| <!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/git/checkouts/incubator-teaclave-crates-c8106113f74feefc/ede1f68/rusty-machine/src/analysis/cross_validation.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>cross_validation.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="../../../rusty_machine/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="../../../rusty_machine/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">//! Module for performing cross-validation of models. |
| |
| </span><span class="kw">use </span>std::cmp; |
| <span class="kw">use </span>std::iter::Chain; |
| <span class="kw">use </span>std::slice::Iter; |
| <span class="kw">use </span>linalg::{BaseMatrix, Matrix}; |
| <span class="kw">use </span>learning::{LearningResult, SupModel}; |
| <span class="kw">use </span>learning::toolkit::rand_utils::in_place_fisher_yates; |
| |
| <span class="doccomment">/// Randomly splits the inputs into k 'folds'. For each fold a model |
| /// is trained using all inputs except for that fold, and tested on the |
| /// data in the fold. Returns the scores for each fold. |
| /// |
| /// # Arguments |
| /// * `model` - Used to train and predict for each fold. |
| /// * `inputs` - All input samples. |
| /// * `targets` - All targets. |
| /// * `k` - Number of folds to use. |
| /// * `score` - Used to compare the outputs for each fold to the targets. Higher scores are better. See the `analysis::score` module for examples. |
| /// |
| /// # Examples |
| /// ``` |
| /// use rusty_machine::analysis::cross_validation::k_fold_validate; |
| /// use rusty_machine::analysis::score::row_accuracy; |
| /// use rusty_machine::learning::naive_bayes::{NaiveBayes, Bernoulli}; |
| /// use rusty_machine::linalg::{BaseMatrix, Matrix}; |
| /// |
| /// let inputs = Matrix::new(3, 2, vec![1.0, 1.1, |
| /// 5.2, 4.3, |
| /// 6.2, 7.3]); |
| /// |
| /// let targets = Matrix::new(3, 3, vec![1.0, 0.0, 0.0, |
| /// 0.0, 0.0, 1.0, |
| /// 0.0, 0.0, 1.0]); |
| /// |
| /// let mut model = NaiveBayes::<Bernoulli>::new(); |
| /// |
| /// let accuracy_per_fold: Vec<f64> = k_fold_validate( |
| /// &mut model, |
| /// &inputs, |
| /// &targets, |
| /// 3, |
| /// // Score each fold by the fraction of test samples where |
| /// // the model's prediction equals the target. |
| /// row_accuracy |
| /// ).unwrap(); |
| /// ``` |
| </span><span class="kw">pub fn </span>k_fold_validate<M, S>(model: <span class="kw-2">&mut </span>M, |
| inputs: <span class="kw-2">&</span>Matrix<f64>, |
| targets: <span class="kw-2">&</span>Matrix<f64>, |
| k: usize, |
| score: S) -> LearningResult<Vec<f64>> |
| <span class="kw">where </span>S: Fn(<span class="kw-2">&</span>Matrix<f64>, <span class="kw-2">&</span>Matrix<f64>) -> f64, |
| M: SupModel<Matrix<f64>, Matrix<f64>>, |
| { |
| <span class="macro">assert_eq!</span>(inputs.rows(), targets.rows()); |
| <span class="kw">let </span>num_samples = inputs.rows(); |
| <span class="kw">let </span>shuffled_indices = create_shuffled_indices(num_samples); |
| <span class="kw">let </span>folds = Folds::new(<span class="kw-2">&</span>shuffled_indices, k); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>costs: Vec<f64> = Vec::new(); |
| |
| <span class="kw">for </span>p <span class="kw">in </span>folds { |
| <span class="comment">// TODO: don't allocate fresh buffers for every fold |
| </span><span class="kw">let </span>train_inputs = inputs.select_rows(p.train_indices_iter.clone()); |
| <span class="kw">let </span>train_targets = targets.select_rows(p.train_indices_iter.clone()); |
| <span class="kw">let </span>test_inputs = inputs.select_rows(p.test_indices_iter.clone()); |
| <span class="kw">let </span>test_targets = targets.select_rows(p.test_indices_iter.clone()); |
| |
| model.train(<span class="kw-2">&</span>train_inputs, <span class="kw-2">&</span>train_targets)<span class="question-mark">?</span>; |
| <span class="kw">let </span>outputs = model.predict(<span class="kw-2">&</span>test_inputs)<span class="question-mark">?</span>; |
| costs.push(score(<span class="kw-2">&</span>outputs, <span class="kw-2">&</span>test_targets)); |
| } |
| |
| <span class="prelude-val">Ok</span>(costs) |
| } |
| |
| <span class="doccomment">/// A permutation of 0..n. |
| </span><span class="kw">struct </span>ShuffledIndices(Vec<usize>); |
| |
| <span class="doccomment">/// Permute the indices of the inputs samples. |
| </span><span class="kw">fn </span>create_shuffled_indices(num_samples: usize) -> ShuffledIndices { |
| <span class="kw">let </span><span class="kw-2">mut </span>indices: Vec<usize> = (<span class="number">0</span>..num_samples).collect(); |
