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feat: adds serialization/deserialization methods

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This commit is contained in:
Volodymyr Orlov
2020-04-03 11:12:15 -07:00
parent 5766364311
commit eb0c36223f
16 changed files with 555 additions and 159 deletions
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src/algorithm/neighbour/bbd_tree.rs
+3 -3
View File
@@ -2,7 +2,7 @@ use std::fmt::Debug;
use crate::math::num::FloatExt;
use crate::linalg::Matrix;
use crate::math::distance::euclidian;
use crate::math::distance::euclidian::*;
#[derive(Debug)]
pub struct BBDTree<T: FloatExt> {
@@ -79,10 +79,10 @@ impl<T: FloatExt> BBDTree<T> {
let d = centroids[0].len();
// Determine which mean the node mean is closest to
let mut min_dist = euclidian::squared_distance(&self.nodes[node].center, &centroids[candidates[0]]);
let mut min_dist = Euclidian::squared_distance(&self.nodes[node].center, &centroids[candidates[0]]);
let mut closest = candidates[0];
for i in 1..k {
let dist = euclidian::squared_distance(&self.nodes[node].center, &centroids[candidates[i]]);
let dist = Euclidian::squared_distance(&self.nodes[node].center, &centroids[candidates[i]]);
if dist < min_dist {
min_dist = dist;
closest = candidates[i];
src/algorithm/neighbour/cover_tree.rs
+43 -42
View File
@@ -3,25 +3,26 @@ use std::iter::FromIterator;
use std::fmt::Debug;
use core::hash::{Hash, Hasher};
use serde::{Serialize, Deserialize};
use crate::math::num::FloatExt;
use crate::algorithm::neighbour::KNNAlgorithm;
use crate::math::distance::Distance;
use crate::algorithm::sort::heap_select::HeapSelect;
pub struct CoverTree<'a, T, F: FloatExt>
where T: Debug
#[derive(Serialize, Deserialize, Debug)]
pub struct CoverTree<T, F: FloatExt, D: Distance<T, F>>
{
base: F,
max_level: i8,
min_level: i8,
distance: &'a dyn Fn(&T, &T) -> F,
distance: D,
nodes: Vec<Node<T>>
}
impl<'a, T, F: FloatExt> CoverTree<'a, T, F>
where T: Debug
impl<T: Debug, F: FloatExt, D: Distance<T, F>> CoverTree<T, F, D>
{
pub fn new(mut data: Vec<T>, distance: &'a dyn Fn(&T, &T) -> F) -> CoverTree<T, F> {
pub fn new(mut data: Vec<T>, distance: D) -> CoverTree<T, F, D> {
let mut tree = CoverTree {
base: F::two(),
max_level: 100,
@@ -43,7 +44,7 @@ where T: Debug
} else {
let mut parent: Option<NodeId> = Option::None;
let mut p_i = 0;
let mut qi_p_ds = vec!((self.root(), (self.distance)(&p, &self.root().data)));
let mut qi_p_ds = vec!((self.root(), D::distance(&p, &self.root().data)));
let mut i = self.max_level;
loop {
let i_d = self.base.powf(F::from(i).unwrap());
@@ -82,6 +83,18 @@ where T: Debug
node_id
}
pub fn find(&self, p: &T, k: usize) -> Vec<usize>{
let mut qi_p_ds = vec!((self.root(), D::distance(&p, &self.root().data)));
for i in (self.min_level..self.max_level+1).rev() {
let i_d = self.base.powf(F::from(i).unwrap());
let mut q_p_ds = self.get_children_dist(&p, &qi_p_ds, i);
let d_p_q = self.min_k_by_distance(&mut q_p_ds, k);
qi_p_ds = q_p_ds.into_iter().filter(|(_, d)| d <= &(d_p_q + i_d)).collect();
}
qi_p_ds.sort_by(|(_, d1), (_, d2)| d1.partial_cmp(d2).unwrap());
qi_p_ds[..usize::min(qi_p_ds.len(), k)].iter().map(|(n, _)| n.index.index).collect()
}
fn split(&self, p_id: NodeId, r: F, s1: &mut Vec<T>, s2: Option<&mut Vec<T>>) -> (Vec<T>, Vec<T>){
let mut my_near = (Vec::new(), Vec::new());
@@ -102,7 +115,7 @@ where T: Debug
let p = &self.nodes.get(p_id.index).unwrap().data;
let mut i = 0;
while i != s.len() {
let d = (self.distance)(p, &s[i]);
let d = D::distance(p, &s[i]);
if d <= r {
my_near.0.push(s.remove(i));
} else if d > r && d <= F::two() * r{
@@ -156,7 +169,7 @@ where T: Debug
let q: Vec<&Node<T>> = qi_p_ds.iter().flat_map(|(n, _)| self.get_child(n, i)).collect();
children.extend(q.into_iter().map(|n| (n, (self.distance)(&n.data, &p))));
children.extend(q.into_iter().map(|n| (n, D::distance(&n.data, &p))));
children
@@ -180,7 +193,7 @@ where T: Debug
}
#[allow(dead_code)]
fn check_invariant(&self, invariant: fn(&CoverTree<T, F>, &Vec<&Node<T>>, &Vec<&Node<T>>, i8) -> ()) {
