a7fa0585eb
* First draft of the new n-dimensional arrays + NB use case * Improves default implementation of multiple Array methods * Refactors tree methods * Adds matrix decomposition routines * Adds matrix decomposition methods to ndarray and nalgebra bindings * Refactoring + linear regression now uses array2 * Ridge & Linear regression * LBFGS optimizer & logistic regression * LBFGS optimizer & logistic regression * Changes linear methods, metrics and model selection methods to new n-dimensional arrays * Switches KNN and clustering algorithms to new n-d array layer * Refactors distance metrics * Optimizes knn and clustering methods * Refactors metrics module * Switches decomposition methods to n-dimensional arrays * Linalg refactoring - cleanup rng merge (#172) * Remove legacy DenseMatrix and BaseMatrix implementation. Port the new Number, FloatNumber and Array implementation into module structure. * Exclude AUC metrics. Needs reimplementation * Improve developers walkthrough New traits system in place at `src/numbers` and `src/linalg` Co-authored-by: Lorenzo <tunedconsulting@gmail.com> * Provide SupervisedEstimator with a constructor to avoid explicit dynamical box allocation in 'cross_validate' and 'cross_validate_predict' as required by the use of 'dyn' as per Rust 2021 * Implement getters to use as_ref() in src/neighbors * Implement getters to use as_ref() in src/naive_bayes * Implement getters to use as_ref() in src/linear * Add Clone to src/naive_bayes * Change signature for cross_validate and other model_selection functions to abide to use of dyn in Rust 2021 * Implement ndarray-bindings. Remove FloatNumber from implementations * Drop nalgebra-bindings support (as decided in conf-call to go for ndarray) * Remove benches. Benches will have their own repo at smartcore-benches * Implement SVC * Implement SVC serialization. Move search parameters in dedicated module * Implement SVR. Definitely too slow * Fix compilation issues for wasm (#202) Co-authored-by: Luis Moreno <morenol@users.noreply.github.com> * Fix tests (#203) * Port linalg/traits/stats.rs * Improve methods naming * Improve Display for DenseMatrix Co-authored-by: Montana Low <montanalow@users.noreply.github.com> Co-authored-by: VolodymyrOrlov <volodymyr.orlov@gmail.com>
894 lines
29 KiB
Rust
894 lines
29 KiB
Rust
//! # Logistic Regression
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//!
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//! As [Linear Regression](../linear_regression/index.html), logistic regression explains your outcome as a linear combination of predictor variables \\(X\\) but rather than modeling this response directly,
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//! logistic regression models the probability that \\(y\\) belongs to a particular category, \\(Pr(y = 1|X) \\), as:
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//!
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//! \\[ Pr(y=1) \approx \frac{e^{\beta_0 + \sum_{i=1}^n \beta_iX_i}}{1 + e^{\beta_0 + \sum_{i=1}^n \beta_iX_i}} \\]
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//!
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//! SmartCore uses [limited memory BFGS](https://en.wikipedia.org/wiki/Limited-memory_BFGS) method to find estimates of regression coefficients, \\(\beta\\)
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//!
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//! Example:
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//!
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//! ```
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//! use smartcore::linalg::basic::matrix::DenseMatrix;
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//! use smartcore::linear::logistic_regression::*;
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//!
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//! //Iris data
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//! let x = DenseMatrix::from_2d_array(&[
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//! &[5.1, 3.5, 1.4, 0.2],
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//! &[4.9, 3.0, 1.4, 0.2],
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//! &[4.7, 3.2, 1.3, 0.2],
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//! &[4.6, 3.1, 1.5, 0.2],
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//! &[5.0, 3.6, 1.4, 0.2],
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//! &[5.4, 3.9, 1.7, 0.4],
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//! &[4.6, 3.4, 1.4, 0.3],
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//! &[5.0, 3.4, 1.5, 0.2],
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//! &[4.4, 2.9, 1.4, 0.2],
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//! &[4.9, 3.1, 1.5, 0.1],
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//! &[7.0, 3.2, 4.7, 1.4],
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//! &[6.4, 3.2, 4.5, 1.5],
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//! &[6.9, 3.1, 4.9, 1.5],
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//! &[5.5, 2.3, 4.0, 1.3],
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//! &[6.5, 2.8, 4.6, 1.5],
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//! &[5.7, 2.8, 4.5, 1.3],
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//! &[6.3, 3.3, 4.7, 1.6],
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//! &[4.9, 2.4, 3.3, 1.0],
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//! &[6.6, 2.9, 4.6, 1.3],
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//! &[5.2, 2.7, 3.9, 1.4],
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//! ]);
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//! let y: Vec<i32> = vec![
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//! 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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//! ];
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//!
