use std::fmt::Debug;
use std::marker::PhantomData;
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::Failed;
use crate::linalg::basic::arrays::{Array1, Array2};
use crate::linalg::traits::cholesky::CholeskyDecomposable;
use crate::linalg::traits::svd::SVDDecomposable;
use crate::numbers::basenum::Number;
use crate::numbers::realnum::RealNumber;
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone, Eq, PartialEq)]
pub enum RidgeRegressionSolverName {
Cholesky,
SVD,
}
impl Default for RidgeRegressionSolverName {
fn default() -> Self {
RidgeRegressionSolverName::Cholesky
}
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct RidgeRegressionParameters<T: Number + RealNumber> {
pub solver: RidgeRegressionSolverName,
pub alpha: T,
pub normalize: bool,
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct RidgeRegressionSearchParameters<T: Number + RealNumber> {
#[cfg_attr(feature = "serde", serde(default))]
pub solver: Vec<RidgeRegressionSolverName>,
#[cfg_attr(feature = "serde", serde(default))]
pub alpha: Vec<T>,
#[cfg_attr(feature = "serde", serde(default))]
pub normalize: Vec<bool>,
}
pub struct RidgeRegressionSearchParametersIterator<T: Number + RealNumber> {
ridge_regression_search_parameters: RidgeRegressionSearchParameters<T>,
current_solver: usize,
current_alpha: usize,
current_normalize: usize,
}
impl<T: Number + RealNumber> IntoIterator for RidgeRegressionSearchParameters<T> {
type Item = RidgeRegressionParameters<T>;
type IntoIter = RidgeRegressionSearchParametersIterator<T>;
fn into_iter(self) -> Self::IntoIter {
RidgeRegressionSearchParametersIterator {
ridge_regression_search_parameters: self,
current_solver: 0,
current_alpha: 0,
current_normalize: 0,
}
}
}
impl<T: Number + RealNumber> Iterator for RidgeRegressionSearchParametersIterator<T> {
type Item = RidgeRegressionParameters<T>;
fn next(&mut self) -> Option<Self::Item> {
if self.current_alpha == self.ridge_regression_search_parameters.alpha.len()
&& self.current_solver == self.ridge_regression_search_parameters.solver.len()
{
return None;
}
let next = RidgeRegressionParameters {
solver: self.ridge_regression_search_parameters.solver[self.current_solver].clone(),
alpha: self.ridge_regression_search_parameters.alpha[self.current_alpha],
normalize: self.ridge_regression_search_parameters.normalize[self.current_normalize],
};
if self.current_alpha + 1 < self.ridge_regression_search_parameters.alpha.len() {
self.current_alpha += 1;
} else if self.current_solver + 1 < self.ridge_regression_search_parameters.solver.len() {
self.current_alpha = 0;
self.current_solver += 1;
} else if self.current_normalize + 1
< self.ridge_regression_search_parameters.normalize.len()
{
self.current_alpha = 0;
self.current_solver = 0;
self.current_normalize += 1;
} else {
self.current_alpha += 1;
self.current_solver += 1;
self.current_normalize += 1;
}
Some(next)
}
}
impl<T: Number + RealNumber> Default for RidgeRegressionSearchParameters<T> {
fn default() -> Self {
let default_params = RidgeRegressionParameters::default();
RidgeRegressionSearchParameters {
solver: vec![default_params.solver],
alpha: vec![default_params.alpha],
normalize: vec![default_params.normalize],
}
}
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug)]
pub struct RidgeRegression<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + CholeskyDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> {
coefficients: Option<X>,
intercept: Option<TX>,
_phantom_ty: PhantomData<TY>,
_phantom_y: PhantomData<Y>,
}
impl<T: Number + RealNumber> RidgeRegressionParameters<T> {
pub fn with_alpha(mut self, alpha: T) -> Self {
self.alpha = alpha;
self
}
pub fn with_solver(mut self, solver: RidgeRegressionSolverName) -> Self {
self.solver = solver;
self
}
pub fn with_normalize(mut self, normalize: bool) -> Self {
self.normalize = normalize;
self
}
}
impl<T: Number + RealNumber> Default for RidgeRegressionParameters<T> {
fn default() -> Self {
RidgeRegressionParameters {
solver: RidgeRegressionSolverName::default(),
alpha: T::from_f64(1.0).unwrap(),
normalize: true,
}
}
}
impl<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + CholeskyDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> PartialEq for RidgeRegression<TX, TY, X, Y>
{
fn eq(&self, other: &Self) -> bool {
self.intercept() == other.intercept()
