Merge potential next release v0.4 (#187) Breaking Changes
* 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>
This commit is contained in:
Lorenzo
+145
-99
@@ -23,31 +23,33 @@
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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::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::BaseVector;
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use crate::linalg::Matrix;
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use crate::linalg::basic::arrays::{Array1, Array2, ArrayView1};
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use crate::linear::lasso_optimizer::InteriorPointOptimizer;
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use crate::math::num::RealNumber;
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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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/// Lasso 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 LassoParameters<T: RealNumber> {
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pub struct LassoParameters {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Controls the strength of the penalty to the loss function.
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pub alpha: T,
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pub alpha: f64,
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#[cfg_attr(feature = "serde", serde(default))]
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/// If true the regressors X will be normalized before regression
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/// by subtracting the mean and dividing by the standard deviation.
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pub normalize: bool,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The tolerance for the optimization
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pub tol: T,
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pub tol: f64,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The maximum number of iterations
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pub max_iter: usize,
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@@ -56,14 +58,16 @@ pub struct LassoParameters<T: RealNumber> {
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug)]
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/// Lasso regressor
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pub struct Lasso<T: RealNumber, M: Matrix<T>> {
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coefficients: M,
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intercept: T,
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pub struct Lasso<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> {
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coefficients: Option<X>,
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intercept: Option<TX>,
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_phantom_ty: PhantomData<TY>,
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_phantom_y: PhantomData<Y>,
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}
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impl<T: RealNumber> LassoParameters<T> {
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impl LassoParameters {
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/// Regularization parameter.
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pub fn with_alpha(mut self, alpha: T) -> Self {
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pub fn with_alpha(mut self, alpha: f64) -> Self {
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self.alpha = alpha;
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self
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}
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@@ -73,7 +77,7 @@ impl<T: RealNumber> LassoParameters<T> {
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self
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}
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/// The tolerance for the optimization
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pub fn with_tol(mut self, tol: T) -> Self {
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pub fn with_tol(mut self, tol: f64) -> Self {
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self.tol = tol;
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self
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}
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@@ -84,34 +88,52 @@ impl<T: RealNumber> LassoParameters<T> {
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}
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}
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impl<T: RealNumber> Default for LassoParameters<T> {
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impl Default for LassoParameters {
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fn default() -> Self {
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LassoParameters {
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alpha: T::one(),
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alpha: 1f64,
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normalize: true,
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tol: T::from_f64(1e-4).unwrap(),
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tol: 1e-4,
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max_iter: 1000,
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}
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}
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}
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impl<T: RealNumber, M: Matrix<T>> PartialEq for Lasso<T, M> {
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impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> PartialEq
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for Lasso<TX, TY, X, Y>
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{
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fn eq(&self, other: &Self) -> bool {
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self.coefficients == other.coefficients
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&& (self.intercept - other.intercept).abs() <= T::epsilon()
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self.intercept == other.intercept
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&& self.coefficients().shape() == other.coefficients().shape()
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&& self
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.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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}
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}
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impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, LassoParameters<T>>
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for Lasso<T, M>
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impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>>
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SupervisedEstimator<X, Y, LassoParameters> for Lasso<TX, TY, X, Y>
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{
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fn fit(x: &M, y: &M::RowVector, parameters: LassoParameters<T>) -> Result<Self, Failed> {
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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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_phantom_ty: PhantomData,
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_phantom_y: PhantomData,
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}
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}
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|
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fn fit(x: &X, y: &Y, parameters: LassoParameters) -> Result<Self, Failed> {
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Lasso::fit(x, y, parameters)
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}
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}
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impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for Lasso<T, M> {
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fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
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impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> Predictor<X, Y>
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for Lasso<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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@@ -119,34 +141,34 @@ impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for Lasso<T, M> {
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/// Lasso 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 LassoSearchParameters<T: RealNumber> {
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pub struct LassoSearchParameters {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Controls the strength of the penalty to the loss function.