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//! let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
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//!
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//! let y_hat = lr.predict(&x).unwrap();
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//! ```
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//!
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//! ## References:
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//! * ["Pattern Recognition and Machine Learning", C.M. Bishop, Linear Models for Classification](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
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//! * ["An Introduction to Statistical Learning", James G., Witten D., Hastie T., Tibshirani R., 4.3 Logistic Regression](http://faculty.marshall.usc.edu/gareth-james/ISL/)
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//! * ["On the Limited Memory Method for Large Scale Optimization", Nocedal et al., Mathematical Programming, 1989](http://users.iems.northwestern.edu/~nocedal/PDFfiles/limited.pdf)
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//!
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//! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
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//! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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use std::cmp::Ordering;
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use std::fmt::Debug;
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use std::marker::PhantomData;
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#[cfg(feature = "serde")]
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use serde::{Deserialize, Serialize};
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use crate::api::{Predictor, SupervisedEstimator};
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use crate::error::Failed;
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use crate::linalg::basic::arrays::{Array1, Array2, MutArrayView1};
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use crate::numbers::basenum::Number;
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use crate::numbers::floatnum::FloatNumber;
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use crate::numbers::realnum::RealNumber;
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use crate::optimization::first_order::lbfgs::LBFGS;
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use crate::optimization::first_order::{FirstOrderOptimizer, OptimizerResult};
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use crate::optimization::line_search::Backtracking;
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use crate::optimization::FunctionOrder;
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, Clone, Eq, PartialEq)]
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/// Solver options for Logistic regression. Right now only LBFGS solver is supported.
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pub enum LogisticRegressionSolverName {
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/// Limited-memory Broyden–Fletcher–Goldfarb–Shanno method, see [LBFGS paper](http://users.iems.northwestern.edu/~nocedal/lbfgsb.html)
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LBFGS,
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}
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impl Default for LogisticRegressionSolverName {
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fn default() -> Self {
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LogisticRegressionSolverName::LBFGS
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}
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}
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/// Logistic Regression parameters
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, Clone)]
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pub struct LogisticRegressionParameters<T: Number + FloatNumber> {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Solver to use for estimation of regression coefficients.
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pub solver: LogisticRegressionSolverName,
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#[cfg_attr(feature = "serde", serde(default))]
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/// Regularization parameter.
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pub alpha: T,
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}
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/// Logistic Regression grid search parameters
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, Clone)]
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pub struct LogisticRegressionSearchParameters<T: Number> {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Solver to use for estimation of regression coefficients.
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pub solver: Vec<LogisticRegressionSolverName>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// Regularization parameter.
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pub alpha: Vec<T>,
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}
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/// Logistic Regression grid search iterator
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pub struct LogisticRegressionSearchParametersIterator<T: Number> {
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logistic_regression_search_parameters: LogisticRegressionSearchParameters<T>,
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current_solver: usize,
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current_alpha: usize,
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}
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impl<T: Number + FloatNumber> IntoIterator for LogisticRegressionSearchParameters<T> {
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type Item = LogisticRegressionParameters<T>;
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type IntoIter = LogisticRegressionSearchParametersIterator<T>;
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fn into_iter(self) -> Self::IntoIter {
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LogisticRegressionSearchParametersIterator {
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logistic_regression_search_parameters: self,
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current_solver: 0,
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current_alpha: 0,
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}
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}
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}
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impl<T: Number + FloatNumber> Iterator for LogisticRegressionSearchParametersIterator<T> {
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type Item = LogisticRegressionParameters<T>;
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fn next(&mut self) -> Option<Self::Item> {
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if self.current_alpha == self.logistic_regression_search_parameters.alpha.len()
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&& self.current_solver == self.logistic_regression_search_parameters.solver.len()
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{
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return None;
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}
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let next = LogisticRegressionParameters {
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solver: self.logistic_regression_search_parameters.solver[self.current_solver].clone(),
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alpha: self.logistic_regression_search_parameters.alpha[self.current_alpha],
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};
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if self.current_alpha + 1 < self.logistic_regression_search_parameters.alpha.len() {
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self.current_alpha += 1;
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} else if self.current_solver + 1 < self.logistic_regression_search_parameters.solver.len()
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{
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self.current_alpha = 0;
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self.current_solver += 1;
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} else {
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self.current_alpha += 1;
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self.current_solver += 1;
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}
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Some(next)
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}
