Struct MultiTaskElasticNet

pub struct MultiTaskElasticNet<F> { /* private fields */ }

MultiTask Elastic Net model

This struct contains the parameters of a fitted multi-task elastic net model. This includes the coefficients (a 2-dimensional array), (optionally) intercept (a 1-dimensional array), duality gaps and the number of steps needed in the computation.

Model implementation

The block coordinate descent is widely used to solve generalized linear models optimization problems, like Group Lasso, MultiTask Ridge or MultiTask Lasso. It cycles through a group of parameters and update the groups separately, holding all the others fixed. The optimization routine stops when a criterion is satisfied (dual sub-optimality gap or change in coefficients).

Implementations

impl<F: Float> MultiTaskElasticNet<F>

View the fitted parameters and make predictions with a fitted elastic net model

pub fn hyperplane(&self) -> &Array2<F>

Get the fitted hyperplane

pub fn intercept(&self) -> &Array1<F>

Get the fitted intercept, [0., …, 0.] if no intercept was fitted Note that there are as many intercepts as tasks

pub fn n_steps(&self) -> u32

Get the number of steps taken in optimization algorithm

pub fn duality_gap(&self) -> F

Get the duality gap at the end of the optimization algorithm

pub fn z_score(&self) -> Result<Array2<F>>

Calculate the Z score

pub fn confidence_95th(&self) -> Result<Array2<(F, F)>>

Calculate the confidence level

impl<F: Float> MultiTaskElasticNet<F>

pub fn params() -> MultiTaskElasticNetParams<F>

pub fn ridge() -> MultiTaskElasticNetParams<F>

Create a multi-task ridge only model

pub fn lasso() -> MultiTaskElasticNetParams<F>

Create a multi-task Lasso only model

Trait Implementations

impl<F: Clone> Clone for MultiTaskElasticNet<F>

fn clone(&self) -> MultiTaskElasticNet<F>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for MultiTaskElasticNet<F>

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

Formats the value using the given formatter.

impl<F: Float, D: Data<Elem = F>> PredictInplace<ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>> for MultiTaskElasticNet<F>

fn predict_inplace(&self, x: &ArrayBase<D, Ix2>, y: &mut Array2<F>)

Given an input matrix X, with shape (n_samples, n_features), predict returns the target variable according to elastic net learned from the training data distribution.

fn default_target(&self, x: &ArrayBase<D, Ix2>) -> Array2<F>

Create targets that predict_inplace works with.