fn check_invariant(&self, invariant: fn(&CoverTree<T, F, D>, &Vec<&Node<T>>, &Vec<&Node<T>>, i8) -> ()) {
let mut current_nodes: Vec<&Node<T>> = Vec::new();
current_nodes.push(self.root());
for i in (self.min_level..self.max_level+1).rev() {
@@ -193,7 +206,7 @@ where T: Debug
}
#[allow(dead_code)]
fn nesting_invariant(_: &CoverTree<T, F>, nodes: &Vec<&Node<T>>, next_nodes: &Vec<&Node<T>>, _: i8) {
fn nesting_invariant(_: &CoverTree<T, F, D>, nodes: &Vec<&Node<T>>, next_nodes: &Vec<&Node<T>>, _: i8) {
let nodes_set: HashSet<&Node<T>> = HashSet::from_iter(nodes.into_iter().map(|n| *n));
let next_nodes_set: HashSet<&Node<T>> = HashSet::from_iter(next_nodes.into_iter().map(|n| *n));
for n in nodes_set.iter() {
@@ -202,11 +215,11 @@ where T: Debug
}
#[allow(dead_code)]
fn covering_tree(tree: &CoverTree<T, F>, nodes: &Vec<&Node<T>>, next_nodes: &Vec<&Node<T>>, i: i8) {
fn covering_tree(tree: &CoverTree<T, F, D>, nodes: &Vec<&Node<T>>, next_nodes: &Vec<&Node<T>>, i: i8) {
let mut p_selected: Vec<&Node<T>> = Vec::new();
for p in next_nodes {
for q in nodes {
if (tree.distance)(&p.data, &q.data) <= tree.base.powf(F::from(i).unwrap()) {
if D::distance(&p.data, &q.data) <= tree.base.powf(F::from(i).unwrap()) {
p_selected.push(*p);
}
}
@@ -216,11 +229,11 @@ where T: Debug
}
#[allow(dead_code)]
fn separation(tree: &CoverTree<T, F>, nodes: &Vec<&Node<T>>, _: &Vec<&Node<T>>, i: i8) {
fn separation(tree: &CoverTree<T, F, D>, nodes: &Vec<&Node<T>>, _: &Vec<&Node<T>>, i: i8) {
for p in nodes {
for q in nodes {
if p != q {
assert!((tree.distance)(&p.data, &q.data) > tree.base.powf(F::from(i).unwrap()));
assert!(D::distance(&p.data, &q.data) > tree.base.powf(F::from(i).unwrap()));
}
}
}
@@ -228,28 +241,12 @@ where T: Debug
}
impl<'a, T, F: FloatExt> KNNAlgorithm<T> for CoverTree<'a, T, F>
where T: Debug
{
fn find(&self, p: &T, k: usize) -> Vec<usize>{
let mut qi_p_ds = vec!((self.root(), (self.distance)(&p, &self.root().data)));
for i in (self.min_level..self.max_level+1).rev() {
let i_d = self.base.powf(F::from(i).unwrap());
let mut q_p_ds = self.get_children_dist(&p, &qi_p_ds, i);
let d_p_q = self.min_k_by_distance(&mut q_p_ds, k);
qi_p_ds = q_p_ds.into_iter().filter(|(_, d)| d <= &(d_p_q + i_d)).collect();
}
qi_p_ds.sort_by(|(_, d1), (_, d2)| d1.partial_cmp(d2).unwrap());
qi_p_ds[..usize::min(qi_p_ds.len(), k)].iter().map(|(n, _)| n.index.index).collect()
}
}
#[derive(Debug, Clone, Copy, PartialEq)]
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub struct NodeId {
index: usize,
}
#[derive(Debug)]
#[derive(Debug, Serialize, Deserialize)]
struct Node<T> {
index: NodeId,
data: T,
@@ -280,13 +277,19 @@ mod tests {
use super::*;
struct SimpleDistance{}
impl Distance<i32, f64> for SimpleDistance {
fn distance(a: &i32, b: &i32) -> f64 {
(a - b).abs() as f64
}
}
#[test]
fn cover_tree_test() {
let data = vec!(1, 2, 3, 4, 5, 6, 7, 8, 9);
let distance = |a: &i32, b: &i32| -> f64 {
(a - b).abs() as f64
};
let mut tree = CoverTree::<i32, f64>::new(data, &distance);
let mut tree = CoverTree::new(data, SimpleDistance{});
for d in vec!(10, 11, 12, 13, 14, 15, 16, 17, 18, 19) {
tree.insert(d);
}
@@ -306,10 +309,8 @@ mod tests {
#[test]
fn test_invariants(){
let data = vec!(1, 2, 3, 4, 5, 6, 7, 8, 9);
let distance = |a: &i32, b: &i32| -> f64 {
(a - b).abs() as f64
};
let tree = CoverTree::<i32, f64>::new(data, &distance);
let tree = CoverTree::new(data, SimpleDistance{});
tree.check_invariant(CoverTree::nesting_invariant);
tree.check_invariant(CoverTree::covering_tree);
tree.check_invariant(CoverTree::separation);
src/algorithm/neighbour/linear_search.rs
+32 -29
View File
@@ -1,16 +1,28 @@
use crate::algorithm::neighbour::KNNAlgorithm;
use crate::algorithm::sort::heap_select::HeapSelect;
use std::cmp::{Ordering, PartialOrd};
use num_traits::Float;
use std::marker::PhantomData;
use serde::{Serialize, Deserialize};
pub struct LinearKNNSearch<'a, T, F: Float> {
distance: Box<dyn Fn(&T, &T) -> F + 'a>,
data: Vec<T>