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}
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impl<T: Number + FloatNumber> Default for LogisticRegressionSearchParameters<T> {
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fn default() -> Self {
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let default_params = LogisticRegressionParameters::default();
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LogisticRegressionSearchParameters {
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solver: vec![default_params.solver],
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alpha: vec![default_params.alpha],
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}
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}
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}
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/// Logistic Regression
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug)]
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pub struct LogisticRegression<
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TX: Number + FloatNumber + RealNumber,
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TY: Number + Ord,
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X: Array2<TX>,
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Y: Array1<TY>,
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> {
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coefficients: Option<X>,
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intercept: Option<X>,
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classes: Option<Vec<TY>>,
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num_attributes: usize,
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num_classes: usize,
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_phantom_tx: PhantomData<TX>,
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_phantom_y: PhantomData<Y>,
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}
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trait ObjectiveFunction<T: Number + FloatNumber, X: Array2<T>> {
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///
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fn f(&self, w_bias: &[T]) -> T;
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///
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#[allow(clippy::ptr_arg)]
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fn df(&self, g: &mut Vec<T>, w_bias: &Vec<T>);
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///
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#[allow(clippy::ptr_arg)]
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fn partial_dot(w: &[T], x: &X, v_col: usize, m_row: usize) -> T {
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let mut sum = T::zero();
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let p = x.shape().1;
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for i in 0..p {
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sum += *x.get((m_row, i)) * w[i + v_col];
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}
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sum + w[p + v_col]
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}
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}
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struct BinaryObjectiveFunction<'a, T: Number + FloatNumber, X: Array2<T>> {
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x: &'a X,
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y: Vec<usize>,
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alpha: T,
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_phantom_t: PhantomData<T>,
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}
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impl<T: Number + FloatNumber> LogisticRegressionParameters<T> {
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/// Solver to use for estimation of regression coefficients.
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pub fn with_solver(mut self, solver: LogisticRegressionSolverName) -> Self {
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self.solver = solver;
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self
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}
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/// Regularization parameter.
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pub fn with_alpha(mut self, alpha: T) -> Self {
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self.alpha = alpha;
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self
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}
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}
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impl<T: Number + FloatNumber> Default for LogisticRegressionParameters<T> {
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fn default() -> Self {
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LogisticRegressionParameters {
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solver: LogisticRegressionSolverName::default(),
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alpha: T::zero(),
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}
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}
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}
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impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
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PartialEq for LogisticRegression<TX, TY, X, Y>
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{
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fn eq(&self, other: &Self) -> bool {
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if self.num_classes != other.num_classes
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|| self.num_attributes != other.num_attributes
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|| self.classes().len() != other.classes().len()
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{
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false
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} else {
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for i in 0..self.classes().len() {
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if self.classes()[i] != other.classes()[i] {
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return false;
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}
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}
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self.coefficients()
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.iterator(0)
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.zip(other.coefficients().iterator(0))
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.all(|(&a, &b)| (a - b).abs() <= TX::epsilon())
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&& self
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.intercept()
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.iterator(0)
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.zip(other.intercept().iterator(0))
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.all(|(&a, &b)| (a - b).abs() <= TX::epsilon())
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}
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}
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}
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impl<'a, T: Number + FloatNumber, X: Array2<T>> ObjectiveFunction<T, X>
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for BinaryObjectiveFunction<'a, T, X>
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{
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fn f(&self, w_bias: &[T]) -> T {
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let mut f = T::zero();
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let (n, p) = self.x.shape();
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for i in 0..n {
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let wx = BinaryObjectiveFunction::partial_dot(w_bias, self.x, 0, i);
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f += wx.ln_1pe() - (T::from(self.y[i]).unwrap()) * wx;
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}
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if self.alpha > T::zero() {
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let mut w_squared = T::zero();
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for w_bias_i in w_bias.iter().take(p) {
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w_squared += *w_bias_i * *w_bias_i;
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}
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f += T::from_f64(0.5).unwrap() * self.alpha * w_squared;
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}
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f
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}
