Lmm/add seeds in more algorithms (#164)

* Provide better output in flaky tests

* feat: add seed parameter to multiple algorithms

* Update changelog

Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>

src/tree/decision_tree_classifier.rs

@@ -77,6 +77,7 @@ use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
+use crate::rand::get_rng_impl;
 
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
@@ -90,6 +91,8 @@ pub struct DecisionTreeClassifierParameters {
     pub min_samples_leaf: usize,
     /// The minimum number of samples required to split an internal node.
     pub min_samples_split: usize,
+    /// Controls the randomness of the estimator
+    pub seed: Option<u64>,
 }
 
 /// Decision Tree
@@ -197,6 +200,7 @@ impl Default for DecisionTreeClassifierParameters {
             max_depth: None,
             min_samples_leaf: 1,
             min_samples_split: 2,
+            seed: None,
         }
     }
 }
@@ -467,14 +471,7 @@ 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,
-            &mut rand::thread_rng(),
-        )
+        DecisionTreeClassifier::fit_weak_learner(x, y, samples, num_attributes, parameters)
     }
 
     pub(crate) fn fit_weak_learner<M: Matrix<T>>(
@@ -483,7 +480,6 @@ 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();
@@ -497,6 +493,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
             )));
         }
 
+        let mut rng = get_rng_impl(parameters.seed);
         let mut yi: Vec<usize> = vec![0; y_ncols];
 
         for (i, yi_i) in yi.iter_mut().enumerate().take(y_ncols) {
@@ -531,13 +528,13 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
 
         let mut visitor_queue: LinkedList<NodeVisitor<'_, T, M>> = LinkedList::new();
 
-        if tree.find_best_cutoff(&mut visitor, mtry, rng) {
+        if tree.find_best_cutoff(&mut visitor, mtry, &mut 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, rng),
+                Some(node) => tree.split(node, mtry, &mut visitor_queue, &mut rng),
                 None => break,
             };
         }
@@ -874,7 +871,8 @@ mod tests {
                     criterion: SplitCriterion::Entropy,
                     max_depth: Some(3),
                     min_samples_leaf: 1,
-                    min_samples_split: 2
+                    min_samples_split: 2,
+                    seed: None
                 }
             )
             .unwrap()

src/tree/decision_tree_regressor.rs

@@ -72,6 +72,7 @@ use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
+use crate::rand::get_rng_impl;
 
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
@@ -83,6 +84,8 @@ pub struct DecisionTreeRegressorParameters {
     pub min_samples_leaf: usize,
     /// The minimum number of samples required to split an internal node.
     pub min_samples_split: usize,
+    /// Controls the randomness of the estimator
+    pub seed: Option<u64>,
 }
 
 /// Regression Tree
@@ -130,6 +133,7 @@ impl Default for DecisionTreeRegressorParameters {
             max_depth: None,
             min_samples_leaf: 1,
             min_samples_split: 2,
+            seed: None,
         }
     }
 }
@@ -357,14 +361,7 @@ 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,
-            &mut rand::thread_rng(),
-        )
+        DecisionTreeRegressor::fit_weak_learner(x, y, samples, num_attributes, parameters)
     }
 
     pub(crate) fn fit_weak_learner<M: Matrix<T>>(
@@ -373,7 +370,6 @@ 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());
 
@@ -381,6 +377,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
         let (_, num_attributes) = x.shape();
 
         let mut nodes: Vec<Node<T>> = Vec::new();
+        let mut rng = get_rng_impl(parameters.seed);
 
         let mut n = 0;
         let mut sum = T::zero();
@@ -407,13 +404,13 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
 
         let mut visitor_queue: LinkedList<NodeVisitor<'_, T, M>> = LinkedList::new();
 
-        if tree.find_best_cutoff(&mut visitor, mtry, rng) {
+        if tree.find_best_cutoff(&mut visitor, mtry, &mut 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, rng),
+                Some(node) => tree.split(node, mtry, &mut visitor_queue, &mut rng),
                 None => break,
             };
         }
@@ -699,6 +696,7 @@ mod tests {
                 max_depth: Option::None,
                 min_samples_leaf: 2,
                 min_samples_split: 6,
+                seed: None,
             },
         )
         .and_then(|t| t.predict(&x))
@@ -719,6 +717,7 @@ mod tests {
                 max_depth: Option::None,
                 min_samples_leaf: 1,
                 min_samples_split: 3,
+                seed: None,
             },
         )
         .and_then(|t| t.predict(&x))