gam_models/transformation_normal/

config.rs

#[derive(Clone, Debug)]
pub struct TransformationNormalConfig {
    /// B-spline degree for the response-direction deviation basis (default 3).
    pub response_degree: usize,
    /// Number of interior knots for the response-direction deviation basis (default 10).
    pub response_num_internal_knots: usize,
    /// Difference penalty order for the response-direction roughness penalty (default 2).
    pub response_penalty_order: usize,
    /// Additional penalty orders for the response-direction (default [1]).
    pub response_extra_penalty_orders: Vec<usize>,
    /// Whether to add a global identity (ridge) penalty (default true).
    pub double_penalty: bool,
    /// When true, `response_num_internal_knots` is treated as an already-resolved
    /// effective value: `fit_transformation_normal` uses it verbatim instead of
    /// re-running `effective_response_num_internal_knots`. This is required by the
    /// cross-fit Stage-1 calibration, which pins the knot count once at the
    /// smallest fold complement so `p_resp` (and hence `p₁ = p_resp · p_cov`)
    /// is fold-invariant; the data-driven complexity cap would otherwise round
    /// to different counts on each fold's response subsample (workflow.rs §3).
    pub response_num_internal_knots_pinned: bool,
}

impl Default for TransformationNormalConfig {
    fn default() -> Self {
        Self {
            response_degree: 3,
            response_num_internal_knots: 10,
            response_penalty_order: 2,
            response_extra_penalty_orders: vec![1],
            double_penalty: true,
            response_num_internal_knots_pinned: false,
        }
    }
}

/// Baseline cap for the tensor-product width used by the transformation-normal
/// response basis. Small datasets should stay compact because the fit
/// repeatedly factorizes dense penalized Hessians.
pub(crate) const BASE_TRANSFORMATION_TENSOR_WIDTH: usize = 160;

/// Large samples can support a richer response basis without the aggressive
/// underfitting forced by the small-sample cap above. This upper cap keeps the
/// tensor width bounded even when the covariate side is narrow.
pub(crate) const LARGE_SAMPLE_TRANSFORMATION_TENSOR_WIDTH: usize = 320;

/// E[log |Z|] for Z ~ N(0, 1), used to put local log-absolute residual
/// projections on the standard-normal scale.
pub(crate) const STANDARD_NORMAL_MEAN_LOG_ABS: f64 = -0.635_181_422_730_739_1;

/// Strict-feasibility margin for `h' > 0` on the monotonicity grid. Used
/// both by the fit-time fraction-to-boundary line search (so accepted β
/// keeps `h'(grid) ≥ EPS`) and by the predict-time monotonicity check
/// in `inference::predict_input` (which rejects predictions whose minimum
/// `h'` on the response grid drops below this threshold). Keeping these
/// in sync prevents the predict path from rejecting fits that the
/// optimizer accepted as feasible — and vice versa.
pub const TRANSFORMATION_MONOTONICITY_EPS: f64 = 1.0e-8;

/// Absolute bound for feasible transformation scores on the standard-normal
/// scale. The CTN likelihood targets `h(Y|x) ~ N(0,1)`; accepting exact-Newton
/// iterates with finite positive `h'` but astronomical `|h|` lets curvature
/// diagnostics overflow into meaningless values. This is a numerical runaway
/// guard, not a statistical plausibility filter: startup seeds can temporarily
/// land outside practically observable normal quantiles before the line search
/// moves them back into the likelihood's high-density region.
pub const TRANSFORMATION_NORMAL_H_ABS_MAX: f64 = 1.0e6;

/// Number of dense-spectral factor columns processed per exact ψψ HVP row pass.
/// At large-scale CTN dimensions p≈800, this keeps the per-worker accumulator well
/// under 1 MiB while reducing repeated SCOP row-invariant work by 32× relative
/// to one-column HVP dispatch.
pub(crate) const SCOP_PSI_PSI_HVP_TILE_COLS: usize = 32;

/// Exact dense SCOP coefficient Hessian cache limit for the inner `H·v` path.
///
/// The large-scale CTN calibration fit has many rows but a moderate coefficient
/// dimension (for example n=20k, p=264). In that regime repeated PCG products
/// against the same Hessian should pay the row-streaming chain rule once, then
/// serve subsequent products as dense BLAS matvecs. Keep the cache restricted to
/// genuinely moderate p so wide CTN fits remain row-streamed.
pub(crate) const SCOP_HESSIAN_HVP_DENSE_CACHE_MAX_DIM: usize = 384;

pub(crate) const SCOP_HESSIAN_HVP_DENSE_CACHE_MAX_BYTES: usize = 64 * 1024 * 1024;