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fn df(&self, g: &mut Vec<T>, w_bias: &Vec<T>) {
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g.copy_from(&Vec::zeros(g.len()));
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let (n, p) = self.x.shape();
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for i in 0..n {
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let wx = BinaryObjectiveFunction::partial_dot(w_bias, self.x, 0, i);
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let dyi = (T::from(self.y[i]).unwrap()) - wx.sigmoid();
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for (j, g_j) in g.iter_mut().enumerate().take(p) {
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*g_j -= dyi * *self.x.get((i, j));
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}
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g[p] -= dyi;
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}
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if self.alpha > T::zero() {
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for i in 0..p {
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let w = w_bias[i];
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g[i] += self.alpha * w;
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}
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}
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}
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}
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struct MultiClassObjectiveFunction<'a, T: Number + FloatNumber, X: Array2<T>> {
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x: &'a X,
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y: Vec<usize>,
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k: usize,
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alpha: T,
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_phantom_t: PhantomData<T>,
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}
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impl<'a, T: Number + FloatNumber + RealNumber, X: Array2<T>> ObjectiveFunction<T, X>
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for MultiClassObjectiveFunction<'a, T, X>
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{
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fn f(&self, w_bias: &[T]) -> T {
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let mut f = T::zero();
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let mut prob = vec![T::zero(); self.k];
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let (n, p) = self.x.shape();
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for i in 0..n {
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for (j, prob_j) in prob.iter_mut().enumerate().take(self.k) {
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*prob_j = MultiClassObjectiveFunction::partial_dot(w_bias, self.x, j * (p + 1), i);
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}
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prob.softmax_mut();
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f -= prob[self.y[i]].ln();
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}
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if self.alpha > T::zero() {
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let mut w_squared = T::zero();
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for i in 0..self.k {
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for j in 0..p {
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let wi = w_bias[i * (p + 1) + j];
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w_squared += wi * wi;
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}
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}
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f += T::from_f64(0.5).unwrap() * self.alpha * w_squared;
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}
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f
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}
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fn df(&self, g: &mut Vec<T>, w: &Vec<T>) {
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g.copy_from(&Vec::zeros(g.len()));
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let mut prob = vec![T::zero(); self.k];
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let (n, p) = self.x.shape();
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for i in 0..n {
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for (j, prob_j) in prob.iter_mut().enumerate().take(self.k) {
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*prob_j = MultiClassObjectiveFunction::partial_dot(w, self.x, j * (p + 1), i);
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}
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prob.softmax_mut();
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for j in 0..self.k {
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let yi = (if self.y[i] == j { T::one() } else { T::zero() }) - prob[j];
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for l in 0..p {
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let pos = j * (p + 1);
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g[pos + l] -= yi * *self.x.get((i, l));
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}
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g[j * (p + 1) + p] -= yi;
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}
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}
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if self.alpha > T::zero() {
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for i in 0..self.k {
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for j in 0..p {
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let pos = i * (p + 1);
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let wi = w[pos + j];
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g[pos + j] += self.alpha * wi;
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}
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}
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}
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}
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}
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impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
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SupervisedEstimator<X, Y, LogisticRegressionParameters<TX>>
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for LogisticRegression<TX, TY, X, Y>
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{
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fn new() -> Self {
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Self {
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coefficients: Option::None,
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intercept: Option::None,
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||
classes: Option::None,
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||
num_attributes: 0,
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num_classes: 0,
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_phantom_tx: PhantomData,
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_phantom_y: PhantomData,
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}
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}
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fn fit(x: &X, y: &Y, parameters: LogisticRegressionParameters<TX>) -> Result<Self, Failed> {
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LogisticRegression::fit(x, y, parameters)
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}
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}
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impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
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Predictor<X, Y> for LogisticRegression<TX, TY, X, Y>
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{
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fn predict(&self, x: &X) -> Result<Y, Failed> {
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self.predict(x)
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}
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}
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impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
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LogisticRegression<TX, TY, X, Y>
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{
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/// Fits Logistic Regression to your data.
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/// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
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/// * `y` - target class values
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/// * `parameters` - other parameters, use `Default::default()` to set parameters to default values.