| in_place_fisher_yates(<span class="kw-2">&mut </span>indices); |
| ShuffledIndices(indices) |
| } |
| |
| <span class="doccomment">/// A partition of indices of all available samples into |
| /// a training set and a test set. |
| </span><span class="kw">struct </span>Partition<<span class="lifetime">'a</span>> { |
| train_indices_iter: TrainingIndices<<span class="lifetime">'a</span>>, |
| test_indices_iter: TestIndices<<span class="lifetime">'a</span>> |
| } |
| |
| <span class="attribute">#[derive(Clone)] |
| </span><span class="kw">struct </span>TestIndices<<span class="lifetime">'a</span>>(Iter<<span class="lifetime">'a</span>, usize>); |
| |
| <span class="attribute">#[derive(Clone)] |
| </span><span class="kw">struct </span>TrainingIndices<<span class="lifetime">'a</span>> { |
| chain: Chain<Iter<<span class="lifetime">'a</span>, usize>, Iter<<span class="lifetime">'a</span>, usize>>, |
| size: usize |
| } |
| |
| <span class="kw">impl</span><<span class="lifetime">'a</span>> TestIndices<<span class="lifetime">'a</span>> { |
| <span class="kw">fn </span>new(indices: <span class="kw-2">&</span><span class="lifetime">'a </span>[usize]) -> TestIndices<<span class="lifetime">'a</span>> { |
| TestIndices(indices.iter()) |
| } |
| } |
| |
| <span class="kw">impl</span><<span class="lifetime">'a</span>> Iterator <span class="kw">for </span>TestIndices<<span class="lifetime">'a</span>> { |
| <span class="kw">type </span>Item = <span class="kw-2">&</span><span class="lifetime">'a </span>usize; |
| |
| <span class="kw">fn </span>next(<span class="kw-2">&mut </span><span class="self">self</span>) -> <span class="prelude-ty">Option</span><<span class="kw-2">&</span><span class="lifetime">'a </span>usize> { |
| <span class="self">self</span>.<span class="number">0</span>.next() |
| } |
| } |
| |
| <span class="kw">impl </span><<span class="lifetime">'a</span>> ExactSizeIterator <span class="kw">for </span>TestIndices<<span class="lifetime">'a</span>> { |
| <span class="kw">fn </span>len(<span class="kw-2">&</span><span class="self">self</span>) -> usize { |
| <span class="self">self</span>.<span class="number">0</span>.len() |
| } |
| } |
| |
| <span class="kw">impl</span><<span class="lifetime">'a</span>> TrainingIndices<<span class="lifetime">'a</span>> { |
| <span class="kw">fn </span>new(left: <span class="kw-2">&</span><span class="lifetime">'a </span>[usize], right: <span class="kw-2">&</span><span class="lifetime">'a </span>[usize]) -> TrainingIndices<<span class="lifetime">'a</span>> { |
| <span class="kw">let </span>chain = left.iter().chain(right.iter()); |
| TrainingIndices { |
| chain: chain, |
| size: left.len() + right.len() |
| } |
| } |
| } |
| |
| <span class="kw">impl</span><<span class="lifetime">'a</span>> Iterator <span class="kw">for </span>TrainingIndices<<span class="lifetime">'a</span>> { |
| <span class="kw">type </span>Item = <span class="kw-2">&</span><span class="lifetime">'a </span>usize; |
| |
| <span class="kw">fn </span>next(<span class="kw-2">&mut </span><span class="self">self</span>) -> <span class="prelude-ty">Option</span><<span class="kw-2">&</span><span class="lifetime">'a </span>usize> { |
| <span class="self">self</span>.chain.next() |
| } |
| } |
| |
| <span class="kw">impl </span><<span class="lifetime">'a</span>> ExactSizeIterator <span class="kw">for </span>TrainingIndices<<span class="lifetime">'a</span>> { |
| <span class="kw">fn </span>len(<span class="kw-2">&</span><span class="self">self</span>) -> usize { |
| <span class="self">self</span>.size |
| } |
| } |
| |
| <span class="doccomment">/// An iterator over the sets of indices required for k-fold cross validation. |
| </span><span class="kw">struct </span>Folds<<span class="lifetime">'a</span>> { |
| num_folds: usize, |
| indices: <span class="kw-2">&</span><span class="lifetime">'a</span>[usize], |
| count: usize |
| } |
| |
| <span class="kw">impl</span><<span class="lifetime">'a</span>> Folds<<span class="lifetime">'a</span>> { |
| <span class="doccomment">/// Let n = indices.len(), and k = num_folds. |
| /// The first n % k folds have size n / k + 1 and the |
| /// rest have size n / k. (In particular, if n % k == 0 then all |
| /// folds are the same size.) |
| </span><span class="kw">fn </span>new(indices: <span class="kw-2">&</span><span class="lifetime">'a </span>ShuffledIndices, num_folds: usize) -> Folds<<span class="lifetime">'a</span>> { |
| <span class="kw">let </span>num_samples = indices.<span class="number">0</span>.len(); |
| <span class="macro">assert!</span>(num_folds > <span class="number">1 </span>&& num_samples >= num_folds, |
