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, ArrayView1};
use crate::linear::lasso_optimizer::InteriorPointOptimizer;
use crate::numbers::basenum::Number;
use crate::numbers::floatnum::FloatNumber;
use crate::numbers::realnum::RealNumber;
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct LassoParameters {
#[cfg_attr(feature = "serde", serde(default))]
pub alpha: f64,
#[cfg_attr(feature = "serde", serde(default))]
pub normalize: bool,
#[cfg_attr(feature = "serde", serde(default))]
pub tol: f64,
#[cfg_attr(feature = "serde", serde(default))]
pub max_iter: usize,
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug)]
pub struct Lasso<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> {
coefficients: Option<X>,
intercept: Option<TX>,
_phantom_ty: PhantomData<TY>,
_phantom_y: PhantomData<Y>,
}
impl LassoParameters {
pub fn with_alpha(mut self, alpha: f64) -> Self {
self.alpha = alpha;
self
}
pub fn with_normalize(mut self, normalize: bool) -> Self {
self.normalize = normalize;
self
}
pub fn with_tol(mut self, tol: f64) -> Self {
self.tol = tol;
self
}
pub fn with_max_iter(mut self, max_iter: usize) -> Self {
self.max_iter = max_iter;
self
}
}
impl Default for LassoParameters {
fn default() -> Self {
LassoParameters {
alpha: 1f64,
normalize: true,
tol: 1e-4,
max_iter: 1000,
}
}
}
impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> PartialEq
for Lasso<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: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>>
SupervisedEstimator<X, Y, LassoParameters> for Lasso<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: LassoParameters) -> Result<Self, Failed> {
Lasso::fit(x, y, parameters)
}
}
impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> Predictor<X, Y>
for Lasso<TX, TY, X, Y>
{
fn predict(&self, x: &X) -> Result<Y, Failed> {
self.predict(x)
}
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct LassoSearchParameters {
#[cfg_attr(feature = "serde", serde(default))]
pub alpha: Vec<f64>,
#[cfg_attr(feature = "serde", serde(default))]
pub normalize: Vec<bool>,
#[cfg_attr(feature = "serde", serde(default))]
pub tol: Vec<f64>,
#[cfg_attr(feature = "serde", serde(default))]
pub max_iter: Vec<usize>,
}
pub struct LassoSearchParametersIterator {
lasso_search_parameters: LassoSearchParameters,
current_alpha: usize,
current_normalize: usize,
current_tol: usize,
current_max_iter: usize,
}
impl IntoIterator for LassoSearchParameters {
type Item = LassoParameters;
type IntoIter = LassoSearchParametersIterator;
fn into_iter(self) -> Self::IntoIter {
LassoSearchParametersIterator {
lasso_search_parameters: self,
current_alpha: 0,
current_normalize: 0,
current_tol: 0,
current_max_iter: 0,
}
}
}
impl Iterator for LassoSearchParametersIterator {
type Item = LassoParameters;
fn next(&mut self) -> Option<Self::Item> {
if self.current_alpha == self.lasso_search_parameters.alpha.len()
&& self.current_normalize == self.lasso_search_parameters.normalize.len()
&& self.current_tol == self.lasso_search_parameters.tol.len()
&& self.current_max_iter == self.lasso_search_parameters.max_iter.len()
{
return None;
}
let next = LassoParameters {
alpha: self.lasso_search_parameters.alpha[self.current_alpha],
normalize: self.lasso_search_parameters.normalize[self.current_normalize],
tol: self.lasso_search_parameters.tol[self.current_tol],
max_iter: self.lasso_search_parameters.max_iter[self.current_max_iter],
};
if self.current_alpha + 1 < self.lasso_search_parameters.alpha.len() {
self.current_alpha += 1;
} else if self.current_normalize + 1 < self.lasso_search_parameters.normalize.len() {
self.current_alpha = 0;
self.current_normalize += 1;