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pub alpha: Vec<T>,
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pub alpha: Vec<f64>,
|
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#[cfg_attr(feature = "serde", serde(default))]
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/// If true the regressors X will be normalized before regression
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/// by subtracting the mean and dividing by the standard deviation.
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pub normalize: Vec<bool>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The tolerance for the optimization
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pub tol: Vec<T>,
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pub tol: Vec<f64>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The maximum number of iterations
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pub max_iter: Vec<usize>,
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}
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/// Lasso grid search iterator
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pub struct LassoSearchParametersIterator<T: RealNumber> {
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lasso_search_parameters: LassoSearchParameters<T>,
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pub struct LassoSearchParametersIterator {
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lasso_search_parameters: LassoSearchParameters,
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current_alpha: usize,
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current_normalize: usize,
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current_tol: usize,
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current_max_iter: usize,
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}
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impl<T: RealNumber> IntoIterator for LassoSearchParameters<T> {
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type Item = LassoParameters<T>;
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type IntoIter = LassoSearchParametersIterator<T>;
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impl IntoIterator for LassoSearchParameters {
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type Item = LassoParameters;
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type IntoIter = LassoSearchParametersIterator;
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fn into_iter(self) -> Self::IntoIter {
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LassoSearchParametersIterator {
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@@ -159,8 +181,8 @@ impl<T: RealNumber> IntoIterator for LassoSearchParameters<T> {
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}
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}
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impl<T: RealNumber> Iterator for LassoSearchParametersIterator<T> {
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type Item = LassoParameters<T>;
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impl Iterator for LassoSearchParametersIterator {
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type Item = LassoParameters;
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fn next(&mut self) -> Option<Self::Item> {
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if self.current_alpha == self.lasso_search_parameters.alpha.len()
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@@ -203,7 +225,7 @@ impl<T: RealNumber> Iterator for LassoSearchParametersIterator<T> {
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}
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}
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impl<T: RealNumber> Default for LassoSearchParameters<T> {
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impl Default for LassoSearchParameters {
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fn default() -> Self {
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let default_params = LassoParameters::default();
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@@ -216,16 +238,12 @@ impl<T: RealNumber> Default for LassoSearchParameters<T> {
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}
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}
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impl<T: RealNumber, M: Matrix<T>> Lasso<T, M> {
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impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> Lasso<TX, TY, X, Y> {
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/// Fits Lasso 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 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: &M,
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y: &M::RowVector,
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parameters: LassoParameters<T>,
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) -> Result<Lasso<T, M>, Failed> {
