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Adds DecisionTree algorithm

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This commit is contained in:
Volodymyr Orlov
2020-01-10 09:07:04 -08:00
parent a4ff1cbe5f
commit a54f7be867
8 changed files with 631 additions and 38 deletions
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src/algorithm/sort/mod.rs
+2 -1
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@@ -1 +1,2 @@
pub mod heap_select;
pub mod heap_select;
pub mod quick_sort;
src/algorithm/sort/quick_sort.rs
+116
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@@ -0,0 +1,116 @@
pub trait QuickArgSort {
fn quick_argsort(&mut self) -> Vec<usize>;
}
impl QuickArgSort for Vec<f64> {
fn quick_argsort(&mut self) -> Vec<usize> {
let stack_size = 64;
let mut jstack = -1;
let mut l = 0;
let mut istack = vec![0; stack_size];
let mut ir = self.len() - 1;
let mut index: Vec<usize> = (0..self.len()).collect();
loop {
if ir - l < 7 {
for j in l + 1..=ir {
let a = self[j];
let b = index[j];
let mut i: i32 = (j - 1) as i32;
while i >= l as i32 {
if self[i as usize] <= a {
break;
}
self[(i + 1) as usize] = self[i as usize];
index[(i + 1) as usize] = index[i as usize];
i -= 1;
}
self[(i + 1) as usize] = a;
index[(i + 1) as usize] = b;
}
if jstack < 0 {
break;
}
ir = istack[jstack as usize];
jstack -= 1;
l = istack[jstack as usize];
jstack -= 1;
} else {
let k = (l + ir) >> 1;
self.swap(k, l + 1);
index.swap(k, l + 1);
if self[l] > self[ir] {
self.swap(l, ir);
index.swap(l, ir);
}
if self[l + 1] > self[ir] {
self.swap(l + 1, ir);
index.swap(l + 1, ir);
}
if self[l] > self[l + 1] {
self.swap(l, l + 1);
index.swap(l, l + 1);
}
let mut i = l + 1;
let mut j = ir;
let a = self[l + 1];
let b = index[l + 1];
loop {
loop {
i += 1;
if self[i] >= a {
break;
}
}
loop {
j -=1;
if self[j] <= a {
break;
}
}
if j < i {
break;
}
self.swap(i, j);
index.swap(i, j);
}
self[l + 1] = self[j];
self[j] = a;
index[l + 1] = index[j];
index[j] = b;
jstack += 2;
if jstack >= 64 {
panic!("stack size is too small.");
}
if ir - i + 1 >= j - l {
istack[jstack as usize] = ir;
istack[jstack as usize - 1] = i;
ir = j - 1;
} else {
istack[jstack as usize] = j - 1;
istack[jstack as usize - 1] = l;
l = i;
}
}
}
index
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn with_capacity() {
let mut arr1 = vec![0.3, 0.1, 0.2, 0.4, 0.9, 0.5, 0.7, 0.6, 0.8];
assert_eq!(vec![1, 2, 0, 3, 5, 7, 6, 8, 4], arr1.quick_argsort());
let mut arr2 = vec![0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4];
assert_eq!(vec![9, 7, 1, 8, 0, 2, 4, 3, 6, 5, 17, 18, 15, 13, 19, 10, 14, 11, 12, 16], arr2.quick_argsort());
}
}
src/classification/decision_tree.rs
+468
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@@ -0,0 +1,468 @@
use std::default::Default;
use std::collections::LinkedList;
use crate::linalg::Matrix;
use crate::algorithm::sort::quick_sort::QuickArgSort;
#[derive(Debug)]
pub struct DecisionTreeParameters {
criterion: SplitCriterion,
max_depth: Option<u16>,
min_samples_leaf: u16
}
#[derive(Debug)]
pub struct DecisionTree {
nodes: Vec<Node>,
parameters: DecisionTreeParameters,
num_classes: usize,
classes: Vec<f64>,
depth: u16
}
#[derive(Debug)]
pub enum SplitCriterion {
Gini,
Entropy,
ClassificationError
}
#[derive(Debug)]
pub struct Node {
index: usize,
output: usize,
split_feature: usize,
split_value: f64,
split_score: f64,
true_child: Option<usize>,
false_child: Option<usize>,
}
impl Default for DecisionTreeParameters {
fn default() -> Self {
DecisionTreeParameters {
criterion: SplitCriterion::Gini,
max_depth: None,
min_samples_leaf: 1
}
}
}
impl Node {
fn new(index: usize, output: usize) -> Self {
Node {
index: index,
output: output,
split_feature: 0,
