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 allow_missing_splits: bool,
    pub monotone_constraints: Option<ConstraintMap>,
    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.
• allow_missing_splits - Allow for splits to be made such that all missing values go down one branch, and all non-missing values go down the other, if this results in the greatest reduction of loss. If this is false, splits will only be made on non missing values.

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

allow_missing_splits: bool

monotone_constraints: Option<ConstraintMap>

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, allow_missing_splits: bool, monotone_constraints: Option<ConstraintMap> ) -> 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.
• monotone_constraints - Constraints that are used to enforce a specific relationship between the training features and the target variable.

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

Fit the gradient booster on a provided dataset without any weights.

• data - Either a pandas DataFrame, or a 2 dimensional numpy array.
• y - Either a pandas Series, or a 1 dimensional numpy array.

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 predict_contributions( &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 value_partial_dependence(&self, feature: usize, value: f64) -> f64

Given a value, return the partial dependence value of that value for that feature in the model.

• feature - The index of the feature.
• value - The value for which to calculate the partial dependence.

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.

pub fn set_objective_type(self, objective_type: ObjectiveType) -> Self

Set the objective_type on the booster.

• objective_type - The objective type of the booster.

pub fn set_iterations(self, iterations: usize) -> Self

Set the iterations on the booster.

• iterations - The number of iterations of the booster.

pub fn set_learning_rate(self, learning_rate: f32) -> Self

Set the learning_rate on the booster.

• learning_rate - The learning rate of the booster.

pub fn set_max_depth(self, max_depth: usize) -> Self

Set the max_depth on the booster.

• max_depth - The maximum tree depth of the booster.

pub fn set_max_leaves(self, max_leaves: usize) -> Self

Set the max_leaves on the booster.

• max_leaves - The maximum number of leaves of the booster.

pub fn set_l2(self, l2: f32) -> Self

Set the l2 on the booster.

• l2 - The l2 regulation term of the booster.

pub fn set_gamma(self, gamma: f32) -> Self

Set the gamma on the booster.

• gamma - The gamma value of the booster.

pub fn set_min_leaf_weight(self, min_leaf_weight: f32) -> Self

Set the min_leaf_weight on the booster.

• min_leaf_weight - The minimum sum of the hession values allowed in the node of a tree of the booster.

pub fn set_base_score(self, base_score: f64) -> Self

Set the base_score on the booster.

• base_score - The base score of the booster.

pub fn set_nbins(self, nbins: u16) -> Self

Set the nbins on the booster.

• nbins - The nummber of bins used for partitioning the data of the booster.

pub fn set_parallel(self, parallel: bool) -> Self

Set the parallel on the booster.

• parallel - Set if the booster should be trained in parallels.

pub fn set_allow_missing_splits(self, allow_missing_splits: bool) -> Self

Set the allow_missing_splits on the booster.

• allow_missing_splits - Set if missing splits are allowed for the booster.

pub fn set_monotone_constraints( self, monotone_constraints: Option<ConstraintMap> ) -> Self

Set the monotone_constraints on the booster.

• monotone_constraints - The monotone constraints of the booster.

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.