Struct CoxModel 

pub struct CoxModel { /* private fields */ }

cox model w/ elastic net regularization

Implementations

impl CoxModel

pub fn new() -> Self

new cox model w/ defaults

pub fn with_l1_penalty(self, penalty: f64) -> Self

add lasso penalty (L1) - encourages sparsity

pub fn with_l2_penalty(self, penalty: f64) -> Self

add ridge penalty (L2) - shrinks coefficients

pub fn with_elastic_net(self, alpha: f64, penalty: f64) -> Self

elastic net mixing: alpha=0 -> pure ridge, alpha=1 -> pure lasso

pub fn with_max_iterations(self, max_iter: usize) -> Self

max iterations before giving up

pub fn with_tolerance(self, tol: f64) -> Self

how close is close enough for convergence

pub fn with_feature_names(self, names: Vec<String>) -> Self

give names to your features for nicer output

pub fn with_optimizer(self, optimizer: OptimizerType) -> Self

choose which optimizer to use

pub fn with_learning_rate(self, lr: f64) -> Self

set learning rate for Adam/RMSprop optimizers

pub fn with_adam_params(self, beta1: f64, beta2: f64) -> Self

set Adam/RMSprop parameters (beta1 for Adam momentum, beta2 for decay rate)

pub fn with_epsilon(self, eps: f64) -> Self

set Adam/RMSprop numerical stability parameter

pub fn fit(&mut self, data: &SurvivalData) -> Result<&mut Self>

fit the model to data - this does the actual work

pub fn coefficients(&self) -> Result<ArrayView1<'_, f64>>

get the fitted coefficients (betas)

pub fn predict(&self, covariates: ArrayView2<'_, f64>) -> Result<Array1<f64>>

predict risk scores for new patients

pub fn predict_hazard_ratios( &self, covariates: ArrayView2<'_, f64>, ) -> Result<Array1<f64>>

predict hazard ratios (exp of risk scores)

pub fn predict_survival( &self, covariates: ArrayView2<'_, f64>, times: ArrayView1<'_, f64>, ) -> Result<Array2<f64>>

predict survival probs at specific time points

pub fn feature_importance(&self) -> Result<Array1<f64>>

feature importance = abs value of coefficients

pub fn summary(&self) -> Result<CoxModelSummary>

get a nice summary of the fitted model

pub fn is_fitted(&self) -> bool

has this model been fit to data yet?

pub fn regularization_params(&self) -> (f64, f64)

what regularization penalties are we using?

Trait Implementations

impl Clone for CoxModel

fn clone(&self) -> CoxModel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CoxModel

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

Formats the value using the given formatter.

impl Default for CoxModel

fn default() -> Self

Returns the “default value” for a type.