Struct ScoreEstimate 

#[non_exhaustive]
pub struct ScoreEstimate {
    pub score: LinearScore,
    pub predictions: Vec<(Name, Array1<f64>)>,
    pub sensitivity: Option<SensitivityParts>,
    pub targets: Vec<NuisanceTarget>,
    pub cate: Option<CateSignal>,
}

Score elements plus the cross-fitted nuisance predictions behind them.

Build it with ScoreEstimate::new (the struct is #[non_exhaustive], so fields can be added without breaking custom models).

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

score: LinearScore

predictions: Vec<(Name, Array1<f64>)>

(name, out-of-fold predictions) — e.g. ("ml_l", ℓ̂).

sensitivity: Option<SensitivityParts>

Ingredients for sensitivity analysis, if the model supports it.

targets: Vec<NuisanceTarget>

What each nuisance learner predicts, for out-of-sample diagnostics.

cate: Option<CateSignal>

Signal for CATE / GATE inference, if the model supports it.

Implementations

impl ScoreEstimate

pub fn new<N: Into<Name>>( score: LinearScore, predictions: Vec<(N, Array1<f64>)>, ) -> Self

predictions pairs a name (a literal such as "ml_l", or a String) with the out-of-fold predictions.

pub fn with_cate_signal(self, cate: CateSignal) -> Self

Attach the signal for CATE / GATE inference.

pub fn with_targets(self, targets: Vec<NuisanceTarget>) -> Self

Attach the nuisance targets used for learner diagnostics.

pub fn with_sensitivity(self, parts: SensitivityParts) -> Self

Attach the model’s sensitivity ingredients.

Trait Implementations

impl Clone for ScoreEstimate

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ScoreEstimate

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

Formats the value using the given formatter.