split_value: std::f64::NAN,
split_score: std::f64::NAN,
true_child: Option::None,
false_child: Option::None
}
}
}
struct NodeVisitor<'a, M: Matrix> {
x: &'a M,
y: &'a Vec<usize>,
node: usize,
samples: Vec<u32>,
order: &'a Vec<Vec<usize>>,
true_child_output: usize,
false_child_output: usize,
level: u16
}
fn impurity(criterion: &SplitCriterion, count: &Vec<u32>, n: u32) -> f64 {
let mut impurity = 0.;
match criterion {
SplitCriterion::Gini => {
impurity = 1.0;
for i in 0..count.len() {
if count[i] > 0 {
let p = count[i] as f64 / n as f64;
impurity -= p * p;
}
}
}
SplitCriterion::Entropy => {
for i in 0..count.len() {
if count[i] > 0 {
let p = count[i] as f64 / n as f64;
impurity -= p * p.log2();
}
}
}
SplitCriterion::ClassificationError => {
for i in 0..count.len() {
if count[i] > 0 {
impurity = impurity.max(count[i] as f64 / n as f64);
}
}
impurity = (1. - impurity).abs();
}
}
return impurity;
}
impl<'a, M: Matrix> NodeVisitor<'a, M> {
fn new(node_id: usize, samples: Vec<u32>, order: &'a Vec<Vec<usize>>, x: &'a M, y: &'a Vec<usize>, level: u16) -> Self {
NodeVisitor {
x: x,
y: y,
node: node_id,
samples: samples,
order: order,
true_child_output: 0,
false_child_output: 0,
level: level
}
}
}
fn which_max(x: &Vec<u32>) -> usize {
let mut m = x[0];
let mut which = 0;
for i in 1..x.len() {
if x[i] > m {
m = x[i];
which = i;
}
}
return which;
}
impl DecisionTree {
pub fn fit<M: Matrix>(x: &M, y: &M::RowVector, parameters: DecisionTreeParameters) -> DecisionTree {
let y_m = M::from_row_vector(y.clone());
let (_, y_ncols) = y_m.shape();
let (x_nrows, num_attributes) = x.shape();
let classes = y_m.unique();
let k = classes.len();
if k < 2 {
panic!("Incorrect number of classes: {}. Should be >= 2.", k);
}
let mut yi: Vec<usize> = vec![0; y_ncols];
for i in 0..y_ncols {
let yc = y_m.get(0, i);
yi[i] = classes.iter().position(|c| yc == *c).unwrap();
}
let mut nodes: Vec<Node> = Vec::new();
let samples = vec![1; x_nrows];
let mut count = vec![0; k];
for i in 0..y_ncols {
count[yi[i]] += samples[i];
}
let root = Node::new(0, which_max(&count));
nodes.push(root);
let mut order: Vec<Vec<usize>> = Vec::new();
for i in 0..num_attributes {
order.push(x.get_col_as_vec(i).quick_argsort());
}
let mut tree = DecisionTree{
nodes: nodes,
parameters: parameters,
num_classes: k,
classes: classes,
depth: 0
};
let mut visitor = NodeVisitor::<M>::new(0, samples, &order, &x, &yi, 1);
let mut visitor_queue: LinkedList<NodeVisitor<M>> = LinkedList::new();
if tree.find_best_cutoff(&mut visitor) {
visitor_queue.push_back(visitor);
}
while tree.depth < tree.parameters.max_depth.unwrap_or(std::u16::MAX) {
match visitor_queue.pop_front() {
Some(node) => tree.split(node, &mut visitor_queue),
None => break
};
}
tree
}
pub fn predict<M: Matrix>(&self, x: &M) -> M::RowVector {
let mut result = M::zeros(1, x.shape().0);
let (n, _) = x.shape();
for i in 0..n {
result.set(0, i, self.classes[self.predict_for_row(x, i)]);
}
result.to_row_vector()
}
fn predict_for_row<M: Matrix>(&self, x: &M, row: usize) -> usize {
let mut result = 0;
let mut queue: LinkedList<usize> = LinkedList::new();
queue.push_back(0);
while !queue.is_empty() {
match queue.pop_front() {
Some(node_id) => {
let node = &self.nodes[node_id];
if node.true_child == None && node.false_child == None {
result = node.output;
} else {
if x.get(row, node.split_feature) <= node.split_value {
queue.push_back(node.true_child.unwrap());
} else {
queue.push_back(node.false_child.unwrap());
}
}
},
None => break
};
}
return result
}
fn find_best_cutoff<M: Matrix>(&mut self, visitor: &mut NodeVisitor<M>) -> bool {
let (n_rows, n_attr) = visitor.x.shape();
let mut label = Option::None;
let mut is_pure = true;
for i in 0..n_rows {
