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feat: refactors packages layout

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This commit is contained in:
Volodymyr Orlov
2020-03-13 14:30:45 -07:00
parent 4f8318e933
commit 87b6fab795
12 changed files with 10 additions and 27 deletions
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src/tree/decision_tree.rs
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use std::default::Default;
use std::collections::LinkedList;
use crate::linalg::Matrix;
use crate::algorithm::sort::quick_sort::QuickArgSort;
#[derive(Debug)]
pub struct DecisionTreeParameters {
pub criterion: SplitCriterion,
pub max_depth: Option<u16>,
pub min_samples_leaf: u16
}
#[derive(Debug)]
pub struct DecisionTree {
nodes: Vec<Node>,
parameters: DecisionTreeParameters,
num_classes: usize,
classes: Vec<f64>,
depth: u16
}
#[derive(Debug, Clone)]
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
}
}
}
pub(in crate) 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 (x_nrows, num_attributes) = x.shape();
let samples = vec![1; x_nrows];
DecisionTree::fit_weak_learner(x, y, samples, num_attributes, parameters)
}
pub fn fit_weak_learner<M: Matrix>(x: &M, y: &M::RowVector, samples: Vec<u32>, mtry: usize, parameters: DecisionTreeParameters) -> DecisionTree {
let y_m = M::from_row_vector(y.clone());
let (_, y_ncols) = y_m.shape();
let (_, 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 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, mtry) {
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, mtry, &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()
}
pub(in crate) 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>, mtry: usize) -> 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..mtry {
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>, mtry: usize, 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, mtry) {
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, mtry) {
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_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_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/tree/mod.rs
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pub mod decision_tree;