Struct FitRequestConfigDocument 

pub struct FitRequestConfigDocument {

    pub adaptive_regularization: Option<bool>,
    pub baseline_makeham: Option<f64>,
    pub baseline_rate: Option<f64>,
    pub baseline_scale: Option<f64>,
    pub baseline_shape: Option<f64>,
    pub baseline_target: Option<String>,
    pub ctn_stage1: Option<CtnStage1Document>,
    pub expectile_tau: Option<f64>,
    pub family: Option<String>,
    pub firth: Option<bool>,
    pub flexible_link: Option<bool>,
    pub frailty_kind: Option<String>,
    pub frailty_sd: Option<f64>,
    pub gpu: Option<String>,
    pub group_metadata: Option<BTreeMap<String, Value>>,
    pub hazard_loading: Option<String>,
    pub latent_coordinates: Option<LatentCoordinatesDocument>,
    pub link: Option<String>,
    pub logslope_formula: Option<String>,
    pub negative_binomial_theta: Option<f64>,
    pub noise_formula: Option<String>,
    pub noise_offset: Option<String>,
    pub offset: Option<String>,
    pub outer_max_iter: Option<usize>,
    pub analytic_penalties: Option<AnalyticPenaltiesDocument>,
    pub pilot_subsample_threshold: Option<usize>,
    pub precision_hyperpriors: Option<BTreeMap<String, PrecisionHyperpriorDocument>>,
    pub precompute_conformal: Option<bool>,
    pub persistent_warm_start_root: Option<PathBuf>,
    pub ridge_lambda: Option<f64>,
    pub scale_dimensions: Option<bool>,
    pub logslope_time_degree: Option<usize>,
    pub logslope_time_k: Option<usize>,
    pub sigma_time_degree: Option<usize>,
    pub sigma_time_k: Option<usize>,
    pub smooth_descriptors: Option<SmoothDescriptorsDocument>,
    pub survival_distribution: Option<String>,
    pub survival_likelihood: Option<String>,
    pub survival_time_anchor: Option<f64>,
    pub threshold_time_degree: Option<usize>,
    pub threshold_time_k: Option<usize>,
    pub time_basis: Option<String>,
    pub time_degree: Option<usize>,
    pub time_num_internal_knots: Option<usize>,
    pub time_smooth_lambda: Option<f64>,
    pub training_table_kind: Option<String>,
    pub transformation_normal: Option<bool>,
    pub weights: Option<String>,
    pub z_column: Option<String>,
}

Serializable model configuration shared by Rust, Python, and the CLI.

Optional fields mean “use the core gam_models::fit_orchestration::FitConfig default”. The document deliberately has one spelling for each concept; the parser does not carry aliases or legacy wire formats.

Fields

adaptive_regularization: Option<bool>

baseline_makeham: Option<f64>

baseline_rate: Option<f64>

baseline_scale: Option<f64>

baseline_shape: Option<f64>

baseline_target: Option<String>

ctn_stage1: Option<CtnStage1Document>

expectile_tau: Option<f64>

family: Option<String>

firth: Option<bool>

flexible_link: Option<bool>

frailty_kind: Option<String>

frailty_sd: Option<f64>

gpu: Option<String>

group_metadata: Option<BTreeMap<String, Value>>

hazard_loading: Option<String>

latent_coordinates: Option<LatentCoordinatesDocument>

link: Option<String>

logslope_formula: Option<String>

negative_binomial_theta: Option<f64>

noise_formula: Option<String>

noise_offset: Option<String>

offset: Option<String>

outer_max_iter: Option<usize>

analytic_penalties: Option<AnalyticPenaltiesDocument>

pilot_subsample_threshold: Option<usize>

precision_hyperpriors: Option<BTreeMap<String, PrecisionHyperpriorDocument>>

precompute_conformal: Option<bool>

Whether to precompute the distribution-free conformal substrates (#942 jackknife+, #1098 exact full-conformal) at fit time and persist them on the saved model. Omit to keep the default of precomputing whenever the fit is eligible; false skips both.

Measured on y ~ s(x1,k=6) + s(x2,k=6) (#2633): the two substrates are 94% of a saved Gaussian model at n=20,000 (10.2 MB of 10.85 MB) and grow linearly with the training rows. Rebuilding both costs ~5.6 ms, 0.3% of the fit, and stays under half a second out to p=253. So turning this off yields a ~16x smaller model (10.85 MB -> ~0.65 MB at n=20,000).

It is opt-OUT because rebuilding needs the training design AND response back, which a saved model deliberately does not carry: a model shipped to a host that never sees the training data must keep them or it cannot produce a conformal interval at all. Turn it off when the caller retains its training data, fits in batch, or never asks for conformal intervals.

persistent_warm_start_root: Option<PathBuf>

Explicit root for cross-process warm starts. Omit to disable on-disk persistence. The path is used exactly as supplied; no temp/cache discovery or environment fallback is performed.

ridge_lambda: Option<f64>

scale_dimensions: Option<bool>

logslope_time_degree: Option<usize>

logslope_time_k: Option<usize>

Number of B-spline basis functions on the log t margin of the survival marginal-slope log-slope block (gam#2765, gam#2767). Omitted = a slope that does not move along follow-up.

sigma_time_degree: Option<usize>

sigma_time_k: Option<usize>

smooth_descriptors: Option<SmoothDescriptorsDocument>

survival_distribution: Option<String>

survival_likelihood: Option<String>

survival_time_anchor: Option<f64>

Explicit centering anchor for the survival baseline time basis, in the data’s own time units. Omit to let the fit pick it from the likelihood mode and the truncation shape of the data — the robust interior median exit for marginal-slope and for any genuinely left-truncated dataset (#751/#1790), the earliest entry age otherwise.

The CLI’s --survival-time-anchor declares a conflict with --request on the premise that this document carries the complete scientific model configuration; until #2631 the document had no field for it, so the premise was false.

threshold_time_degree: Option<usize>

threshold_time_k: Option<usize>

time_basis: Option<String>

time_degree: Option<usize>

time_num_internal_knots: Option<usize>

time_smooth_lambda: Option<f64>

training_table_kind: Option<String>

Container type of the caller’s training table ("pandas", "polars", "pyarrow", "numpy", …), passed through opaquely into the saved model payload for the predict-time output-container fallback (#394).

transformation_normal: Option<bool>

weights: Option<String>

z_column: Option<String>

Trait Implementations

impl Clone for FitRequestConfigDocument

fn clone(&self) -> FitRequestConfigDocument

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for FitRequestConfigDocument

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for FitRequestConfigDocument

fn default() -> FitRequestConfigDocument

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for FitRequestConfigDocument

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for FitRequestConfigDocument

fn eq(&self, other: &FitRequestConfigDocument) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl Serialize for FitRequestConfigDocument

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for FitRequestConfigDocument