use crate::math::num::FloatExt;
use crate::math::distance::Distance;
use crate::algorithm::sort::heap_select::HeapSelect;
#[derive(Serialize, Deserialize, Debug)]
pub struct LinearKNNSearch<T, F: FloatExt, D: Distance<T, F>> {
distance: D,
data: Vec<T>,
f: PhantomData<F>
}
impl<'a, T, F: Float> KNNAlgorithm<T> for LinearKNNSearch<'a, T, F>
{
fn find(&self, from: &T, k: usize) -> Vec<usize> {
impl<T, F: FloatExt, D: Distance<T, F>> LinearKNNSearch<T, F, D> {
pub fn new(data: Vec<T>, distance: D) -> LinearKNNSearch<T, F, D>{
LinearKNNSearch{
data: data,
distance: distance,
f: PhantomData
}
}
pub fn find(&self, from: &T, k: usize) -> Vec<usize> {
if k < 1 || k > self.data.len() {
panic!("k should be >= 1 and <= length(data)");
}
@@ -19,14 +31,14 @@ impl<'a, T, F: Float> KNNAlgorithm<T> for LinearKNNSearch<'a, T, F>
for _ in 0..k {
heap.add(KNNPoint{
distance: Float::infinity(),
distance: F::infinity(),
index: None
});
}
for i in 0..self.data.len() {
let d = (self.distance)(&from, &self.data[i]);
let d = D::distance(&from, &self.data[i]);
let datum = heap.peek_mut();
if d < datum.distance {
datum.distance = d;
@@ -41,43 +53,34 @@ impl<'a, T, F: Float> KNNAlgorithm<T> for LinearKNNSearch<'a, T, F>
}
}
impl<'a, T, F: Float> LinearKNNSearch<'a, T, F> {
pub fn new(data: Vec<T>, distance: &'a dyn Fn(&T, &T) -> F) -> LinearKNNSearch<T, F>{
LinearKNNSearch{
data: data,
distance: Box::new(distance)
}
}
}
#[derive(Debug)]
struct KNNPoint<F: Float> {
struct KNNPoint<F: FloatExt> {
distance: F,
index: Option<usize>
}
impl<F: Float> PartialOrd for KNNPoint<F> {
impl<F: FloatExt> PartialOrd for KNNPoint<F> {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
self.distance.partial_cmp(&other.distance)
}
}
impl<F: Float> PartialEq for KNNPoint<F> {
impl<F: FloatExt> PartialEq for KNNPoint<F> {
fn eq(&self, other: &Self) -> bool {
self.distance == other.distance
}
}
impl<F: Float> Eq for KNNPoint<F> {}
impl<F: FloatExt> Eq for KNNPoint<F> {}
#[cfg(test)]
mod tests {
use super::*;
use crate::math::distance::euclidian;
use crate::math::distance::Distances;
struct SimpleDistance{}
impl SimpleDistance {
impl Distance<i32, f64> for SimpleDistance {
fn distance(a: &i32, b: &i32) -> f64 {
(a - b).abs() as f64
}
@@ -87,13 +90,13 @@ mod tests {
fn knn_find() {
let data1 = vec!(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
let algorithm1 = LinearKNNSearch::new(data1, &SimpleDistance::distance);
let algorithm1 = LinearKNNSearch::new(data1, SimpleDistance{});
assert_eq!(vec!(1, 2, 0), algorithm1.find(&2, 3));
let data2 = vec!(vec![1., 1.], vec![2., 2.], vec![3., 3.], vec![4., 4.], vec![5., 5.]);
let algorithm2 = LinearKNNSearch::new(data2, &euclidian::distance);
let algorithm2 = LinearKNNSearch::new(data2, Distances::euclidian());
assert_eq!(vec!(2, 3, 1), algorithm2.find(&vec![3., 3.], 3));
}
@@ -116,7 +119,7 @@ mod tests {
};
let point_inf = KNNPoint{
distance: Float::infinity(),
distance: std::f64::INFINITY,
index: Some(3)
};
src/algorithm/neighbour/mod.rs
+1 -10
View File
@@ -1,12 +1,3 @@
pub mod cover_tree;
pub mod linear_search;
pub mod bbd_tree;
pub enum KNNAlgorithmName {
CoverTree,
LinearSearch,
}
pub trait KNNAlgorithm<T>{
fn find(&self, from: &T, k: usize) -> Vec<usize>;
}
pub mod bbd_tree;
src/cluster/kmeans.rs
+4 -4
View File
@@ -8,7 +8,7 @@ use serde::{Serialize, Deserialize};
use crate::math::num::FloatExt;
use crate::linalg::Matrix;
use crate::math::distance::euclidian;
use crate::math::distance::euclidian::*;
use crate::algorithm::neighbour::bbd_tree::BBDTree;
#[derive(Serialize, Deserialize, Debug)]
@@ -130,7 +130,7 @@ impl<T: FloatExt + Sum> KMeans<T>{
let mut best_cluster = 0;
for j in 0..self.k {
let dist = euclidian::squared_distance(&x.get_row_as_vec(i), &self.centroids[j]);
let dist = Euclidian::squared_distance(&x.get_row_as_vec(i), &self.centroids[j]);
if dist < min_dist {
min_dist = dist;
best_cluster = j;
@@ -156,7 +156,7 @@ impl<T: FloatExt + Sum> KMeans<T>{
// the distance from each sample to its closest center in scores.
