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 subsample: f32,
    pub top_rate: f64,
    pub other_rate: f64,
    pub seed: u64,
    pub missing: f64,
    pub create_missing_branch: bool,
    pub sample_method: SampleMethod,
    pub grow_policy: GrowPolicy,
    pub evaluation_metric: Option<Metric>,
    pub early_stopping_rounds: Option<usize>,
    pub initialize_base_score: bool,
    pub evaluation_history: Option<RowMajorMatrix<f64>>,
    pub best_iteration: Option<usize>,
    pub prediction_iteration: Option<usize>,
    pub trees: Vec<Tree>,
    /* private fields */
}

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 - Should the algorithm allow splits that completed seperate out missing and non-missing values, in the case where create_missing_branch is false. When create_missing_branch is true, setting this to true will result in the missin branch being further split.
• monotone_constraints - Constraints that are used to enforce a specific relationship between the training features and the target variable.
• subsample - Percent of records to randomly sample at each iteration when training a tree.
• top_rate - Used only in goss. The retain ratio of large gradient data.
• other_rate - Used only in goss. the retain ratio of small gradient data.
• seed - Integer value used to seed any randomness used in the algorithm.
• missing - Value to consider missing.
• create_missing_branch - Should missing be split out it’s own separate branch?
• sample_method - Specify the method that records should be sampled when training?
• evaluation_metric - Define the evaluation metric to record at each iterations.
• early_stopping_rounds - Number of rounds where the evaluation metric value must improve in to keep training.
• initialize_base_score - If this is specified, the base_score will be calculated using the sample_weight and y data in accordance with the requested objective_type.

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>

subsample: f32

top_rate: f64

other_rate: f64

seed: u64

missing: f64

create_missing_branch: bool

sample_method: SampleMethod

grow_policy: GrowPolicy

evaluation_metric: Option<Metric>

early_stopping_rounds: Option<usize>

initialize_base_score: bool

evaluation_history: Option<RowMajorMatrix<f64>>

best_iteration: Option<usize>

prediction_iteration: Option<usize>

number of trees to use when predicting, defaults to best_iteration if this is defined.

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: Option<f64>, nbins: u16, parallel: bool, allow_missing_splits: bool, monotone_constraints: Option<ConstraintMap>, subsample: f32, top_rate: f64, other_rate: f64, seed: u64, missing: f64, create_missing_branch: bool, sample_method: SampleMethod, grow_policy: GrowPolicy, evaluation_metric: Option<Metric>, early_stopping_rounds: Option<usize>, initialize_base_score: bool ) -> Result<Self, ForustError>

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. If set to None the parameter initialize_base_score will automatically be set to true, in which case the base score will be chosen based on the objective function at fit time.
• 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.
• parallel - Should the algorithm be run in parallel?
• allow_missing_splits - Should the algorithm allow splits that completed seperate out missing and non-missing values, in the case where create_missing_branch is false. When create_missing_branch is true, setting this to true will result in the missin branch being further split.
• monotone_constraints - Constraints that are used to enforce a specific relationship between the training features and the target variable.
• subsample - Percent of records to randomly sample at each iteration when training a tree.
• top_rate - Used only in goss. The retain ratio of large gradient data.
• other_rate - Used only in goss. the retain ratio of small gradient data.
• seed - Integer value used to seed any randomness used in the algorithm.
• missing - Value to consider missing.
• create_missing_branch - Should missing be split out it’s own separate branch?
• sample_method - Specify the method that records should be sampled when training?
• evaluation_metric - Define the evaluation metric to record at each iterations.
• early_stopping_rounds - Number of rounds that must
• initialize_base_score - If this is specified, the base_score will be calculated using the sample_weight and y data in accordance with the requested objective_type.

pub fn fit( &mut self, data: &Matrix<'_, f64>, y: &[f64], sample_weight: &[f64], evaluation_data: Option<Vec<EvaluationData<'_>>> ) -> 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], evaluation_data: Option<Vec<EvaluationData<'_>>> ) -> 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>, method: ContributionsMethod, parallel: bool ) -> Vec<f64>

Predict the contributions matrix for the provided dataset.

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_nbins(self, nbins: u16) -> Self

Set the number of nbins on the booster.

• max_leaves - 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 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_initialize_base_score(self, initialize_base_score: bool) -> Self

Set the base_score on the booster.

• base_score - The base score 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.

pub fn set_subsample(self, subsample: f32) -> Self

Set the subsample on the booster.

• subsample - Percent of the data to randomly sample when training each tree.

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

Set the seed on the booster.

• seed - Integer value used to see any randomness used in the algorithm.

pub fn set_missing(self, missing: f64) -> Self

Set missing value of the booster

• missing - Float value to consider as missing.

pub fn set_create_missing_branch(self, create_missing_branch: bool) -> Self

Set create missing value of the booster

• create_missing_branch - Bool specifying if missing should get it’s own branch.

pub fn set_sample_method(self, sample_method: SampleMethod) -> Self

Set sample method on the booster.

• sample_method - Sample method.

pub fn set_evaluation_metric(self, evaluation_metric: Option<Metric>) -> Self

Set sample method on the booster.

• evaluation_metric - Sample method.

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

Set early stopping rounds.

• early_stopping_rounds - Early stoppings rounds.

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

Set prediction iterations.

• early_stopping_rounds - Early stoppings rounds.

pub fn insert_metadata(&mut self, key: String, value: String)

Insert metadata

• key - String value for the metadata key.
• value - value to assign to the metadata key.

pub fn get_metadata(&self, key: &String) -> Option<String>

Get Metadata

• key - Get the associated value for the metadata key.

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.