Struct gbdt::gradient_boost::GBDT

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pub struct GBDT { /* private fields */ }

Expand description

The gradient boosting decision tree.

Implementations

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impl GBDT

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pub fn new(conf: &Config) -> GBDT

Return a new gbdt with manually set config.

Example

use gbdt::config::Config;
use gbdt::gradient_boost::GBDT;

// set config for algorithm
let mut cfg = Config::new();
cfg.set_feature_size(3);
cfg.set_max_depth(2);
cfg.set_min_leaf_size(1);
cfg.set_loss("SquaredError");
cfg.set_iterations(2);

// initialize GBDT algorithm
let mut gbdt = GBDT::new(&cfg);

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pub fn fit(&mut self, train_data: &mut DataVec)

Fit the train data.

First, initialize and configure decision trees. Then train the model with certain iterations set by config.

Example

use gbdt::config::Config;
use gbdt::gradient_boost::GBDT;
use gbdt::decision_tree::{Data, DataVec, PredVec, ValueType};

// set config for algorithm
let mut cfg = Config::new();
cfg.set_feature_size(3);
cfg.set_max_depth(2);
cfg.set_min_leaf_size(1);
cfg.set_loss("SquaredError");
cfg.set_iterations(2);

// initialize GBDT algorithm
let mut gbdt = GBDT::new(&cfg);

// setup training data
let data1 = Data::new_training_data (
    vec![1.0, 2.0, 3.0],
    1.0,
    1.0,
    None
);
let data2 = Data::new_training_data (
    vec![1.1, 2.1, 3.1],
    1.0,
    1.0,
    None
);
let data3 = Data::new_training_data (
    vec![2.0, 2.0, 1.0],
    1.0,
    2.0,
    None
);
let data4 = Data::new_training_data (
    vec![2.0, 2.3, 1.2],
    1.0,
    0.0,
    None
);

let mut training_data: DataVec = Vec::new();
training_data.push(data1.clone());
training_data.push(data2.clone());
training_data.push(data3.clone());
training_data.push(data4.clone());

// train the decision trees.
gbdt.fit(&mut training_data);

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pub fn predict(&self, test_data: &DataVec) -> PredVec

Predict the given data.

Note that for log likelyhood loss type, the predicted value will be normalized between 0 and 1, which is the possibility of label 1

Example

use gbdt::config::Config;
use gbdt::gradient_boost::GBDT;
use gbdt::decision_tree::{Data, DataVec, PredVec, ValueType};

// set config for algorithm
let mut cfg = Config::new();
cfg.set_feature_size(3);
cfg.set_max_depth(2);
cfg.set_min_leaf_size(1);
cfg.set_loss("SquaredError");
cfg.set_iterations(2);

// initialize GBDT algorithm
let mut gbdt = GBDT::new(&cfg);

// setup training data
let data1 = Data::new_training_data (
    vec![1.0, 2.0, 3.0],
    1.0,
    1.0,
    None
);
let data2 = Data::new_training_data (
    vec![1.1, 2.1, 3.1],
    1.0,
    1.0,
    None
);
let data3 = Data::new_training_data (
    vec![2.0, 2.0, 1.0],
    1.0,
    2.0,
    None
);
let data4 = Data::new_training_data (
    vec![2.0, 2.3, 1.2],
    1.0,
    0.0,
    None
);

let mut training_data: DataVec = Vec::new();
training_data.push(data1.clone());
training_data.push(data2.clone());
training_data.push(data3.clone());
training_data.push(data4.clone());

// train the decision trees.
gbdt.fit(&mut training_data);

// setup the test data

let mut test_data: DataVec = Vec::new();
test_data.push(data1.clone());
test_data.push(data2.clone());
test_data.push(data3.clone());
test_data.push(data4.clone());

println!("{:?}", gbdt.predict(&test_data));

Panic

If the training process is not completed, thus, the number of trees that have been is less than the iteration configuration in self.conf, it will panic.