if visitor.samples[i] > 0 {
if label == Option::None {
label = Option::Some(visitor.y[i]);
} else if visitor.y[i] != label.unwrap() {
is_pure = false;
break;
}
}
}
if is_pure {
return false;
}
let n = visitor.samples.iter().sum();
if n <= self.parameters.min_samples_leaf as u32 {
return false;
}
let mut count = vec![0; self.num_classes];
let mut false_count = vec![0; self.num_classes];
for i in 0..n_rows {
if visitor.samples[i] > 0 {
count[visitor.y[i]] += visitor.samples[i];
}
}
let parent_impurity = impurity(&self.parameters.criterion, &count, n);
let mut variables = vec![0; n_attr];
for i in 0..n_attr {
variables[i] = i;
}
for j in 0..n_attr {
self.find_best_split(visitor, n, &count, &mut false_count, parent_impurity, variables[j]);
}
!self.nodes[visitor.node].split_score.is_nan()
}
fn find_best_split<M: Matrix>(&mut self, visitor: &mut NodeVisitor<M>, n: u32, count: &Vec<u32>, false_count: &mut Vec<u32>, parent_impurity: f64, j: usize){
let mut true_count = vec![0; self.num_classes];
let mut prevx = std::f64::NAN;
let mut prevy = 0;
let node_size = 1;
for i in visitor.order[j].iter() {
if visitor.samples[*i] > 0 {
if prevx.is_nan() || visitor.x.get(*i, j) == prevx || visitor.y[*i] == prevy {
prevx = visitor.x.get(*i, j);
prevy = visitor.y[*i];
true_count[visitor.y[*i]] += visitor.samples[*i];
continue;
}
let tc = true_count.iter().sum();
let fc = n - tc;
if tc < node_size || fc < node_size {
prevx = visitor.x.get(*i, j);
prevy = visitor.y[*i];
true_count[visitor.y[*i]] += visitor.samples[*i];
continue;
}
for l in 0..self.num_classes {
false_count[l] = count[l] - true_count[l];
}
let true_label = which_max(&true_count);
let false_label = which_max(false_count);
let gain = parent_impurity - tc as f64 / n as f64 * impurity(&self.parameters.criterion, &true_count, tc) - fc as f64 / n as f64 * impurity(&self.parameters.criterion, &false_count, fc);
if self.nodes[visitor.node].split_score.is_nan() || gain > self.nodes[visitor.node].split_score {
self.nodes[visitor.node].split_feature = j;
self.nodes[visitor.node].split_value = (visitor.x.get(*i, j) + prevx) / 2.;
self.nodes[visitor.node].split_score = gain;
visitor.true_child_output = true_label;
visitor.false_child_output = false_label;
}
prevx = visitor.x.get(*i, j);
prevy = visitor.y[*i];
true_count[visitor.y[*i]] += visitor.samples[*i];
}
}
}
fn split<'a, M: Matrix>(&mut self, mut visitor: NodeVisitor<'a, M>, visitor_queue: &mut LinkedList<NodeVisitor<'a, M>>) -> bool {
let (n, _) = visitor.x.shape();
let mut tc = 0;
let mut fc = 0;
let mut true_samples: Vec<u32> = vec![0; n];
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 {
true_samples[i] = visitor.samples[i];
tc += true_samples[i];
visitor.samples[i] = 0;
} else {
fc += visitor.samples[i];
}
}
}
if tc < self.parameters.min_samples_leaf as u32 || fc < self.parameters.min_samples_leaf as u32 {
self.nodes[visitor.node].split_feature = 0;
self.nodes[visitor.node].split_value = std::f64::NAN;
self.nodes[visitor.node].split_score = std::f64::NAN;
return false;
}
let true_child_idx = self.nodes.len();
self.nodes.push(Node::new(true_child_idx, visitor.true_child_output));
let false_child_idx = self.nodes.len();
self.nodes.push(Node::new(false_child_idx, visitor.false_child_output));
self.nodes[visitor.node].true_child = Some(true_child_idx);
self.nodes[visitor.node].false_child = Some(false_child_idx);
self.depth = u16::max(self.depth, visitor.level + 1);
let mut true_visitor = NodeVisitor::<M>::new(true_child_idx, true_samples, visitor.order, visitor.x, visitor.y, visitor.level + 1);
if tc > self.parameters.min_samples_leaf as u32 && self.find_best_cutoff(&mut true_visitor) {
visitor_queue.push_back(true_visitor);
}
