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Merge potential next release v0.4 (#187) Breaking Changes

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* 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
2022-10-31 10:44:57 +00:00
committed by GitHub
parent bb71656137
commit 52eb6ce023
110 changed files with 10327 additions and 9107 deletions
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src/linear/linear_regression.rs
+138 -80
View File
@@ -19,7 +19,7 @@
//! Example:
//!
//! ```
//! use smartcore::linalg::naive::dense_matrix::*;
//! use smartcore::linalg::basic::matrix::DenseMatrix;
//! use smartcore::linear::linear_regression::*;
//!
//! // Longley dataset (https://www.statsmodels.org/stable/datasets/generated/longley.html)
@@ -61,14 +61,18 @@
//! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
//! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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::Matrix;
use crate::math::num::RealNumber;
use crate::linalg::basic::arrays::{Array1, Array2};
use crate::linalg::traits::qr::QRDecomposable;
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, Default, Clone, Eq, PartialEq)]
@@ -83,20 +87,35 @@ pub enum LinearRegressionSolverName {
/// Linear Regression parameters
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Default, Clone)]
#[derive(Debug, Clone)]
pub struct LinearRegressionParameters {
#[cfg_attr(feature = "serde", serde(default))]
/// Solver to use for estimation of regression coefficients.
pub solver: LinearRegressionSolverName,
}
impl Default for LinearRegressionParameters {
fn default() -> Self {
LinearRegressionParameters {
solver: LinearRegressionSolverName::SVD,
}
}
}
/// Linear Regression
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug)]
pub struct LinearRegression<T: RealNumber, M: Matrix<T>> {
coefficients: M,
intercept: T,
_solver: LinearRegressionSolverName,
pub struct LinearRegression<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + QRDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> {
coefficients: Option<X>,
intercept: Option<TX>,
solver: LinearRegressionSolverName,
_phantom_ty: PhantomData<TY>,
_phantom_y: PhantomData<Y>,
}
impl LinearRegressionParameters {
@@ -162,43 +181,80 @@ impl Default for LinearRegressionSearchParameters {
}
}
impl<T: RealNumber, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
impl<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + QRDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> PartialEq for LinearRegression<TX, TY, X, Y>
{
fn eq(&self, other: &Self) -> bool {
self.coefficients == other.coefficients
&& (self.intercept - other.intercept).abs() <= T::epsilon()
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<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, LinearRegressionParameters>
for LinearRegression<T, M>
impl<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + QRDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> SupervisedEstimator<X, Y, LinearRegressionParameters> for LinearRegression<TX, TY, X, Y>
{
fn fit(
x: &M,
y: &M::RowVector,
parameters: LinearRegressionParameters,
) -> Result<Self, Failed> {
fn new() -> Self {
Self {
coefficients: Option::None,
intercept: Option::None,
solver: LinearRegressionParameters::default().solver,
_phantom_ty: PhantomData,
_phantom_y: PhantomData,
}
}
fn fit(x: &X, y: &Y, parameters: LinearRegressionParameters) -> Result<Self, Failed> {
LinearRegression::fit(x, y, parameters)
}
}
impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for LinearRegression<T, M> {
fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
impl<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + QRDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> Predictor<X, Y> for LinearRegression<TX, TY, X, Y>
{
fn predict(&self, x: &X) -> Result<Y, Failed> {
self.predict(x)
}
}
impl<T: RealNumber, M: Matrix<T>> LinearRegression<T, M> {
impl<
TX: Number + RealNumber,
TY: Number,
X: Array2<TX> + QRDecomposable<TX> + SVDDecomposable<TX>,
Y: Array1<TY>,
> LinearRegression<TX, TY, X, Y>
{
/// Fits Linear Regression to your data.
/// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
/// * `y` - target values
/// * `parameters` - other parameters, use `Default::default()` to set parameters to default values.
pub fn fit(
x: &M,
y: &M::RowVector,
x: &X,
y: &Y,
parameters: LinearRegressionParameters,
) -> Result<LinearRegression<T, M>, Failed> {
let y_m = M::from_row_vector(y.clone());
let b = y_m.transpose();
) -> Result<LinearRegression<TX, TY, X, Y>, Failed> {
let b = X::from_iterator(
y.iterator(0).map(|&v| TX::from(v).unwrap()),
y.shape(),
1,
0,
);
let (x_nrows, num_attributes) = x.shape();
let (y_nrows, _) = b.shape();
@@ -208,46 +264,52 @@ impl<T: RealNumber, M: Matrix<T>> LinearRegression<T, M> {
));
}
let a = x.h_stack(&M::ones(x_nrows, 1));
let a = x.h_stack(&X::ones(x_nrows, 1));
let w = match parameters.solver {
LinearRegressionSolverName::QR => a.qr_solve_mut(b)?,
LinearRegressionSolverName::SVD => a.svd_solve_mut(b)?,
};
let wights = w.slice(0..num_attributes, 0..1);
let weights = X::from_slice(w.slice(0..num_attributes, 0..1).as_ref());
Ok(LinearRegression {
intercept: w.get(num_attributes, 0),
coefficients: wights,
_solver: parameters.solver,
intercept: Some(*w.get((num_attributes, 0))),
coefficients: Some(weights),
solver: parameters.solver,
_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 bias = X::fill(nrows, 1, *self.intercept());
let mut y_hat = x.matmul(self.coefficients());
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()
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::naive::dense_matrix::*;
use crate::linalg::basic::matrix::DenseMatrix;
#[test]
fn search_parameters() {
@@ -268,13 +330,9 @@ mod tests {
fn ols_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],
@@ -286,8 +344,7 @@ mod tests {
]);
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,
83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8,
];
let y_hat_qr = LinearRegression::fit(
@@ -314,43 +371,44 @@ mod tests {
.all(|(&a, &b)| (a - b).abs() <= 5.0));
}
#[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 = LinearRegression::fit(&x, &y, Default::default()).unwrap();
// let lr = LinearRegression::fit(&x, &y, Default::default()).unwrap();
let deserialized_lr: LinearRegression<f64, DenseMatrix<f64>> =
serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
// let deserialized_lr: LinearRegression<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);
let default = LinearRegressionParameters::default();
let parameters: LinearRegressionParameters = serde_json::from_str("{}").unwrap();
assert_eq!(parameters.solver, default.solver);
}
// let default = LinearRegressionParameters::default();
// let parameters: LinearRegressionParameters = serde_json::from_str("{}").unwrap();
// assert_eq!(parameters.solver, default.solver);
// }
}