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pub fn fit(
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x: &X,
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y: &Y,
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parameters: LogisticRegressionParameters<TX>,
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) -> Result<LogisticRegression<TX, TY, X, Y>, Failed> {
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||
let (x_nrows, num_attributes) = x.shape();
|
||
let y_nrows = y.shape();
|
||
|
||
if x_nrows != y_nrows {
|
||
return Err(Failed::fit(
|
||
"Number of rows of X doesn\'t match number of rows of Y",
|
||
));
|
||
}
|
||
|
||
let classes = y.unique();
|
||
|
||
let k = classes.len();
|
||
|
||
let mut yi: Vec<usize> = vec![0; y_nrows];
|
||
|
||
for (i, yi_i) in yi.iter_mut().enumerate().take(y_nrows) {
|
||
let yc = y.get(i);
|
||
*yi_i = classes.iter().position(|c| yc == c).unwrap();
|
||
}
|
||
|
||
match k.cmp(&2) {
|
||
Ordering::Less => Err(Failed::fit(&format!(
|
||
"incorrect number of classes: {}. Should be >= 2.",
|
||
k
|
||
))),
|
||
Ordering::Equal => {
|
||
let x0 = Vec::zeros(num_attributes + 1);
|
||
|
||
let objective = BinaryObjectiveFunction {
|
||
x,
|
||
y: yi,
|
||
alpha: parameters.alpha,
|
||
_phantom_t: PhantomData,
|
||
};
|
||
|
||
let result = Self::minimize(x0, objective);
|
||
|
||
let weights = X::from_iterator(result.x.into_iter(), 1, num_attributes + 1, 0);
|
||
let coefficients = weights.slice(0..1, 0..num_attributes);
|
||
let intercept = weights.slice(0..1, num_attributes..num_attributes + 1);
|
||
|
||
Ok(LogisticRegression {
|
||
coefficients: Some(X::from_slice(coefficients.as_ref())),
|
||
intercept: Some(X::from_slice(intercept.as_ref())),
|
||
classes: Some(classes),
|
||
num_attributes,
|
||
num_classes: k,
|
||
_phantom_tx: PhantomData,
|
||
_phantom_y: PhantomData,
|
||
})
|
||
}
|
||
Ordering::Greater => {
|
||
let x0 = Vec::zeros((num_attributes + 1) * k);
|
||
|
||
let objective = MultiClassObjectiveFunction {
|
||
x,
|
||
y: yi,
|
||
k,
|
||
alpha: parameters.alpha,
|
||
_phantom_t: PhantomData,
|
||
};
|
||
|
||
let result = Self::minimize(x0, objective);
|
||
let weights = X::from_iterator(result.x.into_iter(), k, num_attributes + 1, 0);
|
||
let coefficients = weights.slice(0..k, 0..num_attributes);
|
||
let intercept = weights.slice(0..k, num_attributes..num_attributes + 1);
|
||
|
||
Ok(LogisticRegression {
|
||
coefficients: Some(X::from_slice(coefficients.as_ref())),
|
||
intercept: Some(X::from_slice(intercept.as_ref())),
|
||
classes: Some(classes),
|
||
num_attributes,
|
||
num_classes: k,
|
||
_phantom_tx: PhantomData,
|
||
_phantom_y: PhantomData,
|
||
})
|
||
}
|
||
}
|
||
}
|
||
|
||
/// Predict class labels for samples in `x`.
|
||
/// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
|
||
pub fn predict(&self, x: &X) -> Result<Y, Failed> {
|
||
let n = x.shape().0;
|
||
let mut result = Y::zeros(n);
|
||
if self.num_classes == 2 {
|
||
let y_hat = x.ab(false, self.coefficients(), true);
|
||
let intercept = *self.intercept().get((0, 0));
|
||
for (i, y_hat_i) in y_hat.iterator(0).enumerate().take(n) {
|
||
result.set(
|
||
i,
|
||
self.classes()[if RealNumber::sigmoid(*y_hat_i + intercept) > RealNumber::half()
|
||
{
|
||
1
|
||
} else {
|
||
0
|
||
}],
|
||
);
|
||
}
|
||
} else {
|
||