let mut false_visitor = NodeVisitor::<M>::new(false_child_idx, visitor.samples, visitor.order, visitor.x, visitor.y, visitor.level + 1);
if fc > self.parameters.min_samples_leaf as u32 && self.find_best_cutoff(&mut false_visitor) {
visitor_queue.push_back(false_visitor);
}
true
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::linalg::naive::dense_matrix::DenseMatrix;
#[test]
fn gini_impurity() {
assert!((impurity(&SplitCriterion::Gini, &vec![7, 3], 10) - 0.42).abs() < std::f64::EPSILON);
assert!((impurity(&SplitCriterion::Entropy, &vec![7, 3], 10) - 0.8812908992306927).abs() < std::f64::EPSILON);
assert!((impurity(&SplitCriterion::ClassificationError, &vec![7, 3], 10) - 0.3).abs() < std::f64::EPSILON);
}
#[test]
fn fit_predict_iris() {
let x = DenseMatrix::from_2d_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.];
assert_eq!(y, DecisionTree::fit(&x, &y, Default::default()).predict(&x));
assert_eq!(3, DecisionTree::fit(&x, &y, DecisionTreeParameters{criterion: SplitCriterion::Entropy, max_depth: Some(3), min_samples_leaf: 1}).depth);
}
#[test]
fn fit_predict_baloons() {
let x = DenseMatrix::from_2d_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.];
assert_eq!(y, DecisionTree::fit(&x, &y, Default::default()).predict(&x));
}
}
src/classification/logistic_regression.rs
+2 -37
View File
@@ -354,45 +354,10 @@ mod tests {
assert_eq!(y_hat, vec![0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]);
}
}
#[test]
fn lr_fit_predict_iris() {
let x = DenseMatrix::from_2d_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., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.];
let lr = LogisticRegression::fit(&x, &y);
let y_hat = lr.predict(&x);
assert_eq!(y_hat, vec![0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]);
}
#[test]
fn tt() {
fn lr_fit_predict_iris() {
let x = arr2(&[
[5.1, 3.5, 1.4, 0.2],
[4.9, 3.0, 1.4, 0.2],
src/classification/mod.rs
+1
View File
@@ -2,6 +2,7 @@ use crate::common::Nominal;
pub mod knn;
pub mod logistic_regression;
pub mod decision_tree;
pub trait Classifier<X, Y>
where
src/linalg/mod.rs
+4
View File
@@ -18,6 +18,10 @@ pub trait Matrix: Clone + Debug {
fn get(&self, row: usize, col: usize) -> f64;
fn get_row_as_vec(&self, row: usize) -> Vec<f64>;
fn get_col_as_vec(&self, col: usize) -> Vec<f64>;
fn set(&mut self, row: usize, col: usize, x: f64);
fn qr_solve_mut(&mut self, b: Self) -> Self;
src/linalg/naive/dense_matrix.rs
+16
View File
@@ -135,6 +135,22 @@ impl Matrix for DenseMatrix {
self.values[col*self.nrows + row]
}
fn get_row_as_vec(&self, row: usize) -> Vec<f64>{
let mut result = vec![0f64; self.ncols];
for c in 0..self.ncols {
result[c] = self.get(row, c);
}
result
}
fn get_col_as_vec(&self, col: usize) -> Vec<f64>{
let mut result = vec![0f64; self.nrows];
for r in 0..self.nrows {
result[r] = self.get(r, col);
}
result
}
fn set(&mut self, row: usize, col: usize, x: f64) {
self.values[col*self.nrows + row] = x;
}
src/linalg/ndarray_bindings.rs
+22
View File
@@ -28,6 +28,14 @@ impl Matrix for ArrayBase<OwnedRepr<f64>, Ix2>
self[[row, col]]
}
fn get_row_as_vec(&self, row: usize) -> Vec<f64> {
self.row(row).to_vec()
}
fn get_col_as_vec(&self, col: usize) -> Vec<f64> {
self.column(col).to_vec()
}
fn set(&mut self, row: usize, col: usize, x: f64) {
self[[row, col]] = x;
}
@@ -509,4 +517,18 @@ mod tests {
assert_eq!(res.len(), 7);
assert_eq!(res, vec![-7., -6., -2., 1., 2., 3., 4.]);
}
#[test]
fn get_row_as_vector(){
let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
let res = a.get_row_as_vec(1);
assert_eq!(res, vec![4., 5., 6.]);
}
#[test]
fn get_col_as_vector(){
let a = arr2(&[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]);
let res = a.get_col_as_vec(1);
assert_eq!(res, vec![2., 5., 8.]);
}
}