extern crate xgboost;

use xgboost::{parameters, DMatrix, Booster};

fn main() {
    // training matrix with 5 training examples and 3 features
    let x_train = &[1.0, 1.0, 1.0,
                    1.0, 1.0, 0.0,
                    1.0, 1.0, 1.0,
                    0.0, 0.0, 0.0,
                    1.0, 1.0, 1.0];
    let num_rows = 5;
    let y_train = &[1.0, 1.0, 1.0, 0.0, 1.0];

    // convert training data into XGBoost's matrix format
    let mut dtrain = DMatrix::from_dense(x_train, num_rows).unwrap();

    // set ground truth labels for the training matrix
    dtrain.set_labels(y_train).unwrap();

    // test matrix with 1 row
    let x_test = &[0.7, 0.9, 0.6];
    let num_rows = 1;
    let y_test = &[1.0];
    let mut dtest = DMatrix::from_dense(x_test, num_rows).unwrap();
    dtest.set_labels(y_test).unwrap();

    // configure objectives, metrics, etc.
    let learning_params = parameters::learning::LearningTaskParametersBuilder::default()
        .objective(parameters::learning::Objective::BinaryLogistic)
        .build().unwrap();

    // configure the tree-based learning model's parameters
    let tree_params = parameters::tree::TreeBoosterParametersBuilder::default()
            .max_depth(2)
            .eta(1.0)
            .build().unwrap();

    // overall configuration for Booster
    let booster_params = parameters::BoosterParametersBuilder::default()
        .booster_type(parameters::BoosterType::Tree(tree_params))
        .learning_params(learning_params)
        .verbose(true)
        .build().unwrap();

    // specify datasets to evaluate against during training
    let evaluation_sets = &[(&dtrain, "train"), (&dtest, "test")];

    // overall configuration for training/evaluation
    let params = parameters::TrainingParametersBuilder::default()
        .dtrain(&dtrain)                         // dataset to train with
        .boost_rounds(2)                         // number of training iterations
        .booster_params(booster_params)          // model parameters
        .evaluation_sets(Some(evaluation_sets)) // optional datasets to evaluate against in each iteration
        .build().unwrap();

    // train model, and print evaluation data
    let bst = Booster::train(&params).unwrap();

    println!("{:?}", bst.predict(&dtest).unwrap());
}