for i in 0..n {
// compute the distance between this sample and the current center
let dist = euclidian::squared_distance(&data.get_row_as_vec(i), &centroid);
let dist = Euclidian::squared_distance(&data.get_row_as_vec(i), &centroid);
if dist < d[i] {
d[i] = dist;
@@ -183,7 +183,7 @@ impl<T: FloatExt + Sum> KMeans<T>{
for i in 0..n {
// compute the distance between this sample and the current center
let dist = euclidian::squared_distance(&data.get_row_as_vec(i), &centroid);
let dist = Euclidian::squared_distance(&data.get_row_as_vec(i), &centroid);
if dist < d[i] {
d[i] = dist;
src/decomposition/pca.rs
+52 -1
View File
@@ -1,8 +1,11 @@
use std::fmt::Debug;
use serde::{Serialize, Deserialize};
use crate::math::num::FloatExt;
use crate::linalg::{Matrix};
#[derive(Debug)]
#[derive(Serialize, Deserialize, Debug)]
pub struct PCA<T: FloatExt, M: Matrix<T>> {
eigenvectors: M,
eigenvalues: Vec<T>,
@@ -11,6 +14,22 @@ pub struct PCA<T: FloatExt, M: Matrix<T>> {
pmu: Vec<T>
}
impl<T: FloatExt, M: Matrix<T>> PartialEq for PCA<T, M> {
fn eq(&self, other: &Self) -> bool {
if self.eigenvectors != other.eigenvectors ||
self.eigenvalues.len() != other.eigenvalues.len() {
return false
} else {
for i in 0..self.eigenvalues.len() {
if (self.eigenvalues[i] - other.eigenvalues[i]).abs() > T::epsilon() {
return false
}
}
return true
}
}
}
#[derive(Debug, Clone)]
pub struct PCAParameters {
use_correlation_matrix: bool
@@ -366,5 +385,37 @@ mod tests {
assert!(us_arrests_t.abs().approximate_eq(&expected_projection.abs(), 1e-4));
}
#[test]
fn serde() {
let iris = DenseMatrix::from_array(&[
&[5.1, 3.5, 1.4, 0.2],
&[4.9, 3.0, 1.4, 0.2],
&[4.7, 3.2, 1.3, 0.2],
&[4.6, 3.1, 1.5, 0.2],
&[5.0, 3.6, 1.4, 0.2],
&[5.4, 3.9, 1.7, 0.4],
&[4.6, 3.4, 1.4, 0.3],
&[5.0, 3.4, 1.5, 0.2],
&[4.4, 2.9, 1.4, 0.2],
&[4.9, 3.1, 1.5, 0.1],
&[7.0, 3.2, 4.7, 1.4],
&[6.4, 3.2, 4.5, 1.5],
&[6.9, 3.1, 4.9, 1.5],
&[5.5, 2.3, 4.0, 1.3],
&[6.5, 2.8, 4.6, 1.5],
&[5.7, 2.8, 4.5, 1.3],
&[6.3, 3.3, 4.7, 1.6],
&[4.9, 2.4, 3.3, 1.0],
&[6.6, 2.9, 4.6, 1.3],
&[5.2, 2.7, 3.9, 1.4]]);
let pca = PCA::new(&iris, 4, Default::default());
let deserialized_pca: PCA<f64, DenseMatrix<f64>> = serde_json::from_str(&serde_json::to_string(&pca).unwrap()).unwrap();
assert_eq!(pca, deserialized_pca);
}
}
src/ensemble/random_forest_classifier.rs
+57 -2
View File
@@ -4,12 +4,13 @@ use std::default::Default;
use std::fmt::Debug;
use rand::Rng;
use serde::{Serialize, Deserialize};
use crate::math::num::FloatExt;
use crate::linalg::Matrix;
use crate::tree::decision_tree_classifier::{DecisionTreeClassifier, DecisionTreeClassifierParameters, SplitCriterion, which_max};
#[derive(Debug, Clone)]
#[derive(Serialize, Deserialize, Debug, Clone)]
pub struct RandomForestClassifierParameters {
pub criterion: SplitCriterion,
pub max_depth: Option<u16>,
@@ -19,13 +20,34 @@ pub struct RandomForestClassifierParameters {
pub mtry: Option<usize>
}
#[derive(Debug)]
#[derive(Serialize, Deserialize, Debug)]
pub struct RandomForestClassifier<T: FloatExt> {
parameters: RandomForestClassifierParameters,
trees: Vec<DecisionTreeClassifier<T>>,
classes: Vec<T>
}
impl<T: FloatExt> PartialEq for RandomForestClassifier<T> {
fn eq(&self, other: &Self) -> bool {
if self.classes.len() != other.classes.len() ||
self.trees.len() != other.trees.len() {
return false
} else {
for i in 0..self.classes.len() {
if (self.classes[i] - other.classes[i]).abs() > T::epsilon() {
return false
}
}
for i in 0..self.trees.len() {
if self.trees[i] != other.trees[i] {
return false
}
}
true
}
}
}
impl Default for RandomForestClassifierParameters {
fn default() -> Self {
RandomForestClassifierParameters {
@@ -171,4 +193,37 @@ mod tests {
}
#[test]
fn serde() {
let x = DenseMatrix::from_array(&[
&[5.1, 3.5, 1.4, 0.2],
&[4.9, 3.0, 1.4, 0.2],
&[4.7, 3.2, 1.3, 0.2],
&[4.6, 3.1, 1.5, 0.2],
&[5.0, 3.6, 1.4, 0.2],
&[5.4, 3.9, 1.7, 0.4],
&[4.6, 3.4, 1.4, 0.3],
&[5.0, 3.4, 1.5, 0.2],
&[4.4, 2.9, 1.4, 0.2],
&[4.9, 3.1, 1.5, 0.1],
&[7.0, 3.2, 4.7, 1.4],
&[6.4, 3.2, 4.5, 1.5],
&[6.9, 3.1, 4.9, 1.5],
&[5.5, 2.3, 4.0, 1.3],
&[6.5, 2.8, 4.6, 1.5],
&[5.7, 2.8, 4.5, 1.3],
&[6.3, 3.3, 4.7, 1.6],
&[4.9, 2.4, 3.3, 1.0],
&[6.6, 2.9, 4.6, 1.3],
&[5.2, 2.7, 3.9, 1.4]]);
let y = vec![0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.];
let forest = RandomForestClassifier::fit(&x, &y, Default::default());
let deserialized_forest: RandomForestClassifier<f64> = bincode::deserialize(&bincode::serialize(&forest).unwrap()).unwrap();
assert_eq!(forest, deserialized_forest);
}
}
src/ensemble/random_forest_regressor.rs
+47 -2
View File
@@ -4,12 +4,13 @@ use std::default::Default;
use std::fmt::Debug;
use rand::Rng;
use serde::{Serialize, Deserialize};
use crate::math::num::FloatExt;