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pub fn predict_multiclass(
 &self,
 test_data: &DataVec,
 class_num: usize
) -> (Vec<usize>, Vec<Vec<ValueType>>)

Predict multi class data and return the probabilities for each class. The loss type should be “multi:softmax” or “multi:softprob”

test_data: the test set

class_num: the number of class

output: the predicted class label, the predicted possiblity for each class

Example

use gbdt::gradient_boost::GBDT;
use gbdt::input::{load, InputFormat};
use gbdt::decision_tree::DataVec;
let gbdt =
    GBDT::from_xgoost_dump("xgb-data/xgb_multi_softmax/gbdt.model", "multi:softmax").unwrap();
let test_file = "xgb-data/xgb_multi_softmax/dermatology.data.test";
let mut fmt = InputFormat::csv_format();
fmt.set_label_index(34);
let test_data: DataVec = load(test_file, fmt).unwrap();
let (labels, probs) = gbdt.predict_multiclass(&test_data, 6);

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pub fn print_trees(&self)

Print the tress for debug

Example

use gbdt::config::Config;
use gbdt::gradient_boost::GBDT;
use gbdt::decision_tree::{Data, DataVec, PredVec, ValueType};

// set config for algorithm
let mut cfg = Config::new();
cfg.set_feature_size(3);
cfg.set_max_depth(2);
cfg.set_min_leaf_size(1);
cfg.set_loss("SquaredError");
cfg.set_iterations(2);

// initialize GBDT algorithm
let mut gbdt = GBDT::new(&cfg);

// setup training data
let data1 = Data::new_training_data (
    vec![1.0, 2.0, 3.0],
    1.0,
    1.0,
    None
);
let data2 = Data::new_training_data (
    vec![1.1, 2.1, 3.1],
    1.0,
    1.0,
    None
);
let data3 = Data::new_training_data (
    vec![2.0, 2.0, 1.0],
    1.0,
    2.0,
    None
);
let data4 = Data::new_training_data (
    vec![2.0, 2.3, 1.2],
    1.0,
    0.0,
    None
);

let mut dv: DataVec = Vec::new();
dv.push(data1.clone());
dv.push(data2.clone());
dv.push(data3.clone());
dv.push(data4.clone());

// train the decision trees.
gbdt.fit(&mut dv);

// print the tree.
gbdt.print_trees();

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pub fn save_model(&self, filename: &str) -> Result<(), Box<dyn Error>>

Save the model to a file using serde.

Example

use gbdt::config::Config;
use gbdt::gradient_boost::GBDT;
use gbdt::decision_tree::{Data, DataVec, PredVec, ValueType};

// set config for algorithm
let mut cfg = Config::new();
cfg.set_feature_size(3);
cfg.set_max_depth(2);
cfg.set_min_leaf_size(1);
cfg.set_loss("SquaredError");
cfg.set_iterations(2);

// initialize GBDT algorithm
let mut gbdt = GBDT::new(&cfg);

// setup training data
let data1 = Data::new_training_data (
    vec![1.0, 2.0, 3.0],
    1.0,
    1.0,
    None
);
let data2 = Data::new_training_data (
    vec![1.1, 2.1, 3.1],
    1.0,
    1.0,
    None
);
let data3 = Data::new_training_data (
    vec![2.0, 2.0, 1.0],
    1.0,
    2.0,
    None
);
let data4 = Data::new_training_data (
    vec![2.0, 2.3, 1.2],
    1.0,
    0.0,
    None
);

let mut dv: DataVec = Vec::new();
dv.push(data1.clone());
dv.push(data2.clone());
dv.push(data3.clone());
dv.push(data4.clone());

// train the decision trees.
gbdt.fit(&mut dv);

// Save model.
// gbdt.save_model("gbdt.model");

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pub fn load_model(filename: &str) -> Result<Self, Box<dyn Error>>

Load the model from the file.

Example

use gbdt::gradient_boost::GBDT;
//let gbdt = GBDT::load_model("./gbdt-rs.model").unwrap();

Error

Error when get exception during model file parsing or deserialize.

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pub fn from_xgoost_dump(
model_file: &str,
objective: &str
) -> Result<Self, Box<dyn Error>>

Load the model from xgboost’s model. The xgboost’s model should be converted by “convert_xgboost.py”

Example

use gbdt::gradient_boost::GBDT;
let gbdt =
    GBDT::from_xgoost_dump("xgb-data/xgb_binary_logistic/gbdt.model", "binary:logistic").unwrap();

Error

Error when get exception during model file parsing.