} else if self.current_tol + 1 < self.lasso_search_parameters.tol.len() {
self.current_alpha = 0;
self.current_normalize = 0;
self.current_tol += 1;
} else if self.current_max_iter + 1 < self.lasso_search_parameters.max_iter.len() {
self.current_alpha = 0;
self.current_normalize = 0;
self.current_tol = 0;
self.current_max_iter += 1;
} else {
self.current_alpha += 1;
self.current_normalize += 1;
self.current_tol += 1;
self.current_max_iter += 1;
}
Some(next)
}
}
impl Default for LassoSearchParameters {
fn default() -> Self {
let default_params = LassoParameters::default();
LassoSearchParameters {
alpha: vec![default_params.alpha],
normalize: vec![default_params.normalize],
tol: vec![default_params.tol],
max_iter: vec![default_params.max_iter],
}
}
}
impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> Lasso<TX, TY, X, Y> {
pub fn fit(x: &X, y: &Y, parameters: LassoParameters) -> Result<Lasso<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 parameters.alpha < 0f64 {
return Err(Failed::fit("alpha should be >= 0"));
}
if parameters.tol <= 0f64 {
return Err(Failed::fit("tol should be > 0"));
}
if parameters.max_iter == 0 {
return Err(Failed::fit("max_iter should be > 0"));
}
if y.shape() != n {
return Err(Failed::fit("Number of rows in X should = len(y)"));
}
let y: Vec<TX> = y.iterator(0).map(|&v| TX::from(v).unwrap()).collect();
let l1_reg = TX::from_f64(parameters.alpha * n as f64).unwrap();
let (w, b) = if parameters.normalize {
let (scaled_x, col_mean, col_std) = Self::rescale_x(x)?;
let mut optimizer = InteriorPointOptimizer::new(&scaled_x, p);
let mut w = optimizer.optimize(
&scaled_x,
&y,
l1_reg,
parameters.max_iter,
TX::from_f64(parameters.tol).unwrap(),
)?;
for (j, col_std_j) in col_std.iter().enumerate().take(p) {
w[j] /= *col_std_j;
}
let mut b = TX::zero();
for (i, col_mean_i) in col_mean.iter().enumerate().take(p) {
b += w[i] * *col_mean_i;
}
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,
TX::from_f64(parameters.tol).unwrap(),
)?;
(X::from_column(&w), TX::from_f64(y.mean_by()).unwrap())
};
Ok(Lasso {
intercept: Some(b),
coefficients: Some(w),
_phantom_ty: PhantomData,
_phantom_y: PhantomData,
})
}
pub fn predict(&self, x: &X) -> Result<Y, Failed> {
let (nrows, _) = x.shape();
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,
))
}
pub fn coefficients(&self) -> &X {
self.coefficients.as_ref().unwrap()
}
pub fn intercept(&self) -> &TX {
self.intercept.as_ref().unwrap()
}
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))
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::basic::matrix::DenseMatrix;
use crate::metrics::mean_absolute_error;
#[test]
fn search_parameters() {
let parameters = LassoSearchParameters {
alpha: vec![0., 1.],
max_iter: vec![10, 100],
..Default::default()
};
let mut iter = parameters.into_iter();
let next = iter.next().unwrap();
assert_eq!(next.alpha, 0.);
assert_eq!(next.max_iter, 10);
let next = iter.next().unwrap();
assert_eq!(next.alpha, 1.);
assert_eq!(next.max_iter, 10);
let next = iter.next().unwrap();
assert_eq!(next.alpha, 0.);
assert_eq!(next.max_iter, 100);
let next = iter.next().unwrap();
assert_eq!(next.alpha, 1.);
assert_eq!(next.max_iter, 100);
assert!(iter.next().is_none());
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn lasso_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 = Lasso::fit(&x, &y, Default::default())
.and_then(|lr| lr.predict(&x))
.unwrap();
assert!(mean_absolute_error(&y_hat, &y) < 2.0);
let y_hat = Lasso::fit(
&x,
&y,
LassoParameters {
alpha: 0.1,
normalize: false,
tol: 1e-4,
max_iter: 1000,
},
)
.and_then(|lr| lr.predict(&x))
.unwrap();
assert!(mean_absolute_error(&y_hat, &y) < 2.0);
}
}