use rand::Rng;
use std::default::Default;
use std::fmt::Debug;
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
use crate::api::{Predictor, SupervisedEstimator};
use crate::error::{Failed, FailedError};
use crate::linalg::basic::arrays::{Array1, Array2};
use crate::numbers::basenum::Number;
use crate::numbers::floatnum::FloatNumber;
use crate::rand_custom::get_rng_impl;
use crate::tree::decision_tree_regressor::{
DecisionTreeRegressor, DecisionTreeRegressorParameters,
};
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct RandomForestRegressorParameters {
#[cfg_attr(feature = "serde", serde(default))]
pub max_depth: Option<u16>,
#[cfg_attr(feature = "serde", serde(default))]
pub min_samples_leaf: usize,
#[cfg_attr(feature = "serde", serde(default))]
pub min_samples_split: usize,
#[cfg_attr(feature = "serde", serde(default))]
pub n_trees: usize,
#[cfg_attr(feature = "serde", serde(default))]
pub m: Option<usize>,
#[cfg_attr(feature = "serde", serde(default))]
pub keep_samples: bool,
#[cfg_attr(feature = "serde", serde(default))]
pub seed: u64,
}
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug)]
pub struct RandomForestRegressor<
TX: Number + FloatNumber + PartialOrd,
TY: Number,
X: Array2<TX>,
Y: Array1<TY>,
> {
trees: Option<Vec<DecisionTreeRegressor<TX, TY, X, Y>>>,
samples: Option<Vec<Vec<bool>>>,
}
impl RandomForestRegressorParameters {
pub fn with_max_depth(mut self, max_depth: u16) -> Self {
self.max_depth = Some(max_depth);
self
}
pub fn with_min_samples_leaf(mut self, min_samples_leaf: usize) -> Self {
self.min_samples_leaf = min_samples_leaf;
self
}
pub fn with_min_samples_split(mut self, min_samples_split: usize) -> Self {
self.min_samples_split = min_samples_split;
self
}
pub fn with_n_trees(mut self, n_trees: usize) -> Self {
self.n_trees = n_trees;
self
}
pub fn with_m(mut self, m: usize) -> Self {
self.m = Some(m);
self
}
pub fn with_keep_samples(mut self, keep_samples: bool) -> Self {
self.keep_samples = keep_samples;
self
}
pub fn with_seed(mut self, seed: u64) -> Self {
self.seed = seed;
self
}
}
impl Default for RandomForestRegressorParameters {
fn default() -> Self {
RandomForestRegressorParameters {
max_depth: Option::None,
min_samples_leaf: 1,
min_samples_split: 2,
n_trees: 10,
m: Option::None,
keep_samples: false,
seed: 0,
}
}
}
impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> PartialEq
for RandomForestRegressor<TX, TY, X, Y>
{
fn eq(&self, other: &Self) -> bool {
if self.trees.as_ref().unwrap().len() != other.trees.as_ref().unwrap().len() {
false
} else {
self.trees
.iter()
.zip(other.trees.iter())
.all(|(a, b)| a == b)
}
}
}
impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
SupervisedEstimator<X, Y, RandomForestRegressorParameters>
for RandomForestRegressor<TX, TY, X, Y>
{
fn new() -> Self {
Self {
trees: Option::None,
samples: Option::None,
}
}
fn fit(x: &X, y: &Y, parameters: RandomForestRegressorParameters) -> Result<Self, Failed> {
RandomForestRegressor::fit(x, y, parameters)
}
}
impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
Predictor<X, Y> for RandomForestRegressor<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 RandomForestRegressorSearchParameters {
#[cfg_attr(feature = "serde", serde(default))]
pub max_depth: Vec<Option<u16>>,
#[cfg_attr(feature = "serde", serde(default))]
pub min_samples_leaf: Vec<usize>,
#[cfg_attr(feature = "serde", serde(default))]
pub min_samples_split: Vec<usize>,
#[cfg_attr(feature = "serde", serde(default))]
pub n_trees: Vec<usize>,
#[cfg_attr(feature = "serde", serde(default))]
pub m: Vec<Option<usize>>,
#[cfg_attr(feature = "serde", serde(default))]
pub keep_samples: Vec<bool>,
#[cfg_attr(feature = "serde", serde(default))]
pub seed: Vec<u64>,
}
pub struct RandomForestRegressorSearchParametersIterator {
random_forest_regressor_search_parameters: RandomForestRegressorSearchParameters,
