//! This module implements the process of gradient boosting decision tree
//! algorithm. This module depends on the following module:
//!
//! 1. [gbdt::config::Config](../config/): [Config](../config/struct.Config.html) is needed to configure the gbdt algorithm.
//!
//! 2. [gbdt::decision_tree](../decision_tree/): [DecisionTree](../decision_tree/struct.DecisionTree.html) is used
//!    for training and predicting.
//!
//! 3. [rand](https://docs.rs/rand/0.6.1/rand/): This standard module is used to randomly select the data or
//!    features used in a single iteration of training if the
//!    [data_sample_ratio](../config/struct.Config.html#structfield.data_sample_ratio) or
//!    [feature_sample_ratio](../config/struct.Config.html#structfield.feature_sample_ratio) is less than 1.0 .
//!
//! # Example
//! ```rust
//! use gbdt::config::Config;
//! use gbdt::gradient_boost::GBDT;
//! use gbdt::decision_tree::{Data, DataVec};
//!
//! // 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(Data::new_test_data(
//!     vec![2.0, 2.0, 1.0],
//!     None));
//! test_data.push(Data::new_test_data(
//!     vec![2.0, 2.3, 1.2],
//!     None));
//!
//! println!("{:?}", gbdt.predict(&test_data));
//!
//! // output:
//! // [1.0, 1.0, 2.0, 0.0]
//! ```

#[cfg(all(feature = "mesalock_sgx", not(target_env = "sgx")))]
use std::prelude::v1::*;

use crate::config::{Config, Loss};
use crate::decision_tree::DecisionTree;
#[cfg(feature = "enable_training")]
use crate::decision_tree::TrainingCache;
use crate::decision_tree::{DataVec, PredVec, ValueType, VALUE_TYPE_MIN, VALUE_TYPE_UNKNOWN};
#[cfg(feature = "enable_training")]
use crate::fitness::{label_average, logit_loss_gradient, weighted_label_median, AUC, MAE, RMSE};
#[cfg(feature = "enable_training")]
use rand::prelude::SliceRandom;
#[cfg(feature = "enable_training")]
use rand::thread_rng;
use std::error::Error;

#[cfg(not(feature = "mesalock_sgx"))]
use std::fs::File;

#[cfg(feature = "mesalock_sgx")]
use std::untrusted::fs::File;

use std::io::prelude::*;
use std::io::{BufRead, BufReader};

use serde_derive::{Deserialize, Serialize};

#[cfg(feature = "profiling")]
use time::PreciseTime;

/// The gradient boosting decision tree.
#[derive(Default, Serialize, Deserialize)]
pub struct GBDT {
    /// The config of gbdt. See [gbdt::config](../config/) for detail.
    conf: Config,
    /// The trained decision trees.
    trees: Vec<DecisionTree>,
    /// The bias estimated.
    bias: ValueType,
}

impl GBDT {
    /// Return a new gbdt with manually set config.
    ///
    /// # Example
    /// ```rust
    /// 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);
    /// ```
    pub fn new(conf: &Config) -> GBDT {
        GBDT {
            conf: conf.clone(),
            trees: Vec::new(),
            bias: 0.0,
        }
    }

    /// Return true if the data in the given data vector are all valid. In other case
    /// returns false.
    ///
    /// We simply check whether the length of feature vector in each data
    /// equals to the specified feature size in config.
    #[cfg(feature = "enable_training")]
    fn check_valid_data(&self, dv: &DataVec) -> bool {
        dv.iter().all(|x| x.feature.len() == self.conf.feature_size)
    }

    /// If initial_guess_enabled is set false in gbdt config, this function will calculate
    /// bias for initial guess based on train data. Different methods will be used according
    /// to different loss type. This is a private method and should not be called manually.
    ///
    /// # Panic
    /// If  specified length is greater than the length of data vector, it will panic.
    ///
    /// If there is invalid data that will confuse the training process, it will panic.
    #[cfg(feature = "enable_training")]
    fn init(&mut self, len: usize, dv: &DataVec) {
        assert!(dv.len() >= len);

        if !self.check_valid_data(&dv) {
            panic!("There are invalid data in data vector, check your data please.");
        }

        if self.conf.initial_guess_enabled {
            return;
        }

        self.bias = match self.conf.loss {
            Loss::SquaredError => label_average(dv, len),
            Loss::LogLikelyhood => {
                let v: ValueType = label_average(dv, len);
                ((1.0 + v) / (1.0 - v)).ln() / 2.0
            }
            Loss::LAD => weighted_label_median(dv, len),
            _ => label_average(dv, len),
        }
    }

