Struct PopulationBasedTrainingCV 

pub struct PopulationBasedTrainingCV<E, X, Y> { /* private fields */ }

Population-based Training with cross-validation

Implementations

impl<E, X, Y> PopulationBasedTrainingCV<E, X, Y>

where E: Clone + Debug,

pub fn new( base_estimator: E, config: PBTConfig, parameter_space: PBTParameterSpace, config_fn: PBTConfigFn<E>, ) -> Self

Create a new PBT with cross-validation

pub fn cv<CV>(self, cv: CV) -> Self

where CV: CrossValidator + 'static,

Set cross-validation strategy

pub fn scoring(self, scoring: Scoring) -> Self

Set scoring function

impl<E> PopulationBasedTrainingCV<E, Array2<f64>, Array1<f64>>

where E: Clone + Debug + Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>> + Score<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>>, E::Fitted: Clone + Debug + Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>> + Score<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>>,

pub fn fit( self, x: &Array2<f64>, y: &Array1<f64>, ) -> Result<PBTResult<E::Fitted>>

Fit the PBT optimizer with cross-validation