current_max_depth: usize,
current_min_samples_leaf: usize,
current_min_samples_split: usize,
current_n_trees: usize,
current_m: usize,
current_keep_samples: usize,
current_seed: usize,
}
impl IntoIterator for RandomForestRegressorSearchParameters {
type Item = RandomForestRegressorParameters;
type IntoIter = RandomForestRegressorSearchParametersIterator;
fn into_iter(self) -> Self::IntoIter {
RandomForestRegressorSearchParametersIterator {
random_forest_regressor_search_parameters: self,
current_max_depth: 0,
current_min_samples_leaf: 0,
current_min_samples_split: 0,
current_n_trees: 0,
current_m: 0,
current_keep_samples: 0,
current_seed: 0,
}
}
}
impl Iterator for RandomForestRegressorSearchParametersIterator {
type Item = RandomForestRegressorParameters;
fn next(&mut self) -> Option<Self::Item> {
if self.current_max_depth
== self
.random_forest_regressor_search_parameters
.max_depth
.len()
&& self.current_min_samples_leaf
== self
.random_forest_regressor_search_parameters
.min_samples_leaf
.len()
&& self.current_min_samples_split
== self
.random_forest_regressor_search_parameters
.min_samples_split
.len()
&& self.current_n_trees == self.random_forest_regressor_search_parameters.n_trees.len()
&& self.current_m == self.random_forest_regressor_search_parameters.m.len()
&& self.current_keep_samples
== self
.random_forest_regressor_search_parameters
.keep_samples
.len()
&& self.current_seed == self.random_forest_regressor_search_parameters.seed.len()
{
return None;
}
let next = RandomForestRegressorParameters {
max_depth: self.random_forest_regressor_search_parameters.max_depth
[self.current_max_depth],
min_samples_leaf: self
.random_forest_regressor_search_parameters
.min_samples_leaf[self.current_min_samples_leaf],
min_samples_split: self
.random_forest_regressor_search_parameters
.min_samples_split[self.current_min_samples_split],
n_trees: self.random_forest_regressor_search_parameters.n_trees[self.current_n_trees],
m: self.random_forest_regressor_search_parameters.m[self.current_m],
keep_samples: self.random_forest_regressor_search_parameters.keep_samples
[self.current_keep_samples],
seed: self.random_forest_regressor_search_parameters.seed[self.current_seed],
};
if self.current_max_depth + 1
< self
.random_forest_regressor_search_parameters
.max_depth
.len()
{
self.current_max_depth += 1;
} else if self.current_min_samples_leaf + 1
< self
.random_forest_regressor_search_parameters
.min_samples_leaf
.len()
{
self.current_max_depth = 0;
self.current_min_samples_leaf += 1;
} else if self.current_min_samples_split + 1
< self
.random_forest_regressor_search_parameters
.min_samples_split
.len()
{
self.current_max_depth = 0;
self.current_min_samples_leaf = 0;
self.current_min_samples_split += 1;
} else if self.current_n_trees + 1
< self.random_forest_regressor_search_parameters.n_trees.len()
{
self.current_max_depth = 0;
self.current_min_samples_leaf = 0;
self.current_min_samples_split = 0;
self.current_n_trees += 1;
} else if self.current_m + 1 < self.random_forest_regressor_search_parameters.m.len() {
self.current_max_depth = 0;
self.current_min_samples_leaf = 0;
self.current_min_samples_split = 0;
self.current_n_trees = 0;
self.current_m += 1;
} else if self.current_keep_samples + 1
< self
.random_forest_regressor_search_parameters
.keep_samples
.len()
{
self.current_max_depth = 0;
self.current_min_samples_leaf = 0;
self.current_min_samples_split = 0;
self.current_n_trees = 0;
self.current_m = 0;
self.current_keep_samples += 1;
} else if self.current_seed + 1 < self.random_forest_regressor_search_parameters.seed.len()
{
self.current_max_depth = 0;
self.current_min_samples_leaf = 0;
self.current_min_samples_split = 0;
self.current_n_trees = 0;
self.current_m = 0;
self.current_keep_samples = 0;
self.current_seed += 1;
