8000 Implement the feature importance for Decision Tree Classifier by tushushu · Pull Request #275 · smartcorelib/smartcore · GitHub
[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to content

Implement the feature importance for Decision Tree Classifier #275

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 11 commits into from
Feb 25, 2024
Merged

Implement the feature importance for Decision Tree Classifier #275

Changes from all commits
Commits
Show all changes
11 commits
Select commit Hold shift + click to select a range
4cf2d8c
store impurity in the node
tushushu Jan 21, 2024
e8aabbf
add number of features
tushushu Jan 21, 2024
656bd23
add a TODO
tushushu Jan 21, 2024
965a19f
draft feature importance
tushushu Jan 27, 2024
aca711c
feat
tushushu Jan 28, 2024
1746a7f
n_samples of node
tushushu Jan 28, 2024
0162901
compute_feature_importances
tushushu Jan 28, 2024
2c2da12
unit tests
tushushu Feb 2, 2024
584389b
always calculate impurity
tushushu Feb 3, 2024
1faae64
fix bug
tushushu Feb 3, 2024
42c4949
fix linter
tushushu Feb 7, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
108 changes: 85 additions & 23 deletions src/tree/decision_tree_classifier.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -116,6 +116,7 @@ pub struct DecisionTreeClassifier<
num_classes: usize,
classes: Vec<TY>,
depth: u16,
num_features: usize,
_phantom_tx: PhantomData<TX>,
_phantom_x: PhantomData<X>,
_phantom_y: PhantomData<Y>,
Expand Down Expand Up @@ -159,11 +160,13 @@ pub enum SplitCriterion {
#[derive(Debug, Clone)]
struct Node {
output: usize,
n_node_samples: usize,
split_feature: usize,
split_value: Option<f64>,
split_score: Option<f64>,
true_child: Option<usize>,
false_child: Option<usize>,
impurity: Option<f64>,
}

impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> PartialEq
Expand Down Expand Up @@ -400,14 +403,16 @@ impl Default for DecisionTreeClassifierSearchParameters {
}

impl Node {
fn new(output: usize) -> Self {
fn new(output: usize, n_node_samples: usize) -> Self {
Node {
output,
n_node_samples,
split_feature: 0,
split_value: Option::None,
split_score: Option::None,
true_child: Option::None,
false_child: Option::None,
impurity: Option::None,
}
}
}
Expand Down Expand Up @@ -507,6 +512,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
num_classes: 0usize,
classes: vec![],
depth: 0u16,
num_features: 0usize,
_phantom_tx: PhantomData,
_phantom_x: PhantomData,
_phantom_y: PhantomData,
Expand Down Expand Up @@ -578,7 +584,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
count[yi[i]] += samples[i];
}

let root = Node::new(which_max(&count));
let root = Node::new(which_max(&count), y_ncols);
change_nodes.push(root);
let mut order: Vec<Vec<usize>> = Vec::new();

Expand All @@ -593,6 +599,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
num_classes: k,
classes,
depth: 0u16,
num_features: num_attributes,
_phantom_tx: PhantomData,
_phantom_x: PhantomData,
_phantom_y: PhantomData,
Expand Down Expand Up @@ -678,16 +685,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
}
}

if is_pure {
return false;
}

let n = visitor.samples.iter().sum();

if n <= self.parameters().min_samples_split {
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 {
Expand All @@ -696,7 +694,15 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
}
}

let parent_impurity = impurity(&self.parameters().criterion, &count, n);
self.nodes[visitor.node].impurity = Some(impurity(&self.parameters().criterion, &count, n));

if is_pure {
return false;
}

if n <= self.parameters().min_samples_split {
return false;
}

let mut variables = (0..n_attr).collect::<Vec<_>>();

Expand All @@ -705,14 +711,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
}

for variable in variables.iter().take(mtry) {
self.find_best_split(
visitor,
n,
&count,
&mut false_count,
parent_impurity,
*variable,
);
self.find_best_split(visitor, n, &count, &mut false_count, *variable);
}

self.nodes()[visitor.node].split_score.is_some()
Expand All @@ -724,7 +723,6 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
n: usize,
count: &[usize],
false_count: &mut [usize],
parent_impurity: f64,
j: usize,
) {
let mut true_count = vec![0; self.num_classes];
Expand Down Expand Up @@ -760,6 +758,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>

let true_label = which_max(&true_count);
let false_label = which_max(false_count);
let parent_impurity = self.nodes()[visitor.node].impurity.unwrap();
let gain = parent_impurity
- tc as f64 / n as f64
* impurity(&self.parameters().criterion, &true_count, tc)
Expand Down Expand Up @@ -827,9 +826,9 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>

let true_child_idx = self.nodes().len();

self.nodes.push(Node::new(visitor.true_child_output));
self.nodes.push(Node::new(visitor.true_child_output, tc));
let false_child_idx = self.nodes().len();
self.nodes.push(Node::new(visitor.false_child_output));
self.nodes.push(Node::new(visitor.false_child_output, fc));
self.nodes[visitor.node].true_child = Some(true_child_idx);
self.nodes[visitor.node].false_child = Some(false_child_idx);

Expand Down Expand Up @@ -863,6 +862,33 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>

true
}

/// Compute feature importances for the fitted tree.
pub fn compute_feature_importances(&self, normalize: bool) -> Vec<f64> {
let mut importances = vec![0f64; self.num_features];

fo 8000 r node in self.nodes().iter() {
if node.true_child.is_none() && node.false_child.is_none() {
continue;
}
let left = &self.nodes()[node.true_child.unwrap()];
let right = &self.nodes()[node.false_child.unwrap()];

importances[node.split_feature] += node.n_node_samples as f64 * node.impurity.unwrap()
- left.n_node_samples as f64 * left.impurity.unwrap()
- right.n_node_samples as f64 * right.impurity.unwrap();
}
for item in importances.iter_mut() {
*item /= self.nodes()[0].n_node_samples as f64;
}
if normalize {
let sum = importances.iter().sum::<f64>();
for importance in importances.iter_mut() {
*importance /= sum;
}
}
importances
}
}

#[cfg(test)]
Expand Down Expand Up @@ -1016,6 +1042,42 @@ mod tests {
);
}

#[test]
fn test_compute_feature_importances() {
let x: DenseMatrix<f64> = 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<u32> = 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()).unwrap();
assert_eq!(
tree.compute_feature_importances(false),
vec![0., 0., 0.21333333333333332, 0.26666666666666666]
);
assert_eq!(
tree.compute_feature_importances(true),
vec![0., 0., 0.4444444444444444, 0.5555555555555556]
);
}

#[cfg_attr(
all(target_arch = "wasm32", not(target_os = "wasi")),
wasm_bindgen_test::wasm_bindgen_test
Expand Down
0