let mut y_hat = x.matmul(&self.coefficients().transpose());
|
||
for r in 0..n {
|
||
for c in 0..self.num_classes {
|
||
y_hat.set((r, c), *y_hat.get((r, c)) + *self.intercept().get((c, 0)));
|
||
}
|
||
}
|
||
let class_idxs = y_hat.argmax(1);
|
||
for (i, class_i) in class_idxs.iter().enumerate().take(n) {
|
||
result.set(i, self.classes()[*class_i]);
|
||
}
|
||
}
|
||
Ok(result)
|
||
}
|
||
|
||
/// Get estimates regression coefficients, this create a sharable reference
|
||
pub fn coefficients(&self) -> &X {
|
||
self.coefficients.as_ref().unwrap()
|
||
}
|
||
|
||
/// Get estimate of intercept, this create a sharable reference
|
||
pub fn intercept(&self) -> &X {
|
||
self.intercept.as_ref().unwrap()
|
||
}
|
||
|
||
/// Get classes, this create a sharable reference
|
||
pub fn classes(&self) -> &Vec<TY> {
|
||
self.classes.as_ref().unwrap()
|
||
}
|
||
|
||
fn minimize(
|
||
x0: Vec<TX>,
|
||
objective: impl ObjectiveFunction<TX, X>,
|
||
) -> OptimizerResult<TX, Vec<TX>> {
|
||
let f = |w: &Vec<TX>| -> TX { objective.f(w) };
|
||
|
||
let df = |g: &mut Vec<TX>, w: &Vec<TX>| objective.df(g, w);
|
||
|
||
let ls: Backtracking<TX> = Backtracking {
|
||
order: FunctionOrder::THIRD,
|
||
..Default::default()
|
||
};
|
||
let optimizer: LBFGS = Default::default();
|
||
|
||
optimizer.optimize(&f, &df, &x0, &ls)
|
||
}
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod tests {
|
||
use super::*;
|
||
use crate::dataset::generator::make_blobs;
|
||
use crate::linalg::basic::arrays::Array;
|
||
use crate::linalg::basic::matrix::DenseMatrix;
|
||
|
||
#[test]
|
||
fn search_parameters() {
|
||
let parameters = LogisticRegressionSearchParameters {
|
||
alpha: vec![0., 1.],
|
||
..Default::default()
|
||
};
|
||
let mut iter = parameters.into_iter();
|
||
assert_eq!(iter.next().unwrap().alpha, 0.);
|
||
assert_eq!(
|
||
iter.next().unwrap().solver,
|
||
LogisticRegressionSolverName::LBFGS
|
||
);
|
||
assert!(iter.next().is_none());
|
||
}
|
||
|
||
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||
#[test]
|
||
fn multiclass_objective_f() {
|
||
let x = DenseMatrix::from_2d_array(&[
|
||
&[1., -5.],
|
||
&[2., 5.],
|
||
&[3., -2.],
|
||
&[1., 2.],
|
||
&[2., 0.],
|
||
&[6., -5.],
|
||
&[7., 5.],
|
||
&[6., -2.],
|
||
&[7., 2.],
|
||
&[6., 0.],
|
||
&[8., -5.],
|
||
&[9., 5.],
|
||
&[10., -2.],
|
||
&[8., 2.],
|
||
&[9., 0.],
|
||
]);
|
||
|
||
let y = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
|
||
|
||
let objective = MultiClassObjectiveFunction {
|
||
x: &x,
|
||
y: y.clone(),
|
||
k: 3,
|
||
alpha: 0.0,
|
||
_phantom_t: PhantomData,
|
||
};
|
||
|
||
let mut g = vec![0f64; 9];
|
||
|
||
objective.df(&mut g, &vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
|
||
objective.df(&mut g, &vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
|
||
|
||
assert!((g[0] + 33.000068218163484).abs() < std::f64::EPSILON);
|
||
|
||
let f = objective.f(&vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
|
||
|
||
assert!((f - 408.0052230582765).abs() < std::f64::EPSILON);
|
||
|
||
let objective_reg = MultiClassObjectiveFunction {
|
||
x: &x,
|
||
y: y.clone(),
|
||
k: 3,
|
||
alpha: 1.0,
|
||
_phantom_t: PhantomData,
|
||
};
|
||
|
||
let f = objective_reg.f(&vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
|
||
assert!((f - 487.5052).abs() < 1e-4);
|
||
|
||
objective_reg.df(&mut g, &vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