} else {
self.current_max_depth += 1;
self.current_min_samples_leaf += 1;
self.current_min_samples_split += 1;
self.current_n_trees += 1;
self.current_m += 1;
self.current_keep_samples += 1;
self.current_seed += 1;
}
Some(next)
}
}
impl Default for RandomForestRegressorSearchParameters {
fn default() -> Self {
let default_params = RandomForestRegressorParameters::default();
RandomForestRegressorSearchParameters {
max_depth: vec![default_params.max_depth],
min_samples_leaf: vec![default_params.min_samples_leaf],
min_samples_split: vec![default_params.min_samples_split],
n_trees: vec![default_params.n_trees],
m: vec![default_params.m],
keep_samples: vec![default_params.keep_samples],
seed: vec![default_params.seed],
}
}
}
impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
RandomForestRegressor<TX, TY, X, Y>
{
pub fn fit(
x: &X,
y: &Y,
parameters: RandomForestRegressorParameters,
) -> Result<RandomForestRegressor<TX, TY, X, Y>, Failed> {
let (n_rows, num_attributes) = x.shape();
if n_rows != y.shape() {
return Err(Failed::fit("Number of rows in X should = len(y)"));
}
let mtry = parameters
.m
.unwrap_or((num_attributes as f64).sqrt().floor() as usize);
let mut rng = get_rng_impl(Some(parameters.seed));
let mut trees: Vec<DecisionTreeRegressor<TX, TY, X, Y>> = Vec::new();
let mut maybe_all_samples: Option<Vec<Vec<bool>>> = Option::None;
if parameters.keep_samples {
maybe_all_samples = Some(Vec::new());
}
for _ in 0..parameters.n_trees {
let samples: Vec<usize> =
RandomForestRegressor::<TX, TY, X, Y>::sample_with_replacement(n_rows, &mut rng);
if let Some(ref mut all_samples) = maybe_all_samples {
all_samples.push(samples.iter().map(|x| *x != 0).collect())
}
let params = DecisionTreeRegressorParameters {
max_depth: parameters.max_depth,
min_samples_leaf: parameters.min_samples_leaf,
min_samples_split: parameters.min_samples_split,
seed: Some(parameters.seed),
};
let tree = DecisionTreeRegressor::fit_weak_learner(x, y, samples, mtry, params)?;
trees.push(tree);
}
Ok(RandomForestRegressor {
trees: Some(trees),
samples: maybe_all_samples,
})
}
pub fn predict(&self, x: &X) -> Result<Y, Failed> {
let mut result = Y::zeros(x.shape().0);
let (n, _) = x.shape();
for i in 0..n {
result.set(i, self.predict_for_row(x, i));
}
Ok(result)
}
fn predict_for_row(&self, x: &X, row: usize) -> TY {
let n_trees = self.trees.as_ref().unwrap().len();
let mut result = TY::zero();
for tree in self.trees.as_ref().unwrap().iter() {
result += tree.predict_for_row(x, row);
}
result / TY::from_usize(n_trees).unwrap()
}
pub fn predict_oob(&self, x: &X) -> Result<Y, Failed> {
let (n, _) = x.shape();
if self.samples.is_none() {
Err(Failed::because(
FailedError::PredictFailed,
"Need samples=true for OOB predictions.",
))
} else if self.samples.as_ref().unwrap()[0].len() != n {
Err(Failed::because(
FailedError::PredictFailed,
"Prediction matrix must match matrix used in training for OOB predictions.",
))
} else {
let mut result = Y::zeros(n);
for i in 0..n {
result.set(i, self.predict_for_row_oob(x, i));
}
Ok(result)
}
}
fn predict_for_row_oob(&self, x: &X, row: usize) -> TY {
let mut n_trees = 0;
let mut result = TY::zero();
for (tree, samples) in self
.trees
.as_ref()
.unwrap()
.iter()
.zip(self.samples.as_ref().unwrap())
{
if !samples[row] {
result += tree.predict_for_row(x, row);
n_trees += 1;
}
}
result / TY::from(n_trees).unwrap()
}
fn sample_with_replacement(nrows: usize, rng: &mut impl Rng) -> Vec<usize> {
let mut samples = vec![0; nrows];
for _ in 0..nrows {
let xi = rng.gen_range(0..nrows);
samples[xi] += 1;
}
samples
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::basic::matrix::DenseMatrix;
use crate::metrics::mean_absolute_error;
#[test]
fn search_parameters() {
let parameters = RandomForestRegressorSearchParameters {
n_trees: vec![10, 100],
m: vec![None, Some(1)],