    /// Fit the train data.
    ///
    /// First, initialize and configure decision trees. Then train the model with certain
    /// iterations set by config.
    ///
    /// # Example
    /// ```rust
    /// 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);
    /// ```
    #[cfg(feature = "enable_training")]
    pub fn fit(&mut self, train_data: &mut DataVec) {
        self.trees = Vec::with_capacity(self.conf.iterations);
        // initialize each decision tree
        for i in 0..self.conf.iterations {
            self.trees.push(DecisionTree::new());
            self.trees[i].set_feature_size(self.conf.feature_size);
            self.trees[i].set_max_depth(self.conf.max_depth);
            self.trees[i].set_min_leaf_size(self.conf.min_leaf_size);
            self.trees[i].set_feature_sample_ratio(self.conf.feature_sample_ratio);
            self.trees[i].set_loss(self.conf.loss.clone());
        }

        // number of samples for training
        let nr_samples: usize = if self.conf.data_sample_ratio < 1.0 {
            ((train_data.len() as f64) * self.conf.data_sample_ratio) as usize
        } else {
            train_data.len()
        };

        self.init(train_data.len(), &train_data);

        let mut rng = thread_rng();
        // initialize the predicted_cache, which records the predictions for training data
        let mut predicted_cache: PredVec = self.predict_n(train_data, 0, 0, train_data.len());

        #[cfg(feature = "profiling")]
        let t1 = PreciseTime::now();

        // allocat the TrainingCache
        let mut cache = TrainingCache::get_cache(
            self.conf.feature_size,
            &train_data,
            self.conf.training_optimization_level,
        );

        #[cfg(feature = "profiling")]
        let t2 = PreciseTime::now();

        #[cfg(feature = "profiling")]
        println!("cache {}", t1.to(t2));

        for i in 0..self.conf.iterations {
            #[cfg(feature = "profiling")]
            let t1 = PreciseTime::now();

            let mut samples: Vec<usize> = (0..train_data.len()).collect();
            // randomly select some data for training
            let (subset, remaining) = if nr_samples < train_data.len() {
                samples.shuffle(&mut rng);
                let (left, right) = samples.split_at(nr_samples);
                let mut left = left.to_vec();
                let mut right = right.to_vec();
                left.sort();
                right.sort();
                (left, right)
            } else {
                (samples, Vec::new())
            };

            // Update the target for training
            match self.conf.loss {
                Loss::SquaredError => {
                    self.square_loss_process(train_data, train_data.len(), &predicted_cache)
                }
                Loss::LogLikelyhood => {
                    self.log_loss_process(train_data, train_data.len(), &predicted_cache)
                }
                Loss::LAD => self.lad_loss_process(train_data, train_data.len(), &predicted_cache),

                _ => self.square_loss_process(train_data, train_data.len(), &predicted_cache),
            }
            // train a new decision tree
            self.trees[i].fit_n(train_data, &subset, &mut cache);

            // update the predicted_cache for the data in the `subset`
            let train_preds = cache.get_preds();
            for index in subset.iter() {
                predicted_cache[*index] += train_preds[*index] * self.conf.shrinkage;
            }
            // update the predicted_cache for the data in the `remaining`
            let predicted_tmp = self.trees[i].predict_n(train_data, &remaining);
            for index in remaining.iter() {
                predicted_cache[*index] += predicted_tmp[*index] * self.conf.shrinkage;
            }

            //output elapsed time
            #[cfg(feature = "profiling")]
            let t2 = PreciseTime::now();
            #[cfg(feature = "profiling")]
            println!(
                "iteration {} {} nodes: {}",
                i,
                t1.to(t2),
                self.trees[i].len()
            );
        }
    }

