Struct RandomTreesEmbedding 

pub struct RandomTreesEmbedding<F> {
    pub n_estimators: usize,
    pub max_depth: Option<usize>,
    pub min_samples_split: usize,
    pub random_state: Option<u64>,
    /* private fields */
}

Random trees embedding for unsupervised feature transformation.

Builds an ensemble of randomly split trees (ignoring targets) and represents each sample as a one-hot encoding of its leaf index in each tree, concatenated across all trees.

This is useful for creating a nonlinear feature representation that can be fed into linear models.

Type Parameters

• F: The floating-point type (f32 or f64).

Fields

n_estimators: usize

Number of random trees to build.

max_depth: Option<usize>

Maximum depth of each tree. None means unlimited.

min_samples_split: usize

Minimum number of samples required to split a node.

random_state: Option<u64>

Random seed for reproducibility. None means non-deterministic.

Implementations

impl<F: Float> RandomTreesEmbedding<F>

pub fn new() -> Self

Create a new RandomTreesEmbedding with default settings.

Defaults: n_estimators = 10, max_depth = Some(5), min_samples_split = 2, random_state = None.

pub fn with_n_estimators(self, n_estimators: usize) -> Self

Set the number of random trees.

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

Set the maximum tree depth.

pub fn with_min_samples_split(self, min_samples_split: usize) -> Self

Set the minimum number of samples required to split a node.

pub fn with_random_state(self, seed: u64) -> Self

Set the random seed for reproducibility.

Trait Implementations

impl<F: Clone> Clone for RandomTreesEmbedding<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for RandomTreesEmbedding<F>

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

Formats the value using the given formatter.

impl<F: Float> Default for RandomTreesEmbedding<F>

fn default() -> Self

Returns the “default value” for a type.

impl<'de, F> Deserialize<'de> for RandomTreesEmbedding<F>

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F: Float + Send + Sync + 'static> Fit<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ()> for RandomTreesEmbedding<F>

fn fit( &self, x: &Array2<F>, _y: &(), ) -> Result<FittedRandomTreesEmbedding<F>, FerroError>

Fit the random trees embedding on the training data.

Builds an ensemble of trees with purely random splits (random feature, random threshold between feature min and max in the current node), ignoring any target variable.

Errors

Returns FerroError::InsufficientSamples if there are no samples. Returns FerroError::InvalidParameter if hyperparameters are invalid.

type Fitted = FittedRandomTreesEmbedding<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F: Float + Send + Sync + 'static> PipelineTransformer<F> for RandomTreesEmbedding<F>

fn fit_pipeline( &self, x: &Array2<F>, _y: &Array1<F>, ) -> Result<Box<dyn FittedPipelineTransformer<F>>, FerroError>

Fit this transformer on the given data.

impl<F> Serialize for RandomTreesEmbedding<F>

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.