| <span class="string">"Require num_folds > 1 && num_samples >= num_folds"</span>); |
| |
| Folds { |
| num_folds: num_folds, |
| indices: <span class="kw-2">&</span>indices.<span class="number">0</span>, |
| count: <span class="number">0 |
| </span>} |
| } |
| } |
| |
| <span class="kw">impl</span><<span class="lifetime">'a</span>> Iterator <span class="kw">for </span>Folds<<span class="lifetime">'a</span>> { |
| <span class="kw">type </span>Item = Partition<<span class="lifetime">'a</span>>; |
| |
| <span class="kw">fn </span>next(<span class="kw-2">&mut </span><span class="self">self</span>) -> <span class="prelude-ty">Option</span><<span class="self">Self</span>::Item> { |
| <span class="kw">if </span><span class="self">self</span>.count >= <span class="self">self</span>.num_folds { |
| <span class="kw">return </span><span class="prelude-val">None</span>; |
| } |
| |
| <span class="kw">let </span>num_samples = <span class="self">self</span>.indices.len(); |
| <span class="kw">let </span>q = num_samples / <span class="self">self</span>.num_folds; |
| <span class="kw">let </span>r = num_samples % <span class="self">self</span>.num_folds; |
| <span class="kw">let </span>fold_start = <span class="self">self</span>.count * q + cmp::min(<span class="self">self</span>.count, r); |
| <span class="kw">let </span>fold_size = <span class="kw">if </span><span class="self">self</span>.count >= r {q} <span class="kw">else </span>{q + <span class="number">1</span>}; |
| <span class="kw">let </span>fold_end = fold_start + fold_size; |
| |
| <span class="self">self</span>.count += <span class="number">1</span>; |
| |
| <span class="kw">let </span>prefix = <span class="kw-2">&</span><span class="self">self</span>.indices[..fold_start]; |
| <span class="kw">let </span>suffix = <span class="kw-2">&</span><span class="self">self</span>.indices[fold_end..]; |
| <span class="kw">let </span>infix = <span class="kw-2">&</span><span class="self">self</span>.indices[fold_start..fold_end]; |
| <span class="prelude-val">Some</span>(Partition { |
| train_indices_iter: TrainingIndices::new(prefix, suffix), |
| test_indices_iter: TestIndices::new(infix) |
| }) |
| } |
| } |
| |
| <span class="attribute">#[cfg(test)] |
| </span><span class="kw">mod </span>tests { |
| <span class="kw">use super</span>::{ShuffledIndices, Folds}; |
| |
| <span class="comment">// k % n == 0 |
| </span><span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_folds_n6_k3() { |
| <span class="kw">let </span>idxs = ShuffledIndices(<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>, <span class="number">4</span>, <span class="number">5</span>]); |
| <span class="kw">let </span>folds = collect_folds(Folds::new(<span class="kw-2">&</span>idxs, <span class="number">3</span>)); |
| |
| <span class="macro">assert_eq!</span>(folds, <span class="macro">vec!</span>[ |
| (<span class="macro">vec!</span>[<span class="number">2</span>, <span class="number">3</span>, <span class="number">4</span>, <span class="number">5</span>], <span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>]), |
| (<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">4</span>, <span class="number">5</span>], <span class="macro">vec!</span>[<span class="number">2</span>, <span class="number">3</span>]), |
| (<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>], <span class="macro">vec!</span>[<span class="number">4</span>, <span class="number">5</span>]) |
| ]); |
| } |
| |
| <span class="comment">// k % n == 1 |
| </span><span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_folds_n5_k2() { |
| <span class="kw">let </span>idxs = ShuffledIndices(<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>, <span class="number">4</span>]); |
| <span class="kw">let </span>folds = collect_folds(Folds::new(<span class="kw-2">&</span>idxs, <span class="number">2</span>)); |
| |
| <span class="macro">assert_eq!</span>(folds, <span class="macro">vec!</span>[ |
| (<span class="macro">vec!</span>[<span class="number">3</span>, <span class="number">4</span>], <span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>]), |
| (<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>], <span class="macro">vec!</span>[<span class="number">3</span>, <span class="number">4</span>]) |
| ]); |
| } |
| |
| <span class="comment">// k % n == 2 |
| </span><span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_folds_n6_k4() { |
| <span class="kw">let </span>idxs = ShuffledIndices(<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>, <span class="number">4</span>, <span class="number">5</span>]); |