..Default::default()
};
let mut iter = parameters.into_iter();
let next = iter.next().unwrap();
assert_eq!(next.n_trees, 10);
assert_eq!(next.m, None);
let next = iter.next().unwrap();
assert_eq!(next.n_trees, 100);
assert_eq!(next.m, None);
let next = iter.next().unwrap();
assert_eq!(next.n_trees, 10);
assert_eq!(next.m, Some(1));
let next = iter.next().unwrap();
assert_eq!(next.n_trees, 100);
assert_eq!(next.m, Some(1));
assert!(iter.next().is_none());
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn fit_longley() {
let x = DenseMatrix::from_2d_array(&[
&[234.289, 235.6, 159., 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., 110.929, 1950., 61.187],
&[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
&[346.999, 193.2, 359.4, 113.27, 1952., 63.639],
&[365.385, 187., 354.7, 115.094, 1953., 64.989],
&[363.112, 357.8, 335., 116.219, 1954., 63.761],
&[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
&[419.18, 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, 121.95, 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_hat = RandomForestRegressor::fit(
&x,
&y,
RandomForestRegressorParameters {
max_depth: Option::None,
min_samples_leaf: 1,
min_samples_split: 2,
n_trees: 1000,
m: Option::None,
keep_samples: false,
seed: 87,
},
)
.and_then(|rf| rf.predict(&x))
.unwrap();
assert!(mean_absolute_error(&y, &y_hat) < 1.0);
}
#[test]
fn test_random_matrix_with_wrong_rownum() {
let x_rand: DenseMatrix<f64> = DenseMatrix::<f64>::rand(17, 200);
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 fail = RandomForestRegressor::fit(
&x_rand,
&y,
RandomForestRegressorParameters {
max_depth: Option::None,
min_samples_leaf: 1,
min_samples_split: 2,
n_trees: 1000,
m: Option::None,
keep_samples: false,
seed: 87,
},
);
assert!(fail.is_err());
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
fn fit_predict_longley_oob() {
let x = DenseMatrix::from_2d_array(&[
&[234.289, 235.6, 159., 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., 110.929, 1950., 61.187],
&[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
&[346.999, 193.2, 359.4, 113.27, 1952., 63.639],
&[365.385, 187., 354.7, 115.094, 1953., 64.989],
&[363.112, 357.8, 335., 116.219, 1954., 63.761],
&[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
&[419.18, 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, 121.95, 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 regressor = RandomForestRegressor::fit(
&x,
&y,
RandomForestRegressorParameters {
max_depth: Option::None,
min_samples_leaf: 1,
min_samples_split: 2,
n_trees: 1000,
m: Option::None,
keep_samples: true,
seed: 87,
},
)
.unwrap();
let y_hat = regressor.predict(&x).unwrap();
let y_hat_oob = regressor.predict_oob(&x).unwrap();
println!("{:?}", mean_absolute_error(&y, &y_hat));
println!("{:?}", mean_absolute_error(&y, &y_hat_oob));
assert!(mean_absolute_error(&y, &y_hat) < mean_absolute_error(&y, &y_hat_oob));
}
#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
)]
#[test]
#[cfg(feature = "serde")]
fn serde() {
let x = DenseMatrix::from_2d_array(&[
&[234.289, 235.6, 159., 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., 110.929, 1950., 61.187],
&[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
&[346.999, 193.2, 359.4, 113.27, 1952., 63.639],
&[365.385, 187., 354.7, 115.094, 1953., 64.989],
&[363.112, 357.8, 335., 116.219, 1954., 63.761],
&[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
&[419.18, 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, 121.95, 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 forest = RandomForestRegressor::fit(&x, &y, Default::default()).unwrap();
let deserialized_forest: RandomForestRegressor<f64, f64, DenseMatrix<f64>, Vec<f64>> =
bincode::deserialize(&bincode::serialize(&forest).unwrap()).unwrap();
assert_eq!(forest, deserialized_forest);
}
}