Struct Hyperparameters

pub struct Hyperparameters { /* private fields */ }

Hyperparameters for a FactorizationMachine

Implementations

impl Hyperparameters

pub fn new(dim: usize, num_components: usize) -> Hyperparameters

Creates new Hyperparameters.

The complexity of the model is controlled by the dimensionality of the factorization matrix: a higher num_components setting will make the model more expressive at the expense of training time and risk of overfitting.

pub fn learning_rate(&mut self, learning_rate: f32) -> &mut Hyperparameters

Set the initial learning rate.

During fitting, the learning rate decreases more for parameters which have have received larger gradient updates. This maintains more stable estimates for common features while allowing fast learning for rare features.

pub fn l2_penalty(&mut self, l2_penalty: f32) -> &mut Hyperparameters

Set the L2 penalty.

pub fn l1_penalty(&mut self, l1_penalty: f32) -> &mut Hyperparameters

Set the L1 penalty.

pub fn rng(&mut self, rng: StdRng) -> &mut Hyperparameters

pub fn build(&self) -> FactorizationMachine

Build a two-class model.

pub fn one_vs_rest(&self) -> OneVsRestWrapper<FactorizationMachine>

Build a one-vs-rest multiclass model.

Trait Implementations

impl<'de> Deserialize<'de> for Hyperparameters

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for Hyperparameters

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

where __S: Serializer,

Serialize this value into the given Serde serializer.