|
||
assert!((g[0].abs() - 32.0).abs() < 1e-4);
|
||
}
|
||
|
||
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||
#[test]
|
||
fn binary_objective_f() {
|
||
let x = DenseMatrix::from_2d_array(&[
|
||
&[1., -5.],
|
||
&[2., 5.],
|
||
&[3., -2.],
|
||
&[1., 2.],
|
||
&[2., 0.],
|
||
&[6., -5.],
|
||
&[7., 5.],
|
||
&[6., -2.],
|
||
&[7., 2.],
|
||
&[6., 0.],
|
||
&[8., -5.],
|
||
&[9., 5.],
|
||
&[10., -2.],
|
||
&[8., 2.],
|
||
&[9., 0.],
|
||
]);
|
||
|
||
let y = vec![0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1];
|
||
|
||
let objective = BinaryObjectiveFunction {
|
||
x: &x,
|
||
y: y.clone(),
|
||
alpha: 0.0,
|
||
_phantom_t: PhantomData,
|
||
};
|
||
|
||
let mut g = vec![0f64; 3];
|
||
|
||
objective.df(&mut g, &vec![1., 2., 3.]);
|
||
objective.df(&mut g, &vec![1., 2., 3.]);
|
||
|
||
assert!((g[0] - 26.051064349381285).abs() < std::f64::EPSILON);
|
||
assert!((g[1] - 10.239000702928523).abs() < std::f64::EPSILON);
|
||
assert!((g[2] - 3.869294270156324).abs() < std::f64::EPSILON);
|
||
|
||
let f = objective.f(&vec![1., 2., 3.]);
|
||
|
||
assert!((f - 59.76994756647412).abs() < std::f64::EPSILON);
|
||
|
||
let objective_reg = BinaryObjectiveFunction {
|
||
x: &x,
|
||
y: y.clone(),
|
||
alpha: 1.0,
|
||
_phantom_t: PhantomData,
|
||
};
|
||
|
||
let f = objective_reg.f(&vec![1., 2., 3.]);
|
||
assert!((f - 62.2699).abs() < 1e-4);
|
||
|
||
objective_reg.df(&mut g, &vec![1., 2., 3.]);
|
||
assert!((g[0] - 27.0511).abs() < 1e-4);
|
||
assert!((g[1] - 12.239).abs() < 1e-4);
|
||
assert!((g[2] - 3.8693).abs() < 1e-4);
|
||
}
|
||
|
||
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||
#[test]
|
||
fn lr_fit_predict() {
|
||
let x: DenseMatrix<f64> = DenseMatrix::from_2d_array(&[
|
||
&[1., -5.],
|
||
&[2., 5.],
|
||
&[3., -2.],
|
||
&[1., 2.],
|
||
&[2., 0.],
|
||
&[6., -5.],
|
||
&[7., 5.],
|
||
&[6., -2.],
|
||
&[7., 2.],
|
||
&[6., 0.],
|
||
&[8., -5.],
|
||
&[9., 5.],
|
||
&[10., -2.],
|
||
&[8., 2.],
|
||
&[9., 0.],
|
||
]);
|
||
let y: Vec<i32> = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
|
||
|
||
let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
|
||
|
||
assert_eq!(lr.coefficients().shape(), (3, 2));
|
||
assert_eq!(lr.intercept().shape(), (3, 1));
|
||
|
||
assert!((*lr.coefficients().get((0, 0)) - 0.0435).abs() < 1e-4);
|
||
assert!(
|
||
(*lr.intercept().get((0, 0)) - 0.1250).abs() < 1e-4,
|
||
"expected to be least than 1e-4, got {}",
|
||
(*lr.intercept().get((0, 0)) - 0.1250).abs()
|
||
);
|
||
|
||
let y_hat = lr.predict(&x).unwrap();
|
||
|
||
assert_eq!(y_hat, vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]);
|
||
}
|
||
|
||
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||
#[test]
|
||
fn lr_fit_predict_multiclass() {
|
||
let blobs = make_blobs(15, 4, 3);
|
||
|
||
let x: DenseMatrix<f32> = DenseMatrix::from_iterator(blobs.data.into_iter(), 15, 4, 0);
|
||
let y: Vec<i32> = blobs.target.into_iter().map(|v| v as i32).collect();
|
||
|
||
let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
|
||
|
||
let y_hat = lr.predict(&x).unwrap();
|
||
|
||
assert_eq!(y_hat, vec![0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]);
|
||
|
||
let lr_reg = LogisticRegression::fit(
|
||
&x,
|
||
&y,
|
||
LogisticRegressionParameters::default().with_alpha(10.0),
|
||
)
|
||
.unwrap();
|
||
|
||