/// CTN-scoped ceiling on the custom-family inner exact-Newton cycle budget.
///
/// The global `DEFAULT_CUSTOM_FAMILY_INNER_MAX_CYCLES = 1200` exists for the
/// large-scale survival marginal-slope path, whose inner mode has a long,
/// rank-deficient KKT tail that genuinely needs hundreds of cycles. CTN is a
/// different regime: its coefficient block is a *bounded-dimension* Khatri–Rao
/// tensor (capped by `BASE/LARGE_SAMPLE_TRANSFORMATION_TENSOR_WIDTH`), and the
/// objective is strictly convex by construction — the `double_penalty` ridge
/// plus the order-2/order-1 roughness penalties make the penalized Hessian
/// positive definite even where the likelihood is flat on weakly-identified
/// shape×covariate directions. An exact-Newton iteration on a strictly convex,
/// bounded-dimension block converges in a handful of cycles; the only way the
/// fit reaches 1200 inner cycles is by polishing weakly-identified directions
/// that contribute nothing to the likelihood (the #720 timeout). Scaling the
/// cap with the realized coefficient dimension keeps a generous margin for a
/// genuinely nonlinear, high-dimensional transformation while refusing to grind
/// the production large-scale cap on an easy near-Gaussian shift.
pub(crate) const CTN_INNER_MAX_CYCLES_BASE: usize = 64;

pub(crate) const CTN_INNER_MAX_CYCLES_PER_DIM: usize = 2;

pub(crate) const CTN_INNER_MAX_CYCLES_CEILING: usize = 400;

/// Numerical floor on a Gram/penalty diagonal scale before it enters the
/// `likelihood_scale / penalty_scale` ratio that seeds the outer log-λ search.
/// A genuinely zero diagonal (an all-zero penalty block, or a degenerate
/// likelihood Gram) would otherwise produce a `0/0` or `x/0` seed; flooring
/// both scales at a value far below any meaningful curvature keeps the ratio
/// finite without perturbing well-posed problems.
pub(crate) const CTN_SEED_SCALE_FLOOR: f64 = 1.0e-8;

/// Lower bound on the cold-start seed log-λ (i.e. λ ≥ 1). Keeps the outer
/// optimizer out of the under-regularized regime where the CTN inner solve is
/// structurally rank-deficient (small-n / p > n); the optimizer is free to step
/// below this once the data support it. See `ctn_penalty_scale_log_lambdas`.
pub(crate) const CTN_SEED_LOG_LAMBDA_MIN: f64 = 0.0;

/// Upper bound on the cold-start seed log-λ, matching the outer ρ-bound used
/// across the location-scale families: λ ≈ e¹² caps the seed in the strongly
/// over-smoothed regime so a tiny penalty scale cannot seed an absurd λ.
pub(crate) const CTN_SEED_LOG_LAMBDA_MAX: f64 = 12.0;

/// Floor on the warm-start global residual scale `sqrt(weighted_ss / Σw)`.
/// Guards the degenerate near-perfect-fit case (residuals collapse to numerical
/// zero) so the per-residual `residual_floor` below — and the subsequent
/// `ln(|y−μ|)` log-scale target — stay finite. Well below any real response
/// spread, so it never perturbs a genuine fit.
pub(crate) const WARMSTART_GLOBAL_SCALE_FLOOR: f64 = 1e-6;

/// Per-residual floor used to form the log-scale warm-start target
/// `ln(|y−μ|) − E[ln|N(0,1)|]`. Built as `global_scale · WARMSTART_RESIDUAL_REL_FLOOR
/// + WARMSTART_RESIDUAL_ABS_FLOOR`: the relative term keeps an exactly-fit point
/// (|y−μ| = 0) from sending `ln(0) → −∞` at 1/1000 of the data scale, and the
/// absolute term backstops the case where `global_scale` itself sits at its floor.
pub(crate) const WARMSTART_RESIDUAL_REL_FLOOR: f64 = 1e-3;

pub(crate) const WARMSTART_RESIDUAL_ABS_FLOOR: f64 = 1e-12;

/// Floor on a per-row warm-start scale τ before forming `1/τ` when building the
/// affine transformation seed targets. A degenerate τ = 0 (a collapsed warm-start
/// scale block) would otherwise produce a non-finite reciprocal; the floor sits
/// far below any meaningful scale so it only fires on the degenerate path.
pub(crate) const WARMSTART_INV_SCALE_FLOOR: f64 = 1e-12;

/// Ridge stabilization floor for the penalized least-squares projections that
/// produce the default warm-start location and log-scale coefficients. These
/// seeds only need to land in the right basin (the outer solver refines them),
/// so a mild ridge that keeps the projection well-posed under a near-rank-
/// deficient covariate design is preferable to the tighter floor used for the
/// production inner solve.
pub(crate) const WARMSTART_PROJECTION_RIDGE_FLOOR: f64 = 1e-8;