fix: minor refactoring

src/ensemble/random_forest_classifier.rs

@@ -9,7 +9,7 @@ use crate::tree::decision_tree_classifier::{DecisionTreeClassifier, DecisionTree
 pub struct RandomForestClassifierParameters {  
     pub criterion: SplitCriterion,   
     pub max_depth: Option<u16>,
-    pub min_samples_leaf: u16,    
+    pub min_samples_leaf: usize,    
     pub min_samples_split: usize,          
     pub n_trees: u16,    
     pub mtry: Option<usize>
@@ -97,7 +97,7 @@ impl RandomForestClassifier {
         
     }  
     
-    fn sample_with_replacement(y: &Vec<usize>, num_classes: usize) -> Vec<u32>{
+    fn sample_with_replacement(y: &Vec<usize>, num_classes: usize) -> Vec<usize>{
         let mut rng = rand::thread_rng();
         let class_weight = vec![1.; num_classes];
         let nrows = y.len();

src/tree/decision_tree_classifier.rs

@@ -7,7 +7,7 @@ use crate::algorithm::sort::quick_sort::QuickArgSort;
 pub struct DecisionTreeClassifierParameters {           
     pub criterion: SplitCriterion,
     pub max_depth: Option<u16>,
-    pub min_samples_leaf: u16,
+    pub min_samples_leaf: usize,
     pub min_samples_split: usize
 }
 
@@ -68,14 +68,14 @@ struct NodeVisitor<'a, M: Matrix> {
     x: &'a M,
     y: &'a Vec<usize>,
     node: usize,
-    samples: Vec<u32>,
+    samples: Vec<usize>,
     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 {
+fn impurity(criterion: &SplitCriterion, count: &Vec<usize>, n: usize) -> f64 {
     let mut impurity = 0.;
 
     match criterion {
@@ -112,7 +112,7 @@ fn impurity(criterion: &SplitCriterion, count: &Vec<u32>, n: u32) -> f64 {
 
 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 {
+    fn new(node_id: usize, samples: Vec<usize>, order: &'a Vec<Vec<usize>>, x: &'a M, y: &'a Vec<usize>, level: u16) -> Self {
         NodeVisitor {
             x: x,
             y: y,
@@ -127,7 +127,7 @@ impl<'a, M: Matrix> NodeVisitor<'a, M> {
 
 }
 
-pub(in crate) fn which_max(x: &Vec<u32>) -> usize {
+pub(in crate) fn which_max(x: &Vec<usize>) -> usize {
     let mut m = x[0];
     let mut which = 0;
 
@@ -149,7 +149,7 @@ impl DecisionTreeClassifier {
         DecisionTreeClassifier::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: DecisionTreeClassifierParameters) -> DecisionTreeClassifier {
+    pub fn fit_weak_learner<M: Matrix>(x: &M, y: &M::RowVector, samples: Vec<usize>, mtry: usize, parameters: DecisionTreeClassifierParameters) -> DecisionTreeClassifier {
         let y_m = M::from_row_vector(y.clone());
         let (_, y_ncols) = y_m.shape();
         let (_, num_attributes) = x.shape();
@@ -270,7 +270,7 @@ impl DecisionTreeClassifier {
 
         let n = visitor.samples.iter().sum();        
 
-        if n <= self.parameters.min_samples_leaf as u32 {
+        if n <= self.parameters.min_samples_leaf {
             return false;
         }
         
@@ -297,7 +297,7 @@ impl DecisionTreeClassifier {
 
     }    
 
-    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){
+    fn find_best_split<M: Matrix>(&mut self, visitor: &mut NodeVisitor<M>, n: usize, count: &Vec<usize>, false_count: &mut Vec<usize>, parent_impurity: f64, j: usize){
 
         let mut true_count = vec![0; self.num_classes];
         let mut prevx = std::f64::NAN;
@@ -351,7 +351,7 @@ impl DecisionTreeClassifier {
         let (n, _) = visitor.x.shape();
         let mut tc = 0;
         let mut fc = 0;        
-        let mut true_samples: Vec<u32> = vec![0; n];
+        let mut true_samples: Vec<usize> = vec![0; n];
 
         for i in 0..n {
             if visitor.samples[i] > 0 {
@@ -365,7 +365,7 @@ impl DecisionTreeClassifier {
             }
         }
 
-        if tc < self.parameters.min_samples_leaf as u32 || fc < self.parameters.min_samples_leaf as u32 {
+        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 = std::f64::NAN;
             self.nodes[visitor.node].split_score = std::f64::NAN;
@@ -384,13 +384,13 @@ impl DecisionTreeClassifier {
 
         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) {
+        if 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) {
+        if self.find_best_cutoff(&mut false_visitor, mtry) {
             visitor_queue.push_back(false_visitor);
         }