&& self.coefficients().shape() == other.coefficients().shape()
&& self
.coefficients()
.iterator(0)
.zip(other.coefficients().iterator(0))
.all(|(&a, &b)| (a - b).abs() <= TX::epsilon())
}
}
impl<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + CholeskyDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> SupervisedEstimator<X, Y, RidgeRegressionParameters<TX>> for RidgeRegression<TX, TY, X, Y>
{
fn new() -> Self {
Self {
coefficients: Option::None,
intercept: Option::None,
_phantom_ty: PhantomData,
_phantom_y: PhantomData,
}
}
fn fit(x: &X, y: &Y, parameters: RidgeRegressionParameters<TX>) -> Result<Self, Failed> {
RidgeRegression::fit(x, y, parameters)
}
}
impl<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + CholeskyDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> Predictor<X, Y> for RidgeRegression<TX, TY, X, Y>
{
fn predict(&self, x: &X) -> Result<Y, Failed> {
self.predict(x)
}
}
impl<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + CholeskyDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> RidgeRegression<TX, TY, X, Y>
{
pub fn fit(
x: &X,
y: &Y,
parameters: RidgeRegressionParameters<TX>,
) -> Result<RidgeRegression<TX, TY, X, Y>, Failed> {
let (n, p) = x.shape();
if n <= p {
return Err(Failed::fit(
"Number of rows in X should be >= number of columns in X",
));
}
if y.shape() != n {
return Err(Failed::fit("Number of rows in X should = len(y)"));
}
let y_column = X::from_iterator(
y.iterator(0).map(|&v| TX::from(v).unwrap()),
y.shape(),
1,
0,
);
let (w, b) = if parameters.normalize {
let (scaled_x, col_mean, col_std) = Self::rescale_x(x)?;
let x_t = scaled_x.transpose();
let x_t_y = x_t.matmul(&y_column);
let mut x_t_x = x_t.matmul(&scaled_x);
for i in 0..p {
x_t_x.add_element_mut((i, i), parameters.alpha);
}
let mut w = match parameters.solver {
RidgeRegressionSolverName::Cholesky => x_t_x.cholesky_solve_mut(x_t_y)?,
RidgeRegressionSolverName::SVD => x_t_x.svd_solve_mut(x_t_y)?,
};
for (i, col_std_i) in col_std.iter().enumerate().take(p) {
w.set((i, 0), *w.get((i, 0)) / *col_std_i);
}
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;
}
let b = TX::from_f64(y.mean_by()).unwrap() - b;
(w, b)
} else {
let x_t = x.transpose();
let x_t_y = x_t.matmul(&y_column);
let mut x_t_x = x_t.matmul(x);
for i in 0..p {
x_t_x.add_element_mut((i, i), parameters.alpha);
}
let w = match parameters.solver {
RidgeRegressionSolverName::Cholesky => x_t_x.cholesky_solve_mut(x_t_y)?,
RidgeRegressionSolverName::SVD => x_t_x.svd_solve_mut(x_t_y)?,
};
(w, TX::zero())
};
Ok(RidgeRegression {
intercept: Some(b),
coefficients: Some(w),
_phantom_ty: PhantomData,
_phantom_y: PhantomData,
})
}
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 - TX::zero()).abs() < TX::epsilon() {
return Err(Failed::fit(&format!(
"Cannot rescale constant column {}",
i
)));
}
}
let mut scaled_x = x.clone();
scaled_x.scale_mut(&col_mean, &col_std, 0);
Ok((scaled_x, col_mean, col_std))
}
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(&X::fill(nrows, 1, self.intercept.unwrap()));
Ok(Y::from_iterator(
y_hat.iterator(0).map(|&v| TY::from(v).unwrap()),
nrows,
))
}
pub fn coefficients(&self) -> &X {
self.coefficients.as_ref().unwrap()
}
pub fn intercept(&self) -> &TX {
self.intercept.as_ref().unwrap()
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::basic::matrix::DenseMatrix;
use crate::metrics::mean_absolute_error;
#[test]
fn search_parameters() {
let parameters = RidgeRegressionSearchParameters {
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,
RidgeRegressionSolverName::Cholesky
);
assert!(iter.next().is_none());
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn ridge_fit_predict() {
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<f64> = 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_hat_cholesky = RidgeRegression::fit(
&x,
&y,
RidgeRegressionParameters {
solver: RidgeRegressionSolverName::Cholesky,
alpha: 0.1,
normalize: true,
},
)
.and_then(|lr| lr.predict(&x))
.unwrap();
assert!(mean_absolute_error(&y_hat_cholesky, &y) < 2.0);
let y_hat_svd = RidgeRegression::fit(
&x,
&y,
RidgeRegressionParameters {
solver: RidgeRegressionSolverName::SVD,
alpha: 0.1,
normalize: false,
},
)
.and_then(|lr| lr.predict(&x))
.unwrap();
assert!(mean_absolute_error(&y_hat_svd, &y) < 2.0);
}
}