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pub fn fit(x: &X, y: &Y, parameters: LassoParameters) -> Result<Lasso<TX, TY, X, Y>, Failed> {
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let (n, p) = x.shape();
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if n <= p {
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@@ -234,11 +252,11 @@ impl<T: RealNumber, M: Matrix<T>> Lasso<T, M> {
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));
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}
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if parameters.alpha < T::zero() {
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if parameters.alpha < 0f64 {
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return Err(Failed::fit("alpha should be >= 0"));
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}
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if parameters.tol <= T::zero() {
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if parameters.tol <= 0f64 {
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return Err(Failed::fit("tol should be > 0"));
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}
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@@ -246,71 +264,98 @@ impl<T: RealNumber, M: Matrix<T>> Lasso<T, M> {
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return Err(Failed::fit("max_iter should be > 0"));
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}
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if y.len() != n {
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if y.shape() != n {
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return Err(Failed::fit("Number of rows in X should = len(y)"));
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}
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let l1_reg = parameters.alpha * T::from_usize(n).unwrap();
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let y: Vec<TX> = y.iterator(0).map(|&v| TX::from(v).unwrap()).collect();
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let l1_reg = TX::from_f64(parameters.alpha * n as f64).unwrap();
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let (w, b) = if parameters.normalize {
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let (scaled_x, col_mean, col_std) = Self::rescale_x(x)?;
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let mut optimizer = InteriorPointOptimizer::new(&scaled_x, p);
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let mut w =
|
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optimizer.optimize(&scaled_x, y, l1_reg, parameters.max_iter, parameters.tol)?;
|
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let mut w = optimizer.optimize(
|
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&scaled_x,
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&y,
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l1_reg,
|
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parameters.max_iter,
|
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TX::from_f64(parameters.tol).unwrap(),
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)?;
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|
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for (j, col_std_j) in col_std.iter().enumerate().take(p) {
|
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w.set(j, 0, w.get(j, 0) / *col_std_j);
|
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w[j] /= *col_std_j;
|
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}
|
||||
|
||||
let mut b = T::zero();
|
||||
let mut b = TX::zero();
|
||||
|
||||
for (i, col_mean_i) in col_mean.iter().enumerate().take(p) {
|
||||
b += w.get(i, 0) * *col_mean_i;
|
||||
b += w[i] * *col_mean_i;
|
||||
}
|
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|
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b = y.mean() - b;
|
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(w, b)
|
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b = TX::from_f64(y.mean_by()).unwrap() - b;
|
||||
(X::from_column(&w), b)
|
||||
} else {
|
||||
let mut optimizer = InteriorPointOptimizer::new(x, p);
|
||||
|
||||
let w = optimizer.optimize(x, y, l1_reg, parameters.max_iter, parameters.tol)?;
|
||||
let w = optimizer.optimize(
|
||||
x,
|
||||
&y,
|
||||
l1_reg,
|
||||
parameters.max_iter,
|
||||
TX::from_f64(parameters.tol).unwrap(),
|
||||
)?;
|
||||
|
||||
(w, y.mean())
|
||||
(X::from_column(&w), TX::from_f64(y.mean_by()).unwrap())
|
||||
};
|
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|
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Ok(Lasso {
|
||||
intercept: b,
|
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coefficients: w,
|
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intercept: Some(b),
|
||||
coefficients: Some(w),
|
||||
_phantom_ty: PhantomData,
|
||||
_phantom_y: PhantomData,
|
||||
})
|
||||
}
|
||||
|
||||
/// Predict target values from `x`
|
||||
/// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
|
||||
pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
|
||||
pub fn predict(&self, x: &X) -> Result<Y, Failed> {
|
