Struct MemoryEfficientEnsemble 

pub struct MemoryEfficientEnsemble<State = Untrained> { /* private fields */ }

Memory-efficient ensemble that uses incremental learning and streaming

Implementations

impl<State> MemoryEfficientEnsemble<State>

pub fn memory_usage(&self) -> usize

Get current memory usage estimate in bytes

pub fn active_model_count(&self) -> usize

Get number of active models in memory

pub fn total_models_created(&self) -> usize

Get total number of models created

pub fn needs_cleanup(&self) -> bool

Check if memory cleanup is needed

impl MemoryEfficientEnsemble<Untrained>

pub fn new() -> Self

Create a new memory-efficient ensemble

pub fn max_estimators_in_memory(self, max_estimators: usize) -> Self

Set maximum number of estimators to keep in memory

pub fn batch_size(self, batch_size: usize) -> Self

Set batch size for incremental learning

pub fn window_size(self, window_size: Option<usize>) -> Self

Set window size for streaming data

pub fn lazy_evaluation(self, enabled: bool) -> Self

Enable lazy evaluation

pub fn memory_threshold_mb(self, threshold: usize) -> Self

Set memory threshold in MB

pub fn compress_models(self, enabled: bool) -> Self

Enable model compression

pub fn use_disk_cache(self, enabled: bool, cache_dir: Option<String>) -> Self

Enable disk caching

pub fn learning_rate_decay(self, decay: Float) -> Self

Set learning rate decay

pub fn forgetting_factor(self, factor: Float) -> Self

Set forgetting factor for streaming

pub fn adaptive_batch_size(self, enabled: bool) -> Self

Enable adaptive batch sizing

pub fn for_large_datasets() -> Self

Create a memory-efficient ensemble with optimal settings for large datasets

pub fn for_streaming() -> Self

Create a streaming ensemble with optimal settings

impl MemoryEfficientEnsemble<Trained>

pub fn predict_lazy(&self, x: &Array2<Float>) -> Result<Array1<Float>>

Predict with lazy evaluation

pub fn partial_fit(&mut self, x: &Array1<Float>, y: Float) -> Result<()>

Update the ensemble with new streaming data

Trait Implementations

impl Default for MemoryEfficientEnsemble<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MemoryEfficientEnsemble<Untrained>

type Config = MemoryEfficientConfig

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for MemoryEfficientEnsemble<Untrained>

type Fitted = MemoryEfficientEnsemble<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &Array1<Float>) -> Result<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for MemoryEfficientEnsemble<Trained>

fn predict(&self, x: &Array2<Float>) -> Result<Array1<Float>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals