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<!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Source of the Rust file `/root/.cargo/registry/src/github.com-1ecc6299db9ec823/gbdt-0.1.1/src/gradient_boost.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>gradient_boost.rs - source</title><link rel="preload" as="font" type="font/woff2" crossorigin href="../../SourceSerif4-Regular.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../FiraSans-Regular.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../FiraSans-Medium.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../SourceCodePro-Regular.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../SourceSerif4-Bold.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../SourceCodePro-Semibold.ttf.woff2"><link rel="stylesheet" href="../../normalize.css"><link rel="stylesheet" href="../../rustdoc.css" id="mainThemeStyle"><link rel="stylesheet" href="../../ayu.css" disabled><link rel="stylesheet" href="../../dark.css" disabled><link rel="stylesheet" href="../../light.css" id="themeStyle"><script id="default-settings" ></script><script src="../../storage.js"></script><script defer src="../../source-script.js"></script><script defer src="../../source-files.js"></script><script defer src="../../main.js"></script><noscript><link rel="stylesheet" href="../../noscript.css"></noscript><link rel="alternate icon" type="image/png" href="../../favicon-16x16.png"><link rel="alternate icon" type="image/png" href="../../favicon-32x32.png"><link rel="icon" type="image/svg+xml" href="../../favicon.svg"></head><body class="rustdoc source"><!--[if lte IE 11]><div class="warning">This old browser is unsupported and will most likely display funky things.</div><![endif]--><nav class="sidebar"><a class="sidebar-logo" href="../../gbdt/index.html"><div class="logo-container"><img class="rust-logo" src="../../rust-logo.svg" alt="logo"></div></a></nav><main><div class="width-limiter"><nav class="sub"><a class="sub-logo-container" href="../../gbdt/index.html"><img class="rust-logo" src="../../rust-logo.svg" alt="logo"></a><form class="search-form"><div class="search-container"><span></span><input class="search-input" name="search" autocomplete="off" spellcheck="false" placeholder="Click or press ‘S’ to search, ‘?’ for more options…" type="search"><div id="help-button" title="help" tabindex="-1"><a href="../../help.html">?</a></div><div id="settings-menu" tabindex="-1"><a href="../../settings.html" title="settings"><img width="22" height="22" alt="Change settings" src="../../wheel.svg"></a></div></div></form></nav><section id="main-content" class="content"><div class="example-wrap"><pre class="src-line-numbers"><span id="1">1</span>
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</pre><pre class="rust"><code><span class="doccomment">//! 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(&quot;SquaredError&quot;);
//! cfg.set_iterations(2);
//!
//! // initialize GBDT algorithm
//! let mut gbdt = GBDT::new(&amp;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(&amp;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!(&quot;{:?}&quot;, gbdt.predict(&amp;test_data));
//!
//! // output:
//! // [1.0, 1.0, 2.0, 0.0]
//! ```
</span><span class="attribute">#[cfg(all(feature = <span class="string">&quot;mesalock_sgx&quot;</span>, not(target_env = <span class="string">&quot;sgx&quot;</span>)))]
</span><span class="kw">use </span>std::prelude::v1::<span class="kw-2">*</span>;
<span class="kw">use </span><span class="kw">crate</span>::config::{Config, Loss};
<span class="kw">use </span><span class="kw">crate</span>::decision_tree::DecisionTree;
<span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">use </span><span class="kw">crate</span>::decision_tree::TrainingCache;
<span class="kw">use </span><span class="kw">crate</span>::decision_tree::{DataVec, PredVec, ValueType, VALUE_TYPE_MIN, VALUE_TYPE_UNKNOWN};
<span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">use </span><span class="kw">crate</span>::fitness::{label_average, logit_loss_gradient, weighted_label_median, AUC, MAE, RMSE};
<span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">use </span>rand::prelude::SliceRandom;
<span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">use </span>rand::thread_rng;
<span class="kw">use </span>std::error::Error;
<span class="attribute">#[cfg(not(feature = <span class="string">&quot;mesalock_sgx&quot;</span>))]
</span><span class="kw">use </span>std::fs::File;
<span class="attribute">#[cfg(feature = <span class="string">&quot;mesalock_sgx&quot;</span>)]
</span><span class="kw">use </span>std::untrusted::fs::File;
<span class="kw">use </span>std::io::prelude::<span class="kw-2">*</span>;
<span class="kw">use </span>std::io::{BufRead, BufReader};
<span class="kw">use </span>serde_derive::{Deserialize, Serialize};
<span class="attribute">#[cfg(feature = <span class="string">&quot;profiling&quot;</span>)]
</span><span class="kw">use </span>time::PreciseTime;
<span class="doccomment">/// The gradient boosting decision tree.