use crate::linalg::Matrix;
use crate::tree::decision_tree_regressor::{DecisionTreeRegressor, DecisionTreeRegressorParameters};
#[derive(Debug, Clone)]
#[derive(Serialize, Deserialize, Debug, Clone)]
pub struct RandomForestRegressorParameters {
pub max_depth: Option<u16>,
pub min_samples_leaf: usize,
@@ -18,7 +19,7 @@ pub struct RandomForestRegressorParameters {
pub mtry: Option<usize>
}
#[derive(Debug)]
#[derive(Serialize, Deserialize, Debug)]
pub struct RandomForestRegressor<T: FloatExt> {
parameters: RandomForestRegressorParameters,
trees: Vec<DecisionTreeRegressor<T>>
@@ -36,6 +37,21 @@ impl Default for RandomForestRegressorParameters {
}
}
impl<T: FloatExt> PartialEq for RandomForestRegressor<T> {
fn eq(&self, other: &Self) -> bool {
if self.trees.len() != other.trees.len() {
return false
} else {
for i in 0..self.trees.len() {
if self.trees[i] != other.trees[i] {
return false
}
}
true
}
}
}
impl<T: FloatExt> RandomForestRegressor<T> {
pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, parameters: RandomForestRegressorParameters) -> RandomForestRegressor<T> {
@@ -180,4 +196,33 @@ mod tests {
}
#[test]
fn serde() {
let x = DenseMatrix::from_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());
let deserialized_forest: RandomForestRegressor<f64> = bincode::deserialize(&bincode::serialize(&forest).unwrap()).unwrap();
assert_eq!(forest, deserialized_forest);
}
}
src/linear/linear_regression.rs
+1 -1
View File
@@ -21,7 +21,7 @@ pub struct LinearRegression<T: FloatExt, M: Matrix<T>> {
impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
fn eq(&self, other: &Self) -> bool {
self.coefficients == other.coefficients &&
self.intercept == other.intercept
(self.intercept - other.intercept).abs() <= T::epsilon()
}
}
src/linear/logistic_regression.rs
+13 -4
View File
@@ -42,10 +42,19 @@ struct BinaryObjectiveFunction<'a, T: FloatExt, M: Matrix<T>> {
impl<T: FloatExt, M: Matrix<T>> PartialEq for LogisticRegression<T, M> {
fn eq(&self, other: &Self) -> bool {
self.num_classes == other.num_classes &&
self.classes == other.classes &&
self.num_attributes == other.num_attributes &&
self.weights == other.weights
if self.num_classes != other.num_classes ||
self.num_attributes != other.num_attributes ||
self.classes.len() != other.classes.len() {
return false
} else {
for i in 0..self.classes.len() {
if (self.classes[i] - other.classes[i]).abs() > T::epsilon(){
return false
}
}
return self.weights == other.weights
}
}
}
src/math/distance/euclidian.rs
+29 -10
View File
@@ -1,20 +1,39 @@
use serde::{Serialize, Deserialize};
use crate::math::num::FloatExt;
pub fn distance<T: FloatExt>(x: &Vec<T>, y: &Vec<T>) -> T {
return squared_distance(x, y).sqrt();
use super::Distance;
#[derive(Serialize, Deserialize, Debug)]
pub struct Euclidian {
}
pub fn squared_distance<T: FloatExt>(x: &Vec<T>,y: &Vec<T>) -> T {
if x.len() != y.len() {
panic!("Input vector sizes are different.");
impl Euclidian {
pub fn squared_distance<T: FloatExt>(x: &Vec<T>,y: &Vec<T>) -> T {
if x.len() != y.len() {
panic!("Input vector sizes are different.");
}
let mut sum = T::zero();
for i in 0..x.len() {
sum = sum + (x[i] - y[i]).powf(T::two());
}
sum
}
let mut sum = T::zero();
for i in 0..x.len() {
sum = sum + (x[i] - y[i]).powf(T::two());
pub fn distance<T: FloatExt>(x: &Vec<T>, y: &Vec<T>) -> T {
Euclidian::squared_distance(x, y).sqrt()
}
}
impl<T: FloatExt> Distance<Vec<T>, T> for Euclidian {
fn distance(x: &Vec<T>, y: &Vec<T>) -> T {
Self::distance(x, y)
}
return sum;
}
@@ -27,7 +46,7 @@ mod tests {
let a = vec![1., 2., 3.];
let b = vec![4., 5., 6.];
let d_arr: f64 = distance(&a, &b);
let d_arr: f64 = Euclidian::distance(&a, &b);
assert!((d_arr - 5.19615242).abs() < 1e-8);
}
src/math/distance/mod.rs
+16 -1
View File
@@ -1 +1,16 @@
pub mod euclidian;
pub mod euclidian;
use crate::math::num::FloatExt;
pub trait Distance<T, F: FloatExt>{
fn distance(a: &T, b: &T) -> F;
}
pub struct Distances{
}
impl Distances {
pub fn euclidian() -> euclidian::Euclidian{
euclidian::Euclidian {}
}
}
src/neighbors/knn.rs
+88 -18
View File
@@ -1,26 +1,83 @@
use serde::{Serialize, Deserialize};
use crate::math::num::FloatExt;
use crate::math::distance::Distance;
use crate::linalg::{Matrix, row_iter};
use crate::algorithm::neighbour::{KNNAlgorithm, KNNAlgorithmName};
use crate::algorithm::neighbour::linear_search::LinearKNNSearch;
use crate::algorithm::neighbour::cover_tree::CoverTree;
pub struct KNNClassifier<'a, T: FloatExt> {
#[derive(Serialize, Deserialize, Debug)]
pub struct KNNClassifier<T: FloatExt, D: Distance<Vec<T>, T>> {
classes: Vec<T>,
y: Vec<usize>,
knn_algorithm: Box<dyn KNNAlgorithm<Vec<T>> + 'a>,
k: usize,
knn_algorithm: KNNAlgorithmV<T, D>,
k: usize
}
