Allow setting seed for RandomForestClassifier and Regressor (#120)

* Seed for the classifier.

* Seed for the regressor.

* Forgot one.

* typo.

src/tree/decision_tree_classifier.rs

@@ -68,6 +68,7 @@ use std::fmt::Debug;
 use std::marker::PhantomData;
 
 use rand::seq::SliceRandom;
+use rand::Rng;
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
@@ -328,7 +329,14 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
     ) -> Result<DecisionTreeClassifier<T>, Failed> {
         let (x_nrows, num_attributes) = x.shape();
         let samples = vec![1; x_nrows];
-        DecisionTreeClassifier::fit_weak_learner(x, y, samples, num_attributes, parameters)
+        DecisionTreeClassifier::fit_weak_learner(
+            x,
+            y,
+            samples,
+            num_attributes,
+            parameters,
+            &mut rand::thread_rng(),
+        )
     }
 
     pub(crate) fn fit_weak_learner<M: Matrix<T>>(
@@ -337,6 +345,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
         samples: Vec<usize>,
         mtry: usize,
         parameters: DecisionTreeClassifierParameters,
+        rng: &mut impl Rng,
     ) -> Result<DecisionTreeClassifier<T>, Failed> {
         let y_m = M::from_row_vector(y.clone());
         let (_, y_ncols) = y_m.shape();
@@ -384,13 +393,13 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
 
         let mut visitor_queue: LinkedList<NodeVisitor<'_, T, M>> = LinkedList::new();
 
-        if tree.find_best_cutoff(&mut visitor, mtry) {
+        if tree.find_best_cutoff(&mut visitor, mtry, rng) {
             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),
+                Some(node) => tree.split(node, mtry, &mut visitor_queue, rng),
                 None => break,
             };
         }
@@ -443,6 +452,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
         &mut self,
         visitor: &mut NodeVisitor<'_, T, M>,
         mtry: usize,
+        rng: &mut impl Rng,
     ) -> bool {
         let (n_rows, n_attr) = visitor.x.shape();
 
@@ -482,7 +492,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
         let mut variables = (0..n_attr).collect::<Vec<_>>();
 
         if mtry < n_attr {
-            variables.shuffle(&mut rand::thread_rng());
+            variables.shuffle(rng);
         }
 
         for variable in variables.iter().take(mtry) {
@@ -566,6 +576,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
         mut visitor: NodeVisitor<'a, T, M>,
         mtry: usize,
         visitor_queue: &mut LinkedList<NodeVisitor<'a, T, M>>,
+        rng: &mut impl Rng,
     ) -> bool {
         let (n, _) = visitor.x.shape();
         let mut tc = 0;
@@ -614,7 +625,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
             visitor.level + 1,
         );
 
-        if self.find_best_cutoff(&mut true_visitor, mtry) {
+        if self.find_best_cutoff(&mut true_visitor, mtry, rng) {
             visitor_queue.push_back(true_visitor);
         }
 
@@ -627,7 +638,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
             visitor.level + 1,
         );
 
-        if self.find_best_cutoff(&mut false_visitor, mtry) {
+        if self.find_best_cutoff(&mut false_visitor, mtry, rng) {
             visitor_queue.push_back(false_visitor);
         }
 

src/tree/decision_tree_regressor.rs

@@ -63,6 +63,7 @@ use std::default::Default;
 use std::fmt::Debug;
 
 use rand::seq::SliceRandom;
+use rand::Rng;
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
@@ -242,7 +243,14 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
     ) -> Result<DecisionTreeRegressor<T>, Failed> {
         let (x_nrows, num_attributes) = x.shape();
         let samples = vec![1; x_nrows];
-        DecisionTreeRegressor::fit_weak_learner(x, y, samples, num_attributes, parameters)
+        DecisionTreeRegressor::fit_weak_learner(
+            x,
+            y,
+            samples,
+            num_attributes,
+            parameters,
+            &mut rand::thread_rng(),
+        )
     }
 
     pub(crate) fn fit_weak_learner<M: Matrix<T>>(
@@ -251,6 +259,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
         samples: Vec<usize>,
         mtry: usize,
         parameters: DecisionTreeRegressorParameters,
+        rng: &mut impl Rng,
     ) -> Result<DecisionTreeRegressor<T>, Failed> {
         let y_m = M::from_row_vector(y.clone());
 
@@ -284,13 +293,13 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
 
         let mut visitor_queue: LinkedList<NodeVisitor<'_, T, M>> = LinkedList::new();
 
-        if tree.find_best_cutoff(&mut visitor, mtry) {
+        if tree.find_best_cutoff(&mut visitor, mtry, rng) {
             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),
+                Some(node) => tree.split(node, mtry, &mut visitor_queue, rng),
                 None => break,
             };
         }
@@ -343,6 +352,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
         &mut self,
         visitor: &mut NodeVisitor<'_, T, M>,
         mtry: usize,
+        rng: &mut impl Rng,
     ) -> bool {
         let (_, n_attr) = visitor.x.shape();
 
@@ -357,7 +367,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
         let mut variables = (0..n_attr).collect::<Vec<_>>();
 
         if mtry < n_attr {
-            variables.shuffle(&mut rand::thread_rng());
+            variables.shuffle(rng);
         }
 
         let parent_gain =
@@ -432,6 +442,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
         mut visitor: NodeVisitor<'a, T, M>,
         mtry: usize,
         visitor_queue: &mut LinkedList<NodeVisitor<'a, T, M>>,
+        rng: &mut impl Rng,
     ) -> bool {
         let (n, _) = visitor.x.shape();
         let mut tc = 0;
@@ -480,7 +491,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
             visitor.level + 1,
         );
 
-        if self.find_best_cutoff(&mut true_visitor, mtry) {
+        if self.find_best_cutoff(&mut true_visitor, mtry, rng) {
             visitor_queue.push_back(true_visitor);
         }
 
@@ -493,7 +504,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
             visitor.level + 1,
         );
 
-        if self.find_best_cutoff(&mut false_visitor, mtry) {
+        if self.find_best_cutoff(&mut false_visitor, mtry, rng) {
             visitor_queue.push_back(false_visitor);
         }