    /// Predict the first `n` data in data vector with the [`begin`, `begin`+iters) trees.
    ///
    /// The output will be a vector, having same size as the `test_data`. The first n elements are the predicted values, the others are `VALUE_TYPE_UNKNOWN`
    ///
    /// Note that the result will not be normalized no matter what loss type is used.
    ///
    /// # Panic
    /// If n is greater than the length of test data vector, it will panic.
    ///
    /// If the iterations is greater than the number of trees that have been trained, it will panic.
    fn predict_n(&self, test_data: &DataVec, begin: usize, iters: usize, n: usize) -> PredVec {
        assert!((begin + iters) <= self.trees.len());
        assert!(n <= test_data.len());

        if self.trees.is_empty() {
            return vec![VALUE_TYPE_UNKNOWN; test_data.len()];
        }

        // initialize the vector with bias/initial_guess
        let mut predicted: PredVec = if !self.conf.initial_guess_enabled {
            vec![self.bias; n]
        } else {
            test_data.iter().take(n).map(|x| x.initial_guess).collect()
        };

        // inference the data with individual decision tree.
        let subset: Vec<usize> = (0..n).collect();
        for i in begin..(iters + begin) {
            let v: PredVec = self.trees[i].predict_n(&test_data, &subset);
            for (e, v) in predicted.iter_mut().take(n).zip(v.iter()) {
                *e += self.conf.shrinkage * v;
            }
        }
        predicted
    }

    /// 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
    /// ```rust
    /// 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.
    pub fn predict(&self, test_data: &DataVec) -> PredVec {
        assert_eq!(self.conf.iterations, self.trees.len());
        let predicted = self.predict_n(test_data, 0, self.conf.iterations, test_data.len());

        match self.conf.loss {
            Loss::LogLikelyhood => predicted
                .iter()
                .map(|x| {
                    //if (1.0 / (1.0 + ((-2.0 * x).exp()))) >= 0.5 {
                    //    1.0
                    //} else {
                    //    -1.0
                    //}
                    1.0 / (1.0 + ((-2.0 * x).exp()))
                })
                .collect(),
            Loss::BinaryLogistic | Loss::RegLogistic => {
                predicted.iter().map(|x| 1.0 / (1.0 + (-x).exp())).collect()
            }
            _ => predicted,
        }
    }

    /// 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
    ///
    /// ```rust
    /// 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);
    /// ```
    pub fn predict_multiclass(
        &self,
        test_data: &DataVec,
        class_num: usize,
    ) -> (Vec<usize>, Vec<Vec<ValueType>>) {
        assert_eq!(self.conf.iterations, self.trees.len());
        assert_eq!(self.trees.len() % class_num, 0);

        // this api is used for xgboost's model, so shrinkage is 1.0
        // and config.initial_guess is false
        let mut probs: Vec<Vec<ValueType>> = Vec::with_capacity(test_data.len());
        // initialize the vector with bias value
        for _index in 0..test_data.len() {
            probs.push(vec![self.bias; class_num]);
        }

        // compute the raw predicted values for each class
        for (index, tree) in self.trees.iter().enumerate() {
            let preds = tree.predict(test_data);
            for (x, y) in probs.iter_mut().zip(preds.iter()) {
                x[index % class_num] += y;
            }
        }
        let mut labels = vec![0; test_data.len()];
        // normalize the predicted probilities and compute the label
        for (elem_index, elem) in probs.iter_mut().enumerate() {
            let mut sum: ValueType = 0.0;
            let mut max_value = VALUE_TYPE_MIN;
            let mut max_index = 0;
            let mut prob_vec = vec![0.0; class_num];
            for (index, item) in elem.iter().enumerate() {
                let v = item.exp();
                prob_vec[index] = v;
                sum += v;
                if v > max_value {
                    max_index = index;
                    max_value = v;
                }
            }
            for item in prob_vec.iter_mut() {
                *item /= sum;
            }
            *elem = prob_vec;
            labels[elem_index] = max_index;
        }
        (labels, probs)
    }