impl<'a, T: FloatExt> KNNClassifier<'a, T> {
pub enum KNNAlgorithmName {
LinearSearch,
CoverTree
}
pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, k: usize, distance: &'a dyn Fn(&Vec<T>, &Vec<T>) -> T, algorithm: KNNAlgorithmName) -> KNNClassifier<'a, T> {
#[derive(Serialize, Deserialize, Debug)]
pub enum KNNAlgorithmV<T: FloatExt, D: Distance<Vec<T>, T>> {
LinearSearch(LinearKNNSearch<Vec<T>, T, D>),
CoverTree(CoverTree<Vec<T>, T, D>)
}
impl KNNAlgorithmName {
fn fit<T: FloatExt, D: Distance<Vec<T>, T>>(&self, data: Vec<Vec<T>>, distance: D) -> KNNAlgorithmV<T, D> {
match *self {
KNNAlgorithmName::LinearSearch => KNNAlgorithmV::LinearSearch(LinearKNNSearch::new(data, distance)),
KNNAlgorithmName::CoverTree => KNNAlgorithmV::CoverTree(CoverTree::new(data, distance)),
}
}
}
impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNAlgorithmV<T, D> {
fn find(&self, from: &Vec<T>, k: usize) -> Vec<usize>{
match *self {
KNNAlgorithmV::LinearSearch(ref linear) => linear.find(from, k),
KNNAlgorithmV::CoverTree(ref cover) => cover.find(from, k)
}
}
}
impl<T: FloatExt, D: Distance<Vec<T>, T>> PartialEq for KNNClassifier<T, D> {
fn eq(&self, other: &Self) -> bool {
if self.classes.len() != other.classes.len() ||
self.k != other.k ||
self.y.len() != other.y.len() {
return false
} else {
for i in 0..self.classes.len() {
if (self.classes[i] - other.classes[i]).abs() > T::epsilon() {
return false
}
}
for i in 0..self.y.len() {
if self.y[i] != other.y[i] {
return false
}
}
true
}
}
}
impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
pub fn fit<M: Matrix<T>>(x: &M, y: &M::RowVector, k: usize, distance: D, algorithm: KNNAlgorithmName) -> KNNClassifier<T, D> {
let y_m = M::from_row_vector(y.clone());
let (_, y_n) = y_m.shape();
let (x_n, _) = x.shape();
let data = row_iter(x).collect();
let data = row_iter(x).collect();
let mut yi: Vec<usize> = vec![0; y_n];
let classes = y_m.unique();
@@ -32,14 +89,9 @@ impl<'a, T: FloatExt> KNNClassifier<'a, T> {
assert!(x_n == y_n, format!("Size of x should equal size of y; |x|=[{}], |y|=[{}]", x_n, y_n));
assert!(k > 1, format!("k should be > 1, k=[{}]", k));
assert!(k > 1, format!("k should be > 1, k=[{}]", k));
let knn_algorithm: Box<dyn KNNAlgorithm<Vec<T>> + 'a> = match algorithm {
KNNAlgorithmName::CoverTree => Box::new(CoverTree::<Vec<T>, T>::new(data, distance)),
KNNAlgorithmName::LinearSearch => Box::new(LinearKNNSearch::<Vec<T>, T>::new(data, distance))
};
KNNClassifier{classes:classes, y: yi, k: k, knn_algorithm: knn_algorithm}
KNNClassifier{classes:classes, y: yi, k: k, knn_algorithm: algorithm.fit(data, distance)}
}
@@ -74,8 +126,8 @@ impl<'a, T: FloatExt> KNNClassifier<'a, T> {
#[cfg(test)]
mod tests {
use super::*;
use crate::math::distance::euclidian;
use crate::linalg::naive::dense_matrix::DenseMatrix;
use crate::math::distance::Distances;
use crate::linalg::naive::dense_matrix::DenseMatrix;
#[test]
fn knn_fit_predict() {
@@ -85,10 +137,28 @@ mod tests {
&[5., 6.],
&[7., 8.],
&[9., 10.]]);
let y = vec![2., 2., 2., 3., 3.];
let knn = KNNClassifier::fit(&x, &y, 3, &euclidian::distance, KNNAlgorithmName::LinearSearch);
let y = vec![2., 2., 2., 3., 3.];
let knn = KNNClassifier::fit(&x, &y, 3, Distances::euclidian(), KNNAlgorithmName::LinearSearch);
let r = knn.predict(&x);
assert_eq!(5, Vec::len(&r));
assert_eq!(y.to_vec(), r);
}
#[test]
fn serde() {
let x = DenseMatrix::from_array(&[
&[1., 2.],
&[3., 4.],
&[5., 6.],
&[7., 8.],
&[9., 10.]]);
let y = vec![2., 2., 2., 3., 3.];
let knn = KNNClassifier::fit(&x, &y, 3, Distances::euclidian(), KNNAlgorithmName::CoverTree);
let deserialized_knn = bincode::deserialize(&bincode::serialize(&knn).unwrap()).unwrap();
assert_eq!(knn, deserialized_knn);
}
}
src/tree/decision_tree_classifier.rs
+89 -16
View File
@@ -3,11 +3,13 @@ use std::fmt::Debug;
use std::marker::PhantomData;
use std::collections::LinkedList;
use serde::{Serialize, Deserialize};
use crate::math::num::FloatExt;
use crate::linalg::Matrix;
use crate::algorithm::sort::quick_sort::QuickArgSort;
#[derive(Debug)]
#[derive(Serialize, Deserialize, Debug)]
pub struct DecisionTreeClassifierParameters {
pub criterion: SplitCriterion,
pub max_depth: Option<u16>,
@@ -15,7 +17,7 @@ pub struct DecisionTreeClassifierParameters {
pub min_samples_split: usize
}
#[derive(Debug)]
#[derive(Serialize, Deserialize, Debug)]
pub struct DecisionTreeClassifier<T: FloatExt> {
nodes: Vec<Node<T>>,
parameters: DecisionTreeClassifierParameters,
@@ -24,24 +26,62 @@ pub struct DecisionTreeClassifier<T: FloatExt> {
depth: u16
}
#[derive(Debug, Clone)]
#[derive(Serialize, Deserialize, Debug, Clone)]
pub enum SplitCriterion {
Gini,
Entropy,
ClassificationError
}
#[derive(Debug)]
#[derive(Serialize, Deserialize, Debug)]