| <span class="kw">let </span>folds = collect_folds(Folds::new(<span class="kw-2">&</span>idxs, <span class="number">4</span>)); |
| |
| <span class="macro">assert_eq!</span>(folds, <span class="macro">vec!</span>[ |
| (<span class="macro">vec!</span>[<span class="number">2</span>, <span class="number">3</span>, <span class="number">4</span>, <span class="number">5</span>], <span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>]), |
| (<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">4</span>, <span class="number">5</span>], <span class="macro">vec!</span>[<span class="number">2</span>, <span class="number">3</span>]), |
| (<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>, <span class="number">5</span>], <span class="macro">vec!</span>[<span class="number">4</span>]), |
| (<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>, <span class="number">4</span>], <span class="macro">vec!</span>[<span class="number">5</span>]) |
| ]); |
| } |
| |
| <span class="comment">// k == n |
| </span><span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_folds_n4_k4() { |
| <span class="kw">let </span>idxs = ShuffledIndices(<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>]); |
| <span class="kw">let </span>folds = collect_folds(Folds::new(<span class="kw-2">&</span>idxs, <span class="number">4</span>)); |
| |
| <span class="macro">assert_eq!</span>(folds, <span class="macro">vec!</span>[ |
| (<span class="macro">vec!</span>[<span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>], <span class="macro">vec!</span>[<span class="number">0</span>]), |
| (<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">2</span>, <span class="number">3</span>], <span class="macro">vec!</span>[<span class="number">1</span>]), |
| (<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">3</span>], <span class="macro">vec!</span>[<span class="number">2</span>]), |
| (<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>], <span class="macro">vec!</span>[<span class="number">3</span>]) |
| ]); |
| } |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_folds_rejects_large_k() { |
| <span class="kw">let </span>idxs = ShuffledIndices(<span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>]); |
| <span class="kw">let _ </span>= collect_folds(Folds::new(<span class="kw-2">&</span>idxs, <span class="number">4</span>)); |
| } |
| |
| <span class="comment">// Check we're really returning iterators into the shuffled |
| // indices rather than into (0..n). |
| </span><span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_folds_unordered_indices() { |
| <span class="kw">let </span>idxs = ShuffledIndices(<span class="macro">vec!</span>[<span class="number">5</span>, <span class="number">4</span>, <span class="number">3</span>, <span class="number">2</span>, <span class="number">1</span>, <span class="number">0</span>]); |
| <span class="kw">let </span>folds = collect_folds(Folds::new(<span class="kw-2">&</span>idxs, <span class="number">3</span>)); |
| |
| <span class="macro">assert_eq!</span>(folds, <span class="macro">vec!</span>[ |
| (<span class="macro">vec!</span>[<span class="number">3</span>, <span class="number">2</span>, <span class="number">1</span>, <span class="number">0</span>], <span class="macro">vec!</span>[<span class="number">5</span>, <span class="number">4</span>]), |
| (<span class="macro">vec!</span>[<span class="number">5</span>, <span class="number">4</span>, <span class="number">1</span>, <span class="number">0</span>], <span class="macro">vec!</span>[<span class="number">3</span>, <span class="number">2</span>]), |
| (<span class="macro">vec!</span>[<span class="number">5</span>, <span class="number">4</span>, <span class="number">3</span>, <span class="number">2</span>], <span class="macro">vec!</span>[<span class="number">1</span>, <span class="number">0</span>]) |
| ]); |
| } |
| |
| <span class="kw">fn </span>collect_folds<<span class="lifetime">'a</span>>(folds: Folds<<span class="lifetime">'a</span>>) -> Vec<(Vec<usize>, Vec<usize>)> { |
| folds |
| .map(|p| |
| (p.train_indices_iter.map(|x| <span class="kw-2">*</span>x).collect::<Vec<<span class="kw">_</span>>>(), |
| p.test_indices_iter.map(|x| <span class="kw-2">*</span>x).collect::<Vec<<span class="kw">_</span>>>())) |
| .collect::<Vec<(Vec<usize>, Vec<usize>)>>() |
| } |
| } |
| </code></pre></div> |
| </section></div></main><div id="rustdoc-vars" data-root-path="../../../" data-current-crate="rusty_machine" data-themes="ayu,dark,light" data-resource-suffix="" data-rustdoc-version="1.66.0-nightly (5c8bff74b 2022-10-21)" ></div></body></html> |
