Struct forust_ml::gradientbooster::GradientBooster

pub struct GradientBooster {
    pub objective_type: ObjectiveType,
    pub iterations: usize,
    pub learning_rate: f32,
    pub max_depth: usize,
    pub max_leaves: usize,
    pub l2: f32,
    pub gamma: f32,
    pub min_leaf_weight: f32,
    pub base_score: f64,
    pub nbins: u16,
    pub parallel: bool,
    pub trees: Vec<Tree>,
}

Gradient Booster object

• objective_type - The name of objective function used to optimize. Valid options include “LogLoss” to use logistic loss as the objective function, or “SquaredLoss” to use Squared Error as the objective function.
• iterations - Total number of trees to train in the ensemble.
• learning_rate - Step size to use at each iteration. Each leaf weight is multiplied by this number. The smaller the value, the more conservative the weights will be.
• max_depth - Maximum depth of an individual tree. Valid values are 0 to infinity.
• max_leaves - Maximum number of leaves allowed on a tree. Valid values are 0 to infinity. This is the total number of final nodes.
• l2 - L2 regularization term applied to the weights of the tree. Valid values are 0 to infinity.
• gamma - The minimum amount of loss required to further split a node. Valid values are 0 to infinity.
• min_leaf_weight - Minimum sum of the hessian values of the loss function required to be in a node.
• base_score - The initial prediction value of the model.
• nbins - Number of bins to calculate to partition the data. Setting this to a smaller number, will result in faster training time, while potentially sacrificing accuracy. If there are more bins, than unique values in a column, all unique values will be used.

Fields

objective_type: ObjectiveType

iterations: usize

learning_rate: f32

max_depth: usize

max_leaves: usize

l2: f32

gamma: f32

min_leaf_weight: f32

base_score: f64

nbins: u16

parallel: bool

trees: Vec<Tree>

Implementations

impl GradientBooster

pub fn new(
    objective_type: ObjectiveType,
    iterations: usize,
    learning_rate: f32,
    max_depth: usize,
    max_leaves: usize,
    l2: f32,
    gamma: f32,
    min_leaf_weight: f32,
    base_score: f64,
    nbins: u16,
    parallel: bool
) -> Self

Gradient Booster object

• objective_type - The name of objective function used to optimize. Valid options include “LogLoss” to use logistic loss as the objective function, or “SquaredLoss” to use Squared Error as the objective function.
• iterations - Total number of trees to train in the ensemble.
• learning_rate - Step size to use at each iteration. Each leaf weight is multiplied by this number. The smaller the value, the more conservative the weights will be.
• max_depth - Maximum depth of an individual tree. Valid values are 0 to infinity.
• max_leaves - Maximum number of leaves allowed on a tree. Valid values are 0 to infinity. This is the total number of final nodes.
• l2 - L2 regularization term applied to the weights of the tree. Valid values are 0 to infinity.
• gamma - The minimum amount of loss required to further split a node. Valid values are 0 to infinity.
• min_leaf_weight - Minimum sum of the hessian values of the loss function required to be in a node.
• base_score - The initial prediction value of the model.
• nbins - Number of bins to calculate to partition the data. Setting this to a smaller number, will result in faster training time, while potentially sacrificing accuracy. If there are more bins, than unique values in a column, all unique values will be used.

pub fn fit(
    &mut self,
    data: &Matrix<'_, f64>,
    y: &[f64],
    sample_weight: &[f64]
) -> Result<(), ForustError>

Fit the gradient booster on a provided dataset.

• data - Either a pandas DataFrame, or a 2 dimensional numpy array.
• y - Either a pandas Series, or a 1 dimensional numpy array.
• sample_weight - Instance weights to use when training the model. If None is passed, a weight of 1 will be used for every record.

pub fn predict(&self, data: &Matrix<'_, f64>, parallel: bool) -> Vec<f64>

Generate predictions on data using the gradient booster.

• data - Either a pandas DataFrame, or a 2 dimensional numpy array.

pub fn save_booster(&self, path: &str) -> Result<(), ForustError>

Save a booster as a json object to a file.

• path - Path to save booster.

pub fn json_dump(&self) -> Result<String, ForustError>

Dump a booster as a json object

pub fn from_json(json_str: &str) -> Result<Self, ForustError>

Load a booster from Json string

• json_str - String object, which can be serialized to json.

pub fn load_booster(path: &str) -> Result<Self, ForustError>

Load a booster from a path to a json booster object.

• path - Path to load booster from.

Trait Implementations

impl Default for GradientBooster

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GradientBooster

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl Serialize for GradientBooster

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

Serialize this value into the given Serde serializer.