pub struct Node<T: FloatExt> {
index: usize,
output: usize,
split_feature: usize,
split_value: T,
split_score: T,
split_value: Option<T>,
split_score: Option<T>,
true_child: Option<usize>,
false_child: Option<usize>,
}
impl<T: FloatExt> PartialEq for DecisionTreeClassifier<T> {
fn eq(&self, other: &Self) -> bool {
if self.depth != other.depth ||
self.num_classes != other.num_classes ||
self.nodes.len() != other.nodes.len(){
return false
} else {
for i in 0..self.classes.len() {
if (self.classes[i] - other.classes[i]).abs() > T::epsilon() {
return false
}
}
for i in 0..self.nodes.len() {
if self.nodes[i] != other.nodes[i] {
return false
}
}
return true
}
}
}
impl<T: FloatExt> PartialEq for Node<T> {
fn eq(&self, other: &Self) -> bool {
self.output == other.output &&
self.split_feature == other.split_feature &&
match (self.split_value, other.split_value) {
(Some(a), Some(b)) => (a - b).abs() < T::epsilon(),
(None, None) => true,
_ => false,
} &&
match (self.split_score, other.split_score) {
(Some(a), Some(b)) => (a - b).abs() < T::epsilon(),
(None, None) => true,
_ => false,
}
}
}
impl Default for DecisionTreeClassifierParameters {
fn default() -> Self {
@@ -60,8 +100,8 @@ impl<T: FloatExt> Node<T> {
index: index,
output: output,
split_feature: 0,
split_value: T::nan(),
split_score: T::nan(),
split_value: Option::None,
split_score: Option::None,
true_child: Option::None,
false_child: Option::None
}
@@ -238,7 +278,7 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
if node.true_child == None && node.false_child == None {
result = node.output;
} else {
if x.get(row, node.split_feature) <= node.split_value {
if x.get(row, node.split_feature) <= node.split_value.unwrap_or(T::nan()) {
queue.push_back(node.true_child.unwrap());
} else {
queue.push_back(node.false_child.unwrap());
@@ -299,7 +339,7 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
self.find_best_split(visitor, n, &count, &mut false_count, parent_impurity, variables[j]);
}
!self.nodes[visitor.node].split_score.is_nan()
self.nodes[visitor.node].split_score != Option::None
}
@@ -336,10 +376,10 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
let false_label = which_max(false_count);
let gain = parent_impurity - T::from(tc).unwrap() / T::from(n).unwrap() * impurity(&self.parameters.criterion, &true_count, tc) - T::from(fc).unwrap() / T::from(n).unwrap() * impurity(&self.parameters.criterion, &false_count, fc);
if self.nodes[visitor.node].split_score.is_nan() || gain > self.nodes[visitor.node].split_score {
if self.nodes[visitor.node].split_score == Option::None || gain > self.nodes[visitor.node].split_score.unwrap() {
self.nodes[visitor.node].split_feature = j;
self.nodes[visitor.node].split_value = (visitor.x.get(*i, j) + prevx) / T::two();
self.nodes[visitor.node].split_score = gain;
self.nodes[visitor.node].split_value = Option::Some((visitor.x.get(*i, j) + prevx) / T::two());
self.nodes[visitor.node].split_score = Option::Some(gain);
visitor.true_child_output = true_label;
visitor.false_child_output = false_label;
}
@@ -360,7 +400,7 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
for i in 0..n {
if visitor.samples[i] > 0 {
if visitor.x.get(i, self.nodes[visitor.node].split_feature) <= self.nodes[visitor.node].split_value {
if visitor.x.get(i, self.nodes[visitor.node].split_feature) <= self.nodes[visitor.node].split_value.unwrap_or(T::nan()) {
true_samples[i] = visitor.samples[i];
tc += true_samples[i];
visitor.samples[i] = 0;
@@ -372,8 +412,8 @@ impl<T: FloatExt> DecisionTreeClassifier<T> {
if tc < self.parameters.min_samples_leaf || fc < self.parameters.min_samples_leaf {
self.nodes[visitor.node].split_feature = 0;
self.nodes[visitor.node].split_value = T::nan();
self.nodes[visitor.node].split_score = T::nan();
self.nodes[visitor.node].split_value = Option::None;
self.nodes[visitor.node].split_score = Option::None;
return false;
}
@@ -477,4 +517,37 @@ mod tests {
assert_eq!(y, DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x));
}
#[test]
fn serde() {
let x = DenseMatrix::from_array(&[
&[1.,1.,1.,0.],
&[1.,1.,1.,0.],
&[1.,1.,1.,1.],
&[1.,1.,0.,0.],
&[1.,1.,0.,1.],
&[1.,0.,1.,0.],
&[1.,0.,1.,0.],
&[1.,0.,1.,1.],
&[1.,0.,0.,0.],
&[1.,0.,0.,1.],
&[0.,1.,1.,0.],
&[0.,1.,1.,0.],
&[0.,1.,1.,1.],
&[0.,1.,0.,0.],
&[0.,1.,0.,1.],
&[0.,0.,1.,0.],
&[0.,0.,1.,0.],
&[0.,0.,1.,1.],
&[0.,0.,0.,0.],
&[0.,0.,0.,1.]]);
let y = vec![1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0.];
let tree = DecisionTreeClassifier::fit(&x, &y, Default::default());
let deserialized_tree: DecisionTreeClassifier<f64> = bincode::deserialize(&bincode::serialize(&tree).unwrap()).unwrap();
assert_eq!(tree, deserialized_tree);
}
}
src/tree/decision_tree_regressor.rs
