (block_rss[l] / dims[l] as f64)).ln();
        assert!(
            (log_lambda[l] + steps[l] - root).abs() < 1e-12,
            "block {l}: start+step {} != root {root}",
            log_lambda[l] + steps[l]
        );
    }
}

/// A block with no residual variance is unidentifiable: both the EFS step and the
/// analytic gradient contribution must hold it (step 0), matching the M1 driver's
/// `identifiable = false` gate.
#[test]
fn unidentifiable_block_is_held() {
    let p_x = 4usize;
    let rss_x = 12.0_f64;
    let block_rss = [0.0_f64, 6.0]; // block 0 perfectly reconstructed
    let dims = [2usize, 3];
    let log_lambda = [0.0_f64, 0.0];

    let steps =
        profiled_reml_block_efs_log_lambda_steps(p_x, rss_x, &block_rss, &dims, &log_lambda);
    assert_eq!(steps[0], 0.0, "unidentifiable block must be held");
    assert!(steps[1] != 0.0, "identifiable block must still move");
}

/// The appended block sub-vector survives a `to_flat` → `from_flat` round-trip in
/// both ARD-sharing modes, and an EMPTY block vector reproduces the plain-SAE flat
/// length exactly (byte-identical layout, #2231 §2a).
#[test]
fn rho_flat_round_trip_carries_block_coordinates() {
    let log_ard = vec![arr1(&[0.1_f64]), arr1(&[0.2_f64])];
    let plain = SaeManifoldRho::new(-0.3, 0.5, log_ard.clone());
    let plain_len = plain.to_flat().len();
    assert_eq!(plain_len, 1 + 2 + 2, "plain PerAtom flat length");
    assert_eq!(plain.num_blocks(), 0);

    let block = plain.clone().with_log_lambda_block(vec![0.7_f64, -0.4]);
    let flat = block.to_flat();
    assert_eq!(flat.len(), plain_len + 2, "block coords appended after ARD");
    // The block tail is the last (L-1) entries.
    assert_eq!(flat[plain_len], 0.7);
    assert_eq!(flat[plain_len + 1], -0.4);

    let recovered = block.from_flat(flat.view());
    assert_eq!(recovered.log_lambda_block, vec![0.7, -0.4]);
    assert_eq!(recovered.log_lambda_sparse, -0.3);
    assert_eq!(recovered.log_lambda_smooth, vec![0.5, 0.5]);

    // Shared-ARD mode appends the block tail after the single shared axis.
    let shared =
        SaeManifoldRho::new_shared_ard(-0.3, 0.5, log_ard).with_log_lambda_block(vec![1.1_f64]);
    assert_eq!(shared.ard_sharing(), ArdSharing::Shared);
    let sflat = shared.to_flat();
    // 1 sparse + 2 smooth + 1 shared axis + 1 block.
    assert_eq!(sflat.len(), 1 + 2 + 1 + 1);
    assert_eq!(sflat[sflat.len() - 1], 1.1);
    let srecovered = shared.from_flat(sflat.view());
    assert_eq!(srecovered.log_lambda_block, vec![1.1]);
}

/// An empty-block rho perturbed through the flat vector is byte-identical to the
/// historical layout: `from_flat` of a plain flat vector yields an empty block
/// tail (the plain-SAE outer path never sees a block coordinate).
#[test]
fn empty_block_flat_is_plain_sae_layout() {
    let rho = SaeManifoldRho::new(0.0, 0.0, vec![arr1(&[0.0_f64])]);
    let flat: Array1<f64> = rho.to_flat();
    assert_eq!(flat.len(), 1 + 1 + 1);
    let back = rho.from_flat(flat.view());
    assert!(back.log_lambda_block.is_empty());
}