</span><span class="attribute">#[derive(Default, Serialize, Deserialize)]
</span><span class="kw">pub struct </span>GBDT {
<span class="doccomment">/// The config of gbdt. See [gbdt::config](../config/) for detail.
</span>conf: Config,
<span class="doccomment">/// The trained decision trees.
</span>trees: Vec&lt;DecisionTree&gt;,
<span class="doccomment">/// The bias estimated.
</span>bias: ValueType,
}
<span class="kw">impl </span>GBDT {
<span class="doccomment">/// 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(&quot;SquaredError&quot;);
/// cfg.set_iterations(2);
///
/// // initialize GBDT algorithm
/// let mut gbdt = GBDT::new(&amp;cfg);
/// ```
</span><span class="kw">pub fn </span>new(conf: <span class="kw-2">&amp;</span>Config) -&gt; GBDT {
GBDT {
conf: conf.clone(),
trees: Vec::new(),
bias: <span class="number">0.0</span>,
}
}
<span class="doccomment">/// 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.
</span><span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">fn </span>check_valid_data(<span class="kw-2">&amp;</span><span class="self">self</span>, dv: <span class="kw-2">&amp;</span>DataVec) -&gt; bool {
dv.iter().all(|x| x.feature.len() == <span class="self">self</span>.conf.feature_size)
}
<span class="doccomment">/// 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.
</span><span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">fn </span>init(<span class="kw-2">&amp;mut </span><span class="self">self</span>, len: usize, dv: <span class="kw-2">&amp;</span>DataVec) {
<span class="macro">assert!</span>(dv.len() &gt;= len);
<span class="kw">if </span>!<span class="self">self</span>.check_valid_data(<span class="kw-2">&amp;</span>dv) {
<span class="macro">panic!</span>(<span class="string">&quot;There are invalid data in data vector, check your data please.&quot;</span>);
}
<span class="kw">if </span><span class="self">self</span>.conf.initial_guess_enabled {
<span class="kw">return</span>;
}
<span class="self">self</span>.bias = <span class="kw">match </span><span class="self">self</span>.conf.loss {
Loss::SquaredError =&gt; label_average(dv, len),
Loss::LogLikelyhood =&gt; {
<span class="kw">let </span>v: ValueType = label_average(dv, len);
((<span class="number">1.0 </span>+ v) / (<span class="number">1.0 </span>- v)).ln() / <span class="number">2.0
</span>}
Loss::LAD =&gt; weighted_label_median(dv, len),
<span class="kw">_ </span>=&gt; label_average(dv, len),
}
}
<span class="doccomment">/// 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(&quot;SquaredError&quot;);
/// cfg.set_iterations(2);
///
/// // initialize GBDT algorithm
/// let mut gbdt = GBDT::new(&amp;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(&amp;mut training_data);
/// ```
</span><span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">pub fn </span>fit(<span class="kw-2">&amp;mut </span><span class="self">self</span>, train_data: <span class="kw-2">&amp;mut </span>DataVec) {
<span class="self">self</span>.trees = Vec::with_capacity(<span class="self">self</span>.conf.iterations);
<span class="comment">// initialize each decision tree
</span><span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..<span class="self">self</span>.conf.iterations {
<span class="self">self</span>.trees.push(DecisionTree::new());
<span class="self">self</span>.trees[i].set_feature_size(<span class="self">self</span>.conf.feature_size);
<span class="self">self</span>.trees[i].set_max_depth(<span class="self">self</span>.conf.max_depth);
<span class="self">self</span>.trees[i].set_min_leaf_size(<span class="self">self</span>.conf.min_leaf_size);
<span class="self">self</span>.trees[i].set_feature_sample_ratio(<span class="self">self</span>.conf.feature_sample_ratio);
<span class="self">self</span>.trees[i].set_loss(<span class="self">self</span>.conf.loss.clone());
}
<span class="comment">// number of samples for training
</span><span class="kw">let </span>nr_samples: usize = <span class="kw">if </span><span class="self">self</span>.conf.data_sample_ratio &lt; <span class="number">1.0 </span>{
((train_data.len() <span class="kw">as </span>f64) * <span class="self">self</span>.conf.data_sample_ratio) <span class="kw">as </span>usize
} <span class="kw">else </span>{
train_data.len()
};
<span class="self">self</span>.init(train_data.len(), <span class="kw-2">&amp;</span>train_data);
<span class="kw">let </span><span class="kw-2">mut </span>rng = thread_rng();
<span class="comment">// initialize the predicted_cache, which records the predictions for training data
</span><span class="kw">let </span><span class="kw-2">mut </span>predicted_cache: PredVec = <span class="self">self</span>.predict_n(train_data, <span class="number">0</span>, <span class="number">0</span>, train_data.len());
<span class="attribute">#[cfg(feature = <span class="string">&quot;profiling&quot;</span>)]
</span><span class="kw">let </span>t1 = PreciseTime::now();
<span class="comment">// allocat the TrainingCache
</span><span class="kw">let </span><span class="kw-2">mut </span>cache = TrainingCache::get_cache(
<span class="self">self</span>.conf.feature_size,
<span class="kw-2">&amp;</span>train_data,
<span class="self">self</span>.conf.training_optimization_level,
);
<span class="attribute">#[cfg(feature = <span class="string">&quot;profiling&quot;</span>)]
</span><span class="kw">let </span>t2 = PreciseTime::now();
<span class="attribute">#[cfg(feature = <span class="string">&quot;profiling&quot;</span>)]
</span><span class="macro">println!</span>(<span class="string">&quot;cache {}&quot;</span>, t1.to(t2));
<span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..<span class="self">self</span>.conf.iterations {
<span class="attribute">#[cfg(feature = <span class="string">&quot;profiling&quot;</span>)]
</span><span class="kw">let </span>t1 = PreciseTime::now();
<span class="kw">let </span><span class="kw-2">mut </span>samples: Vec&lt;usize&gt; = (<span class="number">0</span>..train_data.len()).collect();
<span class="comment">// randomly select some data for training
</span><span class="kw">let </span>(subset, remaining) = <span class="kw">if </span>nr_samples &lt; train_data.len() {
samples.shuffle(<span class="kw-2">&amp;mut </span>rng);
<span class="kw">let </span>(left, right) = samples.split_at(nr_samples);
<span class="kw">let </span><span class="kw-2">mut </span>left = left.to_vec();
<span class="kw">let </span><span class="kw-2">mut </span>right = right.to_vec();
left.sort();
right.sort();
(left, right)
} <span class="kw">else </span>{
(samples, Vec::new())
};
<span class="comment">// Update the target for training
</span><span class="kw">match </span><span class="self">self</span>.conf.loss {
Loss::SquaredError =&gt; {
<span class="self">self</span>.square_loss_process(train_data, train_data.len(), <span class="kw-2">&amp;</span>predicted_cache)
}
Loss::LogLikelyhood =&gt; {
<span class="self">self</span>.log_loss_process(train_data, train_data.len(), <span class="kw-2">&amp;</span>predicted_cache)
}
Loss::LAD =&gt; <span class="self">self</span>.lad_loss_process(train_data, train_data.len(), <span class="kw-2">&amp;</span>predicted_cache),
<span class="kw">_ </span>=&gt; <span class="self">self</span>.square_loss_process(train_data, train_data.len(), <span class="kw-2">&amp;</span>predicted_cache),
}
<span class="comment">// train a new decision tree
</span><span class="self">self</span>.trees[i].fit_n(train_data, <span class="kw-2">&amp;</span>subset, <span class="kw-2">&amp;mut </span>cache);
<span class="comment">// update the predicted_cache for the data in the `subset`
</span><span class="kw">let </span>train_preds = cache.get_preds();
<span class="kw">for </span>index <span class="kw">in </span>subset.iter() {
predicted_cache[<span class="kw-2">*</span>index] += train_preds[<span class="kw-2">*</span>index] * <span class="self">self</span>.conf.shrinkage;
}
<span class="comment">// update the predicted_cache for the data in the `remaining`
</span><span class="kw">let </span>predicted_tmp = <span class="self">self</span>.trees[i].predict_n(train_data, <span class="kw-2">&amp;</span>remaining);
<span class="kw">for </span>index <span class="kw">in </span>remaining.iter() {
predicted_cache[<span class="kw-2">*</span>index] += predicted_tmp[<span class="kw-2">*</span>index] * <span class="self">self</span>.conf.shrinkage;
}
<span class="comment">//output elapsed time
</span><span class="attribute">#[cfg(feature = <span class="string">&quot;profiling&quot;</span>)]
</span><span class="kw">let </span>t2 = PreciseTime::now();
<span class="attribute">#[cfg(feature = <span class="string">&quot;profiling&quot;</span>)]
</span><span class="macro">println!</span>(
<span class="string">&quot;iteration {} {} nodes: {}&quot;</span>,
i,
t1.to(t2),
<span class="self">self</span>.trees[i].len()
);
}
}
<span class="doccomment">/// 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.
</span><span class="kw">fn </span>predict_n(<span class="kw-2">&amp;</span><span class="self">self</span>, test_data: <span class="kw-2">&amp;</span>DataVec, begin: usize, iters: usize, n: usize) -&gt; PredVec {
<span class="macro">assert!</span>((begin + iters) &lt;= <span class="self">self</span>.trees.len());
<span class="macro">assert!</span>(n &lt;= test_data.len());
<span class="kw">if </span><span class="self">self</span>.trees.is_empty() {
<span class="kw">return </span><span class="macro">vec!</span>[VALUE_TYPE_UNKNOWN; test_data.len()];
}
<span class="comment">// initialize the vector with bias/initial_guess
</span><span class="kw">let </span><span class="kw-2">mut </span>predicted: PredVec = <span class="kw">if </span>!<span class="self">self</span>.conf.initial_guess_enabled {
<span class="macro">vec!</span>[<span class="self">self</span>.bias; n]
} <span class="kw">else </span>{
test_data.iter().take(n).map(|x| x.initial_guess).collect()
};
<span class="comment">// inference the data with individual decision tree.
</span><span class="kw">let </span>subset: Vec&lt;usize&gt; = (<span class="number">0</span>..n).collect();
<span class="kw">for </span>i <span class="kw">in </span>begin..(iters + begin) {
<span class="kw">let </span>v: PredVec = <span class="self">self</span>.trees[i].predict_n(<span class="kw-2">&amp;</span>test_data, <span class="kw-2">&amp;</span>subset);
<span class="kw">for </span>(e, v) <span class="kw">in </span>predicted.iter_mut().take(n).zip(v.iter()) {
<span class="kw-2">*</span>e += <span class="self">self</span>.conf.shrinkage * v;
}
}
predicted
}
<span class="doccomment">/// 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(&quot;SquaredError&quot;);
/// cfg.set_iterations(2);
///
/// // initialize GBDT algorithm
/// let mut gbdt = GBDT::new(&amp;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(&amp;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!(&quot;{:?}&quot;, gbdt.predict(&amp;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.
</span><span class="kw">pub fn </span>predict(<span class="kw-2">&amp;</span><span class="self">self</span>, test_data: <span class="kw-2">&amp;</span>DataVec) -&gt; PredVec {
<span class="macro">assert_eq!</span>(<span class="self">self</span>.conf.iterations, <span class="self">self</span>.trees.len());
<span class="kw">let </span>predicted = <span class="self">self</span>.predict_n(test_data, <span class="number">0</span>, <span class="self">self</span>.conf.iterations, test_data.len());
<span class="kw">match </span><span class="self">self</span>.conf.loss {
Loss::LogLikelyhood =&gt; predicted
.iter()
.map(|x| {
<span class="comment">//if (1.0 / (1.0 + ((-2.0 * x).exp()))) &gt;= 0.5 {
// 1.0
//} else {
// -1.0
//}
</span><span class="number">1.0 </span>/ (<span class="number">1.0 </span>+ ((-<span class="number">2.0 </span>* x).exp()))
})
.collect(),
Loss::BinaryLogistic | Loss::RegLogistic =&gt; {
predicted.iter().map(|x| <span class="number">1.0 </span>/ (<span class="number">1.0 </span>+ (-x).exp())).collect()
}
<span class="kw">_ </span>=&gt; predicted,
}
}
<span class="doccomment">/// Predict multi class data and return the probabilities for each class. The loss type should be &quot;multi:softmax&quot; or &quot;multi:softprob&quot;
///
/// 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(&quot;xgb-data/xgb_multi_softmax/gbdt.model&quot;, &quot;multi:softmax&quot;).unwrap();
/// let test_file = &quot;xgb-data/xgb_multi_softmax/dermatology.data.test&quot;;
/// 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(&amp;test_data, 6);
/// ```
</span><span class="kw">pub fn </span>predict_multiclass(
<span class="kw-2">&amp;</span><span class="self">self</span>,
test_data: <span class="kw-2">&amp;</span>DataVec,
class_num: usize,
) -&gt; (Vec&lt;usize&gt;, Vec&lt;Vec&lt;ValueType&gt;&gt;) {
<span class="macro">assert_eq!</span>(<span class="self">self</span>.conf.iterations, <span class="self">self</span>.trees.len());
<span class="macro">assert_eq!</span>(<span class="self">self</span>.trees.len() % class_num, <span class="number">0</span>);
<span class="comment">// this api is used for xgboost&#39;s model, so shrinkage is 1.0
// and config.initial_guess is false
</span><span class="kw">let </span><span class="kw-2">mut </span>probs: Vec&lt;Vec&lt;ValueType&gt;&gt; = Vec::with_capacity(test_data.len());
<span class="comment">// initialize the vector with bias value
</span><span class="kw">for </span>_index <span class="kw">in </span><span class="number">0</span>..test_data.len() {
probs.push(<span class="macro">vec!</span>[<span class="self">self</span>.bias; class_num]);
}
<span class="comment">// compute the raw predicted values for each class
</span><span class="kw">for </span>(index, tree) <span class="kw">in </span><span class="self">self</span>.trees.iter().enumerate() {
<span class="kw">let </span>preds = tree.predict(test_data);
<span class="kw">for </span>(x, y) <span class="kw">in </span>probs.iter_mut().zip(preds.iter()) {
x[index % class_num] += y;
}
}
<span class="kw">let </span><span class="kw-2">mut </span>labels = <span class="macro">vec!</span>[<span class="number">0</span>; test_data.len()];
<span class="comment">// normalize the predicted probilities and compute the label
</span><span class="kw">for </span>(elem_index, elem) <span class="kw">in </span>probs.iter_mut().enumerate() {
<span class="kw">let </span><span class="kw-2">mut </span>sum: ValueType = <span class="number">0.0</span>;
<span class="kw">let </span><span class="kw-2">mut </span>max_value = VALUE_TYPE_MIN;
<span class="kw">let </span><span class="kw-2">mut </span>max_index = <span class="number">0</span>;
<span class="kw">let </span><span class="kw-2">mut </span>prob_vec = <span class="macro">vec!</span>[<span class="number">0.0</span>; class_num];
<span class="kw">for </span>(index, item) <span class="kw">in </span>elem.iter().enumerate() {
<span class="kw">let </span>v = item.exp();
prob_vec[index] = v;
sum += v;
<span class="kw">if </span>v &gt; max_value {
max_index = index;
max_value = v;
}
}
<span class="kw">for </span>item <span class="kw">in </span>prob_vec.iter_mut() {
<span class="kw-2">*</span>item /= sum;
}
<span class="kw-2">*</span>elem = prob_vec;
labels[elem_index] = max_index;
}
(labels, probs)
}
<span class="doccomment">/// 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(&quot;SquaredError&quot;);
/// cfg.set_iterations(2);
///
/// // initialize GBDT algorithm
/// let mut gbdt = GBDT::new(&amp;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(&amp;mut dv);
///
/// // print the tree.
/// gbdt.print_trees();
/// ```
</span><span class="kw">pub fn </span>print_trees(<span class="kw-2">&amp;</span><span class="self">self</span>) {
<span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..<span class="self">self</span>.trees.len() {
<span class="self">self</span>.trees[i].print();
}
}
<span class="doccomment">/// This is the process to calculate the residual as the target in next iteration
/// for squared error loss.
</span><span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">fn </span>square_loss_process(<span class="kw-2">&amp;</span><span class="self">self</span>, dv: <span class="kw-2">&amp;mut </span>DataVec, samples: usize, predicted: <span class="kw-2">&amp;</span>PredVec) {
<span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..samples {
dv[i].target = dv[i].label - predicted[i];
}
<span class="kw">if </span><span class="self">self</span>.conf.debug {
<span class="macro">println!</span>(<span class="string">&quot;RMSE = {}&quot;</span>, RMSE(<span class="kw-2">&amp;</span>dv, <span class="kw-2">&amp;</span>predicted, samples));
}
}
<span class="doccomment">/// This is the process to calculate the residual as the target in next iteration
/// for negative binomial log-likehood loss.
</span><span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">fn </span>log_loss_process(<span class="kw-2">&amp;</span><span class="self">self</span>, dv: <span class="kw-2">&amp;mut </span>DataVec, samples: usize, predicted: <span class="kw-2">&amp;</span>PredVec) {
<span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..samples {
dv[i].target = logit_loss_gradient(dv[i].label, predicted[i]);
}
<span class="kw">if </span><span class="self">self</span>.conf.debug {
<span class="kw">let </span>normalized_preds = predicted
.iter()
.map(|x| <span class="number">1.0 </span>/ (<span class="number">1.0 </span>+ ((-<span class="number">2.0 </span>* x).exp())))
.collect();
<span class="macro">println!</span>(<span class="string">&quot;AUC = {}&quot;</span>, AUC(<span class="kw-2">&amp;</span>dv, <span class="kw-2">&amp;</span>normalized_preds, dv.len()));
}
}
<span class="doccomment">/// This is the process to calculate the residual as the target in next iteration
/// for LAD loss.
</span><span class="attribute">#[cfg(feature = <span class="string">&quot;enable_training&quot;</span>)]
</span><span class="kw">fn </span>lad_loss_process(<span class="kw-2">&amp;</span><span class="self">self</span>, dv: <span class="kw-2">&amp;mut </span>DataVec, samples: usize, predicted: <span class="kw-2">&amp;</span>PredVec) {
<span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..samples {
dv[i].residual = dv[i].label - predicted[i];
dv[i].target = <span class="kw">if </span>dv[i].residual &gt;= <span class="number">0.0 </span>{ <span class="number">1.0 </span>} <span class="kw">else </span>{ -<span class="number">1.0 </span>};
}
<span class="kw">if </span><span class="self">self</span>.conf.debug {
<span class="macro">println!</span>(<span class="string">&quot;MAE {}&quot;</span>, MAE(<span class="kw-2">&amp;</span>dv, <span class="kw-2">&amp;</span>predicted, samples));
}
}
<span class="doccomment">/// 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(&quot;SquaredError&quot;);
/// cfg.set_iterations(2);
///
/// // initialize GBDT algorithm
/// let mut gbdt = GBDT::new(&amp;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(&amp;mut dv);
///
/// // Save model.
/// // gbdt.save_model(&quot;gbdt.model&quot;);
/// ```
</span><span class="kw">pub fn </span>save_model(<span class="kw-2">&amp;</span><span class="self">self</span>, filename: <span class="kw-2">&amp;</span>str) -&gt; <span class="prelude-ty">Result</span>&lt;(), Box&lt;Error&gt;&gt; {
<span class="kw">let </span><span class="kw-2">mut </span>file = File::create(filename)<span class="question-mark">?</span>;
<span class="kw">let </span>serialized = serde_json::to_string(<span class="self">self</span>)<span class="question-mark">?</span>;
file.write_all(serialized.as_bytes())<span class="question-mark">?</span>;
<span class="prelude-val">Ok</span>(())
}
<span class="doccomment">/// Load the model from the file.
///
/// # Example
///
/// ```rust
/// use gbdt::gradient_boost::GBDT;
/// //let gbdt = GBDT::load_model(&quot;./gbdt-rs.model&quot;).unwrap();
/// ```
///
/// # Error
/// Error when get exception during model file parsing or deserialize.
</span><span class="kw">pub fn </span>load_model(filename: <span class="kw-2">&amp;</span>str) -&gt; <span class="prelude-ty">Result</span>&lt;<span class="self">Self</span>, Box&lt;Error&gt;&gt; {
<span class="kw">let </span><span class="kw-2">mut </span>file = File::open(filename)<span class="question-mark">?</span>;
<span class="kw">let </span><span class="kw-2">mut </span>contents = String::new();
file.read_to_string(<span class="kw-2">&amp;mut </span>contents)<span class="question-mark">?</span>;
<span class="kw">let </span>ret: <span class="self">Self </span>= serde_json::from_str(<span class="kw-2">&amp;</span>contents)<span class="question-mark">?</span>;
<span class="prelude-val">Ok</span>(ret)
}
<span class="doccomment">/// Load the model from xgboost&#39;s model. The xgboost&#39;s model should be converted by &quot;convert_xgboost.py&quot;
///
/// # Example
///
/// ```rust
/// use gbdt::gradient_boost::GBDT;
/// let gbdt =
/// GBDT::from_xgoost_dump(&quot;xgb-data/xgb_binary_logistic/gbdt.model&quot;, &quot;binary:logistic&quot;).unwrap();
/// ```
///
/// # Error
/// Error when get exception during model file parsing.
</span><span class="kw">pub fn </span>from_xgoost_dump(model_file: <span class="kw-2">&amp;</span>str, objective: <span class="kw-2">&amp;</span>str) -&gt; <span class="prelude-ty">Result</span>&lt;<span class="self">Self</span>, Box&lt;Error&gt;&gt; {
<span class="kw">let </span>tree_file = File::open(<span class="kw-2">&amp;</span>model_file)<span class="question-mark">?</span>;
<span class="kw">let </span>reader = BufReader::new(tree_file);
<span class="kw">let </span><span class="kw-2">mut </span>all_lines: Vec&lt;String&gt; = Vec::new();
<span class="kw">let </span><span class="kw-2">mut </span>has_read_score = <span class="bool-val">false</span>;
<span class="kw">let </span><span class="kw-2">mut </span>base_score: ValueType = <span class="number">0.0</span>;
<span class="kw">for </span>line <span class="kw">in </span>reader.lines() {
<span class="comment">// read base score
</span><span class="kw">if </span>!has_read_score {
has_read_score = <span class="bool-val">true</span>;
base_score = line<span class="question-mark">?</span>.parse::&lt;ValueType&gt;()<span class="question-mark">?</span>;
<span class="kw">continue</span>;
}
<span class="comment">// read trees
</span><span class="kw">let </span>value: String = line<span class="question-mark">?</span>;
all_lines.push(value);
}
<span class="kw">let </span>single_line = all_lines.join(<span class="string">&quot;&quot;</span>);
<span class="kw">let </span>json_obj: serde_json::Value = serde_json::from_str(<span class="kw-2">&amp;</span>single_line)<span class="question-mark">?</span>;
<span class="kw">let </span>nodes = json_obj.as_array().ok_or(<span class="string">&quot;parse trees error&quot;</span>)<span class="question-mark">?</span>;
<span class="kw">let </span><span class="kw-2">mut </span>cfg = Config::new();
cfg.set_loss(objective);
cfg.set_iterations(nodes.len());
cfg.shrinkage = <span class="number">1.0</span>;
<span class="kw">let </span><span class="kw-2">mut </span>gbdt = GBDT::new(<span class="kw-2">&amp;</span>cfg);
gbdt.bias = base_score;
<span class="comment">// load trees
</span><span class="kw">for </span>node <span class="kw">in </span>nodes.iter() {
<span class="kw">let </span>tree = DecisionTree::get_from_xgboost(node)<span class="question-mark">?</span>;
gbdt.trees.push(tree);
}
<span class="prelude-val">Ok</span>(gbdt)
}
}
</code></pre></div>
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