let reg_coeff_sum: f32 = lr_reg.coefficients().abs().iter().sum();
|
||
let coeff: f32 = lr.coefficients().abs().iter().sum();
|
||
|
||
assert!(reg_coeff_sum < coeff);
|
||
}
|
||
|
||
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||
#[test]
|
||
fn lr_fit_predict_binary() {
|
||
let blobs = make_blobs(20, 4, 2);
|
||
|
||
let x = DenseMatrix::from_iterator(blobs.data.into_iter(), 20, 4, 0);
|
||
let y: Vec<i32> = blobs.target.into_iter().map(|v| v as i32).collect();
|
||
|
||
let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
|
||
|
||
let y_hat = lr.predict(&x).unwrap();
|
||
|
||
assert_eq!(
|
||
y_hat,
|
||
vec![0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
|
||
);
|
||
|
||
let lr_reg = LogisticRegression::fit(
|
||
&x,
|
||
&y,
|
||
LogisticRegressionParameters::default().with_alpha(10.0),
|
||
)
|
||
.unwrap();
|
||
|
||
let reg_coeff_sum: f32 = lr_reg.coefficients().abs().iter().sum();
|
||
let coeff: f32 = lr.coefficients().abs().iter().sum();
|
||
|
||
assert!(reg_coeff_sum < coeff);
|
||
}
|
||
|
||
// TODO: serialization for the new DenseMatrix needs to be implemented
|
||
// #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||
// #[test]
|
||
// #[cfg(feature = "serde")]
|
||
// fn serde() {
|
||
// let x = DenseMatrix::from_2d_array(&[
|
||
// &[1., -5.],
|
||
// &[2., 5.],
|
||
// &[3., -2.],
|
||
// &[1., 2.],
|
||
// &[2., 0.],
|
||
// &[6., -5.],
|
||
// &[7., 5.],
|
||
// &[6., -2.],
|
||
// &[7., 2.],
|
||
// &[6., 0.],
|
||
// &[8., -5.],
|
||
// &[9., 5.],
|
||
// &[10., -2.],
|
||
// &[8., 2.],
|
||
// &[9., 0.],
|
||
// ]);
|
||
// let y: Vec<i32> = vec![0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 1, 1, 0, 0, 1];
|
||
|
||
// let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
|
||
|
||
// let deserialized_lr: LogisticRegression<f64, i32, DenseMatrix<f64>, Vec<i32>> =
|
||
// serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
|
||
|
||
// assert_eq!(lr, deserialized_lr);
|
||
// }
|
||
|
||
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||
#[test]
|
||
fn lr_fit_predict_iris() {
|
||
let x = DenseMatrix::from_2d_array(&[
|
||
&[5.1, 3.5, 1.4, 0.2],
|
||
&[4.9, 3.0, 1.4, 0.2],
|
||
&[4.7, 3.2, 1.3, 0.2],
|
||
&[4.6, 3.1, 1.5, 0.2],
|
||
&[5.0, 3.6, 1.4, 0.2],
|
||
&[5.4, 3.9, 1.7, 0.4],
|
||
&[4.6, 3.4, 1.4, 0.3],
|
||
&[5.0, 3.4, 1.5, 0.2],
|
||
&[4.4, 2.9, 1.4, 0.2],
|
||
&[4.9, 3.1, 1.5, 0.1],
|
||
&[7.0, 3.2, 4.7, 1.4],
|
||
&[6.4, 3.2, 4.5, 1.5],
|
||
&[6.9, 3.1, 4.9, 1.5],
|
||
&[5.5, 2.3, 4.0, 1.3],
|
||
&[6.5, 2.8, 4.6, 1.5],
|
||
&[5.7, 2.8, 4.5, 1.3],
|
||
&[6.3, 3.3, 4.7, 1.6],
|
||
&[4.9, 2.4, 3.3, 1.0],
|
||
&[6.6, 2.9, 4.6, 1.3],
|
||
&[5.2, 2.7, 3.9, 1.4],
|
||
]);
|
||
let y: Vec<i32> = vec![0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
|
||
|
||
let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
|
||
let lr_reg = LogisticRegression::fit(
|
||
&x,
|
||
&y,
|
||
LogisticRegressionParameters::default().with_alpha(1.0),
|
||
)
|
||
.unwrap();
|
||
|
||
let y_hat = lr.predict(&x).unwrap();
|
||
|
||
let error: i32 = y
|
||
.into_iter()
|
||
.zip(y_hat.into_iter())
|
||
.map(|(a, b)| (a - b).abs())
|
||
.sum();
|
||
|
||
assert!(error <= 1);
|
||
|
||
let reg_coeff_sum: f32 = lr_reg.coefficients().abs().iter().sum();
|
||
let coeff: f32 = lr.coefficients().abs().iter().sum();
|
||
|
||
assert!(reg_coeff_sum < coeff);
|
||
}
|
||
}
|