Make base_tree_regressor faster

src/ensemble/base_forest_regressor.rs

@@ -5,6 +5,7 @@ use std::fmt::Debug;
 use serde::{Deserialize, Serialize};
 
 use crate::error::{Failed, FailedError};
+use crate::linalg::basic::arrays::MutArrayView1;
 use crate::linalg::basic::arrays::{Array1, Array2};
 use crate::numbers::basenum::Number;
 use crate::numbers::floatnum::FloatNumber;
@@ -120,6 +121,14 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
             })
             .transpose()?;
 
+        // Compute the order of each attribute once
+        let mut order: Vec<Vec<usize>> = Vec::with_capacity(num_attributes);
+
+        for i in 0..num_attributes {
+            let mut col_i: Vec<TX> = x.get_col(i).iterator(0).copied().collect();
+            order.push(col_i.argsort_mut());
+        }
+
         for tree_idx in 0..parameters.n_trees {
             if parameters.bootstrap {
                 samples = BaseForestRegressor::<TX, TY, X, Y>::sample_with_replacement(
@@ -139,7 +148,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
                 min_samples_leaf: parameters.min_samples_leaf,
                 min_samples_split: parameters.min_samples_split,
                 seed: Some(parameters.seed.wrapping_add(tree_idx as u64)), // give each tree its own fixed seed
-                splitter: parameters.splitter.clone(),
+                splitter: parameters.splitter,
             };
             // Only use sample weights on base tree if not already applied during bootstrapping
             let sample_weights_for_base_tree = if parameters.bootstrap {
@@ -153,6 +162,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
                 sample_weights_for_base_tree,
                 samples.clone(),
                 mtry,
+                &order,
                 params,
             )?;
             trees.push(tree);

src/ensemble/extra_trees_regressor.rs

@@ -277,6 +277,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
 #[cfg(test)]
 mod tests {
     use super::*;
+    use crate::error::FailedError;
     use crate::linalg::basic::matrix::DenseMatrix;
     use crate::metrics::mean_squared_error;
 
@@ -470,6 +471,30 @@ mod tests {
         assert_eq!(yhat.err(), Some(Failed::predict(msg)));
     }
 
+    #[cfg_attr(
+        all(target_arch = "wasm32", not(target_os = "wasi")),
+        wasm_bindgen_test::wasm_bindgen_test
+    )]
+    #[test]
+    fn fit_with_empty_x_should_not_panic() {
+        let parameters = ExtraTreesRegressorParameters::default()
+            .with_n_trees(5)
+            .with_seed(42);
+        // (rows, columns, y): no rows, no columns, or both
+        let cases: Vec<(usize, usize, Vec<f64>)> =
+            vec![(0, 0, vec![]), (0, 3, vec![]), (3, 0, vec![1.0, 2.0, 3.0])];
+        for (nrows, ncols, y) in cases {
+            let x: DenseMatrix<f64> = DenseMatrix::new(nrows, ncols, vec![], false)
+                .expect("Construction of empty x should work");
+            let result = ExtraTreesRegressor::fit(&x, &y, parameters.clone());
+            let expected = Failed::because(
+                FailedError::ParametersError,
+                "Training data must contain at least one sample and one feature.",
+            );
+            assert_eq!(result.err(), Some(expected), "shape: ({nrows}, {ncols})");
+        }
+    }
+
     mod sklearn_parity {
         use super::*;
         // sklearn parity tests.

src/ensemble/random_forest_regressor.rs

@@ -470,6 +470,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
 #[cfg(test)]
 mod tests {
     use super::*;
+    use crate::error::FailedError;
     use crate::linalg::basic::matrix::DenseMatrix;
     use crate::metrics::mean_absolute_error;
 
@@ -801,6 +802,30 @@ mod tests {
         assert_eq!(yhat.err(), Some(Failed::predict(msg)));
     }
 
+    #[cfg_attr(
+        all(target_arch = "wasm32", not(target_os = "wasi")),
+        wasm_bindgen_test::wasm_bindgen_test
+    )]
+    #[test]
+    fn fit_with_empty_x_should_not_panic() {
+        let parameters = RandomForestRegressorParameters::default()
+            .with_n_trees(5)
+            .with_seed(42);
+        // (rows, columns, y): no rows, no columns, or both
+        let cases: Vec<(usize, usize, Vec<f64>)> =
+            vec![(0, 0, vec![]), (0, 3, vec![]), (3, 0, vec![1.0, 2.0, 3.0])];
+        for (nrows, ncols, y) in cases {
+            let x: DenseMatrix<f64> = DenseMatrix::new(nrows, ncols, vec![], false)
+                .expect("Construction of empty x should work");
+            let result = RandomForestRegressor::fit(&x, &y, parameters.clone());
+            let expected = Failed::because(
+                FailedError::ParametersError,
+                "Training data must contain at least one sample and one feature.",
+            );
+            assert_eq!(result.err(), Some(expected), "shape: ({nrows}, {ncols})");
+        }
+    }
+
     mod sklearn_parity {
         use super::*;
         // sklearn parity tests.