    /// Print the tress for debug
    ///
    /// # Example
    /// ```rust
    /// 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();
    /// ```
    pub fn print_trees(&self) {
        for i in 0..self.trees.len() {
            self.trees[i].print();
        }
    }

    /// This is the process to calculate the residual as the target in next iteration
    /// for squared error loss.
    #[cfg(feature = "enable_training")]
    fn square_loss_process(&self, dv: &mut DataVec, samples: usize, predicted: &PredVec) {
        for i in 0..samples {
            dv[i].target = dv[i].label - predicted[i];
        }
        if self.conf.debug {
            println!("RMSE = {}", RMSE(&dv, &predicted, samples));
        }
    }

    /// This is the process to calculate the residual as the target in next iteration
    /// for negative binomial log-likehood loss.
    #[cfg(feature = "enable_training")]
    fn log_loss_process(&self, dv: &mut DataVec, samples: usize, predicted: &PredVec) {
        for i in 0..samples {
            dv[i].target = logit_loss_gradient(dv[i].label, predicted[i]);
        }
        if self.conf.debug {
            let normalized_preds = predicted
                .iter()
                .map(|x| 1.0 / (1.0 + ((-2.0 * x).exp())))
                .collect();
            println!("AUC = {}", AUC(&dv, &normalized_preds, dv.len()));
        }
    }

    /// This is the process to calculate the residual as the target in next iteration
    /// for LAD loss.
    #[cfg(feature = "enable_training")]
    fn lad_loss_process(&self, dv: &mut DataVec, samples: usize, predicted: &PredVec) {
        for i in 0..samples {
            dv[i].residual = dv[i].label - predicted[i];
            dv[i].target = if dv[i].residual >= 0.0 { 1.0 } else { -1.0 };
        }
        if self.conf.debug {
            println!("MAE {}", MAE(&dv, &predicted, samples));
        }
    }

    /// Save the model to a file using serde.
    ///
    /// # Example
    /// ```rust
    /// 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");
    /// ```
    pub fn save_model(&self, filename: &str) -> Result<(), Box<Error>> {
        let mut file = File::create(filename)?;
        let serialized = serde_json::to_string(self)?;
        file.write_all(serialized.as_bytes())?;

        Ok(())
    }

    /// Load the model from the file.
    ///
    /// # Example
    ///
    /// ```rust
    /// 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.
    pub fn load_model(filename: &str) -> Result<Self, Box<Error>> {
        let mut file = File::open(filename)?;
        let mut contents = String::new();
        file.read_to_string(&mut contents)?;
        let ret: Self = serde_json::from_str(&contents)?;
        Ok(ret)
    }

    /// Load the model from xgboost's model. The xgboost's model should be converted by "convert_xgboost.py"
    ///
    /// # Example
    ///
    /// ```rust
    /// 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.
    pub fn from_xgoost_dump(model_file: &str, objective: &str) -> Result<Self, Box<Error>> {
        let tree_file = File::open(&model_file)?;
        let reader = BufReader::new(tree_file);
        let mut all_lines: Vec<String> = Vec::new();
        let mut has_read_score = false;
        let mut base_score: ValueType = 0.0;
        for line in reader.lines() {
            // read base score
            if !has_read_score {
                has_read_score = true;
                base_score = line?.parse::<ValueType>()?;
                continue;
            }
            // read trees
            let value: String = line?;
            all_lines.push(value);
        }
        let single_line = all_lines.join("");
        let json_obj: serde_json::Value = serde_json::from_str(&single_line)?;

        let nodes = json_obj.as_array().ok_or("parse trees error")?;

        let mut cfg = Config::new();
        cfg.set_loss(objective);
        cfg.set_iterations(nodes.len());
        cfg.shrinkage = 1.0;
        let mut gbdt = GBDT::new(&cfg);
        gbdt.bias = base_score;

        // load trees
        for node in nodes.iter() {
            let tree = DecisionTree::get_from_xgboost(node)?;
            gbdt.trees.push(tree);
        }
        Ok(gbdt)
    }
}