Struct gbdt::config::Config

pub struct Config {
    pub feature_size: usize,
    pub max_depth: u32,
    pub iterations: usize,
    pub shrinkage: ValueType,
    pub feature_sample_ratio: f64,
    pub data_sample_ratio: f64,
    pub min_leaf_size: usize,
    pub loss: Loss,
    pub debug: bool,
    pub initial_guess_enabled: bool,
    pub training_optimization_level: u8,
}

The config for the gradient boosting algorithm.

Fields

feature_size: usize

The size of features. Training data and test data should have the same feature size. (default = 1)

max_depth: u32

The max depth of a single decision tree. The root node is considered to be in the layer 0. (default = 2)

iterations: usize

The iterations to train, which is also the number of trees in the gradient boosting algorithm. (default = 2)

shrinkage: ValueType

The learning rate parameter of the gradient boosting algorithm.(default = 1.0)

feature_sample_ratio: f64

Portion of features to be splited. (default = 1.0)

data_sample_ratio: f64

Portion of data to be splited. (default = 1.0)

min_leaf_size: usize

The minimum number of samples required to be at a leaf node during training. (default = 1.0)

loss: Loss

The loss function type. (default = SquareError)

debug: bool

Whether the debug information should be outputed. (default = false)

initial_guess_enabled: bool

Whether initial guess for test data is enabled. (default = false)

training_optimization_level: u8

Training optimization level (default = 2).

0: least memory, slowest speed.

1: more memory usage, faster speed.

2: most memory usage, fastest speed.

Implementations

impl Config

pub fn new() -> Config

Return a new config with default settings.

Example

use gbdt::config::Config;
let mut cfg = Config::new();

pub fn set_feature_size(&mut self, n: usize)

Set feature size.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.set_feature_size(10);

pub fn set_shrinkage(&mut self, eta: ValueType)

Set learning rate.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.set_shrinkage(1.0);

pub fn set_training_optimization_level(&mut self, level: u8)

Set training optimization level (default = 2).

0: least memory, slowest speed.

1: more memory usage, faster speed.

2: most memory usage, fastest speed.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.set_training_optimization_level(2);

pub fn set_max_depth(&mut self, n: u32)

Set max depth of the tree.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.set_max_depth(5);

pub fn set_iterations(&mut self, n: usize)

Set iterations of the algorithm.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.set_iterations(5);

pub fn set_feature_sample_ratio(&mut self, n: f64)

Set feature sample ratio.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.set_feature_sample_ratio(0.9);

pub fn set_data_sample_ratio(&mut self, n: f64)

Set data sample ratio.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.set_data_sample_ratio(0.9);

pub fn set_min_leaf_size(&mut self, n: usize)

Set minimal leaf size.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.set_min_leaf_size(3);

pub fn set_loss(&mut self, l: &str)

Set loss type: “SquaredError”, “LogLikelyhood”, “LAD”, “reg:linear”, “binary:logistic”, “reg:logistic”, “binary:logitraw”, “multi:softprob”, “multi:softmax”, “rank:pairwise”

Example

use gbdt::config::{Config, Loss, loss2string};
let mut cfg = Config::new();
cfg.set_loss("LAD");
// Alternative way
cfg.set_loss(&loss2string(&Loss::SquaredError));

pub fn set_debug(&mut self, option: bool)

Set debug mode.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.set_debug(true);

pub fn enabled_initial_guess(&mut self, option: bool)

Set whether initial guess of test data is enabled.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
cfg.enabled_initial_guess(false);

pub fn to_string(&self) -> String

Dump the config to string for presentation.

Example

use gbdt::config::Config;
let mut cfg = Config::new();
println!("{}", cfg.to_string());

Trait Implementations

impl Clone for Config

fn clone(&self) -> Config

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Default for Config

fn default() -> Config

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Config

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

Deserialize this value from the given Serde deserializer.

impl Serialize for Config

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

Serialize this value into the given Serde serializer.