Adds RandomForest

src/classification/decision_tree.rs

@@ -5,9 +5,9 @@ use crate::algorithm::sort::quick_sort::QuickArgSort;
 
 #[derive(Debug)]
 pub struct DecisionTreeParameters {           
-    criterion: SplitCriterion,
-    max_depth: Option<u16>,
-    min_samples_leaf: u16
+    pub criterion: SplitCriterion,
+    pub max_depth: Option<u16>,
+    pub min_samples_leaf: u16    
 }
 
 #[derive(Debug)]
@@ -19,7 +19,7 @@ pub struct DecisionTree {
     depth: u16
 }
 
-#[derive(Debug)]
+#[derive(Debug, Clone)]
 pub enum SplitCriterion {
     Gini,
     Entropy,
@@ -125,7 +125,7 @@ impl<'a, M: Matrix> NodeVisitor<'a, M> {
 
 }
 
-fn which_max(x: &Vec<u32>) -> usize {
+pub(in crate) fn which_max(x: &Vec<u32>) -> usize {
     let mut m = x[0];
     let mut which = 0;
 
@@ -142,9 +142,15 @@ fn which_max(x: &Vec<u32>) -> usize {
 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 (x_nrows, num_attributes) = x.shape();
+        let (_, num_attributes) = x.shape();
         let classes = y_m.unique();        
         let k = classes.len(); 
         if k < 2 {
@@ -159,7 +165,6 @@ impl DecisionTree {
         }
 
         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 {
@@ -186,13 +191,13 @@ impl DecisionTree {
 
         let mut visitor_queue: LinkedList<NodeVisitor<M>> = LinkedList::new();
 
-        if tree.find_best_cutoff(&mut visitor) {
+        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, &mut visitor_queue),
+                Some(node) => tree.split(node, mtry, &mut visitor_queue,),
                 None => break
             };     
         }        
@@ -212,7 +217,7 @@ impl DecisionTree {
         result.to_row_vector()
     }
 
-    fn predict_for_row<M: Matrix>(&self, x: &M, row: usize) -> usize {
+    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();
 
@@ -240,7 +245,7 @@ impl DecisionTree {
         
     }   
     
-    fn find_best_cutoff<M: Matrix>(&mut self, visitor: &mut NodeVisitor<M>) -> bool {
+    fn find_best_cutoff<M: Matrix>(&mut self, visitor: &mut NodeVisitor<M>, mtry: usize) -> bool {
 
         let (n_rows, n_attr) = visitor.x.shape();        
 
@@ -282,7 +287,7 @@ impl DecisionTree {
             variables[i] = i;
         }
 
-        for j in 0..n_attr {
+        for j in 0..mtry {
             self.find_best_split(visitor, n, &count, &mut false_count, parent_impurity, variables[j]);            
         }        
 
@@ -340,7 +345,7 @@ impl DecisionTree {
 
     }
 
-    fn split<'a, M: Matrix>(&mut self, mut visitor: NodeVisitor<'a, M>, visitor_queue: &mut LinkedList<NodeVisitor<'a, M>>) -> bool {
+    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;        
@@ -377,13 +382,13 @@ impl DecisionTree {
 
         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) {
+        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) {
+        if fc > self.parameters.min_samples_leaf as u32 && self.find_best_cutoff(&mut false_visitor, mtry) {
             visitor_queue.push_back(false_visitor);
         }
 

src/classification/mod.rs

@@ -3,6 +3,7 @@ use crate::common::Nominal;
 pub mod knn;
 pub mod logistic_regression;
 pub mod decision_tree;
+pub mod random_forest;
 
 pub trait Classifier<X, Y>
 where 

src/classification/random_forest.rs

@@ -0,0 +1,160 @@
+extern crate rand;
+
+use rand::Rng;
+use std::default::Default;
+use crate::linalg::Matrix;
+use crate::classification::decision_tree::{DecisionTree, DecisionTreeParameters, SplitCriterion, which_max};
+
+#[derive(Debug, Clone)]
+pub struct RandomForestParameters {  
+    pub criterion: SplitCriterion,   
+    pub max_depth: Option<u16>,
+    pub min_samples_leaf: u16,      
+    pub n_trees: u16,    
+    pub mtry: Option<usize>
+}
+
+#[derive(Debug)]
+pub struct RandomForest {    
+    parameters: RandomForestParameters,
+    trees: Vec<DecisionTree>,
+    classes: Vec<f64>
+}
+
+impl Default for RandomForestParameters {
+    fn default() -> Self { 
+        RandomForestParameters {
+            criterion: SplitCriterion::Gini,
+            max_depth: None,
+            min_samples_leaf: 1,
+            n_trees: 100,
+            mtry: Option::None
+        }
+     }
+}
+
+impl RandomForest {
+
+    pub fn fit<M: Matrix>(x: &M, y: &M::RowVector, parameters: RandomForestParameters) -> RandomForest {        
+        let (_, num_attributes) = x.shape();
+        let y_m = M::from_row_vector(y.clone());
+        let (_, y_ncols) = y_m.shape();
+        let mut yi: Vec<usize> = vec![0; y_ncols];
+        let classes = y_m.unique();                
+
+        for i in 0..y_ncols {
+            let yc = y_m.get(0, i);                        
+            yi[i] = classes.iter().position(|c| yc == *c).unwrap();            
+        }
+              
+        let mtry = parameters.mtry.unwrap_or((num_attributes as f64).sqrt().floor() as usize);        
+
+        let classes = y_m.unique();        
+        let k = classes.len(); 
+        let mut trees: Vec<DecisionTree> = Vec::new();
+
+        for _ in 0..parameters.n_trees {
+            let samples = RandomForest::sample_with_replacement(&yi, k);
+            let params = DecisionTreeParameters{
+                criterion: parameters.criterion.clone(),
+                max_depth: parameters.max_depth,
+                min_samples_leaf: parameters.min_samples_leaf                
+            };
+            let tree = DecisionTree::fit_weak_learner(x, y, samples, mtry, params);
+            trees.push(tree);
+        }
+
+        RandomForest {
+            parameters: parameters,
+            trees: trees,
+            classes
+        }
+    }
+
+    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 = vec![0; self.classes.len()];
+        
+        for tree in self.trees.iter() {
+            result[tree.predict_for_row(x, row)] += 1;
+        }        
+
+        return which_max(&result)
+        
+    }  
+    
+    fn sample_with_replacement(y: &Vec<usize>, num_classes: usize) -> Vec<u32>{
+        let mut rng = rand::thread_rng();
+        let class_weight = vec![1.; num_classes];
+        let nrows = y.len();
+        let mut samples = vec![0; nrows];
+        for l in 0..num_classes {
+            let mut nj = 0;
+            let mut cj: Vec<usize> = Vec::new();
+            for i in 0..nrows {
+                if y[i] == l {
+                    cj.push(i);
+                    nj += 1;
+                }
+            }
+            
+            let size = ((nj as f64) / class_weight[l]) as usize;            
+            for _ in 0..size {
+                let xi: usize = rng.gen_range(0, nj);
+                samples[cj[xi]] += 1;
+            }
+        }
+        samples
+    }
+
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*; 
+    use crate::linalg::naive::dense_matrix::DenseMatrix;
+
+    #[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.];
+
+        RandomForest::fit(&x, &y, Default::default());
+
+        assert_eq!(y, RandomForest::fit(&x, &y, Default::default()).predict(&x));                    
+            
+    }
+
+}