Trait Implementations

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impl Default for GBDT

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fn default() -> GBDT

Returns the “default value” for a type. Read more

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impl<'de> Deserialize<'de> for GBDT

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

Deserialize this value from the given Serde deserializer. Read more

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impl Serialize for GBDT

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

Serialize this value into the given Serde serializer. Read more

Auto Trait Implementations

impl RefUnwindSafe for GBDT

impl Send for GBDT

impl Sync for GBDT

impl Unpin for GBDT

impl UnwindSafe for GBDT

Blanket Implementations

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impl<T> Any for Twhere
T: 'static + ?Sized,

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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more

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impl<T> Borrow<T> for Twhere
T: ?Sized,

const: unstable · source

fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more

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impl<T> BorrowMut<T> for Twhere
T: ?Sized,

const: unstable · source

fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more

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impl<T> From<T> for T

const: unstable · source

fn from(t: T) -> T

Returns the argument unchanged.

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impl<T, U> Into for Twhere
U: From<T>,

const: unstable · source

fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

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impl<T, U> TryFrom for Twhere
U: Into<T>,

type Error = Infallible

The type returned in the event of a conversion error.

const: unstable · source

fn try_from(value: U) -> Result<T, <T as TryFrom>::Error>

Performs the conversion.

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impl<T, U> TryInto for Twhere
U: TryFrom<T>,

type Error = >::Error

The type returned in the event of a conversion error.

const: unstable · source

fn try_into(self) -> Result<U, >::Error>

Performs the conversion.

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Struct gbdt::gradient_boost::GBDT

Implementations

impl GBDT

pub fn new(conf: &Config) -> GBDT

pub fn fit(&mut self, train_data: &mut DataVec)

pub fn predict(&self, test_data: &DataVec) -> PredVec

pub fn predict_multiclass( &self, test_data: &DataVec, class_num: usize) -> (Vec<usize>, Vec<Vec<ValueType>>)

pub fn print_trees(&self)

pub fn save_model(&self, filename: &str) -> Result<(), Box<dyn Error>>

pub fn load_model(filename: &str) -> Result<Self, Box<dyn Error>>

pub fn from_xgoost_dump( model_file: &str, objective: &str) -> Result<Self, Box<dyn Error>>

Trait Implementations

impl Default for GBDT

fn default() -> GBDT

impl<'de> Deserialize<'de> for GBDT

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

impl Serialize for GBDT

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

Auto Trait Implementations

impl RefUnwindSafe for GBDT

impl Send for GBDT

impl Sync for GBDT

impl Unpin for GBDT

impl UnwindSafe for GBDT

Blanket Implementations

impl<T> Any for Twhere T: 'static + ?Sized,

fn type_id(&self) -> TypeId

impl<T> Borrow<T> for Twhere T: ?Sized,

fn borrow(&self) -> &T

impl<T> BorrowMut<T> for Twhere T: ?Sized,

fn borrow_mut(&mut self) -> &mut T

impl<T> From<T> for T

fn from(t: T) -> T

impl<T, U> Into<U> for Twhere U: From<T>,

fn into(self) -> U

impl<T, U> TryFrom<U> for Twhere U: Into<T>,

type Error = Infallible

fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

impl<T, U> TryInto<U> for Twhere U: TryFrom<T>,

type Error = <U as TryFrom<T>>::Error

fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

impl<T> DeserializeOwned for Twhere T: for<'de> Deserialize<'de>,

pub fn predict_multiclass(
&self,
test_data: &DataVec,
class_num: usize
) -> (Vec<usize>, Vec<Vec<ValueType>>)

pub fn from_xgoost_dump(
model_file: &str,
objective: &str
) -> Result<Self, Box<dyn Error>>

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

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

impl<T> Any for Twhere
T: 'static + ?Sized,

impl<T> Borrow<T> for Twhere
T: ?Sized,

impl<T> BorrowMut<T> for Twhere
T: ?Sized,

impl<T, U> Into<U> for Twhere
U: From<T>,

impl<T, U> TryFrom<U> for Twhere
U: Into<T>,

impl<T, U> TryInto<U> for Twhere
U: TryFrom<T>,

impl<T> DeserializeOwned for Twhere
T: for<'de> Deserialize<'de>,