+78 -15
View File
@@ -2,31 +2,33 @@ use std::default::Default;
use std::fmt::Debug;
use std::collections::LinkedList;
use serde::{Serialize, Deserialize};
use crate::math::num::FloatExt;
use crate::linalg::Matrix;
use crate::algorithm::sort::quick_sort::QuickArgSort;
#[derive(Debug)]
#[derive(Serialize, Deserialize, Debug)]
pub struct DecisionTreeRegressorParameters {
pub max_depth: Option<u16>,
pub min_samples_leaf: usize,
pub min_samples_split: usize
}
#[derive(Debug)]
#[derive(Serialize, Deserialize, Debug)]
pub struct DecisionTreeRegressor<T: FloatExt> {
nodes: Vec<Node<T>>,
parameters: DecisionTreeRegressorParameters,
depth: u16
}
#[derive(Debug)]
#[derive(Serialize, Deserialize, Debug)]
pub struct Node<T: FloatExt> {
index: usize,
output: T,
split_feature: usize,
split_value: T,
split_score: T,
split_value: Option<T>,
split_score: Option<T>,
true_child: Option<usize>,
false_child: Option<usize>,
}
@@ -48,14 +50,46 @@ impl<T: FloatExt> Node<T> {
index: index,
output: output,
split_feature: 0,
split_value: T::nan(),
split_score: T::nan(),
split_value: Option::None,
split_score: Option::None,
true_child: Option::None,
false_child: Option::None
}
}
}
impl<T: FloatExt> PartialEq for Node<T> {
fn eq(&self, other: &Self) -> bool {
(self.output - other.output).abs() < T::epsilon() &&
self.split_feature == other.split_feature &&
match (self.split_value, other.split_value) {
(Some(a), Some(b)) => (a - b).abs() < T::epsilon(),
(None, None) => true,
_ => false,
} &&
match (self.split_score, other.split_score) {
(Some(a), Some(b)) => (a - b).abs() < T::epsilon(),
(None, None) => true,
_ => false,
}
}
}
impl<T: FloatExt> PartialEq for DecisionTreeRegressor<T> {
fn eq(&self, other: &Self) -> bool {
if self.depth != other.depth || self.nodes.len() != other.nodes.len(){
return false
} else {
for i in 0..self.nodes.len() {
if self.nodes[i] != other.nodes[i] {
return false
}
}
return true
}
}
}
struct NodeVisitor<'a, T: FloatExt, M: Matrix<T>> {
x: &'a M,
y: &'a M,
@@ -169,7 +203,7 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
if node.true_child == None && node.false_child == None {
result = node.output;
} else {
if x.get(row, node.split_feature) <= node.split_value {
if x.get(row, node.split_feature) <= node.split_value.unwrap_or(T::nan()) {
queue.push_back(node.true_child.unwrap());
} else {
queue.push_back(node.false_child.unwrap());
@@ -207,7 +241,7 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
self.find_best_split(visitor, n, sum, parent_gain, variables[j]);
}
!self.nodes[visitor.node].split_score.is_nan()
self.nodes[visitor.node].split_score != Option::None
}
@@ -240,10 +274,10 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
let gain = (T::from(true_count).unwrap() * true_mean * true_mean + T::from(false_count).unwrap() * false_mean * false_mean) - parent_gain;
if self.nodes[visitor.node].split_score.is_nan() || gain > self.nodes[visitor.node].split_score {
if self.nodes[visitor.node].split_score == Option::None || gain > self.nodes[visitor.node].split_score.unwrap() {
self.nodes[visitor.node].split_feature = j;
self.nodes[visitor.node].split_value = (visitor.x.get(*i, j) + prevx) / T::two();
self.nodes[visitor.node].split_score = gain;
self.nodes[visitor.node].split_value = Option::Some((visitor.x.get(*i, j) + prevx) / T::two());
self.nodes[visitor.node].split_score = Option::Some(gain);
visitor.true_child_output = true_mean;
visitor.false_child_output = false_mean;
}
@@ -264,7 +298,7 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
for i in 0..n {
if visitor.samples[i] > 0 {
if visitor.x.get(i, self.nodes[visitor.node].split_feature) <= self.nodes[visitor.node].split_value {
if visitor.x.get(i, self.nodes[visitor.node].split_feature) <= self.nodes[visitor.node].split_value.unwrap_or(T::nan()) {
true_samples[i] = visitor.samples[i];
tc += true_samples[i];
visitor.samples[i] = 0;
@@ -276,8 +310,8 @@ impl<T: FloatExt> DecisionTreeRegressor<T> {
if tc < self.parameters.min_samples_leaf || fc < self.parameters.min_samples_leaf {
self.nodes[visitor.node].split_feature = 0;
self.nodes[visitor.node].split_value = T::nan();
self.nodes[visitor.node].split_score = T::nan();
self.nodes[visitor.node].split_value = Option::None;
self.nodes[visitor.node].split_score = Option::None;
return false;
}
@@ -357,4 +391,33 @@ mod tests {
}
#[test]
fn serde() {
let x = DenseMatrix::from_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<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 tree = DecisionTreeRegressor::fit(&x, &y, Default::default());
let deserialized_tree: DecisionTreeRegressor<f64> = bincode::deserialize(&bincode::serialize(&tree).unwrap()).unwrap();
assert_eq!(tree, deserialized_tree);
}
}