||||
let (nrows, _) = x.shape();
|
||||
let mut y_hat = x.matmul(&self.coefficients);
|
||||
y_hat.add_mut(&M::fill(nrows, 1, self.intercept));
|
||||
Ok(y_hat.transpose().to_row_vector())
|
||||
let mut y_hat = x.matmul(self.coefficients());
|
||||
let bias = X::fill(nrows, 1, self.intercept.unwrap());
|
||||
y_hat.add_mut(&bias);
|
||||
Ok(Y::from_iterator(
|
||||
y_hat.iterator(0).map(|&v| TY::from(v).unwrap()),
|
||||
nrows,
|
||||
))
|
||||
}
|
||||
|
||||
/// Get estimates regression coefficients
|
||||
pub fn coefficients(&self) -> &M {
|
||||
&self.coefficients
|
||||
pub fn coefficients(&self) -> &X {
|
||||
self.coefficients.as_ref().unwrap()
|
||||
}
|
||||
|
||||
/// Get estimate of intercept
|
||||
pub fn intercept(&self) -> T {
|
||||
self.intercept
|
||||
pub fn intercept(&self) -> &TX {
|
||||
self.intercept.as_ref().unwrap()
|
||||
}
|
||||
|
||||
fn rescale_x(x: &M) -> Result<(M, Vec<T>, Vec<T>), Failed> {
|
||||
let col_mean = x.mean(0);
|
||||
let col_std = x.std(0);
|
||||
fn rescale_x(x: &X) -> Result<(X, Vec<TX>, Vec<TX>), Failed> {
|
||||
let col_mean: Vec<TX> = x
|
||||
.mean_by(0)
|
||||
.iter()
|
||||
.map(|&v| TX::from_f64(v).unwrap())
|
||||
.collect();
|
||||
let col_std: Vec<TX> = x
|
||||
.std_dev(0)
|
||||
.iter()
|
||||
.map(|&v| TX::from_f64(v).unwrap())
|
||||
.collect();
|
||||
|
||||
for (i, col_std_i) in col_std.iter().enumerate() {
|
||||
if (*col_std_i - T::zero()).abs() < T::epsilon() {
|
||||
if (*col_std_i - TX::zero()).abs() < TX::epsilon() {
|
||||
return Err(Failed::fit(&format!(
|
||||
"Cannot rescale constant column {}",
|
||||
i
|
||||
@@ -327,7 +372,7 @@ impl<T: RealNumber, M: Matrix<T>> Lasso<T, M> {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::linalg::naive::dense_matrix::*;
|
||||
use crate::linalg::basic::matrix::DenseMatrix;
|
||||
use crate::metrics::mean_absolute_error;
|
||||
|
||||
#[test]
|
||||
@@ -402,39 +447,40 @@ mod tests {
|
||||
assert!(mean_absolute_error(&y_hat, &y) < 2.0);
|
||||
}
|
||||
|
||||
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||||
#[test]
|
||||
#[cfg(feature = "serde")]
|
||||
fn serde() {
|
||||
let x = DenseMatrix::from_2d_array(&[
|
||||
&[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
|
||||
&[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
|
||||
&[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
|
||||
&[284.599, 335.1, 165.0, 110.929, 1950., 61.187],
|
||||
&[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
|
||||
&[346.999, 193.2, 359.4, 113.270, 1952., 63.639],
|
||||
&[365.385, 187.0, 354.7, 115.094, 1953., 64.989],
|
||||
&[363.112, 357.8, 335.0, 116.219, 1954., 63.761],
|
||||
&[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
|
||||
&[419.180, 282.2, 285.7, 118.734, 1956., 67.857],
|
||||
&[442.769, 293.6, 279.8, 120.445, 1957., 68.169],
|
||||
&[444.546, 468.1, 263.7, 121.950, 1958., 66.513],
|
||||
&[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
|
||||
&[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
|
||||
&[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
|
||||
&[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
|
||||
]);
|
||||
// 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(&[
|
||||
// &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
|
||||
// &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
|
||||
// &[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
|
||||
// &[284.599, 335.1, 165.0, 110.929, 1950., 61.187],
|
||||
// &[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
|
||||
// &[346.999, 193.2, 359.4, 113.270, 1952., 63.639],
|
||||
// &[365.385, 187.0, 354.7, 115.094, 1953., 64.989],
|
||||
// &[363.112, 357.8, 335.0, 116.219, 1954., 63.761],
|
||||
// &[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
|
||||
// &[419.180, 282.2, 285.7, 118.734, 1956., 67.857],
|
||||
// &[442.769, 293.6, 279.8, 120.445, 1957., 68.169],
|
||||
// &[444.546, 468.1, 263.7, 121.950, 1958., 66.513],
|
||||
// &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
|
||||
// &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
|
||||
// &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
|
||||
// &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
|
||||
// ]);
|
||||
|
||||
let y = vec![
|
||||
83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
|
||||
114.2, 115.7, 116.9,
|
||||
];
|
||||
// let y = vec![
|
||||
// 83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
|
||||
// 114.2, 115.7, 116.9,
|
||||
// ];
|
||||
|
||||
let lr = Lasso::fit(&x, &y, Default::default()).unwrap();
|
||||
// let lr = Lasso::fit(&x, &y, Default::default()).unwrap();
|
||||
|
||||
let deserialized_lr: Lasso<f64, DenseMatrix<f64>> =
|
||||
serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
|
||||
// let deserialized_lr: Lasso<f64, f64, DenseMatrix<f64>, Vec<f64>> =
|
||||
// serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
|
||||
|
||||
assert_eq!(lr, deserialized_lr);
|
||||
}
|
||||
// assert_eq!(lr, deserialized_lr);
|
||||
// }
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user